esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Communication Technologies
### RFID Based  
Another possibility of communication between the nodes of the monitoring system and trains is using RFID tags. Broadly speaking, RFID is a wireless technology for short range and low power consumption that has two components: RFID tags and RFID readers. An RFID tag can store and send information to a reader via radio waves. The tag consists of an antenna, a wireless transducer and a microchip to store data obtained from sensors. Moreover, the reader is able to automatically read data recorded on the tag when it is physically close to it (in the range of centimeters or even meters).  
In this proposed architecture, shown in Figure 10, the data will be read by the train at no additional cost to the energy monitoring system since RFID tags are typically passive elements. The RFID reader is therefore the element that is installed on the train and passes over each of the RFID tags deployed in the slab track to collect the information. The Arduino over which the system has been built needs a way to record data on the RFID tag prior to being read by the train. One proposal for this is using the Monza chip from the company Impinj [23]. These RFID chips, in addition to their normal function, can be  
Figure 9: 868 MHz radios without using groups.  
read and written via an I2C line. This line would be used by the Arduino to program the tags with data from the sensors.  
In order to analyze the feasibility of this option it is necessary to take into account the maximum distance at which you can read the labels and the speed at which you can perform the reading. Other factors to consider in this feasibility analysis are:  
1. Consumption of I2C protocol for recording data.
2. Read speed.
3. Maximum reading distance.
4. Device capability.  
This architecture was discarded due to the high costs of the initial design and manufacturing of the tags. However, we believe that, if a high number of tags were manufactured and deployed, this solution would be feasible.
## 3 Results
### Spatiotemporal Trends in GPP, ER, and NEP  
During 2000-2020, the annual GPP and ER increased in 69% and 84% of the alpine grasslands on the QTP, respectively, with significant increases found in 29% and 30% of them for GPP and ER, respectively (Figure 4a,b). The annual NEP increased in 52% of the alpine grasslands, with a significant increase found in 15% of them (Figure 4c). Although all carbon budget indicators generally increased across most alpine grasslands from 2000 to 2020, they exhibited significant differences in the spatial patterns of their trends (Figure 4a-c). In the alpine grasslands, areas with increasing GPP were mainly concentrated in the northeastern part and scattered in the western region (Figure 4a), while upward trends in ER were widely distributed throughout the plateau (Figure 4b). The  
Figure 3: Overall dynamics of annual GPP (gross primary productivity) (**a**), ER (ecosystem respiration) (**b**), and NEP (net ecosystem productivity) (**c**) of the alpine grassland on the Qinghai–Tibet Plateau (AGQIP), and the spatial distribution of the average GPP (**d**), ER (**e**), and NEP (**f**) during 2000–2020.  
spatial pattern of trends in NEP showed pronounced heterogeneity, with areas of increasing NEP mainly concentrated in the northeast and areas of decreasing NEP mainly distributed in the west (Figure 4c). In addition, grasslands with significantly decreased NEP accounted for more than 5% of the total grassland area (inset map in Figure 4c), while only a very small percentage (<2%) of grasslands experienced a significant decrease in GPP or ER (inset maps in Figure 4a,b).  
The analysis results based on the sliding window method indicated that in the majority of windows (70%), the alpine grassland area where GPP increased was larger than the area where it decreased (Figure 4d). In almost all windows (92%), the proportion of grasslands with increasing ER was significantly greater than the proportion with decreasing ER (Figure 4e). The variability in the proportion of grasslands exhibiting different trends in NEP was relatively large. For instance, from the window of 2008 to 2012, the proportion of grasslands with increasing NEP dropped sharply from nearly 80% to 20% (Figure 4f). Despite these differences, overall, the proportion of grasslands with increasing GPP, ER, or NEP generally decreased from 2000 to 2020.  
