esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Hydroponic Cultivation System for Lettuce
## 2 Materials and Methods
### NFT Hydroonic System  
The hydroponic system shown in Figure 2 consists of two channels with a trapezoidal cross-section made of polyvinyl chloride (PVC), each featuring five planting cavities spaced 20 cm apart. The systems also include a frame constructed from chlorinated polyvinyl chloride (CPVC), designed with a 2% slope [35], which is essential for maintaining a continuous flow and ensuring the proper operation of the Nutrient Film Technique (Figure 2,E). Several components of this system were custom designed and manufactured using 3D printing technology, as suitable commercial alternatives were not available to meet the specific functional requirements. The dimensions of each system are 100 cm in length, 50 cm in width and 27 cm in height.  
The NFT hydroponic system operates as follows: the nutrient solution flows from a 20 L tank (Figure 2,F) through a 10 mm diameter hose (Figure 2,D) and into a 12 V DC diaphragm water pump (model TOPINCN, 0.48 MPa, 3.5 L/min; TOPINCN, Shenzhen, China) and then into the hydroponic channels (Figure 2,A), where the plant roots (Figure 2,B) absorb the essential nutrients for growth while the remaining solution returns to the tank by gravity.  
Figure 1: Schematic diagram of the plant growth chamber used in the experiment, measuring 3 m \(\times\) 2 m \(\times\) 2.5 m (length \(\times\) width \(\times\) height). Inside, there are three independent NFT-type hydroponic systems. The system includes an IoT monitoring and control module with Wi-Fi connectivity, environmental sensors (air temperature/RH, \(CO_{2}\) and photosynthetically active radiation), ultrasonic humidifiers, water pumps, LED growth lamps, a mini split air conditioning unit and video monitoring.  
The proposed system was specifically designed for lettuce cultivation but also has the ability to adapt and be applied to the other vegetables such as spinach (_Spinacia olercea_ L), cabbage (_Brassica olerca var. Capitata_) and aromatic plants like ciantro (_Coriandrum sativum_) due to their similar dimensions.
## 4 5G and V2X Deployment Projects
### 5 Gcar  
The 5 GCAR project was initiated in 2019 at the UTAC-TEQMO test track in France. It implemented a 5G-based communication system to address various automotive use cases, with the core of the system comprising 5G new radio sideline communication, 5G new radio positioning, and cellular communication using 5G deployment models like edge computing and network slicing [129].  
To optimize the merging process for vehicles entering a lane, 5 GCAR utilizes an IoT platform that includes a maneuver planning system. This system communicates with connected vehicles by sending information from a central maneuver planning system. It also collects data from roadside cameras to provide real-time video between nearby vehicles through vehicle-to-vehicle (V2V) communication, facilitating safe overtaking maneuvers. Connected vehicles within the 5 GCAR system transmit collected Lidar information, which estimates the position and speed of detected vehicles, to the vehicle-to-infrastructure (V2I) server infrastructure in the IoT platform. The server analyzes these data to predict the real-time trajectories of the vehicles and estimate the risk of collision. It then provides warnings to affected drivers to safely and comfortably avoid potential collisions [130].  
Another significant aspect of 5 GCAR is its road user protection system. This system is designed to detect and identify pedestrians crossing the street behind obstacles. It can halt the vehicle when a pedestrian is in close proximity and issue collision warnings to the driver in advance [131].
