esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Test Method
### Data Collection  
The data acquisition equipment used to measure the surface texture of the pavement slab samples was the AMES Laser Texture Scanner 9500, which is shown in Figure 5a.  
The AMES Laser Texture Scanner 9500 system can acquire highly accurate texture data of the surface. The instrument comprises a laser-camera assembly, which incorporates a camera and a line laser, working in tandem to capture the data. The collection of 3D pavement data was accomplished through the implementation of the laser pulse reflection principle and the laser triangulation method [38].  
Figure 4: Relationship between degree of compaction and number of roller passes.  
Figure 5: (**a**) Texture data acquisition using AMES Laser Scanner 9500; (**b**) Three-dimensional display of data on software.  
With a scanning resolution of approximately 0.04 mm for horizontal direction and 0.01 mm for vertical direction, the instrument can detect micro-scale with a resolution of 0.1 mm. The sampling frequency of the scanning is 1 mm. At one time, the horizontal range of the scan is 101.6 mm \(\times\) 101.6 mm. After scanning, the texture was cropped to the size of 100 mm \(\times\) 100 mm to ensure that each measurement was taken in the same position.  
During data collection, the instrument was not affected by ambient light sources. To display the raw cloud point data, the manufacturer's software was used, which presented the data on a 3D display, as shown in Figure 5b. The 3D display allowed us to visualize the texture of the pavement surface preliminarily, enabling us to evaluate the quality of the data. The instrument is highly accurate and has a short collection time, making it an effective tool for acquiring pavement texture data.
# A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring
## 1 Introduction
### State of the Art
#### 1.1.3 Trend Detection Models  
In the context of this publication a trend is defined as the gradual change in future events from past data in a time series [30]. Trend detection can be differentiated from remaining useful life (RUL) estimation by several aspects. In contrast to RUL estimation, trend detection methods do not extrapolate existing time series into the future. Furthermore, no thresholds for the extrapolated time series are defined which describe the end of lifetime of an asset. Trend detection methods have different purposes. It is possible to differentiate between models for change point detection, trend description and identification of trend presence in a time series. For the considered application, a model is required that answers the question of whether a trend is present. This is why the remainder of this subsection focuses on the field of trend presence identification. Here, various statistical tests exist. The Mann-Kendall test (MK) is a sign test based on pairs of all samples of a time series and their predecessors [31] to detect trends. The Cox-Stuart (CS) test uses a reduced amount of data pairs for a sign test [32] to achieve the same objective. The Wilcoxon-Mann-Whitney trend test builds a test statistic based on the signs of the slopes between samples and the rank sums of the samples with an increasing and decreasing slope [33] for this purpose. The Durbin-Watson test checks for auto-correlation in the residuals of a regression fit. If the residuals do not show autocorrelation, a trend can be assumed [34]. Furthermore, slope based approaches in combination with thresholds exist. The most simple approach from this field is to fit a linear or quadratic function to the time series data, calculate the slope of this function and compare it with a threshold. This model will be named linear regression model, short LR, for the rest of the publication. If the slope exceeds the threshold value, a trend can be assumed. A more complex approach for trend detection is based on the clustering of a time series. In a first step, a clustering algorithm (e.g., Fuzzy-K-Means) is used to detect clusters within the time series. Then, the slope between the cluster centres is determined. Finally, the slope values of the cluster centres are compared with a threshold to decide, whether a trend exists [35]. The last approach for trend detection presented in this section is based on the comparison of the time series' moving average with its overall mean (moving average model, short MA). In a first step, these two quantities are calculated. Afterwards, the time series' standard deviation multiplied by a factor is added to the overall mean to determine a threshold. Then, it is determined, whether the moving average of the signal rises above this threshold for a defined time window. If this is the case, it can be assumed that a trend is present in the signal. The principle behind this method is also illustrated in Figure 1.
# Study on Bending Performance of Tightly Spliced Truss-Reinforced Plate-Honeycomb Flat Beam
## 3 Test Results and Discussions
### Finite Element Analysis
#### 3.4.2 Parametric Analysis  
Based on the validated numerical analysis model mentioned above, parameterized analysis was conducted, mainly considering factors such as hole-to-height ratio and, where the range of hole-to-height ratio is 0.45-0.75 and taking into account the bearing characteristics of honeycomb beams with a cross-sectional height of 160-230 mm at a hole-to-height ratio of 0.5. A total of 16 specimens were selected, and their variation parameters and bending bearing capacity calculation values are shown in Table 4.  
