esa-sceva/satcom-chunk-collection huggingface dataset:
# Building Fire Evacuation: An IoT-Aided Perspective in the 5G Era
## 2 Overview of IoT Technology for Smart Buildings
Moreover, the penetration losses of 4G LTE signals in indoor building areas were also demonstrated by [PERSON] et al. [23] and [PERSON] et al. [24]. Therefore, networking devices such as access points, wireless repeaters, and routers should be applied to ensure the reliability of wireless communication in indoor environments. Furthermore, middleware is followed to build the connection between physical and application layers. Middleware is usually described as a software system designed to be the intermediary between IoT devices and applications, so that seamless connectivity and data management for distributed applications can be enabled [12]. To realize IoT application in smart buildings, middleware based on events with database storage is gaining popularity due to the easiness of deployment and lightness of resource utilization. Message Oriented Telemetry Transport (MQTT), Constrained Application Protocol (CoAP), and BLE (Bluetooth Low Energy) are some popular connectivity protocols explicitly designed for this aim [25]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Characteristics** & **NB-IoT** & **4G LTE** & **Wi-Fi 6** & **ZigBee 3.0** & **Bluetooth 5** \\ \hline Spectrum & Licensed & Licensed & Unlicensed & Unlicensed & Unlicensed \\ Range & 1 km (urban), 10 km (rural) & 1 km (urban), 5 km (rural) & 50–100 m & 10–100 m & 10–100 m \\ Connected devices & Over 1,000,000/km\({}^{2}\) & 100,000/km\({}^{2}\) & 254/gateway & 240/gateway & 7/gateway \\ Bandwidth & 200 kHz & 5–20 MHz & 160 MHz & 2 MHz & 1 MHz \\ Peak data rate & 200 Kbps & iGbps & 9.6 Gbps & 250 Kbps & 2 Mbps \\ Battery life & \textgreater{}10 years & Hours & Days & \textgreater{}2 years & Days \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specifications of mobile communication technology used for IoT. The service layer lies at the upper level of the IoT architecture. Owing to the strong analytical power of IoT, the service layer can strongly interact with the application layer to cater to the design and demands of the application layer for providing effective utilization of the collected data. The service layer is established in smart building applications to provide various services, including device management, information analyses, and security control. Finally, with the development of user interfaces in the application layer to access various IoT-based services, monitoring and control of the smart building facilities can be enabled.
## 2 Data and Methods  
### Tectonic Setting of Afghanistan Earthquakes  
The 800 km long Hindukush Range in Afghanistan is part of a collision zone between the Eurasian and Indian tectonic plates, incorporating several active faults such as the Chaman fault system and Kabul fault system; it has high potential to generate earthquakes. For this reason, the earthquake cases studied are as follows.  
A 7.5 magnitude earthquake struck northeast Afghanistan (36.524\({}^{\circ}\)N, 70.3676\({}^{\circ}\)E) on 26 October 2015. The quake occurred at an intermediate depth of 210 km due to reverse faulting [43].  
Between 7 and 15 October 2023, a series of earthquakes and aftershocks struck Herat Province in western Afghanistan, killing 1482 people, injuring 2100, and damaging 3330 dwellings. The aftershocks lasted until 28 October 2023. On 7 October 2023, an earthquake with a magnitude of 6.3 struck 32 km from Qarabag, 34 km from Zendijan, and 41 km from Gurian in Herat, Afghanistan. The event occurred at 34.5982\({}^{\circ}\)N and 61.9263\({}^{\circ}\)E at a depth of 14 km. On 11 October 2023, a 6.3 magnitude earthquake struck 28 km north of Herat city in Herat Province, western Afghanistan, at 34.5364\({}^{\circ}\)N and 62.0509\({}^{\circ}\)E. On 15 October 2023, another 6.3 magnitude earthquake struck 34 km north of Herat city in Herat Province, western Afghanistan, at 34.6529\({}^{\circ}\)N and 62.1244\({}^{\circ}\)E [44].  
The earthquake cases selected in the research were acquired from the United States Geological Survey (USGS) ([[https://earthquake.usgs.gov/earthquakes/search/](https://earthquake.usgs.gov/earthquakes/search/)]([https://earthquake.usgs.gov/earthquakes/search/](https://earthquake.usgs.gov/earthquakes/search/)), accessed on 1 January 2024). This research focuses on studying primary shocks within earthquake clusters in Afghanistan's active fault zones (Figure 1) in order to reduce risks, improve the understanding of earthquake dynamics, and develop effective seismic mitigation measures.
