esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Dentin hypersensitivity (DH) is characterized by brief and intense pain, occurring in teeth with exposed cervical dentin. This pain is triggered by different external stimuli, including thermal changes, cold air evaporation, tactile pressures (e.g., probing), electrical sensations, osmotic influences, or chemical exposures. Importantly, this pain cannot be attributed to any other dental pathology, defect, or disease [1, 2, 3, 4].  
A widely accepted theory that explains the sensation of pain resulting from external stimuli is the \"hydrodynamic theory\", initially proposed by [PERSON] et al. [5]. This theory suggests that the movement of fluids within dentin tubules is responsible for the sensation of pain [5, 6]. To induce DH, two surface changes must occur on the tooth: the exposure and removal of dentin due to receding gums, coupled with the loss of cementum, and the enlargement of dentin tubules [7, 8]. Gum recession and dentin exposure can arise from factors such as excessive or incorrect brushing techniques, as well as periodontal disease [7].  
Accurately diagnosing DH requires a substantial amount of time, as it involves obtaining a comprehensive dental history and relies on the process of eliminating other comparable conditions. When symptoms of DH are present, it is imperative to conduct a differential diagnosis in order to distinguish it from other forms of orofacial pain, such as pulp inflammation, periodontal discomfort, tooth cracking, microleakage, and atypical ootnalgia, among others [7; 8].  
Although DH is most frequently observed in premolars and canines, all teeth and surfaces are susceptible to experiencing hypersensitivity. The prevalence of DH has been documented to vary significantly across various studies, even reaching up to 100%. This variability arises from the diverse population groups studied and variations in assessment methodologies [9].  
Despite the numerous methods proposed for treating DH, a definitive solution to the issue has yet to be identified. Clinical studies evaluating the efficacy of different treatment approaches yield contradictory results [10; 11; 12]. This discrepancy stems from the fact that all these studies assess pain intensity, a parameter that proves notably challenging to evaluate due to its inherently subjective and intricate nature [13].  
Therefore, the objective of this literature review was to furnish dental practitioners with comprehensive information concerning DH. This includes outlining its etiology, diagnostic methods, and treatment options, while also addressing the effectiveness of the approaches employed to manage DH.
# Multi-Source Data Fusion for Vehicle Maintenance Project Prediction
## 3 Method  
### Notations  
To facilitate a comprehensive depiction of the maintenance protection prediction task at hand, we provide a set of notations in Table 1. The base information of a vehicle is represented by \(B=\{B_{1},B_{2},\ldots,B_{n}\}\), where \(n\) is the types of base information. During the vehicle maintenance process, numerous maintenance projects are necessary. All maintenance projects are systematically encoded, forming the set of project codes denoted as \(P=\left\{p_{1},p_{2},\ldots,p_{|P|}\right\}\), comprising \(|P|\) different project types. Each vehicle possesses multiple maintenance records, which encapsulate two essential pieces of information: the maintenance projects and the corresponding mileage. The maintenance projects in the \(t\)-th maintenance are defined as a multi-hot column vector \(V_{t}\in\{0,1\}^{|P|}\); \(V_{t}^{i}=1\) means that the maintenance project \(p_{i}\) was carried out at the \(t\)-th maintenance; \(t=1,2,\ldots,T\); and \(i=1,2,\ldots,|P|\). Multi-hot column vector is a type of binary vector representation where each element of the vector corresponds to a specific maintenance project. If a certain maintenance project is performed during a maintenance event, the corresponding element in the vector is set to 1; otherwise, it is set to 0. For example, if a maintenance record includes projects \(p_{1}\) and \(p_{3}\), the multi-hot vector could be [1, 0, 1,..., 0]. The mileage is denoted by \(\{M_{t}\}_{t=1,2,\ldots,T}\), with \(M_{t}\) representing the mileage achieved during the \(t\)-th maintenance, where \(T\) is the number of maintenance. In this paper, maintenance project prediction is based on the historical maintenance projects \(V_{1},V_{2},\ldots,V_{T}\); mileage data \(M_{1},\ M_{2},\ldots,M_{T}\); and base information \(B\). The objective is to predict the maintenance projects \(V_{T+1}\) for the \((T+1)\)-th maintenance.
