esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview of the Basic Concepts of Wind Farm Design and Optimization
### Adverse Factors and Conflicting Objectives
#### 2.2.2 Wind Farm Infrastructure
It is also desirable to have airports and/or seaports nearby to improve logistic operations during the construction process. Additionally, it should provide a way (e.g., radio frequency-based, telecommunication, Internet-based, wired connection-based or satellite connection-based) through which important information (e.g., wind resource and wind farm real-time operation) can be consulted and monitored, however it mostly depends on the coverage of the local network service providers. Civil engineering and telecommunication infrastructure is commonly not encountered within the wind farm potential areas, thus the wind farm designer usually incur additional expenses [74, 76] by building auxiliary infrastructure (e.g., suitable auxiliary roads), which depends exclusively on the wind farm layout, local orography and restricted areas.
# Evolution of Hybrid LiFi-WiFi Networks: A Survey
## 3 Performance of HWLNets  
The HWLNets performance is measured with SINR coverage, spectral efficiency, energy efficiency, network capacity, and quality of service (QoS). This section provides an overview of these metrics.
# Test-Retest Reliability of a 6 DoF Marker Set for Gait Analysis in Cerebral Palsy Children
## 2 Materials and Methods
### Gait Protocol  
The trials were performed on two different days within a period of 7.5 \(\pm\) 1.4 days to minimize the assessor memory bias and short enough to prevent a change in the children's gait pattern or clinical condition [21]. Upon the participants' arrival, instruction was given about the protocol, the risks and benefits, as well as the informed consent.  
The initial clinical exam consisted of a sequence of measures to assess bone and joint deformities, muscle length, muscle force, selective motor control and spasticity [2]. Two experienced researchers performed the clinical assessment while the same assessor was responsible for the placement of the markers in all the sessions. Palpation was used to locate the subcutaneous anatomical landmarks on the participants [30] and subsequently to place the marker set. These were 1.25 cm spherical reflective markers with a 1.8 cm semi-flexible width base. Four marker clusters were attached to the lateral part of the thigh and shank to independently track anatomical landmarks of each segment allowing rotational and translational motion at the joints [19]. These types of markers were adequate for the general height of these children given the smaller body parts. Motion capture data were collected with 14 infrared, high-speed cameras (Qualisys Oqus 300, Qualisys AB, Gothenburg, Sweden) at a frequency rate of 100 Hz. This system was synchronized in time and space with two force plates (FP4060-07, FP4060-10, Bertec, Columbus, OH, USA) embedded into the laboratory walkway [31]. Before each dynamic trial, a barefoot static trial in the standing position was recorded in order to determine the participant's joint centres and segmental reference systems, as well as segments' length [19]. Afterwards, the participant was instructed to walk along a 10 m corridor, unassisted at a self-selected pace. The dynamic trials ended when the child successfully achieved a minimum of five complete kinematic and kinetic walking cycles for each side [14; 32; 33], considering the natural variation in kinematic and kinetic gait parameters [34].
## 4 System Model and Description of the Proposed Protocols
### Transmission and Receiving Energy Calculations in BF-SPR-Three
#### 4.2.5 How Are Neighbors Selected in BF-SPR-Three?  
In BF-SPR-Three protocol, the neighbors selection is similar to EP-VIR-Three. Whereas, distance from source to destined node is computed using Euclidean Equation (15):  
\[Distance_{(i,j)}=\sqrt{(i_{1}-i_{2})^{2}+(j_{1}-j_{2})^{2}}, \tag{15}\]  
whereas \((i_{1},i_{2})\) and \((j_{1},j_{2})\) are the coordinates of source and receiver node. Coordinates \((i,j)\) are obtained from the beacon message and depth (yd) is obtained by calculating the depth of the node from the sea surface. The value of yd is used to find that the next forwarder node lies closer to the sink. The following constraints must be satisfied to find the optimum forwarders:  
