esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Pollution Estimation and Risk Assessment
#### 2.3.1 Pollution Evaluation Indicators  
To assess soil contamination levels, important indices such as the enrichment factor (EF), geo-accumulation index (I\({}_{\text{geo}}\)), and contamination factor (C\({}_{\text{f}}\)) are widely used [5; 73; 74; 75]. The indicators are calculated using the Equations below for completeness:  
\[\text{EF}=\frac{\left(C_{i}/C_{r}\right)_{soil}}{\left(C_{i}/C_{r}\right)_{ background}} \tag{1}\]The EF is a valuable geochemical instrument for assessing the degree of PTE contamination in soil, where \"_soil_\" denotes surface soil, \"_background_\" denotes background, \"\(C_{i}\)\" is the concentration of the selected PTE, and \"\(C_{r}\)\" denotes the concentration of the reference element.  
Because of the non-mobile character during weathering, Al, Ti, V, Zr, and Sc are often chosen as reference elements [57; 76; 77; 78]. EF \(<2\) indicates depletion to a minimal contamination; EF = 2 \(<\) EF \(<5\), moderate contamination; EF = 5 \(<\) EF \(<20\), significant contamination; EF = 20 \(<\) EF \(<40\), very strong contamination; EF \(>40\), extreme contamination, according to the classification [70].  
\[\mathrm{I_{geo}}=\log 2\left[\frac{C_{i}}{1.5C_{b}}\right] \tag{2}\]  
where \"\(C_{i}\)\" is the concentration of a selected PTE in surface soil, and \"\(C_{b}\)\" is the background level of that PTE [71]. \(\mathrm{I_{geo}}\leq 0\): uncontaminated; 0 \(<\mathrm{I_{geo}}\leq 1\): slightly contaminated; 1 \(<\mathrm{I_{geo}}\leq 2\): moderately contaminated; 2 \(<\mathrm{I_{geo}}\leq 3\): moderately to highly contaminated; 3 \(<\mathrm{I_{geo}}\leq 4\): highly contaminated; 4 \(<\mathrm{I_{geo}}\leq 5\): highly to extremely contaminated; I\({}_{geo}>5\): extremely contaminated, according to the classification [71].  
\[\mathrm{C_{f}}=\frac{C_{i}}{C_{b}} \tag{3}\]  
where \"\(C_{i}\)\" denotes PTE concentration in surface soil, \"\(C_{b}\)\" denotes the background PTE concentration. This indictor represents the impact or contribution of an individual PTE on soil contamination. The \(\mathrm{C_{f}}\) can be divided into four categories: \(\mathrm{C_{f}}<1\): low contamination; 1 \(<\mathrm{C_{f}}<3\): reasonable contamination; 3 \(<\mathrm{C_{f}}>6\): considerable contamination; \(\mathrm{C_{f}}>6\): high or very high contamination [72]. The background level was measured using the deep soil from the 1:250,000 scale land quality geochemistry data from the Baoshan area, since it is considerably less likely to be impacted by human activity [54; 79]. The median values of the PTEs in the deep soil of the Baoshan area were used as the background values to calculate Equations (1)-(3).
###### Abstract  
Ultraviolet (UV) irradiation is used to disinfect water and food and can be classified as UVA (detected at wavelengths 320-400 nm), UVB (280-320 nm), and UVC (<280 nm). We developed a method for UVA sterilization of equipment with a UVA-light-emitting diode (LED); however, a high rate of fluence was needed to promote pathogen inactivation. The aim of this study was to identify genes associated with UVA sensitivity with the goal of improving UVA-LED-mediated bactericidal activity. We constructed a transposon-mutant library of _Vibrio parahaemolyticus_ and selected six mutants with high sensitivity to UVA irradiation. Genes associated with this phenotype include F-type H\({}^{+}\)-transporting ATPases (_atp_), as well as those involved in general secretion (_gsp_), and ubiquinone and terpenoid-quinone biosynthesis (_ubi_). Gene complementation resulted in decreased sensitivity to UVA-LED. The _atp_ mutants had lower intracellular adenosine triphosphate (ATP) concentrations than the wild-type treatment, with 20 mM L-serine resulting in elevated ATP concentrations and decreased sensitivity to UVA-LED. The _gsp_ mutants exhibited high levels of extracellular protein transport and the _ubi_ mutants exhibited significantly different intracellular concentrations of ubiquinone-8. Taken together, our results suggest that the protein products of the _atp_, _gsp_, and _ubi_ genes may regulate sensitivity to UVA irradiation.  
