esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 QoE-Aware Geographic Routing Protocol for Video Streaming over VANETs
### QoS Metrics  
In the proposed protocol, three QoS metrics are used: packet loss rate, end-to-end delay, and jitter. These factors may introduce distortions in the video that affect the quality of the received video. The delay affects the quality of the video, especially in the beginning by causing the start-up delay. In the rest of the video transmission, its effects could be avoided by the use of a buffer. Jitter makes video packets waiting in the queue to be displayed in the correct order which may freeze the most recent frame until the belated frame arrives. The belated frame would be played briefly to preserve the timing of other arrived frames. Packet loss degrades the quality of received video frames. It can cause frame loss, which produces the freezing of a recent frame and the jump to the next arrived frame. This may make the video useless. Packet loss is usually caused by congestion or corrupted packets [45]. The three metrics are used to estimate the \(MOS_{routing}\) of the vehicle and serves together with mobility metrics for the selection of the next forwarding vehicle.
# Multi-Controller Deployment in SDN-Enabled 6G Space-Air-Ground Integrated Network
###### Abstract  
The space-air-ground Integrated Network (SAGIN) is considered to be a significant framework for realizing the vision of \"6G intelligent connection of all things\". The birth of 6G SAGIN also brings many problems, such as ultra-dense dense networks, leading to a decrease in the efficiency of traditional flat network management, and traditional satellite networking solidified network functions, etc. Therefore, combining the 6G SAGIN network with the software-defined network (SDN) is an excellent solution. However, the satellite network topology changes dynamically and the ground user unbalanced distribution leads to the unbalanced load of the SDN controller, which further leads to the increased communication delay and throughput drop, etc. For these problems, a hierarchical multi-controller deployment strategy of an SDN-based 6G SAGIN is proposed. Firstly, the delay model of the network, the load model of the SDN controller, and a loss value as a measure of whether the network delay and controller load are optimal are defined. Then, using the distribution relationship between the SDN controller and the switch node as the solution space, and taking the loss value as the optimization goal, a multi-controller deployment strategy based on the simulated annealing algorithm is used to search for the optimal solution space. Lastly, considering the network topology changes dynamically and the SDN controller imbalance, a switch migration strategy oriented toward load balancing is proposed. We aimed to determine the controller deployment plan through the above two points, balance the controller load, and then improve the network performance. The simulation results show that the controller load is increased by about 7.71% compared to OCLDS, and the running time is increased by 17.7% compared to n-k-means.  
space-air-ground integrated network (SAGIN); software-defined network (SDN); multi-controller deployment; switch migration; load balancing +
Footnote †: journal: remote sensing
# Identification of Land Use Mix Using Point-Based Geospatial Data in Urban Areas
## 3 Results
### Experimental Results
The main motivation was to investigate whether the scores increase proportionally to the significance level, and it is confirmed that when the significance of LMI increases, our classification scores also become better. For instance, for \(p\)-values less than and equal to 0.01, we saw 71% classification \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline Overall & 0.51 & 0.48 & 0.69 & 0.57 \\ Baseline all 0 s & 0.57 & 1.00 & 0.57 & 0.72 \\ Baseline all 1 s & 0.43 & 0.00 & NA & NA \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overall accuracy, precision, recall, and F1-score. Figure 9: AUC curves for different cutoff points. The top-left AUC plot presents cutoff = 1 and the bottom-right plot presents cutoff = 0.9.  
