esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In this context, ML models are adept at learning intricate patterns within data to optimize process parameters, predict equipment wear, and inform proactive maintenance strategies [18]. This article builds on research into monitoring the severity of damage to rubber-textile conveyor belts, which is closely related to their impact resistance and the monitoring of their wear and damage. The article systematically compares four different ML approaches (regression analysis, decision tree regression, random forest and ANN) to predict the impact resistance of rubber-textile conveyor belts based on experimental data. It uses a complex set of input parameters and evaluates not only the accuracy of the models, but also their practical applicability. The primary aim of the article is to provide a complete and comprehensive comparison of selected ML approaches, which can significantly aid the development of a methodology for predicting the impact resistance of conveyor belts. Furthermore, our work aims to:  
* Identify and quantify the relationships between diverse input parameters and conveyor belt impact resistance, leveraging the strengths of various ML models (regression for linear relationships, tree models and neural networks for complex, nonlinear interactions). * Increase prediction reliability through systematic cross-model testing on identical, real-world experimental data, fostering confidence in the chosen model. * Precisely determine the significance of input variables (e.g., drop height, load weight, impactor type, belt strength), offering a deeper understanding of underlying damage mechanisms and enabling optimized belt design and operational parameters. * Demonstrate the flexibility and adaptability of various algorithms to different conditions and belt types, enhancing practical applicability across diverse industrial environments. * Contribute to increased efficiency and reduced costs by enabling more accurate maintenance planning, minimizing unplanned downtime, and extending belt life. The paper is organized as follows: In Section 2 (Related Work) is presented an overview of the current state of knowledge in the field of machine learning and diagnostics in the area of conveyor belts. In Section 3 (Materials and Methods) is described the method of data acquisition and the evaluation methods used. Section 4 (Result) and Section 5 (Discussion) present the results of the analysis and comparison of the predictive capabilities of the selected models, including the evaluation of the significance of input variables. Section 6 (Conclusions) summarizes the main findings and contributions of the study, while Section 7 (Future study opportunities and limitations) is devoted to practical implications and directions for future research.
# A Detailed Review on Foam Concrete Composites: Ingredients, Properties, and Microstructure
## 6 Durability of Foam Concrete
### Acoustic  
Acoustic properties are the least studied ones for foam concrete. Factors such as the foam content, the amount, size and distribution of pores and the inclusion of their uniformity can affect the sound insulation of foam concrete. Compared to normal concrete wall, foamed concrete cellular walls transmit sound frequency with up to 3% higher value, and foamed concrete has 10 times higher sound absorption rate than dense concrete [57]. It was reported that sound absorption increases at 800-1600 Hz in foam concrete containing FA. This was attributed to the altered pore properties with the addition of FA. In addition, the increase in foam dosage has less of an effect at low frequencies. Medium-frequency foam concretes (600-1000 Hz) were reported to be a more efficient material [17].  
[PERSON] et al. [17] reported that thin GFC samples of 20-25 mm exhibit an impressive acoustic absorption rate (\(\alpha\) = 0.7-1.0) in the low frequency region of 40-150 Hz, and that the average sound absorption of the GFC is better than dense concrete. [PERSON] et al. [59] showed that alkali active slag foam concretes developed using 25-35% foam content in their study exhibited excellent maximum acoustic absorption coefficients (0.8-1) in medium- and high-frequency regions. It was reported that there is a linear correlation between the density and acoustic properties of the alkali active slag foam concretes used in the study. In other words, the acoustic properties are improved by decreasing the density.
# Connectivity Maintenance Based on Multiple Relay UAVs Selection Scheme in Cooperative Surveillance
## 3 Multiple Relay UAVs Selection Scheme Based on Fuzzy Optimum Selection
### Multiple Relay UAVs Selection Scheme in Cooperative Surveillance
#### 3.3.1 Multiple Relay UAVs Selection Stage  
It is emphasized that different UAV roles are appropriately allocated according to relay UAV alternatives sequence \(\mathbf{V}_{Seq}\). Firstly, the membership degrees of UAV alternatives are assessed according to four proposed attributes representing network connectivity. Then, 1 st-\(N_{R}\)th UAVs are selected as RUAVs to establish communication links, and \((N_{R}+1)\)th UAV serves as AUAV connecting with remote SUAVs swarm and RUAVs. As SUAV, the remaining UAVs continue performing surveillance tasks and expanding searching scope. For the purpose of surveillance scope expansion, it is assumed that supplying relay UAV is more important than removing relay UAV at the same instant. After multiple relay UAVs are selected based on \(\mathbf{V}_{Seq}\), the role conversion stage is designed to dynamically revise relay UAVs number for detection scope adjustment.
