esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Elevation Accuracy Assessment  
The elevation accuracies of these DEMs were estimated by comparing with the high qualityICESat-2 altimetry data. Because the ICESat-2 reference data are a series of discrete points, the inverse distance weighting (IDW) interpolation method [49; 50] was selected to calculate the DEM elevation of each ICESat-2 data footprint. Formally, the IDW method is used to estimate the unknow value \(\hat{y}(S_{0})\) in location \(S_{0}\), given the observed \(y\) values at sampled locations \(S_{k}\) in the following manner:  
\[\hat{y}(S_{0})=\sum_{k=1}^{n}\lambda_{k}y(S_{k}) \tag{3}\]  
where the weight value \(\lambda_{k}\) is calculated by:  
\[\lambda_{k}=d_{0k}/\sum\
olimits_{k=1}^{n}d_{0k}\text{ with }\sum_{k=1}^{n} \lambda_{k}=1 \tag{4}\]  
Then, the elevation differences were calculated by subtracting the DEM elevation from the ICESat-2. The Mean Error (ME), Root Mean Square Error (RMSE) and Standard Deviation (STD) were calculated as follows:  
\[ME=\frac{1}{n}\sum_{i=1}^{n}(x_{i}-y_{i}) \tag{5}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(x_{i}-y_{i})^{2}}{n}} \tag{6}\]  
\[STD=\sqrt{\frac{\sum_{i=1}^{n}\left[(x_{i}-y_{i})-\overline{(x_{i}-y_{i})} \right]^{2}}{n-1}} \tag{7}\]  
where \(n\) is the number of reference data points; \(x\) is the reference elevations, while \(y\) is the elevation values of DEM. The unit of the elevation data is meter.
## 4 Results and Discussion
### Results of Random Component Analysis of the Models  
In the least squares method, as mentioned earlier, the Gauss-Markov assumptions must be met.  
The random components of the residuals of the obtained models (21), (22), and (24) were analyzed, and the results are shown in Table 10.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Linear Model**} & \multicolumn{4}{c}{**Inverted Linear Model**} & \multicolumn{4}{c}{**Power Model**} \\ \hline
**Observation** & \(\mathbf{Y_{1}}\) & **Residual** & \multicolumn{2}{c}{\(\mathbf{Std.}\)} & \(\mathbf{Y_{2}}\) & **Residual** & \multicolumn{2}{c}{\(\mathbf{Std.}\)} & \(\mathbf{Y_{3}}\) & **Residual** & \multicolumn{2}{c}{\(\mathbf{Std.}\)} \\  & & & **Residual** & & & **Residual** & & **Residual** & \\ \hline
1 & 66,647,389 & 1452.611 & 0.584 & 1.473 \(\times\)\(10^{-5}\) & \(-\)5.244 \(\times\)\(10^{-8}\) & \(-\)0.142 & 11.154 & \(-\)0.025 & \(-\)0.603 \\
2 & 57,428.175 & \(-\)1028.175 & \(-\)0.413 & 1.738 \(\times\)\(10^{-5}\) & 3.409 \(\times\)\(10^{-7}\) & 0.924 & 10.948 & \(-\)0.008 & \(-\)0.182 \\
3 & 71,838.350 & 561.650 & 0.226 & 1.334 \(\times\)\(10^{-5}\) & 4.688 \(\times\)\(10^{-7}\) & 1.270 & 11.257 & \(-\)0.067 & \(-\)1.591 \\
4 & 63,218.056 & 5081.944 & 2.042 & 1.531 \(
# Evaluation of Design Parameters on Drying of Kiwi Fruit
###### Abstract  
In this study, the drying of kiwi fruit in a convective dryer is examined experimentally. Air velocity, thickness of the slices and drying time are the varied parameters in drying process. In the experimental study, kiwi slices having thickness of 4 and 8 mm are dried in the dryer under the conditions of 45 \({}^{\circ}\)C temperature of the drying air, 10% relative humidity and 0.5-1.5 m/s drying air velocity. In addition to the tests, the effects of mentioned drying parameters on drying are analyzed by ANOVA (Analysis of Variance) method to find the contribution rate of each parameter. For this aim, an orthogonal array is arranged by using Taguchi design of experiment method. As the results of experiments and variance analysis, it has been determined that, drying air velocity plays a significant effect on total drying time. The minimum drying time (225 min) is achieved for 1.5 m/s for 4 mm slices, and the maximum drying time (750 min) is achieved for 0.5 m/s for the kiwi sample sliced 8 mm thick. It is also concluded that, working with the convective dryer at different velocities is advantageous in terms of preserving brightness and color quality.  
