esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions  
### Performance of the Models during Training  
Figures 8 and 9 demonstrate the losses and mAP, respectively, of the models during training. As can be seen in Figure 8, the losses of MobileNet Edge TPU and MobileDet Edge TPU are more stable than those of the other two models. Furthermore, MobileDet Edge TPU performs better in terms of mAP on the test set at PC, although it has a larger overall loss than the other models at the end of training.  
Since the mAP improvement of MobileNet-V1 and MobileNet-V2 ended at about 25k steps (Figure 9), their training was terminated at 30k steps, whereas the other models were trained for 35k steps.  
Table 1 shows the training time (s) of the models per step. The training time of the models developed for the Edge TPU was shorter than that of the other models, which is due to the fact that the number of parameters of these models is less than that of the other two models. Although the SSD with MobileNet Edge TPU and MobileDet Edge TPU have fewer parameters, their performance is superior to the other two types (see Figure 9).  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Backbones** & **MobileNet-V1** & **MobileNet-V2** & **MobileNet** & **MobileDet** \\  & & & **Edge TPU** & **Edge TPU** \\ \hline Training time (s) & 0.23 & 0.30 & 0.19 & 0.19 \\ \hline No. Parameters (million) & 5.49 & 4.57 & 2.99 & 3.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The total number of parameters and the training duration for one step.  
Figure 8: Loss of models during training. Top left: MobileNet-V1, top right: MobileNet-V2, bottom left: MobileNet Edge TPU and bottom right: MobileDet Edge TPU.
## 6 Applications of VSNs
### Vehicle Telematics  
VSNs play an important role in intelligent transportation systems. Incorporated into roads, vehicles, and traffic signals, they contribute to the improvement of safety of transporting systems. Through traffic or air-quality monitoring, they increase the possibilities in terms of traffic flow optimization and help reducing road congestion. Let us consider the following scenario for further clarification: scattered sensor nodes are included in roads during their construction for motion monitoring. When a car passes over of these nodes, the possibility is then given to track the trajectory and velocity of cars for safety purposes. The lifetime of the sensor nodes incorporated into roads is expected to be as long as the lifetime of the roads. Multihop communication is possible between sensor nodes, and the network should be able to cope with the deterioration over time of the node density due to power failures. Sink nodes placed at the road side are mains-powered; sensor nodes in the roads run on battery. Power savings schemes might intermittently disconnect the nodes. A rough estimate of four nodes per square meter is needed. Other applications may involve car-to-car communication for increased road safety. This scenario can be better handled by the concept of sensor network virtualization.
# SubvectorS_Geo: A Neural-Network-Based IPv6 Geolocation Algorithm
## 1 Introduction
There are three main challenges: (1) a lack of a reasonable evaluation of the true physical distance of the target IP; (2) how to reduce the negative impact of weak connectivity, high latency, and circuitous routing on measurement-based IP geolocation in IPv6 networks; (3) how to interpret the IPv6 geolocation model under deep learning. To address these issues, we designed an IPv6 geolocation algorithm called SubvectorS_Geo, which improves the performance of IP geolocation algorithms, under the assumption of similarity in host latency in the same region. SubvectorS_Geo is first based on a priori knowledge of IPv6 geolocation, using a rule-based approach combined with SubvectorS_Geo. It first constructs a \"layer by layer approximation\" region constraint based on the a priori knowledge of IPv6 geolocation and, then, uses a rule-based approach to combine landmark measurement data to construct a \"layer by layer approximation\" region constraint. Within the trusted region, SubvectorS_Geo evaluates the delay similarity from the delay sequences near the target IP, effectively reducing the possibility that delayed similar hosts are not in the same geographic location. In summary, the main contributions of this work are as follows:  
* We propose a new IPv6 geolocation algorithm, which solves the problem of the low accuracy of existing geolocation and the lack of reasonable and effective constraints on regional delay similarity. * We apply residual paths (measured paths in trusted regions) to IPv6 geolocation models, and residual path features have a strong geographic correlation with the target IP. A region constraint strategy is added based on IPv6 prefix similarity to improve the fine-grained trusted region constraint scheme, and IPv6 prefix similarity has a high geographic correlation. To the best of our knowledge, we are the first to introduce residual paths and IPv6 prefix similarity in the IPv6 geolocation domain. * The final experimental results of our method show that our method outperforms current IPv6 geolocation algorithms in IPv6 geolocation tasks under noncollaborative conditions. The rest of the paper is organized as follows: Section 2 introduces and analyzes existing IP geolocation algorithms. Section 3 introduces the basic principle and main steps of the hierarchical clustering IPv6 geolocation algorithm, SubvectorS_Geo, based on network features. Section 4 analyzes and discusses the performance of SubvectorS_Geo and existing IP geolocation algorithms by comparing experimental results. Finally, Section 5 conclude this paper. The source code of this model has been open sourced to Github [[https://github.com/Hxh1863819/SubvectorS_Geo/](https://github.com/Hxh1863819/SubvectorS_Geo/)]([https://github.com/Hxh1863819/SubvectorS_Geo/](https://github.com/Hxh1863819/SubvectorS_Geo/)) (accessed on 4 January 2023).
