esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Results and Analysis
### Soil Strengthening Mechanism
#### 3.7.2 SEM  
The microstructure of strengthened soil further reflects that the solidification effect is closely related to the compactness and uniformity of calcium carbonate cemented soil particles. The surface sampling of the test group JPY (Figure 19a,b) shows that the calcium carbonate is dense and uniform, completely covering the soil. Therefore, the disintegration rate of the immersion sample is the lowest due to the dense consolidation layer on the surface. It can be seen from the JR samples of the test group (Figure 19c,d) that after multiple grouping, the calcium carbonate crystals were evenly distributed and formed solid cubes. This is similar to relevant research results [37]. This is the reason why the effect of the peristaltic pump is the best. It can be seen from the JK sampling (Figure 19e,f) that the soil particles are also tightly wrapped by calcium carbonate. However, the consolidation is uneven, there are many plate-like crystals, and there are many voids between the soil samples. This may lead to a poor groutiing effect of the air compressor.  
Figure 18: XRD pattern: (**a**) Uncured; (**b**) JR; (**c**) JK; (**d**) JRY; (**e**) JKY; (**f**) JPY.  
Figure 19: Microstructure: (**a,b**) [PY; (**c,d**)] \(\rm{R}_{\rm{}}\); (**e,f**) \(\rm{JK}\).
# Assessment of Simplified Rating Factor Methodology for Shallow Cover Metal Culverts
## Appendix A Example and Comparison
### Refined ODOT Approach  
Structure Information  
Depth of fill: H = 13 in/12 = 1.083 ft  
Span length: S = 16.67 ft \begin{tabular}{l c} Mechanical Properties \\ \hline Minimum yield point of the metal: F\({}_{y}\) = 33 ksi \\ Minimum tensile strength of the metal: F\({}_{u}\) = 45 ksi \\ Modulus of elasticity of metal: E\({}_{m}\) = 29,000 ksi \\ Resistance Factors \\ \end{tabular}   
Percent of thickness remaining based on condition state ([1], Table 8.7): \(\varphi_{\rm area}\) = 100% Note: The number of tables here refers to the corresponding table in the reference paper. Buckling factor based on condition state ([1], Equation (8.8)): \(\varphi_{\rm buckling}\) = 1.00 Note: The number of equations here refers to the corresponding equation in the reference paper. Seam strength reduction based on condition state ([1], Table 8.6): \(\varphi_{\rm seam}\) = 100% Resistance factor for wall area and buckling (ASHTO LRFD Table 12.5.5-1): \(\varphi_{1}\) = 1.00 Resistance factor for seam strength (ASHTO LRFD Table 12.5.5-1): \(\varphi_{2}\) = 0.67 Soil stiffness factor (AASHTO LRFD 12.7.2.4): k = 0.22  
Pipe crown deflection: 0% Buckling strength reduction factor (NCSPA Design Data Sheet No. 19 Appendix B.1): f = 1.00  
Calculate the Critical Buckling Stress (AASHTO LRFD 12.7.2.4) \begin{tabular}{l} If \(S<\frac{r}{\ell}\sqrt{\frac{24E_{m}}{T_{m}}}\), then \(f_{cr}=F_{u}-\frac{F_{u}^{2}}{48E_{m}}\left(\frac{k5}{r}\right)^{2}\) \\ If \(S>\frac{r}{\ell}\sqrt{\frac{24E_{m}}{T_{m}}}\), then \(f_{cr}=\frac{12E_{m}}{(kS/r)^{2}}\) \\ Compare: \(S(in)=200.04<\frac{r}{\ell}\sqrt{\frac{24E_{m}}{T_{m}}}=283.55\) \\ Therefore, \(f_{\rm cr}\) = 33.80 ksi \\ \end{tabular}   
Thrust Capacity of the Wall  
Wall yield strength: \(\varphi_{1}\varphi_{\rm area}\)F\({}_{y}\)A = 109.6 k/ft  
Wall buckling strength: \(\varphi_{1}\varphi_{\rm area}\varphi_{\rm buckling}\)f\({}_{\rm cr}\)A = 112.3 k/ft  
Seam strength: \(\varphi_{2}\varphi_{\rm seam}\)(Seam strength) = 88.4 k/ft  
Therefore, the thrust capacity of the wall: T\({}_{\rm cap}\) = Min[\(\varphi_{1}\varphi_{\rm area}\)F\({}_{y}\)A, f\(\varphi_{1}\varphi_{\rm area}\varphi_{\rm buckling}\)f\({}_{\rm cr}\)A, \(\varphi_{2}\varphi_{\rm seam}\)(Seam strength)] = 88.4 k/ft  
Pipe Wall Thrust due to Earth Cover  
Pipe wall thrust due to earth cover: T\({}_{\rm E}\) = 8 HS/2 = 1.01k/ft  
Compare the factored T\({}_{\rm E}\) = \(\eta_{\rm RYEVmax}\)T\({}_{\rm E}\) in simplified approach with CANDE result: \begin{tabular}{l c} \hline
**Simplified** & **CANDE** \\ \hline
2.07 k/ft & 2.75 k/ft \\ \hline \end{tabular}   
The CANDE value is obtained at the footing of the culvert. Calculate the Pipe Wall Thrust due to Live Load plus Dynamic Load Allowance  
To incorporate the moment equation in the Duncan method for load rating, the live load effects should be calculated based on the live load over the quarter point.
