esa-sceva/satcom-chunk-collection huggingface dataset:
# UV-Curable Silicone-Modified Polyurethane Acrylates for Food Freshness Monitoring
## 3 Results and Discussion
### Performance of UV Coatings
#### 3.2.4 Monitoring the Freshness of Shrimp and Pork  
During the course of storage and transportation, some high-protein foods such as shrimp and pork deteriorate easily. Eating deteriorated high-protein food by mistake will cause significant harm to health [38; 39]. So, it is crucial to develop intelligent materials to monitor food spoilage. If a certain food is contaminated by pathogenic microorganisms, the pH value of the food will change irreversibly because of the metabolite production such as organic acids, CO\({}_{2}\), and sulfur derivatives [14]. So, color variation in intelligent materials corresponding to pH changes can be adopted to monitor food spoilage [39; 40]. As discussed above, the UV coatings exhibited quite good pH-sensitive performance, so an investigation into monitoring spoilage of high-protein food such as shrimp and pork was carried out, as shown in Figure 8. When monitoring the food spoilage of shrimp (Figure 8a),  
Figure 6: Color change in coating-3 immersed in aqueous solutions of different pH values for different times.  
Figure 7: Color change in coating-3 immersed in aqueous solutions of different solvents (v:v = 50:50).  
the color of the pH-sensitive material gradually changed from deep red (fresh) to wine red (day 1), peach red (day 2), light red (day 3), orange-yellow (day 4), and light yellow (day 5 and day 6). Similarly, the color of the pH-sensitive material gradually changed from deep red (Fresh to day 2) to peach red (day 3), orange-yellow (day 4), and light yellow (day 5 and day 6) when the material was applied to monitor the freshness of pork (Figure 8b) So, the pH-sensitive intelligent materials can be used to monitor the food spoilage of shrimp and pork.  
Figure 8: Images taken during the monitoring of the freshness of (**a**) shrimp and (**b**) pork.
## 3 Parabola-Catenary Hybrid DLO Geometrical Model  
The catenary equation \(y=a\cosh\frac{x}{a}\) is derived from well-known fundamental equations of applied mechanics as the shape that takes a flexible but non-elastic DLO hanging from two extremes under its own weight. Parabola equation \(y=ax^{2}\) has been used as a surrogate geometric model approximation of the shape of hanging cables for both static or kinematics analysis [30, 54, 55], because it is more robust to extreme conditions that induce singularities in the catenary equation. In general applications, such as modeling the dynamic behavior of cables or bridge structures, the dynamic simulations of objects modeled alternatively as a parabola or the catenary are very similar, except under very heavy payloads where the differences among these functions might introduce substantial differences on analysis results [24]. Moreover, the lower computational cost of a parabola is another reason for its use in the mathematical modeling of cables [25]. In the case of robotics, this simplification has been contested in some applications [47]. Figure 2 visualizes the approximation of a catenary by a parabola, which may be good enough for some applications [55, 56, 57, 58], especially in the development of cable-driven robots [59].  
This article proposes a hybrid between catenary and parabola geometric models for a DLO section hanging between two drones. Automated switching from one model to the other occurs when the Euclidean distance between the drones is too short relative to the actual length of the DLO section. In this situation, the catenary is no longer a good approximation to the shape of the DLO. Heuristically, we have set the threshold for the shift between models at \(d<L/3\), where \(L\) is the length of the DLO section and \(d\) the Euclidean horizontal distance between the drones supporting it. In drone team operation modeling, there are some previous studies on hybrid modeling for control strategies and their formation [60, 61, 62], but there are no previous studies on hybrid payload modeling.
## 4 Simulations and Results Analysis
### Security Degree
#### 4.6.2 Accuracy Analysis of \(g\)  
To validate the security degree estimated by SDSE, we simulate scenarios for different values of \(x\) (the number of captured nodes) and \(P_{M}\) (the network base rate). Then, to compute the accuracy, we compare the security degree computed by SDSE (the product of estimated security metrics) to the actual security degree found in the simulations (the product of the fractions, regarding each metric, measured from the network).  
