esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Sub-Indices of BCI  
#### 3.3.1 Structural Index  
The structural index describes the bridge condition in terms of the damages in the structural elements. In other words, it evaluates the structural condition of the bridge. Figure 4 shows that the structural index consists of four sub-indices, including deck, girder, bent-abutment-wall, and foundation. It should be noted that the joints are classified under the deck sub-index and bearing and support in the sub-index of bent-abutment-wall. These four sub-indices have various relative importance. Table A2 in the questionnaire was designed for determining the relative importance of these four sub-indices. Similar to Table A1, the experts were asked to conduct a dual comparison between the four sub-indices and assign a value between 0 and 10 in cells of Table A2. Table 8 presents the averaged viewpoints of experts. By inserting these values into Expert Choice software, the authors calculated the relative weights of the sub-indices. Table 9 indicates these relative weights. Next, these four sub-indices must be assessed for different states of damages. Table A3 had been designed for this purpose. Experts were asked to enter the appropriate scores in the blank cells based on their views about the various damage intensity in each sub-index. Their scores were a number in the range of 0 to 100. A score of 100 is related to the best condition, and a score of 0 is related to the worst condition. Table 10 shows the assigned score of each sub-index for different conditions. These scores are the average of experts' viewpoints.  
Figure 4: The flow chart of AHP used in this study.  
The structural index score is the sum of weighted scores of its four sub-indices. In the bridge maintenance program, usually, more attention was allocated to structural sub-indices, and they were included with more details in the assessment. In other indices, however, evaluations were more general.
# Computationally Efficient Robust Color Image Watermarking Using Fast Walsh Hadamard Transform
## 5 Conclusions  
This paper did a detailed analysis of digital image watermarking, its various properties and applications. Proposed a CDMA based spread spectrum technique of watermarking using customized 8 \(\times\) 8 Fast Walsh Hadamard Transform for color images and compared the performance with similar existing DCT based algorithm. Simulation results showed that the proposed algorithm exhibits better robustness against attacks at a specified gain and is highly resilient to low quality compression compared to DCT algorithm. Analyzed in detail the effect of number, length and type of spreading codes on the robustness of the algorithm. Another major advantage of the proposed technique is low computational complexity and thus the low cost. The only drawback of FWHT is its low information  
Figure 11: Compression Quality Vs BER in the case of different types of spreading codes.  
Figure 12: BER after different attacks in the case of different types of spreading codes.  
hiding capacity and thus the low perceptual quality. So, the proposed algorithm is a good candidate for applications that require computationally efficient, low cost and robust watermarking.  
[PERSON] conceived, designed and performed the experiments; [PERSON] and [PERSON] analyzed the data. [PERSON] and [PERSON] involved in the discussions and analysis of the results. [PERSON] wrote the paper and [PERSON] reviewed the paper.  
The authors declare no conflict of interest.
# Word Sense Disambiguation Using Cosine Similarity Collaborates with Word2 vec and WordNet
## 4 Experiments and Results
### Comparing with Other Systems
The simplified Lesk computes overlapping words between word sense definitions and the context word instead. The adapted Lesk extends a sense definition with sense relations. LSA Lesk makes a model analyzing distributional semantic relationships between a set of documents and terms in documents from a large text corpus, and converts a context and sense signatures using the model to vectorial forms then computing similarity using cosine similarity. The results show that our method (50.9% or 48.7% without the probability of sense distribution) outperforms many baselines (i.e., original Lesk-9.7%, simplified Lesk-31.1%, adapted Lesk-24.7%, and LSA Lesk-40.8%) and many unsupervised systems participating in SESEVAL-3 English lexical sample task. Notice that wsdit gives better performance compared to wsdw2v (our method) because wsdw2v uses only the cosine similarity, but wsdit uses various similarity measures. So, our method could benefit from using other similarity measures [41] as well. Another reason is that some words that cannot be retrieved from Word2 vec, although the Google News Dataset provides 3,000,000 word-embedding vectors but does not cover all the words in WordNet. This problem affected the results as well. Training word-embedding vectors from bigger datasets could solve this problem but, on the other hand, it requires more training time and resources (e.g., CPU and RAM). It could also be solved by using other word-embedding approaches [42; 43; 44] that can handle out of dictionary issues.
