esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
### Background  
High altitude platform stations (HAPSs) are emerging as telecommunication infrastructures that have gained considerable interest, offering direct communication services to ground-based smartphones over a wide area from an altitude of 20 km in the stratosphere [1, 2]. HAPS mobile communication is expected to be extensively utilized not only in rural areas lacking terrestrial network coverage but also in disaster-hit or event-held areas that require temporary networks [1]. A HAPS provides mobile communication services commonly employed in terrestrial mobile communication networks, including fourth-generation long-term evolution (4G LTE) and fifth-generation new radio (5G NR).  
The platform, stationed at a 20 km altitude, employs unmanned aerial vehicles (UAVs), such as balloons and airships. Satellite communications are also employed for sky-based communications; however, HAPSs distinctly offer lower latency owing to their lower altitude than that of satellites. Satellite communications employ Geostationary Earth Orbit, Medium Earth Orbit, and Low Earth Orbit satellites, which have altitudes of 36,000 km, 2000-36,000 km, and <2000 km, respectively. The round trip time (RTT) from the user to these satellites is approximately 240-280 ms, 54-86 ms, and 6-30 ms, respectively [3], whereas the RTT for HAPSs is 360 \(\upmu\)s and 680 \(\upmu\)s to 50 km and 100 km radius points, respectively. Conversely, the altitude of the ground base stations (BSs) is lower than that of HAPSs, although their area coverage is much narrower than that of HAPSs, necessitating multiple BSs for wide area coverage. Consequently, HAPSs offer lower latency than satellite BSs and broader coverage than ground BSs. Figure 1 shows the conceptual diagram of the HAPS mobile communication infrastructure. A HAPS can provide communication services up to 100 and 200 km. To achieve wide coverage and high capacity, it employs multicell configurations by reusing a single-cell frequency. Through multicell configurations, a narrow beamwidth can be created within each cell, thereby extending the coverage area. However, because the user distribution in a HAPS is not uniform, antenna parameters, such as the beam directions and beamwidths, must be optimized for each cell according to the distribution. Additionally, because a HAPS may be used in emergency scenarios, such as disasters, they should have the flexibility to respond to abrupt changes in user distribution. Therefore, it is necessary to optimize the HAPS antenna parameters and to enable their prompt adaptation to different user distribution patterns.
# Awareness to Action: Student Knowledge of and Responses to an Early Alert System
## 3 Results
### What Recommendations Do Students Have to Improve the System?  
This section pertains to our research question: What recommendations do students have to improve the system? Participants were lastly asked how they would improve ASRs (Table 16). The most common response to this question was to not change anything about ASRs (26.8%; n = 45). An additional 10.1% (n = 17) reported that they simply did not know what changes should be made (leaving open that changes could be made). Recommendations for change made by at least 5% of respondents included to better help students respond to the ASR (11.3%; n = 19), have an instructor talk to the student after submitting an ASR for them (7.1%; n = 12), and have ASRs be more positive (6.0%; n = 10) and less negative (5.4%; n = 9).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Change** & **Example** & **n** & **\%** \\ \hline No changes & “I wouldn’t change anything” & 45 & 26.8 \\ Don’t know & “I am not sure”. & 17 & 10.1 \\ Have an instructor & “I would make sure the instructor or TA has a requirement to reach & 12 & 7.1 \\ talk to you & out or the student should to understand the report’s specificity”. & 10 & 6.0 \\ More positive & “More feedback for positive ASR and not just negative ones” & 9 & 5.4 \\ Less negative & “I would make it sound less threatening as I thought at first I was & 8 & 4.8 \\ Send ASRs via different & “I would make an announcement on canvas, as im more likely to & 8 & 4.8 \\ communication streams & look at it”. & 8 & 4.8 \\ No response & “n/a” & 8 & 4.8 \\ \hline \hline \end{tabular}
\end{table}
Table 16: How would you change Academic Status Reports (ASRs) to improve the experience of using or receiving them? (n = 168).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Advice** & **Example** & **n** & **\%** \\ \hline Read/listen/ & & 47 & 28.0 \\ understand the ASR & “To listen to the report and work on it”. & 41 & 24.4 \\ Get external help & “To talk to the professor about what they can do to get their grade up”. & 41 & 24.4 \\ Don’t stress & “To not stress about it because it could be good and bad and overall its & 41 & 24.4 \\ Don’t stress & just to help you” & 41 & 24.4 \\ Do more & “Work hard, change now! sooner rather than later!” & 27 & 16.1 \\ Do something specific & “take a step back, reevaluate, and come back stronger than before”. & 25 & 14.9 \\ Persist & “to not give up” & 13 & 7.7 \\ Don’t know & “no clue” & 9 & 5.4 \\ Other & “It’s a way of improvement”. & 6 & 3.6 \\  & “If it is positive: Keep doing what you are doing. If it is negative: Read & 3 & 1.8 \\ Do nothing & it, and & & \\  & really sit with what your professor is trying to tell you”. & & \\ \hline \hline \end{tabular}
\end{table}
Table 15: What advice would you give someone who had just received an Academic Status Report (ASR)? (n = 168).
