esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Review of Micro UAV Charging Techniques
## 8 Potential Challenges in UAV Charging Techniques
### Propagation Losses in the Optical Beam  
The performance of optical systems is limited due to three characteristics of free space:  
* Water and dust particles which absorb and scatter photons.
* The wave fronts are distorted due to the refraction index of air, which is affected by the gradients in the air caused by temperature and wind.
* Waves expanding beyond the optic's ability to collimate.  
Scattering and absorption coefficients vary according to the airborne particle size. In general, atmospheric articles such as fog, rain and smoke affect beam transmission. In normal conditions, scattering and absorption do not cause severe impacts like distance and atmospheric gradients. Distance is also an important factor which highly influences the beam transmission. Laser mode structure is the largest determinant of beam energy over distance. In such cases, two types of lasers are chosen: single mode and multimode laser. Single mode lasers, such as disk lasers, can be used for LPT up to 1 km. However, multimode lasers cannot even reach the diffraction limit.
# Stacked Ensemble of Recurrent Neural Networks for Predicting Turbocharger Remaining Useful Life
## 2 Data Presentation  
### Dataset Decriptions  
In this work we focus on vehicles that have encountered turbocharger failures during their operations--the goal is predicting the time to failure. Therefore, we limit the investigation only to this subset of the complete fleet.  
As with most of the vehicles predictive maintenance tasks, the data comes from two separate sources [14]. One dataset contains vehicle on-board sensor information which is timestamped and indexed by vehicle ids. This dataset, which in our case is called Logged Vehicle Data (LVD), includes sensors measurements collected bi-weekly. There are more than 400 features, 83% of them numerical and the rest categorical. Numerical features represent trucks' usage-related measurements and categorical features represent trucks' configurations. Examples of usage features are \"engine time\", \"fuel consumption\", \"time in top gear\", \"cruise driving distance\", etc. Examples of configurations features are \"engine type\", \"front axle type\", \"cabin version\", etc.  
Another dataset is the repairs dataset, which in our case study was limited to turbocharger replacement information. In this dataset at each row, the vehicle id and repair date are registered. If a vehicle gets repaired more than once, there will be more that one record with the same vehicle id.  
One difficulty with this dataset is that the failures are very rare. Therefore, if we were to assign binary labels to LVD readouts by aligning the two datasets, only a very small portion of readouts would get labels. Hence, as a classification problem, it would be a very imbalanced dataset.
# A Novel Cell-Based Adaptive Cartesian Grid Approach for Complex Flow Simulations
## 2 Adaptive Cartesian Grid Generation Method
### Flow Field Adaptation  
Since the initial computational grid is usually adaptively generated based on the geometry of the object, it tends to be denser around the surface of the object and lower in resolution in areas far from the object, making it difficult to accurately reflect complex flow characteristics. Therefore, to capture features such as vortices, shock waves, and shear layers within the flow, it is necessary to dynamically adjust the grid resolution based on the flow field solution, refining the grid where needed to more precisely capture flow characteristics.  
First, as referenced in [10; 12], it is necessary to compute the velocity divergence \(d_{i}\), and vorticity \(r_{i}\), for each cell, incorporating the grid scale:  
\[\begin{split} d_{i}&=|\
abla\cdot U|h_{i}^{\frac{k+1 }{k}}\\ r_{i}&=|\
abla\times U|h_{i}^{\frac{k+1}{k}}\end{split} \tag{4}\]  
Here, \(h\) denotes the side length of the target grid cell, with \(k=2\) for two-dimensional and \(k=3\) for three-dimensional spaces. Upon computing the velocity divergence and vorticity across all \(N\) grid cells, the standard deviations for both parameters are:  
\[S_{d}=\sqrt{\frac{\sum{d_{i}}^{2}}{N}},\ S_{r}=\sqrt{\frac{\sum{r_{i}}^{2}}{N}} \tag{5}\]  
Herein, \(S_{d}\) and \(S_{r}\) represent the standard deviations of velocity divergence and vorticity, respectively. Ultimately, this determines whether the target grid requires refinement or coarsening:  
\[\begin{split} if:d_{i}>r_{1}S_{d}\ \text{or}\ r_{i}>r_{2}S_{r},\ \text{then}:\ REFINE\\ if:d_{i}<c_{1}S_{d}\ \text{and}\ r_{i}<c_{2}S_{r},\ \text{then}:\ COARSEN\end{split} \tag{6}\]  
Figure 3: Geometric adaptive refinement illustration. (**a**) Initial uniform grid; (**b**) refinement of intersecting grids; (**c**) addition of buffer layers.  
