esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 RANKL Produced by PDL Cells and Cementoblasts in ORR  
Previous studies have reported that excessive compressive force enhanced PGE2, IL-1, IL-6, and TNF-\(\alpha\) expression in PDL cells [23; 24; 25; 26]. These factors stimulate RANKL expression in hPDL cells. Therefore, it can be concluded that PGE2, IL-1, IL-6, TNF-\(\alpha\), and RANK play important roles in the orthodontic tooth movement [27; 28; 29]. Furthermore, [PERSON] et al. [22] reported that compressive force resulted in RANKL expression and caused an increase in osteoclastogenesis in vitro. According to [PERSON] et al. [28], excessive orthodontic forces stimulated the process of ORR via RANKL and IL-6 expression in hPDL cells. Therefore, PDL cells that are exposed to excessive compressive forces express inflammatory cytokines, which exacerbates the process of ORR.  
Recent studies have demonstrated that both PDL cells and cementoblasts express RANKL. [PERSON] et al. [30] demonstrated that IL-1\(\beta\) and compressive forces induced significant RANKL expression in cementoblasts. [PERSON] et al. [31] and [PERSON] et al. [32] reported that exposure of cementoblasts to excessive orthodontic forces leads to the expression of large amounts of RANKL and IL-6, which could induce ORR; therefore, OIIRR is caused by both PDL cells and cementoblasts. In addition, [PERSON] et al. [22] reported that ORR could be due to both PDL cells and cementoblasts, probably via RANKL expression in these cells (Figure 3).  
Figure 3: The role of RANKL in ORR.
# An Image-Based Fall Detection System for the Elderly
## 2 Related Works
### Environmental-Sensor-Based Methods  
Several research efforts have been made to detect falls by environmental sensors, such as pressure, acoustic, and radar sensors. [PERSON] et al. [4] used a smart floor that was embedded with pressure-sensitive fiber sensors to detect fall events by feature-specific pressure images containing motion features for human activity analysis. The system could be set up indoors, even in the bathroom. Similarly, [PERSON] et al. [19] used force sensors and accelerometers that were concealed under intelligent tiles to locate, track, and recognize user activities, such as walking, standing, sitting, lying down, and falling. However, the proposed method was costly and could only be used indoors.  
[PERSON] et al. [20] proposed a method, named acoustic-FADE, that detected falls by acoustic sensors. The system consists of a circular microphone array that can capture sounds indoors. When a sound is detected, the system locates the source, enhances the signal, and classifies the event as a \"fall\" or a \"non-fall\" by machine learning approaches. However, it is unrealistic to set up a circular microphone array in each and every room. In addition, the classification becomes challenging when there is more than one person in the room.  
[PERSON] et al. [21] detected human falls by using a ceiling-mounted Doppler range control radar. Due to the Doppler effects, the radar senses any motions from falls as well as non-falls. They used wavelet transform (WT) coefficients at several scales over continuous frames to form a feature vector for the \"fall\" versus \"non-fall\" classification. Similarly, [PERSON] et al. [22] proposed a system based on a microwave Doppler sensor. The system calculates the trajectories of the frequency distributions that correspond to the velocities of the movements while falling and classifies the events by a hidden Markov model. However, a signal's analysis can be easily affected by the number of people inside the room.  
[PERSON] et al. [23] designed WiFall, which takes advantage of the physical layer channel state information (CSI) in WiFi infrastructure. WiFall can also effectively recognize common daily activities, such as walking, standing up, sitting down, and falling. [PERSON] et al. [24] proposed a similar method, named RT-Fall, to exploit CSI in WiFi devices to improve WiFall. However, neither WiFall nor RT-Fall can work if there is more than one person in the room. [PERSON] et al. [25] used thermal imaging sensors to differentiate between the normal activity and the falling activity in a toilet room where the temperature should be less than 31 \({}^{\circ}\)C. However, the applicability of the proposed method may be limited due to privacy issues.  
In summary, the accuracy of environmental-sensor-based methods can be easily affected by objects in the environment. In addition, pervasively deploying sensors is costly and impractical.
