esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### NTP Application Case Studies  
Tests on decomposition of endocrine disrupting chemical 17\(\beta\)-estradiol (E2) in different aqueous solutions (ultrapure and tap water, landfill leachate) by non-thermal plasma were reported by [PERSON] et al. [29]. After 30 min treatment with maximum voltage of 12 kV at pH 5.6, 100% removal was achieved from an initial compound concentration of 100 ug/L in ultrapure water (Figure 3(a)-c). Figure 4 summarizes experimental results in the different conditions: Figure 3(a) illustrates the effect of peak voltage at E2\({}_{0}\) = 100 ug/L and pH 5.6, Figure 3(b) the effect of initial concentration (V\({}_{\mathrm{max}}\) = 12 kV, pH 5.6), Figure 3(c) shows the effect of pH value (V\({}_{\mathrm{max}}\) = 12 kV, E2\({}_{0}\) = 100 ug/L), Figure 3(d) describes the effect of different solution matrices (V\({}_{\mathrm{max}}\) = 12 kV, E2\({}_{0}\) = 100 ug/L, pH 5.6), clearly showing the interferences with the process due to the complex nature of the landfill leachate solution. [PERSON] et al. [35] studied the degradation of trace organic compounds such as PPCPs and endocrine disrupting chemicals (meprobamate, dilantin, primidone, carbamazepine, atenol, trimethoprim) by pilot-scale non-thermal plasma (NTP) AOP at 500 Hz frequency and 8.0 kV voltage, in tertiary effluents and spiked surface water. Trimethoprim and carbamazepine were highly susceptible to NTP treatment, while dilantin and atenol degraded more slowly. Meprobamate, a compound known to be highly resistant to chlorination and ozonation, required nearly double the energy to degrade to the specified level. Primidone, although not as resistant as the former, showed slower and more variable degradation than the other compounds. Contrary to EB, which may cause TOC reduction due to the extended mineralization carried out by highly energy intensive radicals, no TOC significant change was observed over the duration of the experiment. This may be a cause of the process' limited energy consumption, low achieved dissolved O\({}_{3}\) concentrations, and limited ozone contact time in the NTP reactor. In addition NTP has shown capacity for N-nitrosodimethylamine (NDMA) destruction, a potent carcinogen in animals classified as probable carcinogen to humans through water ingestion [21]. [PERSON] et al. reported results of a literature review on degradation of PPCPs by non-thermal plasma [14]. Table 5 summarizes some of the data presented by those authors. Different values for the same compound may reflect experimental uncertainty, or non-factored operating conditions of experiments. Figure 4: Effect of different parameters on E2 removal efficiency after treatment time of 30 min, showing the interferences with the process due to the complex nature of a landfill leachate solution (**a**) effect of peak voltage at E2\({}_{0}\) = 100 \(\upmu\)g/L and pH 5.6; (**b**) effect of initial concentration (Vmax = 12 kV, pH 5.6); (**c**) effect of pH value (Vmax = 12 kV, E20 = 100 \(\upmu\)g/L); (**d**) effect of different solution matrices (Vmax = 12 kV, E20 = 100 \(\upmu\)g/L, pH 5.6) [29].
# Multimodal Unsupervised Speech Translation for Recognizing and Evaluating Second Language Speech
## 3 Proposed Fluency Scoring and Automatic Proficiency Evaluation Method
### DTW-Based Feature Extraction for Fluency Scoring
#### 3.1.2 Stress and Rhythm Scoring  
Given the aligned phonemic sequences, the proposed method computes the word stress score, sentence stress score, and rhythm scores. In order to measure the word and sentence stress scores, word stress patterns are computed for each content word in the given sentence, and sentence stress patterns are computed for the entire sentence. Then, the word and sentence stress scores are measured by computing the similarity between the learner's stress patterns and the native speaker's stress patterns.  
