A SysRq is a sequence of keys understood by the Linux operation system kernel, which can trigger a set of pre-defined actions. These commands are often used when virtual machine troubleshooting or recovery can't be performed through traditional administration (for example, if the VM is not responding). Using the SysRq feature of Azure Serial Console will mimic pressing of the SysRq key and characters entered on a physical keyboard.
The Mimic key serial
The study of serial killers has been dominated by an individualised focus on studying the biography of offenders and the causes of their behaviour. Popular representations of Jeffrey Dahmer, Harold Shipman, John Wayne Gacy and other notorious figures emphasise the sociopathic tendencies of the lone serial killer, presented in accounts that accentuate how assorted personality traits and risk factors ostensibly contribute to their otherwise unfathomable behaviour. While this emphasis on personal biography lends itself to much needed psychological analysis, the cumulative effect of such accounts is that serial killing can appear a-historical and a-cultural, as though such predispositions might manifest themselves in identical ways irrespective of context.
Here we briefly identify three aspects of serial killing that are often taken for granted, but that are intimately tied to the emergence of serial murder in its contemporary guise. These include the rise of a society of strangers, the development of a culture of celebrity, and cultural frameworks of denigration and marginalisation.
The rise of capitalism and related processes of mass migration to urban centres resulted in individuals being immersed in a sea of strangers (Nock, 1993). This development also proved to be a key precondition for the emergence of serial murder, given that a defining attribute of serial killers is that they prey on strangers (something that distinguishes them from the vast majority of homicides, which typically involve some form of prior relationship between killer and victim). Thus dense modern urban environments represent ideal settings for the routinised impersonal encounters that operate as a hallmark of serial killing.
While serial killing is routinely presented as the unfathomable behaviour of the lone, decontextualised and sociopathic individual, here we have emphasised the unnervingly familiar modern face of serial killing. Several distinctively modern phenomena, including anonymity, a culture of celebrity enabled through the rise of mass media, and specific cultural frameworks of denigration, each provide key institutional frameworks, motivations and opportunity structures for analysing such acts. To exclusively focus on aetiology and offender biography systematically ignores this larger social context, and elides a more nuanced understanding of the hows and whys of serial killing.
Once you have the Locker Key, make your way to the Barracks where you'll find several lockers that are located between the beds.Using the key, begin opening the lockers until a Mimic spawns. After killing the mimic you will have the chance at receiving the Barrel Assembly. Do keep in mind that the amount of lockers and mimics that you need to kill to receive the Barrel Assembly is currently random. So if you don't happen to receive the Barrel Assembly straight away, just keep trying.
OpenSSL APIs allow us to extract and use origin server certificateproperties when generating a fake server certificate. In general, wewant to mimic all properties, but various SSL rules make micking of someproperties technically infeasible, and browser behavior makes mimickingmost properties undesirable under certain conditions. We detail theseexceptions below. Squid administrator can tweak mimicking algorithmsusing sslproxy_cert_adapt and sslproxy_cert_sign configurationoptions.
All certificates generated by Squid are signed using the configuredtrusted CA certificate private key. This, along with the serial numbergeneration algorithm, allows independent but identically configuredSquids (including but not limited to Squid SMP workers) to generateidentical certificates under similar circumstances.
It is important to understand that Squid can be configured to ignore ortolerate certain SSL connection establishment errors usingsslproxy_cert_error.If the error is allowed, Squid forgets about the error, mimics truebroken certificate properties, and continues to talk to the server.Otherwise, Squid does not mimic and terminates the server connection asdiscussed above. Thus, if you want users to see broken certificateproperties instead of Squid error pages, you must tell Squid to ignorethe error.
