esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Hybrid Organisation Structures  
The challenges show that existing production structures must be adapted to meet the requirements for increasing flexibility while ensuring cost-effective production. Therefore, hybrid organisation structures represent a possible approach. They offer the opportunity to utilise the advantages of different production principles and position oneself strategically between them. However, the term _hybrid_ is used in different contexts in production and assembly, so a definition is given first: \"_A hybrid organisation structure describes a combination of at least two interlinked different organisation structures_\" [8].  
When using the term _hybrid assembly_, it is crucial to differentiate this term, as it also describes semi-automatic assembly as a combination of manual and automated assembly [12]. Although the degree of automation is a significant influencing factor when considering economic production, it should not be considered further in the context of the presented approach. This approach finds hybrid organisation structures in the form of a combination of two different principles, in this case, the flow principle and the job shop principle. Figure 1 shows a hybrid production structure that combines a flow line with a flex zone, which is organised according to the job shop principle. The dimensioning of all zones depends on the assembly operations of the products to be assembled, and the positioning of the flex zone depends on the assembly precedence relationships.  
The advantage of hybrid organisation structures is that they allow a certain degree of flexibility for producing many variants in just one assembly system. However, due to the prevailing principle as a line, they have a higher output than the job shop principle.  
Accordingly, similar assembly operations for the variants are carried out in the line, and the special operations for a particular variant are carried out at a workstation in the flex zone. The challenge with hybrid organisation structures is that the different systems are interlinked and not decoupled by a warehouse as in the production stage concept [13]. Interlinking offers the challenge of balancing the variation in throughput times, but the logistical effects are not transparent due to complex interrelationships and dependencies between influencing and control variables. Furthermore, previous research has not sufficiently investigated the optimal configuration for hybrid organisation structures and how existing assembly systems can be converted into a hybrid organisation structure. For this purpose, the approach presented in this paper offers a systematic procedure and provides investigations into the existing effective relationships.
## 3 Facility and Experimental Section
### Impact AE Signal Processing and Feature Extraction  
Impact AE characterization was realized by AE signal processing and feature extraction.  
Processing and analysis of impact AE signals were operated based on power spectrum density (PSD) method, it is a frequency spectral analysis based on the Fast Fourier Transformation (FFT) method. This method transfer AE signals from the time domain to the frequency domain, in which the frequency response distributions of impact are demonstrated as peaks.  
The measurement threshold of the soundcard was 0-22 kHz, hence according to the Nyquist-Shannon sampling theorem the soundcard was set with a sampling rate of 44.1 kHz as well as a sampling number of 4096 for each AE signal data. Hanning window function was chosen for FFT operation in order to reduce side lobes and make the main feature peaks more significant. The original impact AE signal system contained white noise from electronic devices as well as AE signals from passive impact stone plate. Both kinds of noises could be eliminated by using an adaptive filter and band stop filter. For the facilities in this research, system electronic noise was determined to concentrate at around 500 Hz while the main lobe of stone plate impact AE responses focused on a range of 4350-4600 Hz. PSD spectra of a scrap impact AE signal before and after de-noising are shown in Figure 3.  
Figure 3 shows that the impact frequency response of this impact scrap concentrated on the position of the main peak/main lobe, around 5800 Hz. The lower peak around 10,000 Hz could be an  
Figure 3: Power spectrum density (PSD) spectrum of an impact AE signal before and after filtration.  
interharmonic of the impact AE signal, which is commonly generated by impulse signal and nonlinear frequency response models.  
Feature extraction for impact AE recognition was realized by peak detection on the PSD spectral domain. With the help of programing in MATLAB, the coordinates of the main peaks and side peaks could be easily extracted. The main peaks indicated the concentration of frequency response energy, named as the eigen-frequency of a specific scrap. The feature extraction is shown in Figure 4.  
Generally, the main peak position in the PSD spectral domain indicated the impact response frequency of an impact scrap, described by Equation (5). Further by using the thickness of the scrap determined by laser-triangulation, the extracted eigen-frequency could be normalized to the eigen-frequency response \(f_{\mathrm{norm}}\). With \(f_{\mathrm{norm}}\) from impact scraps of the same material, the material coefficient \(kc\) could be obtained by data fitting and regression analysis according to the impact frequency response model of Equation (6).
