esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The history of agricultural yield prediction is very long [1], and the research area is very wide [2]. The various methods can be divided into two main categories: physical models (also known as process-based or mechanistic) and data-driven (statistical and machine learning) models [3, 4]. Process-based models use physical laws that simulate the growth of crops and can be used to predict yield under a variety of conditions. Data-driven models use statistical methods to learn from historical data [3]. Additionally, some models, such as deterministic and stochastic models, can fall into either category [1]. Deterministic models provide a single, specific output for a given set of inputs, assuming no randomness in the process, and are often based on precise relationships derived from physical laws [5]. Stochastic models, on the other hand, incorporate randomness or uncertainty, providing a range of possible outcomes based on probabilistic inputs, which can better reflect the inherent variability in agricultural systems, such as weather or biological processes [6]. Data-driven models (especially the statistical ones) are the most used [7] since they can be developed more easily and, if well-articulated, can guarantee reliable results.  
In recent years, remote sensing has become essential for providing timely, high-quality information on crop growth [8], while machine learning (ML) techniques have gained prominence in this field [9; 10]. Convolutional Neural Networks (CNNs) have been particularly effective in extracting features from satellite imagery time-series [11], often combined with Long Short-Term Memory (LSTM) networks to capture spatial and temporal growth patterns [12]. Among the most used methods for yield prediction are those based on decision trees [2]. Ensemble methods, which combine multiple models, either process-based or data-driven, have also demonstrated good performance [13; 14; 15; 16; 17].
## 4 System Implementation and Simulation Results  
This section explains and discusses the system implementation and simulation results, along with an analysis of the achieved efficiency.
# Superpixel Segmentation of Hyperspectral Images Based on Entropy and Mutual Information
###### Abstract  
Superpixel segmentation (SS) methods have been proven to be feasible in improving the performance of hybrid algorithms on hyperspectral images (HSIs). In this paper, a superpixel segmentation algorithm based on the information measures with color histogram driving (IM-CHD) was proposed. First, Shannon entropy was applied to measure the image information and preliminarily select spectral bands. Mutual information (MI) is derived from the concept of entropy and measures the statistical dependence between two random variables. Also, MI can effectively identify the redundant spectral bands. Therefore, in this paper, both MI and color matching functions (CMF) were used to select the most useful spectral bands. Second, the selected spectral bands were combined into a false color image containing the main spectral information. A local optimization algorithm named \"hill climbing\" was used to achieve the superpixel segmentation. Finally, parameter selection experiments and comparative experiments were performed on two hyperspectral data sets. The experimental results showed that the IM-CHD method was more efficient and accurate than other state-of-the-art methods.  
hyperspectral images; superpixel segmentation; entropy; mutual information; color matching functions +
Footnote †: journal: Medical Image Computing
# Dynamic Modeling and Response Analysis of an Optical Machining Robot with Prismatic Joint Clearance
###### Abstract  
The accuracy and stability of robotic systems are significantly influenced by joint clearances, especially in precision applications like optical mirror polishing. This study focuses on a 5-DOF (Degree of Freedom) parallel manipulator designed for optical mirror polishing. The study conducts dynamic modeling by incorporating prismatic joint clearance and examines the resulting dynamic response. Previous studies on dynamic modeling have primarily focused on planar mechanisms with rotational or ball joint clearances, whereas research on parallel manipulators with spatial prismatic joint clearances remains limited. This study introduces a comprehensive dynamic modeling framework for parallel manipulators with prismatic joint clearance, utilizing the Lagrange multiplier method (LMD). First, the prismatic joint models of the guideway and slider in the parallel manipulator are simplified, enabling the determination of different contact states and the calculation of friction and contact forces for various contact types. Second, the dynamic equations of the parallel manipulator are derived by establishing system constraint equations. Finally, the dynamic responses of various clearance-related factors are determined through a combination of theoretical calculations and ADAMS simulations. This study provides a framework for modeling the dynamics of parallel manipulators with prismatic joint gaps, offering valuable insights into the design and control of high-precision robotic systems.  
parallel manipulator; dynamic model; prismatic joint clearance; LMD
## 2 Circular Trajectory Generator Measurement System  
The circular trajectory generator measurement system mainly adopts a man-machine interface to realize operation, and the principle is servo system to realize speed control. PLC through the control sent pulse control servo motor, Servo motor and reducer are connected by flange, the reducer through a certain reduction ratio control mechanical parts. The circular trajectory generator measurement system consists of a mechanical system and an electronic control system. The former is composed of the main body of the standard circular trajectory generator, a rotating arm, and a microsphere holder, while the latter is mainly a rotational velocity control system. The main body of the standard circular trajectory generator is made up of a high-precision spindle motor, a motor holder, and a transmission system. The dynamic measurement system based on the circular trajectory generator is shown in Figure 1, where Figure 1a is the CAD simulation diagram and Figure 1b is the on-site measurement diagram. Figure 1c,d are schematic diagrams at different measuring distances.  
