esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical and Dielectric Properties of Aligned Electrospun Fibers
## 1 Introduction
### Working Principle of Electrospinning  
The working principle for electrospinning is shown in the Figure 3. A sufficiently high voltage is applied at the location of the liquid droplets formed at the tip of the needle. The local body of the liquid becomes charged. Electrostatic repulsion counteracts the surface tension. Thus, the droplet is stretched, and at a critical point, a stream of liquid erupts from the surface. The point of eruption is called a Taylor Cone. Sir [PERSON] developed the equation which shows the relationship between the critical voltage and the surface tension as shown in Equation (1) [16; 17].  
\[V_{c}^{2}=\frac{4\mathrm{H}^{2}}{\mathrm{L}^{2}}\left(ln\frac{2\mathrm{L}}{ \mathrm{R}}-\frac{3}{2}\right)\left(0.117\prod\gamma\mathrm{R}\right) \tag{1}\]  
where \(V_{c}\) is the critical voltage, \(\mathrm{H}\) is distance between the needle tip and the collector, \(\mathrm{L}\) is the length of the needle with radius \(\mathrm{R}\), and \(\gamma\) is the surface tension of the liquid (units: \(V_{c}\) in kilovolts; \(\mathrm{H}\), \(\mathrm{L}\), and \(\mathrm{R}\) in cm; and \(\gamma\) in dyne per cm). [PERSON] (2017) showed that electrostatic forces play a key role on the electrospinningning of polymer solutions [18]. As such, the Coulomb's force is considered as the driving factor for better design. In Equation (2) shown below, \(\mathrm{F}\) is the Coulomb's force, \(k\) is the constant of proportionality, \(q_{1}\) and \(q_{2}\) are charges, and \(\mathrm{r}\) is the distance between the charges. In principle, the smaller the distance, the greater the electrostatic force on a charged particle.  
\[\mathrm{F}=\mathrm{k}\frac{q_{1}q_{2}}{\mathrm{r}^{2}} \tag{2}\]  
When an electric field is applied, the liquid jet ejected from the tip of the nozzle/needle travels on a straight line for a short distance. The diameter of the jet, in the straight line, decreases monotonically with the distance from tip, after that a radially outward bending instability happens. The electrostatic force from the charge carries with the jet causes the jet to continue to elongate as it coils and the thin fluid jet solidifies into nanofiber [19].  
Figure 2: Electrospinning setup by [PERSON] [11].
## 4 Study Case--Northern Area of Argentina  
Based on the proposed, the aim was to analyze the behavior of Bayesian techniques for prediction and better result exploitation to prove field experiments. In this case, it is in the northern area of Argentina, where the weather is primarily subtropical, dry, and warm; however, there are some areas with a wet season in which heavier rainfall events occur in the summer, with relative humidity >50%.  
The experiment was performed in six companies from different businesses (veneer plants, and textile, metal-mechanic, and tobacco sectors) in the northern area of Argentina by observing and recording:  
* Season of the year (summer/winter);
* Time schedule.  
For the summer season, cooling loads varied according to solar time; so, data mining was performed three times, at 10.00 h, 15.00 h, and 18.00 h, to consider the variable impact.  
For the winter season, solar time contributes to heating loads, so data mining was performed only at 10.00 h. Variables included:  
* Activity (Light-Moderate-Heavy);
* Indoor Temperature (\({}^{\circ}\)C);
* Indoor Relative Humidity (%);
* Geographical region;
* Thermal comfort status--1 to 10 range where:  
1: completely uncomfortable;  
10: completely comfortable.  
Data mining was performed by a simple interview about thermal comfort, with the following form, named Table 3.  
