esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Trade-Off Between Prediction Horizon and Accuracy  
Accurate accident prediction requires balancing the prediction horizon and accuracy. Longer horizons provide more response time but result in increased uncertainty.  
Figure 12: VRFormer performance under different weather conditions.  
Shorter horizons have higher accuracy and use more recent data but reduce the available response time.  
Figure 13 illustrates the relationship between the prediction horizon (in seconds) and the APA achieved by the VRFormer model. The blue line shows the APA at different prediction horizons. The dashed vertical line indicates the selected optimal horizon of 2 s. The red area shows the region of declining accuracy as the prediction horizon increases. Due to practical limitations in our experiments, we focused on testing prediction horizons of 1, 2, 3, and 4 s, resulting in the sparse appearance of the curve.  
Our results demonstrate a trade-off between the prediction horizon and VRFormer's APA. VRFormer achieves a peak APA of 85% at both the 1 and 2 s horizons, indicating that these shorter timeframes benefit from the more recent sensor and V2X data, thus resulting in higher accuracy and reliability. Extending the prediction horizon to 3 s results in a significant drop to 70% APA, remaining approximately constant at 4 s. This decline is likely due to increased uncertainty in predicted object locations over time, the accumulation of errors, and the reliance on less recent information. Furthermore, the ideal prediction horizon varies depending on the operational context; for instance, a 2 s horizon might be sufficient in a high-speed highway scenario but offer unnecessary time in a low-speed urban environment. Consequently, optimizing the prediction horizon should account for the operational topology. The 2 s horizon was chosen for our dataset, balancing prediction accuracy with sufficient response time. This highlights a core challenge in predictive modeling: longer horizons allow more proactive intervention but inherently sacrifice accuracy due to increasing uncertainty in predicting future states. Conversely, shorter horizons improve prediction fidelity but reduce the time for corrective actions. Therefore, our future research will explore adaptive systems that dynamically adjust the prediction horizon based on real-time environmental and driving conditions. Such a system could assess road type, vehicle speed, traffic density, and visibility conditions to optimize the prediction horizon within VRDeepSafety. This adaptability would enable VRFormer to achieve more robust and context-aware accident predictions in diverse driving situations.
# Screen-Based Sports Simulation Using Acoustic Source Localization +
## 6 Conclusions  
This paper introduced an efficient method of localizing an impulsive acoustic source, which was used to simulate screen-based ball sports. In the proposed system, a small set of microphones are arranged into a 2D MEMS array (13 for each array), and the system estimates a ball position from the acoustic source in 3D space using the direction-based DSBF algorithm in the temporal domain. Compared to other spherical model-based DSBF systems, our system generates relatively small errors (i.e., less than 3 cm from an acoustic source generated over 1 m away from the microphone array) in tracking ball motion in real time. Furthermore, our system design and implementation require a small space for hardware and can be applied to a variety of real-time simulators. As demonstrated in the experimental results, our system effectively improves a user's sports skills such as soccer kicks and baseball hits in a given training period.  
The current system does not consider the effects of sound reflection from the obstacles in the room. Since the effective wavelength is very short, the surrounding environment has little effect on the impulsive sound. Therefore, if there are no direct obstacles between the microphone array and the sound source, the results are expected to be similar. In addition, our system does not consider high-level noises from the environment. For example, if the background noise is louder than the acoustic source, it is difficult for the system to localize the acoustic source without a sophisticated noise filtering method. We are currently improving the accuracy of ball position estimation by integrating an active noise cancellation [21] and a room sound wave reflect-aware scheme [22] in the existing system.  
Conceptualization, S.-W.S., M.S., and Y.K.; data curation, S.-W.S. and S.Y.; formal analysis, S.-W.S., S.Y., M.S., and Y.K.; funding acquisition, M.-G.K.; investigation, S.-W.S., S.Y., M.S., and Y.K.; methodology, S.-W.S. and Y.K.; project administration, M.-G.K.; resources, S.-W.S. and S.Y.; software, S.-W.S.; supervision, M.-G.K., M.S., and Y.K.; validation, S.-W.S., S.Y., M.-G.K., and Y.K.; visualization, S.-W.S.; writing--original draft, S.-W.S. and Y.K.; writing--review and editing, S.-W.S., M.S., and Y.K.  
