esa-sceva/satcom-chunk-collection huggingface dataset:
# Interference Signal Feature Extraction and Pattern Classification Algorithm Based on Deep Learning
## 3 Interference Monitoring Research
### Interference Monitoring Technology Based on Artificial Feature Extraction
#### 3.3.6 Discussion  
In the section, we present an external discussion. Firstly, the experiment proved that the algorithm has excellent detection effect, the accuracy and recall rate of 5 types of interference signals are nearly 100%, the prediction error is less than 4 ms and the prediction error is less than 6 KHz. Secondly, the monitoring bandwidth range reaches 2 MHz. In addition, the algorithm has successfully solved the detection problem of long duration monotone interference and small slope interference, making the detection accuracy and recall rate close to 100%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Initial Time** & **Cut-off Time** & **Initial Frequency** & **Cut-off Frequency** \\  & **Error (ms)** & **Error (ms)** & **Error (KHz)** & **Error (KHz)** \\ \hline Single frequency & 2.8 & 2.3 & 1.64 & — \\ interference & 2.2 & 2.0 & 5.3 & 4.6 \\ interference & 3.7 & 1.1 & 4.4 & 5.0 \\ \hline Sweep interference & 1.9 & 2.7 & 2.6 & — \\ with small slope & 1.9 & 2.7 & 2.6 & — \\ \hline \hline \end{tabular}
\end{table}
Table 2: Root mean square error of interference identification parameter test.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Initial Time** & **Cut-off Time** & **Initial Frequency** & **Cut-off Frequency** \\  & **Error (ms)** & **Error (KHz)** & **Error (KHz)** \\ \hline Single frequency & 1.6 & 1.5 & 0.46 & — \\ interference & 1.4 & 1.5 & 3.8 & 3.7 \\ interference & 1.8 & 2.2 & 4.7 & 3.8 \\ \hline Sweep interference & 1.4 & 1.4 & 1.7 & — \\ with small slope & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average absolute error of interference identification parameter test.
## 3 Parametric Study for Determining the Optimal Sampling Strategy
### Sampling from a Domain
#### 3.2.1 Effect of Spatial Correlation Length \((\theta)\)  
Example charts showing the variation of \(p_{f}\) with respect to \(d_{d}/H\) for various \(\theta/H\) and x/\(H\) values are given in Figure 11, both for the case of sliding and rotation of the wall; it is reminded that _FS_ was set equal to 1.3 (recall Equation (2)). From this figure, it is inferred that the optimal horizontal sampling distance from the wall is again for x/\(H\) = 0, although for very small theta values, the horizontal sampling distance makes no noticeable difference. However, as the theta value increases, the role of horizontal distance becomes more significant. Given now that soil samples will be taken from x/\(H\) = 0, it is advisable, as it is inferred from Figure 11, that the entire domain length along the wall is considered, especially for the rotational mode of failure. This practice may significantly reduce the statistical error. It is also interesting that, extending the sampling domain beyond the maximum depth of wall (i.e., \(d_{d}/H\) > 1), the statistical error remains constant. Finally, from Figure 11, it is inferred that a \"worst case theta\" exists. This is more obvious in Figure 12, showing the variation of \(p_{f}\) with \(d_{d}/H\) for various \(\theta/H\) values and x/\(H\) = 0.  
Figure 10: \(p_{f}\) versus \(d_{p}/H\) example curves for various x/\(H\) values for (**a**) sliding and (**b**) overturning wall considering anisotropic soil (to be compared with Figure 3c and Figure d respectively).  
Figure 11: \(p_{f}\) versus \(d_{d}/H\) example relationships for different values of scaled correlation length (\(\theta/H\)) and lateral distance from the wall face (x/\(H\)). Figure (**a**,\(c\),**e**) refer to the case of sliding wall whilst figure (**b**,\(d\),**f**) to the case of overturning wall.
# Channel and Bit Adaptive Power Control Strategy for Uplink NOMA VLC Systems
## 3 Adaptive Power Control Strategy for Uplink NOMA VLC  
In this section, to manage the multiuser interference and achieve efficient communication, we first propose an efficient channel and bit adaptive power control strategy which has a favorable property about the structure of the received constellation. Then, thanks to the nice constellation structure of the received signals, we develop a low-complexity demodulation and decoding algorithm for signal detection.
