esa-sceva/satcom-chunk-collection huggingface dataset:
# An Interface to Monitor Process Variability Using the Binomial ATTRIVAR SS Control Chart
## 3 Application Development
### Inputs and Outputs Definition  
Regarding the development of a B-ATTRIVAR SS control chart application aimed to monitor the variability of univariate processes through variance, the code should be capable of generating control limits for the attribute (\(W_{Y}\) and \(CL_{Y}\)) and variable (\(LCL\) and \(UCL\), or just \(CL_{S^{2}}\) if \(LCL\leq 0\)) inspections. Based on these four outputs, the application can effectively perform the primary function of a control chart: either accepting or rejecting samples to classify the process as being in control or out of control.  
For such a purpose, the initial necessary inputs would be sample size (n), mean (\(\mu_{0}\)), and standard deviation (\(\sigma_{0}\)) of the process, given that they are essential parameters for the program to perform normal sample simulations.  
The user would have to provide the maximum desired probability of a type I error (\(\alpha_{max}\)) and the maximum desired probability of a type II error (\(\beta_{max}\)) for a given variation (\(\lambda\)) in standard deviation to obtain the attribute and variable control limits. Thus, \(\alpha_{max}\) would define the min \(ARL_{0}\) and \(\beta_{max}\) for a given \(\lambda\), which would define the max \(ARL_{1}\). Then, it would be possible to determine the acceptance intervals, as \(ARL_{0}\) and \(ARL_{1}\) are directly related to the control limits.  
To start the first stage of monitoring with attribute inspection, The user would have to determine the discriminant limits \(LDL\) and \(UDL\), which were also defined as outputs. Thus, these values could be configured using a go/no-go gauge device, for instance. Afterwards, they could conduct inspections and input the number of nonconforming items in each sample into the application to proceed to the next stage.  
Furthermore, the user would have to provide the maximum percentage of samples to be submitted to variable inspection (\(\%S^{2}{}_{max}\)) to define the discriminant limits (\(LDL\) and \(UDL\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{\(\lambda\) (Lambda)} & \multicolumn{3}{c}{**ARL**} \\  & \(\mathbf{S^{2}}\) & **np** & **B-ATTRIVAR SS \(\mathbf{S^{2}}\)** \\ \hline
1.0 & 367.28 & 371.71 & 370.71 \\
1.1 & 107.93 & 118.87 & 120.12 \\
1.2 & 42.79 & 50.26 & 51.54 \\
1.3 & 21.21 & 26.72 & 26.31 \\
1.4 & 12.32 & 15.61 & 15.48 \\
1.5
## 3 Results and Discussion  
In this section, the proposed deblurring method is verified by both simulated motion-blurred images and atmospheric turbulence blur in real-life applications. For simulations, the test images were taken from the Kodak Lossless True Color Image Suite [34], and were numbered as \"Img01,\" \"Img02,\" etc., for ease of comprehension. Figure 3 shows examples of the ground truth images for which the testing process is outlined in the following subsections.  
We compared the proposed method with several other blind image deblurring methods. [PERSON] et al. used both the image gradient domain and pixel domain in the cost function for the data fitting term, together with a natural image prior and uniform prior [35]. [PERSON] and [PERSON] used the shock filter and bilateral filter with multiple orientations of images to enhance the structures, with the aim of ensuring that the histograms of the gradient domain of the reconstructed images are similar to those of the original [36]. [PERSON] et al. proposed an image prior model based on a piecewise function that approximates the statistical characteristics of natural images using a sparsity representation technique [37]. They solved the deblurring problem using an iterative shrinkage-thresholding algorithm. [PERSON] and [PERSON] used the graph Laplacian matrix as the cost function and obtained the clear image by an iterative solution [38].
## 3 Research Methodology
### Energy Potential Analysis Using Transformer Model  
This section provides a detailed explanation of the Transformer model architecture implemented in Python, used to predict energy output in solar-hydrogen systems. The Transformer model was first created for natural language processing, but because of its capacity to identify long-term dependencies in data, it has grown in popularity for time series forecasting. Because of the self-attention mechanisms built into its architecture, the model is able to assess the importance of different time steps in the input data, which makes it a powerful tool for studying complex, multivariate datasets, such as those found in the energy production industry.  
A dataset of historical energy production data, meteorological data, and solar irradiance levels gathered from the university's solar energy installations is used to train the Transformer model. Subsets of the dataset are used for testing, validation, and training to make sure the model learns well and can be applied to fresh, untested data. To maximize model performance, important training parameters including learning rate, batch size, and number of epochs are carefully adjusted. Using patterns from past data, the model is trained to predict future energy output. Overfitting is avoided by using validation procedures to monitor the model's development.  
