esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Literature
### Failure Mode and Effects Analysis (FMEA)  
MEA is a tool widely used in the automotive, aerospace, and electronics industries that is often used to identify, prioritize, and eliminate known potential failures, problems,and errors in a process or product. In FMEA, the potential failure modes and their effects are brainstormed, including the severity of the effect of the failure mode, the occurrence of the failure mode, the detection of the failure mode, and the expected cost, which is to be scaled from 1 to 10 [22]. Therefore, a larger set of empirically derived failures are collected, and unanticipated failures are identified [23]. In addition, FMEA is also a risk management technique, whose purpose can be to prioritize potential failures in accordance with their \"risk\". The prioritized risks enable risk managers to eliminate or reduce the occasions of possible failures, problems, and errors [19]. There are often four risk indicators, which are described in detail as follows [19; 22; 23]:  
* Occurrence (O)--how likely is the failure cause to occur in the risk calculations? It is crucial to evaluate the probability of occurrence, the probability of the cause and immediate failure mode, and the probability of the cause event and end effects. However, the result could be different from the probability of the end effects in the failure mode. The occurrence is estimated using a 1-10 scale.
* Severity (S)--refers to a severity of effect, which is used to measure the seriousness of the effects of a failure mode, as well as to describe how serious the end effects of the failure mode are. The severity categories are estimated using a 1-10 scale; for example, 10 is defined as \"total lack of function\", 5 is \"moderate degradation in performance\", and 1 is \"effect almost no noticeable\".
* Detection (D)--refers to a difficulty of detection, which describes how likely the failure mode is to be detected. There are several definitions for detection, which generally fall into one of the following categories: (1) detection during the design and development process; (2) detection during the manufacturing process; and (3) detection during operation. These detection scores are based on that the lower the 1-10 detection score is, the better the detection capability.
* Expected cost (EC)--as a measure of risk, it explains how risk contains two basic elements: chance and consequence. Probability is a universal measure of chance, and cost is an accepted measure of consequences. Given a failure scenario, risk can be calculated as a failure or expected cost. In fact, the expected cost is widely used in the fields of risk analysis, economics, decision theory, etc. Both probability and cost are ratio scales, which are estimated by using a 1-10 scale.
# Comparison of Various Models for Optimal Water Distribution in Canal Systems
## 2 Materials and Methods
### Irrigation Model 3 (Continuous Irrigation Between Groups, with Rotational Irrigation Within Groups)  
The research of [PERSON] and [PERSON] utilized the method of continuous irrigation between groups, with rotational irrigation within groups. This paper adopts this approach to develop Model 3 [24; 25]. The model is established as follows:  
(1) Calculation of rotational irrigation groups. The number of irrigation groups is calculated as follows:  
\[ceil[\frac{Q_{d}}{q_{d}}]\leq M\leq floor[\frac{Q_{d}}{0.6\overline{q_{d}}}] \tag{1}\]  
Figure 1: Schematic layout of a simplified two-level canal system.  
where \(M\) is the number of irrigation groups, \(Q_{d}\) is the design flow of the upper canal (m\({}^{3}\)s\({}^{-1}\)), and \(\overline{q_{d}}\) is the average design flow rate of all lower-level canals (m\({}^{3}\)s\({}^{-1}\)). _ceil_ indicates that the integer is fetched upward; _floor_ indicates that the integer is fetched down.  
(2) Decision variables  
The decision variables are as follows: the group conditions of canals and the water distribution flow rate of canals. The decision variable \(X_{ij}=\{0,1\}\) represents the switch state of the outlet \(j\) of irrigation group \(i\). \(X_{ij}=0\) indicates that the water outlet is closed. \(X_{ij}=1\) indicates that the water outlet is open. The decision variable \(q_{j}\) represents the flow rate of canal \(j\) (m\({}^{3}\)s\({}^{-1}\)). (\(i\) = 1, 2,... M, is the number of irrigation groups; \(j\) = 1, 2,... N is the number of lower-level canals).  
(3) Objective function  
The minimum duration of the water distribution is described thus:  
\[minT=max\sum_{i=1}^{M}X_{ij}t_{j} \tag{2}\]  
\[t_{j}=\frac{w_{j}}{q_{j}} \tag{3}\]  
where \(t_{j}\) is the water distribution time of canal \(j\) (h), and \(w_{j}\) is the water requirement of canal \(j\) (m\({}^{3}\)).  