drying; kiwi; air velocity; slice thickness Article
# A Novel Energy-Efficient Contention-Based MAC Protocol Used for OA-UWSN
## 4 OA-UWSN MAC Protocol Simulation Environment Settings
### Optical Properties of Underwater Channels
#### 4.1.2 The Model of Energy Attenuation  
The loss of light waves during their propagation is shown in Equation (7) [28]:  
\[c(\lambda)=a(\lambda)+b(\lambda). \tag{7}\]  
In Equation (7), \(a(\lambda)\) is the total absorption coefficient, \(b(\lambda)\) is the total scattering coefficient, and \(\lambda\) is the wavelength.  
The attenuation law of energy transmitted by light in water is exponentially distributed [29], and the average power received by the receiver is shown in Equation (8):  
\[P_{r}=P_{t}exp[-c(\lambda)r]. \tag{8}\]  
In Equation (8), \(P_{t}\) is the transmitted optical power, \(r\) is the underwater communication distance of the optical signal, and \(c(\lambda)\) is the total attenuation coefficient of the light beam propagating in seawater.  
Considering the loss of the system device and optical path expansion, the received power of underwater wireless optical communication is shown in Equation (9) [30; 31]:  
\[P_{r}=P_{t}\eta\eta_{t}\eta_{r}[1-exp(-\frac{2\partial_{r}^{2}}{\theta^{2}R^{ 2}})]exp[-c(\lambda)r]. \tag{9}\]  
In Equation (9), \(\eta_{t}\) is the light-emitting power generated by the emitting device represented, \(\eta_{r}\) is the light source representing the received power generated by the receiving device of the light source, \(\eta\) represents the noise power, and \(\partial_{r}\) is the aperture radius of the optical receiving antenna. The parameter value settings are shown in Table 2.
## 2 Materials and Methods
### Synchronization of the Measuring Signals
Given a maximum rotation frequency of \(f_{\rm rot}\) = 330 Hz according to Equation (9), the maximum precision is given at a temporal uncertainty of \(\delta t_{\rm min}\) = 40 \(\upmu\)s. As this uncertainty is smaller than the exposure time used for high-speed recordings during the experiments of \(\Delta t_{\rm HS}\) = 59 \(\upmu\)s, the setup allows for synchronization between temperature data and high-speed recordings within the accuracy of a single high-speed frame.
## 1 Introduction
A particle generator with a mean diameter of 0.05 \(\upmu\)m was used to measure face masks' filtering effectiveness in [18]; the authors equipped each face mask with sampling probes to take particle samples within the face mask, and used condensation particle counters to monitor particles (0.02-3.00 \(\upmu\)m) in the environment and behind the face mask. Although the studies previously mentioned supported face masks' potential beneficial effect, the substantial impact of face masks on the spread of laboratory-diagnosed respiratory viruses has remained controversial due to their characteristics in terms of materials and quality [3], thus requiring new analyses with different approaches, making use of deep learning and image processing. The upper airway heats and humidifies the air reaching the lungs at the internal body temperature (37 \({}^{\circ}\)C) during inhalation. When exhaling, the mucosa only recovers part of the heat and humidity added during inhalation, which causes a significant loss of heat and moisture to the environment; therefore, both the loss of heat and moisture changes can demonstrate the ineffectiveness of a face mask [19]. This research presents a computer vision system based on infrared thermography and deep learning to estimate the filtration quality of different consumer face masks. This system allows images of the air distribution that pass through the face masks; once the images are obtained, they are compared with the training images selected for the CNN to know which face masks provide the best protection against the dispersion of SARS-CoVo2 in the air. The main research contributions are as follows:  
* A novel method for the estimation, evaluation, and custom classification of face masks. * The image acquisition and processing system are fast, low-cost, versatile, and scalable to other platforms and thermal sensors. * A qualitative demonstration of filtration efficiency is presented. The rest of the article organize as follows:  
In Section 2, the materials and methods used in this work are presented, and the experimental process is described. Section 3 describes the results and discuss them. Finally, Section 5 presents the conclusions obtained in this article.
