esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Overall mission definition and optimization
A three-frequency radiometer is of course required; however, using external data such as SSMI or numerical weather prediction analysis to improve the wet tropospheric correction may be considered, especially at low latitudes and in the middle of large storms where the errors can reach more than 2 cm more than 20 % of the time (see Section II.2.b). The only interest in enlarging the chirp bandwidth would be to get a better resolution in the range direction, which would cost a lot in terms of link budget (leading to degraded performances), power budget and telemetry budget (see [32]). Furthermore, if we limit the mission objectives to oceanographic, coastal and large rivers and basins applications, the current resolution offered by a 20 MHz bandwidth is adequate. Furthermore, the size of the final pixel is in any case mainly constrained in the azimuth direction by the RAR (Real Aperture Radar) resolution around 8.4 km, related to the length of the antennas (see Figure 24). Conversely, the pulse length should be further checked. It was constrained on Jason-2 by the presence of the nadir altimeter and the telemetry budget. Keeping the nadir altimeter in the new proposed mission freezes the PRF and then constrains the pulse length as well. However, the telemetry budget can be improved and therefore frees a degree of freedom on the pulse length (see [32]). The 120 W TWTA designed onboard Jason-2 has specifications enabling it to function with a maximum duty cycle of 27 %. Chronogram and power budget constraints lead to the selection of a pulse length of a hundred microseconds. As it was done in Section 3.4, the worst cases external errors are brought up in the overall height budget related to the instrument operating characteristics, which can then be compared to the one on Jason-2 (see Figure 24). By changing the instrument operating characteristics and the dedicated mission parameters, performances equivalent to a WSOA on a 16*14 km pixel are now likely to be obtained on 12*1 km  
Figure 24: New operating characteristics for improving the height budget. pixel. The decreasing of the intrinsic surface area has been done by almost 95 %. This huge profit enables to get more hits to phenomena at Rossby radius of deformation scales. Furthermore, coastal observations are improved in the worst case of a perpendicular approach as now the limit distance is no more than 8.4 km. Finally, the 4-5 cm noise level specified in section I is almost reached throughout the whole swath for a 12*4 km pixel; FR data errors fill the constraint when averaging up to 6 km in the range direction (keeping 12 km in azimuth) or up to 13 km in the azimuth (keeping 1 km in range).
# Semi-Autogenous Mill Power Consumption Prediction Based on CACN-LSTM
## 4 Experiments
### Evaluation Indicators  
In order to quantitatively assess the performance of the model, this paper selects root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R\({}^{2}\)) as the evaluation metrics.  
The RMSE is a statistic used to measure the difference between the predicted results of a model and the actual observations [28]. The RMSE is calculated as shown in Equation (13).  
Figure 7: Experimental results of hyperparameter settings. (**a**) R\({}^{2}\) of different units. (**b**) R\({}^{2}\) of different layers. (**c**) R\({}^{2}\) of different batch sizes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **TCN** & **LSTM** & **GRU** & **CNN-LSTM** & **CNN-GRU** \\ \hline Batch size & 128 & 64 & 64 & 64 & 64 \\ Neural layers & 3 & 2 & 2 & 2 & 2 \\ Hidden units & 32 & 64 & 128 & 64 & 64 \\ Epochs & 100 & 100 & 100 & 100 & 100 \\ Learning Rate & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 \\ Loss function & MSE & MSE & MSE & MSE & MSE \\ Optimization & Adam & Adam & Adam & Adam & Adam \\ \hline \hline \end{tabular}
\end{table}
Table 5: Baseline Model Parameter Settings for Comparative Experiments.  
\[RMSE(y,\hat{y})=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}} \tag{13}\]  
The MAE denotes the mean of absolute errors, denoting the mean magnitude of discrepancies between two sets of measurements [29]. The MAE is calculated as shown in Equation (14).  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\left|y_{i}-\hat{y}_{i}\right| \tag{14}\]  
The MAPE is a metric that quantifies the relative error between actual and predicted power consumption [30]. The calculation of MAPE is shown in Equation (15). A smaller MAPE value indicates more stable prediction results and better predictive performance.  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{y_{i}-\hat{y}_{i}}{y_{i}}\right| \tag{15}\]  
The R\({}^{2}\) signifies the extent of fit between the predicted values and the actual values [31]. The calculation of R\({}^{2}\) is shown in Equation (16). The higher the R\({}^{2}\), the better the fit.  
\[R^{2}=1-\frac{\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}}{\sum_{i=1}^{n} \left(y_{i}-\overline{y}_{i}\right)^{2}} \tag{16}\]  
where \(\hat{y}\) represents the predicted values, \(y_{i}\) represents the original actual values, \(n\) is the sample size, and \(\overline{y}_{i}\) represents the mean value of SAG power consumption samples.