(4) Constraints  
Grouping constraint: each water outlet can only be opened once during the entire duration of water distribution.  
\[\sum_{i=1}^{M}X_{ij}=1 \tag{4}\]  
Flow rate constraint for lower-level canals: the flow rate of each canal should be within 0.6 to 1.0 times the design flow rate.  
\[0.6q_{jd}\leq q_{j}\leq q_{jd} \tag{5}\]  
where \(q_{jd}\) is the design flow rate of canal \(j\) (m\({}^{3}\)s\({}^{-1}\)).  
Water balance constraint: at any given time, the sum of the flow rates of the lower-level canals must equal the flow rate of the upper-level canal.  
\[Q_{t}=\sum_{t=1}^{T}q_{j} \tag{6}\]  
where \(Q_{t}\) is the flow rate of the upper-level canal at time \(t\) (m\({}^{3}\)s\({}^{-1}\)).  
Flow rate constraint for the upper-level canal: at any given time, the flow rate of the upper-level canal must be within 0.6 to 1.0 times the design flow rate.  
\[0.6Q_{d}\leq Q_{t}\leq Q_{d} \tag{7}\]
# A Framework for Real-Time 3D Freeform Manipulation of Facial Video
## 3 Experimental Results  
### Implementation  
The application programming interface of the proposed framework was designed using Python. Facial landmark detection was implemented in Python using Theano, a deep learning framework, and other functions such as face model fitting and mesh deformation were implemented in C++ and invoked via Python bindings. Partial parallel processing with OpenMP was applied where extensive computation was required, as for example in visibility checking of all vertices of the face model to search for corresponding 3D landmarks on the model of 2D facial landmarks along the boundary of the face.  
In the proposed framework, moreover, each module was assigned to separate threads for more speed-up, allowing the entire framework to operate in real time. However, it was difficult for each module to operate completely in parallel because of data sharing requirements. For instance, the manipulation module needed the results of fitting from the registration module, and the rendering module needed both the camera pose from the registration module and the deformed model from the manipulation module. To handle such dependencies more efficiently, the manipulation and rendering modules instantly used data from the previous frame rather than waiting those from the current frame. Figure 5 illustrates our inter-module parallelization scheme. While the registration and rendering modules perform their processes synchronously with the frame (orange and gray rectangles), in the manipulation module, processes run asynchronously based on a user's input regardless of frame (green rectangles). Whenever a user picks a handle anchor with the mouse and moves the mouse pointer, the mouse events are pushed to a queue, and the processes in the manipulation module run repeatedly while emptying the queue.
## 2 Materials and Methods
### Plasmid Construction  
To construct a plasmid coding the Nluc gene (pEBMulti-Nluc), the fragment of the Nluc gene was amplified by polymerase chain reaction (PCR) using primers (forward:5\({}^{\prime}\)-CGGGTACCATGAACTCCTTCTCACA-3\({}^{\prime}\), reverse:5\({}^{\prime}\)-CCGATATCTTACGCCAATGCTTC-3\({}^{\prime}\)) from the pNL2.3 [secNluc/Hygro] plasmid. Subsequently, the amplified PCR product and pEBMulti-hyg vector were digested using _KpnI_-HF and _EcoRV_-HF restriction enzymes overnight at 37 \({}^{\circ}\)C; then, the product was isolated by electrophoresis with 1% agarose gel to obtain the specific sequence. This sequence was cleaned using NucleSpin Gel and PCR Clean-up. The PCR product was then incorporated into the pEBMulti-Hyg vector using Ligation high Ver.2 at 16 \({}^{\circ}\)C for 2 h. The constructed pEBMulti-Nluc plasmids were transformed into competent NEB 10-beta _E. coli_ using the heat shock method. The transformed _E. coli_ were then spread onto LB agar plates and incubated for 12 h at 37 \({}^{\circ}\)C. _E. coli_ picked from colonies were cultured in LB medium, and plasmids in _E. coli_ were collected using a PureLink HiPure Plasmid Midiprep Kit.
# Efficient Coverage Path Planning for a Drone in an Urban Environment
## 6 Performance Evaluation
### Compared Algorithms  
As the GPP is a newly introduced problem, there are no algorithms available for comparison in the literature. For the examination of the suggested SST algorithm, we used the **ES** algorithm presented in Section 4 as a baseline. In addition, several variants of the suggested algorithm were examined, keeping the score-select-traverse structure. The variants differ in how \(\mathcal{V}\), the candidate waypoints set, is constructed:  
* **SST--All Cells (ACs)--\(\mathcal{V}\)** contains all the cells that belong to the free area.
* **SST--Cluster Every Call (CEC)--\(\mathcal{V}\)** is determined at the planning stage by clustering the cells belonging to the free area according to the drone footprint size.
