esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Traffic Sensor Deployment Model with Communication Consideration for Highways
## 2 The Optimization Model for the Hybrid Sensor Deployment
(4) Capacity constraint  
Since the data-processing time is limited by an MEC server's capacity, multiple sensors send requirements simultaneously to a server, which might bring about a negative effect on computation speed. To improve computational efficiency and reduce latency, the number of sensors associated with a server is constrained, as shown in Equation (7). \[\sum_{i\in\mathbf{N}}y_{iu}(\sum_{j\in\mathbf{N},k\in\mathbf{S}}\alpha_{k}x_{ij}^{k})\leq z _{u}F,\forall u\in\mathbf{N} \tag{7}\]  
where \(\alpha_{k}\) represents the required storage space of the first sensor of combination type \(k\), \(F\) denotes the capacity of an MEC server, \(y_{iu}\) utilizes a binary value to represent the service relationship between sensors in location \(i\) and servers in location \(u\) cell. Equation (8) expresses the connection between the locations of sensors and servers. \[\sum_{j\in\mathbf{N},k\in\mathbf{S}}x_{ij}^{k}=\sum_{u\in\mathbf{N}}y_{iu},\forall i\in \mathbf{N} \tag{8}\]  
Equation (9) implies that only if a server is deployed at cell \(u\), the value of \(y_{iu}\) can be equal to \(1\). \[\sum_{i\in\mathbf{N}}y_{iu}\leq z_{u}M,\forall u\in\mathbf{N} \tag{9}\]  
Equation (10) indicates the constraint of the service scope of a server. \[y_{iu}\leq\lambda_{iu},\forall i,u\in\mathbf{N} \tag{10}\]  
where \(\lambda_{iu}\) is a \(0-1\) index, \(1\) if cell \(i\) is included in the service scope of an MEC server placed on cell \(u\) and \(0\); otherwise, \(M\) is a very large number. (5) Binary constraint Equation (11) imposes the binary constraints for variables \(x_{ij}^{k}\), \(z_{u}\), and \(y_{iu}\). \[ \begin{split}& x_{ij}^{k}\in\{0,1\},\forall k\in\mathcal{S},i,j, \in\mathbf{N}\cup\{0,n+1\}\\ & z_{u}\in\{0,1\},\forall u\in\mathbf{N}\\ & y_{iu}\in\{0,1\},\forall i,u\in\mathbf{N}\end{split}  \tag{11}\]  
Therefore, the entire formulation of the hybrid sensor deployment is outlined as follows. \[ \begin{split}& max\sum_{i,j\in\mathbf{N}\cup\{0,n+1\},k\in \mathcal{S}}\pi_{ij}^{k}x_{ij}^{k}\\ & s.t.\quad\text{Equations}\quad\eqref{eq:sys}\sim\eqref{eq:sys} \end{split}  \tag{12}\]
###### Abstract  
A federated learning (FL) framework for cloud-edge-client collaboration performs local aggregation of model parameters through edges, reducing communication overhead from clients to the cloud. This framework is particularly suitable for Internet of Things (IoT)-based secure computing scenarios that require extensive computation and frequent parameter updates, as it leverages the distributed nature of IoT devices to enhance data privacy and reduce latency. To address the issue of high-computation-capability clients waiting due to varying computing capabilities under heterogeneous device conditions, this paper proposes an improved resource allocation scheme based on a three-layer FL framework. This scheme optimizes the communication parameter volume from clients to the edge by implementing a method based on random dropout and parameter completion before and after communication, ensuring that local models can be transmitted to the edge simultaneously, regardless of different computation times. This scheme effectively resolves the problem of high-computation-capability clients experiencing long waiting times. Additionally, it optimizes the similarity pairing method, the Shapley Value (SV) aggregation strategy, and the client selection method to better accommodate heterogeneous computing capabilities found in IoT environments. Experiments demonstrate that this improved scheme is more suitable for heterogeneous IoT client scenarios, reducing system latency and energy consumption while enhancing model performance.  
federated learning; heterogeneous clients; model dropout Article
# Laminar _Ulva_ Species: A Multi-Tool for Humankind?
## 5 Commercial Potential
### Bioplastic  
Plastic pollution is a major problem across the world, with approximately 20% of plastic in the oceans coming from marine sources and 80% coming from terrestrial sources. Plastic is a unique substance because it is inexpensive, adaptable, light, and resistant. It is utilized in various applications such as medicinal equipment, packaging, 3D printing, home appliances, autos, electronics, and so on, making people dependent [212].  
