esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Saving Strategy of UAV in MEC Based on Deep Reinforcement Learning
## 7 Conclusions  
This paper studies the problem of maximizing the residual energy of maritime search and rescue UAVs based on MEC. The research objective is formalized as a residual energy maximization problem by jointly optimizing communication and time resources. After analysis, the problem is found to be a non-convex problem, which is difficult to solve. However, when the offloading decision is given, it can be transformed into a convex problem and solved by convex optimization techniques. Therefore, we divide the target problem into two sub-problems, namely the time resource allocation problem and the task offloading decision problem. For the time resource allocation problem, the relevant optimal parameters can be obtained by using the convex optimization method. For the task offloading problem, the DOTO algorithm is proposed based on the idea of deep reinforcement learning. To balance the performance and computational complexity of the algorithm, an adaptive \(K\) value method is proposed. Simulation experiments show that the DOTO algorithm can provide users with an online computing offloading strategy that is superior to other traditional benchmark schemes. When there is a UAV in the MEC system that exits the system due to insufficient power or failure, or a new UAV is connected to the system, the DOTO algorithm can also quickly converge to provide a new and effective online offloading strategy for the MEC system. There is no need for manual participation in the whole process, resulting in good stability. Deep reinforcement learning provides a new solution for MEC. In the future, we will explore the application of deep reinforcement learning for the joint offloading of multiple APs. We will also focus on the balance between the residual energy and computing rate of mobile devices.  
Figure 9: The convergence performance of the training loss when randomly turning one UAV off and then reopening it.  
**Author Contributions:** Conceptualization, Z.D. and J.G.; methodology, Z.D. and G.X.; software, Z.D. and Z.L.; validation, Z.D. and Z.L.; investigation, Z.D. and Z.L.; resources, J.G. and W.W.; data curation, Z.D.; writing--original draft preparation, Z.D.; writing--review and editing, Z.D., J.G., Z.L. and W.W.; visualization, Z.D.; supervision, G.X.; project administration, G.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Development Project of Jilin Province of China (No. 20200401076 GX) and Jilin University of China.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Research on Fault Recognition of Roadheader Based on Multi-Sensor and Multi-Layer Local Projection
## 4 Multi-Sensor and Multi-Layer Fault Recognition Based on LPP
### KNN Classification  
The enhanced feature analysis results are fed into the classifier for classification and recognition. The classification process typically involves two steps: the initial step utilizes known instance data for training, learning, and constructing a classifier under supervision, while the subsequent step employs the classifier's test data for sample classification and recognition. As is evident in Figures 11 and 12, the low-dimensional feature model exhibits clarity, with the overall data volume being modest, thereby eliminating the need for intricate operations. Consequently, k-nearest neighbors (KNN) stands out as the prevalent classification method. The fundamental concept of KNN classification is to choose the closest neighbor to the sample based on the distance from the sample to be partitioned and each training sample. Subsequently, the class of the sample is determined by these nearest neighbors. The computation process of KNN [30; 31] involves (1) calculating the distances between the object to be classified and every object in the training set; (2) employing the Euclidean distance metric to define the nearest object as the nearest neighbor of the test object; and (3) categorizing the test object based on the attributes of these neighbors. The number of samples for each state in the sample set is 16, and thus we choose \(k=12\).  
This study employs low-dimensional features of training signals (shown as Figure 11) as KNN training sets, while low-dimensional features of test signals (shown as Figure 12) are used as the samples for classification. It is crucial to choose an appropriate value during the classification calculations.  
The recognition rates for each operational state in the test signal sample are eventually obtained, as presented in Table 3. Following the application of the KNN classifier, only one  
Figure 11: Double-layer low-dimensional feature (training sample).  
Figure 12: Double-layer low-dimensional feature (test sample).  
sample in Fault 3 is misclassified as Fault 1, while the remainder are classified correctly. The overall recognition success rate across 80 samples stands at 98.75%
## 5 Conclusions and Future Work  
An auto-configuration protocol for Mobile _Ad Hoc_ Networks called D2 HCP _Distributed Dynamic Host Configuration Protocol_ (D2 HCP) has been designed. This protocol is classified as a _stateful_ protocol. This is an IPv4 address auto-configuration protocol for isolated Mobile _Ad Hoc_ Networks.  
