esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatiotemporal Assessment and Correction of Gridded Precipitation Products in North Western Morocco
## 2 Materials and Methods  
### Study Area and Rain Gauge Datasets  
This study focuses on the Bouregreg watershed, which is situated in the central-western part of Morocco, spanning latitudes 32.82\({}^{\circ}\) N to 34.04\({}^{\circ}\) N and longitudes 5.48\({}^{\circ}\) W to 6.86\({}^{\circ}\) W (Figure 1). The watershed is bordered by the Saiss and Ghareb ditches to the north, the phosphate plateau to the south, the Middle Atlas Mountains to the east, and the Atlantic Ocean to the west. Administratively, it encompasses provinces from the Rabat-Sale-Kenitra and Beni Mellal-Khenifra regions. The total area of the watershed is approximately 9462 km\({}^{2}\), consisting of four sub-basins. The hydrographic network is formed by four main rivers: Bouregreg (264 km), Grou (278 km), Medior (94 km), and Korifla (110 km). Geomorphologically, the area comprises three natural units: the Middle Atlas Mountains (covering the upper Bouregreg), the Oulmes-Zaers plateau (encompassing the middle Bouregreg), and the Chaouia plain [35]. The climate of the study area is semi-arid to sub-humid. The average annual rainfall ranges from 350 mm in the plain to nearly 600 mm in the mountainous zone. The monthly distribution of precipitation shows two distinct seasons: (a) a rainy season from October to April, accounting for the majority of the annual rainfall; (b) a dry season from May to September. Average temperatures in the area range from 12 \({}^{\circ}\)C to 18 \({}^{\circ}\)C in the mountainous zone and to around 20 \({}^{\circ}\)C in the Bouregreg plain.  
In this study, daily data from 11 rain gauge stations, provided by the Bouregreg and Chaouia Hydraulic Basin Agency (ABHBC), were used to assess the GPPs. To ensure the reliability of the evaluation, a threshold of 20% missing data was set, beyond which the records were not considered. The threshold for excluding missing values was justified by referring to recent research works [36; 37; 38]. These authors defined a threshold of 20% of missing data not to be exceeded to ensure the reliability of the assessment. Therefore, to avoid the consequences and impact of missing values on the Sidi Amar rain gauge and other rain gauges, as well as to preserve the integrity of the assessment and ensure its reliability, the missing days in each station were excluded from both ground stations and satellite products.  
Figure 1: Geographical location of the Bouregreg watershed and rain gauge network.  
Table 1 provides the coordinates of the used stations on a daily time step, along with the percentage of missing values.  
The spatial distribution of the 11 rain gauges is characterized by their concentration downstream of the watershed. Indeed, nine stations are located below 300 m, and the two highest rain gauges are situated at 557 m (Ouljet Haboub) and 687 m (Tsalat), respectively. The higher regions, up to the peak at 1624 m, lack ground observation stations. In this context of sparse ground-based observations, satellite products will be particularly useful.
# Calculation Method of New Assembled Corrugated Steel Initial Support Structure of Highway Tunnel
## 4 Practical Engineering Application  
To further validate the accuracy of the simplified calculation method proposed in Section 3.4, a two-dimensional plane strain simplified calculation model was established based on the Qipanshan Tunnel project and compared with the monitoring data.
## 4 Results
### Accuracy Evaluation of Different Models
#### 4.2.2 Uncertainty Analysis
Based on the above analysis, FS-ConvLSTM shows better median accuracy than GPM and outperforms ground interpolation data and LSTM in most cases. The distribution width of FS-ConvLSTM is narrower than that of F-SVD and, in most cases, is narrower than  
Figure 6: Boxplot of the evaluation indicators for different rainfall events. (**a**) Small rain; (**b**) Moderate rain; (**c**) Heavy rain; (**d**) Torrential rain. that of the GPM. Overall, FS-ConvLSTM improves both the accuracy and the stability of the rainfall data.
