esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The biodortification of lettuce using complexes of chitosan with KI or KIO\({}_{3}\) (Cs-KI and Cs-KIO\({}_{3}\)) did not induce negative effects on the mineral content, chlorophyll content, enzymatic activity, antioxidant content, nor the phenol content of lettuce. Moreover, Cs-KIO\({}_{3}\), either at 5 mg I kg\({}^{-1}\) or 25 mg I kg\({}^{-1}\), applied prior to transplanting increased plant biomass and led to iodine levels of 6.4 and 23.1 mg I kg\({}^{-1}\) dry weight, respectively. Plant biomass was reduced after split-dose application (SDA) of Cs-KI before and after transplanting. The manner of treatment application as a split dose before and after transplant was a determining factor for greater iodine accumulation. Application before and after transplanting of KIO\({}_{3}\) led to the highest iodine concentrations (103.2 mg I kg\({}^{-1}\) dry weight), although biomass was considerably reduced compared to single applications of Cs-KIO\({}_{3}\) at either concentration (5 or 25 mg I kg\({}^{-1}\)). Although Cs-KI and Cs-KIO\({}_{3}\) did not increase iodine absorption compared to the free salts, in some cases they caused increases in biomass while maintaining levels of iodine accumulation in lettuce leaves. As far as we know, there are no similar reports on crop biofortification with chitosan-iodine complexes applied to either soil or substrate. Further biofortification studies with chitosan-iodine complexes in crop species other than lettuce are required in order to evaluate the effects they may have when treatment times are longer.  
I.E.D.R., experimentation, chemical analysis, analysis of data and draft writing; H.O.O., design of experiments and synthesis of Cs complexes; L.I.T.T., design of experiments and chemical analysis; A.J.M., methodology, experimentation; S.G.M., methodology, experimentation; B.C.G., chemical analysis; M.C.D.I.F., data analysis; A.B.M., conceptualization, methodology, data analysis. All authors were responsible for review and editing. All authors have read and agreed to the published version of the manuscript. This research did not receive external funding. Special thanks go to [PERSON] and [PERSON] from the UAAAN Department of Animal Nutrition for their support in mineral determination. Also, to [PERSON] from the UAAAN Department of Horticulture for his help in determining the greenhouse climate conditions.
# Attacks and Preventive Measures on Video Surveillance Systems: A Review
### Motivation and Our Contribution  
The usage of VSS is ubiquitous in today's scenario. As these systems are more beneficial and available at affordable cost, they are widely utilized to protect the public and private network systems. Attackers are continuously targeting these systems with new attacks and vulnerabilities. When we use an internet-connected camera from a computer or smartphone, there is a chance that someone else can access it. That person can be a \"hacker\" or an application to which we may have knowingly or unknowingly given access permission. For example, when a simple search word such as \"webcamXP\" is given on Shodan.io [3], an IoT search engine, one can access random video  
Figure 1: Security camera market size.  
Figure 2: Overview of video surveillance system.  
city centers, boating docks, and domestic spaces. The large scale, restricted resources, outdated firmware, poorly secured IoT devices and inbuilt vulnerabilities have attracted bad actors to perform various attacks on the IoT ecosystem. Table 1 shows examples of real-time attacks that occurred on VSS.  
The reasons for above-mentioned attacks may be due to the usage of default login credentials, no access privileges and poor security and privacy features. The motivation for an attacker could be blackmailing, the ability to observe live video feed, access to video footage, access to VSS network, disabling video feeds, violating privacy, remotely disabling the connection, and performing DoS attacks, etc. As VSSs are used in important places, only authorized agents should have the access to monitor and control it. Privacy and security are the foremost concerns while using such systems. Considering all this, this paper first identifies the possible attacks on such systems and then discusses the measures that can be incorporated to prevent security attacks. Additionally, a comparison is carried out on existing VSS frameworks along with their advantages and disadvantages.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  
After the launch of the Mirai attack and its consequences in the year 2016, there has been a dramatic increase in studies related to attacks and vulnerabilities in the VSS domain. The articles have discussed the loopholes and flaws in the IoT networks. For our review, we have considered the peer-reviewed articles from the year 2016 to 2021. Keywords used for the literature survey are as follows: video surveillance systems, attacks on VSS, security frameworks for VSS, privacy issues with IP camera and botnet. To understand the security loopholes and possible solutions to mitigate the threats in VSS, this paper follows the following steps (Figure 3) to articulate the security issues of VSS.  
