esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Four data sets from the Norwegian Deepwater Programme (NDP), which were obtained from tests on a densely instrumented model riser, are employed to test and compare the different methods. Riser response and fatigue damage over the entire riser length are estimated using strain measurements from a limited number of sensors. The mathematical formulation and basis for each method is briefly presented in Section 3; in addition, underlying assumptions, advantages, and disadvantages of each method are discussed with examples. By comparing the estimated riser response and fatigue damage at selected locations with those obtained directly from the measurements, it is found that all the empirical methods discussed, despite the different underlying assumptions in each of them, may be employed to estimate fatigue damage rates quite well from limited measurements. We note here that the subject of vortex-induced vibration is of interest in a wide range of applications beyond its relevance for deep water marine risers that can experience fatigue damage in some current velocity fields. An example of a subject where there is such interest is with concentrated solar power (CSP) collectors, where vortex shedding from one row of collectors influences neighboring rows due to similar vibration [12].
## 2 Materials and Methods
### Antibacterial Activity  
For the determination of antimicrobial activity, the honey samples were preliminarily diluted in sterile deionized water to achieve different concentrations, representing 100, 75, 50, 25, 12.5, and 6.25% (expressed as \(w/v\)). The pathogenic bacterial strains included three Gram-negative bacteria, _Escherichia coli_ (Migula) (ATCC 8739), _Klebsiella aerogenes_ (ATCC 13048), and _S. typhimurium_ (ATCC 14028), as well as three Gram-positive bacteria, _Staphylococcus aureus_ subsp. _aureus_ (ATCC 25923), _Streptococcus pyogenes_ (ATCC 19615), and _Listeria monocytogenes_ (ATCC 13932).  
The studied bacteria were grown on nutrient agar at a temperature of \(37\pm 1\)\({}^{\circ}\)C for a total of 24 h. The bacterial inoculum for each species was standardized to 1.0 \(\times\) 10\({}^{8}\) CFU/mL in physiological saline using a 0.5 McFarland's standard.  
The antibacterial activity of all the honey samples against each of the bacterial organisms was examined using the agar well diffusion method [23]. For this, a volume of 200 \(\upmu\)L of inoculum from each microorganism was mixed in Mueller-Hinton agar plates and solidified. After, four 8 mm diameter wells were opened in each Petri dish, and the analysis was carried out in two parallels. The prepared wells were filled with the honey sample itself and the previously diluted honey solutions. The dishes were incubated for 18-24 h at 37 \(\pm\) 1 \({}^{\circ}\)C for bacterial growth. The zone of inhibition (in millimeters) was measured using a digital caliper.  
The approximate minimum inhibitory concentrations (MICs) were determined by the agar well diffusion method [24]. Each honey sample was used to determine the lowest honey concentration (expressed as %) that provides an inhibition zone with a diameter greater than 8 mm.
## 2 Key Factors Contributing to Spatial Resolution in PET Imaging
### Image Reconstruction  
Image reconstruction in PET plays a crucial role in determining the spatial resolution of the final reconstructed images [40]. Reconstruction algorithms convert raw coincidence data into spatial maps of the radiotracer distribution within the body. The main purpose of the image reconstruction algorithm is to realize the desired reconstructed spatial resolution goal with a high reconstructed image SNR. Thus, for example, a higher-sensitivity system design can achieve a higher reconstruction resolution goal.  
A common cause of resolution loss during reconstruction is the use of smoothing filters to reduce noise, which can blur edges and diminish fine details [40]. Although smoothing improves image quality by suppressing statistical noise, it must be carefully balanced to preserve spatial resolution.  
The sampling rate of the reconstructed image grid also impacts resolution. An insufficient sampling grid can lead to aliasing artifacts and detail loss, whereas a finer grid increases computational demands without necessarily improving resolution if it exceeds the scanner's intrinsic capabilities [41].  
Advanced reconstruction algorithms like OSEM can incorporate models of the scanner's point spread function (PSF) to compensate for resolution-degrading effects, provided there is adequate SNR. Including PSF modeling can enhance spatial resolution by iteratively deconvolving for detector blurring and system imperfections. However, excessive resolution recovery can amplify noise and introduce artifacts if not properly regularized.
