esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Challenges and Future Opportunities  
Based on the reviewed and analyzed research, several challenges associated with XR solutions for robotic telemanipulation and telemanufacturing have been identified. One major challenge lies in the technological integration of extended reality technologies such as AR, VR, and XR with robotic systems, giving rise to issues such as synchronization problems, latency, and the need for real-time data processing. Additionally, designing interfaces that offer an intuitive and immersive user experience proves to be a daunting task, demanding seamless interaction with augmented or virtual environments while simultaneously controlling robotic systems in real time. Providing accurate and meaningful haptic and sensory feedback to users presents another obstacle, as replicating real-world touch and feel in virtual environments adds complexity to system design. Furthermore, ensuring the safety of both users and the environment remains paramount, necessitating solutions for challenges like collision detection, emergency shutdown mechanisms, and real-time hazard recognition. Moreover, the cost associated with developing and implementing robust XR-enhanced robotic systems poses a significant barrier, limiting accessibility for smaller organizations or research initiatives aiming to delve into this innovative technology.  
Given the dynamic nature of XR technology, we anticipate numerous exciting opportunities in XR-enhanced telerobotics. Advancements in immersive XR interfaces, incorporating enhanced gesture recognition, voice commands, and brain-computer interfaces, are expected, making teleoperation more seamless and intuitive. Customized interfaces tailored to individual preferences, coupled with advancements in MR and haptic feedback integration, promise a realistic and detailed sense of touch and force feedback, enhancing the operator's ability to perform delicate tasks remotely. Additionally, developments in AI and machine learning could enhance robots' autonomy and adaptability by learning from human operators, fostering more efficient and versatile telerobotic systems. In summary, innovations in immersive interfaces, haptic feedback, AI integration, and interdisciplinary research are poised to revolutionize remote teleoperation and human-robot collaboration, unlocking the full potential of XR technology in telemanipulation and telemanufacturing domains.
# Security and Privacy in Physical-Digital Environments: Trends and Opportunities
## 2 The Metaverse and Mixed Reality
### Areas of Application
#### 2.2.3 Medicine  
As for healthcare, the Metaverse is transforming the industry by enabling medical professionals to communicate with patients more effectively through virtual interactions. This technology also provides a platform for creating calming virtual environments that can help alleviate patients' stress during treatment. Additionally, creating virtual representations of patients enables simulations of different treatments to determine the most appropriate course of action. The high level of realism of these virtual representations also allows for simulated and safe training of medical students without any risk of harm to patients. As a result, Metaverse technology is improving patient outcomes and revolutionizing healthcare education.  
In healthcare, MR plays a transformative role by enhancing how medical professionals interact with patients and medical data. MR allows real-time collaboration between healthcare providers, enabling remote consultations and more interactive patient care. By overlaying virtual data on real-world medical environments, MR facilitates the visualization of patient information, such as MRI scans or 3D models, directly during physical examinations or surgeries. This augmented view helps clinicians make better-informed decisions. Additionally, MR is revolutionizing medical training by enabling simulations that combine physical practice with virtual guidance, allowing medical students to practice surgeries or procedures without having any real-life consequences while receiving immediate feedback. A notable component of this innovation is the creation of virtual patient scenarios, a concept that has been around for nearly 40 years [9]. The American Association of Medical Colleges (AAMC) defines these scenarios as \"a specific type of computer-based program that simulates real-life clinical scenarios; learners emulate the roles of healthcare providers to obtain a patient history, conduct physical exams, and make diagnostic and therapeutic decisions\" [10]. This approach facilitates the simulation of a variety of medical conditions, which can be derived from case studies available on websites or CD-ROMs, virtual reality simulations, or even robotic human-scale mannequins equipped with sensors [9], and can incorporate individual patient parameters such as age, medical history, and associated diseases. This realistic training environment helps to reduce errors and improve skill proficiency, all without risking patient safety. MR also aids in rehabilitation, where immersive experiences can support recovery by simulating real-world scenarios for therapeutic purposes. As a result, MR enhances both the quality of care and educational experiences in healthcare.
