esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Prediction Performance  
To ascertain the prediction performance of the proposed model, we conducted a comparative analysis against the following four methods:  
* LSTM-F [32]: LSTM-F is an AD progression model based on a standard LSTM network. It utilizes the most recently observed values to impute missing data.
* LSTM-M [5]: LSTM-M is a variant of LSTM-F that differs in its approach to imputing missing data as it uses the mean values from the training set for imputation.
* MinRNN [13]: MinRNN is a model based on MinimalRNN which employs a technique called 'Model Filling' to impute missing data for predicting AD progression.
* GRU-MF [33]: GRU-MF is the variant of MinimalRNN in which the backbone is replaced with GRU.  
Table 2 presents a comparison of various methods in clinical diagnosis and cognitive score prediction tasks. A higher value of BCA and mAUC indicates better performance of the model in classification task, while adasMAE represents the mean absolute error of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **mAUC (\(\uparrow\))** & **BCA (\(\uparrow\))** & **adasMAE (\(\downarrow\))** & **Inference Speed** **(ms) (\(\downarrow\))** \\ \hline EDL & **0.942 \(\pm\) 0.0091** & 0.879 \(\pm\) 0.0098 & 4.280 \(\pm\) 0.40 & **0.78** \\ Dropout & 0.939 \(\pm\) 0.0135 & **0.885 \(\pm\) 0.0224** & 4.518 \(\pm\) 0.36 & 2.57 \\ Deep Ensemble & 0.933 \(\pm\) 0.0202 & 0.863 \(\pm\) 0.0162 & **4.263 \(\pm\) 0.38** & 2.99 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of different uncertainty methods.  
Figure 4: The variation in accuracy concerning the uncertainty threshold for EDL.  
cognitive score prediction, with lower values indicating better performance in regression task. Among all the methods compared, our approach outperforms most existing methods and is only slightly inferior to MinRNN. This demonstrates that our method can accurately predict clinical diagnosis and cognitive scores, with a predictive performance comparable to similar models. Additionally, considering that our method provides a reliable measure of uncertainty in predicting results, which is lacking in similar models, it offers valuable auxiliary decision making support for AD progression prediction. Therefore, we believe that the slight shortfall in performance is acceptable, given the additional benefits our method provides.
## 1 Introduction  
Traffic accidents are a major problem that endangers personal safety and are also a development challenge in countries around the world. In all age groups, road traffic injuries are the 12 th leading cause of death [1]. Nearly 1.35 million people die or become disabled in traffic accidents each year, with about 3700 deaths per day in fatal accidents alone [2]. As the main cause of death and disability, road traffic injuries have also caused huge economic losses to society. To address this issue, many researchers are currently focusing on how to improve the transportation system. Among them, vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), and infrastructure-to-vehicle (I2V) communication based on wireless communication technology are considered effective means for lane keeping, collision warning, assisted braking, dynamic direction stability assistance, and traction control. However, the additional hardware facilities will lead to a significant increase in the cost and energy consumption of vehicles and infrastructures. Therefore, visible light communication (VLC), with numerous advantages, has attracted widespread attention in vehicle communication.  
VLC, as one of the emerging candidate technologies for wireless access to intelligent transportation systems (ITSs) [3], uses light-emitting diodes (LEDs) to transmit information and illuminate. Visible light is a part of the spectrum of electromagnetic waves with a wavelength range of 380-750 nm and a frequency range of 430-790 THz. As a result, with its huge available bandwidth, VLC is currently able to achieve data transfer rates of up to tens of Gb/s [4]. According to the usage scenario, VLC can mainly be used for communication in indoor and outdoor scenes. Indoor VLC has gained more popularity and expansion due to the success of Light Fidelity (Li-Fi) [5]. However, outdoor VLC has grown slowly owing to challenging environments and mobility and weather constraints [6].  
In the context of vehicle communication, V2V-VLC or V2I-VLC systems mostly use LEDs as transmitters (Txs), including LED-based vehicle headlights and taillights, traffic signals, or street lights. Photodetectors (PDs) or complementary metal-oxide semiconductor (CMOS) camera image sensors are used as the receivers (Rxs). In this paper, the focus is on the modeling and performance of V2I-VLC, considering both line-of-sight (LOS) and non-line-of-sight (NLOS) links for mountain roads. Realistic outdoor propagation characteristics are also considered, which is an important area for traffic safety. To illustrate the contribution of the work in this paper, the related works will be first discussed.
