esa-sceva/satcom-chunk-collection huggingface dataset:
# InnCube--A Wireless Satellite Platform to Demonstrate Innovative Technologies
## 3 Spacecraft Overview
### Thermal-System  
In general, InnoCube's thermal control design has to keep the satellite's internal temperature at least within the range of \(-40\) to \(+85\)\({}^{\circ}\)C. This range derives from the extensive use of industrial grade COTS components for the internal electronics. External units (such as the sun-sensors) may be subjected to much more extreme temperatures and have to be considered separately later in the development process. The internal battery has more strict thermal requirements depending on its current operational state: Charging and discharging the Li-Ion batteries are constraint to a tighter margin in their operational temperature range in order to prevent damage or degradation. For the used Panasonic 18,650 cells charging requires 0 to \(+45\)\({}^{\circ}\)C while discharging allows \(-20\) to \(+60\)\({}^{\circ}\)C, considering the anticipated charge and discharge currents provided by the power analysis [17].  
Typically, CubeSat architectures already provide a reasonable passive thermal design baseline which can be optimized using suitable thermal coatings as finishes [18; 19]. Since more than 85% of the external surfaces are covered by solar cell panels, these finishes are only applicable on the end faces and to some degree on the remaining structural elements. Because of the very low power dissipation of the selected integrated circuits and other electrical components, internal contributions to the thermal energy balance are anticipated to be relatively low. Notable contributions are only expected to be the ADCS actuator systems (magnetorquers and reaction wheels) as well as the UHF transmitters. Hence, placing the battery in the center of the satellite near the ADCS and Tx/Rx hardware is considered advantageous. However, moving the Up/Downlink hardware away from its current location at the end of the satellite near the antenna is currently unsuitable and has to undergo additional considerations. Therefore, using an active heating system in order to keep the battery in its thermal operation range is being considered. The selected battery  
Figure 9: Modular structure showing subsystem integration.  
pack already has an integrated autonomous heater system which will be used to heat the battery if necessary [11]. Thermal analyses with finite element methods (FEM) and the thermal analysis software ESATAN-TMS (Thermal Modeling Suite) using the final design of the satellite has to be conducted in order to verify the given thermal condition later in the development.
## 3 Synthesis of Amine-Functionalized Silica/Organosilica Membranes  
In general, amine-functionalized silica-based membranes are mainly fabricated via either (i) direct sol-gel processing from amine-functionalized silica precursors [6; 7; 30] or (ii) post-grafting or impregnation of mesoporous silica membranes [11; 12; 13]. In addition, the chemical vapor deposition (CVD) method is also sometimes used for the direct deposition of amine-functionalized silica precursor on the substrates, which suggests advantages to prepare defect-free membranes compared with the sol-gel route [10]. However, this method consists of a thermal decomposition step for the silica precursor at higher temperatures (generally \(>400^{\circ}\)C) prior to the deposition process, which could result in the degradation of most amine groups.
###### Abstract  
With the development of unmanned aerial vehicles (UAVs) and global navigation satellite system (GNSS), the accurate camera positions at exposure can be known and the GNSS-assisted bundle block adjustment (BBA) approach is possible for integrated sensor orientation (ISO). This study employed ISO approach for camera pose determination with the objective of investigating the impact of a good sensor pre-calibration on a poor acquisition geometry. Within the presented works, several flights were conducted on a dike by a small UAV embedded with a metric camera and a GNSS receiver. The multi-lever-arm estimation within the BBA procedure makes it possible to merge image blocks of different configurations such as nadir and oblique images without physical constraints on camera and GNSS antenna positions. The merged image block achieves a better accuracy and the sensor self-calibrated well. The issued sensor calibration is then applied to a less preferable acquisition configuration and the accuracy is significantly improved. For a corridor acquisition scene of about 600 m, a centimetric accuracy is reached with one GCP. With the provided sensor pre-calibration, an accuracy of 3.9 cm is achieved without any GCP.  
photogrammetry; sensor calibration; GNSS; integrated sensor orientation +
Footnote †: This paper is an extended version of our paper published in [PERSON]; [PERSON]; [PERSON]; [PERSON] GNSS-assisted accurate corridor mapping with small UAV. In Proceedings of the Conference Francaise de Photogrammetrie et Teledetection (CFPT), Paris, France, 25-28 June 2018.