Figure 9 presents the accuracy obtained in simulations for \(1000\) nodes. In that figure, we can see that the accuracy is always greater than 92.5%. Notice that, as occurred with \(P_{R}\), the accuracy of the security degree has varied a little more significantly.  
Figure 8: Security degree of a node for which a global alarm is not generated (\(A-\)) _vs._ the amount \(x\) of captured nodes, for different values of base rate (\(P_{M}\)) and a network with \(1000\) sensor nodes.
## 2 Gait and Balance Rehabilitation
### Rehabilitation Approaches and Training Modes
#### 2.2.2 Standing/Walking-Type Rehabilitation Devices
For gait rehabilitation, the end effectors usually consist of two footplates that simulate different phases of natural gait (i.e., swing and stance). Based on the trajectory generated [38], two types of end effectors can be distinguished: end effectors with fixed trajectories and end effectors with adjustable trajectories. In the former ones, the trajectory of the footplates can only be adjusted by changing the size of the mechanism components that transfer the movement to the footplates. Crank-rocker mechanisms, such as the Gait Trainer-GT I [84] and the LOKOIRAN [85], and the five-linkage and six-linkage mechanism proposed by [PERSON] et al. [86] and [PERSON] et al. [87] are examples of end effectors with fixed trajectories. In contrast, in the end effector with adjustable trajectories, the trajectory is not predefined and it can be adjusted by programmable systems to generate different types of human gait patterns (e.g., stair climbing and flat walking). HapticWalker [88] and G-EO [89] systems are the most common examples of end effectors with adjustable trajectories. The end effector systems for balance rehabilitation usually consist of a unique standing footplate. This standing surface can challenge the standing balance of the patients by tilt movements or, in general, by ground perturbations. Hunova(r) [90] and the Balance System(tm) SD (Biodex Medical Systems Inc., Shirley, NY, USA) are examples of end effectors for standing balance. Besides, it is possible to combine these perturbation footplates with a treadmill, to assess and rehabilitate the balance function of the patients in walking conditions [91].
## 5 Simulation and Result Analysis
Figure 9: \(\Delta f_{i}(i=1,2,3,4)\) of each power system under load disturbance in Case 3. Figure 10: \(ACE_{i}(i=1,2,3,4)\) of each control area under load disturbance in three different cases: (**a**) \(ACE_{1}\) of Control Area 1; (**b**) \(ACE_{2}\) of Control Area 2; (**c**) \(ACE_{3}\) of Control Area 3; and (**d**) \(ACE_{4}\) of Control Area 4. Figure 10 shows the control effect comparison of four areas in three different situations. The control effect is evaluated according to whether the value of the reference performance index \(ACE_{i}(i=1,2,3,4)\) is close to zero. The expression of \(ACE_{i}(i=1,2,3,4)\) is composed of terms \(\Delta P_{titi}\) and \(\Delta f_{i}\), \(ACE_{i}=\Delta P_{titi}+\varphi_{i}\Delta f_{i}\). The closer \(ACE_{i}\) is to zero, the better is the control effect. Figure 10a-d, respectively, shows the simulation results of the Control Areas 1-4 in three different cases. Comparing and analyzing the output waveform of the system, when the load disturbance occurs in Case 1, the \(ACE_{i}\) values of each region oscillate greatly and it takes more time to recover the stability near the zero value. Comparing PID control and H\({}_{\infty}\) robust state feedback control, it can be clearly seen in Figure 10 that H\({}_{\infty}\) robust state feedback controller can effectively enhance the robustness and stability of the system in the condition of disturbance, keep the system output \(ACE_{i}\) fluctuating within the range of \(\pm 0.0006\) Hz near zero and restore the system frequency stability within 10 s.  
The design goal of this paper is to return the stability of the load frequency of the power system as quickly as possible in the event of external load disturbances, namely, make the value of area control error (\(ACE_{i}\)) stable at zero or within a minimum range near zero. Therefore, through the analysis of simulation results, it can be shown that the stability of power system and the control performance of the LFC can be improved effectively by introducing the HESS.
