esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Contribution of All 36 MISR Data Channels  
The addition of all off-nadir bands to the nadir-only bands (Scenario 4, all 36 MISR data channels) showed a significant improvement of the predictive model performances compared to the nadir two spectral bands only (Scenario 0) or to the nadir four spectral bands (Scenario 1). Scenario 4 improved the relative rRMSE performance by 22% and 25% for H\({}_{\text{mean}}\) and CC, respectively, compared to Scenario 0, and by 11.3% and 13.7% compared to Scenario 1 (Figure 6a). The highest coefficient of determination were also obtained for Scenario 4 with 0.73 and 0.64 for H\({}_{\text{mean}}\) and CC, respectively (Table 2, Figure 5). This compared to the modest prediction improvement obtained between Scenario 1, and Scenario 2 suggests that the performance of multi-angle data is enhanced when using multispectral data. The model rBias decreased marginally for H\({}_{\text{mean}}\) from 1.2-1.4 to 0.3% and from 2.8 to 0.9% for CC. While we obtained a lower rBias for H\({}_{\text{mean}}\), in the scatterplot of the validation model we still observed an overestimation of the prediction at the lower end of H\({}_{\text{mean}}\) or CC, and an underestimation at the higher end (Figure 6b,c). The differences between the Scenario 0 and Scenario 4 models, and the Scenario 1 and Scenario 4, were both statistically significant and for both parameters (\(F_{34,\,7308}=1.63\), \(p<0.05\); F\({}_{32,\,7308}=1.63\), \(p<0.05\)).
# Feature Map Analysis of Neural Networks for the Application of Vacant Parking Slot Detection
## 2 Materials and Methods
### Experimental Processes  
The experimental processes are divided into three phases: data preparation, feature analysis preparation, and feature analysis. The processes are shown in Figure 7.  
Data preparation is the phase in which the new datasets needed for the experiments are prepared. The original dataset in the data acquisition section consisted of five subsets based on ROI levels. New datasets were constructed by uniting parts of the subsets; only subsets with successive ROI levels were unified. As a result, ten new datasets were prepared by controlling the number of unified subsets.  
Feature analysis preparation is the phase in which an optimal dataset is prepared and the networks are optimally trained. New datasets were input into networks for training and testing after grayscaling and resizing. The experiments were repeated 40 times because there were ten datasets and four networks. One of the new datasets, ROI levels 0 to 1, was chosen as an optimal dataset, since the networks trained by the dataset showed good performance. The optimal dataset and optimally trained networks were deployed for the last phase.  
Feature analysis is the phase in which the feature maps obtained from parking slot images are compared and analyzed. Feature maps were extracted by the feature extraction part of the networks and changed into human-recognizable images using the feature map visualization method. When one image was input into the networks, 16 feature maps were extracted. There were various patterns on the 16 feature maps, and the tenth feature map showed prominent pattern differences according to the parking slot states. Thus, that feature map was employed for feature map comparisons.  
Figure 7: Overall processes of feature analysis.
# Estimation of Antenna Pose in the Earth Frame Using Camera and IMU Data from Mobile Phones
## 3 Problem Formulation and Method Overview  
Our goal is to estimate the antenna pose in the Earth frame from multi-view data, which consist of multi-view images of the antenna and IMU (accelerometer and magnetometer) measurements recorded at the exact same instant as each image capture. Below, we first formally define the problem and then present an overview of our solution.
# Quantum Coding via Quasi-Cyclic Block Matrix
###### Abstract  
An effective construction method for long-length quantum code has important applications in the field based on large-scale data. With the rapid development of quantum computing, how to construct this class of quantum coding has become one of the key research fields in quantum information theory. Motivated by the block jacket matrix and its circulant permutation, we proposed a construction method for quantum quasi-cyclic (QC) codes with two classical codes. This simplifies the coding process for long-length quantum error-correction code (QECC) using number decomposition. The obtained code length \(N\) can achieve \(O(n^{2})\) if an appropriate prime number \(n\) is taken. Furthermore, with a suitable parameter in the construction method, the obtained codes have four cycles in their generator matrices and show good performance for low density codes.  
