esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background
### Neural Networks for Non-Linear Regression Problems  
In general, a neural network model is always composed of three parts [15]:  
* network architecture (number of layers, activation functions, etc.),
* cost function,
* optimizer (a method or algorithm used to minimize the cost function).  
The neural networks considered in this work have a feed-forward architecture, as it is typical in regression problems. The details of the networks are described in Section 3. The cost function needs to be chosen depending on the problem to be solved. For example, the cross-entropy is a common choice when solving classification problems [15]. For regression problems, as the one studied in this work, the most common cost function is the mean square error (MSE), which is defined as  
\[MSE=\frac{1}{n}\sum_{j=1}^{n}\sum_{k=1}^{d}(y_{k}^{[j]}-\hat{y}_{k}^{[j]})^{2} \tag{1}\]  
where \(n\) is the number of observations in the input dataset; \(y^{[j]}\in\mathbb{R}^{d}\) is the measured value of the desired quantity for the \(j\)th observation (indicated as a superscript between square brackets), with \(j=1,...,n\); \(\hat{y}^{[j]}\in\mathbb{R}^{d}\) is the output of the network, when evaluated on the \(j\)th observation. The optimizer affects the learning performance of the network but does not determine the type of problems the network can solve and therefore will not be discussed here.  
A regression problem consists of minimizing the cost function, in this case the MSE (Equation (1)), with respect to the learnable parameters of the network, which are defined in the architecture. The implicit assumption done is that there is an underlying albeit unknown function that describes the relationship between the \(y^{[j]}\) and the input observations (the measurements \(x^{[j]}\)). Assuming its existence, the neural networks try to approximate it, by composing a big number of non-linear functions. This approach relies on the implicit assumption that a network can approximate any function. For FFNN, this assumption is legitimate since it was proved mathematically [16; 17; 18; 19; 20; 21; 22; 23]. This mathematical proof thus justifies the use of neural networks for regression problems. Unfortunately, not being a constructive proof, it provides neither the number of layers nor the number of neurons per layer needed to approximate this unknown function. It just tells that, with enough neurons, a neural network is able to approximate any function.
# Low-Profile High-Efficiency Transmitarray Antenna for Beamforming Applications
## 3 Beamforming Partial H-Plane Waveguide Slot Array Antenna
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
0 degree & Row1 & Row2 & Row3 & Row4 & Row5 & Row6 & Row7 & Row8 \\ \hline \(pw_{1}\) & 6.3 & 6.3 & 6.3 & 6.3 & 6.3 & 6.3 & 6.3 & 6.3 \\ \hline \(pw_{2}\) & 7.1 & 7.1 & 7.1 & 7.1 & 7.1 & 7.1 & 7.1 & 7.1 \\ \hline +20 degree & Row1 & Row2 & Row3 & Row4 & Row5 & Row6 & Row7 & Row8 \\ \hline \(pw_{1}\) & 0.1 & 5 & 6.9 & 6.9 & 6.9 & 3 & 6 & 6.6 \\ \hline \(pw_{2}\) & 0.1 & 5 & 6.9 & 7.3 & 7.5 & 3 & 6 & 7 \\ \hline +40 degree & Row1 & Row2 & Row3 & Row4 & Row5 & Row6 & Row7 & Row8 \\ \hline \(pw_{1}\) & 7.7 & 3.0 & 6.9 & 6.9 & 4.7 & 6.9 & 6.9 & 6.4 \\ \hline \(pw_{2}\) & 7.7 & 3.0 & 6.9 & 7.4 & 4.7 & 7.3 & 7.5 & 6.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Dimensions of the unit cell of the FSS for 8 rows against the beamforming angle (0\({}^{\circ}\), +20\({}^{\circ}\), and +40\({}^{\circ}\)) (Unit: mm). Figure 6: Structure of the beamforming partial H-plane waveguide slot array antenna. (**a**) Front view and (**b**) top view.
