esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Research Findings
### RQ1: What Techniques Are Utilized to Abuse the CPLS as C&C Communication Channels?
#### 5.3.2 GCM Flow-Based Detection  
[PERSON] et al. [113] propose a detection approach for identifying Android applications that exploit Google Cloud Messaging (GCM) as a C&C channel. This approach uses GCM flows as features in a machine learning model to differentiate between malicious and benign applications.
# Design of WPT System Based on Interleaved Boost Converter
## 2 Circuit Analysis
### Output Gain Analysis  
When the duty cycle of the interleaved boost circuit is different, that is (a) \(D\leq 0.5\) and (b) \(D>0.5\), the output voltage waveform is shown in Figure 2. \(G(Q_{\rm X})\) is the driving signal of the switch tube \(Q_{\rm X}\).  
According to Figure 2, the dead time angle of the output voltage \(U_{\rm AB}\) can be expressed as:  
\[\theta_{\rm dead}=\begin{cases}\pi-2\pi D&(D<0.5)\\ 0&(D=0.5)\\ 2\pi D-\pi&(D>0.5)\end{cases} \tag{1}\]  
From the waveform in Figure 2 and Formula (1), the relationship between the fundamental RMS value of the output voltage of the interleaved boost converter and the parameters of the circuit can be obtained:  
\[U_{\rm ABI}=\frac{2\sqrt{2}U_{\rm in}}{\pi(1-D)}\cos\Bigl{(}\pi D-\frac{\pi}{2 }\Bigr{)} \tag{2}\]  
According to the controlled source equivalent model of the loosely coupled transformer, the equivalent model of the WPT system can be obtained. When the compensation capacitance and self-inductance of the primary side resonate in series, the expression of the RMS current value \(I_{\rm LS}\) of the secondary side can be deduced:  
\[I_{\rm LS}=-\frac{2\sqrt{2}U_{\rm in}}{\rm j\omega M\pi(1-D)}\cos\Bigl{(}\pi D -\frac{\pi}{2}\Bigr{)} \tag{3}\]  
Figure 1: Schematic diagram of WPT system based on interleaved Boost converter.  
Figure 2: Waveforms of \(U_{\rm AB}\) under different duty cycles. (a) \(D\leq 0.5\); (b) \(D>0.5\).  
From this, the expression of the output voltage can be deduced as:  
\[U_{\text{O}}=\frac{8 RU_{\text{in}}}{\omega k\sqrt{L_{\text{P}}L_{\text{S}}}\pi^{2 }(1-D)}\cos\left(\pi D-\frac{\pi}{2}\right) \tag{4}\]
# Influence of Soil Heterogeneity on the Contact Problems in Geotechnical Engineering
## 2 Implementation of the Penalty Method in the FEM  
In this paper, the commonly used method, i.e., the penalty method, is utilised to solve the contact problems. In the penalty method, a small amount of penetration is allowed, and the contact force is proportional to the amount of penetration. In order to combine the penalty method with the FEM, the slave-master concept is introduced in the FEM. In the slave-master concept, one of the two contact bodies is designated as a slave body and the other one is a master body, as shown in Figure 1. In FEM, one node from the slave body and two nodes from the master body are defined as one contact pair. In one contact pair, the first objective is to check whether the slave node \(\mathsf{X}_{\mathsf{c}}\) is in contact with the master segment \(\mathsf{X}_{1}\mathsf{X}_{2}\), i.e., the gap \(g_{n}\) is positive (separated) or negative (in contact). If it is in contact, the contact forces at the slave node and the master nodes will be calculated by the penalty method. In addition, the contact stiffness at the three nodes will also be calculated. After the contact search is finished, the stiffness and forces calculated by the penalty method will be added into the matrix equation as [26]:  
\[(\mathbf{K}+\mathbf{K}_{\mathbf{c}})\Delta\mathbf{t}_{\mathbf{g}}=\mathbf{F}_{\mathbf{res}}+F_{\mathbf{c}} \tag{1}\]  