long-length quantum codes; stabilizer codes; jacket matrix; quasi-cyclic codes +
Footnote †: journal:
## 4 Discussion  
Since the volume estimation method by alpha shape was based on point cloud data, using appropriate points was critical. Although point clouds had the advantage of easily generating large amounts of data, they could contain outliers and unnecessary data, so a separate editing process was required. In this process, there was no need to use millions of data points to extract regular and correct objects. However, care was to be taken not to delete points necessary for shape implementation during the outlier removal process. Many studies have researched methods on how to remove outliers to extract the minimum necessary data. This process significantly influences the success or failure of the research.  
In this study, the SOR technique to remove outliers was used. Data collection took place outdoors, and since the desired object was not of a fixed shape, data editing was completed cautiously. Additionally, efforts were made to ensure that necessary data were not deleted during the extensive editing process. In this study, the volume of the vegetation patch was estimated using point clouds refined through a minimal SOR process. Comparing it with the initial data in Table 2 confirmed that the size did not change significantly even after successive SOR processes. Since the edges did not change significantly, it was judged that there would be no significant difference when estimating the volume. However, when actually calculating the volume, it was observed that the estimated volume decreased as the SOR process progressed (Figure 11). For the proposed optimal alpha radius of 0.75 for the research subject, there was a difference of 17,347 m\({}^{3}\) in volume after the 1 st and 2 nd SOR, and a difference of 36,312 m\({}^{3}\) after the 3 rd SOR.  
However, caution is clearly needed when considering the volume estimation results by cross-sectional area for each SOR process (Figure 12) as the required area during the SOR process may not be included. In fact, the vegetation patch on the left side of the image was not interrupted, but as the SOR process progressed, necessary points were removed, resulting in a disconnected shape. Thus, there was no universal definition of how many times the SOR process should be undertaken to be optimal, and depending on the target object and measurement environment, the optimal results could vary significantly, so caution was essential. Using the volume estimation results to discern the optimum would result in the usage of an inappropriate alpha radius, which could produce inaccurate results.  
Figure 11: Volume estimation results by alpha radius (\(\alpha\)) for each SOR degree.
## 2 Materials and Methods
### Acid, Peroxide, \(p\)-Anisidine, and TOTOX Values  
The physicochemical quality of oils was determined based on the lipid values. To evaluate the degree of the hydrolytic changes, the acid values (AV) were determined according to the EN ISO 660:2010 standard [5]. The peroxides content, expressed as the peroxide value (PV), was tested according to the EN ISO 3960:2012 standard [6]. The degree of secondary oxidation products was given in \(p\)-anisidine values (\(p\)-AnV) according to the EN ISO 6885:2008 standard method [7]. Based on the AnV and PV, the overall oil oxidation rates were calculated as TOTOX values (TOTOX = 2 PV + \(p\)-AnV).
## 5 Results and Analysis
### Metrics
#### 5.1.2 Partial Accuracy  
Partial Accuracy (\(PA\)) is composed of two categories:  
* Incorrect Answer and Correct Explanation  
It is defined as follows:  
\[PA=\frac{N(C,IE)+N(IC,E)}{N(total)} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Temperature**} & \multicolumn{6}{c}{**Synonyms**} & \multicolumn{6}{c}{**Homophones**} \\ \cline{2-11}  & **1.0** & **0.8** & **0.6** & **0.4** & **0.2** & **1.0** & **0.8** & **0.6** & **0.4** & **0.2** \\ \hline Number of Incorrect Answers & 6 & 11 & 11 & 12 & 12 & 5 & 7 & 5 & 6 & 6 \\ Number of Correct Answers & 194 & 189 & 189 & 188 & 188 & 195 & 193 & 195 & 194 & 194 \\ Accuracy (\%) & 97.0 & 94.5 & 94.5 & 94.0 & 94.0 & 97.5 & 96.5 & 97.5 & 97.0 & 97.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: GPT-4o’s accuracy by temperature settings for synonyms and homophones.  
where \(N(C,IE)\) is the number of cases where the model selected the correct answer but provided an incorrect explanation, and \(N(IC,E)\) is the number of cases where the model selected an incorrect answer but provided a correct explanation.
