esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 On-Site Monitoring Results Analysis
### Stress State Analysis of Steel Arches Based on Material Science  
According to the material-metal strengthening theory, steel-based materials undergo work hardening during tension, compression tests, or processing. During the deformation process, the deformation of the cross section of the I-beam was not synchronized. Usually, the center line of the I-beam is used as the dividing line, and the tension and compression are alternately carried out. It can be seen from Figure 27 that when the steel arch was initially in contact with the surrounding rock, the values of the four measuring points fluctuated slightly due to the influences of blasting disturbance and shotcrete. In the second stage, with the passage of time, the steel arch began to bear the pressure of surrounding rock, and the values at the vault (A), spandrel (C), and haunch (D/E) increased sharply. According to the characteristics of the steel itself, when the steel arch was subjected to surrounding rock stress and plastic deformation (stress lower than its yield strength), the material crystallized due to internal grain slip, edge dislocation, and screw dislocation. The deformation and fragmentation of grains led to hardening. As shown in Figure 27, the steel arch could not withstand large deformation, but the stress value remained basically unchanged and stable. In the subsequent deformation curve analysis, the A, C, D, and E curves demonstrated multiple hardening phenomena, reflecting cycle fatigue characteristics similar to steel. However, the fluctuation range tended to decrease over time, and finally, the deformation at the four measuring points tended towards stability.
## 4 The System Model  
In order to analyze the performance and security attributes of WSNs under the threat of timing attacks quantitatively, we need to combine the behaviors of an attacker who is trying to gain the sensitive information in conjunction with the countermeasures taken by the system. Therefore, we developed a hybrid CTMC and queueing model, shown in Figure 2, that takes into account the behavior of both sides. The goal is to identify optimal system settings to maximize the security and performance tradeoff metric described in Section 5.3.  
The state transition model represents the system behavior under a specific attack and given system configuration that depends on the actual security requirements; whereas, the queueing model is proposed to exhibit the data transmission between sensors and gateway nodes. A coming job joins a queue before it is processed and forwarded to a command node. The states and parameters of the CTMC state transition model are summarized here:  
* \(G\) Good state in which the WSN works properly  
Figure 1: Architecture of a multi-gateway clustered sensor network.  
Figure 2: State transition diagram for a WSN under timing attacks.  
* \(T\) Timing attack state
* \(C\) Compromised state after the attacker knows the secret of the system
* \(R\) Rekeying state in which the system renews its cluster key
* \(\lambda_{1}\) rate at which the system launches the rekeying process in state \(G\) and state \(T\)
* \(\lambda_{2}\) rate at which an attacker triggers a timing attack on the system
* \(\lambda_{3}\) rate at which a timing attack succeeds to break the cluster key
* \(\lambda_{4}\) rate at which the WSN is brought back to the good state by the rekeying process
* \(\lambda_{5}\) rate at which the system launches the rekeying process in the compromised state \(C\)
* \(\lambda_{6}\) rate at which the attacker successfully breaks the key, while failing to access legitimately-authorized information.
## 3 Results and Discussion  
### Morphological and Quality Parameters  
Japanese silverberry produced almost spherical fruits (6.92 \(\pm\) 0.11 mm in width and 6.46 \(\pm\) 0.08 mm in length) with a mean weight of 0.32 \(\pm\) 0.02 g for each berry at fully ripe stage (Table 1). Results are in accordance with previous research [6; 7; 11], except for the weight, which turned out to be slightly higher [4].  
Analysis on quality traits revealed a TSS mean value of 16.23 \(\pm\) 0.15\({}^{\circ}\) Brix (Table 1). These findings are partially confirmed in the literature: a study on _Elaagnus umbellata_ berries cultivated in fivedifferent localities showed lower values of TSS [23], while the results of six autumn olive genotypes grown in Cookeville (TN, USA) recorded TSS values ranging from 10.6 to 18.4\({}^{\circ}\) Brix [5].  
Free sugars are among the principal constituents and are essential to determine the quality of fruits [24]. Within the sugar class, many studies have reported fructose and glucose as the major contributors of fruit flavor. As proof of this, fructose turned out to be 1.8 time sweeter than sucrose [25]. Many studies on oleaster fruits revealed fructose and glucose as the main sugars during ripening stage [5; 26], highlighting the palatability of _Elaengmus_ fruits.  
