esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Large-Scale Floods Using Google Earth Engine and Google Colab
## 2 Materials and Methods
### Processing Chain
#### 2.3.3 Radar Image Filtering  
Radar image filters play a crucial role in image processing, enhancing the quality of radar imagery for various applications. That is why [PERSON] et al. [43] developed a speckle filtering tool in GEE. However, this tool is not available in Google Colab. Therefore, it was rewritten in Python while developing this processing chain. Speckle filter performance is generally assessed by analysing how much speckle noise is reduced and how well features are preserved in the filtered image [44]. Each filter is assessed in terms of the accuracy with which water bodies are detected. Five filters have been incorporated in the processing chain. These filters include the boxcar filter, which smooths images by averaging neighbouring pixels; the gamma map [45], a technique for adjusting brightness and contrast; the Lee filter [46], designed for speckle noise reduction; the Lee sigma filter [47], an enhanced version with adjustable noise reduction; the refined Lee filter [48], which further refines speckle noise reduction; and the Quegan multitemporal filter [49], utilising information from multiple radar images taken at different times to reduce noise.
Traditional crestal placement: Historically, implants were placed such that the implant collar was flush with the alveolar bone crest [1]. This crestal placement was thought to maximize the osseeintegration surface and facilitate soft tissue management [14]. Sub-crestal placement: More recent advancements in implant design and understanding of peri-implant tissues have led some clinicians to place implants in a sub-crestal position. Here, the implant is positioned slightly below the crest of the bone [15]. Advocates believe that this promotes better esthetic outcomes, especially in the anterior region, by preserving the interdental papilla and soft tissue contour. Additionally, by positioning the implant--abutment junction below the bone, there might be enhanced protection against bacterial infiltration and peri-implantitis [16]. Supra-crestal placement: This less-common method involves positioning the implant above the alveolar bone crest. Proponents argue that this placement reduces crestal bone stress and allows better distribution of occlusal loads, potentially minimizing bone resorption [17]. Several factors can influence bone behavior, whether it pertains to growth or loss, during dental implant placement. These include the implant's macro- and micro-design [18; 19; 20; 21], the spacing between implants [22],the quality of the surrounding periodontal tissue and bone [23], occlusal forces [24], the presence of microgaps that allow bacterial colonization between implant and abutment, and the position of the connection in relation to the bone crest [25; 26; 27; 28; 29]. Bone-level dental implants after 10-year follow-up had success rates of 99.4% in healthy patients without systemic disease and 95.9% with systemic disease [30]. Tissue-level dental implants in 4-year follow-ups had a success rate of 100%, with a mean marginal bone loss of 0.99 mm [31]. According to a recent study by Chacun 2024 where 301 tissue-level dental implants were placed, a follow-up of 4.5 years had a survival rate of 98.8% [32]. A study comparing bone and tissue-level implants with a follow-up of 4 years had a success rate of 100% and a mean marginal bone loss of 1.03 mm and 0.95 mm with non-significant difference [33]. For many clinicians, the placement of bone-level/tissue-level implants remains a dilemma. The purpose of this systematic review is to bring some insight into this controversial dichotomy; the next question is raised: Is there any significant difference in marginal bone loss (MBL) around the neck of bone-level and tissue-level implants?