accuracy, 63% precision, 92% recall, and 75% F1-score, with the baseline of all 0 s being 58%. Additionally, we look at the classification accuracy, precision, recall, and F1-score concerning the different numbers of POIs residing on fishnets. In order to specify the bins, we first started with equal-depth binning and then tweaked the ranges to make sure that distributions of LUM values in each bin are different from the others. We used a non-parametric Mann-Whitney U test with a \(p\)-value \(\leq\) 0.05 to make sure that they were not coming from the same distributions. As a result, we created five bins depending on the POI counts with LUM values coming from different distributions. In Table 4, the results are presented for different bins such as [4; 5; 6; 7; 8; 9; 10; 11; 12; 17], [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28], and [29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60]; as a result, we found that the accuracy of finding out whether a fishnet has mix land use or no-mix land use increases up to 65% followed by the 67% precision, 89% recall, and 77% F1-score when the POI count is between 29 and 60. In other words, considering that we are working with fishnets with an area of 0.2655 km\({}^{2}\), if the POI density ranges between 109 POI/km\({}^{2}\) and 226 POI/km\({}^{2}\), we can reach the highest mixed land use classification accuracy of 77% and F1-score of 80%. Finally, we compared the results from Ankara to Kadikoy with the same assumption of having between 29 and 60 points within a fishnet eye, we find the following results presented in Table 5.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Bins** & **Accuracy** & **Precision** & **Recall** & **F1** & **\# of Places** & **Baseline** \\ \hline
[4; 5; 6; 7; 8; 9; 10; 11] & 0.42 & 0.26 & 0.51 & 0.35 & 156 & 0.54 \\
[12; 13; 14; 15; 16; 17] & 0.51 & 0.53 & 0.55 & 0.54 & 107 & 0.65 \\
[18; 19; 20; 21; 22; 2  
Comparing the results from Ankara to those from Kadkoy, we observe that the latter demonstrates a slightly better accuracy and F1-score. This is likely due to Kadkoy's higher density of mixed-use developments which aligned well with our POI-based methodology.
## 3 Connectivity in Wireless Ad Hoc and Sensor Networks
### Techniques to Improve Connectivity  
As connectivity is so important to function a sensor network, some techniques have been proposed to improve the connectivity by extending the transmission range of sensors. These techniques include cooperative transmission [9; 48], and the application of directional antennas [49].  
Cooperative transmission is an exploitation of distributed beam-forming. With cooperative transmission, it is possible to accumulate the transmission power from different nodes to achieve a higher power to transmit identical information, and hence the transmission range can be greatly enlarged, and the connectivity of the whole network can be improved. To implement cooperative transmission, all nodes that are transmitting the same message should synchronize their transmission and superimpose the emitted waveforms on the physical medium, so that the power can be summed up to help the detection at the receiver side. This type of techniques are especially helpful to eliminate the nodes isolation and network separation for large and sparse networks. [PERSON] _et al._[9] built a prototype platform to demonstrate that using cooperative transmission can actually increase the network connectivity.  
Another technique with renewed interests in the network connectivity area is the use of directional antennas [49]. It was proposed to equip wireless devices with directional antennas, which can focus transmission energy in one or two directions, as a large portion of transmission energy from an omnidirectional antenna is wasted in the directions without receivers. Directional antenna can greatly reduce the transmission power while maintaining the same level of connectivity. Accordingly, the sensors with directional antennas can transmit to a receiver with longer distance if the transmission power remains the same. [PERSON] _et al._[49] showed that when highly-directional antennas are applied to sensors, significant savings on the energy can be achieved, compared to that with omnidirectional antennas. This result indicated that under the same energy consumption constraints, sensors with directional antenna nodes can reach longer, and achieve better connectivity. In [49], the authors also derived the theoretical bounds on the maximum beam width to achieve the energy saving. Examples of applications of directional antennas include [50], and references therein.  
Given the complexity of these two techniques on one hand, and the requirements of simple design of sensors on the other, clearly the challenge of improving connectivity in sensor networks still exists.
## 4 Open Issues and Challenges
### Trajectory Planning of the UAV  
As aforementioned, mobile vehicle based technologies are effective methods addressing localization issue because of its mobility and flexibility. Path planning is object to improve the localization accuracy with a best possible trajectory of the UAV. Proper path planning can guarantee good coverage of the whole sensing field as well as keeping the minimum path length. Study in [66] is an application to UAV path planning. An adaptive operator quantum-behaved pigeon-inspired optimization algorithm along with logistic mapping method were proposed in [66] for the application to UAV path planning. A comparison result presents that the performance of their proposed algorithm is better than parts of the existing algorithms in terms of convergence and accuracy. Indeed, according to different applications, many investigations with different objective functions and optimization methods have been done (we summarize them in Table 6).  
