esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Estimation Retrieval of Aerosol Fine-Mode Fraction from Ground-Based Sky Light Measurements
## 4 Results
### Retrieval from Experimental Data
#### 4.2.3 Fitting Residuals  
The OE inversion algorithm finds the best fit between the forward simulations and measurements at all of the wavebands. The disagreement between the measurements and the simulations with best inversion results can be characterized by the fitting residual, which is defined as,  
\[\delta(\lambda)\ =\ (I_{\rm fit}(\lambda)-I_{\rm meas}(\lambda))/I_{\rm meas}( \lambda). \tag{12}\]  
The spectral residual errors are the mean value of \(\delta(\lambda)\) for all measurement data. Although there were slight differences between different measurement channels, we set the same weighting in the retrieval. Therefore, when the cost function reached a minimum value, the fitting residuals were not the same at different wavelengths. The spectral residual errors were 9.28%, 1.45%, 8.14%, 4.42%, and 1.46% for 490, 550, 670, 870, and 1610 nm, respectively. Obviously, the errors for 490 and 670 nm were much larger than the other wavelengths, due to the calibration error and the forward model error in these  
Figure 7: The scatter plot of retrieved (**a**) AOD at 500 nm, (**b**) FMF\({}_{\rm o}\) at 500 nm, and (**c**) Angström Exponent compared to the AERONET direct solar products. The solid line, dotted line, and dashed line represent the fit line, 1:1 line, and the expected error line (\(\Delta x=\pm 0.05\pm 0.15x\)), respectively.  
wavelengths. The total residual error was the average of the five wavebands, which was as low as 4.95%, showing a good fit between the final model results and the real observations.
# Multiscenario Simulation and Prediction of Land Use in Huaibei City Based on CLUE-S and PLUS Models
## 3 Results
### Multiscenario Simulation of Land Use Change in Huaibei City, 2020-2025
#### 3.4.2 Multiscenario Simulation under PLUS Model
Water area increased the most with 972.81 hm\({}^{2}\) and the area of building land decreased by 1157.13 hm\({}^{2}\). Under scenario FEP, the construction land is clustered inward and the internal grassland is occupied. The expansion of forestland in the northwest partly encroached on the construction land. The farmland increases by 1727.91 hm\({}^{2}\) and the forestland increased by 86.22 hm\({}^{2}\). The construction land decreases the most by 2001.96 hm\({}^{2}\). Under the FEP scenario, both farmland and forestland are protected, food security is guaranteed, and the ecological environment is preserved and improved. The expansion of construction land is restrained and the development sustainability of the urban is stronger compared to the NG scenario. Additionally, the area of the water is increased in all four scenarios. Table 6 below shows the area of each LU type under the four scenarios compared with the status of LU in 2020.
## 6 EEG Signal Pre-Processing and Feature Extraction
### EEG Signal Pre-Processing
#### 6.1.3 EEG Channels Selection  
Optimizing the number of EEG channels used for recognition is crucial to enhancing recognition performance. This involves experimenting with various combinations of EEG channels, conducting tests, and selecting the combination that provides the best results. Additionally, the impact of different algorithms and methods of feature extraction and selection on performance can be evaluated to further refine recognition accuracy.  
The placement of sensors on the scalp is vital for minimizing artifacts. Some studies utilize all available sensors in a dataset, regardless of the number [79], while others focus on optimizing the channels to reduce noise and increase system efficiency [104].
## 3 More Recent Technology: Non-Thermal Plasma  
### Non-Thermal Plasma Overview  
Plasma refers to the fourth state of matter: ionised gases. In this phase, electrons possess sufficient energy to escape from the atom. This can be achieved by using high temperatures to energise electrons (thermal plasma) or by using an electric field at lower temperatures (non-thermal plasma). The generation of non-thermal plasma requires significantly less energy [35].
