esa-sceva/satcom-chunk-collection huggingface dataset:
# Controlled Drainage Effectiveness in Reducing Nutrient Outflow in Light of Climate Changes
## 3 Results
### Effect of CD on Subsurface Drainage Outflows  
The average sum of drainage outflows for the start of the CD on 1 March for the variants of all three rainfall scenarios and the adopted climate change models GFDL, MPI, and UKESM is the lowest value. The obtained modeling results indicate homogeneous groups a and b (Table 5). In addition, for the withholding of outflows on 15 March, for the MPI model, lower outflows were again obtained, indicated for homogeneous groups a-d. Starting CD practice after 1 April and 15 April, the adopted year scenarios do not indicate significant differences for wet, normal, and dry years and are similar to FD results.  
Figure 6: Number of GWT days above the depth of the drainage network under FD and different variants of CD practice for wet (**A**), normal (**B**), and dry (**C**) years for GFDL, MPI, and UKESM climate change models (bar charts show the average values and standard deviation (SD); different letters indicate significant differences (\(p\leq 0.05\)) between variants of drainage according to the Tukey test).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Drainage** \\ **Variants** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Initial GWT** \\ **(cm b.s.l.)** \\ \end{tabular} } & \multicolumn{4}{c}{**Average Subsurface Drainage Outflows (mm)**} \\ \cline{3-8}  & & \multicolumn{3}{c}{**Wet**} & \  
The lowest number of days with drainage runoff was indicated when the practice of stopping runoff began on 1 March for all modeling scenarios (Figure 7). In wet years, for the adopted scenarios, the number of days with drainage outflow is 17-19, 18-21 and 15 for the GFDL, MPI, and UKESM models, respectively. In normal years, it is 12-14, 15-17, and 16-18 days for the GFDL, MPI, and UKESM models, respectively. Similarly, in dry years, it is 12-14, 13-15, and 11-13 days for the GFDL, MPI, and UKESM models, respectively.  
The greatest reduction in outflows was obtained at the 1 March start date of the CD practice for all assumed modeling scenarios (Figure 8). Regardless of the assumed initial GWT in wet, normal, and dry years, the reduction results obtained were 100%. Moving the date to two weeks later (15 March) yields a significant reduction for the MPI model (above 85%). At this date, the other two models indicate much lower reductions, at 10-55%. The later withholding of outflows from 1 April to 15 April indicates the lowest reductions and/or none at all. No effect was observed when outflows were blocked from 15 April onwards for the GFDL and MPI models.  
Figure 7: Number of days with drainage outflow for FD and different variants of CD practice under wet (**A**), normal (**B**), and dry (**C**) years for GFDL, MPI, and UKESM climate change models (bar charts show the average values and SD; different letters indicate significant differences (\(p\leq 0.05\)) between variants of drainage according to the Tukey test).
# Additive Manufacturing in Underwater Applications
## 4 Technologies
### Remotely Operated Vehicles (ROVs) and Autonomous Underwater Vehicles (AUVs)  
The 3D printing technology is quite popular for the manufacturing of specific parts for remotely operated underwater vehicles (ROVs) or autonomous underwater vehicles (AUVs) [16; 40]. This technology is useful in the case of investigations of these vehicles, including their technical improvement in different fields. It also helps to obtain a high level of flexibility in their design and gives the possibility of adding individual solutions while ensuring a reasonable cost and speed of execution [66; 67].  
The area of usage of these vehicles is expanding each year. Currently, they are applied in biological research [68; 69], rescue activities [68], technical education [67; 70], fishery inspections [71], exploration [72], and many others. These devices are also very often inspired by biological solutions, such as fish motion, to increase their efficiency [68; 73]. ROV and AUV systems are particularly used in hard-to-reach places, including those located at considerable depths [74]. These vehicles can perform most of the needed tasks autonomously.  
However, technologies such as ROVs and AUVs seem to be naturally suitable for underwater 3D-printed applications, especially for maintenance work at significant depths. Nonetheless, today, there is no information in the literature about such usage of these vehicles. It seems to be a promising area of further research and exploitation for this technology, especially taking into consideration that their air equivalents--drones--are widely applied in the construction industry [75].
# Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer
## 2 Load Period Partitioning Method Based on Improved Fuzzy C-Means Clustering  
Considering the temporal characteristics of distributed generation and load profiles, this paper proposes an improved fuzzy C-means (FCM) clustering algorithm to effectively capture the dynamic variations in distribution network loads in response to real-time system status changes. The proposed method divides the typical load characteristics of the distribution network into representative time periods.  
