esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Stability under Different Noise Levels  
Our subsequent analysis focused on analyzing the noise stability of the SVM, RF and ANN models. Each model was meticulously trained on noisy data at varying SNRs of 30,  
Figure 4: Comparison of the F1-Score between SVM and RF for noiseless data with tumor radii from 6 mm to 12 mm.  
Figure 5: Influence of noise for different tumor radii on the SVM.  
Figure 6: Influence of noise for different tumor radii on the RF.  
50 and 70, followed by testing with noise levels ranging from 10 to 70. The resulting data is visually illustrated in Figures 7-9 for SVM, RBF and ANN, respectively. For all models, it is apparent that lower SNRs during testing, compared to the SNR during training, lead to a significant reduction in the corresponding F1-Score. Notably, the F1 score showed a consistent downward trend as the SNR decreased. In particular, during training with an SNR of 30, the SVM model reached its peak performance when tested with the same noise level in the test data. Deviations from this optimal SNR resulted in a subsequent decrease in the F1 score. Meanwhile, at an SNR of 50, the SVM model showed a slight increase in F1-Score when applied to higher SNRs, reaching values of 0.85 and 0.87, respectively. However, training datasets with an SNR of 70 showed a deterioration in F1-Score when subjected to higher noise levels, dropping from its peak value of 0.87. Conversely, the RF model performed consistently better at higher SNRs, regardless of the level of noise present in the training data. Despite minor variations, the F1 scores remained closely distributed, with a maximum deviation of 0.07 However, it is worth noting that the overall F1 scores for the RF model were relatively lower compared to the other models considered. In contrast, the ANN model proved to be highly sensitive to any deviations in the unseen data, despite showing the highest overall scores of all the models. An increase or decrease in the trained SNR
## 5 Discussion
### The Selection of the Spectral Range for the SIFSpec System  
At the present stage, although the spectral range of spectrometers used in the SIFSpec system is about from 650 nm to 800 nm, the other existing automated systems mainly cover the near-infrared (NIR) region (\(\sim\)730-780 nm), as introduced above. Thus, the information of red SIF retrieval is not available for other SIF observation systems only covering the NIR region. In practice, it is more difficult for red SIF signals to escape from canopy because of the strong re-absorption and scattering effects inside the vegetation canopy [60]. Therefore, the red SIF is much weaker and is also more difficult to measure at canopy level than that at the NIR band, which may be the main reason to neglect the red band in the systems. However, the red SIF may be more directly linked to photosynthetic activity because it contains more information of the photosystem II [60; 61; 62]. In addition, although the spectral resolution will decrease for the instrument with a wider spectral range (concluding red and NIR bands) compared with a shorter range (only concluding NIR band), which decreases from 0.15 nm to 0.34 nm, the integration time will be reduced and the SNR will increase to some extent because of the increased photons that reach each detector per unit time [26]. The previous work has also demonstrated that the difference of the retrieved SIF with spectral resolution of 0.1 nm and 0.3 nm at the O\({}_{2}\)-A band can be negligible if tested using the 3 FLD SIF retrieval method [48]. Furthermore, the red SIF signal will be much more enhanced if it is downscaled from the canopy level to the photosystem level [63], which may improve the correlation with photosynthetic activity. Therefore, in this context, the red band is suggested to be concluded in the SIFSpec system.
# Iterative Positioning Algorithm for Indoor Node Based on Distance Correction in WSNs
## 5 Experimental Results
### Experimental Analysis
#### 5.2.1 The First Experiment: \(F_{n}\) is Constant  
When \(F_{N}\) is a constant value, considering the effects of the actual noise on the signal, three typical \(F_{N}\) values are used for the experiments, these being 0.1, 0.2, and 0.3, respectively.  
The experiments for the three parameters are shown as follows.  
* \(F_{N}\) = 0.1 The positioning errors are shown in Figure 6 in the case of the noise impact factor of 0.1.  
The \(x\)-coordinate represents the target node sequence, the unit is the number of nodes, and the \(y\)-coordinate represents the positioning error, with the unit being m (meters).  
When \(F_{N}\) is 0.1, the average positioning errors of the three algorithms are 0.77, 0.70, and 0.17, respectively, and the positioning accuracy of the iterative positioning algorithm is improved by 77.92% and 75.71% compared with the centroid algorithm and the weighted centroid algorithm.  
* \(F_{N}\) = 0.2 The positioning errors are shown in Figure 7 in the case of the noise impact factor of 0.2.  
Figure 6: Positioning errors of three positioning algorithms in the case of the noise impact factor of 0.1.  
