esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Point Cloud Registration Method with Incorporation of RGB Image Information
## 2 Methods  
The flow of the research method proposed in this article is shown Figure 1. Firstly, SIFT algorithm was applied to detect feature points in the RGB image corresponding to the point cloud, and the feature descriptors were calculated. Then, a bidirectional KNN algorithm was used to match the feature point pairs based on their feature descriptors. The RANSAC algorithm was utilized to eliminate erroneous feature point pairs and obtain optimized feature point pairs. From the selected feature point pairs, four non-collinear pairs were chosen to solve for the homography matrix H. Based on the coordinate system transformation relationship between the laser scanning device and the coaxial visible light camera, the camera motion parameters decomposed from H were converted to the motion parameters of the point cloud laser scanning device in order to form the initial transformation matrix for point cloud registration. The initial alignment was completed by applying the initial transformation matrix to the point cloud data that had undergone noise removal through a pass-through filter and transited down the voxel grid. This resulted in a rotation and translation transformation of the point cloud data. Finally, based on the initial alignment, the improved FR-ICP algorithm using Anderson acceleration was employed for further optimization, resulting in accurate point cloud registration.
## 3 Results and Discussion  
### Ultrasound-Assisted Extraction of Soluble Phenols and Flavonoids from Pigmented Corn Powder  
To investigate the effect of independent variables [\(X_{ET}\) = extraction time (2, 4, and 6 min), \(X_{PC}\) = pulse cycle (1:1, 2:1, and 3:1 s on/off), and \(X_{UP}\) = ultrasonic power (80, 90, and 100%)] on the extraction of soluble phenols (TSP) and flavonoids (FLA) by UAE from the native Mexican pigmented corn powder, fifteen experiments were performed using a three-factor and three-level Box-Behnken design, and to avoid systematic errors, all experiments were conducted in a randomly ordered manner. The results are shown in Table 1, which includes the experimental and predicted values, error rate, and extraction yield. Statistical differences were detected among treatments for TSPs and FLAs in an experimental-conditions-dependent manner (\(p\) < 0.01). Results show that the yield ranged from 5.54 to 6.97% for TSPs and 3.31 to 5.45% for FLAs, similar to those reported in other plant materials when applying UAE for extracting TSPs [34]. The highest TSP content was obtained at 4 min of extraction time and 100% of ultrasonic power, considering a 1:1 s on/off (34.87 mg GAE/g DW) or 3:1 s on/off (34.69 mg/g) pulse cycle. Furthermore, the highest FLA content was obtained at 80% of ultrasonic power at an extraction time of 4 min and 3:1 s on/off pulse cycle (27.49 mg CE/g DW) or 80% of ultrasonic power, 6 min extraction time, and 2:1 s on/off pulse cycle (27.94 mg/g). The lowest TSP and FLA contents were found under the experimental conditions of 2 min extraction time, pulse cycle of 3:1 s on/off, and ultrasonic power of 90% (27.72 mg/g and 16.59 mg/g, respectively). These values are higher than those reported in blue (TSP of 3.07 mg/g) and purple (TSP of 7 mg/g) native pigmented corn kernel powders by traditional extraction methods, including maceration and magnetic stirring [35; 36]. UAE has been successful in extracting bioactive molecules from plant tissues [37; 38; 39]. This success can be attributed to the fact that ultrasound waves, through the cavitation phenomenon, improve solvent penetration into the plant material, increasing the contact surface area between liquid and solid phases, which facilitates the extraction of phenolic compounds [11; 16]. Additionally, during the UAE, the temperature is controlled at 25 \({}^{\circ}\)C (\(\pm\)2 \({}^{\circ}\)C), which reduces polyphenol degradation and increases the extraction yield; nonetheless, the temperature control during UAE is vital to enhance the cavitation effects [40].