In this protocol each node is responsible for managing a range of addresses. When a new node wants to begin participating in the network, one of the nodes within the network gives half of its address range to the new node. In the case of any adjacent node not having free addresses, but free addresses do exists, a request to a network node that has free addresses is done. In this operation mode is based on distributed nature of the protocol.  
To keep updated information about free addresses owned by each node, the traffic of control packets from OLSR protocol. Such protocol at each node tries to keep updated knowledge of the whole topology from the network. This protocol has been designed to work together with OLSR; although it could operate with any proactive protocol by the flexibility of its design.  
D2 HCP warrants uniqueness for IP addresses in a wide variety of network conditions including message loss, concurrent requests and network partition. The simulation results show that the protocol has low latency and overhead. Worth noting is the protocol scalability features compared to other proposals in the literature, its flexibility that facilitates the protocol extension with new features, as well as synchronization process introduces null overhead.  
Possible future work can be identified as follows:  
* Detection of the _merging_ to allow reassigning addresses that enters in conflict (something relatively easy since it would introduce a new message).  
Figure 15: Latency D2 HCP _versus_ Thoppian and Prakash Protocol.  
* Extension of the protocol to subordinate networks with access to the Internet or other networks, for which it should take into account the network topology to perform address auto-configuration process.
* Study protocol performance in cooperation with other proactive routing protocols (the first version D2 HCP is designed to work together with OLSR).
* Add a security module that protects against different attackers to proportionate a safe auto-configuration.
## 2 System Model
### Multi-Objective Optimization Model  
Based on the previously mentioned system optimization objectives and model constraints, the energy management model, which is a Mixed Integer Programming problem with multiple objectives, is constructed as follows:  
\[\begin{split}\min_{\mathbf{x}}\mathbf{C}(\mathbf{x})=\left(C_{1}( \mathbf{x}),C_{2}(\mathbf{x})\right)\\ \text{s.t.}\left(\ref{eq:1}\right)\text{--}\left(\ref{eq:2}\right) \end{split} \tag{21}\]  
Here, \(\mathbf{x}\) represents the decision variables, and the list of decision variables is shown in Table 2. The optimization objectives of the model are to minimize the system operating cost \(C_{1}\) and minimize the dependence on the main grid \(C_{2}\). These two objectives are conflicting--optimizing one objective typically leads to degradation in the value of the other objective; it is not possible to simultaneously optimize both objectives. Therefore, multi-objective optimization results in a set of compromise solutions, known as the Paretooptimal solution set. Each solution in the Pareto optimal solution set is a non-dominated solution, meaning that each solution does not dominate the others. If solution \(\mathrm{x}_{1}\) dominates solution \(\mathrm{x}_{2}\) (\(\mathrm{X}_{1}\prec\mathrm{X}_{2}\)), it is only when the following rules are satisfied:  
\[\begin{array}{c}f_{k}(\mathrm{x}_{1})\leq f_{k}(\mathrm{x}_{2}),k=1,\cdots,M \\ f_{k}(\mathrm{x}_{1})<f_{k}(\mathrm{x}_{2}),\exists k\in\{1,\cdots,M\}.\end{array} \tag{22}\]  
In other words, if solution \(\mathrm{x}_{1}\) is non-dominated by solution \(\mathrm{x}_{2}\) in both objectives and is strictly better than solution \(\mathrm{x}_{2}\) in at least one objective, then solution \(\mathrm{x}_{1}\) dominates solution \(\mathrm{x}_{2}\). Therefore, by solving this multi-objective optimization model, a set of solutions with different emphasis on system cost and dependence on the main grid can be obtained, allowing decision-makers to choose the most suitable solution for different demand scenarios.
## 5 Proposed Framework
### Integration of ZTA and FL
#### 5.4.3 Implementation Details  
The integration architecture leverages several key O-RAN interface points for security enforcement. The A1 interface provides policy control capabilities for xApp governance,  
\begin{table}
\begin{tabular}{l l l l} \hline
**Metric** & **Standard FL [58]** & **Our Framework** & **Improvement** \\ \hline Poisoning Success Rate & 28\% & 9\% & 67.9\% \(\downarrow\) \\ \hline False Accept Rate & 3.2\% & 0.7\% & 78.1\% \(\downarrow\) \\ \hline Detection Latency & 320 ms & 85 ms & 73.4\% \(\downarrow\) \\ \hline Model Accuracy & 88\% & 94\% & 6.8\% \(\uparrow\) \\ \hline \end{tabular}
\end{table}
Table 11: Comparison of security metrics.  
enabling the dynamic adjustment of permissions based on real-time risk assessments. E2 message interception allows for the runtime validation of RIC communications, while O1 telemetry streaming feeds monitoring data to our anomaly detection subsystems.  
