esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Drying Methods and Temperatures on the Quality of Chestnut Flows
## 3 Results and Discussion
### Toxic Volatile Compounds by SPME-GC-MS  
The type of cooking and drying can lead to the formation of toxic volatile compounds [11]. In cooked chestnuts, aromatic components have often been found to derive from the degradation and caramelization of saccharides, degradation of proteins and lipids, and the Maillard reaction [13]. An example of these compounds is furfural, which is usually associated with hints of caramel, roasted coffee, and baked bread [51]. In Figure 5, the furfural peak appears in the profile of traditionally dried non-defective flour (light blue chromatogram, peak number 2). This compound is commonly present in many foods but becomes toxic if it exceeds certain quantities (in mice and rats, for example, it has been found to be toxic with LD50 of 400-500 mg/kg and LD50 of 50-149 mg/kg, respectively) [52]. The present study detects the presence of furfural in all the samples and, although not absolutely quantified, the peak for the traditionally dried non-defective flour presents an area that is more than three times larger than the average and it is significative different (Table 1).  
Other compounds typically found in traditionally dried flours are guaiacol (phenol, 2-methoxy) and o-cresol (phenol, 2-methyl), which are formed during the thermal degradation of lignin [9]. These two compounds can be perceived by consumers as a smoky aroma or, when in high concentrations, associated with the smell of burning [9]. There is evidence that cresols, as well as guaiacol, when reaching high concentrations, can be toxic (for guaiacol there is toxicity in rats with LD50 of 520 mg/kg, while for o-cresol there is acute toxicity in mice and rats with LD50 of 344 mg/kg and 121 mg/kg, respectively) [14; 53]. The o-cresol was found in traditionally dried defective flour (black chromatogram), while guaiacol was in traditionally dried non-defective flour (light blue chromatogram). Even in this case, the peak of both o-cresol (peak number 4) and guaiacol (peak number 3) is more than twice larger than the average of all flours and these are significative different.  
Another relevant substance is 1-octen-3-ol, which is commonly associated with the fungal aroma [51] and has shown some toxicity in tests on human cells [54]. This compound is found in 70 \({}^{\circ}\)C hot air-dried defective flour (purple chromatogram) and therefore derived from visibly defective and moldy chestnuts. The area of the peak (peak number 1) is more than four times larger than the average area of all the flours and it is significative different.  
As for the flours dried with 40 \({}^{\circ}\)C hot air and for 70 \({}^{\circ}\)C hot air non-defective, there are no peaks of interest from a toxicological point of view. Ethanol (retention time: 7.5 min) is found only in defective flour, perhaps a sign of alcoholic fermentation due to molds.
# Fault Detection in Industrial Equipment through Analysis of Time Series Stationarity
## 3 Materials and Methods
### Dataset  
The dataset used in this project contains sensor data from three industrial wood chip pumps, which are operating in a large Portuguese paper industry. The data were collected by a set of sensors installed in the pumps and from logging readings with 1 min sampling rate. The dataset contains data for the years 2017, 2018, and 2019. The number of variables monitored is 31, but for the simplicity of this analysis only three were used in this project: the vibration of chip pump 1 (VCP 1), vibration of chip pump 2 (VCP 2), and vibration of chip pump 3 (VCP 3). Vibration was chosen because it of its importance for condition monitoring. It is one of the most telling variables and also one of the most difficult to analyse because of the influence of noise. Increasing vibration in industrial equipment is a serious risk, so this is one variable where a stationarity analysis may be more important.  
Some statistical results from different years and different chip pumps, taken from the provided dataset, are shown in Table 2. As the table shows, there are some negative numbers, which obviously correspond to noise and outliers in the data. The table also shows that VCP1 has the lowest mean and the highest standard deviation in 2017, which means that it potentially had problems in this year. VCP2 has the highest mean of the three chip pumps. It also reaches its highest values in 2017 and 2018, which means that it may need maintenance interventions to improve its working condition and prevent potential failures.  
Table 2 also shows there are some missing samples, otherwise the AF (absolute frequency) would be 525k for all these variables, considering the sampling period is 1 min. Some of the missing samples correspond to periods of planned maintenance, when the equipment and sensors are turned off. Other missing values, however, correspond to failures in the process of reading or recording the data. When this happens, there are still records in the database; however, they are incomplete and may pose challenges to data analysis. Table 3 summarizes the number of missing values that may need attention during the data analysis process.