Annual plastic production has increased from 2 million metric tons in 1950 to 460 million metric tons in 2019 [212]. However, these plastics will take thousands of years to degrade and will eventually accumulate in landfills or the natural environment.  
There are many methods and materials for making bio-based plastics, and the essential ingredients are polysaccharides, proteins, and lipids. Several substances utilized today include com, potatoes, vegetable oils, wood, food waste, beetroot, sugar cane, and cereals, as the major forms of bioplastics nowadays are starch-based, followed by polylactic acid, (PLA--a highly versatile and biodegradable aliphatic polyester), poly-3 hydroxybutyrate (PHB--a polymer that belongs to the polyester class), polyamide 11 (PA 11--polymer from the nylon family), and polyethylene (PE--organic polymer) [213].  
Seaweed is promising to produce bioplastics as it can form films. _Ulva_ spp. can also be used to generate polylactic acid (PLA) film because of their starch content, which acts as biomass for fermentation with _Lactobacillus plantarum_. Some green algae taxa are underused for the manufacture of bioplastics, whereas others are solely employed to make polymers, such as _Caulerpa_ sp., _Codium_ sp., and _Cladophora_ sp. (Chlorophyta) [213].  
Bioplastic properties and characterization details tend to be categorized into the following categories: physical (thickness, solubility, water vapor permeability, conveyance of water vapor rate, and moisture content); optical (transparency, opacity, and light transmittance value); mechanical (thickness); structural, thermal, antioxidant, antimicrobial, and biodegradable [213]. However, generic methods are not sustainable to produce bioplastics due to yield, time, and costs; hence it is advised to do more research with different species.
## 3 Methods
### Instruments  
For the Augmented Reality application in this study, a commercially available AR system (UINCARE, Uincare Co., Ltd., Seoul, Republic of Korea) was utilized. This system comprises a 3D depth camera, a 64-inch monitor, operating software, and approximately 300 types of body exercise content [9].  
Upon system initiation, the participant stands motionless at a distance of 1.5 m in front of the 3D depth camera. The camera identifies the participant's joints and generates overlaid joint segments on the participant's image displayed on the screen. When pre-set exercise content is activated, a model demonstration for the participant to emulate appears on the left side of the monitor, while the target and completed repetition counts are shown on the right (Figure 1). The user performs the exercise by following the on-screen model. The target range of motion and guiding lines to achieve it are displayed.  
The participant moves their body along the guidelines towards the target. Accurate target achievement triggers a success sound, and the repetition count increases. This process is repeated continuously [9].  
In this study, a sensor-based 3D motion capture system (Utium Motion Sensor, No-raxon, Scottsdale, AZ, USA) was employed to measure the accuracy of exercise movements both with and without the AR system. This system utilizes an array of multiple inertial measurement units (IMUs) to measure and analyze anatomical joint angles, orientation, and linear acceleration during specific tasks performed in daily activities or natural environments. The system comprises 9 inertial sensors, a data receiver, an experimental laptop, and analysis software.  
The placement of the nine sensors was as follows: upper thoracic spine (midline below C7), lower thoracic spine (midline between L1 and T12), pelvis (sacral region), intact upper arm (midpoint between shoulder and elbow joints, lateral to the bone axis), forearm (posterior and distal), hand (dorsum), intact thigh (anterior and distal half), calf (anterior and slightly medial, along the tibia), and foot (dorsum, slightly below the ankle). The sensors for the upper thoracic, lower thoracic, and pelvis regions were placed directly on the bare skin beneath the participants' clothes.  
To analyze the users' subjective satisfaction based on the presence or absence of the AR system, a five-item questionnaire was administered, assessing interest, motivation, exercise effectiveness, accuracy of movement, and overall satisfaction. Each item was rated on a 5-point Likert scale, yielding a total possible satisfaction score of 25 points.
