esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Coordination of PV Active Power Curtailment and EVs Charging among Aggregators
## 4 Case Study  
In order to demonstrate the performance of the proposed convex optimisation problem, it has been applied to two residential communities supplied by individual feeders with a rated capacity of 149 kVA, which are derived from a 350 kVA distribution transformer. Each feeder supplies power to 55 and 75 households, respectively, with a lagging power factor of 0.95. Every feeder is managed by an aggregator \((K=2)\). Data of residential load and network parameters were collected from [23].  
The battery of a Nissan Leaf [24] was selected for all EVs \((BC=24\) kWh and \(ECR=0.1778\) kWh/km). The rated capacity of each EV charger \(p_{ch}^{max}\) is 3.7 kW with a charging efficiency of 92%. Both the daily travelled distance and the arrival time are stochastic. The first parameter follows a log-normal distribution \(\mathcal{N}(\mu_{ln}=2.89257,\sigma_{ln}=0.91779)\)[25], and the second one follows a truncated normal distribution function \(\mathcal{N}(\mu=16{:}00,\sigma=3\,h,t_{min}=11{:}00,t_{max}=23{:}00)\)[26]. It is assumed that all EVs owners would expect and objective \(SOC\) equal to 95% at the end of the charging period.  
Considering that residential PV systems can vary in a range from 2 to 5 kW [27], the PV units size is defined with the last value, operating with a unity power factor. The yearly ambient temperature and solar irradiance data were collected from [28] to model the uncertainty of PV generation.  
A moderate and critical penetration level (PL) for both the EVs and PVs in each feeder was considered, i.e., 50% and 100%, respectively. For example, a moderate PL in the first feeder means that 27 of the 55 households have both a PV and EV operating at certain time slots of the test period. Moreover, it is considered a time step \(t_{s}=10\) min for an evaluation period of \(T=30\) h, i.e., \(N_{slots}=180\). Both weight parameters \(\omega\) and \(\gamma\) are adjusted to 1000 to satisfy the control choices of the DSO. The proposed optimisation model is solved via CPLEX 12.9 under its API in Python (DOcplex) [29], employing a laptop with a four cores Intel Core i7 processor and 12 GB memory. It takes less than 0.1 s to solve the proposed optimisation problem for both penetration levels.
# Numerical Investigations of Pile Group Foundations under Different Pile Length Conditions
## 5 Displacement Analysis  
In Figure 4, the displacements of each pile in the Y-direction are displayed for both the group pile foundation with the same length and the group pile foundation with different lengths. From the graphs, it can be observed that the overall displacement patterns are similar in both cases. The changes in displacement are not significant in the region between 30 m and 65 m from the base of the foundation. However, between 65 m and 80 m, the displacement gradually increases at a slow rate. Beyond approximately 80 m, the displacement of each pile starts to grow rapidly. This indicates that the equivalent sliding surface of the slope is likely located in the range of 80 m to 85 m.  
The maximum displacement at the top of the pile is recorded as 48.5 mm, with Pile 9 exhibiting a significantly larger displacement compared to Pile 8. The displacement values at the pile tops, ranging from Pile 1 to Pile 9, display a linear increase. Furthermore, the graph indicates that Pile 9 experiences a negative displacement segment between 65 m and 80 m from the base, followed by a recovery to its original position.  
There are two possible explanations for this observation. Firstly, it can be inferred that this section of negative displacement occurs because the outermost pile (Pile 9) is subjected to the largest foundation reaction force within the embedded section. This force surpasses the landslide thrust force, resulting in the generation of negative displacement. Secondly, the finite element simulation process may not have considered characteristics such as slip and detachment between the pile and soil. Consequently, when the soil sinks, it creates a backward pulling force on the pile, contributing to the negative displacement.  
Figure 4: The displacement of different conditions: (**a**) the same length pile; (**b**) the different length pile.