The rest of the paper is organized as follows: various types of attacks in VSS are given in Section 2. The security measures for VSS are summarized in Section 3. In Section 4, a detailed review and analysis of the latest advances in VSS frameworks are presented and tabulated. Concluding remarks are made in Section 5.
# Application of a High-Power Reversible Converter in a Hybrid Traction Power Supply System
## 4 Analysis of the DC Short-Circuit Characteristics of the Reversible Converter
### Stage of Resistance-Inductance-Capacitor (RLC) Second-Order Response
At this moment, it can be considered that the RLC second-order circuit is in the process of zero-state response, and from Kirchhoff Current Laws (KCL),  
\[v_{\rm c}=\frac{V_{0}\omega_{0}}{\omega}e^{-\delta t}\sin(\omega t+\beta)- \frac{I_{0}}{\omega C_{\rm d}}e^{-\delta t}\sin(\omega t) \tag{7}\]0  
Equation (13) can be solved the same way as calculating the zero-state response. The zero-state response of the DC inductor current can be obtained as  
\[ \begin{array}{l}i_{\rm L2}=I_{\rm s}-I_{\rm s}e^{-\delta t}\sin(\omega t+ \beta)-\frac{I_{\rm s}(R+R_{\rm c})}{2L\omega}e^{-\delta t}\sin(\omega t)\\ =I_{\rm s}-I_{\rm s}e^{-\delta t}\sin(\omega t+\beta)-\frac{I_{\rm s}(R+R_{\rm c })}{2}\sqrt{\frac{C}{L}}e^{-\delta t}\sin(\omega t)\end{array}  \tag{14}\]  
where the \(\delta\), \(\omega\), \(\beta\) is the same as in Equation (8). The DC inductor current in this stage is the sum of the zero-input response component and the zero-state response component from Equations (12) and (14), that is  
\[i_{\rm L}=i_{\rm L1}+i_{\rm L2}. \tag{15}\]  
From Equations (12), (14) and (15), it can be seen that the DC inductor current \(i_{\rm L}\) is composed of two parts in this stage. Since the zero-input response component is of a large proportion, when the zero-input response component reaches its peak value, the total DC inductor current also reaches the peak value, and the following conclusions can be obtained: When a DC short-circuit fault occurs, the larger the initial DC voltage \(V_{0}\) of \(C\) and the initial current \(I_{0}\) of \(L\) are, the higher the peak value of the DC inductor will be. When the initial values of DC voltage and DC inductor current are constant, the peak current of the DC inductor will increase with the increase of DC capacity C, but decrease with the decrease of DC line inductance \(L\). This is because, under a certain DC voltage, the larger the capacitance value is, the more electric field energy can be released by the capacitor C after the short-circuit fault, and the more electromagnetic energy stored in the inductor, the larger the DC inductor current peak value can reach. The smaller the inductance value is, the greater the current required to store the same energy, and the larger the current is, the greater the total inductor current peak value will be.
## 3 Results and Discussion
### System Performance According to Quality Variation
35 \({}^{\circ}\)C. As shown in Figure 14, wet compression is located closer to the isentropic line in the diagram, indicating a higher COP than SH 10 \({}^{\circ}\)C. Compared to the value of SH 10 \({}^{\circ}\)C, the rate of the increase in the evaporating pressure is higher than that of condensing pressure, as wet compression with the condensing pressure for the latter is larger than that of SH 10 \({}^{\circ}\)C. This results in a decrease in the pressure ratio, leading to an increase in the mass flow rate for wet compression. The isentropic efficiency increased by ca. 5%, when compared to that of SH 10 \({}^{\circ}\)C.  
Figure 3:3 Effects of vapor-liquid compression from the experiment for different EWT of OD HEX. Figure 3:4 COP (Coefficient of Performance) as a function of quality for different EWT of OD HEX. To sum up, an improvement in the performance of the vapor-liquid mixture compression on the heating capacity and COP was attainable and a drop in the discharge temperature was very effective. However, despite the reduction in the discharge temperature, the reliability of the compressor cannot be warranted, as the droplets of liquid during compression may wash the lubricating oil from the walls of the cylinder. However, the vapor-liquid mixture compression could be useful for performance improvement and a reduction in the discharge temperature, if the liquid droplets are fully vaporized by an internal heat transfer process that requires a finite amount of time [22]. Among the three most widely used positive displacement compressors (reciprocating, rolling-piston rotary, and scroll), the scroll compressor could also be appropriate for the wet compression technique, since the scroll compressor is less likely to have liquid slugging problems [4]. Thus, more extensive research, such as a durability test, is needed in this regard in the future.
