esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Triggered Access for BSM Dissemination in 802.11 bd V2V Networks
## 4 Performance Evaluation
Even in non-saturated condition, where the vehicle density is low (0.3) and the BSM traffic is high (400 B, 20 Hz), the number of EDCA transmission is decreased by 71% while the total number of transmission success is greatly increased. This incurs that the scheduling of DTA efficiently relieves the channel access intensity of nearby VSTAs. Moreover, under extremely saturated condition (high vehicle density (0.12) and heavy data traffic (\(R_{BSM}\): 400 B, \(\lambda\): 20 Hz)), upto 87% of successful transmission is served by triggered access, while the transmission success rate is increased by 94% than legacy EDCA only case. The result proves that the proposed scheme effectively raise the channel use. Figure 11 shows the portion of active and non-active BSM among successful transmission. The figure shows that non-active BSM takes a large portion of transmission success when the medium is not saturated. As we discussed before, non-active BSM helps VSTAs to collect BSMs much faster. Additionally, this can be advantageous since scheduled RU can be used adaptively depending on the situation. For example, if a platooning scenario requires VSTAs to maintain the neighbor information with short valid time, VSTAs can be configured to transmit non-active BSMs aggressively. On the other hand, if the BSM requirement is not demanding but VSTAs are running multiple applications, the non-active BSM portion can be used to transmit data which is lower prioritized than BSM. Figure 11: Ratio between active BSM and non-active BSM. Figure 12 shows the performance impact of the payload size and the arrival rate. Figure 12a depicts the transmission success rate of active BSM with variable payload size. According to the figure, with greater payload size (\(R_{BSM}\): 400 B), DTA always outperforms EDCA, and the performance gap increases as the vehicle density increases. The result shows that DTA with greater payload size (\(R_{BSM}\): 400 B) has almost same performance as EDCA with smaller payload size (\(R_{BSM}\): 200 B). Figure 12b also shows that DTA provides enhanced performance regardless of the arrival rate. From lower arrival rate case (\(\lambda\): 10 Hz), it is observed that DTA and EDCA have similar gradient in low vehicle density region. In such case, the probability that more than one VSTA accesses the channel at the same timeslot is extremely low. Therefore, it is expected that the performance gap is purely caused by the protection of MU-RTS from hidden VSTA. As the vehicle density increases, channel gets busier, so the performance gain from triggered access multiplexing increases as we already discussed in Figure 9.
# PPA-SAM: Plug-and-Play Adversarial Segment Anything Model for 3D Tooth Segmentation
## 3 Methodology
### VNet Structure  
VNet is a convolutional neural network structure designed for three-dimensional image segmentation. It has been widely applied in the field of medical image segmentation, especially showing significant advantages in segmentation tasks involving three-dimensional medical images such as CT (computed tomography) and MRI (magnetic resonance imaging). In the medical domain, precise and reliable image segmentation is crucial as it directly relates to accurate diagnosis and treatment decisions by medical professionals. VNet introduces skip connections, allowing end-to-end information transfer from input to output, which is crucial for handling complex data structures like medical images. In the encoder, features are progressively extracted and compressed through multiple convolutional blocks, concurrently reducing the size of feature maps. In the decoder section, upsampling and deconvolution operations restore the feature space, enabling the network to reconstruct and retain detailed image information. Skip connections are established between each layer of the decoder and its corresponding encoder layer, creating a close relationship between the encoder and decoder, allowing low-level features to be directly transmitted to the decoder, thereby enhancing the network's ability to integrate multi-scale feature information. Additionally, VNet utilizes residual connections, a mechanism that helps the network better learn complex medical image features, particularly in accurately  
Figure 1: PPA-SAM integrates the enhanced SAM encoder into the VNet network, creating a dual-encoder structure. The outputs from both the VNet encoder and the enhanced SAM encoder are combined and input into the decoder, allowing PPA-SAM to achieve three-dimensional segmentation of teeth.  
understanding organ boundaries and local features, leading to superior performance in medical image segmentation tasks.
