esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving RNN Performance by Modelling Informative Missingness with Combined Indicators
## 2 Materials and Methods
### Description of Data  
Repeated survey data spanning a 7-year period (2011-2017) was collected from a mobile application. This application was made available to 7098 athletes in Japan. Of these athletes, 5970 had at least one recorded outcome. These were randomly distributed into a train (80%), validation (10%), and test set (10%), each containing surveys from different athletes.  
The survey consisted of 65 questions regarding the athletes' physical activity, food intake, and biometric data (e.g., weight, body fat percentage) on a particular day. The outcome variable was the self-assessed condition on that day, ranging from 1 (very poor) to 10 (very good). Since the data are sparse and contain a considerable amount of missing data, the outcome variable was discretized to good condition (\(\geq\)6) or poor condition (<6) to simplify the prediction problem to binary classification.  
In total, 70.1% of survey answers on filled in surveys were missing. Questions which were answered less than 5% of the time were omitted, since these bear little information about the average user of the application. On average, participants only filled in the survey 2.9% of all days, leaving a large number of missing days (Figure 1).  
Figure 2: Example of a sequence of surveys. Dashed surveys are missing (**red**: prior to first entry; **blue**: between entries; **black**: variable sequence length until the participant’s last entry).
# Crowdsourcing Based Performance Analysis of Mobile User Heterogeneous Services
## 3 Campaign Test and Data Description  
### Campaign Description  
Before evaluating the network and service performance, we will present first in this section the data collection procedure. The latter is composed of three parts: (i) the test campaign description, (ii) the collection of the data traces, and (iii) the raw data presentation. Then, we will detail the achieved data pre-processing phase.  
To begin, we illustrate in Figure 1 an overview of the achieved campaign.  
In this campaign, we used the \"5 Gmark\" tool for a variety of reasons, including the simplicity and the efficiency in evaluating several services with many mobility test modes. In particular, \"5 Gmark\" allows for measuring the cellular connection through three modes: \"Speed Test\", \"Full Test\", and \"Drive test\". In practice, the \"Speed Test\" presents one test cycle to assess the connection quality by measuring just the latency test (in terms of milliseconds), download data test (during 10 s) and upstream data transfer (uploading during 10 s). \"Full Test\" is also one test cycle that integrates, in addition to the \"Speed Test\" data, the measurement of two additional service: YouTube streaming (display YouTube HD video during 30 s) and web browsing (test connection during 10 s of 6 websites). The \"Drive Test\" represents a test cycle, set of \"Speed Tests\" or \"Full Tests\", which runs automatically with a test number counter (5, 10, 20, etc.) and an interval in each test in minutes (by default 0). Note that the server is selected for each service according the user position regardless of the operator.  
To study the impact of the user terminal, we consider two Android smartphone named Xiaomi Note Pro 9 and Samsung A10 that have different characteristics. During our tests, three different access technologies, named 2G (_EDGE_), 3G (_UMTs_, _HSPAP_ (_HSPA_\(+/3G++\)) and 4G (_LTE_/_LTE_A_), are evaluated. The collection and analysis methodology are applied to 5G or the beyond generations. In this evaluation, a wide variety of parameters are collected including application, radio indicators and context information. These parameters are gathered using an active data collection procedure that concerns five services named latency test (ping), download data test, upstream data transfer, web browsing and video streaming as illustrated in Figure 1.  
The measurements are effectuated for six months, from March to September (except August) 2021 in the Ile-de-France region using two main categories of mobility modes. The first mode is car mode with a maximum speed of 130 (km/h). This mode includes travel by car on some highways around Paris as well bus's lines in Paris center. The second mode is trained with a maximum speed of 120 (km/h)). This mode includes a regional train in Paris (RER) and the subway (metro).
