esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
With a growing number of elderly people with disability and chronic diseases [1], long-term care of bedridden elderly people remains a big challenge due to various complications, such as muscular wasting, joint stiffness, nutritional imbalance, and skin problems [2]. One of the most serious complications is pressure ulcers, which are injuries to skin and underlying tissue resulting from prolonged pressure on the skin [3]. The care burden of pressure ulcers is tremendous, and, thus, prevention of pressure ulcers is the most important measure [4]. A systematic review [5] discussed many methods of pressure ulcer prevention and suggested that a non-contact monitoring method is preferred considering the comfort of the patient.  
To reduce the occurrence of pressure ulcers, frequent redistribution of the pressure on bedridden patients by regular repositioning is essential [6]. It is commonly recommended to set a repositioning schedule for changing the position of patients every two hours [7]. However, due to a shortage of long-term care workers, repositioning might not be implemented as often as required. Posture monitoring by using modern health/medical caring technology can potentially solve this problem. In this paper, we present a novel approach based on a remote RGB-D camera to continuously detect and monitor the posture of bedridden elders. The main goal is to notify the caregivers to reposition the patients if they have been staying in the same posture for a long time. In this way, we achieve the following goals: (1) evaluation of the care quality (enough release of pressure) and (2) long-term record of patient's posture variations.  
In our system design, a RGB-D (depth) camera is used to overcome the challenge of posture recognition using incomplete skeletons, since elderly people in bed are usually covered with a blanket and only the head and partial shoulders are visible. Recognition of lying postures (left-side, right-side, and supine) solely from traditional RGB information might be difficult and inaccurate. The adoption of depth information from the RGB-D camera will be helpful in determining the 3D skeleton and, hence, the lying postures.
## 3 Results
### Crop Yield Prediction
#### 3.1.1 RTM Applications  
There is great potential to develop CYMs based on RTMs, because these RTMs can provide more reliable information on the basis of vegetation reflectance [13; 14; 15]. Moreover, these RTMs require limited ground information and reliable prediction is possible on the basis of inversion methods of a look-up table (LUT), Gaussian process regression (GPR),  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Database**} & \multicolumn{2}{c}{**Crop Yield Prediction**} & \multicolumn{3}{c}{**Crop Traits Retrieval**} \\ \cline{2-7}  & **RTM** & **Integrated with CGMs** & **LAI** & **LAI and Others (CWC, CCC, etc.)** & **Other than LAI** & **Total** \\ \hline ScienceDirect & 2 & 5 & 11 & 16 & 19 & 53 \\ \hline MDPI & 1 & 4 & 2 & 5 & 1 & 13 \\ \hline Scopus & 1 & 2 & 3 & 6 & 6 & 18 \\ \hline IEEEXplore & 0 & 0 & 1 & 0 & 5 & 6 \\ \hline Nature & 0 & 0 & 0 & 0 & 1 & 1 \\ \hline Total & 4 & 11 & 17 & 27 & 32 & 91 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Database source-wise distribution of selected articles.  
Figure 2: Taxonomical arrangement of selected articles.  
and a few others. Still, there were very limited studies (4) on RTM-based models for crop yield prediction.  
The RTM models used in these studies were PROSAIL [21; 43] SCOPE [44] and PI-LOTE [45]. These articles explored the application of RTMs on wheat (2) and maize (2) crops. Crop traits used for crop yield modeling were leaf area index (LAI) (4), chlorophyll (2), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) (1), fractional ground cover (Fcover) (1), and nitrogen deficiency-related photosynthesis traits (1). Regional distribution of only RTMs used for CYP described their application in the USA (1), Mexico (1), Germany (1) and Italy (1). Graphs showing the percentage distribution of research papers in terms of RTM models, crop traits, crops and countries are given below in Figure 3.  
The higher number of crop traits used is due to more than one trait being used per study. LAI (100%) means all studies used LAI, whereas only one study [43] used four crop traits (LAI, Cab, FAPAR and Fcover). The same is the case for crop traits in other studies of CYP and crop trait retrieval sections.
