esa-sceva/satcom-chunk-collection huggingface dataset:
# One-Shot Distributed Generalized Eigenvalue Problem (DGEP): Concept, Algorithm and Experiments +
## 5 Numerical Experiments
### DGEP on Synthetic Datasets  
Considering the general settings of the GEP in (1), we generate a symmetric matrix \(A_{i}\in R^{d\times d}\) and a positive definite matrix \(B_{i}\in R^{d\times d}\) randomly and independently, which compromise data-mixed information in the \(i\)-th local server with \(i=1,\dots,N\). Then, we first evaluate Algorithm 1 including DGEP-SVD and DGEP-PM in running time and error.  
The running time concludes communication time and optimization time for DGEP-PM and the maximum iteration step of PM is set to be 10. The error is calculated as  
\[Error=\sin(subspace(\hat{W},W_{c}))=\|\hat{W}-W_{c}(W_{c}^{*}\hat{W})\|,\]  
where \(W_{c}\) is the ground truth, and \(\hat{W}\) is obtained in the central server. The performance of DGEP in time and error is depicted in Figure 3 with \(d=1000\) and \(N\) varying from 2 to 100 in intervals of 2. As the number of local servers (\(N\)) increases, the running time of DGEP goes up, and SVD as a solver in MATLAB is efficient if the complexity \(\mathcal{O}(d^{3})\) is acceptable. The errors of DGEP-PM and DGEP-SVD are similar and keep in a low level as \(N\) varies.  
Then, we investigate Algorithm 3 (DCCA-PM) and compare it to Algorithm 1 (DGEP-PM). Considering the settings of mutiple CCA in (8), we generate the self-adjoint symmetric \(A_{i}\) and \(B_{i}\) randomly and independently with \(i=1,\ldots,N\). The other settings are in the same. The performance in running time and error is depicted in Figure 4, with \(d=1000\) and \(N\) varying from 2 to 100 in intervals of 2. The errors of DGEP-PM and DCCA-PM stay at a low level as \(N\) varies, and DCCA-PM performs better. However, excluding the abnormal experiment, the running time that DCCA-PM costs is eight times of that DGEP-PM costs on average. That is, when using PM, DCCA-PM achieves higher accuracy but more running time than DGEP-PM, which is a trade-off in real-world scenarios. It needs to be evaluated by the performance on real-world data to validate whether it is worthwhile.  
Figure 3: Performance of DGEP-SVD and DGEP-PM in running time (\(T/s\)) and error (\(E\)) with respect to the number of local severs (\(N\)).
## 1 Introduction
The data normalization technique converts raw measurements with any arbitrary length to a specified size of free vibration, preserving the excitation quality of the measurements, and the 1-D CNN detects and localizes damage on a structure. For training and validation, the beam was excited with random and traffic loadings for 20 s, and the damage was simulated as 20%, 30%, and 50% of the single elemental stiffness loss. After training, the trained network was tested considering an untrained input size as well as a randomly several damage severities between 20 and 50%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Literature** & **Input Data** & **Deep-Learning Network Model** & **Measurement Type** & **Applications** \\ \hline [PERSON] et al. [10] & 3-s raw measurement & 1-D CNN & Multiple acceleration &
\begin{tabular}{c} Damage localization- \\ tion/quantification on a \\ simple beam model \\ Damage state \\ Classification of a bridge \\ Prestress tendon damage \\ identification \\ Damage state \\ classification \\ Damage location and \\ quantity \\ Multiressed loss identification \\ of a beam/frame structure \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Deep neural network for vibration-based structural damage detection. The remainder of this report is organized as follows. In Section 2, the proposed data normalization technique and CNN structure used are explained. In Section 3, datasets for training and validation are generated through numerical simulations conducted on a 10-element beam model, under 20%, 30%, and 50% of damage to a single random element on a beam. In this study, random and traffic loading excitations were used to validate the performance of the proposed network under nonstationary traffic loading to simulate the environment of ambient field vibration testing. In Section 4, the application of the proposed trained model to training datasets that have different measurement times and damage severities is described, and the accuracy of the proposed method is discussed. Finally, in Section 5, a summary and the conclusions drawn from the study are provided.
