esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Randomized Text Files  
We generated eight random text files with different alphabet sizes, namely, \(|\Sigma|\) = 2, 4, 8, 16, 32, 64, 128 and 256. The size of each random text file was fixed at 100 MB. Patterns were randomly chosen from these files with 19 varying lengths: \(m\) = 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 30  
* When \(m\geq 100\) and \(|\Sigma|=128,256\), QS is the best algorithm; FQS is similar to FJS, ranked as the second. Figure 9 shows the experimental results. When the length of the pattern is longer than 800, QS, FJS and FQS all have a very similar performance.  
Figure 7: Execution time _versus_ pattern length, \(m\) (\(10\leq m\leq 100\)), using randomized text files when \(|\Sigma|=32,64,128,256\). FJS, Franek–Jennings–Smyth; FQS, faster quick search; HOR, Horspool.  
When the alphabet size is small or medium (\(|\Sigma|\) = 2, 4, 8, 16, 32 and 64, respectively), the performance of FQS is significantly better than others. When the alphabet size is large (\(|\Sigma|\) = 128 or 256), FQS still has a competitive running performance. FQS is suitable to be used with a small or medium alphabet size (not more than 128). The longer the pattern is, the better FQS performs.  
Figure 8: Execution time _versus_ pattern length, \(m\) (\(100\leq m\leq 1,000\)), using randomized text files when \(|\Sigma|=2,4,8,16,32,64\).  
We took a closer look at the impact of alphabet sizes on the performance. Figure 10 shows the average execution time plotted against alphabet size when the pattern length is fixed, for the cases with \(m=10\), \(m=50\), \(m=100\) and \(m=800\), respectively.  
Figure 10: The variation of execution time with alphabet size \(\Sigma\), (\(2\leq|\Sigma|\leq 256\)) using randomized text files, when \(m\) = 10, 50, 100 and 800.  
Figure 9: Execution time _versus_ pattern length, \(m\) (\(100\leq m\leq 1,000\)), using randomized text files when \(|\Sigma|=128,256\).  
From the figure, we can observe the overall trend for all of the algorithms: with increasing alphabet size \(|\Sigma|\), the execution time decreases. FQS has better performance when \(|\Sigma|\) is small, especially for cases of long patterns (see \(m=800\) in Figure 10, for example). This suggests that FQS will have important potential applications in the analysis of a genomic database, since the alphabet size is usually very small, typically four (for DNA or RNA sequences) or 20 (for protein sequences).  
We summarize our observations on random texts as follows.  
* The longer a pattern is, the faster the FQS algorithm runs;
* When the alphabet size is small or medium (\(|\Sigma|\) = 2, 4, 8, 16, 32 and 64), FQS outperforms the other QS variants: Horspool (abbreviated as HOR), FJS and the classic QS;
* When \(|\Sigma|\geq 128\), FQS is competitive with the other QS variants: HOR, FJS and classic QS.
## 2 Data Analysis and Preprocessing
### Data Preprocessing
#### 2.4.3 Abnormal Data Repair  
In the process of data sampling, in addition to losing data, abnormal data often appear. The characteristics of these data obviously deviate from the data in other locations, and may introduce significant interference to the prediction algorithm. Therefore, it is necessary to repair these abnormal data. Because these abnormal points generally occur with low frequency and short duration [36], after abnormal data are detected, the LSTM time series algorithm can be used to predict the location data of abnormal data according to historical data information, and the predicted value can replace the abnormal data to complete the data repair process [37; 38; 39]. The specific process is shown in Figure 4.
# Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid
## 1 Introduction
By combining machine learning tools (ANN and ELM) and signal processing technique, a more accurate fault detection, classification, and location method is proposed in this paper. Wavelet decomposition and statistical analysis are used to extract useful features from the measured current signals. Training data, consisting of such features for each fault, is generated by considering variations in location, loading, fault type, and fault impedance. By increasing the size of training data, a more accurate and robust fault detection can be achieved which will facilitate the fault clearance and maintenance process. This improves the reliability of the distribution network. To evaluate the performance of fault location and classification, confusion matrices, Root mean squared error (RMSE), Mean absolute percentage error (MAPE), coefficient of determination (\(R^{2}\)), [PERSON]'s index of agreement (WIA), and the Nash-Sutcliffe model efficiency coefficient (NSEC), are used. The performance measures are also used to compare the accuracy of MLP and ELM in locating faults. This paper focuses on fault classification and location in a radial distribution network with a single source at the sending end. Both balanced and unbalanced loading are considered. Variationsin the loading and fault impedance are considered, and both balanced and unbalanced loading are studied. All types of faults are considered. Comparisons between MLP and ELM are deliberate. The proposed methodology can be complemented by considering meshed networks and smart grids with high penetration of distributed generation. The remaining part of this paper is organized in four sections. Section 2 presents the theoretical background of machine learning. Section 3 explains the specifics of the discussions. Different case studies, results, and discussions are discussed in Section 4. Finally, the concluding remarks are summarized in Section 5.