Moreover, according to whether there is interaction between UAVs and nodes, path planning of U-WSN has two categories. First is static path planning in which the path is predefined and second is dynamic path planning in which the path can be changed. The existing algorithms are summarized and compared in Table 7.
## 2 Materials and Methods
### Characterization of Plasma Source: E-Field Measurement Set-Up  
For the electrical field (E-field) measurements, we used a 1 transverse electrical field component optical probe (Kapteos, EoProbe ET5-air, low permittivity \(\sim\)3.6, sensitivity 250 mV/m, up to 10 MV\({}_{rms}\)/m, bandwidth 10-12 GHz, spatial resolution of 1 mm, 5 m optical fiber cord) coupled to an electromagnetic field measurement system (Kapteos, EoSense LF 100U-1). The ET5-air is an electrooptic (EO) probe based on an isotropic EO crystal that acts as a transducer (due to the Pockels effect) converting the E-field to be determined into an optical modulation of a laser beam that can be measured via a polarizer and a photodiode [40; 44; 52; 53; 54; 55].  
The EO probe was moved axially to the CAP tube and measurements were made at the nozzle exit (marked as 'b', 0 mm), at 5 mm away from nozzle ('c'), at 10 mm away from nozzle ('d') and near the ground electrode, on the tube at \(-5\) mm from the nozzle exit (positioned instead of the letter 'a'), exactly as in Figure 1 and further on presented in the electrical field characterization paragraph.  
Figure 1: Overview of the experimental arrangements: **(top)** CAP, E-field and VUV set-up with a photo of the CAP and E-field probe; **(bottom)** CAP facing the VUV MgF\({}_{2}\) window with a list of performed diagnostics throughout the manuscript and the description of w/o surface. Leakage current circuit in accordance with [51].
# Heat Transfer Correlations for Star-Shaped Fins
## 1 Introduction
### Influence of Fin Thickness  
Several investigators have considered the influence of fin thickness (tf) on the heat transfer coefficient. [PERSON] and [PERSON] [20] concluded that the Nusselt number increases with increasing fin thickness. The correlation they gave for the pressure drop in the tube bundle with the staggered tube arrangement was valid for the range \(103<Re<3\cdot 10^{4}\). [PERSON] and [PERSON] [11] tested three different thicknesses of spiral fins (0.457 mm, 1.06 mm, and 2.02 mm) and found that the heat transfer coefficient for the thickest fin was about 8% lower compared to the thinnest fin. So, they concluded that heat transfer is less dependent on fin thickness and decreases with increasing fin thickness.  
[PERSON] and [PERSON] [21] have analytically shown that the thickness of the fin does not affect the convective heat transfer coefficient. It has also been found that the pressure drop increases with increasing fin thickness.
# Contrasting Dual Transformer Architectures for Multi-Modal Remote Sensing Image Retrieval
## 2 Materials and Methods  
Let us consider a \(D^{(I)}=\{X_{i},t_{i}\}_{i=1}^{N}\) be a set of \(N\) training image-text pairs in an archive. The goal of a text-to-image retrieval task is to find the most relevant image from \(X_{i}\) to the provided text query. Similarly, in image-to-text retrieval, we are interested in retrieving the sentence \(t_{i}\) that is most similar to the query image.  
