esa-sceva/satcom-chunk-collection huggingface dataset:
# Commonsense-Guided Inductive Relation Prediction with Dual Attention Mechanism
## 3 Methodology
### Experimental Results
#### 4.2.1 Main Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**WN18 RR**} & \multicolumn{4}{c}{**FB15k-237**} & \multicolumn{4}{c}{**NELL-995**} \\  & **v1** & **v2** & **v3** & **v4** & **Avg.** & **v1** & **v2** & **v3** & **v4** & **Avg.** & **v1** & **v2** & **v3** & **v4** & **Avg.** \\ \hline GraIL & 82.45 & 78.68 & 58.43 & 73.41 & 73.24 & 64.15 & 81.80 & 82.83 & 89.29 & 79.51 & 59.50 & 93.25 & 91.41 & 73.19 & 79.33 \\ TACT & 84.04 & 81.63 & 67.97 & 76.56 & 77.55 & 65.76 & 83.56 & 85.20 & 88.69 & 80.80 & 79.80 & 88.91 & 94.02 & 73.78 & 84.12 \\ CoMPILE & 83.60 & 79.82 & 60.69 & 75.49 & 74.90 & 67.64 & 82.98 & 84.67 & 87.44 & 80.68 & 58.38 & 93.87 & 92.77 & 75.19 & 80.05 \\ SNRI & 87.23 & 83.10 & 67.31 & 83.32 & 80.24 & 71.79 & 86.50 & **89.59** & 89.39 & 84.32 & — & — & — & — \\ ConGLR & 85.64 & **92.93** & 70.74 & **92.90** & **85.55** & 68.29 & 85.98 & 88.61 & 89.31 & 82.93 & 81.07 & **94.92** & 94.36 & 81.61 & 87.99 \\ RMPI & 82.45 & 78.68 & 58.68 & 73.41 & 73.31 & 65.37 & 81.80 & 81.10 & 87.25 & 78.88 & 59.50 & 92.23 & 93.57 & **87.62** & 83.23 \\ CNIA (ours) & **89.36** & 85.94 & **72.06** & 83.03 & 82.60 & **74.20** & **86.51** & 89.43 & **89.39** & **84.88** & **85.22** & 94.11 & **94.55** & 82.03 & **88.98** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The Hits@10 metric values (%) of inductive relation predictions on twelve dataset versions. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**WN18 RR**} & \multicolumn{4}{c}{**FB15k-237**} & \multicolumn{4}{c}{**NELL-995**} \\  & **v1** & **v2** & **v3** & **v4** & **Avg.** & **v1** & **v2** & **v3** & **v4** & **Avg.** & **v1** & **v2** & **v3** & **v4** & **Avg.** \\ \hline GraIL & 94.32 & 94.18 & 85.80 & 92.72 & 91.75 & 84.69 & 90.57 & 91.68 & 94.46 & 90.35 & 86.05 & 92.62 & 93.34 & 87.50 & 89.87 \\ TACT & 95.43 & 97.54 & 87.65 & 96.04 & 94.16 & 83.15 & 93.01 & 92.10 & 94.25 & 90.62 & 81.06 & 93.12 & 96.07 & 85.75 & 89.00 \\ CoMPILE & 98.23 & 99.56 & 93.60 & 99.80 & 97.79 & 85.50 & 91.68 & 93.12 & 94.90 & 91.30 & 80.16 & **95.88** & 96.08 & 85.48 & 89.04 \\ SNRI & 99.10 & **99.92** & 94.90 & 99.61 & 98.38 & 86.69 & 91.77 & 91.22 & 93.37 & 90.76 & — & — & — & — \\ ConGLR & 99.58 & 99.67 & 93.78 & 99.88 & 98.22 & 85.68 & 92.32 & **93.91** & **95.05** & 91.74 & 86.48 & 95.22 & 96.16 & 88.46 & 91.58 \\ RMPI & 95.00 & 95.96 & 88.53 & 95.78 & 93.82 & 85.25 & 92.19 & 92.09 & 92.80 & 90.58 & 81.12 & 93.46 & 95.35 & **91.77** & 90.43 \\ CNIA (ours) & **99.89** & 99.91 & **94.95** & **99.90** & **98.66** & **86.99** & **92.75** & 93.42 & 94.80 & **91.99** & **95.50** & 95.43 & **96.25** & 88.13 & **93.83** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The AUC-PR metric values (%) of inductive relation predictions on twelve dataset versions.
