esa-sceva/satcom-chunk-collection huggingface dataset:
# Late Line-of-Sight Check and Prioritized Trees for Path Planning
## 3 Forward Estimation and Online Optimizing
### Theta*  
Theta* adds the online LoS-Check into A* when the successors of the current vertex are generated. Assume the current vertex is \(v\) and its parent vertex is \(p\). If there is the \(Lineof\,Sight\) between vertex \(p\) and any successor of \(v\), set vertex \(p\) as the parent of the successor. If the \(Lineof\,Sight\) condition is not satisfied, maintain \(v\) as the parent of the successor. Figure 2 (left) shows the extension process of Theta*, and when the vertices \(\{A2,A3,B3,C3,A4,B4\}\) are generated, there will be LoS-Check between their grandparent vertices and them.  
Algorithms 1 and 2 review the specific implementation of Theta* and Lazy Theta*. The sentences in the square brackets mean that they are used in other algorithms, and the annotation gives some instruction and explanation. If the code in Line 12 of Algorithm 2 is activated, Algorithms 1 and 2 will become Theta*. Line 4 in Algorithm 1 means popping out the state with the least \(f\) value from the \(open\) list to act as the current state.  
```
1\(g(v_{start})\gets 0;\qquad closed\leftarrow\varnothing;\qquad open\leftarrow\varnothing\);
2\(open.\text{Add}(v_{start},g(v_{start})+h(v_{start}))\);
3 while\(open\
eq\varnothing\)do
4 current \(\leftarrow\)\(open.\text{Pop\_least}()\);
5 [current \(\leftarrow\) UpdateParent(current);] // Lazy Theta*
6 if current == goal then
7 returnpath;
8\(closed.\text{Add}(current)\);
9 for each \(v\in\text{Succ}(\text{current})\)do
10 if\(v\
otin closed\)then
11 if\(v\
otin open\)then
12\(g(v)\leftarrow\infty\);
13\(Parent(v)\leftarrow\text{NULL}\);
14 UpdateNode(\(v\), current);
```  
**Algorithm 1**Theta* and Lazy Theta*  
```
1 UpdateNode(\(v\), current)
2 g_old \(\gets g(v)\);
3 CompareCost(\(v\), current);
4 if\(g(v)\) < g_old then
5 if\(v\in open\)then
6\(open.\text{Delete\_old}(v)\);
7\(open.\text{Add}(v,g(v)+h(v))\);
8 CompareCost(\(v\), current)
9 if\(g(\text{current})\) + Cost(current, \(v\)) < \(g(v)\)then// path1
10\(g(v)\gets g(\text{current})\) + Cost(current, \(v\));
11 Parent(\(v\)) \(\leftarrow\) current;
12[UpdateParent(\(v\));] // Theta*
13 UpdateParent(\(v\))
14 grandpa \(\leftarrow\) Parent(Parent(\(v\)));
15 if\(Lineof\,sight(v\), grandpa) then
16 if\(g(\text{grandpa})\) + Cost(grandpa, \(v\)) < \(g(v)\)then//path2
17\(g(v)\gets g(\text{grandpa})\) + Cost(grandpa, \(v\));
18 Parent(\(v\)) \(\leftarrow\) grandpa;
19 return\(v\)
```  
**Algorithm 2**Relative procedures
## 4 Experiments
### Objective Evaluation
\begin{table}
\begin{tabular}{c c c c} \hline \hline  &