# A V2V Identity Authentication and Key Agreement Scheme Based on Identity-Based Cryptograph
## 1 Introduction  
The Internet of Vehicles connects vehicles, roads, people, and other traffic participants through wireless networks to reduce the accident rate, improve driving safety, improve traffic efficiency, save energy, and reduce carbon emissions. The European Union, the United States, and Japan attach importance to the development of the Internet of Vehicles and have made a strategic development plan of establishing a cooperative vehicle infrastructure system. The 3 GPP (3 rd Generation Partnership Project), ITU (International Telecommunication Union), ETSI (European Telecommunications Standards Institute), ISO (International Organization for Standardization), and SAE (Society of Automotive Engineers) have carried out Internet of Vehicles international standard research and formulation. The U.S. Department of Transportation predicts vehicle to vehicle (V2V) communication can avoid 82% of light collisions in the United States.  
There are mainly two Internet of Vehicles standards worldwide. One is C-V2X (cellular vehicle to everything, including LTE-V2X and its evolved NR-V2X); another is dedicated short range communication (DSRC, IEEE802.11p). The C-V2X is a vehicle wireless communication technology based on a cellular network which supports V2V (vehicle to vehicle), V2I (vehicle to infrastructure), V2N (vehicle to network), and other communication modes. Based on cellular mobile communication networks (4G or 5G), the C-V2X technology is innovatively designed to provide high reliability, low latency, and large bandwidth communication capabilities and support many application scenarios, such as formation driving, semi-automatic driving, extended sensor, remote driving, etc. For example, vehicles can share sensed information through V2V communication and provide reminders of road conditions, such as an accident ahead, defective vehicle, slippery road, or low visibility,and other security events to remind other vehicles to avoid dangers. The C-V2X is the most important supporting technology to achieve an automatic driving and intelligent transportation system [1; 2].  
In the C-V2X system, it is necessary to authenticate the identity of the communication equipment to ensure the equipment participating in the communication are legal equipment, and it is necessary to encrypt the communication data to ensure the communication data is not stolen or forged. This paper focusing on the V2V communication scenario, proposes a lightweight equipment identity authentication and key agreement scheme.  
The rest of the paper is organized as follows: Section 2 summarizes the C-V2X security risk and security technology. Section 3 analyzes the V2V identity authentication and key agreement requirement and explains the significance of this study. Section 4 introduces the identity-based cryptography technology. Section 5 proposes a lightweight V2V identity authentication and key agreement scheme. Section 6 analyzes the scheme security and builds an experimental validation environment to verify the scheme feasibility. Finally, Section 7 concludes the paper.
# Energy Performance of LR-FHSS: Analysis and Evaluation
## 3 Overview of LoRaWAN and LR-FHSS  
Created by the LoRa Alliance [26], the LoRaWAN protocol defines the media access control (MAC) layer and provides extensive networking capabilities to enable long-range, low-power communication on top of several physical layer (PHY) alternatives. The original PHY used in LoRaWAN, known as LoRa, was developed by Semtech, a founding member of the alliance. This section presents the main LoRaWAN protocol concepts and the LoRa and LR-FHSS underlying PHY layers.