# Cooperative Data Collection Mechanism Using Multiple Mobile Sinks in Wireless Sensor Networks
## 3 Network Environment and Problem Formulation
### Problem Formulation
The value of \(s_{ij}^{max}\) can be derived by applying Equation (4)  
\[s_{ij}^{max}=\arg(_{T_{i}\in T,p_{ij}\in T_{i}}E_{ij}) \tag{4}\]  
Expression (5) reflects the goal of this paper. Objective:  
\[\underset{1\leq i\leq k}{Min}\underset{1\leq i\leq k}{Max}s_{ij}^{max} \tag{5}\]  
When achieving the objective (5), the following constraints (6)-(9) must be satisfied. Let \(|\,L\,|\) denote the length of path \(L\). Let \(L_{min}\) denote the shortest Hamilton path passing through every CP in Tree \(T_{i}\) and \(L_{max}\) denote the maximal length of a valid path. Let \(d_{i}\) denote the rendezvous point of global mobile sink \(m_{0}\) and local mobile sink \(m_{i}\). Let path \(\tau_{0}\) denote the path that passes all rendezvous points. Constraint (6) illustrates that the length of each path \(\tau_{i}\) should be smaller than or equal to the maximal length that mobile sinks can move and larger than the minimal length. (1) Distance Constraint:  
\[|\ L_{min}|\ <\ |\tau_{i}|\ \leq|L_{max}\,|,\ 0\ \leq\ i\ \leq\ k \tag{6}\]  
Each mobile sink \(m_{i}\) should collect data from all CPs in \(T_{i}\) and then arrives at the rendezvous point \(d_{i}\) to forward its data to the global sink \(m_{0}\). Let \(m_{0}\) and \(m_{i}\) arrived at rendezvous point \(d_{i}\) at time points \(t_{i}^{1}\) and \(t_{i}^{2}\), respectively. To guarantee that the collected data are fresh, the _rendezvous delay constraint_, as shown in Constraint (7), should be satisfied. (2) Rendezvous Delay Constraint:  
\[t_{i}^{1}\ -\ t_{i}^{2}\ \leq\eta \tag{7}\]  
where \(\eta\) is an acceptable time delay at each rendezvous point. Let one round, denoted by notation \(\varsigma_{i}\), represent the time period required for collecting one data packet from each sensor in \(T_{i}\). Let \(\Omega\) denote the lifetime of a given WSN. The number of rounds that mobile sinks can visit \(T_{i}\) is \(\Omega/\varsigma_{i}\). Since the energy consumption of \(p_{ij}\) is \(E_{ij}\) in each round. The total energy consumption of CP \(p_{ij}\) is \(E_{ij}\ \times\ (\Omega/\varsigma_{i})\). Constraint (8) checks if the total energy consumption is less than or equal to the battery capacity \(B\). **(3)**_Battery Constraint:_  
\[E_{ij}\ \times\ \left(\frac{\Omega}{\varsigma_{i}}\right)\leq\ B\ \ \ \ 1\ \leq\ i\ \leq\ k,\ 1\ \leq\ j\ \leq\ k_{i} \tag{8}\]  
where \(B\) denotes the battery capacity. Let \(S_{ij}\) denote the set of sensors rooted by CP \(p_{ij}\) in subtree \(T_{ij}\). Let \(f_{ij}^{out}\) denote the total number of packets sent by CP \(p_{ij}\) in each round and let \(f_{i}^{in}\) denote the number of packets created by each \(s_{i}\) in set \(S_{ij}\) in each round. To guarantee that all packets of \(S_{ij}\) can be received by the CP \(p_{ij}\), and then \(p_{ij}\) forwards the received packets and its own packet to the mobile sink, the following Flow Constraint (9) should be satisfied. (4) Flow Constraint:  
\[f_{ij}^{out}=(\sum_{\forall s_{i}\in S_{ij}}f_{i}^{in})\ +\ 1 \tag{9}\]
## 6 Discussion  
The system proposed in this paper is differentiated from other edge computing-based healthcare systems proposed in the existing literature. Most smart healthcare systems primarily use sensors and electronic devices connected to the Internet to remotely monitor the patient's health and transmit this information to the doctor. In particular, for remote medical care, the health status is confirmed by transmitting minimal biosignal data, such as those for SpO2, body temperature, and pulse. Such biometric data is also important. However, data such as SpO2 and pulse rate are analyzed from raw data, so the collection of raw data is also important. Therefore, the system proposed in this paper employes an edge computing-type monitoring system that reliably collects, stores, and analyzes all data (raw data and analyzed data) generated from the IoT pulse oximeters. In particular, the goal was to create a system that could be applied to hospital intensive care units or recovery rooms. However, the collection of such raw data possesses potential risks, such as increased data maintenance and management costs.  
