esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization of a Novel Emaravirus Affecting Ash Species (_Fraximus_ spp.) in Europe
## 3 Results
### Geographical Distribution of ASaV and Association with Disease Symptoms  
Following its discovery in 2016, further infections of ASaV in _F. excelsior_ were confirmed in Switzerland (Basel) and at new locations discovered in Crans Montana (Switzerland), Southern Sweden (Dalby Soderskog, Sigtuna, Bromma, Vreta, Krusenstern Herrgard, Hallekis Blomberg and Vasterplana), and Germany (Berlin, Hamburg). Infections in the second host species, _F. onus_, were also found in Germany (Hamburg), including trees at a local tree nursery and Northern Italy (Bolzano). Sample location, host species and ASaV detection are summarized in Figure 5 and Table 4.  
_Fraxinus_ samples collected during the vegetation periods from 2016 to 2019 displayed various suspected virus symptoms, including chlorotic ringspots, line pattern, checking and a range of leaf deformations, including curling and a narrowing of the leaflets termed shoestring. Figure 1 shows a selection of documented symptoms. In total, 52 _F. excelsior_ and 57 _F. onrus_ samples were tested by RT-PCR applying ASaV specific primer sets. The RNA3-5 primer sets were found to be most reliable. ASaV infections were confirmed by Sanger sequencing of PCR products (data not shown). Of 41 symptomatic _F. excelsior_  
Figure 4: Detection of the five genomic RNAs of the putative novel emaravirus ASaV identified in _Fraxinus_ spp. By RT-PCR using segment-specific primer sets in different leaf samples. (1) CORaV-infected common oak (2) MaMaV-infected map; (3) AsMaV-infected aspen (4) E55269 and (5) E55270 _F. excelsior_ exhibiting chlorotic ringspots and shoestring of leaves, Basel; (6) FMV-infected fig; (8) = E55268 _F. excelsior_, Basel; H\({}_{2}\)O = non-template control (NTC).  
Figure 5: Current known distribution of putative emaravirus ash shoestring-associated virus (ASaV) in diseased _Fraxinus excelsior_ and _F. onus_. Key: _F. excelsior_: red, _F. onus_: blue. 1 = Sigtuna, Bromma, Vreta, Krusenstern-Herrgard, Sweden, 2 = Hallekis Blomberg, Vasterplana, Sweden, 3 = Dalby Soderskog, Sweden, 4 = Hamburg, Germany, 5 = Berlin, Germany, 6 = Basel, Switzerland, 7 = Crans Montana, Switzerland, 8 = Bolzano, Italy.  
samples, 37 (90%) were found to be infected with ASaV. ASaV was also detected in 44 of 45 (98%) symptomatic _F. ornus_ samples (Table 4).  
ASaV was also detectable in 6 of the 12 (50%) asymptomatic _F. excelsior_ samples and 3 of the 12 (25%) samples from _F. ornus_ with no observable disease symptoms. Our results show a strong correlation between suspected virus symptoms and ASaV infection in _Frazinus_ spp. ASaV was detected in 94% of the symptomatic samples overall, including all 21 samples displaying the shoestring symptom. The current known geographical range of ASaV extends across Northern and central Europe, from Southern Sweden in the north to Northern Italy in the south (Figure 5).
## 4 Feedback Control System and Field-Null Implementation  
Section 3 introduces the scheme for field-null optimization and gives the control target waveforms of CS and PF currents for various field-null configurations. To realize these currents in real experiments, precise feedback (FB) control must be used. In this section, a FB controller based on the Low-Pass Filtered PID principle [26; 27] is introduced and used for simulating the controlling process of CS and PF coil currents. The result shows that the designed target coil current waveforms can be accurately tracked by the controller, which further validates the feasibility of these waveforms as control targets to be used in actual HL-3 experiments.
