esa-sceva/satcom-chunk-collection huggingface dataset:
# A Refined Approach to Segmenting and Quantifying Inter-Fracture Spaces in Facial Bone CT Imaging
## 1 Introduction
Several studies have explained that in the case of small structures, even minor absolute errors can lead to large relative errors [22; 23; 24; 25], adversely affecting the Dice score. This sensitivity poses a significant limitation in segmentation research for irregularly shaped targets. Nevertheless, in clinical practice, accurate identification of the locations and shapes of fractures, as well as the anatomical positions and quantitative measurements of the spaces between fractured bones, is critical for diagnosis and treatment planning. To address this need, the present study aims to develop and validate an automated algorithm for segmenting the spaces created by facial bone fractures in CT images. To achieve more consistent patterns and improved reliability, we employed a stepwise annotation approach. First, clinical specialists marked the bounding boxes of fracture areas, followed by trained specialists creating the initial unrefined pixel-level ground truth. Finally, the refined ground truth was established by training a model on the unrefined ground truth and using this model to predict and replace the entire dataset, thereby correcting human errors and enhancing segmentation accuracy. Furthermore, this study evaluates the applicability of automated algorithms in radiology by comparing the performance of expert-segmented regions and refined segmented regions using radiomics analysis.
## 6 Experiments and Results
### Evaluation of the Framework with Basic Classifiers  
Different classifiers were tested with the \(k\)-means having different values of \(k\). The values of 2, 3, and 4 were considered for \(k\) and the SVM, SGD, LR, and SLFN were considered as a classifier. Table 2 shows the ACC, PREC, REC, and GM values for different classifiers and different values of \(k\). We ignore the results achieved by the NB classifier because the high values of the \(\text{PREC}_{\text{N}}\) and \(\text{REC}_{\text{I}}\) do not reflect high-quality prediction as the majority of the normal activity instances are classified as intrusion having very low values of \(\text{REC}_{\text{N}}\) and GM. Thus, by ignoring these values, the maximum values from the other techniques are determined in bold. Considering this, SLFN classifier outperformed the other classifiers for the IoTID20 dataset and thus was considered for the next stages of the evaluation of the framework. In addition, the \(\text{PREC}_{\text{N}}\), \(\text{REC}_{\text{I}}\), and GM values for SLFN for the \(k\) value of 3 outperformed the other values of \(k\). With imbalanced datasets, GM measure ensures that the lack of instances for the minority class does not affect the quality of the results generated by the classifier. In addition, predicting the largest amount of intrusion from the instances that are labeled as intrusion, which is reflected by the \(\text{REC}_{\text{I}}\) measure, is critical. Thus, the SLFN classifier with the \(k\) value of 3 was considered for the framework for the IoTID20 dataset.
## 6 Experiments and Results
### Discussion  
We would like to discuss several limitations of our model. First, the hyperparameters associated with observed frame lengths (currently 10) and prediction frame lengths (current 5) may limit the capacity of our model. For example, if we only observe three frames, the model may fail to predict well on the following frames due to the lack of information. This means that the current model only allows the input length of 10. Second, our model is trained only using front-camera images from Waymo cars and may not be easily generalized to other car models whose cameras are mounted with different heights and angles. For example, if an AV manufactured by other companies has its front camera positioned a bit higher than that on Waymo's, our model may not perform well. Thus, the transferrability of our model needs to be further studied.
# Attenuation of DC-Link Pulsation of a Four-Wire Inverter during Phase Unbalanced Current Operation
## 4 Implementation of the Algorithm in Inverter System  
In order to evaluate properties of the AC current compensation algorithm, the laboratory stand was built. First, current pulsation was evaluated in a system with no compensation, as presented in (Figure 4). A battery charge-discharge curve was programmed into a bidirectional DC voltage supply ITECH-IT6012C. The voltage supply was connected to the three-phase four-wire inverter through the DC/DC converter using DAB topology.  
Figure 11: Control algorithm for compensating the DC link pulsating current (DAB Control Module 1).  
Figure 12: Bode plot of control algorithm for compensating the DC link pulsating current.  
Based on the results, the authors decided to add an identical DC/DC converter in parallel, connected to the capacitor bank. Parameters of the final test bench are presented in Table 1.  
