esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
However, increasing the number of nodes is not a robust solution, due to the economic, energetic, and computational costs involved. In the described situation, the aim of this work is to provide farm managers with a friendly tool that improves situational awareness of their crop by using a WSN linked through a mobile node, carried by an UAV. The combined use of both systems has been already reported, showing their benefits and extendable applications by using fixed-wing aircraft [28]. This approach could present some drawbacks when typical short range and low consumption nodes are used. These nodes will probably require complex trajectories to be performed by fixed-wing aircraft. However, using aircrafts with hovering capabilities allows determining optimal static positions for bridging WSN nodes independently of the required download time. Additional facilities have been reported about the combined use of WSN and UAV's, as the improvement of the navigation system by using nodes positions [29].
## 3 Methodology
### Data Modeling  
To impute missing values, temporal interpolation is applied to intermediate time values \(T-1\), \(T_{\text{imputed}}\), and \(T+1\) in combination with Bayesian regression. Before and after imputation, the average time series are examined to ensure that key properties--such as seasonality and trend--are preserved, using visual inspection techniques. Additionally,it is verified that statistical metrics such as the mean, median, maximum, and minimum values remain consistent with those observed prior to imputation.  
We normalize the data to the range [0, 1]. To prevent data leakage, only the training set is used to compute the minimum and maximum values, ensuring that the model has no access to information from the validation or test sets. Normalization is performed using the standard min-max scaling formula:  
\[x^{\prime}=\frac{x-x_{\min}}{x_{\max}-x_{\min}},\]  
where \(x\) is the original value; \(x_{\min}\) and \(x_{\max}\) are the minimum and maximum values from the training set, respectively; and \(x^{\prime}\) is the normalized value.
# Robust Design of 3D-Printed 6-18 GHz Double-Ridged TEM Horn Antenna
## 5 Prototype and Measured Results  
Figure 10 shows a photograph of the antenna fabricated with dimensions obtained by the robust design procedure. The frame of the horn antenna is built by the Form2 (a 3D printer with SLA), and its surface is plated by EM-271S silver paste with conductivity of 2.22 \(\times\) 10\({}^{6}\) S/m. As this silver paste uses a phenoxy resin as its binder, it has favourable adhesion to hard materials. The thickness of the metal deposition is typically less than 10 \(\upmu\)m. Finally, the plated frame is attached to a SMA connector.  
The simulated and measured results of the prototype antenna are depicted in Figures 11 and 12. The reflection coefficient is measured using an Agilent 8510C network analyser and the realized gain and radiation patterns are measured in an anechoic chamber. Good agreement between the simulation and measurement results is observed. As shown in Figure 11a, the measured \(-10\) dB reflection coefficient bandwidth of the antenna can cover 6-18 GHz. In Figure 11b, the simulated and measured gains are obtained in the \(+z\)-direction. The measured gain varies from 4.42 to 10.75 dBi. The measured gain is lower than the simulated gain owing to the rough surface of the frame and the low conductivity of the nano silver plating [20]. The radiation patterns at 6, 12, and 18 GHz are observed on the \(xz\)- and \(yz\)-planes and recorded with an angular resolution of 1\({}^{\circ}\) in the 360\({}^{\circ}\) spatial range, as shown in Figure 12. The difference between the measurement and the simulation at the edge angle is due to the different boundary conditions during the simulation and measurement processes. The boundary condition in the simulation scenario is a numerically infinitely extended space, whereas it is given a finite volume with weak scattering in the measurement setup.  
Figure 9: Noise test results of the (**a**) initial and (**b**) optimized TEM horn antennas.  
Figure 11: Simulated and measured results of the robustly optimized TEM horn antenna: (**a**) reflection coefficient and (**b**) realized gain.  
Figure 12: Simulated and measured radiation patterns of the robustly optimized TEM horn antenna: (**a**) at 6 GHz; (**b**) at 12 GHz; and (**c**) at 18 GHz.  
Figure 10: Photograph of the fabricated antenna with dimensions obtained by the robust design procedure.