Several metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Error Squared (MAE2), are used to assess the Transformer model's performance. An easy-to-understand indicator of prediction accuracy, MAE yields an average of the absolute disparities between values that were anticipated and those that were observed. By accounting for the square of the differences, MSE helps to discover notable outliers by assigning greater weight to larger errors. The MAE2 variance, which squares the MAE, highlights bigger disparities even more, enabling a more thorough assessment of the model's forecasting performance. When combined, these measures offer a thorough evaluation of how well the Transformer model predicts energy output in a solar-hydrogen system.
## 1 Introduction
Motivated by the lack of airborne imaging devices specifically designed to retrieve SIF, and the increasing interest amongst the scientific community in terms of measuring this signal, the HyPlantimaging spectrometer was developed and built by Forschungszentrum Julich in cooperation with the company Specim Ltd. (Finland) [26]. Over the years, HyPlant has been operated several times, together with a TASI-600 hyperspectral thermal imager (ITRES Research Ltd., Canada) and an LMS-Q780 full-waveform airborne LiDAR (RIEGL Laser Measurement Systems GmbH, Austria), onboard an aircraft from the Global Change Research Institute (CzechGlobe) during different preparatory campaigns for FLEX and other upcoming ESA satellite missions. Simultaneous recording of hyperspectral, thermal and LiDAR data enables the acquisition of a comprehensive set of structural and physiological measurements to facilitate the calculation of ecosystem functions (i.e., GPP and transpiration) and vegetation stress responses. Since SIF is a very small signal and constitutes only 2%-5% of the reflected light of plants at canopy level, its retrieval from at-sensor radiances is a complicated process. Several issues have been found to compromise SIF-retrieval accuracy, requiring specific processing routines and tools. These include (i) conversion of raw digital measurements to calibrated at-sensor radiance, and compensation for any sensor non-uniformity (i.e., spectral shift or distortions of the point-spread functions); (ii) compensation for atmospheric absorption and scattering effects; (iii) the geolocation of observations; (iv) retrieval of SIF; and (v) retrieval of additional data to facilitate interpretation of SIF and calculation of higher-level products. An automatized processing chain has been developed in recent years, which enables all these points to be taken into account in a consistent way, so that numerous HyPlant data cubes can be processed in a short space of time, guaranteeing the availability of the processed HyPlant data soon after data acquisition. The aim of this paper is to provide a detailed overview of this processing chain and the processing steps that must be executed to generate the final products from HyPlant DUAL and FLUO raw image data. We describe the HyPlant sensor system and outline the general structure of the processing chain. The individual components of the processing chain are detailed, addressing the challenges listed above. Using an example dataset, we demonstrate the applicability of the implemented processing infrastructure and critically discuss open points and ways forward.
# Soft Decision Decoding with Cyclic Information Set and the Decoder Architecture for Cyclic Codes
## 5 BER Performance Verification Based on Hardware Platform
### BER Performance of the Decoders with Different I and C  
Figures 7-9 present the bit error rates of (23, 12) Golay decoder, the (31, 21) BCH decoder, and the (63, 30) BCH decoder with different parameters, \(I\) and \(C\). The hardware implementation results show that increasing the number of candidate codewords or information sets can significantly improve the decoding performance. As the number of information sequences \(I\) increases, the performance of the decoding algorithm improves. Similarly, when \(I\) is fixed, the performance of the decoding algorithm also improves with \(C\). After reaching the target decoding performance, it tends to be flat. Furthermore, we presented the BER performance with \(I\cdot C\). It can be observed in Figures 7-9 that the proposed algorithms can achieve target performance using different combinations of \(I\) and \(C\) if the overall candidate codewords are enough to find the optimal. Some typical results are listed in Table 4 for convenience. The appropriate values of the parameters \(I\) and \(C\) can be obtained according to the simulation results. When the target decoding performance is satisfied, the parameters corresponding to the minimum \(I\cdot C\) are selected as the appropriate values to reduce the computational complexity. Table 4 presents the optimization results. With the BER performance of \(10^{-4}\) as criterion, for the (23, 12) Golay code, the target performance can be achieved with only 48 candidate codewords (\(I=3,C=16\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cyclic Code** & **BER** & \(I\) & \(C\) \\ \hline (23, 12) Golay code & \(9\times 10^{-5}\) & 3 & 16 \\ (31, 21) BCH code & \(1.1\times 10^{-4}\) & 2 & 64 \\ (63, 30) BCH code & \(7.3\times 10^{-5}\) & 12 & 512 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Appropriate parameters selection for \(I\) and \(C\) when BER is about \(10^{-4}\).  
