esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Thermal Transfer Modelling of BPB-CFEH
### Basic Assumptions and Boundary Conditions for the BPB-CFEH Thermal Transfer Model  
Many different thermal transfer processes are involved in the heating and ice-melting process of the bridge pylon crossbeams, as shown in Figure 4. In order to obtain better simulation results, the following assumptions are made in this study:  
(1) In the thermal transfer process, the model is surrounded by convective boundary conditions, and the solar radiation process is not considered on the surrounding surfaces because the indoor tests are carried out in a laboratory with a constant temperature without solar radiation. The model has an adiabatic boundary in the direction along which the heating cable is arranged. This is because the cross-sectional region of the heating cable is much smaller than that of the beams, and the temperature change is minimal along the direction where the heating cable is located [31].  
(2) The carbon fiber heating cable is a cylindrical thermal source encased in a PVC layer. The PVC, carbon fiber heating cable, and concrete form a homogeneous medium, and their thermal properties remain constant regardless of temperature fluctuations. And the carbon fiber hot wire, PVC and concrete are in close contact, without considering the contact thermal resistance.  
The thermal transfer of BPB-CFEH is a three-dimensional non-stationary thermal transfer process with coordinates \(x-y-z\). Defining the thermal generating cable as the thermal source term \(H(t)\), the thermal transfer process follows [PERSON]'s law as in Equation (1):  
\[\frac{\partial T}{\partial t}=a\bigg{(}\frac{\partial^{2}T}{\partial X^{2}}+ \frac{\partial^{2}T}{\partial Y^{2}}+\frac{\partial^{2}T}{\partial Z^{2}} \bigg{)}+\frac{H(t)}{Cp} \tag{1}\]  
The boundary conditions around the simulation model (excluding the two faces along the direction of the heating cable arrangement) conform to Equation (2):  
\[-\lambda\frac{\partial T}{\partial\overrightarrow{n}}=h(T-AT) \tag{2}\]  
Adiabatic boundary conditions are set along the two faces in the direction of the heating cable arrangement, in accordance with Equation (3):  
\[\lambda\frac{\partial T}{\partial X}\bigg{|}_{X=0}=0,\,\lambda\frac{\partial T }{\partial X}\bigg{|}_{X=L}=0 \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material Parameter** & **Concrete** & **Carbon Fiber Heating Cable** & **Polythene** \\ \hline Thermal conductivity (W/m °C) & 2.2 & 6.8 & 0.4 \\ Densities (kg/m\({}^{3}\)) & 2500 & 1200 & 930 \\ Specific thermal capacity (J/kg °C) & 970 & 1440 & 2300 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material physical parameters.  
Figure 4: Three-dimensional diagram of BPB-CFEH thermal transfer mechanism.  
In order to stream the cable to the boundary conditions of current thermal exchange, this study assumes that the ambient temperature remains constant and does not undergo any changes.  
The thermal exchange coefficient (\(h_{c}\), W\(\cdot\)m\({}^{-2}\) K\({}^{-1}\)) is defined as shown in Equation (4):  
\[h_{c}=\begin{cases}6+4v(v\leq 5\text{ m/s})\\ 7.41v^{0.78}(v\geq 5\text{ m/s})\end{cases} \tag{4}\]
# Time Domain Room Acoustic Solver with Fourth-Order Explicit FEM Using Modified Time Integration
## 4 Discretization Error Characteristics
### Theoretical Dissipation Error Characteristics  
The dissipation error in the proposed explicit TD-FEM is evaluated theoretically using Equation (41). As an example of the evaluation, Figure 5a shows whether or not the dissipation error has anisotropic behavior in terms of the direction of sound wave propagation. It presents results at 2 kHz and 3 kHz with the following settings of \(c_{0}=340\) m/s, \(h=0.0125\) m, and \(\Delta t=1/59,224\) s. The results indicate that the dissipation is isotropic in terms of wave propagation direction; higher frequencies include larger energy dissipation. Then, Figure 5b shows the convergence of dissipation error with respect to the time interval. The error was calculated at 3 kHz for plane wave propagation at an axial direction with time length of 0.1 s. Two element sizes of 0.0125 m and 0.00625 m were tested. The results revealed that the dissipation error is dependent only on the time interval. Also, the use of smaller time intervals produces lower dissipation errors. These findings suggest that the proposed method can control the dissipation error easily, merely by adjustment of the time interval using Equation (41).  
