esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### The Cooling and Ventilation System  
The installed cooling and ventilation system consists of the EAHX, an AHU, the distribution piping buried between the AHU and the building and the diffusion of air and temperature control in the cooled rooms. An outline schematic of this system is presented in Figure 2. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Architectural** & \\ \hline Layout & **(a)** A laboratory located at the west with larger ceiling height takes approximately 40\% of the total building floor area. The remaining area—office area—with lower ceiling height extends towards the east, holding a technical room supporting the laboratory, research and administrative offices, a large meeting/training room, a reception, a restroom, WC and corridors connecting these spaces. Technical room, reception, and corridors lie on the north side of the building whilst offices and meeting room lie on the south side. The building is characterized by a small window-to-wall ratio. \\ Floor area & **(b)** Laboratory: 300 m\({}^{2}\); Office area: 450 m\({}^{2}\). \\ Room height & **(c)** For the cooled office area: 3 m. \\ \hline \multicolumn{2}{p{142.3 pt}}{**Envelope (thermal characteristics)**} \\ \hline Exterior walls & **(d)** From the outside to the inside: mortar (30 mm), extruded polystyrene insulation board (XPS, 80 mm), cement bricks (300 mm), mortar (30 mm). U-value of 0.36 W/(m K) [26]. \\ Pavement floor & **(e)** From the outside to the inside: concrete slab (200 mm), XPS insulation board (60 mm), screed (60 mm). U-value of 0.39 W/(m K) [27]. \\ Ceiling & **(f)** Occupied spaces separated from an inverted roof (insulated on the outside with 60 mm XPS) by a false ceiling comprising (from the outside to the inside): mineral wool (60 mm), gypsum board (12 mm). U-value of 0.6 W/(m K) [26]. \\ Windows and shading & **(g)** Double-glazed (6 mm + 12 mm + 8 mm) window with solar control (solar factor, 0.33; U-value of 1.4 W/(m K)) and aluminium frame with thermal break. In the summer, slightly recessed windows and photovoltaic panels placed over the windows on the south facade block out direct solar radiation. \\ \hline \multicolumn{2}{p{142.3 pt}}{**Occupancy**} \\ \hline  & **(h)** Low occupancy density (lower than 0.03 person/m\({}^{2}\)), weekdays from 8 to 18 h. \\ \hline \end{tabular}
\end{table} 
Table 1: Building characteristics relevant to the study of the built EAHX. Figure 2 represents (from left to right) the EAHX, the AHU and the distribution piping. Supply of cooled air to a room along the exterior wall at floor level, as well as a room temperature controller, are also depicted. Detailed specifications of the cooling and ventilation system are provided in Table 2.
## 2 Materials and Methods
### Preprocessing and Feature Extraction
```
1: Resize the image to 600 \(\times\) 400 px for light processing
2:if Excess_of_protein:
3: Histogram equalization
4:endif
5: Obtain a binarized Image
6: Image dilation
7: Image invert
8: Image erosion
9: Column = 1
10: If Lane detection:
11: MAXWIDE = 400 px
12:else: # band detection
13: MAXWIDE = 50 px
14:endelse
15:endif
16: Apply Binary Mask on the resized image
17: Initialize Array to zero
18:whileColumn = MAXWIDE:
19: Get the Histogram_of_image
20: Otsu_Segmentation_Applied_to_Binary_Mask_Size_zone
21: Get the number of white pixels in the Histogram of the segmented region,
21: Histogram[white_position]
2:# get the quantity of white color in the histogram binarized
22: Array [Column] = Number_White_Pixels_Histogram [255]
23: Column++
24:endwhile
25: Plot Array
26:if Lane_Analysis:
27: Multiple_Minimum_correlate_Lane_Separation(Array)
28: Multiple_Maximus_related_Band_Separation(Array)