\begin{tabular}{c} **Conversion** \\ **Direction** \\ \end{tabular} & \multicolumn{1}{c}{**MaskCycleGAN**} & \multicolumn{1}{c}{**wav2 wav**} \\ \hline \multirow{4}{*}{Intra-gender} & F1\(\rightarrow\)F2 & 7.68 \(\pm\) 0.29 & 6.08 \(\pm\) 0.26 \\  & F2\(\rightarrow\)F1 & 7.44 \(\pm\) 0.23 & 6.01 \(\pm\) 0.26 \\  & M1\(\rightarrow\)M2 & 7.96 \(\pm\) 0.27 & 6.04 \(\pm\) 0.22 \\  & M2\(\rightarrow\)M1 & 7.04 \(\pm\) 0.17 & 5.88 \(\pm\) 0.18 \\  & \multicolumn{1}{c}{} & Average & 7.53 \(\pm\) 0.13 & 6.01 \(\pm\) 0.11 \\ \hline \multirow{4}{*}{Inter-gender} & F1\(\rightarrow\)M2 & 8.48 \(\pm\) 0.21 & 5.86 \(\pm\) 0.16 \\  & M2\(\rightarrow\)F1 & 8.58 \(\pm\) 0.21 & 5.75 \(\pm\) 0.16 \\ \cline{1-1}  & M1\(\rightarrow\)F2 & 8.70 \(\pm\) 0.28 & 6.47 \(\pm\) 0.24 \\ \cline{1-1}  & F2\(\rightarrow\)M1 & 8.36 \(\pm\) 0.23 & 6.38 \(\pm\) 0.22 \\ \cline{1-1}  & \multicolumn{1}{c}{Average} & 8.53 \(\pm\) 0.12 & 6.12 \(\pm\) 0.11 \\ \hline \multicolumn{1}{c}{Average} & \multicolumn{1}{c}{8.03 \(\pm\) 0.10} & 6.06 \(\pm\) 0.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average MCDs and 95% confidence intervals of the speeches converted by MaskCycleGAN and wav2 wav. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{1}{c}{**Feature**} & \multirow{2}{*}{**Vocoder**} & \multirow{2}{*}{**MCD**} \\  & **Extractor** & & \\ \hline MaskCycleGAN & DFT & MelGAN & 8.03 \(\pm\) 0.10 \\ spec2 spec & DFT & HiFi-GAN & 7.88 \(\pm\) 0.12 \\ wav2 spec & CNN & HiFi-GAN & 7.52 \(\pm\) 0.11 \\ spec2 wav & DFT & N/A & 6.82 \(\pm\) 0.10 \\ wav2 wav & CNN & N/A & 6.06 \(\pm\) 0.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average MCDs and 95% confidence intervals for MaskCycleGAN, spec2 spec, wav2 spec, spec2 wav, and wav2 wav.
## 3 Results
### Differences between Alloys  
Two-factor ANOVA showed that the interaction of bacteria and agent was significant and with a greater effect for NiTi (\(p<0.001\); \(\eta^{2}=0.609\)) than for SS (\(p<0.001\); \(\eta^{2}=0.380\)). The inhibition of _V. parvula_ depended on the solution. On SS, there was a greater inhibition of adhesion in streptococci, whereas, on NiTi, there was a greater inhibition of adhesion in _A. actinomycetemcomitans_. After CHX DG therapy, the inhibition of adhesion to SS was greater than that to NiTi in _S. oralis_ (96% vs. 89%; \(p<0.001\); r = 0.303) and _V. parvula_ (81% vs. 69%; \(p<0.001\); r = 0.532). The inhibition of adhesion to SS after CHX DG in _S. mutans_ was higher, but not significant, owing to the large scattering of the data. The inhibition of _A. actinomycetemcomitans_ was equal for both metals after the application of CHX DG. An excellent inhibition of adhesion was observed for both metals after Curasept therapy in _A. actinomycetemcomitans_ (99% vs. 98%; \(p=0.001\); r = 0.486), although it was higher for NiTi than for SS. A greater inhibition was observed for _V. parvula_ (84% vs. 78%), although the difference was not statistically significant, because of the scattering of the data. After therapy with Curasept for streptococci, no differences were observed between the alloys. Perio Plus therapy was the least dependent on the type of alloy used. _V. parvula_ showed a greater inhibition of adhesion to NiTi than to SS alloy after Perio Plus therapy (99.8% vs. 99.3%; \(p<0.001\); r = 0.811), and there was no difference between the alloys for other bacteria (100% inhibition).