When \(F_{N}\) is 0.2, the average positioning errors of the three algorithms are 1.68, 1.30, and 0.42, respectively, and the positioning accuracy of the iterative positioning algorithm is improved by 75% and 67.69% compared with the centroid algorithm and the weighted centroid algorithm.  
* \(F_{N}\) = 0.3  
The positioning errors are shown in Figure 8 in the case of a noise impact factor of 0.3.  
When \(F_{N}\) is 0.3, the average positioning errors of the three algorithms are 2.63, 1.85, and 0.79, respectively, and the positioning accuracy of the iterative positioning algorithm is improved by 69.96% and 57.29% compared with the centroid algorithm and the weighted centroid algorithm.
# Thermal and Mechanical Behavior of Elastomers Incorporated with Thermoregulating Microcapsules
## 1 Introduction
Recently, some authors have incorporated PCMs into elastomers. The most relevant examples found are related to the addition of PCMs into the polymeric matrix. [PERSON] et al. [14] introduced form-stable PCMs from fatty acids in elastomer copolyesters called phase-change elastomer gels (PCEGs). They explored the thermal characteristics of PCEG films, wherein the copolyester grade, gel composition and the fatty acid concentration from 30 to 70 wt.% obtained latent heats within 10-100 J/g and without hysteretic thermal cycling. On the other hand, [PERSON] et al. [15] added microencapsulated PCMs (melting temperature of 52 \({}^{\circ}\)C) using concentrations from 1 to 10 wt.% on an elastomeric matrix of styrene-ethylene/butylene-styrene (SEBS) material with a remarkable effect on the thermal regulation of SEBS while keeping good resistant and ductile properties for PCM concentrations of 1-5 wt.%. [PERSON] et al. [16] investigated the SEBS elastomer using shape-stabilized phase change material by absorbing hexadecane into the network of the SEBS and coating it with a low-density polyethylene (LDPE). They studied four mass fractions of hexadecane/SEBS (80/5, 75/10, 65/20, 55/30, \(w/w\) %), employing 15% LDPE with respect to the melt-mixing method. They found that the thermal properties of the elastomers improved with the PCMcontent, varying from 106.15 kJ/kg to 179.76 kJ/kg for the composite containing 80% PCM and maintaining good properties despite the huge PCM content. Considering the feasibility of LDPE for producing elastomer materials, in this work, thermoregulating microcapsules containing the paraffin wax Rubitherm(r) RT27 with a shell from LDPE and ethyl-vinyl acetate (EVA) with an average particle size of 10 \(\upmu\)m (mSD-(LDPE-EVA-RT27)) were selected [13]. On the other hand, for first time, a polyurethane elastomeric matrix was chosen for the microencapsulated PCMs' incorporation, since it is one of the most versatile polymers. mSD-(LDPE-EVA-RT27) contents from 0.0 to 20.0 wt.% were incorporated, also varying the main constituent proportion, isocyanate and polyol for [NCO]/[OH] molar ratio values of 1.05 and 1.1. The effect of these variables on the structural, mechanical and thermal properties was evaluated, looking for the elastomer with the best thermal energy storage capacity that maintained good values for the mechanical properties.
# Long-Term Effects of a Soft Robotic Suit on Gait Characteristics in Healthy Elderly Persons
## 2 Overview of a Soft Wearable Robotic Suit  
We have presented a soft wearable robotic suit for energy-efficient walking for elderly persons in daily physical activities. Figure 1 illustrates the entire design of the device, which consists of a waist support brace, knee braces, actuators, controllers, stiff and elastic belts, load cells, and gyroscopes. For each leg, one actuator and one controller were affixed to the front and the back sides of the waist support brace, respectively. The upper end of the stiff belt was pinned to the actuator, and the bottom two ends of the inverted Y-shaped elastic belt were attached to the lateral two sides of the knee brace, respectively. In addition, the bottom end of the stiff belt and the upper end of the elastic belt were connected through the load cell. Moreover, the gyroscope was attached to the thigh. The total mass of the device was 2.7 kg, excluding the power supply system (the power of the device was supplied by an external power supply by a power cable.).  
The robotic suit assists hip flexion through a small, yet effective tension force, as shown in Figure 2. Specifically, a tension force is generated on the stiff and elastic belts by the winding up of the belts during the swing phase, and it is transmitted to the wearer's hip joint. In the case of stand phase, the device provides a small enough tension force that permits \"creeping\" of the belts along the thigh without influencing the hip extension.  
