esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analytical Description of an Episode
### Patient Arri  
Emergency departments have to deal with unknown and unbalanced demand. Knowing the demand's distribution along every day of the week and the respective Manchester's priorities represent a key factor for planning purposes [25]. Moreover, having an overview of the behavior of arrivals to the emergency department allows for better resource planning and preventive preparation of the system [16].  
The hospital monitors the emergency episode for every patient by registering the times when changes occur in the episode state. For investigating the patient arrival distribution, it is only necessary to know the time when each patient was admitted to the emergency department, which is the first record in the hospital database. Therefore, the authors had access to the 2018 database which contained 92,978 entries referencing 0.2% red priorities, 8.2% orange priorities, 46.9% yellow priorities, 41.2% green priorities and 3.5% blue priorities, as can be seen by the pie chart presented in Figure 5.  
Orange, yellow and green priorities are the most common patient type that arrives in the emergency department. Since green and blue patients are not usually considered urgent episodes, they are treated together in the same area, and so treated as only one type of patient medical state in this study. Red patients (for brevity, the patient priority state will be referred by the color) have a distinct flow inside the emergency departments not being considered in the study here presented.  
Despite the unknown nature of the arrivals, it is important to describe the arrival pattern per priority (based on historic records) in order to identify how the demand is distributed throughout the day. For that purpose, the arrival pattern was determined for each hour of every weekday per priority, as shown in Figure 6.  
Figure 5: Pie chart representing the distribution of entries per priority assigned in the triage process.  
Figure 6: Average arrival rates for every hour per priority for: (**a**) Monday; (**b**) Tuesday; (**c**) Wednesday; (**d**) Thursday; (**e**) Friday; (**f**) Saturday; (**g**) Sunday.  
Discriminating the emergency department demand patterns is important to identify several characteristics: the demand at night shift is considerably lower in comparison to the rest of the day; the morphology of the arrival pattern of yellow and green priorities is identical. As can be seen, two peaks occur during the day, one generally between 08:00 and 10:00 and another between 14:00 and 16:00 (for yellow and green). Orange patients are considered very urgent episodes with severe clinical states which converts to far fewer entries, and, although there are also two peaks, their magnitude is smoother than in the yellow and green pattern. The demand during the weekdays is considerable higher in comparison to the weekend, highlighting Monday as the most demanded day. This study analytically proved an idea shared within the staff members who mentioned Monday as the worst day in terms of performance.
# Fault Estimation for Semi-Markov Jump Neural Networks Based on the Extended State Method
## 5 Conclusions  
This paper investigated the dissipative fault estimation problem for discrete-time semi-Markov jump neural networks (s-MJNNs) under the influence of actuator and sensor faults, incorporating the implementation of a round-robin protocol (RRP). To accurately estimate the system states and faults, an extended state observer was developed. By employing the RRP, network congestion was effectively mitigated, and active sensors were dynamically scheduled at each sampling instance, optimizing system resource utilization. A series of sufficient conditions were derived, and the estimator gain parameters were designed to ensure that the error system achieves mean square exponential stability (MSES) and strict \((\chi_{1},\chi_{2},\chi_{3})-\varpi\)  dissipative performance. The results of the numerical simulations further validate the effectiveness and feasibility of the proposed method.  
Despite its demonstrated theoretical and simulation-based advantages, there remain several open questions for further exploration. For instance, building upon the proposed fault estimation framework to achieve fault-tolerant control that reduces conservatism and enhances robustness remains a critical area of future research. Additionally, extending this approach to more complex network systems and integrating advanced communication protocols with real-time control strategies will provide new opportunities to enhance its practical engineering applicability.  
Conceptualization, L.R.; methodology and software, L.R. and Y.P.; validation, Y.P. and L.R.; formal analysis, L.R.; investigation and resources, Z.T.; data curation, Y.P.; writing--review and editing, L.R.; visualization supervision, project administration and funding acquisition, L.R. and Z.T. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Qingdao Agricultural University Doctoral Start-Up Funds (1122031, 1121005), the Talent Training Project of Heilongjiang Bayi Agricultural University (XDB201815), the Supported by the Open Project Program of Key Laboratory of Smart Agricultural Technology in Tropical South China, Ministry of Agriculture and Rural Affairs, P. R. China (HNZHNY-KFKT-202206), the 2023 Shandong Province Science and Technology based Small and Medium sized Enterprises Innovation Capability Enhancement Project (2023 TSGC0741). and the National Natural Science Foundation of China (32073029).  
Not applicable.  
The research presented in this article did not involve the use of any specific data or datasets.  
The authors express their sincere gratitude to the editors and reviewers for their insightful suggestions and constructive feedback, which have significantly enhanced the quality and clarity of this manuscript.  
The authors declare no conflicts of interest.
