esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Indoor physical modeling experiments are commonly used for studying slope stability process and mechanism as they provide several advantages. One such advantage is the ability to create homogeneous soil slopes and control experimental conditions, ensuring that the results are not influenced by unknown factors [28; 29; 30]. Additionally, devices such as sensors can be utilized to monitor changes in micro-parameters during the simulation process [31]. Many researchers have conducted numerous studies on slope stability, instability, and failure processes using this method [32; 33]. For example, [PERSON] et al. [34] investigated the occurrence patterns and mechanisms of shallow landslides in loess under continuous heavy rainfall and intermittent heavy rainfall conditions through multiple sets of experimental comparisons. Likewise, [PERSON] et al. [35] analyzed the influence of rainfall characteristics on the instability process of soil slopes by establishing a model under both low (40 mm/h) and high intensity (80 mm/h) rainfall conditions, and provided recommendations for selecting rainfall warning benchmark parameters. Furthermore, [PERSON] et al. [36] examined the instability process of slopes under sidewall seepage conditions and analyzed the slope instability mechanism using sensor data. Ten physical modeling experiments were also carried out to investigate the similarities and differences in slope instability and failure modes under various rainfall intensities ranging from low (18 mm/h) to high (63.5 mm/h) by [PERSON] et al. [37], and scholars have also replicated the initiation process of landslides, debris flows, and other geohazards by setting specific experimental conditions indoors [36]. For example, [PERSON] et al. [38; 39] reproduced the process of debris flow disasters caused by runoff in the Wenchuan earthquake zone through indoor modeling experiments and analyzed the initiating mechanism. Moreover, some scholars have studied the influence of specific factors on slope stability through comparative experiments, including soil initial moisture content [40], soil particle size distribution [39; 41], and fine particle content [41]. This study is focused on the HLFS failure mode and process triggered by precipitation under different mitigation measures. We conducted indoor physical model experiments and analyzed the differences in failure modes and mechanisms using sensor data, 3D laser scanner data, and direct observations of the experimental phenomena, the results can provide a reference for optimization mitigation measures for the \"Gully Stabilization and Tableland Protection\".
# Resource Allocation in 5G Cellular IoT Systems with Early Transmissions at the Random Access Phase
## 3 System Model
### Deployment and Resources  
We consider a single base station (BS) serving end devices (ED) located in its coverage and requiring communications in both uplink and downlink directions; see Figure 1. Orthogonal frequency-division multiple access (OFDMA) is assumed. The available bandwidth is \(B\) Hz. The system operates in terms of frames with frame duration of \(\Delta\)ms. The frame is divided into two parts--random access (RA) and data transmission (DT) phase--as shown in Figure 2. The time-frequency resources are measured in \(n\) channels (resource units, RU), where \(n\) is calculated according to the Nyquist theorem, \(n=\Delta/(1/2B)=2\Delta\). Thus, out of \(n\) RUs, \(n_{1}=\Delta_{1}/(1/2B_{1})=2\Delta_{1}B_{1}\) RUs are allocated to the RA phase, whereas the remaining \(W=n-n_{1}\) RUs are assigned to the DT phase. A single RU corresponds to the time required to transmit a single packet.  
Figure 1: The considered mMTC deployment with downlink and uplink traffic for an early data transmission operation.  
Figure 2: The frame structure, resources, and packet passing through RA and DT phases in the considered 5G mMTC system.
## 2 Quasi-Static and Dynamic Tensile Experiments
### Experimental Results and Analysis  
In the quasi-static tensile tests, the controlled displacement load was imposed at a velocity of 0.01 mm/min. Figure 2 shows the experimental crack trajectories. For h = 5 and 6 mm, the crack starts to propagate from the pre-crack to the hole, then a new crack was initiated and propagated from the hole to the right edge, and for h = 9 mm the crack propagated horizontally from the pre-crack to the right edge.  
Figure 1: Geometry of the pre-cracked specimen.  
Figure 2: Experimental crack paths for quasi-static tensile test with a loading velocity of 0.01 mm/min for: (**a**) h = 5 mm; (**b**) h = 6 mm; (**c**) h = 9 mm.  