## 2 Fully Coupled Traffic-Bridge-Wind (TBW) Simulation
### Modelling of Wind Forces on Road Vehicles  
The total wind force on running vehicles can be formulated based on quasi-static approach [27], as follow:  
\[\left\{\begin{array}{l}F_{S}=0.5\rho U_{T}^{2}(t)C_{S}(\Psi)A_{0};\quad F_{ L}=0.5\rho U_{T}^{2}(t)C_{L}(\Psi)A_{0};\quad F_{D}=0.5\rho U_{T}^{2}(t)C_{D}( \Psi)A_{0};\\ M_{P}=0.5\rho U_{T}^{2}(t)C_{P}(\Psi)A_{0}l_{v};\ M_{Y}=0.5\rho U_{T}^{2}(t)C_{ Y}(\Psi)A_{0}l_{v};\ M_{R}=0.5\rho U_{T}^{2}(t)C_{R}(\Psi)A_{0}l_{v};\end{array}\right., \tag{4}\]  
where \(F_{S}\), \(F_{L}\), \(F_{D}\), \(M_{P}\), \(M_{Y}\), and \(M_{R}\) = side force, lift force, drag force, pitching moment, yawing moment, and rolling moment on the vehicle, respectively; \(C_{S}(\Psi)\), \(C_{L}(\Psi)\), \(C_{D}(\Psi)\), \(C_{P}(\Psi)\), \(C_{Y}(\Psi)\), and \(C_{R}(\Psi)\) = wind aerodynamic coefficients with respect to six wind forces; \(A_{0}\) = the reference area of the vehicle; \(h_{v}\) = the distance from the vehicle gravity center to the road surface; \(U_{r}\) is the relative wind velocity to the vehicle; and \(\psi\) = the yaw angle defined as the angle between the direction of relative wind speed and the vehicle driving direction. The \(U_{r}\) and \(\Psi\) are given by the following:  
\[\left\{\begin{array}{l}U_{r}=\sqrt{\left(U+u(x,\ t)\right)^{2}+U_{ve}^{2}(t) }\\ \Psi=\arctan[U+u(x,t)/U_{\rm{tr}}(t)]\end{array}\right., \tag{5}\]  
where \(U\) = the mean wind speed; \(u(x,\ t)\) = the horizontal wind turbulence at the \(x\) coordinate along the bridge axis at time step \(t\); and \(U_{ve}\) = vehicle speed.  
It is noted that the wind aerodynamic coefficients for the vehicles can be experimentally determined using the following equation:  
\[\left\{\begin{array}{l}C_{S}=\frac{F_{S}}{0.5\rho U^{2}A_{0}}\quad C_{L}= \frac{F_{L}}{0.5\rho U^{2}A_{0}};\quad C_{D}=\frac{F_{D}}{0.5\rho U^{2}A_{0}}; \\ C_{P}=\frac{M_{P}}{0.5\rho U^{2}A_{0}l_{v}};\ C_{Y}=\frac{M_{Y}}{0.5\rho U^{2}A _{0}l_{v}};\ C_{R}=\frac{M_{R}}{0.5\rho U^{2}A_{0}l_{v}};\end{array}\right., \tag{6}\]  
where \(\overline{F}_{s}\), \(\overline{F}_{L}\), and \(\overline{F}_{D}\) are the mean values of the aerodynamic side force, lift force, and drag force during the wind tunnel test and \(\overline{M}_{P}\), \(\overline{M}_{Y}\), and \(\overline{M}_{R}\) are the mean values of the pitching moment, yawing moment, and rolling moment during the wind tunnel test.
## 4 Discussion
### Input Data Biases and Possible Impacts on GPP Simulations  
Evaluating the quality of input data and understanding the impact of data biases on GPP simulation are prerequisites for improving GPP simulation accuracy. In this study, we found that the data from the spatial data fields at the EC-tower sites had various systematic deviations that seriously affected GPP estimates. For example, the spatial data fields of PAR explained only 57% of the PAR variation observed at the EC-tower sites. Reasons for the data biases are mainly rooted in the influence of sensor errors and atmospheric factors (e.g., cloud and snow) [50; 57; 58]. In addition, attempts unifying data from different spatial and temporal resolutions also bring biases as we found that data at different resolutions sometimes had poor correlations. The reason for the existence of resolution mismatch (i.e., spatial product data pixels cannot be unified in temporal or spatial scale) is mainly caused by mixed pixels and/or different time scales (e.g., daily, eight-day, or monthly data), compared to the spatio-temporal resolution that applies to ground conditions [51; 59]. In general, spatial data biases are an important cause of the uncertainty of GPP simulation, which is an important reason for the large differences in GPP estimated among existing GPP models [60; 61].  
