esa-sceva/satcom-chunk-collection huggingface dataset:
reduction methods. At the same time, the presence of strong temporal correlation among data points that belong to the same time series confuses many dimensionality reduction methods which operate under the assumption that the input data is uniformly sampled in the manifold space.  
The key motivation behind this work arises from the massive and high-dimensional datasets that are produced by computational models that simulate material morphology evolution during the fabrication process of organic thin films (see Section 5.1), by solving nonlinear partial differential equations. Organic thin film fabrication is a key factor that controls the properties of organic electronics, such as transistors, batteries, and displays. However, this requires expensive search campaigns of the design space. Choice of the fabrication parameters impacts the trajectory that the process follows, which eventually determines the properties of the material being fabricated. Scientists and engineers are interested in using dimensionality reduction on the resulting big data to explore the material design space, and optimize the fabrication to make devices with desired properties.  
In our prior work, we proposed S-Isomap, a spectral dimensionality reduction technique for nonlinear data [1] that can scale to massive data streams. While this method can efficiently and reliably process high-throughput data streams, it assumes that the input data are weakly correlated. Consequently, it fails when applied directly to process data. S-Isomap was derived from the standard Isomap algorithm [2], which is frequently used and favored in scientific computing data analysis [3; 4; 5; 6; 7; 8]. Unfortunately, while there is some prior work on applying Isomap to spatio-temporal data [9], the focus has been on segmentation of data trajectories rather than discovering a continuous latent state. In our recent work, we proposed a new spectral method to handle high-dimensional process data [10].  
This paper significantly expands on our past work [10] and makes two key contributions. The first contribution is to show how the standard linear and nonlinear dimensionality reduction methods, e.g., PCA, Isomap, etc., fail when dealing with process data. The poor performance can be attributed to the fact that every observation is highly correlated with the temporally close observations belonging to the same trajectory, and there is a lack of _mixing_ (or _cross-talk_) between different trajectories of a process. The second contribution of this paper is a new method, Entropy-Isomap, which induces the cross-talk between different trajectories by adaptively modifying the neighborhood size for every data instance. The proposed method is both easy to implement and effective, and could likely be applied to other spectral dimensionality reduction methods, besides Isomap.
## 4 Results and Observations
### Unprotected Slim Floor Specimens
The predicted deflections are in good agreement with the test data for both beam assemblies. The mid-span deflections predicted using 3D modelling (using ABAQUS) are better as compared to those obtained from 2D modelling using SAFIR, however, both approaches yield good results. Hence the proposed FEM approach is not only capable of predicting the thermal behaviour, but it also predicts the structural response of slim floor beams with considerable accuracy. These results also show that the simplified 2D FE approach provides conservative results for structural response in terms of mid-span deflections. This may probably result due to more severe thermal field evaluated for the section B-B', which is the widely distributed section along the span of the slim floor beam assemblies resulting from the shape of steel docking. The good agreement of 2D models not only in terms of temperatures but also in terms of displacements both with 3D model and experimental results further emphasises the possibility of using a simple 2D model in the design and verification of the slim floor beams, according to the technical fire regulation. Figure 7: Thermal comparisons for WFRC 67756: (a) Thermal contours at section AA’ after 90 min, (b) Thermal contours at section BB’ after 90 min, (c) Test vs. FEM at section AA’ (d) Test vs. FEM at section BB’. For example, the European codes [25] define five safety performance levels for the building fire resistance design, depending on the building intended use and its importance [29]:  
* Level I: no external consequences for structural collapse;
* Level II: maintaining the fire resistance requirements for a period of time sufficient for the evacuation of occupants. * Level III: maintaining the fire resistance requirements for the natural fire duration;
* Level IV: limited damage of the structure after fire exposure;
* Level V: complete serviceability of the structure after fire exposure. Focusing on Level IV or Level V, which in many cases are requested to the structures, the Italian Code for Fire Safety fix the displacement capacity corresponding to the achievement of the value of \(1/100\) and \(1/250\) of the length of the structural components, respectively. Applying these limit values (50 mm and 20 mm) to the analysed beams, both the performance levels are not satisfied and designing a protection material that can reduce the temperatures in the structural sections and improve the fire response of the beam, is necessary. In the following, the results of the simulation of the slim floor protected with intumescent coating are shown, modelling this reactive material according to the method described before and in [17]. Figure 8: Experimental vs. predicted mid-spand deflections for 2D and 3D FEM: (**a**) Deflected shape of WFRC 66162 after 102 min, (**b**) Deflected shape of WFRC 67756 after 79 min, (**c**) Mid-span deflection, Test vs. FEM for WFRC 66162, (**d**) Mid-span deflection, Test vs. FEM for WFRC 67756.
