esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Clinical Assessment Results  
For FMA, all patients gained at least 1 point, with a maximum pre-post difference of 9 points. According to the WMFT scale, every subject reached at least 1 point of improvement, with a maximum difference of 5 points. The mean scores (\(\pm\)STD) are increased for both the measures: from 42.4 \(\pm\) 19.9 to 47 \(\pm\) 21.0 points for FMA (\(p\) = 0.06) and from 52.4 \(\pm\) 24.4 to 56 \(\pm\) 24.0 for WMFT (\(p\) = 0.06). Clinical scales scores are reported in Table 1 and synthetically described in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & **Patient 1** & **Patient 2** & **Patient 3** & **Patient 4** & **Patient 5** \\ \hline \multirow{8}{*}{Clinical scales} & & T0 & 56.00 & 36.00 & 11.00 & 61.00 & 48.00 \\  & FMA & T1 & 57.00 & 44.00 & 12.00 & 65.00 & 57.00 \\  & (0–66) (a. u.) & \(\Delta\) & 1.00 & 8.00 & 1.00 & 4.00 & 9.00 \\  & & \(\Delta\)res\% & 10.00 & 26.67 & 1.82 & 80.00 & 50.00 \\ \cline{2-8}  & & T0 & 66.00 & 47.00 & 12.00 & 71.00 & 66.00 \\  & WMFT & T1 & 70.00 & 51.00 & 16.00 & 72.00 & 71.00 \\  & (0–75) (a. u.) & \(\Delta\) & 4.00 & 4.00 & 4.00 & 1.00 & 5.00 \\  & & \(\Delta\)res\% & 44.44 & 14.29 & 6.35 & 25.00 & 55.56 \\ \hline \multirow{8}{*}{Kinematics} & & T0 & 62.82 & 31.36 & 48.50 & 5.55 & 39.51 \\  & & T1 & 59.04 & 31.80 & 78.75 & 15.68 & 50.25 \\  & & \(\Delta\) & \(-\)3.78 & 0.44 & 30.25 & 10.13 & 10.73 \\ \cline{2-8}  & & T0 & 135.99 & 123.14 & 122.63 & 124.24 & 107.36 \\  & & T1 & 132.62 & 125.07 & 116.74 & 124.64 & 101.05 \\  & & \(\Delta\) & \(-\)3.37 & 1.93 & \(-\)5.88 & 0.41 & \(-\)6.31 \\ \cline{2-8}  & & T0 & 1.25 & 1.42 & 2.35 & 1.08 & 2.03 \\  & & T1 & 1.20 & 1.18 & 2.27 & 1.24 & 1.62 \\  & & \(\Delta\) & \(-\)0.05 & \(-\)0.24 & \(-\)0.09 & 0.16 & \(-\)0.41 \\ \cline{2-8}  & & T0 & 80.73 & 126.39 & 332.54 & 143.65 & 290.71 \\  & & T1 & 60.92 & 71.93 & 248.34 & 81.46 & 163.03 \\  & & \(\Delta\) & \(-\)19.81 & \(-\)54.46 & \(-\)84.20 & \(-\)62.19 & \(-\)127.68 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the results: clinical scales and multi-domain approach. For each parameter X of each domain the values at \(T0\), \(T1\) and the difference \(\Delta\) = \(X(T1)-X(T0)\) are reported. As far as the clinical scales are concerned, the variation over the residual recovery potential \(\Delta\)res% = \(\frac{X(T1)-X(T0)}{\max(X)-X(T0)}\) % are listed as well. In bold, variables that differ significantly (\(p\) < 0.05) comparing values at T1 and T0 (two-sided Wilcoxon rank sum test).
# Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques
## 3 Model Development
### ElasticNet (ENet)  
To understand how ENet finds a solution, we need to make some assumptions. Consider a set of samples \(\{(x_{i},y_{i}),\,i=1,\,2,\,\ldots\,\,N\}\), where each \(x_{i}\in R^{p}\) and \(y_{i}\in R\). Also, consider \(y=(y_{1},y_{2},\,\ldots\,\,y_{n})^{T}\) and \(X\in R^{n\times p}\) as denoting the vector that is called \"_response vector_\" and the set design matrix, respectively. During the model analyzing, ENet (as described in [PERSON] and [PERSON] [54]) establishes a linear program of two parameters (\(K_{1}\) and \(K_{2}\)) to estimate the target. To do this, ENet should minimize the squared loss with \(K_{2}\)-regularization and \(K_{1}\)-_norm_ constraint,  
\[\underset{\beta\in R^{p}}{min}\|X\beta-y\|_{2}^{2}+\mu_{2}\|\beta\|_{2}^{2}\ \textbf{such that}\ |\beta|_{1}\leq g, \tag{3}\]  
where \(\beta=[\beta_{1},\,\beta_{2},\,\ldots\,,\,\beta_{Z}]^{T}\in R^{p}\) denotes the weight vector, \(\mu_{2}\geq 0\) is the \(P_{2}\)-regularization factor, and \(g>0\) represents by \(P_{1}\)-_norm_ budget. The \(K_{1}\) constraint encourages the method to be sparse. The presence of the \(K_{2}\) regularization factor causes the acquisition of a unique solution by making the problem severely convex, and if \(P\gg N\) the optimization continues as stable for noticeable values of \(g\). Furthermore, it  
Figure 8: Variation of the correlation coefficient (R\({}^{2}\)) versus number of iterations, in alternating model tree (AMT) technique.  
helps the solution to be more stable when there is a high correlation between the features. For the number of the models (i.e., the set length of the lambda sequence to be generated), the value of 100 was used. For the number of decimal places (i.e., the number of decimal places to be used for the output of numbers in the model), as usual, the value of 2 was selected. The batch size was considered to be 100. The values of alpha and epsilon were set to be 0.001 and 0.0001, respectively. Along with the above-mentioned structure, a unique linear regression equation can also be found from the ENet as shown in Equation (4):  
\[\begin{split}\text{HL}=-0.049\times\text{X2}+0.100\times\text{ X3}+-0.075\times\text{X4}+0.144\times\text{X5}+-0.003\times\text{X6}+0.051 \times\text{X7}+\\ 0.161\times\text{X8}+35.597.\end{split} \tag{4}\]
## 3 Test Results and Discussion
### Results of Repeated Impact Tests
#### 3.2.4. Dynamic Failure Pattern of Specimens under Repeated Impacts  
Figure 11 reveals the change of the sandstone from being intact to fractured under repeated impacts. The study found that the crack development of the specimen cannot be observed from the naked eye when the impact number is less than seven times, and only the decrease in the longitudinal wave velocity can be measured by an acoustic detector. When the impact number reaches seven and above, a random distribution of cracks appears on the surface of the specimen. The randomly distributed cracks penetrate with the increase in the number of impacts, but as long as a penetrating crack is formed, the new crack is formed and expands around the existing penetrating crack as the core in the subsequent impact, then the penetrating crack is extended until the whole specimen is penetrated. Finally, the specimen cracks along this crack under multiple impacts. Differing from the  
Figure 10: The fitting curve of cumulative sandstone damage under repeated impacts.  
Figure 9: Dynamic stress–strain curves of sandstone specimens under repeated impact tests.  
specimen failure caused by a single impact, the failure pattern caused by repeated impacts has only one failure surface. The fundamental reason for this phenomenon is that the damage caused by repeated impacts is the result of cumulative damage development. The dominant crack is taken as the core, and a new crack is developed, resulting in the expansion of the crack and the formation of a single fracture surface. When the specimen is damaged under a single impact, it has multiple fracture surfaces. As long as the amplitude of the incident wave is large enough, multiple cracks will be randomly generated in the specimen, and based on the propagation and development of these cracks, damage will occur. There is no dominant crack, nor will one develop with an existing crack as the core.