Specifications for the survey were as follows:  
* To state, before starting the survey, that the answers are anonymous for employees and company, and that the results are only part of a research;
* Minimum answering time to not interfere with personal activities;
* To contemplate that the respondents have similar clothing or wear a uniform;
* To contemplate that air velocity has no determining influence [8];
* Temperature and relative humidity measurements are taken with a hygrometer specially designed for long-term follow-up of indoor climate conditions. This device used for air quality has a temperature sensor with a measuring range of 0-50 \({}^{\circ}\)C--resolution 0.1 \({}^{\circ}\)C (\(\pm\)0.15 \({}^{\circ}\)C from 0 to 20 \({}^{\circ}\)C and \(\pm\)0.1 \({}^{\circ}\)C from 20 to 50 \({}^{\circ}\)C) and a relative humidity (RH) sensor with a measuring range of 0-100% RH--resolution 0.1% RH (\(\pm\)1.5% RH from 0 to 80% RH).
* Comfort status is divided into three categories:
* Low Comfort: Status from 1 to 4;
* Medium Comfort: Status 5, 6, and 7;
* High Comfort: Status 8, 9, and 10.  
Subsequently, the Bayesian network techniques proposed were applied, with WEKA [37] and ELVIRA [38] tools.  
Finally, the results obtained were validated, thus offering thermal comfort recommendations.
# Enhanced Effective Filtering Approach (eEFA) for Improving HSR Network Performance in Smart Grids
## 5 Simulations and Discussion
### Simulation Description
#### 5.1.2 Simulation 2: Bidirectional Communication  
In this simulation, the communication between the source and destination DANH was bidirectional. When the destination DANH receives a unicast frame sent by the source DANH, it replies by sending a unicast frame back to the source DANH. The source DANH sent \(N\left(N=10,\ 20,\ \dots,\ 100\right)\) unicast frames to the destination DANH. Network frames (including network traffic frames and control frames) were recorded to validate and compare the network performance.
## 3 Materials and Methods
### FaceNet Architecture  
The original FaceNet architecture is a convolutional neural network (CNN), which has been trained to map face images to a compact and meaningful representation in Euclidean space, where the distances between points indicate the similarity between faces [60].  
The architecture of FaceNet can be divided into three main parts: The first part of the model is the convolutional neural network (CNN) that is used to extract features from the input face image. This CNN is typically based on an architecture called Inception, which is a variant of GoogleNet. The Inception architecture uses a combination of 1 \(\times\) 1, 3 \(\times\) 3, and 5 \(\times\) 5 convolutional filters, as well as max pooling layers, to extract features from the input image. The output of the Inception CNN is a 512-dimensional feature vector. The second part of the model is the embedding layer, which is a fully connected (FC) layer that maps the 512-dimensional feature vector to a 128-dimensional embedding vector. The embedding vector is used to calculate the distance between faces. The embedding layer is defined as:  
\[E(x)=W_{2}\times max(0,W_{1}\times x+b_{1})+b_{2} \tag{8}\]  
where \(x\) is the 512-dimensional feature vector, \(W_{1}\) and \(W_{2}\) are the weight matrices, and \(b_{1}\) and \(b_{2}\) are the bias vectors.  
The third part of the model is the triplet loss function, which is used to train the model. The triplet loss function is defined as:  
\[L(A,P,N)=max(\left\|E(A)-E(P)\right\|^{2}-\left\|E(A)-E(N)\right\|^{2}+\alpha,0) \tag{9}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyper Parameter** & **Value** \\ \hline Batch size & 8 \\ Epoch & 5001 \\ Seed & 2020 \\ Optimizer & Adam \\ Loss function & Perpetual loss \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameters of SR-GAN.  
where \(A\), \(P\), and \(N\) are anchor, positive, and negative images, respectively, \(E(A)\), \(E(P)\), and \(E(N)\) are embedding vectors of anchor, positive, and negative images, respectively, and \(\alpha\) is a margin constant. The triplet loss function is used to ensure that the embedding vectors of the same person are closer to each other than the embedding vectors of different people.
## 3 Results
### Energy Balance Closure and Uncertainty  
The slope of the regression between the energy fluxes (H + LE) and the available energy (Rn-G-Gs) (Figure 2) indicates an energy balance closure of 0.84 for Rainfied and 0.72 for Irrigated. These values are consistent with several studies made more than two decades ago, which concluded that the range of suitable ratios of the linear regression slope lies between 0.7 and 1 [37; 38; 39]. In this study, the imbalance may be mainly attributed to nighttime low turbulence conditions, as has been found in previous studies [37; 39].  