Figure 4: Spatial distributions of trends (**a–c**) and the changes in the proportion of the area of different trends (**d–f**) in gross primary productivity (GPP) (**a,d**), ecosystem respiration (ER) (**b,e**), and net ecosystem respiration (NEP) (**c,f**) over the alpine grassland on the Qinghai–Tibet Plateau (AGQIP) during 2000–2020. The inset maps (**a,b**) show significant (\(p<0.05\)) decreases (red), and significant increases (green). The lower panels (**d–f**) display the proportion of the grassland areas with different trends in each window on the vertical axis, while the horizontal axis indicates the year in the middle of each window (e.g., 2004 represents the window of 2000–2008).
# Improvement of Light Extraction in Deep Ultraviolet GaN Light Emitting Diodes with Mesh P-Contacts
## 3 Results  
Figure 2 shows the optical microscope (OM) and scanning electron microscope (SEM) images of the mesh p-GaN/ITO DUV LED. In Figure 2a, the OM image is taken from the substrate side. Large circular array and smaller circular mesh are n-contacts and holes in the p-GaN/ITO layers, respectively. In Figure 2b, the SEM image is taken from the p-side. The two large pads are for bonding with the n and p electrodes. The mesh holes in the p-GaN/ITO layers are colored with blue. Figure 2c is the enlarged SEM image of the area marked by a red rectangular dashed box in Figure 2b. We cut the sample along the dashed line shown in Figure 2c on the p-contacts and the corresponding cross-sectional image is shown in Figure 2d. We can clearly see that the p-GaN/ITO was etched down by 50 nm, and the passivation layer indeed covered the sidewalls of the p-GaN layer. To further improve the characteristics of the DUV LEDs, the mesh p-GaN/ITO structures were investigated.  
Figure 2: The OM and SEM images of the mesh p-GaN/ITO DUV LED. (**a**) OM image taken from the substrate side (**b**) SEM image taken from the p-side. (**c**) Enlarge SEM image of mesh p contact (**d**) Cross-sectional image of mesh p contact.  
Figure 3a shows the light output L-I characteristics and the I-V characteristics of two kinds of DUV LEDs. The mesh p-GaN/ITO DUV LED shows the output power of approximately 5.64 mW at 20 mA, which is 12% higher than the output power from the conventional DUV LED. The improvement of light output emission may result from the novel structure that reduces the volume of the P-GaN layer and the ITO layer, which can decrease the light absorption in the chip. The mesh p-GaN/ITO DUV LED shows the voltage of approximately 6.65 V at 20 mA, which is higher than the voltage of the conventional DUV LED due to the smaller contact area. Figure 3b shows External Quantum Efficiency (EQE) versus the current characteristics and the emission spectra of the two kinds of DUV LED. The EQE is defined by the ratio of the number of emitted photons to the number of electrons injected into the device, and can be calculated by the output power divided by the input current multiplied with the photon energy to the electronic charge ratio. The mesh p-GaN/ITO DUV LED showed that the EQE value was as high as 6.3% at 20 mA. In addition, the notorious efficiency droop was not observed up to 120 mA and the output power could reach 32 mW at 120 mA. The wavelength peaks of the two kinds of DUV LED were almost the same, which were at 280 nm, and the intensity of the mesh p-GaN/ITO DUV LED was higher than the one for the conventional DUV LED.  
Figure 3: (**a**) The measured L–I characteristics and I–V characteristics of the two kinds of DUV LEDs. The inset shows L-I characteristics from 0 to 20 mA. (**b**) EQE versus current characteristics of the two kinds of DUV LED. The inset shows the emission spectra of the two kinds of DUV LEDs. The solid line and dashed lines are data curves for the mesh p-GaN/ITO DUV LED and the conventional DUV LED.
## 3 Related Methods
### Dirichlet Process  
One of the main problems in clustering is determining the number of clusters [31]. [PERSON] [32] introduced the Dirichlet Process (DP), which is used in Bayesian nonparametric clustering. In DP, the number of clusters is not predefined like in Gaussian Mixture Modeling [33]. Dirichlet Process consists of infinitely larger number clusters [34].  
