esa-sceva/satcom-chunk-collection huggingface dataset:
# GNSS Spoofing Detection and Mitigation Based on Maximum Likelihood Estimation
## 4 MLE-Based GNSS Anti-Spoofing Method  
Since the MLE cost function is directly connected with the PVT parameters, spoofing signals that mislead the PVT results will cause distortion to the MLE cost function. Hence, the MLE-based positioning technique has a potential ability for spoofing detection. In this section, based on the technique, an estimation-cancellation algorithm is proposed, and a test statistic is derived for the detection of evil signals. Then, a validation method is proposed based on the decomposition of MLEcost function to determine whether the evil signals are spoofing ones or not. Finally, the overall implementation architecture is provided at the end of the section.
## 3 Proposed Solutions
### Follow-Up Clock Synchronization and Optimization of Listening Window's Size
#### 3.2.1 Algorithms Selecting the Listening Moment  
* An algorithm that synchronizes the receiver clock after each packet received (A1) When the packet is correctly received, the receiver clock is synchronized to its arrival time (i.e., the time of arrival becomes the \"zero\" time from which the time to the next expected packet arrival is counted).
* An algorithm of receiver time synchronization based on the statistics from the N most recent received transmissions (A2) The expected time of arrival of the next packet \(t_{i+1}\) is determined on the basis of the receiver's clock \(\widetilde{t}_{i}\) (the degree of desynchronization of the receiver's clock towards the transmitter changes with the passage of time) and the calculated period \(o_{i}\) after which the next transmission will occur: \(t_{i+1}=\widetilde{t}_{i}+o_{i}\). The algorithm recalculates the  
Figure 3: Shifted time windows.  
interval \(I\), the value of which is added to the expected time of arrival of the next packet \(\widetilde{t}_{i+1}=t_{i+1}+I=\widetilde{t}_{i}+o_{i}+I\). The interval is determined according to the formula: \[I=\widehat{B}\cdot(t_{i+1}-\widetilde{t}_{i}),\] (1) where \[\widehat{B}=\frac{\sum_{i=1}^{N}\Delta B_{i}}{\widetilde{t}_{N}-\widetilde{t }_{1}},\] (2) \[\Delta B_{i}=tr_{i}-\widetilde{t}_{i},\] and \[tr_{i}\] is the time when \[i\] -th message actually arrives at the receiver.
###### Abstract  
Trees in proximity to power lines can cause significant damage to utility infrastructure during storms, leading to substantial economic and societal costs. This study investigated the effectiveness of non-parametric machine learning algorithms in modeling tree-related outage risks to distribution power lines at a finer spatial scale. We used a vegetation risk model (VRM) comprising 15 predictor variables derived from roadside tree data, landscape information, vegetation management records, and utility infrastructure data. We evaluated the VRM's performance using decision tree (DT), random forest (RF), k-Nearest Neighbor (k-NN), extreme gradient boosting (XGBoost), and support vector machine (SVM) techniques. The RF algorithm demonstrated the highest performance with an accuracy of 0.753, an AUC-ROC of 0.746, precision of 0.671, and an F1-score of 0.693. The SVM achieved the highest recall value of 0.727. Based on the overall performance, the RF emerged as the best machine learning algorithm, whereas the DT was the least suitable. The DT reported the lowest run times for both hyperparameter optimization (3.93 s) and model evaluation (0.41 s). XGBoost and the SVM exhibited the highest run times for hyperparameter tuning (9438.54 s) and model evaluation (112 s), respectively. The findings of this study are valuable for enhancing the resilience and reliability of the electric grid.  
power outages; tree-related outage modeling; machine learning; decision tree; random forest; support vector machines; extreme gradient boosting; k-Nearest Neighbor +
Footnote †: journal: Applied sciences
# Multi-UAVs Communication-Aware Cooperative Target Tracking
## 2 Problem Description and System Model
### UAV Motion Model  
We used the following UAV motion model:  
\[\left[\begin{array}{c}\dot{x}_{i}\\ \dot{y}_{i}\\ \dot{\varphi}_{i}\end{array}\right]=\left[\begin{array}{c}v_{i}\cos\varphi_ {i}\\ v_{i}\sin\varphi_{i}\\ \alpha_{\varphi}(\varphi_{i}^{c}-\varphi_{i})\end{array}\right] \tag{16}\]  
s.t.  
\[-\omega_{\max}\leq\dot{\varphi}_{i}\leq\omega_{\max}. \tag{17}\]  
\(X_{i}=[x_{i},y_{i},\varphi_{i}]^{T}\) is the state of UAV, in which \((x_{i},y_{i})\) is the position and \(\varphi_{i}\) the heading angle. In this paper, the speed of UAV is constant. \(\alpha_{\varphi}\) is the heading gain, \(\varphi_{i}^{c}\) the control input which represents the expected heading angle, and \(\omega_{\max}\) the maximum angular velocity.
