esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Proteomic Analysis
#### 2.7.2 Isoelectrofocalization (IEF)  
Isoelectric focusing was performed in Immobiline IPG strips (Immobiline(tm) DryStrip pH 4-7, 7 cm, Cytiva) on an IPGphor system (Cytiva) in triplicate for each sample. Strips were placed for 12 h in a rehydration solution with a final concentration of 8 M urea, 2 M thiourea, 4% CHAPS (\(w/v\)), 65 mM DTT, traces of bromophenol blue and 0.5% (\(v/v\)) of IPG Buffer (Cytiva). A total of 150 \(\upmu\)g of protein samples was mixed with DTT and IPG buffers (Cytiva) to final concentrations of 65 mM and 0.5% (\(v/v\)), respectively. The mixture was pipetted onto the strip holder. Isoelectric focusing (IEF) was conducted at 20 \({}^{\circ}\)C on the IPCphor unit, with a maximal intensity of 50 \(\mu\)A per strip, under the following steps: (1) Grad 300 V for 1 h, (2) 1000 V for 2 h, (3) 5000 V for 2 h (4) and Step 5000 V for 2 h.  
After focusing, the strips were soaked for 15 min in reduction solution (Tris pH 8.8, 2% SDS, 6 M urea, 30% glycerol and 125 mM DTT), followed by 15 min in an alkylation solution (Tris pH 8.8, 2% SDS, 6 M urea, 30% glycerol, 125 mM iodoacetamide and traces of bromophenol blue).
## 3 Results and Discussion  
### Bending and Torsional Stress Analysis  
Table 6 presents the analysis results of the bending and torsional stresses for each torsional beam configuration. The stresses vary depending on the shape of the reinforcement. Among the convex shapes, Type 1, with a curvature of +40 mm, showed higher stresses than those of the baseline configuration (Base). As the curvature increased, the stresses decreased, but there was no significant difference compared with the baseline configuration.  
In contrast, for the concave shapes with added reinforcements (Types 4, 5, and 6), the stresses were lower than those of the base configuration. The torsional stress did not show significant variations with curvature, whereas the bending stress decreased as the curvature decreased. A lower bending stress indicates that the torsional beam can withstand the applied load with less deformation and is considered more stable under a load. Similarly, a lower torsional stress indicates that the torsion beam is less affected by twisting forces. Therefore, among all the configurations, the concave shape with added reinforcements exhibited the most stable performance. Specifically, the Type 4 configuration with the lowest bending stress was identified as the most stable structure.
# Efficient High-Dimensional Quantum Key Distribution with Hybrid Encoding
## 5 Conclusions  
In this paper, we proposed a schematic configuration of \(d\)-dimensional QKD based on hybrid encoding over two different DoFs. Qudits are exploited in the setup to improve a secret key rate, since a qudit can carry more classical information and it has enhanced robustness against eavesdropping compared with a qubit. We investigated possible practical implementations of the proposed QKD protocol with current optical technologies. OAM modes of a single photon is exploited as a high-dimensional information carrier. OAM modes are suitable for quantum communication because of their resilience against perturbation effects. We showed that a cyclic transformation of OAM modes can be implemented within the reach of current technologies as well. We analyzed security of the proposed protocol and showed there is improvement compared with original qubit protocol in an ideal situation. We found the condition that \(d\)-QKD with hybrid encoding has a higher secret key rate than the original DDI-QKD in the consideration of realistic experimental parameters as well.  
Figure 6: The secret key rate of \(3d\)-QKD hybrid encoding (black solid line) and a prepare-and-measure \(3d\)-QKD (red dashed line). Dark count rate of single photon detectors is assumed as \(10^{-5}\) per pulse.  
Finally we compared our protocol with existing \(d\)-QKDs and showed our protocol has advantages regarding the prevention of side channel attacks against detectors and experimental feasibility.  
[PERSON] designed and analysed the protocols. [PERSON] and [PERSON] provided guidance. [PERSON] supervised the whole project. All authors reviewed the manuscript.  
This work was supported by the R&D Convergence Program of the National Research Council of Science and Technology (NST) (Grant No. CAP-15-08-KRISS) of Republic of Korea.  
[PERSON] thanks to the Agency for Defense Development (ADD) for their graduate student scholarship program. [PERSON] acknowledges the University of Oxford and the Korea Institute for Advanced Study (KIAS) for their visitorship program.  
The authors declare no conflict of interest.