where, \(\mathbf{K}_{\mathbf{c}}\) is the contact stiffness matrix in the global coordinate, \(\mathbf{F}_{\mathbf{c}}\) is the contact force in the global coordinate, while \(\mathbf{K}\) is the global stiffness matrix in the FEM and \(\mathbf{F}_{\mathbf{res}}\) is the residual force in the FEM generated from boundary conditions. In each contact pair (one slave node with one master segment), the contact stiffness matrix \(\mathbf{k}_{\mathbf{c}}\) can be calculated as:  
\[\mathbf{k}_{\mathbf{c}}=\mathbf{k}_{\mathbf{N}}+\mathbf{k}_{\mathbf{T}} \tag{2}\]  
in which, \(\mathbf{k}_{\mathbf{N}}\) and \(\mathbf{k}_{\mathbf{T}}\) are the stiffness matrices due to normal contact and consideration of friction, respectively. \[\mathbf{k}_{\mathbf{N}}=\omega_{n}\mathbf{C}_{\mathbf{n}}\mathbf{C}_{\mathbf{n}}^{\mathbf{T}} \tag{3}\]  
For the calculation of \(\mathbf{k}_{\mathbf{T}}\), there are two different situations: the stick situation and the slip situation. For the stick condition,  
\[\mathbf{k}_{\mathbf{T}}=\omega_{\mathbf{t}}\mathbf{C}_{\mathbf{t}}\mathbf{C}_{\mathbf{t}}^{\mathbf{T}}+\frac{ \omega_{\mathbf{t}}g_{\mathbf{t}}}{l}\Big{(}\mathbf{C}_{\mathbf{n}}\mathbf{P}^{\mathbf{T}}+\mathbf{P}\mathbf{C }_{\mathbf{n}}^{\mathbf{T}}-\mathbf{C}_{\mathbf{t}}\mathbf{Q}^{\mathbf{T}}-\mathbf{Q}\mathbf{C}_{\mathbf{t}}^{\mathbf{ T}}\Big{)} \tag{4}\]  
\[g_{\mathbf{t}}=l^{0}\Big{(}\xi-\xi^{0}\Big{)} \tag{5}\]  
in which, \(\xi\) is the local coordinate of the projection point of the slave node on the master surface (see Figure 1). The superscript \({}^{0}\)' denotes the previous time step. \(l^{0}\) is the length of the master segment. Figure 1: Contact of two bodies.
# Key Recovery for Content Protection Using Ternary PUFs Designed with Pre-Formed ReRAM
## 3 Session Key Recovery with Ternary PUFs
### Session Key Recovery  
In the key recovery cycle, see Figure 4, the challenges (**T**, _IDAccess_) are retrieved by the client device, and used to generate the responses **K**\" from the PUFs that are not necessarily identical to **K**' due to the drifts of the parameters driving the PUF. The search engine, such as RBC, uses the message digest H(**K**') to retrieve **K**' from **K**''.  
Mainstream ECC schemes can replace the search engine in the architecture presented in Figure 4, whereby the message digest is replaced by a data helper. The session key **Sk** is recovered by decrypting the ciphertext with **K**'. The opponent cannot recover the session key without having access to the PUF, which can strengthen the system's tamper resistance. The session key can be used in various symmetrical cryptographic schemes such as AES and DES, or in public key infrastructures such as Elliptic curves, RSA, and Post Quantum Cryptographic codes such as Dilithium, Kyber, NTRU, Falcon, Saber, Rainbow, and Classic McEliece [35; 36; 37; 38; 39; 40; 41; 42].  
Figure 3: Block diagram of the encapsulation of the session key **Sk**. _IDAccess_ is computed from the image of the PUF. The challenges (**T**, _IDAccess_) generate **K**’ from the PUF. The client devices store the challenges, the ciphertext of **Sk** encrypted with **K**’, and the message digest **H**(**K**’).
## 2 Characteristics of OMWW
### Microbiological Traits of OMWW  
The microbial community present in OMWW is strongly influenced by several parameters, among which the ripeness state and the olive variety are the most influential [42; 43]. The microbial density in OMWW varies between \(10^{5}\) and \(10^{6}\) CFU/mL (CFU: colony-forming unit) and is mainly composed of yeasts, bacteria, and molds [42; 43; 44; 45]. The yeast population includes species belonging to _Pichia_, _Candida_, and _Saccharomyces_ genera [44; 45]. A survey carried out on OMWW revealed the presence of over 100 identified fungi, mainly belonging to the genera _Acemonium_, _Alternaria_, _Aspergillus_, _Bionectria_, _Byssochlamys_, _Chalara_, _Cerrena_, _Fusarium_, _Lasiodiplodia_, _Lcythophora_, _Paecilonyces_, _Penicillium_, _Phycomyces_, _Phoma_, _Rhinocladiella_, and _Scopulariopsis_[46].  
