esa-sceva/satcom-chunk-collection huggingface dataset:
# Semi-Supervised Deep Subspace Embedding for Binary Classification of Sella Turcica
## 2 Materials and Methods
### Proposed Semi-Supervised Deep Subspace Embedding (SSLDSE) Architecture
#### 2.3.2 Semi-Supervised Learning with Deep Subspace Descriptor
A deep subspace descriptor with t-SNE refines the feature representations, and the outputs are classified by a zero-shot classifier (\(ZSc\)) with KL divergence loss, enabling the model to handle unseen or unlabeled ST structures. * Deep Embedding Clustering: The deep subspace descriptor is employed to combine the strengths of supervised and unsupervised learning. This involves using a deep embedding clustering algorithm, which reduces the dimensionality of the feature space while preserving its essential characteristics. The algorithm generates embeddings \(SL_{i}\) representing the data in a lower-dimensional space, facilitating the clustering of labeled and unlabeled data. This clustering helps form a more coherent and discriminative feature space, improving the overall classification performance. **[**Deep Embedding and Clustering Process**] The deep embedding layer operates similarly to t-SNE, as it seeks to find a lower-dimensional representation that preserved the clustering structure of the data. It accomplishes this by employing a clustering loss that refines the embeddings for better clustering as measured by the similarity between an embedding \(Z_{i}\) and the mean of a cluster \(\mu_{j}\):  
\[p_{i,j}=\frac{\left(1+\|Z_{i}-\mu_{j}\|^{2}\right)/\beta^{\frac{-(\beta+1)}{2} }}{\sum_{j}\left(\frac{1+\|Z_{i}-\mu_{j}\|^{2}}{\beta}\right)^{\frac{-(\beta+1) }{2}}}, \tag{5}\]  
where \(p_{i},j\) represents the probability that data point \(i\) belongs to cluster \(j\) and functions as prediction \(p\). This layer learns the means or centroids of different clusters from the embeddings of the penultimate layer, while a dense layer learns the mapping between embeddings and predictions. The deep embedding clustering algorithm is applied to the feature vectors in the embedding space. This algorithm is chosen due to its ability to handle complex, high-dimensional data and its efficiency in forming well-separated clusters. It complements the deep learning framework by effectively grouping similar feature vectors, thus enhancing the overall performance of the SSL model. The use of non-linear embeddings, analyzed with KL divergence, ensures that the essential data structures are preserved while making the embeddings robust to shifts and variations. Non-linear embeddings capture complex patterns in the data, effectively reducing dimensionality and preserving important relationships. KL divergence, used as a clustering loss, helps maintain the structural integrity of the data by aligning the learned embeddings with the true data distribution. This combination of techniques enhances the clustering performance and ensures robust, shift-invariant embeddings. In all the experiments, the hyperparameter \(\beta\) is set to 1.
## 4 Discussion
This is particularly true at the main reservoir level (blue rectangle in Figure 9 and dashed line in Figure 10), in which the highly porous interval is better predicted with the 10 attributes selected from all available attributes (Group 4). From Figure 10, the porosity seismic sections for all groups match the validation porosity log to some extent. The Groups 1 and 2 sections show continuous layers of the same porosity, while predicted porosity sections from Groups 3 and 4 show layers with great lateral variability. This last behavior is typical of a fan delta depositional environment. When comparing average porosity at the main reservoir level (Figure 11), Groups 2 to 4 seem to follow the same trend as the interpolated well logs. However, the Group 4 map shows more apparent detail and features that are possibly small channels (Figure 12). The areal extent of the predicted porous reservoir appears greatest for Group 4. When compared to well logs, the porosity predicted with attributes in Group 4 shows more significant detail and is quantitatively closer to the measured porosity. The resulting metrics from the blind-well validation data not included in the training process (Table 2) show that Group 4 predictions are more accurate, more precise, and most statistically significant, with the highest correlation coefficient and F-value, as well as the lowest absolute and standard errors and lowest Significance F. Adding only relative geologic age (Group 2) increases the correlation coefficient and decreases error relative to Group 1. Adding only spectral decomposition components to the conventional attributes (Group 3) increases the accuracy and statistical significance of the prediction. The differences in the metrics between the groups are more drastic when predicting average porosity at the reservoir level (Table 3), with a smaller error and both an increased correlation coefficient and statistical significance for the predictions made with Groups 3 and 4. Figure 15 shows a comparison of the measured and predicted porosity at one well location used in the validation, in which three intervals of thicknesses that are barely above the \(\lambda/4\) of 10.04 m are highlighted. The Group 3 and 4 predictions can better resolve these thin layers and the predicted values are closer to the actual values as compared to the predictions made with Groups 1 and 2. The Group 4 predicted porosity volume can also be useful in identifying and delineating geological features of interest. Figure 16 shows three examples of channel-like features with high predicted porosity. It also shows a comparison to the corresponding spectrally broadened seismic section in which the same features are not obvious. Figure 17 shows a similar predicted high porosity penetrated by a well in which the logs validate the porosity prediction and channel interpretation.
