esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancements for Snowmelt Monitoring by Means of Sentinel-1 SAR
## 3 Methods  
The reduced backscattering coefficient (\(\sigma^{0}\)) of wet snow compared to surfaces that are snow-free or covered by dry snow provides the basis for mapping snowmelt areas [9; 10; 11; 12; 13; 14; 15]. Due to the high dielectric losses of water, \(\sigma^{0}\) of snow decreases with increasing liquid water content [24; 25; 26]. The typical C-band one-way penetration depth of dry seasonal snow is about 20 m, whereas for wet snow with liquid water content of 5% by volume, it is only 3 cm [25]. Whereas for wet snow the radar signal at C- and X-band frequencies is reflected and scattered at the surface and within the uppermost centimeters of the snowpack, in the case of dry seasonal snow, a main part of the observed signal is contributed by the snow/ground interface. Consequently, the contrast in backscatter intensity of dry snow areas and snow-free surfaces is small and not suitable for reliable mapping of dry snow. The combined use of optical sensors and SAR enables the mapping of total snow area and the monitoring of the temporal dynamics of snowmelt [14].  
The backscatter intensity of wet snow packs is subject to temporal variations, which may affect the snow retrievals. Refreezing of the surface layer, for example during a cloudless night, causes an increase in \(\sigma^{0}\) due to the high scattering albedo of the frozen layer [27; 28]. This results in a decrease in the contrast of snow _versus_ snow-free surfaces. Backscatter theory and experimental data show that this effect is less pronounced at C-band than at X-band because of the smaller scattering efficiency and the better penetration through the frozen layer at the longer wavelength [28].  
Another issue that plays a role for snow retrievals is the angular dependence of backscatter. The backscatter contrast between melting snow and snow-free surfaces changes with the local incidence angle of the radar beam, which refers to the normal surface. For co-polarized backscatter, the contrast in \(\sigma^{0}\) decreases significantly towards low incidence angles because of a comparatively strong rise of the backscatter signal of wet snow surfaces [25; 29]. Cross-polarized backscatter shows less angular dependence, maintaining a clear difference between wet snow and snow-free surfaces also at low incidence angles. On the other hand, at high incidence angles cross-polarized backscatter may approach the noise floor. For these reasons, it is advisable to use both the co- and the cross-polarized channel for snow retrievals if available.
# Evaluation and Comparison of Spatial Clustering for Solar Irradiance Time Series
## 3 Characterization of Solar Irradiance Time Series  
Seventy-six GHI time series (one per location) measured between 1 January 2017 and 31 December 2020 were used. These observations were collected every 30 min. Each GHI series comprises 70,128 elements, for a total of 5,329,728 measurements. In addition, for each site and each 30 min, the GHIcs and kt (GHI/GHIcs) are computed; that is, more than 15 million data are created. As mentioned in the paper's Introduction, each site must be characterized from the available-solar-energy point of view, not only with the mean value or the cumulative GHI but mainly by the distribution or variation of the solar radiation. The notions of variability, forecastability, and dynamics of the series must be considered. The statistical parameters that are the most appropriate for this study are divided into two classes: the first one concerns static parameters (mean, standard deviation, skewness, and kurtosis), and the second one concerns the dynamic parameters (Hurst exponent (H) and forecastability coefficient (F)).
