esa-sceva/satcom-chunk-collection huggingface dataset:
# From Discourse Relations to Network Edges: A Network Theory Approach to Discourse Analysis
## 2 Discourse Relations and Discourse Units
### Elementary Discourse Units  
The EDUs are linguistic utterance nodes on the discourse graph and are considered the basic building blocks of a discourse representation. The intricate semantic and pragmatic manner that the EDUs are related is expressed through a number of discourse relations, such as _Narration_, _Elaboration_, _Explanation_, _Result_ and _Narration_.  
All the above-mentioned discourse theories have a different theoretical standpoint regarding both the definition criteria of discourse relations and the set of discourse relations that characterize a coherent text. However, for the purposes of this paper, these heated theoretically driven debates can be safely ignored, since the main target is to unveil deep properties of the discourse structure viewed as a network and not on the type of discourse relations that create them.  
Moreover, all discourse theories acknowledge the need of a discourse segmentation algorithm that tokenizes the discourse into EDUs, and they are more or less aligned regarding the segmentation criteria.  
The above-mentioned theoretical differences aside, another important classification axis of discourse relations pertaining to whether a discourse relation should be considered as coordinating or subordinating ([38]). Briefly, coordinating relations, such as _Narration_ and _Continuation_, relate EDUs that share a general common topic and can be thought of as providing information on the same level of detail on that topic, while subordinating relations, such as _Elaboration_ and _Explanation_ between two EDUs are asymmetrically related, since one of the two plays a subordinate role relative to the other. Ref. [38] argues that it is not an easy task to classify discourse relations as either coordinating or subordinating and presents linguistic tests that help decide which discourse relations are subordinating and which are coordinating in a given context.  
The relevant part of the distinction between coordinating and subordinating relations for the construction of the discourse representation is that the first type of relation is indicated through horizontal connections, as in the simple graph in Figure 1 (1), for the coordinating relation _Narration_ between \(\pi_{1}\) and \(\pi_{2}\) and the second type through vertical ones, as in Figure 2 for the subordinating relation _Explanation_ between \(\pi_{1}\) and \(\pi_{2}\) in (1).
###### Abstract  
In performing various missions using various types of vehicles or other moving objects, the positioning of each agent within the swarm is essential. In particular, for missions that require precise location estimation, the case of malicious attacks through data forgery cannot be excluded. In this paper, we propose a highly secure and accurate localization framework utilizing a Directed-Acyclic-Graph (DAG)-based distributed ledger as an intelligent vehicle network for an Ultra-Wideband (UWB) positioning system. When performing UWB positioning, the data generated from each node are used to calculate the Time of Flight (ToF), and if any of them are tampered with, the overall positioning performance is greatly reduced. We prevented positioning performance degradation by ensuring the safety and integrity of the data by applying a chain-based logical network between each node utilized for UWB positioning. The experimental results indicated that the proposed framework was effective at providing system stability and security without affecting the UWB positioning performance. In addition, the performance of the framework was verified by presenting defense indicators for various attack scenarios.  
vehicle network; UWB; accurate positioning; DAG; consensus algorithm Article
# One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction
## 7 Proposed 1D CNN Regression for Cutter Wear Prediction
### Data Preprocessing  
Before model training and data splitting, the EPB TBM machine parameters were standardized using Equation (3).  
\[z=\frac{x-\mu}{\sigma} \tag{3}\]  
where \(\mu\) is the mean, \(\sigma\) is the standard deviation of the input parameters, and \(z\) is an element of the normalized training data.
## 4 Multimodal Fusion for Infrastructure Inspection
### Applications in Practice
#### 3.4.2 Archeology and Environmental Studies  
GPR is invaluable for non-invasive archeological prospecting. AI tools reduce the time and subjectivity involved in manually interpreting vast GPR grids. One case study, employing a U-Net model trained on 3D GPR volumes from Roman villa excavation sites, successfully segmented buried walls and foundations--as validated by ground-truth excavations. In environmental science, AI helps detect tree root networks, buried waste, and voids beneath forest floors or urban green spaces [61, 62, 63].