## 3 Results and Discussion
### Ascorbic Acid Content  
The confitures produced in an open vessel as well as under reduced pressure were characterized by similar ascorbic acid content (Table 2). [PERSON] et al. [15] obtained a considerably greater difference in vitamin C content in their jams as a result of the different production process. They indicated that bilberry jams concentrated under vacuum contained from 8.6 to even 18.3% more vitamin C than those concentrated conventionally, which is likely related to the different temperature levels. Depending on the variety of blue honeysuckle berry used, the puree made from them contained less ascorbic acid (10.7 to 19.5 mg per 100 g) [22] than our confitures.  
The honeysuckle berry fruits contain more ascorbic acid than processed products due to the degradation of this compound during heat treatment and presence of oxygen. Regarding the berry origin or variety, the literature indicates different content of ascorbic acid in fresh honeysuckle fruits: 1628-2855 mg in 100 g dry weight (Czech Republic) [2], 178-421 mg/100 g fresh weight (Canada) [25], 250 mg/100 g fresh weight (Portugal) [5] or 90-300 mg/100 g fresh weight (Poland) [4].  
Figure 1: The scores of the sensory evaluation of different confitures recipes. R1—recipe without guar gum; R2, R3, R4, R5—recipes with 0.15, 0.30, 0.40, 0.50% of guar gum content.  
In both types of confitures, ascorbic acid has gradually degraded. However, in confiture cooked under reduced pressure, the degradation was significantly lower, and finally after 180 days of cold storage it contained about two times more ascorbic acid than other confiture types. A similar effect of the vacuum cooking method was found by other researchers in bilberry jams [15]. This was probably due to the lower oxygen concentration in the confiture mass, which causes oxidation of ascorbic acid [20]. In addition, the low storage temperature allows the preservation of more ascorbic acid [26].
## 1 Introduction
Therefore, in this study, since time-sequential inertial sensor data is used to minimize training time and computing resources, it is judged that the use of a single LSTM model using time series data is more suitable than a hybrid of CNN based on spatial correlation. The development of motion classification algorithms based on deep-learning models generally requires a long training time and high-specification PC. The model type and  
Figure 1: Categorization of proposed deep-learning models [5]. architecture as well as the number of training iterations also exert a considerable influence on the outcome. Factors affecting the accuracy of motion classification can be very diverse, not only by the type of model but also by data acquisition methods (number and attachment locations of sensors, sampling rate, etc. ), pre-processing, etc. [11; 12]. However, if we look only at the application of the same model under the same conditions, the main factors influencing the training time and accuracy of deep-learning models are the number of hidden units and epochs. An epoch is a single training iteration of the overall data, and an increase in the number of epochs corresponds to an increase in the number of training iterations, and hence an increase in the accuracy of the deep-learning model [13]. A hidden unit acts as a neuron in the hidden layer and performs data computation. An increase in the number of hidden units indicates an increase in the level of computation and hence an increase in the accuracy; however, the training time per epoch also increases [14]. Thus, in order to construct an efficient deep-learning model, it is necessary to properly control these two factors: the numbers of epochs and hidden units. In this study, motion classification was performed using low-sampling single accelerometer data using a deep-learning model on a common PC with typical specifications. In addition, the training time and classification accuracy according to the adjustment of the number of epochs and hidden units were compared.