29: else/band_analysis
30: Average_Multiples_Maximums_between_separations_To_Get_One_Maximum
31:endelse
32:endif
```  
**Algorithm 1** Pseudocode to find the number of lanes and bands in the polyacrylamide gel image.
## 2 Datasets
### Ground-Based Data  
AERONET, a global network of calibrated sun photometers, can provide near-truth AOD measurements every 15 min at 340, 380, 440, 500, 670, 870, and 1020 nm wavelengths with an expected uncertainty of approximately 0.01 to 0.02 [54]. In this study, AERONET Level 2.0 (cloud-screened and quality-assured) version 3.0 AOD (Washington, DC, USA)measurements from 22 observation sites are adopted to serve as a reference for accuracy comparison between the satellite-based AOD retrievals and merged AODs. It is also used to quantify the discrepancy among satellite-based AOD products. MAN is mainly a periodical marine aerosol observation network using handheld Microtops II sun photometers on research vessel ships. MAN Level 2.0 AOD measurements are employed as a complementary part of the AERONET AOD over islands and oceans. It is notable that MAN measurements are often taken irregularly in time lags that vary with location and are of similar uncertainty (<0.02) to that of AERONET [55]. The aforementioned datasets are released on the website [[http://aeronet.gsfc.nasa.gov](http://aeronet.gsfc.nasa.gov)]([http://aeronet.gsfc.nasa.gov](http://aeronet.gsfc.nasa.gov)) (accessed on 5 March 2022). The valid ground-based sites of AERONET and MAN are shown in Figure 1, and the elevation and geographic location are listed in Supporting Information Tables S1 and S2.
# Genetic Algorithm-Based Optimization of Clustering Algorithms for the Healthy Aging Dataset
## 2 Materials and Methods
### Clustering Algorithms
#### 2.4.1 KMeans/KMeans++  
KMeans++ is a centroid-based clustering algorithm designed to partition a dataset into clusters, ensuring that each data point is assigned to the cluster with the closest centroid. K-means++ is a modified version of the K-means method that enhances the initial selection of cluster centroids. In the normal K-means algorithm, the initial centroids are often selected randomly from the dataset. However, this random selection can result in clustering outcomes that are not ideal. K-means++ resolves this problem by employing a more sophisticated initialization technique. The process begins by randomly selecting a single initial centroid from the available data points. Successive centroids are thereafter selected based on a probability that is directly proportionate to the squared distance from the nearest centroid that already exists. This guarantees that the initial centroids are evenly distributed and enhances the likelihood of discovering improved final centroids.  
After the centroids are initialized, the next steps of the K-means algorithm follow the normal procedure. Data points are allocated to the centroid that is closest to them, and the  
Figure 2: A fundamental operational premise of a genetic algorithm.  
centroids are updated by calculating the mean of the points in each cluster. This process continues until the centroids reach stable positions and the cluster assignments remain mostly unchanged.
## 3 Part 1 of the Study
### Results for Part 1  
This subsection summarizes the findings of our first survey of participants' cybersecurity and phishing expertise based on their school categories and residences.  
We analyzed teenagers' phishing and cybersecurity knowledge according to school categories and residence places. Participants in international schools were found to have more knowledge about phishing (58%) than those in private schools (25%) and the government (19%), based on high scores on questions measuring knowledge about phishing, as shown in Figure 1.  
Furthermore, teenagers living in urban areas are more knowledgeable than those in suburban and rural areas by 59%, 28%, and 13%, respectively, as indicated in Figure 2. The reasons could be having more exposure, better social-economic background, and different social culture than their counterparts. [PERSON] et al. [40] discovered that participants with a higher socioeconomic status had greater knowledge levels. Good social-economic background enables them to frequently access mobile phones, the internet, and social networks, allowing them to learn several issues from others and gain self-experience.  
Although a few participants showed better knowledge for those living in town and from international schools, a significant proportion of participants still had very little cybersecurity awareness. This is indicated by the participants (54%, \(n=65\)) who said that they would likely download applications and attachments from untrustworthy sources, which is a common way to launch phishing attempts. Furthermore, 53.7% of the participants who reported sharing their mobile phones with friends described the most threatening risky behavior and poor cybersecurity hygiene. Sharing devices creates a loophole for many users to be easily exploited once their confidential information is accessed by third parties and shared publicly, either intentionally or accidentally. In addition, it is not easy  
\begin{table}
\begin{tabular}{c c} \hline \hline
**School Categories** & **Participants** \\ \hline International & 33.88\%, \(n=41\) \\ Private & 33.06\%, \(n=40\) \\ Government & 33.06\%, \(n=40\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Participants in school categories.  
