esa-sceva/satcom-chunk-collection huggingface dataset:
# Underlay Loosely Coupled Model for Public Safety Networks Based on Device-to-Device Communication
###### Abstract
Moreover, the quantity and the diversity of the deployed equipment, as well as the complexity of offering a new LTE-based network based on another LTE Cell (or Satellite), make the use of such a solution very expensive to deploy, especially in countries with large territories and limited resources. On the other hand, in the last few years, the Third Generation Partnership Project (3 GPP) has introduced device-to-device (D2D) technology [1,2], which enables a direct connection where the data plane can be handled by User Equipment (UE) without crossing the cellular network infrastructure. This D2D technology can be coupled with a Proximity-based Service (ProSe) [3] that provides many functions, including discovery, communication, and the relay mode. ProSe technology allows UE in proximity to discover itself and establish direct communication channels as soon as cellular operators enable the public safety mode. D2D technology enables out-of-coverage UE to join the network through another UE that plays the role of a relay. Compared with the proposed PSMB solutions, the deployment of D2D in a public safety context offers several benefits:  
* Benefits from proximity, with higher throughput, lower delays, and optimized power consumption. * Avoids overloading the central cellular antenna by dispatching media streams directly between participants. * Extends the cell range using an in-band D2D relay function to connect out-of-range devices. This paper explores underlay network models based on D2D by focusing on loosely coupled topology. User membership and network management are studied, and simulations are conducted to analyze the effects of user density and group connectivity as well as assess the optimization effect in terms of the CQI and the number of link updates. In addition to carrying out simulations using the \"less cost\" and \"optimized tree\" approaches, it was possible to obtain relevant results by modifying the CQI threshold and the optimization interval. The combination of these two parameters made it possible to reduce the number of updates while keeping an average CQI close to the results obtained with the \"optimized tree\" approach. The remainder of this paper is organized as follows: Section 1 provides a survey of some existing solutions for the public safety context; Section 2 presents an overview of the underlay D2D network based on ProSe service; Section 3 focuses on the loosely coupled model; Section 4 includes the material and methods used for simulations; and Section 5 presents the simulation results and comments followed by concluding remarks.
## 5 Energy-Harvesting Applications
### Cognitive Radio for Wireless Sensor Networks
#### 5.1.4 Challenges and Solutions  
As cognitive radio provides notable advantages, various obstacles are necessary to address:  
* Interference Management: Making sure that the secondary users (SUs) do not obstruct primary users (PUs) or other secondary users.
* Security Concerns: Keeping CR systems safe from malicious attacks and certifying steady spectrum access.
* Complexity in Implementation: It would be complex and costly to integrate the CR technology into existing WSN infrastructure.
## 3 Results
### Comparison with Urban Related Indices  
The methodology proposed in the present study is based on the integrated analysis of several remote sensing indices. Considering the fact that, for the digital mapping of the built surfaces, based on the analysis of satellite images, there are a multitude of remote sensing indices applicable and applied  
Figure 3: Results of the analysis based on proposed method.  
for various research topics or territorial analysis purposes, we consider that a comparative analysis of results is necessary to validate the obtained results.  
All the results have been quantitatively and qualitatively compared to other urban related remote sensing indices, such as:  
\[\text{NDBI}=\left(\text{SWIR1}-\text{NIR}\right)/\left(\text{SWIR1}+\text{NIR}\right) \tag{4}\]  
\[\text{UI}=\left(\text{SWIR2}-\text{NIR}\right)/\left(\text{SWIR2}+\text{NIR}\right) \tag{5}\]  
\[\text{EBBI}=\left(\text{SWIR1}-\text{NIR}\right)/\left(10\sqrt{\left(\text{ SWIR1}+\text{TIR}\right)}\right. \tag{6}\]  
\[\text{IBI}=\frac{2\text{SWIR1}/\left(\text{SWIR1}+\text{NIR}\right)\;-\;\left[ \text{NIR}/\left(\text{NIR}+\text{Red}\right)\;+\;\text{Green}/\left(\text{ Green}+\text{SWIR1}\right)\right]}{2\text{SWIR1}/\left(\text{SWIR1}+\text{NIR}\right)\;+\;\left[ \text{NIR}/\left(\text{NIR}+\text{Red}\right)\;+\;\text{Green}/\left(\text{ Green}+\text{SWIR1}\right)\right]} \tag{7}\]  
As we outlined above, most of the remote sensing indices related with built-up area extraction have a lot of noise in the results and erroneously include various land classes, such as bare land, uncultivated agricultural land, water bodies, etc. By comparing the results of NDBI, UI, EBBI and IBI (Figure 4) with the results obtained using the proposed methodology, we noticed a significant improvement in the delimitation of built-up areas with less noise and errors present (Figure 3).  