## Nomenclature  
\begin{tabular}{l l} _Symbols_ & \\ \(A\) & Branch area (m\({}^{2}\)) \\ _c\({}_{\text{P}}\)_ & Specific heat (J/(kg K)) \\ _D\({}_{\text{h}}\)_ & Hydraulic diameter (m) \\ \end{tabular}  
\begin{tabular}{l l} \(E_{\rm{r_{h}}}\) & Percentage error (\(\%\)) \\ \(f\) & Darcy friction factor \\ \(g\) & Gravity acceleration (m/s) \\ \(G\) & Mass flux (kg/(m\({}^{2}\) s)) \\ \(h\) & Enthalpy (J/kg), heat transfer coefficient (W/(m\({}^{2}\) K)) \\ \(h_{\rm{lv}}\) & Enthalpy of evaporation (J/kg) \\ \(h_{\rm{lv}}^{\prime}\) & Equivalent enthalpy of evaporation (J/kg) \\ \(k\) & Thermal conductibility (W/(m K)) \\ \(K_{\rm{rot}}\) & Rotation factor between fluid and surrounding surface (-) \\ \(L\) & Branch length (m) \\ \(m\) & Mass (kg) \\ \(m\) & Mass flow rate (kg/s) \\ \(n\) & Number of elements in the network (-) \\ \(n_{\rm{fb}}\) & Number of fluid-to-fluid branches in the network (-) \\ \(n_{\rm{fn}}\) & Number of fluid nodes in the network (-) \\ \(n_{\rm{fn}}\) & Fluid nodes adjacent to node \(i\) (-) \\ \(n_{\rm{sb}}\) & Number of fluid-to-solid branches in the network (-) \\ \(n_{\rm{fitn}}\) & Number of internal fluid nodes in the network (-) \\ \(n_{\rm{s}}\) & Number of species in the network (-) \\ \(n_{\rm{sb}}\) & Number of solid-to-solid branches in the network (-) \\ \(n_{\rm{sn}}\) & Number of solid nodes in the network (-) \\ \(n_{\rm{sn}_{i}}\) & Solid nodes adjacent to node \(i\) (-) \\ \(p\) & Pressure (Pa) \\ \(Pr\) & Prandtl number (-) \\ \(Q\) & Heat flux (W) \\ \(\dot{q}\) & Heat flux (W/m\({}^{2}\)) \\ \(Re\) & Reynolds number (-) \\ \(T\) & Temperature (K) \\ \(t\) & Time (s) \\ \(u\) & Velocity (m/s) \\ \(V\) & Volume (m\({}^{3}\)) \\ \(x\) & Quality (-) \\ _Greaks_ \\ \(\beta\) & Mass fraction (-) \\ \(\delta\) & Solid node distance (m) \\ \(\Delta t\) & Time interval between time steps (s) \\ \(\varepsilon\) & Absolute roughness (m) \\ \(\theta\) & Branch inclination angle respect to gravity (rad) \\ \(\mu\) & Viscosity (Pa - s) \\ \(\rho\) & Density (kg/m\({}^{3}\)) \\ \(\sigma\) & Surface tension (N/m) \\ \(\chi\) & Molar fraction (-) \\ \(\psi\) & Overall gas fraction (-) \\ \(\omega\) & Angular velocity (rad/s) \\ _Subscripts and superscripts_ \\ annular & Annular flow \\ CHF & Critical heat flux \\ disp & Dispersed flow \\ f & Friction, fluid node variable (or unspecified) \\ ff & Fluid-to-fluid branch variable (or unspecified) \\ fs & Fluid-to-solid branch variable \\ \(i\) & Node index \\ \(ij\) & Branch index from node \(i\) to node \(j\) \\ \(j\) & Node index adjacent to cell \(i\) \\ \(ji\) & Branch index from node \(j\) to node \(i\) \\ k & Specie, Local \\
1 & Saturated liquid, line \\ MHF & Minimum heat flux \\ \end{tabular}
## 1 Introduction
### Paper's Contribution  
Among the proposed approaches, a novel millimeter-wave (mm-Wave) ISAC system based on photonics technology and spectrum spreading phase-coding has been explored in [10]. The work in this paper aligns with that study, and the added contribution to the scientific field in comparison to other existing approaches is elaborated as follows.  