# Satellite-Based Estimation of Carbon Dioxide Budget in Tropical Peatland Ecosystems
## Materials and Methods  
### Study Area  
Southeast Asia (SE Asia) region is known as a hotspot for tropical peatlands, accounting for half of the global peatland emissions due to rapid deforestation and drainage [14]. Within SE Asia, Indonesia has the highest CO\({}_{2}\) emission rate due to peatland decomposition and fires. The target area for this study was the peatlands in Indonesia and Malaysia in the Borneo Island region (6\({}^{\circ}\)N-11\({}^{\circ}\)S, 95-141\({}^{\circ}\)E) (Figure 1). In the study area, the total peat forest area was 205,408 km\({}^{2}\), as reported by the Global Forest Watch collected from the Ministry of Agriculture (2012) and Wetlands International (2004) for Indonesia and Malaysia [35; 36]. The locations of flux towers are shown in Figure 1. The information of latitude and longitude is as follows: undrained forest (_Uf_) (2\({}^{\circ}\)20\({}^{\prime}\)42\({}^{\prime}\)S, 114\({}^{\circ}\)2\({}^{\prime}\)11\({}^{\prime}\)E), drained forest (_DF_) (2\({}^{\circ}\)20\({}^{\prime}\)59\({}^{\prime\prime}\)S, 114\({}^{\circ}\)8\({}^{\prime}\)13\({}^{\prime\prime}\)E), and burned forest (_BF_) (2\({}^{\circ}\)20\({}^{\prime}\)14\({}^{\prime\prime}\)S, 114\({}^{\circ}\)2\({}^{\prime}\)13\({}^{\prime\prime}\)E).
# A Few-Shot Learning-Based Material Recognition Scheme Using Smartphones
## 5 Performance Evaluation
### Performance Evaluation of FSMR
#### 5.2.3 Comparison with Previous Research  
Table 4 compares the performance of FSMR, proposed in this paper, with that of VibePhone, a study that classifies objects in contact with a smartphone using vibrations and accelerometers. Both models were evaluated using six classes. VibePhone trained on the entirety of data collected over 180 s, whereas FSMR was trained only on datasets of 80 s or less and used a tripled evaluation support set for testing.  
The results show that the VibePhone achieved an average classification accuracy of 85%, demonstrating high performance. However, it is important to note that the training dataset size was significantly large at 180 s. FSMR, when trained on data collected over 2 s, achieved a 71.94% accuracy, which appears insufficient for practical use. However, with 5 s of data, FSMR achieved 76.34% accuracy, and with 10 s of data, it reached 83.01%. The training durations of 5 and 10 s are reasonably short for practical user registration, and the nearly 80% accuracy achieved with 10 s makes FSMR suitable for real-world applications. For longer training durations of 30, 50, and 80 s (although less practical for users) the results can serve as benchmarks for comparison with previous studies. FSMR achieved 90.73%, 91.79%, and 91.91% accuracy for these durations, respectively. These figures are comparable to the VibePhone's performance and demonstrate that FSMR significantly reduces the amount of data required for training, while maintaining a competitive accuracy. Although FSMR did not outperform the VibePhone in any scenario, the ability to achieve a similar accuracy with substantially less training data highlights its potential as a more practical solution for real-world applications.  
Table 5 presents the results of response time measurements for VibePhone and FSMR. The same models and training datasets as in the previous experiments were used, with the input data size for VibePhone set to 1 s. The results show that VibePhone achieved a response time of approximately 19 ms for 1 s input data, which is faster than FSMR's response times of 22 ms and 26 ms for input sizes of 2 s and 5 s, respectively. However, even for larger input sizes, FSMR maintained a response time within 80 ms for 50 s input data and 115 ms for 80 s input data, a difference of only 30 ms. This response time is  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **VibePhone [6]** & \multicolumn{6}{c}{**FSMR**} \\ \hline Training data size (s) & 180 & 2 & 5 & 10 & 30 & 50 & 80 \\ \hline Accuracy (\%) & 85 & 71.94 & 76.34 & 83.01 & 90.73 & 91.79 & 91.91 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Material recognition accuracy comparison with VibePhone.  