## 5 Microbial Dysbiosis
### Salivary Microbiome  
Research into the oral microbiota of patients with OSA has revealed notable changes in salivary microbial communities, particularly an increase in bacteria associated with gingivitis and periodontal inflammation, although the specific roles of these microbes remain unclear [29; 34; 61]. A cross-sectional study by [PERSON] et al. analyzed salivary samples from male patients with moderate to severe OSA, reporting a higher prevalence of _Prevotella_ and other periodontal pathogens, such as _Aggregatibacter actinomycetemcomitans_, _Prevotella intermedia_, _Campylobacter rectus_, _Fusobacterium nucleatum_, and _Treponema deticola_. While these bacteria appeared more frequently in OSA patients than in controls, the differences were not always statistically significant. Importantly, OSA patients exhibited reduced microbial diversity, suggesting that shifts in microbial community composition may contribute to periodontal disease by fostering inflammation and tissue breakdown rather than by introducing new pathogens [28].  
Pathogenic bacteria of the orange complex, such as _Prevotella_ and _Candida albicans_, appear to further alter the diversity of culturable oral microbiota in patients with both OSA and periodontal disease, potentially exacerbating the severity of periodontal conditions [61; 62]. [PERSON] et al., however, found no significant differences in the salivary microbiome between OSA and non-OSA patients, although microbial richness was reduced, and habitat variability increased in OSA patients. Despite no direct evidence linking OSA's hypoxic conditions to an increase in anaerobic bacteria, the study noted a marked rise in _Prevotella_, a Gram-negative bacterium prevalent in periodontal disease [63]. This bacterium facilitates biofilm expansion by attaching to existing colonies rather than directly to the tooth surface, driving a shift from a Gram-positive-dominated microbiome to a more diverse pathogenic system.  
These findings suggest that OSA may indirectly contribute to PD by disrupting the oral microbial community structure, creating an environment that promotes inflammation and periodontal tissue degradation.
# Radon Transform Based on Waveform for AVO-Preserving Data Construction
## 4 Conclusions  
In this paper, we proposed a waveform Radon transform (WRT) method considering both wave shape characteristics and energy characteristics of seismic data, which can preserve AVO. The proposed method used an upper envelope instead of the seismic waveform to obtain the energy and wave cycle information. The forward Radon transform incorporated the prior information of the waveform to improve the resolution in the Radon model. The inverse Radon transform based on the least-squares inversion was implemented in the time domain, incorporating the coefficients of low-order polynomials to describe the main energy of the amplitude, which can preserve a good AVO.  
We compared the performance of traditional sparse Radon transform (CGRT) and wave Radon transform (WRT). WRT obtained a high resolution in the Radon panel, and the inverse transform was more similar to the original seismic data, preserving a good AVO. In addition, computational efficiency is an important advantage of WRT, especially for field data. In the future, WRT can be further applied for multiple scenarios.  
