esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Hybrid Vibration Isolation System
### Rubber Vibration Isolator
In an ideal state, the function output can be written as  
\[f\Big{(}\mathbf{w}^{T}\mathbf{x}\Big{)}=\left\{ \begin{array}{cc}\tau\cdot sign(\mathbf{w}^ {T}\mathbf{x});&\left|\mathbf{w}^{T}\mathbf{x}\right|\geq\tau\\ \mathbf{w}^{T}\mathbf{x}&\left|\mathbf{w}^{T}\mathbf{x}\right|<\tau\end{array} \right. \tag{4}\]  
where \(\mathbf{\tau}\) is the constraint threshold, and \(sign(\mathbf{w}^{T}\mathbf{x})\) is the round operation; that is, when the threshold is exceeded, amplitude limiting is carried out; and when the threshold is not exceeded, the update is originally carried out according to Formula (2). Based on the nonlinear output of Formula (4), the Lagrange function is introduced to obtain the extreme value to ensure the accuracy and convergence speed of the active control. The objective function is set as  
\[ \begin{array}{l}F(w,\lambda)=\frac{1}{2}\|\mathbf{w}(n+1)-\mathbf{w}(n)\|_{2}^{2}- \lambda\big{[}d(n)-\hat{\mathbf{s}}^{T}\cdot f\big{(}\mathbf{w}^{T}\mathbf{x}\big{)}\big{]} \\ =\frac{1}{2}\sum\limits_{i=0}^{L-1}[w_{i}(n+1)-w_{i}(n)]^{2}-\lambda\big{[}d( n)-\hat{\mathbf{s}}^{T}\cdot f\big{(}\mathbf{w}^{T}\mathbf{x}\big{)}\big{]}\end{array}  \tag{5}\]  
The derivatives of \(\mathbf{w}\) and \(\lambda\) are taken, respectively, as:  
\[\left\{ \begin{array}{l}\frac{\partial F(w,\lambda)}{\partial w}=w(n+1)-w(n) -\lambda\hat{\mathbf{s}}^{T}\frac{\partial f\big{(}\mathbf{w}^{T}\mathbf{x}\big{)}}{ \partial w}\mathbf{x}\\ \frac{\partial F(w,\lambda)}{\partial\lambda}=d(n)-\hat{\mathbf{s}}^{T}f\big{(} \mathbf{w}^{T}\mathbf{x}\big{)}\end{array} \right. \tag{6}\]  
The results of Formula (6) are all set as zero to obtain:  
\[w(n+1)=w(n)+\lambda\frac{\partial f\big{(}\mathbf{w}^{T}\mathbf{x}\big{)}}{\partial w }\hat{\mathbf{r}} \tag{7}\]  
\[e(n)=d(n)-\hat{\mathbf{s}}^{T}f\Big{(}\mathbf{w}^{T}\mathbf{x}\Big{)} \tag{8}\]  
The suppression factor can be written as \(\Delta f=\frac{\partial f(w^{T}\mathbf{x})}{\partial w}\), as can be seen from Formula (4), in an ideal state as  
\[\Delta f=\left\{ \begin{array}{cc}0&\left|\mathbf{w}^{T}\mathbf{x}\right|\geq\tau\\ 1&\left|\mathbf{w}^{T}\mathbf{x}\right|<\tau\end{array} \right. \tag{9}\]  
Next, \(\Delta f\) is introduced in Formula (2) to modify the update of the weight coefficient, and the formula for the ASFXLMS algorithm is obtained as  
\[\mathbf{w}(n\!+\!1)=\mathbf{w}(n)+\mu\Delta f(n)\hat{\mathbf{r}}(n) \tag{10}\]  
Figure 16: Block diagram of the FXLMS algorithm. According to the output form of Formula (10), an arc tangent function can be adopted, for example  
\[f(x)=A\cdot\frac{2}{\pi}\text{arctan}(\theta x) \tag{11}\]  
The derivative function is obtained as  
\[f^{\prime}(x)=\frac{A}{1{+}\theta x^{2}} \tag{12}\]  
where \(A\) is the coefficient related to the threshold, and \(\theta\) can adjust the window width of the derivative function.