## 4 Experiments
### Model-in-the-Loop Simulations
On the straight road, the torque distribution strategy under PSO without battery balancing exhibited the best energy consumption rate: 75.21 Wh/km. However, the front battery was depleted after 167.6 km, and energy consumption efficiency was 67.5%. Thus, the rear battery packs had only 32.5% energy remaining when the EV stopped. The proposed TBD strategy of partly using torque distribution by PSO had the highest driving range: 214.4 km, i.e., approximately 248 UDCs. This was attributed to the SOC of the three battery packs being kept in balance to avoid unexpected battery depletion. Therefore, approximately 28% more driving range was extended by the TBD strategy than by the PSO strategy without battery balancing. It was also found in the simulation that the battery energy was fully utilized by the TBD strategy for the EV on a straight road of UDC. The 99.9% energy consumption efficiency for the TBD strategy was calculated in Table 4. The energy of three battery packs can be effectively distributed and utilized to extend the driving range, when the SOC gap of the three battery packs remains within a prescribed limit during vehicle operation. Other strategies with a PTR, \(P_{r}\) from 0 to 1, presented lower travel distances and less energy consumption efficiency than observed for the proposed TBD strategy. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Battery** & **Front** & **Rear Left** & **Rear Right** \\ \hline Energy capacity (kWh) @ 100\% SOC & 10.45 & 5.2 & 5.2 \\ Initial SOC (\%) & 90 & 90 & 90 \\ Final SOC (\%) & 0 & 1.75 & 1.75 \\ Initial battery energy (kWh) & \(A=(10.45)(0.9)+(5.2)(0.9)(2)=18.77\) \\ Total energy consumption (kWh) & \(B=(10.45)(0.9-0)+(5.2)(0.9-0.0175)(2)=18.75\) \\ Energy consumption efficiency & \(A/B=18.75/18.77=99.89\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Energy consumption efficiency for the EV on a straight road of UDC by the TBD strategy. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Strategy** & **Energy Consumption (\&Wh)** & **Travel Distance** & **Energy Consumption Rate (Wh)/km** & **Energy Consumption Efficiency (\%)** \\ \hline \multicolumn{5}{c}{Clockwise UDC along a Circular Path (Radius 100 m)} \\ \hline TBD & 16.52 & 142.6 & 115.9 & 88.0 \\ PSO & 13.89 & 132.4 & 104.9 & 73.5 \\ \(P_{r}=0\) & 10.11 & 85.9 & 117.7 & 53.3 \\ \(P_{r}=0.25\) & 11.10 & 90.6 &
# Mobility Management Based on Beam-Level Measurement Report in 5G Massive MIMO Cellular Networks
###### Abstract  
Massive multiple-input-multiple-output (MMIMO) in the mmWave band is an essential technique to achieve the desired performance for 5G new radio (NR) systems. To employ mmWave MMIMO technology, an important challenge is maintaining seamless mobility to users because we need to consider beam-switching within a cell besides the handover between cells. For mobility management in 5G NR systems, 3 GPP specified a beam-level-mobility scheme that includes beam pairing and maintenance between a transmitter (Tx) and receiver (Rx) pair. We propose a unific-measurement report based mobility management scheme for improved radio-link-failure (RLF) rate and the accuracy of the Tx-Rx-beam-pair (TRP) selection with low overhead in 5G mmWave MMIMO networks where both handover and beam-switching are required. Furthermore, we modeled a finite-state-machine (FSM) for a user terminal to evaluate performance gain based on a system-level-simulation (SLS). We use the FSM-based Monte-Carlo SLS for the experiment and compare the performance of the proposed scheme with that of existing schemes in the scenario where both beam and cell-level-mobility are necessary. We show that the proposed scheme achieves an improvement in terms of the 3-dB loss probabilities representing the accuracy of the TRP selection, signal-to-interference-and-noise-ratio (SINR), and RLF rates with a lower signaling overhead compared to existing methods.  
5G NR systems; mmWave; massive MIMO; mobility management; handover; beam management; beam switching +
Footnote †: journal: Electronics  
_Article_
# Enhanced Solar Photovoltaic System Management and Integration: The Digital Twin Concept
## 3 Digital Twins for Solar Photovoltaics
### PV DT Technologies
#### 3.3.3 Electronic Components  
Digital twins of electronic components applied to the integration of PV have been covered by a few research studies [86; 87; 88]. The main inverters and the maximum power point trackers (MPPTs) [89; 90] are the elements most often considered. However, the list of associated electronic components is extensive and includes surge protectors [91], circuit breakers [92], transformers [93], sub-components in the PV distribution board, DC/AC cables, and more [94]. A DT of the PV electronics is an essential aspect of the overall DT and it is implemented towards the physical structure or internal process monitoring of the electronic asset itself [95; 96; 97] or analysis of the impact of the asset on the PV performance at various operational points [89; 90]. Most multi-physics software used in the industry for PV plant design includes the mathematical models of various electronic components, particularly the power conversion efficiency of the main inverter at different operational points [98; 99; 100]. By including electronics models in a PV DT, the authors of [101; 102] implemented early fault detection of DC-DC converters in renewable energy systems. While most DTs are focused on monitoring the performance of the PV modules/array, it is crucial to monitor fault scenarios in the associated electronic components.