Figure 3 shows the control architecture. The hip angle was calculated through the numerical integration of the hip angular velocity, which was obtained by the gyroscope. Estimations of average gait cycle and three events of heel contact, minimum hip angle, and maximum hip angle were conducted. Then, these estimated values were applied for the desired assistive force generation. In order to track the desired force smoothly and safely, a proxy-based sliding mode controller [29] was conducted with a control cycle of \(T=0.001\) s. Figure 4 illustrates the typical data of hip angle and assistive force.  
Figure 1: Entire design of a soft wearable robotic suit for hip flexion.  
Figure 2: Assistance strategy for hip flexion. Blue-filled circles express reflective markers attached to the subjects in the experiment of Section 3 (1. approximation of the center of mass, 2. hip, 3. knee, 4. ankle, and 5. toe).
## 4 Results and Discussion
### Comparison between the Lignin-Treated Soil and the Lime-Treated Soil
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Stabilizing Agent Content (\%) and Type** & **CBR (\%)** & **CII (\%)** \\ \hline \multicolumn{3}{c}{0 days of curing} \\ \hline
0\% (untreated soil) & 2.5 & - \\
0.4\% lignin-treated soil & 6.2 & CII\({}_{\text{Lg-U}}\) = +60 \\
2.5\% lime-treated soil & 14.2 & CII\({}_{\text{Lm-U}}\) = +82 \\ \hline \multicolumn{3}{c}{7 days of air drying + 4 days of soaking} \\ \hline
0\% (untreated soil) & 2.2 & - \\
0.4\% lignin-treated soil & 3.4 & CII\({}_{\text{Lg-U}}\) = +35 \\
2.5\% lime-treated soil & 22.5 & CII\({}_{\text{Lm-U}}\) = +90 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison between the results of the CBR test carried out on the untreated soil, the lignin-treated soil, and the soil treated with lime.
## 3 Results and Discussion
### Compression Properties
#### 3.3.3 Optimization Results  
Figure 18 demonstrates the outcomes of the RSM optimization performed on the yield strength (Figure 18a), compression strength (Figure 18b), and fracture strain (Figure 18c), alongside the factors that were pertinent to this analysis. The optimization objective was configured to \"in range,\" using \"maximize\" as the solution destination, and the anticipated result of the desirability function was in the format of \"larger-is-better\" attributes. The optimal AMC conditions were found to be P (A) = 884.2 MPa, (B) H/D = 0.762, and (C) Al\({}_{2}\)O\({}_{3}\)% = 8.57% for the yield strength. This led to the highest value of 341.372 MPa for the yield strength. Additionally, for a maximum compression strength of 410.93 MPa, the appropriate AMC parameters were P (A) = 506.142 MPa, H/D (B) = 0.873, and Al\({}_{2}\)O\({}_{3}\)% (C) = 9.71%. Finally, the optimum fracture strain of 13.54 was reached at (A) P = 501.692 MPa, (B) H/D = 0.757, and (C) Al\({}_{2}\)O\({}_{3}\)% = 0.017%.  
The AMC's boundary condition, which acted as the fitness function, regulated the maximization of the mechanical properties outlined in Equations (7)-(9). According to the results of the GA, the optimal yield strength value was found to be 350.399 MPa, which was achieved at 900 MPa, a 0.75 H/D, and 10% Al\({}_{2}\)O\({}_{3}\). The highest number for the GA's estimation of the compression strength was 421.906 MPa at 500 MPa, a 0.75 H/D, and 10% Al\({}_{2}\)O\({}_{3}\). The best fracture strain value was found to be 13.0851, and it was obtained at 501.12 MPa, a 0.758 H/D, and 0% Al\({}_{2}\)O\({}_{3}\). This value is displayed in Figure 19a,c,e.  
The hybrid DOE-GA data are displayed in Figure 19b, and they reveal that the greatest yield strength of 350.398 MPa was achieved at 900 MPa, a 0.75 H/D, and 10% Al\({}_{2}\)O\({}_{3}\). Furthermore, the maximum possible value of compression strength was 421.906 MPa, which was achieved at 500 MPa, a 0.75 H/D, and 10% Al\({}_{2}\)O\({}_{3}\), as illustrated in Figure 19d. As can be seen in Figure 19f, the maximum fracture strain was found to be 13.464 when the material was subjected to 510 MPa of pressure, a 0.75 H/D, and 0% Al\({}_{2}\)O\({}_{3}\). Table 9 summarizes the comparison of the AMC response values of the experimental, RSM, GA and hybrid RSM-GA techniques.  
Figure 18: RSM optimization results of mechanical properties (**a**) \(\sigma_{y}\), (**b**) \(\sigma_{\text{uc}}\), and (**c**) \(\varepsilon_{\text{f}}\) for AMC.  
