esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Cement Mortars
#### 3.2.2 Dry State Density  
The values obtained for the dry state density of the mortars are presented in Figure 8. As it can be seen, although they are, in all the cases, lower than the reference composite, the dry state density values were irregular. However, it was confirmed that the new mixtures  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & **Fresh State Density** & **Dry State Density** & **Water Absorption** & **Flexural Strength** & **Compressive Strength** \\  & **[kg/m\({}^{3}\)]** & **[kg/m\({}^{3}\)]** & **[kg/m\({}^{2}\)\(\cdot\)min\({}^{0.5}\)]** & **[MPa]** & **[MPa]** \\  & **(CoV [\%])** & **(CoV [\%])** & **(CoV [\%])** & **(CoV [\%])** & **(CoV [\%])** \\ \hline
**Reference (R)** & 2096.74 (0.26) & 1989.82 (0.18) & 0.42 & 4.68 (5.26) & 23.66 (10.23) \\
**CBC-0.1** & 2040.12 (0.74) & 1937.97 (0.15) & 0.29 & 5.38 (7.00) & 24.59 (22.73) \\
**CBC-0.25** & 2066.01 (1.33) & 1876.31 (0.23) & 0.41 & 4.92 (10.70) & 20.21 (26.22) \\
**CBC-0.4** & 2074.38 (1.16) & 1956.00 (0.05) & 0.32 & 5.68 (13.52) & 27.71 (25.03) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary table with all the test results obtained for cement mortars.  
Figure 6: SEM images obtained for gypsum plasters (**a**) Reference material; (**b**) GBC-1.  
were lighter than the reference material. The sample CBC-0.25 showed the biggest decrease, with a reduction of 5.7% compared to the reference density value.
# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 4 Experimental Results and Analysis
### Performance Evaluation
#### 4.3.1 Comparative Analysis with State-of-the-Art Methods
Based on the experimental results from both the MOT17 and MOT20 datasets, our proposed tracking model achieves optimal balance between speed and detection performance through innovative algorithm design and optimization. This balance holds significant implications for practical applications, particularly in intelligent surveillance scenarios where real-time processing of extensive video data and accurate tracking of multiple tar  
Figure 5: Performance-Speed Trade-offs Comparison on The MOT17 Dataset. gets are essential. In autonomous driving applications, vehicles must make split-second decisions while maintaining precise real-time tracking of surrounding objects. The exceptional performance of our proposed model provides robust technical support for these application scenarios, demonstrating considerable potential for practical implementation and promising to advance the development of multi-object tracking technology in real-world applications. Moreover, there are significant differences in the performance of different multi-object tracking (MOT) methods on the MOT17 and MOT20 datasets. On the MOT17 dataset, our proposed method achieves scores of 63.7, 79.8, and 79.4 in the HOTA, MOTA, and IDF1 metrics, respectively, ranking just behind StrongSORT++, and it significantly outperforms other methods with only 1134 identity switches, demonstrating its advantage in maintaining object identity consistency. With an FPS of 61.4, it far surpasses StrongSORT++ in processing speed, balancing accuracy and efficiency. In contrast, on the MOT20 dataset, our method achieves HOTA, MOTA, and IDF1 scores of 61.5, 77.1, and 76.5, with slightly lower overall accuracy compared to MOT17, where BoostTrack++ leads with a HOTA of 66.4 and an IDF1 of 82.0. However, our method still maintains a low number of identity switches (1162 times) and has an FPS of 27.6, significantly higher than BoostTrack++. Overall, our method demonstrates robust performance on both datasets, especially excelling in reducing identity switches and improving processing speed, and it remains competitive in practical applications despite the slightly lower accuracy on MOT20.
# Panoramic Digital Image Correlation for 360-Deg Full-Field Displacement Measurement
## 2 Materials and Methods
### Epipolar Geometry-Based Extrinsic Parameters Determination
#### 2.3.1 Stereo Point Matching Registration  
Based on the robust subset-based digital image correlation method, the stereo point correspondences between two distinct camera views are realized, through which a set of accurate point matches can be registered for subsequent extrinsic parameters determination.  