A comparison of existing global GPP products shows a huge variation from 92.7 to 178.9 Pg C yr\({}^{-1}\), which is not only affected by input data biases, but also by the model structure [13; 62]. One should realize that input data biases affect the model outputs differently, depending on the model structure. In our study, it was found that the LUE-EF model is more susceptible to data errors, while the LUE-NDWI model is less affected by data biases (Figure 3d). The sensitivities of the GPP models to data deviations should be systematically investigated in future research as the sensitivities have not been effectively evaluated and compared.
# Conversion of Stearic Acid into Bio-Gasoline over Pd/ZSM-5 Catalysts with Enhanced Accessibility
## 3 Results and Discussion
### Reactions of Stearic Acid
#### 3.2.4 Effect of the Reaction Temperature over Hierarchical ZSM-5  
The effect of the temperature on the stearic acid conversion was determined over the Pd/h-ZSM-5 (122) catalyst by operating at 250, 275, 300, and 325 \({}^{\circ}\)C. Figure 9 illustrates the conversion and selectivities by groups so obtained, whereas Figure 10 shows the corresponding selectivities by carbon atom.  
At the lowest temperature (250 \({}^{\circ}\)C), the conversion was very low (6%) and the product comprised solely C\({}_{2}\)-C\({}_{4}\) hydrocarbons. With the increase in temperature, a strong enhancement of the stearic acid conversion took place, reaching almost total conversion at 325 \({}^{\circ}\)C. This confirms the high activity of the catalytic system here investigated, although some contribution of the thermal reaction cannot be discarded when working at the highest temperature.  
It is noteworthy that the selectivity towards C\({}_{1}\)-C\({}_{4}\) hydrocarbons dropped steadily with temperature, until reaching a minimum value of 12% at 325 \({}^{\circ}\)C. In the same way, the selectivity to products within the diesel was reduced when working at higher temperature. As a consequence, the selectivity towards the gasoline fraction was significantly improved with the temperature, rising from 46% at 275 \({}^{\circ}\)C to a remarkable value of 86% at 325 \({}^{\circ}\)C.  
Figure 8: Influence of the Si/Al atomic ratio of the Pd/h-ZSM-5 catalysts over the selectivity by atom carbon number (T = 300 \({}^{\circ}\)C).  
Figure 10: Effect of the reaction temperature on the selectivity by atom carbon number in the stearic acid conversion over the Pd/h-ZSM-5 (122) catalyst.  
Figure 9: Effect of the reaction temperature on the stearic acid conversion and selectivity by groups over the Pd/h-ZSM-5 (122).  
Regarding the selectivity by carbon atom, shown in Figure 9, the main hydrocarbon obtained was C\({}_{3}\) at 250 and 275 \({}^{\circ}\)C, changing to C\({}_{8}\) and C\({}_{11}\) at 300 \({}^{\circ}\)C. The selectivity towards n-paraffins in the C\({}_{5}\)-C\({}_{12}\) fraction was mostly 26% at 325 \({}^{\circ}\)C and lower at 250 and 275 \({}^{\circ}\)C, pointing out that the main components of this bio-gasoline were branched, naphthenes, and aromatic hydrocarbons formed as a consequence of isomerization, cyclization, and aromatization reactions. The enhanced relevance of the OCA pathway was revealed by the relatively plain product distribution obtained in terms of atom carbon number when increasing the reaction temperature up to 300 \({}^{\circ}\)C.  
In summary, these results indicate that by adjusting the properties of the ZSM-5 support, as well as the operation conditions, it is possible to achieve almost total conversion of stearic acid with a high selectivity towards gasoline-range hydrocarbons.
