esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Analysis of Experimental Results  
#### 4.3.1 Ablation Experiments  
In this paper, ablation experiments of the Backbone, Head, and loss function were carried out in turn. The experimental results are shown in Table 1.  
The first row of Table 1 shows the unimproved YOLOv5s model, where the mean average precision of rail surface defects is 95.4%. Then, the trunk and header were improved, respectively. After the addition of the CDConv module and C3 STR in the second and third lines, the mAP was significantly improved, reaching 96.1% and 96.3%, respectively. The reason is that the CDConv module greatly reduces the calculation amount of network parameters, and the model detection speed is improved; however, some information may be lost. The Swin Transformer combined with the C3 module completed the global information and richer context information. Then, the attention mechanisms, CBAM and GAM, were added to the Head for comparison. It was found that the network model with the GAM achieved better detection accuracy. Although both CBAM and GAM are mixed attention mechanisms, CBAM does not consider the interaction between the two dimensions, which may lead to the separation of feature vectors and the loss of inter-dimensional information. Therefore, we chose the GAM for its superior performance. Finally, the Soft-SIoUNMS loss function, which is a combination of SIoU and Soft-NMS, was added, providing a better effect than a single loss function. The experimental results showed that the mAP of our proposed method reached 96.9%, achieving the best detection results. The detection diagram in Figure 8 corresponds to the different methods used in the ablation experiments in Table 1. In order to better demonstrate the benefits of the improved model on the rail surface defect dataset, each improvement point will be validated separately.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Name** & **P** & **R** & **[EMAIL_ADDRESS]** \\ \hline YOLOv5s & 0.965 & 0.933 & 0.954 \\ YOLOv5s + CDConv (Method1) & 0.968 & 0.936 & 0.961 \\ YOLOv5s + CDConv + C3 STR (Method2) & 0.951 & 0.936 & 0.963 \\ YOLOv5s + CDConv + C3 STR + CBAM (Method3) & 0.963 & 0.938 & 0.964 \\ YOLOv5s + CDConv + C3 STR + GAM (Method4) & 0.967 & 0.940 & 0.965 \\ YOLOv5s + CDConv + C3 STR + GAM + Soft-SIoUNMS (Ours) & 0.971 & 0.945 & 0.969 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of results of ablation experiments.  
Figure 8: The different methods used in ablation experiments.
# Fire Detection and Geo-Localization Using UAV's Aerial Images and Yolo-Based Models
## 2 Related Work
### UAVs for Forest Fire Detection and Monitoring  
Fire mapping is among the most common tasks in forest fire remote sensing, a process that generates maps indicating fire locations within a specific timeframe using geo-referenced aerial images. Fire maps can also be processed to determine ongoing fire perimeters and estimate positions in unobserved areas. When this mapping process is carried out continuously to provide regular fire map updates, it's referred to as monitoring [15]. Drones play a crucial role in generating comprehensive and accurate fire data through high-resolution cameras, facilitating the characterization of fire geometry. The remote 3D reconstruction of forest fires contributes substantial information for firefighters to assess the fire's severity at specific locations safely [16].  
As mentioned in [17], the progression of drone applications within the realm of fire-fighting is primarily focused on the remote sensing of forest fires. Aerial monitoring of such fires is not only expensive but also fraught with significant risks, especially in the context of uncontrolled blazes. The authors in [18] presented an autonomous forest fire monitoring system with the goal of rapidly tracking designated hot spots. Through a realistic simulation of forest fire progression, the authors introduced an algorithm to guide drones toward these hot spots. Their algorithm's performance was then compared against a rudimentary strategy for circling around the fire's existing outline. To test the algorithm, a mixed reality experiment was conducted involving an actual drone and a simulated fire scenario. Drones are poised to perform tasks beyond remote sensing, as discussed in [19], including tasks like aerial prescribed fire lighting. However, the operational implementation of the latter remains underdeveloped due to the logistical challenge of transporting substantial amounts of water and fire retardant. In [20], the authors presented a comprehensive framework utilizing mixed learning techniques, involving the YOLOv4 deep network and LiDAR technology. This cost-effective approach enabled control over the UAV-based fire detection system, which can fly over burned areas and provide precise information. The development of a real-time forest fire monitoring system as described in [21], utilized UAV and remote sensing techniques. Equipped with sensors, a mini processor and a camera, the drone processed data from various on-board sensors and images.
