esa-sceva/satcom-chunk-collection huggingface dataset:
# Unsupervised Temporal Adaptation in Skeleton-Based Human Action Recognition
## 3 Method
### Preliminary
#### 3.1.2 Domain Adaptation in Skeleton Data  
However, end-to-end supervised learning on \(F\) requires the collection and annotation of a large volume of skeleton sequences \(\{X,Y\}\), which is extremely time-consuming in practice. To circumvent the need for such data, this work proposes Unsupervised Domain Adaptation (UDA) based on two fundamental considerations:  
1. The action recognition model, \(F=\mathcal{C}\circ\mathcal{M}\), is modular such that its subnetworks (\(\mathcal{M}\) and \(\mathcal{C}\)) can be treated individually and separately, which allows an adaptation framework to optimize them step by step.
2. In practical engineering applications, a target deployment environment (target domain \(\mathcal{D}_{t}\)) often presents misaligned data distributions, given the fact that the image acquisition configuration can differ from that of the original data collection domain (source domain \(\mathcal{D}_{s}\)). At the same time, individuals coming from different environments performing the same action may have significant variations in their movement patterns, leading to another form of discrepancy in data distribution. Either data domain shift (i.e., the data distribution variations across \(\mathcal{D}_{s}\) and \(\mathcal{D}_{t}\)) tends to corrupt the model's performance when the model is well trained on \(\mathcal{D}_{s}\) but is evaluated on \(\mathcal{D}_{t}\).  
To this end, UDA considers a source domain, i.e., \(\mathcal{D}_{S}=\{(X_{s},y_{s})\}\), which is composed of the sequence \(X_{s}\) and its annotation \(y_{s}\), and a target domain, \(\mathcal{D}_{t}=\{X_{t}\}\), where the skeleton sequence \(X_{t}\) is unlabeled. UDA aims to utilize a significant quantity of labeled data from \(\mathcal{D}_{s}\) to pretrain an intermediate model (e.g., feature encoder) and then refine a target-adaptive model \(F\) by using only unlabeled data from the target environment. By transferring skeleton action knowledge across different domains, UDA effectively avoids learning from scratch and saves the labor of annotating videos in the target domain.  
Figure 1 illustrates the proposed framework. **Contrastive Prototype Learning (CPL)** optimizes the subnetwork \(\mathcal{M}\) over the labeled data from \(\mathcal{D}_{S}\). The pre-learned \(\mathcal{M}\) is recycled into a successive **Temporal Adaptation Modeling (TAM)** phase that models temporal adaptation knowledge (pseudo-labels) on the unlabeled data from the target domain. Finally, the proposed **CPL** and **TAM** models are submitted to an optimization phase to progressively refine an action classifier.
# A Hybrid Robust-Learning Architecture for Medical Image Segmentation with Noisy Labels
## 3 Methods
### Segmentation Module  
Considering the complementary relationship among slices of CT (or MRI) data, we prefer to choose the 3D neural network as the basic segmentation module to leverage the spatial relationship between slices, instead of the 2D segmentation model. Considering the promising performance of U-net, we select 3D U-net as the backbone [35], which has proven its effectiveness for 3D volumetric images. Further, we extend our backbone with two important modules. First, we adopt a residual skip connection for each convolutional block to aid the total training [36]. Second, we introduce 3D squeeze-and-excitation (SE) blocks for better feature representation. Inspired by the recent proposed 2D SE module for image classification [37], we introduce the 3D extension for our image segmentation.
## 2 Channel Model
### Link Budget  
Link budgeting is a method which analyzing the performance of wireless communications. A link budget is a tool which predicts signal-to-noise ratio (SNR) at the receiver side by considering multiple parameters: transmit power, gain, losses and interference etc. Figure 5 shows a plot for the link budget of wireless communications. A simplified link budget equation for wireless communication is modeled by the expression [43] as:  
\[P_{re}=P_{tr}+G_{tr}+G_{re}+L_{s}+L_{n}, \tag{12}\]  
where \(P_{re}\) is received power, \(P_{tr}\) = transmitted power, \(G_{tr}\) = transmitter gain, \(G_{re}\) = receiver gain, \(L_{s}\) = path loss and \(L_{n}\) is the noise factors. Figures 4 and 5 shows the simplified block diagram and plot of the link budget.  