# How Will Rwandan Land Use/Land Cover Change under High Population Pressure and Changing Climate?
## 5 Conclusions  
Based on the analysis of LULC transfer in Rwanda during the historical period (1990-2015), this study found that the LULC in Rwanda was dominated by the decrease in forest land and the expansion of cropland. Moreover, the wetland, water and unused land areas fluctuated with the annual increase in grassland and urban land. Before 2000, LULC transfer in Rwanda primarily consisted of the conversion of forest and grassland to cropland, with 72% of the newly generated cropland coming from forest land, and 28%coming from grassland. From 2000 to 2010, the land transfer was balanced. After 2010, 83% of the transferred forest land was converted to cropland, and 17% was converted to grassland. Changes in forest, grassland, and cropland areas were driven by multiple factors, whereas changes in wetland, water, urban land and unused land areas were dominantly influenced by a single factor. Moreover, the factors driving different LULC types changed with the passage of time.  
The PLUS model was applied to simulate the LULC pattern in Rwanda during the next 30 years. The simulation results showed that the PLUS model has high simulation accuracy and generates a reliable LULC pattern. In Rwanda, the LULC will continue to exhibit a trend of decreasing forest and increasing grassland and cropland. Moreover, the model indicated that the cropland in the western part has increased and the forest has decreased. The grassland in the eastern part has increased, but the cropland has decreased. Thus, the trend observed is that of cropland'moving westward' and grassland'moving eastward'.  
Conceptualization, C.L.; methodology, C.L.; data curation, C.L.; writing--original draft preparation, C.L.; writing--review and editing, M.Y. and B.W.; visualization, C.L.; funding acquisition, Z.L.; All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the International Cooperation and Exchange Program of the National Natural Science Foundation of China (41561144011).  
Not applicable.  
All
## 4 Discussion
### Interpretation of Changes in Textile Characteristics Due to APPJ Treatment  
The results of this study indicate that APPJ treatment increased the mean fiber diameter (MFD) and spinning fineness (SF) while reducing the curvature index (CI). These findings are consistent with those of previous studies. For instance, a study conducted by [PERSON] and [PERSON] [44] investigated the effect of plasma treatment on textile properties and characteristics. After subjecting the wool and cotton fabrics to oxygen plasma, significant changes were observed in the mechanical properties, which were measured using the KES-FB system. Additionally, plasma treatment improved the wettability and surface adhesion of the fabrics, affecting their feel and appearance. Other authors, including [PERSON] and [PERSON] [14], demonstrated that oxygen plasma treatment modifies the surface properties of wool, such as roughness and chemical structure, without significantly affecting the fundamental textile characteristics, such as MFD, FC, CI, and SF. However, unlike these studies, the present work reveals that the APPJ has a significant impact on CI, suggesting that the APPJ might be more effective in modifying the fiber structure than oxygen plasma.  
The initial hypothesis of this study was that the APPJ improves the textile characteristics of wool. These results partially confirmed this hypothesis. Although the APPJ didnot improve all textile characteristics, it increased the spinning fineness and reduced the curvature index, which could be beneficial for the textile industry.  
These findings are relevant to the textile industry, as they suggest that the APPJ can be a useful tool for improving the quality and durability of fabrics. An increase in spinning fineness could enhance fabric strength, while a reduction in the curvature index could improve softness and hand feel. Future studies are recommended to investigate the effect of the APPJ on the mechanical strength of the fiber and its impact on the hand feel and appearance of fabrics. It would also be interesting to explore the effect of the APPJ on the dye absorption and wash fastness of fabrics.
## 5 Conclusions  
In this paper, the forming ability of LAM with coaxial wire feeding and the microstructure and mechanical properties of the fabricated component were investigated. Vertical wire feeding with an axisymmetric multi-laser source can eliminate the anisotropic forming problem in the deposition process. No matter whether the tool path is round or polyline, the forming morphology of the sample deposited by the vertical wire feeding method is smooth and there is no difference in all directions. A conclusion can be drawn that vertical wire feeding with an axisymmetric multi-laser source has a strong forming ability.  