# Stacked Ensembles Powering Smart Farming for Imbalanced Sugarcane Disease Detection
## 2 Methodology
### Stacking Ensemble Framework  
The stacking ensemble approach integrates multiple machine learning classifiers trained on feature embeddings extracted from InceptionV3, SqueezeNet, and DeepLoc. The stacking process consists of two main stages, which are explained in Figure 2. In the first stage, multiple classifiers are trained independently on the feature embeddings to capture diverse representations of sugarcane leaf patterns. The selected base classifiers include SVM, KNN, RF, GB, and an ANN. Each classifier processes the extracted embeddings and outputs class probabilities.  
In the next stage, in the meta layer, a median-based fusion strategy is used to aggregate the predictions from the base models. This ensures robustness against outliers by preventing extreme predictions from dominating the final classification. Additionally, it improves the performance on imbalanced classes by reducing the effect of overconfident model outputs, leading to a more stable decision-making process. Median aggregation also simplifies the computation, eliminating the need for additional training layers.  
Figure 1: Flowchart of the sugarcane classification pipeline. All different categorizations of the classes from the aforementioned dataset are mentioned in [41].  
Each feature extraction method (InceptionV3, SqueezeNet, and DeepLoc) is processed independently, meaning that three separate classification pipelines are created. The final classification decision is obtained after the median aggregation step, allowing the direct evaluation of the effectiveness of the different embeddings.
# Wireless Remote Control for Underwater Vehicles
## 3 Requirements and Definition of Working Modes for ROV Control
### Long-Range Minimum Control Wireless Mode  
Also in the long-range minimum control wireless mode (LRM), real-time remote control is not supported. Similarly to MRM, the ROV position is controlled through the transmission of way-points but the low-quality video stream cannot be transmitted. This mode supports only position monitoring, LBL positioning, and the transmission of status updates. It makes use of an MF underwater multihop network to extend the control range up to 10 km from the control station. The operator in this case can only receive information about the ROV position and can transmit the next position that the vehicle needs to reach. In this mode, the average time between the transmission of 2 consecutive way-points is 60 s. Also in this mode, the gripper cannot be maneuvered due to the lack of video monitoring; still, it is possible for the ROV to take some high-definition pictures once it reaches a way-point. Moreover, an automatic identification system can decide to take some pictures in some areas where there might be damage to the underwater asset. These pictures will be stored in the ROV and buffered until the system switches to SRM mode. LRM requires traffic up to 50 bps from the control station to the ROV (that includes way-point transmission and control of lights and cameras) and at least 75 bps from the ROV to the control station to transmit periodic status information.  
In both the MRM and LRM modes, the remote control requires a high level of autonomy for the underwater vehicle, that behaves more like an AUV than an ROV.
# Optimizing Charging Pad Deployment by Applying a Quad-Tree Scheme
## 2 Related Works  
### WPT for WSNs  
WPT, a technique for transmitting energy over long distances, finds applications in various domains such as portable devices, vehicles, and UAVs [16]. In the context of wireless sensor networks (WSNs), researchers have explored methods for leveraging WPT to directly supply energy to sensor nodes experiencing energy deficits, thus addressing the challenge of limited energy resources [17]. Wireless charging technology facilitates energy transmission to sensors.  
