esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Proposed Methods Based on FTSP  
### System Oscillator  
The motes used in this work have been TelosB (Rev. b) [16]. These motes use a Citizen CMR200T oscillator working at 32.768 kHZ and an internal digitally controlled oscillator (DCO) operating at 1 MHZ. The DCO may be turned on and off. When the DCO is off, the microcontroller (MSP430F1611) operates an external 32.768 kHZ watch crystal. Although the DCO frequency changes with voltage and temperature, it may be calibrated using the external oscillator [16].  
Thanks to the work of [PERSON] and [PERSON] [17; 18], an accuracy of microseconds can be achieved by using a timer linked to the internal DCO. This clock is calibrated by means of the frequency changes occurring in the external 32.768 kHZ oscillator.  
That external oscillator is classified within the tuning-fork [4] family. The tuning-fork oscillators are characterized by a quadratic relationship between the frequency and the temperature. This relationship is shown in Equation (1) and Figure 2.  
\[f=f_{0}(1+\beta(T-T0)^{2}) \tag{1}\]  
where \(f_{0}\) is the nominal frequency, \(\beta\) is a constant, known as the temperature coefficient, \(T\) is the ambient temperature and \(\mathrm{T}_{0}\) is the reference temperature (25 \({}^{\circ}\)C). In real scenarios, the actual frequency (\(f_{0}^{s}\)) is slightly different when compared to \(f_{0}\) for a given temperature, \(T_{0}\). This deviation is known as frequency tolerance. In the CMR200T oscillator, \(\mathrm{f}_{0}=32\),\(768\) Hz, \(\beta=-0.034\pm 0.006\) ppm, and the frequency tolerance is about \(\pm 20\) ppm. This working interval is mainly due to the effects of the cut of the crystal.  
This work proposes two new approaches to obtain a more precise clock skew, taking into consideration the temperature variations [19]. Including the actual temperature of the motes in the computations, it is possible to obtain a correction factor that minimizes the effects of the temperature in the oscillators frequency.  
Figure 2: Clock skew in Citizen CMR200T.  
The TelosB model includes the Sensirion SHT11 temperature sensor [20], which allows for an accurate measure, and its performance is guaranteed by the manufacturer.
## 4 Model Formulation and Specification
### Proposed Joint Framework Formulation  
The overall methodology for this study is depicted in Figure 6. It is important to note that the two outcomes, consideration (\(Y_{1}\)) and footfall (\(Y_{2}\)), are distinct but not mutually independent. Footfall is a measure of the number of travelers using the station. Thus, footfall itself is observed only for those users who considered the train and is, therefore, conditional on consideration. Hence, common unobserved variables may persist across the two outcomes, leading to endogeneity.  
Figure 5: Distribution of footfall across some key factors.  
Figure 6: Proposed joint modeling framework for consideration propensity and hourly footfall.  
Furthermore, consideration and footfall outcomes are captured using different data sources with varying levels of information and aggregation about respondents. Therefore, the two dimensions and datasets are made compatible with each other for joint modeling. In this joint framework, first, the individual consideration decision \(Y_{1}\) is modeled using the binary logit model. Next, to explicitly link the potential demand to realized demand and to account for potential self-selection bias, the average consideration propensity among those who considered train within the influence area of each metro station is used as an instrumental variable in the hourly football model \(Y_{2}\) to represent the conditional mean of the errors for the users (riders).  
Another research issue addressed by this framework is distinguishing how different factors influence travel behavior across the two stages. Some factors might primarily affect consideration, influencing football only indirectly through this consideration stage (a mediation effect). Others might affect both stages directly, and only the footfall stage directly. To address this aspect, the set of explanatory variables is classified into three categories of vectors: \(X_{1}\), \(X_{2}\), and \(X_{3}\). The vector of variables denoted as \(X_{1}\) affects only consideration (an indicator of the potential demand) directly but has no direct effect on footfall (a measure of the realized demand). This vector of variables (\(X_{1}\)) is referred to as a pure mediation variable. The vector \(X_{2}\) represents the set of variables that affect both dimensions (partially mediated). The vector \(X_{3}\) influences the footfall but not the consideration probability (unmediated variables). The specifications of models \(Y_{1}\) and \(Y_{2}\) are discussed in detail in Sections 4.2 and 4.3 and are shown schematically in Figure 6.