## 8 Conclusions  
In conclusion, this paper provides a thorough study of the security concerns associated with Lua applications in IoT firmware. This paper introduces an innovative progressive taint analysis method that precisely identifies service handlers, performs segmented taint analysis, and utilizes graph search to identify vulnerability paths. Furthermore, we developed a prototype system, FLuaScan, and conducted experiments to demonstrate its superiority over current state-of-the-art tool TscanCode in terms of correctness, effectiveness, and practicality. FLuaScan discovered 21 vulnerabilities during the experiment, of which 7 are unknown. The work presented in this paper provides a solid foundation for future research on vulnerability detection in other language scripts programs of IoT firmware.  
Conceptualization, X.L. and Z.W.; Methodology, X.L., W.G. and Q.W.; Software, X.L. and Q.W.; Validation, W.G.; Writing--original draft, X.L.; Writing--review & editing, X.L. and W.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable  
Readers could send emails to us to access the related data.  
The authors declare that they have no conflict of interest and no competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Correctness** & **Effectiveness** & **Practicability** \\ \hline Tscancode & 0\% TP Rate & 0 actual vulnerabilities & time cost: average 1.51 s \\  & & & output: simple text \\ \hline FLuaScan & 42.50\% TP Rate & 21 vulnerabilities(9 unknown) & time cost: average 25.16 s (acceptable) \\  & & moderate to high severity risk & output: two visual output assist manual analysis \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of FLuaScan and Tscancode in terms of correctness, effectiveness, and practicability.
# Comparison of Nonlinear and Linear Controllers for Magnetic Levitation System
## 3 Control Design
### Robust Pole Placement--Pole Region Approach
#### 3.1.1 Robust Control Problem Formulation  
Robust pole-placement controller is designed for a polytopic uncertain linear discrete-time dynamic system.  
\[x(k+1)=A(\xi)x(k)+B(\xi)u(k), \tag{7}\]  
where \(x(k)\in R^{n}\) is the state vector, \(u(k)\in R^{m}\) is the control input, \(\xi\in R^{N}\) denotes the vector of uncertainty parameters corresponding to uncertainties belonging to the convex polytope described as follows  
\[(A(\xi),B(\xi))\in\{\sum_{i=1}^{N}(A_{i},B_{i})\xi_{i},\quad\sum_{i=1}^{N}\xi_ {i}=1,\xi_{i}\geq 0\} \tag{8}\]  
matrices \(A_{i},B_{i}\) are constant and have corresponding dimensions. It is assumed that all states can be used for feedback control  
\[u(k)=Kx(k) \tag{9}\]  
The control design goal is to find such feedback gain matrix \(K\) that the corresponding closed-loop system (10) is stable and conform to the prescribed performance limits, in our case it has all poles in the prescribed region in the convex plane.  
\[x(k+1)=(A(\xi)+B(\xi)K)x(k)=A_{CL}(\xi)x(k). \tag{10}\]
## 5 Testing and Development of Hybrid Connections (PRESSS Research Program 1989)
### Outcome and Discussion of the PRESSS Program  
In total, 18 precast post-tensioned specimens were tested. The variables included the amount and location of post-tensioning steel, the type of bonding (fully, partial, or unbonded), and the type and amount of low-strength steel. The strands performed better than the high-strength bar. The prestressing steel location at the centroid of beam resulted in more energy dissipation and less strain in PT steel. Low-strength steel was located at top and bottom, which acted as energy dissipators. This steel was partially debonded to delay the fracture of the bars.  
The experimental studies showed that hybrid connections could perform like monolithic connection in terms of strength, ductility, and energy dissipation, with minimal damage to the structure because the plastic hinge is in the connection region. The damping of monolithic specimens was greater compared to that of precast frames. This was due to complete damage of monolithic beams, whereas in precast specimen failure and yielding was only in the connection region. Beam and column damage was minimal in precast specimens. Even with the fracture of energy-dissipating bars, there was no actual  
Figure 20: Four test specimens with various variables.  
connection failure. The connection attained 6% drift before failure, indicating that even at an unanticipated drift level, the structure would survive. Overall, the hybrid connection performed well against seismic loads.  
