esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Carrier Tracking Loop and the Phase Error
### Clock Phase Noise  
The vibration-induced oscillator phase noise \(\sigma_{\
u}\) and the Allan deviation oscillator phase noise \(\sigma_{A}\) are the two main types of clock noise phase jitter, which originates from the satellite and receiver clock phase noise. The contribution of the clock noise (correlated errors) to GNSS phase tracking loops can be determined by [14]:\[\begin{split}\sigma_{c}^{2}&=\frac{1}{2\pi}\int_{0}^{ \infty}|H_{e}(\omega)|^{2}G_{c}(\omega)d\omega\\ &=\frac{1}{2\pi}\int_{0}^{\infty}|1-H(\omega)|^{2}G_{c}(\omega)d \omega\\ &=\frac{1}{2\pi}\int_{0}^{\infty}\frac{\omega^{2n}}{\omega_{n}^{ 2n}+\omega^{2n}}\,G_{c}(\omega)d\omega\end{split} \tag{10}\]  
where \(H_{e}(\omega)=1-H(\omega)\) is the error transfer function of the loop, and \(G_{c}(\omega)\) is the Power Spectral Density (PSD) of the colored noises. Due to the high pass characteristic of the error transfer function of the loop in the equation, the clock noise phase jitter becomes larger with decrease of the loop bandwidth. Consequently, the impact of this error should be considered when reducing the loop bandwidth.
# Creating a Regional MODIS Satellite-Driven Net Primary Production Dataset for European Forests
## 2 Materials and Methods
### Analysis of NPP Results  
We thus have three NPP sources: two using the MOD17 algorithm with different daily climate data: (i) MODIS GLOB produced by the Numerical Terradynamic Simulation Group (NTSG) at University of Montana and (ii) MODIS EURO by running the original MOD17 algorithm and the latest BPLUTs parametrized by [9] with downscaled daily climate data from Europe [25] as well as (iii) Terrestrial NFI NPP using forest inventory data from the 12 countries (Table S1) and local carbon estimation methods [32].  
We compared the three NPP datasets across Europe, by our 4 regions (Figure 1) and the 12 countries to analyze our results across different spatial scaling. We extracted for each forest inventory plot at the corresponding MODIS cell the average NPP from MODIS GLOB and MODIS EURO for 2000 to 2012. We next computed for all plots the difference between the two MODIS NPP estimates and the Terrestrial NFI NPP (ANPPGLOB = MODIS GLOB minus NFI NPP and ANPPEURO = MODIS EURO minus NFI NPP).  
We used each NFI plot separately and did not compute average values for MODIS pixels. This avoided smoothing effects due to different spacing between inventory grid points and the plot clusters used in some countries (Table S1).  
To analyze the effect of gradients on the NPP results, we collected potentially meaningful meta-information such as plot location (Longitude and Latitude in WGS1984), Elevation (EU-DEM 30 m resolution), MODIS Land Cover type or forest characteristics (dominant tree species, mean age, stand density, tree height, etc.) and analyzed patterns of ANPPGLOB and ANPPEURO across these gradients.  
Terrestrial and remote sensing NPP estimates exhibited discrepancies in previous research [15; 18] and as explanation the authors suggested changes in stand density, which are commonly caused by forest management and disturbances [15; 18]. Since major parts of the forests in Europe are managed [7] and affected by natural disturbances such as wind damage or forest fire [35], they should have experienced changes in stand density as compared to unmanaged forests. Stand density directly affects terrestrial NPP estimates by its impact on the development of DBH and H of the remaining trees after forest management operations until canopy closure is reached. On the other hand MODIS NPP is based on the \"big leaf\" concept and assumes a full coverage of forest area. We thus use Stand density index (SDI) [36] in the analysis of our NPP estimates.