## 4 Design Procedure and Application Example
### Capacitors and High-Pass Filter Design  
The reference current \(i_{R}\) in the switching function (24) is generated by a high-pass filter \(G_{hp}\) processing the load current \(i_{o}\). Equation (47) describes the first-order high-pass filter considered in this solution.  
\[i_{R}=G_{hp}\cdot i_{o}\quad\text{where}\quad G_{hp}=\frac{s}{s+2 \cdot\pi\cdot F_{safe}} \tag{47}\]  
The most extreme transient corresponds to a step current \(i_{o}=\frac{\Delta i_{o}}{s}\), which produces the following time-domain waveform obtained from the inverse Laplace transformation of Equation (47):  
\[i_{R}=\Delta i_{o}\cdot e^{-2\cdot\pi\cdot F_{safe}\cdot t} \tag{48}\]  
The charge extracted from \(C_{2}\) during a positive load transient (increment in \(i_{o}\)) is obtained by integrating the previous time-domain waveform:  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Description** & **Variable** & **Value** \\ \hline Battery voltage & \(v_{b}\) & 48 V \\ \hline Nominal C\({}_{2}\) voltage & \(v_{R}\) & 48 V \\ \hline Maximum deviation of C\({}_{2}\) voltage & \(MO_{\text{C2}}\) & 3\% \\ \hline Settling time of C\({}_{2}\) voltage & \(t_{s,C2}\) & 100 ms \\ \hline Maximum derivative in the load current & \(\max\Bigl{(}\bigl{|}\frac{di_{o}}{dt}\bigr{|}\Bigr{)}\) & 70 A/ms \\ \hline Maximum load transient magnitude & \(\Delta i_{o}\) & 2 A \\ \hline Maximum safe frequency for the battery & \(F_{safe}\) & 500 Hz \\ \hline Maximum switching frequency & \(F_{sw}\) & 120 kHz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of the application example.  
\[Q_{\text{C2}}=\int_{0}^{\infty}\Delta i_{o}\cdot e^{-2\cdot\pi\cdot F_{ safe}\cdot\text{t}}\ dt=-\frac{\Delta i_{o}}{2\cdot\pi\cdot F_{safe}} \tag{49}\]  
During a positive load transient, the \(v_{\text{C2}}\) voltage is decreased and the voltage deviation is negative, which produces the following charge extraction from \(C_{2}\):  
\[Q_{\text{C2}}=-C_{2}\cdot(v_{R}\cdot MO_{\text{C2}}) \tag{50}\]  
Finally, \(C_{2}\) capacitance is obtained by relating Equations (49) and (50), as follows:  
\[C_{2}=\frac{\Delta i_{o}}{2\cdot\pi\cdot F_{safe}\cdot(v_{R}\cdot MO_{\text{ C2}})} \tag{51}\]  
The design of \(C_{1}\) is performed to provide a balance with the energy stored in \(C_{2}\), which is equal to \(E_{C_{2}}=\left(C_{2}\cdot v_{R}^{2}\right)/2\). Taking into account that the energy stored in \(C_{1}\) is \(E_{C_{1}}=\left(C_{1}\cdot v_{\text{C1}}^{2}\right)/2\), the condition for \(C_{1}\) to achieve \(E_{C_{1}}=E_{C_{2}}\) is calculated using Equations (18) and (19), as follows:  
\[C_{1}=C_{2}\cdot\left(\frac{v_{R}}{v_{b}}\right)^{2} \tag{52}\]  
Evaluating Equation (51) with the values given in Table 2 produces a capacitance equal to 442.1 \(\upmu\)F, which, adjusted to a commercial value, results in \(C_{2}=470\)\(\upmu\)F. Similarly, evaluating Equation (52) produces the commercial value \(C_{1}=470\)\(\upmu\)F.
