esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The use of fiber-reinforced polymer (FRP) jackets as external confinement is becoming popular, especially in seismic areas, because of its ability to enhance the strength and ductility of reinforced concrete to perform as a sustainable symmetric structural member. Therefore, various researchers have worked out for the prediction of strength and strain models of FRP-confined concrete. This study presents the improved strain models for the FRP confined cylindrical concrete members. Different previously proposed models of axial strain of FRP-confined concrete were evaluated based on a large database of 678 specimens from previous experiments and an improved model was proposed using the general regression analysis technique. Furthermore, the proposed model was validated using the previous experimental work of FRP-wrapped concrete cylinders and their finite elements analysis (FEA) using the ABAQUS software. The accuracy of the proposed strain model was quite satisfactory in comparison with the previous experimental and FEA results of the present study. Moreover, the proposed empirical strain model was used for the parametric study to investigate the effect of different geometric and material parameters such as the compressive strength of unconfined concrete, diameter of the cylinder, elastic modulus and thickness of the FRP layers, on the axial strain of FRP-wrapped cylinders. A close agreement among the proposed strain models and experimental outputs was observed. This study will help in understanding the behavior of sustainable FRP-confined symmetric concrete members.  
symmetry; FRPs; confined concrete; strain model; sustainability +
Footnote †: journal: Applied Mechanics and Physics Letters
## 2 Substrate Materials for Wearable Antennas
The low permittivity of fleece allows design of textile wearable antennas with large gain and high efficiency which claims fleece fabric as a very good candidate for textile substrate material. The firmness of a textile material substrate can affect the performance of the textile antenna. An embroidered textile antenna (Sierpinski Carpet Antenna) was designed in [16] using fractal antenna technology. Two antennas were fabricated. One antenna was embroidered on denim and the other on felt. The first one showed the best performance--denim fabric has a firm surface compared to felt fabric that has a soft fluffy surface which gives disadvantages to felt--_fluffy_ surfaces are easily compressed and the variable thickness affects the antenna properties. Both fabricated antennas are very good candidates for wearable applications. For good antenna performance homogeneity of the textiles is a requirement [10]. The accurate characterization of the textile properties is also a requirement for \begin{table}
\begin{tabular}{c c c c c} \hline
**Material** & **Permittivity (\(\varepsilon_{x}\))** & **Loss Tangent (tan\(\delta\))** & **Frequency (GHz)** & **Reference** \\ \hline Polystyrene foam & 1.02 & 0.00009 & & \\ Felt & 1.36 & 0.016 & & \\ Fleece & 1.2 & 0.004 & & \\ Neoprene rubber & 5.2 & 0.025 & & \\ Silk & 1.2 & 0.054 & & \\ Cotton & 1.54 & 0.058 & & \\ Leather—different & 1.8–2.4 & 0.049–0.071 & & \\ types & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._good agreement between the antenna design and manufacture [10]. In [17] a novel method to measure the relative permittivity of textile substrates and eventually to design and manufacture a textile patch antenna is described and proposed. Once a patch has been designed by using an estimated value for the dielectric constant and then when the antenna is manufactured, a different resonant frequency occurs which means that the initial estimation was wrong. Then there is a need of tuning the permittivity and using the right value for the design. One method of characterizing the dielectric properties is by using split post dielectric resonators at the respective frequencies as seen in Figure 1. The dielectric properties can be measured for example using a split post dielectric resonator [18]. However, in practice an effective permittivity can be found by comparison with measurements. A novel polymer substrate was developed, produced and used as an RF substrate [19]; it is known as Polydimethylsiloxane (PDMS) (\(\epsilon_{\mathrm{r}}=3\), tan \(\delta<0.02\) from 0-1 GHz). PDMS has a good mechanical flexibility and low loss performance. By adding ceramic powder to the PDMS, the polymer composite is capable of providing tunable permittivity.