## 2 Materials and Methods  
### Experimental Design  
Fresh skinless and boneless raw chicken breasts were purchased from the local market (Reggio Emilia, Italy) supplied by the same producer within 24 h postmortem and transported to the Department of Life Sciences, University of Modena and Reggio Emilia, Italy using a thermocol box filled with ice and used immediately. Surface fat was trimmed off and samples were cut into pieces with 125 \(\pm\) 5 g weight and 2.5 \(\pm\) 0.2 cm thickness. Samples were randomly assigned into the 13 groups. Twelve groups were vacuum-sealed in the food-grade nylon-polyethylene plastic pouches (150 \(\times\) 200 mm\({}^{2}\)) using a vacuum sealer (La Grandispensa, Elegen, Reggio Emilia, Italy) with a pump flow rate of 30 L per minute to create 98% vacuum degree inside the pouches. Plastic pouches had wide thermal stability (\(-\)40 \({}^{\circ}\)C\(+\)120 \({}^{\circ}\)C) with O\({}_{2}\) permeability of 9 cm\({}^{3}\)/day m\({}^{2}\) (4 \({}^{\circ}\)C/80% relative humidity), and water vapor permeability of 1.2 g/day m\({}^{2}\) (Joelplas SL, Barcelona, Spain). As a control group, chicken breast fillets sealed in plastic pouches without a vacuum (0% vacuum degree) were boiled at 100 \({}^{\circ}\)C for 60 min. The samples were cooked in a sous vide cooker (Elegen, Reggio Emilia, Italy). Three independent replicate trials with two repeatsbased on different combinations of temperature (60, 70, and 80 \({}^{\circ}\)C) and time (60, 90, 120, and 150 min) were analyzed (Table 1). Overall, a total of 78 chicken breast fillets were analyzed (13 groups of samples \(\times\) 3 independent replicate \(\times\) 2 repeats). The sous vide chicken breast fillets were cooled in an ice bath for one hour and overnight in the fridge at 2-4 \({}^{\circ}\)C. Moisture content, cooking loss, pH, color, TBARS, and shear force were measured the day after the cooking process [7].
## 1 Introduction
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline  & \multicolumn{1}{c}{**Partnership for Global Infrastructure (BRI)**} & \multicolumn{1}{c}{**Free and Open**} & \multicolumn{1}{c}{**Indo-Pacific Strategy**} & \multicolumn{1}{c}{**Three Seas Initiative (3 SI)**} \\ \hline Inception & 2013 & 2022 & 2021 & 2016 & 2015 \\ \hline \multirow{5}{*}{Funding} & USD 1.175 trillion (invested and contracted) & USD 600 billion by 2027 (pledged) & USD 318 billion between 2021 and 2027 (pledged) & FOIP: USD 318 billion between 2021 and 2027 (pledged) & USD 75 billion by 2030 (pledged) and EUR 928 million (committed), with target budget of 2015 to 2020 (budgeted), increased to USD 200 billion in 2016 & USD 690 billion by 2030 (required) and EUR 928 million (committed), with target budget of 2015 to 2020 (budgeted), increased to USD 200 billion in 2016 & USD 690 billion by 2030 (required) and EUR 928 million (committed), with target budget of 2015 to 2020 (budgeted), EUR 3-5 billion (Seno, 2023; Wilczek & Wilczek & and Rudowski, 2024) \\ \hline \multirow{5}{*}{Regional Focus} & Global (Global with strongest operational presence in Asia) & Global (Global South-focused) & Global (Africa-focused, Latin America, Asia, Eastern Europe) & Central and Eastern Europe & Central and Eastern Europe (Baltic, Adriatic, and Black Sea countries) \\ \hline \multirow{5}{*}{Objectives} & Infrastructure; energy, trade, connectivity & Quality infrastructure; domestic job creation and social expenditure, climate