In this context, \(r_{1}\), \(r_{2}\), and \(c_{1}\), \(c_{2}\) are empirical coefficients that should be specified for specific flow problems, as indicated in references [10; 12]. Due to the varying dominant flow features in different flow problems, the coefficients for velocity divergence and vorticity can be adjusted accordingly. Generally speaking, larger coefficient values increase the threshold for grid refinement and decrease the threshold for coarsening, resulting in fewer total grid cells within the computational domain; conversely, smaller values increase the number of adaptive grid cells. This adjustment allows for better adaptation to the requirements of different flow problems, effectively enhancing the accuracy and efficiency of numerical simulations.
## 3 Methodology
### Accurate Transformation Estimation
#### 3.3.1 Feature Matching
\[d_{S}^{j^{\prime}}=\underset{d_{S}^{j}\in D_{S}}{min}d(d_{T}^{i},d_{S}^{j})\] (25) \[d_{S}^{j^{\prime\prime}}=\underset{d_{S}^{j}\in D_{S}\backslash d_{S}^{j^{ \prime}}}{min}d(d_{T}^{i},d_{S}^{j})\] (26) where \(D_{S}\backslash d_{S}^{j^{\prime}}\) is the set \(D_{S}\), excluding the covariance matrix descriptor \(d_{S}^{j^{\prime}}\), and then the NNDR \(r_{dist}\) is defined as follows: \[r_{dist}=\frac{d\left(d_{T}^{i},d_{S}^{j^{\prime}}\right)}{d\left(d_{T}^{i},d_ {S}^{j^{\prime\prime}}\right)}\] (27) if the ratio \(r_{dist}\) is less than a threshold \(\varepsilon_{r}\), \((d_{T}^{i},d_{S}^{j^{\prime}})\) can be thought of as a candidate-matched feature pair, and \((kp_{T}^{i},kp_{S}^{j^{\prime}})\) can be also thought of as a candidate-matched keypoint pair, with keypoint \(kp_{T}^{i}\) corresponding to \(d_{S}^{i}\) and keypoint \(kp_{S}^{j^{\prime}}\) corresponding to \(d_{S}^{j^{\prime}}\). Then, \(d_{S}^{j^{\prime}}\) is also matched against all the covariance matrix descriptors in \(D_{T}\) to generate its corresponding feature descriptor \(d_{T}^{j^{\prime}}\) following the preceding procedures, and the matched keypoint pair \((kp_{S}^{j^{\prime}},kp_{T}^{j^{\prime}})\) can be acquired by the corresponding covariance matrix descriptor pair. Finally, \((d_{T}^{i},d_{S}^{j^{\prime}})\) is a matched feature pair if \(kp_{T}^{j^{\prime}}\) is the same keypoint as \(kp_{T}^{i}\); otherwise, \((d_{T}^{i},d_{S}^{j^{\prime}})\) is an unmatched feature pair.
## 2 Results and Discussion
### Effect of Wine Fining Treatments on Wine Chemical Composition  
As described before [27], bentonite fining in wines always resulted in a significant decrease in total phenolic concentration. In contrast, the DCMC treatment had little influence on the wine phenolic content and for any dose tested, it only removed a smaller fraction of the phenolic compounds (Figure 4).  
