esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Polar Subdivision
### Subdivision in radial layers  
The subdivision in radial direction is the key procedure to map a polar structure to a refined version. The refinement rules in radial layers for polar subdivision (see [17]) can be described as:  
\[\begin{split}& e_{i,j}^{\prime}=\frac{1}{2}(v_{i-1,j}+v_{i,j}) \quad where\ i>1,\quad e_{j}^{\prime}=(1-\beta)v+\sum_{k=0}^{n-1}v_{k}\gamma_ {k-j},\\ & v_{i,j}^{\prime}=\frac{1}{8}v_{i-1,j}+\frac{6}{8}v_{i,j}+\frac{ 1}{8}v_{i+1,j},\quad v^{\prime}=(1-\alpha)v+\frac{\alpha}{n}\sum_{k=0}^{n-1}v_ {k}.\end{split} \tag{1}\]  
where \(\alpha=\beta-\frac{1}{4}\), \(\beta=\frac{1}{2}\), \(\gamma_{k}=\frac{1}{n}(\beta-\frac{1}{2}+\frac{5}{8}c_{n}^{k}+(c_{n}^{k})^{2}+ \frac{1}{2}(c_{n}^{k})^{3})\), \(c_{n}^{k}=\cos(\frac{2\mathrm{i}\pi}{n})\), n is the valence of polar vertex \(v\), the meaning of subscribe i and j are the same as they are shown in Figure 2.  
We need to perform some transform operations on the refinement rules, so that we can reverse them to construct lazy wavelets analysis. The transformed refinement rules in radial layers:  
\[\begin{split}& e_{i,j}^{\prime}=e_{i,j}+\frac{1}{2}(v_{i-1,j}+v_{i,j}) \quad\forall j,\ \forall i>1,\quad e_{j}^{\prime}=e_{j}+(1-\beta)v+\sum_{k=0}^{n-1}v_{k} \gamma_{k-j},\\ & v_{i,j}^{\prime}=\frac{1}{2}v_{i,j}+\frac{1}{4}(e_{i-1,j}^{ \prime}+e_{i,j}^{\prime})\quad\forall j,\ \forall i>1,\quad v_{j}^{\prime}=\frac{1}{8}v+\frac{5}{8}v_{j}+\frac{1}{4}e_{2,j}^{\prime},\\ & v^{\prime}=[(1-\alpha)-\frac{\alpha}{C}(1-\beta)]v+\frac{ \alpha}{nC}\sum_{k=0}^{n-1}e_{k}^{\prime}.\end{split} \tag{2}\]  
where \(C=\sum\limits_{k=0}^{n-1}\gamma_{k}=\beta\), other symbols have the same meanings as expression (1). We can verify that the results of expressions (2) are equal to the original rules, if \(e_{i,j}\) and \(e_{j}\) in expression (2) are initialized as zero. By inverting the subdivision process in radial layers, we can get the \(e_{i,j}\), \(e_{j}\), \(v_{i,j}\), \(v_{j}\) and \(v\) in reverse order:  
\[\begin{split}& v=\frac{1}{U}(v^{\prime}-\frac{\alpha}{n\beta}\sum_{k= 0}^{n-1}e_{k}^{\prime}),\quad v_{j}=\frac{8}{5}\times(v_{j}^{\prime}-\frac{1}{8} v-\frac{1}{4}e_{2,j}^{\prime}),\\ & v_{i,j}=2\times(v_{i,j}^{\prime}-\frac{1}{4}(e_{i-1,j}^{\prime }+e_{i,j}^{\prime}))\quad\forall j,\forall i>1,\\ & e_{j}=e_{j}^{\prime}-(1-\beta)v-\sum_{k=0}^{n-1}v_{k}\gamma_{ k-j},\quad e_{i,j}=e_{i,j}^{\prime}-\frac{1}{2}(v_{i-1,j}+v_{i,j})\quad\forall j,\ \forall\ i>1.\end{split} \tag{3}\]
# Goal-Conditioned Reinforcement Learning within a Human-Robot Disassembly Environment
## 3 Method
### Markov Decision Process
#### 3.1.3 Reward Function  
The agent must perform the disassembly of the peg considering the location of the co-worker. Hence, two objectives can be distinguished; one related to the disassembly task itself and the second one that minimizes the robot's collision risk with the human operator. Therefore, the complete reward function can be defined as follows:  
\[r=r_{disassembly}+r_{risk}, \tag{4}\]  
\(r_{disassembly}\) is a continuous reward provided at each timestep \(t\) and is defined by the displacement of the peg with respect to the base, \(d\), and penalized by the number of timesteps required to perform the task.  
