esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Equipment
### Experimental Materials
#### 2.1.3 Blockage  
In order to study the chemical composition, particle size composition and other properties of the silt, this experiment collected road surface silt from Renmin Street in Changchun City. The collected sludge sample is shown in Figure 1. In order to study the composition of sludge, after drying the collected samples, we took a small part of the samples for an XRF(X-Ray Fluorescence) test. At the beginning of the test, X-rays were emitted to the sample under test by using the excitation source of the XRF instrument. The different elements contained in the excited sample emit secondary X-rays with specific energy and specific wavelength. The intensity of the radiation varies with the concentration of the elements contained in the sample. The software converts the characteristic quantity information detected by the detection system into the type and content of each element in the sample. XRF testing has a wide range of applications, and can detect elements 9 (F) to 92 (U). The type of chemical element contained in the silt and the mass ratio of each element can be obtained through the XRF test.  
Using XRF test to detect road blockages, the chemical elements in the blockages and the quality of each element are shown in Table 3.  
Through the analysis of the sources of various elements in the silt, it is concluded that heavy metal ions such as Pb and Zn come from automobile exhaust pollutants, fuel or lubricating oil leaked during driving, and deicing agents. Petroleum pitch contains C, H, S, N and some metal elements. Tires are generally made of rubber, and their wear contains elements such as C, H, S, Zn, and SiO\({}_{2}\) comes from stones and tire reinforcement.  
It can be seen from the content of each element in the table that the road surface blockage mainly comes from the abrasion of the road material. There is also a small part of it coming from tire wear and other road deposits generated during driving, heavy metal ions in rainwater, pollutants leaked from driving, snow removal agents, etc.  
The experiment of simulating the process of pavement siltation needs to prepare a certain grade of stone to simulate the blocking agent. Therefore, the collected road surface  
Figure 1: Blockage sample.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Element Name** & **Content (\%)** & **Element Name** & **Content (\%)** \\ \hline O & 60.960 & Ca & 7.450 \\ Na & 1.475 & Ti & 0.487 \\ Mg & 1.158 & Mn & 0.068 \\ Al & 4.819 & Fe & 3.400 \\ Si & 17.675 & Zn & 0.041 \\ P & 0.099 & Sr & 0.017 \\ S & 0.279 & Zr & 0.018 \\ Cl & 0.159 & Ba & 0.113 \\ K & 1.770 & Pb & 0.012 \\ \hline \end{tabular}
\end{table}
Table 3: Proportion of chemical elements in silt.  
silt is screened with a geosieve to obtain the mass proportion of each particle size of the silt in the actual road surface, as shown in Table 4.
## 4 Discussion
The implication is that manufacturers can only determine the opening and closing times based on the process itself, ignoring the response times. The Matrix HS 211.20 injector had the shortest opening time (calculated from the start of the control pulse), which is 1.54 ms. The Barracuda 115 injector's time was 29.3% longer (2.18 ms), as was the AC W01-4 injector's one, with an elongation of 36.6% (2.45 ms). The pressure waveforms shown in Figure 12 show lower values for the Matrix HS 211.20 injector, which translated into lower average pressure per cycle (Table 6). In this case, the AC W01-4 injector's pressure was 1.8% higher, while the Barracuda 115 injector's one was 9.94% higher. Figure 11: Comparative data: (**a**) maximum flow; (**b**) power from one cylinder. Based on Refs. [58; 70]. All this helps indirectly explain the differences in volumetric flow rate of the tested injectors when using nozzles with the same bore diameter. The supply pressure was maintained at \(1\times 10^{5}\) Pa. An increase in supply pressure increases the maximum values at the outlet while increasing the opening time and decreasing the closing time, as shown in study [71]. The maximum stroke of the actuator (plunger, plate) and the resulting relationship of the flow field within the injector valve and the flow field in the outlet nozzle bore may be responsible for the nature of the \"inflection\" of the flow characteristic (Figure 10a). Unfortunately, the stroke of the actuators is not given in the manufacturers' technical materials, so it requires further research. The overall study showed a significant effect of the diameter of the outlet nozzle bore \(d_{on}\) of the injector on its flow characteristics. In addition, it was proven that the volumetric flow rate \(Q\) is also affected by the process of opening and closing the injector, so that different injectors with the same nozzle diameter have different \(Q\). The topic addressed in the study requires further research, especially in the aspect of the effect of actuator pitch on flow characteristic.
