esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Preparation  
### Optimal Sampling  
Source rocks in the Majiagou Formation in the Ordos Basin mainly include the thin-layered dark dolomite mudstones and argillaceous dolomite. The LT1 well has full-section coring and plenty of TOC measured data. There are relatively complete conventional logging data, including natural gamma, interval transit time, neutron porosity, density and resistivity. Therefore, the core of LT1 well in the eastern Ordos Basin was chosen and logging data were used as the basic data. After core location and logging normalization, 309 TOC measured data and corresponding logging data were gained.  
During the sedimentary period of the Majiagou Formation in the eastern Ordos Basin, sea level changed frequently, thus resulting in the very complicated lithology and common thin interbedded formations. It can be seen from Figure 3 that gypsum rock and dolomite are in thin alternating deposit and thickness of some lithologic strata is smaller than 1 cm, lower than the logging resolution. Although core lithology changes, the logging curve shows no obvious responses, thus causing mismatching rock-electricity relations. Although there's relatively high TOC in some thin strata, they are too thin to generate obvious logging responses (Figure 3).  
Figure 2: Formation histogram of Majiagou Formation in the eastern Ordos Basin.  
Moreover, TOC logging responses are not significant due to the general inadequate TOC measured data of source rock in the study area. Moreover, external factors (e.g., lithology) influence logging response greatly. As a result, the correlation between TOC measured data and logging data is very poor (Figure 4). It is necessary to process TOC sample data.  
To assure consistency of resolution between core data and logging data and increase the correlation between TOC measured data and corresponding logging data, the laboratory TOC measured data were processed by observing the following of two principles.  
1. For the section with relatively homogenous lithology, actual core lithology and the mean TOC was used.  
2. For the section with thin complicated lithology, and due to a thin stratum being difficult to be used as an effective source rock (although there's a very high TOC content),  
Figure 4: Scatter plot of intersection between original TOC data and some logging data: (**a**) natural gamma ray and (**b**) organic matter density.  
Figure 3: Schematic diagram of core logging.  
the core lithology in this section was used to represent the lithology and TOC of this section was the mean after exclusion of singular values.  
Based on above principles, 158 sample points were chosen as the sample data to construct the TOC quantitative log interpretation model.
# A New Resource Allocation Protocol for the Backhaul of Underwater Cellular Wireless Networks
## 3 Description of UBSI Resource Allocation Protocol
### Protocol Messages and Procedures
#### 3.2.1 Protocol Messages  
All messages of the UBSI resource allocation protocol is equivalently applied as in [16]:  
* A Resource Information Request (RIREQ) is a message that a UBSC asks all UBSs to send their traffic information as specified in Section 3.1.1.
* A Resource Information Response (RIREP) is a response message to RIREQ sent by a UBS which includes its traffic information.
* A Resource Allocation Information Send (RAIS) is a message sent by a UBSC to inform a UBS sending a RIREQ of new backhaul capacity. During the IP, this message contains allocated backhaul capacity of each UBS and is broadcasted to all the UBSs. However, during the NP, this message contains reallocated backhaul capacity of a UBS which requested reallocation of its backhaul capacity. Thus, in this case, this message is sent to only one UBS.
* A Resource Allocation Information Send Acknowledgement (RAISACK) sent by a UBS is an acknowledgement message of RAIS.
* A Resource Reallocation Request (RAREQ) is a message that a UBS asks a UBSC to adjust its backhaul capacity.
* A Resource Reallocation Acknowledgement (RAACK) sent by a UBSC is an acknowledgement message of the RAREQ.  
Table 2 shows the summary of the messages used in the UBSI protocol. For each message, the tables shows the direction of message, the phase when the message is used, and the transmission type.
# Assessing the Potential of PlanetScope Imagery for Iron Oxide Detection in Antimony Exploration
## 5 Discussion
### Validation and Evaluation  
All spectra display a reflectance peak at approximately 750 nm. This peak is slightly weaker in the sample from ROI 5 (Figure 9i,j) and significantly weaker in the sample from point 3 (Figure 9e,f). Given that Hematite typically exhibits a reflectance peak at 750 nm and Goethite around 660 nm, and that all spectra display a peak at 750 nm, the results suggest that Goethite is absent in the analyzed soil samples, with Hematite being the predominant iron oxide identified.  
