esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Procedures  
The data collection was conducted in a controlled environment, with each task being performed by a single researcher to minimize inter-rater variability. Prior to data collection, anthropometric measurements, including body mass and height, were taken for each participant. Based on these measurements, the body mass index (BMI), expressed in kg/m\({}^{2}\), was calculated using the standard formula (BMI = body mass/height\({}^{2}\)).  
To ensure accuracy and precision in measurements, a single investigator was responsible for the preparation and meticulous placement of the markers [66]. Additionally, a pilot study was conducted to familiarize participants with the experimental protocol and to refine the experimental setup, enhancing data robustness and alignment with the latest technological advancements [67].  
Data collection started with the participant standing in an upright position on top of the first force platform, assuming a comfortable posture with their arms resting at their sides and feet in their natural stance position, while looking forward at an eye-level point two metres away [54]. This initial stance position was marked on the top of the force platform to ensure consistency across trials [68]. Gait initiation was performed upon hearing the voice command \"Please, you may walk.\", after which the participant walked at a self-selected speed. Data recording began 30 s before the voice command onset and continued until the participant reached the end of the calibrated walkway, which allowed for approximately 3-5 steps [69]. Each participant was free to choose which foot to use for the first step, with the swing leg being self-selected by the participant [70]. There was a one-minute rest between each trial, and 10 trials were performed [69]. A physiotherapist stood nearby to ensure safety in case the participant required physical assistance during the trial. If participants hesitated due to a misunderstanding of the instructions or required physical assistance from the physiotherapist, the trial was repeated [71]. Prior to data collection, participants were given the opportunity to complete several practice trials to warm up and familiarize themselves with the experimental setup and procedures [50; 54]. All participants used standardized footwear (flat shoes with rubber soles and laces), appropriately sized [54; 72]. No assistive devices or orthotics were used by the participants during data collection [54].
# Trellis Tone Modulation Multiple-Access for Peer Discovery in D2D Networks
## 2 System Model and Conventional FDMA-Based D2D Discovery
### System Model  
We assume that D2D networks are peer-to-peer ad hoc networks that operate in a distributed manner and no central coordinator exists. Every UE uniformly locates in a circular region with radius R. Each UE broadcasts a message through a discovery signal to its proximity. At the same time, each UE receives a signal sent by other UEs and identifies the discovery messages of the neighbor UEs. The resources for discovery are periodically allocated based on the OFDM physical layer. Synchronous transmission is assumed for efficient resource utilization as in the FlashLinQ [16]. The synchronization can be achieved in several ways [28].  
In this D2D network, communications can be initiated by knowing the existence of proximity UEs. Therefore, each UE needs to discover as many peers as possible. To accomplish this, each UE tries to find all UEs around itself, but the goal is not always possible nor desirable. Depending on the discovery scheme, different numbers of UEs will be found within a given resource. The average number of discovered UEs is a major performance metric of the peer discovery in D2D networks. A good discovery scheme can achieve high desired discovery performance with low resource usage.  
In this paper, we evaluate the performance of the discovery scheme by using the observation UE at the center of the circular region, we let the uniformly distributed UEs transmit the discovery signal through the given discovery resource, and we observe the number of discovered UEs by detecting the observation UE.
## 2 Material and Methods  
The adhesion characteristics and stress distributions of the thin films were analyzed based on the change in the deposition temperature by using the DC magnetron sputtering method; the deposition conditions are summarized in Table 1. Before the deposition, the Si wafer substrate was cleaned ultrasonically in acetone and an IPA solution for 5 min, respectively, and it was dried by blowing nitrogen gas. The TiN target had a purity of 99.99% and the initial vacuum degree was 3.0 \(\times\) 10\({}^{-6}\) Torr; Ar and N\({}_{2}\) (purity 99.9995%) were used as the working gas, and the flow rate was fixed at 55 sccm. The operating pressure was maintained at 3 mTorr, and the distance from the target to the substrate was set at approximately 7 cm. The DC power was fixed at 6.5 kW, and the substrate temperature was changed to approximately the ambient temperature, 100, 200, and 300 \({}^{\circ}\)C, and deposition was performed for approximately 10 min.  
