esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 First Order Theory of Propulsion Generation by Means of Rotation Energy
For fixed values of unbalance mass \(m_{u}\), rotor radius \(R\) and rotation frequency \(f\) propulsion force \(F_{x}\) will become its maximum if the unbalance is present in an angle range of \(2\alpha_{x}=180^{\circ}\). Furthermore, due to our intermittent operation of rotor unbalance, only the wanted outward force component \(F_{x}\) (propulsion force) occurs in the rotation plane. The propulsion force, \(F_{x}\), is orthogonal to rotation axis A and points exactly in the wanted direction, \(x\), which is in a symmetrical position within an unbalance activity angle \(2\alpha_{x}\). On the other hand, the orthogonal force component \(F_{y}\) is extinguished over each rotation period. If there is no other restriction, the unchained rotor will move exactly in direction \(x\) of the only acting force \(F_{x}\). Its start point is \(x=0\) at time \(t=0\). Here follows our description of the takeoff scenario:  
The UAV containing the well-balanced rotor on board is initially fixed in a constraining device at the start position. After electrical loading, the rotor spins with a high rotation frequency. The translational energy of the fixed rotor is zero at the beginning of the start phase because all kinetic energy is stored in rotation energy. If we now generate a permanent unbalance mass on the spinning rotor, it will produce only rotating centrifugal force and unwanted vibrations of the cell and constraining device. But by controlled on/off-unbalance activation always in the same selected angle range during each revolution, a sequence of force pulses in the wanted direction is generated due to permanent rotation which can be used now to produce directed propulsion of the vehicle cell. Thus, after opening the constraining device, the directed force pulse sequence will produce the translational acceleration and cause movement of the UAV away from its start position. For simplicity in the dynamic situation, we are assuming here the subcritical operation of the rotor system itself: the lowest resonance frequencies are all above the rotational frequency f of the rotor. All critical system components (actuators, rotor bearings, etc.) are assumed to be sufficiently hard; thus, superimposed elastic forces and friction forces in the system are eliminated. During free flight, only inertial forces act on the rotor bearing, as the elastic mounting forces are eliminated when the rotor is detached. As a result, the load on the bearings due to unbalance is significantly lower during flight than when operating a tethered rotor, which has a favorable effect on the bearing life.
## 2 Materials and Methods
### Data Sources
#### 2.2.1 NDVI Data  
The NDVI data were derived from the MOD13Q1 and MYD13Q1 products on the GEE platform from 2000 to 2020. These two products are produced from Moderate Resolution Imaging Spectroradiometer (MODIS) data by NASA's Land Processes Distributed Active Archive Center (LP DAAC) at the USGS EROS Center [60,61]. The NDVI data have a temporal resolution of 16 days and a spatial resolution of 250 m. We first used the quality bands (SummaryQA) to remove the influence of cloud-, snow-, and ice-based pixels for each image. Secondly, since there were three or four remote sensing images for each month (since the Aqua satellite was not launch yet, before July 2002, there is only MOD13Q1 data from Terra satellite, with one or two images each month), the maximum-value composite (MVC) procedure was applied to transfer the 16-day NDVI data to monthly data. Moreover, we re-projected the NDVI data to Universal Transverse Mercator (UTM) 49 N Projection based on the WGS-84 datum and extracted the data for the research area through the use of the Inner Mongolia administrative boundary vector file.
# Classification and Regression of Muscle Neural Signals on Human Lower Extremities via BP_AdaBoost
## 2 Materials and Methods
### Feature Extraction for Weak Classification--Muscle Neural Activation  
The neural dynamic model of EMG signal is defined as the nerve convulsion generated by action potential. There are many criteria for evaluating nerve activity (iEMG, RMS, VAR, et al.) [18]. The sliding standard deviation is used to evaluate nerve activity in this article. Setting the length of the collected original EMG signal sequence as \(n\) and the length of the time window as \(N\) (\(N<\frac{n}{2}\)). The window is slid backward after calculating the standard deviation of the signal in a long time (the method is shown in Formulas (1) and (2)). The neural activation sequence of the original EMG signal will be obtained after successive calculation.  