# Model Parallelism Optimization for CNN FPGA Accelerator
## 4 Optimizations
### Channel Communication  
The OpenCL-based FPGA accelerator data flow is as follows. First, the kernel receives the data in the input buffer; then, it stores the result in the output buffer after computing; finally, the host side reads the result into memory. The whole process needs to ensure that the kernel has stored the complete data in the output buffer when the host side reads the content in the output buffer. For neural networks with several different layers, such as convolutional layers and pooling layers, the host side and the kernel side need to constantly exchange data, resulting in a significant amount of communication time. In this case, the Intel FPGA SDK for the OpenCL channel provided a flexible way to allow data to be passed from one kernel to another, reducing time-consuming interactions and enabling a \"write once, compute many\" programming model. Note that it was necessary to add #pragma OPENCL EXTESION cl_intel_channels: enable before running channels to allow extensions. This was because the default behavior of channels is blocking, and the cl_intel_channels extension provided a way to access data stored in multiple channels with a single instruction. This allowed for the more efficient accessing and processing of data stored in multiple channels.
## 3 Results
### Empirical Algorithm
The SVM model maintains a high level of prediction accuracy when using remote-sensing reflectance data that do not exist in the training set for SD prediction, although the prediction accuracy decreases, and for the shorter time window dataset, the prediction accuracy remains stable. In conclusion, the machine-learning algorithm was suitable for using longer datasets to obtain more stable prediction results. Figure 4: Scatter plot of the measured Secchi depth (SD) and predicted SD of the optimal solutions for different groups of spectral variables corresponding to MLP and SVM models. (**a**) The optimal solutions for the MLP model for different groups of spectral variables. (**b**) The optimal solutions for the SVM model for different groups of spectral variables. The experiment number corresponds to Table 3. The blue points represent models using spectral variables in a single band or band combination. The orange points represent models using spectral variables in band combination and band ratio combination.
# On the Optimal Calculation of the Rice Coding Parameter
## 3 General Selection of the Golomb Parameter for Rice Coding
### Pre-Calculation of \(Q\)  
Any non-negative integer value \(a\) is represented in binary by a sequence of bits \(\mathbf{c}=\{c_{j}\}\) such that:  
\[a=\sum_{0\leq j\leq L(a)}c_{j}\cdot 2^{j}\text{, }c_{j}\in\{0,1\}. \tag{22}\]  
Let us denote by \(q(x,r)\) the function performing the integer division of \(x\) by a power of two--viz. right bit shift. Such an operation can be defined as follows in terms of (22):  
\[q(a,r)=\left\lfloor\frac{a}{2^{r}}\right\rfloor=\sum_{j\geq r}c_{j}\cdot 2^{j- r}. \tag{23}\]  
It is not difficult to notice that our definition fulfils the following recursive property of bit shifting:\[q(a,r)=\begin{cases}q(q(a,r-1),1),\text{ for }1\leq r<L(a)\\ a,\text{ for }r=0.\end{cases} \tag{24}\]  
Considering the encoding problem of this article, we can redefine \(Q(r)\) as follows:  
\[Q(r)=\sum_{i}\left\lfloor\frac{n_{i}}{2^{r}}\right\rfloor=\sum_{i}q(n_{i},r). \tag{25}\]  
We are interested in designing an algorithm that efficiently updates the value of \(Q(r)\) as new samples are added to the set \(n\). Let us denote by \(n^{\prime}\) the set of samples \(n\) enlarged by a new sample \(\hat{n}\). The value of \(Q(r)\) for the new set is therefore defined as:  
\[Q(r,\mathbf{n^{\prime}})=Q(r,\mathbf{n})+q(\hat{n},r). \tag{26}\]  
The next section presents an algorithm that is defined based on (24) and on (26).
# Terahertz Emission Enhanced by a Laser Irradiating on a T-Type Target
## 6 Conclusions  
In summary, we have proposed a novel scheme with an ultra-intense laser pulse irradiating on a T-type target to enhance the THz emission, and the related processes were investigated by using the PIC simulations performance. Compared with the single-layer target, our results show that such a T-type target could not only increase the intensity of terahertz emission by an order of magnitude but also could significantly tune its spatial distribution. The reason for the above results is that the fast electrons controlling the THz emission originate from the longitudinal portion of the T-type target. The direct laser acceleration modulated the electrons very significantly. Finally, the robustness of our scheme was studied with respect to the thickness and length of the longitudinal part. The proposed scheme and simulation results may provide important theoretical and data support for the enhancement of terahertz emission efficiency based on the ultra-intense laser irradiation of solid targets.  