# Solar Radiation Parameters for Assessing Temperature Distributions on Bridge Cross-Sections
## 2 State of Art of Estimation Models for Solar Radiation
### Estimation Models for Global Solar Radiation
#### 2.1.1 Estimation Models for Daily Global Solar Radiation
It should be noted that the estimation error for models based on ambient temperature is larger than for those based on sunshine duration [22]. The extent of cloud cover was chosen by some researchers to establish estimation models for global solar radiation due to the lack of measured sunshine duration in some regions, especially in the oceans, mountains and deserts, where few meteorological stations exist. However, cloud cover is usually noted visually, which may have great uncertainty and error. An increase in cloud cover would reduce the beam solar ration and increase the diffuse solar radiation. A linear equation for the clearness index and average total cloud cover was proposed by [PERSON] based on the measured data from many meteorological stations in the U.S. [31]. The relationships between the clearness index and the sunshine duration fraction, and between the clearness index and the extent of cloud cover, were discussed by [PERSON] in 1965 [32]. It is reported that the relationship between the clearness index and the sunshine duration fraction is more evident compared to other parameters. Three types of estimation models were presented by [PERSON] based on the sunshine duration fraction, the extent of cloud cover, and the combination of the two [33]. Results indicated that the model considering the sunshine duration fraction had the highest accuracy. The artificial intelligence approach has been utilized by some researchers to predict the global solar radiation [34; 35; 36; 37; 22]. However, more factors are involved in the artificial intelligence techniques compared to empirical models. Consequently, the computational effort is significantly higher, making them less convenient for engineering applications. Overall, the empirical model for daily global solar radiation based on the sunshine duration fraction is preferred for engineering applications.
# Valorization of Date Fruit (_Phoenix dactylifera_ L.) Processing Waste and By-Products: A Review
## 4. Valorization Approaches of Date Processing Waste  
The valorization approaches of date processing waste are essential for several reasons. First and foremost, the disposal of date processing waste can have significant environmental impacts. The process of organic waste decomposition in landfills produces methane, a powerful greenhouse gas that contributes to climate change [71]. By utilizing valorization approaches for date processing waste, it will be possible to redirect waste away from landfills and decrease the emission of greenhouse gases. In addition to the environmental benefits, valorization approaches for date processing waste can also lead to economic opportunities. Furthermore, the extraction of high-value compounds from date processing waste can create new markets for the food, cosmetic, and pharmaceutical industries [72]. Moreover, valorization approaches for date processing waste can also become a source of renewable energy. The production of biofuels and organic waste can be subjected to anaerobic digestion to produce electricity and heat, which can be used to power homes, buildings, and industrial processes [73]. Another reason to utilize date processing waste is to improve the sustainability of the date industry and reduce costs. Disposing of date processing waste can be expensive, so finding ways to convert it into valuable products may reduce costs and make the industry more sustainable [17]. The valorization of date processing waste, by transforming it into value-added products, represents an innovative solution that addresses both waste reduction and the creation of new economic opportunities (Figure 4).  
Overall, the valorization approaches of date processing waste are crucial for sustainable development. These approaches may create a source of renewable energy, animal feed, and materials and help to reduce the environmental impact of waste disposal and create economic opportunities. The possible valorization methods of date by-products are listed in Table 4.  
Figure 4. Opportunities of date waste valorization.
###### Abstract  
In DC systems, DC resistive type superconducting fault current limiters (R-SFCLs) can respond within a few hundred milliseconds and limit the fault current to a very low level to protect the power equipment in DC systems. The main part of R-SFCLs are superconducting tapes. When short-circuit faults occur in the system, the superconducting tapes will quench and become a large quenched resistor to limit the fault current. The surrounding magnetic fields and the magnetic fields caused by the superconducting tapes itself influence the quench characteristics of the superconducting tapes of R-SFCLs. Thus, the current limiting characteristics of R-SFCLs will also be affected. Until present, very few studies have investigated the effects of magnetic fields on quench characteristics of superconducting tapes for DC R-SFCL. The objective of this paper is to obtain the effects of magnetic fields on quench characteristics of superconducting tapes for DC R-SFCL. Two different kinds of YBa2 Cu3O7-8 (YBCO) tapes are studied under a permanent magnetic field of 0, 42.4, 75.9, 122.9 mT, respectively. One is from Shanghai Superconductor Technology Co., Ltd., Shanghai, China, type ST-12-L (named SC_SH) and the other is from American Superconductor Inc. Boston, MA, USA, type 8602 (named SC_8602). The research results show that the magnetic fields influence both the amplitude and the rising rate of the quenched resistance of an SC_SH tape. Under the same magnetic field, both the speed of quenching and the quenching resistance of SC_SH tape are larger than them of SC_8602 when the prospective current exceeds 800 A. Thus SC_SH tape can limit the fault current faster and to a lower level.  
