esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Influence of Unmeasurable Load Branches on Protection and Countermeasures
### Influence of Unmeasurable Load Branches on Protection
#### 4.1.3. Faults Occur outside a Protected Feeder and Fault Point Is Located Downstream of ULB  
When a fault occurs outside the feeder \(MN\), fault point \(f_{3}\) is located downstream of the ULB, and its composite sequence network of the positive-sequence fault component is shown in Figure 9.  
In Figure 9, \(y\) is the ratio value of the distance from the ULB to the bus \(M\) to the feeder \(MN\)'s total length. The relationship between \(\Delta\dot{I}_{\text{m}}\) and \(\Delta\dot{I}_{\text{n}}\) can be obtained as  
\[\Delta\dot{I}_{\text{m}}=-\frac{Z_{\text{b}}}{Z_{\text{m}}+yZ_{\text{mn}}+Z_{ \text{b}}}\Delta\dot{I}_{\text{n}} \tag{14}\]  
From Equation (14), due to \(Z_{\text{m}}<<Z_{\text{b}}\), \(Z_{\text{mn}}<<Z_{\text{b}}\), it can be deduced that \(\varepsilon\approx 1\), so the influence of the ULB upstream of the fault point on the protection can be ignored when the external fault occurs.  
1.4. Faults Occur outside Protected Feeder and Fault Point out of Operation during Faults Test Is Located Upstream of ULB  
When a fault occurs outside the feeder \(MN\), fault point \(f_{2}\) is located upstream of the ULB, and its composite sequence network of the positive-sequence fault component is shown in Figure 10.  
Figure 10. Composite sequence network of the positive-sequence fault component: the UBL is upstream of the fault point in case of an external fault.  
Figure 9. Composite sequence network of the positive-sequence fault component: the UBL is downstream of the fault point in case of an external fault.  
From Figure 10, the relationship between \(\Delta\dot{I}_{\rm m}\) and \(\Delta\dot{I}_{\rm n}\) can be obtained as  
\[\Delta\dot{I}_{\rm n}=-\frac{Z_{\rm b}}{Z_{\rm L}+(1-y)Z_{\rm mn}+Z_{\rm b}} \Delta\dot{I}_{\rm m} \tag{15}\]  
Due to \(Z_{\rm m}<<Z_{\rm b}\), \(Z_{\rm mn}<<Z_{\rm b}\), Equation (15) can be simplified as  
\[\varepsilon=\frac{\left|\Delta\dot{I}_{\rm m}\right|}{\left|\Delta\dot{I}_{ \rm n}\right|}=1+\frac{Z_{\rm L}}{Z_{\rm b}} \tag{16}\]  
From Equation (16), due to the uncertainty of \(Z_{\rm b}\) and \(Z_{\rm L}\), \(\varepsilon\) may be larger. When \(\varepsilon\) is greater than \(1.5\), \(g(\varepsilon)=1\), and the proposed protection will compensate for the phase of the measured current on the DG side of the feeder, it will malfunction, making the protection less reliable.  
In summary, for the feeder section with the ULB, when a fault occurs outside the feeder section, the ULB located downstream of the fault point may cause the protection device to malfunction in this feeder section. Therefore, it is necessary to establish corresponding auxiliary criteria to improve the reliability of protection.