## 1 Introduction  
Because of their large surface area, large volume to surface ratio, and potential for high sensitivity sensor applications, nanoporous structures have drawn great attention in electronics, sensor and storage devices, and photonics [1, 2, 3, 4, 5, 6]. Electron or ion beam patterning can be used for nanopore fabrication by direct writing [7, 8, 9, 10]. However, direct writing is a rather expensive and time-consuming method due to its serial nature. Therefore, it may not translate to a preferred manufacturing process. Meantime, metal anodization processes, i.e., anodized aluminum oxide (AAO) and anodized titanium oxide (ATO) processes, are used for large-area nanopore templates [11, 12, 13, 14], but their array patterns tend to contain locally disordered portions, which would limit their usage for electronic memory devices or photonic devices requiring perfectly ordered arrays over a large area. Multibeam interference lithography (MIL), however, could be a good candidate for the patterning of perfectly ordered large-area photonic crystals and can be applied for photonic, electronic applications [15, 16, 17, 18, 19]. MIL can form interference patterns on photosensitive polymer. In our previous work, MIL and two-photon lithography were combined by two-step exposures and a single development process. This new approach, adding patterns on the repeated nanoporous structures, suggested a new path from restricted usages of nanoporous structures to various applications but essentially, the newly fabricated structures were still made of the same material, polymer, and there was a limit to their usage for diverse applications [20]. To overcome its limitations and construct a functional electronic or photonic device, pattern transfer to the underneath layers or substrates is required.  
In this work, three-beam interference lithography with a visible light source is used to form hexagonal symmetric interference patterns on a large photoresist (PR) layer. Subsequently, various pattern transfer processes to different substrates such as reactive ion etching (RIE) to glass and Si substrates, nanomolding to a polydimethylsiloxane (PDMS) substrate, and anodization to an aluminum-coated substrate were performed. By combining MIL and RIE/nanomolding, nanoporous polymer structures could be transferred to various substrates and this can provide the opportunity to be used for a variety of applications for electronic and photonic devices.
## 2 Materials and Methods  
### Databases  
In this work, two datasets were used to train and test the different architectures that are reviewed. The first one (Figure 1) contained MR and CT head pairs from 19 healthy women (34.96 \(\pm\) 5.23 y/o). MR images were acquired on a GE Signa HDxt 3.0-T MR scanner, and imaging was performed using a 3D T1-weighted sequence with a repetition time of 10.024 ms, echo time of 4.56 ms, inversion time of 600 ms, 1 excitation acquisition matrix of 288 \(\times\) 288, isotropic 1 mm resolution, and a flip angle of 12\({}^{\circ}\). Low-dose CT images were acquired on a Siemens Somatom Sensation 16 CT scanner with a matrix of 512 \(\times\) 512, resolution of 0.48 \(\times\) 0.48 mm, slice thickness of 0.75 mm, pitch of 0.7 mm, acquisition angle of 0\({}^{\circ}\), voltage of 120 kV, and radiation intensity of 200 mA.  
The second database (Figure 2) contained MR and CT images from the pelvis of 13 colorectal and 6 prostate cancer patients (61.42 \(\pm\) 10.63 y/o, mean BMI 22.3 \(\pm\) 2.88, 12 males/8 females). Additionally, images from follow-up visits for 9 of the colorectal  
Figure 1: Head Data set example.  
cancer patients were also included in this study. MR and CT scans were performed on the same day with an average delay of 66 min. CT images were acquired on a Discovery PET/CT 710 scanner (GE Healthcare) with a matrix of \(512\times 512\), resolution of \(1.37\times 1.37\) mm, slice thickness of 3.75 mm, pitch of 0.94 mm, acquisition angle of 0, voltage of 120 kV, and radiation intensity of 150 mA. MR data were acquired on a Biograph mMR scanner (Siemens Healthcare, Erlangen, Germany). The sequence was a dual echo Dixon-VIBE, which is the standard image for attenuation correction purposes. Dixon-Vibe acquisitions are composed by 4 sets of images: water, fat, in-phase and out-of-phase.