## 3 Results and Discussion
### Determination of Design Horizontal Response Spectra and Site Coefficient
#### 3.3.1 Design Horizontal Response Spectra
The design horizontal response spectral acceleration was proposed for the average and envelope under three seismic conditions--near-field, far-field, and a combination of near-field and far-field seismic events. This eased the related parties to decide which scenario best fit the site condition. Based on the results, it was found that soil type D had a lower value compared to soil type E. The same findings can be seen in work by [PERSON] et al. (2007) [43], where soil type E produced higher spectral acceleration compared to stiffer soil, such as soil type D. It was suggested that response spectral acceleration is strongly related to soil parameters, the depth of soil, and PGa. [PERSON] et al. (2015) [44] stressed that the response spectra due to different seismic events (but having the same input acceleration) would be different due to different frequency content. It should be noted that both maximum acceleration and frequency content must not be higher than standard response spectra used in the construction of the structure because it will be severely damaged. In the current study, far-field seismic events produced longer periods of constant acceleration compared to near-field. This is supported by previous researchers, who found  
Figure 14: Comparison of DHRSa and ISO for soil types D and E.  
the same patterns in their studies [20; 45]. In both soil types, all conditions showed higher design horizontal response spectral acceleration except for the average of far-field for soil type D. In Dave far-field, the spectral acceleration was slightly higher, but the constant acceleration period was higher.
# Fault-Free Protection Level Equation for CLAS PPP-RTK and Experimental Evaluations
## 3 Proposed Fault-Free Protection Level Equations for CLAS PPP-RTK Service
### Fault-Free Protection Level Equations of SBAS  
The SBAS L1 frequency-only protection-level equation uses the broadcast correction message uncertainties and user-defined multipath and noise uncertainties. For an individual SBAS pseudo range error, the combined range error variance for the \(i\)th satellite was constructed as follows:  
\[\sigma_{i}^{2}=\sigma_{flt,i}^{2}+\sigma_{UIRE,i}^{2}+\sigma_{tropo,i}^{2}+ \sigma_{air,i^{\prime}}^{2} \tag{3}\]  
where \(\sigma_{flt,i}^{2}\) is the variance of the fast and long-term satellite clock error corrections. \(\sigma_{UIRE,i}^{2}\) is the variance of the user ionosphere range error correction, and \(\sigma_{tropo,i}^{2}\) is the variance of the tropospheric error correction, and \(\sigma_{air,i}^{2}\) is the variance of the multipath error excluding multipath from ground. It should be noted that each variance is determined from a zero-mean Gaussian distribution that overbounds a non-ideal Gaussian distribution of correction errors.  
The pseudo range variance is used as a weighting matrix to compute an SBAS position solution such that  
\[\mathbf{W}=\left[\begin{array}{cccc}\frac{1}{\sigma_{1}^{2}}&0&\dots&0\\ 0&\frac{1}{\sigma_{2}^{2}}&\dots&0\\ \vdots&\vdots&\ddots&0\\ 0&0&\dots&\frac{1}{\sigma_{2}^{2}}\end{array}\right]. \tag{4}\]  
The direction vector from a user to satellite can be formulated as  
\[\mathbf{G}_{i}=[\cos(El_{i})\sin(Az_{i})\ \ \cos(El_{i})\cos(Az_{i})\ \ \ \sin(El_{i})\ \ 1]. \tag{5}\]  
Then, the standard deviation of the position estimate uncertainty in East/North/Up (ENU) coordinates is  
\[\sigma_{p}=\sqrt{\left(\mathbf{G}^{T}\mathbf{W}\mathbf{G}\right)^{-1}}. \tag{6}\]  
With a missed-detection probability and its corresponding Gaussian tail value of \(K_{ffmd}\), the vertical protection level (VPL) bounding vertical position errors is  
\[\text{VPL}_{\text{SBAS}}=K_{ffmd}\sqrt{\left[\left(\mathbf{G}^{T}\mathbf{W}\mathbf{G}\right) ^{-1}\right]_{(3,3)}}. \tag{7}\]  
Similarly, the horizontal protection level (HPL) is computed.
# A Framework for the Design of Privacy-Preserving Record Linkage Systems
## 3 System Design Framework for PPRL
### Qualified Expert Evaluations  
A key part of the PPRL system is an evaluation performed by qualified experts to ensure that privacy protections are in place throughout the record linkage process. This kind of evaluation is similar to the Expert Determination as defined under the HIPAA Privacy Rule [26]. The expert is not only looking for whether the resulting linked data is de-identified but also looking through the linkage process to ensure that data is not identifiable in any part of the linkage process. As such, the expert typically evaluates the following:  
1. If any PII gets transferred out of a controller's environment during the tokenization process;
2. Whether the tokens generated through the tokenization process could still be considered PII;
3. Whether the information transferred by the Honest Broker back to data controllers or the data platform could be considered PII;
4. Any additional re-identification risk to the de-identified datasets arising from the data linkage;Whether the controls implemented on the system ensure privacy protection throughout the PPRL process.  