## 4 Open Challenges and Future Research Directions
### Holistic Future Challenges and Strategic Opportunities  
IoE systems operate within complex, multi-layered infrastructures that integrate a wide array of heterogeneous devices, platforms, and protocols. As IoE ecosystems continue to evolve, several critical challenges remain unresolved, particularly in areas such as the integration of decentralized technologies [93; 33], quantum-resilient security solutions [38; 52; 53], and scalable AI models [49; 59; 108; 113]. Addressing these challenges is not only essential for building secure, sustainable, and future-proof systems, but also requires interdisciplinary approaches to develop ethical, inclusive, and trustworthy IoE applications. Table 18 presents a structured overview of these fundamental challenges alongside potential strategic solutions.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Challenge** & **Problem Description** & **Potential Solutions** & **References** \\ \hline Scalability of Blockchain and AI & High latency in blockchain transactions and FL model training in large-scale IoE networks & Optimized consensus mechanisms, lightweight privacy-preserving AI techniques, DAG and Layer-2 solutions for high-throughput IoE security & [33; 49; 66; 84; 92; 108] \\ \hline Quantum Threats to Security & [PERSON]’s and Grover’s algorithms threat classical encryption and blockchain integrity & Quantum-safe cryptography (lattice-based, hash-based signatures); hybrid quantum-secure AI integration & [38; 52; 53; 54; 108] \\ \hline Privacy-Preserving AI Optimization & FL, Differential Privacy (DP), and Secure Multi-Party Computation (SMPC) require high computational overhead & Hybrid privacy models combining DP, Edge AI, and Homomorphic Encryption & [59; 113] \\ \hline Decentralized Identity in IoE & Lack of standard security protocols across heterogeneous IoE domains & DID and SSI-based authentication models supported by blockchain trust layers & [93; 127; 130] \\ \hline Cross-Chain IoE Data Security & Data interoperability and security challenges across different IoE platforms & Cross-chain privacy protocols and SMPC-based inter-blockchain security mechanisms & [175; 176; 177] \\ \hline \hline \end{tabular}
\end{table}
Table 18: Future research challenges and potential solutions.  
The challenges summarized in Table 18 highlight the need for not only technological advances but also holistic strategies that address scalability [49; 84], privacy [59; 113], interoperability [93; 127], and post-quantum security [38; 53]. Given the inherent complexity of IoE ecosystems, comprising diverse platforms, device types, and varying levels of data sensitivity, solutions must be hybrid and adaptive rather than singular or static [70; 108; 120]. In this context, the proposed solutions are designed to both strengthen current systems and enhance resilience against future threats. The mappings provided in the table offer a concrete perspective on aligning technological priorities with research strategies, enabling the development of next-generation IoE security frameworks.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### gMIMO
#### 3.1.8 State of the Art of the Beamformer-Antenna Array System  
In the following section, the beamformer-antenna array state of the art is presented in Table 3. The notation used is as follows: PY stands for the publication year. In the beamformer column, A, B, and MB stand for active, Butler, and modified Butler beamformers, with the symbol (4 \(\times\) 8) meaning a matrix of four inputs and eight outputs. In the number of beams column, CS means continuum scanning, while 2 \(\times\) 4 means four beams with two different polarizations. SD stands for scanning dimensions, and A(D)BF stands for an analog (digital) beamformer. T stands for technology and MS, SIW, and WG indicate microstrip, surface integrated waveguide, and waveguide. FBW stands for the fractional bandwidth. Regarding the antenna element, V and P stand for vertical or planar positioning of the antenna element axis with respect to the host surface of the array. TSA means the tapered slot antenna, and WG-A indicates the waveguide aperture.  
Given the state of the art indicated in the table, we conclude that while noticeable efforts are present, nevertheless, a scaling of 4 or 5 is still needed to deliver 256 to 512 beams in order for a system to be classified as a gMIMO enabler.  
Next, in Table 4, we present various noticeable published MIMO prototypes, following a chronological order, to better demonstrate the relative evolution.
# Zeroes: Robust Derivative-Based Demodulation Method for Optical Camera Communication
## 2 Optical Camera Communication
### Image Conversion  
With the information transmitted through the brightness variation of a white LED, images contain a redundancy of the information through the three color channels. Usually, RGB images are firstly converted in another representation to reduce the image dimensionality, such as grayscale [6] or value [19] (from hue saturation value representation), therefore reducing the whole processing time. Hence, in the grayscale representation, each pixel is a linear combination of the three RGB pixel values, whereas only the maximum pixel value is retained in the value representation. Thus, the value representation provides a better contrast between bright and dark strips on colorful object while adding a reasonable amount of noise, whereas the grayscale representation is more resilient against chromatic aberration [20]. In fact, this phenomenon occurs when the lens fails to focus the color light in a same pixel due to a refractive index changing with wavelength, which is even more pronounced when the contrast between the strips is high. Hence, as illustrated in Figure 2a, chromatic aberration makes the dark strips boundaries colorful, which are therefore considered as bright strips in value representation (Figure 2c), while the impact of the color boundaries on the strips detection is less important in grayscale (Figure 2b). Moreover, as images are captured with rolling shutter, a pixel column therefore contains a redundancy of the transmitted information. Thus, processing all the pixels of a column is not required to correctly recover the transmitted information. The same remains true to a lesser extend for the pixel rows when the transmitted signal is oversampled, resulting in wider bright and dark strips in the image.  