FCM is a clustering algorithm grounded in fuzzy set theory. In contrast to traditional hard clustering techniques such as K-means, FCM does not rigidly assign each data point to a single cluster. Instead, it employs a membership function that allows each sample to simultaneously belong to multiple clusters with varying degrees of membership. This soft partitioning approach enhances FCM's flexibility and representational capability, particularly when handling data with ambiguous boundaries or indistinct structures, thereby making it a popular tool in load analysis.  
To address the limitations of the traditional FCM algorithm--specifically its deficiencies in clustering accuracy, stability, and feature representation when applied to load period division--this study introduces an improved FCM method. The improvement lies in incorporating a comprehensive similarity measure and a time-weighted similarity matrix, enabling more precise and reliable segmentation of distribution network load curves into distinct time intervals.
## 3 Results and Discussion
### Impacts of Fire
#### 3.4.2 Successful Patterns of Land Cover Following Fire  
Boreal forest climax communities are dominated by coniferous species [29, 34], occupying nearly 70% of their total area (Figure 4B). Following fire, the coniferous component markedly decreases while deciduous and herbaceous regrowth dominate, particularly in severely burned areas [15, 53]. Alternatively, low intensity ground fires can occur in the organic layers [4, 14]; these areas are typically self-replaced by conifers unless the soil organic layer thickness is less than 3 cm [29, 52].  
Early-succession, mid-succession, deciduous forest, and coniferous forest had their peak percentage of area 5, 10-15, 35, and \(>\)50 years following fire, respectively (Figure 4B). The time period 30-40 years after fire had the greatest landscape diversity (Figure 4B). Postfire vegetation is commonly deciduous stands that persist for 10-50 years following disturbance before mixing with and then gradually being replaced by coniferous stands [4, 29, 43, 52], similar to our results. Land cover proportions of fire scar area were nearing pre-fire conditions 50 years after fire (Figure 4B), suggesting that secondary vegetation successional processes were nearly complete.  
In general, yearly average EPA values following fire were positively associated with deciduous percentage of fire scar area (R\({}^{2}\) = 0.47, \(p<0.01\)), unrelated to coniferous and mid-successful percentage, and negatively related to early successional land cover (R\({}^{2}\) = 0.22, \(p<0.01\)). The successional patterns in vegetation following fire are also manifested in soils, where vegetation regrowth following fire allows for accumulation of the soil organic layer, critical to the recovery of permafrost [8] and coniferous stands. A warming temperature and changing fire frequencies could cause deviation from these successional patterns observed in our chronosequence, specifically with an increased proportion and duration of deciduous components [52].
## 3 Data Pre-Processing and Feature Analysis
### Data Decomposition  
The CEEMDAN method is utilized to decompose the aggregated charging load at low- and high-frequency waves, which correspond to daily and seasonal, respectively. All graphs in Figure 6 are on the same scale, thus enabling the evaluation of the contribution of each extracted IMF on the daily, weekly, monthly, and seasonal scales (Figure 6b-e). It can be seen that increasing the range of data for decomposition from daily to seasonally would decrease the amplitude of the fluctuation. Therefore, it presents improvements in the extracted IMFs and provides a valuable abstract for data visualization. For load forecasting, the generated IMFs were divided into training and testing sets, with the ratio selected as 76/24 (16 months of training data, and 5 months of testing data).  
For time interval processing, Anaconda, which is a distribution of the Python programming language, was used to integrate all the charging load event data based on the time stamps and split them into 24 h time stamps every single day.
# Formal Modeling and Improvement in the Random Path Routing Network Scheme Using Colored Petri Nets
## 4 Experimental Results
### Comparison of CRPR with Other Similar Routing Techniques  
The comparison of our proposed CRPR model with other similar routing techniques is shown in Table 3. The phantom routing-based techniques are not formally modeled by Petri nets, and it also does not use any guard value. It is a single-path routing scheme, and it can have repetitive packets sent. The fake source-based techniques are also not modeled yet, and it is a multi-path routing technique. Dynamic source routing is a secure network technique, but not formally modeled yet, and it is a multi-path routing technique. Associativity-based routing is also not modeled yet, and it is a multi-path routing scheme. Our proposed technique models the shortest selective random path routing technique and is formally modeled by using the guard technique. SSRPR is a multi-path outing scheme and its reachability is 100%. However, this model does not have a liveness property. The comparison of the proposed model with the existing techniques shows relatively good results so far as the security of the packets is concerned.