## 3 Results and Analysis
### Evaluation of Soil Nutrient Status by LightGBM Model  
#### 3.3.1 Confusion Matrix  
A confusion matrix, also known as an error matrix, is a standard format for expressing accuracy evaluation in integrated algorithm model and is also a method for judging the classification of algorithm model [36]. It is a matrix in which rows represent actual classes and columns are prediction classes. In the calculation process of the LightGBM model, 1038 samples and 9 nutrient factors in Baise, Hechi and Hezhou City were taken as a data set, and 70% of the samples were taken as the training sample set and 30% as the validation sample set according to the ratio of 7:3, for multi-classification prediction.  
As shown in Figure 4, 127 of 134 samples in the class I nutrient state were predicted to be true, with a validation rate of 94.8%. 34, 37, 61 and 35 samples in class II, III, IV and V were predicted to be true, with a validation rate of 91.9%, 92.5%, 92.4% and 94.6%, respectively. Of the 312 validation sample sets in the five nutrient status levels, 294 were verified as true categories, with an overall accuracy of 94.2%. The model has a small calculation error and high classification accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Nutrient** & **Ph** & **Organic** & **Total** & **Available** & **Available** & **Exchangeable** & **Exchangeable** & **Available** & **Available** \\
**Factors** & **Value** & **Matter** & **N** & **K** & **P** & **Cs** & **Mg** & **B** & **Zn** \\ \hline pH & 1 & & & & & & & & \\ value & 1 & & & & & & & & \\ Organic & 0.341 ** & 1 & & & & & & & \\ matter & 0.323 ** & 0.588 ** & 1 & & & & & & \\ N & -0.107 & -0.133 * & -0.182 ** & 1 & & & & & \\ K & -0.107 & -0.133 * & -0.182 ** & 1 & & & & & \\ Available & 0.092 & 0.201 ** & 0.031 & 0.042 & 1 & & & & \\ P & 0.092 & 0.201 ** & 0.031 & 0.042 & 1 & & & & \\ Exchangeable & 0.631 ** & 0.525 ** & 0.411 ** & -0.198 ** & 0.078 & 1 & & & \\ Exchangeable & 0.479 ** & 0.387 ** & 0.316 ** & 0.052 & 0.099 & 0.563 ** & 1 & & \\ Available & -0.356 ** & -0.005 & -0.002 & 0.031 ** & 0.075 & -0.330 ** & -0.082 & 1 & \\ Available & 0.078 & 0.204 ** & 0.213 ** & -0.051 & 0.237 ** & 0.099 & 0.041 & 0.060 & 1 \\ \hline \end{tabular} Note.: * \(p\) < 0.05, **_p_ < 0.01.  
\end{table}
Table 4: Correlation analysis of tobacco planting soil.  
Figure 4: Prediction results of the LightGBM model confusion matrix.
## 3 Results and Discussion
### Assessment of the Technical Condition of the Pipeline under Operational Conditions  
In order to determine the areas of damage to the inner surface of the pipeline walls during operational loads, a methodology that takes into account the effect of the hydrogen environment has been developed. To implement this approach, a special test chamber made of 17G1C steel with different wall thicknesses and loaded with internal pressure was developed from 17G1C steel (Figure 6).  