After performing a holistic evaluation on the entire PPRL system, the expert provides their report, which acts as evidence that the system can successfully conduct PPRL. They may also recommend some mitigations to be put into place to ensure privacy preservation in the report if they discover issues during their evaluation. While these evaluations have no set timeframe for expiry and renewal, due to changing technologies and the evolution of privacy regulations, setting up a regular schedule of reviews (i.e., annually) is recommended. Should there be significant changes to the system, such as the addition of new data controllers participating in the linkage system, the addition of new data to link, the switching of Honest Brokers, changing tokenization methods or providers, changing methods by which data users access and analyze the data, or changes to the data flow between different parties, then a re-evaluation may need to be performed.
## 4 Methodology
### Previous Research
#### 4.1.3 Reliability with Cronbach's Alphas  
Cronbach's Alpha is a method for assessing and evaluating questionnaires [49]. It is calculated as the mean of the correlations of the questionnaire items that are part of a scale. It is a measure of internal consistency of the items comprising the questionnaire, that is, how closely related a set of items are as a group. It varies from 0 to 1. The closer it is to 1, the more consistent the items related to that scale will be. It is considered to be a measure of scale reliability. Therefore, to analyze the reliability of the instruments, the values of Cronbach's alphas were calculated for each dimension of each instrument, for the student sample. The average value for each dimension was 0.8121, which means that the reliability of the instruments used here are solid [48].
# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 3 Fundamentals and Basic Definitions
### Types of GANs
#### 3.3.5 WGAN  
The Wasserstein GAN (WGAN), introduced by [PERSON] et al. [49], addresses key training instabilities and mode collapse issues observed in traditional GANs by replacing the Jensen-Shannon (JS) divergence with the Earth Mover (EM) distance, also known as the Wasserstein-1 distance.  
Given two probability distributions \(P_{r}\) (real data) and \(P_{g}\) (generated data), the Wasserstein-1 distance is defined as:  
\[W(P_{r},P_{g})=\inf_{\gamma\in\Pi(P_{r},P_{g})}\mathbb{E}_{(x,y)\sim\gamma}[ \|x-y\|] \tag{4}\]  
where \(\Pi(P_{r},P_{g})\) denotes the set of all joint distributions \(\gamma(x,y)\) whose marginals are \(P_{r}\) and \(P_{g}\).  
Using the Kantorovich-Rubinstein duality, the WGAN objective becomes:  
\[\max_{D\in\mathcal{D}}\mathbb{E}_{x\sim P_{r}}[D(x)]-\mathbb{E}_{z\sim P_{z}}[ D(G(z))] \tag{5}\]  
Here, \(D\) (often referred to as the critic instead of a discriminator) is constrained to be 1-Lipschitz, originally enforced via weight clipping. The generator \(G\) is trained to minimize this objective.  
The use of the Wasserstein distance provides a smoother and more meaningful loss metric that correlates better with the quality of generated samples. As a result, WGAN improves convergence and offers more stable training, even in cases of poor discriminator performance.  
In practice, the WGAN critic loss is:  
\[L_{D}=-\mathbb{E}_{x\sim P_{r}}[D(x)]+\mathbb{E}_{z\sim P_{z}}[D(G(z))] \tag{6}\]  
and the generator loss is:  
\[L_{G}=-\mathbb{E}_{z\sim P_{z}}[D(G(z))] \tag{7}\]  
In summary, WGAN and its variants have become valuable tools in medical image processing, enabling artifact correction, anomaly detection, and synthetic data generation, ultimately contributing to improved diagnostic performance and model reliability [50; 51]. These models address key challenges in the field such as the scarcity of labeled data, noise suppression, and the synthesis of realistic medical images.
###### Abstract
According to [PERSON] et al. [23], in recent times, more attention has been paid to portable NIR than to laboratory models, due to reduced weight and ease of use, allowing direct, non-destructive and in-situ sample measurement. Portable sensors have the advantage that the sensor itself is taken to samples and not the other way around, which allows for a win-time in the process of obtaining results and reducing costs and labour in collection, transport, processing and laboratory analysis [31]. [PERSON] et al. [28] refer that real-time analysis with the micro NIR overcomes the spatial and temporal variability of the pasture. According to [PERSON] et al. [29], in samples with large amounts of water, strong absorption signals are generated, which overlap and darken other spectral characteristics, which can lead to non-linear responses, which has led to this technique being used mainly with dry samples. Also, according to [PERSON] et al. [36], spectra are often disturbed by different interferences in the signal acquisition, which is a practical problem of this technique. Another problem associated with these portable sensors is their narrow wave-length range [35]. According to [PERSON] et al. [11] the main advantage of portable NIRS is that it allows a more frequent analysis of the pasture, reducing the time interval between sequential nutritive value readings, thus contributing to more timely decision making. [PERSON] et al. [33] report that in field pasture analysis allows a reduction in costs and time, compared to laboratory analyses. Portable NIRS makes it possible to analyse and provide the farmer with real-time data relating to pasture and soil nutrients in a soil-pasture-animal system [11]. The aim of this study was to calibrate and validate the use of a portable near-infrared spectrometer to predict pasture quality (crude protein and neutral detergent fibre) in the Montado ecosystem during spring peak.