## 5 Simulations Results and Analysis
### Results of Switch Control Strategy  
According to the optimization results mentioned in Table 5, the RCSMC and the HDSMC are obtained. A comparison between the proposed switch control SMC (Switched SMC) and two conventional SMCs (HDSMC and RCSMC) is discussed, which can better assert that the proposed control is superior. In order to demonstrate the effectiveness of the proposed switch control strategy, different steering maneuvers including J-turn and Fishhook are tested. Furthermore, to better illustrate the adaptability of control strategy, two different typical road conditions including random road and bump road are built. The related parameters of the vehicle driving state and the corresponding road input are listed in Table 6.  
\begin{table}
\begin{tabular}{c c c|c c c} \hline  & \multicolumn{2}{c|}{**RCSMC**} & \multicolumn{2}{c}{**HDSMC**} \\ \hline  & PACSA & GA & & PACSA & GA \\ \hline \(n_{1}\) & \(-0.575\) & \(-2.55\) & \(\rho\) & 5 & 4.73 \\ \(n_{2}\) & \(0.6559\) & \(9.7\) & \(\mu\) & 3 & 2.99 \\ \(n_{3}\) & \(0\) & \(-0.063\) & \(\eta\) & 25 & 24.92 \\ \(\eta\) & \(11\) & \(14.939\) & \(\kappa_{1}\) & 0.0012 & 0.73 \\ \(\mu\) & \(0.0005\) & \(0.15\) & \(\kappa_{2}\) & 0.0014 & 0.0025 \\ \(\zeta\) & \(0.444\) & \(0.39\) & & & \\ \hline \end{tabular}
\end{table}
Table 5: Optimized parameters of the PACSA and GA.  
Figure 12: SMC control comparison optimized by PACSA and GA: (**a**) the objective function of ride comfort; (**b**) the objective function of handling stability; (**c**) the body vertical acceleration; (**d**) the roll angle.
## 3 Results and Discussion
### Oil-Film Pressure  
In Section 3.2, oil-film temperature under different load cases has been analyzed and oil-film temperature determines the safety of operation [28; 29]. In order to avoid repeatedly analyzing the influence of load cases on the operating performance of oil-film bearing oil, the emphasis in Sections 3.3 and 3.4 will be on the comparison between two kinds of oil-film bearing oil, and 150 rpm-6 MPa is selected as the analyzing load case [30; 31].  
Figure 10 is the comparison of oil-film pressure between S220 and M220 at 150 rpm-6 MPa. P1 and P2 (P means pressure transducer) are in the same circumferential direction, and P1 and P3 are in the same axial direction, e.g., the theoretical oil-film pressures at 150 rpm-6 MPa were P1 = 1.528069, P2 = 0.475358, and P3 = 0.701255 by numerical calculation, which is obvious that P1 \(>\) P3 \(>\) P2. As shown in Figure 10, the experimental results of oil-film pressure of both oils conform to the theoretical distribution. P1 is the nearest to the loading point, so it is the maximum. P3 is near to the axial end of oil-film bearing, so it is smaller. P2 locates in the unload-carrying area, so it is the smallest. During the whole operation stage, the oil-film pressure always keeps constant, and both oils have the equal oil-film pressure at the corresponding points. Therefore, both oils have the approximate same oil-film pressure.
# An Optimization Model for a Desert Railway Route Scheme Based on Interval Number and TOPSIS
## 2 Research Methods
### Construction of the Evaluation Index System of Desert Railway Route Scheme
#### 2.2.1 Analysis of the Evaluation Factors
Due to the sparse rainfall, low vegetation coverage, and shallow groundwater depth in the desert area, the project construction can easily damage the fragile geographical environment [26]. Ecologically sensitive areas along the railway include natural reserves, key cultural relics' protection areas, water conservation districts, and crucial ecological function zones. The noise and vibration produced by the train during the operation period will overshadow the survival of animals in protection districts [27]. Vegetation destruction is chiefly caused by the permanent occupation of forest lands, grasslands, wetlands, and temporary facilities (i.e., those in the construction stage by the railway and stations) [28]. The wildlife migration corridor plays a vital role in maintaining communities. However, the subgrade height of the railway is generally placed in the range of 2-6 m, which divides the migration activities of animals, thus affecting the reproduction and foraging of wild animals. The desert railway route should be designed to occupy less basic farmland and make use of wasteland and gravel beaches as much as possible for line development. (4) Operating condition  
Sand hazard affects the safe operation of desert railways. The track structure of railway in desert areas is usually ballasted track. Sand grains filled in the ballast reduce the damping and drainage performance of the ballast, which increases the dynamic response between wheels and rails when the train passes. In order to ensure the safe operation of the train, ballast needs to be cleaned regularly. The sand entering the track bed shortens the maintenance period and increases the maintenance cost of the ballast. The wind-sand flow reduces the visual distance of the train. The train should operate at low speed when the wind-sand flow is serious. The operation speed of train is related to the railway alignment parameters. The minimum curve radius limits the minimum speed of the train passing through the track. The more curves, the lower the passenger comfort and driving safety.
