esa-sceva/satcom-chunk-collection huggingface dataset:
# Autonomous Mutual Authentication Protocol in the Edge Networks
## 3 Methodology
### Registration Phase  
The users follow the instruction for the edge device registration on the national portal _RS_. The user's personal unique ID (UID) and device ID are required to complete the registration process on the national website. Each device is assigned a unique public key that can be an automobile car, a computer/laptop, autonomous drones, ships, robots, etc. The user will also receive the public keys of all the registered devices within that state/region. Henceforth, the regional-level portal server will possess a list of local registered edge devices, which will be accessible while traveling to other regions and can access new public keys without the need to re-register his/her devices. The national central server will also possess the state wise edge devices public key's, which are accessible to the trusted authorities. A distributed database stores the UID linked device details in either cloud or blockchain based server because of immutability and distributed ledger feature.
## 3 Proposed Quality of Service Routing Protocol (QoSRP) in UWSNs
### Packets Forwarding Scheme (UIWPF)
#### 3.4.3 Reliable Packets Forwarding Process  
At this stage, each source node has the multiple constructed data paths information towards the sea surface sink. The source node selects the best path among the available and sends a ready data (\(re\_d\_\&\ell\_\)) to neighbors and the potential forwarder, so that it can tune its channel to the requested one for receiving data packets. The \(re\_d\_\&\ell\_\) message includes the sender identity and channel information. This mechanism avoids excessive acknowledgment message sharing to the individuals and thus minimize energy consumption, but also provides information to neighbors about the usage of an occupied channel in the network. The packets transmission begins as soon as the acknowledgment message is received from the receiver in the network. This procedure repeats at each hop until the data packets are successfully delivered to the sea surface sink. After receiving the packets, the sink sends a confirmation message to the senders in the reverse routing path to notify the source node that its data has been received for further processing and sent to a remote user in the network. In addition, the similar procedure is repeated in the reverse packets forwarding from the sink towards the source node in UWSNs. Thus, the entire data packets are greedily forwarded to the neighbors' relay candidates closest to the destination in a multi-hop manner.
# A Review on the Prospects of Mobile Manipulators for Smart Maintenance of Railway Track
## 2 Current Technologies for Railway Track Maintenance
### Push-Trolley  
Inspection trolleys used in railway maintenance are capable of on-track navigation because of their rail-shaped wheels. The size of the trolley used depends on the payload requirements. They can be used for different purposes, whether carrying materials for maintenance or carrying people. Depending on their size and design, these simple trolleys can carry up to 60 kg of material [30].  
Inspection trolleys are typically trimmed to reduce mass and increase sensor payload capability. They are ideal for use in ultrasonic inspection systems because of their slower operating speed. Moreover, they are widely used for track geometry validation on short track lengths [31]. GPS capability provides the defect location [32]. However, as humans typically push these devices, their maximum speed is limited, and they are only suitable for use over short distances. Figure 2 is an example of a track geometry inspection trolley. The disadvantage of using a push-trolley for smart maintenance is that it can only carry a limited payload before it becomes cumbersome. Moreover, it lacks the capability for payload deployment.
## 2 Materials and Methods
### Sample Preparation  
[PERSON] et al. [45] measured the damping ratio of rubber-reinforced clay and suggested an optimum rubber content of 10%. When the fiber content exceeded 20% during the preparation of the specimens, the fibers clumped together and a uniform specimen could not be obtained. Similarly, [PERSON] et al. [46] showed that specimens with high fiber content were not uniform. To avoid non-uniformity of the specimens, the rubber fiber content used in this study was 10% (ratio of fiber mass to dry expansive clay mass). Air-dried expansive clay was uniformly mixed with rubber fibers, and distilled water was added to increase the moisture content to 16% of the dry expansive clay mass. The mixture was stored in a sealed bag for 24 h to an even moisture distribution. Specimens with a diameter of 39.1 mm and height of 80 mm were obtained using the standard compaction effort [39]. Because it is more difficult to bring clay to saturation than sand or silt, the rubber fiber-reinforced expansive clay specimens were treated using the vacuum saturation method [47]. Some rubber fiber-reinforced expansive clay specimens are shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Soil Parameters** & **Results** & **Reference Standards** \\ \hline Maximum dry density & 1.858 g/cm\({}^{3}\) & ASTM D698-12 [39] \\ Optimum moisture content & 15.71\% & ASTM D2216-19 [40] \\ Natural moisture content & 6.06\% & ASTM D2216-19 [40] \\ Liquid limit & 57.9\% & ASTM D4318-17e1 [41] \\ Plastic limit & 22.3\% & \\ Free swell ratio & 71\% & [PERSON] et al. [42] \\ Specific gravity ds & 2.72 & ASTM D854-14 [43] \\ Uniformity coefficient (\(C_{\text{u}}\)) & 9.3 & ASTM D7928-17 [44] \\ \hline \end{tabular}
\end{table}
Table 1: Characteristic parameters for the expansive clay.  