## 4 Results from Case Studies
### Validation of Synchrophasor Applications Using Field Measurements  
#### 4.2.1 Results from Spatiotemporal Synchrophasor Data Compression  
The evaluation of the _spatiotemporal data compression module_ is based on the _LVField_ dataset. The spatiotemporal compression algorithm is applied on randomly selected measurement records with a fixed time range of 100 s (1000 observations). Within the data pre-processing, the raw measurement values are normalized into the range [0,1] and filtered using backward sliding moving averages to eliminate high-frequency oscillations. To avoid signal discontinuities, the voltage and current phase angles are unwrapped before normalization. Regarding Section 3.2, the evaluation is focused on PCA as spatial and DWT as temporal compression algorithm whereas the chosen DWT hyperparameters are given in Table 6. The number of principle components is varied during the evaluation. Original and reconstructed voltage magnitude and frequency values are compared in Figure 12 for one and three principle components. Increasing the number of principle components reduces the compression rate roughly from 17 to 5. At the same time, the reconstruction error raises from 3.47 to 17.30 for voltage magnitudes and from 0.64 to 2.59 for frequencies. As a result, high deviations between original and reconstructed signals can be observed when reducing the number of principle components.  
\begin{table}
\begin{tabular}{c c} \hline
**Hyperparameter** & **Value** \\ \hline Wavelet function & Db5 \\ Decomposition level & 2 \\ Coefficient threshold & 0.05 \\ \hline \end{tabular}
\end{table}
Table 6: Hyperparameters of the spatiotemporal synchrophasor data compression module.  
Figure 11: Exemplary simulations of four PMUs for a short circuit at line L19 (90% line length) including frequencies (**top and bottom left**) and voltage magnitudes (**top and bottom right**) for two operational points.  
Spatial, temporal and total compression rates \(r_{C}\) as well as reconstruction errors \(e_{R}\) are summarized for the different measurement channels in Table 7 assuming three principle components used within the spatial compression stage.  
Using three principle components reduces the compression rate up to 5.8 and the reconstruction error up to 0.04. The highest reconstruction errors can be observed especially during the spatial compression and for voltage magnitude signals. The reconstruction errors heavily depend on the signal variability and the deviations within the different measurement channels. Assuming 1000 observations, a single measurement record of one voltage and current phasor as well as one frequency and ROCOF signal comprises 240 kByte memory space allocation. Specific file formats or additional over-head information are not considered here. The size of the compressed dataset varies between 14 kByte and 40 kByte depending on the number of principle components used in the spatial compression stage. The necessary calculations are performed in Matlab(r) with the implementation of an additional toolbox for wavelet decomposition [63].
# Large Field-Size Throughput/Area Accelerator for Elliptic-Curve Point Multiplication on FPGA
###### Abstract  
This article presents a throughput/area accelerator for elliptic-curve point multiplication over \(GF(2^{\mathcal{N}1})\). To optimize the throughput, we proposed an efficient hardware accelerator architecture for a fully recursive Karatsuba multiplier to perform polynomial multiplications in one clock cycle. To minimize the hardware resources, we have utilized the proposed Karatsuba multiplier for modular square implementations. Moreover, the Itoh-Tsuji algorithm for modular inverse computation is operated using multiplier resources. These strategies permit us to reduce the hardware resources of our implemented accelerator over a large field size of 571 bits. A controller is implemented to provide control functionalities. Our throughput/area accelerator is implemented in Verilog HDL using the Vivado IDE tool. The results after the place-and-route are given on Xilinx Virtex-6 and Virtex-7 devices. The utilized slices on Virtex-6 and Virtex-7 devices are 6107 and 5683, respectively. For the same FPGA devices, our accelerator can operate at a maximum of 319 MHz and 361 MHz. The latency values for Virtex-6 and Virtex-7 devices are 28.73 \(\upmu\)s and 25.38 \(\upmu\)s. The comparison to the state-of-the-art shows that the proposed architecture outperforms in throughput/area values. Thus, our accelerator architecture is suitable for cryptographic applications that demand a throughput and area simultaneously.  
