esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Research Methods  
#### 2.4.1 Method for Applicability Verification  
To verify the applicability of multisource SIF satellite products for GPP estimation in arid regions, linear regression fitting analysis is conducted using measured GPP data from each site and their corresponding SIF data. Our data used for comprehensive calculations have complete and stable time series, with a clear number of variables and the exclusion of outliers and outliers. The adjusted R\({}^{2}\) is used for comprehensive verification and evaluation, which avoids the irrationality, overfitting, bias, and inconsistency of model predictions. We set the suitability of SIF satellite products for arid regions when R\({}^{2}\) is greater than 0.6 in desert vegetation areas and greater than 0.8 in the cultivated and farmland and pasture grassland areas. The parameter calculation formula is as follows:  
\[RMSE =\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}} \tag{3}\] \[MB =\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i}) \tag{4}\]\[SD=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(x_{i}-\overline{x}_{i}\right)^{2}} \tag{5}\]  
\[R^{2}=1-\left(\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}/\sum_{i=1}^{n} \left(y_{i}-\overline{y}_{i}\right)^{2}\right) \tag{6}\]  
where \(n\) represents the number of samples, \(y_{i}\) represents the observed values, \(\hat{y}_{i}\) represents the predicted values, and the predicted value was calculated comprehensively based on the slope and intercept.
## 3 Results
### Ablation Experiments  
#### 3.3.1 WRPM  
To verify the effectiveness of the WRPM, we conducted ablation experiments on the UGSAM framework. The experimental group removes the WRPM component while retaining other identical network structures, with comparative tests conducted on mixed datasets containing both normal and severe weather conditions and adverse weather street scenes in a ratio of 2:1. This design aims to isolate the specific contribution of the WRPM in enhancing segmentation robustness.  
As shown in Table 7, the experimental results demonstrate significant performance improvements after incorporating the WRPM. In the defogging module of the UGSAM, the use of the WRPM leads to superior performance in all four metrics--OA, recall, precision, and mIoU--compared to using FFA-Net or LS-UNet alone. Specifically, OA improves by 1.17% and 0.89%, respectively. Meanwhile, experiments on FCN and PointNet++ also validate the generalizability of these results. Although the recall of WRPM applied to FCN is slightly lower than that of FFA-Net, the difference is only 0.39%, and all other metrics show improvement. This experiment indicates that the WRPM, which integrates FFA-Net and LS-UNet, is the optimal choice as a defogging module.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **WRPM** & **OA** & **Recall** & **Precision** & **mIoU** \\ \hline \multirow{3}{*}{FCN} & FFA-Net & 0.6809 & **0.8077** & 0.7820 & 0.6968 \\  & LS-UNet & 0.6872 & 0.7914 & 0.7793 & 0.6935 \\  & WRPM & **0.6935** & 0.8038 & **0.7878** & **0.7028** \\ \hline \multirow{3}{*}{PointNet++} & FFA-Net & 0.7529 & 0.7596 & 0.8171 & 0.6903 \\  & LS-UNet & 0.7471 & 0.7544 & 0.8204 & 0.6880 \\  & WRPM & **0.7562** & **0.7665** & **0.8242** & **0.6989** \\ \hline \multirow{3}{*}{UGSAM} & FFA-Net & 0.8515 & 0.9152 & 0.8941 & 0.8316 \\  & LS-UNet & 0.8543 & 0.9116 & 0.8935 & 0.8274 \\ \cline{1-1}  & WRPM & **0.8632** & **0.9277** & **0.9048** & **0.8452** \\ \hline \hline \end{tabular}  
* The best results are highlighted in bold.  
\end{table}
Table 7: Performance comparison of FFA-Net, LS-UNet, and the WRPM in the defogging module of UGSAM.  
Figure 13: Visualization of the correction effects of SCP-Net on image segmentation results. The arrows indicate a magnified view of the details within the red box in the original images. The black boxes highlight the areas where SCP-Net has performed corrections.
