esa-sceva/satcom-chunk-collection huggingface dataset:
# LPCP: An efficient Privacy-Preserving Protocol for Polynomial Calculation Based on CRT
## 8 Performance and Evaluation
### Theoretical Analysis  
For the computation aspects, the computation overhead comprises three parts--the one-way trapdoor permutation, arithmetic ring operations and hash operations. Both the one-way trapdoor permutation encryption and the one-way trapdoor permutation decryption are implemented twice. The main overhead of the arithmetic ring operations lies in the re-encryption phase. The hash operations is implemented six times. Therefore, the computational complexity of LPCP is \(\mathcal{O}(1)\cdot T_{trapdoor}+\mathcal{O}(max(log_{2}^{p+q},term_{F})) \cdot T_{multiplication}+\mathcal{O}(1)\cdot T_{hash}\), where \((p,q)\) is the big prime parameters, \(term_{F}\) is the polynomial term number,\(T_{trapdoor}\) is the time of executing a trapdoor permutation encryption, \(T_{multiplication}\) is the time of executing the multiplication arithmetic operations, and \(T_{hash}\) is the time of executing a hash function.  
For the communication aspects, the communication packages contain two trapdoor permutation ciphertexts, five 3\(\lambda\)-long ciphertexts and three hash signature messages. To sum up, the communication complexity of LPCP is reduced to \(\mathcal{O}(1)\cdot L_{trapdoor}+\mathcal{O}(1)\cdot L_{ciphertext}+\mathcal{O}( 1)\cdot L_{hash}\), where \(L_{trapdoor}\)is the length of the trapdoor permutation ciphertext, \(L_{ciphertext}\) is the length of the ciphertext and \(L_{hash}\) is the length of the hash signature.
## 3 Materials and Methods
### Model Deployment, Fine-Tuning, and Predictive Evaluation
#### 3.3.3 CNN Model Deployment and Fine-Tuning  
In this phase, we designed a CNN specifically aimed at classifying news articles into fake and non-fake categories. For data manipulation, we employed the Pandas library in Python, which facilitated the efficient handling and preprocessing of our datasets. The model development was carried out using TensorFlow's Keras API, a powerful tool for building and training deep learning models. To streamline our methodology, we created a custom class named CNNTraining, which encompassed essential functions such as hyperparameter initialization--setting the learning rate, number of epochs, batch size, and maximum sequence length--as well as mechanisms for storing training history and performance metrics.  
The dataset was sourced from CSV files stored on Google Drive, enabling easy access and management. We employed Keras's Tokenizer for the text tokenization process, which converted the articles into sequences of integers based on word frequency [43]. Following tokenization, we applied the pad_sequences function to standardize the input length across all samples, ensuring compatibility with the neural network. The architecture of our CNN included an embedding layer to transform word indices into dense vector representations, followed by a 1D Convolutional layer designed to extract local features from the text. This was complemented by a Global Max Pooling layer to reduce dimensionality and capture the most salient features, and two dense layers, one with a ReLU activation function for hidden representations and another with a sigmoid activation function for outputting probabilities of class membership (Figure 2).  
The model was compiled using the Adam optimizer, known for its efficiency in training deep learning models, and the binary cross-entropy loss function, which is suitable for binary classification tasks. The training process was executed over three epochs with predefined batch sizes, and validation metrics, such as loss and accuracy, were monitored throughout the training to assess model performance. Upon completion of the training phase, we assessed the model's performance by utilizing the validation dataset, which allowed us to gauge its accuracy and effectiveness in classifying news articles. Following this evaluation, the trained model was then employed to make predictions on the test set, providing insights into its ability to generalize to unseen data. This two-step evaluation process ensured that we not only understood how well the model performed on the data on which it was trained, but also how reliably it could classify new articles, further validating its practical applicability in real-world scenarios.
