esa-sceva/satcom-chunk-collection huggingface dataset:
# An Outdoor Navigation System for Blind Pedestrians Using GPS and Tactile-Foot Feedback
## 3 System Description
### User Interface  
A vibrotactile interface provides the user with navigational directions to reach a destination. The main novelty of this approach is that directions are conveyed via tactile foot-stimulation.  
Based on the physiology of the plantar surface of the foot [43], a tactile interface consisting of a four-point array of actuators was conceived to stimulate the fast adapting type I (FAI) mechanoreceptors sensitive to low frequency vibrotactile stimulation on the medial, lateral, and tibial plantar areas (Figure 3a).  
Figure 3: On-shoe tactile display: (**a**) Distribution of fast adapting type I (FAI) mechanoreceptors sensitive to vibrotactile stimulation in the foot sole, Reproduced with permission from [PERSON], [PERSON], Journal of Physiology; published by John Wiley and Sons, 2004.; (**b**) Prototype; and (**c**) Fully wearable device with wireless connection.  
Figure 3b shows the prototype developed. This device integrates four vibrating actuators in a commercial, inexpensive foam shoe insole. Actuators provide axial forces up to 13 mN and vibrating frequencies between 10 and 55 Hz. Each actuator can be independently controlled with a specific vibrating frequency command. Dots of an epoxy paste cover the actuators' entire upper surfaces and ensure a 133 mm\({}^{2}\) contact surface with the foot sole. Vibrations are correctly transmitted through the dots, while the absorption characteristics of the foam prevent them from having an expanding vibration effect throughout the insole.  
The tactile display is meant to be used on the right foot, and it is completely wearable (Figure 3c). Note that battery, RF transmission module, and control circuitry are all embedded in an electronic module that the user carries comfortably attached to the ankle. Experimental tests revealed a 6 h continuous operation of the device, and a 200-m communication distance range with the controlling computer. Note that the tactile display is further inserted into the shoe, and the electronic module can be covered by the user's clothing, thus becoming an inconspicuous, visually unnoticeable assistive device.  
This device is the third version of tactile interfaces for the foot that we have developed. It integrates technological and tactile rendering improvements of the two previous prototypes [44; 45].
# Transmission Line Fault Classification of Multi-Dataset Using CatBoost Classifier
## 3 Methodology  
It was possible to achieve fault classification by using phase and zero-sequence current fault data obtained from simulated models. The diagram in Figure 3 shows the data processing model for machine learning used for this article. It involved accessing and loading the data collected from the simulated model into the trainer. Next, the data collected were processed by looking for the data points outside the fitted end of the rest of the data to see if they could be ignored or considered [34].  
The next step was to derive features by turning the information into a machine-learning algorithm to improve accuracy, boost model performance, improve model interpretability and prevent overfitting. This was preceded by building and training the model where a confusion matrix was plotted to compare the classification made by design with the actual data collected. Next, we improved the model by checking the correlation matrix to remove variables that were not correlated. The fault data type was introduced in the 500 km, 500 kV transmission line and the dataset was divided into three categories: training, testing, and validation. Each dataset was trained and analysed for final validation, accuracy, errors and performance.
# Traffic-Predictive Routing Strategy for Satellite Networks
## 4 Route Discovery
### Implementation of Path Replacement Mechanism  
The G-AODV protocol uses a path replacement mechanism to avoid heavy-load nodes in the path. The G-AODV protocol adds routing congestion status (TAG_CONGEST) and congestion control signal (RCS) based on the original AODV routing protocol. The congestion control signal RCS contains relevant information about the source node and the destination node. The upstream node will use this information to reselect neighbor nodes to forward the routing request packet. If the satellite node receives the congestion control signal, it will reselect the neighbor node to change the route for sending the route request packet.  
The structure of the congestion control signal RCS packet is shown in Table 3.  
The satellite node sends a congestion control signal to inform the upstream node that its downstream node may become a congested node and therefore cannot accept new business traffic. After the downstream node constructs a congestion control signal and sends it out, it sets the routing status in the routing table to the TAG_CONGEST status. In the route congestion state, the upstream node will cache the data packets sent to the downstream node, and the downstream node will not accept any RREQ packets. Therefore, the downstream nodes have enough time to relieve their heavy load conditions. If the congestion condition of the downstream node is relieved, it will continue to receive RREQ messages.  
