esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Implementation
### A Load Increment Algorithm Based on Fictitious Density  
The governing equation in motion form cannot be directly applied to quasi-static problems. Rather, substitute the artificial damping coefficient into Equation (1), which can be expressed as Equation (21).  
\[\rho\ddot{u}+C\dot{u}=L+b \tag{21}\]  
where \(C\) represents the artificial damping coefficient.  
Previous studies often report that boundary particles may break prematurely when the model boundary is subjected to external load. Hence, the boundary region is usually designated as a nonfailure region to prevent the boundary material points from tearing apart from the model [16]. However, this is obviously not in accordance with the laws of physics, which limits the application of PD in quasi-static analysis to some extent.  
Based on the concept of load increment proposed by [PERSON] [16], a load increment algorithm based on fictitious density is proposed to solve this problem. The fictitious density, \(M\), in the adaptive dynamic relaxation (ADR) scheme [40] has the ability to improve convergence and maintain the calculation stability. The purpose of setting the fictitious density is to accelerate the convergence by setting different virtual densities for each material point so that they have the same convergence time step. The algorithm is divided into the following steps:  
1. The external load is gradually applied to the object in increments. The load increment is calculated in Equation (22). \[\Delta\lambda=0.8\times\frac{2 Ms\Delta x}{\Delta t^{2}\mathbf{b}},\ \ \ M=\frac{1}{4}\Delta t^{2}\sum\limits_{j=1}^{p}\left|K_{ij}\right|\] (22)  
in which 0.8 is a safety factor. \(M\) is fictitious density [40], \(K_{ij}\) is the stiffness matrix and \(p\) is the amount of points in \(H_{x_{i}}\).\(\Delta t\) is the time step, which must satisfy the stability condition [24].
2. The time step integral describes how points move in space and interact with their horizon points. The velocity and displacement of points are obtained by employingthe central difference formula combined with the damping relaxation method, seen in Equation (23). \[\left\{\begin{array}{l}\ddot{\mathbf{u}}=(\frac{\dot{\mathbf{u}}^{n+1/2}-\dot{\mathbf{u}}^{ n}}{1/2\Delta t}+\frac{\dot{\mathbf{u}}^{n}-\dot{\mathbf{u}}^{n-1/2}}{1/2\Delta t})/2= \frac{\dot{\mathbf{u}}^{n+1/2}-\dot{\mathbf{u}}^{n-1/2}}{\Delta t}\\ \dot{\mathbf{u}}^{n+1/2}=\frac{\dot{\mathbf{u}}^{n-1/2}}{1+C\Delta t/M}+\frac{(L+b)}{C +M/\Delta t}\end{array}\right.\] (23). After numerical iteration, the model would come to equilibrium when the convergence criterion is reached, which indicates that the next time step and load increment can be practiced in simulation. The convergence criterion is \[\frac{\sum\limits_{i=1}^{N}RE_{x}+\sum\limits_{i=1}^{N}RE_{y}}{N}\leq\gamma\] (24) where \(N\) stands for total number of material points. \(RE_{x/y}\) represents relative error of displacement between the current iteration and the last iteration. \(\gamma\) is a given convergence index.
# Comparative Study of Machine Learning Classifiers for Modelling Road Traffic Accidents
## 3 Methodology
### Missing Data Strategies
#### 3.2.1 Mean and Median  
Some methodological strategies replace the missing values for given data with the mean or median of all the known values by adding available values and dividing their sum with by the average [51].
###### Abstract  
Due to the limitations on the capabilities of current robots regarding task learning and performance, imitation is an efficient social learning approach that endows a robot with the ability to transmit and reproduce human postures, actions, behaviors, etc., as a human does. Stable whole-body imitation and task-oriented teleoperation via imitation are challenging issues. In this paper, a novel comprehensive and unrestricted real-time whole-body imitation system for humanoid robots is designed and developed. To map human motions to a robot, an analytical method called geometrical analysis based on link vectors and virtual joints (GA-LVVJ) is proposed. In addition, a real-time locomotion method is employed to realize a natural mode of operation. To achieve safe mode switching, a filter strategy is proposed. Then, two quantitative vector-set-based methods of similarity evaluation focusing on the whole body and local links, called the Whole-Body-Focused (WBF) method and the Local-Link-Focused (LLF) method, respectively, are proposed and compared. Two experiments conducted to verify the effectiveness of the proposed methods and system are reported. Specifically, the first experiment validates the good stability and similarity features of our system, and the second experiment verifies the effectiveness with which complicated tasks can be executed. At last, an imitation learning mechanism in which the joint angles of demonstrators are mapped by GA-LVVJ is presented and developed to extend the proposed system.  