Firstly, the specimen is decorated with a stable, random, high-contrast scatter pattern, or the inherent natural texture of the test specimen is exploited and captured by stereo-camera pairs. Secondly, in the image on the left, a ROI is selected, consisting of a grid of equidistant computed points that correspond to the sample area visible from both camera views. Then, based on the selected subset size and the calculated grid points within the ROI, the corresponding points are detected on the right image using the subset-based DIC. Briefly, for the initial computed points, called seed points, a square reference subset centered on them is selected and, then, iteratively minimized using the least squares correlation criterion \(C_{LS}\) as the correlation function to match them to the target subset of the right-hand image. A first-order shape function is used to describe the transformation of the subset, an inverse synthetic Gauss-Newton method (IC-GN) is used as a non-linear optimizer, and a reliability-oriented approach is used to transfer the calculation to the ROI to achieve a series of image point correspondences between the left and right cameras. The detailed principles and step-by-step implementation of the algorithm are presented in [39; 40].
# Computationally Efficient Channel Estimation in 5G Massive Multiple-Input Multiple-output Systems
## 2 System Model
### Minimum Mean Square Error Signal Detection  
The main task of signal detection was to accurately determine user transmission vector \(s\) at the base station through received signal vector \(y\). The transmitted signal vector \(\hat{s}\) detected by the MMSE algorithm could be expressed as:  
\[\hat{s}=\left(H^{H}H+\sigma^{2}I_{2K}\right)^{-1}\!H^{H}y=W^{-1}\hat{y} \tag{4}\]  
where \(\hat{y}=H^{H}y\). The filter matrix \(W\) of the MMSE detector could be expressed as:  
\[W=G+\sigma^{2}I_{2K} \tag{5}\]  
Figure 1: Massive multiple-input multiple-output system model.  
where \(G=H^{H}H\) was the Gram matrix. In massive MIMO systems, the computational complexity of \(W^{-1}\) is \(O\big{(}K^{3}\big{)}\), which makes the implementation of the MMSE algorithm very complex.
# Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring
## 5 Fiber Optics  
Fiber Optic Sensors (FOSs) have found their practical use in civil engineering applications for structural health monitoring, including crack detection, measuring strains, pH level, vibration, corrosion, temperature monitoring, etc. The FOSs are designed to manipulate transmitted or reflected light response, namely, intensity, polarization, and spectral content of the phase from the measured object. A FOS is a cylindrical symmetric structure, which comprises a central core with a diameter between 4 and 600 \(\upmu\)m and a uniform refractive index [31]. The FOS is enclosed in cladding with a relatively lower refractive index. In this way, the light waves propagating within the core are trapped by the cladding due to the reflection at the interface between the core and the cladding [32]. The cladding is then covered with an external plastic coating, which provides environmental and mechanical protection to the FOS. A FOS is shown in Figure 5.  
Figure 4: Schematics of the proposed hybrid image scanning (HIS) system [30].  
Figure 5: An optical fiber along with light guiding and reflection [32].  
The FOSs offer a number of advantages compared to traditional sensors, such as strain gauges. Firstly, the light signal in the FOS is transmitted over a very long length leading to insensitivity to electromagnetic radiation and very low signal transmission loss [3]. FOSs are very durable since they are made of glass, and the protection cover is free from corrosion, which increases their durability [33; 34]. FOSs are not non-conductive, which means that they are free of electromagnetic and radio-frequency interference. Thus, they can be used in urban areas, in which the problem of signal interferences is severe. FOS are light and of small size, and for this reason, they are suitable for embedding them permanently into structures [3; 33]. In addition, many FOSs can be joined together into one array, so one cable is able to work for approximately 10 sensors, which is an advantage over the traditional electrical strain gauges and accelerometers that require a larger amount of wiring for one sensor. This results in simplification of the cable layout, and thus the installation period of FOSs is less than that of traditional sensors, which leads to less installation costs [33]. FOSs can be divided into three main techniques, i.e., fiber Bragg grating (FBG) sensors, distributed sensors and interferometric sensors [35]. In the following, the principles of each method are provided, along with some recent application in Civil Engineering.