Figure 4: Block diagram of the link budget as expressed in Equation (12).  
Figure 3: Ambient noise (dB re \(\mu\) Pa) level in seawater.
## 4 The Trust Evaluation Process
### Trust Calculation for One-Hop Neighbor Nodes
#### 4.2.2 Recommendation Trust Calculation for One-Hop Neighbor Nodes  
If there are not enough information interactions between two neighbor nodes, the trust relationship between them needs to be established based on the third party nodes' recommendations. As shown in Figure 4, the first step of recommendation calculation is to select reliable recommender nodes among all the neighbor nodes. If node **a** wants to obtain the recommendations of node **b**, it first broadcasts recommendation request to all the common neighbor nodes of them. The neighbor nodes which have the trust value of node **b** will reply related acknowledgment packet to node **a**. The reliable recommenders are selected among the reply nodes according to the following principle: (1) the selected recommender should be trusty; (2) the recommendation value should be reliable. Whether a recommender is trusty or not can be easily judged by its trust value. The reliability of a recommendation value is decided by the familiarity degree between the recommender and the sponsor node. In general, people are more dependent on more familiar things and think they are reliable. Similarly, the more frequently two neighbor nodes communication with each other, the higher credibility of the recommendation trust value is. That is, the reliability of a recommendation value is judged by the familiarity among neighbor nodes.  
If there are \(m\) recommenders selected for target node, we can obtain \(m\) recommendation values as \(\left\{\left.T_{r1}(E_{x1},E_{n1},E_{e1}),T_{7,2}(E_{x2},E_{n2},E_{e2}), \cdot\cdot\cdot,T_{rm}(E_{xm},E_{mm},E_{em})\right\}\right.\). Therefore, the total recommendation trust \(T_{r}(E_{x},E_{n},E_{e})\) can be calculated by  
\[T_{r}=T_{r1}\oplus T_{r1}\oplus\cdot\cdot\cdot\oplus T_{rm} \tag{11}\]  
\[T_{r}=T_{r1}(E_{x1},E_{n1},E_{e1})\oplus T_{r2}(E_{x2},E_{n2},E_{e2})\oplus \cdot\cdot\cdot\oplus T_{rm}(E_{xm},E_{mn},E_{em}) \tag{12}\]  
\[\left\{\begin{array}{l}E_{x}=\frac{1}{m}\prod\limits_{i=1}^{m}E_{xi}\\ E_{n}=\min(\frac{1}{m}\sum\limits_{i=1}^{m}E_{ni},1)\\ E_{e}=\min(\frac{1}{m}\sum\limits_{i=1}^{m}E_{ei},1)\end{array}\right. \tag{13}\]
## 3 Efficient OFDM BER Floor with Joint Near-Optimal Time Sampling and CFO
### CFO Abstraction by Time Dispersion  
If we observe the received OFDM symbol with offset \(\Delta f_{\text{CFO}}\) of the subcarrier frequency recovered at the receiver, with respect to the transmitted frequency \(m\cdot\frac{2\pi}{M\text{s}}\), the \(i\)-th echo in Equation (15) of the received OFDM symbol transforms to:  
\[\hat{r}_{\text{CFO}}=\sum_{m=1}^{M}s_{m}e^{ip\cdot}e^{j(m\cdot\frac{2\pi}{M \text{s}}+\Delta f_{\text{CFO}})\cdot(\overline{\tau}+\Delta\tau_{\text{opt} }-\tau_{i})};\quad i=1,2,\ldots,N \tag{18}\]  
Now, the residual CFO being a non-AWGN impairment, can be abstracted not by conventional AWGN, but by, in this case more convenient, equivalent additional delay spread that would cause the same phase and thus BER floor degradation, described exclusively by time dispersion [12].  
Figure 1: Carrier lock angle error [16].  
the model applicability is not limited by any specific \"m\" scenario (i.e., use case) of OFDM-based wireless networks.  
The efficient BER floor estimation model with near-optimal sampling at the mean delay of the power delay profile, as well as with CFO abstraction by time dispersion, was verified by comparing the results achieved two-fold: first by Equations (12)-(14) and (21), and then by software simulations.
