esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Investigation  
### Investigated Structure  
The composite sandwich structure with the dimension of \(1\times 0.38\times 0.016\) m\({}^{3}\) represents a simplified 2:1 model of a large civil aircraft. Figure 2 shows the sandwich structure composed of glass fiber reinforced plastic face layers, a Nomex(r) Aramid honeycomb core (Grade 5) and adhesively bonded aluminum brackets. The glass fiber reinforced polymer (GFRP) face layer laminate is built up of four prepreg fabric plies [0, 45, \(-\)45, 0], with a total thickness of \(0.5\times 10^{-3}\) m. The Nomex honeycomb core has a total thickness of \(0.015\) m.  
This paper focuses on the detection of face layer debondings in sandwich structures. The severity of such type of damage is typically non-critical for structural integrity if it is smaller than a specific critical size, that is, the structure is damage tolerant to a certain extent. Typical definitions of critical sizes for circular shaped damages have a diameter between one and two inches [31]. In the sandwich panel under study a face layer debonding with a diameter of \(0.025\) m (approximately one inch) was artificially introduced during the manufacturing process via an insert. Using an aluminum or Teflon insert to imitate a delamination or face layer debonding for thermography investigations is a well established technique [10; 15]. For the present investigation a circular aluminum foil with a thickness of \(0.01\) mm was used as insert. Compared to Teflon foils, aluminum foils disturb the laminate layup less because they are thinner. Aluminum also has a higher thermal conductivity, which avoids heat accumulation. Furthermore, untreated aluminum foil gets bonded to the laminate when cured, and thus, a single and well defined surface of separation can  
Figure 2: Investigated structure: (**a**) The investigated sandwich structure with the face layer debonding, excitation devices and surface temperature sensors (**b**) Cross-section of the investigated structure at the artificially introduced face layer debonding.  
be produced by applying release agent on only one side, the outer (negative \(z\)-direction, side oriented to the sandwich core) of the inserted foil. Figure 2b shows a section of the manufactured artificial debonding. Nonetheless, it has to be noted that the contact situation as well as thermal properties, of the artificially introduced debonding, deviate from a real delamination or debonding.
# Prediction of Tooth Profile Deviation for WEDM Rigid Gears Based on ISSA-LSSVM
## 4 Tooth Profile Deviation Prediction Model Based on ISSA-LSSVM
### Model Validation  
The performance of the rigid gear tooth profile deviation prediction model based on ISSA-LSSVM is demonstrated by comparing it with other models including PSO-LSSVM, GWO-LSSVM, and SSA-LSSVM. Figure 7 presents the links between real and simulated values, respectively, showing the performance of the prediction models. The results show that the ISSA-LSSVM model closely aligns with the actual prediction curve, with minimal error between the actual and predicted values.  
The predictive indicators of the four models are presented in Table 7. The \(R^{2}\) value of the ISSA-LSSVM model (\(R^{2}=0.9828\)) significantly exceeds 0.95, whereas the \(R^{2}\) values of the other models fall below 0.95. The comparison shows that the ISSA-LSSVM model has the highest degree of fit. Additionally, the \(RMSE\) and \(MAPE\) of the ISSA-LSSVM model are smaller than other models (\(RMSE=0.0029\), \(MAPE=0.0156\)). In comparison to the SSA-LSSVM model, the ISSA-LSSVM model shows an increase in \(R^{2}\) value by 0.0389, a decrease in \(RMSE\) by 0.0009, and a decrease in \(MAPE\) by 0.0070. The above analyses also validate the feasibility and superiority of the ISSA, which incorporates multiple strategies for improvement. Both Table 7 and Figure 7 demonstrate that the ISSA-LSSVM model outperforms other models in terms of prediction accuracy and stability.
