esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Training and Performance Evaluation of the CCBA Model
### Performance Evaluation and Results  
This Section presents comprehensive experimental results that highlight the effectiveness of our proposed method for urban sound classification across various SNRs. We evaluated multiple classifiers and feature extraction techniques, including benchmark classifiers built using our CNN-Audio block integrated with several models. We comparedthem with two existing methods reported in the literature. One existing method is the CNN-LSTM using MFCC with a window length of 1024 and a hop length of 512, proposed in our previous study [21], was designed to deliver real-time urban sound classification on edge devices with outstanding accuracy on clean audio, where MFCC is one of the most widely used features. The other method involves our replication of the system proposed by [PERSON] et al. [38], who used multi-stream CNN to classify MFCC extracted from multiple time-frequency distributions, including STFT, Stockwell, WVD, and Wigner-Ville Filtered (WVF). The existing methods are trained on the same pre-processed UrbanSound8K as introduced. All end-to-end classifiers that use the CNN-Audio block are trained following the process introduced in Section 4.2 by distilling knowledge and features from the dual-stream ResNet50-CNN-LSTM that uses SPWVD-MFCC features. Notably, in all models utilizing LSTM layers, only the output from the final time step of the sequence is used for the final classification. In models using BiLSTM, except for the proposed CCBA, only the outputs from the last time steps of both the forward and backward directions are utilized for final classification.
# Unmanned Aerial Vehicle Landing on Rugged Terrain by On-Board LIDAR-Camera Positioning System
## 5 Experimental Results
### Real-World Outdoor Experiments
#### 5.2.4 Runtime Analysis  
Five tests were conducted in each real-world scenario to verify the overall reliability of the proposed algorithm. As shown in Table 7, a significant proportion of time was allocated to scene recognition due to the high computational complexity of some algorithms; however, this was a necessity for ensuring a safe landing. Moreover, new modules and factors can be seamlessly integrated or substituted within the landing framework proposed in this study, thereby further enhancing the landing performance of UAVs.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **(a) Turf** & \
\begin{tabular}{c} **(b) Rooftop** \\ **Parking Lots** \\ \end{tabular} & **(c) Cottage Area** & **(d) Footbridge** &
\begin{tabular}{c} **(e) Swimming** \\ **Pools** \\ \end{tabular} \\ \hline Obstacle distance (m) & 4.52 & 2.12 & 1.58 & 1.86 & 4.33 \\ Average level of site selection (\({}^{\circ}\)) & 3.52 & 2.32 & 4.11 & 2.45 & 3.18 \\ Speed at landing (m/s) & 0.13 & 0.12 & 0.15 & 0.13 & 0.10 \\ Type of landing site & Lawn (safe) & Path (safe) & Road (safe) & Road (risky) & Field (safe) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Autonomous UAV landing results for multiple experimental scenarios.  
Figure 12: Comparison of UAV landing positions with different methods.
# Spatial Deformation Calculation and Parameter Analysis of Pile-Anchor Retaining Structure
## 4 Engineering Background and Numerical Simulation Model
### Comparative Analysis of Results  
During on-site construction, horizontal monitoring points were arranged at the crown beam and pile. The horizontal monitoring points of the crown beam were measured using a level gauge with a horizontal spacing of 20 m, and the monitoring points were symmetrical about the middle of the foundation pit. The deep horizontal deformation of the retaining pile was monitored using an inclinometer, which consists of an inclinometer tube, a clinometer, and a digital readout. The inclinometer tube is made of 90 mm PVC and is buried and fixed in the pile wall, which is placed into the pile hole together with the reinforcement cage. The clinometer used is a CX-03E type borehole inclinometer with high sensitivity (total system accuracy: \(\pm 4\) mm/15 m) and strong stability. The spacing of the inclinometers is 8 m, with five horizontal inclinometers within a depth of 40 m. The horizontal layout position is the same as the position of the horizontal monitoring points of the crown beam.  
In the theoretical calculations, it is assumed that the strata are uniformly distributed, and the equivalent soil layer method can be used to convert non-uniform strata into uniformly distributed soil layers. Numerous studies have proven the applicability of the equivalent soil layer method [29; 30; 31]. Other parameters can be calculated based on design data, but currently, there is no research indicating a clear value for the pile curve shape coefficient \(\xi\). In this study, a value of 1.25 was chosen for \(\xi\).  
