esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Stability in Simulated Gastric Fluid and Release in Simulated Intestinal Fluid
If the bacteriophages are released more slowly, encapsulated bacteriophages may also enter the colon or trigger a kind of long-term effect, whereby bacteriophages are gradually released again and again. [PERSON] et al., reported a long-term effect of alginate/calcium carbonate encapsulated _Salmonella enterica_ ssp. _entrica_ serovar Typhimuriumium (abbreviated as _Salmonella_ Typhimurium) bacteriophages in an in vivo study in broiler chickens, referring to the mucoadhesive property of the capsules [93]. To counteract the rapid expansion of alginate, a denser or stronger crosslinking of the polymer could be obtained or an additional polymer could be used [94]. This was already shown by [PERSON] et al., by comparing alginate/wheyprotein matrices containing either 0.8 or 1.5% alginate with a constant protein content of 3%; full bacteriophage release was obtained after 120 or 180 min, respectively [88]. Another possibility to prevent the early release of bacteriophages is a consecutive encapsulation process. The substance or bacteriophage to be encapsulated is entrapped in the first matrix by spray drying. The obtained dry particles are then suspended in a second matrix and spray dried again. This results in a so-called coating of the primary matrix and the bacteriophages are encapsulated twice. [PERSON] et al., encapsulated a virus-based vaccine in disaccharides (primary matrix) in this way and coated these particles with the polymer Eudragit L100(r) (secondary matrix) in a second spray drying process [95]. [PERSON] et al., used the polymer chitosan as a coating material for alginate capsules produced using extrusion technology. The bacteriophages encapsulated therein were successively released within 6 h [52]. Further development of the process of this study should pay attention to the size of the resulting particles (should be \(<\)100 \(\mu\)m, see Section 3.4) and to the bacteriophage loss during two spray drying steps.
# Enhanced K-Nearest Neighbor for Intelligent Fault Diagnosis of Rotating Machinery
## 3 Proposed Method
### Stage 2: Discriminative Feature Extraction  
This stage makes use of the powerful and automatic feature extraction capability of parameter-based methods to obtain fault-sensitive features from time-domain sample \(\mathbf{z}^{i}\) for further feature classification. Generally, the features are extracted through two steps.  
First, FFT is applied in \(\mathbf{z}^{i}\in R^{2N_{in}\times 1}\) to obtain its corresponding frequency-domain sample \(x^{i}\in R^{N_{in}\times 1}\), and all frequency-domain samples compose the training dataset \(\mathbf{X}\in R^{N_{in}\times M}\).  
Second, the sparse filtering (SF) is applied to the training dataset \(\mathbf{X}\) to extract discriminative features, which is a two-layer unsupervised network aiming to extract discriminative features of each input. The L-BFGS [37] algorithm is used to optimize the network.  
Finally, with the optimized parameter matrix \(\mathbf{W}^{1}\in R^{N_{1}\times N_{in}}\), \(x^{i}\in R^{N_{in}\times 1}\) can be transformed to its feature vector \(f^{i}\in R^{N_{2}\times 1}\). Additionally, it is also observed in our experiments that the samples transformed by SF can be both quite discriminative and consistent, which means that feature vectors of all samples can be quite similar and activated in the same elements. This property would be likely to make further testing sample reconstruction-based nearest neighbor location more robust.  
Figure 1: The flowchart of the proposed method.
# BEAC-Net: Boundary-Enhanced Adaptive Context Network for Optic Disk and Optic Cup Segmentation
## 3 Methods
### Efficient Boundary Pixel Attention  
The fundus shows that the OC boundary regions are usually complex and blurred, and it is impossible to specify the boundary location. To solve this problem, we propose an efficient boundary pixel attention (EBPA) to collect contextual information in the horizontal and vertical directions to improve the boundary pixel feature capability, which effectively exploits the information of adjacent regions around the OD and OC boundaries to compensate for inadequate feature capture and to enhance the accuracy of segmented boundaries. We focus on acquiring pixel features near the OD and OC boundary region, and boundary detection provides powerful complementary information for semantic segmentation.  