1. \(Distance_{(i,j)}<\) Transmission Range \((R_{t})\),
2. Depth of the current forwarder should be greater than the next elected forwarder node.
# Algorithms in Tomography and Related Inverse Problems--A Review
## 5 A Deep Learning-Based 3D Ground-Penetrating Radar Data Inversion
#### DENOISING, Inverter, and Training
Finally, the following outlines a three-step process for the 3 DInvNet, a two-stage scheme designed for denoising GPR C-scans and reconstructing 3D subsurface permittivity maps. The three steps involve denoiser pre-training, inverter pre-training, and fine-tuning the pre-trained networks using transfer learning. Here is a summary of each step:  
* Denoiser Pre-training
* Objective: Train the denoiser component using a diverse dataset of noisy and noise-free C-scans. * Loss Function: Mean Squared Error (MSE) between the predicted denoised C-scan \(\mathbf{y_{D}}\) and the corresponding ground truth (\(\hat{y}_{D}\)). * Loss Function Formula: \(L_{1}(\mathbf{y_{D}},\hat{\mathbf{y}_{D}})=\frac{1}{D\cdot H\cdot W}\sum_{d=1}^{D} \sum_{h=1}^{H}\sum_{w=1}^{W}\left(y_{Dd,h,w}-\hat{y}_{Dd,w}\right)^{2}\)
* Optimizer: Adam optimizer. * Inverter Pre-training
* Objective: Pre-train the inverter using noise-free C-scan ground truth (\(\hat{y}_{D}\)) as input data. * Loss Function: Mean Absolute Error (MAE) between the predicted permittivity map \(\mathbf{X}\) and the ground truth \(\hat{\mathbf{X}}\). * Loss Function Formula: \(L_{2}\left(\mathbf{X},\hat{\mathbf{X}}\right)=\frac{1}{D\cdot H\cdot W}\sum_{d=1}^{ D}\sum_{h=1}^{H}\sum_{w=1}^{W}\left|\left(X_{d,h,w}-\hat{X}_{d,h,w}\right)\right|\)
* Optimizer: [PERSON] optimizer. * Fine-tune the Pre-trained Networks (Transfer Learning)
* Additional Data Creation: Generate a small dataset containing new scenarios. * Initial Network States: Utilize the pre-trained networks as the starting point for fine-tuning. * Parameter Updates: Further refine the network parameters by minimizing the loss functions \(L_{1}(\mathbf{y_{D}},\hat{\mathbf{y}_{D}})\) and \(L_{2}(\mathbf{X},\hat{\mathbf{X}})\) using the new training dataset until convergence. * Enhanced Networks: After fine-tuning, the networks are better suited to handle a broader range of scenarios. * Objective: Improve networks' adaptability and robustness. In Summary:  
Denoiser: Captures informative features from subsurface objects and mitigates environmental noise in GPR C-scans. Inverter: Establishes a relationship between discriminative features extracted from denoised C-scans and corresponding subsurface scenarios. Comprehensive Testing: Validates the capability of the proposed method to accurately and efficiently reconstruct 3D permittivity maps across various subsurface scenarios using both numerical simulations and real measurement data.
## 3 System Model of FGSM and Traditional EDAS
### Edas  
TAS improves the transmit diversity of traditional MIMO and SM systems, as described in Section 2. EDAS is the most popular and optimal TAS technique, with the objective of choosing antenna combinations that enhance the minimum ED of received constellations. It is shown that EDAS has superior EE and ABER compared to other conventional methods [12,15]. The channel matrix \(\mathbf{H}\) is derived from \(\mathbf{H_{T}}\) when TAS is incorporated in FGSM. The important steps in the EDAS process are listed below:  
1. Determine the total number of distinct feasible subsets \(N_{p}=\begin{pmatrix}N_{T}\\ N_{t}\end{pmatrix}\).
2. Find all of the possible transmit vectors \(2^{\hat{c}_{FGSM}}\) for each antenna subset.
3. For each antenna subset, calculate their least possible ED using: \[\lambda=\left(\min_{\mathbf{x}_{j}\
eq\mathbf{x}_{k}\in\mathbf{\mathcal{X}}}\|\mathbf{H_{T}}( \mathbf{x}_{j}-\mathbf{x}_{k})\|^{2}\right)\] (3) In (3), \(\mathbf{\mathcal{X}}\) represents the set of all possible transmit vectors.