DC superconducting fault current limiter; magnetic field; quench; YBCO
## 3 Motivational Scenario and System Model  
### Problem Statement  
The system under consideration consists of a three-tier hierarchy: end users, small-cell base stations, and a remote cloud. SBSs are typically deployed in very densely populated urban areas where many people use mobile devices, i.e., shopping centers, sports venues, airports, and train stations. Moreover, mobile devices are operated with various applications and data packet information such as pictures, email, web browsing, video, gaming, and live streaming. When the mobile device user's density is high, a single SBS-MEC server will face many challenges in handling a large number of service requests because of its confined computing resources. As a result, a single SBS-MEC server is overloaded which degrades the QoE.  
To explore the neighboring small-cell base stations, we observed the following:  
1. It is possible to utilize a nearby SBS-MEC server which has spare computing resources or is lightly loaded to overcome the overloaded problem of a single SBS-MEC server.
2. There is a function that is a trade-off of the resource capacities of all the SBS-MEC servers. We should then decide whether it should be offloaded to the remote cloud.
3. By sharing the resource capacity among the SBS-MEC server, the total processing time can be reduced.  
Figure 1 shows the limited capacity and the overload problem that exists in the SCNs. User #1 has two tasks: one task, \(T_{1}^{1}\), can be processed locally; the remaining task, \(T_{2}^{1}\), needs to be offloaded for processing in another place. Generally, user #1 will offload this task for performing its computation to the SBS-MEC server #1, SBS-MEC-1. However, in that time, SBS-MEC-1 cannot handle this server as it is already overloaded. Consequently, task \(T_{2}^{1}\) will fail. Meanwhile, user #2 has three tasks which need to offload because of the confined capacity and weak performance, namely, \(T_{1}^{2}\), \(T_{2}^{2}\), and \(T_{3}^{2}\). Basically, all the tasks will be offloaded to SBS-MEC server #4. Nevertheless, SBS-MEC server #4 can handle two tasks, i.e., \(T_{1}^{2}\) and \(T_{2}^{2}\). Therefore, task \(T_{3}^{2}\) will fail.  
Figure 1: Limited capacity and overload problems.
# A Virtual Network Resource Allocation Framework Based on SR-IOV
## 5 Evaluation
### File Transfer Time  
In the end, we tested the performance of the file transfer system under mixed load conditions. We use the command scp for remote file copying in Linux to test how long the VM transfers files. In this test, we gradually increase the number of VMs, of which 70% run the _Lookbusy_ program, allowing all VMs to copy local 1G files to the server at the same time, measuring the average transfer time of the VM. Figure 7d shows a comparison of the average file transfer time before and after the system optimization when the number of VMs is gradually increased from 1 to 48. From the experimental results, it can be seen that using SR-IOV-based virtual resource allocation framework can effectively reduce the average file transfer time of VMs, with the increase of the number of VMs, the average transfer time after optimization is less than before optimization. For example, when the number of VMs is 48, the average transmission time before optimization is 295.95 s, and after optimization is 210.81 s.  
The reason SR-IOV-based virtual resource allocation framework can reduce the average file transfer time of VMs is similar to the principle of system network delay reduction. The demand of VMs for network resources is concentrated on a period of time after being scheduled, when the VMs are perceived as network-intensive and will be assigned VF to ensure their network performance.
## 2 Related Work
In PEGASIS, each node communicates only with a close neighbor and takes turns transmitting to the base station, thus reducing the amount of energy spent per round.
## 5 Experimental Setup and Results
### Experiment 3--Machine Learning Algorithm Repeatability  
The accuracy of a model is a metric for determining which model is the best at recognizing correlations and patterns between variables in a dataset based on the input or training data. Accuracy is defined as the percentage of correct predictions, which is simply the ratio of correct guess or detection to the total number of predictions. This is presented in Equation (1).  
\[\text{Accuracy}=\frac{Correct\ Detection}{Total\ number\ of\ predictions} \tag{1}\]  
In this experiment, the repeatability of the algorithm is evaluated. The system is validated by processing the gestures of all letters and numbers. For validation, the most accurate gesture is selected and processed. The results are presented in Table 2.