# Microencapsulation of Polyphenols and Their Application in Food Technology
## 4 Microencapsulation of Polyphenols
### Encapsulation Carriers
#### 4.2.1 Carbohydrates  
Carohydrates are among the most commonly used materials in microencapsulation, especially in the food industry, due to their low cost, availability, and safety. For instance, maltodextrin and various starches are commonly employed as efficient encapsulation agents for polyphenols, ensuring their stability and controlled release in food products. [156; 157; 158]. Notably, maltodextrin, a polysaccharide derived from starch, is widely used for spray-drying encapsulation. Its high water-solubility, low viscosity, and neutral taste make it an ideal carrier for polyphenols, particularly in the creation of powders. As a result, maltodextrin forms a protective barrier around the polyphenol molecules, shielding them from environmental factors such as heat, oxygen, and light, which can lead to degradation. Additionally, maltodextrin improves the flow properties and stability of encapsulated products, which are critical during storage [154; 158; 159]. Similarly, starch, another commonly used carbohydrate, offers diverse structural forms (e.g., native, modified, or resistant starches) and has excellent film-forming capabilities. In particular, starch-based carriers protect polyphenols from enzymatic breakdown and environmental stressors while also enabling the gradual release of the active compounds during digestion. Moreover, the encapsulation efficiency of starch is often enhanced by modifying it with other agents, such as \(\beta\)-cyclodextrintrins, which form inclusion complexes with polyphenols, further improving their solubility and protection [154; 158; 159; 160; 161].
## 6 Evaluation Experiment of the Proposed Approach
### Feasibility of Crowdsourced Requirements  
Feasibility has obtained the highest overall assessment score equal to 4.45. The experts have rated most of the crowdsourced requirements as being feasible. According to the heat map in Figure 11, only 2% of the crowdsourced requirements have an overall averaged low score in feasibility. The score attained by this factor reflects that the crowd are reasonable in the ideas and requirements they contribute. They have not asked for requirements that could not be technically implemented or difficult to accommodate in terms of cost or time.  
This finding surpasses the anticipated outcome in which the selected crowd--especially mobile apps' users--were expected to come up with a larger number of requirements that are beyond the capabilities supported by technology. Moreover, this provides promising implications about the performance of the crowd in providing realistic requirements. This observation encourages leveraging crowds in requirements elicitation tasks with minimal concerns pertaining to requirements' feasibility.
# My Smartphone tattles: Considering Popularity of Messages in Opportunistic Data Dissemination
## 6 Keetchi--Case Studies
### Use Case 3: The Lifetime of Good and Bad Jokes  
This use case follows a single message, containing a joke. Even if the full complexity of popularity of messages cannot be easily shown in a simple example, the difference between Epidemic and Keetchi can be clearly explained. Figure 7a shows the initial scenario, where a single person in Bremen has heard a joke he likes and posts it. Figure 7b shows the behaviour of Epidemic which simply does not consider whether people like the joke or not. It disseminates the joke to its pre-defined dissemination area, controlled by the TTL of messages and the maximum number of hops. Here, we defined this area to be restricted to Bremen (motivated by the default settings of the Jodel app, see more information about it in the next section).  
Figure 7c shows the behaviour of Keetchi if the people do not like the joke. One will receive it (the lady with the hat in Bremen), but will not like it and its propagation will be stopped soon. The example here is exaggerated, but underlines the main property of Keetchi: if a message is not popular, it will not get far. On the other side, if the joke is very good, it will continue being propagated irrespective of distances or time. This is depicted in Figure 7d, where even people in Australia will get it. The only person left out is the guy in Munich. This is a general property of all OppNets protocols: mobility connects people.
## 4 Privacy-Preserving Smart Metering with Authentication Capability
### Scheme Construction
#### 4.2.1 System Setup  
In the initial system setup phase, system security parameters and users' secret keys are set up. Aggregation paths are then set up as follows.  