\[r_{disassembly}=5\cdot e^{log(d)+4}-w_{1}\cdot\frac{timestep}{timestep_{max}}, \tag{5}\]  
As shown in Figure 2, the resulting function increases as the extraction is performed in fewer timesteps while it decreases as the timesteps elapse and the robot has not displaced the peg.  
Figure 2: Representation of the disassembly reward function and how it depends on the displacement of the peg with respect to the base and the number of time steps spent for it.  
\(r_{risk}\) only takes a value if the robot approaches the obstacle. For this purpose, a comparison is made between the previous and current timestep Euclidean distance. This function penalizes the agent according to whether its displacement, \(d\), is greater and, therefore, its Euclidean distance to the obstacle is smaller.  
\[r_{risk}=-w_{2}\cdot d, \tag{6}\]  
In addition, if certain conditions are met, a series of sparse rewards are provided to the agent and the episode is ended.  
\[r_{disassembly}=\begin{cases}+200&\text{if }d>dis_{th}\\ -100&\text{if }F>F_{th}\\ -100&\text{if }rot>rot_{th}\\ -100&\text{if }timestep>timestep_{max}\end{cases}, \tag{7}\]  
\[r_{risk}=\left\{-100\quad\text{if }\text{d}(ee_{x,y},obs_{x,y})>safety_{th}\,,\right. \tag{8}\]  
where \(dis_{th}\) is the disassembly threshold, \(F\) is the resultant force applied on the base, \(F_{th}\) is the maximum force threshold that can be exerted, \(rot\) and \(rot_{th}\) are the rotation of the peg in its longitudinal axis at each timestep and the maximum rotation threshold, respectively, \(\text{d}(ee_{x,y},obs_{x,y})\) is the Euclidean distance between the end-effector and the obstacle, and \(safety_{th}\) is a set safety distance.
# Route Optimization for Open Vehicle Routing Problem (OVRP): A Mathematical and Solution Approach
###### Abstract  
In the everchanging landscape of human mobility and commerce, efficient route planning has become paramount. This paper addresses the open vehicle routing problem (OVRP), a major logistical challenge in route optimization for a fleet of vehicles serving geographically dispersed customers. Using a heuristic approach, we explore the complexities of OVRP, comparing the results with advanced optimization methods. This study not only highlights the effectiveness of mathematical modeling, but also explores the practicality of heuristic algorithms such as Greedy, Nearest Neighbor and 2-opt to provide quality solutions. The findings highlight the nuanced interplay between solution quality and computational efficiency, providing valuable insights for addressing real-world logistics challenges. Recommendations delve into optimization opportunities and the integration of emerging technologies, ensuring adaptable solutions to the intricate the problem of open vehicle routing.  
mathematical model; heterogeneous fleet; single depot; open vehicle routing problem +
Footnote †: journal: Applied sciences
# LSSDNF: A Lightweight Secure Software Defined Network Framework for Future Internet in 5G-6G
###### Abstract  
As information technology advances quickly, so does the 5G-6G network management system, which is moving toward greater integration, decentralization, diversity, and intelligence. As flexibility is a crucial criterion for 5G-6G network architecture, we use the Software Defined Network (SDN) paradigm to make the programmability more flexible. Due to their ability to replace the current TCP/IP architecture with one that separates the control plane and data plane, software-defined networks have gained much popularity. However, they are susceptible to routing attacks. Therefore, this work proposes Lightweight Security Framework that combines blockchain technology with Software-Defined Networking (LSSDNF) to address this problem. The proposed framework adds the routing data that the controller withheld to the multichain blockchain. Here, a mininet network simulator is used to model the proposed framework. The data transfer rate or network throughput, bandwidth variation, and jitter have all been used to assess the performance of single-controller-SDN networks and multi-controller-SDN networks. The results demonstrate that the proposed framework performs better than the conventional single-controller-SDN architecture in terms of throughput, bandwidth fluctuation, and jitter.  