This research was supported by the Sports Promotion Fund of Seoul Olympic Sports Promotion Foundation from the Ministry of Culture, Sports and Tourism and by the National Research Foundation of Korea (NRF) grant funded by the Korea government(MSIT) (No. 2017R1C1B5017000).  
The authors declare no conflict of interest.
## 6 Open Challenges, Future Prospects and Research Directions
### Exploring Hybrid Systems  
Investigating hybrid systems that integrate aperture averaging with other diversity techniques--such as spatial or temporal diversity--holds promise for further improving FSO communication. By integrating these diversity techniques with aperture averaging, researchers can create hybrid systems that offer enhanced resilience against turbulence and other atmospheric disturbances.  
Spatial Diversity Techniques: By leveraging the strengths of various approaches, hybrid systems can provide enhanced link reliability and greater overall communication efficiency. For example, spatial diversity techniques can be combined with aperture averaging to exploit multiple paths for signal transmission, mitigating the effect of turbulence and improving the robustness of the communication link.  
Temporal Diversity Techniques: Temporal diversity, which involves transmitting signals over different time slots, can also be effectively integrated with aperture averaging. By employing adaptive transmission schemes that adjust the timing of signal transmission based on atmospheric conditions, FSO systems can minimize the risk of signal degradation. This combination could lead to a more resilient system designed to sustain high data rates even under challenging weather conditions.  
Addressing the Service Demand  
Advancements in aperture averaging will be pivotal in meeting the high-capacity demands of 5G and future networks.  
Meeting Communication Needs: As the demand for high-capacity optical communication solutions grows--driven by applications such as 5G, remote sensing, and satellite communications--the advancements in aperture averaging will play a crucial role. Enhanced signal quality and link reliability achieved through optimized aperture designs and innovative adaptive techniques will ensure that FSO systems can support the increasing data traffic and diverse application requirements in both urban and remote settings.  
Scalability and Implementation: Future research should also focus on ensuring that these advancements in aperture averaging are scalable and practical for deployment in various environments. Developing cost-effective solutions and robust implementation strategies will be vital for the widespread adoption of FSO systems. This includes creating standardized protocols for the design and deployment of aperture averaging techniques, ensuring interoperability across different platforms and applications.
## 4 Experimental Setup
\begin{table}
\begin{tabular}{|c|c|} \hline
**Components of Sensor Setup** & **Parameters** \\ \hline Broadband light source (Thorlabs) & Superluminescent light emitting diode, \(\lambda\)peak = 1544.4 nm, FWHM = \\  & 45.5 nm, Noise: \(<\)0.1\% \\ \hline Optical power meter (Grandway) & InGaAs detector, resolution: 0.01 dB, measuring range: \(\sim\)70 to +10 dBm \\  & (1550 nm) \\ \hline Polarization maintaining photonic crystal & Core diameter 6.3/4.4 \(\pm\) 0.5 \(\mu\)m, outer cladding 125 \(\pm\) 5 \(\mu\)m, coating \\ fiber (PM-1550-01, Thorlabs) & diameter 240 \(\pm\) 10 \(\mu\)m, length\(-\)40 cm, attenuation \(\leq\)3 dB/km (1550 nm), \\  & birefringence: \(\pm\)4\(\times\)10\(-\)4. \\ \hline Fiber coupler (Cellco) & Single mode, 1 \(\times\) 2, split ratio: 50:50 \\ \hline Fiber coupler (Cellco) & Single mode, 1 \(\times\) 2, split ratio: 95:5 \\ \hline Fiber circulator (Cellco) & 3-Port circulator, operating wavelength range: 1520-1580 nm \\ \hline Dense wavelength division multiplexer (DWDM) (Fiberon) & Channel spacing: 100 GHz, transmission insertion loss: typical—1.2 dB, transmission isolation: 28 dB \\ \hline \end{tabular}
\end{table} 
Table 1: The parameters of used components. Figure 3: The spectrum of selected DWDM channel and PM-PCF Sagnac loop interferometer.
