esa-sceva/satcom-chunk-collection huggingface dataset:
# Error Distribution Pattern Analysis of Mobile Laser Scanners for Precise As-Built BIM Generation
## 6 Conclusions  
This study proposed and validated an analysis method to identify the error distribution patterns in point clouds acquired from mobile laser scanning (MLS) devices. Three MLS devices were evaluated using terrestrial laser scanning (TLS) data as a reference. Wooden columns were fabricated as analysis targets, taking advantage of their closed shape in the XY-plane. The analysis method integrated the characteristics of iterative closet points (ICP) registration and cloud-to-mesh (C2M) distance measurement. Random consensus sampling (RANSAC) was used for generating reference planes and verification of dimensional correction using error distribution patterns. The results demonstrate that the proposed method effectively revealed the unique error distribution patterns of each MLS device. The standard deviation of the measured C2M distances allowed a comparative assessment of the data precision and consistency among devices. Furthermore, the application of representative mean error values showed potential for improving dimensional accuracy in as-built modeling.  
Through this study, the two assumptions presented in the introduction were validated, and the following conclusions were drawn:Point clouds from each MLS device exhibited distinct error distribution patterns, with errors biased either inward or outward relative to the actual surface. Notably, two devices of the same model demonstrated opposite error directions (Assumption 1 is satisfied).
* Representative values derived from the identified error patterns were effective in enhancing the dimensional accuracy of reconstructed BIM components (Assumption 2 is satisfied).  
This study contributes to addressing the limitations associated with MLS point cloud inaccuracies in as-built BIM generation. By developing an optimized correction method based on the identified error patterns, the accuracy of as-built BIMs generated from MLS data can be significantly improved. This approach is not limited to specific devices or scenarios and holds potential for generalization across a wide range of MLS-based applications in construction management. Furthermore, these findings highlight the importance of accounting for MLS-specific error patterns in point cloud processing, particularly in tasks that require high geometric accuracy in as-built BIM generation.
## 3 Materials and Methods
### Characterization  
The surface morphology and structure of PS seed particles and porous P(S-DVB) spheres were observed by using scanning electron microscopy (SEM, JEOL JSM-6309 LV, Beijing, China) and transmission electron microscopy (TEM, JEOL JEM-1200). An accelerated surface area and porosimetry analyzer (ASAP, Micromerities 2020, Micromerities Instrument Corp., Shanghai, China) was used for the BET tests. Chromatographic study was carried out using a HPLC (SEV P500, Qingdao Qicai Sunshine Information Technology Co., Ltd., Qingdao, China) equipped with a UV detector.
# Sixth-Generation (6G) Networks for Improved Machine-to-Machine (M2M) Communication in Industry 4.0
## 5 Conclusions  
Sixth-generation networks hold great promise in revolutionizing M2M communications in Industry 4.0/5.0, offering the increased reliability, low latency, high bandwidth, adaptability, and security features that are essential to realizing the full potential of intelligent, connected industrial systems. Sixth-generation networks will have an impact at the intersection of many technologies: IoT, artificial intelligence/machine learning, virtual and augmented reality, cloud computing, and cybersecurity. To take full advantage of this, new solutions, architectures, and concepts for their use must be developed. Joint initiatives between interested investors, standardization bodies, and academia are needed to overcome current limitations and ensure the full use of 6G technology in industrial applications.  
Conceptualization, I.R., P.K., J.D. and D.M.; methodology, I.R., P.K., J.D. and D.M.; software, I.R., P.K., J.D. and D.M.; validation, I.R., P.K., J.D. and D.M.; formal analysis, I.R., P.K., J.D. and D.M.; investigation, I.R., P.K., J.D. and D.M.; resources, I.R., P.K., J.D. and D.M.; data curation, I.R., P.K., J.D. and D.M.; writing--original draft preparation, I.R., P.K., J.D. and D.M.; writing--review and editing, I.R., P.K., J.D. and D.M.; visualization, I.R., P.K., J.D. and D.M.; supervision, I.R.; project administration, I.R.; funding acquisition, I.R. and D.M. All authors have read and agreed to the published version of the manuscript.  
