esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Actual Predictions for Selected Monitoring Sites  
To further validate the validity of this method, data from different periods of several monitoring sites in the field were selected for validation. Figure 10 shows the measured and predicted horizontal displacements of the enclosure structure at the HD measurement points CX-1 and CX-58. The dashed line is the predicted value, and the measured value is the actual value. It can be seen that the predicted values obtained by the RF prediction model are consistent with the actual values in terms of numerical values and overall trends. As shown in Table 8, the accuracy assessment achieves an R\({}^{2}\) of 0.9646, an MAE of 0.5625 mm, and an RMSE of 3.6403 mm, which proves the feasibility of machine learning in HD prediction.  
Figure 10: Horizontal displacement of the enclosure at CX-1 and CX-58; (**a**) CX-1; (**b**) CX-58.  
Figure 9: Heat map of correlation coefficients of characterization factors.
## 4 Methods for Mitigating Jamming
### ABER Calculation
#### 4.3.1 Analysis of the ABER for SIMO in Scenario 1
When the jamming is idle, according to [34], and assuming perfect Channel State Information at the receiver, the expression for BER is as follows:  
\[ \begin{split} P_{e1i-SIMO}(\overline{\gamma}_{D})=\int_{0}^{ \infty}\int_{0}^{\infty}\cdots\int_{0}^{\infty}f_{\gamma_{11}}(\gamma_{11})f_{ \gamma_{12}}(\gamma_{12})\cdots f_{\gamma_{13}}(\gamma_{13})\\ \times Q\left(\frac{1}{2\sqrt{N}}\sqrt{\sum\limits_{n=1}^{N} \gamma_{1n}}\right)d\gamma_{11}d\gamma_{12}\cdots d\gamma_{1N}\end{split}  \tag{41}\]  
where, \(\gamma_{1n}\) represents the SNR from the transmitter to the nth receiver, while N denotes the total number of receivers. The Q-function can be approximated as \(Q(x)\approx\frac{1}{12}e^{-x^{2}/2}+\frac{1}{4}e^{-2x^{2}/3}\), we can obtain  
\[ \begin{split} P_{e1i-SIMO}(\overline{\gamma}_{D})\approx\frac{1} {12}\prod\limits_{n=1}^{N}\int_{0}^{\infty}f_{h_{in}}(h_{in})\exp\left(- \frac{\overline{\gamma}_{D}h_{in}^{2}}{4N}\right)dh_{in}\\ +\frac{1}{4}\prod\limits_{n=1}^{N}\int_{0}^{\infty}f_{h_{in}}(h_{in })\exp\left(-\frac{\overline{\gamma}_{D}h_{in}^{2}}{3N}\right)dh_{in}\\ =\frac{1}{12}\prod\limits_{n=1}^{N}\Gamma_{1}+\frac{1}{4}\prod \limits_{n=1}^{N}\Gamma_{2}\end{split}  \tag{42}\]  
where  
