esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Dual-Path Speech Enhancement Model
## 5 Results and Discussion
### Ablation Test
M refers to the case when only the masking branch is used, and S refers to the case when only the mapping branch is used. M + S is the case when a masking branch and a mapping branch are combined. Proposed is our dual-path SE model. These observations motivated the design of our proposed dual-path model (Proposed), which combines the strengths of masking-based approaches (M) to address the limitations identified in the M + S model. The final model was constructed by independently training the M and M + S and then integrating their learned features. We believe this separation plays a crucial role in the effectiveness of our model, as can be clearly observed in Figure 3. Notably, the model was primarily designed to achieve a high-quality SE by jointly addressing noise suppression and speech intelligibility. As shown in Table 1, PESQ increases by 0.11, enhancing speech intelligibility, while CSIG, CBAK, and COVL improve by approximately 0.07, 0.06, and 0.1, respectively. STOI also increases by 1%, confirming better speech clarity. Additionally, as we can see in Figure 3, the auxiliary path mitigates the LLR degradation observed in the M + S model, stabilizing the spectral tilt and formant amplitude preservation while enhancing fwSNRseg (16.61 in Figure 3) and SNRseg (10.44 in Table 1). Although the exact reason for the decrease in the LLR score is unclear, it is clear that constructing a dual-path model by joining two single-path models after independently training them gives an improvement since it can average the outputs of the two paths. This is also supported by the results, which show clear performance gains in Figure 3.  
The proposed model's WSS score increased slightly compared with M + S, indicating a relative decline in the preservation of the formant location. However, the improved LLR and fwSNRseg scores suggest that the auxiliary path enhances the modeling of spectral tilt and formant amplitude and reflects a more accurate reconstruction in the perceptually important frequency regions, which play a critical role in subjective speech quality. Consequently, the overall enhancement performance becomes more balanced. Additionally, the WER is further reduced, confirming that speech content is more effectively restored. In summary, the proposed dual-path model achieves balanced improvements across all metrics. It records a WB-PESQ of 3.33 (+0.11), with CSIG, CBAK, and COVL improved by 0.07, 0.06, and 0.10, respectively, compared with the single-path masking model. STOI increases by 1%, and WER is further reduced. The model achieves a fwSNRseg of 16.61 and SNRseg of 10.44, indicating the effective preservation of the perceptually important frequency components. While the WSS slightly increased, better LLR and fwSNRseg confirm a more stable modeling of the spectral tilt and formant amplitude, leading to a more perceptually coherent enhancement.
# Physical Movement Demands of Training and Matches across a Full Competition Cycle in Elite Netball
## 2 Materials and Methods
### Data Collection  
This was a longitudinal single-cohort observational research design for one team. Data collection occurred over an entire pre-season and competitive season of elite netball. Catapult T6 devices containing a 10 Hz LPS receiver tag and 100 Hz inertial sensors (tri-axial accelerometer, gyroscope, and magnetometer) (Catapult Sports, Melbourne, Australia) were worn by each athlete for every training session and match. Devices were held in a fitted pouch built into a sports vest (upper-back) to ensure a close fit for the sensors and unencumbered movement for the athletes. The Catapult ClearSky LPS (Catapult Sports, Melbourne, Australia) consisted of a pre-installed network of anchors in the team's training centre and home competition venue to communicate with the LPS receiver tags in the T6 devices and track positional movement data using ultra-wideband technology. The ClearSky LPS has shown acceptable validity for time-motion analysis in team sport applications within an indoor environment [17]. Training sessions and matches recorded in stadia with no LPS system were limited to accelerometer-derived variables only.  
Data were recorded from all coach-prescribed, on-court, pre-season training sessions (\(n\) = 29), pre-season practice matches (\(n\) = 8), pre-season tournament matches (\(n\) = 4), in-season training sessions (\(n\) = 21), in-season practice matches (\(n\) = 5), and competition matches (\(n\) = 15). A total of 744 unique sessions for all athletes, across all session types over the course of the full season were recorded. LPS anchors were only available in one competition stadium, thereby limiting available LPS data capture to matches played at this venue (\(n\) = 6). All pre-season and in-season training sessions (\(n\) = 50) were captured using LPS technology, with anchors installed in the training stadium. The total number of unique sessions collected containing LPS variables was 566 of the 744 individual samples.  