Among them, research has found that the reduction coefficient of bearing capacity exhibits a nonlinear relationship under different hole-height ratios, which can be used for subsequent simplified calculation and analysis, as shown in Figure 18.
## 4 Evaluation Model Based on AH-Entropy and Normal Cloud Model
### Determination of Combination Weights  
Prior to employing cloud models to assess pavement performance conditions, it is crucial to determine appropriate indicator weights. Weighting methods encompass subjective, objective, and comprehensive approaches. Subjective methods include the AHP [31], the Delphi method [32], and the scoring method [33], among others. Objective methods comprise the entropy method [34], CRITIC method [35], and principal component analysis (PCA) [36], among others. Subjective methods can reasonably establish weights, ensuring alignment with actual importance; however, they may lack objectivity. Objective methods offer clarity in the calculation process and objective weighting but may fail to capture evaluators' varying degrees of importance placed on different indicators, potentially leading to disparities between attribute weights and actual importance. Upon reviewing existing research findings, the AHP method was selected for subjective weighting, entropy weighting for objective weighting, and a combination of both methods to derive comprehensive weights.
## 4 Support Vector Machines
### The Standard Model for SVM  
An SVM is a supervised classification algorithm that allows a binary decision to be performed, assigning an \(M\)-dimensional feature-vector to one of two classes. Being a supervised approach, an SVM needs to be trained using an appropriate dataset, which should be sufficiently large and representative of the two classes, with respect to the selected features. A training phase is then needed to determine a subset of the training vectors (called support vectors), which will actually be used for solving the classification problem. One important advantage of SVMs resides in the fact that the number of support vectors is generally much smaller than the cardinality of the training dataset. Hence, while the training of the SVM can be a resource-intensive task, the actual classification algorithm can be very slender. The standard formulation for a two-class classification problem is:  
\[y(\mathbf{x})=\mathbf{w}^{T}\phi(\mathbf{x})+b \tag{1}\]  
which is a linear model where \(\mathbf{x}\) is the \(M\)-dimensional input vector, \(M\) is the size of the feature space, \(\mathbf{w}=\{w_{1},\,w_{2},\,\ldots,\,w_{M}\}\) is the vector of coefficients for the linear model, \(\phi\) is a general feature-space transformation function (which can eventually be non-linear) and \(b\) represents the bias of the model.  
Hence, the training set for the SVM is composed of a set of \(N\) training feature-vectors \(\mathbf{x}_{1},\,\ldots,\,\mathbf{x}_{N}\) where each vector is associated with one of the two classes \((C_{1},\,\,C_{2})\) via the parameters \(t_{n}=\{-1,\,\,1\}\), which are the class labels for the training vectors. The decision logic is then the following: an unknown vector \(\mathbf{x}^{*}\) belongs to class \(C_{1}\), if \(y(\mathbf{x}^{*})<0\) and to class \(C_{2}\) if \(y(\mathbf{x}^{*})>0\). The implicit assumption is that the training data are linearly separable, so that the coefficient vector \(\mathbf{w}\) and the parameter \(b\) can be determined (i.e., there exists at least one feasible combination of \(\mathbf{w}\) and \(b\)).
###### Abstract  
A polarization-independent electro-optical (EO) laser beam steerer based on a bulk relaxor ferroelectric polycrystalline Pb(Mg\({}_{1/3}\)Nb\({}_{2/3}\))O\({}_{3}\)-PbTiO\({}_{3}\) (PMN-PT) was developed in this study to steer light ranging from visible to mid-IR wavelengths. A large number of the resolvable spots was achieved with this EO steerer. A Fourier-transform infrared (FTIR) spectroscopy was employed to determine the refractive index of the polycrystalline PMN-PT over a wide range of optical wavelengths. Besides measuring the transmission of this material, the capacitance bridge analysis was used to characterize the effect of temperature on the dielectric constant of PMN-PT. The performance of the steerer over a variety of wavelengths was simulated using COMSOL. Multiphysics. The deflection angle for the wavelengths of 532, 632.8, 1550, and 4500 nm was measured in the lab in terms of mrad.mm/kV at two different temperatures and compared to the simulation results. The quadratic Kerr electro-optic coefficient and the halfwave electric field were determined for those four wavelengths at two different temperatures. The results showed polycrystalline PMN-PT has a large quadratic EO coefficient for visible light, almost as large in the near IR, but drops significantly in the mid-IR. No significant temperature dependency for the EO coefficients was observed for any of those four wavelengths.  
