esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
The International Prospective Register of Systematic Reviews (PROSPERO) was searched for studies that compared postendodontic pain and flareups between single and multiple visit RCTs. Finding no similar studies, protocol for the present review was registered (CRD42020216029). The review was carried per the guidelines for preferred reporting items for systematic reviews and meta-analyses (PRISMA).  
Research question: Is there a significant difference in the incidence and intensity of postendodontic flareup between a single-visit and multiple-visit RCT?  
Literature search: The electronic databases PubMed, Medline, Embase, Cochrane, Scopus, and Web of science were searched. Grey literature was searched in Google Scholar and Saudi digital library. Articles published in English between 1 January 2000 and 15 April 2020 were included. The keywords combinations used were \"postoperative pain\" or \"post obturation pain\" and \"single visit\" or \"multiple visits\" or \"one visit\" or \"two visits\" and \"flareup\". The selection was based on PICOS format (Population, Intervention, Comparison, Outcome and Study).  
The criteria included: Population--patients with at least one tooth indicated for endodontic treatment; intervention--root canal treatment; comparison--single and multiple visits; outcome--incidence and/or intensity of postoperative pain or flareup; study design--randomised controlled trial.  
Selection process: Three reviewers (SV, PM, and SK) screened the studies independently and compared the results. In case of any disagreement, studies were included/excluded after discussion. The articles selected were included for a full text reading to examine their eligibility. The following inclusion criteria were applied in addition to PICOS.  
Inclusion criteria:  
1. The study must be conducted on permanent vital teeth.
2. The outcome must have been measured post treatment or post initiation of treatment.
3. Clinical procedure was described in detail.  
Collection of data: Data from studies was extracted. Characteristics such as number of samples, type of treatment, use of intracanal medicament, the incidence, and the intensity of the postendodontic flareup were identified. The details were tabulated. Figure 1 summarizes the search strategy employed in the study.  
Risk of bias: The Joanna Briggs institute critical appraisal tool for randomised controlled trials was used to determine the risk of bias. This tool evaluates trials on the basis of baseline characteristics and randomisation of groups, allocation, blinding, outcome measures, statistical analysis and method of conduct of trial. The results of the assessment are reported in a tabular and narrative form.  
Meta-analysis: Quantitative data was extracted from all studies and organised into an extraction sheet for cleaning. Studies found eligible for a meta-analysis were analysed using a random effects model.
## 3 On Orbit Operations and Anomaly Analysis
### UHF Communication Anomalies  
While operating, the IDEASSat FSW would monitor the operating status of each spacecraft subsystem in the background, and restart a subsystem if its operational telemetry exceeded the defined operating limits. Figure 19 shows the time distribution of the UHF transceiver reboot count from spacecraft deployment to 14 February. Before 2 February, the UHF transceiver was restarted a total of four times. By 14 February, the number of UHF reboot counts had grown to 13 times, surpassing the number of resets of all of the other spacecraft subsystems. Conditions that would trigger a reset of the UHF transceiver by FSW are command timeout, electrically erasable programmable read-only memory (EEPROM) error, anomalous frequency settings, or undervoltage/overcurrent as detected by the EPS. The number of resets after 2 February shows a linear distribution, suggesting that the reason for the restarts was likely to be from a persistent source, such as that caused by electromagnetic interference within the spacecraft or abnormalities in the UHF module itself. Unfortunately, FSW was configured in such a way that the cause for any subsystem reset was only logged in beacon packets for the 15 min following the reset. This is a lesson learned, with later revisions of FSW logging reboot causes in a rolling stack.  
Another non-critical anomaly identified from IDEASSat flight data was a persistent increase in the command reject count logged by FSW. Figure 20 shows the variation in  
Figure 19: IDEASSat UHF transceiver reboot count as a function of time and linear regression.  
command reject count as a function of time. The IDEASSat UHF transceiver identified valid received signals as those prefixed with the appropriate sync word. The bit stream from such signals was then passed to the OBC and FSW for processing, If the received bitstream did not match the formatting or length of a valid IDEASSat command, it would be rejected by FSW and the command reject count incremented.  
According to the linear distribution trend in Figure 20, it can be seen that the spacecraft encountered about 45 command rejections per day. Assuming that the content of what is identified as a command packet by UHF using the sync word did not correspond to any valid command, that is, only the first three bytes of the command string were correct, the expected probability according to the binomial distribution is \(1/2^{8\times 3}\). Considering that the data rate of UHF was 9600 bps, the expected command reject count in a day due to random noise matching the sync word would be 9600 bps \(\times\) 86, 400 s day\({}^{-1}\times\frac{1}{2^{24}}\cong\) 49.34 day\({}^{-1}\). The result can be regarded as being slightly over-estimated, but is nonetheless close to the command reject rate in Figure 20. Random noise in the environment may have been the main reason for linear growth in the command reject count.