## 2 Related Work
However, the NB-IoT terminals will not assume that NPSS and NSSS are transmitted on the same antenna port and the CFRs of NPSS may be different from NSSS [2]. The non-coherent method can be subdivided into differential correlation-based and partial correlation-based algorithms. Multifarious differential solutions have been proposed to accomplish the detection, for example, [PERSON] and [PERSON] [21] adopt this method to reduce the effect of the dispersive channel. Nevertheless, the differential correlation would deteriorate SNR with the autocorrelation of received signals. The idea of partial correlation can be found in [22; 23]. However, since NPSS has no repeatability among signals, this method is invalid in NB-IoT application. In addition to the above-mentioned methods, the authors of [24] search the correct SSS sequence by minimizing the Euclidian distance which is the basic application of ML detection and the performance will be reduced in the context of low SNR. 3. A communication link would be set up as soon as the initial cell search has been completed. However, because of the residual frequency offset after CFO recovery and the continuous frequency drift caused by the oscillator, frequency offset tracking is essential for NB-IoT systems. Besides, one uplink transmission feature of NB-IoT is that a 40 ms downlink gap must be inserted between two 256 ms uplink transmission time units [2]. This is because the frequency drift value may exceed the tolerance of the synchronization circuits in base stations [25]. Typically, LTE frequency tracking algorithms mainly employ the references signal (RS) as auxiliary data. In some OFDM systems, due to multiple crystals being used, residual carrier frequency offset and sampling clock frequency offset need to be estimated separately [26]. This is impractical for NB-IoT terminals with the consideration of cost constraints. The authors of [27] adopt the autocorrelation of two identical symbols in the same subcarrier to calculate phase difference. The fatal drawback is that the number of repetitive preamble pairs may be inadequate to achieve adequate frequency tracking accuracy at low SNRs. By using the cross correlation process, the solution derived in [28] can make use of different pilots. Two extra multiplications are needed in the calculation of each correlation pair. Considering the frequency estimation precision demanded by the NB-IoT system, the ML frequency estimation solution proposed in [9] is unsuitable for frequency tracking. That is because when the tracking accuracy is 40 Hz, the number of FFT points should be no less than eight times of 1024.
# Natural Frequencies of a Tethered Satellite System
## 3 Dynamic Responses of the ANCF Model
The frequency of 0.0020 rad/s corresponds to the libration frequency of the satellite system, while the frequency of 0.0215 rad/s is expected to represent the natural frequency of transverse vibration of the tether. \begin{table}
\begin{tabular}{l l l} \hline \hline  & **Symbols** & **Values** \\ \hline Masses of satellites \(m_{1}\) and \(m_{2}\) & \(m_{1},\ m_{2}\) & 1000 kg \\ Length of tether & \(L\) & 10 km \\ Mass density of tether & \(\rho\) & 1440 kg/m\({}^{3}\) \\ Young’s modulus of tether & \(E\) & 131 GPa \\ Cross-sectional area of tether & \(A\) & 3.1416 mm\({}^{2}\) \\ Area moment of inertia & \(I\) & 0.7854 mm\({}^{4}\) \\ Initial orbital radius of \(m_{1}\) & \(r_{1}(0)\) & 6600 km \\ Initial orbital radius of \(m_{2}\) & \(r_{2}(0)\) & 6610 km \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Reference values for the physical properties and dimensions of the satellite system. Figure 9: Dynamic response of the libration angle: (**a**) dynamic response and (**b**) frequency spectrum. Meanwhile, Figure 11b illustrates that the prominent peak frequencies in the time response for the longitudinal deformation are 0.0020 and 0.2858 rad/s. Similar to the transverse case, the frequency of 0.0020 rad/s is associated with the libration motion, and the frequency of 0.2858 rad/s is presumed to correspond to the natural frequency of longitudinal vibration of the tether. Figure 11: Dynamic response of the longitudinal deformation of the tether at node 31: (**a**) time response and (**b**) frequency spectrum. Figure 10: Dynamic response of the transverse deformation of the tether at node 31: (**a**) time response and (**b**) frequency spectrum.