For all wine samples treated as well as in the controls, the pH was measured before and after treatment to evaluate the potential effect exerted by bentonite or DCMC. In all trials, the maximum increase in pH, after each treatment, was 0.1 pH units. In the Encruzado and Viosinho sample wines, DCMC had a significantly smaller impact on the wine pH than bentonite, but exerted a mixed pattern in the highly unstable wine Moscatel de Setubal (Figure 5).  
Figure 4: Changes in the amount of total wine phenolics detected by the absorbance at 280 nm for increasing amounts of bentonite or DCMC applied to samples of the wines under study. Different letters represent statistically significant differences among different homogeneous subsets for \(p=0.05\). (**a**) Encruzado, (**b**) Viosinho, and (**c**) Moscatel de Setubal monovarietal wines.  
Figure 3: Untreated (**left**) and treated (**right**) with 0.5 g/L of DCMC samples of Moscatel de Setübal wine following a HST.  
Protein removal by cationic exchange typically results in increasing the wine content in inorganic cations. In bentonite, the negative net charge of the natural clay is partially balanced by exchangeable cations, mainly Ca2+, K+, and Mg2+, and the commercially available products are usually modified through an activation process, which enriches the calcium-dominant clay with sodium [28]. Calcium can contribute to tartaric instability since calcium tartrate is 10 times less soluble than potassium bitartrate [15].  
For all three varietal wines under analysis, DCMC produced significant differences in the decrease in wine calcium content when compared to bentonite, producing significant differences when compared to untreated and treated with bentonite wines (Figure 6). In contrast, wines treated with bentonite always showed an increase in calcium content, in contrast to DCMC-treated wines in which wine calcium decreased with increasing DCMC applied. The increase in wine sodium content by treatment with DCMC was not determined once DCMC is an organic compound with a low content in sodium (0.62%). The maximum amount of sodium that this DCMC can transfer to wine in a dosage of 2 g/L of wine is 12.4 mg of sodium, which is irrelevant for the wine quality.  
DCMC has a high capacity to remove positively charged molecules from solution, therefore, it can carry out a cation exchange involving the removal of proteins and is also able to remove calcium. This decrease may result in more stable wines in terms of tartaric stabilization.
###### Abstract  
This study presents an effective control algorithm to improve the robustness of fast steering mirror (FSM)-based laser-beam steering systems against dynamic disturbances, such as repetitive disturbances resulting from operating conditions. A stable control system must be able to maintain the required high-precision control, even when dynamic disturbances affect the FSM system. In this study, an improved control method is proposed using an internal model principle (IMP)-based nonlinear controller with a disturbance observer (DOB) for the FSM system. This IMP-based controller with DOB can attenuate the residual control-error signal under dynamic disturbance conditions.  
laser-beam steering; fast steering mirror; adaptive feedforward control; tip-tilt control system; internal model principle; disturbance observer
## 3 Proposed Methodology
### HAR Datasets
#### 3.2.1. HAPT: Human Activities and Postural Transition Dataset  
Extending the UCI HAR dataset [45], the HAPT dataset [31] enables an extra six postural changes in addition to the six standard activities. Unprocessed tri-axial signals from the inbuilt accelerometer and gyroscope sensors in a Samsung Galaxy II mobile make up the dataset, which is sampled at a rate of 50 Hz.  
The findings are based on a study in which 30 people, ages 19 to 48, were asked to wear a smartphone around their waists. Twelve different tasks were given to these participants (six primary and six transitions). Moving from one activity to the next is documented as \"standing to sit\" and \"lying to sit\", as well as the transitions between them. Walking is also included in the list of fundamental activities. Meanwhile, videos and photos of the investigation are being prepared for later data marking efficiency. Figure 2 depicts a summary of the HAPT dataset's occurrences.
