esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Results
### Comparison of Segmentation Results for the Mongolian Horse Test Set  
To further evaluate and analyze the performance of the model used in this paper for horse contour segmentation and extraction, the segmentation effect of the model on the Mongolian horse test set dataset was explored. The pixel accuracy (PA) and the mean intersection ratio (MioU) were used as evaluation indexes to measure image segmentation accuracy and compared with the representative models developed by [PERSON] et al. [32] and [PERSON] et al. [47], namely, the original Mask R-CNN [37], YOLACT [48], and SOLO [49], in order to validate and analyze the performance of the improved model in horse silhouette extraction. The results for the same set of test images are shown in Table 2.  
Figure 9: Layout of shooting environment.  
As shown in Table 2, the model described in this paper can segment the target Mongo-lian horse silhouette in the image relatively accurately, with a pixel accuracy and intersection ratio of 98.72% and 95.36%, respectively. Compared with the other models, namely, Mask R-CNN, YOLACT, [PERSON] et al.'s model [32], SOLO, and [PERSON] et al.'s model [47], the pixel accuracy provided by our model was 7.26, 6.39, 8.57, 5.28, and 0.46 percentage points higher, and the Mean intersection over union was 10.64, 9.85, 9.99, 8.90, and 0.39 percentage points higher, respectively. Although the method proposed in this paper requires more processing time for a single image, Figure 10 shows that our method yields higher pixel accuracy and intersection ratios. As a result, our method can achieve higher accuracy in extracting the boundary contours of Mongolian horse images. In addition, the segmentation accuracy of the target horse contour edges exceeds that of the original Mask R-CNN model.
# Design of a Dual-Path Speech Enhancement Model
###### Abstract  
Although both noise suppression and speech restoration are fundamental to speech enhancement, many Deep neural network (DNN)-based approaches tend to focus disproportionately on one, often overlooking the importance of their joint handling. In this study, we propose a dual-path architecture designed to balance noise suppression and speech restoration. The main path consists of an encoder and two specialized decoders: one dedicated to estimating the clean speech spectrum and the other to predicting a noise suppression mask. To reinforce the joint modeling of noise suppression and speech restoration, we introduce an auxiliary refinement path. This path consists of a separate encoder-decoder structure and is designed to further refine the enhanced speech by incorporating complementary information, learned independently from the main path. By using this dual-path architecture, the model better preserves fine speech details while reducing residual noise. Experimental results on the VoiceBank + DEMAND dataset show that our model surpasses conventional methods across multiple evaluation metrics in the causal setup. Specifically, it achieves a PESQ score of 3.33, reflecting improved speech quality, and a CSIG score of 4.48, indicating enhanced intelligibility. Furthermore, it demonstrates superior noise suppression, achieving an SNRseg of 10.44 and a CBAK score of 3.75.  
Keywords:speech enhancement; speech restoration; dual-path; multi-decoder +
Footnote †: journal: Applied sciences
## 4 Identified Factors and Discussed Discussion
### Factors: York ICT-2  
Figure 5b shows that two factors were identified, for which expert sets 7, 6, 2, 5, and 4 loaded factor one with a maximum PM of 0.928 and SM of 0.944, whilst sets 1 and 8 loaded factor two with a PM of 0.882 and an SM of 0.972 (Tables A2 and A3). It should be noted here that responses from set 3 showed \"zero\" variance.  
2.1 Time and Resource Efficiency: Addressing the Obvious on Priority Basis Whilst Putting Minimum Effort to Enquire about the out of Sight  
York experts showed concern that although authorities had enough resources vs. a controllable number of flood victims in the flooded area, even then it was a challenge for them to decide where to start rescuing people. The usual practice was that after every flood, the data of each vulnerable person were acquired and added to a record and each activity was evaluated [40; 42] to utilize time and resources in a better way next time.  
The identified factor indicated information about the provision of EM to the people by using different resources including ER vehicles. Authorities usually did not try to reach the far-flung flooded regions by themselves, but rather addressed the obvious and easy-to-access areas to utilize time and resources efficiently. This leaves a few gray areas in the system that cause either loss of life, property, or other resources.  
This situation could be avoided by engaging ICTs to provide easy options to investigate and help neglected flood victims who probably could not engage in any contact with anyone by themselves. For example, if a local community group of flooded people was created on any social media, then the absence of someone's response may indicate that the person was in trouble or had no access to any communication sources. Also, while evacuating, sharing of vehicles and other necessary transport-flood information might improve the safety of the citizens and develop community strength.