## 3 Spiking Neural Network
### Gabor Feature Based Edge Detector  
Figure 2 displays the Spiking Neural Network architecture designed for spike processing with four layers of edge detection. The first layer in the architecture is a receptor layer R with dimensions identical to the input image. The second layer is made of six sets of Gabor filers, \(W_{nu}^{L}\) (L = A to F, \(nu\) = _ex_ or _in_) where a set consists of a filter each for excitatory (_ex_) neurons and inhibitory (_in_) neurons [8; 14]. The third layer is an intermediate layer, consisting of six parallel layers of neurons \(N_{i}\) (\(i\) = 1 to 6) with dimensions identical to the image. \(S_{A}\), \(S_{B}\), \(S_{C}\), \(S_{D}\), \(S_{E}\) and \(S_{F}\) are the output spike trains from neuron layers \(N_{1}\), \(N_{2}\), \(N_{3}\), \(N_{4}\), \(N_{5}\) and \(N_{6}\), respectively. The fourth and final layer is the spike output layer \(S_{out}\) with dimensions identical to the input image. The output of the image is then an edge map generated by the SNN edge detector. As shown in Figure 2, the framework combines six Gabor filters to generate an edge map of the image. The synaptic conductance of the Gabor feature is calculated by combining the spikes from the six Gabor filters using the equation:  
\[g_{ex1}(t)=g_{ex}(|S_{A}(t)-S_{B}(t)|+|S_{B}(t)-S_{C}(t)|+|S_{C}(t)-S_{D}(t)|+|S _{D}(t)-S_{E}(t)|+|S_{E}(t)-S_{F}(t)|) \tag{14}\]  
and the synaptic current is then evaluated as:  
\[I_{z1}(t)=-g_{ex1}(t)E_{ex}. \tag{15}\]  
Figure 2: Spiking Neural Network Architecture showing the neuron spike processing layers of the edge detector.  
The dynamics of the membrane potential is then calculated by solving Equation (9) with \(g_{ex}=g_{ex1}\) and \(g_{in}=0\). The analytical solution with \(V_{Na}=0\) and \(V_{K}=0\) is then given by:  
\[v_{2}=(\frac{1}{g_{l}})\{(-exp(\frac{g_{l}t}{C_{m}}))\times(I_{z1}+70g_{l}+g_{l} E_{l})+I_{z1}+g_{l}E_{l}\}, \tag{16}\]  
which is iteratively solved by updating the synaptic current using:  
\[I_{z1}=g_{ex1}(v_{2}-E_{ex}) \tag{17}\]  
over the time window T to solve for the output spikes \(S_{2}(t)\). The SNN edge map of the input image is calculated using the 1's complement of the normalized sum of spikes:  
\[SNNEdge=1-\sum_{t=1}^{T}\frac{S_{2}(t)}{T}. \tag{18}\]  
Figure 3 shows a flow chart with the computational steps in the MATLAB implementation of the SNN edge detector.
## 2 The Proposed Method
### Automatic Template Extraction
#### 2.1.2 Standard Template Extraction  
We now want to find regularly repeated regions inside the initial template (as shown in Figure 3). The process of finding such a region is described in detail as follows.  
Figure 5: Results of binary-thresholding followed by the opening from the correlation map.  
1. The initial template is first smoothed using a two-dimensional Gaussian filter with a kernel size of \(5\times 5\) pixels. Because the weights are effectively zero out of a \(5\times 5\) filter when approximating to Gaussian function with a standard deviation \(\sigma=1.0\), we select this kernel size in this study.
2. Thresholding is applied to the filtered template, where the threshold is determined using [PERSON]'s well-known method [20]. Figure 6 shows the results of this step.
3. After labeling all bright objects, the largest one is found and its centroid \((x_{\mathrm{L}},y_{\mathrm{L}})\) is recorded.
4. A patch centered at \((x_{\mathrm{L}},y_{\mathrm{L}})\) is cropped to a size of \(\left(w^{*}_{\mathrm{Tpl}},h^{*}_{\mathrm{Tpl}}\right)\) pixels from the initial template. This cropped image can be considered the standard template. In Figure 7, the green rectangle in subplot (a) shows the extracted template and (b) shows its close-up.  