Figure 4: Waveforms at the receivers in the axial direction (upper row) and the diagonal direction (lower row) between the reference solution and the proposed method for three FE meshes (Mesh-1, -2 and -3) listed in Table 1.  
Figure 3: Modulated Gaussian pulse with upper-limit frequency of 2.5 kHz: (**a**) waveform and (**b**) frequency characteristics.
## 5 Results and Discussion
### Long-Term Evolution of Drought Indices  
In this study, time series were generated to analyze the long-term drought values obtained through the CDI. As shown in Figure 11, the CDI and SPEI time series are presented on the left y-axis, while the scPDSI time series are depicted on the right y-axis. The reason for using two different vertical axes is that the scPDSI varies between \(-5\) and \(+5\), which might pose challenges in the multi evaluation of it with CDI and SPEI, which fluctuate between \(-2\) and \(+2\). One of the most significant findings is the similar behavior exhibited by the CDI and SPEI. The fluctuations in scPDSI values are less pronounced compared to the other two indices. SPEI, in terms of its composition, utilizes precipitation and ET calculations, which are largely based on temperature. This similarity in inputs results in SPEI displaying a similar trend to CDI. However, SPEI focuses only on PET, which is insufficient to represent surface water processes fully [116], and lacks input that provides information about vegetation conditions, leading to significant divergences in some cases. For instance, in the Altimekin district, SPEI values indicate a mild drought and fewer fluctuations compared to CDI values (Figure 11).  
The scPDSI generally exhibits a similar pattern to CDI and SPEI, but significant differences are observed in certain years. For example, scPDSI indicates the presence of a prolonged drought from 2003 to 2009, with drought values remaining constant over several months. A similar pattern is observed between 2016 and 2020. In the districts of Aksehir, Beysehir, and Ilgin, a long-term drought with consistent values is present. One of the reasons for this could be that scPDSI values are calculated using the drought values from the previous month, which contributes to the persistence of similar values over extended periods.  
Figure 11: _Cont._
## 7 Conclusions  
This paper reports experimental results on the flexural behavior of RC T-section beams strengthened by CFRP sheets and proposes new analytical models to predict its behavior. Based on the test results, the failure mechanisms were analyzed, and the effects of various parameters were discussed. Robust analytical models were developed to predict the cracking resistance \(P_{cr}\), yielding resistance \(P_{y}\), and ultimate resistance \(P_{u}\) of the T-section beams strengthened by CFRP. Model accuracy was verified though validation against the experimental data. Based on the experimental and analytical study, the following conclusions can be drawn;  
1. The RC T-section beam specimens strengthened with CFRP sheets failed in a flexural failure. Increasing the thickness of the strengthening CFRP did not enhance the cracking resistance of the beam, but significantly improved the yielding and ultimate resistance.
2. Increasing the flexural reinforcing ratio of the beam by 36% from 0.95% to 1.29% increased the \(P_{cr}\), \(P_{y}\), and \(P_{u}\) by 17%, 13%, and 48%, respectively.
3. Increasing the CFRP U-wrap spacing had little effect on the resistance of the RC T-section beams. Moreover, increasing the compressive strength of concrete only had marginal influence on the yielding and ultimate resistance of the CFRP strengthened RC T-section beams.
4. The T-section beams strengthened by CFRP sheets exhibited lower ductility at the final failure stage due to brittle failure of the bond between the CFRP sheet and concrete.
5. The developed analytical models offered reasonable predictions of the cracking resistance, yielding and ultimate resistance of the RC T-section beams strengthened with CFRP sheets. The average test-to-prediction ratios (or COVs) for \(P_{cr}\), \(P_{y}\), and \(P_{u}\) were 1.01 (0.12), 1.10 (0.12), and 1.08 (0.10), respectively.  