Key Setup  
The \(Setup\) algorithm chooses random generator \(g\in\mathbb{G}\) and random \(\alpha_{1},\alpha_{2}\in\mathbb{Z}_{p}\), and cryptographic hash functions \(H_{1},H_{2}:\{0,1\}^{*}\rightarrow\mathbb{G}\) and \(H_{3}:\{0,1\}^{*}\rightarrow\mathbb{Z}_{p}\). It then returns \((g,g^{\alpha_{1}},g^{\alpha_{2}},H_{1},H_{2},H_{3})\) as \(mpk\), and \((\alpha_{1},\alpha_{2})\) as \(msk\).  
The KGC then runs the \(KeyDer\) algorithm on the input of \(msk\) and \(ID\in\{0,1\}^{*}\), and returns secret key \(sk_{ID}=(H_{1}(ID)^{\alpha_{1}},H_{2}(ID)^{\alpha_{2}})\) to the smart meter associated with identity \(ID\). Henceforth, \(ID_{i}\) denotes the identity of a smart meter \(s_{i}\).  
Aggregation Path Setup  
The aggregation path is constructed so that all of the smart meters in a neighborhood are covered. Figure 2 shows an example of the aggregation path constructed in a neighborhood. If we consider the smart meter network as a connected graph where smart meters are vertices and wireless links between any two meters are edges, the aggregation tree is constructed as a spanning tree of the graph rooted at the concentrator. The aggregation task is performed on the basis of each aggregation path. All or a subset of the smart meters on the aggregation path participate in the aggregation task. The construction of an efficient aggregation path is beyond the scope of this study.  
Without a loss of generality, we will describe data encryption/decryption and signature generation/verification algorithms below under the assumption that a set of smart meters are deployed on the aggregation path to the concentrator in the order \(s_{1},\ldots,s_{n}\).
## 2 PV Model of the Power System  
### Definition of the HMM  
The hidden Markov model (HMM) is a statistical model used to describe a Markov process with hidden, unknown state variables [12]. The state sequence of the system is recorded as \(I=\{I_{1},I_{2},\ldots,I_{T}\}\), where \(T\) is the length of the observation sequence. Each state sequence is assigned a unique emission probability, which generates the observation sequence denoted by the symbol \(O=\{O_{1},O_{2},\ldots,O_{T}\}\).  
The HMM can be uniquely determined by the initial state probability vector \(\pi\), the state transition probability matrix \(A\), and the emission probability matrix \(B\).  
HMM is represented as:  
\[\lambda=(\pi,\mathbf{A},\mathbf{B}) \tag{1}\]  
where initial state probability vector \(\pi\):  
\[\pi_{i}=P(I_{1}=i),\;1\leq i\leq N \tag{2}\]  
State transition probability matrix \(\mathbf{A}\):  
\[a_{ij}=P(I_{t+1}=j\;\big{|}\;P(I_{t}=i),1<i,j<N \tag{3}\]  
The two basic assumptions of the HMM are:  
**Assumption 1**.: _The state \(I_{t}\) of the hidden Markov chain at any given time \(t\) depends only on the state \(I_{t-1}\) of the previous time and is independent of the state or observations at other times._  
\[P(I_{t}\;|\;I_{t-1},O_{t-1},\cdots,I_{1},O_{1})=P(I_{t}\;|\;I_{t-1}),t=1,2, \cdots,T \tag{4}\]  
**Assumption 2**.: _The observation \(O_{t}\) at any given time is dependent only on the state \(I_{t}\) of the Markov chain at time \(t\) and is independent of the observations and states at other times._  
\[P(O_{t}\;|\;I_{T},O_{T},\cdots,I_{t-1},O_{t-1},\cdots,I_{1},O_{1})=P(O_{t}\;| \;I_{t}) \tag{5}\]
# A Thermogravimetric Analysis of Biomass Conversion to Biochar: Experimental and Kinetic Modeling
## 3 Results and Discussion  
### Thermal Decomposition of Biomass Waste  
#### 3.1.1 Proximate Analysis Results  
The proximate analysis of apple and potato peel waste is presented in Table 1. The selection of an effective conversion procedure is significantly influenced by the moisture content of the biomass. The processing, transportation, and storage of biomass feedstock are all impacted by the moisture content. For pyrolysis, biomass with a moisture content of less than 15% is generally favored for conversion [47]. The moisture content of the two samples varies from 2.1% for potato peel to 5.48% for the apple peel and, thus, suggests their suitability for the pyrolysis process.  