* **SST--K-Means Dilution (DIL--N)--\(\mathcal{V}\)** is calculated as in the vanilla algorithm and is then diluted at the planning stage to a predefined size \(N\) by using the k-means algorithm [37].
## 3 Results and Discussion  
### Validation of Experimental Setup  
To validate the experimental setup, the pool boiling curve for a plain copper surface, obtained using Novec-7100 as the working fluid, was compared with the [PERSON] correlation curve [49] and similar pool boiling curves in the related literature [50; 51]. The widely used correlation proposed by [PERSON] is expressed in Equation (10) as follows:  
\[q^{\prime\prime}\;_{nuclate}=\mu_{l}h_{l\
u}\Big{[}\frac{g(\rho_{l}-\rho_{\
u} )}{\sigma}\Big{]}^{0.5}\Bigg{(}\frac{c_{pl}\Delta T_{sat}}{C_{sf}h_{l\
u}P^{ rt}}\Bigg{)}^{3} \tag{10}\]  
where \(\mu_{l}\) is the dynamic viscosity of the liquid, \(h_{l\
u}\) is the latent heat, \(g\) is the gravitational acceleration, \(\rho_{l}\) is the density of the liquid, \(\rho_{\
u}\) is the density of the vapor, \(\sigma\) is the surface tension, \(c_{pl}\) is the specific heat of the liquid, \(\Delta T_{sat}\) is the wall superheat, \(Pr\) is the Prandtl number, \(C_{sf}\) is the surface-fluid factor, and \(n\) is the experimental constant.  
In the current study, \(n\) equals 1.7 for fluids other than water. The surface-fluid combination, \(C_{sf}\), equals 0.0036 based on the experimental data for Novec-7100 [52; 53]. As depicted in Figure 11, the boiling results obtained for the plain surface in the present work are almost consistent with the Rohsenow correlation and those obtained for the plain surface in the related studies. However, owing to the prediction limit of the Rohsenow correlation, the experimental data diverge in the high heat-flux regime. A vapor film gradually forms from the intensified bubble aggregation in this regime and deteriorates  
\begin{table}
\begin{tabular}{c c c} \hline \hline \
\begin{tabular}{c} **Specific Heat-Flux Interval** \\ **(W/cm2)** \\ \end{tabular} & \multicolumn{2}{c}{
\begin{tabular}{c} **Uncertainty Parameter** \\ **T\({}_{\mathbf{w}}\) (K)** \\ \end{tabular} } & \multicolumn{1}{c}{**h (W/cm2\({}^{\circ}\)K)**} \\ \hline \(q^{\prime\prime}\approx 2.09\) & 0.27 & 0.02 \\ \(q^{\prime\prime}\approx 10.23\) & 0.56 & 0.03 \\ \(q^{\prime\prime}\approx 19.14\) & 0.87 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Uncertainty analysis for a plain copper surface (S1), using Novec-7100 as the working fluid at different heat-flux intervals.  
the heat transfer rate. Therefore, the boiling curve diverges to the right in the end. Similar phenomena can be found in the above literature [50; 51].
## 2 Data and Methods
### Methods
#### 2.2.2 Empirical Orthogonal Function Decomposition (EOF)  
The EOF method has been widely used in the spatial and temporal analysis of marine variables [20; 21]. In this study, we employed the EOF method to investigate the spatial and temporal characteristics of multi-year monthly mean sea surface temperature anomalies in the central and western South China Sea. Specifically, we first calculated the multi-year monthly mean moment level values of SST in the study area, and then applied the EOF method to obtain the spatial modes and temporal coefficients of the monthly mean sea surface temperature anomalies from 2000 to 2018. Finally, we calculated the variance contribution of each mode to better understand the underlying variability in the data.  
\[R_{k}=\frac{\lambda_{k}}{\sum\limits_{i=1}^{m}\lambda_{i}},k=1,2,\cdots,p(p<m) \tag{2}\]  
The variance contribution of the kth mode is represented by \(R_{k}\), where \(k\) is the mode number. The spatial point or grid point is denoted by \(m\), while \(p\) denotes the number of eigenvectors. The eigenvalue of the ith mode is represented by \(\lambda\).
# On Ad Hoc Communication in Industrial Environments
## 5 Examination of the Industrial Ad Hoc Channels  
Measurements with the proposed tracing protocol were performed in different environments and under varying conditions. The goal of the measurements and the analysis is to characterize the industrial environment in terms of effects on ad hoc communication channels. Knowing the characteristics of a communication channel allows for a more effective selection and configuration of applied routing solutions. The networks examined by [PERSON] et al. [28] are fundamentally different from the network examined in this work. The number of clients, the kind of mobility, and the environment are the most obvious differences. Different metrics for these network characterizations are therefore applied in this work.  