**Author Contributions:** Conceptualization, Y.Z., M.L.N.; methodology, Y.Z., M.L.N.; validation, Y.Z., M.L.N.; formal analysis; Y.Z., M.L.N.; investigation, Y.Z.; resources, Y.Z., M.L.N.; data curation, Y.Z.; writing original draft, Y.Z.; writing review and editing, M.L.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** No funding received.  
**Conflicts of Interest:** The authors have no conflict of interest related to this research publication.
# Fall Recognition System to Determine the Point of No Return in Real-Time
## 2 Materials and Methods  
### Sub-Division of the Critical Phase  
A fall, defined in an existing study, is a sequential process of a person losing balance during his or her daily activities, colliding with the ground, lying on the ground, and standing up again. In this study, the entire phases of falling were divided into pre-fall, critical, post-fall, and recovery phases [3]. This study aimed to detect the critical phase, as presented in Table 1, i.e., the point where a person cannot recover his or her position during the fall process. The pre-fall phase, which refers to daily activities, was limited to falls generated by standing and walking. The critical phase, which refers to a fall progress process, was sub-divided into two specific phases, as shown in Figure 1. In this figure, the x- and y-axes indicate the time flow and location of the COM in the direction of gravity, respectively. T\({}_{0}\) indicates the moment when balance is disrupted by a causal factor, leading to a fall, and T\({}_{0}\) to T\({}_{\alpha}\) is the period during which a person can recover balance, according to his or her exercise ability. The T\({}_{\alpha}\) point indicates when the imbalance has progressed to some extent; after this, it is difficult to recover the posture using exercise ability. This study aimed to detect the point of no return (PONR), i.e., when a person cannot recover his or her balance based on his or her exercise ability. T\({}_{1}\) refers to the point at which the chest or bottom of a person touches the ground. The time after this point indicates a complete fall.  
To develop a system for real-time detection of PONR, the process of falling in an ADL situation is conceptualized as multiple frames and each frame is mapped to the result inferred by the learning model. Then, the fall progress is expressed as a result value inferred by the learning model (e.g., the continuous output value of a fall learning model, such as 333334444, where the value of the imbalance in our system is 3 and the falling down is 4). Through continuous result values inferred from the learning model, entering the falling down stage can be detected and the PONR time can be determined in real-time. This system was performed every 10 ms in consideration of the time required for inference of the learning model and the operating time of the system collecting data from several sensors.
# Energy Ratio Variation-Based Structural Damage Detection Using Convolutional Neural Network
## 5 Experimental Validation
### Experimental Results and Analysis  
In order to verify the feasibility of the numerical model, a total of three types of working scenarios, namely, working scenarios S0, S2, and S7, are selected for the steel-beam. 54 sample data are collected for each type of working scenarios, and 162 test data sets are formed. The CNN model for training finite element data is used for further testing, and the accuracy of the experimental data is finally obtained. Firstly, the acceleration signals  
Figure 12: The notch position and size diagram.  
Figure 13: Experimental facilities: (**a**) Piezoelectric acceleration sensor. (**b**) Rubber hammer. (**c**) Laptop. (**d**) DH5927N dynamic signal test and analysis system.  
under two damage working scenarios are obtained by vibration test, as shown in Figure 14, and then the acceleration signals are decomposed into each frequency band by wavelet packet. The ERV values obtained by comparison experiment and numerical simulation are shown in Figure 15. The results show that the matching degree between experimental data and numerical simulation data can reach about 95%. It can be seen from the figure that when the damage occurs at the position of the second unit, the ERV value at this position is significantly different from the ERV value of other units. When the damage occurs at the mid-span position of the second unit and the eighth unit, the change of the energy ratio of these two units can also be observed, and other units also show a small range of fluctuations.  
After obtaining the energy ratio variation damage index of S2 and S7, the experimental data are used to verify the damage detection effect of CNN on real structures. Firstly, the grid is trained to obtain a sample database, and then the experimental data is used as a test sample for prediction. The final accuracy of the validation set is 91.67%. The network training process and the final detection results of single damage and multiple damage are shown in Figure 16.  