Figure 4 shows that the feature map \(X\) is shaped \(R^{C\times W\times H}\). EBPA first uses two convolutional layers with \(1\times 1\) convolution on \(X\) and then generates two feature maps, \(Q\in R^{C_{1}\times W\times H}\) and \(K\in R^{C_{2}\times W\times H}\), where \(C_{1}\), \(C_{2}\) denotes the number of channels and \(C_{1},C_{2}<C\). We use the affinity operation according to \(Q\) and \(K\) to generate attention map \(B\in\mathbb{R}^{
## 4 Routing Protocols Using Ant-Like Mobile Agents
### Ant-Aodv  
In order to ameliorate the dilemma between proactive routing protocols and reactive routing protocols, it is natural to conceive of AODV routing protocol that does not consume network resources as DSDV does but integrate proactive ant-like mobile agents. [PERSON] _et al._ have proposed such routing protocol called Ant-AODV hybrid routing protocol [46]. As the base protocol is an on-demand type, when the source node does not have the route information to the destination, it starts an on-demand route discovery procedure. It provides, however, proactive mobile agents, and they update the routing tables of participating nodes so that they decrease the frequency of the on-demand route discoveries. In such a compromise, it is most important to discover the appropriate parameters based on intensive simulations reflecting the real situation. In real world, constructing a hybrid system based on real compromise data may produce good enough MANET.
## 4 Materials and Methods
### SLC10A1 Sequencing and Genotyping
#### 4.5.1 Next Generation Sequencing (NGS)  
NGS was conducted at the Center for Genomics and Transcriptomics (CeGaT GmbH, Tuebingen, Germany) [63]. In brief, a custom-made ADME panel including _SLC10A1_ was designed and manufactured for use with Agilent in-solution target capture technology (Agilent Technologies, Santa Clara, CA, USA). Target regions of _SLC10A1_ used for library preparation are given in Supplementary Table S8. The DNA libraries were generated from the genomic DNA of 143 blood samples. NGS was performed at a high depth (459-fold mean coverage) with 2 \(\times\) 100 bp paired-end reads on the Illumina HiSeq2500 platform (Illumina Inc., San Diego, CA, USA). Raw reads were mapped to the human reference genome (hg19) and variations were called and annotated using a custom bioinformatics pipeline, also covering previously published biomarkers. Filtering of germline variants was performed as described previously [91].
## 4 Discussion
### Selection of sEMG Low-Pass Cutoff Frequency  
The low-pass cutoff frequency is one factor that affects the sEMG processing results. The low-pass filter is used to acquire a smooth muscle activation envelope [39]. It has been demonstrated that applying a low-pass filter is highly convenient in the study of muscle force and limb motion prediction, as the muscle behaves like a low-pass filter [46]. Thus far, even though the low-pass filter has been extensively investigated, no standard results have been reported to act as guidelines for how to set or obtain appropriate values. Values from 1 to 10 Hz in 1 Hz increments are tested in this study. Figure 17 shows the comparison among the different cutoff frequencies for subject A. As the cutoff frequency drops, the evidence becomes smoother. Clear fluctuations can be found by using a 10 Hz cutoff frequency. Almost no fluctuation could be observed below 3 Hz. The envelope turns into a smooth line with a cutoff frequency of 1 Hz.  
Figure 16: Diagram of implementation of the proposed method. The mean values of reflex gains are used as initial gains for a robotic device controller. An iterative calibration is applied to modified the reflex gains.  
Figure 15: Muscle activation prediction results using different reflex gains. The gains are calculated from different lengths of data. The dotted line is the measured muscle activation. The results show that the prediction results are not acceptable if the length of the data is too short.  
