esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Structural Monitoring via Global Navigation Satellite System: The Case of High Wind Turbines
### The Role of Frequency in Measurement
It can be observed that the performance trend of the control system with varying frequency is not very different for the different load cases, but the curves become gradually \"flatter\" as the intensity of the wind decreases. This aspect is highlighted even more in Table 4, where the range of variability in the structural response against the three load cases is reported for the frequency range 10-1000 Hz that was investigated. This outcome can be explained by the less intense wind gust which causes the control system to intervene less. For this reason, the acquisition frequency plays a less significant role in influencing the effectiveness of the mitigation system. Conversely, when the wind intensity is high, the more severe structural response requires a much more frequent intervention by the control system, which therefore requires high-frequency monitoring to work. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Maximum Base** & **Maximum Top** & **No. of State** & **Maximum Base** \\  & **Stress** & **Displacement** & **Changes (On/Off)** & **Rotation** \\  & **[MPa]** & **[mm]** & **[rad]** \\ \hline _Fixed base_ & _90_ & 1320 & - & - \\ SA @1000 Hz & 14 & 752 & 1648 & 0.0054 \\ SA @700 Hz & 16 & 730 & 1782 & 0.0051 \\ SA @500 Hz & 15 & 756 & 1158 & 0.0054 \\ SA @300 Hz & 22 & 840 & 1050 & 0.0061 \\ SA @100 Hz & 22 & 731 & 366 & 0.0053 \\ SA @70 Hz & 34 & 703 & 260 & 0.0049 \\ SA @50 Hz & 36 & 656 & 220 & 0.0046 \\ SA @20 Hz & 40 & 685 & 104 & 0.0049 \\ SA @10 Hz & 45 & 659 & 62 & 0.0052 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Peak response of the wind turbine by varying the frequencies of measurements. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Load Case vs. Full EOG** & \(\sigma_{\text{max, SA/}}\sigma_{\text{max, FB}}\) & \(\mathbf{x_{\text{max, SA/}}}\mathbf{x_{\text{max, FB}}}\) \\ \hline
50\% & 16\%–39\% & 36\%–38\% \\
75\% & 15\%–47\% & 34\%–55\% \\
100\% & 15\%–50\% & 50\%–64\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Range of demand reduction against the three load cases for the range 10-1000 Hz of SA control frequency. Figure 6: (**a**) Displacement and (**b**) stress response for different frequencies of measures for control, (**c**) zoom of panel (a), (**d**) zoom of panel (b).
## 3 System Overview
### Building Topology  
Room level localisation is defined in terms of cell-based localisation, i.e., locations are represented as cells. A cell may correspond to a room, or a section of a hallway. In the test area depicted in Figure 2, for instance, the main corridor is divided into four cells. In addition, the segmentation rule classifies the floor area into three categories: rooms, corridors and entrance/exits. It constructs logical links between rooms and corridors and models the constraints to movement imposed by the building's layout. Note that surveying the scales or true dimensions of the floor is not needed for space segmentation.  
Figure 1: Framework of the system.  
Figure 2: A schematic of the Bolz Hall, Ohio State University (not to scale) and topology.  
The segmentation will define the transition matrix in the HMM such that only adjacent cells have non-zero transition probability while the transition probability between isolated cells is zero. The system does not attempt to determine the exact grid position of the mobile user but the cell that the user is in.
## 4 Seepage Simulation Based on the Navier-Stokes Equation
### Principles and Methods of Seepage Simulation  
Absolute permeability \(K\) refers to the intrinsic properties of a porous material transporting a single-phase fluid at a certain pressure difference, regardless of any external conditions. It is commonly used in square microns (\(\mu\)m\({}^{2}\)), which is approximately equal to one Darcy unit (1D = 0.98 \(\mu\)m\({}^{2}\)) [21], and 1D = 10\({}^{3}\) m, D = 1.03 \(\times\) 10\({}^{-3}\) cm/s. In [PERSON]'s law, absolute permeability is a constant related to fluid, flow, and material parameters. The relationship between Darcy velocity and absolute permeability satisfies Equation (7).  
\[V=\frac{Q}{S}=-\frac{k}{\mu}\cdot\frac{\Delta P}{L} \tag{7}\]  
where \(Q\) is the total flow rate through the porous medium, \(S\) is the cross-sectional area of the sample through which the fluid passes, \(k\) is the absolute permeability, \(u\) is the dynamic viscosity of the fluid, \(\Delta P\) is the pressure difference applied around the sample, \(L\) is the length of the sample in the flow direction, and \(V\) is often used to explain the surface or mean fluid velocity or Darcy velocity through the porous medium.  