When loaded with internal pressure from hydraulic unit 4, the walls of the sample of sections I and II are deformed. The method involving the optical-digital speckle correlation of images is used to register the number of movements and determine the strain of the walls [20]. When the sample is loaded with internal pressure, the amount of local strain is determined on the basis of 0.5 mm by using a video camera in sections I and II with different wall thicknesses, and by using a computer. The amount of specific strain energy  
Figure 5: Change in the specific fracture energy of 17 KhH1C steel samples pre-deformed to 60% of the limit strains at different concentrations of hydrogen \(C_{H}\).  
Figure 6: Test chamber for internal pressure loading P: 1—camera, 2—nut, 3—gasket, 4—hydrostation UNGR-2000, 5—manometer, 6—video camera, 7—computer.  
is determined by the amount of strain in the two sections with wall thicknesses \(t_{1}\) and \(t_{2}\), using the true fracture diagram of a tubular sample made of 17G1C steel in air (Figure 2). A similar procedure for determining strains is carried out after reducing the wall thickness by boring the inner surface.  
Table 1 presents the results of the experimental studies conducted on tubular samples with varying wall thicknesses. For each wall thickness ratio, five samples were tested, with a result error margin of up to 5%.  
When determining the magnitude of local plastic deformations in the material of the pipeline through which a hydrogen mixture is transported using an optical-digital correlator, hydrogen saturation occurs in the wall.  
To establish the stress-strain state, it is necessary to use the true fracture diagram of 17G1C steel, taking into account the previous elastic-plastic strain (Figure 3).
## 1 Introduction  
For decades, end-stage osteoarthritis of the hip has been successfully treated with metal-on-polyethylene (MoP) total hip arthroplasty (THA) [1, 2, 3]. A major limitation, especially in young patients, is the polyethylene wear particles produced by the bearing surfaces and associated with osteolysis and aseptic loosening of the implant [4, 5, 6, 7]. Metal-on-metal (MoM) THA and hip resurfacing arthroplasty (HRA) were expected to lower dislocation rates and increase range-of-motion and functional capacity as the femoral component mimic the natural human anatomy [8]. MoM THA was previously recommended in younger patients. Furthermore, the MoM THA/HRA was hoped to enable patients to return to pre-osteoarthritic potential. However, due to concerns regarding adverse reaction to metal debris (ARMD), the success for MoM hip articulations was short-term [9, 10, 11, 12, 13, 14]. Thus, the use of MoM THA/HRA has fallen to \(<\)1% [10, 15]. Studies have investigated the short-term differences in patient-reported outcomes, gait patterns, and performance-based tests between patients undergoing MoM THA/HRA and patients undergoing MoP THA [16, 17, 18, 19, 20]. A few of these studies suggest that a better anatomical preservation and a larger femoral head may lead to superior outcomes in performance-based tests [20; 21]. However, it is unclear if the potential benefits of MoM THA/HRA with large femoral heads contribute to superior outcome in performance-based tests compared to MoP THA with smaller femoral heads. Furthermore, mid- and long-term results are missing. We reported that even young, active patients undergoing MoM THA had not fully regained muscle mass or muscle power at 5-7 years postoperatively [22]. Additionally, it is unclear whether patients with MoM hip articulations achieve a better recovery of muscle mass and leg power compared to patients with MoP THA at mid- and long-term follow-up. Overall, we wanted to investigate if MoM THA/HRA contributes to superior functional outcomes compared to MoP THA at mid- to long-term follow-up. We investigated three hypotheses: (1) muscle mass and muscle power are higher in patients undergoing MoM THA/HRA compared to patients undergoing MoP THA, (2) the inter-limb difference in muscle mass and muscle power is smaller in patients undergoing MoM THA/HRA compared to patients undergoing MoP THA, and (3) patients undergoing MoM THA/HRA experience less step test asymmetry compared to patients undergoing MoP THA.
