esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Debonding Detection with Electromechanical Impedance Technique
Nevertheless, one can say that an RMSD value of 0.31 can be set as a threshold value and any value above this value can be used to identify debonding damage. From past studies conducted by various authors in the field of the EMI technique, it has been experimentally found that damage detection using the EMI technique is more difficult for non-homogenous materials (such as composite structures or concrete) compared to homogenous materials such as steel. One reason is due to the damping effect where the impedance signature change will be larger for steel structures compared to composite structures subjected to same damage. Thus, it is vital for one to acquire a large difference in the impedance signature subjected to damage to avoid false alarms as a small change in the surrounding temperature can also cause the impedance signatures to change. For this reason, the authors have introduced a way of causing the impedance signatures to change as large as possible subjected to debonding damage by biomimicking a spider and its web.
###### Abstract  
The aim of this study was to evaluate the influence of clarification treatments on volatile composition and aromatic attributes of wine samples. 'Italian Riesling' icewines from the Hexi Corridor Region of China were clarified by fining agents (bentonite (BT) and soybean protein (SP)), membrane filtration (MF), and centrifugation (CF) methods. The clarity, physicochemical indexes, volatile components, and aromatic attributes of treated wines were investigated. Both the fining agents and mechanical clarification treatments increased the transmittance and decreased the color intensity of icewine samples. Bentonite fining significantly influenced the total sugar content, total acidity and volatile acidity. Total acidity decreased 2-3.5% and volatile acidity 2-12%. MF showed the greatest influence on total phenol content, decreasing the initial content by 12%, while other treatments by less than 8%. Volatile analysis indicated that both the categories and contents of volatile compounds of wine samples decreased. MF treatment showed the most significant influence, while SP fining showed much lower impact. Odor activity values indicated the compound with the highest odor activity in Italian Riesling icewines was \(\beta\)-damascenone. For this compound, BT and SP did not show significant differences, however, in MF and CF it decreased by 20% and 63%, respectively. Furthermore, with high impact on aroma were: ethyl hexanoate which reduced by 20-80% especially in MF; rose oxide which extremely reduced in MF and undetected in BT, SP, and CF; isoamyl acetate which reduced by 3-33% and linalool decreased by 10-20% and undetected for BT. Principle component analysis indicated that icewine clarified by different methods could be distinguished and positively correlated with odor-active compounds. Floral and fruity were the dominant aroma series in icewine samples followed by fatty, earthy, spicy, vegetative and pungent flavor. The total odor active value of these series significantly (\(p<0.5\)) decreased in different clarification treatments. Sensory evaluation showed similar results, but the SP and CF wine samples achieved better sensory quality. This study provides information that could help to optimize the clarification of ice wines.  
Keywords:fining; filtration; icewine; volatile compounds; aroma series; sensory analysis +
Footnote †: journal: molecules
## 4 Discussion
### Limitations of the Current Study  
The first limitation is the selection of data resolution. Using higher resolution data at the provincial scale will make the results more accurate, and subsequent research needs to use higher resolution data for analysis.  
The second limitation is land surface temperature data. Because land surface temperature is affected by radiation balance, evapotranspiration, and land type, it is not a primary variable. Subsequent studies will explore the differences between weather station data and land surface temperature data.  
The third limitation is the data limitation. There are a few driving factors involved in this paper. In subsequent work, more driving factors should be considered as much as possible, as well as data related to national policies [46]. The effect of the classification method on the final result should be taken into account in the geographical detector.
# Cable Temperature Prediction Based on RF-GPR for Digital Twin Applications
## 4 Results and Discussion
### Analysis of Prediction Results of Cable Temperature in Cable Trench  
A comparison between the predicted cable temperature based on the RF-GPR cable temperature prediction model and the cable temperature calculated by the finite element is shown in Figure 7. It can be seen from Figure 7 that the distribution trend of the model prediction results trained by the machine learning method is basically the same as that of the finite element calculation results. Through the error analysis of the finite element calculation temperature value and the machine learning prediction temperature value, the relative error percentage distribution of the cable temperature distribution predicted by the RF-GPR cable temperature prediction model in the cable trench is obtained, as shown in Figure 8. The relative error of the cable temperature predicted by the machine learning model is minimum in the cable core part, and the relative error distribution is basically within 0.1%. The relative error distribution outside the cable shows an increasing trend, and the average relative error of the temperature prediction value is 0.17%. Near the cable sheath, the machine learning prediction results are not as accurate as the finite element calculation results. This is due to the fact that the cable jacket is located on the outer layer of the cable, and the temperature gradient drops considerably compared to the cable core temperature. The decrease in temperature gradient near the cable bottom skin is particularly obvious; thus, the machine learning model near the cable bottom skin has poor temperature prediction accuracy, and the relative error reaches the maximum of 1.54%. At the same time, the training feature data near the cable sheath are less than the data set near the cable core, which causes the prediction results to not be as accurate as those near the cable core. In the research on cable temperature calculation, the most noteworthy value is the temperature of the cable core. The influence of the temperature distribution at the cable sheath on cable operation is much smaller than that at the cable core temperature, so the prediction accuracy of a few points at the cable sheath is negligible.  