Figure 6: The BER performance of the BCH code and the Golay code.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cyclic Code** & **BER = 10\({}^{-4}\)** & **BER = 10\({}^{-5}\)** \\ \hline (15, 5) BCH code & 5.7 dB & 6.6 dB \\ (31, 21) BCH code & 4.9 dB & 5.7 dB \\ (63, 30) BCH code & 3.4 dB & 4.2 dB \\ (23, 12) Golay code & 4.8 dB & 5.5 dB \\ \hline \hline \end{tabular}
\end{table}
Table 3: The SNR comparison for different cyclic codes.
# Route Prefix Caching Using Bloom Filters in Named Data Networking
## 3 Route Prefix Caching
### Design of Route Prefix Cache Architecture  
The name lookup system is composed of two parts, a Bloom filter-based route prefix cache and an FIB table, as shown in Figure 6. At the first step of the name lookup, a given name is looked up in the route prefix cache. If a cache hit occurs, the lookup procedure terminates at the step. Otherwise, on a cache miss, the FIB table is consulted. Note that the cache miss occurs not only in the event that there is no matching prefix in the cache, but also in the event that the Bloom filter of the matching prefix asserts a positive. On a cache miss, the matched entry is fetched from the FIB and stored in the cache. An associative memory is provided for storing prefixes to facilitate prefix matching in the cache. The other fields, such as the length, the Bloom filter, and the output face of a prefix, are stored in a conventional but fast memory. Those fields are accessed when the corresponding prefix is matched with a given name.  
Figure 5: Type of matching prefix for a given name \(c\).  
Ternary content addressable memory (TCAM) may be a candidate for the associative memory of the cache. TCAM supports a parallel associative search, and a variable-length key can be stored in it and searched for. Using the TCAM, the variable-length prefix can be stored and searched for in our cache. The TCAM is an expensive memory; however, the proposed cache can be configured with a feasible size. Figure 7 shows how our Bloom filter-based route prefix cache works to find a matching result. For a given name,/ride/wagon/se/ty, the longest matching prefix/ride/wagon is found in the TCAM. The corresponding information, the length, the Bloom filter, and the face, are extracted from an SRAM. The length value is used for extracting the next component, \"se\". Then, the bits in the Bloom filter indicated by \(h_{1}\) (\"se\") and \(h_{2}\) (\"se\") are checked to see if those bits are all set to 1. If not, a cache hit is raised, and the face number is delivered as the result.
# Dynamic Monitoring of the Water Flowing Fractured Zone during the Mining Process under a River
## 5 Conclusions  
This paper first monitored the development of the WFFZ in the process of mining beneath a river with the TEM method. Detailed analysis found that the WFFZ reached the middle Jurassic coarse sandstone aquifer and the K\({}_{\text{lzh}}\) unconfined aquifer; in other words, the height of the WFFZ was over 97.25 m, which was quite different from the 49.9-67.7 m calculated by empirical formula. Hence, it is not appropriate to calculate the height of the WFFZ only by the empirical formula when the mining height exceeds 3 m. During the mining process, the rock fractures were developed in advance, and the downward seepage of water started before the longwall face reached the corresponding area. Combined with the numerical simulation results, we found that the water flowed downward quickly when the mining-induced water flowing fractures reached the overlying aquifer. The K\({}_{\text{lzh}}\) unconfined aquifer under the riverbed had a strong lateral recharge capacity, and when the water declined at the position closest to the longwall face, lateral seepage occurred to recharge this position. This caused a drop in the water level of the entire aquifer. As the river was in a dry spell, there was no surface water supply, the water storage of the aquifer was limited, and the flow slowed down after a short-term rapid downward movement. Finally, the water distribution of the overlying strata tended to be stable. During that period, roof water spraying occurred during the mining process. The harm was mainly caused by the lateral recharge of aquifer. Although it had a certain interference with the mining of the 8101 longwall face, the harm was limited. If the mining process had been carried out in the rainy season, the surface water would have continually moved downward into the longwall face, and the risk of water inrush accidents would be extremely high.  