Equation (8) gives the relationship between the permeability coefficient \(K\) and the permeability \(k\), as shown in the following equation:  
\[K=\frac{\rho\cdot g\cdot k}{\mu} \tag{8}\]  
where \(\rho\) is the density of the liquid; \(u\) is the hydrodynamic viscosity coefficient; \(g\) is the acceleration due to gravity.
# FEAROL: Aging Flow Entries Based on Local Staircase Randomized Response for Secure SDN Flow Tables
## 2 Background and Related Work  
### SDN Flow Setup and Flow Entry Aging Procedures  
While the flow setup procedure in SDN systems is responsible for generating and installing new flow entries for SDN switches, the flow-entry-aging procedure is responsible for maintaining the lifespans of flow entries in the flow tables. As shown in Figure 2, the flow setup procedure relies on an SDN switch and its controller. An SDN switch looks up its flow table to find the matching flow entry for a received flow packet, and the received packet is directly forwarded out if a matching entry is found, otherwise a flow entry request is sent to the controller. The received packet will not be forwarded until the matching flow entry generated by the controller arrives at the switch. This procedure provides network programmability and flexibility through fine-grained flow-entry management;however, it introduces an interaction between switches and controllers that increases the network-forwarding latency.  
Regarding the flow-entry-aging procedure, each SDN switch contains a timer to maintain the lifespan of its entries. Whenever the timer fires, the flow-entry-aging procedure is triggered, as shown in Figure 2, to update the current existence and idle time of each entry in the flow table. The current existence and idle time of an entry is compared to the hard and idle timeouts set by the controller and stored in the entry, respectively. Entries are timed out and removed from the flow table if their current existence and idle time are greater than their hard and idle timeouts. Such a procedure ensures that each entry in the flow table ages correctly and that the flow table consists of only the active entries to save space for the incoming entries.
# Biosensors Based on Bivalent and Multivalent Recognition by Nucleic Acid Scaffolds
## 2 Biosensors Based on Bivalent Binding Modes
### Biosensors Based on DNA Nanodevices  
DNA displacement strategies were further modified to create new, improved biosensing systems, including a DNA displacement-based sensor combined with DNA nanostructures or nanodevices [82]. In 2013, [PERSON] et al. demonstrated dynamic DNA assemblies triggered by output DNA released from binding-induced DNA displacement [83]. In this design, the output DNA strand (O) acts as an initiator strand of the two-step DNA circuit (Figure 9). As a result, the DNA circuit produces a fluorescent signal in the presence of streptavidin. The detection threshold of the combined system was lower than one nanomolar, owing to the greater than 10-fold amplification effect of the DNA circuit. This combined DNA circuit system was also utilized for the detection of thrombin [84; 85; 86] and other proteins [87], for the identification of antibodies [88], and for developing a strip biosensor capable of the amplified detection of nerve growth factor-beta (NGF-\(\beta\)) [89].  
Encouraged by this result, more complex DNA nanodevices were recruited to transduce signals from bivalent biomolecular interactions to readable signals. One recent example was achieved by [PERSON] et al. in 2019 [90], where the authors proposed a combined system comprising a DNA scaffold and a hybridization chain reaction (HCR) for bivalent recognition and reporting, respectively, to provide single-step cancer cell identification (Figure 10). HCR is a process where two metastable nucleic acid hairpins are iteratively hybridized to each other to form a long stretch of a double helix [91]. Cell-type identification was achieved using aptamers that recognize biomarkers specific to individual types of cancer cells. In the presence of the target biomarkers on the cell, with the aid of a connector strand, two aptamer sequences form a DNA bridge-bearing associated toehold. The generated toehold then becomes an initiator of the HCR, and the fluorescent oligonu  
Figure 8: Design of a fluorescent biosensor based on proximity-induced displacement for monitoring protein dimerization. Adapted with permission from _J. Am. Chem. Soc._ 2018, 140, 4186–4190, doi:10.1021/jacs.7b11311. Copyright (2018) American Chemical Society [55].  
cleotide is elongated to visualize the cells. Using such an AND logic gate strategy based on bivalent interactions, a specific cancer cell was identified among several cancer cell lines. Furthermore, a triple-aptamer-based system using trivalent interactions has been suggested for more elaborate cell identification. These results demonstrate that such devices can visualize target proteins with excellent selectivity and sensitivity. Inspired by this work, an HCR system initiated by bivalent interactions was recently applied to the sensitive electrochemical sensing of thrombin [92] and was used in viral sensors based on cascade amplification using DNAzyme assemblies [93] or in the detection of antibodies [94].