## 7 Results and Discussions  
### Consumer Scenarios  
In the present work, four types of consumer scenarios were simulated and discussed. In the first case, a single home was taken, and its hourly load, hourly energy cost, PAR and waiting time were determined both in the unscheduled and scheduled (with ACO, CSA, GA, FA, MFO and TG-MFO) environment for a single day. In the second case, a residential building with thirty homes having users with different habits with different LOTs and different power ratings of appliances were considered. Again, their hourly average load, hourly cost, PAR and waiting time were determined both for unscheduled and scheduled (with ACO, CSA, GA, FA, MFO and TG-MFO) scenarios for a single day. In the third case, we considered a single home, found all four of its parameters for unscheduled and scheduled scenarios for a complete month, i.e., thirty (30) days, and found its monthly bill. In the fourth case, a residential building with thirty homes was considered, and we determined its daily average load, daily cost, daily PAR and average waiting time for a complete month, i.e., thirty (30) days, as well as calculated its monthly electricity consumption and electricity bill. In all four cases, the RTP scheme was used.  
For the system's stability, further reduction of electricity consumption and maximum user comfort, RES and BSUs were also integrated. Additionally, for photovoltaic cells' electricity generation, temperature, solar irradiance, battery charging/discharging rates and its storage system, different assumptions were considered from [40].
## 2 Results and Discussion
### Nephroprotective Effect of APTM
#### 2.4.3 Effect of APTM on Serum and Urinary Creatinine, Uric Acid, and Urea Levels  
In GM-induced acute nephrotoxicity, the activities of aqueous APTM extract on renal biomarkers were investigated (Table 3). In comparison to rats in the NCG, rats receiving GM (80 mg/kg) for 14 days had significantly higher serum creatinine (\(p<0.001\)), uric acid (\(p<0.05\)), and urea (\(p<0.01\)). In addition, GM caused a substantial reduction in creatinine (\(p<0.001\)), uric acid (\(p<0.01\)), and urea (\(p<0.01\)) levels in the urine. Changes in renal biochemical marker levels caused by GM suggest acute renal toxicity [25; 26]. Since urea and creatinine are produced during purine metabolism, their elevated serum levels could indicate filtration rate alterations and be linked to kidney injury [27; 28]. These outcomes are in line with those of other researchers, who found that giving GM to rats caused an increase in urea and creatinine concentrations in the serum with a lowering in those levels in the urine [29; 30; 31]. As shown in Table 3, before 3 h of the GM injection, daily administration of the aqueous extract of APTM (for 14 days of treatment) reversed the GM-induced alterations in kidney biomarkers to near-normal levels. When compared to GM-poisoned rats not treated with extract, the 200 and 400 mg/kg doses of the APTM extract substantially lowered serum creatinine (\(p<0.01\), \(p<0.001\)), urea (\(p<0.05\)), and significantly increased urine creatinine (\(p<0.01\), \(p<0.001\)), uric acid (\(p<0.05\), \(p<0.01\)), and urea (\(p<0.05\)). Various plant extracts have been demonstrated to have similar anti-GM protective properties in other research [25; 32; 33]. According to several studies, GM can cause nephrotoxicity by increasing reactive oxygen species [34; 35]. Natural antioxidants like polyphenols and flavonoids are known to capture free radicals in vivo and in vitro [17]. In addition, the results of our study indicate that the aqueous extract of APTM is rich in phenolic compounds, suggesting that our extract improved kidney function in GM-poisoned by removing free radicals brought on by GM in rats.
# Shared-Custodial Wallet for Multi-Party Crypto-Asset Management
## 4 System Operation
### Key Generation  
As shown in Figure 1, we employ Distributed Key Generation (DKG) to produce cryptographic key shards for the client, wealth manager, and the system. Key shards are generated locally for all users and are never exposed to one another, effectively eliminating any single point of failure. If a malicious actor compromises one of the shares, the client's funds cannot be spent because no single shard provides access to the private key.  
The process begins with each participant independently generating their own key shard. These shards are collaboratively verified using cryptographic commitments to ensure consistency and integrity. Once verification is complete, the shards are mathematically combined to derive a public key. This public key is then used to generate a shared wallet address for the wealth manager and the associated client.  