## 2 Mechanisms Specific to QoS and Multicast in Ad Hoc Networks
### Mechanisms for Multicast Routing
#### 2.1.1 Routing Type  
The routing decision can be made centrally or distributively among all nodes. In a distributed scheme, all nodes take part in the routing process and determine the multicast tree/mesh structure. Typically, a request-reply process is performed to select multicast routes. The source floods a route request. Intermediate nodes make the decision of whether to forward the route request or discard it. A route reply is sent by the receiver and forwarded to the source. When an intermediate node receives the route reply, it is a confirmation that the node is part of the tree/mesh for the multicast group. Otherwiseany state the node is in will time out. Protocols in [15, 19, 29, 32, 38] are examples of schemes using the distributed approach.  
In the central method, the source (or a dedicated node) collects the topology and resource information from the network and computes the multicast tree/mesh. The next step is to distribute the calculated multicast tree/mesh topology to other nodes.  
The central decided routing is used in [17, 24, 25, 41]. The protocol proposed by [PERSON] [17] is designed to correctly estimate interference between different flows. Therefore, all routes satisfying the QoS requirement must concurrently be determined by the source. In MPT [24], the source first discovers the topology, it then uses available bandwidth information of the links in topology to select the multicast routes. The protocol EQMGA [25] uses an entropy-based genetic algorithm to support QoS multicast routing. Here the entropy metric is computed based on the direction and the speed of the nodes and its neighbors. The protocol MACO [41] proposes a biology based ant algorithm where the direction towards the destination is determined by a heuristic method based on location.  
In ad hoc networks the topology and resource information changes quite frequently. As a result, the resource information and the calculation of the multicast tree/mesh may be outdated in centralized schemes. The majority of the proposed schemes is therefore based on the distributed approach.
## 2 Methodology
### Spatiotemporal Adaptive Prediction Network
#### 2.2.2 Self-Attention Mechanism  
To further enhance the representation capability of assembly steel strut axial force data features, a self-attention mechanism is integrated into the LSTM network to refine the hidden states across time steps by focusing on the most relevant information. As illustrated in Figure 3, the self-attention mechanism not only captures long-range dependencies but also highlights critical temporal features, overcoming the limitations of traditional methods that predominantly focus on local characteristics. By applying self-attention to the LSTM's hidden states, the model selectively accentuates key information, yielding a more comprehensive and focused depiction of the global dynamic changes in the data. Moreover, this approach enables the model to uncover and prioritize potential spatial correlations within the axial force data, thereby optimizing the feature representation derived from the LSTM. The enhanced representation effectively combines the temporal dynamics captured by the LSTM with the essential dependencies revealed by the self-attention mechanism, ultimately providing enriched and precise feature information for the subsequent prediction module.  
The core computation process of the self-attention mechanism is as follows: For an input feature matrix \(X\in\mathbb{R}^{n\times a}\) (where \(n\) represents the number of time steps and \(d\) is the feature dimension), linear transformations are first applied to obtain the query vector (\(Q\)), key vector (\(K\)), and value vector (\(V\)):  
\[Q=XW_{Q},\quad K=XW_{K},\quad V=XW_{V} \tag{7}\]  
where \(W_{Q}\), \(W_{K}\), \(W_{V}\in\mathbb{R}^{d\times d^{\prime}}\) are learnable weight matrices and \(d^{\prime}\) is the reduced feature dimension. Next, the similarity between the query and key vectors is computed using a dot product, and the result is normalized with the Softmax function to generate the attention weight matrix \(A\):  
\[A=\text{Softmax}\left(\frac{QK^{\top}}{\sqrt{d^{\prime}}}\right) \tag{8}\]  
the attention weight matrix \(A\in\mathbb{R}^{n\times n}\) represents the strength of spatial correlations within the input data. Finally, the attention weight matrix is multiplied with the value vector to produce the weighted output features \(O\):  
\[O=AV \tag{9}\]  
the output \(O\) is the global feature representation processed by the self-attention mechanism, which incorporates the spatial correlation information within the input data.  
Figure 3: Structure of the self-attention mechanism.
