esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussions  
The novelty of this paper is to propose an image process that can possibly detect the abnormality of a solar module quickly and accurately. The whole process includes grayscale conversion, filtering, 3-D temperature representation, probability density function, and cumulative density function. Different kinds of faults may be detected in this process. Unlike the preceding report [2], this article presents real-size outdoor PV array monitoring by drone. The health condition and defect ratio of each panel can be monitored, which is different from the previous report [8; 9]. The advantage of the innovative method proposed in this paper is to apply an infrared imaging system with drone functions to detect faults in the solar power system and MATLAB image analysis software is adopted to analyze the IR image. Results show that in addition to the instantaneous acquisition of IR images, the health and defect area of the cell can also be determined clearly and defective solar panels can be compared through the cumulative chart data promptly. Furthermore, the procedure of IR image analysis can search panels that have cells exceeding the temperature threshold effectively, as well as panels that have been confirmed to be abnormal.  
The cumulative chart is a convenient way to represent the temperature distribution on the module. The grayscale of the thermal image may show the abnormal area on the module. However, the representation is qualitative rather than quantitative. It is difficult to determine how severe the damage is based on qualitative representation. The cumulative curve proposed in this paper is a quantitative way to represent the temperature distribution on the module. One characteristic of the cumulative curve, which was not shown in the paper, is that we can mark any point on the accumulation curve to show the percentage of the area below the specified temperature. The maintenance staff may make decisions quickly and accurately based on the cumulative curve of each image. Ten cases have been tested in this paper and the results have confirmed the validity of this method. More cases of testing will be carried out in the future.  
The detection of faults in solar modules by AI is a future trend. However, the original image contains too much information that it takes time to inspect the image by machine vision or other AI technology. The cumulative distribution function proposed in this paper is a new tool for the preliminary examination of thermal images. It is a much easier way to detect the abnormality of a solar module than the direct inspection with machine vision. Once it is determined by the cumulative distribution function that there is something wrong with the module, the machine vision can then be applied to inspect the thermal image to find out the location as well as the severity of faults.
# Metamorphic Testing of Relation Extraction Models
## 3 Approach
### Metamorphic Relations for RE
#### 3.2.3 MRs with the Combination Operation on Multiple Source Inputs  
In a sentence, it is common that there exist more than two entities. Moreover, different pairs of entities exhibiting varying relations may have common entities. In particular, entities with a symmetrical relation play equal roles in a sentence. Motivated by this, it is feasible to combine two pairs of entities (one of which has the symmetric relation) to construct a new pair of entities. This type of MR involves two source inputs,\((s^{s},e^{s}_{h1},e^{s}_{t1})\) and \(t_{s2}=(s^{s},e^{s}_{h2},e^{s}_{t2})\)), and one follow-up input, the example MGs for which are shown in Table 3.  
**MR-C\({}_{1}\): Combining two pairs of entities sharing the same head entity.**  
We first focus on the combination of two source inputs sharing the same head entity; that is, \(e^{s}_{h1}=e^{s}_{t2}\).  
Suppose that \(e^{s}_{h1}=e^{s}_{h2}\) and \(\mathcal{R}(t_{s1})\) is a symmetrical relation. Then, \(t_{f}\) is constructed as \((s^{s},e^{s}_{t1},e^{s}_{t2})\), which takes the tail entity of \(t_{s1}\) as the head entity and uses the tail entity of \(t_{s2}\) as the tail entity. Thus, \(\mathcal{R}(t_{f})\) is expected to be identical to \(\mathcal{R}(t_{s2})\).  
**MR-C\({}_{2}\): Combining two pairs of entities sharing the same tail entity.**  
Similarly, the combination can also be performed on two pairs of entities sharing the same tail entity.  
Suppose that \(e^{s}_{t1}=e^{s}_{t2}\) and \(\mathcal{R}(t_{s1})\) is a symmetrical relation. Then, \(t_{f}\) is constructed as \((s^{s},e^{s}_{h2},e^{s}_{h1})\), which takes the head entity of \(t_{s2}\) as the head entity and uses the head entity of \(t_{s1}\) as the tail entity. Thus, \(\mathcal{R}(t_{f})\) is expected to be identical to \(\mathcal{R}(t_{s2})\).  
