esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Ranging-Compensated Temperature-Sensing Sensor Design Method
### Data Preprocessing Flow and Method
\tag{5}\]  
We sum up \(j\) times fitting sequences \(\widetilde{\tau_{i}^{T}}\) and \(\widetilde{\tau_{i}^{R}}\) and calculate their respective arithmetic means to obtain transmission time-delay fitting sequence \(\widetilde{\tau_{i}^{T}}\) and reception time-delay sequence \(\widetilde{\tau_{i}^{R}}\) corresponding to the final temperature range, i.e.,  
\[\left\{ \begin{array}{l}\widetilde{\tau_{i}^{T}}=\frac{\sum\limits_{n=1}^{j }\widetilde{\tau_{i,n}^{T}}}{j}\\ \widetilde{\tau_{i}^{R}}=\frac{\sum\limits_{n=1}^{j}\widetilde{\tau_{i,n}^{T}} }{j}\end{array} \right.;\;\;i=\left(0,1,2\cdots,2^{N}-1\right). \tag{6}\]  
We assume that the ADC sampling value corresponding to the stabilization temperature in the satellite cabin is \(V_{c}\). Then, \(\widetilde{\tau_{i}^{T}}\) and \(\widetilde{\tau_{i}^{R}}\), which correspond to \(V_{c}\), are \(\widetilde{\tau_{c}^{T}}\) and \(\widetilde{\tau_{c}^{R}}\). The delay deviation sequence of the entire temperature range relative to \(V_{c}\) in the satellite cabin is expressed as follows:  
\[\left\{ \begin{array}{l}\Delta\tau_{i}^{T}=\widetilde{\widetilde{\tau_{c}^{T }}}-\widetilde{\widetilde{\tau_{i}^{T}}}\\ \Delta\tau_{i}^{R}=\widetilde{\tau_{c}^{R}}-\widetilde{\tau_{i}^{R}}\end{array}  \right.;\;\;i=\left(0,1,2\cdots,2^{N}-1\right). \tag{7}\]  
We convert sequences \(\Delta\tau_{i}^{T}\) and \(\Delta\tau_{i}^{R}\) into pseudo-code delay phase values \(\Delta P_{i}^{T}\) and \(\Delta P_{i}^{R}\) and store them in the read-only memory (ROM) of the FPGA, which is the DBD data-processing chip. The read address of the ROM is ADC sample value \(V_{i}\), and the read data are the delay compensation data. By assuming that the pseudo-code rate of the ISL ranging is \(C_{V}\) and the bit width of the pseudo-code numerically controlled oscillator is \(M\), we have  
\[\left\{ \begin{array}{l}\Delta P_{i}^{T}=2^{M}\cdot\Delta\tau_{i}^{T}\cdot C_ {V}\\ \Delta P_{i}^{R}=2^{M}\cdot\Delta\tau_{i}^{R}\cdot C_{V}\end{array} \right. ;\;\;i= \left(0,1,2\cdots,2^{N}-1\right). \tag{8}\]  
We need to open up a single block of \(2^{N}\times M\) ROM capacity in the FPGA. All of these tasks are performed in advance on the ground. The onboard ISL payload only needs to read the compensation data at the time of transmission or reception as delay correction, where the read address is the integral result of the ADC sample value. When the ISL payload is received, modified ranging phase-delay value \(P_{modify}^{R}\) is expressed as  
\[P_{modify}^{R}=P_{measure}^{R}+\Delta P_{i}^{R}, \tag{9}\]  
where \(P_{measure}^{R}\) is the measured phase delay. When the ISL payload is transmitted, modified preset pseudo-code initial phase value \(P_{modify}^{T}\) is expressed as  
\[P_{modify}^{T}=P_{initial}^{T}+\Delta P_{i}^{T}, \tag{10}\]  
where \(P_{initial}^{T}\) is the initial preset pseudo-code transmission phase of the ISL payload. The diagram of the whole ranging compensation temperature-sensing sensor data processing is shown in Figure 4.