# Aerodynamic Benefits by Optimizing Cycling Posture
## 2 Methods
### Post-Processing of the 3D Scan, Meshing, Validation  
For the scanned HH position, a STL-file was generated which then was imported into the open-source graphics software Blender 2.9. Postprocessing work and meshing the body structure were performed in Blender as well. It is known from a comparable case [17] that an accuracy of 2% (in terms of cdA) may be reached after refinement from a volume mesh containing 2 M cells to a 55 M mesh. Our mesh-dependency study is summarized in Table 2. Deviation was extrapolated form a mutual exact value by a _Richardson_ extrapolation. Here drag was fitted as a function of inverse mesh-size (1/ms) by a quadratic polynomial, and then the limit \(ms\rightarrow\infty\) was taken. As a result, accuracy in CdA of a few percentage points was reached with _medium-sized_ meshes according Table 2.  
The finally used surface mesh contained 78,490 vertices and 156,976 triangles with a side length below 9 mm, and it fits well into the category of _medium-sized_ mesh described by [17].  
The meshed cyclist's head is shown in Figure 3. After preparing the original position, a so-called metarig was implemented and connected to the 3D geometry. A metarig is a skeleton of the human body reduced to its essential bones and moving joints. DB position is shown in Figure 4. As mentioned above, some bone constraints were set for arms and legs to ensure accurate changes in the target positions. For the simulation using Nabla Flow, all models were rotated around the z-axis to account for yaw angles ranging from 5\({}^{\circ}\) to 20\({}^{\circ}\) in steps of 5\({}^{\circ}\).  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Dimensions and Geometry** & **HH** & **HD** & **DB** & **TT** \\ \hline Cyclist’s height [m] & 1.50 & 1.40 & 1.22 & 1.10 \\ Cyclist’s length [m] & 1.02 & 1.02 & 1.02 & 1.17 \\ Cyclist’s height to shoulders [m] & 1.21 & 1.10 & 1.00 & 0.94 \\ Exaggeration [m] & 0.16 & 0.26 & 0.16 & 0.16 \\ Upper arm length [m] & 0.32 & 0.32 & 0.32 & 0.32 \\ Lower arm length [m] & 0.32 & 0.32 & 0.32 & 0.32 \\ Shoulder width [m] & 0.44 & 0.44 & 0.44 & 0.44 \\ Arm width [m] & 0.44 & 0.44 & 0.44 & 0.15 \\ Hands above feet [m] & 0.59 & 0.49 & 0.59 & 0.59 \\ Hip angle [\({}^{\circ}\)] & 37 & 21 & 13 & 4 \\ Shoulder angle [\({}^{\circ}\)] & 95 & 85 & 76 & 108 \\ Arm angle [\({}^{\circ}\)] & 163 & 150 & 97 & 112 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cyclist’s dimensions and geometry.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & **nVC (M)** & **nSC (k)** & **1/nVC** & **Drag (N)** & **CdA** & **Dev. (\%)** \\ \hline coarse & 0.24 & 4.167 & 2.7 & 2.24 & 0.036 & 65.0 \\ medium & 0.67 & 1.493 & 69.0 & 6.14 & 0.100 & 3.2 \\ medium & 0.71 & 1.416 & 69.5 & 6.61 & 0.108 & 4.3 \\ fine & 2.55 & 0.392 & 279 & 6.49 & 0.106 & 2.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data used in mesh dependency study. nVC: number of volume cells (tets), nSC: number of surface cells (triangles) of cyclist’s body.
# Megalos: A Scalable Architecture for the Virtualization of Large Network Scenarios +
## 7 Evaluating the Scalability
### Latency Scalability  
To measure the latency overhead induced by Megalos, using the same cluster of the previous experiment, we realized a network scenario consisting of a chain of \(n+2\) VDs, for different values of \(n\), where:  
1. The first and the last VD of the chain are a client and a server, respectively;
2. All the intermediate VDs are routers; and
3. Two VDs that are consecutive in the chain are connected by a virtual LAN.  
Also, we tampered the Kubernetes scheduler so that two VDs that are consecutive in the chain are never allocated to the same worker. Using plain ping we measured the Round-Trip Time (RTT) between the client and the server. We performed the above experiment ranging \(n\) from 0 to 200. The measured average RTTs are in Figure 4 with the green curve (standard deviation is also shown).  
