esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Isolation and Identification of LAB from Mango Peel  
No growth was observed on MRS, M17, and SDB media from fresh mango peel dilutions. Consequently, 10 g of mango peel were directly added to 50 mL of MRS and M17 media. Colonies with morphologies consistent with LAB grew on the MRS medium. Phenotypically, the strains were catalase-negative and lacked an external membrane. RAPD-PCR was used to differentiate the strains of presumptive LAB. All isolates sharing a given RAPD profile were considered to represent the same strain. Thus, the dominant LAB populations in the mango peel samples included only four strains. These selected strains underwent 16S rRNA gene sequencing, which revealed the presence of three LAB species. Specifically, three obligate homofermentative LAB were identified as _Latilactobacillus sakei_. This LAB plays a crucial role in fermenting meat products due to its ability to produce lactic acid and enhance food safety and flavour [32]. _Latilactobacillus sakei_ has also been used to enhance flavour impact in fermented wheat gluten [33], but its application in bread making remains unexplored.
# Burst Channel Error Reduction Based on Interleaving for Efficient High-Speed Wireless Communication
## 5 Simulation Results
### BER Performance Evaluation over Vehicular A Channel  
In this experiment, the BER performances of FFT-OFDM and DWT (Haar)-OFDM with and without helical and FRF interleavers are evaluated over the vehicular A outdoor channel model in the presence of AWGN. The multipath profile of the vehicular A outdoor channel model is summarized in Table 4 [35].  
Figure 11: BER performance for the FFT\(-\)OFDM and DWT\(-\)OFDM systems over SUI-3 channel.  
Figure 12: BER performance for the FFT\(-\)OFDM and DWT\(-\)OFDM systems over SUI-6 channel.  
Figure 10: BER performance for the FFT\(-\)OFDM and DWT\(-\)OFDM systems over SUI-2 channel.  
Figure 13 shows the BER vs. \(E_{b}\)/\(N_{0}\) for the helical (FFT/DWT)-OFDM and FRF (FFT/DWT)-OFDM systems compared with the conventional (FFT/DWT)-OFDM over the vehicular A outdoor channel model. As can be seen in the figure, the proposed (FFT/DWT)-OFDM systems outperform the conventional (FFT/DWT)-OFDM systems. Hence, this confirms that the proposed systems have a high immunity to burst errors regardless of the channel models. From Figure 13, it is also clear that the proposed FFT-OFDM system with an FRF interleaver provides a better BER performance than that with the helical interleaver, but the proposed DWT-OFDM system with the FRF interleaver provides approximately the same BER performance as that with the helical interleaver for the same reasons mentioned above. Therefore, it is better to use the proposed FRF interleaver with the FFT-OFDM system for the same reasons described above. From the results obtained from Figure 13, at \(E_{b}\)/\(N_{0}\) = 25 dB, the proposed FFT-OFDM system with an FRF interleaver achieves a BER of about \(3.62\times 10^{-6}\). On the other hand, the BER value achieved by the FFT-OFDM system with a helical interleaver is \(1.335\times 10^{-5}\), while it is \(0.8369\times 10^{-3}\) in the case of the conventional FFT-OFDM system. This means that the proposed FRF interleaver can improve BER performance compared to those achieved by the FFT-OFDM system with the helical interleaver and the conventional FFT-OFDM system.
## 3 Model Training and Testing
### Evaluation of Model Metrics Before and After Improvement  
In the semantic segmentation training and validation phases, the loss function serves as a critical metric for evaluating the performance of deep learning networks. It is primarily used to assess both the model's actual convergence behavior and its overall training performance. To ensure the reliability of the comparison, the cross-entropy loss function was employed for loss value calculation in both the pre- and post-improvement models. It is worth noting that this function remains the most commonly used classification loss function in convolutional neural networks. Cross-entropy is primarily utilized to measure the actual distance between two probability distributions. The calculation formula is as follows:  
\[H(p,q)=-\sum_{x}\ p(x)\text{log}\,q(x) \tag{2}\]  
Here, \(p\) represents the true values, and \(q\) represents the predicted values. Both \(p\) and \(q\) refer to the values obtained after the output of the original neural network has been processed by softmax regression.  
During the testing phase, this study applied the Mean Intersection over Union (MIoU) to measure the recognition performance of each pixel [39]. Due to space limitations, the detailed calculation process of MIoU is not elaborated here.  
