esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Search Methodology  
Between November 2020 and February 2021, an electronic search was conducted on the following databases: PubMed/MEDLINE, Google Scholar, SCOPUS and Embase. On PubMed/MEDLINE, the Medical Subject Headings (MeSH) keywords used were: \"orthodontic tooth movement\" OR (\"tooth\"(All Fields) AND \"tooth movement\" (All Fields)) OR \"tooth movement\"(All Fields)) AND (\"parathyroid hormone\" OR \"teriparatide\"(All Fields) OR \"orthodontic\"(All Fields)). Suitable filters were applied on the other database search engines to use the keywords: \"orthodontic tooth movement\" AND \"parathyroid hormone\" OR \"teriparatide\". On Google Scholar and Embase databases, the relevant filters were applied to exclude articles not relevant to our study. Prior to our search, inter-examiner calibration and piloting exercises were carried out to reduce bias and improve inter-examiner reliability.  
The inclusion criteria included randomized clinical trials, case series, case reports and animal studies, as well as dissertations on topics meeting our focused question. Letters to the editor, in vitro studies, ongoing trials, conference proceedings, reviews and commentaries were excluded. Articles published on and after the year 2000 were searched for. The language of the articles search was English. Reference lists of the studies found were also searched for any articles matching our inclusion criteria. The search was a conducted independently by the author and another investigator. Both were blinded to each other's research results. Any disagreements were solved by discussion. Following the completion of the search, the general characteristics, outcomes, and overall quality of the articles were summarized and assessed by the investigators. The grey literature was searched with the assistance of the library services at College of Dentistry, King Faisal University (KFU), KSA. The outcomes of studies were assessed in terms of the effect of PTH administration on the rate orthodontic tooth movement, periodontal tissues, orthodontic relapse and root and/or bone resorption.
## 4 Research Methodology
### FLO-2D Model and Governing Equations  
The FLO-2D model utilizes several governing equations to simulate debris flow behavior. These equations include the following.  
(1) Continuity equation:  
\[\frac{\partial{h}t}{\partial{t}}+\frac{\partial{(uh)}}{\partial{x}}+\frac{ \partial{(yh)}}{\partial{y}}=\mathrm{I} \tag{1}\]  
where \(h\) represents the deposition depth; \(\mathrm{I}\) denotes the rainfall intensity during the mudslide event; \(u\) and \(v\) indicate the average flow velocities in the x and y directions, respectively; and \(t\) is the time.  
(2) Equation of motion:  
\[S_{fx}=S_{Ox}-\frac{\partial{h}}{\partial{x}}-\frac{\partial{u}}{g\partial{t} }-u\frac{\partial{u}}{g\partial{x}}-v\frac{\partial{u}}{g\partial{y}} \tag{2}\]  
\[S_{fy}=S_{Oy}-\frac{\partial{h}}{\partial{y}}-\frac{\partial{v}}{g\partial{t} }-u\frac{\partial{v}}{g\partial{x}}-v\frac{\partial{v}}{g\partial{y}} \tag{3}\]  
where \(g\) represents the gravitational acceleration; \(S_{fx}\) and \(S_{fy}\) are the frictional slopes in the \(x\) and \(y\) directions, respectively; and \(S_{Ox}\) and \(S_{Oy}\) are the slopes at the bottom of the channel in the \(x\) and \(y\) directions, respectively.  
(3) Rheological equations:  
\[\tau=\tau_{c}+\tau_{mc}+\tau_{v}+\tau_{l}+\tau_{d} \tag{4}\]  
where \(\tau_{c}\) represents the viscous yield force, \(\tau_{mc}\) denotes the Mohr-Coulomb shear stress, \(\tau_{v}\) indicates the viscous shear stress, \(\tau_{l}\) is the turbulent shear stress, \(\tau_{d}\) represents the dispersive shear stress, and \(\tau\) is the total shear stress.  
Figure 4: Drainage network map of Dashiling Gully.  
