esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Neural Network Design and Implementation  
Our study designed and implemented ANNs to investigate the predictive power of nonlinear gait measures (LyE\({}_{\text{R}}\), DFA, ApEn, CD, SpEn, LyE\({}_{\text{W}}\)) for metabolic cost. The process commenced by loading the nonlinear gait data and setting up the neural network parameters and architecture. The networks were trained using the Levenberg-Marquardt backpropagation algorithm [1], renowned for its efficiency in solving nonlinear problems [2]. We chose ANNs over other machine learning models due to their effectiveness in capturing the complex, nonlinear relationships inherent in gait dynamics, which are key to our analysis [14; 15; 37]. Moreover, studies in biomechanics demonstrate their utility with small datasets [10; 14; 15]. Additionally, ANNs are well established in gait and exoskeleton research, offering reliable performance in predicting movement-related outcomes, further supporting our choice for this specific application [10; 14; 15]. The data division for training, validation, and testing was set at 70%, 15%, and 15% of the dataset to ensure a balanced approach to learning and generalization. A subject-wise splitting strategy was applied, ensuring all trials from a single subject were included exclusively in the training, validation, or testing set. This prevented data leakage and ensured the model was tested on entirely new subjects, enhancing generalizability. The network's performance was closely monitored throughout the training process, focusing on the error distribution, response correlation, and predictive accuracy across the training, validation, and testing phases. This involved analyzing prediction errors and their correlation with input variables, offering insights into the network's behavior and areas for improvement. Regression analysis was employed to quantify the alignment between the network's outputs and the actual metabolic rates, offering a comprehensive evaluation of the model's effectiveness. The networks were trained, validated, and tested using a subset of the dataset, with performance metrics such as the mean squared error (MSE) and correlation coefficients (R-values) as key evaluation criteria [37]. This methodical approach to ANN design and implementation enabled us to harness the power of neural networks by analyzing complex biomechanical data to enhance our understanding of the relationship between gait nonlinearities and metabolic cost.
## 3 Design of Hybrid Control Scheme
### Robust Performance Controller  
Equation (13) can be transformed into  
\[G_{F_{\rm L}u_{\rm c}}(s)=P_{\rm n}(s)\left[1+\frac{1-Q(s)}{1+Q(s)\Delta_{\rm L }(s)}\Delta_{\rm L}(s)\right] \tag{18}\]  
The controlled plant is changed because of the feedback of the DOB loop. However, from the relationship between \(u_{\rm c}\) to \(F_{\rm L}\) in Equation (18), it is obvious that the outer loop controlled plant is still a model with multiplicative perturbation, and its structure can be equivalent to a virtual controlled plant \(P_{\rm n}(s)(1+\Delta_{1})\), where \(P_{\rm n}(s)\) is the nominal model and \(\Delta_{1}=\frac{1-Q(s)}{1+Q(s)\Delta_{\rm L}(s)}\Delta_{\rm L}(s)\).  
In our loading system with dynamic load superimposed on static load, the force signal to be tracked is in the sinusoidal form or the form of sine superposition offset; a feedback controller is therefore adequate for the outer loop. Therefore, a robust feedback controller based on the H\(\infty\) theory is designed, and the structure of the outer loop is shown in Figure 8.  
If the \(Q(s)\) is appropriately designed, the design specifications of the outer loop feedback controller may not consider the disturbance attenuation requirement. Therefore, the outer loop controller \(G_{\mathrm{c}}(s)\) may not include an integrator. From the small gain theorem, the sufficient and necessary condition for the stability and robustness of \(G_{\mathrm{c}}(s)\) is  
\[\left\|\Delta_{1}T_{\mathrm{ou}}\right\|<1\text{ and }T_{\mathrm{ou}}=\frac{P_{ \mathrm{n}}G_{\mathrm{c}}}{1+P_{\mathrm{n}}G_{\mathrm{c}}} \tag{19}\]
# Detection of Helminthosporium Leaf Blotch Disease Based on UAV Imagery
## 2 Materials and Methods  
### Study Site  
The study site was located in Xinxiang city, Henan province, China (35\({}^{\circ}\)8\({}^{\prime}\)4 N, 113\({}^{\circ}\)46\({}^{\prime}\)56 E). Wheat was planted within this field, with a row spacing of 1 meter. Two fields were selected for the experiment. One is infected with HLB, and the other is asymptomatic (AS), as shown in Figure 1. UAV flights and field campaigns were conducted under cloud-free conditions on April 22 nd, 2017, when the wheat was in its heading period. The HLB infection was in its initial stage when most of the infected leaves started to turn yellow.  
