esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Models and Tools  
#### 2.4.1 CCR Model  
The CCR model was the first DEA model proposed by [PERSON], [PERSON], [PERSON] and some other scholars in 1978 [37], which is widely used to evaluate the relative effectiveness of the same type of departments or units. The CCR model is a method that is used to study the relationship between input and output based on the production function. It extends the efficiency concept of a single input and single output to the effectiveness evaluation system of similar decision-making units with multiple inputs and multiple outputs.  
Assuming that there are \(n\) DMUs, each having \(m\) types of \"input\" (denoting the \"resource\" consumption of the DMU) and \(s\) types of \"output\" (they are some indexes of \"effectiveness\" after the consumption of \"resources\" by the DMU), the input and output data of each DMU are as follows:  
\(x_{ij}\) denotes the \(i\)th input of the \(j\)th DMU, \(y_{rj}\) denotes the \(r\)th output of the \(j\)th DMU, \(v_{i}\) represents the corresponding weight of \(x_{ij}\), \(u_{r}\) represents the corresponding weight of \(y_{rj}\); \(x_{ij_{0}}\) and \(y_{rj_{0}}\) denote the input and output vectors of the DMU under evaluation, respectively. Therefore, the CCR model can be expressed as follows:  
\[\max\,\frac{\sum\limits_{i=1}^{s}u_{r}y_{rj_{0}}}{\sum\limits_{i=1}^{s}v_{i}x_ {ij_{0}}}=V_{j_{0}}^{CCR}\]  
\[(P_{CCR})\,s.t.\left\{\begin{array}{l}\frac{\sum\limits_{i=1}^{s}u_{r}y_{rj _{0}}}{\sum\limits_{i=1}^{s}v_{i}x_{ij_{0}}}\leq 1\\ v_{i}\geq 0,\,i=1,2,\cdots,m\\ u_{r}\geq 0,\,r=1,2,\cdots,s\\ j=1,2,\cdots,n\end{array}\right. \tag{1}\]  
The objective function of the CCR model is to find the optimal efficiency value of the \(j_{0}\)th DMU-\(V_{j_{0}}^{CCR}\), and the variables are the weights \(v_{i}\) (\(i=1,\,2,\,\ldots,\,m\)) of the input index and weights \(u_{r}\) (\(r=1,\,2,\,\ldots,\,s\)) of the output index. The DMU was evaluated using the CCR model. With the optimum weight, the evaluation result can achieve the best efficiency. If \(V_{j_{0}}^{CCR}=1\), the \(j_{0}\)th DMU is CCR-effective; otherwise, the \(j_{0}\)th DMU is not CCR-effective.  
The weights used in the CCR model are all optimal weights, which are not necessarily equal to the actual weights, so they suggest a certain level of idealization. When evaluating corn production performance, the CCR model can only distinguish which kind of cultivation measures are effective or ineffective and cannot compare or rank the production performance of cultivation measures. Therefore, based on the CCR model, the GDM-DEA (group decision method of DEA) model is proposed.  
The GDM-DEA model can not only ensure that the obtained GDM-DEA-effective cultivation measures are CCR-effective but can also reduce the number of effective cultivation measures, reduce the workload of the actual calculation, and select environment-friendly cultivation measures on the premise of ensuring the effectiveness and high yield of the cultivation measures.
## 1 Introduction  
With developments of multimedia telecommunication networks, increased speed of information processing and power saving are required for networks and telecommunication equipment, including personal computers (PCs). To this purpose, various high-speed and low-voltage operation techniques have been developed and applied in many devices [1; 2; 3]. However, high-speed and low-voltage operations in communication equipment bring an undesired electromagnetic emission to other equipment and cause malfunction trouble by emission from other equipment, respectively [4; 5; 6]. Therefore, regulations [7] and countermeasure techniques [8; 9] have been investigated and studied for electromagnetic compatibility (EMC) between the equipment. Emission and conduction of electromagnetic noise are also significant problems in the field of information leakage and tapping. Information reconstruction techniques from electromagnetic noise have been studied from the viewpoint of information security [10; 11; 12; 13]. Display images on PC monitors are particularly important for security because they may contain a lot of secret and personal information [14; 15; 16]. The common-mode noise containing the information displayed on the monitor seems to appear not only from the signal cable but also from the power cable of the monitor. While the signal cable, which connects the PC and the monitor, is usually near the users, the power cable is connected to the outside electrical network of the office or the building. Thus, the power cables may become dominant gateways and/or antennas for noise emission and conduction, which may lead to a serious security issue. Studies on noise emitting from the power cables of the PC and its applications are not only important for research that may be used in military technology, but also contribute in increasing the awareness of the users and the device makers regarding information security issues. In this work, the distributions of common-mode noise due to the PC monitor signal are examined for the power cables of a PC and a monitor and for the monitor signal cable between the PC and the monitor. Then, we propose an algorithm for signal processing to reconstruct small size characters displayed on the monitor from common-mode noise on the power cable.  