Therefore, the image can be downsampled in height (keeping 1 pixel row over \(\Delta_{row}\), with \(\Delta_{row}\) being the decimation factor along the pixel rows) and width (keeping 1 pixel  
Figure 1: Schematic representation of OCC reception side.  
column over \(\Delta_{col}\), with \(\Delta_{col}\) being the decimation factor along the pixel columns) in order to reduce the processing time aiming for a real-time execution [21].
## 2 Establishing the Mathematical Model for Controlling Spot Size  
### Establishment of the Mathematical Model  
For curved parts, the complex surface morphology has an important influence on the scanning spot size, to determine the diameter of laser beam in the cladding process. [PERSON] et al. [18] proposed the equal bow height method, where the laser beam can be simplified as a cylinder. Due to the curved parts having a complex curvature, the laser spot size on the surface is irregular; however, the variable spot size can cause the inhomogeneous distribution of laser energy, which affects the final coating quality. To solve the problem, the surface which is scanned by the laser spot is simplified, as a part of curvature sphere (shown in Figure 1), and the radius of sphere is the reciprocal of the maximum curvature of the point on the surface [19; 20]--the problem is therefore transformed into solving the intersection area of the sphere and cylinder. At the beginning of the laser cladding, the laser beam is in the normal direction of the sphere and the axis of the cylinder passes through the centre of the sphere (shown in Figure 2). When the curvature of the workpiece changes, the position and the radius of the curvature sphere changes. In the wake of the moving of the laser beam (cylinder) with its initial attitude, the relative position between the cylinder and the sphere changes; at this time, the second case is formed as follows: the axis of the cylinder does not pass through the centre of the sphere (shown in Figure 3).  
Figure 1: The curvature sphere fitting the complex surface.  
When the axis of the cylinder passes through the centre of the sphere, (as shown in Figure 2), the process of solving the intersection area of the sphere and cylinder is as follows: the radius of the cylinder is set as '\(r\)', the radius of the curvature ball is '\(R\)' and the area of the laser beam irradiated on the workpiece is '\(A\)'. According to mathematical knowledge, the equation of the sphere is x\({}^{2}+\)y\({}^{2}+\)z\({}^{2}=\) R\({}^{2}\), and the equation of the cylinder is x\({}^{2}+\)y\({}^{2}=\) r\({}^{2}\). The equation of the irradiated surface is z = \(\sqrt{\text{R}^{2}-\text{x}^{2}-\text{y}^{2}}\).  
Figure 3: The cylinder passing through the sphere without crossing its centre: (**a**) the relative position between cylinder and sphere, (**b**) the side view of the model, (**c**) the model after rotating on the \(Z\) axis.  
Figure 2: The cylinder passing through the sphere centre.
## 3 Experiment and Result
### Gust Front and Vortex Observations  
During the experiment, many complex flow phenomena were observed, which pose potential threats to aircraft. Figure 8 shows a continuous process of gust front and vortex structure on 1 July, 3:46-4:29 (UTC).  
Figure 8: The windshear event detected by the CDWL on July 1. (UTC). (**a–d**) Radial speed, (**e–h**) Horizontal speed. (**a,e**) 3:46:31-3:47:12, (**b,f**) 3:57:17-3:57:58, (**c,g**) 4:08:03–4:08:45, (**d,h**) 4:29:34–4:30:17.  
The maximum detection distance exceeded 12 km. At 3:46, one gust was blowing from the northwest, the other was from the south. The radial velocity was divided into three zones, and each gust front was accompanied by a low velocity zone at the junction of streams, as shown in Figure 8a-d. The two streams of air squeezed each other, resulting in a vortex that spined in reverse time, as shown in Figure 8e-h. The vortex has an influence range of 10 km in the south glide path. As the two air flows come closer, the vortex structure becomes more obvious.  