# New Approach of Blind Adaptive Equalizer Based on Genetic Algorithms
## 3 Methods
### Adaptive Blind Equalization
#### 3.3.2 The Constant Modulus Algorithm  
The Constant Modulus Algorithm (CMA), proposed by the authors of [6], is one of the most well-known blind equalization schemes for QAM systems. The CMA cost funtion, \(J_{CMA}\), is expressed by  
\[J_{CMA}(\mathbf{w}(k))=E[e(k)^{2}] \tag{11}\]  
where \(E[\cdot]\) denotes statistical expectation, and  
\[e(k)=\frac{E\{|a_{k}|^{2p}\}}{E\{|a_{k}|^{p}\}}-|\overline{a}(k)|^{p} \tag{12}\]  
where \(a_{k}\) is a symbol of the alphabet of the modulation employed. Using the stochastic gradient of the cost function \(J_{CMA}\), the filter parameters are updated at each instant \(k\) using the stochastic gradient descent rule for the filter coefficients, which is given by  
\[\mathbf{w}(k)=\mathbf{w}(k-1)+\mu e(k)\mathbf{u}(k) \tag{13}\]  
where \(\mu\) is the adaptation step, and  
\[\mathbf{u}(k)=[u(k)\cdots u(k-l)\cdots u(K-N+1)]^{T}. \tag{14}\]  
CMA is extremely simple to implement and is able to characterize the level of ISI at the equalizer output regardless of the carrier phase. However, it is highly dependent of the adaptation step being small enough to be stable, and it can lead to a misconvergence due to the existence of local minimum points in the Godard criterion [6; 7].
## 3 Motivation
### Dependencies among the Probe Surfaces  
There are hierarchical dependencies among the three-layer probe surface. Individual ports or port combinations can be used to identify the device brand but not the model  
Figure 1: Examples of protocol responses.  
Figure 2: Examples of web features.  
or firmware version. The protocol-response layer must determine whether the port is open before probing the protocol response. Protocol responses can be used to identify the model and firmware version. The web-feature layer must determine whether the target device opens the HTTP/HTTPS protocol port (such as 80 and 443) and runs a web service. Then, web features such as special URLs, page content, and SSL certificates can be further gathered.  
Device identification is a dynamic process. We can improve the RL algorithm to update the identification status and obtain the optimal probe sequence. Furthermore, interactions between the agent and the environment enable evaluation of whether the information received is sufficient to complete the identification task and adjust the capture policy accordingly. In this way, data capture and device identification can be performed simultaneously, preventing the need to send additional probe requests.
## 3 Experiments and Results  
### Experimental Design  
The test was conducted at the Equine Research Technology Base of Inner Mongolia Agricultural University, Hohhot, Inner Mongolia Autonomous Region. The test subjects were 20 adult Mongolian horses, which were numbered. First, the horses to be tested were fixed in the enclosure, and their body parameters were measured manually using measuring sticks, tape measures, and other measuring tools, and each parameter was measured three times to allow us to determine the average value. Secondly, the breeder used a halter to pull the horse from right to left, walking it at a constant speed past the camera, to capture a complete side view of the horse; the corresponding shooting schematic as shown in Figure 9. The average length of each captured video of a horse walking is 10 s, so each captured video was set filmed such that an image would be intercepted every 150 frames to ensure the integrity of the Mongolian horse outline in the image, and the intercepted images were used as the test set for the experimental model. After a horse contour image is obtained from the test set through the convolutional neural network model input, the key points of its measurement were automatically extracted, and the scale parameters were extracted to obtain the real body parameter values, which were compared with the data obtained from manual measurement to verify the effectiveness of the test.
# Intelligent Edge-Cloud Framework for Water Quality Monitoring in Water Distribution System
## 5 Results and Discussion  
### Network Topology  
The proposed framework consists of three different layers: IoT in WDS, edge layer, and cloud layer as explained in the previous section. The network topology used to represent this framework is as follows. The case study (WDS) was divided into three different zones: Area-1 (B), Area-2 (C), and Area-3 (F) as shown in Figure 9. Each area contains a number of drinking water quality sensors, and these sensors were positioned in the area using optimisation method that was applied in the WDS [49]. A separate edge server is assigned for each area, and it is responsible for all water quality sensors located in its area. Water quality sensors need to establish a connection with edge servers, and edge servers establish communication with cloud servers. Another connection is also required, as cloud servers require establishing a connection with edge servers.  