# Calculation of Guided Wave Dispersion Characteristics Using a Three-Transducer Measurement System
## 5 Finite Element Analysis
Overlapping of pulses is an issue for this technique, as it changes the phase and waveform of the analyzed mode, highly distorting the results and impeding its application. The results for the phase velocity are better than for the group velocity, more notably when overlapping occurs. This is because of the phase-shift term in the denominator in (9). This contribution in the phase velocity equation minimizes the erroneous calculated value of the propagation time and  
Figure 11: Comparison of the phase velocity and group velocity of S\({}_{0}\), A\({}_{0}\) and SH\({}_{0}\) between the results from the synthesized signals (black circles) and theoretical values (grey lines). Figure 10: Signal acquired at 33 cm from excitation point at 60 kHz. also provides more resolution on the calculation of the phase velocity, removing the stepped shape seen in the group velocity.
# Detection of Suicide Ideation in Social Media Forums Using Deep Learning
## 5 Experimental Results
### Data Analysis Results  
To compare dissimilarities in the lexicon, we examine the entire dataset to investigate the presence of suicidal thoughts. We compute the frequencies of all the unigrams and bigrams in both suicide-indicative posts and non-suicidal posts. We select the top 200 unigrams and bigrams from each category to examine their nature and connection with suicide ideation. We use a visual support of the word cloud. We support the analysis with top 20 most frequent n-grams in both dataset categories. Figures 3 and 4 present the top 200 unigrams and bigrams for both datasets generated in our experiment. Emerged words indicated by a high frequency are illustrated in both figures. Examining the posts from the SuicideWatch forum, we identify the features with a suicidal intent align with the findings supported in suicide literature [2]. Specifically, we observe evidence of manifestation of hopelessness and frustration (\"fucking life\", \"tired living\", \"hate tired\", \"I'm tired\"), anxiety (\"I'm afraid\"), sense of guilt (\"I am sorry\"), regret (\"never again\"), signs of loneliness (\"no friend\"). Concerning the mental focus of the users, we identify the self-oriented references and attention turned towards themselves (\"I'm\", \"I'm not\", \"I've never\"). This result is supported by [81]. After that, we detect the user's tendency for the preoccupation with their feelings (\"feel like\", \"make feel\"), strengthened by the words of negation (\"no one\", \"anymore\", \"I've never\", \"would never\"). In the suicide-indicative posts, we observe quite a high frequency of question marks (\"Concerned but don't know what to say?\", \"Why is mankind afraid of death?\"). It might originate in a frequent usage of rhetorical questions to emphasize the ideas consciously and intensify the sentiment [11].  
Another interesting observation is found in users' depiction of suicidal tendencies. It is mostly expressed by the words with death connotations (\"suicide\", \"want die\", \"die fucking\", \"suicide wish\", \"wish die\", \"want kill\", \"want end\", \"want go\"). Furthermore, sense of urgency and a manifestation of hopelessness is also visible (\"hope help\", \"life hope\", \"help ended\").  
In contrast to the suicide-indicative posts, the unigrams and bigrams examined in the non-suicidal posts contain predominantly the words describing happy moments, positive attitude and feelings (\"want joke\", \"want fun\", \"go out\", \"laugh thought\", \"want happy\"). The users have a tendency to strive towards maintaining positive spirits (\"get better\"). They often mention social relations activities (\"best friend\", \"high school\") or express their feelings (\"make feel\", \"feel like\").
# Intelligent Handover Decision-Making for Vehicle-to-Everything (V2X) 5G Networks
## 4 Performance Evaluation
### Performance Measures
#### 4.2.3 Latency  
In the context of V2X communications, latency plays a vital role in enabling real-time safety applications [36] and ensuring service continuity. The reduction in the packet loss ratio, as discussed earlier, can directly affect the E2E latency. However, it can be subject to significant QoS changes owing to the high mobility of the UE. The latency model for 5G V2X E2E for time-critical applications deployment should consider variations in traffic load and transmission delay [37]. In this study, the latency is modeled by considering the queuing delay \(D_{q}\), propagation delay \(D_{p}\), and handover delay \(D_{h}\). The queuing delay is the time taken by the packets waiting in the queue before transmission.  
The calculation of the total end-to-end latency (\(L_{total}\)) of \(n\) packets is subject to minimizing the following parameters, as expressed below:  
\[L_{total}=D_{q}+D_{p}+D_{h} \tag{7}\]  
where\(D_{q}\) represents the Queue delay(s) of 5 ms.