relaxor ferroelectrics; Kerr effect; beam steerer; polycrystalline PMN-PT; halfwave electric field; Fourier-transform infrared (FTIR) spectroscopy; mid-IR +
Footnote †: journal: Applied Physics Letters
## 3 Results and Discussion
### Characterization
#### 3.2.2 BET Analysis  
The specific surface area of the nZVI@ATP was analyzed to obtain the specific surface parameters of the material, as well as the N\({}_{2}\) adsorption and desorption curves (Figure 3 and Table 1). The N\({}_{2}\) adsorption and desorption curves were isotherms of type IV(a). The isotherm and adsorption model analysis revealed that the nZVI@ATP had a mesoporous  
Figure 1: Removal efficiency of Ni\({}^{2+}\) by different adsorbents (initial concentration of 200 mg/L; initial pH of 5; temperature of 298 K; dosage of 1 g/L; and stirring time of 12 h).  
Figure 2: (a) SEM image of nZVI@ATP; and (b) EDS image of nZVI@ATP.  
structure (2-50 nm), with a specific surface area and an average pore size of 51.79 m\({}^{2}\)/g and 9.22 nm, respectively; the specific surface area of the nZVI@ATP exceeded that of the nZVI (15-34 m\({}^{2}\)/g) [29]. This finding indicates that ATP exerts a good dispersion effect on nZVI and can effectively alleviate its easy agglomeration.
###### Abstract  
Fiber-optic sensors, such as fiber Bragg grating (FBG) sensors and fiber-optic interferometers, have excellent sensing capabilities for industrial, chemical, and biomedical engineering applications. This paper used machine learning to enhance the number of fiber-optic sensing placement points and promote the cost-effectiveness and diversity of fiber-optic sensing applications. In this paper, the framework adopted is the FBG cascading an interferometer, and a deep belief network (DBN) is used to demodulate the wavelength of the sampled complex spectrum. As the capacity of the fiber-optic sensor arrangement is optimized, the peak spectra from FBGs undergoing strain or temperature changes may overlap. In addition, overlapping FBG spectra with interferometer spectra results in periodic modulation of the spectral intensity, making the spectral intensity variation more complex as a function of different strains or temperature levels. Therefore, it may not be possible to analyze the sensed results of FBGs with the naked eye, and it would be ideal to use machine learning to demodulate the sensed results of FBGs and the interferometer. Experimental results show that DBN can successfully interpret the wavelengths of individual FBG peaks, and peaks of the interferometer spectrum, from the overlapping spectrum of peak-overlapping FBGs and the interferometer.  
fiber Bragg grating sensor; interferometer; deep belief network; wavelength detection Article  
31 July 2024  
15 August 2024  
Accepted: 19 August 2024  
Published: 20 August 2024
## 4 Results and Discussion
### Ablation Study on Different Attention Mechanisms
Furthermore, the proposed method incorporates a stripe-cross attention mechanism that enhances interactions between features, enabling simultaneous optimization of local and global information representations in multimodal tasks. These mathematical innovations not only improve the overall model performance but also significantly enhance adaptability to complex scenarios. The results conclusively demonstrate the theoretical and practical superiority of the proposed sliding-window attention mechanism. To further evaluate the effectiveness of different attention mechanisms in real-world applications, two qualitative case studies were conducted. In a financial time-series anomaly detection task, the sliding-window attention mechanism successfully localized short-term fluctuations, improving sensitivity to rapid changes. In contrast, standard self-attention showed limited responsiveness, and CBAM lacked adequate sequential modeling. In a spatial small-object recognition task, the sliding-window mechanism provided focused local attention and improved foreground-background separation, significantly enhancing \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **Accuracy** & **F1-Score** & **FPS** \\ \hline Time-Series—Standard & & & & & \\ Self-Attention [49] & 0.73 & 0.70 & 0.72 & 0.71 & 31 \\ Time-Series—CBAM [50] & 0.85 & 0.81 & 0.83 & 0.83 & 35 \\ Time-Series—Proposed Method & 0.95 & 0.91 & 0.93 & 0.93 & 46 \\ Spatial Data—Standard & & 0.71 & 0.68 & 0.70 & 0.69 & 33 \\ Self-Attention [51] & & & & & \\ Spatial Data—CBAM [52] & 0.83 & 0.80 & 0.82 & 0.81 & 40 \\ Spatial Data—Proposed Method & 0.93 & 0.90 & 0.92 & 0.91 & 49 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation study on different attention mechanisms. recall and precision. These findings validate the practical advantage of the proposed attention design.