Keywords:light-emitting diode; ultraviolet A; _Vibrio parahaemolyticus_
# Time-Sensitive Networking Technologies for Industrial Automation in Wireless Communication Systems
## 3 3 GPP 5G Technology for TSN
### Periodic Deterministic QoS  
Scheduled traffic and per-stream filtering and policing (PSFP) defined in IEEE 802.1Q are supported by 5 GS. For this purpose, the policy control function (PCF) maps the TSN QoS received from TSN AF to the 5 GS QoS flow. Moreover, 5 GS supports periodic deterministic QoS by defining TSCAI with TSN traffic pattern information transmitted between next generation NodeB (gNB) and UE. Table 2 lists the TSCAI parameters that exist per QoS flow. The session management function (SMF) creates TSCAI with QoS flow information received from PCF and delivers it to gNB along with the QoS profile information. In this case, the TSC QoS flow uses a delay-critical guaranteed bit rate (GBR) resource type. In addition, for de-jittering the traffic flows transmitted in 5 GS, DS-TT/NW-TT supports a hold and forward buffering mechanism.  
To handle TSN-scheduled traffic, 5 GS must provide a 5 GS bridge delay as defined in IEEE 802.1 Qc. Therefore, in 3 GPP, the 5 GS bridge delay is defined as the sum of UE-DS-TT residence time and per traffic class minimum and maximum delays between the UE and the UPF/NW-TT. The UE-DS-TT residence time corresponds to the time spent to transfer a packet between the UE and the DS-TT port. The per traffic class delay is pre-configured by TSN AF.
# LNSNet: Lightweight Navigable Space Segmentation for Autonomous Robots on Construction Sites
## 4 Method
### Model Architecture
#### 4.1.3 Model Compressing  
A well-trained CNN model usually has significant redundancy among different filters and feature channels. Therefore, an efficient way to cut down both computational cost and model size is to compress the model [52].  
For a layer whose kernel size is \(K\), it transforms an \(H_{in}\times W_{in}\times C_{in}\) input feature map into an output feature map whose shape is \(H_{out}\times W_{out}\times C_{out}\). The shape of the kernel matrix is \(C_{in}\times K\times K\times C_{out}\). Each single \(C_{in}\times K\times K\) filter in the kernel could generates one layer of the output feature map. \(L1\) norm of the filter is used to evaluate the efficiency of the filter. The \(L1\) norm of a vector \(x\) is calculated using Equation (5), where \(x_{i}\) represents each value of the kernel.  
\[||\mathbf{x}||_{1}=\sum|x_{i}| \tag{5}\]  
If the \(L1\) norm is close to zero, it generates negligible output because the output feature map values tend to be close to zero. To improve efficiency, removing these filters would reduce the computational expense and model size. After removing the filters, the corresponding output feature map channels would also be removed.  
Network compressing consists of the following steps:  
1. For each filter, calculate the \(L1\) value.
2. Sort filters by \(L1\) values calculated in Step1.
3. Remove the filters and corresponding feature maps.
4. Fine-tune the whole network.  
Since the architecture of LNSNet is much more complex compared to CNNs without residual connections, such as AlexNet [47] and VGG [48], the first several layers are very sensitive and could have a huge impact on the layers behind and the overall performance.  
However, it is hard to predict how many filters and which filters can be removed without damaging the performance. As shown in Figure 5, by calculating and sorting the \(L1\) values of every filter in the model, the 128 filters (denoted as 128-kernels) generally have smaller \(L1\) values and have the largest number of redundant filters. 128-kernels are less sensitive to the changes compared to other kernels. Therefore, removing 128-kernels have a negligible impact on performance. As shown in Figure 5, the \(L1\) values for half of them is less than one, which indicates that they have a small impact on the overall performance. As a result, these filters could be removed without harming the accuracy. As it is shown in Figure 4 half of the filters of 128-kernels are removed (is indicated with * in the figure) and the model is fine-tuned on the same dataset.  
Figure 5: \(L1\) Norm visualization.
# Cognitive Diagnosis Method via Q-Matrix-Embedded Neural Networks
## 2 Materials and Methods
### Related Concepts and Symbol Definition
#### 2.1.2 Interaction Q-Matrix  
According to the polynomial expansion theorem [18], there are \(2^{K}\) types of interactive relationships for an item that examines \(K\) skills. Subtracting the zero-order and first-order interactive relationships yields the remaining \(k^{\prime}\) interactive relationships, where the quantity of \(K^{\prime}\) is calculated using the formula shown in Equation (2).  
