esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Timescale-Scalable Posture Detection Algorithm
### TSPDA--Graph Aligner  
The Graph Aligner module aligns the candidates calculated through the Graph Comparer module in ascending order and iterates through such that the item is added to the final result.  
Additionally, when adding the item, the Graph Aligner module checks whether the item frame area of the already recorded item overlaps the area of the frame area to be newly registered. If the area overlaps, it is excluded from the results.  
Candidates are sorted by ascending order by accuracy, which means an item recorded earlier has a higher accuracy. Thus, checking a previous registered item's area before adding an item to the result enables extraction of the item with the highest accuracy among the items that overlap the area.  
The Graph Aligner module is represented by the Algorithm 4 below.  
Figure 2 shows the posture of an ordinary person walking two steps, and Figure 3 presents data of a user imitating the posture of Figure 2 three times. Visually, it can be seen that the detected graph and the reference graph are different in length; however, the waveform itself is very similar.  
Table 3 lists the data used to derive the results of the two pictures. All areas with the same properties as the reference graph were derived from the recorded posture data using the TSPDA.  
Figures 4-8 show the TSPDA results when the slice count was nil (the optimal case) and 10, 25, 50, and 100. The results were similar.  
Figure 5: The result with a slice count of 10.  
Figure 6: The result with a slice count of 25.  
Figure 7: The result with a slice count of 50.  
Figures 9 and 10 show that slice counting afforded accurate results (both visually and numerically) compared to the use of reference posture data only. Specifically, at a slice count of 10, execution was 7.67-fold faster than when slice counting was absent (the optimal case), but the accuracy was 96.5%. When the slice count was 25, execution was 6.24-fold faster and the accuracy 97.2%. When the slice count was 50, execution was 3.5-fold faster and the accuracy 98.7%. When the slice count was 100, execution was 1.9-fold faster and the accuracy 99.8%.  
When slice counting is absent (the optimal case) the accuracy is 100% but execution is slow. The TSPDA slice count method thus facilitates individualized balancing of execution time and accuracy.  
Figure 8: The result with a slice count of 100.  
Figure 9: Accuracy compared to result in the absence of slice counting.
# Probing Dust and Water in Martian Atmosphere with Far-Infrared Frequency Spacecraft Occultation
## 2 Materials and Methods
### Atmospheric Losses  
#### 2.2.1 Gas Absorption  
The main absorbers of the Martian atmosphere in the far-infrared wavelengths are CO\({}_{2}\), N\({}_{2}\), H\({}_{2}\)O, CO, and O\({}_{3}\). In order to assess the contribution of absorbers in the THz band, we compute the maximum expected losses in the range of 0.1 to 10 THz from the HITRAN database [48] for the surface conditions of Mars. Line absorption and broadening for the gases are computed using the Reference Forward Model (RFM) [49]. H\({}_{2}\)O and O\({}_{2}\) are expected to be the main absorbers below 1 THz [50]. In addition, the continuum absorption for water vapor is taken into account using the Mlawer-Tobin-Clough-Kneizys-Davies (MT\({}_{C}KD\)) water vapor continuum model [51]. We investigate the absorption sources and identify possible sources of background absorption.
## Abbreviations  
The explanations for some abbreviations mentioned in this paper are shown in the following table:  
\begin{tabular}{l l} Abbreviation & Full English Name \\ OFDM & Orthogonal frequency division multiplexing \\ SR & Stochastic resonance \\ MPFT & The mean first passage time \\ SUUBSR & Second-order underdamped unsaturated bistable stochastic resonance \\ FUBSR & First-order unsaturated bistable stochastic resonance \\ SNR & Signal-noise ratio \\ BER & Bit error rate \\ \end{tabular}
## 3 Method
### Arabic Task-Oriented DS Dataset  
Because Arabic is a low-resource language, no human-annotated Arabic dataset for task-oriented DS has been produced (to the best of our knowledge). To obtain a good-quality dataset, we decided to use an existing dataset, translating a benchmark dataset for task-oriented DS (BiToD [1]) to develop a suitable training dataset for Arabic task-oriented DS.  
Translating existing datasets is a practice frequently observed in the literature for low-resource languages, with examples including [46; 47; 48]. Recent translation techniques for crowd-sourced annotated datasets have produced reasonable results on training data for different languages, enabling many studies to address the lack of datasets by translating existing datasets for many downstream tasks in NLP. For example, for question answering (QA), the SQuAD dataset has been translated into Arabic [46] and Bengali [47], and for conversation generation, the EmpatheticDialogues dataset has been translated into Arabic [48].  