The important elements of the patient management system proposed in this paper were evaluated. According to the evaluation results, good outcomes were achieved by analyzing the results measured for 24 h using five IoT pulse oximeters and one edge node. However, the evaluation method used in this paper is the result of testing in a limited environment. Therefore, it is necessary to set up various test bed environments for further analyses.
## 7 Model Predictive Control (MPC) Strategy Used in T1 DM Therapy
Consequently, for the first time in history, an automated model was approved by the FDA as a substitute for the preclinical trials of certain insulin treatments, including closed-loop algorithms [162]. This UVA/Padova T1 DM simulator (S2008) emulated meal challenges and included a population of the 300 in silico subjects (100 children, 100 adults, and 100 adolescents) for validation. Each virtual subject was represented by a model's parameter vector, which was randomly extracted from an appropriate joint parameter distribution. At that time, the S2008 was successfully used by 32 research groups in academia, as well as by the companies active in the field of the T1 DM treatment. The simulation results were explained by 63 publications in peer-reviewed journals. [PERSON] et al. [163] presented new insights about the non-linearities of insulin action in the glucagon kinetics and hypoglycemic range and developed an updated version of the  
Figure 14: Conceptual overview of a model predictive control (MPC) strategy used in T1 DM treatment and control (adopted from [150]). UVA/Padova T1 DM simulator (S2013), which was submitted to the FDA for approval in 2013. It yields better results compared to the S2008 simulator, and it has been extensively used in T1 DM treatment until now. Later, [PERSON] et al. [164] assessed the rationality of the S2013 simulator against the clinical data and compared its performance with that of the S2008. Recently, the S2013 simulator has been enriched by three novel features [165], which are: (i) assimilation of intra- and inter-day variability of the insulin sensitivity (SI), (ii) different distributions of the carbohydrate-to-insulin ratio (CR) at lunch, breakfast, and dinner, and (iii) dawn phenomenon. [PERSON] et al. [166] presented an upgraded version of the UVA/Padova S2013 simulator. The presented simulator reproduces the intra-day glucose variability observed in the clinical data, and also describes the nocturnal glucose increase, and the simulated insulin profiles reflecting real-life data. The FDA's approved UVA/Padova simulator has been tested and improved for fully automated BG control with announced/unannounced meal intake [167]. [PERSON] et al. [168] evaluated the potential benefits and risks of different insulins or dosing schemes to suggest the design of the clinical studies for T1 DM. For this, a module describing the pharmacokinetics of different basal insulins is incorporated into the UVA/Padova T1D simulator. Authors verified the feasibility results through extensive simulations with alternate parameters. Furthermore, the MPC and PID control strategies have been extensively used with the UVA/Padova simulator for the insulin regulation to the target individual in the presence of disturbances and model uncertainties to effectively monitor the T1 DM's patients [169; 170; 171].
# Predicting Employee Absence from Historical Absence Profiles with Machine Learning
## 2 Background and Related Work
### Formulating the Prediction of Employee Absenteeism as a Machine Learning Task  
Predicting employee absenteeism can be translated into different machine learning tasks, each of which fulfills specific prediction requirements and provides unique insights. Depending on the desired result and the level of detail, this prediction task can be approachedas _binary classification_, _regression_[9], _multi-target regression_, _multi-label classification_[10] or _hierarchical regression_[11]. The selected task also influences the data representation used in the learning process, which is described in more detail in Section 3. Here, we present the different problem formulations in more detail.  
In the **binary classification** approach to predicting employee absence, the task is to determine whether an employee will be absent (1) or present (0) on a given day or week. This formulation is simple yet powerful for immediate, short-term planning requirements. This model can use characteristics such as the day of the week, public holidays, individual workload, and historical absence patterns to make predictions.  
