esa-sceva/satcom-chunk-collection huggingface dataset:
Additionally, the past decade has seen remarkable advances in RS technologies and methods, making this period central to capturing the latest developments. During this period, significant improvements have been observed in sensor capabilities, data processing techniques and the broader integration of machine learning (ML) in RS applications. The unique characteristics of almond trees, their response to water stress, susceptibility to disease, and distinctive planting and production methods demand a dedicated investigation into the applications of RS in this economically important crop. The specific objectives of this study include exploring RS applications associated with almond crops, identifying the most applied RS platforms and sensors, and understanding how RS techniques can be leveraged for effective tree segmentation and parameter extraction in almond orchards. Furthermore, it aims to examine the role of RS in crop classification, monitoring almond orchards, assessing crop health, managing water resources, and controlling diseases and pests. Additionally, RS-based approaches for estimating almond crop yield and gaining insights from phenological studies are also investigated. Before delving into the specifics of RS applications in almond cultivation, a broader context for RS in agriculture needs to be established. RS serves as a valuable tool for monitoring various aspects of plants, offering insights into vegetation health, stress conditions, and growth dynamics [12]. This sets the stage for understanding its applicability \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Producers**} & \multicolumn{3}{c}{**Consumers**} \\ \hline
**Producer** & **Production (Tons)** & **Evolution of Production (2012–2022)** & **Consumer** & **Consumption (Tons)** \\ \hline
11 & Algeria & 62,988 & \(-\)3499 (\(-\)5\%) & South Korea & 36,571 \\
12 & Chile & 47,388 & \(+\)18,492 (64\%) & Australia & 34,471 \\
13 & Portugal & 46,220 & \(+\)39,042 (544\%) & Turkey & 30,828 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._to almond cultivation and justifying its role in addressing the unique challenges faced by almond producers. Previous review articles have explored the use of RS in horticulture [13], olive cultivation [14], and viticulture [15]. However, there is a lack of a comprehensive review specific to almond crops, with only one identified study conducted by [PERSON] and [PERSON] [2]. Their review focused on RS platforms, sensors, and applications in nut crop management, providing a foundation that this study seeks to build upon and extend. In the subsequent sections, the methodology employed in this systematic review is outlined, followed by an analysis and discussion of the results obtained, exploring research gaps and opportunities, and presenting key findings. This comprehensive review aims to provide valuable insights for researchers and practitioners in the fields of agriculture and RS, guiding future studies and applications to optimize almond crop management.
# In Situ Study of Surface Morphology Formation Mechanism During Laser Powder Bed Fusion
## 2 Materials and Methods  
### Experimental Equipment  
The experiments were conducted using a self-developed LPBF system, equipped with a single-mode continuous fiber laser (MFSC-500 W, Max photonics) with a wavelength of 1080 nm and a maximum laser power of 500 W. The dual coaxial imaging monitoring system was integrated into the laser optical path module, with the system architecture, optical layout, and actual machine setup as shown in Figure 1. The system employed two monitoring devices: (a) A high-speed camera in the visible-light spectrum (Revealer, Hefei, China) with a resolution of 2.0 Mpix and a frame rate of 2000 fps was used to coaxially capture, in real time, the dynamic changes in the melt pool's melting and solidification, as well as the evolution of surface morphology formation. The optical path utilized a laser light source with a wavelength of 808 nm and a narrow-band filter to eliminate interference from the self-emission of the melt pool. (b) An infrared thermometer (Mikron, Milpitas, CA, USA) with a resolution of 0.6 Mpix and a frame rate of 60 fps was used to coaxially monitor the temperature distribution and trends of high-temperature areas such as the melt pool on the working surface in real time. The working wavelength range was 780-1080 nm in the near-infrared spectrum, enabling monitoring and imaging within a temperature range of 800-3000 \({}^{\circ}\)C.  