# Algorithmic Coverage Quantification and Visualization in Range-Free Sensor Networks
## 2 Materials and Methods
### Analyzing Coverage Zones  
Having calculated the complete set of \(Q_{i}\) coverage zones \(\{L_{i,q},q=1,\ldots,Q_{i}\}\) for each \(SR_{i}\), \(i=1,\ldots,n\), each list \(L_{i,q}\) is transformed into a \"shapely Polygon\" object denoted as \(P_{i,q}\). In the sensor class (please refer to the class diagram of Figure 2), the complete list of these polygon objects \(P_{i,q}\) of each \(SR_{i}\) are defined as the \"beams\" of \(SR_{i}\).  
Subsequently, the program identifies the intersections \(P_{i,q}\cap P_{j,s}\cap P_{i,p}\) among the beams of all combinations of three sensors \(SR_{i}\), \(SR_{i}\) and \(SR_{i}\) (where \(i,j,t=1,\ldots,n,j\
eq i\), \(t\
eq i,j\) and \(q=1,\ldots,Q_{i},s=1,\ldots,Q_{j},\)\(v=1,\ldots,Q_{i}\)). Finally, the multi-polygon \(I\), formed as the union of all these intersections, defines the non-overlapping coverage zones of three or more SRs (strong coverage, \(k\geq 3\)):  
\[I=\bigcup P_{i,q}\cap P_{j,s}\cap P_{t,p} \tag{1}\]  
Figure 5: Flowchart describing the process of approximating the q-th circular sector coverage zone of \(SR_{i}\), \(q=1,\ldots,Q_{i}\), as a list of polygon vertices \(L_{i,q}\).  
\[i,j,t=1,\ldots,n,j\
eq i,t\
eq i,j \tag{2}\]  
\[q=1,\ldots,Q_{i},\;s=1,\ldots,Q_{j},\;v=1,\ldots,Q_{t} \tag{3}\]  
To ensure effective visualization of coverage, the multi-polygon \(I\) is highlighted on the map with a transparent color, and the total area of \(I\) is calculated. Since shapely requires projected coordinates in meters, geographic coordinates (latitude/longitude) are transformed before any area calculations. Folium, which specializes in visualizing geographic data, typically assumes coordinates in degrees (ellipsoidal axes, WGS84 format--EPSG code 4326, unless specified otherwise), automatically converting them to meters in the pseudo-Mercator projection (cartesian axes, EPSG code 3857) for map display [23; 24]. To perform these coordinate transformations and accurately compute the coverage area in square meters, pyproj is used to establish a transformer that converts coordinates from the WGS84 system to the projection used for the Universal Transverse Mercator (UTM)-specific zone that corresponds to the area under study.
# Research on Improved Lightweight YOLOv5s for Multi-Scale Ship Target Detection
## 3 Data and Methods
### The Improved Yolov5_ESGW Model
#### 3.3.3 Wise-IoU Loss Algorithm  
In the target detection task, bounding box loss, as one of the important loss functions, plays an important role in improving the accuracy and stability of model prediction. The bounding box loss is mainly used to optimize the difference between the bounding boxes predicted by the model and the true bounding boxes. Specifically, this loss function optimizes the model by calculating the distance or difference between the predicted and true bounding boxes. For the ship visible light dataset, the scenes included are complex, and the target scales vary greatly. This study uses Wise-IoU (Intersection over Union) [25] as the bounding box loss function for ship target detection to achieve more accurate detection of ship multi-scale targets. Wise-IoU is based on IoU loss, giving different weights to samples of each category to reduce the difference between better and worse quality categories. This improves the generalization performance of the model.  
