esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Single Motor Hybrid Powertrain Configuration with Dual Planetary Gears
### Topology Design Method
#### 2.2.2 Mode Screening and Classification  
Equation (2) shows that if there is only one non-zero element in row \(i\) (\(i>3\)) of matrix \(T_{con}\), the corresponding node of this row can be determined to be locked. Therefore, the row can be deleted in matrix \(T_{con}\), and the row of the corresponding node in matrix \(D\) can also be deleted. The transformation process is shown in Equation (6). When the node of row \(i\) is connected to the node of row \(j\) (\(i<j\)), the element of row \(j\) is added to row \(i\), and row \(j\) is deleted in matrix \(T_{con}\) and matrix \(D\). The transformation process is shown in Equation (7).  
\[\left\{\begin{array}{l}T_{con\ i,:\;}=[\ ]\\ D_{i,:\;}=[\ ]\end{array}\right. \tag{6}\]  
\[\left\{\begin{array}{l}T_{con\ i,:\;}=T_{con\ i,:\;}+T_{con\ j,:\;}\\ T_{con\ j,:\;}=[\ ]\\ D_{i,:\;}=D_{i,:\;}+D_{j,:\;}\\ D_{j,:\;}=[\ ]\end{array}\right. \tag{7}\]  
According to Equations (6) and (7), matrix \(T_{con}\) is transformed into matrix \(T_{con}\)*, and its size is (\(3n-q\)) \(\times\)\(3n\), \(q\) is the number of clutches that lock and connect PG nodes. Similarly, matrix \(D\) is transformed into matrix \(D\)*, and its size is (\(3n-q\)) \(\times\)\(n\). According to [\(T_{con}\)*, \(D\)*]\(\cdot\)\(T\) = 0, the torque relationship of each component can be obtained as shown in Equation (8). The matrix \(S_{con}\) represents the speed relationship of the system. Solving [\(S_{con}\), \(D^{T}\)]\(\cdot\)\(W\) = 0 by applying Gauss elimination method, the speed relationship of each component can be obtained as shown in Equation (9).  
\[\left\{\begin{array}{l}T_{e}=f_{1,1}(k_{1},k_{2})T_{pg}+f_{1,2}(k_{1},k_{2}) T_{mg}\\ T_{pg}=f_{2,1}(k_{1},k_{2})T_{e}+f_{2,2}(k_{1},k_{2})T_{mg}\end{array}\right. \tag{8}\]  
\[\left\{\begin{array}{l}\omega_{e}=g_{1,1}(k_{1},k_{2})\omega_{pg}+g_{1,2}(k _{1},k_{2})\omega_{mg}\\ \omega_{pg}=g_{2,1}(k_{1},k_{2})\omega_{e}+g_{2,2}(k_{1},k_{2})\omega_{mg} \end{array}\right. \tag{9}\]  
where \(k\) is the radius ratio of the ring gear to sun gear; \(k_{i}=R_{i}/S_{i}\); \(f_{1,1}\), \(f_{1,2}\), \(f_{2,1}\), \(f_{2,2}\) are the torque coefficients; \(g_{1,1}\), \(g_{1,2}\), \(g_{2,1}\), \(g_{2,2}\) are the speed coefficients.  
The torque and speed coefficients are defined as mode characteristic parameters, and all operating modes can be classified according to mode characteristic parameters and criteria, as shown in Table 1.
# Vibration Energy Harvesting from Raindrops Impacts: Experimental Tests and Interpretative Models
###### Abstract  
The kinetic energy of raindrops is a large and renewable source of energy that nowadays can be exploited by means of piezoelectric harvesters. This study focuses on a new cantilever harvester that uses the impact of a drop on a liquid surface created on the harvester in order to improve the conversion from kinetic energy to electric energy. Experimental tests, carried out both outdoors and indoors, were performed to assess the validity of the proposed design. The voltage obtained with the impact on the liquid surface was about four times larger than the one obtained with the impact on a dry surface. The phenomena that lead to the increased performance of the harvester were analyzed both experimentally, by means of a high-speed camera, and analytically, by means of a mathematical model. The camera footage showed a clear relationship between the waveform of the generated voltage and the various phases of the impact (crown formation, crown collapse, and sloshing). The mathematical model developed herein, which was based on the oscillation of the liquid mass caused by the impact and on the linear momentum equation, is simple and can be used to estimate the measured voltage within a good approximation.  