# Towards the Detection of GPS Spoofing Attacks against Drones by Analyzing Camera's Video Stream
###### Abstract  
A Global Positioning System (GPS) spoofing attack can be launched against any commercial GPS sensor in order to interfere with its navigation capabilities. These sensors are installed in a variety of devices and vehicles (e.g., cars, planes, cell phones, ships, UAVs, and more). In this study, we focus on micro UAVs (drones) for several reasons: (1) they are small and inexpensive, (2) they rely on a built-in camera, (3) they use GPS sensors, and (4) it is difficult to add external components to micro UAVs. We propose an innovative method, based on the video stream captured by a drone's camera, for the real-time detection of GPS spoofing attacks targeting drones. The proposed method collects frames from the video stream and their location (GPS coordinates); by calculating the correlation between each frame, our method can detect GPS spoofing attacks on drones. We first analyze the performance of the suggested method in a controlled environment by conducting experiments on a flight simulator that we developed. Then, we analyze its performance in the real world using a DJI drone. Our method can provide different levels of security against GPS spoofing attacks, depending on the detection interval required; for example, it can provide a high level of security to a drone flying at altitudes of 50-100 m over an urban area at an average speed of 4 km/h in conditions of low ambient light; in this scenario, the proposed method can provide a level of security that detects any GPS spoofing attack in which the spoofed location is a distance of 1-4 m (an average of 2.5 m) from the real location.  
drones; GPS spoofing countermeasures +
Footnote †: journal: Sensors
## 3 General Opportunities, Challenges, and Applications
### Opportunities
#### 3.1.5 Decentralization, Robustness, and Optimized Network Traffic  
Unlike centralized systems, FL's decentralized nature improves resilience by eliminating single points of failure. Despite failures in individual nodes, the system remains functioning, demonstrating resilience [25; 26]. FL distinguishes itself in a regulatory context centered on data protection by guaranteeing compliance without sacrificing data-driven insights. Its emphasis on data localization enables organizations to function within stringent regulatory constraints. FL optimizes network traffic by transmitting model updates instead of substantial raw data, ensuring effective bandwidth use and decreasing the latency [5]. The practical impact of FL across diverse sectors, from healthcare to smart cities, underscores its transformative role in data analytics.
# Efficient End-to-End Sentence-Level Lipreading with Temporal Convolutional Networks
## 5 Experiment
### Convergence Speed  
What we should emphasize is that replacing the recurrent neural network (e.g., LSTM, GRU) with a Temporal Convolutional Network (TCN) [36] is for adequate training, speeding up the convergence speed, and preventing the disappearance of the gradient. We implemented a comparison of four different architectures to illustrate the role of TCN in serialization learning, such as lipreading, machine translation, and so on. The four different architectures are 3D-2D-CNN-BGRU-CTC, 3D-ResNet50-BGRU-CTC, 3D-2D-CNN-TCN-CTC, and 3D-ResNet50-TCN-CTC. Figure 6 shows the diagrams of ablation experiments with different architectures.  
We trained 45 epochs for each architecture, and calculate its training and evaluate loss under one epoch for each iteration. To describe the differences more vividly, we use curve graphs to represent these data, as shown in Figures 7 and 8. From the figure, it can be seen that the losses of the four architectures are steadily decreasing until they approach a fixed value. Comparing the 3D-2D-CNN-BGRU-CTC and 3D-ResNet50-BGRU-CTC architectures, the 3D-2D-CNN-TCN-CTC and 3D-ResNet50-TCN-CTC architectures converge faster, there is no vanishing gradient, and minimal losses can be achieved. Comparing the 3D-2D-CNN-BGRU-CTC and 3D-2D-CNN-TCN-CTC architectures, the 3D-ResNet50  
Figure 6: Diagrams of ablation experiments with different architectures. (**a**) 3D-2D-CNN-BGRU-CTC, (**b**) 3D-ResNet50-BGRU-CTC, (**c**) 3D-2D-CNN-TCN-CTC, (**d**) 3D-ResNet50-TCN-CTC.  
BGRU-CTC and 3D-ResNet50-TCN-CTC architectures use efficient feature extractors, so the final loss is relatively small, and the accuracy rate is relatively improved.  
The above experiments prove that the proposed architecture has back propagation paths in different sequence time directions, thereby avoiding gradient explosion/ disappearance in RNNs (such as LSTM, GRU). In addition, the use of an efficient feature extractor combining 3D and ResNet50 [37] convolutional networks has improved the performance of our architecture.
