esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
We considered first-order features based on the image gray levels histogram and second-order features based on the gray level co-occurrence matrix (GLCM) [1; 23] (Table 1). GLCM features were extracted using first-order neighbors and \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\) and \(135^{\circ}\) directions. Feature values extracted from each slice were averaged into a single value. The same averaging operation was performed for the feature values obtained along the different GLCM calculation directions (\(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\) and \(135^{\circ}\)). With these averaging operations, a single value for each feature was associated with a single DBT image set. Descriptions and mathematical details of the relevant features for this study can be found in Supplementary Materials. In our DBT system, image gray levels had 14 bits and this resolution was maintained for histogram-based features. However, resampling was needed for GLCM features to avoid the risk of excessively large and sparse matrices that would inhibit a robust statistical analysis [24; 25]. We extracted GLCM features after reducing the bits per pixel to 6 bits (MaZda default value), repeating the analysis with 5 bits and 7 bits to confirm the robustness of extracted features in relation to the bits rescaling. Figure 1: Workflow followed for texture analysis. DBT, digital breast tomosynthesis; ROI, region of interest; GLCM, gray level co-occurrence matrix. The features reported in Table 1 were extracted from the DBT images obtained with both ST (15\({}^{\circ}\) acquisition angle) and HR (40\({}^{\circ}\) acquisition angle) scan angles. All statistics comparisons were conducted considering these two paired datasets. Populations were compared with boxplots and the difference was assessed with a Wilcoxon-signed rank test (R-software version 3.5.3 Lucent Technologies, Murray Hill, New Providence, NJ, USA). A \(p\)-value < 0.05 was deemed statistically significant. Correlation between features that showed a significant difference between ST and HR images was inferred with Spearman rank correlation test. The datasets generated and/or analyzed during the current study are available from the corresponding authors on reasonable request.
## 4 Discussion
Excluding the time of sample preparation and calibration, each MALDI-ToF mass spectrometer has an estimated maximum capacity of generating approximately 620 results per day, with a potential increase if optimizing for speed of data acquisition. At this rate, only 102 MALDI-ToF machines working full-time would be required to screen the entire UK female population over 30 in one year [34]. Thus, the approach could make it feasible to screen an entire relevant population group with an initial investment for laboratory setup or hiring specialized companies that already have MALDI-ToF technology. In our work, we also optimized, tested, and compared the potential application of CHCA and SA matrices for ovarian cancer diagnostics. These have not been compared before for this purpose [16]. Our results have pinpointed better diagnostic potential for SA-derived models in comparison to the CHCA. This was unexpected as CHCA matrix is known to generate more robust spectra in comparison to SA in urine, blood, serum, and culture media [18, 20, 21, 22, 35]. Comparatively, mass spectral data acquired using SA matrix result in richer data; however, the acquisition simultaneously produces more background noise, often leading to the complex baseline that requires more sophisticated corrections [18]. This has previously been solved using automated bioinformatics pipelines and is successfully applied now for ovarian cancer mass spectral analysis [18]. There are several limitations to our study. First, our predictive models were not capable of providing an accurate classification between stages. This was evident during testing with multiple models on all data regardless of the diagnosed stage. Nevertheless, the low intra-stage specificity observed is irrelevant for screening purposes as other confirmatory methods such as TVUS offer support to further clinical diagnostics and therapy assessment [2, 4, 5]. These can be applied as a second-stage evaluation following positive scoring by inexpensive serum MALDI-ToF MS screening [2, 4]. Secondly, huge deviations in predictions can be obtained if experimental protocols, matrices, and mass spectrometer settings are not exactly followed, including sample preparation and storage. This has been observed before for other studies of data modeling using both CHCA and SA matrices [18, 19, 20, 21, 36]. The reason for this is the high precision needed for our pattern recognition. Peak widening on a mass spectrum with experimental conditions and calibrations is detrimental [18, 32, 37]. Third, the number of volunteers in different cancer stages and the control were limited for conducting more robust validations of predictive models [26]. As the numbers for stages 1 and 2 were reduced, we had to aggregate these stages into one to have statistical meaning. In an ideal scenario, the numbers of volunteers within each group should be approximately a thousand, in order to split into large training and validation subsets [26]. Consequently, the reported performance may slightly change if more data were to be tested.