Autumn olive berries had a pH mean value of \(3.53\pm 0.20\), falling within the range found in a previous study (3.30-3.90) [23], although in contrast with the study of [PERSON] on the same species [27], TA ranged from 252 to 402 meg/L, with a mean value of \(342\pm 79.37\) meg/L (Table 1). In order to make this value comparable, it was converted in percentage of malic acid equivalent. Therefore, titratable acidity was found to be 1.64% (equal to 1.64 g of malic acid/100 g), considerably lower than the findings of [PERSON] [23] and [PERSON] [27], which were 2.20-2.94% and \(3.1\pm 0.1\%\), respectively. The differences in terms of TA could be due to many factors, among which locality, weather conditions, genotype (cultivar), and agronomic techniques play a key role. Considering the non-climacteric attitude of the berries, the time of the harvest is also essential in determining the TSS:TA ratio. All of these aspects have to be taken into account when comparing fruits grown in tropical or sub-tropical areas rather than in temperate regions [28].  
The TSS:TA ratio of 9.90 observed in this study further emphasized the large intraspecific variability of the _E. umbellata_ fruits, partially confirming previous studies [5; 23; 27].
# Review of Unmanned Aerial Vehicle Swarm Communication Architectures and Routing Protocols
## 3 Routing Protocols
### Swarm Intelligence-Based Routing Protocols  
Swarm intelligence (SI) is a multi-agent self-organizing system. Inspired by the swarming patterns of fish, birds, insects, etc., we can apply SI to mobile robots. Additionally, the SI brings new ideas for optimizing complex collaborative tasks. In the routing protocol of UAV swarm systems, there are also embodiments of SI. In order to solve the problems of communication range expansion and information leakage, which are caused by the increasing scale of swarm, one study [95] proposed an Improved Artificial Fish-Swarm Algorithm (IAFSA). By adjusting the topology of the group, the proposed wireless communication model achieves secure communication purposes. In addition, there are other routing protocols specifically designed for UAV communications such as the Bee colony algorithm-based Ad hoc network (BeeAdhoc) [96], and Ant colony optimization-based Polymorphism-Aware Routing (APAR) [97].
# A Study on the Evaluation Method of Highway Driving Assist System Using Monocular Camera
## 2 Proposed Formulation for HDA System with Monocular Camera
### Formulation  
Using the aforementioned geometric relationships and the corresponding figures, Equations (1) and (2) can be obtained.  
\[h_{2}\ :b=\ h_{1}\ :(a+c) \tag{1}\]  
\[I_{2,left}\ :I=\ L_{left}\ :L. \tag{2}\]  
On the basis of the camera angle of view and mounting height, Equations (3)-(6) can be obtained.  
\[L_{2,left}=\ 2(a+c)\frac{L_{2,left}}{L}\ tan\frac{\theta}{2} \tag{3}\]  
\[tan\ \beta=\ \frac{a+c}{h_{1}} \tag{4}\]  
\[l\ :(l+l^{\prime})=\ H_{2}\ :(H_{1}+H_{2}) \tag{5}\]  
\[tan\ (\alpha+\beta)=\ \frac{a+c+d_{Image}}{h_{1}}. \tag{6}\]  
The geometric relationships of the camera-equipped vehicle and lead vehicle can be used to derive Equations (7) and (8).  
\[d_{front}=a+d_{Image}-d_{O,H.} \tag{7}\]  
\[d_{Image}\ :l=\left(d_{Image}+a+c\right):(l+l^{\prime}). \tag{8}\]  
The geometric relationships of the camera-equipped vehicle and lane in terms of the heading angle can be used to derive Equations (9)-(12).  