Although many studies report that the culturable microbial population is represented by only a few bacterial communities, such as: _Firmicutes_, _Actinobacteria_, _Alphaproteobacteria_, _Betaproteobacteria_, and _Gammaproteobacteria_, recently, microarray analyses have revealed a high-density of a larger microbial population, including _Proteobacteria_, _Bacteroidetes_, _Chloroflexi_, _Cyanobacteria_, and _Actinobacteria_. However, the most commonly reported microbial communities, representing 50% of the 16S rRNA gene sequences deposited in GenBank, include _Gammaproteobacteria_ (_Enterobacteriaceae_, _Moraxellaceae_, _Xanthomonadaceae_, and _Pseudomonasaceae_) with a percentage of almost 30%, and _Betaproteobacteria_ (_Oxalobacteraceae_ and _Commonandaceae_) with a percentage of 21.5% [46]. _Alphaproteobacteria_ and _Actinobacteria_ (_Micrococcaceae_, _Microbacteriaceae_, and _Propionibacterium_) together comprised 20%, whereas _Firmicutes_ (_Bacillusaceae_, _Clostridiaceae_, _Lactobacillusaceae_, and _Paenibocallaceae_) and _Bacterioides_ (_Prevotellaceae_, _Porphyromondaceae_, and _Splingobatteriaceae_) phyla accounted for approximately 6.8%, respectively. Furthermore, differences in microbial population have been detected, highlighting that only 15% of operational taxonomic units (OTUs) are commonly detected [47]. In addition, high densities of enteric bacteria belonging to _Porphyromondaceae_, _Prevotellaceae_, _Lachospiraceae_, _Eubacteriaceae_, _Peptococcaceae_, and _Ruminococcaceae_ spp. or to genera _Actinetobacter_, _Enterobacter_ spp., _Pseudomonas_, _Citroobacter_, _Escherichia_, _Klebsiella_, and _Serratia_ spp. have been reported [48].
## 3 System Architecture
### Logical Layer  
The data generated by the sensors should be processed and stored so that they are persistent and accessible from different networks and for different applications. Given the benefits introduced in Section 2.2, the IoT platforms rely on computing environments in the cloud and architectures oriented to a service for storing and processing data, enabling the user to build applications that incorporate these services, forgetting effects on maintenance and how or where they are deployed.  
Among other equally valid alternatives [24], such as Carriots (Madrid, Spain, [[https://www.carriots.com/](https://www.carriots.com/)]([https://www.carriots.com/](https://www.carriots.com/))) or Temboo (New York, NY, USA, [[https://temboo.com/](https://temboo.com/)]([https://temboo.com/](https://temboo.com/))), we have opted for ThingSpeak (Marlborough, MA, USA, [[https://thingspeak.com/](https://thingspeak.com/)]([https://thingspeak.com/](https://thingspeak.com/))) as the IoT platform for the logic layer. ThingSpeak [25] is an open source platform for IoT that offers a full and intuitive REST API to store and retrieve data using the HTTP protocol on the Internet or a local area network. In addition to the storage and retrieval of data, the ThingSpeak API allows the processing of numerical data such as timeline, averages, medians, or summations and creating expression data analysis easily in MATLAB. ThingSpeak organizes information in eight input channels that support numeric and alphanumericdata each. Each data is stored along with the date, time, and a unique identifier, which makes it a uniquely accessible object via a URI. Each channel supports CSV, XML, and JSON data interchange formats for its use in third party applications formats. In addition, the API provides functionality for generating statistical graphics from the cloud and its integration into an external application. To read or write data to a ThingSpeak channel, an application should take an HTTP GET or POST request via the provided API and the URI that identifies the object to update. Thus, each field is a REST object accessible via REST HTTP requests and POST and GET methods. In addition, we have experience with this platform in other projects [26].  
In the proposed architecture, the data supplied by the sensors is organized in fields, which, in turn, are organized into channels. Figure 3 shows the interaction between different components of the SmartResidents architecture.
# A Reliability Scheduling Algorithm for the Static Segment of FlexRay on Vehicle Networks +
## 2 Proposed Methodology
### Scheduling Output Module  
After the message reliability processing module, the next step will estimate the scheduling of all messages. The scheduling output module is used to make sure all messages can be transmitted before their deadline. [PERSON] et al. [8] proposed bounding retransmissions to bound the minimum number of retransmissions required that must be done in order to achieve the system reliability goal. In the proposed method, this module is denoted with the following upper and lower bound constraint parameters:  
* Maximum retransmission times (\(RT_{i(\max)}\)): slot utilization is also an important factor for successful transmission. When slot utilization is bigger than 1, it will cause message transmission failure.  
Figure 3: Flowchart of the message reliability processing module.  