\[ \begin{split}\text{F}_{1}=\frac{\xi_{p}^{2}}{\Gamma(x_{p})\Gamma( \beta_{p})}\frac{\xi_{q}^{2}}{\Gamma(x_{q})\Gamma(\beta_{q})}\frac{2(x_{p}+ \beta_{p}+x_{q}+\beta_{q}-\gamma)}{\sqrt{\pi}(\pi)^{2}}\\ \times\xi_{12,5}^{1,12}\left(\frac{64\overline{\gamma}_{D}}{N} \left(\frac{a_{p}\beta_{p}a_{q}\beta_{q}}{\Lambda_{0p}\Lambda_{0q}}\right)^{-2 }\right|\begin{array}{c}\mathbf{E_{1}},\mathbf{E_{2}},\mathbf{E_{3}}\\ 0,\frac{-\xi_{q}^{2}}{2},\frac{1-\xi_{q}^{2}}{2},\frac{-\xi_{p}^{2}}{2},\frac{1 -\xi_{p}^{2}}{2}\end{array}\right)\end{split}  \tag{43}\]  
\[ \begin{split}\text{F}_{2}=\frac{\xi_{p}^{2}}{\Gamma(x_{p})\Gamma( \beta_{p})}\frac{\xi_{q}^{2}}{\Gamma(x_{q})\Gamma(\beta_{q})}\frac{2(x_{p}+ \beta_{p}+x_{q}+\beta_{q}-\gamma)}{\sqrt{\pi}(\pi)^{2}}\\ \times G_{12,5}^{1,12}\left(\frac{256\overline{\gamma}_{D}}{3N} \left(\frac{a_{p}\beta_{p}a_{q}\beta_{q}}{\Lambda_{0p}\Lambda_{0q}}\right)^{-2 }\right|\begin{array}{c}\mathbf{E_{1}},\mathbf{E_{2}},\mathbf{E_{3}}\\ 0,\frac{-\xi_{p}^{2}}{2},\frac{1-\xi_{p}^{2}}{2},\frac{-\xi_{q}^{2}}{2},\frac{ -\xi_{q}^{2}}{2}\end{array}\right)\end{split}  \tag{44}\]  
where \(\mathbf{E_{1}}=\frac{1-\xi_{p}^{2}}{2},\frac{2-\xi_{p}^{2}}{2},\frac{1-a_{p}}{ 2},\frac{2-a_{p}}{2},\mathbf{E_{2}}=\frac{1-\beta_{p}}{2},\frac{2-\beta_{p}}{ 2},\frac{1-\xi_{q}^{2}}{2},\frac{2-\xi_{p}^{2}}{2},\mathbf{E_{3}}=\frac{1-a_{q} }{2},\frac{2-a_{q}}{2},\frac{1-\beta_{q}}{2},\frac{2-\beta_{q}}{2}\). Now, we can calculate the ABER for Scenario 1 of SIMO-FSO as follows  
\[P_{e1-SIMO}=\rho P_{e1a-SIMO}\Big{(}\overline{\gamma}_{J}\Big{)}+(1-\rho)P_{ e1i-SIMO}(\gamma_{D}) \tag{45}\]
# Performance Analysis of a Novel TCP Protocol Algorithm Adapted to Wireless Networks
## 3 Results
### Long Distance Scenario  
For this experiment, we considered a 92 km PtP link with line-of-sight between the Crocker hill in Santa Cruz island (0\({}^{\circ}\)38\({}^{\prime}\)50.0\({}^{\prime\prime}\) S 90\({}^{\circ}\)19\({}^{\prime}\)21.0\({}^{\prime\prime}\) W) and San Joaquin tower in San Cristobal island (0\({}^{\circ}\)53\({}^{\prime}\)26.9\({}^{\prime\prime}\) S 89\({}^{\circ}\)29\({}^{\prime}\)40.0\({}^{\prime\prime}\) W), using the IEEE 802.16-2009 WiMAX as communication standard, and employed 1, 2, 3 and 4 Mb/s bandwidth. First, we validated the link by simulation through the \"SImulation of Radio-Electric NETworks\" (SIRENET) software and the Motorola PTP LINKPlanner software, with the aim of eliminating most design assumptions. Eventually, we used two parabolic antennas with 24 dBi gain, two computers with the TCP-NACK modified kernel, and the Motorola PTP 58,600 proprietary radio equipment to implement the network. Figure 7 shows the PtP topology used for this experiment. More details about this experiment are presented in [24].
