esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Image Semantic Segmentation Method Based on Superpixels and Conditional Random Fields
## 3 Key Techniques
### Feature Extraction  
Different from the classic CNN model, the FCN model can take images of arbitrary size as inputs and generate correspondingly-sized outputs, so we employ it to implement the feature extraction in our semantic segmentation method. In this paper, the VGG-based FCN is selected due to its best performance among various structures of FCN model. The VGG-based FCN is transformed from the VGG-16 [28] by replacing the fully connected layers with convolutional ones and keeping the first five layers. After multiple iterations of convolution and pooling, the resolution of the resulting feature map gets lower and lower. Upsampling is required to restore the coarse feature to the output image with the same size as the input one. In the implementation procedure, the resolution of the feature maps is reduced by 2, 4, 8, 16, and 32 times, respectively. Then, upsampling the output of the last layer by 32 times can get the result of FCN-32s. Because the large magnification leads to the lack of image details, the results of FCN-32s are not accurate enough. To improve the accuracy, we added more detailed information of the last few layers, and combined them with the output of FCN-32s. By this means, the FCN-16s and the FCN-8s can be derived.  
An important problem that needs to be solved in semantic segmentation is how to combine \"where\" with \"what\" effectively. In other words, semantic segmentation is to classify pixel-by-pixel and combine the information of position and classification together. On one hand, due to the difference in receptive fields, the resolution is relatively higher in the first few convolutions, and the positioning of the pixels is more accurate. On the other hand, in the last few convolutions, the resolution is relatively  
Figure 1: The flow chart of the proposed method.  
lower and the classification of the pixels is more accurate. An example of three models is shown in Figure 2.  
It is can be observed from Figure 2 that the result of FCN-32s is drastically smoother and less refined. This is because the receptive field of FCN-32s model is larger and more suitable for macroscopic perception. In contrast, the receptive field of FCN-8s model is smaller and more suitable for feeling the details. From Figure 2 we can also see that the result of FCN-8s, which is closest to the ground truth, is significantly better than those of FCN-16s and FCN-32s. Therefore, we choose FCN-8s as the front-end to extract the coarse features of images. However, the results of FCN-8s are still far from perfect and insensitive to the details of images. In the following, the two-step optimization is introduced in detail.
## 3 Methodology
### Model Training by Machine Learning Classifiers
#### 3.6.4 Gaussian Naive Bayes (GNB)  
GNB is based on the Bayes theorem. It is more effective with continuous data sets, continuous features, and models that have a normal (Gaussian) distribution. Equation (6) is derived as:  
\[p(w_{i}|r)=\frac{1}{\sqrt{2\pi\sigma_{r}^{2}}}e(\frac{-(w_{i}-\mu r)^{2}}{2 \sigma_{r}^{2}}) \tag{6}\]  
We assume that r follows a Gaussian or normal distribution; we must substitute the probability density of the normal distribution and name it GNB. To compute this formula, one needs the mean and variance of w. In Equation (6), \(\sigma\) and \(\mu\) are the variance and mean of the continuous variable w, computed for a given class c of r.  
The variance in Equation (6) is assumed as  
* Independent of w (i.e., \(\sigma_{i}\));
* Independent of r (i.e., \(\mu_{i}\));
* Or independent of both.  
GNB:  
* Handles continuous large data sets with high flexibility and it works well with large data sets.
* It can be applied to complex and nonlinear problems.
* It is highly independent.
* Good results can be obtained only on the large data set.
# Ground-Borne Vibration Model in the near Field of Tunnel Blasting
## 4 Vibration near Blasting Area  
### Vibration Model Based on Sadovsky Formula  
Through the comprehensive analysis of the monitoring data, the proportional distance and vibration velocity are taken as independent variables and dependent variables, respectively. The obtained cut hole data are regressed and fitted by using the Sadovsky formula with the Origin mathematical software. The variation in ground vibration in the near field of small-spacing tunnel blasting is shown in Figures 3-6.  