Conceptualization, S.W., L.H. and X.G.; methodology, S.W.; formal analysis, S.W. and X.G.; writing--original draft preparation, S.W.; writing--review and editing, X.G. and P.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers 42074154, 42074151 and 42004106, the Natural Science Foundation of Jilin Province, grant number YDZJ202101 ZYTS020, the \"Thirteenth Five-Year Plan\" Science and Technology Project of Education Department of Jilin Province, grant number JKH20201001 KJ, the Lift Project for Young Science and Technology Talents of Jilin Province, grant number QT202116.  
The authors declare no conflict of interest.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 3 Overview of Telemedicine
### Components of Telemedicine  
Figure 3 depicts the different parts that constitute a telemedicine system, its the activities and processes, consisting of the care seeker whose data is collected via sensors or through any other means, stored in an Electronic Health Record (EHR) or local server, and transmitted through the telecommunication network to the destination [45]. Among the wireless technologies used for short-range communication between wireless sensors are Bluetooth, ZigBee, and Wi-Fi. Transmission of patient data to a remote caregiver is carried out with long-range communication technologies such as LoRaWAN, internet, GSM, 3G, 4G, LTE, and 5G [19]. The connectivity between components is established by communication protocols during a medical procedure, such as taking the output of a test from a patient to the user interface control for display in a computer or other relevant display device [44]. Telemedicine systems consists of four basic units, which include the patient unit, a communications technology unit driven by one or more wireless communication technologies (such as ZigBee, Bluetooth, WIMax, etc.), the virtual or remote server unit, and the medical professional unit. Together, such systems can improve easy access to specialist health care delivery [46].  
Figure 2: Block Diagram of Telemedicine System.
# Tensile Specim Circular Grid Pattern and AI-Based Strain Calculation Method
## 1 Introduction
### Research Background
Specifically, DIC requires high computing performance to measure strain by analyzing correlations over the entire image area captured by the camera and necessitates the application of a speckle pattern on the specimen surface [5]. Additionally, to measure the strain on the tensile specimen surface using DIC, a speckle pattern must be applied to the specimen using spray, allowing the tracking of image changes. However, this application process is time consuming and, during the drying process, the paint may peel off depending on the specimen's surface roughness and material properties, making measurement impossible. The technical issues of the various measurement methods discussed can be summarized as follows. Contact methods offer high accuracy and precision but are limited by the specimen's thickness and dimensions. Non-contact methods minimize damage to thin specimens and allow measurements in various environments, but they require expensive equipment, are sensitive to environmental conditions, and involve lengthy preparation and calculation times. Therefore, a need exists for a strain measurement method that reduces calculation and preparation time and does not require additional equipment.
# HTSA-LSTM: Leveraging Driving Habits for Enhanced Long-Term Urban Traffic Trajectory Prediction
## 3 Experiments and Evaluation
### Analysis of Temporal and Spatial Attention Allocation
#### 3.5.1 Temporal Attention Allocation  
This analysis uses three 15 min subsets from the NGSIM US101 dataset to examine the allocation of temporal attention, calculating the attention weights for 15 historical time steps (from \(t-14\) to \(t\)). Figure 6 illustrates the average temporal attention weights from \(t-5\) to \(t\). It is evident that the weights before \(t-5\) are nearly zero, indicating minimal influence on the prediction results, and thus, the model opts to ignore them. The temporal attention weight at the current time step \(t\) is the highest, suggesting that the future trajectory of the target vehicle is primarily influenced by the most recent state of the target and neighboring vehicles. This result also explains why the CS-LSTM model, which incorporates spatial but not temporal attention, underperforms compared to HTSA-LSTM.
# Satellite Measured Ionospheric Magnetic Field Variations over Natural Hazards Sites
## 2 Magnetic Field Measurements at Low Earth Orbit Altitudes
### Magnetic Field Models and Multi-Experiment Observations  
The generation of geomagnetic field models including secular variations is an important application of space based measurements to study long term behaviour in terms of month and timescales beyond, e.g., [8]. In case of distinct natural hazards we rely on observations immediately before and after the events. The time frame could be from a few weeks, days, up to hours--in any case as much as possible steady conditions--and also depends on the time of the preparation phase of an, e.g., earthquake. This implies, at first glance, that carrying out and comparing such models are not fully useful, narrow pre- and post-spatio-temporal high-quality samplings in multi experiment settings and complementary measurements are preferable [9; 10]. The 1 Hz magnetic field data from CSES HPM permit the analysis of large scale structures, whereas the _Swarm_ VFM 50 Hz high rate values enable the study of, e.g., fine structured details during boundary crossings in the ionosphere above target objects on the ground.