In order to obtain the RTT overhead actually introduced by Megalos, we need to subtract from the above described values the intrinsic latency of the containers and the latency due to the physical network. Therefore, we performed the following additional measures.  
1. We repeated the above experiment placing the entire network scenario on a single host without using Megalos and implementing each VD with a single Docker container. The measured average RTT, reported in Figure 4 with the blue curve, provide us with an estimation of the intrinsic latency of the containers.
2. We measured the RTT in the cluster between each pair of workers and computed their average. This provided us with an estimation of the average latency of our cluster physical network.  
Finally, for each value of \(n\), we summed up the RTT measured in the single host scenario with the average latency of the physical network multiplied by \(n\). The red curve of Figure 4 shows the result. The difference between the green curve and the red curve is the overhead latency introduced by Megalos. It appears that such overhead is linear with the number of consecutive VDs traversed by the traffic that are allocated to different workers.
###### Abstract  
In recent years, significant advancements in nanotechnology have facilitated the synthesis of zinc oxide (ZnO) nanoparticles with tailored sizes and shapes, offering versatile applications across various fields, particularly in biomedicine. ZnO's multifunctional properties, such as semiconductor behavior, luminescence, photocatalytic activity, and antibacterial efficacy, make it highly attractive for biomedical applications. This study focuses on synthesizing ZnO nanoparticles via the microwave-assisted hydrothermal method, varying the precursor concentrations (0.3488 mol/L, 0.1744 mol/L, 0.0872 mol/L, 0.0436 mol/L, and 0.0218 mol/L) and reaction times (15, 30, and 60 min). Characterization techniques, including X-ray diffraction, scanning electron microscopy, transmission electron microscopy, BET surface area analysis, and Fourier transform infrared spectroscopy were employed to assess the structural, morphological, and chemical properties. The predominant morphology is observed to be platelets, which exhibit a polygonal shape with beveled corners and occasionally include short rod-like inserts. The thickness of the platelets varies between 10 nm and 50 nm, increasing with the concentration of Zn\({}^{2+}\) in the precursor solution. Preliminary antimicrobial studies indicated that all strains (_S. aureus_, _E. coli_, and _C. albicans_) were sensitive to interaction with ZnO, exhibiting inhibition zone diameters greater than 10 mm, particularly for samples with lower precursor concentrations. Cell viability studies on human osteoblast cells demonstrated good compatibility, affirming the potential biomedical applicability of synthesized ZnO nanoparticles. This research underscores the influence of synthesis parameters on the properties of ZnO nanoparticles, offering insights for optimizing their design for biomedical applications.  
zinc oxide; microwave-assisted hydrothermal method; antimicrobial activity; particle size 1  
[PERSON]  
1 Department of Science and Engineering of Oxide Materials and Nanomaterials, Faculty of Chemical Engineering and Biotechnology, National Polytechnic University of Science and Technology of Bucharest, 011061 Bucharest, Romania; irina, [EMAIL_ADDRESS] (L.E.D.); manuela.peda@stud fm.upb.ro (M.D.P.); [EMAIL_ADDRESS] (V.L.E.); [PERSON]DRESS] (A.C.B.); [EMAIL_ADDRESS] (E.A.)
