esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 System Analysis
### Impact on the Energy Bottleneck  
With conventional S2 data collection, there is an energy bottleneck at the sink node. This occurs in multihop wireless sensor networks because most of the data are accumulated at the sink node, and nodes near the sink node are likely to consume more energy than nodes far removed from the sink node. The impact of the S3 model is that data are aggregated to the maximum degree before being delivered to the sink node and the sink proxy is selected from various sensor nodes, so that energy consumption is much balanced.  
We analyze the impact of the S3 model on the energy bottleneck, with a specific focus on the energy reduction at the one-hop neighbour nodes of the sink node. Given a sensor network consisting of N nodes, and average energy consumption of a one-hop transmission of a packet assumed to be \(E_{0}\). Note \(\alpha\) is the ratio of sensor nodes that will respond to a query message. Then, energy consumption \(E_{S2}(\textit{lhop})\) at one-hop neighbour nodes of the sink in the conventional S2 model can be denoted as:  
\[E_{S2}(\textit{lhop})=\alpha*N*E_{0} \tag{10}\]  
where \(\alpha*N\) is the number of sensor nodes corresponding to a query. The energy consumption at the one hop neighbour nodes of sink in S3 model is denoted as:  
\[E_{S3}(\textit{lhop})=\alpha*N*E_{0}*\beta+\gamma*E_{0} \tag{11}\]  
where \(\beta\) is the ratio of aggregated data size to un-aggregated data size, \(\gamma*E_{0}\) is the energy consumed at one-hop nodes for relaying a packet from a sensor to the sink proxy before collecting data at the sink. When the network is large, the number of one-hop nodes is small compared with the number of nodes in the network, the relay probability is very small, and \(\gamma*E_{0}\) can be ignored, and so:  
\[E_{S3}(\textit{lhop})\doteq\alpha*N*E_{0}*\beta \tag{12}\]  
This means that energy consumption is \(\beta\) times the S2 model (\(\beta\leq 1\)). The greater the aggregation capability of the network, the smaller the \(\beta\) is, leading to more energy savings. For example, when 10 packets each with 5 bytes of payload data are aggregated into one packet with a 10 \(\times\) 5 payload, sharing one header with 28 bytes, \(\beta\) is \((10\times 5+28)(5+28)\times 10=25\%\).
# Automated Multiple Concrete Damage Detection Using Instance Segmentation Deep Learning Model
## 2 Mask and Region-Based Convolutional Neural Network (Mask R-CNN)
### Region Proposal Network  
From the feature map extracted by CNN and FPN, Mask R-CNN detects an object and estimates shape of detected objects. Region Proposal Network (RPN), which is highlighted with red color in Figure 1, detects the existence and location of objects in an image. The core idea of RPN is to determine the existence and location of objects in the form of bounding boxes using a small FCN as an extension of the normal CNN [31]. In the step of the region proposal, an n \(\times\) n sliding window scans multiple feature maps created by FPN and feed the scanning result in the intermediate layer. The intermediate layer is followed by two layers: the classification layer and regression layer. They are created by a 1 \(\times\) 1 convolution from the intermediate layer. The classification layer and regression layer have 2k and 4k elements, respectively, for each sliding window that is scanned the feature map, where k denotes the number of anchors of a sliding window. The role of the anchor is to create various-sized candidate boxes from each sliding window on the feature map. The 2k elements of the classification layer represent object/non-object classes of the anchors, and the 4k elements of the regression layer  
Figure 1: Overall architecture of Mask R-CNN.  
represent the horizontal and vertical (i.e., x and y) coordinates of the top left corners of the bounding boxes and the width and height of the anchors.