# Federating Medical Deep Learning Models from Private Jupyter Notebooks to Distributed Institutions
## 3 Notebook Federator
### Architecture  
The main goal of this work is to simplify the process of federation setup and make it less burdensome and time-consuming for domain-specific users such as AI experts and pathologists. In this work, we propose our FL setup solution, as depicted in Figure 1, illustrating an overview of its architecture. It comprises the following five steps:  
1. **Model and aggregator definition:** Suppose a Jupyter Notebook user, e.g., an AI expert, develops a cutting-edge model architecture, aggregation function, and other machine learning-related code fragments for the FL training process.  
2. **Create FL pipeline building blocks:** For flexibility purposes when it comes to handling collaborating institutions and updates, as well as reducing the complexity of FL deployment and execution, we propose to adopt one Jupyter Notebook extension named component containerizer on the local experimental environment, e.g., NaaVRE [8], to encapsulate FL pipeline building blocks as reusable services, such as model and aggregator.
3. **Build and push job automation:** Once the model and aggregator are ready, with the workflow of the GitHub project and docker registry, such components can be automatically built and pushed to the Docker Hub.
4. **Deliver FL building blocks to distributed resources:** The model can be delivered to distributed client users worldwide, i.e., geographically distributed clients in institutions A, B, and C, by pulling the model to local sites. At the same time, the aggregator can be easily delivered to the cloud infrastructure by pulling the aggregator to the cloud virtual machine.
5. **Federation setup:** Once the aggregator is assigned to the cloud infrastructure, e.g., a cloud virtual machine, it is easy to start the Docker container with specific IP and port number. For geographically distributed users, i.e., clients across wide-area institutions, the local AI model training process mainly contains (A) feeding with local data, (B) assigning suitable computation, and (C) starting the model training with specific data and computation (e.g., GPU resources) on site. By this time, the FL starts.  
Specifically, the proposed solution enables a standard centralized AI environment to a decentralized (e.g., raw data) federated setup, flexibly supporting \"using AI at the edge\". In such a setting, only the models' general characteristics are shared. The Notebook Federator provides a Jupyter notebook template that gives the overall structure of the different functions and classes that will be further integrated into the FL pipeline code ([[https://github.com/QCDIS/Notebook-Federator](https://github.com/QCDIS/Notebook-Federator)]([https://github.com/QCDIS/Notebook-Federator](https://github.com/QCDIS/Notebook-Federator)), accessed on 2 January 2023).
## 3 Materials, Methods, and Findings
### DistilBERT  
DistilBERT is configured as a lighter version of BERT. For binary classification, the model was trained with parameters num_labels = 2 and max_len = 128. The model has the advantage of a low hardware requirement and a fast training process. The 'binary_crossentropy' loss function and Adam optimization algorithm were used during training. Figure 11 shows the complexity matrix of the DistilBERT algorithm.  
The model correctly classified 94 instances in the \"no verbal violence\" class and incorrectly labelled only 8 instances as \"verbal violence present\" (false positive). In the \"verbal violence present\" class, the model correctly predicted 101 instances and incorrectly labelled only 7 instances as \"verbal violence absent\" (false negative). These results show that the DistilBERT model provides a balanced performance in both classes. The low false negative rate (6.48%) and the low false positive rate (7.84%) indicate that the model can reliably discriminate both the \"no verbal violence\" and \"verbal violence present\" classes. Moreover, since the accuracy rate (92.86%) and sensitivity of the model are high, DistilBERT offers a similar performance to BERT with a lower computational cost.
## 3 The Proposed Optimization Algorithm  
### User Association  
With the fixed receive beamforming vector \(\mathbf{v}_{m,k}\) and transmit power \(p_{m,k}\), problem (7) is reduced to the following form  
\[\begin{split}\max_{\mathbf{a}_{m,k}}&\ R_{sum}=\sum_{k \in\mathcal{K}}R_{k}\\ \text{s.t.}&\ \text{C2,C3,C4,C5,}\end{split} \tag{8}\]  
which is a non-convex integer programming that is difficult to solve. Here, we temporarily ignore the QoS constraint C5 and design an approach to determine user association.  
