esa-sceva/satcom-chunk-collection huggingface dataset:
# Landslide Prediction with Model Switching +
## 3 Model Switched Landslide Prediction System
### Prediction Model Design
```
Input:\(T_{data}\): Data of FS values used for training BPNN; \(P_{input}\): Inputs of FS values for prediction; \(E_{threshold}\): Training error threshold; Output:\(P_{output}\): Prediction output of FS values; Variable:\(W\): Weights of BPNN; \(E\): Training error between training outputs and target; \(\delta\): Error value for adjusting weights; \(c_{sat}\): 1: Training cycle is complete, 0: Training cycle is incomplete;
1 Set hidden layer number of cells \(\widehat{N}\) ;// Equation (6)
2 Set the maximum iteration number for training \(Epochs\);
3 Randomly initialize weight \(W\); // Training model
4\(i=0\);
5 while\((E>E_{threshold})\&\&(i<=Epochs)\)do
6\(c_{sat}=0\);
7 while\(c_{sat}=0\)do
8\(CalculateBPNN(T_{data},W)\) ;// Equations (2) and (3)
9 Calculate output network error \(\delta\) ;// Equation (4)
10 Calculate adjustment weight \(W\) ;// Equation (4)
11 ifall samples are trainedthen
12\(c_{sat}=1\);
13 Calculate training error \(E\) ;// Equation (5)
14\(i++\); // Do prediction
15\(P_{output}=CalculateBPNN(P_{input},W)\); return\(P_{output}\);
```  
**Algorithm 1**Prediction Model Algorithm. After the training phase, the training error \(E\) is calculated by Equation (5), to determine whether the training step has reached convergence. If it is not less than the training error threshold, \(E_{threshold}\)-the training phase is restarted. \[E=\frac{1}{N}\sum_{i=1}^{N}(T_{i}-O_{i})^{2}, \tag{5}\]  
where:  
* \(T_{i}\): Target FS value of training sample \(i\);
* \(O_{i}\): BPNN output FS value of training sample \(i\); and
* \(N\): Number of training samples. Considering the training time in our proposed re-training process, the number of outputs, \(Num_{output}\), is set to 1 to avoid a long training time. This means that only one prediction result per iteration will be obtained, by taking the past FS value as input. The number of neurons in the hidden layer, denoted as \(Num_{neuron}\), is determined based on the following experience rule [13]:* The number of neurons in the first hidden layer is calculated using (6): \[Num_{neuron}=\sqrt{Num_{input}*Num_{output}}.\] (6)
## 1 Introduction
Although recent studies focused on the synthesis of polymer networks in the presence of RDRP controllers, such as the synthesis of soft, elastomeric, non-tacky polymer networks; combining RAFT synthesis of the network followed by photo-induced ATRP grafting of side chains [59]; the development of a novel class of acrylic acid (AA)-based superabsorbent polymers (SAPs) with improved absorption performance by applying iodine transfer polymerization (ITP) [60]; the assessment of the advantages of RDRP polymer networks over conventional FRC ones for 3D printing [61]; and the development of a photoinduced free radical-promoted cationic RAFT polymerization method using the direct photolysis of the RAFT agent as a radical source, which was further combined with photo-INIFERTER RAFT polymerization to prepare polymer networks [62], have been reported lately (2019-2022), none of them have been carried out in \(\mathrm{s\mathrm{cCO_{2}}}\), and none of them have used NMP chemistry. In this contribution we report the nitroxide-mediated copolymerization with crosslinking of STY and DVB in \(\mathrm{s\mathrm{cCO_{2}}}\) using 2,2,6,6-Tetramethylpiperidine-1-oxy (TEMPO) and dibenzoyl peroxide (BPO) as the nitroxide controller and initiator, respectively. A high-pressure cell with lateral sapphire windows at 120 \({}^{\circ}\)C and 207 bar was used as the reaction vessel. The motivation and background for this study is contained in a patent generated in our group [55].