# Mobility-Enhanced Reliable Geographical Forwarding in Cognitive Radio Sensor Networks
## 7 MROR Comparison
Another factor responsible for the energy efficiency of MROR is the utilization of idle listening to make critical network decisions, which was not considered in CRP. Furthermore, the extension in hop length offered by MROR has also helped in conserving energy by the reduction of hops to the sink. As in the case of ROR, the main reason that can be attributed to the energy inefficiency is the frequent initiation of the route request and route maintainability that were caused by the speed introduced into the network. Figure 17: Goodput _vs._ speed of the mobile node. Figure 18: Consumed energy/packet _vs._ speed of the mobile node.
## 1 Introduction
The geometry distance detrending can be also carried out using filters such as the sixth order Butterworth Filter or polynomial fitting. Although this method is effective in monitoring large carrier phase anomalies such as ionospheric scintillations [16], the detrending performance is dependent on how the filter parameters are accurately estimated. Initial measurements should be used for this parameter estimation process, and the magnitude of the variations in the detrended carrier is generally larger than the expected noise of the measurements, making the measurement monitoring less effective. The SBDD, on the other hand, can be an option to mitigate the aforementioned issues, as all the noise and multipath errors are not cancelled out due to separate antennas as much as the ZBDD, and the detrended carrier is not as filter parameter-dependent. In order to carry out effective carrier phase noise and multipath modeling and monitoring, other GNSS common error terms should be effectively differentiated. To extract the site-dependent error terms, we suggest utilizing the SBDD-based residuals. The precise and accurate relative vector between two GNSS antennas of a reference station can be used to calculate the residuals instead of using their absolute positions. Since the obstacle geometry and its multipath effect cannot be exactly identified, the residual error regression process is necessary to estimate and model the nominal residuals. The azimuth angle as well as elevation should be considered as the input parameters for the model, and C/No from both satellites used in the residual computation can be added. In order to be employed to a safety-critical system by ensuring correct false alarm probability, inflation of the standard deviation for residual errors with respect to the tail portion needs to be considered. Finally, the inflation factors should be computed so that the normal distribution over-bounds the actual residual distribution to meet the false alarm requirement. In Section 2, we discuss the GNSS measurements that are used for PVT computation and assess the various methods that can be used for GNSS residual computation and modeling. In Section 3, our proposed methodology is introduced and how it is implemented to model and monitor the residuals is described. In Section 4, we describe how the training and test data were collected. In Section 5, we validate our methodology using a validation set of data. Finally, we end with a conclusion about our study and how it can be applied for accurate and reliable GNSS PNT.
## 3 Experiments and Results
### Simulated Data Experiments
In relation to _NDVI_, the best _PSNR_ value was achieved in scenario 3 with a value of 74.3175, the _SSIM_ with the best result of 0.8622 for scenario 2 and the _RMSE_ reached 0.0489 as the best result for scenario 3, all of these best results using the LaMa network. Figure 15: Scatter plots of the image reconstruction using the Big LaMa model. The black straight line represents the original image, while the red line represents the analysis of the reconstructed image versus the original image for three different scenarios. Each row corresponds to a specific scenario and each column corresponds to the reconstructed image, its _LST_ and _NDVI_, respectively.
# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 4 Proposed Fast CU Size Selection Algorithm
### Level I(L-1) SVM Models: Features and Optimal Weight Calculation
#### 4.1.1 Data Collection  
L-1 constitutes two weighted SVM models (Section 3.1.2) for each CU depth level using the data collected during an initial training phase for each video sequence. During the initial training phase, \(N\) data points are accumulated for each CU depth level (i.e., 0, 1, 2, 3). In this phase, CU/PU size decisions are obtained through traditional RD optimisation.  
Here, \(N\) is set at \(N=2000\), which gives the best trade-off between the encoding time reduction and model accuracy based on the empirical observation.  
The SVM models for a particular CU depth level are generated once the required \(N\) number of data points are collected during the encoding process. This enables the models to be generated at different times depending on the content, resolution, and Quantisation Parameter (QP). For example, SVM models for lower CU depths (i.e., 0, 1) for certain sequences with low resolution (e.g., 416 \(\times\) 240) are typically generated after encoding several frames, whereas SVM models for higher CU depths (i.e., 2, 3) typically become available much sooner.  