[PERSON] et al. [36] showed that after sufficient time all nodes of a network encounter each other if they move randomly on the same area. AGVs do not move randomly, but for the examined small networks the same behavior was observed. It is expected that, in bigger AGV systems, it may not be true all AGVs encounter all other AGVs. Certain AGVs could, for example, exclusively transport goods within specified disjoint areas.  
The industrial environment where the measurements took place involves electric drives and gear production that adhere to Industry 4.0 paradigms, although it is a brown-field factory. Thus, a typical industrial environment by means of the amount of mobility and conductive material is present. The AGVs that were equipped with the measurement equipment facilitate intra-logistic processes of half-finished and finished products and drive up to 1.2 m/s. They cover an area of \(\approx\)25,000 m\({}^{2}\).
# Learning-Based Traffic Scheduling in Non-Stationary Multipath 5G Non-Terrestrial Networks
## 1 Introduction
### Related Work
#### 1.1.2 RL-Based Traffic Scheduling  
Recently, there has been great interest in applying RL in transmission networks. As a result, various RL-based models have been proposed for traffic control and scheduling. In [14], the authors presented a scheduling framework based on RL for satisfying the bandwidth requirements of Wi-Fi users. In [15], another scheduler using the RL model was proposed for multipath QUIC in Wi-Fi and cellular transmissions. An AC framework was proposed in [16] for dynamic single-user and multi-user access to multiple links in wireless networks which avoids collisions by selecting suitable links. An RL framework was proposed in [17] to improve data rates and mitigate E2E delay in IoT networks. The work in [18] proposed an AC-based scheduling framework for UAV cellular integrated networks.  
Inspired by this development, but different from these works, we propose a learning-based framework that performs path selection and traffic protection by repeating traffic over multiple links to provide redundancy. Using an AC-based algorithm, our agent searches for a policy to select suitable satellite links in terms of LOS availability and determine how much redundancy is required to protect traffic against channel losses due to a varying LOS probability, which depends on the changing satellite elevation angle. Redundancy estimation is carried out in such a way as to provide enough protection without wasting bandwidth. Moreover, the AC algorithm used by our agent is a model-free RL algorithm that does not require knowing in advance the model's underlying transmission channels.
# STOD: Towards Scalable Task-Oriented Dialogue System on MultiWOZ-API
## 5 Experiment and Analysis
### Multi-Domain Dialogue Modeling
#### 5.1.2 Automatic End-to-End Evaluation  
Table 2 displays the results of models on the multi-domain end-to-end dialogue modeling. We find our model MSTOD achieves state-of-the-art Inform, Success, and Combined scores, demonstrating the effectiveness of our proposed API-focused framework. MSTOD achieves slightly worse BLEU than Galaxy because Galaxy uses an auxiliary large-scale dialogue corpus UnPreDial for pre-training along with MultiWOZ. Further human evaluation results in Table 3 indicate that both systems have good fluency due to prior syntactic knowledge from pre-trained language models, but our MSTOD significantly outperforms Galaxy on the rate of task success and coherency. The task completion ability is essential for a task-oriented system, which we focus on in this paper.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Inform** & **Success** & **BLEU** & **Combined** \\ \hline DAMD \(\uparrow\) & 57.9 & 47.6 & 16.4 & 84.8 \\ AuGPT \(\uparrow\) & 76.6 & 60.5 & 16.8 & 85.4 \\ MinTL \(\uparrow\) & 73.7 & 65.4 & 19.4 & 89.9 \\ SOLOIST \(\uparrow\) & 82.3 & 72.4 & 13.6 & 90.9 \\ UBAR \(\uparrow\) & 83.7 & 70.3 & 17.6 & 94.4 \\ PPTOD \(\downarrow\) & 83.1 & 72.7 & 18.2 & 96.1 \\ BORT \(\downarrow\) & 85.5 & 77.4 & 17.9 & 99.4 \\ MTTOD \(\uparrow\) & 85.9 & 76.5 & 19.0 & 100.2 \\ GALAXY \(\uparrow\) & 85.4 & 75.7 & **19.6** & 100.2 \\ \hline MSTOD & **89.0** & **78.6** & 18.4 & **102.2** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Multi-domain end-to-end evaluation. \(\uparrow\): we cite these results from the official benchmark of MultiWOZ 2.1. \(\downarrow\): we cite these results from [PERSON] et al. [14]. Note that SOLOIST, PPTOD, and GALAXY use auxiliary dialogue corpus to perform pre-training.