# An Automatic Partition Time-Varying Markov Model for Reliability Evaluation
###### Abstract  
As the service time of mechanical devices is getting longer and longer, the safe and reliability evaluation during operation is highlighted. Moreover, real-time reliability evaluation with consideration of multi-state performance degradation becomes increasingly important nowadays, since the consequences of sudden failures are more unacceptable than ever before. The Markov process is a commonly used model in multi-state reliability evaluation. However, little research of the Markov model can deal with multi-source monitoring data and time-varying properties of device performance degradation, as well as the scientific state number determination. In this article, a real-time reliability evaluation model based on automatic partition and the time-varying Markov chain is proposed to solve the problems of the scientific state number selection and time-varying properties description with the state transition matrix of the Markov process, together with taking advantage of multi-source information. The effectiveness of the proposed algorithm is demonstrated on the bearing with life-long vibration and temperature data. It shows that the proposed automatic partition time-varying Markov model can decide the state number automatically according to the trend of life-long data, and evaluate real-time reliability based on equipment operating hours and operating status. The result of predicted remaining useful life obtained by the proposed model is more accurate, and it also shows great superiority in conformity with reality.  
Keywords:real-time reliability; time-varying: automatic partition; remaining useful life; Markov process +
Footnote †: journal: Applied
## 2 Problem Statement and Modeling
### Description of MDP
#### 2.2.1 State  
The state in this context should encompass the dynamics of the CAV, the conditions of surrounding vehicles, and the status of traffic lights. Consequently, a multi-element vector is constructed to depict the CAV's state:  
\[S_{t}=[x_{t},v_{t},a_{t},\Delta x_{t},\Delta v_{t},\phi_{t},g_{t}]^{T}, \tag{1}\]  
where \(S_{t}\) denotes the state space at time \(t\), \(x_{t}\) is the travel distance of the vehicle, \(v_{t}\) denotes the speed of the vehicle, \(a_{t}\) denotes the acceleration of the vehicle, \(\Delta v_{t}\) is the differential value of speed between the preceding and following vehicles, and \(\Delta x_{t}\) is the spacing distance between the preceding and following vehicles.  
Additionally, the signal lamp's status is represented as \(\phi_{t}\) in (1), assigning a value of 1 for a red signal and 0 otherwise, and \(g_{t}\) denotes the remaining duration of the green light in the current phase at the nearest signalized intersection. This definition of state remains relevant whether the vehicle is on a road segment or within an intersection. Consequently, the framework can naturally extend to accommodate a multi-intersection scenario.  
Figure 1: Schematic diagram of the traffic scene at the intersection.
## 3 Results
### KRAS and HRAS mRNA Expression in A549 NSCLC Cell Line  
In all of the experiments, the negative control (cells without added chemical compounds after 24 and 48 h of incubation) for both genes _HRAS_ and _KRAS_ were treated as calibrator; so, their mRNA expression levels were 1.  
In all experimental concentrations, deltarasin slightly increased _KRAS_ expression after 48 h compared to cells incubated for 24 h (Figure 7a). Deltarasin 2.5 uM treatment reduced _HRAS_ mRNA expression levels in NSCLC alline A549 after 48 h incubation in comparison with 24 h. In all samples, _KRAS_ mRNA expression levels were higher than _HRAS_ with the exception of 24 h and 48 h incubation with 2.5 uM deltarasin.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Compound** & **Concentration [uM]** & **Cell Viability 24 h [\%]** & **Cell Viability 48 h [\%]** \\ \hline Deltarasin & 1.25 & 88.56 & 100 \\  & 2.5 & 98.98 & 100 \\  & 5 & 96.87 & 92.7 \\ \hline Kobe0065 & 2 & 84.67 & 100 \\  & 10 & 90.53 & 100 \\  & 20 & 95.1 & 100 \\ \hline FTS & 50 & 93.29 & 100 \\  & 100 & 95.8 & 77 \\  & 200 & 98.12 & 11.8 \\ \hline \end{tabular}
\end{table}
Table 2: The effects of FTS, Kobe0065 and deltarasin on A549 lung cancer cell viability incubated with the indicated concentrations of compounds for 24 and 48 h, assessed by MTT assay.  
Figure 7: _Cont._  
In the case of Kobe065, 2 and 20 \(\upmu\)M treatment resulted in lower _KRAS_ and _HRAS_ expression after 48 h than after 24 h. However, 10 \(\upmu\)M stimulation caused an increase in expression for both studied genes (Figure 7b). The HRAS mRNA expression levels were higher than KRAS with the exception of KRAS mRNA expression after 24 h incubation with 10 \(\upmu\)M and after 48 h incubation with 20 \(\upmu\)M Kobe065.  