ContikiOS with CooJa simulator was used to implement the communication of our proposed framework. Contiki OS is an open-source operating system for resource-constrained hardware devices with low power and less memory [50]. It was developed mainly for Internet of Things applications. It also has many features, such as the communication module encompassing uIP and 6 LowPAN [51]. uIP is, known as \"micro IP\", has been designed to integrate a minimum set of components needed for TCP / IP stacks [52]. 6 LowPAN is a growing technology that allows the use of IPv6 over IoT devices [53]. ContikiOS also has an extra function that supports the resource constraint hardware with the following features: small size, limited memory, lower power, low bandwidth, short-range, and communication using radio [54]. On the other hand, Cooja is a wireless sensor network emulator based on Java across layers, and it is distributed with Contiki OS. It simulates different levels from the physical to the application layer and enables the emulation of the hardware of a group of sensor nodes [55].
# Algorithms for Drug Sensitivity Prediction
## 4 Predictive Models
### Logistic Regression and Principal Component Regression-Based Techniques  
Logistic regression refers to a regression model where the response or dependent variable is categorical [63; 64]. The model estimates the probability of the categorical dependent variable based on the predictor variables using the logistic function: \(g(t)=\frac{1}{1+e^{-t}}\). To explain logistic regression, consider a binary outcome \(Y\), and we want to model \(Pr(Y=1|X=x)\). The logistic regression model based on logit transformation of the probability and subsequently equating it to a linear combination of predictors will consist of:  
\[\ln\frac{p(x)}{1-p(x)}=\beta_{0}+x\beta \tag{8}\]  
where \(p(x)\) can be computed as:  
\[p(x)=\frac{1}{1+e^{-(\beta_{0}+x\beta)}} \tag{9}\]Logistic regression has been used as one of the comparison methods for drug sensitivity prediction in multiple studies [65; 66; 25; 67].  
Principal Component Regression (PCR), as the name suggests, is based on principal component analysis. The principal components \(\mathbf{Z}\) of the dependent matrix \(\mathbf{X}\) are calculated, and the regression of \(\mathbf{Y}\) on \(\mathbf{Z}\) is fitted using ordinary least squares. The regression coefficients in the transformed space are mapped back to the original space to arrive at the principal component regression coefficients. The primary difference with ordinary least squares is that the regression coefficients are computed based on the principal components of the data. When a subset of the principal components is used for generating the regression coefficients, it acts similar to a regularization process. Principal component regression can also overcome the problem of multicollinearity in ordinary least squares [60].  
Principal component regression has been used as one of the comparison methods for drug sensitivity prediction in multiple studies, as well [52; 57; 25].  
Partial Least Squares (PLS) regression [68] has similarities to principal component regression, but projects both \(\mathbf{X}\) and \(\mathbf{Y}\) to new spaces before generating the regression model. The intent is that the model might have few underlying factors that are important, and using these latent factors in prediction can avoid overfitting [69]. Similar to PCR, PLS is often used as a comparison method in multiple drug sensitivity prediction studies [52; 25; 39].  
PCR and PLS utilize derived input directions and select a subset of the derived directions for modeling. PCR keeps the high variance directions and discards the low variance ones, whereas PLS also tends to shrink the low variance directions, but can inflate the high variance regions [70]. Both PCR and PLS have similarities with ridge regression that shrink in all directions and more so in low variance directions. In terms of predictive performance, ridge regression is usually preferable over PLS and PCR [71].
## 4 Experimental Verification
### Test Results and Analyses
#### 4.2.3. Creep Curve of Two-Layered CGS  
Figure 12 shows the tested creep strains (\(\varepsilon\)-\(t\)) of the two-layered CGS (L1 and L2, L2 and L3, L1 and L3) as well as the predicted \(\varepsilon\)-\(t\) curves with different interface-layer heights, (\(h\) = 0\(\sim\)0.5 \(H\), \(H\) is the designed height of each layered CGS, \(H\) = 300 mm for the two-layered CGS, the total specimen height is 600 mm). They all have the same stages as those of the one-layered CGS: instantaneous deformation, attenuation deformation, and stable creep deformation. In addition, they all decrease as the interface-layer height (\(h\)) increases. That is because the interface-layer has a smaller viscoelastic parameter than the average values of the two one-layered CGS, e.g., \(I_{\rm L1/L2}<(I_{\rm L1}+I_{\rm L2})/2\), the total creep strain (\(\varepsilon\)) is decreased with increase of \(h\) according to the following formula (Equation (21)), when \(H\) is given. The higher the interface-layer, the weaker the rheological behavior of the two-layered CGS.  