Figure 4: Results obtained using other urban related indices, calculated on Landsat-5 image, acquired in 2010.
## 4 Discussion
They both report the \(t\)-statistics and the relative \(p\)-value of the null hypothesis of no contribution of hydrophilicity (polarity) of the multiple linear regression having score for different classes as dependent variable and different chemico-physical indexes relative to the simplices as regressors. As evident, especially hydrophilicity enters a significant contribution to all models as the most important predictor (i.e., the estimated coefficient for average hydrophilicity is approximately one order of magnitude higher with respect to other coefficients). Another interesting aspect is that all models show a negative coefficient for average hydrophilicity and a positive sign for its standard deviation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Polarity** & **Hydrophilicity** & **Polarity** & **Hydrophilicity** \\  & **(avg)** & **(avg)** & **(std)** & **(std)** \\ \hline
**Polarity (avg)** & 1 & 0.99818 & \(-\)0.01869 & \(-\)0.06879 \\
**Hydrophilicity (avg)** & 0.99818 & 1 & \(-\)0.03705 & \(-\)0.08582 \\
**Polarity (std)** & \(-\)0.01869 & \(-\)0.03705 & 1 & 0.99397 \\
**Hydrophilicity (std)** & \(-\)0.06879 & \(-\)0.08582 & 0.99397 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Pearson correlation coefficients between polarity and hydrophilicity. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **EC1** & **EC2** & **EC3** & **EC4** & **EC5** & **EC6** & **Not Enzymes** \\ \hline \(R^{2}\) & 0.0250 & 0.0239 & 0.0212 & 0.0199 & 0.0239 & 0.0170 & 0.0250 \\ \(p\) & \(<\)0.0001 & \(<\)0.0001 & \(<\)0.0001 & \(<\)0.0001 & \(<\)0.0001 & \(<\)0.0001 & \(<\)0.0001 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Variance explained and statistical significance for the seven models. In conclusion, beside the confirmation of the pivotal role of residue hydrophilic character in determining the protein structure, it is well known [91] that when shifting from a single residue to an entire protein level, new organisation principles arise and 'context-dependent' features largely overcome single residue level properties. The 2% of variance explained is the percentage that can be imputed to the plain chemico-physical properties of individual simplices and one might ask whether the same analyses can be carried out by considering 'groups of simplices' instead of individual simplices and scoring their relevance for the problem at hand. This paves the way for new granulation-based studies which should also take into account these aspects. All in all, the observed results confirm the actual biochemical theory, thus providing a 'lateral validation' to the pattern recognition procedure, while at the same time pushing biochemists to look for non-local chemico-physical properties for getting rid of protein folding and structure-function relations.
# A Location-Aware Waypoint-Based Routing Protocol for Airborne DTNs in Search and Rescue Scenarios
## 4 Protocol Design
### General Description  
The forwarding decision on a node is taken if (a) a new message is available at the node or if (b) a new contact with another node takes place. In these cases, the node that owns the message analyzes the future movement path of the considered contact to check if it will crosses the position of the destination. If so, the node transmits the message to that contact node, otherwise, it does nothing. In the following, we detail the two specific cases, and illustrate the forwarding mechanism. For the sake of clarity we provide some notation:  
* The current node that is processing the forwarding decision of a message is denoted by \(c\);
* The considered message is denoted by \(m\);
* The destination of \(m\) is denoted by \(d_{m}\)
* The contact that sent \(m\) to \(c\) is denoted by \(p\);
* The contact that \(c\) is currently checking is denoted by \(u\).  