The DP is usually represented by the Chinese Restaurant Process [35]. Imagine a restaurant with infinitely many tables. In this metaphorical example, customers and tables symbolize data points and clusters, respectively. A customer will sit at a new table with a probability proportional to \(\alpha\), or sit at a table which already people sitting there with a probability proportional to the number of people at that table. In particular, the \(k\)thcustomer chooses the table \(i\) with a probability of \(\frac{k_{i}}{\pi+k-1}\) for \(1\leq i\leq I\) and a new table with a probability of \(\frac{\alpha}{\pi+k-1}\) in which \(k_{i}\) represents the number of customers already sitting at table \(i\), and \(I\) represents the total number of tables. In our model, draws from a multinomial distribution of which parameters are derived from a Dirichlet distribution represent the data to cluster. There are \(c_{i}\) indicators of data points for each cluster. Let \(\theta_{i}\) be an associated cluster parameter derived from a base distribution \(G_{0}\). The following DP describes a collection of observations \((x_{1},\ldots,x_{n})\) with \(\theta=(\theta_{1},\ldots,\theta_{n})\) latent variables:  
\[G\sim DP(G_{0},\alpha) \tag{1}\]  
\[\theta_{i}\sim G \tag{2}\]  
\[x_{i}\sim\mathbb{N}(\cdot|\theta_{i}) \tag{3}\]  
where \(G\) is probability measure drawn from DP, \(\alpha\) is the scaling parameter, \(\theta_{i}\) is generated from \(G\), and \(x_{i}\) is data points derived from a normal distribution with parameter \(\theta_{i}\).  
Figure 1 presents the wind-speed time series clustering with the Dirichlet process for the last 1000 h of one of our data sets. In Figure 1, each line style represents a different cluster type, and vertical lines form the clustering blocks. In this data set, the DP model results in five clusters.
# Optimization of Intelligent Maintenance System in Smart Factory Using State Space Search Algorithm
## 2 Materials and Methods
### A New Design of an Intelligent Maintenance System (IMS)
Data Integration: Combining data from various sources and formats into a cohesive system. 2. Real-Time Processing: Ensuring the system can process and analyze data in real-time to provide timely maintenance alerts. 3. Predictive Accuracy: Developing algorithms that accurately predict maintenance needs to prevent unexpected failures. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Literature**} & \multirow{2}{*}{**Maintenance Method**} & \multicolumn{5}{c}{**Algorithms Objectives**} \\ \cline{3-6}  & & **Optimization** & **Cost** & **Time** & **Sustainable** \\ \hline
[3; 4] & Preventive & & ✓ & ✓ & \\
[5; 13] & Predictive & & ✓ & ✓ & \\
[7] & Reactive & & & ✓ & \\
[19; 31] & CMMSs & ✓ & ✓ & ✓ & \\
[14; 16; 32] & IMSs & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Various maintenance approaches in comparison to their functional applications. Figure 14: A new design of an intelligent maintenance system (IMS). Scalability: Designing the system to handle increasing amounts of data and more complex operations as the company grows. * User Interface: Creating an intuitive and user-friendly interface for maintenance personnel to interact with the system. * Cost Efficiency: Balancing the cost of implementing and maintaining the IMS with the benefits it provides. Addressing these challenges effectively can lead to a robust and efficient IMS that significantly improves maintenance operations [35].
## 3 Results and Discussion
### Methodology  
#### 3.3.1 Preprocessing Levels of PRISMA Hyperspectral Data  
Figure 8 shows the conversion of three different levels of PRISMA data [34].  
* Level 0: the raw binary files, comprising instrument data and cloud percentage, are included in the L0 data product.
* Level 1: the L1 data product contains radiometrically calibrated data for hyperspectral and panchromatic radiance cubes on the top of the atmospheric radiance image.
* Level 2: the L2 data product is further split into three different groups:
* L2B: atmospheric calibration and geolocation of the L1 data product, i.e., atmospheric radiance at the bottom.
* L2C: atmospheric calibration and geolocation of the L1 data product, i.e., bottom of atmospheric radiance including aerosol thickness and map of water vapour.
* L2D: orthorectification of L2C with significantly less cloud coverage.