throughput/area; hardware accelerator; elliptic-curve; point multiplication; FPGA 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
# Optimizing Automated Detection of Cross-Recessed Screws in Laptops Using a Neural Network
## 3 Results
### Screw Characteristic Analysis
These groupings were selected based on the \"All Screws Detection percentage\" rates in Table 7. Each of these categories were then labeled with a letter. Depth was labeled as \"A\", taper was labeled as \"B\", position was labeled as \"C\", and color difference was labeled as \"D\". Each of these characteristics was combined to get all of the possible combinations of screw characteristics. The Venn diagram shown in Figure 12, shows the percentage detection for the different combinations. These results show that of all the combinations, the system had the best detection rates when a screw was a surface screw, regardless of its other characteristics. The data for the Venn diagram can be viewed in the S4-Screw Characteristics in the Binary Data Expanded tab. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Categories**} & \multirow{2}{*}{\begin{tabular}{c} **Poor Detection Rate** \\ **Sample \%** \\ \end{tabular} } & \multicolumn{2}{c}{**Perfect Detection Rate**} & \multirow{2}{*}{\
\begin{tabular}{c} **All Screws Sample \%** \\ **Sample \%** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **All Screws Detection** \\ **Percentage** \\ \end{tabular} } \\ \hline Deep screw & 100 & 43.9 & 68.7 & 68.8 \\ Surface screw & 0.0 & 56.1 & 31.3 & 94.1 \\ \hline Has a taper & 34.8 & 9.8 & 17.4 & 56.9 \\ No taper & 65.2 & 90.2 & 82.6 & 80.9 \\ \hline Exterior screw & 60.9 & 31.7 & 40.0 & 69.8 \\ Interior screw & 21.7 & 51.2 & 38.3 & 85.2 \\ Corner screw & 17.4 & 17.1 & 21.7 & 74.3 \\ \hline Large color difference & 26.1 & 29.3 & 26.1 & 80.0 \\ Medium color difference & 47.8 & 34.1 & 34.8 & 68.6 \\ No color difference & 26.1 & 36.6 & 39.1 & 81.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Sample percentages and detection percentages of different screw groupings. The factors compared are (1) a deep/surface screw, (2) screws for which the hole has/does not have a taper, (3) screws with an exterior/interior/corner location, and (4) screws for which the color difference between the screw and the laptop was none/medium/large.
## 2 Cluster Head Selection
### Self-Organized Schemes
#### 2.1.2.3 Sensing Coverage  
As the main objective of implementing sensor nodes is to sense a physical phenomenon, avoiding coverage holes within the network should be a top priority. In [27], the paper proposes coverage area in addition to residual energy as combined metrics for the selection of CHs. The algorithm divides the sensor nodes into two groups of normal and critical nodes. The nodes with overlap coverage percentage over a threshold value (70% proposed by the paper) by other sensor nodes are considered as normal nodes and have normal sleeping intervals, but the nodes with less overlap percentage are marked as critical nodes and have longer sleeping intervals. To determine the overlap coverage percentage, each node, at the initial phase, broadcasts a message with the transmission power equal to its sensing range (usually considers as half the transmission range) and the nodes, which receive the message, response to the message during a certain time period. In this way, each node calculates its overlap coverage percentage. In CH selection phase, each node sets a timer based on remaining power and coverage overlap and thus the nodes having the greater residual energy and more overlap coverage have more chances to be selected as CH. The critical nodes do not participate in CH competition to retain the sensing coverage level of the network, even if they have more battery power than normal nodes.  