Before testing was conducted, the capacitor bank was pre-charged to 60 V, to ensure stable startup of the DC/DC converter. The DC link current was observed using a Tektronix MSO5034B oscilloscope. Data were logged and then processed using Matlab/Simulink. Figure 11 presents the results of the system operation. The system was run first without the AC current compensation, then the additional DC/DC converter was turned on. The results show initial current spike, caused by a converter launch, but it drops in less than 10 ms. Afterwards, the system operation is stable. Further, frequency analysis of the signal was performed. THD presented in Figure 13 is THD1 value in relation to the DC component. In the case without active AC current compensation THD1 in relation to DC component was 155.89%. With active compensation THD1 drops to 4.3%. AC component observed on power inverter DC link drops by 97%. This compensation comes at a cost of pulsation appearing in capacitor bank (Figure 14), but this situation is plausible, as the electrochemical energy storage is prevented from heating up.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline \multicolumn{3}{c}{T-NPC 3-phase, 4-wire inverter} \\ Converter output power & 10 & [kVA] \\ DC link & 10 & [mF] \\ DC link voltage & 750 & [V] \\ Filter LCL & 1.2 + 5 + 0.38 & [mH, uF, mH] \\ Switching frequency & 20 & [kHz] \\ DAB-converter 1, DAB-converter 2 & & \\ DAB converter power & 6 & [kW] \\ Energy storage and capacitor bank voltage & 60 & [V] \\ Switching frequency & 60 & [kHz] \\ Medium frequency transformer ratio & 12.5 & [n1/n2] \\ Capacitor bank capacity & 1.2 & [F] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table of main converters parameters.  
Figure 13: The DC Link current pulsation and FFT analysis for cases without (before Time = 0.15 s) and with (after Time = 0.15 s) proposed compensation algorithm operation (experimental results, measured at i\({}_{\text{stor}}\) point in Figure 1).
# Efficient Mobile Vehicle Data Sharing Scheme Based on Consortium Blockchain
## 4 Problem Formulation and Solving
### Stackelberg Game Solution  
This paper models the problem as a Stackelberg game, \(RSU_{j}\) deployed on the DSSC using the Stackelberg game to find the amount of data \({d_{k}}^{*}\) that the seller \(s_{k}\) should supply to the buyer \(b_{i}\) and the final unit data bid \({p_{i}}^{*}\). It is a common leader-follower game and includes both the follower's decision and the leader's decision. The model of the Stackelberg game can be formally summarized as:  
\[\Gamma=\{(S,\{b_{i}\}),\ \{D_{k}\}_{1\leq k\leq n},\ \{U_{s_{k}}\}_{1\leq k \leq n},\ p_{i},\ U_{b_{i}}\} \tag{5}\]  
In Equation (5), \((S,\{b\})\) is a set of all participants, the buyer \(b_{i}\) is the leader, and the set \(S\) contains all of the sellers who are followers, \(D_{k}\) is the strategy vector of \(s_{k}\). \(U_{s_{k}}\) is the benefit function when \(s_{k}\) provides \(d_{k}\) quantities of data to \(b_{i}\), \(U_{b_{i}}\) is a function of the benefit obtained by buying data for \(b_{i}\) bids and \(p_{i}\). After the game is completed, it is possible to determine the optimal value of the amount of data \({d_{k}}^{*}\) traded by the seller \(s_{k}\) in the transaction and the bid of \(b_{i}\) the buyer's final unit data \({p_{i}}^{*}\).  
For the convenience of research, we divide the entire game process into two stages: firstly, the buyer \(b_{i}\) who plays the role of leader, \(RSU_{j}\) defines the strategy of \(b_{i}\), that is, the bid \(p_{i}\). Secondly, \(RSU_{j}\) determines the seller's strategy, the amount of data it is willing to trade is mainly considered. For this game \(\Gamma\), the purpose is to achieve Stackelberg equilibrium, \({d_{k}}^{*}\) determines \(s_{k}\) the maximum benefit and \({p_{i}}^{*}\) determine \(b_{i}\) the greatest benefit, which is defined as follows.  