## Results
### Moisture Influence on the Electrical Response  
Moisture influence on the electrical conductivity of the membranes was evaluated and is displayed in Figure 9a, where Nyquist plots for different RH concentrations (varying from 0% to 100%) for the 90 PAA sample are plotted. Impedance spectra obtained in the whole RH range exhibit two well-distinguished arcs, indicating grain and grain boundary conductivities according to the Bauerle circuit for polycrystalline materials [48]. The resistance of the grain component (at high frequencies) is less than 100 Ohm, indicating high bulk conductivity of the obtained membrane (-3 \(\times\) 10\({}^{-2}\) S/cm). Moreover, it remained almost unchanged with the RH increase from 0% to 100%, whereas the resistance of grain boundaries for the 90 PAA sample decreased significantly from \(\sim\)3 \(\times\) 10\({}^{-5}\) to \(\sim\)10\({}^{-5}\) Ohm. Specific conductivities of the grain boundary component were calculated using the obtained data (Figure 9b). It can be seen that conductivity values varied significantly with RH concentration: indeed, they displayed an almost-exponential growth from 1.8 \(\times\) 10\({}^{-5}\) to 4.5 \(\times\) 10\({}^{-5}\) S/cm.
# An Accurate Platform for Investigating TCP Performance in Wi-Fi Networks
## 5 Conclusions  
This paper has presented an accurate platform for evaluating TCP performance in Wi-Fi networks. The platform leverages the Mininet-WiFi emulator to construct different Wi-Fi networks and modify the network configurations in a time- and cost-effective manner. Moreover, we have added measurement tools to assess the network performance conveniently. We have compared and verified the platform's accuracy by evaluating 14 TCP CCAs. The results show that the emulator platform provides results that are as reliable as those from the actual network. We have also identified whether the bufferbloat issue has occurred within the platform (e.g., a small queue size at a bottleneck link). In addition, we have shown that the platform's evaluation can be configured with different Wi-Fi settings,  
Figure 13: CUBIC flows with different bandwidth values on the S1–S2 link (each color representing a flow throughput).  
such as various IEEE 802.11 standards or signal levels. Using the platform, we can compare TCP CCAs with different performance metrics, including RTT, throughput, and Jain's fairness index. Moreover, we can collect details on the dynamic behaviors of multiple TCP flows. Utilizing the proposed platform, we evaluated CCAs and found that BBR performed best in the Wi-Fi network, where the bufferbloat event happened. Moreover, Reno achieved the best performance in the Wi-Fi environment with a constantly changing number of flows.  
In the future, we will verify the accuracy of emulated Wi-Fi networks against the real ones, which supports IEEE 802.11n, IEEE 802.11 ax, and other Wi-Fi standards.  
Conceptualization, S.A., Y.H. and K.N.; methodology, S.A., Y.H. and K.N.; software, S.A., Y.H. and D.T.T.H.; writing--original draft preparation, S.A., Y.H. and K.N.; writing--review and editing, S.A., Y.H., D.T.T.H., T.D.N., D.-D.L., K.N. and H.S.; supervision, K.N. and H.S.; project administration, K.N.; funding acquisition, K.N. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Japan Society for the Promotion of Science (JSPS) under Grant 20H0417, 23H03377 and in part by the Japan Science and Technology Agency (JST), with the establishment of university fellowships towards the creation of science technology innovation, Grant Number JPMJFS2107.  
Not applicable.  
The authors declare no conflict of interest.
# Side-Channel Vulnerabilities of Unified Point Addition on Binary Huff Curve and Its Countermeasure
## 3 Vulnerabilities of Unified Point Addition
### Vulnerabilities of Binary Huff Curve  
In this section, we find Type 1 and Type 2 vulnerabilities of unified point addition on the binary Huff curve from [9] during the computations of \(P_{1}+P_{2}\) for \(P_{1}\
eq P_{2}\) and \(P_{1}=P_{2}\). Let \(P_{1}=(X_{1}:Y_{1}:Z_{1})\) and \(P_{2}=(X_{2}:Y_{2}:Z_{2})\). In each case, the unified point addition formula can be evaluated as shown in Table 1.  