Figure 1: General overview of the study location.
## 3 Results and Discussion
### Evaluation of NIRS Spectra Data  
Figure 3 shows the average spectrum of each batch of 10 fish after being subjected to depuration with clean water at 0 h, 12 h, 24 h, 48 h, 72 h, and 96 h. The initial and final bands of the infrared spectrum have not been taken into account since absorptions of the functional groups were not observed in those characteristic regions. The main peaks originate from the stretching vibrations of -CH, -OH, and -NH groups. Thus, the characteristic bands of the asymmetric stretching of the methyl groups (-CH\({}_{3}\)) are observed at 400 nm, while the asymmetric and symmetric stretching of the methylene groups appear at 362 nm and 374 nm, respectively. The C-H absorption bands of the aliphatic chains of fatty acids are also observed at 440 nm.  
Figure 2: Concentrations of VOCs (\(\mu\)g/g) obtained in tench during washing treatments. TMH: 3,5,5-trimethyl-1-hexene; GEO: dimethyl-8-hydronaphtalen; OCT: 1-octen-3-ol; DEP: diethyl phthalate; MIB: 2-methylisoborneol; IML: \(\alpha\)-isomethylionone.  
Another characteristic band, corresponding to the carbonyl group found in aldehydes, ketones, and esters, appears at 578 nm (Figure 3). Methylene and methyl scissor vibrations are observed at 685.1 nm, and the asymmetric C-O-C stretching of lipid esters is observed at 1020 nm. Other lipid-related vibrations present in the fish were methylene bending and C-O stretching and are observed at 1125 nm, while symmetric PO\({}_{4}\)\({}^{2-}\) stretching along with lipid (C-C) and C-O vibrations appear at 900 nm and 880 nm [21; 22]. Other key lipid absorbance peaks are C-H and CH\({}_{2}\) vibrations using FT-NIR spectroscopy. These signals, related to the fatty acid content, appear around 1680-1710 nm [28; 29; 30]. The first overtone of the C-H stretching is observed in the range of 770-998 nm, and the second overtone between 719 and 900 nm. At around 1150-1200 nm, a band can be seen that is the harmonic of the C-H groups of lipid methylenes. This is associated with the second overtone of the C-H bonds that include the CH, CH\({}_{2}\), and CH\({}_{3}\) groups. Finally, the bands at 890 and 940 nm correspond to the third harmonic of the C-H stretching of saturated and unsaturated fats [31]. At around 720 nm, the first harmonic of the O-H group's stretching is observed. This is attributed to water absorption bands, as the samples analyzed were fresh fish. These bands also overlap with those of the C-H group's stretching and C-H deformations that correspond to -CH\({}_{2}\) and -CH\({}_{3}\) bonds [32]. The stretching of N-H bonds due to proteins is also observed at 760 nm. At around 1528 nm, we can see thin, sharp bands that are responsible for the N-H bonds of protein molecules [33; 34].  
Figure 3 shows three key regions where differences between spectra occurred with the washing time, classified as 400-650 nm, 700-900 nm, and 1000-1350 nm, respectively. Absorbance was lowered as the washing time increased. It is in these regions that the absorption bands of the characteristic families of VOCs included in Figure 2 produced significant differences in the absorption intensity.