# Simple and Efficient Non-Contact Method for Measuring the Surface of a Large Aspheric Mirror
## 2 Methodology
### Pad Form and Size  
The spherical air-bearing with a matching radius (the radius of the air-bearing surface is equal to or close to the vertex curvature radius of the aspheric surface) is an ideal choice for making a non-contact probe. However, it may not be commercially available. Instead of customizing spherical air-bearing, a flat round air bearing can be used in most circumstances. In this case, the size of the air-bearing pad must be considered. For a large mirror with a radius of curvature R, the surface sag is given by the following formula:  
\[Sag_{PV}\approx R-sqrt\left(R^{2}-r^{2}\right) \tag{1}\]  
where \(r\) is the radius of the air-bearing pad. Therefore, the mean sag can be expressed as:  
\[Sag_{average}\approx\frac{\int_{0}^{2\pi}\int_{0}^{r}\left(R-sqrt\left(R^{2}- \rho^{2}\right)\right)\rho d\rho d\theta}{\pi r^{2}} \tag{2}\]  
Figure 2 shows the relationship between the radius of curvature of the measured surface and the average sag for a \(\O 25\) mm and a \(\O 50\) mm pad (calculated by Formula (2)). In order to make the probe float on the surface, the flying height of the air-bearing should exceed the average sag, which means that the size of the pad is proportional to the surface's radius. For most large aspherical mirrors, the asphericity is on the order of several hundred microns to several millimeters; thus, the sag difference between the center and outer edge of the mirror can be neglected for the pad with the appropriate size.
# Using Interval Analysis to Compute the Invariant Set of a Nonlinear Closed-Loop Control System
## 6 Application Scenarios
### Pendulum Motion on a Circle in the Phase Plane
#### 6.1.2 Verification of Stability  
The stability of the closed-loop control structure can be verified by computing the total time derivative of the sliding surface \(s(\mathbf{x})\) defined in (35) according to:\[\dot{s}(\mathbf{x}) =\frac{\partial s(\mathbf{x})}{\partial x_{1}}\cdot\dot{x}_{1}+\frac{ \partial s(\mathbf{x})}{\partial x_{2}}\cdot\dot{x}_{2}\] \[=2x_{1}\dot{x}_{1}+2x_{2}\dot{x}_{2}\] \[=2x_{1}x_{2}+2x_{2}\cdot\left(-\sin(x_{1})-x_{2}+\left(-\left(x_{ 1}^{2}+x_{2}^{2}-1\right)\cdot p(\mathbf{x})-2x_{1}x_{2}\right)\cdot\frac{1}{2x_{ 2}}+\sin(x_{1})+x_{2}\right)\] \[=-s(\mathbf{x})\cdot p(\mathbf{x})\quad\text{with}\quad p(\mathbf{x})=4x_{2} ^{2}>0\quad\text{for}\quad s(\mathbf{x})\
eq 0\ \wedge\ \ x_{2}\
eq 0 \tag{42}\]  
along the trajectories of the controlled system. Hence, \(\dot{V}(\mathbf{x})=s(\mathbf{x})\cdot s(\mathbf{x})<0\) is guaranteed for all states that do not belong to the desired invariant set, except for the fixed point at the origin of the phase space.  