It is convenient to obtain a smooth value in engineering applications. However, too much information loss also has profound influences on the results. Table 6 summarizes the reflex gains and VAF values of muscle activation prediction under different cutoff frequencies. From this table, one can observe that \(p_{a}\) and VAF values increase, whereas \(d_{a}\) value first increases (until 3 Hz) and then decreases in the case where the cutoff frequency decreases. We assume that the fluctuation influences the reflex gain calculation and model prediction results seriously in the 10 Hz case. In this case, the reflex gains are inaccurate, and the VAF value is low. With the smoothing of fluctuation, its influence on the reflex gains and VAF decreases. This improvement for \(d_{a}\) stops when the cutoff frequency is lower than 3 Hz. In the 1 Hz case, the \(d_{a}\) value decreases to about 40% compared with the 3 Hz case, and in some cases, the value becomes negative, which means that the physiological meaning has been lost. The increase of \(p_{a}\) value may be explained by the amplitude (or the muscle activation level) increasing with the decreasing of the cutoff frequency (except the 1 Hz case). The maximum muscle activation amplitude and the \(p_{a}\) values increase by 24% (0.55 in 10 Hz and 0.68 in 3 Hz) and 30%, respectively. \(p_{a}\) value remains almost unchanged when cutoff frequency is lower than 3 Hz. This result indicates that the \(d_{a}\) component dominates the 3-5 Hz range and the \(p_{a}\) component dominates the lower frequencies.
# Efficient Pruning of Detection Transformer in Remote Sensing Using Ant Colony Evolutionary Pruning
## 3 Methodology
### Population Initialization
Within the Transformer layer, for the pruning of attention heads, the option to eliminate the complete attention head or to eradicate an identical number of parameters from each attention head is at one's disposal. The latter choice offers finer pruning granularity but poses challenges in implementation as it does not assure an equal level of sparsity across all attention heads, especially under high pruning ratios, potentially undermining the capabilities of all attention heads substantially. Hence, to guarantee pruning effectiveness and efficiency, this study designates attention heads as the smallest pruning unit. Based on the definition of attention mechanisms provided in [50], Equation (2) is revised as follows:  
\[a=\sum_{k=1}^{N}\sum_{i=1}^{M_{k}}H_{k}^{(i)}Head_{k}^{(i)}(Q,K,V), \tag{5}\]  
where \(H_{k}^{(i)}\in\{0,1\}\) embodies the attention head sparse operator, and \(Head_{k}^{(i)}(\cdot)\) denotes the \(i\)-th attention head in the \(k\)-th layer. We utilize attention head sparse operators to log the ants' path selections within the Transformer layer. Furthermore, within the Transformer layer, there are feed-forward layers  
Figure 3: (**a**) DS Block and (**b**) ST Block structures in Resnet 50.  
exhibiting centralized parameter distributions, comprising two linear layers. In this context, the identical channel sparse operators utilized in the Backbone are used to document the path selections of the ants.
# Hybrid Multimodal Medical Image Fusion Method Based on LatLRR and ED-D\({}^{2}\)GAN
## 5 Conclusions  
This paper proposed a hybrid multimodal medical image fusion method based on LatLRR and ED-D\({}^{2}\)GAN. Firstly, the CT and PET source images were decomposed into low-rank region images, salient region images, and noise region images by LatLRR, respectively. Secondly, the decomposed CT low-rank region image was enhanced, and a dual discriminators GAN (ED-D\({}^{2}\)GAN) was used to fuse the low-rank region images. Thirdly, the threshold adaptive weighting algorithm based on a region energy ratio was used as the salient region images' fusion rule. Finally, the final fused image was obtained by linear addition. Subjective and objective experiments demonstrate the effectiveness of the proposed fusion rules. The proposed method can not only highlight the lesion information in PET source images, but also obtain fused images with obvious contrast and edge intensity. It is of great significance to effectively alleviate the shortcomings of the single fusion method.  