# Low-Cost Data-Driven Robot Collision Localization Using a Sparse Modular Point Matrix
## 2 Materials and Methods  
### Dataset Description  
The SMPM proposed in this study was constructed based on the collision point matrix template introduced in ref. [4]. To investigate the effectiveness of the proposed SMPM, the data used in this study were consistent with our previous work [4]. Specifically, the datasets comprised vibration signals encompassing 3-axis acceleration and 3-axis angular velocity, collected by the IMU mounted on the charger at a sampling frequency of 1500 Hz. An AUBO-i5 robot, a commercially available general-purpose 6-DOF robotic arm, was employed as the automatic charging equipment. It was connected to the charger via a flexible wrist, as depicted in Figure 1. During the data collection process, the end-effector robot moves in a linear motion at a speed of 15 mm/s to execute the collision. Each collision point in the collision point matrix template attached to the charging port undergoes five collisions in the same pattern to minimize the impact of robot positioning errors on the results. Additionally, we considered the impact of different joint configurations on the collision localization results, thereby constructing three independent collision vibration signal datasets named D1, D2, and D3. Each dataset corresponds to three sets of distinct joint configurations, with each dataset containing 4335 samples. For more details, please refer to the table entitled \"Joint configuration of the datasets\" in ref. [4].
## 2 Results
### Fano-Feshbach Analysis of the Internal Modes
\begin{cases}\mathrm{TE}_{mpq}&=N_{mpq}J_{m}(\frac{\chi^{\prime}_{mp}}{R}r) \sin(\frac{q\pi}{hn})\exp(im\phi)\\ \mathrm{TM}_{mpq}&=M_{mpq}J_{m}(\frac{\chi_{mp}}{R}r)\cos(\frac{q\pi}{hn})\exp (im\phi)\end{cases}  \tag{3}\]  
where \(J_{m}\) is a Bessel function of the first-kind of order \(m\); \(N_{mpq},M_{mpq}\) are normalization constants; and where \(\chi_{mp},\chi^{\prime}_{mp}\) denote the \(p\)-th zero of the Bessel function \(J_{m}\) and its derivative \(J^{\prime}_{m}=\partial J_{m}/\partial\rho\), respectively. The resonance frequencies of the internal modes are defined as:  
Figure 2: **Local density of states and interacting resonant modes**. (**a**,**b**) Local density of state for the (**a**) \(H_{z}\) and (**b**) \(E_{z}\) field components, corresponding to the transverse electric (TE) and transverse magnetic (TM) modes of the silicon nanostructure, respectively. The resonant wavelengths (vertical dashed lines) in both configurations are computed by means of Equation (4). \[ \begin{cases}\omega_{mpq}^{2}&=\frac{c^{2}}{n^{2}}\left[\left(\frac{q\pi}{ln} \right)^{2}+\left(\frac{\chi_{mp}^{\prime}}{R}\right)\right]&\text{TE modes},\\ \omega_{mpq}^{2}&=\frac{c^{2}}{n^{2}}\left[\left(\frac{q\pi}{ln}\right)^{2}+ \left(\frac{\chi_{mp}}{R}\right)\right]&\text{TM modes}.\end{cases}  \tag{4}\]  
The choice of PEC boundary conditions for the internal modes is not fixed, as it is only dictated by the mathematical partitioning scheme adopted. As thoroughly discussed in [11; 18; 20], they can be easily exchanged with perfect magnetic conductor (PMC) boundary conditions, which are usually employed to analyse high refractive index nanoparticles [21; 22]. A preliminary analysis of the system in terms of Fano-Feshbach internal resonances allows us to unveil some fundamental properties of the anapole excitation. By solving Equation (4), we computed the Fano-Feshbach resonant frequencies \(\omega_{mpq}\) of the system, which are reported as sets of dashed vertical lines on Figure 2. Interestingly, the anapole state is generated in the proximity of a few orthogonal resonances. Among all the available candidates, the characteristic three-lobes mode profile of the anapole state can be obtained by superimposing two TM modes (Figure 3): a cylindrically symmetric TM\({}_{020}\) and a quadrupolar TM\({}_{210}\). Remarkably, these modes constitute the three-dimensional version of the eigenmodes composing the anapole state in two-dimensional cylinders (cfr. Figure 3 of [11]).