# Smart Healthcare Applications over 5G Networks: A Systematic Review
###### Abstract  
Provisioning of health services such as care, monitoring, and remote surgery is being improved thanks to fifth-generation cellular technology (5G). As 5G expands globally, more smart healthcare applications have been developed due to its extensive eMBB (Enhanced Mobile Broadband) and URLLC (Ultra-Reliable Low Latency Communications) features that can be used to generate healthcare systems that allow minimizing the face-to-face assistance of patients at hospital centers. This powerful network provides high transmission speeds, ultra-low latency, and a network capacity greater than that of 4G. Fifth-generation cellular technology is expected to be a means to provide excellent quality of medical care, through its technological provision to the use of IoMT (Internet of Medical Things) devices. Due to the numerous contributions in research on this topic, it is necessary to develop a review that provides an orderly perspective on research trends and niches for researchers to use as a starting point for their work. In this context, this article presents a systematic review based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), with article selection based on inclusion and exclusion criteria that avoid bias. This research was based on research questions that were answered from the included works. These questions focus on technical characteristics, health benefits, and security protocols necessary for the development of smart healthcare applications. We have identified that a high percentage of existing works in the literature are proposals (56.81%, \(n=25\)) and theoretical studies (22.73%, \(n=10\)); few implementations (15.91%, \(n=7\)) and prototypes (4.55%, \(n=2\)) exist, due to the limited global deployment of 5G. However, the panorama looks promising based on proposals and future work that these technological systems allow, all based on improving healthcare for people.  
mobile network 5G; smart healthcare; telemedicine; eHealth; IoMT
# Numerical Simulation of Red Blood Cell-Induced Platelet Transport in Saccular Aneurysms
## 3 Results and Discussions
### Blood Flow and Platelets Migration in Aneurysms
#### 3.2.1. Bulbous Saccular Aneurysm  
The RBCs concentration in the bulbous saccular aneurysm during the initial filling (dimensionless time \(\tau=100\). \(\tau=tu_{in}/h\), where \(t\) is the time, \(u_{in}\) is the inlet velocity and \(h\) is the vessel height.) is shown in Figure 5a, illustrating that the RBCs are drawn into the aneurysm by a vortex within, as seen in Figure 5d. Figure 5b illustrates the near-wall excess of the platelets from the parent vessel drawn into the aneurysm in a similar fashion. In addition, for the results shown in Figure 5a,b, the RBCs concentration is low while the platelets concentration is quite high near the flow circulation center in the aneurysm. This phenomenon agrees with the platelets transportation model used in this paper. In the model, the platelets tend to move to the region where the RBCs concentration is relatively low. The velocity of whole blood and corresponding streamlines are provided in Figure 5c,d. In the bulbous aneurysm, a purely circulation flow is observed. In such a flow field, the platelets entering into the aneurysm have a large resident time due to the circulation. These resided platelets can be activated and coagulate with a higher possibility compared to the platelets in the mainstream flow outside of the aneurysm.  
The progression of initial mixing of the RBCs and the platelets in the bulbous saccular aneurysm at different dimensionless time steps is shown in Figure 6a. We can see that as time increases, the platelets concentration in the aneurysm increases gradually and approaches the concentration near the wall of the straight vessel upstream of the aneurysm. Figure 6b shows the RBCs concentration fields at different dimensionless times. Compared to the Figure 6a, the similarity is that both the concentration fields show a circulation pattern; while the difference is that, the amount of the RBCs entering into the aneurysm is much lower than the platelets. This can be attributed to the shear lift force that the RBCs experienced (see Equation (10)): near the neck of the aneurysm the shear lift force still has the tendency of transporting the RBCs to the center of the straight vessel.  
Figure 4: Schematic of the aneurysm vessels. (**a**) Bulbous saccular aneurysm with neck size of 125 \(\upmu\)m; (**b**) Bulging saccular aneurysm with neck size of 250 \(\upmu\)m. The inlet velocity is 0.5 m/s (Re = 50). The hematocrit is 42%. The platelets bulk concentration is \(3.0\times 10^{14}\) PLTs/m\({}^{3}\).  