If Squid receives a valid true certificate, Squid does not try toenforce CN length limit and simply mimics true certificate fields asdescribed in the table above. However, when Squid fails to connect tothe origin server or fails to receive a usable true certificate, Squidhas to generate a minimal fake certificate from scratch and has to dealwith long domain names of the sites a user intended to visit. To shortenthe name, Squid tries to replace the lower level domain label(s) with awild card until the CN length no longer exceeds the 64 character limit.If that replacement results in a TLD wildcard such as *.com or,worse, in a bare * wildcard, then Squid produces a certificate withno CN at all. Such certificates are usually rejected by browsers withvarious, often misleading, errors. For example,
A user may type SSL server IP address in the address bar. Some browsers(e.g., Rekonq browser v0.7.x) send IP addresses in CONNECT requests evenwhen the user typed a host name in the address bar. Currently, Squidcannot distinguish the two cases and assumes that an IP address in theCONNECT request implies that the user typed that address in the addressbar. Besides assuming user input, Squid overall behavior here is meantto mimic what would happen if Squid was not in the loop. Here are a fewcases when the user enters something like of :
Not all true certificate properties are mimicked. Initially, we thoughtit is a good idea to mimic everything by default, but we quickly raninto problems with browsers rejecting fake certificates due tomismatching or otherwise invalid combination of properties (e.g.,alternative names not matching CN). We now mimic only the propertiesthat are unlikely to cause problems. However, a few other properties maystill be investigated for mimicking: Certificate Policies, SubjectDirectory Attributes, Extended Key Usage, Freshest CRL, and SubjectInformation Access.
The following properties are probably not applicable because they dealwith CA or other specialized certificates (or are too vague to bemimicked safely): Basic Constraints, Name Constraints, PolicyConstraints, and Inhibit anyPolicy.
Users with motor control impairments can manipulate the mouse cursor by pressing keys of the numeric pad located on the right of the keyboard. Below is a list of numeric pad keys and their mimic mouse moves:
The key to finding sources of jitter lies in the ability to time-correlate jitter measurement results with high-speed serial data signals, as well as other possible sources of uncorrelated periodic jitter. A real-time oscilloscope with jitter analysis along with the appropriate stimulus meet that critical time-correlation requirement to relate jitter trend measurement results to measured signals. Once you are able to time-correlate particular real-time timing error measurements to particular bits within a serial data pattern, it usually becomes a routine troubleshooting task to solve your deterministic jitter problems.
In Figure 4 is shown an example of a period jitter measurement floor measurement for a PCI Express application. The target signal to be measured is a 1.0-Vp-p, 2.5-GHz clock signal with a nominal slew rate of 10 V/ns. In this example, two cycles of a 0.6-Vp-p, 5.0-GHz sinewave are used to mimic the period and slew rate of the target PCI Express signal. The oscilloscope is configured to measure the time interval between two clock transitions that are two cycles apart. Measurement statistics are then used to calculate the standard deviation of a large number of these two-cycle time interval measurements.
Figure 4. An example of a period jitter measurement floor measurement for a PCI Express application. The target signal to be measured is a 1.0-Vp-p, 2.5-GHz clock signal with a nominal slew rate of 10 V/ns. In this example, two cycles of a 0.6-Vp-p, 5.0-GHz sinewave are used to mimic the period and the slew rate of the target PCI Express signal. The measured jitter measurement floor is 900 femotseconds rms.
NOTE: I will be using a DHT11 temperature sensor to produce data on the Arduino end. Since this is a tutorial on reading data from the serial port using Python, not Arduino, I recommend visiting a DHT11 tutorial to learn how to print temperature data from the sensor to the serial port (see here, or here).
Now we have a working datalogger! This is as simple as it gets, and it's remarkably powerful. The three lines that start as: '' with open("test_data.csv","a") as f: '' look for a file called 'test_data.csv' and create it if it doesn't exist. The "a" in parentheses tells Python to append the serial port data and ensure that no data is erased in the existing file. This is a grand result because it not only takes care of saving the data to the .csv file, but it creates one for us and prevents overwriting and (most times) corruption. How considerate!
NOTES: while I was using Raspberry Pi, I came across an issue between reading the serial port, saving to .csv, and updating the plots. I found that updating the plot occupied a lot of processing time, which resulted in slower reading of the serial port. Therefore, I advise anyone who is using the method below to assess whether you are reading all the bytes that are being outputted by the Arduino. I found that I was missing bytes or they were getting backed up in the queue in the buffer. Do some tests to verify the speed of your loop. This will prevent lost bytes and dropouts of data. I found that my loop took roughly half a second to complete, which means that my serial port should not be outputting more than 2 points per second. I actually used 0.8 seconds as the time between data records and it appeared to catch all data points. The slow loop is a result of the plotting, so once you comment out all of the plot code, you will get a much higher data rate and .csv save rate (just as above). 2ff7e9595c
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