## 3 Non-Enzymatic Defensive Mechanisms
### Carotenoids  
Carotenoids are tetraterpene antioxidants found in the plastids of photosynthetic and non-photosynthetic tissues of plants [255; 256] and are synthesised by geranyl pyrophosphate (GPP) in the course of isoprenoids [26]. Carotenoids containing pure hydrocarbons (carotene) and those with one or more oxygen atoms (xanthophyll) are two main types of carotenoids in plant tissues [257]. Apart from their presence in plants, carotenoids have also been found in algae and photosynthetic microorganisms [152; 256]. Their light-harvesting behaviour in chloroplasts encompasses not only light absorption by antenna molecules (450-570 nm) and transfer of energy to chlorophyll (Chl) molecules but also photosynthetic machinery protection [258; 259; 260]. Carotenoids exert their antioxidative functioning in photosynthetic apparatus through scavenging singlet oxygen activity, reacting with LPOs to halt the chain reaction of ROS production, quenching triplet (3 Chl*), and exciting (Chl*) Chl molecules to prevent the formation of singlet oxygen, and dissipating excess excitation energy in the xanthophyll cycle [26; 261; 262; 263]. Depending on plants' resistance threshold to stressful conditions, the amounts of carotenoids are affected [264; 265]. Sugarcane plants with high carotenoids contents exhibited a better adaptation to saline conditions [266].
# Evaluation of the SOA Formation in the Reaction of Furfural with Atmospheric Oxidants
###### Abstract  
An experimental product study of the reactions of furfural with the main tropospheric oxidants (CI, OH and NO\({}_{3}\)) has been carried out using a Fourier Transform Infrared spectrophotometer (FTIR) and a gas chromatograph-mass spectrometer with a time of flight detector (GC-TOFMS). The main gas-phase products detected were 5-chloro-2(5\(H\))-furanone, maleic anhydride, 2-nitrofuran and CO. Molar yields were quantified for the detected products in these reactions, thus suggesting the existence of nongaseous products that could not be observed with the analytical techniques employed. The formation of Secondary Organic Aerosol (SOA) from the oxidation of furfural with CI atoms, OH, NO\({}_{3}\) and ozone was investigated in a smog chamber in the absence of inorganic seed aerosols. The experimental results show the formation of ultrafine particles (less than 1 \(\upmu\)m in diameter) for all of the studied reactions except for the nitrate radical. Given their small size, these ultrafine particles (<1 \(\upmu\)m) can easily penetrate into the respiratory tract and reach the alveolar region. These particles, therefore, have the potential to cause severe damage to the respiratory system. The aerosol yield obtained, Y, was low (<0.04) in all cases, which means that the aerosols generated from furfural, under atmospheric conditions, could have little impact.  
Keywords:furfural; atmospheric degradation; aerosol; reaction products; mechanism +
Footnote †: journal:
## 4 Taxonomy of the Existing Multipath Routing Protocols
### Concurrent Multipath Routing
#### Energy-Efficient Multipath Routing Protocol
LIEMRO improves the performance demands of event-driven applications through distributing network traffic over high-quality paths with minimum interference. This protocol utilizes a dynamic path maintenance mechanism to monitor the quality of the active paths during network operation and regulates the injected traffic rate of the paths according to the latest perceived paths quality. Therefore, it accounts for the temporal variations of the low-power wireless links and adjusts traffic distribution accordingly. However, similar to the most of the previously discussed protocols, LIEMRO does not consider the effects of buffer capacity and service rate of the active nodes to estimate and adjust the traffic rate of the active paths.
# Fractional Order Processing of Satellite Images
## 3 Results
### Overall Performance with Fixed Parameters  
One crucial objective of this work is to assess if it is possible to find and easily tune parameters, in an automatic way, without loosing performance. A search for optimal fixed parameters was conducted sorting performance results using fixed parameters for all the images in the data set. Table 7 and Figure 26 present the best fixed parameters results for each detector; they are discussed in the next section.  
\begin{table}
\begin{tabular}{l c} \hline
**Detector** & \(\overline{J}\) \\ \hline Fractional Derivative Op. & 0.9623 \\
**Color** Fractional Derivative Op. & 0.9596 \\
**Color** Laplacian of Gaussian & 0.9554 \\
**Color** [PERSON] & 0.9537 \\ Laplacian of Gaussian & 0.9531 \\
**Color** Sobel & 0.9478 \\ [PERSON] & 0.9474 \\
**Color** CRONE & 0.9461 \\
**Color** Canny & 0.9448 \\ CRONE & 0.9447 \\ Canny & 0.9342 \\ Sobel & 0.9282 \\ \hline \end{tabular}
\end{table}
Table 6: Ranking of the best results average for all images.  
Figure 25: Bar chart with best results average for all images (Varying parameters).