## 2 Full-Scale Tests
### Measurements  
The measuring system contains GPS devices, pressure sensors, shield signal wires, multi-channel amplifiers and A/D converters. Before the test, the local time recorded by the IMC data acquisition is standardized by the GPS devices. Pressure sensors produced by Endevoco and Head-Artemis are utilized (see in Figure 2) to detect TPL acting at tunnel surface and MPW emitted at tunnel portal, with measuring range respectively being \(-8.0\)-\(8.0\) kPa and \(-500.0\)-\(500.0\) Pa. Along the longitudinal direction, two set measuring points are respectively mounted at \(x_{itin}\) = 200 m away from the tunnel entrance and tunnel center (\(x_{itin}\) = 314 m). Besides, each set holds two measuring points respectively installed at \(z_{itin}\) = 1.2 m and 2.0 m. Outside the tunnel, two pressure sensors are mounted at the distance of 20 m and 50 m away from the tunnel exit. Measured values will be captured by the IMC data acquisition system at the collecting frequency being 1000 Hz for TPL and 500 Hz for MPW. Each test was repeated at least twice to ensure the reliability of the measurements. As shown in Figure 3, pressure history for a given location shows very good agreement, indicating the test data being reliable.
# Bearing Behavior of Cold-Formed Thick-Walled Steel Plates with a Single Bolt
## 2 Experimental Investigation  
### Subsection Specim Preparation  
Single-bolt connections in double shear were investigated in the tests. In total, 54 tests were conducted, and Table 1 lists the main parameters of test specimens. The tests were designed to investigate the effects of different end/edge distance combinations on failure modes of connections. These tests used 24 mm bolts with 26 mm bolt holes. Grade 8.8 and 10.9 bolts were applied to connect the test specimens with 6 mm and 10 mm thick steel plates, respectively. Figure 1 is the schematic diagram of the specimen.  
The specimen ID is expressed by the type of shear test, the thickness of steel plate, the ratio of end distance to bolt diameter, and the ratio of edge distance to bolt diameter. For example, D6.0-1.0-3.0 refers to double shear test (D), steel plate thickness of 6.0 mm, end distance \(e_{1}=1.0d_{0}\), and edge distance \(e_{2}=3.0d_{0}\). The remaining specimens were numbered the same way.  
Steel plates used in the connections were taken from the middle of flat parts of hollow tubes that were cold-formed, with structural steel sheets with a nominal yield strength of 235 MPa. The nominal dimensions of hollow tubes were 200 \(\times\) 200 \(\times\) 10 mm and 150 \(\times\) 150 \(\times\) 6 mm, and the inside radii of rounded corners were 8 mm and 6 mm, respectively. The plates were all cut in the longitudinal direction.
## 3 Design and Implementation of the Smart Campus System
### LoRaWAN Testbed Implementation  
A LoRaWAN network consisting on a gateway and several nodes was deployed. A RisingHF RHF0M301 module [108] installed on a Raspberry Pi 3 was selected as gateway. Such a module is equipped with a dual digital radio front-end interface with a typical sensitivity of \(-137\) dBm. The module is capable of simultaneous dual-band operation and supports Adaptive Data Rate (ADR), automatically changing between LoRa spreading factors. It can use a maximum of 10 channels: 8 multi Spreading Factor (SF) channels (SF7 to SF12 with 125 KHz of bandwidth), one FSK channel and one LoRa channel. Another interesting feature of the module is its ability to operate with negative Signal-to-Noise Ratios (SNRs), with a Co-Channel Rejection (CCR) of up to 9 dB. It also supports LoRaWAN classes A, B, and C and its maximum output power is 24.5 dBm.  
A 0 dBi antenna was used for the tests. The module is installed on a Raspberry Pi 3 using the provided bridge (RHF4T002). To access the LoRaWAN RFID module, configure the node, and access the message and the transmission parameters, the following software was installed on the Raspberry Pi 3: Raspbian Stretch (Linux raspberrypi 4.14.70-v7+), LoRa Gateway v5.0.1, LoRa Packet Forwarder v4.0.1 and LoRaWAN-Server v0.6.0.  
The LoRa gateway was configured as follows:  
* Coding Rate: 4/5.
* RX1 delay: 1.
* RX2 delay: 2.
* Power: 14 dBm.
* RX Frequency: 869.5 MHz.  
A RisingHF RHF76-052 module [109] was used by the LoRaWAN nodes (one of such nodes can be observed on the right of Figure 3). The module has a maximum sensitivity of \(-139\) dBm with spreading factor 12 (SF12) and 125 kHz of bandwidth, channels (0-2) at 868.1, 868.3 and 868.5 MHz, and a maximum output power of 14 dBm. During the experiments presented in this paper, the module made use of the pre-installed wired antenna.