# Telerehabilitation--A Viable Option for the Recovery of Post-Stroke Patients
## 8 Engagement and Motivation in Telerehabilitation  
Patients' perception of TR is generally good, with positive satisfaction reports [11]. Keeping the post-stroke patient engaged and motivated through the rehabilitation process seems to be one of the most challenging parts of telerehabilitation [6]. The lack of compliance with the therapy protocol can reach 70% of cases when the training is performed at home without direct supervision [12]. Many factors such as a small variety of games or exercises, poor structure of the intervention, too challenging or too easy tasks, the lack of feedback or poor-quality feedback can negatively affect patients' motivation [9]. Therefore, when it comes to telerehabilitation, it seems that the focus should not only be on high-performance technology, but also on therapists who should be ready to adapt and modify the therapy and handle a disengaged, demotivated, frustrated or bored patient. The communication skills of therapists may be challenged in different ways from situations encountered during face-to-face rehabilitation, and their training in this field may be opportune. One study showed that the system used for TR motivated the patient if it reported their real-time progress to the therapist and permitted the latter's prompt and regular intervention [17]. Another benefit of the patient's constant contact with a therapist has been the improvement of the social isolation feeling and overall mood [12]. The absence of a social network and human interaction represents a risk factor for stroke recurrence, its presence being associated with better clinical progress during TR due to socially offered support and motivation [2]. The consistency of post-stroke patients' treatment dependson the integration of the training in their daily routine, and for this, shorter trainings with a higher frequency were preferred [9].  
During our literature review, we identified a variety of reasons that explained the cases of low compliance with TR that specialists faced. The most frequent are: an elderly population with no experience with technology or reluctance to it, lack of time for exercise at home, absence of a caregiver to provide assistance during training, lack of specialist supervision, cognitive impairments after stroke and bad internet connection. Therefore, one important aspect is the thorough selection of patients with additional attention given to aspects that may lead to low compliance.
# Exploring Synergy of Denoising and Distillation: Novel Method for Efficient Adversarial Defense
## 1 Introduction
Through this research, we devised a technique that could fundamentally defend against adversarial attacks. The contributions of this study are as follows:  
1. We propose that the knowledge distillation method for adversarial robustness enhances the effect of denoising, thereby enabling a more efficient response to adversarial attacks. 2. We propose that the use of smoothing in distillation can strengthen defense performance under adversarial attacks while maintaining high model accuracy. 3. We achieved a notable defense success rate of 72.7%, which represents a significant improvement over the 41.0% success rate of models with only denoising defenses.