Figure 7: Theoretical calculation process steps.
## 4 Results
### Scenario Discription  
* Simulation flow and parameters  
The scenario is as follows: Patients of varying severity levels visit the ED for treatment. Nurses classify the patients' condition into three levels based on the KTAS (1, 2; 3; 4, 5). All patients undergo a similar treatment process. Depending on their severity, patients receive medical tests (ultrasound/X-ray) before seeing a doctor. There are three treatment rooms tailored to the severity of the patients' conditions. After triage, nurses transfer patients to the appropriate beds, and doctors provide the necessary treatment. Patients are discharged upon completion of their treatment. Parameters used in the simulation of the ED research are listed in Table 4, with settings based on average durations and references from existing studies.  
* Patient arrival scenario  
Experimental analysis is based on the 2022 NEDIS Emergency Medical Statistics Yearbook, utilizing emergency department (ED) data from three regions within South Korea. NEDIS provides comprehensive statistical information on ED facilities, equipment, personnel, and training. It offers hourly patient admission times categorized by the Korean Triage and Acuity Scale (KTAS) and provides a year's worth of statistical data. However, simulating a full year would be too extensive, so daily admission rates are calculated instead. For each region, statistics for a single facility including medical staff, equipment, and facility capacity are used to estimate daily patient admissions. This approach allows for general application across various emergency departments, not just specific ones. Therefore, daily data from three regional EDs are used to construct the scenarios. These scenarios incorporate differences in patient arrival times, KTAS ratings, patient residence time, and medical staff availability across regions.  
Ethical approval is not required for this study because the statistical data is publicly available.  
The actual patient arrival data for the three regions of NEDIS are shown in Table 5. This is the average daily visit of patients recorded for one regional emergency center.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Process** & **Actor** & **Duration (min)** \\ \hline Registration & Nurse & Triangular (3, 5, 10) \\ Triage & Nurse & Triangular (3, 7, 10) \\ Medical Test (X-ray, Ultrasound) & Technician & Normal (3, 15, 30) \\  & & KTAS 1, 2: Triangular (8, 20, 30) \\  & General Doctor & KTAS 3: Triangular (10, 25, 35) \\ Diagnosis & & KTAS 4, 5: Triangular (20, 35, 45) \\  & & KTAS 1, 2: Triangular (5, 15, 25) \\  & Senior Doctor & KTAS 3: Triangular (5, 20, 30) \\  & & KTAS 4, 5: Triangular (15, 30, 40) \\  & & KTAS 1, 2: Triangular (480, 600, 720) \\ Bed & Patient & KTAS 3: Triangular (240, 360, 480) \\  & & KTAS 4, 5: Triangular (60, 120, 240) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation experiments parameters (T: Triangular) [36; 37; 38; 39].
# Efficient Algorithm for Providing Live Vulnerability Assessment in Corporate Network Environment
## 4 System Data Life Cycle and Analysis
### Asset Collector Module  
Asset Collector Module is responsible for collecting, integrating, and filtering data that involve detected and defined assets for monitored network. The module collects data from two data sources. The first source is Configuration Management Database (CMDB) [59]. The second source is vulnerability scanner [60; 61] that is not only able to scan but also has the functionality of detecting the components. In consequence, it is possible for VMC to inform the operator about data incompatibility between CMDB and scanning results. That is one of the first knowledge enrichment manifestation the VMC can present while analyzing collected data. In order to explicitly determine an asset identifier (id), the universally unique identifier (uuid) generator version 3 [62] was used, based on the asset IP address and id value received from CMDB. The proposed document, except for fields needed by CVSS standards, contains also business and technical owner fields, which hold information about the person responsible for the monitored asset.
## 2 Literature Review
### IoT Technologies in Health Monitoring
#### 2.1.1 Key Advancements and Applications of IoT in Healthcare  
IoT-enabled devices facilitate the 24/7 real-time health monitoring of vital signs like blood pressure, heart rate, and SpO\({}_{2}\). This enables more effective patient care by facilitating the early detection of medical conditions, improved chronic disease management, and timely alerts for healthcare professionals [17].  
Advanced sensors are used by wearable devices like smartwatches, fitness trackers, and biosensors to monitor a variety of parameters, including heart rate and rhythm, ECG, sleep patterns (duration and quality of sleep), activity levels (steps, total movement, motion tracking), physiological signs of stress, etc. Algorithms are used to gather huge amounts of real-time health data [18]. Medical practitioners can take preventative measures for early warning systems for metabolic and cardiovascular disorders.  