# The Influence of the Quality of Digital Elevation Data on the Modelling of Terrain Vehicle Movement
## 4 Discussion
### Usability of Soil Databases in CCM Analyses  
Soil databases with sufficient quality are not available in the areas of foreign military operations. Despite a lower accuracy in comparison with elevation models, soil databases can be used for terrain analyses. However, to achieve the required detail of data, a more accurate mapping of soil type boundaries would be necessary. For example, in the territory of the Czech Republic, the Digital Soil Map 1:50,000 need to be updated [20]. Since DTM 4 was identified as a sufficiently accurate terrain model, the optimal density of measurement points for soil characteristics is 5 m \(\times\) 5 m at the boundaries between soil types. Apart from geometric accuracy improvement, other information needs to be added to the assessment of soil types, such as an actual soil moisture level and an assessment of the quality of the surface itself. Vector databases that contain surface roughness and vegetation coverage parameters with sufficient detail and area coverage do not exist. Accurate soil databases would not be much use without this additional surface information.  
From the global perspective, universal soil data is represented by the Harmonized World Soil Database (HWSD), which combines various national soil databases. Its detail is very low and represents a scale of 1:500,000. It can only be used for lower-accuracy estimates of passability analysis for the operational and up to the strategic level of military planning. A detailed study of improvements of the HWSD and its usability in national systems was given in [49]. One of the possible options to replace low-detailed surface data is the Destination Earth initiative. It is a project of the European Commission with the aim of collecting and evaluating satellite images. One of the important use cases of this project for the CCM analysis domain is the evaluation of surface temperatures to determine soil and surface types. Even though this method is not accurate enough, it can improve data availability in territories of potential conflicts across the world [50].  
A study [51] showed that if the terrain has arid or semi-moist conditions, then the influence of soil types in the CCM can be disregarded. The only significant exception was the alluvial soil type, which occurs in some humid valleys. Except for arid conditions, these valleys are impassable for most types of vehicles (track or wheeled). The other exception is a sandy surface, which substantially limits the movement of wheeled vehicles. A vast majority of soil types are different from the alluvial and sandy types. Therefore, the focus should be put on distinguishing correct humidity conditions of the surface and properly processing a utilised elevation model.
## 6 Conclusions  
(1) An engineering geological assessment of the deep hard-rock mass was carried out at three drifts in the Sanshandao Gold Mine in China. Discontinuity data collected from field investigations were used in laboratory Unwedge software for wedge analysis. The Q, RMR, and GSI classification systems were used to estimate rock mass quality and support requirements for the drift.  
(2) The numerical method (RS2) was used to the quantify plastic zone and principal stress of the drift. After the installation of the proposed supports, the plastic zone areas of the\(-915\) m, \(-945\) m, and \(-960\) m drift decreased by 8.882 m\({}^{2}\), 3.605 m\({}^{2}\), and 28.773 m\({}^{2}\), respectively, and the stress state surrounding the drift was effectively improved. The final field monitoring displacements of the two monitoring sites were 4.2 mm and 4.9 mm, respectively, indicating that the support system effectively exhibited control failure and deformation in the deep hard-rock drift.  
(3) According to the results from different methods, the standard support system of 2.2 m length rock bolts with a spacing of 1 m-1.5 m, a diamond-shaped galvanized wire mesh of 50 mm \(\times\) 50 mm net size, and 100 mm thick shotcrete is recommended. The results prove that both numerical and empirical methods complement each other and can be effectively used for optimizing the support design of deep hard-rock mines.  
Conceptualization, X.Z.; methodology, X.Z.; software, N.Z.; validation, Y.Z.; formal analysis, N.Z. and L.D.; investigation, N.Z., L.D., S.Y. and Q.Z.; data curation, N.Z., L.D. and S.Y.; writing--original draft preparation, N.Z. and L.D.; writing--review and editing, X.Z., Q.Z. and Y.Z.; visualization, N.Z. and L.D.; supervision, X.Z.; project administration, X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a project of the NSFC Shandong United Fund (U1806208) and Key Program of the National Natural Science Foundation of China (52130403), the Fundamental Research Funds for the Central Universities (N2001033 and N2101022).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
Special thanks to Sanshandao Gold Mine for facilitating the case study.  
