esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
### Result of Threshold Calculation
#### 6.2.2 Curve Type of Road Variable  
As shown in Table 6, from the results of calculating the RORC severity threshold according to the type of curved section, as shown in Table 6, the compound curve showed the greatest change in probability with a total CoO of 1.9. For roads with compound curves, the proportion of serious accidents and fatal accidents was high, and on the road sections without reverse and continuous curves, the proportion of fatal accidents was actually high. The reasons for the above result can be interpreted as follows.  
1. In the schematic diagram of the TAN structure, the compound curve is linked as a variable that is correlated with the radius of the curve and the vertical slope.
2. The effect of the vertical slope is greater than that of the radius of the curve.
3. The roads with compound curves involve a high proportion of serious accidents.
4. Because the reverse and continuous curves are greatly affected by the radius of the curve, a result similar to the threshold calculation result of the radius of the curve is obtained.
5. Most sections with reverse and continuous curves have a small radius of the curve, which reduces the driving speed. This subsequently causes a decrease in the probability of fatal accidents and affects only serious accidents.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & & **Slight** & **Serious** & **Fatal** \\ \hline \multirow{4}{*}{Vertical slope} & \(\geq\)4\% & \(-\)63.8 & 135.7 & 79.1 \\  & 2–4\% & 48.6 & \(-\)13.4 & \(-\)22.5 \\  & \(<\)2\% & 78.5 & \(-\)51.1 & \(-\)10.2 \\  & None & 2.0 & \(-\)1.8 & \(-\)0.2 \\ \hline Total & & 65.3 & 69.4 & 46.2 \\ \hline Average (class = 12) & \multicolumn{4}{c}{(65.3 + 69.4 + 46.2)/12 = 15.1} \\ \hline \multirow{4}{*}{Radius of curve} & \(\geq\)500 m & \(-\)5.9 & 5.9 & 1.1 \\  & 250–500 m & \(-\)4.1 & \(-\)11.5 & 20.4 \\ \cline{1-1}  & \(<\)250 m & 2.0 & 23.0 & \(-\)15.2 \\ \cline{1-1}  & None & 4.6 & \(-\)5.7 & 2.7 \\ \hline Total & & \(-\)3.4 & 11.7 & 9.1 \\ \hline Average (class = 12) & \multicolumn{4}{c}{(\(-\)3.4 + 11.7 + 9.1)/12 = 1.5} \\ \hline Compliance status of minimum radius of curve design criteria & NC & 1.5 & 16.9 & \(-\)12.4 \\ \cline{1-1}  & C & \(-\)0.6 & \(-\)4.3 & 5.4 \\ \hline Total & & 1.0 & 12.6 & \(-\)7.0 \\ \hline Average (class = 6) & \multicolumn{4}{c}{(\(1.0+12.6-7.0\))/6 = 1.1} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Result of the road geometry variable threshold calculation.
## 4 Experiment  
In this section, the necessity of multiple CNNs, multiple layers and feature recalibration are analyzed first. Extensive plant recognition experiments are then conducted on eight simple and complex plant databases in order to evaluate the effectiveness of our learned features thoroughly; the datasets are Swedish, Flavia, MEW2012, ICL, ICL compound, CVIP100, Leafsnap and Turkey Plant. The dimensionality reduction is not performed on Leafsnap and Turkey Plant. The species, training ratio and image number of train and test set are summarized in Table 1. What is more, leave-one-out test plant leaf image retrieval experiments are also carried out. The evaluation metrics are accuracy and mean average precision. Each experiment is repeated for five rounds with different training sample random selections; the average results and standard deviations are reported. The performances of our method are compared with seven handcrafted methods and nine neural network-based methods. The handcrafted methods include SC+DP [9], IDSC+DP [10], MDM [24], MTCD [13], MFD [14], MTD+LBP-HF [27] and MMNLBP [28]. The deep learning related methods are AlexNet+relu5 [58], VGG16+relu5_2 [54], Dual-Path CNN [19], Deep Plant [18], HGO-CNN [20], MaskCOV [35], KernelPool [36], SWP-LeafNET [37] and IMTD+relu5_2 [38]. The results of all the other comparative methods are quotedfrom the original articles or the references [3; 27; 38]. The software, framework and hard configuration of our implementation are MATLAB 2020a, MATCONVNET, Intel(R) Xeon(R) Silver 4210R CPU, 2.4 GHz with 64 GB. The feature extraction time for one image, the linear SVM classifier training time and the prediction time for one image are 8.89, 2.35 and 0.001 seconds, respectively. Because the leaf feature extraction processes of our method for different CNNs and different layers are independent, we believe the feature extraction time can be greatly reduced if parallel computing is applied.