# Pathological Gait Classification Using Early and Late Fusion of Foot Pressure and Skeleton Data
## 2 Related Works
### Vision-Based Approaches  
In [28], the Kinect motion system is utilized to collect spatiotemporal gait data from seven healthy subjects across three walking trials: normal, pelvic-obliquity, and knee-hyperextension walking. Four classifiers--LSTM, SVM, KNN, and CNN--are employed. Notably, SVM and KNN perform well, achieving classification accuracies of 94.9% and 94.0%, respectively. Ref. [29] introduces a deep CNN that employs 3D convolutions for gait recognition from multiple viewpoints, capturing spatiotemporal features. This approach is evaluated across three different datasets, accounting for variations in clothing, walking speeds, and viewing angles. Additionally, ref. [30] proposes gait recognition using an enhanced CNN with the incorporation of a Gabor filter.  
In [31], the researchers propose a model based on Gait Energy Images (GEIs) to automatically extract robust and discriminative spatial gait features for human identification. They leverage deep 3-dimensional CNNs to learn the temporal gait features, referred to as Convolutional 3D (C3D) representations. In [32], a classifier based on Gated Recurrent Units (GRUs) is proposed. It is used to classify six different gaits, including one normal and five pathological gaits (antalgic, stiff-legged, lurching, steppage, and Trendelenburg), achieving an accuracy of 93.67%.  
A study detailed in [33] that introduces an RNN-based model for the classification of pathological gaits based on skeleton data has garnered significant attention. Utilizing bidirectional LSTM, the model achieves an accuracy of 88.90%. It categorizes gaits into six categories: normal, in-toeing, out-toeing, drop foot, pronation, and supination. Furthermore, in [34], features extracted by an LSTM-based autoencoder are employed in a GRU classifier. This approach accurately distinguishes between normal, limping, and knee-stiff gaits across various levels, achieving an impressive accuracy rate of 95.90%.
# Applications of Pulsed Light Decontamination Technology in Food Processing: An Overview
## 6 Effect on Food Quality Characteristics
### Effects on Nutrients and Bio-Active Components
Pulsed UV exposure of blueberries enhanced their antioxidant capacity, phytochemicals and enzyme activity [99]. Pulsed UV treatment of blueberry wine enhanced the total anthocyanins (as mg of Cyanidin-3-glucose equivalents (C-3-GE)/L of wine) and total flavonoids (as mg of Catechin equivalents (CE)/L of wine), but did not significantly reduce total phenolics (as mg of Gaelic acid equivalents (GAE)/L of wine) and antioxidant activity (as mmol Trolox equivalent/L of wine) [99]. There was increase in bio-active components like \(\beta\)-carotene, \(\alpha\)-carotene, and total lycopene contents with some lycopene isomerization in PL-treated tomatoes [97]. Green tomatoes showed increase in phenolic compounds, antioxidant activity, lycopene content, and total carotenoids after treatment [139]. [PERSON] et al. [110] observed a slight increase in polyphenolic content and antioxidant activity after the PL treatment of spinach. There was an increase in antioxidant activity in mango pulp [129]. The researchers found an increase in phenylalanine ammonia lyase enzyme content indicating increase in phenolic content. There was also little reduction in Vitamin C content in the mango pulp on storage after PL exposure. [PERSON] et al. [140] shows that using pulsed UV as a pre-treatment for drying mangoes can reduce vitamins loss as compared to untreated dried mangoes. They found that the level of Vitamin C and carotenoids in untreated dried mangoes were 10% and 40% lower than mangoes treated with fluences between 3.6-10.8 J/cm\({}^{2}\). In addition, in samples subjected to fluences between 3.6 and 7.2 J/cm\({}^{2}\) the level of vitamins B\({}_{1}\), B\({}_{2}\) and B\({}_{5}\) risen by 10 to 25%. However, Vitamin B\({}_{6}\) was decreased by 40%-50% [140]. In a study, our research group showed the effect of PL on gallic acid solution (model solution of phenolic compounds in fruits and vegetables) [141]. It was observed that gallic acid solution turned brownish due to photo-degradation and a critical fluence of 3.82 J/cm\({}^{2}\) at 2 cm distance was identified, below which the gallic acid solution showed little to no degradation. Thus, as bioactive compounds being important from nutritional point of view, studies on the effect of PL on these compounds remain very important.