## 4 Results and Discussion
### MSW Prediction Model
#### 4.1.2 Kriging Spatialization  
After establishing a high-precision domestic waste production prediction model, this paper spatialized the overall waste production in Beijing to visualize its distribution across the city and verify whether there is any error in the model. The spatialization results are shown below, revealing a distribution pattern similar to the ring pattern of Beijing. This similarity indicates that there are no significant errors in the data indicator system or the regression model construction. In Figure 5, the darker the color the higher the garbage production. According to our analysis, the average waste generation in the city center is approximately 961,100 tons, while the average waste generation in the peripheral areas is about 321,700 tons. The waste generation in the city center exceeds that of the peripheral regions by approximately 639,400 tons, which corresponds to an increase of 198.77%. This finding further corroborates the spatial distribution of waste generation and its consistency with Beijing's \"ring\" model, thereby supporting the validity of our model.
# Surface Defect Detection Methods for Industrial Products: A Review
## 4 The Key Problems  
### Real-Time Problem  
In surface defect detection tasks in real industrial scenes, real-time problems are not to be ignored. In some special scenarios, such as online analysis, online monitoring and so on, real-time problems are in an extremely important position. The goal of dealing with real-time problems is to reduce the detection time and improve the detection efficiency under the premise of roughly the same accuracy. At present, some scholars have carried out certain research on real-time problems. For example, literature [79] designed a new type of 11-layer CNN model for welding defect detection in robotic welding manufacturing. The proposed method provides guidance for the online detection of metal additive manufacturing (AM), that is, the method can meet certain real-time requirements. Literature [80] proposed a two-stage algorithm combining SSIM and MobileNet to detect surface defects on printed circuit boards, which is at least 12 times faster than Faster RCNN while maintaining high accuracy.  
At present, model acceleration is one of the important ideas to solve the real-time problem. Model acceleration can be carried out mainly from two aspects: algorithm and hardware, as follows:  
1. Algorithm: for the network algorithm level, lightweight network can be used to accelerate the model. Commonly used lightweight models include MobileNet, ShuffleNet, SqueezeNet, and EfficientNet. In addition, distillation and pruning can also be used in accelerating the network at algorithm level. In terms of calculation algorithms, the convolution operation can be optimized to achieve the purpose of model acceleration. Typical algorithms include FFT, Winograd, etc.
2. Hardware: the use of GPU, FPGA, DSP, etc. is the main way to accelerate the model through hardware at present.
## 2 Materials and Methods
### Choosing \(\varepsilon\)  
Let \(P\) be a MIP with objective function \(\sum_{i=1}^{n}c_{i}x_{i}\) such that \(c_{i}\
eq 0\) and \(x_{i}\in\mathbb{Z}\) for at least one index \(i\). We assume that each variable has type \(\mathtt{vtype}(x)\), which is one of three possible types: _Binary_, _Integer_, or _Continuous_. In MIPLIB instances, each variable \(x\) has specified lower and upper bounds that we denote by \(\mathtt{lb}(x),\mathtt{ub}(x)\) respectively. The MadHitter instances we used for testing are ILP selection problems in which every variable in the objective function is of type _Binary_ with lower bound \(0\) and upper bound \(1\).  
Define \(S=\sum_{i=1}^{n}f(c_{i},x_{i})\) where  
\[f(c,x)=\begin{cases}|c|\cdot\max(|\mathtt{lb}(x)|,\ |\mathtt{ub}(x)|),&\mathtt{ vtype}(x)\
eq Continuous,\\ 0,&\mathtt{vtype}(x)=Continuous,\end{cases}\]  
Then \(\varepsilon=\frac{1}{2S}\).  
For example, suppose the objective function is \(2x+3y+z\), all variables are of type _Integer_, and the variables are constrained to be in the following intervals \(x\in[-2,1],y\in[1,4],z\in[-4,2]\). Then, \(S=(2\times\ 2)+(3\times\ 4)+(1\times 4)=20\), and \(\varepsilon=1/40=0.025\). If we scale the coefficients up by a factor of \(5\) to make the objective function \(10x+15y+5z\), then \(S=100\) and \(\varepsilon=1/200=0.005\), decreasing by a factor of \(5\). If we scale the coefficients down by a factor of \(5\) to make the objective function \(0.4x+0.6y+0.2z\), then \(S=4\) and \(\varepsilon=1/8=0.125\), increasing by a factor of \(5\). In general, scaling the objective function coefficients by a factor of \(k>0\) scales \(\varepsilon\) by a factor of \(1/k\).  