# SDebrisNet: A Spatial-Temporal Saliency Network for Space Debris Detection
## 5 Experimental Results and Analysis
### Sensitivity Analysis to the Video Clip 0  
The video clip length \(T\) is a hyperparameter that affects the space debris detection performance of SDebrisNet. It denotes the number of video frames that are fed into the spatial-temporal saliency network for each batch during the training stage. First, a subset of the training set is divided and then it is trained without the pretrained model for each video clip length. The performance of SDebrisNet trained with different \(T\) is shown in Table 5. As shown in the table, SDebrisNet achieves better detection performance from the increase in the video clip length. However, when the value of \(T\) is too large, the detection performance will decrease. This is because SDebrisNet cannot sufficiently learn the spatial-temporal information when the value of \(T\) is too small. However, a large value of \(T\) may cause objects to disappear from the reference window, which can be detrimental to the PCSA's ability to learn motion cues. In addition, the computational cost will increase with longer video clip lengths. \(T=4\) is the most suitable choice for spatial-temporal information propagation.
## 5 Empirical Analysis
### Analysis of the Results
From an industrial perspective, the carbon  
Figure 4: Comparison of carbon quota allocation results for regional industries. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Pre-Quota Allocation**} & \multicolumn{4}{c}{**Post-Quota Allocation**} \\ \cline{2-9}
**Province** & **Primary** & **Secondary** & **Tertiary** & **Total** & **Primary** & **Secondary** & **Tertiary** & **Total** \\  & **Industry** & **Industry** & **Industry** & **Industry** & **Industry** & **Industry** & **Total** \\ \hline Northern coastal & 2765.40 & 161,074.26 & 26,030.49 & 189,870.16 & 2636.15 & 162,031.04 & 25,046.79 & 189,713.98 \\ Southern coastal & 1647.83 & 61,125.40 & 19,122.09 & 81,895.33 & 1731.28 & 64,260.37 & 20,163.69 & 86,155.34 \\ Eastern coastal & 1729.28 & 99,438.25 & 25,456.03 & 126,623.55 & 1847.71 & 108,199.42 & 25,542.28 & 135,589.42 \\ Middle Yangtze River & 2722.40 & 76,495.25 & 22,352.14 & 101,569.79 & 2433.94 & 82,619.76 & 22,133.61 & 107,097.30 \\ Middle Yellow River & 3130.43 & 146,374.71 & 23,571.16 & 173,049.30 & 2608.11 & 146,633.34 & 21,027.45 & 170,268.90 \\ Northwest & 1750.41 & 61,884.26 & 7301.47 & 70,936.14 & 1703.93 & 64,437.61 & 7496.61 & 73,638.14 \\ Southwest & 1796.32 & 102,543.21 & 18,827.13 & 123,166.66 & 1582.31 & 118,003.30 & 23,215.38 & 142,801.00 \\ Northeast & 2420.28 & 66,982.99 & 16,938.82 & 86,342.09 & 2287.77 & 67,911.84 & 14,436.31 & 84,635.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Regional industry carbon quota results in China. quotas of all regions in the secondary industry rise. The reason for this is that the secondary industry occupies the largest share of the national economy, and its corresponding carbon emissions will also rise when the overall carbon emissions rise. Secondly, the carbon quotas for the primary and tertiary industries in the northeastern, Middle Yangtze River, Middle Yellow River, and northern coastal regions shrink, which means these regions are under greater pressure to reduce carbon emissions. In contrast, the southern coastal and eastern coastal regions have a positive growth in carbon quotas for all industries, which could be due to better geographical location, along with the advantages of early opening-up policies.
# Signals Intelligence System with Software-Defined Radio
## 2 Materials and Methods
### System Architecture  
The implemented system has an architecture based on the symbiosis between the two components: hardware and software. They intertwine to create a homogeneous and efficient system. Figure 1 shows how the system components are interconnected.  
Figure 1: Component interconnections.  
How the components of the system work and communicate can be seen in Figure 2. In the upper part of Figure 2, the components that are used to train and validate the artificial intelligence network are presented. The database, after it is prepared, is loaded in the Microsoft Azure Auto ML Service, where the artificial intelligence network is configured. Following the training and validation part, the trained model is realized. In the lower part of the image, the components used to test the trained model are presented. The SDR platform is used to send and receive modulated signals. The modulated samples received are inserted into the integrated trained model (integrated into Microsoft Azure cloud) that generates the answer with the identified modulation type.