# Power Optimization in Multi-Tier Heterogeneous Networks Using Genetic Algorithm
## 9 Results and Discussion
### Power Consumption Performance
The figure shows that the power consumption using the modified genetic algorithm was relatively lower than the other two optimization algorithms.
# A Semi-Random Elliptical Movement Model for Relay Nodes in Flying Ad Hoc Networks
## 4 Node Distribution in SREM
### Numerical Analysis
This can be attributed to the numerical instability caused by the \(\arctan(y/x)\) term, which fluctuates sharply near \(x\approx 0\), as well as the limitations introduced by mathematical simplifications such as constant normalization factors and the omission of time-based weighting. As a result, both SREM and SRCM models face intrinsic limitations when it comes to analytically deriving or precisely interpreting their distribution functions. To overcome these challenges and better understand the actual node density characteristics, we used the Monte Carlo method. By repeatedly sampling node trajectories under various combinations of initial angles \(\theta_{i}^{(0)}\) and angular increments \(\delta\), we statistically estimate the frequency of node occurrence at each spatial location.
## 5 Conclusions  
This study focuses on developing the \(p\)-multipliers and group effect parameters for squared vertical pile groups in granular soil using a 3D nonlinear FE model. The \(p\)-multipliers are derived directly from numerical simulation by calculating the ratio of averaged soil resistances within a prescribed depth of group and single piles. The effect of \(S\)/\(D\), number of group piles, \(\varphi\), and pile fixity conditions are examined and quantified. Based on the simulation results, an empirical functional form for the \(p\)-multipliers is proposed for pile groups.  
The proposed \(p\)-multipliers generated from the numerical simulations exhibit the following trends. The \(p\)-multipliers decrease with an increase in \(\varphi\) and decrease in \(S\)/\(D\). The number of piles is shown to have marginal influence on the values of the \(p\)-multipliers. The \(p\)-multipliers are shown to be highly influenced by the pile fixity conditions. The results demonstrate that the \(p\)-multipliers are lower for fixed-head pile groups because of higher group interactions compared with the free-head pile groups. Based on the numerical outputs, an empirical functional form conditioned on \(S\)/\(D\), \(\varphi\), and fixity condition is proposed to estimate the \(p\)-multipliers.  
The numerically calculated \(p\)-multipliers and group effect parameters are compared with the previous experimental studies. The \(p\)-multipliers calculated in this study are in line with that measured from field tests. Further comparison of group effect parameters with the group effect parameters presented in AASHTO [33] and FEMA [34] depicts that the calculated values for \(\varphi=30^{\circ}\) yield the upper limit values for free-head condition. The parameters of AASHTO [33] and FEMA [34] do not account for the fixity condition, and therefore produce significantly higher parameters for fixed-head condition.  
Figure 10: Comparison among calculated group effect parameters for 3 \(\times\) 3 free-head and fixed-head pile groups in this study, AASHTO [33], and FEMA [34].  
**Author Contributions:** Conceptualization and methodology, D.P.; formal analysis, resources, and writing--original draft preparation, M.B.A.; numerical simulations, M.B.A., M.A.J. and M.A.J.; review and editing, D.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant (20 SCIP-B146946-03) from Smart Civil Infrastructure Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government, Korea Institute of Energy Technology Evaluation and Planning (KETEP), and the Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea (No. 20183010025580).  
**Acknowledgments:** The authors would like to thank [PERSON] and [PERSON] for providing the user material subroutine (UMAT) of the nonlinear soil model used in the analyses, along with a MATLAB code to calibrate its input parameters.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Deterioration Process of Cementitious Material Properties under Internal Sulphate Attack
## 4 Conclusions  
The deterioration process of concrete exposed to ISA was investigated. The main conclusions are as follows:  
(1) The results of the swelling rate and compressive flexural strength of mortar specimens show that the higher the SO\({}_{3}\) content in the aggregate, the greater the swelling rate of mortar specimens, the lower the strength and the more significant the strength loss, and the more significant the effect of ISA on the mortar.  