# A Location-Aware Waypoint-Based Routing Protocol for Airborne DTNs in Search and Rescue Scenarios
## 4 Protocol Design  
Our proposed solution uses two information sources regarding the nodes involved: the current location and the mission planned path under the form of waypoints. The UAVs move along predetermined paths, and follow an _a priori_ known schedule. This information is used by the protocol to predict the future locations of each relaying node and the time it will reach the locations (time of arrival or time of intercept), in order to transmit any message destined to a certain recipient crossing the path of the relay node. It is hence clear that a node waits for specific selected contacts and transmits the packet only to those. This aspect makes our proposal a partial replication-based strategy, like the Spray and Wait protocol. For this reason, we name our protocol _Geographic Spray and Wait_ (GeoSaW).
# Techno-Economic Analysis of the Peak Shifting Strategy Based on Time-of-Use Tariff for Cold Stores
## 4 Results and Discussion
### Analysis of Control Effects  
The comparison of the cooling capacity before and after peak shifting is shown in Figure 9, where red, blue, and green represent the ratio of the cooling capacity provided in the peak, flat, and valley periods to the total amount of the day, respectively. The cooling capacity of the peak period accounts for 3-14% and the valley period accounts for only about half in the original mode. Mode A achieves complete shifting, and the cooling load is met in the valley period. The cooling capacity ratio of the valley period in Mode B has increased by about 15% on average, while that of the flat period remains almost the same as before. To sum up, both modes can achieve a good peak shifting effect.  
The variation in the operating cost for 5 days is shown in Figure 10, in which the bars represent the cost and the lines indicate the cost reduction rate. Mode A is a more aggressive mode with an average cost reduction of about 40%. As a comparison, the average cost reduction rate of Mode B is only 13.4%. The cost reduction rate of both modes suffers a significant decrease on day 5 because the economy of the peaking strategy is mainly due to load shifting of the peak period while the cooling capacity of the peak period accounts for only 3% out of the total amount. In general, the strategy proposed in this paper can achieve a satisfactory reduction in operation costs without requiring changes in the physical structure of the system.  
Figure 10: Economy comparison of peak shifting strategy.  
Figure 9: Effect of cooling capacity shifting.
# A Simple Model of Ballistic Conduction in Multi-Lead Molecular Devices
## 4 Symmetric Devices with Complete Structure
### Analysis of the Complete Symmetric Device  
In our previous work on two-lead devices, we were able to describe 11 specific cases for conduction with leads attached to distinct vertices [60; 63; 70]. In particular, we identified _inert_ MOs (or Dyson orbitals for correlated molecular calculations) that had no effect on the transmission at any electron energy, \(E\). On the other hand, _active_ orbitals--more correctly active shells, allowing for possible degeneracy-- each produce a maximum, or a shoulder, in the transmission spectrum. This feature is produced by the presence of a pole in the complex energy plane in the expression for the transmission. The width of the feature associated with the pole depends upon the size of the imaginary part of the pole associated with the eigenstate. Inert states do not have poles in the transmission formula, and hence, they produce no such features.  
In that treatment, we were also able to identify states i where the transmission vanished at \(E=\epsilon_{\rm Mi}\). Such _pointwise_ insulation effects depend upon non-cancellation of factors \(\Delta_{\rm i}\) between numerators and denominators in the expressions for the transmission, Equation (39). The separation into the 11 cases depended solely on the properties (specifically, the numbers of zero roots) of the SPs in the expression for the two-lead transmission. In the multi-lead case, the quantities \(\rm D_{L}\) and \((\rm D_{L})_{\rm P1}\) contain many more SPs of higher rank. The number of different cases allowed by the Interlacing Theorem would be very large. For the present, therefore, we restrict ourselves to a more limited analysis.  