# Two-Level Sharing and Extraction of Sensing Information with Hybrid V2X Communications
## 4 Two-Level Sharing and Extraction of Sensing Information with Local Cooperation among Vehicles
The state machine at a vehicle is illustrated in Figure 4. The set of states includes the Initiation state, the MinFactor unchanged state, the MinFactor updated state, and the Global report state. The condition of transition from the MinFactor unchanged state to the MinFactor updated state is that the MinFactor at a vehicle is updated due to the reception of a new message in the exchange of sensing information. The vehicle has a state of a global report when it confirms that it has a sensing value less than all received sensing values after all rounds of sensing-data exchange. Figure 2: Method overview. When the edge server has collected the report(s) of sensing information from vehicles, the edge server carries out a new extraction procedure of sensing information if there are multiple reports from the vehicles. The edge server searches for the MinFactor from the reported sensing values as the final result of extraction. The final extraction result at the edge server, such as the minimum RSSI among vehicles, will be utilized in the network management, e.g., for reliable communications and the coordination of the maneuver of vehicles. Figure 4: State machine at each vehicle that employs the proposed scheme. Figure 3: Flowchart of the proposed scheme.
# Photonic Integrated Circuits for NGPON2 ONU Transceivers (Invited)
## 4 PIC for Next-Generation PON
### Control Complexity, Power Dissipation and Form Factor
XGSPON, besides being already stressed in price, may require O-band and L-band integration, which can entail extra processing and packaging steps, thus increasing the limitations of PIC viability in this wavelength range. DWDM is also included in the wavelength plan of NGPON2 and is already an ITU-T standard being used for implementing several telecommunication systems, which also poses a good opportunity for the use of the PIC technology.
## Materials and Methods
### Non-Negative Matrix Factorization  
NMF is a low-rank approximation technique that decomposes the original data matrix \(X\in R_{+}^{\text{ID}\times m}\) in the product of two non-negative matrices \(W\) and \(H\) (\(X\approx WH\), with \(W\in R_{+}^{\text{ID}\times k}\) and \(H\in R_{+}^{\text{ID}\times m}\)) called basis and encoding matrix, respectively, where \(k\) is the factorization rank that indicates the number of hidden genes (metagenes) to extract.  
An explicative example is reported in Figure 1. A microarray data matrix with 30 genes (rows) and 10 HMCLs (columns) was decomposed in the matrices \(W\in R_{+}^{\text{30}\times 2}\) and \(H\in R_{+}^{\text{2}\times 10}\).  
The basis matrix \(W\), contains the latent factors that are automatically extracted by the algorithm. In the context of the microarray data analysis, these factors are called metagenes: groups of genes that emerged from the computation and used to describe the original data in a sub-dimensional space. Each column of \(W\) represents a metagene that is described by the same genes used in the original data. Each entry \(w_{\text{ij}}\) is the membership values of the \(i\)-th gene to the \(j\)-th metagene. The metagenes represent the relationships among the genes. From the mathematical point of view, these are linear relationships that emerge from data. One gene can be involved in the definition of different bases (metagenes); indeed, these are not exclusive relationships. This well represents the biological nature of the metagenes that could actually involve different genes depending on their role.  
The encoding matrix \(H\) represents the original data (HMCLs) in terms of the metagene expression profile. Each entry \(h_{\text{ij}}\) denotes the importance of the \(i\)-th metagene in reconstructing the \(j\)-th HMCLThe non-negativity constraint of NMF allows describing the HMCLs as a linear combination of these metagenes, weighted by the coefficients in \(H\) (part-based decomposition).  
Graphical representations of the metgene expression profiles could reveal different metgene behaviors for the considered HMCLs, as in Figure 1. Therefore these patterns are used to automatically group the HMCLs based on their similarities.  
For this study, the standard multiplicative update algorithm [39] based on the Euclidean distance was used. Moreover, since the NMF algorithms are strictly dependent on the initial starting point, the non-negative double singular value decomposition (NNDSVD) algorithm was applied since it was proven to improve the quality of clustering results [40].  
The rank of the factorization is an input parameter of the problem. It is crucial for the results since it indicates the number of metagenes and the clusters that should be extracted. The correlation matrix gives a qualitative measurement of the stability of the algorithm. Quantitative measurement of this stability is given by the cophenetic correlation coefficient that in gene expression analysis it is used to quantify the stability of the clustering results of microarray gene expression [10].
## 4 Experiments
### Datasets  
The BCDD dataset was collected in New Zealand and covered Christchurch. It contains two high-resolution remote sensing images with a registration error of 1.6 pixels. The imaging dates were 2012 and 2016, respectively, the resolution is 0.3 m/pixel, and the size is 32,507 \(\times\) 15,354 pixels. To make the training more convenient, we cropped the images into non-overlapping 256 \(\times\) 256 image pairs, for a total of 7434 pairs, and divided them randomly in the ratio of 8:1:1 into a training set, validation set, and testing set.  