\[K^{\prime}=\sum_{k=2}^{K}\left(\begin{array}{c}K\\ k\end{array}\right)=2^{K}-\sum_{k=0}^{1}\left(\begin{array}{c}K\\ k\end{array}\right)=2^{K}-(1+K) \tag{2}\]  
where \(\left(\begin{array}{c}K\\ k\end{array}\right)\) is denoted as a combinatorial number that picks \(k\) skills at random from \(K\) skills. In a given Q-matrix comprising \(J\) question items and \(K\) skills, the upper limit for the number of possible interactions between skills is \(K^{\prime}\), while during an instructional assessment, the actual number of interactions is significantly smaller, represented as \(K^{*}\), where \(K^{*}<<K^{\prime}\). For example, in the case of five skills, the maximum number of potential interactions between these skills is 26 (e.g., \(2^{5}-(1+5)=26\)), while fewer than ten may actually exist. Hence, a skill interaction matrix is established to depict the skill interactions in the real assessment and is defined as shown in Equation (3).  
\[\mathbf{Q}^{\prime}=\left(q^{\prime}_{k^{*}}\right)_{k^{*}=1,2,\ldots,K^{*},k= 1,2,\ldots,K} \tag{3}\]where \(q^{\prime}_{k^{*}k}\in\{0,1\}\), and each row \(\mathbf{q}^{\prime}_{k*}\) in the skill interaction matrix \(\mathbf{Q}^{\prime}\) represents an actually existing skill interaction. For example, \(\mathbf{q}^{\prime}=(1,0,1,0)\) indicates that there is an interdependence or influence between skill 1 and skill 3. \(K^{*}\) denotes the number of skill interactions actually present in the instructional assessment. In light of this, the interactions among skills associated with each item in a given Q-matrix \(\mathbf{Q}\) can be characterized by an interactive Q-matrix \(\mathbf{Q}^{*}\), which is defined as shown in Equation (4).  
\[\mathbf{Q}^{*}=\left(q^{*}_{jk^{*}}\right)_{j=1,2,\ldots,J,k^{*}=1,2,\ldots,K*} \tag{4}\]  
where each column in the interaction matrix-\(\mathbf{Q}\)\({}^{*}\) corresponds to a specific skill interaction type, which aligns with a row item in the skill interaction matrix \(\mathbf{Q}^{\prime}\). Each row \(\mathbf{q}^{*}_{j}\) denotes a tangible skill interaction with item \(j\). \(q^{*}_{jk^{*}}\in\{0,1\}\), \(q^{*}_{jk^{*}}=1\) indicates the presence of the \(k\)th skill interaction type in item \(j\); otherwise, it signifies its absence. The computation of \(q^{*}_{jk^{*}}\) follows the formula in Equation (5).  
\[q^{*}_{jk^{*}}=\prod_{k=1}^{K}q^{q^{\prime}_{jk}}_{jk},j=1,2,\ldots,J,k^{*}=1, 2,\ldots,K^{*} \tag{5}\]  
It is important to note that both the Q-matrix \(\mathbf{Q}\) and the skill interaction matrix \(\mathbf{Q}^{\prime}\) are predefined by the experts who construct the questions.
## 2 Materials and Methods  
### Plant Materials  
The experiment was conducted in 2019 on a 10 year-old commercial vineyard of Negro Amaro _cv_. located in the Salice Salentino wine grape area (Masseria Filippi, Apulia region, Southern Italy) and a 6 year-old commercial vineyard of Primitivo _cv_. located in the Gioia del Colle D.OC. (Denomination of Controlled Origin) area (Azienda Benagiano, Apulia region, Southern Italy). Both Negro Amaro and Primitivo vines, grafted onto _Vitis berlandieri \(\times\) Vitis rupestris_ 1103 Paulsen rootstock, were planted in north-south oriented rows; they were spaced 2.15 m between rows and 0.90 m on the row, trained to a Vertical Shoot Positioned (VSP) system and spur-pruned with 14 buds per vine.  
The experimental design for the current study was a randomized complete block with four replicates, with three rows of each treatment (80 vines per row) separated by a buffer row. Treatment consisted in two canopy spraying times, the first at the beginning of veraison (5%) and the second 12 days later, with Lalvigne(r) MATURE diluted in water without adjuvant at 1 kg/ha dose (YE) in comparison to untreated rows used as control (C). Lalvigne(r) MATURE is a formulation of 100% natural inactivated wine yeast (_Saccharomyces cerevisiae_) derivatives, non-pathogenic, non-hazardous, food grade and non-GMO, which are specifically designed to be used with the patented foliar application technology WO/2014/024039 (Lallemand Inc., Montreal, QC, Canada).  
