esa-sceva/satcom-chunk-collection huggingface dataset:
# Trajectory Clustering and k-NN for Robust Privacy Preserving Spatiotemporal Databases
## 2 Materials and Methods
### Classification  
Classification is an unsupervised machine learning approach and concerns the assigning of class labels to a new sample on the basis of a training dataset whose sample class membership is known. The principle behind nearest neighbor method is to find a number of training samples closest in distance to the new point, and predict the class label from these. The number of \(k\) nearest neighbors can be user-defined, constant or varying, based on the local density of points (radius-based neighbor learning). The most common distance measure is Euclidean. The \(k\)-Nearest Neighbor (Algorithm 2) [9] is a non-parametric method and by far the simplest of all machine learning algorithms. The use of \(k\)-NN solely has great calculation complexity. This means that the classification of a new data point needs the calculation of distances between it and all training dataset points, with the ultimate goal choosing the \(k\) nearest neighbors. To overcome this issue, we combine it with a clustering method, namely K-means, which reduces the size of training sets efficiently as well as the computational time of \(k\)-NN. It is worth mentioning that the application of \(k\)-NN inside a cluster has no sense if the cluster size is less than the number of \(k\) nearest neighbors we are looking for, inside it. Hence, the appropriate combination of parameters K, since K influences cluster size, and \(k\) is crucial. Despite the aforementioned advantages, it gives to each labeled sample the same importance for classification, in contrast with what fuzzy classifier considers [10]. Finally, in a recent work [11], the authors described and suggested an efficient method in which kernel fuzzy clustering is combined with harmony search algorithm for scheme classification.  
```
1:Input:X:training dataset, \(Cl_{X}\):class labels of \(X\), \(p\):testing point to classify
2:Method:Compute distances \(d(X_{i},p)\) to every training point \(X_{i}\) and keep the indexes \(I\) of the \(k\) smallest distances.
3:Select the \(k\) labels \(Cl_{X}(I)\)
4:return\(I\) and the majority class \(cl_{b}\) in \(Cl_{X}(I)\)
```  
**Algorithm 2**\(k\)-nearest neighbor.
## 2 Problem Statement
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Crystal** & **THG Matching Angle \(\theta(^{\circ})\)** & \(\Delta_{21}\bar{k}\) (mm\({}^{-1}\))** & \(\chi^{(2)}\) (pm/V) & \(I_{0(\gamma=1)}\) (GW/cm\({}^{2}\)) \\ \hline KDP [62] & 64.97 & 147.403 & \(0.76\sin\theta\sin(2\varphi)\) & 6.06 \\ \hline ADP [62] & 66.58 & 163.755 & \(0.76\sin\theta\sin(2\varphi)\) & 6.177 \\ \hline BBO [62] & 37.48 & 140.934 & \(4.32(\sin\theta-0.07\cos\theta\sin(3\varphi))\) & 0.695 \\ \hline KBBF [63] & 31.16 & 87.371 & \(0.98\cos\theta\cos(3\varphi)\) & 3.78 \\ \hline RBBF [61] & 33.77 & 86.507 & \(0.9\cos\theta\cos(3\varphi)\) & 4.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Phase matching angle \(\theta\) of the process \(\omega(o)+\omega(o)+\omega(o)=3\omega(e)\), dimensional phase mismatching between FW and SH. The second-order susceptibility of some crystals and the incident pulse intensity \(I_{0}\), corresponding to value \(\gamma=1\), in dependence of the laser pulse propagation direction with respect to the crystal axis: (\(\theta\), \(\varphi\)) are spherical coordinates. Phase matching angles are provided for FW length \(\lambda=1064\) nm. Let us notice that the integration limits change on \(\{-\infty,\infty\}\), respectively, if the laser pulse interaction is analysed in the unbounded time domain.