## 3 Results and Discussion
### Dimensional Stability  
From a practical viewpoint, the dimensional stability in different electrolyte solutions is the key criteria for any commercial ion exchange membrane product. For this purpose, the 3D changes in four classical electrodialysis system solutions (deionized water, 4.0 wt% NaCl, 4.0% wt% HCl and 4.0 wt% NaOH) were compared. The results are displayed in Figure 8, in which a small downtrend of 3D changes for the Na-form CAM membrane was observed in the order of HCl, water, NaOH and NaCl  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**DVB** (wt\%)} & \multicolumn{8}{c}{\(R_{\text{st-DVB/PVDF}}\) (–)} \\ \cline{2-9}  & 0.5 & 0.6 & 0.7 & 0.8 & 0.9 & 1.0 & 1.1 \\ \hline
1.0 & + & + & + & + & + & + & + \\
2.0 & + & + & + & + & + & + & + \\
3.0 & + & + & + & + & + & + & + \\
4.0 & + & + & + & + & + & + & - \\
5.0 & + & + & + & + & + & + & - \\
6.0 & + & + & + & + & - & - & - \\
7.0 & + & + & - & - & - & - & - \\
8.0 & + & - & - & - & - & - & - \\ \hline \end{tabular} a  
\end{table}
Table 1: The possibility of membrane formation by the thermoforming process a.  
Figure 7: SEM image of the prepared cation exchange membrane.  
solutions. Although the dry-wet change in length was close to 10%, the maximum difference in the four solutions was no more than 0.5% (from 8.93% in 4% NaCl to 9.39% in 4% HCl). This indicates that the alloy membrane has an outstanding dimensional ability in possible ED applications. On the other hand, 3D changes of the H-form CAM membrane in water exceeded 10%, and thus, the maximum length difference from 4% NaCl to water was up to 2.29%. However, the maximum 3D change criterion for the practical application of ion exchange membranes, particularly in length and in width, is generally believed to be less than 1%. A too large of a 3D change may lead the adopted membranes to puckering, breaching or even falling to pieces, particularly when the concentration or kind of running solution is suddenly alternated [23]. To overcome this drawback, it is necessary for the H-form CAM membrane to be transferred into the Na-form (e.g., using 4% NaCl solution) before washing with water.
## 6 Conclusions  
In view of the difficulties associated with the lack of transparency and ease of data tampering that exist in the present centralized storage methodology of spatiotemporal data, a spatiotemporal data-oriented blockchain is proposed, which integrates the decentralized, tamper-proof, and traceable characteristics of blockchain technology with spatiotemporal data management. Methods for constructing and querying the blockchain are also presented. To provide secure storage of spatiotemporal big data classification based on blockchain technology, a novel CSSoB scheme is proposed. The experimental results demonstrate that the storage rate of CSSoB gradually declines as the proportion of sensitive data increases. The performance of the CSSoB scheme is evaluated by comparing its read and write capabilities in both local and LAN environments. The results indicate that the absolute read and write speed is slower for smaller files, while larger files have greater speed. A comparison of the TPS between CSSoB and HDFS under various thread numbers reveals that when the thread number is 100, CSSoB outperforms HDFS by 7.8% in terms of TPS. Adopting the IPFS solution not only enhances storage performance, but also significantly bolsters storage security. These results suggest that the CSSoB scheme has substantial practical value. In the future, we will continue to study how to improve the efficiency of secure storage and improve the throughput of spatiotemporal big data secure storage.  
Conceptualization, B.Z.; methodology, B.Z. and Y.Y.; software, B.Z. and G.C.; validation, G.C. and B.Z.; formal analysis, G.C.; investigation, Y.Y. and G.C.; resources, J.Z.; data curation, B.Z.; writing--original draft preparation, B.Z. and G.C.; writing--review and editing, B.Z. and J.Z.; visualization, G.C. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China (No. 41761081).  
Informed Consent Statement: Informed consent has been obtained from all subjects for this study.  
The data used to support the findings of this study are available within the article.  