Figure 10: Material recognition accuracy of FSMR when augmenting the evaluation support set.  
imperceptibly short for human users. Considering that FSMR achieved over 90% accuracy in the previous experiment with a training dataset of only 30 s, these results demonstrate the exceptional performance of FSMR.
## 5 Limitations and Further Research  
It is necessary to mention some limitations of the current study. Firstly, it was impossible to link the adolescents who did the platform and those who filled out the post-measurement questionnaire in the EG, preventing the identification and removal of adolescents who filled out the questionnaire but did not use the platform. Secondly, the study period was at the end of the academic year (exam preparations), and fewer adolescents were available for the platform and the pre-post study. Additionally, the COVID-19 restrictions required students to use the platform at home, limiting their ability to ask any questions they had while using the platform and decreasing the response rate in post-measurement questionnaires (less control, more internet problems). It is suggested that to evaluate this platform further, it is crucial to involve more adolescents (from different cities in Pakistan) to better emphasize the benefits of using the online platform. In addition to this, the opinions of other stakeholders, i.e., parents and teachers, about road safety education platforms should also be investigated to determine their acceptability in the region. Also, long-term testing is necessary to fully understand its impact on adolescents' traffic safety behaviors. This will involve extended follow-up studies to assess retention of traffic knowledge and changes in behavior over time. Also, a longitudinal approach in the future is needed to verify the platform's practical implications. Lastly, the current outcomes already provide insights into how gamified platforms can help participants develop their skills and knowledge regarding traffic safety in Pakistan.
## 2 Methods
### GPS PPP Observation Models
#### 2.1.3 Ionosphere-Constrained Single-Frequency GPS PPP Observation Model  
The ionosphere-constrained SF GPS PPP can add the GIM product as a virtual observation to constrain the ionospheric parameters as compared with the standard SF GPS PPP. The constraint equation for the observation is as follows [35]:  
\[\left[\begin{array}{c}p_{r}^{1}\\ l_{r}^{1}\\ \vdots\\ p_{r}^{m}\\ l_{r}^{m}\\ \Lambda^{1}\\ I_{r}\\ \vdots\\ \Lambda^{m}\\ I_{r}\end{array}\right]=\left[\begin{array}{cccc}-\mathbf{\mu}_{r}&\mathbf{I }_{t}&M_{w}&\mathbf{K}&\mathbf{R}\\ \mathbf{0}&\mathbf{0}&\mathbf{0}&\mathbf{I}_{l}&\mathbf{0}\\ \vdots\\ \Lambda^{m}\\ \Lambda^{1}\\ \Lambda^{m}\\ I_{r}\end{array}\right]+\left[\begin{array}{c}\varepsilon_{r}\\ \varepsilon_{r}\\ \varepsilon_{r,ion}\\ \end{array}\right],\quad\mathbf{Q}_{L},\quad\mathbf{Q}_{l}\enspace. \tag{9}\]  
where \(\Lambda^{s}_{r}\) is derived from external ionospheric products with the corresponding noise \(\varepsilon_{r,ion}\); \(\mathbf{I}_{l}\) is a unit vector of \(m\) rows and one column; and, \(\mathbf{Q}_{l}\) denotes the stochastic model of virtual ionospheric observables.
# Degradation State Recognition of Piston Pump Based on ICEEMDAN and XGBoost
## 3 Performance Degradation Test of Piston Pump  
In this paper, the piston pump performance degradation test bench could collect the vibration signals of the pump with different wear degrees. During the test, the slipper with different wear degrees was replaced to simulate the phenomenon that the wear of the piston pump gradually increases in actual work. Figure 4 is the physical picture of the test bench. The vibration sensor is installed by magnetic base adsorption, and the vibration signal of pump shell is collected synchronously.  