## 3 Results
### Grazing Flow  
[PERSON] et al. experimentally investigated the effects of grazing flows on the acoustic impedance of four orifice liners [23]. Figure 10 illustrates the configuration of the test rig in the reported experiment, and Table 2 lists the geometric features of the orifice liners that were included in the tests. The grazing flow duct was a 120 \(\times\) 120 mm square tube. A closed 150 mm-long, 35 mm-diameter cylindrical cavity was fixed on the side wall of the grazing flow duct. A loudspeaker was installed on the duct wall just opposite to the cavity. The sound signal was a sine signal of 200 Hz. Transient pressure data were collected and processed by the method described in [PERSON]'s work [48].  
CFD simulations of the experiments were obtained following the method described in Section 2. Acoustic impedances of the four orifice liners from the experiment of [PERSON] [23] were obtained under various grazing flow conditions, with the PVPM model representing the liner region. Results from the derived normalized specific acoustic impedance from the numerical simulations are compared with experimental results in Figure 11. Both numerical and experimental methods suggest that the acoustic resistance \(r\) rises linearly with the grazing flow Mach number. More precisely, the experimental results indicate the linear proportional constant Cg to be 0.39, 0.41, 0.5 and 0.48 for liners JG1, JG2, JG3 and JG4, respectively. By comparison, the numerical results indicate 0.385, 0.417, 0.47 and 0.45 for these four liners, values which are in close agreement to the experimental values.  
In addition, the simulation and experimental results are in close agreement in terms of the change in acoustic reactance with the grazing flow speeds. The difference between the experimental and numerical results is within 11% within the investigated grazing flow ranges. Both methods indicate that the magnitude of acoustic reactance \(x\) reduces with the rising grazing flow speeds, and the reduction tends to slow down at high grazing flow speeds. Therefore, the further developed PVPM model is able to represent acoustic impedance under the influence of grazing flows. Acoustic energy absorption coefficients are not shown here because the absorption data were not provided in the original experimental work [23].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Liner No.** & \begin{tabular}{c} **Hole Diameter** \\ **(mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Liner Thickness** \\ **(mm)** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Orifices** \\ \end{tabular} & **Porosity** \\ \hline JG1 & 3 & 2 & 4 & 2.94\% \\ JG2 & 4.5 & 2 & 1 & 1.65\% \\ JG3 & 7 & 0.5 & 1 & 4\% \\ JG4 & 7 & 2 & 1 & 4\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Geometric specifications of the orifice liners from the experiment by [PERSON] et al. [23].  
Figure 10: Test rig configuration for the investigation of grazing flow, [PERSON] et al. [23].
## 2 Principles and Methodologies
### OSP Methods
#### 2.3.4 Evaluation Criteria  
1. Modal assurance criterion  
The modal assurance matrix can effectively evaluate the goodness of orthogonality between the mode shape vectors and is defined as follows [20]:  
\[MAC_{ij}=\frac{{{\left({\Phi_{i}^{T}\Phi_{j}}\right)}^{2}}}{{{\left({\Phi_{i} ^{T}\Phi_{i}}\right)}\left({\Phi_{j}^{T}\Phi_{j}}\right)}} \tag{20}\]When MAC is 0, it indicates that the \(i\)-th and \(j\)-th order mode shape vectors have the best orthogonality; when MAC is 1, it indicates that the \(i\)-th and \(j\)-th order mode shape vectors have the worst orthogonality.  
(2) Singular value ratio criterion  
Maximum singular value ratio is an evaluation metric for the singular value decomposition of modal matrices [31]. It can be represented by the following equation:  
\[S=\frac{\sigma_{d}}{\sigma_{m}} \tag{21}\]  
where \(\sigma_{d}\) and \(\sigma_{m}\) are the maximum and minimum values of the singular values of the modal matrix, respectively. The smaller \(S\) is, the better the results obtained.