Preparation: All the UEs establish their preference lists \(\{Pre_{k}\}\) in accordance with the descending order of the channel gain towards the APs.  
Judge and Join-in: Define \(\mathcal{X}_{1}\) as the set of UEs that have already accessed an AP and \(\mathcal{X}_{2}\) as the set of UEs that have not yet accessed any AP. Each UE in \(\mathcal{X}_{2}\) sequentially sends the request signal to the APs in order of priority based on \(\{Pre_{k}\}\). The AP determines whether the inclusion of the current user would cause the sum rate to exceed the backhaul link rate. If not, the AP allows the current user access. This judgment step is to ensure that the constraint C4 in (8) is satisfied. However, a challenging problem is that the subsequent UE association strategy, receive beamforming design, and power allocation can all affect the achievable sum rate of the current AP. To address this, an approximate upper bound on the achievable sum rate is derived as a criterion for making the decision.  
Let \(\mathbf{\alpha}_{m}=[\alpha_{m,1},\alpha_{m,2},\cdots,\alpha_{m,K}]^{T}\) and define \(\mathbf{H}_{m}=[\mathbf{h}_{m,1},\dots,\mathbf{h}_{m,K}]\text{diag}(\mathbf{ \alpha}_{m})\) as the channel matrix from the UEs served by AP \(m\) to AP \(m\), where \(\text{diag}(\mathbf{\alpha})\) represents a diagonal matrix constructed from the vector \(\mathbf{\alpha}\). Denote the interference channel matrix from the UEs served by AP \(i\), \(i\in\mathcal{R}\backslash m\) to AP \(m\) by  
\[\overline{\mathbf{H}}_{m}=[\mathbf{H}_{1}\text{diag}(\mathbf{\alpha}_{1}),\cdots, \mathbf{H}_{m-1}\text{diag}(\mathbf{\alpha}_{m-1}),\mathbf{H}_{m+1}\text{diag}( \mathbf{\alpha}_{m+1})\cdots,\mathbf{H}_{M+1}\text{diag}(\mathbf{\alpha}_{M+1})], \tag{9}\]  
then, according to the minimum mean square error (MMSE) criterion, the upper bound of the achievable sum rate for AP \(m\) is  
\[R_{m}^{up}=\log\det\left(\mathbf{I}+\frac{\mathbf{H}_{m}\mathbf{P}\mathbf{H} _{m}^{H}}{\mathbf{H}_{m}\mathbf{P}\mathbf{H}_{m}^{H}+\sigma_{0}^{2}\mathbf{I }}\right), \tag{10}\]  
where \(\mathbf{P}=\text{diag}(\mathbf{p})=\text{diag}\Big{(}[p_{1},p_{2},\cdots,p_{K} ]^{T}\Big{)}\) and \(p_{k}=\sum\limits_{m\in\mathcal{R}}\alpha_{m,k}p_{m,k}\). The value of \(p_{m,k}\) is set to the maximum, and \(\alpha_{m,k}\) for \(k\in\mathcal{X}_{2}\) is set to 0.  
After the last AP reaches its saturation rate, if \(\mathcal{X}_{2}\
eq\emptyset\), the remaining UEs reset their preference lists and repeat the above process.
