esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Datasets  
Our experiments were conducted on the following 5 public datasets [41]:  
* MR (Movie Review) ([[http://www.cs.cornell.edu/people/pabo/movie-review-data/](http://www.cs.cornell.edu/people/pabo/movie-review-data/)]([http://www.cs.cornell.edu/people/pabo/movie-review-data/](http://www.cs.cornell.edu/people/pabo/movie-review-data/))): This dataset is often refered to as polarity dataset, it intends to divide movie reviews into positive or negative reviews.
* CR (Custom Review) ([[https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html#datasets/](https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html#datasets/)]([https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html#datasets/](https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html#datasets/))): This dataset intends to predict whether a review is good or bad.
* Subj (Subject) ([[http://www.cs.cornell.edu/people/pabo/movie-review-data/](http://www.cs.cornell.edu/people/pabo/movie-review-data/)]([http://www.cs.cornell.edu/people/pabo/movie-review-data/](http://www.cs.cornell.edu/people/pabo/movie-review-data/))): It is known as subjectivity dataset and its categorization purpose lies in predicting whether the sentence is subjective or objective.  
* MPQA (Multiple Perspective QA) ([[http://mpqa.cs.pitt.edu/](http://mpqa.cs.pitt.edu/)]([http://mpqa.cs.pitt.edu/](http://mpqa.cs.pitt.edu/))): This dataset contains new articles from a wide variety. In this paper, we choose opinion polarity detection subtask of the MPQA dataset, it aims to determine whether an opinion sentence is positive or negative.
* TREC (Text Retrieval Conference) ([[https://cogcomp.seas.upenn.edu/Data/QA/QC/](https://cogcomp.seas.upenn.edu/Data/QA/QC/)]([https://cogcomp.seas.upenn.edu/Data/QA/QC/](https://cogcomp.seas.upenn.edu/Data/QA/QC/))): This dataset is composed of 6 types of problems, which aims to predict which category of problem it belongs to.  
The detailed information about these five datasets are demonstrated in Table 2, including their name, abbreviation, total sample number (TAN), class number (CN), average sentence length (ASL), and dictionary length (DL), and number of validation set sample (NVSS).  
Among the 5 datasets, only the TREC dataset has been divided into training set and testing set. For the other four datasets MR, CR, Subj, and MPQA, there is no standard partition of training set and testing set available. Therefore, 10-fold cross-validation is applied in the experiments for the above four datasets. That is we will randomly divide a data set 10 times, and each time the ratio of the training set and testing set is 9:1. The average result of 10 experiments will be taken as the final result.
## 4 Proposed Dual Control Strategy Based on Load Detector and Offset Corrector
In fact, the voltage is controlled by the first loop. Therefore, the cost function of the FCS-MPC algorithm is simplified from (5) to the following:  
\[CF(n)=\left(i_{L,ref}-\ i_{LP}(n)\right)^{2} \tag{8}\]  
This eliminates the need for tuning the parameter \(\lambda\), which is removed from the algorithm. Regarding the PI controller in the first loop, it can be designed with any classical techniques, such as Ziegler-Nichols tuning method, since its role is the standard one of adapting the reference current to control the output voltage. Given that the main component of the reference current is calculated by the IRC, which operates with fast dynamics, the PI controller plays a secondary role, handling only minor steady-state discrepancies. This allows for increasing the phase and gain margins without compromising the system's dynamics. By increasing the phase and gain margins of the PI controller, the system becomes more robust, allowing for greater tolerance to variations in system parameters without compromising performance. Therefore, a fine-tuning method is used in this study to determine the proportional and integral gains. This fine-tuning is justified because the PI controller has a low-speed response, which leaves large phase and gain margins. This dual approach optimizes the overall performance of the control system in both transient and steady-state conditions. However, a drawback of this method is the need for an extra sensor to measure the load current when the load is not known, which adds complexity and cost to the system. In the absence of a physical load sensor, observer-based methods offer an alternative for load current estimation, though they may introduce noise-related issues. [PERSON] et al. [24] demonstrated the effectiveness of an adaptive sliding mode observer (ASMO) for the sensorless control of DC-DC boost converters. The ASMO accurately estimated load resistance and output voltage using the system's mathematical model, maintaining the controller performance even under load variations. However, this method can be sensitive to noise, which might affect the accuracy of the estimations, particularly in systems with a high switching frequency. Similarly, [PERSON] and [PERSON] [25] proposed a Luenberger observer for load current estimation in UPS systems,  
Figure 5: Flowchart of the proposed dual-loop control strategy, integrating PI-based offset tuning for steady-state correction and FCS-MPC for fast dynamic response. which replaces sensors with model-based estimations of the load current. While the Luenberger observer improves system reliability and reduces complexity, it is also prone to noise issues, especially during sudden load changes or in non-linear conditions. These observer-based approaches could be integrated into the proposed algorithm to estimate the load current without a dedicated load sensor, though noise mitigation strategies should be considered to ensure precise control and system performance, making the whole system more complex.