# A Multiple Criteria Decision Analysis Framework for Dispersed Group Decision-Making Contexts
## 4 Scenarios of Application
### Explaining the Reasons Behind the Different Recommendations/Proposals (to Reject or to Accept)
#### 4.2.3 Step 3  
The third and last step consists of ordering the criteria list for the proposed alternative from the most to the less important. In our example, zingara is the proposed alternative because of the criteria transportExpenses, qualityOfTheFood, atmosphere, wine and healthy.  
Decision-makers are usually interested in understanding the reasons why an alternative was proposed instead of another. Let us imagine we have to explain why zingara was proposed insteadof ThaiPalace. For that, we use the consistency matrix (\(CM_{dm_{k}}\)) of each decision-maker, which in our example are the following:  
\[CM_{dm_{1}}=\left[\begin{array}{cccccccc}0&0&0&0&0&0&0&0&0\\ 0&0&0&0&0&0&0&0&0\\ 0&0&0&-3&0&-4&-4&0&-4\end{array}\right]\]  
\[CM_{dm_{2}}=\left[\begin{array}{cccccccc}-2&0&0&0&-4&0&0&0&0\\ -4&-4&0&0&-1&0&0&0&0\\ 0&0&0&0&0&-4&-4&0&0\end{array}\right]\]  
\[CM_{dm_{3}}=\left[\begin{array}{cccccccc}0&0&0&0&0&0&0&0&0\\ 0&0&0&0&0&0&0&0&0\\ 0&0&0&-3&0&-4&-4&0&-4\end{array}\right]\]  
Remember that each cell on each consistency matrix refers to the calculus of \(F_{CriterionConsistency}\big{(}a_{i},c_{j},dm_{k}\big{)}\), the columns to the criteria, and each row to an alternative. So, only the rows of the alternatives being compared are needed:  
\[CM_{dm_{1}}=\begin{array}{c}zingara\\ \textit{thaiPalace}\end{array}\left[\begin{array}{cccccccccccc}0&0&0&0&0&0&0&0 &0\\ 0&0&0&0&0&0&0&0&0\end{array}\right]\]  
\[CM_{dm_{2}}=\begin{array}{c}zingara\\ \textit{thaiPalace}\end{array}\left[\begin{array}{cccccccc}-2&0&0&0&-4&0&0&0&0 \\ -4&-4&0&0&-1&0&0&0\end{array}\right]\]  
\[CM_{dm_{3}}=\begin{array}{c}zingara\\ \textit{thaiPalace}\end{array}\left[\begin{array}{cccccccc}0&0&0&0&0&0&0&0&0 \\ 0&0&0&0&0&0&0&0\end{array}\right]\]  
Now, let us sum the corresponding cells of each decision matrix:  
\[\left[\begin{array}{cccccccc}-2&0&0&0&-4&0&0&0\\ -4&-4&0&0&-1&0&0&0&0\end{array}\right]\]  
Finally, we just need to select all the criteria with a higher value for the proposed alternative. So, we can say that zingara is a better option than thaiPalace due to the distance and transportExpenses. In addition, decision-makers could have interest in knowing what reasons could make thaiPalace better than zingara, which according to this strategy, would be the mealPrice.
# Algorithm for Extraction of Reflection Waves in Single-Well Imaging Based on MC-ConvTasNet
## 3 Results
### Wave Separation for Field Logging Data
The \(\text{R}_{\text{snr}}\) values corresponding to the four methods are 4.9 dB, 13.5 dB, 18.2 dB and 58.1 dB, respectively. In this well section, MC-ConvTasNet achieves both a direct wave suppression ratio and signal-to-noise ratio exceeding 55 dB, demonstrating superior direct wave suppression capability. Although MC-ConvTasNet is trained on the dataset derived from theoretical calculations, the comprehensive analysis of \(\text{R}_{\text{dsr}}\), \(\text{R}_{\text{snr}}\) and imaging results demonstrates that  
Figure 12: The reflected waves extracted from field logging data. (**a**) is a COG variable density map of field logging data. (**b–e**) are COG variable density maps of the reflected waves extracted by parameter estimation, the median filter, the F-K filter and MC-ConvTasNet, respectively. In each of these maps, the area containing the reflected waves is highlighted with a solid red box. (**f–i**) are imaging results of reflected waves extracted by these four methods, respectively. In each of these maps, the area the reflector is highlighted with a dotted red box. The values displayed in the variable density maps (**a–h**) are clipped to a range of \(+/-70\). The values displayed in the variable density map (**i**) are clipped to a range of \(+/-250\). MC-ConvTasNet can not only effectively suppress the majority of direct waves but also maintain most of the reflected wave signals.