# hLSTM-Aging: A Hybrid LSTM Model for Software Aging Forecast
## 6 Results Analysis
### Metrics
\begin{table}
\begin{tabular}{c c c} \hline
**Architecture** & **MSE-Train/Test** & **MAPE-Train/Test** & **MAE-Train/Test** \\ \hline Vanilla-LSTM & \(1\times 10^{-4}/1.4\times 10^{-2}\) & \(4.99\times 10^{-1}/2.4194\times 10^{5}\) & \(4\times 10^{-3}/7.3\times 10^{-2}\) \\ Stacked-LSTM & \(1\times 10^{-4}/5.7\times 10^{-2}\) & \(5.55\times 10^{-1}/4.6351\times 10^{5}\) & \(4\times 10^{-3}/1.44\times 10^{-1}\) \\
**Bi-LSTM** & \(\mathbf{9\times 10^{-5}/3.73\times 10^{-1}}\) & \(\mathbf{4.78\times 10^{-1}/1.2014\times 10^{6}}\) & \(\mathbf{4\times 10^{-3}/3.65\times 10^{-1}}\) \\ CNN-LSTM & \(1\times 10^{-4}/1.3\times 10^{-2}\) & \(6.59\times 10^{-1}/2.3476\times 10^{5}\) & \(5\times 10^{-3}/1.3\times 10^{-2}\) \\ Conv-LSTM & \(1\times 10^{-4}/7\times 10^{-3}\) & \(5.25\times 10^{-1}/1.6974\times 10^{5}\) & \(4\times 10^{-3}/5.2\times 10^{-2}\) \\ AR & \(8.937\times 10^{0}/2.30\times 10^{-1}\) & \(3\times 10^{-3}/\)div by zero & \(3\times 10^{-3}/2.98\times 10^{-1}\) \\
**MA** & \(\mathbf{3\times 10^{-4}/2.30\times 10^{-1}}\) & \(\mathbf{1.6\times 10^{-2}/div by zero}\) & \(\mathbf{1.3\times 10^{-2}/2.98\times 10^{-1}}\) \\ ARMA & \(9.125\times 10^{0}/2.29\times 10^{-1}\) & \(3\times 10^{-3}/\)div by zero & \(2\times 10^{-3}/2.96\times 10^{-1}\) \\ ARIMA & \(9.397\times 10^{0}/3.68\times 10^{-1}\) & \(2\times 10^{-3}/\)div by zero & \(2\times 10^{-3}/4.32\times 10^{-1}\) \\ SARIMA & \(9.401\times 10^{0}/2.72\times 10^{-1}\) & \(2\times 10^{-3}/\)div by zero & \(2\times 10^{-3}/3.52\times 10^{-1}\) \\ \hline
**hLSTM** & \(\mathbf{6\times 10^{-5}/1.5\times 10^{-2}}\) & \(\mathbf{4.34\times 10^{-1}/1.5\times 10^{-2}}\) & \(\mathbf{3\times 10^{-3}/7.7\times 10^{-2}}\) \\ \hline \end{tabular}
\end{table} 
Table 3: Results of evaluation metrics for VmRSS. Vanilla-LSTM in [6] had good results, the combination of the MA and the Conv-LSTM model also demonstrated a way of improving the forecasting of the software aging problems with hybrid models.
# Review of Indoor Positioning: Radio Wave Technology
## 2 Parameter Based Positioning
### Toa  
TOA is also known as TOF [28] and is described as the first period within which the signal reaches the receiver. It can estimate the distance to the node by computing the broadcast time travel of a wireless radio signal [57; 58] as shown in Figure 3. The traditional TOA schemes needs a minimum of two or three reference nodes in a LoS situation with a target, to support a high level of position accuracy [59]. The nodes can be synchronized or the miss-synchronization in TOA and the signal must consist of the timestamp data [60]. To solve these issues, the TDOA method, as well as the round trip time of arrival (RTOA) method, also called RTOF, is implemented. RTOA ranging mechanisms are identical to TOA, but it does not need a corporate time reference within nodes. TOA is influenced by multipath and additive noise. Additive noise imitates the precision of the signal arrival time. This problem can be fixed by applying the TDOA instead of TOA [61].  