The volatile content of the samples exhibited high values of 76.73% and 75.79% for apple peel and potatoes peel, respectively. This feature is desirable since it suggests that biomass waste samples can be easily broken down through pyrolytic conversion [49]. Potato peel had the highest ash content at 5.88%. The potato peel has a higher content than the apple peel, but this value is not a large one, which influences the structural characteristics of the coal in a favorable way. This characteristic is favorable because a high ash content can cause aggregation and limit heat and mass transfer, which yields low conversion and operation costs [49].  
The energy contained in carbon-carbon bonds, which contributes to biochar production is mainly represented by the fixed carbon content [50]. The fixed carbon content was recorded to be in the range 17.23 to 18.35%. According to different studies [47; 51] good development of the material's surface, structure, and textural qualities is dependent on the high volatile matter content and low ash content of the fixed carbon. Therefore, based on the above affirmation, the two biomass waste samples have good potential to produce high yields of biochar.  
The chemical composition of the potato peel, according to [PERSON], [PERSON], [PERSON], and [PERSON] [48] consists of moisture around 6.6%, protein 11%, lipids 1.75%, total carbohydrates 62.4% from which starch 20%, cellulose 32.4%, and hemicellulose 10%. Meanwhile, the mean values of moisture, minerals (ash), proteins, fats, carbohydrates, and dietary fibers (non-starch polysaccharides and lignin) determined from three batches of summer apples grown in hilly areas of Romania, according to [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [45] were as follows: 5.48 \(\pm\) 0.76 moisture; 3.01 \(\pm\) 0.45 ash; 4.31 \(\pm\) 0.48 proteins; 3.48 \(\pm\) 0.40 fats; 36.39 \(\pm\) 1.23 dietary fibers; and 63.84 \(\pm\) 1.53 carbohydrates.
# A Novel Named Entity Recognition Algorithm for Hot Strip Rolling Based on BERT-Imseq2 seq-CRF Model
## 3 Model Design
### Analysis of Experimental Results
The specific recognition results of this model are shown in Table 5. The mixed expression of Chinese characters, letters, and numbers in some sentences does not affect the prediction performance of the model, and almost all entity tags are accurately predicted as a whole. At the same time, the prediction results of the specific entity types of the hot strip rolling text by this model are shown in Figure 6. There is a phenomenon that the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** \\ \hline CRF & 82.66\% & 72.59\% & 77.30\% \\ seq2 seq-CRF & 37.93\% & 8.38\% & 13.72\% \\ BiLSTM-CRF & 84.17\% & 89.06\% & 86.56\% \\ Imseq2 seq-CRF & 88.72\% & 86.08\% & 87.38\% \\ BERT-BiLSTM-CRF & 90.22\% & 87.04\% & 88.60\% \\ BERT-Imseq2 seq-CRF & **93.16\%** & **89.85\%** & **91.47\%** \\ CRF & \multicolumn{1}{c}{} & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Identification results of hot strip rolling. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**WeiBo Dataset**} & \multicolumn{4}{c}{**News Dataset**} \\ \cline{2-7}  & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline CRF & 84.31\% & 69.54\% & 76.22\% & 82.51\% & 71.83\% & 76.80\% \\ seq2 seq-CRF & 42.50\% & 8.63\% & 14.35\% & 45.12\% & 9.39\% & 15.55\% \\ BiLSTM-CRF & 87.47\% & 84.48\% & 85.95\% & 89.06\% & 85.58\% & 87.29\% \\ Imseq2 seq-CRF & 85.14\% & 87.32\% & 86.21\% & 90.32\% & 87.32\% & 88.73\% \\ Model 1 4 & 88.51\% & 87.76\% & 88.13\% & 91.70\% & 87.84\% & 89.73\% \\ Model II 3  & **90.02\%** & **88.58\%** & **89.30\%** & **93.05\%** & **88.01\%** & **90.46\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Identification results of the third party dataset. prediction results of individual entity types are lower than the average value. Through the verification text, it is found that the number of entity types of 'defect type DEF' is relatively small. Different statements express the entity type differently, which affects the prediction level of the model to a certain extent. Inspired by the seq2 seq model, this paper constructs the NER model applied to the hot strip rolling field. Except for some entity types, the F1-Score is more than 85%, and some experimental indexes are close to 100%. It improves the efficiency and quality of entity recognition and labeling, and provides solutions and technical support for information extraction and atlas construction of hot strip rolling.