# An Accurate, Efficient, and Stable Perspective-n-Point Algorithm in 3D Space
## 3 Experiments and Analysis
### Testbed and Test Synthesis Data Generation
For all figure groups, the four sub-figures are: a. mean error of the rotation matrix; b. median error of rotation matrix; c. mean error of translation vector; d. median error of translation vector. To avoid repetitive expressions, only the different cases are given in the title of the figure. Figure 4: Error comparison in the Planar Case. Figure 3: Error comparison in the Ordinary 3D case. According to the above simulation tests, due to the iterative method used and the sensitivity of the initial value problem, the LHM algorithm exhibited higher error and rapid fluctuation of accuracy error in Ordinary 3D, Planar Case, and the Quasi-Singular case, and especially so in the latter, compared with other PnP algorithms in the presence of fewer spatial redundant points. The EPnP+GN algorithm, which was also a non-iterative algorithm, also produced a large error when there was noise and few spatial redundant points, which came down to the linear solution method adopted. Meanwhile, it was found that, if the Gauss-Newton method was not introduced to optimize the solutions of EPnP+GN, the performance of the EPnP algorithm alone was as unsatisfactory as that of the LHM algorithm. Interested readers could conduct experiments to ascertain the performance gap. The authors no longer tested the difference alone. In the above three cases, the DLS algorithm fluctuated greatly, which could be explained by the lack of proper treatment regarding the singularity of the CGR parameters, and, thus, the poor accuracy error and stability of its solution. The RPnP algorithm used linearization to transform non-linear equations into linear equations, and its optimization was still non-global, which led to its less favorable solution error and stability, compared with OPnP, SRPnP, RDLT, ASPnP, and Hidden PnP, etc. The OPnP, SRPnP, RDLT, and ASPnP, etc. exhibited favorable stability, and the accuracy error and stability of the four algorithms were similar. Through a detailed comparison of the simulation data, it was easy to see that the best performance among the above nine algorithms was the Hidden PnP, which had the smallest error in solution accuracy. In addition, this algorithm provided the best stability in solving the three cases, without large fluctuations. The reason for this was that the Hidden PnP could quickly calculate the solution near the optimal solution. On this basis, the algorithm used this solution as the initial value of the Gauss-Newton method to achieve the precise location of the solution in one or two iterative steps. This process avoided the problem of unreasonable choice of initial values when using the Gaussian-Newton method directly,  
Figure 5: Error comparison in the Quasi-Singular case. and also avoided the operational instability and large computational burden caused by the need to construct a large matrix elimination template using the Grobner basis technique. The Hidden PnP, therefore, had the high accuracy and universality sought by the PnP algorithm.
