esa-sceva/satcom-chunk-collection huggingface dataset:
# Tunnel-Free Distributed Mobility Management (DMM) Support Protocol for Future Mobile Networks
###### Abstract  
The number of wireless services and devices have remarkably increased, especially since the introduction of smart phones. The population of mobile nodes (MNs) is now exceeding the traditional non-mobile nodes. Mobility is a key factor in mobile core networks as it is responsible for providing continuous communication when a MN is on the move. Currently, a centralized mobile core network architecture is implemented, which has certain limitations. Distributed mobility management (DMM) is often seen as a solution to the problems associated with centralized mobility management (CMM). Address and tunneling management are big challenges for current DMM-based mobility protocols. Keeping in mind the current advancement of mobile network architecture, this paper proposes a novel tunnel-free distributed mobility management support protocol intended for such an evolution. In addition, the performance of the existing DMM IPv6 mobility management protocols in the context of handover latency, handover blocking probability, and data packet loss is analyzed and compared to the proposed framework. The performance analyses show that the proposed tunnel-free method can reduce about 12% of handover latency, 71% of handover blocking probability, and 82% of data packet loss.  
Keywords:distributed mobility management; DMM; IP mobility; flat network architecture +
Footnote †: journal:  
_Article_
## 2 Materials and Methods
### Uniaxial Tensile Testing and Sample Preparation  
After optimizing the pellet extruder for both non-medical- and medical-grade PCL, square-shaped and diagonal-shaped meshes were printed and subjected to mechanical testing. In the uniaxial tensile testing machine, a preload of 0.1 N was applied to remove any slack from the mesh. A constant elongation rate of 10 mm/min was applied to load the mesh until failure. Figure 4 illustrates the dimensions of the specimens used for the uniaxial tests.  
The printed meshes were cut into 50 \(\times\) 10 mm strips, ensuring that the pore pattern was respected, with 10 mm allocated on each side for specimen fixation. This preparation followed the procedure outlined in previous studies [11; 18]. The sample dimensions were selected to maintain a constant length-to-width aspect ratio of 4, minimizing nonlinear clamping effects on the uniaxial mechanical properties.  
In this study, the Secant modulus (\(E_{s}\)), given in Equation (3), was analyzed to gain insight into the mechanical behavior of the tested meshes within a specific region of the stress-strain curve-comfort zone [34].  
\[E_{s}=\frac{\Delta_{stress}}{\Delta_{strain}} \tag{3}\]  
The Secant modulus is conventionally used to estimate stiffness over a range of deformation, which helps in assessing how well the mesh materials behave under physiological conditions [34]. This approach allows for the approximation of the mesh's performance in replicating the mechanical properties of vaginal tissue.  
The medical-grade meshes produced were compared with a commercially available synthetic mesh, Restorelle(r) (Figure 5). This mesh is constructed from polypropylene knitted monofilaments with a density of 19 g/m\({}^{2}\). It features pores approximately 2 mm in diameter and is composed of three filaments, each with a diameter of 80 \(\upmu\)m (see Figure 5) [35; 36; 37].  
Continuous variables were described using the mean and standard deviation (SD). The maximum stress values between the two groups (non-medical and medical grade) were compared using the Mann-Whitney U test. A \(p\)-value of less than 0.05 was considered statistically significant. All analyses were conducted with IBM SPSS  
Figure 4: The standardized dimensions for the meshes used in the uniaxial tensile tests are as follows: length of 50 mm (A), width of 10 mm (B), and fixation area (C).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Diameters** & **Temp (\({}^{\circ}\)C)** & **Trans Speed (mm/min)** & **Volt (kV)** & **Dist (mm)** \\ \hline
240 \(\upmu\)m & 200 & 1000 & 7 & 2 \\ \hline
160 \(\upmu\)m & 200 & 1000 & 6.25 & 2 \\ \hline
80 \(\upmu\)m & 200 & 1000 & 5.25 & 2 \\ \hline \end{tabular} Note: Dist: distance (mm); Temp: temperature (\({}^{\circ}\)C); Trans Speed: translational speed (mm/min); Volt: voltage (Kv).  
\end{table}
Table 5: Calibrated printing parameters for medical-grade PCL meshes.