We see the need to educate teenagers on how to prevent phishing attacks using a mobile-based educational game. The research by [21, 25, 38] found that the game is an effective tool to teach various cybersecurity topics such as safe and cautious online habits, threats and attacks, malware, and other cybersecurity topics. The game has been considered a useful training tool and gives encouragement for a change in player habits. However, it always gives learners interactive methods only if some features, such as user needs and requirements, and the specific issue, have been incorporated within. Therefore, we developed a customized mobile game to teach teenagers some common parameters that attackers mostly use to initiate phishing, such as emails, messages, and phone calls.
# NTPP-MVSNet: Multi-View Stereo Network Based on Neighboring Tangent Plane Propagation
## 3 Methods
### Depth Hypothesis
#### 3.2.2 Neighboring Pixels  
Owing to our deep sampling approach that is heavily reliant on the spatial disposition of neighboring pixels, and mandating the neighborhood pixels to maintain similar tangent planes to the current pixel in 3D space as shown in Figure 5, the algorithm necessitates the spatial surface of the neighboring points to remain contiguous with the current pixel to avoid any significant discrepancies between the sampling findings and the actual value. To tackle this challenge, we incorporated the reference image feature map information within the model. As shown in Figure 7, by leveraging deformable convolutional networks, we ascertained the position offset of the neighboring pixels and overlaid the original neighborhood pixels against said offset, thereby achieving a computation of spatially continuous neighborhood positions. This methodology facilitates a deep sampling procedure predicated on neighborhood tangent plane propagation.  
Figure 6: In this approach, the cost volume is fed into the 3D-UNet network to regularization, ultimately producing the cost volume with three channels. The resultant channels serve different purposes: the first channel fosters the regression of the probability volume for each depth sampling of the target image, while the second and third channels facilitate the regression of the normal volumes. The depth sampling and normal volumes demand a weightage and cumulative summation alongside the probability volume to arrive at the ultimate predictive outcome.
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 4 Routing Protocols
### Position Aware Routing Protocols  
This type of routing protocol is based on geographic location information, and each node can use GPS to define geographic locations and broadcast the location and speed at which they move to their neighbors. Nodes make local decisions without having to explore the state of the entire network, so it is suitable for a wide range and high-speed mobile networks. The advantage is to solve the problem of frequent disconnection between network nodes due to node unpredictable movement.
# Dam Deformation Monitoring Model Based on Deep Learning and Split Conformal Quantile Prediction
## 2 Methodologies
### The Evaluation Indicators of Model Performance  
In this paper, the point prediction performance of the model is evaluated by three metrics: the correlation coefficient (\(R^{2}\)), mean absolute error (_MAE_), and root mean square error (_RMSE_). These indicators can be expressed as follows:  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}{(y_{i}-\hat{y}_{i})}^{2}}{\sum\limits_{i=1}^ {n}{(y_{i}-\overline{y_{i}})}^{2}} \tag{15}\]  
\[MAE=\frac{1}{n}{\sum\limits_{i=1}^{n}{|y_{i}-\hat{y}_{i}|}} \tag{16}\]  
\[RMSE=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{(y_{i}-\hat{y}_{i})}}} \tag{17}\]  
where \(\hat{y}_{i}\) denotes the predicted value, \(y_{i}\) is the actual value, and \(n\) is the total length of the sample. For _MAE_ and _RMSE_, the smaller their values, the better. The range of \(R^{2}\) is between 0 and 1, with higher values indicating that the fitted displacements of the model are closer to the observed displacements. \(R^{2}\) reflects the model's fitting performance for deformation trends, while _MAE_ and _RMSE_ indicate the model's ability to control local and global error.  
Similar to the point prediction model, the interval prediction model also has specific evaluation indicators. Prediction interval effective coverage (_PICP_) and average interval coverage width (_MPIW_) tend to best visualize the accuracy and effectiveness of PIs. They are defined as, respectively:  
\[PICP=\frac{1}{n}{\sum\limits_{t=1}^{n}{k_{t}}} \tag{18}\]  
\[MPIW=\frac{1}{n}{\sum\limits_{t=1}^{n}{(UB_{i}^{\alpha}-LB_{i}^{\alpha})}} \tag{19}\]  
where \(n\) is the number of target samples. \(k_{t}\) is 1 if the samples fall within the PIs, otherwise \(k_{t}\) is 0. \(UB_{i}^{\alpha}\) and \(LB_{i}^{\alpha}\) denote the upper and lower bounds of the PIs, respectively; \(\alpha\) is the error coverage.  