# Slotted ALOHA on LoRaWAN-Design, Analysis, and Deployment
## 10 Conclusions  
We have presented an analysis of the LoRaWAN protocol focused on channel throughput and issues associated with its scalability. Moreover, we have implemented a reliable synchronization method, used to develop an S-LoRaWAN protocol suitable for low-cost and low-power IoT devices. This approach gives a theoretical \(2\times\) network throughput improvement, but in high traffic set-up, the measured improvement is up to 5.8\(\times\) with a demonstrated reduction of packet collisions of 26% in a real-life deployment with 24 nodes operating for hours. The overall overhead of the proposed S-LoRaWAN is only eight bytes in the downlink packet, with a negligible consequence in term of power consumption, which is a fundamental factor for battery operated devices. Finally, our S-LoRaWAN does not require any change to the LoRaWAN software stack and can be deployed on top of unmodified LoRaWAN firmware.  
**Supplementary Materials:** The LoRa data rate calculator is available online at [[http://www.rfwireless-world.com/calculators/LoRa-Data-Rate-Calculator.html](http://www.rfwireless-world.com/calculators/LoRa-Data-Rate-Calculator.html)]([http://www.rfwireless-world.com/calculators/LoRa-Data-Rate-Calculator.html](http://www.rfwireless-world.com/calculators/LoRa-Data-Rate-Calculator.html)); the LoRaWAN stack is available online at [[https://www.st.com/en/wireless-connectivity/lorawan-technology.html](https://www.st.com/en/wireless-connectivity/lorawan-technology.html)]([https://www.st.com/en/wireless-connectivity/lorawan-technology.html](https://www.st.com/en/wireless-connectivity/lorawan-technology.html)).  
**Author Contributions:** [PERSON] proposed the idea of a Slotted LoRaWAN. [PERSON] and [PERSON] performed the experiments and the analysis of the results. The article was written by [PERSON] and [PERSON], with the assistance of [PERSON] on the revision of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The end nodes used to evaluate the S-LoRaWAN in this paper were designed by [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(T_{\text{b}}\) [ms] & 50 & 100 & 150 & 200 & 250 & 300 \\ \hline N slot errors & 328 & 27 & 20 & 6 & 5 & 5 \\ Success rate [\%] & 74 & 97 & 98 & 99 & 99 & 99 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The number of slots failed respect to tolerance interval shrinkage.  
Figure 14: **(a)** Synchronization error in ms respect to the beginning of each slot. The maximum error allowed is 400 ms, which is exceeded five times. Two overflows are generated from end node reboot due to firmware issues; **(b)** number of slots overlapped as a function of tolerance interval length.
## 3 Results and Discussion
### Air Analysis
#### 3.2.1 Case Selection Using Vanadium (V) Concentration
Furthermore, the Mn and Fe concentrations (Figure 7) were distributed between Fe = 3.8 \(\times\) Mn and Fe = 43 \(\times\) Mn and bisected near the two lines, with an r of 0.74; however, in SCs, r was 0.92 and the Mn and Fe ratio converged to 1:3.8. Especially, when the airflow affected by the industrial complex was extracted using V, the Mn/Fe ratio converged on 1:3.8, and those concentrations were distributed adjacent to the 1:3.8 line, and data near the 1:43 line had been removed. The linear regression analysis of the concentrations of the four heavy metals between ALL and SCs showed that the _p_-values were <0.05, thereby implying that they were statistically significant. Considering these variations in the heavy metal concentrations, we conclude that the air inflow to the air quality monitoring station of the SC is affected by the specific emission sources.
## 6 Conclusions  
Significant progress has been made in detecting and mapping slums. The resulting data is invaluable to policymakers in designing urban improvement initiatives. However, the physical information obtained from RSI has limitations in describing the overall condition of the urban environment, such as building size, population density, roofing material type, and street network pattern. Moreover, other parameters related to the quality of residential areas, such as the type of building material and drainage system, are crucial for identifying slums, but are difficult to measure using RSI.  