## 5 Simulation Results and Water Tank Experiments  
### Simulation Results  
The simulation verification of the designed controllers is as follows. Start the VRX environment, open Simulink, set the simulation step size and sensor sampling interval to 0.05 s, and run simulations in three modes: yaw rate, heading, and path-following control. Both the yaw rate and heading modes consider two scenarios: no wind, no waves, and wind-wave interference (wind speed 3 m/s, wind direction 90\({}^{\circ}\), wave gain 0.5, wave period 5 s, average wave direction 90\({}^{\circ}\)). The parameters of each reference model and controller are shown in Table 3.
# Earth Observation and Biodiversity Big Data for Forest Habitat Types Classification and Mapping
## 3 Results
### Classification
#### 3.2.2 Supervised Machine Learning Model
Finally, of the 240 million pixels potentially detectable (spatial extension of the Forest Mask layer), approximately 9.15% was not classified. Most of the area not classified were distributed on mountainous regions.
## 2 Models  
### Problem Description and Model Assumptions  
Due to the characteristics of high aggregation and strong repeatability of terminal demand points, it is difficult to directly use logistics UAVs for door-to-door distribution between the distribution warehouse and the demand points. In this paper, a UAV parcel-receiving station is set up in an area with intensive customer demand to receive and temporarily store logistics packages. The \"last-mile\" logistics transportation can be split into two parts: \"distribution warehouse-parcel-receiving station\" and \"parcel-receiving station-demand point\". Logistics UAV are used to perform the transportation task of \"distribution warehouse-parcel-receiving station\". Therefore, the problem solved in this paper can be described as follows: According to the location and demand volume of each demand point on the landslide, reasonably locate the UAV parcel-receiving stations to obtain an economical, practical, and fair logistics UAV parcel-receiving station location scheme. Based on the location results, plan the public air-route configuration of logistics UAVs, and obtain a safe and efficient public air-route planning scheme for logistics UAVs.  
To solve the transportation problem of \"distribution warehouse-parcel-receiving station\", the transportation network diagram \(G=(N,E)\) can be defined, where \(N=W\cup S\). \(N\) refers to the set of all nodes (UAV takeoff and landing points) in the transport network diagram, \(W\) refers to the set of distribution warehouses, \(S\) refers to the set of selected UAV parcel-receiving stations, and \(E\) refers to the set of connecting edges between nodes. The model proposed in this paper determines the layout scheme of the UAV parcel-receiving stations set \(S\) and designs the connection mode of the public air routes \(E\) simultaneously.  
The assumptions of the logistics UAV parcel-receiving station and public air-route planning bi-layer optimization model are as follows:  
1. The location of the distribution warehouse and the location and demand volume of each logistics demand point are known.
2. Take the multi-rotor UAV as the operating user, and the layered route strategy is adopted in air-route planning. The UAV can only adjust its flight altitude at the takeoff and landing area; the air routes with different operation directions are at different altitudes.
3. The distribution warehouse does not provide external services. Users can only pick up goods at the parcel-receiving station.
4. Due to the high accessibility and connectivity of ground-road junctions, all junctions are selected as alternative locations for parcel-receiving stations. Each alternative location is biased towards the open space beside the road to ensure that the construction of the parcel-receiving station does not affect the normal operation of ground traffic.
## 3 Results  
### Automated Positive CAC Score Prediction Using DL  
In this section, we report the results of applying the DL model over fundus images to automatically predict patients presenting a CAC score \(>400\) and thus a potential cardiovascular risk. The included subjects were enrolled in the PRECISED study (ClinicalTrial. gov NCT02248311). The study was conducted according to the declaration of Helsinki and was approved by the local ethics committee. All subjects provided written informed consent before study entry.  
We performed a 5-Fold stratified Cross Validation, where the image set is split in 5 folds, being 4 folds used for training and 1 for testing. The process is repeated 5 times until each sample has appeared in test once. We report the mean accuracy along the 5 trials and the 95% confidence intervals. We used VGG16 as the DL architecture (see Section 2). Training time was 141 s using NVidia GPU 12 Gb 2080 Ti 12 Gb and inference time 0.85 s on the same hardware.  