# Analysis of Maritime Wireless Communication Connectivity Based on CNN-BiLSTM-AM
## 3 Principles of the Deep Learning Models
### The Attention Mechanism  
The attention mechanism (AM) is inspired by the simulation of the attention characteristics of the human brain and is an important tool for addressing the issue of information overload [20]. It has been widely applied in the field of deep learning. The fundamental principle of the AM is to allocate weights based on the varying impact of the input features on the output results, thereby enhancing the model's feature extraction capability through differentiated weights. In this paper, the AM first generates feature representations from the hidden states of the previous layer (i.e., the BiLSTM layer) and calculates the importance scores for each time step using a trainable weight matrix. Subsequently, the scores are normalized using the Softmax function to obtain the weights for each time step. These weights are then used to perform a weighted sum of the hidden states to generate the context vector, which highlights the significant temporal features. During the training process, the AM dynamically adjusts the weight distribution by optimizing the network parameters, improving the model's ability to capture non-linear relationships while also  
Figure 4: The structure of BiLSTM.  
enhancing its robustness and generalization capability. The structure of the AM unit is shown in Figure 5.  
The attention state transformation process is shown in Equations (27)-(29):  
\[S_{ii}=V\tanh(Wh_{t}+Ulh_{i}+b),\quad i=1,2,\ldots,t-1 \tag{27}\]  
\[a_{ti}=\frac{\exp(S_{ti})}{\sum_{i=1}^{t}\exp(S_{ik})},\quad i=1,2,\ldots,t-1 \tag{28}\]  
\[F=\sum_{i=1}^{t}a_{ti}\times h_{i},\quad i=1,2,\ldots,t-1 \tag{29}\]  
In the above equations, \(V\), \(W\), \(U\), and \(b\) are the network model parameters; \(h_{i}\) is the hidden state of the corresponding input sequence \(Y_{i}\); and \(a_{ti}\) is the weight of the BiLSTM hidden state \(h_{i}\) with respect to the current input \(Y_{i}\). And \(F\) is the attention layer's output. The overall workflow for CNN-BiLSTM-AM is shown in Figure 6.
# An Evaluation of Orbital Angular Momentum Multiplexing Technology
## 2 Background and Theoretical Performance Evaluation
### Mode-Dependent Power Distribution  
In the work we report here, we also considered two key issues regarding the mode-dependent power distribution among different OAM modes. These issues are as follows.  
* Peak Rx Power Degradation: As the number of OAM modes increases, the radiation becomes wider, the angle from the beam axis at the peak Rx power becomes wider, and the SNR at its peak Rx power becomes smaller. Accordingly, the performance is degraded as the number of OAM modes increases.
* Non-identical Peak Rx Power Locations: The peak Rx power locations of each OAM mode are not identical because their radiation patterns are distinct. Therefore, the mode-dependent performance degradation becomes more severe when a single Rx UCA is used because some OAM modes might not have the peak Rx power at a certain location.
## 3 UAV AG Communication Channel Modeling and Characterization
### A UAV AG Multi-Mobility Multi-Trajectories Channel Model
#### 3.1.2 Generation of Rx Trajectories  
In the daily ground driving process, vehicles have fewer sharp turns and sharp driving routes; therefore, in order to better simulate the trajectory of the Rx movement, a ST mobility model was introduced [35]. The basic principle of the model is that the vehicle moves in a circle along a point in a straight line perpendicular to the forward direction, until the  
Figure 3: Simulation of the Tx and Rx trajectories: (**a**) Tx for different trajectories based on Markov chain; (**b**) Rx for different trajectories based on ST mobility model.  