# MANET Routing Protocols' Performance Assessment Under Dynamic Network Conditions
## 5 Results and Comparative Analysis
### Scenarios with Homogeneous Nodes
#### 5.1.3 Simulation Time  
A detailed comparison of the eight network protocols is shown in Figure 3 below. The data reveal significant patterns in simulation duration with increasing network size. At 20 nodes, the protocols show comparatively short simulation periods at first, providing  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Size** & **Proactive Protocols (OLSR,** & **Reactive Protocols (AODV,** & **Hybrid Protocols (ZRP, GRP)** \\ **(Nodes)** & **DSDV)** & **DSM, AODV, TORA)** & **Hybrid Protocols (ZRP, GRP)** \\ \hline
20 Nodes & Consistent throughput, efficient for smaller networks & Varying throughput, generally higher than proactive protocols & Moderate throughput, balanced performance \\
80 Nodes & Slight decrease, consistent performance & Noticeable decrease due to route discovery overhead & Balanced performance, effective management of increased load \\
200 Nodes & Sharp increase, high throughput & Sharp increase, demonstrating scalability and adaptability & Sharp increase, balanced throughput, effective integration of strategies \\
500 Nodes & Stabilizes with slight variations, less optimal due to maintenance overhead & Stabilizes with similar high values, indicating efficiency & Stabilizes, maintains high throughput, most suitable for high-density networks \\ \hline \hline \end{tabular}
\end{table}
Table 6: Protocol Suitability for Different Scenarios Considering Throughput.  
Figure 2: Throughput across the Four Scenarios.  
a starting point for comparing performance. Due to their continuous route maintenance, proactive protocols typically exhibit moderate simulation times; which makes them appropriate for smaller networks. For very large networks, however, the overhead of maintaining routing tables reduces their performance. Under moderate traffic conditions, the overhead of route discovery causes reactive protocols to noticeably increase their simulation time as the network size grows to 80 nodes. These protocols' on-demand route discovery method can efficiently handle the increasing load in situations with moderate to high node densities (80 to 200 nodes).  
When all protocols reach their peak simulation times at 200 nodes, the biggest shift is shown, underscoring the difficulties with scaling and adaptation that come with higher node density. Due to their balanced performance, hybrid protocols--which blend proactive and reactive tactics--are especially well-suited for very high-density networks (500 nodes), where they can effectively handle the additional load. Most protocols show a return to efficiency at the highest network densities, with simulation times tending to steady or drop at 500 nodes. This analysis highlights each protocol category's effectiveness and scalability while offering important insights into their operational performance and possible limitations in a range of network scenarios. Given the simulation time, Table 7 below provides a summary of the procedure's applicability for the four distinct scenarios.
# Secure Speech Content Based on Scrambling and Adaptive Hiding
## 3 Proposed Scheme
### Recovery Module
#### 3.2.3 Descrambling  
The purpose of this block consists in organising the samples obtained from the previous block, according to the positions comprised in the key. Without the information from the key, it is computationally and statistically impossible to determine the secret content in a proper way. By computationally, it is meant that the time to test all options takes several years, given the fact that, thanks to the features of the key, the total number of possible solutions is \(m\) factorial, where \(m\) is the total number of voice signal samples. This way, if \(m\) were equal to 8000 (e.g., a one-second signal with fs = 8 kHz), the total number of possible solutions would be 8000 factorial, a value that greatly exceeds 105,000. Statistically means that if all possible keys that can be used are similarly probable and there is not any preference towards a subset out of them, uncertainty about which of the possible keys is the right one is complete.  
In this context, when applying the correct key, a voice signal with (perceptually) intelligible content equal to the original secret message is obtained. Eventual differences between these two signals comply with the normalisation processes of dynamic ranges in the transmission and reception modules.
# Analysis of Controllability in Cyber-Physical Power Systems under a Novel Load-Capacity Model
## 2 Methods
### Controllability of Cyber-Physical Power System  
Representing real power information physical systems using unified equations is difficult due to their nonlinear nature. Nevertheless, there are many similarities between nonlinear and linear systems. Therefore, when dealing with nonlinear and time-invariant CPPSs, it is possible to explore their linear dynamics. The dynamics equations for these linear time-varying systems can be formulated as follows:  
\[\frac{dx(t)}{t}=\begin{bmatrix}G_{p}&\mathbb{C}\\ \mathbb{C}^{T}&G_{c}\end{bmatrix}x(t)+\begin{bmatrix}B_{\tau_{p}}&B_{\tau_{c}} \end{bmatrix}u(t) \tag{7}\]  
where \(x(t)=[x_{1}(t),x_{2}(t),...,x_{N+M}(t)]^{\prime}\) represent the state of nodes, \(x(t)\in\mathbb{R}^{\prime}N+M\)). And \(u(t)=[u_{1}(t),u_{2}(t),...,u_{s}(t)]^{\prime}\) represents a set of independent input signals, \(u(t)\in\mathbb{R}^{s}\). A \(=\begin{bmatrix}G_{p}&\mathbb{C}\\ \mathbb{C}^{T}&G_{c}\end{bmatrix}_{\begin{subarray}{c}(N+M)\times(N+M)\end{subarray}}\) is the transpose adjacency matrix of the CPPS, which reflects its internal dynamics. \(B=\begin{bmatrix}B_{\tau_{p}}&B_{\tau_{c}}\end{bmatrix}_{s}\) defines how the input signals are coupled to the system, and \(B_{\tau_{p}}\) represents the input signals in the power network, while \(B_{\tau_{c}}\) represents the input signals in the information network.  