**Remark 3**.: _The control law defined in (41) ensures perfect trajectory tracking as soon as the system has reached the sliding surface \(s(\mathbf{x})=0.\) However, it does not possess the variable structure characteristics defined in (5). Therefore, typically, it only leads to an asymptotic convergence towards the desired invariant set. However, here, and analogously in all other examples shown in the remainder of the paper, finite time convergence towards \(s(\mathbf{x})=0\) can be achieved if \(u\) is replaced by:_  
\[\tilde{u}=u-\frac{\eta}{2x_{2}}\cdot\text{sign}\left(s(\mathbf{x})\right)\quad \text{with}\quad\eta>0 \tag{43}\]  
_for states that are not contained in the invariant set. Then, the control \(\tilde{u}\) turns into the equivalent control \(u\) as soon as \(s(\mathbf{x})=0\) starts to hold, and hence, the computation procedure for invariant sets derived in Section 4 becomes again applicable._
# Edge Couplers in Silicon Photonic Integrated Circuits: A Review
## 3 Structural Transformations of Edge Couplers in Horizontal Direction
### Edge Couplers Based on Subwavelength Gratings Structure
Multiple variables in the SWG structure provide a high degree of design freedom; hence, it is possible to obtain excellent coupling performance including high coupling efficiency, compact size, low sensitivity to polarization status, and broad bandwidth. Take the fork-shape coupler and trident coupler shown below in Figure 9 [72; 73] as an example; they both achieve an ultra-small footprint with a length of 36.5 \(\upmu\)m and 35 \(\upmu\)m respectively, which are much more compact than traditional edge couplers based on a single solid taper with a length around 300 \(\upmu\)m or even longer. This remarkable merit  
Figure 8: **(a)** Top view of a taper based on subwavelength gratings; **(b)** propagation of light through a periodic waveguide under different regimes, from top to bottom: subwavelength regime, Bragg reflection, and radiation. Reproduced with permission from [71], Copyright John Wiley and Sons, 2014. Figure 7: **(a)** Schematic of subwavelength gratings; **(b)** the relationship between effective index and wavelength-to-pitch ratio. Reproduced with permission from [71], Copyright John Wiley and Sons, 2014.  
of ultra-compact size makes SWG-assisted couplers suitable for application scenarios where dense integration is highly demanded. The presented SWG fork-shape coupler in Figure 9a achieves a low coupling loss of about 1 dB and an ultra-broad 0.5-dB bandwidth over 100 nm near the 1550 nm wavelength. Furthermore, it can be fabricated via one-step EBL, which is simple for fabrication. In fact, the fabrication process of a SWG structured edge coupler can be regarded as a synthetic procedure of conventional gratings and inverse tapers, which is feasible and compatible with the CMOS process. Additionally, the performance of an edge coupler based on subwavelength gratings can be further optimized if varied pitches and duty cycles are taken into consideration.
# Effect of Tide Level Change on Typhoon Waves in the Taiwan Strait and Its Adjacent Waters
## 4 Model Setup and Validation
### Model Setup
#### 4.1.3 Tide Level Operation  
By simulating the tide level at each calculation time during the Typhoon Mekkhala process, the tide level simulation results of the calculation area within the calculation time can be obtained. Figure 3 shows the simulation results of the tide level in the calculation area spanning from 10 August 2020 to 11 August 2020. During this period, the tide level in the Taiwan Strait reached its lowest tide at 00:00 UTC on 10 August 2020. Six hours later, it reached its highest tide, followed by a cyclic pattern of reaching the highest tide every twelve hours. The simulated results are consistent with the semi-diurnal tide pattern near the Taiwan Strait.
## 3 Materials and Methods  
### Dataset Construction and Preprocessing  
#### 3.1.1 Data Source and Label Consolidation  
The CrisisMMD (Multimodal Crisis Dataset) [2] was adopted as the primary dataset, containing 8460 multimodal tweets (text-image pairs) collected across seven natural disasters (e.g., Hurricane Harvey, California Wildfires, and the Mexico Earthquake). Among these, 8079 samples exhibited consistent text-image labels, covering eight humanitarian categories.  
As illustrated in Figure 2a (Random Image-Text Label Statistics), Figure 2b (Image Label Statistics), and Figure 2c (Text Label Statistics), the original labels exhibited a long-tail distribution--a minority of categories (e.g., Not_humanitarian) dominated the dataset, while critical disaster-related categories suffered from severe scarcity. For instance, the missing_or_found_people category appeared only 41 times in text annotations, accounting for merely 0.22% of the total labels. This long-tail imbalance posed challenges for classification models, which often struggle to recognize rare but critical disaster events.  
To address this issue and align with disaster response frameworks, we reconstructed the label system based on the United Nations OCHA Humanitarian Response Framework and FEMA standards, via three key steps:  
1. [label=(1)]
2. Merge low-frequency human impact labels: Injured_or_dead_people and missing_or_found_people--semantically related and collectively <15% of data--were merged into affected_individuals.