All sessions were coded using the accompanying proprietary software--Openfield v1.22.0 (Catapult Sports, Melbourne, Australia). The times when a player was on the bench and breaks between quarters were excluded from match-play data. During training sessions, coaching stops and breaks between drill blocks were excluded from analysis. However, planned rest intervals in drills were included. Spatiotemporal and accelerometer-derived data are reported in this manner to reflect the actual volume and intensity of the drills, quarters, and prescribed tasks--rather than as an average of the entire session. No resistance training or off-court conditioning sessions were included in this analysis, as the focus of this study was on-court workloads.
# Pressure-Driven Nitrogen Flow in Divergent Microchannels with Isothermal Walls
## 6 Conclusions  
Two-dimensional numerical simulations based on the direct simulation Monte Carlo (DSMC) method were performed to predict thermal and flow fields of pressure-driven nitrogen flow in divergent microchannels. A wide range of Knudsen numbers was studied covering the slip to the free molecular rarefaction regimes. Moreover, the influences of microchannel divergence angle, inlet-to-outlet pressure ratio, and incomplete surface accommodation were investigated. The following conclusions were drawn based on the present study.  
The influence of thermal gradient on net heat flow direction increases with an increase in the divergence angle of the microchannel and can lead to the occurrence of thermal separation even when hydrodynamic separation is absent. The contribution of the pressure gradient in determining the heat flow direction enhances with an increase in the Knudsen number. At sufficiently large Knudsen numbers, the heat flow is only toward the channel inlet. Cold-to-hot heat transfer (also known as anti-Fourier heat transfer) was observed in the microchannel as a result of nonequilibrium effects. Viscous dissipation in the Knudsen layer diminishes with decreasing applied pressure ratio, which results in the domination of the pressure gradient influence on heat flow close to the channel walls.  
Further studies may focus on realising the effects of the molecular weight and structure, or different gas mixtures on thermal and fluid flow fields inside micro- and nanochannels with with complex geometries.  
Conceptualization, A.E. and E.R.; methodology, A.E.; software, A.E. and V.S.; validation, A.E.; formal analysis, A.E.; investigation, A.E.; resources, A.E., V.S. and E.R.; data curation, A.E. and V.S.; writing--original draft preparation, A.E.; writing--review and editing, A.E., V.S. and E.R.; visualization, A.E. and V.S.; supervision, A.E. and E.R.; project administration, A.E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding. The APC was funded by the Delft University of Technology.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} DSMC & Direct simulation Monte Carlo \\ NTC & No time counter \\ TAS & Transient adaptive sub-celling \\ VHS & Variable hard sphere \\ PPC & Particle per cell \\ NSF & Navier-Stokes-Fourier \\ MEMS & Micro-electromechanical systems \\ OpenFOAM & Open Source Field Operation and Manipulation \\ \end{tabular}
# Human-AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM Era +
## 3 Identifying Challenges in RSA: Literature Review
### Challenges in Localization and Orientation  
One of the biggest challenges identified for RSA agents is accurately localizing users and orienting themselves. For this task, the agent mainly depends on the two sources of information--users' live video feed and GPS location. The agents put them together to localize the users on a digital map on their dashboard [1; 10]. However, the agents frequently get confused perceiving which direction the user is facing from the user's camera feed and GPS location [9; 13; 42]. The trained agents who participated in a prior study [13] also reported that losing track of users' current location is a challenging problem. RSA agents' lack of environmental knowledge and their unfamiliarity with the area, the scarcity and limitations of the map, and the inaccuracy of GPS are found to be the main causes of location- and orientation-related challenges.
## 5 Computational Experiments
### Problem Parametrization  
Taking the results of [4] as a reference, the best exploration strategy corresponds to a back and forward movement strategy and a weighted orientation criteria given by \(\alpha\) = {0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8}. The experiments in this section are designed to determine the best refueling strategy in terms of the exploration route duration. The parameterization of the experiments is as follows:  
* Ten random seeds.
* Different levels of disruption, given in {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7}. Disruptions are classified into three levels, low = {0.1, 0.2, 0.3}, medium = {0.4, 0.5}, and high = {0.6, 0.7}.