# Deep Learning in Food Image Recognition: A Comprehensive Review
## 4 Application  
### Nutrient Intake Assessment  
Food computing is one of the main interdisciplinary studies of food science and computer science at present. It aims to use artificial intelligence, data processing, and data analysis technology and integrate information such as nutritional characteristics of food itself, changes in nutritional attributes of raw materials, and manufacturing processes. By using these data, the nutritional content of corresponding food can be predicted and estimated, providing a scientific basis for people to eat healthily. In addition, diet quality assessment is also one of the important measures to prevent and treat various chronic diseases. Through the application of food image recognition, classification, and retrieval technology, it can help people to complete the purpose of food calculation and has important practical value for the study of people's eating habits and health management. At present, the main related applications include intelligent health management software, food calorie prediction platforms, and so on. For example, [PERSON] et al. [PERSON] et al. (2016) developed \"Smart Diet Diary,\" an application for diet tracking, using a pre-trained R-CNN model that uses deep learning to identify foods and calculate their nutritional value based on calories.
## 5 Demonstration of Pump-Probe Experiment  
We performed X-ray diffraction measurements of a matter under shock-compression in the reflection geometry. The experimental setup is shown in Figure 4a. A multi-layered sample was irradiated by a high-power laser pulse with a pulse energy of 30 J in a 10-ns duration. The time delay between the laser and the XFEL pulses was 11 ns. The XFEL pulse was line-focused to be 30 \(\upmu\)m (vertical) in FWHM by using one elliptical mirror of the KB optics. As shown in Figure 4b, owing to the wide observation range of the FPD, multiple diffraction lines from the corundum (Al\({}_{2}\)O\({}_{3}\)) were observed in a single-shot measurement. Figure 4c shows line profiles of the diffraction pattern of ambient and in-situ. The most intense peak at 45.5 degrees is the diffraction line of the Al\({}_{2}\)O\({}_{3}\)(116). The peak broadening to the large angle side is observed in the in-situ data that is consistent with the compression of the corundum due to the laser irradiation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Flat Panel Detector (FPD)** &
\begin{tabular}{c} **Dual MultiPort Charged** \\ **Coupled Device (Dual-MPCCD)** \\ \end{tabular} \\ \hline Active area & \(204\times 153\) mm\({}^{2}\) & \(51\times 51\) mm\({}^{2}\) \\ Pixel size & 99 m & 50 m \\ Number of pixels & \(2064\times 1548\) & \(1024\times 1024\) \\ Place & Inside chamber (in vacuum) & Outside chamber (in air) \\ Typical distance from sample & 130 mm & 640 mm \\ Observation range at typical distance & 60 degrees & 4.6 degrees \\ Angular resolution of a pixel on detector & 0.04 degrees & 0.004 degrees \\ center at typical distance & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detectors for XRD measurements.
## 4 Numerical Simulation of Developmental Range of Water-Conducting Fracture Zone
### Analysis of Overburden Fracture Field  
By analyzing overburden movement and the caving process in the 8103 working face of a 3D model, the developmental height and range of the water-conducting fracture zone in the working face were studied. The evolution of the 3D-model overburden is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Rock Character** & \begin{tabular}{c} **Density/** \\ **(kg/m3)** \\ \end{tabular} & \begin{tabular}{c} **Bulk Modu-** \\ **lus/GPa** \\ \end{tabular} & \begin{tabular}{c} **Shear** \\ **Modu-** \\ **lus/GPa** \\ \end{tabular} & \begin{tabular}{c} **Internal Friction** \\ **Angle/(C)** \\ \end{tabular} & \begin{tabular}{c} **Cohesion/** \\ **MPa** \\ \end{tabular} & \
\begin{tabular}{c} **Tensile** \\ **Strength/MPa** \\ \end{tabular} &
\begin{tabular}{c} **Compressive** \\ **Strength/MPa** \\ \end{tabular} \\ \hline Silstone & 2637 & 12.6 & 1.98 & 36.20 & 4.2 & 1.16 & 18.9 \\ Coarse-grained sandstone & 2621 & 23.2 & 3.75 & 28.45 & 14.6 & 1.68 & 71.3 \\ Sandy mudstone & 2572 & 5.8 & 1.37 & 33.32 & 9.4 & 0.97 & 15.8 \\ Medium-grained sandstone & 2619 & 13.2 & 1.98 & 36.29 & 11.6 & 1.28 & 39.9 \\ Fine grained sandstone & 2526 & 17.8 & 2.53 & 36.29 & 10.1 & 1.67 & 63.0 \\ Mudstone & 2631 & 10.6 & 2.03 & 43.02 & 0.4 & 1.13 & 7.5 \\ No. 2 coal seam & 1360 & 5.44 & 2.37 & 30.37 & 2.35 & 1.98 & 13.26 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical parameters of the coal seam and its roof and floor.  