# SKVOS: Sketch-Based Video Object Segmentation with a Large-Scale Benchmark +
###### Abstract
This approach enhances the model's ability to handle temporal variations and improves segmentation accuracy. * Comprehensive Evaluation: We conduct extensive experiments on our SKVOS datasets, demonstrating that our method outperforms existing approaches in terms of segmentation accuracy and temporal consistency. Our results also highlight the effectiveness of sketches as a reference modality compared to traditional photo masks and language-based references. By addressing these challenges, our work not only advances the state-of-the-art in SKVOS but also provides a robust framework for future research in sketch-based video understanding. The proposed TRM and STCL modules offer new insights into the fusion of sketch and video modalities, paving the way for more efficient and accurate video object segmentation methods. The structure of this paper is as follows: Section 2 reviews the related research on video object segmentation (VOS) and sketch-based methods. Section 3 introduces the Sketch-VOS dataset, including its collection methodology and statistical information. Section 4 provides a detailed description of the proposed framework, including the sketch-video fusion models, the Temporal Relation Module (TRM), and the Spatiotemporal Contrastive Learning (STCL) module. Section 5 presents the experimental results and their analysis. Finally, Section 6 summarizes the paper and discusses future research directions.
# Deep-Sea Underwater Cooperative Operation of
## 3 Cooperative Operational Modes
### Pure Research Vessel R/V-Based Mode  
Figure 4 shows the scene diagram of the cooperative operational mode based on the pure research vessel R/V. It comprises the research vessel R/V, manned submersibles, AUVs, and other equipment. The research vessel R/V is equipped with command-and-control systems, a UACS, and a USBL. The command-and-control systems are used to guide the underwater cooperation of the HOVs and AUVs. The UACS is used for information transfer between the research vessel R/V and different types of submersibles. The USBL is used for precise underwater positioning of different types of submersibles. AUV clusters can be used for subsea micro-topography and geomorphological mapping can be used for the detection of key hydrological data.  
Manned submersibles are mainly used for field surveys in hotspot areas, accurate sampling, and accurate detection. Manned submersibles and AUV clusters can achieve three-dimensional operations in a local underwater area. The AUV cluster conducts scanning and surveying operations in an area 80-120 m from the bottom, while the HOV conducts fixed-point operations, such as observation and sampling on the seabed. To ensure the time consistency of the measured data of each submersible, unified timing is required before diving operations. In the field of seabed resource exploration, the AUV cluster detection data is transmitted to the HOV in real-time, and the detection data can provide underwater guidance for the HOV, which could improve the efficiency of underwater exploration.  
Figure 5 shows a schematic diagram of the cooperative operational mode of manned and unmanned submersibles based on the pure research vessel R/V. During the under  
Figure 4: Scenario of a cooperative operation based on the pure research vessel R/V.  
water cooperative operation, the HOV and the AUV cluster send the submersible status information to the research vessel R/V into a certain time period through the UACS. This status information includes mainly information about the submersible attitude, motion status, energy, early warnings, and life support systems (only provided by HOVs).  
The UACS of the research vessel R/V decodes the communication after receiving it and sends it to the surface command and monitoring system of the research vessel R/V through the network link. In the feedback communication, the UACS of the research vessel R/V sends control instructions and USBL positioning information to each submersible, to realize the command and dispatch of the underwater submersible cluster. However, owing to the constraints of underwater acoustic communication and positioning distance, when this cooperative operational mode is adopted, submersibles, such as HOVs and AUVs, are relatively close, and the distance from the research vessel R/V is less than 10 km.
# Efficient FPGA Implementation of an ANN-Based Demapper Using Cross-Layer Analysis
## 4 Hardware Implementation
### Reasonability of Demapper Fine-Tuning  
As most previous works analyze the communication performance of the complete end-to-end AE system, the question arises how much gain can be achieved by fine-tuning the receiver for channel fluctuations during runtime. In [21], an end-to-end system is trained over an AWGN-based channel model. Afterward, it is tested in a real-world scenario using SDRs in combination with fine-tuning of the receiver. The results show that for over-the-air fine-tuning, it can improve the BLER by up to 1 dB, compared to an AE that is only trained for the channel model. Furthermore, in [35], it is shown that fine-tuning increases the communication performance by up to 1 dB, which can be explained by quantization noise, phase offset, and hardware impairments not considered in the channel model. Those works indicate that the adaption of the receiver to a real channel leads to significant gains and therefore justifies the implementation of a trainable receiver ANN.