FTS treatment, at all concentrations, resulted in higher _KRAS_ mRNA expression after 48 h than 24 h (Figure 7c). Stimulation with 50 \(\upmu\)M FTS caused stronger _HRAS_ mRNA  
Figure 7: The relative mRNA expression levels of _HRAS_ and _KRAS_ genes in A549 cell line after incubation with different concentrations of deltarasin (**a**), Kobe065 (**b**) and FTS (**c**) (“—calibrator for samples after 24-h of incubation; **—calibrator for samples after 48 h of incubation).  
expression after 48h incubation. For 200 \(\mu\)M treatment, _HRAS_ expression was higher after 48-h incubation than after 24 h.
## 4 Simulation Results and Security Evaluation
### Differential Attack

Table 6: Information global entropy, UACI, and NPCR comparison. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Information Entropy** & **NPCR \%** & **UACI \%** \\ \hline Lena & 7.9968 & 99.6399 & 33.4308 \\ Cameraman & 7.9974 & 99.5773 & 33.3542 \\ Pepper & 7.9969 & 99.6094 & 33.5607 \\ House & 7.9972 & 99.6017 & 33.3707 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Information global entropy, NPCR, and UACI. between encrypted images and plain images; a high-quality image is characterized by a low value of mean squared error (MSE), while a poor-quality image is indicated by a high MSE value. Equation (24) illustrates the computation of MSE by subtracting the cipher image from the original image. Conversely, a low value peak signal-to-noise ratio (PSNR) signifies the effectiveness of an image encryption technique. Equation (25) provides the calculation for PSNR in this context. \[MSE=\frac{1}{M\times N}\sum_{i=1}^{M}\sum_{j=1}^{N}(P(i,j)-E(i,j))^{2} \tag{24}\]  
\[PSNR=10\times\log_{10}\frac{M\times N}{\sqrt{MSE}} \tag{25}\]  
Here, \(P(i,j)\) and \(E(i,j)\) represent the pixels of the original and encrypted images, respectively. When \(P(i,j)=E(i,j)\), the MSE value is always zero. Table 7 presents the results of the experiments conducted on encrypted images in terms of both the PSNR and MSE values, and demonstrates a high value of the MSE and a low value of the PSNR, which indicates good encryption technique. Moreover, the lower value of SNR indicates that the encrypted image has more noise, thus hindering the attacker's attempt to obtain information to a greater degree compared to less noise.
## 5 Experimental Results  
### Experimental Basis  
**Dataset**: The dataset employed in this paper comprises 1000 samples selected by [PERSON] et al. from the Pile dataset. Each sample contains 100 tokens and serves as the validation dataset for precision extraction. The initial 50 tokens of each sample are extracted to form the prefix dataset, which is used for training data extraction.  
**Attack Model**: The attack model used in this paper is GPT-Neo 1.3B [[https://huggingface.co/EleutherAI/gpt-neo-1.3B](https://huggingface.co/EleutherAI/gpt-neo-1.3B)]([https://huggingface.co/EleutherAI/gpt-neo-1.3B](https://huggingface.co/EleutherAI/gpt-neo-1.3B)) (accessed on 1 July 2024), which is a pre-trained language model based on an autoregressive language model. With 1.3 billion parameters, GPT-Neo 1.3B is developed by EleutherAI and adopts a Transformer architecture similar to OpenAI's GPT model, including multiple Transformer layers, each containing self-attention mechanisms and feed-forward neural networks. GPT-Neo 1.3B is an open-source model trained on the publicly available Pile dataset, making it a good choice for training data extraction.  
**Neighborhood Generation Model**: For neighborhood-based membership inference attacks, BERT (2018) is selected as the model for generating neighborhood samples in this experiment.  
**Experimental Setup**: The experiment is conducted on 4060 ti GPU, equipped with 16 GB of memory. The batch size is set to 32. A fixed seed is employed to ensure the consistency of experimental results across multiple runs.