## 6 Discussion
The configuration process for these newer communication protocols is much simpler because they are based on open communication standards. Usually, the user is required to enter the IP address of the device and the appropriate port with which they want to communicate. In addition, serial communication has a low baud rate and is used over shorter distances. Last but not least, we only performed experiments inside one of our laboratories. More experiments have to be realized to compare use, further demonstration, and the functionality of the proposed project in a diversity of conditions.
# Convolution Accelerator Designs Using Fast Algorithms
## 3 Architecture Design
### Conventional Design  
There are eight convolutions between the input feature maps and the kernels for the convolution of a \(2\times 2\) matrix. Figure 2 shows the architecture of our design with eight convolution modules (ConvNxN) instantiated in parallel. We divide the conventional algorithm into two stages. In Stage 1, input feature maps and kernels are sent to the module ConvNxN, which is designed for the convolution between the feature map and the kernel. In Stage 2, the output results from the convolutions are summarized to obtain the output feature maps.  
Figure 2: Schematic representation of the architecture for the conventional design.  
Figure 3 shows the architecture of the module ConvNxN. Usually, a convolution requires high memory bandwidth. To reduce this bandwidth demand, we designed a data buffer to maximize data reuse and stored all kernels in memory on chip. Four pixels of the feature map are sent into the buffer module, which is an FIFO consisting of a shift registers group, every clock cycle. The width of the FIFO is 4 pixels, and the depth of the FIFO is 112 + 2 pixels. The data buffering process can be divided into three stages as follows:  
Stage a: as Figure 4a shows, four pixels are written into the FIFO each clock cycle (the FIFO is not full during this stage). No valid pixels are sent to the Conv module during this clock cycle.  
Figure 4: _Cont._  
Figure 3: Schematic representation of the ConvNxN architecture.  
Stage b: as Figure 4b shows, four pixels are written into the FIFO, and four pixels are read out simultaneously (the FIFO is full in this stage). The eight earliest written pixels and the eight latest written pixels constitute a group of \(4\times 4\) pixels, shown as the dotted box in Figure 4b. This group of pixels is sent to the Conv module for the convolution operation.  
Stage c: as Figure 4c shows, the eight earliest written pixels and the eight latest written pixels cannot constitute a group of \(4\times 4\) pixels. No valid pixels are sent to the Conv module during this clock cycle.  
The Conv module is designed to compute the convolution with Figure 5 showing its data flow, which completes the multiplications and additions in the convolution. The Conv module is designed in the pipeline mode. Four convolutions between the \(4\times 4\) input feature map and the \(3\times 3\) kernel are executed in parallel in the Conv module. Thus, the process requires \(4\times 3\times 3=36\) multipliers. There are eight ConvNxN modules in Figure 2, which need a total of \(8\times 36=288\) multipliers.
# A Review of Ku-Band GaN HEMT Power Amplifiers Development
## 2 Ku-Band GaN HEMT HPA Technology
### High Power Amplifier Design
Several studies have employed eutectic die attachments with good thermal conductivity, mounting them on Cu-Mo-Cu flanges, which serve as excellent heat sinks, rather than the typical copper jig [44]. Figure 9 illustrates the heat sink structure of a GaN HEMT die, frequently used in IM-PAMs. Reference [49] reports improvements in output power and PAE by using chemical vapor deposition (CVD) diamond materials, which possess the best thermal conductivity, as the thermal interface on top of the copper heat sink, as shown in Figure 10.  
Figure 7: Example photos of (**a**) a fabricated Ku-band GaN HEMT IM-PAM and (**b**) a GaN HEMT power amplifier module with wire bonding. Figures reproduced or reworked with permission from refs. [38; 43]. (**a**) is Copyright 2018 MDPI and (**b**) is Copyright 2023 IEEE. In this subsection, we investigated two methods for implementing Ku-band GaN HEMT HPAs, MMICs and internally matched MIC design methods, by comparing the reported papers. As mentioned above, MMIC HPAs can be configured in a multistage arrangement with a compact size to achieve high gain, offering a low input power burden and wide bandwidth. In contrast, MIC HPAs are advantageous for obtaining high output power within a narrow bandwidth; however, due to size constraints, they are typically limited to a single-stage configuration and require high input power. Nonetheless, advancements in MMIC processes and design technologies have been significant in recent years, suggesting that we can expect numerous studies reporting output powers exceeding 100 W in the near future, such as in [31]. Figure 8: Temperature distribution of a 20 W class GaN HEMT HPA bare die (**a**) with only DC power applied and (**b**) with DC power and RF power applied using a high-resolution IR scope. Figure 9: Heat sink structure of a GaN HEMT die [44]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Composition** & **Thermal Conductivity (W/mK)** \\ \hline Mo & 140 \\ W & 170 \\ Al & 230 \\ Cu & 400 \\ CMC 1  & 270–320 \\ CVD Diamond & 1000–1800 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of thermal conductivity of heat sink or thermal interface materials. (Data from [49]).