The control system of this study is divided into two subsystems for motion control and data acquisition. The motion control subsystem includes the rotating servo of the rotating arm, with a speed ranging from 0 to 5 r/s, where r represents the number of revolutions. The speed is steplessly adjustable to satisfy the requirements of different measurements. The data acquisition subsystem samples and saves the data installed in the servo motor encoder.  
Supposing the linear velocity of the laser tracker is v, we have the equation  
\[\mathrm{v}=\mathrm{R}\times\mathrm{w} \tag{1}\]  
where R denotes the radius (unit: m) of the circular trajectory generator, and w denotes the angular velocity (unit: rad/s) of the circular trajectory generator (1r/s = 2\(\pi\) rad/s). After measurement, the radius of the circular trajectory generator is 0.5013 m.
# 3D Approaches and Challenges in Facial Expression Recognition Algorithms--A Literature Review
## 7 Role of Time  
Facial expressions play an important role in the recognition of human emotions. At first, facial expressions analysis interested only psychologists, but later it had a wide diffusion in the field of scientific research.  
In the past, many techniques, including neural networks, have been applied to recognize facial expressions in still images. The strategies developed had some limitations; one of these is the possibility to view only an instant of the expression, and usually, the still images capture the moment at which it is most marked, without considering that in their daily lives people show the apex of their facial emotions only for particular cases and for very brief periods of time. The subtle facial expressions that are used for most of the communication activities are not identifiable in still images but became visible in video sequences. For this reason, dynamic facial expressions became increasingly important in recent years. Temporal dynamics of facial expression provide additional relevant information that is not available in static 2D or 3D images. Indeed, an emotion lasts from 250 milliseconds to 5 seconds [154]; a dynamic method may be useful for evaluating the intensity level of muscle activities and for classifying emotions.  
Referring to the work of [PERSON] and [PERSON] [155], a majority of spontaneous smiles reach onset faster and show more action units than the posed smiles. In contrast to the 18 types of smile described by [PERSON], during spontaneous smiles \"the appearance of AU 12 was either simultaneous with or closely followed by one or more associated action units\", such as AU 6 (cheek raiser), AU 15 (lip corner depressor) or AU 17 (chin raiser). The dynamic properties of spontaneous human facial expressions have significant implications for human-computer interaction to describe naturalistic interactive behavior.  
Most of the works that choose video sequences as input use 2D FER algorithms; only a few attempts have been made to analyze facial expression using 4D data (i.e., 3D videos), partly due to the lack of public databases having three-dimensional dynamic sequences. Nowadays, thanks to the incredible development of capturing, reconstruction, alignment, and tracking techniques, dynamics 3D recordings are increasingly used in expression analysis research [9].  
The public availability of the BU-4 DFE database contributed to this innovation process. This database includes 3D sequences of the six universal emotions, but unfortunately, it does not provide AU annotations.
# Evaluation of Grid-Based Rainfall Products and Water Balances over the Mekong River Basin
## 4 Results and Discussion
### Evaluation of Precipitation Products' Hydrological Performance using SWAT Model
#### 4.2.2 Water Balance Components at the Sub-Region Scale
The distribution of total runoff depth in the MRB closely reflects the spatial patterns in rainfall, with much higher proportions of rainfall (approximately 60%) translated into streamflow in R2c and R4, and with the greatest runoff of up to 1305.7 in R2c as the annual average for the 7-year period. The precipitation in R2a, R2b, and R3 is lower than that in R2c and R4, but is still relatively high--from 1615 mm in R2a to approximately 2000 mm in R2b and R3. This R2a and R2b area is almost entirely mountainous and is covered with natural forest (nearly 90% in R2a and around 60% in R2b). These areas have high evapotranspiration and low surface runoff due to the high subsurface flow. Since R2a is still dominated by characteristics of R1, the evapotranspiration in R2a occupies upto 60%, and the surface runoff occupies only 4% in the total runoff depth. Additionally, R2b has a wide scope for agriculture (30% of the area), therefore much higher proportions of rainfall are translated into streamflow compared with R2a (up to 53%). Agriculture dominates in R3 and is reported to account for more than 80% of the area [32; 33]. Consequently, the surface runoff is high, accounting for approximately 60% of the total flow. This region also has a high evaporation rate, accounting for 47%. The rainfall in R1 decreases as the altitude increases toward the north; in this region, the mean rainfall is only 857 mm, and evapotranspiration is around 60%. Land use in this region is mainly divided into two major components: The majority is covered primarily by grassland (50%), while a further 40% is covered primarily by forest. This region has a very low runoff coefficient, but the streamflow-precipitation ratio accounts for up 42% of the total rainfall due to the major source of water flowing into the river coming from the melting snow on the Tibetan Plateau.
