esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Root canal shaping is essential to achieve optimal results in the subsequent cleaning and filling procedures [1, 2]. This process aims to create an adequate space for irrigation while preserving the original anatomy [3].  
Maintaining the original canal shape through the use of a minimally invasive technique is associated with better endodontic outcomes [1, 4, 5]. Nickel-titanium (NiTi) rotary systems have been shown to reduce shaping time, operator fatigue, and root canal transportation [6, 7]. A reciprocatingsingle instrument approach has been introduced to simplify root canal shaping [8; 9; 10]. This technique uses only one file after the initial canal scouting and glide path to create a continuous tapered preparation [11; 12]. The reciprocating movement limits the torsional stress placed on the shank of the single file during shaping, reducing the risk of instrument fracture [9; 10; 13; 14]. Recently, the WaveOne Gold (WOG) reciprocating system has been introduced. The evolved parallelogram cross section and the Gold-Wire technology are claimed to optimize root canal centering ability and fracture resistance [15; 16]. This WOG directions of use suggest utilizing a brushing motion on the outstroke to eliminate coronal interferences and to enhance shaping results [17].  
Therefore, brushing motion is widely suggested to remove restrictive coronal dentine using a painting movement towards the outside of the canal curvature. It was clinically adopted for multi-file shaping systems to improve the efficiency of the finishing files after deleting coronal constriction, especially for mesial canals of mandibular molars [18]. However, brushing motion was also proposed for single-file reciprocating systems to eliminate coronal interferences before filling with a warm gutta-percha technique, or to improve shaping outcomes in canals that exhibit irregular cross-sections [19]. There is currently insufficient information regarding the shaping ability and the maintenance of the original canal anatomy of reciprocating single-file instruments when used in a brushing motion.  
Micro-computed tomography (micro-CT) has emerged as a powerful device for the in vitro evaluation of root canal shaping outcomes. It enables the analysis of volume changes, cross-sectional shape, taper, and proportion of prepared surface by matching reconstructed sample volumes of pre-operative and post-operative canal systems [20; 21].  
The aim of this study was to evaluate the ability of reciprocating WaveOne Gold single files to maintain the original root canal anatomy when used with or without an intentional brushing motion. The correct clinical use of the reciprocating instrumentation could improve the maintenance of the sound tooth structure, reducing the risk of root canal transportation and positively influencing the long-term prognosis of the endodontic treatments. The null hypothesis is that both instrumentation techniques will equally maintain the original root canal anatomy.
# Synthetic Image Generation Using Conditional GAN-Provided Single-Sample Face Image
## 6 Discussion on Results and Advantages of the Approach  
The application of Conditional Generative Adversarial Networks (CGANs) to generate synthetic facial images has proven highly effective for enriching the datasets with diverse facial variations, overcoming the key challenge of data scarcity often encountered in SSPP scenarios. The model exhibits robust performance against variability by accurately generating facial expressions across a range of lighting conditions and disguises. Furthermore, the model also demonstrates dataset independence; once trained on the baseline dataset, it can produce accurate variations from a single neutral image, thereby potentially eliminating the need for extensive and diverse training datasets. The proposed model not only matches but also surpasses various state-of-the-art methods, particularly in its ability to handle complex facial expressions and adapt to uncontrolled shooting conditions.
# Spatial Downscaling of Soil Moisture Based on Fusion Methods in Complex Terrains
## 2 Study Area and Data
### Data  
In this study, the SM data included Soil Moisture Active and Passive (SMAP) satellite products for the downscaling and in situ SM data for the evaluation. Predictors were divided into land-surface environmental variables and spatiotemporal information. Land-surface environmental variables included the vegetation index (VI), land-surface temperature (LST), LCT, evapotranspiration (ET), slope, and aspect. Spatial information included longitude, latitude, and elevation, and time information referred to the day sequence and AM/PM. All the data files contained latitude, longitude, and time labels.
# Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers
###### Abstract  
In the past few years, educational data mining (EDM) has attracted the attention of researchers to enhance the quality of education. Predicting student academic performance is crucial to improving the value of education. Some research studies have been conducted which mainly focused on prediction of students' performance at higher education. However, research related to performance prediction at the secondary level is scarce, whereas the secondary level tends to be a benchmark to describe students' learning progress at further educational levels. Students' failure or poor grades at lower secondary negatively impact them at the higher secondary level. Therefore, early prediction of performance is vital to keep students on a progressive track. This research intended to determine the critical factors that affect the performance of students at the secondary level and to build an efficient classification model through the fusion of single and ensemble-based classifiers for the prediction of academic performance. Firstly, three single classifiers including a Multilayer Perceptron (MLP), J48, and PART were observed along with three well-established ensemble algorithms encompassing Bagging (BAG), MultiBoost (MB), and Voting (VT) independently. To further enhance the performance of the abovementioned classifiers, nine other models were developed by the fusion of single and ensemble-based classifiers. The evaluation results showed that MultiBoost with MLP outperformed the others by achieving 98.7% accuracy, 98.6% precision, recall, and F-score. The study implies that the proposed model could be useful in identifying the academic performance of secondary level students at an early stage to improve the learning outcomes.  
educational data mining; supervised learning; secondary education; academic performance
## 3 Materials and Methods
### Peptide Synthesis
#### 3.1.1 Synthesis of N-Methyl Peptides  
After placing Fmoc-Rink Amide resin (100-200 mesh, 1% DVB, 0.75 mmol/g) in a peptide synthesis vessel, it was swelled in DMF and deprotected with a 20% piperidine/DMF solution (5 mL, 1 \(\times\) 5 min and 1 \(\times\) 25 min). Protected amino acid (3 eq.), HBTU (3 eq.), and HOBt (3 eq.) in DMF (1-3 mL/g resin) were sequentially added to the resin, and the mixture was irradiated in an automated microwave peptide synthesizer (Biotage Initiator + Alstra(tm)) for 20 min with a maximum temperature of 35 \({}^{\circ}\)C.  
The following protected N-methyl amino acids were then added stepwise: Fmoc-N-Me-Lys(Boc)-OH, Fmoc-N-Me-His(Trt)-OH, Fmoc-N-Me-Leu-OH, Fmoc-N-Me-Asp(OtBu)-OH, Fmoc-N-Me-Cys(Trt)-OH, Fmoc-N-Me-Sert(Bu)-OH.  
The peptide resin was washed with DCM (3\(\times\)), DMF (3\(\times\)), and DCM (3\(\times\)), and the Fmoc deprotection protocol described above was repeated after each coupling step.  
Two treatments with piperidine/DMF (2:8, \(v/v\)) for 10 min and two extra 5 min treatments with piperidine/DBU/toluene/DMF (5:5:20:70, \(v/v\)) were used. Additionally, the chloranil test was carried out following each phase of deprotection and coupling to validate, respectively, the complete removal of the Fmoc protective group and to confirm that complete coupling has taken place on all the free amines on the resin. After removing the N-terminal Fmoc group, the peptides were acetylated by adding a solution of Ac\({}_{2}\)O/DCM (1:3) and agitating for 30 min. The final step was cleaving the peptides from the resin using the following cleavage cocktails: TFA/DCM (95:5 \(v/v\)) for 90 min (10 mL/g resin). By adding cold diethyl ether, peptides were precipitated, the solution was decanted, the solid was triturated, and the mixture was then again decanted. This procedure was carried out twice. The yield for the crude peptides was 30%.
## 4 Evaluation of Model Developments
### Observational Data to Evaluate Model Developments
#### 4.2.4 Worofost  
WOFOST is a crop growth model to estimate yields quantitatively. It is developed by the Department of Theoretical Production Ecology (Wageningen Agricultural University, The Netherlands) and the Center for Agrobiological Research and Soil Fertility (Wageningen, The Netherlands). This model is implemented currently within the MARS Crop Growth Monitoring System (CGMS), allowing the estimation of biophysical variables related with crop yields such as potential biomass production, crop development stage, and so forth. WOFOST raw simulations are done at soil unit level, that is, for each of the soil types within a soil polygon. These data were provided by JRC under the EU FP7 ImagineS project framework.
# Wireless Mesh Networking: An IoT-Oriented Perspective Survey on Relevant Technologies
## 5 Mesh Networking with IEEE 802.15.4
### Applications: Comparison between ZigBee Pro and LWMesh Protocols  
To demonstrate the interest in adopting the previous detailed IEEE 802.15.4-oriented protocols, we recall the comparison between ZigBee Pro and LWMesh presented in [73]. The authors mainly focus on self-healing capability, application layer, and routing latency, and highlight how LWMesh has specific drawbacks, although being better in several aspects with respect to ZigBee Pro.  