## 4 Conclusions  
Incorporation of barium (0.5 wt%) into TiO\({}_{2}\) was successfully accomplished using a microwave irradiation method. With respect to Ba content, its incorporation modulated the band gap of TiO\({}_{2}\) by decreasing particle size, leads to increased specific surface area and consequently, superior dye uptake and high photocurrent, resulting in higher DSSC power conversion efficiency. The higher band gap value of Ba/TiO\({}_{2}\) (3.26 eV) as compared to TiO\({}_{2}\) photoanode (3.21 eV) was attributable to the quantum confinement effect, which was observed due to the reduced size of the as-synthesized samples. EDS results confirmed the presence of barium in Ba/TiO\({}_{2}\) without any impurities; this was further confirmed by XRD results. A higher efficiency of Ba/TiO\({}_{2}\) photoanode was observed as compared to pure TiO\({}_{2}\) (3.4 vs 2.1%). Barium doped titania (Ba/TiO\({}_{2}\)) could be employed in DSSCs owing to its relevant photoactive properties, with further prospects for other applications including photocatalysis as well as wastewater treatment.  
Conceptualization, A.A. and S.K.; methodology, M.K.; software, M.K.; validation, M.K.; writing--original draft preparation, A.A.; writing--review and editing, R.L.; visualization, M.J.; supervision, R.L.; project administration, M.A.A.; funding acquisition, M.A.A. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Najran University, and the grant number is NU/IFC/INT/01/003. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.  
The authors would like to acknowledge the support of the deputy for research and innovation-Ministry of Education, Kingdom of Saudi Arabia for this research through grant (NU/IFC/INT/01/003) under the institutional funding committee at Najran University, Kingdom of Saudi Arabia.  
The authors declare no conflict of interest.
# Integrated Hydrological Analysis of Little Akaki Watershed Using SWAT-MODFLOW, Ethiopia
## 2 Materials and Methods
### Groundwater Modelling Method and Procedures
#### 2.3.3 Hydraulic Conductivity  
Groundwater flow rate in the saturated zone depends on the hydraulic conductivities of the aquifer. For the current study, the horizontal hydraulic conductivity values were obtained from the calibration process in PEST [24], considering that there is no available data. Zonation for hydraulic conductivity area was based on the available geologic and geomorphologic information. Then, the hydraulic conductivity map obtained from the calibration is overlaid on the model grid, and then respective average values are assigned to each model cells.
# SASLedger: A Secured, Accelerated Scalable Storage Solution for Distributed Ledger Systems
## 4 SASLedger: A Secured, Accelerated Scalable Storage Solution
### Proving Data Integrity through VC  
To prove the integrity of the data in a block, Jidar [13] attaches a proof for each transaction, which requires extra storage space and generates significant network overhead. Compared to Jidar, in our solution, nodes only store a concise VC of a block, the proof for a transaction is generated and provided by the remote DB server only when the transaction is requested.  
A Merkle tree [43] is used to generate VC for the set of transactions in a block and proofs for the transactions. As shown in Figure 3, the hashes of the transactions in a block \((tx_{1},\ldots,tx_{n})\) form the leaf nodes of the Merkle tree, the root of the Merkle tree is considered to be VC (\(\mathbf{C}\)) of the block (\(7\)). When the proof of the \(i\)th transaction \(tx_{i}\) is requested, the list  
Figure 2: System architecture of our solution. The entire blockchain network is divided into multiple Consensus Unit (CU), in which nodes combine their resources to maintain the blockchain data together.  
which includes adjacent sibling nodes of the corresponding leaf and its ancestor nodes is returned as the proof \(\mathbf{\pi}\) (8), in Figure 3, the list \([Hash_{3},Hash_{12},Hash_{5678}]\) (shown in yellow) is the proof of transaction \(tx_{4}\).  
