esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 7 Hydrological Application
### Combined with Conventional Methods for Rainfall Measurement  
[PERSON] et al. [104] verified that microwave links in any direction can be used to compensate the attenuation effect of radar reflectivity through experimental methods. A new method of CML-based rainfall retrieval adjusted by RGs is proposed by [PERSON] et al. [105], which does not require installing RGs near the CMLs intentionally. Using RGs with different spatial and temporal resolutions, even if they are relatively far away, can improve the performance of CML-based rainfall measurement. [PERSON] et al. [44] integrated the RG, radar and microwave links to improve the measurement accuracy of the spatial distribution of rainfall and rainfall intensity, and the Gauss-Newton method is used to minimize the cost function of all sensing methods. CML also has a great potential in the calibration of rainfall measured by radar or satellite images [106].
## 4 Discussion
The complex VR environments require users to process multiple types of sensory information simultaneously (visual, auditory, tactile), leading to improved sensory integration skills. This ability to synthesize and respond to multi-sensory inputs is crucial in cognitive tasks that require attention, problem-solving, and multitasking [71]. Recent findings by [PERSON] et al. [72] highlight that stereoscopic objects in VR can impact motor performance,particularly in reaching tasks, by introducing additional depth-processing demands. Their study revealed that young participants exhibited differences in movement smoothness when interacting with 2D versus 3D objects, whereas older participants struggled with predictive control, suggesting that stereoscopic VR environments challenge the brain's ability to integrate spatial and motor cues effectively. Enhanced sensory integration improves the brain's capacity to handle complex information and perform tasks that demand high levels of cognitive flexibility and coordination. Furthermore, the immersive nature of VR compels users to remain highly engaged mentally. This sustained mental engagement serves as a form of cognitive exercise, improving overall mental agility and capacity [73]. Engaging in complex VR tasks increases cognitive endurance and flexibility, particularly in executive functions. Tasks in VR often require planning, problem-solving, and strategy, enhancing executive function capabilities crucial for managing information, controlling impulses, and exhibiting flexible thinking [74]. These results underline the potential benefits of incorporating immersive VR games into cognitive training interventions and support the notion that virtual reality can positively impact cognitive functions. Another explanation for the success of VR training involves the moderate physical activity in games like Beat Saber [75], which can lead to a rise in neurotransmitters such as dopamine and serotonin, enhancing mood and cognitive functions. Dopamine plays a critical role in reward-motivated behavior and learning, while serotonin contributes to mood regulation and cognitive functions such as memory and learning [76,77]. Additionally, VR environments provide instant feedback and rewards, further stimulating the brain's reward systems and reinforcing learning and task engagement, which are also associated with increased dopamine release [78]. This biochemical change indicates that even a single session of VR training can improve cognitive functions through these neuro-chemical pathways. The increase in neurotransmitter levels during VR activities enhances synaptic plasticity, facilitating better learning and memory [79]. The dopamine release fosters heightened motivation, which is crucial for maintaining engagement and sustained effort throughout the training period. Motivation, which precedes engagement, fuels the initial desire to participate and persist in the VR-based interventions, thereby amplifying their cognitive benefits.
# Improving Waste Management System Efficiency and Mobility with Efficient Path MANET
## 3 Intersection GA-Based Routing Algorithm
### Initialisation  
The initialization stage presents the initial population as a research space to find the feasible path. The representation of network topology in a symmetric matrix, in addition to the intersection GA-based algorithm, is the proposed intersection node table presented in Table 1. The proposed intersection node table is used to generate the chromosomes and prevent invalid paths in the initial population. The proposed relation table is generated based on the connectivity matrix. All connected devices are presented as state nodes in the first row and neighbors for each node in the second row, which are considered the next hope for the current state, as shown in Table 1.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c c c c c} \hline \hline
**State** & **1** & & **2** & & & **3** & & & **4** & & & & **5** & & & & **6** & & & **7** \\ \hline
\begin{tabular}{c} Next \\ state \\ \end{tabular} & 2 & 3 & 5 & 1 & 4 & 5 & 1 & 5 & 6 & 7 & 2 & 5 & 8 & 1 & 2 & 3 & 4 & 8 & 3 & 7 & 3 & 6 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Intersection node table.  
According to the intersection node table, the selected state in the first gen in the chromosome presents the source node as the starting point towards the destination point on the last gene of the chromosome. A random selection of intermediate nodes is made from the neighboring active nodes connected to the current state node, starting with the source node and progressing until the destination. Each chromosome in the initial population is generated using the same generation process according to the number of chromosomes, which is identified by the population size.
