esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This research endeavors to ameliorate acidic soils contaminated with copper sulfide via the application of a composite of \(L\). _perenne_ in conjunction with vermicompost and steel slag, either administered singularly or in tandem. The findings posit that \(L\). _perenne_ demonstrates notable resilience in adverse environments, rendering it a prospective phytoremediation candidate for soils afflicted by both acidity and copper pollutants. The solitary or combined application of vermicompost and steel slag substantively elevates soil parameters such as pH, electrical conductivity (EC), soil organic matter (SOM), and cation exchange capacity (CEC). This augmentation engenders an amplification in the abundance of bacterial communities concomitant with a diminution in soil redox potential (Eh), total copper content, and available copper content. It is noteworthy that the application of steel slag exhibits superior efficacy in immobilizing copper within the soil matrix, while vermicompost conduces to an augmentation in the diversity of soil bacterial communities. The ensuing alterations in soil physicochemical properties further precipitate a transformative impact on the composition of bacterial communities, and the joint application of steel slag and vermicompost serves to both enrich and stabilize the structure of soil bacterial communities. Thus, the synergistic application of vermicompost and steel slag in soils contaminated with heavy metals emerges as an environmentally conscientious and economically viable strategy for remediation.  
Conceptualization, J.X.; formal analysis, S.W.; data curation, H.Q.; writing--original draft preparation, X.W.; writing--review and editing, X.W., J.X., M.H., H.Q., and S.W.; supervision, J.X. and M.H.; funding acquisition, J.X. and X.W. All authors have read and agreed to the published version of the manuscript.  
This work was grateful to the Key R&D Program of Jiangxi Province, China (20212 BBG73013), and Jiangxi Province Graduate Innovation Special Fund Project, China (XY2022-S211).  
Not applicable.  
The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.  
Thanks to Jiangxi University of Science and Technology for providing experimental facilities and instruments.  
The authors declare no conflicts of interest.  
Figure 7: Heatmap of the correlation between soil physicochemical properties and the dominant bacterial phyla in soil. Note: *** \(p\leq 0.001\).
# Tire Defect Detection via 3D Laser Scanning Technology
## 2 Materials and Methods
### Data Preprocessing  
We establish a new dataset for tire defect detection consisting of 3D tire surface data captured by a laser scanner. Our mechanical environment in the production line is shown in Figure 2a, where the laser scanner is fixed during operation for accurate data acquisition. The scanning angle can be adjusted by changing the tire orientation, and each tire is rotated at a regular speed. The entire system works automatically by loading tire samples onto the transport line.  
The resulting data of each inspected tire are represented as a sidewall image in the form of a 3D point cloud. The length of the point cloud is too long to process. Additionally, because of the unbalanced aspect ratio, tiny defects are invisible, hindering their detection. To tackle this problem, we first divide the point cloud into several patches. As shown in Figure 2, all point cloud patches possess the same size for convenience. Notably, adjacent patches slightly overlap each other to avoid incompleteness of defects. All subsequent steps of our method are based on these patches.  
Next, to ease the unexpected dislocation and scanning errors, we employ normalization and reorientation to fine-tune the distribution of points. After normalization, the centroid of each patch is located at the original point of the 3D coordinate system. In the reorientation process, we first calculate the mean normal of the whole patch, and then we align the mean normal with the \(z\)-axis in the 3D coordinate system, making it more convenient to project the 3D points to the 3D plane along the \(z\)-axis. Lastly, to amplify the height of tiny defects, we multiply the \(z\) coordinate of each point with a coefficient \(\alpha\) (\(\alpha=10\) in our implementation).
## 5 Conclusions  
This paper has proposed and discussed an original REB fault diagnosis method based on rough set and dependent feature vector (RS-DFV). First, this method employed dependent feature vector (DFV) to describe rolling element bearing faults. And this fault characterization method greatly improved the accuracy of fault description and laid a reliable data base for the follow-up fault diagnosis. Afterwards, a tree heuristic feature selection method (THFS) is proposed for selecting the effective features and building the dependent feature vector structure. THFS perfectly overcame the difficult problem in dependent feature vector building and realized feature reduction as well as feature optimization. Above all, a feature extraction method based on rough set and the dependent feature vector extraction tree was designed for RS-DFV extraction, and it ensured that RS-DFV had the same structure advantage as that of dependent feature vector and solved the abnormal termination problem in DFV extraction. Therefore, RS-DFV not only inherits the advantages of DFV, but also cleverly overcomes the defects of DFV. The results of the contrast tests showed that: the diagnostic accuracy of RS-DFV are (100%, 100%, 99.51%, 100%, 99.47%, 100%), while those of the feature vector constituted by the same feature items and DFV are only (93.97%, 100%, 99.33%, 100%, 88.09%, 93.81%) and (100%, 100%, 99.51%, 100%, 99.47%, 100%); and there was no significant difference in training time and test time. In general, the fault diagnosis method presented in this paper ameliorated the algorithm performance in terms of accuracy, complexity and timeliness and greatly advanced the efficiency as well as the practicability of REB fault diagnosis. Therefore, RS-DFV is a very useful fault description method for REB fault diagnosis.  