Remote diagnosis and seamless communication are made possible by integrating IoT devices with telemedicine platforms. This decreases needless hospital stays, increases access to healthcare in underserved and rural areas, and improves senior care through remote monitoring [19].  
Large volumes of data are produced by IoT devices, and machine learning and AI are used to integrate and analyze these data. Insights from predictive analytics are utilizedto prevent disease, provide personalized treatment plans, and make better operational decisions in hospitals [20].  
Real-time patient, staff, and medical equipment tracking is made possible by IoT-enabled infrastructure, which speeds up emergency response times and optimizes resource use in hospital and smart home systems [21].  
Key IoT applications in the healthcare sector include the following:  
a. Chronic Disease Management--In order to effectively manage chronic conditions, IoT devices like blood pressure monitors, connected inhalers, and blood glucose monitors are essential [22]. This includes continuous blood pressure monitoring for patients with hypertension, smart inhalers for monitoring COPD, and real-time glucose tracking for managing diabetes [23].  
b. Remote Patient Monitoring (RPM)--Heart rate, blood pressure, temperature, and other health data can be automatically collected through wearables and portable ECG monitors and sent to a software program where algorithms can evaluate the information to recommend treatments or generate alarms [24]. RPM makes it easier to track health in real time, which improves care for older people who live independently and enables preventive treatments for high-risk patients [25]. Healthcare professionals may monitor patient health in real time thanks to the continuous flow of data, which reduces the need for frequent in-person visits.  
c. Preventive Healthcare--Fitness trackers and smartwatches aid in health maintenance by monitoring physical activity, sleep patterns, and stress levels through heart rate variability analysis [26]. These devices promote proactive well-being management and healthier lifestyle choices.
# Syn2 Real Detection in the Sky: Generation and Adaptation of Synthetic Aerial Ship Images
## 4 Experiments  
### Target Datasets  
The TGRS-HRRSD-Dataset [9] is specifically designed for object detection in high-resolution remote sensing images. It comprises over 20,000 images with spatial resolutions ranging from 0.15 to 1.2 m. The dataset contains a total of more than 50,000 object instances, spanning across 13 categories of remote sensing imaging (RSI) objects. Each category consists of a relatively balanced number of instances. For our study, we extracted the subset that focuses on ships, resulting in 2165 images and 3954 ship objects. We randomly partitioned the data, allocating 60% (1299 images) for the training set, 20% (433 images) for the validation set, and another 20% (433 images) for testing.  
The LEVIR [21] dataset contains a large collection of high-resolution Google Earth images, each with dimensions of 800 \(\times\) 600 pixels. The dataset comprises over 22,000 images and covers various terrestrial features of human habitats, including cities, villages, mountains, and oceans. However, extreme terrestrial environments such as glaciers, deserts, and the Gobi Desert are not included. The dataset consists of three object types: airplanes, ships (both offshore and seaward), and oil tanks. A total of 11,000 individual bounding boxes were annotated for all the images, resulting in an average of 0.5 objects per image. For our analysis, we extracted the subset that focuses on ships, resulting in 1494 images and 2961 ship objects. Similar to the previous dataset, we randomly divided the dataset into three sets: 60% (896 images) for training, 20% (299 images) for validation, and 20% (299 images) for testing.  
The airbus-ship-detection [26] dataset was released in 2019 for the \"Airbus Ship Detection Challenge\" on Kaggle. It is one of the largest publicly available datasets for ship detection in aerial images. The original dataset provides instance segmentation labels in mask form. However, for our experiment, these labels were transformed into horizontal bounding boxes to simplify the representation of ship locations. The original training dataset consists of 192,556 images, out of which 150,000 are negative samples (no ships present). We selected only the positive samples, resulting in a subset of 42,556 images that contain a total of 81,011 ships. The dataset was randomly divided into a training set (60%--25,533 images), a validation set (20%--8511 images), and a test set (20%--8511 images).  
Figure 9: The architecture of the mean teacher model. The loss of pseudo-labels generated by the teacher model was used to update the student model, which updates the teacher model with the EMA formula.