and health security, gender equality and equity & Quality infrastructure, resilience, climate, health, and food security, transport, education and research & Infrastructure; energy, transport, digital connectivity \\ \hline \multirow{2}{*}{Finance Model} & Primarily state-funded & Private sector mobilisation + G7 public funding & EU grants/loans + private capital mobilisation & ODA loans + private partnerships & Private + national/EU public funds \\ \hline \multirow{2}{*}{Goverance} & Bilateral (state-led) & Multilateral, rules-based rules-based (G7-led coalition) & Multilateral, rules-based rules-based institutions & Multilateral, rules-based (Japan-led, U.S.-supported) & Multilateral (3 SI-led coalition) \\ \cline{1-1} \cline{2-5}  & Primarily state-funded & Private sector mobilisation + G7 public funding & EU grants/loans + private capital mobilisation & ODA loans + private partnerships & Private + national/EU public funds \\ \hline \multirow{2}{*}{Goverance} & Bilateral (state-led) & Multilateral, rules-based rules-based (G7-led coalition) & Multilateral, rules-based rules-based (EU institutions) & Multilateral, rules-based (Japan-led, U.S.-supported) & Multilateral (3 SI-led coalition) \\ \cline{1-1} \cline{2-5}  & \multicolumn{1}{p{56.9 pt}}{} & \multicolumn{1}{p{56.9 pt}}{} & \multicolumn{1}{p{56.9 pt}}{} & \multicolumn{1}{p{56.9 pt}}{} & \multicolumn{1}{p{56.9 pt}}{} \\ \end{tabular}
\end{table} 
Table 1: General comparison between global and regional infrastructure initiatives.
# Mixed-Species Cover Crop Biomass Estimation Using Planet Imagery
## 3 Results
### Planet Imagery and Cover Crop Biomass Relationship  
Imagery bands and indices from March and April were better correlated with cover crop biomass compared to November (Table 3). Generally, the correlation was highest in March, and the NIR data were most strongly correlated (\(r\) = 0.74) with biomass during this time period. Within the bands (blue, green, red, and NIR), NIR was the only one strongly and positively correlated with biomass during March. However, out of the eight VIs (Table 2) explored in this study, five VIs (NDVI, EVI, EVI2, OSAVI, and SR) were positively correlated with CC biomass (Table 3). These five VIs again showed a significant correlation with biomass during the first half of April; however, their coefficient decreased by this time period, except for NDVI. Correlation further decreased for these five VIs by the second half of April, and these VIs were no longer statistically correlated with biomass. Biomass correlation with the visible bands (blue, green, and red) increased in April compared to November and March. Biomass correlation with MSR increased during the latter part of the cover crop season, and it was the only VI correlated during the second half of April.  
Since biomass and seeding rate were highly correlated (Figure 3), we tested another hypothesis that the seeding rate was related to bands and VIs. Correlation with seeding rate (Table 3) showed that the same five VIs (NDVI, EVI, EVIZ, OSAVI, and SR) and NIR were correlated with biomass and seeding rate during March.
## 3 Resource Allocation Based on a Multi-Agent Reinforcement Learning Algorithm
### Multi-Agent Environment Modeling
#### 3.1.2 Action Space  
The ideal action space \(\mathcal{A}\) for the proposed environment must include every subchannel allocation decision. Indeed, the action space \(a_{i}^{t}\) is featured as follows:  