The LEVIR-CD dataset originated from the Beihang LEVIR team, and the imaging locations were 20 different areas in several cities in Texas, USA. The imaging time varied from 2002 to 2018. Over 31,000 individual instances of change were fully labeled in 637 image pairs of 1024 \(\times\) 1024 pixels and a resolution of 0.5 m, among which the change in land use types such as urban expansion was more significant. For the convenience of training, we cropped the image into small nonoverlapping blocks of 256 \(\times\) 256 pixels, and the dataset was randomly partitioned, with 7120 image pairs as the training set, 2048 image pairs as the validation set, and 1024 image pairs as the testing set.  
The SYSU-CD dataset includes 20,000 pairs of 0.5 m aerial images collected in Hong Kong in 2007 and 2014. The primary change types in the dataset comprised suburban sprawl, new urban construction, pre-construction groundwork, road expansion, vegetation changes, and marine construction. In this experiment, we partitioned the whole dataset into a training set, a validation set, and a testing set in the proportion of 6:2:2.  
Table 3 shows the sizes of the three datasets used.
# Experimental Demonstration of Single-Channel EEG Signal Using 32 x 32 Pixel OLED Screen and Camera
## 3 VL-OCC System Architecture and Applications
### VL-OCC System Architecture  
The basic simplified block diagram of VL-OCC is shown in Figure 2 and it is comprised of three sections, namely transmitter, optical channel, and receiver. The transmitter section is comprised of input data module, modulator, and LED driver. The input data bits can be transmitted through the LED using a modulation scheme such as OOK, UPSOOK, etc.; The LED driver circuitry is essential to drive the LED, used as transmitter. The free space optical channel is the second module where the intensity-modulated light signals propagates from the transmitter to the receiver.  
The third module is the receiver section whereby the receiver is a camera, used to capture the transmitted light signal in the form of images or video. Thereafter, the offline processing or image processing is done in MATLAB to decode the bits and to extract the original transmitted bits, and finally to calculate BER, resulting in reliability and stability of the communication system.  
The image bits received have distortions which can be minimized at a large extent by accurately focusing the camera. Furthermore, the interference from neighboring sources can be significantly mitigated using region of interest signaling, where the source is detected at a low frame rate. Another method to reduce the interference is the selective capturing technique, in which the selective LED region is captured by the image sensor/camera, hence minimizing the probability of interference. In the proposed system modelling and experimental work, the selective capture technique was used to minimize the interference [44; 46].
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 4 Commercial UWB Modules and Chips for IPSs
### NXP Trimension UIWB Chips Family  
The Trimension UWB family (NXP Semiconductors, Eindhoven, The Netherlands) has many products that are well customized to suit different applications. Trimension SR150 is used for UWB tags and anchors in IoT applications, and it is ideal for applications with large-scale infrastructure. It has a pre-installed FiRaTM stack (which stands for \"fine ranging\"), which has the integrated FiRaTM MAC, supporting interoperability with a set of UWB devices in the market. It adds angle-of-arrival (AoA) technology for high-precision applications, and it is interoperable with IEEE 802.15.4z higher-pulse-repetition UWB. Other members of the Trimension family of UWB chips, such as SR150, SR040, OL23D0, NCJ29D5, and NCJ29D6, can be used with different technologies and applications [35; 36].
## 3 Results
### Different Surfaces  
One of the crucial parameters for signal simulation is the proper surface choice. Here, we compare fully absorbing, lambertian, rough (with roughness of 0.29 in the Cook-Torrence BRDF), and specular tungsten surfaces. The latter three models have 10%, 45%, and 54% of effective reflectivity averaged over the angles of incidence. The absorbing and specular cases give the most extreme cases setting the range of possible influence of the reflections. The lambertian and rough models are supposed to describe tungsten-coated and bulk tungsten PFCs on the JET tokamak, respectively [8]. As most of the WEST PFCs were coated with tungsten, but not made from bulk tungsten, the lambertian case might be the closest to the real optical properties of WEST components.  
