esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The System Performance
### The Outage Probability (OP)  
**Theorem 1** (OP--Closed Form).: _The closed-form expression of the OP of the proposed model system can be formulated as_  
\[\begin{split}& P_{out}=1-2\exp\left[-\frac{z_{rd}^{dm}}{\Omega_{|h \
u|^{2}}(1-\rho)\psi}\right]^{m_{Ty}-1}\sum_{t=0}^{t}\sum_{n=0}^{K-1}\frac{( -1)^{p}K\times C_{K-1}^{p}\frac{n+t+1}{2}(\lambda_{rd})^{\frac{n-t-1}{2}}}{( (t-n)\
u t\left(\Omega_{|h\
u|^{2}}\right)^{\frac{n+t+1}{2}}(p+1)^{\frac{n-t+ 1}{2}}} \tag{17}\]  
**Proof of Theorem 1.** See Appendix A. \(\Box\)
# Characterization of Narrowband Noise and Channel Capacity for Powerline Communication in France
## 3 Main Experimental Results and Discussion
### Estimation of the Channel Capacity
#### 3.5.2 Estimation of the Channel Capacity in the FCC Band  
Figure 20 shows the results of the estimation of the channel capacity in the FCC band i.e., from 150 to 487 kHz. The noise power can also be extracted from the measurements of each site. In that case, the signal power of the transmitter is equal to 66 \(\upmu\)V. The number of subcarriers is fixed (\(N=72\)) with the \(\Delta f\)-parameter equal to 4.7 kHz.
## 2 Related Work and Contributions
### Contributions  
This work is complementary to existing RSSI-based technology recognition solutions, in the sense that the targeted technologies and application scenarios are different. The goal of this work is to achieve DSA capabilities for NGWN; hence, we focus on streaming technologies beyond the well-studied coexistence problem in the ISM bands. Eventually, prior solutions can be combined with this work to extend the range of recognizable technologies and reduce complexities. To the best knowledge of the authors, this work is the first effort (i) to explain the multi-modal distribution of RSSI with the properties of the signals, other than external factors such as fading, (ii) to systematically explore the features of RSSI from real-life wireless networks and (iii) to apply novel features of RSSI distribution for technology recognition, using a computationally simple and technology agnostic classification algorithm for constrained devices.
## 3 Methodology  
The assessment of rockfall hazard is an extremely complicated work. The procedure of our study is shown as follows: firstly, identify the source areas of rockfalls; secondly, analyze the passage number, passing height, and kinetic energy of falling blocks along the trajectory by numerical simulation; finally, analyze the rockfall hazard in the study area by using the RHV method with an improved reclassification criterion of evaluation parameters.  
In this study, we identified the typical source areas of rockfalls firstly based on aerial photos, remote sensing interpretations, and field investigations. Using a Digital Elevation Model (DEM) with a resolution of 10 m, we summarized the distribution characteristics of typical rockfall source areas in lithology, elevation, slope aspect, slope angle, and other factors in the study area. Then, we used these characters to identify other unstable blocks semi-automatically by inputting the distributing thresholds of rockfall source areas into ArcGIS platform [30; 31]. This method was used to identify the rockfall sources in the area from Nang County to Jiacha County in detail.  
RHV is a rockfall hazard evaluation method proposed by [PERSON] and [PERSON] based on a three-dimensional numerical simulation. This method mainly considers three factors, including the passage number, kinetic energy, and passing height of falling blocks. The values of the three factors can be obtained by the 3D numerical simulation, and reclassified to facilitate the comprehensive evaluation [18]. In this study, Rockyfor3D numerical simulation software was used to obtain the kinematic characteristics of falling blocks. The parameters mentioned above were re-classified by an improved re-classification standard based on the one proposed by [PERSON] et al. [18]. Finally, a rockfall hazard assessment map of the study area was obtained based on comprehensive analysis of each parameter after re-classification.  
