esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed MTU-Net-Based Speech Enhancement
### Model Architecture  
In previous studies, multiple-target-based SE was proposed to improve speech enhancement performance [5; 29; 30]. Inspired by these approaches, the proposed MTU-Net-based single-channel SE has a network architecture as shown in Figure 2. First, the encoding network in the MTU-Net has the same structure as in the conventional U-Net, which is described in Section 2. However, there is a difference in the decoding network in which an additional decoding path is attached to estimate the noise spectra. In other words, the output of the encoding network is decoded through two paths that are used to estimate the clean speech and noise spectra, respectively. The architecture for the noise decoding path is identical to that for the speech decoding path, and the layers of each decoding path are reversely composed of those in the encoding network. Here, a spectrogram of the noisy input speech is used as the input feature, while its corresponding clean speech and noise spectrograms are used as target features. Note that there is a difference between the U-Net and proposed MTU-Net in mask estimation. The IRM of the U-Net is the output of the network; however, the mask of the proposed MTU-Net is calculated by using estimated speech and noise from the two outputs of the network, which will be explained in Section 3.3.
## 3 Methodology
### Evaluation Datasets  
This study utilized two publicly available HAR datasets, UCI-HAR and WISDM, to assess the proposed deep learning model. These datasets were selected because they encompass diverse real-world scenarios in HAR employing wearable sensors. They feature different sensor placements, sampling rates, and types of activities. Both datasets include measurements from inertial sensors (accelerometer and gyroscope) collected via smartphones, making them ideal for assessing HAR systems based on wearable devices.
## 3 Materials and Methods
### Experimental Design
#### 3.3.2 Experiments Performed on Synthetic Data  
A complexity analysis was performed recurring to synthetic data by generating multiple instances of the problem by increasing the considered number of products (P), inspection stations (S), product characteristics (K), and time periods (T). These sets were adjusted within the ranges defined in Table 2 to create progressively larger problem instances. Each generated instance was solved using the MILP model, and the computational time required to solve each instance was recorded in seconds. The experiments thus aimed to measure how the model's performance (in terms of solution time) is affected as the problem size increases. These tests provide valuable insights into the model's scalability, helping to understand its efficiency and identify potential bottlenecks as the problem grows.
## 3 Analysis and Discussion
### Analysis of AFEM Setting
#### 3.3.1 Method of Weight Constraint  
As shown in Table 4, different types of constraints are used to constrain the weight matrix of the AFEM module: exponential constraint and weight clipping. An exponential constraint is used to ensure that the weights are positive by applying an exponential function to each element in the weights. Furthermore, weight clipping is calculated as follows:  
\[W^{\prime}_{ij}=max(a,min(W_{ij},b))\quad\ a<b \tag{7}\]  
where the current weights of \(W_{ij}\) are constrained by the ranges \((a,b)\) after the mini-batch training update. The results in Table 4 show that weight clipping is more beneficial for feature extraction, especially for \(a=1\) and \(b=1\). The network has the highest validation accuracy, which is 1.8% higher relative to the validation accuracy of hand-crafted features.
## 3 Results and Discussion
### Earthquake Displacement
#### 3.2.1 Deformation of Tohoku-Oki Earthquake
Figure 3: Time series for the three directions of GPS stations after 05:40:21 (UTC time), where in (**a**–**c**) are the position time series in the north, east, and up components. The RMSs of the GAMIT/TRACK and SF-PPP position errors are enumerated in Table 2. After removing the solutions under poor satellite-observing conditions and during the convergence period, the position errors are calculated by comparing the solutions from the mean value between 0 and 5 h before the earthquake. Accordingly, it can be found that the accuracy of GAMIT/TRACK in terms of RMS is much higher than that of SF-PPP. Meanwhile, GAMIT/TRACK captures lower amplitude changes than that of SF-PPP because of its higher positioning accuracy character. Nevertheless, the results afforded by Figures 3 and 4 illustrate that SF-PPP provides the coseismic displacement with similar accuracy as that from GAMIT/TRACK. It is because the detection of coseismic displacement just requires high accuracy position solutions in the relative term. In addition, such an SF-PPP model also can provide ionospheric VTEC distribution. Hence, we can adopt SF-PPP to calculate and analyze the VTEC variation during the earthquake. Moreover, for both methods, the precision in horizontal directions is much higher than that in the vertical direction. This circumstance is engendered by the spatial distribution of GPS satellite constellations.