This means that the number of slots used for message retransmission is over the total number of slots in the static segment (\(N_{slot}\)). Therefore, we can now denote \(RT_{i(\text{max})}\) as Equation (8): \[\sum_{i=1}^{n}(RT_{i(\text{max})}+1)\ \leq\ N_{slot}\] (8)
* Minimum retransmission times (\(RT_{i(\text{min})}\)): the proposed method improved the reliability of the system using a message re-transmission mechanism. Essentially, there are too few re-transmission times to achieve the system reliability goal (\(SR\)). \(RT_{i(\text{min})}\) relates with \(SR_{mi}\) and \(PF_{i}\), as in Equation (9): \[RT_{i(\text{min})}\ \geq\ \frac{\log(1-SR_{mi}^{\frac{T_{i}}{T_{i}}})}{\log PF_{i }}\ -\ 1\] (9)
# Forecasting Ionospheric foF2 Based on Deep Learning Method
## 4 Conclusions
LSTM Long-Short-Term Memory  
foF2 the critical frequency of the ionosphere F2 layer  
LT Local Time  
SSN the Sunspot Number  
BPNN a Back-Propagation Neural Network  
GABP a Genetic Algorithm optimized Backpropagation neural network  
IRI the International Reference Ionosphere model  
HF High Frequency  
RNN a Recurrent Neural Network  
GRU Gated Recurrent Unit  
GA Genetic Algorithm  
TEC Total Electron Content  
URSI Union of Radio Science International
## 5 Measurements and Results  
Before placing the PM in the cryostat, the mirrors were mounted on it in a dust-proof box. The mirrors were fastened at the ends of the PM in special clips (holders) (Figure 1). The compression of the mirror to the bottom of the holder was carried out by adjusting screws at four points along the periphery of the mirror. At the same time, the remaining gap between the clip and the lateral (cylindrical) surface of the mirror allowed it to react freely to radial thermal deformations. Only in one small area of the side surface did the mirror have a contact with the temperature sensor. When installing the mirrors, the initial tuning of the FP cavity using the auxiliary laser was carried out to find the FP resonances and the main TEM\({}_{00}\) mode. In this process, an optimal mutual axial orientation of the mirrors was selected by rotating them relative to the axis of the FP cavity to compensate for the initial deformation asymmetry (removing of the deformation of mode splitting) After this operation, the PM was moved to an open cryostat and hung on a special construction (the so-called \(\Omega\)-loop) with a loop coverage in the center of the PM. This design provided automatic maintenance of the  
Figure 2: Experimental setup configuration.  
thermal contact of the PM with the cryostat at all stages of cooling (for further details, see [27]) A closed cryostat was placed on the atmosphere pumping out and it then underwent a cooling procedure. At the beginning of the optical measurements, fine tuning of the optical resonances of the FP-cavity was carried out by changing the angle of inclination of the incoming beam using an alignment platform in front of the cryostat. In the course of the experiment, the power of the radiation sent to the FP resonator could be changed stepwise from 450 mW to 5 mW using a set of absorbing filters. The measurements that we performed gave the following results.
## 3 The Design Process for an Aging Sensor with Multiple Points of Frequency Detection
### Aging Detection Process  
The process for detecting the delay is illustrated in Figure 7. The clock signal frequency is initially set for the system to be operated on a PGT or NGT, depending on the system requirement. The _Vi_ is subsequently injected with a random delay signal to test the aging detection circuit. In the Verilog coding, the injected delay signal is present in the time unit using the '#' symbol. For example, '#3' equals three time units, equal to a 10.8\({}^{\circ}\) phase difference from the original signal. This injected delay signal signified the degradation caused by the variation of the threshold voltage, carrier mobility, NBTI, and HCI, as discussed in the previous section. Therefore, when the aging sensor detects the adjacent injected delay signals, these injected delay signals will fall in the same phase delay range and produce an identical output category. As shown in Figure 7, the _Vi_ signal was preset with a delay of 220\({}^{\circ}\) for \(\theta_{1}\) and a _Clk_Delay_ of 200\({}^{\circ}\) for \(\theta_{2}\).  
Consequently, both \(\theta\) values will be compared with the _Vi_ signal independent of whether it exceeds the _Clk_Delay_ frequency. To obtain an aging value, \(\theta_{1}\) should be greater than \(\theta_{2}\). For example, the 220\({}^{\circ}\) delay (_Vi_ signal mentioned above) exceeded the 200\({}^{\circ}\) delay, which subsequently caused the delay detection to go HIGH. The output triggered a _hex_ output signal as a reference value. The _hex_ output signal is a data representation that is based on seven detection points, as previously described in Figure 5. The results obtained were in binary format and converted to a hexadecimal format called _hex_ for a more straightforward implementation for the following process. However, if the value of \(\theta_{1}\) is less than or equal to \(\theta_{2}\), the aging sensor will produce a value of 0, indicating the aging condition is null.  