[PERSON] contributed to the conception of the reported research. [PERSON] contributed significantly to the design and conduct of the experiments and the analysis of results, as well as contributed to the writing of the manuscript. [PERSON] and [PERSON] helped design the experiments and perform the analysis with constructive discussions.  
This project is partially supported by the National Natural Science Foundation of China (Grant no. 51609088), the Provincial Natural Science Foundation of Jiangxi (Grant no. 20161 BAB212053), the postdoctoral fund of Jiangxi Province (2015 KY19), and the National Natural Science Foundation of China (Grant no. 61663011).  
The authors declare no conflict of interest.
## 2 Theoretical Background  
### Carriers (Organizational Buyers) versus Owner-Operators (Individual Buyers)  
According to [PERSON] [29], the trucking industry can be divided into two major components: private trucking and for-hire trucking. Private trucking refers to companies that maintain a fleet of trucks and trailers and employ drivers to haul and distribute their freight, but whose principal business is not trucking. For-hire trucking companies (organizational buyers) carry freight for a fee to domestic and/or international markets. There are two common driver employment modes in for-hire trucking: company (or carrier) drivers and owner-operators. Carrier drivers are hired under open-ended employment contracts to drive vehicles provided and maintained by the carrier, using fuel for which the carrier pays. A carrier usually assigns a driver to the same truck for an extended period of time.  
Owner-operators (individual buyers) are self-employed truck drivers who work under contract for either for-hire trucking companies or companies engaged in private trucking. They typically own the road tractors and trailers they drive. Contractual relationships with owner-operators range from single-haul transactions to long-term contracts, with the typical arrangement resembling repeated short-term contracts. Owner-operators are paid on either a per-mile basis or a per-haul basis where the driver receives a fixed percentage of haul revenue. These drivers may find it necessary to work long weeks to cover fuel, maintenance, insurance, and rental or leasing expenses [29].  
Owner-operators receive greater market opportunities from taking on greater economic risks than other drivers, and they should, thus, receive a higher return than employees in similar positions. However, they also face the risk of earning less. In fact, there is no evidence that owner-operators earn significantly different total incomes compared to employed drivers [29; 30]. Because of their personal stake in their trucking activity, owner-operators have strong incentives to properly care for their vehicles. In contrast, carrier drivers have fewer incentives to care for the equipment they operate. Further, because carrier drivers do not fully internalize the cost of idle equipment, they have less of an incentive to expend effort than do owner-operators [31]. Because owner-operators directly bear all the costs associated with the operation of their vehicles, they have a stronger incentive than carrier drivers to minimize such costs [27].
## 3 Research Model and Hypotheses
### Perceived Enjoyment  
Based on the TAM model, perceived usefulness and perceived ease of use are considered to be important factors influencing attitudes and intention to use. The TAM model has been further improved with the increasing number of studies that used TAM theory as a theoretical foundation. The study by [PERSON] [37] indicated that \"perceived enjoyment\" and \"perceived ease of use\" are strong determinants of behavioral intention to accept technology. In addition, students' intrinsic motivation is influenced by their learning enjoyment [34]. Consequently, this study will investigate the relationship between students' perceived enjoyment of and their intention to participate in an online gamified classroom. As such, the following hypotheses are proposed:  
**Hypothesis 4 (H4):**_Perceived enjoyment has a significant positive influence on students' intention to participate in the online gamified classroom._
## 4 Experiments
### Experiments on Registration Effectiveness  
#### 4.3.1 Dataset  
To verify the registration of point clouds using our proposed covariance matrix descriptor, we conduct experiments on partial point clouds from the Stanford 3D Scanning Repository, the SpaceTime [10; 37; 38] dataset, and the Kinect dataset [10; 37; 38]. In the Stanford 3D Scanning Repository, we test registration effectiveness for eleven of Armadillo's incomplete point clouds, six of Bunny's incomplete point clouds, fifteen of Happy Buddha's incomplete point clouds, and fifteen of Dragon's partial point clouds. In the SpaceTime dataset, we test registration effectiveness between two models and fifteen scenes, where models and scenes are regarded as source point clouds and target point clouds, respectively. In the Kinect dataset, we test registration effectiveness between partial views of six models and fifteen scenes, where partial views of six models and scenes are regarded as source point clouds and target point clouds, respectively. Some of the point clouds taken from these datasets are presented in Figure 9.