## 1 Introduction
The method of compressing data based on binary binomial numbers has properties shown in Figure 1:  
Figure 1: Properties of the method of compressing data based on binary binomial numbers. In addition, an essential advantage of the method is that compressed sequences are endowed with numerical characteristics that are expressed by their corresponding binomial numbers. Thus, information is compressed without loss, adapting to speed and error detection. The paper is structured as follows. Section 2 describes the general analysis of the binomial adaptive compression method. Section 2.1 describes the binomial decomposition of information sources, while Section 2.2 describes the analysis of the proposed compression method. Section 3 describes the lossless adaptive data compression method based on the binomial number system (BNS). Section 3.1 describes the data compression method with BNS, and Section 3.2 describes the methods for converting binomial code to binary. Section 3.3 describes the methods for converting binary sequences to binomial code and vice versa. Theoretical aspects of the lossless adaptive binomial data compression method are described in Section 4. Finally, we conclude this paper in Section 5.
# A Multi-Degree of Freedom Tuned Mass Damper Design for Vibration Mitigation of a Suspension Bridge
## 1 Introduction
In this paper, we propose a synthetic approach based on both the graphical approach and parameterized compliance for the concrete design of the TMDs with the expected DOFs and we verify their feasibility and performance by numerically and experimentally way on a laboratory suspension bridge mock-up. The paper is organized as follows: Section 2 describes the vibration characteristics of the bridge mock-up and builds a concept model of bridge with two 2 DOFs TMDs. Based on the equations of motion, the decentralized control technique is directly used to optimize the stiffness and damping coefficients of the springs and dampers to obtain the optimum frequency ratios in Section 3. Section 4 presents the detailed design process of the 2 DOFs TMDs based on the graphical approach and compliance analysis. Section 5 mainly focuses on evaluating the damping performance and verifying the proposed design method. Finally, findings and conclusions of the study are summarized at the end.
## 4 Results
### Example Analysis
Comparative analysis of the similarities and differences of the distribution of node degree, node strength, and correlation of strength and degree and edge weights by changing the value of extra traffic load \(\alpha\) is shown in Figures 8 and 9. Figure 7: Analysis of the influence of local-world \(M\) on network structure performance: (**a**) Distribution of edge weights; (**b**) Distribution of the correlation of strength and Degree. As shown in Figure 8, keeping the other parameters unchanged, give the assignments \(\alpha=1\) and \(\alpha=6\) to observe the changes in the power law exponential distribution. From Figure 8a,b, it can be seen that the magnitude of \(\alpha\) has little effect on the strength of nodes and nodes in the case of other parameter assignments. Figure 8: Analysis of the influence of extra traffic load \(\alpha\) on network structure performance: (**a**) Distribution of node strength. (**b**) Distribution of node degree. As shown in Figure 9, it can be seen from the Figure 9a that the larger the value of \(\alpha\) is, the higher the degree of precision between the nodes is. From Figure 9b, it can be seen that although there is still a preferred choice of connection, the larger the value of \(\alpha\) is, the greater the additional traffics burden brought about by the newly added node. The correlation value is in the correlation line and the larger the left and right fluctuations, the greater the instability of the network's preferences. Figure 9: Analysis of the influence of extra traffic load \(\alpha\) on network structure performance: (**a**) Distribution of edge weights; (**b**) Distribution of the correlation of strength and Degree. To sum up, this section considers three different scenarios to verify the simulation test, and the example simulation results and theoretical analysis results coincide exactly, thus verifying the effectiveness and feasibility of the proposed model.