###### Abstract  
An analytic method for a complementary metal-oxide-semiconductor (CMOS) terahertz plasmon detector operating in the subthreshold region is presented using the equivalent circuit model. With respect to design optimization of the detector, the signal transmission from the antenna port to the output of the detector is described by using the proposed circuit model, which does not include a complicated physical operating principle and mathematical expressions. Characteristics from the antenna port to the input gate node of the detector are analyzed through the superposition method by using the characteristic impedance of transmission lines. The superposition method shows that the effect of interconnection lines at the input is simplified with the optimum bias point. The characteristics of the plasmon detection are expressed by using small-signal analysis of the single transistor at the sub-threshold operation. The results of the small-signal analysis show that the unity gain preamplifier located between the detector core and the main amplifier can improve the detection performances such as the voltage responsivity and the noise equivalent power. The measurement results using the fabricated CMOS plasmon detector at 200 GHz suggest that the unity gain preamplifier improves the detector performances, which are the same results as we received from the proposed analytic method.  
Keywords:equivalent circuit model; CMOS plasmon detector; terahertz detector; detector optimization; small-signal analysis; sub-threshold operation; quasi-static +
Footnote †: journal: Sensors
# Automatic Segmentation of Mandible from Conventional Methods to Deep Learning--A Review
## 3 Results
### Methodology  
In this paper, the mandible segmentation approaches have been broadly divided into six categories, namely, (1) statistical shape model-based (SSM-based), (2) active shape model-based (ASM-based), (3) active appearance model-based (AAM-based), (4) atlas-based, (5) level set-based, (6) classical machine learning-based and (7) deep learning-based approaches, as depicted in Table 2. There are several hybrid segmentation methods proposed in the collected literature. The hybrid methods are introduced in the listed seven categories.
# Design and Implementation of a Passive Optical Network for a Small Town +
## 3 Methodology
### Topology Selection (Point-to-Multipoint Tree)  
Based on the service and traffic analysis, we adopted a point-to-multipoint tree topology for the access network. In this GPON FTTH architecture, each optical line terminal (OLT) port in the central office feeds a single fiber that is split to serve multiple optical network units (ONUs/ONTs) at subscriber premises. This topology was chosen over a point-to-point fiber design for reasons of cost and scalability--a P2 MP PON significantly reduces the amount of fiber and central office transceivers needed by sharing one trunk fiber among many users. The trade-off is that the 2.5 Gbps/1.25 Gbps bandwidth is shared but, as shown above, this shared capacity is sufficient for the aggregate demand. The traffic and service profile of the users (largely bursty internet traffic and streaming, with intermittent peak usage) aligns well with a TDM shared medium solution. GPON's MAC layer and dynamic bandwidth allocation allow multiple users to efficiently share the common fiber while meeting their individual quality of service needs. In practice, PON technology streamlines the physical network by eliminating active electronics in the field and using passive splitters, which lowers maintenance costs and power consumption. Given the small-town geography, a tree topology with splitters located near clusters of users (neighborhoods or buildings) was ideal to reach all subscribers with minimal fiber deployment. Each branch of the tree carries combined traffic, which we engineered not to exceed GPON capacity as discussed. Furthermore, in theory, the chosen GPON standard (ITU-T G.984.x series) supports up to 128 ONT connections per port, providing flexibility for future expansion if user demand grows [17]. For our design, one GPON port serves up to 64 premises initially, leaving headroom in the split ratio for adding more users or higher-bandwidth services in the future, if needed.