In detail, the performance comparison has been made using an IEEE 802.15.4-compliant physical device equipped with a 2.4 GHz radio transceiver and 8-bit MCU (8 KB RAM, 128 KB Flash). Each experimental scenario has been operated transmitting 1000 packets at each realization and varying various parameters (e.g., number of network nodes, payload size, and security policies). To be able to cover a small area with a multi-hop network, all physical antennas have been removed, obtaining a centimeters radio coverage range, while, in terms of security, no hardware-accelerated XTEA and AES-128 algorithms have been adopted [73].  
Figure 6: General LWMesh frame format.
## Enhanced Stiffness Model of the 3 DJM
### Parametric Stiffness Formulation of the 3 DJM  
In Figure 2, \(\mathbf{a}_{i}=\overrightarrow{OA_{i}},\mathbf{b}_{i}=\overrightarrow{OB_{i}}\in \mathbb{R}^{3}\) (\(i=1,2,\ldots,6\)) are the position vectors of points \(A_{i}\) and \(B_{i}\), respectively. The vector from \(A_{i}\) to \(B_{i}\) along the \(i^{th}\) cable, denoted as \(\mathbf{c}_{i}=\overrightarrow{A_{i}B_{i}}\), yields  
\[\mathbf{c}_{i}=\mathbf{b}_{i}-\mathbf{a}_{i}=c_{i}\mathbf{u}_{i}\in\mathbb{R}^{3}, \tag{12}\]  
where \(c_{i}=\|\mathbf{c}_{i}\|\in\mathbb{R}\) is the length of the cable from \(A_{i}\) to \(B_{i}\) and \(\mathbf{u}_{i}=\mathbf{c}_{i}/c_{i}\in\mathbb{R}^{3}\) is the unitary vector of \(\mathbf{c}_{i}\). Let \(\mathbf{t}_{i}=t_{i}\mathbf{u}_{i}\in\mathbb{R}^{3}\) (\(i=1,2,\ldots,6\)) be the cable tension vector of the \(i^{th}\) cable, where \(t_{i}=\|\mathbf{t}_{i}\|\in\mathbb{R}^{3}\) is the value of the cable tension. According to the equilibrium equation, the moment load \(\mathbf{\tau}\) applied on the moving platform with respect to the point \(O\) satisfies  
\[\mathbf{\tau}+\sum_{i=1}^{6}\mathbf{a}_{i}\times\mathbf{t}_{i}=\mathbf{0}. \tag{13}\]  
Denoting \(\mathbf{T}=(t_{1},t_{2},\ldots,t_{6})^{T}\in\mathbb{R}^{6}\) as the vector of six cable tension values, the moment load \(\mathbf{\tau}\) can be written as  
\[\mathbf{\tau}=-\sum_{i=1}^{6}\mathbf{a}_{i}\times\mathbf{t}_{i}=-\sum_{i=1}^{6}(\mathbf{a}_{i} \times\mathbf{u}_{i})t_{i}=\mathbf{J}^{T}\mathbf{T}, \tag{14}\]where \(\mathbf{J}\in\mathbb{R}^{6\times 3}\) is called the Jacobian matrix of the 3 DJM and yields  
\[\mathbf{J}=-(\mathbf{a}_{1}\times\mathbf{u}_{1},\mathbf{a}_{2}\times\mathbf{u}_{2},\cdots,\mathbf{a}_{6} \times\mathbf{u}_{6})^{T}. \tag{15}\]  
Denoting \(\mathbf{S}=\mathbf{J}^{T}\in\mathbb{R}^{3\times 6}\) and \(S_{ji}\) (\(j=1,2,3;i=1,2,\ldots,6\)) as the components of \(\mathbf{S}\), the components of \(\mathbf{\tau}\) yield  
\[\tau_{j}=\sum_{i=1}^{6}S_{ji}t_{i}. \tag{16}\]  
Then, the expression \(\mathbf{L}_{k}\circ\tau_{j}\) in (10) can be written as  
\[\mathbf{L}_{k}\circ\tau_{j}=\lim_{\Delta t\to 0}\frac{\tau_{j}|_{\mathbf{R}(t+\Delta t )}-\tau_{j}|_{\mathbf{R}(t)}}{\omega^{k}\Delta t}=\frac{\partial\tau_{j}}{\partial \xi^{k}}|_{\mathbf{R}(t)}=\sum_{i=1}^{6}\left(\frac{\partial S_{ji}}{\partial\xi^ {k}}t_{i}+S_{ji}\frac{\partial t_{i}}{\partial\xi^{k}}\right). \tag{17}\]  
where \(\tau_{j}|_{\mathbf{R}(t)}\) (\(j=1,2,3\)) are the components of \(\mathbf{\tau}\) when the 3 DJM stays at the pose \(\mathbf{R}(t)\). Writing the six cable lengths as a vector \(\mathbf{c}=(c_{1},c_{2},\ldots,c_{6})^{T}\in\mathbb{R}^{6}\), then we have the following equation according to the principle of virtual work, i.e.,  
\[\mathbf{T}^{T}\delta\mathbf{c}=\mathbf{\tau}^{T}\delta\mathbf{\xi}. \tag{18}\]  
Substituting (14) into (18), we have  
\[\delta\mathbf{c}=\mathbf{J}\delta\mathbf{\zeta}. \tag{19}\]  
It shows that \(\frac{\partial c_{i}}{\partial\xi^{k}}=J_{ik}\) (\(i=1,2,\ldots,6;k=1,2,3\)), where \(J_{ik}\in\mathbb{R}\) is the component of \(\mathbf{J}\in\mathbb{R}^{6\times 3}\).