Its implementation process is as follows. The first source input \(t_{s1}\) is known. We need to construct the second source input \(t_{s2}\) and build \(t_{f}\) from \(t_{s1}\) and \(t_{s2}\). First, we create a relation set {R\({}_{1}\), R\({}_{2}\)} with the conditions for combination, where R\({}_{1}\) has symmetry. Then, we obtain the type information of the tail entity in R\({}_{2}\) and apply the named entity recognition tool Stanford NER to identify all entities that are consistent with the tail entity type in R\({}_{2}\), forming a set E. Next, we remove the entities \(e^{s}_{h1}\) and \(e^{s}_{t1}\) (if any) from the set E to obtain E\({}^{\prime}\). We construct a candidate source input \(t_{cs}=(s^{s},e^{s}_{h1},e^{s}_{t2})\) for each entity \(e^{s}_{t2}\) in set E\({}^{\prime}\) and obtain \(\mathcal{R}(t_{cs})\) via RE. If \(\mathcal{R}(t_{cs})\in\) R\({}_{2}\), then the candidate source input satisfies the condition and is used as the second source input \(t_{s2}=(s^{s},e^{s}_{t2},e^{s}_{t2})\). Finally, \(t_{f}\) is constructed as \((s^{s},e^{s}_{t1},e^{s}_{t2})\), which is generated from the original source input \(t_{s1}\) and the constructed source input \(t_{s2}\).
## 4 Discussion  
The practical difficulties in effectively pruning infested trees have led to the decision to use chemical agents to control _O. linearis_. Spraying with chemical insecticides has been the only route to efficiently manage this pest in walnut and hazelnut orchards [8]. However, the obvious negative impacts of chemicals (risks to the environment and humans, development of resistance, etc.) and the EU's promoted policy (Farm to Fork strategy) of phasing out chemicals in sensitive environments (forests, cities, etc.) make chemical control increasingly unfeasible [40]. It has, therefore, become evident that biological control agents can play a pivotal role in protecting forest and agricultural ecosystems from wood borers such as _O. linearis_. The isolate that stood out was C. _funosorosea_ (Is) as it induced 100% mortality on both larvae and adults of _O. linearis_. In contrast, other isolates caused the same effect but only on one of the insect stages. The great variation in the lethal action of some EPF isolates on larvae in comparison with adults is a very interesting outcome. For instance, _P. lawendulum_ (101) demonstrated efficacy against larvae (100%) yet exhibited limited activity against adults (72.8%). Similarly, a screening of 27 EPF isolates on larvae and adults of the onion maggot _Delia antiqua_ ([PERSON]) (Diptera: Muscidae: Anthomyiidae), revealed that only three isolates controlled larvae effectively, while twelve isolates controlled adults [41]. This discrepancy can be attributed to the role of insect microbiota, which can vary between the developmental stages of the insect [42]. The importance of gut microbiota in regulating the interactions between EPF and host insects has been well documented [43]. The only published work investigating biological methods of managing _O. linearis_ is the one of [PERSON] and [PERSON] in 2007 [44], in which the bacterial flora of the insect was investigated, and the findings were used to assess the bacteria's impact. The authors found that _Serratia marcescens_ Bizio (Enterobacterales: Yersiniaceae) induced the highest mortality (65%). Conversely, a spore and crystal mixture of _Bacillus thuringiensis_ Berliner (Bacilluses: Bacillaceae), isolated from the European cockchafer _Melodottha melodottha_ (L.) (Coleoptera: Scarabeidae), achieved a remarkable 90% mortality against the pest within a four-day period. There is a paucity of information on the biological control of _O. linearis_ with the majority of the existing literature pertaining to hazel tree infestation, while our study was conducted on walnut. To the best of our knowledge, this is the first study to
# Graphene-Based Functional Hybrid Membranes for Antimicrobial Applications: A Review
## 5 Antimicrobial Applications
### Antimicrobial Mechanism
#### 5.1.1 Sharp Edge Cutting  
In brief, graphene-based membrane nanocomposites have sharp edges that physically damage the cell membrane when in direct contact with bacterial cells [91]. [PERSON] et al. found that factors affecting the antibacterial activity induced by the sharp edges of graphene-based nanomaterials include edge density and the angle at which flakes are in contact with the cell membrane. The edge length is the lateral dimension of the two sides of the exfoliated graphene film [92]. The density of graphene edge length is called edge density, which is positively correlated with the antibacterial activity of graphene-based nanomaterials. They concluded that graphene with smooth edges has a greater edge density and better antibacterial effect than rough graphene. They concluded that when the contact angle was 37\({}^{\circ}\), the graphene-based nanomaterials began to show antibacterial effects, and reached the biggest consequent at 90\({}^{\circ}\). Besides the direct cleavage effect, which affects the  
Figure 4: Schematic diagram of the interaction between bacteria (platelets) and graphene films covered with graphene substrates. (Printed image with permission from Ref. [84], copyright 2016, Nature Publication).  