## 3 Mathematical Models
### Direct Heat Transfer Problem  
On the basis of the assumptions presented in the previous section, the macroscopic mathematical model of the heat transfer problem in granular material, based on [PERSON]'s law, as the one-dimensional axisymmetric task in the circle (the cross-section of the cylinder) of radius \(R\) [m] (ignoring the thin-walled metal sheet of the can) can be formulated in the form [14; 15]  
\[c\rho\frac{\partial T(r,t)}{\partial t}=\frac{1}{r}\frac{\partial}{\partial r }\bigg{(}\lambda r\frac{\partial T(r,t)}{\partial r}\bigg{)},\quad 0<r<R,\quad t>0, \tag{1}\]  
where \(T\) denotes temperature [\({}^{\circ}\)C], \(t\) and \(r\) denote time [s] and the radial coordinate [m], \(c\), \(\rho\) and \(\lambda\) are the specific heat capacity [J/(kg \({}^{\circ}\)C]), density [kg/m\({}^{3}\)] and thermal conductivity [W/(m \({}^{\circ}\)C)] of the granular material, respectively. Equation (1) is supplemented by the following initial boundary conditions  
\[T(r,t)\big{|}_{t=0}=T_{init}, \tag{2}\]  
\[\left.\frac{\partial T(r,t)}{\partial r}\right|_{r=0}=0, \tag{3}\]  
\[T(r,t)\big{|}_{r=R}=T_{b}. \tag{4}\]  
Figure 1: Schematic diagram of the measurement system.  
Assuming that the thermophysical parameters \(c,\rho\) and \(\lambda\) are constants, and \(a=\lambda/(c\rho)\) is the thermal diffusivity [m\({}^{2}\)/s] (that describes the speed of propagation of thermal energy in the material during changes in temperature over time), then Equation (1) takes the form  
\[\frac{\partial T(r,t)}{\partial t}=a\left(\frac{\partial^{2}T(r,t)}{\partial r^ {2}}+\frac{1}{r}\frac{\partial T(r,t)}{\partial r}\right),\quad 0<r<R,\quad t>0. \tag{5}\]  
The analytical solution of the above direct problem using Green's function in the general form has been presented in [14; 36], but by adopting the constant values of \(T_{init}\), \(T_{b}\) and \(a\), then the solution reduces to the simpler form and can be represented as  
\[T(r,t)=T_{b}+(T_{init}-T_{b})\sum_{k=1}^{\infty}\frac{2}{\mu_{k}I_{1}(\mu_{k}) }\exp\left(-\mu_{k}^{2}\frac{at}{R^{2}}\right)J_{0}\left(\mu_{k}\frac{r}{R} \right), \tag{6}\]  
where \(J_{0}\) and \(J_{1}\) are called the Bessel functions of the first kind of orders zero and one. The values of \(\mu_{k}\) in Equation (6) are positive zeros of the Bessel function of order zero, i.e., \(J_{0}(\mu)=0\). Usually, the roots of this equation are determined numerically (by using one of many known root-finding algorithms [37]) and some of them with a precision of six decimal places are presented in Table 1. In the numerical calculations of the values of function \(T\), it is worth taking the values of \(\mu_{k}\) with much greater precision.  
In practical computations, the infinite sum in Equation (6) can be reduced to about 100-200 terms, giving fairly good approximate results.
## 3 Results
### Analysis of THR
#### 3.1.2 Tablets with Phenoxymethylpenicillin  
For this preparation, the lowest values of THR were demonstrated for the UV range (335-380 nm) and infrared (from 1000 nm to 2500 nm). The THR values for the 700-1100 nm infrared range were similar to THR within the total visible light range.  
For non-blister phenoxymethylpenicillin tablets, the THR values differed between day 0 and day 3 for all spectral bands except for 335-380 nm, and between day 3 and day 7 for all spectral bands except for 1000-1700 nm. For blister tablets in the range 335-380, THR was significantly lower on day 0 compared to day 3 and day 7 (\(p<0.001\)) (Figure 4). Comparisons of THR on day 3 between non-blister and blister tablets demonstrated that blister tablets had significantly higher THR than non-blister tablets in all ranges (\(p<0.001\)). In turn, the mean THR value after 7 days of experiment was considerably higher for non-blister tablets in most of the analyzed spectral ranges except for the 335-380 nm and 400-540 nm ranges (Figure 4).  