However, in most cases, _PICP_ and _MPIW_ are two opposing evaluation metrics. It is difficult for interval prediction models to maintain a balance between high _PICP_ and small _MPIW_ in constructing PIs. To validate the quality of the deformation PIs constructed by our proposed method, we use an evaluation metric called coverage width-based criterion (CWC), which assesses the comprehensive quality of PIs as follows:  
\[CWC=MPIW[1+\gamma(PICP)\cdot\exp(-\eta(\mu-PINC))] \tag{20}\]  
\[\gamma(PICP)=\left\{\begin{array}{l}1,PICP<\mu\\ 0,PICP\geq\mu\end{array}\right. \tag{21}\]  
where \(\eta\) and \(\mu\) are the penalty parameter and the _PINC_ of constructed PIs, respectively. When the PICP is lower than \(\mu\), the effective coverage rate of the PIs does not meet the predetermined probability requirement; then, \(\gamma(PICP)=1\). The _CWC_ will be determined by the PICP and MPIW together; on the contrary, if the PICP is higher than \(\mu\), the effective coverage rate of the PIs meets the requirement, and the _CWC_ is only related to MPIW. The \(\eta\) will be set to 10 in this paper [21].
## 4 Satellite Self-Organizing Network Simulation Platform Based on OMNeT++
### Function Domain
#### 4.3.4 Data Generation and Forwarding  
Each member node has a certain probability of generating a data packet every second. The node sends this data packet to the cluster head within its time slot. If the cluster head's queue is full when it receives the data packet, the packet is discarded. If the data queue is not full when receiving a data packet, the data are added to the queue for processing. Once all the data packets ahead in the queue have been forwarded, the cluster head parses the destination node of the data packet, calls the routing algorithm function to calculate the next hop, and forwards it. In the previous text, the specific process of the SQL-CBRP algorithm has been introduced. To implement this algorithm on the OMNeT++ platform, nodes need to be able to obtain the location information of other nodes. Since the nodes inherit from the INET Framework's NodeBase module, which includes a mobility module, the location information of each node can be obtained from it. In addition, the algorithm requires maintaining a neighbor table locally at each node. Therefore, in this platform, a neighbor class that includes the neighbor's ID and its queue length is created, and a neighbor list composed of neighbor objects is created in the network layer module of each satellite node. Each time a neighbor sends back the queue length information, the queue length of the corresponding neighbor object is updated for routing decisions. When a cluster head receives a data packet forwarded from another cluster head, it executes the same queuing and forwarding process, until the packet arrives at its destination node.  
The packet queue and routing calculation functions are deployed at network layer modules, and forwarding is implemented by Mac layer modules.
## 2 Materials and Methods
### Vegetation Indices (VIs)  
The vegetation indices are an important indicator to measure the growth of crops, monitor the crop development process, and estimate the crop yield potential. In combination with the physical significance of spectral indices, the selection of VIs was based on the spectral characteristics of crops and the available literature from China and abroad.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & \multicolumn{5}{c}{**DAS**} & \multirow{2}{*}{**Time (UTC)**} & \multirow{2}{*}{**Sun Elevation**} & \multirow{2}{*}{**Sun Azimuth**} \\ \cline{2-2} \cline{5-8}  & **Period** & & & & & & \\ \hline
1 May 2022 & A & 193 & 194 & 185 & 13:57 & 55.4\({}^{\circ}\) & 115.6\({}^{\circ}\) \\
6 May 2022 & B & 198 & 199 & 190 & 14:44 & 64.2\({}^{\circ}\) & 129.1\({}^{\circ}\) \\
15 May 2022 & C & 207 & 208 & 199 & 14:31 & 63.7\({}^{\circ}\) & 119.9\({}^{\circ}\) \\
23 May 2022 & D & 215 & 216 & 207 & 14:33 & 64.7\({}^{\circ}\) & 116.8\({}^{\circ}\) \\
15 June 2022 & E & 223 & 224 & 215 & 13:58 & 58.9 & 104.1\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of PlanetScope satellite imageries (3 m resolution) on different dates of the grain-filling stage.  
Figure 3: Location of georeferenced sampling plots in the field and five-point sampling method.  