The rhythm scores are measured by computing the mean and standard deviation of the time intervals between the stressed phonemes. An example of computing the rhythm score in the sentence \"I am still very sick. I need to take some pills.\" is as follows:  
* Compute the stress patterns from the aligned phonemic sequences. Table 1 shows an example of the sentence stress pattern. The start times of the stressed phonemes, including the start and end times of the sentence, are highlighted (bold in Table 1) to compute the rhythm features.
* Select the stressed phonemes (highlighted point in Table 1), and compute the mean and standard deviation of the time intervals between them: (1) Mean time interval: \((0.2+0.2+0.6+0.5+1.5)/5=0.6\) (2) Standard deviation of the time interval: \((0.16+0.16+0.0+0.01+0.81)/5=0.23\)  
Figure 5: Example of DTW-based phonemic sequence alignment in sentence “I am still very sick. I need to take some pills.” (**a**) Aligned alignment path of the phonemic sequences. (**b**) Aligned phonemic sequence and stress values.  
Table 2 shows an example of the mean values of the mean time interval and the standard deviation of the time interval for each pronunciation proficiency level evaluated by human raters. Proficiency scores 1, 2, 3, 4, and 5 indicate very poor, poor, acceptable, good, and perfect, respectively. As shown in Table 2, the lower the proficiency level, the greater the mean and standard deviation values of the time intervals between the stressed phonemes. Two stress scores and rhythm scores are used for spoken English proficiency evaluation with other features.
# A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management
## 2 Methodology and Findings
### Analysis of Identified Practical Use Cases
Together with up-to-date information on weather forecasts, traffic scenarios, and other important factors, which can have a direct or indirect influence on transport, the systems provide real-time insight into the supply chain. This allows delivery delays to be predicted and modified routes to be developed if necessary ([PERSON], 2018). The ML-based 'Supply Watch' system, used by DHL, also monitors more than 140 different risk categories, including financial, environmental, and social factors--e.g., risks due to crime, labour violations, quality defects--and dangers within the supply chain such as general bottlenecks, capacity bottlenecks, and delays ([PERSON], 2018). If a disruption is predicted, proactive measures can be taken, and customers can be informed about changes earlier on. Walmart Inc., a US retail group, uses ML to process orders from approximately 2000 stores (www.walmart.com, accessed on 21 May 2021). Delivery is optimized considering order quantities, staffing levels, available delivery vehicle types, and the estimated distance between stores and homes ([PERSON], 2018; [PERSON] et al., 2018). In addition, ML is used especially in the sales area to better predict customer behavior. McDonald's, which suggests further additional sales to the customer during the purchase transaction, processes information using ML and considers weather data, times of day, local traffic, events in the vicinity, and historical sales data ([PERSON], 2018). By addressing supply and demand imbalances and triggering automated responses using ML applications, companies cannot only improve the customer experience, but also limit sales risks. They can also reduce costs through better forecasting of freight and warehouse processes and improve collaboration with logistics service providers ([PERSON], 2018). In addition, examples of ML applications in the production area could also be found. Robert Bosch GmbH, a manufacturer of industrial technology and consumer goods, uses ML to test components on the test bench and to recognize error patterns based on collected data, thus distinguishing relevant from non-relevant error messages. As a result, risks regarding product quality and delays in the process flow can be reduced ([PERSON], 2018). Figure 4 summarizes the risks that are addressed in the practical examples when integrating ML into the SCRM. While the examples from the production area mainly refer to internal risks, the purchasing, transport, and sales areas show a focus on supply chain-wide risks.