[PERSON] and [PERSON] [29] further refined the equations by introducing an improved slope representation:  
\[S_{f}=S_{y}+S_{v}+S_{td}=\frac{\tau_{y}}{\gamma_{m}h}+\frac{K\eta u}{8\gamma_{m} h^{2}}+\frac{n^{2}u^{2}}{h^{4/3}} \tag{5}\]  
\[\tau_{y}=\alpha_{2}e^{\theta_{2}C_{v}} \tag{6}\]  
where \(S_{f}\) represents the total friction slope drop; \(S_{y}\) denotes the yield slope drop; \(S_{v}\) indicates the viscous slope drop; \(S_{td}\) is the turbulent-discrete slope drop; \(\tau_{y}\) represents the yield stress; \(\gamma_{m}\) denotes the relative density of the earth and rock fluid; \(h\) indicates the deposition depth; \(K\) represents the laminar resistance coefficient; \(\eta\) denotes the viscous coefficient; \(n\) indicates the Manning's coefficient; and \(u\) is the movement velocity.
## 5 Experimental Result and Analysis
### Performance Comparison with the Existing Methods
To provide a more precise evaluation of the model's detection capability under varying embedding rates, we report the recall values separately for each embedding level. Since the primary objective in steganalysis is to correctly detect steganographic samples, recall is an appropriate metric for measuring performance in this context. The results are presented in Figure 6.  
As illustrated in Figure 6, two key observations can be drawn: (i) When the embedding rate is below 60%, our method consistently achieves the highest recall among all compared approaches, demonstrating superior detection capability in low embedding rate conditions. Specifically, for the Geiser steganography method at an embedding rate of 10%, the DST-based method yields a recall below 15%, while SVM-MSDA and XGBoost-MSDA achieve recall values below 40%. In contrast, our method attains a recall exceeding 70%. (ii) A positive correlation is observed between the embedding rate and recall, and the performance gap between the proposed method and the DST-based method gradually decreases as the embedding rate increases. To further evaluate the effectiveness of our approach and compare it with existing steganalysis methods, we present receiver operating characteristic (ROC) curves for detecting four steganographic techniques, as shown in Figure 7. The results clearly show that our method is effective and consistently outperforms the baseline approaches. Figure 6: The recall result with each embedding rate on multiple steganography methods. (**a**) The recall result with each embedding rate on [PERSON]’s steganography method; (**b**) the recall result with each embedding rate on [PERSON]’s (\(\eta=1\)) steganography method; (**c**) the recall result with each embedding rate on [PERSON]’s (\(\eta=2\)) steganography method; (**d**) the recall result with each embedding rate on [PERSON]’s (\(\eta=4\)) steganography method.
# Vision-Based Distance Measurement in Advanced Driving Assistance Systems
## 4 Distance Measurement between the Target and the Subject Vehicle
### Target Distance Measurement
#### 4.2.1 Distance Measurement of the Target Vehicle  
If the target is a vehicle, we first fit a plane in the camera body coordinate system using object points corresponding to the pixels of the vehicle and the RANSAC algorithm [31]. Suppose that \(\left(x_{l}^{i},y_{l}^{i},z_{l}^{i}\right)\) is the coordinate of the point corresponding to each pixel of the target vehicle in the camera body coordinate system, \(S\) is the number of these object points, and \(i=1,\ldots,S\). By using RANSAC, a fitted plane in the camera body coordinate system can be determined and expressed as \(z=ax+by+c\). Secondly, by projecting the image points \(\left(x_{l^{\prime}}^{i},y_{l^{\prime}}^{i},z_{l}^{i}\right)\) onto this plane, specifically, \(z_{l}^{i}=ax_{l}^{i}+by_{l}^{i}+c\), we can obtain a set \(\hat{Z}=\left\{\hat{z}_{l}^{i}\right|\hat{i}=1,2,\ldots,S\right\}\). Finally, the distance between the target vehicle and the subject vehicle is equal to the minimum value of this set \(\hat{Z}\).
## 4 Experiment and Analysis
### The Experiment with the Enhanced YOLO Model  
#### 4.2.1 Comparison of Activation Functions in Slope Scenarios  
Initially, based on the original model, this section employed four types of activation functions for adjustments: FReLU, SiLU, LeakyReLU, and MetaAconC. The training epochs were consistently maintained at 300 rounds.  
This article primarily employs mAP_0.5: mean average precision (IoU = 0.5), training/validation set object confidence loss (obj_loss), and detection speed (FPS) as the performance evaluation metrics for the model. mAP_0.5 indicates the average detection precision at an IoU threshold of 0.5; obj_loss assesses the disparity between the model's predicted bounding boxes (rectangles enclosing objects) and the actual boxes; FPS measures how many images the target detection network can process per second, evaluating the model's detection speed.  