Column fabrication for the hybrid frame to accommodate connections requires ducts for mild steel bars and post-tensioning steel, and is an easy process. However, beams require troughs at the top and bottom and additional shear detailing at this reduced section, which will increase congestion and costs. Embedment of straight PT ducts during fabrication is easy. The erection of beams require temporary corbels, and beams can be easily erected due to large tolerance, reducing the crane time. The weight of mild steel bars can easily be handled by a single person. The grouting and tensioning process requires a skilled workforce and can be expensive in some countries. Co-ordination between precast and post-tensioning contractors is required. PT steel and mild steel bars have sufficient cover for protection from deterioration. However, the unbonded portion of PT steel and mild steel at the beam-column interface can be at risk of damage. It is difficult to repair damaged energy-dissipating bars in hybrid systems. Special consideration needs to be given to hybrid frames for connecting the floor diaphragm at the beam-column interface where the gap is designed to occur. The floor diaphragm can either be isolated at the gap area or connected to the beam where expect local damage is expected.
# Utilization of Merge-Sorting Method to Improve Stitching Efficiency in Multi-Scene Image Stitching
## 3 Image Registration
Then, the RANSAC algorithm is used to remove the incorrectly matched points, keeping the correctly matched points, and to calculate the affine transformation matrix for the images. After several iterations, the final image feature point matching result is obtained. We used three methods (SIFT, SURF, and A-KAZE) to detect and match the feature points of the images respectively and compared the time taken by the three methods, which is discussed in Section 5. In Figure 1, four images (a,b) and (c,d) were obtained from [28]. It shows the results for the feature point detection and matching obtained using the A-KAZE method for the pairs of images (a,b) and (c,d), respectively. The pair of images (a,b) is a close-up view with shade, and the pair of images (c,d) is a distant view with roads and pedestrians. It can be seen from the results that the method not only resulted in correctly matching the points for buildings in all images, but matching points in the shade of trees were also correctly matched in images (a,b), and matching points on the road were correctly matched in images (c,d).
# Effect of Water Content on Light Nonaqueous Phase Fluid Migration in Sandy Soil
## 4 Discussion  
The present study provides a comprehensive analysis of the impact of water content on the migration of light nonaqueous phase liquids (LNAPLs) in sandy soil. The findings contribute to the understanding of LNAPL behavior in the subsurface environment, which is crucial for developing effective remediation strategies for contaminated sites. The experimental results clearly demonstrate that water content plays a pivotal role in LNAPL migration. In the unsaturated zone, LNAPL migration is influenced by both capillary action and gravity, with the latter becoming more dominant as the water content decreases. This observation is consistent with previous studies that highlighted the dual influence of capillary forces and gravity on LNAPL movement [27]. However, our study extends  
Figure 10: LNAPL flow rate versus time.  
these findings by providing a quantitative relationship between water content and LNAPL migration rates under controlled laboratory conditions.  
The residual saturation of LNAPL in the soil was found to be negatively correlated with water content, indicating that as water content increases, the amount of LNAPL retained in the soil decreases. This finding is significant as it suggests that in wetter conditions, a greater proportion of LNAPL may migrate into the groundwater, posing a higher risk of contamination. This relationship between water content and residual LNAPL saturation has implications for the design of monitoring wells and the timing of remediation efforts. The nonlinear migration patterns observed in this study, particularly the L-shaped trend during subsurface imbibition, underscore the complexity of LNAPL movement in heterogeneous soil structures. These patterns may be attributed to the varying degrees of soil saturation and the interaction between LNAPL and water within the soil matrix. The nonlinear relationship between LNAPL migration rate and time suggests that migration processes are not constant and are influenced by changing soil conditions, such as water content and oil content.  
While our study aligns with previous research in terms of the influence of water content on LNAPL migration, it offers new insights into the specific mechanisms governing this process in sandy soils. For example, our findings regarding the residual LNAPL saturation in different water content conditions contribute to a more nuanced understanding of LNAPL retention and release in the subsurface environment [26]. Despite the valuable insights gained from this study, there are limitations that should be acknowledged. The experiments were conducted under controlled laboratory conditions, which may not fully replicate the complexity of field conditions. Future research should aim to validate these findings in real-world settings and consider the impact of other factors, such as soil type, temperature, and the presence of other contaminants.