Still, it is imperative for the research community to develop multi-lingual benchmarks to evaluate the cross-lingual transferability of end-to-end systems in general and task-oriented DS in particular [49]. For task-oriented DS, many multi-lingual datasets can be obtained by translating the English datasets. Table 2 presents some of these alongside their corresponding tasks and domains. Translation represents a good choice for low-resource languages to support the reuse of resources and save time spent creating and annotating long dialogues. Additionally, this enables the development of multi-lingual benchmarks for the research community to use.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Task** & **Language** & **Domains** \\ \hline \multirow{2}{*}{Chinese ATIS [50]} & Intent classification & ZH & Flight bookings \\  & Slot extraction & & \\ \hline Multi-lingual WOZ 2.0 [51] & DST & EN, DE, IT & Restaurant bookings \\ \hline \multirow{2}{*}{SLU-IT [52]} & Intent classification & IT & 7 domains (Restaurant, \\  & Slot extraction & & Weather, Music,... ) \\ \hline \multirow{2}{*}{Almawave-SLU [53]} & Intent classification & IT & 7 domains (Restaurant, \\  & Slot extraction & & Weather, Music,... ) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Datasets translated from English within the field of task-oriented DS. EN: English, ES: Spanish, DE: German, IT: Italian, TH: Thai, VI: Vietnamese, ZH: Chinese.  
Figure 1: High-level view of our approach.
## 2 Preliminaries  
For a given constant \(\epsilon\geq 0\), the \(\epsilon\)-subdifferential of function \(f\) at \(x\) is defined by (see Reference [34])  
\[\partial_{\epsilon}f(x):=\{g\in\mathbb{R}^{n}:\;f(y)\geq f(x)+\langle g,y-x \rangle-\epsilon,\;\forall y\in\mathbb{R}^{n}\},\]  
with \(\partial f(x):=\partial_{0}f(x)\) being the usual subdifferential in convex analysis [35]. Each element \(g\in\partial f(x)\) is called a subgradient. For simplicity, we use the following notations:  
\(f_{x}\): the approximate \(f\) value at \(x\), that is, \(f_{x}\approx f(x)\);  
\(g_{x}\): an approximate subgradient of \(f\) at \(x\), that is, \(g_{x}\approx g(x)\in\partial f(x)\);  
\(F_{x}\): the approximate \(F\) value at \(x\), that is, \(F_{x}:=f_{x}+h(x)\).  
Aiming at the special structure of problem (1), we present a slight variant of the oracles with on-demand accuracy proposed in Reference [23] as follows: for a given \(x\in\mathbb{R}^{n}\), a descent target \(\gamma_{x}\) and an error bound \(\varepsilon_{x}\geq 0\), the approximate values \(f_{x},g_{x}\) and \(F_{x}\) satisfy the following condition  
\[\begin{cases}f_{x}=f(x)-\eta(\gamma_{x})\;\;\text{with unknown}\;\eta(\gamma_{x}) \geq 0,\\ g_{x}\in\partial_{\eta(\gamma_{x})}f(x),\;\text{and}\\ \text{whenever}\;\;F_{x}\leq\gamma_{x}\;\text{({\it descent target reached}),\;the relation}\;\eta(\gamma_{x})\leq\varepsilon_{x}\;\text{holds}.\end{cases} \tag{6}\]From the relations in (6), we see that although the error \(\eta(\gamma_{x})\) is unknown, it has to be restricted within the error bound \(\varepsilon_{x}\) whenever the descent target \(F_{x}\leq\gamma_{x}\) is reached. This ensures that the exact and inexact function values satisfy  
\[f_{x}\in[f(x)-\varepsilon_{x},f(x)]\;\;\text{and}\;\;f(x)\in[f_{x},f_{x}+ \varepsilon_{x}],\;\;\text{ whenever}\;\;F_{x}\leq\gamma_{x}. \tag{7}\]  
The advantages of oracle (6) are that: (1) if the descent target is not reached, the calculation of oracle information can be rough without an accuracy control, which can potentially reduce the computational cost; (2) by properly choosing the parameters \(\gamma_{x}\) and \(\varepsilon_{x}\), the oracle (6) covers various existing oracles:  
* Exact (Ex) [12; 21]: set \(\gamma_{x}=+\infty\) and \(\varepsilon_{x}=0\);
* Partially Inexact (PI) [24]: set \(\gamma_{x}<+\infty\) and \(\varepsilon_{x}=0\);
* Inexact (IE) [11; 25; 32; 36; 37]: set \(\gamma_{x}=+\infty\) and \(\varepsilon_{x}\equiv\varepsilon>0\) (possibly unknown);
* Asymptotically Exact (AE) [38; 39]: set \(\gamma_{x}=+\infty\) and \(\varepsilon_{x}\to 0\) along the iterative process;
* Partially Asymptotically Exact (PAE) [23]: set \(\gamma_{x}<+\infty\) and \(\varepsilon_{x}\to 0\).