Unlike traditional Verifiable Secret Sharing (VSS), DKG eliminates the need for a trusted centralised party [15]. Instead, each participant runs their own VSS instance, distributing pieces of their share to the other participants. At the end of the sharing phase, these pieces are verified for consistency using cryptographic commitments. This decentralised approach ensures that no single entity ever has access to the full private key, enhancing both security and trust among participants [16].  
The resulting setup is a 2-of-3 MPC-TSS wallet, requiring the collaboration of at least two parties (e.g., the client and wealth manager, the wealth manager and system, or the client and system) to generate a valid transaction signature. This distributed approach ensures that the private key is never fully reconstructed, thereby protecting against compromise and maintaining system security.
## 3 Proposed Ensemble of Depthwise Separable Convolutional Neural Networks
### Beat Detection and Segmentation  
In this paper, beat detection, QRS detection, or R-peak detection are performed based on analysis of gradient, amplitude, and duration of the ECG signals similar to [39]. R-peak detection of ECG signals can be done through wavelet transforms with a detection accuracy of more than 99% [39; 40]. R-peak detection was performed on 48 records of the MIT-BIH database. With a 360 Hz sampling rate, each record contains 650,000 samples or a duration of 30 min [41]. Each sample is a conversion of a range of 10 mV using an 11-bit ADC [41].  
Figure 1 shows examples of ECG pieces of 3000 samples or 8.33 s from the 2 nd, 18 th, and 36 th records. The RR intervals on ECG chunk from the 2 nd and 36 th files tend to be uniform, while the RR interval on the ECG chunk from the 18 th file looks more diverse. Hence, the R-peak detection and the RR interval measurement affect the beat segmentation process.  
In this paper, after R-peak detection, beat segmentation starts from \(\frac{1}{4}L_{i}\) to \(\frac{3}{4}R_{i}\), in which \(L_{i}\) is the RR-interval right before the detected R-peak, while \(R_{i}\) is the RR-interval right after the detected R-peak. This automatic segmentation window is necessary to ensure that there is only a single beat or R-peak in each segment. The maximum number of samples taken for each segment is 256 points which is equivalent to a duration of 256/360 s or 711 ms. The MIT-BIH database used a sampling rate of 360 Hz, so the 256 sample segment size will be more than adequate as the typical RR interval is around 500 ms [42].  
Figure 2 shows the example of our proposed automatic beat segmentation. In our segmentation method, if the RR-interval is too short, or there is more than one R-peak at the segment, then zero-paddings are performed to keep the segment size to 256 samples with only one R-peak. If required, this segment size could be downsampled to 128 or even 64 samples to further reduce the computational cost. However, there will be a slight decrease in the classification accuracy. More experiments are conducted on the sample sizes and its accuracy in the next section.  
Figure 1: The ECG chunk contains 3000 samples from 3 files, i.e., the 2 nd, 18 th, and 36 th files. The vertical axis is the voltage in mV, while the horizontal axis is time with a scale of 500 samples or 1388 s. The text on the horizontal axis contains the type of beat, and its R-peak position, for example, _f_: 608, means that the beat is Paced Beat (see Table 1) with R-peak position at the 1608 th sample from 650,000 samples in the 2 nd file.
## 5 Discussion of Measurement Results: Model of Charged Defects Motion Near a Waveguide
### When the Temperature is Stabilized, Waveguide Properties Are Restored over Time  
When the temperature is stabilized, mobile charges existing within the crystal and on its surface (as well as charged particles attracted by the electric field from the air surrounding the crystal) shield the pyroelectric charges, leading to a decrease in the pyroelectric field in the crystal. Thus, after some time and at a constant temperature, the pyroelectric field drops to a value at which the waveguide contrast sufficient for operation in the single mode is restored.
## 5 Experimental Setup and Results Discussion
### Comparison of Algorithm
The proposed IMA achieves a well-balanced trade-off between computational efficiency and positioning accuracy, making it particularly suitable for real-time indoor localization applications.