\[d_{left}=d_{left}^{\prime\prime}\times cos\ \psi \tag{9}\]  
\[d_{left}\prime=k\ \times cos\ \psi \tag{10}\]  
\[k\ :d_{left}\prime=\left(k+a+d_{f,wh}\right):d_{left}^{\prime\prime}\right. \tag{11}\]  
\[d_{left}\prime=L_{left}-\frac{1}{2}w_{car}. \tag{12}\]  
Equation (13) can be used to calculate the distance to the lead vehicle via application of Equations (1)-(12). In addition, Equations (14) and (15) can be used to determine the distances from the closest front tires to the left and right lanes, respectively, and Equation (16) can be used to obtain the distance to the center of the lane, \(d_{\text{center}}\), via combination of Equations (14) and (15).  
\[d_{front}=a-d_{O,H.}+(a+c)\frac{I_{2}-I_{1}}{I_{1}} \tag{13}\]  
\[d_{left}=\left\{2(a+c)\frac{I_{2,left}}{I}\ tan\ \frac{\theta}{2}-\frac{1}{2}w_{ car}\right\}cos\ \psi+\left(a+d_{f,wh}\right)sin\ \psi \tag{14}\]\[d_{right} =\left\{2(a+c)\frac{I_{2,\,right}}{I}\ tan\ \frac{\theta}{2}-\frac{1}{2}w_{car} \right\}\!\!\cos\psi-\left(a+d_{f,\,sub}\right)sin\psi \tag{15}\] \[d_{center} =\left\{(a+c)\frac{I_{2,left}-I_{2,\,right}}{I}\ tan\ \frac{\theta}{2}\ \right\}\cos\psi+\left(a+d_{f,\,sub}\right)sin\psi. \tag{16}\]  
The following distance between the vehicles can be maintained via longitudinal control on the basis of Equation (13), and lane keeping can be achieved via lateral control on the basis of Equation (16). Moreover, the proposed formulation can be used for safety and function evaluations of an HDA system relying on a monocular camera.
## 4 Research on the Effectiveness of Mineral Exploration
### Geology-Geoelectric Prospecting Model  
#### Indicators in Geological Exploration  
The Middle Devonian Xindu Formation (D2x) and Tangjiawan Formation (D2t) constitute the principal uranium-hosting strata in the study area. The F1 interlayer fracture zones and the secondary fractured zones--developed along their contact interface--serve as critical ore-bearing structures and primary migration pathways for mineralized fluids. Uranium mineralization bodies, strictly constrained by lithology and stratigraphic position, exhibit stratiform and lenticular geometries along these fractured zones. Three essential prospecting indicators for carbonaceous-silicocus-argillaceous uranium deposits in this region have been identified: 1 elastic rocks enriched with phosphates, vanadates, and Fe-Mn constituents, intercalated with variegated carbonate and silicicous lithologies; 2 interlayercompression fracture zones and cross-cutting fault zones; and 3 wall-rock alterations, including carbonatization, silicification, bleaching, and iron mineralization. These geological features collectively provide diagnostic criteria for carbonaceous-siliceous-pelitic uranium deposits exploration in the study area [40; 41].  
2.2 Geo-Electrochemical Extraction and Measurement Markers of Soil Ionic Conductivity and Thermally Released Mercury  
The geo-electrochemical extraction survey revealed distinct elemental anomalies of U, Th, W, Mo, As, and Pb with local overlaps, effectively indicating the presence of deep-seated concealed uranium mineralization. Th, as a decay product of U, shares similar geochemical affinities with its parent element, while Mo demonstrates the most effective geochemical signature for carbonaceous-silicous-argillaceous uranium mineralization [42]. Therefore, three critical prospecting indicators can be established: 1 U-Th-Mo anomalies as direct exploration criteria; 2 W-As-Pb anomalies exhibiting strong correlation with U as indirect indicators; and 3 coincident lambda-shaped soil ionic conductivity anomalies and rabbit-ear-shaped thermally released mercury anomalies serving as significant structural-geochemical markers (Figure 6) [43].  
Figure 6: Schematic diagram of the geological-geo-electrochemical prospecting model in the study area.