Figure 19: Optimal mechanical properties by GA (**a**,\(c\),**e**) and hybrid DOE-GA (**b**,\(d\),_f**) of \(\sigma_{y}\) (**a**,_b_), \(\sigma_{\rm{uc}}\) (**c**,_d_), and \(\epsilon_{\rm{f}}\) (**e**,_f_), (**b**,_c_).
# AUV Adaptive Sampling Methods: A Review
## 1 Introduction  
Autonomous underwater vehicles (AUVs) are unmanned marine robots. Owing to their mobility and increased ability to accommodate sensors, they have been used for a broad range of oceanographic missions, such as surveying underwater plumes and other phenomena, collecting bathymetric data and tracking oceanographic dynamic features. As the name suggests, autonomous behaviours [1] are one of their capabilities. Although defining autonomy in robotics can be ambiguous and the minimum level of autonomy depends on context, this paper is only concerned with decision autonomy [2] of unmanned robots. [PERSON] [1] discriminated between autonomous behaviour and intelligent behaviour. The former is a collection of actions for known situations in which the robot is not allowed to adapt its tasks in situ. The latter, on the other hand, is the capability that the robot can adapt and complete its mission by reacting to unforeseen events. Intelligent behaviours of an AUV require a high level of autonomy. They include the ability to make decisions, interpret in-situ sensor data, diagnose the problems, make inference from data, suggest solutions and adapt the planned mission.  
Demand for a high level of autonomy is increasing as the application of underwater robotics has expanded. Depending on the dynamics of a feature, different strategies and sampling methods may apply. Some ocean features evolve highly dynamically over time, some others relatively less so. If the spatial and temporal scales are known, the primary perspective of sampling will be how to adjust sampling distribution for measurements in accordance with the local variance. Scales also determine the number of survey agents that need to be involved. The mesoscale oceanographic features (in theorder of 10 km) may require the coordinated effort of a fleet of platforms rather than a single vehicle, in order to obtain a more synoptic and cohesive data set [3].  
This paper reviews the state-of-the-art adaptive sampling methods that have been used in the last few decades in AUV operations. The review aims to put into context the various methods that have been proposed outlining their advantages and disadvantages for future researchers in the field. Section 2 elucidates the process of adaptive sampling. Sections 3-5 describe various adaptive sampling techniques. They are described in accordance with subcategories: _Targets_, _mission objectives_ and _multi-vehicle networking_, as listed in Table 1. Section 6 concludes this study.
# Energy Efficient Resource Allocation for M2M Devices in LTE/LTE-A
## 4 Results and Analysis  
### Simulation Environment  
We used a round-robin (RR) scheduler, and CBC and RME schedulers which are channel aware schedulers, as references to evaluate our algorithms. The RR scheduler, which is one of the classic schedulers used in many older systems, consists of distributing RBs to all UEs that require data transmission equally. It assigns resource blocks to users in a circular order.  
In order to evaluate the performance of our algorithms, we used the LTE-Sim simulator [35]. In our simulation, we considered a single cell, in which one eNB was positioned in the centre, whereas M2M and H2H devices were distributed randomly. In this study, voip, video and constant bit rate (CBR) flows were used for H2H communication; 30 H2H devices were used, 10 for each flow. As mentioned in Section 2.1, the majority of M2M applications are either event-driven or time-driven applications, so in this study, these two types of M2M applications were simulated. The distribution of the M2M devices was considered in our scenario as follows: 70% of devices were time-driven applications and 30% of MTC devices were event-driven applications. The burst transmission of M2M event-driven applications was modelled with a Poisson process with rate \(\lambda=50\) ms. However, the transmission interval of time-driven application was fixed and it was selected randomly within the range \([0.05,5]\) s. Both M2M applications had a packet size of 125 bytes [36]. Table 3 shows the simulation parameters and Table 4 summarises the information about the traffic model.  
Besides, for RME-M scheduler, the delay threshold was fixed to one frame (10 ms), which is higher than the minimum tolerable delay of M2M applications.
## 2 Preliminaries
### Gadget Decomposition  
Gadget decomposition, originally introduced by [27], is a fundamental technique extensively employed in FHE schemes (e.g., [5; 6; 7; 8; 9; 10; 15; 16; 17; 18; 19]) to mitigate the escalation of noise during homomorphic evaluations and bootstrapping. Intuitively, it expresses a ring element as a vector of polynomials with relatively small coefficients, thereby facilitating key switching and other noise-control procedures.  