# Simulation Analysis of Nanosecond Laser Processing of Titanium Alloy Based on Helical Trepanning
## 3 Results and Discussion
### Simulation Results
#### 3.2.2 Variation Trend Analysis of Titanium Alloy Micro Hole  
The variations in the micro hole's width and depth with the laser processing time are shown in Figure 3. As shown in Figure 3a, the micro hole's width first increased quickly, and then the increase slowed down rapidly. This could be due to the high energy in the center of the laser beam that could easily cause the material to reach the gasification temperature. Therefore, in the initial stage, the laser could easily remove the material. However, it took a longer time for the temperature of the material far from the center of the laser spot to reach the gasification temperature. As a result, with laser processing, the width of the micro hole increased gradually, but the width change rate decreased gradually. In contrast, the depth change rate of the micro hole did not change significantly during the entire process, as shown in Figure 3b. This could be attributed to the Rayleigh range of the Gaussian beam that was larger than the simulated thickness of 60 \(\upmu\)m, while the energy loss of the laser along the optical axis was lower. In addition, the reason for the slow change in the depth change rate of the micro hole was that the processing heat residue caused a rapid increase in the downward speed.
# Energies of Mechanical Fractional-Order Elements: Causal Concept and Kernel Effects
## 2 Lorenzo-Hartley Concept: A Thorough Evaluation and New Causal Interpretation
### Force-Displacement Relationship: Causal Formulation  
More rationally, following the causality principle [10; 11] (see Section 3), because _the displacement is a result of a force application_, we may write the relationship as an integral relation:  
\[x(t)=\frac{1}{k_{\lambda}}{}_{0}D_{t}^{-\lambda}F(t) \tag{3}\]  
where \({}_{0}D_{t}^{-\lambda}F(t)\) is the Riemann-Liouville fractional integral [7]  
\[{}_{0}D_{t}^{-\lambda}F(t)=\frac{1}{\Gamma(\lambda)}\int_{0}^{t}\frac{F(t)}{( t-\tau)^{1-\lambda}}d\tau \tag{4}\]  
Regarding (1) and the mechanics of the simple spring-dashpot element (a Maxwell model with a spring-dashpot in a series), we can see that with \(\lambda=0\) we have a spring with pure elastic properties, while \(\lambda=1\) corresponds to a viscous dashpot.  
The relation (3) is a causal version of the second Newtonian law. It is more fundamental in the description of causality relations (force-displacement or force-velocity), while (1) is a consequence of it through the availability of semi-group properties in the Riemann-Liouville derivative and integral [7]. We will refer to (3) again when a more general concept regarding the definitions of \({}_{0}D_{t}^{-\lambda}F(t)\) applying various memory kernels is discussed.  
**Remark 1**.: _The non-causal Formulation (1) concerning the force-displacement relationship in terms of fractional operators can be traced back to [PERSON]'s law [12; 13]_  
\[\frac{k(t)}{\text{Const.}}\equiv\sigma^{n}t^{\alpha},\quad 0<\alpha<1, \tag{5}\]  
_reformulated by [PERSON] [14] as_  
\[\sigma(t)=E_{\lambda}D_{t}^{\lambda}[\varepsilon(t)], \tag{6}\]  
_where \(E_{\lambda}\) with dimension \(\left[F\cdot t^{1-\lambda}/L^{2}\right]\) is the anomalous (fractional) [PERSON]'s modulus._  
_However, as deformation is caused when stress is exerted on the body, the physically correct (causal) formulation should be through a memory integral, that is,_  
\[\varepsilon(t)=\frac{1}{E_{\lambda}}D_{t}^{-\lambda}[\sigma(t)]. \tag{7}\]  
_Formally, the causal and non-causal formulations are largely equivalent; however, this is true only mathematically due to the semi-group properties of the Riemann-Liouville operators [7]. It needs to be stressed that only a limited group of materials exhibits power-law relaxation properties. Thus, the integral formulation (3) draws a more general law, where other types of relaxation functions (memories) could approximate material relaxation more adequately than the power law._
## 3 Network Model and Network Initialization  
### Network Model  
The considered 3D network model is shown in Figure 1, where one source node is deployed underwater and it is responsible for periodically collecting and sending data. Numerous nodes (forwarding nodes) are randomly deployed underwater to receive a DATA packet and transmit it from the source node [17]. A sink node deployed on the water surface collects DATA packets and delivers the packets to the server in a data center. For understanding, we made the following assumptions.  