# SybilHP: Sybil Detection in Directed Social Networks with Adaptive Homophily Prediction
## 4 Methodology
### Adaptive Homophily Estimator  
In this subsection, we build adaptive estimators to predict the assortativity [41; 42] for each node, i.e., the likelihood that an individual will form connections with other individuals. Here, we do not distinguish between homophily and assortativity, although the former is descriptive, and the latter is predictive. Furthermore, we call the estimators homophily estimators.  
Our idea comes from an observation that it is uncommon for Sybils to be actively followed by human users, so the Sybil heads and benign tails tend to play more informative roles compared to benign heads and Sybil tails. To better capture this information, we maintain a pair of homophily estimators to measure a benign user's capability to resist the Sybil attack, and the Sybil's capability to make a benign user compromise. These assortative capabilities can be reflected in the statistics of the neighboring nodes' states. Therefore, for unlabeled nodes, we take their posterior probabilities in the previous iteration as their state; thus, the estimators should be updated in each iteration.  
If a benign user \(u\) has already followed a certain number of Sybils, then it is safe to say that he/she will do it again. In other words, its discernment depends on the percentage of benign nodes among the nodes it follows as shown on the left of Figure 4. We thus define adaptive homophily estimator \(c_{u\_b}^{(t)}\) as follows:  
\[c_{u\_b}^{(t)}=\frac{\sum_{v\in N_{\text{out}}(u)}p^{(t)}(x_{v}=-1)}{|N_{ \text{out}}(u)|}, \tag{7}\]  
where \(N_{\text{out}}(u)\) is the set of outgoing neighbors of \(u\). Furthermore, \(p^{(t)}(\cdot)\) is the temporary posterior probability distribution at iteration \(t\), which is calculated by the aggregation of propagated label information at iteration \(t-1\):  
\[p^{(t)}(x_{u})=\frac{1}{Z}\phi_{u}(x_{u})\prod_{k\in N(u)}m_{ku}^{(t-1)}(x_{u}). \tag{8}\]  
Similarly, a Sybil \(u\) who has already managed to obtain many benign followers can also entice one more benign follower at a small price. In other words, its deception capability depends on the percentage of benign nodes among its followers as shown on the right of Figure 4. We thus define adaptive homophily estimator \(c_{u\_s}^{(t)}\) as follows:  
\[c_{u\_s}^{(t)}=\frac{\sum_{v\in N_{\text{in}}(u)}p^{(t)}(x_{v}=1)}{|N_{\text{ in}}(u)|}, \tag{9}\]  
where \(N_{\text{in}}(u)\) is the set of incoming neighbors of \(u\). \(p^{(t)}(\cdot)\) is the label propagation from iteration \(t-1\) defined in Equation (8). Note that we count the bidirectional linked neighbors in both \(N_{\text{in}}(u)\) and \(N_{\text{out}}(u)\).
## 1 Introduction
### Maritime Emissions and Their Effects  
Table 1 presents a summary of the kinds of maritime ships' emissions in terms of their chemical composition and their effects on climate change, human health, and food production [25; 26; 27; 28; 29; 30; 31].  
Black carbon is emphasized in the present article because BC emissions are:  
* The second leading contributors to global warming, equaling about 55% of carbon dioxide's contribution, and more potent per ton than carbon dioxide by a factor on the order of thousands of times over a 20-year period [26; 27].
* A principal contributor to the global public health impacts of air pollution and the most important component of particulate matter pollutants, and they are especially important in large urban areas [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16].
* The greatest contributor per ton to the damages to food crops caused by climate change pollutants, and an especially threatening air pollutant for crops near coastal areas [7; 8; 18; 19; 20; 21].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Emissions** & **Comments** \\ \hline  
Two combinations of pollutants complicate the net effect of reducing some kinds of emissions. As noted in Table 1, one of the many types of emissions from maritime diesel engines is organic carbon (OC), which is a global _coolant_. The ratio of BC to OC in the ships plumes is therefore important because of ratio of BC to OC and because of different impacts. However, since the BC:OC ratio for large marine diesel engines is about 9:1, this particular combination is not a serious complication in maritime BC emissions analysis or policymaking [28]. See the paper Annex for further details about this issue.  