## 3 Methodology
### Dynamic Feature Samples Construction
#### 3.2.2 Time-Series Samples Dataset (\(TS\))  
\(TS\) is defined as Equation (12), where \(TS^{i_{m}}\) denotes the set of all sub-trajectories of \(T^{i_{m}}\), as shown in Figure 7. Space-based AIS data has weak data continuity, and there are data points between which the time interval is large in \(T^{i_{m}}\), which cannot reflect the ship movement correctly. As such, we break \(T^{i_{m}}\) into a series of sub-trajectories \(TS^{i_{m}}_{n}\). For \(T^{i_{m}}\), the steps for constructing \(TS^{i_{m}}\) are as follows.  
\[TS=\left\{TS^{i_{1}},TS^{i_{2}},\dots,TS^{i_{m}}\right\} \tag{12}\]  
1. Calculate the upper quartile \(Q^{i_{m}}_{u}\), the lower quartile \(Q^{i_{l}}_{l}\), and the quartile distance \(IQR^{i_{l}}\) of the field \(\delta t\) on the whole dynamic dataset.
2. Traverse the points on \(T^{i_{m}}\) in time order. For \(d^{i_{m}}_{i}\), if \(\delta t^{i_{m}}_{j}\) is outside \([Q^{i_{l}}_{l}-3 IQR^{i_{l}},\ Q^{i_{l}}_{u}+3 IQR^{i_{l}}]\), break \(T^{i_{m}}\) at \(d^{i_{m}}_{j-1}\), then set \(\delta t^{i_{m}}_{j}\), \(\delta lng^{i_{m}}_{j}\), \(\delta lat^{i_{m}}_{j}\), \(\delta COG^{i_{m}}_{j}\), and \(\delta SOG^{i_{m}}_{j}\) to zero. The sequence from the last interrupted point to \(d^{i_{m}}_{j-1}\) constructs the sub-trajectory \(ST^{i_{m}}_{n}\). All sub-trajectories of \(T^{i_{m}}\) construct \(TS^{i_{m}}\).
3. Apply the second step on all trajectories, and then we obtain \(TS\).
# Study on the Vibration Effects of Cyclic Blasting on Bridge Structures under Construction
## 6 Conclusions  
Our study highlights the significant impact of cyclic tunnel blasting on the stability of adjacent bridge structures under construction. Utilizing FLAC3D software, we developed a 3D numerical model to simulate the vibration response of the bridge. The results demonstrate that the peak vibration velocities induced by cyclic blasting on bridge components are within safe limits, ensuring structural integrity. Specifically, the vibration velocity at the bridge deck decreases more rapidly with increasing distance from the tunnel compared to the pier base.  
Key findings include the following:  
1. The results show that vibration velocity decreases more rapidly at the bridge deck than at the pier base with increasing distance from the tunnel.
2. Peak vibration velocities were recorded at 0.235 cm/s for the bridge deck and 0.081 cm/s for the pier base, with a predicted 0.209 cm/s at the middle column.
3. The peak vibration velocity induced by blasting remains within safe limits, and regression analysis indicates that eight daily blasting cycles do not compromise the safety of bridge structures 43 m away.
4. The optimized blasting scheme is Scheme 2, with a hole depth of 1.9 m, a linear charge density of 0.33 kg/t, and a blasting delay of 50 ms, making it the optimal blasting scheme for this project. This scheme ensures both construction efficiency and compliance with safety standards.  
Our research provides a systematic evaluation of the differential impacts of cyclic blasting on various bridge components and introduces a highly accurate three-dimensional numerical model. This model serves as a scientific reference for ensuring safety in blasting operations. Future research should focus on the long-term effects of repeated blasting on different materials and under varying environmental conditions. Additionally, the development of real-time monitoring and dynamic adjustment technologies for blasting parameters will further enhance construction safety and efficiency.  
Conceptualization, Y.C. and E.C.; funding acquisition, E.C.; methodology, Y.C.; software, Y.C.; validation, E.C.; writing--original draft, Y.C.; writing--review editing, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors declare no conflicts of interest.