# Deep-Learning-Based Stream-Sensing Method for Detecting Asynchronous Multiple Signals
## 5 Learning-Based Asynchronous Stream Sensing
### Losses and Objectives for Learning ASensNet  
For training samples, \(\mathcal{I}_{\text{R}}\) and \(\mathcal{I}_{\text{I}}\) denote input domains for \(\mathbf{I}_{\text{R}}\) and \(\mathbf{I}_{\text{I}}\), respectively. Samples of \(\mathcal{I}_{\text{R}}\) and \(\mathcal{I}_{\text{I}}\) are followed by a data distribution, denoted as \(\mathbf{I}_{\text{R}}\sim p_{\text{data}}(\mathbf{I}_{\text{R}})\) and \(\mathbf{I}_{\text{I}}\sim p_{\text{data}}(\mathbf{I}_{\text{I}})\), respectively. The input domain of \(\mathbf{I}_{\text{eig}}\) is represented by \(\mathcal{I}_{\text{eig}}\), and samples are followed by \(\mathbf{I}_{\text{eig}}\sim p_{\text{data}}(\mathbf{I}_{\text{eig}})\). The label domain is represented by \(\mathfrak{L}\). Feature domains of \(\mathbf{z}_{\text{R}}\), \(\mathbf{z}_{\text{I}}\), \(\delta_{\text{R}}\), and \(\delta_{\text{I}}\) are represented as \(\mathcal{I}_{\text{R}}\), \(\mathcal{I}_{\text{I}}\), \(\Delta_{\text{R}}\), and \(\Delta_{\text{I}}\), respectively. In addition, \(\mathfrak{F}\) denotes a domain constructed by all feature vectors \(\mathbf{z}_{\text{R}}\), \(\mathbf{z}_{\text{I}}\), \(\delta_{\text{R}}\), and \(\delta_{\text{I}}\). The domains for \(\mathbf{w}_{\text{e}}\), \(\mathbf{w}_{\text{c}}\), and \(\mathfrak{s}\) are represented by \(\mathfrak{W}_{\text{e}}\), \(\mathfrak{W}_{\text{c}}\), and \(\mathfrak{S}\), respectively. The domain for \((\mathbf{w}_{\text{e}}\circ\mathbf{w}_{\text{c}})\) is denoted as \(\mathfrak{W}_{\text{eoc}}\). Finally, \(O(\cdot)\) represents a loss function, and then this work uses the support vector machine (SVM) for multi-class classification [42]. This SVM loss function takes as input a score vector and a ground truth.  
For our ASensNet framework, we have four subnetworks for extracting features from input data and a fully connected-based classifier. For two subnetworks of the AutoEncoder Extractor, two Encoding Blocks can be represented as mapping functions: \(E_{\text{R}}\): \(\mathcal{I}_{\text{R}}\rightarrow\mathcal{I}_{\text{R}}\) and \(E_{\text{I}}\): \(\mathcal{I}_{\text{I}}\rightarrow\mathcal{I}_{\text{I}}\), respectively, while two Decoding Blocks are also two mapping functions: \(D_{\text{R}}\): \(\mathcal{I}_{\text{R}}\rightarrow\mathcal{I}_{\text{R}}\) and \(D_{\text{I}}\): \(\mathcal{I}_{\text{I}}\rightarrow\mathcal{I}_{\text{I}}\), respectively. For the inception layer-based extractor, the real and imaginary subnetworks are also mapping functions: \(F_{\text{R}}\): \(\mathcal{I}_{\text{R}}\rightarrow\Delta_{\text{R}}\) and \(F_{\text{I}}\): \(\mathcal{I}_{\text{I}}\rightarrow\Delta_{\text{I}}\). The Linear extractor 1 and 2 are represented as: \(K_{\text{c}}\) : \(\mathfrak{F}\rightarrow\mathfrak{W}_{\text{c}}\) and \(K_{\text{e}}\) : \(\mathcal{I}_{\text{eig}}\rightarrow\mathfrak{W}_{\text{e}}\), respectively. Finally, the Classifier is a mapping function: \(K\) : \(\mathfrak{W}_{\text{eoc}}\rightarrow\mathfrak{S}\).