\begin{table}
\begin{tabular}{c c} \hline
**Method** & DC-magnetron sputtering \\ \hline
**Thin film** & TiN thin film \\ \hline
**Substrate** & 4-inch Si wafer \\ \hline
**Source gases** & Argon gas flow rate: 55 sccm \\  & N\({}_{2}\) gas flow rate: 55 sccm \\ \hline
**Working pressure** & 3 mTorr \\ \hline
**Substrate temperature** (\({}^{\circ}\)**C**) & 24, 100, 200, 300 \\ \hline \end{tabular}
\end{table}
Table 1: Process conditions for TiN thin film.  
The thickness of the deposited TiN thin film was measured using a scanning electron microscope (UH8010, Hitachi, Tokyo, Japan); the results are shown in Figure 1. TiN thin films (311, 222, 228, and 282 nm) were deposited at the ambient temperature, 100, 200, and 300 \({}^{\circ}\)C, respectively.  
The crystal structure was measured using XRD (D/MAX-2500/PC, Rigaku, Tokyo, Japan), and the diffraction pattern, full width half maximum (FWHM) value, grain size, and residual stress of the thin film were also measured to analyze the growth characteristics of the TiN thin film based on the change in substrate temperature. For atomic force microscopy (AFM), surface roughness was measured using an ultrasonic-AFM manufactured in-house in this laboratory; it was measured in the range of 50 \(\times\) 50 \(\upmu\)m. The adhesion properties were measured using the NanoTest for the nanoindenter scratch test. Each correlation was derived by measuring the LSAW using the V(z) curve measurement technique of the ultrasonic microscope Olympus and UH-3 equipment.
# Seismic Performance Evaluation of Highway Bridges under Scour and Chloride Ion Corrosion
## 3 Effect of Cl\({}^{-}\) Corrosion on the Seismic Performance of Bridge Column
### Degradation of Material Properties by Cl\({}^{-}\) Corrosion
#### 3.2.2 Effect of Cl\({}^{-}\) Corrosion on the Material Properties of Steel Reinforcement  
The damage pattern of columns shows that relatively severe corrosion appeared on the steel reinforcement in corroded specimens. After a quasi-static test of three groups of comparative specimens, six longitudinal rebars divided into two groups from each specimen with middle height were cut out to be subjected to tensile tests. A total of five valid load-displacement curves of uniaxial tensile were obtained apart from one longitudinal rebar broken due to the clamp. The comparison of mechanical properties among longitudinal rebars before and after Cl\({}^{-}\) corrosion is shown in Figure 22. It was found that the average elongation of tensile longitudinal rebars without Cl\({}^{-}\) corrosion was 26.16% and the average elongation of tensile longitudinal rebars subjected to Cl\({}^{-}\) corrosion was 16.5%. The tensile strength of longitudinal rebars changed significantly, with a 4.69% decrease in ultimate breaking strength and a 4.26% decrease in yield strength of corroded rebars.  
Figure 21: Damage pattern in the plastic hinge region of column specimens: (**a**) LL, (**b**) LLE, (**c**) MM, (**d**) MME, (**e**) MH, and (**f**) MHE.  
Figure 22: Comparison of load–displacement curves for rebars before and after Cl\({}^{-}\) corrosion.
## 2 STAR Algorithms to Convert Carrier Phase to Excess Phase and Bending Angle Profiles
### LEO Orbit and Receiver Clock Bias Determination  
COSMIC-2 has two POD antennas. However, the front and rear antennas do not point to the zenith direction (against nadir) but at a tilted angle. Thus, the POD observed GNSS satellite could only be viewed in a cone in a rear or front direction. This reduces the total number of observations for one POD antenna compared to viewing the whole hemisphere when the antennas point to the zenith direction.  