Rockyfor3D numerical simulation software was developed based on a large number of field investigations and physical experiments [14]. By inputting the parameters such as locations, shapes and sizes of the unstable blocks, and terrain condition, roughness of slope materials, and vegetation factors into the software, we can obtain the trajectory, passing height, kinetic energy, reach probability, and other kinematic characteristics of the falling blocks [14]. Combined with Rockyfor3D numerical simulation and the parameters calibrated by deposition conditions of historical rockfalls in Zhaxilin Village, we analyzed the trajectory and other kinematic parameters of falling blocks in the study area.  
Figure 2: Elevation distributions of different lithology and distribution of rockfall source areas in study area (**a**). Distribution of rockfall source areas and positions of typical profiles (**b**,**c**). Elevation distributions of different lithology are in profile 1-1\({}^{\prime}\) and 2-2\({}^{\prime}\).  
The methodology flow chart of this study is shown in Figure 3.
# Evaluation of Workability and Mechanical Properties of Bottom Ash Aggregate Concrete
## 4 Results and Discussion  
### Initial Slump (\(S_{i}\)) and Air Content (\(v_{A}\))  
The results of the initial slump \(S_{i}\) and air contents \(v_{A}\) for specimens in which the water-reducing agent was mixed in equal proportion to cement weight are shown in Table 4. A-LWAC showed the lowest slump value when using BAA as both fine aggregate and coarse aggregate at the same \(W\)C, and a medium value when using BAA only as either fine aggregate or coarse aggregate. In general, a number of researchers have reported that the slump is decreased when using BAA instead of natural aggregate [29]. This indicates that the influence of the physical properties of BAA, such as its irregular shape and rough surface texture, are the greatest. In addition, some studies have reported that for fine BAA, \(R_{S}\) has a greater influence on the slump than \(R_{G}\) because it contains a large percentage of particles 600 \(\upmu\)m or below in radius that are difficult to accurately control in the process of grain size  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Specimens**} & \multirow{2}{*}{\(W\)/C} & \multirow{2}{*}{\(R_{S}\) (\%)} & \multicolumn{6}{c}{**Unit Volume Weight (kg/m\({}^{3}\))**} \\ \cline{5-8}  & & & & \(W\) & \(C\) & \(F_{S}\) & \(F_{B}\) & \(C_{G}\) & \(C_{B}\) \\ \hline L-0-0 & & 0 & 0 & & 0 & 614 & 0 & 654 \\ L-100-0 & & 100 & 0 & 389 & 756 & 0 & 0 & 654 \\ L-0-100 & & 0 & 100 & & 0 & 614 & 932 & 0 \\ L-100-100 & & 100 & & 768 & 0 & 946 & 0 \\ \hline M-0-0 & & 0 & 0 & & 0 & 590 & 0 & 629 \\ M-100-0 & & 0 & 100 & & 728 & 0 & 0 & 629 \\ M-100-100 & & 100 & & 0 & 0 & 590 & 896 & 0 \\  & & 100 & 100 & & 739 & 0 & 910 & 0 \\ \hline H-0-0 & & 0 & 0 & & 0 & 555 & 0 & 592 \\ H-100-0 & & 100 & & 583 & 684 & 0 & 0 & 592 \\ H-100-100 & & 0 & 100 & & 0 & 555 & 843 & 0 \\ H-100-100 & & 100 & & 696 & 0 & 857 & 0 \\ \hline \hline \end{tabular} Note: \(R_{S}\) = natural sand content ratio for the replacement of fine bottom ash aggregates; \(R_{G}\) = natural crushed granite content ratio for the replacement of coarse bottom ash aggregates; \(F_{S}\) = natural sand; \(F_{B}\) = fine bottom ash aggregate; \(C_{G}\) = natural crushed granite; \(C_{B}\) = coarse bottom ash aggregate.  
\end{table}
Table 3: Mixture proportions of concrete.  
adjustment [16]. The results of the present experiment also show a greater decline in slump when using BAA as fine aggregate than as coarse aggregate.  