# Range-Free Localization Approaches Based on Intelligent Swarm Optimization for Internet of Things
## 3 Proposed Localization Algorithms
### Motivation Behind Using Intelligent Swarm Optimization  
DV-Hop is known to suffer from localization errors caused by uncertainty in distance estimation, particularly as relates to the minimum hop count and average hop size. This leads to significant deviations between estimated and actual distances that greatly affect its localization precision. Localization in WSNs is a challenging NP-hard optimization problem. Nature-inspired approaches have emerged as suitable solutions for this problem, with swarm intelligence optimizations being particularly promising thanks to their adaptability, simplicity, self-organizing capacity, decentralization, and robustness. These algorithms draw inspiration from social behaviors observed in animal communities such as ants, fish, birds, and even chickens. Swarm intelligence optimization encompasses various approaches, including the PSO, Ant Colony Optimization, Chicken Swarm Optimization, and Artificial Bee Colony algorithms [28; 45; 46; 47]. Recent research comparisons indicate that CSO and PSO are among the most effective optimization algorithms for localization problems in WSNs, offering numerous advantages over other techniques. For instance, PSO is advantageous in its utilization of minimal parameters to adjust the particle population, resulting in simplified parameter tuning. Moreover, it exhibits low spatial complexity thanks to its efficient utilization of small temporary storage to minimize memory requirements. The fast convergence speed of PSO is attributed to its selective sharing of solutions, where only the most optimistic particle can influence others. This enhances the algorithm's ability to rapidly converge towards optimal solutions. Furthermore, as demonstrated by its successful use in tackling an extensive variety of problem areas, PSO has excellent adaptability. Lastly, PSO offers an intuitive approach to interpreting and adapting solutions to specific problem contexts, facilitating ease of implementation and customization.  
The aim of this paper is to improve the localization accuracy of algorithms based on chicken swarm optimization while achieving faster convergence. Our goal is to refine the positions of nodes within a specific area. To achieve this, we introduce extensions to the existing DV-Hop technique, which we call W-DV-Hop and HW-DV-Hop. Thesemodifications incorporate biologically inspired optimization techniques by integrating CSO into the localization process. The process of localization consists of four key stages: (i) broadcasting and minimal hop computation; (ii) determining the average hop distance; (iii) localization for estimating the positions of unknown nodes; and (iv) CSO integration for determining the optimal positions of unknown nodes. Through this work, we endeavor to demonstrate the efficacy of leveraging CSO to improve the localization process and achieve improved accuracy in locating unknown sensor positions within WSNs.
# A Heart Rate Variability-Based Paroxysmal Atrial Fibrillation Prediction System
###### Abstract
In ECG-based methods, the detection of features such as P-wave morphology and P-wave variability is essential for premature atrial complexes (PAC) detection [8; 9]. However, ECG monitoring devices that provide accurate signals for such features are hardly accessible outside health centers. Although ECG signals can be acquired from some wearable devices, they have not met the medical-grade demands. On the other hand, HRV has become a widely used signal to diagnose/predict several cardiac diseases because it can be more easily extracted from ECG through successive QRS complexes than those features through P waves. In addition, HRV signals have been found to provide useful clues about the underlying cardiac diseases [10; 11; 12; 13; 14], not to mention that they require much less computation and storage than ECG. A feature-classification approach is commonly adopted in HRV-based methods. Features representing subtle changes in an HRV signal that are associated with the underlying cardiac disease are computed and then sent to a classifier for state determination. These features are usually derived from the time, frequency, and state-space domains [6; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. Some approaches that used a combination of features [11; 12] in different domains may require a considerable amount of computations and, hence, are not suitable for quick diagnosis. Moreover, previous approaches seldom considered inter- and intra-person differences. However, features such as absolute and relative heart rate variations may both contribute to the disease onset, perhaps in different facets. Recently, deep learning models have been widely applied to various fields, including computer vision [28], speech recognition [29], and natural language processing [30], where their performances are comparable to, and in some cases surpass, human experts. One of the most popular models is the convolutional neural network (CNN), which has appealing performances, particularly in image classification. It outperforms many conventional classifiers, such as artificial neural network (ANN), k-nearest neighbors (KNN), and support vector machine (SVM), in medical image analysis [31]. Since we converted an HRV sequence into multiple 2-dimensional matrices to reveal useful features, CNN was adopted in our system for feature selection and classification. In this work, we developed a PAF prediction system that achieved accurate prediction with 400-point (\(\sim\)5 min) HRV sequences. Intra- as well as inter-person features that are easy to compute and interpret were proposed. The developed system was trained, validated, and tested using three datasets excerpted from PhysioNet [32]. The system performance and some medical implications derived from the proposed features are discussed herein.