An advantage of this method is that it can be executed by simply examining the structure of the instance. If variable bounds are not available, then one can solve the instance to obtain a single optimum and use the absolute values of the variables in that solution to define \(\varepsilon\), which can then be used to search for other optima.
## 4 Results and Discussion
### SOH Estimation Results  
Divide experimental data into training and test sets, and the ridge regression model is constructed by the training set, while its performance is assessed by the test set. Specifically, the first 100 cycle data from B0005 and B0006 of NASA battery datasets are used as training sets, and subsequent cycle data are used as test sets. Similarly, the first 50 cycles of Cell1 and Cell2 in the Oxford battery dataset are chosen as training sets, followed using the remaining cycle data as test sets. Throughout each prediction process, three selected health features are selected from the charging voltage curve and utilized as inputs for the ridge regression model, with SOH estimates as outputs.  
Figure 6: Flowchart of SOH and RUL estimation.  
Our method shows superior performance in predicting battery SOH, as shown in Figure 7. Its prediction curve closely tracks the real SOH degradation curve, accurately capturing the trend of battery capacity decline. Moreover, Our method effectively adapts to the deterioration process of the battery and its capacity regeneration phenomenon. Compared with the SVR and LSE methods, Our method exhibits a notable enhancement in predictive precision. Most of its predicted SOH values closely match the real values, making it well suited for predicting the real SOH trend. However, a careful comparison can be found in Figure 7c,d, where the simulated results are slightly different from the actual results and appear to jump up and down. This may be due to errors in data collection or processing resulting in bias in the input data used for model training. Therefore, the datasets used for training and testing need to be further reviewed to minimize noise disturbances and outliers and to ensure data accuracy.  
Table 4 shows the MAE and RMSE calculation results, and Our method prediction error results on the four battery datasets show extremely low values, which remain below 0.02. Moreover, under the same conditions, Our method exhibits the smallest prediction error. Especially for Cell1 and Cell2, the MAE and RMSE are maintained within 0.004 and 0.005. This shows that the ridge regression model proposed in this paper not only adapts effectively to different datasets but also exhibits superior prediction accuracy.  
Among them, the estimation errors of both Cell1 and Cell2 are relatively small, because there is no obvious fluctuation during battery decline and the overall degradation trend is relatively smooth. Therefore, all three prediction methods effectively capture the real SOH curve trends for Cell1 and Cell2 batteries. However, for battery B0006, the degradation trend is more drastic, and fluctuations up and down are more obvious. Within 60 cycles, the SOH of this battery decreased by about 15% and significant capacity regeneration occurred, resulting in a large error in the prediction effect for battery B0006. However, Our method outperforms the other two methods in predicting the real degradation trend with the smallest error, and this also reflects the applicability of Our method in dealing with battery data with drastic and fluctuating degradation trends.
# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 6 Performances Analysis
### The Column Mobility Model (CMM)
#### 6.1.5 Average Power Consumption  
Figure 6 depicts a comprehensive examination of APC dynamics in a network environment employing the CMM mobility model, particularly under various conditions involving the Hello Flood attack. A crucial distinction lies in the categorization of nodes: mobile nodes denote scenarios devoid of the attack, while attack nodes signify the presence of the Hello Flood attack within the same mobility model. Across different percentages of mobile nodes, ranging from 10% to 40%, the APC remains relatively stable in the absence of the attack. However, when the network contends with the Hello Flood attack, a notable surge in APC becomes evident, nearly doubling compared to scenarios without the attack. This substantial increase highlights the significant impact of the Hello Flood attack on energy consumption within the network.  