The whole process lasted 43 min. Complex flows such as gust front and vortex can easily lead to a loss of wind speed. Sufficient intensity of front and vortex structures will affect the aircraft takeoff/landing for their wide influence. Through the two-dimensional wind field retrieval, these complex flows can be accurately depicted and identified, which is important to ensure the safety of aircraft.
## 2 Background and Related Work
### Tangibles in VR
#### 2.1.1 Active  
[PERSON] et al. [11] implemented plush toys with various sensors including pitch and roll sensors, gyroscope, and magnetometers as well flexion and squeeze sensors, which enabled them to sense various kinds of interactions with the toys. Although the usage of the toys was not in immersive VR but rather in front of a screen, this project demonstrates an interesting aspect of adapting existing toys: they are familiar to users and they can easily be \"diegetic tangibles\" that \"exist within the space and time of a narrative's world and can be an effective strategy for interaction design and for narrative design\" [12].  
[PERSON] et al. [13] employed Sifteo Cubes as tangible objects in a VR game--Maze Commander--where two players, one using a VR headset and another manipulating the Sifteo Cubes, collaborate to escape a maze. The player with the Sifteo Cubes, moves them around to form and manipulate a virtual maze, while the VR user can identify enemies and obstacles. Sifteo Cubes are small computers (cubes with about 4 cm) with screens and sensors that enable them to react to movements and proximity with one another. Maze Commander demonstrates an important aspect of tangible interaction: part of the state of the VR system is visible and physically manipulable by users outside of VR.  
Another example is Snake Charmer by [PERSON] et al. [6] where a robotic arm acts as physical object to provide haptic feedback. The arm tracks the movements of the user's hand and is able to reposition itself so as to provide haptic feedback when the user touches a virtual object. It is also capable of picking up different endpoints to provide different surface textures, temperatures, etc. Snake Charmer highlights the importance of the detail of the haptic feedback when interacting in immersive VR.  
All these are examples of active tangibles: objects instrumented with sensing capabilities that need power to operate. Active tangibles are usually more expensive and/or require considerable effort to produce.
## 2 Materials and Methods
### Data Sources  
The road network data for the Mingcheng District of Xi'an are sourced from the 2022 Chinese road dataset published by the Open Street Map website. The centerline is established using road network land information, with sampling points selected at 30 m intervals. After eliminating sampling points such as intersections and expressways, street view images are retrieved by utilizing the Baidu API interface through Python. Subsequently, location information for each viewpoint is inputted to acquire panoramic views, along with details such as location and viewing angles for each sampling point. Following this, the viewpoint location information is utilized to generate panoramic views for each collection point, along with relevant details such as location and viewing angles. The acquired street panoramic images are then processed in batches, resulting in a total of 6784 effective street view images.  
This study employs the large-scale ADE20K dataset released by MIT as the model training dataset, selecting the top 150 categories based on their total pixel ratios within the ADE20K dataset for annotation purposes. The labeled pixels across 150 classes constitute 90.16% of the total pixels in the dataset, thereby linking the resulting pixel images to an expanded knowledge base. This makes the ADE20K dataset more versatile and better suited to a variety of urban environments [31]. Upon comparing the coverage of various models (Table 1), it is evident that DeepLabV3+ outperforms others in street view image segmentation. This flexible and scalable model leverages dilated convolution and the ASPP module to capture multi-scale contextual information, adapting to objects of varying sizes and shapes in street view images. Additionally, it can be fine-tuned for specific tasks and datasets. So, utilizing DeepLabv3 semantic segmentation, the model and training parameters were configured in PyCharm. The ADE20K dataset was employed to classify street scene images of Mingcheng District at the pixel level, enabling the completion of the semantic segmentation task and the extraction of segmentation result data (see Figure 2), allowing for the extraction of street scene metrics that quantify pixels across various categories, including road, sky, vegetation, buildings, and railings. In terms of model validation, the agreement between the model's segmentation and human segment recognition at randomly selected locations in GSV images was 85% [32] (kappa coefficient = 0.78, \(p<0.001\)). Python ([[https://www.python.org/](https://www.python.org/)]([https://www.python.org/](https://www.python.org/))) is employed to compute the ratios of these different elements within the street scene.