# Improving Real-Time Position Estimation Using Correlated Noise Models +
## 2 Data Analysis
### Noise Correlations  
The datasets were analysed in detail in Reference [8]. Here, we briefly present the findings relevant for this study. When positioned in a new location, the GPS device gives readings offset from the correct location over a distance scale of approximately 100 m. The readings tend to the correct location within a time scale of seconds to minutes. In this paper, all further analysis and inference is performed on  
Figure 1: Map of GPS receiver locations for the datasets considered. A single GPS receiver was located at different times at the labelled locations: Dunedin (New Zealand) and North Stradbroke Island, Port Douglas and Thursday Island (Australia). The map demonstrates the spread of the four testing sites, indicating that the observed correlation patterns cannot be attributed solely to an unfortunate “bad” geographic location. The partial autocorrelation functions, \(\hat{\rho}_{p}(h)\), for latitude, longitude and altitude are shown on the left for the four geographical locations as a function of the lag, \(h\), measured in seconds.  
datasets with the first 300 s truncated in order to ignore this initial transient behaviour. Subsequently, the readings take continual excursions with typical length scales of metres around what we assume to be the \"true\" location (see Figures 2 and 3). These excursions have typical time scales of minutes to hours, causing the data to be autocorrelated [8]. Note that the GPS data are recorded in degrees to eight significant figures; these data are converted to meters, which, at the locations analysed in this paper, leads to an apparent quantisation of approximately 0.1 m. This quantised length scale is larger than the typical length scales for the processes, and thus, the data apparently jump between these quantised locations in tens to hundreds of seconds. None of the further treatment in this paper considers this apparent quantisation, which may be an avenue for further work.  
Figure 3: Residuals of latitude, longitude and altitude time-series at Port Douglas, Australia. The grey dashed lines guide the eye and highlight the mean-reverting nature of the time-series.  
Figure 2: (**a-c**) First 600 s of GPS time-series residuals taken at Port Douglas, illustrating the rapid initial convergence towards the “true” value; (**d–f**) corresponding later GPS time-series residuals illustrating the time-correlated deviations from the “true” value (zero). The apparent position quantization due to rounding is also visible.
# Two Novel Multidimensional Data Analysis Approaches Using InSAR Products for Landslide Prone Areas
## 3 Methodology
### Obtaining InSAR Outputs (Deformation Products)  
While the InSAR method can only measure surface deformations in the line-of-sight (LOS) direction, the ascending and descending orbit geometries allow for the decomposition of LOS time series into vertical and east-west (E-W) components. However, due to the near-polar orbit of satellites like Sentinel-1, the north-south (N-S) component of displacement is nearly parallel to the satellite's azimuth direction, and contributes minimally to the LOS measurements. Therefore, the N-S deformation component cannot be reliably resolved with conventional InSAR geometry. In this study, the decomposition was carried out using the method proposed in [39], which enables the estimation of vertical and E-W deformation time series by combining coherent data from both ascending and descending tracks. Only pixels with sufficient coherence in both acquisition geometries were selected for 3D decomposition to ensure reliability [40; 41].  
Decomposition of the LOS Velocity Maps  
Figure 5 shows the spatial distribution of the vertical (U-D) and east-west (E-W) deformation components calculated using the results derived from the cumulative LOS velocity maps. Figure 5a represents the vertical deformation component, with values ranging from \(-\)19.4011 mm/year to +23.1739 mm/year, while Figure 5b displays the east-west ground motion, with velocities varying between \(-\)28.5156 mm/year and +29.1044 mm/year. The color scale in both maps enables the distinction between uplift/subsidence and east-ward/westward movements. These decomposed velocity maps offer clearer insights into the directional characteristics of the surface deformation, and are particularly valuable for identifying the orientation of mass movements. The data are obtained through a joint analysis of the ascending and descending Sentinel-1 orbits, contributing to a more comprehensive three-dimensional understanding of ground displacement.  
Figure 4: Cumulative LOS velocity maps obtained from LiCSBAS processing (**a**) the deformation pattern based on the descending orbit data, and (**b**) the deformation pattern based on the ascending orbit data.  
The results are visualized in the form of deformation rate maps and time series graphs. These data can be exported to GeoTIFF, KMZ, or text format and analyzed via a user-friendly interface [38]. The primary processing steps of the LiCSAR and LiCSBAS tools are illustrated in Figure 3.  
The velocity and component maps obtained from the cumulative velocity and LOS decomposition analyses were combined with the vector-based landslide inventory data [17] using ArcGIS 10.0 software [42]. These datasets were exported to the GeoTIFF format and prepared as input for subsequent analysis using the MIRA and InCROSS approaches.
## 3 Experimental Results Analysis
### Effect of Loading Rate and Inclination Angle on Shear Stress Component-Shear Displacement Curve  
According to the method described in Section 2.2, the relationship between the shear stress component and shear displacement of coal samples under different loading rates and inclination angles are obtained, as shown in Figure 7. When the inclination angle is \(0^{\circ}\), the vertical load applied by the testing machine is parallel to the axial direction of coal samples, and the shear stress component of coal samples is 0. According to the analysis in Figure 7, it is obtained that with the increase of inclination angle, the shear displacement corresponding to the peak shear stress component of the sample increases gradually, indicating that inclination angle has a significant influence on the shear performance. Meanwhile, at the same loading rate, with the increase of the inclination angle, the peak shear stress also increases gradually, indicating that the shear stress component of the sample is closely related to its inclination angle. At the same time, in general, the shear stress component-shear displacement curve of coal samples has four stages at any loading rate, namely the initial compression stage, the near-linear elastic stage, the nonlinear crack growth stage and the post-peak drop stage. The characteristics of this curve are consistent with the axial stress-strain relation curve. Moreover, at any loading rate, with the increase of inclination angle, the compaction stage increases, indicating that the shear stress component is not conducive to the closure of the original crack in coal samples. Lastly, at any inclination angle, with the increase of the loading rate, the peak shear stress and curve characteristics are changed, indicating that the loading rate has a significant impact on the shear properties of coal samples.  