4. Determine the subset of antennas with the maximum ED. \[\hat{c}=\underset{c\in 1,2\ldots N_{p}}{\text{arg max}}\lambda,\] (4) here \(\lambda=\left[\lambda_{1},\lambda_{2}\ldots\lambda_{N_{p}}\right]\).  
On the basis of the antenna subset index acquired in step 4, the antenna indices and their corresponding \(N_{t}\times N_{t}\) channel matrix \((\mathbf{H})\) are computed.
# Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques
## 3 Results
### Vegetation Indices
The corn yield maps after kriging and resampling to a 10 m pixel size with mostly correlated GNDVI and corresponding R\({}^{2}\) graph are shown in Figure 4. In Figure 4, GNDVI and yield maps are shown with the same color map stretched with the histogram equalization method.
## 3 Principles of Speed Estimation for Inter-Symbol Processing  
Figure 1 represents the block diagram of the flow of velocity estimation of radiating sources for motion communication across symbol processing. The received signal is first divided into two paths, one of which is demodulated to obtain the transmitted symbol sequence after carrier phase synchronization using MMSE. The other channel performs modulation phase balancing between different symbols and combines the signals in segments according to the symbol period under the condition that the transmitted symbol sequence is known to be used as a data auxiliary, and the speed of the radiated source can be estimated by taking the same corresponding point between different symbols and carrying out the vertical FFT.
## 3 Results and Discussion
### Determination of DA and UA in Human Serum Samples  
To validate practicability, the concentrations of DA and UA in human serum samples were also detected on MnO2 NFs/NrGO/GCE. The determination results were calculated from the calibration curves (Table 2). To further validate the precision of the proposed sensor, a series of known concentration solutions of DA and UA were spiked with the serum samples to figure out the recovery. The recoveries are 96.2-105.6% and 96.2-104.9% for DA and UA respectively, verifying that biological matrixes, like human serum, do not influence the simultaneous detection of DA and UA.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Electrodes**} & \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Detection Range (\(\upmu\)M)**} & \multicolumn{2}{c}{**LOD (\(\upmu\)M)**} & \multirow{2}{*}{**Ref.**} \\ \cline{3-3} \cline{5-6}  & & **DA** & & **UA** & & **DA** & **UA** \\ \hline Au/Cu\_O/FOO/GCE & a  & DPV & 10–90 & 100–900 & 3.9 & 6.5 & [7] \\ NrGO/GCE & DPV & 0.5–170 & 0.1–20 & 0.25 & 0.045 & [46] \\ Pd/RGO/GCE & DPV & 0.45–71 & 6–469.5 & 0.18 & 1.6 & [47] \\ Pt/RGO/GCE & DPV & 10–170 & 10–130 & 0.25 & 0.45 & [48] \\ ZnO/SPCE & DPV & 0.1–374 & 0.1–169 & 0.004 & 0.00849 & [49] \\ PtNi@MoS2/GCE & DPV & 0.5–150 & 0.5–600 & 0.1 & 0.1 & [50] \\ Au-Pt/GO–ERGO & DPV & 0.0682–49,800 & 0.125–82,800 & 0.0207 & 0.0407 & [51] \\ HFP/GCE & DPV & 1–200 & 20–400 & 0.016 & 0.218 & [52] \\ MoS2/GCE & DPV & 1–900 & 1–60 & 0.15 & 0.06 & [53] \\ ZnO/PAN/NrGO/GCE & DPV & 0.1–90 & 0.5–90 & 0.017 & 0.12 & [54] \\ MoS2/GO/GCE & DPV & 5–545 & 25–2745 & 0.05 & 0.46 & [55] \\ NCNF/GCE & DPV & 1–10; 10–200 & 5–200 & 0.5 & 1 & [56] \\ PTPCNs/GCE & DPV & 1–100 & 5–200 & 0.078 & 0.17 & [57] \\ MnO2 NFs/NrGO/GCE & LSV & 0.2–6.0; 6.0–100 & 0.2–6.0; 6.0–100 & 0.036 & 0.029 & This work \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison sensing performance between previous reports and the proposed MnO2 NFs/NrGO/GCE for the simultaneous detection of DA and UA.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{\({}^{\text{a}}\)**Samples**} & \multicolumn{1}{c}{**Detected (\(\upmu\)M)**} & \multicolumn{1}{c}{**Added (\(\upmu\)M)**} & \multicolumn{1}{c}{**Found (\(\upmu\)M)**} & \multicolumn{1}{c}{**RSD (\%)**} & \multicolumn{1}{c}{**Recovery (\%)**} \\ \hline \multirow{4}{*}{**Serum**} & DA & b  & ND & 20 & 18.98 & 4.25 & 94.9 \\ \cline{3-6}  & & & 40 & 38.48 & 3.76 & 96.2 \\ \cline{3-6}  & & & 20 & 42.92 & 2.98 & 105.6 \\ \cline{2-6}  & UA & 21.8 & 40 & 63.74 & 2.45 & 104.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detection results of DA and UA in human serum samples using MnO2 NFs/NrGO/GCE (\(n=3\)).