## 1 Introduction  
Inspired by the success of unconventional hydrocarbon exploration in the U.S., China's unconventional hydrocarbon has developed rapidly in recent years and has continuously obtained industrial oil and gas production from transitional [1], continental, and marine shale fields [2; 3; 4]. In recent years, the annual output of marine shale gas has accounted for more than 10% of the total annual output of natural gas in China [2]. Despite the steady and rapid promotion of annual output, the current production of shale gas in China is mainly derived from shallow-medium-buried (<3500 m) marine shale [5; 6; 7; 8; 9], and there is still a big gap compared with the shale gas production of the U.S. [10; 11; 12]. Deep-marine shale (buried depth of > 3500 m) gas resources in the southern Sichuan Basin have great potential, a large distribution area, and good preservation conditions, which is of great importance for the exploration and development of unconventional hydrocarbon in China [13; 14; 15].  
Compared with conventional reservoirs, a shale reservoir is characterized by abundant nanopores and a complex pore structure, possessing ultra-low porosity and permeability [16], which increases the difficulty of the efficient exploitation of hydrocarbon. The burial depth of the main shale gas production layers of North America is rarely more than 3500 m [17; 18; 19; 20]. The gas production layers with burial depths exceeding 4100 m are relatively low after fracturing, and the cost of a single well-drilling and fracturing project is more than USD 14 million [21]. It is difficult to achieve an economic and effective use of resources. Therefore, there is less experience with deep-marine shale exploration and exploitation.  
Pores of various sizes provide reservoir space for natural gas in deep-marine shale [22]. It is significant to illustrate pore structure when evaluating the heterogeneity of shale reservoirs and improving shale gas well recovery [23]. Pore structure has a certain indication for the maintenance of abnormally high pressure in deep-marine shale reservoirs and even for the preservation and accumulation of shale gas, which is a key study topic related to the preservation and accumulation of deep-marine shale reservoirs. Moreover, the occurrence of pore fluid in reservoir spaces influences the adsorption capacity and flow capacity of natural gas to a large extent [24], which increases difficulties when evaluating shale gas resources and predicting commercial production [25; 26; 27; 28; 29; 30; 31].  
In this study, taking the Wufeng (O\({}_{3}\)w)-Longmaxi Fm. (S\({}_{1}\)I) deep-marine shale as an example, pore structure and pore fluid in deep-shale reservoirs are evaluated on the basis of X-ray diffraction (XRD), scanning electron microscopy (SEM), nuclear magnetic resonance (NMR), and low-temperature nitrogen adsorption (LTNA).
# Difficulties and Challenges of Anomaly Detection in Smart Cities: A Laboratory Analysis
## 2 Background
### One-Class Support Vector Machines (OC-SVM)  
Basically, classification techniques based on machine learning require two steps. First, a dataset is used to train a learning model. Then, the trained model is used to classify new data samples. Several features define each sample of the datasets. Classification techniques based on support vector machines (SVM) have proven to be effective in several contexts related to intrusion detection [12; 30]. The SVM classification process represents the training dataset in a \(n\)-dimensional vector space, \(n\) being the number of features of the training data. Then, it defines a hyperplane (i.e., a \(n\)-\(1\) dimensional plane) that separates (with a maximum margin) the samples from the different classes. The support vectors are the subset of training samples that are near the hyperplane and that define it. Finally, the hyperplane acts as a frontier to classify other samples.  
In our work, we have used one-class support vector machines (OC-SVM), which are a special case of semi-supervised SVM that do not require labeled data. OC-SVM build a frontier to classify new samples as normal or outlier. In SVM, different types of kernel functions are available to build the most adequate hyperplane for each application. In this work, we have used a radial basis function (RBF) kernel, which can learn complex regions [31]. In order to build the frontier, RBF kernel OC-SVM use basically two parameters [32]: \(\
u\) and \(\gamma\). On the one hand, \(\
u\) defines the maximum fraction of outliers present in the training data. On the other hand, \(\gamma\) establishes the influence area of the support vectors on the classification. Generally, increasing the value of \(\gamma\) implies adjusting the frontier closer to the training samples. This reduces the number of misclassified outliers as normal samples. However, increasing \(\gamma\) too much causes the training data to be overfitted. A usual approach to select optimum parameters is grid search [33]. This method uses a grid with parameter values that is exhaustively explored in order to select the values that give the best performance of the SVM over a set of samples.