\[\overline{x}=\frac{1}{N}\sum_{n=0}^{N-1}x_{i} \tag{1}\]  
\[u=\sqrt{\frac{1}{N}\sum_{n=0}^{N-1}(x-x_{i})^{2}} \tag{2}\]  
where \(x_{i}\) is the original EMG value, \(\overline{x}\) is the integrated EMG, and \(u\) is the nerve activation. In order to eliminate the individual differences of the experimenters, the resting EMG signal \(x_{0}\) of each experimenter's test muscle in the natural state is recorded as the neural activity value \(u_{0}\) of the muscle in the resting state. Muscle neural activation is the response of muscle to nerve electrical stimulation. The relationship between muscle neural activation \(a\) and nerve activation \(u\) in the literature is shown in Formula (3) [19]:  
\[a=\frac{e^{Au}-1}{e^{A}-1}\ (-3\ <\ A\ <\ 0) \tag{3}\]  
where \(A\) is a constant, indicating the degree of nonlinearity, and the value range is \(-3\)-\(0\). The following Figure 3 shows the relationship curve between muscle activity and nerve activity under the influence of different constants \(A\). It shows that muscle activity is positively correlated with nerve activity. The smaller \(A\) is, the more obvious the linear relationship between muscle activity and nerve activity. Muscle neural activation will not change too much during the surge of nerve activity, which ensures the smoothness of muscle strength eigenvalues. Taking the EMG signal of a thigh raising cycle as an example, Figure 4 compares the difference between the original EMG signal of biceps  
Figure 2: Comparison before and after signal noise reduction.  
demoris (after filtering) and its muscle neural activation (\(A\) is taken as \(-0.2\)). It illustrates that the calculated EMG signal retains the characteristics of the original EMG signal, further filters the signal, and increases the recognizability of the signal. This also shows that EMG signals can reflect the strength of neural signals.
# Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks
## Appendix B Proof of Theorem 2  
Since \(\mathcal{D}_{i}(0)\) satisfies the constraint of data queue upper-bound, Theorem 2 can be proved by mathematical induction. Assume that \(0\leq\mathcal{D}_{i}(t)\leq\mathcal{D}_{\max}\) holds in time slot \(t\). According to (6), we have \(0\leq\mathcal{D}_{i}(t+1)\). Then, we focus on proving Formula (25) holds at time slot \(t+1\), i.e., \(\mathcal{D}_{i}(t+1)\leq\mathcal{D}_{\max}\).  
1. If \(\varphi_{i}(t)=0\) at time slot \(t\), no data is collected by satellite \(i\). We have \(\mathcal{D}_{i}(t+1)\leq\mathcal{D}_{i}(t)\leq\mathcal{D}_{\max}\).
2. If satellite \(i\) collected space data in time slot \(t\), i.e., \(\varphi_{i}(t)\geq 0\). Note that the DACO problem is a convex problem, we have \((1/\tau)V\mathcal{H}^{\prime}(\varphi_{i}^{*}(t))=\mathcal{D}_{i}(t)-(P_{r}/ \varphi_{\max})(E_{i}(t)-\mathcal{B})\). Since \(E_{i}(t)-\mathcal{B}\leq 0\) and \(\mathcal{H}^{\prime}(\varphi_{i}^{*}(t))\leq\mathcal{H}_{\max}^{\prime}\), we have \(\mathcal{D}_{i}(t)\leq(1/\tau)V\mathcal{H}_{\max}^{\prime}\). According to (6), we can obtain \(\mathcal{D}_{i}(t+1)\leq\mathcal{D}_{i}(t)+\tau\varphi_{i}(t)\). Therefore, we have \(\mathcal{D}_{i}(t+1)\leq(1/\tau)V\mathcal{H}_{\max}^{\prime}+\tau\varphi_{ \max}\).
