esa-sceva/satcom-chunk-collection huggingface dataset:
# Proving Properties of [PERSON]'s Algorithm for Mutual Exclusion of N Processes
## 3 [PERSON]'s Mutual Exclusion for Any Number of Processes
### Model Checking the Dekker-N Model with Atomic Registers  
The model in Figure 5 was exhaustively verified by separately using NCS as a normal location (with arbitrary stay time) and as an urgent location (zero stay time). The use of NCS as normal location enables us to check that the algorithm correctly behaves when processes possibly terminate in NCS or re-enter the system after an arbitrary time. The use of NCS urgent ensures the worst case arrival of ready processes. Making NCS urgent favors the model checker because it eliminates a source of time behavior.  
Figure 7 reports a snapshot of the Uppaal verifier when NCS is normal and N is set to three processes. As one can see from the green circle generated in response to each query, the model was found correct from all the mutual exclusion queries. In addition, the overtaking bound (\(\mathit{ov}\)) emerged to be \(\mathit{ov}=2\).  
The model of Figure 5 was verified for N ranging in [2.4]. The same green answers as in Figure 7 were found for each value of N. Table 1 collects the observed overtaking bound \(\mathit{ov}\) (see last query in Figure 7), the elapsed time (ET) in sec, and the peak of memory usage (M) of the model checker. For \(N=5\), the model with NCS normal suffers from state explosions in the state graph, and the verifier was stopped as soon as the virtual memory exceeded the available RAM (32 GB).  
The Dekker-N model becomes more scalable when NCS is made urgent. The model was verified for \(N\) in [2.5]. For N = 6, the model is still affected by state explosions. All the queries, as shown in Figure 7, achieved the same responses as in Figure 7. Table 2 reports the overtaking bound results in the new scenario.  
Figure 6: The try() function of the model in Figure 5.  
From Tables 1 and 2, it emerges that Dekker-N under atomic registers is fair with an overtaking bound \(ov=N-1\), that almost coincides with the \(N\) indication of informal analysis in [1].
# Comprehensive Analysis of Advanced Techniques and Vital Tools for Detecting Malware Intrusion
## 9 Case Study 2: Cisco System Breach by Yanluowang Ransomware Gang  
Cisco Systems, the largest provider of networking solutions in the world, experienced a security breach. In an announcement released on Wednesday, 10 August 2022, Cisco Systems divulged that a cyber intrusion had taken place at the company on 24 May 2022. The networking equipment manufacturer divulged its discoveries, announcing that the cyber criminals had attained entry to an employee's Google account through the synchronization of the account's passwords with Cisco's web browser.  
Figure 11: Facebook Shares’ Price Drop.  
The company revealed that the Yanluowang ransomware group had used a Google account that had been hacked by one of their employees to gain access to their system, subsequently posting a list of files they had obtained on their breach notification website.  
Due to the security breach in the Google account, the aggressors were able to gain access to the corporation's virtual private network. The individual in question had linked their Cisco login information to the Chrome web browser, where they had stored them previously. Consequently, assailants could utilize this information to harmonize their Google accounts. On the 10 th of August, the Yanluowang ransomware collective released documents that were taken during the security breach, tacitly implying culpability for the infraction. The malicious actor indicated that a total of 2.75 gigabytes of data, which encompassed 3100 individual files, had been pilfered. A profusion of these records encompasses non-disclosure agreements, data disgorements, and technical representations.
## 8 System Design
### Onboard Processing Design, Software and Operations
#### 8.5.2 Onboard Computing System Operations  
The operations cycle for OpenOrbiter starts with the identification of a tasking need by ground controllers. The controller enters this tasking need into the ground station software which evaluates it for compatibility with current tasks and spacecraft capabilities. The tasking request is then sent as a message to the spacecraft and received and processed by the flight computer. Some requests (e.g., the execution of a specific literal command) are processed directly by the flight computer. Tasking requests for payload operations are sent to the payload processing unit where they are decomposed, based upon task requirements and existing pending jobs, into a set of jobs to be executed by the flight computer. These jobs are then sent back to the flight computer, which performs them and, when they are completed, sends the data to the payload processing unit for selected processing actions. The processed data, possibility stamped with a transmission priority, is sent back to the flight computer and queued for transmission to the ground. During a transmission window, this data is sent back to the ground station where it is received, supplemental processing is performed and it is sent to its requesting-users. Figure 4 depicts the system operations of the OpenOrbiter mission.
