esa-sceva/satcom-chunk-collection huggingface dataset:
# A Robust Controller for Upper Limb Rehabilitation Exoskeleton
## 5 Conclusions  
The exoskeletons have shown that they can contribute to a better rehabilitation process by improving movements and increasing the rehabilitation time and the number of sessions. To achieve a good performance requires controllers that are robust to disturbances (endogenous and exogenous) that can occur in the system.  
In this paper, a robust GPI controller is proposed, which only requires output measurements (angular position of the link) for tracking desired trajectories. The simulation results show a good performance of the GPI controller with respect to the PID controller, with these results being obtained in the MSC Adams(r) software in co-simulation with Matlab-Simulink(r).  
In reference to the development of the exoskeleton for the rehabilitation of upper limbs, (a) it has to perform passive movements without reaching extremes that can generate excessive loads of muscle or joint stretching; the degrees of freedom that were designed will initially allow an active reach for limb stretching in order to regain movement and motor control; (b) it is safe, as the movement is assisted, and it has designed mechanical limits to avoid unwanted singularities; (c) portability is essential in this design, since in many cases receiving specialized assistance is complex due to the country or region where patients are located; (d) the cost of acquiring an exoskeleton is high.  
There are various limitations, or rather there are challenges to be covered as the design, tests, mechanisms, etc., progresses, because emulating the movements of the upper extremities is very complex. It is not possible to cover all the movements carried out by an upper limb individually in real time and, if the movements carried out in combination with both upper limbs are annexed, the degrees of freedom increase, but exponentially with increases in the difficulty of adding mechanical, electrical and electronic components to the design. This only adds to the weight, and would make the exoskeleton no longer portable. The higher the portability, the fewer the degrees of freedom that can be added by the aforementioned.  
In future work, the exoskeleton will be conditioned and instrumented to help patients to perform voluntary movements, and not only the previously programmed movements.  
[PERSON] worked in the conceptualization, validation, simulations and control; [PERSON] worked in the simulations, design and control, and wrote the initial manuscript; [PERSON] and [PERSON] worked in the conceptualization and performed the experiments; [PERSON] and [PERSON] worked on mechanical design; [PERSON] worked on electrical design and control. All of the above authors analyzed the results and revised the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 14: Elbow flexor torque (0.17 Nm/\({}^{\circ}\)) in function of \(\theta_{3}\).
## 3 Data and Model Configuration  
### Datasets  
To verify the validity of our proposed approach, we conducted experiments on the NIST LRE 2017 dataset [38] (LRE17). This data set mainly consists of three parts: narrowband telephone training set, development set, and evaluation set. In addition, it also provides [PERSON] corpus [39] and switch corpus [40]. The development and evaluation sets  
Figure 2: Fusion module frame diagram. (**a**) Add Merge Module, (**b**) Concat Merge Module, (**c**) Depth-wise Convolution Merge Module, (**d**) Attention Merge Module, (**e**) Gated Merge Module, (**f**) Inter–Attention Merge Module.  
are composed of two different types of data, narrowband MLS14 data and wideband VAST data. Among them, narrowband MLS14 data contain three fixed time segments, namely 3 s, 10 s, and 30 s, while broadband VAST data consist of speech in the range of 10 to 600 s. We trained our model on the training and development sets and tested it on narrowband MLS14 data on the evaluation set to compare the performance of our model on speech of different durations.
## 3 Experiments and Evaluation
### Overall Results  
An overall view of the regularized wall boundaries detected from dense points is shown in Figure 5. The areas include individual courtyards like (a) to (c), and connected courtyards like (d) to (f). It can be seen that the building outline obtained by the proposed algorithm is very complete, and it can effectively avoid the interference of eaves and corridors on the accuracy of building boundaries. Additionally, both building outline and courtyards are included. The qualitative results are also provided in Table 3. It can be seen that the average RMSE of the proposed method can be about 8.4 cm and the correctness index of corners, edges, and polygons are above 92%.  
Figure 5: Overall view of the regularized boundaries detected from dense points. Red lines: boundary edges from wall points, and blue lines: extracted doorway boundaries.