For CSD it is straightforward to identify _two_ inertness classes. _Strong_ inertness implies that a specific MO (or Dyson orbital) does not affect the transmission in _any_ lead at _any_ energy, \(E\). From Equation (70), we can see that strong inertness for pole i obtains when the vertex in contact with the source lead 1 is at a node in all orbitals of the shell i,  
\[\rm U_{\rm ii}=0, \tag{72}\]as the denominator \(\mathrm{L_{i}}\) does not then appear in the transmission formula. We can also have inertness restricted to a specific sink lead, p. This we call _weak_ inertness, and it occurs when the connection vertex p is at a node for all orbitals of shell i:  
\[\mathrm{U_{pi}}=0. \tag{73}\]  
As noted earlier, the transmission is strictly positive across the energy range allowed by the band-pass factor. The maxima in the CSD transmission spectra are close to the poles of the device GF, which in this case are the zeroes of the denominators,  
\[|\mathrm{L_{i}}|^{2}=\beta_{\mathrm{L}}^{2}(E-\epsilon_{\mathrm{Mi}})^{2}-E \beta_{\mathrm{LM}}^{2}(E-\epsilon_{\mathrm{Mi}})+\beta_{\mathrm{LM}}^{4}. \tag{74}\]
## 3 Methodology
### Temporal Modeling
#### 3.4.2 Temporal Decoder  
The decoder also adopts a multilayer structure. In the \(l\)-th decoder, the overall equations can be summarized as: \(X_{de}^{l}\), \(\mathcal{T}_{de}^{l}=\text{Decoder}\) (\(X_{de}^{l-1},\mathcal{T}_{de}^{l-1},K_{en},V_{en}\)), where \(l\in\{1,\cdots,L\}\). The details can be formalized as:  
\[K_{en}=X_{en}^{L},\ \ \ V_{en}=X_{en}^{L}+\sum_{i=1}^{L}V_{en}^{i} \tag{15}\]  
\[S_{de}^{l,1},\mathcal{T}_{de}^{l,1}=\text{SeriesDecomp}\left(\text{LSA}\left( X_{de}^{l-1}\right)+X_{de}^{l-1}\right) \tag{16}\]  
\[S_{de}^{l,2},\mathcal{T}_{de}^{l,2}=\text{SeriesDecomp}\left(\text{CFA}\left( S_{en}^{l,1},K_{en},V_{en}\right)+S_{en}^{l,1}\right) \tag{17}\]  
\[S_{de}^{l,3}=\text{FeedForward}\left(S_{en}^{l,2}\right)+S_{en}^{l,2},\ \ \ X_{de}^{l}=S_{de}^{l,3} \tag{18}\]  
\[\mathcal{T}_{de}^{l}=\mathcal{T}_{de}^{l-1}+W_{l,1}\mathcal{T}_{de}^{l,1}+W_{l,2}\mathcal{T}_{de}^{l,2} \tag{19}\]  
where \(S_{de}^{l,j}\), \(\mathcal{T}_{de}^{l,j}\), \(i\in\{1,2,3\}\),\(j\in\{1,2\}\) are the seasonal and trend part component after the \(i\)-th decomposition block in the \(l\)-th layer. LSA is locality-aware sparse attention operation, and CFA is cross-full attention operation. \(W_{l,j}\), \(i\in\{1,2\}\) is the projector for the \(j\)-th trend component \(\mathcal{T}_{de}^{l,j}\). The feed-forward also consists of two linear transformations with a ReLU activation in between. The final prediction output is the sum of the two refined decompositions after a linear transformation and can be expressed as:  
\[Y=\text{Linear}\left(X_{de}^{L^{\prime}}+\mathcal{T}_{de}^{L^{\prime}}\right) \tag{20}\]where Linear denotes a linear transformation.
# GNSS Spoofing Detection Based on Wavelets and Machine Learning
## 2 Related Works
This framework is validated throughexperiments using the Texas Spoofing Test Battery (TEXBAT) dataset, a benchmark in GNSS spoofing detection research [32]. TEXBAT is also employed by [33] to validate the Signal Quality Monitoring (SQM) technique, which assesses the quality of the correlation function peak under realistic spoofed conditions. Finally, ref. [34] introduces a GNSS spoofing detection method based on simulations under ideal conditions for RFF identification. That approach extracts RF fingerprints from incoming signals using deep learning, eliminating the need for manual feature selection. Two deep learning-based classification methods for RFF identification are compared: one focuses on analyzing the signal's physical layer characteristics, while the other works in a way that extracts RFF in the time-frequency domain. Both techniques demonstrate high effectiveness in spoofing detection. The authors in [35] investigate the use of convolutional neural networks (CNNs) for classifying various unintentional interference signals while excluding spoofing attacks. The proposed method demonstrates high effectiveness in two scenarios: ground-station monitoring and classification via low Earth orbit (LEO) satellites, achieving 99.69% accuracy even for low-power interference and enabling real-time monitoring of jammers. Inputs to the CNN include time-frequency visual representations of raw samples and derived statistical metrics. The methodology employs signal analysis using the short-term Fourier transform (STFT) and Wigner-Ville transform and evaluates two CNN architectures, AlexNet and ResNet, in terms of accuracy, F1-score, inference time, and model size. That work extends prior research by integrating extracted features to enhance classification performance, particularly for low-power signals, and classifies multiple interference types. The method proves efficient for real-time applications in GNSS interference detection. According to the state of the literature and to the authors' knowledge, different types of Daubechies wavelets db4 and db8 in combination with machine learning models and different types of classification data for spoofing detection have not been proposed yet. Similar approach is presented in [35] for jamming detection and classification of different jamming types based on a CNN and Wigner-Ville transform, which is computationally more complex than a DWT. This paper proposes an approach for spoofing attack detection and signal-type classification based on the combination of discrete wavelet transform and machine learning classifiers. Furthermore, the authors in this research aim to reduce the computational complexity by using pre-extracted features for the classification. All results in this paper are presented for approximation coefficients on decomposition level 1 and for the SVM model, since the SVM shows the best performance for both types of wavelets.