## 5 Discussion
### Experimental Results and Fitted Parameters  
After material characterization, the properties of the steels under study are obtained. The measured grain sizes are not very large, since grain boundaries are defined with a relatively small angle and the bainite and martensite phases present a lath structure. Ellipsoid axes aspect ratios follow a clear distribution around an average value, while the directionality of these ellipsoid appear to be randomly distributed. Regarding crystallographic textures, the results are comparable to typical rolled textures [63; 64]. Results of the tensile experiments are shown in Figure 5, together with the fitted curves. The results are in the expected range for the materials studied. While there are bainitic steels with better mechanical properties [65; 66], this is usually due to higher carbon content or a nanostructured bainite. Other bainitic steels also with a low carbon content present a similar behavior to the one observed here [67; 68].  
The fitted parameters are presented in Table 4. Although the fitting algorithm used does not search for the global minimum, using sensible initial values and randomly applying small perturbations to the obtained solutions allows reaching a good fitting. When compared with the experimental scatter, the predicted curves show good accordance with the fitting data.  
The first parameter, \(C_{1}\), was expected to be of the same order of magnitude of the Taylor factor, and indeed it is very close to a typical Taylor factor value. The value found for the Hall-Petch constant \(K_{\rm HP}\) is close to values commonly used for steel (the same constant is being used to account for the Hall-Petch effect of bainite grain size and martensite lath width). The value fitted for \(\alpha\) is lower than usual. This may be explained because of the latent hardening coefficients introduced in (18), imprecisions in the measurement of dislocation densities or additional effects not considered in the model. The annihilation distance and \(\beta\) parameter take values that are close to other values in the literature [17]. The carbides shape constant \(c_{p}\) and strain rate hardening exponent \(n\) take sensible values too. On the other hand, the \(C_{2}\) and \(\sigma_{\rm imb}^{2}\) parameters do not vary too much from the initial values used for the fitting (taken from [17]). These parameters are primarily related with stage IV hardening, and the maximum strain reached in the tensile experiments used for the fitting was relatively low.  
It was shown that the presented model with the fitted parameters can reproduce tensile behavior at quasi-static and high strain rates. Since the model relies on a combination of physical principles, general phenomenological relationships and microstructural data, it can be used to simulate a wide range of different materials and loading conditions.
# Channel Model for Estimating Received Power Variations at a Mobile Terminal in a Cellular Network
## 2 The Channel Model
### Directional Derivative of Received Power with Respect to Distance R  
In an ideal scenario, the signal power should decrease predictably as the distance increases, following a theoretical propagation loss model. However, in real environments, the received power behavior is affected by various factors. Calculating the partial derivative with respect to \(R\) enables the assessment of the power's sensitivity to distance changes, which is essential for optimizing base station placement and ensuring adequate signal quality throughout the coverage area.  
\[\frac{\partial P_{Rx}}{\partial R}=\frac{d}{dR}\big{(}-20\log_{10}R\big{)} \tag{15}\]  
To solve this partial derivative, the logarithmic differentiation rule is applied straightforwardly, as shown in Equation (16):  
\[\frac{d}{dx}(\log_{n}(x))=\frac{1}{x\ln(a)},\quad a>0,\ a\
eq 1 \tag{16}\]  
Therefore, the derivative of the given expression is calculated as follows:  
\[\frac{\partial P_{Rx}}{\partial R}=-\frac{20}{R\ln(10)}\quad[W/\mathrm{m}] \tag{17}\]  
This equation represents the partial derivative of the received power with respect to the distance \(R\), illustrating that power decreases inversely with distance, consistent with free-space propagation laws.  
Figure 1: Graph of the theoretical power derivative with respect to the theta variable vs. the HPBW.  
Following the procedure outlined earlier and using Equation (17), Figure 2 depicts the relationship between the partial derivative of power with respect to distance and the distance itself. In this figure, the blue points correspond to measured data, while the red line represents the modeled trend of power variation based on the derivative.  