Within a network environment, the interaction between the CMM and the Hello Flood attack has a significant impact on the APC. The CMM's structured mobility patterns lead to increased energy consumption as nodes converge, resulting in higher contention for communication resources and heightened transmission power to maintain connectivity. Also, the constant movement in CMM causes the topology of the network to change often, which means that devices in the network have to keep recalculating routes and using more energy to adapt. Concurrently, the Hello Flood attack inundates the network with excessive traffic, saturating communication channels and consuming network resources, further elevating energy consumption. Moreover, the structured movement patterns of CMM can exacerbate the impact of the Hello Flood attack by concentrating nodes, intensifying its effects on energy consumption. Conversely, the increased energy consumption caused by the attack can strain network resources, amplifying the effects of the CMM on APC.
# Optimization of Storage Medium for Hematological Reference Samples in External Quality Assessment
## 2 Materials and Methods
### Evaluation of the Experimental Model  
The optimal storage media conditions for blood cells were chosen based on RSM-CCD analysis and the accuracy of the predictive model was assessed by calculating the percentage of similarity between the results from the model and the actual data. The experiment was repeated three times.  
Moreover, the stability and homogeneity of RBCs, WBCs, and PLTs in the storage medium were validated during the storage time of 3 months at 2-8 \({}^{\circ}\)C. Firstly, a total of 50 samples were equally divided into two lots and one of them was randomly collected from each lot. Each collected sample was tested for its stability and homogeneity three independent times. A \(t\)-test (\(\alpha\) = 0.05) and F-test (\(\alpha\) = 0.05) were used to evaluate the stability and homogeneity of samples [15; 16]. The number of RBCs, WBCs, and PLTs in the stored samples was identified by ABX (Horiba-Franch). Simultaneously, the stability of the stored samples was also validated by other machine models, including CellDyn 1700 and Mindray BC3000 once a month for 3 months.
# Scheduling the Exchange of Context Information for Time-Triggered Adaptive Systems
## 5 Results and Discussion
### Statistical Consistency Across GA Executions  
To assess the consistency of the genetic algorithm (GA) under repeated executions, we conducted a statistical stability analysis using a 10-job application. The GA was executed 100 times independently for both the proposed method and a benchmark configuration without context exchange. For each run, the resulting makespan was recorded, and key statistical metrics were computed, including the mean, standard deviation, variance, and the coefficient of variation (CoV).  
The CoV, defined as the ratio of standard deviation to the mean, serves as a normalized measure of dispersion. As shown in Table 5, the makespan CoV for the 10-job for our proposed method and the benchmark case remained well below 7%, indicating that the GA consistently converges to similar solutions across runs. This analysis exemplifies the statistical stability of the proposed method and supports its applicability in reliable and repeatable scheduling scenarios.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Job Size** & **2 TU Samp.** & **4 TU Samp.** & **6 TU Samp.** & **8 TU Samp.** & **10 TU Samp.** \\ \hline
10J & 6783 & 4360 & 3553 & 3149 & 2907 \\
20J & 263,672 & 169,503 & 138,114 & 122,419 & 113,002 \\
30J & 621,897 & 399,791 & 325,755 & 288,738 & 266,527 \\
40J & 1,096,581 & 704,945 & 574,399 & 509,127 & 469,963 \\
50J & 2,769,347 & 1,780,294 & 1,450,610 & 1,285,768 & 1,186,863 \\
60J & 6,807,524 & 4,376,265 & 3,565,846 & 3,160,636 & 2,917,510 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Makespan for different sampling methods and job sizes.  
Figure 8: Makespans for the proposed algorithm and the no-context-exchange scenario across increasing job sizes.
# Enhancing Target Speech Based on Nonlinear Soft Masking Using a Single Acoustic Vector Sensor
## 4 Experiments and Results
### Experiments on Recorded Data in an Anechoic Chamber  
In this section, two experiments have been carried out with the recorded data captured by an AVS in an anechoic chamber [25]. Every set of recordings lasts about six seconds, which is made by the situation that the target speech source and the interference source are broadcasting at the same time along with the background noise, as shown in Figure 1. The speech sources taken from the Institute of Electrical and Electronic Engineers (IEEE) speech corpus [26] are placed in the front of the AVS at a distance of one meter, and the SIR-input is set to 0 dB, while the SNR-input is set to 10 dB, and the sampling rate was 48 kHz, and then down-sampled to 16 kHz for processing.