raindrop harvester; piezoelectric; impulse vibrations Article
# Frequency-Dependent Acoustic Reflection for Soil Classification in a Controlled Aquatic Environment
###### Abstract
By systematically executing these tasks, this study aims to establish spectral acoustic analysis as a viable, scalable, and non-invasive method for soil classification in aquatic environments. The present research is conducted in a controlled laboratory environment, ensuring the precise measurement of acoustic interactions with sediment samples. The controlled setting eliminates unpredictable marine influences, allowing an accurate assessment of intrinsic spectral properties. The findings from this study will provide the first experimentally validated spectral fingerprints for sediment classification, demonstrating whether spectral-based acoustic reflectance analysis can reliably distinguish between similar sediment types. The results presented in the following sections will detail the spectral response of SP and GP soils, compare their reflection coefficients, and evaluate the effectiveness of this approach in differentiating sedimentary soils based on acoustic energy interactions. The Section 4 will critically analyze these findings to assess their implications for marine geophysics and remote seabed classification.
## OpenOrbiter: A Low-Cost, Educational Prototype CubeSat Mission Architecture  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 2, [PERSON] 1, [PERSON] 3, [PERSON] 4 and [PERSON] 1**  
1 Department of Computer Science, 3950 Campus Road, Stop 9015, Grand Forks, ND 58202-9015, USA; E-Mails: [EMAIL_ADDRESS] (C.K.), [EMAIL_ADDRESS] (A.M.), [EMAIL_ADDRESS] (D.T.)  
2 Department of Entrepreneurship, 293 Centennial Drive, Stop 8098, Grand Forks, ND 58202-8098, USA; E-Mail: [EMAIL_ADDRESS]  
3 Department of Electrical Engineering, 243 Centennial Drive, Stop 7165, Grand Forks, ND 58202-7165, USA; E-Mail: [EMAIL_ADDRESS]  
4 Department of Space Studies, 4149 University Ave, Stop 9008, Grand Forks, ND 58202-9008, USA; E-Mail: [PERSON]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-701-777-4107; Fax: +1-701-777-3330.  
_Received: 16 December 2012 / Accepted: 21 January 2013 / Published: 24 January 2013_  
**Abstract:** The preliminary design for the Open Prototype for Educational NanoSats (OPEN) demonstration spacecraft, OpenOrbiter, is presented. OPEN is designed to facilitate the formation of CubeSat development programs nationally and worldwide via providing a publically-available set of spacecraft design documents, implementation and testing plans. These documents should allow the creation of a 1-U CubeSat with a parts budget of approximately $ 5,000. This allows spacecraft development to be incorporated in regular curriculum and supported from teaching (as opposed to research) funds. The OPEN design, implemented by OpenOrbiter, has an innovative internal structure, separates payload and operations processing and includes features to ease and highlight errors in integration.  
**Keywords:** small spacecraft; CubeSat; educational NanoSat; open hardware designs; open source software; mission design
## 3 Validation of the Model
### Description of the Experimental Tests  
The experimental tests of the erosion-induced water and mud inrush disaster were performed by using a self-designed large-scale triaxial testing system (Figure 2). The experimental system consisted of a pressure control system, a particle transfer permeability testing system, data collection equipment, and water and mud collection equipment. A hollow base pedestal with a hole-based flow piston was designed for particle transfer and water flow.  
The testing soil was obtained from a fault rupture zone during the construction of tunnels (i.e., Yonglian tunnel in Jiangxi, China) [36] ([PERSON] et al. 2020). The diameter and height of the sample were 15 cm and 20 cm, respectively. The stress conductions of these tests were determined according to the burial depth of the tunnel (around \(-100\) m), namely the axial pressure of 2 MPa and the confining pressure of 1 MPa. Different water pressures were considered to investigate their influence on the evolution of water and mud inrush. The basic procedure for these tests included: (i) sample installation and saturation; (ii) application of the axial and confining loads; and (iii) application of the water pressure and data collection. The criteria for the test termination is that no particles are eroded.  
Three tests with different water pressure were conducted in this study, namely, 0.4, 0.7, and 1.0 MPa. Through these tests, the transient seepage-erosion behavior can be obtained, e.g., porosity, permeability, and water and mud inflow. More details of these experiments (e.g., experimental device and procedure, soil properties, parameter measurement) can be found in the previous studies [6; 36].  