The organization of this paper is as follows: Section 2 presents the measurement system set-up for the common-mode noise of the power line. Examination of the common-mode noise and the proposed signal processing method for reconstruction of the PC display are presented in Section 3 with the discussions. Section 4 shows the experimental results for the elimination of undesired beats and reconstruction of a character string from the common-mode noise. Conclusions are presented in Section 5.
# SROR: A Secure and Reliable Opportunistic Routing for VANETs
## 2 Related Work
### Secure Routing for VANETs  
The V2V communication is vulnerable to some attacks due to open communication environments in VANETs. While routing is one of the targets of attackers. A major line of work [27, 28, 29, 30] concentrated on the security of router data. [PERSON] et al. [28] proposed an ant colony optimization-based secure routing protocol. Some feasible routes are constructed by the ant colony optimization technique. [PERSON] et al. [29] put forward a geographic position-based secure routing. They employed a trust model to resist black hole attack in order to choose the secure routing path. In [27], the authors put forward an intelligent cluster-based routing protocol. In the routing protocol, an artificial neural network (ANN) model was used to detect the malicious nodes.  
Trust-based solutions [31, 32] are common methods to deal with the routing security issue. [PERSON] et al. [30] first studied the trust properties and put forward a novel trust inference model. [PERSON] et al. [33] also employed a trust model. In the trust model, the cloud is in charge of evaluating the trustworthiness of each individual. The evaluation results are used to choose the reliable relay node. Nevertheless, evaluation of trustworthiness of vehicles is very complete due to dynamic topology, and the trustworthiness of vehicles depend on many factors. Hence, it is still a challenge to objectively evaluate the behavior of vehicles by trustworthiness alone. In addition, in [34], the authors proposed an RSU-assisted trust-based routing for VANETs. The routing provides a more reliable monitoring process for trust management in order to increase resistance to trust-based attacks.
## 5 Experiment Results and Discussion
### Scenario 2: Loading Rate at 0.3 MPa/s  
In order to examine the influential effects of different loading speeds on failure strain, another series of experiments with a speed of 0.3 MPa/s were carried out. To ensure an effectual investigation, in the following experiments, the test conditions were controlled to be comparable with that in Scenario 1 (where the loading rate was 0.1 MPa/s), except for loading speed difference. The sandstone test-piece had the equal dimension sizes as the one used above.
# The Status of Space Weather Infrastructure and Research in Africa
## 3 Human Capital Development
### The Abdus Salam International Centre for Theoretical Physics/Boston College/INGV  
Every year since 2009, the ICTP and Boston College have been organizing training workshops on GNSS and space weather for developing countries. Recently, INGV has been contributing by organizing additional workshops in collaboration with the aforementioned institutions. Many African lecturers and students have been trained in space weather monitoring equipment installation and maintenance and data acquisition, reduction, and interpretation. Some of these works have been organized in Africa and have greatly impacted space weather research capacity in Africa.
## 3 Results
### Changes in the Harvest Yield  
The above-ground part of the plants was cut 14 weeks after sowing, dried and used for the analysis of morphometric plant parameters, seed yield per plant and nutritional seed value. The results showed (Figure 4) that CP5, CP7 and EMF15 treatments increased the number of matured seeds per plant of the 'VB Vokaii' cultivar by 46%, 41% and 44%, respectively compared to the control; and seed weight per plant in CP5 and EMF15 groups was increased by 70 and 56%, respectively. Seed treatments with CP5 and EMF15 also improved plant biomass production: plant weight increased by 37% and 33% and leaf weight by 80% and 47%, respectively. EMF had no effect on seed number for 'VB Nojai' cultivar but EMF15 increased plant and leaf weight by 41 and 71%, respectively, compared to the control. The effect of CP was stronger for 'VB Nojai', especially in the case of CP5 treatment which resulted in an 85% increase in the number of matured seeds per plant, a 97% increase in seed and plant weight and as much as 129% increase in leaf weight. CP7 increased the number of seeds by 45%, seed weight by 55%, and plant weight-by 44%. It is important to note that the mean weight of a single seed did not differ between the experimental groups for both 'VB Vokai' and 'VB Nojai' (data not shown).