The characteristic iron absorption near \(\sim\)670 nm is absent in all spectra. However, all of them show a second diagnostic absorption feature ranging from 852 nm to 890 nm, which strongly indicates the presence of Hematite.  
The spectra from sample 3 exhibit very uniform spectral behavior and low reflectance for iron diagnostic features. This may suggest that the sample is homogeneous. Upon analyzing the spectra of the field-collected samples, all exhibit spectral characteristics marked by a prominent diagnostic iron absorption feature, indicating the predominance of Hematite in the study area. The soil sample from ROI 3 displays a comparatively weaker spectral response associated with iron oxides. This location was identified in both fuzzification Tests 1 and 2, indicating consistent detection across all satellite datasets. However, before classifying this point as a false positive, a more detailed analysis of rock samples is planned for future work.  
The analysis of the field samples confirms the effectiveness of the proposed method in accurately delineating zones enriched in iron oxides.
## 3 Results and Discussion
### Pollutant Disappearance Experiments
#### 3.1.2 Decolorization of Anthraquinone-Type Dyes  
The three-ring structure of anthraquinone (the metabolite of the fungal degradation of anthracene), occupies the central part of a range of dyes, the so-called anthraquinone-type dyes. These dyes are considered to be the most stable among the different classes of dyes currently used [52, 53]. Their entry to the environment with waste water is a serious problem. Many industrial dyes are toxic mutagenic and carcinogenic [54, 55]. Although anthraquinone dyes are a large group of synthetic dyes, PolyR-478 and Remazol Brilliant Blue R (RBBR) are most often used as models of similar dyes in biodegradation studies [56].  
In our study, two other dyes of this type (Acid Blue 62 and Reactive Blue 4), which differed by substituents at the anthraquinone structure, were chosen. All the studied fungi decolorized anthraquinone dyes, and both the chromophore part of the molecule and the aromatic ring were available for the fungal degradation. Figure 2 (top) shows the chemical structures of these dyes. This evidenced by a decrease in absorption at the UV and visible regions of the spectra of Acid Blue 62 and Reactive Blue 4. Most strains of basidiomycetes decolorized both dyes after 6 days (Figure 2). An exception was _S. commune_, which decolorized Reactive Blue 4 only by 60%.  
The decolorization of Acid Blue 62 resulted in the change of color from dark blue to light reddish-brown. This destruction of the molecule was accompanied by the formation of precipitate, which may indicate the polymerization of the products formed, similar to data obtained by [PERSON] et al. [54]. The degradation of Reactive Blue 4 was more complete, as can be judged by the complete decolorization of the cultivation medium (Figure 2, photo).  
Both dyes were less accessible to the ascomycetes. Decolorization varied from 40% for _G. candidum_ to 70% for _L. aphanocladii_. An exception was _C. herbarum_, which decolorized both studied dyes completely, without marked sorption by mycelia.  
As seen from Figure 2, the ligninolytic fungi (_P. ostreatus_ strains, _Trametes_ species, _B. alusta_, _L. betulina_, and _Str. rugosonnulata_) and ascomycetes (_F. oxysporum_ and _C. herbarum_) actively decolorized anthraquinone-type dyes. This property can be indirect evidence of
# Assessment of Self-Supervised Denoising Methods for
## 2 Materials and Methods
### Loss Functions  
To calculate the training loss of the two models above, two different loss functions were used: the basic loss and the regularization loss, which were defined and described in [20] as follows:  
\[\mathcal{L}=\mathcal{L}_{\text{basic}}+\gamma\cdot\mathcal{L}_{\text{reg}} \tag{3}\]  
where \(\gamma\) denotes a tunable parameter.  