To accurately characterize the macroscopic surface morphology of the samples, an ultra-depth-of-field 3D microscope (VHX-7000, Keyence, Osaka, Japan) was employed for observation and characterization, and surface roughness was measured. To further obtain high-magnification features, an optical microscope (Axio Imager M2m, Zeiss, Oberkochen, Germany) was used to observe the cross-sectional and internal microstructures of the samples after wire cutting and grinding.
## 4 Discussion
### Limitations  
This study has several limitations. One key limitation is the potential for visual inaccuracies in tracking shoulder displacement. Due to participants wearing gi uniforms, markers could not be placed directly on their bodies, necessitating manual tracking of reference points using Kinovea software. This method may have introduced small errors in displacement measurements, particularly in high-velocity scenarios. Additionally, the absence of a precise definition for the \"point of impact\" may have affected the consistency of data calibration, as the exact moment and location of shoulder contact with the tatami could vary between throws. These factors could have influenced the reliability and precision of the recorded kinematic values.  
Another limitation is the small sample size, consisting of six elite male judokas recruited from a single judo club. This limits the representativeness of the findings, as training methodologies and techniques can vary across clubs. Future studies should include athletes from multiple clubs and varying competitive levels to ensure broader applicability. The small sample size also reduces the statistical power of the study, potentially missing variability in shoulder kinematics. Furthermore, the lack of female athlete representation restricts the generalizability of the findings. Future research should prioritize recruiting a larger and more diverse sample, including athletes of different genders, skill levels, and competitive backgrounds, to provide comprehensive insights into the biomechanical aspects of judo throws.  
To improve data accuracy, future studies should consider using three-dimensional motion analysis or marker-based systems to enhance the precision of kinematic tracking. Direct measurements of impact forces are also recommended to validate inferences drawn from velocity and acceleration data regarding injury risks. Additionally, the lack of control over participants' weight categories may have introduced variability into the results, as body mass significantly influences the dynamics of judo throws. Including weight categories in future research would help reduce variability and improve the robustness of findings.
## 4 Conclusions  
In this study, we proposed a deep learning model called RiceDRA-Net for identifying rice leaf diseases in the context of complex rice fields. We incorporated the Res-Attention module into the deep residual structure of the deep residual network to form RiceDRA-Net, making the residual connections more dense and more suitable for identifying rice leaf diseases in rice fields. We also constructed a test dataset, SBG-Dataset, consisting of a single background of rice leaf diseases. We also experimented with the effects of different optimizers, different learning rates, and different convolutional kernel sizes in Res-Attention in terms of the recognition effect of the model.  
RiceDRA-Net extracts image features while retaining as much information as possible that may be lost during the convolution process. This greatly improves the accuracy in identifying rice leaf diseases with complex rice backgrounds. We compared RiceDRA-Net with various classical models on two datasets and found that RiceDRA-Net achieved higher accuracy in identifying rice leaf diseases with complex rice backgrounds and accurately locating the diseases in rice leaves.  
In conclusion, our study presents a novel deep learning model, RiceDRA-Net, that performs well in identifying rice leaf diseases with complex rice backgrounds. The Res-Attention module incorporated into the model improves the ability of the model to identify rice leaf diseases and retain important information. The SBG-Dataset is a useful dataset to further study rice leaf diseases. Our results show that RiceDRA-Net outperforms classical models in identifying rice leaf diseases and locating diseases in rice leaves. These findings  
Figure 16: Heat map of disease pictures.  
have important implications for the development of effective tools to diagnose and manage rice leaf diseases in rice fields.  
The study's main limitation was the small dataset size, which restricted the comparative analysis of the models. This may affect the reliability and generalizability of the study's findings. To address this, future research could focus on collecting additional data. Furthermore, data augmentation techniques could be explored to expand the dataset, which would increase its diversity and size. Additionally, employing more lightweight algorithms could reduce the dependence on a large volume of data.  
