esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Report Estimation-Based Bandwidth Allocation Problem Formulation  
In frequency division duplex (FDD)-based LTE, the downlink and uplink sub-frames are time-aligned at BBU, as illustrated in Figure 2. To meet the timing requirements imposed by the hybrid automatic repeat request (HARQ) protocol, every received MAC protocol data unit (PDU) has to be acknowledged (ACK) or non-acknowledged (NACK) back to the UE transmitter within the deadline of 4 ms. Thus, all the processing including air interface uplink and downlink processing, front-haul processing uplink and downlink processing and BBU processing should be completed within this 4 ms deadline. Within this 4 ms, BBU requires around 2.3-2.6 ms [13] to finish preparing and sending the downlink ACK/NACK back to UE in such a way that when it receives the uplink data from UE in sub-frame n, it will send ACK/NACK to the UE in sub-frame n+4.  
Figure 2: Long-term evolution–based radio access network processing latency breakdown.  
Our idea is to take advantage of this 2.3 ms BBU processing time for collecting data statistics (i.e., buffer occupancy reports) from ONUs. Then we will utilize these reports as training examples (samples) to train a prediction model to predict the next reports for the ONUs. We can utilize these predicted reports to generate the scheduling grants for the ONUs and formulate the final bandwidth map for the upcoming XGS-PON frames.  
The number of ONU reports that can be collected within one BBU processing time is given by:  
T = BBU processing time/(one-way propagation delay to transmit one frame from ONU to OLT) + (125 \(\upmu\)s: the time required for transmitting the previous frame). For example, for 10 km mobile front-haul, the number of reports will be equal to = 2.3 ms/(50 + 125) \(\upmu\)s = 13 reports.  
Assuming that we collect until \(T+1\) reports from \(j\)th ONU during one BBU processing cycle, if we extend backward from \(T\)th report to the beginning of cycle, we will have a time series containing the following reports \(\{R_{T}^{j},\,R_{T-1}^{j},\,R_{T-2}^{j}\,\cdots\,\cdots\,\,\,R_{0}^{j}\}\) for each ONU (i.e., \(j=1,\ 2,\ 3\ldots K\), where \(j\) is the index of the ONU). Given this time series, if we want to estimate \(R_{t}^{j}\) at some future time \(T+s\), we can write this problem as follows:  
\[\hat{R}_{T+s}^{j}=f(R_{T}^{j},\ R_{T-1}^{j},\ R_{T-2}^{j},\ldots R_{0}^{j}), \tag{1}\]  
where \(s\) is the prediction horizon. Assuming \(s\) = 1, we can predict one time step or sample into the future \(\hat{R}_{T+1}^{j}\), if we can find an optimal function estimator that can accurately approximate the function \(f\). Finding such an estimator is a machine learning function approximation problem, which can be solved using machine learning function approximation models such as feedforward artificial neural network. The next section introduces a multi-layered feedforward artificial neural network that employs adaptive learning approach to solve this problem.
# A Survey of the Effects of Vehicle Configuration on Urban Air Mobility +
## 2 Aerial Urban Vehicle Configurations and Concepts
### Hybrid
#### 2.3.1 Dual Systems
The big advantage of the dual system in fault conditions is separated VTOL and HTOL propulsion units and the absence of any transition actuators like other hybrid configurations. With this advantage, the aircraft can handle the fault condition easier than other types [80,81]. Furthermore, hybrid gas-electric systems can be applied to dual systems where an internal combustion engine simultaneously drives a propeller for thrust and generates electricity for electric motors powering lift-generating rotors. This electrical coupling between thrust and lift components allows for reduced overall weight as a battery is primarily needed for emergency landings.
## 2 Appendix Segmentation with Double-Layered Outlier Rejection FCM
### Double-Layered Pixel Clustering  
Another well-known limitation of FCM is its tendency to fall into local maximum due to the gradient descent method it is based on. A recent study [36] overcome that limitation by making the FCM learning structure as double-layered as shown in Figure 1.  