Figure 5: The distribution of (**a**) the RBCs concentration, (**b**) the platelets concentration, (**c**) the velocity and (**d**) the streamlines of the whole blood in the bulbous saccular aneurysm after simulating for \(\tau=100\). \(\tau=tu_{in}/h\) is the dimensionless time, where \(t\) is the time, \(u_{in}\) is the inlet velocity and \(h\) is the vessel height. The units of the scale bars in (**b–d**) are \(1\times 10^{14}\)/m\({}^{3}\), m/s and m/s.
# A New Method for Calculating Prediction Parameters of Surface Deformation in the Mining Area
## 6 Discussions  
### Influence of Hidden Layer Nodes on the Prediction of Subsidence Coefficient  
The number of hidden layer neurons plays a key role in the prediction accuracy of the M-CM-GA-ELM neural network model. Too many or too few hidden layers will directly affect the prediction results. Repeated experiments and their own experience are generally used to determine the number of hidden layer neurons. The experiment found that when nodes of the hidden layer are greater than 30, the error has a large mutation. The range of nodes selected in this paper was 1 to 30. The number of hidden layer nodes was judged by the size of the predicted average relative error MeaRE value of the test set. The prediction results are given in Figure 14.  
Figure 14: Selection of the number of hidden layer nodes.  
Figure 13: Contour map of surface subsidence of 1613 working face.  
In Figure 14, the prediction accuracy of the test set varies greatly with the number of hidden layers. Through multiple experiments, when the number of hidden layers was 14, the average relative mean square error of the test set reached the minimum of 5.699%, and the accuracy was the highest. Therefore, the number of hidden layers was 14 in this paper.
## 1 Introduction
The control input for each small spherical robot is determined by solving the local linear MPC optimization problem based on the state information of itself and the neighbors. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Advantages** & **Disadvantages** \\ \hline PID & Simple; independent of system models & Single input and single output \\ \hline LQR & Multiple input and multiple output & Dependent on system model \\ \hline Sliding mode control & Robust to uncertain models and time-varying parameters & Chattering \\ \hline Model predictive control & Deals with constraints; Multiple input and multiple output & Dependent on system model \\ \hline Backstepping & Independent of accurate models; Suitable for nonlinear systems & As the complexity of the control problem increases, more parameters need to be adjusted \\ \hline Fuzzy control & Independent of models & Over-dependence on expert experience \\ \hline Neural network control & Adaptability, learning ability, and robustness & Dependent on a large number of training examples and needs a long training time \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the different control methods. 3. A multi-small spherical robot formation simulation system is built based on a gazebo, and it is used for physical simulation to verify the effectiveness of the proposed algorithm. The remaining parts of this article are organized as follows. The set up and modeling of the small spherical robot are described in Section 1. Next, the control approach was designed, and is detailed in Section 2. Then, the simulation analysis is described, including numerical and physical simulations, in Section 3, and this is followed by the experimental verification in Section 4. In Section 5, the simulation and experiment results are discussed. Finally, the conclusion are summarized in Section 6. Notations: Bold symbols represent vectors and diag(\(\cdot\)) denotes a diagonal operation.
# GCBAM-UNet: Sun Glare Segmentation Using Convolutional Block Attention Module
## 2 Related Work
### Approaches-Based Deep Learning
[38] proposed a U-Net variant that employs a pre-trained MobileNetV2 model as its encoder to facilitate multi-scale feature extraction at different spatial resolutions. [PERSON] and [PERSON] [39] further enhanced the U-Net architecture by integrating CBAM attention mechanisms, residual blocks with embedded coordinate attention, and a max-pooling layer for improved segmentation accuracy.
# Leveraging Blockchain and Consignment Contracts to Optimize Food Supply Chains Under Uncertainty
## 3 Problem Definition, Notations and Assumptions
### Assumptions  
1. Under the make-to-order policy, a single-period supply chain model is taken into account. Without enough supplies on hand to meet client demand, the manufacturer must produce and distribute goods.
2. Although no specific probability distribution is taken into account, customer demand is viewed as random. The distribution of demand is one of the probability distribution functions. Despite this, the customer's demand needs to be compared to a known mean and standard deviation.
3. In the traditional structure, the manufacturer just bears the manufacturing costs; the retailer bears the full cost of inventory carrying. The retailer pays the manufacturer a wholesale price for the order quantity.