antibacterial activity, the charge transfer between the microbial cell membrane and the edge of graphene-based nanomaterials may also be beneficial to increase the antibacterial activity. [PERSON] et al. showed that the reduced graphene-based nanomaterials are more damaging to bacterial cell membranes than the unreduced graphene-based nanomaterials, because of the stronger charge transfer effect between the edges of the reduced graphene-based nanomaterials and the cell membrane [93].
## 4 Discussion
### Limitations  
Using OAEs to monitor auditory status will always be limited by their reliance on healthy outer hair cells and a functioning cochlear amplifier. Thus, they will never be useful to characterize the status of other auditory structures or higher level auditory processing. However, their role as a part of a comprehensive test battery to determine site of lesion should not be ignored, particularly for ISSNHL. Measurable OAEs in the presence of more than a moderate SNHL should prompt referral for imaging to assess retrocchelare function [36]. OAE's utility as a monitoring tool is also limited by their absence with greater than moderate SNHL. The majority of ISSNHL cases occur in middle age individuals [1], while the prevalence of hearing loss increases with age and measurable OAE declines [1].  
A second category limiting OAE's ability to monitor auditory status relates to the equipment currently available to clinicians. Commercially available OAE measurement systems (which were used in all of the studies reviewed herein) limit the frequency regions tested (up to \(\sim\)4000 Hz for TEOAE and \(\sim\)8000 Hz for DPOAE). Commercial equipment typically also has relatively high system distortion levels (e.g., \(-\)10 dB SPL at many frequencies and up to \(-\)5 dB SPL using moderate level stimuli), which means that a DPOAE must be \(>\)\(-\)5 dB SPL in level to be designated as present. Many commercial systems have licensed the normative data published by [PERSON] et al. [37], and display their 95% confidence intervals for the DPOAE level. However, the commercial system's high system distortion essentially eliminates the possibility of monitoring smaller amplitude DPOAE, labelled \"present but reduced\" in this normative data set [38]. Lastly, monitoring OAE within the same patient over time is still hampered by commercial systems calibrating stimulus levels at the plane of the OAE probe, which does not control for the effect of individual ear canal acoustics [38; 39; 40]. Small changes in OAE probe insertion depth from one test to another can result in \(\pm\)20 dB differences in stimulus level arriving at the eardrum [41].
# The Necessity of LED to Ambient Light Ratio Optimization for Vehicular Optical Camera Communication
## 3 LED to Ambient Light Ratio Optimization
### Constraint Optimization Problem of VOCC System
#### Formulation of the L2A Ratio Optimization Problem  
The L2A ratio optimization in this paper is a constraint optimization problem in which the variable to be optimized is the L2A ratio, the objective function to be maximized is the B2W variance ratio, and the soft constraint is the LED interval. More specifically, the L2A ratio optimization is formulated as the problem of maximizing the B2W variance ratio with respect to the L2A ratio, subject to a given LED interval:  
\[\begin{array}{ll}\max_{r_{L2A}}&R_{B2W}(r_{L2A})\\ \text{subject to}&\text{dens}_{LED}=0.5,...,3\\ &0.2\leq r_{L2A}\leq 4\end{array} \tag{17}\]  
where \(r_{L2A}\) is the L2A ratio defined as:  
\[r_{L2A}=\frac{L_{v}}{E_{v}R}, \tag{18}\]  
\(R_{B2W}\) is the B2W variance ratio defined as:  
\[R_{B2W}=\frac{Bvar}{Wvar}, \tag{19}\]  
and \(intv_{LED}\) is the normalized LED interval defined as:  
\[intv_{LED}=\frac{d_{L2L}}{d_{LED}}, \tag{20}\]  
Figure 9: The idea behind vehicular optical camera communication (VOCC) system optimization.  
where \(d_{L2L}\) is the distance between LEDs in the panel and \(d_{LED}\) is the diameter of each LED.  