## 2 Background Knowledge
### Wireless Sensor Network (WSN)  
A sensor network comprises many sensor nodes, which are randomly deployed in inaccessible areas around a phenomenon without predetermination. A sensor node consists of four basic components namely sensing unit, processing unit, transceiver unit and power unit. The sensing units usually comprise two subunits, namely sensors and analog-to-digital converters (ADCs). The analog signals produced by the sensors are converted into digital signals by the ADC, and then fed into the processing unit. The processing unit, which is generally linked with a small storage unit including ROM and RAM, manages the procedures to execute the assigned jobs. A transceiver unit connects the node to the network, and communicates with other nodes. One of the most important components of a sensor node is the power unit. Power units may be only supported by batteries, or by solar cells that act like a power generator without recharging. Moreover, some other application-dependent components may be attached. A mobilize is needed when the sensor nodes need to move to carry out the assigned jobs. Advances in hardware technology mean that nodes, including all subunits are now smaller matchboxes device. Some additional stringent constraints for sensor nodes are low power consumption, operation in high dense sensor network, low production cost and adaptation to the environment.
## 5 System Simulation and Results Analysis
### Model Control Block Diagram  
Once the corresponding relationships between input resistance and output resistance in continuous and discontinuous conduction time are determined, the goals of impedance matching are clarified. The control block diagram of the impedance matching model is shown in Figure 10, where Vb represents the output voltage across Rb; Ib represents the output current through Rb; MD represents the multiplier and divider; Rb represents the output resistance; PWM represents the PWM controller; K represents the coupling coefficient; Q represents the expression defined in Section 2; R_(b-Pmax) represents the maximum power point; R_(b-pmax) represents the maximum efficiency point; GD represents the gate driver module; MOS represents the metal-oxide-semiconductor field-effect transistor; D represents the duty cycle; CCM represents the continuous conduction time; DCM represents the discontinuous conduction time; IM represents impedance matching; and R_(b-out) represents the matched output resistance.  
The model control block diagram analysis was as follows: In the first step, the output voltage and current through Rb were detected, multiplied, and divided to obtain the output resistance before impedance matching. In the second step, the obtained output resistance was input to the PWM controller, which adjusted the K and Q parameters derived from the maximum power and efficiency expressions in Section 2 to achieve maximum power or efficiency. The PWM controller controlled the GD gate driver module, which drove the MOS transistor. In the third step, the MOS transistor adjusted duty cycle D based on the expression derived from Section 3.2 to switch between CCM and DCM modes. Finally, the matched output resistance was obtained. Through these three steps, the model was effectively controlled. The model was simulated in MATLAB to validate the results.
## 3 Motion Vehicle Target Extraction
### Improvement of FFD-ViBe Algorithm  
The frame difference method and the ViBe algorithm are commonly used for motion target detection, but they have limitations in extracting complete vehicle outlines and eliminating ghost shadows. To address these issues, the FFD-ViBe algorithm with a hash value is proposed. This algorithm combines frame difference and ViBe, using a hash value to fill the voids in the vehicle foreground and effectively eliminate ghosting. It has shown improved performance in vehicle target detection compared to the individual methods.  
The perceptual hash algorithm [24] is an image search algorithm that generates a hash value for an image and finds similar images based on this value.  
First, we select \(g_{\alpha}(x,y)\) as the standard frame and calculate its hash value. We set the threshold value T based on the hash value of the image and the Hamming distance between them frame by frame and set the corresponding \(g_{b}(x,y)\) frame as the standard frame when the Hamming distance is greater than the threshold value. Then, the operation is similar to finding the \(g_{c}(x,y)\), \(g_{d}(x,y)\), and \(g_{e}(x,y)\) frames. Differentiate the five frames two by two:  
\[\left\{\begin{array}{l}T_{(g_{\alpha}(x,y),g_{c}(x,y))}=|g_{\alpha}(x,y)-g_{ c}(x,y)|\\ T_{(g_{\alpha}(x,y),g_{c}(x,y))}=|g_{b}(x,y)-g_{c}(x,y)|\\ T_{(g_{\alpha}(x,y),g_{c}(x,y))}=|g_{d}(x,y)-g_{c}(x,y)|\\ T_{(g_{\epsilon}(x,y),g_{c}(x,y))}=|g_{\epsilon}(x,y)-g_{c}(x,y)|\\ \end{array}\right. \tag{8}\]  
To address the issue of uneven illumination in images, this paper proposes an image binarization algorithm that utilizes the concept of image chunking. Specifically, the algorithm partitions the image into smaller regions and determines the optimal threshold for each region using the Otsu method. \(t_{i}\) and \(B_{i}(x,y)\) denote the optimal threshold and resulting binary image, respectively. This approach can effectively mitigate the effects of uneven illumination.  