\[V_{X}=269.97\Big{(}\sqrt[3]{Q}/R\Big{)}^{2.146}\Big{(}R^{2}=0.77 \Big{)} \tag{16}\] \[V_{Y}=158.5\Big{(}\sqrt[3]{Q}/R\Big{)}^{1.853}\Big{(}R^{2}=0.60 \Big{)}\] (17) \[V_{Z}=237.89\Big{(}\sqrt[3]{Q}/R\Big{)}^{2.221}\Big{(}R^{2}=0.65 \Big{)}\] (18) \[V_{X}=18.759\Big{(}\sqrt[3]{Q}/R\Big{)}^{1.175}\Big{(}R^{2}=0.69 \Big{)}\] (19) \[V_{Y}=5.1194\Big{(}\sqrt[3]{Q}/R\Big{)}^{0.512}\Big{(}R^{2}=0.41 \Big{)}\] (20) \[V_{Z}=19.622\Big{(}\sqrt[3]{Q}/R\Big{)}^{1.38}\Big{(}R^{2}=0.64 \Big{)}\] (21) \[V_{X}=229.36\Big{(}\sqrt[3]{Q}/R\Big{)}^{2.285}\Big{(}R^{2}=0.48 \Big{)}\] (22) \[V_{Y}=77.685\Big{(}\sqrt[3]{Q}/R\Big{)}^{1.871}\Big{(}R^{2}=0.51 \Big{)}\] (23) \[V_{Z}=292.35\Big{(}\sqrt[3]{Q}/R\Big{)}^{2.427}\Big{(}R^{2}=0.53 \Big{)}\] (24) \[V_{X}=9.442\Big{(}\sqrt[3]{Q}/R\Big{)}^{0.781}\Big{(}R^{2}=0.47 \Big{)}\] (25) \[V_{Y}=9.4618\Big{(}\sqrt[3]{Q}/R\Big{)}^{0.711}\Big{(}R^{2}=0.44 \Big{)}\] (26) \[V_{Z}=11.129\Big{(}\sqrt[3]{Q}/R\Big{)}^{0.995}\Big{(}R^{2}=0.66 \Big{)} \tag{27}\]  
Figure 2: Box chart of three-direction vibration velocity.  
Figure 4: _Cont_.  
Figure 3: Vibration velocity in three directions of cutting hole blasting in front of the working face: (**a**) X direction; (**b**) Y direction; (**c**) Z direction.  
Figure 4: Vibration velocity in three directions of cutting hole blasting behind the working face: (a) X direction; (b) Y direction; (c) Z direction.  
Figure 5: Vibration velocity in three directions of other blast holes in front of the working face: (a) X direction; (b) Y direction; (c) Z direction.  
It is found that when the Sadovsky formula is used, the data of the cutting holes and other holes are obviously different, and the data when the measuring points are in front of and behind the face are obviously different [22]. By comparing the parameters in the above 12 expressions, it can be seen that the values of \(K\) and \(\partial\) when the measuring points are in front of the tunnel face are significantly higher than those when the measuring points are behind the tunnel face. This shows that the vibration velocity of the measuring points in front of the tunnel face will attenuate relatively quickly due to the influence of the cavity effect, and the stratum will be greatly affected. The same difference also exists in the data of the cut hole and other holes. Therefore, it is necessary to distinguish the data when using the Sadovsky formula.
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 3 Integrated Management Framework
### Scheduler Module Technical Implementation  
The scheduler module employs a CSP approach using an open-source framework built upon a constraint satisfaction engine. The mathematical formulation addresses a multi-objective optimization problem specifically tailored for satellite task scheduling in NTN environments.
## 1 Introduction
Therefore, this model is suitable for the study of relative-location-based solar irradiation. This research is motivated by the necessity for understanding and mitigating the effects of solar irradiance on O-ISLs, particularly in the context of satellite's mega-constellations. This work presents three key contributions: (i) a systematic methodology for assessing the impact of solar irradiance on O-ISLs, integrating practical parameters, such as the receiver's FoV (at both the azimuth and elevation angles), directionality, transmission power, and noise characteristics; (ii) a detailed analysis of how solar irradiance affects O-ISL performance in mega-satellite constellations, incorporating seasonal and statistical evaluations to capture real-world variability; and (iii) an investigation into the benefits of inter-shell O-ISL configurations for path optimization under solar interference, supported by verifications using intercontinental O-ISL scenarios. By providing these insights, this work aims to enhance the design and operational planning of O-ISLs, contributing to more resilient and efficient inter-satellite communication networks. Section 2 discusses the system modeling framework for evaluating solar irradiance's impact on LEO O-ISLs, presenting a multi-directional FoV model and theoretical approaches, such as Heron's law and signal-to-noise-ratio (SNR) analysis. Section 3 outlines the simulation results, with performance comparisons between inter-plane and inter-shell setups under various seasonal and geographical conditions, followed by Section 4, with a discussion and conclusions.