Figure 2: RSSI-based trilateration.
# FFNN-TabNet: An Enhanced Stellar Age Determination Method Based on TabNet
## 5 Experiments and Results  
### Loss Functions and Evaluation Metric  
The loss functions used in this paper are MAE, The formula is delineated in Equation (10).  
\[MAE=\frac{1}{n}\sum\limits_{i=1}^{n}\lvert y_{i}-\hat{y}_{i}\rvert \tag{10}\]  
RMSE, the formula is delineated in Equation (11).  
\[RMSE=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{11}\]  
The evaluation metrics used in this paper are the Coefficient of Determination (R\({}^{2}\)). The formula is delineated in Equation (12).  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}}{\sum \limits_{i=1}^{n}\left(y_{i}-\overline{y}\right)^{2}} \tag{12}\]  
Here, \(n\) is the number of samples, \(y_{i}\) is the target value of the \(i\)-th sample parameter, \(\hat{y}_{i}\) is the FFNN-TabNet's determination for \(y_{i}\), and \(\overline{y}\) is the average predicted age of the red giants by the model.  
MAE represents the average absolute error between the predicted ages of red giant stars by the model and the actual ages in the sample, indicating the actual size of the error in the model's determination. RMSE is the square root of the ratio of the sum of the squared differences between the predicted and actual ages of red giants and the number of samples n, measuring the deviation of the model's determination from the actual ages. Smaller values of MAE and RMSE indicate better model performance. R\({}^{2}\) reflects the degree of fit between the model's predicted ages for red giant stars and the actual ages in the sample. Generally, the closer R\({}^{2}\) is to 1, the better the model's fit.
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 7 Case Study and Results
### Experimental Setup
#### 7.2.1 Production Line Description and Case Study Design  
The case study environment consists of eight machines organized in three groups (A, B, C) processing twelve jobs with varying priorities and due dates. This configuration was carefully designed to balance experimental complexity with practical relevance to real-world manufacturing scenarios. The setup mirrors typical production environments where multiple parallel machines perform identical tasks, allowing for operational flexibility and workload distribution.  
- Group A: Three machines (\(A_{1}\), \(A_{2}\), \(A_{3}\)) for Task A
- Group B: Three machines (\(B_{1}\), \(B_{2}\), \(B_{3}\)) for Task B
- Group C: Two machines (\(C_{1}\), \(C_{2}\)) for Task C
* Task sequences (e.g., Task A \(\rightarrow\) Task B)
- Due dates and priority levels (1-9, where 9 is highest)
- Processing times: Task A (3 units), Task B (2 units), Task C (3 units)  
This experimental setup offers several key advantages for validating the proposed methodology. The parallel machine configuration within each group enables thorough testing of fault-response strategies and load balancing capabilities. When faults occur, the system can explore various task reallocation options among identical machines, which is similar to actual manufacturing environments. The number of jobs and their varying characteristics create a sufficiently complex scheduling problem with approximately 30 total tasks, generating a large solution space that challenges the scheduling algorithm while remaining computationally manageable.  
Furthermore, this configuration provides an ideal testbed for evaluating both the fault prediction and scheduling optimization components. The controlled complexity of three machine groups maintains clarity in process flow while offering enough scheduling alternatives to demonstrate the system's ability to handle resource conflicts, priority-based decisions, and fault avoidance strategies. The inclusion of multiple parallel machines in each group particularly enhances the study of fault-adaptive behaviors, as it allows for realistic scenarios where workload can be redistributed among alternative resources when faults are predicted.
## 1 Introduction
Therefore, this paper focuses on finding out a proper method to predict the radiated noise of a panel under the fluctuating surface pressure (FSP) due to an idealized side mirror. The FSP of the idealized side mirror is firstly analyzed by the incompressible and compressible numerical simulations. Then, based on the FSP of the incompressible and compressible numerical simulations, the aerodynamic noise analysis of the panel is respectively performed by the six methods. By validating with the experimental data and comparing the computing time of all six methods, the method of random modal force coupling boundary element method (BEM) under the incompressible numerical simulation is proposed to predict the radiated noise of the panel under the aerodynamic load. This method can be used to predict the vehicle interior noise at high speed. At different stages of automobile development, an appropriate method can be selected for different purposes in terms of the accuracy of results and computing time.