# A Multi-Bandwidth Reconfigurable Patch Antenna for Devices in WLAN and UWB Technology Applications
## 3 Experimental Results and Discussion
In contrast, in the E-plane, the radiation pattern is bidirectional at the first resonant frequency (2.5 GHz) and quasi-omnidirectional at the higher frequency (4.4 GHz), as shown in Figure 10. Changes in the radiation pattern at high frequencies can be attributed to the influence of the high frequencies on the FR-4 substrate used in the antenna. Overall, the results demonstrate that the proposed antenna is well-suited for WLAN and low-UWB applications, providing stable and wideband radiation patterns in both E and H planes. Figure 11: Comparison of the measured and simulated 2D radiation patterns of the proposed PA. (**a**) Radiation patterns at a frequency of 2.5 GHz. (**b**) Radiation patterns at a frequency of 4.4 GHz. _Comparison with the State-of-the-Art WLAN and UIWB Antennas_  
The proposed antenna has a large bandwidth range of up to 1.22 GHz (2.35-2.74 GHz) and a reflection coefficient value of \(-34.9\) dB. In addition, it has a compact design, and it offers a better impedance bandwidth and gain when compared to the other works. Other antennas in the literature do not exceed 0.42 GHz in the band, as reported in [18; 40]. The antenna proposed in [41] presents a frequency response with an impedance bandwidth ranging from 2.4 GHz to 2.485 GHz. However, the antenna proposed in this paper still outperforms this result in terms of bandwidth range. Table 2 presents a comparison of our proposed antenna's performance with previously published works from recent years. The table highlights various measured parameters, such as the frequency responses and the impedance bandwidth ranges of the reflection coefficient, and the size of the patch antenna. From the table, it is evident that the proposed antenna outperforms all other comparative literature works in terms of its compact size, better impedance bandwidth, and reflection coefficient.
## 1 Introduction
Although the above methods do accelerate the electrolyte renewal and the products removal, they still suffer the disadvantage of a long flow path, meaning that fresh electrolyte has relatively far to travel to reach the reaction area. Herein, we propose a novel pulsating radial electrolyte supply for WECM, which utilized a tube electrode with a line of micro-holes on surface. The electrolyte is pumped into the tube electrode from two ends and is ejected into the machining gap from the radial micro-holes of the tube. In this way, the electrolyte refreshment has a relatively short path, being independent of the workpiece thickness. The micro-hole size as well as its distribution will indeed have an important effect on the radial flow field uniform and the process stability. As a common micro-structure, micro-holes have been used widely in various parts, but their fabrication has invariably been a difficult problem in drilling thin-walled parts. Mechanical drilling has the disadvantages of severe tool wear and deformation, and defects such as thermal deformation, heat-affected layers and recast layers arise at the edges of the holes processed by electrical discharge machining and laser micromachining [16; 17]. By contrast, electrochemical micro-drilling (ECMD) is more suitable for fabricating micro-holes because of its good surface quality and absence of stress. [PERSON] et al. [18] found using a micro helical tool could improve the accuracy of micro-holes significantly. [PERSON] et al. [19] machined taper free micro-holes with a diameter of 186 \(\upmu\)m on the GH4169 plate of 500 \(\upmu\)m thickness using a rotating helical electrode and ultra-short voltage pulse. However, there is a lack of research on drilling micro-holes on cylindrical surface. Herein, we present research into the processing of micro-holes on cylindrical surface fabricated by a rotating helical electrode. After determining the quantitative relationship among the feed rate, the applied voltage, and the hole diameter, a tube with holes of varying diameters is machined by regulating the applied voltage on-line. Then, experiments are carried out to verify whether a tube electrode with varying micro-holes offers a better processing capacity for supplying pulsating radial electrolyte in WECM of a 30 mm-thick workpiece.
# A Dynamic Risk Assessment and Mitigation Model
## 4 Methodology
### Information Collection  
The primary objective of our methodology is to detect vulnerabilities and use the identified CVEs to collect all necessary data. Following the identification of CVEs, our model utilizes the NVD API to retrieve CVSS 3.x metrics, retaining only the exploitability and impact subscores. These subscores are then normalized to enable their integration into the third module of our methodology.  