## 2 Related Works  
The predictive model holistically uses academic and non-academic characteristics. Most prediction models have used a combination of different domains; however, not much research has been focused on improving the accuracy of models with specific domains. In the study of [PERSON] et al. [14], model accuracy was compared when using both academic and non-academic data and only academic data. It has been shown that the combination of both academic and non-academic data can obtain better results for prediction models. The highest F1 Score obtained using all parameters is 93.8% (using the Random Forest classifier), which is much higher than the maximum F1 Score obtained using academic parameters only, which is 79.6% (using Logistic Regression, multilayer perceptron, and voting meta classifiers). The study of [PERSON] et al. [12] found that having more than one domain combination obtains better results than when only one domain is used. Domains that exist in the dataset are demographic, academic, behavioral, and additional characteristics. This research analyzes different domain combinations and the accuracy obtained by the prediction model. This study also shows that one domain by itself, without the combination of other domains, does not obtain high accuracy. The model obtained the highest accuracy when using a combination of academic, behavioral, and additional characteristics. The model obtained an accuracy of 75.47% with that combination. Referring to Table 1, most studies have evaluated student performance with their scores. Student performance has been evaluated by predicting the final cumulative grade point average (CGPA) [15; 18; 19]. In addition, some studies predict students categorically according to their scores, i.e., high or low performance [20; 21] or high, medium, and low performance [12; 15; 19; 22] pass or fail [23; 24]. In the study of [PERSON] et al. [25], student performance was evaluated by the mean score of the course and the range of student positions in the course, which was divided into five categories, namely those who scored the top 20% as the first category and the last 20% as the fifth category. Next, in the study of [PERSON] et al. [23], student performance is defined by the student's academic standing at the end of the year, whether the student passes, fails, fails conditionally, or has to repeat the semester/year. Meanwhile, there are some studies evaluating student performance and whether students complete the course [14] or their studies [3; 16]. Data mining methods can be divided into classification and clustering. Commonly used algorithms are shown in Table 2. For classification, the most commonly used are naive bayes (NB), decision tree (DT), neural network (NN), ensemble learning, SVM, and Logistic Regression (LR). Ensemble learning consists of Random Forest, bagging, voting, and boosting. Most studies have tried more than one algorithm for the prediction model and made a comparison of the accuracy of those algorithms. Meanwhile, there have also been studies that prioritize the improvement of one algorithm or the importance of
# Design of a Dual-Path Speech Enhancement Model
## 3 Proposed Multi-Branch SE Model
### Overall Architecture  
Figure 2 illustrates the overall architecture of the proposed Dual-Path Complex Nested U-Net (DPCNU) model. It consists of two parallel processing paths--a main path and an auxiliary path--that collaboratively enhance noisy speech signals.  
The enhancement process begins with the short-time Fourier transform (STFT) module, which converts the noisy time-domain signal \(y(t)\) into a complex-valued spectrogram \(Y(k,l)\). This spectrogram serves as the input for both the major and auxiliary paths, where each path extracts distinct yet complementary features through its respective encoder-bottleneck-decoder structures.  
The main path consists of one encoder (Encoder 1), one bottleneck (Bottleneck 1), and two decoders (Decoder 1 and Decoder 2). This path plays the primary role in enhancing the speech signal by producing two separate estimates: (1) A noise suppression mask \(\bar{M}(k,l)\), which is multiplied element-wise with \(Y(k,l)\) to generate a denoised spectrogram. (2) A spectral estimate \(\bar{S}(k,l)\), which directly represents an enhanced version of the target signal. The processed spectrograms are then transformed back into the time domain, producing \(\bar{x}_{M}(t)\) and \(\bar{x}_{S}(t)\).  
Figure 2: Overall architecture of proposed multi-branch SE model called DPCNU.  
The auxiliary path consists of a separate encoder (Encoder 2), bottleneck (Bottleneck 2), and decoder (Decoder 3). Unlike the main path, it focuses solely on estimating a noise suppression mask \(\tilde{M}(k,l)\). This mask is also multiplied element-wise with \(Y(k,l)\) to generate a filtered spectrogram, which is then transformed back into the time domain, producing \(\hat{x}(t)\). The auxiliary path serves as a complementary mechanism, refining noise suppression by capturing additional spectral cues.  