Figure 2: Large-scale triaxial testing system [6; 36].
# Mechanisms Underlying the Changes in Sporadic E Layers During Sudden Stratospheric Warming
## 3 Results
### Mechanism of Planetary Wave Modulation on Sporadic E Layers
This nonlinear interaction principally involves the modulation of the frequency of diurnal tides. To further elucidate the nonlinear interactions between planetary waves and diurnal tides, the original wavelet spectra from the foEs data were analyzed,  
Figure 10: Similar to Figure 9, but the y-axis represents the amplitude variations in the semidiurnal tidal component (s_amp) of sporadic E layers. as depicted in Figure 12. The original foEs data, with a time resolution of 0.25 h, effectively captured the high-frequency components of the sporadic E layer. Figure 12 clearly demonstrates that the diurnal tidal components of the sporadic E layers at the three stations experienced significant increases after the transition from easterly to westerly winds, aligning with the observations presented in Figures 9 and 10. Additionally, Figure 12 highlights the presence of other frequency components within the black dashed rectangular box: both Okinawa and Yamagawa stations showed a component with a 21 h period, Yamagawa had an additional component with a 27 h period, and Kokubunji displayed a component with a 22 h period. These observations are consistent with the findings reported by the authors of reference [40]. Therefore, during SSW periods, planetary waves influence the sporadic E layers not only by modulating the amplitude of diurnal tides but also through their nonlinear interactions with these tides, affecting the frequency and structure of the sporadic E layers. Figure 11: Wavelet spectra of the diurnal tidal component amplitude (left) and semidiurnal tidal component amplitude (right) of sporadic E layers at Okinawa, Yamagawa, and Kokubunji from 9 January to 28 February 2009 (frequency range: 0.05–1). The vertical white dashed lines indicate the time when the winds shifted from easterly to westerly, and the horizontal white dashed lines represent the y-coordinates corresponding to the peak values of the enclosed structures in the figure. The y-coordinates corresponding to the horizontal white dashed lines in (**a–c**) are 19 days, 8 days, and 12.5 days, respectively; in (**d**), the upper white dashed line corresponds to 18 days and the lower white dashed line corresponds to 8 days; the y-coordinates in (**e,f**) are 7.5 days and 15 days, respectively. Figure 12: Wavelet spectra of the raw data of sporadic E layers at Okinawa, Yamagawa, and Kokubunji from 9 January to 28 February 2009. The vertical white dashed lines indicate the time when the winds shifted from easterly to westerly, and the horizontal white dashed lines correspond to the y-coordinate at 24 h.
###### Abstract  
**Purpose**: Maintaining the surface quality of ceramic restorations after clinical adjustments is critical for both aesthetic outcomes and long-term oral health, yet the optimal approach to restoring gloss and smoothness remains unclear. The purpose of this study is to investigate the effect of different surface finishing and grinding procedures on the surface gloss and roughness of three different monolithic lithium disilicate ceramics and one monolithic ultra-translucent zirconia ceramic. **Materials and Methods**: A total of 104 specimens (1.5 \(\times\) 12 \(\times\) 14 mm) were prepared from four ceramic materials: LiSi CEREC Tessera (CT), GC Initial LiSi (LS), IPS e.max CAD (EC), and zirconia disc (KATANA UTML (KAT)). Each was divided into two subgroups based on surface finishing (mechanical polishing or glazing; \(n\) = 10). Gloss and surface roughness were measured using a gloss-meter and a profilometer, respectively. One specimen per subgroup was analyzed under SEM at \(\times\)1000 magnification. **Results**: Gloss and roughness values were analyzed with the two-way robust ANOVA test and multiple comparisons were made with Bonferroni correction. The significance level was set at \(p\) < 0.05. Mechanical polishing, glazing, and repolishing increased the gloss values of the materials, with the KAT group achieving the highest gloss in the repolishing groups. The lowest gloss values were observed in the grinding groups. Additionally, these surface treatments reduced the roughness of the surface of all the materials. **Conclusions**: Surface finishing procedures significantly influenced the gloss and roughness of monolithic lithium disilicate and zirconia ceramics. Mechanical polishing systems performed similarly or better than glazing. However, selecting an appropriate polishing system for each material is essential.  