# Assessment of the Structural State of Dissimilar Welded Joints by the Acoustic Emission Method
## 1 Introduction
Moreover, the AE method does not require a probing action, which allows it to be effectively used in structure health monitoring systems for loaded structures in the operating environment [12]. The use of the AE testing method for diagnostics of dissimilar welded joints is insufficiently studied nowadays. Few studies are devoted to the application of AE directly in the process of dissimilar welding [13; 14; 15; 16] and only few experiences have been successful. In [17], the authors monitored the development of a crack in a dissimilar welded joint made of low-alloy pearlitic and austenitic steels. The study was carried out under laboratory conditions for a pre-hydrogenated specimen. The AE method showed high sensitivity since it was able to detect a crack with an opening of no more than 50 \(\upmu\)m.  
The high sensitivity of the AE testing method to the microstructure parameters reveals a great variety of changes in the material structure and composition. For example, in [18; 19], the authors implemented AE to identify such microscopic AE sources as the destruction of carbide particles in high-strength steels. In [20], the authors studied martensitic transformations in steel with an austenitic structure, AISI 304. The influence of microstructure parameters of the parent metal and the weld metal was analyzed in [21] using the example of low-alloy pearlitic steel. The authors investigated the impact of the grain size, structural composition and the presence of non-metallic inclusions on the AE parameters and mechanical properties of the welded joint. We studied the dissimilar welded joints of 12 Kh18N10T austenitic steel to 09G2S pearlitic steel. In [22], AE monitoring under static tension of the dissimilar welded joint is investigated by the authors. Therefore, this research is devoted to the study of AE technique application under cyclic loading of dissimilar welded joints. The main goal of this work was to analyze the possibility of detecting diffusion decarburized and carbide interlayers based on AE data. The specimens of dissimilar welded joints with and without diffusion interlayers were tested using the AE method. The AE signature was analyzed at various stress values for defect-free specimens; some patterns of AE data were determined, indicating diffusion interlayers in the welded joints.
## 3 Methodology
### EMS Performance  
The objective is to achieve the optimal utilization of the PV energy while achieving a generation-demand balance maintaining the grid frequency. In addition, it ensures that the minimum frequency (\(f^{mm}\)) reached after a severe generation-load imbalance is within the limits (see Section 3.4 and the frequency constraint explained later for more detail).  
The output variables (the setpoints to the generation and storage units) of the EMS are: (i) number of diesel generators to be connected (\(D^{*}_{con}\)); (ii) the setpoint to the battery (\(P^{*}_{bat}\)); and (iii) the maximum PV power setpoint (\(P^{*}_{PV_{max}}\)). They are calculated for the remaining of the day at each optimization execution period. On the other hand, the inputs are: (i) the load forecast (\(L^{c}\)); (ii) the available PV power forecast (\(L^{PV}\)); and (iii) the initial state of charge (SOC). Forecasts include the mean and standard deviation.  
Figure 3 shows the time periods used. \((T_{for})\) represents the time periods when forecasts are updated. \((T_{EMS})\) is the period between EMS executions. Finally, \(T_{intra}\) is the optimization problem time resolution. When the EMS is executed, the output variables (decision variables) are calculated for the rest of the day. While \(P^{*}_{bat}\) and \(P^{*}_{PV-max}\) are calculated with a time resolution of \(T_{intra}\), the resolution of \(D^{*}_{con}\) is \(T_{EMS}\).  
Figure 3: Temporal description of the daily execution cycle.