## 5 Comparative Analysis of Incremental Launching Monitoring Results
### Temporary Pier Concrete Foundation Settlement Monitoring  
The settlement of the concrete foundation of incremental launching pier L3 under unfavorable working condition 4 is selected for analysis. At this time, the incremental launching process is relatively regular. There are four incremental launching cycles. The five points of each incremental launching process are, respectively, expressed as the falling of the previous cycle, jacking, pushing, correcting, and falling. The settlement of the concrete foundation of incremental launching pier L3 during the four incremental launching cycles is presented in Figure 12. The diagram indicates that the foundation displacement of each incremental launching cycle exhibits an initial increase followed by a decrease; the variation between the lowest values remains within 4 mm, indicating that the incremental launching process has a negligible influence on the settlement of the concrete foundation at the incremental launching pier.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Condition** & **Position** & **Monitoring Data (mm)** & **Calculated Value (mm)** & **Limiting Value (mm)** \\ \hline \multirow{3}{*}{1} & Front & 0.402 & 0.267 & \\  & End & \(-\)14.016 & \(-\)12.29 & \\  & Front & \(-\)30.581 & \(-\)19.506 & \\  & End & \(-\)0.108 & \(-\)0.074 & \\  & Front & \(-\)5.245 & \(-\)7.906 & \\  & End & \(-\)0.071 & \(-\)0.013 & \(\pm\)20 \\  & Front & \(-\)4.558 & \(-\)6.974 & \\  & End & \(-\)19.487 & \(-\)18.097 & \\  & Front & 0.468 & 0.279 & \\  & End & \(-\)0.319 & \(-\)0.654 & \\ \hline \hline \end{tabular} Note: During the incremental launching process, if the monitoring data exceed the limiting value, an early warning will be issued. At this time, measures need to be taken for the support to correct the deviation and remove the limit warning to ensure the smooth progress of the incremental launching construction.  
\end{table}
Table 2: Contrast between measured and calculated displacements of steel box girder.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Condition** & **Monitoring Data (mm)** & **Calculated Value (mm)** & **Limiting Value (mm)** \\ \hline
1 & \(-\)14.022 & \(-\)16.068 & \(\pm\)115 \\
2 & \(-\)39.303 & \(-\)66.683 & \(\pm\)177.5 \\
3 & 4.515 & 3.435 & \(\pm\)30 \\
4 & 2.596 & 5.095 & \(\pm\)60 \\
5 & \(-\)9.867 & \(-\)7.566 & \(\pm\)115 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Contrast between measured and calculated deflections of guide girder.
## 4 Results and Discussion
### Transfer Learning Process  
By leveraging transfer learning techniques, we aim to capitalize on existing knowledge and patterns learned from one task or domain and apply them to another related task or domain--thereby potentially improving our model's ability to accurately classify land use categories within Handan City. This strategic shift towards transfer learning reflects our commitment to continuously refining our approach in order to achieve more robust and reliable results in land use classification using remote sensing data. The constructed data set comprises four cities, and the weights obtained after training the model on data set for one city are applied to the next city data set for transfer learning. Studies have shown that transfer learning is more effective in domains with higher data set similarity and requires fewer iterations for the same classification [32]. In order to achieve such notable improvements, a series of strategies and methods were employed. Firstly, the pre-trained model was used for training on the Handan data set, and the resulting weights were applied to the next city data set for transfer learning. Then, continuous training was carried out on the Shijiazhuang data set to obtain the model result of this city, and it was applied to the next city--the Xingtai data set. Finally, further training was conducted on the Tangshan data set to obtain the Tangshan model result. Through transfer learning, there was a gradual improvement in accuracy of each city's land use classification testing set: 81.44% for Handan City testing set, 84.67% for Shijiazhuang City testing set, 86.58% for Xingtai City testing set, and 88.22% for Tangshan City testing set. The comparison of using pre-training weights with transfer learning for each data set is shown in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methodologies** & **Accuracy (\%)** \\ \hline AlexNet & 61.00 \\ ResNet & 72.98 \\ DenseNet & 75.00 \\ Transformer & 77.96 \\ EfficientNet & 75.99 \\ EfficientNetV2 & 80.97 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Various methods and their accuracy of testing set.  
In the quantitative analysis part of this paper, the accuracy, precision, recall, and F1 score are used to evaluate the recognition and classification effect of the model on each data set. For the training results of four city data sets, we can see that the results of each average index are improved after the transfer learning in turn. The results are shown in Table 5.