In order to simulate the process of slipper wear degradation that affected the performance of the piston pump, the wear amounts of slipper were 0 mm (no wear), 0.5 mm (slight wear), 1.5 mm (moderate wear) and 2.5 mm (severe wear) in turn. The test adopts 20 kHz sampling frequency to collect the vibration signal. The test hydraulic system was under a pressure of 15 MPa, and the vibration signals of the slipper with different degrees of wear were sequentially collected. In our experiment, the data collected in the last 3 s are taken as effective degr  
Figure 4: Test bench physical map.  
Figure 3: Flow chart of XGBoost.  
obtained by each degradation degree are divided into 50 groups. Among the 50 sets of data, 30 sets are used for training and 20 sets are used for testing.
# Combination of Models to Generate the First PAR Maps for Spain
## 5 Conclusions  
A new PAR model has been introduced for Spanish territories. This new model was developed for each point of the field study from two previous models as a linear combination of their estimates. The model was validated at seven stations in Spain, four of them with Mediterranean climates and the remaining three with oceanic climates.  
The most significant contribution of this study is the development of a PAR model that is suitable for any location in mainland Spain, regardless of its climate, to develop the first PAR maps over Spain. These maps graphically show daily PAR averages at all locations for each month and for the whole year.  
Conceptualization, J.M.V. and R.X.V.; methodology, J.M.V.; software, F.F-C.; validation, F.F.-C., J.M.V., R.X.V., A.A.N. and L.F.Z.; formal analysis, J.M.V. and F.F.-C.; investigation, R.X.V. and L.F.Z.; resources, R.X.V. and A.A.N.; data curation, F.F.-C., J.M.V., R.X.V., A.A.N., L.F.Z. and O.W.; writing--original draft preparation, F.F.-C., J.M.V., R.X.V., A.A.N., L.F.Z. and O.W.; writing--review and editing, F.F.-C., J.M.V., R.X.V., A.A.N., L.F.Z. and O.W.; visualization, R.X.V. and A.A.N.; supervision, J.M.V.; project administration, J.M.V. and R.X.V.; funding acquisition, J.M.V. and R.X.V. All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank the funding from the Ministry of Economy, Industry, and Competitiveness (MINECO) (Project CGL2016-79284-P AEI/FEDER/UE). FF-C acknowledges its funding to MINECO for its grant (BES-2017-082043), and also to the Autonomous Community of Madrid, Spain, and co-financed by the FEDER \"A way of making Europe\" ALGATEC-CM (S2018/BAA-4532) and to the CYTED-IberoAmerican Program on Science and Technology for Development (Red RENUWAL P320 RT005 CYTED).  
The authors gratefully acknowledge EPS Lugo--USC, NEIKER, Estacion Experimental de Aula Dei--CSIC, CIALE--USAL, ITAP, IFAPA--Cordoba for maintaining the stations. We are also grateful for the data taken from MODIS, particularly products from NASA Level-1 and Atmosphere Archive and Distribution System (LAADS) Distributed Active Archive Center (DAAC), located in the Goddard Space Flight Center in Greenbelt, Maryland ([[https://ladsweb.nascom.nasa.gov/](https://ladsweb.nascom.nasa.gov/)]([https://ladsweb.nascom.nasa.gov/](https://ladsweb.nascom.nasa.gov/)), accessed on 27 February 2020), as well as for the SRI product of CM-SAF provided by EUMETSAT.  
The authors declare that they have no conflict of interest.