Another algorithm that considers the sensing coverage is presented in [28]. The paper calculates the estimated normalized effective sensing area of the nodes through finding the overlapped sensing nodes. Each node broadcasts a beacon message to its neighbouring nodes to discover them. The transmission power of the beacon message is set to reach the nodes within two sensing range times. Therefore, each node can find its overlapped sensing area and estimates its effective sensing range. It is concluded that demise of the nodes with larger value of effective sensing range causes greater coverage hole and further degrades the network performance. Hence, the algorithm multiplies the original probability in LEACH by the estimated normalized overlapping area parameter. Thus, the nodes with large value of effective sensing range have less probability to be selected as CH. In other words, the CH role is mainly carried by nodes with small normalized overlapping area values.
## 2 Experimental Arrangements  
An Ultrafast high-performance Yb-doped fiber laser (UFFL_300_2000_1030_300; Active Fiber System), with a central wavelength of 1030 nm, a repetition rate of 150 kHz, and a pulse duration of 40 fs, was used to irradiate the surfaces (Figure 1a). The laser polarization was linear with beam quality close to the diffraction limit (M\({}^{2}<1.3\)), and average power stability <1% of the root mean square (RMS) value. The laser beam was directed to a computer-driven scan head (FARO Scan System). The scan head had the flexibility to control the scanning speed along the metal surface, line spacing between the markings, and some other features such as the shape of the irradiated area, angle of incidence, direction of the laser beam, and others. The laser system controller maintained the parameters of laser pulses like repetition rate and pulse duration. A half wave plate (HWP) and a thin film polarizer (TFP) were used to control the power of laser pulses on the target surfaces (Figure 1a). A 3-D motorized stage (3 DMS) was used to adjust the position of the sample under the scan head. The scan head was equipped with an F-Theta lens, which provided almost constant spot size across the entire image plane (90 \(\times\) 90 mm\({}^{2}\)). The spacing between two lines was maintained at 50 \(\upmu\)m, which nearly corresponded to the spot size of the focused beam. The sample position was kept constant and the scan head software controlled the beam motion. The optimal laser irradiation of surfaces for different metallic samples was determined by varying the scan speed of the laser beam.  
Stainless steel, brass, aluminum, and copper plates were irradiated at different speeds of the scan head. Except for the 5 mm thick brass sample, the thickness of the processed samples was -2 mm. The surfaces of samples were cleaned with isopropanol before irradiation. A scanning electron microscope (SEM) was used to characterize the surfaces of different treated samples. The surface optical characteristics were analyzed using the experimental arrangement shown in Figure 1b. The horizontally mounted sample was illuminated by an un-polarized light source with continuum spectrum in the visible spectral range, which was transformed to a parallel beam by two lenses and an aperture. In our study, the incident position angle (\(\alpha\)) was fixed at 20\({}^{\circ}\). To inspect the colorizing effect of the treated area, the spectra of the scattered light from different angles (\(\beta\)) were measured by a fiber optics spectrometer (Flame, Ocean Optics).
## 4 Conclusions  
The addition of glycerol in the cultures of _A. platensis_ had negative impact on biomass production due to the growth of bacteria that apparently outcompeted and repressed growth of A. platensis. Glycerol was not an inhibiting factor because _A. platensis_ did not alter its photosynthetic efficiency (Quantum Yield). However, since the increase of glycerol impacted the biochemical composition (increase of protein and lipid and decrease of carbohydrate and photosynthetic pigment content) it is suggested that _A. platensis_ was able to assimilate glycerol towards metabolic process for protein and lipid synthesis instead of biomass production.  
Conceptualization, G.M.; Methodology, G.M., E.K.; Formal analysis, G.M., E.K.; Investigation, G.M., E.K.; Resources, G.M.; Data curation, G.M.; Writing--Original draft preparation, G.M.; Writing--Review and editing, G.M., I.K., V.T., D.A., I.C.; Visualization, G.M.; Supervision, G.M.; Funding acquisition, G.M., D.A., I.C.  