Figure 8: Relative error percentage of cable temperature predicted by machine learning.  
Figure 7: Comparison of finite element calculation results (**left**) and machine learning prediction results (**right**) of cable temperature distribution in cable trench.
## 3 Simulation of Underwater Electric Field Communication
### Analysis of Underwater Electric Field Communication Simulation Results
#### 3.2.3 Underwater Electric Field Communication End-to-End Delay Simulation Test
Since there was only one underwater communication channel with a large number of communication nodes, the signals emitted by the robots through the electric field would collide and cause coupling in the underwater channel, resulting in error codes. Therefore, this section aims to solve the backoff problem of underwater electric field multi-node communication in the underwater channel.
## 6 Conclusions and Future Work  
In this paper, the problem of designing an LPV state-feedback controller that takes into account the time-varying saturation limits has been investigated. The design procedure corresponds to checking the feasibility of an appropriate set of LMIs, which can be solved efficiently using available solvers. Finally, the results obtained in the illustrative example correspond to the case where the LPV state-feedback controller designed following the proposed methodology is evaluated in an LPV mathematical system with time-varying boundaries, showing that the controller guarantees the closed-loop stability and its capacity of adjusting the system's performance in front of the variability of the saturation limits.  
Future work will focus on applying the procedure described in this paper to design an LPV controller using robust control techniques combined with a model reference control for UAV vehicles.  
Figure 5: Scenario II: adaptability of control signal \(u_{1}\).  
Figure 6: Scenario II: Lyapunov function and guaranteed decay rate.  
Moreover, in order to deal with exogenous disturbances, for example, wind gusts in the application of UAV control, the results presented in this paper will be extended to the case where disturbance rejection is considered.  
Conceptualization, Methodology, Supervision and Writing--original draft, review & editing, A.R., D.R. and B.M.  
This work has been partially funded by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project SCAV (ref. MINECO DPI2017-88403-R). This work has also been partially funded by AGAUR of Generalitat de Catalunya through the Advanced Control Systems (SAC) group grant (2017 SGR 482) and by the Spanish State Research Agency through the Maria de Maeztu Seal of Excellence to IRI (MDM-2016-0656). [PERSON] is also supported by Formacio Personal Investigador a la Universitat Politecnica de Catalunya under an FPI-UPC 2018 grant (ref. 20 FPI-UPC 2018). [PERSON] is also supported by [PERSON] - Formacion grant (FJCI-2016-29019).  
The authors declare no conflict of interest.
# An Edge-Based LWM2M Proxy for Device Management to Efficiently Support QoS-Aware IoT Services
## 4 QoS-Oriented Extended Functions
### Requests-Shaping  
The Proxy can alleviate network congestion by controlling the flow of requests to the Client [7]; consequently, it can guarantee a better QoS. To do so, the Proxy implements a congestion window that limits the number of outstanding requests over the constrained network up to a fixed maximum. When the Proxy receives a message request for a Client, it enqueues it in a buffer, and forwards it only if the window is open, i.e., if the number of requests forwarded to any Client and still waiting for a response is less than the maximum window size.
# Opportunities and Limitations in Recycling Fossil Polymers from Textiles
## Recycling Fossil Polymers
### Polyamide  
The two main polyamide fibre groups, wool (natural fibre) and PA6 (or PA6,6) (synthetic fibre), have very different recycling options, even though they both have amide bonds in their polymer backbone. Wool can be recycled mechanically, whereas for PA6, being a polycondensation polymer and a thermoplastic material, recycling options similar to those for polyesters are viable. PA6 can be melted and reshaped into new fibres of appropriate length and strength. As in the case of PET, a drying process is suggested before melting; it is revealed that the drying process prevents hydrolytic chain scission in wet materials and the intrinsic properties of PA6 polymer are preserved [106]. [PERSON] et al. [107] observed that the reprocessing up to 16 times of polyamide 6 resulted in a progressive decrease in molecular weight, but the polymeric structure, investigated by infrared analysis, was not significantly modified. Generally, thermal nonoxidative degradation of polyamide 6 and 6,6 results in the evolution of some CO\({}_{2}\), ammonia and formation of insaturations, nitrile and isocyanate groups [108]. However, these reactions were observed in experiments carried out for a long time (hours). On the contrary, reprocessing operations such as extrusion or injection-moulding are generally very fast (seconds).  