Figure 14: (**a**) Acceleration signals of S2. (**b**) Acceleration signals of S7.  
Figure 15: (**a**) ERV value of working scenario S2. (**b**) ERV value of working scenario S7.
## 3 Experimental Results and Analysis  
### Experimental Devices and Materials  
The data used in the article were collected using a detection train. The detection device consists of a detection probe, a conditioning circuit, a signal data acquisition card, and a computer. The detection probe is mainly composed of a magnetizer, a sliding shoe, and a Hall sensor array. The Hall sensor array is situated between the sliding shoe and magnetizer, as shown in Figure 7. The device is installed on the train and scans along the railway. During detection, the lift off will vary within 1-3 mm.  
Figure 8 shows a defect, with a length of approximately 15 mm in the x-direction and a width of approximately 13 mm in the y-direction. The detection speed was 45 km/h. The original signal collected via the probe is shown in Figure 9, and the defect MFL signal is marked with a rectangle on the graph.  
Figure 8: Defect sample.  
Figure 7: Detection device.
###### Abstract  
The increase in the world population has led to new needs for food. Precision Agriculture (PA) is one of the focuses of these policies to optimize the crops and facilitate crop management using technology. Drones have been gaining popularity in PA to perform remote sensing activities such as photo and video capture as well as other activities such as fertilization or scaring animals. These drones could be used as a mobile gateway as well, benefiting from its already designed flight plan. In this paper, we evaluate the adequacy of remote sensing drones to perform gateway functionalities, providing a guide for choosing the best drone parameters for successful WiFi data transmission between sensor nodes and the gateway in PA systems for crop monitoring and management. The novelty of this paper compared with existing mobile gateway proposals is that we are going to test the performance of the drone that is acting as a remote sensing tool to carry a low-cost gateway node to gather the data from the nodes deployed on the field. Taking this in mind, simulations of different scenarios were performed to determine if the data can be transmitted correctly or not considering different flying parameters such as speed (from 1 to 20 m/s) and flying height (from 4 to 104 m) and wireless sensor network parameters such as node density (1 node each 60 m\({}^{2}\) to 1 node each 5000 m\({}^{2}\)) and antenna coverage (25 to 200 m). We have calculated the time that each node remains with connectivity and the time required to send the data to estimate if the connection will be bad, good, or optimal. Results point out that for the maximum node density, there is only one combination that offers good connectivity (lowest velocity, the flying height of 24 m, and antenna with 25 m of coverage). For the other node densities, several combinations of flying height and antenna coverage allows good and optimal connectivity.  
drone; remote sensing; sensor network; WiFi; precision agriculture +
Footnote †: journal: Applied Sciences
# Why Do Tethered-Bilayer Lipid Membranes Suit for Functional Membrane Protein Reincorporation?
## 4 Different Types of Tethered-Bilayer Lipid Membranes
### Vesicle Fusion Method to Form tBLMs: A \"Top-Down\" Approach  
In order to circumvent the drawbacks of the \"bottom-up\" procedure to prepare tBLMs, another concept for forming tethered membranes, based on the fusion of lipid vesicles containing both the anchor and the spacer molecules on a functionalized surface has been proposed as an easier and versatile approach. Subsequently, the two leaflets of the tethered bilayers originate from the membranes of the vesicle, and are never built independently. This approach combines functionalized/spacer lipids and their use for receptor/ligand binding. This approach is expected to overcome some limitations of the different constructions developed so far, as the tether density, for instance [126]. This \"top-down\" approach can be performed with PEG [35; 39; 211; 212; 213], specific recognition/ligand receptors [81; 118; 214] (Figure 3) or peptides [43; 200] as anchoring spacers (Figure 2).