## 3 Compositional Data on Date Seeds and Date Seed Coffee Products
### Macronutrients
#### 3.1.2 Carbohydrates  
Compared to date flesh, date seeds only contain a small amount of soluble sugars, but a rather high amount of fiber [26; 27; 28]. A total of 16-35% of the total mass consists of glucose, fructose, raffinose stachyose, sucrose, and galactose [26].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Material/Date Variety** & **Raw/Roasted** &
\begin{tabular}{c} **Carbohydrates** \\ **(Fiber)** \\ \end{tabular} & **Ash** & **Fat** & **Protein** & **Reference** \\ \hline Date seeds [2; 3] & Roasted & 84.3 (29.7) & 1.4 & 8.5 & 5.8 & [9] \\ _Hillavey_ (Iraq) [1] & Raw & 80.5 (29.1) & 1.5 & 5.7 & 6.4 & [29] \\ _Saidy_ (Egypt) [2] & Raw & 73.6 & 3.4 & 9.6 & 6.2 & [25] \\ _Degla-Baidha and Tafezoune_ [1] & Raw & - & - & 5.5 & - & [30] \\ _Deglet Nour_, _Allig_ (Tunisia) [2] & Dried at 50 \({}^{\circ}\)C & 83.1; 81.0 & 1.1 & 10.2; 12.7 & 5.6; 5.2 & [11] \\ Summary: 23 date varieties [1] & n.a. & 70.9–86.9 & 0.9–1.2 & 5.0–12.5 & 2.3–6.9 & [31] \\ _Red Sayer_ (Emirates) [2; 3] & Roasted at & 78.5 & 1.2 & 7.3 & 8.6 & [32] \\ _Kabkab_ (Iran) [1] & Raw; Date seed flour & 77.3 (22.1) & 1.1 & 9.6 & 5.5 & [33] \\ _Soukari_ (Saudi Arabia) [1; 3] & Raw; & & & & & & \\ _P. canariensis_ (Emirates) [2] & Dried at 60 \({}^{\circ}\)C for 24 h & 72.6 & 1.2 & 10.4 & 5.7 & [23] \\ Roasted seeds (Oman) [1; 3] & Roasted at 220 \({}^{\circ}\)C for & 83.7 (21.4) & 1.0 & 8.1 & 7.1 & [2] \\ Roasted date seeds & & 15–20 min & & & & & \\ (commodity: India) [1; 3] & Roasted & 76 & 8.8 & 0.3 & 11.6 & [34] \\ Roasted date seeds & & & & & & \\ (Jordan) [2; 3] & Roasted & 60.8 (6.7) & 17.2 & 0.8 & 11.7 & [35] \\ Heated date seed powder & & & & & & \\ (Tunisia) [2] & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Macronutrients of date seeds, pure and roasted (g/100 g \({}^{1}\) or % fresh weight/d.w. \({}^{2}\) or roasted \({}^{3}\)).  
Different heating techniques, and heating in general, can affect the levels of macronutrients. The carbohydrate, fat, and protein values are lower after heating in the oven than after solar thermal treatment [28].
# SatScope: A Data-Driven Simulator for Low-Earth-Orbit Satellite Internet
## 2 Research Status  
### LEO Satellite Internet Architecture  
A typical LEO satellite internet system comprises two main segments [9]: the space segment and the ground segment, as illustrated in Figure 1. The ground segment encompasses backbone routers, ground stations, end-user equipment, and control centers for both satellite operations and network management. The space segment primarily consists of satellites.  