# An Augmented Warning System for Pedestrians: User Interface Design and Algorithm Development
## 3 Result  
A total of 8 s of data were used to test the algorithm. The projected points inside the warning window were calculated for each second of the data. Table 1 shows the projected vehicle and conflict points inside the warning window. For each second, the x-axis and the y-axis locations for the points were calculated.  
Once the projected points were calculated, the vehicle path was formed and was overlaid on the video images based on the x and the y locations in the warning window. An ideal vehicle path was visually identified and manually drawn on the video image based on the location of the vehicle in the image and the direction of the road. Figure 11 shows an example of the projected vehicle path and the ideal vehicle path in the video at 1 s, 3 s, 5 s, and 7 s.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Time**} & \multicolumn{2}{c}{**Vehicle Point**} & \multicolumn{2}{c}{**Conflict Point**} \\ \cline{2-5}  & **x-Axis** & **y-Axis** & **x-Axis** & **y-Axis** \\ \hline
1 s & 0.000 & 0.542 & 1.155 & 0.460 \\
2 s & \(-\)0.834 & 0.580 & 1.155 & 0.218 \\
3 s & \(-\)1.155 & 0.523 & 1.155 & \(-\)0.078 \\
4 s & \(-\)1.155 & 0.288 & 0.171 & \(-\)0.091 \\
5 s & \(-\)1.155 & 0.255 & 0.089 & \(-\)0.107 \\
6 s & \(-\)1.155 & 0.175 & \(-\)0.108 & \(-\)0.143 \\
7 s & \(-\)1.155 & 0.063 & \(-\)0.355 & \(-\)0.194 \\
8 s & \(-\)1.155 & 0.272 & 0.029 & \(-\)0.278 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vehicle projection points and conflict projection points.  
Figure 11: Projected video path (red) and ideal vehicle path (green) in the video image. (**a**) Vehicle path at 1 s; (**b**) vehicle path at 3 s; (**c**) vehicle path at 5 s; (**d**) vehicle path at 7 s.  
The distances from the projected vehicle and conflict points to the ideal path were calculated for each second of the data. The average of these two distances was calculated as well. An overall average among all 8 s of the data was then calculated. As shown in Table 2, two distances from the projected vehicle path to the ideal vehicle path and the average of these two distances were calculated. The overall average of the calculated distance was 0.253.  
As described above, the slope of the vehicle path was calculated based on vehicle points and conflict points for both the projected vehicle path and the ideal vehicle path. The difference between the projected slope and the ideal slope was calculated to measure the accuracy of the projected vehicle path direction. Table 3 shows the result of the slope and the differences. Overall, both projected and ideal paths had slopes less than 0.3. The largest slope was observed from the projected vehicle path at 8 s, which also caused the largest slope difference between the projected and the ideal path.
# Robust Symbol and Frequency Synchronization Method for Burst OFDM Systems in UAV Communication
## 3 Improved Burst OFDM Synchronization Method
### High-Efficiency Frame Structure Design  
Frame efficiency is a measure of how effectively the available time slots within a frame are used for transmitting actual data, as opposed to overhead information such as synchronization or control signals. The frame efficiency is typically defined as the percentage of time slots in the frame that contain useful data. In this work, a novel low-overhead frame structure for a burst OFDM communication system is designed, as illustrated in Figure 4. Here, the AGC guard interval is eliminated, unlike in the traditional frame structure.  