## 6 The Mathematical Approach for Computing the Dependability of the Solar Tracking System  
This section describes the numerical method for determining the reliability and maintainability of the solar tracking prototype which makes use of software and hardware testing methods to enhance its durability during everyday operation. Whereas the reliability-oriented metrics are supported by fault coverage derived from various test strategies, the maintenance-oriented metric system targets the modularity and accessibility of the solar tracking prototype hardware structure.
# Image Deblurring Based on an Improved CNN-Transformer Combination Network
## 4 Experiments and Analysis
### Runtime Comparison  
In recent years, deblurring models have made significant progress in accuracy and processing speed. In this paper, we balance efficiency and speed. Namely, we focus on exploring an efficient deblurring model to obtain relatively high accuracy and consume less testing time as well. In general, under the premise of similar accuracy, the model with smaller parameters is more suitable for resource-limited equipment. For a fair comparison, we tested all the compared models in the same environment. The runtime was measured by using the released code of each model on a single TIAN RTX GPU. Table 3 compares our model and some advanced deblurring models in terms of accuracy, model parameters, and process speed.  
As we can see, our model achieves competitive PSNR and SSIM performance to other models. It is fast and has relatively small parameters. Notably, SDWNet [16] has the highest SSIM but the processing speed is much slower than our model. Compared with DeblurGAN-V2, our model parameters are only one-third of DeblurGAN-V2's, while our model performs better. This shows that our model has significant advantages in terms of model parameters, process speed, and deblurring performance.
## LVRT Control Strategy for Renewable Energy Systems
### LVRT Control Strategy in the SEG System  
Figure 3 shows the configurations of the SEG systems, which are typically classified into two topologies: the one-state topology without a DC/DC converter (see Figure 3a) and the two-stage topology with a DC/DC converter (see Figure 3b). The SEG system comprises photovoltaic (PV) arrays, a power conversion system (comprising a DC/DC converter and a grid-side inverter), a filter, and a three-phase grid.  
If the grid voltage drops in the SEG system, regardless of the topology, reactive power is injected in the grid-side inverter to comply with the LVRT requirement, which is similar to the case of WPG system. However, the LVRT control strategies for the SEG systems are different from those for the WPG systems. In the SEG system, the LVRT control strategy with maximum power generation is widely used. Using the LVRT control strategy, the \(d\)-axis current (\(I_{de}\)) required for injecting the reactive power into the grid having a low voltage is determined based on the LVRT requirement depending on the level of low voltage. In addition, to transfer the maximum power from the PV arrays to the grid with low voltage, the active current is calculated by (1). Unlike the LVRT control strategy for the WPG systems, in the SEG systems, the LVRT control strategy focuses on the power generation.  
In Figure 3b, the DC/DC converter transfers the input power corresponding to the output power calculated using \(I_{qe}\) and the low grid voltage.
# A Green Approach for Preparing High-Loaded Sepiolite/Polymer Biocomposites
## 3 Results and Discussion
### Thermal, Spectroscopic and Morphological Characterization of Sep/NR Biocomposites
The strong peak at q = 0.5 A\({}^{-1}\) is related to the channel structure of the fillers [46]. The scattering of the Sep fibers can be analyzed by means of the Guinier approach (Figure S8 and Table S2) assuming a rod-like form factor. In principle, this approach allows the determination of both radius and length of the scattering bodies. However, the latter parameter can only be assessed if the forward scattering at very low q vectors is visible, i.e., the system is high diluted. Therefore, since the Sep fibers form here bundle-like aggregates (steep power law at q \(<\) 0.002 A\({}^{-1}\)), only the radii of the fibers, from the intermediate part of the curve, can be determined. These values are 11.5 nm and 11 nm for B5 and S9 samples, respectively, in good agreement with the SEM and TEM analyses. Regarding the biocomposite samples (light gray lines), the peak at q = 0.5 A\({}^{-1}\) indicates that the filler is incorporated in the SepX/NR material. With respect to the curves of the single systems, the patterns show a different course. This effect is particularly visible for the SepS9/NR biocomposite at small scattering vectors. The higher scattering intensity is directly related with an enhancement of the interface area between the SepS9 fibers, thus suggesting that these inorganic fillers are much more homogeneously distributed in the polymer than SepB5, in accordance with the TEM results. In addition, the signals at _approx_. q = 10\({}^{-1}\) A\({}^{-1}\) in the NR curve, proper of the crystalline domain of the rubber, disappear in the SepS9 biocomposite system, but not completely in the SepB5 one. This suggests that, once in contact with the SepS9 fibers, the polymer chains unfold much better  
Figure 6: Scattering curves of (**a**) NR (black line), SepS9 (dark gray), SepS9/NR biocomposite (light gray), (**b**) NR (black line), SepB5 (dark gray), SepB5/NR biocomposite (light gray). generating a more homogeneous material, further showing that little interactions are established between organic and inorganic components. The SAXS analysis supports the TEM investigation, showing the formation of different structural arrangement depending on the type of filler used.