# Windowed Hamming Distance-Based Intrusion Detection for the CAN Bus
## 6 Discussion
### Other Attacks  
The proposed method adopts unsupervised learning, which helps limit zero-day attacks to some extent, and has excellent detection performance against injection attacks. Moreover, in addition to considering changes in the ID field, our method also takes into account variations in the data field. So in theory, our method has the capability to detect attacks that specifically target and modify the data field.
# Spatial Small Target Detection Method Based on Multi-Scale Feature Fusion Pyramid
###### Abstract  
Small target detection has become an important part of space exploration missions. The existence of weak illumination and interference from the background of star charts in deep and distant space has brought great challenges to space target detection. In addition, the distance of space targets is usually far, so most of them are small targets in the image, and the detection of small targets is also very difficult. To solve the above problems, we propose a multi-scale feature fusion pyramid network. First, we propose the CST module of a CNN fused with Swin Transformer as the feature extraction module of the feature pyramid network to enhance the extraction of target features. Then, we improve the SE attention mechanism and construct the CSE module to find the attention region in the dense star map background. Finally, we introduce improved spatial pyramid pooling to fuse more features to increase the sensory field to obtain multi-scale object information and improve detection performance for small targets. We provide two versions and conducted a detailed ablation study to empirically validate the effectiveness and efficiency of the design of each component in our network architecture. The experimental results show that our network improved in performance compared to the existing feature pyramid.  
small target detection; attention mechanism; feature fusion pyramid
## 3 Results
### Application of Pretensioner and Force Limiter Systems
#### 3.2.2 Biomechanical Values  
The average maximum value of each parameter is shown in Table 2. In test D, the maximum resultant accelerations of the head (312.0 m/s\({}^{2}\)), chest (666.2 m/s\({}^{2}\)), and pelvis (320.4 m/s\({}^{2}\)), as well as the neck extension moment (38.2 Nm) and maximum loads of the lap (11.1 kN) and shoulder (9.2 kN) belts, were the highest among tests D-G. In test E, the maximum resultant accelerations of the head, chest, and pelvis, as well as the neck extension moment and maximum belt load, were lower than those in test D. In test F, the maximum resultant accelerations of the head and chest, neck flexion, extension moments, and maximum lap belt load were lower than those in test D, and the maximum shoulder belt load (3.8 kN) was the lowest among tests D-G. In test G, all parameter values were lower than those in test D, and the maximum values of the head and chest resultant accelerations, neck flexion, extension moments, chest deflections, and lap seatbelt load were the lowest among tests D-G. The largest and smallest chest deflections were obtained in tests D and G, respectively.  
Figure 7: Time courses of the chest displacement, using a correctly fitted seatbelt, optionally equipped with a pretensioner and/or force limiter.
# Experimental Investigation and Mechanism of Fly Ash/Slag-Based Geopolymer-Stabilized Soft Soil+
## 2 Experimental Work
### Testing Procedures  
To obtain a uniform soil structure, geopolymer-stabilized soil specimens were prepared according to the following procedure. The collected soft soil was dried on site at a low temperature (50-60 \({}^{\circ}\)C), crushed using a pulverizer, and passed through a 2-mm sieve. The corresponding masses of dry soil, fly ash, and slag were mixed well. Then, the corresponding masses of water and the compound activator solution (which had been left to stand for 24 h) were added, and the mixture was stirred again until it reached a homogeneous state. According to Chinese standard JGJ/T 233-2011 [24], the soil mixture was poured into a 70.7 \(\times\) 70.7 \(\times\) 70.7 mm\({}^{3}\) cubic mold in three stages, and the air bubbles in the specimens were eliminated by vibrating each layer for 2 min. The soil specimens were sealed into the standard curing box (temperature of 20 \(\pm\) 2 \({}^{\circ}\)C, relative humidity of \(>\)95%), stabilized for 24 h, and then demolded. After curing for one day, the soil specimens were demolded and sealed in plastic wrap and then placed into the curing box until the tests were performed.  