## 3 Design with Constant Input Saturation
Thereupon, let us introduce the ellipsoidal region \(\mathcal{V}\), which corresponds to the unit level curve of the Lyapunov function \(V(x)\)  
\[\mathcal{V}=\{x\in\mathbb{R}^{n}:\;x^{T}P^{-1}x\leq 1\}. \tag{18}\]  
By introducing an inclusion relation between \(\mathcal{E}\) and \(\mathcal{V}\), one can guarantee that, as long as the system is working in the linear region of the saturation function, any state trajectory \(x(t)\) which starts from an initial state contained in \(\mathcal{E}\) will necessarily remain inside region \(\mathcal{V}\). In particular, the inclusion \(\mathcal{E}\subseteq\mathcal{V}\) can be expressed by the following inequality  
\[x^{T}P^{-1}x\leq x^{T}Rx, \tag{19}\]  
which, by means of appropriate manipulations, leads to  
\[R-P^{-1}\succeq 0, \tag{20}\]  
and, by means of Schur complements, leads to (14). Finally, taking into account the inclusion \(\mathcal{V}\subseteq\mathcal{U}(\theta)\), we can guarantee that any state trajectory contained in the unit level curve of the Lyapunov function will also lie in the region of linearity of the actuators, such that no saturation occurs and, hence, convergence of \(x\) to \(0\) when \(t\to\infty\) is ensured for any \(x(0)\in\mathcal{V}\) (hence, for any \(x(0)\in\mathcal{E}\)). In particular, the above inclusion is described by  
\[x^{T}K_{i}{}^{T}QK_{i}x\leq x^{T}P^{-1}x. \tag{21}\]  
Applying (17) to (21), the following inequality is obtained  
\[P-\Gamma_{i}{}^{T}Q\Gamma_{i}\succeq 0, \tag{22}\]  
and applying the Schur complement to (22) one gets (15)  
**Remark 3**. : _Note that the quadratic Lyapunov function used in the proof of the Theorem 1 introduces conservativeness due to the constant matrix \(P\). The conservativeness can be decreased by modifying \(V(x)\) through a parameter-dependent matrix \(P(\theta)\), although such modification would add computational complexity to the LMI problem._
# A Secure Link-Layer Connectivity Platform for Multi-Site NFV Services
## 2 Background on Multi-Site VNF Communications
### Layer-3 Inter-Site Communications
As VNFs will typically communicate using IP addresses from private address spaces, the realization of these communications among NFV sites (that is, across the Internet) may require the use of NAT functionalities at the edge routers and/or the establishment of VPN-based overlay networks among the NFV sites. Although this may not necessarily be a limiting factor from a performance perspective, it increases the complexity of the network configuration at each site, being this an aspect that needs careful consideration when designing an effective layer-3 inter-site communications model.
## 3. Results and Discussion
### Experimental Results
#### 3.1.4 Aipical Displacement  
Given the difficulties in achieving a common initial pressure for all the taken measurements, only the corneas to which it was possible to apply an average initial pressure of 1.61 kPa (12.09 mmHg) (with a 0.06-kPa standard deviation) were taken into account to obtain the pressure versus apical displacement and stress-strain relations (Corneas 1-5). Figure 4 depicts the behavior of the average obtained cornea. This behavior presented an approximately linear region between 2.6 and 5.0 kPa. In accordance with this value, the cornea rigidity slightly increased.  
Figure 4: Aipical displacements versus applied pressure for the experimental and biomechanical modeling results.  
Figure 3: Optical power behavior versus pressure for the experimental results.  
A wide range of reported results has been observed for this parameter [13; 22; 24; 25; 28] using porcine corneas extracted from eyeballs. The results obtained in our experiments fell within the range of the results reported in the consulted bibliography.
# Multi-Component Temporal-Correlation Seismic Data Compression Algorithm Based on the PCA and DWT
## 4 Case Study  
A case study was performed to assess the compression efficacy of the proposed approach using a dataset of active seismic data acquired during an oil and gas reservoirmonitoring survey employing ocean bottom nodes (OBNs). The following sections will offer an overview of the dataset employed for evaluation, the experiments conducted, the performance evaluation metrics employed, and the findings from the experiments, accompanied by a discussion of the results.