To address this shortcoming, street view imagery (SVI) can serve as an effective complement, especially if these two data sources are integrated. In the Bandung City case study, the integration of RSI and SVI data demonstrated better results compared to the use of either data separately. Our experiment also revealed that dataset quality and network architecture play an important role in improving the accuracy of the analysis. The FCN-DK network used in this study showed significant improvement in recall, F1 score, and intersection over union (IoU) metrics. Furthermore, further research is needed to explore more efficient dataset collection and integration methods, as well as to find effective ways to interpolate the SVI classification information into the broader spatial coverage provided by RSI.  
Conceptualization, K.R.S., M.A., M.C.W.A. and I.A.D.; Methodology, M.A.; Software, K.R.S. and M.A.; Validation, M.C.W.A.; Formal analysis, I.A.D.; Investigation, K.R.S.; Resources, I.A.D.; Data curation, K.R.S.; Writing--original draft, K.R.S.; Writing--review & editing, I.A.D.; Supervision, M.A., M.C.W.A. and I.A.D.; Project administration, I.A.D.; Funding acquisition, I.A.D. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request. The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
NDRI Normalized Difference Built-up Index NDVINormalized Difference Vegetation Index NDWINormalized Difference Water Index NDMINormalized Difference Moisture Index NDBaINormalized Difference Bareness Index MNDWIModified Normalized Difference Water Index EVIEnhanced Vegetation Index SAVISoil Adjusted Vegetation Index UIUrban Index FCNFully Convolutional Network FCN-DK Fully Convolutional Neural Network with a Dilated Kernel
## 3 Results and Discussion
### Morphology  
Figure 2 provides a characteristic SEM image of the pure BST nanocrystalline powder, with particle size in the range of a few \(\mu\)m.  
By observing the SEM images provided in Figures 3-5, corresponding to composites with 30 wt.% and 20 wt.% BST, respectively, it becomes apparent that in both cases, the BST additive has been successfully distributed within the PANI matrix, either as highly dispersed particles or as larger BST agglomerates up to tens of \(\mu\)m in size. In addition, BST-free areas could also be visualized. Moreover, it seems there is no clear difference in the morphological characteristics of the samples processed at higher (Figure 3) and lower (Figure 4) temperatures. Conclusively, the SEM analysis verified the successful incorporation of BST within the polymer (PANI) matrix, while the presence of BST agglomerates suggested that highly homogeneous PANI-BST composites in which the inorganic BST additives are present as individual, non-agglomerated particles cannot be obtained via simple mechanical mixing.  
Furthermore, Figure 5a-e with EDS mapping (20 kV, standardless ZAF-matrix corrected method) provides information on the distribution of the BST additive (as seen from the secondary electron detector (SE) and back-scattered electron detector (BSE) images), whereas in Figure 5f, the elemental contrast between the polymer matrix and the additive can be clearly observed. Additionally, the formation of cracks in the samples is apparent, and it could be attributed to either the cooling process applied after pellet formation or to the sample preparation prior to the measurements. Apparently, the presence of both BST agglomerates and cracks within the composites may affect their thermoelectric properties.  
Figure 3: SEM (SE) images of sample 30-T80-P2-D75 (processed at 80 \({}^{\circ}\)C) with (**a**) 2 k and (**c**) 10 k magnification and corresponding BSE images with (**b**) 2 k and (**d**) 10 k magnification.  
Figure 2: SEM image of the pure BST nanocrystalline powder (secondary electron (SE) image; 10 k magnification).