## 2 Related Work and Multilingual Research
Moreover, the literature suggests deep learning and neural network-based techniques to consume more computational resources, data, and time, thus making them less appealing for real-time applications such as cyberbullying and hate speech detection. Roman Urdu is a resource-scarce language because of its huge morphological complexity and recent adoption. Because of the inadequacy of resources, despite being a frequently used language globally and in South Asia, only a few efforts have been put forward, as evident from the current scientific literature. The existing systems are also lacking in addressing the challenges such as word variants, naturally occurring highly unbalanced Roman Urdu content on social media, irregular use of capitalization, domain-specific standardized preprocessing methods, and so on. Motivated from this, we address the limitations by considering inherent writing patterns of colloquial Roman Urdu language users and grammatical structure variabilities, focused on novel preprocessing techniques for free text forms. The study has used a comparatively balanced Roman Urdu corpus hand annotated by linguistic experts and validated using [PERSON]'s kappa statistic. We have developed machine learning and ensemble-based models over many features. Instead of merely considering accuracy, we checked the performance of models and evaluated their time complexity. The performance of models is then compared for efficiency and time complexity.
## 2 System Model and Problem Formulation
### MIMO Radar Sensing  
For MIMO radar sensing, we assume that the transmitted probing signal is a narrow-band signal and that the propagation path is non-dispersive, i.e., within the line of sight (LoS) [23; 24; 25; 26]. Then, the received baseband signal at an angular direction \(\theta\) can be expressed as  
\[y^{\text{Rad}}(s;\theta)=\mathbf{a}^{H}(\theta)\mathbf{x}(s),\ \forall s\in \mathcal{S}, \tag{3}\]  
where \(\mathbf{a}(\theta)\in\mathbb{C}^{N\times 1}\) is the array steering vector of the direction \(\theta\). We adopt uniform linear array (ULA) antennas on the UAV, so the array steering vector is given by [11; 27]  
\[\mathbf{a}(\theta)=\big{[}1,e^{-j2\pi\frac{d_{a}}{\lambda_{c}}\sin(\theta)}, \ldots,e^{-j2\pi(N-1)\frac{d_{a}}{\lambda_{c}}\sin(\theta)}\big{]}^{T}, \tag{4}\]  
where \(\lambda_{c}\) is the carrier wavelength and \(d_{a}\) is the spacing between adjacent antennas.  
From (3), the power of the probing signal at the angular direction \(\theta\) is obtained as [23; 24]  
\[P^{\text{Rad}}(\theta;\mathbf{X})=\mathbf{a}^{H}(\theta)\mathbf{X}\mathbf{a}( \theta). \tag{5}\]  
The spatial spectrum in (5), as a function of the generic direction \(\theta\), is termed the beam pattern. The primary purpose of MIMO radar is to direct the transmitted beam toward multiple specified directions, which are usually known to the transmitter, thereby allowing for the collection of more information about the targets illuminated by these beams. To this end, transmit beamforming for MIMO radar probing signals aims to optimize the transmit power over sectors of interest by matching the desired transmit beam pattern to focus the spatial power in the target directions [23; 24]. To achieve this, the loss function is defined as the weighted sum of the deviations from the desired beam pattern. The details are given below.  
Let \([\theta_{l}]_{l=1}^{L}\) and \(\chi(\theta_{l})\) be the sample angle grids and the desired transmit beam pattern that covers the location sectors of interest, respectively, where \(L\) denotes the number of sample angle grids. Then, the MIMO radar waveform is designed to minimize the mean square error (MSE) between the desired and designed beam patterns, which is given as follows [23; 24]:  
\[L_{\text{Rad}}(\mathbf{X},\boldsymbol{\gamma})=\frac{1}{L}\sum_{l=1}^{L}\big{(} \gamma\chi(\theta_{l})-P^{\text{Rad}}(\theta_{l};\mathbf{X})\big{)}^{2}, \tag{6}\]  
where \(\gamma\) is the scaling factor to be optimized. Such a design is called a beam pattern matching design for MIMO radar sensing.
## 3 Results and Discussion
### Comparison of Leaf Segmentation Performance
#### 3.2.4 Comparison of the Execution Time  
The execution time of 11 segmentation algorithms is given in Table 14. Most of the thresholding algorithms, except Local thresholding, have a fast execution time of about 0.2 s. Since Local thresholding computes the thresholding throughout the local windows, the computation time is longer. The execution time of K-Means, an iterative approach, is longer than that of the thresholding approach; it took 2.463 s for two clusters (K = 2) and 3.483 s for three clusters (K = 3). With regard to its application to plant monitoring, the execution time is still acceptable for real-time implementation.
