esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
As a filter-denoising algorithm based on the steepest gradient descent, the LMS algorithm requires fewer computer resources and is easy to implement. Since its introduction, it has been widely used for signal denoising. Reference [21] proposed an improved LMS algorithm for system noise reduction, which successfully solved the interference problem of complex signal components. The authors of [22] found that the LMS technique has a fast detection speed for signal noise reduction. Reference [23] proposed an improved adaptive algorithm for LMS to perform adaptive filtering and noise reduction processing on fault signals to improve the SNR of the fault signals. To sum up, the existing traditional noise reduction methods have disadvantages such as mode aliasing, end effect, residual noise, and so on. Therefore, this paper proposes a new hybrid method consisting of ICEEMDAN's signal decomposition algorithm and IMF's selection method. This method achieves good denoising accuracy and completeness, which can fully make up for the shortcomings of the above methods. In order to improve the steady-state performance of the noise reduction method, LMS is introduced to improve the steady state of the algorithm. Therefore, ICEEMDAN, combined with the improved LMS method, can not only make the noise reduction more thorough and achieve the ideal noise reduction effect, but also have better robustness. This study aims to propose a new method to reduce the noise of pipeline defect signals, so as to improve the ability to identify pipeline defects, evaluate the effect of noise reduction, and verify the effectiveness of this method through an example. The rest of this article is organized as follows. In Section 2, we introduce the basic theory of ICEEMDAN, the LMS, and the basic theory of the improved LMS. Section 3 introduces the ICEEMDAN-LMS method and its noise reduction performance on analog signals. In Section 4, the denoising of pipeline defect signals is evaluated experimentally. Section 5 puts forward the conclusion of this paper and the future research direction.
## 3 Improve L1 Optimization to Correct 100% of Corrupted Propagated Errors in Network Coding
### Overcome \"All-or-Nothing\" Problem with Reputation-Based Trust Model of Social Network
#### 4.2.3 Network Coding Based on Reputation-Based Trust  
In the stage of network coding which is based on reputation-based trust, we will set a threshold value for the trust value. The relay nodes with trust value larger than the threshold value will perform network coding. On the contrary, the relay nodes with trust value smaller than the threshold value  
Figure 5: The building procedure of reputation-based trust about an intermediate node. The node \(i1\) is another node which is similar to \(i2\). The red ACKs are part of all the ACKs which are sent by the \(i1\).  
will not perform network coding and go into hibernation. The above scheme will select some nodes as active nodes and other nodes as hibernation nodes which will save energy.  
In Figure 6, the trust values are divided into three levels: the highest trust value in the relay nodes with the most black color, the median value in the relay nodes with the light black color, and the lowest trust value in the relay nodes with the white color. Only the relay nodes with the most black color will perform network coding. Compared with the situation where all the relay nodes perform network coding that situation where only a part of nodes perform network coding will save energy. In the sense of network coding, the relay nodes with high trust value are similar with the active nodes in social network.  
Theoretically, this model does not always select the optimal nodes, which in reality will be the \"key\" nodes. Because the scheme which is based on social network theory is completely decentralized and distributed, we cannot always reach the ideal situation: the selected nodes with highest trust value will help completely overcome the \"all-or-nothing\" problem in network coding. However, many works about social networks illustrate that, if the trust model is defined reasonably, the model which is based on social network theory really can reach an acceptable result even if the model is decentralized and distributed. In our work, the definition about \"trust\", which is also illustrated in Figure 5, is really a reasonable and novel model which captures the point of the \"all-or-nothing\" problem in random network coding. The following experiment results confirm our conclusion.
# Efficient Resource Allocation in Blockchain-Assisted Health Care Systems
## 4 Proposed Technique  
In this work, an efficient resource allocation mechanism for transferring data blocks to mining machines to compute their nonce within a health care system \(ESM_{HCS}\) is proposed. \(ESM_{HCS}\) comprises two algorithms that assist LBMs in smartly distributing the received data blocks to multiple backwardly connected mining machines with a uniform distribution, when the received data block requests are within the caching capacity of the mining machines. The second algorithm efficiently scrutinizes the requested data blocks when the requested data blocks exceed the caching capacity of the mining machines.  