## 2 SE-TransUNet Water Seepage Detection Model
### Overall Model Architecture of Water Seepage Recognition  
The TransUNet network forms the foundational architecture, leveraging both UNet's capability to extract leakage region features and ViT's capacity to encode global feature positions in tunnel lining images. This dual mechanism enables TransUNet to accurately capture global contextual information of leakage areas. The proposed architecture consists of three key elements: an encoder, a decoder, and skip connections. The proposed model structure is illustrated in Figure 1. Specifically, Transformers excel at extracting global features through their self-attention mechanism, but are intrinsically limited to unidirectional positional awareness, neglecting multi-faceted perspectives of local features. Conversely, traditional CNN-based UNet architectures demonstrate proficiency in local feature extraction but lack comprehensive capacity for capturing fine-grained details.  
To address these limitations, Vision Transformer (ViT) layers were integrated into the UNet framework, forming a TransUNet architecture. This integration refines leakage feature maps within the Transformer module, enabling more nuanced and precise global feature extraction. Furthermore, the SE-Block incorporated in skip connections optimizes feature transmission from the encoder, facilitating more accurate reconstruction of leakage feature maps during decoding.  
Consequently, the SE-TransUNet architecture synergizes the advantages of Vision Transformer and UNet frameworks. By enhancing both local and global information processing, it yields a comprehensive leakage identification model capable of effective feature extraction from leakage images.
# Stable Matching of Users in a Ridesharing Model
## 2 The Proposed Model
### Travel Savings  
The _cost_ of the ridesharing service provided by \(p\) and \(d\) depend on the spatial and temporal characteristics of the trip. Consider a user \(u\) (of the type \(p\) or \(d\), interchangeably) traveling through the route \(R_{u}\). The _transfer cost_ of \(u\) traveling by \(R_{u}\) is expressed in Equation (2) by considering the unit price of fuel (gas), the vehicle performance measured by miles per gallon (mpg), and the payment of services such as tolls, parking fees, or public transportation (fees). Note that if \(u\) does not have a particular vehicle, the transfer cost only depends on the fees.  
\[c(R_{u})=\tilde{w}(R_{u})\cdot\texttt{gas/mpg}+\texttt{fees}. \tag{2}\]  
Figure 1: Types of ridesharing based on the routes. (**a**) identical. (**b**) inclusive. (**c**) partial.  
Let \(z_{u\to R_{uv}}\) be the _cost savings_ when \(u\) shares its trip with another user \(v\) through the joint route \(R_{uv}\). The cost savings depend on the type of user, as expressed in Equation (3).  
\[z_{u\to R_{uv}}=\begin{cases}c(R_{u})-c(R_{uv})/2,&\text{if $u$ is a passenger;}\\ c(R_{uv})/2,&\text{if $u$ is a driver.}\end{cases} \tag{3}\]  
Let \(t_{u\to R_{uv}}\) be the estimated _length of time_ in which \(u\) travel from its origin to its destination through \(R_{u}\). Then, the time savings of \(u\) following a joint route \(R_{uv}\) with a user \(v\) (instead of its original route) is expressed as \(t_{u\to R_{uv}}-t_{u\to R_{uv}}\). On the other hand, a _slack time_\(\eta_{u}\) indicates the time interval in which \(u\) is willing to wait for \(v\) at the origin point of \(R_{uv}\). Let \(t_{u\to R_{uv}}^{0}\) be the _instant of time_ in which \(u\) estimates to arrive to the origin point of \(R_{uv}\). Equation (4) computes the _total time savings_ for \(u\) considering the joint route \(R_{uv}\).  
\[t_{u}(R_{uv})=t_{u\to R_{u}}-t_{u\to R_{uv}}+(\eta_{u}-|t_{u\to R_{uv}}^{0}-t_ {v\to R_{uv}}^{0}|). \tag{4}\]  
Note that, traditionally, \(\eta_{u}-|t_{u\to R_{uv}}^{0}-t_{v\to R_{uv}}^{0}|\) is positive; however, it could be negative depending of the times saving estimated for the joint route \(R_{uv}\). Thus, the _travel savings_ generated by \(u\) when sharing its trip with \(v\) through \(R_{uv}\) are calculated by Equation (5).  
\[s_{u}(R_{uv})=z_{u\to R_{uv}}+\alpha t_{u}(R_{uv}), \tag{5}\]  
where the coefficient \(\alpha\) weights the importance assigned by the user about the times, and it allows the comparison between different magnitudes.