Figure 5: An activity diagram illustrating the proposed overall encoding algorithm (RDO: Rate-Distortion optimisation).  
The ratio between the training and predicted samples is kept at 1:\(\Gamma\). Once the number of predictions per model (\(\Gamma\)) is exceeded, the model is expired and recreated following a similar data collection approach. This process ensures the SVM models are kept relevant to the content being encoded and are hence, content-adaptive. In this case, \(\Gamma\) is maintained as \(\Gamma=400\), which results in a minimal BDBR increase for a set of test video sequences evaluated at \(QP\in\{22,27,32,37\}\) for a range of ratios where \(\Gamma\in\{100,200,300,400,500\}\). Experimental results reveal that the models for smaller CU sizes are retained for a longer period of time before they are discarded when encoding less complex, high-resolution sequences using large QPs. This is due to the lower presence of smaller CUs in such scenarios. Similar behaviour is observed for the SVM models corresponding to large CU sizes when encoding complex sequences using smaller QPs. Retaining models for a longer period impacts the content adaptiveness of the SVM models and results in coding efficiency losses. However, the lower number of decisions made by such models facilitates the algorithm to maintain any adverse impacts on the coding efficiency at a lower level. In this context, \(\Gamma\) remains a design parameter that can trade-off the encoding time reduction to the coding efficiency, depending on the requirement.
## 1 Introduction  
Metamaterials have unique advantages for the regulation of electromagnetic waves. It is a powerful structure formed by the periodic arrangement of many micro-nano structures, such as concentric rings [1, 2], nano-rods [3, 4] and the metal split-ring resonators [5, 6]. Metamaterial absorbers have received extensive attention from researchers since [PERSON] et al. [7] proposed metamaterial-based perfect absorber (MPA) in 2008. The work based on metamaterials has promoted the development of many functional devices, such as optic cloaks [8, 9], sensors [10], optical switches [11] and so on. However, most of these proposed MPAs still have a strict working bandwidth. It is hard to change the structure in integrated devices.  
Graphene, as a two-dimensional material with many excellent properties, is widely used in the design of metamaterials. The graphene nanoring or nanodisk is used to realize the functions of light modulation, switching and sensing in 2013 [12]. Graphene multilayer wave absorbers [13, 14, 15] have been proposed many times, but the fabrication of multilayer structures is complex and multilayers cannot be guaranteed. Therefore, much research has conducted extensive explorations on single-layer graphene structures [16, 17, 18], whereas most graphene metamaterials have narrow bands or have very limited frequency adjustments, especially the frequency of adjustment is not flexible enough. The frequency changes at the same time, which greatly limits the application of graphene metamaterials. Researchers have done many experiments to explore graphene metamaterial absorbers, [PERSON] et al. utilize an array of closely packed graphene nanodisks, realizing absorption efficiency from 3% to 30% in the infrared region of the spectrum [19]. In addition, a cavity-coupled nanopatterned graphene absorber has achieved high absorption theoretically and experimentally between 8-12 \(\upmu\)m, and the adjustable range is about 2 \(\upmu\)m [20]. These experiments show that the graphene absorber is technically feasible. Therefore, we have proposed a graphene absorber that can effectively control a single frequency.  
In this work, we propose a triple-band graphene structure that can be adjusted separately. Our device is a sandwich structure which consists of a single-layer of graphene concentric triple ring (GCTR) array and a metallic mirror separated by SiO\({}_{2}\) layer. Compared with the previous work of others before [21], our device has a wider range of adjustment freedom, and our proposed absorber can achieve a Tri-band and Dual-band conversion. FDTD is used to simulate the response frequency of 0.5-2.5 THz, and the single-frequency or multi-frequency conversion can be achieved by separately adjusting the Fermi level of a single pattern. In addition, we use a hybridization model to explain the coupling of the graphene rings in a proposed absorber.