Here, we calculate the COSMIC-2 excess phase in a post-processing mode (two-week latency). We used the Bernese GNSS software to compute individual satellite POD and clock biases (Figure 3). The input parameters, GNSS final orbit, clock, and earth orientation products are from the CODE, and the attitude and RINEX files are from CDAAC. The COSMIC-2 clock bias solution is produced simultaneously with the orbital kinematic solution with every other observation epoch of 2 Hz. This clock bias is mainly used for correcting the time tag calculation (from the local receiver time to the GPS time) in the excess phase calculation.  
COSMIC-2 clock bias is generally larger than COSMIC-1 (see Figure 3a-f). The receiver clock bias of COSMIC-1 is on the order of microsecond level (\(10^{-6}\) s) ([28]), and the clock stability (using Allan deviation to remove the time drift) of COSMIC ranges from \(10^{-9}\) to \(10^{-11}\) over 1 to 104 s. Figure 3 depicts that the COSMIC-2 clock bias can be as large as a few milliseconds with a maximum drift of a few hundred microseconds per day. This requires a frequent clock bias adjustment to keep it within the millisecond range.  
Figure 3: COSMIC-2 FM1-FM6 clock bias and drift time series in October 2019. The left panel (**a–f**) shows COSMIC-2 Clock Bias (millisecond) from Bernese for 6 COSMIC-2 satellites, and the right panel (**g-l**) shows the clock drift rate (millisecond/day, non-smooth, time derivative from clock bias) derived from the clock bias.  
Figure 3g-1 also show that COSMIC-2 clock drift is not stable. We removed the receiver clock bias in the excess phase using the single differencing method between POD and OCC observations. Currently, we used the POD solution from the UCAR data products as orbital inputs for the excess phase model. The GNSS orbital interpolation and LEO Clock solution (for excess phase time tag) are still from the output of the Bernese software.
# Data Access Based on a Guide Map of the Underwater Wireless Sensor Network
## 6 Experiments and Analysis
### Experiment Results and Comparative Analysis
(**a**) The performance in global query; (**b**) The performance in partial query. Our DAGM provided data storage and query mechanism for UWSNs, at the same time through center ring structure and metadata, data aggregation (mainly large volume non-real time data) and data discovery(include real time and non-real time data and event) can be carried conveniently. Compared with some previous works ([15] (2016), [16] (2016), [19] (2016), [20] (2014), [21] (2015), [31] (2014), [35] (2014), [36] (2015) and [37] (2015)), the functions of the DAGM can be employed as shown in Table 1 (in the Table, \(\surd\) represents'strong support', - represents 'can support', \(\times\) represents 'cannot support'). From the experimental results, compared with GHT, DAGM has a great advantage when considering from the data access time and energy consumption, although the data storage time is relatively long. Because DAGM query data is directly sent through the metadata to locate storage nodes purposefully, compared with GHT, DAGM achieves a good balance and optimization in energy consumption and data access time.
# Robustness, Stability, and Fidelity of Explanations for a Deep Skin Cancer Classification Model
###### Abstract  
Skin cancer is one of the most prevalent of all cancers. Because of its being widespread and externally observable, there is a potential that machine learning models integrated into artificial intelligence systems will allow self-screening and automatic analysis in the future. Especially, the recent success of various deep machine learning models shows promise that, in the future, patients could self-analyse their external signs of skin cancer by uploading pictures of these signs to an artificial intelligence system, which runs such a deep learning model and returns the classification results. However, both patients and dermatologists, who might use such a system to aid their work, need to know why the system has made a particular decision. Recently, several explanation techniques for the deep learning algorithm's decision-making process have been introduced. This study compares two popular local explanation techniques (integrated gradients and local model-agnostic explanations) for image data on top of a well-performing (80% accuracy) deep learning algorithm trained on the HAM1000 dataset, a large public collection of dermatoscopic images. Our results show that both methods have full local fidelity. However, the integrated gradients explanations perform better with regard to quantitative evaluation metrics (stability and robustness), while the model-agnostic method seem to provide more intuitive explanations. We conclude that there is still a long way before such automatic systems can be used reliably in practice.  