3. Consolidate infrastructure labels: Vehicle_damage was integrated into infrastructure_and_utility_damage per FEMA guidelines.
4. Retain core semantic categories: Rescue_volunteering_or_donation_effort and other_relevant_information were preserved to maintain granularity.  
This process reduced the label set to five classes, aligning with disaster assessment priorities (\"human impact\" and \"infrastructure impact\"), as shown in Figure 2d (Consolidated Label Statistics). The final categories include the following:  
Figure 2: Label distribution in CrisisMMD: original (8 classes) vs. consolidated (5 classes).  
1. Affected individuals: Merged from \"injured_or_dead_people\" and \"missing_or_found_people\";
2. Rescue_volunteering_or_donation_effort: Retained as original;
3. Infrastructure_and_utility_damage: Merged from \"vehicle_damage\" and the original infrastructure category;
4. Other_relevant_information: Retained as original;
5. Not_humanitarian: Retained as original.
# Towards Bio-Inspired Anomaly Detection Using the Cursory Dendritic Cell Algorithm
## 4 Cursory Dendritic Cell Algorithm
### Online Dendritic Cell Algorithm  
As previously mentioned, DCA does not support an online anomaly detection capability. The DCA can internally process input data in real-time, but it requires an analysis stage,  
Figure 7: Modular approach for DCA. Each modules consists on a step in the data-flow of the algorithm.  
which is performed offline. To reduce the impact of this limitation, the DCA is updated to allow intermediate classification of data and thus, enable near real-time anomaly detection.  
The classification of _antigens_ is accomplished at the end of the algorithm's data-flow, by calculating the _MCAV_ value, which consists of the ratio of matured _DCs_ that collect a given _antigen_, to the total number of times an _antigen_ is collected. Thus, the classification method was updated in two ways: (i) each time a _DC_ matures, all _antigens_ it has collected have their _MCAV_ values derived; (ii) each _antigen_ has three possible states: inconclusive, safe, or dangerous. The finite state diagram of the _antigens_ is depicted in Figure 8, alongside the _DC_ states and their interaction.  
All _antigens_ begin as inconclusive. If their _MCAV_ value increases beyond the predetermined threshold, the _antigen_ is immediately classified as dangerous, i.e., anomalous. If all _DCs_ that collected the _antigen_ have matured, but the _MCAV_ value has not yet increased beyond the predetermined threshold, the _antigen_ is classified as safe. The introduction of an \"inconclusive\" state leads to a lack of immediate classification. Classification will only occur after a sufficient number of _DCs_ have migrated. On the other hand, classification of \"dangerous\" _antigens_ is achieved faster, as this classification is attributed to the _antigen_ as soon as the _MCAV_ increases above the threshold, rather than waiting for the entire data set to be processed. The classification of \"safe\" _antigens_ is also faster than waiting for the entire dataset but is slower than the classification of \"dangerous\" _antigens_.
# Edge Computing Approach for Vessel Monitoring System
## 3 Proposed Approach
### Direction Analysis  
We intend to explore the possibility of establishing an angular velocity profile of a vessel in order to be able to identify whether it belongs to a specific type of fishing technique, as each technique requires a particular vessel behavior. The purse-seiner fishing technique is characterized precisely by performing a circular route in the fishing. This approach is used only for purse-seiner identification.  
Purse-seiners are long nets deployed hanging vertically from floats around schooling fish on or near the surface by the vessel or by a separate skiff. To avoid fish from escaping, the circle is performed quickly at high speeds averaging around 10 knots. The circle radius depends on the vessel and is detected from direction velocity. After the circle is defined, the net is closed around the fish to the end of the fish bailing operation when the net is lifted out of the water. During this last operation, the vessel stays more or less stationary with speed less than 2.5 knots.  
If the vessel is detected as conducting a movement similar to the one required by the purse-seiner technique and it is not licensed to it, an alert is issued.