Keywords:humanoid robot; whole-body imitation; social learning; motion mapping; teleoperation for tasks; similarity evaluation +
Footnote †: journal: Applied Sciences
## 3 Results  
### Extracted Ion Data for Unburned Neat Ignitable Liquids  
The total ion chromatogram obtained for unburned neat gasoline sample (Figure 1a) was consistent with what was reported in the literature for neat gasoline analyzed using HS-SPME in terms of the retention time and abundance of the volatile organic compounds [13; 14].  
The extracted ion profiles could not be found in the literature for the SPME technique; however, the extracted profiles (Figure 1b-f) were visually comparable with those provided in the literature using the activated charcoal strip (ACS) method [11]. It should be noted that the extracted profiles obtained by the SPME method can be different in terms of abundance of the peaks with those obtained using activated charcoal strip due to the differences in both the physical properties and the capacity of the extraction phases used in the two methods [5]. Nevertheless, the extracted profiles of gasoline were clearly distinguishable and characteristic of neat gasoline. The gasoline target compounds were also identified in the chromatograms: toluene (RT = 3.28 min, RSI = 929), \(m\)- and \(p\)-xylene (RT = 5.85 min, RSI = 924), \(o\)-xylene (RT = 6.51 min, RSI = 931), trimethylbenzene (RT = 10.54 min, RSI = 904), and indane (RT = 10.92 min, RSI = 921).  
Figure 1: GC-MS extracted ion data related to neat gasoline. (**a**) Total ion chromatogram; (**b**) extracted alkane profile; (**c**) extracted cycloalkanes and alkenes profile; (**d**) aromatics (alkylbenzenes) profile; (**e**) extracted polynuclear aromatics profile; (**f**) extracted indane profile.  
The TIC pattern and the extracted profiles for kerosene using HS-SPME were not found in the literature but the obtained pattern (Figure 2a) presents the disproportionality abundant homologous series of n-alkanes and the less abundant cycloalkanes, which are the characteristics for kerosene. The extracted profiles were also comparable with those obtained using the ACS method provided in the literature [5]. The following target compounds of kerosene were identified: nonane (RT = 6.80 min, RSI=881), decane (RT = 10.09 min, RSI = 931), undecane (RT = 13.19 min, RSI = 922), and dodecane (RT = 16.00 min, RSI = 926).  
Figure 2: _Cont._
# Windowed Hamming Distance-Based Intrusion Detection for the CAN Bus
## 3 Background and Attack Model
### Threat Model
#### 3.2.3 Spoofing Attack  
This attack uses a large number of injected specific IDs to deceive a specific ECU. When an attacker spoofs the key components of the vehicle, it brings great security risks to the driver and passengers in the vehicle. The dataset we use performs this attack on two key parts (gear and rpm). The pre-set data frames are continuously injected into the CAN bus, and various states such as the dashboard of the car are found to change, which illustrates the danger and feasibility of this attack. In the Spoofing attack, each time interval is 2 ms, and we inject CAN IDs 0x316 and 0x43F to the bus, corresponding to gear and rpm, respectively, for 10 s.
## 3 Materials and Methods
### Case Study
#### 3.3.2 User Study Setup  
In each session, three user study participants were located in different rooms, each wearing a VR head-mounted display. Two HTC Vive and one HP Reverb VR sets were used. One on-site facilitator was present in each of the three rooms to provide assistance and ensure the safety of the participants. The user study procedure was moderated by one of the on-site facilitators via Microsoft Teams on a laptop, connecting with the participants, asking them to accomplish the tasks, and encouraging their full participation in the tests. Teams established an audio connection between the rooms; the audio from the VR sets was muted so there was no interference with the audios. Teams also streamed the video of the physical space of the main facilitator and the participants from two rooms. The VR view from one user test participant was also streamed to Teams; the VR computer was used for sharing this stream.  