## 2 Literature Review
### Ssa
The set \((\lambda_{i},\,U_{i},\,V_{i})\) is referred to as a value of the matrix X called the \(i\)-th eigentriple, in which \(\mathrm{s_{i}}\) represents the singular value of X or its \(i\)-th value (whose value is equal to the square root of the \(i\)-th eigenvalue of matrix \(\mathrm{XX^{T}}\)); \(\mathrm{U_{i}}\) is the \(i\)-th left singular vector of X (equivalent to the \(i\)-th eigenvector of \(\mathrm{XX^{T}}\)); and \(\mathrm{V_{i}}\) is the \(i\)-th right singular vector of X (equivalent to the \(i\)-th eigenvector of \(\mathrm{X^{T}X}\)). A group of r eigenvectors determines an r-dimensional hyperplane in the L-dimensional space, RL of vectors, Xj. The second stage is the reconstruction stage, which consists of two steps: grouping and diagonal averaging. The grouping step refers to dividing the elementary matrices, \(\mathrm{X_{i}}\), into some categories and adding the matrices in each group. Imagine that \(\mathrm{I}=\left\{\mathrm{i_{1}},\ldots,\mathrm{i_{p}}\right\}\) is a set of indices \(\mathrm{i_{1}},\ldots,\mathrm{i_{p}}\). Therefore, the matrix, \(\mathrm{X_{I}}\), related to the set \(\mathrm{I}\), is presented as \(\mathrm{X_{I}}=\mathrm{i_{1}}+\ldots+\mathrm{i_{p}}\). The decomposition of the set of  
Figure 1: The standard Singular Spectrum Analysis (SSA) steps. indices, \(J=1,\ldots,d\), into the disjoint subsets, \(I_{1},\ldots,I_{m}\), corresponds to the matrix called \(X\), which is represented by \(X=X_{I_{1}}+\ldots+X_{I_{m}}\). Eigentriple grouping is the process of selecting the sets \(I_{1},\ldots,I_{m}\). Diagonal averaging turns every single matrix \(I\) into a time series. A time series is an element commonly added to the initial series \(Y_{T}\). If \(z_{ii}\) represents a component of a matrix \(Z\), then the k-th term of the obtained series is calculated by averaging \(z_{ij}\) over all \(I\), \(j\) in such a way that \(i+j=k+2\). The above procedure is known as diagonal averaging or, to put it differently, the Hankelization of the matrix \(Z\). By applying diagonal averaging or the Hankelization procedure to all matrix elements of \(X_{I_{1}},\ldots,X_{I_{m}}\), another expansion is achieved: \(X=\widetilde{X_{I_{1}}}+\ldots+\widetilde{X_{I_{m}}}\). This expansion is equal to the decomposition of the first original series \(Y_{T}=\left(y_{1},\ y_{2},\ \text{Thiy}_{T}\right)\) into a total m series: \(y_{t}=\sum_{k=1}^{m}\widetilde{y_{t}}^{(k)}\), where \(\widetilde{Y_{T}}^{(k)}=(\widetilde{y_{1}}^{(k)},\ldots,\widetilde{y_{T}}^{( k)})\) matches the matrix \(X_{I_{k}}\). Two parameters are needed for the SSA method: the number of elementary matrices \(r\) and the window length \(L\). It is necessary that we choose an appropriate window length \(L\) since it is essential for improving the accuracy of the SSA. If the points of different vectors, \(X_{i}\) and \(X_{j}\) (\(i\
eq j\)) are linearly independent, then the value of \(L\) is calculated. The structure of the data and how we perform the analysis determine our choice of these parameters following particular selection rules.
# Combining Machine Learning and Social Network Analysis to Reveal the Organizational Structures
## 2 Materials and Methods
### Social Network Analysis  
The field of social network analysis can be understood as a set of techniques deriving knowledge about human relationships based on the relations they form--usually by being members of social networks of different kinds. These networks can relate to family, friends, companies or organizations they are employees members of, or social media they participate in. More formally, social network consists of a finite set or sets of actors and the relation or relations defined on them [33]. To help understanding this definition of a social network, some other concepts that are fundamental in this case should be explained. An actor is a discrete individual, corporate or collective social unit [33]. This can be a person in a group of people, a department within a company or a nation in the world system. Actors are linked to each other by social ties and these relations are the core of the social network approach. Social networks are presented using graph structures, where nodes are actors and edges are connections between them. Hence, all graph theory methods and measured can be applied. A graph may be undirected, which means that there is no distinction between the two vertices associated with each edge, or its edges may be directed from one vertex to another. A graph is being defined usually as an ordered pair \(G:=(V,E)\), where \(V\) are vertices or nodes and \(E\) are edges. On top of that multiple methods are being used in order to derive knowledge about network members or network itself, such as centrality measures [34], community detection [35], modelling evolution [36], or detection of influential nodes [37]. As described in Section 1, social network analysis also became present in organizations where it is often being referred to as organizational network analysis [38].