# A Semantic Segmentation Algorithm Using FCN with Combination of BSLIC
###### Abstract  
An image semantic segmentation algorithm using fully convolutional network (FCN) integrated with the recently proposed simple linear iterative clustering (SLIC) that is based on boundary term (BSLIC) is developed. To improve the segmentation accuracy, the developed algorithm combines the FCN semantic segmentation results with the superpixel information acquired by BSLIC. During the combination process, the superpixel semantic annotation is newly introduced and realized by the four criteria. The four criteria are used to annotate a superpixel region, according to FCN semantic segmentation result. The developed algorithm can not only accurately identify the semantic information of the target in the image, but also achieve a high accuracy in the positioning of small edges. The effectiveness of our algorithm is evaluated on the dataset PASCAL VOC 2012. Experimental results show that the developed algorithm improved the target segmentation accuracy in comparison with the traditional FCN model. With the BSLIC superpixel information that is involved, the proposed algorithm can get 3.86%, 1.41%, and 1.28% improvement in pixel accuracy (PA) over FCN-32s, FCN-16s, and FCN-8s, respectively.  
Keywords:semantic segmentation; FCN; BSLIC; superpixel +
Footnote †: journal: Applied Sciences
## 4 Experimental Results and Discussion
### Vision Capabilities Implementation
#### 4.1.2 Training Strategy  
The actual training and validation of the neural network for object detection on grape clusters has followed three main lines of implementation, in order to be able to control the improvement in the average precision of the network itself, both globally (i.e., on the whole set of 20 PASCAL VOC classes enriched with the \"grapes\" class) and at the specific \"grapes\" class level. The YOLOv2 model has been trained with image pre-processing/resize at 416x416 pixels, on the PASCAL VOC 2007+2012 aggregate dataset and the original WGISD (**416@VOC+WGISD**), then with image pre-processing/resize at 416 \(\times\) 416 pixels, on the PASCAL VOC 2007+2012 aggregate dataset and the Weather-Augmented WGISD (**416@VOC+WA-WGISD**) and finally with image pre-processing/resize at 608 \(\times\) 608 pixels, on the PASCAL VOC 2007+2012 aggregate dataset and the Weather-Augmented WGISD (**608@VOC+WA-WGISD**).  
An example of the graphical monitoring for training the **608@VOC+WA-WGISD** network is shown in Figure 5.  
Each training cycle completed successfully underwent an optimization procedure for OpenCL and translation into OpenCL Caffe standard to be integrated with the developed software components (Section 3.2.2). The main evaluation parameters on the quality of the resulting neural networks are reported in Table 4.  
Comparing, in particular, the medium average precision parameters on the entire set of classes (**mAP@.5**) and the average precision on the \"grapes\" class (**AP@.5**), the adopted choices for augmenting the WGISD dataset and a lower resi  
Figure 5: Average precision/loss curve for 608@VOC+WA-WGISD training.  
positively influenced the learning curve. All this without exceeding in quality percentages on the accuracy, which could indicate a potential overfitting of the network with respect to the training data.
# Reviews Based on the Reconfigurable Intelligent Surface Technical Issues
## 2 Technical Considerations for RIS
### Hardware Implementation of RIS
As described earlier, it is important to determine the optimized RIS size according to the scenario; if the RIS does not reach a certain size, it is not possible to obtain excellent SNR characteristics by using the currently used relays [21]. As shown in Figure 25, the RIS is frequency-independent, but the number of unit cells per surface area increases with wavelength to the fourth power. A Decode and Forward (DF) relay can have a much smaller size than the RIS, except in cases where very high spectral efficiency is required. Finally, we need to consider the price of implementing the RIS hardware. The cost of manufacturing the hardware board for an RIS can be broken down into the following three parts: the PCB; the electronic components such as RF switches, MCUs, and resistor; and the cost of assembling all the components on the PCB. The costliest of these are low-loss substrates for use in the high-frequency band, high-frequency band components for reconfiguration such as PIN diodes or varactors, and additive PCB fabrication with a build-up process including vias. Therefore, engineers can reduce costs by using simplified reconfigurable elements such as FET switches, unit cell designs that do not use vias, etc. Figure 26a shows the RIS structure using FET switches [99] and Figure 26b shows the RIS unit cell realized with printing technology without using vias [100]. Figure 16.5 Spectral efficiency as a function of RIS and relay surface area. Adapted with permission from [21]. Copyright 2020 IEEE.