The results revealed that adjustments to the activation function caused noticeable differences in the model's accuracy and detection speed, with these differences primarily manifesting in terms of detection speed. Moreover, when observing the entire detection process, the variance in accuracy altered as the training progressed. Throughout the training phase, as visualized in Figure 6, even though the distinctions between models narrowed after 250 rounds, the performance of SiLU and LeakyReLU was significantly superior to that of FReLU and MetaAconC functions in terms of accuracy. Simultaneously, as shown in Table 2, regarding detection speed, the model utilizing the SiLU function achieved a detection speed of 81.45 fps, and the model employing the LeakyReLU function reached a speed of 79.10 fps, both significantly outpacing the model with the FReLU function at 44.63 fps and the model with the MetaAconC function at 27.49 fps.
# mmS-TCP: Scalable TCP for Improving Throughput and Fairness in 5G mmWave Networks
## 1 Introduction
Applying CoDel [20], an active queue management (AQM) algorithm, to the base station buffers lowered the end-to-end latency, but due to packet drops by CoDel, CUBIC stayed in the congestion avoidance phase and was unable to fully utilize the link. We evaluated whether the existing TCP CCAs produced similar results and confirmed that Scalable TCP (S-TCP) provides the highest throughput in a single flow [21]. We judged that the 5G design goals could be achieved if high throughput were provided using S-TCP and if low latency were guaranteed through the CoDel scheme. However, because S-TCP increases the cwnd quite dramatically, TCP fairness problems and excessive packet loss may occur. Therefore, in this study, we propose a congestion control algorithm that guarantees sufficient TCP throughput in mmWave networks while providing stability to the network by redesigning the existing S-TCP. The mmWave Scalable TCP (mmS-TCP), redesigned through a simple modification of the cwnd increment/decrement mechanism of S-TCP, can provide excellent performance in mmWave networks in conjunction with CoDel. The main contributions of this study are as follows:  
* The mmS-TCP algorithm simultaneously improves intra-protocol and inter-protocol fairness without degrading the throughput of a single TCP in mmWave networks;
* mmS-TCP greatly enhances network stability through a simple modification of the cwnd increase/decrease mechanism of S-TCP. Therefore, it is easy to deploy and implement;
* By using the mmS-TCP proposed in this study and CoDel scheme together, TCP can guarantee adequate performance in mmWave networks. The remainder of the paper is organized as follows. In Section 2, we investigate the characteristics of 5G mmWave networks and studies related to TCP in mmWave networks. We introduce the details of the redesigned mmS-TCP in Section 3. Subsequently, we evaluate the mmS-TCP algorithm in various scenarios by conducting a simulation experiment in Section 4. Finally, in Sections 5 and 6 we discuss our proposed algorithm and conclude the paper.
# Two-Dimensional Modelling Approach for Electrokinetic Water Transport in Unsaturated Kaolinite
## 3 Results and Discussion
### Influence of the Electrode Positioning Type
#### 3.1.1 Void Ratio Sensitivity
Finally, it is important to highlight the significant increase observed in the magnitude of both fluxes in the vicinity of the electrolyte reservoirs. In the case of the hydraulic flux, this occurs because the highest liquid pressure gradient is present in this zone. However, in the case of the electroosmotic flow, it is mainly due to a simple geometrical effect associated with the two-dimensional configuration. The area of origin/arrival of the electric current lines (the circular orifice corresponds to the electrolyte reservoirs) is smaller than the total area of the domain through which they flow. Consequently, a flow concentration is produced around these areas, thus increasing the current density and consequently the electric potential gradient. The second scenario corresponds to electrodes inserted directly into the soil. In this case, no addition or withdrawal of electrolyte from the system was carried out. Figure 5 shows the evolution of the degree of saturation for the three different void ratios. Figure 3: Temporal variation of anolyte (empty markers) and catholyte (filled marker) volume. Void ratio: 0.7 \(\blacktriangle\), 0.9 \(\blacksquare\), 1.2 \(\bullet\). Figure 4: 2D maps of hydraulic and electroosmotic volumetric fluxes evaluated at two times, 86,400 (1 d) and 864,000 s (10 d), and three void ratios, 0.7, 0.9, and 1.2, when the electrodes were in the electrolyte reservoirs, as shown in Figure 1a. Electric potential gradient applied: 1 Vcm\({}^{-1}\). The behaviour observed in this scenario was totally different from that obtained when electrolyte reservoirs are used. In this case, there was a redistribution of the water content of the system. There was progressive drying of the area near the anode and, on the other hand, an increase in the degree of saturation in the area where the cathode was located. This phenomenon has also been shown in experimental tests evaluating the electrokinetic removal of phenolic compounds in partially saturated soils. The normalised evolution of the water content shows a desiccation near the anode and an increase at the cathode side [33, 34]. This behaviour can be explained by taking into account the direction of water transport by electroosmosis, which is the predominant transport phenomenon when using this technique (see Supplementary Material Figure S2).
