esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Extraction of High-Voltage Power Transmission Objects from UAV Lidar Point Clouds
## 3 Method  
Figure 1 illustrates the whole workflow of the proposed method, which consists of five steps: data preprocessing, feature calculation, object extraction, optimization, and reconstruction.  
* Preprocessing. The first step is to remove the sparse noisy points and divide the data into grids at different levels. First, \(5\times 5\) m\({}^{2}\) grids were constructed in the 2D horizontal plane considering the horizontal diameter of HV pylons, then were further divided into \(0.5\) m\({}^{3}\) voxels for detailed analysis. The grid structure contains not only the point set within its range, but also grid features including the values of height difference, inclination, and point density within the grid.
* Dimensional feature generation. Based on the analysis of several geometrical distribution features of transmission lines locally (linear, parallel, consistent) and globally (elongate), PLs were extracted using 3D grid features; four further features were then extracted based on 2D grids, i.e., large height difference, continuous height distribution, local extremely high value, and limited range in planar projection map, were calculated to differentiate pylons and poles from the other objects.
* Extraction of individual lines and towers. First, PL segments were labeled based on the local linear geometric distribution in 3D. Consecutive PL segments were then merged as PL candidates. Next, the HV pylons were detected using the 2D grid features calculated above within the whole strip of the scanned transmission corridor.
* Optimization. The preliminary result was optimized by a feature of positional relationship between the HV towers and PLs in the 3D space, on the basis that they successively connect to each other around individual lines at the point of attachment (POA) [40].
* Reconstruction. The 3D PL was modeled using a 2D line in a projected ground plane and a catenary curve in the vertical plane that is perpendicular to the projection plane. The pylons were constructed by fitting with models from an existing library.
# Near-Optimal Weather Routing by Using Improved A* Algorithm
###### Abstract  
With soaring oil prices worldwide, determining the most optimal routes for economical ship operation has become an important issue. Optimizing ship routes is economically important for ship operation, but it is also essential to meet the standards of environmental regulations recently imposed by the International Maritime Organization. For this purpose, various algorithms for determining ship routes have been developed to ensure the economical operation of ships via utilization of marine climate data and Automatic Identification System (AIS) data. However, such algorithms require a large amount of computational time and do not provide optimal routes because they do not consider practical operating conditions, such as weather and ocean conditions. In this study, an improved A* algorithm using AIS and weather data is proposed to overcome the limitation of the original A* algorithm, one of the most widely used path-finding algorithms. The improved A* algorithm uses an adaptive grid system that efficiently explores nodes according to map grid deformation by latitude. It finds economical routes by minimizing the estimated time of arrival generated by machine learning through 16-way node exploration. For verification of the proposed method, the original A* algorithm and improved A* algorithm were compared through a case study.  
A* algorithm; weather routing; ship speed; machine learning; economical ship operation +
Footnote †: journal: Applied sciences
# Comparative Theoretical Analysis of Halden Reactor Creep Tests on M5 Cladding Material
###### Abstract  
This study provides a comparative analysis of two creep tests performed on M5 cladding material at the Halden reactor under different stress conditions in the IFA-617 and IFA-663 test rigs. The objective was to evaluate the creep behavior of the M5 cladding material, a crucial component in nuclear reactors, under both compressive and tensile stress environments. The data generated from these experiments were analyzed to assess the diameter changes in the cladding over time, considering factors such as stress, temperature, and neutron flux. The results revealed a significant difference in creep rates between the two tests, with IFA-663 showing a considerably higher rate. This disparity is attributed to the combined effects of creep and corrosion/crud deposition phenomena, which were more pronounced under tensile stress conditions. A model was developed to account for these factors, providing a better understanding of the M5 cladding behavior in different operational environments. These findings contribute to improving the predictive capabilities of the TRANSURANUS code for simulating M5 cladding creep, highlighting the need for further post-irradiation examinations to refine the understanding of corrosion and crud deposition effects.  
M5; creep; Halden; neutron flux; corrosion; crud deposition; TRANSURANUS
# Modeling of Historical Marine Casualty on S-100 Electronic Navigational Charts
## 5 Discussion and Limitation
### Representation and Integration with Other S-100 Layers  
The current implementation represents all casualty features as point objects. While appropriate for simple incident visualization, this approach may oversimplify the spatial and temporal extent of certain accident types. For instance, events such as drifting, collision tracks, or grounding zones may be more effectively portrayed using lines or polygons to capture the full geographic scope of the event.  
