esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methodology
### Multi-Preference Search Mode Based on Evolutionary Demand
#### 3.2.2 Search Mode 2: Co-Evolutionary Search Model  
In Mode 2, the main population and the auxiliary population are respectively used to explore the satisfaction demands of constraint conditions and the optimization demands of objective values. After the main population P and the auxiliary population A independently generate offspring solutions, the main population selects the solution with a lower CV value to approach the constrained Pareto front, and the auxiliary population selects the solution with a better objective value to approach the unconstrained Pareto front. In the population update operation, the main population P is first combined with the auxiliary population A. For updating P, feasible solutions are prioritized; if insufficient feasible solutions exist, the solution with the smallest CV value is selected. For updating A, non-dominated sorting and crowded distance are used to select individuals with better diversity and convergence to help the population cross the feasible region and improve diversity.
## 4 ADMM Formulation and Solution
### Penalty Parameter Selection
One standard method is to relate the values of the residual norms with the tolerances by using the concept of'residual balancing' as given by Equation (36). \[\rho_{k+1}=\left\{ \begin{array}{lcl}\eta\rho_{k}&\text{if}&d_{pr1}^{m+1}>\mu \epsilon_{1}^{pri}\\ \rho_{k}/\eta&\text{if}&e_{1}^{pri}>\mu d_{pr1}^{m+1}\\ \rho_{k}&\text{otherwise}&\end{array} \right. \tag{36}\]  
where \(\rho_{k}\) is the penalty parameter, and \(\mu>1\) and \(\eta>1\) are constants, \(d_{pr1}^{m+1}\) is the primary residual, and \(\epsilon_{1}^{pri}\) is the tolerance.
# PUF and Chaotic Map-Based Authentication Protocol for Underwater Acoustic Networks
## 5 Informal Security Analysis
### Perfect Forward Secrecy  
This secrecy means that the disclosure of a long-term master key will not lead to past session key disclosure. In the proposed scheme, if the \(GWN_{k}\)'s long-term private key, \(S_{k}\), is leaked to the attacker, it does not help the adversary to reveal the past session keys. The session key is computed as \(SK=h\big{(}T_{ru}(M_{9})\parallel K_{l}\parallel M_{9}\parallel Gid_{j} \parallel Sid_{j}\parallel h(id_{i}\parallel r)\big{)}=h\big{(}T_{rs}(K_{l}) \parallel K_{l}\parallel M_{9}\parallel Gid_{j}\parallel Sid_{j} \parallel h(id_{i}\parallel r)\big{)}\). The parameters \(ru\) and \(rs\) are generated randomly and uniquely for every session. Meanwhile, it is computationally infeasible to compute \(T_{rs}(T_{ru}(x))\) according to \(T_{rs}(x)\) and \(T_{ru}(x)\) due to the hardness of CMDHP. Therefore, our scheme can achieve perfect forward secrecy.
## 2 Foundational Concepts
### Privacy-Preserving Techniques
Stronger privacy guarantees (lower \(\epsilon\)) often correlate with a decrease in model utility or accuracy. The exact curve depends heavily on the dataset, model, and specific DP mechanism. Secure Multi-Party Computation (SMPC): SMPC enables multiple parties to jointly compute a function, such as the sum of updates, using cryptographic protocols like secret sharing, without revealing their private inputs [54]. The primary advantage of SMPC lies in its strong privacy guarantees achieved by distributing trust among participants, including potentially the server and clients, with no impact on model accuracy [55]. However, SMPC protocols often require complex multi-round interactions, leading to significant communication overhead. Furthermore, assumptions of synchronous participation or the need for fault tolerance mechanisms add complexity, posing challenges for deployment in dynamic IoT environments [3]. Secure Aggregation: Secure Aggregation utilizes specialized protocols, often based on secret sharing or lightweight cryptography, optimized specifically for the FL aggregation task [56]. These protocols allow the server to securely compute only the sum or average of client updates [57]. Compared to general HE or SMPC, Secure Aggregation is significantly more efficient computationally and communication-wise for this specific task, leading to its widespread adoption in practical FL systems. Nevertheless, while it protects individual updates from the server during the aggregation phase, it does not shield the final aggregated result from potential inference attacks, nor does it secure the updates during transmission unless combined with additional encryption methods. Table 3 provides a comparative summary of these key privacy-preserving techniques, highlighting their mechanisms, pros, and cons within the FL context. The practical choice often involves secure aggregation, potentially combined with DP for stronger client-level guarantees, or relies on trust in the server, depending heavily on the threat model, system capabilities, and regulatory environment (e.g., GDPR, HIPAA constraints on data processing and transfer) [3; 58].