PA6 waste is available as carpets and fishing nets, and these materials are an important feedstock for PA6 recycling [109]. The recycled polyamide shows similar characteristics compared to commercial PA6. Recycling carpets is also possible by blending various carpet components through a reactive extrusion and compatibilization process, yielding lower-quality products [89; 110]. The low number of PA6 suppliers and recyclers makes recycled polyamide fibres more expensive than PET fibres.
# Kinematic and Workspace Analysis of RRU-3 RSS: A Novel 2T2R Parallel Manipulator
## 2 Description of the Manipulator  
Figure 1 shows the schematic representation of the mechanism presented in this work. It is a 4-DOF fully PM that consists of two rhomboid-shaped platforms--one mobile and the other one fixed--connected by four kinematic chains (KCs). The first KC (\(i=1\)) consists of two revolute joints and a universal joint (RRU), while the three remaining KCs (\(i=2\), \(3\), \(4\)) consist of a revolute joint and two spherical joints (RSS).  
The revolute joints connected to the fixed platform are the active joints of the manipulator. Hence, the kinematic topology of the manipulator can be described as RRU-3 RSS. As depicted in Figure 1, we use two reference frames. The fixed reference frame (\(O_{X,Y,Z}\)) is located at the center of the fixed platform, and the mobile reference frame (\(P_{u,v,w}\)) is attached to the mobile platform, with the \(w\)-axis normal to the platform plane and the \(u\)-axis pointing towards \(A_{3}\) point. Accordingly, the PM provides two translations (\(x\), \(z\)) along \(X\)- and \(Z\)-axes and two rotations (\(\varphi\) and \(\theta\)) about \(Y\)- and \(u\)-axes. Both output and input variables ( \(\beta_{1}\), \(\beta_{2}\), \(\beta_{3}\), \(\beta_{4}\)) are represented in Figure 1b.  
In terms of mobility, the RRU chain is the only one that imposes motion constraints on the mobile platform and, thus, it can provide the 4-DOF by itself. The rest of the KCs present full mobility, and, even if they do not constrain the motion space of the PM, they allow it to fulfill other design requirements, such as a higher stability, the possibility of arranging all the actuators in the fixed frame or a lower energy consumption. Due to the SS link, each RSS chain presents an idle rotation (local DOF) that could be avoided by substituting one of the S joints with a U joint.  
To provide this specific 2T2R motion, all the axes of the revolute joints must be arranged parallel to the \(Y\)-axis, and the first and second axes--starting from the fixed platform--of the universal joint must be parallel to \(Y\)- and \(u\)-axes, respectively (R'R'U'u-3R'SS). Consequently, the first link of each KC is always parallel to the \(XZ\) plane. Besides, performing a \(\theta\) rotation does not involve any change in the input variables associated with the first (\(C_{1}B_{1}A_{1}\)) and third (\(C_{3}B_{3}A_{3}\)) KCs. In fact, the whole RSS limb corresponding to the third KC always remains parallel to \(XZ\) plane, which allows us to substitute its topology with another RRU chain. This would lead to an equivalent symmetrical PM (2R'R'U'u-2R'SS), but with two kinematic redundancies.  
We will consider the length of all the lower and upper links to be \(R\) and \(L\), respectively, and the size of the fixed and mobile platforms to be the same. That is, the length of the rhombus diagonals that connect the first and the third KCs (\(A_{1}A_{3}\) and \(C_{1}C_{3}\)) is \(2\cdot F\), while the length of the diagonals that connect the second and the fourth KCs (\(A_{2}A_{4}\) and \(C_{2}C_{4}\)) will be \(2\cdot N\).
# BFE-Net: Bidirectional Multi-Scale Feature Enhancement for Small Object Detection
## 2 Related Work  
### Deep Object Detectors  
**Two-stage Detectors:** The two-stage object detection algorithm generates object candidate regions and refines the regions of interest using high accuracy but relatively inefficient classifiers and regressors. Fast R-CNN [3] proposed a pooling layer structure for ROI pooling, which efficiently solves the operation of R-CNNs that must crop and scale image regions to the same size. Faster R-CNN [4] proposed a Region Proposal Network (RPN) and developed an end-to-end framework that significantly improves the detector's efficiency. The proposed RPN integrates proposal generation with a single convolutional network. Cascade R-CNN [24] introduced multi-level refinement in Faster R-CNN to achieve more accurate target location prediction. Mask R-CNN [5] replaced the ROI Pooling layer with ROIAlign and added a branch FCN layer for semantic mask recognition on top of the edge recognition. The detection process of this type of algorithm is complex. Although the algorithm has high accuracy, the detection speed is relatively slow.  