## 3 Classical Routing Protocols
### Modification of LEACH Based on CH Selection
#### 3.2.1 Leach-C  
In LEACH Centralized, proposed by [PERSON] [67], the base station performs decisions for cluster formation, CH selection, and data distributions. It ensured uniform energy distribution among all sensor nodes and created better clusters than LEACH. The overhead during the cluster formation phase is reduced due to control by \(BS\). CH selection process considers residual energy and evaluates the optimal number of clusters \(K\) using the formula  
\[K=\sqrt{\frac{N\in_{fs}}{2 TI\in_{mp}}\frac{M}{d_{toBS}^{2}}} \tag{2}\]  
The node distance to \(BS\) is given as \(d_{toBS}\) and \(M\) is the sensing area of interest. The \(BS\) communicates the CH ID to each node, and if the CH id matches a sensor node, it is declared as CH; otherwise, it behaves like a normal node. To achieve this, the location of each node is known to \(BS\) with the help of a Global Positioning System (GPS) receiver. The steady-state phase is the same as that of the LEACH protocol. Since the entire process is controlled and processed by _BS_, LEACH-C has proved more energy-efficient than LEACH. However, the usage of GPS increases the cost and also consumes energy.
## 2 Background
### Block Chain  
The block chain concept is based on a decentralized, distributed ledger for storing time-stamped transactions between many computers in a peer-to-peer network. Thus, any involved record cannot be tampered with retroactively. This concept allows block chain users to audit and verify transactions, independently and transparently. Nowadays, this technology is applied in cryptocurrencies, processing records called \"blocks\", which are connected using cryptographic techniques. Each block must have a hash code of the previous block, a timestamp, and a set of confirmed transactions [22; 23].  
This technology has many applications in precision agriculture and/or smart agriculture. The adoption of IoT brought a need to develop smart peer-to-peer systems capable of verifying, securing, monitoring, and analyzing agricultural data. These data must be secure and reliable. Block chain is changing the classical methods of storing, sorting and sharing agricultural data into a more reliable, immutable, transparent and decentralized manner. However, the adoption of blockchain in smart farming or precision agriculture still needs to mature to become a reality. Currently, there are several projects using this technology. A total of 40% are in the concept phase, 53% are in the pilot phase, and 7% do not have available information [22; 23].
## 4 Experiments and Results
### Teacher and Student Model Comparison  
The comparison between the teacher and the student model highlights the effectiveness of knowledge distillation in balancing performance and computational efficiency. The teacher model achieved a 99.3% validation accuracy, while the student model reached 98.7%. Despite the slight performance difference, the student model offered a significant advantage in inference time and model size, with 30% faster inference and 40% smaller memory footprint.  
Table 2 summarizes the comparative performance and computational metrics of the EfficientNet-B0 teacher model and the DeiT-Tiny student model. While the teacher model exhibited slightly higher accuracy, the student model achieved nearly comparable performance with a notable reduction in inference time and model size. Figure 6 visually represents these efficiency gains, emphasizing the student model's suitability for real-time deployment in resource-constrained environments like chicken farms.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Metric** & **EfficientNet-B0 (Teacher)** & **Deit-Tiny (Student)** \\ \hline Validation accuracy & 99.3\% & 98.7\% \\ Test accuracy & 99.0\% & 98.5\% \\ AP & 0.995 & 0.990 \\ Inference time (ms/frame) & 15 & 10 \\ Model size (MB) & 28.4 & 17.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance metrics and computational efficiency comparison.  