1. We propose the use of DSSS for integrating radar and communication systems. Leveraging photonics-based technology, our system demonstrates significant advantages over 16 previously reported schemes, offering superior performance compared to alternative ISAC methods; see Table II in [10].
2. The system architecture we propose is simplified compared to the prior work, making it more suitable for practical applications. Specifically, our system reduces the number of components, such as by avoiding the use of optical filters.
3. Additionally, the proposed system in this paper is also fundamentally different from other reported works in that it considers transmission over an RoF channel. This technology directly modulates light with an RF signal and transmits it over optical fibers. Typically, an RoF system comprises a central unit (CU), optical fibers, and remote antenna units (RAUs). The CU serves as a hub for system resources, facilitating resource sharing among multiple RAUs, thereby simplifying network architecture. In addition, RoF technology inherits the benefits of optical fibers, which include low attenuation and longer reach distance compared to other media.  
The work in this paper is divided into three distinct parts. The first part involves simulating our ISAC concept using MATLAB to produce results under various scenarios. The second part will utilize VPI Photonics Transmission Makter 11.4, a specialized software tool for designing and simulating optical transmission systems and their components [24] to simulate the proposed ISAC system. The third and final part will involve experimentally testing our simulation and design in the laboratory. In particular, our research work will consider (i) the use of one waveform to perform both sensing and communication, (ii) the use of spread spectrum and binary phase-shift modulation to generate the system waveform, as in [10], (iii) and the use of photonics technology to simulate and implement the proposed system over the RoF channel.  
This research is intended to develop an ISAC system to achieve a data rate of 50 Mbps, a radar maximum unambiguous range of 1200 m, and a radar range resolution of less than 0.5 m. A scenario ideally suited for this application is the detection of slow-moving ground targets, including vehicles, pedestrians, or wildlife, attempting to breach borders or access protected areas and simultaneously promptly sending warning messages to security personnel, ensuring a swift response to potential threats.
# Hybrid Trust Model for Node-Centric Misbehavior Detection in Dynamic Behavior-Homogeneous Clusters
## 3 Methodology
### Overview of the Proposed Scheme  
Assuming the evaluation of composite trust values and misbehavior detection for vehicles, the computation process of the composite trust value in the hierarchical trust model based on behavior-homogeneous vehicle clusters proceeds as follows:  
Initially, at the vehicle node layer, the total trust value of the vehicle is derived by calculating its direct and indirect trust values. Subsequently, at the cluster head layer, the average total trust value of a vehicle within the vehicle cluster is computed based on the reported total trust values of multiple vehicles and the weights of the association between member vehicles and the vehicle cluster. Then, at the RSU layer, the global hybrid trust value of the vehicle is calculated by considering the average total trust values of multiple vehicles provided by different vehicle clusters and the weights of the association between these clusters. Finally, through the RSU, the cloud controller collects the hybrid trust values of various vehicle nodes within the region. Utilizing a consistency parameter, a trust threshold range is computed. Through a series of decision-making logic, nodes are classified into three categories: trustworthy, malicious, and pending inspection. Nodes identified as malicious are recognized as exhibiting misbehavior within the vehicular network. Figure 2: Hierarchical trust model based on dynamic behavior-homogeneous clusters. Our proposed scheme consists of four main stages:
* Segmentation of Dynamic Behavior-Homogeneous Vehicular Clusters In this stage, vehicles are grouped based on behavioral similarity to enhance trust evaluation. * Mining of Complex Relationship Weights in Dynamic Vehicle Clusters This stage focuses on extracting and modeling vehicle association relationships, ensuring accurate trust propagation during the trust computation process. * Hybrid Trust Value Computation Based on Weighted Vehicle Behavior Trust values are computed hierarchically across multiple layers in this stage, and direct and indirect trust assessments are integrated. * Collaborative Node-Centric Misbehavior Detection This process occurs in three layers, implementing a multi-layered mechanism to identify and mitigate malicious behavior within the vehicular network. The notations employed in the scheme are detailed in Table 1.