The air contents of all the specimens were not greatly affected by \(R_{S}\), \(R_{G}\), or W/C, as shown in Table 4. The air content of all specimens was within 4.0-5.5%, and met the recommended requirements of LWAC not exposed to freezing [19].
## 3 D2 HCP (Distributed Dynamic Host Configuration Protocol)
### Joining and Departure of Nodes  
The protocol uses a specific message number for each operation. All the operations are defined seeking optimum operation and low latency. This section discusses how communication is established between the nodes and the messages transmitted during the joining and departure of nodes in the network.
# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
### Survey Methodology  
#### 1.4.1 Scope and Overview  
This paper focuses on critically reviewing research articles, in the form of journals, conferences, or theses, on the application of deep-learning techniques for Arabic handwritten OCR and text correction from 2020 to 2023. It does not include in its analysis works that do not apply deep-learning techniques or works that do not consider the Arabic language. The research questions are the initial points of departure for the investigation. The keywords were searched in the sources, and the studies were screened and selected according to the inclusion-exclusion criteria.
# Vision-Based Hand Rotation Recognition Technique with Ground-Truth Dataset
## 5 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Task (N)** & **Evaluation Metric** & **Number of Rotations** & **Angle of Rotation** & **Time of Rotation** \\ \hline \multirow{2}{*}{Task 1 (18)} & Accuracy & 100.00 (0.00)\% & 94.88 (3.49)\% & 85.13 (1.29)\% \\ \cline{2-5}  & MAE & 0.00 & 9.21 \({}^{\circ}\) & 4.52 (ms) \\ \hline \multirow{2}{*}{Task 2 (18)} & Accuracy & 100.00 (0.00)\% & 94.16 (3.15)\% & 74.06 (2.84)\% \\ \cline{2-5}  & MAE & 0.00 & 7.13\({}^{\circ}\) & 5.47 (ms) \\ \hline \multirow{2}{*}{Task 3 (18)} & Accuracy & 100.00 (0.00)\% & 94.88 (2.50)\% & 69.51 (4.69)\% \\ \cline{2-5}  & MAE & 0.00 & 5.26\({}^{\circ}\) & 5.65 (ms) \\ \hline \multirow{2}{*}{Task 4 (18)} & Accuracy & 100.00 (0.00)\% & 93.80 (5.26)\% & 85.13 (9.75)\% \\ \cline{2-5}  & MAE & 0.00 & 5.16\({}^{\circ}\) & 4.83 (ms) \\ \hline \multirow{2}{*}{Task 5 (18)} & Accuracy & 100.00 (0.00)\% & 91.54 (3.73)\% & 74.06 (6.94)\% \\ \cline{2-5}  & MAE & 0.00 & 5.41 \({}^{\circ}\) & 4.53 (ms) \\ \hline \multirow{2}{*}{Task 6 (18)} & Accuracy & 100.00 (0.00)\% & 80.60 (4.52)\% & 69.51 (7.88)\% \\ \cline{2-5}  & MAE & 0.00 & 8.66\({}^{\circ}\) & 3.82 (ms) \\ \hline \multirow{2}{*}{Task 7 (18)} & Accuracy & 100.00 (0.00)\% & 94.93 (3.45)\% & 76.12 (1.46)\% \\ \cline{2-5}  & MAE & 0.00 & 9.13\({}^{\circ}\) & 7.25 (ms) \\ \hline \multirow{2}{*}{Task 8 (18)} & Accuracy & 100.00 (0.00)\% & 89.76 (2.71)\% & 64.08 (2.65)\% \\ \cline{2-5}  & MAE & 0.00 & 12.50\({}^{\circ}\) & 7.68 (ms) \\ \hline \multirow{2}{*}{Task 9 (18)} & Accuracy & 100.00 (0.00)\% & 93.72 (2.03)\% & 59.46 (3.75)\% \\ \cline{2-5}  & MAE & 0.00 & 6.57\({}^{\circ}\) & 7.57 (ms) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy and MAE results between ground-truth dataset and predicted values by task.
# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
## 4 Experiments
### Hyper-Parameters Analysis
#### 4.2.1 Compound Impact of Learning Rate and Output Feature Vector Dimensions  
The results indicate that the impact of the learning rate and the dimension of the output feature vector on the results is complex. When the learning rate is large, the convergence speed of the model will be faster, but the model accuracy will be worse. The reason is that increasing the MLP output feature vector dimension also increases the number of model parameters, improving the model complexity and convergence difficulty. It can achieve acceptable training results when the learning rate is large. By contrast, the model is relatively hard to be suboptimal when the learning rate is low. Due to feature redundancy caused by an excessment MLP structure, the optimal model parameters can be obtained in a lower output vector dimension.  
Different learning rates and output dimensions are tested to obtain the best model structure and the corresponding learning rate. The learning rate of the AdamW optimizer  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Learning** & **Feature** & **Similarity** & **R@1** & **R@5** & **R@10** & **AP** \\
**Rate** & **Dimension** & **Calculation** & & & & \\ \hline \(1\times 10^{-4}\) & 1000 & cosine & 78.52 & 94.46 & 96.99 & 82.07 \\ \(1\times 10^{-4}\) & 4000 & cosine & 80.42 & 95.16 & 97.30 & 83.67 \\ \(1\times 10^{-5}\) & 1000 & cosine & **86.57** & **69.48** & **97.93** & **88.78** \\ \(1\times 10^{-5}\) & 4000 & cosine & 86.46 & 96.53 & 97.82 & 88.74 \\ \hline \(1\times 10^{-4}\) & 1000 & euclid & 81.65 & 95.69 & 97.62 & 84.77 \\ \(1\times 10^{-4}\) & 4000 & euclid & 91.12 & 98.73 & 99.57 & 92.89 \\ \(1\times 10^{-5}\) & 1000 & euclid & **91.57** & **99.29** & **99.68** & **93.31** \\ \(1\times 10^{-5}\) & 4000 & euclid & 88.58 & 98.32 & 99.24 & 90.73 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The comparisons between different hyperparameters. (Bold numbers mean the best results).  
is confirmed to perform best at 0.00001, while the feature vector dimension is 1000. An excessive learning rate in this model structure will cause training fluctuation, resulting in model instability. In the following experiment, the learning rate is maintained at 0.00001, which is consistent with the learning rate when the model achieves the best performance.
## 2 Test Design
### Controlled Samples' Design  
The engineering background for this work is the Bojitian #1 gold mine of the Zijin Mining Group (in Guizhou province). The arc arch tunnel is used in underground mining engineering. In addition, this area belongs to typical Karst Plateau stratum, and the karst phenomenon is extremely developed (forms many original caverns in rock mass).  
There were two types of samples in the controlled test: the cavern-free sample and the cavern-containing sample. Figure 1 illustrates the basic geometric design of the samples; the cubic specimen was applied to conduct the true triaxial compression test, where the arc arch tunnel was built in the center of the specimen (the geometric similarity ratio of the simulated and real tunnel section is 1:1000). The tunnel was formed by the 'Embedded extraction method': the steel tunnel model was inserted into the uncured gypsum slurry and pulled out after the initial setting of the gypsum.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Paremeters Name** & **Notes** \\ \hline Chemical composition of gypsum powder & a-CaSO\({}_{4}\)+1/2H\({}_{2}\)O \\ Mesh number & 2000 \\ Initial setting period/min & \(\geq\)3.0 \\ Final setting period/min & \(\leq\)30.0 \\
2 h strength/MPa & 4.0 \\ Uniaxial compressive strength/MPa & 26.63 \\ Tensile strength/MPa & 1.13 \\ Elastic modulus/GPa & 17.19 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The related parameters of the rock-like sample.  
Figure 1: Design of the sample and the prefabricated cavern.  