The second layer takes outputs of the first layer as input. Subsequent parameters of the first and second layers of clustering are not necessarily the same. In [36], they used conventional FCM in both layers but with different parameter initializations and the number of clusters.
# Generative Adversarial CT Volume Extrapolation for Robust Small-to-Large Field of View Registration
## 3 Results
### Registration Test  
Figure 8 displays the registration results of the full moving images with all three variants of the fixed images-full, thin and extrapolated. The blue boxplots display the results of landmark-based registration which is then used as an initialization for the intensity-based registration, depicted in red.  
As expected, the best performance was obtained when the full-moving images are registered with full-fixed images (having a median SD of 0.20(\(\pm\)0.08) mm after intensity-based registration), and the worst results were obtained when the full-moving images were registered with thin-fixed images (5.66(\(\pm\)20.56) mm). However, we obtained a registration performance comparable to the one corresponding to full-fixed images (0.57(\(\pm\)2.05) mm) by using the proposed extrapolation method as a prior step, thus reducing the thin slab registration error with a factor of 10 (\(p\)-value = \(4.18\times 10^{-6}\)). The same holds true when considering the DICE score (Figure 7(b)), which increased due to the extrapolation from 0.67 to 0.88 (median).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Mean (\(\pm\)std) [mm]**} \\ \hline
**Image** & **All Detected Landmarks** & **Landmarks Detected on Actual Info** \\ \hline Full volume & 4.64(\(\pm\)8.02) & 4.04(\(\pm\)7.05) \\ Thin volume & 19.51(\(\pm\)43.0) & 19.51(\(\pm\)43.0) \\ Extrapolated volume & 18.62(\(\pm\)22.96) & 12.08(\(\pm\)16.86) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Landmark detection results on the test set.  
Figure 8: Registration results: landmark-based registration in blue, intensity-based registration in red. Each figure has three groups: left—registration of full-fixed with full-moving images; middle—registration of thin-fixed with full-moving images; right—registration of extrapolated-fixed with full-moving images.  
Figure 7: Detection errors. For the blue boxplots all detected landmarks were considered, while the red boxplots only take into account landmarks detected on the region containing the actual information.  
Figure 9 displays a comparison between the registration of thin, full and extrapolated images. While the first row presents a poor registration of the thin image, the third row demonstrates the benefits of our approach, showing a much better overlap between the extrapolated-fixed and full-moving images. However, the best performance was obtained when aligning the full-fixed with the full-moving images (second row).
## 2 Materials and Methods
### Data Analysis  
Vicon data were labelled and processed with Vicon Nexus 2.14. Raw Vicon data were filtered with a fourth-order (zero lag) low pass Butterworth filter with a cut-off frequency of 6 Hz, which was determined based on residual analysis [30; 34; 36]. XY-coordinates of the filtered 100 Hz Vicon data were used for analysis; the z-coordinates (vertical-axis) were neglected in the calculations as the used GNSS tracking system does not collect data in the third dimension. The Vicon data were subjected to six different processing steps to calculate the acceleration data; for clarity and reproducibility, all processing steps are detailed and presented in Table 2. To select a suitable smoothing technique for the Vicon speed data, frequency analysis using fast Fourier transform and time-frequency analysis using a spectrogram were performed on the unfiltered Vicon speed data. The characteristics of team sport-specific human movement patterns and potential sources of error or variability in the data were studied and taken into consideration. The frequency bandwidth containing 99% of the total power of the speed signal was identified to be at 2 Hz. Therefore, a fourth-order (zero lag) low pass Butterworth filter with a cut-off frequency of 2 Hz was performed on the Vicon speed data. This ensured that the selected smoothing for the Vicon data were relevant and suitable for the specific type of data, making the Vicon data an accurate representation of the real world. The raw (not smoothed in any way by the manufacturer software) GNSS Doppler-shift speed data were exported and retrieved from the Catapult software file folder. It was chosen to use the speed derived from Doppler-shift as it is more frequently used and preferred to the speed of