Results suggest that even with a small sample size effect, the VGG16 can model better than change both classes (see Table 3). Notice that the model does not perform equally using images from each eye. Although differences are not statistically significant, the retinas differ from the left to the right eye.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1** & **Conf Mat** \\ \hline RFI (Left Eye) & 0.67 & 0.48 & 0.67 & 0.56 & \begin{tabular}{c} 35 \\ 17 \\ \end{tabular} & \begin{tabular}{c} 8 \\ 16 \\ \end{tabular} \\ RFI (Right Eye) & 0.72 & 0.52 & 0.77 & 0.62 & \
\begin{tabular}{c} 38 \\ 16 \\ \end{tabular} &
\begin{tabular}{c} 5 \\ 17 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy, Precision, Recall and F1-measure and confusion matrix \(\begin{pmatrix}TN&FP\\ FN&TP\end{pmatrix}\) using fundus image and DL.
## 3 Results
### Trade-off between Fuel Consumption and Pollutant Emissions  
In the previous section, we presented a pollution centered scenario where the weighting parameter \(\alpha\) gives priority to pollutant emissions. In this section, we made several runs with different values of \(\alpha\). This balances the fuel economy compared to pollutant emissions. Results are presented in Figure 27.  
First, it can be noticed that a low weighting parameter (\(\alpha=0.1\)) of the objective function makes it possible to reduce pollutant emissions to a large extent without altering fuel consumption. In this case, the heating strategy of the catalyst is already implemented. With a hot catalyst, combustion is stoichiometric and a slight delay is observed compared to optimal spark advance (\(2^{\circ}\) in this particular case).  
When the value of \(\alpha\) increases, NO emission can be further reduced by lowering exhaust gas temperature. As we can see in Figure 27, a good trade-off can be obtained by choosing a value of \(\alpha\) between 1 and 2.
## 3 Results
### Eye-Tracking Results of the Pairwise Comparison
#### 3.1.1 Results of the Wireframe Button  
The observed frequencies of the wins and losses of the different wireframe buttons (see Buttons BW) in a pair comparison are shown in Table 2. Table 2 shows that the third version (V3) won the most pairwise comparisons (78 out of a total of 320; see the last column of the sum of wins). This is followed by the fourth version (V4; 76 out of 320), the second version (V2; 65 out of 320), and the fifth version (V5; 57 out of 320). The first version (V1) resulted in the fewest wins (44 out of 320).  
Figure 5: Course of the study from the participants’ perspective.  
After determining the frequencies, the simple Bradley-Terry model was estimated. The estimation of the pair comparison data via generalized linear models provides the effects from the individual pair comparisons of the five button versions. Similarly, the percentage of glances at the AOIs provides a clear ranking. The estimators for the item parameters and the ranks assigned to the wireframe button versions are shown in Table 3.  
The parameter for the first button version (V1) was set to zero in this case and therefore acts as a reference object. Based on the coefficient estimators, the study participants most frequently preferred the third button version (V3). The fourth button version (V4) is in second place, followed by the second (V2) and fifth (V5) button versions. In last place was the most angular and therefore first button version (V1). For visualization, a graphical representation of the coefficient estimators is shown with the gray line in Figure 6. The first button version (V1) also represents the reference object.
## 4 Algorithms to Determine the Diameter of the DG Tree and Minimum Delay for Data Aggregation
### Examples to Compute the Minimum Aggregation Delay of Data Gathering Trees  
Figure 1 illustrates examples to compute the minimum aggregation delay of two different data gathering trees that have the same diameter. In both the examples, the number inside the circle indicates the node ID and the number outside the circle indicates the minimum aggregation delay for the node. DG trees that have a larger fraction of leaf nodes (and hence fewer intermediate nodes) are more likely to have a larger number of child nodes per intermediate node. For such DG trees, it would take relatively more time slots for an intermediate node to sequentially aggregate data from its child nodes (especially for intermediate nodes at higher levels), resulting in a larger aggregation delay at the leader node. Hence, even if two DG trees have the same diameter (as shown in Figure 1), it could take a relatively longer time to aggregate data from the DG tree that has a larger number of child nodes per intermediate node. This is the trend of performance that is also observed with the DG trees in the simulations, especially for networks of high-very high node density.