In order to obtain more ideal experimental results (the prefabricated cavern shows strong influence on the top vault and sidewall of tunnel), the invisible cavern (radius is 10 mm) was placed on the right side of the tunnel vault (in the x-o-z plane) and the minimum distance between the cavern and tunnel was 5 mm; the prefabricated cavern distributes at the central position in the y direction. The position of prefabricated cavern (polymer model) was fixed by a thin fish wire, the x and z coordinates were located by the fixed point of the wire on the model mold, and the y coordinate was controlled by the length of wire.  
To ensure the reliability of the experimental results, each group of experiments contained three identical test blocks. The cavern diameter was controlled by the polymer ball (three sets of diameter errors not exceeding 1%).
# Energy Efficient Data Transmission for Sensors with Wireless Charging
## 4 Transmission in an Unlimited Number of Periods  
If sensors stay in the range of wireless charging for a long enough time, then all the data can be transmitted to the base station. In such a situation, we try to solve the problem to minimize the number of the used periods. This is reasonable, since it needs to store as much energy as possible to transmit packets arriving in the future. Similar to the last section, we first consider a simple situation where \(C=E\), i.e., the energy received in each period equals the battery capacity. We then study the problem with \(C=2E\), where the battery can be filled in two periods of wireless charging. Finally, we discuss the general problem with \(C=mE\), where the battery can be filled in \(m\) periods of wireless charging.
# Brain-Like Navigation Scheme based on MEMS-INS and Place Recognition
## 4 Experimental Test and Comparison  
### Experiment  
To verify the proposed brain-like navigation scheme and the SI-GMS algorithm, experiments were carried out. The experimental results fully demonstrate the reliability of the proposed scheme and the superiority of the proposed algorithm. The experimental platform used in the experiment is a six-rotor UAV equipped with a self-made brain-like navigation system, which consists of a LattePanda (a kind of card type computer), a MEMS INS (consisted by STIM 202 and 1521 L), a camera and a high-precision GPS device (NovAtel ProPak6) used as reference (Figure 10). We used a laser ranging module (TW10S) to make the height of the UVA relative to the ground unchanged. Detailed equipment parameters are given in Tables 1-3.  
Figure 8: Two groups of images from KITTI.  
Figure 9: The successfully matching points after using the grid-based motion statistics (GMS) algorithm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Gyroscope (STIM202)**} & **Bias** & **0.5\({}^{\circ}\)/h** \\  & **Scale Factor** & **200 ppm** \\  & **Random Walk** & **0.2\({}^{\circ}\)/h** \\ \hline Accelerometer(1521L) & Bias & 0.5\(\%\)\(\%\  
mainly acquire the position, velocity and direction information. The INS communicate with LatrePanda through the serial port protocol, so that the precise position information fed back by LatrePanda after place matching can be received in real time and its position can be corrected when the place cell is activated. The power of the entire device is supplied by a USB and placed at the front of the six-rotor UAV. A camera is connected to the LatrePanda to capture RGB images at 12 fps, which is then used for matching with the visual template when the UAV is flying. High-precision GPS is applied to provide the reference coordinate while acquiring the template images.  
We conducted two sets of experiments. The experimental sites are located on a direct flight area about 2 m from the ground and 500 m in length for experiment 1 and an approximately 280-meter-long folding airspace for experiment 2 (shown as the red tracks in Figure 11), and the UAV flight at a speed of around 5 m/s. The position of the nodes of place cells for the visual matching is as shown in Figure 11. The place cells are activated for three times throughout the entire process for each experiment (at the green points in the Figure 11).
## 4 The Performance Analysis
### The In-VDM Performance Analysis  
To analyze the performance of In-VDM, three experiments were conducted. In two experiments, NNVP was compared with multi-layer perceptron (MLP) and long short-term memory (LSTM) in computation time and accuracy, while in the other experiment, RPS was compared with a support vector machine (SVM) and fuzzy in test loss. According to the experimental environment, the number of test data sets was 100, 150, 200, 300, 400, and 500 sets, the computation speed was 3.20 GHz, and the RAM memory size was 16 GB.  