\[\begin{array}{ll}\varepsilon&=\varepsilon_{\rm I1}+\varepsilon_{\rm I1/2}+ \varepsilon_{\rm I2}\\ &=\frac{u_{\rm I1}+u_{\rm I1/2}+u_{\rm I2}}{2H}\\ &=(\sigma_{\rm 1}-\sigma_{\rm 3})\frac{\int_{\rm I1}(H-h/2)+\int_{\rm I1/2}(t)h+ \int_{\rm I2}(H-h/2)}{2H}\\ &=\frac{(\sigma_{\rm 1}-\sigma_{\rm 3})}{2H}\Big{[}(I_{\rm I1}+I_{\rm 2})H- \Big{(}\frac{\int_{\rm I1}+I_{\rm 2}}{2}-J_{\rm 11/2}\Big{)}h\Big{]}\end{array} \tag{21}\]  
It is also found that most of the tested creep strain are between the predicted \(\varepsilon\)-\(t\) curves of 20%\(H\) and 30% \(H\), which can prove the existence of the interface-layer and validity of the interface-layer calculation model. The suitable interface-layer height is \(h=(20  
Figure 12: Tested and predicted creep curves of the two-layered CGS with different interface-layer heights. (**a**) L1/L2, (**b**) L2/L3, (**c**) L1/L3.
###### Abstract  
In response to issues such as low resolution, severe occlusion, and insufficient fine-grained feature extraction in tea plantation disease detection, this study proposes an improved YOLOv10 network based on low-altitude unmanned aerial vehicle remote sensing for the detection of diseases in Yunnan large-leaf tea trees. Through the use of a Shape-IoU optimized loss function, a Wavelet Transform Convolution to enhance the network's Backbone, and a Histogram Transformer to optimize the network's Neck, the detection accuracy and localization precision of disease targets were significantly improved. Through testing of common diseases, the research results indicate that, for the improved YOLOv10 network, the Box Loss, Cls Loss, and DFL Loss were reduced by 15.94%, 13.16%, and 8.82%, respectively, in the One-to-Many Head, and by 14.58%, 17.72%, and 8.89%, respectively, in the One-to-One Head. Compared to the original YOLOv10 network, precision, recall, and F1 increased by 3.4%, 10.05%, and 6.75%, respectively. The improved YOLOv10 network not only effectively addresses phenomena such as blurry images, complex backgrounds, strong illumination, and occlusion in disease detection, but also demonstrates high levels of precision and recall, thereby providing robust technological support for precision agriculture and decision-making, and to a certain extent promoting the development of agricultural modernization.  
Keywords:improved YOLOv10 network; low-altitude unmanned aerial vehicle remote sensing; Shape-IoU; wavelet transform convolution; histogram transformer +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Procedures
#### 2.3.3. sEMG Measurements  
EMG signals for the LG and MG were recorded bilaterally during HREs and overground sprints. The skin was shaved and cleaned with alcohol to reduce impedance and ensure optimal signal quality. Electrodes were positioned over the belly of the MG and LG of both legs, placed parallel to the muscle fibers following SENIAM guidelines [43]. Pre-gelled silver chloride bipolar electrodes (KendallITM Medi-Trace, Covidien, Barcelona, Spain) were used with a 10 mm interelectrode distance with the reference electrode placed on the lateral malleolus of the fibula. Two synchronized two-channel units (Shimmer, 16-bit analog-to-digital) recorded EMG at 1024 Hz--one per leg (Realtime Technologies Ltd., Dublin, Ireland). Sensors were positioned on the mid-medial tibia, aligned with the electrodes. A compressive tubular bandage was placed over the lower leg and the ankle to prevent electrode and sensor displacement.  
EMG signals were acquired via validated mDurance software (mDurance Solutions S.L., version 2.4.2, Granada, Spain) [45]. Raw data were stored for subsequent evaluation and digitally filtered using a fourth-order \"Butterworth\" bandpass filter (20 and 450 Hz) to attenuate movement artifacts [46]. Signals of <0.05 V or >2 V were flagged as dropouts or spikes. After normalization, values of <5% or >100% were also flagged [38]. All flagged signals were visually inspected and retained if artifact-free. In addition, full-wave rectification was applied to preserve amplitude characteristics, and signal smoothing was performed using a moving average (5-10 Hz) to extract the linear envelope, following validated procedures from previous studies using the mDurance system.  
After completing the HREs, participants performed two 40 m sprints from a standing start at maximal effort, with a 5 min rest between attempts. Peak EMG values for each muscle were extracted from both sprints and averaged. These served as reference values to normalize EMG activity from the HREs, calculated as the mean of the three middle repetitions. Consequently, all EMG values are expressed as a percentage of sprint-based maximal activation [10]. This dynamic normalization followed standardized procedures [38].