End-users worldwide connect to LEO satellites to access the satellite internet service. Satellites function as data relays within the communication network, leveraging their robust capabilities for signal reception, processing, and retransmission. This enables global communications, cross-regional data transfers, and various applications, providing reliable and efficient communication services. Ground stations act as the interface between satellites and terrestrial networks, handling data transmission, reception, and processing. The backbone ground network is interconnected via fiber optics. The satellite operation control center oversees satellite monitoring and control, whereas the network management center manages and optimizes the overall operation and service quality of the satellite communication network.  
The commercial LEO satellite internet sector is experiencing rapid growth, with notable players including OneWeb [17], Starlink [18], Kuiper [19], and Iridium [20]. Among these,Starlink stands out as the LEO satellite internet system with the largest constellation of operational satellites, serving the most users across the broadest geographical area, and undergoing the most frequent updates. Founded by SpaceX in 2015, Starlink aims to establish a global internet network through the deployment of thousands of small satellites operating at altitudes between 550 and 1200 km. These satellites move at an average velocity of 7.6 km/s, resulting in continuously dynamic inter-satellite and satellite-to-ground links. This dynamic topology can compromise communication stability for end-users, leading to frequent interruptions, higher latency, and packet loss during peak times [21]. Optimization efforts for LEO satellite communication systems primarily rely on simulations to address these challenges.
# The Role of Artificial Intelligence in Sports Analytics:
## 4 Discussion  
The present review synthesizes empirical evidence on the application of artificial intelligence (AI) in sports performance analysis. The compiled data encompass studies across 13 sports disciplines, employing a broad range of AI techniques, from conventional machine learning classifiers to advanced deep learning architectures and computer vision methods. The performance metrics reported, including accuracy, precision, recall, F1-score, mean absolute error, and mean average precision, reflect the multifaceted approaches used to quantify athletic performance.  
To address concerns regarding methodological heterogeneity, we conducted additional subgroup analyses stratified by AI methodology (classical ML vs. deep learning), sport type (individual vs. team sports), and overall study quality. These subgroup comparisons revealed notable differences in pooled accuracy, with deep learning models achieving markedly higher performance (92.3%) compared to classical ML (78.6%). Similarly, team sports studies and high-quality studies outperformed individual sport analyses and lower-rated studies, respectively. These findings reinforce the importance of methodological rigor and AI selection tailored to sport-specific contexts and data structures.
# Discrete-Time Asymptotic Tracking Control System for a Satellite with a Solar Panel
## 3 Plant State Space Models and Their Properties
### Properties of the Discrete-Time State Space Model
#### 3.3.4 Zeros and Invertibility of \(\Sigma_{G}^{d}\)  
As the transfer function of \(\Sigma_{G}^{d}\) is given by  
\[G^{d}(z)=\frac{L^{d}(z)}{M^{d}(z)}=\frac{C^{d}\text{adj}\,(zI-A^{d})B^{d}+ \det(zI-A^{d})D^{d}}{\det(zI-A^{d})}\,, \tag{53}\]  
then the zeros of \(\Sigma_{G}^{d}\) are the roots \((z_{l})_{l=1}^{l=2}\) of the polynomial  
\[L^{d}(z)=C^{d}\text{adj}\,(zI-A^{d})B^{d}+\det(zI-A^{d})D^{d}\,. \tag{54}\]  
Using (52), they can be computed as follows:  
\[z_{l}(\Sigma_{G}^{d})=\frac{\mu+s_{l}(\Sigma_{G})}{\mu-s_{l}(\Sigma_{G})}\,, \quad l=1,2\,, \tag{55}\]  
where \((s_{l}(\Sigma_{G}))_{l=1}^{l=2}\) are the zeros of \(\Sigma_{G}\), given by (18).  