# Enhancement of Ultrasonic Guided Wave Signals Using a Split-Spectrum Processing Method
## 2 Split-Spectrum Processing (SSP)  
### Theory of SSP  
SSP is an advanced signal processing technique that was initially developed from the frequency agility techniques used in radar [28]. This method was then considered for SNR enhancement in NDT applications such as conventional UT, to reduce grain scatter in the received signal. A significant amount of research has been undertaken over the last few decades in this area with respect to the reduction of non-random noise (coherent noise) in NDT applications, due to ultrasonic scattering. The application of SSP in GWT is relatively new and, to the best of the authors' knowledge, this technique and its limitations have not been previously investigated in this field.  
SSP splits the spectrum of a received signal in the frequency domain using a bank of bandpass filters to generate a set of sub-band signals at incremental centre frequencies. These sub-band signals are normalised and then subjected to a number of possible non-linear processing algorithms to generate an output signal. Figure 2 illustrates a block diagram for SSP with its step-by-step implementation. It shows that the input time domain signal, x(t), is transformed into the frequency domain, X(f), using a fast Fourier Transform (FFT), and is then filtered by a bank of band-pass filters. Subsequently, the outputs from the filter banks, \(\mathrm{X_{k}(f)}\)\((\mathrm{k=1,\ 2,\ \ldots,n})\), are converted back into the time domain using the inverse FFT (IFFT) and normalised by a weighting factor, \(\mathrm{w_{k}}\). Then one of the recombination algorithms will be employed to combine these non-linear signals to produce the output signal \(\mathrm{y(t)}\). In general, SSP application shows a great potential to reduce those signal components that vary across a frequency range, in particular dispersive wave modes, and to suppress the regions of the signal of interest that are constant in that frequency range [29].
## 5 Discussion
The challenges faced in the work presented here, including differences in bandwidth, location of bands, orbital considerations, and data sharing policy _etc._ are common to any compositing of virtual constellations. The proposed method can therefore also be used as a reference for generating higher density image products in these situations. Because the HJ-1A/B imagery can be freely accessed and still runs well on the orbit, the composited methodology can be used to generate HJ composited products for increasingly more areas in the foreseeable future. Particularly, another two small optical satellites will be launched in the \"4 + 4\" (four optical satellites and four synthetic aperture radar satellites) constellation in the second HJ constellation construction procedure [25]. This paper is a case study to demonstrate the feasibility and potential of using the 30 m HJ-1A/B constellation imagery during a 2-day repeat cycle to create time series (_i.e.,_ 8 days, 16 days and monthly) 30-m composites which can better reflect the variability of vegetation in heterogeneous regions. The detailed methodology and performance of the results were systemically introduced. The composited method in this paper is general and can be easily implemented for other areas. Efforts in the future will focus on testing the proposed method for large scale areas, and more image process procedures such as radiometric topographic correction should be included to improve the radiometric consistency [56, 69].
# Review of CMOS Integrated Circuit Technologies for High-Speed Photo-Detection
## 4 CMOS Transimpedance Amplifier (TIA)
### Integrating Receiver  
The aforementioned topologies are based on an amplifier which offers a high-bandwidth and low-noise characteristics generally at the cost of an increased power consumption. In order to overcome this, an integrating receiver is proposed using a different method for achieving a high sensitivity with a low-power consumption [92]. The basic concept is briefly explained in Figure 28. The photocurrent  
Figure 27: Inverter-based TIA.  
Figure 26: Various implementations of shunt-shunt feedback TIA. (**a**) CS amplifier with resistor feedback, (**b**) Cascaded CS amplifier and source follower with resistor feedback, and (**c**) multi-stage amplifier with resistor feedback.  
is first integrated by a sampler using two non-overlapping phases, thus charging or discharging \(V_{in}\) according to the incoming data. After the integrating phase, the current value, \(V_{n}\), is compared with the previous value, \(V_{n-1}\), to make a proper decision. Based on this scheme, the work in [92] achieves a 1.6-Gb/s operation while consuming considerably low power. The works in [93; 94] further extend this work, and achieve much higher operation speeds of 16 and 24 Gb/s, respectively.
