esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Methods  
Before the start of the experiment, the researchers first precisely preset the core operating parameters such as drying temperature, wind speed and relative humidity in the central control system of the experimental device to ensure the consistency and accuracy of the experimental conditions. The parameter control operation page is shown in Figure 1d.  
Figure 1: Multi-parameter controlled in situ precision drying test bench. Note: 1. wind shunt; 2. axial flow fan; 3. material bin door; 4. material triay; 5. sensor bin; 6. electric sealing valve; 7. heating pipe; 8. inner tank; 9. wet exhaust fans; 10. test-bed shell; 11. sensor group; and 12. humidifier.  
After the preset parameters were entered, a 20-min preheating procedure was performed to fully achieve and maintain the set operating stable state. At the same time, the experimental rice samples were removed from the low temperature storage environment (cold storage) and left in a constant indoor environment to acclimate to room temperature conditions. In the experimental stage, 1000 g of rice samples were accurately weighed (using a digital electronic balance with an accuracy of \(\pm\)0.01 g) and then placed in the material chamber of the drying equipment to start the drying process. Throughout the drying process, the rice samples were weighed every 15 min to monitor real-time water loss. At the end of the drying stage, the samples were transferred to an oven with the same set temperature for tempering treatment according to the preset tempering ratio. The time length of the tempering ratio was strictly calculated according to the preset tempering ratio. In order to simulate a real tempering environment, the special tempering box used in this study has good sealing performance to ensure that the influence of external environmental factors on the sample is minimized during the tempering process.  
All rice-drying experiments were continued until the samples reached the predetermined moisture content threshold of 14% (w.b.). The dried rice samples were kept stable at room temperature for 48 h, and then a series of quality indicators were determined, including the percentage of burst waist growth, the percentage of whole milled rice, the content of resistant starch and the percentage of germination. After that, the samples were stored in a cold storage environment at 4 \({}^{\circ}\)C, so as to detect the deep nutritional quality of the rice such as fatty acid value, protein content and fat content. In order to ensure the reliability and statistical validity of the experimental data, all the quality tests under each group of experimental conditions were repeated in three parallel experiments.
# Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection
## 3 Training Data Selection
### Importance of Training Data Update  
As discussed in Section 2 the clutter statistics are estimated for each red sub-region within the green region (cf. Figure 4 top). This is how the training data is extracted locally and updated first along range and then along azimuth. Training data update is an important step because of two main reasons. The first reason was already explained in Section 2 where the contribution of the aircraft's Euler angles is discussed (cf. Figures 3 and 13). The second reason is due to the varying motion of the ocean surface. With a moving ocean surface, the clutter statistics tend to change significantly over range and time. To show this behavior, the texture (or shape) parameter from the K-distribution (cf. Section 4.1 and cf. (21)) is estimated. The variation of the texture both along range and azimuth is shown in Figure 17 for X-band HH polarized data acquired during a linear and circular flight. Lower values of texture indicate spiky clutter and higher values indicate Rayleigh like characteristics of the clutter intensity.  
From Figure 17, it can be clearly observed that the K-distribution based texture parameter varies both over range and azimuth time. Significant changes are observed along range (incidence angle) where the texture values vary from very low to very high. Along the azimuth, the variation tends to follow a sinusoidal pattern which may be aligned with the wind direction [35]. Therefore, to consider these changes accurately, the training data need to be updated frequently in order to obtain bias free local sea clutter statistics [23].  
Figure 17: Estimated texture parameter of the K-distribution along (**a**,**c**) range and (**b**,**d**) azimuth using data acquired during a linear and circular flight.
## 3 System Model  
### Assumptions  
We consider that the distance between suspicious UAV transmitter (\(UAV_{ST}\)) and receiver (\(UAV_{SR}\)) is denoted as \(D\) meters. The distance can be calculated in the subsequent time slot, considering the dynamic mobility of the two UAVs. Without loss of generality, we consider legitimate eavesdropper (\(UAV_{L}\)) patrols in a predetermined circular trajectory between \(UAV_{ST}\) and \(UAV_{SR}\) with a diameter \(D\), particularly, the wireless link dynamics that are affected by the distance between \(UAV_{L}\) and the suspicious UAVs are identical on a semi-circle of the trajectory. As a result, we consider the trajectory of \(UAV_{L}\) as a semi-circle, even though the distance between \(UAV_{L}\) is dynamic with time-depend.  
The suspicious communication between \(UAV_{ST}\) and \(UAV_{SR}\) consists of \(m\) number of time slots, and each time slot is denoted as \(x\). We assume that \(UAV_{ST}\) communicates with \(UAV_{SR}\) in a TDMA fashion, however, it should be noted that our method is generalized and thus agnostic of the MAC protocol in use. In our proposed model, we assume that the suspicious UAVs consider the \(UAV_{L}\)'s eavesdropping signal as interference during the wireless communication.  