positional differentiation due to its increased accuracy, lower error, and default measure of speed of most manufacturers [4; 7]. The smoothed GNSS acceleration data (smoothed by the manufacturer software) were exported from the manufacturer's software (Openfield version 3.9.0, Catapult Innovations, Melbourne, Australia). Each GNSS file was partitioned to align with the 3D motion capture Vicon trials based on the coded timestamps of the trials and standing still for 3 s at the start and end of each trial. Due to the difference in sampling frequencies between the Vicon and GNSS, the files could not be directly combined. As this study was to investigate how closely the GNSS acceleration data matched the VICON data, the 10 Hz GNSS data were used for further analysis to avoid any potential additional noise in the data due to upsampling, while downsampling the Vicon data. The additional noise due to upsampling would have been created by introducing new data points that did not exist in the original dataset through interpolation. This could have led to inaccuracies because the data that were filled in were never measured, making it less reliable. Downsampling the 100 Hz VICON data retained the real measured data by selecting a subset of the original data. Furthermore, this approach ensured that the GNSS data are a true representation of that used in the field by
# Application of the Segmented Correlation Technology in Seismic Communication with Morse Code
## 3 Experiment  
In the course of experiment, a seismic communication system is first set up with a 500 N vibroseis source, 5 CDJS2C-2 geophones and 5 GEIWS RII seismometers in the Geological Palace of Changchun, Jilin, China. A 500 N vibroseis source sends out seismic signals encoded in Morse code, whose volume is no more than 8000 cm\({}^{3}\) so that it is easy to carry. The vibroseis, whose power supply system can be adjusted according to the actual situation, is powered by alternating current (AC) in the urban power grid or the lithium battery system with voltage of 220 V and frequency of 50 Hz. The CDJS2C-2 geophones are used to collect signals through the earth, whose sensitivity is \(2+10\%\) v/cm/s. The GEIWSR-II seismometers are employed to record and store seismic data from the geophones at a sampling rate of 1000 Hz. In this system, the distance between the first geophone and the source is 20 m, the distance between geophones is 10 m and the distribution of their  
Figure 4: The direct cross-correlation in signal simulation; (**a**) The 200 Hz SOS signal in the simulation diagram; (**b**) The direct cross-correlation about SOS signal in the simulation diagram.  
locations as shown in Figure 5. Finally, a series of instructions (including a 200 Hz SOS signal) are sent at the appointed time in the environment with various electromagnetic equipment, people and elevators. According to the Morse table, the binary code of the SOS signal should be 1010100110110110010101.
## 2 Prior Work
### Calibration and Rectification of Binocular Camera  
#### 2.2.1 Calibration of Binocular Camera  
The ZED2 binocular camera producing an RGB image with a resolution of 1280 \(\times\) 720 pixels at 60 fps was used in this paper. Since the internal and external parameters of the camera are important for restoring 3D spatial location information, they can be obtained through the camera calibration. 30 pairs of pictures (60 images) with the calibration chessboard collected by a ZED2 binocular camera were put into the camera calibration box of MATLAB to calibrate the ZED2 binocular camera, as shown in Figure 4.  
The camera parameters were calibrated by identifying the corners on the chessboard picture collected by the camera. After both cameras were calibrated, the calibration error was analysed by re-projecting the calibration results of the left and right cameras, respectively. The error analysis found that the errors of the left camera in the \(X\) and \(Y\) directions were 0.48406 and 0.39770 pixels, and the errors of the right camera in the \(X\) and \(Y\) directions were 0.4940 and 0.36554 pixels, which meet the accuracy requirements of the research. After calibrating the rotation matrix and translation matrix of the two cameras, the rotation matrix \(R\) was found to be similar to the identity matrix, which showed that the optical axes of the left and right cameras were nearly parallel. Meanwhile, the rotation angle between them was very small. The coordinate system was established by the optical centre position  
Figure 4: Image pairs used in binocular camera calibration. (**a**) Image taken by left camera. (**b**) Image taken by right camera.  