## 3 Case Studies of Pilot and Full-Scale STRBs
### Water Works Sludge (England)
#### 3.4.1 WW Sludge and Drained Water Quality  
The feed sludge of both the Whitacre and Hanningfield WW had a dry content between 0.1 and 0.5% (Figure 9) and a suspended solids content between 100 and 8000 mg/L (Table 4), respectively. As Figure 9 shows, there is a wide range of the ds applied on the beds; hence, it is crucial to know the ds percentage in each load so that the exact areal load to be applied can be calculated (kg ds/m\({}^{2}\)/year). The use of an average ds value may result in an overestimated load in some beds and underestimated in others. Moreover, the sludge from the two WW had a relatively low organic content (i.e., low LOI), between 20 and 40% ds (Table 4). It should be noted that the respective figures for sewage sludge are usually between 50 and 70%.  
Figure 8: (**a**) Full-scale and (**b**,**c**) pilot-scale STRB systems [38; 59] at Hanningfield (England) for water works sludge dewatering (courtesy of Orbicon).  
Figure 9: Dry solids (%) in the feed sludge at the Hanningfield WW [53].  
The feed sludge at Hanningfield WW had a very low fat and oil content (average, 0.600 g/kg ds) but high iron content (average, 250 g/kg ds). The pH range was within the acceptable limits for plant growth. The presence of nutrients in the sludge implies that the use of fertilizer can be limited or even avoided if the treated sludge is reused. The drained water at both WW had significantly low dry solids, suspended solids, iron, and total phosphorous contents (Table 5).
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 5 SR in Wi-Fi 6
### Related Works
#### 5.4.4 Performance Evaluation  
Most of the evaluations were conducted with a network simulator. [PERSON] et al. [144] developed a simulator in NS-2 to evaluate the OBSS PD mechanism and throughput of each BSS. They showed that the SR improves performance by 34%.  
[PERSON] et al. [145] evaluated the Dynamic Sensitivity Control (DSC) and BSS coloring mechanisms with NS-3 simulations. They observed that DSC improves the network throughput and preserves fairness among the stations. Moreover, BSS coloring improves the network performance if the number of Wi-Fi 6 stations is larger than legacy stations. [PERSON] et al. [146] analyzed dynamic sensitivity control and BSS coloring with the same simulator while the network was modeled by the capture effect model. They highlighted that the throughput improves with larger frame sizes, whereas the high data rate and large frame size negatively affect the throughput. SR's throughput was analyzed in [147] in NS-3. They reached the conclusion that SR performance depends on the CCA threshold, power, and the distance between the BSSs.  
[PERSON] et al. [148] evaluated the SR mechanisms with the Komondor simulator. They considered up to 400 scenarios by varying the number of stations, topology, OBSS PD threshold, and traffic load. Its efficiency is measured by throughput, delay, and channel occupancy. By alleviating the inter-neighbor WLAN' contentions successfully, SR reveals its significance in high-density and high-load networks.  
The authors of [21] evaluated the BSS color efficiency also with NS-3. They proved that BSS coloring increases the throughput per access point up to 47%. [PERSON] et al. [149] mainly focused on BSS color. Seven BSSs were simulated with 70 stations and various threshold values were applied to find the optimal value. They concluded that up to a certain value, the throughput increases and thereafter the performance degrades because of the interference from other BSSs.  
[PERSON] et al. [150] investigated the impact of the **PSR!** (**PSR!**) feature on transmission latency using a testbed. The evaluations revealed that this feature could reduce the latency considerably in worst-case scenarios and enhance the throughput.