Additionally, the same API is used to retrieve the associated CWE-ID for each detected CVE. The CWE-ID is subsequently employed to identify related CAPEC entries by leveraging the CAPEC 3.9.xml file. This approach establishes a pathway from each identified CVE to the CAPEC framework via CWE. The CAPEC framework is then utilized to provide mitigation recommendations for each identified CVE.  
Furthermore, the proposed methodology integrates the CISA KEV API, which is queried using the detected CVEs to identify whether the vulnerabilities are included in the CISA KEV Catalog. This process allows for the determination of whether a clear remediation action, such as the availability of a patch, has been specified for the identified vulnerabilities. This information is then presented to the user along with corresponding mitigation recommendations. Finally, the EPSS API is utilized to retrieve EPSS scores for each detected CVE. These scores are subsequently integrated into the Bayesian network within the third module.
###### Abstract
Above all, despite the great progress achieved by the above methods, accurate extraction of buildings from remote sensing images is still a challenge. Presently, attention mechanisms [38] and multi-task learning [39] are widely used in image processing; among them, the goal of applying attention mechanisms is to help select effective information used in the network. The aim of multi-task learning is to leverage useful information contained in related tasks to help improve the generalization performance of all tasks. Inspired by attention mechanisms and multi-task learning, in this paper, to address the aforementioned problems based on the traditional UNet++ architecture combined with attention mechanisms and multi-task learning, we designed an improved UNet++ architecture with an attention block and edge preservation (AEUNet++) for accurate extraction of buildings from HRSI. Based on UNet++, we exploited deep structured feature fusion techniques to enhance the feature fusion by giving a trainable weight to the different feature maps using the convolutional block attention module (CBAM) [40]. Further, in order to address the problem of segmentation prediction results with poor boundaries, we incorporated the boundary information of the building mask into the network by introducing a multi-task loss based on the distance class map. The aim was to produce accurate semantic segmentation results in homogeneous regions and preserve image details. The main advantages of the AEUNet++ are as follows:  
1. An improved UNet++ for the wise fusion of extracted feature maps is proposed, in which the CBAM, including the spatial attention and channel attention gates, is introduced to learn 'where' and 'what' the meaningful representations of the given features are. It significantly suppresses the drawbacks of direct concatenation by averaging the operations in the UNet++ models, thus improving the segmentation accuracy. 2. To improve the boundary precision of extracted buildings, the boundary geometric information of the building is introduced into the proposed AEUNet++ by using a multi-task loss based on the proposed distance class map. 3. The proposed AEUNet++ achieved 1.62% and 1.8% F1 and 2.0% and 3.24% intersection over union (IoU) improvements compared with UNet++ on the Massachusetts building data set [41] and the WHU data set [36] and outperforms two other SOTA methods on the two data sets.
## 3 Results and Discussion  
### Isolation and Screening of Odor Degrading Microorganisms  
Fifty-two microbial strains were isolated from pig manure using a modified culture method in Transwell plates containing R\({}_{2}\)A, MRS, and Sabouraud agar media. Furthermore, 16 highly efficient strains with the ability to remove ammonium-nitrogen from the first screening were molecularly identified and re-screened, as shown in Table S1. To identify these strains (TP1, TP3, and TP5), the 16S rRNA genes were sequenced and analyzed. The most similar strains were identified using the NCBI database. This study selected three strains among the 16 strains for further odor reduction study: Saccharomyces cerevisiae (TP1), Lactococcus lactis (TP3), and Lactobacillus argentoratensis (TP5). These three bacterial strains were selected based on their ability to remove NH\({}_{3}\) and the influence of surfactant foam on their growth. The 16S rRNA gene sequence of strains TP3, TP5, and the 18S rRNA gene sequence of strain TP1 were deposited at the GenBank database under the accession number SUB10993114 TP3 OM370997, SUB10993114 TP5 OM370998 and SUB10995932 TP1 OM417178, respectively. Phylogenetic analysis based on 16S rRNA and 18S rRNA gene sequence showed high sequence similarities with Saccharomyces cerevisiae NRRL Y-12632--TP1 (99.88%), Lactococcus lactis subsp. herdariae NBRC100931T--TP3 (99.93%), and Lactobacillus argentoratensis DSM 16365T--TP5 (100%).  