## 3 Results  
### Statistical Overview of Results  
Based on the statistical evaluation, the observed parameters have a statistically significant effect, with particularly strong statistical significance observed for the \(\mathbf{v_{t}}\) factors across all tested plywood thicknesses. The cutting process for technical beech plywood included two key components. Firstly, the AWJ, which carries the energy needed to split the material itself, gives resistance to the cutting force. The energy from AWJ depends on the traverse speed and the abrasive flow rate. Secondly, the material's resistance depends upon its properties and the specific cutting conditions. A vital indicator of the interaction between these two components is the width of the kerf created by the water jet. Increasing energy supplied or reducing material resistance can decrease the width of the kerf. Conversely, reducing energy or increasing material resistance can result in increasing the width of the cutting joint. This relationship between energy and material resistance is essential for optimizing the cutting parameters to achieve the desired surface quality.  
The importance of monitored factors (according to Table 2) was the following:  
Figure 5: Measurement of surface roughness and marking of measurement traces. (**a**) shows the complete measurement apparatus, including the individual samples. (**b**) highlights one of the samples in detail, with clearly visible measurement traces.  
Figure 6: Screenshot view of the working window of the LPM View 2.0 program during measurement of the characteristics of the machined surface.  
**Material thickness:** This factor has the most significant influence on the AWJ cutting process. As the plywood thickness increases, the material's resistance to cutting also increases, which requires more energy. The material's thickness also affects the cutting quality. Thicker materials often require finer adjustment to achieve an optimal result.  
**Traverse speed:** The speed at which the material moves under the cutting beam dramatically affects both the quality and efficiency of the cut. A higher traverse speed can lead to lower cut quality, as the beam may not have enough time to fully cut through the material. On the other hand, too slow a traverse speed increases processing time and costs.  
**Abrasive flow:** The amount of abrasive material supplied to the cutting process affects the depth and quality of the cut. An intense abrasive flow rate can improve cutting efficiency, but exceeding the optimum value leads to excessive equipment wear and increased consumables costs.  
**Cutting direction:** This factor was evaluated as statistically insignificant, which means that changes in the cutting direction do not significantly affect the quality of the cut or the efficiency of the process. Nevertheless, the direction of cutting can affect certain applications, where wood grain structure or aesthetic requirements must be taken into account.
## 2 Methods and Materials
### Detail of Dataset and Analysis Method
#### 2.1.2 L-RFA Evaluation  
In the L-RFA, two lasers are used: an impact laser to induce vibration on the screw and a measurement laser to evaluate the induced vibration. An Nd: YLF laser was used as the impact laser. A laser Doppler vibrometer (PDV-100, Polytec GmbH, Baden-Wurttemberg, Germany) was used as a measurement laser. Both lasers were irradiated at the neck of the screw, and the measurement laser was irradiated. The vibration time-series data were synchronized to the Q-switched signal of the laser pulse irradiation, and the data (1.6 s) for 16 pulse irradiations were stored. The obtained signals were divided into 16 pulses of laser-induced vibration for each pulse irradiation, and the laser-induced vibrational frequency spectrum was obtained using a fast Fourier transform with a rectangular window function by trimming the data for 4.5 ms after the impact laser pulse irradiated. The purpose of the data trim was to obtain a clear frequency peak arising from the natural vibrations in the audible region. This was achieved by eliminating the noise associated with the burst sound caused by plasma formation arising from laser ablation.
# Review of sEMG for Robot Control: Techniques and Applications
## 3 Processing and Pattern Recognition Methods
### Feature Extraction  
Before feature extraction, the signal segmentation should first be determined. An overlapping window is the most widely used method; however, the window width and overlapping width are different. The width of the window directly affects the effectiveness of the feature extraction and delay time. The larger the window width, the more information will be obtained, and the less the deviation of the extracted features will be, but more computing time will be required. In references [121; 122; 123], for the first time, it was suggested that the maximum delay in a closed-loop real-time control system should be less than 300 ms. A delay time of 100 ms-250 ms is appropriate, and the classifier's performance should precede the delay time.  
