esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Novel Algorithms  
The same system was used as in [19]. This ESS consists of fixed sensor nodes (SNs) and mobile communication nodes (CNs). The sensors are pre-deployed at fixed locations in the building, having short range wireless communication capability so they can directly communicate with CNs in range. SNs are only utilized for civilian localization, _i.e._, to tell the portable CNs about where is the mobile user in the building, as in an indoor environment GPS localization is not reliable. Therefore, SNs do not form a conventional wireless sensor network and hazard information is not disseminated among SNs, because the limited energy and physical capabilities of SNs. The communication nodes (CNs) form a network in an opportunistic manner as devices come into contact. Opportunistic communication enables the dissemination of messages in order to gather and convey information for situational awareness, such as the condition and location of the hazard. The described ESS assumes that each CN stores the graph representation of the area (_i.e._, the layout of a shopping centre) and can communicate each other via its radio interface.  
Our proposed algorithms, a path finding algorithm and a communication protocol, are based on the same idea: always to maintain the direction from the hazardous area (_i.e._, fire area) to the exits. With the help of this direction our navigation system can guide the civilians not through the shortest path but through a safer one. Once a CN (which is carried by a civilian) observes hazard in its local environment it generates a new emergency message (EM) that includes the location of the fire (gathered from the SNs), the hazard intensity, the device ID and the observation timestamp. Each EM is identified by its (device ID, timestamp) pair. CNs use oppcomms to disseminate EMs to other CNs. From the received EMs a CN can update its own local graph (which is stored on the device) and calculates a path to the determined exit. The algorithm for the path selection will be explained in Section 2.1 while the protocol, which is used for data dissemination among CNs, will be defined in Section 2.2.
## 4 Analysis and Findings of Research Questions
#### 4.1.3. Smartphone User Activities Based on Physical Modality  
Some studies discovered that motion and orientation sensors (gyroscope, accelerometer) might be breached due to a user's behavior [96; 98]. Nevertheless, the user uses mobile web browsers, carries out touch actions (tapping, scrolling, holding, and zooming), and uses PINs. Such actions enable a remote website to learn about the user's client-side activities [96].  
Other activities potentially leading to mobile phone exploitation include the use of smartwatches and wearable accessories, which expose mobile devices to vulnerabilities [101; 112]. Furthermore, people frequently hold mobile phone devices with essential data assets [107]. Apparently, users are inclined to download attachments from unknown sources, share their location through social networking sites, and browse the web while monitoring their devices' most recent downloads and interests [92; 93; 109].
## 3 Robot Functionalities: People Perception, Human Following and Pick and Placing
### Pick and Placing
#### 3.4.3 Visual-Servo Control  
After estimating the object (\(\hat{g}^{c}\)) and end-effector (\(\hat{h}^{c}\)) poses in the camera-frame, the error is computed as the difference between the two and minimized by the following proportional control law [37]:  
\[u^{0}=-k\ \hat{T}^{0}_{c}(\hat{h}^{c}-\hat{g}^{c}) \tag{1}\]  
where \(\hat{T}^{0}_{c}\) is the transformation matrix from camera to root frame, based on measurements; \(k\) is a proportional gain parameter; and \(u^{0}\) is the resulting root-frame velocity required for the arm to approach the object.  
Since both poses are subject to the same camera calibration, the accuracy of the system is independent from calibration errors. The system is also robust to joint sensor errors as the end-effector is being continuously observed in the camera frame. To control the arm in joint-velocity mode, the end-effector's Cartesian velocity must be translated into six joint velocities for the arm's revolute joint actuators. This is done through _inverse differential kinematics_ where the arm's Jacobian matrix is inverted through singular value decomposition and multiplied by the end-effector velocity vector, resulting in a column vector of joint velocities, sent to the arm driver. Figure 6 shows the diagram for the described architecture.  
Visual servoing is used on the final grasp approach, after the end-effector is already visible to the MBot's head camera. We developed a complete grasping pipeline, making use of the MoveIt! ROS framework, that begins by placing the arm in a pregrasp pose (selected based on a modular criterion, currently the object's height), which subsequently activates visual servoing, stopping when an acceptable distance \(s\) (configurable, currently 1.2 cm) is reached, at which point the fingers are closed and the object is grasped.