# Managing Redundancy in a Multiple-Robot Orienteering Problem for Equilibrium and Robustness
## 2 Materials and Methods
### Algorithm for RMOPWB
#### 2.3.2 Algorithm Analysis  
The guarantee of optimality for our proposed algorithm is conditional upon the characteristics of the employed RMOP Solver. As illustrated in the workflow of Figure 4 and the procedure in Algorithm 1, if the RMOP Solver is optimal, each execution will yield a random optimal solution \(\mathcal{S}_{1}\), where \(\mathcal{S}_{1}\in\mathcal{S}_{1}^{*}\), the set of all optimal solutions for stage #1. Under this condition, our Monte Carlo sampling framework is theoretically guaranteed to identify the globally optimal RMOPWB solution. Specifically, for each random optimal solution drawn from the RMOP solver, there is a probability of at least \(1/|\mathcal{S}_{1}^{*}|\) of obtaining the solution that corresponds to the global optimum of the RMOPWB problem. While this provides a theoretical guarantee, achieving the optimal trade-off between the sample size and the associated computational cost remains an essential practical consideration, particularly when the cardinality of \(\mathcal{S}_{1}^{*}\) is large.  
In Algorithm 1, the main computational expenses come from the execution of the functions denoted as _RMOP_ and _PR_. The total computation time is \(K\times(R_{1}+R_{2})\), where \(K\) symbolizes the quantity of Monte Carlo samples. \(R_{1}\) and \(R_{2}\) correspond to the respective computational time required for _RMOP_ and _PR_. Upon the implementation of parallel sampling, the computation time is significantly reduced, culminating in an expression of \(R_{1}+R_{2}\). \(R_{2}\) consists of two processes: the _CutCircle_ function and _the BackWalks_ function. Suppose that basic operations like sorting and comparison take one unit of time. _CutCircle_ determines for each vertex whether there is a repeated cycle. If there is, remove the cycle. So there is  
\[CC(\mathcal{W}_{i})=\sum_{l=1}^{M_{i}}(l+l)=M_{i}(M_{i}+1). \tag{5}\]  
In the above formula, \(M_{i}\) represents the length of \(\mathcal{W}_{i}\). \(CC(\mathcal{W}_{i})\) represents the time taken by _CutCircle_ to process the \(\mathcal{W}_{i}\). Then consider an upper bound on the computation time of the _BackWalks_ function:  
\[BW=\sum_{j=1}^{n}M_{j}\bigg{[}\sum_{k_{1}=1}^{j-1}M_{k_{1}}+(M_{j}-1)+\sum_{k_ {2}=j+1}^{n}M_{k_{2}}\bigg{]}\leq\sum_{j=1}^{n}M_{j}\sum_{k=1}^{n}M_{k}=\bigg{(} \sum_{j=1}^{n}M_{j}\bigg{)}^{2}. \tag{6}\]  
Utilizing (5) and (6), deduce the upper bound of the time complexity when employing parallelization techniques:  
\[O(R_{1}+R_{2})=O(R_{1}+\sum_{l=1}^{n}CC(\mathcal{W}_{i})+BW)=O\bigg{(}R_{1}+ 2\bigg{[}\sum_{j=1}^{n}M_{j}\bigg{]}^{2}\bigg{)}.\]  
Figure 5: Robots \(\alpha\) and \(\beta\) start at the blue and yellow \(S\) vertices, respectively. The table presents five examples illustrating how our algorithm reduces path redundancy when the two robots, \(\alpha\) and \(\beta\), perform tasks in different regions.