## 3 Results
### Dataset and Preprocessing  
The dataset consists of a training set of 10,000 _.uav_ files, a validation set of 800 files, and a test set of 50 files. The speech signals are originally sampled at 48 kHz, and in this study, they are downsampled to 8 kHz to match practical bandwidth constraints in wireless communication, as speech intelligibility is largely contained within the 4 kHz frequency range.  
Before transmission, all speech samples undergo amplitude normalization to mitigate level variations across recordings to achieve a consistent dynamic range. The selection of dataset emphasizes phonetic and acoustic diversity to evaluate the system's generalization ability across different speaker profiles and speech characteristics.  
For evaluation, each test speech sample undergoes five independent transmission iterations, each initialized with a different random seed. By doing so, we introduce a controlled randomness in the transmission process which ensures robustness against initialization biases. Additionally, performance is assessed across SNR levels and fading channels for simulating real-world wireless conditions.  
To ensure a fair comparison between semantic-aware transmission and conventional digital speech communication, we enforce a bit allocation constraint based on conventional PCM encoding. In traditional digital speech transmission, an audio signal is represented by its sampling rate and bit depth, which determine the total number of bits required for transmission. For a speech signal of 2.048 s, sampled at 8 kHz with a 16-bit resolution, the total bit allocation is given by the following:  
\[B_{\text{total}}=\text{sampling rate}\times\text{duration}\times\text{ bit depth} \tag{14}\]  
Numerically, in our case, this becomes the following:  
\[262144\text{ bits}=(8000\text{ samples}/\text{s})\times(2.048\text{ s})\times(16\text{-bit}) \tag{15}\]  
This bit allocation serves as a reference for conventional communication, where each sample is explicitly quantized into a 16-bit PCM format for digital transmission. To maintain a fair and analytically rigorous comparison, we impose the same bit burden constraint on our proposed model. Specifically, the output of the semantic compressor, after flattening and discretization, is mapped into 16-bit integers to match the bit depth of conventional PCM encoding. This ensures that the overall number of bits used for semantic-aware transmission aligns with conventional digital transmission, enabling an equitable performance evaluation. Furthermore, this structured feature discretization prevents uncontrolled increases in transmission overhead while preserving semantic integrity.  
By enforcing this constraint, we confirm that our semantic-aware model aligns with the same bit-rate requirements as conventional methods, allowing for a meaningful comparison in terms of transmission efficiency, perceptual quality, and robustness to channel impairments.
## 2 Background
### Underwater Light Intensity  
Figure 3 was adopted from an article published in an IEEE international conference [10]. The horizontal axis is the depth of water from 0 to 20 m, and the vertical axis is light intensity (%). In general, blue and green lights attenuate the slowest and second slowest in intensity, but red light attenuates the fastest. At 12 m underwater, the intensities of blue, green, and red lights are greater than 60%, approximately 60%, and nearly 0%, respectively. At 20 m underwater, the intensities of blue, green, and red lights are greater than 45%, greater than 40%, and 0%, respectively. Colors of objects observed in underwater environments are different from those in air.  
Figure 3: Spectrum of the horizontal axis which is around the water depth from 0 to 20 m, and the vertical axis is light intensity (%). Blue and green lights attenuate the slowest and second slowest in intensity; red light attenuates the fastest. This diagram is from reference [10].  