## 3 Results and Discussion
### Wine Composition
The cause of this interaction was the particularly great positive effect of the combination of CT and HC on the increase in alcohol content, which was observed in both years of this study (Figure 4). Moreover, the interaction also occurred for the concentration of total anthocyanins in 2018 as, in full crop conditions, severe shoot trimming obtained a mild increase in the concentration of total anthocyanins, whereas in the reduced crop conditions obtained via cluster thinning, severe shoot trimming strongly decreased the concentration of total anthocyanins in wine. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Season 2017**} & \multicolumn{6}{c}{**Season 2018**} \\ \cline{2-13}  & \multicolumn{3}{c}{**Cluster Thinning**} & \multicolumn{3}{c}{**Shoot Trimming**} & \multicolumn{3}{c}{**Int. b**} & \multicolumn{3}{c}{**Cluster Thinning**} & \multicolumn{3}{c}{**Shoot Trimming**} & \multicolumn{3}{c}{**Int.**} \\ \cline{2-13}  & **FC** & **CT** & **Sign. a** & **HC** & **SST** & **Sign.** & **Sign.** & **FC** & **CT** & **Sign.** & **HC** & **SST** & **Sign.** & **Sign.** \\ \hline Alcohol (vol.\%) & 12.6 & 13.3 & **11.3** & 12.6 & **12.6** & **13.0** & 13.8 & **13.0** & 13.8 & **13.0 & **13.0** & **13.0** & **13.0** & **13.0** & **13.0** \\ Titractable acidity (g/L) & 6.5 & 6.3 & ns & 6.4 & 6.4 & ns & ns & 6.4 & 6.2 & ns & 6.3 & 6.2 & ns & ns \\ pH & 3.33 & 3.43 & * & 3.41 & 3.36 & ns & ns & 3.38 & 3.46 & * & 3.41 & 3.43 & ns & ns \\ Color intensity & 0.77 & 0.87 & ns & 0.84 & 0.80 & ns & ns & 0.81 & 0.90 & ns & 0.88 & 0.83 & ns & ns \\ Total anthocyanins & 248 & 295 & ** & 281 & 262 & ns & ns & 353 & 426 & *** & 398 & 381 & ns & * \\ Total phenolics (mg/L) & 1074 & 1171 & ns & 1136 & 1110 & ns & ns & 1277 & 1454 & ** & 1359 & 1372 & ns & ns \\ \hline \hline \end{tabular}  
* Data were analyzed via two-way ANOVA in randomized blocks design (ns, not significant; \({}^{*}\), \(p\leq 0.05\); **, \(p\leq 0.001\)). b Interaction between cluster thinning and shoot trimming.  
\end{table} 
Table 3: Wine composition obtained from grapes of Merlot vines subjected to cluster thinning (FC—full crop; and CT—cluster thinning) and shoot trimming (HC—high canopy; and SST—severe shoot trimming).
# Autoencoder-Based Semantic Novelty Detection: Towards Dependable AI-Based Systems
## 8 Autoencoder-Based Fully Semantic Novelty Detection  
### Architecture  
According to the evaluation results in Section 7, the semi-semantic approach with semantic topology of the autoencoder has shown great performance for minimizing the false negative errors in novelty detection. During the evaluation, we found that the current classification criterion of the threshold based on MSE could not segment known and novel input data, which were similarly structured but had different semantic meanings, for example, in the case of known digits versus unknown digits (cf. Figure 10). This could also be another reason for the false negative errors in addition to the topology of the autoencoder.  
Since the current classification criterion for novelty detection is the threshold solely based on the reconstruction MSE of raw input data, it means that in the case of camera image data, the threshold is determined based on pixel-wise deviations between the raw and reconstructed image without any semantic meaning. In this case, the false negative errors due to similarly structured but semantically different novel data are obviously remaining.  
With this hindsight, we propose our next improvement: autoencoder-based fully semantic novelty detection based on a semantic error instead of the threshold based on MSE. The architecture of the quantitative monitor using this approach is shown in Figure 11.  