## 3 Proposed Method
### Adaptive Bin Estimator
#### 3.3.3 Bin-Splitter MLPs (\(S_{n}\))  
After bin initialization, the initial bin division is sent to the bin-splitter MLPs to perform the coarse-to-fine bin generation strategy. The bin-splitter MLPs play an important role in determining the distribution of bins, based on their respective bin embeddings. In essence, they act as decision-makers to determine how the previous bin division should be divided for improving the precision and the resolution of bins. As shown in Figure 6, the interpolated embedding features of the previous layer are element-wisely added with the embedding features of the current layer and then fed into the next bin-splitter MLP. After the FC layer in each bin-splitter MLP, the features are passed through a sigmoid activation function to obtain a split score vector of dimension \(2^{(n-1)q}\) denoted by \(\mathbf{\alpha}=\left[\alpha^{0},\alpha^{1},\ldots,\alpha^{(2^{n-1}q-1)}\right]\). The scores in this vector determine how each previous bin is divided into two bins. The bin-splitter MLP imposes the \(n\)th bin division on \(\mathbf{b}_{n-1}\) with \(\mathbf{\alpha}\) to obtain the bin-division vector the \(n\)th bin-division vector \(\mathbf{b}_{n}\) as  
\[\mathbf{b}_{n}=\left[\alpha^{0}b_{n-1}^{0},\left(1-\alpha^{0}\right)b_{n-1}^{0}, \alpha^{1}b_{n-1}^{1},\left(1-\alpha^{1}\right)b_{n-1}^{1},\ldots,\alpha^{(2^{ n-1}q-1)}b_{n-1}^{(2^{n-1}q-1)},\left(1-\alpha^{(2^{n-1}q-1)}\right)b_{n-1}^{(2^{n-1}q- 1)}\right]. \tag{5}\]  
This binning processing increases granularity and resolution as the layers progress, ultimately improving the quality of the output. Similar to INIT, the bins of the output bin-division vector are re-scaled by the renewing their centers to fit the depth range of the training dataset, given by  
\[c\left(b_{n}^{i}\right)=d_{\min}^{n}+\left(d_{\max}^{n}-d_{\min}^{n}\right) \cdot\left(\vec{b}_{n}^{i}/2+\sum_{j=1}^{i-1}\vec{b}_{n}^{j}\right),i=0,1, \ldots,(2^{n}q-1), \tag{6}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer Type** & **Input Channels** & **Output Channels** & **Activation** \\ \hline FC & 32 & 64 & GeLU \\ FC & 64 & \(q\) & RLU \\ Global AvgPool & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of INIT MLPs while keeping the same \(H_{n}\) and \(W_{n}\).  
where \(d_{\min}^{n}\) and \(d_{\max}^{n}\) are the predicted minimal depth and maximal depth of bias-prediction MLP \(B_{n}\). Table 3 details the architecture of the \(n\)th splitting MLPs.
# Numerical Simulation and Analysis of Heart-Aorta Fluid-Structure Interaction Based on S-ALE Method
## 2 Materials and Methods
### Analysis of Heart-Aorta FSI at Different Physiological States  
In actual physiological processes, blood viscosity changes in response to factors such as erythrocyte specific volume and plasma protein concentration (especially fibrin content). Meanwhile, blood viscosity is also a core parameter that determines the WSS in the aortic wall. In order to investigate the effect of changes in blood viscosity on the hemodynamic response of the aorta, heart-aorta FSI models were established with gradient variations ranging from 0.0015 Pa\(\cdot\)s, 0.0035 Pa\(\cdot\)s, 0.0055 Pa\(\cdot\)s, 0.0075 Pa\(\cdot\)s, and 0.0095 Pa\(\cdot\)s.  
Figure 5: Boundary load curves at normal physiologic state. (**a**) LV motion displacement curve; (**b**) relative volume curve obtained from outlet pressure conversion.  