In the dynamic tests, the loading velocity is 1 m/s as shown in Figure 3, and for h = 5 and 9 mm the crack trajectories are the same as those achieved in the quasi-static case. However, for h = 6 mm, the crack propagated horizontally without passing through the hole.  
In this test, we can see the effect of loading velocity on the crack trajectory. In fact, the inertial effect can change the crack path. The crack has no time to deviate to the hole and propagate directly toward the opposite edge.
## 2 Performance Assessment
### Signal-to-Noise Ratio Simulations
#### 2.2.3 Signal-to-Noise Ratio Simulations at 905 nm
In this case, there is no gain (\(M=1\)) and so the excess noise factor is \(F=1\). The noise equivalent power is 10.49 \(\,\mathrm{pW}/\sqrt{\mathrm{Hz}}\), a value calculated through Equation (10). Optical transmissivity. Three values for the transmission factor were simulated, corresponding to high (\(\xi_{0}=0.5\)), moderate (\(\xi_{0}=0.25\)) and low (\(\xi_{0}=0.1\)) optical transmissivity. Background-radiance system constant. For \(K_{b}\) a value of 6.17 \(\times\) 10\({}^{-9}\) m\({}^{2}\cdot\)nm\(\cdot\)sr is assumed. This parameter was calculated by applying Equation (8), taking a reception diameter of 50 mm, a field of view of 1 mrad and an interference filter width of 1 nm. In Figure 2, the signal-to-noise ratio is simulated as a function of the system constant \(K_{s}\) (variants 1 to 6 in Table 2) in response to the lidar backscattered signal of Equation (9) and the optical coefficients of Table 1. It can be seen that when using an APD module (in red, Figure 2), it is possible to achieve an SNR of 5 with system constants (\(K_{s}\) between 16.33 and 81.58 W\(\cdot\)m\({}^{3}\) in Table 2) two orders of magnitude lower than those required when using a PIN module (\(K_{s}\) between 852.3 and 4261 W\(\cdot\)m\({}^{3}\) in Table 2). Therefore, using a Si-APD module and assuming reception diameters \(D_{r}\) of between 50 and 100 mm, it is concluded that the required pulse energy must be between 14 and 277 \(\mu\)J (Equation (7)).
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Variation** & \
\begin{tabular}{c} **Caffeine** \\ **Dose** \\ \end{tabular} & **Time** & **Mean \(\pm\) SD** & **F** & \(p\) & **Factor 1** & **Factor 2** &
\begin{tabular}{c} **Mean Difference \(\pm\) SE** \\ **(Factor 1-Factor 2)** \\ \end{tabular} & **t** & \(P_{\text{{Shkey}}}\) \\ \hline \multirow{4}{*}{Accuracy rate} & 0 min & 1 & & & & 30 min & \(-\)0.01305 \(\pm\) 0.00932 & \(-\)1.400 & 0.356 \\  & 100 mg & 30 min & 1.013 \(\pm\) 0.04842 & 3.31 & 0.044 \({}^{*}\) & 0 min & 60 min & 0.00903 \(\pm\) 0.00947 & 0.953 & 0.613 \\ \cline{2-10}  & & 60 min & 0.991 \(\pm\) 0.04923 & & 30 min & 60 min & 0.02207 \(\pm\) 0.00684 & 3.226 & 0.009 \({}^{*}\) \\ \cline{2-10}  & & 0 min & 1 & & & 30 min & 0.00537 \(\pm\) 0.00586 & 0.917 & 0.634 \\ \cline{2-10}  & 200 mg & 30 min & 0.995 \(\pm\) 0.03218 & 5.44 & 0.007 \({}^{*}\) & 0 min & 60 min & 0.02034 \(\pm\) 0.00708 & 2.874 & 0.020 \({}^{*}\) \\ \cline{2-10}  & & 60 min & 0.979 \(\pm\) 0.04009 & & 30 min & 60 min & 0.01497 \(\pm\) 0.00618 & 2.423 & 0.055 \\ \hline \multirow{4}{*}{Response time} & 0 min & 1 & & & & 30 min & 0.0445 \(\pm\) 0.0094 & 4.740 \(<\)0.001 \({}^{*}\) \\ \cline{2-10}  & 100 mg & 30 min & 0.963 \(\pm\) 0.04238 & 30.4 & \(<\)0.001 \({}^{*}\) & 0 min & 60 min & 0.0701 \(\pm\) 0.01048 & 6.690 \(<\)0.001 \({}^{*}\) \\ \cline{1-1} \cline{2-10}  & & 60 min & 0.934 \(\pm\) 0.04590 & & 30 min & 60 min & 0.0256 \(\pm\) 0.00706 & 3.620 & 0.003 \({}^{*}\) \\ \cline{1-1} \cline{2-10}  & 200 mg & 30 min & 