As shown in Figure 4, the comparison of the cross-entropy loss values and MIoU calculations provides an intuitive demonstration of the performance differences between the models before and after the improvement throughout the training, validation, and testing stages. The study found that significant enhancements were observed when the model was applied to real-world reinforcing mesh scenarios. The improved model exhibited faster convergence, with the cross-entropy loss value decreasing more rapidly, leading to increased training efficiency. Moreover, overfitting was reduced on the validation set, enhancing the model's generalization capability. Segmentation accuracy also improved, with IoU showing excellent performance across the training, validation, and testing sets.  
Particularly in complex scenes, the model accurately identified detailed portions of the reinforcing mesh.  
The improved model also demonstrated stronger robustness and stability, proving its adaptability to scenarios with complex backgrounds. While maintaining high accuracy, computational efficiency was preserved, with no significant increase in inference time or resource usage. Overall, the enhanced model showed notable improvements in accuracy, efficiency, and robustness, providing reliable technical support for practical engineering applications.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 7 Hydrological Application  
Rainfall measured by CML has a broad application prospect in radar rain attenuation correction, urban rainfall-runoff simulation, drainage pipe flow prediction, flash flood warning, and so on [102; 103]. On the one hand, CML-based measurement provides a new source of rainfall data for hydrology research; on the other hand, the results of hydrological application can verify the performance of CML-based rainfall measurement.
# Grating-like Terahertz Metasurface for Large-Deflection-Angle Beam Manipulations
## 4 Conclusions  
A frequency-scanning metasurface grating operating in the terahertz band has been designed and manufactured. The metasurface grating shows a large diffraction angle of up to 59.5\({}^{\circ}\) and an angle range of \(\pm\)(59.5-47.3\({}^{\circ}\)) as the working frequency varies in the range of 0.87-1.02 THz. It is also numerically demonstrated that the metasurface grating based on CSRR has higher first-order diffraction intensity compared with metallic strip grating with the same grating constant and slit width. The experimentally measured diffraction angles are in good agreement with the theoretical and simulated results. Moreover, the proposed reflective grating-like metasurface shows the functions of beam scanning and  
Figure 4: The measured results of the diffraction wave (+1 st order mode) compared with the theoretical and simulated results.  
beam splitting and has important applications in the fields of moving target detection, wireless communication, spectral separation, and other fields.  
Conceptualization and supervision, X.F. and T.J.C.; software, Y.F. and L.S.; validation, X.F., Y.F. and L.S.; writing--original draft preparation, Y.F.; writing--review and editing, X.F. and T.J.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Key Research and Development Program of China (Grant Nos. 2018 YFB1801505, 2017 YFA0700201, 2017 YFA0700202, and 2017 YFA0700203), in part by the National Natural Science Foundation of China (Grant Nos. 61871127, 61735010, 61731010, 61890544), and in part by the 111 Project (Grant No. 111-2-05).  
The data presented in this study are available upon request from the corresponding authors.  
The authors declare no conflict of interest.