* Level 1 and Level 2 data are generated in the Hierarchical Data format release 5 (HDF5 or he5). In this work, we have used Level2D data to identify paddy production in a particular area.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Product Name** & **PRS\_L2D\_STD\_20201211052010\_** & **Product Id** & **PRS\_L2D\_STD** \\  & **20201211052014 0001.HE5** & & \\ \hline Processor Version & 02.04 & L1 Processor Version & - \\ \hline Processor Name & L2D & Processing Time & 2021-08-11T04:56:29.000749 \\ \hline Processing Level & 2D & Acquisition Type & EARTH OBSERVATION \\ \hline Acquisition Size & 30 KM & Acquisition Purpose & NOT SPECIAL PRODUCT \\ \hline Number of Bands (VNIR + SWIR) & 234 & Image Id & 15549 \\ \hline Product Start Time & 2020-12-11T05:20:10.524999 & Product Stop Time & 2020-12-11T05:20:14.830690 \\ \hline Sun Zenith Angle & 39.602\({}^{\circ}\) & View Zenith Angle & 15.724\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Dataset descriptions for PRISMA data.  
Figure 8: Mapping ground truth raw soil data with PRISMA hyperspectral data.
# Fusion of Different Image Sources for Improved Monitoring of Agricultural Plots
## 2 Materials and Methods
### Measurement of Similarity and Validation  
As stated in the introduction, the SSIM index has been the chosen metric for measuring similarity between two images (x and y). This index is the combination of three functions that independently compare luminance (\(l(x,y)\)), contrast (\(c(x,y)\)) and correlation (\(s(x,y)\)) of both images:  
\[SSIM(x,y)=[l(x,y)]^{\alpha}\cdot[c(x,y)]^{\beta}\cdot[s(x,y)]^{\gamma} \tag{5}\]The index was implemented assuming \(\alpha=\beta=\gamma=1\) and coefficients \(C_{1}=C_{2}=C_{3}=0\) (not shown in Equation (5)), because sums of squared means and sums of squared standard deviations of our images are far from being 0 in our images. As a consequence, we use a particular case of SSIM implementation that corresponds to the Universal Quality Index described in [13]. The sliding window size was set to 11 \(\times\) 11 pixels for high resolution images and 5 \(\times\) 5 for Sentinel 2 images. These sizes were selected to keep a ratio image size/window size analogous to the one described in [11] and [13]. Values of _SSIM_, \(l\), \(c\) and \(s\) were calculated for each band of the compared images  
Several similarities have been calculated to assess the robustness of the proposed methodology:  
1. Similarity between original drone images (July, original August and original September), in order to have an approximate idea of the SSIM initial values.
2. Similarity between the reference July image and the homogenized August and September images, in order to assess the effect of the homogenization procedure on similarity.
3. Similarity of the Sentinel 2 images during the overlapping period.
4. Similarity of the NDVI, GNDVI and GCI images calculated from Sentinel 2 images during the overlapping period.
5. Similarity of the NDVI, GNDVI and GCI images obtained from merging the original August and the original September images during the overlapping period.
6. Similarity of the NDVI, GNDVI and GCI images obtained from merging the original August or the original September images outside the overlapping period.
## 3 Integrated User-Oriented Framework  
Although these open 3D printing applications are compatible with several 3D printers, each one has its specific parameters, and final users need to have specific knowledge to print a part. To carry out a 3D printing job, the user has to be aware of some necessary principles for management, maintenance, optimization, etc. Moreover, CAM software is needed to generate the trajectory files, and therefore the user has to be aware of how to command it. Furthermore, for the specific case of working with recycled material, the 3D printing application has to be adapted to work with the variability of the new filaments.  
However, marine industry and port activity scenarios have specific challenges; users have limited knowledge of 3D printing techniques, and there is the need to adapt the system to recycled plastics coming from a wide variety of sources and having a variety of properties. The surveys highlighted the need for a simple and intuitive \"user procedure\" or \"service\" to introduce 3D printing to the maritime sector, which, although technified, does not have qualified personnel in additive manufacturing.  