Another complication of the multiplicity of maritime emissions is that sulfur dioxide is also a global _coolant_. Thus, reducing ships' emissions of sulfur dioxide emissions by reducing the sulfur content of their diesel fuel has the unintended consequence of reducing emissions of a global _coolant_. Moreover, reductions of the sulfur content of marine fuels may _increase_ the black carbon content [27].  
For more on the chemical and physical basics of BC, see Box 1.
## 3 Numerical Model
### Meshing  
Figure 6 shows the numerical computational grid model of the high-speed centrifugal pump. The computational model consists of six parts: inducer, impeller, vaned diffuser, guide section, inlet, and outlet pipes. The computational model considers the clearances present in actual operation, with a clearance of 0.5 mm set for the inducer tip gap and 0.35 mm for the impeller tip gap. To generate the structured hexahedral mesh for the fluid domains, the commercial software Turbo-Grid and ICEM CFD are employed, respectively, as shown in Figure 6. The quality of the mesh is rigorously assessed to ensure the reliability of numerical calculation. A grid independence verification was performed using the pump head as a reference. Figure 7 indicates that the computational model with a total grid count of 18.46 million satisfies the computational requirements.  
Figure 8 illustrates the numerical computational model and boundary conditions for the flow domains of the radial and thrust hydrodynamic bearings used in the prototype. The bearings' inlet and outlet pressure boundary values are set by the results from the impeller outlet. In actual operation, the rotor system consisting of the impeller, main shaft, and thrust bearing remains in a dynamic equilibrium process in both the radial and axial directions. Therefore, this study conducted numerical simulations for the thrust bearings with different film thicknesses and the radial bearings with various eccentricities at the designed rotating speed.
## 3 Improved ORB-Based Matching Method for Pose Estimation  
### The Pinhole Camera Model and Camera Calibration  
The imaging system is the most important part of the whole pose-estimation system for obtaining high quality images. The schematic diagram of the imaging model is shown in Figure 2. A large format CCD camera with wide-angle lens provides high resolution images with more than 2000 pixels in three dimensions. The keypoint \(P_{n}\) in the 3D world coordinate is projected to the image plane. The lenses used in the system sustain a small amount of distortion since they are not perfectly manufactured. The distortion will be corrected in the camera calibration process.  
The pinhole camera model [13; 14] relates the correspondences between n reference points of the scene in a 3D world coordinate system and their projections on the image plane, as is shown in Figure 3. The model assumes that all the rays of light generated by these pairs of corresponding points would converge to the projection center point, away from the image plane at a distance of \(f\) which is the focal length. The found scene keypoint \(P_{c}(x_{c},y_{c},z_{c})\) in a camera coordinate system, and the corresponding point \(p(u,v)\) on image plane are related by:  
Figure 1: System layout of pose-estimation system; the pose-estimation module is fixed on the robot manipulator; The pose-estimation module, composed of Charge Coupled Device (CCD) camera, two laser rangefinders and two LED light sources; The battery of electric car with printing pattern.  
Figure 2: Schematic diagram of the imaging model of the pose-estimation system.  
\[\left[\begin{array}{c}u-u_{0}\\ v-v_{0}\end{array}\right]=\frac{f}{z_{c}}\left[\begin{array}{c}x_{c}\\ y_{c}\end{array}\right] \tag{1}\]  
The 3D transformation between the real-world coordinate \(P_{c}(x_{w},y_{w},z_{w})\) and camera coordinate \(P_{c}(x_{c},y_{c},z_{c})\) can be defined by a \(3\times 3\) rotation matrix \(R\) and the translation vector \(T\):  
\[\left[\begin{array}{c}x_{c}\\ y_{c}\\ z_{c}\\ 1\end{array}\right]=\left[\begin{array}{cc}R&T\\ 0^{T}&1\end{array}\right]\left[\begin{array}{c}x_{w}\\ y_{w}\\ z_{w}\\ 1\end{array}\right] \tag{2}\]  
The final projection can be expressed as Equation (3),  
\[Z_{c}\left[\begin{array}{c}u\\ v\\ 1\end{array}\right]=A\left[\begin{array}{cc}R&T\end{array}\right]\left[ \begin{array}{c}x_{w}\\ y_{w}\\ z_{w}\\ 1\end{array}\right] \tag{3}\]  
where \(A\) is the intrinsic matrix, \((u_{0},v_{0})\) is the intersection pixel point of the optical axis and image plane, \(s\) is the skew angle, which is very close to 0 in most cases, \(d_{u}\) and \(d_{v}\) represent the scale factors in each direction since the pixels in the CCD are not absolutely square. The camera intrinsic parameters listed above, link the keypoint 3D coordinates to pixel coordinates on the captured image.  