# A Smart and Mechanized Agricultural Application: From Cultivation to Harvest
## 3 Materials and Methods
### Cultivation Phase  
As stated before, with the increase in the world population, the need for agricultural and food products is also increasing. Therefore, the importance of smart farming systems and methods has increased. In this context, the first step in the mechanism and a sustainable model is the planting (cultivation) phase. Here, it is very important to place each product in the correct location in order to minimize or remove manpower and manual interventions. The metaheuristic approach is useful because the solution to this problem is a type of NP-Hard problem to be solved systematically. Therefore, the SCSO algorithm was used in the first phase of this study. Due to its nature, this algorithm can provide good performance both locally and globally, as its transitions are balanced in the exploration and exploitation phases, and therefore this new algorithm was used to solve the first problem case in this study.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Approach, Technique** & **Task and Goals** \\ \hline (31) & GIS system & Monitoring process based on map analysis and reduced data \\ \hline (23) & IoT and autonomous robot & Monitoring process and yield forecasts by IoT devices and mobile/web app \\ \hline (24) & IoT & Process management based on a farm management information system and architecture \\ \hline (26) & IoT & Product management based on data analysis \\ \hline (36) & Single autonomous robot & Monitoring process based on efficient water usage, controlling amounts of phosphate (PO4) and nitrate (N03), and detecting drainage pipes \\ \hline (37;49) & Single autonomous robot & Monitoring process based on monitoring vegetation state \\ \hline (32) & Multi autonomous robots & Monitoring process based on providing a multiple UAV system for aerial imaging \\ \hline (33) & Single autonomous robot, metaheuristic & Spraying process based on spraying fruits and trees \\ \hline (34) & Multi autonomous robots & Spraying process based on path-planning algorithm \\ \hline (42) & Metaheuristic & Food logistics chain and crop planning based on minimizing cost and maximizing geographic diversity \\ \hline (44) & Metaheuristic & Convenient and efficient planting and cultivation \\ \hline (39) & Metaheuristic & Annual crop planning \\ \hline (43) & Metaheuristic & Rent and share agricultural equipment based on a smart-engine-based decision system \\ \hline (45) & Metaheuristic and IoT & Best planting model based on optimal deployment \\ \hline (48) & Metaheuristic & Optimal plant \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characterization of current studies in the literature.
## 6 Open Challenges, Future Prospects and Research Directions
### Future Directions
#### 6.4.1 Modulation Schemes  
The future of FSO communications is poised for considerable advancements through the continuous enhancement and integration of various modulation schemes. These advancements will enable FSO systems to achieve higher capacities, enhanced reliability, and greater resilience against atmospheric challenges. Several key directions in the enhancement and integration of traditional, advanced, and SM techniques promise to significantly improve the performance of FSO communication networks.
# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 2 CO2 Observation, Datasets, Global Related Research
### Satellite Remote Sensing Observations
#### 2.2.1 Development of Thermal Infrared Hyperspectral Sensors  
The High-spectral Resolution Infrared Sensor (HIRS) developed in the 1970s in the United States marked a significant advancement in satellite remote sensing for atmospheric detection. Initially, infrared sensors were primarily used to monitor atmospheric temperature and humidity profiles. However, with advancements in sensor technology and inversion theory in atmospheric remote sensing, as well as the growing need to understand the atmospheric environment, thermal infrared hyperspectral sensors began to play a crucial role in atmospheric detection.  
In August 1996, the Advanced Earth Observation Satellite (ADEOS) was launched, featuring the Interferometric Measurement of Greenhouse Gases (IMG) [18]. IMG was the first onboard hyperspectral Fourier sensor dedicated to gas detection via space-borne observations and demonstrated the feasibility of high-resolution sensors for detecting trace gases [19; 20]. In May 2002, NASA's Aqua satellite was launched, equipped with the Atmospheric Infrared Sounder (AIRS) and NOAA's Advanced Microwave Sounder Unit (AMSU). AIRS was designed to enhance global climate research and weather prediction by improving the accuracy of tropospheric temperature profiles and atmospheric humidity measurements [21]. Subsequently, the Aura satellite, launched in June 2004, carried the high-resolution Tropospheric Emission Spectrometer (TES). In October 2006, the METOP-A satellite, part of the polar-orbiting meteorological satellite series, was launched  
Figure 5: Developments of carbon dioxide remote sensing satellites.  
with the next-generation hyperspectral atmospheric sounder, the Infrared Atmospheric Sounding Interferometer (IASI). In November 2017, China's FY-3D satellite launched with the Hyperspectral Infrared Resolution Atmospheric Sounder (HIRAS) and the Greenhouse Gas Absorption Spectrometer (GAS). The following year, the GF-5 satellite was launched, featuring the Atmospheric Environment Infrared Ultraspectral Sounder (AIUS), the first occultation observational sensor with ultraspectral resolution in the infrared band developed by China.  