## 3 Results  
### Spatiotemporal Changes in Cropland CWUE, GPP, NPP, and ET  
#### 3.1.1 Spatiotemporal Changes in Cropland CWUE  
Figure 3 illustrates the spatial distribution and spatiotemporal trends of the cropland CWUE in Henan Province from 2001 to 2019. During this period, the multi-year average values of the CUE and WUE\({}_{\text{NPP}}\) were predominantly high in the western region and low in the northeastern region, while the WUE\({}_{\text{GPP}}\) exhibited higher average values mainly in the central and southwestern regions. Overall, the cropland CWUE in Henan showed a primarily decreasing trend between 2001 and 2019, with the CUE, WUE\({}_{\text{NPP}}\), and WUE\({}_{\text{GPP}}\) declining over approximately 78.26%, 82.13%, and 76.77% of the cropland area, respectively. Significant reductions in the CUE and WUE\({}_{\text{NPP}}\) were mainly concentrated in the southeastern region, accounting for about 22.08% and 19.72% of cropland, respectively, while there were significant decreases in the WUE\({}_{\text{GPP}}\) over approximately 11.36% of the area. Conversely, the proportion of cropland experiencing a significant increase in CWUE was minimal, with the CUE, WUE\({}_{\text{NPP}}\), and WUE\({}_{\text{GPP}}\) rising over only about 1.71%, 1.57%, and 1.18% of the cropland area, respectively.  
Figure 3: Spatial distribution and spatiotemporal change dynamics of cropland CWUE in Henan Province from 2001 to 2019. CUE mean (**a**), WUE\({}_{\text{NPP}}\) mean (**b**), WUE\({}_{\text{GPP}}\) mean (**c**), CUE annual mean change dynamics (**d**), WUE\({}_{\text{NPP}}\) annual mean change dynamics (**e**), and WUE\({}_{\text{GPP}}\) annual mean change dynamics (**f**).
## 3 Methods
### Data Processing Flow  
The data were processed to be prepared as machine learning input. The flow encompassed several steps: signal filtering, scaling, movement event exporting, and feature extraction. Following the feature extraction process, two mechanisms were employed to evaluate the performance of machine learning models. The first mechanism was inter-subject cross-validation, hereinafter referred to as ISCV, which consisted of a data resampling process to address imbalanced dataset conditions. Subsequently, the data were directly deployed in machine learning after the feature extraction step. Another mechanism that was employed is data-scaled inter-subject cross-validation, hereinafter referred to as DS-ISCV, which added data scaling before the classification process. The signal filtering process until feature extraction was performed individually for each participant's data. Subsequently, the classification processes of both mechanisms were performed using inter-subject cross-validation. Finally, we observed and analyzed the classification outcomes of both mechanisms for each FMA-based movement task. The data process flow is shown in Figure 2.
# Contrasting Dual Transformer Architectures for Multi-Modal Remote Sensing Image Retrieval
## 3 Experimental Results and Discussion
### Comparisons to State-Of-The-Art RS Methods
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Approach**} & \multicolumn{3}{c}{**Text Retrieval**} & \multirow{2}{*}{**mR**} & \multicolumn{3}{c}{**Image Retrieval**} & \multirow{2}{*}{**mR**} \\ \cline{2-2} \cline{5-8}  & **R@1** & **R@5** & **R@10** & & **R@1** & **R@5** & **R@10** \\ \hline SAM t-i [30] & 9.60 & 34.60 & 55.80 & 33.53 & 5.80 & 32.70 & 48.10 & 30.57 \\ VSE++ [31] & 24.4 & 53.45 & 67.24 & 48.36 & 6.21 & 33.56 & 51.03 & 30.27 \\ MTFN [33] & 20.69 & 51.72 & 68.97 & 47.13 & 13.79 & 55.51 & 77.59 & 48.05 \\ SCAN [32] & 20.69 & 55.17 & 67.24 & 47.7 & 15.52 & 57.59 & 76.21 & 49.77 \\ AMFMN-soft [29] & 20.69 & 51.72 & 74.14 & 48.85 & 15.17 & 58.62 & 80.00 & 51.26 \\ AMFMN-fusion [29] & 24.14 & 51.72 & 75.86 & 50.57 & 14.83 & 56.55 & 77.89 & 49.76 \\ AMFMN-sim & 29.31 & 58.62 & 67.24 & 51.72 & 13.45 & 60.00 & 81.72 & 51.72 \\
**Ours** & 26.76 & 57.59 & 73.53 & 52.62 & 24.95 & 57.44 & 72.32 & 51.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison between the state-of-the-art retrieval methods in the Sydney dataset in terms of R@K. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Approach**} & \multicolumn{3}{c}{**Text Retrieval**} & \multirow{2}{*}{**mR**} & \multicolumn{3}{c}{**Image Retrieval**} & \multirow{2}{*}{**mR**} \\ \cline{2-2} \cline{5-8}  & **R@1** & **R@5** & **R@10** & & **R@1** & **R@5** & **R@10** \\ \hline Bi-LSTM [28] & 19.02 & 55.25 & 71.72 & 48.66 & 22.95 & 59.52 & 77.23 & 53.23 \\ Triplet [21] & 12.55 & 41.62 & 62.09 & 38.75 & 12.55 & 39.53 & 59.53 & 37.20 \\
**Ours** & 24.55 & 65.66 & 80.60 & 56.93 & 24.08 & 66.04 & 80.78 & 56.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison between the state-of-the-art retrieval methods in the TextRS dataset in terms of R@K.