1. The subchannel decision made by the CV indicated as \(\xi_{i}^{t}\);
2. The mode of communication decision made by the CV indicated as \(\theta_{i}^{t}\).  
Therefore, the CV's action space, where \(i\in\mathcal{P}\) and \(\mathsf{a}_{i}^{t}\in\mathcal{A}\), can be given as  
\[\mathsf{a}_{i}^{t}=\{\xi_{i}^{t},\theta_{i}^{t}\}\]
## 1 Introduction  
In recent years, several bio-based monomers were studied for the improvement of polycondensate properties or for the replacement of petrochemical monomers. The 1,4:3,6-dianhydrohexitols (DAHs), namely isomannide, isosorbide, and isoidide are produced in two steps: hydrogenation of glucose and dehydration of the corresponding hexitol. Of the three known isohexide isomers, only dianhydro-1,4:3,6-p-sorbitol (DAS) or isosorbide is currently produced with sufficient purity for polymerization on an industrial scale. Due to their rigidity and their harmlessness, these molecules are gaining more interest for their use as building blocks in various polymers in the field of thermoplastic materials and for curable resin applications [1, 2]. As a diol, isosorbide found its place as a monomer suitable for polycondensate synthesis like polyesters, polycarbonates, and thermoplastic polyurethane [3]. In polycarbonate, isosorbide not only allows the substitution of toxic bisphenol A (BPA), but offers a significant increase in the mechanical strength, heat resistance, UV resistance, and optical properties, with resulting properties between common BPA polycarbonate and PMMA.  
In polyesters, isosorbide was copolymerized in polymers such as poly(ethylene terephthalate) (PET) [4, 5, 6], poly(trimethylene terephthalate) (PTT) [7], poly(butylene terephthalate) (PBT) [8, 9], and poly(cyclohexanedimethylene terephthalate) (PCT) [10] for its ability to increase the heat resistance of the polymer. As an example, in the case of PET, isosorbide allows for an outstanding increase in Tg. In the 1990s, [PERSON] et al. [11] demonstrated that the Tg followed a linear increase with a slope of 1.2 \({}^{\circ}\)C per 1 mol.% of isosorbide incorporated in the substitution of ethylene glycol.  
Depending on the amount of isosorbide introduced in the polymer, different final applications can be targeted. As an example for the lower amounts of isosorbide, the semi-crystalline properties of the polymer can be preserved, allowing its modification via solid-state post-condensation (SSP), followed by its use in injection stretch blow molding (ISBM) of a bottle or the melt spinning of fibers. In the final shaped objects, the gain in thermal stability due to the addition of isosorbide extends their range of applicability (i.e., hot filling of bottles or making heat-resistant fibers). When isosorbide is used in higher proportions (>20 mol.%), it induces larger variations in the crystallization behavior of PET, thus resulting in amorphous copolymers with a Tg above 100 \({}^{\circ}\)C. These fully amorphous polymers can then compete with transparent high-Tg polymers such as polycarbonates (PC) or poly(methyl methacrylate) (PMMA).  
The aim of this paper is to provide a deeper study of the crystallization kinetics of various PEITs with isosorbide molar contents (0% to 20%) in a large temperature range from 130 \({}^{\circ}\)C to 190 \({}^{\circ}\)C. These data are particularly of interest in order to precisely identify in which range of isosorbide content could poly(ethylene-_co_-isosorbide terephthalate) be used in semi-crystalline applications.
## 2 Methodology
### Estimation of PV Potential from PV-Powered EV
#### 2.3.2 Estimation of PV Potential Considering the Shadow Effect  
The results derived thus far do not reflect the shadow effect defined by the surrounding environment, such as terrain and buildings. However, shadows have a significant effect on reducing the solar power output. Therefore, we attempted to predict the actual solar power output potential by reflecting the shadow effect of the surrounding topography or buildings in the study area.  
The study area was photographed using a DJI Phantom 4 Pro V2.0 drone. Thereafter, Pix4D Mapper Pro software was used to create a 3D digital surface model (DSM) data with a spatial resolution of 0.5 m for the study area. The positions of the sun (altitude and azimuth) at 9:00, 12:00, and 15:00 in December were obtained from the Korea Astronomy Space Science Institute ([[http://www.kasi.re.kr/](http://www.kasi.re.kr/)]([http://www.kasi.re.kr/](http://www.kasi.re.kr/)), accessed on 20 December 2022). By entering the DSM and sun's position data in the hillshade tool (shading modeler) of the ArcGIS Pro software, the hourly shaded areas for the seventeen sections were modeled. The criterion for shaded area analysis was determined to adopt a conservative approach in estimating the potential power output because the ratio of shaded area is highest in December. Finally, the actual power output potential reflected by the shadow effect was evaluated by calculating the shaded-area ratio overlapping with the road.