Formally, let \(Q\) be a positive integer, and consider the ring \(R_{Q}\). We define a gadget vector \(\mathbf{g}=(g_{0},g_{1},\ldots,g_{t-1})\in R_{Q}^{t}\). The gadget decomposition function \(h(\cdot)\) maps each element \(a\in R_{Q}\) to a vector \(\mathbf{v}=(v_{0},v_{1},\ldots,v_{t-1})\in R^{t}\) such that: \(\langle h(a),\mathbf{g}\rangle=\langle\mathbf{v},\mathbf{g}\rangle=a(\text{ modQ})\). In this decomposition, the parameter \(t\) is a positive integer referred to as the decomposition degree. The resulting vector \(\mathbf{v}\) is typically constrained to have a small \(l_{\infty}\)-norm, i.e., \(\|\mathbf{v}\|_{\infty}\leq B_{h}\), where \(B_{h}>0\) is a predetermined bound significantly smaller than \(Q\). For \(x\in R_{Q}\) and \(\mathbf{y}\in R_{Q}^{t}\), the external product is defined as \(x\boxdot\mathbf{y}=\langle h(x),\mathbf{y}\rangle\pmod{Q}\), where \(\langle\cdot,\cdot\rangle\) denotes the inner product between two vectors.  
Over the years, multiple gadget decomposition variants have been employed in FHE schemes tailored to diverse design needs and constructions, including bit decomposition [5; 6], residue-number-system (RNS)-oriented decomposition [28; 29], base decomposition [10; 30], and homomorphic gadget decomposition [20].
## 4 Results, Discussions and Achievements
### RMSE Analysis against Multiple Snapshots  
In this analysis, we investigate the value of RMSE against multiple snapshots. Therefore, this analysis is also known as robustness against snapshots. In Figure 7, we examine that value of RMSE of CS is decreasing as the number of snapshots increases as compared to PSO. In the region of 0 to 100 snapshots, RMSE is falling off rapidly for both optimization schemes, and the overall performance of CS is better than PSO. In the scenario of nine and twelve targets estimation, the version of CS is better than PSO. Therefore, the required degree of freedom is achieved using CS, as shown in Figures 8 and 9.  
Figure 6: Robustness against noise for twelve sources.  
Figure 7: Estimation of robustness using six sources in terms of snapshots.
# VidBlock: A Web3.0-Enabled Decentralized Blockchain Architecture for Live Video Streaming
## 4 Experiment and Evaluation
### Security Analysis
#### 4.3.1 Attack Model  
In VidBlock, one of the primary threats is the 51% attack, where a malicious actor controlling the majority of network nodes can manipulate the blockchain and generate counterfeit blocks. By doing so, the attacker can generate a fraudulent chain of blocks and replace the legitimate chain, compromising the integrity of the system. This type of attack exploits the fundamental consensus mechanism of the blockchain, making it one of the most critical security concerns in decentralized networks [35; 36].  
To understand the mechanics of a 51% attack, the security of the blockchain system can be modeled using the following parameters:  
* \(N\): The total number of nodes participating in the network, representing its level of decentralization.
* \(M\): The number of nodes or hash power controlled by the attacker.
* \(P_{m}=\frac{M}{N}\): The proportion of malicious nodes in the network (attacker's power ratio).
* \(1-P_{m}\): The proportion of honest nodes in the network.
* \(k\): The number of nodes required for the attacker to control a majority (\(k\geq\lceil N/2\rceil\)).  
The likelihood of a successful 51% attack increases significantly as \(P_{m}\), the attacker's power ratio, approaches or exceeds 0.5. When the attacker's computational power surpasses that of honest nodes, they can generate blocks faster than the legitimate chain, eventually overtaking and replacing it. This probability is particularly high in networks with a small number of participating nodes (\(N\)), where the attacker's relative control of the network is amplified.  
The relationship between \(N\), \(P_{m}\), and \(k\) is critical for understanding blockchain security. For instance, the probability of the attacker overtaking the network can be modeled probabilistically based on these variables, as shown in prior research [35; 37]. Specifically, as \(N\) increases, the relative power of the attacker (\(P_{m}=\frac{M}{N}\)) diminishes, exponentially reducing the probability of a successful attack. Similarly, larger values of \(k\), representing the majority threshold (\(k=\lceil N/2\rceil\)), increase the difficulty for the attacker to control the network, further mitigating the risk of attack success.  
In decentralized networks such as VidBlock, ensuring a high \(N\) is essential for security. A highly decentralized network with a large number of participating nodes reduces the feasibility of an attack by diluting the attacker's relative power. Conversely, in early-stage networks or low-participation environments, decentralization is limited, making the network more vulnerable to 51% attacks. This dynamic highlights the critical importance of robust decentralization strategies in VidBlock's architecture.