# Design and Verification of a New Fixture for Machining of Porous Blocks for Medical CAD/CAM Systems
## 1 Introduction
In addition, fixtures also play a critical role in many medical tests such as providing sample stabilization [28], sample handling [29], setting consistent test conditions, and accurate load placement [30]. These various tests are essential for understanding the biomechanical properties of bones, muscles, tendons, and joints, which further contribute to the development of more effective therapeutic approaches and medical devices. The use of appropriate fixtures in medical testing is key to ensuring accurate test results and guaranteeing their functionality in real-world conditions. As can be seen, although today there are various applications of fixtures in the medical field, their application is still somewhat limited and narrowly specialized to specific uses where the focus is mainly put on testing purposes. Fixtures are widely used in many fields, including medical applications, but their design is typically tailored for rigid and durable materials, leaving a significant gap in addressing the needs of fragile, porous materials. Porous biocompatible blocks, due to their delicate structure, are prone to breakage under excessive force, making traditional fixture designs unsuitable for machining such materials. This limitation significantly restricts the potential of CAD/CAM systems for manufacturing complex, patient-specific implants. Current research on fixtures primarily focuses on their applications in testing, diagnostics, and rehabilitation, with little emphasis on adapting them for machining sensitive materials. This presents a critical challenge on how to securely clamp and position porous materials for precise machining while preserving their structural integrity. To address this challenge, this study introduces a novel fixture specifically designed for machining porous biocompatible blocks. Unlike traditional fixtures, which are optimized for rigid materials, this fixture applies minimal force to securely clamp and position the porous material without causing damage. The design incorporates silicone tabs to enable soft clamping, further reducing the likelihood of damage during the machining process. The fixture also integrates seamlessly with CAD/CAM systems, facilitating the precise machining of osseous materials. This approach opens new possibilities for machining delicate biocompatible materials in medical applications. Porous bone blocks,due to their sensitivity and delicate porous structure, can easily break. Excessive force risks compromising their integrity, often rendering them unusable. The fixture proposed in this paper specifically targets these challenges, combining minimal clamping force with advanced design features to preserve the material's structure during machining. By addressing these limitations, this innovative solution expands the possibilities for safely machining fragile biocompatible materials, ensuring both precision and material integrity in medical applications.
## 1 Introduction  
Time synchronization is critical in most networks, especially in WSNs [1]. Clock accuracy in these types of networks is difficult to achieve, due to energy and processing constraints in the motes. Synchronization protocols [2, 3] allow the application of several different policies, such as sensor data fusion, coordinated actuation between nodes and power-efficiency.  
The work of [PERSON] [4] demonstrates that the \"characteristics of crystal units are determined primarily by the angles of cut of the crystal plates with respect to the crystallographic axes of quartz\" and \"a temperature variation can also cause a frequency change due to the energy dissipation in the active area of the resonator\". More recently, [PERSON] _et al_. [5] demonstrate how the temperature variations affect the TelosB oscillator (CMR200T). Using a set of motes with the above mentioned oscillator, this work demonstrates that the synchronization error of FTSP increases, due to temperature variations. A more accurate clock skew is obtained when temperature variations are included in synchronization protocols.  
As is described in Section 3.1, there are several types of oscillators. The common Crystal Oscillator (XO) does not compensate for its frequency on temperature, providing a clock stability of \(\pm 20\) ppm at \(25\,^{\circ}\)C (e.g., Citizen CMR200T). On the other hand, the Temperature Compensated Crystal Oscillators (TCXO) are able to manage temperature variations in order to enclose the errors within a certain range. For example, the Kyocera KT3225T TCXO improves the frequency stability to \(\pm 5\) ppm. However, the enhancement obtained by TCXO requires more complexity, energy consumption and economic costs. For the previously given models, prices are US$3.10@3K for the TCXO and US$0.25@2K for the XO model. For compensating temperature with an XO, it is mandatory to include a temperature sensor, e.g., a thermistor probe with a cost of US$0.06@2K, which sum up to a total of US$0.31@2K. Therefore, as [PERSON] _et al_. [6] state, \"even with a TCXO, the clock skew is still non-negligible (e.g., up to \(\pm 7.5\) ppm)\". Thus, flexible software solutions working on low-cost hardware systems are a strong and feasible research area in order to improve time synchronization protocols in WSNs.  
This work proposes an innovative correction factor to obtain a more precise clock skew, based on temperature variations between the global and the local clocks. With this adjustment, two new synchronization approaches for WSNs are proposed: the Adjusted Temperature FTSP (AT-FTSP) and the Advanced Adjusted Temperature FTSP (A2T-FTSP). Experiments have been carried out in a network of TelosB (Rev.b) motes running TinyOS. Both AT-FTSP and A2T-FTSP improve the average synchronization error with respect to FTSP under temperature variation scenarios.