When a transaction is requested, the transaction is retrieved from the remote DB server with its proof and verified by calculating the Merkle tree root using the received proof and comparing the root with the previously stored commitment (9). If the root equals the VC, the node can believe that the transaction has not been subject to tampering on the remote DB server. In the case of a block being requested, the node retrieves the block from the remote DB server and validates its integrity by calculating its hash and comparing it with the previously stored hash of the block. The size of a VC is 32 B when SHA256 is used as the hash calculating algorithm while the size of a block with default settings containing 10 transactions is 34 KB [20]. Since the size of a VC is much less than the size of a block, storing its VC instead of the original block can save much storage space.  
\[\mathbf{\mathrm{C}}\leftarrow\mathit{Commitment}(tx_{1},...,tx_{n}) \tag{7}\]  
\[\mathbf{\mathrm{\pi}}\leftarrow\mathit{Open}(i,tx_{1},...,tx_{n}) \tag{8}\]  
\[\{0,1\}\leftarrow\mathit{Verify}(\mathbf{\mathrm{C}},i,tx,\mathbf{\mathrm{\pi}}) \tag{9}\]  
Since the transactions in a block are fixed, the \"Update\" and \"ProofUpdate\" algorithms in conventional VC definition are not applicable. The \"KeyGen\" algorithm is unnecessary neither since there are no other public parameters to be generated. The algorithms are shown in detail with Algorithms A1-A4 in Appendix A.
## 2 Results
### Principal Component Analysis
#### 2.2.2 PDMS/DVB Fiber  
The biplot constructed by the first two principal components that are showing the distribution of investigated _S. romana_ and _S. montana_ populations and VOCs identified with PDMS/DVB fiber is presented in Figure 4. Principal component 1 (PC1) accounted for 55.88% and principal component 2 (PC2) for 33.21% of total variance in the data. Clear separation between _S. romana_ and _S. montana_ was obtained. Distinctive components observed by using PDMS/DVB fiber for all _S. romana_ populations, except for _S. romana_ M, were bicyclogermacrene (C15\({}^{\prime}\)), isocaryophyllene (C13\({}^{\prime}\)), _trans_-\(\beta\)-farnesene (C12\({}^{\prime}\)) and bicycloelemene (C9\({}^{\prime}\)). _S. romana_ M population was characterized by viridiflorol (C17\({}^{\prime}\)), \(\gamma\)-terpinene (C7\({}^{\prime}\)), \(\alpha\)-pinene (C1\({}^{\prime}\)), \(p\)-cymene (C4\({}^{\prime}\)), benzyl alcohol (C6\({}^{\prime}\)) and \(\alpha\)-terpinene (C3\({}^{\prime}\)). Distinctive components for discrimination of _S. montana_ were germacrene D (C14\({}^{\prime}\)), \(\alpha\)-copaene (C10\({}^{\prime}\)) and \(\delta\)-cadinene (C16\({}^{\prime}\)).  
Figure 4: Biplot obtained by principal component analysis of VOCs composition of investigated populations of _S. romana_ and _S. montana_, based on major components with average percentages \(\geq\) 1% in at least one of the samples, detected with PDMS/DVB fiber.
# Generation of Orbital Angular Momentum Modes Using Fiber Systems
## 4 Advances in Fiber OAM Generation Systems
### Micro-Structured Optical Fiber-Based OAM Generation System
With such a phase difference distribution, when the spatial-phase-modulated multi-beams converge in a section of ring core fiber (RCF), the OAM mode with TC \(l\) can be effectively generated [50]. For PCF, the air-holes in silica are arranged for the transverse subwavelength grating in [51]. The air-hole diameter across the transverse dimension can be varied to create an arbitrary ERI profile \(N_{\textit{eff}}(r,\theta)=N_{0}+\triangle N_{\textit{eff}}(r,\theta)\), where \(N_{0}\) and \(\triangle N_{\textit{eff}}(r,\theta)\) are the  
Figure 12: Schematic of proposed micro-structured optical fiber to generate OAM mode in [49]. Reprinted with permission from [49], copyright 2015 The Optical Society. constant and varying parts of \(N_{\textit{eff}}(r,\theta)\). To generate OAM with TC \(l\), the introduced ERI perturbation \(\bigtriangleup N_{\textit{eff}}(r,\theta)\) must satisfy the phase-matching condition:  
\[\Delta N_{\textit{eff}}(r,\theta)=l\frac{\lambda}{z}\frac{\theta}{2\pi} \tag{17}\]  
where \(z\) is the length of the PCF. The OAM generation methods based on the fiber design not only have the advantages of the fiber device such as low loss and small size, but also have the advantages such as directly converting the fundamental mode to the OAM mode and the TCs of generated OAM being controllable. However, the structures of the designed fiber are usually complicated and difficult to fabricate in reality.