Figure 8: Evolution of three-dimensional model overburden: (**a**) working face mining at 167 m; (**b**) end of mining face.  
Figure 7: The numerical simulation model.  
Figure 8a shows the distribution characteristics of the fissure field when the working face was mined to 167 m. At this point, the upward expansion of the fissure field in the roof strata was particularly obvious, and the concentrated distribution area of the fissure field formed a \"bridge arch\". The fissure development model in the middle of the goat was higher than both ends of the working face, and the deformation of the roof strata was obvious, mainly concentrated within the model widths of 22.5-177.5 m and lengths of 10-177 m. The height of the water-conducting fissure zone in the middle of the goat was the highest, about 97.2 m, and the lower fissure field at both ends of the goat was about 94.4 m Although there were tiny fissures above the model, a continuous fissure field was not formed.  
As shown in Figure 8b, the evolution of overlying strata at the end of mining shows that the height of the fracture field in roof strata is stable, and the concentrated distribution area of the fracture field is still in the shape of a \"bridge arch\", mainly within model widths of 22.5-177.5 m and lengths of 10-344 m. The height of the water-conducting fracture zone in the middle area of the goat was the highest, about 122.3 m. At this point, the water-conducting fracture zone was stable.
# Ontology-Based Framework for Cooperative Learning of 3D Object Recognition
## 3 Proposed Cloud and Ontology-Based Framework Architecture
### The Ontology-Based Framework
This standard allows robots to draw inferences about the geometry of an object and its position and orientation with respect to the robot. With a point cloud, the robot can perform autonomous actions such as moving to and grasping an object. In our ontology-based framework, we use the Point Cloud Library (PCL) to formally describe the geometry of objects in the 3D Object Repository so that robots in the cooperative learning environment can reuse those data when performing object learning and recognition [32; 33; 34]. We allow association of multiple partial point clouds to form a single object template to represent different viewpoints of the object. Our prototype application generally uses template matching to identify the object in a given score that best matches a given template. Hence, the object template must be linked with the ROD ontology to formally describe the object represented by the template. Figure 3: Physical and mechanical characteristics of an example robot. (**a**) Visualization of a physical robot from a URDF file. (**b**) Description of physical and mechanical characteristics of the same robot using the URDF XML schema. **The Object Grasping strategy module** is used for object learning and recognition. Consider the use case of grasping and moving an object. The grasping strategy module uses a SVM with grasping rectangles (i.e., a set of coordinates at which it is possible to grasp a specific object) and point cloud data to determine how to grasp a particular object. This task is computationally intense, so we offload it to cloud infrastructure to reduce compute time, robot cost, and robot power consumption.
## 4 Methodology
### Numerical Simulation and Result Analysis
#### 4.4.3 Simulation Analysis of Rotary Drum Velocity Change  
By setting the initial rotational velocity of the drum to 20 r/min and the initial air velocity to 14 m/s, a comprehensive analysis of how varying the drum rotational velocity affects the flow field is conducted. Furthermore, the rotational velocity increases systematically to 25 r/min, 30 r/min, 35 r/min, and 40 r/min to assess the corresponding impact on the internal flow field and the efficiency of winnowing in the device. The cross-sectional sampling method remains consistent with the previous approach, where the average values of pressure and velocity are calculated for each cross-section. These values are then used to plot curves illustrating the changes in pressure and velocity distribution within the flow field under different drum velocities. Figure 13 displays the variation in flow field pressure along the length of the cavity when the drum velocity is altered, while Figure 14 depicts the change in fluid velocity across the dimensions of the cavity during the drum velocity changes.  
Figure 13: The change of fluid pressure along the cavity under different drum rotational velocities.  