# Can We Exploit Machine Learning to Predict Congestion over mmWave 5G Channels?
## 3 Scenarios and Dataset Generation
### Metrics Definition and Analysis
In Figure 7 we plot the cross-correlation matrix of all metrics (including the congestion itself) for a distance and data rate of 30 m and 30 Mbps respectively. It is worth noting that the same relationship has been observed for other scenarios. The figure shows a matrix where each cell indicates the correlation between the metrics in the corresponding row and column. As mentioned before, we include the congestion metric itself (with symbol \(\zeta\)), so the figure yields both the correlation with the buffer congestion, as well as among the different metrics. For instance, the correlation between the congestion and average delay (\(\zeta\) and \(\delta\) respectively) corresponds to cell (1,2). Since the matrix is symmetric, the same result can be seen in cell (2,1). To make the results more visual, we use different colors and circle sizes the correlation values of each pair of metrics, being the diagonal the results corresponding to the auto-correlation of each metric (which equal 1). As can be seen, the greatest correlation value is obtained between delay and congestion. Furthermore, the moving standard deviations of both delay and IaT also show high values. Nevertheless, if we observe the relationship of the delay metric (\(\delta\)) and those related with the moving standard deviation, we can also perceive high correlation (row 2 with columns 5 and 7). According to these observations, we can conclude that the use of the moving standard deviation metrics would not add much information to the learning process. Based on this analysis, the learning evaluation will be based on: (1) delay metric \(\delta\), and (2) moving standard deviation of the IaT. Including this latter metric would in addition allow us to consider _jitter_ information.
# A System-Level Methodology for the Design of Reliable Low-Power Wireless Sensor Networks
## 3 Outdoor Measurements and Wireless Link Characterization
### Path-Loss Model  
For outdoor applications, the most common multipath signals are caused by ground reflections [11]. The fitting factor proposed in [38] for LOS communications systems is also used with a variable fitting coefficient \(l_{f}\). The proposed path-loss model is expressed as follows  
\[PL=20\log\left(\frac{4\pi d}{\lambda}\right)-20\log\left[2\sin\left(\frac{2\pi h _{t}h_{r}}{\lambda d}\right)\right]+l_{f}\log(50d), \tag{12}\]  
where \(d\) is the distance between the two nodes, \(\lambda\) is the wavelength, \(h_{t}\) and \(h_{r}\) are the distances that separate the transmitter and the receiver from the ground, respectively, and \(l_{f}\) is a fitting coefficient.  
Figure 13 shows that the proposed model can accurately estimate the power at the receiver when compared with the free space path loss (FSPL) model. The latter is given by [11],  
\[PL=20\log\left(\frac{4\pi d}{\lambda}\right). \tag{13}\]  
In the urban area, a fitting coefficient, \(l_{f}=6\), was used. However, in the suburban area, a \(l_{f}=1.5\) was found to model the path loss more accurately. Moreover, the ground reflection is well-modeled at a distance \(d\approx 10\) m when the transmitter and the receiver are 1.1 m and 1.4 m away from the ground, respectively. As no antenna gains were considered on the transmitter and receiver sides, the experimental path-loss \(PL_{exp}\) was calculated using  
\[PL_{exp}=P_{t}-RSSI_{avg}, \tag{14}\]  
where \(P_{t}\) is the output power level. Figure 13 also shows that the FSPL can provide an acceptable estimation of the received power up to 30 m in the suburban area. For longer distances, the FSPL presents an unacceptable error. However, in the urban area, the FSPL model drastically deviates from the experimental results and the proposed model fits.  