The user study was observed by three observers; two were present on site observing the participants, and one was observing the procedure remotely with the Teams video stream. Observers were watching for certain behaviors and taking notes on the things that they observed the participants doing. The user study setup and the view of the Teams streams can be observed in Figure 4.  
Figure 3: Task step visualized in the DocPanel Tool (**left**), the same step in the original PDF instruction (**right**).  
The user study participants were immersed in VR, and the Teams stream was not visible to them; it was only used for audio and observation purposes. The Teams sessions were recorded for future reference. Figures 5 and 6 show user tests in progress.  
Figure 4: User study setup, showing both the video streams in Teams and the setups of the rooms and locations.  
The test setup, researchers' roles as well as the instructions and tasks for the participants were tested in a separate pretest session. Based on the learnings from the pretest, some modifications were made. For example, in the VR environment, the participant names and department were added above each avatar to make it easier to recognize participants in VR. Some details of the tasks for the participants were also modified to bring clarity to the test sessions. After the pretest, no modifications were made between actual user tests; thus, they were all equal.  
Figure 5: User tests in progress. Laptop streaming the participant’s VR view can be observed in the background. An USB camera is connected to the laptop in the background, streaming the physical space of the room to Teams.  
Figure 6: User test participant and VR view displayed in a monitor.
## 4 Experimental Setup
### Performance Metrics
#### 4.4.1 Hypervolume (HV)  
Hypervolume (HV) serves as a comprehensive indicator for assessing multi-objective evolutionary algorithms, capable of concurrently reflecting the convergence and distribution characteristics of the algorithm [30]. It is computed by determining the hypervolume of the region bounded by the Pareto front and the reference point. The calculation formula is provided below:  
\[HV=\lambda\left(\bigcup_{i=1}^{|s|}v_{i}\right) \tag{12}\]  
where \(\lambda\) denotes the Lebesgue measure, \(v_{i}\) signifies the hypervolume formed by the reference point \(Ref\) and a point on the Pareto front, and \(s\) denotes the Pareto front generated by the algorithm.  
In selecting the reference point \(Ref\), we use the worst value of each objective function observed on the Pareto front. Hence, a larger hypervolume (\(HV\)) corresponds to superior algorithm performance.
# Suitability of Low-Cost Sensors for Submicron Aerosol Particle Measurement
## 4 Conclusions  
In this work, the measurement capability of low-cost sensors for particle mass concentration in aerosols in the submicron range was investigated and compared to SMPS and AQ Guard sensors. Furthermore, the influence of humidity and the drying process were determined as variables. It was shown that the measurement performance of the two different low-cost sensors (SDS011 and HPMA compact) is comparable. However, in comparison to the laboratory devices, different PM2.5 values were obtained by low-cost sensors, which can be explained by different measuring ranges and methods. The impact of an increase in the relative humidity to over 65%, using water-soluble sodium-chloride particles, on the measurement behavior of the sensors had a negative effect on PM measurements due to hygroscopic droplet growth. A simple electrical heating stage, which can be installed in front of the low-cost sensor, improved the measurement of the PM values significantly. Despite some disadvantages of the low-cost sensors, they have the potential to detect indoor aerosol spreading. Based on these data, hygiene concepts for air-quality improvement and infection prevention can be developed.  
Conceptualization, D.S., M.K. and S.P.; methodology, D.S., M.K. and S.P.; software, D.S. and S.P.; validation, D.S. and S.P.; formal analysis, D.S. and S.P.; investigation, D.S. and S.P.; resources, D.S. and S.P.; data curation, D.S. and S.P.; writing--original draft preparation, D.S. and S.P.; writing--review and editing, D.S.; visualization, D.S. and S.P.; supervision, S.A.; project administration, D.S.; funding acquisition, M.K. and S.A.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the VolswagenStiftung (99604).  
Not applicable.  
**Acknowledgments:** This work was performed within the project \"Development of a low-cost sensor for aerosol spread monitoring\". The project (99604) was funded via the VolkswagenStiftung \"Corona Crisis and Beyond--Perspectives for Science, Scholarship and Society\".  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Results and Discussion
### CH/CNC and CH/CD Edible Films
#### 3.1.6 Summary of Results for CH/CNC and CH/CD Edible Films  
The overall evaluation of the above results shows that the addition of CNC as well as CD had an enhancing effect on the CH films, both decreased the viscosity, leading to better coating applicability. Moreover, they both improved the barrier properties of the final formed edible films. Furthermore, the addition of CNC led to films with a more stable structure and higher mechanical strength, while CD improved their barrier and optical properties by increasing the transparency and reducing the color index. Finally, the 50/50 proportions for both CH/CNC and CH/CD films provided the best overall results.  