# Object Semantic Grid Mapping with 2D LiDAR and RGB-D Camera for Domestic Robot Navigation
## 4 Back-End
### Room Dominant Direction Detection
However, in order to reduce the sensitivity of thresholds and improve the detection accuracy of the dominant directions, we use the direction, distance, and coincidence constraints to detect and merge these line segments. Suppose the points of the first line segment are \(\mathbf{P}_{\mathrm{s1}}=\left[x_{\mathrm{s1}},\ y_{\mathrm{s1}}\right]^{ \mathrm{T}}\) and \(\mathbf{P}_{\mathrm{e1}}=\left[x_{\mathrm{e1}},\ y_{\mathrm{e1}}\right]^{ \mathrm{T}}\), and the second line segment are \(\mathbf{P}_{\mathrm{s1}}=\left[x_{\mathrm{s1}},\ y_{\mathrm{s1}}\right]^{ \mathrm{T}}\) and \(\mathbf{P}_{\mathrm{e1}}=\left[x_{\mathrm{e1}},\ y_{\mathrm{e1}}\right]^{ \mathrm{T}}\), the direction constraint requires the angle between the two line segments to be less than the angle threshold \(\delta_{\phi}\):  
\[\mathrm{acos}\left(\frac{a1a2+b1b2}{\sqrt{a_{1}^{2}+b_{1}^{2}}\sqrt{a_{2}^{2}+ b_{2}^{2}}}\right)<\delta_{\phi}. \tag{10}\]  
The distance constraint requires that the distance from the first segment points to the second line be smaller than the distance threshold \(\delta_{\mathrm{d}}\):  
\[\frac{\left|x_{\mathrm{s1}}a_{2}+y_{\mathrm{s1}}b_{2}+c_{2}\right|+\left|x_{ \mathrm{e1}}a_{2}+y_{\mathrm{e1}}b_{2}+c_{2}\right|}{2\sqrt{\left(a_{2}^{2}+b_{ 2}^{2}\right)}}<\delta_{\mathrm{d}}. \tag{11}\]  
The coincidence constraint requires the distance between the midpoints \(\mathbf{P}_{\mathrm{c1}}\) and \(\mathbf{P}_{\mathrm{c2}}\) of the line segments be less than the sum of the half-lengths of the line segments:  
Figure 6:0  
The segment merging method is shown in Figure 6:1 Based on the first line segment, the projection points of the second line segment are calculated. At last, the points and the first line segment length are recalculated as the merge result. To count the length of line segments, the directions of all line segments are used as the vote box, the direction constraint threshold is regarded as the box width, and the cumulative length of each direction is the number of votes. The voting result is calculated as shown in Figure 6:2 The dominant directions of the room \(\phi_{\mathrm{m1}}\) and \(\phi_{\mathrm{m2}}\) are:  
Figure 6:3  
Figure 6:4 Line segments merging: (**a**) schematic diagram and (**b**) merging result based on the gird map. The points of line segment are \(\mathbf{P}_{\mathrm{s}}\) and \(\mathbf{P}_{\mathrm{e}}\). The line parameters are [a, b, c]\({}^{\mathrm{T}}\). The line segment length is \(l\). The projection point is \(\mathbf{P}_{\mathrm{e}}^{\prime}\). The recalculated line segment length is \(l^{\prime}\).
## 3 Materials and Methods
### Classification  
In Figure 3, from 5 to 7, classification was performed using 10 classic machine learning models and their default parameters (if not specified otherwise), including the following:  
1. K-Nearest Neighbours, where K = 3;
2. Support vector classifier with a linear kernel;
3. Support Vector classifier with a radial basis kernel;Support vector classifier with a radial basis kernel and a balanced approach to automatically modify the weights with an inverse proportion to the number of occurrences of each class in the training data;  
Decision tree classifier with a maximum depth settled in 5;  
Random forest classifier with a maximum depth settled in 5 and a maximum number of estimators fixed in 10;  
Multi-layer perceptron classifier with a learning rate of 0.1 and a maximum number of iterations settled in 1000;  
AdaBoost classifier;  
Gaussian naive Bayes classifier;  
XGBoost classifier, with a maximum depth settled in 5, a maximum number of estimators fixed in 10, and a learning rate of 0.1.  
This list of classifiers was validated using hold-out validation, with a training set using 80% of the data and the remaining 20% used for testing data to obtain a general scope of how the different classification models performed.  
Considering that hold-out validation represents an adequate opportunity to evaluate a group of different models if the training and testing processes are performed under the same initial conditions (i.e. if the training and testing splits do not change), 10 classifiers were trained on the same training set and then evaluated with the same testing set.  
Based on hold-out validation results, the best-performing model was selected and then evaluated under a stratified K-fold cross-validation strategy for the following reasons:  
* To verify that the selected model's results were independent of the training set used during hold-out validation;
* To keep the original label distribution in the training and testing sets in the K splits;
* To thoroughly evaluate the impact of different input vector dimensions (inferred output of the Doc2 Vec models);
* To quantitatively analyze the impact of dimensionality-reduction techniques applied to the input vectors, for example, principal component analysis (PCA), with different numbers of computed features.  
Finally, the K value for the stratified K-fold cross-validation was fixed at 10. This means the fitting procedure was performed ten times, each with a training set of 90% of the data and a testing set of the remaining 10%. Additionally, this validation strategy ensures that the testing instances are not repeated between folds and that the training and testing sets preserve the same label proportion as the original input data.