## 4 Discussion
### Intraoperative DUS Assessment for Definition of Technical Success  
In the majority of the studies reported in the literature, technical success is defined by angiography as the completion of the endovascular procedure with \(<\)50% residual stenosis of the treated lesion by visual estimate and the absence of flow-limiting dissection [2,3]. The accurate definition of technical success has relevant consequences since the degree of residual stenosis and the presence of long and/or flow-limiting dissections at the index procedure may affect the long-term patency of femoro-popilteal artery angioplasty [8,9,14]. However, this type of evaluation has two main issues: first, the angiographic bi-dimensional evaluation of a complex tri-dimensional environment such as a dissection or an eccentric stenosis can be challenging; second, angiography does not provide any functional evaluation of the PTA, which may help the healthcare professional to discriminate more effectively between the success and failure of PTA, especially in ambiguous cases of stenosis with a lumen reduction very close to 50% or in the case of a long dissection. Indeed, the functional relevance of stenosis can be indirectly supposed with angiography by means of an observation of a slow flow downstream of a dissection; furthermore, it is supposed that stenosis \(>\) 50% is producing a trans-stenoic gradient according to the physical laws of fluid hemodynamics. However, the slow flow itself might also have different causes such as the distal embolization of debris. Another important issue is that the current classifications of dissections have many caveats, and even the recent DISFORM (diameter reduction, spiral shape, flow impairment, or adverse morphology) classification system for peripheral vessel dissections, using the Delphi consensus methodology, has the inherent limitations of being based on experts' suggestions and not being directly supported by any clinical or functional data [9,13].  
We found that one-third (5/14) of the grade C dissections had functional relevance at the DUS assessment despite no clear signs of flow impairment at angiography. Similarly, we demonstrated that one-third of residual intermediate stenoses \(<\)50% had functional relevance at the DUS assessment and should therefore be considered as a sub-optimal result in a multi-imaging evaluation perspective. In conclusion, in the present cohort of patients, characterized by long and calcific femoro-popilteal lesions, one-third of angiography-based successes should be reclassified as failures after an intraoperative DUS assessment.
## 2 Model, Theoretical Setup and Linear Limit  
Motivated by the partially \(\mathcal{PT}\)-symmetric setting of [19], we consider the complex potential \(U(x,y)=V(x,y)+iW(x,y)\) where  
\[V = \left(ae^{-(y-y_{0})^{2}}+be^{-(y+y_{0})^{2}}\right)\left(e^{-(x-x _{0})^{2}}+e^{-(x+x_{0})^{2}}\right)\] \[W = \beta\left(ce^{-(y-y_{0})^{2}}+de^{-  
In [19], it is shown that the spectrum of the potential \(U\) can be all real as long as \(|\beta|\) is below a threshold value, after which a (\(\mathcal{PT}\)-) phase transition occurs; this is a standard property of \(\mathcal{PT}\)-symmetric potentials. For the case of \(a=3,b=c=2,d=1\), the spectrum is real below the threshold value of \(|\beta|\approx 0.214\); we focus on \(\beta=0.1\), i.e., we operate well below this critical point. Figure 1 shows plots of the potential \(U\). The real part of the potential is shown on the left, while the imaginary part associated with gain-loss is on the right; the gain part of the potential corresponds to \(W<0\) and occurs for \(x<0\), while the loss part with \(W>0\) occurs for \(x>0\). Figure 2 shows the spectrum of \(U\), i.e., eigenvalues for the underlying linear Schrodinger problem \((\
abla^{2}+U)\psi_{0}=\mu_{0}\psi_{0}\). The figure also shows the magnitude of the corresponding eigenvectors for the three discrete real eigenvalues \(\mu_{0}\). It is from these modes that we will seek bifurcations of nonlinear solutions in what follows. It is worthwhile to mention here, in comparison, e.g., with the real four-well potential of [23] that the latter possessed four localized modes, with the fourth antisymmetric, quadrupolar one being absent from the point spectrum in the case of interest herein.