1 & & & 30 min & 0.0232 \(\pm\) 0.00660 & 3.520 & 0.005 \({}^{*}\) \\ \cline{1-1} \cline{2-10}  & 200 mg & 30 min & 0.976 \(\pm\) 0.03408 & 32.6 & \(<\)0.001 \({}^{*}\) & 0 min & 60 min & 0.0637 \(\pm\) 0.00928 & 6.860 \(<\)0.001 \({}^{*}\) \\ \cline{1-1} \cline{2-10}  & & 60 min & 0.938 \(\pm\) 0.04713 & & 30 min & 60 min & 0.0404 \(\pm\) 0.00784 & 5.160 \(<\)0.001 \({}^{*}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of results of the two-back test using RM ANOVA. Figure 7: Comparison of changes in SpO\({}_{2}\) at each time point (the error bars indicate standard error). \({}^{*}p\) < 0.05. At a caffeine intake of 100 mg, the VAS score was 3.13 \(\pm\) 2.19 at 0 min, 3.07 \(\pm\) 2.03 at 30 min, and 2.80 \(\pm\) 2.04 at 60 min. This value decreased by 0.06 at 30 min compared to
## Materials and Methods
### Primer Design and Real-Time Quantitative PCR  
Primer Premier 5.0 (Palo Alto, CA, USA) was used to design primers. All primer sequences are listed in (Table 1). All candidate genes were selected from the terpenoid biosynthetic pathways in plants (Figure 1). We employed iQ5 real-time quantitative PCR (Bio-Rad, Hercules, CA, USA) to explore the expression profiles of candidate genes. In this experiment, _UBQ_ and _TIF_ were used as housekeeping reference genes as in a previous study these genes were stably expressed during different developmental stages of _Z. bungeanum_ fruit [2]. The reaction included: 10 \(\upmu\)L of 2 \(\times\) SYBR Green Mix (Bioer Technology, Hangzhou, China); 5 \(\upmu\)L of cDNA template; 0.6 \(\upmu\)L of Forward Primer; 0.6 \(\upmu\)L of Reverse Primer; and 3.8 \(\upmu\)L of DEPE water. The amplification procedure started with 95 \({}^{\circ}\)C for one cycle of 3 min. This was followed by 40 cycles of: 95 \({}^{\circ}\)C for 15 s; 60 \({}^{\circ}\)C for 15 s; and 72 \({}^{\circ}\)C for 30 s. The reaction was terminated at 95 \({}^{\circ}\)C for 10 s and 65 \({}^{\circ}\)C for 5 s. There was three biological repetitions for each template. The relative mRNA abundance was calculated using the formula: 2\({}^{-\Delta\Delta\up
# A Resilient Large-Scale Trajectory Index for Cloud-Based Moving Object Applications
###### Abstract  
The availability of location-aware devices generates tremendous volumes of moving object trajectories. The processing of these large-scale trajectories requires innovative techniques that are capable of adapting to changes in cloud systems to satisfy a wide range of applications and non-programmer end users. We introduce a Resilient Moving Object Index that is capable of balancing both spatial and object localities to maximize the overall performance in numerous environments. It is equipped with compulsory, discrete, and impact factor prediction models. The compulsory and discrete models are used to predict a locality pivot based on three fundamental aspects: computation resources, nature of the trajectories, and query types. The impact factor model is used to predict the influence of contrasting queries. Moreover, we provide a framework to extract efficient training sets and features without adding overhead to the index construction. We conduct an extensive experimental study to evaluate our approach. The evaluation includes two testbeds and covers spatial, temporal, spatio-temporal, continuous, aggregation, and retrieval queries. In most cases, the experiments show a significant performance improvement compared to various indexing schemes on a compact trajectory dataset as well as a sparse dataset. Most important, they demonstrate how our proposed index adapts to change in various environments.  