###### Abstract  
Estimating a pile shaft's frictional capacity is challenging and has been a controversial subject among researchers. In this study, the shaft friction resistance of non-displacement (pre-installed) model piles under axial load was investigated. Four different model piles were used, including steel, timber, and two composite piles (FRP and PVC filled with concrete). The angle of interface friction (\(\delta\)) between test sand, and pile materials was determined using an interface shear test (IST) at four relative densities. Axial pile load experiments were implemented in a soil tank and piles were embedded into loose to very dense sand. Model pile load tests were performed in such a way that there was no end (point) bearing capacity (only friction was generated), and lateral friction resistance between the pile material and the soil along the pile shaft formed the complete bearing capacity of the model pile. According to experimental results, it was observed that, with increasing sand relative density and surface roughness of the pile material, the shaft friction resistance of the model pile increases. A back-calculation analysis was also performed to find the values of lateral earth pressure coefficient (K) using [PERSON]'s (1973) equation with the help of measured shaft friction capacity of the model pile load test. By performing multivariate regression analysis, an equation was obtained between the back-calculated lateral earth pressure coefficient (K) and other parameters. The obtained equation was used to calculate the K values given in other studies in the literature. It was determined that the obtained equation was in good agreement with the data in other studies. This equation can be beneficial in practice and can be advantageous for further study in the future.  
shaft friction resistance; lateral earth pressure coefficient (K); interface friction angle (8); relative density (Dr); interface shear test (IST) +
Footnote †: journal: Applied Sciences
# A Lightweight Chaotic Map-Based Key Agreement Scheme for the Advanced Metering Infrastructure
## 3 AMI System Model  
In the AMI, the smart meter is located at the user side, and the collected measurement, illegal power consumption, fault and other data are uploaded to the data concentrator through RS485, PLC and other communication media. It is an intelligent instrument with a microprocessor as the core, and its memory and computing power are relatively limited. The data concentrator is generally installed in the substation, which processes and packages the collected data and forwards it to the measurement data management system through optical fibers, GPRS and other communication media. The measurement data management system is located in the power grid company to store, analyze and use all the data recorded by the smart meter. The communication mode in the uplink network is generally unicast communication, such as smart meter sending measurement data to a data concentrator. In the downlink network, there is also a multicast message transmission method, such as the measurement data management system sending the electricity price information to multiple smart meters simultaneously through the data concentrator. The architecture of the AMI system is shown in Figure 1.  
The AMI has terminal access to a large number of smart meters, which record the power consumption information of each user in real time and upload the power consumption data to the measurement data management system regularly. Once the data are eavesdropped on and tampered with by adversaries, this will lead to the leakage of the user's privacy information and affect the normal charging of the power company, which will cause conflicts and disputes between the supply and demand sides. The measurement data management system remotely controls the smart meter by transmitting control instructions, such as determining whether to cut off the power according to the user's electricity bill balance, configuring parameters during the installation of the smart meter, and rewriting the electricity price parameter. Once the control instruction is issued wrongly, it will cause disorders in the power system and affect people's normal life. In addition, attackers can impersonate legitimate entities and inject false messages into the channel to disrupt the normal operation of the system and even control the entire AMI system.  
To sum up, in order to enable entities in AMI to transmit data and exchange information through a secure channel, a lightweight key agreement scheme based on chaotic map is  
Figure 1: AMI system structure.  
designed in this article. Each entity generates a common session key through authentication and key agreement mechanisms and transmits information through encrypted channels to achieve secure data transmission, preventing unauthenticated devices from obtaining effective information. We also designed the generation of multicast keys to address the characteristics of multicast message transmission in AMI. In addition, the identity information of smart meters bound to users will not be leaked during the interaction process.