# Evaluation of Three-Dimensional Environmental Hydraulic Modeling in Scour Hole
## 2 Methodology
### Numerical Study
#### 2.2.2 Turbulent Model  
Based on the previous research [8; 9], the \(k-\omega\)\(SST\) turbulence scheme, which was proved to be the best to investigate the turbulence properties of the flow in the scour hole, was employed in the present study. Both OpenFOAM and Ansys Fluent models use the \(k-\omega\)\(SST\) formula that was developed from [PERSON] [19]. The idea of this approach is blending or switching the \(k-\epsilon\) model with the \(k-\omega\) model. This task can be done by the addition of a blending function, which can switch from one in the near-wall region to activate the \(k-\omega\) model to zero while in the far field to change to the \(k-\epsilon\) model. The standard \(k-\omega\) model is a low Reynolds number model and can be used where the boundary layer is thick and the viscous sublayer can be resolved. On the other hand, the standard \(k-\omega\) is best used for near-wall treatment. Meanwhile, the \(k-\epsilon\) model is ideal for predicting the flow away from the wall. Therefore, the combination is less sensitive to the free stream conditions compared to the standard \(k-\omega\) model and also avoids build-up of excessive turbulent kinetic energy near stagnation points as compared to the standard \(k-\epsilon\) model.  
In general, the transport equations for turbulent kinetic energy, \(k\), and specific dissipation rate \(\omega\) can be written in the simple forms as:  
\[\frac{\partial}{\partial t}(\rho k)+\frac{\partial}{\partial x_{i}}(\rho u_{ i}k)=\frac{\partial}{\partial x_{j}}\Bigg{[}\bigg{(}\frac{\mu_{t}}{\sigma_{k}}+ \mu\bigg{)}\frac{\partial k}{\partial x_{j}}\Bigg{]}+G_{k}-Y_{k}+S_{k} \tag{5}\]  
\[\frac{\partial}{\partial t}(\rho\omega)+\frac{\partial}{\partial x_{i}}(\rho u _{i}\omega)=\frac{\partial}{\partial x_{j}}\Bigg{[}\bigg{(}\frac{\mu_{t}}{ \sigma_{\omega}}+\mu\bigg{)}\frac{\partial\omega}{\partial x_{j}}\Bigg{]}+G_{ \omega}-Y_{\omega}+S_{\omega}+D_{\omega} \tag{6}\]  
where \(G_{k}\) and \(G_{\omega}\) are the generations due to the mean velocity gradient, and \(Y_{k}\) and \(Y_{\omega}\) are the dissipations due to turbulence of \(k\) and \(\omega\), respectively; \(S_{k}\) and \(S_{\omega}\) denote the moduli of the mean rate-of-strain tensors, while \(\sigma_{k}\) and \(\sigma_{\omega}\) present the diffusion rates of turbulent kinetic energy and specific dissipation rate, respectively; \(D_{\omega}\) is the cross-diffusion term; \(\mu_{t}\) is the dynamic turbulent viscosity.
## 4 Algorithms
### Overdub Detection  
This second detection step deals with the identification of an overdub in the analyzed tapes. It is positioned after the registration module, as it requires the spectrogram pair to be already aligned.  
1. Compute the residual spectrogram as the pixel-wise absolute difference of \(P_{i}^{\prime}\) and \(P_{j}\) (Figure 4a).  
\[P_{r}(u,v)=|P_{i}^{\prime}(u,v)-P_{j}(u,v)| \tag{5}\]Define the function \(e(v)\) representing the energy content of the residual spectrogram over time. \[e(v)=\sum_{u}P_{r}(u,v),\qquad v=1,\ldots,V\] (6)
3. Look for strong variations in the residual energy by computing the first derivative \(e^{\prime}(v)\) and applying an outlier detector (three scaled MAD from the median, where MAD denotes the median absolute deviation), obtaining a set of points \(\mathcal{O}=\{v_{k}\}\) (Figure 4b).