Figure 1: Particle size distribution curve of the expansive clay.
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
This article will help equip researchers with better knowledge of clustering-based optimization techniques, their classification and function based on the applied optimization methods, and an understanding of the basic limitations of the existing clustering techniques. This study also focuses on clustering protocols in WSNs from the perspective of optimization algorithms. Recent studies on optimized clustering solutions were extensively reviewed and examined in detail for protocols regarding the methodology and properties of the considered algorithms. These techniques were evaluated based on the clustering standard parameters and optimization process parameters used with respect to their compatibility with different network characteristics in order to compare cluster protocols. Optimization parameters were used in this review for a better evaluation of the techniques and a general understanding of the clustering protocols. The parameters are presented considering various classes of optimization techniques, such as meta-heuristic, fuzzy and hybrid algorithms. The following are our major contributions:  
* Provision of a novel perspective and method for conducting a review of the existing optimization techniques for clustering protocols;
* Provision of a novel optimization algorithm-based classification method;
* Provision of a comprehensive review and evaluation of the available literature based on clustering parameters and optimization for WSNs to understand the protocols and their related methodologies. The remainder of the article is organized as follows. Section 2 describes how the review was conducted. Section 3 presents the existing literature surveys, followed by Section 4, which discusses clustering fundamentals. Section 5 provides an overview of Clustering Process Optimizations. In Section 6, clustering approaches are classified into different categories, and a summary of each clustering technique is presented in order to highlight the objectives and evaluation functions. Moreover, the section provides tables comparing the protocols that are examined and discussed with respect to classification. Finally, Section 7 details the conclusions.
## 2 Reverse Conversion for General View Moduli  
Most methods of reverse conversion are based on the use of CRT and MRC. Reconstruction of a number \(X\) from its modular form \((x_{1},x_{2},\ldots,x_{n})\) using the classical form of the Chinese Residue Theorem (CRTc) [7] is possible by the formula:  
\[X=\left|\sum_{i=1}^{n}\left|P_{i}^{-1}\right|_{p_{i}}\cdot P_{i}\cdot x_{i} \right|_{p}, \tag{1}\]  
where \(p_{1},p_{2},\ldots,p_{n}\) are RNS moduli, \(P=\prod_{i=1}^{n}p_{i}\) is RNS dynamic range, \(P_{i}=\frac{p}{p_{i}}\), \(\left|P_{i}^{-1}\right|_{p_{i}}\) is multiplicative inversion of \(P_{i}\) modulo \(p_{i}\). To illustrate the considered methods, let us consider examples of RNS with 8-bit range, in particular {7,8,9} with range \(P=7\cdot 8\cdot 9=504\). For the given RNS the auxiliary coefficients are:  
\[P_{1}=\frac{504}{7}=72,\qquad P_{2}=63,\qquad P_{3}=56,\] \[\left|P_{1}^{-1}\right|_{p_{1}}=4,\quad\left|P_{2}^{-1}\right|_{p _{2}}=7,\quad\left|P_{3}^{-1}\right|_{p_{3}}=5.\]  
Consider the reconstruction of the number \(X=(3,2,1)=10\). Then, using the Formula (1), we obtain:  
\[X=\left|4\cdot 72\cdot 3+7\cdot 63\cdot 2+5\cdot 56\cdot 1\right|_{504}=\left| 2026\right|_{504}=10.\]  