## 4 Discussion
### The Relationship between AIRT and Physical Temperature  
According to the Stefan-Boltzmann's law, the object's radiant energy can be expressed as:  
\[M(T)=\varepsilon\varepsilon T^{4} \tag{7}\]  
where \(M(T)\) is the radiantitance (W\(\cdot\)m\({}^{-2}\)), \(\varepsilon\) is the emissivity in the range of 0-1, \(e\) is the Stefan-Boltzmann constant, and \(T\) is the physical temperature (K). For blackbody, the radiant temperature is equal to \(T\). For an actual object, the relationship between the two temperatures is:  
\[T_{s}=\varepsilon^{1/4}\ T \tag{8}\]  
where \(T_{s}\) is the radiant temperature (K). Based on the TIR theory, the TIR intensity changes linearly with the physical temperature change with the correlation coefficient greater than 0.99, and it was confirmed by the experimental results in the laboratory condition with slight changes in ambient temperature [35; 36]. As the emissivity is less than 1, the value of radiant temperature change is lower than that of the physical temperature, though both of the temperatures could be used to represent the thermal change. Figure 4a shows that the ambient temperature change was 0.33 K, whereas the \(\Delta\mathrm{AIRT}_{\mathrm{low}}\) was 0.27 K (Figure 5a). Similarly, the ambient temperature change in Figure 6a was 0.19 K,whereas the \(\Delta\)AIRT\({}_{\rm low}\) was 0.17 K (Figure 7a). When taking the emissivity of the rock samples (\(\varepsilon\approx 0.9\)) into consideration, the two temperatures were generally consistent according to Equation (8). It is noted that the emissivity of the rock was changed obviously in the stress condition in the range of 8.0-10.0 \(\upmu\)m [38]. However, as the AIRT was obtained in the range of 3.7-5.0 \(\upmu\)m, the emissivity change was not considered here.
# TSCH Multiflow Scheduling with QoS Guarantees:
## 3 Results  
This section includes the results of the simulation of the four TSCH scheduling methods, for each of the proposed metrics. In test 1 the end-to-end delay and packet delivery ratio (Section 3.1), radio duty cycle (Section 3.2), packet inter-arrival time and deadline satisfaction ratio (Section 3.3) have been evaluated. Test 2 includes the multi-flow tests (Section 3.4).
# Cooperative Caching and Resource Allocation in Integrated Satellite-Terrestrial Networks
## 4 Problem Formulation
### Cooperative Caching and Resource Allocation Problem
#### 4.1.4 Problem Description
In the case of the requiredcontent deployed on the current satellite, a feasible strategy \(\{a,True,True\}\) will be obtained when the constraints in Equations (13)-(15) are satisfied. Strategy \(\{a,True,True\}\) indicates that the required content was deployed on the current satellite and the content service can be provided by the path from the candidate \(a\). Furthermore, in the case of the required content not deployed on the current satellite, when both of the available onboard storage and network bandwidth resources, as well as the constraints of service delay and service provisioning, are satisfied, the other feasible strategy \(\{a,True,False\}\) can be just obtained. Strategy \(\{a,True,False\}\) indicates that the current satellite with sufficient storage resources can cache and provide the required content by the path from the candidate \(a\). Then, if the current hops are less than \(h_{sub}\), the available neighbors and paths for the current candidate will be updated into \(A_{layer}\). When all the candidates in \(A_{layer}\) are searched, it will be viewed as \(A_{layer}\leftarrow A_{layer}\). The process of neighborhood search will be terminated when \(A_{layer}=\varnothing\). When \(s_{u}=\varnothing\) or the access satellite \(v_{dc,a}\) is one of the satellites in the current neighboring sub-network, the \(L\) shortest paths \(P^{L}_{u,dc}\) will be traversed to obtain a feasible strategy \(\{path,False,False\}\) if the network bandwidth resources are satisfied for the \(path\), where \(\{path,False,False\}\) indicates that the required content can be retrieved by the \(path\) from cloud center \(dc\). Furthermore, the feasible strategy with minimum deployment costs from \(s_{u}\) can be viewed as the optimal strategy \(s_{u}^{*}\), that is, \(s_{u}^{*}\gets min\ s_{u}\). For each time slot, \(M\) users can access the integrated satellite-terrestrial network and require the content services. According to the proposed CCRA algorithm based on neighborhood search, there are \(V_{v,sub}\) satellites and \(E_{v,sub}\) SLs, as well as \(L\) shortest paths \(P^{L}_{u,dc}\), to be searched for each user in the worst scenario. Therefore, the computation complexity of the proposed CCRA algorithm can be viewed as \(O(M(|V_{v,sub}|+|E_{v,sub}|+L))\).