After receiving the congestion control signal, the upstream node caches the data packets of the route, extracts the relevant information of the destination node and source node in the congestion control signal, and sends an RREQ request to the neighbor node.  
The G-AODV route discovery process is shown in Table 4.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{2}{c}{**G-AODV Route Discovery Process:**} \\ \hline Step 1: & Determine whether there is a path from the source node to the destination node. If satisfied, the data are transmitted. \\  & Otherwise, execute Step 2. \\  & The local node sends a HELLO packet to the neighbor node to obtain the neighbor node traffic information and record it in the neighbor list of this node. \\  & \\ \hline Step 3: & Calculate the network traffic threshold according to Formula (1). \\ Step 4: & Determine whether the traffic prediction value is less than the network traffic threshold. If satisfied, execute Step 5; \\  & otherwise, execute Step 8. \\ Step 5: & Forward the RREQ packet to the downstream node. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Route discovery process.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{2}{c}{**Fields of RCS Packet**} \\ \hline \hline \multicolumn{2}{c}{rc\_dst} \\ \hline \hline \multicolumn{2}{c}{rc\_S\_address} \\ \hline \hline \multicolumn{2}{c}{rc\_D\_address} \\ \hline \end{tabular}
\end{table}
Table 3: RCS package structure.
# Investigation of Guidewire Deformation in Blood Vessels Based on an SQP Algorithm
###### Abstract  
This paper proposes a solution to the simulated deformation of guidewires when they come in contact with the blood vessel in an interventional surgery simulation training system. Starting from the principle of minimum energy, the guidewire is evenly dispersed into a rigid light bar articulation model. A sequential quadratic programming (SQP) algorithm is used to nonlinearly optimize the deflection angle of each light bar. When the elastic potential energy of the guidewire reaches a minimum, we can get the guidewire deformation we want to solve. The method proposed in this paper avoids necessity of delving into contact deformation caused by the contact force between the guidewire and the blood vessel wall, while solving the problem of the deformation of the guidewire due to the pose of the contact points. We use an ABAQUS (finite element software) simulation to verify that this solution has a theoretical simulation accuracy of 5.11%, and the designed experiments prove that the actual simulation accuracy is about 11%. Moreover, we also simulated the bending stress state of the guidewire by using the deflection angle of each bar. In addition, in order to achieve the most suitable simulation results, we discuss the discrete density of the guidewire model from the perspective of algorithm time consumption and simulation accuracy.  
interventional surgery; guidewire; deformation simulation; SQP algorithm; simulation accuracy
## 4 Experiment
### Parameter Optimization  
#### 4.2.1 Threshold Search  
This section compares the traffic classification accuracy and speed when the incremental strategy is adopted, and the threshold varies from 70% to 95%. The aim is to evaluate the impact of these thresholds on the algorithm's performance. XGBoost is used as the benchmark classifier, taking the VPN-nonVPN dataset as an example. The analyzed indicators include classification accuracy, precision, recall, F1-score, and classification time. The detailed results are shown in Figure 2.  
Overall, it is evident from the experimental results presented in the tables that adjusting the confidence threshold has a significant impact on traffic classification, accuracy, and speed. Both classification accuracy and classification time are positively correlated with the threshold, which is consistent with the inference we provided in Section 3. Taking all factors into comprehensive consideration, it is more appropriate to select a confidence threshold in the range of 0.8 to 0.9. Although a threshold lower than 0.8 may result in a faster speed, it may reduce the accuracy. In contrast, a threshold higher than 0.9 can improve accuracy in some cases, but it is likely to cause excessive data packet processing and an extension of waiting time. Considering both the classification time and classification accuracy, the threshold used in the following examples in this paper is 85%.  
In addition, we also list in Table 4 the changes in the average number of data packets when different thresholds are used to obtain the classification results of the method in  
Figure 2: Classification performance with different confidence thresholds in the incremental strategy.  
the VPN dataset. As the threshold increases, an increasing number of flows are classified by classifiers with larger K-values. As can be seen from the experiments in Section 4.4, classifiers with larger K-values have a higher classification accuracy compared to those with lower K-values. Therefore, as the threshold increases, the classification accuracy is correspondingly enhanced.