Figure 4: Comparison of mean THR values of tablets with phenoxymethylpenicillin between subsequent days of experiment. THR—total hemispherical reflectance; B—blister tablets. Only significant differences are shown.  
Figure 3: The course of mean THR spectra at subsequent time points (days 0, 3, and 7) for non-blister clindamycin tablets (on the left) and clindamycin tablets stored in blisters (on the right). THR—total hemispherical reflectance; B—blister tablets.  
THR for non-blister phenoxymethyllpenicillin tablets decreased slightly on day 3 of UV exposure for the ranges 400-540 nm, 480-600 nm, 590-720 nm, 700-1100 nm, and 1000-1700 nm, and then on day 7 of UV exposure, THR values increased to a level close to the baseline. For the remaining ranges, i.e., 335-380 nm and 1700-2500 nm, THR values were close to baseline values on day 3 and increased on day 7. Figure 5 shows the course of the spectra for the above-mentioned bands throughout the experiment (time points 0, 3, and 7). In the case of blister tablets, THR values remained at a similar level throughout the experiment for all ranges except 335-380 nm, for which THR is the highest after 7 days of UV exposure.
## 2 Numerical Simulation Method
### Model Validation  
The matrix and bedding micro-parameters obtained through parameter calibration are presented in Table 1. Figure 4 illustrates the numerical simulation and experimental stress-strain curves for uniaxial compression when the bedding angle is 60\({}^{\circ}\). As shown in Figure 4a, the elastic modulus and compressive strength of the numerical and experimental  
Figure 3: Schematic representation of shale numerical model.  
results are in close agreement, and the trend of the acoustic emission (AE) count aligns well with the stress curve. Since the model lacks defect structures such as pores and microcracks, the experimental curve does not display the typical concave compaction section during the deformation stage. However, the pre-peak loading section and the post-peak brittle failure process are consistent with the experimental observations. Figure 4b presents the failure modes of the experimental and simulated specimens, showing shear failure along the bedding plane, with the macroscopic crack morphology being identical. In summary, while there are minor differences in specific details between the numerical simulation and the experimental curves, the overall agreement is good, demonstrating that RFPA3D is highly applicable for studying the failure of brittle rocks.
# Time-Series Prediction of Failures in an Industrial Assembly Line Using Artificial Learning
## 3 Materials and Methods
### Modelling Using MLP  
MLP is one of the earliest ANN techniques developed for supervised learning, with initial applications dating back to 1954. It was not originally designed for time-series analysis; however, it has been included in this study for performance comparison. MLP consists of layers. The main layers are the input, hidden, and output layers. The neurons in each layer process weighted inputs from the previous layer using a nonlinear activation function and transmit the resulting output to the subsequent layer. The input layer receives raw data, providing the foundation for network operation, while the hidden layers learn nonlinear relationships and generate high-level representations of the data. Finally, the output layer performs either classification or regression tasks.  
The MLP network topology shown in Figure 10 illustrates the interconnections among the layers and the flow of information within the network.  
Since MLP networks have the same topology as NAR networks with their layered structure, their hyper-parameters and detection methods are also the same. To ensure an enhancement in generalisation ability and a reduced risk of overfitting, the number of hidden  
Figure 10: MLP network topology.  
layers and neurons per layer are limited through a trial-and-error process based on validation error trends. Also, early-stopping is applied to determine the optimum epoch number. The early-stopping method is based on catching the best iteration number just before the validation curve starts to divergence. Also, simpler architectures generalise better.