## 3 Results and Discussion
### Analysis of LM Canola Survey Data from 2009 to 2023  
We analyzed the LM canola monitoring results from 2009 to 2023, focusing on the number of survey sites and the collection of suspicious samples. The initial number of LMO monitoring sites was 159 in 2009, 169 in 2010, and 177 in 2011 (Figure 2A), with a focus on natural environments along major transportation routes from harbors to feed and food factories. Based on the monitoring results from the first three years, the number of survey sites increased annually from 2012 onwards, with a temporary spike in 2017 due to a massive spill of LM canola nationwide. The survey sites continued to increase through 2023, reflecting the areas of LM canola re-collection and the monitoring results from the previous year. Over the 15-year period, a total of 10,571 survey sites were monitored, and 4326 suspicious canola samples were collected (Figure 2A). The number of monitoring sites significantly increased in 2012, leading to a temporary rise in suspicious samples, but the number of samples collected between 2009 and 2016 remained relatively consistent. However, since 2017, they have increased due to the intensive survey of LMO collection areas. The NIE increased the number of samples from 2021 due to concerns about gene flow from LMOs to related species in the natural environment. As a result, the number of suspicious samples gradually increased, and the number of samples collected in 2023 was approximately four times higher than in 2020. A total of 2403 suspicious samples were  
Figure 1: LMO monitoring project and typical monitoring sites. (**A**) Processing scheme for the LMO monitoring project in natural environments. Field images of suspected LM canola growing near (**B**) roadside, (**C**) stockbreeding farm, (**D**) feed factory, and (**E**) other areas, such as a festival site.  
collected from 2021 to 2023, accounting for 55.5% of the suspicious LM canola samples collected over the 15 years.
# Explainable Internet Traffic Classification
## 3 Materials and Methods
### The Proposed Traffic Classification System
#### 3.2.2 Feature Extraction  
The feature extraction phase has been designed and implemented so as to process real-time traffic captured by means of the pcap libraries. First of all, the traffic is reconstructed to identify the flows, defined by the 5-uple: source and destination IP addresses, source and destination ports, and protocol (note that, in this work, we consider bidirectional flows). Then, each 5-uple is transformed in a vector of features to be used as input of the FRBC, which is in charge of estimating the type of traffic.  
In this work, we have experimented two distinct typologies of traffic features:  
* Statistical features: the flow is described by a set of statistical values (namely 21), reported in Table 3. It is important to highlight that in this work, such features have only been computed for flows made of five or more packets.
* Composite features: the flow is described by an array \(\mathbf{x}\in\mathbb{R}^{3H-1}\), where \(H\) is the number of analyzed flow packets, of higher granularity (i.e., packet level) features [34]: \[\mathbf{x}=(d_{1},l_{1},d_{2},l_{2},t_{2},\ldots d_{H},l_{H},t_{H})\] (2) where
* \(d_{i}\in[0,1]\) with \(i=1,2,\ldots H\) is the direction of the \(i^{th}\) packet
* \(l_{i}\) with \(i=1,2,\ldots H\) is the dimension in Byte of the \(i^{th}\) packet, normalized with respect to \(l_{MAX}\)
* \(t_{i}\) with \(i=1,2,\ldots H\) is the time in seconds between packet \(i\) and packet \(i-1\) Clearly, such features depend on the parameter \(H\) and can only be computed for those flows made of at least \(H\) packets. In the experimental results, we will consider \(H\in[3,5,10]\).
# A Novel 2D Micro-Displacement Measurement Method Based on the Elliptical Paraboloid
## 3 Experimental Results
### Calibration Experiments  
The relationship between the displacement to be measured and the displacement of the light spot detected by the CCD detector in two directions was required to calibrate the experiments. The coordinate-measuring machines (CMM) (Hexagon, Stockholm, Sweden) and a Renishaw XL80 (Renishaw, Gloucestershire, UK) laser measurement system were used in the calibration experiments. The experiments in two directions were repeated several times, and the results are shown in Figures 6 and 7. Figure 6 shows the calibration result in the \(x\) direction, from which we can work out that the relationship between the measured displacement and the relative displacement of the light spot is \(x=3.5045x^{\prime}+199.8730\). Furthermore, the linear correlation coefficient \(\text{R}^{2}\) is 0.9999. Figure 7 shows the result obtained from the \(y\) direction calibration experiments. It can be seen from Figure 7 that the  
Figure 4: Image of the light spot.  
Figure 5: Experimental configuration in the \(x\) direction.  
relationship between the measured displacement and the relative displacement of the light spot is \(y=3.5023y^{\prime}+250.5680\), and the linear correlation coefficient R\({}^{2}\) equals 0.9999.
## 4 Results and Validation  
In this section, we first assess the proposed method with the first network of homogeneous receivers. The other network of heterogeneous receivers is then used to further validate the method.
# Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network
## 2 Related Work
### 5G Network Environment  
#### 2.2.1 Using GTP Tunneling Packet  
[PERSON] et al. [20] conducted a study on ML-based IoT botnet traffic detection in a 5 GC network path using binary classification for detecting benign and malicious traffic and multiclass classification for benign and malicious traffic types. All 5G Core GTP packets were used, and free5 GC, an open-source project for 5 th generation (5G) mobile core networks, was used to configure the GTP tunneling testbed. MedBIoT was used as the dataset, labeled \"benign\" or \"malicious\", and the GTP packet was generated by dumping the packet into the free5 GC testbed. Four algorithms were used for anomaly detection (KNN, SVM, RF, and stacking). When evaluating ML performance, IP+GTP packets were used simultaneously for IoT botnet detection in a 5 GC environment to obtain performance comparable to that of a wired network. The results showed that stacking achieved 99.924% in binary classification and 97.5% in multiclass classification.  
Ref. [20] performed detection using GTP-U packets collected from the 5G core network path; however, high performance was obtained using both IP and GTP packets. In addition, all features were used without selecting significant features during feature selection. Therefore, ref. [20] demonstrated a high performance with high time complexity.
## 2 Modeling Assumptions
### Problem Definition
\begin{array}{l}\delta T=\int_{A}\int_{-h/2}^{h/2}\rho(z,T)\Big{(}\Big{(}-z ^{2}\frac{\partial^{4}w}{\partial x^{2}\partial t^{2}}-z^{2}\frac{\partial^ {4}w}{\partial y^{2}\partial t^{2}}-\frac{\partial^{2}w}{\partial x^{4} \partial t^{2}}-B^{2}\frac{\partial^{4}w}{\partial y^{4}\partial t^{2}}-2A \frac{\partial^{4}w}{\partial x^{3}\partial t^{2}}+\\ -2B\frac{\partial^{4}w}{\partial y^{4}\partial t^{2}}-2 AB\frac{\partial^{4}w}{ \partial x^{2}\partial y^{2}\partial t^{2}}\Big{)}\delta w\Big{)}dzdA=0\end{array}  \tag{6}\]  
The enforcement of \(\delta V=0\) yields to the following governing equations of the problem  
\[ \begin{array}{l}\delta w=0\ ;\\ -\frac{\partial^{2}M_{y}}{\partial x^{2}}-\frac{\partial^{2}M_{y}}{\partial y ^{2}}-2\frac{\partial^{2}M_{y}}{\partial x\partial y}+A\frac{\partial^{3}Q_{ x}}{\partial x^{3}}+B\frac{\partial^{3}Q_{y}}{\partial x\partial y}+A\frac{ \partial^{3}Q_{y}}{\partial x^{2}\partial y}+B\frac{\partial^{3}Q_{y}}{ \partial y}+\\ +N_{x}\Big{(}A2\frac{\partial^{2}\theta w}{\partial x^{2}}+B^{2}\frac{\theta w }{\partial x^{2}\partial y^{4}}+\frac{\partial^{2}w}{\partial x^{2}}+2 AB\frac{ \theta^{2}w}{\partial x^{4}\partial y^{2}}+2A\frac{\partial^{4}w}{\partial x ^{4}}+2B\frac{\partial^{4}w}{\partial x^{2}\partial y^{2}}\Big{)}+\\ +N_{y}\Big{(}A2\frac{\partial^{2}\theta w}{\partial x^{2}\partial y^{2}}+B^{2} \frac{\theta w}{\partial x^{2}\partial y^{2}}+2 AB\frac{\theta w}{\partial x ^{2}\partial y^{2}}+2A\frac{\partial^{4}w}{\partial x^{2}\partial y^{2}}+2B \frac{\theta^{2}w}{\partial y^{4}\partial y^{2}}\Big{)}+\\ +N_{xy}\left(2A^{2}\frac{\partial^{2}\theta w}{\partial x^{3}\partial y^{4}}+4 AB\frac{\partial^{2}w}{\partial x^{3}\partial y^{4}}+4A\frac{\partial^{4}w}{ \partial x^{3}\partial y^{4}}+2B^{2}\frac{\theta^{2}w}{\partial x\partial y^{ 2}}+4B\frac{\partial^{4}w}{\partial x\partial y^{3}}+2\frac{\partial^{2}w}{ \partial x\partial y}\right)+\\ -I_{2}\left(\frac{\partial^{2}w}{\partial x^{2}\partial t^{2}}+\frac{\partial^ {4}w}{\partial y^{4}\partial t^{2}}\right)-I_{0}\left(\frac{\partial^{2}w}{ \partial x^{2}}+A^{2}\frac{\partial^{4}w}{\partial x^{4}\partial t^{2}}+B^{2} \frac{\theta^{2}w}{\partial y^{4}\partial t^{2}}+\\ +2A\frac{\partial^{4}w}{\partial x^{2}\partial t^{2}}+2B\frac{\partial^{4}w}{ \partial y^{2}\partial t^{2}}+2 AB\frac{\theta w}{\partial x^{2}\partial y^{ 2}\partial t^{2}}\right)=0\end{array}  \tag{7}\]  