Figure 2: General visible spectrum. The wavelength of visible light ranges from 380.00 to 760.00 nm. Violet, indigo, blue, green, yellow, orange, and red light have wavelengths from short to long.  
Generally speaking, the light intensity decreases with the distance from objects in liquid by light attenuation depending on the wavelength of light. Figure 3 shows the light attenuation in water. For example, the intensity of red color decreases to about a half at the point whose distance from the light source is 2 m, although that of blue color hardly changes. Red color disappears at 20 m distance. In this way, colors of objects in water look different from those in air. The measurement system and its set-up is described in [10].
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 2 Research Status of the Precision Formation of Distributed Spacecraft Project
### Trend Analysis of the Distributed Precise Spacecraft Formation Flying Project  
With the continuous development of space exploration missions, distributed spacecraft precision formation flying has become a research hotspot in the field of aerospace, and its related technologies have also achieved remarkable progress. It has promoted in-depth engineering practice and theoretical research in numerous international universities and institutions. As shown in Figure 12 and Table 6 below, the international precision spacecraft formation-flying program began to develop in 1993 and reached its peak period of development in 2005. The task requirements can be roughly divided into three stages. Phase 1 is the technical verification of a low-Earth-orbit precision spacecraft formation flying project, RF precision relative measurement technology verification, visual precision relative measurement technology verification, space laser precision relative navigation technology verification, etc. Phase 2 is the formation of large virtual satellites, such as SAR, hard X-ray modulation telescopes, and infrared interferometers, to complete Earth, sun, and other observation tasks. Phase 3 is progress toward the development of scientific research, such as space gravitational wave detection, which requires higher precision, whereas deep space exploration needs greater autonomy. The mission orbit also begins to develop from a low-Earth orbit to a high orbit, L2 point, and deep space. This type of development is indistinguishable from the progress of relative measurement technology. It also reveals the development direction of technology, which will be explained below. The accuracy of relative state measurement is indivisible from the formation configuration. Different baseline length ranges will produce results with different accuracies. Moreover, due to different technical characteristics, relative state measurement methods have different valid ranges and applicable scenarios, as shown in Figure 13. Mission requirements determine the configuration, orbit, and accuracy, which, in turn, define the appropriate relative navigation method. As shown in Figure 14 below, from the \(y\)-axis to the \(x\)-axis, the accuracy required by the mission is constantly improving: precision formation flying technology verification, space synthetic aperture radar, an infrared interferometer, and laser gravitational wave detection. For example, transferring from CanX-4, with a baseline of 500 m and cm-level accuracy, to LISA and other future formations with a baseline of a million kilometer-level and pm-level accuracy is a very major leap for laser-based relative measurement technology. The specific accuracy is as follows. Figure 11: Schematic diagram of IRASSI.
# The Forecast of the Wind Turbine Generated Power Using Hybrid (LTC + XGBoost) Model
## 4 Methodology
### Feature Selection
#### 4.1.1 Feature Importance Evaluation by Random Forest  
Random Forest, a robust ensemble-based ML algorithm, measures feature importance based on how much each feature contributes to reducing impurity in the _feature selection_ model. Such an approach is very practical when capturing non-linear data point relationships and non-linear interactions among the features [53].  
To ensure enough data for both algorithm development and evaluation, each monthly dataset was split into training (70%), validation (15%), and testing (15%) subsets.  
Once the training process was completed, the importance scores of the features were extracted to analyze the impact of each feature on model predictions. Feature importance  
Figure 3: Steps of models’ development.  
was calculated based on the mean decrease in impurity (MDI), also referred to as Gini importance. Specifically, the importance of a feature is determined as an average reduction in node impurity contributed by that feature across all decision trees within the ensemble. Impurity reduction is weighted by the number of samples reaching each node and then averaged across the forest [54].  
Random Forest algorithm-based calculations (see Table 2) showed that four most influential parameters for _power_kw_ variable are predicted: wind speed (_predict_wind_speed_), predicted wind direction (_predict_wind_direction_), predicted wind (_predict_wind_, defined as the sum of predicted wind speed and predicted wind gust), and predicted sea level pressure (_predict_sea_level_pressure_).