# A Multi-Channel Data Simulator Based on the Time Unification System
## 3 The Design of the Time Unification
### The Time Unification Design in Real-Time Mode  
The time unification design of the simulator in real-time mode is shown in Figure 4. It includes the PPS control module, host computer, 10 GbE module, UDP packet decode module, Data FIFO module, and output interface module. The PPS control module sends the host computer signal of the time reference generator output to the host computer through a fixed UDP port. The host computer generates real-time simulated data based on the operating status of the target system, then it sends the multi-channel simulated data to the 10 GbE module through different threads and multiple ports simultaneously. The UDP packet decode module parses the data packets from different ports, buffers the real-time output through the Data FIFO module, and sends it to the output interface module to the target system.  
In the real-time mode, the host computer generates simulated data based on the received PPS data packet and sends the simulated data to the target system through different threads and ports. The target system sends the processing results back to the host computer through the simulator. The host computer can construct the next batch of simulated data to be sent based on the generated results, achieving closed-loop simulation. The PPS triggers the sending of UDP data packets and outputs them to the target system, achieving time synchronization between the host computer and the target system, which unifies the working rhythm and achieves the simulation of a real system.
## 3 Experiments and Results  
In this section, we describe experiments carried out to verify the effectiveness and efficiency of the REI and ACE modules and the overall RIIAnet model designed in this paper. We first conducted experiments to analyze the roles of the above two modules in the network by varying the percentages of the different modules in the depth of the baseline network to select the appropriate positions of both in the network and ensure the network performance while maintaining inference speed. We then compare the performance of our RIIAnet network model with current advanced models to check the efficiency of our model. To comprehensively evaluate the performance of the module and the model, the metrics used in this paper are MIOU, MPA, Recall, F1 Score, and PR curve.
# User Utility Maximization in Narrowband Internet of Things for Prioritized Healthcare Applications
## 5 Performance Evaluation
### Simulation Results
#### 5.3.2 Impacts of Varying Number of User Equipment (UEs)  
In this experiment, we choose the number of UEs randomly (with uniform distribution) from a range of 3-15 and the performance of varied numbers of UEs keeping the bandwidth, bit error rate, and the number of uplink channels at 66 kbps, \(10^{-3}\) BER, and 2 with a 1 ms time slot, respectively. The high priority users having a higher data rate and less interference requirements are more valuable for data transmission than others, which is not addressed in \(bRRM\) and \(iRRM\). As a result, the graphs in Figure 4(a) illustrate that in the case of PAUM, even a little increase in UE can maintain a steady utility for all UEs. Thus, the PAUM outperforms the \(bRRM\) and \(iRRM\) systems significantly due to smart user classification and prioritization.  
The graphs in Figure 4(b) demonstrate that as the number of UEs grows, the average packet delivery ratio (PDR) decreases consistently for all examined approaches, indicating that the PAUM is effective for all classified events. It avoids interference and prioritizes UEs with higher PDR over UEs with lower PDR, such as \(bRRM\) and \(iRRM\). It occurs because the priority-sorted event minimizes the average hop distance of delivered data packets. Despite the fact that \(iRRM\) and \(bRRM\) reduce interference while managing the radio resources, the packet delivery ratio of PAUM demonstrates the benefits of employing prioritized classified events, which outperforms both of them.  
Figure 4: Impacts of varying data generation rates from sensor devices.  
Finally, when compared to other systems, the proposed PAUM has the highest resource sharing fairness for changing numbers of UEs, as shown in Figure 4(c). This is due to the network's bandwidth being divided with suitable event urgency, using channelization on time by Equations (8) and (9). Due to a lack of priority and correct coordination with channelization and time slot with bandwidth, \(bRRM\) and \(iRRM\) do not adequately share resource allocation. The resources are equitably distributed across the UEs following their priorities, and the bandwidth is distributed in such a way that data transfer from the assigned node is reduced. As a result, PAUM achieved a higher fairness index over \(bRRM\) and \(iRRM\).