The second moments of inertia \(I_{0}\), \(I_{2}\) in Equation (7) are defined as  
\[\left(I_{0},I_{2}\ \right)=\int_{A}\rho(z,T)\Big{(}1,z^{2}\Big{)}\ dA \tag{8}\]  
The additional quantities \(N\), \(M\) and \(Q\) in Equation (7) refer to the axial, flexural and shear internal actions along the \(x\), \(y\) and \(z\) axes, namely  
\[ \begin{array}{l}\left(N_{x},N_{y},N_{xy}\right)=\int_{-h/2}^{h/2}\left(\sigma _{x},\sigma_{y},\sigma_{xy}\right)dz\\ \left(M_{x},M_{y},M_{xy}\right)=\int_{-h/2}^{h/2}\left(\sigma_{x},\sigma_{y}, \sigma_{xy}\right)zdz\\ \left(Q_{x},Q_{y}\right)=\int_{-h/2}^{h/2}\left(\sigma_{xz},\sigma_{yz}\right)dz \end{array}  \tag{9}\]
## 3 Experimental Verification of Chatter Suppression Effect with TMDs
### Design for TMDs in Case of Multiple Modes of Chatter Generation
Therefore, the suppression effect of multiple chatter modes was examined by respectively installing two TMDs with natural frequencies close to chatter frequencies of (3,1) mode and (4,1) mode. The circumferential installation pit  
Figure 16: Workpiece vibration displacement at number 2 measurement point for mounting arrangement A: Comparison of (**a**) vibration waveforms with and without TMDs, (**b**) frequency analysis with and without TMDs. Figure 17: Workpiece vibration displacement at number 2 measurement point for mounting arrangement B: Comparison of (**a**) vibration waveforms with and without TMDs, (**b**) frequency analysis with and without TMDs. tuned to the (3,1) and (4,1) modes were determined such that they did not coincide with the pitch of the nodes of the (3,1) and (4,1) modes, respectively. The natural frequencies and circumferential installation pitches of each of the four TMDs are listed in Table 6. The circumferential mounting arrangement of the TMDs were superimposed on the (3,1) and (4,1) modes of chatter, as depicted in Figure 18. Moreover, the position of each TMD in the \(z\)-axis was \(l_{\text{d}}=35\) mm from the workpiece tip. The workpiece vibration waveforms measured by the eddy current sensor of number 2 measuring point and the RMS values with and without the TMDs during the turning process for mounting arrangement C are presented in Figure 19a. The frequency analysis of the time waveform for the 0.05 s period in the initial 28 s after cutting (vertical dotted line in Figure 19a) is presented in Figure 19b, wherein the peak of the chatter frequency in the (3, 1) mode slightly appears for the case of mounting arrangement C with the four TMDs. However, the RMS values of the workpiece vibration displacement with the TMDs (Figure 19a) were significantly reduced in comparison to those in Figures 16a and 17a. Although the task of investigating the TMDs' mounting arrangements to have a further chatter suppression effect remains, the experimental results in Figure 19 suggested that, in order to suppress the multiple chatter modes using small mass ratio TMDs, the TMDs are required to be placed at unequal intervals and at least two TMDs ought to be attached for each chatter vibration mode. \begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Mounting Arrangement C**} \\ \cline{2-3}  & **Natural Frequency (Hz)** & **Position (\({}^{\circ}\))** \\ \hline TMD 1 & 1600 & 0 \\ TMD 2 & 2490 & 80 \\ TMD 3 & 1600 & 210 \\ TMD 4 & 2490 & 280 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Natural frequencies and circumferential installation pitch of the four TMDs. Figure 18: Mounting arrangement C of four TMDs.