Conceptualization, J.P. and Y.W.; methodology, J.P. and Y.W.; software, R.Z.; validation, J.P., R.Z. and H.C.; data curation, H.C.; writing--original draft preparation, J.P.; writing--review and editing, Y.W.; visualization, P.J.; funding acquisition and supervision, Y.W. and P.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China under the subproject \"Research and System Development of Navigation Technology for Harvesting Machine of Special Economic Crops\" (No. 2022 YFD2002001), within the key program \"Engineering Science and Comprehensive Interdisciplinary Research\".  
Not applicable.  
Data availability Statement: Data are available on request due to privacy.  
The authors declare no conflict of interest.
## 4 New TIRA System Design
### Access to the Global Reference Frame and Time Scale
The deviation between the axis and the local vertical is determined by measurement, while the so-called deflection of the vertical between the reference surface normals are evaluated from the geoid model. Although the axes of the antenna were aligned to the local vertical or plumb line during construction, the azimuth axis tilted during the first decades, probably due to settlement. Figure 20 shows the history [25] of the measured deviation of the azimuth axis from the true vertical. The symbol \(\delta\) denotes the magnitude of the angular deviation in arcseconds, \(\delta_{\rm N}\) and \(\delta_{\rm E}\) give its components in the north and east direction, respectively. Until recently, the measurement was performed using an automatic level in combination with a parallel plate micrometer observing the vertical movement of a point fixed to the turning azimuth platform over a full revolution. The amplitude and phase of that sinusoidal quantity along the azimuth angle then gave the magnitude and direction of the axis deviation. Today, this deviation is regularly monitored using a high-performance accelerometer with a noise density of \(2\,\mathrm{\SIUnitSymbolMicro m}\,\mathrm{s}^{-2}\,\mathrm{Hz}^{-0.5}\). Mounted near the center of the azimuth rotation, it directly observes the precession of the vector of gravity, yielding measurements with arcsecond accuracy. The deflection of the vertical is estimated as a gradient of the German Combined Quasigeoid (GCG2016), yielding \(\zeta=5.35\)'' in the north-south direction and \(\eta=-1.13\)'' in the west-east direction, respectively. These values are in good agreement with evaluations by ICGEM [26] using the global EIGEN-6C4 model to degree 2190 giving \(\zeta=5.04\)'' and \(\eta=-1.42\)''. As the target positions are now available in local tangent plane coordinates on the reference ellipsoid of the International Terrestrial Reference Frame (ITRF), they can be directly converted to Cartesian coordinates. The origin of the local tangent plane system is maintained by GNSS-PPP and monitored by GNSS-real-time kinematics (RTK) to the nearest SAPOS station. To estimate satellite positions from measurements in the local system, tidal effects at the ground have to be taken into account to compute the instantaneous station position. Utilizing the Earth rotation parameters (ERPs), these positions [27] may be transformed finally to the inertial ICRF.
## 4 Experiments
### Datasets and Metrics
#### 4.1.3 Pairwise Text Classification  
**MNLI** The Multi-Genre Natural Language Inference Corpus [28] is a corpus of sentence pairs with textual entailment annotations. A pair of sentences is comprised of a premise sentence and a hypothesis sentence. The task is to determine whether the premise entails the hypothesis, contradicts the hypothesis, or neither. Accordingly, it has three labels: entailment, contradiction, and neutral.  
**RTE** The Recognizing Textual Entailment is a textual entailment dataset, which is a combination of RTE1 [29], RTE2 [30], RTE3 [31], and RTE5 [32]. GLUE's authors modified some datasets annotated with three kinds of labels to two: entailment and not_entailment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Dataset** & **The Number of Training Examples** \\ \hline MMLI & 393 k \\ QQP & 364 k \\ QNLI & 108 k \\ SST-2 & 67 k \\ CoLA & 8.5 k \\ STS-B & 7 k \\ MRPC & 3.7 k \\ RTE & 2.5 k \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of training examples.  