## 3 Results  
Several optical coherence tomography (OCTA) images were used in the experiment to test whether the scheme we proposed could locate the choroidal neovascularization (CNV). The result of each processing stage is shown in Figure 7. We compared our results with the image of FAG/ICGA (Figure 8). Some detection results for different patients are shown in Figure 9. As can be seen in Figure 9, most of the images contain a lot of noise. Even when using the naked eye, it is quite difficult to find the region containing the blood vessels. For Figure 7, since the original image is a color image, when an image is input into the computer, we used a CIELAB color transform to extract the L-channel, and then used that as a grayscale image for the next processing step. As can be seen from the image, even if we convert to a grayscale image, the color of the image does not change much, implying that this conversion method strongly retains the original color brightness information. Figure 7 also shows that [PERSON]'s method can separate the blood vessels from the background, but unfortunately they will contain some of the noise. However, when compared to the noise of the original image, this is quite clear. Since there still remain some noise points which interfere with the blood vessel region, we used the majority and Size filter to solve this problem. This was quite effective for noise cancellation, and retained the blood vessel region as best as possible.  
Finally, we used eccentricity as a feature to search for CNV. Through research done on various cases, we know that CNV has a variety of different appearances. Even so, the scheme proposed in this paper can be used to identify them. It is a pity that the last case in Figure 9 was not found in our experiment. However, this image has CNV on the FA image, but in fact no such vascular structure was found on the OCTA images. This is mainly because this case does not have any clear CNV on the OCTA image, such that our scheme was unable to find it. This also means that our method did not find the CNV with the indefinite shape. Thus, no hard to define areas will be found. In fact, our approach can reduce the possibility of excessive misjudgment, thus indicating that the proposed method is indeed effective.  
Figure 6: An example of choroidal neovascularization (CNV) detection: (**a**) Thinning, (**b**) endpoint connection, (**c**) eccentricity, and (**d**) output image.  
Figure 8: An example of the comparison of (**a**) FA and (**b**) OCTA image. (**a**) Dye leakage on FAG (red arrow) indicates presence of CNV. Orange square indicates the area scanned by OCTA. (**b**) The CNV was detected by our method. FA: fluorescein angiography; OCTA: optical coherence tomography angiography; CNV: choroidal neovascularization.  
Figure 7: Schematic diagram of each step process. The original image uses the CIELAB transform to extract the L channel, removes noise after passing through [PERSON]’s method, Majority, and Size filter, and finally finds CNV through thinning, endpoint connection, and eccentricity feature.
## 4 State-of-the-Art Review
### Coal Gasification  
In the continuous demand for more efficient and more environmentally friendly methods to produce hydrogen, [PERSON] and his co-authors point to coal gasification [114].  
This thermochemistry transformation generates gas from coal [115], i.e., converts solid fuel to gas fuel; the aim of this process is mainly to decrease harmful emission occurring during the traditional burning of coal and also to increase the fuel's density. Their study in particular analyzes the gasification process' performance of different types of coal, concluding that the gasification of Tuncbilek coal followed by Soma coal provide the highest energy efficiency processes, with 41% and 38%, respectively. Other studies [116] have analyzed the impact of moisture contents on the gasification process, concluding through numerical simulations that coal gasification time increases with increasing moisture content--since high moisture content causes a decrease in temperature, which reduces the reaction rates.  
[PERSON] et al. have gone a step further and carried out a deep analysis of the carbon footprint of hydrogen production from sub-bituminous coal and lignite, using two gasification technologies--GE Energy/Texaco and Shell [117]. Among the analyzed variants of hydrogen production, sub-bituminous coal gasified with Shell technology was the one holding the lowest carbon footprint, at around 19 kg CO\({}_{2}\)/kg H\({}_{2}\); on the other hand, Shell technology used to gasify lignite held the highest, at 25.30 kg CO\({}_{2}\)/kg H\({}_{2}\). This technology was included in this analysis despite not being renewable--and not comparable with SMR, which can already be used to produce blue hydrogen--because it is on the verge of doing it too: as expected, the authors concluded that the use of capture and sequestration of CO\({}_{2}\) decreases the overall carbon footprint of all the processes.  