* Number of refueling stations available in the disrupted network: (1) only the DMC as refueling station, (2) the DMC and 50% of the victim nodes operate as potential refueling stations, or (3) the DMC and all victim nodes operate as potential refueling stations.
* Three refueling strategies: extremely conservative refueling strategy (_EConservative_), moderately conservative refueling strategy (_MConservative_), and conservative refueling strategy (_Conservative_). This section presents the following experiments:
* Comparison of the refueling strategies: _EConservative_, _MConservative_, _Conservative_, seven \(\alpha\) values, low, medium, and high disruption levels.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Instance**} & \multirow{2}{*}{**Nodes**} & \multicolumn{2}{c}{**Total Distance of**} & \multicolumn{2}{c}{**Longest Aerial**} & \multirow{2}{*}{**Relation**} & \multirow{2}{*}{**Cluster**} \\  & & & **Road Network (km)** & & & \\ \hline p1.2.b & 32 & 84,990.26 & 120.07 & 220 & A \\ p2.2.a & 20 & 24,710.51 & 126.84 & 42 & A \\ p3.2.a & 32 & 95,884.38 & 169.03 & 350 & A \\ \hline p4.2.a & 99 & 1,481,730.60 & 257.97 & 25,539 & B \\ \hline p5.2.a & 65 & 351,215.83 & 109.66 & 7128 & C \\ p6.2.a & 63 & 232,134.88 & 130.38 & 5533 & C \\ p7.2.a & 101 & 344,621.25 & 49.92 & 5042 & C \\ \hline \hline \end{tabular}  
* Longest aerial edge connecting to the DMC node  
\end{table}
Table 2: Characteristics of instances and cluster type.  
* Comparison of the performance of refueling strategies considering in the disrupted network: (1) only the DMC operate as refueling station, (2) the DMC and 50% of the victim locations as potential refueling stations and the DMC, and (3) the DMC and all victim locations as potential refueling stations and the DMC.
* For each disruption level, 10 different disrupted road networks are evaluated per instance (10 seeds).
# Comprehensive Study of IoT Vulnerabilities and Countermeasures
## 2 Preliminary Materials
### Commonly Used Protocols
#### 2.7.19. Open Platform Communications (OPC)  
Open Platform Communications (OPC) is a machine-to-machine protocol that facilitates real-time data exchange between control systems and devices and allows seamless connectivity. OPC supports classic specifications such as OPC DA (Data Access) and newer advances such as OPC UA (Unified Architecture), increasing platform security and independence. The protocol plays a key role in the Industry 4.0 environment, supporting the integration of the Internet of Things (IoT) and computer-physical systems (CPS). OPC is useful for implementing industrial systems and optimizing data exchange in heterogeneous environments [57].
# Mobile Robot Path Planning Considering Obstacle Gap Features
## 2 Path Planning Method with Clearance Characteristics
### Real-Time Robot Control Method for Optimization of Clearance Angle  
#### 2.2.1 Obstacle Avoidance Angle Generation Method Based on Gap Extraction  
The distance measurement between the robot and the obstacle is very important to determine the heading angle and speed of the robot during motion. The circular robot is regarded as a point robot in Cartesian space, and the distance between the robot and the obstacle boundary is calculated, represented by \(d\). The Pythagorean theorem is used in the computational method by considering the geometry of the robot and the obstacles, as shown in Equation (7).  
\[d=\sqrt{(x_{o}-x_{r})^{2}+(y_{o}-y_{r})^{2}} \tag{7}\]  
The heading angle of the robot is the angle adopted by the mobile robot to go to the goal position, which is an element composed of the clearance angle and the goal angle. The final heading angle of the robot is calculated based on the gap array so that the robot selects the position in the largest gap center during the motion. It is also considered that the robot may become unstable when there are multiple gaps of similar size; a suitable gap distance coefficient is used to eliminate the interference of redundant gaps. The optimal angle of each gap is deduced, and the new feasible gap is selected as the moving target of the robots. There are two key variables in the utility function: the size of the gap \(d_{n}\) and the angle \(\delta_{n}\) between the target point and the center of the gap. Which are formulated by Equation (8).  
\[U_{f}=m_{1}d_{n-gap}+m_{2}(\pi-\delta_{n}) \tag{8}\]  
where, \(d_{n}\)-gap and \(\delta_{n}\) are, respectively, the size of the gap and the angle between the target point, and n represents the number of gaps. \(m_{1}\) and \(m_{2}\) are the weight coefficients of gap size and angle, respectively.  