Figure 3: Binocular stereo vision imaging model.  
of the left camera as the origin, and the translation vector was acquired. This showed that the distance between the two cameras in the \(X\)-axis direction was 119.79 mm, which was very close to the given baseline length of 120 mm. At the same time, the deviation in the \(Y\)- and \(Z\)-axis directions was very small (0.138 mm and 0.504 mm, respectively). Through calibration, the camera was confirmed as meeting the needs of sugarcane location.
# Energy-Saving Routing Protocols for Smart Cities
## 3 Results
### Determining the Safety Margin of the proposed RPA Routing Protocol  
With the aim of providing more reliability during the computation of \(P_{TX}\), a safety margin in the range from 0 to 5% was added to the proposed RPA algorithm. Thus, the total energy consumption was evaluated as a function of the variation of the safety margin. For this purpose, three simulation experiments were performed for a scenario composed by 25 nodes, one source sensor node, 23 router nodes, and one gateway.  
In this experiment, the source node moves at a fixed speed of 18 km/h and the others remain static. The source sensor node sends data to the gateway at every 30 s. At the end, the value of energy savings is calculated from the average consumption value comparing the simulations of the DSR protocol with and without the RPA algorithm.  
As shown in Figure 3, when the adjustment margin is greater than 2.5% the energy savings are less than 10%. Therefore, to obtain energy savings of at least 10%, we decided to keep the safety margin fixed at 2%.
## 3 Background
### Hotspot
#### 3.4.4 Hotspot Gateway  
It is a network device that is responsible for providing authentication, authorization, and accounting (AAA) for a given wireless network infrastructure. Despite a possible intrusion by an eavesdropper, such a gateway can prevent malicious users from accessing a private network [94]. It helps users access the Internet instantly, without requiring any changes to the configuration of the user's mobile device or its internal client-side network software. With the existing network settings, a user can easily access different Internet networks through hotspot gateways. The location of the gateway can be identified by integrating the GPS-based antenna.
## 3 Results
### Baseline Characteristics  
All 22 subjects were female and White, with one subject reporting Hispanic/Latinx ethnicity. The median (IQR) age was 61.5 (19.0) years, with a median BMI of 28.4 (14.2) kg/m\({}^{2}\). The median MET-minutes/week were 590.0 (1065.0), or 118% of the Physical Activity Guidelines for Americans [49]. Most of the population was highly educated, with 87% holding a bachelor's degree or higher. The predominant cancer type was breast cancer (64%), followed by blood and bone marrow cancers (18%). Half of the subjects received 1-2 treatment modalities for their cancer, with the most common treatments being surgery (n = 18, 82%), chemotherapy (n = 16, 73%), and radiation (n = 11, 50%). Forty-one percent had previously met with a dietitian at least once prior to the cooking class. Further details on subject demographics are shown in Table 2.  
Figure 1: Flowchart of participant inclusion. Abbreviations: DHQIII: Diet History Questionnaire; FACT-Cog: Functional Assessment of Cancer Therapy–Cognitive Function.  
Median values of baseline dietary factors and cognitive scores are presented in Table 3. Median (IQR) dietary intakes at baseline consisted of 1513.7 (452.3) calories, 16.8 (11.7) g of fiber, 1.0 (1.0) cup of fruit, 1.4 (1.7) cups of vegetables, 0.7 (0.8) oz of whole grains, and 1.1 (1.6) oz of nuts/seeds/legumes. Therefore, at baseline, participants consumed 56% of the AICR recommended amount of fiber. The median (IQR) inflammatory potential of diet via the E-DII score was \(-\)2.3 (4.3). The median baseline cognitive factor scores of COG-PCI, COG-PCA, COG-OTH, and COG-QOL were 17.5 out of 72, 22 out of 28, 0 out of 16, and 4 out of 16, respectively.  