SDN; blockchain; 6G; 5G; routing; performance modeling; internet of things Article
###### Contents
Based on the \"stress weakening coefficient\" indicator, they conducted a back analysis on the deformation and crack distribution characteristics of the surrounding rock in the roadway, thereby revealing the deformation and failure mechanisms of the roadway. [PERSON] et al. [44] presents a comprehensive review on numerical simulation methods for large deformations in geotechnical engineering. Alternatively, [PERSON] et al. [45] investigated the cracking behavior of straight-wall tunnel linings under varying burial depths using the PFC2D numerical simulation model. With the increasing maturity of Optical Frequency Domain Reflectometry (OFDR) technology, its application in underground geotechnical engineering measurements has expanded significantly [46; 47; 48]. A critical research question is whether OFDR can be integrated into inclined shaft lining construction to provide more comprehensive and accurate stress-strain measurements. Based on similarity theory, this study calculated the dimensions of the shaft lining model and surrounding rock thickness to construct scaled physical models. To obtain model shaft linings with deformation characteristics consistent with prototype conditions, three experimental groups were designed, Group No. 1: direct contact between the shaft lining and surrounding rock; Group No. 2: a polytetrafluoroethylene (PTFE) plate inserted at the shaft lining-surrounding rock interface; Group No. 3: composite filling with both a PTFE plate and fine sand at the interface. Four key monitoring points were established along the inner circumference of the shaft lining section, with one strain gauge installed at each point. Distributed optical fibers were deployed along both the inner and outer circumferences of the monitoring section. Hydraulic lacks apply bidirectional loading in the X and Y directions, simulating stress-strain conditions under asymmetric ground pressure. The experimental study recorded strain-time histories from both conventional strain gauges and distributed optical fiber sensors throughout the loading process. A comprehensive verification was performed by comparing synchronous strain measurements obtained from colocated fiber optic and strain gauge sensors. This comparative analysis assessed the potential of OFDR technology to substitute for conventional strain gauges in the application of inclined shaft lining deformation monitoring. Through systematic analysis of model shaft lining strain monitoring data and prototype deformation characteristics, the investigation identified the most appropriate contact condition from the three predefined experimental configurations. The experimental results informed subsequent numerical simulations, with comparison between numerical simulations and physical measurements serving to further validate the measurement capabilities of OFDR technology for shaft lining strain monitoring.
## 3 Results  
After the experimentation phase, the outcomes were measured with precision and accuracy. The investigation evaluated parameters such as the surface roughness, the flank wear of the cutting tool, and the dimensions of burrs in down milling. A summary of these results is presented in Table 5. Burr width was selected as the primary measurement for quantifying burrs due to its ease of microscopic measurement, as depicted in Figure 4. Notably, the profile measurement inherently encompassed the burr's height. Therefore, to determine the burr's length, a calculation method based on the Pythagorean theorem was applied. It is worth noting that the literature has investigated three types of burrs: entry, exit, and top burrs [36]. This study focused solely on the top burr, using the worst-case scenario for all measured burr parameters. The DSX software employs line characteristics to identify the top burr width, with the blue segment on the red line indicating the measured burr width, as illustrated in Figure 4. Surface roughness was evaluated at the slot's center, with three values recorded for each slot and their average utilized for analysis. The tool's condition was assessed both before and after experimentation to ensure precise analysis of the tool flank wear. All of the results are presented in Table 5.
## 1 Introduction  
Identifying frequent items in a massive data stream has many applications in data mining and network monitoring, and the problem has been studied extensively [2; 3; 4; 5]. Recent interest has been shifted from the statistics of the whole data stream to that of a sliding window of recent data [6; 7; 8; 9]. In most applications, the amount of data in a window is gigantic when compared with the amount of memory available in the processing units. It is impossible to store all the data and then find the exact frequent items. Existing research has focused on designing space-efficient data structures to support finding the _approximate_ frequent items. The key concern is how to minimize the space so as to achieve a required level of accuracy.
# Whole-Genome Alignment: Methods, Challenges, and Future Directions
## 2 Classification of Whole-Genome Alignment Algorithms
### Graph-Based Homology Mapping Methods
#### 2.3.2. Mugsy  
Mugsy is a multiple-whole-genome alignment tool, primarily designed to align multiple genomes or genomic regions simultaneously, enabling researchers to compare and analyze evolutionary relationships, genomic rearrangements, and structural variations across different species or individuals. It is a graph-based algorithm that is mainly used for whole bacterial genome alignment and multiple human-chromosome alignment [46]. It first performs pairwise local alignment between sequences. Then, it constructs and uses an alignment graph to identify LCBs.  