# Multi-Fidelity Gradient-Based Optimization for High-Dimensional Aeroelastic Configurations
## 3 Results
### Summary and Comparison
Again, this was due to the expected improvement ratio being negative for both cases. With the poor agreement between high- and multi-fidelity models, the multi-fidelity optimization convergence was slow compared to single-fidelity, leading to the minimal cost benefit or penalty. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Case** & **Threshold (\%)** & **Reduction (\%)** & **NF (SF)** & **NF (MF)** & **Savings (\# Evals)** & **Savings (\% of SF)** \\ \hline \multirow{8}{*}{P2a} & 50.0000 & 48.9775 & 3 & 3 & 0 & 0.0000 \\  & 75.0000 & 73.4662 & 3 & 3 & 0 & 0.0000 \\  & 90.0000 & 88.1594 & 3 & 3 & 0 & 0.0000 \\  & 95.0000 & 93.0572 & 6 & 8 & \(-2\) & \(-33.3333\) \\  & 98.0000 & 95.9958 & 10 & 14 & \(-4\) & \(-40.0000\) \\  & 99.0000 & 96.9754 & 18 & 20 & \(-2\) & \(-11.1111\) \\ \hline \multirow{8}{*}{P2b} & 50.0000 & 49.0545 & 4 & 4 & 0 & 0.0000 \\  & 75.0000 & 73.5818 & 5 & 5 & 0 & 0.0000 \\  & 90.0000 & 88.2981 & 7 & 8 & \(-1\) & \(-14.2857\) \\  & 95.0000 & 93.2036 & 9 & 9 & 0 & 0.0000 \\  & 98.0000 & 96.1468 & 12 & 10 & 2 & 16.6667 \\  & 99.0000 & 97.1279 & 16 & 21 & \(-5\) & \(-31.2500\) \\ \hline \multirow{8}{*}{P3a} & 50.0000 & 49.3826 & 3 & 3 & 0 & 0.0000 \\  & 75.0000 & 74.0739 & 3 & 6 & \(-3\) & \(-100.0000\) \\ \cline{1-1}  & 90.0000 & 88.8887 & 6 & 7 & \(-1\) & \(-16.6667\) \\ \cline{1-1}  & 95.0000 & 93.8270 & 7 & 7 & 0 & 0.0000 \\ \cline{1-1}  & 98.0000 & 96.7899 & 11 & 12 & \(-1\) & \(-9.0909\) \\ \cline{1-1}  & 99.0000 & 97.7776 & 14 & 13 & 1 & 7.1429 \\ \hline \multirow{8}{*}{P3b} & 50.0000 & 48.8109 & 4 & 3 & 1 & 25.0000 \\ \cline{1-1}  & 75.0000 & 73.2163 & 5 & 5 & 0 & 0.0000 \\ \cline{1-1}  & 90.0000 & 87.8596 & 6 & 8 & \(-2\) & \(-33.3333\) \\ \cline{1-1}  & 95.0000 & 92.7407 & 9 & 10 & \(-1\) & \(-11.1111\) \\ \cline{1-1}  & 98.0000 & 95.6693 & 10 & 12 & \(-2\) & \(-20.0000\) \\ \cline{1-1}  & 99.0000 & 96.6455 & 11 & 13 & \(-2\) & \(-18.1818\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Cost summary for optimization cases including structural sizing.
## 4 Electromagnetic Performance Analysis
### Air-Gap Magnetic Density  
The air-gap magnetic field region is the core region of motor energy conversion. Therefore, it is necessary to analyze the distribution of the air-gap magnetic field inside the motor. Generally, the air-gap magnetic density at each diameter is taken as a reference. After post-processing, the air-gap flux density waveform at different radii distributed along the circumferential direction is obtained, as shown in Figure 6.  
In Figure 6, we can see that the magnetic density amplitude of the air gap at its innermost diameter is about 0.54 T. The air-gap flux density amplitude at the average radius of the air gap is about 1.06 T. The air-gap magnetic density amplitude at its outermost diameter is about 0.55 T. The air-gap flux density waveforms \(B_{\delta}\) are different at different positions. The air-gap flux density amplitudes at the innermost and outermost sides are smaller than those at the average radius. Due to the special structure of the AFPMM, the permanent magnet has an edge effect on both inner and outer diameters, which makes the air-gap magnetic density smaller.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Rated power/\(P\) & 240 W \\ Rated speed/\(n\) & 4800 rpm \\ Outer diameter/\(D_{o}\) & 65 mm \\ Inner diameter/\(D_{i}\) & 25 mm \\ Number of slots/\(q\) & 12 \\ Number of poles/\(p\) & 10 \\ Number of turns of winding/\(N\) & 14 turns \\ Length of air gap/\(\delta\) & 0.8 mm \\ Polar arc coefficient/\(\alpha_{i}\) & 0.8 (initial value) \\ Arc radius of permanent magnet/\(r_{h}\) & 19 (initial value) \\ Permanent magnet thickness/\(h_{m}\) & 3.4 (initial value) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design Parameters of AFPMBLDCM.  
Figure 5: Half-Subdivision of AFPMBLDCM.
# Adap-UIL: A Multi-Feature-Aware User Identity Linkage Framework Based on an Adaptive Graph Walk
## 4 Adaptive Graph Walk Based on Restarted Affinity Coefficients
### Restarted Affinity Coefficients  
In order to better capture the local features of the nodes, this paper introduces the restart probability \(\alpha\) in Random Walk with Restart (RWR) and affinity coefficients to construct the RAC.  
Figure 1: Convergency of transition matrix as k increases.  
Figure 2: Convergency of transition probabilities.  