Figure 1: Fourier-transform infrared spectroscopy (FTIR) of CH/CNC and CH/CD final formed edible films.  
Figure 2: SEM images of the surface of different CH, CH/CNC and CH/CD edible films.  
All the above-mentioned findings supported the idea of combining the two materials (CNC and CD) in a ternary mixture with CH, in order to examine whether each material retains its beneficial properties in the final mixture. The results of this research are analyzed below.
# Performance Evaluation of UAV-Enabled LoRa Networks for Disaster Management Applications
## 2 Network Setup and Architecture
### UAV Functionality  
The drones are the most complex elements in the system, given the variety of tasks they need to perform. First of all, each UAV must be equipped with all the necessary sensors, controls and software for navigation and stabilization. Typical sensors include altimeters, speed meters, accelerometers, and tilt sensors. The details of the navigation and control system are outside the scope of the paper and it is simply assumed, realistically, that drones already come with all the necessary components and with a software API to interact with them. The UAVs are able to hover over a specific location or target while the specific type of engine and fuel for UAV depends on the application and is outside the scope of this work.  
As shown in Figure 3, the end-to-end packet delivery over WiFi is accomplished by resorting to a traditional TCP/IP stack, with the assistance of a Mobile Ad hoc Network (MANET) routing protocol. Even though any MANET routing protocol can be used, in the current implementation of the system some mechanisms rely on the knowledge of the whole topology of the network, therefore a table-driven link-state protocol like Optimized Link State Routing (OLSR) protocol becomes necessary for the correct operation of UAV-enabled LoRa network. The module contains also a LoRa-to-WiFi relay application, responsible for adapting uplink LoRa packets received by the gateway and send them over the WiFi ad hoc network to the intended destination. The most important and complex part of the whole system is the virtual spring force (VSF) distributed mobility algorithm. This algorithm is responsible for planning the movements of the drone according to VSF computed based on parameters collected from positioning sensors, routing tables and messages exchanged with GNs and other UAVs. Given the amount of information needed, the algorithm needs to interface with almost all the previously described components to work properly.
## 3 Datasets and Methodology
### Methodology
#### 3.2.3 Bayesian Hyperparameter Optimization for SVM  
Support vector machine (SVM) is a supervised machine learning approach that is based on the kernel method, which is often used for classification and regression missions [56; 57; 58; 59]. As we aimed to predict whether an earthquake event was about to occur or not, we used the SVM to classify ionospheric TEC enhancements prior to earthquake events during low and high solar cycle activities, where we introduced the pre-earthquake TEC time series sample as a positive instance of an earthquake event, and its corresponding quiet day time series as a negative instance (i.e., an earthquake would not occur). As such, we sought to optimize the SVM hyperparameters in order to find the kernel function and parameters yielding the most accurate model. To do this, we employed MATLAB's 'fitcsvm' function, which automatically compares different kernels, such as the radial basis function and polynomials of different degrees, and also tunes the kernel hyperparameters, such as the penalties for false negatives, and false positives, and the inner trade-off between smaller sample errors and larger margins. The optimization process searches for the optimal hyperparameters using the Bayesian method [60], which initializes a random point in the hyperparameter space and then iteratively evaluates promising hyperparameter configuration based on the current model; if these new parameters yield improvements, the current model is updated with the new hyperparameters [61]. Bayesian optimization aims to gather observations revealing as much information as possible about the function and the location of its optimum [62].  
We found that the optimal result was obtained by a polynomial kernel function of degree 2.
## 2 Acquisition of Tunnel Face Images
### Stitching and Fusion of Partitioned Photography Images  
After obtaining the six partitioned photographic images of the tunnel face, stitching them together to form a complete and clear tunnel face image is a prerequisite for the next step of joint extraction.  
When taking the images, to ensure complete coverage of each partition, the area covered by each partition image is often slightly larger than the actual corresponding partition area. This inevitably leads to overlapping images in adjacent regions, making it impossible to achieve image stitching through simple positioning. The following example illustrates the stitching and fusion algorithm of partitioned images on the tunnel face, using the right arch foot region and the floor region of a tunnel face as examples.