# Music Similarity Detection Through Comparative Imagery Data
## 4 Experimental Results: Model Training, Validation, and Testing
### Testing with Independent Data  
In order to test the ensemble model and its sub-models, we selected two pieces of music that differ significantly from the training data. One is a Chinese folk song called \"Mo Li Hua\" (Jasmine Flower), which dates back to the 18 th century. The song has multiple verses, each with the same basic melodic and rhythmic structure. Its melody is gentle, graceful, and lyrical. The other is a Turkish march called \"Izmir Marsi\" (Izmir March), dating back to the period of the Turkish War of Independence (1919--1923). It is a fast paced, bold, and highly energetic song. The march has a main melody and accompanying counter melodies, and these are repeated throughout the song.  
For each of the two pieces of music, we selected eight bars as the original version and created five variants, namely (a) _transpose_, (b) _chord altered_, (c) _swing genre_, (d) _ornaments added_, and (e) _combination_ of several of these changes. Let us denote the original \"Mo Li Hua\" and its five variants as \(C_{o},C_{a},C_{b},C_{c},C_{d},C_{e}\), and those of \"Izmir Marsi\" as \(T_{o},T_{a},T_{b},T_{c},T_{d},T_{e}\). With the 12 music pieces, there are 66 pairwise comparisons, among which 30 are similar (positive) and 36 are dissimilar (negative). Table 9 shows the results of applying the four sub-models and the ensemble model to heatmaps depicting these 66 pairwise comparisons.  
Figure 12: The confusion matrix for the validation set for the ensemble model.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Results:** Ensemble Model & \\ \hline Loss & 0.06 \\ F1 Score & 1.00 \\ Accuracy & 1.00 \\ AUC & 1.00 \\ \hline
**Feature Weights:** & \\ \hline MI Histogram & 0.39 \\ FFPC Histogram & 0.20 \\ WVI Histogram & 0.33 \\ DBTMCVI & 0.08 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Summary of model ensemble performance on the same validation set used in the training of the individual models. Feature names are abbreviated.  
From Table 9, we can observe that the ensemble model has performed the best in dealing with music data that differ significantly from the training data. It is closely followed by the WVI (vertical interval) sub-model, then MI (melodic interval), FFPC (folded fifths pitch class), and DBTMCVI (distance between two most common vertical intervals). Among the 66 pairwise comparisons, there are ten cases where MI and WVI sub-models disagree with each other. In eight (out of ten) cases, the FFPC sub-model helps the ensemble model to make correct decisions. The testing provided concrete validation evidence, confirming that training ML models with comparative imagery data can produce effective models for determining music similarity.
## 3 Results
### Feature Analysis Using Dimension Reduction  
To provide a visual representation of the performance of the various models using deep representation learning, we used a dimension-reduction method, uniform manifold approximation and projection (UMAP). In this, we reduced the feature vector into a 2-dimensional space using UAMP and displayed the vectors as a series of points on this plane, as shown in Figure 4. Red points represent thermophilic proteins (positive samples), while blue points indicate non-thermophilic proteins (negative samples). The decision function line of LR were also drawn in Figure 4. As shown in Figure 4A-C, there was considerable overlap between positive and negative samples when protein prediction was carried out using UniRep, SSA, or W2V. Consequently, classifying samples accurately using these features is almost impossible in a 2-dimensional space. However, as shown in Figure 4D, most positive and negative samples projected in the 2D representation of BERT-bfd vectors concentrated into two distinct areas, with positive samples mostly in the yellow area and negative samples in the cyan area in Figure 4D. This finding visually indicated that LR classified most of the samples correctly in this 2-dimensional space, indicating the superiority of BERT-bfd-based feature extraction.  
As described in Section 3.4, feature selection could further improve the performance of the BertThermo model. Therefore, we also introduced dimension reduction using the top 90 BERT-bfd features and displayed the results as a 2D figure, shown in Figure 4E. The distribution of positive and negative samples identified under these conditions was concentrated into smaller areas. However, there are still considerable overlap between positive and negative samples. Therefore, better feature selection methods or feature extraction methods were still needed for better identification in the future. Additionally, as with the improvement of computational resources, for training, interpreting and predicting, a better performance model will be developed.