The samples fabricated by the vertical wire feeding method at the current process parameters exhibited fine equiaxed grains and some short columnar grains (parallel to each other). The average size of the equiaxed grains was about 91.7 \(\upmu\)m. The average size of the short columnar grains was 339.6 \(\upmu\)m in width and 122.5 \(\upmu\)m in height. The microstructure of the samples exhibited fine basket-weave and martensite morphology.  
The static tensile tests showed that the average strain of the samples was about 6%, and the average ultimate tensile strength of the L direction and T direction was 1140.33 MPa and 1114.67 MPa, respectively. The ultimate tensile strength difference of the two directions (L and T) becomes smaller. Therefore, vertical wire feeding with an axisymmetric multi-laser source at the current process parameters can improve the ultimate tensile strength, and the anisotropy of the mechanical properties of the fabricated parts can be largely eliminated.  
This research is supported by the National Natural Science Foundation of China (No. 51405018), the Discipline Group Innovation Collaboration Project of China Association for Science and Technology (No. 2016 XXYL05), Fundamental research of the State Ministry (No. JCKY2016602B007) and the Excellent Young Scholars Research Fund of the Beijing Institute of Technology (No. 2016 CX04026). The authors would like to thank the anonymous reviewers for their helpful comments and suggestions to improve the quality of this paper.  
[PERSON], [PERSON], [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed reagents/materials/analysis tools; [PERSON], [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.  
Figure 11: The schematic of the grain morphology of the fabricated sample.
# Coal and Gas Outburst Risk Prediction and Management Based on WOA-ELM
## 5 Results and Discussion
### WOA-ELM Model Prediction Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Training Datasets**} & \multicolumn{4}{c}{**Testing Datasets**} \\ \cline{2-9}  & **MAE** & **SSE** & \(\mathbf{R^{2}}\) & **MRE (\%)** & **MAE** & **SSE** & \(\mathbf{R^{2}}\) & **MRE (\%)** \\ \hline WOA-ELM & \(3.9\times 10^{-2}\) & \(4.7\times 10^{-3}\) & 0.982 & 1.7 & \(4.5\times 10^{-2}\) & \(4.0\times 10^{-3}\) & 0.871 & 1.5 \\ GA-ELM & \(1.04\times 10^{-1}\) & \(5.69\times 10^{-3}\) & 0.895 & 3.65 & \(1.30\times 10^{-1}\) & \(5.72\times 10^{-3}\) & 0.83 & 4.36 \\ CS-ELM & \(1.26\times 10^{-1}\) & \(5.8\times 10^{-3}\) & 0.882 & 3.7 & \(1.29\times 10^{-1}\) & \(5.24\times 10^{-3}\) & 0.823 & 4.41 \\ ELM & \(1.17\times 10^{-1}\) & \(6.9\times 10^{-3}\) & 0.863 & 3.9 & \(1.56\times 10^{-1}\) & \(6.2\times 10^{-3}\) & 0.704 & 5.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of the performance of WOA-ELM and ELM. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Functions** & **Range** & **Minimum Value** \\ \hline \(f_{1}(x)=\sum\limits_{i=1}^{D}x_{i}^{2}\) & [\(-100,100\)] & 0 \\ \(f_{2}(x)=\sum\limits_{i=1}^{D}|x_{i}|+\prod\limits_{i=1}^{D}|x_{i}|\) & [\(-10,10\)] & 0 \\ \(f_{3}(x)=\max\limits_{i}\{|x_{i}|,1\leq x_{i}\leq D\}\) & [\(-100,100\)] & 0  
when it approaches 150 iterations, falling into the local optimum. The CS-ELM algorithm converges to the global optimum when it iterates 180 times, as shown in Figure 6d. When processing the F6 function, the ELM algorithm has a downward trend at the beginning of the iteration. However, it converges slowly in the iteration process and quickly falls into the local optimum. The WOA-ELM algorithm has a fast iteration speed and completes the iteration when the number of iterations exceeds 50. The GA-ELM and the CS-ELM complete the iteration 150 times, as shown in Figure 6f. In conclusion, after improving the ELM with the WOA, the GA, and the CS algorithms, the accuracy of its operation is higher, the error is minor, and the convergence speed is faster than that of the ELM algorithm. By comprehensively comparing the three improvement methods, the performance of the WOA-ELM algorithm is better than that of other methods [40]. In this paper, five populations of different scales are used to analyze the whale population's impact on the WOA-ELM model's prediction results. The prediction values of gas outburst risk under different population sizes are compared in the experiment, and the experimental results are shown in Figure 7.  