## 4 Results and Case Study
### UX Expert Base Evaluation  
We compared the extracted dimensions using UXWE-LDA analysis with those manually extracted by our human experts for validation. We used the Jaccard coefficient similarity [46] to check the degree of dimensions overlapping between automatic extraction using UXWE-LDA and human experts. The Jaccard coefficient is calculated as Equation (9)  
\[JC=\frac{|D_{UXWE-LDA}\cap D_{Exp}|}{|D_{UXWE-LDA}\cup D_{Exp}|} \tag{9}\]  
where \(D_{UXWE-LDA}\) dimensions are extracted using automatic UXWE-LDA analysis and \(D_{EXP}\) are dimensions extracted by human experts through a rigorous manual process. The higher the Jaccard coefficient's value, the higher the degree of overlap between the two sets  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Topics**} & \multicolumn{3}{c}{**LDA**} & \multicolumn{3}{c}{**UXWE-LDA**} \\ \cline{2-7}  & **Precision** & **Recall** & **F1 Score** & **Precision** & **Recall** & **F1 Score** \\ \hline Attractiveness & 0.71 & 0.46 & 0.55 & 0.83 & 0.72 & 0.77 \\ Dependability & 0.78 & 0.49 & 0.60 & 0.80 & 0.91 & 0.85 \\ Efficiency & 0.73 & 0.60 & 0.66 & 0.76 & 0.77 & 0.76 \\ Perspicuity & 0.80 & 0.47 & 0.59 & 0.80 & 0.72 & 0.76 \\ Novelty & 0.76 & 0.51 & 0.61 & 0.81 & 0.76 & 0.78 \\ Stimulation & 0.75 & 0.47 & 0.58 & 0.87 & 0.81 & 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Topic-wise performance measures.  
Figure 12: Average F1 Score, Precision, and Recall of LDA and UXWE-LDA.  
of dimensions, as shown in Table 5. Three researchers were invited, having hands-on NLP and text mining experience, to extract the UXDs from the randomly selected online reviews. Each researcher selected 50 reviews randomly; finally, a total of 150 reviews were selected for UXWE-LDA validation. We compared the UXDs extracted from UXWE-LDA with the UXDs extracted by the human experts to check the reliability of the result generated by UXWE-LDA.  
The Jaccard coefficient for both the researchers' extracts and UXDs extracted by UXWE-LDA model are 0.3, 0.5, and 0.4, respectively. This concludes that our study inferred new latent variables or dimensions from the online reviews. We claim that our study outcomes are more reliable for generalization due to large corpus textual data. Due to the complexity and ambiguity involved in UXD extraction from online reviews, the results show that UXWE-LDA is a reliable and suitable approach for UXD extraction from online reviews.
## 2 Related Works
### RESTful Protocols  
HTTP is the most popular application-layer protocol used in the Internet and it is clearly an attractive solution for IoT applications. However, due to its synchronous client/server pattern and high header overhead, it is often left aside in favor of more lightweight options. HTTP Quality of Services relies on the underlying transport layer provided by TCP, and it is therefore not explicitly provided.  
Differently, CoAP was created with the purpose of adapting the well-known HTTP functionalities to resource-constrained devices typical of IoT scenarios. CoAP is based on UDP at the transport layer: on the one hand, this choice makes it a very lightweight protocol, while on the other hand it forces to manage QoS directly at the application layer. For this reason CoAP provides a two-level QoS support for messages, which can be of unconfirmable (not acknowledged) or confirmable (acknowledged) type. In case a confirmable message is lost, it is retransmitted after a retransmission timeout (RTO), whose value is insensitive of network conditions. Indeed, the legacy version of COAP does not adapt the RTO on the basis of the network Round Trip Time (RTT). Therefore, if the RTO chosen by CoAP congestion control is below the actual RTT, CoAP will incur spurious retransmissions, which contribute to increasing network congestion. To address such shortcomings, several works proposed modification to the CoAP congestion control protocol for what concerns RTO managing [11; 12].  
The authors of [13] proposed a CoAP QoS support for timeliness, i.e., the end-to-end transmission delay. Clients requesting a resource to a CoAP server may express the priority (among four different levels) with which they wish to be notified by the server, which can accept or negotiate such priority. Furthermore, servers differentiate between critical and non-critical notifications, and each client may select what type of notification it is interested in. The authors show through experiments in a real wireless sensor network that the proposed modification is able to deliver on-time critical notification in the context of a e-health application.