Figure 7: Shear stress component–shear displacement curve under different loading rates and inclination angles: (**a**) 0.05 mm/min; (**b**) 0.5 mm/min; (**c**) 1.0 mm/min; (**d**) 2.0 mm/min; (**e**) 3.5 mm/min; (**f**) 5.0 mm/min; (**g**) 7.5 mm/min; (**h**) 10.0 mm/min.
## 1 Introduction  
The growing need for intelligent machines, the outreach for more efficient use of the machines in industry, and the development of Industry 4.0 and 5.0 ideologies have pushed forward the field of machine fault diagnosis. Research in the field is receiving significant attention in academia and industry due to the importance of identifying underlying causes of machine faults. The overall objective of different machine fault diagnosis methods is to develop an effective diagnosis procedure. Recent methodological advances permit compressive machine fault diagnosis, providing detailed information, essential for the prevention of future machine failures.  
Some of the most promising approaches for the continuous advancement of fault detection and diagnosis technologies are advanced digital signal processing, vibration-based condition monitoring, modal and operational mode analysis, neural network analysis, and machine learning. Moreover, there is often a combination and cross-usage of the methods. With the development of different IT solutions, the number and usability of methods is rapidly growing. Even some of the older and known methods, proposed years ago but discarded due to problems with automation or the need for additional computational resources, have found new life and promising results due to advances in measurement technologies and computational power. The opportunities opened by Internet of things (IoT) and cloud computing services are breaking new ground and shifting paradigms in the field.  
Artificial intelligence (AI) has become one of the most transformative technological revolutions since, e.g., the invention of the steam or electric engines. Robustness, precision automated (online) learning, and the capacity to handle complex data are some of the AI attributes that hold significant potential for machine fault diagnosis. In hand with IoT and cloud computing, the emerging AI-based diagnostic methods are proving themselves to be powerful tools for the future.  
The main objective of this Special Issue (SI) was to gather state-of-the-art research contributing recent advances in machine fault diagnosis and, hopefully, to outline future research directions in the field.
# A Novel Global Key-Value Storage System Based on Kinetic Drives +
## 5 Design and Architecture
### Indexing Scheme for Original Data  
Several _key_ indexing schemes for a cluster of Kinetic Drives in one location were discussed in [16]. This paper, in contrast, focuses on a global KVS system with multiple regions. In this paper, we generally follow one approach in [16] to assign key-value pairs of the _original data_ to drives. Generally speaking, it maps the key-value pairs to drives based on _key_ ranges. Essentially, a _key_ consists of a series of bits 0 and 1. Furthermore, we apply the same idea to map key-value pairs to different regions. The impacts of other indexing schemes in [16] are beyond this paper's scope and will be considered as our future work.  
In this paper, the _keys_ are assumed to be randomly generated (e.g., a common way to generate a _key_ is to apply a hash function on the _value_), so that they are more or less uniformly distributed. We use the prefix of a _key_ to decide a key-value pair's region. We assume there are \(N\) regions so that the first \(\lceil log_{2}N\rceil\) bits are used to make the decision. For example, if there are eight regions in total, then the region is decided by the first three bits of the _key_. The _original data (primary copy)_ of the key-value pair whose _key_ starts with \"000\" is stored in Region 0. Therefore, the mapping between prefixes of _keys_ and regions works in the following way.  
\[\begin{array}{l}\underline{000}\ldots\longrightarrow\text{Region 0}\\ \underline{001}\ldots\longrightarrow\text{Region 1}\\ \ldots\ldots\longrightarrow\ldots\ldots\\ \underline{111}\ldots\longrightarrow\text{Region 7}\end{array}\]  
We apply the same method to locate which Kinetic Drive stores the key-value pair within a region. In one region, a part of the all the drives is used for storing _original data (primary copies)_, and the rest of the drives are for backup and other possible usages. The metadata server in each region maintains a table to map _key_ ranges to drives. If there are \(M\) drives for storing original data in a region (of course, the number \(M\) is variable among different regions), the additional \(\lceil log_{2}M\rceil\) bits after the first \(\lceil log_{2}N\rceil\) bits are used for locating the specific drive. In Table 1, we show an example of Region 4 (eight regions in total), assuming 128 drives are used for storing _original data_. In that case, we know that all the  
Figure 5: A novel global key-value store system based on Kinetic Drives.  
key-value pairs whose _keys_ start with \"100\" are stored in this region. The fourth to 10 th bits decide the specific drive. Hence, the key-value pair whose _key_ starts with \"100 0000011\" (indicating the _key_ range from \"100 0000011 0...\" to \"100 0000011 1...\") is stored in the third drive (IP: 135.37.118.6) in Region 4 (drive numbers start from 0).