To obtain data from Figure 5, deuterium and tungsten radiation is taken into account. Time moments of \(t=0.26\) and \(4.48\) s are used as the most representative ones, covering both limiter and divertor phases of the discharge. According to Figure 5, the signals diverge both for divertor and central channels. This is due to the complex geometry of the PFCs, which allows reflected light from various locations to reach the detectors. Such a difference can be important both for a simulation results benchmark as well as for the interpretation of the experimental results. For example, this is crucial for tomography inversions, because  
Figure 4: Bolometer cameras benchmark. (**a**) Two-dimensional radiation isolines, obtained with Cherab and used for signals calculation. (**b**) Synthetic signals from Cherab and SYNDI comparison. (**c**) Absolute relative difference between Cherab and SYNDI calculated signals.  
reflected light can be counted as the real plasma radiation. The proper optical description of the WEST tokamak should be implemented during further studies together with a more precise CAD model of the WEST tokamak.
# Path Optimization Using Metaheuristic Techniques for a Surveillance Robot
## 5 Simulation Results
### Results in Scenario (b): Medium-Complexity Room  
In the case of a space of medium complexity, the Pattern Search algorithm has been the most efficient in terms of optimization time, iterations, and final trajectory, as in the previous case. The GA has turned out to be the slowest. This observation is a direct extension of the explanation described above. In the context of a moderately complex environment, the preference for a local search algorithm remains the best option, reflecting the need for detailed adjustments within this specific environment. However, it is worth noting that the performance disparities between GA, PSO, and PS are not as pronounced as in the low-complexity scenario.  
The results in this medium-complexity environment suggest that as the complexity and dimensions of the space increase, global search algorithms begin to show more favorable final results. Furthermore, it can be noted that in this case, as well as in the other environments studied in this article, the GA exhibits a relatively longer convergence time and a greater propensity to require more iterations to find solutions.  
The graph shown in Figure 12 matches the data in Table 3. PS is the fastest to find a good solution, while the GA is the slowest. In general terms, after eight hours of simulation, the best results are also offered by PS.  
Figure 13a-c show that the best trajectory is the one obtained with PS, that is simpler and more straightforward, in contrast to the GA and PSO, where the robot makes many inefficient displacements. In particular, the final trajectory of the GA is the worst.  
Figure 11: Experiments in the low-complexity room with an object. Real environment (**a**), and mapped environment (**b**).  
Figure 12: Medium-complexity room. Cost function evolution.
## 3 Results
### Determination of Crucial Components  
The contribution of different variables to the overall classification can be explained by the variable importance for the projection (VIP) [29]. The more notable the difference in variables across groups, the higher the VIP value is. When the VIP value is higher than 1.00, the corresponding variable can be defined as the key variable of the discriminant model. To make the analysis more accurate, a Kruskal-Wallis nonparametric test was used to further examine the compounds with VIP greater than 1.00, along with the P-values less than 0.05 as the selection criteria. 14 crucial components were eventually identified (Table 3), which were consistent with the results of the PLS-DA loading scatter plot, indicating statistical variations of the three species. Moreover, the olfactory properties of the 14 crucial components are varied, primarily manifesting as citrus, woody, floral.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Components Name** & **VIP** & \(p\)**-Value** & **Aroma Characteristics** \\ \hline
1 & \(\alpha\)-Farmesene & 3.70 & 0.02 & Citrus, herbal, lavender \\
2 & D-Limonene & 2.76 & 0.03 & Citrus, orange \\
3 & Germacrene D & 2.50 & 0.02 & Woody, spice \\
4 & Linalool & 2.47 & 0.02 & Citrus, woody, floral \\
5 & Cyclohexene, 1-methyl-4-(1- & 2.11 & 0.02 & Citrus, woody \\  & methylethylene)- & & & \\
6 & Phenyltly Alcohol & 1.55 & 0.02 & floral, rose \\
7 & \(\gamma\)-Terpinene & 1.38 & 0.02 & Oily, woody, lemon \\
8 & NI-11 & 1.33 & 0.03 & - \\
9 & Methyleugenol & 1.21 & 0.02 & Spicy, cinnamon, clove \\
10 & Benzeneacetaldehyde & 1.16 & 0.02 & Honey, floral, cocoa \\
11 & Estragole & 1.10 & 0.02 & Spice, herbal, anise \\
12 & NI-6 & 1.09 & 0.02 & - \\  & (IR)-2,6,6- & & & \\
13 & Trimethylylicy[3.1.1]hept- & 1.06 & 0.02 & Terpene, aromatic, minty \\  & 2-ene & 1.04 & 0.02 & - \\ \hline \end{tabular}
\end{table}
Table 3: Characteristics of the 14 crucial components in the PLS-DA model.  
Figure 4: Permutation test of PLS-DA model. (**a**) _T. cordata_; (**b**) _T. miqueliana_; (**c**) _T. tomentosa_.  
A heat map was utilized for the analysis to visualize the distribution of the 14 crucial components at the different flowering stages of the three species, as shown in Figure 5. The three species could be totally separated from one another by the 14 crucial components, which had varying distributions and high abundances.