## 4 Results from Case Studies
### Validation of Synchrophasor Applications Using Field Measurements
#### 4.2.2 Results from Synchrophasor Disturbance Extraction
In all cases, LOF, COP and SiLiOd mainly agree when detecting far-away outliers (data points with high distance to center), but disagree when detecting near-by outliers (data points with medium or low distance to center). Due to their low distances to neighboring data points, near-by outliers are more difficult to detect and to distinguish from the majority signal behavior. As stated in Section 3.3, the outlier scores of the different base detectors are aggregated via rank transformation and passed to the clustering stage. For a better illustration, Figure 15 shows the rank transformed _top-10 outliers_ for all base detectors in the time and frequency domain. Low outlier ranks correspond to high outlier scores and vice versa. A high agreement among all base detectors and domains can be observed for the samples 8, 9, 97 and 98 indicating a high outlier degree and a potential disturbance. These samples are assigned with low total rank values. Other samples like 43, 65 or 85 are only detected by a minority of the base detectors which results in a lower certainty of the results and higher total rank values. Figure 16 gives some exemplary results for disturbed patterns (low total ranks) and undisturbed patterns (high total ranks). In case of the disturbed signal patterns, sample 6 corresponds to a voltage sag and sample 10 corresponds to a voltage oscillation. From Figure 15, sample 10 with the voltage oscillation shows low total ranks among all base detectors in the time- and frequency-domain. In contrast to that, sample 6 with the voltage sag can only be detected by a few base detectors in the time-domain resulting in high total ranks. This maybe a result of the short time span of the disturbance compared to the window size. The necessary calculations are performed in Matlab(r) with an additional open-source implementation for the S transform [63; 64] and a JAVA(r) based library for Isometric mapping [65]. Figure 14: Outlier scores for different combinations of outlier detection techniques (LOF at **top and bottom left**, SiLiOd at **top right** and COP at **bottom right**) and dimension reduction techniques (PCA at **top left and right**, Isomap at **bottom left and right**) using features from time-domain. The outlier detection methods were taken from the JAVA(r) based ELKI (Environment for Developing KDD-Applications Supported by Index-Structures) data mining framework [66].
# A Study of Cislunar-Based Small Satellite Constellations with Sustainable Autonomy
## 4 Cislunar Dynamics (Constellation Scenario)
### Constellation Modelling  
The constellations can be designed with a variety of mathematical models according to the mission objectives and the configurational capabilities of the satellites. In this research, a leap has been taken towards building the hybrid form of constellation designs to reduce the number of operational small satellites and enhance their performance and longevity. Similar to the Drain orbit concept defined in [27] from this research, the optimal hybrid constellation design is proposed, defined, designed, studied, and simulated for analysis for this paper. The main objective here is to develop a seamless, cost-effective, and safe environment for missions to operate and deliver the tasks as required utilizing the Cislunar region optimally for long-term sustenance.  
Some portion of their dynamics with a combination of flower and walker constellation is given in the form of equations below.
## 3 CB-Tree: A B-Tree Employing Cascade Memory Nodes  
As mentioned in Section 2.2, the performance degradation is inevitable when directly constructing the B-tree index on flash storage devices. To address this problem, buffer-based B-tree approach (Section 2.3) and structure-modified B-tree approach (Section 2.4) have been proposed. They have their own advantages and disadvantages as mentioned in Sections 2.3 and 2.4. The key idea of CB-tree employs only the advantages of each approach. That is, it yields good performance with small memory resources and also guarantees high reliability by storing all updated records into the flash memory.  
In this section, we present a novel B-tree index structure, which is called CB-tree, improving sequential writes with cascade memory nodes. The design goals of CB-tree are as follows. The first goal is to quickly create the index structure with small memory resources. The second goal is to quickly find a record without visiting extra area irrelevant to the B-tree nodes. CB-tree improves the write throughput by employing the cascade memory node to keep inserted or deleted records in the main memory and later apply them into the flash memory in a batch process. Additionally, it reduces the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Buffer-Based B-Trees** & **Structure-Modified B-Trees** \\ \hline Performance & High & Low \\ Reliability & Low & High \\ Memory Usage & High & Low \\ \hline \hline \end{tabular}
\end{table}
Table 1: Buffer-based B-Trees vs. Structure-modified B-Trees.  
number of write operations by not splitting the leaf nodes when records are sequentially inserted in continuous key order.
## 7 Conclusions  
The limitations of existing research on highway passenger volume prediction are overcome in this paper. The main conclusions of this paper are as follows:  
1. A two-stage method is proposed. In the first stage, the significant influencing factors are extracted. In the second stage, the BP neural network model is developed to realize the prediction, and the data set of the past 30 years is used to predict the highway passenger volume.
2. In the first stage, a data set including the four important influencing factors of GDP, population, private car ownership, and air passenger volume is extracted by using a multilayer perceptron, which proves that they are the significant influencing factors of road passenger volume.