The work presented in this paper has been financed under grant to maintain the research potential of Kazimierz Wielki University.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflicts of interest.
# Solar Filament Detection Based on an Improved Deep Learning Model
## 4 Method
### CNN Encoder  
The CNN Encoder is responsible for extracting local detail information from the input full-disk H\(\alpha\) image and reducing its size while preserving the feature maps for skip connections.  
Solar filaments exhibit various sizes in full-disk H\(\alpha\) images, making it challenging to select an appropriate convolution kernel size. Large-sized solar filaments have their  
Figure 7: The structure of the detection model.  
information distributed over a larger area, making larger convolution kernels suitable for feature extraction. Smaller-sized solar filaments have their information concentrated in a smaller area, necessitating the use of smaller convolution kernels for feature extraction. It is necessary to enhance the multi-scale feature extraction capability of the CNN Encoder. Therefore, this article proposes using a multi-scale residual block (MRB). The MRB utilizes parallel convolution and pooling kernels of different sizes to extract the features of solar filaments. Its structure is illustrated in Figure 8.  
In the MRB, the input image is first processed through three different paths, involving 3 \(\times\) 3 convolution, 3 \(\times\) 3 pooling, and 5 \(\times\) 5 convolution, respectively. The use of different kernel sizes allows for the extraction of solar filament features at various scales. \(1\times 1\) convolutions after these convolution and pooling layers adjust the output depth, which helps reduce the overall number of network parameters and improves computational efficiency. The solar filament features processed through each of the three paths are then concatenated, forming a multi-scale feature map. Using a 1 \(\times\) 1 convolution after feature concatenation enables the fusion of solar filament features across different scales, assisting the network in better understanding the global patterns and structures of solar filaments. The addition of a residual path addresses the issues of gradient vanishing and gradient explosion in deep network training, overcoming the problem of overfitting caused by a limited number of data samples.  
The detailed structure of the CNN Encoder is presented in Table 1. It employs three sets of MRBs for feature extraction, gradually increasing the number of feature channels.
## 3 The F-Channel Platform
Special care was given to the security of the server infrastructure, the question whose relevance has continuously been growing in the previous several years with the increasing number of information leaks and hacker attacks even on some of the largest and best-protected organizations, whose field of work covers power systems and their management. Therefore, the server access codes for the development environment were encrypted with the appropriate SSH (Secure Shell) keys. Furthermore, access to different parts of the platform was restricted in a way to only give permission to enter to the specified IP address ranges. In line with this approach, access to the web application on the user side also was limited to verified accounts created by users, with each of the users, depending on the designated type assigned to them, only seeing the appropriate page of the F-channel platform. The most prominent example of applying the AI tools is seen in the part of the platform related to the Forecasting Module. Here, the specifically designed and trained ANN was combined with the high-resolution measurements of the climatic conditions to provide potential users with forecasts of the selected system state parameters with a remarkable level of accuracy. These parameters include, for example, the production of the wind and solar units in the area of interest or the ampacities of the chosen lines in the system. In line with this, the platform was developed in such a way to have those functionalities already implemented; thus, there is no need to accommodate the mentioned features in the future. They are already in the F-channel platform, giving the users the opportunity of harvesting the benefits in a friendly and intuitive environment.
## 2 Materials and Methods
### The Methodology
#### 2.1.3 Fault Diagnosis
In order to evaluate the performance of each classifier, several parameters can be defined. Here, precision and recall are considered. Precision measures how many samples classified in the positive class are positive, and it is computed through Equation (2). Finally, the Recall, also named Sensitivity or Probability of Detection, measures how well the classifier recognizes the positive samples, and is defined in Equation (3). \[Precision=\frac{TP}{FP+TP} \tag{2}\]  
\[Recall=\frac{TP}{FN+TP} \tag{3}\]  
where:  
* \(TP\) (True Positive) is the number of observations correctly classified as positive;
* \(FP\) (False Positive) is the number of negative observations classified as positive;
* \(FN\) (False Negative) is the number of positive observations classified as negative.