In addition, the integration of casualty layers with other S-100-based datasets--such as ENC, S-111 surface currents, or S-124 navigational warnings--raises concerns about symbol prioritization, overlap resolution, and portrayal consistency. Ensuring compatibility with existing portrayal catalogs and maintaining interpretability across multiple overlapping layers will require the development of clear rendering rules and interaction logic. Addressing these issues is essential for embedding the proposed model within a broader ecosystem of safety-critical S-100 services.
# Advanced Strategies for Mitigating Particulate Matter Generations in Poultry Houses
## 6 Particulate Matter Emission Mitigating Strategies
### Other Management Practices  
#### 6.7.1 Aeration and Ventilation System  
Aeration (Airflow speed) exhibited a significant and inverse relationship with PM and NH\({}_{3}\) concentrations but a significant and direct relationship with temperature [80]. The contradiction between maintaining temperature and increased ventilation must be implemented to improve the air quality in the layer house. When there is insufficient ventilation, the concentration of air pollutants can build up to dangerous levels [147]. During the colder seasons, ventilation is used to remove moisture and manage humidity levels, whereas during the warmer seasons, ventilation is used to keep interior temperatures within the poultry barn's thermal neutral zone. Furthermore, mechanical and natural ventilation systems help to dilute NH\({}_{3}\) and PM concentrations by delivering fresh air into the indoor environment [148]. The mixing of air induced by the air inlets and exhaust fans might impact PM levels within the barn when mechanical ventilation is used. Decreased ventilation can cause dust concentrations to drop because of increased moisture content in air and litter compared to high ventilation [70]. However, houses with extremely high ventilation rates or natural ventilation resulted in decreased dust levels.
## 2 Class-D PA for Polar-Mode Architectures
### Class-D PA Design Proposal  
From the previous subsection, it is logical to consider that the class-D voltage switching topology is potentially the most suitable for PAs operating within a wideband polar-mode architecture. Then, a VHF PA demonstrator with up to \(P_{OUT}=100\) W (PEP) was proposed to evaluate its linearity.  
The implementation of voltage-switching class-D PAs for polar-mode transmitters operating in the VHF band has already been suggested [19], but at that time, the capabilities of existing solid-state devices were a limitation for the wideband operation of the envelope input (\(v_{E}\)) port. Semiconductor technologies such as the gallium nitride (GaN) high-electron-mobility transistor (HEMT) can help overcome these limitations. Their smaller parasitic capacitances and shorter switching times compared with their counterparts based on previous solid-state technologies improve the PA performance in terms of linearity (both in the envelope and in the phase path) and power management (as it can provide higher-energy-efficiency figures).  
The specifications given for this class-D voltage-switching PA led to a load network design based on [19] that comprises a hybrid coupler (T2), an LC matching circuit (LCMN), and two low-Q resonators (LC1 and LC2), followed by an output balun (T3) [11]. Since this class-D hybrid-coupled voltage-switching (HCVS) topology (Figure 4) is a slight variation of the TCVS topology, it retains its main linearity benefits when used as a high-level modulator:  
* The envelope-input-related currents (\(i_{E1}\) and \(i_{E2}\)) flowing in opposite directions in T2 windings result in magnetic flux cancellation, which provides an almost noninductive wideband envelope input (\(v_{E}\)) port. Only some small inductances related to T2 leakage and the circuit-board layout are present in this path;
* The drain-to-source voltages (\(v_{DS1}\) and \(v_{DS2}\)) described are as follows: \[v_{DS1}(t) =v_{E}(t)\cdot[1+s_{C}(t)]\] (5) \[v_{DS2}(t) =v_{E}(t)\cdot[1-s_{C}(t)]\] (6)  
These result in a differential voltage defined by the following expression:  
\[v_{DS1}(t)-v_{DS2}(t)=2v_{E}(t)\cdot s_{C}(t) \tag{7}\]  
which is scaled by the 1:N ratio of the LC matching network (LCMN) and filtered by the LC resonators (LC1 and LC2), yielding an output voltage that can be expressed as follows:  
\[v_{O}(t)=\frac{8N}{\pi}v_{E}(t)\cdot\sin(\omega t) \tag{8}\]  
where the \(s_{C}\) square signal harmonics of (1) were removed. It must be noted that the envelope input (\(v_{E}\))-independent term is cancelled, which confirms the desired envelope input (\(v_{E}\)) rejection effect on the amplified output signal (\(v_{O}\)).  