LV ejection is the driving force of aortic blood flow. To investigate the effect of changes in LV ejection volume on the hemodynamic response of the aorta, changes in physiological state were achieved by scaling the time-displacement loads applied to the LV (Figure 5a). The ejection volume gradually increases as the LV motion displacement rising, as shown in Table 2. In our study, the normal physiologic state peak LV displacement was 5.1 mm with an ejection volume of 66.6 mL. The weakened systole pattern simulated the heart insufficiency with reduced ejection volume; the enhanced systole pattern simulated the exercise state with increased ejection volume.  
The FSI model calculations were performed on a workstation equipped with an AMD EPYC 7R3 64-Core Processor (2.45 GHz). The LS-DYNA 971 R12.0 (LSTC, Livermore, CA, USA) version of the solver was used for the solution. The model required approximately 15 h of computation time during the cardiac cycle (0.8 s).
## 2 Materials and Methods
### Testing Procedures  
The density measurements on test samples were recorded in accordance with _AS 1012.5:2014--Determination of mass per unit volume of freshly mixed concrete_[47] and the workability was measured using a flow table apparatus, in accordance with _ASTM C230/230M-08--Standard Specification for Flow Table for Use in Tests of Hydraulic Cement_[48]. The compressive strength testing was conducted in accordance with _AS 1012.9.2014--Methods of testing concrete Method 9: Compressive strength tests--Concrete, mortar and grout specimens_[49] using a 100 kN _Instron_ 1195 testing machine, at a loading rate of 20 \(\pm\) 2 MPa/min. The cubical specimens were tested for initial compressive strength at 24 h after casting. No samples were subjected to a rest period, which meant that all samples were assumed to be immediately cured after casting. The measurements were done in triplicate and the average values are quoted. The mass loss for each test sample was measured using two methods: by using a balance and through thermo-gravimetric (TGA) runs. In the first method, cube samples were accurately weighed in an electronic balance before and after exposure to elevated temperature levels from which the difference in weights were calculated. In the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mix Components** & **RPC01 +** & **RPC02 +** & **RPC01 +** & **RPC02 +** \\
**Mix Components** & **Gladstone** & **Gladstone** & **Gladstone** & **Gladstone** \\  & **GP-0.4/2.5** & **GP-0.4/2.5** & **GP-0.57/2.5** & **GP-0.57/2.5** \\
**Sample ID** & **RPGC 01** & **RPGC 02** & **RPGC 03** & **RPGC 04** \\ \hline Volume needed (mm\({}^{3}\)) & 1.00 & 1.00 & 1.00 & 1.00 \\ GP cement (kg/m\({}^{3}\)) & — & — & — & — \\ Fly ash (kg) & 680 & 955 & 680 & 955 \\ Silica fume (kg/m\({}^{3}\)) & 204 & 229 & 204 & 229 \\ Silica flour 200 G (kg/m\({}^{3}\)) & 204 & 10.0 & 204 & 10.0 \\ Silica sand 50 N (kg/m\({}^{3}\)) & 974 & 1051 & 974 & 1051 \\ Alkaline solution (kg) & 272 & 382 & 388 & 544 \\
8 M NaOH (kg) & 77.7 & 109 & 111 & 156 \\ Sodium silicate grade D (kg) & 194 & 273 & 277 & 389 \\ SP (L/m\({}^{3}\)) & — & — & — & — \\ Water (kg/m\({}^{3}\)) & — & — & — & — \\ Added water due to absorption (kg) & 11.7 & 10.6 & 11.7 & 10.6 \\ Added water due to workability (kg) & — & — & — & — \\ Alkaline solution/fly ash ratio & 0.40 & 0.40 & 0.57 & 0.57 \\ Na\({}_{2}\)SiO\({}_{3}\)/NaOH & 2.50 & 2.50 & 2.50 & 2.50 \\ Aggregate/binder ratio & 2.03 & 1.35 & 2.03 & 1.35 \\ Total weight (kg) & 2334 & 2627 & 2450 & 2789 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mix Design—RPGC Trial and Error.  
second method, the Mettler Toledo TGA was used to determine the loss of mass. Powdered samples (obtained passing through the 0.425 \(\upmu\)m (ca. 20 mg) sieve were placed in silica crucibles and subjected to a constant heating rate of 10 \({}^{\circ}\)C/min up to 800 \({}^{\circ}\)C, under a nitrogen atmosphere and the mass loss of the samples (in wt. %) were recorded with increasing temperatures (from 30 to 800 \({}^{\circ}\)C).