The main advantage of this algorithm is that it identifies genomic regions that are homologous, collinear, free of rearrangements, and suitable for multiple alignment. In addition, Mugsy can align 57 _E. coli_ genomes (299 Mb) in \(<\)1 day on a single CPU. It can also perform an alignment of four assembled human chromosomes, completing the LCB identification and multiple alignment in \(<\)1 h. Mugsy's complexity is \(O(n^{3})\), where \(nm\) is the length of the input sequences.
## 4 Results and Analysis
### Flow Pressure
The extreme pressure \(P_{e}\) (or \(P_{\rm min}\) and \(P_{\rm max}\)) is associated with the flow condition (\(q\)), step geometry (\(h\), \(l\), and \(\theta\)), flow property (\(\rho\)), and gravity (\(g\)). Therefore, these parameters are selected to describe \(P_{e}\)  
\[P_{e}=f_{1}(q,h,l,\theta,\rho,g) \tag{13}\]  
As \(d_{c}=(q^{2}/g)^{1/3}\) and \(\theta\) is dependent on \(H\), Equation (13) is expressed in a dimensionless form  
\[\frac{P_{e}}{\rho gd_{c}}=f_{2}\left(\frac{d_{c}}{h},\frac{h}{l},\frac{H}{h}\right) \tag{14}\]  
where \(h/l\) represents the channel slope and is a constant in a given spillway. Thus, \(P_{\theta}/(\rho gd_{c})\) is a function of \(d_{c}/h\) and \(H/h\). Figure 7a-d present correlation of \(P_{\rm min}\) and \(P_{\rm max}\) with \(d_{c}/h\) under different \(\theta\). On the horizontal step surface (Figure 7a,c), both \(P_{\rm min}\) and \(P_{\rm max}\) become larger as the flow rate increases, which is consistent with the findings by [PERSON] et al. [11]. The pressures at \(\theta=-20^{\circ}\) correspond to the lowest values. If implemented in a prototype, a large downward angle is more prone to cavitation, as its \(P_{\rm min}\) is more likely to drop below the vapour pressure. The layout at \(\theta=20^{\circ}\) exhibits the highest pressures. Ranging from \(\theta=-20^{\circ}\) to \(20^{\circ}\), the \(P_{\rm max}\) values differ on average by 49.4%. On the vertical step surface, the extreme pressures are compared in Figure 7b,d. For a fixed \(\theta\) value, both \(P_{\rm min}\) and \(P_{\rm max}\) become higher if \(d_{c}/h\) gets smaller, which is in agreement with the results by [PERSON] et al. [11]. Similar to the horizontal steps, to gradually change from a downward to upward angle contributes to an ascending \(P_{\rm min}\). However, an increase in \(\theta\) also leads to an increase in the maximum pressure load. It is even noted that \(P_{\rm max}\) at \(\theta=-20^{\circ}\) is negative, indicating that the entire vertical face is subjected to sub-pressure. Figure 6: Flow pressure distribution along step surfaces at step no. 43 (dc/h = 4.8): (a) horizontal and (b) vertical. Based on the dimensional analysis from Equation (14), a correlation is proposed to estimate the extreme pressures on the horizontal and vertical step face  
\[\frac{P_{e}}{pgd_{c}}=a\ln\left(\frac{d_{c}}{h}\right)+\left(\frac{H}{h}\right)^ {b} \tag{15}\]  
where \(a\) and \(b\) are coefficients. Their values are shown in Table 3. For \(P_{e}\) on the horizontal step surface (Figure 7e), Equation (15) presents a satisfactory estimation, with the coefficient of determination  
Figure 7: Extreme pressures acting on step surfaces at step no. 43: (**a**,**c**,**e**) horizontal face, and (**b**,**d**,**f**) vertical face. \(R^{2}>0.91\). The discrepancy of most data points is less than 10%. For \(P_{e}\) on the vertical step surface (Figure 7f), its prediction by Equation (15) shows a generally good agreement with the simulation (\(R^{2}>0.80\)).
