esa-sceva/satcom-chunk-collection huggingface dataset:
# A Spam Filtering Method Based on Multi-Modal Fusion
## 4 Experimental Results and Discussion
### Results and Discussion
In general, an AUC of 0.5 indicates no discrimination (i.e., ability to diagnose whether an email is spam based on the testing), 0.7 to 0.8 is considered acceptable, 0.8 to 0.9 is considered excellent, and more than 0.9 is considered outstanding. Thus, we also use an ROC chart to further illustrate the performance of the MMA-MF model. We give four figures, Figure 4 shows the ROC chart for the text dataset and Figure 5 shows the ROC chart for the image dataset. Figure 6 shows the ROC chart for the hybrid dataset 1, for which the number of text dataset is equal to the number of image dataset. Figure 7 shows the ROC chart for the hybrid \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset Type** & **Model** & **Accuracy** & **Recall** & **F1-Score** & **Precision** \\ \hline \multirow{4}{*}{Text} & Char-CNN & 0.9563 & - & - & - \\  & BiLSTM & 0.9640 & - & - & - \\  & Naive Bayes & 0.9600 & - & 0.9600 & - \\  & ICRM & 0.9400 & - & 0.9500 & - \\  & \(\textbf{MMA}-\textbf{MF}\) & **0.9848** & **0.9796** & **0.9743** & **0.9850** \\ \hline \multirow{4}{*}{Image} & Naive Bayes & 0.8504 & - & 0.9103 & - \\  & ID3 Decision Tree & 0.8900 & - & 0.9000 & - \\  & \(\textbf{MMA}-\textbf{MF}\) & **0.9264** & **0.9263** & **0.9263** & **0.9170** \\ \hline \multirow{4}{*}{Hybrid Dataset 1} & SVM & 0.9825 & 0.9825 & 0.9825 & 0.9810 \\  & Classical k-NN & 0.9783 & 0.9781 & 0.9783 & 0.9770 \\ \cline{1-1}  & Enhanced k-NN & 0.9825 & 0.9830 & 0.9825 & 0.9820 \\ \cline{1-1}  & Random Forest & 0.9833 & 0.9833 & 0.9833 & 0.9820 \\ \cline{1-1}  & \(\textbf{MMA}-\textbf{MF}\) & **0.9842** & **0.9845** & **0.9841** & **0.9840** \\ \hline \multirow{4}{*}{Hybrid Dataset 2} & SVM & 0.9842 & 0.9844 & 0.9841 & 0.9830 \\ \cline{1-1}  & Classical k-NN & 0.9808 & 0.9813 & 0.9808 & 0.9790 \\ \cline{1-1}  & Enhanced k-NN & 0.9842 & 0.9844 & 0.9842 & 0.9820 \\ \cline{1-1}  & Random Forest & 0.9833 & 0.9834 & 0.9833 & 0.9810 \\ \cline{1-1}  & \(\textbf{MMA}-\textbf{MF}\) & **0.9846** & **0.9852** & **0.9845** & **0.9830** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of experimental results on different models. dataset 2. From the above four figures, we can clearly see that the AUC indicators of the model on the four datasets are all greater than 0.93, which indicates that the performance of MMA-MF on the four datasets is outstanding for the spam detection. In conclusion, it shows that the performance of the MMA-MF model is indeed better than other models that we mentioned, and the dropout technology does enable the MMA-MF model to be able to deal with hybrid spam, but also filter spam with only text or image data. Figure 4: ROC chart for Text Dataset. Figure 5: ROC chart for Image Dataset.
# Business Intelligence through Machine Learning from Satellite Remote Sensing Data
## 4 System Design and Implementation  
We designed and implemented a software system that provides information for business intelligence and policymaking through the use of spatial satellite data and machine learning. Our proposed system is open-source and open-architecture, and its main feature is its ability to detect abandoned buildings over an area of interest and display them to the user over map imagery. What differentiates it from similar recent software [4; 53] is the fact that it uses freely available satellite imagery and explores areas where ground-truth data are scarce.  