## 6 Conclusions  
We surveyed the format of 360-degree-related metadata followed by different camera makers. We found that the GPano metadata format is followed by most cameras. JPEG WG defined JPEG 360, a new standard format for 360-degree metadata. As a result of comparing GPano metadata properties and JPEG 360 metadata properties, we found that the JPEG 360 metadata format covers more than the GPano metadata format. Therefore, it is necessary to convert a huge number of legacy 360-degree images in the market to a standard format, which requires a converter tool. We presented our own developed tool to convert 360-degree images from the conventional JPEG-1 format to the JPEG 360 standard format. This helps users convert their images to a standardized format efficiently to achieve more benefits of metadata in designing different VR/AR applications. Our implementation can be used to extract and view JPEG 360 standard metadata values. Similarly, our tool can be used as a basic unit for developing image-rendering software for omnidirectional content. As a practical-use case of our converter, we also proposed and implemented a system of a JPEG 360-compatible panorama from a single PTZ camera. This panorama will be suitable for broadcasting and recording a conference or meeting. A participant attending or watching the conference/meeting remotely can see and observe the environment more effectively through the wide-FOV, standard JPEG 360 panorama. The proposed PTZ panorama solution is also cost-effective compared with state-of-the-art omnidirectional solutions and cameras.  
In the future, the JPEG 360 converter tool can be made intelligent to overcome the limitations presented in Section 5 above. This will enable the tool to recognize the 360-degree image programmatically and assess the input metadata values according to the constraint definition. Similarly, in research regarding PTZ panoramas, the stitching time can be improved to apply a non-feature-based stitching approach as the pan, tilt angles, and camera parameters can be obtained directly from the camera for homography estimation. Thus, features-finding, matching, and homography estimation can be skipped to optimize and speed up stitching time.  
Conceptualization, F.U. and O.-J.K.; data curation, F.U.; formal analysis, S.C.; funding acquisition, O.-J.K.; project administration, S.C.; resources, S.C.; software, F.U.; supervision, O.-J.K.; writing--original draft, F.U.; writing--review and editing, O.-J.K. and S.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Institute for Information and Communications Technology Promotion (IITP) grant number 2020-0-00347 And The APC was funded by Korea Government.  
Not applicable.  
The authors declare no conflict of interest.
# Implementation of Pavement Defect Detection System on Edge Computing Platform
## 5 Experimental Results and Quantitative Analysis
We will attempt to use the YOLOv4 model with another tracking algorithm in the future and believe that it will have a much better recognition effect. Figure 12: Pavement defect detection results using different deep learning models. (**a**) YOLOv4; (**b**) YOLO-mobilenet; (**c**) TF-YOLO; (**d**) RetinaNet. Figure 14 demonstrates the miss rate analysis of the DL object detection model compared in this study using TPDID. It is obvious that YOLOv4 focused on the condition of \(10^{-1}\) FPPI and had a smaller miss rate, implying that the model corresponds effectively with the proposed road pavement defect detection system. In the future, we will continue to build up sample dataset repositories to further enhance the detection accuracy and reliability of the proposed detection system. Figure 14: Comparison of miss rate for different deep learning models. Figure 5:0 Quantitative comparison of mAP and FFS for different deep learning models. Finally, the AP of Category 0 for each model in different distance intervals with respect to 70\({}^{\circ}\) FoV was evaluated. The results are shown in Figure 15; when the defect is approximately 20-30 m away from the vehicle, the vertical (\(Y\)-axis) size of the image is about 400 pixels. YOLO series resulted in a better detection accuracy rate than others when IOU is 0.5, especially in the interval of 0-2, i.e., within 0-16 m, which means that the YOLO series can have better detection results at both medium and short distances.
[20] established a unified reconstruction method for the beam-like structure based on the framework of iFEM, which can reduce the structure identification error by introducing some generalized quantities. [PERSON] et al. [21] proposed the inverse-plate quadrilateral area coordinates method to replace the Jacobian matrix, such that the error caused by numerical integration can be avoided. [PERSON] et al. [22] expanded the applicability of the iFEM beam model to include prismatic beams with arbitrary cross-sections. [PERSON] et al. [23] combined the Green-Lagrange strain theory to extend the iFEM for nonlinear deformation. Due to some uncertain factors, including sensor installation position errors and strain measurement errors presented in actual engineering, the accuracy of shape sensing with the iFEM is affected. [PERSON] et al. proposed to use a fuzzy network to approximate the relationship between the measured strains and the inverse solution strains. They established a fuzzy calibration network through 13 sets of working condition data, and the calibrated strain was used to solve the frame deformation displacement [24]. [PERSON] et al. proposed a fuzzy calibration network with support vectors to generalize the use of the network [25]. The network is obtained by training with 10 sets of working condition data. Since the above-mentioned approaches use less data for training the calibration network, the resulting network covers less information, which will directly influence the accuracy of the calibration network [26]. In addition, [PERSON] et al. [27] customize a calibration method using a fuzzy self-framework, which can effectively solve the interference caused by the sensor paste error in iFEM. However, the reconstruction accuracy of the partial evaluation position was only improved by the above calibration method. In addition, the errors between calibration results and the actual deformation displacement were increased when the faint noise interfered with the working condition data. Considering the reduced influence of external factors on the real-time reconstruction of structural deformation, an online measurement and calibration method for structural deformation based on small samples is proposed in this paper, which can calibrate the reconstruction values of the sub-reflector position and altitude in real-time to improve the reconstruction accuracy. The main original aspects of the proposed method are twofold. Firstly, a reconstruction model between the FBG strain measurements and the deformation displacements of a sub-reflector support structure is established. Secondly, the sample measurement data is extended by the NURBS to provide the training data for the SSFN, which can effectively correct random noise data.