## 2 Experimental Procedures  
Different InP\({}_{1-x}\)Sb\({}_{x}\) epilayers of thickness d (\(\equiv\)-1 \(\upmu\)m) with diverse Sb (see Table 1) compositions x are prepared on n-InAs substrate by using GS-MBE technique (Section 2.1). Hall effect and van der Pauw measurements were reported earlier on several Be- and Si-doped InP\({}_{1-x}\)Sb\({}_{x}\) samples to assess their electrical properties [39; 40]. Several experimental characterization tools are exploited (viz., RSS and PL) here to investigate the fundamental features of InP\({}_{1-x}\)Sb\({}_{x}\)/n-InAs epilayers (Sections 2.2 and 2.3), including the phonon and optical traits. Careful analyses of RSS, PL, and SE results are performed (Section 3) to attain valuable characteristics for the binary InP, InSb and ternary InP\({}_{1-x}\)Sb\({}_{x}\) alloys.  
\begin{table}
\begin{tabular}{c c c} \hline  & \multicolumn{2}{c}{**GS-MBE-Grown**} \\  & \multicolumn{2}{c}{**InP\({}_{1-x}\)Sb\({}_{x}\)/n-InAs Samples**} \\ \hline
**Sample** & **x (a)** & **1 \(-\) x** \\
**\#** T0 & 0.10 & 0.90 \\
**\#** T1 & 0.16 & 0.84 \\
**\#** T2 & 0.17 & 0.83 \\
**\#** T3 & 0.30 & 0.70 \\
**\#** T4 & 0.33 & 0.67 \\
**\#** T5 & 0.36 & 0.64 \\
**\#** T6 & 0.37 & 0.63 \\
**\#** T7 & 0.48 & 0.52 \\ \hline \end{tabular} \({}^{(a)}\) Refs. [39; 40].  
\end{table}
Table 1: Different high-quality, undoped samples of nearly one-micron-thick InP\({}_{1-x}\)Sb\({}_{x}\) on n-InAs (001) substrate (see text) grown using the gas-source molecular beam epitaxy (GS-MBE) technique (see text).
# Computer Aided Detection of Pulmonary Embolism Using Multi-Slice Multi-Axial Segmentation
## 2 Materials and Methods
### Network Architectures  
#### 2.2.1 \(2d\) Network  
The \(2D\) network was based on the U-Net structure [20]. The input to the network consisted of axial slices of the training data, and the output was emboli segmentation. We used the reference standard segmentations with \(\epsilon=\)0 mm tolerance level as ground truth. The U-Net was composed of four convolution and polling levels in the down-sampling encoder. Each level was composed of the \(3\times 3\) convolution operations that doubled the number of filters with respect to the previous level. The first level started with 32 filters and the last encoder block had 256. Each encoder level was followed by a \(2\times 2\) Max-Pooling operation. At the end of the encoder, we performed two further \(3\times 3\) convolutions with 512 filters each. The decoding was performed by four up-sampling blocks consisting of an up-sampling operation, followed by the concatenation with the output of the corresponding encoder level data and two \(3\times 3\) convolution operations, with the same number of filters as its equivalent encoder level. To finish, a last \(1\times 1\) convolution was performed to obtain the network output. All of the convolution outputs were activated with a \"ReLU\" function except the last \(1\times 1\) convolution, which used a sigmoid activation to concentrate the output values in the extremes of the range \([0,1]\). In addition, batch normalization was used for the convolution activations, to maintain the mean close to zero and its standard deviation close to one.
## 3 Method and Material
### Multi-Objective Optimization Model for Polygeneration System
#### 3.2.2 Objectives
The operation and maintenance expenses include the labor costs, material costs, and equipment depreciation required by polygeneration technologies to address the by-products. The economic benefit of by-products is contributed by energy and resources saved, as well as the symbiotic products. In short, the economic benefit of polygeneration systems can be calculated by Equation (3). \[EB=CSO\times\sum_{pro}\sum_{bp}\sum_{t}SR_{pro}\times GF_{pro,bp}\times Ratio_{ pro,bp,t}\times(\sum_{bp}B_{t-bp}+\sum_{e}B_{t,e}+\sum_{res}B_{t,res}-OC_{t}-FI_{t} \times AR_{t}) \tag{3}\]  
where \(EB\) represents the energy benefit of polygeneration system; \(B_{t-bp}\) is the economic benefit of by-product \(bp\) tackled by symbiotic technology \(t\); \(B_{t,e}\) is the economic benefit of technology \(t\) through energy conservation; \(B_{t,res}\) is the economic benefit of technology \(t\) through resource conservation; \(FI_{t}\) and \(OC_{t}\) are the fixed investment cost and operation and maintenance (O&M) cost of technology \(t\), respectively; and \(AR_{t}\) is the annualized ratio of fixed investment in technology \(t\). As mentioned above, even for the same waste/by-product, different utilization approaches will create different energy, environmental, and economic benefits. Consequentially, determining the use approaches and amounts of various waste/by-products is the key to completing the optimization of the polygeneration system. Given this situation, in this study, the optimization variables in the polygeneration system are the utilization ratio of by-products \(bp\) from the production process \(pro\), i.e., \(Ratio_{pro,bp,t}\).