## 2 Smart Supply Chain with Additive Manufacturing
### Flexibility in the Smart Supply Chain with Additive Manufacturing
#### 2.2.1 Design Flexibility  
A 3D printer using additive manufacturing technology adds layers using raw materials such as polymers, ceramics, and metals [25]. 3D printers can produce products in the desired shape without molds when the raw materials necessary for the production are ready; the variety of product shapes is therefore very broad. Design flexibility refers to the flexibility to produce a product with a variety of designs. 3D printers can be used to produce customized products with various designs performing the same function [4].  
Production through 3D printouts can be based on CAD drawings, and do not require expensive tools used for subtractive manufacturing processes such as drilling, grinding, and molding [26]. The process of product design is less constrained by manufacturing processes, and has a high degree of freedom. With 3D printers, hollow-core structures are easily formed while maintaining or optimizing the characteristics and performance of products. For example, [PERSON] et al. [27] propose a honeycomb-cell structure providing strength in tension with minimal material cost. [PERSON] et al. [28] investigate the properties of spider web design. These new types of design allow companies to make products that perform the same function while reducing the weight of the parts compared to solid type designs, through topology optimization [29; 30]. Moreover, in order to manufacture lighter parts, fewer raw materials are used and a manufacturer can save on raw materials costs. Lighter products consume less energy and produce less CO\({}_{2}\), and are therefore of significant interest to companies in automobiles and aviation.
# Improved Ensemble Learning for Wind Turbine Main Bearing Fault Diagnosis
## 3 Methodology
### Isolation Forest
#### 3.2.3 Isolation Forest Setup  
Isolation forest has been introduced by [PERSON] et al. (2019) as an efficient anomaly detection algorithm based on widely known decision trees. Unlike most model-based algorithms, no normal condition training set is required. Points lying at the edges of the data distribution are to be intended as anomalies. Having this definition of anomalous point, isolation forest uses binary search trees to recursively split data. Various fully developed trees, i.e., having single instance terminal nodes, are trained. Isolation of anomalies is promoted by using random partitions when separating data, as indicated in [PERSON] et al. (2019). The anomaly score used to determine the likelihood of a point to be anomalous is based on the average path length required by the trained trees to isolate the cited point from the rest of the data. Equation (2) is the canonical definition for an isolation forest anomaly score.  
\[s(x,n)=2^{-\frac{E(h(x))}{c(n)}} \tag{2}\]  
where \(E(h(x))\) is the average of the path length \(h(x)\) required to isolate the given point. The denominator \(c(n)\) is the average path length of unsuccessful search in binary search tree. Given a distribution \(d\), three cases are typical:  
* \(\mathrm{s}\sim\)1 \(\implies\) x is anomalous
* \(\mathrm{s}<0.5\implies\) x is normal
* \(\mathrm{s}\approx 0.5\ \forall\ \mathrm{x}\ \mathrm{in}\ \mathrm{d}\implies\) no anomalies in \(\mathrm{d}\)  
In this research, the implementation of Isolation Forest from Sklearn, for Python programming language is utilized [PERSON] et al. (2019). The following parameters are set to run the algorithm:  
* number of estimators \(=250\)
* contamination \(=0.1\)
* max samples \(=0.3\)  
The number of estimators determines the quantity of decision trees of which the isolation forest is composed. More trees lead to more reliable estimations of the path length, at the cost of higher computational time. The contamination coefficient represents the expected amount of anomalies in the data. The points in the analyzed distribution are sorted by their anomaly score and the most anomalous ones, according to the contamination value, are selected. Finally, the maximum number of samples to use to train each estimator, i.e., each decision tree, is defined by the homonymous parameter.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Variable** & **Description** & **Range** & **Units** \\ \hline AmbientTemp & Ambient temperature & \([-5,\ 40]\) & \({}^{\circ}\)C \\ MainBearingTemp & Main bearing temperature & \([0,\ 120]\) & \({}^{\circ}\)C \\ RotorSpeed & Rotor speed & \([0,\ 50]\) & rpm \\ \hline \hline \end{tabular}
\end{table}
Table 4: Selected SCADA variables used to develop the anomaly detection model, its description, range of possible values, and units. All of them are related to the mean value over a 10-min period.