Figure 12: BER, PER, and RSSI field measurements in (**a–c**) the urban and (**d–f**) the suburban areas.
## 1 Introduction
### State of the Art
To determine the transition position with subpixel accuracy, each chord-wise intensity profile in the thermographic image was approximated with the Gaussian error function. However, turbulence wedges were not detected reliably due to the low CNR between laminar and turbulent flow regions. Similarly, [PERSON] et al. [14] used intensity gradients to also detect flow transitions in thermograms of a wind turbine in operation. Since chord-wise intensity gradients are not sufficient for the reliable detection of turbulence wedges, the thermogram was at first rotated to such a degree that the subsequently evaluated intensity gradient was perpendicular to the premature transition lines. As a result, the rotation increased the sensitivity for the flow transition detection with regard to premature flow transitions. However, turbulence wedges with low CNR to the surrounding laminar flow still remain a challenge. The current algorithms for detecting premature transitions only make use of image information such as intensities, which are often evaluated in single pixel lines without context or comparison to neighboring image parts. Therefore, when premature transitions are detected, they account for single points in the transition front. However, neighboring premature transition points are not grouped together. Not recognizing turbulence wedges as a whole complicates the quantification of the wedges' features, such as their sizes. Only [PERSON] et al. [14] and [PERSON] et al. [13] quantify figure of merits related to additional turbulent area due to premature transitions, by calculating the differences between the positions of the natural and premature transition lines instead of adding up the turbulent area of turbulence wedges. Furthermore, pattern recognition has not been utilized for turbulence wedge detection by any of the discussed approaches. Pattern recognition would allow for the detection of each turbulence wedge as a whole, based on certain features such as their shape. An algorithm which specifically uses pattern recognition to reliably detect turbulence wedges even under low CNR conditions has not been reported yet, although such an algorithm seems promising for extracting and quantifying the wedges' features such as the additional turbulent area with low uncertainty.
## 5 Attenuation Method Experiment
### Phase II: Attenuation of Service Channels
#### 5.2.1 Crosstalk or Raman Noise?  
To determine the reason for the decreasing key rate, a quick test was performed as part of the phase. From the configurations in Table 2, the one with a +7 dB attenuation added was selected. This is a configuration where the system is in a stable state and at the same time a change in Visibility, Key Rate and QBER can be observed.  
Subsequently, the 20 km fiber was removed, and Filters I and II were connected directly. By bending the interconnecting cable of negligible length between the filters, the same attenuation was added as the originally connected route (approximately 6 dB).  
If the quantum channel interference is caused by crosstalk, its value no longer increases with channel length, and the system parameters remain the same in both cases. In contrast, Raman noise increases rapidly with channel length. Thus, from the results shown in Table 3, Raman noise is the main cause of quantum channel interference. The possible contributions of other nonlinear phenomena are neglected.
# Integrative Assessment of Sediment Quality in the Sao Francisco River (Mina Gerais, Brazil)
## 2 Materials and Methods
### Benthic Community Analysis  
A total of 25 sediment samples were collected with a 0.0928 m\({}^{2}\) Petersen grab. Five replicate samples were taken at each station for benthic faunal analysis. After collection, samples were sieved in situ with a 0.025 mm mesh, and the retained organisms were transported to the laboratory in buffered formalin (4%). There, these samples were washed and transferred to 70% isopropyl alcohol. Then, they were sorted and identified to the lowest taxonomic level.  
We used the biological monitoring working party (BMWP) score to study the tolerance of the benthic community. It is based on the principle that different aquatic invertebrates have different tolerance to pollutants, especially organic (i.e., nutrient enrichment that can affect the availability of dissolved oxygen). The presence of mayflies or stoneflies indicated the best water quality, with a score of 10, while worms indicated the worse water quality, with a score of 1 [24].  
Moreover, the Shannon-Wiener diversity (H\({}^{\prime}\)), species richness (d\({}^{\prime}\)[25; 26]), Pielou evenness (J\({}^{\prime}\)[27]), total abundance (A), and abundance of taxa (S) classic indices were calculated using the PRIMER-E (Plymouth Routines in Multivariate Ecological Research, v6) [28; 29] suite of computer programs developed at Plymouth Marine Laboratory, UK.