## 5 Train-to-Ground Communication Architectures with Leaky Waveguide
### Mobile Relays  
As is shown in Figure 4a, same as that of current HSR [43], the on-board MRs that are usually installed on the train roof can solve the issues of high penetration loss and group handover. These MRs are usually connected to access points (APs) installed inside the train through wired links such as optical fiber. In the uplink, the traffic from all user equipments (UEs) and the on-train equipments are first congregated at the on-board MRs through the APs, then the relay stations forward all the congregated data to the leaky waveguide by the roof antenna. In the downlink, the leaky waveguide transmits all communication data to the MRs, which then deliver the traffic to their intended UEs and train equipments through the APs inside the capsule. As the MRs are connected to the antenna installed outside of the train, it eliminates the penetration loss [43].  
Figure 3: Field distribution of the leaky waveguide method: (**a**) Field distribution (\(E_{z}\)) with different \(\alpha\) (Np/m). (**b**) The variations of field distribution (\(E_{z}\) component) with different heights x when \(\alpha=0\)[42].  
The key challenge of this solution for vactrain is the wireless resource allocation for safety-related operational services and non-secure passenger services, which have different transmission requirements in terms of bandwidth and QoS. Thus, the train-to-ground wireless link involved with physical layer needs to be created specifically. Besides, the train relay is responsible for the aggregation and distribution of all UEs' traffic, which leads to the complex design of MR's system structure to ensure the transmission performance. This implies that the complexity of the train-to-ground communication system will be increased sharply, resulting in the declines of transmission robustness. In addition, the delay caused by the MR's forwarding will reduce the stability of the train operational data transmission. As is mentioned above, the communication channels between leaky waveguide and train are stationary, thus there is no need to use MRs with the complex system design. Instead, the solutions related to direct radio coverage for passengers could be available in vactrain scenario.
# Analysis of Pre-Driver and Last-Stage Power--Ground-Induced Jitter at Different PVT Corners
## 3 Jitter Analysis under PVT Corners: Simulation Setups and Results
### Induced jitter: Shared vs. Decoupled PG Noise  
The two simulation configurations, which are used to estimate the p2p eye jitter, are shown in Figure 4. The PGSV settings are described in Table 4. The simulation results of both configurations are shown in Table 5. Furthermore, Figure 7 illustrates the eye diagrams of the pre-driver and the last-stage-induced jitter at the SS\(-\)40 \({}^{\circ}\)C corner.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Center** & **PG Noise** & **Jitter p2p (ps)** & **Jitter/UI (\%)** & **EW (ns)** & **EH (V)** \\ \hline \multirow{2}{*}{SS\(-\)40 \({}^{\circ}\)C} & Shared & 328.50 & 16.43 & 1.80 & 2.50 \\ \cline{2-5}  & Decoupled & 275.00 & 13.75 & 1.77 & 2.57 \\ \hline \multirow{2}{*}{SS 125 \({}^{\circ}\)C} & Shared & 150.78 & 7.54 & 1.88 & 2.51 \\ \cline{2-5}  & Decoupled & 195.20 & 9.76 & 1.85 & 2.51 \\ \hline \multirow{2}{*}{TT 25 \({}^{\circ}\)C} & Shared & 124.17 & 6.21 & 1.88 & 2.83 \\ \cline{2-5}  & Decoupled & 186.25 & 9.31 & 1.82 & 2.87 \\ \hline \multirow{2}{*}{FF\(-\)40 \({}^{\circ}\)C} & Shared & 115.30 & 5.77 & 1.91 & 3.17 \\ \cline{2-5}  & Decoupled & 133.04 & 6.65 & 1.89 & 3.16 \\ \hline \multirow{2}{*}{FF 125 \({}^{\circ}\)C} & Shared & 177.60 & 8.88 & 1.91 & 3.15 \\ \cline{2-5}  & Decoupled & 150.78 & 7.54 & 1.90 & 3.21 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Eye Diagram Metrics: Shared vs. Decoupled Cases.  