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 6 Nanoparticle Toughening Method
### CNTs  
[PERSON] et al. [102] investigated the effects of incorporating toluene-functionalized carbon nanotubes (CNTs) into epoxy resin. The study demonstrated that the introduction of functionalized CNTs significantly enhanced the glass transition temperature (Tg), thermal conductivity, tensile strength, and fracture strength of the epoxy resin. Furthermore, the incorporation of functionalized CNTs resulted in improvements of 60%, 27%, and 80% in tensile, flexural, and fracture strengths, respectively.  
[PERSON] et al. [103] functionalized carbon nanotubes (CNTs) via ball milling and incorporated them into epoxy resin to fabricate high-performance nanocomposites. The study revealed that non-covalently modified CNTs (M-CNTs), compared to pristine CNTs, significantly enhanced the elastic modulus, tensile strength, and fracture toughness of the epoxy composites. Notably, the 2 wt% M-CNTs/epoxy composite exhibited a 64% increase in elastic modulus, a 22% improvement in tensile strength, and a remarkable 95% enhancement in fracture toughness.
## 5 Measurement Campaign
### Performed Flights  
The measurement campaign was held in France in the Champagne--Ardenne region, near Saint Hilaire le Grand (latitude 49\({}^{\circ}\)10\({}^{\prime}\)43.45\({}^{\prime\prime}\) N and longitude 4\({}^{\circ}\)28\({}^{\prime}\)3.72\({}^{\prime\prime}\) E) to be precise. During this measurement campaign organized in two periods (the first one from the 3 rd to the 12 th of September 2014 and the second one from the 22 nd of September till the 3 rd of October 2014) a total of 17 flights were carried out with six of them to test all the components while the rest where intended to measure the CO\({}_{2}\) vertical profile up to 10 km altitude under Meteo-France responsibility, flight direction and with the Meteorological Mobile Measurement Means facilities (4M, Meteo France Institut National des Sciences de I'Univers INSU). Note that the pre-positioning system of Meteo-France was 100% efficient so that we pre-localized the recovery site and had 100% recovery of the instrument.  
The main objective of this campaign was to show the feasibility of measuring the CO\({}_{2}\) profile during several periods of the day, in particular to study the boundary layer between early morning time and afternoon. A second objective was to slightly exceed the tropopause level in order to test the instrument in harsh environments. On 1 October 2014, we realized four successive flights up to 10 km altitude from the same launch site with the same instrument at 08h07, 10h18, 12h23 and 14h11 Paris Local Time. Figure 10 shows the results recorded in the upward sounding at a rate of 1 Hz (i.e., spatial resolution < 5 m), to the left, the inverted pressure scale vs. the concentration of the measured CO\({}_{2}\), then the temperature according to the pressure and finally the relative humidity depending on the pressure. We also present the trajectories of these 4 flights with the help of Google Earth.  
Figure 10: The four successive flights realized on 1 October 2014 launched from the same site from 08h07 till 14h11 Paris Local Time (approximately 2 h separating 2 successive flights). From left to right: Inverted pressure vs. the concentration to show the vertical profile of the CO\({}_{2}\) up to 10 km altitude, the second panel is dedicated for pressure vs. temperature dependency, the third one is for relative humidity dependency. The fourth panel shows the launching site for all those flights and the recovery site according to the GPS position and plotted with the help of Google Earth.  
Figure 9: The different flight stages of AMULE. (**a**) The instrument is securely attached to the two-balloon system and ready to fly. Stages (**b,c**) corresponds to the beginning of the flight while (**d**) shows the pre-positioning determined by the flight simulation software in order to recover the instrument (**e**).