## 2 Methods
### Adaptive Collocation  
On the other hand, in the TLSC algorithm, the coordination between the variance-covariance of the signal and the observation may not be unbalanced. To solve this problem, an adaptive factor can be introduced to adjust contribution of the observation error and signal in the process of solving model parameters. The function model of the adaptive collocation algorithm (ALSC) based on Helmert VCEM is [PERSON] (1975); [PERSON] (1980):  
\[V^{T}P_{e}V+\alpha S^{T}P_{s}S=min \tag{15}\]  
where \(\alpha\) is the adaptive factor; \(V\) is the correction value of the observation; \(S\) is the correction value of the signal; \(P_{e}\) and \(P_{s}\) are weight matrices of the observation and signal vectors, respectively. Equation (15) takes the signal as the virtual observation value and combines the observations to form two kinds of observation values [PERSON] (1980):  
\[V=AX+BS-L\]  
\[V_{s}=S-0\]  
The normal equation can be obtained [PERSON] (1980)  
\[\left[\begin{array}{cc}S_{11}&S_{12}\\ S_{12}&S_{22}\end{array}\right]\left[\begin{array}{cc}\sigma_{e}{}^{2}\\ \sigma_{s}{}^{2}\end{array}\right]=\left[\begin{array}{cc}V^{T}P_{e}V\\ S^{T}P_{s}S\end{array}\right] \tag{17}\]  
where \(S_{ii}=n_{i}-2\textit{tr}\Bigl{(}N^{-1}N_{i}\Bigr{)}+\textit{tr}\Bigl{(}N^{- 1}N_{i}N^{-1}N_{i}\Bigr{)}\), \(n_{1}\) is the number of observations; \(n_{2}\) is the number of signals; \(N=\left[\begin{array}{cc}A^{T}P_{e}A&A^{T}P_{e}B\\ B^{T}P_{e}A&B^{T}P_{e}B+P_{s}\end{array}\right]\), \(N_{1}=\left[\begin{array}{cc}A^{T}P_{e}A&A^{T}P_{e}B\\ B^{T}P_{e}A&B^{T}P_{e}B\end{array}\right]\), \(N_{2}=\left[\begin{array}{cc}0&0\\ 0&P_{s}\end{array}\right]\); \(\sigma_{s}{}^{2}\) represents the variance of the signal, \(\sigma_{e}{}^{2}\) represents the variance of the noise.  
Then \(\sigma_{e}{}^{2}\) and \(\sigma_{s}{}^{2}\) can be solved based on the Equation (17), and the adaptive factor can be constructed:  
\[\alpha=\sigma_{e}{}^{2}/\sigma_{s}{}^{2} \tag{18}\]  
Using the adaptive factor to calculate the iterative adjustment makes the signal weight matrix increase or decrease, which ensures coordination between the observation and the signal weight matrix:  
\[P_{s}=\alpha P_{s} \tag{19}\]Combined with the above adjusted observed weight matrix, the posterior Euler parameters and posterior signal correction values can be recalculated, as follows:  
\[\hat{\Omega}=\left(A^{T}\overline{C}^{-1}A\right)^{-1}A^{T}\overline{C}^{-1}V_{o} \tag{20}\]  
\[\hat{V}_{s}=C_{uo}\overline{C}^{-1}\Big{(}V_{o}-A\hat{\Omega}\Big{)} \tag{21}\]  
where \(\overline{C}=\left(C_{uo}+C_{m}\right)\); \(\hat{\Omega}\) is the posterior Euler parameter; \(\hat{V}_{s}\) is the posterior signal; \(C_{uo}\) is the covariance matrix of observation noise; \(C_{m}\) is the covariance matrix of signal.
# Pointer Meter Reading Recognition by Joint Detection and Segmentation
###### Abstract  
To handle the task of pointer meter reading recognition, in this paper, we propose a deep network model that can accurately detect the pointer meter dial and segment the pointer as well as the reference points from the located meter dial. Specifically, our proposed model is composed of three stages: meter dial location, reference point segmentation, and dial number reading recognition. In the first stage, we translate the task of meter dial location into a regression task, which aims to separate bounding boxes by an object detection network. This results in the accurate and fast detection of meter dials. In the second stage, the dial region image determined by the bounding box is further processed by using a deep semantic segmentation network. After that, the segmented output is used to calculate the relative position between the pointer and reference points in the third stage, which results in the final output of reading recognition. Some experiments were conducted on our collected dataset, and the experimental results show the effectiveness of our method, with a lower computational burden compared to some existing works.  