## 3 Results
### Dielectric Spectroscopy
#### 3.8.2 Impact of ZnO Filler on Dielectric Properties of Samples during AWE Treatment  
As AWE treatment progressed, \(\Delta\)tan\(\delta\) of ZnO-loaded samples compared to bare untreated samples (C0/INT0) was mostly unaffected, excluding an increase at INT576 (\(1\times 10^{7}\)-\(1\times 10^{8}\) Hz) and at INT1728 in the whole range, both attributed to high moisture content (maximum at INT1728). In the end of the treatment (INT2880), tan\(\delta\) was reduced compared to INT0/C0, due to the stabilization effect of ZnO on epoxy resin photodegradation (Figure 13a and Figure S3.3a,c Addendum). On the other hand, \(\Delta\)k' also underwent maximum increase during AWE treatment at INT1728, but in the end, it returned to the initial INT0 levels, which is also indicative of the stabilization of the material (Figures 13b and S3.3b,d Addendum).  
Figure 14a,b indicate the impact of ZnO on the epoxy resin loaded with 0.5 wt% ZnO during each AWE treatment interval (\(\Delta\)tan\(\delta_{\text{AWE}}\) and \(\Delta\)k'AWE' values of C0.5 samples were compared to C0 samples of the same interval). According to Figure 14a, ZnO presence did not significantly affect \(\Delta\)tan\(\delta_{\text{AWE}}\) of the composite, which is also true for C0.1 and C2 samples (Figure S3.4a,c Addendum). Regarding \(\Delta\)k'AWE' values, the presence of ZnO was responsible for maintaining the initial C0.5 low k' values up to INT1152, after which an increase was observed at INT1728 that persisted until INT2880 (Figure 14b). Despite this increase, INT2880 k' values were still lower than the bare resin's values, which is explained by the remaining moisture inside the samples' bodies after maximum SM at INT1728 (Figure 7b). C0.1 samples exhibited the same dielectric behaviour (Figure S3.4b Addendum) in contrast to C2 samples, whose \(\Delta\)k'AWE' values remained positive since INT0 (Figure S3.4d Addendum), indicating k' increases due to the increased content of ZnO.
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 5 Enhancing the EPR Effect
### Physical Strategies
#### 5.2.2 Hyperthermia (HT)  
Hyperthermia (HT) (Figure 3b) is another widely studied physical strategy that enhances the EPR effect by heating tumors to mild temperatures (typically 40-43 \({}^{\circ}\)C). This mild heat induces vasodilation, increasing blood flow to the tumor and improving the delivery of nanodrugs. The enhanced blood flow is the principal mechanism through which HT boosts the EPR effect [180,181].  
Recent studies have explored the combination of HT with ultrasound to enhance the effectiveness of nanomedicine. For example, [PERSON] et al. developed a nanomedicine system combining ferrate and doxorubicin NPs, which exploit the EPR effect for tumor accumulation. Ultrasound-induced hyperthermia triggered the simultaneous release of ferrate and doxorubicin, boosting tumor oxygenation and enhancing doxorubicin's ability to induce tumor cell death. Their study demonstrated a significant reduction in tumor  
Figure 3: Schematic illustration of (**a**) radiation therapy (RT), (**b**) hyperthermia (HT), and (**c**) photodynamic therapy (PDT) approaches (created with BioRender.com).  
volumes in mice with osteosarcoma, particularly when using NPs containing both ferrate and doxorubicin in combination with ultrasound [182].  
Thermosensitive liposomes have also been extensively researched for their potential applications in oncology, particularly in breast cancer treatment. [PERSON] et al. demonstrated that combining paclitaxel and liposomal doxorubicin with hyperthermia as neoadjuvant therapy is a viable and well-tolerated treatment approach for patients with locally advanced breast cancer (LABC) [183]. Similarly, [PERSON] et al. found that combining liposomal doxorubicin with mild local hyperthermia was safe and resulted in objective responses in heavily pretreated patients with chest wall recurrences of breast cancer [184].  
Despite its potential, HT is not widely adopted in clinical practice due to challenges such as temperature control and inadequate monitoring. Addressing these issues is critical for the broader application of HT in enhancing the EPR effect [5].