# Design and Optimization of Non-Coplanar Orbits for Orbital Photovoltaic Panel Cleaning Robots
## 2 Construction of Models
### Modeling and Simulation of Non-Coplanar Orbits  
According to the Bezier curve equations, both before and after optimization, the 3D models of the photovoltaic (PV) panel and the non-coplanar orbit were created and imported into ADAMS for simulation to verify the passing ability. Figure 9A illustrates the non-coplanar track 3D model that connects the PV panels, while Figure 9B depicts the simulation of the spanning motion phase of the cleaning robot, which includes three scenarios: (a), (b), and (c). By observing the simulation animation, it is evident that the cleaning robot spanning the non-coplanar track does not experience jamming or falling off the track. Furthermore, it successfully executes the spanning motion in a complete and smooth manner.  
Figure 8: The optimization effects: (**a**) optimization results of Bezier curve 1; (**b**) optimization results of Bezier curve 2.
## 3 Process for Training a Neural Network
### A Supervised Learning Algorithm  
Given the obtained data set, Definition 3.1 is transformed into a supervised learning task, which is solved by Algorithm 1. Firstly, we determined the relevant parameters of the supervised learning algorithm, such as learning rate, iteration times, batch size, etc. Secondly, we randomly initialized the weights \(W_{i}\) and thresholds \(b_{i,j}\). Thirdly, to balance memory efficiency and memory capacity, the training data set was divided into several batches of elements. Fourthly, we started an iterative process in which the network parameters were continuously updated until the loss value dropped to an acceptable value.  
```
0: Collected human database \(\Lambda\), cross entropy loss function (7), initial network weights \(W_{i}\) and thresholds \(b_{i,j}\), total number of training data \(\eta\), batch size \(n_{batch}\), and iteration epoch number \(n_{epoch}\).
0: A trained model with \(W_{i}\) and \(b_{i,j}\).
1:initialization: Set training epoch number \(i=1\).
2: Randomly select \(\eta\) elements of \(\Lambda\) to a training set \(\Lambda_{train}\) and the rest to a testing set \(\Lambda_{test}\).
3: Cut \(\Lambda_{train}\) into \(\text{int}(\eta/n_{batch})\) batches of elements with batch size \(n_{batch}\).
4:while\(i\leqslant n_{epoch}\)do
5:for\(j\leqslant\text{int}(\eta/n_{batch})\)do
6: Calculate the sum of the cross entropy loss (7) of all data in the \(j\)-th batch.
7: Compute the gradients of \(W_{i}\) and \(b_{i,j}\) with respect to the obtained sum.
8: Perform Adaptive Moment Estimation (Adam) gradient descent training to update \(W_{i}\) and \(b_{i,j}\) according to loss back-propagation algorithm.
9:endfor
10: Calculate the accuracy rates of the updated model in \(\Lambda_{train}\) and \(\Lambda_{test}\) by Equation (16).