# A Review on Reconfigurable Liquid Dielectric Antennas
## 3 Liquid-Assisted Antennas
### Other Tuning Techniques
#### 3.3.3 Thermal Tuning  
The dielectric permittivity of water depends on the ambient temperature and this allows to use temperate as another mean for frequency tuning [71]  
\[\epsilon(\
u,T)=\epsilon_{\infty}(T)+\frac{\epsilon_{0}(T)-\epsilon_{\infty}( T)}{1-i2\pi\
u\tau(T)}, \tag{5}\]  
where \(T\) is the water temperature (\({}^{\circ}\)C), \(\
u\) is the frequency of the electromagnetic field (Hz), \(\tau\) is the characteristic time of the relaxing/resonating system (s), and \(\epsilon_{\infty}\) is the water permittivity at an \"infinite\" frequency [71]. For example, in Reference [107], the frequency response of a dipole antenna was manipulated by changing the local temperature of a water-based substrate.
## 3 Framework
### Architecture
#### 3.1.1 vUE Probes  
Open radio access network (RAN) technologies will allow the resources to be converted into base stations or user terminals. The first step is to create containers with the execution of base stations or UEs. For this, software implementations of such elements are available, making use of the SDR platform where all the RF and PHY-related functions take place [35; 36]. In this way, through configuration files, different virtual interfaces where the instantiated entity must connect (i.e., core functions [37]; IP address in the case of gNodeB) are set and all the necessary parameters (Mobile Country Code (MCC), Mobile Network Code (MNC), access point name (APN), eSIM, encryption type, frequencies, etc.) in the case of a UE. Therefore, the deployment of vUE probes is performed by instantiating the necessary hardware resources (including signal and computing processing units) selected to take the measurements with a proper parameter configuration to connect to the RAN.  
Once established and tested, the operation of the vUE, including the connection to the container-based management system, can launch isolated virtualized scripts to take samples as needed.  
Therefore, it is necessary to develop scripts to run on the vUEs and schedule them accordingly or on-demand, depending on whether a specific situation is found where the operator wants to troubleshoot more deeply from a UE perspective. Additionally, the inclusion of virtualization software in the probes (such as Docker [5]) allows instances to use the different radio functions to connect to different operators. Similarly, the use of a sniffer of the radio interface of the user could be instantiated to obtain a detailed capture of the signaling exchanged with the base station (using some of the above-mentioned open source tools such as Mobile Insight [22]).
## 3 Results and Discussion  
### Effect of Cold Plasma Treatment on Physical Change of Seed's Surface  
#### 3.1.1 Effect of Cold Plasma Treatment Time on Germination Rate and Root Length  
Seed germination is a physiological process that begins with the seeds' water absorption (imbibition) and ends with the root emerging. To study the effect of the plasma energy dosage on seed germination, we applied different plasma treatment times (1, 2, 4, 6, 8, 10, and 15 min) and monitored both the germination rate and the lengths of the sprout roots after germination. Seed germination and root length were significantly enhanced after plasma treatment (Figure 2a). After 12 h of sowing on cotton under the same conditions, the germination rate, defined as the number of germinated seeds per the total number of seeds (Equation (1)), increased from 8% to 88% (11 times) between untreated and 15-min plasma-treated seeds (Figure 2b). After 24 h, all seeds were germinated, and the length of the root also significantly increased, from 70% in the range of 0.25-0.75 cm (group 2-red) for untreated seeds, to 70% in the range of 0.75-1.25 cm (group 3-green) for 4-min plasma-treated seeds, and up to 60% in the range of longer than 1.25 cm (group 4-blue) for 10-min plasma-treated seeds (Figure 2c). These results were obtained from five replications (a total of 125 seeds per a certain treatment time). Effect of plasma enhancement on the length of 24-h sprout root was saturated after 10 min of treatment, since 10-min and 15-min plasma treatments showed similar results. Our observation also agreed with a recent report [20], which indicated that short-time plasma treatment promoted seed germination and seedling growth, while long-time plasma treatment could have inhibitory effects. For the highestgermination rate after 12 h and the longest extrusion root after 24 h, a plasma treatment time of 10 min for 25 seeds with a plasma power of 1.2 W was optimal, which equaled a plasma energy dosage of 0.08 Wh per seed.  