Conceptualization and supervision, C.L. and F.W.; simulations, J.Z., X.B. and C.L.; data analysis, C.L. and F.W.; writing--original draft preparation, J.Z. and X.B.; writing--review and editing, C.L., F.W. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 12005305, 11905169, and the Continue Basic Scientific Research Project under Grant Nos. WDJC-2019-02, and BJ20002501.  
Not applicable.  
Figure 8: Influence of the different laser durations with \(3T_{0}\), \(5T_{0}\), and \(10T_{0}\) on THz emission for the TTT. (**a**) The time-domain waveforms of the radiated electromagnetic field. (**b**) The frequency-domain spectra.  
Figure 9: The radiated electromagnetic field passing through the space point (\(146.67\lambda_{0},-20.0\lambda_{0}\)) at the (\(x\), \(y\)) plane for the different laser pointing deviations. (**a**) The time-domain waveforms of the radiated electromagnetic field. (**b**) The frequency-domain spectra.  
Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors would like to thank the anonymous reviewers for their constructive suggestions. [PERSON] would like to thank [PERSON] from the Research Center of Laser Fusion, China Academy of Engineering Physics for their fruitful discussions. The PIC simulations were performed on the Tianhe-2 supercomputer (China). The authors are particularly grateful to CFSA at University of Warwick for allowing us to use the EPOCH. The EPOCH code was used under UK EPSRC contract (EP/G055165/1 and EP/G056803/1).  
The authors declare no conflict of interest.
# Case Study of Power Plants in the Slovak Republic and Construction of Microgrid and Smart Grid
## 2 Current Situation in Slovakia  
The Slovak Republic has one transmission system, which is managed by the Slovak Electricity Transmission System, a.s. (SEPS). SEPS manages all transmission lines with a total length of 3008 km and a total transformation power of 11,730 MVA [17]. As shown in Figure 2 current grid map.  
Furthermore, the Slovak Republic was divided into three distribution companies, namely West Slovak Distribution, a.s. (ZSD), Middle Slovak Distribution, a.s. (SSD), and East Slovak Distribution, a.s. (VSD). As shown in Figure 3.  
Figure 2: Grid map of the power system of The Slovak Republic for the state of 2021 [18].  
The organization of power supply management in Slovakia was divided according to Figure 4. The transmission and distribution of power at the voltage level of 400 kV and 220 kV are controlled by SEPS at the central dispatching Slovak electricity dispatching (SED). The majority production of electricity is managed by Slovak power plants through dispatching stations in individual power plants, and the management of all hydroelectric power plants in the V\(\acute{a}\)zska cascade is managed by a separate dispatching center. The supply of electricity to households and businesses is provided by local dispatch centers as ZSD, SSD, and VSD according to the point of consumption, as shown in Figure 3. These dispatching manage voltage level of 110 kV, 22 kV, and 0.4 kV. During the transition to the intelligent network, it will be necessary to redistribute the management of Slovak power and hydropower plants between the SED and the local dispatching stations ZSD, SSD and VSD.  
Slovak power plants operate 31 hydro, 2 nuclear, 2 thermal, and 2 solar power plants with a total capacity of 4112 MW [19]. The total installed capacity of the Slovak power plant in 2019 is 7716 MW. The full electricity consumption for the Slovak Republic in 2019 was 30,309 GWh [17]. When we examined more detailed information about the location of power plants, we found the following divisions of power plants in Slovakia, which is shown in Table 1 and Figure 5.  
Figure 4: Scheme of distribution of energy system management.  
Figure 3: Division of the Slovak Republic according to electricity distributors.