At the core lies a sophisticated security policy engine containing over 500 rule templates covering known vulnerabilities (including protections against CVE-2023-1234 and similar threats). The engine performs adaptive threshold tuning every five minutes, automatically adjusting sensitivity based on current network conditions. Policy generation is handled by the SMO, ensuring centralized management while preserving distributed enforcement.  
Forensic capabilities have been deeply integrated throughout the system. Model lineage tracking via a provenance chain mechanism provides complete audit trails of all training data and parameters. Tamper-evident logging using Merkle trees guarantees the integrity of all security events, with logs retained for one year to support post-incident investigations. These features operate within standard O-RAN interfaces without requiring modifications to existing xApp or rApp implementations, as confirmed through testbed deployments with commercial O-RU and O-DU equipment.
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 6 Mathematical Models
### Free Space Loss  
In addition to the previously mentioned parameter, free space loss (_FSL_) in wireless communication quantifies the attenuation of electromagnetic waves as they propagate through a vacuum. This attenuation is a critical factor in designing and analyzing wireless systems, directly influencing the received signal strength (_RSL_) over distance [29; 30]. It can be calculated using the following formula:  
\[L_{FSPL}=-20\text{log}_{10}\bigg{(}\frac{4\pi D}{\lambda}\bigg{)} \tag{1}\]
## 1 Introduction  
Medical imaging is one of the most investigated use cases for machine learning in healthcare [1]. While effort remains consistent in developing and improving algorithms, data availability is crucial for deploying efficient machine learning solutions [2]. The recent COVID-19 pandemic has demonstrated, for instance, how the availability of a large annotated dataset could significantly boost the power of machine learning [3]. However, in most clinical practices, such an initiative to share a large dataset is still limited.  
The machine learning community has developed several workaround approaches to compensate for the lack of data. This compensation can be obtained using algorithmsthat learn faster, such as in few-shot learning approaches [4]. The lack of data can also be compensated by automatically generating fake data which are realistic enough to boost the training of algorithms. This includes the generation of synthetic data via simulators [5], generative models [6] or via data augmentation [7]. The last approach, known as transfer learning, uses pre-trained models on similar datasets [8]. In this article, we focus on this transfer learning approach.  
While largely used in computer vision, transfer learning is still actively investigated (especially in the medical domain [9; 10]). One of the most common transfer learning approaches in computer vision is using pre-trained models from 2D color outdoor natural images. However, for specific application domains, such as medical imaging, this approach is neither optimal nor possible, due to the difference in data structure between medical images and 2D color natural images (3D images instead of 2D images, size of images, bit depth of images, etc.) [11]. In addition, the efficiency of transfer learning has been shown to be optimal when images share similar content [12]. Lastly, transfer learning helps when the data used for the pre-training cost less by comparison than the images from the target domain. This analysis brings us to the simple and yet innovative idea of investigating the value of a pre-trained model on healthy subjects' data when transferred to unhealthy patients. We propose testing and illustrating this transfer learning idea in a specific clinical use case. The rationale for the selection of this use case is: (i) we need a use case for which very few public datasets are indeed currently available, (ii) we need a use case for which the impact of the disease on the image is not too large to minimize the shift between healthy and unhealthy data, and (iii) we need a use case for which imaging modality is non-invasive so that the acquisition of images from healthy controls is relatively easy. This brought us to the selection of the clinical use case presented in the next section to test our original idea of transfer learning from healthy subjects' data to unhealthy patient data.
## 4 APSP and Routing Tables
### Distance Products and Routing Tables  
Definition 5: A routing table for a node \(v\) is a function \(R_{v}:V\to V\) mapping a vertex \(u\) to the first node visited in the shortest path from \(v\) to \(u\), other than \(v\) itself.  