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 1 Introduction
* Multisource dataset construction: We assemble a regional forest carbon stock dataset by integrating Sentinel-2 spectral variables with field-based carbon density measurements from 150 inventory plots in the Liuchong River Basin, Guizhou. * Comprehensive performance evaluation: We benchmark the proposed model against a suite of classical and advanced models (RF, GBDT, ViT, Swin Transformer), demonstrating its superiority in accuracy and generalization across heterogeneous forest conditions. To guide this study, we formulated the following four research questions:  
1. Can an enhanced ResNet architecture improve the accuracy of forest carbon stock estimation compared to conventional machine learning and deep learning models? 2. How effectively can multispectral Sentinel-2 data represent structural and spectral heterogeneity of forest ecosystems in mountainous terrain? 3. Is the proposed model generalizable across different forest types and elevation zones within the study area? 4. What is the spatial mapping capability of the proposed framework in producing continuous, high-resolution carbon stock estimates?
# Classification of Children's Sitting Postures Using Machine Learning Algorithms
## 4 Results  
### Results Comparison with Individual Validation  
The maximum and minimum accuracy for NB were 0.995 and 0.656, respectively. The average accuracy for NB was 0.871 (Std. = 0.113). For MLR, the minimum and maximum accuracies were 0.737 and 0.977, respectively. The average accuracy was 0.845 (Std. = 0.091). In case of DT, all the average accuracies were less than 80% for the selected parent sizes. The minimum and maximum average accuracies for each parent size were 0.787 and 0.794, respectively, with a difference of less than 1%. The highest average accuracy was 0.794 (Std. = 0.083) when the parent size was 15, and the smallest standard deviation was obtained for a parent size of 10 (Std. = 0.077). For NN, training and test accuracies were obtained under the conditions of number of layers and neurons. Except for the accuracy of the combination of one layer and 20 neurons, all the results showed that the classification performance was higher than 90%. The highest value was 0.921 for the combination of one layer and 40 neurons. In the case of this combination, the standard deviation also showed the smallest value (Std. = 0.057), which could indicate that this combination is optimal for this data. In case of SVM, the best accuracies of test were acquired under the condition of the combination of the values of c (lambda) and gamma. The average accuracy was found to be greater than 0.9 for all the conditions except for the gamma value of 0.5. The overall accuracy was highest at 0.942 (Std. = 0.057) for the condition in which the lambda was 0.05 and gamma was 0.05.  
Table 4 shows the results of the CNN model in comparison with the rest of the models used in this study. For DT, NN, and SVM, the accuracy is presented for combinations of parameters that  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Actual Class**} \\ \cline{3-4}  & & **Positive** & **Negative** \\ \hline \multirow{2}{*}{Predicted class} & Positive & True positive (TP) & False positive (FP) \\  & Negative & False negative (FN) & True negative (TN) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix.  
showed the best results. The individual validation results of CNN showed that the accuracy was at least 0.902, and the maximum accuracy was 1.000. The average accuracy was 0.953 (Std. = 0.037). Among learning algorithms, the performance of DT was the lowest. NB and MLR showed an accuracy of more than 80% but not more than 90%. NN, SVM, and CNN showed an accuracy of more than 90%. The performance of CNN was better than all the other learning algorithms in terms of average accuracy and standard deviation.
## 4 Case Study
### Characteristics Analysis of the Dam Deformation Zoning
With the advance of the dam operation, the deformation zoning gradually extends to the outside, and the level of the zoning is gradually improved. The sensitive deformation measuring points with more changes of the dam mainly include PL11-3, PL11-4, PL11-5, PL13-3, PL13-4, PL16-3, PL16-4, and PL16-5. Dam managers need to focus on the monitoring status of measuring points in the middle of the dam body and study and judge the safety status of dam operation in real-time. In addition, since the overall displacement of the areas on both sides of the dam (Zone VI) is small, managers need to pay attention to the measuring points in Zone VI with fast displacement change rate to evaluate the behavior of the dam, especially the measuring points with abnormal data in Zone VI. Managers need to comprehensively evaluate the performance of the dam according to the state of each zone of the dam.