moving objects; big data; spatial indexing; machine learning for indexing +
Footnote †: journal: Applied Sciences
## 3 Data Analysis Setup
### Inspiration and General Framework
Figure 7 indicates the feature map of the wavelet coefficient in the form of a scalogram for two different time series with various noise levels, whereas Figure 8 shows the mapping of K vector for each image in new space. The intention is to indicate how the difference in normalized averaged feature value is manifested differently. In fact, the map in Figure 7 illustrates the fine spectral/temporal representation while the projection in Figure 8 indicates the condensed features of the respective map in the temporal direction of \(1=8000\) samples. In Figure 7, left image, the scalogram is superimposed with white Gaussian noise, characterized by a variance of \(\sigma^{2}=0.01\), equating to a signal-to-noise ratio (SNR) of 8.99. This noise appears as scattered white pixels across the scalogram. Figure 7 illustrates how this noise affects the new feature space, leading to increased variation and spread of features, particularly below an intensity of 0.5. Additionally, features are shifted within the range below 0.5 due to the higher average value of the vector. Consequently, there exists a qualitative and quantitative correlation between noise variance and feature spread in the new space. Upon closer inspection of the primary low-frequency components in Figure 7, which appear prominently as a broad white region, their consistent influence can  
Figure 7: Representation of features for wave response in the form of greyscale scalograms for two different segments of data (wavelet coefficients). be observed across the diagonal spread in Figure 8 above 0.5, akin to salient low-frequency features. It is crucial to acknowledge the reverse spectral direction in the new feature space; the upper regions in the image's height direction correspond to lower frequency constitution, whereas lower regions denote higher frequencies. Hence, the lower spectral elements, represented by white regions at the bottom of the image, manifest as higher heights in Figure 8.  
Overall, the new process has compacted the temporal features while maintaining the spectral composition through normalized wavelet coefficients, not only in the temporal dimension but also accounting for the intensity of feature amplitudes in the spectral dimension. This condensation encapsulates nuanced information within the 40-s time series, encoded into a vector as a unified data packet, and suitable for subsequent classification or regression tasks. This data transformation offers fresh perspectives on feature distribution and composition, enabling them to be manipulated, organized, sorted [7], and ultimately correlated with the inputs.
# On the Performance Analysis for CSIDH-Based Cryptosystems
## 3 CSIDH-Based Schemes
### Collisions for CSIDH-Based Algorithms
: _When the prime \(p_{2}\) is used on the surface, the secret exponents_  
\[(e_{0},e_{1},\ldots,e_{73})\qquad\text{and}\qquad(e_{0}+2,e_{1}+3,e_{2}+1,e_{3 }+1,e_{4}+2,e_{5}+1,\ldots,e_{73}+1)\]  
_represent the same ideal class._  
Since collisions in a secret exponent reduce the size of the private keyspace, we must either avoid the collisions or endure the risk for collisions by counting the number of possible public keys. Theorem 3 deals with the number of ideal classes that a secret exponent can represent, assuming that a collision exists. Theorem 3: Assume that a secret exponent \((e_{0},e_{1},\ldots,e_{n})\) has the collision so that it represent the same ideal class as \((e_{0}+c_{0},e_{1}+c_{1},\ldots,e_{n}+c_{n})\), where \(e_{i}\in[-m_{i},m_{i}]\), and \(c_{i}\in\mathbb{Z}\). Then, there are \(\prod_{i=0}^{n}(2m_{i}+1-c_{i})\) collisions. Therefore the order of the private key space \(\left\{[t_{0}^{e_{0}}t_{1}^{e_{1}}\cdots