# Energy and Spectrally Efficient Modulation Scheme for IoT Applications
## 3 OSP-MSK Performance Analysis
### OSP-MSK ABER Performance
Therefore, the probability of correctness due to the two arms can be expressed as follows:  
\[p_{c}=(1-p_{e})^{2}=1-2p_{e}+{p_{e}}^{2}, \tag{25}\]  
Hence, the probability of error due to the first two arms can be expressed as follows:  
\[p_{e\;1,2}=1-p_{e}=erfc\Bigg{(}\sqrt{\frac{E_{b}}{N_{o}}}\Bigg{)}-\frac{1}{ 4}erfc^{2}\Bigg{(}\sqrt{\frac{E_{b}}{N_{o}}}\Bigg{)}. \tag{26}\]Likewise, the probability of error due to the other two arms \(p_{e\;3,4}\) can be acquired, and it equals to the probability of error due to the first two arms, \(p_{e\;1,2}\). Lastly, the OSP-MSK demodulator can be interpreted as two independent correlation receivers, each with a probability of error of \(p_{e\;1,2}\) and \(p_{e\;3,4}\), respectively. Therefore, the ABER of the proposed OSP-MSK over AWGN can be expressed as follows:  
\[ABER_{G}=\frac{1}{2}p_{e\;1,2}+\frac{1}{2}p_{e\;3,4}=erfc\bigg{(}\sqrt{\frac{ E_{b}}{N_{o}}}\;\bigg{)}-\frac{1}{4}erfc^{2}\bigg{(}\sqrt{\frac{E_{b}}{N_{o}}} \bigg{)}. \tag{27}\]  
To obtain the ABER of the proposed OSP-MSK modulation over different fading channels, the previous analysis can be expanded by averaging the ABER of AWGN provided by Equation (27) over the PDF of fading channels [28]. More specifically, when the fading is present, the received signal amplitude is attenuated by the fading amplitude \(\varepsilon\), and the instantaneous signal power will be attenuated by \(\varepsilon^{2}\). It is should be noted here that \(\varepsilon\) is a random variable (RV) with a mean square value \(\beta=\overline{\varepsilon^{2}}\), and its PDF depends upon the type of fading channel. As such, the signal to noise ratio (SNR) per bit in the presence of fading can be expressed as follows:  
\[\Psi=\varepsilon^{2}\frac{E_{b}}{N_{o}}, \tag{28}\]  
and the average SNR per bit can be expressed as follows:  
\[\overline{\Psi}=\overline{\varepsilon^{2}}\frac{E_{b}}{N_{o}}=\beta\frac{E_{ b}}{N_{o}}. \tag{29}\]  
Therefore, the ABER performance of the OSP-MSK over any fading channel can be determined by integrating the channel fading effect in Equation (28) in the formula of the ABER over AWGN in Equation (27) to acquire the conditional ABER (i.e., conditioned on the channel). Then, the ABER due to the presence of fading can be expressed as follows:  
\[ABER_{f}=\int_{0}^{\infty}ABER_{E|\text{H}}\;p_{\Psi}(\Psi)d\Psi, \tag{30}\]  
where \(ABER_{E|\text{H}}\) denotes the conditional ABER, and \(p_{\Psi}(\Psi)\) is the PDF of the fading channel.
# Design of Multi-Chain Traceability Model for Pepper Products Based on Traceability Code
## 5 Conclusions  
### Summaries  
This paper first introduces the ways to improve the efficiency of blockchain traceability and then chooses to use multi-chain storage to improve the query efficiency and constructs a multi-chain traceability model for pepper products based on the design idea of \"main chain + side chain\" on the basis of the traditional blockchain traceability model. According to the multi-chain model, the design of the corresponding smart contract is completed, and the function of the model is improved. Eventually, through the model comparison test analysis, the multi-chain model becomes more applicable to the actual traceability scenario of the pepper industry chain and further improves the efficiency of traceability querying.  
1. The proposed multi-chain traceability model compared to the previous blockchain single-chain traceability model will have higher query efficiency than the traditional single-chain model when the total quantity of data is greater than 1000 items. When the number of batch traceability data entries is high and the total quantity of data is large, the query efficiency of the multi-chain model is higher than that of the  
Figure 5: Throughput comparison chart.  
single-chain model. The batch traceability data volume of pepper products is about 150 items, and the query efficiency of the multi-chain model is higher than that of the single-chain model when the total data volume is greater than 7000 items. The multi-chain model is more suitable for real traceability scenarios of pepper products.
2. The model enables full on-chain data storage, making traceability data secure. The nodes in each link of the pepper supply chain can be easily uplinked with data to ensure that consumers can query the complete, accurate, and comprehensive pepper traceability information.