This extracted template is used to detect the die patterns in the initial template to check whether the number of detected die patterns is sufficient. If the number of patterns is insufficient, the algorithm of automatic template extraction is re-conducted.
# Asymmetric Encryption of Invisible Structured Light 3D Imaging
## 4 Conclusions  
In this research, we propose a new imaging method based on the combination of invisible structured light 3D imaging technology and elliptic curve cryptography. The proposed scheme has the following advantages: first, it combines cryptography and optics, constructs a scheme that combines digital encryption and 3D imaging, and provides a new method for the safe transmission of 3D object information. Second, using elliptic curve cryptography, a typical asymmetric encryption algorithm, to encrypt and decrypt eight images generated by four-step phase shift was shown to greatly improve the security of object encryption. Moreover, the elliptic curve cryptography algorithm solves the challenging key distribution problem to achieve asymmetric encryption. Third, with invisible structured light as the projection light, the adverse impact of the background light is suppressed, thus significantly reducing the system error. Fourth, the device of the system is simple, and automation is easy. The encryption system has a large fault tolerance, which can effectively resist a noise attack and known plaintext attack. Last, the encryption strength was proven using the image histogram, the correlation of adjacent pixels, correlation coefficient of adjacent pixels, structural similarity, and information entropy. It showed that the system is robust to noise attacks, and can effectively restore the 3D information of the original object. It is expected that the proposed solution will have practical secure 3D imaging applications.  
Conceptualization and methodology, A.Y. and J.Z.; writing--original draft preparation, J.Z.; reviewing and editing, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Nature Science Foundation of China under grant, grant number 62075134.  
Not applicable.  
The authors declare no conflict of interest.
# Utilization of a Mobile Application for Motor Skill Evaluation in Children +
## 2 Materials and Methods
### Data Analysis  
The app has been designed so that physiotherapists can retrieve all the data recorded by the app for further processing. Before the device (tablet or phone) is given to a child, the therapist must first select the name or identification (ID) from a list previously configured in the app. After the desired number of attempts are done for a given track, a commanded value (CSV) file is generated in the external storage with the data from all the attempts. Each attempt in the file starts with one line for the configuration and the global results of that attempt (track ID, speed in cm/s, whether the attempt is valid or not, the obtained score E and the DPI of the display of the device) followed by as many lines as polls were done during the attempt: time in ms from the start of the attempt, the coordinates \((x_{t}(t),y_{t}(t))\) of the target in cm, the coordinates \((x_{u}(t),y_{u}(t))\) of the detected touch event in cm (or \((-1,-1)\) if no touch was detected), the instantaneous error in cm and the estimated user speed in cm/s.  
For a better understanding of the results, we displayed them using a boxplot. This plot is a common way of displaying data based on the sample median and the first and third quartiles. This boxplot is constituted by the box, drawn from the first quartile (Q1) to the third quartile (Q3), and two whiskers corresponding to data below Q1 and above Q3. Outliers are represented by a plus sign when below Q1 - 1.5 IQR or above Q3 + 1.5 IQR, and by a circle when below Q1 - 3 IQR or above Q3 + 3 IQR, with IQR = Q3 - Q1 the interquartile range. The limits of the whiskers correspond to the minimum and maximum values, not counting outliers. The red line inside the box represents the median.