# A Mathematical Study of COVID-19 Spread by Vaccination Status in Virginia
## 2 Materials and Methods
### Basic and Effective Reproduction Number  
The basic reproduction number (\(R_{0}\)) of an infectious disease is the expected number of secondary infections produced by a single infectious person entering a fully susceptible population, assuming minimal public health interventions [34]. The effective reproduction number (\(R_{t}\)) additionally takes into account mitigating factors such as public health interventions, natural immunity through infection, and vaccination. These values are considered key epidemiological parameters since \(R_{0}>1\) (\(R_{t}>1\)) suggests that a disease has the capacity to spread in the population while \(R_{0}<1\) (\(R_{t}<1\)) suggests that it will die out. We compute the basic and effective reproduction number of the \(n\)-stage vaccination model (2) using the next generation method [35; 36].  
To compute the basic reproduction number, we use the disease free equilibrium \(S_{0}^{*}=N-V_{1}(0)\), \(S_{n}^{*}=V_{n}(0)\), \(S_{i}^{*}=V_{i}(0)-V_{i+1}(0)\), for \(i=1,\ldots,n-1\), and \(I_{i}^{*}=0\) for \(i=0,\ldots,n\), where \(V_{i}(0)\) is the number of people who have received the \(i\)th shot by the initial time. This gives the following:  
\[R_{0}=\frac{\beta(0)}{N}\Bigg{(}\frac{N-V_{1}(0)}{\gamma_{0}+h_{0}}+\frac{(1- \alpha_{n})V_{n}(0)}{\gamma_{n}+h_{n}}+\sum_{i=1}^{n-1}\frac{(1-\alpha_{i})(V_{ i}(0)-V_{i+1}(0))}{\gamma_{i}+h_{i}}\Bigg{)}. \tag{5}\]  
Note that this number assumes no vaccination beyond the initial time and does not account for natural immunity obtained by previous COVID-19 infections. To account for these mitigating effects, we also compute the effective reproduction number:  
\[R_{t}=\frac{\beta(t)}{N}\Bigg{(}\frac{S_{0}(t)}{\gamma_{0}+h_{0}}+\sum_{i=1}^{ n}\frac{(1-\alpha_{i})S_{i}(t)}{\gamma_{i}+h_{i}}\Bigg{)}. \tag{6}\]  
In general, \(R_{t}\) (6) is more challenging to compute since it requires time-course simulations of \(S_{i}(t)\), \(i=0,\ldots,n\), whereas \(R_{0}\) (5) only requires the initial conditions. Note that the outbreak containment condition \(R_{t}<1\) is explicitly dependent on the effectiveness of the vaccine shots (i.e., \(\alpha_{i}\)), the recovery rates (i.e., \(\gamma_{i}\)), and the hospitalization rates (i.e., \(h_{i}\)). The condition is also implicitly dependent on the number of individuals who have received each dose of the vaccine (i.e., \(V_{i}(t)\)) as these functions affect \(S_{i}(t)\) through (2).
# Adaptive Constant False Alarm Detector Based on Composite Fuzzy Fusion Rules
## 5 Simulation Experiment Results and Analysis
### CFVI-CFAR Detection Performance
#### 5.2.1 Homogeneous Environment  
The performance comparison of each detector in a homogeneous environment, as well as the probability of determination of the type of environment of the VI-type detectors are shown in Figure 4.  