The deployment of wireless charging by drones enables continuous energy replenishment for sensors deployed in inaccessible outdoor environments, eliminating the need for maintaining a physical charging infrastructure. For instance, [PERSON] et al. [18] investigated the efficiency enhancement of a system providing wireless power for rail transportation equipment. They analyzed electromagnetic characteristics using finite element methods, including magnetic equivalent resistance, inductance, magnetic coupling rate, and magnetic core loss. Furthermore, they applied the results of magnetic field finite element analysis to equivalent circuit modeling, analyzing voltage transmission ratio and input/output characteristics of the CLLC resonant converter designed for wireless power transmission.  
In recent years, the advancement of wireless power transfer (WPT) technology has enabled its use in providing additional energy for wireless sensor networks [2]. Such wireless rechargeable sensor networks can serve as development platforms [19], comprising a base station (BS), several wireless communication sensor nodes capable of wireless charging, wireless charging vehicles (WCVs), and unmanned aerial vehicles (UAVs) equipped with WPT devices. The base station collects sensor data and offers rapid battery charging services for WCVs or UAVs, which can then wirelessly charge sensor nodes to replenish their energy.
## 6 Proposed System Model and Machine Learning Prediction Models
### Feature Selection Methods  
Feature selection enables a reduction in the computational complexity, as well as the computational time [47]. Feature selection reduces the number of features that will be applied to the classifier model by keeping only features that are more important for call failure/success.  
In this work, we will consider two different supervised feature selection methods, namely: the Filter method and Wrapper method combined with four different classifiers. The feature selection methods are as follow;  
1. **Filter method:** In the Filter method, the relationship between each input feature and output feature is measured, and each feature is granted a scoring based on statistical tests. Then, the features with a high score are considered. Filter methods are preferred in terms of their easiness in implementation and very low computational time. Several algorithms are present in the Filter method feature selection technique. Algorithms are chosen based on dataset properties such as data dimensions, percentage of redundant, irrelevant features and interaction between features [48]. Since the features present in IMS calls are highly dependent on each other, we use the Relief algorithm [49].
2. **Wrapper method**: In the wrapper method, evaluation of features is performed through training a model using ML algorithms, then selecting the best performing features in the model [50]. Moreover, Wrapper methods contain different algorithms. Swarm-based meta-heuristic algorithms have been widely used in several engineering fields due to the fact that they have an advantage over other classes of nature-inspired algorithms [51]. It offers an edge over evaluation-based algorithms by preserving the search space information after each subsequent iteration and fewer operators for successful execution [52]. We have used two of these Swarm Algorithms, WOA and SMA. We have chosen both algorithms for their high capability of solving complex optimization problems compared with other algorithms. In addition, we had a large number of features (256 features) inside our dataset, which led to complex optimization problems.
# Effect of Data Augmentation on Deep-Learning-Based Segmentation of Long-Axis Cine-MRI
## 2 Materials and Methods
### Methodology
#### 2.3.2 Data Augmentation  
Training was performed with and without data augmentation to observe the influence on the segmentation accuracy. The augmentation was performed following a hierarchical procedure as outlined in Figure 3, where original data were duplicated into multiple groups. Each group is associated with a specific image alteration and follows a set of predefined rotations.  
The first stage comprised generating rotated versions of the input image \(r\) times, where \(r\in\{3,7,11\}\) depending on the chosen configuration. The rotation angles took on values from the following sets:  
\[R_{1} \in\{-30^{\circ},0^{\circ},+30^{\circ}\}\] \[R_{2} \in\{-45^{\circ},0^{\circ},+45^{\circ}\}\] \[R_{3} \in\{-60^{\circ},-45^{\circ},-15^{\circ},0^{\circ},+15^{\circ},+ 45^{\circ},+60^{\circ}\}\] \[R_{4} \in\{-90^{\circ},-60^{\circ},-30^{\circ},0^{\circ},+30^{\circ},+ 60^{\circ},+90^{\circ}\}\] \[R_{5} \in\{-90^{\circ},-75^{\circ},-60^{\circ},-45^{\circ},-15^{\circ}, 0^{\circ},+15^{\circ},+45^{\circ},+60^{\circ},+75^{\circ},+90^{\circ}\}\]  
where \(|R_{1}|=3\), \(|R_{2}|=3\), \(|R_{3}|=7\), \(|R_{4}|=7\) and \(|R_{5}|=11\). This resulted in \(Nr\) images, where \(N\) is the total number of images in a given set.  