# Autonomous Intersection Management by Using Reinforcement Learning
## 3 Methodology
### Reinforcement Learning for Traffic Control
#### 3.1.3 Reward Definition  
The reward \(r\) can be either a fixed value or a variable. For a fixed reward, it can be positive or negative, e.g., \(r=1\) represents a prize whereas \(r=-1\) represents a penalty. A variable reward, it can consist of multiple sub-rewards. There are two main definitions for reward.  
**Variation of vehicular delay**. The agent is rewarded if the total cumulative delay (i.e., cumulative delay of all vehicles) is reduced. The cumulative delay is the total time a vehicle spends in a queue. The reward definition can be the difference of the total cumulative delay of vehicles at time \(t+1\) and time \(t\), or a reference value \(r_{t}=td_{ref}-ttd_{t}\), where \(r_{t}\) is the reward value at time \(t\), \(td_{ref}\) is the user-defined reference of total cumulative delay, and \(ttd_{t}\) is the total cumulative delay of vehicles at time \(t\)[21].  
**Variation of queue size**. If the queue size is reduced within a time interval, the agent is rewarded.
## 6 Efficiency and Security Analysis
### Efficiency  
A common metric for quantifying the qubit efficiency of quantum protocols is the ratio \(\eta\), which compares the total number of transmitted \"useful\" classical bits to the total number of qubits employed [71; 72]. For multiparty quantum private comparison protocols, this measure must be suitably adjusted and defined as follows (see, for instance, [36; 73]):  
\[\eta=\frac{\eta_{cb}}{\eta_{\eta_{q}}}\;, \tag{25}\]  
where \(\eta_{cb}\) stands for the number of classical bits required to represent the fortunes of the millionaires, and \(\eta_{\eta_{q}}\) is the number of qubits the protocol uses. At this point, we must clarify that the measure \(\eta_{\eta_{q}}\) does not include the decoys, as is the norm in the literature.  
In the case of the GHZ\({}_{3}\)MQPEC protocol, we assumed that each millionaire requires \(m\) bits to store her fortune. Taking into account the fact that there are \(n\) millionaires, we see that  
\[\eta_{cb}=nm\;. \tag{26}\]  
To accomplish this task, our protocol requires \(3m+2\) qubits for each millionaire, which means that for \(n\) millionaires, it requires  
\[\eta_{tq}=3 nm+2n \tag{27}\]  
qubits in total. Therefore, the efficiency \(\eta\) of the GHZ\({}_{3}\)MQPEC protocol is  
\[\eta=\frac{nm}{3 nm+2n}=\frac{m}{3m+2}\approx\frac{m}{3m}=\frac{1}{3}=33.33\%\;. \tag{28}\]  
In the derivation of the above result, we used the fact that for large values of \(m\), a realistic assumption in a real-life scenario, it holds that \(3m+2\approx 3m\). These efficiency results are what is to be expected in multiparty quantum private comparison protocols based on GHZ entanglement. This fact can be verified by consulting the relevant literature [36; 73; 74; 75; 76].  
Let us emphasize that the quantum resources required by our GHZ\({}_{3}\)MQPEC protocol, as given by Formula (27), grow linearly in both parameters \(n\), expressing the number of millionaires, and \(m\), quantifying the number of qubits that suffice to represent in binary any of the millionaires' fortunes. This can be more formally stated as follows:  
\[\eta_{tq}=\Theta(n+m)\;. \tag{29}\]  
This proves the ability of the GHZ\({}_{3}\)MQPEC protocol to scale in the most efficient way possible with both parameters \(n\) and \(m\) and thus be amenable to practical implementation.  
It should be noted though that, given the modest number of qubits in modern quantum computers, there are practical limitations to scaling. For instance, higher \(m\) values may prove challenging. We should point out that as \(m\) increases, it becomes increasingly difficult to ensure the desired entanglement states and to maintain control over the entire composite system. This could potentially restrict the practical application of our protocol on today's quantum computers.
# Design and Performance Analysis of Navigation Signal Based on OFDM
## 4 Analysis of ACF and PSD  
The ACF and PSD of the navigation signal are directly related to the quality of the navigation signal and reception performance of the receivers. This section first analyzes the  
Figure 2: Comparison of the ACF and PSD simulation results and the theoretical results of the OFDM navigation signals. **(a)** The ACF of the OFDM navigation signal in cyclic prefix mode; **(b)** The ACF of the OFDM navigation signal in zero-padding mode; **(c)** The PSD of the OFDM navigation signal in cyclic prefix mode; **(d)** The PSD of the OFDM navigation signal in zero-padding mode.  
influence of the OFDM modulation parameters on the navigation characteristics. Then, the OFDM navigation signal is compared with several different navigation signals to analyze the navigation performance.