# Hard SyDR: A Benchmarking Environment for Global Navigation Satellite System Algorithms +
## 6 Discussion and Open Challenges
### Perspectives of Using AxC  
Our prime objective in this research is to enable a design space exploration of using hardware-level AxC techniques in GNSS algorithms to provide more realistic power/energy estimates than from software. However, given the challenges encountered during our work with HLS, especially regarding the FFT, we acknowledge that integrating techniques, like approximate arithmetic [10; 11], is non-trivial and requires an analysis of the affected algorithms or processing stages to identify their error-resilient elements [48]. We expect to pursue such an analysis, beginning with software models of simple approximate adders and multipliers.
# Effect of Ozonized Water against Pathogenic Bacteria and Filamentous Fungi on Stainless Steel
## 4 Conclusions  
Aqueous ozone has a Generally Regarded as Safe (GRAS) designation from the U.S. Food and Drug Administration (FDA, 2001), and it is allowed in many countries, including Japan, Australia, France, and Canada [59]. On the contrary, in Europe, no regulation exists about the use of ozonized water for agro-industrial purposes, with a recent EFSA report (European Union, 2021) indicating that \"no conclusion could be drawn concerning the risk assessment to non-target species from potentially occurring by-products of the chemical interaction between ozone and substances occurring in water.\" Nevertheless, the use of ozone in its liquid form for sanitization purposes is gaining an increasing interest in the food and feed industry due to its undeniable advantages compared with chemicals, such as the absence of by-products that should affect human health or the possibility to generate it when needed.  
In our paper, ozonized water gave different results based on the tested microorganism, as observed yet for gaseous ozone [35]. Pathogenic bacteria proved markedly more sensible to ozone than filamentous fungi, even if great differences were observed at the inter- and intra-specific level for both categories of microorganisms, as in the case of _L. monocytogenes_ and _P. nordicum_. For a better interpretation of the obtained results, it could be taken into consideration the European Standard EN 13697:2015+A1 [36], which establishes that any product with bactericidal and/or fungicidal activity used in food, industrial, domestic, and institutional areas should demonstrate at least a four-decimal log reduction for bacteria and at least a three-decimal log reduction for fungi. In this perspective, an appropriate number of log reductions were achieved only for three out of four pathogenic strains when higher concentrations of ozonized water were used or for some _P. nordicum_ and _H. burtonii_ at different treatment times, depending on their spatial arrangement of the conidia and the use of neutral or acidified pure water. Nevertheless, the non-linear inactivation kinetics of the studied strains made the calculation of a punctual F-value difficult, so in industrial practice, adequate tailoring of the treatments to be applied, based on the registered extrinsic factors and the industrial bio-burden, would be appropriate.  
E.B.: conceptualization (filamentous fungi), microbiological software, and writing; C.M.: execution of microbiological analyses and acquisition of data; M.C.: conceptualization (pathogens); D.B.: execution of chemical analyses and acquisition of data (ozone treatments); C.C.C.: conceptualization and writing (ozone treatments); D.I.: project supervision, conceptualization, statistical analysis, and review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Feature Separation and Fusion to Optimise the Migration Model of Mural Painting Style in Toms
## 5 Conclusions  
This paper proposes a generative model for a tomb mural style based on feature separation and fusion. The model aims to address the high cost of digital restoration and the lack of training samples, while also improving the restoration of tomb mural style samples. First, it is proposed to separate image features and detail features before fusion to improve the performance of the model, and to adjust the noise insertion module of the parsing layer in the generator framework to preserve the noise, cracks, etc., that are unique to tomb murals, and to optimise the generation quality of the generator in a targeted way. Then, texture loss, colour loss and semantic loss functions are used in the original discriminator to constrain the model to learn the style of tomb murals. Extensive experiments on synthetic and real datasets proved the effectiveness of our method and achieved the most excellent results; objectively, our model, in terms of generative effectiveness, improved with SSIM by 0.05, FID optimisation was second only to the CUT model, and the LPIPS and NIQE were also optimised by reductions of 0.125 and 1.364. When using our model for digital restoration, SSIM was improved by 0.052, the FID optimisation index was second only to the AdaIN model, and LPIPS and NIQE also obtained 0.2 and 0.506 reduction optimisation, which fully proves the feasibility of our method.  
In the future, we will focus on more detailed and precise stylistic migration of chamber murals, such as generating stylised images in a given cave or dynasty.  