In the case of the hardware implementation, the product calculations are performed in parallel, and the summation can be performed by tree doubling, which speeds up the calculations, but it is necessary to find the remainder of the division by a large number \(P\). Although in some cases it helps to simplify the scheme by using the Formula (1) to find the remainder for each summand \(\left|\left|P_{i}^{-1}\right|_{p_{i}}\cdot P_{i}\cdot x_{i}\right|_{p}\), the hardware implementation of the classical CRT leads to large areas and delays. Another approach to reverse conversion is using the mixed-radix conversion [7], according to which the number \((x_{1},x_{2},\ldots,x_{n})\) can be transformed into a positional number system based on the formula:  
\[X=d_{1}+d_{2}\cdot p_{1}+d_{3}\cdot p_{1}\cdot p_{2}+\ldots+d_{n}\cdot p_{1} \cdot\ldots\cdot p_{n-1}, \tag{2}\]  
However, the coefficients \(k_{i}\) can rarely be represented as a finite fraction, so the question of the accuracy of rounding the coefficients is acute. In the article [7], there is an approximate modification of the method based on the CRT scaling by \(2^{N}\), where \(N=\left\lceil\log_{2}P\mu\right\rceil-1\), \(\mu=\sum_{i=1}^{n}(p_{i}-1)\). Then the Formulas (3) and (4) can be written as:  
\[X^{\prime}=\left|\sum_{i=1}^{n}k_{i}^{\prime}\cdot x_{i}\right|_{2^{N}}, \tag{5}\]  
where the coefficients \(k_{i}^{\prime}\) are rounded upwards  
\[k_{i}^{\prime}=\left\lceil\frac{\left|P_{i}^{-1}\right|_{p_{i}}}{p_{i}}\cdot 2 ^{N}\right\rceil,i=1,2,\ldots.n.\]  
Then, the recovered number can be obtained by the formula with rounding down:  
\[X=\left\lfloor\frac{X^{\prime}\cdot P}{2^{N}}\right\rfloor. \tag{6}\]  
For RNS {7,8,9} the required accuracy is \(N=13\), so the calculation is performed modulo \(2^{N}=8192\).
# Intelligent Dynamic Power Control with Cell Range Expansion for Small-Cells in 5G HetNets
## 2 System Model
### SINR Model, System Capacity and Outage Probability  
In order to analyze the system performance of 5G HetNets, we first calculate the signal-to-interference plus noise ratio (SINR) of MUEs and SUEs. Let \(\gamma_{mn}^{k}\) and \(\gamma_{mst}^{k}\) denote the SINR of MUE \(n\) (\(\forall n\in\mathbf{N}\)) served by MBS \(m\) (\(\forall m\in\mathbf{M}\)) and the SINR of SUE \(t\) served by SBS \(s\) (\(  
where \(h_{mn}=P^{MBS}L_{mn}\) and \(h_{mst}=P^{SBS}L_{mst}\) are the RSSI measurements between MBS \(m\) and MUE \(n\) and between SBS \(s\) and SUE \(t\) in the coverage of MBS \(m\), respectively. \(P^{MBS}\) and \(P^{SBS}\) are the transmission power for each subchannel of MBSs and SBSs, respectively. \(L_{mn}\) and \(L_{mst}\) denote the path losses between MBS \(m\) and MUE \(n\) and between SBS \(s\) and SUE \(t\) in the coverage of MBS \(m\) in dB, respectively. Further, \(\psi_{mn}^{k}\) and \(\psi_{ist}^{k}\) are indicator variables for the channel assignment, e.g., \(\psi_{mn}^{k}=1\) and \(\psi_{ist}^{k}=1\) if MBS \(m\) assigns subchannel \(k\) to MUE \(n\) and if SBS \(s\) of MBS \(i\) assigns subchannel \(k\) to SUE \(t\), respectively, and otherwise is 0. The SINR of CUEs served by SBSs is also calculated by (2). \(A(\theta)\) is the azimuth antenna pattern from MBSs to MUEs (or SUEs) in dB while \(\sigma^{2}\) is the white noise power. Then, \(A(\theta)\) can be expressed as  
\[A(\theta){=}A_{mg}-\min\bigg{[}12\bigg{(}\frac{\theta}{\theta_{3 \text{dB}}}\bigg{)}^{2},A_{ma}\bigg{]},-180^{\circ}\leq\theta\leq 180^{ \circ}, \tag{3}\]  
where \(A_{mg}\) and \(A_{ma}\) are the maximum antenna gain and maximum attenuation in dB, respectively, while \(\theta\) and \(\theta_{3 dB}=70^{\circ}\) are the azimuth antenna pattern of MBSs and 3 dB beamwidth, respectively [40].  