# Vibration Response Analysis of Overhead System Regarding Train-Track-Bridge Dynamic Interaction
### Overhead System Model
#### 1.1.2 Model Building and Boundary Processing  
The overhead system has a total length of 80 m and a standard span of 8 m. For the consideration of calculation efficiency, the influence of the three-span overhead system was calculated. The total length of the overhead architecture model along the track line direction is 24 m, which is divided into 12 m and 9 m along the road direction of the frame bridge. The 12 m transverse girder was arranged in two spans of 7 m + 5 m. A B32 three-dimensional beam element was used to discretize the structure. The concrete sleeper has little effect on the stiffness of the structure, which is simplified in the model as a mass element applied to the relevant rail beam joints. The joints are rigidly joined, and nodal plates and rivets were not taken into consideration. The finite element numerical models of the two types of overhead systems are shown in Figure 5.
# Modeling Network-Controlled Device-to-Device Communications in SimuLTE
## 5 Modeling Device-to-Device Communications in SimuLTE
### Modifying Protocol Layers to Accommodate D2D Data Processing
* At the RLC layer, whether to flush the RLC buffer (which contains data ciphered with the wrong session key, see [18]) and switch mode instantly, or to defer switching until the RLC buffer has been drained by the MAC. * At the PDCP layer, whether to buffer SDUs in anticipation of a possible future MS operation, and whether to relay them to the PDCP entity that manages the new direction upon MS (see again [18]).
# Immersive Virtual-Reality System for Aircraft Maintenance Education: A Case Study
## 3 Materials and Methods
### Gameful Experience
#### 3.2.2 VR Experience  
The tasks necessary to carry out the maintenance of the aircraft were determined by the staff of Castello Airport (Aerocas). With the purpose of providing students with the opportunity to practice with experiences close to reality, the entire real-world environment was replicated. Figure 9 shows samples of the result.  
Figure 8: Gameful experience.  
Four operations, which are necessary for students to practice during their training with the Boeing 737 Next Generation, were simulated as follows:  
* Landing gear downlock pins installation.
* Hiding height and temperature sensors.
* Removing gear downlock pins installation.
* Unhiding height and temperature sensors  
The simulation starts when the aircraft has landed, and its maintenance has to be performed at the hangar. From that moment, the user is faced with five challenges that must be overcome in a specific order, although they can repeat those that they have previously completed.  
The **first** challenge is designed as a training scene, where an introduction to the virtual environment and its main interactions are presented. In order to become acquainted with the application management, the user has to move outside the hangar area, reach the specific hangar door, pick up a card, and open the door by using it. The **second** mission is already carried out inside a hangar where the user has to secure the landing gear by performing the landing gear downlock pins installation maneuver. The **third** mission consists of protecting the aircraft's temperature and altitude sensors with protective hoods. Therefore, users have to use a ladder that facilitates access to them. For the last two missions, the narrative changes; instead of receiving the landing of the plane, the user has to prepare it for take-off. In the **fourth** mission, the landing gear pin has to be removed, and, finally, in the **fifth** mission, the objective is to uncover the sensors.  
Figure 10 shows details of the digital environment and objects that were modeled to perform these processes.