Therefore, the existing carrier loop performance needs to be improved for better performance in indoor weak signal environment. Currently, the methods to improve the carrier loop performance are divided into three categories: the KF-based PLL [19], the algorithms based on the Maximum likelihood estimation (MLE) algorithm [20], and the FLL-assisted PLL [21]. The KF-based PLL includes Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) algorithms [22; 23]. The observation model in the carrier loop is nonlinear, which means the observed signal vector and the estimated state vector are nonlinear in the carrier loop. The EKF and UKF are utilized to expand the nonlinear observation functions using Taylor series and keep the linear part of the function while ignoring the higher order nonlinear part. However, the EKF and UKF based on linear minimum mean square error estimation criterion represent a sub-optimal carrier loop scheme, which still produces a large parameter estimation error for weak received signals. Methods based on FLL-assisted PLL can ensure the dynamic of the carrier loop but the estimation accuracy is limited in a weak signal environment [24]. Reference [25] uses the MLE to adjust the Doppler frequency in a high dynamics receiver carrier loop, but it does not make a detailed analysis for weak received signals. Iterative and non-iterative MLE are proposed to estimate the signal Doppler frequency and code phase in [26; 27]. These two algorithms need to deal with real-time IF data, which means a heavy calculation burden. This paper proposes a novel carrier loop algorithm based on MLE and KF for weak TC-OFDM signals. The main idea is to combine the MLE with KF to effectively estimate the carrier loop parameters and smooth the estimation results, so as to obtain an accurate estimation of the residual carrier for weak TC-OFDM signals and improve the receiver tracking sensitivity indoors. With the novel algorithm, the MLE discriminator function replaces the current carrier loop discriminator. The Levenberg-Marquardt (LM) algorithm is utilized to obtain the MLE cost function consisting of signal amplitude, residual carrier frequency and carrier phase, and the MLE discriminator function is derived from the corresponding MLE cost function. The KF is used to smooth the MLE discriminator function results, which takes the carrier phase estimation, the angular frequency estimation and the angular frequency rate as the state vector. The rest of this paper is organized as follows: Section 2 presents the signal model. The proposed carrier loop algorithm is introduced in Section 3. Section 4 presents the simulation and real data test results of the comparison between different carrier loop algorithms. Finally, Section 5 concludes the work.
# Monitoring Safron Crops with UAVs
## 3 Field Study Design Methodology
### Estimations  
Estimating cultivation production is one of the most important issues for agricultural management and one of the areas in which precision farming techniques can offer the greatest benefit. The estimation step enables the visualization of the whole field in a user-friendly modelized image where the cultivator can immediately locate the mouse traces, the weeds to be harvested, and a percentage of the estimated production per day in the cultivation.  
The method used in the present study aims to create images with distinct objects which are the result of the predictions of the machine learning models. Based on the predictions made by the model for each pixel of the orthophoto of the field, an image marked with different colors for each category is created. In this way, saffron crops, weeds, and mouse traces are located and estimated for the cultivation features to become better perceived. Figure 5 presents the initial RGB orthophoto obtained from a field (Figure 5a) and the output of the estimation model for the particular field (Figure 5b). In the predicted image (Figure 5b), the estimated values are depicted with different colors, e.g., red for the saffron flower, green for the weeds, blue for the mouse tracks, and black for pixels associated with the soil class (anything not belonging to the three classes).
## 3 The Proposed Model
### Steps Involved in CMLOA-NLA Technique  
The CMLOA-NLA algorithm involves the following steps to localize the sensor in WSNs. \(N\) AN and \(M\) TN are randomly placed from the device part [28]. All the ANs are spatially localized and helped to identify the residence of other nodes. All the targets and ANs comprise the transmission range \(R\).  
(1) The distance between the ANs and TNs is altered and evaluated using preservative Gaussian noise. The TN is used to control distance as \(\hat{d_{i}}=d_{i}+n_{i}\), where \(d_{i}\) signifies the actual distance, viz., calculated amongst the place of beacon \((x_{i},y_{i})\) and the place of TN \((x,y)\). It is defined in Equation (13).  
\[d_{i-}\sqrt{\left(x-x_{i}\right)^{2}+\left(y-y_{i}\right)^{2}}. \tag{13}\]  
The parameter \(n_{j}\) controls the noise that traces the evaluated distance in \(d_{i}\pm d_{i}(P_{n}/100)\), where \(P_{n}\) signifies the sound connection within the predicted distance.  
(2) The chosen node is called a localizable node once it takes three ANs in the communication radius (CR) of TN.  