RWR has been widely used in graph data mining. In RWR, it walks along the edges from a given source node \(i\); for each node visited, it will return to the source node with the probability \(\alpha\) to restart or visit its neighbors with the probability \((1-\alpha)\). \(\alpha\) is the restart probability or the restart coefficient for RWR. So, the probability that node \(j\) is visited by source node \(i\) by \(k\) steps is represented as:  
\[P_{ij}^{k}=(1-\alpha)^{k-1}\cdot p_{ij}^{k}\cdot\alpha \tag{9}\]  
where \(p_{ij}^{k}\) denotes the \(k\)-order transition probability; here, the number of transfers is \((k-1)\) and the \((k-1)\) steps are not restarted, which is why the power of \((1-\alpha)\) is \((k-1)\). For the case where a restart occurs at step x in \((k-1)\), it is equivalent to the case where k is replaced with \((k-1-x)\).  
According to the affinity calculation formula, with the introduction of the restart coefficient, the RAC of any node \(j\) with source node \(i\) can be expressed as:  
\[m_{i,j}=\sum_{k=1}^{\infty}P_{ij}^{k}=\sum_{k=0}^{\infty}(1-\alpha)^{k-1} \cdot p_{ij}^{k}\cdot\alpha \tag{10}\]  
Taking all the nodes as sources nodes, performing an unlimited number of RWR, the restarted affinity coefficients matrix M can be obtained as:  
\[M=\sum_{k=1}^{\infty}(1-\alpha)^{k-1}\cdot P^{k}\cdot\alpha \tag{11}\]  
It can be proved that for any node \(i\) in the graph as the source node, we set the sum of its restarted affinity coefficient scores with all other nodes to be 1, that is :  
\[\sum_{j=1}^{|N_{C}|}m_{i,j}=1 \tag{12}\]
# On the Utilization of Reversible Colour Transforms for Lossless 2-D Data Compression
## 4 Experimental Setup And Results
### Lossless Compression of CFA Images  
CFA images undergo lossless compression to lower down the computational complexity in later stages. [PERSON] et al.'s in [50] proposed a scheme working on the lossless compression of HDR imagery. Initially, the images are de-interlaced into individual channels followed by the weighted template matching based prediction is performed compressing the high-resolution images at a very  
Figure 6: Set of RASTER maps used for compression.  
Figure 7: Comparative analysis of RASTER maps of different compression sizes. Original image size is 843 KB. Values on y-axis are displayed on the logarithmic scale for a better visual representation.  
low computational cost. [PERSON] et al. in [51] proposed a method to use context matching accompanied with reduction techniques in the spectral domain for colour difference estimation.  
CFA images utilized for the evaluation of the designed system are given in Figure 8. Bits Per Pixel (BPPs) achieved by the schemes in the discussion are represented in Figure 9. JPEG-LS, JPEG2000, LCOI, CMBP and HPCM techniques [51] are used as a benchmark scheme to draw a comparison with the proposed system. YUV RCT based BBWCA of [PERSON] et al. in [14] is also compared with the proposed method to determine its compression efficiency and performance.  
The proposed YA6 UA6 VA6 RCT based BBWCA performs the compression reducing the data with an average BPP of 3.35. In comparison to the YUV BBWCA, HPCM and CMBP schemes, it shows an increase of 18, 20, and 26 percent increase in the effective lossless compression.  
The transform mainly offers the advantage of its ability to produce a redundant number of grey scale values lying among the U and V channels. Once the redundant data is transformed by BWT, it generates data with correlation matrix among the 2-D image which is efficiently compressed by the later stages.  
Figure 8: Colour Filter Array (CFA) images utilized for the evaluation of the proposed system.  
Figure 9: Bits Per Pixel (BPPs) employed by the benchmark and BBWCA for lossless compression of CFA images.
## 3 Case Study  
### Study Area  
The Rizhao Reservoir was used as a case study for the application of the proposed DT-based rule in this study. This reservoir is located 16 km west of Donggang District, Rizhao City in Shandong Province, China, and belongs to the upper and middle reaches of the Futuan River. Construction of the Rizhao Reservoir began in October 1958 and was completed in June 1959. It is a large type-II reservoir with multi-year regulation used mainly to provide flood control and irrigation in combination with aquaculture, power generation, water supply, and other secondary objectives. The climate of the Rizhao Reservoir basin generally exhibits the characteristics of humid and semi-humid regions. Other basic parameters of the reservoir are shown in Table 1.  