# Blockchain-Embedded Strategic Options for Satellite Operators: Sell, SataS, or Dual?
## 1 Introduction
### Research Goals  
This paper aims to provide guidelines for SOs to choose the optimal strategy and demonstrate the proper conditions for adopting BCT. We build three channel models to derive the satellite operator's strategy by capturing the characteristics of the satellite data business. Our paper delves into the traditional satellite selling channels, SataaS, and the combination of both through dual-channel strategies. Given the distinctive attributes of commercial satellites, including their vulnerability to launch risks and data transmission concerns, our study evaluates optimal strategies in scenarios with and without blockchain technology using game theoretical analysis.
# Use of Binary Classification in Non-Invasive Load Monitoring
## 4 Feature Selection--mRMR Method  
Feature selection is a very important stage of the NILM. Its aim is to select, from the available set of features, those that carry as much information about the receiver as possible. In the case of a large set of potential identification features, feature selection also leads to a reduction in the dimensions of the input data. For the purposes of this study, the mRMR selection method was used.  
The mRMR method is based on issues in information theory and the correlations between features describing an element (in our case, an electricity receiver). The algorithm aims to select the most relevant features that are, at the same time, as least dependent among themselves, so it selects features with a high correlation with the class (in our case, the receiver understood as an entity) and low correlation among themselves.  
The significance, \(D\), of the feature set, \(S\), for class c is determined by the average value of all mutual information values, \(G\), between individual feature, \(f\), and class, \(c\), as follows:  
\[D(S,c)=\frac{1}{|S|}\sum_{f_{i}\in S}G(f_{i},c) \tag{1}\]  
The redundancy, \(R\), of all features in the set, \(S\), is determined as the mean value of all mutual information values between the features \(f_{i}\) and \(f_{j}\), as follows:  
\[R(S)=\frac{1}{|S|^{2}}\sum_{f_{i},f_{j}}G\big{(}f_{i},\,f_{j}\big{)} \tag{2}\]  
Using the measures of significance and redundancy defined above, the relevance, \(M\), of each feature to the class description is determined as follows:  
\[M=\max_{S}\left(\frac{1}{|S|}\sum_{f_{i}\in S}G(f_{i},c)-\frac{1}{|S|^{2}}\sum _{f_{i},f_{j}}G\big{(}f_{i},\,f_{j}\big{)}\right) \tag{3}\]  
The features are selected one at a time via a greedy algorithm to maximize the objective function, which is a function of the relevance and redundancy.  
The selection of features of the electrical appliances via the mRMR method was carried out on the entire learning set, maintaining an independent description of each appliance. As a result of this study, we obtained significant values for individual parameters, as shown in Figure 3. An analysis of the figure allows us to conclude that most of the features are, according to the mRMR method, completely insignificant, as their significance coefficient values are close to 0.  
It was decided that the following features would be used to learn and test a classifier for identifying electrical equipment:  
Figure 3: Significance coefficient values of identification features determined via the mRMR method: (**a**) all features in order of occurrence; (**b**) features in order of significance.  
* Reactive power of the first harmonic, \(\text{Q}_{1}\);
* Rms value of current, I;
* Reactive power of the third harmonic, \(\text{Q}_{3}\);
* Rms value of the twenty-ninth harmonic current, \(\text{I}_{9}\);
* Reactive power of the fifth harmonic, \(\text{Q}_{5}\).