###### Abstract  
Observation angles are of great importance with respect to Earth observation platforms. The richness of angular combination, i.e., the combination of three observational angles (viewing zenith angle, solar zenith angle, and relative azimuth angle), is an important parameter to illustrate the angle sampling capability of an Earth observation platform. Here, the angular combination characteristics of a Moon-based platform were investigated and compared with existing artificial satellites. Furthermore, the effects of the time sampling interval on the angular combination characteristics were analyzed using a newly established angular combination number index (ACNI). Results show that a Moon-based platform can complement angular sampling observations from existing satellites. We found that the time sampling interval has different effects on the angular combination for different observed points. Accordingly, the Earth's surface can be divided into two zones with respect to its sensitivity to the time sampling interval. When the time sampling interval increased from 10 min to 2 h, the maximum loss of the angular combination reached 50% for the observed points in the mid-low latitude zone.  
M _remote sensing_ 2022, 14, 1623. [[https://doi.org/10.3390/rs14071623](https://doi.org/10.3390/rs14071623)]([https://doi.org/10.3390/rs14071623](https://doi.org/10.3390/rs14071623))  
1  
Citation: [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Effects of the Time Sampling Interval on the Angular Combination Characteristics of Moon-Based Earth Observations. _Remote Sens._**2022**, _14_, 1623. [[https://doi.org/10.3390/rs14071623](https://doi.org/10.3390/rs14071623)]([https://doi.org/10.3390/rs14071623](https://doi.org/10.3390/rs14071623))  
Academic Editor: [PERSON]  
Received: 22 February 2022  
Accepted: 24 March 2022  
Published: 28 March 2022  
Published: **Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
## 3 Results
### Quality Assessment  
The reviewers conducted a quality assessment of the 23 included studies. Nine studies were characterized by a moderate risk-of-bias scoring of 3/6 [32,37] or 4/6 [41, 42, 43, 44, 45, 49, 51] points. Furthermore, twelve studies were considered high-quality studies with a low risk of bias, scoring 5/6 [17, 34, 35, 36, 38, 40, 46, 47, 48, 50, 52, 53] or 6/6 [33, 39] points. None of the studies was excluded due to a high risk of bias. A summary of the conducted quality assessment is presented in Figure 3.
## 2 System Model  
We consider a point-to-point wireless communication system in which a cooperative relay assists in transmitting data from the source to the destination. An AF relay operating in half-duplex mode is employed, and all nodes--including the source, relay, and destination--are assumed to be equipped with a single antenna. The relay node is considered energy-constrained and lacks a dedicated power supply. Instead, it operates by harvesting energy from the RF signal transmitted by the source. To enable simultaneous EH and IP at the relay, a PS receiver architecture is adopted. Specifically, the received signal power at the relay is split into two parts: one portion is used for EH, and the remaining portion is used for IP, as in [27; 28].
# QoS-Aware Scheduling Algorithm Enabling Video Services in LTE Networks
## 4 Proposed PrOEXPRule Scheduler Design  
In this paper, we propose a novel resource scheduling algorithm for the LTE downlink that optimises the EXPRule scheduler more efficiently. EXPRule was selected due to its high-level effect on transmissions across LTE networks, which accounts for throughput optimisation based on [22]. The EXPRule algorithm employs queues and channel data, but lacks statistical data concerning traffic channel use. The approach utilises information on the channel that is queued without previous information on arrival or channel statistics for traffic. The approach proposed in this paper offers improvements in QoS for video traffic under heavy load conditions. User channel state is basically analysed with consideration of the recorded jitter/delay characteristics and the maximum permissible delay for all packet transmissions to the end user, wherein scheduling decisions are performed every 1 ms TTI as required in 3 GPP specifications. The method of our proposed approach is to apply the calculated jitter values with the head-of-line (HOL) delay and required delay for all packets,so as to improve channel states for all users at each TTI for every transmitted packet, even where channel conditions are too degraded to satisfy QoS requirements.
# Validation and Parametric Study of a Soil-Structure Interaction Nonlinear Simplified Model
## 2 Improved Penzien Model  
### Model Details  
The improved Penzien model consists of two parts: a structural system, including the superstructure, pile, and equivalent soil, and the free field system. The free field system is divided into multiple soil layers based on soil properties and actual conditions, with half of the mass taken from each of the adjacent two soil layers and concentrated at their interface. The superstructure, the pile, and the soil-pile interaction are the same as in the Penzien model. The improved Penzien model is based on four assumptions: (1) only the horizontal degrees of freedom between the foundation soil and the free field are considered, (2) the vertical and torsional effects of the structure are neglected, (3) the group piles corresponding to the raft foundation move synchronously and can be combined into a single pile, and (4) interactions between piles are ignored. The dynamic equation of the improved Penzien model under seismic effects is represented by Equation (1):  
\[M\ddot{U}+C\dot{U}+KU=-M\ddot{U}_{g}+C_{h}\dot{U}^{G}+K_{h}U^{G} \tag{1}\]  
where \(U,\dot{U},\ddot{U}\) denote the displacement, velocity, and acceleration of the structural system, respectively. \(U^{G}\) and \(\dot{U}^{G}\) represent the displacement and velocity of the free field system. These are solved using the ANSYS finite element model. \(M\), \(C\), and \(K\) are the mass, damping, and stiffness matrices of the structural system. \(C_{h}\) and \(K_{h}\) are the damping and stiffness matrices of the pile-soil interaction. \(\ddot{U}_{g}\) denotes the input acceleration due to seismic forces.  