(2) The increase of the curing temperature makes the mortar specimen swell rapidly and steadily on the one hand and promote increased strength on the other hand. In general, it improves the resistance to internal sulphate attack.  
(3) The increased contact area for the reaction between gypsum and slurry accelerates the dissolution rate of gypsum and deepens the erosion of the specimen. This suggests that ISA tests can be accelerated by reducing the particle size of the aggregates.  
(4) The concentration of sulphate ions dissolved from gypsum aggregates in the pore solution environment of cementitious materials is low enough to generate sulphate reaction products of gypsum, and the main reaction product of ISA is calcium alumina.  
(5) The generation of gypsum during ISA does not produce expansion, and the main cause of cracking and damage of cementitious materials is the calcium alumina generated during the reaction.  
C.Z.: Conceptualization, Methodology, Validation, Writing--original draft preparation. B.H.: Conceptualization, Methodology, data curation. All authors have read and agreed to the published version of the manuscript.  
We are grateful to the financial support from National Key Research and Development Program of China (2021 YFB3802002).  
Not applicable.  
The authors declare no conflict of interest.
# Rearrangeable Nonblocking Conditions for Four Elastic Optical Data Center Networks
## 4 RNB DCN2 and DCN4 Networks
In addition, numerical results are given in Table 2, which shows that \(k^{\prime}_{\text{RNB}}\) can reduce \(k^{\prime}_{\text{SNB}}\) down to as low as one third, for example, all the cases with \(m_{\max}\geq 4\), \(s=3\) and \(q\geq 4\). Again, due to the limited resource of FSUs per fiber in practical systems, to have an RNB DCN2(\(s\), \(r\), \(q\), \(k\)) (or DCN4(\(s\), \(r\), \(q\), \(k\), \(p\))) network for \(m\)-slot connections with \(1\leq m\leq m_{\max}\) in the real word, we need \(sq\cdot m_{\max}\leq 350\) due to Property 5 (or Property 7). \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{\(m_{\max}\)} & \multicolumn{2}{c}{\(s\) = 3, \(q\) = 4} & \multicolumn{2}{c}{\(s\) = 3, \(q\) = 8} & \multicolumn{2}{c}{\(s\) = 3, \(q\) = 10} \\ \cline{2-7}  & \(k^{\prime}_{\text{SNB}}\) & \(k^{\prime}_{\text{RNB}}\) & \(k^{\prime}_{\text{SNB}}\) & \(k^{\prime}_{\text{RNB}}\) & \(k^{\prime}_{\text{SNB}}\) & \(k^{\prime}_{\text{RNB}}\) \\ \hline
2 & 68 & 24 & 140 & 48 & 176 & 60 \\ \hline
4 & 158 & 48 & 326 & 96 & 410 & 120 \\ \hline
6 & 248 & 72 & 512 & 144 & 644 & 180 \\ \hline
8 & 338 & 96 & 698 & 192 & 878 & 240 \\ \hline
10 & 428 & 120 & 884 & 240 & 1112 & 300 \\ \hline \end{tabular}
\end{table} 
Table 2: Numerical results of \(k\) required for being an SNB or RNB DCN2(\(s\), \(r\), \(q\), \(k\)) network for \(m\)-slot connections with \(s=3\), \(q=4\), \(8\), \(10\) and \(1\leq m\leq m_{\max}\), where \(k^{\prime}_{\text{SNB}}\) and \(k^{\prime}_{\text{RNB}}\) are given in Equations (2) and (4), respectively.