As shown in Figure 4a, the curves, from top to bottom, represent CA-CFAR, CFVI-CFAR, VIHCEMOS-CFAR, KLQ-CFAR, and OS-CFAR, respectively. Since both CFVI-CFAR and VIHCEMOS-CFAR use CA-CFAR as the detector in a homogeneous environment, their detection performances are nearly identical. Meanwhile, as shown in Figure 4b, due to the probability of approximately 0.1 of the VI-type detectors misjudging the homogeneous environment as another type, a slight difference in detection probability is observed between CFVI-CFAR and VIHCEMOS-CFAR.  
To further analyze the detection performance of each detector in a homogeneous environment, the SNR of each detector is selected when the detection probability \(P_{d}\) = 0.5, the specific data are shown in Table 10.  
As shown in Table 10, when the detection probability \(P_{d}\) is 0.5, the SNR required for CFVI-CFAR is 0.02 dB lower than that for VIHCEMOS-CFAR, the SNR loss of CFVI-CFAR relative to CA-CFAR is 0.18 dB. In comparison, the SNR loss of VIHCEMOS-CFAR compared with CA-CFAR is 0.20 dB. CFVI-CFAR exhibits a smaller SNR loss in a homogeneous environment, maintaining detection performance close to that of CA-CFAR.
# Vulnerabilities and Limitations of MQTT Protocol Used between IoT Devices
## Security of the Internet of Things  
### The Message Queue Telemetry Transport (MQTT) Protocol  
As stated in Reference [3], the Message Queue Telemetry Transport (MQTT)is a protocol used for communication within an IoT environment that functions on top of the Transport Control Protocol. The protocol was created by IBM as a machine-to-machine, lightweight communication method. MQTT was standardized by ISO/IEC 20922 and was further accepted as part of OASIS. At its core, MQTT is a messaging protocol that uses the publish-subscribe communication model, where the clients themselves do not require updates, thus in turn causing a reduction of used resources, which makes this model optimal for use in a low-bandwidth environment.  
Furthermore, the protocol functions on a server-client system where the server, called a broker, pushes updates to MQTT clients. The clients won't send messages directly to each other, instead relying on the broker for this. Every MQTT message contains a topic, organized in a tree-like structure, to which the clients can subscribe or publish. The broker receives published messages from clients that contain a certain value or command and relays the information to every client that has subscribed to that specific topic. As can be seen, the MQTT protocol was designed for asynchronous communication, where subscriptions or publishing to or from different entities take place in a parallel order. The protocol is also able to provide reliable transfers by choosing between three types of reliability mechanism, also called Quality of Service (QoS).  
When compared to other protocols like HTTP, the MQTT protocol has a considerably smaller footprint, making MQTT, as stated above, much more suitable for resource-constrained environments. Although the MQTT protocol has many advantages, not every MQTT-based broker has similar or comparable abilities for entity authentication or encryption. Eclipse's open-source application, called Mosquito, is able to provide most of standardized features of the MQTT protocol, such as SSL/TLS and client certificate support. The Mosquito broker, by default, does not provide security for its messaging scheme and authentication information is sent in plaintext; therefore, it requires security mechanisms to protect the transferred information.
## 3 Sliding Mode Controller Design  
The control goal of this study was altered due to the stability issue of the error model (7) at the origin under the tracking error modeling in the preceding section. It has been demonstrated that sliding mode control is an effective and user-friendly method for controlling UAVs [24], unmanned underwater vehicles [25], unmanned vehicles [26], and other domains. Nevertheless, it has not been reported that sliding mode control has been used in agricultural tractor-trailer vehicles. There are generally two major states in sliding mode control: the approaching state and the sliding mode state. When a vehicle exhibits an initial deviation, it might be considered that an acceptable reaching law would be necessary to ensure that the sliding mode variable approaches the designed sliding mode surface. The sliding mode variable will slide along the predetermined path when positioned on the sliding mode manifold. A sliding mode variable will escape the designed sliding mode surface due to unknown factors in an unstructured farmland environment. Hence, the traditional sliding mode controllers contain the discontinuous symbol function term, which is capable of suppressing the disturbance and forcing the sliding mode variables to operate on the designed sliding mode surface, but they can result in controller chattering. Controller chattering may irreversibly damage the mechanical structure of the vehicle, so this phenomenon should be avoided [27; 28; 29]. In this section, the linear sliding mode surface is built, which guarantees the tracking error, and the articulated angle asymptotically converges to the origin. Second, this part uses the power-reaching law to create a continuous sliding mode controller to lessen the chattering phenomena. In addition, the fuzzy control concept is employed in this research to create the fuzzy rules for the controller's parameters and realize controller parameter tuning, thereby easing the load of controller parameter tuning.