Through (1) and (2), we use [PERSON]'s formula to calculate the capacities of MUE \(n\) served by MBS \(m\), \(C_{mn}\), and SUE \(t\) served by SBS \(s\) of MBS \(m\), \(C_{mst}\). Then, \(C_{mn}\) and \(C_{mst}\) can be expressed as  
\[C_{mn}{=}\sum_{\forall\in\mathbf{K}}W\psi_{mn}^{k}\cdot\log_{2}(1+\gamma_{mn} ^{k}),\ \forall m\in\mathbf{M},\forall n\in\mathbf{N}, \tag{4}\]  
\[C_{mst}{=}\sum_{\forall\in\mathbf{K}}W\psi_{mst}^{k}\cdot\log_{2}(1+\gamma_{ mst}^{k}),\ \forall m\in\mathbf{M},\forall s\in\mathbf{S},\forall t\in\mathbf{T}, \tag{5}\]  
where \(W\) is the bandwidth of each subchannel.  
In addition, we calculate the outage probability of MUEs and SUEs (including CUEs), \(P_{out}^{MUE}\) and \(P_{out}^{SUE}\). Then, \(P_{out}^{MUE}\) and \(P_{out}^{SUE}\) can be expressed as  
\[p_{out}^{MUE}=Pr(\gamma_{mn}^{k}<\Gamma_{th}),\ \forall m\in\mathbf{M}, \forall n\in\mathbf{N}, \tag{6}\]  
\[p_{out}^{SUE}=Pr(\gamma_{mst}^{k}<\Gamma_{th}),\ \forall m\in\mathbf{M}, \forall s\in\mathbf{S},\forall t\in\mathbf{T}, \tag{7}\]  
where \(\Gamma_{th}\) is a given target SINR threshold of MUEs and SUEs.
## 1 Introduction
### Motivation--Why BPL?  
Common communication technologies, such as cellular networks, sometimes deal with the limitation of increased attenuation due to the landscape profile. In built-up areas, problems related to communication, especially high attenuation levels under the ground level, and interference issues are experienced. Another drawback of cellular network solutions is the absolute dependence on services of the third party, therefore, ensuring a desired level of cybersecurity becomes more complicated.  
Particularly, electric utilities consider the future of the Smart Grid concept in using power lines as communication media for reading out values of key measuring points in the power grid. Deployment of BPL technology would be beneficial due to the independence on telecommunication service providers and, thus, lower operational costs and own management over service operations when needed [13]. As a primary communication technology, BPL is also being considered due to its sufficient throughput for enabling asymmetric cryptography and usage of certificates.
# Reliability-Aware Cooperative Node Sleeping and Clustering in Duty-Cycled Sensors Networks
## 5 RCR Algorithm Design  
The algorithm flowchart of RCR-selection is shown in Figure 4(a), and the OPNET protocol for RCR-selection and RCR-delivery is shown in Figure 4(b). Specifically, the pseudocode of the RCR scheme is presented in Algorithm 1.  
```
begin notation \(n_{m}\) denotes the total number of neighbor nodes of m; \(V_{m}\) denotes the array of neighbor nodes of m; \(t\) is the sink node; \(R_{RCR}\) denotes the cooperative nodes search region of m; \(Q\) is array of priority for every neighbor nodes of m; \(C_{m}\) is array of cooperative nodes of m; initialization for\(i=0\) to \(K-1\)do \(Q_{Max}\gets 0\); \(idx\gets-1\); for\(j=1\) to \(n_{m}\)do if\(V_{m}[j]\) is \(t\)then  break; endif if\((V_{m}[j]\) in \(C_{m})\) OR \((V_{m}[j]\) not in \(R_{RCR})\)then  continue; endif if\(Q[j]>Q_{Max}\)then \(Q_{Max}\leftarrow Q[j]\); \(idx\gets j\); endif end if \(C_{i}^{*}\gets V_{m}[idx]\); if\(\overline{C_{m}}\) is \(t\)then  break; endif endfor Return C_{m};
```  
**Algorithm 1**\(C_{m}\) choosing algorithm.  
Figure 5: The algorithm flowchart and OPNET protocol for the RCR scheme.  
Figure 5(a) shows the data structure of the node to store the PROB message when choose CNs. The field named \"CN_Table\" is an array where the PROB information of which node received is stored, and each CN includes node ID, coordinates \((x,y)\) and node residual energy \(e\_res\).  