[PERSON] and [PERSON] take yet another approach and, in their study, conducted a parametric study to find the best steam-to-carbon ratio that yields the maximum performance of an integrated gasifier system for hydrogen production [118]. They found evidence that, in general, increasing this value makes the system work at its most optimal performance; at a 0.9 steam-to-carbon ratio, the maximum energy efficiency is reached: 53.80%. The authors then conclude that further increasing the proportion does not yield much more performance improvements (only incurring in higher costs). The same conclusion is reached regarding ambient temperature--it is best to operate this system in low-temperature climate areas. If that is achieved, the authors state, gasification of coal presents itself as the cleanest and most efficient method of utilizing coal for hydrogen production.
# Fine-Grained Detection of Pavement Distress Based on Integrated Data Using Digital Twin
## 3 Methodology
### Fine-Grained Pavement Distress Detection Based on Deep-Object Detection Networks
#### 3.2.4 Overall Loss  
The purpose of training is to optimize the performance of the model, which is achieved by reducing the loss function. The loss function of YOLOv3 is mainly composed of three components: bounding box regression loss, confidence loss and classification loss. The loss function is shown in Equation (1). The bounding box regression loss function usually uses Mean Squared Error (MSE) to directly perform regression on the center point coordinates and height and width of bounding box [70]. The confidence and classification loss function are analyzed by binary cross-entropy loss. Compared with YOLOv3, the loss function of YOLOv5 only innovates in bounding box regression by replacing MSE with CIOU [71], and the other two parts do not change substantially. The CIOU considers the scale informationof border overlap, center distance and aspect ratio on the basic of IOU. The CIOU loss function is shown in Equation (2).  
\[loss=lbox+lobj+lcls\] \[lbox=\lambda_{coord}\sum\limits_{i=0}^{S^{2}}\sum\limits_{j=0}^{B}l_{ij }^{obj}(-\omega_{i}\times h_{i})\times\left[(x_{i}-\delta_{i})^{2}+(y_{i}- \hat{y}_{i})^{2}+(\omega_{i}-\hat{\omega}_{i})^{2}+\left(h_{i}-\hat{h}_{i} \right)^{2}\right]\] \[lcls=\lambda_{class}\sum\limits_{i=0}^{S^{2}}\sum\limits_{j=0}^{B} l_{ij}^{obj}\sum\limits_{c\in classes}p_{i}(c)log(\hat{p}_{i}(c)) \tag{1}\] \[lobj=\lambda_{noobj}\sum\limits_{i=0}^{S^{2}}\sum\limits_{j=0}^{ B}l_{i,j}^{noobj}(c_{i}-\xi_{i})^{2}+\sum\limits_{i=0}^{S^{2}}\sum\limits_{j=0}^{B}l_{i,j}^ {obj}(c_{i}-\xi_{i})^{2}\]  
where the meaning of \(x,y,w,h\) is as follows: \(x\) coordinate location of the box center point, \(y\) coordinate location of the box center point, weight of the box, and height of the box, respectively. \(l_{i,j}^{obj}\) is a symbol that indicates the value is \(1\) if the box has an object; otherwise it is \(0\).  
\[L_{CIOU}=1-IOU(A,B)+\frac{\phi^{2}(A_{obj},B_{ctrl})}{c^{2}}+\alpha\cdot v \tag{2}\] \[v=\frac{4}{\pi^{2}}\left(\arctan\frac{\omega^{gt}}{h^{gt}}- \arctan\frac{\omega}{h}\right)^{2}\] \[\alpha=\frac{v}{(1-IOU)+v^{gt}}\]  
In which, \(\omega^{gt}\) and \(h^{gt}\) are the weight and height of the ground truth bbox respectively; \(\omega\) and \(h\) are the weight and height of the prediction box respectively. And \(\alpha\) and \(v\) are penalty terms for the aspect ratio, \(\alpha\) is a positive number and \(v\) measures the consistency of aspect ratio.