In practice, a VOCC system operates in an outdoor environment having a wide range of illuminance. Given a specific LED interval \(intv_{LED}\) of a particular system, based on the optimal L2A ratio \(\bar{r}_{L2A}(intv_{LED})\) corresponding to that LED interval, the luminance of the LED is adjusted to the optimal level \(\bar{L}_{v}\) to obtain the lowest BER of the VOCC system:  
\[\bar{L}_{v}=\bar{r}_{L2A}(intv_{LED})E_{v}R. \tag{21}\]
## 6 Results and Discussion
### Comparison between Various Versions of BHHO with Basic and Time Varying V-Shaped TFs
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Dataset** & **BHHO\({}_{\mathbf{V1}}\)** & **BHHO\({}_{TV1}\)** & **BHHO\({}_{\mathbf{V2}}\)** & **BHHO\({}_{TV2}\)** & **BHHO\({}_{\mathbf{V3}}\)** & **BHHO\({}_{TV3}\)** & **BHHO\({}_{\mathbf{V4}}\)** & **BHHO\({}_{TV4}\)** \\ \hline Breastcancer & 0.0361 & **0.0258** & **0.0046** & 0.0130 & 0.0276 & **0.0186** & **0.0104** & 0.0274 \\ BreastEW & 0.0318 & **0.0204** & **0.0114** & 0.0141 & 0.0212 & **0.0110** & 0.0277 & **0.0235** \\ CongressEW & **0.0079** & 0.0224 & 0.0133 & **0.0032** & 0.0361 & **0.0200** & 0.0252 & **0.0019** \\ Exactly & **0.0046** & 0.0218 & 0.0897 & **0.0081** & **0.0050** & 0.0050 & 0.0053 & **0.0048** \\ Exactly2 & 0.2097 & **0.1886** & **0.1863** & 0.2758 & 0.2460 & **0.2046** & 0.2311 & **0.2128** \\ HeartEW & **0.0661** & 0.1137 & 0.1047 & **0.0993** & 0.1326 & **0.1076** & 0.0957 & **0.0933** \\ IonosphereEW & 0.0389 & **0.0318** & 0.0591 & **0.0502** & 0.0414 & **0.0394** & 0.0477 & **0.0280** \\ KrvskpEW & 0.0300 & **0.0249** & 0.0326 & **0.0306** & **0.0307** & 0.0344 & 0.0300 & **0.0247** \\ Lymphography & **0.0199** & 0.0891 & 0.1141 & **0.0538** & **0.0508** & 0.0749 & 0.0372 & **0.0215** \\ M-of-n & 0.0048 & **0.0048** & **0.0046** & 0.0046 & **0.0046** & 0.0047 & 0.0047 & **0.0046** \\ penglungEW & 0.0003 & **0.0003** & **0.0004** & 0.0556 & 0.0069 & **0.0003** & 0.0004 & **0.0003** \\ SonarEW & 0.0596 & **0.0464** & 0.0527 & **0.0184** & **0.0428** & 0.0671 & 0.0509 & **0.0268** \\ SpectEW & **0.0967** & 0.1128 & 0.1465 & **0.1234** & 0.1403 & **0.0732** & **0.0905** & 0.0922 \\ Tic-tac-toe & 0.1771 & **0.1535** & 0.1661 & **0.1461** & 0.1739 & **0.1654** & 0.1884 & **0.1707** \\ Vote & 0.0386 & **0.0026** & 0.0192 & **0.0155** & **0.0150** & 0.0171 & 0.0158 & **0.0137** \\ WaveformEW & **0.2005** & 0.2053 & 0.1985 & **0.1947** & **0.1968** & 0.2105 & **0.2016** & 0.2047 \\ WineEW & **0.0031** & 0.0033 & **0.0032** & 0.0125 & 0.0027 & **0.0025** & **0.0023** & 0.0033 \\ Zoo & **0.0019** & 0.0580 & **0.0019** & 0.0019 & 0.0019 & **0.0013** & **0.0025** & 0.0029 \\ \hline \(W|T|L\) & \(8|0|10\) & \(10|0|8\) & \(7|0|11\) & \(11|0|7\) & \(7|0|11\) & \(11|0|7\) & \(5|0|13\) & \(13|0|5\) \\ Rank (F-Test) & 4.44 & 4.75 & 4.92 & 4.33 & 5.03 & 4.31 & 4.75 & **3.47** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of BHHO with the basic and time-varying V-shaped variants in terms of fitness rates.