As shown in Figure 6, \([x,\ y,\ w,\ h]\) represents the center point coordinates and width and height of the anchor box; \([x_{gt},\ y_{gt},\ w_{gt},\ h_{gt}]\) represent the center point coordinates, width, and height of the target frame, respectively; \(W_{g}\) and \(H_{g}\) represent the width and height of the smallest enclosing rectangle of the anchor box and target box, respectively; and \(W_{i}\) and \(H_{i}\), respectively, represent the width and height of the overlap between the anchor box and the target box. The calculation method of \(L_{Wise-IOU1}\) is as shown in Formulas (1)-(3). Among them, \(R_{Wise-IOU}\) can significantly increase the \(L_{IOU}\) of ordinary quality anchor frames, and \(L_{IOU}\) can significantly reduce the \(R_{Wise-IOU}\) of high-quality anchor frames.  
\[L_{Wise-IOU1}=R_{Wise-IOU}L_{IOU} \tag{4}\]  
\[L_{IOU}=1-\frac{W_{i}H_{i}}{wh+w_{gt}h_{gt}-W_{i}H_{i}} \tag{5}\]  
\[R_{Wise-IOU}=\exp(\frac{(x-x_{gt})^{2}+(y-y_{gt})^{2}}{(W_{g}^{2}+H_{g}^{2})^ {*}}) \tag{6}\]  
In addition, an outlier degree (\(\beta\)) is introduced to characterize the quality of the anchor box, and its calculation method is shown in Formula (9). When \(\beta\) is smaller, it represents higher anchor box quality. At the same time, a small gradient gain is assigned to it so that the bounding box regression focuses on the normal quality anchor box. For a larger \(\beta\), assigning a smaller gradient gain can effectively suppress the risk of low-quality examples generating large harmful gradients. Finally, the non-monotone focusing factor \(r\) is used to optimize \(L_{\textit{Wise-IOUv1}}\), and the specific calculation method is shown in Formulas (7) and (8).  
\[L_{\textit{Wise-IOUv3}}=rL_{\textit{Wise-IOUv1}} \tag{7}\]  
\[r=\frac{\beta}{\delta\alpha^{\beta-\delta}} \tag{8}\]  
\[\beta=\frac{L_{\textit{LoU}}^{*}}{L_{\textit{IoU}}}\in[0,+\infty) \tag{9}\]
## 5 Production History Matching Using Limited Production History  
Hindcasting is performed by matching a portion of the known production history and comparing the remaining portion of known production history to the forecast. Overall, the main objective for production history matching is to forecast production with high accuracy given only a short early production period. Therefore, production history matching was done using different early production periods (three months, six months, one year, and two years) and then compared to find the shortest production period needed to achieve reliable forecasts of EUR. Tables 5 and 6 summarize the results from production history matching between different early production periods and Arps hyperbolic decline and SEPD models, respectively. Figures 6 and 7 present examples of the outcomes of production history matching using early production data with Arps hyperbolic decline and SEPD models, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Decline** & **Formation** & \begin{tabular}{c} **Matching** \\ **Period** \\ \end{tabular} & **MAPE (\%)** & \begin{tabular}{c} **Correlation** \\ **Coefficient (\(\mathbf{r}\))** \\ \end{tabular} & **NRMSE** \\ \hline \hline \multirow{8}{*}{Niobra} & & 2 years & 5.9 & 0.991 & 9.30 \\ \cline{2-6}  & \multirow{2}{*}{Niobra} & 1 year & 15.4 & 0.946 & 21.4 \\ \cline{2-6}  & & 6 months & 30.8 & 0.814 & 49.5 \\ \cline{2-6}  & \multirow{2}{*}{3 months} & 67.9 & 0.735 & 92.1 \\ \cline{2-6}  & & 2 years & 7.0 & 0.981 & 14.8 \\ \cline{2-6}  & \multirow{2}{*}{Barnett} & 1 year & 18.3 & 0.896 & 35.6 \\ \cline{2-6}  & \multirow{2}{*}{6 months} & 42.3 & 0.780 & 80.9 \\ \cline{2-6}  & \multirow{2}{*}{3 months} & 80.6 & 0.743 & 128.2 \\ \cline{2-6}  & \multirow{2}{*}{Haynesville} & 2 years & 9.4 & 0.938 & 20.3 \\ \cline{2-6}  & \multirow{2}{*}{Haynesville} & 1 year & 29.5 & 0.772 & 64.5 \\ \cline{2-6}  & \multirow{2}{*}{6 months} & 86.6 & 0.652 & 136.8 \\ \cline{2-6}  & \multirow{2}{*}{3 months} & 146.2 & 0.626 & 172.5 \\ \cline{2-6}  & \multirow{2}{*}{\begin{tabular}{c} 2 years \\ \end{tabular} } & 10.5 & 0.974 & 20.6 \\ \cline{2-6}  & \multirow{2}{*}{\begin{tabular}{c} 1 year \\ \end{tabular} } & 25.5 & 0.841 & 71.6 \\ \cline{2-6}  & \multirow{2}{*}{6 months} & 77.8 & 0.791 & 179.2 \\ \cline{2-6}  & \multirow{2}{*}{3 months} & 149.2 & 0.757 & 231.8 \\ \cline{2-6}  & \multirow{2}{*}{\
\begin{tabular}{c} 2 years \\ \end{tabular} } & 11.0 & 0.964 & 17.9 \\ \cline{2-6}  & \multirow{2}{*}{
\begin{tabular}{c} 1 year \\ \end{tabular} } & 24.0 & 0.861 & 37.0 \\ \cline{2-6}  & \multirow{2}{*}{6 months} & 44.7 & 0.711 & 58.8 \\ \cline{2-6}  & \multirow{2}{*}{3 months} & 68.3 & 0.615 & 94.6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results from production history matching on all wells from each formation using different production data periods with the SEMD model.  
Figure 6: Example of production history matching using different production history data with the Arps hyperbolic decline model for a well in the Niobrara formation.
# NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training
## 5 Discussion  
The experimental results in the previous section showed promising outcomes for training PINNs using the proposed NSGA-PINN method. As described in Section 3, when solving the inverse problem using the traditional ADAM optimizer, the algorithm became trapped in a local optimum after running for 400 epochs. However, by using the NSGA-PINN method, the loss value continued to decrease, and the predicted solution was very close to the true value. Additionally, when dealing with noisy data, the traditional ADAM optimizer had difficulty learning quickly and making accurate predictions. On the other hand, the proposed NSGA-PINN algorithm learned efficiently and converged to a better local optimum for generalization purposes.  
However, the main drawback of the proposed method is that it requires an ensemble of neural networks (NNs) during training. Consequently, the proposed NSGA-PINN incurs a larger computational cost than traditional stochastic gradient descent methods. Therefore, reducing the computational cost of NSGA-PINN is a goal for our future work. For instance, some of the training computational cost could be mitigated by using parallelization. Additionally, we will attempt to derive effective methods for finding the best trade-off between NSGA and ADAM.  
More specifically, in our future work, we will focus on balancing the parent population (\(N\)), max generation number (\(\alpha\)), and number of epochs used in the ADAM optimizer. These values are manually initialized in the proposed method. The parent population determines the diversity in the algorithm, and we ideally want high diversity. The max generation number determines the total learning time. Increasing this time allows the algorithm to continue learning from previous generations, but it may lead to overfitting if the number is too large. Note that there is a trade-off between the max generation number and the epoch number used in the ADAM optimizer. A higher generation number allows the NSGA algorithm to perform better, helping the ADAM optimizer escape the local optima, but this comes at a higher computational cost. Meanwhile, increasing the number of epochs used in the ADAM optimizer helps the model decrease the loss value quickly, but it reduces the search space and may lead to the algorithm becoming trapped in the local minima.