## 3 Problem Formulation  
The objective of the proposed system is to maximize the achievable rate of the users. Therefore, the problem can be formulated as below  
\[\max_{\mathbf{w}_{k},b_{k},\text{max},\Theta_{k}}\!\log_{2}\!\left( 1+\frac{\left|\mathbf{F}_{r}^{H}\mathbf{\[PERSON]}_{r}\mathbf{G}\mathbf{w}_{r} \right|^{2}}{\left|\mathbf{F}_{t}^{H}\mathbf{\[PERSON]}_{t}\mathbf{G}\mathbf{w}_ {r}\right|^{2}+\sigma_{r}^{2}}\right)+\log_{2}\!\left(1+\frac{\left|\mathbf{F}_ {t}^{H}\mathbf{\Theta}_{t}\mathbf{G}\mathbf{w}_{t}\right|^{2}}{\sigma_{t}^{2} }\right)\!, \tag{9a}\] \[\text{s.t.}\quad\sum_{k\in\{t,r\}}\left\|\mathbf{w}_{k}\right\|^{ 2}\leq P_{\text{max}}\] (9b) \[R_{k}\geq R_{k}^{\text{min}}\quad\forall k\in\{t,r\},\] (9c) \[0<\beta_{m}^{t},\beta_{m}^{r}<1,\beta_{m}^{t}+\beta_{m}^{r}=1, \beta_{m}^{k}\in(0,1),\quad\forall k\in\{t,r\}\quad\forall m\in\mathcal{M},\] (9d) \[\beta_{m}^{k}\in(0,1),\forall k\in\{t,r\},\quad\forall m\in \mathcal{M},\] (9e) \[\phi_{k}^{m}\in[0,2\pi),\quad\forall k\in\{t,r\},\quad\forall m \in\mathcal{M}, \tag{9f}\]  
where (9b) denotes that the maximum total transmit power is \(P_{\text{max}}\); \(R_{r}\) and \(R_{t}\) denote the achievable rate of U1 and U2, respectively; (9c) denotes that the minimum required communication rate of each user is \(R_{k}^{\text{min}}\); and \(\mathcal{M}\triangleq\{1,2,\ldots,M\}\), \(a_{k}\), and \(\beta_{m}^{k}\) are fixed parameters that remain constant throughout the optimization process. The UAV altitude \(h_{uw}\) plays a critical role in determining the characteristics of the channels. Specifically,it influences both the path loss exponents and the Rician factors of the channels \(\mathbf{F}_{k}\) and \(\mathbf{G}\). As the UAV descends to a lower altitude, it is more likely to experience blockages and scattering from surrounding buildings and obstacles in the urban environment. This leads to a higher path loss exponent and a lower Rician factor. Therefore, the UAV altitude is crucial for the overall system performance and is explicitly considered in our joint optimization. Ultimately, \(R_{t}+R_{r}\) is collectively determined by these three variables through their joint effects on channel characteristics.
## 3 Renewal and Revocation Protocols
### Renewing the Network Key  
Changing the network key \(NK\) is a decision made by the sink. We propose a secure way for the sink to distribute this new key to all authenticated nodes. This protocol, denoted RNK, is described in Figure 6. It allows the sink to be sure that all nodes receive the new key before starting to use it. It works as follows: the sink generates a new network key \(NK^{\prime}\) and a nonce \(n_{s}\). Then, it encrypts \(NK^{\prime}\) and \(n_{(S,I)}\) (one new nonce per node \(I\)) using the shared symmetric key \(K_{DH}(S,I)\) and sends the encrypted message to each node. Then, it collects all nonces \(n_{(S,I)}\) before starting to communicate with the new network key \(NK^{\prime}\).  
Figure 5: Two protocols for renewing the new asymmetric keys of a node \(I\), where \(S\) uses the symmetric shared key \(K_{DH}(S,I)\) to deliver \(pk^{\prime}(S)\) to \(I\). (**a**) Protocol RAKdh\({}_{a}\): \(S\) and \(I\) compute \(K^{\prime}_{DH}(S,I)\); (**b**) protocol RAKdh\({}_{b}\): \(S\) computes \(K^{\prime}_{DH}(S,I)\) and sends it to \(I\).