* \(D_{p}\) represents the Propagation delay(s) = distance/speed of light.
* \(D_{h}\) is the handover delay(s), radio resource control time+ path switch time+ procedure time.
## 1 Introduction  
Matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) is a soft ionization method used to determine the molecular weight of an analyte. MALDI MS enables us to detect nonvolatile compounds, such as proteins and peptides, which are difficult to detect by other methods [1]. Some organic acids--such as \(\alpha\)-cyano-4-hydroxycinnamic acid (CHCA), sinapinic acid (SA), 2,5-dihydroxybenzoic acid (DHB), and 2,4,6-trihydroxyacetophenone (THAP)--are often used as the matrix. However, these organic matrices dissociate during the desorption/ionization process, thereby limiting the application of MALDI MS to low molecular weight compounds. In addition, the peak intensity of a protonated analyte is sometimes suppressed by an alkali metal ion conducted species. In order to overcome those drawbacks, several matrix-free techniques have been developed, including silicon nanowire (SiNW) [2], desorption/ionization on silicon (DIOS) [3; 4], and nanostructure-initiator mass spectrometry (NiMS) [5]. The application of nanometer-sized particles--including Au [6], Ag [7], Pt [8], and Si [9]--instead of an organic matrix has also been proposed. In addition, the use of a co-matrix consisting of ammonium salt [9], phosphoric acid [10], serine [11], saccharide [12], and protein [13] with conventional organic matrix molecules is considered an effective way to improve the mass spectra as well.  
Generally, it is known that fatty acids are difficult to ionize by MALDI MS using conventional organic matrix molecules. In our previous research, we revealed that CdTe nanoparticles ionizedfatty acids efficiently [14]. Electrons ejected from CdTe nanoparticles by Auger recombination after photoexcitation interacted with fatty acids to ionize them into carboxylate anion (-RCOO\({}^{-}\)) as RCOO\({}^{-}\) was stabilized by resonance structures. In addition, CdTe nanoparticles were loaded on a zeolite surface to improve reproducibility of the peak intensity, and the complex of CdTe and zeolite was applied to the quantitative analysis of stearic acid in human serum.  
Because it is understood that electrons ejected from CdTe nanoparticles react with carboxyl groups in fatty acids, we assumed that other compounds containing carboxyl groups could be ionized as well. In this study, CdTe was used to ionize such compounds as glucuronic acid (Glu), salicylic acid (Sal), ibuprofen (Ibu), tyrosine (Tyr), and model peptides, the structures of which contain carboxyl groups. As electrons ejected from a semiconductor are necessary for the ionization, we assumed that another semiconductor (CuO) would be also applicable to the ionization of the above-mentioned compounds. The purposes of this paper are to apply CdTe or CuO nanoparticles to the ionization of those molecules and to clarify the possible desorption and ionization mechanisms after the photoexcitation.
# Forecasting the Spread of COVID-19 in Kuwait Using Compartmental and Logistic Regression Models
## 2 Methods  
Previous studies have established the effectiveness of mathematical modeling in predicting the spread of various diseases such as Ebola and Human Immunodeficiency Virus (HIV) as well as the novel coronavirus COVID-19 [10; 11]. Numerous epidemiological modeling approaches have been discussed in literature including the logistic growth model, deterministic compartmental models (DCM) and agent-based models (ABM). Deterministic compartmental models (DCM) are nonlinear models of differential equations that provide analytical epidemic predictions based on the input parameters. The classical DCM, such as susceptible-infected-recovered (SIR) model, assumes a homogenous and well-mixed population. On the other hand, agent-based modeling (ABM) is an approach to simulate the interactions between individual agents and a group of agents. Therefore, ABM simulations include heterogeneous attributes among individual agents. ABM allows for defining complex networks of interactions for a more realistic representation of epidemic situations. However, one major drawback of ABM is that it requires more detailed information of the dataset and would significantly increase computational complexity. In addition, the interpretation of ABM behavior to its structure tends to be more challenging with sophisticated details and networks [12]. Thus, ABM predictions are useful when detailed data is available and is stable over the required forecast timeframe. In early-stage epidemic modeling, where data is limited, the importance of choosing a modeling approach depends on the purpose. Unlike ABM, the