# Smart Antenna for Application in UAVs
## 2 State of the Art  
### UAV Contextualization  
Unmanned vehicles (UVs) are used on all surface types (water, air, and land) [3; 4]. The following illustration represents the classification of UVs, divided by action.  
The UAVs used for military applications present greater robustness and reliability than a UAV used for civil applications. Factors such as weight, size, volume and input power are important in the construction of a UAV [4].  
One of the many applications of UAVs is in Electronic Warfare (EW). Electronic warfare can be defined as the set of actions that use electromagnetic energy to neutralize the opponent's command and control ability, take advantage of the use of the electromagnetic spectrum by the enemy, and ensure the efficient use of electromagnetic emissions of friendly forces [5].  
Regarding EW, it has easily found utility in UAVs. The aircrafts allow for an approach to enemy forces and interference in their entire communications network. The objective is to use UAVs to replace traditional vehicles, improving interference efficiency, reducing costs, and increasing the speed and safety of their own forces [5].  
With the existence of several applications, it has become necessary to study the communication system of a UAV to increase the range and improve the efficiency of the connection (Figure 1).
# Reducing Videoconferencing Fatigue through Facial Emotion Recognition
## 6 Limitations  
While this project provides interesting insights, much further work is needed. In particular, we are not making a claim with respect to causality. Rather, we take triggering a wider range of emotional expressions of the audience as an indicator of a successful presentation. In order to solidly claim a causal link between triggering a broad range of emotions to produce a great presentation, we would need to conduct controlled experiments, for instance comparing presentations triggering fear by showing horror movie snippets with presentations including humor parts. Nevertheless, there are results from other areas, such as marketing and advertisement, where it has been found that emotional advertisements are more successful [45], suggesting that there might indeed be a causal link between experienced emotionality of the audience and presentation quality.  
An additional limitation is the low number of measurements which number to 41. In an ideal world, the experiments should be repeated with a larger N to gain more statistical significance. Furthermore, our training data set for the CNN was unbalanced as there were ten times more happy and neutral faces in the dataset than fearful faces. However, as shown in the confusion matrices in the results section, this did not negatively impact our accuracy. Additionally, it is well known that facial recognition systems, which are not restricted to emotion recognition, are biased towards Caucasian males [46] and discriminate against females and non-Caucasians. Finally, it is also worth mentioning that video presentations are restricted in communicating emotions since there are only the contents of the PowerPoint slides and the voice and face of the presenter in the little \"talking head\" to get emotionality across to the audience. In real-classroom scenarios, the body language of the presenter and other interaction channels and environmental cues, such as smell, convey a much richer emotional experience for the audience; this introduces other factors that influence the perceived quality of a great presentation.
## 1 Introduction
While several studies [7; 14; 15; 16; 17] have explored the application of FSM for sizing web applications and estimating development efforts through COSMIC, a significant gap remains in the literature regarding the applicability of functional size measurement to web analytics implementations. To date, no research has specifically addressed the application of a measurement framework for web analytics implementations, nor has any study proposed the use of the COSMIC method to quantify such implementations. Web analytics implementations can be complex, particularly because of custom user requirements, and when there is no use of a standardized measurement method for web analytics implementation, practitioners are left to rely mostly on highly subjective opinions, which is a very weak and unreliable basis for effort estimation and project planning and monitoring. The objective of this study is to fill this gap by introducing a COSMIC-based measurement framework tailored specifically for measuring the functional size of Google Analytics implementations and its use in development effort estimation. The proposed framework is designed to equip project managers and web analytics practitioners with a reliable and precise tool for quantifying the size of Google Analytics implementation. This measurement serves as a foundation for more accurate time and budget estimates for implementing Google Analytics in various projects. The structure of this paper is as follows: Section 2 presents a background on the technical fundamentals of web analytics and related work; Section 3 presents the development of COSMIC measurement for web analytics implementation; Section 4 presents two examples of the application of COSMIC measurement on web analytics, followed by an empirical study in Section 5, a discussion in Section 6, and the conclusion in Section 7.