## 5 Numerical Experiments
### Results from Medium-Scale and Large-Scale Transportation Networks  
The real-world cases are primarily present and adopted from the Starlink constellation of publicly available datasets. We use the simulation platform to calculate the set of inter-satellite visible relationships in the constellation and construct a 20 \(\times\) 500 mesoscale and 20 \(\times\) 900 large-scale inter-satellite network where there are 500 satellites and 20,000 links in the mesoscale scenario and 900 satellites and 36,000 links in the large-scale scenario. Meanwhile, the sparse task scenario has been proven to be well solved by the proposed algorithm, so the regular and dense task scenarios are set up separately, as shown in Table 12. In addition, we set the upload time window to 4 and the downlink time window to 8 for each task. The node and link capacity in the network is defined according to the number of tasks. The termination iteration condition of the algorithm is that the \(LB^{*}\) of the subproblems and the \(UB^{*}\) of the original problem reach a small gap. The experimental results are presented in Table 13, where the proposed algorithm can still be solved efficiently by verifying the dense task demand scenario with a large-scale population of satellites. In particular, our algorithm is able to give a near-optimal solution quickly when the tasks are more uniformly distributed. It is worth noting that we set the priority of task requirements to be the same in all experimental cases. However, when the task priorities are different, the degree of differentiation of task attributes will be more obvious and more favorable to the solution.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Scenario** & \(LB^{*}\) & \(UB^{*}\) & **Gap\%** & \begin{tabular}{c} **Number of** \\ **Iterations** \\ \end{tabular} & \begin{tabular}{c} **CPU Running** \\ **Time (s)** \\ \end{tabular} & \
\begin{tabular}{c} **Violated Node** \\ **Constraints** \\ \end{tabular} &
\begin{tabular}{c} **Violated Link** \\ **Constraints** \\ \end{tabular} \\ \hline Random\_I & 276.55 & 276.55 & 0\% & 10 & 95.812 & Null & Null \\ Random\_II & 456.43 & 459.67 & 0.75\% & 20 & 277.538 & [264] & Nullx \\ Random\_III & 553.90 & 553.90 & 0\% & 10 & 119.954 & Null & Null \\ Random\_IV & 828.34 & 828.34 & 0\% & 27 & 401.272 & Null & Null \\ Random\_V & 1055.13 & 1058.63 & 0.33\% & 29 & 482.773 & [606] & Null \\ Random\_VI & 1272.36 & 1276.90 & 0.36\% & 30 & 506.777 & [606] & Null \\ Random\_VII & 1361.89 & 1364.34 & 0.18\% & 20 & 305.205 & Null & [1655,1993], \\ Random\_VIII & 1623.88 & 1623.88 & 0\% & 14 & 259.703 & Null & Null \\ Random\_IX & 2146.54 & 2146.54 & 0\% & 20 & 587.444 & Null & Null \\ Random\_X & 2757.69 & 2761.23 & 0.13\% & 47 & 1811.168 & [2825,2238] & Null \\ \hline \hline \end{tabular}
\end{table}
Table 11: Experimental results of random scenarios.
# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 3 Designed Channel Code-Book MappingNet Based on Vector Quantization
### Channel Code-Book  
In the satellite-ground communication environment, with the change in satellite position, the transmission channel between satellite and ground also changes greatly, so it cannot be expressed by a single channel function. Therefore, we design a CCB to store different channel parameters.  
The proposed CCB uses vector quantization (VQ) technology [38], which can reduce the complexity of channel estimation compared with traditional design methods. Firstly, the wireless channel parameter \(H\) is extracted, where  
\[H=\left(\begin{array}{cccc}h_{11}&h_{12}&\cdots&h_{1N}\\ h_{21}&h_{22}&\cdots&h_{2N}\\ &&\ddots&\\ h_{M1}&h_{M1}&\cdots&h_{MN}\end{array}\right). \tag{7}\]  
Specifically, the parameters of \(H\) are determined by various parameters and SNR values in the SR channel. This process enables the dynamic adjustment of code-book parameters in response to changing channel conditions, thereby facilitating adaptation to different noise environments. This ensures that the model maintains optimal performance under varying signal conditions.  