A random sample of three bunches for each repetition was manually picked between the third and seventh node from random vines at periodic intervals from August until September. Berries (\(\sim\)30 g) were then chosen at random from these bunches and stored in the dark at \(-\)80 \({}^{\circ}\)C until analysis.
## 3 Evaluation
### Results  
We compared the number of correct answers between the non-intervention and intervention courses. Of the 80 students enrolled in the intervention course, 52 (65%) students completed the questionnaire, and 19 (23.8%) indicated that they used WOJR.  
Table 1 shows the number of students with all correct answers and the average number of correct answers by year. Although the difficulty of the assignments was the same in the intervention year and the non-intervention year, some of the problems were slightly different. The intervention involved nine problems, which were similar in both the non-intervention and intervention years. Problems that were drastically different, too easy, or whose reference code was too similar to the answer code of the assignments were excluded. The average number of correct answers increased from 8.37 to 8.77, and the average percentage of correct answers increased from 93.0% to 97.4%. This is about a 4.4% improvement. The percentage of students who answered all the problems correctly increased from 79.8% to 91.3%. This is about an 11.5% improvement.  
A two-sided test was used in the Mann-Whitney U test [11] to verify whether the difference was statistically significant. We used this test because it is unbiased in assuming a normal distribution for the number of correct answers. This non-parametric test examines significant differences between two unpaired groups without assuming a population distribution. For the test, we used the number of correct answers. Since the two groups took the course in different years, there is no correspondence. A significance level of 0.05,  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Non-Intervention Year** & **Intervention Year** \\ \hline Number of Students & 89 & 80 \\ Number of Students Who Answered All Problems Correctly & 71 & 73 \\ Average Number of Correct Answers & 8.37 & 8.77 \\ Percentage of Students Who Answered All Problems Correctly & 79.8\% & 91.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Changes in the number of correct answers to all problems and the average number of correct answers. Each year has nine problems. Problem contents are almost identical between the intervention and non-intervention years.  
which is generally used in education, was employed. The obtained \(p\)-value was 0.033. Thus, the number of correct answers improved significantly in the intervention year compared to that in the non-intervention year.  
In the questionnaire, we asked only the students who answered that they used WOJR what they liked, and what they would improve. We received responses from six students, which are summarized below:  
* There is a similar problem whose statement is difficult to understand.
* It was helpful to refer to similar problems and algorithms with similar ideas.  
We also asked all the students what kind of functionality they wanted. We received responses from 6 students who used WOJR and 14 students who did not use WOJR, which are summarized below:  
* I want the degree of similarity with the assignment to increase.
* Please explain the algorithm using figures.
## 5 Experiment and Analysis
### Evaluation of SAPRM
#### 5.2.3 Experiment on Loss Function  
The loss function we propose consists of two parts: the quality entity recognition loss of continuous casting and the quality relation extraction loss of continuous casting. Therefore, we explore the proportion of these two parts of the loss by setting hyperparameter \(\lambda\) with different value, as shown in Table 6.  
After modifying the proportions of the two parts of the loss function, the performance of the model decreased. Among the different \(\lambda\) values, \(\lambda\) with value 1 still maintains the best performance, while \(\lambda\) with value 0.67 performs second best in all cases. Consequently, we set the hyperparameter \(\lambda\) as 1.
## 1 Introduction
[35; 36], [PERSON] et al. [37; 38], and [PERSON] et al. [39] also presented suitable methods, based on SDM. It should be noted that the uses of VPC, VHC and serrated cutter are three typical methods that affect the time delay characteristics of milling process by changing the tool structure, and then suppress chatter by disturbing the regeneration effect. Another similar method is variable spindle speed (VSS), whose corresponding milling system can be expressed as the DDEs with time-dependent time delays. Around the theme of VSS, [PERSON] and [PERSON] [40], [PERSON] et al. [41], [PERSON] and [PERSON] [42], [PERSON] et al. [43], and [PERSON] et al. [44] respectively used the updated SDM to carry out the associated stability analysis. [PERSON] et al. [45] utilized CCM to obtain SLDs for milling process with VSS efficiently. [PERSON] et al. [46] developed a stability model of VSS turning based on perturbation theory and conducted a study of its real implementation in turning systems. [PERSON] et al. [47; 48] studied the effectiveness of the continuous VSS with different waveforms on chatter suppression in the milling process, based on improved SDM and experimental tests. Recently, based on SMD, [PERSON] et al. [49; 50], [PERSON] et al. [51] and [PERSON] and [PERSON] [52] investigated the milling dynamics of the combination process with VPC and VSS (or called VPCVSS), which was modeled by the DDEs with multiple and time-periodic delays. They pointed out the effectiveness of this combination for further suppression of cutting chatter. However, it should be noted that the VPC in above system, obviously, can be replaced by a VHC in practice [49], so the original system will change into new one with VHC and VSS (or called VHCVSS). In that case, there will be two main problems: (1) what is the suppression effect of this new process on cutting stability? (2) Due to the increase of system complexity, the model and algorithm in [49; 50; 51; 52] would not be applicable anymore. Based on the efficient methods in [15; 32] with the local discretization error \(\mathcal{O}(\Delta t^{3})\), this paper presents an updated method to analyze the stability of milling process with multiple and distributed time delays. The innovation of this paper is mainly reflected in the following points: (1) theoretically, developing the theory in [15; 32] and widening its application scope for above case; (2) applying the method to dealing with the stability prediction problem with regard to a new process, i.e., VHCVSS for the first time. Obviously, the method is not only applicable to the cases associated with VPC, VHC, or VSS, but also to their combination cases, e.g., VFCVSS and VHCVSS. The rest of this paper is organized as follows: Section 2 describes the mathematical model and the method in detail; Section 3 provides the application of the presented method for one-degree-of-freedom (DOF) and 2-DOF models associated with VHCVSS; the influence of the system parameters on chatter is analyzed. Finally, the related conclusions are given.