The rotated images obtained from stage 1 were then subjected to alterations in the image intensity and noise during stage 2. An initial intensity normalizing augmentation was applied by scaling the pixel intensities with a uniformly sampled random value from the interval \([0.6,1]\). Subsequently, two parallel subgroups of images were generated. The first subgroup applied additive zero-mean Gaussian noise with a standard deviation uniformly sampled from the interval \([1\times 10^{3},5\times 10^{3}]\). The second subgroup applied another intensity variation, this time uniformly sampled from the interval \([0.3,1]\).  
The third stage comprised generating horizontally and vertically flipped versions of the already altered images from stage 2.
## 2 Methods
### The Design of the Data Compression Algorithm
#### 2.3.1 Differential Coding Algorithm  
Upon receipt of the profile detection data by the Beidou transparent transmission terminal, it extracts the time, temperature, depth, and salinity information, respectively, preserving the first data and representing the subsequent data as the difference between it and the previous data [19]. Take the time parameter, for example. The difference between the time of the second frame of data and the previous time is 243, which may be used as a substitute for the original value, reducing 9 bytes compared to one frame of data.  
\begin{table}
\begin{tabular}{r} \hline
**Profiling Data (Time, T/\({}^{c}\)C, D/m, C/uS/cm)** \\ \hline
230224071515, 01.3870, 4001.1, 0376 \\
230224073436, 01.3896, 3882.5, 0404 \\
230224074043, 01.3919, 3846.1, 0404 \\ \hline
230224173957, 28.8629, 0002.1, 0872 \\
230224174020, 28.8775, 0001.3, 0872 \\ \hline \end{tabular}
\end{table}
Table 1: Profile detection data table.
# Estimation of the Achievable Performance of Mobile Ad Hoc Networks with Optimal Link State Routing
## 2 Models and Restrictions  
### Network Features and Network Structure Description  
It is assumed that the network is formed in urban or rural environments only with mobile user terminals (smartphones and laptops) using Wi-Fi technology, in conditions of absence or impossibility to provide communications through the infrastructure of other wireless communication networks--in emergency incidents, in regions with a lack of developed communications infrastructure, as well as in the case of failure of the main communication network.  
* The network infrastructure includes only user devices and the communication channels between them.
* The network topology can change, but these changes do not occur faster than the average packet transmission time from source to destination.
* Network devices can be sources of transmitted information or receivers of it, or perform routing functions.
* At the physical layer, the devices are interconnected based on 802.11n and 802.11 ac, at center frequencies of 2.4 GHz and 5 GHz, respectively.
* Nodes in the network are assumed to be located both outside and inside buildings.
* The number of user devices in the network is limited by the requirements of a specified quality of the service level.
* The distance between network nodes is chosen based on the permissible level of signal attenuation.
* The routing algorithms used in the network are OLSR with MPR optimization.
* The traffic to be transmitted is VoIP, dialog data, streaming video, and latency-insensitive data.  
Figure 1 depicts a diagram of a network segment, including communication nodes and connections between them.
# A Multi-Objective Evaluation Method for Smart Highway Operation and Management
## 4 Methods for Assigning Weights to Evaluation Indicators
### CRITIC Method  
The CRITIC [58] method is an objective weight assignment method frequently utilized for analyzing data with strong inter-indicator correlations while accounting for the variability among indicators. Scholars have identified that the critical method can comprehensively measure the contrast strength and conflict between indicators. However, it cannot measure the degree of dispersion between indicators [59]. The entropy weight method determines indicator weights based on the dispersion level between them. Combining the critical method with the entropy weight method can provide a more objective reflection of indicator weights [60]. The calculation steps are as follows:  
Step 1: Data normalization.  