## 5 Experiments
### Practical Evaluation on Resource-Constrained Devices
#### 5.5.2 Network Performance  
The Raspberry Pi 4 was selected for carrying out network performance tests as previously described in Section 5.3. This was due to the fact that the Orange Pi, although it was able to run both consensus protocols, did not have enough power to perform high-load stress tests.  
Thus, Figures 22 and 23 show the TPS obtained under different workload levels for the Raspberry Pi 4 in a network of four nodes and one validator. Specifically, Figure 22 shows the performance of the Raspberry Pi 4 for PoA at different gas limits. For obtaining such a figure, the same number of transactions employed in Section 5.3 was sent but each point was averaged only 20 times due to the notable difference in hardware performance. In addition, it should be indicated that, when using resource-constrained devices, the gas limit cannot be too high: a high gas limit allows for a greater allocation of computational resources, thus avoiding network congestion, but due to the lower computational capacity of the SBCs, the synchronization process becomes slower when more transactions are validated for each block. In contrast, such a computational limitation implies that setting the gas limit too low may cause transactions to run out of gas due to their intrinsic complexity.  
For such reasons, during the tests, the minimum gas limit was set to 5 M and the maximum to 30 M. The obtained results show a similar behavior to the ones depicted in Figure 15, but with a lower TPS performance, demonstrating that increasing the gas limit leads to an increase in TPS.  
Figure 23 shows the difference in performance of PoW and PoA with the previously described test network using a gas limit of 20 M. Like in the non-SBC-based case, the Figure shows, for different amounts of transactions, the remarkable difference in TPS between PoA and PoW, thus indicating that, under the selected experimental conditions, PoA is a better fit for the evaluated SBCs.
## 3 Results and Discussion  
The permutation feature importance technique [56; 57] was used to calculate the importance of the features. The training set for this method is used to train a model, and the validation set is used to measure the increase in the prediction error after a feature is permuted in the validation set, destroying the link between the feature and the true outcome [45]. Since the model depends on the feature for prediction in this scenario, a feature is considered \"important\" if permuting its values results in an increase in the model error. Figure 6 shows the importance of each feature using the BiLSTM model and permutation feature importance technique. The daily ETo was the most-important feature because the permutation of this feature caused the largest increase in the MSE from 0.3 to 1.75.  
The feature with the highest importance (ETo) was selected as the main independent variable. Then, the other features were added to the group of independent variables one by one in order of importance, and the Variance Inflation Factor (VIF) value for this group of variables was calculated. The VIF [58] with a threshold of five was used to remove the multicollinear variables. The variable is retained as a predictor if the VIF value is below the threshold; otherwise, it is removed from the group. Following these processes, the historical data selected as the input to the FTSW forecast model were the month, average RH, minimum VPD, rainfall, and daily ETo.  
Since the VPD (min) was not present in the Quinta de Pancas dataset, a model without this variable was also trained to evaluate the performance of the model on the Quinta de Pancas dataset. In general, with various input variables and various input and output lengths, fifteen BiLSTM models were trained (see Table 4).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Variables** & **Input Length (Look-back)** & **Output Length** \\ \hline Month, ETo, RH (avg), rainfall & \(3,5,7\) & \(1\) \\ \hline Month, ETo, rainfall RH (avg), VPD (min) & \(3,5,7\) & \(1\) \\ \hline Month, ETo, rainfall, RH (avg), VPD (min), irr & \(3,5,7\) & \(3,5,7\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Different inputs of the model and different input and output sizes used to train the BiLSTM model. Look-back indicates the number of days used to predict the FTSW, while irr indicates irrigation.  
Figure 6: Feature importance using the BiLSTM model and the permutation feature importance technique.
## 2 Materials and Methods
### Model Building and Validation Strategy
#### 2.3.2 Modelling and Validation Tools  
Multivariate adaptive regression splines (MARS; [23]), which was successfully applied in a number of recent landslide and soil erosion susceptibility studies [52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85;  
where \(y\) is the dependent variable (the outcome) predicted by the function \(f(x)\), \(\alpha\) is the model intercept, and \(\beta_{i}\) is the coefficient of the \(h_{i}\) basis functions, given the \(N\) number of basis functions. MARS analysis was performed by exploiting the \"earth\" R-package [60].  