SIR modeling approach requires less data with reduced computational requirements which makes it particularly more useful for an early-stage epidemic estimation. A recent study reported that the SIR model provides sufficient and reliable predictions in estimating the early-stage epidemic [13]. In addition, the authors further indicated that complex modeling approaches might not be as reliable as simpler models when data is limited. SIR model consists of divided compartments of susceptible-infected-recovered to forecast the disease spread based on the actual infected cases per day. Hence, the logistic growth regression model is used for fitting the actual infected cases and the growth of the infection per day. In this section, the SIR and the logistic growth models are utilized to estimate the epidemic size of the infected population from the period of 24 February 2020 until 19 April 2020. Furthermore, stochastic simulations were carried out to address the simultaneous changes of the susceptible and infected number of individuals at a given time. The results were analyzed to provide insightful predictions of
# Numerical Simulation on Dynamic Response of Drilling Parameters in Loaded Rock Mass
## 3 Establishment of Numerical Model
### Analysis of Drilling Parameters Under Different Revolution Speed Conditions
Because the slope of time-drilling depth reflects the drilling rate in the physical sense, this phenomenon shows that the drilling rate increases with the increase in the revolution speed. In Figure 13a, it can be seen that the drilling rate also shows an upward trend with the increase in the revolution speed in the same rock, which also shows that the revolution speed and the drilling rate are positively correlated. Through the analysis of Figure 9c, Figure 10c, Figure 11c, and Figure 12c, it can be seen that the torques of coal, mudstone, sandy mudstone, and siltstone are 51.84 N\(\cdot\)m, 56.96 N\(\cdot\)m, 64.91 N\(\cdot\)m, and 72.50 N\(\cdot\)m, respectively, at a low speed of 60 r/min. At this speed, the torque growth is gentle. This is because, at the moment of start-up, the bit is just in contact with the rock surface, and it is necessary to gradually overcome the initial static friction force of the rock. In this process, the speed is relatively slow, making the torque growth rate more gentle; until the torque reaches the first peak, the variation range of the torque is limited to a small range, showing relatively gentle fluctuation characteristics. When the speed is 120 r/min, the torques of coal, mudstone, sandy mudstone, and siltstone are 48.95 N\(\cdot\)m, 51.85 N\(\cdot\)m, 63.04 N\(\cdot\)m, and 67.47 N\(\cdot\)m, respectively. At this speed, the initial fluctuation range for the torque is large. Compared with the case of 60 r/min, the higher speed gives the drill a stronger initial impact force, which can quickly break the initial resistance of the rock surface and break the rock strongly, making the torque explode instantly and grow rapidly. However, due to the higher speed, the cutting frequency is more efficient, and the torque fluctuation range increases when the rock knot is encountered. At a high revolution speed of 240 r/min, the torques of coal, mudstone, sandy mudstone, and siltstone are 44.28 N\(\cdot\)m, 47.41 N\(\cdot\)m, 58.01 N\(\cdot\)m, and 61.13 N\(\cdot\)m, respectively. At this  
Figure 13: Variation curves of torque and drilling rate with revolution speed. (**a**) Different rock revolution speed–drilling rate change curve; (**b**) different rock revolution speed–torque change curve. speed, the torque cuts rapidly and fluctuates violently. The high speed gives the drill a higher kinetic energy, which can quickly break the rock, instantaneously producing a huge crushing force, and the torque also rises sharply. From the above analysis and Figure 13b displaying the speed-torque curve, it can be clearly seen that there is a significant negative correlation between speed and torque. The specific performance is that with the continuous increase in speed, the torque shows a corresponding decreasing trend.
# Study on Disturbance Mechanism of Squeezed and Non-Squeezed Soil Piles on Soft Soil Foundation
## 3 Methodologies
### Bored Pile  
(1) Mechanism  
The construction process of a bored pile includes several stages, such as drilling, placing a reinforcement cage, and pouring concrete (see Figure 5). Unlike the two previously mentioned pile types that cause soil squeezing, the bored pile generates significant disturbance to the surrounding soil during the drilling stage, which is caused by the soil unloading effect. Therefore, to investigate the soil disturbance effect caused by the bored pile, a simulation of the drilling process is conducted.  