Figure 1: Meteorological measurements for Rainfied (12 August-22 November 2020) and Irrigated (3 February–9 June 2021). (a,b) photosynthetic active radiation, \(\mu\)mol photons m\({}^{-2}\) s\({}^{-1}\) (PAR); (**c,d**) air temperature, \({}^{\circ}\)C (Tair); (**e,f**) vapor pressure deficit, kPa (VPD); (i,j) soil water content, cm\({}^{3}\) cm\({}^{-3}\) (SWC), measured at 0–20 cm depth, are shown as daily mean values; (**g,h**) daily and cumulated precipitation, mm (Pp), is shown as daily sum, black dots indicate irrigation times.  
Figure 2: Energy balance closure for (**a**) Rainfied and (**b**) Irrigated sites.  
Carbon Fluxes, Daily Averages, Maximums, and Sums of NEE, GPP, and RECO in the Different Growth Stages for Non-Irrigated and Irrigated Crops  
Table 1 shows the behavior of fluxes throughout the growth stages. In Rainfed, the sum of NEE from sowing to tuber bulking had a positive value, indicating CO\({}_{2}\) emissions to the atmosphere (154.7 g C m\({}^{-2}\)\(\pm\) 30.21). In contrast, Irrigated behaved as a CO\({}_{2}\) sink (sum of NEE = \(-\)366.6 g C m\({}^{-2}\)\(\pm\) 50.30). In Rainfed, the daily NEE average was negative and close to zero during the tuberization stage (\(-\)0.03 g C m\({}^{-2}\) d\({}^{-1}\)\(\pm\) 0.29). Irrigated had negative NEE daily means and sums in all stages, except during sprouting, which can be explained by the absence of aerial biomass in potato plants.  
In general, the highest values for each flux were found during the tuberization stage on both crops. Nevertheless, in Rainfed, the highest RECO was observed during the vegetative stage. GPP in Irrigated (\(-\)1043.6 g C m\({}^{-2}\) d\({}^{-1}\)\(\pm\) 23.96) was 4.37 times higher than in Rainfed (\(-\)239.9 g C m\({}^{-2}\) d\({}^{-1}\)\(\pm\) 5.33), and RECO was 1.71 times higher in Irrigated than in Rainfed, due to the larger amount of respiration contributed by the higher aerial biomass in Irrigated.
## 3 Methodology
### Databases
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Language**} & \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Speaking Style**} & \multirow{2}{*}{**Age Range**} & \multirow{2}{*}{**\#Speakers**} & \multicolumn{2}{c}{**\#Utterances\(\mid\)\#Hours**} & \\ \cline{2-2} \cline{6-9}  & & & & & **Training** & **Validation** & **Test-Read** & **Test-CTS/HMI** \\ \hline Dutch & CGN & Read \(\mid\)CTS & 18–65 & 2897 & 704,293 \(\mid\) 433 & 70,498 \(\mid\) 43 & 409 \(\mid\) 0.45 & 3884 \(\mid\) 1.80 \\  & Jasmin-DC & Read \(\mid\) HMI & 06–13 & 71 & - & - & 13,104 \(\mid\) 6.55 & 3955 \(\mid\) 1.55 \\  & Jasmin-DT & Read \(\mid\) HMI & 12–18 & 63 & - & - & 9061 \(\mid\) 4.90 & 2723 \(\mid\) 0.94 \\  & Jasmin-NnT & Read \(\mid\) HMI & 11–18 & 53 & - & - & 11,545 \(\mid\) 6.03 & 3093 \(\mid\) 1.16 \\ \hline German & CommonVoice & Read & - & 3413 & 330,454 \(\mid\) 452.8 & 55,050 \(\mid\) 75.23 & 55,102 \(\mid\) 75.5 & - \\  & kidsTALC & Read & 3–11 & 47 & 9187 \(\mid\) 9.18 & 1676 \(\mid\) 1.68 & 2413 \(\mid\) 12.2 & - \\ \hline Mandarin & SLT-SetA & Read & 18–60 & 1999 & 196,616 \(\mid\) 276.7 & 22,764 \(\mid\) 31.52 & 23,950 \(\mid\) 33.41 & - \\  & SLT-SetC1 & Read & 7–11 & 927 & 24,109 \(\mid\) 23.38 & 2701 \(\mid\) 12.48 & 3042 \(\mid\) 12.79 & - \\  & SLT-SetC2 & Conversational & 4–11 & 166 & 25,245 \(\mid\) 23.49 & 2955 \(\mid\) 2.85 & - & 3303 \(\mid\) 13.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Details of the Dutch, German, Mandarin adults’ and children’s speech datasets used in this study.