# Gaussian Mixture Modeling for Detecting Integrity Attacks in Smart Grids
## 2 Problem Definition  
We consider a network of interdependent infrastructures composed by \(N\) nodes which can produce either _homogeneous_ measurements, i.e., of the same physical quantity (e.g., only voltages), or _heterogeneous_ meaning that they are associated with different physical quantities (e.g., voltage phasor, frequency, and rotor angle measurements). The underlying assumption in this work is that the produced measurements are related to some extent since altogether they form a framework providing a particular service, e.g., an electrical distribution network. Thus, the data coming from the nodes are indirectly related to each other. The proposed method does not assume the existence of an analytical model of the specific relationship [22], it rather aims at learning the dynamics of the system solely by inspecting the data. In addition, the method is independent of network topology or routing/synchronization protocol.  
Let \(X_{i,T_{0}}=\{X_{i}(t),t=1,\ldots,T_{0}\}:\mathbb{N}\rightarrow\mathbb{R}\) be the scalar-in-time datastream produced by the \(i\)-th node. At an unknown time instant \(T^{*}>T_{0}\), an abnormal situation, i.e., an integrity attack, affects node \(i\). No assumptions are made with respect to the magnitude or the time profile of the incident influencing the data generation process. The ultimate goal is to detect promptly and identify correctly the nature of the occurred incident.
# The Influence of the Load Term of the Strain Energy Sensitivity in the Shell Shape Optimization
## 3 The Derivation of the Strain Energy Sensitivity
### The Derivation of the Strain Energy Sensitivity Including the Load Term  
Taking into account the derivative of Equations (2) and (3), the difference between Sections 3.1 and 3.2 is that the load vector \(\mathbf{F}\) is a function of the nodal \(z\)-coordinates.  
Equations (5) and (6) were obtained, and the new strain energy sensitivity expression, i.e., Equation (7) was obtained after the derivation.  
\[\frac{\partial\mathbf{C}}{\partial z_{j}}=\frac{1}{2}\frac{\partial\mathbf{F}^{T}}{ \partial z_{j}}\mathbf{U}+\frac{1}{2}\mathbf{F}^{T}\frac{\partial\mathbf{U}}{\partial z_{ j}} \tag{5}\]  
\[\mathbf{K}\frac{\partial\mathbf{U}}{\partial z_{j}}+\frac{\partial\mathbf{K}}{\partial z_ {j}}\mathbf{U}=\frac{\partial\mathbf{F}}{\partial z_{j}} \tag{6}\]  
\[\frac{\partial\mathbf{C}}{\partial z_{j}}=\frac{\partial\mathbf{F}^{T}}{\partial z_{j }}\mathbf{U}-\frac{1}{2}\mathbf{U}^{T}\frac{\partial\mathbf{K}}{\partial z_{j}}\mathbf{U} \tag{7}\]  
Because the sensitivity value of one node may be positive or negative, it is difficult to compare the influence of the load term on the strain energy sensitivity. In order to facilitate the comparison, the first term \(\frac{\partial\mathbf{F}^{T}}{\partial z_{j}}\mathbf{U}\) on the right-hand side of Equation (7) is called as the load term, and a vector is made up of the value of the load term of all nodes (the same below), and the vector length value is denoted as \(S_{F}\); similarly, the second term \(-\frac{1}{2}\mathbf{U}^{T}\frac{\partial\mathbf{K}}{\partial z_{j}}\mathbf{U}\) on the right-hand side is called the stiffness term, and its vector length value as \(S_{G}\), the result on the left-hand side of Equation (7) is called the whole value, its vector length value as \(S_{T}\).  
Their vector length, as shown in Equations (8) to (10), is used to measure the macroscopic size of structural sensitivity and its components.  
\[S_{F}=\sqrt{\sum_{j=1}^{n}\Bigl{(}x_{j}^{F}\Bigr{)}^{2}},\alpha_{j}^{F}= \frac{\partial\mathbf{F}^{T}}{\partial z_{j}}\mathbf{U} \tag{8}\]\[\mathrm{S_{G}}=\sqrt{\sum_{j=1}^{n}\left(\alpha_{j}^{C}\right)^{2}},\alpha_{j}^{G }=-\frac{1}{2}\mathbf{U}^{T}\frac{\partial\mathbf{K}}{\partial z_{j}}\mathbf{U} \tag{9}\]  
\[\mathrm{S_{T}}=\sqrt{\sum_{j=1}^{n}\left(\alpha_{j}^{T}\right)^{2}},\alpha_{j}^ {T}=\frac{\partial C}{\partial z_{j}} \tag{10}\]  
where \(n\) is the number of all the nodes as the design variables.