Furthermore, for all \(\mu>0\), the feedthrough term \(D^{d}(\mu)\
eq 0\), which means that it is invertible. This easily follows from the following expression:  
\[D^{d}(\mu)=C(\mu I-A)^{-1}B=\frac{C\,\text{adj}\,(\mu I-A)B}{\det(\mu I-A)}= \frac{\frac{1}{l}(\mu^{2}+\frac{b}{p}\mu+\frac{k}{p})}{\mu^{2}(\mu^{2}+(\frac{ b}{p}+\frac{b}{l})\mu+\frac{k}{p}+\frac{k}{l})}\,. \tag{56}\]  
The existence of the inverse \((D^{d})^{-1}\) implies that the system \(\Sigma_{G}^{d}\) also has an inverse \((\Sigma_{G}^{d})^{-1}\) with the state space matrix:  
\[\left[\begin{array}{c|c}A^{d}-B^{d}(D^{d})^{-1}C^{d}&B^{d}(D^{d})^{-1}\\ \hline-(D^{d})^{-1}C^{d}&(D^{d})^{-1}\end{array}\right]. \tag{57}\]The transfer function of the inverse system \((\Sigma_{G}^{d})^{-1}\) has the following form:  
\[\begin{split}&(G^{d}(z))^{-1}=\frac{M^{d}(z)}{L^{d}(z)}\\ &=\frac{\det(zI-A^{d})}{C^{d}\text{adj}\,(zI-A^{d})B^{d}+\det(zI- A^{d})\text{D}^{d}}\\ &=\frac{-(D^{d})^{-1}C^{d}\text{adj}\,(zI-A^{d}+B^{d}(D^{d})^{-1} C^{d})B^{d}(D^{d})^{-1}+\det(zI-A^{d}+B^{d}(D^{d})^{-1}C^{d})(D^{d})^{-1}}{ \det(zI-A^{d}+B^{d}(D^{d})^{-1}C^{d})}\,.\end{split} \tag{58}\]  
As the polynomials in the denominators coincide, we see that  
\[C^{d}\text{adj}\,(zI-A^{d})B^{d}+\det(zI-A^{d})D^{d}=\det(zI-A^{d}+B^{d}(D^{d}) ^{-1}C^{d})\,, \tag{59}\]  
which means that the zeros of the system \(\Sigma_{G}^{d}\) (recall (54)) coincide with the spectrum \(\sigma(A^{d}-B^{d}(D^{d})^{-1}C^{d})\) of the main matrix of the inverse system \((\Sigma_{G}^{d})^{-1}\).
## 7 Results and Discussion
### Ablation Results  
To study the contribution of the different modules of the U-IntentCapsNet, the ablation test results are presented in Table 15. The \"U-IntentCapsNet \(w/o\) BLSTM\" used the LSTM with only a forward pass; the \"U-IntentCapsNet \(w/o\) Regularizer\" did not include the drop out = 0.3 used in the model to avoid over-fitting.  
From the results of these experiments presented in Table 15, it is clear that every module of the proposed U-Intent CapsNet played a detrimental part in improving the overall performance of the model. In the proposed model, the BLSTM significantly contributed to boosting the performance by 3.35%, and introducing regularization to avoid over-fitting also had a comparable contribution by increasing the accuracy by 1%.  
As shown in Table 16, the proposed U-IntentCapsNet architecture was designed by varying the dimensionality of the input vector \(D_{W}\) and training models with \(D_{W}=200\) and \(D_{W}=100\) dimensions. The results showed a 4% and 5% decrease in the accuracy by using  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{1}{c}{**Model**} & **Prec.** & **Recall** & **F1** & **Acc.** \\ \hline U-IntentCapsNet \(w/o\) BLSTM & 0.8735 & 0.8777 & 0.8741 & 0.8777 \\ U-IntentCapsNet \(w/o\) Regularizer & 0.9018 & 0.9048 & 0.9026 & 0.9047 \\ \hline
**U-IntentCapsNet** & **0.9083** & **0.9112** & **0.9084** & **0.9112** \\ \hline \hline \end{tabular}
\end{table}
Table 15: Intent detection results for the proposed U-IntentCapsNet model in ablation experiments.  