next steering center is selected. The process of the basic ST mobility model during the time interval \(T_{i}\leq t<T_{i+1}\) was as follows:  
\[a_{t,i}(t)=0 \tag{11}\]  
\[a_{n,i}(t)=\frac{\mathbf{V}^{2}}{r(T_{i})} \tag{12}\]  
\[\dot{\Phi}(t)=-\omega(t)=-\frac{V}{r(T_{i})} \tag{13}\]  
\[v_{Q,x}(t)=\mathbf{V}\cos(\Phi(t)) \tag{14}\]  
\[v_{Q,y}(t)=\mathbf{V}\sin(\Phi(t)), \tag{15}\]  
where the \"\(\cdot\)\" was the first-order derivative with respect to time t. The horizontal tangential and centripetal acceleration were denoted as \(a_{t},i(t)\) and \(a_{n},i(t)\), respectively. The velocity along the x and y axes during the time interval \(T_{i}\leq t<T_{i+1}\) were defined as \(v_{Q,x}(t)\) and \(v_{Q,y}(t)\), respectively. The angle and angular acceleration of circular motion during the time interval \(T_{i}\leq t<T_{i+1}\) were denoted as \(\Phi(t)\) and \(\omega(t)\), respectively. The speed vector was \(\mathbf{V}\), and the turning radius was \(r(T_{i})\). The inverse of the turning radius followed a Gaussian distribution with zero mean and variance \(\sigma_{s}^{2}\). The turning radii \(r(T_{i})>0\) represented turning right and \(r(T_{i})<0\) represented turning left. The time interval \(\tau_{i}=T_{i+1}-T_{i}\) was subject to exponential distribution. The coordinates of Rx were expressed as  
\[\mathbf{A}_{q}^{R}(t)=\mathbf{A}_{ini,q}^{R}+\mathbf{V}_{Q}t \tag{16}\]  
\[\mathbf{V}_{Q}=\begin{bmatrix}v_{Q,x}\\ v_{Q,y}\\ v_{Q,z}\end{bmatrix}=\begin{bmatrix}V_{q,x}\cos(\Phi(t))\\ V_{q,y}\sin(\Phi(t))\\ V_{q,z_{0}}\end{bmatrix}, \tag{17}\]  
where \(\mathbf{A}_{ini,q}^{R}\) was the initial coordinate of the \(q\)th Rx antenna. The height of the ground Rx end was constant, and remained \(V_{q,z_{0}}=0\) m/s.
## 4 Numerical Results and Discussion
### Analysis of Numerical Simulation Results of Main Tunnel Excavation  
After the pilot tunnel excavation, the main tunnel excavation shall be carried out after the surface and surrounding rock deformation were stable. The main tunnel was excavated by the three-bench reserved core soil method. Before the formal excavation, the long pipe shed at the portal was arranged first. During the excavation, the long pipe shed and small conduit of the tunnel body were arranged. The excavation of the main tunnel was carried out in the order of excavating the left main tunnel first and then the right main tunnel. The staggering distance between the upper bench face of the left main tunnel and the right main tunnel was 30 m, and the distance between the upper bench and the middle bench, and the middle bench and the lower bench, was 20 m and 15 m, respectively, and the secondary lining of the main tunnel lagged behind the inverted arch by 10 m.  
Figure 12: Vault settlement of three pilot tunnels during pilot tunnel excavation.  
Figure 13: Deformation of supporting structure (unit: m): (**a**) Normal supporting bolt; (**b**) Advance supporting pipe.
# Performance of Dense Wireless Networks in 5G and beyond Using Stochastic Geometry
###### Abstract  
Device density in cellular networks is expected to increase considerably in the near future. Accordingly, the access point (AP) will be equipped with massive multiple-input multiple-output (mMIMO) antennas, using collimated millimeter-wave (mmW) and sub-THz communications, and increasing the bandwidth to accommodate the growing data rate demands. In this scenario, interference plays a critical role and, if not characterized and mitigated properly, might limit the performances of the network. In this context, this paper derives the statistical properties of the aggregated interference power for a cellular network equipping a mMIMO cylindrical array. The proposed statistical model considers the link blockage and other network parameters such as antenna configuration and device density. The findings show that the characteristic function (CF) of the aggregated interference power can be regarded as a weighted mixture of two alpha-stable distributions. Furthermore, by analyzing the service probability, it is found that there is an optimal configuration of the array depending on the AP height and device density. The proposed statistical model can be part of the design of dense networks providing valuable insights for optimal network deployment and resource management and scheduling.  