4. The consignment policy uses the Stackelberg (leader-follower) game method to simulate a consignment contract. Manufacters and retailers assume the positions of leaders and followers, respectively. Under the MTO manufacturing approach, the manufacturer provides the retailer with a consignment contract.
5. The manufacturer keeps ownership of the goods even after they are transferred to the store or warehouse of the retailer. As the follower, the retailer would prefer to sign the agreement knowing that it will receive a specified fee as well as a commission for each item sold.
6. The consignment contract divides the holding cost into two components: financial and operational. The manufacturer carries the financial portion, whereas the retailer bears the operational portion of the cost.
7. A consignment contract may have a positive or negative sign associated with the fixed charge. A positive fixed fee is charged for guidance from the manufacturer to the retailer and vice versa.  
Figure 1: Consumer behavior under blockchain and no-blockchain adoption.
## 4 Results and Analysis
### Analysis of Compliance Behavior
#### 4.1.3 Protection Motivation Behaviors and Compliance
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Research Method** & **Sample Size** & **Findings** \\ \hline \multirow{3}{*}{[60]} & Mixed method research design & & \multicolumn{1}{c}{} & Fear appeal, response efficacy, \\  & structural equation modeling & 275 usable responses from & self-efficacy, and social influence have \\  & used for framework testing & collected from a university & significant effects on information \\  & & & security behavior \\ \hline \multirow{3}{*}{[14]} & The quantitative research & & \multicolumn{1}{c}{} & Self-efficacy, attitude toward \\  & methodology adopted and SEM & 124 business managers and & compliance, subjective norms, response \\  & used for research model testing & professionals participated & efficacy, and perceived vulnerability \\ \hline \multirow{3}{*}{[61]} & The hypothetical research design & 210 participants responded & Past behavior (habit) has a significant \\  & adopted (pre- and post-test), and & correctly from a & effect on protection motivation and \\  & SEM used for research & Finnish organization & employees’ intentions to comply \\  & model testing & & with ISP \\ \hline \multirow{3}{*}{[37]} & A six steps Mixed (Qualitative & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\  & and Quantitative) Methodology & 67 classes identified for the & \multicolumn{1}{c}{} & PMBs taxonomy will provide a \\ \cline{1-1}  & adopted to develop a taxonomy & \multicolumn{1}{c}{} & protection motivation behaviors & nomenclature that will increase \\ \cline{1-1}  & for protection motivation & \multicolumn{1}{c}{} & of insiders & practitioners and academicians’ \\ \cline{1-1}  & behaviors (PMB) & & understanding of IS security behaviors \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Studies on PMB’s and ISPC.
# Tibetan Speech Synthesis Based on Pre-Trained Mixture Alignment FastSpeech2
## 3 Experiments and Results
### Evaluation Indicators and Experimental Results
#### 3.5.3 Objective Assessment Method  
Root Mean Square Error (\(RMSE\))  
\(RMSE\) measures the differences between the original and synthesized speech in the time domain sequence. It quantifies the average magnitude of errors between the predicted and observed values. The calculation method is depicted in Equation (9):  
\[RMSE=\sqrt{\frac{\sum_{t=1}^{n}(x_{1,t}-x_{2,t})^{2}}{n}}, \tag{9}\]  
where \(x_{1,t}\) and \(x_{2,t}\) represent the speech sequences of the original speech and the synthesized speech at moment \(t\). The smaller the \(RMSE\) value, the greater the similarity between the original and synthesized speeches, and the higher the quality of the synthesized speech.  
The real-time factor (\(RTF\)) measures the efficiency of the speech synthesis process. It represents the ratio of the time required to process a given duration of audio to the actual duration of the audio. The \(RTF\) is calculated using the following equation:  
\[RTF=\frac{\text{Processing Time}}{\text{Audio Duration}}, \tag{10}\]  
where \"Processing Time\" is the total time taken by the system, including all components, to process the audio, and \"Audio Duration\" is the length of the audio that is being processed. A smaller \(RTF\) value indicates more efficient synthesis, meaning the system processes speech faster and is more suitable for real-time applications.