## Appendix A User Authority Calculation Method Based on Multidimensional Attribute Weighted Fusion  
To represent authority, principal component analysis (PCA) was used to fuse multiple metadata features of users as  
\[w_{i}=\lambda_{1}VF_{i}+\lambda_{2}FL_{i}+\lambda_{3}FR_{i}+\lambda_{4}D_{i}+ \lambda_{5}GEO_{i}+\lambda_{6}F_{i} \tag{1}\]  
where \(\lambda_{i}\) represents the weight coefficient of the user's \(i\)th meta-feature. \(VF_{i},D_{i}\), and \(GEO_{i}\) represent whether the elements of \"verified,\" \"geo,\" and \"homepage introduction\" exist, respectively. \(FL_{i},FR_{i}\), and \(F_{i}\) represent the numbers of followers, friends, and favorites, respectively.  
Table 1 lists the metadata and other information of users participating in discussion. For Boolean features, when the value was true we converted it to a value of one, and when the value was false we converted it to a value of zero. Because the value ranges of each feature were very different, we applied the min-max normalization method to make the values of the metadata features of the six-dimensional users dimensionless while keeping their feature distribution characteristics unchanged.  
The PCA method was adopted to convert six-dimensional metadata user features into multiple comprehensive indicators to calculate user authority while minimizing the loss of metadata information. Figure A1 presents the relationship between the number of principal components and the variance contribution rate.  
One can see that the variance contribution rate of the first principal component on both datasets exceeded 0.8. Therefore, to simplify our calculations, we directly represented the authority of users involved in a discussion. For the two datasets, the weights of the six-dimensional features in the first principal component are presented in the third column of Table A1.
## 3 Fe-MOF Synthesis  
In this section, each of the synthesis methods of these materials is explained, especially Fe-MOFs, due to their application as catalysts in several AOPs. Fe-MOFs provide a greater number of catalytic sites, allowing them to more effectively generate free radicals due to the iron extranuclear electrons undergoing hybridization during bonding. Hence, Fe-MOFs are crucial to the development of AOP systems, including photo-electrocatalytic Fenton reactions, photo and electrocatalytic processes, ozone oxidation and advanced oxidation systems containing sulphate radicals.  
It is important to emphasize the complexity of the synthesis of MOFs since their elaboration depends on many parameters. Normally, these are divided into two classes: operational and compositional [81; 82]. The most important operational factors are the temperature and pressure to which they are subjected in the synthesis reaction and the time taken for this reaction [81; 82]. The other, but no less important, are the compositional parameters, in which pH, solvents and organic ligands used, etc., could be highlighted [81; 82]. Small changes in some of these parameters can lead to different kinds of MOFs. In Figure 3, the different methods of synthesis of Fe-MOFs are shown and several related operational parameters are introduced.  
Before starting to explain each of the methods shown in Figure 3, it is important to emphasise once again the importance of the chemical compounds involved, i.e., the source used to obtain the metal ions, in this case, the iron ions, the solvent and the organic ligand. As mentioned in Section 2.1, depending on the organic ligand and the metal ion used, a certain family of MOF (MIL, ZIF, etc.) can be obtained [84]. Despite this, the solvents used in the formation of MOFs play an important role in the distribution of the compounds since they are considered structure-directing agents or participate in the coordination of the structure with the metal ions [85]. Each of the synthesis methods will be explained with examples illustrating the components identified and operating conditions.
## 2 Related Work  
### Problem of LS-Wsn for Big Data Collection  
LS-WSNs are a set of thousands of low-cost, low-power sensors with limited computing, memory storage, communication and battery power capabilities that often have to cooperate in order to route the collected data to a single central node or sink [1; 20]. In recent years, these networks have seen their fields of application broaden to penetrate smart cities (see Figure 1), in several areas including monitoring, measurement and data collection, health applications, and precision agriculture. For these types of applications, the general purpose of a LS-WSN is the collection of a set of data, from an extended environment, in order to route them, through multi-hop routing, to processing points called base stations (or sinks).  