# Fuzzy-Based Multi-Modal Query-Forwarding in Mini-Datacenters
## 4 Modeling the Query Flow Within the Mini-Datacenter
### Modeling Fine-Grain Forwarding
#### 4.2.2 Modeling Reliability  
We define the reliability (_Re_) of query-forwarding in the mini-datacenter as the probability that the query-forwarding has been performed successfully from CC to sensors through the gateways, which depends on the number of fitted gateways \(\left|G_{fitted}(t)\right|_{t}\) redundancy \(R(t)\), and the probability of individual gateway reliability. We assume a reliability of \(P\) for a query flow with a single gateway availability, where \(P\) is the probability that a single gateway works successfully. We define reliability as in Equation (6), where it is varied between 0 and 1. Here, the term \(G_{fitted}(t)\) represents the fitted gateways enumerated using Equations (2) and (3). If no fitted gateways are available, query-forwarding becomes entirely unreliable (Re = 0), and no queries are forwarded. If there is only one fitted gateway, the reliability is calculated as \(P\), and if there are more than one fitted gateway, the failure probability of each gateway is raised to the power of the redundancy. If there are \(n\) gateways available, each with individual reliability \(P\), the system fails only if all gateways fail. As the redundancy (\(R\)) increases, the term \((1-P)^{R}\) becomes smaller, thus the overall reliability (_Re_) approaches 1 (the maximum reliability).  
\[\begin{array}{ccc}0&if\ \left|G_{fitted}\right|=0\\ Re=&P&if\ \left|G_{fitted}\right|=1\\ 1-(1-P)^{R}&if\ \left|G_{fitted}\right|>1\end{array} \tag{6}\]
## 5 Discussion  
The proposed optimal feature models have a lesser number of features compared to our baseline model and outperformed the performance of our baseline model which had a F1-score of 0.871. Table 5 depicts quantitatively the improvement made by our research when compared with the baseline model.  
From Table 5, we can see the accuracy and F1-score increment in our optimal feature models when compared to the baseline model. This experimentation found that the combination of a random forest for feature selection and a support vector machine for training our model on those selected features provides the highest accuracy and F1-score increment. Moreover, it was found that the optimal number of features for our problem was 30 features as increment or decrement in feature count reduced the accuracy of our models. The results discussed above have some major overlapping drawbacks as the model  
Figure 8: F1-score comparison of optimal feature set model and baseline model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **(\%) Accuracy** & **F1-Score** & \begin{tabular}{c} **Accuracy** \\ **Increment** \\ \end{tabular} & \
\begin{tabular}{c} **F1-Score** \\ **Increment** \\ \end{tabular} &
\begin{tabular}{c} **Features Used** \\ **(\%)** \\ \end{tabular} \\ \hline LR Baseline & & & & & \\ (121 Features) & 87.20 & 0.871 & - & - & - \\ AB + SVM & 98.87 & 0.988 & 11.664 & 0.117 & 49.58 \\ RF + SVM & **99.08** & **0.990** & **11.874** & **0.119** & 24.79 \\ GB + SVM & 98.77 & 0.987 & 11.564 & 0.116 & **16.52** \\ (20 Features) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of optimal feature set models with baseline model.  
is not fully capable of identifying zero-day attack, the performance of the model is also very dependent on the train-test data distribution and how the hyperparameters are tuned.  
Pertaining to these results, it can be inferred that integrating artificial intelligence into intrusion detection systems (IDSs) will largely help industry and organisations, as they will not have to depend on rule-based systems to identify potential threats to the system, and any new signatures classified as threats may be added to the database for future analysis and detection. This will automate the workflow of identifying threats and reduce the manual intervention required to perform such detection, which is also prone to manual error at times.