## 3 First-Stage Problem Formulation
### Objective Function  
In order to obtain a trade-off among the reconfiguration time, effectiveness, operational constraints, and economic costs, the objective function at the first (or pre-positioning) stage should take into account the outage time of loads, the number of loads to be shed, the number of switching operations, and the total purchasing capacity of MES, as illustrated in Equation (1). Since the four terms of the optimization objective have diverse dimensions, they are normalized and correspondingly labeled in the above equation. In essence, the outage time of loads is largely driven by the time required to re-allocate the MES (i.e., moving the pre-positioned MES to other nodes), denoted as \(\sum\limits_{p\in I_{1}}\sum\limits_{k\in I_{2}}\sum\limits_{m\in M}\beta_{ mpkh}T_{skn}\), and the time required for operating the switches is a negligible factor. The aim at this stage is to pick up weighted loads as much as possible by using a strategy that minimizes the weighted loads to be shed, denoted as \(\sum\limits_{i\in I}Z_{i}sl_{in}\). Furthermore, the impact of the switching operations of boundary lines, measured based on the number of operations, \(\sum\limits_{(ij)\in BL}bl_{ijn}\), is also considered. As the cost of MES devices is closely related to their battery capacity, it is economically preferred to minimize the total capacity of the purchased MES devices, represented as \(\sum\limits_{m\in M}G_{m}\). Though the problem is to efficiently restore as many loads as possible with the shortest time and lowest cost, the impact of switching operations and the weighting of each scenario \(W_{n}\) are also taken into consideration. The weights of four terms in the optimization objective, denoted as \(\eta_{1}\) to \(\eta_{4}\), are determined by the decision makers.  
\[\min\sum\limits_{n\in N}W_{n}\Bigg{\{}\eta_{1}\frac{\sum\limits_{p\in I_{1}} \sum\limits_{k\in I_{2}}\sum\limits_{m\in M}\beta_{mpkn}T_{skn}}{BM\cdot T_{ \max}}+\eta_{2}\frac{\sum\limits_{i\in I}Z_{i}sl_{in}}{\sum\limits_{i\in I}Z _{i}P_{i}}+\eta_{3}\frac{\sum\limits_{(ij)\in BL}bl_{ijn}}{N_{bl}}+\eta_{4} \frac{\sum\limits_{m\in M}G_{m}}{BM\cdot G_{M}}\Bigg{\}} \tag{1}\]
# STGAT: Spatial-Temporal Graph Attention Neural Network for Stock Prediction
## 4 Experiments
### Experimental Results
#### 4.3.2 Performance of Portfolio Optimization Results
These temporal variations may arise from structural differences in market characteristics and cross-market spillover effects, such as the policy-driven retail dominance in A-shares versus the institutional liquidity dynamics in US equities or sentiment transmission between stock markets. However, apparently STGAT achieves superior cumulative returns in both markets during periods of relatively stable market conditions. The results reveal that STGAT outperforms both the market index and other deep learning methods, reflecting the feasibility of integrating spatial-temporal features and the advantages of incorporating graph attention mechanisms into temporal analysis. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Cumulative** & \multirow{2}{*}{**Volatility**} & **[PERSON]** & **Max** & **Calmar** & **Information** & **Treynor** \\  & **Return** & & **Ratio** & **Drawdown** & **Ratio** & **Ratio** & **Ratio** \\ \hline S\&P500 & 20.16\% & 0.1179 & 3.9280 & 4.73\% & 10.0456 & - & 0.4631 \\ GCN & 26.08\% & 0.2673 & 2.4776 & 7.68\% & 8.7771 & 0.0849 & 0.4403 \\ GRU & 23.98\% & 0.2381 & 2.4938 & 9.42\% & 6.4303 & 0.0590 & 0.5473 \\ LSTM & 16.77\% & 0.2686 & 1.5486 & 10.00\% & 4.2790 & \(-\)0.0212 & 0.2874 \\ MLP & 32.42\% & 0.2244 & 3.6718 & 7.28\% & 11.4829 & 0.1745 & 0.6852 \\ RNN & 19.86\% & 0.2539 & 1.9307 & 10.31\% & 4.8712 & 0.0121 & 0.3990 \\ TCN & 16.12\% & 0.1784 & 2.0939 & 7.00\% & 5.5078 & \(-\)0.0476 & 0.8754 \\ Transformer & 25.71\% & 0.2602 & 2.4944 & 7.68\% & 8.6104 & 0.0786 & 0.4885 \\ STGAT & 36.87\% & 0.2306 & 4.1513 & 6.57\% & 14.7553 & 0.2197 & 0.8031 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance of investment portfolios in fixed time interval on US stock market.