# Smartphone-Based NFC Potentiostat for Wireless Electrochemical Sensing
## 3 Results
### NFC Potentiostat Performance
#### 3.2.2 Chronoamperometry  
To evaluate the performance of the potentiostat in chronoamperometric detection, an experiment was performed with ferricyanide at concentrations ranging from 25-100 \(\upmu\)M. For chronoamperometric detection, a fixed potential was applied at \(-\)100 mV and the measurement period was 30 s. Although the signal response from the NFC potentiostat showed current noise, as shown in Figure 5a, while the current signals from the commercial potentiostat were smooth and free of noise (Figure 5b), there was good agreement between data obtained by the NFC potentiostat and the commercial potentiostat at different concentrations of ferricyanide (Figure 6a). The final steady-state current after 25 s, calculated as the mean current due to the steady-state value, was proportional to the concentration of the analyte [49,50]. Figure 6b demonstrates the theoretically expected linear relationship between the current and concentration of ferricyanide. The calibration curve obtained from the commercial potentiostat corresponds to Equation (2) with the regression coefficient as R\({}^{2}\) = 0.999,  
\[\text{y (I($\upmu$A))}=0.0207\times\text{x($\upmu$M(mol/L))}+0.0303, \tag{2}\]  
while the calibration curve for the NFC potentiostat was obtained as the following Equation (3) with R\({}^{2}\) = 0.998,  
\[\text{y (I($\upmu$A))}=0.0212\times\text{x($\upmu$M(mol/L))}+0.0169. \tag{3}\]  
The reliability of the potentiostat in detection of the analyte by chronoamperometry was calculated by comparing the slopes between our device and the standard device [51]. The result demonstrated that the reliability was greater than 97%. The average (AVG) and relative standard deviation (RSD) values of the current obtained from both potentiostats are shown in Table 2. The current values obtained from the commercial potentiostat were reproducible with RSD of \(\pm\)2.55% and \(\pm\)3.40% corresponding to chronoamperometric measurement for 25 \(\times\) 10\({}^{-6}\) M and 1000 \(\times\) 10\({}^{-6}\) M, respectively, while the RSD obtained from the NFC potentiostat gave values \(\pm\)0.91% and \(\pm\)7.74% for 25 \(\times\) 10\({}^{-6}\) M and 1000 \(\times\) 10\({}^{-6}\) M, respectively.  
Figure 4: (a) Cyclic voltammograms of 1 mM potassium ferricyanide/0.1 M KCl on the NFC potentiostat sweeping the potential between \(-\)300 and 600 mV, at various scan rates. (b) The linear relationship of peak current and square root of scan rate obtained from the NFC potentiostat (red) and the commercial potentiostat (black).
## 3 Results
### Velocity
#### 3.1.2 Phase Velocity  
The longitudinal phase velocities along the three directions are shown in Figure 3. According to Kramer-Kronig [32] relationship, an inverse-asymptotic behavior is expected between phase velocity and frequency. The shape of the phase velocity curve is consistent with previously published data for high attenuation samples including polymer composites [32]. The dispersion, i.e., frequency dependence of phase velocity signifies the dependence on attenuation including porosity and other features. Between the different directions, the DED sample shows strong dispersion and the build direction; D3 shows a higher dispersion compared to D1 and D2.
# Analyzing the Performance of the S3 Object Storage API for HPC Workloads
## 2 Materials and Methods
### Benchmarks  
We extend two HPC benchmarks, the IO500 and MD-Workbench, to analyze the potential peak performance of the S3 API on top of the existing HPC storage infrastructure.  
The IO500 [12] uses IOR and MDTest in \"easy\" and \"hard\" setups, and hence performs various workloads and delivers a single score for comparison, and the different access patterns are covered in different phases:  
* IOEasy simulating applications with well-optimized I/O patterns.
* IOHard simulating applications that utilize segmented input to a shared file.
* MDEasy simulating metadata access patterns on small objects.
* MDHard accessing small files (3901 bytes) in a shared bucket.  
We justify the suitability of these phases as follows: [PERSON]. [PERSON] and [PERSON] [25] found that, inside HPC, between 25% and 90% of all files are 64 Kbytes or less in size, as such a typical study of the performance of object storage inside HPC should also address this range, rather than only focusing on large sizes, which are expected to deliver better performance and only be limited by the network bandwidth [21; 23]. This is why, using the IOR benchmark, we highlight this range when exploring the performance for files of size up to 128 MB in Sections 3.2 and 4. Large file sizes are also addressed since the performed IO500 benchmarks operate on 2 MiB accesses creating large aggregated file sizes during a 300 s run.  