## 4 Findings
### Providing Security Metadata  
Table 2 effectively highlights the key findings of our study, organized by significant themes. From Task-1, the overarching theme identified was 'attack points', where the participants discussed possible attacks on smartphones, electronic health records, gateway, cloud etc. 'Security controls' was the theme for Task-2, where participants discussed on various security mechanisms and existing security practices. For Task 3, we identified two compelling themes: 'topic based security evidence' and'relevant security metadata'. The \"topic-based security evidence\" theme captured broader discussions on security, while \"relevant security metadata\" focused on crucial information that can be effectively recorded in provenance graphs. The following paragraphs will provide concrete examples to illustrate the depth and progression of this vital discussion. While analyzing security attacks in smartphones, EHRs, bluetooth, fitness trackers, and gateways used for communication, the participants shared views on different access control mechanisms as prospective security controls. According to **Expert-7**,  
_\"I think the simplest solution is a digital signature. In every five minutes, data will be aggregated and assigned either by the device itself or from the smartphone, and then it will pass on to the nearest endpoint for the doctors...\"_  
Other experts (expert-6, 8, and 10) raised the concern that digital signatures cannot be easily checked if there is no end-to-end security relationship between the devices and end-users. As a result, we divide our findings from the ongoing discussion into two categories. We first illustrate relevant security metadata observed for trusted computing, encryption mechanisms, policy, and human-centric perspectives.
# NuCap: A Numerically Aware Captioning Framework for Improved Numerical Reasoning
## 4 Experiments
### Counting Performance
#### 4.5.2 Comparative Performance Analysis  
As shown in Table 4, NuCap achieves the highest counting accuracy of 34.18%, outperforming all baseline models. This represents a roughly 3\(\times\) improvement over competitive captioning models such as GRIT and ExpansionNet v2, and a significant margin over large-scale VLMs, including mPLUG-Owl3 (2B: 11.39%, 7B: 13.36%). Among VLMs, BLIP (large) achieves the next-best accuracy at 28.29%, but still lags behind NuCap. Notably, NuCap also attains the highest F1 score (0.342), confirming its ability to both generate numeric tokens more frequently and reduce severe miscounts.  
These results highlight NuCap's core advantage: it bridges the gap between caption fluency and accurate numerical grounding. By combining visually grounded features with count-aware supervision, NuCap addresses a key limitation in existing models. This positions NuCap as a reliable framework for real-world applications requiring numerically informative vision-language generation.
## 2 Materials and Methods
### Method  
#### 2.2.1 Selection of Downscaling Algorithm  
Extreme Gradient Boosting (XGBoost) is an algorithm based on Gradient-Boosted Decision Trees (GBDT) [37]. While sharing the core concept of GBDT, XGBoost incorporates a number of optimizations, making it particularly well-suited for large-scale datasets and high-dimensional features. It is a tree-based ensemble learning algorithm that enhances model performance by optimizing tree structure and node splitting. As a boosting algorithm, XGBoost iteratively builds models, where each iteration re-weights the samples based on the prediction errors from the previous iteration. With each iteration, the residual error decreases, reducing model bias and effectively preventing overfitting. Tree-based models have been widely used in various remote sensing regression tasks, including the spatial downscaling of SIF data [9; 38]. Therefore, in this study, the XGBoost regression model was employed to perform spatial downscaling on the sparsely distributed GOME-2 SIF products.  
To evaluate the performance of the XGBoost model, we also introduced Multivariate Linear Regression (MLR), Random Forest (RF), and MLPRegressor (multilayer perceptron Regressor) as comparative models during training. The accuracy of these regression models reflects the degree to which the selected explanatory variables explain the sample variance, and can be evaluated using metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R\({}^{2}\)), with their formulas defined as follows:  
\[RMSE=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{(\hat{y_{i}}-y_{i})^{2}}}} \tag{2}\]  
\[MAE=\frac{1}{n}{\sum\limits_{i=1}^{n}{|\hat{y_{i}}-y_{i}|}} \tag{3}\]  
\[R^{2}=1-\frac{\sum\
olimits_{i=1}^{n}{(\hat{y_{i}}-y_{i})^{2}}}{\sum\
olimits _{i=1}^{n}{(\hat{y_{i}}-\overline{y_{i}})^{2}}} \tag{4}\]  
where \(\hat{y_{i}}\) represents the SIF predicted value, \(y_{i}\) represents the SIF true value, \(\overline{y_{i}}\) is the sample mean, and \(n\) is the sample size.
## 5 Application of Marine Accident Occurrence Prediction Model and Results
### Training  
All the simulations, including model training and prediction analyses, were performed using Google Colaboratory, which is well suited to ML, data science, and education, and thus, served as a platform for simulating our proposed ML method. The experiments were performed on a machine equipped with GPU NVIDIA V100 and 60 GB of RAM.  