## 3 Results and Discussion
### Comparison and Evaluation of Prototype DPIs
#### 3.2.3 Comparison of Injection Rate Results  
Figures 17 and 18 compare the injection rate results between Types 1, 2, 3, and the conventional type DPI. To confirm the reliability of the injection rate meter, the injection rate obtained from the meter used in this study was first validated with reference data from Delphi [10]. It was observed that the injection quantity of the Type 1 DPI is 3.14 mg, which is relatively smaller than Type 2 or Type 3, as shown in Table 9. Since the spring constant K is relatively higher than the leaf spring, the length of the compressed spring is shortened when the needle is lifted, so the distance of travel of the needle in the Type 1 DPI is also shortened, and the injection quantity is expected to be reduced. In the case of Type 2, the injection amount is high after the start of injection, but then falls sharply. Compared to Type 3, the end of the injection (EOI) of the Type 2 DPI occurs early and pressured fuel moves freely in the needle. So, the Type 2 DPI has a higher injection rate than other DPI types, until it reaches the EOI. This is a key cause of decreasing injection quantity due to the quick closing operation.  
The effect of the injection rate on combustion is discussed in Section 3.2.4. It predicts that the Type 2 DPI has a higher NO\({}_{x}\) emission due to its higher injection quantity at the beginning of the combustion. It can be inferred that the Type 3 DPI might have the advantage of multiple injections, because the test results indicated that it had a lower injection delay and faster injection start, with a higher initial injection quantity. Therefore, a pilot injection strategy was applied and described, as shown in Figure 19b.
## 2 Materials and Methods
### Model Selection, Implementation, Training and Optimization  
A CNN architecture based on ResNet-50 was implemented [28]. ResNet-50 was chosen due to its proven effectiveness in medical image classification and its ability to extract hierarchical features from radiographic images [29]. The model was initialized with ImageNet weights and refined using transfer learning techniques to optimize the performance for DFO detection [30].  
The training dataset consisted of manually labeled radiographs, categorized as either osteomyelitisitis-positive or osteomyelitisitis-negative cases (Figure 1a,b). The data were divided into training (80%) and validation (20%) subsets. To boost diagnostic precision, we used supervised learning with balanced classes to confidently differentiate between the DFO-positive and DFO-negative cases. Hyperparameters, such as the batch size and the learning rate, were optimized for convergence.  
A web-based interface was created using Flask, permitting users to upload radiographic images, receive real-time AI-based diagnostic predictions, and download structured reports (Figure 2). The system was established in a Python-based environment, integrating Torchvision and PIL for image preprocessing [31]. To streamline model deployment, ChatGPT (GPT-4.5, OpenAI) was used to support the integration of the AI model into the web-based diagnostic interface, focusing on code refinement and system functionality.  
Figure 1: (**a**) A radiographic example of an osteomyelitisitis-positive case. The affected region is located at the fifth metatarsal. The observed radiographic signs include the loss of cortex with bony erosion and affected bone marrow (focal loss of trabecular pattern or marrow radiolucency). (**b**) A radiographic example of an osteomyelitisitis-negative case. The analyzed region is located beneath the second metatarsal head. No radiographic signs indicative of osteomyelitisitis, such as cortical disruption and medullary involvement, were observed.