The **(single-target) regression** task focuses on predicting the number of days an employee will be absent within a certain time frame, e.g., a week. With this task, we not only predict attendance or absence, but also quantify the extent of absence. It is suitable for creating detailed forecasts that help with resource allocation and strategic planning in companies. The key features that can be used to create a regression model include aggregated absence data over a specific historical period.  
**Multi-target regression** involves predicting the number of absence days for multiple future periods simultaneously, such as several consecutive weeks. Multi-target regression extends predictions to multiple future intervals, providing a comprehensive view that aids in long-term strategic planning. This approach is beneficial for understanding patterns over extended periods, which can be critical for addressing systemic issues. For this approach, it is useful to use lagged features. Lagged features are variables derived from past data points. For example, if you are trying to predict employee absences for the week ahead, lagged features could include data such as the number of absence days in previous weeks. This approach was also used in our work.  
In a **multi-label classification** scenario, we want to predict multiple binary outcomes (present or absent) for each unit of time within a period, such as daily predictions over a week. This approach is suitable for weekly planning where daily absence predictions are needed. This approach requires both day-specific and general predictive features.  
In a **hierarchical regression** task, we build models to predict absences at multiple levels of aggregation, from more general (e.g., quarterly) to more specific (e.g., monthly, weekly). This model helps to understand and plan for patterns that occur on different timescales.
# A Topology Optimization Method for Reducing Communication Overhead in the Kalman Consensus Filter
## 4 Performance Evaluation and Discussion
To further evaluate the performance of the proposed network topology optimization method, three performance metrics, the total communication overhead \(J_{\text{total,overhead}}(k)\), the mean square estimate error \(m(k)\), and the mean square estimate disagreement \(\delta(k)\) in the network are adopted, where  
\[m(k)=\sum\limits_{i=1}^{I}\left\|\mathbf{x}_{i}(k)-\mathbf{x}(k)\right\|_{2}^{2}\] \[\delta(k)=\sum\limits_{i=1}^{I}\left\|\mathbf{\dot{x}}_{i}(k)-\frac{ 1}{N}\sum\limits_{j=1}^{I}\mathbf{\dot{x}}_{j}(k)\right\|_{2}\]  
The performance of three cases, (1) the traditional KCF algorithm [5] under original topology \(\mathcal{G}\), (2) the traditional KCF algorithm [5] under optimized topology \(\mathcal{G}^{\prime}\), (3) the RC-KCF algorithm [14] under original topology \(\mathcal{G}\), is evaluated, and \(P\) denotes the probability that each node receives messages from its neighboring nodes. Figure 5 shows the total communication overhead at time \(k\). From Figure 5, we observe that the number of iterations for three cases is 2425, 1556, and 3010, respectively, which means that the proposed method in this paper improves the convergence rate of the KCF algorithm effectively. Figures  
and  
\[\delta(k)\leq 0.2\text{max}(\delta(k))\]  
Therefore, from Figures 5-7, we conclude that the proposed network topology optimization method achieves a good trade-off between the estimation error and communication overhead. We also evaluated the performance of the proposed network topology optimization method using 20 WSNs with \(I=15\), \(K=40\), and \(D=60\) m, and the results are given in Table 2. In these simulations, the number of iterations of the KCF algorithm under the optimized network topologies is reduced by 10-40% compared with that of the original network topologies. In the meantime, the proposed network topology optimization method  
Figure 6: The mean square estimate error. Figure 7: The mean square estimate disagreement.
# Application of a Hybrid Model in Landslide Susceptibility Evaluation of the Western Tibet Plateau
## 2 Materials and Methods
### Conditioning Factors  
Based on the literature, effectiveness, availability of data and relevance with respect to landslide occurrence, seven factors were chosen to analyze the landslide: slope, aspect, elevation, lithology, distance to faults, distance to roads, and distance to rivers to produce the landslide susceptibility map.