In Figure 2, it can be observed that as the distance between the mobile terminal and the base station increases, the variation in the received power gradually decreases. This behavior aligns with Equation (17), which indicates that the rate of change of power with respect to distance is inversely proportional to the distance and decreases following a logarithmic pattern. In other words, at greater distances, power changes occur more gradually, reflecting reduced sensitivity to small positional variations. In the figure, the blue points represent the experimental data collected from field measurements, while the red curve corresponds to a regression fitted to those data to reveal the overall trend. This regression highlights the expected logarithmic decrease in the power variation with distance. The method used to obtain and process the experimental data is described in detail in a later section.
# Potential Benefit of Carbon Benchmarking: Analysis of Semiconductor Industry in Taiwan
###### Abstract
Of the three approaches, the directional distance function approach has emerged in recent years as the main method for measuring TE with undesirable outputs included, because it avoids the technical problems outlined above [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. However, to correct the possible potential estimated bias of TE under the directional distance function, a bootstrap algorithm was proposed by [PERSON] et al. [24]. This algorithm can not only capture the possible bias of TE estimates, it can also calculate the confidence intervals. For these reasons, studies that have used the directional distance function to measure TE in recent years have innovatively combined it with the bootstrap procedure to overcome the limitations mentioned above and improve the robustness of TE estimates [25; 26; 27]. Specifically, the main focus of this study is the use of a TE method based on the bootstrapped directional distance function approach to measure the TE of carbon reduction management in one of Taiwan's key industries: the semiconductor industry. In addition, the study estimates the monetary benefit potential of the carbon reduction efforts, so that the results of carbon reduction can be presented in the form of a benefit indicator to support the implementation of carbon benchmarking management in the semiconductor industry. This paper is structured as follows: following this introduction, Section 2 explains the evaluation method, Section 3 discusses the sources of empirical data and the data-processing procedures, Section 4 presents the empirical results and the analysis of these results, and Section 5 concludes this study.
## 2 Materials and Methods
### Models' Architecture Setup
#### 2.2.3 Fourier Neural Operator  
The Fourier Neural Operator (FNO) has one important reported advantage over classical methods (all CFD as well as ML methods). The FNO models the solution operator, for not only one instance but also multiple instances at a time [27]; this means that its tuning is not restricted to a specific solution case, being more generalizable than other ML approaches. In our work, we implemented this capability by training and testing the Physics-Informed Neural Network (PINN) for different Reynolds numbers.  
The structure of the tested models was mounted by stacking contiguous FNO layers sequentially, up to 3. The current research evaluated the number of Fourier modes to keep (modes) in each layer (up to 16, in powers of 2), as well as the number of transforms to apply to these Fourier modes (width, up to 32, in powers of 2). It was expected that the FNO could capture the intrinsic PDE information, because its mapping occurs in the Fourier space (based on frequency modes), not Euclidean (based on spatial coordinates), as usual. This commonly may represent, or be near, the real solution of the PDE.
## 3 Materials and Methods
### Software Features and Algorithm
#### 3.1.1 Input Video  
Multi-threaded processing was implemented in the algorithm. In this procedure, the video is read in a thread independent of the processing thread, and the frames are stored in a stack (Figure 1; step 1.1; Appendix A). This avoids the delay between frame reading and other processing steps of the algorithm, whereby frames are always available to obtain better rates in frames per second (fps).
## 1 Introduction  
Multiple sclerosis (MS) is a chronic inflammatory, demyelinating and degenerative disease of the central nervous system which mostly affects females, with an age of onset between 20 and 45 years. MS shows an extremely variable clinical and temporal evolution [1, 2]. The visual disturbances that MS patients frequently experience include visual acuity (VA) reduction, altered contrast sensitivity, visual field (VF) abnormalities, dyschromatopsis and diplopia. A frequent acute neuro-ophthalmological manifestation of the disease is represented by the optic neuritis (ON) that occurs in \(\sim\)20% of MS patients as a first clinical manifestation and in more than 60% of cases during the course of the disease [3, 4, 5, 6].  
The etiology of MS is still largely unknown; however, a key pathogenic aspect is represented by the peripheral activation and proliferation of autoimmune myelin-targeted lymphocytes [7, 8].  