The gap angle between the target point and the center of the nth gap is expressed as Equation (9), as follows:  
\[\delta_{n}=|\alpha_{n}-\beta| \tag{9}\]  
where, \(\alpha_{n}\) is the angle of the sampling path to the center line of the gap, and \(\beta\) is the angle of the current sampling path to the target point.  
By considering both the size of the gap and its heading angle to the target point, the feasible gap of the robot can be calculated. And guiding the robot to choose the closest feasible gap to reach the goal position quickly, the shorter path is created while ensuring a collision-free trajectory. The optimal angle search is shown in Figure 4.  
At the same time, the evaluation function of the dynamic window algorithm is improved, which is shown in Equations (10) and (11), as follows:  
\[G(v,w)=\alpha\cdot head(v,w)+\beta\cdot dist(v,w)+\gamma\cdot vel(v,w)+\eta \cdot gap(v,w) \tag{10}\]\[gap(v,w)=\frac{1}{\theta-U_{f}} \tag{11}\]  
where \(gap(v,w)\) represents the reciprocal of the difference between the angle from the robot guidance angle to the end point and the optimal obstacle avoidance. Angle is the sample path heading angle for the robot.
## 4 Conclusions  
At present routing in large-scale WSNs is a hot research topic, with a limited but rapidly growing set of efforts being published. In this paper we have conducted a comprehensive survey of the various routing protocols in large-scale WSNs, which is the first attempt in the area. We categorized the routing protocols as control overhead reduction, energy consumption mitigation and energy balance ones, depending on their design objectives. We presented a comparison of the routing protocols discussed in the work in terms of message complexity, memory requirement, localization, data aggregation, clustering manner, intra-cluster topology, cluster head selection and multi-path routing. Through these metrics, the reasonable explanations of their strengths and weaknesses were given.  
Although the performances of these protocols are encouraging for improving scalability of large-scale WSNs, some issues remain to be considered. First of all, as the number of nodes in large-scale WSNs increases, the density of the network is increased. Therefore, more redundant information is created and this makes the network congestion more serious. On the other hand, in some increment and unstable environments, a certain degree of redundancy may be desirable to provide the network with reliability. A trade-off between the redundancy reduction and the redundancy utilization is challenging. In addition, data transmission delays are an unavoidable problem when time-sensitive tasks such as fire alarms are assigned to an entire network. In this case, routing must be prepared in advance and maintained constantly. Embedding this consideration in the routing design is desirable. Furthermore, in a large-scale network, communication links become longer and the deployment of the nodes becomes denser. The possibility of link-failure becomes more frequent [52]. Work towards developing techniques for quickly re-establishing valid routes is likely to be of higher importance for improving the robustness of large-scale wireless sensor networks.  
Further research should consider other network performance criteria such as the quality of service (QoS) issues posed by the use of video and imaging sensors for the real-time applications, and node mobility in some special environments. Nonetheless, with the increasing functionalities available to a wireless sensor node, more complicated tasks which involve more energy consumption and network overhead may be assigned to the sensor nodes, so how to increase energy efficiency and scalability of the network remains a challenging research area.
## 3 YAMNet: An Efficient CNN for Sound Event Detection  
YAMNet [55; 56; 57] is a pre-trained CNN developed for SED tasks. The network is trained with AudioSet [48], a dataset containing 632 classes of sound events drawn from more than 2 million YouTube(r) clips. Environmental sounds, human sounds, music genres, and musical instruments are examples of classes included in the dataset.  
YAMNet is built on the MobileNet [58] architecture. This is an efficient framework designed to fit low latency artificial intelligence models in mobile applications, where the availability of computational resources is quite restrained. In MobileNet architectures, the standard convolution is replaced with the depthwise separable convolution. This particular approach is further detailed in the original paper provided by Google Inc. in 2017 [58], where it is showed that the computational cost may be reduced up to nine times compared with standard convolution. In addition to input and output layers, YAMNet contains 27 convolutional layers, 1 global average pooling layer, and 1 fully connected layer. Standard convolution and depthwise separable convolutions are sequentially stacked up to the pooling layer. The convolutional layers employ ReLU activation functions and batch normalization processes [59]. Finally, the output layer brings the sound class prediction with a Softmax activation function.  