**Definition 1**.: _For Stackelberg game \(\Gamma\), the strategy \(({d_{k}}^{*},{p_{i}}^{*})\) meets the following conditions to achieve Stackelberg equilibrium:_  
\[\begin{array}{c}{U_{s_{k}}({d_{k}}^{*},{p_{i}}^{*})\geq U_{s_{k}}({d_{k}},{d _{-k}}^{*},{p_{i}}^{*})}\\ {U_{b_{i}}({d_{k}}^{*},{p_{i}}^{*})\geq U_{b_{i}}({d_{k}}^{*},{p_{i}})}\end{array} \tag{6}\]  
_where \({d_{k}}^{*}\) is the strategy set of all sellers that \(s_{k}\) are not included. In the game \(\Gamma\), when \(b_{i}\) bid \({p_{i}}^{*}\) determined, the seller \(s_{k}\) can obtain the most considerable benefit in the case of sharing \({d_{k}}^{*}\) data, so the amount of shared data cannot be adjusted to improve the seller's income by \(RSU_{j}\). In addition, in the case of \(s_{k}\) shared \({d_{k}}^{*}\) data, the buyer \(b_{i}\) earns the most considerable income \({p_{i}}^{*}\), and even if the bid strategy is adjusted, it cannot improve the income._
## 1 Introduction
This analysis is relevant vis-a-vis the application of nighttime light images in impermeable surface extraction for complex geomorphological regions and the in-depth mining of nighttime light image data. At the same time, topography and geomorphology are two key environmental factors governing human use and modification of land resources, which are deeply involved in the formation and change process of regional land use. Therefore, based on the geomorphological characteristics of the Karst mountains, this paper divides the study area into different geomorphological regions for analysis. Considering the complexity of the types of geomorphological areas in the Karst mountains and the data availability, three data sources are used:  
1. [label=(0)]
2. Luojia1-01 nighttime light data, which do not have saturation and blooming issues;
3. NPP-VIIRS nighttime light data, which are the most widely used in current research; and
4. \"Flint\" nighttime light data, which can smooth out disturbances other than surface factors. This study analyzes the potential of different nighttime light image datasets for impermeable surface extraction in topographically fragmented areas. It contributes to the extension of the application potential of nighttime light imagery and supports subsequent research on mitigating urban climate change and reducing the impact of global warming on humans, which is important for the sustainable development of the region.
## 4 Future Perspectives  
In this section, we review some recently proposed GNSS-R techniques for soil moisture and snow property determination, and discuss how they could be combined with other techniques, such as ALS, to collect information about the three-dimensional surface surroundings (which may be dynamic, e.g., in the case of snow) and therefore enable the use of GNSS-R over complex terrains or valleys.
# Beam Propagation Factor of a Cosh-Airy Beam
###### Abstract  
Based on the second-order moments, the analytical expression of the beam propagation factor of a cosh-Airy beam has been derived. The beam propagation factor was determined by the decay factor and the cosh parameter. Because the beam propagation factors in the \(x\)- and \(y\)-directions of the cosh-Airy beam have the same form, only the beam propagation factor in the \(x\)- direction was selected as the object of numerical calculation and analysis. The effects of the decay factor and the cosh parameter on the beam propagation factor were investigated. When the decay factor was greater than 1, the beam propagation factor first increased and then decreased with the increase of the cosh parameter, and finally, tended to a minimum value. Under the condition that the decay factor was less than 1, the beam propagation factor always increased with the increase of the cosh parameter. As the decay factor increased, the beam propagation factor decreased and tended to a minimum value. Finally, the effects of the decay factor and the cosh parameter on the squares of the beam waist and the divergence were analyzed in more detail.  
cosh-Airy beam; beam propagation factor; second-order moment; decay factor; cosh parameter +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion
### Impact Assessment
#### 4.2.2 Establishment of Quantification Model  
In order to link the scale value in the analytic hierarchy process with the numerical results of the wind parameter factors, a quantification model was established. The coefficient of factor sensitivity was defined, which represents the ratio of different wind parameter factors under the same boundary condition, and was used to evaluate the sensitivity of wind parameters. The coefficient of factor sensitivity can be calculated according to Equations (12)-(14):  
\[\{\overline{u},\overline{\alpha},\overline{\beta}\}\in\psi \tag{12}\]  
\[\Delta\psi_{i}=\psi_{i\_max}-\psi_{i\_min} \tag{13}\]  
\[\lambda_{i}=\frac{\Delta\psi_{i}}{min\Delta\psi_{i}} \tag{14}\]  
where \(\psi\) is the wind parameter factor including \(\overline{u},\overline{\alpha},\overline{\beta}\), which are wind speed factor, wind attack angle factor, and wind azimuth angle factor, respectively; \(\psi_{i\_max}\) and \(\psi_{i\_min}\) represent the maximum and minimum values, respectively, of the wind parameter factor corresponding to the \(i\)th boundary condition, which are marked in Figures 6-9; \(\Delta\psi_{i}\) is the value range of the wind parameter factor; and \(\lambda_{i}\) is the coefficient of factor sensitivity.  