As described in Section 4.4, training was conducted for the related accident-occurrence prediction algorithm on the training set, which accounted for 80% of the SMOTE Data U set. The importance of the derived features is illustrated in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Characteristic** & **Data** & **Characteristic** & **Data** \\ \hline
**Categorical Variables** & **Count** & **Percentage** & **Categorical Variables** & **Count** & **Percentage** \\ \hline \multicolumn{5}{c}{Year} \\ \hline
2022 & 101 & 100.00\% & & \\ \hline Annual sailing distance & & & Number of operations per year & \\ \hline Less than 50 km & 56 & 55.45\% & Less than 800 & 36 & 35.64\% \\ \hline
50 km to 100 km & 13 & 12.87\% & 800 to 1600 & 27 & 26.73\% \\ \hline More than 100 km & 32 & 31.68\% & More than 1600 & 38 & 37.62\% \\ \hline \multicolumn{5}{c}{Operating minutes per sea route} & Number of port calls per sea route & \\ \hline Less than 60 & 23 & 22.77\% & Less than 3 & 22 & 21.78\% \\ \hline
60 to 120 & 36 & 35.64\% & 3 to 5 & 57 & 56.44\% \\ \hline More than 120 & 42 & 41.58\% & More than 5 & 22 & 21.78\% \\ \hline \multicolumn{5}{c}{Passenger capacity} & Gross tonnage & \\ \hline Less than 200 people & 30 & 29.70\% & Less than 300 tons & 46 & 45.54\% \\ \hline
200 to 400 people & 40 & 39.60\% & 300 tons to 500 tons & 34 & 33.66\% \\ \hline More than 400 people & 31 & 30.69\% & More than 500 tons & 21 & 20.79\% \\ \hline \multicolumn{5}{c}{Passenger ship} & Regional sea route & \\ \hline Ship managed by C & 12 & 11.88\% & D sea routes & 15 & 14.85\% \\ \hline Ship managed by P & 5 & 4.95\% & L sea routes & 13 & 12.87\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Characteristic distribution of the extracted variables in the 2022 MTSA-related route information.  
The six most important features of the ML algorithm are ranked as follows: number of operations per year (0.158288474), annual sailing distance (0.125514964), operating minutes per sea route (0.124485911), gross tonnage (0.099032723), number of port calls per sea route (0.095755849), and passenger capacity (0.073789485).  
An assessment of the main characteristics associated with these high-importance features reveals that the frequency of vessel operation, distance traveled, time spent operating, and number of port calls significantly influence the related accidents. In particular, the frequency can be considered from a probabilistic perspective to be the number of attempts made at an event under the same conditions. The likelihood of an event occurring increases with the frequency of attempts.
## Materials and Methods
### Analytical Methods
#### 2.2.2 Enzymatic Activity  
The bromelain assay was determined according to [PERSON] et al. (2014) [10] with some modifications reported by [PERSON] et al. (2020) [28]. The assay consisted in mixing the enzyme extract (50 \(\mu\)L) and 1150 \(\mu\)L casein 1% (_w/v_) (Sigma-Aldrich, Germany) solution in 0.1 mol L\({}^{-1}\) glycine (Sigma-Aldrich, Germany) and 25 mmol L\({}^{-1}\) cysteine. The mixture was incubated in a shaking water bath (10 min at 37 \({}^{\circ}\)C) and the reaction was stopped by adding 5% (_w/v_) trichloroacetic acid (1.8 mL). The assay mixture was filtrated (0.45 \(\mu\)m) and the absorbance was measured at 280 nm. The bromelain activity was calculated using a tyrosine (Alfa Aesar, Lancashine, United Kingdom) standard curve and expressed as the amount of tyrosine on a dry weight basis (\(\mu\)mol tyrosine\(\cdot\)min\({}^{-1}\cdot\)g\({}^{-1}\) of dry weight).  
Phenylalanine Ammonia-Lyase (PAL) Activity  
The PAL activity determination was performed as described in [PERSON] et al. (2016) [29] with few modifications. The reaction was performed by addition of borate buffer (2 mL), 100 mmol L\({}^{-1}\) L-phenylalanine (Alfa Aesar, United Kingdom) substrate solution (600 \(\mu\)L) and crude enzyme extract (400 \(\mu\)L). Samples' absorbency was read before and after incubation in a water bath (40 \({}^{\circ}\)C, 1 h) in a spectrophotometer at 290 nm. PAL activity was expressed as the amount of synthesized t-cinnamic acid on a dry weight basis (\(\mu\)mol t-cinnamic acid\(\cdot\)g\({}^{-1}\) of dry weight\(\cdot\)h\({}^{-1}\)).  