# Syn2 Real Detection in the Sky: Generation and Adaptation of Synthetic Aerial Ship Images
## 2 Related Works
### Object Detection from the Synthetic World to the Real World  
Taking the number of stages as a dimension, object detection can be categorized into one-stage algorithms and two-stage algorithms. One-stage algorithms have the advantages of fast speed and good instantaneous performance, which are important in scenarios that require real-time detection. However, they tend to have lower accuracy and may produce less satisfactory results for small object detection. Examples of one-stage algorithms include the You Only Look Once (YOLO) series [50; 51; 52; 53; 54], RetinaNet [55], and Fully Convolutional One-Stage Object Detection (FCOS) [56].  
On the other hand, two-stage algorithms offer high accuracy and perform well in detecting small objects. However, they tend to be slower due to the need to generate a large number of candidate regions. Examples of two-stage algorithms include Faster R-CNN [57], Mask R-CNN [58], and TridentNet [59].  
Using synthetic image datasets directly for object detection can improve results to some extent. However, it can also introduce issues such as dataset shift or domain shift due to the inherent differences between synthetic and real image datasets. To address this problem, domain-adaptation methods can be employed to make better use of existing synthetic datasets.  
Early cross-domain object detection methods have primarily focused on Faster R-CNN, which is the most representative algorithm among two-stage detectors. One notable example is the DA-Faster algorithm proposed by [PERSON] et al. [13], who introduced a gradient-reversal layer [60] and were the first to incorporate image-level and instance-level alignment techniques to enhance cross-domain performance. The Stacked Complementary Losses (SCL) method developed by [PERSON] et al. [61] utilized a gradient-detachment approach to tackle the domain shift issues in object detection. More recently, there have been breakthroughs in the application of single-stage detectors to domain adaptation for object detection (DAOD) problems, as demonstrated by works such as [13; 14; 15; 16; 17; 18; 19]. The EP-MDA method proposed by [PERSON] et al. [62] made adjustments to FCOS [56], and they drew inspiration from semi-supervised learning. Additionally, the Mean Teacher with Object Relations (MTOR) [63] and Unbiased Mean Teacher (UMT) [15] methods built upon the mean teacher framework for unsupervised domain adaptation.
## 1 Importance of Plant Management in Speciality Crop Production  
Effective plant management encompasses a range of practices aimed at maximizing crop health, yield, and quality. In specialty crop production, where market demands often emphasize superior taste, appearance, and nutritional value, precise management strategies are essential. Integrated pest management (IPM) is a cornerstone of sustainable specialty crop production. By combining biological, cultural, physical, and chemical control tactics, IPM minimizes the use of synthetic pesticides while effectively managing pests and diseases. Techniques such as crop rotation, beneficial insect releases, and monitoring systems are integral to successful IPM programs [3]. Meanwhile, precision agriculture technologies, including GPS-guided machinery, remote sensing, and data analytics, offer precise insights into soil variability, crop health, and water management. These technologies enable farmers to optimize input use, reduce resource wastage, and enhance overall productivity [4]. Also, efficient water management is critical for specialty crop production, particularly in regions prone to water scarcity. Techniques such as drip irrigation, soil moisture sensors, and deficit irrigation strategies help conserve water while maintaining optimal crop growth and yield [5]. It is worth noting that specialty crops often have specific nutrient requirements for optimal growth and quality. Precision fertilization based on soil testing, tissue analysis, and nutrient modeling ensures that crops receive adequate nutrients without excess, resulting in reduced environmental impacts such as nutrient leaching and runoff [6].  
Despite advancements, several challenges persist in plant management for specialty crops. Such specialty crops are susceptible to a wide range of pests and diseases, necessitating vigilant monitoring and rapid response strategies. Many specialty crops require labor-intensive management practices, which can be costly and challenging in regions with labor shortages. At the same time, high temperatures, overse of antibiotics, altered precipitation patterns, and extreme weather events pose risks to crop production, necessitating adaptive management practices [7]. Fortunately, recent innovations are transforming plant management into specialty crop production. Advances in biological control agents, such as predatory insects and microbial biopesticides, offer effective alternatives to conventional pesticides [8]. Breeding for disease resistance, drought tolerance, and improved nutritional profiles through genetic technologies is enhancing crop resilience and quality. Moreover, robotics and automation technologies are revolutionizing labor-intensive tasks such as harvesting and precision application of inputs, resulting in reduced costs and improved efficiency [9].