## 6 Practical Applications and Future Research Perspectives  
Despite the considerable progress made in recent years in the development of rare-earth-based nanomaterials for biotechnological applications, which is reflected in a large number of metal-organic frameworks and NPs accessible with many different compositions, morphology, size, properties, and applications, some challenges and improvements have yet to be addressed before these systems can be used clinically. The development of multifunctional systems is certainly one of the most useful research focuses on the design of rare-earth-based nanomaterials for biotechnological applications. In fact, numerous luminescent rare-earth based nanomaterials are developed to provide at least one additional functionality, either as MRI thus providing a clear diagnosis by combining the advantages of each modality. Rare-earth-based nanomaterials can act as very efficient contrast agents in a variety of applications. Medical imaging methods and provide a wide range of options for modern cancer therapy. Y\({}_{2}\)O\({}_{3}\) NPs is an air-stable, solid substance white in color. It is used in the field of material sciences to make phosphors that are used in imparting the red color of the picture tubes in televisions. Another major use of the yttrium oxide nanoparticles is in inorganic synthesis. In the field of materials science, these particles find a number of applications such as imparting color to the television picture tubes. It is also used in the making of plasma and flat panel displays. Yttrium iron garnets are derived from yttrium oxide are used as powerful microwave filters. Y\({}_{2}\)O\({}_{3}\) is a vital starting point in the inorganic synthesis of compounds. The property of red light emission is used in making fluorescent lamps. They are also used as additives in the coatings used in high-temperature applications, paints, and plastics for guarding against UV degradation and also in making permanent magnets and in ultrafast sensors that are used in g-ray and X-rays. Some of the other applications include additives in steel, non-ferrous alloys, and iron. Y\({}_{2}\)O\({}_{3}\) NPs are an excellent host material for rare earth metals and have high luminescence efficiency providing a potential application in photodynamic therapy and biological imaging.
## 2 Theory and Methods
### Model Improvements  
The existing YOLOv8 algorithm is effective for target detection. However, it still struggles with small target detection. To enhance the detection of small target objects, this study improves the YOLO algorithm, naming it Lightweight Small Object Detection YOLOv8 (LSOD-YOLOv8). This model accurately extracts target information while maintaining a lightweight network structure. The architecture of the model is illustrated in Figure 1c, with specific algorithm details provided below.  
In the LSOD-YOLOv8 model, we introduce a lightweight adaptive weight downsampling module (LAWDS) inspired by the Focus network. This module replaces the standard convolution in the original backbone, compensating for the information loss that occurs during conventional convolution and downsampling operations while achieving a more lightweight architecture. Additionally, we incorporate the concepts of a P2 layer and shared convolution in the head section, resulting in a lightweight small target detection head called P2-LSCSBD, which facilitates multi-scale information fusion while maintaining a lightweight design. Finally, recognizing the limitations of the original loss function, we combine the concepts of WiseIoU, InnerIoU, and MPDIoU to propose a new loss function, WMIIoU. This new loss function allows the entire model to achieve a significant improvement in accuracy without compromising performance.
# Adaptive Bit-Labeling Design for Probabilistic Shaping Based on Residual Source Redundancy
## 3 Analysis and Design of Bit-Labeling
### An Adaptive Design Scheme of Bit-Labeling
```
1:\(p,\,B_{s},R,\Delta,\phi_{ini}\)
2:\(C\gets H(p)R\);
3:\(\mathrm{snr}_{\min}\gets 10\log_{10}\big{[}\big{(}2^{C}-1\big{)}/R\big{]}\); \(\backslash*\) Shannon limit \(*\backslash\)