## 5 Discussion and Conclusions  
Airborne lidar measurements were performed over several temperate and tropical forests sites, which allowed for building a representative database of lidar vertical profiles. From these lidar measurements, a semi-empirical approach was applied using the radiative transfer equation applied to the laser beam propagation into a scattering medium. An end-to-end simulator was developed to simulate and analyze the performance of both air- and space-borne lidar systems. The uncertainties on structural and optical parameters (tree top height, quadratic mean canopy height, and extinction coefficient) for spaceborne observations were estimated. The surface slope and the multiple scattering effects on the lidar signal were discussed, and proved to be not negligible for spaceborne observations leading to a relative error \(\sim\)10%-30% on the retrieved parameters. The optimal signal-to-noise ratio was discussed for both ultraviolet (UV) and near infrared (NIR) wavelengths. The link budget for several platforms was built up for the two selected wavelengths (355 and 1064 nm), first under ideal atmospheric conditions (_i.e._, no cloud and medium aerosol content: aerosol optical thickness of 0.15), and then considering more realistic atmospheric scattering properties.  
We confirm that the UV wavelength is suitable for airborne lidar measurements. However, UV lidar is not a good candidate for spaceborne missions due to low atmospheric transmission and strong absorption by the vegetation in the UV domain. The required energy in UV is \(\sim\)30 times larger than in NIR through our simulations. It may be possible to use a UV lidar with \(\sim\)80 mJ energy onboard the ISS platform, but only for forests with an optical thickness less than 1, corresponding to temperate deciduous forests in winter or temperate conifer forests. Hence, a wavelength in the NIR is preferred for a spaceborne lidar system dedicated to forest survey at the global scale, as medium dense forests (e.g., \(FOT\)\(\sim\)2) can be well detected for all considered orbits from the ISS to the CALIPSO missions. But for the denser forests (e.g., temperate deciduous forests in summer or tropical forest with \(FOT\)\(>2\)), a lower orbit is preferred and the number of satellite revisits should be increased to reach a good detection probability.  
Spaceborne lidar dedicated to canopy can also be used for atmospheric studies because the emitted energy needed for forest study is comparable to the one of missions as CALIPSO. It can be a continuation of the CALIPSO/CALIOP or further the ADM-Aeolus/ALADIN or EARTHCARE [76] missions. A specific spaceborne canopy lidar mission has been considered as a priority in the medium term by the French space agency following its prospective seminary held at La Rochelle in 2014.
## 5 Constraints Employed While Formulating Optimization Problems in WSNs  
In many practical problems, the input parameters can not be selected arbitrarily, rather they are prescribed by some physical limitations. Different configuration of the input variables can lead to different nature of optimization problem and can largely affect the output of the optimization. Figure 4 highlights various constraints which have been considered in the articles mentioned against each while formulating the optimization problem.  
* For example, the constraint of ensuring connectivity between different nodes of sensor network has been considered in [85] while obtaining a trade-off between coverage rate, percentage of active sensor nodes and unbalanced energy consumption. Connectivity and coverage constraints have been considered in [134], where authors have used a multi-objective optimization to achieve a trade-off between coverage, load balance, economic efficiency and interference. In [163], the authors have used an evolutionary approach to minimize the cost of the network and maximize the system reliability while considering the constraints of coverage and connectivity.
* Energy consumption is a critical parameter which influence the overall performance of wireless sensor network and its effective lifetime. Therefore, abundant literature is available considering power or energy consumption as their design objective or considering it as the constraint while formulating the optimization problem. For example, in [164], a multi-objective optimization technique has been discussed while considering the constraint of energy consumption to maximize the prediction accuracy and minimize the latency. The constraint of energy consumption has also been considered in [165], where the authors have proposed a technique to maximize the the coverage and efficiency of tracking the mobile targets.
* Some monitoring or measuring applications of wireless sensor networks require to provide with real time sensing capability in order to facilitate protection of those persons who are at risk to potentially harmful environments, including soldiers, first responders, and deep-sea and space explorers [166]. Some bio medical sensors require low data rate, e.g., heartbeat, blood pressure and electroencephalogram but the data may be delay sensitive and must be delivered to the main processor with in some specified time limits. In [42], a multi-objective routing algorithm has been proposed to ensure QoS for different traffic types while ensuring the delay constraints. Reliability of information transmission, interference, QoS, radio resource, coverage, topology, transmission range, number of hops, spatial density, cost and storage are few other constraints which have been considered in numerous articles while formulating the multi-objective optimization problem for the optimization of wireless networks.