In fact, our policy proposed in Section IV is general and can support other shapes of flight trajectory since we have considered different fading channels with path loss that is affected by the distance between hostile UAV pairs, regardless of trajectories of UAVs. Moreover, Table 1 lists the fundamental variables that have been used in our system model.
# DYNASTI--Dynamic Multiple RPL Instances for Multiple IoT Applications in Smart City
## 4 DYNAATIC Multiple RPL Instances for Multiple IoT ApplicatIons (DYNASTI)
### Concepts and Definitions: Applications, Traffic Classes, and RPL Instances  
The communication of different types of traffic occurs concurrently in IoT scenarios with multiple applications, and each application has different requirements and disputes the resources available on the network. This demands a differentiation of traffic, resulting in a range of scenarios, which motivates the study of these multiple applications and how much their requirements can be supported differently even when computational and network resources are scarce in LLNs.  
Each application can have more than one instance option in our proposal; however, an active application (running) is associated with a single instance in an instant of time. The instances are employed selectively, so that there is adaptation to the emergence of new applications with requirements of different priorities. To enable this adaptation, we use traffic classes, which are composed of a routing metric and a message sending interval for each instance. Figure 1 illustrates a scenario with multiple applications, where there are three traffic flows. The communication of each flow is supported by different instances of the RPL and therefore different classes of traffic. This allows each stream to have a particular routing metric and transmission interval. For example, application 1 has flows B and C associated with instances 2 and 3, respectively. Consequently, application 1 has two classes of traffic at different times.  
Figure 2 demonstrates the relationship between application, instance, and traffic class. Each application can have more than one instance, and, for each instance, there is a class of traffic. These classes have different configurations to meet the requirements of critical applications that may appear sporadically.  
Figure 1: Diagram of the linkage between applications, instances, and traffic classes.  
Each instance of the application employs a distinct metric that allows applications to be more flexible: if the network infrastructure lacks resources, the application can switch from the current instance to another with more appropriate metrics and message sending intervals. This change directly impacts decision-making for the choice of routes and thus frees up resources to meet the demand of other competing applications with critical traffic. The applications, therefore, are not permanently associated with a single class of traffic, making them more dynamic, since they can adapt to coexist with traffic that has more priority.
# A Survey of Candidate Waveforms for beyond 5G Systems
## 4 Performance Results and Discussion
### PSD and Spectral Efficiency
\begin{table}
\begin{tabular}{c c} \hline \hline
**Waveform** & **Bandwidth** \\ \hline OFDM & \(\frac{R_{b}}{2\log_{2}(M)}\Big{(}1+\frac{N_{cp}}{N}\Big{)}\) \\ \hline F-OFDM & \(\frac{R_{b}}{2\log_{2}(M)}\Big{(}1+\frac{N_{cp}}{N}\Big{)}\) \\ \hline GFDM & \(\frac{R_{b}}{2\log_{2}(M)}\Big{(}1+\frac{N_{cp}}{NN_{s}}\Big{)}\approx\frac{R _{b}}{2\log_{2}(M)}\) \\ \hline TIBWB-OFDM with WTO & \(\frac{R_{b}}{2\log_{2}(M)}\Big{(}1+\frac{\beta N+N_{Zp}}{NN_{s}}\Big{)}\approx \frac{R_{b}}{2\log_{2}(M)}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Required bandwidth for the different waveforms. In summary, the TIBWB-OFDM with WTO and GFDM, from the point of view of spectrum demand, are the most spectrally efficient waveforms, since they require the least amount of signal overhead. On the other hand, the waveforms that provide a higher degree of spectrum confinement are the TIBWB-OFDM and F-OFDM, while TIBWB-OFDM with WTO still provides considerably low OOB emissions. All things considered, the TIBWB-OFDM with WTO is among all the one that with the better spectral efficiency, presenting a good trade-off between effective throughput, while still achieving low OOB radiation.