# IoTBystander: A Non-Intrusive Dual-Channel-Based Smart Home Security Monitoring Framework
## 8 Limitations and Ethical Considerations
### Consent and Transparency  
A major ethical challenge in the deployment of IoTBystander is ensuring that homeowners and household members are fully informed of the system's capabilities and the type of data it collects. There is a risk that users may not be fully aware that their behavior, conversations, and movements could be monitored in their own homes. This lack of informed consent could lead to ethical issues, especially if the system is deployed by third parties such as landlords or employers rather than the residents themselves.  
To mitigate these issues, we propose the implementation of clear transparency protocols and consent management mechanisms. This includes providing users with an easy-to-understand summary of how the system works, what data it collects, and how it will be used. Users should be able to opt in or out of data collection at any time, and the system should offer clear options for deactivating or limiting data collection. Furthermore, clear data usage agreements should be established, outlining the ethical boundaries of data collection and usage. Such policies will ensure that IoTBystander is used only for its intended purposes, thus protecting user privacy and autonomy.
## 4 Implementation and Results
### Linear Classification  
Initially, we trained a backbone model using both SimSiam and SimMIM. We followed the default scheme from original papers in both cases, and we trained the models for 100 epochs. Afterwards, we implemented the standard evaluation protocol for the pretrained models; we added a linear layer at the top of the model, with the rest of the layers of the backbone frozen. We trained this classification model on the target dataset with critical and minor defects. We established a baseline using a model with ImageNet pretrained weights. For SimSiam, we used ResNet50 [45] as the feature extractor, generating a 2048-dimensional feature vector for the classification layer. Conversely, for SimMIM, we used the original Swin Transformer [38] configured with a 12-pixel window size. The feature vector in the case of Swin Transformer was 1024 dimensional. The results of this linear classification on our target dataset are detailed in Table 2.  
Table 2 compares the performance of different backbone architectures using ImageNet weights versus weights obtained through self-supervised pretraining methods with X-ray images. From the results, we observed that, for the ResNet backbone, the SimSiam pretraining method outperforms the supervised approach in both Average Precision (AP) and accuracy. Specifically, SimSiam achieves a higher AP of 75.9 compared to 72.5 and an improved accuracy of 71.2 over the 68.4 seen with the supervised method. However, for the Swin-T backbone, the supervised method demonstrates superior performance with a high AP of 87.9 and an accuracy of 82.2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Critical Defects** & **Minor Defects** \\ \hline Train & 1424 & 3200 \\ Test & 360 & 802 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of images for critical and minor defects in the target dataset.  
It is important to note, though, that the results from pretraining with SimMIM are not as satisfactory. This approach appears to highlight the global features of the image rather than the more relevant characteristics associated with defects. Since defects are often quite small relative to the overall image resolution, the features pertinent to these defects tends to be overshadowed by the more prominent structural features of the manufactured product. This tendency tends to diminish the effectiveness in specific defect detection unless fine-tuning is applied to the backbone to accurately teach characteristics of the defects. In such cases, the model learns both the structural features of the product and the local characteristics of the defects, contrasting with the lower AP and accuracy values observed in SimMIM pretraining without such targeted fine-tuning. However, as we explained in Section 4.1, fine-tuning is the optimal protocol for manufacturing defect detection.
# Urban Logistics through River: A Two-Echelon Distribution Model
###### Abstract  
Studies that use rivers in a last-mile delivery context are scarce. This research considers the first multimodal alternative based on a barge for parcel delivery activities. It proposes two sustainable network designs for a two-echelon distribution. The efficiency of scenarios is assessed through performance indicators. A three-stage decomposition heuristic is used. Allocation of the customers to the closest satellite at the first stage uses a non-supervised machine learning clustering method, 2D-\(k\)-means. The last two stages, comprising the two echelons routing, are solved using a heuristic based on the nearest neighbor procedure. The fixed costs decrease by 41% and energy consumption by 92% when applying a river transportation mode and e-cargo bikes in the distribution network's first and second echelon, respectively. Future research avenues are to render the results more realistic with the consideration of other costs and a larger network.  