\[A=\left[\begin{array}{ccc}d_{u}f&s&u_{0}\\ 0&d_{v}f&v_{0}\\ 0&0&1\end{array}\right] \tag{4}\]  
The intrinsic matrix is assumed to be known before estimating the pose of objects. Therefore, the camera needs to be calibrated with Matlab calibration toolbox [15] by exploiting a chessboard.
## 3 Methodology
### NN Sensitivity Tests
#### 3.3.1 Selection of an Optimized Threshold for the NN Output  
The first test is conducted to optimize the output threshold to distinguish water from non-water pixels. Figure 7 shows the histogram of the output of the NN, separating the water and non-water pixels according to the related Landsat-8 surface water map. The histograms of the water and non-water clusters intersect around 0.9, meaning that the optimal threshold to separate water from non-water pixels is close to this number. Different thresholds on the NN output values were tested (0.80, 0.85, and  
Figure 6: Examples of the five inputs and the target for the NN. (**a**) SAR backscatter coefficient VH polarization; (**b**) SAR backscatter coefficient VV polarization; (**c**) SAR incidence angle; (**d**) SAR standard deviation of backscatter coefficient VH polarization; (**e**) SAR standard deviation of backscatter coefficient VV polarization; and (**f**) Target surface water map based on NDVI from Landsat-8. The white areas are cloud-covered pixels detected by the Landsat quality flags, and they have been removed. Sentinel-1 and Landsat-8 images were acquired on 16 and 14 April 2015, respectively.  
0.90): for each one, the confusion matrix and the overall accuracy are calculated, with the corresponding Landsat-8 images as references. The overall accuracy and the spatial correlation increase from 98% to 99% when the threshold increases from 0.80 to 0.90 (Table 3), but the true positive pixel detection decreases from 92% (with threshold 0.80) to 89% (with threshold 0.90) and the false negative pixel detection increases from 8% to 11%. A threshold of 0.85 is selected here because of its good water detection performance and because it results in the predicted water surface closest to the reference map: 4430 km\({}^{2}\) from the Landsat-8 versus 4420 km\({}^{2}\) from the SAR results, i.e., a limited overestimation of 0.4% as compared to the reference map.
# Effects of Specific Training Using a Rowing Ergometer on Sport Performance in Adolescents
## 2 Materials and Methods  
### Subjects  
A sample of students/rowers (N = 56; boys: n = 32; girls: n = 24) with a mean age of 11.73 \(\pm\) 1.4 years, belonging to two categories (10-11 years old: n = 25 and 12-13 years old: n = 31), completed an eight-week training program, with four weekly sessions of two hours each one, consisting of one hour of specific training followed by an hour of rowing on the water.  
The sample was divided into the following two groups:  
1. A control group (CG: \(n=26\)) received specific training based on general strength circuit training with exercises using their own body weight at six stations (squat jump, push-up, plank, back-squat, pull-ups with elastic bands, side-plank). They performed three 30-s sets of each exercise in a rotating circuit with 30-s rests between each set.
2. An experimental group (EG: \(n=30\)) whose training consisted of a specific training protocol using a rowing ergometer (rowing machine model CONCEPT 2 D PM5, Concept-2, Morrisville, VT, USA) [18] consisted of two blocks of five sets of 90 s, at 18 strokes at maximum power, resting 60 s between sets and 4 min between each of the blocks. A drag factor (DF) of 140 was established (higher than what is usually set during training in the juvenile and youth categories, which is around 90-100 DF or the resistance of the water when paddling). The DF measures how quickly the fan blades slow down between each pull.