## 1 Introduction
### Pattern Matching with Mismatches  
For pattern matching with mismatches, a naive algorithm computes the Hamming distance for every alignment of the pattern in the text, in time \(O(nm)\). A faster algorithm, in the absence of wild cards, is [PERSON]'s algorithm [10] that runs in \(O(n\sqrt{m\log m})\) time. [PERSON]'s algorithm can be extended to solve pattern matching with mismatches and wild cards, as we prove in Section 2.2.1. The new algorithm runs in \(O(n\sqrt{g\log m})\) time, where \(g\) is the number of non-wild card positions in the pattern. This gives a simpler and faster alternative to an algorithm proposed in [11].  
In the literature, we also find algorithms that approximate the number of mismatches for every alignment. For example, an approximate algorithm for pattern matching with mismatches, in the absence of wild cards, that runs in \(O(rn\log m)\) time, where \(r\) is the number of iterations of the algorithm, is given in [12]. Every distance reported has a variance bounded by \((m-c_{i})/r^{2}\) where \(c_{i}\) is the exact number of matches for alignment \(i\).  
Furthermore, a randomized algorithm that approximates the Hamming distance for every alignment within an \(\epsilon\) factor and runs in \(O(n\log^{c}m/\epsilon^{2})\) time, in the absence of wild cards, is given in [13]. Here, \(c\) is a small constant. We extend this algorithm to pattern matching with mismatches and wild cards, in Section 2.3. The new algorithm approximates the Hamming distance for every alignment within an \(\epsilon\) factor in time \(O(n\log^{2}m/\epsilon^{2})\) with high probability.  
Recent work has also addressed the online version of pattern matching, where the text is received in a streaming model, one character at a time, and it cannot be stored in its entirety (see, e.g., [14; 15; 16]). Another version of this problem matches the pattern against multiple input streams (see, e.g., [17]). Another interesting problem is to sample a representative set of mismatches for every alignment (see, e.g., [18]).
# Fall Detection Approaches for Monitoring Elderly HealthCare Using Kinect Technology: A Survey
## 4 Kinect-Based Fall Detection Approaches
### Interpretation in Control  
Unfortunately, falls can lead to severe and even fatal consequences. These issues are compounded by the presence of a long time spent lying after a fall. This aspect occurs when an older adult is unable to stand up alone and spends more than an hour on the ground before someone comes to help [107; 108]. A static study has shown that staying immobilized on the ground for more than an hour brings a 50% risk of death within six months [109]. More than 50% of those who fall cannot get up from the floor by themselves [110; 111], leading to the necessity of effective and rapid automatic alerting in each approach to anticipate the consequences of prolonged immobilization on the ground. A long lie time can lead to complications such as muscle weakness, pressure area development, pneumonia, dehydration, missing medication, and hypothermia [112]. Thus, automatic notification via SMS-Mail-Call to emergency services, family members, or neighbors must occur immediately after the accident. In the literature, we find that there are several strategies for warning that researchers have used as a condition for the period of inactivity of the fallen person [68]. Several durations have been implemented: 0.7 s [78], 5 s [73], 10 s [59; 105], and 60 s [54; 75; 91]. Voice exchange has been used via the Kinect's voice recognition system [56]. This solution without a timer is not recommended, especially if the person loses consciousness. In certain works, the authors have indicated the alert system's presence in their methods without details on the triggering process [44; 50; 57; 58; 62; 74; 83; 84; 90; 94; 95; 96; 97]. Sometimes, an older person who has fallen lives with the anxiety of falling again. This behavior can impoverishes social life and increase isolation. The integration of neighbors, family members, and volunteers in the intervention after the fall minimizes the psychological and social consequences of a fall [55]. This functionality appears in the form of audio, message [76], or visual exchange [55] of information between the target and the caregivers (family, neighbors, professional). We can name this task through feedback; moreover, one of its objectives is to reduce the error rate of the approach. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Paper** & **Data** & **Preprocessing** & **Features** & **Algorithm** & \begin{tabular}{c} **Threshold** \\ **Values** \\ \end{tabular}