# Aggregation Dispatch and Control Strategies for Multi-Type Loads in Industrial Parks
## 2 Aggregated Dispatch and Control Framework for Multi-Type Loads in Industrial Parks
### Aggregated Control Framework for Multi-Type Loads in Industrial Parks  
Due to the different control characteristics of multi-type loads in industrial parks at different time scales, as well as their behaviors being affected by daily production plan changes, it is necessary to establish a real-time closed-loop predictive control model for controllable loads based on their control characteristics and response delays. Based on this, an aggregated control model for multi-type loads in industrial parks can be established to achieve precise unified aggregation and control of multi-type loads in industrial parks.  
As shown in Figure 2, the three-level aggregated control framework belongs to a funnel structure of controllable loads: for controllable loads whose start-up delay and start-up response time sum is less than one day, they can all participate in day-ahead optimal dispatch; for controllable loads whose start-up delay and start-up response time sum is less than one hour, they can continue to participate in intra-day rolling optimal dispatch after participating in day-ahead optimal dispatch. The real-time predictive control part of a single load determines its control power.
## 5 Conclusions and Suggestion  
(1) The equation for predicting the plastic zone range of rock mass in the gateway-like structure tunnel under the thermo-mechanical coupling effect is derived, and the accuracy of the equation is verified to a certain extent via numerical simulation. In addition, compared with the intact rock mass, the joints in the rock mass significantly reduce the temperature variation area of the rock mass and increase the constant temperature area. The vertical joint group and horizontal joint group have a far greater influence on the temperature field than the inclined joint group.  
(2) At various joint spacings, the influence of the horizontal and inclined joint groups on the plastic zone morphology decreases, while the influence of the vertical joint group on the plastic zone morphology increases. Similar to the influence of temperature field, the influence of the vertical and horizontal joint groups on the plastic zone range is far greater than the inclined joint group.  
(3) The influence of the joint internal friction angle and thermal expansion coefficient on the plastic zone range of jointed rock mass is relatively small, mainly manifested as decreasing with the increase in the internal friction angle and increasing with the increase in the thermal expansion coefficient.  
(4) For the three types of joint groups, under the same rock mass temperature, the plastic zone range of the vertical joint group is the largest, followed by the horizontal joint group, and finally the inclined joint group. In terms of the plastic zone morphology, the plastic zone shows uneven expansion along the direction of the joint dip angle, which changes the potential failure direction of rock mass and intensifies the potential destruction degree of rock mass. However, in the direction perpendicular to the joint dip angle, smaller uniform expansions occur, and the boundary of the plastic zone also coincides with the joint surface, which relatively hinders the potential destruction degree of the rock mass.  
Data curation, editing, software, and analysis, K.L.; methodology, project administration, and guidance, H.J.; supervision, validation, methodology, and guidance, G.W.; writing--review and editing, P.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 52369021 and 51769031; the Regional Innovation Guidance Plan project of the XPCC, grant number 2021 BB004; and the Science and technology plan project of Zhejiang Provincial Department of Water Resources, Grant number RB2213.
# Research on Fault Diagnosis of Marine Diesel Engines Based on CNN-TCN-ATTENTION
## 3 Experiment
### Contrast Experiment
The other three models exhibit poor convergence of the loss rate curve and lower accuracy, which can also be seen in the confusion matrices in Figure 16. Compared to the confusion matrix of the combined model in Figure 14, the comparison models show more light blue blocks, indicating a higher number of misdiagnosed samples and thus lower accuracy. Furthermore, compared to traditional algorithms, CNN-TCN-ATTENTION shows faster convergence, and the model evaluation results demonstrate that it effectively identifies fault types in marine diesel engines. The Transformer model is sensitive to positional information and requires additional positional encoding to provide sequence information to improve accuracy. LSTM has  
Figure 16: Confusion matrices of different models: (**a**) Transformer model, (**b**) LSTM model, (**c**) GRU model, and (**d**) DBN model. strong feature extraction capabilities but tends to lose the correlation between channels, which can lead to overfitting. The GRU model has a simpler structure and, like LSTM, experiences overfitting issues when training data are limited or the model has many parameters. The DBN has a complex structure and training algorithm, involving numerous parameters, and improper parameter selection may cause learning to converge to a local optimum, negatively impacting model performance. On the other hand, the CNN model is simple, with flexible layers and powerful global feature extraction capabilities, while the TCN is effective in capturing temporal dependencies and sequence patterns and is less prone to overfitting than the RNN and its variants. The CNN-TCN-ATTENTION model combines the advantages of both the CNN and TCN, utilizing the attention mechanism to highlight significant features that increase model accuracy while extracting features at various scales. This suggests that the CNN-TCN is effective at feature extraction, and the attention mechanism further enhances model accuracy. Thus, compared to more conventional models, the CNN-TCN-ATTENTION model is more reliable, accurate, and efficient in identifying problems in marine diesel engines.