## 3 Results and Discussion
### Characterization of the RL
#### 3.2.2 Elemental Analysis of the RL
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **N (\%)** & **C (\%)** & **H (\%)** & **S (\%)** & **O (\%)** & **HHV (MJ kg\({}^{-1}\)) \({}^{*}\)** \\ \hline Kraft lignin & 1.02 & 59.88 & 5.62 & 1.22 & 32.26 & 24.48 \\ \hline RL at 110 \({}^{\circ}\)C/8 h & 0.46 & 60.65 & 5.93 & 1.06 & 31.9 & 25.10 \\ RL at 170 \({}^{\circ}\)C/8 h & 0.62 & 62.17 & 5.91 & 1.04 & 30.26 & 25.76 \\ RL at 190 \({}^{\circ}\)C/8 h & 0.71 & 62.89 & 6.24 & 0.72 & 29.44 & 26.44 \\ RL at 210 \({}^{\circ}\)C/8 h & 0.88 & 61.75 & 5.89 & 0.99 & 30.49 & 25.57 \\ \hline RL at 190 \({}^{\circ}\)C/4 h & 0.93 & 60.51 & 5.85 & 1.01 & 31.70 & 24.99 \\ RL at 190 \({}^{\circ}\)C/8 h & 0.71 & 62.89 & 6.24 & 0.72 & 29.44 & 26.44 \\ RL at 190 \({}^{\circ}\)C/8 h & 0.83 & 62.03 & 6.14 & 0.85 & 30.15 & 25.98 \\ \hline RL at 190 \({}^{\circ}\)C/8 h (no DES) & 0.61 & 60.39 & 5.77 & 1.13 & 32.10 & 24.82 \\ \hline D:L of 1:1 at 190 \({}^{\circ}\)C/8 h & 0.71 & 62.89 & 6.24 & 0.72 & 29.44 & 26.44 \\ D:L of 3:3 at 190 \({}^{\circ}\)C/8 h & 0.74 & 61.94 & 5.86 & 1.09 & 30.37 & 25.62 \\ D:L of 5:5 at 190 \({}^{\circ}\)C/8 h & 1.12 & 60.01 & 5.70 & 1.18 & 31.99 & 24.65 \\ \hline \hline \end{tabular} \({}^{*}\) HHV (MJ kg\({}^{-1}\)) = [(34 \(\times\) C) + (124.3 \(\times\) H) + (6.3 \(\times\) N) + (19.3 \(\times\) S) - (9.8 \(\times\) O)]/100.  
\end{table} 
Table 1: Elemental analysis of the raw KL and RL (RL = recovered lignin). Figure 6: Van Krevelen diagram comparing KL and RL (**A,B**). The O/C vs. H/C and S/C vs. H/C ratios of (a, a\({}^{\prime}\)) KL. The O/C vs. H/C and S/C vs. H/C ratios of RL obtained at (b, b\({}^{\prime}\)) 190 \({}^{\circ}\)C for 8 h without DES, and (c, c\({}^{\prime}\)) 190 \({}^{\circ}\)C for 8 h using DES.
# Extraction, Characterization, and Applications of Pectins from Plant By-Products
## 3 Pectin Extraction Methods
### Green methods for Pectin Extraction
#### 3.2.1 Microwave-Assisted Extraction  
Microwave-assisted extraction (MAE) is a green method in which a polar solvent absorbs microwave energy and consists of two oscillating perpendicular fields: electric and magnetic fields [73]. MAE is considered as a fast extraction method that involves electromagnetic radiation in the microwave frequency range in the sample to produce thermal energy in the solvent. Microwave energy initiates the electrophoretic transfer of ions and electrons which generates an electric field responsible for particle movements, while dipole rotation is caused by the alternate displacement of polar molecules [45]. Microwave energy increases the efficiency of the extraction process, compared with conventional heating techniques [41]. The heat generated is an important factor in the extraction process as the higher temperatures result in an increase in diffusion rate and, therefore, enhance extraction yields. In addition, the dielectric properties of the sample and solvent and solubility of the interesting compounds in the solvent also affect the extraction rate and quality of extracted compounds [2].  