# Automatic Roadway Features Detection with Oriented Object Detection
###### Abstract  
Extensive research efforts have been devoted to identify and improve roadway features that impact safety. Maintaining roadway safety features relies on costly manual operations of regular road surveying and data analysis. This paper introduces an automatic roadway safety features detection approach, which harnesses the potential of artificial intelligence (AI) computer vision to make the process more efficient and less costly. Given a front-facing camera and a global positioning system (GPS) sensor, the proposed system automatically evaluates ten roadway safety features. The system is composed of an oriented (or rotated) object detection model, which solves an orientation encoding discontinuity problem to improve detection accuracy, and a rule-based roadway safety evaluation module. To train and validate the proposed model, a fully-annotated dataset for roadway safety features extraction was collected covering 473 km of roads. The proposed method baseline results are found encouraging when compared to the state-of-the-art models. Different oriented object detection strategies are presented and discussed, and the developed model resulted in improving the mean average precision (mAP) by 16.9% when compared with the literature. The roadway safety feature average prediction accuracy is 84.39% and ranges between 91.11% and 63.12%. The introduced model can pervasively enable/disable autonomous driving (AD) based on safety features of the road; and empower connected vehicles (CV) to send and receive estimated safety features, alerting drivers about black spots or relatively less-safe segments or roads.  
Computer vision; object detection; road transportation; safety management Article
## 4 Results
### Trace Elements Analysis by LA-ICP-MS  
Chemical characterization of samples in terms of trace elements was performed using the LA-ICP-MS technique on cross-sections with polished surface.  
This method made it possible to determine the trace elements in both the black crusts and the unaltered substrate (i.e., marbles), selecting a statistically valid number of analyses as a function of the sample thickness. Exceptionally, the assays were only carried out on the crust in SM-P1, due to the lack of representativeness of the substrate resulting from the insufficient thickness available.  
The average concentrations (in ppm) of the most representative chemical elements, both in the unaltered substrates (US) and in the black crusts (BCs), is reported in Table 4.  
Chemical elements such as Ba, Cu, Pb, Ti and Zn achieved the major concentrations in the black crusts (Table 4).  
Specifically, the BCs (SD2 and SD3) taken from the complex of _San Domenico Maggiore_ show higher average maximum values in Pb (3525 ppm), Zn (1580 ppm) and Cu (224 ppm) than the substrate, where they are considerably lower (i.e., Pb is 82.09 ppm; Zn is 32 ppm; Cu is 18.18 ppm). In the same samples of BCs, similar and sometimes lower values of Ba, As and V were detected with respect to the unaltered substrate (Table 4).  
The BCs from the cloister of _San Marcellino e Festo_ (SM-S1, SM-A1 and SM-P1) display lower concentrations in heavy metals compared to the previous ones (i.e., SD2 and SD3). In detail, BCs of SM-S1, SM-A1 and SM-P1 show higher average maximum values in Ba, Ti, Zn and Cu than the unaltered substrate (Table 4). Specifically, in SM-A1, Ba increases from 9.15 ppm in the US to 288 ppm in the BC; in SM-S1, Ti varies from 12.38 ppm in US to 417 ppm in BC, Zn from 19.25 ppm in US to 321 ppm in BC, Cu from 4.4 ppm in US to 93.55 ppm in BC (Table 4).  
The chemical compositions determined for the samples from the complex of _San Domenico Maggiore_ suggest a certain correlation between the concentrations of the trace elements and the position of the samples in terms of height and exposure to washout (Figure 2 and Table 1). In particular, the trace elements detected in SD2, taken at 2.30 m high, show greater overall concentrations than SD3, sampled at 1.60 m high (Figure 2). In fact, this latter one is located in an area only partially exposed to washout (i.e., on a vertical and internal surface), thus favoring a major accumulation of pollutants over time.  
Conversely, the black crusts belonging to the three historical artifacts from the cloister of _San Marcellino e Festo_ (SM-S1, SM-A1 and SM-P1) do not show a clear correlation between the concentration of the trace elements and their sampling position.  
Overall, the samples of the cloister exhibit lower concentrations than those of _San Domenico Maggiore_, which is attributable both to different sampling heights and to exposure conditions. In fact, the artifacts in _San Marcellino e Festo_ are located in a more protected micro-environment with respect to the samples of _San Domenico Maggiore_, which are directly exposed in a higher vehicular traffic area.