# The Health-Promoting Potential of Fruit Pomace and Its Application in the Confectionery Industry
## 5 The Methods Used for Preserving the Nutritional Benefits of Pomace as Candy Fillings
### Spray Drying  
Spray drying enables the efficient use of fruit pomace in food production while preserving its nutritional value and improving the functionality of the final product. This method transforms fruit pomace into a fine powder that can be easily incorporated into candy fillings. A study on the microencapsulation of phenolic extract from Vitis vinifera grape pomace using maltodextrin demonstrated that the resulting powder enriched gummy candies with retained antioxidant properties and acceptable sensory qualities [PERSON] et al. (2019). Spray drying is especially effective at preserving antioxidants due to its low processing temperatures and short drying time. Moreover, it is cost-effective and suitable for large-scale applications [PERSON] et al. (2019) (Table 4).  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Method** & **Description** & **Nutritional Impact** & **Technological Feasibility** & **Reference** \\ \hline Aqueous Extraction & Extracts bioactives (e.g., phenolics, carotenoids from pomace. & Improves antioxidant and antidiabetic properties. & Simple, suitable for clean-label products. & [PERSON] et al. (2024) \\ \hline Spray Drying & Converts extract into powder using low-temp drying. & Preserves antioxidants, improves sensory properties. & Scalable, cost-efficient, industrial-friendly. & [PERSON] et al. (2019); [PERSON] et al. (2019) \\ \hline Freeze Drying & Low-temp dehydration preserving structure and compounds. & Retains color, flavor, fiber, and polyphenols. & Premium quality, higher cost, aseptic. & [PERSON] et al. (2019) \\ \hline Oven/Air Drying & Drying at 58–60 \({}^{\circ}\)C; used for pomace flour production. & Some nutrient loss, but increased fiber and phenolics remain. & Economical, slower, widely accessible. & [PERSON] et al. (2019) \\ \hline Wet Planetary Ball & Reduces particle size in oil suspension. & Maintains fiber and polyphenol stability. & Industrially viable, enhances texture and shelf-life. & [PERSON] et al. (2019) \\ \hline Grinding & Fine powder preparation from dried pomace. & Increases polyphenol, flavonoid, and betalin content; retains stability. & Simple, compatible with standard confectionery processing. & [PERSON] et al. (2019) \\ \hline Enzymatic Treatment & Enzymes break down polysaccharides to increase solubility and fiber content. & Enhances probiotic viability; improves swelling and hydration capacity. & Scalable, low-cost, functionally beneficial. & [PERSON] et al. (2019) \\ \hline Heat and Infrared Treatment & Infrared at 140–150 \({}^{\circ}\)C for 10 min with wheat bran. & Increases fiber and polyphenols with minimal degradation. & Easy to implement in small-scale settings, eco-friendly. & [PERSON] et al. (2019) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of techniques for preserving nutritional benefits of pomace use in confectionery.
# Enhancements and Challenges in CoAP--A Survey
## 3 Applications of CoAP
### Interoperability and Integration
#### 3.1.2 CoAP Interoperability with MQTT for Healthcare  
In [10], [PERSON] et al. design and implement a messaging system for IoT devices used in healthcare sector. Although the use of Message Queuing Telemetry Transport (MQTT) protocol in IoT devices has advantages of supporting many-to-many communication and with a small overhead; however, the limited rest time of devices due to constant wait for request is disadvantageous for these constrained devices. Therefore, the proposed system implements the messaging system with MQTT model using CoAP. The architecture consists of publisher, subscriber, and message broker where publisher and subscriber are CoAP clients and broker is a CoAP server. The use of CoAP observer method is employed to implement the subscription method of MQTT and GET, POST, PUT and DELETE methods are used for topic resource discovery, topic resource registration, publishing measured medical data, and resource deletion respectively. The use of CoAP in implementing the MQTT based model is beneficial for keeping the advantages of MQTT protocol as well as providing rest to constrained IoT devices enabling them to consume minimum energy and drain less battery.