In the proposed healthcare system for smart cities, multiple fog mining machines are placed in different areas to compute valid hash values for a patient's health data block. It is obvious that some of these mining machines will be under a heavy load and some will have a lesser or zero computing load. For a uniform and fair distribution of data blocks to all fog mining machines, a smart load balancing mechanism is required. Furthermore, there is another probability, where the number of nonce computing requests at any time instant is greater than the caching limits of the mining machines, and all the requested data blocks can not be allocated to the associated mining machines. It is therefore required to scrutinize the data block requests to be cached on these mining machines.In this work, LBMs collect patients' data and forward these data to fog mining machines placed in the cluster. This algorithm is designed for LBMs. The algorithm not only ensures the load balancing of fog mining machines within a cluster but also allocates data blocks by considering the sensitivity of patients' data.  
The main characteristics of this proposed algorithm are mentioned below:  
* Compute the priorities of each data block by considering the time of origin and data severity;
* Smartly allocate these data blocks to mining nodes.
# Privacy-Preserving Transfer Learning Framework for Kidney Disease Detection
## 3 Material and Methods
### Evaluation Methods  
Evaluating the performance of the proposed framework with transfer learning architectures is an essential process to measure how well the models have acquired the ability to classify test data. The architectures generate predicted labels, which are then compared with the true labels to determine the performance outcomes. This section examines various evaluation methodologies.  
1. Confusion matrix  
As a typical practice, the performance evaluation of a classification algorithm involves comparing the actual and predicted values of the classes with the confusion matrix. An analysis of the predicted labels against the actual labels yields four outcomes: true positive (TP), true negative (TN), false positive (FP), and false negative (FN). TP indicates the number of positive class instances that are accurately identified as positive. TN represents the number of instances belonging to negative class instances that are accurately predicted as negative. FP is the count of negative class instances that the algorithm classifies incorrectly as positive. FN represents the number of positive class instances that the algorithm predicts as negative. A confusion matrix for two classes is presented in Figure 8.  
Figure 8: Confusion matrix [38].  
Classification metrics are also used to measure the performance of the models. Metrics such as accuracy, precision, recall, and F1 score are calculated as follows [39].  
Accuracy is the ratio of correct predictions to total predictions and is given in Equation (3):  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{ FN}} \tag{3}\]  
Precision is the ratio of true positive class predictions to the sum of true positive and false positive, shown in Equation (4):  
\[\text{Presicion}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{4}\]  
Recall is defined as the ratio of correct positive class predictions to the sum of correct positive class predictions and false negative class predictions and is given by Equation (5):  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{5}\]  
The F1 score is defined as the weighted average of precision and sensitivity and is given by Equation (6):  
\[\text{F1 Score}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{6}\]  
The area under the process characteristic curve, or AUC, is another evaluation metric used in classification problems. The ROC curve plots the TP ratio (TPR) against the FP ratio (FPR). The ROC curve has the TP ratio on the \(Y\)-axis and the FP ratio on the \(X\)-axis. These ratios are presented in Equation (7) and Equation (8), respectively:  
\[\text{TPR}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{7}\]  
\[\text{FPR}=\frac{\text{FP}}{\text{FP}+\text{TN}} \tag{8}\]
# Multi-Source Satellite Imagery and Machine Learning
## 5 Experiments and Results
### Combination of OLI Spectrometric Parameter Data with PCA-Derived Data (DPCAP-DOLIP)  
Several algorithms were used to evaluate the combination of OLI spectrometric parameter data with PCA-derived data. Table 9 presents the performances of these algorithms according to different criteria.  
The results show that Random Forest provides the best performance, with an accuracy of 45.05%, an MCC of 31.81%, and the lowest execution time (3.38 ms). Gradient Boosting follows with 41.29% accuracy and 27.12% MCC but significantly higher execution time (15.99 ms). XGBoost, while fast (5.61 ms), has a slightly lower accuracy (40.27%) and an MCC of 26.34%.  
These results demonstrate the Random Forest algorithm's superior performance and speed, highlighting the effectiveness of combining OLI spectrometric parameters and PCA data for classification tasks.
## 3 Literature Review
### Privacy  
Privacy concerns related to census data have become increasingly significant with the advancement of technology. In the past, the primary worry was the possibility of data leakage to enemy states, which could provide them with valuable information for planning attacks. For example, a cyber-attack on the Census Bureau exposed personnel data for approximately 4200 individuals, highlighting the need for stronger data protection measures [4].  
In more recent times, the concerns are similar but pertain to both the use and storage of census data. In the Australian census, there was a public outcry when the option to opt-out of retaining the original names of users was removed without sufficient consultation or prior knowledge [17]. These privacy concerns can lead to reluctance or even boycotts of the census.  