## 4 Verification and Comparison of Theoretical and Experimental Results
### Comparison of Results  
By substituting the parameters of G3-2 and G3-3 into Equations (15) and (16) and making them equal, the limit inclination angle of surface slip can be obtained. These results were compared with the test, as shown in Table 3.  
The data in Table 3 show that the theoretical results are in good agreement with the test results and that the relative error of the results was controlled within 15%. In addition, with the same number of iron hoops, the shear strength and the limit inclination angle of the interface gradually increased with the increase in the vertical load.
# Highly Integrated Wideband Transmit/Receive Module for X-Band SAR Applications
## 3 Radio Frequency Design
### RF Transition between Stripline/Microstrip and Coaxial Connector  
The design of the RF transitions from the stripline (calibration line) and the microstrip (input TX and out RX) toward the coaxial connectors will be described in this section. The stripline to coaxial transition is shown in Figure 10a, whereas the microstrip to coaxial transition is shown in Figure 10b.  
As performed in Section 3.2 for the microstrip-stripline-microstrip transition, the vertical transition was designed to have a characteristic impedance as close as possible to 50 \(\Omega\); additionally, the diameter of the pads at the signal was optimized to avoid an abrupt impedance mismatch at the external layer where a large pad is required for brazing the pin of the coaxial connector.  
The optimization of the transition was performed in two steps. The first one involves the vertical transition and a small piece of stripline up to the point where a TEM mode is propagating. A waveguide port [35] was set at the stripline, whereas the output port was defined at the coaxial interface on the substrate external layer; the two-port S-parameter of the transition was obtained through a full-wave simulation. The S-parameter dataset was used within the circuit simulation environment [34], where three transmission line sections were added. Such three sections were optimized for length and width (characteristic impedance) to achieve optimized performances of the S\({}_{11}\) and S\({}_{21}\). Once the optimized results were satisfactory, the obtained geometrical parameters (length and width) of the three lines were  
Figure 7: Return loss (**a**) and insertion loss (**b**) of the optimized microstrip–stripline–microstrip transition.  
translated into actual stripline sections added to the original full-wave model to verify the optimized performances. The final results are reported in Figure 11 for the stripline to coaxial transition, with a comparison between the circuit and full-wave simulation. Good agreement was obtained, thus demonstrating the feasibility of the quick circuit-based optimization compared to the much more time-consuming full-wave optimization.  
Figure 8: Overview of the equivalent circuit model and the corresponding layout within the full-wave simulator.  
Figure 9: S-parameters results and comparison between the circuit and the full-wave model. Specifically, the return loss at Port 1 and at Port 2 (and 3) are shown in **(a,b)**, whereas the insertion loss and coupling factor are given in **(c,d)**, respectively.  
Figure 11: Stripline to coaxial connector transition: (**a**) S\({}_{11}\) and S\({}_{22}\), (**b**) S\({}_{21}\).  
Figure 10: Three-dimensional overview of the transitions: (**a**) stripline to coaxial connector, (**b**) microstrip to coaxial connector.
## 2 Health Condition Monitoring
### Data Processing
#### 2.2.3 Data Fusion
Most methods use high efficiency as the main evaluation criterion, and the applicability and safety of the methods need to be improved. Due to the harsh and complex working environment of MSAE, the diversity, dynamics, and high-latitude information monitored by sensors will be the main challenges for feature processing in the future.