explainable artificial intelligence; interpretable machine learning; skin cancer; convolutional neural network; deep learning; integrated gradients; local model-agnostic explanations +
Footnote †: journal: Medical Image Analysis  
1  
[PERSON]  
1 Faculty of Information Technology, University of Jyvaskyla, P.O. Box 35, FI-40014 Jyvaskyla, Finland 1  
2 School of Mathematical & Computer Sciences, Heriot Watt University, Edinburgh EH14 4 AS, UK 2
# Upmix B-Format Ambisonic Room Impulse Responses Using a Generative Model
###### Abstract
By training on raw FOA and HOA RIRs without decomposition, our model implicitly learns to preserve the complete spatial sound field. We further adopt a multi-resolution Short-Time Fourier transform (STFT) loss, enabling the generation of upmixed RIRs that retain both temporal precision and spectral accuracy. We demonstrate significant improvements in upmixing quality through extensive objective evaluation metrics. The main structure of this paper is as follows. Section 1 discusses the limitations of FOA compared to HOA and reviews existing FOA upmixing methods. Section 3 introduces the fundamental structure of GAN networks and explains how we utilize the HiFi-GAN model for upmixing RIRs. Section 4 outlines the preprocessing steps for training data. In Section 5, we demonstrate the effectiveness of our proposed upmixing algorithm, in comparison to the baseline. Additionally, we showcase the synthesis of Binaural Room Transfer Functions (BRTFs) to highlight the potential advantages of our algorithm. Finally, Section 6 provides a summary of our proposed algorithm and the experimental results.
## 4 Proposed Model
Second, if it is on the right-hand (RH) side, it will choose the RHS's adjacent nodes; for example, let's say it selects the L and M nodes as its right-side MPR. However, L is not related to target Q any further. Therefore, there will only be one MPR, node M. For the intention of sending data packets to Q, in the MPR set of E. The data will be relayed to node M by node E. Thirdly, the right-hand side MPRs of node M shall now be discovered. Let's assume that these are nodes I and Q, but Q has not subsequently been associated with node Z. Accordingly, the MPR set of node M will only contain node I. ```
if Broadcast then  Node sends a signal to find other nodes within range else  Updating routing table and synchronization between nodes endif  MPR selection with cross and dot product if Node is ready then  Route reply message within = 1 s. else  Update neighbor hold duration = 1 s. endif if destination node then  Transmission begin  Successful Transmission else  Check for the readiness of the destination node endif
```  
**Algorithm 1** Proposed OCI-OLSR Algorithm  
Figure 4: OLSR Working Mechanism—Flowchart. The data packet will then be routed by node M to node I, who will then deliver it yet another time to node Q. Therefore, the route taken will be E, M, I, and Q. The step-by-step working of the proposed OCI-OLSR is discussed below:  
* Step1: Select a new scenario of 100 \(\times\) 100 m scale. * Step2: Then create a network by placing nodes from the object. * Step3: Synchronizing all network nodes and modifying the routing table periodically. * Step4: MPR selection with cross and dot product. * Step5: Run the simulation for the desired time to collect the results
* Step6: Exchange of message through chosen route from source to destination
* Step7: Receiving node sens back ready signal route reply message. * Step8: Finally, transmission begins successfully.
# A Study on the Mechanism of the Precipitation-Induced Slope Instability of Colluvium
## 7 Conclusions  
The following conclusions are drawn from this study:  
1. The instability process of the 40\({}^{\circ}\) slope can be described as follows: rainwater infiltration; soil settlement causes tensile cracks on the trailing edge; rainwater drives the soil to move downward along the slope direction; tensile cracks accelerate expansion; rainwater infiltration causes the formation of aquitard at the foundation overburden interface; overall damage. The 40\({}^{\circ}\) slope creeps at first, and overall sliding occurs when the rainfall reaches the critical value, so when the slope is 40\({}^{\circ}\) or larger, the occurrence and development rate of the trailing edge tensile crack should be monitored so as to effectively predict the safety of the slope.