## 3 Methods  
### In Situ Data Collection  
The water sampling data were taken at the same time as the multispectral images taken on site and further analyzed and verified by SGS (Societe Generale de Surveillance) Ltd., Taiwan. Four water quality parameters (i.e., chlorophyll a concentration, total phosphorus, transparency and dissolved oxygen) were collected based on the water environment monitoring norms. The collected water samples were sent to SGS, Taiwan, for inspection. The test standards of water quality parameters in Taiwan are readily available [35]. For chlorophyll a inspection, the water sample was filtered using glass-fiber filter paper. The filter paper was then ground with a tissue grinder in 90% acetone solution to extract chlorophyll a. Subsequently, the extract absorbance was measured with a spectrophotometer to calculate the chlorophyll a concentration in the water sample. The detection method for the total phosphorus is as follows: the water sample was digested with sulfuric acid and persulfate to convert phosphorus into orthophosphate; ammonium molybdate and potassium antimony tartrate were added to react with orthophosphate to generate phosphomolybdic acid, which was reduced to blue by ascorbic acid; and finally, the color complex molybdenum blue was quantified by measuring its absorbance at 880 nm wavelength using a spectrophotometer. Transparency refers to the degree to which light can penetrate water. This method uses a Secchi disk or tube to measure the distance from the water surface to its visibility, which is the transparency of the water body, known as Secchi transparency. The dissolved oxygen electrode method uses a selective membrane to allow the dissolved oxygen in the water to pass through to isolate it from liquids, ions and other interfering substances. The molecular oxygen passing through the membrane is then reduced at the electrode cathode. The current intensity generated in a steady state is proportional to the dissolved oxygen concentration; thus, the current value can be converted to the dissolved oxygen concentration in the water. Table 1 presents the detailed geo-temporal information and testing results with respect to six sample-collecting sites in the pond. The sampling process of three experimental sets was completed on different dates and times, respectively. As the pond is small and we assume that water quality parameters are considered stably constant during a short period, the sampling date/time for S1\(\sim\)S6 in each sampling process, thus, can be assumed the same as well.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sampling** & **Coordinate** & **Sampling** & **Chlorophyll a** & **Total Phosphorus** & **Transparency** & **Dissolved Oxygen** \\
**Point** & **Position** & **Date/Time** & **(\(\mu\)g/L)** & **(\(\mu\)g/L)** & **(\(\mu\)g/L)** & **(m)** & **(mg/L)** \\ \hline S1 & 24\({}^{\circ}\)08\({}^{\prime}\)38\({}^{\prime\prime}\) N & 5/15 AM 10:00 & 69.8 & 94 & 0.47 & 16.7 \\
## 1 Introduction
In this work, the authors repeatedly (with average time interval 32.50 months) acquired sustained vowel /a/ in 32 female and 48 male PD patients (age in session 1: 66.28 \(\pm\) 8.11 years; PD duration in session 1: 6.10 \(\pm\) 4.63 years; UPDRS III in session 1: 20.16 \(\pm\) 10.96; UPDRS III in session 2: 19.58 \(\pm\) 8.29). The voice was quantified by jitter, shimmer, noise-to-harmonic ratio, and mean fundamental frequency. Based on the paired \(t\)-test, the authors identified significant changes in shimmer and noise-to-harmonic ratio. In both cases, the values of these parameters increased. Another interesting finding is that, although some phonatory features significantly changed, UPDRS III was held widely stable over time. The authors provide two possible explanations: (1) voice impairment could be the result of an escalation of axial dysfunction too subtle to be mirrored by UPDRS III; (2) alterations of speech parameters could becompletely independent of motor performance that may be based upon non-dopaminergic mechanisms. Inconsistencies in terms of the L-dopa effect on HD are further discussed in [PERSON] et al. [9]. To sum it up, although the scientific community frequently addresses phonation in association with HD (especially when diagnosing or assessing PD), to the best of our knowledge, there is only one study that focuses on HD phonatory disorders from a longitudinal perspective. Moreover, the work deals with the analysis of phonation just partially, it considers only the sustained vowel /a/, and it does not explore a possibility of PD progress prediction based on a combination of acoustic analysis and machine learning. Therefore, in the frame of our two-year follow-up study, we are going much further with the following aims:  
1. to identify phonatory acoustic features at baseline that are significantly correlated with changes in various clinical rating scales,
2. to investigate relationship between changes in the phonatory acoustic features and the clinical rating scales after the two-year follow-up,
3. to establish mathematical models that will estimate the change in clinical rating scales based on the change in acoustic measures,
4. to compare results based on five vowels: /a/, /e/, /i/, /o/, /u/. The rest of this article is organized as follows: Section 2 describes a dataset of PD patients as well as methodology in terms of acoustic analysis, statistical analysis and machine learning. Results are reported in Section 3 and consequently discussed in Section 5. Finally, conclusions are given in Section 4.