**Citation:** [PERSON] [PERSON]; [PERSON] [PERSON] Performance of Dense Wireless Networks in 5G and beyond Using Stochastic Geometry  
**Mathematics 2022**, _10_, 1156. [[https://doi.org/10.3390/math10071156](https://doi.org/10.3390/math10071156)]([https://doi.org/10.3390/math10071156](https://doi.org/10.3390/math10071156))  
19 February 2022  
Accepted: 30 March 2022  
Published: 2 April 2022  
**Published:** **Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
# Understanding and Classifying Permanent Denial-of-Service Attacks
## 3 Case Studies of PDoS Attacks
### CVE-2022-23968--Xerox VersaLink  
A vulnerability in Xerox VersaLink devices was identified as CVE-2022-23968 [11]. The vulnerability allows remote attackers to brick these devices using a crafted TIFF file in an unauthenticated HTTP POST request, leading to a PDoS situation. The issue arises due to image parsing causing a reboot, which then restarts as soon as the boot process finishes, creating a loop. This can be resolved by a field technician.
## 2 Materials and Methods
### Numerical Simulations  
Numerical simulations of EMR emission parameters (operating parameters of the modeled MIFA) and the related SAR were performed using the commercially licensed software Sim4 Life v7.2.4 (Zurich Med Tech, Zurich, Switzerland) based on the finite difference time domain (FDTD) method compliant with relevant standardized requirements [15; 16; 33]. The standard uncertainty of the simulations was estimated at \(\pm\)(20-25)%, relevant to the state-of-the-art in this field [1].  
The finest resolutions of the numerical models were 0.005 mm for the antenna and 0.5 mm for models of particular body parts (better than 1/15 of the wavelength in tissues at 2.45 GHz, i.e., better than 1 mm), which is the minimum resolution for SAR assessment required by standardization requirements (e.g., IEC 62232:2022) [33].  
The simulations' domain was extended by 200 mm in each direction from the nearest surface of the considered models. The absorption boundary conditions (ABCs) were applied to all of its walls.
## 5 Challenges and Future Directions
### Technical Challenges  
UAVs face multiple technical constraints that can limit their effectiveness for climate studies in complex urban environments. Restricted flight endurance poses challenges for the extended duration sampling of heat patterns, air quality, and other dynamics across metro regions [36]. As discussed above, most commercial UAVs have less than 30 min of flight time; though, advances in batteries and efficiency help extend this. Payload restrictions also constrain the size, weight, and combinations of scientific sensors used to characterize urban climate factors. While rotary-wing UAVs are highly agile and adaptable, their flight endurance remains limited due to battery life constraints. A single battery charge often does not provide sufficient power for long-distance missions. Factors, such as hovering, maneuvering, payload weight, speed, and headwinds, can negatively impact the battery's life and range [204]. In practice, rotary drones may need to stop for recharging multiple times before reaching faraway destinations.
## 5 Results and Discussion
### LSTM-RNN Model Verification  
An approximation of the transcendental equation roots of the prismatic cantilever beam based on the datasets is performed in the proposed LSTM-RNN network. As described above, a total of \(n=20\),000 datasets have been generated for evaluation purposes, containing input and output data values. The input datasets depend on the parameters of beams and natural frequencies (\(f_{n}\)), while output target datasets contain roots (\(\beta_{n}l\)) of the transcendental equation. In this study, 80% of datasets are used in the training set, while the remaining 20% is used for the testing stage.  
Figure 8 shows the approximation of transcendental equation roots with 500 epochs. The proposed model performance is examined in terms of both training accuracy and training loss with respect to validation accuracy. Hence, from the results, it has been found that the roots approximation of the prismatic cantilever beam has an accuracy of 96.25% with a means squared error of 0.063% without an overfitting problem.  