Another key difference is its extensibility, since new applications do not require changes in the core architecture. Thus, the software is suitable for SMEs that do not have the expertise to implement their own solutions but also do not have the ability to invest in commercial solutions. Leveraging our platform, they can make informed decisions about optimal site selection and thus reduce the costs of outsourcing it.
## 2 Literature Review
### IoT Challenges in the MS  
The IoT is associated with several aforementioned potential benefits; simultaneously, there are several adoption challenges surrounding it [55]. These challenges hinder wide IoT technology deployment, which reduces its delivered value [56]. The literature review found that IoT adoption challenges in MS were raised in three different contexts: te technological, organizational, and environmental contexts [27]. Technological challenges emerge from the technical characteristics of the technology itself. Security implications present a significant obstacle to wide IoT adoption in MS [18; 38]. The potential for IoT security hazards is escalated due to the lack of collective IoT security standards and the development of numerous layers that require diverse security systems [19; 24; 57; 58]; furthermore, the IoT requires the integration of numerous heterogeneous devices, which leads to increasing the complexity of devices and data management [32; 57]. Given that the IoT is also a new technology, its compatibility with manufacturing legacy systems is highly challenging [4; 44]; therefore, the IoT requires significant information technology (IT) infrastructure expansion [18; 45] and a considerable initial investment in hardware, software, skilled staffing, training, operation, and maintenance [27].  
Several organizational challenges can emerge during IoT implementation [12], mainly from internal organizational processes. Many decision-makers remain uninformed of the IoT and how it can positively change their businesses [18; 32], or they are inclined towards closed thinking and are entrenched in traditional industry practices. Adopting IoT technology may require redefining the organizational business model to adapt to it and profit from its distinctive functions [2; 12]. Most decision-makers are reluctant to take risks in modifying their business models, which leads to unanticipated interruptions and major alterations in the usual work practices and employees' resistance [29]. Most employees are disinclined to acquire new technology skills or alter their routines [18; 59] due to their feeling of ease and accord with the current technology and apprehension about their incapability to use the IoT [3].  
External factors related to the surrounding environment can also be a substantial challenge to IoT adoption in the MS. To date, there are no clear IoT use policies and regulations, which result in the emergence of security, data-sharing, protection, and ownership challenges [37; 53]. Governments should consider how new technologies, such as the IoT, can be supported and establish legislation and liability regulations to integrate such innovations into organizations to increase economic competitiveness systematically [9]; furthermore, market monopolies and competitive relations between IoT suppliers and vendors have hindered the adoption of the IoT [58]. IoT vendors use different architectures and protocols for their devices, making IoT solutions complex and impractical due to integration problems and difficulty of communication between devices [55].
# Internet of Things Applications as Energy Internet in Smart Grids and Smart Environments
## 2 IoT-Based Applications for Smart Grids
In NB-IoT technology, while orthogonal frequency-division multiplexing access (OFDMA)  
Figure 1: Communication technologies utilized in IoT applications: (**a**) Data rate versus range comparison of wireless technologies, (**b**) comparison of a low power wide area network (LPWAN) with IEEE 802.15.4 and 3G/4G/5G cellular communication technologies. is utilized for downlink, single-carrier frequency division multiple access (SC-FDMA) is exploited for uplink connections. The maximum data rate of the NB-IoT is typically 1 Mbps over 1.08 MHz bandwidth, which is approximately six-fold of the LTE systems. In spite of LTE-M, NB-IoT serves by employing tail-biting convolutional coding for the downlink connections, which eliminates complex decoding requirements at the user equipment [29; 30; 33; 34]. The main characteristics and detailed comparisons of leading LPWAN technologies are listed in Table 1. Furthermore, we have depicted EI infrastructure combining with the IoT and SG concepts in Figure 2. The presented EI infrastructure covers the combination of several application scenarios, where smart city operations, smart home automation systems, and energy-harvesting systems are included by considering metering, monitoring, and management processes. Furthermore, this figure covers all layers of the IoT technology.
# Optimisation of 2D U-Net Model Components for Automatic Prostate Segmentation on MRI
## 2 Background
### The Activation Layer  
Activation layers are typically applied after convolution layers in order to decide whether a particular neuron should be activated or not. Nonlinear activation functions, e.g., a rectified linear unit (ReLU), a leaky ReLU or a parametric ReLU [48], are used to enable the network to approximate most nonlinear functions.  