(3) For the localized node, the CMLOA-NLA method was separately executed to identify the place of TN. The CMLOA-NLA technique is implemented by the centroid of AN inside a CR, as provided in Equation (14).  
\[\left(x_{c},y_{c}\right)=\left(\frac{1}{N}\sum_{i=1}^{N}x_{i},\frac{1}{N}\sum _{i=1}^{N}y_{i}\right) \tag{14}\]  
Now \(N\) shows the entire AN counts in the communication range of restricting TNs.  
(4) The CMLOA-NLA method is used to detect the \((x,\text{ and }y)\) coordinates as TN which decreases the localized error. The primitives applied in the localized problem are a mean 4-sided detachment amongst TN as well as AN, as given in Equation (15):  
\[f(x,y)=\frac{1}{N}\left(\sum_{i=1}^{N}\sqrt{\left(x-x_{i}\right)^{2}+\left(y-y _{i}\right)^{2}}-\hat{d}\right)^{2} \tag{15}\]  
where \(N\geq 3\) signifies the AN counts are confidential and a transmission radius of TN.  
(5) When the maximal repetition counts are acquired, then optimal location coordination \((x,y)\) is defined by the CMLOA-NLA technique.  
The localizing error is defined after calculating the localized TN \(N_{L}\). It could be assessed as a mean 4-sided of coldness in the node \((X_{i},\text{ }Y_{i})\) coordinates in the real node \((x_{i},y_{i})\) coordinates, as given in Equation (16).  
\[E_{1}=\frac{1}{N_{1}}\sum_{i=1}^{N}\sqrt{\left(x_{i}-X_{i}\right)^{2}+\left(y _{i}-Y_{i}\right)^{2}}. \tag{16}\]  
(6) Steps 2-6 are reiterated until the TN can be localized. The localization technique is based on the mistake-controlled \(E_{1}\), and the amount of unlocalized bulges \(N_{N_{L}}\) is determined as \(N_{N_{L}}=M-N_{L}\). The lowest score of \(E_{1}\) and \(N_{N_{L}}\) shows a restricted approach.
## 3 ML for SatCom RRM: Architecture and Techniques
### Reinforcement Learning for RRM
#### 3.3.3 Double Deep Q Learning  
Each agent in DDQL uses two neural networks with the same architecture to learn and predict what action to perform at each step (Figure 8c). A DDQL model includes two deep learning networks, called generated Q-network \((Q_{b}(s^{b}_{t},a^{b}_{t},\omega_{t}))\) and target Q-network \((Q_{b}(s^{b}_{t},a^{b}_{t},\omega_{t}^{-}))\), where \(\omega_{t}^{-}\) includes the parameters of the current target neural network.  
DDQN can produce more accurate value estimates and leads to better overall deep neural network performance. The generated Q-network is used to generate actions, and the target Q-network is used to train from randomly selected observations from the replay memory. The replica memory of the DDQN stores the state transitions received from the environment, allowing the reuse of these data. By randomly sampling it, the changes forming a batch are related to the decorrelation, stabilizing DDQN. The generated Q-network is used to calculate the \(b\)th value \(Q_{b}\) for the \(b\)th agent. At the same time, the target Q-network aims to produce the target \(Q^{\prime}_{b}\) value to train the parameters of the generated Q-network. According to the basic idea of DDQL, the target value \(Q^{\prime}_{b}\) can be defined as follows.  
\[Q^{\prime}_{b}=r_{t}(s^{b}_{t},a^{b}_{t},\omega_{t-1})+\gamma Q_{b}(s^{b}_{t+1 },\arg\max_{a^{b}_{t}}Q_{b}(s^{b}_{t+1},a^{b}_{t},\gamma_{t-1}),\omega_{t}^{-}) \tag{15}\]
# Simulation of Test Arch Based on Concrete Damage Plasticity Model and Damage Evolution Analysis
## 3 Concrete Plastic Damage Model
### CDP Model Parameterization  
ABAQUS requires input for the stress-strain relationships of \(\sigma_{t}-\bar{\epsilon}_{t}^{ck}\) and \(\sigma_{c}-\bar{\epsilon}_{c}^{ck}\). However, effective experimental data are often elusive when employing this model. Consequently, actual structural analysis typically incorporates the concrete design code [20]. This integration facilitates specific calculation methodologies.  