## 3 Test Results and Analysis
### Changes in Permeability  
The curves of the permeability coefficient plotted versus the fly ash dosage variation are displayed in Figure 14. The permeability coefficient of the plain soil is \(5.29\times 10^{-5}\) cm/s, while the permeability coefficients of the granite residual soil with fly ash dosages of 5%, 10%, 15% and 20% are 6.68 \(\times\) 10\({}^{-6}\), 6.92 \(\times\) 10\({}^{-6}\), 7.13 \(\times\) 10\({}^{-6}\) and 6.83 \(\times\) 10\({}^{-6}\) cm/s, respectively. As shown in Figure 14, the permeability coefficient of the improved soil mixed with fly ash decreases by an order of magnitude. The permeability coefficients of the improved soils with different fly ash dosages do not differ significantly, and the permeability coefficient of the granite residual soil cannot be significantly decreased by adding more fly ash to the mixture. According to the results obtained by [PERSON] [38], CaO in fly ash reacts with water to produce Ca(OH)\({}_{2}\), while SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\) in fly ash react with Ca(OH)\({}_{2}\) to produce gelling substances such as calcium aluminum and calcium silicate minerals, reducing the effective pore space of granite residual soil. The permeability coefficient of granite residual soil decreases due to the cementitious materials and fly ash filling the pore space in the soil, leading to some improvement in the resistance of the soil to rainwater erosion. Notably, when the fly ash dose was changed from 5% to 20%, the enhanced soil permeability coefficients increased. This may be because the optimal water content is greater when the fly ash dosage is 5% compared to other fly ash dosages of improved soil, and when the reaction between fly ash and granite residual soil occurs, a sufficient amount of water is available around the fly ash particles, leading to a more sufficient reaction compared to the other dosages of improved soil; therefore, a lower permeability coefficient is obtained.
## 4 Results and Discussion
### Defect Detectability and SNR Evaluation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multicolumn{2}{c}{**Roundness**} & \multicolumn{2}{c}{**Roundness**} \\  & **Raw** & **ATC** & **ID** & **Raw** & **ATC** \\ \hline A1 & 0.82 & 0.82 & B1 & 0.74 & 0.71 \\ A2 & 0.83 & 0.81 & B2 & 0.75 & 0.78 \\ A3 & 0.84 & 0.82 & B3 & 0.78 & 0.76 \\ A4 & 0.82 & 0.84 & B4 & 0.74 & 0.81 \\ A5 & 0.74 & 0.72 & B5 & 0.85 & 0.79 \\ A6 & 0.80 & 0.80 & B6 & 0.81 & 0.80 \\ A7 & 0.78 & 0.75 & B7 & 0.79 & 0.73 \\ A8 & 0.84 & 0.89 & B8 & - & 0.70 \\ A9 & - & 0.90 & B9 & - & - \\ A10 & - & - & B10 & - & - \\ \hline \hline \end{tabular} Note. ‘-’ indicates that the defect is not detected. All defects in rows C to E (defect depths of 6 mm, 6 mm, and 5 mm) were not detected.  
\end{table} 
Table 4: Roundness metric results in the amplitude image for each defect. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multicolumn{2}{c}{**Roundness**} & \multirow{2}{*}{**ID**} & \multicolumn{2}{c}{**Roundness**} & \multirow{2}{*}{**ID**} & \multicolumn{2}{c}{**Roundness**} \\  & **Raw** & **ATC** & & & & **Raw** & **ATC** \\ \hline A1 & 0.81 & 0.77 & B1 & 0.80 & 0.76 & C1 & 0.77 & 0.68 \\ A2 & 0.80 & 0.81 & B2 & 0.78 & 0.71 & C2 & 0.70 & 0.76 \\ A3 & 0.79 & 0.79 & B3 & 0.81 & 0.74 & C3 & 0.76 & 0.70 \\ A4 & 0.81 & 0.77 & B4 & 0.79 & 0.76 & C4 & 0.64 & 0.67 \\ A5 & 0.70 & 0.67 & B5 & 0.80 & 0.67 & C5 & 0.65 & 0.69 \\ A6 & 0.65 & 0.73 & B6 & 0.76 & 0.70 & C6 & 0.84 & 0.73 \\ A7 & 0.38 & 0.76 & B7 & 0.71 & 0.78 & C7 & - & 0.99 \\ A8 & - & 0.56 & B8 & - & 0.75 & C8 & - & - \\ A9 & - & - & B9 & - & 0.57 & C9 & - & - \\ A10 & - & - & B10 & - & - & C10 & - & - \\ \hline \hline \end{tabular} Note. ‘-’ indicates that the defect is not detected. All defects in rows C to E (defect depths of 6 mm and 5 mm) were not detected.  