m achenical polishing; monolithic ceramics; surface finishing; surface gloss Article
# Application of Enhanced YOLOX for Debris Flow Detection in Remote Sensing Images
## 5 Discussion
### Overall Performance Analysis of RS-YOLOX-Nano
#### 5.1.3 Introducing Various Attention Mechanisms  
The YOLOX-Nano model exhibits relatively low accuracy in detecting and recognizing debris flow geological disasters, with a mean average precision (\(mAP\)) of only 82.51%. This is attributed to the complex and multifaceted nature of debris flow disaster characteristics. Different attention mechanisms can impart distinct features to the network's various channels, selectively amplifying the weight of channels associated with debris flow characteristics. The SE attention mechanism compresses global information into channel weights, effectively discerning the importance among different channels. The CA attention mechanism captures long-range dependencies along one spatial direction while preserving precise positional information along another. The CBAM attention mechanism assigns varying weights to different feature points within the same feature map, distinguishing in  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline _Mixup_ & _Mosaic_ & \(EIoU\) & **Focus** & SPP & \multicolumn{3}{c}{**PAFPN**} & \multirow{2}{*}{\(mAP_{0.5}\)/\%**} \\ \cline{5-6} \cline{8-9} \cline{7-9} \cline{10-10} \cline{10-10} \cline{5-6} \cline{  
ternal pixel points. After integrating various attention mechanisms, the model's parameter size increased from 3.71 MB to 3.83 MB and single-sample inference time rose by 18 ms, but the \(mAP\) value improved from 82.51% to 84.56%, significantly enhancing the accuracy of debris flow geological disaster detection. Using the Grad-CAM tool [27], the feature extraction layers post-attention mechanism processing were visualized [28], elucidating their impact on feature extraction. As demonstrated in Figure 11, before the introduction of attention mechanisms, the network's feature extraction from samples was somewhat arbitrary, lacking adequate focus on characteristic points in debris flow disaster areas. After the implementation of attention mechanisms, the network increasingly prioritized important feature channels during forward propagation, allowing it to focus on essential parts. This enabled the improved RS-YOLOX-Nano model to more efficiently extract features from complex images.
# Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data
## 4 Results and Discussions
### Spatial Textural Feature Analysis
#### 4.1.2 Feature Vector Selection
For serpentinite-granite and gabbro-serpentinite, which can be well distinguished when only spectral vectors are used, the increase in class separability is insignificant. The statistical results indicate that integrating spatial textural features with spectral features yields an improvement in rock separability, especially for granite and quartzose rock, which may result in improved classification accuracy. To confirm this premise, the vector combinations that achieved the most significant J-M distances were compared with the classification results obtained using only spectral vectors.
## 3 System Design
### Transmission Mechanism
#### 3.2.2 Adaptive Data Transmission at the Protocol Layer
SRT protocol also implements a predictive adaptive buffer management mechanism. The receiver's buffer size dynamically adjusts based on network jitter and packet loss patterns:  
\[BufferSize=BaseSize+\gamma\cdot\sqrt{Var(RTT)}+\delta\cdot E[Loss_{burst}] \tag{7}\]  
where \(Var(RTT)\) represents the variance of RTT, \(E[Loss_{burst}]\) is the expected value of burst loss, and \(\gamma\) and \(\delta\) are weighting coefficients. This dynamic buffering mechanism effectively balances latency and reliability requirements, ensuring smooth playback even when network conditions deteriorate.
## 4 Experiment
### Dataset and Preprocessing  
For the experiment, we used a private bulk commodity dataset, (Commodity), Eshop dataset [27], Yoochoose dataset ([[https://recsys.acm.org/recsys15/challenge](https://recsys.acm.org/recsys15/challenge)]([https://recsys.acm.org/recsys15/challenge](https://recsys.acm.org/recsys15/challenge)), accessed on 6 August 2022), and Taobao dataset ([[https://tianchi.aliyun.com/dataset/649](https://tianchi.aliyun.com/dataset/649)]([https://tianchi.aliyun.com/dataset/649](https://tianchi.aliyun.com/dataset/649)), accessed on 6 August 2022) [28; 29; 30]. These datasets are from different platforms, each with unlabeled records of many users.  
The Commodity dataset was provided by a partner company and included bulk commodity user transaction data for users from 2014 to 2015. We added the ratio of the historical price to the current price to the data. The Eshop dataset comprises click stream data of an online shopping website. We treated each connection as a user. The Yoochoose dataset is also a visit dataset from a shopping website. We only kept records with the main categories between 1 and 12. The Taobao dataset contains user behavior data, including click, star, buy, etc., from Taobao, a Chinese e-commerce platform.  