# An Optimal Scheduling Model for Connected Automated Vehicles at an Unsignalized Intersection
###### Abstract  
The application of connected automated vehicles (CAVs) provides new opportunities and challenges for optimizing and controlling urban intersections. To avoid collisions of vehicles in conflicting directions at intersections and improve the efficiency of intersections, an optimal scheduling model for CAVs at an unsignalized intersection is proposed. The model develops a linear programming model of intersection vehicle timing with the minimum average vehicle delay within the optimization time window as the optimization objective and the minimum safe time interval for vehicles to pass through the intersection as the constraint. A rolling optimization algorithm is designed to improve the efficiency of the algorithm solution. Finally, the effects of different traffic demand conditions on the results are investigated based on numerical simulation experiments. The results show that both the proposed algorithm and the Gurobi solver can significantly reduce the average vehicle delay compared with the first-come-first-served (FCFS) control method, and the proposed model and algorithm can reduce the average vehicle delay by 76.22% at most. Compared with the Gurobi solver, the proposed model and algorithm can reduce the solution time and ensure the optimization effect to the greatest extent. Therefore, the proposed model and algorithm provide theoretical support for managing CAVs at unsignalized intersections.  
c 25 February 20
###### Abstract  
To evaluate peri-implant bone formation following single or combined systemic administration of alendronate and simvastatin in healthy and osteoporoporotic rats, eighty female Wistar rats were ovariectomized (n = 40) or sham-operated (n = 40). At six weeks, implants were placed in femoral condyles. Then, ovariectomized (OVX) and sham-operated (SHAM) animals received daily subcutaneous alendronate (50 ug/kg), simvastatin (5 mg/kg), or both, for three weeks. Control animals received subcutaneous saline. Thereafter, specimens were retrieved for biomechanical testing, histological evaluation, and bone area (BA%) and bone-to-implant contact (BIC%). In healthy and osteoporoporotic rats, similar (\(p>0.05\)) push-out values were observed for all groups. For BA% analysis, control rats showed similar results for OVX (9.2% \(\pm\) 2.4%) and SHAM (11.1% \(\pm\) 3.5%) animals. In contrast, single or combined drug therapy significantly increased BA% compared to controls in both healthy and osteoporoporotic conditions (\(p<0.05\)). In osteoporoporotic conditions, alendronate alone showed a superior effect on BA% compared to simvastatin alone, or their combination. Systemic alendronate, simvastatin, or both showed a similar BIC% compared to controls (\(p>0.05\)). The present study demonstrates that single or combined systemic alendronate and simvastatin increases bone formation around implants (i.e., distance osteogenesis) in healthy and osteoporoporotic bone conditions. However, these drugs showed no beneficial effect on direct bone-to-implant contact or implant fixation.  
d 2020 10 4298 10.3390/app10124298
## 3 System Model
### Service Model  
The QoS category of services is defined in ITU-T G.1010 [27]. Referring to the description of service delay tolerance in ITU-T, we divide service into four grades, as shown in Table 1. Each grade of services will be processed differently according to the maximum delay tolerance. Services with different delay tolerance have different requirements to access LEO satellites.