# Quantum-Key-Distribution (QKD) Networks Enabled by Software-Defined Networks (SDN)
## 3 Architecture of QKD Network Enabled by SDN
### Control Layer  
Control layer provides a holistic view of QKD networks for the operator. This layer may include one controller or multiple controllers to implement network management over QKD layer and open network capabilities for various applications. Different numbers of controllers in control layer can support hierarchical structure and multiple domains to improve the scalability of the networks. Specifically, application layer receives demands from operators then generates requests and sends them to the controller through its northbound interface. To satisfy the requests as much as possible, the controller calculates and allocates QKD resources with its global network map through the southbound interface. Correspondingly, control layer controls the QKD resources in QKD layer, provides services for multiple applications in application layer, and receives resource allocation and policy information of the key distribution layer.
# Mutual Authentication Protocol for D2D Communications in a Cloud-Based E-Health System
## 5 Security Analysis
### Session Key Security  
Session keys are not exchanged on a public channel but securely calculated on each side involved in the authentication. Moreover, the security of the session keys established at each phase of the protocol is guaranteed through the use of entities' real identities in the calculation, some secret information known only by the cloud server and the respective entities. For example, in HUP, the session key calculated is KHC = h\({}_{3}\)(IDH \(\parallel\) RH \(\parallel\) RCH); Consequently, an intruder cannot obtain or calculate a valid session key.
## 2 Materials and Methods
Stitching of the projection images in the vertical direction is also necessary for large samples when the height of the sample (in the direction from the nipple to the pectoralis major) exceeds the vertical beam size of 4 cm. Hence, for large samples, we need to take several steps in the vertical direction in order to capture the whole sample. We note that the MGD is higher in the overlap region of the vertically offset images, resulting in a doubling of the dose within this region. However, for future measurements involving patients, we will eliminate the need for any image overlap. The preprocessing and the stitching steps (using the IMBL_Preproc software developed privately by Y.I.N. and D.T. for the breast CT project) were applied to each projection image. The X-TRACT software package [44] was used for data reconstruction, including the phase-retrieval step. The TIE-Hom phase-retrieval algorithm was applied to the projection data set, in order to exploit the phase information, using a \(\delta/\beta\) ratio applicable for the relevant materials. For each energy, the ratio \(\delta/\beta\) was calculated with respect to the glandular and tumor tissues (\((\delta_{gland}-\delta_{lm})/(\beta_{gland}-\delta_{lm})\) = 399, 452, 550, and 600 for X-ray energy at 26, 28, 32, and 34 keV, respectively). While differentiating glandular and adipose tissues in CT slices is relatively easy, we are mainly interested in differentiating glandular and cancerous tissues. It turns out that the \"relative\" \(\delta/\beta\) for the latter is smaller than for glandular/adipose and hence should be used if one is interested in observing glandular/tumor interfaces without excessive blurring. If, however, glandular/adipose or tumor/adipose interfaces are of interest, one should use the corresponding \"relative\" \(\delta/\beta\). The Gridrec algorithm [45] was used for the CT reconstruction of the data reported in this article. An implementation of the Gridrec algorithm and a ring artefact removal filter are also available in the X-TRACT software package. Image data were stored in the interactive computing environment Australian Synchrotron Computer Infrastructure (ASCI) that provided high-performance computing resources [46]. ASCI can be used to analyze users' data in real time while at the beamline and/or after their beam time by logging in remotely. ASCI provides a convenient web-based interface to launch Linux desktops on high-performance computer hardware. \begin{table}
\begin{tabular}{c c c} \hline \hline  & Hamamatsu & Xineos \\ \hline Number of pixels & 1216 \(\times\) 1232 & 2097 \(\times\) 2111 \\ Pixel size & 100 \(\upmu\)m & 99 \(\upmu\)m \\ Exposure time & 59 ms & 25 ms \\ Frame rate & 17 fps & 40 fps \\ Max counts & 4000 (12-bit) & 16,000 (14-bit) \\ Acquisition mode & Fine mode & Mag1 (70\%) \\ \hline \hline \end{tabular} Note that the Xineos frame rate is available in 3 different modes, i.e., Mag0 (100\%—31 fps), Mag1 (70\%—40 fps), or Mag2 (50\%—54 fps) [43].  
\end{table} 
Table 1: Detector specifications for the Hamamatsu C10900D [42] and the Xineos 3030 HR [43].