## 3 Test Results
### Water Discharge  
Figure 7 illustrates the water discharge volume of the tested slurries over time during the vacuum preloading test. At 24 h after the test began, the water discharge volumes of the lime-floculated slurry, dual use of PAC and APAM-floculated slurry, APAM-floculated slurry were 10.8 kg, 10.7 kg, 9.6 kg, and 3.5 kg, respectively. The flocculated slurries exhibited a significantly higher water discharge volume than the untreated slurry, with the lime-floculated slurry demonstrating the largest water discharge volume at 24 h. However, by the end of the vacuum preloading test, the final water discharge volumes ranked from highest to lowest were dual use of PAC and APAM-floculated slurry with 15.2 kg, lime-floculated slurry with 13.4 kg, APAM-floculated slurry with 12.1 kg, and untreated slurry with 10.1 kg. The final water discharge volume of the lime-floculated slurry was notably lower than that of the dual use of PAC and APAM-floculated slurry. This is attributed to the high permeability and low compressibility of lime-floculated slurry [45]. The high permeability of lime-floculated slurry facilitates rapid initial dewatering, allowing pore water to be efficiently discharged under vacuum pressure in the early stage of the test. However, due to its low compressibility and the formation of a relatively stable soil structure, further volume reduction and dewatering become challenging in the later stages. Additionally, the dewatering completion times for the lime-floculated slurry, dual use of PAC and APAM-floculated slurry, and APAM-floculated slurry were 46 h, 60 h, and 97 h, respectively, all significantly shorter than that of the untreated slurry. This confirms that flocculation treatment enhances both dewatering efficiency and the consolidation rate in slurry.
# Text Indexing for Faster Gapped Pattern Matching
## 3 An Improved Solution
### Query Time Analysis  
We now analyze the run time of Algorithm 2. Our first step can be seen as a modification of the Kraft-McMillan inequality. **Lemma 6**. : _For a rooted binary tree \(\mathcal{T}=(V,E)\), let \(h(v)\) denote the height of node \(v\) in \(\mathcal{T}\). Then_  
\[\sum_{v\in V}2^{-h(v)}\leq 1+\log|V|.\]  
Proof. : We use induction on the tree height. The base case holds with a single node having height \(0\) since \(2^{0}=1\leq 1+\log 1=1\). For an arbitrary tree \(\mathcal{T}=(V,E)\) with \(|V|>1\) nodes, let the left subtree of the root be \(\mathcal{T}_{\mathcal{L}}=(V_{L},E_{L})\) with relative height function \(h_{L}\), and the right child of the root, \(\mathcal{T}_{R}=(V_{R},E_{R})\), with relative height function \(h_{R}\). Then,  
\[\sum_{v\in V}2^{-h(v)} =2^{0}+\sum_{v\in V_{L}}2^{-(h_{L}(v)+1)}+\sum_{v\in V_{R}}2^{-(h_ {R}(v)+1)}\] \[=1+\frac{1}{2}\sum_{v\in V_{L}}2^{-h_{L}(v)}+\frac{1}{2}\sum_{v \in V_{R}}2^{-h_{R}(v)}\] \[\leq 1+1+\frac{1}{2}\log|V_{L}|+\frac{1}{2}\log|V_{R}|\qquad \qquad\text{ (By Inductive Hypothesis)}\] \[=1+1+\log\sqrt{|V_{L}||V_{R}|}\] \[\leq 1+1+\log\frac{|V_{L}|+|V_{R}|}{2}\qquad\text{(Inequality of Arithmetic and Geometric Means)}\] \[=1+\log(|V_{L}|+|V_{R}|)\] \[\leq 1+\log(1+|V_{L}|+|V_{R}|)=1+\log|V|.\qed\]  