**Type 1 vulnerability**: Let us consider  
\(Z_{1})(X_{1}+Z_{1})=X_{1}X_{1}+Z_{1}Z_{1}+X_{1}Z_{1}+X_{1}Z_{1}\). Since the value of \(X_{1}Z_{1}+X_{1}Z_{1}\) is zero in \(\mathbb{F}_{2^{n}}\), then \(m_{4}=X_{1}X_{1}+Z_{1}Z_{1}=m_{1}+m_{3}\) for \(P_{1}=P_{2}\). Also, \(m_{5}m_{8}=(Y_{1}^{2}+Z_{1}^{2})(X_{1}^{2}Y_{1}^{2}+Z_{1}^{4})=m_{11}\) for \(P_{1}=P_{2}\). Thus, the operands of \([m_{11}]\cdot[(m_{1}+m_{3})]\), \([m_{4}]\cdot[m_{11}]\), and \([m_{5}m_{8}]\cdot[(m_{1}+m_{3})]\) for \(Z_{3}\), \(X_{3}\), and \(Y_{3}\) are the same for \(P_{1}=P_{2}\) but different for \(P_{1}\
eq P_{2}\). Similarly, consider \([m_{8}]\cdot[(m_{2}+m_{3})]\) and \([m_{5}]\cdot[m_{8}]\) for \(m_{11}\) and \(Y_{3}\). Since \(m_{5}=(Y_{1}+Z_{1})(Y_{2}+Z_{2})=Y_{1}Y_{1}+Z_{1}Z_{1}=m_{2}+m_{3}\), \([m_{8}]\cdot[(m_{2}+m_{3})]\) and \([m_{5}]\cdot[m_{8}]\) have the same inputs for \(P_{1}=P_{2}\) but different inputs for \(P_{1}\
eq P_{2}\).  
In this section, we have defined the two types of vulnerabilities and highlighted them in unified point addition on the binary Huff curve. These vulnerabilities can also be found in unified point additions on other elliptic curves. We explain how to find these vulnerabilities of unified point addition on other elliptic curves in the Appendix A.
## 2 Measurement Principles and Methods
### Sub-Pixel Edge Detection and Fitting  
The sub-pixel edge detection step is pivotal in the slanted-edge method, as accurate determination of the sub-pixel edge position is essential for proper alignment and fitting in subsequent stages. Presently, sub-pixel edge detection techniques can be broadly classified into three categories: moment-based, interpolation-based, and edge model function fitting-based approaches. In this paper, we employ one method from each category for sub-pixel edge detection using the slanted-edge method: gray-level co-occurrence matrix (GLCM) [35], bilinear interpolation (BLI) [36], and hyperbolic tangent fitting (HTF) [37]. Since sub-pixel edge detection is executed for each row of data points, the methods used are inherently one-dimensional. We simulate, compare, and analyze the accuracy and influencing factors of these methods to identify the most effective sub-pixel edge detection technique for the slanted-edge method.
# Beam Scanning and Capture of Micro Laser Communication Terminal Based on MEMS Microm mirrors
## 4 MEMS Micromirror Scanning Capture System  
The microsized laser communication terminal scanning system based on MEMS micromirrors is illustrated in Figure 3. The beam emitted by the laser diode is reflected by the MEMS micromirror and then passes through an optical telescope for emission. The optical system magnifies the beam scanning angle. Similarly, the received light is focused onto a photodetector through an optical coupling lens. The detected beam deflection error is fed back to the micromirror controller for real-time correction of the beam deflection angle.  
Figure 2: Schematic diagram of the principle of the MEMS micromirror scanning-expansion angle.