To understand the effect of enhancing germination and stem growth, we studied changes in the seed's surface morphology, wettability, as well as the regulation of gibberell acid induced by plasma treatment.  
Figure 2: (**a**) Photo of batches of 25 bean sprout seeds with different plasma treatment time after 24 h of sowing on cotton on a 9-cm petri dish. The ruler on the right is 10-cm long. (**b**) Seed germination rate after 12 h and 24 h. (**c**) Effect of plasma treatment time on the roots at germination state. Plot of percentage of seeds divided into different categories of roots’ length: (1-black): shorter than 0.25 cm; (2-red): 0.25–0.75 cm; (3-green): 0.75–1.25 cm; and (4-blue): longer than 1.25 cm. The error bar was calculated from five replications (total 125 seeds per a certain treatment time).
## 3 Results
### Partial Shielding of Electrical Actuator  
In the target system, the main electrical actuator is realized by dual stepper motor sets, an active one and a backup. Each set of stepping motors contains two symmetrically arranged two-phase stepping motors, which respectively drive the mechanical arm to rotate and lift. The straight-line distance between the motor and the center of the target is \(\sim\)50 cm. The active and standby motors on the same side are connected by a dual-input  
Figure 13: Visualization of FLUKA 3D radiation field simulation data based on ParaView.  
speed reducer, which is connected to the revolution axis through a coupling. The schematic diagram is illustrated in Figure 15.  
The winding insulation materials and semiconductor devices in stepper motors are demonstrated to be susceptible to ionizing radiation damage. For organic insulating materials, chemical reactions can occur when irradiated, affecting the mechanical and insulating properties [22]. Ionization and displacement damage also occur inside semiconductor materials when irradiated, changing the microstructure of the material, affecting its overall performance [23; 24]. Therefore, partial shielding was designed to prevent the short-term failure of the stepper motor components in the intense radiation field. Taking into account  
Figure 14: Induced ambient dose equivalent rate (\(\upmu\)Sv/h) at beamline level (y = [\(-\)4.7, 1.6]) at 0 s post-EOB.  
Figure 15: (**a**) Two-dimensional diagram of the motor region, (**b**) motor region geometry in Flair. Distances are given in cm.  
the load bearing of the motor support and the low cost, an iron alloy (Q235A) was selected to shield secondary radiation.  
Because the internal structure of stepping motors is very complex, the motor and the reducer models were simplified, the size of the motors is 8.58 cm \(\times\) 8.58 cm \(\times\) 15.6 cm, and the material is iron. The sidewall thickness of the shielding hoods is 0.8 cm and the material is Q235A. The top shielding consists of eight detachable plates, each with a thickness of 1 cm, for ease of installation purposes.  
The USRBIN card was used to score the variation in the absorbed dose rate of the motor region with 8 cm shielding plates. The prompt absorbed dose rate of the motor region is \(\sim\)2.5 Gy/h, a drop of about 63% compared to that without shielding (6.8 Gy/h). Thus, the maximum allowable exposure time increases by 2.7 times.
# Quasi-All-Passive Thermal Control System Design and On-Orbit Validation of Luojia 1-01 Satellite
## 5 Experimental Verification
### Testing Working Conditions  
High temperature conditions in the thermal balance test simulate the external heat flux during flight on the December solstice, under the condition that all the instruments are working at their maximum power consumption status and the properties of coatings are at the end of their life expectancy. The parameters of the high temperature conditions in the thermal balance test are set as follows:  
* The camera continues to work for a duration of 120 s in the shadow region and the data transmission unit continues to work for a duration of 300 s in the sunlight region. All the rest of the instruments keep on working at their maximum power consumption except the navigation unit which is working at the minimum power consumption
* Active thermal control is working as designed. The temperature of the camera remains stable at 12 \({}^{\circ}\)C and the battery temperature is stable at 18 \({}^{\circ}\)C.
* The external heat flux is set the same as the high temperature condition in the thermal analysis using FEM.
* The properties of the coatings and MLI is set up as the ones at the end of their life expectancy.  
Low temperature conditions in the thermal balanced test simulate the external heat flux during flight on the June solstice, under the condition that all the instruments are working at their minimum power consumption status and the coatings are at a good status in the beginning of its life expectancy. The low temperature condition parameters in the thermal balance test are set as follows:  
* The camera is off and the data transmission is off. All the rest of the instruments keep on working at their minimum power consumption.
* Active thermal control is working as designed. The temperature of the camera is stable at 12 \({}^{\circ}\)C and the battery is stable at 18 \({}^{\circ}\)C.
* The external heat flux is set the same as the low temperature conditions in the thermal analysis using FEM.
* The properties of the coatings and MLI is set up as the ones at the beginning of their life expectancy.