# From Patient to Musician: A Multi-Sensory Virtual Reality Rehabilitation Tool for Spatial Neglect
## 7 Results
### Scales Task  
The time to play the following key in the scale is extracted during the _Scales_ task. Figure 15 shows the aggregated values of all sessions for patients and test users. The data in the graphs are the median time it took to play each key for all sessions, regardless of the difficulty level and the Q1 and Q3 quartiles. The time to play the first note on the scale is omitted in these figures. The value of this first keystroke represents the reaction time to the presentation of the visual stimulus on the first key, while all other values represent the time in between two keys being played. Leaving out the first key allows for a better comparison of the data. Patient 1 had difficulties with the last note in the sequence. It took them significantly longer to play that note than any other note. Moreover, the spread of the time values for this key is significantly wider; this suggests that they were not consistent in the time to play this key. For all patients, except Patient 1, the time to play the following key on the scale gradually reduces as they move further towards the neglected side. The time needed by the test user to play each key is significantly lower than that of the patients. In addition, the spread of the time values is much narrower for the test users compared to the patients.
## 4 Simulation and Experimental Analysis
### Simulation Results and Discussion
Table 6 shows the adjustment times \(\Delta t_{d1}\) and \(\Delta t_{d2}\) of the three control methods for interference \(\tau_{d1}\) and \(\tau_{d2}\). Compared with the PID-CTM, the maximum adjustment time of the DP-SMC was reduced by 16.61% and 14.46%, and the maximum adjustment time of the SACDP-SMC was reduced by 33.19% and 30.54%. Both the DP-SMC and SACDP-SMC effectively reduced the interference adjustment time and accelerated the error convergence speed, however, the convergence speed of SACDP-SMC was the best. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Indicators**} & **PID-CTM** & **DP-SMC** & \multicolumn{2}{c}{**SACDP-SMC**} \\ \cline{2-6}  & **Value\({}^{\circ}\)** & **Value\({}^{\circ}\)** & **Reduction** & **Value\({}^{\circ}\)** & **Reduction** \\ \hline \(e_{a}\) & 2.3405 & 1.9533 & 16.54\% & 0.5474 & 76.61\% \\ \(e_{m}\) & 0.1679 & 0.1418 & 15.54\% & 0.0474 & 71.77\% \\ \(r_{e}\) & 0.5012 & 0.4199 & 16.22\% & 0.1331 & 73.44\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The metrics of control accuracy. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\(\Delta e\)} & **PID-CTM** & \multicolumn{2}{c}{**DP-SMC**} & \multicolumn{2}{c}{**SACDP-SMC**} \\ \cline{2-6}  & **Value\({}^{\circ}\)** & **Value\({}^{\circ}\)** & **Reduction** & **Value\({}^{\circ}\)** & **Reduction** \\ \hline \(\Delta e_{d1}\) & 1.5400 & 1.0120 & 34.29\% & 0.4153 & 73.03\% \\ \(\Delta e_{d2}\) & 2.3350 & 1.9350 & 17.13\% & 0.5425 & 76.77\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The error variation of the three control methods. It can be seen from the error variation \(\Delta e\) and the interference adjustment time \(\Delta t\) that the proposed SACDP-SMC improves the anti-interference ability of the lower limb rehabilitation exoskeleton robot by a large margin.
## 3 Results and Discussion
### FT-IR Analysis  
In Figure 7, FT-IR spectra of agarose powder, crystal violet and agarose aerogel and cryogel after CV adsorption tests are reported.  