The MARS statistical modelling of landslide susceptibility conditions requires the random extraction of a sample made of a balanced number of stable and unstable cases to be split into calibration and validation subsets: the first is exploited for regressing the outcome status on the set of covariates that express the adopted controlling factors, while the latter furnishes the unknown-to-model target pattern whose status has to be blindly predicted. In a pixel-based method, where the number of stable cases is typically largely greater than the unstable, balanced samples are obtained by merging all the positives to an equal number of randomly extracted negatives. To account for any potential unrepresentativeness of the extracted negatives, by adopting recurrent random selection routines, multiple samples were produced. Similarly, to control the influence of the specific cases which feed the calibration subsets, multiple (75/25%) calibration/validation splitting was applied to each sample as well. In this way, one hundred samples were split one hundred times so that each pixel was classified ten thousand times, allowing us to estimate the model resolution and precision. Finally, to fully evaluate the prediction skill of the model, the regression coefficients gained in the calibration/validation subset were applied to the whole investigated area.  
Receiver operating curve (ROC) [61; 62; 63] and confusion matrices analyses were the tools employed to investigate the model's accuracy. In particular, ROC plot analysis is based on evaluating true- versus false-positive rates for decreasing susceptibility scores, with a larger area under the curve (AUC) [64; 65] attesting to more effective classifications. The score at the maximum gradient of the ROC is then used as an optimized cut-off [66] for building a binarized (positive/negative-observed/predicted) confusion matrix. In this way, the accuracy of the model can be evaluated both with score-independent (ROC_AUC) and -dependent (ACC) indices.
## 3 Configuration of the System and Conditions
### Recompressed S-Co\({}_{2}\) System Layout
 \\ \hline \multirow{8}{*}{CO\({}_{2}\)} & 1 & 2.64 & 7.6 & 32 & 315.09 \\  & 2 & 2.64 & 30 & 81.66 & 354.02 \\  & 3 & 1.36 & 7.6 & 104.76 & 529.05 \\  & 4 & 1.36 & 30 & 251.02 & 644.00 \\  & 5 & 2.64 & 30 & 251.02 & 644.00 \\  & 6 & 4 & 30 & 2 two compressors, C1 and C2. \(Q\) is the heat power input into the system, and in the current S-CO\({}_{2}\) recompression cycle, it was simply the heat generated by H:  
\[Q=\dot{m}_{7}\cdot(h_{8}-h_{7}) \tag{11}\]  
where \(\dot{m}_{7}\) is the mass flow into H, and \(h_{7}\) and \(h_{8}\) are the enthalpies corresponding to the inflow and outflow S-CO\({}_{2}\) of H (see Figure 3). Based on the current design values, the RTE of the system turned out to be 43.55%, slightly higher than the design given by [PERSON] et al. [3], who used a lower estimation for efficiencies for the key devices in their system. As for a real S-CO\({}_{2}\) power plant, however, the actual value of RTE should be lower.
# STGAN: Swin Transformer-Based GAN to Achieve Remote Sensing Image Super-Resolution Reconstruction
## 4 Experiments
### Quantitative and Qualitative Comparison of Different Methods
Taken together, STGAN performs excellently in terms of perceptual quality, image details, structural similarity, and spectral consistency, demonstrating its advantages in super-resolution reconstruction tasks. In the mean square error (MAE) difference between the output panchromatic sharpening results and the ground truth, as shown in Figure 9, the red areas indicate poorer generation while the blue areas indicate better results. Compared to other competing methods, our model exhibits smaller values of spatial and spectral distortion, further validating the effectiveness of the method. The experimental results show that the method successfully reduces the information redundancy and significantly improves the quality of the pan-sharpening results. Figure 8: Visual contrast of the different methods, while the red box is a magnification of the localized area.