Figure 6: Eye diagram: pre-driver vs. last-stage PGSIJ at the SS\(-\)40 \({}^{\circ}\)C corner.  
Figure 7: Eye diagram: shared vs. decoupled PGSIJ at SS\(-\)40 \({}^{\circ}\)C.  
Tables 3 and 5 show that the worst-case eye jitter is observed at the SS\(-\)40 \({}^{\circ}\)C corner for this specific transistor technology and node at which usually the worst jitter performance is observed. Since IO buffers are more sensitive to jitter noise at the SS corner, it is usually recommended to run the high-speed IO link SiPI simulation at the SS corner to determine the timing margins at the receiver's input. Therefore, the accuracy of the power-aware IO buffer behavioral model should be guaranteed at the SS corner, where it presents the worst-case PGSIJ performance. In addition to that, the p2p jitter for shared and decoupled PG supply noise at the SS corner shows \(\sim\)53 ps difference, which depends on the PG noise frequency content and amplitude variations.
## 3 Used Datasets and Training Tricks
### Loss Functions  
The loss functions are used by machine learning algorithms to compare the difference between the solution proposed by the model and the ground truth. In this paper, two loss functions are used: generalized intersection over union and focal loss.  
The _IoU_ loss \(L_{IoU}\) used for the regression task, computes the dissimilarity between the proposed bounding box and the ground truth bounding box as \(L_{IoU}=1-IoU\). However, this method does not work properly when the two bounding boxes do not intersect (\(IoU=0\)), as it does not consider the distance between them. To solve this, [PERSON] et al. [54] proposed the generalized intersection over union loss function (\(L_{Global}\)\(=1\ -\ GloU\)), which uses a minimum surface rectangle \(C\) that encloses both bounding boxes to compute the relative distance between them. The equation for the generalized intersection over union (\(GIoU\)) is:  
\[GIoU=IoU-\frac{A_{C}-A_{U}}{A_{C}}, \tag{2}\]  
where \(A_{C}\) is the area of the rectangle \(C\) and \(A_{U}\) is the area of the union of the two bounding boxes. Unlike _IoU_, _GIoU_ outputs values in the range (-1,1), the negative values being  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Type** & **Value** & **Meaning** \\ \hline \(\alpha\) & input & 0.001 & learning rate \\ \(\beta_{1}\) & input & 0.9 & first moment estimates decay rate \\ \(\beta_{2}\) & input & 0.999 & second moment estimates decay rate \\ \(\lambda\) & input & 0.01 & parameters vector decay rate \\ \(t\) & - & 0 & current time step \\ \(m_{t}\) & - & \(m_{t=0}=0\) & first moment estimates vector \\ \(v_{t}\) & - & \(v_{t=0}=0\) & second moment estimates vector \\ \(\eta_{t}\) & input & computed & learning rate schedule multiplier \\ \(\theta_{t}\) & output & \(\theta_{t=0}=\) random values & optimized parameter vector \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used by [PERSON] with L2 regularization and AdamW.  
obtained when _IoU_ = 0, which causes the loss to increase the further the two bounding boxes are from each other.  
For the classification task, focal loss [42] is one of the most popular choices, as it alleviates the problems caused by a class imbalance in an image (foreground vs. background), especially in the case of single-stage object detectors, and is defined as:  
\[FL(p_{t})=-\alpha_{t}(1-p_{t})^{\gamma}log(p_{t}), \tag{3}\]  
where:  
* \(p_{t}\) is the confidence of the model that the bounding box contains an object of class \(t\);
* \(\alpha_{t}\) is the weighting factor for class \(t\);
* \(\gamma\) is the focusing parameter.  
It addresses the problem of class imbalance by introducing the scaling factor \((1-p_{t})^{\gamma}\), which increases the impact of misclassified proposals (hard negatives) on the total loss, while also decreasing the contribution of correctly classified examples (easy negatives or true positives).