Regarding the linear stress-strain relationship within the elastic phase, the modulus of elasticity \(E_{0}\) can be inferred from the crack initiation slope depicted in Figure 3, which is mathematically expressed as follows:  
\[E_{0}=\frac{f_{tk}}{\varepsilon_{tk}} \tag{2}\]  
Among them, \(f_{tk}\) is the standard value of uniaxial tensile strength, which can be obtained directly from the field specimen test under the condition; \(\varepsilon_{tk}\) is the corresponding strain value, and the recommended calculation formula is \(\varepsilon_{tk}=f_{tk}^{0.54}\times 65\times 10^{-6}\).  
The inelastic stress-strain relationship is specified as follows:  
When pulled:  
\[\begin{cases}y=1.2x-0.2x^{6}&x\leq 1\\ y=\frac{x}{\alpha_{t}(x-1)^{1/2}+x}&x>1^{,}\end{cases}x=\varepsilon/\varepsilon _{tk}\,,y=\sigma/f_{tk} \tag{3}\]  
When pressurized:  
\[\begin{cases}y=\frac{nx}{n-1+x^{n}}&x\leq 1\\ y=\frac{x}{\alpha_{c}(x-1)^{2}+x}&x>1^{,}\end{cases}x=\varepsilon/\varepsilon _{ck}\,,y=\sigma/f_{ck},n=\frac{E_{c}\varepsilon_{ck}}{E_{c}\varepsilon_{ck}- f_{\sigma}} \tag{4}\]  
Among them, \(f_{ck}\) is the standard value of uniaxial compressive strength; \(\varepsilon_{ck}\) is the corresponding strain value, and the recommended calculation formula is \(\varepsilon_{ck}=(700+172\sqrt{f_{ck}})\times 10^{-6}\); \(\alpha_{t}\) and \(\alpha_{c}\) are the parameter values of the descending section of uniaxial tensile and compressive stress-strain curves, respectively, and the corresponding values are shown in the specification.  
Figure 5: Variation of stiffness under cyclic loading for the CDP model [18].  
In the CDP model, it is necessary to specify the damage factors \(d_{t}\) and \(d_{c}\), which can be derived from Equation (2) and introduced into the scale factor \(\eta\):  
\[\left\{\begin{array}{l}d_{t}=\frac{(1-\eta_{t})\frac{\eta_{c}^{2 \eta_{c}}E_{0}}{\eta_{c}-(1-\eta_{t})\eta_{c}^{2\eta_{c}}E_{0}}}{\eta_{c}-(1- \eta_{t})\frac{\eta_{c}^{2\eta_{c}}E_{0}}{\eta_{c}-(1-\eta_{c})\eta_{c}^{2\eta _{c}}E_{0}}}\end{array}\right. \tag{5}\]  
among them, \(\eta_{t}\) was 0.9 and \(\eta_{c}\) was 0.6.  
From the above, the calculated parameters of the test arch concrete CDP model are shown in Tables 3 and 4.
# Performance Analysis of P2P Networks with Light Communication Links: The Static Managed Case
## 4 LiFi- and WiFi-Based Communication P2P Architectures
For the case of WiFi-based networks, we could assume that all nodes inside the transmission range are reachable by a given peer in the network, i.e., nodes do not require a strict alignment, as in the case of LiFi-based transmissions. Nevertheless, sometimes nodes cannot reach all nodes in the network due to obstacles, power restriction, and propagation of the signals. In general, the transmission range is adjusted in such a way as to avoid interference among neighbor systems or clusters, and also to provide an adequate service in terms of bit error rate and data rate transmissions. This WiFi scenario is illustrated in Figure 7.  
It is important to mention that in centralized networks, unlike P2P networks, all clients are reachable from the server whose capacity allows for connecting and sharing information with up to four nodes at the same time, allowing information to be propagated through the network directly from the server. Figures 7-9 outline scenarios for the proposed LiFi and WiFi networks, stressing that for centralized LiFi 2.0 and WiFi 7 networks, a single server is used. Figure 6: P2P-LiFi NLOS propagation scenario: nodes in level 1 (green nodes) can download information from upper nodes in level 0 (blue nodes) or upload information to immediate lower nodes in level 2 (yellow nodes) and upload data from the peers in the same level (red nodes). Figure 7: In the proposed P2P-WiFi scenarios, a node that acts as a server (green) can connect with the nodes that are within its coverage radius (blue) but not with nodes that are out of its range (gray nodes). It is important to mention that for the LiFi case, and in order to have an adequate comparison with WiFi-based networks, we consider a fixed number of nodes without mobility to provide the required alignment in light-based communications. In the literature, it is common to consider an infinite population model where nodes can enter and leave the system. Hence, we develop a new mathematical model considering these restricted mobility conditions where the system administrator initiates the file-sharing procedure in the fixed population network.