Definition 6: The distance product between two \(n\times n\) matrices \(A\) and \(B\) is defined as the \(n\times n\) matrix \(A\star B\) with entries:  
\[(A\star B)_{ij}=\min_{k}\{A_{ik}+B_{kj}\}. \tag{1}\]  
The distance product is also sometimes referred to as the min-plus or tropical product. For shortest paths, we will repeatedly square the graph adjacency matrix with respect to the distance product. For a \(n\times n\) matrix \(W\) and an integer \(k\), let us denote \(W^{k,\star}:=W\star(W\star(\ldots(W\star W)))\ldots)\) as the \(k^{th}\) power of the distance product. For a graph \(G=(V,E,W)\) with weighted adjacency matrix \(W\) (assigning \(W_{uv}=\infty\) if \(uv\
ot\in E\)), \(W^{k,\star}_{uv}\) is the length of the shortest path from \(v\) to \(u\) in \(G\) using at most \(k\) hops. Hence, for any \(N\geq n\), \(W^{N,\star}\) contains all the shortest-path distances between nodes in \(G\). As these distance products obey standard exponent rules, we may take \(N=2^{\lceil\log n\rceil}\) to recursively compute the APSP distances by taking \(\lceil\log n\rceil\) distance product squares:  
\[W^{2,\star}=W\star W,\ \ W^{4,\star}=\left(W^{2,\star}\right)^{2,\star}, \ldots,\ \ W^{2^{\lceil\log n\rceil},\star}=\left(W^{2^{\lceil\log n\rceil-1},\star} \right)^{2,\star}. \tag{2}\]  
This procedure reduces computing APSP distances to computing \(\lceil\log n\rceil\) distance products. In the context of the CONGEST-CLIQUE model, each node needs to learn the row of \(W^{n}\) that represents it. As we also require nodes to learn their routing tables, we provide a scheme in Section 4.3 that is well-suited for our setting to extend [7] to also compute routing tables.
## 2 Methodology
### Preprocessing
#### 2.1.2 Camera to Scanner Co-Alignment  
Integration of the two data sources requires securing their co-alignment. The relative/boresight transformation between the mounted camera and scanner body consists of rotation, **R**, and a 3D offset,  
Figure 1: Polar representation of the color cue projected onto the range data. (**a**) The range data in polar representation (encircled is a no-reflectance area). (**b**) Selected region of the seven individual images for the construction the color channel. (**c**) Color content as projected to the scanner system.  
\(t\), and can be accommodated via the projection matrix \(\mathbf{P}_{3\times 4}\) between a 3D point, \(X\), and a 2D image point, \(x\), in homogeneous coordinates,  
\[x=\mathbf{KR}[\mathbf{I}\,|-t]\,X=\mathbf{P}X \tag{2}\]  
where  
\[\mathbf{K}=\left[\begin{array}{ccc}f_{x}&s&x_{0}\\ 0&f_{y}&y_{0}\\ 0&0&1\end{array}\right]\]  
\(f_{x}\) and \(f_{y}\) are the focal lengths in the \(x\) and \(y\) directions respectively, \(s\) is the skew value, and \(x_{0}\) and \(y_{0}\) are the principal point coordinates [23]. The parameters in \(\mathbf{K}\) account for the intrinsic camera parameters, while radial and decentering lens distortions are also calibrated and corrected for prior to their use in Equation (2). For each scan, \(n\) images are acquired at predefined \"stops\" (every 360/\(n\) degrees), thus \(n\) transformation matrices are computed. Assuming that, (i) the camera is rigidly mounted to the scanner, (ii) the intrinsic camera parameters are fixed and calibrated in advance, and (iii) the acquisition position (of the \"stop\") is fixed across all scanning positions, enabling use of the same projection matrices for all images of the same \"stop\" within different scans.  
The camera-to-scanner linkage enables rendering the color content on the 3D laser points. Relating each 3D point to the best of the \(n\) images is implemented by projecting the point to all images and selecting the one where the projected 3D point's position is the closest to its perspective center (Figure 1(b)). It is noted that within the point's projection computation, points that appear at the back of the camera are avoided. The image projection into 3D space leads to some content loss due to change in resolution, but allows for processing of both information sources in a single reference frame (Figure 1(c)).