# NR Sidelink Performance Evaluation for Enhanced 5G-V2X Services
## 5 Conclusions  
We analyzed 5G-V2X sidelink packet transmission performance for V2X communication considering the differences between highway and urban scenarios. Specifically, we focused on the impact of a varying number of vehicles as well as vehicles' density and speed. The performance evaluation demonstrates the significant impacts of transmission interference on vehicle speed estimation, communication efficiency, and distance between vehicles. On the other hand, the random allocation of BSs and larger packets can also degrade performance and increase the average interference. These findings will open new directions into resource allocation strategies that consider performance requirements.  
Conceptualization, M.T., F.H.B., A.O. and A.K.; methodology, M.T. and F.H.B.; software, M.T. and F.H.B.; validation, M.T. and F.H.B.; formal analysis, M.T. and F.H.B.; investigation, M.T. and F.H.B.; resources, M.T. and F.H.B.; data curation Tabassum; writing--original draft preparation, M.T., F.H.B., A.O. and A.K.; writing--review and editing, M.T.; visualization, M.T., F.H.B., A.O. and A.K.; supervision, A.O. and A.K.; project administration, M.T., F.H.B., A.O. and A.K.; funding acquisition, A.K. All authors have read and agreed to the published version of the manuscript.  
This work was partially financed by the Project Smart 5G Core And MUltiRAn Integration (SAMURAI) (MCTIC/CGLbr/FAPESP under Grant 2020/05127-2).  
Figure 9: Latency for different NR-V2X configurations with varying vehicle speeds without retransmissions and varying inter-vehicle distances. (**a**) Highway and (**b**) Urban.  
Data Availability StatementWe are dedicated to facilitating data sharing for the purpose of transparency, verification, and further research following \"MDPI Research Data Policies\". The implementation codes and data of the simulations can be found on the GitHub repository ([[https://github.com/[PERSON]/NR-Sidelink-Performance-Evaluation-for-Enhanced-5G-V2X-Services](https://github.com/[PERSON]/NR-Sidelink-Performance-Evaluation-for-Enhanced-5G-V2X-Services)]([https://github.com/[PERSON]/NR-Sidelink-Performance-Evaluation-for-Enhanced-5G-V2X-Services](https://github.com/[PERSON]/NR-Sidelink-Performance-Evaluation-for-Enhanced-5G-V2X-Services))), ensuring easy access for further examination.
# Additive Manufacturing in Underwater Applications
## 5 Areas of Application
### Artificial Reefs  
Artificial coral reefs aid in restoration and reformation strategies that support coral ecosystems. The use of 3D printing techniques, in this case, helps in supporting structural complexity and biodiversity through better-fixing structures for the needs of local flora and fauna [21; 27]. The usage of AM supports provides additional space for organisms to settle and grow. It increases the local biodiversity and stimulates the recovery of ecosystems [27; 83; 84]. AM technology improves the efficiency of the recultivation process in comparison with traditional artificial reefs (concrete molds with simple geometric shapes) [85].  
Despite the abundance of different materials suitable for use for artificial reef creation, there is only a small number of investigations into how the physical and chemical properties of different materials influence particular species [86]. Some works in this area were provided by [PERSON] et al. [86]. They investigated the coral recruitment success and early benthic community development depending on the used materials, such as 3D-printed concrete, polyvinyl chloride (PVC) with and without a chitosan coating, a fiberglass polymer, flax-based polylactic acid, and Portland concrete [86]. The results suggest that different materials should be selected according to the recultivation purposes. Meanwhile, porous concrete was characterized by the high abundance of non-coralling encrusting red algae, and the PVC with a chitosan layer supported coral recruitment [86].  
[PERSON] et al. [27] investigated different cementitious materials, such as geopolymers and cement concrete, taking into consideration biofouling (the colonization of a structure by biological organisms) and the durability of these materials in seawater [27]. The results showed that cement seems to be a better material for artificial reef construction in the 3D printing method, because of its better mechanical properties and lesser biofouling in the medium term [27]. Contrasting results were obtained by [PERSON] et al. [34]. They showed, after three months of underwater tests, that geopolymers had better biological receptivity compared to cement [34].  
There is a relatively large amount of investigation into the use of 3D printing for reef restoration [35]. However, there is no information in the literature about possible restoration in situ, as well as works connected with so-called mezzo reefs (the deepest reef formations).