It can be seen in Figure 7 that the population size has no noticeable impact on the gas outburst risk prediction value. Through research, when using the WOA-ELM model for prediction, the whale population can be 30-50, which ensures the accuracy of prediction results and the operation speed [41]. Figure 6: Convergence plots under each benchmark function.
## 2 Related Works and Motivations
### Asymmetry in RDMA Unilateral Communication  
The core of the RDMA protocol lies in its one-sided read-and-write capabilities, which provide the foundation for efficient data transmission. RDMA's one-sided operations can be categorized into active and passive operations [28]. In active operations, the server initiates RDMA operations to write data to or read data from the client. In passive operations, the client initiates requests for data reads or writes, and then the server merely responds to these requests passively. However, the resources consumed and latency incurred by actively initiating RDMA operations differ from those involved in passively responding to RDMA operations.  
Due to the unique nature of RDMA communication, some data processing functions are directly integrated into the RDMA NIC (RNIC) hardware, allowing RDMA operations to bypass the operating system kernel and occur directly at the hardware level [29]. During one-sided RDMA transmissions, passive RDMA operations (receive operations) are entirely handled by the RNIC hardware. In contrast, active RDMA operations (initiate requests) involve interaction between hardware and software. This process includes sending a work request, waiting for an acknowledgment (ACK) from the remote RNIC, and generating a completion queue entry (CQE) upon request completion, which requires data tracking throughout to ensure successful operation.  
This asymmetry, referred to as the asymmetry of RDMA one-sided communication, implies that during data transmission, the initiating side (active party) bears most of the processing burden, while the responding side (passive party) only needs to provide simple responses. Consequently, under the same conditions, the cost of processing passive RDMA operations is significantly lower than that of initiating active RDMA operations.  
In edge computing, although servers work collaboratively, once the RDMA connection is established, the receiving server often does not need to send large amounts of data to the sending server. Therefore, in an RDMA-based data transmission system, the sending server uses active RDMA operations exclusively, while the receiving server exclusively uses passive RDMA operations. This approach not only conserves significant CPU and RNIC resources for the receiving server but also improves throughput and supports a higher number of RDMA connections.
# Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges
## 7 Discussion
### Performance  
Machine learning models have consistently demonstrated superb performance, with median accuracy levels of approximately 99% on binary and multiclass tasks. This performance is best illustrated using the BoT-IoT dataset, most likely due to it having fewer classes and properly designed features, which make it highly trainable. Decision Tree (DT) and Random Forest (RF) models tend to perform well, particularly in binary classification, where overfitting resistance is an asset. In multiclass scenarios, tree-based ensemble techniques are also used very effectively, although their application differs across datasets. However, the inconsistencies observed with datasets such as TON_IoT suggest that dataset characteristics, including class distribution and feature diversity, significantly impact model performance. Cross-validation across multiple datasets remains crucial to ensuring robustness and the ability to adapt to different conditions.