# Volumetric Analysis of Aortic Changes after TEVAR Using Three-Dimensional Virtual Modeling
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multicolumn{1}{c}{**Variable**} & \multicolumn{1}{c}{**Cut-Off**} & \multicolumn{1}{c}{**AUC**} & \multicolumn{1}{c}{**Std. Error**} & \multicolumn{1}{c}{**Asymptotic**} & \multicolumn{1}{c}{**CI (95\%)**} & \multicolumn{1}{c}{**Sensitivity**} & \multicolumn{1}{c}{**Specificity**} \\ \hline  & \multicolumn{5}{c}{**Diameter change—net (mm)**} \\ \hline Proximal Neck (1) & 4.0 & 0.667 & 0.265 & 0.505 & 0.147–1.000 & 0.500 & 1.000 \\ Proximal Landing Zone (2) & 1.0 & 0.465 & 0.139 & 0.783 & 0.193–0.737 & 0.500 & 0.688 \\ Aneurysm (3) & 2.0 & 0.680 & 0.130 & 0.159 & 0.424–0.935 & 0.750 & 0.688 \\ Distal Landing Zone (4) & 3.0 & 0.641 & 0.138 & 0.270 & 0.370–0.911 & 0.625 & 0.813 \\ Distal Neck (5) & 4.0 & 0.647 & 0.155 & 0.270 & 0.343–0.952 & 0.429 & 1.000 \\ \hline \multicolumn{1}{c}{**Diameter change—relative**} & & & & & & \\ \hline Proximal Neck (1) & 10.4\% & 0.667 & 0.265 & 0.505 & 0.147–1.000 & 0.500 & 1.000 \\ Proximal Landing Zone (2) & 4.6\% & 0.438 & 0.135 & 0.624 & 0.137–0.702 & 0.375 & 0.688 \\ Aneurysm (3) & 2.6\% & 0.656 & 0.138 & 0.221 & 0.386–0.927 & 0.750 & 0.688 \\ Distal Landing Zone (4) & 8.1\% & 0.637 & 0.138 & 0.284 & 0.366–0.907 & 0.625 & 0.813 \\ Distal Neck (5) & 11.6\% & 0.634 & 0.156 & 0.316 & 0.328–0.940 & 0.429 & 1.000 \\ \hline \multicolumn{1}{c}{**Volume change—net (mL)**} & & & & & & \\ \hline Proximal Neck (1) & 4.4 & 1.000 & 0.000 & 0.064 & 1.000–1.000 & 1.000 & 1.000 \\ Proximal Landing Zone (2) & 5.4 & 0.688 & 0.150 & 0.161 & 0.393–0.982 & 0.714 & 0.813 \\ Aneurysm (3) & \(-7.3\) & 0.727 & 0.115 & 0.076 & 0.501–0.952 & 0.875 & 0.563 \\ Distal Landing Zone (4) & 14.0 & 0.733 & 0.145 & 0.102 & 0.499–1.000 & 0.667 & 0.933 \\ Distal Neck (5) & 4.6 & 0.875 & 0.073 & 0.005 & 0.731–1.000 & 1.000 & 0.688 \\ Descending Thoracic Aorta (1, 2, 3, 4, 5) & 40.4 & 0.906 & 0.062 & 0.001 & 0.785–1.000 & 0.750 & 0.938 \\ \hline \multicolumn{1}{c}{**Volume change—relative**} & & & & & & \\ \hline Proximal Neck (1) & 30.5\% & 0.625 & 0.296 & 0.643 & 0.044–1.000 & 0.500 & 1.000 \\ Proximal Landing Zone (2) & 40.3\% & 0.616 & 0.154 & 0.385 & 0.314–0.919 & 0.429 & 1.000 \\ Aneurysm (3) & \(-3.7\) & 0.617 & 0.119 & 0.358 & 0.383–0.851 & 0.875 & 0.563 \\ Distal Landing Zone (4) & 42.3\% & 0.611 & 0.141 & 0.436 & 0.334–0.888 & 0.333 & 0.933 \\ Distal Neck (5) & 9.8\% & 0.857 & 0.079 & 0.008 & 0.703–1.000 & 1.000 & 0.688 \\ Descending Thoracic Aorta (1, 2, 3, 4, 5) & 3.2\% & 0.703 & 0.107 & 0.111 & 0.493–0.913 & 1.000 & 0.625 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of ROC curve analysis for all diameters and volumes considered in the study. Figure 5: ROC curve analysis for net changes in descending thoracic aortic volumes: a cut-off of 40 mL demonstrated high specificity and good sensibility in predicting TEVAR-related complications (AUC of 0.906, sensibility of 0.750, and specificity of 0.938, \(p=0.001\)).