To benefit from these advantages, the EA should be placed as close as possible to the envelope supply port of the RF PA to reduce any inductance.  
It is also important to note that the use of GaN HEMTs at these frequencies would simplify the input network by reducing it to just an input balun (T1) and some power resistors located at the transistor gates to improve input return losses and device stability.
# Seismic Performance of LRS-FRP-Concrete-Steel Tubular Double Coupling Beam
## 3 Results and Discussion  
### Failure Modes  
The failure modes of the specimens are shown in Figure 7. Notably, only the failure behavior of specimen DST-DCB-89 appears in Figure 7 due to the similarity of the failure modes in each of the three coupled beams with inner steel tubes. Three different kinds of failure modes are illustrated in Figure 7. Compared to cross diagonal cracks observed in specimen RC-DCB (Figure 7a), specimen LRS-DCB was noticeably strengthened with the PET-FRP wrap as evidenced by the appearance of more flexural cracks and fewer shear cracks at both ends. Relatively speaking, specimen DST-DCB-89 presented one main bending crack in the plane perpendicular to the force direction and yet the concrete was crushed.  
As for the control specimen RC-DCB, some shear cracks were generated one by one before the longitudinal reinforcements yielded. Then, one small bending crack was generated after longitudinal bar yield. Finally, with the deformation increase, the spalling of concrete resulted in a sudden drop in bearing capacity.  
The damage processes of the other specimens during testing were similar to each other. With the extra wrapped PET-FRP material, no obvious shear crack appeared during the testing process. As shown in Figure 7b,c, the location of the observed first crack was at the junction of the coupling beam and end block. Then all these specimens reached the peak strength. With the deformation increase, the horizontal cracks were generated near the end of the coupling beam.  
Figure 5: Locations of strain gauges: (**a**) for longitudinal steel bar; (**b**) for PET sheet.  
Figure 6: Diagrammatic sketch of shear deformation (**a**) locations of LVDTs; (**b**) calculation sketch of shear deformation.  
After artificially removing the PET-FRP jacket and concrete, the specimens LRS-DCB and DST-CB-89 presented two different failure modes in Figure 7. In Figure 7b, many cracks are shown as generated at the plastic hinge areas at both ends of the specimen LRS-DCB. Furthermore, one main crack appeared at the junction of the beam and on the end block. Figure 7c shows serious concrete crushing and local buckling of the steel tube occurring in those specimens with inner steel tubes. The primary analysis concluded that the local buckling of the inner tube invalidated any blame on the constraining effect to concrete provided by the PET-FRP material; then, concrete started crushing as the deformation increased. When the crushed concrete filled the gap and the deformation increased, the constraining effect of the PET-FRP sheet controlled the attenuation of the bearing capacity.
# A Bayesian Network-Based Information Fusion Combined with DNNs for Robust Video Fire Detection
## 3 Proposed Method
### Network Architecture and Timing Diagram
Because all results of the averaged location classifier and the long-term smoke detector were available at every \(T_{vot}\), the final decision in our Bayesian net was made at the same time interval as \(T_{vot}\). In other words, \(T_{vot}=T_{Bayes}\). In addition, the areas of flame and smoke were calculated for each frame and smoothed by taking the average over \(T_{ave}\). Changes in the average area in video frames were reported with other additional information at every time instant \(T_{rep}\). For convenience, we assumed that \(T_{rep}=T_{vot}=T_{Bayes}\). The details of this interpreter of long-term dynamic behavior were identical to our previous method [3], except that here the identified location was accounted for [26]. The exact timing for each block depended on the situation where the fire detection system with our proposed method operates, and could be adjusted accordingly.