# Enhanced Distributed Dynamic Skyline Query for Wireless Sensor Networks
## 4 Performance Evaluation
### Evaluation against SkySensor
#### 4.4.4 Resilience to Node Failure  
In this experiment, the resilience to node failure of EDDS is studied against SkySensor. In SkySensor a local replication method is adopted in order to circumvent data loss due to a storage node failure. On the other hand, EDDS integrates a Decentralized Distributed Erasure Coding (DDEC)  
Figure 16: (**a**) Data uniformity; (**b**) data loss; and (**c**) success rate.  
algorithm instead of simple replication to achieve similar level of reliability with less redundancy [33]. In this study, the number of dimensions in attribute space was fixed at five and a fraction \(f\) of nodes from the network were removed at random point of simulation. The simulation was run for 20 rounds.  
In the first sub-experiment (Figure 17a), the data generation rate was varied from 0.02 to 0.1 packets/node/second and \(f\) was fixed at 20%. Figure 17a shows that DDEC consumed almost 50% less resources to maintain a similar or even higher magnitude of redundancy in a normal situation (packet rate < 0.08). However, due to the limited number of clusters, the storage space of SkySensor depleted with packet rate greater than 0.08 packets/node/second.  
In the second sub-experiment, data generation rate was fixed at 0.05 packets/node/second and \(f\) was varied from 5% to 30%. Figure 17b shows the percentage of the correct skyline result after a query is issued to the network at the end of each round. It is shown that it is possible to get false skyline results. It is shown in Figure 17b that EDDS performance is similar to SkySensor under a lower data production rate. Therefore, EDDS achieves similar or higher level of reliability with less redundancy.
## 2 Approach Description
### Optical Path Design  
This section details how the laser signal of the device is projected from the laser transmitter onto the wall of the hole and ultimately reflected back to the CCD. Figure 2 shows the optical path designed for our measuring system. The laser emitted by the laser transmitter is at an angle of 45\({}^{\circ}\) to the plane of the beam splitter. Due to the working principle of the beam splitter, when the laser is directed at the beam splitter, half of the light is projected in the original direction, and the other half of the light is reflected [22]. Therefore, by using two parallel splitters, the laser beam emitted from the laser transmitter can be projected perpendicular to the wall of the measured hole. At this time, the laser beam reflected by the hole wall can also be projected onto the CCD through these two beam splitters. In order to converge the laser beam, which will gradually diverge in the projection process, a convex lens is placed at the laser exit end, as shown in Figure 2, and another one is placed in front of the CCD to focus the laser beam on it.  
Figure 1: Schematic diagram of the probe movement process.  
A problem in the process is that the diameter of the probe is only 2 mm, which cannot accommodate a laser transmitter with diameter of 6 mm. Therefore, the ultimate goal of the optical path design is to project the laser beam emitted from the laser transmitter onto the hole wall. Beyond that, the light reflected back from the wall of the hole can be collected by the CCD through this optical path and processed in the next step.