## 4 Algorithm Design  
### Optimal Traveling Speed Calculation Strategy  
When the feeding volume \(q\) exceeds the rated feeding volume \(Q\) of the harvester, the traveling speed should be reduced appropriately. The traveling speed should be increased when the feeding volume \(q\) is too low. The ideal target speed can be calculated from real-time feed rates while ensuring rice harvester efficiency and quality and reducing equipment failure.  
\[v_{n}=v_{0}+\frac{Q-q}{3\sigma} \tag{12}\]  
where \(v_{n}\) and \(v_{0}\) are the ideal target velocity and real-time velocity (in m/s), respectively.  
Considering the many contingent factors when rice harvesters operate in southern China's complex field environments, the target speed calculated from the input quantity alone was not well adapted to the system. Therefore, we propose the traveling speed optimization formula, as shown in Equation (13). In the following exposition, the traveling speed of the harvester is optimized based on the tilt conveyor's power shaft torque \(T_{s}\) and rotational speed \(n_{s}\).  
* When \(T_{s}\) and \(n_{s}\) are both in a reasonable state, the absolute value of the relative error between its value and the rated value belongs to [0, 0.1]; then, the traveling speed at this time is \(v_{n}\);
* When there is an abnormality between \(T_{s}\) and \(n_{s}\), i.e., the absolute value of the relative error between at least one set of values and the rated value belonging to [0.1, 0.2], adjust the speed to \(0.8v_{n}\) and observe the subsequent values. If all values return to a reasonable state within 3 s, adjust the speed to \(v_{n}\); otherwise, decelerate to \(0.4v_{n}\);
* When the harvester fails, i.e., the absolute value of the relative error between any value and the rated value is constantly \(>\)20%, the speed is immediately reduced to 0 m/s and the machine is stopped for inspection. \[V=\left\{\begin{aligned} v_{n}.0\leq|n_{i}|\leq 0.1N_{i}\\ 0.8v_{n}.01N_{i}<|n_{i}|\leq 0.2N_{i};T\leq 5\\ 0.4v_{n}.01N_{i}\langle|n_{i}|\leq 0.2N_{i};T\rangle 5\\ 0|n_{i}|>0.2N_{i}\end{aligned}\right.\] (13) where \(V\) is the optimal traveling speed of the harvester, m/s; \(i\) = 1,2, \(n_{i}\) are the error values of \(T_{s}\) and \(n_{s}\), respectively; \(N_{i}\) are the rated values of \(T_{s}\) and \(n_{s}\), respectively; and \(T\) is the data update time, s.
## 4 Results
### Initial Path Planning Effect of IGWO Algorithm  
Due to the fact that the fusion algorithm in non-free space path planning is based on the point closest to the UAV on the path generated by the IGWO algorithm for the next step computation, it is essential for the IGWO algorithm to consistently generate high-quality paths under different starting points. In this paper, three points (1, 1, 1), (12, 28, 30), and (20, 40, 61) in the task environment are selected as the starting planning points of the IGWO algorithm to validate the effectiveness of the IGWO planning from various aspects. As shown in Figure 11, it depicts the effect of the initial path planning by the IGWO-IAPF fusion algorithm.
## 3 Results and Discussion
### Status of Waste-to-Energy Energy and Biomass Energy in Southeast Asian Countries  
Table 2 shows the status of waste-to-energy and biomass energy in Southeast Asian countries. The status of the technologies in most Southeast Asian countries is at an emerging stage. However,  
in the near future, the development of waste-to-energy technologies could be seen to be comparatively widespread across the region due to the promotion of renewable energy supplies in the region, particularly in Indonesia, Malaysia, Thailand, and Singapore. For instance, all twelve waste-to-energy plants in Indonesia are expected to produce 234 MW of electricity in 2022. In Malaysia, the current installed capacity from incineration and landfills is 18.8 MW. Its overall waste-to-energy potential is 400 MW. In Thailand, the total capacity of the current waste-to-energy potential is 43.324 MW and it targeted to reach 160 MW by 2021. Overall, the total amount of capacity from landfill biogas plants, incineration plants, anaerobic digestion, and gasification in the region is over 323 MW at present and is expected to reach to 700 MW by 2022. Meanwhile, by 2030, the realizable generation potential from renewable municipal waste in six Southeast Asian countries (Indonesia, Malaysia, Philippines, Singapore, Thailand, and Vietnam) will be 17.26 TWh.