## 5 Conclusions  
In this study, a dimension-reduced ANN model is proposed to predict the cell voltage distributions of a 100-cell automobile PEMFC stack. The cell voltage consistency is analyzed with varied ANN model structures under different fuel cell operating and assembly conditions. The primary findings are as follows:  
1. Five grouping strategies for the output values are applied for the ANN model. All of the model structures have a good prediction accuracy, with \(R^{2}\) values greater than 0.999. The predictions under different stack assembly torques fit the experimental results, with margins of error less than 2 mV.
2. The ANN model can accurately predict each group's average voltage under different group strategies. The _MSE_ values of the proposed models are lower than \(8\times 10^{-7}\). With the reduced output dimension of the model, details of the individual cell voltages are missed, but the distribution trends of the cell voltage distributions are retained.
3. The _training time_ decreases as the number of cells in each group decreases. The minimum _training time_ for the five data groups is only six seconds.
4. The \(U_{cell}\) and _range_ values positively correlate with the load currents under different assembly torques, while the curve slope declines as the model output dimension reduces.  
Conceptualization, C.Y.; Investigation, J.C., C.Y., Y.F., Y.S., and P.L.; Methodology, J.C. and C.Y.; Software, P.L.; Validation, Y.S. and P.L.; Writing--original draft, J.C., Y.F., and Y.S.; Writing--review & editing, C.Y. and H.T.; Supervision, [PERSON]'s Funding acquisition, H.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China (No. 2018 YFB1502500) and the Science and Technology Program of Sichuan Province (No. 2022 NSFSC1218, No. 2019 ZDZX0002).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Results and Discussion
### Effects of Reed Carbon Source on COD and pH of Constructed Wetland Effluent
#### 3.2.2 Changes in pH of Wetland Effluent  
As shown in Figure 6, the influent pH of RW, CW and N-RW was 6.96-7.18; the effluent pH of wetland was 7.51-7.72, 7.86-8.08 and 7.16-7.41, respectively, and the corresponding mean values were 7.60 \(\pm\) 0.04, 7.94 \(\pm\) 0.05 and 7.27 \(\pm\) 0.07, respectively. It was evident that the pH of the effluent from the three groups of constructed wetlands was higher than that of the influent. This was mainly because the conversion of NO\({}_{3}\)\({}^{-}\)-N in the wetland into N\({}_{2}\) under the action of denitriying bacteria generated alkalinity at the same time. The reed char in the substrate contained mineral elements like Ca and Mg which would also make the water basic after hydrolysis [32].  
Comparison between RW and CW showed that the addition a of reed carbon source could significantly decrease the pH of effluent from constructed wetlands (\(p<0.05\)). Hydrolysis of the reed carbon source in RW could liberate some organic acids. As the concentration of organic matter in the system increased, anaerobic fermentation became the main way to remove organic matter, and acidic metabolites would be produced, leading to a decrease in pH of wetland effluents [33]. However, the pH of the RW effluent was still in the range of 7.51 to 7.72, which was suitable for metabolic reproduction of denitriying microorganisms.  
The comparison between RW and N-RW showed that reed planting could significantly improve the pH of the wastewater in constructed wetlands (\(p<0.05\)). The oxygen secretion function of reed roots could improve the dissolved oxygen content and redox potential in the wetland substrate, which promoted the degradation of organic matter in the system by aerobic microorganisms and reduced the production of organic acids. At the same time, the exudates from the reed roots could be used as organic carbon sources to promote microbial denitrification, increasing the alkalinity generated by the denitrification reaction [34]. Therefore, the pH of the effluent from RW is higher than that from N-RW.
# A Versatile Resilience Packet Ring Protocol Model for Homogeneous Networks
## 2 Related Work
### VRPR Packet Flow When a Packet Enters a GW Router
When an acknowledgment arrives at the router from the outer network router, the router again checks the traces of the path and sends this acknowledgment towards that link from whence it comes. In our proposed network model, we have made some scenarios. To check the delay in the complete LAN network, we discuss these scenarios in detail as follows:  
Scenario A:  
In this scenario, all the links are functional, and each device can communicate without any interruption. In the subsequent two scenarios, we introduce wired and wireless link failures and explain how the network handles the situation in the case of link failure. Scenario B:  
In this scenario, the link from the server to the access switch is down. The traffic from the server is sent only via the wireless link and it has been reached at the access point. The access point will send traffic towards the GW router. Here, the gateway router will check: if the traffic is coming from a wireless link, then the GW router will send it to the external network. In Scenario C, the traffic flow is from the external network to the internal server. If the packet reaches the switch and the link from the switch to the server is down, traffic will be routed towards the access point, and an access point will send the traffic to the server. Figure 7 illustrates the logical diagram of scenario B.  