## 3 Internal Load Monitoring
### Physiological Markers
Due to the ease of collecting and examining this enzyme, serum creatine kinase (CK) activity has become an important marker widely used in sports and fitness, with increases in CK activity indicating structural muscle damage at the level of the sarcolemma and Z-disks [68; 69]. Therefore, this biochemical marker has been used to assess muscle damage since its levels increase acutely in response to training loads [69]. Other muscle enzymes, such as serum lactate dehydrogenase (LDH), myoglobin, and troponin, can also be measured to indicate the extent of muscle damage with excessive exercise training [69]. For instance, LDH activity is a marker of cell damage, with increases helping to assess adaptation to training, while myoglobin release indicates degradation of protein structures within muscle and correlates with neutrophil response induced by stress [69]. In addition, cardiac and skeletal muscle troponins are released with high-intensity exercise, indicating disruption of the actin-myosin structure [69]. Given these molecules' structural and functional importance, they are considered valuable markers for monitoring the effects of training load on muscle tissue. Immunological and inflammation markers can be used to assess physiological stress in response to training load. Excessive training can lead to immunosuppression and inflammation, increasing the individual's risk of illness [70]. Several immunological and inflammatory markers are associated with physical exercise and sports; among them, cytokines play a significant role in acute and chronic responses to physical training. The most commonly measured cytokines include interleukin-6 (IL-6), interleukin-8 (IL-8), interleukin-10 (IL-10), and interleukin-18 (IL-18) [71; 72]. Hence, cytokines are molecules that modulate inflammation and immune responses. According to [PERSON] [71], increases in interleukin IL-1 ra, IL-6, IL-8, and IL-10 appear to be more closely related to exercise intensity (physiological load/stress) than to muscle damage. Furthermore, long-duration exercise tends to elicit a significantly higher response of these cytokines compared to short-duration intensive exercise. Even with the complexity of the cytokine network, the pro-inflammatory cytokines (e.g., IL-18, IL-6, IL-8, TNF-\(\alpha\)) and the anti-inflammatory cytokines, also with immunosuppressive function (e.g., IL-1 ra, IL-10, and IL-4), contribute to muscle injury and susceptibility to infections. Therefore, the inflammatory cytokines, neutrophils, and macrophages can be markers of exhaustive exercise-induced muscle tissue damage [71].
## 3 Methodology
### Bias Correction Methods  
This paper implemented two bias correction methods: simple multiplicative bias correction (SMBC) and ratio bias correction (RBC). As the Fuji River has sufficient rainfall stations, the robust and straightforward bias correction techniques were primarily selected.
# Deterministic Coresets for \(k\)-Means of Big Sparse Data +
###### Abstract  
Let \(P\) be a set of \(n\) points in \(\mathbb{R}^{d}\), \(k\geq 1\) be an integer and \(\varepsilon\in(0,1)\) be a constant. An \(\varepsilon\)-_coreset_ is a subset \(C\subseteq P\) with appropriate non-negative weights (scalars), that approximates _any_ given set \(Q\subseteq\mathbb{R}^{d}\) of \(k\) centers. That is, the sum of squared distances over every point in \(P\) to its closest point in \(Q\) is the same, up to a factor of \(1\pm\varepsilon\) to the weighted sum of \(C\) to the same \(k\) centers. If the coreset is small, we can solve problems such as \(k\)-means clustering or its variants (e.g., discrete \(k\)-means, where the centers are restricted to be in \(P\), or other restricted zones) on the small coreset to get faster provable approximations. Moreover, it is known that such coreset support streaming, dynamic and distributed data using the classic merge-reduce trees. The fact that the coreset is a subset implies that it preserves the sparsity of the data. However, existing such coresets are randomized and their size has at least linear dependency on the dimension \(d\). We suggest the first such coreset of size _independent_ of \(d\). This is also the first deterministic coreset construction whose resulting size is not exponential in \(d\). Extensive experimental results and benchmarks are provided on public datasets, including the first coreset of the English Wikipedia using Amazon's cloud.  
coreset; clustering; KMeans; big data; streaming +
Footnote †: journal:  
0000-0001-6000-0000-0000-0000-0000-00000-00000-0000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-000000-000  
\(M\) machines (e.g., on the cloud) and reduce the running time by a factor of \(M\). The communication between the machines is also small, since each machine needs to communicate to a main server only the coreset of its data.  
In practice, this technique can be implemented easily using the map-reduce approach of modern software for Big Data such as [5].  
Coresets can also be used to support the dynamic model where points are deleted/inserted. Here the storage is linear in \(n\) since we need to save the tree in memory (practically, on the hard drive), however the update time is only logarithmic in \(n\) since we need to reconstruct only \(O(\log n)\) coresets that correspond to the deleted/inserted point along the tree. First such coreset of size _independent_ of \(d\) was introduced by [6]. See [1,2] for details.