**Author Contributions:** Writing--review and editing, T.Z.; project administration, T.Z.; funding acquisition, T.Z.; Conceptualization, Q.L.; writing--original draft preparation, Q.L.; methodology, Q.L.; software, Q.L.; validation, Q.L., X.Z. and Q.C.; investigation, H.L.; data curation, H.L.; supervision, H.L.; visualization, X.Z.; formal analysis, Q.C. 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, grant number 62062003; Natural Science Foundation of Ningxia, grant number 2022 AAC03149; and North Minzu University Research Project of Talent Introduction, grant number 2020 KYQD08.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.  
The authors declare no conflict of interest.
###### Abstract  
Alterations in metabolism caused by SARS-CoV-2 infection have been highlighted in various investigations and have been used to search for biomarkers in different biological matrices. However, the selected biomarkers vary greatly across studies. Our objective is to provide a robust selection of biomarkers, including results from different sample treatments in the analysis of volatile organic compounds (VOCs) present in urine samples from patients with COVID-19. Between September 2021 and May 2022, urine samples were collected from 35 hospitalized COVID-19 patients and 32 healthy controls. The samples were analyzed by headspace (HS) solid phase microextraction (SPME) coupled to gas chromatography-mass spectrometry (GC-MS). Analyses were conducted on untreated urine samples and on samples that underwent specific pretreatments: lyophilization and treatment with sulfuric acid. Partial Least Squares Linear Discriminant Analysis (PLS-LDA) and Subwindow Permutation Analysis (SPA) models were established to distinguish patterns between COVID-19 patients and healthy controls. The results identify compounds that are present in different proportions in urine samples from COVID-19 patients compared to those from healthy individuals. Analysis of urine samples using HS-SPME-GC-MS reveals differences between COVID-19 patients and healthy individuals. These differences are more pronounced when methods that enhance VOC formation are used. However, these pretreatments can cause reactions between sample components, creating additional products or removing compounds, so biomarker selection could be altered. Therefore, using a combination of methods may be more informative when evaluating metabolic alterations caused by viral infections and would allow for a better selection of biomarkers.  
COVID-19; SARS-CoV-2; urine volatileome; VOCs; GC-MS; metabolomics; biomarkers Article Analysis of the Urine Volatilome of COVID-19 Patients and the Possible Metabolic Alterations Produced by the Disease  
1
# A General Statistical Physics Framework for Assignment Problems
## Appendix C Monotonicity of \(F_{\beta}^{mf}\)  
The effective free energy \(F_{\beta}(\lambda,\mu,x)\) defined in Equation (8) is a function of the cost matrix \(C\) and of real unconstrained variables \(\lambda(i)\), \(\mu(j)\), and \(x\). For the sake of simplicity, for any \((i,j)\in[1,N_{1}]\times[1,N_{2}]\), we define  
\[y(i,j) = C(i,j)+\lambda(i)+\mu(j)+x,\] \[y^{MF}(i,j) = C(i,j)+\lambda^{MF}(i)+\mu^{MF}(j)+x^{MF}.\]  
The effective free energy is then \[F_{\beta}(\lambda,\mu,x)= -\frac{1}{\beta}\sum_{i}G(\beta\lambda(i),p_{min}(i),p_{max}(i))\] \[-\frac{1}{\beta}\sum_{j}G(\beta\mu(j),q_{min}(j),q_{max}(j))\] \[+\frac{1}{\beta}\sum_{i,j}H(\beta y(i,j))-kx.\]  