Figure 6: Embedding vector representation with W2V for sample two-term queries.  
200 or 100 input vector dimensions. Further experiments were conducted by varying the routing iterations, e.g., iter. to two, three and five, in the proposed model.  
Iterative routing computes and updates the coupling co-efficient \(c_{kt}\) that determines the contribution the \(t\)-th word \(w_{t}\) has with the \(k\)-th intent, \(y_{k}\). In order to determine the best number of iterations for the coupling coefficients, experiments were conducted with a different number of routing iterations of two, three, and five. The accuracy and F1 scores of the model with the varying number of iterations are given in Table 17, and the influence of the outing on the proposed model is visually presented in Figure 7. It is evident that the proposed model with three iterations converged faster and gave the best results.
## 4 Available Capacity Modeling Based on Terminal Voltage Drop
### Available Capacity Prediction Model of Battery Based on the SVM  
The steps of available capacity estimation modeling based on the SVM are as follows:  
Step 1: For the cycle life test of the No. 1 battery, obtain the health factors according to the method of Section 4.2 and adopt it as input \(x_{i}\) and adopt the discharging capacity as output \(y_{i}\).  
Step 2: Normalize the input and output data;  
Step 3: Use the cross-validation method to select the optimal parameters \(C\) and \(g\) of the regression;  
Step 4: Use the optimal parameters obtained in Step 3 to train the SVM and get the model.  
The model is used to estimate the available capacity and the corresponding SOH. The following Equations (23)-(25) are used to calculate the performance indices, included the mean squared error (MSE), squared correlation coefficient \(R^{2}\) and relative error \(Error\).  
\[MSE=\frac{1}{l}\sum_{i=1}^{l}{(f(x_{i})-y_{i})^{2}} \tag{23}\]\[R^{2}=\frac{\left(l\sum_{i=1}^{l}f(x_{i})y_{i}-\sum_{i=1}^{l}f(x_{i})\sum_{i=1}^{l} y_{i}\right)^{2}}{\left(l{\sum_{i=1}^{l}f(x_{i})}^{2}-\left(\sum_{i=1}^{l}f(x_{i}) \right)^{2}\right)\left(l{\sum_{i=1}^{l}y_{i}^{2}-\left(\sum_{i=1}^{l}y_{i} \right)^{2}}\right)} \tag{24}\]  
\[Error_{i}=\frac{\left|f(x_{i})-y_{i}\right|}{y_{i}} \tag{25}\]  
In Equations (23)-(25), \(l\) represents the total number tests,\(f\) represents the estimated available capacity of the \(i\)th test, i.e., the \(i\)th health factor and, \(y_{i}\) represents the real available capacity of the battery.
# Assessing the Potential of PlanetScope Imagery for Iron Oxide Detection in Antimony Exploration
## 5 Discussion  
### Spectral Analyses  
The spectral analyst results indicate that iron oxides are less representative than sheet silicates in the study area. Goethite was identified in four spectra and Hematite in two spectra. Despite not being so representative, mapping iron oxides is a basic method of finding hydrothermal deposits.  
Therefore, after spectral analysis results, a new spectral analysis was conducted focusing on Goethite and Hematite. Figure 3 compares both Hematite and Goethite spectra, and it is possible to observe that there is a slight shift between their absorption features and reflectance peaks. However, due to the limited spectral resolution of multispectral satellites, both minerals often fall within the same satellite bands, making it challenging to differentiate between them [46]. As a result, the same processing methods used to map Goethite can also be applied to Hematite [46]. Therefore, while the Goethite spectrum was selected for analysis, it can be used to represent both minerals in this context.