## 3 Vision, Key Features and Challenges of 6G
### Vision and Key Features of 6G
#### 3.1.1 Intelligent Network  
As 6G is envisioned as a fully automated and smart network, the incorporation of AI, MLand quantum machine learning (QML) makes the future wireless networks more intelligent and predictive by limiting human efforts [176, 187]. AI and ML are the transforming technologies and data analytics tools in the modern era of wireless communication that bring new research challenges in the field of 6G IoT [186]. By using big data and ML, a more precise performance prediction model can be implemented in a 6G IoT network to make smart decisions for security, optimization, resource allocation, network management, self-organization, etc., [155, 156, 157, 158, 188]. Due to the high veracity/volume data and complex 6G IoT network structure, it is necessary to instigate more futuristic learning/training frameworks for high-dimension neural networks (HDNN) [165].  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{3}{c}{**Technological Standards**} \\ \cline{2-3}  & **5G** & **6G** \\ \hline _Frequency Band_ & Sub 6 GHz, 30–300 GHz [155] & Sub 6 GHz, 30–300 GHz, 0.3–3 THz [155] \\ \hline _Average Data Rate_ & 100 Mbps [155] & 1 Gbps [155] \\ \hline _Latency_ & 1 ms [155] & \textless{}1 ms [155] \\ \hline _Mobility_ & \textgreater{}500 kmph [155, 156] & \textgreater{}1000 kmph [155, 156] \\ \hline _Maximum Channel Bandwidth_ & 1 GHz [156] & 100 GHz [156] \\ \hline _Connection Density_ & \(10^{6}\) devices/km\({}^{2}\)[156] & \(10^{7}\) devices/km\({}^{2}\)[156] \\ \hline _Reliability_ (Packet Error Rate) & \(10^{-5}\)[156] & \(10^{-9}\)[156] \\ \hline _Area Traffic Capacity_ & 10 Mbps/m\({}^{2}\)[155, 156] & 10 Gbps/m\({}^{2}\)[155, 156] \\ \hline _Service Types_ & eMBB, mMTC, uRLLC [155] & mbRLLC, muRLLC, HCS, MPS [155] \\ \hline \multirow{3}{*}{_Multiplexing_} & CDMA [157, 158], OFDM, GFDM [158], & Smart OFDMA + Index Modulation, \\  & FBMC [159], Adaptive & OMA [161], NOMA [161], OAM [162], \\  & Time–Frequency Multiplexing [160] & Spatial Multiplexing [163] \\ \hline _Power Consumption_ & Low to Medium & Ultra-low [164] \\ \hline _Downlink Spectral Efficiency_ & 30 bps/Hz [165] & 100 bps/Hz [165] \\ \hline _Energy Efficiency Gains in Comparison With_ & \multirow{2}{*}{10\(\times\)[165]} & \multirow{2}{*}{1000\(\times\)[165]} \\
4G & & \\ \hline _Network Architecture_ & Centralized [155] & Decentralized [155, 166] \\ \hline \hline \end{tabular}
\end{table}
Table 5: A comparative analysis between 5G and 6G.  
Figure 5: Vision and key features of 6G [22; 26; 154; 165; 166; 167; 168; 169; 172; 173]
# Design of an Energy Efficient Future Base Station with Large-Scale Antenna System
## 2 System Model  
This section describes the power consumption model and the LS-MIMO model, which are the core models used in the performance analysis of the proposed BS.
# Plasmonic and Dielectric Metasurfaces: Design, Fabrication and Applications
## 2 Plasmonic Metasurfaces
### N-Fold OAM Multicasting Using V-Shaped Antenna Array [120]
The power distribution of OAM channels generated by the designed V-shaped antenna array is also calculated and displayed in Figure 11d. The crosstalks for all multicast OAM channels are less than \(-\)15 dB, as shown in Figure 11d. Figure 9: Concept and principle of N-fold multicasting of OAM beams using a V-shaped antenna phase array. Reproduced with permission from [120], Copyright Nature Publishing Group, 2015. Figure 11: **(a)** Intensity distribution of the four collinearly-superimposed OAM beams; **(b)** intensity distribution of the undesired OAM channels after demultiplexing; **(c)** intensity distribution of the multicasting OAM channels after demultiplexing; **(d)** power distributions of the OAM channels generated by the designed V-shaped antenna array and the theoretical continuous phase pattern. Reproduced with permission from [120], Copyright Nature Publishing Group, 2015. Figure 10: **(a)** Discretization of the ideal continuous phase pattern; **(b)** power distributions of OAM channels generated by the continuous phase pattern and the discrete phase pattern; **(c)** top view of the designed V-shaped antenna array based on the discrete phase pattern; it repeats with a periodicity of 2 \(\upmu\)m in both the \(x\) and \(y\) directions; **(d)** details of the designed V-shaped antenna array. Reproduced with permission from [120], Copyright Nature Publishing Group, 2015. Fabrication: The proposed V-shaped antenna array to generate N-fold OAM multicasting can be fabricated using the existed fabrication techniques. Figure 12 shows the schematic diagram of the fabrication process of the V-shaped antenna array: (1) using standard electron-beam lithography to generate patterns on the silicon substrate; (2) 5 nm Cr and 50 nm Au layers are then respectively deposited on the substrate using electron-beam evaporation; (3) removing the photoresist and undesired metal layers.