Figure 7: Scenario C [11]. Scenario C:  
In this scenario, we assume that the wireless links are down from the server to the access point, and packets go outside of the network. The delay factor becomes very important when running time-sensitive applications such as voice, video, etc. To check the delay in the whole LAN network, we have made different scenarios which are discussed below. We have tested our proposed model in two different media, namely, wired and wireless media. The whole traffic goes from the wired link to the access switch and from the access switch to the router. As depicted in Figure 8, the router will keep traces of the path of the traffic and will forward the traffic to the outer network.
## 3 Materials and Methods
### Data Processing
#### 3.2.1 Distance Calculation  
The distance calculation of latitude and longitude using the Haversine equation is a fundamental tool in the field of geolocation and navigation. The Haversine equation is a mathematical formula that allows for estimating the distance between two points on the Earth's surface, considering their location in terms of latitude and longitude coordinates. This equation takes into account the curvature of the Earth and considers the average radius of the planet to calculate the distance on a sphere. Equation (1) calculates the distance between two geolocations [19]:  
\[D_{r}=\sqrt{\left(2r\cdot arcsin\left(\sqrt{sin^{2}\left(\frac{\varphi_{2}- \varphi_{1}}{2}\right)+cos\varphi_{1}cos\varphi_{2}sin^{2}\left(\frac{\lambda _{2}-\lambda_{1}}{2}\right)\right)}\right)^{2}+h^{2}} \tag{1}\]  
where \(D_{r}\) represents the distance between Tx-Rx, r is the average radius of the Earth (e.g., approximately 6371 km); \(\varphi_{1}\) and \(\lambda_{1}\) are the latitude and longitude coordinates of the first point, respectively; \(\varphi_{2}\) and \(\lambda_{2}\) are the latitude and longitude coordinates of the second point, respectively; \(h\) is the height difference between Tx and Rx relative to the ground.
# Design of a Tri-Band Wearable Antenna for Millimeter-Wave 5G Applications
## 2 Antenna Design Methodology
### Antenna Geometry  
The proposed antenna's top, back, and side views are shown in Figure 3a-c. The antenna design is modelled using Rogers R/Duroid5880 with a relative permittivity of 2.2, and loss tangent of 0.12. The thickness of Rogers material is 0.25 mm. This material has a low dielectric constant and low dielectric loss, which makes it suitable for high frequency/broadband applications. This material is isotropic and absorbs low moisture. The overall size of the proposed antenna is \(\rm L_{s}\times W_{s}\times H_{s}\). Copper is used on the top side of the substrate and has an effective length of \(\lambda/2\). A mm-wave tri band antenna is proposed which resonates at 28 GHz, 38 GHz, and 60 GHz operating frequencies. The U-shaped and C-shaped slots are inserted in the radiator, and the center side of the rectangular monopole antenna is truncated. The truncated side, top invert C-shaped slot, and bottom U-shaped slots are used to resonate at 28 GHz, 38 GHz, and 60 GHz, respectively. A Computer Simulation Technology (CST) Studio suite is used to model the antenna design. Several antenna parameters are optimized during the design process to provide the best possible radiation characteristics. Table 1 indicates the proposed dimension parameters of the optimized antenna.  
Figure 3: Microstrip feedline and an umbrella-shaped radiator comprise the antenna’s geometry; (**a**) front view, (**b**) back view, and (**c**) side view.