As compared with the semi-semantic novelty detection, the component autoencoder with the semantic topology (cf. Section 7) remains the same. The output of the autoencoder (reconstructed raw sensor data) is forwarded to the _environmental and self-perception_ components with the identical interpretation mechanism acting as the perception part of the monitored autonomous system, which delivers its own interpreted semantic classes as output (cf. Figure 11), such as the object list in the case of the autonomous driving system on the vehicle. These interpreted semantic classes are used as inputs of the component _discriminator_ in the quantitative monitor. The discriminator detects novel input by com  
Figure 10: Applying the rules: novelty detection with a semantic topology.  
paring both identified semantic classes, delivered by the components environmental and self-perception, respectively, in the autonomous system and the quantitative monitor.  
Thus, it can be understood that the quantitative monitor with the fully semantic approach has novelty detection on two different levels. First, the identical component environmental and self-perception classifies the reconstructed raw data provided by the autoencoder. Second, both sets of interpreted semantic classes are compared within the component discriminator on a higher semantic level due to the consideration of integrated semantic errors. Thus, the false negative errors due to similarly structured but semantically different novel data can be minimized.
# RSSI-Based MAC-Layer Spoofing Detection: Deep Learning Approach +
## 5 Experiments and Results
### Model Classification Performance  
We have evaluated our novel spoofing detection approach against the Support Vector Machine (SVM) one-class anomaly detection technique described in [23] (as a baseline detection model) and the state-of-the-art Log-likelihood ratio test approach proposed in [13]. We have evaluated all three approaches against two real-world datasets (refer to Section 5.1) using 10-fold cross validation. The average classification/detection performance is reported in Table 2.  
We have trained two classifiers for our autoencoder as well as each of the other two approaches (SVM [23] and Log-likelihood [13]): one for the period of high volatility (e.g., environmental moving objects--daytime) and another for the period of low volatility (e.g., minimal environmental moving objects--nighttime) as reported in Table 1. All three classifiers perform relatively better during the low volatility period (i.e., nighttime) than the high volatility period--with our approach performing the best in both categories significantly. Figure 10: (**a**) Case where the adversary starts transmitting right after the legitimate node terminated its transmission; (**b**) The adversary gains access to the channel while the legitimate node has not finished transmitting all of its frames. Figure 9: \(s^{\prime}\) RSSI stream as received by \(r\) during \(s^{\prime}\) deployment inside of the property. We have also evaluated the classification performance of the three models against an adversarial traffic mix (as explained in Section 5.2). We can observe in Table 2 (also refer to Figures 11 and 12, that our approach slightly loses classification accuracy (by 1%) when 20% of RSSI values in a given window is generated by an adversary while the performance of the other two classifiers deteriorates significantly. This can partly be explained by the fact that our LSTM (Long Short-Term Memory) autoencoder approach takes into consideration the order in which RSSI samples appear (i.e., are collected), while the other two approaches treat RSSI values in a window as independent data points. It is clear from the obtained results that our approach is well equipped to deal with an active adversary that transmits during the transmission period of the legitimate node while such overlap of traffic is not well protected using existing approaches.
# Exploiting the Capture Effect to Enhance RACH Performance in Cellular-Based M2M Communications
## 5 Simulation Results
### Performance of the Msg3 PR Scheme
#### 5.1.3. The Connection Failure Probability  
Figure 9 presents the relationship between the \(PRS\) and the connection failure probability when \(V=20\) and \(N_{max}=8\). From Figure 9, we can see that, by increasing the number of accesses, the connection failure probability increases due to severe collisions. In the conventional case, almost 70% of devices experience a connection failure when the number of arrivals surges (\(N=60\)). The Msg3 PR scheme reduces the connection failure probability significantly. For example, under heavy access load (\(N=60\)), the connection failure probability could be reduced by approximately 42% when Msg3 PR with \(PRS=3\) dB is used, as compared with the conventional case. In addition, the connection failure probability of the 3 dB-case is lower than that of the 4 dB-case.  
Figure 8: Steady-state RA throughput in terms of \(V\).  
Figure 7: Steady-state RA throughput in terms of \(N_{max}\).