The ReLU is a nonlinear function defined by  
\[f(x)=\begin{cases}x&\text{if }x>0\\ 0&\text{if }x\leq 0.\end{cases} \tag{1}\]  
The ReLU restricts the activation of the neuron when the input is less than or equal to zero, which simplifies the network and reduces the computational time during the training process. This advantage however, comes with an issue, i.e., since the gradient of the ReLU function in the negative region of \(x\) is zero, once the neuron is inactive, the neuron will not be activated again throughout the training process (i.e., dying ReLU problem [49]).  
A Leaky ReLU (LReLU) attempts to solve the dying ReLU problem by setting the gradient of the negative region as a small constant value, \(c\), i.e.,  
\[f(x)=\begin{cases}x&\text{if }x>0\\ cx&\text{if }x\leq 0,\end{cases} \tag{2}\]  
where, for example, c = 0.1.  
Although the LReLU (and other modified ReLU activation functions) are shown to be superior to ReLU [48], many state-of-the-art models [18; 19; 39] still use ReLU as it produces satisfactory results and is simple to implement.
###### Abstract  
Rotating stall, which is a common phenomenon in turbomachinery, strongly relates to the flow rate condition. In centrifugal impellers, rotating stall was induced by the incidence angle on blade leading-edge at partial-load. The blade leading-edge shape also influences the rotating stall because of the subtle change of local flow-field. In this study, the influence of blade leading-edge shape on rotating-stalled flow characteristics was studied in a six-blade centrifugal pump impeller. The stall pattern was \"alternating\": Three passages were stalled, three passages were well-behaved, and the stalled and well-behaved passages occurred alternately. The stalled flow characteristics can be studied without the interruption of stall cell movement. Four types of blade leading-edge (blunt, sharp, ellipse, and round) were numerically compared based on the initial typical impeller and the numerical-experimental verification. The numerical comparison shows that the leading-edge shape has a strong influence on the stalled flow pattern, velocity, pressure, turbulence kinetic energy, and flow-induced noise inside impellers. The blunt and sharp leading-edge impellers had a similar internal pattern; the ellipse and round leading-edge impellers were also similar in the internal flow-field. Pressure pulsation analysis showed more obvious differences among these impellers. The main frequency and the pulsation peak-peak values were completely different because of the slight leading-edge shape differences. It revealed the impact of leading-edge geometry on the transient flow-field change under the same incidence angle conditions. It also provided reference for influencing or controlling the rotating stall by blade profile design.  
centrifugal pump; leading-edge shape; rotating stall; flow simulation; pressure pulsation
# Evaluation of Impact Energy Absorption by Natural Fiber Composites in Motorcycle Helmets
## 4 Results and Discussion
relative to pure PU, indicating that their addition positively affected the properties and efficiency of the composites, thereby highlighting their potential as a promising alternative to the reference material. Those containing agave sisalana fibers displayed the lowest variability among the reinforced composites, indicating a consistent and predictable response. Notably, the 10% agave sisalana proportion achieved the best performance among the samples, suggesting that this concentration is particularly advantageous for this fiber. The 5% and 15% proportions also demonstrated satisfactory performance, reinforcing the potential of sisal as an effective reinforcement for PU composites. Sugarcane bagasse fiber exhibited good stability, particularly at the 5% proportion. Although higher concentrations performed within acceptable limits, the results suggest that larger proportions may reduce energy absorption efficiency. This finding indicates that lower concentrations are more suitable for optimizing the performance of composites with this fiber. Coconut fiber yielded good results at the 5% and 10% proportions; however, it exhibited the highest variability among all the tested fibers. Furthermore, the composite with 15% coconut fiber showed the poorest results in terms of both variability and effectiveness, suggesting that lower concentrations are preferable for achieving more efficient performance.