# An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds Environments
## 4 The Proposed Approach
### ACOSIMv2--Ant Colony Optimization for Sensor Selection Based on Similarity
#### 4.2.1 Ant Colony Optimization  
Ant colony optimization is a class of algorithms inspired by how some ant species forage for food [24]. The ants deposit a chemical substance, named pheromone, on the ground, which influences the choices they take. While spreading around the area, searching for food, the ants tend to use paths with higher pheromone concentration. Besides the complex behavior of foraging, other collective  
Figure 7: ACOSIM’s Flowchat.  
behaviors of real ants that have been proposed and applied include the division of labor, cemetery organization, brood care, and construction of nests. ACO has successfully been applied to several NP-hard optimization/combinatorial problems [52; 53]. The emergence of shortest-path selection in foraging behavior is explained by the differential path length effect and autocatalysis (positive feedback, reinforcement learning through pheromone deposit, etc.) [54; 55].  
The ACO algorithm involves two basic procedures:  
1. Procedure for building a solution. \(N_{A}\) (the number of ants) ants build \(N_{A}\) solutions to the problem.
2. Procedure for updating the pheromone concentration. The solutions are evaluated through an evaluation function in order to measure their quality. The update of the pheromone concentration is based on the evaluation function. Thus, better solutions cause more pheromone to be deposited in its region.
## 3 RS Applications in Ocean
### Ocean Wave Height (OWH)
#### 3.3.4. HF Radar  
HF radar is another RS system to measure OWH. This parameter can be calculated based on the first- and second-order sea surface scattering mechanism. Generally, OWH depends on the integral of the second-order part of the radar-measured Doppler spectra [180]. Beamforming is required to obtain the OWH over various ranges and azimuths. Thus, a narrow-beam system along with a large aperture is essential for retrieving a wave map using HF radars [181]. In this regard, [PERSON] et al. [182] proposed a method to calculate OWH based on the second-order harmonic peaks of radar Doppler spectra and its ratio to the Bragg peak power. The comparison of the results with buoy data over a 30-day experiment depicted that the RMSE was between 0.33 and 0.77 m. It is worth mentioning that the second-order scattering is vulnerable to the external clutters and noise which may reduce the robustness of the OWH measurement [183; 184; 185]. On the other hand, the first-order radar backscatter has stronger power compared to the second-order harmonic peaks. In this regard, [PERSON] and [PERSON] [186] proposed an empirical method to calculate OWH from the power of the first-order peaks. Subsequently, [PERSON] et al. [187] proposed a technique using the power ratio of the first-order peaks acquired at two radar frequencies to estimate OWH. By utilizing the first-order peaks instead of the second-order peaks to calculate the OWH,the detection range improves because of the high SNR value. Nevertheless, this method can be affected by low sea state conditions due to the saturation of the Bragg waves [182].
# Long-Term Visitation Value for Deep Exploration in Sparse-Reward Reinforcement Learning
## Appendix A Experiment Details
### Q-Learning with Augmented Reward  
This version follows the same scheme of Algorithm A1 with \(\epsilon\)-greedy exploration. The only difference is that the reward used to train the behavior policy is augmented with the exploration bonus proposed by [PERSON] and [PERSON] [30]. In our experiments, \(\alpha=0.1\), as used by [PERSON] and [PERSON] [30] and [PERSON] et al. [15].  