## 3 Results
### Unsaturated Seismic Analysis  
Another case was the analysis of the same slope and same material properties in the case of an unsaturated condition. In this section, the same method (Fellenius) is followed for slope stability analysis, computing the shear strength, shear stress and factor of safety. The measurement of soil suction is performed through the Slide software. It is measured in terms of the height of the water column (h) suspended in the soil. Figure 9 presents the slope model considered in this phase. Figure 10 presents the factor of safety achieved in the case of Material 1 for slope angles ranging from 70 to 88 degrees, respectively.  
The FS values for Material 1 in the case of a saturated seismic condition are 0.981, 0.954, 0.941, 0.920, 0.896, 0.877, 0.866, 0.849, 0.825 and 0.812, respectively. Similar analyses, considering the material properties mentioned in Table 1, were performed for all material types, and its summary is provided in Tables 5 and 6. All the input values were analyzed in Slide and Tables 5 and 6 are achieved, which presents the shear strength (\(\tau\)) and shear stress (\(\sigma\)) along with FS values for Materials 1 to 5 and 6 to 10, respectively, in the case of an unsaturated seismic condition. A total number of 100 analyses were performed in this case.  
In both the saturated and unsaturated conditions of the slope, the variation of the shear stress and shear strength are related. Similarly, with changing slope angle, both these values were also related. All these values underwent SPSS to analyze correlations between all these parameters. It is noted that almost all soil parameters are closely related, and they depend on each other. In future work, other soil parameters could also be considered to identify correlations. Furthermore, it will be an easy way to use these correlations in any design work or analysis. Table 7 presents the correlations developed using SPSS for all the ten types of material used in the case of an unsaturated seismic analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Material 6**} & \multicolumn{3}{c}{**Material 7**} & \multicolumn{3}{c}{**Material 8**} & \multicolumn{3}{c}{**Material 9**} & \multicolumn{3}{c}{**Material 10**} \\ \cline{2-13}
**Slope Angle** & \(\tau\) & \(\sigma\) & FS & \(\tau\) & \(\sigma\) & FS & \(\tau\) & \(\sigma\) & FS & \(\tau\) & \(\sigma\) & FS & \(\tau\tau\) & \(\sigma\) & FS \\  & **(kPa)** & **(kPa)** & & **(kPa)** & & **(kPa)** & & **(kPa)** & & **(kPa)** & & **(kPa)** & & **(kPa)** & & **(kPa)** & \\ \hline
70  
From Table 7, the final correlation is calculated by computing the average value, which is:  
\[\tau\left(\text{kPa}\right)=49.637-0.606\beta\left(\text{deg.}\right)+0.947 \sigma\left(\text{kPa}\right) \tag{10}\]  
the average R\({}^{2}\) comes out to be 99.6%.
# Aero-Engine Remaining Useful Life Estimation Based on CAE-TCN Neural Networks
## 3 Experiments
### Model Configuration and Training  
In the experiment, the model adopts a standard CAE and TCN structure. CAE is mainly composed of convolution block and up sampling block. Each convolution block was stacked by convolution, batch normalization, average pooling and activation function. The upsampling block is composed of transposed convolution, normalization and activation function. The encoder realizes dimension reduction through the pooling layer, and the decoder realizes dimension increase through transposed convolution. The details of CAE are shown in Table 3.  
TCN is mainly composed of temporal block. Each temporal block is stacked by dilated causal convolution, weight norm, activation and dropout. In addition, skip connection is used to learn better. The details of TCN are shown in Table 4.  
During the training process, the batch size was 256, Mean Square Error (MSE) and Adam were used as the loss function and optimizer. The initial learning rate was 0.01, and decayed the learning rate of each parameter group by 0.9 every 30 epochs.