Similarly, Figure 9 demonstrates the model performance with 1000 epochs. In this case, the training model and validation accuracy is over 98.55% with a mean squared error of 0.042%, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(f_{n}\) **(Hz)** & \(f_{n}\) **(Numerical)** & \(f_{n}\) **(ANSYS)** & **\% Error** \\ \hline \(f_{1}\) & 25.1 & 25.1 & 0.00 \\ \(f_{2}\) & 157.1 & 156.6 & 0.32 \\ \(f_{3}\) & 439.8 & 436.1 & 0.84 \\ \(f_{4}\) & 861.9 & 847.6 & 1.46 \\ \(f_{5}\) & 1424.8 & 1403.9 & 1.59 \\ \(f_{6}\) & 2128.4 & 2141.1 & 0.60 \\ \(f_{7}\) & 2972.7 & 3005.3 & 1.10 \\ \(f_{8}\) & 3957.7 & 3935.6 & 0.56 \\ \(f_{9}\) & 5083.5 & 5035.8 & 0.94 \\ \(f_{10}\) & 6350.0 & 6421.1 & 1.12 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of MATLAB (numerical) and ANSYS results.  
Figure 7: Graphical comparison of \(f_{n}\) results for ten modes.  
In this study, simulations are performed on Intel Core i-7 with a 1.8 GHz CPU clock and 8 GB RAM. Moreover, the Python programming language version 3.7.4 has been used to implement the LSTM-RNN model.
# Rationale for the Combined Use of Biological Processes and AOPs in Wastewater Treatment Tasks
## 3 Results and Discussion
### Modeling the Effects of Antibiotics and Surfactants on the Activity of Bioprocesses
Development of an Integrated Model for Combining Biological Processes and AOPs in Wastewater Treatment  
In Table 2, we list the main technological disadvantages for the systems of physical (mechanical), chemical, and biological methods of purification. As can be seen from Table 2, the physical methods include filtration, centrifugation, ultrasound, ozonation, etc., while the chemical methods include the use of various reagents, realization of processes of coagulation, flocculation, chlorination, etc. Thus, in the very principles of their implementation, AOPs, in a broad sense, are a set of chemical treatment procedures designed to remove organic (and sometimes inorganic) materials from water and wastewater. However, in actual wastewater treatment applications, the term usually refers more specifically to a subset of such chemical processes that use ozone (O\({}_{3}\)), hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), and/or UV light. One of these types of processes is called in situ chemical oxidation. Figure 8: Systemic combination of biological processes and AOPs in wastewater treatment (averaging devices, sand traps, and other pretreatment elements are not specified). From the technological point of view, the development of new methods of using water treatment and water purification technologies is prompted by their known operational drawbacks, which have been investigated in several works [10; 11; 13; 19]. It is worth noting that a common disadvantage of these methods is that dozens of water quality and process parameters must be monitored in real time, and only a few automated measuring devices exist and work reliably at industrial facilities. It is necessary to take into account the qualitative and quantitative parameters of the operation of the activated sludge biosystem in order to form a dynamic equilibrium in its species diversity for the most effective destruction of organic pollutants. Thus, a promising direction is the use of methodological approaches of aggregation of different basic approaches to water treatment. For example, the analysis of industrial systems, in the case of a combination of basic methods (necessary for the treatment of industrial effluents), showed the overlapping effect of different equipment on the same pollutants: the cross-effect of different methods on one type of pollutant varies from 5 to 40% [7]. Based on the results obtained, the sequence of creating water treatment systems with enhanced functionality, and their ability to effectively counteract emergencies, the method of combining solutions for removing pollutants from effluents is justified [36; 37]. For an example of a combination of different basic solutions, consider the use of biological processes as the technological core, while reducing their disadvantages with AOPs. Ozone, chlorine, sodium hypochlorite, hydrogen peroxide, and Fenton's reagent are the most commonly used oxidants. Each of these reagents has its advantages and