# Navigating the Maps: Euclidean vs. Road Network Distances in Spatial Queries
## 3 Methodology
### Data Source  
The dataset for hospital and ambulance addresses was obtained from OpenStreetMap (OSM), which is a comprehensive database containing information on hospitals and ambulances across various Australian states. In contrast, a segment of the residential addresses data originated from the Geospace Geocoded National Address File (G-NAF) [33], which adheres to the Australian Statistical Geography Standard (ASGS) [34] and encompasses addresses located within the residential mesh blocks (MBs) framework. Specifically, residential addresses were randomized, and ten addresses from each Statistical Area Level 2 (SA2s) area were selected, thereby ensuring a representative sample for the study.  
To classify the query points, the mesh blocks shapefile was employed to assess their characteristics based on the density of the POIs. Mesh blocks, defined by the Australian Bureau of Statistics [34], are small geographical areas designed for statistical and data aggregation purposes. They offer a structured way to organize and analyze fine-grained data. These mesh blocks served as a crucial framework for categorizing the query points, thereby enabling the evaluation of POI density near these points.  
In our research, the residential address dataset was deliberately structured by mesh blocks, thereby ensuring the absence of duplicate addresses. This strategic alignment is a precautionary measure to eliminate the potential bias that might arise from duplicate addresses within the dataset. By selecting the query point in this manner, the experimental  
Figure 7: Methodology process with arrows representing the sequential flow of the process and the process explanation shown in each box.  
data were able to accurately evaluate the accuracy of the Euclidean distance method in identifying the closest hospital to a given residential address.
## 4 Simulation
### RL Routing Simulation  
This simulation allows studying of the performance of RL enabled UAV path planning for remote sensing applications in smart farms. The simulation addresses a \(\varepsilon\)-greedy routing algorithm aiming to maximize the reward value according to the environment exploration-exploitation paradigm. Through this, UAV starts to explore the area and update the reward using random flight plans. This takes the random actions (depending on \(\varepsilon\) value, which is exploration probability) and moves throughout the farm and receives a positive reward if the route ends at an SP zone. In contrast, a negative reward is recorded if the UAV blindly flies over either a fly or no-fly zone with no SP visit. This algorithm is repeated until either all the SPs are visited or simulation time is finished. Then, this aims to forward the UAV for remote sensing according to an exploitation fashion. According to Algorithm 1, Q-learning values are computed using Equation (1), where either a positive or negative reward (rt) is applied with learning rate (\(\alpha=0.01\)), future reward discount factor (\(\gamma=0.1\)) and exploration probability (\(\varepsilon=0.1\)) [47].  
```
/*initialize Q table at base station*/  Initialize Q (S, A); /*Repeat Q-learning calculation until the whole set of target region is explored.*/ while\(R_{st}\
eq Null\)do /*initialize States for a particular exploring region.*/  Initialize S,/*Repeat Q-learning value calculation until available states are addressed.*/ while\(S_{s}\)\(\varepsilon\
eq Null\)do  Select action (A) from state (S) according to exploration probability (\(\varepsilon\));  Measure R and observe \(S_{next}\); \(Q(S_{t},A_{t})\)\(\leftarrow\)\(\{Q(S_{t},A_{t})+\alpha\)\([R_{t}+\gamma\) max\({}_{next}\)Q(\(S_{next},A_{next})\) - Q(\(S_{t},A_{t})]\}\) S\(\leftarrow\)\(S_{next}\);  end while  end while
```  
**Algorithm 1**Q-learning for UAV Routing.
# Intersection Routing Based on Fuzzy Multi-Factor Decision for VANETs
## 3 Intersection Routing Based on Fuzzy Multi-Factor Decision
### System Components  
In this paper, we suppose the vehicles and SNs have the following features:  
1. Vehicles and SNs are equipped with a GPS receiver and a digital map. A wireless transceiver also exists and the transmission coverage of SNs can cover the area of the intersections.
2. Each vehicle maintains a neighbor table to record the information about other vehicles based on the beacon messages they received. Table 2 shows the format of the beacon message. The link sequence maintains the information about the reverse route to the source intersection node. The lifetime is the minimum time between two vehicles in the link path.