This research has been co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH--CREATE--INNOVATE (T1 EDK-02142).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Glycerol Concentration g/L** & **5 Klux** & **10 Klux** & **15 Klux** & **NH3-15 Klux** \\ \hline
0 & 7.22 \(\pm\) 0.49 & 7.64 \(\pm\) 1.00 & 5.89 \(\pm\) 0.58 & 4.96 \(\pm\) 1.27 \\
0.5 & 9.23 \(\pm\) 0.27 & 7.32 \(\pm\) 0.20 & 6.40 \(\pm\) 0.43 & 5.84 \(\pm\) 0.41 \\
1 & 8.76 \(\pm\) 0.33 & 8.32 \(\pm\) 0.43 & 6.36 \(\pm\) 0.26 & 5.77 \(\pm\) 0.23 \\
1.5 & 9.22 \(\pm\) 0.38 & 8.44 \(\pm\) 0.85 & 5.70 \(\pm\) 0.43 & 5.40 \(\pm\) 0.48 \\
3 & & & 9.46 \(\pm\) 1.05 & 9.16 \(\pm\) 0.97 \\
6 & & & 11.13 \(\pm\) 1.45 & 10.03 \(\pm\) 0.20 \\
9 & & & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Lipids content of _A. platensis_ cultivated under different glycerol concentrations and light intensities.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Glycerol Concentration g/L** & **5 Klux** & **10 Klux** & **15 Klux** & **NH3-15 Klux** \\ \hline
0 & 12.99 \(\pm\) 0.55 & 14.71 \(\pm\) 0.25 & 14.20 \(\pm\) 1.38 & 15.73 \(\pm\) 1.45 \\
0.5 & 11.22 \(\pm\) 0.37 & 12.86 \(\pm\) 0.1 & 12.84 \(\pm\) 2.95 & 14.21 \(\pm\) 2.58 \\
1 & 12.10 \(\pm\) 0.75 & 10.64 \(\pm\) 0.14 & 14.83\(\pm\) 0.25 & 14.07 \(\pm\) 1.27 \\
1.5 & 11.42 \(\pm\) 1.11 & 10.55 \(\pm\) 0.46 & 12.85 \(\pm\) 0.05 & 12.60 \(\pm\) 0.79 \\
3 & & & 6.60 \(\pm\) 0.25 & 6.85 \(\pm\) 0.5 \\
6 & & & 8.19 \(\pm\) 2.12 & 6.13 \(\pm\) 0.34 \\
9 & & & 1.36 \(\pm\) 1.58 & 2.85 \(\pm\) 0.55 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Phycocyanin content of _A. platensis_ cultivated under different glycerol concentrations and light intensities.
# Efficient Multipath Clutter Cancellation for UAV Monitoring Using DAB Satellite-Based PBR
## 8 Conclusions  
In this paper, the detection of UAVs is addressed using PBR with a DAB satellite as an illuminator. The main work contained three parts. First, an advanced ECA version is designed to improve the efficiency of multipath clutter suppression via batch-level parallelism and precompression for close-range monitoring. Second, the proposed algorithm's performance is analyzed and compared with the existing ECA versions, and the selection of its parameters is discussed and summarized. Theoretical derivation and numerical simulation show that the algorithm has better efficiency and preservation of slow targets. Finally, the experiments validate the feasibility of DAB satellite signals for UAV monitoring, in which the results demonstrate the influence of observation configuration and target attitude on the target's detectability. For targets in non-forward beams, the system performance is vulnerable to fluctuation due to weak scattering while the forward geometry can significantly enhance the energy and thus improve the continuity of detection. The limitation of a fixed surveillance beam will be overcome with multi-channel antennas in future works.  