This research is focused on the development of the WFFZ of shallow buried coal seams. Further studies are needed in regard to the coal seam under the goat, the deep buried coal seam or the repeated mining of close-distance coal seams. The data and results of this paper can provide a basis for the research of mining-induced water seepage mechanisms based on big data and the further study of the flow mechanisms of groundwater resources affected by mining.  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] conceived and designed the field survey; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] carried out the TEM detection; [PERSON], [PERSON] and [PERSON] processed and analyzed the TEM data; [PERSON] and [PERSON] designed and carried out the numerical experiment; [PERSON], [PERSON] and [PERSON] wrote this paper.  
**Funding:** This research was funded by National Natural Science Foundation of China (No. 51174193).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Loss Function
#### 2.4.1 Loss Function of SGAN  
In this paper, I\({}^{\text{LR}}\) and I\({}^{\text{HR}}\) are used as the LR image and HR image, respectively. We construct a robust loss function to force generator G to generate an HR image similar to I\({}^{\text{HR}}\),  
\[\text{L}_{\text{SGAN}}=\text{L}_{\text{mse}}+\alpha_{1}\text{L}_{\text{adv}}+ \alpha_{2}\text{L}_{\text{tv}} \tag{5}\]  
where \(\alpha_{1}\) and \(\alpha_{2}\) represent the coefficients of \(\text{L}_{\text{adv}}\) (adversarial loss) and \(\text{L}_{\text{tv}}\) (regularization loss), with values of 1 \(\times\) 10\({}^{-3}\) and 2 \(\times\) 10\({}^{-8}\). The network requires the reconstruction effect at the pixel level through \(\text{L}_{\text{mse}}\), trains the discriminator D to minimize the adversarial loss, and encourages G to generate a reconstructed image close to the I\({}^{\text{HR}}\). It is well known that adversarial learning strategies can maintain the visual authenticity of the generated images; however, it is also vulnerable to noise pollution. In order to restrain the noise and keep the image smooth, TV regularization loss is introduced in this paper.  
\[\text{L}_{\text{mse}}=\frac{1}{\text{r}^{2}\text{WH}}\text{\sum}_{\alpha=1}^{ \text{rW}}\text{\sum}_{\text{y=1}}^{\text{rH}}\left(\text{I}_{\text{x,y}}^{ \text{HR}}-\text{G}_{\text{\theta}_{\text{G}}}\left(\text{I}^{\text{LR}} \right)_{\text{x,y}}\right)^{2} \tag{6}\]  
\[\text{L}_{\text{adv}}=\text{\sum}_{n=1}^{\text{N}}\text{ }-\text{logD}_{\text{O}_{\text{D}_{\text{D}_{\text{D}_{ \text{D}}}}}}\left(\text{G}_{\text{\theta}_{\text{G}}}\left(\text{I}^{\text{LR} }\right)\right) \tag{7}\]  
\[\text{L}_{\text{tv}}=\frac{1}{\text{r}^{2}\text{WH}}\text{\sum}_{\alpha=1}^{ \text{rW}}\text{\sum}_{y=1}^{\text{rH}}\parallel\
abla\text{G}_{\text{\theta} _{\text{G}}}\left(\text{I}^{\text{LR}}\right)_{\text{x,y}}\parallel \tag{8}\]  
where N denotes the number of images, \(\text{\theta}_{\text{G}}\) denotes a set of model parameters in G, \(\text{\theta}_{\text{D}}\) denotes a set of model parameters in D, \(\text{G}_{\text{\theta}_{\text{G}}}\left(\text{I}^{\text{LR}}\right)\) denotes the reconstructed image, \(\text{D}_{\text{\theta}_{\text{D}}}\left(\text{G}_{\text{\theta}_{\text{G}}} \left(\text{I}^{\text{LR}}\right)\right)\) denotes the probability that the \(\text{G}_{\text{\theta}_{\text{G}}}\left(\text{I}^{\text{LR}}\right)\) is a real HR image, and \(\
abla\text{G}_{\text{\theta}_{\text{G}}}\left(\text{I}^{\text{LR}}\right)\) represents the gradient of the output of G.
## 4 Security and Performance Analyses
### Authentication Proof of the Proposed Scheme Using BAN Logic  
This subsection shows that the proposed scheme satisfies the session-key security and mutual authentication by using BAN logic [34]. Table 2 lists the notations of BAN logic.
## 2 Materials and Methods
### Geometry and Mass Measurements  
To verify the change in the geometry of the products, the thickness of the samples (nominal 2 mm) was measured immediately after manufacture and one month later. The measurements were carried out using a Baker thickness gauge type J 130/7 (Baker, Pune, India). Similarly, the weight of the samples was measured using a Radwag AS 220.X2 laboratory balance (Radwag, Radom, Poland).