As formulated in Equation (4), the basic loss function consists of weighted SDR loss \(\mathcal{L}_{\text{wSDR}}\)[24], frequency domain loss \(\mathcal{L}_{\text{F}}\), and time domain loss \(\mathcal{L}_{\text{T}}\).  
\[\mathcal{L}_{\text{basic}}=(\alpha\cdot\mathcal{L}_{\text{F}}+(1-\alpha) \cdot\mathcal{L}_{\text{T}})\cdot\beta+\mathcal{L}_{\text{wSDR}} \tag{4}\]  
\(\alpha\) and \(\beta\) are hyper-parameters that regulate the strength of \(\mathcal{L}_{\text{F}}\) and \(\mathcal{L}_{\text{T}}\).  
Figure 3: Schema of DCUNET-cTSTM architecture as proposed in [20].  
Figure 2: Schema of DCUNET architecture.  
The measure of the loss in the time domain is the average of the squared differences between the output and the clean waveforms.  
\[\mathcal{L}_{\text{T}}=\frac{1}{N}\sum_{i=0}^{N-1}(s_{i}-\hat{s}_{i})^{2} \tag{5}\]  
In this context, \(s_{i}\) and \(\hat{s}_{i}\) represent the i-th instance of unprocessed speech and its corresponding denoised version, with N referring to the total number of audio samples.  
The model can gather additional information which can lead to improved speech intelligibility and better-perceived quality using frequency domain loss, which is given by Formula (6) as follows:  
\[\mathcal{L}_{\text{F}}=\frac{1}{T_{F}}\sum_{t=0}^{T-1}\sum_{f=0}^{F-1}\bigl{[} \left(\left|S_{r}(t,f)\right|+\left|S_{i}(t,f)\right|\right)-\left(\left|\hat{ S}_{r}(t,f)\right|+\left|\hat{S}_{i}(t,f)\right|\right)\bigr{]} \tag{6}\]  
\(S\) and \(\hat{S}\) symbolize the clean and processed spectrogram, while \(r\) and \(i\) represent the real and imaginary components of the complex variable. \(T\) and \(F\), on the other hand, denote the respective quantities of frames and frequency bins.  
Also, \(\mathcal{L}_{\text{wSDR}}\), given by Equation (7), helps to improve the well-known assessment metrics specified in the temporal domain:  
\[\mathcal{L}_{\text{wSDR}}(x,y,\hat{y})=-\alpha\frac{\langle y,\hat{y}\rangle}{ \|y\|\|\hat{y}\|}-(1-\alpha)\frac{\langle x-y,x-\hat{y}\rangle}{\|x-y\|\|x- \hat{y}\|} \tag{7}\]  
and  
\[\alpha=\frac{\|y\|^{2}}{\|y\|^{2}+\|x-y\|^{2}} \tag{8}\]  
\(x\) represents a noisy sample, while \(y\) and \(\hat{y}\) represent the target sample and the predicted outcome. The symbol \(\alpha\) denotes the energy ratio between the target speech and the noise.
# Digital Twin Framework for Road Infrastructure Management
## 4 Digital Twin Framework for Road Infrastructure Management
### Analysis of Existing DT Frameworks for Road Infrastructure Management
#### 4.1.3 Road Tunnels
The data layer provides foundational data required for all other layers, including asset life cycle data and supplementary inputs (such as weather information and publicly available datasets). The semantic web layer serves as the core of the system, enabling data description, knowledge organization, and the application of reference rules for defect diagnosis. The function layer processes and analyzes the information from the semantic web layer and transmits the results to the decision-making layer. It incorporates modules for decision-making, reasoning, query generation, and visualization. The decision-making layer receives these outputs and enables users to interact directly with the system. Key applications of the system include information retrieval, diagnosis, evaluation, and maintenance services. Table 3 summarizes the findings from DT layered approaches in road tunnel applications. In Section 4.1.3, we conducted a thorough review of five papers focused on DT applications in road tunnels. These papers primarily addressed issues related to underground utilities and tunnels, DT decision support for tunnels, tunnel traffic and safety management, and tunnel assessment and diagnosis. However, the research concerning the layered approach to applying DT was insufficient. While not all studies explicitly utilized the DT layered approach, some employed a layer-based strategy to solve problems without referencing the DT concept. Although these studies do not identify themselves as being related to DTs, they hold potential for enhancing the application of DTs. The number of layers in tunnel DT was comparatively lower than those found in road infrastructure and bridges, which typically consist of three to five layers. Similar to the DT in road infrastructure and bridges, inconsistencies in naming and the roles of different layers must be addressed when developing a comprehensive and integrated DT  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
\begin{tabular}{c}  
framework. Additionally, it is important to consider the corresponding layer components and functionalities in order to establish a robust DT framework for road management.