# Sensor Observation Service API for Providing Gridded Climate Data to Agricultural Applications
## 2 Basic Concepts and Related Information
### IoT and Interoperable Sensor Network  
Internet-connected devices and services have been growing rapidly. The concept of IoT is defined as a \"global infrastructure for the information society, enabling advanced services by interconnecting (physical and virtual) things based on existing and evolving interoperable information and communication technologies\" [8]. Field sensor networks that collect data from various kinds of sensors in agricultural fields are considered as one of the IoT platforms. Consequently, according to IoT growth, protocol and data format become heterogeneous, which causes the problem of data  
Figure 1: Coverage of six areas in AmGSD. Reproduced with permission from [PERSON], User’s manual of the Agro-Meteorological Grid Square Data; NARO/CRARC, 2014 [1].  
exchanging and application development. To enable information interoperability among IoT devices and applications, the open standard web API plays an important role.  
The Open Geospatial Consortium (OGC) is an international consortium established in 1994 that has been contributing geospatial interoperability, including sensor interoperability. According to the growing demand for information interoperability, OGC developed SWE (Sensor Web Enablement) for enabling sensors, transducers and sensor data repositories to be discoverable and accessible via the web [9]. The main concept of SWE is to develop a global framework of standards that integrate diverse sensors and network technologies, then to achieve sensor network interoperability. The SWE framework includes, i.e., Sensor Model Language (SensorML), Observation and Measurements (O&M) and the web API named Sensor Observation Service (SOS).
## 2 Split Spectrum Processing  
The schematic diagram of split spectrum processing (SSP) is illustrated in Figure 1. First, the fast Fourier transform (FFT) is used to transform an input signal \(x(t)\) in time domain into a frequency-domain signal \(X\)(\(f\)), which is split into a sub-band signal group \(X_{i}\)(\(f\)) (\(l\) = 1,2, \(\ldots\), \(N\), \(N\) denotes the number of filters) in the frequency domain by an intersecting bandpass filter bank (increasing center frequency). Then, the inverse FFT (IFFT) is employed on \(X_{i}(f)\) and normalization to obtain a time-domain sub-band signal group \(y_{i}(t)\). Finally, nonlinear signal recombination methods are utilized to produce an output signal \(y(t)\). The function principle behind SSP is the frequency sensitivity among the UGWs with desired and other modes; that is, a different \(X_{i}(f)\) contains the desired UGW mode with similar amplitude and the modes of other UGWs with different amplitudes.  
Gaussian bandpass filters are commonly used in SSP due to the ease of implementation [21]. On the one hand, the cross-correlation coefficient of coherent noise components in adjacent \(X_{i}(f)\) should be controlled to be as small as possible for better coherent noise elimination, indicating that the overlapping region of adjacent filters is required to be as small as possible. On the other hand, the non-overlapping filter bank should be able to avoid the \"picket fence\" effect, wherein substantial information will be lost, which is not conducive to the signal interpretation [22]. Hence, the filter bank design should take into account the above requirements.
# Differentially Private XGBoost Algorithm for Traceability of Rice Varieties
## 2 Related Work
### XGBoost  
Xgboost is widely used in data mining analysis as a popular classifier [33]. [PERSON] et al. [34] proposed a distributed privacy-protection boosting algorithm suitable for various classifiers. The algorithm uses local differential privacy (LDP) as a building block to build the basic learner using the aggregation of the perturbed data shares to ensure that the privacy of the participating data owners is well protected.  
For better data mining, many people combine privacy protection with a classifier [35; 36], for example, [PERSON] et al. proposed the DiffPRF algorithm [35], which is mainly a combination of differential privacy protection and random forest, but the algorithm can only process discrete features, which need to be discretized before processing continuous features.  