**QQP** The Quora Question Pairs [2] is a dataset of question pairs drawn from social question-answering domain. This task's goal is to predict whether question pairs are semantically equivalent or not. This dataset consists of two labels: 0 is negative, implying that the questions in a pair are not semantically equivalent, and 1 is positive  
**MRPC** The Microsoft Research Paraphrase Corpus [33] is a corpus of sentence pairs comprised of sentences automatically extended from online news. As in QQP, the task is to determine whether the sentences in the pair are semantically equivalent or not. Each sentence is annotated 0 or 1, where 0 is negative, implying that the sentences in a pair are not semantically equivalent, and 1 is positive.
###### Abstract
Since each feature reflects the unique physical property of the objects, multi-dimensional features including entropy, height variation, intensity, normalized height, and standard deviation extracted from LiDAR data were utilized for building detection with convolutional neural network (CNN) model [14]. Combining slope, aspect, and shaded-relief generated from DSM with infrared (IR) images could improve semantic segmentation performance of the DL model [15]. This paper aimed to improve extracting point clouds belonging to buildings using semantic segmentation with PointNet++ model. The assumption of this study is that the training datasets conveying building information, such as building boundaries, could provide better performance of DL models. In this regard, number of returns of the laser beams recorded during the LiDAR data acquisition was utilized in creating training datasets. To analyze the accuracy of building segments, we compared the results from various configurations of the training datasets. One of the unique and important properties of the laser scanner is multiple returns. Figure 1 illustrates concept of the return number and number of returns. The number of returns depends on the characteristics and situation of the objects' surfaces. If the laser beams hit the building roofs or terrain surfaces, only one return pulse is generated (i.e., number of returns is 1). On the other hand, multiple return pulses could be generated if the laser beams hit edge of the building roofs or branches of the trees. Specifically, two returns (i.e., number of returns is 2) and several returns are to be generated for the building edges and trees, respectively. Multiple returns are capable of determining heights  
of the objects, such as buildings and trees, by measuring the time of flight between the first and the last returns within a laser footprint if the laser pulse reaches the ground [3]. In this paper, two returns were utilized for training the DL model because edges of the building provide important features of the buildings that could differentiate them from other objects. Two returns could be generated not only from the building edges but also from parts of other objects such as trees. In fact, utilizing number of returns is beneficial in extracting buildings. The results show that the accuracy of the building extraction was improved by taking the number of returns into account instead of using all point cloud data for training the DL model.
# Micro-Satellite Systems Design, Integration, and Flight
## 3 ChipSat RTOS and RF Development
### Ground Station  
To complete a successful RF transmission as required by 7.0 (see Table 4), a ground station had to be set up to receive the signal. Two ways of setting up a ground station were attempted, and both will be used for Alpha. The first way was to use software-defined radio (SDR). This would let Alpha team members and other enthusiasts try to pick up the signal. The second way was to use a receiver network. This could allow a larger network to track the signal and report back any data gathered.
# Feasibility of Using Low-Cost Dual-Frequency GNSS Receivers for Land Surveying
###### Abstract  
Global Navigation Satellite Systems (GNSS) have revolutionized land surveying, by determining position coordinates with centimeter-level accuracy in real-time or up to sub-millimeter accuracy in post-processing solutions. Although low-cost single-frequency receivers do not meet the accuracy requirements of many surveying applications, multi-frequency hardware is expected to overcome the major issues. Therefore, this paper is aimed at investigating the performance of a u-blox ZED-F9P receiver, connected to a u-blox ANN-MB-00-00 antenna, during multiple field experiments. Satisfactory signal acquisition was noticed but it resulted as \(>\)7 dB Hz weaker than with a geodetic-grade receiver, especially for low-elevation mask signals. In the static mode, the ambiguity fixing rate reaches 80%, and a horizontal accuracy of few centimeters was achieved during an hour-long session. Similar accuracy was achieved with the Precise Point Positioning (PPP) if a session is extended to at least 2.5 h. Real-Time Kinematic (RTK) and Network RTK measurements achieved a horizontal accuracy better than 5 cm and a sub-decimeter vertical accuracy. If a base station constituted by a low-cost receiver is used, the horizontal accuracy degrades by a factor of two and such a setup may lead to an inaccurate height determination under dynamic surveying conditions, e.g., rotating antenna of the mobile receiver.  