Conceptualisation, M.W.; methodology, M.W.; writing--review and editing, M.W.; software, M.L.; writing--original draft preparation, M.L.; validation, Q.Z.; resources, Q.Z.; data curation, Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 61701388), the Cross-disciplinary Fund of Xi'an University of Architecture and Technology (No. X2022082), (No. X20230085) and the Fund of the Ministry of Housing and Urban-Rural Development (No. Z20230826).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **SSIM\(\uparrow\)** & **FID\(\downarrow\)** & **LPIPS\(\downarrow\)** & **NIQE\(\downarrow\)** \\ \hline Gatys & 0.819 & 481.126 & 0.272 & 4.205 \\ Artflow & 0.863 & 527.819 & 0.257 & 4.096 \\ CycleGAN & 0.822 & 511.409 & 0.198 & 4.173 \\ ChipGAN & 0.855 & 501.745 & 0.222 & 4.414 \\ AdaIN & 0.859 & **425.532** & 0.366 & 4.305 \\ SANet & 0.867 & 437.335 & 0.208 & 4.275 \\ CUT & 0.864 & 459.213 & 0.250 & 4.148 \\ LseSim & 0.868 & 476.218 & 0.225 & 4.538 \\ \hline Ours & **0.871** & 458.260 & **0.166** & **4.032** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Quantitative effects of different models for digital restoration. Best results are shown in bold.  
Figure 14: Digital restoration effects for various styles of migration models.
# ACT-FRCNN: Progress Towards Transformer-Based Object Detection
## 3 Method and Materials
### Adaptive Clustering Transformer  
#### 3.2.1 Determine Prototypes  
The locality sensitive hashing (LSH) prototype is used to join queries that are close together based on Euclidean distance. This method effectively solves the nearest neighbor search problem. When vectors are close, they are likely to have the same hash, while those far apart do not. By adjusting the hash function's parameters and the number of hashing iterations, to ensured that all vectors with a distance less than \(p\) end up in the same hash bucket. Specifically, the used the exact Euclidean locality sensitive hashing (E2 LSH) as our hash function [19].  
\[h(\overrightarrow{v})=[\frac{\overrightarrow{a}.\overrightarrow{v}+b}{r}] \tag{2}\]  
where \(h:R^{d}\to Z\) is the hash function, \(r\) is a hyper-parameter, and \(\overrightarrow{a},b\) are random variables satisfying \(\overrightarrow{a}=(a_{1},a_{2},\ldots\ldots,a_{d})\), with \(a_{i}~{}\sim~{}N~{}(0,1)\) and \(b~{}\sim~{}u~{}(0,r)\). The results carry more weight when LSH is applied iteratively. The final hash value obtained by  
\[h(\overrightarrow{v})=\sum_{i=0}^{L-1}B^{i}h_{i}(\overrightarrow{v}) \tag{3}\]  
where each \(h_{i}\) is calculated using Equation (3) with parameters \(\overrightarrow{a}\) and \(b\) drawn at random, and where \(B\) is a constant equal to 4 in our trials. The random normal vectors \(\overrightarrow{a}_{1}\overrightarrow{a}_{1}\) and offset \(b_{i}\) in each hash function \(h_{i}\) can be visualized as a series of parallel hyperplanes. The hyper-parameter \(r\) controls the spacing of the hyperplanes. More significant separation indicates greater importance. In addition, \(L\) hash functions partition the search area into a number of cells, each of which contains vectors that are hashed to the same value. The likelihood that the vectors enter the same cell increases as the Euclidean distance decreases.  
To obtain the prototypes, the hash value for each query was first computed. The questions that have the same hash value will be clustered together, with the prototype query serving as the group's hub. The \(Q\in R^{N\times D_{k}}\) and the queries \(P\in R^{C\times D_{k}}\) sets as prototypes, where \(N\) and \(C\) are the number of groups for queries and prototypes, respectively. The \(G_{i}\) represent the index of the cluster to which \(Q_{i}\) belongs. The Prototype of the \(j\)th group can be obtained by Equation (4).  
\[P_{j}=\frac{\sum_{i}G_{i}=j\,Q_{i}}{\sum_{i}G_{i}=j\,1} \tag{4}\]  
Figure 2: The encoder outputs are connected to the detection module using the intermediary residual convolution blocks. Batch normalization and GeLU activation functions are used in all studies.