This paper proposed a privacy-protected XGBoost algorithm for rice variety traceability. Compared with the random forest algorithm, this algorithm has better performance in preventing overfitting. The results show that the fusion of the XGBoost algorithm and differential privacy protection method can not only maintain data privacy, but also analyze the privacy-protected data, which can well solve the privacy protection requirements in the process of rice variety traceability.
# Remote Sensing Multimodal Image Matching Based on Structure Feature and Learnable Matching Network
## 3 Template Matching Based on PCA-LMN
### Learnable Matching Network  
The gradient reversal among remotely sensed multimodal images is considerably common. This causes orientation reversal (Figure 1), which is a critical problem for the similarity measurement of structure orientation.  
Figure 5: Dominant structure orientation and local gradient vectors.  
To solve this problem, some methods remap the structure orientation to range [0-180\({}^{\circ}\)] by adding 180\({}^{\circ}\) to the negative value [9; 10; 35]. However, miscalculations can result if the orientation is not appropriately reversed. For example, suppose that a structure orientation vector in one image is 5\({}^{\circ}\). The orientation of the corresponding structure in the other image should be changed to \(-5^{\circ}\) because of the noise effect. According to the remapping rule, the orientation difference between them is 170\({}^{\circ}\), which is evidently unreasonable. The SAR and infrared images sometimes contain intense noise; hence, these methods may encounter problems in matching the images.  
Instead of changing the orientation mapping, a learnable matching network (LMN) is proposed, which consists of a correlation layer and a regression network.
## 5 Conclusions  
This study evaluated SCN impact on crop vigor and yield as well as the viability of using SUAS aerial multispectral imagery integrated with statistical and ML models for SY estimation. Crop vigor and yield showed significant differences between the resistant and susceptible varieties. Lower yield and crop vigor recovery as well as higher SCN population (20 to 1080) was observed for the susceptible varieties. Similarly, resistant varieties had higher yield, higher crop vigor recovery, and lower SCN population (0 to 340). The blue band (r = 0.58) and GNDVI (r = \(-\)0.6) showed the best correlation with the SCN rating. GDNVI, GCI and NDA were able to differentiate between soybean plants with and without SCN symptoms.  
SUAS imagery derived VIs such as NDRE, GCI, and GOSAVI, among others, had the strongest correlation (r = 0.59-0.75) with SY. REFs in blue, green, red, RE, NIR, and VIs like NDRE, GCI, GOSAVI, GSAVI, GDVI and VARI were identified as key inputs for statistical and ML modeling of SY. The statistical models and ML model performance for estimating SY increased as training dataset sizes increased. RF showed the best overall performance (r: 0.84-0.97, rRMSE: 20.60-8.72%). The input groups (REFs or REFs+VIs) had a significant impact on model performance. When models were validated over the train and entire datasets, the 95:5 train-test split ratio performed the best. While the 60:40 split ratio performed best when models were validated over the test dataset.  
This study showed SCN impact on crop vigor and yield and SY estimation using a combination of SUAS aerial multispectral imagery and ML models. This would be vital for implementing timely and precision management practices by farmers to maximize crop yield and profitability.  
Conceptualization, P.J. and A.K.C.; methodology, P.J., A.K.C. and D.B.L.; software, P.J. and A.K.C.; validation, P.J., A.K.C. and D.B.L.; formal analysis, P.J. and A.K.C.; investigation, P.J., A.K.C. and D.B.L.; resources, A.K.C. and D.B.L.; data curation, P.J., A.K.C. and D.B.L.; writing--original draft preparation, P.J. and A.K.C.; writing--review and editing, P.J., A.K.C. and D.B.L.; visualization, A.K.C. and D.B.L.; supervision, A.K.C.; project administration, A.K.C. and D.B.L.; funding acquisition, A.K.C. and D.B.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by USDA NIFA Project # 420110, Hatch Project # VA160181, Multistate Hatch Projects # VA136412 and VA136438, and Faculty startup.  