# Event-Based Path-Planning and Path-Following
## 3 Two-Level Control System
### Event-Based Path-Planning
#### 3.3.4 Path-Planning Algorithm for Building a Path to a Given Segment  
The path-planning algorithm that is proposed in this section generates a path \(\mathcal{P}\) that connects the current position \(P_{V}\) of the AUV oriented along the vector \(e_{v}\) with a segment \(S\), thus solving the problem of returning the AUV to the reference path \(\mathcal{P}_{ref}\). It is based on Dubins curves [39]. The generic Dubins algorithms generate the shortest path that connects two points with a constraint on the curvature of the path. However, in our case, it is also necessary to determine the final path point, which is on a line or arc segment of \(\mathcal{P}_{ref}\), as well as to limit the set of allowable types for the path, specifying allowable motion primitives \(\mathcal{D}_{s},\mathcal{D}_{f}\subseteq\{\text{'L'},\text{'R'}\}\) for the start and final segments of the path respectively. Here, as before, the symbol 'L' corresponds to a left-arc turn and the symbol 'R' to a right-arc turn. Note that there are algorithms that build a Dubins path to a circle [40; 41], but they cannot be directly adapted for our purposes.  
1. Set \(L=\infty\) and \(\mathcal{P}=\emptyset\).
2. For each start turning direction \(d_{s}\in\mathcal{D}_{s}\) and final turning direction \(d_{f}\in\mathcal{D}_{f}\) perform Steps 3-8.
3. Find the center \(C_{1}^{d_{s}}\) of the arc \(S\) for start turning direction \(d_{s}\).
4. Shift segment \(S\) in direction \(d_{f}\) by \(R_{\min}\), resulting in a new segment \(S_{d_{f}}\).
5. Find a point \(P_{f}\) on segment \(S_{d_{f}}\) which is closest to point \(P_{V}\). If the distance between points \(P_{V}\) and \(P_{f}\) is greater than \(2R_{\min}\), then build a Dubins path \(\mathcal{P}_{Dub}\) of type \(d_{s}S_{d_{f}}\) that connects points \(P_{V}\) and \(P_{f}\) and go to step 8.
6. Find the point \(C_{2}^{d_{f}}\) of intersection of the circle with the center \(C_{1}^{d_{s}}\) and radius \(2R_{\min}\) with the segment \(S_{d_{f}}\). If there are no such points, go to Step 2 and continue with the next \(d_{f}\).
7. Build a path \(\mathcal{P}_{Dub}\) of type \(d_{s}S_{d_{f}}\) connecting point \(P_{V}\) with point \(P_{f}\), which is obtained by projecting point \(C_{2}^{d_{f}}\) onto segment \(S\).
8. If the path \(\mathcal{P}_{Dub}\) is successfully built (\(\mathcal{P}_{Dub}=\emptyset\)) and the length \(L_{Dub}\) of the path \(\mathcal{P}_{Dub}\) is less than \(L\), then \(L=L_{Dub}\) and \(\mathcal{P}=\mathcal{P}_{Dub}\).  
The described algorithm is illustrated in Figure 7.  
Figure 6: Building a path that wraps around a middle obstacle point.
## 3 Development of RF WPT-Enabled IoT Application
### Design Methodology
Specifically, these analyses determine such important design factors as the general system architecture and protocols, the used energy harvesting mechanism and the requirements to the harvester (e.g., size, weight, efficiency), the need of using an energy buffer and its volume. Following these steps, the development of the system following the standard procedure can commence. Unlike the opportunistic energy harvesting based applications discussed above, the availability of energy in the RF WPT-based applications has a more predictable nature. Given that the schedule of WPT charger is known, the power of the radio signal received by an IoT device depends on the distance to the charger, the antennas used and the radio channel conditions (e.g., channel blocking). At the very same time, the WPT introduces a new design challenge, which is the development of WPT transmitter and positioning of the WPT infrastructure. Specifically, based on the analysis of the target functionalities and the operation environment, one needs to determine whether RF WPT-based supply is technically and economically feasible. Next, the number and the location of the WPT chargers, the frequency of operation, the shape of the WPT radio signal and the antenna polarization need to be determined. Finally, all respective components  
Figure 1: Traditional design flow for a wireless sensor node (based on methodology presented in Ref. [54]). need to be designed, instrumented and evaluated in real life. In what follows, we illustrate step-by-step how this methodology can be employed to develop an illustrative WPT application.