###### Abstract  
In the present study, a total of 12 extracts of _Rlinanthus angustifolius_ subsp. _grandiflora_, an understudied hemiparsistic species, were obtained using different extraction techniques, namely, homogenizer-assisted extraction (HAE), maceration (MAC), soxhlet (SOX), infusion, and solvents (ethyl acetate, methanol, ethanol, and water), and were evaluated for their in vitro antioxidant and enzyme-inhibiting properties. Additionally, untargeted profiling based on high-resolution mass spectrometry targeted different phytochemical classes, namely, polyphenols, terpenoids, and alkaloids. The highest total phenolic and flavonoid contents were detected using methanol as the extraction solvent. Multivariate statistics following the untargeted profiling revealed that the extraction solvent had a hierarchically higher impact than the extraction method when considering the recovery of bioactive compounds. The methanolic extracts displayed the highest radical-scavenging antioxidant capacity, as provided by CUPRAC and FRAP assays. On the other hand, the water extracts (MAC and HAE) and the infusion extract showed the highest activity as metal chelators (25.66-27.51 mg EDTAE/g). Similarly, the water extract obtained by HAE and the infusion extract revealed the highest phosphopolhydenum activity (3.92 \(\pm\) 0.14 and 3.71 \(\pm\) 0.01 mmol TE/g, respectively). The different extracts also exhibited different enzyme inhibition patterns. For instance, HAE and MAC ethanolic extracts inhibited only \(\alpha\)-amylase (0.69 \(\pm\) 0.01 and 0.70 \(\pm\) 0.01 mmol ACAE/g), while all the other extracts showed a dual inhibition against both carbohydrate-hydrolyzing enzymes tested (i.e., \(\alpha\)-amylase: 0.07-0.69 mmol ACAE/g: \(\alpha\)-glucosidase: 0.03-1.30 mmol ACAE/g). Nevertheless, the other extracts inhibited acetyl-, butyryl-cholinesteraseerases, or both; MAC-water extract displayed no inhibition against the enzymes. Additionally, all the studied extracts were found to inhibit tyrosinase, ranging from 10.62 to 52.80 mg KAE/g. In general, the water extracts showed weaker inhibition towards the enzymes than the other extracts. This study demonstrated that _R. angustifolius_ is an excellent source of natural antioxidants and enzyme inhibitors that could be further investigated and exploited for pharmaceutical purposes.  
Rlinanthus angustifolius; solvent extraction; phenolics/flavonoids; antioxidants; enzyme inhibitors; UHPLC-QTOF-MS; metabolomics
## 3 Theoretical Background
### Deep Learning Models
#### 3.2.2 Bayesian Neural Networks  
It is important to note that FFNNs are deterministic: given a certain input, they always provide the same output. They also do not account for the uncertainty of the prediction, i.e., they do not provide any measurement of their certainty of the given prediction, which is known as calibration in the literature [29]. In order to address both issues, we use BNNs, whose main difference to FFNNs is that the weights and biases, instead of being a scalar, are now considered a Gaussian random variable with mean and variance. Thus, the training differs, and it is now based on an algorithm called Bayes By Backpropagation [30], based on the Bayes' Theorem.  
The fact that the weights and biases in a BNN are random variables means that each time that we provide an input to a BNN, the weights are sampled according to their distribution, and thus, the output will differ even if the input is the same. This means that we can predict several outputs for a fixed input, and by studying the distribution of the outputs, we can assess whether the BNN is confident in its prediction (i.e., low variance) or not. That has been shown to improve the calibration of the network [29], and it is useful because it allows identifying the outputs where the BNN has a lower certainty, which is important to our problem, as we will see in Section 4. Moreover, it allows controlling the prediction error, which is something that must be controlled even in the case of using FE [13].
## 8 Conclusions  
The effects of different railway lines and of different mitigation measures have been analysed by 2D thin-layer finite-element (Sections 3-6), 2D boundary-element (Section 7), and by 3D combined finite-element boundary-element methods (Appendix A). The frequency-dependent reduction of the train-induced ground vibrations has been evaluated as an average over far-field surface points in comparison to a surface line without mitigation as the reference situations. Low frequencies are ususally not reduced, and the reduction ratio is decreasing with frequency. The strongest reductions expressed as the ratio of the amplitudes with changes to the amplitudes of the reference system go down to 0.1 at 80 Hz. A more realistic reduction is summarised in Figure 15 as an average over the high frequencies between 40 and 80 Hz. An embankment has an average reduction ratio of 0.7, wheras a cut and a tunnel have a stronger reduction ratio of 0.3 for the railway lines. Mitigation measures in the transmission path are effective with reduction ratios of 0.4 for plates and walls and 0.25 for a massive block. The reduction of a trench depends on the (compressional) stiffness of the infill and reaches 0.2 for very soft materials or for an open trench. Simultaneous measurements at a surface bridge and tunnel line have been performed and will be discussed in a companion paper together with the corresponding track stiffnesses calculated in the appendix of the present article. As pointed out by FEM and BEM calculations (and by measurements), the reduction of a tunnel line is a combination of different effects such as the depth of the excitation and the load distribution by the stiff track structure.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The data of this study are available on request from the author.  
**Acknowledgments:** Thanks to [PERSON] who helped with some FEM calculations.  
**Conflicts of Interest:** The author declares no conflict of interest.  
Figure 15: The reduction effect of different railway lines and different obstacles for frequencies between 40 and 80 Hz (average).
# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 4 Measurement Scenarios and Data Preparation  
In this section, we describe the experimental setup of the evaluations (Section 4.1), the data collection processes including labeling and data separation (Section 4.2), and the feature extraction based on the collected data (Section 4.3) for the three evaluated ML models.