During training, the main and auxiliary paths are trained independently. Training the two paths separately allows each to concentrate on a distinct aspect of the enhancement process, enabling the model to capture complementary information more effectively. However, during inference, their outputs are combined through element-wise summation and averaging, resulting in the final enhanced speech signal \(\hat{x}(t)\). This fusion strategy effectively balances noise suppression and spectral integrity by leveraging the complementary strengths of both paths. More details regarding the design motivation and performance analysis are presented in Section 5.1.
# Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey
## 6 Conclusions  
We investigated the evolutionary advancements in FAS algorithms over the past decade, focusing on the significant achievements and scientific trends of DL-based FAS algorithms in the last five years. Early successful attempts focused on constructing efficient classifiers through robust, handcrafted features. Since 2014, there has been a shift from traditional, handcrafted features to modern, data-driven neural network models. Around 2018, research shifted towards domain generalization, including robustness and generalization to unknown domains and attacks. According to statistical results from this review, research on generalization has achieved significant breakthroughs and has expanded to cover various types of attacks and datasets. However, current research still primarily focuses on single-modal (RGB) photo and replay video attacks. To successfully deploy FAS models in more complex scenarios, such as cross-domains, cross-modalities, and mobile device environments, further and deeper investigations are still required. Finally, we analyze the current issues and future research trends in face anti-spoofing algorithms, providing references for forensic research and development within the face community.  
Conceptualization, H.X. and S.Y.T.; methodology, S.Y.T. and F.Q.; formal analysis, Y.J.; writing--original draft preparation, H.X.; writing--review and editing, H.X., S.Y.T. and F.Q.; visualization, S.Y.T. and F.Q.; supervision, S.Y.T. and F.Q. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the Fundamental Research Grant Scheme under grant number FRGS/1/2022/ICT10/UKM02/2; in part by the Ministry of Education Malaysia and Natural Science Research Project of Anhui Educational Committee Key Project, grant number 2024 AH051339; the Young and Middle-aged Teacher Development Action Project of Anhui Province--Outstanding Young Teacher Training Project, grant number YQYB2024064; and the Natural Science Research Project of Anhui Educational Committee Key Project, grant number 2023 AH052100.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
We would like to acknowledge Mixed Reality and Pervasive Computing Lab for providing access to their facilities and technical support throughout this research.  
The authors declare no conflicts of interest.
# Quality Assessment of DSMs Produced from UAV Flights Georeferenced with On-Board RTK Positioning
## 4 Discussion
### RTK versus NRTK  
It is of practical interest to find out whether, ceteris paribus, the camera station and the ground coordinate accuracies depend on using a local master or a CORS Network to relay the differential corrections. It has already been clarified that a local master is certainly a safer and better choice whenever possible. From an empirical standpoint, both the AAT-only and the AAT with one GCP should be considered, though the discussion above shows that AAT-only is not reliable, especially in elevation. However, as the CORS Network closest station is located just 6 km from the test site, this is too small a distance to really test whether the accuracy levels of the Network and of the local master are critically different. Restricting the comparison to the AAT with one GCP, only a small difference can be noticed between the average RMSE XY of the RTK flights in **RTK+1 GCP** configuration (2.4 cm)  
Figure 7: Mean values (**a**) and standard deviations (**b**) of the DSM error for each flight and each block control configuration measured on about 1600 ground checkpoints.  
against the NRTK flights in the same configuration (2.7 cm); a slightly larger difference is found in elevation, with RMSE Z values of 2.7 cm and 3.7 cm, respectively.  
Keeping the size of the dataset and the limited distance from the closest CORS station in mind, no statistically significant conclusions can yet be drawn. Therefore, more tests are needed in order to clarify this point.