According to [PERSON]'s controllability rank condition [PERSON] (1964), a system described by (7) is deemed fully controllable if and only if it can reach any desired state \(x(t)\) under the control of input signals if and only if the matrix  
\[C=[B,AB,A^{2}B,\ldots,A^{N+M-1}B] \tag{8}\]  
is full rank, that is,  
\[rank(C)=(N+M). \tag{9}\]Here, the concept of driver node density is employed to assess the controllability of the network. In Figure 3, input nodes \(u_{1}\), \(u_{2}\), and \(u_{3}\) send control signals to nodes \(x_{1}\) and \(x_{2}\), which are referred to as controlled nodes. When a controlled node is influenced by only one control signal, it is called a driver node. In Figure 3, \(x_{1}\) serves as a driver node. The driver node density, denoted as \(n_{d}\), is defined as follows [22]:  
\[n_{d}=N_{D}/(M+N) \tag{10}\]  
where \(N_{D}\) is the minimum number of driver nodes in the network, and \((M+N)\) is the total number of nodes in the CPPS, including both power and information network nodes. For a CPPS, the minimum number of driver nodes can be determined by calculating the maximum algebraic multiplicity \(\delta(\lambda_{i})\) of the eigenvalues \(\lambda_{i}\) of the network's adjacency matrix. This can be expressed as follows [23]:  
\[N_{\text{D}}=\max_{i}\{\delta(\lambda_{i})\} \tag{11}\]  
The higher the density of driver nodes \(n_{d}\), the greater the number of driver nodes required for the network, indicating the poorer controllability of the network. Conversely, the lower the density of driver nodes \(n_{d}\), the fewer driver nodes are needed for the network, indicating better controllability.
# Effective Optical Scattering Range Determination Based on Ray Tracing
## 3 Application  
In this section, a superpolished sample is considered. A series of measurements of surface features with different techniques is conducted in order to get precise topographic or statistical surface information. Then, the BRDF of the surface is introduced to a typical Cassegrain telescope. The effective directional range is recorded and the corresponding applications are shown in Section 3.2.
# Research on Tbps and Kilometer-Range Transmission of Terahertz Signals
## 3 Enabling Technologies for THz Communication Systems
### High-Gain Antennas and Lenses
This structure \"folds\" the electromagnetic path via the sub-reflector, positioning the feed near the vertex of the primary reflector (or behind it) rather than at the distant focal point. This shortens the antenna\({}^{\prime}\)s physical length (effective focal length \(\geq 2\times\) physical length), reduces transmission loss, and maintains high gain and superior directivity. The sub-reflector effectively \"relocates\" the feed's phase center to the focus of the parabolic surface [102]. The most prevalent antenna in our long-range THz transmission system is the THz lens antenna. Leveraging its focusing and imaging capabilities, this antenna enhances performance by achieving excellent directivity and high gain. Our lens adopts a plano-convex structure--simple in design and mature in manufacturing--optimized via geometric optics principles. Constructed from polytetrafluoroethylene (PTFE), it utilizes THz-wave refraction to collimate divergent radiation from the feed into directional narrow beams. PTFE is the ideal material for high-performance THz lens antennas, offering unmatched ultra-low loss and a low dielectric constant in THz bands; its refractive index is 1.4 in a wide wavelength range [103]. In long-range THz communication experiments, the lens gain at both the transmitter and receiver reaches 70 dB. In the experimental setup diagram shown in Figure 12, to address the impact of Earth\({}^{\prime}\)s curvature on the transmission system, our transmitter is located atop the Guanghua Tower in Fudan University\({}^{\prime}\)s Handan Campus, while the receiver is situated at an open coastal area. by integrating a Cassegrain antenna (gain: 58 dBi, diameter: 60 cm), a dual-cascaded lens antenna (gain: 40 dB + 6 dB, diameter: 60 cm and 10 cm), and a horn antenna (gain: 25 dBi), we constructed a high-performance D-band wireless link. We use the Friis transmission equation for link power budget analysis. The transmit power is 23 dBm, the transmit antenna gain is 58 dBi, the signal frequencies are 125 GHz and 128 GHz, the transmission distance is 30.2 km, and the atmospheric losses are 28 dB at 125 GHz and 27.6 dB at 128 GHz, respectively. The received signal powers measure \(-\)40 dBm at 125 GHz and \(-\)39.8 dBm at 128 GHz. This system successfully achieved stable transmission of an 18 Gbps signal over 30.2 km (comprising 10.8 km terrestrial and 19.4 km sea-surface paths) under complex rain/fog conditions, establishing a new record rate-distance product of 543.6 Gbps\(\cdot\)km. The integrated antenna system significantly enhanced signal transceiving capability, with the innovative dual-lens design at the receiver contributing an additional 6 dB gain compared to single-lens solutions, effectively overcoming the substantial path loss induced by ultra-long distances and severe weather. Figure 11: Terahertz antenna structure diagram: (a) HA; (b) CA; (c) lens antenna.