Currently, many disease-modifying therapies have been approved for the treatment of MS [9, 10]. Among them, fingolimod, the first oral drug, was approved in 2010 after showing positive results in two phase III randomized clinical trials conducted on relapsing-remitting MS (RR-MS) patients [11, 12]. Fingolimod is a pro-drug that, after phosphorylation, acts as a modulator of the sphingosine1-phosphate receptor by reducing the exit of lymphocytes from lymphoid tissues and consequent self-aggression into the central nervous system [13, 14].  
A rare ocular side effect related to fingolimod is macular edema (ME), which is detectable through optical coherence tomography (OCT) with an incidence of 0.3-1.2% [15, 16, 17, 18].  
OCT is a non-invasive imaging technique that uses low-coherence light to acquire micrometer-resolution, two-dimensional and three-dimensional images of ocular structures. OCT has now become indispensable in clinical practice, being particularly useful in monitoring both the progress of MS and therapy, allowing for the early identification of anatomical alterations, clearly visible after the introduction of the high-resolution spectral domain (SD) technique, and even more visible if OCT is combined with other retinal imaging techniques [19, 20, 21, 22].  
To date, many clinical studies have demonstrated the usefulness of assessing, by OCT, the retinal nerve fiber layer thickness (RNFL-T) and the total macular volume (TMV) in patients with MS, considering the reduction of these parameters as biomarkers of disease progression [18, 23, 24, 25, 26, 27]. Instead, in RR-MS patients treated with fingolimod, there are no long-term studies that analyze macular and RNFL changes and assess the effect of these retinal changes on VA, low-contrast letter acuity (LCLA) and VF. Therefore, the purpose of this work was to describe the long-term anatomical and functional retinal changes observed in RR-MS patients which started on fingolimod and were followed for two years, also taking into account the effect of previous ON.
# Integrating Prior Knowledge into Attention for Ship Detection in SAR Images
## 3 Representing Prior Knowledge
### Generating by Brightness  
As aforementioned in Section 3.1, brightness is important to distinguish ships from the water surface. Therefore, we can use an interval of pixel values to separate ships from the water surface. Only pixels within are considered as ships. Since the images from training set and test set are i.i.d., the interval which fits for the training set suits the test set as well.  
Figure 3: Objects with different densities in the SAR images. The difference in densities comes from the difference in structures. The red boxes in the figure mark the ships while the green boxes mark the areas. The densities of land are significantly different from those of the ships. (**a**) Example 1 of density difference between ships and land. (**b**) Example 2 of density difference between ships and land. (**c**) Example 3 of density difference between ships and land. (**d**) Example 4 of density difference between ships and land.  
Figure 2: Ships with different background in SAR images. Ships are significantly brighter on SAR images than on the sea surface. The ship targets are marked in red boxes, and the strong scattering points on land are marked in green boxes. (**a**) Example 1 of ship’s brightness. (**b**) Example 2 of ships’ brightness. (**c**) Example 3 of the brightness difference between ships and land. (**d**) Example 4 of the brightness difference between ships and land.  
We perform clustering on pixels of each bounding box (annotations of training set) individually. we use K-means [83] clustering with two classes (one for the foreground and the other for the background) to cluster the pixel values. The cluster which has a higher value as the center than that of the other cluster represents the foreground. From the foreground pixels, we find a pair of median value \(med_{i}\) and max value \(max_{i}\), where \(i\) indicates the global index of bounding boxes. The global median value \(med\) and max value \(max\) could be calculated as:  
\[med=\arg\min_{x}\frac{1}{n}\sum_{i}(med_{i}-x)^{2} \tag{1}\]  
\[max=\arg\min_{x}\frac{1}{n}\sum_{i}(max_{i}-x)^{2} \tag{2}\]  
Here, \(n\) is the total number of bounding boxes. The final interval is \([2\cdot median-max,max]\).  
As can be seen from Figure 4, most of the points in the clustering results are distributed at higher pixel value levels. It can be illustrated that most pixels in the bounding boxes represent ships while others are noises. The intervals from Equations (1) and (2) visualized on the second row suggest the effectiveness of our approach.  
Figure 4: Two ship examples and their histograms. In the example, only the pixels in the green box (on the images from first row) are included in the distribution for the second row. (**a**) Ship pixel distribution example 1. (**b**) Ship pixel distribution example 2.