MD-Workbench [13] simulates concurrent access to typically small objects and reports throughput and latency statistics, including the timing of individual I/O operations. The benchmark executes three phases: pre-creation, benchmark, and cleanup. The pre-creation phase setups the working set, while the cleanup phase removes it. A pre-created environment that is not cleaned can be reused for subsequent benchmarks to speed up regression testing, i.e., constant monitoring of performance on a production system. During the benchmark run, the working set is kept constant: in each iteration, a process produces one new object and then consumes a previously created object in FIFO order.
## 4 Discussion  
Patients with erythroderma constitute a heterogeneous group, which in most cases is a challenge both in terms of diagnosis and treatment. For this reason, we wanted to analyze patients' outcomes during the hospitalization due to erythroderma.  
In our study, we observed cases of erythroderma in both adults and children. The median age of the adult population in our study is in close agreement with the results of other studies, as is the median age determined using the etiology of erythroderma, particularly for psoriatic erythroderma, drug-induced and CTCL-induced erythroderma, and idiopathic erythroderma [1; 2; 3; 4; 5; 6]. The age of patients with erythroderma secondary to CTCL, eczema, or idiopathic is higher. The youngest group was patients with AD. Male patients outnumbered females (1.96:1), which also agrees with reports from other authors where the male-to-female ratio varies between 1.5:1 and 2.2:1 [3; 7; 8]. In their report, [PERSON] and [PERSON] observed that erythroderma occurs almost four times more often in men than females (male-to-female ratio--3.6:1); however, the authors did not address the possible reason for such a pronounced male predominance [6].  
We found that almost half of the patients (46.57%) developed erythroderma due to a preexisting skin condition. In other studies, this data varied between 38% and 74.6% [3; 5; 9; 10]. That is why collecting a patient's medical history plays such an important role.  
During the study period, the median length of hospitalization for all patients from the Dermatology Department was 6 days, while for patients with erythroderma, it was much longer--10 days. The longer the hospitalization duration is, the higher financial costs for the hospital are, which makes erythroderma even more demanding both for health workers and the health system in general.
## 4 Simulation Results and Discussion
### Simulation for One-Hop Setup  
In this section, we conduct simulation experiments to study the impact of protocol design parameters on the location estimation accuracy in one-hop setup using Matlab. The simulation environment is a coordinated square map of size 100 m \(\times\) 100 m with \(N_{b}\) = 5 beacons and \(N_{s}\) = 50 sensors randomly deployed in uniform distribution. The five beacons are set into the four corners and the central point of the map.
## 3 Materials and Methods
### Lightweight Expression Recognition System  
#### 3.2.1 Image Preprocessing  
Image preprocessing includes image graying, face detection, data normalization, resizing and dimension adjustment adapting to the requirements of the model, and data augmentation:  
* Image graying. Image graying is necessary for recognition because of the feature complexity in RGB color images. In fact, the facial expression features in the gray image have met the recognition requirements, and it can greatly reduce the amount of calculation compared with color images. The average method in Formula (14) is used to convert RGB images into gray images: \[\text{Gray}(x,y)=[R(x,y)+G(x,y)+B(x,y)]/3\] (14)
* Face detection. Face detection refers to recognizing the region and location of the face using corresponding algorithms. The MTCNN [28] algorithm, which is accepted to do nice work, is adopted. The MTCNN algorithm is a face detection and face alignment method based on deep learning. It can complete the tasks of face detection and face alignment at the same time. Compared with traditional algorithms, it has better performance and faster detection speed. Image pyramid, P-Net, R-Net, and O-Net are the main steps included.
* Data normalization. We normalize each value of the pixel data of the input image to the [\(-\)1,1] interval through Formula (15) in order to better use the standardized data for training: \[\mathbf{N}=(I/255.0-0.5)\times 2\] (15)  
\(N\) is the result of normalization, and \(I\) is the input pixel matrix in Formula (15). After this processing, the illumination and other factors of the image are weakened, the extraction of useful features is easier, and the amount of calculation is reduced, so as to accelerate the convergence.
* Data augmentation. Data augmentation is a technique to expand the dataset and reduce overfitting based on some transformations of the data samples of the existing dataset. The main operations include cutting pictures, stretching, adjusting the brightness and angle, etc.