# A One-Phase Tree-Structure Method to Mine High Temporal Fuzzy Utility Itemsets
###### Abstract  
Compared to fuzzy utility itemset mining (FUIM), temporal fuzzy utility itemset mining (TFUIM) has been proposed and paid attention to in recent years. It considers the characteristics of transaction time, sold quantities of items, unit profit, and transformed semantic terms as essential factors. In the past, a tree-structure method with two phases was previously presented to solve this problem. However, it spent much time because of the number of candidates generated. This paper thus proposes a one-phase tree-structure method to find the high temporal fuzzy utility itemsets in a temporal database. The tree was designed to maintain candidate \(1\)-itemsets with their upper bound values meeting the defined threshold constraint. Besides, each node in this tree keeps the required data of a \(1\)-itemset for mining. We also designed an algorithm to construct the tree and gave an example to illustrate the mining process in detail. Computational experiments were conducted to demonstrate the one-phase tree-structure method is better than the previous one regarding the execution time on three real datasets.  
fuzzy set; data mining; temporal fuzzy utility mining; temporal database; tree structure
# An Image Hashing-Based Authentication and Secure Group Communication Scheme for IoT-Enabled MANETs
## 5 Results and Analysis for Performance Evaluation  
The proposed work was verified through extensive simulation where nodes were deployed at 1200 m \(\times\) 1200 m. A simulation was conducted using simulation tool NS 2.35 on Fedora Core 20. In this scenario, TCL was used to create nodes and deploy the network, and for message initiation at particular timeslots. The results were extracted using AWK scripts applied to the trace files. The C language was used for implementing the send, receive, hash, image hash, and security functions. We created separate classes for different levels of devices, such as the GH and NA member nodes. A list of simulation parameters is shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Network Area & 1200 m \(\times\) 1200 m \\ Group Range & 400 m \\ Sensing Range & 120 m \\ Primary Energy of Node & 1000 J \\ Relaying Power & 0.819 \(\mu\)J \\ Receiving Power & 0.049 \(\mu\)J \\ Channel Type & Wireless \\ Communication Framework & Two Ray \\ Relaying Power of node & 0.5819 \(\mu\)J \\ Receiving Power & 0.049 \(\mu\)J \\ Maximum Queue Packets & 50 \\ Router Trace & ON \\ Mac Trace & OFF \\ Agent Trace & ON \\ Number of Nodes in a Group & 5–30 nodes \\ Original Data Size & 50–400 bytes \\ Total Number of Message & 8–40 messages \\ Set Time Period & 0.1–1.0 s \\ Number of Nodes & 50–250 nodes \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of simulation parameters.  
Figure 3: Scenario of intergroup node migrations.  
In the modeling message, node 0 is a GH at group 1 that forwards the group migration requests of node 4 to the target GH at group 2. Node 6 is a GH at the target group. The message display has a receiving function in node 6 of the target GH, and the message depends on the TCL modeling script sending function. Figure 3 represents the complete setup. However, if the password and image of the node are unverified, then that node will not be permitted from the targeted GH. For example, the source GH (node 0) request for the \(Ni\) (node 4) is received at the target GH (node 6). The denied responses for joining node 4 are replied to by the target GH (node 6). In this setup, node 4 was unverified, as the hash image and password were not similar to endangered standards.  
In this section, the proposed Scheme IHA was examined based on security and efficiency. We compared the proposed IHA scheme with two recent schemes, namely, SRIH [27] and ESIH [28], to prove the supremacy of our proposed scheme. We analyzed our scheme in terms of communication cost, computational cost, memory overhead, failure cost for cluster authentication, and reliability against malicious nodes.