Conceptualization, Y.M. and J.L.; methodology, Y.M. and F.L.; software, Y.M.; formal analysis, Y.M. and Y.B.; writing--original draft preparation, Y.M.; visualization, Y.B.; writing--review and editing, F.L. and C.H.; project administration, C.H. and J.L. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Beam a** & **Beam b** & **Beam c** \\ \hline Maximum distance/m & 452.6 & 551.6 & 416.7 \\ Maximum SNR/dB (CPI = 0.5 s) & 20.57 & 14.25 & 13.51 \\ Maximum SNR/dB (CPI = 2 s) & 25.44 & 17.76 & 16.73 \\ Proportion of detectable CPIs & 85.5\% & 26.0\% & 22.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Detection performance of different beams.  
**Funding:** This work was supported by the Special Fund for Research on National Major Research Instruments (grant No. 31727901) and the National Natural Science Foundation of China (grant No. 62071045 and grant No. 61625103).  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author, [PERSON], upon reasonable request.  
**Acknowledgments:** The authors thank the editors and anonymous reviewers for their help comments and suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Cost Model and Problem Description
### Cost Model  
In CDC, the cost related to computational resources mainly involves hosts, VM migrations, and SLAV penalties.  
Before giving a specific cost model, we first describe the time and objects of the entire system. There are \(N\) heterogeneous hosts in CDCs, forming the host set \(H=\{h_{1},h_{2},\cdots,h_{N}\}\). The total resources provided by a host \(h_{i}\) is denoted as \(C_{i}=(c_{i}^{cpu},c_{i}^{mem},c_{i}^{disk},c_{i}^{net})\), where \(c_{i}^{cpu}\), \(c_{i}^{mem}\), \(c_{i}^{disk}\), and \(c_{i}^{net}\) are the total available usage of CPU, memory, Disk R/W, and network I/O throughput, respectively. There are \(M\) running VMs, forming VM set \(V=\{v_{1},v_{2},\cdots,v_{M}\}\). When a user creates a VM \(v_{j}\), the submitted resource requirements are denoted as \(D_{j}=(d_{j}^{cpu},d_{j}^{mem},d_{j}^{disk},d_{j}^{net})\), where \(d_{j}^{cpu}\), \(d_{j}^{mem}\), \(d_{j}^{disk}\), and \(d_{j}^{net}\) are the total requirements of CPU, memory, Disk R/W, and network I/O throughput, respectively.  
The life cycle of the CDC is divided into \(L\) small and equally long consecutive time segment \(\{t_{1},t_{2},\cdots,t_{L}\}\), and each time segment has a length of \(T\). In a certain time segment \(t_{k}\), if host \(h_{i}\) is in the working state, then we have \(\lambda_{i,k}=1\); otherwise, \(\lambda_{i,k}=0\). The amount of resources that \(h_{i}\) can provide are denoted as \(R_{i,k}=(r_{i,k}^{cpu},r_{i,k}^{mem},r_{i,k}^{disk},r_{i,k}^{net})\). In \(t_{k}\), the resources required by VM \(v_{j}\) are denoted as \(S_{j,k}=(s_{jk}^{cpu},s_{jk}^{mem},s_{jk}^{disk},s_{jk}^{net})\).  
We summarize the total cost of CDC for a given lifetime by analyzing the performance of each computing device in each time segment. In addition to the energy consumption cost of the hosts, it is also necessary to consider the cost of VM migration during each server consolidation and the penalty caused by the occurrence of SLAV. We will discuss them separately in the following subsections.
# Query-Based Cascade Instance Segmentation Network for Remote Sensing Image Processing
## 3 Experiment and Analysis
### Datasets  
**NWPU-VHR-10 dataset**: This dataset [26] consists of 800 labeled high-resolution optical RS images, including 650 images with objects and 150 images with pure backgrounds.  
The dataset comprises a total of 10 categories. We randomly selected 70% of the images with objects to be the training set and 30% of the images to be the test set.  
**SSDD dataset:** The dataset [51] consists of SAR images that contain 1160 images. The dataset includes many small- and medium-sized objects, but relatively few large objects. In the experiment, we randomly allocated the training set and test set according to a 7:3 ratio. The training set contains 812 images, while the test set contains 348 images.