Figure 5: Confusion matrix for teacher and student model on the validation dataset.  
Figure 6: Bar chart comparing inference time and model size.
###### Abstract  
Internet of Remote Things (IoRT) networks utilize the backhaul links between unmanned aerial vehicles (UAVs) and low-earth-orbit (LEO) satellites to transfer the massive data collected by sensors. However, the backhaul links change rapidly due to the fast movement of both the UAVs and the satellites, which is different from conventional wireless networks. Additionally, due to the various requirements of IoRT multiservices, the system performance should be comprehensively considered. Thus, an adjustable wireless backhaul link selection algorithm for a LEO-UAV-sensor-based IoRT network is proposed. Firstly, an optimization model for backhaul link selection is proposed. This model uses Q, which integrates the remaining service time and capacity as the objective function. Then, based on the snapshot method, the dynamic topology is converted into the static topology and a heuristic optimization algorithm is proposed to solve the backhaul link selection problem. Finally, the proposed algorithm is compared with two traditional algorithms, i.e., maximum service time and maximum capacity algorithms. Numerical simulation results show that the proposed model can achieve better system performance, and the overload of the satellites is more balanced. The algorithm can obtain a trade-off between remaining service time and capacity by dynamically adjusting model parameters. Thus, the adjustable backhaul link selection algorithm can apply to multiservice IoRT scenarios.  
Internet of Remote Things (IoRT); unmanned aerial vehicle (UAV); low-earth-orbit (LEO) satellite; backhaul link selection; multiservices support
# Deep Learning-Based End-to-End Carrier Signal Detection in Broadband Power Spectrum
## 3 Methodology
### SCN Architecture
* **The Regression Network Head** The PSD and BW regressions are produced by almost the same structure modules in the RegNet head, as shown in Figure 3. The modules consist of a depthwise separable convolutional layer [30] with 256 channels, rectified linear unit (ReLU) [31], and a \(1\times 1\) Conv with one channel in common. However, we hope that in the PSD regression, all the values in the subcarrier center position are 1 and the others are considered as background and 0. Therefore, we can use the non-linear sigmoid function as the output layer. Moreover, since we directly regress the BW, the ReLU is used for the BW regression output layer to ensure positive results.
## 5 Conclusions
Additionally, the stiffness of specimen S5 decreased by approximately 21.9% following 4 million cycles of constant amplitude fatigue (CAF) loading, whereas specimen S6 experienced a significant stiffness reduction of 71.32% under low-cyclic variable amplitude fatigue (VAF) loading before complete failure. A mathematical model has been developed, grounded in experimental results, to assess the cumulative fatigue damage (CFD) and fatigue resistance (P-N effect) of GFRP-reinforced FDDPs featuring transverse joints. It is observed that the amplification factor related to fatigue loading is inversely related to the frequency of repeated cycles, which corresponds to the lifespan until failure. The three developed UHPC-filled closure strips with projecting GFRP bars exhibited almost identical behavior given their similar punching shear failure pattern and very close experimental ultimate loads. They also developed fatigue parameters for the fatigue prediction model. Based on the outcome of this research, the following recommendations for future research can be made:  
1. Study the monotonic and cyclic pushover effect of the shear pockets and shear stud distribution for the GFRP-reinforced panel-to-girder connection. 2. Conduct static and fatigue tests on a composite girder incorporating the developed zigzag-shaped joint as a transverse joint subjected to pure tension resulting from a global negative bending moment at the piers on the continuous bridge girders. **Author Contributions:** M.S.A. : methodology, validation, formal analysis, investigation, data curation, writing--original draft, visualization, project administration. K.S. : conceptualization, investigation, data curation, writing--review and editing. H.M.A. : formal analysis, investigation, writing--review and editing. All authors have read and agreed to the published version of the manuscript. **Funding:** This study was sponsored by the Ontario Ministry of Transportation's (MTO) Highway Infra-structure Innovation Funding Program through a cash contribution, through Lafarge North America supplying UHPFRC (Ductal JS1000), and through Schoeck Canada providing composite GFRP bars. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request. **Acknowledgments:** Opinions expressed in this paper are those of the authors and do not necessarily reflect the views and policies of the Ministry. The authors thank the technical staff of the Department of Civil Engineering at Toronto Metropolitan University for the support received in executing the experimental program. **Conflicts of Interest:** Author [PERSON] was employed by the company Megastone Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.