Centralized data storage also raises privacy concerns, as it makes data vulnerable to cyber-attacks. The rise in cybercrimes necessitates a more technologically advanced and secure approach to data maintenance, access, and storage to address privacy concerns among the population.  
In the past, privacy concerns have led to legal actions. In Germany, citizens sued the government over a 160-question long census questionnaire, raising concerns about the potential identification of individuals through the collected information. The ruling established a right to \"self-determination\" of the information shareable by citizens, in contrast to the surveillance behavior seen in Nazi-Germany [18].  
Data leakages can occur in four ways, as identified by [PERSON] and [PERSON] [19]. (i) Accidental leakage may result from human errors, oversight, or ignorance in handling data during the traditional census process. The number of individuals involved in the census process increases the risk of such leakages. (ii) The second form of leakage is driven by malicious intent, where involved parties seek unauthorized access to data for criminal purposes. Strict precautions, training, and penalties are necessary to prevent this type of leakage. (iii) The third form of leakage arises from legal obligations, such as court orders which require the disclosure of information publicly. Efficient means of validating personal information without disclosing actual data can help handle such circumstances. (iv) The last form of leakage is related to statistical necessity, where data are disclosed to researchers or the public for analysis and interpretation. While insights derived from data analysis do not violate privacy, disclosing actual identifiable data can raise concerns about data misuse and potential human errors leading to leakage.  
Addressing privacy concerns in census data collection requires robust security measures, data protection protocols, and strict compliance with privacy regulations. Furthermore, involving the public in discussions regarding data collection and ensuring transparency can build trust and confidence in the census process.
# Fast and Accurate Visual Tracking with Group Convolution and Pixel-Level Correlation
## 4 Experiments  
### Implementation Details  
The initial model of the backbone was derived from ResNet50 [4] trained on the COCO [29] dataset, a migration learning approach that is commonly used for network training today. We used the Lasot [30], Got10k [31], ImageNet VID [32], and YouTube Bounding Boxes [33] datasets as training sets. The search region was cropped to 255 \(\times\) 255, and the template region was cropped to 127 \(\times\) 27 for training. The initial learning rate was 0.001, and 20 training epochs were performed using stochastic gradient descent (SGD). In the first 5 epochs, the learning rate increased from 0.001 to 0.005, and in the last 15 epochs, it gradually decreased from 0.005 to 0.0005. Meanwhile, the parameters of the backbone network were frozen in the first 10 epochs, where only the neck and output parts were trained, and in the last 10 epochs, the parameters of the backbone network were unfrozen, and the network was trained as a whole. Finally, the model was tested and evaluated on the UAV123 [34] and OTB100 [35] datasets.  
Figure 8: Different connections between the correlation module and prediction sub-network: (**a**) separate correlation module connected to the classification and regression sub-networks; (**b**) use of a shared correlation module.  
Figure 7: The global to local information fused spatial attention block (GSA).
# Cheating Detection in Online Exams Using Deep Learning and Machine Learning
## 2 Materials and Methods
### Data Normalization  
In this study, min-max and standardization normalization techniques were used. These two normalization techniques vary depending on deep learning models. With the min-max normalization technique, the original data are transformed into the 0-1 range through a linear transformation.  
Standardization, also known as Z-score normalization, transforms the data by setting the standard deviation to 1 and the mean to 0. For values that deviate from the analysis,  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Attribute** & **Explanation** & **Ticket** \\ \hline \multirow{3}{*}{department} & \multirow{3}{*}{Departments in Higher Education Institutions} & 1: Physical Education, 2: Computer Programming, 3: Biomedical, 4: \\  & & Mechatronics, 5: Nursing, 6: Anesthesia, 7: Justice, 8: Banking and Insurance, 9: Veterinary Laboratory \\ \hline class & represents classes & 1: 1 st Class, 2: 2 nd Class \\ \hline gender & expresses gender & 0: Female, 1: Male \\ \hline Al,...,Al5 & Question answers & 0: Student’s wrong answer, 1: Student’s correct answer \\ \hline c,number & Total number of correct answers & Non-categorical value \\ \hline w,number & Total number of errors & Non-categorical value \\ \hline \multirow{3}{*}{supervision} & \multirow{3}{*}{Exam supervision status} & 0: synchronous environment with supervision, 1: unsupervised \\  & & asynchronous environment \\ \hline \multirow{3}{*}{exam,type} & \multirow{3}{*}{Exam type} & 0: real (midterm exam, final exam, etc.), 1: artificial (An exam prepared within the scope of the study and which does not affect their academic success.). Both types of exams contain the same questions. \\ \cline{1-1} R\_per & & The target variable defined by the researcher is the \\  & & sum of the values corresponding to the threshold \\  & & Class label in binary classes 0: “copy, 1: “did not cheat” and Class label in \\  & value determined by the experts. & triple classes 0: “cheat”, 1: “diec”, 2: “did not cheat”. \\ \hline total\_e,time & Total time spent in the exam. & Non-categorical value \\ \hline RT1,...,RT15 & The time taken per question while answering is in \\  & & seconds. \\ \hline \multirow{3}{*}{ques\_solution\_total\_time} & The time spent solving questions during the exam is \\  & & in seconds. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dataset properties.  