# Temperature Dependence of Density and Viscosity of Biobutanol-Gasoline Blends
## 4 Conclusions
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Temperature (\({}^{\circ}\)C)**} & \multirow{2}{*}{**Temperature (K)**} & \multicolumn{6}{c}{**Volumetric Mass Density RON 95 (kg\(\cdot\)m\({}^{-3}\))**} \\ \cline{3-8}  & & **B0** & **B5** & **B10** & **B85** & **B100** \\ \hline \(-\)10 & 263.15 & 776.1 \(\pm\) 1.49 & 768.3 \(\pm\) 1.63 & 773.5 \(\pm\) 1.41 & 817.1 \(\pm\) 1.62 & 824.6 \(\pm\) 1.60 \\ \(-\)5 & 268.15 & 763.8 \(\pm\) 1.56 & 766.7 \(\pm\) 1.53 & 771.5 \(\pm\) 1.49 & 814.8 \(\pm\) 1.81 & 822.8 \(\pm\) 1.50 \\ 0 & 273.15 & 761.9 \(\pm\) 1.27 & 768.4 \(\pm\) 1.40 & 771.1 \(\pm\) 1.52 & 813.1 \(\pm\) 1.58 & 821.3 \(\pm\) 1.31 \\ 5 & 278.15 & 761.1 \(\pm\) 1.42 & 766.2 \(\pm\) 1.48 & 768.3 \(\pm\) 1.50 & 809.7 \(\pm\) 1.58 & 817.9 \(\pm\) 1.60 \\ 10 & 283.15 & 760.6 \(\pm\) 1.61 & 764.0 \(\pm\) 1.61 & 765.2 \(\pm\) 1.45 & 806.5 \(\pm\) 1.81 & 813.9 \(\pm\) 1.27 \\ 15 & 288.15 & 759.6 \(\pm\) 1.53 & 762.4 \(\pm\) 1.52 & 762.6 \(\pm\) 1.39 & 803.0 \(\pm\) 1.83 & 810.5 \(\pm\) 1.41 \\ 20 & 293.15 & 759.0 \(\pm\) 1.48 & 759.7 \(\pm\) 1.38 & 760.0 \(\pm\) 1.61 & 798.7 \(\pm\) 1.53 & 806.3 \(\pm\) 1.44 \\ 25 & 298.15 & 755.8 \(\pm\) 1.42 & 756.5 \(\pm\) 1.29 & 756.6 \(\pm\) 1.41 & 794.6 \(\pm\) 1.57 & 801.7 \(\pm\) 1.47 \\ 30 & 303.15 & 753.8 \(\pm\) 1.29 & 750.3 \(\pm\) 1.47 & 751.3 \(\pm\) 1.48 & 787.9 \(\pm\) 1.55 & 794.2 \(\pm\) 1.78 \\ 35 & 308.15 & 752.3 \(\pm\) 1.25 & 747.2 \(\pm\) 1.62 & 749.4 \(\pm\) 1.37 & 784.2 \(\pm\) 1.58 & 790.8 \(\pm\) 1.52 \\ 40 & 313.15 & 751.1 \(\pm\) 1.55 & 744.2 \(\pm\) 1.60 & 747.6 \(\pm\) 1.24 & 780.2 \(\pm\) 1.48 & 786.6 \(\pm\) 1.57 \\ \hline \hline \end{tabular}   
**Table A2. Values of volumetric mass density of biobutanol-gasoline (RON 98) blends (mean \(\pm\) standard deviation, \(N\) = 5). **  
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Temperature (\({}^{\circ}\)C)**} & \multirow{2}{*}{**Temperature (K)**} & \multicolumn{6}{c}{**Volumetric Mass Density RON 98 (kg\(\cdot\)m\({}^{-3}\))**} \\ \cline{3-8}  & & **B0** & **B5** & **B10** & **B85** & **B100** \\ \hline \(-\)10 & 263.15 & 776.1 \(\pm\) 1.63 & 785.1 \(\
## 4 Discussion
Due to the complexity of ecosystems and the limitations of data and methodologies, the sources of uncertainty in this study mainly include the following aspects: (1) The data used in this study were derived from multiple sources with inconsistent spatial and temporal resolutions. The interpolation methods used to unify the resolutions may introduce a certain degree of uncertainty. (2) The SEM framework was based on a priori assumptions. However, in real ecosystems, causal relationships are often complex and involve feedback mechanisms, making it difficult to fully capture the system using a single SEM. (3) This study primarily focused on the influence of climatic factors such as water, heat, and light on vegetation growth. In recent years, the increasing frequency of extreme climate events and intensified human disturbances (e.g., grazing and land-use changes) might have altered the response patterns of vegetation WUE. Therefore, future research should incorporate extreme climate indices and human activity variables, and combine them with higher-resolution data and field observations, in order to deepen the comprehensive understanding of carbon-water coupling mechanisms in alpine grassland ecosystems and provide more robust scientific support for regional ecological conservation and climate adaptation strategies.