However, the technological challenges to be met by these networks are numerous; beyond the continuous miniaturization of these objects, their communication capacities have given rise to many problems. Unlike traditional networks that seek to guarantee a good quality of service, LS-WSNs place great importance on energy consumption, which remains the major concern of the protocols proposed for these networks [16; 18]. Indeed, sensors are often equipped with non-rechargeable batteries that are difficult to replace. The survival and performance of such a network, therefore, depend on the lifetime of the batteries in its nodes. The main task of a sensor, deployed in an area of interest, is to detect events or measure a quantity, or calculate and aggregate the collected data and transmit it to the Base Station (BS). Energy consumption therefore mainly concerns three operations: acquisition, communication, and data processing. The energy consumption by the acquisition operation depends on the nature of the application. For instance, a sporadic or periodic detection could consume less energy than permanent monitoring of an event. Data communication generally consumes more energy than other operations and therefore needs to be well-controlled [21; 22].  
In smart cities, where almost all the services offered are provided by means of a computer system to such an extent that these cities are considered to be one of the main sources of big data in the world. There is a lot of interest in this data in many sectors. However, in view of the intrinsic characteristics of big data, still considered one of the great challenges of modern computing, collecting them using LS-WSNs is a twofold challenge that we are seeking to meet. Although such a solution is, to our knowledge, a first. The challenges are many. For us, it is a question of proposing a logical architecture for LS-WSN deployment capable of responding to the scale-up and maximizing the life of the network. To do so, in the context of this document, we have chosen to collect big data generated by persons from their multiple activities, and the data from their environment. As set out in our research problematic, we need to review the literature on the different approaches to deploying WSNs in order to adapt it to our context, and then propose mechanisms that will allow us to maximize the life span of the network.
## 3 Results and Discussions
### Odor Activity Value (OAV) and Relative Odor Contribution (ROC)  
Table 6 shows the OAV values and ROC index of individual volatile compounds which contributed at the minimum threshold (OAV > 1) for its identification. Only six compounds from the 67 identified exceeded the threshold values in melon-based wines, the majority group corresponded to fatty acid ethyl ester usually produced by _S. cerevisiae_ during alcoholic fermentation; it possesses a lower threshold value and provides fruity, candy, and perfume-like aroma [40]. Ethyl hexanoate had the highest OAV (19.62 in the Jimbee melon wine and 50.66 in the Okashi wine) and the contribution (ROC) of aroma in both wines was 0.68 and 0.44 in Jimbee and Okashi, respectively, which enhanced the fruity and wine-like aroma. Ethyl octanoate and methyl decanoate were quantified in both melon wines, and the ethyl hexanoate gave the wine a fruity aroma. Ethyl butanoate was found only in the Jimbee melon wine (OAV = 1.81), this compound is associated with pineapple- and apple-like aromas. The ethyl 9-decenoate and phenethyl acetate were concentrated enough to reach the minimum threshold in the Okashi melon wine, 6.96 and 1.04, respectively, both of which contributed to a flower-like aroma.
## 3 Results
### Distribution of Vegetation Drought Recovery Periods
#### 3.3.1 Drought Recovery Periods of Vegetation Under Different Climates  
Figure 9 presents the proportions of drought recovery periods for the five indices under various climatic conditions. It was evident that under humid climatic conditions, vegetation exhibited a faster recovery period, while there was a declining trend in the required recovery periods from arid to humid climates. The semiarid and Mediterranean climates occupied an intermediate state, with recovery periods shorter than those in arid climates but longer than those in humid climates. The proportion of EVI recovery within 16 days varied from 68.26% under extremely arid conditions to 96.80% under extremely humid conditions. Conversely, the percentage of EVI recovery exceeding 128 days decreased from 18.41% under extremely arid conditions to 2.33% under extremely humid conditions. Similar patterns were observed for the other vegetation indices. In 2012, climates characterized as humid and extremely humid exhibited faster vegetation recovery periods, while in 2019, the vegetation recovery periods for humid and extremely humid climates were even faster than those in 2012 (Figure 10).  