The authors declare no conflict of interest.  
Figure 13: CSSoB and HDFS performance comparison TPS.
# Prediction of Intraday Electricity Supply Curves
## 5 Conclusions and Extensions  
We have concluded that the MLP and CNN models are significantly better than the naive model when it comes to predicting energy curves, broadly reducing the error incurred in some cases. These models use information from the same session from the previous day and information from the previous session on the same day. By improving the naive procedure in this way, we have responded positively to the two research questions posed. Between these two models, at the \(\alpha=0.005\) significance level, it is concluded that CNN performs better in the first five markets, while in the last one, the performances are similar. On the other hand, we observe that the LSTM and BiLSTM models give, by far, the worst results. The CNN+LSTM composite model, again, improves the results thanks to the inclusion of the convolutional layers. The Transformer model does not manage to improve on the naive model, except in a couple of markets.  
Non-uniform point grids as an approximation to supply curves have proven effective in obtaining accurate predictions, thereby reducing the size of the objects handled as well as the computational time required. However, this aspect is one of the limitations of our study in the sense that the size of the grid implies a high dimension of the input tensor of the neural network models. Therefore, it would be interesting to study procedures that allow for reducing this dimension.  
Another limitation that also relates to the dimensionality of the input features is that we have not taken into account meteorological variables that influence the availability of electricity produced through solar or wind technologies. Therefore, a possible extension of this work (maintaining the techniques used) could be to increase the amount of market information used for predictions, for example, meteorological variables such as temperature, solar radiation, precipitation, wind, etc., which affect both generators and consumers, could also be included.  
Although this project has focused on predicting supply curves, both the preprocessing and predictive models can be used for demand curves as well. By doing so, an estimate of the energy price would be obtained by intersecting both curves. This will be the subject of future research.  
Conceptualization and methodology, G.V. and A.M.A.; software, validation, data curation and writing--original draft preparation, G.V.; writing--review and editing, supervision and project administration, A.M.A. All authors have read and agreed to the published version of the manuscript.  
The second author acknowledges the partial funding of Ministerio de Ciencia e Innovacion by Grants PID2022-138114 NB-I00 and PID2023-151013 NB-I00 funded by MCIN/AEI/10.13039/501100011033 and by FEDER/UE.  
Figure 5: Boxplot of prediction errors using the naive, MLP, and CNN models.
# Bending Performance of a Prestressed Concrete Composite Girder Bridge with Steel Truss Webs
## 4 Test and Finite Element Result Analysis  
### Evolution Process of the Bending Performance  
The vertical deflection distribution of the composite girder test specimen along the longitudinal bridge direction under different load cases is shown in Figure 7. It can be seen in Figure 7 that under symmetrical loads, the vertical deformation of the test specimen is symmetric along the longitudinal bridge with respect to the mid-span section undergoing the maximum deformation. During the whole loading process, the load-deflection relationship curves at the \(L/12\), \(L/6\), and \(L/2\) sections are shown in Figure 8. As can be seen from Figure 8, the evolution process of the flexural performance of the test specimen can be divided into the elastic stage, cracking elastic stage, elastoplastic stage, and structural failure stage.  
In Figure 8, it can be seen that when the load is less than 95 kN, the structure is in the linear elastic stage where the load-deflection curve changes linearly. When the load reached 95 kN, cracks began to appear in the concrete bottom plate of the test specimen, and the flexural stiffness of the girder decreased slightly. However, before the load reaching 120 kN, that is, before the reinforcement in the bottom plate yields, the load-deformation curve of the specimen presented an approximately elastic relationship, and the structure was in the cracking elastic stage. When the load reached the value of 120 kN, the reinforcement in the concrete bottom plate yielded, and the crack growth rate increased sharply, the load-deformation curve had a relatively obvious turning point, indicating that the reduction rate of flexural stiffness and the increasing rate of deflection were further accelerated, and the structure entered the elastoplastic stage. When the load exceeded 165 kN, the readings from the load-monitoring sensors were no longer stable, but the deformation of the test specimen continued to increase until it exceeded the range of the dial indicator. Considering that the external prestressed strand may break, the loading was stopped, and the structure was considered to have failed.  