```
1 Initialize \(Q_{0}^{\beta}(s,a)\), \(Q_{0}^{\pi}(s,a)=0,n(s,a)=0,i=0\)
2 While \(i<i_{\textsc{weak}}\) do
3 Reset environment to state \(s_{1}\)
4 For \(t=1...H\) or until \(s_{t}\) is terminal
5 Select action according to the \(\epsilon\)-greedy policy of \(Q_{i}^{\beta}(s,a)\)
6 Transition to \(s_{t+1}=\mathcal{P}(s_{t+1}|s_{t},a_{t})\) and receive reward \(r_{t}=\mathcal{R}(s_{t},a_{t})\)
7 Update visitation count: \(n(s_{t},a_{t})\gets n(s_{t},a_{t})+1\)
8 Store tuple \((s_{t},a_{t},r_{t},s_{t+1})\)
9 For all tuples \((s,a,s^{\prime},r)\) in the replay memory
10 \(\delta^{\pi}(s,a,s^{\prime})=\begin{cases}r_{t}-Q_{i}^{\pi}(s,a)&\text{if $s$ is terminal}\\ r_{t}+\gamma\max_{a}Q_{i}^{\pi}(s^{\prime},a)-Q_{i}^{\pi}(s,a)&\text{otherwise} \end{cases}\)
11 \(Q_{i+1}^{\pi}(s,a)\gets Q_{i}^{\pi}(s,a)+\eta\delta^{\pi}(s,a,s^{\prime})\)
12 Augment reward: \(r_{t}^{+}=r_{t}+\alpha\,n(s_{t},a_{t})^{-1/2}\)
13 \(\delta^{\beta}(s,a,s^{\prime})=\begin{cases}r_{t}^{+}-Q_{i}^{\beta}(s,a)&\text{ if $s$ is terminal}\\ r_{t}^{+}+\gamma\max_{a}Q_{i}^{\beta}(s^{\prime},a)-Q_{i}^{\beta}(s,a)&\text{ otherwise}\end{cases}\)
14 \(Q_{i+1}^{\beta}(s,a)\gets Q_{i}^{\beta}(s,a)+\eta\delta^{\beta}(s,a,s^{ \prime})\)
15 Update budget counter: \(i\gets i+1\)
```  
**Algorithm A2**Tabular Q-Learning with Replay Memory and Augmented Reward
# Automated Evaluation of C-ITS Message Content for Enhanced Compliance and Reliability
## 2 Materials and Methods
### Types of C-ITS Messages  
C-ITS messages are critical components of the communication between vehicles, roadside infrastructure, and central systems. These messages serve various purposes, from enhancing safety to optimizing traffic flow and providing real-time information to road users. The types of messages used in C-ITS vary depending on their specific function within the system. Some of the key message types include:  
* Cooperative Awareness Messages (CAMs): provide real-time data about the position, speed, and status of vehicles.
* Decentralized Environmental Notification Messages (DENMs): alert vehicles and infrastructure about hazardous conditions or events.
* In-Vehicle Information Messages (IVIs): convey road sign information and other relevant data directly to drivers.
* Signal Phase and Timing Messages (SPaTs): communicate traffic signal status and timing to vehicles.
* MAP Messages: provide detailed descriptions of intersections, including lane configurations and connections.
* Signal Request Messages (SRMs) and signal Status Messages (SSMs): used for communication between vehicles and traffic signal controllers, often for prioritizing emergency vehicles or public transport.  
Among these, CAMs and DENMs are the most fundamental, as they enable vehicles and infrastructure to share crucial information that supports safe and efficient transportation. This study focuses specifically on the testing and validation of CAMs and DENMs.
# A Named Entity and Relationship Extraction Method from Trouble-Shooting Documents in Korean
## 4 Named Entity Recognition  
The named entities that were to be detected for equipment maintenance knowledge extraction from the generated tokens were defined as follows.  
1. PART: Equipment itself, or the components of the equipment (e.g., pressure gauge, valve, turbine, nozzle).
2. FAILURE: Equipment malfunction or failure mode (e.g., clogging, loosening, damaged, coarse surface).
3. ACTION: Actions that must be taken to resolve a failure mode (e.g., control, replace, disassemble).
4. (ETC): Words that refer to a specific object, but are not included in PART, FAILURE, or ACTION (e.g., supplier, pressure, air).
5. O: Words that are not included in PART, FAILURE, ACTION, or (ETC) (e.g., and, therefore).  
In order to extract the entities as defined in this section, the tokens from the pre-processed data were tagged using the BIO method. The BIO method attaches \"B-named entity\" to the beginning, \"I-entity name\" to the inside of entities, and \"O\" for non-entity tokens. A <BOS> (\"beginning of sentence\") tag was also attached in addition to the BIO tag to indicate the start of a sentence. Figure 2 shows a training data sample from an equipment maintenance document in Korean that was pre-processed and BIO-tagged.  
Figure 2: Entity name tagging example: (**a**) original equipment maintenance document example in Korean; (**b**) input examples for the NER model.  