## 2 Methodology
### Health Risk Assessment  
Health risk assessment can be conducted by considering exposure to PAHs through ingestion, inhalation, or dermal contact (U.S. EPA 2005) [16]. In this study, due to the results concerning the concentration of these substances in the air, the findings related to the inhalation of PAH-contaminated air are presented. Due to the varying carcinogenic potential of individual substances within the PAH group, risk assessment cannot be based solely on their total concentration. To determine their carcinogenic potential relative to benzo(a)pyrene and estimate the benzo(a)pyrene equivalent concentration (BaP\({}_{\text{eq}}\)), toxicity equivalency factors (TEFs) were used [17; 18]. The total carcinogenicity associated with PAHs was calculated using Equation (1).  
\[\text{BaP}_{\text{eq}}=\Sigma\big{(}\text{C}_{\text{PAH}_{i}}\cdot\text{TEF}_ {\text{PAH}_{i}}\big{)} \tag{1}\]  
The incremental lifetime cancer risk (ILCR) in humans can be determined by calculating the lifetime average daily dose (LADD) of PAHs according to USEPA guidelines (USEPA 2013) [19]. The equations used for estimating LADD and ICLR were as follows (Equations (2) and (3)).  
\[\text{LADD}=\frac{\text{Cs}\cdot\text{IR}\cdot\text{ET}\cdot\text{EF}\cdot \text{ED}}{\text{BW}\cdot\text{AT}}\cdot\text{CF} \tag{2}\]  
\[\text{ILCR}=\text{LADD}\cdot\text{CSF} \tag{3}\]  
The average concentration of a specific PAH (Cs) is measured in ng/m\({}^{3}\). The intake rate (IR) varies for different age groups, with adults having an intake rate (IR\({}_{\text{A}}\)) of 0.83 m\({}^{3}\)/h and children under six years (IRc) at 0.5 m\({}^{3}\)/h. Exposure time (ET) is considered to be 21 h per day, and exposure frequency (EF) is assumed to be 350 days per year. The exposure duration (ED) differs by age group: 70 years for adults (ED\({}_{\text{A}}\)) and six years for young children (ED\({}_{\text{C}}\)). A unit conversion factor (CF) of 10\({}^{-6}\) is applied. Average body weight (BW) was estimated at 70 kg for adults (BW\({}_{\text{A}}\)) and 15 kg for children (BW\({}_{\text{C}}\)). For average timing (AT), values of 25,550 days (70 years) and 2190 days (6 years) were assumed for adults (AT\({}_{\text{A}}\)) and children (AT\({}_{\text{C}}\)), respectively, based on [PERSON] et al. (2015) [18]. Carcinogenic slope factor (CSF) values for PAHs were obtained from USEPA (2005) [16]. Using these values, Lifetime Average Daily Dose (LADD) for both adults and children was calculated, alongwith the Incremental Lifetime Cancer Risk (ILCR) for seven PAHs classified as carcinogenic by IARC (2016) [1].
## 5 Power Delay Profile Measurements
The RMS delay spread, \(\sigma_{\tau}\), throughout the tunnel remains smaller than 1 ns. The value of 1 ns is significantly lower than other typical values, as shown in Table 1, but is similar to the values obtained in comparable studies [14]. Since the time resolution in the experiment is 0.2 ns and the value of the RMS delay spread is greater than that, the result obtained is accurate. As noticeable, the RMS delay spread, \(\sigma_{\tau}\), is not a constant value along the tunnel. As mentioned, the antennas that were used are directional. When the range between the transmitter and receiver is small, a small number of rays reach the receiving antenna, but in high scatter, due to the large difference in the optical paths. The farther away from the transmitter the receiver progresses, the smaller the difference in the optical paths becomes and then the scatter becomes smaller. However, due to the directional characteristics of the antenna, at some point, additional rays that bounced off the walls begin to reach the receiving antenna, increasing the RMS delay spread. After this stage, as the distance between the transmitter and the receiver increases, the RMS delay spread, \(\sigma_{\tau}\), dwindles down. The difference in the optical paths between the rays that do not experience strong decay becomes smaller, and the scattering from here on out becomes smaller. In other words, the rays that are reflected several times no longer reach the receiver with any significant power. The rays that remain powerful enough to get to the receiver are the rays that are reflected infrequently or not at all, and their travel time is similar to the time of the LOS ray. A comparison between the average values along the whole tunnel of the experiment and the model is shown in Table 8.