Furthermore, the quantification model was used to describe the relationship between the coefficient of factor sensitivity and the scale value in Equation (15), as shown in Figure 12 and Table 4. It could be found that the relationship between the coefficient of factor sensitivity and the scale value represents a logarithmic base 2. The scale value increased with the coefficient of factor sensitivity, but the increase velocity decreased gradually. In other words, as the scale value rises, the corresponding interval of the coefficient of factor sensitivity is sharply augmented.  
\[n=\log_{2}\biggl{(}\frac{4}{3}\biggl{(}\lambda+\frac{1}{2}\biggr{)}\biggr{)} \tag{15}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Factor Sensitivity** & **0.5-1.5** & **1.5-3.5** & **3.5-7.5** & **7.5-15.5** & **15.5-31.5** & **31.5-63.5** & **63.5-127.5** & **127.5-255.5** & **255.5-** \\ \hline Scale value \(n\) & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Conversion between and coefficient of factor sensitivity and scale value.  
Figure 12: Curve of factor sensitivity coefficient and scale value.
## 5 Evaluation
### Experimental Parameters  
We considered the number of codewords \(C\) and the size of test areas \(A\) and tested the robustness of our localization system by manually adding extra additive white Gaussian noise (AWGN) \(\mathbf{N}\sim\mathcal{N}(0,\sigma^{2})\) to the calculated RSSI, where \(\mathcal{N}\) means Gaussian distribution and \(\sigma\) is the variance [95]. As such, we can use a parameter set \(\{C,A,\sigma\}\) to describe the experiments. The AWGN here refers to some miscellaneous noises, including device noise [96], fading [97], and polarization mismatch [98], which are not discussed in Section 3.1.1 and are able to be modeled by Gaussian processes [99; 100].  
In Section 3.1, we mentioned that most RIS prototypes only support \(C=2/4\)[66; 67], whereas others may realize \(C=64\)[68]. For forward-looking results in this paper, we set an initial resolution of \(15^{\circ}\), and \(R=180^{\circ}\). Then, we had \(C=144\). We set \(A=51.84\) and \(\sigma=0\) by default. We first applied the default experimental parameter set \(\{C=144,A=51.84,\sigma=0\}\) for evaluation to check the feasibility of our solution. Then, we changed one parameter and kept the other two unchanged to separately investigate the impacts of these three experimental parameters on the proposed solution.
# A Secure and Lightweight Group Mobility Authentication Scheme for 6 LoWPAN Networks
## 4 System Models
### The Proposed Scheme (SL_GAS)
#### 4.3.3 Group Handover Phase
It is necessary for each node to register with the AAA; therefore, nodes with fake identities or secret values cannot be authenticated. Additionally, SL_GAS supports group authentication, which helps mitigate DoS attacks. _DoS attacks:_ An attacker may launch DoS attacks by impersonating a legitimate 6 LMNi and sending numerous fake requests to the 6 LMNGL. The SL_GAS scheme uses a MAC tag to ensure the integrity of 6 LMNi without aggregating messages. This prevents DoS attacks, as sending a false MAC to invalidate the group verification will not succeed because only the MACs of two users are aggregated. Thus, the SL_GAS scheme effectively restrains DoS attacks. _Privileged insider attacks:_ An insider attack occurs when a legal insider of a network steals confidential information or injects false information into the network. SL_GAS ensures the security of all messages by using a pairwise key between two parties. If the attacker does not possess the correct secret key, extracting the _SP_ of other parties is difficult. Further, the 6 LMNi, the LMA, and the AAA are the only parties with access to confidential information, such as the individual's real identity. Due to the assumption that LMA and AAA are trustworthy, confidential information about each node cannot be leaked by either party. It is, therefore, impossible for an insider to deduce information from nodes other than what is sent to them.