last-mile delivery; urban logistics; vehicle routing; sustainability; river logistic Article
## 3 Results and Discussion
### Parametric Analysis  
#### 3.2.1 Aspect Ratios (Rock-Bed Shape)  
It is expected that the dimensions of the TES system will affect the configuration of the fluid flow profiles and velocity contours within the porous media, thus impacting the ratio of heat transfer and the capacity of the system to store heat. Due to that, the distinct geometries previously presented were used throughout the study to show the impact of several parameters in the fluid flow and heat transfer of rock beds of different dimensions. Figure 6 depicts the contours for velocity inside the rock bed, in the storage period, for all the proposed aspect ratios. The selected values for inlet velocity being 0.05 m/s, the porosity 0.2, and an average particle size of 1.0 m (here considered the base case).  
Figure 5: Validation of the proposed model for both local thermal equilibrium (LTE) and local thermal non-equilibrium (LTNE) approaches against experimental results from [28]: (**a**) comparison of temperature along the vertical axis; (**b**) comparison of pressure drop along the rock bed.  
It can be seen that having a lower aspect ratio causes higher velocity gradients to happen, leading to a lower exit velocity of air. This is reasonable, since the outlet has a greater area in that case. It should be noted that, for higher mass flows (and consequently higher Reynolds numbers), the exploitation rate, along with the energy efficiency of the system, declines [42]. The reason for this being that the residence time of the HTF inside the bed is reduced as an effect of the lower fluid velocities, which, per se, decreases the opportunity for heat exchange. Therefore, it is important to note that, to improve heat exchange rate within the system, an optimum fluid velocity should be found for each application.
# Augmented Reality, Mixed Reality, and Hybrid Approach in Healthcare Simulation: A Systematic Review
## 3 Results
### Evaluation of Simulators (FQ9-10)
Additionally, construct validity results confirm the ability of a simulator to differentiate performance between experts and novices. With regard to question FQ10 (\"Is a statistical analysis performed?\"), 16 studies out of 23 conducted a statistical analysis [22; 23; 24; 25; 26; 27; 28; 30; 31; 33; 34; 37; 40; 41; 43; 44]. One criticism of these studies is that often the number of participants was small: only five studies involved more than 20 subjects [22; 30; 34; 40; 43], so further studies are needed to generalize the findings obtained.
## 3 Methods  
### Algorithms for Image Classification  
In the present research, three supervised classification algorithms (i.e., random forest (RF), extreme gradient boosting (XGB), and multi-layer perceptron (MLP)) were selected. The algorithms evaluated are those included in the 'randomForest', 'xgboost', and 'keras' packages using R statistical software, respectively.  
The RF classifier works by constructing an ensemble of decision trees that each make predictions about the class label of a given pixel in an image. The decision trees' majority vote determines the pixel's final class label. It was introduced by [PERSON] [48] and has been found to perform well with high-dimensional data and resist overfitting, making it a robust choice for classification tasks [49; 50]. There are two parameters that have to be chosen before training the RF model. The _mtry_ parameter sets the number of variables used to split the nodes where each node in the decision tree is split using a random selection. Furthermore, the _ntre_ parameter defines the number of trees in the forest [49].  
Extreme gradient boosting (XGB) is a regularized version of traditional boosting techniques that belong to the CART (classification and regression trees) family. It is an implementation of the gradient boosting algorithm, which involves training a series of decision trees on the data and combining their predictions to make a final prediction [51]. The final classification is a combination of the improvements made by all of the previous trees. Developed by [PERSON] and [PERSON] [52], XGB is particularly useful in remote sensing because it can handle large datasets with high dimensional features and can also handle missing values in the data.  
Furthermore, an ensemble of feedforward NN, multi-layer perceptron (MLP), was used for the allergenic tree species classification. The architecture of MLP includes three layers of nodes: an input layer, one or more hidden layers, and an output layer [53]. The input layer receives input data (e.g., a dataset of labeled satellite images), which is then processed by the hidden layers using weighted connections and activation functions, and finally produces output data at the output layer. The hidden layers process the input data using a set of weights and activation functions, which are used to transform the input data into a more suitable form for the output layer of the process. In a multi-layer perceptron(MLP), the strength of connections between nodes is adjusted during training to reduce the difference between the predicted output and the actual output [54].