###### Abstract  
Accurate wind shear detection is crucial for aviation safety, especially in landing and departure. A new approach for windshear alerting is proposed and demonstrated. This approach monitors orthogonal wind components in multiple runways using single coherent Doppler wind lidar (CDWL). First, the two orthogonal components of the wind field are retrieved from radial speed by an updated variational method. Then, the heading wind and cross wind on different runways are calculated simultaneously, without the location restriction of the single lidar. Finally, a windshear alerting message is generated through quantitatively evaluating the distribution of shear ramps over the monitoring area. The new CDWL-based approach for windshear alerting is implemented at the Beijing Daxing International Airport. The retrieved horizontal wind from the lidar is consistent with that from anemometers. Thanks to its high spatial/temporal resolution, some meteorological phenomena of aviation hazards, including microburst, windshear, gust front, and vortex are well captured. Particularly, all 10 windshear cases reported by crews are successfully identified during the windshear verification experiment, demonstrating the effectiveness and robustness of the new approach.  
coherent Doppler wind lidar; windshear; 2D wind field; aviation safety 2022141626. [[https://doi.org/10.3390/rs14071626](https://doi.org/10.3390/rs14071626)]([https://doi.org/10.3390/rs14071626](https://doi.org/10.3390/rs14071626))
## 4 Result and Discussion
### Error Recognition
Another interesting result from the analysis of this table is the effect of the projector light on VL53L1X. This sensor measured a higher distance from the estimations of the HC-SR04 and the steel measurement pattern measurements when the projector was on. Errors of faulty sensors can easily affect the overall performance of data acquisition. Consequently, errors should be removed in postprocessing evaluations. If a sensor shows dissonance among other ones, the data output of the faulty sensor or sensors should be deleted and the sensor replaced. During long-term monitoring, when the health status of a sensor that is a part of the sensor combination is suspicious, the data of that sensor can be removed. The overall accuracy and resolution are decreased by removing a sensor from the sensor combination, but the system still works at a lower resolution until the faulty part is replaced. If a traditional single sensor is used for the same purpose, however, its health status is unlikely to be evaluated because there is no other sensor to compare to its results. In such cases, no data can be acquired until the traditional sensor is identified and replaced. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Steel**} & \multirow{2}{*}{**HC-SR04**} & \multirow{2}{*}{**VL53L1X**} & \multirow{2}{*}{**VL53L1X Light**} & \multirow{2}{*}{**VL53L0X**} & \multirow{2}{*}{**VL53L0X Light**} \\ \multicolumn{2}{c}{**Pattern**} & & & & & & & & & \\ \hline
**(mm)** & **Averaged** & **STDEV** & **Averaged** & **Averaged** & **STDEV** & **Averaged** & **(mm)** & **STDEV** \\ \hline
290.0 & 302.2 & 0.9 & 301.7 & 5.1 & 303.5 & 6.0 & 319.1 & 7.3 & 319.5 & 8.3 \\
340.0 & 348.4 & 1.1 & 347.7 & 5.8 & 350.5 & 7.3 & 367.2 & 8.8 & 368.0 & 10.3 \\
440.0 & 448.1 & 1.3 & 448.2 & 7.8 & 452.8 & 11.4 & 471.5 & 13.2 & 475.7 & 18.7 \\
540.0 & 547.8 & 1.4 & 550.8 & 10.0 & 557.7 & 13.8 & 575.0 & 18.1 & & \\
640.0 & 646.9 & 1.5 & 651.4 & 13.2 & 660.8 & 21.4 & 679.7 & 24.4 & & \\
740.0 & 746.1 & 1.9 & 755.9 & 15.4 & 772.7 & 30.9 & & & \\
834.0 & 838.4 & 2.0 & 853.3 & 19.2 & 876.6 & 40.5 & & & \\
940.0 & 945.6 & 2.2 & 980.3 & 22.7 & 1009.4 & 52.1 & & & \\
1040.0 & 1047.7 & 2.7 & 1096.5 & 26.5 & 1126.0 & 60.3 & & & \\
1140.0 & 1147.7 & 3.0 & 1214.1 & 30.5 & 1244.2 & 71.6 & & & \\
1170.0 & 1176.7 & 3.1 & 1241.3 & 30.0 & 1270.4 & 75.1 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The normalized values of similar sensors.