Then the extracted channel characteristic parameters are quantified to generate the feature vector \(v\), that is,  
\[v=f(H)=\sigma(W_{f}H+B), \tag{8}\]  
where \(\sigma\) is a nonlinear activation function, \(W_{f}\) is the weight matrix, and \(B\) is the bias vector. Theoretically, different channel matrices may be mapped to the same feature vector, so the denoising parameters optimized for \(H_{1}\) may not be effective for \(H_{2}\). Therefore, when we perform feature mapping, we use multi-level feature vectors to represent channel information more comprehensively, which can reduce the probability that different channel matrices are mapped to the same feature vector.  
Finally, the CCB is constructed by VQ technology, and the channel feature vector is mapped to code-book index \(c\) for  
\[c=Q(v)=\arg\min_{c_{i}\in C}\parallel v-c_{i}\parallel, \tag{9}\]  
where \(v\) is the feature vector, \(c_{i}\) is the codeword in the code-book, and \(C\) is a set of code-books.
# Ozone Generation by Surface Dielectric Barrier Discharge
## 3 Results and Discussion
### Voltage and Discharge Current Measurements  
Figure 2 shows the typical voltage and discharge current waveforms recorded over one voltage cycle for all three types of SDBD generators (with smooth-edge, serrated, and thin-wire electrodes). Each generator was powered with the same voltage waveform, and the power supply was not equipped with the impedance matching circuit [50; 51]. In each case, the voltage was a sine wave with an 18.5 kV peak-to-peak voltage and a frequency of 1 kHz. In general, the current-voltage waveforms are typical of the SDBD actuators used for flow control [52; 53; 54; 55]. For all three cases, the current waveforms consisted of two batches of pulses appearing in each cycle: a batch of positive pulses occurring at the positive-going voltage half cycle, and a batch of negative pulses at the negative-going half cycle. The batch of positive pulses is characterized by relatively high discharge currents reaching up to about 100 mA and is associated with the formation of positive streamers. On the other hand, the batch of negative pulses is associated with so-called micro-glow discharge and is characterized by less frequent current pulses and smaller currents [56; 57; 58]. The waveforms for the smooth-edge and serrated electrode generators are similar; they consist of dense batches of both positive and negative pulses, which indicates an intense discharge taking place from those electrodes. On the other hand, with the thin-wire electrode, the current pulses in both batches are scarcer and the batch duration itself is noticeably shorter. This suggests that the geometry of the thin-wire electrode inhibits the SDBD discharge.  
Figure 2: Typical voltage and discharge current waveform for the generators with smooth-edge, serrated and thin-wire electrodes (V\({}_{\rm PP}=18.5\) kV, \(f=1\) kHz). The A, B and C rectangles represent the temporal position of the time windows set for the time-resolved imaging.
## 5 LSTM-Based Scheduling
### Prediction Model Architecture  
Figure 7a illustrates the process of the LSTM model learning time series data. It depicts the process of constructing and predicting time series data using the LSTM model. Initially, the time series data is normalized to the [0, 1] range through Min-Max scaling. Subsequently, the data is divided into X values and Y values by setting the Lookback Window and Forecasting Horizon of the sliding window. Here, X values correspond to data from past times, while Y values correspond to data for future times. The data is then split into training and testing sets through Data Split, with a split ratio of 0.2. The LSTM  
sequentially receives X values from the training data, and Y values are used as labels in supervised learning. The LSTM model consists of LSTM layers and a Dense layer. The first LSTM layer has an input shape of (None, 3, 128), which means the input sequence length is 3 and there are 128 features at each time step. This LSTM layer has 128 units, and the total number of parameters is 66,560. The second LSTM layer takes the output of the previous LSTM layer as its input, with an output shape of (None, 128). This LSTM layer has 128 units, and the total number of parameters is 131,584. Finally, a Dense layer follows, with an output shape of (None, 1). This layer has one unit, and the total number of parameters is 129.  
We have set RMSE as the loss function for LSTM training and used the Adam Optimizer to minimize it. The generated LSTM model has a total of 198,273 trainable parameters, and each model undergoes training for 50 epochs.  
Figure 6(b) illustrates the process of using the trained model to make predictions. The X values from the Test Data are sequentially input into the trained LSTM model, and the model predicts the Y values corresponding to the X values through the prediction process, representing the CPU rate. As these predictions are normalized, they undergo a Denormalization process to convert them back into actual prediction values.  
Therefore, in our research, we apply the LSTM model to the scheduling of time series data. This model effectively detects non-linear behavior patterns, understands temporal dependencies, and adapts to chaotic long-term time series data through adaptability and model generalization.