## 5 Evaluation
### Metrics and Scoring
#### 5.2.4 Recall  
The recall defines the ratio of correctly predicted positive samples, that is to say, the proportion of malicious documents that were correctly classified as such. The following definition is provided:  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted**} \\ \hline  & & **Malicious** & **Benign** \\ \hline  & & Malicious & 13 & 1 \\  & & Benign & 1 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix.  
Inserting our test results, we receive the following recall:  
\[\text{Recall}=\frac{13}{13+1}\approx 0.929=92.9\%\]  
The 92.9% represents the proportion of correctly classified malicious documents. It is of particular significance when the objective is to avoid misclassifying any genuinely malicious documents as benign, thereby failing to identify them as such.
# Visualization of Distributed Plasticity in Concrete Piles Using OpenSeesPy
## 1 Introduction and Background
### Contribution of This Work  
The present study addresses lumped plasticity model limitations by developing and applying an open-source, fiber-based seismic model of a typical RC wharf pile. The model is implemented in OpenSeesPy and features 3D nonlinear springs to simulate soil-structure interaction, and fiber-discretized RC sections to capture distributed plasticity. This allows axial-moment interactions and distributed yielding to take place. To aid the interpretation of results, a Python-based post-processing workflow is employed using custom scripts to visualize sectional strains, curvature profiles, and demand-to-capacity ratios.  
Since the validation of OpenSeesPy models against experimental data has been successfully presented by many researchers [11; 35; 36; 37; 46], the main objective of this paper is to compare numerical modeling platforms--specifically, a fiber-based OpenSeesPy model versus a lumped plasticity model built in SAP2000--highlighting the differences in damage progression. Advantages of the approach include the following:  
* Using OpenSeesPy to integrate full 3D soil-pile interaction with distributed plasticity;* Automating result visualization so that fiber strain and curvature demand along the pile length can be plotted;
* Enables engineers to generate automated sequences for modifying model parameters and instantly obtaining updated stress-strain visualizations, thereby streamlining parametric studies, and design optimization;
* Benchmarking the OpenSeesPy fiber model against the commercial SAP2000 frame-hinge model of the same pile.  
By comparing the results from these models, it is shown how fiber-based analysis can reveal damage progression, overlooked by lumped hinges, and can be used to verify pile strain limits prescribed by design standards such as ASCE 61-14.
# Automatic Modulation Classification Using Hybrid Data Augmentation and Lightweight Neural Network
## 2 Signal Model and Problem Formulation
### System Model  
The framework of the deep learning-based AMC system is shown in Figure 1. In this system, the classifier is critical, as it needs to classify the modulation scheme based on the pre-processed data.  
In the training process, the goal is to obtain a mapping function \(f\in\mathcal{F}:\mathcal{X}\rightarrow\mathcal{Y}\) with a low number of parameters. \(\mathcal{X}\) and \(\mathcal{Y}\) represent the sample space and category space, respectively. Due to the limited amount of data, the neural network can approximate the mapping function between \(\mathcal{X}\) and \(\mathcal{Y}\) by finding the mapping relationship between existing data and labels in the database. The optimization target can be expressed as  
\[\min_{f\in\mathcal{F}}\mathbb{E}_{(\mathbf{x},\mathbf{y})\sim D}\{\mathcal{L}_ {ce}[f(\mathbf{x}),\mathbf{y}]\}, \tag{3}\]  
where \(D\) is the existing dataset, \(y\) is the modulation category label of \(x\), and \(\mathcal{L}_{ce}\) is the cross-entropy loss.  
In the testing process, the neural network is employed as the classifier. For a communication system, an accurate and fast classifier facilitates the subsequent correct demodulation and the stability of the whole system. The classifier infers the probability that the input signal belongs to each modulation type in the modulation type pool \(\mathbb{P}=\{p_{j,j}=1,2,\cdots,J\}\)  
Figure 1: System framework of AMC based on the neural network.  
where \(J\) represents the number of modulated types and \(p_{j}\) represents the modulated type. The inference of the classifier is shown as  
\[p=\operatorname*{arg\,max}_{p_{j}\in\mathbb{P}}P(p_{j}|\mathbf{x}), \tag{4}\]  
where \(P(p_{j}|\mathbf{x})\) is the probability that the input signal \(\mathbf{x}\) belongs to modulation type \(p_{j}\) and \(p\) is the classification result.
# Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
## 3 Proposed Approach
### Deep Learning-Based Vial Detection  
The preprocessed images are input into the Lightweight YOLOV4, which detects vials and generates bounding boxes. To make our model better at finding small, visually similar vials, we added more convolutional layers to the feature extraction layers to improve small object recognition. We also used custom anchor boxes made for medical vial sizes and tweaked the bounding box adjustments to cut down on false positives and boost classification accuracy. In the bounding box, \(B\), in Equation (1), each detected vial is represented mathematically as follows:  
\[B=(x,y,w,h) \tag{1}\]  
where \((x,y)\) represents the center of the bounding box, and \((w,h)\) denote the width and height of the detected object. The classification confidence score for each detected object is calculated as \(C\) in Equation (2):  
\[C=P(O)\times\max P(c_{i}), \tag{2}\]  
where \(P(O)\) is the probability of an object being present in the given bounding box, and \(\max P(c_{i})\) represents the highest probability among all object classes.
## 5 Experimental Results and Analysis
### Case Studies
#### 5.3.1 Case of Correct Identification  
In Figure 5(a), the sewer built by Germans in Qingdao a century ago is presented. It is difficult to determine the conveyed information solely based on the image. However, in the text, through the comprehensive semantic information containing the keyword \"sewer\", the interaction between the textual and visual features revealed inconsistent representations across different modalities, which could not complement each other. Hence, the fused features obtained during the rumor detection classified this news as a rumor.  
In Figure 5(b), clouds on Mount Fuji in Japan are depicted. Although the textual description matches the visual description, the shape of the \"clouds\" in the image is exaggerated to the point of incredulity, thereby enabling the identification of the image as a fake and consequently inferring it as a rumor.  
Figure 5: Ablation study of the TDEDA model on Weibo and Twitter datasets. Each color represents a component.
# Impact of One Additional Substitution on Player Load and Coaching Tactics in Elite Football
## 1 Introduction
With regard to exercise intensity, several studies on match analysis have outlined that the volume of high-intensity exercises decreases towards the end of matches in professional and semi-professional football [19; 21]. This has been demonstrated across the board in all age, gender, or performance categories [24; 25]. Therein, the quantity of sprinting, high-intensity running, and distance covered was lower in the second half of a match than in the first half [19; 26]. In addition, high-intensity running was significantly reduced in the last 15 min of an elite football match [19]. Research has indicated that player load progressively increases during a match with peak loads based on multiple factors [27]. Therefore, the physical conditions of the game make use of substitutions effective in reducing the load on the players. Frequent substitutions can reduce fatigue during a match and between matches and help maintain the health of athletes through squad rotation [28; 29]. This becomes even more consequential when the player load is compared to the player rest time. On average, players face 254 h and 196 days of load exposure in up to 60 matches and 220 training sessions per season, which corresponds to a ratio of two days of load to one day of recovery [30; 31]. National team players who also compete in international club-level matches have even more playing hours and, therefore, less recovery time, which may further increase the importance of load reductions for these elite players through substitutions [30; 32; 31]. Existing research on the effects of an additional substitution is based on the observation of the coaches' substitution behaviour in the German Bundesliga and Italian Serie A, when three substitutions, instead of two, were implemented in the national leagues for the 1995/96 season [1; 9]. Both the studies indicated that with an additional substitution opportunity, substitutions were used significantly more often and earlier in the match [1; 9]. [PERSON] et al. [1] and [PERSON] [9] both found similar average substitution times overall in matches with three and two substitutions allowed. Furthermore, the additional substitution resulted in the first two substitutions being made about 5 min earlier [1], and the use of the second substitution was increased by about 20% [9]. However, whether an additional substitution is an effective tool to reduce the player load in a match or over the course of a season or not remains unexplored.
# SMARTerra, a High-Resolution Decision Support System for Monitoring Plant Pests and Diseases
## 2 Methods
### Back-End
#### 2.2.4 Spatial Interpolation of Weather Forecasts  
The nodes in the BOLAM model are arranged according to a Cartesian grid in terms of geographic coordinates, where unnecessary far offshore points have been removed to save computational effort (see Figure 2, right). Interpolating from such a pseudo-structured grid is mathematically easier than interpolating from the irregular pattern of the stations' distribution and can be carried out, for instance, by standard bicubic spline approximation. This enables the interpolation of meteorological quantities on a finer grid, increasing the resolution from approximately 4 km (the BOLAM grid step size) to 100 m over the entire island or to 10 m in regions critical for SMARTerra where farm-scale resolution is required.