The authors declare no conflict of interest.  
Figure 11: Drift displacement duration curve.
## 4 Vibration Signal Analysis
### Frequency Distribution under Different Mass  
The time domain signals from three different time periods (0-120 s, 120-240 s, and 240-360 s) were decomposed by the EMD method, and we used fast Fourier transform to obtain the frequency spectrum of the IMFs. We analyzed 15 main frequencies (those corresponding to the highest 15 peaks) of different time periods to explore the relationship between the main frequencies and mass, as the weight of the whole machine keeps increasing. The main frequencies extracted in different time periods are shown in Table 2.  
It can be seen that the difference between the frequency peaks extracted from different time periods is small, and that the relationship with mass variation is not obvious.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Peak** & **Time** & **0–120 s** & **120–240 s** & **240  
Figure 7: EEMD analysis results of test point 1: **(a)** IMFs of test point 1, **(b)** FFT spectrum of IMFs, and **(c)** enlarged view of FFT spectrum.  
During the harvesting operation, the feed amount of the harvester was 3.67 kg/s, which is a small change in mass compared to the whole vehicle mass, and thus mass time varying had a small effect on frequencies. Within a certain time period of 0-120 s, there are some frequency peaks that did not appear in other time periods (such as 26.0 Hz corresponding to peak 15 in 0-120 s), or some frequency peaks that are lost (such as 21.9 Hz did not appear in 0-120 s). Because the largest 15 peaks were extracted in the analysis, some frequencies with smaller peaks are not extracted, which does not mean that these peaks are wrong, or that these frequency peaks are not present in these time periods. The reason for extracting 15 peaks is that the 15 th order modal frequency is finally obtained by the SSI algorithm calculation.  
Considering jump resonance phenomenon existed in mechanical systems [36], we analyzed the change of vibration frequency, and the connection of frequency and mass is shown in Figure 8.  
It shows that the peaks of the frequencies appeared as straight lines, and the change of frequencies is small. At the same time, it was found that the relationship between the frequency of the harvester and the mass is not obvious, and it can be considered that when the feeding amount situation is small, the influence of weight change on frequency is relatively small.
# Measuring the Effect of Mental Health on Type 2 Diabetes
###### Abstract  
There are many putative risk factors for type 2 diabetes (T2D), and the causal relationship between these factors and diabetes has been established. Socio-environmental and biological approaches are increasingly used to infer causality, and research is needed to understand the causal role of these factors in diabetes risk. Therefore, this study investigated the extent to which the treatment factor of stress induces the risk of diabetes through socio-environmental and biological factors. We present machine learning-based causal inference results generated using DoWhy, a Python library that provides a four-step causal inference method consisting of modeling, identification, estimation, and refutation steps. This study used 253,680 examples collected by the Behavioral Risk Factor Surveillance System (BRFSS), created a causal model based on a socio-environmental model, and tested the statistical significance of the obtained estimates. We identified several causal relationships and attempted various refutations. The results show that mental health problems increase the incidence of diabetes by about 15% (mean value: 0.146). The causal effect was evaluated based on the causal model, and the reliability of causal inference was proved through three refutation tests.  
causal inference analysis; causal machine learning; mental health; type 2 diabetes; socio-environmental model 2020 2024 14 5184 122  
a theoretical model that explains the relationships between environments and chronic diseases, proposed by [PERSON] et al. [1]. Many previous studies suggest that stress affects diabetes, in terms of both its onset and its exacerbation [6]. Therefore, this research also aims to evaluate how the quality of mental health impacts the onset of diabetes within the proposed causal model.  