# Data Fusion Analysis and Synthesis Framework for Improving Disaster Situation Awareness
## 2 Background and Related Work
### UAVs and Data Fusion Techniques
The authors give a detailed comparison of the important algorithms by referring to their performances and propose extending the frameworks with deep learning techniques. Our focus in this article is more on the fusion of heterogeneous data sources but not on the techniques adopted in the implementation of individual data sources. In the literature, data fusion techniques are classified as signal fusion and information/data fusion [39]. Signal fusion is used to combine information from multiple sources that measure the same type of physical phenomena, but with different characteristics. Information fusion combines data/information from multiple sources to provide a more accurate understanding of a situation. This article adopts information fusion techniques. Due to the progress in sensor technologies and related systems, in addition to UAVs, it may be beneficial to adopt multiple data sources to enhance awareness of disaster situations. None of the publications, however, propose analysis and synthesis methods for the purpose of determining the optimal set of data sources, before expensive design and implementation activities are carried out. Moreover, model-based frameworks are in general not supported, meaning that system architectures are fixed and when needed, they can not be easily extended, for example, by introducing new geographical and data source models, and different analysis and synthesis algorithms.
# A Theoretical Study on the Resilience Evaluation Method of Operational Road Tunnel Systems
###### Abstract  
Road tunnel operation will suffer from a lot of uncertain external disturbances, which will greatly affect the operational safety of road tunnels and even block traffic. Focusing on road tunnel operation safety and disaster-resistant ability, the concept of resilience is introduced to provide a scientific and effective basis for road tunnel operation and emergency management. In this paper, the concept of tunnel system resilience was proposed based on the concept of system resilience. A theoretical analysis model of road tunnel resilience was built to describe the change in road tunnel system function over time due to external disturbances (e.g., fires, traffic accidents, floods, earthquakes). Five fundamental attributes of road tunnel system resilience were proposed to describe the resilience level. A resilience evaluation method for road tunnels was proposed based on the functional network. The vulnerable links of road tunnels subjected to external disturbances can be analyzed using this method. This study will provide important references for the resilience evaluation method of road tunnels and risk mitigation strategies.  
road tunnel system; resilience; resilience evaluation model; resilience evaluation method +
Footnote †: journal: Applied sciences
# Coverage Strategy for Small-Cell UAV-Based Networks in IoT Environment
## 6 Further Discussion  
We present here additional insights and directions for future research based on the results and limitations of the study. One potential avenue for future research is to investigate the impact of environmental factors such as weather conditions and terrain on the performance of the proposed strategy. Another area of interest is the integration of multiple types of UAVs with different capabilities and constraints into the network, which may require a more sophisticated coordination mechanism. This section also discusses the potential use of reinforcement learning techniques to improve the performance and adaptability of the proposed strategy. Additionally, this section highlights the need for further evaluation of the proposed strategy in real-world scenarios and the importance of considering ethical and privacy issues related to the use of UAVs in wireless network applications. Finally, this section concludes with a call for collaboration between researchers and industry practitioners to address the challenges and opportunities of UAV-based wireless networks in IoT environments.
###### Abstract  
The study aimed to assess the success and survival rates of post-extraction implants with immediate loading. A systematic search was performed in the Medline (PubMed), Cochrane Library (Wiley), and Scopus (Elsevier) databases to identify randomized and non-randomized studies of intervention (NRSI) on bone loss and success and survival rates in post-extraction implants with immediate, early, and delayed loading. Articles were selected based on predefined inclusion and exclusion criteria, and the risk of bias was assessed following Cochrane guidelines, and the Newcastle-Ottawa Scale was used to assess quality. A fixed-effect meta-analysis was conducted to evaluate bone loss using mean, median, and standard deviation, and to calculate odds ratios for success-survival rates. Of the 13 studies identified, three met the criteria for inclusion in the meta-analysis, involving a total of 178 patients and 296 post-extraction implants. Bone loss was the lowest in the delayed loading group (0.51 mm) compared to immediate (0.55 mm) and early loading (0.54 mm). Implant failures were similar in the immediate and early groups (one case each), while two failures were reported in the delayed group. Delayed loading reduced peri-implant bone loss, but immediate loading showed a slightly higher success-survival rate. Further high-quality studies are needed to strengthen the evidence on the long-term effects of the different implant loading protocols.  