**(a) \(m\) is available at \(c\).** When a new message \(m\) is present in \(c\), it means that either \(m\) is created from \(c\) or \(m\) is arrived at \(c\) from a neighbor node \(p\). \(c\) starts by checking all its current neighbor nodes to find any carrier \(u\) that will reach \(d_{m}\). If \(m\) is created by \(c\), an initial counter field called _Time To Arrival_ (TTA) is set to _infinite_ and stored in \(m\). TTA will be used as the minimum known time \(m\) will take to reach \(d_{m}\) being carried by the current node, in this case \(c\).  
**(b) a new contact \(u\) is available to \(c\).** In this case, \(c\) checks all the messages in its own buffer to find any message \(m\) for which \(u\) could reach \(d_{m}\). Differently from the previous case, only this new contact \(u\) is checked, since the other current contacts have been already processed.
## 4 Research on the Influence of Track Structure Stiffness Change on Track Deformation
### Analysis of Sensitivity Indexes of Vehicle-Track System Dynamics Response
#### 5.2.4 Running Safety Analysis  
The change of the sensitivity indexes of running safety coefficient with different structural stiffness is shown in Figure 27, which shows that:  
1. The derailment coefficient is most obviously affected by the change in the vertical stiffness under block and shows an increasing trend with the increase in the stiffness under block, and its value decreases when the other stiffness changes, indicating that the appropriate vertical stiffness under block is maintained and the remaining stiffness is appropriately increased. The stiffness of the track structure can reduce the risk of train derailment and ensure the safety of operation;  
Figure 26: Lateral ride comfort coefficient sensitivity indexes. (**a**) Lateral displacement of rail and (**b**) Gauge expansion.  
2. The wheel load reduction rate is significantly affected by the under-rail and under-block lateral stiffness, and shows a decreasing trend as it increases, indicating that the larger lateral stiffness of the track structure can reduce the risk of train skipping.
## 5 Research Progress on Membranes for Redox Flow Batteries
### Nafion (Perfluorinated Membrane)
#### 5.3.1 Coating Nafion with Charged Substances (Polycations and Polyanions)  
Interfacial polymerization was used to develop a cationic charged layer on the surface of Nafion 117 membrane in order to reduce the permeation of vanadium ions [81]. On comparing the modified membrane with unmodified Nafion, it can be seen that after modification, there is a dramatic reduction in the crossover of vanadium ions across the membrane and a slight increase in the resistance of the membrane. As a result, the coulombic efficiency of the VRB single cell (based on modified Nafion membrane (which is related to the concentration of the incubation solution of polyelectrolyte polyethylenimine (PEI)) increased significantly. The value was found to be 96.2%-97.3%, which is higher than that obtained with the VRB single cell based on unmodified Nafion (around 93.8%). Due to a slightly higher area resistance caused as a result of modification, the voltage efficiency of modified Nafion is lower than that of pure Nafion. Furthermore, the water transfer across the modified membrane was also reduced.  
Another novel approach involved the fabrication of a barrier layer onto the surface of Nafion by alternate adsorption of a polycation, poly (diallyddimethylammonium chloride) (PDDA) and a polyanion, poly (sodium styrene sulfonate) (PSS) using a polyelectrolyte layer-by-layer self-assembly technique, which can suppress the crossover of vanadium ions [82]. The Nafion-[PDDA-PSS] n membrane (n = the number of multilayers) obtained, exhibited a much lower vanadium ion permeability when compared to the unmodified Nafion membrane. Moreover, VRB with Nafion-[PDDA-PSS] n as the membrane exhibits a higher coulombic efficiency (CE) and energy efficiency (EE) together with a slower self-discharge rate than that of the original Nafion system. Highest CE of 97.6% and EE of 83.9% can be achieved at charge-discharge current densities of 80 mA/cm\({}^{2}\) and 20 mA/cm\({}^{2}\), respectively.  
Polyaniline/Nafion and polypyrrole/Nafion composite membranes were prepared by chemical polymerization and their properties were studied in the VRB [83, 84]. The introduction of cationic polymers reduces the crossover of vanadium ions; however this modification also decreases the proton conductivity. It has been suggested that careful selection of the cationic polymer allows tuning the inherent trade-off between membrane permeability and resistance [84].