# SC-SA: Byte-Oriented Lightweight Stream Ciphers Based on S-Box Substitution
## 2 SC-SA Cipher
### Differential Analysis  
The SC-SA encryption algorithm draws on the design concepts of block cipher algorithms for designing stream cipher algorithms. During differential analysis, we can refer to the differential analysis methods of block cipher algorithms that use S-boxes, specifically by calculating the number of active S-boxes. In Section 2, it is stated that the maximum differential probability of the S-box is 10/256. During the 80 iterations, the diffusion layer is mainly composed of shifts and XOR operations, while the confusion layer is composed of S-box substitutions and modular addition. The number of linear diffusion branches in the iterative process is conservatively set to four. Considering the worst-case scenario, each swap of the S-box increases the maximum differential probability of the S-box. Given an S-box return period of 256, the differential probability increases by 2R/256 after each iteration. We can calculate the number of active S-boxes and the maximum linear differential probability. Let the number of rounds be RR, where R \(\in\) [1,80]. The maximum differential probability of the S-box in the R-th iteration is \((10+2(\mathrm{R}-1))/2561+4(\mathrm{R}-1)\). Differential probability after iteration are shown in Table 3.  
With the advantage of a large S-box, SC-SA can resist differential attacks after multiple iterations.
# Follow-Up Control and Image Recognition of Neck Level for Standard Metal Gauge
## 3 Image Segmentation Algorithm Based on Edge Detection and K-Means Clustering Algorithm
### K-Means Clustering of Edge Information  
Once image segmentation is complete, the disorganized line sets must be accurately classified to identify the left and right edges of the scale and liquid tube indicator. After the original image is divided into liquid tube and scale regions, K-means clustering is used to identify structures of interest [22]. Given a random dataset \(X=\{x_{1},x_{2},\ldots\ x_{n}\}\), the algorithm randomly selects \(K\) objects as initial clustering centers and a corresponding subset of data \(C=\{c_{i},I=1,2,\ldots,K\}\). Each cluster object corresponds to a cluster center \(\mu_{i}\). The Euclidean distance from this center to a given point mi can then be calculated using:  
\[J(c_{i})=\sum_{x_{i}\in c_{i}}^{m_{i}}\|X_{i}-\mu_{i}\|^{2} \tag{8}\]  
The K-means clustering algorithm can be iteratively repeated to minimize the square of the distance from a given data set to the clustering center as follows [23]:  
\[J(C)=\sum_{i=1}^{k}J(c_{i}) \tag{9}\]  
The accurate clustering of multidimensional data requires an optimal value for \(K\). In order to separate the two effective regions, the liquid tube and the scale, four straight lines are required (left edge of the scale, right edge of the scale, left edge of the liquid tube, and right edge of the liquid tube), so the value of \(K\) is 4 in this study. Segmented lines were classified by identifying the peripheral edges of the scale and indicator tube, rather than the clustering centers. The x coordinates detected by the Hough transform were selected as a test data set, for the convenience of representing a fixed y coordinate in a 2D image. Clustering results are shown in Figure 9, where it is evident that the combination of edge detection and clustering produced an accurate segmentation, with edges of the scale and liquid tube being clearly identified. This approach is simple and efficient when provided with an optimal value for \(K\).  
After processing and segmentation, the original image is divided into scale and indicator tube regions. The following section discusses the scale image and the corresponding relationship between scale digits and pixel coordinate positions. The location of the concave meniscus in the liquid tube is then used to calculate the true height h of the liquid level.  
Figure 8: (**a**) Improved edge and (**b**) Hough linear detection.