# Machine Learning for Predicting Neurodevelopmental Disorders in Children
## 2 Materials and Methods
### Analysis Workflow  
Our analysis workflow has been divided into the following fundamental phases to achieve accurate and meaningful results.  
In the first phase, we paid particular attention to data preprocessing, a crucial step to ensuring data quality and cleanliness. During this phase, we handled any missing values, removed outlier data, and standardized variables to make the data consistent and homogeneous.  
Next, we performed a cluster analysis in the second phase to group similar variables into homogeneous clusters. This procedure allowed us to identify patterns and structures within the data, revealing any groupings of participants with similar characteristics.  
In the third phase, we conducted a reliability analysis to assess the consistency and reliability of our variables' measurements. This analysis allowed us to verify the stability of the measures over time and ensure the validity of the obtained results.  
Subsequently, in the fourth phase, we engaged in a factor analysis on the initial 13 variables, which led to the identification of five latent factors. These factors precisely aligned with the clusters identified in the cluster analysis, providing deeper insights into the underlying dimensions of our data.  
After completing the factor analysis, we moved on to the fifth and final phase of our workflow, where we developed a predictive mdel based on machine learning techniques. Using the logistic regression model, we predicted the presence of an ND based on the latent factors identified in the cluster analysis. This prediction model allowed us to obtain accurate and clinically relevant results, providing valuable insights for early diagnosis and treatment of NDs. The methodological workflow is illustrated in Figure 1.  
Through this comprehensive and in-depth analysis workflow, our study aims to provide a deeper understanding of NDs and develop a precise and reliable predictive model for their identification. The results obtained could significantly contribute to clinical practice and neurological research, enabling the early detection of at-risk subjects and providing targeted and personalized support for their well-being and development.
# Optimization of Multi-Mission CubeSat Constellations with a Multi-Objective Genetic Algorithm
###### Abstract  
The increasing demand for low-cost space-borne Earth observation missions has led to small satellite constellation systems development. CubeSat platforms can provide a cost-effective multiple-mission space system using state-of-the-art technology. This paper presents a new approach to CubeSat constellation design for multiple missions using a multi-objective genetic algorithm (MOGA). The CubeSat constellation system is proposed to perform multi-missions that should satisfy global Earth observation and regional disaster monitoring missions. A computational approach using a class of MOGA named non-dominated sorting genetic algorithm II is implemented to optimize the proposed system. Pareto optimal solutions are found that can minimize the number of satellites and the average revisit time (ART) for both regional and global coverage while maximizing the percentage coverage. As a result, the study validates the feasibility of implementing the CubeSat constellation design with an acceptable level of performance in terms of ART and percentage coverage. Moreover, the study demonstrates CubeSat's ability to perform a multi-missions.  