1. Each node in the network has a unique ID.
2. All the legal nodes in the network are isomorphic and have the same parameter settings, such as receiving power, transmitting power, idle power, sleeping power, and communication radius.
3. The source node generates packets periodically.
4. The CTV of each node in the network is initialized to 0.5 in the network initialization. Moreover, the value range of the CTV/DTV/ITV is \([0,1]\).
# DataMatrix Code Recognition Method Based on Coarse Positioning of Images
## 3 Method of Adaptive Sampling Grid Partitioning
### Peak Detection  
From the gray deviation value distribution map in Figure 4, it can be seen that at the junction of black and white grids, there is a peak value of gray deviation. Therefore, as long as the position of the peak is detected, the position of the dividing line can be found. It is necessary to calculate the first-order difference of the waveform to obtain the gradient direction map of the waveform. Among them, \(1\), \(0\), and \(-1\), respectively, represent the rising, maintaining, and falling trends of the waveform. Then, one must take the difference in the gradient direction. When the results are \(1\) and \(2\), there are corresponding valleys; when the result is \(0\), the corresponding state remains the same; when the result is \(-2\), there is a peak with a single extreme value; when the result is \(-1\), there is a trapezoidal peak [39]. Figure 5 is a peak detection process diagram in the horizontal direction based on Figure 4b.  
Figure 4: Grayscale distribution diagram: (**a**) standard DM code; (**b**) horizontal grayscale distribution diagram; (**c**) vertical grayscale distribution diagram.  
Figure 5: Process diagram of horizontal peak detection in Figure 4b.  
However, in real application scenarios, DM code images often have problems such as wear, corrosion, geometric distortion, and strong background interference. Figure 6a is a DM image obtained in an actual scene. There is a lot of interference in both horizontal and vertical directions, as shown in Figure 6b,c. Figure 7 is a peak detection process diagram in the horizontal direction based on Figure 6b. It can be seen that in actual application scenarios, it is necessary to screen the detected peaks to accurately find the peaks representing the position of the dividing line.  
Figure 6: Grayscale distribution diagram: (**a**) DM code in actual scenario; (**b**) horizontal grayscale distribution diagram; (**c**) vertical grayscale distribution diagram.  
Figure 7: Process diagram of horizontal peak detection in Figure 6b.
## 5 Analytical Techniques for Detection and Quantification of PFAS
### Analytical Detection Techniques
In this study, rice samples were grown in artificially contaminated soils with perfluoroalkyl substance soils to validate the method. This method improved the use of extraction methods to aid in untargeted characterization, but it is also possible to improve chromatographic conditions for the purpose. A novel analytical method for the non-targeted detection of total PFAS was developed by [PERSON] et al. [155] for the analysis of fire-fighting AFFFs. The researchers performed the untargeted identification due to a LCxLC chromatography system coupled to a high-resolution mass spectrometer. The peak capacity of chromatographic systems was increased by using the 2D-LC method and this was essential to map the chemical space of complicated mixtures such as AFFFs. For screening all possible PFAS on AFFFs, [PERSON] et al. [156] tested a method based on the Fourier-transform (FT) ion cyclotron resonance (ICR) MS. One of the purposes of this study was to show the effectiveness of FT-ICR MS direct infusion for suspect and non-target detection of PFAS in a complex mixture of AFFFs. Molecular structure cannot be determined definitively by direct infusion MS (or any mass analyzer) by detecting the molecular ion alone, but the study shows that the list of known PFASs can be expanded during subsequent analyses using accepted structural identification standards. Another alternative approach for untargeted analysis of perfluorinated compounds is the inductively-coupled plasma (ICP) MS. This method allows [PERSON] et al. (1977) to identify all fluorinated compounds whether or not they are ionized under electro spray conditions, and a fluorinated standard is available. This non-targeted approach is well adapted to assist in identifying a large proportion of organofluorines not detected.