The suggested model's overall architecture, which is built mostly on language and vision transformers, is depicted in Figure 2. In order to generate visual and textual characteristics for the transformer encoders during training, we sample a mini-batch of images from the training set with their corresponding sentences. Next, we compute the similarity between each potential pair of text-images in the mini-batch to create a similarity matrix. Then, by maximizing an image-to-text and text-to-image contrastive classification loss, we learn the model weights. During the test, based on a query text we retrieve the most similar images by computing the similarity between the textual feature of the query and the visual features of the training images. In a similar manner, we can retrieve the most similar textual descriptions in the case of an image query. Detailed descriptions of the overall architecture are provided in next subsections.
# Offline Payment of Central Bank Digital Currency Based on a Trusted Platform Module
## 4 Results and Discussion
### Token Forgery and Non-Repudiation  
All tokens transmitted and received offline in this study adhere to the JWT structure.  
Token format (JWT) = _Header.Payload.Signature_  
Tokens require the signature of a guarantor (the receiver), thereby preventing attackers (the sender) from generating a valid token on their own. The guarantor's signature is produced by a signing key located within the TPM, accompanied by an ID certificate; this process is inherently unforgeable. In a worst-case scenario, if an attacker successfully compromises the TPM and forges a token through collusion with another subscriber, the potential damage inflicted within this digital currency system would be limited to the maximum transaction amount established by policy, even if the counterfeit token is transmitted to a legitimate subscriber. Furthermore, when all offline tokens are eventually shared and reconciled upon establishing an online connection, any compromised or counterfeit tokens can be identified. In terms of non-repudiation, all messages and tokens are equipped with digital signatures. A fundamental characteristic of digital signatures is their provision of non-repudiation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **OPERA Model** & **Proposed Model** \\ \hline Key features & * - The distribution of pre-generated tokens with a predetermined value by a financial institution, similarly to the issuance of coins. * - It is necessary to assemble a minimum combination of tokens that corresponds to the transaction amount and subsequently deliver it to the designated recipient. & * - All valid input tokens are categorized into two distinct output tokens: the sending token and the holding token. & * - The tokens employed are organized in a token chain. \\ \hline Security Mechanisms & * - Specific hardware, characterized as one-time readable memory, is employed to safeguard against the reuse of tokens. & * - The use of a software wallet is essential for the management of tokens, as it securely stores key information, including the encryption key, within a TPM. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with the OPERA model.
# Evaluating the Effect of Road Surface Potholes Using a Microscopic Traffic Model
## 4 Conclusions  
A microscopic traffic model was proposed which considers the road surface conditions. This is important as potholes develop due to road surface deterioration. Potholes affect traffic behavior and can result in accidents. Road surface conditions have not previously been incorporated into microscopic traffic models. The proposed model was used to investigate the impact of potholes on traffic. For this purpose, potholes were divided into three categories, small, medium, and large, and were assumed to have a conical shape. The proposed model was compared with the ID model on a 1 km circular road. The fundamental diagrams showed that with the proposed model, the relationships between flow, density, and velocity vary according to the road surface conditions. These results are more realistic than with the ID mode, which is based on a constant acceleration exponent. In addition, the velocity and acceleration with the proposed model evolve realistically over time based on the road surface conditions and driver sensitivity, and reflect actual traffic conditions.  
The proposed model can be used to explore vehicle movement and interactions when potholes are present. The insights gained with this model can aid in developing strategies to improve traffic flow leading to better efficiency and safety. In addition, the proposed model can be integrated into transportation systems to help reduce fuel consumption and pollution. Future research can consider incorporating real-time data to determine the effect of potholes.  
Conceptualization, F.A. and Z.H.K.; methodology, F.A.; software, F.A.; validation, F.A., Z.H.K., K.S.K. and T.A.G.; formal analysis, F.A., Z.H.K., K.S.K. and T.A.G.; investigation, F.A., Z.H.K., K.S.K. and T.A.G.; writing--original draft, F.A.; writing--review and editing, F.A., Z.H.K., K.S.K. and T.A.G.; visualization, F.A., Z.H.K., K.S.K. and T.A.G.; funding acquisition, Z.H.K., K.S.K. and T.A.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Conclusions  
The purpose of this study was to enhance the detection accuracy for PWDT using TSUI and DLSS techniques. A total of 2350 PWDT were vectorized at 9 sites, and 795 images of 2000 damaged trees at 8 sites were used as training data. For the test dataset, 200 images from Angang-eup, Gyeongju city, Gyeongbuk province, where 350 damaged trees were found, were used.  