3. In the second stage, the BP neural network is developed, and the percentage of the predicted road passenger volume is 62.82%, 73.94%, and 72.97% lower than the actual road passenger volume. The results show that there are many reasons for this phenomenon, but the most important one is the impact of the outbreak of China's epidemic in 2019 on China's road passenger traffic.  
To sum up, the research on highway passenger traffic volume based on neural network regression analysis and prediction provides an accurate and reliable prediction tool for traffic management decision making. Through the use of previous data and related influencing factors, establishing a suitable prediction model can provide a reference for traffic planning and resource management, and improve the efficiency and safety of highway passenger traffic. Therefore, this research has important application value and significance in practice. Because some industries in the statistical yearbook only publish annual statistical data, the highway passenger volume of each quarter or several months cannot be analyzed in detail. In addition to this, the data mutation caused by the change of statistical standards in the statistical yearbook affects the accuracy of the prediction. Therefore, in order to conduct a more accurate analysis in the future, we can consider introducing other new data sets in the study.
# Resource Management and Secure Data Exchange for Mobile Sensors Using Ethereum Blockchain
## 5 Results  
The previous section discussed the methodology adopted for implementing the proposed study agenda. The proposed method targets cost-effective resource optimization for sensory devices operating in a mobility-based environment. Furthermore, the Ethereum blockchain was introduced to secure the participation of sensors and safeguard the transacted information among mobile sensors. Unlike existing blockchain approaches, the proposed model retains an equilibrium between the security and communication of the sensor devices in a mobile environment. Hence, a well-planned assessment strategy is required to test the effectiveness of the proposed model. This section comprehensively discusses the assessment environment adopted and the results.
# Optimization of Active Distribution Network Operation with SOP Considering Reverse Power Flow
## 3 Actual Maximum Transfer Capacity of Reverse Power Flow in the Distribution Network
### Constraints
#### 4.3.3 Reactive Power Compensation Constraint  
\[Q_{m,CB,i,t}=N_{m,CB,i,t}\cdot q_{m,CBs,i} \tag{48}\]  
\[\sum_{t=1}^{24}\lvert N_{m,CBs,i,t}-N_{m,CB,i,t-1}\rvert\leq\Delta CB_{m,i,max} \tag{49}\]  
\[0\leq N_{m,CB,i,t}\leq N_{m,i,max} \tag{50}\]  
In the distribution network \(m\), \(q_{m,CB,i}\) denotes the capacitance at node i for a single capacitor group (and reactor for the high-voltage side). \(N_{m,CB,i,t}\) represents the number of engaged CB groups. \(\Delta CB_{m,i,max}\) signifies the maximum switching operations constraint for CB stages within the scheduling period. \(N_{m,i,max}\) refers to the maximum number of groups that CB can employ.
## 2 The TASIPPS Conceptual Framework
### Application Security  
Within the proposed framework, a pivotal component is the application security layer, which is designed to enforce the security of the prototypes that are instrumental to the framework's functionalities. Application security measures are strategically employed to safeguard these prototypes, ensuring their integrity, confidentiality, and availability in the face of modern-day cyber-attacks, potential threats, and vulnerabilities. Incorporating SHA-512, OAuth 2.0, and strategically positioned firewalls, the application security layer functions as a safeguarding stronghold for the prototypes within the framework. These measures collectively bolster data integrity, control access, and mitigate risks, ensuring that the prototypes remain fortified against a diverse range of security challenges. As the backbone of the framework's security architecture, the application security layer plays an instrumental role in upholding the confidentiality, integrity, and availability of the prototypes and the sensitive data they manage.
# A Low-Cost, Small-Size, and Bluetooth-Connected Module to Detect Faults in Rolling Bearings
###### Abstract  
Condition monitoring techniques have been successfully applied to detect damaged bearings. However, the signal acquisition and the subsequent processing are typically outsourced to expensive data acquisition boards and complex software, resulting in expensive solutions. As a side effect, the integration of condition monitoring systems in wireless sensor networks can be tough to achieve. Aiming to overcome such issues, a low-cost and small-size electronic module to be placed in the proximity of the bearing to be monitored was developed. The acoustic signal delivered by the bearing is acquired, and the corresponding frequency spectrum is evaluated on-board. Based on that, the developed module automatically detects the presence of defects and notifies the remote controller via a wireless connection only when a fault is detected, thus avoiding the use of data cables whilst minimizing the amount of transferred data. Experimental tests carried out on the proposed system assessed the accuracy of the evaluated frequency spectrum, resulting in an amplitude error within \(\pm 0.6\%\), as well as the fault detection capability in the presence of environmental acoustic noise.  
rolling bearings; condition monitoring; reliability; fault detection; wireless node; acoustic emissions; MEMS microphone; embedded system; Bluetooth Low-Energy Article  
A Low-Cost, Small-Size, and Bluetooth-Connected Module to Detect Faults in Rolling Bearings