# Neural Network-Based Analysis and Its Application to Spectroscopy for Mango
## 2 Equipment and Method  
### Experimental Materials and Instrument  
#### 2.1.1 Experimental Materials  
Ninety-five samples of Tai Tainong and Jin Huang Awn mangoes, chosen for their smooth surfaces, the absence of scars, even ripeness, and consistent size distribution, were sourced from Haidian District, Beijing [12]. The samples were randomly divided into distinct ratios and categorized into training and calibration sets. Purified water was employed to cleanse each mango, ensuring the mango's inherent quality remained intact during the cleaning process. Following cleaning, the mangoes were uniformly positioned in a controlled outdoor environment to minimize external factors, such as wind and surface temperature, that could affect the prediction accuracy. Each mango was individually numbered and marked in sequence [13].
# Long-Range Correlations and Natural Time Series Analyses from Acoustic Emission Signals
## 2 Theoretical Background
### b-Value Analysis  
AE data analysis can be based on several criteria: signal amplitude, duration, rise time, acoustic emission energy, and number of AE signals. One of the most used is the \(b\)-value, which is calculated from the AE amplitude distribution data by the power-law relationship between the number of events (\(N\)) that exceeds a given amplitude (\(A\)) and the amplitude of these events [28]. This relationship is commonly known as Gutenberg-Richter [29]:\[N(\geq A)\propto A^{-b} \tag{1}\]  
Figure 1 illustrates how the \(b\)-value varies during a typical damage process. According to [PERSON] [11] and [PERSON] et al. [12; 30], its physical meaning is related to the fractal dimension (\(D\)) of the domain from which the cracks emanate through the expression \(D=2b\).  
At the beginning of the damage process, high \(b\)-values point out the occurrence of many small-amplitude AE events that are caused by micro-crack formation distributed throughout the whole structure's volume, implying \(D=3\) and \(D=1.5\). As the damage process evolves to produce the localization effect, i.e., events tend to emanate from a preferential region and form a \"cloud\" of micro-fissures [26]. Thus, macro-crack nucleation occurs, and the acoustic events tend to originate from a definite surface, i.e., \(D=2\) and \(D=1\). In terms of Equation (1), this is interpreted as an increment of high-amplitude events. The \(b\)-value is a classic parameter in structural monitoring, and therefore, it is used here as a reference for evaluating the proposed indexes' usefulness.
## 1 Introduction
For the analysis of the two \(PV\) module mounting systems under study in real operating conditions, the outdoor photovoltaic energy laboratory of the Department of Electrical Engineering at the University of Oviedo, Spain (latitude 43\({}^{\circ}\)31'22'' N, longitude 05\({}^{\circ}\)43'07'' W, elevation 28 (m) above sea level) was used. To carry out a detailed study of the proposed mounting systems, it was necessary to obtain two fundamental parameters: (i) the hourly incident solar irradiance on the \(PV\) field, and (ii) the daily, monthly, and annual incident solar irradiation on the \(PV\) field. The hourly and daily level of detail is not available in the commercial software (PVsyst, SolarFarmer, RETScreen, etc.) normally used in \(PV\) system projects. To solve this problem, a specific _Mathematica_ code was implemented to calculate the hourly and daily incident solar irradiance on the \(PV\) field. Exploring the advantages and disadvantages of each \(PV\) module mounting system from the point of view of the incident solar irradiance on the \(PV\) field will facilitate the appropriate choice of each system in a given location. The aim of this paper was to provide \(PV\) power plant designers with the knowledge they need to make informed decisions in their \(PV\) power plant projects and answer the following questions: Do they obtain greater energy gain at all times of the year? Do they obtain greater energy gain throughout the day? If all three modes of operation are considered, do they obtain greater energy gain? Will the study under real operating conditions alter the trend of the results? These questions were analysed in this experimental and theoretical study. The main contributions of this work are as follows:  
* A theoretical study of the annual, monthly, and daily energy gain of the horizontal single-axis tracker with respect to the fixed tilt angle system, considering various modes of operation of the solar tracker. * A theoretical study of the hourly incident solar irradiance on the \(PV\  
The remainder of the paper is organised as follows: Section 2 shows the technical considerations for \(PV\) module mounting systems and their differences. The methodology used is presented in Section 3. Section 4 presents the results obtained in the two \(PV\) power plants. Finally, the main conclusions of this work are drawn in Section 5.