Figure 14(a) shows that the computation time of the NNVP was improved by 44.894% and 62.719% more than that of the MPL and the LSTM separately as the number of test data sets increased from 100 to 500 sets. Figure 14(b) shows that the accuracy of the NNVP was higher by about 1% than that of the MLP but similar to that of the LSTM on average. Since there was little difference between them in accuracy but the NNVP was more efficient in computation time, NNVP was more suitable than the MPL and LSTM in vehicle self-diagnosis by using payloads.  
Figure 13: The (**a**) conversion time and (**b**) an error rate per protocol.  
Figure 15 shows a test data loss and over-fitting when RPS, SVM, and fuzzy were used to diagnose parts of a vehicle. The RPS had a loss similar to the SVM and about 0.2 less than the fuzzy. However, since the SVM had over-fitting, RPS was most suitable for part diagnosis of vehicles.
## 2 Related Work
The work [19] introduces a multipath routing protocol using genetic algorithm to select efficient routes that have the shortest route, maximum residual energy and less overhead. Also, we highlight our previous proposal named multipath multimedia dynamic source routing (MMDSR) [3] which considers the specific characteristics of the video when it establishes a QoS-awarepath to forward packets from source to destination looking to provide the best QoS. The QoS metrics considered are available bandwidth, packet losses, packet delay, packet delay jitter, hop count to destination, reliability metric and mobility metric. The MMDSR operation is detailed in Section 4. Regarding _social MANETs_, the work in [20] proposes to combine _social trust_ metrics derived from social networks with _QoS trust_ metrics derived from communication networks to obtain a composite trust metric for evaluating trust of mobile nodes in MANETs. As social trust metrics they use _social ties_ (two nodes have a lot of direct or indirect interaction experiences with each other) and _honesty_ (a belief of whether a node is malicious or not). As QoS trust metrics they use _energy_ (residual energy of a node) and _cooperativeness_ (whether the node is cooperative in routing and forwarding packets). They find the best protocol settings to decrease the trust bias between subjective and objective trust evaluation results, which happens around a 80% of using direct observations (i.e., 20% of indirect observations from trustworthy recommenders) for subjective trust evaluation. In [21], the authors propose a hierarchical cooperation protocol where the forwarding path from source to destination is formed taking into account the nodes' density (\(\gamma\)) and the number of social contacts per node (\(q\)). They model the probability that any two nodes in distance \(d\) away from each other are socially connected is assumed to be proportional to \(d^{-\gamma}\). They analyze the throughput-delay trade-off in dense and sparse networks for different parameters (\(q\), \(d\), \(\gamma\)). Their results show that as \(\gamma\) grows the throughput-delay trade-off improves when considering the social information between nodes. As far as we know, there is still no proposal that addresses the issue of providing video-streaming services through MANETs using a multimetric routing protocol that includes a social metric, which is the objective of our work. In the next two sections we summarize in a nutshell the basics of our QoS-aware multimetric routing protocol MMDSR, previously presented in [4]. Afterwards, in Section 5 we introduce a new social-aware metric named _tie strength_ (TS). This TS will be included in the MMDSR forwarding scheme by means of a new QoS-aware and social-aware multimetric score, presented in Section 6.
## 3 Design of the System with Base Generation (Proposed Study)
### Design of the Base Generation Capacity  
The base load of the target areas was calculated as the lowest load, 32.28 kW, by analyzing the power consumption of the industrial complexes and the residential area at the same time during the past year. The base load is powered using a fuel cell with a high power supply stability. For a fuel cell to supply as much power as the base load for 24 h, it must have a power generation capacity greater than or equal to the base load; therefore, the fuel cell system with a rated capacity of 35 kW, which is greater than the base load, was adopted as the base power source. This base power source will provide 840 kWh of electricity per day (24 h).  
\[P_{min}=32.28\ [kW]\]  
\[F_{0}=35\ [kW]\]  
\[P_{base}=F_{0}\times 24=840\ [kWh]\]