The UCS of the specimen was measured using a WAW-300B electromechanical universal testing machine with a maximum force of 30 kN, and the loading rate was set to 1 mm/min. The average value of the strengths of the three specimens was taken as the representative strength. Selected internal fragments of typical specimens after strength testing were dried at low temperature in an oven at 50 \({}^{\circ}\)C, ground, and passed through a 0.075-mm sieve for XRD analysis. The mineralogical changes were determined using a D8 Advance XRD, with 20 of 10-80\({}^{\circ}\) and a scanning speed of 0.5 s. Small specimens with fresh natural surfaces inside were selected for SEM analysis. The fresh surfaces were not touched or polished to avoid affecting the observations. The microstructures of the gold-sprayed fracture surfaces of the specimen (without polishing) were observed using a Gemini 500 field emission scanning electron microscope (FESEM), with an accelerated voltage of 3 kV. Using nitrogen as the adsorbate, the nitrogen adsorption and desorption tests were carried out at 90 \({}^{\circ}\)C, and the adsorption and desorption times were 12 h. The pore diameter distribution was analyzed by the BJH method dV/dD through the adsorption-desorption curve.
## 5 Case Studies on the OMF-Enabled Testbed
### Case Study 4: Packet Loss and Jitter Analysis in VoIP Applications
The diversity of case studies on the proposed OMF-enabled test environment can be enriched by conducting further experiments such as analyzing the convergence times and scalability of various routing protocols, measuring packet loss under controlled congestion scenarios, and investigating energy consumption in resource-constrained environments, to name just a few. Nonetheless, it is noteworthy that although the test environment is presented in the context of measurement experiments, it can also be used to support researchers in deploying, running, monitoring, and experimenting with any kind of other  
Figure 7: Analyzing packet loss and jitter in VoIP over two-hop 802.11 g wireless ad hoc networks. services, protocols and applications under similarly configurable real-world conditions. Also, new testbed setups such as grid networks or multi-hop scenarios (e.g., for investigating the effect of the number of hops on measurement experiments) can easily be configured on the proposed OMF-enabled test environment.
# Nanotechnology, Nanomedicine, and the Kidney
## 5 Nanotechnology for Renal Therapy
### Tissue Engineering  
#### 5.2.1 Renal Replacement Technology  
Dialysis is the most common treatment for ESRD. Blood is circulated out of the body, and during the extra-corporal phase, excess fluid is removed from blood via special membranes. Waste products and metabolites leave with the water, while the filtration membranes' aim is to retain essential proteins. Being connected to a bulky dialysis machine at home daily or at a dialysis center several times a week, the patients are limited in their range of travel. Nanotechnological solutions aim to develop wearable or implantable dialysis machines that make the patient independent from hour-long treatments on stationary equipment.  
The Kidney Project at the University of California San Francisco (UCSF) is developing a fist-sized implantable artificial kidney (IAK) that performs the main function of the natural kidney [85]. In the IAK, the kidney function is carried out in two steps. First, fluid is removed from the blood by filtration through a membrane of silicon nanoparticles that generate a filtration slit of 10 nm [86]. In the second step, the filtrate is transported over renal tubular cells arrayed on silicon wafers. The cells reabsorb sugars, amino acids, salts, and other components valuable to the body [87]. These compounds are then transferred back to the bloodstream, and the waste fluid is removed via the bladder.  
Nanotechnology is also used to improve the standard hemodialysis. [PERSON] developed light-activated charged gold nanoparticles that exhibit a negative surface charge. When used in a dialysis filtration unit, the particles increased the efficiency of uremic toxin removal. In addition, dialysis-induced effects of coagulation activation, platelet adhesion, and thrombosis were reduced by the charged nanoparticles [88].  
Figure 5: Nanotechnologies for drug delivery.
# Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
## 7 Implementation and Real-World Performance Analysis
### MR Implementation Results  
The Mixed Reality (MR) implementation of the proposed vial identification system enables the real-time detection, classification, and spatial annotation of medical vials within an augmented environment. Figure 7 illustrates how the system overlays bounding boxes and labels onto detected vials, ensuring an interactive and intuitive identification process.  
In the top row of images, users interact with vials in real time using an MR headset. The system successfully recognizes and labels vials such as Midazolam and Amiodron, displaying their respective confidence scores. The detected objects remain anchored in 3D space, allowing users to move around and still maintain correct visual alignment with the vials.  
The bottom row of the images shows the multi-vial detection capability of the system, where multiple vials are identified simultaneously. However, the system experiences detection errors in clustered settings, particularly with visually similar vials such as Epinephrine Pocket and Atropine. These cases suggest that overlapping objects and varying lighting conditions can affect classification accuracy, indicating the need for further occlusion handling and feature refinement.  