Upon analyzing Figure 13, it is observed that the initial pressure inside the model decreases with an increase in drum velocity, while the outlet pressure tends to remain constant. The maximum pressure within the flow field is measured at 26 Pa, with a minimum of 5 Pa. For drum velocity below 25 r/min, the changes in pressure are minimal, indicating a tendency towards pressure stabilization, which facilitates the effective separation of film impurities. However, for drum velocity exceeding 25 r/min, the initial pressure decreases while the outlet pressure increases significantly. This significant pressure change is not conducive to the efficient separation of film impurities.  
After analyzing Figure 14, it is evident that as the drum velocity increases, the rate of increase in cavity internal velocity becomes more significant, exceeding 2 m/s. When the drum velocity is higher than 25 r/min, the velocity distribution becomes uneven, resulting in precarious and in the front end of the model. On the other hand, when the drum velocity is lower than 25 r/min, noticeable velocity changes occur in unstable situations. The difference between the two scenarios is apparent; at lower drum velocity, the airflow becomes more consistent, which is beneficial for effectively separating film impurities. However, when the drum velocity is 25 r/min, the cavity velocity remains below 2 m/s, accounting for 15% of the model. In this case, the pace becomes relatively stable, and the pressure gradually increases, which is advantageous for the smooth delivery of materials.  
In summary, when the drum velocity is 25 r/min, the pressure distribution and velocity distribution of the flow field are improved.
## 2 Cycloid Gear Tooth Surface Geometry
### Tooth Longitudinal Modification  
Research on cycloid drives concerning the geometrical design method [20; 23], kinematic principle [18; 24], and load distribution analysis [4; 11; 12; 14; 15; 25] have all assumed a two-dimensional planar problem without considering the three-dimensional topological structure of the cycloid gear. However, in practice, like involute cylindrical gears [26; 27; 28], the cycloid gear is also crowned in the longitudinal direction to avoid edge contact between the teeth and rollers, considerably improving the contact stress distribution. Three types of tooth longitudinal modifications for cycloid gears are proposed in this paper: fully crowned modification (FCM), partially crowned modification (PCM), and logarithmically crowned modification (LCM), as shown in Figure 4a-c, correspondingly. The total amount of modification along the effective contact length \(L\) of a gear tooth in the longitudinal direction is defined by the following equations.  
1. In the case of FCM,  
\[\Delta f(u)=R_{f}-\sqrt{R_{f}^{2}-u^{2}}, \tag{5}\]  
where \(R_{f}\) is the radius of a circle with origin located on the centerline of a gear tooth.  
2. In the case of PCM,  
\[\Delta p(u)=\left\{\begin{array}{c}R_{p}-\sqrt{R_{p}^{2}-\left(|u|-L_{0}/2 \right)^{2}},-L/2\leq u<-L_{0}/2,L_{0}/2\leq u\leq L/2\\ 0,-L_{0}/2\leq u\leq L_{0}/2\end{array}\right., \tag{6}\]  
where \(R_{p}\) is the radius of a circle tangent to the line with length \(L_{0}\).  
3. In the case of LCM,  
\[\Delta l(u)=f\cdot\ln\left(1/\left[1-\left(2u/L\right)^{2}\right]\right)|u|<L /2, \tag{7}\]  
where \(f\) is the coefficient of modification.  
Tooth longitudinal modification (TLM) of cycloid gears can be carried out by a computer numerical control (CNC) form grinding machine for finishing where the grinding wheel has an accurate modified profile representing the tooth space between two adjacent cycloid gear te  
Figure 3: The four types of tooth profile modifications for cycloid gears.  
performs a radial infeed motion according to the minute amount of longitudinal modification along the gear axis to remove material from the teeth.
## 4 Simulation Results
### Simulation Criteria and Environment Description
The experimental NMEA PVT data used for the simulation and evaluation of our contribution were recorded from six land vehicles that drove concurrently over nonidentical trajectories that endured approximately 2000 epochs at a 1 Hz sampling rate (33.3 min in total) [27]. The collected trajectories' data are GNSS observations from various outdoor areas, including: (i) unobstructed, open space conditions, (ii) urban settings with narrow lanes and tall buildings, and (iii) semi-urban sections with grown trees and thick vegetation, causing a large-scale signal degradation and restricted view of the GNSS satellites [27]. Evidently, all vehicles carry a single low-cost GNSS receiver aboard able to compute the position solution and produce NMEA GNS and GST sentences. The first vehicle (veh. #1) is notated as the target vehicle, and the other five (veh. #2, veh. #3, veh. #4, veh. #5, and veh. #6) are considered the aiding vehicles. The MATLAB(r) toolbox was employed to import the data and establish a simulation environment for the MADM algorithms.