# C-SAR/02 Satellite Polarimetric Calibration and Validation Based on Active Radar Calibrators
## 1 Introduction
[40] proposed an improved method of data processing in polarimetric calibration by reducing the interpolation window of the local region of the point target and using the peak method instead of the integration method to obtain the device amplitude value, which obtained better calibration results, in response to the problems in the external calibration of GF-3. In 2019, [PERSON] et al. [41] found that the cross-polarized manual gain control (MGC) values were significantly different from the co-polarization by analyzing the GF-3 quad-polarization mode transmit and receive signals; in response to this problem, the Freeman method was improved by introducing the gamma factor, which improved the polarimetric calibration accuracy of GF-3 data [29]. After the launch of the C-SAR/02 satellite, there is an urgent need to carry out polarimetric calibration for quantitative applications. To meet this need, in this study, the SAR calibration and validation site in Xilinhot, which was constructed under the support of the Common Application Support Platform project for Land Observation Satellites of China's Civil Space Infrastructure (CASPLOS_CCSI) [42], was used to carry out the polarimetric calibration and validation of the C-SAR/02 satellite. The polarimetric calibration method proposed by [PERSON], based on active calibrators, has been widely applied in the polarimetric calibration of international satellites such as Radarsat-2 [43], as well as Chinese domestic satellites like GF3 and LT-1 [44]. This method is renowned for its simplicity and clarity in computation while achieving high polarimetric accuracy. Based on [PERSON]'s method [30], the polarimetric calibration and validation of C-SAR satellites is carried out using multi-band active calibrators and trihedral CRs. To address the instability of the polarization calibration matrix along the range direction, we deployed several active calibrators and trihedral CRs along different range directions to systematically analyze the polarization calibration aberration matrix and differences in polarization calibration accuracy along the range direction, to further improve the polarization accuracy of the image. This paper is organized as follows: Section 2 describes the experimental area and equipment used. Section 3 describes the polarimetric calibration method using multi-band radar active calibrators. Section 4 shows the results of the experimental images and systematically analyzes the stability of the calibration accuracy along the range direction. The results show that the ARC and polarimetric calibration method used in this study have good stability and that the Chinese domestic C-SAR/02 satellite has good polarization isolation and amplitude-phase imbalance, which can guarantee the subsequent application of polarization quantification.
# A Fast Hole-Filling Method for Triangular Mesh in Additive Repair
## 2 Related Work  
### Additive Manufacturing Repair  
In recent years, a great improvement in 3D scanning techniques contributes to the 3D point cloud-based surface reconstruction. However, due to the occlusion between the scanner and the object,as well as the low reflectance of the object surface, the measured point cloud usually includes many holes which would weaken the ability of post-processing and visual effects of triangular mesh [3]. Besides, the Boolean difference for triangular mesh has been widely used to extract repair volume in additive repair. However, in most cases, the input meshes are required to be complete, i.e., without any holes. [PERSON] et al. [7] presented current barriers, findings, and future trends significantly about additive manufacturing in engineering. [PERSON] et al. [8] also carried out a comprehensive review of the main 3D printing methods, materials and their development in additive repair. [PERSON] et al. [9] utilized a Boolean difference to yield a parameterized geometric representation of the repair volume and then applied the laser direct deposition to repair defective turbine blades. [PERSON] et al. [10] developed a repair volume extraction method that integrates surface reconstruction and repair volume extraction in additive repair. [PERSON] et al. [11] proposed an automatic multi-direction slicing algorithm to deal with components with a large number of holes by additive manufacturing. [PERSON] et al. [12] proposed a systematic framework to aid the reverse engineering-based additive repair process of worn parts, in which they utilized a Boolean operation to yield a parameterized geometric representation of the repair volume of two mesh models. [PERSON] et al. [13] applied a STEP-based numerical control (STEPONC)-based process planning method in additive manufacturing for automating the derivation of the repair section. [PERSON] et al. [14] proposed a method to predict and reconstruct an expected profile of the edge shape in adaptive machining. [PERSON] et al. [15] developed a hybrid method to repair damaged parts by using additive manufacturing techniques, which integrates coordinate measuring machine data collection, defective model regeneration, and decision marking for process selection for the repair. It can be seen that most of the existing methods focus on 3D measurement of damaged parts, repair volume extraction, machining process planning, and repair material development, etc. Therefore, it is necessary to develop an efficient and fast hole-filling algorithm to reduce the gap between the 3D geometry processing and additive manufacturing repair.