pointer meter reading; object detection; deep convolutional network; semantic segmentation Article
## 9 Electrodiagnostic Studies  
Nerve conduction tests and electromyography (EMG) monitor progress and serve as additional tools to supplement clinical findings and imaging [5; 27]. These tests mostly use muscles innervated by C5 and C6 nerves and can inform the clinician about the site,  
Figure 4: (**A**) Illustration of cord to canal area measurement, the ratio of area within the inner circle (orange, [_cord_]) to area within the outer circle (area of pink and orange combined, [_canal_]). (**B**) Cord compression ratio = a/b, the ratio of sagittal [a] to axial [b] cord diameter.  
Figure 3: (**A**) Top–Pavlov ratio, the ratio of canal diameter (distance from the midpoint of the posterior aspect of the vertebral body to the nearest point on the corresponding spino-laminar line,designated as ‘a’) to vertebral body diameter (anteorposterior width of the vertebral body,designated as ‘b’) on a sagittal cervical plain x-ray (TPR = a/b). (**B**) Mean sub-axial cervical space available for the cord is the mean of the values for cord diameter deducted from disk level canal diameter at each level between C3 and C6.  
severity, and regeneration of nerves after injury. Results are based on signals generated from normal and involved muscles or nerves [5,27]. For instance, limb muscle involvement can imply plexus or root injury, while paraspinal muscle involvement implies root injury due to the anatomical orientation of the posterior rami nerves supplying the paraspinal muscles [27]. Sensory amplitudes can also be used to localize injury sites. A normal peripheral sensation signal conduction velocity and amplitude is seen in preganglionic injury. However, normal amplitude does not rule out post-ganglionic injury [27]. The severity of injury also correlates with the presence and magnitude of abnormal spontaneous electrical activities that manifest as positive sharp waves and fibrillation. The absence of positive sharp waves and fibrillations, however, does not rule out nerve root injury or rule in brachial plexus injury [27]. Abnormal electrical activity takes 2 weeks to develop and 3-5 weeks to maximize [27]. Complete nerve transection is known for the absence of muscle unit recruitment and has the worst prognosis. On the other hand, discrete motor unit recruitment that occurs despite an attempt to maximally contract muscle indicates a conduction block, which can result from severe stretch injury (neurapraxia) or axonal degeneration. However, after the first few weeks, when neurapraxia is expected to subside, it is a sign of axonal degeneration [27]. Long polyphasic potentials and nascent potentials (axonal regeneration potentials, ARPs) develop over time and are seen as signs of reinnervation and regeneration of axons, respectively [27]. Terminal collateral sprouts polyphasic potentials that identify chronic and recurrent injury [27]. For these various reasons, even though it is possible to use EMG as early as 7 days post-injury, its specificity increases after 4-6 weeks and remains most effective during the follow-up period rather than the acute phase [5, 6, 7, 27].
# FTIR Characterization of Asphalt SARA Fractions in Response to Rubber Modification
## 2 Materials and Methods
### Methods
#### 2.2.4. FTIR Analysis  
SARA fractions of neat binder and A-RBs were assessed chemically using the Thermo Scientific Nicolet 8700 FTIR-transmission method (Thermo Fisher Scientific, Waltham, MA, USA). Each fraction was diluted into toluene with a concentration of 50 mg of asphalt fraction per mL of toluene [33,36,37,38]. Then, the diluted samples were placed on a potassium bromide disc and kept in air for 15 min before testing to ensure complete toluene evaporation [33]. The test was conducted using 32 scans, four resolutions, and wavenumbers from 4000 to 600 cm\({}^{-1}\). The test was performed using two duplicates, and the average results were assessed. FTIR spectra peak assignments were made according to well-established literature values for functional groups [17]. Baseline correction and Gaussian peak fitting were performed using OMNIC software version 9.13.1224 (Thermo Fisher Scientific, Waltham, MA, USA) to separate overlapping bands and enhance the accuracy of the identification of functional groups. FTIR quantitative analysis aimed to calculate the changes in carbonyl (\(l_{CO}\)), sulfoxide (\(l_{SO}\)), aromatic (\(l_{CC}\)), and aliphatic (\(l_{CH}\)) indices using Equations (2), (3), (4), and (5), respectively [16,17,19,39].  