4:\(\mathrm{snr}\leftarrow\mathrm{snr}_{\min}\), \(\phi\leftarrow\phi_{ini}\)
5:for all gray mappings \(\phi^{\prime}\) (\(\phi^{\prime}\
eq\phi_{ini}\)) do
6: compute \(P_{\mathbf{B}_{s}}(X,\phi)\) with \(p\), \(B_{s}\) and \(\phi\);
7: compute \(C_{\mathbf{B}_{s}}\Big{(}10^{\mathrm{snr}/10},\phi\Big{)}\) with \(P_{\mathbf{B}_{s}}(X,\phi)\), \(\mathrm{snr}\) and \(\phi\);
8:while\(C_{\mathbf{B}_{s}}\Big{(}10^{\mathrm{snr}/10},\phi\Big{)}/H(p)<R\)do
9:\(\mathrm{snr}=\mathrm{snr}+\Delta\)
10:endwhile
11: compute \(C_{\mathbf{B}_{s}}\Big{(}10^{(\mathrm{snr}-\Delta)/10},\phi^{\prime}\Big{)}\) and \(C_{\mathbf{B}_{s}}\Big{(}10^{\mathrm{snr}/10},\phi^{\prime}\Big{)}\);
12:if\(C_{\mathbf{B}_{s}}\Big{(}10^{(\mathrm{snr}-\Delta)/10},\phi^{\prime}\Big{)}\geq C_{ \mathbf{B}_{s}}\Big{(}10^{(\mathrm{snr}-\Delta)/10},\phi\Big{)}\) and \(C_{\mathbf{B}_{s}}\Big{(}10^{\mathrm{snr}/10},\phi^{\prime}\Big{)}\geq C_{ \mathbf{B}_{s}}\Big{(}10^{\mathrm{snr}/10},\phi\Big{)}\)then
13:\(\phi\leftarrow\phi^{\prime}\);
14:\(\mathrm{snr}\leftarrow\mathrm{snr}_{\min}\)
15:endif
16:endfor
17:return\(\phi\);
```  
**Algorithm 1** Adaptive Bit-Labeling DesignTo further verify the merits of the proposed bit-labelings, we present some bit-error-rate (BER) results of the JSCCM systems with 16 QAM over an AWGN channel for different source codes and source probabilities. For all the cases, the same Protograph LDPC code (\(3/4\)-rate-\(\mathbf{B}_{c}^{U}\)[21]) is employed as a channel code. And four Protograph LDPC codes, including the \(1/2\)-rate-\(\mathbf{B}^{s,1}\) code [15], the \(1/2\)-rate-\(\mathbf{B}^{s4/A}\) code [19], the \(3/8\)-rate-\(\mathbf{B}^{s,2}\) code [22], and the \(3/8\)-rate-\(\mathbf{B}^{s,3}\) code [23], are used for source coding as follows:  
\[\mathbf{B}^{s,1}=\left( \begin{array}{cccccc}1&1&3&3&1&0\\ 1&0&5&2&0&1\\ 1&2&3&3&1&1\end{array} \right). \tag{13}\]  
\[\mathbf{B}^{s4/A}=\left( \begin{array}{cccccc}3&1&1&1\\ 1&2&1&2\end{array} \right). \tag{14}\]  
\[\mathbf{B}^{s,2}=\left( \begin{array}{cccccc}2&1&1&1&1&1&1\\ 1&2&0&1&2&1&1&0\\ 1&1&2&1&0&2&0&1\end{array} \right). \tag{15}\]  
\[\mathbf{B}^{s,3}=\left( \begin{array}{cccccc}2&1&0&2&3&1&1&1\\ 1&2&1&2&1&3&0&2\\ 1&1&2&1&1&0&3&2\end{array} \right). \tag{16}\]
## 5 Simulation Results and Discussions
### Performance Comparison  
This subsection compares the performance of SISO and MIMO systems under scenarios with and without ambient noise. Figure 12 illustrates the detrimental effects of ambient light noise on the SISO-VLC system, showing a decrease in coverage distance from 1125 m to 1040 m (a 7.6% reduction) at a log(BER) of \(-50\) dB, using the QPSK technique.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Line Coding**} & \multicolumn{2}{c}{**Achievable Link Distance in Meter**} \\ \cline{2-3}  & **SISO** & **MIMO** \\ \hline NRZ & \textasciitilde{-}1150 & \textasciitilde{-}1250 \\ \hline QPSK & \textasciitilde{-}1125 & \textasciitilde{-}1225 \\ \hline RZ & \textasciitilde{-}1150 & \textasciitilde{-}1250 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Achievable link distance considering the effects of ambient light noise.  
Figure 10: BER performance against distance for both MIMO- and SISO-based VLC systems with ambient light noise.  
Similarly, Figure 13 displays the impact on the MIMO-VLC system, where at the same BER level, the coverage distance drops from 1325 m to 1075 m, marking an 18.9% reduction. Despite this, the overall performance gain provided by MIMO's diversity outweighs the losses caused by ambient noise.  
Figure 11: Received power versus distance for both MIMO- and SISO-based VLC systems with ambient light noise.  
Figure 12: BER performance comparison for the SISO-VLC system with and without ambient noises.  
Figures 14 and 15 illustrate the received power for SISO-VLC and MIMO-VLC systems, respectively, both with and without ambient noise. The effect of ambient noise is evident in the performance degradation. For the SISO-VLC system, the coverage distance decreases from 1087 m to 1020 m, a reduction of 6.2%. In the MIMO-VLC system, the coverage distance drops from 1280 m to 1105 m, marking a 13.6% reduction at a target received power of \(-\)70 dBm.