## 5 Analysis and Discussion
### Analysis of Axial Force During CWR Fractures Caused by Temperature Variations  
The axial force analysis of CWR fractures caused by temperature variations was conducted by examining rail fracture displacement under axial load and rail temperature changes attributed to axial stress. A numerical simulation was performed to model CWR fractures under maximum temperature variation (maximum load). At a maximum load of 1192 kN, a relative displacement difference (left/right) of 0.346-0.429 mm occurred, resulting in a clearance of \(\sim\)0.08 mm at the fractured CWR. Relative displacement differences among the four fracture points remained relatively uniform with temperature variations,  
Figure 15: Temperature influence range near ventilation shafts.  
which indicates that fractures occurred perpendicular to the rail cross-section. The analysis results closely resembled actual CWR weld fractures observed near ventilation shafts, as indicated in Figure 16.  
The displacement analysis of CWR fractures under axial load considered relative displacement differences, and the results indicate that at \(\sim\)535 kN of axial load, the rail weld experienced a displacement of \(\sim\)0.05 mm, leading to fracture. The numerical analysis confirmed that thermal expansion and contraction caused by temperature variations cause minor deformations in rails, which can exceed material strength limits and result in CWR weld fractures. The analysis results of rail axial force and weld displacement under temperature variations are shown in Figure 17.  
The correlation between axial stress and rail temperature variations showed that axial stress at the CWR weld fracture point was \(\sim\)71 MPa, which was \(\sim\)23% lower than the additional axial stress threshold (92 MPa) for the CWR base material. This implies that CWR fractures can occur at stress levels lower than that at the standard axial stress limit. In actual field conditions, the rail installation status, welding quality, and fastening conditions can directly affect CWR weld fractures. The analysis results of temperature variations leading to CWR weld fractures and corresponding axial stresses are shown in Figure 18.  
The analysis demonstrated that CWR weld fractures can occur at temperature variations of \(\sim\)25 \({}^{\circ}\)C in locations with large localized temperature fluctuations, such as near ventilation shafts. This finding is consistent with field investigation results on rail fractures.  
Figure 16: CWR weld fracture near the ventilation shaft.  
Figure 17: Rail axial force and weld displacement during fracture.
## 3 Results
### Optimistic and Pessimistic Forecasts: The Versatility of Quantile Forecasting
A similar phenomena occur with the grid-commitment for the three scheduling strategies, where the AnEn\({}_{\tau=0.1}\) forecast, being the most pessimistic, presents the best results. This aligns with the fact that under-estimative (i.e., pessimistic) PV power forecasts, favor the grid-commitment service. The latter throws an interesting conclusion regarding the effect of forecasts accuracy: highly optimistic or pessimistic forecasts (such as AnEn\({}_{\tau=0.9}\) or AnEn\({}_{\tau=0.1}\)), do affect negatively the performance in some services like EC or CO\({}_{2}\) content, that seem to be rather favored by low-bias forecasts (e.g., PE or NWP). On the other hand, a service, like grid-commitment, is favored by pessimistic forecasts, while the grid contracted power, while not presenting a clearly defined behaviour, is less affected when pessimistic forecasts are used. With the previous results in mind, an EMS is able to favor some services by using different forecasting approaches with errors ranging from under-estimative to over-estimative. This suggests that it does not exist an unique proper forecasting method as such, as it is dependent on the service being targeted. Therefore, if the most suitable forecasts are used for each service, how well can the EMS perform? To what extent can we improve performance in the services proposed? We try to find the answer to this question in the next section  
The results shown so far suggest that there are services that benefit from over or under estimative forecasts. Therefore, at the view of this results and acknowledging the fact that a forecasting error will be always present, a proper forecasting method could be defined as such that, due to its persistent under or over estimative nature, allows for the EMS to produce optimal performance in a particular service by providing certainty on the sign of the imbalance between production and consumption. In the following section, we search for the optimal quantile forecast that must be used in order to optimize the different services.