(2) Assumptions  
The simulation process is based on the theoretical foundation of circular hole contraction. Under natural stress conditions, the soil elements inside the hole are gradually closed from the top to the bottom. Simultaneously, static water gradient pressure is applied to the hole wall to simulate the support of mud on the hole wall. This approach enables the simulation of the contraction behavior of the drilling hole under the combined effect of natural stress and mud pressure. The simulation method is based on the following assumptions:  
a. Ignoring the infiltration of slurry moisture.  
b. Ignoring the vibration effects generated by the drilling machine.  
Figure 4: Reinforcement mechanism of the bag grouting pile [31].  
(3) Simulative steps  
The main steps of the simulation process are as follows:  
i. Establishment of bored pile grids consisting of cylindrical pile components with the same diameter as the pile itself.  
ii. Establishment of soil grids around the pile: the vertical and lateral lengths of the model are both twice the length of the pile, and the grids are slightly dense near the pile.  
iii. Calculation of initial state of soil: define all grids as soil, assign material parameters, set gravity load, and complete the calculation of initial stress and initial modulus.  
iv. Drilling from top to bottom: close the grid at the pile location, apply normal pressure to the hole wall at the drilling location with the pressure being distributed in a hydrostatic gradient in the vertical direction, gradually close the grid from top to bottom, and apply pressure.  
Further, to quantify the deformation of the soil around the pile during the pile driving process, this study selects four characteristic values at distances of 10R, 20R, 30R, and 50R from the center of the pile, based on the calculation results.
# Laser Ablation Manipulator Coverage Path Planning Method Based on an Improved Ant Colony Algorithm
## 4 Improved Ant Colony Algorithm  
### Basic Ant Colony Algorithm  
With the development of numerous metaheuristic algorithms, many scholars have applied these algorithms to solve the path or trajectory optimisation problems. Due to the similarity between CPP and TSP, in this study, an improved ant colony algorithm was adopted to solve the CPP problem to determine an optimal path. The ant colony optimisation (ACO) algorithm is a heuristic random search algorithm based on a distributed autocatalytic process by simulating the collective path-seeking behaviour of ants in nature [43]. In comparison with other metaheuristic algorithms, the ant colony algorithm has the advantages of strong robustness, parallelism, and positive feedback. It has been widely used in path-planning problems.  
There are two essential steps in simulating the foraging behaviour of the ant colony. The first is counting the transition probability from one point to another, according to Equation (17). The second is updating the pheromone according to Equation (18).  
\[P_{ij}^{k}(t)=\left\{\begin{array}{cl}\frac{\left[\tau_{ij}(t)\right]^{a} \cdot\left[\eta_{ij}\right]^{b}}{\sum_{\text{sallowod}_{k}}\left[\tau_{is}(t) \right]^{a}\cdot\left[\eta_{is}\right]^{b}},&\text{if }j\in\text{ allowed}_{k}\\ 0,&\text{otherwise}\end{array}\right. \tag{17}\]  
where \(\text{allowed}_{k}\) is a point set of allowed from point \(i\), \(\tau_{ij}\) is the pheromone between point \(i\) and point \(j\), and \(\eta_{ij}\) is the visibility, which is generally \(\frac{1}{d_{ij}}\). Moreover, \(\alpha\) and \(\beta\) are parameters that control the relative importance of the trail pheromone versus visibility.  
\[\tau_{ij}(t+n)\,=\rho\cdot\tau_{ij}(t)\,+\Delta\tau_{ij} \tag{18}\]  
where \(\rho\) is a parameter, \((1-\rho)\)  denotes the evaporation of the trail pheromone between time \(t\) and \(t+n\), and \(\Delta\tau_{ij}\) follows Equation (19).  
\[\Delta\tau_{ij}=\sum_{k=1}^{m}\Delta\tau_{ij}^{k} \tag{19}\]  
where \(\Delta\tau_{ij}^{k}\) is the amount per iteration of the path of the trail pheromone laid on the edge (\(i\),\(j\)) by the \(k\)-th ant between time \(t\) and \(t+n\). It follows Equation (20):  
\[\Delta\tau_{ij}^{k}(t)=\left\{\begin{array}{cl}\frac{Q}{L_{t}},&\text{if }k_{ th}\text{ant select }(i,j)\text{ in its tour}\\ 0,&\text{otherwise}\end{array}\right. \tag{20}\]where \(Q\) is a constant, and \(L_{k}\) is the tour length of the \(k\)-th ant.