2. The instability process of the 30\({}^{\circ}\) slope can be described as follows: rainwater infiltration; gully at the foot of the slope; sliding at the foot of the slope; tensile cracks in the middle of the slope; creeping down along the slope in the middle of the slope; tensile cracks at the trailing edge; sliding in the middle and sliding as a whole. The 30\({}^{\circ}\) slope slides locally after the gully, and then, the upper soil slides under the traction of gravity as a whole, so the slope monitoring of about 30\({}^{\circ}\) should focus on the change in the slope near the foot of the slope.
3. As the slope angle increases, the more fine particles appear at the foot of the slope, and the influence on drainage gradually increases. When the slope angle is 20\({}^{\circ}\), the migration of fine particles does not change much, and the rainwater can form a stable seepage channel in the slope, while the migration of fine particles in the slope at 30\({}^{\circ}\) only causes rainwater to collect at the foot of the slope, resulting in its softening and destruction. When the slope angle is 40\({}^{\circ}\), a large number of fine particles migrate to the foot of the slope, resulting in the formation of aquitard near the contact surface of the foundation cover and overall sliding.
4. With the infiltration of rainfall, the volume of the slope first decreases and then increases, and the corresponding pore water pressure behaves in the same way. The larger the slope angle, the greater the peak value of pore water pressure. A rapid rise in pore water pressure can be an indicator of slope instability.  
**Author Contributions:** Conceptualization, J.Y.; Writing--review and editing, J.W. and F.S.; Completed the experiment, X.Y.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China grant number U1704243.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 3 Methodology
### Data Processing
#### 3.2.2 Removal of Anomalies/Redundant Points
\[Po_{or}\
eq\{Po_{i}|0\leq cop_{i}\leq 360,0\leq sog_{i}\leq 25,0\leq lon_{i} \leq 180,0\leq lat_{i}\leq 90\} \tag{10}\]  
\[Po_{co}=\left\{Po_{i}|sog_{i}>0,lon_{i}=lon_{j},lat_{i}=lat_{j},i\
eq j\right\} \tag{11}\]  
\[Po_{de}=\{Po_{i}|\frac{dist(Po_{i+1},Po_{i})}{t_{i+1}-t_{i}}\geq V_{\max}\} \tag{12}\]  
\[Po_{re}\
eq\{Po_{i}|b_{min}^{crc}\leq crc_{i}\leq b_{max}^{crc},b_{min}^{crc} \leq crs_{i}\leq b_{max}^{cr}\} \tag{13}\]  
After the removal of anomalous and redundant values, the trajectory data in the collection have been significantly compressed, while ensuring that the original trajectory remains undistorted, as many unnecessary trajectory points as possible have been removed. This greatly preserves valid trajectory points that represent the vessel's motion state.
# Combined Data Augmentation on EANN to Identify Indoor Anomalous Sound Event
## 2 Materials and Methods
### Combined Augmentation Performance  
The results for combined data augmentation methods are presented in Table 7. It is evident that combined data augmentation methods significantly outperformed not using any data augmentation. The TS and BN and TS and PS combined data augmentation methods showed good recognition performance, and the difference between these two was not substantial. The BN and PS combination had relatively lower recognition performance. Among these combinations, TS [0.88, 1.12] and BN [10 dB] achieved the highest recognition performance, with a maximum accuracy of 0.964. When spectrogram masking was combined with TS and BN and TS and PS combined data augmentation techniques, there was a slight improvement in recognition performance. However, when spectrogram masking was combined with PS and BN, the recognition performance decreased. The proposed TS [0.88, 1.12] and BN [10 dB] and Online Aug combined data augmentation technique exhibited the most significant improvement, with an accuracy increase of 10.643% compared to not using any data augmentation techniques, and the recall and specificity were greatly improved. The visualized accuracy is shown in Figure 6.  
Figure 6: The classification accuracy results for combined data augmentation.