## 2 Model Parameter Identification  
### Second-Order RC Equivalent Circuit Models  
Based on the comparison between the first-order and second-order equivalent circuit models used by [PERSON] et al. in the application of SOC estimation for electric vehicle batteries, this paper compares the estimation results of SOC using the second-order model and the first-order model, respectively. The estimation results prove the superiority of the second-order model over the first-order model, especially in terms of describing the dynamic characteristics of lithium-ion batteries. Therefore, this paper uses the second-order equivalent circuit model.  
The second-order RC equivalent circuit model of lithium-ion battery is shown in Figure 1; according to Figure 1, it can be obtained by using [PERSON]'s voltage law (KVL):  
\[U=U_{oc}+U_{1}+U_{2}+IR_{0} \tag{1}\]  
where \(U\) is the terminal voltage; \(U_{OC}\) is the open-circuit voltage, which is polynomial about SOC; \(U_{p1}\) and \(U_{p2}\) simulate multiple electrochemical processes inside the battery; \(R_{0}\) is the ohmic internal resistance; and \(I\) is the current.  
R0 represents the ohmic resistance, while \(R_{p1}\) and \(R_{p2}\) denote the polarization resistances of the battery, and \(C_{p1}\) and \(C_{p2}\) represent the capacitances of the battery.  
Its discretization formula can be expressed as the following [9]:  
\[\begin{cases}U_{1,k+1}=U_{1,k}e^{-\Delta t/\tau_{1}}+R_{p1}I_{k}\Big{(}1-e^{- \Delta t/\tau_{1}}\Big{)}\\ U_{2,k+1}=U_{2,k}e^{-\Delta t/\tau_{2}}+R_{p2}I_{k}\Big{(}1-e^{-\Delta t/\tau_{ 2}}\Big{)}\\ \tau_{1}=R_{p1}C_{p1},\tau_{2}=R_{p2}C_{p2}\end{cases} \tag{2}\]  
where \(U_{1,k+1}\), \(U_{2,k+1}\) are the values of \(U1\) and \(U2\) at the \((k+1)\)th moment; \(R_{p1}\), \(R_{p2}\) are the polarization resistances generated by electrochemical polarization and concentration difference polarization; \(C_{p1}\), \(C_{p2}\) are the polarization capacitances; \(\tau_{1}\), \(\tau_{2}\) are the time constants.  
Figure 1: Second-order RC equivalent circuit model.  
The SOC is calculated by the ampere-time integration method as shown below [12]:  
\[SOC_{AH}=SOC_{0}+\frac{1}{c_{0}}\int_{0}^{k}\eta^{*}Idt \tag{3}\]  
where \(SOC_{AH}\) is the SOC value calculated by the ampere-time integration method, \(SOC_{0}\) is the initial SOC value, \(c_{0}\) is the rated capacity, and \(\eta\) is the Coulomb efficiency.
###### Abstract  
Metal alloy matrix composites are generally lightweight structural materials with a high strength-to-weight ratio. They can be extensively used in various fields of modern engineering applications, such as aerospace and automotive components and biomedical engineering. This study focuses on the development and characterization of lightweight metal alloy matrix composites for industrial applications, with a particular emphasis on magnesium (Mg) alloys as a replacement for aluminum-based alloys. Mg alloys offer significant weight advantages, being 33% lighter than aluminum and 75% lighter than steel, making them highly desirable for use in various engineering fields. In the present study, Mg (AZ91) alloy reinforced with x-Si\({}_{3}\)N\({}_{4}\) composites (\(\kappa\) = 0, 1, 3, 5, 7, 9 wt.%) were fabricated using a liquid state process. The AZ91/x-Si\({}_{3}\)N\({}_{4}\) composites were evaluated through physical, mechanical, wear, and microstructural characterization. The experimental results, supported by statistical analysis, demonstrated that the incorporation of Si\({}_{3}\)N\({}_{4}\) particles amplified the mechanical properties, wear resistance, and porosity of the composites. However, the presence of the reinforced particles resulted in reduced foregability and elongation, limiting certain deformation characteristics. The existence of the reinforced particles within the composites was confirmed through SEM analysis, providing visual evidence of their distribution and interaction within the Mg alloy matrix. Finally, it was concluded that the implication of the study could be sought for the light structural parts of aerospace, automotive, biomedical, and prosthetic applications.  
Mg (AZ91) alloy; stir casting; physical properties; mechanical properties; microstructure 4