the Z-score outlier detection method was applied to the data. The Z-score indicates how many standard deviations a value is away from the mean. Typically, values where \(|z|>3\) are considered outliers. As a result of the analysis, 33 exam records identified as outliers were removed from the dataset. The final dataset consisted of 129 exam records.
## 2 Literature Review
Prior studies provide an excellent source of information for studying the passenger flow risk. Nevertheless, congestion in stations is extremely hard to mitigate because of congestion features such as randomness, diffusivity, and uncertainty. In the last decade, studies have used traffic road safety and time series models as an evaluation method for urban road traffic. However, few studies have focused on passenger safety in rail stations. The recent deep learning models present high performance; however, the modelling has been performed by computer and information technologies rather than the crowd risk and management analysis. Moreover, they often share parallel structures and concepts. Passenger flow has not been completely addressed or is often overlooked, and studies are more focused on vehicles and city congestion. Analysis of the flow will provide knowledge for designing future stations and correcting any deficiency in trains or facilities provided to passengers. This would support the effective planning of essential services, such as transfer services at railway terminals. Many factors must be considered in such cases, including walking speed, crowd density, free-flow speed, and risk level. However, there is still a great need for extensive studies from the perspective of safety and risk. Some of the aspects and methodologies that should be considered while studying flow prediction are listed as follows:  
* The risk and consequences of the unwanted events (crowd) must be considered, such as stamping and falling or panicking. * Real data is essential and will reduce error compared to simulation data. * Time series models usually neglect many factors and specific locations, such as the platforms in railway stations. * Appling AI to existing systems in railway stations, such as closed-circuit television CCTV, will be beneficial and cost efficient to operators for timely flow prediction. * Certain passenger categories, such as the disabled, families, and the elderly, need to be considered to make stations accessible to all. In this study, we propose a method that utilises data from many digital sources in a station. In fact, a large volume of data is collected daily in railway stations, e.g., smart card data, smart phones applications, sensors, Bluetooth, and video surveillance [63; 64; 65; 66; 67]. To monitor the crowd, it is necessary to locate and follow passengers and members of the public whilst they are in the station. This requires extracting crowd information [19] and making a robust prediction of the passenger flow to support the decision maker and assist in managing the operations in the stations. The crowd in the station is related to many characteristics: health and safety, security, ridership, service quality and satisfaction, cost and maintenance, station operation, dwell time, risk assessment, and passenger preferences and travel decisions.
# Dynamic Scheduling and Optimization of AGV in Factory Logistics Systems Based on Digital Twin
## 6 AMODS Method for Complex Discrete Logistics System
### The Process of AMODS Method  
The AMODS method based on the digital twin is shown in Figure 6. The MES system exchanges data with the digital twin of the logistics system, and the data can be recorded and displayed through the visualization platform. Customers send orders through the APP(application). When receiving the new order, the virtual twin production line generates a preliminary scheduling plan according to the original scheduling strategy in the historical knowledge database and feeds it back to the real transportation production line. When the real production line executes the preliminary scheduling plan, the data acquisition center and the decision control center also synchronously send real-time data to the virtual twin production line. When there are multiple dynamic tasks and empty AGVs in the logistics system, it is necessary to determine the routing of the tasks and AGVs. Therefore, the pending orders and transportation loading and unloading signals in the virtual twin production line are dynamically written into the dynamic task list. According to the scheduling of the dynamic task list, empty AGV is scheduled first, and then, based on the principle of zero conflict and shortest path, AGV transportation path planning is carried out,and the optimized scheduling plan is fed back to the real transportation production line. If the scheduling plan does not meet the requirements, the intelligent algorithm scheduling needs to be triggered again.