FT-IR spectrum of agarose powder showed the typical peaks reported in the literature, at a wavelength of approximately 3300 and 2900 cm\({}^{-1}\) that correspond to the stretching vibrations of the -OH and -CH bonds, respectively [38]. There were also peaks at 1161 and 1060 cm\({}^{-1}\) that correspond to the vibrations of C-O-C and C-OH bonds, respectively, and at 770, 890 and 929 cm\({}^{-1}\) that are referred to the flexion of 3.6 anhydro-b-galactose skeleton, constituting the agarose macromolecule. FT-IR spectrum of the dye showed characteristic peaks at wavenumbers of 3400 cm\({}^{-1}\) (-OH vibrations), 2900 cm\({}^{-1}\) (C=C stretching), 1588 cm\({}^{-1}\) (C-H bending), 1300 and 1178 cm\({}^{-1}\), due to tertiary amines [42]. The analysis of FT-IR spectra related to the cryo/aerogels after the adsorption process revealed that cryogel spectrum was very similar to that of agarose powder: only the intensity of the peaks was slightly decreased. In the case of aerogel, a strong increase in the intensity of hydroxyl group (3300 cm\({}^{-1}\)) and the appearance of the positively charged amino group (1330 cm\({}^{-1}\)) can be detected, indicating the presence of physical-chemical interactions between the agarose and the dye. Therefore, an electrostatic interaction between agarose hydroxyl groups and positively charged amino groups of CV was formed, promoting a more effective dye removal during adsorption tests.  
Figure 7: FT-IR analysis of agarose powder (black line), crystal violet (red line), agarose cryogel after adsorption (blue line) and agarose aerogel after adsorption (violet line).
## 3 Results and Discussion
### ITS Response of Slope Anchorage System
During Stage-1 and Stage-3, axial forces at monitoring points ITS-7-ITS-9 exhibited initial reductions followed by subsequent increases. Rainfall-induced softening of the slope surface induced prestress losses, while prolonged water infiltration intensified seepage-induced damage along structural planes, resulting in rock block loosening and increased slip force. As shown in Figure 10a, splash erosion effects intensified at 50 min, coinciding with the phenomenon of axial force increase. During 50-55 min, axial forces at monitoring points ITS-7-ITS-9 exhibited a decreasing trend. Based on the observed propagation of shallow tensile cracks during the model test, it is hypothesized that prestress losses caused by slope infiltration damage exceeded the axial force increases resulting from improved support resistance. During 55-60 min, the axial force at monitoring points ITS-7-ITS-9 increased due to the overall decline of rock blocks, which caused the anchor bolt to play a supporting role (Figure 10b). During 60-90 min, the fractured rock blocks remained predominantly stable. Axial forces at monitoring points ITS-7-ITS-9 continued to decline under the combined effects of surface erosion and groundwater seepage.
###### Abstract  
K-means clustering algorithm is a partitional clustering algorithm that has been used widely in many applications for traditional clustering due to its simplicity and low computational complexity. This clustering technique depends on the user specification of the number of clusters generated from the dataset, which affects the clustering results. Moreover, random initialization of cluster centers results in its local minimal convergence. Automatic clustering is a recent approach to clustering where the specification of cluster number is not required. In automatic clustering, natural clusters existing in datasets are identified without any background information of the data objects. Nature-inspired metaheuristic optimization algorithms have been deployed in recent times to overcome the challenges of the traditional clustering algorithm in handling automatic data clustering. Some nature-inspired metaheuristics algorithms have been hybridized with the traditional K-means algorithm to boost its performance and capability to handle automatic data clustering problems. This study aims to identify, retrieve, summarize, and analyze recently proposed studies related to the improvements of the K-means clustering algorithm with nature-inspired optimization techniques. A quest approach for article selection was adopted, which led to the identification and selection of 147 related studies from different reputable academic avenues and databases. More so, the analysis revealed that although the K-means algorithm has been well researched in the literature, its superiority over several well-established state-of-the-art clustering algorithms in terms of speed, accessibility, simplicity of use, and applicability to solve clustering problems with unlabeled and nonlinearly separable datasets has been clearly observed in the study. The current study also evaluated and discussed some of the well-known weaknesses of the K-means clustering algorithm, for which the existing improvement methods were conceptualized. It is noteworthy to mention that the current systematic review and analysis of existing literature on K-means enhancement approaches presents possible perspectives in the clustering analysis research domain and serves as a comprehensive source of information regarding the K-means algorithm and its variants for the research community.  