# Self-Guided Pixel-Wise Calibration for Low-Light Image Enhancement
## 3 Method
### Loss Function
#### 3.3.2 Total Loss  
The design of the spatial consistency loss \(\mathcal{L}_{spa}\), color constancy loss \(\mathcal{L}_{col}\), and illumination smoothness loss \(\mathcal{L}_{tv,A}\) from Zero-DCE [20] is computed. Specifically, \(\mathcal{L}_{spa}\) promotes spatial consistency of the enhanced image by preserving the difference between adjacent regions of the input image and the enhanced image. \(\mathcal{L}_{col}\) is based on the design principle of the gray-scale world algorithm. This means that in a brightly colored image, the mean values of the three channels R, G, and B should tend to be equal. \(\mathcal{L}_{tv,A}\) ensures the monotone relationship between adjacent pixels and smooth the parameter map \(\mathcal{A}\). Therefore, the proposed total loss function can be summarized as follows:  
\[\mathcal{L}_{\text{total}}\,=\,W_{\text{exp}}\,\mathcal{L}_{\text{exp}}\,+\, \mathcal{L}_{spa}+W_{col}\mathcal{L}_{col}+W_{tv,A}\mathcal{L}_{tv,A}. \tag{7}\]  
where \(W_{exp}\), \(W_{col}\), and \(W_{tv,A}\) represent the weights of losses. In our experiments, weights \(W_{exp}\), \(W_{col}\), and \(W_{tv,A}\) are set to 20, 5, and 1600, respectively, to balance the losses' scale.
###### Abstract  
The performance of a polymeric resin (Sepabeads SP207, from Resindion, Binasco, Italy) was compared with that of an activated carbon (GPP20, from Chemviron Carbon, Feluy, Belgium) in the adsorption of acetaminophen and ibuprofen from either ultrapure or waste water. Kinetic and equilibrium adsorption experiments were carried out under batch operation conditions, and fittings of the obtained results to different models were determined. The kinetic experimental results fitted the pseudo-first and -second order equations, and the corresponding kinetic rates evidenced that the pharmaceuticals adsorption was faster onto GPP20 than onto Sepabeads SP207, but was mostly unaffected by the aqueous matrix. The equilibrium results fitted the Langmuir-Freundlich isotherm model. The corresponding maximum adsorption capacity (\(Q_{\mathrm{m}}\), mg\({}^{-1}\)) was larger onto GPP20 (20 mg g\({}^{-1}\)\(\leq\)\(Q_{\mathrm{m}}\)\(\leq\) 273 mg g\({}^{-1}\)) than onto the polymeric resin (7 mg g\({}^{-1}\)\(\leq\)\(Q_{\mathrm{m}}\)\(\leq\) 18 mg g\({}^{-1}\)). With respect to the parameter \(K_{\mathrm{LF}}\) (mg g\({}^{-1}\) (mg L\({}^{-1}\))\({}^{-1/n}\)), which points to the adsorbent-adsorbate affinity, greater values were determined for the pharmaceuticals adsorption onto GPP20 than onto Sepabeads SP207. For both adsorbents and pharmaceuticals, neither \(Q_{\mathrm{m}}\) or \(K_{\mathrm{LF}}\) were affected by the aqueous matrix.  
emerging contaminants; medicines; paracetamol; sewage; waste water treatment Article
## 2 VDBA Description  
The electrical symbol of the VDBA is shown in Figure 1. The defining characteristic of the VDBA can be described by the following matrix equation:  
\[\left[\begin{array}{c}i_{p}\\ i_{n}\\ i_{z}\\ v_{w}\end{array}\right]\ =\ \left[\begin{array}{cccc}0&0&0&0\\ 0&0&0&0\\ \alpha g_{m}&-\alpha g_{m}&0&0\\ 0&0&\beta&0\end{array}\right].\left[\begin{array}{c}v_{p}\\ v_{n}\\ v_{z}\\ i_{w}\end{array}\right], \tag{1}\]  
where \(g_{m}\) is the transconductance gain of the VDBA. The transconductance \(g_{m}\), as usual, can be tuned by a bias current or voltage, thereby imparting tunability to the structure. Further, \(\alpha\) and \(\beta\) are the non-ideal transconductance gain and nonideal voltage gain, respectively. These non-ideal gains can be defined as \(\alpha=(1+\varepsilon_{\alpha})\) and \(\beta=(1+\varepsilon_{\beta})\), in which the tracking errors are identified as \(\mid\varepsilon_{\alpha}\mid<<1\) and \(\mid\varepsilon_{\beta}\mid<<1\). Accordingly, the values of \(\alpha\) and \(\beta\) are ideally equal to unity.  