## 2 Numerical Approaches
### Modeling of Rock
The characteristics of the materials used in the CD model are suitable for simulating the combined behavior under the stresses in the rock cutting process. \[Y_{dam}=Y\bigg{(}1-\frac{D}{D_{MAX}}\bigg{)} \tag{7}\]  
Figure 7: Cumulative damage as a function of effective plastic strain (from [16]). Linyi sandstone is from China, and it is considered to be a homogeneous and isotropic material. The mechanical properties of Linyi sandstone are presented in Table 1. Its initial elastic behavior is expressed by an approximation of [PERSON]'s law [16]. The properties of the rock are represented using a linear EOS. The bulk modulus and shear modulus are calculated on the basis of elasticity, and the reference temperature is set to room temperature, i.e., 20 \({}^{\circ}\)C. The input parameters for the DP model are obtained from laboratory tests, and the values for the CD model are validated by trial and error. Table 2 summarizes the input parameters of Linyi sandstone for the CD model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Properties** & **Unit** & **Value** \\ \hline Density & g/cm\({}^{3}\) & 2.4 \\ Porosity & \% & 8.2 \\ Uniaxial compressive strength & MPa & 64.0 \\ Brazilian tensile strength & MPa & 4.7 \\ Young’s modulus & GPa & 10.2 \\ Poisson’s ratio & & 0.2 \\ Schmidt hammer rebound hardness \({}^{\star}\) & & 57.3 \\ Shore hardness \({}^{\star}\) & & 43.5 \\ P-wave velocity & m/s & 2317 \\ S-wave velocity & m/s & 1531 \\ \hline \hline \end{tabular} \({}^{\star}\) Schmidt hammer rebound and Shore hardness values were obtained by averaging the upper 10 values from 20 tests performed.  
\end{table} 
Table 1: Mechanical properties of Linyi sandstone. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model (Category)** & **Parameter** & **Input Value** \\ \hline \multirow{2}{*}{Linear (equation of state (EOS))} & Density (g/cm\({}^{3}\)) & 2.41 \\  & Bulk modulus (kPa) & \(5.67\times 10^{6}\) \\  & Reference temperature (K) & 293 \\ \hline \multirow{3}{*}{Drucker–Prager (strength)} & Shear modulus (kPa) & \(4.25\times 10^{6}\) \\  & Pressure hardening type & Stassi \\ \cline{1-1}  & Yield stress in tension (kPa) & \(4.23\times 10^{3}\) \\ \cline{1-1}  & Yield stress in compression (kPa) & \(5.76\times 10^{4}\) \\ \hline \multirow{3}{*}{Cumulative damage (failure)} & Plastic strain (D = 0) & 0.10 \\  & Plastic strain (D = D\({}_{\text{MAX}}\)) & 1.0 \\ \cline{1-1}  & Maximum damage factor & 0.99 \\ \cline{1-1}  & Erosion & None \\ \cline{1-1}  & Minimum density cut-off (SPH) & 0.2 (default) \\ \cline{1-1}  & Maximum density cut-off (SPH) & 1.0 (default) \\ \cline{1-1}  & Maximum sound speed (m/s) & 10,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Input parameters of Linyi sandstone for CD model. Figure 8: Differences in behavior of cumulative damage (CD) model material in compressive and tensile regions (modified from [16]).
# Smart Healthcare Network Management: A Comprehensive Review
## 4 Smart Healthcare Network Management Protocols
### Lightweight Machine to Machine (LWM2M)  
LWM2M (Lightweight Machine to Machine) [81] is a client-server protocol specifically designed for managing IoT (Internet of Things) devices with low power and constrained resources. Developed by the Open Mobile Alliance (OMA), it builds upon protocol and security standards from the Internet Engineering Task Force (IETF). The protocol supports functionalities such as _connectivity monitoring_, _resource monitoring_, and _firmware upgrades_, which are essential for managing IoT ecosystems efficiently.  
The LWM2M architecture follows a hierarchical structure. The _LWM2M server_ resides on the network manager device, while _LWM2M clients_ operate on managed IoT devices. These devices organize their resources into objects. For instance, a _location object_ encompasses resources that provide geolocation information. This modular structure facilitates the efficient management of resources.  
A mathematical representation of LWM2M operations can be modeled as resource mappings:  
\[R=\{r_{1},r_{2},\ldots,r_{n}\} \tag{20}\]  
where \(R\) represents the set of IoT resources organized into objects. The protocol supports operations like read (\(O_{r}\)), write (\(O_{w}\)), and execute (\(O_{e}\)):  
\[O_{r}:R\to V,\quad O_{w}:V\to R,\quad O_{e}:R\to\text{Action} \tag{21}\]  
Here, \(V\) denotes the value space of a resource.  
Applications of LWM2M in healthcare include the remote management of medical devices, ensuring timely software updates, continuous performance monitoring, and secure communication. While LwM2M is designed to manage constrained IoT devices and provide efficient remote device management, it may face performance limitations in large-scale deployments [82].