**One-stage Detectors:** The main difference between the one-stage object detection algorithm and the two-stage object detection algorithm is that the former does not have a candidate region recommendation stage, the training process is relatively simple, the target category can be directly determined, and the position detection frame can be obtained in one stage. YOLOv1 [7] directly performs target coordinate regression and category classification by dividing the grid on the output feature map, omitting the explicit process of extracting candidate regions. This design method greatly reduces the time taken by the detection process. Subsequent research [8; 9; 10] continues to improve based on [7], which further improves the detection accuracy while ensuring the detection speed. SSD [6] and its variants [14; 15] combine YOLO, which has the advantage of fast detection speed, and Faster R-CNN [4], which has the advantage of accurate positioning, and introduce multi-scale and multi-resolution detection technology. The network of different layers detects objects with different scales, which greatly improves the detection effect of small targets. Although the single-stage object detector is significantly faster than the two-stage object detector based on candidate region recommendation, its accuracy has not been comparable to the two-stage object detector. [PERSON] et al. proposed RetinaNet [11] by introducing a focal loss function, which reconstructs the standard cross-entropy loss function so that the detector will pay more attention to the samples that are difficult to classify during the training process. The introduction of focal loss solves the problem of unbalanced sample samples and achieves an accuracy comparable to the two-stage target detection framework.
## 2 Related Works  
### Deep-Learning-Based Visual SLAM  
The integration of deep learning neural networks into visual SLAM has emerged as a promising research direction for end-to-end pose estimation and depth perception. Learning-based SLAM methods can be categorized into supervised and self-supervised approaches based on their supervision types. For instance, DeepVO [5] represents a typical supervised odometry approach that uses convolutional neural networks (CNNs) for visual feature extraction and fully connected layers for pose regression. It follows the local mapping approach of traditional SLAM, optimizing visual features of image sequences using LSTM. DROID-SLAM [16] merges pose and depth estimation tasks, constructing a complete learned SLAM system by integrating a hidden state optimization model based on GRU [17] with traditional bundle adjustment models. NeuralBundler [18] employs both stereo DepthNet and PoseNet, along with monocular and stereo photometric loss functions, to build a scalable framework. It also utilizes a bag-of-words (BoW) model for loop closure detection and constructs a pose graph optimization model to correct positioning errors. DeepSLAM [9] features three modules--mapping net, tracking net, and pretrained loop net--dedicated to depth estimation, pose estimation, and loop closure, respectively.  
In other approaches, [19; 20] leverage the optical flow estimation network PWC-Net [21] to track keypoints between adjacent frames and construct an epipolar geometry model for relative pose estimation. [19] integrates DepthNet's depth estimates to form a photometric loss, using triangulation to maintain depth scale consistency by constructing 3D spatial points corresponding to matching keypoints. The framework adopts a self-supervised training approach, initially constructing photometric loss with PWC-Net and then jointly optimizing DepthNet and PWC-Net parameters.
## 2 MIMO Antenna Design Approaches
### Mean Effective Gain (MEG)  
MEG is an important diversity parameter for MIMO antennas and defined as the ratio of power received by the MIMO antenna to the power received by the isotropic antenna. For the better performance of a MIMO antenna with the equal power level, the ratio \(\frac{\text{MEG}_{j}}{\text{MEG}_{i}}\) must be less than 3 dB. The MEG can be evaluated using Equations (4) and (5) [14]:  
\[\text{MEG}_{i}=0.5\Big{[}1-\left|S_{ii}\right|^{2}-\left|S_{ij}\right|^{2}\Big{]} \tag{4}\]  
and  
\[\text{MEG}_{j}=0.5\Big{[}1-\left|S_{ij}\right|^{2}-\left|S_{jj}\right|^{2}\Big{]} \tag{5}\]
###### Abstract  
In this paper, we have investigated and reported the performance of free-space optical (FSO) links operating in adverse atmospheric conditions. Since FSO links share operational similarity with fiber communication; hence, we believe that a cost-effective FSO framework can play a significant role in the transparent integration of high-speed network access backbones with the end-users. Different modulation formats, complemented with spatial diversity techniques, are discussed in this paper to strategize performance optimization of FSO links. Using bit error rate (BER) and signal-to-noise ratio (SNR) as performance metrics, it was found that binary phase-shift keying (BPSK) qualifies as the best technique modulation technique delivering SNR gain of 10 dB over on-off keying (OOK) operating link under similar channel conditions. Further performance optimization was achieved using space diversity reception wherein SNR witnessed a gain of 3 dB gain over the single-channel FSO link. In terms of application, the proposed model can help in developing a citizen-centric smart city ecosystem that can support seamless communication between heterogeneous smart devices.  
free-space optics (FSO); smart cities; last-mile connectivity; local area network (LAN); gamma-gamma channel model; space diversity reception technique (SDRT); selection combining (SC) +
Footnote †: journal: _photonics_