Despite these challenges, the implementation of MR successfully integrates computer vision and spatial computing, allowing for users to interact with medical vials in a way that improves efficiency and reduces human error compared to traditional barcode scanning methods.
###### Abstract  
The high complexity, repeatability, standardization, and quality requirements of the assembly process presently put forward raised requirements for the unified expression and organization of assembly process knowledge. As one of the core technologies supporting intelligent manufacturing, the construction of an assembly process knowledge graph (KG) becomes a viable method, the inference tasks of which include knowledge judgment, entity, and relation completion, all of which have also become research hotspots. However, most existing knowledge inference methods utilize the triplet form to express the KG, which cannot express all crucial information of assembly process KG construction, including entity, relation, entity type, attributes, etc. Therefore, a KG inference method integrating logical rules and embedded learning is proposed in this paper for KG inference tasks in KG construction. Comprehensively considering the semantic information of entity, relation, and entity type, an improved self-adversarial negative sampling method and logical rules are constructed, which are also introduced in the training process of existing embedded learning models. The proposed method could effectively solve the problems of incomplete assembly process KGs and low construction efficiency. Finally, based on three existing embedded learning models, including DistMult, ComplEx, and RotatE, this paper verifies the effectiveness of the proposed method relative to the above KG inference tasks.  
knowledge inference; knowledge graph construction; assembly process; embedded learning; logical rules; negative sampling
# Vegetation Loss Measurements for Single Alley Trees in Millimeter-Wave Bands
## 3 Measurement Campaign
### Single-Tree Model  
The initial measurements carried out at the measurement site show that the foliage attenuation results are related to the tree size in the alley. This conclusion led to the creation of the single-tree model. The generic model of a park tree is shown in Figure 9. In that model, the height of the corona of the tree is \(C_{h}\), and the diameter of the corona is \(C_{w}\). The total height of the tree is \(T_{h}\), and the height of the trunk is \(T_{h}-C_{h}\). The measurement site is shown in Figure 10. The precise sizes of the trees can be found in Tables 1 and 2. Not only dimensions of trees but also the predicted volume of tree corona can be found there. Most trees have a corona volume from 7 to 16 m\({}^{3}\). There are two exceptions: the third tree for Scenario 1 with a volume of nearly 50 m\({}^{3}\) and the fifth tree in Scenario 2. Tree coronas have greater variability; the diameter of the corona varies from 1.81 m to 3.8 m, while the height of the tree ranges from 4.5 m to 6.38 m.  
Figure 10: Measurement site at Poznan University of Technology.  
Figure 9: Single-tree model. Each tree has its height \(T_{h}\), corona height \(C_{h}\), and corona width \(C_{w}\).
# Optimal Task Offloading Strategy for Vehicular Networks in Mixed Coverage Scenarios
###### Abstract
* We formulate an optimal task offloading problem in which the real-time uncertainty of BS loading, the moving speed of vehicles, and the dynamic transmission channels are primarily considered. * We propose a two-stage algorithm to solve the formulated joint optimization problem. Firstly, a real-time prediction method based on LSTM is proposed to extract BSs' load patterns from historical data. Then, considering the real-time position relationship between vehicles and RSUs, BSs in the mixed coverage scenario, and the real-time load status of BSs, we propose a task allocation offloading strategy based on the PPO algorithm to reduce the task offloading delay. Compared with the inherent optimization mode, the task processing delay reduces by approximately 5.6%, and the offloading strategy has good robustness in the congest traffic conditions. Finally, we conduct extensive experimental analysis to verify the effectiveness of the proposed strategy. The remainder of this article is organized as follows. In Section 2, we review the relevant works. In Section 3, we discuss a task offloading system model in the mixed coverage scenario. In Section 4, a BS load condition prediction scheme based on LSTM and a vehicle task offloading optimization scheme based on PPO are proposed. Section 5 proposes the simulation experiments and performance evaluation. Finally, we summarize our work in Section 6.
## 3 Results and Discussion
### Optimisation  
The topology optimisation isosurface results produced are given in Figure 9. Theoretically, this is the ideal design of a solid crank with increasing levels of weight reduction. The complex geometry shown suggests that ALM would be the favoured, if not the only possible method of manufacture for producing this design.  
Figure 8: Fatigue Test Configuration.  
Figure 9: Topology Optimisation Isosurface Results (Increasing Mass Reduction Left to Right).