# A Review of Advanced Air Lubrication Strategies for Resistance Reduction in the Naval Sector
## 4 Mathematical Models
### Models on ALDR  
In [51], the authors develop a computational model to understand and optimize ALDR on flat surfaces. This is particularly relevant for improving the aerodynamic efficiency of ships.  
The study integrates CFD with linear stability analysis. CFD is used to simulate the air-water interface, focusing on how the air layer behaves under various conditions. The linear stability analysis is crucial for identifying the frequencies at which flow becomes unstable. This combination allows for a more accurate prediction of flow instabilities, which is essential for the effective application of ALDR in marine vehicles.  
The key innovation in this study is the incorporation of a harmonic perturbation into the CFD model. By doing this, the authors can more accurately mimic the real-world conditions under which these air layers operate. This method showed significant improvements in modelling results when compared to experimental data.  
The air layer thickness and velocity profile are extracted from the CFD results and a curve fit is performed.  
The two-phase Orr-Sommerfeld equation is then solved to find the frequency of the most unstable mode (Figure 19).  
This frequency is used to perform another CFD simulation, this time with an air injection fluctuation with the frequency obtained from Orr-Sommerfeld (Figure 20).  
Figure 19: Stability spectrum of linear stability analysis for different air fluxes (Ref. [51] reproduced under a CC BY license).  
Figure 20: Air layer thickness for different air fluxes extracted from properly perturbed flow analysis (Ref. [51] reproduced under a CC BY license).  
The results from this study provide valuable insights into the physics of ALDR, revealing new aspects not previously observed in experimental tests. This has practical implications for the design of more efficient air lubrication systems on marine vessels, potentially leading to significant reductions in fuel consumption and emissions. The study demonstrates a considerable improvement in the theoretical understanding of ALDR phenomena, bridging the gap between experimental observations and numerical modelling.
# Performance of the Transport Layer Security Handshake Over 6 TiSCH
## 2 Background
### Applicability of TCP in the IoT
#### 2.2.2 Selective Acknowledgments  
Selective Acknowledgments (SACK) allow the receiving peer to precisely indicate in its acknowledgment packet which TCP segments were not received. A single SACK block adds 10 B to an acknowledgment packet, and it specifies the TCP sequence numbers of the missing data. This option is only useful when the sender has multiple unacknowledged segments in flight. Upon reception of a SACK, the sender creates a new TCP segment that contains only the data the receiver has requested.
# Wireless Sensor Networks for Big Data Systems
## 3 Technical Approaches to WSN-Based Big Data Systems  
### Big Data Through WSN  
Data generated by the sensors grow exponentially. Conventional information technologies for data processing, storage, and reporting (such as servers and relational databases) are too expensive to deal with these data. Moreover, they cannot cope with the processing needs that can be required for real-time processes. In addition, most of the events monitored at regular intervals are largely redundant or are minor variations leading to a large waste of data storage resources and communication energy at relay and sensor nodes. This implies that much of these data are of no interest, meaningless, and redundant. Thus, unlike the case of typical WSNs, it is essential to gather and transmit a large amount of data while minimizing data latency in WSN-based big data systems. Moreover, it is required to efficiently eliminate data redundancy and improve energy efficiency. The overlap between big data systems and WSNs lies in the use of in-network data processing techniques, as indicated in [19]. For the WSNs side, it would save their limited resources. At the same time, receiving a clean, non-redundant, and relevant data would reduce the excessive data volume at the side of the big data system. Thus, it would reduce overload by discovering values from these data rapidly.  
The research challenges for WSN-based big data systems are addressed in [20; 21]. [PERSON] et al. [20] classified big data challenges in wireless sensor networks into clustering, securing, processing, and energy saving. The proposed strategies of dealing with these challenges are based on the correlation between the data aggregation, clustering, and energy consumption challenges of big sensor data. [PERSON] et al. [21] described big data collection in a large-scale WSN, presented data transferring scheme in the context of large-scale WSN, and discussed the challenges of big data collection. An example architecture for gathering, storage, and analysis of data generated by WSN for monitoring air pollution levels in a city was proposed as a prototype in [22]. The prototype was developed with open source technologies such as Storm for stream processing, Hadoop for distributed computing with MapReduce, and a small WSN on the Arduino platform.