## 1 Introduction
Studies have reported that by increasing WUE, N addition could enhance plant productivity [39, 40, 41]. On the other hand, other researchers have found that N enrichment has no impact on WUE [42, 43, 44] or a negative effect on WUE [45,46]. Consequently, it is difficult to account for the differences in N-induced effects on WUE [47]. Furthermore, fertilization may increase NEP through an increase in GPP, resulting from an increase in resource capture and resource use efficiency, e.g., increase in absorbed photosynthetically active radiation (APAR), light use efficiency (LUE, the ratio of GPP to APAR), and/or ecosystem carbon-use efficiency (CUEe, the ratio of NEP to GPP) [5]. Although LUE models have been extensively used to estimate regional or global GPP through remote sensing techniques [48, 49, 50], the biophysical controls on LUE in terrestrial ecosystems, especially soil N, remain poorly understood, especially in Douglas-fir stands on the west coast of Canada. The three different-aged Douglas-fir stands in this study (DF49, established in 1949, HDF88, established in 1988, and HDF00, established in 2000) are in the Pacific Northwest coastal forest region of Canada. Forests in this region between Oregon and Alaska cover approximately 10\({}^{5}\) km\({}^{2}\) and play an important role in the global C cycle. In this region, due to their considerable distances from heavy industrial activity, there is very little atmospheric N deposition, and N deficiency generally occurs in soils [51]. Fertilization using a single dose of urea in early 2007 in all three of these Douglas-fir stands and on-going long-term measurements of C fluxes using the eddy-covariance (EC) technique offer an opportunity to examine the effects of N fertilization on C and water fluxes. Previous studies on the effects of N fertilization have focused on C fluxes at DF49 in the first post-fertilization year using a process-based model [52], and C fluxes, ET and WUE in all three stands in the two post-fertilization years using an empirical model (multiple linear regression, MLR) [53]. In this study, we have used the measured C fluxes (viz., GPP, ecosystem respiration (\(R\)), NEP (= GPP - \(R\))), ET and climatic variables in the three stands from 1998 to 2010. The objectives of this study are threefold: (1) to estimate the effects of N fertilization in the three stands on the C fluxes, ET, WUE and LUE using two common data-driven models, MLR and an artificial neural network (ANN); (2) to examine the variation of the N fertilization effects during the 4-year post-fertilization period; and (3) to gain insight into the mechanisms of N fertilization controlling the C fluxes, ET, WUE and LUE in the three stands.
# A Review of Performance, Energy and Privacy of Intrusion Detection Systems for IoT
### Scope of Survey and Contributions  
Our research has identified existing efforts to study the state-of-the-art for intrusion detection within IoT systems with [17; 18; 19] being the most notable efforts. Although these efforts present a structured analysis of existing literature within IoT intrusion detection domain, these share a significant limitation in being agnostic of the performance overhead and privacy implications. For instance, using the _interaction ability_ proposed by [PERSON] et al. [17], [PERSON] and [PERSON] [20] scores high (three) indicating its efficiency to protect an IoT system. However, a deeper analysis of the system highlights that it is remarkably CPU intensive, which will affect its suitability for an IoT system. A detailed analysis of existing studies into intrusion detection for IoT is presented in Section 2. In view of these limitations,we undertake rigorous analysis of the intrusion detection systems taking into account a number of performance metrics to present an exhaustive evaluation of existing approaches for intrusion detection in IoT systems.  
In view of the limitations of existing studies, this paper makes the following contributions:  
* A comprehensive attack model for IoT systems is presented that is envisaged to inform the state-of-the-art for intrusion detection within IoT. The attack model comprises of threats across different dimensions of an IoT system aiming to aid improved classification.
* An extensive review of intrusion detection literature for IoT is conducted highlighting their performance with respect to critical attributes such as CPU and memory overheads, and privacy implications. As detailed in Section 2, existing studies have ignored these attributes while analysing the state-of-the-art.
* Identification of open challenges to achieve effective intrusion detection for IoT systems. These challenges include resource constraints, attack complexity, experimentation rigor and unavailability of relevant security data. This is informed by extensive review and analysis of existing intrusion detection research within IoT systems, and highlights significant research directions.  
The rest of the paper is organized as follows: Section 2 includes a detailed discussion about the existing surveys of intrusion detection research within IoT highlighting unanswered questions. Section 3 introduces a comprehensive attack model for IoT systems. Extensive review of existing literature within intrusion detection for IoT is presented in Section 4 which is organized into different types of IDS and therefore provides a linkage with classification introduced in Section 3. Section 5 presents privacy implications of intrusion detection systems followed by Section 6, which provides a thorough analysis of the existing literature with respect to a number of attributes including intrusion detection and performance efficiency. Through the findings of our research, Section 7 discusses open challenges which require further attention followed by conclusions and future aims of our research in Section 8.