As written above, \(F_{\beta}(\lambda,\mu,x)\) is a function of the independent variables \(\beta\), \(\lambda(i)\), \(\mu(j)\), and \(x\). However, under the saddle point approximation, the variables \(\lambda(i)\), \(\mu(j)\), and \(x\) are constrained by the conditions  
\[\frac{\partial F_{\beta}(\lambda,\mu,x)}{\partial\lambda(i)}=\frac{\partial F _{\beta}(\lambda,\mu,x)}{\partial\mu(j)}=\frac{\partial F_{\beta}(\lambda, \mu,x)}{\partial x}=0,\]  
and the free energy under those constraints is written as \(F_{\beta}^{MF}\). In the following, we use the notations \(\frac{dF_{\beta}^{MF}}{d\beta}\) and \(\frac{3F_{\beta}^{MF}}{d\beta}\) to differentiate between the total derivative and partial derivative of \(F_{\beta}^{MF}\) with respect to \(\beta\), respectively. It can be shown easily that (see Appendix C of [15])  
\[\frac{dF_{\beta}^{MF}}{d\beta}=\frac{\partial F_{\beta}\big{(}\lambda^{MF}, \mu^{MF},x^{MF}\big{)}}{\partial\beta},\]  
namely that the total derivative with respect to \(\beta\) is, in this specific case, equal to the corresponding partial derivative, which is easily computed to be  
\[\frac{\partial F_{\beta}^{MF}}{\partial\beta}=\] \[\frac{1}{\beta^{2}}\sum_{i}G(\beta\lambda^{MF}(i),p_{min}(i),p_{ max}(i))-\] \[\frac{1}{\beta}\lambda^{MF}(i)g(\beta\lambda^{MF}(i),p_{min}(i),p _{max}(i))+\] \[\frac{1}{\beta^{2}}\sum_{j}G(\beta\mu^{MF}(j),q_{min}(j),q_{max}( j))-\] \[\frac{1}{\beta}\mu^{MF}(j)g(\beta\mu^{MF}(j),q_{min}(j),q_{max}( j))\] \[-\frac{1}{\beta^{2}}\sum_{i,j}\Big{[}H(\beta y^{MF}(i))-\beta y^{ MF}(i,j)h(\beta y^{MF}(i,j))\Big{]}.\]  
Let \(t(x)=H(x)-xh(x)\). The function \(t(x)\) is continuous and defined over all real values \(x\) and is bounded above by \(0\), i.e., \(t(x)\leq 0\quad\forall x\in\mathbb{R}\). Similarly, let us define \(l(x,a,b)=G(x,a,b)-xg(x,a,b)\). In Appendix H, we show that \(l(x,a,b)\) is positive over \(\mathbb{R}\). As \[\frac{dF_{\beta}^{MF}}{d\beta}= \frac{1}{\beta^{2}}\sum_{i}l(\beta\lambda^{MF}(i),p_{min}(i),p_{ max}(i))+ \tag{104}\] \[\frac{1}{\beta^{2}}\sum_{j}l(\beta\mu^{MF}(j),q_{min}(j),q_{max}(j))\] \[-\frac{1}{\beta^{2}}\sum_{i,j}t(\beta\mu^{MF}(i,j)),\]  
we conclude that  
\[\frac{dF_{\beta}^{MF}}{d\beta}\geq 0,\]  
namely that \(F_{\beta}^{MF}\) is a monotonically increasing function of \(\beta\).
## 4 Generative Modeling as a Distribution Approximation Process
### Experimental Validation in Image Compression  
To demonstrate the practical implications of our theoretical findings, we conducted experiments on image compression using the RDP framework. We implemented several learning-based compression methods that incorporate perceptual measures and compared them against traditional compression codecs.  
We evaluated the performance of five compression methods: BPG (baseline), VAE-based compression [93], attention-based compression [15], GAN-based compression [7], and diffusion-based compression [88] at a low bpp regime (\(R\approx 0.2\) bpp). We used the CLIC2020 dataset [94], which contains 428 high-quality color images, for our experiments. The performance metrics include PSNR, MS-SSIM, and the perceptual-oriented LPIPS.  
The results are summarized in Table 2. The GAN-based method shows the most significant improvement in perceptual quality metrics while maintaining competitive distortion metrics. The attention-based method provided better PSNR than traditional methods but showed less improvement in perceptual metrics.