# Towards Robust Representations of Spatial Networks Using Graph Neural Networks
## 4 Methods
### Results
An interesting direction for future work will be to investigate the impact of _type_ imbalance in heterogeneous graph representations to model performance. It may be that in the presence of _type_ imbalance, using the homogeneous graph representation may be better for model performance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Split** & **GCN** & **GAT** & **GraphSAGE** & **HetSpatial** \\ \hline \multirow{4}{*}{**Dublin**} & 20\% & 0.33 \(\pm\) 0.012 & **0.36**\(\pm\) 0.029 & 0.32 \(\pm\) 0.002 & 0.33 \(\pm\) 0.010 \\  & 40\% & 0.33 \(\pm\) 0.014 & **0.40**\(\pm\) 0.031 & 0.34 \(\pm\) 0.050 & 0.33 \(\pm\) 0.004 \\  & 50\% & **0.57**\(\pm\) 0.045 & **0.57**\(\pm\) 0.037 & 0.32 \(\pm\) 0.002 & 0.33 \(\pm\) 0.007 \\  & 70\% & 0.33 \(\pm\) 0.011 & **0.54**\(\pm\) 0.047 & 0.39 \(\pm\) 0.063 & 0.33 \(\pm\) 0.007 \\ \hline \multirow{4}{*}{**Frankfurt**} & 20\% & 0.50 \(\pm\) 0.018 & **0.51**\(\pm\) 0.019 & 0.47 \(\pm\) 0.014 & 0.41 \(\pm\) 0.010 \\  & 40\% & 0.49 \(\pm\) 0.025 & **0.50**\(\pm\) 0.030 & 0.45 \(\pm\) 0.006 & 0.41 \(\pm\) 0.008 \\  & 50\% & 0.49 \(\pm\) 0.029 & **0.52**\(\pm\) 0.030 & 0.45 \(\pm\) 0.005 & 0.41 \(\pm\) 0.012 \\  & 70\% & **0.50**\(\pm\) 0.026 & 0.48 \(\pm\) 0.024 & 0.47 \(\pm\) 0.022 & 0.41 \(\pm\) 0.007 \\ \hline \multirow{4}{*}{**Toronto**} & 20\% & **0.65**\(\pm\) 0.035 & 0.49 \(\pm\) 0.014 & 0.48 \(\pm\) 0.002 & 0.49 \(\pm\) 0.001 \\  & 40\% & **0.62**\(\pm\) 0.027 & 0.51 \(\pm\) 0.020 & 0.48 \(\pm\) 0.003 & 0.49 \(\pm\) 0.002 \\  & 50\% & **0.66**\(\pm\) 0.020 & 0.47 \(\pm\) 0.013 & 0.48 \(\pm\) 0.003 & 0.49 \(\pm\) 0.001 \\  & 70\% & **0.63**\(\pm\) 0.028 & 0.48 \(\pm\) 0.002 & 0.48 \(\pm\) 0.002 & 0.49 \(\pm\) 0.002 \\ \hline \multirow{4}{*}{**Manchester**} & 20\% & **0.50**\(\pm\) 0.225 & 0.36 \(\pm\) 0.080 & 0.40 \(\pm\) 0.163 & 0.42 \(\pm\) 0.028 \\  & 40\% & 0.36 \(\pm\) 0.080 & **0.58**\(\pm\) 0.234 & 0.42 \(\pm\) 0.162 & 0.42 \(\pm\) 0.022 \\  & 50\% & **0.74**\(\pm\) 0.227 & 0.52 \(\pm\) 0.215 & **0.73**\(\pm\) 0.250 & 0.41 \(\pm\) 0.019 \\  & 70\% & 0.58 \(\pm\) 0.252 & **0.74**\(\pm\) 0.254 & 0.49 \(\pm\) 0.199 & 0.41 \(\pm\) 0.027 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Table showing the macro F1-score results for building type inference. The values in bold and underlined represent the best and second best performing approaches for each city/split, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Split** & **GCN** & **GAT** & **GraphSAGE** & **HetSpatial** \\ \hline \multirow{4}{*}{**Dublin**} & 20\% & 0.29 \(\pm\) 0.001 & 0.32 \(\pm\) 0.006 & 0.29 \(\pm\) 0.001 & **0.56**\(\pm\) 0.348 \\  & 40\% & 0.29 \(\pm\) 0.001 & 0.31 \(\pm\) 0.005 & 0.29 \(\pm\) 0.002 & **0.64**\(\pm\) 0.345 \\  & 50\% & 0.29 \(\pm\) 0.001 & 0.32 \(\pm\)
# A Novel Method for Modeling the Electromagnetic Characteristics of Switched Reluctance Motors
## 4 Dynamic Simulation and Experiment  
In this section, to further verify the effectiveness of the proposed modeling method, the comparison studies between the simulation and experiment are carried out by using a three-phase 12/8 pole 1.5-kW SRM. The details of the motor parameters are listed in Table 2.