Figure 8: Distribution of drought recovery times for different indices in 2019. (**a–e**) Represent the recovery time distributions for EVI, FVC, GPP, LAI, and RSVI during the drought year of 2019, respectively. The stacked bar chart shows the proportion of recovery periods for the five indices, with values below 5% not displayed numerically.  
Figure 7: Distribution of drought recovery times for different indices in 2012. (**a–e**) Represent the recovery time distributions for EVI, FVC, GPP, LAI, and RSVI during the drought year of 2012, respectively. The stacked bar chart shows the proportion of recovery periods for the five indices, with values below 5% not displayed numerically.
# Blockchain-Based Quality Assurance System for Academic Programs
## 3 The Proposed Blockchain-Based QAS
### Implementation Environment
#### 3.7.3 Implementing Cryptographic Algorithms for Enhanced Security in QMS  
The current research investigated the use of various cryptographic algorithms to provide confidentiality, integrity, and non-repudiation services at the application layer. This aims to make all users confident that sensitive quality information is confidential and not manipulated by unauthorized parties. Moreover, the provided security services, in particular digital signatures, aim to ensure that the user who created and signed the quality report cannot deny his responsibility about that report.  
To provide confidentiality, quality reports are encrypted using AES before sending them to other parties or storing them on any storage areas of cloud service providers. On the other hand, integrity and non-repudiation services were provided through digital signatures. The current research applied two popular digital signature algorithms (DSAs), which are the Elliptic curve cryptography DSA (ECCDSA) and RSA DSA. It has been found that the ECCDSA takes less time to generate the signature and hence achieves better performance. The rationale behind the enhanced performance level for the ECCDSA is that it can provide the desired security level while using much smaller key sizes compared to other public key cryptosystems. This means that the ECCDSA needs to perform less computations and hence consumes less time. The used encryption scheme is depicted in Figure 7.  
As can be noticed from Figure 7, the implemented encryption scheme utilizes the ECCDSA to digitally sign the sensitive quality files. The use of digital signatures aims to ensure the integrity of such sensitive files and prevent source repudiation behaviors. This research study implemented both the RSADSA and ECCDSA. The later scheme was adopted because it has less computational complexity and takes less time. The code used to implement the ECCDSA to sign and encrypt quality files can be accessed through [37].  
The following section briefly describes the GUIs of the proposed QAS.  
3.7.4. Developing the GUIs of the Proposed QAS  
We used javascripts and HTML to develop the web application interface. For the integrated development environment (IDE), a visual studio was used to write the HTML and Javascript codes.  
Figure 8 shows the login interface for the proposed blockchain-based QAS. The user needs to login using his wallet ID, email address, and password, as previously discussed in the process model presented in Figure 8.  
The following section introduces the experimental results and analysis in terms of the performance and cost of the blockchain transactions and cryptographic operations, along with elaborations of the main research findings.
# Blockchain Solutions for Enhancing Security and Privacy in Industrial IoT
## 4 Blockchain-Based Architecture and System Design
### System Controller and Modules  
The system controller is the central component, ensuring data security, integrity, and availability. It consists of four key modules:  
* Blockchain Management Module: This module generates transactions by encapsulating data, SHA-256 (Secure Hash Algorithm) hashes, device IDs, and database identifiers. These transactions are added to the blockchain, ensuring immutability and traceability.
* Database Management Module: Responsible for calculating the SHA256 hashes of transmitted data, this module securely stores the data in an indexed database. It ensures that the data is well organized and readily accessible to authorized users.  
Figure 4: Flowchart of data flow and blockchain integration in the proposed IIoT system architecture.  
Edge-Allowing Hub Module: This module manages communication between controllers and hubs, enforcing data transmission schedules and timing policies. Filtering noise and correcting errors ensure the validity and reliability of the data.
* Access Control Module: This module authenticates users through token-based mechanisms, preventing unauthorized access. It ensures that only authorized entities interact with the system, maintaining security and integrity.