3. Each SN also maintains a neighbor table to record the information of its neighbors within 2-hop. Table 3 shows the format of an SN neighbor table. Assume the SN is \(SN_{c}\). Then, \(SN_{a}\) is its 1-hop neighbor and \(SN_{b}\) is its 2-hop neighbor. \(SN_{a}\) is the joint node between \(SN_{c}\) and \(SN_{b}\). Vehicle quantity \(n_{b}\) is the sum of vehicles in links from \(SN_{c}\) to \(SN_{b}\). Lifetime \(t_{b}\) is the minimum time between \(link_{ac}\) (the link between \(SN_{c}\) and \(SN_{a}\)) and \(link_{ab}\) (the link between \(SN_{a}\) and \(SN_{b}\)).
## 4 Experimental Evaluation
### Text Prompted Verification  
Text prompted verification was done with an external binary classifier following the next procedure: a client provides samples of speech and face data; with these samples the network produces a bimodal embedding and a transcription of the speech data; the bimodal embedding is matched with a previously stored template to generate a verification score; the WER value is calculated from the transcription; and, finally, the verification score and WER are fed to the binary classifier, which decides whether to accept or reject the identity claim. This framework reinforces the security because the identity verification is based on the characteristics of two biometric traits and whether a client correctly uttered a digit sequence that acts as a randomly generated password or not. This way the system makes it difficult for an impostor to use prerecorded audio to falsely authenticate his/her identity [32].
# A Networked Sensor System for the Analysis of Plot-Scale Hydrology
## 2 Materials and Methods
### Hydraulic Properties from Soil Moisture Measurements  
One of the benefits of in situ plot-scale hydrology studies is the ability to estimate the hydraulic properties that govern the region. These hydraulic properties are important in characterizing a region with estimates to pedotransfer function (PTF) parameters that are utilized by hydrologic models to predict soil water retention properties based on available soil survey data [54; 55]. In this work, two PTFs are examined. The first PTF is the Clapp-Hornberger equation, which is given by the following power curve [56]:  
\[s=\left(\frac{\psi}{\psi_{s}}\right)^{\frac{-1}{b}}, \tag{1}\]  
where \(\psi\) is the hydraulic conductivity, \(\psi_{s}\) is the hydraulic conductivity at saturation, and \(s=\theta/\theta_{s}\) is the soil wetness, a ratio of the SM, \(\theta\), to the saturated SM, \(\theta_{s}\) (i.e., total porosity).  
The second PTF is the van Genuchten equation, given by the following expression [57]:  
\[\frac{\theta-\theta_{r}}{\theta_{s}-\theta_{r}}=\left[1+\left(\alpha|\psi| \right)^{n}\right]^{-m}, \tag{2}\]  
where \(\theta_{r}\) is the residual SM, \(m=1-1/n\), and \(n\) and \(\alpha\) are fitting parameters. Assuming, for simplicity, that \(\theta_{r}\) is zero, the left-hand side of Equation (2) may be expressed in terms of \(s\), such that:  
\[s=\left[1+\left(\alpha|\psi|\right)^{n}\right]^{-m}, \tag{3}\]Field measurements of \(\theta\) and \(\psi\) are used for fitting values of \(\psi_{s}\) and \(b\) in Equation (1) and \(\alpha\) and \(n\) in Equation (3). The value for \(\theta_{s}\) was determined experimentally based on the following methodology. A soil core sample was taken at a depth of 10 cm and 30 cm at the two node locations surrounding the validation data loggers (Figure 5). The soil samples were weighted under field conditions and then dried in an oven at 100 \({}^{\circ}\)C for 48 h before being weighted again. The bulk density was calculated as the dry weight divided by the volume of the soil sample core. The porosity was calculated as one minus the bulk density divided by the particle density, which was assumed as 2.65 g\(\cdot\)cm\({}^{-3}\).
## 2 Materials and Methods
### Characterization of MIL-88A Carriers
#### 2.2.4 Thermogravimetric Analysis (TGA)  
The MIL-88A samples' thermal stability was characterized using a Thermogravimetric Analyzer (TGA) Setsys 1200 (Setaram, Caluire, France). Measurements of the samples' stability (approximately 10 mg) were performed in airflow from ambient to 1000 \({}^{\circ}\)C at a constant rate of 5 \({}^{\circ}\)C/min. TGA analysis was conducted utilizing a sample pan made of platinum.