t_{n}^{e_{n}}]E\mid e_{i}\in[-m_{i},m _{i}]\right\}\) is \(\prod_{i=0}^{n}(2m_{i}+1)-\prod_{i=0}^{n}(2m_{i}+1-c_{i})\). Proof: If every exponent \(e_{i}\) is equal to or greater than \(c_{i}-m_{i}\), then the secret exponent \((e_{0},e_{1},\ldots,e_{n})\) and \((e_{0}-c_{0},e_{1}-c_{1},\ldots,e_{n}-c_{n})\) represent the same ideal class. So, there are \(\prod_{i=0}^{n}(m_{i}-(c_{i}-m_{i})+1)=\prod_{i=0}^{n}(2m_{i}+1-c_{i})\) collisions and we must contain \(e_{i}\) smaller than \(c_{i}-m_{i}\) for at least one \(i\in[0,n]\). Thus, \(\left|\left\{[t_{0}^{e_{0}}t_{1}^{e_{1}}\cdots t_{n}^{e_{n}}]E\mid e_{i}\in[-m _{i},m_{i}]\right\}\right|=\prod_{i=0}^{n}(2m_{i}+1)-\prod_{i=0}^{n}(2m_{i}+1- c_{i})\). To avoid this type of collision, we can consider two options - dropping some degree of an isogeny or adding supplementary factors to the prime of the base field. The former method is used in [13; 15] and has the advantage of avoiding computation of some large odd-degree isogenies. However, the interval adjustment of a secret exponent is inevitable to guarantee the security of the protocol. The latter method is to let exponent \(r_{i}\) of \(\ell_{i}\) in \(p\) for at least one \(i\) to be bigger than \(2m_{i}\). In this case, we must choose the prime having those factors. The advantage of this method is that we can expect that CSIDH protocols to have a certain level of the resistance for subexponential quantum attack [2; 17; 18] by expanding the size of the base field.
# Continuously Beam-Steered Phased Array Antenna Using GaN Varactors for Millimeter-Wave Applications
## 3 Design of Analog GaN-Based Phase Shifter
The phase shift versus frequency is plotted at \(-\)2 V and 2 V to compare the measured and simulated results in Figure 6. The simulation results show a continuously variable phase shift of up to 108\({}^{\circ}\), while the measurements resulted in 110\({}^{\circ}\) tunability from 40 GHz to 42 GHz. As was almost expected, a good match between simulation and measurement is achieved at 40.5 GHz. However, it is shown that as the frequency becomes higher, more deviation is significant, while the phase difference between the maximum and the minimum values remains almost unchanged. This deviation can be caused by parasitic elements that are not captured in the proposed model. In Figure 7, the return loss of the phase shifter under the two bias conditions is presented and is greater than \(-\)15 dB at 41 GHz. As can be clearly seen, a good match is achievable over the range of 40-42 GHz in simulations as well as in measurements. Figure 5: Simulated performances of hybrid coupler versus frequency. Figure 8 shows the simulated and measured insertion loss of the phase shifter at the two DC control voltages. The worst case of the insertion loss is about \(-4.5\) dB and occurs at 40 GHz in the measurements. This value could be due to the higher series resistor value of the used HEMT device at this bias voltage [15; 20] and also due to the wire bonding technique that is not included in the used HEMT varactor model. The amplitude imbalance between those two extreme DC bias states (i.e., \(-2\) V and \(2\) V) is about 1.9 dB at 41.6 GHz and 2.7 dB at 40.6 GHz in simulations and in measurements and is therefore better than those values over the extended band from 40 to 42 GHz. Figure 6: Simulated (continuous line) and measured (dotted line) phase states vs. frequency. Figure 7: Simulated (continuous line) and measured (dotted line) \(|\)S11\(|\) versus frequency.
## 3 Proposed Methodology
### Feature Selection Techniques
#### 3.3.1 Principal Component Analysis (PCA)  
In [41], PCA has been presented as an efficient feature selection method with lower computational complexity in comparison to other approaches, like colony optimization [41]. PCA as a feature selection approach is implemented as follows [21].  