GNSS; low-cost; land-surveying; RTK; PPP; positioning accuracy +
Footnote †: journal: Sensors
# An All-Mach Number HLLC-Based Scheme for Multi-Phase Flow with Surface Tension
## 3 Numerical Methodology  
### Finite Volume Dual-Cell Mesh  
In this work, the chosen discretisation method is the finite volume vertex-centred median dual-cell mesh variant since it is applicable to general meshes. As illustrated in Figure 1, a computational median dual cell, \(\Omega_{l}\), is constructed around node \(l\) by connecting the mid-points and centroids of the adjacent edges and elements, respectively. The cell, of volume \(\mathcal{V}_{l}\), is bounded by facets of the set \(\partial\Omega_{l}\) with normals \(\mathbf{n}_{l,m_{i}}\) and boundary faces of the set \(\mathcal{A}_{b}\) with normals \(\mathbf{n}_{b,f_{i}}\).  
In Elemental(r), in the interest of computational efficiency, the spatial discretisation is done in an edge-wise manner. For \(\Omega_{l}\), the governing equations read in semi-discrete form as follows:  
\[\frac{\partial\mathbf{U}_{l}}{\partial t}+\frac{1}{\mathcal{V}_{l}}\sum_{f\in \partial\Omega_{l}}(\mathbf{F}\mathbf{n}\mathcal{A})|_{f}+\frac{1}{\mathcal{V}_{l}} \sum_{b_{f}\in\mathcal{A}_{b}}(\mathbf{F}\mathbf{n}\mathcal{A})|_{b_{f}}=\mathcal{S}_ {l}, \tag{16}\]  
where for an edge connecting nodes \(l\) to \(m\), denoted \(\mathbf{t}_{l,m}\), the face coefficient is given by the sum of the product of the area and the normals of the shared face segments that straddle  
Figure 1: 2-D computational median dual-cell around node \(l\).  
the edge. Mathematically, this reads for an internal edge as \(\mathcal{A}\mathbf{n}|_{\partial\Omega_{l,m}}=\sum\limits_{i}\mathcal{A}_{\partial \Omega_{l,m_{i}}}\mathbf{n}_{l,m_{i}}\) and for a boundary face, \(\mathcal{A}\mathbf{n}|_{b_{f}}=\sum\limits_{i}\mathcal{A}_{b_{f_{i}}}\mathbf{n}_{b_{f_ {i}}}\).  
For the purpose of computing the face-flux values, HLLC is employed. The involved left and right states are reconstructed via a second-order accurate method as detailed in the next section.
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 4 ViMindXAI: A Scalable and Cognitive AI Platform for VBDA
### Deployment Considerations and Solutions
#### 4.4.2 Proposed Optimization Strategies  
ViMindXAI addresses these challenges through a multi-tiered optimization strategy designed for real-world resilience:  
* **Model Compression for Edge Inference:** Techniques such as pruning, quantization, and knowledge distillation are applied to compress models without compromising accuracy. This enables real-time inference on edge hardware like NVIDIA Jetson and Intel Movidius for tasks such as anomaly detection and object tracking [100; 134].
* **Tiered and Intelligent Storage Architecture:** The platform combines edge-level buffering, AI-driven caching, and cloud-based archiving. Semantic indexing using models like CLIP, FAISS, and VideoBERT enables rapid, context-aware retrieval [144; 30; 145], while tiered orchestration minimizes redundancy and optimizes bandwidth usage.
* **Metadata Optimization and Context-Aware Allocation:** Rich metadata, including object categories, spatial context, and scene attributes, is used to prioritize content for storage and retrieval. This improves responsiveness and reduces data transfer volumes, especially in bandwidth-constrained scenarios [11; 142].  
Together, these strategies equip ViMindXAI with the capability to operate effectively in real-world deployments, delivering scalable, low-latency, and privacy-aware analytics. The system's design supports modular extensibility, aligning with future advancements outlined in Section 5.