## 5 Discussion
### Comparison with Machine Learning and Deep Learning Models
Figure 4: Results for all binary classification models: (**a**) binary classification on NSL-KDD; (**b**) binary classification on UNSW-NB15. Figure 5: Results for the confusion matrix: (**a**) confusion matrix on NSL-KDD; (**b**) confusion matrix on UNSW-NB15. By combining the performance of different classification algorithms on the two datasets, we find that both the machine learning and deep learning models can achieve good results in attack detection accompanied by a low FAR.
# A Topology Map Generation Algorithm for Optimal Path Finding for Image-Based Maps
## 5 Experimental Results
### Very Simple  
When finding a path to a destination, when the same pathfinding algorithm was performed on the preprocessed topology map, 39 nodes were visited, representing a reduction of 99.92% compared with the average of 46,062.5 nodes visited with the existing image map. If path finding was performed using an image map with only the existing A* algorithm, the number of nodes visited was very high, resulting in performance degradation. When the topology map was generated and applied through preprocessing, the number of unnecessary and repetitive operations to the destination was reduced significantly. Preprocessing through the CNA took 7 ms, compared with 75. 8 ms when only A* was used; applying the same algorithm to the topology map generated by the CNA took 1.2 ms. The processing time decreased by 90.77% when the preprocessing and pathfinding processes were performed together relative to when they were performed alone.
## 6 Future Challenges and Open Research Issues
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Reference** & **Technology** & **WSN-CFS** & **Intelligent Data** & **QoS** & **Mobility** & **Cross Layer** & **Level of** & **Localization** & **IP-Based** & **Energy** & **Applications** \\  & **Specifics** & **Integration** & **Processing** & & & & & & & & & \\ \hline \multirow{4}{*}{[18]} & Multi Application & Yes, Middle & Yes, Dedicated & Quality Of & & Yes, Multi-Application & & Yes & Yes & Not, & \\  & Quality & Level Resource & Computing & Monitoring & No & Allocation and & High & Yes & Yes & Available & Multi Application \\  & Monitoring & Sharing & Resources & & & Deployment & & & & & \\ \hline \multirow{4}{*}{[25]} & \multirow{4}{*}{General Survey} & \multirow{4}{*}{Yes} & \multirow{4}{*}{Yes} & \multirow{4}{*}{Yes} & Event Handling, & & & Yes (Domain & & & Intelligent \\  & & & & Network & Intelligence) & High & Yes & (Knowledge & No & Low & Transportation, \\  & & & & Coverage & & & & & & & Social Networking \\ \hline \multirow{4}{*}{[44]} & Cloud Software & \multirow{4}{*}{Yes} & \multirow{4}{*}{Yes} & \multirow{4}{*}{Yes} & Yes, User B Based & Yes, Implementation & Yes, Implementation Abstraction & Yes, Software & Yes & Not, & Network Monitoring, IP Based Services \\  & & & & & Level & & & & & & & \\ \hline \multirow{4}{*}{[57]} & Unmanned & \multirow{4}{*}{Yes} & \multirow{4}{*}{Yes} & \multirow{4}{*}{Yes} & \multirow{4}{*}{Sebware Vision & Yes, Vehicle & & & & & & & \\  & Vehicle & & & System & Vehicle & Yes & Very High & Yes, GPS-0.67 & No & Energy & \\  & Detection & & & & Navigation & & & & & & & \\ \hline \multirow{4}{*}{[58]} & Structure & \multirow{4}{*}{Yes} & Multi level & Operation, & & Flexible Network & & & & & & & \\  & Condition & & & Multi-resolution & & & Integration & High & Yes & (Damage & \\  & Reporting & & & & & & & & & & \\ \hline \multirow{4}{*}{[60]} & Locating & \multirow{4}{*}{Yes} & \multirow{4}{*}{Partial} & \multirow{4}{*}{Yes} & 64.8\% Latency & & & & & & & & \\  & Structure & & & & Reduction & & & & & & & \\  & Damages & & & & & & & & & & \\  & Internet of & & & & & & & & & & \\  & Things & Limited & Yes & & Algorithm & Yes & Yes & Yes & & & \\ \hline \multirow{4}{*}{[66]} & Sensor Node & \multirow{4}{*}{Limited} & \multirow{4}{*}{Yes} & \multirow{4}{*}{Yes} & \multirow{4}{*}{Limited} & \multirow{4}{*}{High} & Range Free, & & & & Cyber \\  & Localization & & & & & & & & & & \\  & & & & & & & & & & & \\ \hline \multirow{4}{*}{[70]} & Soil Moisture & \multirow{4}{*}{Yes, Full} & Yes, Collection, & Confirm with & & & & & & & & \\  & Monitoring & & & Sensor Web & Yes & Yes & Uniform & Yes & Yes & Not, & Environment \\ \hline \multirow{4}{*}{[70]} & Soil Moisture & \multirow{4}{*}{Yes, Full} & Yes, Collection, & & & & & & & & & \\  & Management, & & Sensor Web & & & & & & & & \\  & Visualization & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of CPSN architectures for infrastructure monitoring.