## 4 Results
### Test Case 2: Industrial Area of Houston  
The second case study is analogous to the one presented above. The area of interest is centered on an industrial area of Houston, where the chemical plant Arkema is located. The exact location of the chemical plant is highlighted on all maps shown in Figure 6 by a blue square. Even though there are no crowdsourcing points available for this location, flooding by Hurricane Harvey is confirmed by aerial photos acquired over this area (Figure 6f), as well as by information published on social media and official news outlets. From the SAR intensity information displayed in Figure 6a,b, it is not possible to assess whether the chemical plant was flooded or not. The backscattering variations recorded in that specific area are not high enough to determine this. However, if we analyze the variations in \(\rho\), an important drop-off is noticeable in both the IW and SM maps, as shown in Figure 6c,d. This allows the delineation of a flooded area that comprises the chemical plant, as highlighted in dark blue in Figure 6g,h. It is worth noting that flooded areas on bare soil are larger on the SM map than on the IW map. This is probably because the IW image was acquired 12 h after the SM image, when flooding had already receded. This is also confirmed by the GeoEye-1 images acquired the day after, where some flooded areas present on August 30 had already disappeared.
## 2 Basic Theory  
### Contact Stiffness  
The actual surface of a mechanical structure is inherently rough, characterized by randomly distributed rough peaks of varying heights. As shown in Figure 1, when observed at a microscopic scale, the seemingly smooth surface reveals the presence of randomly distributed micro-convex bodies.  
The distribution of rough peaks on the surface of a mechanical structure is random, including the distribution of their heights. Consequently, the characterization of a rough surface primarily relies on statistical quantities. Physical quantities utilized to describe rough surfaces can be categorized into three groups: amplitude parameters, which depict the heights of peaks and valleys and are independent of the chosen reference level; spacing parameters, which describe the distances between micro-irregularities on the surface; and hybrid parameters, which combine amplitude and spacing parameters to offer a comprehensive description.  
When a contact interface is subjected to normal loads, contact occurs on a rough surface. The initial contact is made at the highest point, which corresponds to the sum of the heights of the micro-convex bodies on both surfaces. As shown in Figure 2, assuming contact between a smooth surface and a rough surface, the first contact occurs at micro-convex peak \"a,\" followed by \"b,\" \"c,\" and \"d.\" As the normal load increases, contact will also take place between micro-convex bodies with smaller combined heights. The region where paired micro-convex peaks come into contact is referred to as the contact area. The aggregate sum of the contact areas for all micro-convex bodies on the rough surface represents the actual contact area between the two rough surfaces.  
Under the influence of the normal load, paired micro-convex bodies undergo deformation. Initially, this deformation is elastic. However, as the load continues to increase, it reaches a critical contact area where plastic deformation occurs. Consequently, the number of micro-convex bodies participating in contact on the rough surface increases with the increasing load. This increase in load-bearing, micro-convex bodies leads to an expansion of the contact area and, consequently, an increase in the effective contact area. As a result, the ability to withstand normal loads also increases accordingly. When rough surfaces with micro-asperities come into contact under the influence of normal loads, they experience mutual compression at the peaks of the asperities, resulting in deformation. The contact forces on rough surfaces are generated by the mutual contact and compression of these micro-asperities, transmitting the normal contact force through their interaction.  
Figure 1: Rough surface of mechanical structure.
# Improvement of Hexacopter UAVs Attitude Parameters Employing Control and Decision Support Systems
## 4 Laboratory and In Situ Measurement Results and Discussion
### Laboratory Tests
In the 50-75% rpm range, which is equivalent to operating the drone in hover and light horizontal manoeuvres, the efficiency decreases to a value of 6.49 g/W (\(\geq\)6 g/W--high-efficiency drone). In the speed range of 85-100%, the efficiency further decreases to a minimum value of 4.96 g/W (4 \(\div\) 6 g/W--low-efficiency drone). * With increasing speed, the current consumption increases proportionally, reaching a measured current value at 100% speed of 21.6 Ah. * The mechanical power produced also increases to a value of 346.2 W at 100% speed. The optimum operating temperature of the BLDC motors is of critical importance as high temperatures can lead to premature engine damage and failure. Thus, it is necessary to test the operating temperatures at idle, midthrottle and maximum throttle to determine the RPM ranges where their performances are at best. During the tests on the test stand, motors temperatures at different RPM ranges were measured employing a FLIR E86 thermal imaging camera (Figure 15). By analysing the results, the following points of interest were found:  
* At idle, with the throttle stick at 30%, for a 3-5-min interval, the motor temperature reached 40 \({}^{\circ}\)C.