# A Proposal for a New Python Library Implementing Stepwise Procedure
## 2 Background
### Normality of Residuals  
The normality of the residuals is an essential assumption for the hypothesis tests of the regression models to be validated, such as the \(p\)-value of the \(t\) and F tests [6]. According to [32], this assumption is constantly violated when estimating OLS regression models. However, normality is vital for defining the best functional form and determining confidence intervals for model prediction, with exceptions for sufficiently large samples.  
In this context, the Shapiro-Francia statistical test, applied to the model's error terms, allows for verifying the assumption of the normality of the residuals [32]. This test, proposed by [PERSON] and [PERSON] [39], can be applied to samples of size \(5\ \leq\ n\ \leq\ 5000\). The Shapiro-Francia test assumes the following hypotheses [6].  
**H0**: _The sample comes from a population with normal distribution._
**H1**: _The sample does not come from a population with a normal distribution._  
It is possible to see the Shapiro-Francia statistic (\(W^{\prime}_{cal}\)) calculation in Equation (3):  
\[W^{\prime}_{cal}=\frac{\left[\sum_{i=1}^{n}m_{i}.X_{(i)}\right]}{\sum_{i=1}^{ n}m_{i}^{2}.\sum_{i=1}^{n}\left(X_{i}-\overline{X}\right)^{2}}\,\ \ \ \ \ \ for\ i=1,\ \ldots,\ n \tag{3}\]  
where \(X_{(i)}\) are the statistics of the i-th ordered observation so that \(X_{(1)}\ \leq\ X_{(2)}\ \leq\ X_{(3)}\leq\ \ldots\ \leq X_{(n-1)}\leq X_{(n)};m_{i}\) is the approximate expected value of the i-th observation (\(Z_{score}\)) [6].  
For the diagnosis of multicollinearity, the critical values \(W^{\prime}_{c}\) should be considered, such that \(P(W^{\prime}_{cal}<W^{\prime}_{c})=\alpha\) (considering a unilateral test on the left), with the \(p\)-values being established by a correspondence table, found in [6]; The parameter \(\alpha\) corresponds to the level of statistical significance established. For the null hypothesis \(H0\) to be rejected, \(W^{\prime}_{cal}<W^{\prime}_{c}\); otherwise, \(H0\) is not rejected [32].  
Another form of analysis is the comparison of the \(p\)-value (probability associated with \(W^{\prime}_{cal}\)), obtained in the same table of correspondences [6]. In this case, \(H0\) is rejected if pvalue \(\leq\alpha\). In short, if \(H0\) is rejected, the residues do not adhere to normality.
# Improved ECG Watermarking Technique Using Curvelet Transform
## 8 Conclusions  
A curvelet transform-based ECG-watermarking approach using Euclidean and non-similarity distance clustering technique was presented and a 1D-ECG was converted into a 2D-ECG image by preserving the QRS complex. The curvelet transforms 2D-ECG results from a set of coefficients. A Euclidean, non-similarity distance method was incorporated to make the clusters of the coefficients. Patient information was transformed into the image and embedded into the selected cluster coefficients. Keys were utilized to secure patient information from unauthorized access. The performance of the proposed method was evaluated by metrics such as PSNR, NC, PRD, KL, BER, MSE and SSIM. As the size of the patient's information increase, the quality of the signal decreases, but this does not affect the ability to diagnose disease from the ECG. It was verified by experimental results that the BER was approximately zero, and the NC was zero by a two-fold increase in the patient information. Patient information was also secure and robust from any geometric attacks. The proposed technique can be easily implemented in wearable medical devices to transmission of the ECG. Hence, the proposed technique has provided a better way to transfer patient information without affecting the ability to diagnose, and provides a reliable, secure and robust ECG-watermarking technique. However, there is always room for improvement. Deep learning and fuzzy logic may be applied for non-singularity and varying levels of brightness/noise. The automation of the process can be improved at the early stages for even better results.  
[PERSON] has worked on the mathematical modeling part; [PERSON] has performed data set collection and relational part of the manuscript; [PERSON] has developed the algorithm; [PERSON] has designed the algorithm; [PERSON] has performed coding part; [PERSON] has maintained SQL connectivity and performed troubleshooting; [PERSON]-h.K. has assisted in RDBMS part. All authors have read and agreed to the published version of the manuscript  
This research received no external funding.  
The authors declare no conflict of interest.