Figure 8: Comparison of the load–deflection curves in a typical section.  
Figure 7: Vertical deflection of the composite girder.
# Natural Frequencies and Modes of Electrostatically Actuated Curved Bell-Shaped Microplates
## 3 Results and Discussion  
In all the calculations, plates with a radius of 700 \(\upmu\)m, thickness of 400 nm, and initial elevation-to-thickness ratio varying from 0 to 30, were considered. This specific geometry was adopted since plates of the same dimensions were previously fabricated using the mold-less stamping technique and their bistable behavior was demonstrated theoretically and in the experiment [41].  
Prior to using the ROM for the vibration analysis of the plate, the ROM was verified by comparing its results with those provided by the finite elements (FE) simulation carried out using Ansys package. In the case of mechanical loading, shell elements SHELL181 with four nodes and quadratic interpolation were used in the three-dimensional FE model of the device. To track the unstable branches of the equilibrium curves, arc-length continuation implemented in Ansys was used in the case of mechanical loading. In the coupled electromechanical analysis of an electrostatically loaded structure, PLANE223 elements with eight nodes were used for the air-electrostatic domain between the plate and the electrode. The plate itself was meshed using PLANE183 elements with eight nodes. The plate thickness was subdivided into four layers while the air gap was constructed as a single layer to avoid mesh distortion. Electrostatic boundary conditions included prescribed voltages on the top and bottom surfaces of the air gap. Several mesh refinements were carried out in both mechanical and coupled electromechanical FE simulations to reach convergence with the relative error of less than 0.1% in terms of the fundamental mode frequency. Consequently, 67,840 elements and 68,137 nodes were used for the mechanical simulations. The coupled electromechanical FE model consisted of 5000 elements and 19,012 nodes. In all the cases, both RO and FE models incorporated the leading-order geometric nonlinearities of the plate and, as a result, the key features of the response, including von Karman stiffening/softening and bistability.
# Range-Free Localization Approaches Based on Intelligent Swarm Optimization for Internet of Things
## 1 Introduction
The algorithm obtains improved performance with a high percentage of anchors. The Centroid method [25] considers the barycenter of nearby anchors as the determined positions of nodes, avoiding the need for additional materials. Amorphous [26] is similar to DV-Hop in that it accurately determines the locations of unknown nodes. DV-Hop [23] is a well-known approach that has become the most recommended candidate for the localization of sensors in different kinds of topologies using a limited number of anchor nodes within a wireless sensor network [27]. In this approach, the sensor node position estimate is based on the average distance and the hops number, both of which are derived from each anchor node in the network. In this approach, the positions of nodes are calculated using a multilateration technique, which is applicable even when there are fewer than three neighboring anchor nodes. In the existing literature, a number of localization algorithms that use the DV-Hop technique have been suggested to reduce the errors in estimated sensor node positions. Despite these efforts, localization accuracy remains inadequate, prompting a need for more enhancements. To address this gap, in this study we introduce three novel localization algorithms that combine DV-Hop with Chicken Swarm Optimization (CSO) [28]. These algorithms are developed to enhance and refine the localization accuracy of sensor nodes within WSNs. The key contributions of this study can be stated as follows:  
* Three new algorithms to increase localization accuracy, denoted as DV-HopCSO, W-DV-HopCSO, and HW-DV-HopCSO. * New and enhanced steps to increase localization accuracy. * Evaluation of the presented algorithms considering four distinct types of complex topology. * Verification of the reliability of the proposed localization approaches regarding their accuracy and error considering four distinct types of complex topology through comparisons with existing algorithms (DV-Hop, PSDV-Hop, MDV-Hop, W-DV-Hop, and HW-DV-Hop) involving the communication range, number of anchor nodes, total number of nodes, and maximum number of iterations. The rest of this paper is structured as follows: Section 2 covers the background on localization algorithms from the literature; Section 3 presents the proposed algorithms based on DV-Hop and chicken swarm optimization; Section 4 presents the simulation results and performance analysis' and Section 5 concludes the paper and suggests future work.