As shown in Table S2, the TP1, TP3, and TP5 strains showed NH\({}_{3}\) removal efficiencies of 45% (1000-550 ppm), 82% (1000-180 ppm), and 85% (1000-150 ppm), respectively, under the closed system (capped jar) at ambient temperature (22 \({}^{\circ}\)C). The reduction efficiency of NH\({}_{3}\) by the three strains increased at 30 \({}^{\circ}\)C (still capped jar), as shown in Table S3, where the removal efficiencies of TP1, TP3, and TP5 were 49% (1000 to 510 ppm), 90% (1000 to 100 ppm), and 92% (1000 to 80 ppm), respectively.  
Table 1 shows the changes in NH\({}_{3}\) gas in the open jar when the NH\({}_{3}\) solution-soaked tissue was spiked with the selected microbial consortium. Three selected strains were mixed in a 1:1:1 ratio to investigate their ability in NH\({}_{3}\) removal from the NH\({}_{3}\) source in an open jar. The microbial consortium (TP1 + TP2 + TP3) significantly reduced the initial NH\({}_{3}\) concentration to 3.33 ppm in 1 h after spiking 10 mL of the microbial consortium. The NH\({}_{3}\) concentration was reduced to 320 ppm after 10 mL of water was added to the NH\({}_{3}\)-tissue in the control. However, the NH\({}_{3}\) gas concentration in the open jar increased to 70 ppm during the first experiment and 226.7 ppm after 12 h. This was because ammonia gas was continuously being generated from the NH\({}_{3}\) solution-soaked tissue. The NH\({}_{3}\) gas concentration eventually decreased to 0 ppm with the microbial consortium, while it remained at 60 ppm in the control. This study found that the microbial consortium removed all NH\({}_{3}\) after 48 h. Therefore, this microbial consortium was used for further foam experiments.
## 3 Datasets and Methods Used in SATIDS
### MRMR-Based Feature Selection  
To deal with the high dimensionality of huge datasets, feature selection (FS) approaches are used. FS aids an IDS in both increasing its output and decreasing the amount of time required to train its model. To create better IDS models, a subset of the original features is chosen. It is possible to categorize the methods by the level of supervision that is available: supervised, unsupervised, and semi-supervised. Filtering, wrapping, and embedding are all examples of different FS approaches [21, 22].  
MRMR-FS is one of the most popular FS methods available. Bioinformatics and multimedia processing are only two examples of the many fields where it is widely employed. Using mutual information as an indicator of reliance, it is a forward-selection filter approach. In MRMR, features are picked based on their relevance to the target variable while also minimizing the correlation between them [22, 23].  
MRMR-FS detects the most effective features as follows:  
* The highest relevance feature \(\max\limits_{x\in\Omega}V_{x}\) is selected and added to an empty feature set, \(S\), which is calculated using Equations (6) and (7). \[M_{1}=\sum_{x\in X}\sum_{y\in Y}p(x,y)log\frac{p(x,y)}{p(x)p(y)}\] (6) \[V_{x}(S)=\frac{1}{|S|}\sum_{x\in S}M(C,x_{i})\] (7) where \(x\) and \(y\) are two random variables.
* C: the output
* \(M_{1}\): Mutual information of feature \(x\) and \(y\).
* \(M(C,x_{i})\) : Mutual information of feature \(x_{i}\) with class \(C\).
* Nonzero relevance and redundancy features are chosen in both \(S\) and \(Sc\). In case \(Sc\) does not contain a nonzero relevance and zero redundancy feature, go to Step IV.
* Select the largest relevance feature, \(\max\limits_{x\in S^{c},\mu_{y}=0}V_{x}\) and add the selected feature to set \(S\).
* For all features in set \(Sc\), repeat Step II until nonzero redundancy occurs.
* Choose nonzero relevance and nonzero redundancy feature with the largest mutual information quotient (\(MIQ\)) value within \(Sc\) and add that feature to \(S\) set using Equation (8). \[\max\limits_{x\in S^{c}}MIQ=\max\limits_{x\in S^{c}}\frac{I(x,y)}{\frac{1}{|S| }\sum_{Z\in S}I(x,z)}\] (8) where \(x\) and \(y\) are two random variables and \(I(x,y)\) is the mutual information between \(x\) and \(y\).
* Step IV should be repeated until there is no longer any relevancy for any feature \(Sc\).
* Zero relevance features are added randomly to \(S\).