**Lemma 7**. : _Let \(s\) be the number of leaves for which we have to run the \"report_all_occurrences\" subroutine in Algorithm 2. Let \(\mathcal{V}\) be the set of nodes on which Search is executed in Algorithm 2. Then, \(|\mathcal{V}|=\mathcal{O}(1+s\log N)\)._  
Proof. : The height of the tree constructed by Algorithm 1 is \(\mathcal{O}(\log N)\). Each root-to-\(v\) path for all \(v\) where \"report_all_occurrences\" is called contributes at most \(\mathcal{O}(\log N)\) calls of Search. Thus, the contribution overall of these paths is \(\mathcal{O}(1+s\log N)\). What remains to be counted are nodes where Search is called and \"contains_occurrence\" reports there are no occurrences. For these, observe that each node on the root-to-\(v\) path for all \(v\) where \"report_all_occurrences\" is called has at most two children where this can be the case. Thus, including these nodes at most triples the total number of nodes on which Search is called. We take \(s\) and \(\mathcal{V}\) as defined in Lemma 7. First, we consider the time used by calls to \"contains_occurrence\". Because the size of a subarray for a node \(v\) at height \(h(v)\) is \(\mathcal{O}(N/2^{h(v)})\), by Lemma 1, a call to \"contains_occurrence\" for a node \(v\) at height \(h(v)\) requires time \(\tilde{\mathcal{O}}((N/2^{h(u)})^{\delta})\).
## 3 Experiment and Analysis
### Experimental Setups
#### 3.1.2 Dataset  
Two home-made datasets are provided to demonstrate the applicability of the proposed solder joint quality inspection system, including an ROI extraction dataset and a quality assessment dataset. They are collected from the production line of an astronautics electronics equipment manufacturing factory in Beijing, China. The ROI extraction dataset contains 60 training samples and 60 testing samples with a resolution of 1920 \(\times\) 1080 pixels. The quality assessment dataset consists of 150 training samples and 150 testing samples, whose raw resolutions are about 1500 \(\times\) 160 pixels. High-quality annotations of all samples are provided by the strategy described earlier, and the examples with annotations of two datasets are shown in Figure 6. Meanwhile, to avoid the overfitting problem for the models, the training samples in both datasets are enhanced by the five image enhancement methods described earlier. The luminance of the testing samples in the two datasets is also reduced and increased respectively to test the performance of the proposed method under different lighting conditions.
## 2 Materials and Methods
### Water Quality Assessment Based on Fuzzy Water Pollution Index Method
#### 2.6.2 Calculation of the FCI of Water Quality  
1. Fuzzy Relation Matrix \(R\)  
The fuzzy relation matrix represents the subject degree of each evaluation factor to each water quality grade. In this study, the subject degree matrix \(R\) is calculated by using the simplified half-trapezoidal distribution function and the linear triangle function [42]. The surface water is categorized into six grades (I, II, III, IV, V, worse than V), which is based on the \"Environmental quality standard for surface water\". The standard values for each grade are Q(1), Q(2), Q(3), Q(4), Q(5), Q(6). The membership pollution factor \(r_{ij}\) can be defined as follows in Equation (6):  
\[r_{ij}=\left\{\begin{array}{cc}1&X_{i}\leq Q(j)\\ \frac{Q(j+1)-X_{i}}{Q(j+1)-Q_{j}}&Q_{j}<X_{i}\leq Q(j+1)\\ 0&X_{i}>Q(j+1)\end{array}\right. \tag{6}\]  
where \(x_{i}\) represents the measured value of the \(i\) pollution factor; \(Q_{(j)}\) represents the \(j\) level quality standard of the \(i\) pollution factor; \(Q_{(j+1)}\) represents the \(j\)+1 level quality standard of the \(i\) pollution factor.  