## 2 Materials and Methods
### Training Process Tracking and Model Continuity  
In this study, a customized model training strategy was adopted to efficiently process the large dataset used in the deepfake detection task within the limited session duration of Google Colab. Since the session duration on the Google Colab platform is generally limited to 24 h, a specific saving mechanism was developed to ensure that the model could resume training from where it left off in each session. This mechanism involves recording the current state of the model and tracking the training data step by step.  
The dataset used consists of the DFDC (Deepfake Detection Challenge) dataset divided into 50 different parts, containing a total of 124,000 videos. To prevent the model from focusing solely on fake data, as most of the dataset is labeled FAKE, and to ensure balanced learning, the videos in each part were grouped as one FAKE and one REAL video. As a result of this limitation, the model was trained using a total of 25,000 videos.  
During the training process, the current state of the model was saved to the balanced_model_path after every two videos, ensuring that the most recent state of the model was preserved so that it could continue from where it left off in the next session. In this structure, two main folders were defined: the 00 folder, where the current data parts from the DFDC dataset are loaded during processing, and the ALL folder, where trained video data are stored. After every two-video training cycle, the training data in the 00 folder are transferred to the ALL folder to avoid repetition in training. This ensures that when a session ends and a new session begins, the model continues training only with videos that were not processed in the previous session.  
At this stage, metadata.json files were used to track the data labels used in training. The metadata.json file in the 00 folder holds the labels of the videos to be processed in the current part, while the metadata.json file in the ALL folder accumulates the labels of the processed videos. In case the training process is interrupted for any reason, the model resumes from where it left off by loading the model state from the balanced_model_path using the load_state_dict command. This approach ensures that all videos are included in the training process and prevents redundant processing, achieving high efficiency. Additionally, collecting the processed videos in the ALL folder allows them to be directly used as training data if multiple epochs are desired.  
As shown in Figure 5, this structure enables efficient training of the model under limitations such as a restricted session duration and a large dataset.
# NTARC: A Data Model for the Systematic Review of Network Traffic Analysis Research
## 7 Analysis of NTA Research (NTARC Database)
### Reproducibility  
In [35], ACMpresents a terminology to define to what degree a research work can be repeated by using the information provided by the authors in the paper and in the linked resources. These categories are:  
* Reproducible: Experiments are fully reproducible by a different team based on the information given in the paper. The setup, parameters, tools, and datasets are described and/or provided (references to valid links) in a clear and open way. Results are expected to be the same or very similar.
* Replicable: The experiment can be replicated by a different team, but with a different setup. The methodology is clearly explained, at least at a theoretical level. Not all parameters or tools are provided, but readers obtain enough know-how from the paper and references to develop their own setups based on the provided descriptions.
* Repeatable: The methodologies and setups are clearly described with scientific rigor; however, experiments can only be repeated by the authors given that some resources are not publicly available (e.g., they use datasets that are not openly available).  
Figure 7: Methods for evaluating NTA proposals and the reproducibility of experiments and setups.  
* No: Important information about the part of the methodology is missing in a way that the experiment cannot be repeated in comparable conditions. Papers show findings or results, but it is not clear how they were obtained (information is hidden, omitted, or simply missing).  
Figure 7b shows that 43.3% of the papers were replicable, but only 10.4% were reproducible; while 29.9% were only repeatable, and the remaining 16.4% did not meet the minimum repeatability standards. Considering that the paper selection prioritizes most cited papers and papers published in reputed journals and conferences, there is much room for improving the reproducibility culture in the field. Even when considering completely reproducible works, experiments that can be repeated out-of-the-box are very scarce. One of the most noticeable impediments for reproducibility is the dataset availability (Figure 5a), which is not openly available in almost half of the cases. A second relevant reproducibility hindrance is the use of software, tools, and scripts for preprocessing and analysis that are not publicly available or even not specified in the paper (57.5% of the studied papers did not provide information about the tools used for preprocessing, while 53.8% did not provide information about the analysis tools). Not providing algorithm parameters and descriptions of analysis setups is also common.