* At idle, with the throttle stick at 50% for 3-5 min, the motor temperature reached 60 \({}^{\circ}\)C.
* In maximum mode, with the throttle stick at 100%, for 3-5 min, the temperature reached over 200 \({}^{\circ}\)C, which means that it is only desirable to operate the drone in maximum mode for very short periods, around 10-15 s, to avoid these temperature increases in the motor windings, which can eventually lead to burn-out and thus their permanent damage. Figure 14: Propulsion system tests results. (**a**) Propulsion system efficiency; (**b**) Traction force as a function of RPM; (**c**) Current consumption based on RPM; (**d**) Mechanical power versus RPM.
## 7 Solution of The Digital Beamforming Matrix and The Analog Beamforming Matrix
### MM-AltOpt Algorithm: Solution of \(\mathbf{F}_{BB}\) and \(\mathbf{F}_{RF}\)  
According to \(F=F_{RF}\mathbf{F}_{BB}\) and \(\left|(\mathbf{F}_{RF})_{i,j}\right|=1\), the solution of \(\mathbf{F}_{BB}\) and \(\mathbf{F}_{BB}\) can be modeled as a joint optimization problem with the power and constant modulus constraints, as follows:  
\[P_{1}:\min_{\mathbf{F}_{BB},\mathbf{F}_{RF}}\left\|\mathbf{F}^{pt}-\mathbf{F}_ {RF}\mathbf{F}_{BB}\right\|_{F}^{2}, \tag{82}\]  
\[s.t.\ C_{1}:F_{RF}\in\mathbb{F}, \tag{83}\]  
\[C_{2}:\left\|F_{RF}\mathbf{F}_{BB}\right\|_{F}^{2}=P_{T}, \tag{84}\]  
where \(\mathbb{F}=\left\{\mathbf{F}_{RF}\in\mathbb{C}^{N\times L}\middle|\middle| \mathbf{F}_{RF(i,j)}\right|^{2}=1,1\leq i\leq N,1\leq j\leq L\right\}\) represents the unit modulus constraint, which is determined by the phase shifter in UPA, and the constraint \(C_{2}\) represents the power constraint.  
It is worth noting that the problem \(P_{1}\) is a matrix decomposition problem with the constant modulus constraint and the equality constraint. The objective function is a nonconvex function of variables \(\mathbf{F}_{BB}\) and \(\mathbf{F}_{RF}\), and the constraints \(C_{1}\) and \(C_{2}\) are also nonconvex. Meanwhile, it can be seen that when one of the two variables is given, the objective function is the convex function of the other variable. To solve the problem \(P_{1}\), we invoke the alternating optimization algorithm. The alternating optimization algorithm can decompose the multivariable joint optimization problem into multiple subproblems according to the partial convexity of the problem \(P_{1}\), and one of the variables can be iteratively solved by fixing the residual variables.  
It should be noted that the nonconvexity of constraints is still a challenge. To this end, we first relax the constraint \(C_{2}\), and then use the scale factor to adjust the digital beamforming matrix \(\mathbf{F}_{BB}\) to meet the power constraint. Then, for the solution of the analog beamforming matrix \(F_{RF}\) with the unit modulus constraint, the MM algorithm is adopted [33].
## 3 Methods
### Network and User Processing
#### 3.2.2 Ambiguity Resolution
Compared to the conventional approach of using the Success Rate and the Ratio Test, ambiguities are expected to be fixed faster, as the conventional approach using a fixed Success Rate and Ratio Test threshold generally makes more conservative assumptions. In conjunction with the Ratio Test Integer Aperture estimation algorithm, the resulting Integer Least Squares Problem is solved by the application of the MLAMBDA algorithm. Due to the large number of ambiguities at the network level and the computationally intensive nature of the Ratio Test Integer Aperture estimation algorithm, the ambiguities are fixed at the network level using the conventional Success Rate and Ratio Test approach. The minimum Success Rate is chosen at 99%, and the Ratio Test threshold is 0.5. For the user case, wide-lane ambiguities are first attempted to be fixed with the conventional approach, and then all remaining ambiguities, including the narrow-lane ambiguities, are attempted to be fixed with the Ratio Test Integer Aperture estimation algorithm.