2. Weight Coefficient  
The weight coefficient of pollution factors is used to measure the influence of pollution factors on environmental water quality. In this study, the weight value is determined by calculating the excess ratio. The higher the weight coefficient, the stronger the influence of the factor on water quality. The formula is as follows in Equation (7):  
\[a_{ij}=\frac{C_{i}\sqrt{S_{i}}}{\sum\limits_{i=1}^{n}C_{i}\sqrt{S_{i}}} \tag{7}\]  
where \(C_{i}\) is the measured value of the pollution factor \(i\); \(\overline{S_{i}}\) is the arithmetic mean value of environmental quality standards for pollution factors at all levels in \(i\), such that in Equation (8):  
\[\overline{S_{i}}=\frac{1}{m}\sum\limits_{j=1}^{m}S_{ij} \tag{8}\]  
3. Fuzzy Composite Index \(B^{*}\)  
In this study, we use the weighted average principle to calculate the Fuzzy composite index \(B^{*}\) in Equation (9):  
\[B^{*}=\frac{\sum\limits_{j=1}^{m}b_{j}^{k}\cdot j}{\sum\limits_{j=1}^{m}b_{j}^ {k}} \tag{9}\]  
where \(B^{*}\) is the relative position of the object in each quality grade; \(b_{j}\) is the membership of grade \(j\); and, \(k\) is an undetermined coefficient which is used to control the value of \(b_{j}\).
## 1 Introduction
In previous works, we studied the ASNase adsorption onto pristine [9] and oxidized MWCNTs with a specific diameter [22]. To fully explore the enzyme-CNT complex potential, it is essential to also find the optimal diameter for CNTs. [PERSON] et al. [23] found that adsorbed bovine serum albumin (BSA) stability is determined by the diameter and surface area of CNTs. [PERSON] et al. [24] also reported a selective protein (BSA, carbonic anhydrase, hexokinase) binding depending on the protein and MWCNTs diameter. In this work, the immobilization of commercial ASNase was studied for the first time onto pristine and f-MWCNTs with different diameter ranges (from \(<\)10 to 100 nm). The wide range of diameters tested, together with the functionalization of the MWCNTs surface, are key for the novelty of this work and represent a step forward in the study of enzyme's immobilization over nanomaterials. Two important MWCNTs characteristics were evaluated: the specific surface area and pH of the point of zero charge (\(pH_{PZC}\)), along with the determination of the ASNase isoelectric point (\(yl\)). The adsorption conditions such as contact time, medium pH and ASNase concentration were also improved to reach the maximum _RRA_ of immobilized enzyme and \(IY\). Finally, the ASNase-MWCNT complex was characterized in terms of kinetic properties and operational stability.
## 3 Leakage Abuse Attack
### Attacks on Prior Works
#### 3.3.3 Discussion  
As shown in the above attack scenarios, in addition to the information leakages defined in Type-II backward privacy, there exists extra information that an adversary may learn from the protocols and exploit for extracting a deletion history. A root cause for allowing the additional leakage is the static data structures and static values used for updating and searching a keyword. During the search protocol, for instance, SGX-SE1 [11] and Aura [14] access the same locations of data structures when searching on the same keyword. Thus, the adversary is able to specify whether a certain document is added or deleted when compared with the same information learned from the previous search query on the same keyword. Bunker-B [8], on the other hand, accesses different locations of data structure for every search query on the same keyword, due to the re-encryption of the entries after every search. However, the re-encryption of entries incurs high computational overhead, degrading the practicality of the scheme. It is thus a challenging problem to minimize extra information leakages while achieving high efficiency.