## 2 Validation of the Numerical Model
### Validated Model
The points were chosen in the lower part of the steel profile as follows: point 1 was considered at a quarter of the bottom flange length, point 2 at 10 mm on the web height, and points 3, 4 at 30 mm and 50 mm above the bottom flange, respectively. Figure 5: Cross-section of the WFRC 44174 test. Figure 6: Cross-sections of the WFRC 66162 test: (**a**) cross-section F with a thinner concrete slab depth [28]; (**b**) transition cross-section; (**c**) cross-section G with a concrete slab standing on the lower flange of the steel profile [28]. The temperature comparisons are presented in Figure 7, for both tests, and demonstrate a good fit between the temperatures computed with SAFIR and those measured during the experiments. The displacement-time evolutions are presented in Figure 8, for both tests. Even if the numerical models do not present a perfect fit with the experiments, in both cases, the results are on the safe side. The larger displacements lead to a failure time of 94 min, compared to 107 min obtained in the WFRC 66162 test, while for the WFRC 44174 test the failure time was 52 min, in comparison with the 43 min obtained numerically. This phenomenon was also noticed by [PERSON] et al. [21] during their study for SFB type slim floors, using tests from the same experimental campaign. A possible explanation for the conservative results may be related to the support conditions within the experimental set-up, as the supports were outside the oven, and the assembly was part of its roof. This means that the oven walls might have interfered with the specimen during the test. Therefore, a second numerical analysis was made, in which the support conditions were simulated by considering a reduced span length of 4.25 m. A very good fit was obtained, in terms of both displacement and failure time, as shown in Figure 8. The assumption of the reduced span does not influence the validation of the numerical model, which was proved to be suitable by providing similar time-displacement evolutions and conservative failure times, considering the initial span of 4.5 m mentioned in the experimental report [23; 24]. Figure 8: Vertical displacement at mid-span: comparison between numerical model and experiment: (**a**) WFRC 44174; (**b**) WFRC 66162 [28]. Figure 7: Temperature comparison between the numerical model and the experiment for: (**a**) WFRC 44174; (**b**) WFRC 66162 [28].
# Consistency Analysis and Accuracy Assessment of Eight Global Forest Datasets over Myanmar
## 3 Results
### Factors Influencing on the Spatial Consistency  
#### 3.4.1 Influence of Terrain on the Consistency  
The changes in spatial distribution consistency under different elevations were computed, as shown in Figure 8. In Figure 8a, the proportion of complete consistency is lower in the plain area. While in high mountain areas, the proportion of forests per class decreases with increasing consistency in class. In the regions with hilly, low mountain, and medium mountain (i.e., in the elevation range of 200-3500 m), the proportion of complete consistency in eight datasets is higher. Especially in the medium mountain region, the proportion of complete consistency among datasets exceeds 50%. It indicates that the forests can be identified more accurately from various datasets in these regions. Moreover, the mountains are the main plantation area of forests, the area is vast. It facilitates the interpretation and classification of forests for each dataset. In the areas of plains, the lower consistency among products may be influenced by the confusion of forests with shrubs or cultivated land planted with fruit trees. In the case of high mountain areas, forest extraction is also difficult due to fragmented terrain and fewer planted trees. The distribution pattern of consistency distribution with elevation in the three datasets in 2015 is similar to that of the eight datasets (as shown in Figure 8b). The proportion of complete consistency is also higher in hills, low mountains, and middle mountains.  
Figure 9 illustrates the variation of the spatial distribution consistency under different slopes. As shown in Figure 9a, it can be seen that the eight datasets have the highest proportion of complete agreement in each slope interval. As the slope rises, the proportion of complete consistency among datasets shows a trend of increasing and then decreasing. The highest proportion of complete consistency is 62% in the steep slope (26 deg-35 deg) interval. The percentage of complete consistency in both slope and sharp slope areas is also over 50%. Except for the flat slope areas, the proportion of forest consistency classes 1-4 in the other slope intervals is very low, which does not exceed 10% or even 5%. It is reasonable to assume that as the slope increases, the geographic landscape is more homogeneous and the forest can be better identified across the datasets. Thus, their consistency results are better. The proportion of complete agreement for each slope interval is greatly increased due to the fixed year variable and the reduction in the number of datasets (Figure 9b). The steep slope and sharp slope interval data sets, respectively, have the best consistency, i.e., 26 deg-45 deg, with 74% and 76% complete agreement.  
Figure 8: Consistency distribution among different elevation intervals. (**a**) Eight datasets; (**b**) Three datasets in 2015.
# Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems
## 2 System Model
### Transmitter  
At the transmitter side, the binary data are first encoded and mapped with quadrature amplitude modulation (QAM) by utilizing the modulation block. We suppose that the system transmits data in \(T\) time slots, and the QAM symbols at time slot \(t\), \(t=1,\cdots,T\), are combined to a data vector \(\mathbf{x}(t)\in\mathbb{C}^{N}\) as  
\[\mathbf{x}(t)=[x_{1}(t),x_{2}(t),\cdots,x_{N}(t)], \tag{1}\]  
where \(N\) is the total number of modulation symbols. The encoded data are then separated into the \(N_{T}\) vectors corresponding to the \(N_{T}\) transmit antennas as follows:  
\[\mathbf{x}_{i}(t)=[x_{i}(t),x_{i+N_{T}}(t),x_{i+2N_{T}}(t),\cdots]\quad i=1,2, \cdots,N_{T}. \tag{2}\]  
The data for each antenna are converted from serial to parallel, and then the pilot signals, which are known from both the transmitter and receiver, are inserted along with data in every layer for channel estimation purposes. We denote \(\mathbf{x}_{a}(t)\) with \(a=1,\cdots,N_{T}\) being the signal vector with a pilot inserted into the corresponding data \(\mathbf{x}_{i}(t)\); then, the IFFT (inverse fast Fourier transform) block is applied to \(\mathbf{x}_{a}(t)\) such that the signals are transformed from the frequency domain into the time domain (denoted by \(\bar{\mathbf{x}}_{a}(t)\)) as  
\[\bar{\mathbf{x}}_{a}(t)=\text{IFFT}\{\mathbf{x}_{a}(t)\}. \tag{3}\]  
After that, the cyclic prefix (CP) with the length \(N_{G}\) is inserted as a guard interval to alleviate the ISI (inter-symbol interference) by utilizing the CP insertion block. By including the cyclic prefix, the transmitted signal that is denoted by \(\bar{\mathbf{x}}_{ga}(t)\) is formulated in the time domain as follows:  
\[[\bar{\mathbf{x}}_{ga}(t)]_{n}=\begin{cases}[\bar{\mathbf{x}}_{a}(t)]_{n+N_{ \text{FFT}}}&n=-N_{G},-N_{G}+1,\ldots,-1\\ [\bar{\mathbf{x}}_{a}(t)]_{n}&n=0,1,\ldots,N_{\text{FFT}}-1,\end{cases} \tag{4}\]  
where \(N_{\text{FFT}}\) is the FFT size. This means that the last \(N_{G}\) samples of \(\bar{\mathbf{x}}_{a}(t)\) are used as a cyclic prefix and inserted into the beginning of this symbol, resulting in the signal \(\bar{\mathbf{x}}_{ga}(t)\) with a length of \(N_{\text{FFT}}+N_{G}\).
# A Comprehensive Review of Sensor-Based Smart Building Monitoring and Data Gathering Techniques
## 4 Occupancy Sensing
* Ultrasonic sensors--operate by continuously emitting high-frequency sound waves. When any motion is present, the sensor detects the frequency of sound shifts, called Doppler shift. Ultrasonic sensors are more power-consuming compared to PIR sensors but have a broader coverage area, which means that ultrasonic sensors are more suitable for large spaces like conference rooms while PIR sensors are often used in small rooms and corridors. Also, ultrasonic sensors have a better performance at small movement detection [18]. Other sensors that can be used for occupancy detection include Bluetooth or similar wireless sensors (which detect the presence of people or objects by transmitting a signal between a transmitter and a receiver) [49], wireless microphonic sensors (which detect presence by continuously monitoring for sound), video image sensors (which use a camera and video analytics to determine occupancy), dual-technology sensors (which combine PIR sensors with another technology, typically ultrasonic (active) or microphonic (passive)), and volumetric microwave Doppler sensors (which detect motion by utilizing the Doppler effect, where emitted microwaves bounce off surfaces and return to the sensor) [18; 48]. A combination of various sensors is also common for increasing the precision. Occupancy sensors are usually installed on the ceiling or the wall. The installation of wired sensors can be more expensive compared to wireless sensors because of the complexity of installation, although wireless sensors tend to be more costly than wired ones. Notably, for popular brands, wireless sensors mark a significant premium of about 55% to 100% over wired sensors according to Pacific Northwest National Lab. [18]. Figure 3: Occupancy resolution levels by [PERSON] et al. [45].