For a specific classifier, the selection of the feature sets of the sEMG signals is the most important factor affecting its performance. Signal analysis generally includes Time Domain (TD), Frequency Domain (FD), Time-Frequency Domain (TFD) transformation, and entropy. TD features are used most frequently and are mainly for muscle effort or active-level analysis; FD features are primarily utilized to study the level of muscle fatigue. They can also be recruited as supplement features to form a feature set together with TD features to improve the accuracy of classification. TFD transforms such as Short-Time Fourier Transform (SFTF), Fast Fourier Transform (FFT), and wavelet transform retain the TD and FD characteristics of a signal. However, there are few studies based on TFD, owing to its complexity and poor interpretability. Fifty groups of standard features were recruited from reference [116] to select the most robust single feature and feature set. TD, FD, TFD, and Entropy features were also included. Based on the Linear Discriminant Analysis (LDA) classifier, the feature sets with the best performance are the Sample Entropy (SampEn), Cepstrum Coefficient (CC), Root Mean Square (RMS), and Wave Length (WL). The feature sets used in these studies are listed in Table 2.
## 2 Methods
### CNN Encoder Module  
The design of the CNN encoder in this paper, illustrated in the left half of Figure 2a, draws inspiration from the VGG16 [31], ResNet [32], and Unet [13] architectures. Each stage comprises stacked convolution, Depthwise Separable Convolution (DSC) [33], and _ReLU_ activations.  
Before performing convolution, a max-pooling layer is applied to downsample the feature map, reducing both spatial dimensions and computational demands. The convolution operation utilizes a \(3\times 3\) kernel for feature extraction, with a zero padding [34] operation used to increase the receptive field to a certain extent, preserving the original positional information while capturing local contextual details. Stacking multiple convolutions enables the progressive extraction of complex features and adjustment of channel dimensions as needed. Utilizing multiple small kernels (\(kernel\_size=3\)) instead of a single large kernel (\(kernel\_size=5\) or \(7\)) achieves an equivalent receptive field with fewer parameters and lower computational cost. Specifically, depthwise separable convolutions apply a \(3\times 3\) kernel independently to each input channel, followed by point-wise convolutions that use a \(1\times 1\) kernel across channels. This approach reduces the number of parameters and computational load while effectively capturing feature information. The _ReLU_ activation function introduces non-linearity, allowing the network to learn complex patterns and representations while mitigating gradient vanishing.
# Joint Pricing, Server Orchestration and Network Slice Deployment in Mobile Edge Computing Networks
## 3 System Model
### Cost and Revenue Model  
When a slice deploys its access function on base stations and deploys a VNF chain on MEC servers, the InP will charge deployment fees for the installation of VNFs and subsequent maintenance [17; 19]. Hence, the cost of each slice comprises deployment costs and resource costs. We denote the prices of access function deployment, VNF chain deployment, and radio and computational resources as \(c_{a}\), \(c_{v}\), \(c_{r}\) and \(c_{e}\). Note that once the number of users is determined, the user demand on storage resources will not adapt based on price. In this context, we can ignore storage resource cost. The ranges of \(c_{a}\), \(c_{v}\), \(c_{r}\) and \(c_{e}\) are denoted as \(\left[c_{a}^{min},c_{a}^{max}\right]\), \(\left[c_{v}^{min},c_{v}^{max}\right]\), \(\left[c_{r}^{min},c_{r}^{max}\right]\) and \(\left[c_{e}^{min},c_{e}^{max}\right]\), respectively. We quantize the value of prices into \(L\) discrete levels, and the \(c_{a}\) with level \(l_{a}\) is denoted as:  
\[c_{a}(l_{a})=c_{a}^{min}+\frac{l_{a}\left(c_{a}^{max}-c_{a}^{min}\right)}{L-1},\ l_{a}=0,1,\ldots,L-1. \tag{5}\]  
We denote the cost of each slice as:  
\[c_{n}=\ c_{a}\sum_{m=1}^{M}x_{m,n}+c_{v}\sum_{v=1}^{V}k_{v,n}+c_{r}\sum_{m=1}^ {M}\sum_{q=1}^{Q}w_{n,q}^{m}+c_{e}\sum_{v=1}^{V}\sum_{q=1}^{Q}e_{n,q}^{v}. \tag{6}\]  
Thus, the revenue of the InP is expressed as \(r^{lnP}=\sum_{n=1}^{N}c_{n}\).