immediate loading; marginal bone loss; post-extraction dental implants 2024  
[PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Immediate Loading of Post-Extraction Implants: Success and Survival Rates: A Systematic Review and Meta-Analysis. _Appl. Sci._**2024**, _14_, 11228. [[https://doi.org/10.3390/app142311228](https://doi.org/10.3390/app142311228)]([https://doi.org/10.3390/app142311228](https://doi.org/10.3390/app142311228))
# Medinoid: Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning +
## 2 Methodology
### Convolutional Neural Networks
In our research, however, we made use of the Adaptive Moment estimation (ADAM) optimizer [42], so as to be able to overcome a number of drawbacks of SGD, including a slow convergence and a high error fluctuation. **ResNets:** Microsoft Research introduced ResNets in 2016 [39]. These models obtained the first place in the ImageNet and the COCO2015 competitions, covering the tasks of image classification, semantic segmentation, and object detection. The core idea behind ResNets is the use of a residual network, which leverages skip connections to jump over some layers, reducing the impact of vanishing gradients, as there are fewer layers through which to propagate. As such, the use of residual networks makes it possible to train networks that are deeper, obtaining lower training errors and higher levels of accuracy. Depending on the number of residual networks, ResNets come with certain variations. In our research, we used a ResNet with 152 layers (i.e., ResNet-152), given that this network, among the different ResNets available, is able to obtain some of the highest accuracy levels. **InceptionNets:** Unlike VGGNets and ResNets, InceptionNets [31] make it possible to better focus on the location of features that are important for the purpose of classification. Since salient parts can have different sizes per image, choosing the right receptive field size is difficult. In addition, in deep networks, there is the issue of overfitting and the issue of vanishing gradients. Therefore, Google Research introduced a new module, called \"inception\", having several receptive fields with a different size [43]. Specifically, filter sizes of \(1\times 1\), \(3\times 3\), and \(5\times 5\) were used, making the network wider. After max pooling, each output is concatenated and then sent to the next inception module. Over the course of time, inception modules have been modified and improved, leading to more powerful InceptionNets. Inception-ResNet-v2 and Inception-v4 are currently the most effective networks. By default, InceptionNets take as input images with a size of \(299\times 299\times 3\), with pixel values belonging to \([-1,1]\). The optimizer used is also different from the previous networks: InceptionNets typically make use of the Root Mean Squared Propagation (RMSProp) optimizer [44].
## 5 Intelligent Fault Diagnosis of Sensor Faults
### Artificial Intelligence-Based Fault Diagnosis Methods  
With the widespread adoption of Ag-IoT, the uncertainty and complexity of the sensor system have gradually grown, as has the difficulty of developing an accurate mathematical model. The artificial intelligence-based fault diagnosis method treats the system as a black box, does not need to understand the structure and principle of the system and the precise mathematical model of the diagnostic object, and uses a large amount of sensor system data for fault diagnosis [91]. The method relies primarily on real-time or historical data and comprises artificial intelligence algorithms and statistical data processing. The primary benefit of this method is that it does not require a precise system model, and it has been utilized successfully for agricultural sensor fault diagnosis. Common fault detection techniques based on artificial intelligence include statistical analysis, expert systems, and machine learning.
# HinPhish: An Effective Phishing Detection Approach Based on Heterogeneous Information Networks
## 2 Related Work
### Anti-Phishing Techniques
#### 2.1.2 Site Content-Based Techniques  
Site content-based phishing detection techniques [12; 13; 14] separately extract some common attributes (e.g., URL, text, and image) in the website and then use heuristic or machine learning methods for phishing detection. Although there is no guarantee that all phishing websites have these extracted attributes, there is a certain amount of under-reporting. These technologies can detect the latest phishing attacks that list-based technology cannot. Moreover, some excellent attributes can also lead to a satisfactory under-reporting ratio.  
**Extracting features from URLs** is a commonly used method in this category of techniques. [PERSON] et al. [15] and [PERSON] et al. [16] detected phishing websites by counting the number of characters, hyphens, redirects, numbers, and other information. Similarly, other researchers [17; 18] have detected phishing websites by judging the semantics of URLs [19]; for example, they checked whether the URLs contained an IP address, brand name, https protocol tag, special characters (7,@,*, -/), and specific words.  
**Extracting features from source code** is another technique that is often used. Researchers identify the legitimacy of websites by extracting features from webpage elements, including keywords, tags, and images [9; 20; 21; 22; 23]. In addition, some works have attempted to find a breakthrough, in terms of comparing the visual similarity [24] between suspicious and normal websites. With the development of deep learning technology, this kind of research has made significant progress [25] in recent years. Finally, another line of work [18; 26; 27] attempts to detect phishing websites based on the relationships between hyperlinks, for instance, by analyzing the proportion of foreign links, broken links, common URLs, and null links. Our work, similarly, focuses on relationship of hyperlinks. The difference is that we regard the relationships between links as the different edges in HIN and construct the HIN model of link objects to discover complex semantic information.