## 4 Modeling
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Paroxysm 1**} & \multicolumn{4}{c}{**Paroxysm 2**} & \multicolumn{4}{c}{**Paroxysm 3**} \\ \hline  & \multicolumn{2}{c}{**Measured**} & \multicolumn{2}{c}{**Predicted**} & \multicolumn{2}{c}{**Measured**} & \multicolumn{2}{c}{**Predicted**} & \multicolumn{2}{c}{**Measured**} & \multicolumn{2}{c}{**Predicted**} \\ \hline  & x & y & x & y & x & y & x & y & x & y & x & y \\ PLC & 0.27 & 1.01 & \(-\)0.41 & 2.35 & 0.19 & 0.82 & \(-\)0.05 & 1.95 & 0.05 & 0.50 & 0.98 & 0.17 \\ CDV & 0.67 & \(-\)0.22 & 0.44 & \(-\)0.49 & 0.60 & \(-\)0.20 & 0.35 & \(-\)0.35 & 0.38 & \(-\)0.08 & 0.20 & \(-\)0.26 \\ CBD & 0.69 & 0.21 & 0.20 & 0.10 & 0.54 & 0.26 & 0.13 & 0.07 & 0.35 & 0.10 & 0.29 & 0.05 \\ MNR & 0.21 & 0.45 & 0.14 & 0.37 & 0.21 & 0.45 & 0.10 & 0.24 & 0.11 & 0.23 & 0.58 & 0.75 \\ PDN & 0.44 & 0.44 & 1.88 & 1.95 & 0.27 & 0.29 & 1.19 & 1.01 & 0.08 & 0.10 & 0.92 & 0.08 \\ DAM & 0.31 & 1.17 & 0.04 & 0.27 & 0.13 & 0.89 & 0.03 & 0.18 & 0.07 & 0.50 & 0.31 & 0.78 \\ MGL & \(-\)0.72 & \(-\)0.37 & \(-\)0.25 & \(-\)0.08 & \(-\)0.51 & \(-\)0.35 & \(-\)0.21 & \(-\)0.06 & \(-\)0.36 & \(-\)0.18 & \(-\)0.32 & \(-\)0.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Measured tilt components vs. model predictions for the 3 paroxysms. Tilt data are reported in microrads. The solution converged to an almost vertically dipping (78\({}^{\circ}\)) prolate spheroid, N-S oriented (N185E), located at about 6 km beneath the summit craters, losing about 6.5 million cubic meters during the entire eruptive cycle (Table 3), which is close to the volume (7 million cubic meters) estimated by [2]. This location should represent the original source of magma, that was gradually depleted to feed the shallower single-shot sources, where the gas exsolved and pressurized to charge each paroxysm.
## 2 Materials and Methods
### PSSM Feature Extraction
#### 2.3.2 S-Fpssm  
The S-FPSSM [21] (Sum-Frequency PSSM) method generates a 400-dimensional feature vector derived from the FPSSM matrix by row transformation. In this method, the FPSSM is a matrix where the negative values from the original PSSM have been filtered out (set to zero).  
The S-FPSSM vector is constructed by considering each of the 20 amino acids (rows in a PSSM) across the length of the protein sequence. Each element of the vector \(S_{i}\) is calculated based on the FPSSM and an indicator function that considers amino acid matches. This vector represents a 400-dimensional feature.  
The value of \(S_{i}\) is calculated as follows:  
\[S_{i}=\sum_{j=1}^{L}fp_{ij}\times\delta_{ij}\quad\text{for }1\leq i\leq 20\]where  
* \(L\) is the length of the protein sequence.
* \(fp_{ij}\) represents the element in the \(i\)th row and \(j\)th column of the FPSSM matrix. This means it is the positive part of the PSSM for the \(i\)th amino acid type at position \(j\).
* \(\delta_{ij}\) is an indicator function defined as follows: \[\delta_{ij}=\begin{cases}1&\text{if }r_{j}=a_{i}\\ 0&\text{otherwise}\end{cases}\quad\text{for }1\leq i\leq 20,1\leq j\leq L\]
* \(r_{j}\) represents the amino acid at position \(j\) in the original protein sequence.