Step 2: Calculate indicator variability.  
Employ the standard deviation to represent the variability of internal values for each indicator. A larger standard deviation signifies increased numerical variation for the indicator, reflecting more information. The indicator's evaluation strength is enhanced, leading to a higher weight assignment. \(S_{j}\) represents the standard deviation of the \(j\)-th indicator.  
\[\left\{\begin{array}{l}\overline{x}_{j}=\frac{1}{n}\sum_{i=1}^{n}x_{ij}\\ S_{j}=\sqrt{\frac{\sum_{i=1}^{n}\left(x_{ij}-\overline{x}_{j}\right)^{2}}{n-1 }}\end{array}\right. \tag{19}\]  
Step 3: Calculate the indicator conflict.  
Use the correlation coefficient \(R_{j}\) to represent the correlation between indicators. The stronger the correlation with other indicators, the less conflict there is with those indicators. This reflects duplicated information, to some extent weakening the evaluation strength of the indicator and reducing the weight assigned to it. The formula is as follows:  
\[R_{j}=\sum_{i=1}^{p}\big{(}1-r_{ij}\big{)}. \tag{20}\]  
Step 4: Calculate information quantity.  
The larger \(C_{j}\) is, the greater the impact of the \(j\)-th evaluation indicator in the entire evaluation indicator system; thus, more weight is assigned to it.  
\[C_{j}=S_{j}\sum_{i=1}^{p}\big{(}1-r_{ij}\big{)}=S_{j}\times R_{j} \tag{21}\]  
Step 5: Calculate weights.  
\[w_{j}=\frac{C_{j}}{\sum_{j=1}^{p}C_{j}} \tag{22}\]
# Detecting IoT Attacks Using an Ensemble Machine Learning Model
## 4 Simulation Environment
### Dataset Description  
The NSL-KDD dataset ([[https://www.unb.ca/cic/datasets/nsl.html](https://www.unb.ca/cic/datasets/nsl.html)]([https://www.unb.ca/cic/datasets/nsl.html](https://www.unb.ca/cic/datasets/nsl.html)), accessed on 20 March 2022) is used for the simulation of this work. It contains 41 features to describe each specific entity in an IoT network. Details on network intrusions with these 41 features can be segmented into computational information (service, flag, land, etc.), content-based information (login information, root shell information, etc.), duration-based (such as duration from host to destination transfer, error rates), and host-based information (host and destination ports and counts information).  
In Figure 4, the NSL-KDD dataset is represented by two layers: (1) the inner layer represents different types of IoT attacks in the dataset, such as Probe, DoS, U2R, and R2L; (2) the outer layer represents examples of attacks within each category. Attacks such as Saint, Satan, Nmap, and portsweep, which can be found in Figure 4, come under the Probe IoT attack category. In these attacks, the attacker scans a network device to determine potential weaknesses in its design, which are subsequently exploited in order to gain access to confidential information, as described in Section 4.  
Figure 4: Layerwise NSL-KDD dataset description.  
Likewise, attacks such as Neptune, Teardrop, Worm, and Smurf fall into the category of DoS attacks. These attacks cause a denial of service when an attacker consumes resources unnecessarily, making the service unavailable for legitimate users. Moreover, Sendmail, Multihop, and phf belong to R2L (remote-to-user) attacks, while Perl, text, and sqlattack belong to U2R (user-to-root) attacks. In Figure 4, variables are underlined according to their segment. Most variables in this dataset are nominal. There are three basic protocol types, TCP (transmission control protocol), UDP (user datagram protocol), and FTP (file transfer protocol), that exist in the dataset.