## 4 Results
### Characterization of Digital Identity
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Social Networks/ Tools**} & \multicolumn{2}{c}{**Degree of Use**} & & & & \\ \hline \multicolumn{1}{c}{Facebook} & F & 7.73\% & 8.02\% & 24.38\% & 26.23\% & 33.64\% & \\ \multicolumn{1}{c}{M} & 8.65\% & 11.89\% & 25.41\% & 24.32\% & 29.73\% & 0.1742 \\ \hline \multirow{2}{*}{Instagram} & F & 6.79\% & 2.16\% & 5.56\% & 12.03\% & 73.46\% & \\ \multicolumn{1}{c}{M} & 9.19\% & 4.86\% & 9.73\% & 18.38\% & 57.84\% & \\ \hline \multirow{2}{*}{WhatsApp} & F & 0.93\% & 0.93\% & 3.09\% & 6.79\% & 88.26\% & \\ \multicolumn{1}{c}{M} & 3.24\% & 3.24\% & 4.87\% & 12.97\% & 75.68\% & \\ \hline \multirow{2}{*}{Spotify} & F & 20.99\% & 4.63\% & 8.33\% & 13.89\% & 52.16\% & \\ \multicolumn{1}{c}{M} & 24.32\% & 7.03\% & 10.81\% & 16.22\% & 41.62\% & \\ \hline \multirow{2}{*}{YouTube} & F & 2.78\% & 6.48\% & 18.52\% & 21.60\% & 50.62\% & \\ \multicolumn{1}{c}{M} & 2.16\% & 5.41\% & 13.51\% & 18.92\% & 60.00\% & \\ \hline \multicolumn{1}{c}{**Frecuency of Use**} & & & & & & \\ \hline \multicolumn{1}{c}{**Social Networks/ Tools**} & \multicolumn{2}{c}{**Never**} & **Monthly** & **Fortnightly** & **Weekly** & **Daily** & _p-Value_** \\ \hline \multirow{2}{*}{Facebook} & F & 11.42\% & 11.73\% & 4.94\% & 19.44\% & 52.47\% & \\ \multicolumn{1}{c}{M} & 15.68\% & 8.65\% & 8.65\% & 22.70\% & 44.32\% & \\ \hline \multirow{2}{*}{Instagram} & F & 11.73\% & 8.64\% & 6.17\% & 12.35\% & 61.11\% & \\ \multicolumn{1}{c}{M} & 14.59\% & 8.65\% & 6.49\% & 14.05\% & 56.22\% & \\ \hline \multirow{2}{*}{WhatsApp} & F & 3.09\% & 4.62\% & 2.16\% & 2.78\% & 87.35\% & \\ \multicolumn{1}{c}{M} & 8.65\% & 4.86\% & 1.08\% & 8.11\% & 77.30\% & \\ \hline \multirow{2}{*}{Spotify} & F & 34.88\% & 4.32\% & 2.78\% & 8.33\% & 49.69\% & \\ \multicolumn{1}{c}{M} & 31.89\% & 5.95\% & 5.41\% & 12.43\% & 44.32\% & \\ \hline \multirow{2}{*}{YouTube} & F & 19.75\% & 4.94\% & 4.02\% & 26.85\% & 44.44\% & \\ \multicolumn{1}{c}{M} & 21.08\% & 4.33\% & 3.24\% & 19.46\% & 51.89\% & \\ \hline \multicolumn{1}{c}{(*) Significance when _p-_value < 0.05 increasing scale, where 0 implies non-use and 4 the highest degree of use. F = female; M = male.} & & & & \\ \end{tabular}
\end{table} 
Table 10: Degree and frequency of use of social networks and tools available on the Internet. Table 12 allows us to find that the frequency with which information is usually shared does not differ by gender. Its distribution presents a varied trend according to the type of information and context. Thus, sharing videos with and without a subject line created by others presents similar frequency distributions. When the creator is the same as the one sharing, there are also no differences in the distribution of frequencies whether the created material follows a thematic line or not. In general terms, it is more frequent that they share information created by others than their own or from friends or colleagues, either with or without a defined thematic line.