# Imaging Flow Velocimetry with Laser Mie Scattering
## 1 Introduction
While Rayleigh scattering is an elastic light scattering, which allows one to measure the velocity, the pressure and the temperature of the fluid [19; 20], Mie scattering is an elastic light scattering that only allows one to measure the particle velocity. However, the scattering efficiency is increased by several orders of magnitude, which enables the measurement of flow velocity images with a lower measurement uncertainty at an identical spatial and temporal resolution with the same laser power. The other way around, the higher scattering intensity also allows a higher spatiotemporal resolution, image size and measurement rate at an identical measurement uncertainty with the same laser power. For this reason, the article is focused on optical flow velocity measurement techniques based on Mie scattering, which typically require seeding. A large variety of different measurement techniques based on Mie scattering exists. In particular concerning the improved scattered light energy, one fundamental question is the existence and the magnitude of physical measurement limits, which hold for any measurement technique. Understanding these limits allows one to identify the potential for the future development of each measurement technique. Furthermore, the existence of a possibly superior technique needs to be clarified. Such a unified broad consideration of the different flow velocity measurement techniques based on Mie scattering, starting with a physical categorization of the different techniques, is missing. For this reason, the aim of the article is to summarize and to review the developments, recent advances and fundamental limits of the different imaging flow velocity measurement principles based on Mie scattering. At first, the physical measurement principles are explained in Section 2. Next, the development towards an imaging technique is described for each principle in Section 3. Thereby, it is focused on the development of high-speed imaging measurement systems. Research results regarding fundamental limits of the measurement techniques are then presented in Section 4. Finally, application examples, as well as the respective challenges and perspectives are discussed in Section 5. Note that the focus is set on measurement techniques for meso-scale flow applications with dimensions between millimeter and meter. A current review of micro-scale flow metrology can be found in [27]. However, the present review is not regarding certain flow applications, but the development of the different flow measurement techniques and their fundamental measurement limits.
## 3 Proposed Method
### Loss Function  
We introduce the Charbonnier Loss [46] as our basic reconstruction loss:  
\[\mathcal{L}_{rec}=\mathcal{L}_{c}(\mathcal{I}(X),\mathcal{J}_{gt}) \tag{3}\]  
where \(\mathcal{I}\) is our LPS-Net and \(X\) and \(\mathcal{J}_{gt}\) stand for the input and ground-truth. \(\mathcal{L}_{c}\) denotes the Charbonnier Loss, which can be express as:  
\[\mathcal{L}_{c}=\frac{1}{N}\sum_{i=1}^{N}\sqrt{\left\|X^{i}-Y^{i}\right\|^{2}+ \epsilon^{2}} \tag{4}\]  
where constant \(\epsilon\) was empirically set to \(1\times 10^{-3}\) for all experiments. In addition, the perceptual level of the restored image is also critical. We applied the perceptual loss to improve the restoration performance. The perceptual loss can be formulated as follows:  
\[\mathcal{L}_{perceptual}=\sum_{j=1}^{2}\frac{1}{\mathcal{C}_{j}H_{j}W_{j}}|| \phi_{j}(\mathcal{I}(x))-\phi_{j}(\mathcal{Y})||_{1} \tag{5}\]  
where \(\phi_{j}\) represents the 1-st and the 3-rd layers of VGG19 [47]. \(C_{j}\), \(H_{j}\), and \(W_{j}\) represent the channel number, height, and width of the feature map, respectively.  
In order to better reflect the human visual system's perception of image quality, we adopted the negative SSIM loss to focus on luminance, contrast, and structure. The negative SSIM loss is:  
\[\mathcal{L}_{ssim}=-SSIM(\mathcal{I}(X),\mathcal{J}_{gt}) \tag{6}\]where \(\mathcal{I}(\cdot)\) is LPS-Net and \(X\) and \(\mathcal{J}_{gt}\) stand for the input and the corresponding ground-truth, respectively.  
The overall loss function can be expressed as:  
\[\mathcal{L}=\lambda_{1}\mathcal{L}_{c}+\lambda_{2}\mathcal{L}_{ perceptual}+ \lambda_{3}\mathcal{L}_{ssim} \tag{7}\]  
where the \(\lambda_{1}\), \(\lambda_{2}\) and \(\lambda_{3}\) are set to 1, 0.2 and 0.5, respectively.