# Tablet Scoring: Current Practice, Fundamentals, and Knowledge Gaps
## 4 Tablet Scoring
### Tablet Splitting: Current Practice
Images (**d**) through (**i**) are various tablets splitters that can be used for different shape tablets that are embossed with a score line. Images (**j**) though (**s**) are tablet grinders; these are designed to grind and crush tablets to deliver finely crushed powder medication. Images (**t**) through (**w**) are tablet pulverizers that are designed to crush tablets to a fine powdered medication. They are available as ball and sockets and as motor and pestle. Image reproduced from the article [129]. Permission to include this image in this review has been obtained from PLOS One. Tablet physical properties such as size, curvature and thickness, and the form and deepness of the score line as well as scoring techniques can affect the effectiveness of tablet splitting [118]. To facilitate tablet splitting, recent studies have suggested the use of compression molding [130]. Compression molding is a semi-manual operation where tablets are formed by forcing the wet granulated blend into a mold to achieve tablets of desired shapes and sizes [131]. Designing a mold cavity with a score line could in principle produce tablets that can be scored more effectively. However, this technology, while effective for proof of concept experiments, could have several limitations during large-scale production. Also, since the molded tablets are tray-dried and/or oven-dried, obtaining a desired tablet hardness and a low tablet friability may be an issue [132]. Tablet weights and uniformity of dose are interdependent. Uneven scored tablets can pose great clinical risk, especially when targeting a narrow therapeutic-toxic range [130]. By contrast, tablets with wider therapeutic indices and longer half-life are more forgiving to uneven splitting. In a nutshell, while there are cost benefits associated with tablets splitting/partitioning, this practice is extensively used for clinical reasons. In a larger perspective, using tablet splitting to reduce costs is limited by the number of products suitable for tablet splitting [133]. The practice is largely dependent on the actions of pharmaceutical manufacturers. Factors that can influence tablet splitting practices include: (1) the number of dosages available, (2) tablet properties, (3) tablets that require specialized excipients to target a modulated release profile, and (4) storage conditions. Patients should be able to split tablets easily, either by hand or with the use of a tablet splitter. To achieve the therapeutic and economic benefits from tablet splitting, patients need to be educated on the rationale and procedures of tablet splitting [109]. Tablet splitting/partitioning has played a crucial role in modern medicine, as the ability to dose taper and dose titrate is important across therapeutic areas. Based on some accrued clinical data, some warning has been issued around the practice of tablet splitting/partitioning. According to clinical observations, approximately a third of the partitioned tablets fall outside of the recommended dosages by more than 15 percent [79; 134].
# A Comparative Study of 3D UE Positioning in 5G New Radio with a Single Station
## 4 Performance Comparison
### Performance Comparison of Positioning accuracy in Different Contexts
#### 4.2.2 Impact of the Nearby Reflection  
This section discusses the deterioration caused by a nearby reflected signal. Different from the SRSs from the different UEs, the reflection of the SRS from a nearby object has a limited ToA difference from the original SRS. In this simulation, we placed the signal source and reflection point within the minimal resolvable area (\(1.5^{\circ}\) and \(0.3\) m target range in the simulation when \(8\times 8\) is the antenna scale). Assume the signal source is located at a fixed point with a 12 m distance to the base station with \([20,20]\) azimuth and elevation angles, under 9 dB SNR. The reflecting signal is located at 12.6 m with \([21,21]\) azimuth and elevation angles. Under that case, the ToA detection for both the EM and subspace approaches is influenced. The angle and time increment are one degree and 1 ns, respectively. The details are discussed in the following subsection.  