Hence, the frame efficiency of the traditional frame structure can be expressed by  
\[\eta=\frac{T_{data}}{T_{G}+T_{AGC}+T_{P}+T_{data}}, \tag{7}\]  
and the frame efficiency of the proposed frame structure is  
\[\eta^{\prime}=\frac{T_{data}}{T_{G}+T_{P}+T_{data}}. \tag{8}\]  
The proposed frame structure can improve the frame efficiency by  
\[\Delta\eta=\frac{T_{data}T_{AGC}}{(T_{G}+T_{P}+T_{data})(T_{G}+T_{AGC}+T_{P}+T _{data})}, \tag{9}\]  
when \(T_{p}\) and \(T_{AGC}\) in Equations (7) and (8) are fixed.  
The power of the received preamble may vary over a large dynamic range since the AGC guard interval is eliminated. To regulate the power of the received signal to a desired level before receiving the OFDM payload symbols, we present an AGC strategy based on the symbol synchronization result. The block diagram of the proposed AGC control circuit is illustrated in Figure 5.  
The VGA is used to amplify or attenuate the received signal. The average power calculator computes the mean power of the received signal, which is then compared with a preset reference power and fed into a VGA control module. The VGA control module calculates the VGA adjustment step based on the power difference between the received signal power and the reference power. The control switch governed by the result of the symbol timing synchronization determines whether to update the VGA. A successful symbol synchronization indicates that the preamble has been detected; if so, the control switch is turned off, and the VGA does not need to adjust the gain. Otherwise, the control switch is turned on, and the VGA works according to the VGA control word generated by the VGA control module. With this strategy, the receiver can stop the VGA adjustment after detecting the preamble, thereby reducing frame overhead and improving reception efficiency.  
Figure 4: The proposed frame structure and its comparison with the traditional frame structure.
## 3 Preliminary and Problem Statement
### Concept Stability  
Concept lattice straightforwardly illustrates various relationships between concepts and their subconcepts and superconcepts, which is beneficial for knowledge discovery. However, in practice, the effective use of the concept lattice is limited by the large volume of extracted concepts and noise contained in the dataset. Noise contained in the dataset favors the existence of many similar but distinct concepts, which may excessively impair concept readability. Stability is an effective measure used to select the most useful and interesting concepts. The definition of stability is as follows:  
**Definition 1** ([5] Concept Stability).: _Let \(K=(G,M,I)\) be a formal context. Given a formal concept \((A,B)\) of \(K\), the intentional stability (stability for short) \(\sigma\) of \((A,B)\) is defined as follows:_  
\[\sigma\left(A,B\right)=\frac{\left|\{C\subseteq A\mid f(C)=B\}\right|}{2^{|A|}} \tag{1}\]  
The stability describes the proportion of subsets of extent whose closure is equal to the intent [5]. It also can be computed by locating and counting the associated generator of \(A\). Calculating the stability of the formal concept will explore all elements of the power sets of the extent to check whether it is a generator. The definition of generator is as follows:  
**Definition 2** (Generator).: _Given a formal context \(K=(G,M,I)\) and a concept \((A,B)\) of \(K\). If there is a subset \(P\) of \(A\) satisfy \(f(P)=B\), then \(P\) is a generator of \((A,B)\)._  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(K\) & **a** & **b** & **c** & **d** & **e** \\ \hline
1 & & & & & & \(\times\) \\
2 & & & & & & \(\times\) \\
3 & & & & \(\times\) & & \\
4 & & & & \(\times\) & \(\times\) & \(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example Formal context \(K\).  
Figure 2: The Concept Lattice of \(K\).  
**Corollary 1**: _Given a formal concept \((A,B)\), the stability of \((A,B)\) is the proportion of generator in the power set of \(A\). Therefore, Equation (1) of the stability is simplified as follow:_  
\[\sigma\left(A,B\right)=\frac{|Gen|}{2^{|A|}} \tag{2}\]  
_where \(|Gen|\) is the total number of generators of \((A,B)\)._  
It can be derived naturally from Definition 1 and Definition 2.  
**Example 2**: _Continue the Example 1, let us consider the formal concept \((\{1,2,3\},\{a,b\})\). The number of the subsets of \(\{1,2,3\}\) is 8 and only three subsets \(\{1,3\},\{2,3\},\{1,2,3\}\) satisfy the Definition 2. Therefore, the number of generators \(|Gen|\) is equal to 3 and the stability of \((\{1,2,3\},\{a,b\})\) is equal to 3/8._