## 3 Physical Layer Design
### Low-Power Transceivers
#### 3.3.1 60 GHz Transmitter  
Figure 5a shows the block diagram of an OOK transmitter consisting of a Voltage Controlled Oscillator (VCO), modulator and Power Amplifier (PA). In this section, we briefly discuss the design approaches for these components of the 60 GHz OOK transmitter designed using 45 nm technology.  
Figure 5: Block diagram of the OOK (**a**) Transmitter; (**b**) receiver.
# S-Band Class-C-F Power Amplifier with 2 nd Harmonic Control at the Input
###### Abstract  
Power amplifiers (PAs) are the most power-consuming devices in a transmitter. Their performance in efficiency is crucial to the efficiency of the whole system. Therefore, the issue of high efficiency PA has remained hot over the years. This paper presents an approach to design an S-band single stage class-F PA biased in class-C condition. Through the manipulation of the 2 nd harmonic at the input, shaping the voltage waveform at the gate node, a class-F PA with proper output matching network (OMN) is realized. The proposed class-C-F PA achieved 60% drain efficiency (DE) and 36.3 dBm of output power around the 3 dB compression point. The methodology of taking the input non-linearity into consideration is also presented. Its feasibility has been verified through both the design and measurements.  
power amplifier; class-C; class-F; gate harmonic control +
Footnote †: journal: Applied Physics Letters  
_Applied Physics Letters_
# Survey on Video-Based Biomechanics and Biometry Tools for Fracture and Injury Assessment in Sports
## 3 Case Studies
### Football Soccer  
For this particular demonstration, a participant was filmed instead of using an online video. In this case, a young (18 years) male participant was recruited. The height and weight of the participant at the moment of the experiment were 1.72 m and 65.5 kg, respectively.  
The video was filmed at 50 fps using a Huawei Mate 20 Lite mobile phone (Huawei, Monterrey, Mexico). The procedure involved a lateral view of a soccer ball kick with the dominant leg of the participant. Before the kick, the ball remained in a fixed position, at approximately 0.7 m from the participant. The participant proceeded to reach the soccer ball and kick it using an average force level. Figure 4 shows the position, acceleration, and estimated force at the left ankle of the participant during the kick, as well as two manually digitized frames of the video. Hence, manual digitization entailed forty-six frames (1 s) for this analysis, with 18 points: (LR toes, LR ankles, LR knees, LR hips, LR shoulder, LR elbows, LR wrists, LR fingers, chin, and forehead) per frame. Force was obtained by multiplying the mass times the acceleration estimated by SkillSpector (\(\vec{F}=m\vec{a}\)).  
Figure 4: Temporal tracking of position, acceleration and estimated force of the left ankle of the volunteer during a football soccer kick. The insets at the top of the figure depict the sequence of movements that the volunteer was performing; before and after shooting.  
As shown in Figure 4, Position tracking in the vertical orientation displays the raising of the foot from ground level until reaching its maximum height at the instance of 0.48 s. Subsequently, the foot lowered towards the ground level with higher velocity, as shown during the lapses 0.49 towards 0.8 s.  
Regarding acceleration, the movement began with a positive value close to 50 m/s\({}^{2}\), which decreased to allow the foot to raise. The acceleration reached a maximum negative acceleration level surpassing the region of \(-\)100 m/s\({}^{2}\) and increasing again until reaching a level over 100 m/s\({}^{2}\) at the moment of maximum height of the foot. After this moment, acceleration decreased again until getting the foot in touch with the ball, at 0.72 s.  
In the case of the applied force, the movement began at approximately 4 kN, reaching a minimum value of 0 N at 0.17 s and increasing steeply towards the raising of the foot. The motion achieved a maximum force of 10 kN at 0.32 s to permit the foot to reach its top height, lowering until reaching a value close to the 3 kN at 0.42 s and rising again to a maximum of 10 kN at 0.5 s. The force decreased again while the foot lowered its height, reaching a minimum of 0 N at 0.68 s and rising when impacting the ball towards 4050 N. Similar studies in the literature have been reported maximal forces during in-step kicking in football soccer of 8.5 kN [101].