Table 1 illustrates a sample of the detection process for the aging of the circuit using a sample _Vi_ with delays of 220\({}^{\circ}\) and 270\({}^{\circ}\). For \(\theta_{1}\) = 220\({}^{\circ}\), the _Clk_Delay_ values for 50\({}^{\circ}\), 100\({}^{\circ}\), and 150\({}^{\circ}\) were HIGH, while other frequencies were LOW, hence producing an output of 3C\({}_{16}\). Conversely for \(\theta_{1}\) = 270\({}^{\circ}\), the value of _hex_ was 3E\({}_{16}\) due to the _Clk_Delay_ being HIGH for \(\theta_{2}\) equal to 50\({}^{\circ}\), 100\({}^{\circ}\), 150\({}^{\circ}\), 200\({}^{\circ}\), and 250\({}^{\circ}\).  
Figure 7: Timing diagram to detect the phase delay.
# Dissolved Gas Analysis for Fault Prediction in Power Transformers Using Machine Learning Techniques
## 3 Methodology and Machine Learning Methods (MLMs)
### MLMs
#### 3.1.2 Backpropagation Neural Network (BPNN)  
A neural network (NN) consists of interconnected 'units' organized in a sequence of layers, with each layer being connected to adjacent layers. Neural networks draw inspiration from biological systems, such as the brain, and their information-processing capabilities. NNs, or neural networks, consist of several interconnected processing components that collaborate to address particular issues [26]. The brief main steps of a BPNN are as below:  
Set learning rate (\(\alpha\))  
Set number of epochs (E)  
Define network architecture (number of layers, number of neurons per layer)  
For each layer in the network:  
Initialize weights randomly  
Initialize biases to zero  
For each epoch in range(E):  
Figure 1: Flow chart of the methodology of the DGA technique.  
For each training sample (x, y):  
For each hidden layer:  
Compute weighted sum: z = W1 * x + b  
Apply activation function: H = activation(z)  
For each node in Output layer:  
Compute weighted sum: s = W2 * H + b  
Apply activation function: Out = activation(s)  
Calculate loss using Cross-Entropy loss function  
Compute gradients of loss with respect to output  
For each layer from output to input:  
Compute gradients for weights and biases  
Update weights and biases using gradient descent:  
W = W - \(\alpha\) * gradient_W  
b = b - \(\alpha\) * gradient_b
# An Intelligent Real-Time Driver Activity Recognition System Using Spatio-Temporal Features
## 4 Results and Evaluation
### Evaluation of the LRCN Model Based on Seven-Fold Cross Validation
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Metrics** & **Fold-1** & **Fold-2** & **Fold-3** & **Fold-4** & **Fold-5** & **Fold-6** & **Fold-7** & **Average** \\ \hline Accuracy (\%) & 94.9 & 82.9 & 96.7 & 95.3 & 96.4 & 86.9 & 93.8 & 92.4 \\ Precision (\%) & 92.9 & 78.2 & 95.9 & 93.7 & 97.2 & 86.4 & 95.3 & 91.4 \\ Recall (\%) & 92.6 & 75.2 & 96.3 & 92.7 & 95.9 & 85.0 & 93.4 & 90.2 \\ F\({}_{1}\)-Score (\%) & 92.3 & 74.9 & 96.4 & 92.2 & 96.3 & 84.6 & 94.0 & 90.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The performance metric measures (Accuracy, Precision, Recall, and F1 score) based on seven-fold cross validation for IR images. A similar performance was observed in the drinking class. This could be due to driver activities involving objects such as phones and plastic bottles being easier to detect by the classifier model. In contrast, the talking class showed the worst performance. We found that the talking class is mainly misclassified as smoking. Indeed, based on our class definition, talking and smoking have similar right arm movements and orientation, which leads the classifier model to a wrong prediction. As shown in Table 4 for the IR image results, except for the heading nodding and smoking class, the remaining seven classes achieved accuracies over 85%, as measured by the F1 score. The worst performance appears in the smoking class. To further understand the performance of the classifier model, Figure 8 illustrates the final average confusion matrix of the classifier model for both RGB and IR images. First, a confusion matrix for each fold was obtained. Then, the mean average for all folds was computed. The diagonal of the confusion matrix from left to right shows the maximum result class in their perspective row and column. In general, we observed comparatively low performance in faint and head nodding classes. This result is expected, not only due to the fact that the amount of data collected for faint and head nodding classes was relatively small compared to other classes, but also because the similarity between both actions caused the classifier model to be confused. As such, we performed additional experiments by merging both actions as a single class to explore the combined result. In this case, we observed a significant accuracy rise for both types of images.