MAE could limit the pectin depolymerization that can be observed during acid extraction [74]. Microwave radiation has an impact on the cell wall matrix and leads to the severing of the parenchymal cells and, thereby, the skin tissues are extensively opened up. This could lead to increased interaction between the source material and solvent in the extraction process. Therefore, the permeation of the extracting solvent is increased [75] and extraction efficiency could be increased by increasing microwave power [1,2].  
The increasing of the microwave irradiation energy can enhance the penetration of solvent into the plant matrix and deliver it efficiently to materials through molecular interaction with the electromagnetic field and offer a rapid transfer of energy to the solvent and matrix, allowing the dissolution of components to be extracted. Moreover, microwave irradiation can accelerate cell rupture through an internal pressure increase inside the cells of the plant sample and a sudden rise in temperature, which promotes the destruction of the sample surface and, in turn, the exudation of pectin from the plant cells into the solvent [76].  
Hua et al. (2015) reported that the apparent viscosity of the pectin from microwave heating was greater than that from conventional heating. This could be explained by the difference in the methoxyl content and the heating method. A high methoxyl content means a small number of molecules and a greater distance between molecules, resulting in low viscosity of pectin extracted by conventional heating [77]. Table 2 summarizes the physico-chemical properties of pectin extracted by green methods, including MAE.
Future Internet_**2014**, \(6\), 203-222; doi:10.3390/fi6020203  
_future internet_  
**ISSN 1999-5903**  
www.mdpi.com/journal/futureinternet  
_Article_  
**Routing Diverse Evacuees with the Cognitive Packet Network Algorithm**  
**[PERSON]**  
Intelligent Systems and Networks Group, Department of Electrical and Electronic Engineering,  
Imperial College London, London SW7 2 BT, UK; E-Mail: [EMAIL_ADDRESS];  
Tel.: +44-207-594-7913  
_Received: 19 February 2014; in revised form: 2 April 2014 / Accepted: 3 April 2014 / Published: 17 April 2014_  
**Abstract:** Regarding mobility, health conditions and personal preferences, evacuees can be categorized into different classes in realistic environments. Previous emergency navigation algorithms that direct evacuees with a single decision rule cannot fulfil civilians' distinct service requirements and increase the likelihood of inducing destructive crowd behaviours, such as clogging, pushing and trampling, due to diverse mobility. This paper explores a distributed emergency navigation algorithm that employs the cognitive packet network concept to tailor different quality of service needs to different categories of evacuees. In addition, a congestion-aware algorithm is presented to predict the future congestion degree of a path with respect to the observed population density, arrival rate and service rate of each route segment. Experiments are implemented in a simulated environment populated with autonomous agents. Results show that our algorithm can increase the number of survivors while providing improved quality of service.  
**Keywords:** emergency navigation; diverse evacuees; quality of service; congestion management
# Physicochemical Properties and Oxidation Status of Pork from Three Rearing Systems
## 4 Discussion
### Chemical Composition  
It is important to acknowledge that the impact of pig housing systems on the chemical composition of pork has been the subject of extensive investigation, yet the findings derived from published studies have exhibited a notable lack of consistency and uniformity [32]. In the present study, significant differences in dry matter, protein, fat and carbohydrate content were found in the meat of pigs from different housing systems. In contrast, no significant differences were found in dry matter, protein, fat and ash content in meat from pigs raised in free-range or conventional systems by [PERSON] et al. [37]. The higher protein content in conventional pork and the higher fat content in organic pork are not in accordance with the previous studies, indicating the necessity to carry out more research and compare different breeds, including individual rearing systems in different regions [38; 39; 40]. This statement is supported by other previous studies: higher intramuscular fat content was found in organic pork by [PERSON] et al. [13]. The collagen content of organic pork tended to be higher (not significant, \(p>0.05\)) than that of meat from conventionally and traditionally raised pigs, which could be attributed to the motor activity of the animals [18]. The idea that motor activity prioritizes myogenesis over lipogenesis [25] is more consistent with the results of pork from traditional rearing systems (higher portion vs. lower fat content) than organic pork in the study. These findings raise a question about the extent of the physical activity of animals in outdoor areas under organic production systems. The physical activity of organically raised animals primarily affects the metabolic state of the muscles at slaughter, whereas it has a limited effect on the chemical composition of the meat. The influence of the husbandry system on the chemical composition of the meat is often due to differences in feed composition and intake, as well as the energy required to maintain the animals [32]. Biochemical and metabolic properties inherent to pig muscles undergo significant transformations as a result of the physical activity stimulation imposed upon them, further compounded by the confluence of ambient temperatures within extensive rearing systems [41; 42]. It is crucial to recognize that the interplay between these multifaceted factors encompasses an integration that intricately intertwines the physiological responses of the pig's musculature with the prevailing environmental conditions [43]. The energy content of the feed according to organic standards, combined with a mild climate during animal production, could be the reason for the thicker meat of the organic pigs in this work. The higher fat content of organic pork may also be attributed to the genotype of the pigs used for breeding. In contrast, the genotype of the pigs used in the conventional system is a meat type and the fat content is not as high, even at a higher slaughter weight.