# A Cache Policy Based on Request Association Analysis for Reliable NAND-Based Storage Systems
## 4 A Cache Policy Based on Request Association Analysis for Reliable NAND-Based Storage Systems
### Request Association Cache
#### 4.3.1 Strong/Weak Request Data Management  
When a request will enter the cache from the host, we will first compare whether the request's starting LPN is in the strong request list. If the request's starting LPN is in the strong request list, it is a strong request and its content can be considered as the strong request data; otherwise, the request's starting LPN is in the weak request list and its content is considered as the weak request data. Note that the strong and weak request lists are according to the results of request association pairs management and we use the starting LPN of each request to decide the strong or weak request. If the starting LPN is strong, the entire content contained in the request will be seen as the strong request data. If the starting LPN is weak, the entire content contained in the request will be seen as the weak request data. Therefore, we can modify the original ARC design to consider the strong and weak requests to improve the cache hit ratio, as shown in Figure 8. The proposed cache policy consists of two LRU lists which are a weak request data list (WL1) and a strong request data list (SL2). WL1 can be divided to two parts: WT1 and WB1, where WT1 stores the content (i.e., weak request data) and WB1 (like a ghost buffer) only stores the evicted information from WT1, not to store the content. SL2 can be also divided to two parts: ST2 and SB2, where ST2 stores the content (i.e., strong request data) and SB2 (like a ghost buffer) only stores the evicted information from ST2, not to store the content. As the total size of WT1 and ST2 is the same as the cache, if the size of WT1 is decreased, the size of ST2 will be increased, and vice versa.  
Figure 8: The proposed cache policy.  
Figure 7: Strong/weak request lists.  
When any new weak/strong request data will be input to WT1/ST2, if WT1/ST2 is full, the (LRU) weak/strong request data will be removed from WT1/ST2 and its related evicted information will be checked for existence in WB1/SB2, respectively. If the evicted information exists in WB1/SB2 (i.e., a hit in WB1/a hit in SB2), it means that the removed weak/strong request data could be a miss in WT1/ST2 so that the size of WT1/ST2 is increased by 1; otherwise, the evicted information will be insert to WB1/SB2, respectively. If WB1/SB2 is full, the LRU request information can be removed from WB1/SB2, respectively. There are two differences from the original ARC design. The first difference is that we distinguish between WL1 and SL2 based on the strong and weak request data management, not based on one or more occurrences like the original ARC design. The second difference is that the length of WL1 and SL2 is fixed because we can dynamically adjust the threshold to accurately separate the strong and weak request data according to the current workload. Note that how to dynamically adjust the threshold will be described in the following section.
## 3 Results and Discussion
### Confirmatory Analysis Results
#### 3.2.3 Ruggedness  
As described above for the immunoenzymatic analysis, the HPLC/FLD method's ruggedness was evaluated by using the Youden experimental design under conditions of minor changes [23]. Youden tests were performed for maize and barley samples spiked at 500 ug kg\({}^{-1}\) of the T-2 and HT-2 toxins. An analysis of samples gave a calculated standard deviation of difference (SDi) of 43 ug kg\({}^{-1}\) for T-2 and 45 ug kg\({}^{-1}\) for HT-2. At a confidence level of \(p\) = 0.05, SDi values were not significantly different from the reproducibility deviation (S\({}_{\mathrm{R}}\)) registered at the same level of fortification (46 for T-2 and 41 for HT-2). Consequently, method ruggedness was demonstrated (Supplementary Tables S1 and S2).  
Figure 2: Chromatogram examples: T-2 and HT-at concentration of 500 ug L\({}^{-1}\) (**A**); corn “blank” sample with no residues of T-2 and HT-2 toxins (**B**); corn samples fortified with 250 ug kg\({}^{-1}\) of T-2 and HT-2 toxins (**C**); and corn sample naturally contaminated: 315.9 ug kg\({}^{-1}\) of HT-2 toxin (**D**).