# Collaborative Learning Groupings Incorporating Deep Knowledge Tracing Optimization Strategies
## 2 Related Work
### Student Grouping  
The value matrix \(M_{t}^{v}(i)\) of the DKVMN-EKC model stores the knowledge level mastery vector \(x_{i}=\{x_{i1},x_{i2},\ldots\ldots x_{in}\}\) of each student \(x_{i}\) at time \(t\), where \(n\) represents the number of knowledge points and the K-means algorithm is chosen to assign students with similar states to the same cluster. The K-means algorithm for student clustering iteratively finds a student partitioning scheme with K clusters, such that the loss function corresponding to the clustering result is minimized [25].  
The sizes of clusters generated by the conventional K-means algorithm may vary; nevertheless, in educational settings, it is imperative that groupings are as uniform as possible to maintain consistency in the number of individuals across groups. Therefore, it is advisable to enhance the original K-means algorithm by giving precedence to assigning students to clusters that have not yet attained their maximum capacity. This approach will guarantee uniformity in the size of each cluster. Algorithm 1 outlines the process of clustering students accordingly.  
```
Input: Student set \(S=\{x_{1},x_{2},.....x_{p}\}\), the number of study groups \(q\), then the number of clusters \(k=p/q\) Output: Cluster division \(C=\{c_{1},c_{2},.....c_{k}\}\)
1: From \(S\) randomly selected from \(k\) samples as the initialized mean vector \(\{u_{i},u_{2},.......u_{k}\}\)
```
2:repeat
3: The order \(C_{i}=\emptyset(1\leq i\leq k)\), the number of \(C_{i}\) The number of n in is denoted as n
4:for\(j=1,2,...,p\)do
5:\(d_{ij}=\left\|x_{j}-u_{i}\right\|^{2}\)// Calculate the student \(x_{j}\) with each mean vector \(u_{i}(1\leq i\leq k)\) of the distance
6:\(\lambda_{j}=argmin_{i\in\{i,2,3...k\}}d_{ij}\) and \((n\leq q)\)// The closest cluster that is under the maximum number is marked
7:\(C_{\lambda_{j}}=C_{\lambda_{j}}\cup x_{j}\)// assign the sample \(x_{j}\) into the appropriate cluster
8:endfor
9:for\(i=1,2,...,k\)do
10:\(u_{i}=\frac{1}{|c_{i}|}\sum_{\text{rec}_{i}}x\)// Calculate the new mean vector
11:if\(u_{i}^{\prime}\
eq u_{i}\)then//
12:\(u_{i}=u_{i}^{\prime}\)// update the current mean vector \(u_{i}\) is updated to \(u_{i}^{\prime}\) otherwise, leave the current mean vector unchanged
13:endif
14:endfor ```  
**Algorithm 1** K-means-based clustering algorithm for students.  
Once k clusters are obtained through student clustering, students within each cluster have similar levels of knowledge mastery. Subsequently, to achieve heterogeneous grouping, students from the same cluster are distributed into distinct learning groups, ensuring that group members have different knowledge structures.
# Shear Strength of Trapezoidal Corrugated Steel Webs for Horizontally Curved Girder Bridges
## 3 Finite Element Analysis
### Influence of the Radius of Curvature and Girder Span on the Elastic Shear Buckling Stress  
Theoretically, the radius of curvature \(R\) has only a small influence on the elastic global shear buckling stress and has no influence on the elastic local shear buckling stress. Table 9 shows the elastic shear buckling stress of CSWs for different radii of curvature when \(L/2=15q\). As we can see from Table 9, the difference of the FEA results between curved girders and straight girders with CSWs is very small which agrees with the theoretical expectations. Table 10 shows the elastic shear buckling stress of CSWs for different spans. It can be seen from Table 10 that the shear buckling stress decreases slightly with the increase of span and the buckling stress shows a converging trend with the increase of the span, which agrees with the theoretical expectations. In Tables 9 and 10, the theoretical stress \(\tau_{\sigma}^{e}\) is the minimum of the elastic global and the local shear buckling stress. \(\tau_{FEA}^{e}\) is the shear buckling stress obtained from FEA. Because the difference of elastic shear buckling stress of CSWs between curved girders and straight girders is very small, the shear buckling stress formulas for straight girders can be applied for curved girders.  