Figure 1: The flowchart of the real-time reservoir operation procedure using DT-based rules.  
To ensure safety downstream of the Rizhao Reservoir, two control discharges and two high-volume discharge states have been established: when the water level \(Z\leq 43.46\) m, the control discharge is 1000 m\({}^{3}\)/s; when 43.46 m \(<Z\leq 43.79\) m, the control discharge is 1900 m\({}^{3}\)/s; when 43.79 m \(<Z\leq 44.02\) m, the spillway shuices are completely opened; and when Z \(>\) 44.02 m, the spillway shuices and the north water release tunnel are completely opened together.
# Group Authentication Scheme for Neighbourhood Area Networks (NANs) in Smart Grids
## 3 The Smart Grid Network Model
### Notations  
Having stated the assumptions made, we proceed with detailing the security scheme for the scenario in Figure 1. The following subsection begins with a listing of the notations (Table 2) used to detail the security scheme. This is followed by the details of the authentication process.  
The scheme addresses two cases--smart meters in multi-hop (mesh), and smart meters in star topology.
## 3 Control Strategies  
A nonlinear adaptive multiple-input and multiple-output (NAMIMO) controller has been proposed to control the output power of the compensation system. The biggest difficulty with the proposed controller is that the compensation system has to achieve minimal or no fluctuations in frequency. Meanwhile, it makes it more difficult to obtain frequency parameters for the system without PLL in this paper.
# Towards Federated Learning with Byzantine-Robust Client Weighting
## 2 Materials and Methods
### Preprocessing Client-Declared Sample Sizes
#### 2.2.2 Truncating the Values of N  
Our suggested preprocessing procedure uses element-wise truncation of the values of \(\mathbf{N}\) by some value \(U\), marked \(\text{trunc}(\mathbf{N},U)=(\min(n_{1},U),\ldots,\min(n_{K},U))\). Given \(\alpha\) and \(\alpha^{*}\), we search for the maximal truncation that satisfies Equation (3):  
\[U^{*}\coloneqq\operatorname*{arg\,max}_{U\in\mathbb{R}}\text{mwp}(\text{ trunc}(\mathbf{N},U),\alpha)\leq\alpha^{*}. \tag{4}\]  
Here, \(\alpha\) and \(U^{*}\) present a trade-off. A higher \(\alpha\) means more Byzantine tolerance but requires a smaller truncation value \(U^{*}\), which may cause slower and less accurate convergence, as we demonstrate empirically in Section 3 and theoretically in Theorem 3.  
Finding \(U^{*}\) Given \(\alpha\)  
If one has an estimate for \(\alpha\), it is easy to calculate \(U^{*}\), for example, by going over values in \(\mathbf{N}\) in decreasing order until finding a value that satisfies the inequality in Equation (4). Then we mark the index of this value by \(u\) and use the fact that in the range \([n_{u-1},n_{u}]\), we can express \(\text{mwp}(\text{trunc}(\mathbf{N},U),\alpha)\) as a simple function of the form \(\frac{\alpha+bU}{c+dU}\):  
\[\frac{\sum_{i=u}^{t}n_{i}+|\{n_{i}:i<\min(t,u)\}|U}{\sum_{i=u}^{K}n_{i}+|\{n_ {i}:i<u\}|U}=\frac{\sum_{i=u}^{t}n_{i}+\min(t,u)U}{\sum_{i=u}^{K}n_{i}+uU}\]  
for which we can solve Equation (4) with  
\[U^{*}\leftarrow\frac{a-c\alpha^{*}}{da^{*}-b}. \tag{5}\]  
Optimality  
**Theorem 1**.: _Given Byzantine proportion \(\alpha\) and Byzantine weight proportion \(\alpha^{*}\), then setting \(\bar{\mathbf{N}}=\text{trunc}(\mathbf{N},U^{*})=(\hat{n}_{1},\ldots,\hat{n}_{K})\) for \(U^{*}\) given in Equation (4) gives an optimal solution for the following minimization problem: \(\min_{N^{\prime}=(n_{1}^{\prime},\ldots,n_{K}^{\prime})}\lVert\mathbf{N}^{\prime }-\mathbf{N}\rVert_{1}\) s.t. Equation (3) is satisfied (for \(Preprocess(\mathbf{N})=\mathbf{N}^{\prime}\)) and \(\forall k\in[K]:n_{k}^{\prime}\leq n_{k}\)._  
Proof.: See Appendix A.1.  