Looking ahead, the Meteosat Third Generation (MTG) satellites will continue to contribute significantly into the 2040s. The MTG system will consist of two identical imaging satellites (MTG-I) with advanced capabilities and a sounding satellite (MTG-S) carrying an infrared sounder (IRS) and the Copernicus Sentinel-4 ultraviolet-visible and near-infrared instrument, marking the first deployment of such a system over Europe. Table 1 provides a summary of thermal infrared sensors used for CO\({}_{2}\) detection.
## 2 Materials and Methods
### Illumina Miseq Sequencing  
The samples of anaerobic granular sludge from V1, V3, and V5 were used for metagenomic analysis, due to the assumption that they would represent the most diverse microbial population. DNA extraction was performed using a FastDNA SPIN Kit for Soil (MP Biomedicals), and its purity (A260/280 and A260/230 ratios) and concentration were measured with a NanoDrop One spectrometer (Thermo Scientific, Waltham, MA, USA). Genomic DNA was then detected by 1% agarose gel electrophoresis. Metagenomic analysis of the 16S rRNA encoding gene was performed on the basis of the V3-V4 hypervariable region of the 16S rRNA gene by Genomed Laboratory (Poland). Specific primer sequences 341F and 785R were used to amplify the selected region of Archaea and Bacteria and prepare the library. PCR was performed using Q5 Hotstart High-Fidelity DNA Polymerase (NEBNext), under conditions consistent with the manufacturer's recommendations. Sequencing was carried out on a MiSeq sequencer, in paired-end (PE) technology, 2 \(\times\) 250 nt, using the MiSeq Reagent Kit v2, according to the manufacturer's recommendations (Illumina). For sequence analysis, the 16S Metagenomics protocol was used, which provides a species-level classification of reads based on the Greengenes v13_5 reference sequence database modified by Illumina. The analysis consisted of automatic demultiplexing of samples, generating fastq files containing raw readings, and classification of paired-end readings in particular taxonomic categories.
# Attacks and Preventive Measures on Video Surveillance Systems: A Review
## 2 Attacks on VSS
### Privilege Escalation Attack
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & **Attacks** & **Threats** & **Description** & **Examples** \\ \hline \multirow{4}{*}{**Perception Layer**} & \multirow{4}{*}{Device attack} & Physical Attacks, impersonation, malicious code injection & Someone takes advantage of a bug or inherent vulnerability to gain access to the infrastructure. & Physical access to a security surveillance camera and modifying the design settings. \\ \cline{2-4}  & & & Group of hacked computers, smart devices, and appliances connected to the Internet are known as an IoT botnet. & The Mirai malware is seen as a milestone in the threat landscape and exploits security holes in IoT devices and launches attacks. \\ \hline \multirow{4}{*}{**Network Layer**} & \multirow{4}{*}{Reconnaissance} & \multirow{4}{*}{Routing attacks, data transit attacks} & \multirow{4}{*}{Numerous malicious activities can be performed on devices if an attacker gains physical access to the local network wirelessly.} & In the network level attacks, cybercriminals are able to redirect network traffic; for example, Address Resolution Protocol poisoning (ARP) or by changing the Domain Name System (DNS) settings. \\ \cline{2-4}  & & & The aim is to collect data about an infrastructure, including the network services and devices that are running. \\ \cline{2-4}  & & & It is a type of eavesdropping attack. This attack could permit the attacker to secretly relay and possibly alter the communications between two IoT devices. & Attackers can use a network packet analyzer, i.e., Wireshark for analyzing network traffic. If communications are not encrypted or authenticated, an attacker can easily steal the data. \\ \hline \multirow{4}{*}{**Application Layer**} & \multirow{4}{*}{Data leakage, DoS attacks, malicious code injection} & An IoT device interconnects with back-end cloud services. IoT cloud services might permit the client to select simple passwords. & A lot of cloud services have a logical weakness, which is actually the permission of cloud to a cybercriminal to obtain sensitive information of the customer and also access to the device without any authentication. \\ \cline{2-4}  & & & The attacker takes advantage of programming errors or software flaws to permit cybercriminals to elevate access to an IoT infrastructure. & Grant the cybercriminal elevated access to the IoT ecosystem and its associated data and applications. \\ \cline{2-4}  & & & Electronic devices and its connected devices are deactivated or changed by a cybercriminal, via physical or remote access to the IoT sensors. & An attacker can deny the sensors to send and receive communications. Another example could be battery abuse, device disabling, or device bricking. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: VSS architecture layers, attacks and their examples.