## Materials and Methods
### Selection of VIs and Data Analysis  
A number of statistical methods were employed to determine the VI most strongly related to the measured yield parameters average fruit size (g) per tree and total fruit weight (kg\(\cdot\)tree\({}^{-1}\)). These included a principal component analysis (PCA) and a non-linear regression, both undertaken using the statistical software R [54]. Although PCA is quite commonly adopted to remove the redundancy in a large number of variables, in this study, we applied it as a variable reduction procedure to select the two optimal VIs out of the 18 most related to avocado yield parameters. The VI that produced the highest coefficient of determination (R\({}^{2}\)) and the lowest root mean square error (RMSE) (Equation 1) was selected as optimal.  
\[RMSE=\sqrt{\sum(Observed\ data-Estimated\ data)^{2}/(n-1)} \tag{1}\]  
An additional stepwise regression and Analysis of covariance (ANCOVA) was performed to determine if the linear relationships between the optimal VI and the measured yield parameter for each of the season differed significantly.
## 2 Materials and Methods  
### FEM Model  
Figure 1 shows the FEM model, in which the anterior teeth were moved distally to close an extraction space with a helical loop incorporated into an archive. The only right side was modeled due to the bilateral symmetry.  
Three-dimensional models of the teeth were made based on a dental study model (i21D-400C, Nissin Dental Products Inc., Kyoto, Japan). The teeth were assumed to be rigid bodies because their elastic deformations were negligible compared to that of the periodontal ligament (PDL). The surface of the tooth was divided with rigid shell elements.  
The PDL of 0.2 mm thickness was constructed over the root with solid elements. It was assumed to be an elastic material, which has a linear stress-strain relationship ([PERSON]'s modulus: 0.13 MPa, Poisson's ratio: 0.45) [18]. By using these elastic moduli, the in vivo mobility of human teeth could be simulated by the FEM.  
The archive of 0.016 \(\times\) 0.022 inches (0.41 \(\times\) 0.56 mm) or 0.018 \(\times\) 0.025 inches (0.46 \(\times\) 0.64 mm) is made of a low elasticity material, TMA (Titanium Molybdenum Alloy, Young's modulus: 69 GPa) [19]. The low elastic wire was used to reduce the spring constant and increase the amount of tooth movement, which facilitated visual observations of the movement patterns. A decrease in the elastic modulus of a wire is mechanically equivalent to a decrease in wire size. The archive was divided with three-dimensional beam elements and fixed to the crown with rigid beam elements. While brackets were not modeled, this boundary condition simulated the case where the archive was firmly ligated without a clearance gap to the bracket slot.  
Figure 1: Finite element model for simulating extraction space closure using helical loop spring.  
A helical or vertical loop was incorporated into the archive between the canine and the premolar. The height of the loop was 6, 8, and 10 mm. The posterior part of the archive was bent 10, 20, and 30\({}^{\circ}\) for preventing lingual tipping of the anterior teeth, which was called gable bend (GB).
# Fault Analysis and Protection Principle for the Distribution Networks Integrated with PV and BESS
## 3 The Proposed Fault Direction Detection Principle
### Principle for Detecting Fault Direction Under Symmetric Faults  
#### 3.2.1 Voltage Blocking Criterion for Symmetric Faults  
Similar to the voltage blocking criterion proposed in Section 3.1.1, when the positive sequence voltage and negative sequence voltage at the installation location of the protection device satisfy the equation shown in Equation (8), the reactive current direction criterion should be activated; otherwise, the direction criterion will be blocked.  
\[\left\{\begin{array}{l}U_{p4}^{+}<U_{\text{set}}^{\prime+}=K_{\text{rel}}^{+}U _{\text{max}}^{\prime+}\\ U_{p4}^{-}<U_{\text{set}}^{-}=K_{\text{rel}}^{-}U_{\text{min}}^{-}\end{array}\right. \tag{8}\]  
where \(K_{\text{rel}}^{+}\) and \(K_{\text{rel}}^{-}\) are reliability factors, taking values of 0.98 and 0.95 respectively. \(U_{\text{max}}^{\prime+}\) is the voltage at the installation point of the protection device when the reactive current detected by PR4 is 0 during a forward fault. \(U_{\text{min}}^{-}\) is defined in the same way as in Section 3.1.1. After substituting the system parameters for calculation, \(U_{\text{max}}^{\prime+}\) and \(U_{\text{min}}^{-}\) are set as 4.6126 kV and 0.2952 kV, respectively.