\end{table} 
Table 3: Roundness metric results in the phase image for each defect. between the diameter of the defect and the calculated SNR value. The amount of SNR improvement was about 7.93% (1.57 dB) when ATC was applied to the A1 defect with a defect diameter of 20 mm and a depth of 9 mm. Comprehensively, it is believed that the impact of an uneven heat load was improved through the compensation technique.
# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 3 Real-Time Systems and Real-Time SR
### Deep Learning-Based Models for Real-Time Super-Resolution
#### 3.4.2 Recurrent Neural Networks (RNNs)-Based Models for Real-Time Super-Resolution  
[PERSON] et al. [80] present an explainable technique to SISR based on RNNs known as the model-guided deep unfolding network (MoG-DUN). To overcome the coherence barrier, they employed a well-known image prior, the non-local auto-regressive model, to guide their deep neural network. This technique embeds deep denoising and non-local regularization as trainable modules in a deep learning framework, transforming the iterative model-based SISR process into a three-stage sequence of interconnected modules: denoising, non-local AR, and reconstruction. These modules use sophisticated approaches such as dense/skip connections and fast non-local implementations. MoG-DUN is explainable, highly accurate with less aliasing artifacts, computationally economical with fewer parameters, and capable of managing a wide range of degradations while deploying in real time.
# Deep Point Cloud Facet Segmentation and Applications in Downsampling and Crop Organ Extraction
## 3 Results
### Evaluation Metrics  
To comprehensively assess facet segmentation, three metrics are used: Under-segmentation Error (UE), Achievable Segmentation Accuracy (ASA), and compactness (CPT). UE measures the extent to which generated facets cross true semantic/instance boundaries:  
\[\text{UE}=\frac{1}{|G|}{\sum_{g\in G}}\Big{|}\Big{\{}p\Big{|}p\in g\wedge p \
otin\cup_{f\in F(g)}f\Big{\}}\Big{|} \tag{18}\]  
where, \(G\) is the set of ground-truth regions, \(|G|\) is the total number of ground-truth points, and \(F(g)\) is the set of facets intersecting region \(g\). A lower UE indicates that the facet boundaries are better aligned with the true boundaries.  
The ASA reflects the maximum fraction of each ground-truth region coverable by facets under ideal assignment as follows:  
\[\text{ASA}=\frac{1}{|P|}{\sum_{f\in P}\max_{g\in G}}|f\cap g| \tag{19}\]  
where, \(P\) is the set of all generated facets and \(|P|\) is the total number of facet points. An ASA closer to 1 indicates that the facets are more suitable as semantic/instance units.  
Compactness (CPT) is inspired by the 2D isoperimetric entropy. Each 3D facet is projected to 2D via PCA along its normal. For the projected polygon, compute area \(A\) and perimeter \(L\). Single-facet isoperimetric score:  
\[S=\frac{4\pi A}{L^{2}} \tag{20}\]  
Since among shapes with equal perimeter, the circle has the maximum area, \(S\in(0,1]\). Higher \(S\) means better compactness. For a set of facets, the overall SPR in 2D is a weighted sum of each facet's \(S\) weighted by its area ratio:  
\[\text{SPR}=\sum_{i}S_{i}\frac{|A_{i}|}{\sum_{i}|A_{i}|} \tag{21}\]  
Due to the disorder and the discrete and uneven density of 3D point cloud patches, the 2D SPR index cannot be directly used. In this section, a 3D facet regularity index, CPT, based on the concept of SPR, is proposed. The specific method is as follows: First, Principal Component Analysis (PCA) is applied to each facet to extract the normal vector, and the 3D facet is projected onto the 2D plane along the direction of the normal vector to preserve the main spatial features. Then, edge points are extracted from the projected 2D point cloud by an edge search algorithm, and the perimeter is calculated by connecting the edge points in order \(L\). The area \(A\) is then calculated by a greedy triangulation algorithm. With \(L\) and \(A\) known, the isoperimetric entropy S of a single surface can be calculated. Finally, CPT averages the isoperimetric entropies of all facets according to the proportion of their projected area to obtain the overall regularity score, which is also between 0 and 1; the larger the value, the more regular the 3D geometry of the patch set. Figure 2 shows the isoperimetric entropy calculation method for a single-sided specimen.