# An Asymmetric Encryption-Based Key Distribution Method for Wireless Sensor Networks
###### Abstract  
Wireless sensor networks are usually applied in hostile areas where nodes can easily be monitored and captured by an adversary. Designing a key distribution scheme with high security and reliability, low hardware requirements, and moderate communication load is crucial for wireless sensor networks. To address the above objectives, we propose a new key distribution scheme based on an ECC asymmetric encryption algorithm. The two-way authentication mechanism in the proposed scheme not only prevents illegal nodes from accessing the network, but also prevents fake base stations from communicating with the nodes. The complete key distribution and key update methods ensure the security of session keys in both static and dynamic environments. The new key distribution scheme provides a significant performance improvement compared to the classical key distribution schemes for wireless sensor networks without sacrificing reliability. Simulation results show that the proposed new scheme reduces the communication load and key storage capacity, has significant advantages in terms of secure connectivity and attack resistance, and is fully applicable to wireless sensor networks.  
WSN; security; key distribution; cryptography +
Footnote †: journal:
## 3 Proposed Methodology
### Data Acquisition  
This study aimed to develop a HAR model that effectively deals with real-life situations where people often carry their wearable electronics in different positions. To explore position-independent HAR, three publicly accessible multi-position HAR datasets--Opportunity, PAMAP2, and REALWORLD16--were selected. Several factors support the choice of these datasets: (i) The data are gathered from diverse locations, as shown in Figure 1; (ii) The datasets are sufficiently large to support the application of deep learning algorithms; and (iii) The covered activities are diverse, including both small-scale activities (like gestures in the Opportunity dataset) and everyday daily activities.
# Limitations and Performance Analysis of Spherical Sector Harmonics for Sound Field Processing
## 3 Violation of the Helmholtz Equation
### Elevational Direction  
We first provide our proof in the elevational direction. We use \(\mathbf{U}(\theta)\) to represent the shifted associated Legendre polynomials:  
\[\mathbf{U}(\theta)=\mathcal{P}_{n}^{m}(q_{1}\cos\theta+q_{2}). \tag{11}\]  
where \(\theta\in[\theta_{1},\theta_{2}]\), \(\phi\in[\phi_{1},\phi_{2}]\). Let \(\eta=q_{1}\text{cos}\theta+q_{2}\), and we have \(\eta\in[-1,1]\). Based on the chain rule [32], the left-hand side of (9b) can be rewritten as  
\[\begin{split}&\frac{1}{\sin\theta}\frac{d}{d\theta}(\sin\theta \frac{d\mathbf{U}}{d\theta})+[n(n+1)-\frac{m^{2}}{\sin^{2}\theta}]\mathbf{U}\\ =&\ q_{1}^{2}\frac{d}{d\eta}[(1-(\eta-q_{2})^{2}/q_{ 1}^{2})\frac{d\mathcal{P}_{n}^{m}(\eta)}{d\eta}]\\ &+[n(n+1)-\frac{m^{2}}{1-(\eta-q_  
when \(q_{1}=1\) and \(q_{2}=0\), (12) can be simplified as  
\[\frac{d}{d\eta}[(1-\eta^{2})\frac{d\mathcal{P}_{n}^{m}(\eta)}{d\eta}+[n(n+1)- \frac{m^{2}}{1-\eta^{2}}]\mathcal{P}_{n}^{m}(\eta). \tag{14}\]  
From (13) and (14), we observe that when \(q_{1}=1\) and \(q_{2}=0\), \(\mathbf{U}\) satisfies (9b). However, for \(q_{1}\
eq 1\) or \(q_{2}\
eq 0\), (12) does not match the left-hand side of (13).  
To further prove that SSHs are not solutions of the Helmholtz equation, we take the specific example of \(\mathcal{P}_{2}^{0}(\eta)=(3\eta^{2}-1)/2\)[33] into (12) and have  
\[12q_{2}\eta+3q_{1}^{2}-3q_{2}^{2}-3\
eq 0, \tag{15}\]  
where \(q_{1}\
eq 1\) or \(q_{2}\
eq 0\). Thus, the shifted associated Legendre polynomials in SSHs violate (9b) and are not a solution of the Helmholtz equation.