4. Process the points \(v_{k}\in\mathcal{O}\) in order to obtain the interval \([v_{1},v_{2}]\) corresponding to the candidate overdub. The employed criterion is that of selecting the couple of points which maximizes the average energy ratio between the regions inside and outside those points. \[(v_{1},v_{2})=\arg\max_{(v_{k},v_{k})\in\mathcal{O}^{2}}\frac{\mathbb{E}\left[ e(v)\right]_{v_{k}<v<v_{k}}}{\mathbb{E}\left[e(v)\right]_{v<v_{k}\lor v>v_{k}}}\] (7) where \(\mathbb{E}\left[e(v)\right]_{\mathcal{I}}\) denotes the expectation of \(e(v)\) for \(v\in\mathcal{I}\).  
Given a detected overdub spanning from \(v_{1}\) to \(v_{2}\), the algorithm tries to infer the phylogenetic relation. Again, we compare energy statistics inside and outside the overdub region, but in this case, we consider \(P_{i}^{\prime}\) and \(P_{j}\), instead of \(P_{r}\).
5. Scan through the spectrogram rows \(u=1,\ldots,U\). For each \(u\), compute: \[\begin{array}{l}c_{i}(u)=\left|\mathbb{E}\left[P_{i}\left(u,v\right)\right]_ {v_{1}<v<v_{2}}-\mathbb{E}\left[P_{i}\left(u,v\right)\right]_{v<v_{1}\lor v>v_ {2}}\right|\\ c_{j}(u)=\left|\mathbb{E}\left[P_{j}(u,v)\right]_{v_{1}<v<v_{2}}-\mathbb{E} \left[P_{j}(u,v)\right]_{v<v_{1}\lor v>v_{2}}\right|\end{array}\] (8) which represent the discrepancies between average spectral energy inside and outside the overdubbed region, in \(P_{i}\) and \(P_{j}\), for each frequency sub-band (row), \(u\). The spectrogram presenting a higher \(c(u)\) for the majority of rows \(u\) is assumed to be the overdubbed one, i.e., the phylogenetic descendant.  
In a similar way to what is done in the leader detection step, if \(j\) is chosen as the ancestor of \(i\), the algorithm sets \(d_{i,j}=+\infty\). Otherwise, the dissimilarity value computed in (4) is kept.  
Figure 4: Residual spectrogram and related energy-over-time associated with a track pair \((i,j)\) containing an overdub, which appears in (**a**) as a bright region with clean edges. The red circles in (**b**) represent the detected outliers \(v_{k}\in\mathcal{O}\), and the two points marked with green asterisks are the selected edges \((v_{1},v_{2})\).
## 3 Proposed Solution Method
### Master Stage: GNDO
#### 3.2.1 Local Exploration  
Local exploration is a common search technique in metaheuristic-based optimization methods, the main idea of which is to explore in the vicinity of a current solution (i.e., minimum variations) in order to find a better one. In other words, it is an exploitation stage. The rule defined in the GNDO for this exploration is defined below:  
\[v_{i}^{t}=\mu_{i}+\delta_{i}\eta,\ i=1,2,\cdots,N_{i}, \tag{12}\]  
where \(v_{i}^{t}\) corresponds to the trailing vector associated with the current solution \(i\) at iteration \(t\); \(\mu_{i}\) denotes the generalized mean location of the individual solution \(i\) during iteration \(t\); \(\delta_{i}\) corresponds to the generalized standard deviation of this solution; and \(\eta\) is a penalty factor. It is worth mentioning that \(N_{i}\) is a constant parameter related to the number of individuals that make up the algorithm population.  
For the optimal location and sizing problem regarding D-STATCOMs in radial and meshed distribution networks, the proposed codification is a discrete-continuous representation, where the integer part defines the nodes where the compensators must be installed, and the continuous part of the codification corresponds to their optimal sizes. Equation (13) presents the proposed codification.  