In the research of the monitoring of the crop growth process and yield formation, the normalized differential vegetation index (NDVI), green normalized differential vegetation index (GNDVI), plant senescence reflectance index (PSRI), normalized difference red-edge index (NDRE), normalized difference vegetation index of red-edge/red (NDVI-rededge) and nonlinear vegetation index (NLI), which are six VIs based on visible light, red-edge and near-infrared wavebands, were widely used. ArcMap 10.7 software (ESRI Co., Redlands, CA, USA) was used to extract reflectance data from all bands, and SPSS 25 (IBM Co., Foster City, CA, USA) software was used to calculate the six VIs.  
All VI values were used to correlate with the dry thousand-grain weight, fresh thousand-grain weight and water content at each sampling point, and the linear model, polynomial model and logistic growth model were created to validate the potential of these VIs in predicting winter wheat maturity variability. These indices were selected due to their wide application in monitoring different physiological and agronomical parameters in various crops (Table 2).
# Simulation-Supported Testing of Smart Energy Product Prototypes
## 2 Materials and Methods
### Simulation Environment Testing  
A series of short testing sequences were developed to validate prototype operation using the simulation environment from the Smart Electricity Systems and Technology Services laboratory (SmartEST Lab) at the Austrian Institute of Technology (AIT) [26]. In these tests, energy production and consumption were independently simulated to model different system states, which were interpreted by each prototype in order to set its LED properties accordingly, as seen in Figure 2 below. This was achieved through the following process:  
* Energy **generation** was modelled using a DC voltage/current source, which simulated a residential PV system. Energy **consumption**, on the other hand, was modelled using an RLC controllable load, which consumed the generated power or drew power from the local grid whenever consumption exceeded generation.  
* The laboratory's main measurement system integrated these two inputs and periodically passed them on to the HEMP prototype using the communication infrastructure, which consisted of a custom-built middleware application linking these components.
* The prototype calculated the key indicator's new value and set the corresponding LED properties.
## 3 Results
### Impacts of DSPPs on Adipocyte Lipolysis  
To determine whether DSPPs increase lipolysis to decrease lipid content, researchers looked at the lipolysis of completely differentiated adipocytes. DSPPs (\(p<0.05\)) improved epinephrine-induced lipolysis at a concentration of 100 \(\upmu\)g/mL but only by approximately 13% (Figure 7). Nevertheless, basal lipolysis without epinephrine (\(p<0.05\)) was reduced in response to treatment with 100 \(\upmu\)g/mL DSPPs compared to that of the control (Figure 7) in completely differentiated 3T3-L1 adipocytes. The amount of available glycerol issued into the culture medium fell by approximately 21% after 4 h of incubation. DSPPs reduced basal lipolysis at a dosage of 25 \(\upmu\)g/mL. However, when equated with the control, this decline was statistically insignificant. Meanwhile, the free fatty acid levels in the supernatant have a more convincing readout for lipolysis. Figure 8 outlines the binding between epicatechin (the main polyphenol of DSPPs) and the lipase enzyme (PDB: 1 HPL). In brief, epicatechin showed a weak interaction or did not significantly interact with lipase, thereby not influencing its activity to accelerate the lipolysis process. This behavior of DSPPs toward the lipase enzyme may be one of the key roles of the pills as potential anti-obesity, but more comprehensive studies of this effect are demanded in further studies.  
Figure 4: The impact of DSPPs that have been technically produced on fat accumulation. According to the MDI differentiation procedure, DSPPs or a vehicle were administered to 2 d post-confluent 3T3-L1 cells for 6 d. The values (\(n=3\)) represent means \(\pm\) SD. Typical illustrations of Oil Red O staining and AdipoRed test for measuring lipid content. The lipid accumulation was also calculated based on the cell number counted and dividing by the intensity of the cell number of each sample. Scale bars = 50 \(\upmu\)m. C is the control cells without DSPP. Mean values with different letters are significantly different (\(p<0.05\)).  
Figure 5: The impacts of DSPPs that have been technologically produced on adipocyte differentiation. According to the MDI differentiation procedure, DSPPs or a vehicle were given to two-day post-confluent 3T3-L1 cells for 6 d. The values depicted means \(\pm\) SD. (**A**) The Wise and Green technique was used to assess the GPDH activity (\(n\) = 3). Western blot analysis was used to examine PPAR expression (**B**). Mean values with different letters are significantly different (\(p\) < 0.05).  
Figure 6: The 2D and 3D interaction models between the GPDH enzyme (PDB: 3 DA1) and epicatechin (PubChem CID-72276).