A randomized experiment is a common way to validate a causal model. However, it is challenging to randomize participants in a study, so it is desirable to use an observational study design. For this study, we used a survey dataset collected by the Centers for Disease Control and Prevention (CDC) via the Behavioral Risk Factor Surveillance System (BRFSS). Unlike a randomized experiment, when a causal analysis is inferred by using observational studies, it is challenging to control bias due to confounding variables. To resolve this problem, we employed a machine learning-based causal inference method, called DoWhy, proposed by Microsoft researchers [7]. DoWhy allows the creation of a causal relationship model with a causal graph and structural assumptions; identifies whether or not the causal effect is estimable; estimates the effect using a statistical estimator; and, finally, refutes the obtained estimate through various robustness checks. Existing studies related to health, such as those regarding diabetes, were able to derive important factors related to health, but were unable to quantitatively evaluate the importance of those factors. This study is the first application of causal inference using DoWhy in the health domain and suggests the quantitative treatment effect of stress objectively.
## 3 Results
### Oxidation and Stability Analysis of Ozone-Induced Free Radical Solution
#### 3.1.1 Effect of pH on Oxidizability and Stability  
The ORP value of the ozone radical solution preparation device decreases with an increase in pH value over a period of five minutes in Figure 5. Within the first minute, there is a significant disparity in the ORP values within the solution. The ORP value reaches 1104 mV at a pH of 3, while it stands at 832 mV at a pH of 9. In the subsequent one to five minutes, the ORP value slightly increases, indicating the pH value's impact on the redox potential of the ozone-induced free radical solution, with slightly acidic solutions demonstrating greater oxidative properties.  
Following a 5 min operation of the ozone-induced free radical solution preparation device, the TRO concentration detector measured the TRO mass concentration's attenuation over a 30 min period. Except for a mass concentration of 5.61 mg/L at pH 5, the initial mass concentrations for other pH values were approximately 10 mg/L. When the pH value ranged from 3 to 9, the decay rates were 48.86%, 38.86%, 89.09%, and 98.81%, respectively. As depicted in Figure 6, Acidic ozone radical solutions demonstrated slower decay rates than alkaline solutions.  
Figure 5: Effect of different pH levels on the oxidation of free radical solutions.
## 2 Related Work  
The GAN has been employed to transform multimodal image registration into unimodal registration. [PERSON] et al. [18] employed the CycleGAN to synthesize the missing modalities to enhance the precision of multimodal registration algorithms. By employing the CycleGAN to synthesize 3D images and using two multimodal similarity measures, [PERSON] et al. [19] improved the registration performance effectively. [PERSON] et al. [20] used the CycleGAN to synthesize CT images from MRI based on inverse consistency networks for MRI-CT registration. [PERSON] et al. [21] presented a translation-based image registration network for decomposing the image into shape and appearance spaces, which achieved a competitive performance to other methods. By using a translation-based registration method via disentangled representations, [PERSON] et al. [22] designed similarity measures defined in the image space and content space to achieve a superior registration performance.  
By applying image-to-image translation to multimodal image registration, [PERSON] et al. [23] used unimodal similarity metrics to train the network instead of using cross-modality similarity measures, which alleviated the drawbacks of a hand-crafted similarity measure. [PERSON] et al. [24] implemented MRI-CT image registration using a patch-wise random forest to bridge the appearance gap in two modalities, which employed a bidirectional image synthesis network to generate two DVFs. [PERSON] et al. [25] proposed a method to train the synthetic model through the registration loss, which avoided unstable training when using GAN networks. [PERSON] et al. [26] designed a discriminator-free image-to-image translation method to mitigate inconsistencies and artifacts that arose from the discriminator, thereby improving the performance of image registration.  
To mitigate the coupling challenge between the registration and translation networks, [PERSON] et al. [27] proposed a geometry-consistent adversarial training scheme, which guided the registration network to learn spatial deformation. [PERSON] et al. [28] proposed a self-supervised IMSE method, which evaluated spatial differences in multimodal image registration to establish a novel image-to-image translation paradigm.  
However, some multimodal image registration methods based on translation networks tend to synthesize complex anatomical details into simple image modalities, such as MRI to CT translation, which neglect the complex information in the other modality,leading to potential registration biases. Therefore, the quality of the transformed image in an image-to-image translation network plays a crucial role in the effectiveness of the subsequent image registration.