## 2 Integrated Model of Turbofan Engine and Jet Noise
### Component Level Model of Turbofan Engine with Separate Exhaust  
The structure of the turbofan engine with separate exhaust includes ten components, namely, inlet, fan, booster, high pressure compressor (HPC), combustor, high pressure turbine (HPT), low pressure turbine (LPT), main nozzle, bypass, and bypass nozzle. The component level model (CLM) of the turbofan engine is used most extensively as a performance model. For model construction, each component of the engine is modelled based on dynamic aerothermodynamics principles, rotor dynamics, etc. Then, the operation relationship among components is established by the co-operating equations, including the flow continuity equations, pressure balance equations, and power balance equations or shaft dynamics equations [27]. The Newton-Raphson method is usually used to solve the formulated nonlinear co-operation equations, and the performance parameters, which are the outputs of the CLM, derived.  
A detailed description of CLM is found to in [28], and Figure 3 gives the computation flowchart of the CLM. \(H\) denotes the flight height; \(Ma\) denotes the flight Mach number; \(W_{f}\) represents the fuel flow of combustor; \(\alpha_{Fan}\) is the inlet guide blade angle of fan; and \(A_{microjet}\) is the area of micronozzle throat. \(W_{f}\) and \(\alpha_{Fan}\) can adjust the running state of the engine. The role of micronozzle is to mix a small fraction of gas in the booster with the outlet airflow of the main nozzle. In this paper, the active jet noise control was realized by controlling the flow of the micronozzle, and regulating the inlet guide blade angle of the fan.  
Figure 3: Schematic diagram of computational interaction of turbofan engine model.  
Figure 2: Comparison results between simulation and experiment data.
# Digital Twin-Driven Virtual Network Architecture for Enhanced Extended Reality Capabilities
## 3 Architecture's Proposed Approach
### System-Supporting Architecture
#### 3.2.2 Digital Twin Manager (DTM)
When it receives a \"Network Warning\" from the Status Control, the Builder needs to request to the Modeler to model the most recent stored network model with the aim of re-generating the DTN. This paper introduces to the Builder the capability to receive super users' requests intended to modify the current DTN model; subsequently, the request is forwarded to the modeler for the model updating process. * Status Control: This is in charge of receiving the \"Network Status\" beacons from the Builder with the objective of identifying any anomalies and failures in the DTN based on the stored network model. In the case that any anomaly or failure is detected, the Status Control transmits a \"warning\" signal to the Builder requesting that the DTN must be re-modeled. * Content Delivery Agent: This has the function to receive data packets from the ON and determine whether the packets have to be forwarded to a DTCD or an external proxy server with the objective of storing ON content in a Content Delivery Network fashion. Additionally, it receives data packets from DTCDs to be delivered to the ON. Regarding the DTM operations, it is crucial to emphasize the necessity of implementing failure recovery techniques into the modules. These techniques can ensure that operations are not compromised. AI techniques or multi-core solutions, among others represent effective solutions for achieving self-healing mechanisms. Nevertheless, in the event of a module failure before the self-healing is completed, processes such as ON-DTN communication and synchronization may be compromised; however, the DTN has been deployed and inner DTN communication can operate uninterrupted. The DTN can be self-healed by means of the monitoring process described in Section 3.3.