## 3 Materials and Methods
### Principal Component Analysis (PCA)  
In order to find possible correlations between samples depending on diverse pre-treatments, the volatile compositions were analyzed by PCA using a covariance matrix including 161 variables \(\times\) 7 samples (1127 data). For this purpose, the software Statistica v. 7.1. (Stat Soft Italia, Vigonza, Italy) was used to generate two-dimensional score and loading plots. Eigenvalues were calculated using a covariance matrix among 161 chemical compounds as input, and the two-dimensional PCA biplot, including both samples of different drying and freezing treatments and compounds, was generated. Jam samples and the four compounds detected only in jams were excluded from PCA analysis, because jams did not undergo our drying or freezing treatments.
## 4 Discussion
### The Effects of Pixel/Field Size on the 3D-CNN  
As shown in Figure 9, the 1D-CNN and 3D-CNN show better performance in comparison with other classical ML algorithms and the 1D-CNN shows a slight superiority to 3D-CNN. The superiority of 1D to 3D-CNN was not expected, because 3D-CNN takes advantage of the spatial information besides spectral data, while the 1D-CNN method is only based on spectral data and considers each pixel as independent. To better understand the effect of field size on the performance of these two methods, the fields were classified based on their width into three categories (small when <150 m, moderate in the range 150-250 m, and large when >250 m). The Kappa coefficient of 1D-CNN and 3D-CNN methods for these categories are shown in Figure 13. The Kappa coefficients for large field classifications using 3D-CNN and PRISMA and Sentinel-2 images were 0.839 and 0.74, respectively. For small fields, the Kappa coefficients were reduced to 0.75 and 0.66, respectively. The Kappa coefficient of the 1D-CNN method did not show a significant change between different categories for PRISMA- and Sentinel-2 image-derived maps.  
Figure 13: Kappa coefficient of small (<150 m), moderate (150–250 m), and large (>250 m) fields provided by PRISMA and Sentinel-2 images.  
The limited results of 3D-CNN with respect to the 1D-CNN were hence strictly related to the small field size and, since most of the Macacarese and Jolanda di Savoia fields had widths lower than 200 m (Figure 1d), this made it difficult for the 3D-CNN algorithm to perform well. It is, therefore, expected to overcome this limited performance of the 3D-CNN in the case of future pansharpening of the hyperspectral at 30 m to 5 m resolution offered by the co-registered PRISMA PAN or by new missions like PRISMA-2 (having a higher spatial resolution mode).
# Geographic Routing Decision Method for Flying Ad Hoc Networks Based on Mobile Prediction
## 2 Related Work  
Many researchers have delved into the routing decision methods for MANETs and have achieved fruitful research results. The existing work can be roughly divided into reactive routing, proactive routing, and geographic routing [17]. The following is our analysis and summary.
## 2 Materials and Methods
### Analysis of Caffe by UHPLC-MS/MS  
Milk caffeine levels were measured using a UHPLC (Agilent Technologies 1290 LC, Palo alto, CA, USA) coupled to a triple quadrupole (Agilent Technologies Ultivo, Palo Alto, CA, USA) equipped with a Kinetex 5-\(\mu\)m EVO C18 100 A, 150 mm \(\times\) 2.1 \(\mu\)m column (Agilent Technologies, Palo Alto, CA, USA), and maintained at 25 \({}^{\circ}\)C in a thermostated column compartment. Mobile phases consisted of deionized water containing 0.1% formic acid (A) and acetonitrile containing 0.1% formic acid (B). The chromatographic separation was obtained by an isocratic elution mode consisting of 55% of A and 45% of B. The mobile phase flow rate was set to 0.7 mL/min. The total chromatographic run time was 9.0 min. The mass spectrometer with a jet stream electrospray source was operated in positive ionization mode with the following parameters: nitrogen drying gas temperature 350 \({}^{\circ}\)C, nitrogen sheath gas temperature 325 \({}^{\circ}\)C, nitrogen drying gas flow 7.0 L/min, nitrogen sheath gas flow 11.0 L/min, nebulizer pressure 50 psi, capillary voltage 3500 V, and nozzle voltage 1500 V. The following transition, 195.2 \(>\) 138.2 (CE = 16V), was used for the quantitation of caffeine levels. Calibration curves were calculated with four points and were considered acceptable when \(r^{2}\geq\) 0.998.