In the simplified model based on the improved Penzien model, both the superstructure and the soil can exhibit nonlinear behavior. The superstructure is simplified into a lumped-mass model with layer shear, and its nonlinear behavior is represented through a nonlinear layer shear force model. The nonlinearity of the soil is modeled through a nonlinear soil dynamics simulation within the free field system. The calculation method of specific parameter values of the simplified model under seismic effects are referenced from the paper written by [PERSON] et al. [34].
## 3 Methodology
### Multi-Sensor IF Estimation Algorithm
#### 3.1.2. Estimation of the Highest Energy Frequency Bin  
Fractional Fourier Gaussian windows, i.e., \(w_{\alpha_{l}}(t-t_{0})\), are used to window all signals received at \(M\)-sensors. Then the Fourier transform of the windowed signal at \(k\)-th sensor \(x_{k}(t)w_{\alpha_{l}}(t-t_{0})\) is computed as [29]:  
\[X_{k,\alpha_{l}}(f)=\int e^{-\beta\pi ft}x_{k}(t)w_{\alpha_{l}}(t-t_{0})dt, \tag{9}\]  
where \(t_{0}\) represents the time shift, \(k=1,2,\ldots,M\),  
\[\begin{split} w_{\alpha_{l}}(t)&=\frac{e^{\beta \alpha_{l}/2}}{\sqrt{j\sin(\alpha_{l})}}\\ &\int_{-\infty}^{\infty}e^{-\frac{\mu^{2}}{2\pi^{2}}}e^{j\pi(( \mu^{2}+t^{2})\cos\alpha_{l}-2\mu)/\sin\alpha_{l}}d\mu\end{split} \tag{10}\]  
and \(l=-L,-L+1,\ldots,-1,0,1,\ldots L\), \(\alpha_{l}=\frac{l}{L}\) represents the rotation order and \(L\) is the number of quantization levels for \(\alpha_{l}\). In this study, \(L=100\). To reduce noise, the magnitude of the Fourier transform of the windowed signal is spatially averaged as:  
\[\hat{X}_{\alpha_{l}}(f)=\sum_{k=1}^{M}|X_{k,\alpha_{l}}(f)| \tag{11}\]  
\(\hat{X}_{\alpha_{l}}(f)\) is used to estimate both the peak frequency and rotation order:  
\[(f_{0},\alpha_{0})=\operatorname*{arg\,max}_{f_{A_{l}}}\hat{X}_{\alpha_{l}}(f)| \tag{12}\]  
The IF at time-instant \(t_{0}\) is given as: \(\hat{f}_{i}(t_{0})=f_{0}\).
# Linear Method for Diagnosis of Inter-Turn Short Circuits in 3-Phase Induction Motors
## 2 Overview of Turn-to-Turn Shorts  
In Figure 1, the short circuit in phase-A indicates the case in which a short circuit occurs between Turn 1 and a distant turn. The short circuit in phase-B indicates a short circuit between coils. The short circuit in phase-C indicates a fine short circuit between adjacent Turns 1 and 2.  
Figure 1: Short circuit in a slot.  
These illustrate the two types of short circuits in a stator: coil-to-coil shorts and turn-to-turn shorts. When a fault like this occurs, an induction motor may generate severe vibration and heat. However, a fine turn-to-turn short is difficult to diagnose because there is no difference in the current between steady state and pulsation due to losses of the induction motor such as iron loss and copper loss.  
Nevertheless, research on the detection of ITSCs is required because they can cause serious faults because of local heat generation and dielectric breakdown. Moreover, it must be possible to perform online diagnosis of 3-phase induction motors. This means that the motor is in rotation, and it is difficult to perform analysis in a time-varying state. Analysis can be facilitated using a D-Q transformation to change 3-phase signals to 2-phase.