The proposed solution is based on two aspects: a decentralized local service, where each node, company or entity can have the printer of its choice (top part of Figure 1); and an external CAM service provider, which also produces recycled filaments and adjusts the generation of the G-code to suit the user (bottom part of Figure 1). The design of integrated architectures to manage this variety of technologies, application solutions, etc. has already been proposed in [65; 66], though they do not take into account the additional problem of dealing with non-standard materials and handling by non-expert users.  
With this approach, the end-users need only an interface on their smart devices (or personal computer) where they can select the part to print (the virtual model of the part) and the filament for their local printer. The integrated service then takes care of the rest of the process. Figure 1 lists the three entities that make up the integrated service that will be detailed throughout this section.  
1. Local Service--performs the 3D printed job of the object, so it is a local service for the end-user; consists of a commercial 3D printer connected to a print host (Raspberry Pi with Octoprint) that allows remote management (as illustrated in Figure 1).  
Cloud Service--the service in charge of the CAM process and the management of material data and filament stock at the node; a back-end service running in the cloud by the service provider.
3. End-user App--the user application, with a user-friendly interface, having just the selection of the model to be printed and its requirements as well as basic operation management on the 3D printer.  
Additionally, a source of virtual models of parts given in STL becomes important. This may be through a local repository of the user node, from the service provider itself or through some 3D scanning application.
## 4 Proposed Protocol
### Cluster Head Selection Algorithm  
* Each vehicle inside a cluster announces itself as a \"Cluster Head\" and displays the broadcast signal: \(S[Id_{i},~{}POSITION_{i}]\)
* Every vehicle \(V_{j}\) displays the list of closest vehicles (\(VL_{i}\)) after getting \(S[Id_{i},~{}POSITION_{i}]\) from \(V_{i}\)
* \(DISTANCE_{ij}\) is estimated by \(V_{j}\)
* The weighted sum is calculated by \(V_{j}\)
* \(W_{j}=a~{}NV_{j}+bR+c\theta_{i}+dS_{i}\)
* The vehicle calculates the above equation's arguments, and the range of weighted constants varies from 0 to 1. Since the weighted sum is derived from these arguments, the Cluster Head based on this sum will be the most efficient and trustworthy.
* In the end, the \(V_{i}\) with the lowest \(W_{j}\) is selected as the Cluster Head.  
**Pseudo code for the Cluster Head Selection Algorithm:**  
function select cluster head (clusterVehicles)  
lowest weight = infinity  
selected cluster head = null  
for each vehicle \(V_{i}\) in clusterVehicles do  
\(VL_{i}\) = get Closest Vehicles List (\(V_{j}\))weighted sum = calculate Weighted Sum (\(V_{i}\), \(VL_{i}\)) if weighted sum < lowest weight then lowest weight = weighted sum selected cluster head = \(V_{i}\) return selected cluster head  
**Explanation:**  
* The select cluster head function takes the list of vehicles within the cluster as input and returns the selected cluster head.
* It initializes the lowest Weight variable with a high value (infinity) and sets the selected Cluster Head to null.
* For each vehicle \(V_{i}\) in the clusterVehicles list, the algorithm retrieves the list of closest vehicles \(VL_{i}\) using the get Closest Vehicles List function.
* The weighted sum is calculated using the arguments \(V_{i}\) (the current vehicle) and \(VL_{i}\) (the closest vehicles list).
* If the calculated weighted sum is lower than the current lowest Weight, the lowest Weight is updated, and the corresponding vehicle \(V_{i}\) is selected as the potential cluster head.
* After processing all vehicles, the algorithm returns the selected cluster head with the lowest weighted sum.  
The specific implementations of the get Closest Vehicles List and calculate Weighted Sum functions should be defined separately based on your system's requirements and available information.  
Figure 3: V2V and V2I Communication in a cluster.  
The Cluster Head Selection Algorithm utilizes several notations to describe its functionality, further elaborated in Table 9.