## 5 Experiments and Results
### Results Analysis
#### 5.2.2 Damage Assessment Models  
Four models were selected to compare the damage assessment results in the second stage and ensure the applicability of the proposed method. We selected the Siamese  
Figure 15: Visual comparison of different localization methods used in our paper.  
Base-UNet as a common model (vanilla model) in the damage assessment stage and also expanded it with an SKM. Moreover, in order to further understand the impact of adding the SKM to the networks, SKM was added to the segmentation head of the EfficientNetB7-UNet building damage assessment model. Table 2 summarizes all the models that were compared in the damage assessment stage of our study.  
We trained damage assessment models for 30 epochs on the xBD dataset and produced damage classification maps. F1-score and overall accuracy were observed during the training process and recorded as the evaluation metrics of the damage assessment stage. Figure 16 shows the mentioned metrics for the four models. Training and validation loss values, shown in Figure 16, represent that the learning procedure was almost steady for all models. Furthermore, F1-score and Accuracy values demonstrate that the proposed model with SKM on each localization branch, the EfficientNetB7-SKM-UNet (black line), outperformed other damage assessment models in both F1-score and accuracy. The proposed model performance was improved by at least 5% in the overall F1-score using richer features obtained through the SKM.  
The qualitative comparisons are also displayed in Figure 17, where the damage assessment model outputs for six sample images from the Test set are compared with their corresponding ground truth images. Visual investigation of the results indicated a higher localization and classification performance for the proposed models with EfficientNetB7-UNet architecture. The EfficientNetB7-UNet model with SKM added  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Damage Assessment Models** & **Extensions** \\ \hline
1 & Siamese-Base-UNet & - Basic localization UNets in each branch \\ \hline
2 & Siamese-Base-UNet+SKM & - Basic localization UNets in each branch \\  & - SKM added in segmentation head \\ \hline
3 & Siamese-EfficientNetB7-UNet+SKM & - EfficientNetB7-UNets from localization in each branch \\  & - SKM added in segmentation head \\ \hline
4 & Siamese-EfficientNetB7+SKM-UNet & - EfficientNetB7-UNets from localization in each branch \\  & - SKM added at the end of each branch \\ \hline \hline \end{tabular}
\end{table}
Table 2: The four model architectures that were used for comparison in the second stage building damage assessment.  
Figure 16: Comparison of the classification models in the damage assessment stage. Dashed and connected lines are for training and validation sets, respectively. Models 1 to 4 are colored red, cyan, yellow, and black, respectively.  
at the end of each branch had the best building damage assessment performance and outperformed other models. All models could predict destroyed and intact buildings with higher precision, while minor- and major-damaged buildings were the most challenging classes.
## 2 Theoretical Methods for TBR Prediction and Experimental Methods for TBR Measurement
### Experimental Methods for TBR Measurement
A detector is used to record the reflected probe beam as a function of time. The TBR can be determined by matching the experimental data with the model predictions [69]. In comparison with the ultrafast lasers employed in TDTR, continuous-wave (CW) lasers used in FDTR offer advantages, such as lower cost and greater wavelength flexibility. However, the FDTR can exhibit higher noise levels. In comparison, the TDTR provides superior signal-to-noise ratios, with ultrafast laser pulses. In addition, the optical alignment process is generally simpler for FDTR, except for extremely high frequencies. However, the TDTR is more sensitive to a wide range of TBRs [70].
# GPS-Induced Disparity Correction for Accurate Object Placement in Augmented Reality
## 4 Conclusions  
In this paper, we successfully demonstrate the implementation of an augmented reality (AR) application capable of accurately rendering 3D objects. By employing advanced coordinate system linking techniques and semantic segmentation, we overcome the challenges of GPS inaccuracies and object truncation in AR environments. Our experimental results demonstrate the effectiveness of using semantic segmentation for object recognition and movement in an AR setting, leading to the precise alignment of virtual objects with real-world locations. In addition, our approach for calculating and correcting location errors based on pixel distance proved to be effective in ensuring the accurate placement of AR objects. The findings of this study not only contribute to the field of AR by enhancing the user experience through more accurate and realistic renderings but also pave the way for future innovations in AR applications, particularly in marketing and advertising. The ability of the proposed system to seamlessly integrate virtual objects into the real world, as  
Figure 6: The images where the object is correctly displayed along the building’s boundaries. It shows a video captured while the device is in motion from (**a**–**d**).  
evidenced by comprehensive testing and experimentation, holds great potential for future applications that require high levels of precision and realism in AR environments.  