* \(a_{i}\) represents the \(i\)th amino acid type in the standard amino acid order. This indicates whether the amino acid at position \(j\) in the protein sequence corresponds to the \(i\)th amino acid type.
## 3 Model Calibration and Optimization
### Genetic Algorithm Optimization Framework
#### 3.2.1 Parameterization Strategy  
The algorithm's parameter configuration (Table 6) was determined through systematic sensitivity analysis. Initial testing with population sizes (5-50) and generations (500-2000) revealed that 10 individuals over 1000 generations achieved optimal balance between convergence stability (coefficient of variation < 2.3%) and computational efficiency (R\({}^{2}\) = 0.96 vs. computational time). This configuration aligns with recent agricultural optimization benchmarks [40] while accommodating the 42-dimensional solution space from seven 6-bit encoded fertilization variables.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Stage** & **T1** & **T2** & **T3** & **T4** & **T5** & **T6** & **T7** \\ \hline DAS & 47 & 77 & 82 & 89 & 96 & 103 & 110 \\ Range (kg/ha) & & & 10–900 & & & \\ \hline \hline \end{tabular}  
* DAS: Days after sowing; Uniform nitrogen application ranges established through preliminary soil N-min tests (0–90 cm depth) and historical yield response curves [41].  
\end{table}
Table 6: Genetic algorithm parameter configuration and operational ranges.  
Figure 5: Structure of the DSSAT + GA algorithm.
# On Blockchain-Enhanced Secure Data Storage and Sharing in Vehicular Edge Computing Networks
## 4 Security Analysis and Performance Evaluation
### Performance Evaluation
#### 4.2.2 Block Generation  
Data sharing flow begins with the vehicles sending or uploading all transactions to RSUs in the edge layer. RSUs use their transaction processors (i.e., LSSC and RPSC) to ensure the integrity of the transaction. Then, preselected RSUs conduct the consensus mechanism to generate the block (which consists of transactions) before storing it into the blockchain network. Here, the block size is equivalent to batch size in our simulation. The term batch size refers to the number of transactions that are in every block of the blockchain. We run multiple tests by changing batch size up to 100 tx/block to evaluate the effect of batch size on throughput. As can be inferred from Figure 5, at the total batch size of 100 tx/block, the throughput reaches 543 tx/s. The throughput is shown to increase linearly over different batch sizes.
## 2 Materials and Methods
### Parking Scenarios  
Three scenarios were defined to represent increasing levels of complexity in vehicle placement and rotation:  
Scenario 1: Parallel Parking Without Rotation (Parking Area 1)  
* Focuses on positioning vehicles in a strictly parallel manner, effectively treating each parking spot as having zero rotational freedom.
* Aims to assess how effectively the algorithms place vehicles when rotation is restricted to a single axis. Scenario 2: Parallel Parking with Limited Rotation Up to 45\({}^{\circ}\) (Parking Area 1)
* Extends the first scenario by allowing limited rotation--up to 45\({}^{\circ}\) from the reference axis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vehicle** & **Actual Dimensions (m)** & **Dimensions with Buffers (m)** \\ \hline Citroen Ami [37] & 1.39 \(\times\) 2.41 & 1.69 \(\times\) 2.61 \\ Volkswagen Crafter [38] & 2.43 \(\times\) 5.99 & 2.73 \(\times\) 6.19 \\ Standard Car [39] & 2.05 \(\times\) 4.80 & 2.35 \(\times\) 5.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vehicle dimensions.  
* Highlights how partial rotation flexibility can improve space utilization and influence algorithmic performance. Scenario 3: Perpendicular Parking with Full Rotation (Parking Area 2)
* Introduces the highest level of complexity by permitting any rotation angle between \(0^{\circ}\) and \(180^{\circ}\).
* Includes additional complexity due to the inability of a Volkswagen Crafter to fit perpendicularly into the parking space.