## Materials and Methods
### The Proposed Framework  
The structure of the proposed model is shown in Figure 4. The dataset is first subjected to pre-processing, where data cleansing and outlier removal take place. The following stage is the input selection stage, where EDA and correlation analysis are employed to determine the data to be used as features. Next, the dataset is split into training and testing sets. Subsequently, the process is divided into a training phase and a test phase. In the training phase, the training dataset is divided into \(N\) clusters by SOM, and each cluster is used to train a corresponding forecasting model. Our proposed model uses the SELM for forecasting, but single models, such as MLR, ANN, and SVR, and ensemble models, such as GBM, ADB, and XGB, are also tested to demonstrate the superiority of our model. After the training phase ends, the forecasting performance of the model is evaluated in the test phase. First, we determine the clusters to which each data point in the testing dataset belongs by calculating its BMU, and we then forecast the future load using models trained by the corresponding clusters. The novelty of this method compared to the methods in the literature is that we use the SELM after  
Figure 3: Conceptual diagram of the stacking ensemble learning method.  
clustering in order to mitigate the effect of overfitting caused by the reduced number of samples in each cluster. We did not develop the techniques used in our proposed method. We devised the strategy of handling the overfitting problem, fully utilizing existing techniques. The experimental results demonstrating the effectiveness of our proposed method will be discussed in Section 4.
## 2 Materials and Methods
### Bacterial Community Analysis  
A DNeasy power soil kit (QIAGEN, Inc., Venlo, TheNetherlands) was used to extract DNA samples and determine their purity, concentration, and integrity. Then, Pyrobest DNA polymerase (DR500A, TaKaRa, Kusatsu, Japan) and the primer pairs of 338F (5 ACTCCTACGGGGAGGCAGCA-3\({}^{\prime}\)) and 806R (5\({}^{\prime}\)-GACTACHVGGGATCTAATCC-3\({}^{\prime}\)) amplified the 16S rRNA V3-V4 regions of genomic DNA. After the amplification, Agencourt AMPure beads (Beckman Coulter, Indianapolis, IN) were adopted for the purification of PCR products, and the PicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA) was used for quantification. Equimolar and paired-end sequencing (PE250) was performed via the Illumina Novaseq 6000 platform (Personal Biotechnology Co., Ltd., Shanghai, China). After high-throughput sequencing, QIIME (V 1.8.0) was adopted for the processing of sequenced data. The effective sequences were collected and grouped into operational taxonomic units (OTUs) using UCLUST with a 97 percent similarity criterion after being filtered to remove chimeric and low-quality sequences as indicated. An OTU table was created after a sample sequence from each OTU was chosen for further taxonomic categorization using the Basic Local Alignment Search Tool (BLAST). Sequence data analysis was carried out by QIIME and R software package (V 4.0.0). The \(\alpha\)-diversity was analyzed by the \(\alpha\)-diversity.py script in QIIME, using UniFrac distance measurement and visualization by principal coordinate analysis (PCoA), and the \(\beta\)-diversity analysis was carried out. The PIRUSt database was used to predict the function of microbiome [19].
## 2 WSNs Data Acquisition Model
### Compressive Sensing Theory
#### 2.1.2 Signal Recovery  
Based on the previous work, if the original signal is sparse, it can be recovered with high probability using a method with some optimization techniques, such as \(L1\)-minimization [2,5,6] and BCS [21, 22, 23]. With a sufficient number of measurements, the sink is able to reconstruct the sensor readings by solving an \(L1\)-minimization problem [5,6]. By using \(L1\)-minimization technology [2], we reconstructed the chlorophyll-A signal from a smaller sample with higher accuracy; the mean of the reconstruction error for sampling point-1 was 0.17%, while that for sampling point-2 was 1.59%.  
As for the BCS method [23], its basic idea is to maximize the posterior from a Bayesian perspective, estimate sparse coefficients from the Bayesian framework, and then compute the estimated value of the original signal. In BCS, the estimated parameters are random variables with some prior distribution. By learning the \(i\)th sample \(x^{i}\) and the Bayesian rule, \(P(x^{i}\,|\,\alpha)\) can be transformed into \(P(\alpha\,|\,x^{i}\,)\), a maximum _a posteriori_ (MAP) estimate, where \(\,\alpha\,\) is the estimated parameter. We employ BCS method in our later data acquisition model.