# The Extended SLM Combined Autoencoder of the PAPR Reduction Scheme in DCO-OFDM Systems
## 1 Introduction
A novel deep neural network combined with extended Selected Mapping (ESLM), namely ESLM-AE, is proposed in this paper to mitigate the high PAPR issue of DCO-OFDM signals. It uses an AE structure to represent the constellation mapping and de-mapping of the transmitted symbols. In the network, the ESLM method is added after the constellation mapping to reduce the high PAPR of the DCO-OFDM system. By designing the loss function of neural network and considering both the BER and PAPR performance, autoencoder and SLM can be combined organically. Further, the phase factor of SLM can be determined and optimized accordingly in the network training process. Thus, it is expected that the proposed ESLM-AE method is more efficient in reducing the PAPR without deterioration of the BER performance. The remainder of this paper is organized as follows. Section 2.1 gives an overview of the DCO-OFDM system model. Section 2.2 presents the detailed architecture of the proposed ESLM-AE scheme. Section 3 contains the simulation results and a discussion of the proposed scheme compared to the standard methods in different channels. Finally, conclusions are reported in Section 4.
# Performance and Evaluation of GNSS Receiver Vector Tracking Loop Based on Adaptive Cascade Filter
## 3 Experiment and Analysis  
This section will verify the effectiveness and feasibility of the proposed method through three experimental test scenarios. The test equipment is shown in Figure 3. The NovAtel SPAN-CPT high-precision navigation system is used as the measurement benchmark, and the positioning results of the traditional tracking method and the proposed method are compared.
## Appendix B CNN Model and Training Details  
For the classification CNNs, we used a softmax layer and cross-entropy loss. These models were trained using class weighting so that the on and off classes were represented equally during training. This is necessary because plants were \"on\" in about 80% of the training images.  
For the regression CNNs, we used a sigmoid layer with either mean squared error or Huber loss based on performance. The regression models had a more difficult time converging than the classification ones. We found the simplest solution was to train it as a multi-task model that performs both classification and regression, with weights of 0.02 and 0.98 applied to each, respectively.  
The patch size around each tower or stack was optimized as a hyperparameter for each model type and imagery product. Patch sizes ranged from 8 to 64 pixels, with larger patch sizes selected for regression models where the model can benefit from a full view of the plume.  