## 4 Materials and Methods
### Materials and Nomenclature  
Analysing publications in the field of head numerical modelling and head biomechanics during road accidents, Global Human Body Models Consortium (GHBMC) and Total Human Model for Safety (THUMS) models are the most popular [81; 82]. Even though they are representatives of the group of full-body response models, the grade of details is high concerning their implemented head models. Moreover, unlike other models available in the literature, more recent versions of these models have a division into grey and white matter. Medical cases show that most brain damage occurs at the interface between grey matter and white matter, e.g., diffuse axonal injury. This is because these structures have a different structure, which in turn must affect their mechanical properties. Due to the fact that currently children's models presented in the literature do not distinguish between grey and white matter, the authors based their model on the material properties available for global models GHBMC and THUMS as well as [PERSON]'s model. The approach of this study is to use the above models of materials for either homogeneous brain structure --i.e., without white/grey matter separation--or inhomogeneous brain structure --i.e., with white (W) and grey (G) matter. Thus, for a homogeneous brain model, the whole brain is modelled by the white matter (W), whereas for an inhomogeneous model, the authors used different parameters for both grey and white matter (W/G). Therefore, the nomenclature of the materials used in this publication is as follows:  
1. Hyper-elastic Ogden Rubber is MAT_HYPRELASTIC (W)
2. Viscoelastic GHBMC is MAT_VISCOELASTIC 1 (W or W/G)
3. Viscoelastic THUMS is MAT_VISCOELASTIC 2 (W or W/G)  
The mechanical properties used for this study are tabularized in Table 4, whereas are: \(\
u\)--Poisson's ratio, G\({}_{0}\)--shear modulus, G\({}_{1}\)--Long-time (G\(\infty\)) shear modulus, Mu1--first shear modulus, and alpha-1--first exponent for Ogden Rubber (Hyper-elastic) model material in LS-DYNA code [81].
# A Computational Study of Polymer Solutions Flow Regimes during Oil Recovery from a Fractured Model
## 1 Introduction
For example, adding silicon dioxide nanoparticles (SiO\({}_{2}\)) into the polymer solutions, used for enhancing the oil-recovery factor by the polyacrylamide solutions, was suggested by [PERSON] et. Al. [14]. Studies have shown that the nanocomposites used are capable of forming agglomerations/deposits, which, consequently, leads to a decrease in the expected effect. In doing so, [PERSON] et al. revealed that adding silicon oxide nanoparticles has a significant effect on the viscosity and viscoelastic properties of HPAM [15]. Along with a great number of laboratory experiments, numerical simulations are extensively carried out to study polymer flooding. In the field of numerical simulation of polymer flooding, the two following trends can currently be distinguished. One is treating the porous medium as a continuum with averaged properties and solving the Darcy equation for each phase under study, making the system geometry independent on a microscale [16; 17; 18]. The second approach is based on using the Navier-Stokes equation system over the whole complex network of microchannels in the porous medium. This approach could be beneficial for developing new displacement fluids and considering their effects at a microscopic scale. Subsequently, these studies should be supplemented by reservoir simulation at a certain scale and field testing using the Darcy equation approach. These studies are used mainly for terrigenous reservoirs with well-studied porosity and relative phase permeability. In addition to works where Eulerian effective porosity methods are widely accepted, direct numerical simulation methods with phase-interface resolution [19; 20] have recently begun to be applied in computational studies of polymer flooding. However, at present, studies of polymer-fluid flow regimes during oil recovery using direct numerical simulation are still insufficient. The conducted review has shown that most of the current research on the injection of polymers, as well as polymer solutions containing added nanoparticles, are associated with terrigenous reservoirs. However, there are also a significant number of carbonate fields with fractured reservoirs. To date, the number of studies on using polymer solutions in fractured reservoirs is quite low. Therefore, the effect of polymer solutions on oil-recovery enhancement in fractured reservoirs needs a systematic study conducted. Our work was the first to use a topology with naturally created fracturing, unlike other works in which synthetically generated structures that do not fully reflect the properties of real rocks are used to solve similar problems. To prepare a naturally cracked structure, an original approach was used, based on the evaporation of a suspension of nanodiamonds on a glass substrate. This made it possible to obtain a topology of the pore space close to the topology of real fractured cavern rocks. A diagram showing the stages of the research described in the work is shown in Figure 1.