# Cyber-Secure IoT and Machine Learning Framework for Optimal Emergency Ambulance Allocation
###### Abstract  
Optimizing ambulance deployment is a critical task in emergency medical services (EMS), as it directly affects patient outcomes and system efficiency. This study proposes a cybersecure, machine learning-based framework for predicting region-specific ambulance allocation and response times across South Korea. The model integrates heterogeneous datasets--including demographic profiles, transportation indices, medical infrastructure, and dispatch records from 229 EMS centers--and incorporates real-time IoT streams such as traffic flow and geolocation data to enhance temporal responsiveness. Supervised regression algorithms--Random Forest, XGBoost, and LightGBM--were trained on 2061 center-month observations. Among these, Random Forest achieved the best balance of accuracy and interpretability (MSE = 0.05, RMSE = 0.224). Feature importance analysis revealed that monthly patient transfers, dispatch variability, and high-acuity case frequencies were the most influential predictors, underscoring the temporal and contextual complexity of EMS demand. To support policy decisions, a Lasso-based simulation tool was developed, enabling dynamic scenario testing for optimal ambulance counts and dispatch time estimates. The model also incorporates the coefficient of variation (CV) of workload intensity as a performance metric to guide long-term capacity planning and equity assessment. All components operate within a cyber-secure architecture that ensures end-to-end encryption of sensitive EMS and IoT data, maintaining compliance with privacy regulations such as GDPR and HIPAA. By integrating predictive analytics, real-time data, and operational simulation within a secure framework, this study offers a scalable and resilient solution for data-driven EMS resource planning.  
ambulance deployment; machine learning; response time prediction; IoT integration; simulation; cybersecurity
## 3 Test and Analytical Methods
### Scanning Electron Microscopy--Energy Dispersive X-Ray Spectroscopy Analysis (SEM-EDS)  
The microstructure of RCPB specimens was analyzed using a MIRA4 LMH scanning electron microscope (SEM) (TESCAN, Brno, Czech Republic) fitted with an Ultim Max 40 energy dispersive X-ray spectroscopy (EDS) detector. Following UCS testing, specimens were immersed in anhydrous ethanol for 48 h to terminate hydration and prevent further reactions. Samples were subsequently dried at 45 \({}^{\circ}\)C until constant weight was achieved [29]. To mitigate charging effects caused by low electrical conductivity and high porosity, specimens were coated with platinum via sputter coating prior to SEM analysis. Thin sections (10 mm thick) were cut from the specimens, mounted on SEM stubs, and vacuum-dried to ensure surface stability during imaging [30].
## 3 Results and Discussion
### Compressive Strength  
Three specimens for each mixture were prepared for the compressive strength test. The test results are presented in Figure 7. Comparing Mix 1 and Mix 2, which were corresponded to the cases with 1.2 wt.% and 1.8 wt.% WRA solid content to the cement, the difference in the compressive strength was negligible. However, in the comparison of Mix 2 to 4, which corresponded to the cases with 1.8, 2.4, and 3.0 wt.% WRA solid content to cement, it can be seen that the compressive strength decreased as the amount of WRA increased. These results means that adding WRA up to a certain amount does not adversely affect the compressive strength while providing a beneficial effect on the compressive strength.  
Figure 5: Flow test results.  
Figure 6: Yield stress and plastic viscosity estimated from rheometer test.  
workability. However, an excessive amount of WRA has a negative influence on the compressive strength of UHPFRC.
## 1 Introduction
This paper investigates the impact of different LEO constellation configurations on GNSS CIT, focusing on the effects of varying satellite numbers. Specifically, we focus on global, high-resolution 3D ionospheric electron density reconstruction using a voxel-based model, considering both the configuration of LEO satellites and the distribution of their observational rays. Through simulated observation data, we demonstrate how the spatial distribution and altitude of LEO satellites influence CIT results. This research contributes to improving the accuracy of the global ionospheric 3D model and provides valuable insights for the future design of LEO constellations, particularly in the context of global ionospheric tomography.