Polyphenol Oxidase (PPO) Activity  
The extraction of PPO followed a modified method of [PERSON] et al. (2003) [30] with some modifications. PPO activity was assayed spectrophotometrically by a modified method based on [PERSON] et al. (2008) [31] with some modifications. The assay mixture consisted of 2.5 mL substrate solution (50 mmol L\({}^{-1}\) catechol (Alfa Aesar, United Kingdom) in 0.1 mol L\({}^{-1}\) phosphate buffer, pH 6.5) and enzyme extract (500 \(\mu\)L). The rate of catechol oxidation was followed at 420 nm for 1 min and the enzyme activity unit was defined as the amount of enzyme that causes an increase of 0.1 in absorbance per min and per mL (\(\Delta\)Abs. min mL\({}^{-1}\)).
## 3 System Architecture
### LTE Macro-BS Subsystem and Power Consumption Modeling
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Notation** & **Unit** & **LTE Macro-BS** \\ \hline \multirow{6}{*}{PA} & Max transmit (_rms_) power, \(P_{\max}\) & W & 39.8 \\  & Max transmit (_rms_) power & dBm & 46.0 \\  & Peak average power ratio (PAPR) & dB & 8.0 \\  & Peak output power & dBm & 54.0 \\  & PA efficiency, \(\mu\) & \% & 38.8 \\  & Total PA \(P_{PA}^{DC}=\frac{P_{\max}}{\mu}\) & W & 102.6 \\ \hline \multirow{3}{*}{TRX} & \(P_{TX}\) & W & 5.7 \\  & \(P_{RX}\) & W & 5.2 \\  & Total RF \(P_{RF}^{DC}\) & W & 10.9 \\ \hline \multirow{3}{*}{BB} & Radio (inner _Rx_/_Tx_) & W & 5.4 \\  & Turbo code (outer _Rx_/_Tx_) & W & 4.4 \\ \cline{1-1}  & Processor & W & 5.0 \\ \cline{1-1}  & Total BB \(P_{RF}^{DC}\) & W & 14.8 \\ \hline \multirow{3}{*}{BB} & DC-DC loss, \(\sigma_{DC}\) & \% & 6.0 \\ \cline{1-1}  & Cooling loss, \(\sigma_{cool}\) & \% & 10.0 \\ \hline \multirow{3}{*}{Total per TRX} & Radio (inner _Rx_/_Tx_) & W & 5.4 \\ \cline{1-1}  & Turbo code (outer _Rx_/_Tx_) & W & 4.4 \\ \cline{1-1}  & Processor & W & 5.0 \\ \cline{1-1}  & Total BB \(P_{RF}^{DC}\) & W & 14.8 \\ \hline \multirow{3}{*}{BB} & DC-DC loss, \(\sigma_{DC}\) & \% & 6.0 \\ \cline{1-1}  & Cooling loss, \(\sigma_{cool}\) & \% & 10.0 \\ \hline \multirow{3}{*}{BB} & Total per TRX & \(=\frac{P_{DC}^{DC}+P_{RF}^{DC}}{(1-\sigma_{DC})(1-\sigma_{cool})}\) & W & 151.65 \\ \cline{1-1}  & \(P_{RF}^{DC}\) & W & 151.65 \\ \cline{1-1}  & \(P_{RF}^{DC}\) & W & 151.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Power consumption of the LTE-macro BS hardware elements [21]. In addition, Figure 3 shows the hourly load profiles. The alternating current (AC) load includes a 91 W air conditioner that represents 10% of \(P_{op}\) and 40 W lighting that operates from 7 PM to 7 AM. The direct current (DC) load includes a BS (\(P_{op}\) minus air conditioner equals 819 W and microwave backhaul equals 80 W).
# Seismic Ground Response Estimation Based on Convolutional Neural Networks (CNN)
## 3 Analytical Method
### Convolutional Neural Networks (CNN)  
Deep learning explores complex structures in the large dataset using algorithms to change the internal parameters in order to compute the representation of the dataset in each  
Figure 3: Earthquake data before and after correction (MYGH04).  
layer from the previous layer, based on the artificial neural networks. Convolutional neural networks (CNN), a class of artificial neural network that has become dominant in computer vision works, are designed to learn spatial hierarchies of features in the form of 2D images automatically and adaptively through backpropagation using building blocks, such as convolution layers, pooling layers, and fully connected layers, as shown in Figure 4[21]. A convolution layer has image data abstracted to a feature map, and a pooling layer reduce the dimensions of the feature map.