\(B_{1}(x,y)\) is the operation of the first difference image and the third difference image, and \(B_{2}(x,y)\) is the operation of the second difference image and the fourth difference image. Finally, the obtained \(B_{1}(x,y)\) and \(B_{2}(x,y)\) are combined to obtain \(B(x,y)\).  
\[\left\{\begin{array}{l}B_{1}(x,y)=T_{(g_{x}(x,y),g_{c}(x,y))}\oplus T_{(g_{x} (x,y),g_{c}(x,y))}\\ B_{2}(x,y)=T_{(g_{x}(x,y),g_{c}(x,y))}\oplus T_{(g_{x}(x,y),g_{c}(x,y))}\end{array}\right. \tag{9}\]  
\[B(x,y)=B_{1}(x,y)\otimes B_{2}(x,y) \tag{10}\]  
The morphology of image \(B(x,y)\) is processed to obtain relatively complete background pixels. Then, the motion region in image \(g_{c}(x,y)\) is filled with the corresponding background pixels in the other four frames, and the unfilled motion region is filled using its eight neighboring pixels to obtain the filled background image. Finally, the background sample library can be built using the same modeling method as used in the original algorithm for the background image. The resulting background model is shown in Figure 8. Data taken from CDnet2014.
# IoT-Based Solid Waste Management Solutions: A Survey
## 6 Standard Protocols Used in Waste Management Systems
### Service Discovery Protocols  
Due to a large number of devices connected and given the need to ensure the proper functioning of the applications developed for IoT based systems, a resource management mechanism is essential for excellent coverage of the technology. Thus, the system must be able to discover resources and register the services automatically. The most notorious protocols to meet these needs are domain name system (DNS), multicast (mDNS) and DNS Service Discovery (DNS-SD). Current research studies aim to adapt lighter versions to the IoT environment.  
* **Multicast DNS (mDNS).** mDNS is a very flexible protocol and uses the DNS namespace locally, being a timely option for Internet devices because it does not require manual configuration or an administration that manages the device and is capable of operating without an infrastructure or even in failures. The name query is done through multicast messages, in which the client requests all domain nodes the Internet Protocol (IP) address for a specific name. At that moment, all the ones in the network update the caches with the provided address [102].
* **DNS Service Discovery (DNS-SD).** The DNS-based discovery service (DNS-SD) performs the service delivery function required by clients through mDNS, enabling customers to discover the desired services using standard DNS messages. Like mDNS, DNS-SD does not require a naming configuration [103] and the DNS packets are sent through the UDP transport protocol, having as destination a multicast address. A first step in finding the necessary services is to find the corresponding IP address of the respective host, and then the pairing function is sent, also via multicast, containing the essential details for connection as the IP/Port pair of the connected hosts, so that the names of the instances can be kept constant, increasing reliability.
# An Iterative Guidance and Navigation Algorithm for Orbit Rendezvous of Cooperating CubeSats
###### Abstract  
Modern space missions often require satellites to perform guidance, navigation, and control tasks autonomously. Despite their limited resources, small satellites are also involved in this trend, as in-orbit rendezvous and docking maneuvers and formation flying have become common requirements in their operational scenarios. A critical aspect of these tasks is that these algorithms are very much intertwined with each other, although they are often designed completely independently of one another. This paper describes the design and simulation of a guidance and relative navigation architecture for the rendezvous of two cooperating CubeSats. The integration of the two algorithms provides robustness to the solution, by simulating realistic levels of noise and uncertainty in the guidance law implementation. The proposed guidance law is derived based on the linearized equations of orbital motion, written in terms of spherical coordinates. The trajectory is iteratively corrected at a fixed time step, so that errors from the navigation and the initial orbital condition can be recovered. The navigation algorithm processes the bearing and range measurements from a camera and an intersatellite link through an unscented filter to provide the information required from the guidance law. A Monte Carlo campaign based on a 3-DOF simulation demonstrates the effectiveness of the proposed solution.  
small satellites; cubesat; rendezvous; docking; guidance; navigation +
Footnote †: journal: Appl. Sci.  
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