CubeSat; multi-mission; constellation; multi-objective genetic algorithm Article
# A Sparse Algorithm for Computing the DFT Using Its Real Eigenvectors
## 5 An Example with 5-Point DFT
### Inverse DFT  
As mentioned in Section 3, the eigenvectors for the inverse operation can be constructed from (19) by interchanging the eigenvectors corresponding to the eigenvalues \(j\) and \(-j\). Thus, the eigenvector matrix for the 5-point IDFT is  
\[V_{\text{IDFT}}=\left[\begin{array}{ccccc}0.851&0&0.526&0&0\\ 0.263&0.5&-0.425&0.68&0.193\\ 0.263&-0.5&-0.425&0.193&-0.68\\ 0.263&-0.5&-0.425&-0.193&0.68\\ 0.263&0.5&-0.425&-0.68&-0.193\end{array}\right]\]  
If \(y\) is taken from (20), \(\tilde{y}=V_{\text{IDFT}}y\).  
\[\tilde{y}=\left[\begin{array}{c}-0.387\\ -1.5\\ 3.178\\ 0.294j\\ -1.554j\end{array}\right]\]  
If \(\hat{Y}=V_{\text{IDFT}}\cdot\text{diag}(\tilde{y})\),  
\[\hat{Y}=\left[\begin{array}{ccccc}-0.329&0&1.671&0&0\\ -0.102&-0.75&-1.352&0.12j&-0.3j\\ -0.102&0.75&-1.352&0.057j&1.057j\\ -0.102&0.75&-1.352&-0.057j&-1.057j\\ -0.102&-0.75&-1.352&-0.12j&0.3j\end{array}\right]\]  
The eigenvalue multiplicity for the 5-point IDFT is \(\{2,1,1,1\}\) for eigenvalues \(\{1,-1,j,\)\(-j\}\). Summing the columns of \(\hat{Y}\) using the information from the eigenvalue multiplicity, we get  
\[x=\hat{y}_{0}+\hat{y}_{1}-\hat{y}_{2}+j(\hat{y}_{3}-\hat{y}_{4})\]  
where \(\hat{y}_{k}\) is the \(k^{\text{th}}\) column of \(\hat{Y}\). Thus,  
\[x=\left[\begin{array}{c}-2\\ 0\\ 3\\ 1\\ 1\end{array}\right]\]  
This is the signal that we started with while computing the forward DFT.
# Semi-Empirical Satellite-to-Ground Quantum Key Distribution Model for Realistic Receivers
## Appendix A Derivation of Equation (2)
### Derivation of Equation (3)  
In this section, we show how to obtain the relation between the elevation angle and the distance \(d\), that we used in the communication link geometry study of Section 2.  
Considering \(\triangle TGS\) (Figure 1) we can write the following equation:  
\[\frac{sin(\alpha/2+\theta_{EI})}{h}=\frac{\sin\left(90+\alpha/2\right)}{d} \tag{13}\]  
The result of simplifying this relation is:  
\[\left[\frac{h}{d}-\sin\theta_{EI}\right]\cos\frac{\alpha}{2}=\cos\theta_{EI} \cdot\sin\frac{\alpha}{2} \tag{14}\]  
Inserting the second Equation (10) in the Equation (14) we obtain the following formula:  
\[\left[\frac{h}{d}-\sin\theta_{EI}\right]\cos\frac{\alpha}{2}=\frac{R}{d}\sin \alpha\cdot\sin\frac{\alpha}{2} \tag{15}\]  
That turns into:  
\[h-d\sin\theta_{EI}=R\cdot\left(1-\cos\alpha\right) \tag{16}\]  
Using the relation \(R=R_{E}+h\) according to Figure 1, it can be expressed as follows:  
\[R_{E}+d\sin\theta_{EI}=R\cdot\cos\alpha \tag{17}\]  
Hence, the first Equation (10) and the Equation (17) can be reduced to the following expression:  
\[d^{2}+d\cdot 2R_{E}\sin\theta_{EI}+R_{E}^{2}-R^{2}=0 \tag{18}\]Then, by solving the quadratic equation, we obtain the result:  
\[d=-R_{E}\sin\theta_{EI}+\sqrt{{R_{E}}^{2}\sin^{2}\theta_{EI}+h^{2}+2R_{E}h} \tag{10}\]