## Materials and Methods
### Satellite Data  
The satellite data used in this case are acquired by Planet's PlanetScope (PL) Eatrh-Imager, multispectral Cubsat constellation. Here, the PL Ortho Scenes products which are orthorectified, scaled Top of Atmosphere Radiance image (Level 3B) and are delivered as analytical (4-band) products [42]. PlanetScope captures imagery at a ground sampling distance of 3.7 m at a reference altitude of 475 km and the imagery is then orthorectified to a pixel size of 3 m [43]. The information about its specific attributes is shown in Table 1. A total of 552 cloud-free PL scenes were acquired before (21 August) and after (30 October) the occurrence of rice diseases in study area. The orthorectified scene mosaic were then printed out in the Universal Transverse Mercator (UTM) projection to provide near complete coverage of the study area.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Information** \\ \hline Sensor & PlanetScope \\ Acquisition date & 21 August 2017 and 30 October 2017 \\ Orbit altitude & 475 km \\ Spatial resolution (m) & 3 \\ Revisit time (days) & 1 \\ Wavelength range (nm) & \\ Band 1 & Blue: 455–515 \\ Band 2 & Green: 500–590 \\ Band 3 & Red: 590–670 \\ Band 4 & NIR: 780–860 \\ Signal-to-noise ratio (SNR) & 68.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters and information for the selected PlanetScope imagery.  
Figure 1: A false-color map of study areas and survey plots in Guangxi Province, China. The rice planting areas are revealed as green polygons.
## 4 Design and Stability Analysis of Surrounding Rock-Supporting Structure of Shaft
### Optimization Scheme Numerical Calculation Results Analysis  
The above two optimization schemes are calculated using the convergence-constraint method, and the rock support interaction curves are shown in Figure 13. The optimization of support scheme 1 using both the ideal elastic-plastic model and equivalent effect softening model shows that the obtained GRCs all intersect with the elastic part of the SCCs, and that the support structure meets the support requirements. However, it is worth noting that when adopting the strain-softening model calculation, the optimization of support scheme 1 and the junction between the GRC and SCC are very close to the end of the elastic phase of the SCC. Small safety reserves of support structures are required under the action of the natural rheology of the rock mass and the supporting structure will gradually be destroyed. The risk is, therefore, relatively high, which is not advisable in actual engineering. If optimized support scheme 1 is adopted to support the shaft-surrounding rock, the thickness of the shotcrete can be appropriately increased to provide a stronger support reaction to ensure the inner diameter of the shaft. Alternatively, the distance between anchor cables can be appropriately reduced to allow them to provide greater supporting resistance while keeping the thickness of the shotcrete unchanged. This reduces the pressure on the concrete, which meets the requirements of improved supporting structure safety.  
The support scheme 2 is optimized to enhance the shaft wall structure stiffness by appropriately increasing the grade of the coagulation strength. The intersection point of the GRC and support structure occurs within the elastic part of the SCC, the support system reaches the equilibrium condition calculated by the strain-softening model, and the support system meets the stable requirements and has a large safety reserve. If optimized support scheme 2 is adopted to support the surrounding rock of the shaft, the thickness of the shotcrete can be appropriately reduced to reduce the engineering cost as much as possible and improve the economics of the support system under the requirement of meeting the stability of the surrounding rock.
## 2 Materials and Methods
### Methodology
#### 2.2.4 Phase 4: Data Pre-Processing  
Structured query language (SQL) is written to filter the missing data in the database. SQL is a powerful tool for accessing and manipulating a relational database system [66]. The data pre-processing can be performed with Stored Procedures, which can be recalled for new datasets or re-processing of the data. SQL scripting files can be saved and called in the Linux terminal for a huge amount of data processing. The rain gauge data retrieved from DID were massaged to match the satellite data according to the latitude and longitudeof the study site.  
If there is more than one rain gauge station in the same grid, the rain gauge values will be averaged. A simple SQL selection statement will tell us which grids have more than one rain gauge station. SQL is also used to determine which stations have complete data for the study period. Rain gauge stations that do not have complete data or missing data are excluded from the study. A preliminary examination of the rain gauge observation data using SQL selection indicates the presence of erroneous entries containing special characters such as \(\#\) and \(*\) in the rainfall column. These erroneous data were ignored by removing them from the datasets using SQL execution. The same spatial-temporal IMERG and rain gauge data were mapped using latitude and longitude values and stored in a table for the ease of retrieval using SQL. These data were normalized to obtain daily IMERG rainfall estimates by grid with rain gauge data for the study area for the period from 2011 to 2020.