Not applicable.  
**Data Availability Statement:** All collected data and pertaining analysis has been included in the manuscript.  
We would like to thank the technicians of the plant pathology laboratory as well as Tidewater Agricultural Research and Extension Center, Suffolk, VA for their help in managing trials and collecting ground truth data.  
The authors declare no conflicts of interest.
## 3 Mathematical Equations for Parameters Design  
### Boost-Typed PFC Converter  
From the analysis of the operation mode, it can be known that the voltage across \(L_{p1}\) (\(L_{p2}\)) is equal to \(v_{rec}\) when \(S_{1}\) (\(S_{2}\)) is on. On the contrary, when \(S_{1}\) (\(S_{2}\)) is turned off, \(L_{p1}\) (\(L_{p2}\)) transfers the stored energy to \(C_{B1}\) (\(C_{B2}\)) and \(C_{o}\). \(L_{p1}\) (\(L_{p2}\)) is served as the inductor of a boost converter. In order for the boost converter to operate in DCM throughout the input voltage cycle, \(V_{B}+V_{o}\) must be high enough to satisfy the following equation.  
\[V_{B}+V_{o}\geq\frac{V_{m}}{1-D}, \tag{27}\]  
First, \(k\) is defined as  
\[k\triangleq V_{B}+V_{o}/V_{m}, \tag{28}\]  
When the value of \(k\) is greater than 2, the power factor can reach more than 0.98 and the output power can be expressed as [30]  
\[P_{o}=\frac{\eta D^{2}V_{m}^{2}}{L_{p}f_{s}}\cdot y, \tag{29}\]  
where \(\eta\) represents the circuit efficiency and \(y\) is expressed as  
\[y=\int_{0}^{\pi}\frac{sin^{2}\theta}{1-\frac{1}{k}sin\theta}\,d\theta=\frac{ k^{3}}{\sqrt{k^{2}-1}}\left(1+\frac{2}{\pi}sin^{-1}\frac{1}{k}\right)-k^{2}- \frac{2}{\pi}\cdot k, \tag{30}\]
## 4 Other Si-Based Anode Materials
Just like in the field of SiOCN, for example, [PERSON] et al. [58] synthesized an Sn/N-doped silicon oxycarbide (SiOCN) nanocomposite by pyrolyzing a precursor of poly(vinyl)silazane and tin acetate. The introduction of metallic Sn nanoparticles facilitated charge transfer within the amorphous SiOCN matrix. Despite these improvements, the reversible capacity of SiOC anodes remains significantly below its theoretical value due to the low ICE. One promising strategy to enhance performance is the incorporation of metal compounds, which can mitigate irreversible reactions and promote better charge transport within the SiOC anode [59]. [PERSON] et al. [60] prepared spherical SiOCN materials by using an aldehyde-amine condensation and high-temperature pyrolysis method, and improved the first coulombic efficiency of the SiOCN/Li half-cell from 73.6% to 90.4% by employing a chemical prelithiation method using a simple immersion in Li-BP/2-MeTHF solution. Furthermore, [PERSON] et al. [20] explored the variation in ceramic precursor chemistry to synthesize a series of Si-C-N-O ceramics with different N/O ratios. The results indicate that SiOC exhibits superior performance compared to SiCN when processed under similar conditions (Figure 10). They concluded that the higher covalence of Si-N bonds localizes electron density, making them less effective for Li-ion binding compared to Si-O bonds. Additionally, they investigated the cyclic stability of SiOC as a function of pyrolysis temperature. Specimens pyrolyzed at 800 and 1000 \({}^{\circ}\)C retained nearly 100% of their capacity after 60 cycles, while those processed at 1200 and 1400 \({}^{\circ}\)C experienced a steady decline. Other studies have also reported improved electrochemical performance for SiOC specimens processed at 1100 and 1300 \({}^{\circ}\)C. And we can improve the performance through further investigation into Si-N and Si-O bonding and adjustment of pyrolysis temperature. The practical evidence shows that utilizing SiOCN and SiBCN will become a promising approach to enhance battery performance.