## 2 Sequential Modelling Strategy
### Net Dynamics
#### 2.2.3 Equations of Motion for Net Systems  
The equations of motion (EoMs) which govern each net lumped point mass, \(m_{i}\), can be obtained using [PERSON]'s second law in the inertial reference frame of the chaser:  
Figure 2: Representation of the coordinate system with origin \(O,y\) direction representing the direction of flight, the radial coordinate as \(x\) direction and the \(z\) direction completes the frame.  
\[m_{i}\mathbf{\dot{r}}_{i}=\sum_{j=1}^{N_{t}}\mathbf{T}_{ij}+\sum_{p=1}^{p}\mathbf{F}_{ext,p}, \tag{1}\]  
where \(\mathbf{T}_{ij}\) is the tension force on the \(i^{th}\) mass due to the node \(j\) computed using the Kelvin-Voigt model [22], \(N_{t}\) is the number of adjacent cables connected to the \(i^{th}\) mass and \(\mathbf{F}_{ext,p}\) represents any external force contribution and disturbances present in the space environment, such as solar radiation, aerodynamic drag and contact forces.  
In order to integrate the aforementioned EoMs, the state \(\mathbf{s}=[\mathbf{x}^{T},\ \mathbf{v}^{T}]^{T}\) is defined, where \(\mathbf{x}\) and \(\mathbf{v}\) are the stacked net nodes' position (\(\mathbf{r}_{i}\)) and velocity vectors (\(\mathbf{\dot{r}}_{i}\)). The state derivative \(\mathbf{\dot{s}}\) can, hence, be obtained as [10]:  
\[\mathbf{\dot{s}}=\begin{pmatrix}\mathbf{v}\\ \mathbf{M}^{-1}\mathbf{F}_{sys}\end{pmatrix} \tag{2}\]  
where \(\mathbf{M}\) is the diagonal mass matrix of all net and bullet node masses, and \(\mathbf{F}_{sys}=\mathbf{T}_{sys}+\mathbf{F}_{ext}\) is the stacked, internal (\(\mathbf{T}_{sys}\)) and external (\(\mathbf{F}_{ext}\)) forces exerted on the net. These are stacked as follows:  
\[\mathbf{T}_{sys}=\left(\sum_{j=1}^{N_{t}}\mathbf{T}_{0j}^{T}\ \dots\ \sum_{j=1}^{N_{t}}\mathbf{T}_{Nj}^{T}\right)^{T}, \tag{3}\]  
\[\mathbf{F}_{ext}=\left(\sum_{p=1}^{p}\mathbf{F}_{ext,0p}^{T}\ \dots\ \sum_{p=1}^{p}\mathbf{F}_{ext,N_{p}}^{T}\right)^{T}. \tag{4}\]
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 1 Introduction
### Security Challenges of Smart Cards  
Designing efficient security solutions for smart card-based systems has been an incessant challenge for professionals and the researchers alike, due to the following limitations:  
* Low Computational Capability--The smart cards have a low computational capacity of 1-5 MHz clock rate, less than 1 MB flash memory, and a few hundreds of KB RAM [5]. Owing to these restricted computational resources, it is challenging to develop computationally efficient security solutions that satisfy the required security attributes as well as security functions.
* Less Power--Certain smart cards operate on power, which is limited. The security operations must be carefully selected in a way that avoids heavy computational operations and tasks.  
Figure 1: Architecture of a smart card. CPU: central processing unit, ROM: read-only memory, RAM: random access memory, EEPROM: electrically erasable programmable read-only memory.  
* Unreliable Communication--The messages and data sent by the smart cards and received by them passes through an unreliable wireless medium, and therefore, it is more susceptible to different attacks. Strong security countermeasures must be implemented to defeat these attacks.  
These constraints of smart cards pose two security challenges. The first challenge is to defeat the attacks attempted on smart cards. The second one is to implement the efficient cryptographic solutions that can provide necessary security properties and can resist the attacks on smart cards. [PERSON] and [PERSON] wrote a review of the security of smart card-based systems and categorized the attacks on smart cards as side-channel attacks, logical attacks, physical attacks, and other attacks. [PERSON] et al. [4] pointed out that impersonation attacks, session attacks, password-guessing attacks, and replay attacks can be launched on smart cards. [PERSON] et al. [6] have explored that besides other attacks, power analysis attacks are also a threat to smart cards. The taxonomy of attacks attempted on smart cards has been summarized in Figure 2. Physical attacks attempted on smart cards can be refuted using techniques such as MAC (message authentication code), cryptographic fingerprinting, polarization of the antenna, spread spectrum technologies, and measures of physical security. A resilient and efficient implementation of security schemes and protocols is needed to defeat information security attacks on smart cards.