## 3 Results and Discussions
### Dissociation Enthalpy of CO\({}_{2}\)-CH\({}_{4}\) Gas Hydrate
From the figure, it can be observed that the dissociation enthalpy of mixed gas hydrates increases with the increase in carbon dioxide molar fraction, but the rate of increase gradually diminishes. Unlike the situation above the freezing point, there is no decrease in enthalpy values. In addition, the enthalpy range of mixed gas hydrates below the freezing point is 17.52-20.92 kJ/mol, significantly lower than the values above the freezing point. In order to eliminate hydrate blockage below freezing point, in addition to stimulating the dissociation and release of gas from the hydrate, it is also necessary to heat the ice into water, which consumes a significant amount of energy. In addition, due to the lower formation temperature of hydrates below the freezing point, it is easy to cause the formation of secondary hydrates, making blockage more severe. At present, natural gas hydrate is the main research object, but there are few reports on the technology of carbon dioxide hydrate control. Due to the mild conditions for the formation of carbon dioxide hydrate, CO\({}_{2}\) hydrates are more stable than methane hydrates. Compared with the control of methane hydrate, the thermodynamic inhibition method needs to add a higher concentration of thermodynamic inhibitors. In the kinetic inhibition method, the kinetic inhibitor for CO\({}_{2}\) hydrate must bear a higher degree of undercooling [11; 12; 13]. In addition, since CO\({}_{2}\) is an acidic gas, the effectiveness of current kinetic inhibitors for natural gas systems needs to be further evaluated. In short, the prevention and control of hydrates in the production fluids of CO\({}_{2}\) flooding is a significant challenge.
## 3 Case Study
### SKAB Dataset
#### 3.2.3 Efficacy of Task Sequencing in GOPML  
To verify the effectiveness of task sequencing, GOPML with task-sequencing was compared with the baseline algorithm MAML. The inner network architecture, the dataset's partition, and the training strategy for both algorithms are consistent. The results of the fault diagnosis on the SKAB dataset are shown in Table 9 and Figure 9. Under four different task settings, the accuracy rates of the proposed GOPML were 91.50%, 92.03% for the 3-way 5-shot and 6-shot settings, and 80.78% and 81.30% for the 8-way 5-shot and 6-shot settings, respectively. It can be seen that the meta-learning strategy with task sequencing could obtain better results than the one without sequencing. Moreover, when the number of samples in the support set was smaller, the accuracy improved more obviously. It verified the effectiveness of GOPML in few-shot scenarios. Besides accuracy, precision and F1 score were also introduced as evaluation indicators. As shown in Table 9, the F1 score  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Layer Number** & **Module 2** \\ \hline L1 & Conv1D (5, 64) \\ L2 & ReLU \\ L3 & BN \\ L4 & Maxpool1D (2) \\ L5 & Dropout (0.5) \\ L6 & LSTM (100) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Parameters of each module for the SKAB dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sequence Number** & **Base Learner** \\ \hline
2 & Module 1 \\
3 & Module 2 \\
4 & Module 1 \\
5 & FC (Dense Layer) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Structure of the base learner for the SKAB dataset.  
and precision of GOPML were also excellent. To further explain the training process of GOPML, Figure 10 shows how the accuracy in a task changed as the update step increased. To optimize the efficiency of the experiment, the task-level update step in our experiments was set to 5. But, here, more intuitively, the task-level update step in Figure 9 is 10. It can be seen that the accuracy of the proposed GOPML method is improved more obviously and quickly.
# Retrieving Freeze/Thaw Cycles Using Sentinel-1 Data in Eastern Nunavik (Quebec, Canada)
## 3 Results
### Threshold Distribution through All Selected Stations  
As a result of the validation process with the 100-step threshold test, the accuracy distribution of each test site that varied with the changing thresholds was obtained. The threshold for each soil depth at each site to reach the highest accuracy is summarized in Figure 5. The accuracy of the sites had different forms of distribution, and there was not always a single threshold where the highest accuracy could be reached. This selection below is based on the statistics of the five thresholds that bring the top five accuracies of each depth of each site, and it shows the best-performing threshold or a representative of the best-performing thresholds.  
As expected from the hypothesis, Figure 5 shows that the most effective threshold did not always fall within a fixed range. Many of the most effective thresholds were distributed between 0.55 and 0.65, which fits the commonly used range to a certain extent. However, in actual observations of each site, the fluctuation of the accuracy with the change in threshold was not the same. Meanwhile, there were also a non-negligible number of sites, which reached their optimal accuracy with a threshold that fell in other ranges. This could confirm the hypothesis that the reasonable adjustment of the threshold can help to improve accuracy, and test sites in different environments will respond differently to the threshold.  
Figure 4: Backscatter signal reference of all test sites, **left**: comparison of references of different polarizations in the same season (**top**: winter, **bottom**: summer), **right**: comparison of references of the same polarization in different seasons (**top**: VH, **bottom**: VV).  