# DPtracer: Integrating Log-Driven Accountability into Data Provision Networks
## 2 Background
### Tamper-Evident Logging Using Merkle Trees
#### 2.2.2 Tamper-Evident Logging  
Tamper-evident logging extends the concept of data integrity into log management by ensuring that any changes to log entries are detectable. A tamper-evident log system maintains a log record of events to identify any modification, insertion, or deletion of a log entry. Tamper evidence is used in environments where logs are critical, such as security monitoring and forensic analysis [16].  
For tamper-evident logging, while specialized hardware may be applied [17; 18], cryptographic methods such as hash chains [19] or hash trees [12; 20] are more broadly applicable across various computing environments. The hash-tree method, in particular, has been more favored because of its efficiency; it requires fewer computations and less data during proof generation and verification, making it particularly suitable for systems with resource constraints.
# Fault Diagnosis Algorithm of Beam Pumping Unit Based on Transfer Learning and DenseNet Model
###### Abstract  
The difficulty of collecting fault data samples is one of the application problems of the deep learning method in fault diagnosis of mechanical production; the second is that when the depth of the learning network increases, the network accuracy is saturated or even decreased. Therefore, based on the deep learning algorithm and the DenseNet model, this paper establishes a fault diagnosis model for the beam pumping unit through the transfer learning method. The model uses the global pooling layer as the classifier. The model is used to classify and test various working conditions such as wax deposition, pump leakage, insufficient liquid supply, and pump leakage in oil wells. The results show that the model can obtain a classification model with high accuracy by learning a limited number of sample data; in the case of uneven data samples, the model can also basically complete the task classification task accurately. Through the evaluation of the test set, the model has an average accuracy of more than 95% in identifying various working conditions.  
transfer learning; DenseNet model; fault diagnosis; indicator diagram; beam pumping unit +
Footnote †: journal: Applied Physics Letters  
1
Footnote 1: School of Mechanical Science and Engineering, Northeast Petroleum University, Daqing 163000, China  
2 College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325000, China  
3 Geophysical Institute, China Petroleum Exploration and Development Research Institute, Beijing 100107, China  
4
Footnote 4: Correspondence: [EMAIL_ADDRESS]
# Towards Deep Integration of Electronics and Photonics
## 6 Conclusions  
Integration of electronics and photonics may help to overcome the existing performance-limiting factors and create the ground for the future electro-optical technologies. The deep integration assumes the usage of fundamental physical principles to provide an efficient interplay of electrons and photons. Hybrid quasiparticles mixing light and matter may help to achieve that goal. One such particle is a surface plasmon polariton. Multiple SPP-based devices, which can be used for the construction of future mixed circuits, are demonstrated theoretically and realized experimentally. One of the most important active devices, responsible for the interaction between electronic and photonic parts of a circuit, is an electro-optical modulator. SPP modes, providing subwavelength signal confinement and strong light-matter interaction, are especially useful in this class of devices. The ENZ effect in novel plasmonic materials along with the HPWG concept have proven their efficiency in various designs of modulators. The existing models technically allow the data processing frequency of the order THz. Despite the fact that most of the elements of mixed circuits are already available, the construction of large plasmonic circuits is still in its early phase. Some technical challenges, like plasmonic losses, still remain partially unsolved.  
Switching from macroscopic to microscopic principles in circuit building, involving the implementation of achievements of molecular electronics and quantum plasmonics, is an upcoming task for future technologies. Manipulation of a microscopically small amount of photons and electrons to organize communication and computation requires the implementation of new paradigms and more fundamental physical principles, which may help to reconsider the existing obstacles in electronics. To organize an efficient interaction between single photons and electrons, hybrid quasiparticles may be as productive as they are in macroscopic applications. The successful implementation of plasmons and excitons hybridized with light as well as their combinations in device designing appears to be a productive approach for future technologies.  