We trained our CNN models using the AdamW optimizer that uses weight decay for regularization. We also regularized with dropout and image augmentation, including transformations for random flipping, rotation, brightness, contrast, darkness, Gaussian blur, translation, and zooming. The brightness, contrast, darkness, and Gaussian blur transformations simulate some of the image quality issues that we saw. Many of these issues are caused by natural phenomena like haze and various lighting conditions. As we trained models on a single satellite at a time with a fixed spatial resolution, the amount of translation and zooming augmentation is relatively small but does provide a benefit. We tuned the magnitude of image augmentation during hyperparameter optimization.  
In summary, we optimized the following hyperparameters:  
* Patch size [8, 16, 32, 48, 64];
* Backbone truncation layer [block2_pool, block3_pool, block4_pool];
* Attention heads [1, 8];
* Augmentation magnitude [0.5, 1, 1.5, 2, 2.5, 3];
* Weight decay [\(10^{-6},10^{-5},10^{-4},10^{-3}\)];
* Early stopping patience [10, 20, 30];
* Batch size [32, 54, 128];
* Learning rate [\(10^{-5},10^{-2}\)];
* Number of epochs [40, 50, 60, 70, 80, 90, 100];
* Loss (for regression only) [mean squared error, huber].
# A Comprehensive Study of the Use of LoRa in the Development of Smart Cities
## 5 Discussion
### Impact of LoRa Research Proposals in the Smart City Initiative
#### 5.7.4 LoRa Proposals in the Building of Smart Cities  
A possible reason for not covering all of the indicators or criteria for the circular economy of smart city in the proposals presented from 2015 to 2019 may be the fact that they were not developed within the context of the smart city. According to the analysis carried out of the three hypotheses (loose integration, tight integration, and data), most contributions presented a loose integration. This indicates that although a specific need was being solved, it was not being considered within a macro-plan in the development of a sustainable city. A possible consideration in this regard would be to strengthen the interaction between the academy and local governments; also, research works should contribute to cover the proposed roadmaps for circular economy strategies for cities. Cities indicated in the subsection \"Smart City Strategies Using IoT LoRa\" have established a roadmap for circular economy by the year 2021.  
7.5 Aspects to Consider to Develop IoT Solutions that Contribute to the Development of Smart City Circularity.  
Tight or data integration in IoT-LoRa proposals can contribute to the circular economy in smart cities. Some considerations from the study are presented below.  
1. The criteria or indicators of circular economy or circularity related to the smart city sector for which the solution is developed should be considered.  
2. Identify the infrastructure of the city related to the sector that could use the developed solution.  
3. Analyze the technological, legal, or economic barriers that may affect the development of the solution.  
4. Identify the infrastructure of the city to which it is possible to add the intelligence component for improving decision-making processes.  
5. Design the solution under the scheme of re-using, according to the concepts of circular economy, which will allow adding functionalities to the smart city solution without having to invest in new components.  
6. Configure the technical aspects of the components of the IoT ecosystem (devices, LoRa, fog, or cloud) based on the functional characteristics of the smart city subcategory. Select the most appropriate aspects of SF, data rate, and throughput for each case.  
7. Validate the security requirements that guarantee the confidentiality, integrity, and availability of the implemented solutions.
## 3 Overall Design Architecture
### Routing Protocol and Innovative Dropping Scheme in Network Layer for Wysn  
Network layers play an important role in delivering the multimedia from video source node to the Sink. As a result, energy-efficiency and video quality are influenced by the approaches of this layer that include routing protocol and dropping scheme. The presented routing protocol forwards packets using a hierarchical architecture topology and an adaptive single-path transmission. In used single-path transmission protocol, energy efficiency increases more remarkably than multi-path transmission protocols which were proposed previously. In this layer, a dropping scheme that causes nodes to save energy and to prolong the lifetime of the network is proposed. On the basis of energy level of each node and information that has been provided by video compression layer inside the received packet, dropping scheme decides to discard data packets. As regards the structure of the network layer, video quality is preserved while energy consumption is distributed fairly among all the nodes. Routing protocol and new dropping scheme are described more in Section 6.  
Figure 1: Overall EQV-Architecture