# A General Euler Angle Error Model of Strapdown Inertial Navigation Systems
## 1 Introduction
Since the output attitudes of SINS are Euler angles, it is necessary to analyze the Euler angle error model which can reflect attitude measurement accuracy more directly. To address the above issues, a general Euler angle error modeling method of SINS is proposed. Firstly, based on the operation principles of SINS, the Euler angle solution formulation is given. Then the differential formulation of relative Euler angle errors induced by angular velocity errors can be derived. Secondly, the differential formulation of convected Euler angle errors induced by an inaccurate attitude matrix is proposed based on the Euler angle differential equations. Then, general Euler angle error formulations of SINS are modeled, and the mechanisms and characteristics of Euler angle error propagation in SINS are analyzed. Finally, some simulation experiments are carried out to verify the proposed models. The general Euler angle error model can be used to analyze the attitude movements in real-time, and an optimal control strategy can be designed based on the general Euler angle error model.
# Multi-Sensor Face Registration Based on Global and Local Structures
## 4 Experimental Results
### Registration on Real Face Images with the UTK-IRIS Database  
In this section, the performance of face image registration with our FR-GLS method is evaluated by using the public UTK-IRIS Database. The images in the database have a spatial resolution of \(320\times 240\) pixels and the database contains individuals with various poses, facial expressions and illumination variations. We first give an intuitive impression of registration and fusion results, and then we provide a quantitative comparison with three typical methods such as Coherent Point Drift (CPD) [19], Regularized Gaussian Fields (RGF) [20] and SMM [25], based on the specified landmarks of visible and thermal infrared image pairs. The reasons for choosing these three comparison methods rest on the following two considerations. On the one hand, CPD and SMM use both global structures based on finite mixture models to parameterize the transformations. On the other hand, besides the global relationships in the feature point sets, RGF uses local features descriptor such as SC to recover the accurate transformation.  
However, our FR-GLS has two major advantages compared to RGF: (i) Unlike the local features descriptor used in RGF, we use a more robust local features descriptor such as IDSC to initialize the correspondences between the two shape features; (ii) In order to preserve the global and local structures of feature point sets during the registration process, a heavy tail distribution in our FR-GLS is used to replace the traditional Gaussian distribution in the RGF method. Furthermore, we use the IDSC descriptor to assign the mixing proportion \(w_{ij}\) of the SMM.
## 4 Proposed Approach
### Design Methodology
#### 4.2.3 Reliability  
Our solution utilizes Federated Learning, enabling decentralized data processing across user devices, achieving high reliability and quality of service without central data storage. Additionally, it decentralizes the role of Identity Providers (IdPs) by distributing via virtual IdPs the authentication and digital signatures across multiple entities. Therefore, for an attacker, it would be possible to compromise the integrity of the system only by compromising all the participants in the system. This innovative approach removes single points of failure, significantly improving security, reliability, and overall system resilience.
## 3 Results  
### Spectral Analysis  
In view of the obvious noise at both ends of the original spectrum, this study focused on the analysis of spectral data with wavelengths ranging from 935 to 1720 nm. Figure 2a presents the original spectral profiles of 900 blueberry samples, displaying distinct absorption maxima around 960 nm, 1090 nm and 1280 nm, alongside pronounced minima near 1200 nm and 1400 nm. The prominent peaks at 960 nm and 1090 nm are likely attributed to anthocyanin absorption in blueberries [35], while the 1280 nm peak corresponds to chlorophyll characteristics [36; 37; 38]. Conversely, the absorption troughs at 1200 nm and 1400 nm are likely associated with triple-frequency O-H stretching vibrations and the combined stretching modes of C-H and O-H bonds, respectively [39].  
Figure 2b demonstrates the average spectral curves of the three blueberry varieties. Overall, the general trends of the three curves are basically the same, showing a trend of first high and then low, but there are some differences in the absorbance at different wavelengths. Among them, the spectral absorption intensity of 'Lexi' is significantly lower than that of 'L25' and 'Bluecrop', and the spectral absorption intensity of the latter two is basically the same. This difference may be related to the shape and flesh state of different blueberry varieties. In order to reduce the spectral differences between varieties and improve the robustness of the model, the S-G algorithm was firstly adopted in this study to smooth the data and reduce the spectral variability, and at the same time, the MSC and SNV treatments were combined to further minimize the systematic errors due to the scattering of particles and the uneven size of the samples. The average spectral curves after processing are shown in Figure 2c. The results show that the processed spectral data can effectively support the construction of a universal prediction model for multi-variety blueberry SSC.  
Figure 2: _Cont._