First, the mean of each feature set is calculated then each value is subtracted from the mean. It is a crucial step to ensure that the first principal component describes the direction of the maximum variance [15]. A further step is calculating the covariance matrix, which is a representation of the linear dependency between two values. The third step is the calculation of eigenvectors and eigenvalues of the covariance matrix to determine the principal components of the data. Therefore, the highest eigenvalues are related to the most uncorrelated eigenvectors, which are considered to be the principal components. The main focus of PCA is to put maximum possible information in the direction of the first component, then the maximum remaining data in the second one, and so on. Since a feature component is less significant for feature extraction, it can also be interpreted that this feature is less informative in the original space [15].
## 6 Results and Discussions
### F-K Analysis: Surface Wave Dispersion Curves  
In Figure 10, the dispersion curves estimated from the different deployed arrays are shown. In some places, a single array was enough to obtain a reliable dispersion curve, while, in other places, two arrays of different apertures were implemented, obtaining an average dispersion curve. The frequency range is fixed by the theoretical wavenumber  
Figure 9: (**A**) Fundamental frequencies obtained from the HVSR analysis. (**B**) Amplitudes of the HVSR fundamental frequency peaks. Red circles represent the cities. The blue line represents the surface trace of the El-Asnam fault. OF: Oued-Fodda. AB: El-Abadia. AT: El-Attaf. RO: Rouina. AM: El-Amra. AD: Ain-Defla.  
limits (K\({}_{\rm min}\)/2, K\({}_{\rm max}\)), which depend on the array configuration and aperture (Figure 8). The curves are, therefore, not very reliable outside the theoretical wavenumber limits.  
In the city of Oued-Fodda and its surroundings, the analysis of the array measurements at sites AR1, AR2, AR3, AR4, and AR5 provided dispersion curves between 2.5 and 10 Hz. The dispersion curves obtained from the array recording AR6 in the city of El-Attaf are reliable in the frequency range between 5 and 10 Hz, and between 2.5 and 8 Hz in the city of El-Amra (AR7).
# Design and Implementation of a Passive Optical Network for a Small Town +
## 2 Technologies  
To achieve the goal of this study PON technologies were used [6]. A PON is a network that transports data in an optical domain between an optical line terminal (OLT) and an ONU (optical network unit) or ONT (optical network terminal) (G.984.1) [8,9]. Through the PON network, an optical distribution network (ODN) can be built. It carries out optical data transmission from an OLT to multiple user terminal devices (ONU) [10].  
Three PON schemes are well known, differing in the fiber termination point. Fiber-to-the-curb (FTTC) technology provides broadband, plain old telephone service (POTS), integrated services digital network (ISDN), and shared access with digital subscriber line (DSL) services. The fiber-to-the-building (FTTB) scheme offers similar services to multi-dwelling units (MDUs) and provides private services to businesses. Fiber-to-the-home (FTTH) concepts delivers broadband, POTS, and ISDN directly to individual homes [10,11].  
PON research focuses on the following two areas: the Ethernet First Mile Alliance (EFMA)/Institute of Electrical and Electronics Engineers (IEEE) definition based on Ethernet PONs and the Full Service Access Network (FSAN)/International Telecommunication Union (ITU-T) definition based on ATM and GFP PONs [12, 13, 14, 15].  
A PON uses wavelength-division multiplexing (WDM) to provide full duplex operation over a single non-zero dispersion-shifted fiber (ITU-T G.652). Typically, data is transmitted to the customer at 1490 nm, and back in the opposite direction at 1310 nm, and a signal with a wavelength of 1555 nm may be used for an additional service, such as cable television.  
Our research is based on the Ethernet standard 8.3, E-PON 802.3 ah, which defines a similar passive network with a range of up to 20 km. It uses frequency division multiplexingof the same optical frequencies as a GPON and time-division multiple access (TDMA) [16]. In this case, the speed is 1.25 Cbits/s simultaneously on the forward and reverse channels, which is also known as the Gigabit Ethernet PON (GEPON). There is also a 10-Gbit/s Ethernet (10 GbE) version--802.3 av with a line speed of 10.3125 Gbits/s and 10G EPON (10 Gb/s downstream, 1 and 10 Gb/s upstream).