# Initialisation Approaches for Population-Based Metaheuristic Algorithms: A Comprehensive Review
## 3 Major Initialisation Methods
### Quasirandom Methods  
Quasirandom number generators are known to generate sequences that are proven to have low discrepancy [9]. Low discrepancy sequences, like Van der Corput, Sobol, Faure, and Halton, are potent computational method tools, which have been used to improve the performance of optimisation algorithms. Quasirandom numbers are effective initialisation mechanisms for metaheuristic algorithms to uniformly cover the search space in order to obtain the optimal solution. The particle swarm population in the work of [21] was initialised using the randomized low discrepancy sequences of Halton, Sobol, and Faure. The three modified PSO were applied to the benchmark test functions, and results were then compared with the global best PSO. This showed that PSO was significantly improved with Sobol, while the results showed a varying improvement with Faure and Halton. Similarly, the Van der Corput and Sobol sequences were used to initialise the PSO and were then applied to solve the benchmark functions [8]. The results obtained were promising when compared to the original PSO.  
The krill population in the KH algorithm was initialised using the Faure, Sobol, and Var der Corput sequences [22]. The benchmark test functions were used to test the efficacy of the modified KH. Our findings revealed significant improvements in the performance of the KH algorithm when initialised using Faure, Sobol, and Var der Corput low-discrepancy sequences, which was also the case with the guaranteed convergence particle swarm optimization (GCPSO) algorithm, using the Niching methods to initialise the swarm population [23]; the Niching methods are based on the Faure low-discrepancy sequence, and the benchmark test functions were used to evaluate the performance of GCPSO, with promising results.  
The initialisation schemes that were implemented using low-discrepancy sequences are known to perform poorly, as the problem dimension or graph size scales up. Figure 9 shows how the Halton sequence spreads and fills the search space at the 1000 th iteration, improving the convergence of algorithms. We have noted authors [24] who use the Halton sequence to initialise the search agents of the Wingsuit Flying Search (WFS) algorithm.  
Figure 8: Random numbers after 1000 iterations.  
Table 2 summarises the glossary of efforts that used low discrepancy sequences (quasirandom numbers) to initialise the population of some metaheuristic optimizers. Interested readers can refer to the references for more details about the efforts. In all the papers reviewed in this section, the authors claimed that fine-tuning the initialisation control parameters (population size and diversity and maximum number of iterations or function evaluations) improved the performance of the algorithm.
## 2 Literature Review
The findings showed that wire tension, current, and their interactions significantly influenced surface roughness, while pulse on-time and current had notable effects on material removal rate. Additionally, wire feed and the interaction between current and pulse off-time were found to significantly impact wire consumption rate. Ultimately, a desirability function approach was employed to optimize the process parameters and achieve the desired surface roughness along with a maximum material removal rate and minimum wire consumption rate. In the study conducted by [PERSON] et al. [25], the influence and improvement of machining parameters on cutting speed in wire electrical discharge machining (WEDM) operations were investigated, specifically focusing on a metal matrix composite material. Parameters such as pulse on-time, pulse off-time, gap voltage, wire tension, and wire feed were analyzed. The examination of parameter variations revealed that pulse off-time, gap voltage, pulse on-time, and discharge current significantly impacted WEDM and were identified as the most influential control parameters affecting cutting speed in WEDM. In their work [26], [PERSON] et al. focused on optimizing material removal rate and surface finish in the wire cut electric discharge machining of Inconel 750, considering four input factors: pulse on-time, pulse off-time, voltage, and current. To achieve optimization, they trained a feedforward artificial neural network model using a Taguchi orthogonal array and then utilized particle swarm optimization to optimize the weighting factors of the network within neural power software, aiming to enhance material removal rate and surface roughness. In the study by [PERSON] et al. [27], a Taguchi optimization method was employed for the wire electrical discharge machining (WEDM) of managing steel 350, focusing on material removal rate and surface roughness as key output responses. The cutting parameters optimized included pulse on-time, pulse off-time, peak current, and spark gap voltage. Results revealed that surface roughness tended to increase with higher pulse on-time and peak current, while material removal rate was positively affected by increases in pulse on-time, spark gap voltage, and peak current. Notably, peak current emerged as the most influential factor impacting both material removal rate and surface roughness in the WEDM process.