* RMSE vs. power: The total bandwidth is 60 MHz, and the SRS uses 2.88 MHz. The reflection signal is set at a fixed range 12.6 m and the \([21,21]\) angles. The EM and subspace algorithms' positioning RMSEs versus different signal power-to-reflection power ratio (SPRP) levels are collected in Figure 8. The EM estimation accuracy decreases with the power of the reflection. However, the influence of the SPRP on the subspace algorithm is more severe (constantly around 0.6, as shown in Figure 5, as the subspace algorithm is more sensitive to the correlated sources.  
Figure 7: Positioning accuracy of the EM and subspace algorithms with different bandwidths.
# A Deep Curriculum Learning Semi-Supervised Framework for Remote Sensing Scene Classification
###### Abstract  
In recent years, deep learning has witnessed astonishing success in the field of remote sensing in images. Generally, deep learning requires a large amount of labeled training data. Nevertheless, in remote sensing, sufficient labeled data are scarce because labeled data are often difficult, expensive, or time-consuming to obtain. To address these problems, we propose a deep curriculum learning semi-supervised framework (DCLSSF) for remote sensing image scene classification. This framework employs a multimodal deep curriculum learning method which can realize the classification of images on a range of easy-difficult. Specifically, by utilizing multiple pretrained networks to extract multiple deep features of images as their multimodal feature representations, it can comprehensively mine the information from labeled and unlabeled images from diverse perspectives. Subsequently, a feature fusion method is used on deep features of different modalities to obtain deep fusion features with a strong discrimination ability and low dimensionality. Finally, the multimodal deep features are fed into multimodal curriculum learning methods for classification. Multimodal curriculum learning can integrate the easy curricula recommended by each modal according to the order of the samples of each modal and then learn step by step. Experiments on three publicly available datasets (UC Merced, AID, and NWPU-RESISC45) show that the semi-supervised classification framework achieves high accuracy rates (99.14%, 97.95%, and 93.01%), even surpassing those of the most supervised classification methods. The DCLSSF method can not only fully exploit the rich features extracted by the multimodal deep learning network but can also perform the semi-supervised classification of unlabeled samples in a range of easy-difficult.  
Keywords:convolutional neural network (CNN); feature fusion; curriculum learning; semi-supervised learning; image classification
## 2 Theoretical Behavior of Distributed versus Concentrated Volt/var Local Control Strategy
### Definitions  
Distributed Volt/var local control strategy is when Volt/var local controls (e.g., \(cos\varphi(U)\) or \(Q(U)\)) are applied to reactive power devices (e.g., PV-inverters), which are connected to various locations throughout the length of the feeder in order to mitigate the violation of the upper voltage limit. Distributed var-sinks are reactive power consumptions in various locations of the feeder provoked by the distributed Volt/var local controls. Figure 1 shows an overview of the distributed \(Q(U)\) local control strategy or rather distributed var-sinks.  
Concentrated Volt/var local control strategy is when one reactive power device (e.g., coil or inductance, \(L\)) upgraded with a Volt/var local control (e.g., \(L(U)\)) is applied mostly at the end of the feeder to mitigate the violation of the upper voltage limit. A concentrated var-sink is the reactive  
Figure 1: Overview of the distributed \(Q(U)\) local control strategy or rather distributed var-sinks.  
power consumption of the reactive device at the connection point. Figure 2 shows an overview of the concentrated \(L(U)\) local control strategy or rather concentrated var-sink.  
Both control strategies are theoretically analyzed in the following.