To have an idea of the effect of depthwise convolutions, it is possible to compare one of the early CNNs such as AlexNet [22] with YAMNet. The number of parameters contained in AlexNet amounts to 61 million in 8 layers, whereas YAMNet contains 3.7 million parameters [41] in 30 layers. Fewer learnable weights further reduce overfitting risks in such architectures.
## 3 Model Building
### Numerical Model and Constitutive Parameters
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Soil Layer**} & \multirow{2}{*}{**The Name of the Soil**} & **Thickness (m)** & **Density (kg/m\({}^{3}\))** & **Cohesion (kPa)** & **Angle of Internal** & **Modulus of Elasticity** (MPa)** & **Poisson’s Ratio** \\ \hline \(1\) & \(\lx@math@degree\)1\({}_{1-1}\) miscellaneous fill & 3.4 & 1900 & 9.0 & 15.00 & 10 & 0.33 \\ \hline \(2\) & \(\lx@math@degree\)1 clay & 5.6 & 1805 & 13.4 & 15.06 & 14.4 & 0.33 \\ \hline \(3\) & \(\lx@math@degree\)mucky clay & 19.2 & 1803 & 16.1 & 13.99 & 38.4 & 0.35 \\ \hline \(4\) & \(\lx@math@degree\)2\({}_{2}\) silty clay+ silt & 6.1 & 1880 & 13.0 & 24.50 & 79.5 & 0.31 \\ \hline \(5\) & \(\lx@math@degree\)3\({}_{3}\) silty clay & 7.7 & 1887 & 23.4 & 21.08 & 92.5 & 0.31 \\ \hline \(6\) & \(\lx@math@degree\)2\({}_{2-2}\) silt & 7.9 & 1990 & 6.0 & 33.63 & 214 & 0.26 \\ \hline \(7\) & \(\lx@math@degree\)1\({}_{1}\) clay & 20.1 & 1863 & 24.4 & 18.40 & 80 & 0.33 \\ \hline \multicolumn{2}{c}{The grouting layer} & \multirow{2}{*}{/} & \multirow{2}{*}{0.03} & \multirow{2}{*}{1200} & \multirow{2}{*}{/} & \multirow{2}{*}{/} & \multirow{2}{*}{1.5} & \multirow{2}{*}{0.45} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Basic parameters of soil in the finite element model. of growing drag reduction, the friction resistance per unit area can generally be directly assumed to be a fixed value. In this model, it is assumed that the pipe joint's frictional resistance is equal and opposite to the driving direction. The frictional resistance is set to 3 kPa for simulation, and the grouting pressure is taken as 300 kPa with reference to the actual project. The pipe jacking machine will pass between the Y156 and Y157 piers of the Yan'an viaduct, and the minimum clear distance between the piles of the viaduct is only about 5.6 m. The piles of Yan'an viaduct are \(\Phi\)600 PHC piles with a wall thickness of 110 mm, and the length of the piles is 42 m. The C80 concrete is used. The size of the bearing platform is 8.4 m \(\times\) 6.6 m and 2.0 m thick, and it is made of C25 concrete and has 18 piles. The actual bearing platform and group piles have a certain angle with the pipe jacking's forward direction. In order to simplify the calculation, in the numerical simulation, the bearing platform and group piles are simplified to be orthogonal to the jacking direction, the minimum distance between the piles and the axis of the tunnel is taken to be 6 m, and the distance between the bearing platform and the front and rear model boundaries is 36 m. The overall model is shown in Figure 3b. In the modeling, group pile and bearing platform models are added to the soil body, and the piles are simulated by beam unit and linear elastic models. The top of the pile is bound to the bottom of the bearing platform, and the tangential friction model of the pile foundation and soil contact surface adopts a penalty function. The friction coefficient is 0.3, and the normal direction adopts hard contact. The model parameters are shown in Table 2.