The authors equally contributed to the manuscript.  
This work was financially supported by the Ministry of Science and Higher Education of the Russian Federation, project No. RFMEFI58117X0026.  
The authors declare no conflict of interest.
## 3 Experiments and Results
### Experiments
#### 3.2.5 Comparison with Classic Methods  
To ascertain the practical value of the DNN approach to the IK problem, the results have been compared to those obtained by means of more traditional methods. In this experiment in particular, the best-performing DNN model will be compared against the Orocos KDL IK solvers (See [[https://www.orocos.org/wiki/Kinematic_and_Dynamic_Solvers.html](https://www.orocos.org/wiki/Kinematic_and_Dynamic_Solvers.html)]([https://www.orocos.org/wiki/Kinematic_and_Dynamic_Solvers.html](https://www.orocos.org/wiki/Kinematic_and_Dynamic_Solvers.html)), accessed 24 May 2025). To do this, the kinematic chain of the robotic arm is created and the test dataset used to evaluate the DNN models is loaded. The values are de-normalized, and, for each Cartesian pose input, the IK is computed using a classic solver. The resulting joint space positions are compared to the expected ones and the mean squared error is obtained.  
The tested options include two position IK solvers based on a numerical approach. The first of them is based on the Newton-Raphson method, an iterative algorithm which produces an approximation to the root of a function by refining an initial guess. The second solver is based on the Levenberg-Marquardt algorithm, used to solve non-linear least squares problems. The initial guess for both solvers places every joint at 0 radians, which approximately sets each one of them halfway through its upper and lower limits. It is the default starting point for many manipulation tasks that employ the TIAGo robot. No sensitivity analysis has been performed.  
The results of the comparison can be seen in Table 6. They include the mean squared error, the number of times a solution could not be found for a given input, and the time needed to process the whole test dataset.  
As can be seen, the results suggest that the DNN approach may offer some advantages over the proposed Jacobian-based numerical methods. While the mean squared error favors the DNN method, it might be influenced by the choice of initial guess in the numerical algorithms and should therefore not be deemed as relevant a result. Notably, the numerical methods fail to converge in approximately 13% of the test cases, whereas the DNN consistently provides a solution. Additionally, it takes considerably less computation time. Overall, this experiment indicates that the DNN approach holds promise as an alternative for solving the IK problem. However, further investigation is needed.
## 2 Specimens and Experimental Methods  
### Coal Specimens and Their Characteristics  
#### 2.1.1 Coal Specimen Collection  
In the present study, the sampling site is located at the Zhuxianzhuang coal mine in the Suxian mining area of Huaibei coalfield (Figure 1). The main coal bearing strata are the Permian Shihezi and Shanxi formations. The Shanxi formation, with a thickness of 96 m to 143 m and 120 m on average, is widely distributed throughout the whole area. As one of the main coal bearing sections in the area, the Shanxi formation presents well-developed number 10 and 11 coal seams. The stable number 10 coal seam has a cumulative thickness varying from 2.0 m to 3.5 m, and is the main minable coal seam in the Suxian mining area. The regional control structure of the Suxian mining area is composed of the Sudong syncline in the overlying system, and the Sunan and Namping synclines in the underlying system of the Xishipo thrust fault. The Zhuxianzhuang coal mine is located in the overlying system of the Xisipo thrust fault, with very large compression and shear stress and intense tectonic deformation. Therefore, complete types of tectonically deformed coals have developed there.  
The underground sampling is located near the newly exposed structure of the number 10 coal seam. Tectonically deformed coal samples of different deformation degrees are collected from different distances to the fault plane (Figure 1). The methods of collection and preservation of coal specimens are based on the \"Sampling of coal seams\" (GB/T 482-2008) and \"Sampling of coal petrology\" (GB/T 19222-2003). During sampling, the original structure of the coal body should be maintained as fully as possible. Three tectonically deformed coal specimens and one undeformed coal specimen are chosen as the research objects.