## 3 Results and Discussion
### Bilayer Planar Heterojunction
From the above, we can see that the performances of Dev-B and Dev-C are both far superior to those of Dev-A, reflecting the advantages of PHJ structure over a single layer. Compared to Dev-C, Dev-B has equivalent mobility, higher NIR absorption, and lower exciton dissociation efficiency, which renders Dev-B to perform equivalently to Dev-C (shown in Table 1). The air stability of organic electronic devices has long been a concern. For this, the air stability of Dev-B and Dev-C was investigated. The \(R\), \(P_{\rm max}\), and mobility (\(\mu\)) variations of them with storage time in air were plotted in Figure 5. It is clear that after 24 h, the \(R\), \(P_{\rm max}\), and \(\mu\) of Dev-C plunge from 142 mA/W, 5.5 \(\times\) 10\({}^{3}\) and 3.9 \(\times\) 10\({}^{-2}\) cm\({}^{2}\)/V\(\cdot\)s to 9.4 mA/W, 3.2 \(\times\) 10\({}^{3}\), and 6.7 \(\times\) 10\({}^{-3}\) cm\({}^{2}\)/V\(\cdot\)s, respectively, while after 120 h, the \(R\), \(P_{\rm max}\), and \(\mu\) of Dev-B rise from 139 mA/W, 5.3 \(\times\) 10\({}^{3}\), and 3 \(\times\) 10\({}^{-2}\) cm\({}^{2}\)/V\(\cdot\)s to 473 mA/W, 6.1 \(\times\) 10\({}^{3}\), and 4.2 \(\times\) 10\({}^{-2}\) cm\({}^{2}\)/V\(\cdot\)s, respectively. These results proved that Dev-B has far better air stability than Dev-C. The reason for this is that PbPc and pentacene are relatively stable for O\({}_{2}\) and H\({}_{2}\)O in air, whereas C\({}_{60}\) is highly sensitive to them [32; 33; 34; 35; 36]. When Dev-C exposes to air, the O\({}_{2}\) and H\({}_{2}\)O molecules can permeate into the C\({}_{60}\) layer and react with C\({}_{60}\) to produce hydroxyls, and the hydroxyls would act as electron traps and capture lots of electrons [37], which causes a sharp decline in mobility and a serious deterioration of device performance (see Figure 5c). And when Dev-B is exposed to air, the O\({}_{2}\) and H\({}_{2}\)O molecules are not easy to react with pentacene and PbPc, and thus Dev-B performs stably in air with time. Hence, given the performance and air stability of devices, the structure of Dev-B will be preferred. Figure 4: Output and transfer characteristics of Dev-B (**a**,**b**), Dev-C (**c**,**d**), and Dev-D (**e**,**f**) in the darkness and illumination. Figure 5: The \(R\), \(P_{\max}\), and \(\mu\) variations of the PHJ devices with storage time in air. (**a**) \(R\). (**b**) \(P_{\max}\). (**c**) \(\mu\).
# Research on Multi-Scale Point Cloud Completion Method Based on Local Neighborhood Dynamic Fusion
## 3 Method  
### Network Architecture  
The overall architecture of the multi-scale point cloud completion network based on local neighborhood dynamic fusion (LNDM-Net), as proposed in this paper, is illustrated in Figure 1. Three main components compose the network: a multi-scale feature extractor, a pyramid point cloud generator, and an attention discriminator. Initially, the multi-scale point cloud extractor employed the farthest point sampling method to down-sample the input, generating three incomplete point clouds at varying resolutions. These point clouds then underwent local neighborhood dynamic fusion combined with a series of attention layers to extract feature vectors at each resolution. Next, the feature vectors from all three resolutions were merged in parallel, producing a unified feature vector that captured both the global and local characteristics of the point cloud. This enriched feature vector was fed into the pyramid point cloud generator, which output point clouds at different resolutions, progressing from coarse to fine. Finally, an attention discriminator, which shared the same structure as the multi-scale point cloud extractor, was applied. By utilizing local neighborhood dynamic fusion and attention layers, the discriminator evaluated the generated point clouds, guiding the generator to produce more accurate and realistic point cloud completions.