# Reliability Analysis of Vehicle Braking System Based on Hyperellipsoidal Dynamic Bayesian Network
## 4 Model Validation
### Braking System Reliability Analysis
\begin{table}
\begin{tabular}{c c c c} \hline
**Event Number** & **Event Name** & **Event Number** & **Event Name** \\ \hline x1 & [7.815 \(\times\) 10\({}^{-6}\), 8.859 \(\times\) 10\({}^{-6}\)] & x10 & [1.652 \(\times\) 10\({}^{-5}\), 1.875 \(\times\) 10\({}^{-5}\)] \\ x2 & [1.345 \(\times\) 10\({}^{-6}\), 1.484 \(\times\) 10\({}^{-6}\)] & x11 & [1.945 \(\times\) 10\({}^{-5}\), 2.202 \(\times\) 10\({}^{-5}\)] \\ x3 & [9.702 \(\times\) 10\({}^{-6}\), 1.099 \(\times\) 10\({}^{-5}\)] & x12 & [1.160 \(\times\) 10\({}^{-6}\), 1.299 \(\times\) 10\({}^{-6}\)] \\ x4 & [2.273 \(\times\) 10\({}^{-6}\), 2.556 \(\times\) 10\({}^{-6}\)] & x13 & [2.105 \(\times\) 10\({}^{-6}\), 2.362 \(\times\) 10\({}^{-6}\)] \\ x5 & [4.836 \(\times\) 10\({}^{-6}\), 5.462 \(\times\) 10\({}^{-6}\)] & x14 & [1.849 \(\times\) 10\({}^{-6}\), 2.071 \(\times\) 10\({}^{-6}\)] \\ x6 & [4.828 \(\times\) 10\({}^{-6}\), 5.470 \(\times\) 10\({}^{-6}\)] & x15 & [5.525 \(\times\) 10\({}^{-6}\), 6.235 \(\times\) 10\({}^{-6}\)] \\ x7 & [8.833 \(\times\) 10\({}^{-6}\), 1.003 \(\times\) 10\({}^{-5}\)] & x16 & [1.122 \(\times\) 10\({}^{-5}\), 1.274 \(\times\) 10\({}^{-5}\)] \\ x8 & [1.082 \(\times\) 10\({}^{-5}\), 1.224 \(\times\) 10\({}^{-5}\)] & x17 & [1.063 \(\times\) 10\({}^{-5}\), 1.206 \(\times\) 10\({}^{-5}\)] \\ x9 & [1.446 \(\times\) 10\({}^{-5}\), 1.642 \(\times\) 10\({}^{-5}\)] & x18 & [1.421 \(\times\) 10\({}^{-6}\), 1.586 \(\times\) 10\({}^{-6}\)] \\ \hline \end{tabular}
\end{table} 
Table 8: Failure probability intervals for the bottom event of the superellipsoid model. From the results, it can be clearly seen that the interval length under the hyperellipsoid model (Figure 7a,b) is narrower than that under the interval model (Figure 7c,d), which proves that the hyperellipsoid model can avoid the situation where each bottom event takes the limit value at the same time, i.e., this algorithm of the hyperellipsoid model is able to solve the problem of relatively conservative calculation, and at the same time, it verifies the validity of the simulation process. In order to quantify the advantage of the hyperellipsoid model over the interval model in terms of accuracy, a comparative analysis of the failure interval lengths of the two models during 500 days of operation was conducted [17], and, by calculating the average of the ratio between the failure interval lengths of the hyperellipsoid model and the interval lengths of the interval model, the results showed that the average length of the brake system availability interval under the hyperellipsoid model was reduced by about 54.06%. This significant reduction ratio not only verifies the effectiveness of the hyperellipsoid model in improving the assessment accuracy, but also further confirms the rationality and practicality of the simulation method proposed in this study. Figure 7: (**a**) No maintenance superellipsoid model; (**b**) With maintenance superellipsoid model; (**c**) No maintenance interval model; (**d**) With maintenance interval model.
# Reliability-Aware Cooperative Node Sleeping and Clustering in Duty-Cycled Sensors Networks
## 4 On-Demand ETE Reliability Model  
### Probability Model for Successful Delivery in One Hop  
Assume the duty cycle is \(\mu\) and the number of cooperative nodes is \(K\), and the probability that \(k\) cooperative nodes are awake among RCR is represented by \(P_{k}\). Then, \(P_{k}\) can be calculated by Formula (1):\[P_{k} = C_{K}^{k}\times\mu^{K-k}\times(1-\mu)^{k}. \tag{1}\]  
In that case, for \(k\) awake cooperative nodes, the probability of successful transmission at the current hop is represented by \(W_{k}\), which can be calculated by Formula (2).  