## 5 Conclusions  
In this work, a two-stage centrifugal compressor with three types of interstage pipelines used in a 120 kW HFC were analyzed using experiments and CFD methods. The internal flow field of the interstage pipeline, the overall efficiency of the compressor with different interstage pipelines, as well as the influence of bend section on the inlet distortion of downstream HPS were analyzed. The main conclusions are as follows:  
(1) Due to the influence of the bend section, the EPC model leads to strong distortion at the inlet of the HPS impeller, resulting in overall performance degradation. Affected by centrifugal force and wall viscosity, obvious secondary flow is generated in the bend section of the EPC model. Near the bend outlet, the SPC model has more uniform distribution and smaller total pressure distortion, compared with that of the EPC and TPC model. The high entropy area of the three models is located at the top of the trailing edge of the impeller, which is caused by the leakage vortex of the blade.  
(2) The inner diameter uniformity of the bend section will affect the flow stability and total pressure loss of the interstage pipeline. The more uniform the inner diameter of the bend section, the wider the high total pressure area, and the smaller the corresponding pressure loss. For the flow in impeller, as the blade height gradually decreases, the influence of the leakage vortex gradually weakens. On the main blade, the load becomes more uniform with the decrease of blade height; while for the splitter blade, the load extremum migrates to the trailing edge with the decrease of blade height.  
(3) The experimental and CFD results show that among the three models, the EPC model induces the most intense total pressure distortion at the condition of 80 \(\mathrm{kr}\)/\(\mathrm{min}\) and 130% mass flow rate, resulting in an efficiency reduction of 2 and 1.5 percentage points  
Figure 14: Three kinds of interstage pipes.  
Figure 15: Experimental of three models at different rotational speeds.  
compared with the SPC and the TPC model, respectively. The efficiency and pressure ratio of the SPC model are the best among the three models.Thus, it is recommended that the interstage pipelines similar to SPC models should be recommended in the design of two-stage centrifugal compressor.  
Conceptualization, methodology, software, validation, D.Y.; formal analysis, investigation, D.Y. and H.W.; resources, H.W.; data curation, H.Z.; writing--original draft preparation, Q.Z.; writing--review and editing, D.Y.; visualization, Q.Z.; supervision, Z.Z.; project administration, D.Y. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China grant number 52006027 and the Natural Science Foundation of Hebei Province grant number E2021501028 and the Central University Basic Scientific Research Business Fee Funding Project grant number N2123027.  
Not applicable.  
The authors declare no conflict of interest.  
The efficiency and pressure ratio
## 3 Methodology and Data Sources
### Analysis of the Impact of Individual Factors
#### 4.4.1 Impact of Meteorological Conditions on the Receptor City
Some studies also proved that the conditions at burn sites, especially emission intensity and meteorological factors, as well as transport distance, play significant roles in the regional transport of PM\({}_{2.5}\); moreover, they verified that not only local emission control, but also horizontal atmospheric transport and meteorological conditions can facilitate the cross-regional development of accurate prediction models and effective pollution control measures [54; 55].
# One-Stage Disease Detection Method for Maize Leaf Based on Multi-Scale Feature Fusion
## 2 Materials and Methods
### Anchor-Free Plant Disease Detection Algorithms
#### 2.2.2 CenterNet  
CenterNet is a detection algorithm based on key points estimation, which enables the detection of disease targets by estimating the center point or corner point. CenterNet improves on CornerNet by detecting an additional key point in addition to a pair of corner points, enhancing the ability to synthesize information about the target as a whole. As a result, CenterNet's detection speed and accuracy are considerably improved compared to the frames with both one-stage and two-stage detectors. CenterNet was proven to be applicable to plant disease detection under natural conditions. [PERSON] et al. [34] performed the detection of apples through CenterNet's detection network combined with MobileNet v3, and the detection speed and accuracy were superior to SSD. However, there is still the problem of inaccurate matching of key points in intensive targets, and the results are less satisfactory for small target diseases of plants.