# Energy-Efficient Hip Joint Offsets in Humanoid Robot via Taguchi Method and Bio-inspired Analysis
## 5 Validation of Optimal Hip Joint Offsets
### Forward and Backward Walking  
Table 6 lists the power consumption in the hip, using the original model and the optimized models after the first and second experiments for forward and backward walking. For forward walking, the RMS power at the hip joint reduced by 18.07% and 26.92% for the models after the first and second optimizations, respectively, compared with the original model. For backward walking, the corresponding reductions were 17.26% and 25.68%.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Forward Walking**} & \multicolumn{2}{c}{**Backward Walking**} & \multicolumn{2}{c}{**Sideways Walking**} & \multicolumn{2}{c}{**Turning**} \\ \cline{2-9}  & \(\mathbf{Pov_{\text{rms}}}\) & **Reduction** & \(\mathbf{Pov_{\text{rms}}}\) & **Reduction** & \(\mathbf{Pov_{\text{rms}}}\) & **Reduction** & \(\mathbf{Pov_{\text{rms}}}\) & **Reduction** \\  & **(W)** & **(\%)** & **(W)** & **(\%)** & **(W)** & **(\%)** & **(\%)** & **(W)** & **(\%)** \\ \hline Original & 20.250 & – & 21.013 & – & 19.868 & – & 15.325 & – \\ First Optimization & 16.591 & 18.07 & 17.387 & 17.26 & 13.951 & 29.78 & 11.983 & 21.81 \\ Second Optimization & 14.799 & 26.92 & 15.617 & 25.68 & 12.365 & 37.76 & 10.713 & 30.09 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Root-mean-square (RMS) power reduction using an optimized model in humanoid hip joints during target motions.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameter** & \(\mathbf{P_{z}}\) & \(\mathbf{R_{x}}\) & \(\mathbf{R_{y}}\) & \(\mathbf{Y_{x}}\) & \(\mathbf{Y_{y}}\) & \(\mathbf{Y_{z}}\) & \(\mathbf{P_{x}}\) & \(\mathbf{P_{y}}\) \\ \hline
**Value (mm)** & 0 & 0 & \(-\)15 & 10 & \(-\)15 & 0 & \(-\)10 & \(-\)15 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameter values of the model after the second optimization.  
Figure 7: Hip joint configuration of (**a**) the original model and (**b**) the model after the second optimization.
## 3 Discrete Element Numerical Simulation
### Characteristic Analysis of the Monitoring Points  
#### 4.3.1 Displacement Monitoring Curve in Y Direction  
As shown in Figure 16, the deformation of monitoring point A-2 increases sharply after excavation and then slowly increases, stabilizing at approximately 3.2 cm. The A-3 monitoring points are arranged at the interface between the Q\({}^{\text{edl}}\) sediments and the lower cataclastic loose rock mass. The A-4 monitoring points are arranged at the interface between the cataclastic loose rock mass and its lower toppling-deformed rock mass. The Y-direction displacement of the A-3 and A-4 monitoring points remains near 0 as the number of iteration steps increases in the excavation calculation. No change is observed in the Y-direction displacement trend of monitoring points A-5-7 in the middle of the excavation face. After excavation, the cumulative displacement of the monitoring points increases rapidly, and then the increase rate slows down and finally plateaus. The maximum displacement of the A-5 monitoring point on the slope surface is 13 cm. The Y-direction displacements of the A-8-10 monitoring points are nearly equal, measuring approximately 4 cm. This finding indicates that the cataclastic loose rock mass and its lower toppling-deformed rock mass near the front edge of the cataclastic loose rock mass in the middle and lower sections of the excavation face undergo deformation simultaneously because of the considerable height of the excavation beam and the high excavation slope ratio.  
Figure 14: Vertical displacement nephogram of the A–A\({}^{\prime}\) section (unit: cm). (**a**) Natural state (**b**) Primary excavation (**c**) Secondary excavation (**d**) Three-stage excavation.  
Figure 15: Total displacement nephogram of the A–A\({}^{\prime}\) section (unit: cm). (**a**) Natural state (**b**) Primary excavation (**c**) Secondary excavation (**d**) Three-stage excavation.