\[x_{i}^{t}=\left[15,\ k,\ \cdots,28\ |1.3566,\ Q_{k}^{st},\ \cdots,\ 2.0101\right] \tag{13}\]  
**Remark 3**.: _It is important to observe that with the codification presented in Equation (13), the objective function component related to the D-STATCOM investment costs (\(f_{2}\)) is completely known._  
To determine the value of the parameters \(\mu_{i}\), \(\delta_{i}\), and \(\eta\) defined in Equation (12), the following set of equations is used:\[\mu_{i}=\frac{1}{3}\left(x_{i}^{t}+x_{\text{best}}^{t}+M\right), \tag{14}\] \[\delta_{i}=\frac{1}{\sqrt{3}}\Big{(}\big{(}x_{i}^{t}-\mu\big{)}^{2}+ \big{(}x_{\text{best}}^{t}-\mu\big{)}^{2}+(M-\mu)^{2}\Big{)}^{\frac{1}{2}},\] (15) \[\eta=\left\{\begin{array}{ccc}(-\text{log}(\lambda_{1}))^{\frac{1}{2}} \cos(2\pi\lambda_{2})&\text{if}&a\leq b\\ (-\text{log}(\lambda_{1}))^{\frac{1}{2}}\cos(2\pi\lambda_{2}+\pi)&\text{if}&a> b\end{array}\right. \tag{16}\]  
In Equations (14)-(16), it can be noted that:  
1. The parameters \(a\), \(b\), \(\lambda_{1}\), and \(\lambda_{2}\) are values generated with a uniform distribution in the interval \([01]\);
2. \(x_{\text{best}}^{t}\) represents the best current solution reached as of iteration \(t\);
3. \(M\) is a vector that represents the average (mean value) associated with all the individuals in the current population. To calculate the vector \(M\), Equation (17) is used. \[M=\frac{1}{N_{i}}\sum_{i=1}^{N_{i}}x_{i}^{t}.\] (17)
# Detecting Web-Based Attacks with SHAP and Tree Ensemble Machine Learning Methods
## 3 Proposed Method  
When machine learning is employed to detect malicious URLs and their JS code content, certain considerations have to be addressed, such as privacy and safety concerns, feature learning methods, and the development of a fully representative feature set.  
Given the common vulnerabilities, characteristics, and features employed by malicious JS codes, it is evident that malicious websites are different from benign ones, as they each have different objectives. For example, a malicious JS code contains functions or a combination of functions such as \(document.write\), \(location.replace\), and \(document.getElementById\)Among other things, these functions are used to perform cross-site scripting attacks by injecting malicious JS code into the document object model, direct concatenation of user input with the query string, and building a database query via string concatenation used to perform structured query language injections. Therefore, developing a malicious JS code detection system that can generalize well to detect different attack types is imperative. We propose automatic feature selection for AST-JS features, leveraging the global and local features that contribute to the model performance. Adequate details of a JS code structure are also presented without executing actual code.  
This section details the proposed method for web-based attack detection using SHAP and tree ensemble methods. The method includes the following three main stages: preprocessing, feature selection, and classification, as shown in Figure 1. The following sections explain the JS code preprocessing, feature selection, the SHAP values used for feature selection, and the machine learning classifiers used for classification.
# Modular Design and Decentralized Control of the Recupera Exoskeleton for Stroke Rehabilitation
## 3 Exoskeleton Modeling and Control for Rehabilitation Therapies
### Exoskeleton Control
#### 3.2.2 Mid-Level Control  
Mid-level control architecture implements the kinematic (Equation (1)) and dynamic (Equation (6)) model of the system and associated control approaches for rehabilitation therapies. In the (1) _Gravity Compensation_ (GC) mode, the weight of the system is compensated with the help of an inverse dynamic model of the exoskeleton arms. One can also use GC mode to take into account the dynamics of human arms. The input to this model is the actuator positions read from the position encoders (see Table 2) and the output is the reference torque values which is then converted into motor current and sent to the current controller implemented in the ACU. The GC mode is used to implement a transparent behavior of the system and represents the _basic operation mode_ of the system, on which most of the other modes are based. To support repetitive movement therapies, (2) _Teach & Replay_ (TR) can be used. This mode consists of two phases. First, the affected arm is put in the gravity compensation mode so that a therapist can easily move the arm. The forearm is equipped with a touch sensor (see Figure 3b) which recognizes the intention of the therapist to teach a trajectory and stores the trajectory (position and velocity readings from the involved ACUs) in the system's storage device. Subsequently, the trajectory can be replayed according to a trigger by the patient or therapist. During the replay, the exoskeleton executes the trajectory movement in the cascaded position-velocity control mode in the ACU. Mirror therapy can be supported using the (3) _Master-Slave_ (MS) mode, where movements from the healthy arm can be transferred to the unhealthy arm in a mirrored fashion. In this mode, the healthy arm is kept in the gravity compensation mode and the actuator positions read from the healthy arm are mirrored and sent to the ACUs of the unhealthy arm, which is in cascaded position-velocity control mode. Furthermore, sitting and standing features for the lower part of full body exoskeleton are implemented at this level.