Figure 5(b) shows the time period of sending PROB and DATA. In order to prevent a few CNs from excessively taking the role of data forwarding, the CN list needs to be re-selected periodically to achieve load balancing. The re-selection of the CN list is activated by a source node to initiate a new PROB.  
At each hop, the master CN will choose \(k\) CNs for its next hop and put them into the PROB message and then broadcasts the message. When a node receives the PROB message, it will judge whether it is in \(CN\_Table\) or not. If the node is selected as a CN, its \(CN\_Flag\) will be marked as one, and \(CN\_HopCount\) is recorded. Especially, if its \(CN\_Index\) is equal to zero, the node works as the role of master CN. This procedure repeats at every hop until the sink node receives the PROB message.
## 4 System Model
### Scheduling Model  
The primary purpose of the scheduling model is to receive the tasks and map them to an appropriate server for the execution by considering the load factor and expected completion time while minimizing the makespan. The scheduling algorithm gets executed after a specific time interval and maps all the tasks that arrived. Some of the salient features of the scheduling model are:  
* It considers the number of VMs and containers running in a server and compares it with the maximum number of containers possible in that server.
* It considers the load factor of all the servers and selects an appropriate server in which the container will be deployed in a VM.
* It also takes care of the maximum time taken by individual servers to complete a task and tries to ensure that the task will be completed before its deadline.  
Task generators are responsible for generating different types of tasks. Since the tasks are of different types, their resource requirement is also different. Once the task is generated, they are forwarded to the task manager, responsible for screening and maintenance of all the tasks. The task manager maintains a proper order among tasks and sends them for scheduling. The task scheduler is responsible for selecting an appropriate VM in a server to execute the task. The container mapper creates a container image on a VM on which the task will be executed and started. The number of containers that can be deployed in a VM depends on the number of processor cores.
## 2 Literature Review
### AI for Complaint Management and Pattern Recognition
Top2 Vec automatically identified latent topics (e.g., transportation delays and waste management issues) with minimal preprocessing, outperforming traditional LDA-based methods in coherence scores [27]. However, Top2 Vec's reliance on dense embeddings can struggle with sparse, domain-specific complaint datasets, such as informatics-related issues, where contextual nuances are critical. Post-2023, GPT-powered ticket triage systems have emerged as a frontier in public-sector NLP. A 2024 study by [PERSON] et al. evaluated a GPT-4-based system for triaging citizen complaints in a smart city context. The model, fine-tuned on municipal feedback data, achieved 90% accuracy in categorizing complaints and generating actionable summaries, surpassing traditional classifiers like Random Forest by 10-15% in precision for multi-label tasks [28]. GPT's strength lies in its contextual understanding and ability to handle multilingual and unstructured inputs, making it suitable for diverse public-sector settings. However, its computational cost and need for extensive fine-tuning pose challenges for resource-constrained agencies, unlike the more lightweight BERT-based embeddings used in our framework. Comparative applications outside Europe also provide useful insights. In the United States, several municipal governments have deployed AI tools like NYC's 311 analytics platform to extract insights from unstructured citizen complaints, which has become a benchmark in the field [29]. In South Korea, the Seoul Metropolitan Government has experimented with AI-enhanced e-petition systems to streamline citizen input and prioritize responses [30]. These international cases underscore the growing global convergence in AI-powered service delivery while also reflecting local institutional adaptations. AI plays a crucial role in transforming unstructured citizen feedback into actionable insights for government agencies, particularly in e-government platforms where large volumes of complaints or feedback need to be analyzed [31]. Tools like topic modeling, clustering, and semantic analysis help identify common themes and pain points, enabling agencies to prioritize interventions and allocate resources effectively. For example, text mining of environmental complaints can reveal which issues are most frequently troubling residents, helping agencies target their efforts [32]. Similarly, a framework using network analysis on complaint data can uncover connections, such as noise complaints often coinciding with traffic issues, offering a more holistic view of citizen concerns [32]. However, challenges like ensuring accuracy, fairness, and privacy in AI-driven systems, as well as integrating these tools into existing workflows, must be addressed. Successful integration can lead to faster response times, better issue identification, and cost savings, such as fixing recurring software bugs, making AI a valuable tool for enhancing responsive governance.