## 3 Methodology
### Measurement
#### 3.4.2 Physiological Indices  
(a) EEG  
EEGs were recorded using an EEG-measuring instrument (EEG-1200, Nihonkohden Co., Japan) at a sampling rate of 500 Hz. EEG electrodes were attached on 16 channels (based on the internationally accepted 10-20 system, Fp1, Fp2, F7, F3, F4, F8, T7, C3, C4, T8, P7, P3, P4, P8, O1, O2). Next, raw data were processed using the Fourier transform method, and the spectral power of each frequency band, such as the content of theta wave (4-8 Hz), low-alpha wave (8-10 Hz), high-alpha wave (10-13 Hz), low-beta wave (13-20 Hz), high-beta wave (20-30 Hz), and SMR (12-15 Hz) bands, was calculated in addition to the values of the beta per alpha and alpha per high-beta for each channel.  
(b) ECG  
ECGs were recorded using an ECG-measuring instrument (WEB-7000 and ECG picker, Nihonkohden Co., Japan) at a sampling rate of 1000 Hz. Three electrodes were attached to the chests of subjects using the precordial leads method. The R-R interval (RRI) was calculated from the ECG waveform using MATLAB (Mathwork Co.) programs. The values of the mean of the RRI and the coefficient of variance of RRI [CVRR (100*SD/Mean of RRI)] were calculated from the RRI data. In addition, the spectral power of each frequency band, such as the very low frequency (VLF, 0.001-0.04 Hz), low frequency (LF, 0.04-0.15 Hz), and high frequency (HF, 0.15-0.45 Hz) bands, was calculated from the time series of the RRI using the fast Fourier transform method.  
(c) Respiration  
A thermal picker (WEB-7000, Nihonkohden Co., Japan) was used to measure the temperature of the breath of subjects. The peak values in the time series graph of the temperature were detected, and the mean and standard deviation of the respiratory cycle time were calculated.  
(d) Skin Temperature  
Thermocouples were attached at 7 places on the bodies of subjects based on the Hardy-Du Bois method [16] to measure the skin temperature (LT8, GRAM Co., Japan). Finally, the mean skin temperature was calculated using following Equation (1):  
\[MST=0.07\ T_{1}+0.14\ T_{2}+0.05\ T_{3}+0.35\ T_{4}+0.19\ T_{5}+0.13\ T_{6}+0.07 \ T_{7} \tag{1}\]  
where \(MST\) is the mean skin temperature based on the Hardy-DuBois method [16], and \(T_{1}\), \(T_{2}\), \(T_{3}\), \(T_{4}\), \(T_{5}\), \(T_{6}\), and \(T_{7}\) are the temperatures of the forehead, forearms, hands, abdomen, thighs, legs, and feet, respectively.
## 3 Results
### Quantitative Relationships between \(RE\) and \(Biotic\) and \(Climatic\) Factors
#### 3.1.2 Quantitative Relationships between \(f(T,P,W)\) and Biotic and Climatic Factors  
The regression analyses between \(f(T,P,W)\) and the remote-sensing indexes (Figure 4) indicate that the temporal variability of \(RE\) has a strong exponential dependency on temperature, which could be described using the \(LST_{n}\)-driven Lloyd-Taylor equation. \(RE\) still has a significant linear response to the seasonal dynamics of \(EVI\), which is decided by the behind mechanism that growth respiration supplies energy at the cost of consuming some photosynthetic, causing the \(RE\) to be approximate to a constant fraction of plant productivity [57; 58]. In terms of the response to temporal variation of \(LSWI\), \(RE\) is almost linearly related to \(LSWI\) firstly (when \(LSWI<0.1\)) and then it reaches saturation during the final stage in the Inner Mongolian region with a relatively low soil water content, whereas in the Tibetan region with a relatively high soil water content, the response of \(RE\) to \(LSWI\) follows a gradual saturation. The responses of \(RE\) to moisture under the two different water regimes both agree well with the typical Michaelis-Menten curve.  
The temporal variation of \(RE\) in the alpine grasslands of the Tibetan Plateau is mainly correlated to temperature (\(LST_{n}\), \(R^{2}\) = 0.752), followed by plant productivity (\(EVI_{std}\), \(R^{2}\) = 0.617), while water (\(LSWI\), \(R^{2}\) = 0.451) plays a minor role in regulating the temporal dynamics of \(RE\) in this temperature-limited region (Figure 4). In contrast, water exerts a strong influence (\(LSWI\), \(R^{2}\) = 0.656) on the temporal variability of \(RE\) in the temperate grasslands of Inner Mongolia, which is comparable with the determinant factor (\(LST_{n}\), \(R^{2}\) = 0.687), while plant productivity exerts a relatively weak impact (\(EVI_{std}\), \(R^{2}\) = 0.485).