Conceptualization, S.Y.; methodology, S.Y.; software, S.Y.; validation, S.Y.; formal analysis, S.Y.; investigation, S.Y. and S.G.; writing--original draft preparation, S.Y.; writing--review and editing, S.Y.; visualization, N.J.; supervision, N.J.; project administration, N.J.; funding acquisition, S.G.; resources, N.J.; data curation, N.J. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by Wonkwang University in 2022.  
Not applicable.  
The data presented in this study are available upon reasonable request from the corresponding author. The data are not publicly available due to privacy.  
The authors thank the anonymous reviewers and editors for their insightful comments and suggestions.  
Author [PERSON] was employed by the company DAXIB Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Results and Discussion
### Characterization of Gypsum/RT18 HC@AC Composite Boards
#### 3.2.4 EMI Shielding Properties for GB/RT18 HC@AC Composite Boards
The electromagnetic interference shielding effect (\(SE\)) due to absorption (SER; (**c**)) and reflection (\(SE_{A}\); (**d**)) can also be seen in the frequency range of 3.5–7.0 GHz.
# Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning
## 2 Materials and Methods
### Pretext Task Settings  
We implemented two pretext tasks in this work:  
* Data-**Augmentations (A)**: it consists in choosing data-augmentations according to the invariances our network needs to learn. In our case, it has to identify ships no matter their orientation, size, even if the water is turbulent or if the background is noisy. Therefore, we applied an augmentation function \(aug\) to our query patch, where \(aug\) is one of: color jitter, random rotations, crop and resize with a small scale difference, slight Gaussian noise: \(x^{k_{+}}=aug(x^{q})\)
* **Region**-wise similarity and data **augmentations** (**RA**): learns features that increase the similarity between two patches of the same geographical region. This task is illustrated in Figure 3 and can be formalized as \(x^{k_{+}}=aug(sample\_neighbor(x^{q}))\), where \(aug\) is the same as above and \(sample\_neighbor\) generates a geographically close patch. Inspired by [26], this strategy aims to help the network to better cluster together similar regions (land, water bodies, etc.). The maximum distance can be varied to control the average overlap of sampled patches. Larger distance induces increased diversity but can generate patches that are too different from each other (ex: water and land when applying to littoral regions). We test two variants: high distance (RA) and low (**RA-lo**).  
Figure 3: Region based pretext task. Patches \(x^{q}\) and \(x^{k+}\) (yellow) constitute a positive pair whereas patches \(x^{q}\) and \(x^{k-}\) (red and yellow) form a negative pair.  
Figure 2: MoCo training algorithm [6]: negative embeddings from momentum encoders at previous iterations (\(i-1\), \(i-2\), etc.) are cached and reused at iteration \(i\).
## 2 Transmission Setup and Principles
### Mutual Information and Achievable Information Rate Estimation  
Due to the property of the binary symmetric channel, the hard-decision forward error correction (HD-FEC) based code rate of the QPSK and m-QAM signal transmission over an additive white Gaussian noise (AWGN) channel can be expressed as [28; 29]  
\[R_{C}=1+BER\cdot\log_{2}(BER)+(1-BER)\cdot\log_{2}(1-BER) \tag{6}\]  
In communication systems, mutual information is an indicator of the achievable channel capacity under specific modulation schemes. In dual-polarization coherent optical transmission systems, the \(MI\) can be expressed as  
\[MI=2\cdot\log_{2}(m)\cdot R_{C} \tag{7}\]where \(m\) is the order of the modulation format. The \(AIR\) is also an important indicator, which refers to the net data rate that can be achieved using FEC decoders [30]. In a dual-polarization Nyquist-spaced optical transmission system, AIRs are expressed by MI as  
\[AIR=N_{\text{ch}}\cdot R_{S}\cdot MI \tag{8}\]  
where \(N_{\text{ch}}\) is the number of WDM channels in the system, and \(R_{S}\) is the symbol rate of the transmitted signal.