## 3 Establishment of Numerical Model for the Foundation Pit
### Parameters for Analyzing Stability of the Foundation Pit during Excavation Process  
Lateral displacements of the surrounding rocks and settlement of the surrounding ground are the main direct parameters that are clearly required in GB50007-2011. Therefore, the stability of the foundation pit at different excavation stages could be evaluated based on the above two parameters. In GB50007-2011 [10], the values of the above two parameters should be less than 0.3% and 0.15% of the excavation depth, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Elastic Modulus (GPa)** & **Section Area (m\({}^{2}\))** & **Z-Axial Inertia Moment (m\({}^{3}\))** & **Y-Axial Inertial Moment (m\({}^{3}\))** & **Polar Inertia Moment (m\({}^{3}\))** & **Poisson’s Ratio** \\ \hline
200 & 0.0394 & 0.003 & 0.003 & 0 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Beam unit parameters of steel bracings.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Supporting Type** & **Density (kg/m\({}^{3}\))** & **Elastic Modulus (GPa)** & **Cohesion (MPa)** & **Internal Friction Angle (MPa)** & **Tensile Strength (MPa)** & **Poisson’s Ratio** \\ \hline Drilled grouting piles & 2500 & 31.5 & 20 & 18 & 3 & 0.25 \\ Transverse reinforced & 2500 & 31.5 & 20 & 18 & 3 & 0.25 \\ concrete bracings & 2500 & 31.5 & 20 & 18 & 3 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters used for numerical calculations applied to drilled grouting piles and reinforced concrete bracings.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Prestessed** & **Elastic Modulus** & **Section Area (m\({}^{2}\))** & **Unanchored** & **Anchorage Length (m)** & **Bond Stiffness (MPa)** & **Grouting Perimeter (m)** \\ \hline
200 & 200 & 0.002 & 9 & 8 & 27.5 & 0.565 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Cable unit parameters of prestressed anchorages.  
Apart from the above direct parameters, the stability of the foundation pit at different excavation stages could also be evaluated according to the stress conditions and deformation characteristics of the supporting elements. Therefore, the parameters used to analyze the stability of the deep foundation pit with hard surrounding rocks at different excavation stages included the lateral displacement of the surrounding rocks, settlement of the surrounding ground, axial forces of steel bracings, and displacement at the tops of the drilled grouting piles.
## 5 Security Issues and Cyber Attacks in Wireless Sensor Networks
### Attacks on Various Network Layers
#### 5.3.2 Misdirection Attack  
In the misdirection attack, the information is routed towards fake path. It alters the routing information of network and affects the communication adversely. Misdirection is a network layer attack. Authentication techniques between transmitter and receiver, multi hop routing, etc. can be used to detect misdirection attack.
## 4 Performance Evaluation
### Computational Efficiency of Deep Learning Classification  
Table 13 shows the time execution (in seconds) of the training and testing for each dataset image. In the training set, the number of mammogram images directly alter the time it takes to finish the learning process. Using the TTCNN approach the DDSM dataset takes longer time to train than the INbreast and MIAS datasets. It takes approximately 3.2, 1.4 and 1.1 h to complete the 120 epoch training process on the DDSM, INbreast and MIAS datasets, respectively. For the testing process, the proposed approach took only 1.2 (s) for DDSM, 1.7 (s) for INbreast, and 0.4 (s) for MIAS to classify a single mammogram image. The runtime minimization will be further enhanced in future work, as our main goal is to enhance the detection and classification accuracy.  
In general, the proposed approach improves the performance in comparison. The proposed approach can be employed for real-time assessment and assist the radiologists in automated evaluation of mammogram images.