# HTTP Adaptive Streaming Framework with Online Reinforcement Learning
## 3 Framework Design
### Operation of Framework  
#### 3.4.1 Framework Workflow  
In this study, we propose an HTTP adaptive streaming framework with online reinforcement learning to address the disadvantage that existing RL-based ABR algorithms degrade in performance when not employed in the target network environment. If the QoE of the client decreases, the proposed scheme adapts the ABR algorithm to changes in the network environment by updating the ABR neural network model while video streaming is performed. Here, the network environment change does not mean network fluctuations in the DASH environment but a case in which the ABR neural network model operates in an environment different from the environment in which the learning is performed. Because the proposed scheme is a framework for improving the previously trained model, it is assumed that the client has a pre-trained model when video streaming starts. We use Pensieve as a pre-trained reference model. The operational structure of the proposed scheme is shown in Figure 4, and the pseudocode to describe the complete algorithm in detail is presented in Algorithm 1. The client performs adaptive video streaming using the criteria model. At the same time, the state of the network is stored, and the network is classified using the average and deviation of the bandwidth. At this time, the QoE value calculated during video streaming and the maximum QoE value obtained from the current network state are calculated to normalize the QoE value. The network state and normalized QoE values are stored in a first-in-first-out (FIFO) manner, with a video of length of 2 min. When the normalized QoE value is lower than the threshold set by the algorithm, the client creates an agent and updates the model through reinforcement learning. The network trace data has been classified in the same way that the current network is classified. The client uses a network trace similar to the current network state for training. How to calculate the normalized QoE and how to classify networks will be described in detail later. If a training agent is created based only on the normalized QoE value, the risk of overfitting increases because an agent that conducts training using the same network trace data is generated. Therefore, we make the created agents learn by using different network trace data. Each agent uses an early stopping technique to terminate training early when the increase in validation accuracy becomes small. The neural network model is updated whenever each created agent's training ends. The proposed scheme uses an asynchronous advantage actor-critic (A3C) network because there is no central network, and the model needs to be updated asynchronously every time an agent completes training [27].
## 2 Quasi-Resonance Circuit
### Operating Principles  
Quasi-resonant inverters only require one switching transistor per coil, and they are also sometimes referred to as QR flyback inverters. QR inverters are popular among single-coiled burners and are commonly found in Asian burner markets because of their cost advantage over half-bridge inverters. However, QR inverters have the drawback of not being able to fully control the transferred power to the load, and because of this, their frequency control and EMC issues need to be handled.  
It is known that an inductor and a parallel capacitor will exchange energy with each other, and as a result, according to Equation (2), their current and voltage change in a sinusoidal form with a frequency proportional to the capacitance and inductance of the circuit. In Figure 2, for simplicity and a better understanding of how this arrangement works, we consider a situation where the transistor is turned on momentarily only for a short moment and then stays turned off for a very long time (ignoring the effects that the freewheeling diode might have for now).  
\[f=\frac{1}{2\pi\sqrt{LC}} \tag{2}\]  
where L and C are the inductance [H] and capacitance [F] of the pair, respectively. In a quasi-resonant circuit, when the transistor is turned on, we can assume that the capacitor (Cres) quickly gets charged, and the current flowing through the inductor (Lres) will rise according to the following:  
\[\begin{array}{c}V_{bus}=L\frac{\Delta I_{L}}{\Delta I}\\ I_{L}=\frac{V_{bus}}{L}\Delta t\end{array} \tag{3}\]  
Equation (3) reveals that the inductor current grows linearly in time with a slope proportional to \(\frac{V_{bus}}{L}\) (phase I in Figure 2). Then, when the switch (transistor) gets turned off, the inductor and capacitor start to oscillate with each other due to the stored energy in them, in such a way that the current in the inductor will be manifested as the capacitor's voltage. And in the next half cycle, the capacitor voltage transforms into the inductor's current (phase II in Figure 2). Now, in this oscillating state, the energy of the elements involved starts to degrade (due to the internal resistance of the elements) until the oscillation ceases to exist. Our analysis was based on the assumption that we do not consider the freewheel diode, but the fact is, it will start conducting once the inductor current gets reversed and the capacitor is able to match the \(V_{BUS}\) (the voltage will eventually surpass \(-V_{BUS}\)). Then, the inductor's current is no longer being pushed into the capacitor, and until it gets fully depleted, it will pass through the diode and return back to the source (phase III in Figure 2).  
Therefore, in short, it can be argued that the system contains only the energy of the capacitor after the first cycle because the energy of the inductor has been discharged in the third phase (Figure 2) [24]. This means the majority of the energy transfer takes place only in the first cycle after switching, so theoretically, with the start of the second cycle, switching can be initiated once again [25].