The characteristics of the datasets after preprocessing are shown in Table 1. The detailed dimensions of these datasets are shown in Table 2. We handled enumerated data using one-hot codes whenever possible and embedded only data with a large number of data types. Numeric-type data were normalized to between 0 and 1 and concatenated with the one-hot code data. The above two types of data were processed before the model was trained so that all models in the experiment could use them. Some data used as one-hot codes were also used as embedded data. Because processing embedding-type data requires a specific neural network task, only the neural network models used the embedding-type data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Dimension** & \multirow{2}{*}{**Dimension**} & \multirow{2}{*}{**Meaning**} & \multirow{2}{*}{**Range/Type**} \\  & **Type** & & & \\ \hline \multirow{6}{*}{Commodity} & Decimal & 1 & Trade hour & [0, 24] \\  & Decimal & 1 & Trade number & [1, 46, 191] \\ \cline{1-1}  & Decimal & 9 & History price ratio & [0.494, 2.029] \\ \cline{1-1}  & One-hot & 2 & Trade direction & 2 types \\ \cline{1-1}  & One-hot & 2 & Opening and liquidation & 2 types \\ \cline{1-1}  & Embedding & 1 & Commodity code & 6 types \\ \hline \multirow{6}{*}{Eshop} & Decimal & 1 & Price & [18; 82] \\  & One-hot & 4 & Main category & 4 types \\ \cline{1-1}  & One-hot & 14 & Color & 14 types \\ \cline{1-1}  & One-hot & 6 & Location & 6 types \\ \cline{1-1}  & One-hot & 2 & Model Photography & 2 types \\ \cline{1-1}  & One-hot & 2 & Price 2 & 2 types \\ \cline{1-1}  & One-hot & 5 & Page & 5 types \\ \cline{1-1}  & Embedding & 2 & Country & 47 types \\ \cline{1-1}  & Embedding & 3 & Clothing model & 217 types \\ \hline \hline \end{tabular}
\end{table}
Table 2: The dimension of datasets after preprocessing.
## 4 Corrosion Protection Measures
### Internal Anti-Corrosion Technique
#### 4.3.2 Inner Coating Technology  
Anticorrosive coatings are used not only for the exterior of steel pipelines but also for the interior of pipelines. Anticorrosive inner coatings include epoxy type, modified epoxy type, epoxy phenolic type, polyurethane type, and nylon type. They are difficult to apply due to the limitation of the inside of the pipeline and the method of preparation of the inner coating [119]. [PERSON] [120] used a special method called \"plasma sputtering\" to spray the coating. The inner coating system was applied to reactive sputter deposition on metallic tubes with inner diameters of 30 mm and lengths of 480 mm.
## 4 Results and Discussion
### Performance Metrics  
The main metrics considered for the analysis of the proposed work are discussed in this section.  
1. Precision
2. Precision is defined as the co-variance of the method, which is attained by suitably comparing cases to the overall cases. It is computed using Equation (10); \(TrP\) represents True Positive and \(FlP\) indicates False Positive. \[Precision=\frac{TrP}{TrP+FlP}\] (10)
3. Recall  
Recall is known as a production metric that finds the overall number of accurate positive groups composed of all the optimistic groups, and it is computed using Equation (11); \(TrP\) represents True Positive and \(FlN\) denotes False Negative. \[Recall=\frac{TrP}{TrP+FlN}\] (11)
4. F1-score  
This is also termed the F-measure. It is the weighted harmonic value of precision and recall. It is given in Equation (12),  
\[F1-score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{12}\]
5. Accuracy  
Accuracy can be understood as the degree of systematic organization, which can be exposed by correctly classifying the groups into the overall dataset. It is computed with the use of Equation (13), where \(TrN\) indicates True Negative, \(TrP\) represents True Positive, \(FlN\) denotes False Negative and \(FlP\) indicates False Positive. \[Accuracy=\frac{TrN+TrP}{TrP+TrN+FlN+FlP}\] (13)
6. Specificity  
This is understood as the quality or state of remaining unique and specific to groups or individuals. It is computed by Equation (14), where \(TrN\) indicates True Negative and \(FlN\) denotes False Negative.  
\[Specificity=\frac{TrN}{FlN+TrN} \tag{14}\]