Figure 6: Three shear buckling modes: (**a**) Local shear buckling; (**b**) Global shear buckling; (**c**) Interactive shear buckling.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & \(h\) **(mm)** & \(a\)/\(h\) & \(t\) **(mm)** & \(d\)/\(t\) & \(R\) **(m)** & \(\tau_{\text{FEA}}^{e}\) **(Mpa)** & \(\tau_{\text{cr}}^{e}\) **(Mpa)** & \(\tau_{\text{FEA}}^{e}\)**/\(\tau_{\text{cr}}^{e}\) **(**r**\(e\)**)** \\ \hline \multirow{10}{*}{CSW900} & \multirow{10}{*}{1250} & \multirow{10}{*}{0.2} & \multirow{10}{*}{8} & 30 & 1073.8 & 1069 & 1.00 \\  & & & & & 60 & 1073.6 & 1069 & 1.00 \\  & & & & & 90 & 1073.5 & 1069 & 1.00 \\  & & & & & 120 & 1073.5 & 1069 & 1.00 \\  & & & & & 150 & 1073.5 & 1069 & 1.00 \\  & & & & & 250 & 1073.5 & 1069 & 1.00 \\  & & & & & Straight & 1073.5 & 1069 & 1.00 \\ \cline{2-10}  & & & & & 30 & 1309.4 & 1064.4 & 1.23 \\  & & & & & 60 & 1308.5 & 1064.4 & 1.23 \\  & & & & & 90 & 1308.3 & 1064.4 & 1.23 \\  & & & & & 120 & 1308.3 & 1064.4 & 1.23 \\  & & & & & 150 & 1308.3 & 1064.4 & 1.23 \\  & & & & & 250 & 1308.2 & 1064.4 & 1.23 \\  & & & & & Straight & 1308.2 & 1064.4 & 1.23 \\ \hline \multirow{10}{*}{CSW1200} & \multirow{10}{*}{0.2} & \multirow{10}{*}{8} & \multirow{10}{*}{25} & 30 & 655.7 & 613.5 & 1.07 \\  & & & & & 60 & 653.9 & 613.5 & 1.07 \\  & & & & & 90 & 654.3 & 613.5 & 1.07 \\  & & & & & 120 & 654.5 & 613.5 & 1.07 \\  & & & & & 150 & 654.6 & 613.5 & 1.07 \\  & & & & & 250 &
# A Channel Measurement-Based Listen-Before-Talk Algorithm for LTE-LAA and WLAN Coexistence
## 1 Introduction
Nevertheless, using saturation load models for the IEEE 802.11 distributed coordination function does not allow for accurately predicting an optimal contention window size to maintain fairness between LTE-LAA and WLAN systems, as most real-world applications do not operate under saturation conditions [9; 10]. For instance, consider a scenario where one LTE-LAA eNB and ten WLAN nodes share the unlicensed spectrum. Under saturated traffic conditions, all ten WLAN nodes would always have packets ready for transmission. However, in real wireless networks, a more common scenario might involve only one WLAN node operating under saturation, while the remaining nine nodes transmit sporadically. In such cases, the contention window size of the LTE-LAA eNB must be sufficiently large to balance the traffic load between LTE-LAA and WLAN systems, contrasting with the assumptions made under saturation models. Therefore, in this paper, we propose a channel measurement-based LBT (CM-LBT) mechanism that adaptively controls both the CCA threshold and contention window size with consideration of the congestion status in both LTE-LAA and WLAN systems. Accordingly, the proposed CM-LBT mechanism can balance the traffic between LTE-LAA and WLAN systems with the ultimate intention of enhancing the total system throughput in the unlicensed band. Through simulations, we show that the proposed mechanism improves the overall system throughput compared to existing LBT mechanisms, includingthe fair LBT (F-LBT) algorithm proposed in [5]. The major contributions of this paper are summarized as follows:  
* To control the contention window size and CCA threshold under typical network conditions, we introduce a non-saturated Markov chain model for the LTE-LAA eNB behavior. It captures the channel access activities of both LTE-LAA and WLAN systems in the unlicensed band. * We additionally set the appropriate CCA threshold by jointly considering the transmission and collision probabilities of WLAN nodes inside the CCA range and the interference at an LTE-LAA device from concurrent WLAN transmissions outside the CCA range. * Furthermore, we conduct simulations to evaluate the performance of our CM-LBT scheme by assessing the throughput of both LTE-LAA and WLAN systems, as well as the fairness between them using a reward function. Additionally, we compare the performance of our proposed scheme with F-LBT and conventional LB-LBT schemes. The remainder of this paper is organized as follows. Section 2 provides a brief overview of existing studies on the coexistence of LTE-LAA and WLAN systems. Our proposed CM-LBT scheme is detailed in Section 3. Section 4 discusses the simulation results. Finally, Section 5 concludes this paper.
# Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series
## 3 Data and Methods
### Satellite Data Processing
#### 3.3.2 Derivation of Temporal Metrics  
The basis for the segmentation procedure, shown in Figure 6, consists of multi-temporal metrics that have been calculated for all Sentinel-1 and Sentinel-2 scenes from  
Figure 6: Workflow of the applied method for the mapping of coastal pond aquaculture for Asia, with following main processing blocks: preprocessing of the SAR Sentinel-1 data and optical Sentinel-2 data, generation of time series metrics, image segmentation, masking by topographic features, and object filtering based on geometrical and water features.  
1 January to 31 December 2019. For the Sentinel-1 SAR data cube, we calculated the median; standard deviation; 10 th, 25 th, 50 th, 75 th, and 90 th percentile; and standard deviation of the VH and VV polarized data. The pixel-wise median (50 th percentile) is particularly suitable for identifying permanent and stable high (dams, dikes) and permanent low scatterers (smooth water surface) from the dense annual time series. A per-pixel median image was generated and used for the object-based extraction of pond aquaculture on a single-pond-unit basis. The median of all observations was selected rather than the mean to describe a single and more typical backscatter value because of the strong influence of possible extreme outliers of mean values [32; 50]. By averaging over time, the temporal median image improved the appearance of narrow, elongated line structures such as dams and levees (surrounding ponds), which is very crucial for the detection and mapping of aquaculture on a single-pond level. For the optical multispectral Sentinel-2 data cube, we calculated the median of band 2 (blue), band 3 (green), band 4 (red), band 8 NIR), band 11 (SWIR), and the Normalized Difference Vegetation Index (NDWI) by [PERSON] [51] and [PERSON] [52] and the modified NDWI (MNDWI) [53].
# Additive Manufacturing for Sensors: Piezoresistive Strain Gauge with Temperature Compensation
## 1 Introduction
[22] found a mix of several parameters (nozzle temperature, layer height, space width, and bed temperature) to minimize the final electrical resistance in 3D-printed tracks. This way, the fabrication of embedded sensors will be easier and more reliable. Post-processing treatments are another way to improve the final sensor conductivity. Flash metal ablation (FAM), a non-invasive technique, has been proven to improve the final conductance by two orders of magnitude by dissolving the superficial plastic and improving the bond among conductive fillers scattered into the conductive composite material [23]. Scientific literature has reported at different stages the strong dependence of 3D-printed sensors' performance by temperature [17; 24; 25; 26; 27], suggesting how the study of this effect and its compensation is crucial to push more the role of the AM for the fabrication of sensors. In the present paper, we characterize the low-cost FFF strain sensors fabricated in [21] by performing a calibration to investigate the relationship between the resistance of the sensors and the applied force or displacement. We propose an approach to compensate for the influence of temperature, which strongly affects the electrical resistance, as highlighted in [27]. The obtained results are promising, considering the low cost, ease of fabrication of the proposed sensors, and the possibility of directly integrating them into more complex systems in one fabrication cycle.