# APT Attack Detection Scheme Based on CK Sketch and DNS Traffic
## 4 Experiments
### Model Training and Results
#### 4.4.3 Testing Accuracy Comparison  
This experiment compares the accuracy difference between six machine learning models trained and detected using the original dataset and the dataset compressed and counted by CK sketch. The experimental results are shown in Figure 11, where the horizontal coordinates represent the four metrics used to measure the detection accuracy of the models and the vertical coordinates represent the accuracy values. The experiments show that, except for RandomForest, the other five machine learning models exhibit higher accuracy when trained and detected with the CK sketch structure.
# Application of the Theory of Planned Behavior in Autonomous Vehicle-Pedestrian Interaction
## 6 Discussion
### Surrounding Contextual Effect and Pedestrians Responses  
One of the practical challenges [47] of AVs is predicting pedestrian behavior while relating and considering their surrounding context. Responses revealed that pedestrians' response becomes uncertain if they find garbage and fifth in their path while crossing the road, and can lead to any unexpected action. Only 11% agreed that they would keep focused on the road crossing and would not deviate their path. The remaining participants showed either they will change their track or decide at the time, indicating ambiguity in behavior.  
The second important outcome in this context is pedestrian body action in such scenarios. Research revealed that pedestrians use different gestures when they face an indefinite situation. The majority of the participants confirmed that they would use hand indications to show their intention. Only 13% intended to use head indication, and 11% vowed that they would not perform any action.  
Further insight revealed significant differences in gesture application; participants from Europe and America were more responsive than Asians and Africans.  
To attain human driving perception, AVs have to reach this level of discernment, understanding full context and pedestrian gestures variations.
# A Novel 3D-Printed Training Platform for Ossiculoplasty with Objective Performance Evaluation
## 2 Materials and Methods
### Ossiculoplasty Simulation Hands-On Session  
S Sixty final-year medical students from the Faculty of Medicine and Surgery of the University of Bologna experienced the novel training method based of 3D-printed models between April 2024 and July 2024. The training sessions started with a standardized introductory lecture delivered by two of the authors ([PERSON], [PERSON]), providing a general overview of incus remodeling for OPL and explaining the steps to be followed during the practical exercises. All the students replicated the same type of incus remodeling (drilling of the long process, partial drilling of the short process, smoothing of the superior edge, and creation of the hole for the steps head at the base of the long process) with an operative microscope. The lecture was followed by the practical session, preceded by the demonstration of ossicle drilling by one of the authors ([PERSON], [PERSON]).  
The participants were homogeneously divided into two groups (A and B), each consisting of thirty students.  
Participants from Group A performed a preliminary ossiculoplasty task on the magnified incus (3:1), before replicating it on the real-sized incus (1:1). Participants from Group B performed the ossiculoplasty task only once, directly on real-sized incus (1:1).  
After the completion of the tasks, all the participants had the possibility to insert the remodeled real-sized incudes into the mTB using both a microscope and an endoscope. All trainees managed to complete the whole training experience. Training sessions for group A consisted of 5 4-h sessions, during which every student performed the ossiculoplasty first on the 3:1 incus and immediately afterwards on the 1:1 incus. On the contrary, the training session for group B on the 1:1 model was completed in 3 sessions, each 4 h long.  
At the end of the training session, participants were asked to complete a qualitative questionnaire. The survey was divided into three main sections: The first two regarded the use of the instruments on the 3D-printed incudes (both real-sized and magnified). The third section was general and regarded the interest in using 3D-printed models for specific training purposes.  
The task on the real-sized incus was also completed by an experienced surgeon (G.M.), who remodeled five real-sized incudes and whose performance was taken as a reference for the subsequent evaluation of the two groups.  
Figure 3: (**A**,**B**) Temporal bone model and incudes after printing. (**C**) Real-sized (1:1) and magnified (3:1) incudes ready for use.  
All the real-sized incudes obtained (from the participants and the experienced surgeon) were scanned via CBCT according to three configurations (A, B, C), each corresponding to a different positioning of the parts during the scanning process. The obtained images were segmented using the same thresholding parameters (\(-240\pm+360\) HU) with Mimics.