Equations (2) and (3) represent the \(l_{CO}\) and \(l_{SO}\), which are as follows:  
\[l_{CO}=\frac{\text{area around 1700 cm}^{-1}}{\sum\text{the area around 1460 and 1376 cm}^{-1}} \tag{2}\]  
\[l_{SO}=\frac{\text{area around 1030 cm}^{-1}}{\sum\text{the area around 1460 and 1376 cm}^{-1}} \tag{3}\]  
The \(l_{CC}\) and \(l_{CH}\) are denoted by Equations (4) and (5), respectively, as follows:  
\[l_{CC}=\frac{\text{area around 1600 cm}^{-1}}{\sum\text{area around 1700, 1600, 1460, 1376, and 1030 cm}^{-1}} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Rubber** & **Mixing** & **Mixing** & **Mixing Time** & **Code** \\
**Percentage \({}^{\text{a}}\) (\%)** & **Temperature (\({}^{\text{c}}\)C)** & **Speed (min\({}^{-1}\))** & **Mixing Time** & **Code** \\ \hline \multirow{3}{*}{10} & \multirow{3}{*}{190} & 600 & \multirow{3}{*}{8} & A-RB 190–600 \\ \cline{3-3} \cline{5-5}  & & 3000 & & A-RB 190–3000 \\ \cline{1-1} \cline{3-3} \cline{5-5}  & 220 & 3000 & & A-RB 220–3000 \\ \hline \end{tabular} \({}^{\text{a}}\) The percentage was calculated based on the weight of the neat binder.  
\end{table}
Table 1: Interaction matrix.  
\[I_{CH}=\frac{\sum\text{the area around 1460 and 1376 cm}^{-1}}{\sum\text{area around 1700, 1600, 1460, 1376, and 1030 cm}^{-1}} \tag{5}\]
## 6 Simulation and Analysis
### Simulation Parameters  
The simulation parameters of this paper are shown in Table 1. The variables \(a_{i}\) and \(f_{i,0}\) follow random distribution within the intervals of [0.5, 5] Mbits and [1, 2] Gcycles/s, respectively. \(K\) is the Rice factor. The main references for simulation parameter settings are [16, 24, 25, 27, 30, 31]. To validate our scheme, GWOORAC was compared with other baseline schemes described below. The first scheme uses the GWO algorithm to determine the offloading decision, allocates the resources randomly, and considers inter-satellite cooperation (GWORandRAC). The second scheme obtains the offloading decision randomly, optimizes the resource allocation, and considers inter-satellite cooperation (RandORAC). The third scheme uses the GWO algorithm to make the offloading decision, optimizes the resource allocation, and takes no account of inter-satellite cooperation (GWOORANC). The fourth scheme only has local computing, and the rest is the same as our scheme (OLC). The fifth scheme uses the particle swarm optimization (PSO) algorithm to determine the offloading decision, optimizes the resource allocation, and considers inter-satellite cooperation (PSOORAC). The number of particles is 20. The maximum number of iterations is 200. The inertia weight is 0.8 and both learning factors are 1.5. All simulation results were obtained by averaging 300 times simulations.
# Low-Cost, Compact, and Rapid Bio-Impedance Spectrometer with Real-Time Bode and Nyquist Plots
## 2 Materials and Methods
### Software  
The software was developed in Delphi, and is used for two purposes. First is for controlling the hardware system, and second is for data analysis and display.  
Software for controlling the hardware system is responsible to:  
* Determine the value of the current used (1 \(\mu\)A, 10 \(\mu\)A, 100 \(\mu\)A, or 1 mA). This is related to the selection of the \(\mathbf{R_{\rm S}}\) value that stated in Equation (7).
* Determine low frequency and high frequency limits, and the number of data samples. This is related to the DDS-AD9850 voltage generator programming.
* Determine the voltage gain value of the reference signal and sample signal. This is related to the selection of the \(\mathbf{R_{\rm G}}\) value to produce \(\mathbf{G_{\rm I}}\) and \(\mathbf{G_{\rm Z}}\) that stated in Equation (10).  
Whereas, software for data analysis and display is responsible to:  
* Calculation of values of the current, voltage, phase angle, and impedance (using Equations (15) and (16))
* Calculation of real and imaginary parts of impedance (using Equation (17))
* Plot the impedance value in the Bode (magnitude and phase as a function of frequency)
* Plot the impedance value in Nyquist (real part vs. imaginary parts)