# Characteristics of Deep Groundwater Flow and Temperature in the Tertiary Pohang Area, South Korea
## 4 Results
### Geophysical Log Profiles
#### 4.2.2 Bh-2  
Compared with the lithology data for the other boreholes, more caliper log anomalies were observed at the following depths: 576.4, 636.4, 704-773 m for rhylolite and basic dyke and 900, 960.75, 1065.55, 1087.25, 1124.8, 1181, 1126.9, and 1245 m for sandstone/mudstone (Figure 6a).  
The temperature log zones were identified at depths of 580-610, 618-652, and 736-757 m for the rhylolite section and 955-976, 1178-1189, 1257-1273, and 1287-1356 m for the sandstone/mudstone section (Figure 6b,c). The highest temperature was 69.51 \({}^{\circ}\)C, at a depth of 1502 m. The thermal gradient of BH-2 was 22.89 \({}^{\circ}\)C/km at a depth of 130-370 m, 26.78 \({}^{\circ}\)C/km at a depth of 370-538 m, 64.52 \({}^{\circ}\)C/km at a depth of 538-770 m, and 33.33-35.67 \({}^{\circ}\)C/km at a depth of 770-1502 m.  
The EC values at a depth of 122-1490 m ranged from 801 to 3758 \(\upmu\)S/cm. The most distinctive anomalous zones of the EC were found at depths of 578-581, 622-627, 665, 737, 762, 943-955, 966-972, 1170, 1181, 1251, 1273, 1284, and 1360 m (Figure 6d).  
The semiconsolidated mudstone had an average natural gamma radioactivity of 51 American Petroleum Institute (API) units at a depth of 0-356.7 m; lapilli tuff had 71 API units at a depth of 356.7-423.7 m; tuffaceous sediment had 103 API units at a depth of 423.7-426.4 m and 70 API units at a depth of 450.6-461.8 m; crystal tuff had 93 API units at a depth of 426.4-450.6 m; sandstone/mudstone had 72 APIs unit at a depth of 461.8-521.6 m and 56 API units at a depth of 876.4-1501.3 m; rhylolite had 64 API units at a depth of 521.6-701.1 m, 63 API units at a depth of 708.0-744.5 m, 65 API units at a depth of 775.7-827.9 m, and 66 API units at a depth of 830.6-876.4 m; and, finally, basic dyke had 33 API units at a depth of 701.1-708.0 m, 35 API units at a depth of 744.5-775.7 m, and 36 API units at a depth of 827.9-830.6 m (Figure 6e). The lower natural gamma-ray intensities represented sandstone at a depth of 1200-1440 m based on the core samples, as well as the basic dyke at a depth of 1440-1490 m. The zone of 1200-1440 m approximately corresponded with an anomaly depth of 1245 m based on the caliper log. The trend of the caliper log was different from that for the EC log, which could not distinguish between sandstone and basic dyke at depths greater than 1200 m. Rhyolite at a depth of 520-825 m exhibited a distinct natural gamma-ray intensity. Three basic dykes intruding into the rhylolite could be easily distinguished from the rhylolite, by their low natural gamma-ray values. At a depth of 460-520 m, the natural gamma-ray values of the semiconsolidated mudstone overlying the rhylolite were approximately the same with those of sandstone.
# Evaluating the High Frequency Behavior of the Modified Grounding Scheme in Wind Farms
## 4 Simulation Results and Discussion
### Pair of Turbines with Two Interconnected Turbines  
Since in reality, more than one wind turbine will be working to produce electricity, this study examines the impact of both indirect and direct lightning strikes on a pair of turbines that are connected to each other via a 350 m FD medium-voltage cable (see Figure 15). The simulation was carried out for two states, one in which the turbines' PMGSs are connected to each other through a 350-m horizontal grounding conductor, while in the other, the PMGSs are separated. Furthermore, they are compared in the worst lightning condition, not only to see the effect on overvoltages, but also to examine their performance in diverse strike conditions.  
This section studies the impact of a 200-kA lightning strike, with the following results.  
Figure 14: Impact of direct strike on out-of-service wind turbine overvoltages. (**a**) LV side of the main transformer; (**b**) HV side of the main transformer from node to ground; (**c**) control circuits.  
Figure 13: Impact of direct strike on in-service wind turbine overvoltages. (**a**) LV side of the main transformer; (**b**) HV side of the main transformer from node to ground; (**c**) control circuits.