# An Experimental Tethered UAV-Based Communication System with Continuous Power Supply
## 7 Saving the iptables rules' configuration:
#### 3.2.4 Uav  
A UAV is required to establish the communication system in accordance with the prototype requirements. For this purpose, a quadcopter UAV of the Holybro brand, model S500 V2, is used, as shown in Figure 13. This UAV features a lightweight design, is easy to assemble, and does not require welding; its structure is composed of various materials. The arms are made of polyamide and nylon that are reinforced with carbon fiber in the center, and the landing gear is made of carbon fiber tubes. The characteristics of the landing gear are specified in Table 5.  
Figure 11. All extensions configured in FreePBX.  
Figure 12. Configuration of the Zoiper system in the context of the VoIP communication system.  
The UAV integrates a Pixhawk 6C flight controller and a Holybro SiK V3. The controller provides highly accurate and stable flight control. This controller enables autonomous mission execution, ensuring robust navigation thanks to its advanced processing power and control algorithms. The Pixhawk 6C operates at a frequency of 480 MHz, providing fast processing. Table 6 details the specifications of the Pixhawk 6C flight controller. On the other hand, the telemetry radio establishes a connection between the autopilot and the ground station through an antenna that offers a range of 300 m, which can be extended as needed. This device utilizes open-source firmware specifically designed to operate with MAVLink packages and is compatible with various tools, including Mission Planner, ArduPilot, QGroundControl, and PX4 Autopilot. It operates in two frequency bands depending on the region: (i) 915 MHz in America and (ii) 433 MHz in Europe [37]. Table 7 shows the technical specifications of the telemetry radio.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Detail** \\ \hline
**Speed** & 480 MHz \\ \hline
**Flash memory** & 2 MB \\ \hline
**RAM** & 1 MB \\ \hline
**Weight** & 34.6 g \\ \hline \hline \end{tabular}
\end{table}
Table 6: Pixhawk 6C controller [37].  
Figure 13: UAV Holybro S500 V2 [37].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Detail** \\ \hline
**Weight** & 1230 g \\ \hline
**Dimensions** & 383 × 385 × 240 mm \\ \hline
**Maximum payload weight** & 1.5 kg \\ \hline
**Propellers** & 4 \\ \hline
**Recommended voltage for 1.3 kg payload** & 16 V \\ \hline
**Recommended current for 1.3 kg payload** & 16.37 A \\ \hline
**Flight time** & 18 min in hover flight with a 5000 mAh battery \\ \hline \hline \end{tabular}
\end{table}
Table 5: Holybro S500 V2 UAV features general, based on [37].
## 3 Results and Discussion  
### Results of Force-Displacement Curve  
The results of the test for obtaining the force-displacement diagram are shown in Figure 7. It was confirmed that the same trajectory was drawn when the results of tests repeated more than five times were expressed graphically. The values measured when the suspension was extended and when it was retracted were different because resistance forces are generated in the opposite direction of movement, owing to internal friction and fluid viscosity. The suspension characteristic in which this difference occurs is called hysteresis, and when the difference between the upper and lower curves was measured at the point where the displacement was zero, the magnitude of hysteresis was 179.8 N. Using the test results, the relationship between force and displacement was obtained using Equation (9), which derives the average trend line of the two curves. The slope of 3.617 N/mm was the spring constant, and the constant \(-\)1547 N was the initial retractive force acting by the charging pressure of  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Velocity (m/s)** & **Frequency (Hz)** \\ \hline
1 & 0.025 & 0.114 \\
2 & 0.050 & 0.227 \\
3 & 0.075 & 0.341 \\
4 & 0.100 & 0.455 \\
5 & 0.150 & 0.682 \\
6 & 0.200 & 0.909 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Velocity conditions of test for force-velocity curve.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Command (\%)** & **Current (mA)** \\ \hline
1 & 0 & 280 \\
2 & 20 & 360 \\
3 & 30 & 540 \\
4 & 40 & 720 \\
5 & 50 & 900 \\
6 & 60 & 1080 \\
7 & 75 & 1350 \\
8 & 100 & 1580 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Current conditions of test for force-velocity curve.  
the accumulator when the displacement was zero. In the initial retractive force, a positive number indicated force in the extend direction and a negative number indicated force in the retract direction.  
\[F=3.617x-1547 \tag{9}\]