\[W_{k} = P_{k}\times(1-f^{k}) \tag{2}\] \[= C_{K}^{k}\times\mu^{K-k}\times(1-\mu)^{k}\times(1-f^{k}).\]
# Precise Mission Process Control Based on a Novel Dual-Code Group Network Plan Diagram
## 4 Mission Process Control System Modeling
### System Modeling
\[e(t) =y(t)-y_{e} \tag{28}\] \[=T_{PS}^{m^{*}}(t)-T_{PS}^{m^{*}}\]  
The goal of mission progress control is to minimize system error and ensure that the mission is executed according to plan. Define the system control quantity as \(\mathbf{u}(t)=[u_{m_{1}}(t),u_{m_{2}}(t),\dots,u_{m_{n}}(t)]\), where \(u_{m_{i}}(t)\), \(m_{i}\in\mathcal{M}_{s}\) is the amount of mission duration change for mission \(m_{i}\) at time \(t\). If a delay occurs in the currently executing mission, the plan is changed by adjusting the mission duration of the unsaturated mission to achieve the elimination of the system error. Define the system disturbance as \(\mathbf{\omega}(t)=[\omega_{m_{1}}(t),\omega_{m_{2}}(t),\dots,\omega_{m_{n}}(t)]\). The disturbance of the system \(\mathbf{\omega}(t)\) reflects the progress bias during the execution of the mission, and according to Hypothesises 2 and 4, the mission progress bias refers to the delay of the mission, disregarding the mission advancement, the mission disappearance, and the mission increase. Therefore, \(\omega_{m_{i}}(t),m_{i}\in\mathcal{M}_{s}\) represents the delayed duration of mission \(m_{i}\) at time \(t\). Let the planned completion moment of the mission \(m_{i}\) be \(T_{PF}^{m_{i}}(t)\), we have  
\[\omega_{m_{i}}(t)= \begin{cases}t+\Delta t-T_{PF}^{m_{i}}(t),&\text{if }t\geq T_{PF}^{m_{i}}(t)\text{ and }m_{i}\in\mathcal{M}_{1}(t)\\ 0,&\text{otherwise}\end{cases}  \tag{29}\]  
The physical meaning of the above equation is that when \(t=T_{PF}^{m_{i}}(t)\), the planned end time of the mission \(m_{i}\) is reached, but if the end signal of the mission \(m_{i}\) is not obtained from the observation, it means that there is a delayed disturbance in the mission \(m_{i}\) at this time. Let \(\omega_{m_{i}}(t)=1\), which represents a disturbance of 1 time step. When \(t>T_{PF}^{m_{i}}(t)\), the disturbance is linearly related to the delay time. The disturbance is imposed on the set of executing missions \(\mathcal{M}_{1}(t)\), which is due to various contingencies during mission execution and related to the actual mission execution. From the system point of view, the mission progress disturbance is an unknown quantity that can only be observed in real time and cannot be predicted and modeled, so the whole system is an uncertainty system.
###### Abstract  
The large temperature difference condition in Northwest China threatens a myriad of concrete structures during construction, with the daily temperature varying by around 40 \({}^{\circ}\)C. To investigate the macro-mechanical properties and microstructural characteristics of concrete containing different amounts of mineral admixtures under such harsh conditions, this investigation used an environmental chamber to simulate a saline soil erosion environment with a large temperature difference. Four types of concrete containing different proportions of fly ash and slag were prepared and exposed in the environmental chamber with a daily temperature change of \(-\)5-40 \({}^{\circ}\)C to investigate their compressive strength, flexural strength, and fracture properties. Moreover, the X-ray diffraction (XRD) characteristics, microscopic morphological characteristics, pore structure characteristics, and post-eroson chloride ion distribution characteristics were also observed and recorded. Results showed that the mineral admixture could improve the early strength development of the concrete and effectively improve the fracture performance of the concrete. The average compressive strength growth rate of concrete from day 3 to day 14 was 83.25% higher than that of ordinary concrete (OC) when 15% fly ash and 15% slag were added. In addition, the fracture energy of the concrete was maximized when 15% fly ash and 20% slag were added, which was 50.67% higher than that of OC; furthermore, the internal compactness and pore structure were optimized, and the resistance to saline soil erosion was strong. This provides a basis for the practical application of compounded mineral admixture-modified concrete in an arid environment with a large temperature difference and saline soil erosion.  
large temperature difference; saline soil erosion; microstructure; mechanical properties; mineral admixture +
Footnote †: journal: Applied Sciences