## 5 Hardware Deployment
### Photovoltaic System  
The photovoltaic system is formed by a photovoltaic panel, the battery, the load regulator and an inverter. There are 3 basic types of photovoltaic panels, all of them use silicon: monocrystalline cells, polycrystalline cells and amorphous cells. We have used the polycrystalline cells because they have higher performance than the amorphous cells (between 11% and 13%) and they are cheaper than the monocrystalline cells. There are many types of batteries that can be used in a photovoltaic system: Lead-acid battery, VRLA battery, AGM battery, Gel battery, Nickel-cadmium battery, etc.  
First we studied the solar radiation map of Spain. Taking into account the values provided by references [29] and [30], Spain has high solar radiation values. Concretely, Madrid, the place where we deployed our test bench, has 1,560 hours of sunlight per year, which means a mean value of 4.27 hours of sunlight per day. The battery of the sensor has to be able to provide power during 20 hours at least.  
In our case, the sensor consumes 12V and 100 mA approximately, so the power consumed by the sensor is 1.2 W. Taking into account that a polycrystalline cell has a mean performance of 12%, the power needed to supply the sensor and to charge the battery simultaneously is 60 W. Let us suppose that we use have a battery of 24 Vah fully charged. The sensor will discharge the battery in 240 hours if there is not sunlight. Such number of hours without sunlight is very difficult to happen in Spain.
# A Police and Insurance Joint Management System Based on High Precision BDS/GPS Positioning
## 1 Introduction
### Lane Level Map and Matching Technology  
In the process of vehicle behavior judgment and rapid accident processing, it is necessary for the system to screen accident evidence. Thus, it is of great importance to match the high precision track of the accident with a lane level map. The match helps to identify whether the vehicle has some behaviors such as lane changes and reproduce the accident scene accurately. Afterwards a whole picture of the accident is obtained to accurately assess the accident responsibility.  
In order to develop a steady model of map matching of high precision tracks, there are two main problems to be solved in this process: (1) matching and correction of coordinate coherence for map road network data and BDS/GPS track data; (2) lane matching of BDS/GPS point and discovery of lane changing.  
The high precision road network contains lane collection L = {1, 1, 1, 1,...,  
Data preprocessing: mainly refers to conducting structured process for the data object to meet the latter analysis and calculation.
2. Matching and correction of data consistency: the road data of based drawing and vehicle track data may be not consistent, so it is necessary to use the method of partial affine transformation to deal with the inconsistency.
3. Lane matching and lane changing discovery: BDS/GPS track point has been matched with lane information, and the abnormal information such as lane changing has been detected. Therefore, the following algorithm is proposed: GPS point lane matching algorithm:  
1. Input: vehicle BDS/GPS positioning track data tri = {P1, P2, P3,..., Pn}. And map data of road L = {l1, l2, l3,..., Lm}.
2. Output: the corresponding lane information of each BDS/GPS point as well as the anomalous features reflected by a part of vehicle tracks (e.g., \"lane changing\" behavior). Detailed steps:  
1. Take Pi-1 before the point Pi, connect the two points to get segment piPi-1;
2. Making two rays r1 and rr on both sides of Pi point; the rays are perpendicular to piPi-1; the ray length is radio; (radio value is generally 1.5 times the maximum lane width);
3. Use r1 to detect the lane line that intersected with r1 on the left side; If there are two or more different lane lines, take the shortest distance between the lane line and pi point as the smallest lane line;
4. Use the same method of Step 3 to calculate the lane line that intersected with rr on the right side;
5. After obtaining the lane lines of pi points on both sides, extract relevant properties of two lane lines respectively. If the two lines share common lane number, then the lane number is lane that pi point located; Otherwise, it fails to calculate the pi point lane information; analyze and calculate the next point pi+1 after identifying the result;
6. Repeat steps 1-5; analyze and calculate the results after traversing each GPS point. If the lane line information of adjacent two points such as pj and pj+1 is different, extract the information and identify as lane changing position.