# Suppression of Multiple Reflection Interference Signals in GPR Images Caused by Rebar Using VAE-GAN
###### Abstract  
Due to the rebars layer's shielding effect on Ground Penetrating Radar (GPR) waves, the hyperbolic clutter generated by the rebars interferes with the echoes from void beneath them. The overlapping waveforms of both signals result in attenuation and distortion of the void signals, making it difficult to identify void defects under the rebar. This study proposes an unsupervised generative network model based on Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). Through a shared latent space, mapping is achieved between two image domains, effectively eliminating the multiple reflection interference signals caused by the rebar while accurately reconstructing the void defects, generating GPR B-Scan images without rebar clutter. Additionally, the channel and spatial attention module (CSA) is implemented into the model to help the network to better focus on the essential information in GPR images. The proposed model was validated through ablation and comparative experiments using synthetic data. Finally, real GPR data from the Husa Tunnel were used to verify the model's effectiveness in practical engineering applications. The results showed that this model is highly effective; it improves the visibility of void defects signals, thereby enhancing the interpretability of GPR data for tunnel lining inspections.  
G 2025 15 3728
## 3 The Datasets  
The NeVoCGPS network, operated by the Istituto Nazionale di Geofisica e Vulcanologia (INGV), was installed to monitor ground deformation in Neapolitan volcanic area. In this area, there are three active volcanoes, Somma-Vesuvius, Ischia island and Campi Flegrei caldera. Today, the NeVoCGPS network consists of 37 GNSS sites equipped with choke ring antennae and dual-frequency receivers. Our study concerns a part of this network, the one that covers the Neapolitan and Vesuvius areas. The Somma-Vesuvius is located east of Naples, has a surface of 165 km\({}^{2}\) and is about 1200 m high [21], therefore, it is the highest relief in this area.  
The selection of GNSS sites from NeVoCGPS network used in this study consists of 15 GNSS sites (Table 1), 10 stations located on the volcano and 5 outside the volcanic area. Sites located outside the volcano (Figure 1) are: ENAV, sited on the limestones of Sorrento Peninsula; PACA. placed on the first Apennine foothills, MAFE, TAI1 and FRUL, which are installed in Naples.  
The dataset used in this study is divided into two parts, one used for studying the long-term trends of the tropospheric delay parameters having a climatological focus, and one analyzed to investigate the evolution of delay parameters during severe rainfall events. The relatively long data set consists of Rinex data, covering intervals ranging from 6 to 11 years, from 2006 to 2016. The other part consists of Rinex data collected in time intervals encompassing the thunderstorms that hit Naples on 23 September 2019, 26 September 2019 and 16 November 2020. As regards the last event, we also use the meteorological data from two stations, co-localized with the NeVoCGPS site MAFE (Table 1) and the IGS (International GPS Service) station (MATE-Matera). We even use GNSS Rinex data from a station not belonging to the NeVoCGPS network, located in the city of Naples, named TAI1. The latter has been set up in 2019, under the framework of the EU project TRYAT ([[https://www.tryat.eu/](https://www.tryat.eu/)]([https://www.tryat.eu/](https://www.tryat.eu/)), accessed on 15 September 2021).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Station (ID)** & **Latitude (\({}^{\circ}\)N)** & **Longitude (\({}^{\circ}\)E)** & **Altitude (m a.s.l.)** & **Analysis Type** \\ \hline AGR1 & 40.811 & 14.343 & 70 & long term \\ BKE1 & 40.819 & 14.439 & 864 & long term \\ BKNO & 40.830 & 14.430 & 959 & long term \\ ENAV & 40.582 & 14.335 & 485 & long term \\ FRUL & 40.877 & 14.225 & 233 & thunderstorm \\ MAFE & 40.847 & 14.258 & 57 & long term/thunderstorm \\ NAMM & 40.836 & 14.254 & 3 & thunderstorm \\ ONPI & 40.779 & 14.411 & 123 & long term \\ OSVE & 40.828 & 14.397 & 624 & long term/thunderstorm \\ PACA & 40.870 & 14.556 & 74 & long term/thunderstorm \\ PRET & 40.849 & 14.477 & 209 & long term \\ SANAA & 40.869 & 14.412 & 156 & long term \\ TAI1 & 40.859 & 14.276 & 35 & thunderstorm \\ TERZ & 40.808 & 14.475 & 180 & long term \\ VOLL & 40.883 & 14.348 & 33 & thunderstorm \\ \hline \end{tabular}
\end{table}
Table 1: Geographical coordinates, altitude above sea level, analysis type and the name of the GNSS stations used in this study.