11: Set \(i=i+1\) to the next iteration.
12:endwhile
13:return The trained model with \(W_{i}\) and \(b_{i,j}\).
```  
**Algorithm 1** Training process for neural network model  
To evaluate the training effect of the neural network, the output accuracy of the training network on \(\Lambda_{train}\) and \(\Lambda_{test}\) were computed as  
\[\phi_{train}=\frac{\sum_{\lambda\in\Lambda_{train}}\lambda^{acc}}{\text{len}( \Lambda_{train})},\ \phi_{test}=\frac{\sum_{\lambda\in\Lambda_{test}}\lambda^{acc}}{\text{len}( \Lambda_{test})}, \tag{16}\]  
where \(\lambda_{i}\) is a mark used to determine whether the character corresponding to the maximum value of neural network output was consistent with the label as  
\[\lambda_{i}^{acc}=\begin{cases}1,\ \text{arg}\max z_{i}=l^{i},\\ 0,\ \text{else}.\end{cases} \tag{17}\]  
**Remark 1**.: _Since the elements in testing set \(\Lambda_{test}\) and the training set \(\Lambda_{train}\) are completely independent of each other, the evaluation results are able to examine the training performance in terms of overfitting problems._
## 4 Hardware Design
### Energy Consumption  
Minimizing the energy consumption of our platform is essential, so a dynamic power-management technique is a feature of our system. This technique dynamically reconfigures the system to provide the requested services and performance levels with a minimum number of active components or a minimum activity level on such components. We chose the SP6201 to dynamically control the RF module. The SP6201 is CMOS low dropout regulator (LDO), which offers an extremely low quiescent current. The current of the nRF905 and the SP485E is zero in the sleep mode using the SP6201. Table 3 shows the average current of a data collector under different modes.
## 2 Experimental Section  
### Catalyst Preparation  
Four Mo/ZSM-5 (6 wt. % Mo) were prepared using a commercial ZSM-5 sample with Si/Al molar ratio of 10 (TZP302A, supplied by TRICAT Catalysts Technologies, acquired by Sud-Chemie, now Clariant, [[https://www.clariant.com](https://www.clariant.com)]([https://www.clariant.com](https://www.clariant.com)), accessed on 11 June 2021) as acid support. This zeolite, originally in its ammonium form, has a heterogeneous crystal size distribution (see Figure S1), an average crystal size close to 1 um, and is denoted as Z5. The Mo-loaded catalysts have been prepared following two different metal incorporation procedures, e.g., incipient wetness impregnation (IW) and solid-state ion exchange reaction (SSR). The metal precursor employed for the incipient wetness impregnation is ammonium heptamolybdate tetrahydrate (99% Merck, (NH\({}_{4}\))\({}_{6}\)Mo\({}_{7}\)O\({}_{24}\)-4 H\({}_{2}\)O). After impregnation, the catalyst was dried at 373K overnight followed by a calcination in air at 773 K for 3 h, under static conditions in a muffle (MoZ5-IW-SC) or under dynamic conditions in a fixed-bed reactor (MoZ5-IW-DC). For the solid state reaction, the zeolite was physically mixed with molybdenum (VI) oxide (\(\geq\)99.5% MoO\({}_{3}\), Sigma-Aldrich, [[https://www.sigmaaldrich.com](https://www.sigmaaldrich.com)]([https://www.sigmaaldrich.com](https://www.sigmaaldrich.com)), accessed on 11 June 2021) and calcined in air at 723 K for 10 h in a muffle (Mo-Z5-SSR-SC) or in a fixed-bed reactor (MoZ5-SSR-DC). Part of the two catalysts prepared by SSR has been pre-treated in nitrogen (15 mL/min), by heating from room temperature (Tr) to 923 K at a rate of 10 K/min and cooling down to Tr. These nitrogen-treated samples have been labelled as MoZ5-SSR-SC-N\({}_{2}\) and MoZ5-SSR-DC-N\({}_{2}\).