# DMLU-Net: A Hybrid Neural Network for Water Body Extraction from Remote Sensing Images
###### Abstract  
The delineation of aquatic features from satellite remote sensing data is vital for environmental monitoring and disaster early warning. However, existing water body detection models struggle with cross-scale feature extraction, often failing to resolve blurred boundaries, and they under-detect small water bodies in complex landscapes. To tackle these challenges, in this study, we present DMLU-Net, a U-shaped neural network integrated with a dynamic multi-kernel large-scale attention mechanism. The model employs a dynamic multi-kernel large-scale attention module (DMLKA) to enhance cross-scale feature capture; a spectral-spatial attention module (SSAM) in the decoder to boost water region sensitivity; and a dynamic upsampling module (DySample) in the encoder to restore image details. DMLU-Net and six models are tested and compared on two publicly available Chinese remote sensing datasets. The results show that the F1-scores of DMLU-net on the two datasets are 94.50% and 86.86%, and the IoU (Intersection over Union) values are 90.46% and 77.74%, both demonstrating the best performance. Notably, the model significantly reduces water boundary artifacts, and it improves overall prediction accuracy and small water body recognition, thus verifying its generalization ability and practical application potential in real-world scenarios.  
U-Net; CNN; remote sensing image; deep learning; water extraction 2025  
2025157733
# Introduction of a New Index of Field Operations Efficiency
## 3 Results
### Effect of Driving Direction on FTE  
As it is shown in Figure 12 the driving direction of 90\({}^{\circ}\) yielded the higher average FTE than the other tested directions. Even in the same type of field, the FTE also varied substantially when using different directions. For instance, the discrepancy of the average FTE is up to about 10% when comparing the applied driving direction 0\({}^{\circ}\) and 90\({}^{\circ}\) in R41 field. More tracks require more turns to cover the same field area, and subsequently resulting in lower field efficiency. Taking R41 as an example, the driving direction 0\({}^{\circ}\) produces 158 more tracks than using driving direction 90\({}^{\circ}\) when the operating width is 3 m.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
\begin{tabular}{c} **Direction** \\ **(Degrees)** \\ \end{tabular} \\ \hline
**SQR** & SF & 12 & 6 & 30 & BL & 4.5 & 3 & 0 \\ \hline
**STD** & SF & 12 & 6 & 30 & BL & 3 & 3 & 0 \\ \hline
**R41** & AB & 4.5 & 6 & 30 & AB & 6 & 3 & 90 \\ \hline
**R21** & SF & 12 & 6 & 30 & AB & 4.5 & 3 & 90 \\ \hline
**REN** & SF & 12 & 6 & 30 & BL & 3 & 3 & 0 \\ \hline
**BPL** & SF & 9 & 6 & 60 & AB & 6 & 3 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The scenarios for min, max computed FTE in each test field.  
Figure 11: Average FTE for the three selected fieldwork patterns (AB, SF, and BL) in all template field shapes.
## 2 Related Work
In general, every drift detection method works either on the input data (monitors dissimilarity in data distribution) or on the output of the ML model (drop in accuracy or increase in error rate). We diagrammatically illustrated the input-based drift detector and output-based drift detector in Figure 2a,b. Most of the work presents a variety of drift detection methods in general. Therefore, the experimental study [44] examined eight drift detection approaches in terms of how they perform in the context of sudden and gradual concept drifts. Another work presented in [45] compared four drift detection methods on drifted data streams with noise. The study [46] compared fourteen drift detection methods on binary class data streams. Again, the sudden and gradual drift types were considered in these studies. These experimental studies are limited to sudden and gradual drift types only, and the data streams used in their study were mostly binary classes. Another main limitation is that the effect of the class imbalance issue was not considered in their work. Hence, comprehensive work is required which compares the existing drift detection methods on data streams with various drift types, in presence of a multi-class imbalance problem. A summary of the existing experimental studies which compare drift detection methods is given in Table 1. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & \multicolumn{5}{c}{**Concept Drift**} & \multicolumn{2}{c}{**Multi-Class**} & \multicolumn{1}{c}{**Class**} \\ \cline{2-7}  & **Sudden** & **Gradual** & **Incremental** & **Reoccurring** & **Classification** & **Imbalance** \\ \hline
[44] & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\
[45] & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & \(\surd\) & \(\times\) \\
[46] & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ Ours & \(\surd\) & \(\surd\) & \(\surd\) & To some extent & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative studies on drift detection methods. Figure 2: (a) Input-based drift detection; (b) output-based drift detection.