###### Abstract  
The aim of the study was to evaluate the changes in skeletal, dentalveolar, and airway dimensions induced by the mandibular anterior repositioning appliance (MARA) in the treatment of Class II malocclusion. A group of 24 Class II patients treated with MARA was evaluated with lateral cephalograms at the start (\(13.8\pm 2\) years) and at the end of treatment (\(15.7\pm 1.9\) years) and compared with a matched untreated Class II group of 20 subjects selected from the archive of the AOOF Legacy. Statistical comparisons were performed with the Student's \(t\)-test. No significant differences were observed for the sagittal airway dimensions. The MARA group showed a significant improvement of Wits (\(-1.4\) mm; \(p=0.009\)) and ANB angle (\(-0.6^{\circ}\); \(p=0.038\)), a reduction in the overjet (\(-1.9\) mm; \(p=0.001\)), of the overjet (\(-1.5\) mm; \(p=0.005\)), an improvement of molar relationship (\(1.8\) mm; \(p=0.000\)) and a proclination of the lower incisors (\(2.7^{\circ}\); \(p=0.012\)). During active treatment, MARA did not produce significant favorable changes in the sagittal airway dimensions in Class II subjects when compared with untreated controls. Clinically, these findings highlighted that MARA was able to provide an effective correction of Class II malocclusion with favorable dentoskeletal changes.  
mardiular anterior repositioning appliance (MARA); Class II malocclusion; lateral cephalogram; cephalometrics +
Footnote †: journal: Applied  
such as obstructive sleep apnea (OSAS) [8]. Since functional appliances represent one of the treatment protocols used for the correction of Class II malocclusions, the literature has demonstrated that these devices could increase the airway dimensions and decrease the risk of respiratory disorders [9]. In this context, it has been demonstrated that the advancement of the mandible with functional appliances influences the position of the hyoid bone including tongue, and it improves the morphology of upper airways [10; 11]. In this panorama, the orthodontist will have a crucial role not only in the treatment of craniofacial anomalies but also in the early interception and reduction in incidence of respiratory disorders. Several studies have evaluated the effects of various functional devices on upper airway modification, but only one examined the mandibular anterior repositioning appliance (MARA) [12]. Specifically, except for that study, only skeletal and dentoalveolar changes induced by the MARA have been evaluated in the literature, without considering airway dimensions. The aim of this retrospective controlled clinical study was to evaluate the changes in skeletal, dentoalveolar, and airway dimensions on lateral cephalograms induced by the MARA in the treatment of Class II malocclusion.
# IoT's Tiny Steps towards 5G: Telco's Perspective
## 5 IoT in 5G Enabling Technologies
### Advanced Interface Management  
This technological group includes receiver advances in terms of simultaneous non-unique decoding (SND) and sliding-window superposition coding (SWSC).  
**SND** follows a rule that implies that each receiver attempts to recover the code words from intended and interfering senders [248]. The combination of advanced receivers and joint scheduling provides an improvement of over 50% in cell edge throughput without sacrificing the cell average throughput [5]. This gain demonstrates that if 5G networks incorporate advanced interference management, they will provide a virtually edgeless end-user experience [5].  
**SWSC**[61; 62] combines the theory concept from superposition coding without rate splitting [63], block Markov coding [166; 167], successive cancellation decoding [63; 64; 168], and sliding-window decoding [65; 169; 249]. The sliding-window coded modulation (SWCM) aims to mitigate inter cell interference at the physical layer by achieving simultaneous decoding performance with point-to-point channel codes, low-complexity decoding, and minimal coordination overhead [170]. The realization of the theoretical concept to the practical transmission coding scheme is an important research direction in SWSC [5].