## 4 Results
### Effect of Regularization Parameter on Local Explainability Metrics
\begin{table}
\begin{tabular}{l c c c} \hline
**Explanation Method** & **Point Fidelity \(\uparrow\)** & **Neighborhood Fidelity \(\uparrow\)** & **Stability \(\downarrow\)** \\ \hline No-regularization & 0.8183 \(\pm\) 0.3524 & 0.8050 \(\pm\) 0.1268 & 0.3524 \(\pm\) 0.0175 \\ Reg \(\lambda=0.01\) & 0.8473 \(\pm\) 0.0351 & 0.8553 \(\pm\) 0.1158 & 0.1290 \(\pm\) 0.0010 \\ Reg \(\lambda=0.05\) & 0.8781 \(\pm\) 0.0195 & 0.8923 \(\pm\) 0.1043 & 0.1128 \(\pm\) 0.0009 \\ Reg \(\lambda=0.1\) & 0.9370 \(\pm\) 0.0230 & 0.9353 \(\pm\) 0.0737 & 0.0815 \(\pm\) 0.0019 \\ Reg \(\lambda=0.25\) & 0.9740 \(\pm\) 0.0237 & 0.9903 \(\pm\) 0.0329 & 0.0189 \(\pm\) 0.0041 \\ Reg \(\lambda=0.5\) & 0.9824 \(\pm\) 0.0234 & 0.9953 \(\pm\) 0.0215 & 0.0078 \(\pm\) 0.0010 \\ Reg \(\lambda=1\) & 0.9951 \(\pm\) 0.0117 & 0.9953 \(\pm\) 0.0215 & 0.0078 \(\pm\) 0.0010 \\ \hline \end{tabular}
\end{table} 
Table 3: Effect of explainability regularization parameter \(\lambda_{1}\) on stability and fidelity for the pre hoc framework on the ML-100k dataset. Comparison of the pre hoc framework with \(k=10\), for \(\lambda=\{0.01,0.05,0.1,0.25,0.5,1\}\) in point fidelity, neighborhood fidelity, and stability results. “Reg” means that regularization was used. \(\uparrow\) means higher score is better, \(\downarrow\) means lower score is better. The improvements in fidelity metrics indicate better alignment between the predictor and explainer models, while the reduction in total variation demonstrates more consistent explanations across different instances.
## 1 Introduction
### Titanium Alloys in the Biomedical Field  
The conventionally-fabricated Ti-6 Al-4V alloy is one of the most popular materials for biomedical applications because it is biocompatible, corrosion resistant, has a high strength to weight ratio and displays good fatigue resistance [43; 44]. Its Young's modulus can be controlled by varying the chemical composition [45] and porosity [46; 47] of the alloy. Cast Ti-6 Al-4V alloy is a two-phase \(\alpha+\beta\) alloy, and its Young's modulus is between 100 and 130 GPa. Such a Young's modulus is too large for bone implants, but it can be reduced by controlling the degree of porosity [48]. It is important that the Young's modulus of an alloy for bone implants is close to that of human bones, which is between 10 and 26 GPa. Otherwise, a biomechanical mismatch between the implant and the surrounding tissue may promote stress-shielding phenomena, which either retards bone regeneration [49] or causes bone failure via bone atrophy. The porous and rough surface of the implant ensures stronger adhesion between an implant and a bone tissue by promoting the biomechanical connection [50]. Many studies have shown that implants with a pore diameter between 100 and 400 \(\upmu\)m are best for bone implants, because such pore diameters facilitate cell penetration, tissue ingrowth, vascularization and transport of nutrients to healing tissues [51; 52; 53]. Fortunately, both SLM and EBM machines enable the production of complex three-dimensional titanium structures with controlled porosity. The minimum pore diameters obtained by EBM machines are 450 \(\upmu\)m [30], while SLM methods can achieve a pore size of 150 \(\upmu\)m [54] due to the small size of the beam.
# Efficient Open-Set Recognition for Interference Signals Based on Convolutional Prototype Learning
## 2 Related Work
### Prototype Learning  
Prototype learning belongs to metric learning. It is a clustering algorithm derived from the KNN classifier. Different from unsupervised clustering algorithms, prototype learning uses the supervised information of samples to cluster in the training phase and learns a group of N-dimensional prototypes \(\left\{\,P_{1},P_{2},P_{3}\dots\right\}\) as cluster centers. The training procedure of prototype learning is composed of feature extractors and prototype updating. The deep prototype learning has a powerful automatic feature extraction ability and end-to-end updating policy, so it has a better performance than the traditional prototype learning [20; 21; 22; 33]. In Ref. [33], the center loss was used as the regularization term for the cross-entropy loss to reduce the Euclidean distance between similar samples to obtain stronger intraclass compactness, and the network parameters were jointly trained with the prototype to find a more suitable embedding feature space and prototypes. To realize the OSR, the cross-entropy loss in [33] was modified to the Tuplet loss based on distance in [20], while it was replaced with distance-based cross-entropy loss (DCE), one-vs-all loss (OVA) and prototype loss in [21; 22], which further improved the intraclass compactness and interclass separability. However, because the known class sample features overlapped the unknown class sample features in the interior of the feature space, the OSR performance of prototype learning is limited. To solve this problem, we propose hollow convolutional prototype learning with a new hybrid loss function, which clusters known class signal features to the periphery of the feature space.