# A Cross-Chain Solution to Connect Multiple DNS Blockchains in Consensus Roots System
## 5 Security Analysis
### Security Proof
#### 5.1.3 Node Authorization  
**Definition 3** (Authorization Game).: _An adversary \(\mathcal{A}\) without CA credentials attempts to inject a transaction \(T_{\mathrm{mal}}\) with a forged certificate \(\mathsf{cert}_{\mathrm{mal}}\)._  
**Theorem 3** (CA Authorization Security).: _Let \(\mathcal{N}_{\mathrm{CA}}\) denote the set of CA-authorized nodes. Then, only nodes \(n\in\mathcal{N}_{\mathrm{CA}}\) can influence cross-chain transactions._  
Proof.: The adversary \(\mathcal{A}\) succeeds only if  
\[\mathrm{Verify}(\mathsf{pk}_{\mathrm{node}},T_{\mathrm{mal}},\sigma_{ \mathrm{node}})=1\quad\land\quad\mathrm{Verify}(\mathsf{pk}_{\mathrm{CA}}, \mathsf{cert}_{\mathrm{mal}},\sigma_{\mathrm{CA}})=1. \tag{2}\]  
Since \(\mathcal{A}\) lacks \(\mathsf{sk}_{\mathrm{CA}}\) and \(\mathsf{sk}_{\mathrm{node}}\), we have  
\[\Pr[\mathcal{A}\;\mathrm{wins}]=\mathsf{negl}(\lambda) \tag{3}\]  
by the EU-CMA security of the signature scheme.
## 3 Methodology
### Statistical Analysis and Modeling  
Various statistical methods guide in exploring the relationship between bridge health metrics such as performance, deterioration, and maintenance activities of bridge deck-wearing surfaces. Understanding the correlation between bridge health measurements gives stakeholders insights for making effective decisions regarding budget planning and allocation, maintenance scheduling, and policy development.  
From visual inspection, the performance and deterioration scores distribution resembles a normal distribution. Therefore, appropriate parametric or non-parametric statistical testing will provide insights into differences between the distribution of bridge health metrics across the bridge deck-wearing surfaces. Moreover, other statistical analyses, such as correlation tests between performance, maintenance, and deterioration among all surface wearing types, will suggest if there is a correlation between the bridge health metrics.  
After the data processing phase, it is common to encounter challenges in the dataset, such as class imbalance, outliers, and missing data. To address these challenges effectively, this research study finds tree-based modeling to be a promising approach for several reasons. One advantage is that certain implementations of tree-based models have the ability to handle missing data directly, eliminating the need for imputing missing values. Additionally, tree-based models perform well even when the target classes are imbalanced, which is a prevalent issue in many real-world datasets.  
Another benefit is the inherent interpretability of tree-based models, making them accessible even to non-experts. These models can be easily visualized, allowing for clear and concise explanations when presenting the model to stakeholders. Particularly decision trees are considered the most interpretable models [18; 19]. Furthermore, tree-based models are non-parametric, meaning they do not rely on specific assumptions about the data distribution [9]. This flexibility enables them to effectively handle outliers and capture non-linear relationships within the data.  
By leveraging these features, tree-based models provide a robust and intuitive approach for analyzing datasets with imbalanced classes, outliers, and missing data. Their interpretability and ability to handle diverse data characteristics make them a valuable tool in addressing the challenges often encountered in real-world datasets.  
This research study assesses the performance of models and reports metrics like accuracy, precision, recall, F1 score, AUC, and kappa values for the maintenance classification problem.