## 3 Preliminaries
### System Model  
In this study, we present an opportunistic routing (OR) protocol that integrates a clustering mechanism within the routing layer. This clustering approach enables the protocol to assess local node density, a key factor in accurately computing the routing metric for optimal forwarder selection in wireless sensor networks (WSNs). The clustering scheme enhances the efficiency of forwarder selection by accounting for local network conditions, ensuring that data forwarding decisions are both dynamic and context-aware.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
To establish the system model for the proposed protocol, we operate under the following key network assumptions:  
* The sensor nodes are battery-operated, and each node has a unique ID.
* \(N\{i_{1},i_{2},i_{3},\ldots,i_{n}\}\) nodes are randomly deployed in an \(m\times n\) meter square-shaped sensor field. Thus, the network has an uneven density of nodes (Figure 2) [23].
* All nodes have information about the total number of nodes \(|N|\) in the network.
* We assume that the sink node has enough energy supply. Thus, the sink node is active throughout the time, resulting in duty cycle 1.
* It is assumed that all nodes have access to their own positional information at the time of deployment.
* The sensor nodes are homogenous and have the same transmission range, \(l\). Therefore, nodes with a longer distance from the sink than the transmission range need to transmit through multiple nodes, which leads to a multi-hop network.
* All nodes follow their schedule of duty cycles; however, they share the same sleep interval. There is no scheme of synchronization among them.
* When the nodes are active, they monitor the environment and generate data for any event-triggered activity.
* All nodes are static and have the same initial energy, \(E_{initial}\).
* Following the cluster-formation phase, each node can be a member of only one cluster, and clustering is based on the positions of the forwarder nodes relative to the position of the sender node.
# Infrared and Visible Image Fusion with a Generative Adversarial Network and a Residual Network
## 4 Experiments and Results
### Related Pre-Process and Settings
#### 4.1.2 Training and Testing Details  
As mentioned before, for training, cropped image pairs were put into G network whose batch size was set to 32 and the learning rate was initialized as \(10^{-5}\). Our model was trained with the ADAM optimizer whose internal parameters were set as follows: \(\beta_{1}=0.9\), \(\beta_{1}=0.999\), \(\varepsilon=10^{-8}\). The training of G and D were time-sharing that only one network could update its weights and bias every time. As to the loss functions, the parameters a and b were vectors whose sizes were equal to the batch and randomly created within the scope of 0.7 to 1.2. We used the same way to generate the parameter c, but within the scope of 0 to 0.3. After careful analysis and experiments, the weight \(\omega\) was set to 0.5, the \(\delta_{1}\) was set to 1000 and the \(\delta_{2}\) was set to 5, the parameters \(\gamma_{1}\), \(\gamma_{2}\), \(\gamma_{3}\), and \(\gamma_{4}\) were separately set to 15, 650, 15, and 300, respectively. The GPU platform is Intel E5-2680 V3 processor with TITAN V GPU and 64 Gb memory.  
During testing, only the trained G was used to generate the fused images. When we described the structure of G earlier in the article, we have mentioned the problem of gray blocks around edges caused by convolution with padding. To get rid of these blocks, firstly, we padded the edges of the source images and the padding values were set to 6. The padding value might change according to the number of convolution layers and the actual outputs. Then we put the transformed source image pairs into G and directly got the fused images, of course, whose sizes were the same as the inputs. Then we cut out the final results from the fused imaged with the size of source images. The schematic diagram of the testing process is shown in Figure 6.
## 3 Algorithms for Managing Complex Traffic Scenarios and Violations
### Types of Complex Traffic Scenarios and Violations
#### 3.1.3 Traffic Signal and Sign Violations  
Violations of traffic signals and signs by other road users represent another critical challenge. [PERSON] et al. (2018) discussed the need for robust algorithms to detect and respond to these violations, which often occur with little warning and require immediate action.  
[PERSON] et al. (2018) developed a threat assessment algorithm for detecting red light runners using Hidden Markov Models and Support Vector Machines. Their approach demonstrated the potential to predict violations up to 1.2 s before they occur, giving autonomous vehicles crucial time to respond.