The \(\alpha\)-\(U^{*}\) Trade-Off  
When we do not know \(\alpha\), as a practical procedure, we suggest plotting \(U^{*}\) as a function of \(\alpha\). In order to do so, we can start with \(\alpha\leftarrow\alpha^{*}\), \(U\gets n_{1}\), and alternate between decreasing \(\alpha\) by \(1/K\) (one less Byzantine client tolerated) and solving Equation (4). This procedure can be made efficient by saving intermediate sums and using a specialized data structure for trimmed collections. See Algorithm 3 for the pseudocode and Figure 1 for an example output.
# Deep Learning-Based End-to-End Carrier Signal Detection in Broadband Power Spectrum
## 2 Problem Description  
### The Core Task of Carrier Signal Detection Problem  
At present, in the 2D object detection problem, it is always preferrable to use a rectangle to localize the object in the images. Therefore, most of the end-to-end deep learning-based solutions are engaged in the regression of the corner points or center points position of the rectangle, as well as the width and height [24; 25].  
As Figure 1 shows, inspired by the 2D object detection problem, the 1D power spectrum is regarded as a 1D image and each subcarrier as an object. Moreover, we can use rectangles to localize the subcarriers. Therefore, the carrier signal detection problem turns into a 1D object localization problem. However, contrary to the 2D object detection problem, the core task of the carrier signal detection problem in the broadband power spectrum is the detection of the FC and BW of all subcarriers in the wideband power spectrum. Then, we can easily split the subcarrier signal from the broadband signal to perform modulation classification as well as further research and analysis.  
The proposed SCN directly regresses the FC and BW in this paper and is an end-to-end carrier signal detector. Furthermore, its input is the broadband power spectrum, and the outputs are all of the subcarriers' positions and the corresponding BW of the broadband.
## 4 Development and Simulation of a Predictive S&OP Methodology
### Development of the Conceptual Model
#### 4.1.1 Target System  
To assess the methodological approach, the following key performance indicators (KPIs) were calculated in the simulation:  
* The cumulated potential demand (units): the cumulative sum of the potential car units that can be ordered by the customers over the 1000 simulated days.
* The cumulated demand (units): the cumulative sum of the car units demanded by the customers over the 1000 simulated days. This is the result of the difference between the cumulated potential demand and the cumulated volume loss.
* The cumulated volume loss (units): the cumulative sum of the volume loss due to long customer order lead times.
* The cumulated quantity delivered (units): the cumulative sum of the car units delivered to end-customers over the 1000 simulated production days.
* The average quantity delivered on time per week (%): the average of the weekly fulfillment of delivery goals to end-customers.
* The average WIP stock (units): the average of the units in the warehouses during the manufacturer's production process without considering the supplier and distributor.
* The average capacity utilization of the production plant (%): the average utilization of the available capacities of all production shops of the producer over the 1000 simulated production days.
* The average customer order lead time (days): the average days between the customer order and car delivery to the end-customer over the 1000 simulated production days.
* The average mean absolute deviation (MAD) per week (units): the average deviation between the real demand and forecast values for a week.
* The average order backlog (units): the average number of units in the backlog; the ordered units not yet delivered.
* The cumulated operational savings (M euros): the savings due to adjustments in employees and working shifts.
* Cumulated investment value (M euros): the cumulated investments made to increase the production capacities of the car manufacturer, as well as the supplier's or distributor's capacities based on contract agreements.