## 4 Classification of Experimental Results and Analysis  
### An Analysis of the Comparative Experimental Results of the Transfer Learning Model  
First, experiments were conducted using different neural network models without transfer learning. Subsequently, to evaluate the training performance of the ResNet18 transfer learning model, the test set accuracy, training accuracy, and F1-score were adopted as evaluation metrics. Their respective definitions are as follows:  
\[\left\{\begin{array}{c}Accuracy=\frac{TP+TN}{P+N}\\ \text{Precision}=\frac{TP}{TP+FP}\\ \text{Recall}=\frac{TP}{TP+FN}=\frac{TP}{P}\\ f1=2\times\frac{Recall\times Precision}{Recall+Precision}\end{array}\right. \tag{6}\]  
Here, TP (True Positives) refers to the number of correctly predicted positive cases, FP (False Positives) denotes the number of incorrectly predicted positive cases, and FN (False Negatives) represents the number of incorrectly predicted negative cases. Additionally, P stands for the total number of actual positive cases, and N for the total number of actual negative cases. The F1-score value range is from 0 to 1. Unlike accuracy, it comprehensively incorporates the results of both precision and recall in its calculation. A high F1-score can only be achieved when both precision and recall are high, making it a more balanced metric for evaluating classification performance.  
Figure 6 presents the evaluation metrics for model training using three transfer learning strategies. This experiment focused on residual networks, comparing the training process curves of different classification models to verify the effectiveness of the employed transfer learning strategies. In the figure, the training curves show dotted lines representing the test set accuracy, while solid lines correspond to F1-scores. As the number of training  
Figure 5: A structural diagram of the model after the introduction of the CBAM.  
epochs gradually increases, these metrics stabilize, indicating that the models possess good generalization ability.  
Specific values are provided in the accompanying Table 3. Among the strategies evaluated, the model with the lowest performance (presumably the first strategy, i.e., fine-tuning only the last layer without full transfer learning) achieved an accuracy of 84.5%, precision of 82.9%, and recall of 91.1%.  
Figure 6: Evaluation metrics of network model classification models for different transfer learning strategies. (**A**) Evaluation indices of TL-M1 network classification model. (**B**) TL-M2 network classification model evaluation indicators. (**C**) Evaluation indices of TL-M3 network classification model.  
The model trained with random initialization (second strategy) yielded an accuracy of 90.1%, precision of 90.0%, and recall of 90.8%.  
The model utilizing full-layer weight fine-tuning (third strategy) achieved the highest performance: 92.7% accuracy, 91.2% precision, and 90.8% recall.  
Compared to the poorest-performing model, the third strategy demonstrated improvements of 8.2% in accuracy, 8.3% in precision, and 9.7% in recall. Consequently, the full-layer weight fine-tuning strategy was selected for subsequent studies.
# Development of a New Test-Rig for Wheel-Rail Contact Experiments under Large Slip Conditions
## 1 Introduction  
Wheel-rail contact is an important boundary factor for rail vehicles, which is one of the main characteristics that is different from other transportation. Ordinarily, rail vehicles rely on wheel-rail contact for loading and guiding, traction and braking. Since the wheel and rail are not completely rigid, creep occurs at the wheel-rail contact interface, which generates creep force. This condition is called wheel-rail adhesion [1]. When the rail surface is contaminated by water, oil or leaves, the adhesion between wheel and rail will be reduced, resulting in the loss of braking force and extended stopping distances [2], threatening the driving safety. It has been found that large slip between wheel and rail can alter the contact interface state and recover poor adhesion [3], besides the method of sanding. In order to further theoretical and experimental research on adhesion recovery under large slip conditions, it is necessary to develop a new test-rig for wheel-rail contact experiments under various surface conditions.