## 6 Results and Discussion
### Scenario 2: Barrier Coverage  
In this Scenario, we monitor a barrier within the area in order to detect any intruders. We used a picture with 100 \(\times\) 100 pixels, where the barriers are colored in yellow. In this simulation, we use the same parameters: \(\mu\) = 0.05, \(d_{0}\) = \(R_{S}\) ; \(R_{S}\) = 7 ; \(R_{T}\) = 14; except for \(\
u\) = 0.1, so better performance could be achieved.Figure 16 shows the initial deployment with N\({}_{0}\) = 37. After optimization, the algorithm found the best deployment of nodes at iteration 6485, achieving the collection of the maximum joint interest with N = 16 nodes, as illustrated in Figure 17.  
Figure 18a shows the network before guaranteeing connectivity. Two unconnected groups of sensor nodes are detected. Nodes within the same group are connected as demonstrated by green links. Otherwise, nodes from different groups can't communicate with each other as shown by yellow links. Figure 18b shows a fully connected network as demonstrated by the green lines linking all nodes so they can reach the sink node.  
Figure 19 demonstrates the Rips and Rics values for different designs. The optimal topology was detected at iteration 6485, where 100% of collected interest is reached, while full coverage and connectivity are granted. Besides, the best average of collected interest per sensor is achieved, proving that the algorithm has chosen the best topology between all designs, ensuring that the area is not over- or under-covered.  
Figure 19: Ratio Rips &Ratio Rics “Scenario 2”.  
Figure 17: Optimal sensor deployment “Scenario 2”.
# Wireless Sensor Network Aided Assembly Line Monitoring According to Expectations of Industry 4.0
## 2 Materials and Methods
### Parameters That Influence the Localization Procedure and the Quality of the Assembling Process  
Having a mounted wireless sensor on the base part allows the monitoring of the assembling process, i.e., the position monitoring of the base part in the assembling system. The usage of WSN technology raises some questions due the industrial environment of the assembling systems. The interference, diffraction, and dissipation of the wireless signals in industrial environment can be critical and therefore ask for additional attention during the design of the WSN.  
To be able to localize the mobile sensor mounted on the base part, several referent-fixed sensors, i.e., anchor sensors, are needed as well. By optimally positioning the anchor sensors, the unwanted effects of the industrial environment can be minimized.  
The localization is realized based on predefined resolution. This resolution is also used when the RSSI map of the assembly line is recorded. The localization precision, but also the complexity of the system, is higher if the resolution is higher. Therefore the resolution has to be optimized for every assembling system individually. During the development of the localization system, to be able to define the optimal resolution of the localization system, several characteristics of the system have to be analyzed:  
* Special requirements regarding the accuracy of localization
* The overall dimensions of the assembling line (lengths and width)
* The number of workstations in the assembling line
* The number of used anchors
* The overall dimensions of the product
* Available hardware and software resources implemented into the localization system
* Specific requirements of the assembling system
* Technical specification of the wireless modules.  
Every assembling line has a defined working environment, which is necessary to have a quality product at the end of the assembling process. Usually, the quality of the product is analyzed at the end of the process, not during the process. Several physical parameters could influence the product during the process [18].  
WSN technology allows for monitoring the quality of the product during the whole assembling process. Not only the position of the product can be tracked in time and space, but also a number of physical parameters can be checked if required as well:  
* The temperature of the assembly
* The relative humidity of the surrounding air
* The illumination of the assembly
* The unwanted acceleration of the assembly
* The sound level around the assembly  
Having in mind the specificities of the assembling system, it can be concluded that the monitoring of physical parameters has to be performed not only on the assembly itself, but also independently on each workstation and on each technological system, e.g., the screwing system, the gluing system, the riveting system, etc. For that, separate wireless sensors could be mounted directly on each workstation and on used technological systems.