As mentioned above, the VDBA block comprises two essential circuit blocks: an OTA and a VF [47; 48]. The simple CMOS implementation of the VDBA used in this work is shown in Figure 2, in which the OTA consists of transistors M\({}_{1}\)-M\({}_{6}\); and it is followed by a VF formed by transistors M\({}_{7}\)-M\({}_{14}\). A pair of diode-connected PMOS active load (M\({}_{3}\)-M\({}_{4}\)) is driven by a source couple pair (M\({}_{1}\)-M\({}_{2}\)). The transconductance gain (\(g_{m}\)) of the OTA stage can be externally tuned by the bias current (\(I_{B}\)), as described by the following expression:  
\[g_{m}\ =\ \sqrt{K_{n}\left(\frac{W}{L}\right)I_{B}}, \tag{2}\]  
where \(K_{n}=\mu_{n}C_{ox}\) is the transconductance parameter, and (\(W/L\)) is the ratio of the width-to-length of the transistors M\({}_{1}\) and M\({}_{2}\). Note from Equation (2) that the transconductance \(g_{m}\) is electronically adjustable utilizing \(I_{B}\).  
Further, the voltage drop across the grounded impedance at terminal z (\(v_{z}\)) is then conveyed to the w terminal with a unity voltage gain by the VF. Thus, the negative-feedback loop established by M\({}_{7}\)-M\({}_{11}\) provides a very low output impedance at the w terminal. For the simulation purpose, the TSMC 0.18-\(\mu\)m level 7 CMOS model parameter has been employed, where the transistor aspect ratios are given in Table 3.  
Figure 1: Electrical symbol of the VDBA.
## 1 Introduction  
In the fourth industrial revolution, machines must communicate autonomously to optimize processes. It emerges from the previous literature that the hallmarks of Industry 4.0 are intelligent automation and the integration of new technologies [1]. To have these characteristics, the basic elements of Industry 4.0 are real-time communication and the possibility of making systems communicate with each other and humans. Industry 4.0, therefore, emphasizes digital technology and ubiquitous connectivity. The devices for establishing communication are sensors, and these can be wired or wireless. Connectivity is achieved by using a network of sensors. Wired sensors require a cable for their power supply and data transmission. This makes them less flexible in deployment. Wireless sensors (WS) are more flexible, space-saving, have a low cost, are quite robust, and have little impact on the environment [2]. Due to these many advantages (especially flexibility), the WSs adapt better to the requirements of Industry 4.0. Despite this flexibility, one of the main constraints in deploying WSs is the energy limit, due to the limited size of their battery [3]. Thus, although Industry 4.0 envisages that machines can communicate with each other autonomously, the fact remains that this objective can be achieved if WSs are also guaranteed energy autonomy.
## 4 Conclusions and Future Works  
In this paper, an accurate and robust method is proposed for insulator faults detection in UAV-based aerial images. The proposed method consists of two steps: (1) A novel neural network is developed to obtain accurate insulator positions; (2) An RoI-based method is designed to highlight the insulator fault locations. Experimental results on various insulator aerial images validate the proposed method had higher average precision rates and lower running times compared with two state-of-the-art methods. Most importantly, whether it is in the insulator one fault detection or multi-fault detection, the proposed method can obtain not only high average precision rates, but also high average recall rates. Since the insulator fault is a common accident that damages the operation of the power grid, the proposed method has high prospects for implementation in high-voltage transmission lines inspection applications for unmanned aerial vehicles.  
Although the proposed method indeed promotes the quality of insulator faults detection in most of the aerial images, it is still not a real-time solution. Considering the actual problem that current insulator faults images are not sufficient to directly train a deep network for insulator faults detection, more insulator fault simulated images should be created by our proposed data augmentation method, and the future work can be developed a deep learning framework to detect insulators positions and faults positions simultaneously. In addition, with the development of UAV flight control technology, there is an important and meaningful need for work in the future to explore the insulator faults detection in bad weather conditions, such as foggy days.  
[PERSON] was the main writer of this paper; [PERSON] conceived the methodology; [PERSON] provided plenty of insulator aerial images; [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] labelled the ground-truth for each image in our dataset; [PERSON] responded to the English check; [PERSON] and [PERSON] put forward many valuable suggestions on the algorithm design of this paper.  
This research was supported in part by the Science and Technology Projects of China Southern Power Grid Co. Ltd. (Grant No. 066600 KK52170074), the National Science Foundation of China (Grant No. 61473144), the Aeronautical Science Foundation of China (Key Laboratory) (Grant No. 20162852031), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) and the National Key R & D Plan (Grant No. 2017 YFE0113200).  
The authors declare no conflict of interest.