# Probing Dust and Water in Martian Atmosphere with Far-Infrared Frequency Spacecraft Occultation
## 1 Introduction
Therefore, we consider a frequency range between 1 and 10 THz (30-300 \(\mu\)m) to understand the contribution of dust and water vapor to atmospheric signal loss and test the feasibility of using these bands for dust and water vapor retrieval. The feasibility of orbiter-to-orbiter (O2O) radio occultation (also called cross-link radio occultation) of Mars has been studied in detail [47]. Radio science experiments provide a higher resolution of vertical profiles compared to infrared spectrometer retrievals. They could retrieve high-resolution and accurate measurements of the near-surface environment. Therefore, retrievals of dust opacity from THz-sounding experiments could provide more precise and accurate measurements over current IR dust opacity measurements. Below, we provide an assessment of expected signal loss during occultation and the feasibility of detection with respect to the current state of spacecraft operations and signal processing. Temperature retrieval in radio occultation can resolved at altitudes of \(\sim\)40 km. However, the objective of the work is to investigate the dust activity in the lower atmosphere of Mars and resolve processes in the planetary boundary layer. Therefore, we do not require temperature measurements above 40 km.
# Low-Cost Goethite Nanorods for As (III) and Se (VI) Removal from Water
## 2 Experimental Work  
### Materials and Methods  
FeCl3 (98%, Fisher Scientific, Nagpur, India) and NaOH pellets (98%, Fisher Scientific, Nagpur, India) were used as a precursor and reducing agent, respectively. Besides, NaOH was used as the reducing agent as well as hydroxyl ions' generator in the reaction. In a quite simple procedure, 6.49 g of ferric chloride (FeCl3) was dispersed in 200 mL of distilled water in a 2 L glass-beaker. In the next step, the NaOH solution was slowly added under room conditions with continuous control of pH to be ~11. The reaction was then allowed to continue under rapid stirring at 60 \({}^{\circ}\)C for 2 h. until the color of the resultant solution changed to a yellowish brown-like color. In the subsequent step, the obtained solution was allowed to precipitate and then filtered using a vacuum filtration system. Finally, the obtained precipitate was repeatedly cleaned up with water, and ethanol and then dried at 60 \({}^{\circ}\)C for 6 h. Basically, the plausible reaction is expected to proceed in two steps wherein iron hydroxide (Fe(OH)3) is produced during the first step according to Equation (1):  
\[\text{FeCl}_{3}+\text{NaOH}\rightarrow\text{NaCl}+\text{Fe(OH)}_{3} \tag{1}\]  
Accordingly, during the second step, \(\text{Fe(OH)}_{3}\) transforms to oxy-hydroxide (\(\alpha\)-FeOOH) due to the presence of appropriate hydroxyl ions that could dehydate hydroxide to the oxy-hydroxide phase. Of course, this reaction involves more oxygen molecules for faster kinetics based on Equation (2).  
\[\text{Fe(OH)}_{3}\rightarrow\alpha\text{-FeOOH}+\text{H}_{2}\text{O} \tag{2}\]In fact, maintaining high alkalinity (here pH is \(\sim\)11) during the reaction is expected to speed-up the reaction process. However, precise control of the reaction pH is needed to avoid the formation of other phases such as ferrihydrite at higher pH (\(>\)12) [24]. Herein, conducting the reaction at pH \(\sim\)11 for \(\sim\)2 h was the optimum period for obtaining the goethite phase, which is far less than the required time in other similar synthesis methods to obtain \(\alpha\)-_FeOOH_ NRs [10; 11; 12; 24; 25]. Interestingly, conducting the experiments in large beakers (here, it was 2 L) diminished the reaction time several folds. Indeed, large beakers provide a proper environment to accelerate the oxygen extraction process from the air that is needed for the growth of the goethite phase. In other words, large beakers offer large contact areas between the air and the solution in the reaction, and therefore rapid reaction occurred. Accordingly, the present study claims that an increase in the beaker size could also cut the reaction time, which opens a novel approach for cost-effective continuous production of scalable \(\alpha\)-_FeOOH_ NRs in the air-rich environment.