This study is distinguished in several aspects as follows. First, the semantic segmentation models of U-net, SegNet and DeepLabV3+ were adopted and compared to determine which can best reflect the characteristics of atypical and morphologically diverse tree canopies. Second, to ensure the reliability of DL data in previous studies, felled trees were tracked and pest-controlled trees were used as the ground truth data for the TSUI for a minimum of 2 years.  
The comparison of each algorithm shows that DeepLabV3+ (ResNet50) had the best performance, with an f1-score of 0.742, followed by U-Net with 0.720. For precision, U-Net had the highest value of 0.776, while SegNet and DeepLabV3+ (ResNet50) had similar values. For recall, DeepLabV3+ (ResNet50) showed the highest value (0.742), while  
Figure 5: The sample cases of TP and FP (PWDT falsely detects soil or browned hardwoods) (Red: Ground Truth, Blue: FP).  
DeepLabV3+ (ResNet50) and U-Net showed similar values. The false positives occurred mainly on bare ground, shaded areas, roads and rooftops, and were more common in broadleaf trees than conifers.  
In future, a potential way to minimize overestimation is the construction of accurate learning data with an increase in datasets via data augmentation, which is predicted to further enhance the accuracy. The results of this study will contribute to the forecasting of major forest diseases such as PWD and oak wilt disease (OWD), while supporting more rapid detection and monitoring in addition to reduced manpower and cost of the conventional wilt disease detection.  
Conceptualization, S.-W.K.; Methodology, S.-K.Y. and S.-W.K.; Software, M.-G.L.; Validation, H.-B.C.; Investigation, M.-G.L. All authors have read and agreed to the published version of the manuscript.  
This Study was supported by the 2021 research fund from Wonkwang University.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Result and Discussion
### Flow within the Leakage Vortex
#### 3.2.1 Interaction between Shock Wave and Tip-Leakage Vortex  
Figure 11g shows that for the incident shock, because of the sudden expansion of the vortex core, the supersonic mainstream outside the vortex core is disturbed and a compression shock wave is formed, which is called \"induced separation shock\". Due to the oblique shock wave, the fluid on the surface of the vortex core flows away from the blade surface and then the tip-leakage vortex turns back to the blade surface because of the transverse pressure gradient of the turbine passage. Since the main flow velocity flows to the leakage vortex surface, a third shock wave is formed, which is called the reattachment shock. The flow between the induced separation shock and the reattachment shock gradually expands and forms a series of expansion waves. This process has a direct impact on the shape of the leakage vortex after its breakdown.  
Figure 11g shows that for the leakage vortex in the supersonic flow, there is a series of expansion waves between the throat (\(\text{Ma}=1\)) and the incident shock wave and the flow in the leakage vortex core is under a negative pressure gradient. It accelerates the leakage vortex core flow, which suppresses its flow instability. However, the excessive expansion after the expansion wave leads to shock waves (strong oblique shock waves) in the leakage vortex. Due to the rapid rise of the pressure through the shock wave, the axial velocity of the leakage vortex core decreases. However, the rotating velocity parallel to the shock surface is almost unchanged. Therefore, the vortex core becomes unstable and rapidly expands under the rotating motion. Then, the vortex breakdown occurs almost downstream to it. After the breakdown of the leakage vortex, the velocity in the vortex core drops to zero rapidly, forming a stagnation point and a separation flow region. The above is the breakdown process of the tip-leakage vortex after interaction with the shock wave.  
Therefore, it can be inferred that the increment of the leakage loss is correlated to the interaction between the leakage vortex and the shock wave.