K-means clustering; automatic clustering; nature-inspired metaheuristic algorithms; cluster analysis +
Footnote †: journal: Applied Sciences
# Application of Organic-Inorganic Hybrids in Chemical Analysis, Bio- and Environmental Monitoring
## 2 Classification and Synthesis of Organic-Inorganic Hybrids (OIH)
### Synthesis
#### 2.2.2 (Self) Assembly Method  
Unfortunately, most of OIH prepared via the sol-gel processes are not materials controlled on a meso-, or nano-scopic scale. Therefore, other synthesis strategies should be employed to control hybrid materials on the nanoscale.  
The second strategy of hybrid materials synthesis is based on the (self)assembly of monodispersed nanoscale objects: nanoparticles (metal and semiconductor), core/shell nanocomposites, 1D and 2D nanomaterials [50; 51]. These nanostructures can be modified by different type of organic molecules, functional dendrimers, biomolecules, polymers [2; 52]. This synthesis strategy is often called \"legochemistry,\" because it allows one to build the hybrid assemblies by simply combining organic/inorganic materials. This method provides a number of advantages, such as better control of its structure on the nanoscale, and as a consequence it facilitates the tailoring of physical properties of the final material.
# Quantitative Study on the Impact of Energy Consumption Based Dynamic Selfishness in MANETs +
## 3 Energy Consumption Based Selfish Nodes
### Energy Consumption  
In MANETs, the main energy consumer in the mobile nodes is the transceiver. In this work, energy consumption is based on power consumption values for various transceiver modes, states, and the time the transceiver spends in these states. Transceivers consume a small amount of power when they are idle in receiving mode. They consume more when they are receiving a transmission, and even more when they are transmitting.  
In order to clearly illustrate the energy variation of a node in MANETs, the related data of a normal node are collected from a simple test and plotted in Figure 2.  
In this test, the nominal energy capacity of the node is 0.025 J, initial residual energy is a random value between 0 J and 0.025 J, and the test lasts 100 s. The graph clearly visualizes the changes in energy generation, consumption, and storage. In the figure, the energy generation, consumption, and storage are indicated by the green, red, and blue solid lines, respectively. The node starts from a given charge level, and its energy level constantly decreases from there. It eventually reaches the shutdown capacity (0 J), and when that happens, the node shuts down. Then it starts to charge, and when the charge level reaches the start capacity, the node turns back on. When the transceiver in the node consumes energy by receiving/transmitting operations, the energy storage decreases fast. However, when the node is transmitting and the generator is charging, the energy level does not decrease as fast. In contrast, when the node is in the idle state and the generator is charging, the storage level increases linearly.  
In sum, it is evident that residual energy of a node can represent the limited resources in a dynamic way, which make nodes selfish in MANETs. That is also the reason we define energy consumption based dynamic selfishness in this work.
# Resource Management for Collaborative 5G-NR-V2X RSUs to Enhance V2I/N Link Reliability
## 5 Improved V2I/N Link Reliability with Proposed Collaborative RSU for Resource Management
### Simulation Results
Simulation results show that the average throughput received by the vehicle in the simulator increases by about 282%, from 1.528 [Mb/s] to 5.85 [Mb/s], when transmitting and receiving V2X messages using only a single BS-type RSU and when applying all the proposed techniques. Compare to the case of transmitting and receiving V2X messages using only UE-type RSU as a single unit, it increases by about 133%, from 2.507 [Mb/s] to 5.85 [Mb/s]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & \multicolumn{2}{c}{**Dynamic ICIC +**} & \multirow{2}{*}{**CRE**} & \multirow{2}{*}{**3D Beamforming**} & **Throughput** \\  & & **CS-CoMP** & & **[Mb/s]** \\ \hline \multirow{4}{*}{Hybrid RSU} & X & X & X & 3.02 \\ \cline{2-5}  & O & X & X & 3.768 \\ \cline{2-5}  & O & O & X & 4.537 \\ \cline{2-5}  & O & O & O & 5.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative analysis of throughput with application of various schemes. Figure 6: (a) VUE throughput applying dynamic ICIC + CS CoMP; (b) VUE throughput applying cell range expansion; (c) VUE throughput applying 3D beamforming.