## 4 Discussion  
As discussed above, the HTP-MC criterion exhibits high accuracy in predicting rock strength under high-temperature and high-pressure conditions. Compared to the classical MC criterion, which involves only two fitting parameters (cohesion c and internal friction angle \(\varphi\)), the HTP-MC model introduces only two additional parameters (\(n\) and \(B\)). Theoretically, these parameters can be determined using just four sets of uniaxial or triaxial compression tests. However, in practice, the selection of test data--particularly the number of samples and the temperature and confining pressure conditions--significantly influences the fitting results. These differences reveal the sensitivity of the model parameters to changes in the experimental conditions. If the parameters are highly sensitive to variations in temperature and confining pressure, this may result in decreased predictive accuracy of the model under complex loading environments.
## 3 Methodology
### Utilizing Invariant Rules for Anomaly Detection in ICS  
After generating invariant rules using swarm intelligence algorithms, the next task is to apply these rules for real-time monitoring of an ICS to achieve effective anomaly detection. This section details the application of extracted invariant rules to analyze ICS data, enabling the timely identification and response to deviations from normal operational patterns, thus ensuring system stability and safety.  
SCADA continuously collects data from various sensors and actuators within the ICS, including critical operational parameters such as pressure, temperature, liquid level, flow rate, and valve statuses. These data are compared against the set of invariant rules generated in the previous section. Each invariant rule is considered a physical law or state constraint that the system should adhere to under normal conditions. Any deviation from these invariant rules is flagged as an anomaly.  
The method of anomaly detection based on invariant rules, due to its clarity and explanation, enables operators to quickly understand changes in the system's state and respond promptly. By comparing real-time data collected by the SCADA system with these invariant rules, this approach facilitates early detection and localization of anomalies, reducing the time required for fault diagnosis. Additionally, its ability to clearly distinguish between normal and abnormal states enhances the overall safety of the system, ensuring the continuity and reliability of industrial processes.
# Data Aggregation Based on Overlapping Rate of Sensing Area in Wireless Sensor Networks
## 5 Performance Evaluation
### Parameter Analysis
#### 5.3.2 Analysis of the Threshold of Overlapping Rate of Sensing Area  
In order to show the impacts of the threshold of overlapping rate \(OR_{T}\) on the overall performances of AggOR and AggOR-CP, we conduct a series of experiments in Scenario SP with \(OR_{T}\) ranging from 0 to 1. Figure 10 shows the simulation results. The solid line, intermittent line and chain dotted line indicate the three scenarios with 120, 160 and 200 nodes, respectively. In Figure 10, the results are stable when \(OR_{T}\leq 0.4\) or \(OR_{T}\geq 0.8\), because in our dense WSN, all the overlapping rates of sensing area between two nodes are larger than 0.4 and smaller than 0.8.  
In Figure 10a, when \(OR_{T}\leq 0.4\), all gathering nodes are candidate parent nodes and almost no independent node exists. When \(OR_{T}\) increases to 0.5, the network lifetime prolongs a little, because the gathering areas are constructed efficiently with the larger overlapping sensing area as well as some neighbor nodes selected as gathering nodes. When \(OR_{T}\) continues to increase, less candidate parent nodes and more neighbor nodes are selected as the gathering nodes, which increases energy cost. Additionally, more independent nodes also limit the benefits of data aggregation. Therefore, with a medium value 0.5 of \(OR_{T}\), AggOR reaches the longest network lifetime. Moreover, the network lifetime of AggOR is superior to that of AggOR-CP.  
Figure 9: Results with different sensor densities. (**a**) network lifetime; (**b**) transmission overhead; (**c**) maximum hops to the sink; (**d**) information accuracy.  
Figure 10b depicts that, when there are 120 or 200 nodes, the smallest transmission overhead appears in AggOR when \(OR_{T}\) is 0.5. It is obvious that the larger \(OR_{T}\) is, the higher transmission overhead there is due to less aggregation. In addition, transmission overhead in AggOR is smaller than AggOR-CP, and large scale leads to a bigger difference between them. It is consistent with our previous analysis that the neighbor nodes working as gathering nodes cost less energy for data transmission than independent nodes.  
Data collection and aggregation by the gathering nodes cause a little delay apparently, and the neighbor nodes as the gathering nodes further increase the hops to the sink. When \(OR_{T}\) rises, the median value of maximum hops to the sink in AggOR is bigger than AggOR-CP, as shown in Figure 10c. However, the largest difference between them appears when \(OR_{T}\) is larger than 0.6 and the difference is one hop, which implies that there are no candidate parent nodes suitable to be gathering nodes for some node on the longest path to the sink.