The CRF model involves selecting the BIO tag with the maximum likelihood \(P(y|x)\), formally defined in Equation (1).  
\[P(y|x)=\frac{1}{Z(x)}\exp\Bigl{(}\sum_{t}\sum_{k}\lambda_{k}f_{k}(y_{t-1},\ y_{t},\ x)\Bigr{)} \tag{1}\]  
In this equation, \(\lambda_{k}\) represents the weight given to each feature during the learning phase; \(f_{k}(y_{t-1},\ y_{t},\ x)\) is a feature function whose value could be 0 or 1; \(Z(x)\) is a normalization factor; \(x\) denotes the sequence of words in a sentence of documents; and \(y\) denotes the BIO tag sequence.  
This paper used KLUE [8], which is a BERT model trained on Korean data that has demonstrated remarkable performance in natural language processing. The network takes tokens and BIO tagging values as sequential inputs, while training utilizes pretrained embedding values. The output layer contains a classifier that outputs a tag value for each token. Figure 3a shows a schematic of the model used in this paper, and Figure 3b shows a sample of a successful NER using the model trained on the test dataset.
## 3 Methodology
### Rnn  
RNNs are designed to model sequences in NNs with feedback connections. They are very powerful in computational analysis and are biologically more reliable than other NN techniques given their lack of internal states. The memory of past activations in RNN is very effective with feedback connections, making them suitable for learning the temporal dynamics of sequential data. RNN is very powerful when used to map input and output sequences because it uses contextual information. However, traditional RNNs face the challenge of exploding or vanishing gradients. [PERSON] and [PERSON] [PERSON] and [PERSON] (1996) proposed long short-term memory (LSTM) to tackle this issue.  
Hidden units in LSTM are replaced with memory blocks that contain three multiplicative units (input, output, forget gates) and self-connected memory cells to allow for reading, writing, and resetting through a memory block and behavioral control. A single LSTM unit is shown in Figure 5. \(c_{t}\) is the sum of inputs at time step \(t\) and its previous time step activations. LSTM updates time step \(i\) given inputs \(x_{t}\), \(h_{t-1}\), and \(c_{t-1}\) as reported in [PERSON] and [PERSON] (1996).  
Input gates:  
\[i_{t}=\sigma(W_{xi}\cdot x_{t}+W_{hi}\cdot h_{t-1}+W_{cl}\cdot c_{t-1}+b_{i}) \tag{3}\]  
Forget gates:  
\[f_{t}=\sigma\Big{(}W_{xf}\cdot x_{t}+W_{hf}\cdot h_{t-1}+W_{xf}\cdot x_{t}+W_{ cf}\cdot c_{t-1}+b_{f}\Big{)} \tag{4}\]  
Cell units:  
\[c_{t}=i_{t}\cdot tanh\Big{(}W_{xc}\cdot x_{t}+W_{hc}\cdot h_{t-1}+b_{c}+b_{f} \cdot c_{t-1}\Big{)} \tag{5}\]  
Output gates:  
\[o_{t}=\sigma(W_{x0}\cdot x_{t}+W_{ho}\cdot h_{t-1}+W_{co}\cdot x_{t}+b_{o}) \tag{6}\]  
The hidden activation (output of the cell) is also given by a product of the two terms:  
\[c_{t}=o_{t}\cdot\tanh(c_{t}) \tag{7}\]  
where \(\sigma\) and \(\tanh\) are an element-wise non-linearity, such as a sigmoid function and hyperbolic tangent function, respectively; \(W\) is the weight matrix; \(x_{t}\) refers to input at time step t; \(t\), \(h_{t-1}\) represents the hidden state vector of the previous time step; and \(b_{c}\)denotes the input bias vector. The memory cell unit \(c_{t}\) is a sum of two terms: the previous memory cell unit \(c_{t-1}\), which is modulated by \(f_{t}\) and \(c_{t}\), a function of the current input, and previous hidden state, modulated by the input gate \(i_{t}\) due to  
Figure 5: Structure of a memory cell in long short-term memory (LSTM)-RNN.  
and \(f_{t}\) being sigmoidal. Their values range within [0, 1], and \(i_{t}\) and \(f_{t}\) can be considered as knobs that the LSTM learns to selectively forget its previous memory or consider its current input, whilst \(o_{t}\) is an output gate that learns how much of the memory cell to transfer to the hidden layers.