## 5 Conclusions  
The research in the fields of PAI and non-contact LUI is highly active. The interest in PAI is primarily biomedical (cancer diagnostics, small animal imaging etc.) while the interest in non-contact LUI is primarily industrial (early-stage inspection, additive technologies, inspection in harsh environments etc.). The promising results of these techniques draw considerable attention from the scientific and industrial community. However, today, there is a lack of review papers about the contact LU method. Thus, the purpose of this review is to put a spotlight on the contact LUI and its combinations with PAI, and discuss the algorithms, experimental setups, applications, etc.  
The research in the Laser Optoacoustic Laboratories of ILIT, NUST MISiS, and ILC MSU is primarily concerned with tomographic diagnostics of objects that are of interest for biomedicine and industry. Nowadays, tomographic imaging is a hot topic, since it can provide detailed information about the internal structure, reliability, and safety. One of the important advantages of PAI and LUI is mobility, high speed of operation (up to real time), and moderate cost in comparison with other well-established imaging modalities (like MRI, X-ray CT, neutron imaging etc.). Besides, PAI and LUI are usually safer for humans, since the necessary power of laser radiation can comply with safety standards in PAI, or can be confined inside the setup in contact LUI. Perhaps, PAI and LUI cannot replace the well-established and highly developed techniques completely, however, they can provide valuable complementary information about the object and can be integrated in the existing systems.  
In this paper, we observe methods and algorithms of PAI and LUI imaging first, then we discuss the combined PAI and LUI systems and their biomedical applications in more detail and, finally, we focus on the immersion LU profilometry and tomography as an example of a possible industrial application of LUI.  
[PERSON] reviewed the combined PAI and LUI systems in biomedicine, spatial resolution, sampling, and limited-view issues, and edited the text. [PERSON] reviewed the methods of PAI and LUI, wrote the introduction, and edited the text. [PERSON] reviewed the reconstruction algorithms in PAT and LUT and LUI of solid bodies. [PERSON] and [PERSON] supervised, reviewed and edited the text.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 9: The cross-sectioned 3D model of the sample object: poly (methyl methacrylate) (PMMA) solid of revolution with complex external and internal generatrices fixed on a steel rod and submerged in water (**center**). The reconstructed LU image of the sample object after the first stage of the algorithm without refraction correction (**left**). The final refraction-corrected image (**right**). Black and white stripes show the position of the boundaries that reflect the ultrasonic probe pulse and the sign of the acoustic reflection coefficient (black—negative and white—positive). Adapted with permission from [54], OSA Publishing, 2018.
## 4 Proposed Protocol
### Cluster Formation  
In this subsection, we describe a single round of the proposed EEUCH protocol, which involves dividing the deployed SNs into clusters, where each cluster has a UAV as a CH that collects data from its CMs and sends it to the BS. In each round, UAVs arrive at predefined hovering positions, at a certain altitude from the FoI, from which they can cover the maximum number of SNs. Since unlicensed spectrum is utilized, the UAVs perform CCA first to ensure that the unlicensed channel is free. Once CCA is successful, the UAVs broadcast WuCs to the ground SNs, initiating the cluster formation phase. After the clusters are formed, the SNs of each cluster become CMs of a particular UAV, and the steady-state phase starts, during which data are collected from the ground SNs. At the end of successful data collection, the CMs enable sleep mode. Hence, a single round is completed.
# Group Authentication Scheme for Neighbourhood Area Networks (NANs) in Smart Grids
## 3 The Smart Grid Network Model
### Case Two--Multi-Hop (Mesh)
#### 3.5.2 Authentication of the Smart Meters, \(SM_{ng}\)
Each node will, therefore, have a pre-installed NOC key, a master GW key sent by the NOC, and a random secret R sent by the group gateway. All other keys necessary are derived from this information. Therefore, our scheme is scalable to any number of end devices. However, we realise the need to limit the number of nodes per group to keep the number of paths low, the routing delays low and, consequently, the end-to-end delays low.