Within the same test site, the results from different depths shared almost the same distribution. For example, the 2 cm, 5 cm, and 10 cm layers of site 1 all reached their highest accuracy by setting the threshold as 0.45; the 2 cm, 5 cm, and 10 cm layers of site 2 reached their highest accuracy by setting the threshold as 0.8; the 0 cm, 5 cm, and 10 cm layers of site 6 reached their highest accuracy by setting the threshold as 0.63; the 5 cm and 10 cm layers of site 11 reached their highest accuracy by setting the threshold as 0.61; the 0 cm and 10 cm layers of site 36 reached their highest accuracy by setting the threshold as 0.59. Not only the best-performing threshold but also the overall change trend of the accuracy following the stepwise change in threshold was almost always the same between different testing depths from the same test site. This phenomenon indicates that the surficial 10 cm of soil reacts similarly in this approach without a significant difference in sensitivity to the thresholds.  
This section mainly presents the analysis of the input data and the staged results in the calculation process. How well the seasonal threshold algorithm performs over the test sites, in other words, the exact accuracy, is shown in the next section.
# Post-Disaster Recovery Monitoring with Google Earth Engine
## 3 Results and Discussion  
The proposed approach produced overall accuracies of 92.8%, 88.6%, 93.8%, 94.6%, and 89.0% for land cover classifications of the T0, T1, T2, T3, and T4 images, respectively (Table 3). In addition, Figure 3 shows the original true-color image composites and the results of the land cover classification for the T0-T4 time steps. Visual interpretation of the results and the high accuracy rates demonstrate the robustness of the approach and the Random Forest classifier for land cover classification for the defined classes in such a case study. However, the producer's accuracy values of the built-up and the other (including non-tree vegetation, bare land, and debris) classes for T1 and T4 images were 76.5% and 75.4%, respectively, which shows the relatively high omission errors for those classes. Relocation of palm tree fronds by the typhoon made distinguishing trees, crop land, and other (including debris) classes more difficult for the event time, and resulted in the lowest overall accuracy of 88.6% for the T1 image classification. Furthermore, the generated composite images may still include cloud coverage. For example, the presence of clouds in the T4 image even after image composition generation resulted in some inaccuracies that produced the second-lowest overall accuracy of 89.0% among other images. The developed method produced accurate results in classifying built-up, water body, and forest/trees classes in most of the images. Figure 4 shows the land cover class change trends extracted from the T0 to T4 images. Typhoon Haiyan clearly had a significant impact on the forest/trees and built-up areas, reducing their coverage by about 40% and 30% from T0 to T1 images, respectively. The increase of the other class from T0 to T1 is mostly due to the debris and rubble caused by Haiyan and denuded trees, which led to an increase in non-tree vegetation coverage even in (formerly) forested areas. The storm surge during the typhoon resulted in the increase in water bodies in the T1 image. Monitoring three years after Typhoon Haiyan for Leyte island demonstrates that most of the land cover changes returned to their pre-disaster situation. However, there is an increase in the built-up class that is due to the construction of resettlement sites (as safe zones) mostly around the eastern coastal cities (e.g., Tacloban city--Figure 3). Figure 5 shows the damage and recovery maps for the post-Typhoon Haiyan situation generated at the municipality level for Leyte island. The damage map was produced based on the change analysis of the land cover classes for the T0 and T1 times, and recovery maps (RC) (i.e., RC1, RC2, and RC3) were produced based on change analysis of the damage map and the land cover classes of T2, T3, and T4 times, respectively.
## 5 Method for Calculating Path Loss Based on Local Conditions
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**L (m)** & **PLm (dBm)** & **(PLm \(-\) PLr) (dB)** & **(PLm \(-\) PLr)\({}^{2}\) (dB)\({}^{2}\)** \\ \hline
1 & 73.751 & \(-6.263\) & 39.231 \\
20 & 76.4485 & \(-3.566\) & 12.716 \\
30 & 78.0265 & \(-1.988\) & 3.952 \\
40 & 79.1461 & \(-0. In Figure 15, the adjusted model (in blue) represents the path loss while allowing the influence of topographical elements and obstacles, which are more significant at shorter distances. At the intersection points (marked with red circles), we observe that at 18 m, the adjusted model is more sensitive to these elements than the Okumura-Hata, COST 231-Hata, and COST 231-WI models. However, as the distance increases from 18 to 55 m, the adjusted model falls below the COST 231-Hata model (in purple). This suggests that the topographical and obstacle effects diminish over greater distances, allowing the adjusted model to stabilize its predictions. Beyond 55 m, the behavior of the adjusted model closely aligns with the COST 231-WI (in green) and Okumura-Hata (in orange) models. This trend is consistent with Figure 14, where beyond 55 m, the signal reception levels are more closely matched by the adjusted model (in red). In all scenarios, the free-space model is significantly lower than the adjusted model and the reference models.