\begin{table}
\begin{tabular}{l l} \hline
**Continuous Variables** & **Median (IQR)** \\ \hline Age (Years) & 61.5 (19.0) \\ \hline BMI (Kg/m\({}^{2}\)) & 28.4 (14.2) \\ \hline Physical Activity (MET-Minutes/Week) & 590.0 (1065.0) \\ \hline Categorical Variables & n (\%) \\ \hline Sex & \\ Female & 22 (100) \\ Male & 0 (0) \\ \hline Race & \\ White & 22 (100) \\ Other & 0 (0) \\ \hline Ethnicity & \\ Non-Hispanic/Latinx & 21 (96) \\ Hispanic/Latinx & 1 (4) \\ \hline Education & \\ High school or GED & 2 (9) \\ Associate’s degree & 1 (4) \\ Bachelor’s degree & 9 (41) \\ Master’s, professional, or doctoral degree & 10 (46) \\ \hline Cancer Type & \\ Breast & 14 (64) \\ Endometrial/ovarian/uterine & 1 (4) \\ Leukemia/lymphoma/multiple myeloma & 4 (18) \\ Other & 3 (14) \\ \hline Number of Treatments & \\
1-2 treatments & 11 (50) \\ \(\geq\)3 treatments & 11 (50) \\ \hline Previous Dietitian Encounter & \\ Yes & 9 (41) \\ No & 13 (59) \\ \hline Physical Activity (\(\geq\)500 Met-Minutes/Week) & \\ Yes & 11 (50) \\ No & 11 (50) \\ \hline \end{tabular} Abbreviations: BMI: Body Mass Index; IQR: interquartile range; MET: Metabolic Equivalent of Task; GED: General Educational Development Test.  
\end{table}
Table 2: Baseline characteristics of cancer survivors (N = 22).
# A Real-Time Semi-Supervised Log Anomaly Detection Framework for ALICE O\({}^{2}\) Facilities
## 4 Results and Discussion
### Comparison with Traditional Topic Models
of Topics** & **P** & **R** & **F1** & **FPR** & **FNR** \\ \hline Infologger & LDA & 100 & 0.906 & **0.996** & 0.946 & 0.0003 & 0.0040 \\ (CFRN) & & 200 & 0.903 & **0.996** & 0.947 & 0.0003 & **0.0039** \\  & & 300 & 0.906 & **0.996**  
large datasets. In contrast, LSA achieves the lowest inference time at 14.49 s in Inflogger since it relies on precomputed matrices, enabling efficient topic assignment through simple matrix multiplication. Regarding BERTopic, its training time is generally longer than that of traditional methods due to the integration of clustering algorithms, which involve more complex update rules. However, its inference time remains moderate at 456.91 s, as it still requires embedding and dimensionality reduction, which introduce some computational overhead. Among the models evaluated, NMF emerged as the most effective approach. On the unprocessed InfoLogger dataset, NMF achieved the highest F1-score (0.977) at 300, demonstrating its ability to classify log messages accurately. Similarly, LDA and BERTopic also delivered high precision and recall, making them competitive alternatives to NMF. In the BGL dataset, NMF was still the most effective approach in both before and after prepro  
Figure 4: Comparison of computational efficiency based on training and inference time. Figure 3: Comparison of preprocessed vs. unprocessed performance based on F1-score. cessing. NMF achieved an F1-score of 0.941 at 200 in the unprocessed BGL dataset, while LSA and LDA followed closely with F1-scores ranging from 0.876 to 0.890. These empirical findings demonstrate that preprocessing procedures have more influence than models and the number of topics, highlighting their importance in the analysis workflow. Nevertheless, preprocessing remains inherently dataset-specific and requires careful consideration for each specific case. Despite their performance, traditional methods still incurred substantial computational overheads during inference time. This is because traditional algorithms needed to update their results multiple times to converge to an optimal solution, excluding the LSA matrices, which had already been computed. While this trade-off enhanced performance, it still resulted in significant computational costs. However, BERTopic achieved moderate results in both performance and time consumption. Consequently, the next experiments explored BERTopic's capacity to handle unprocessed data through parameter optimization, potentially providing insights into more robust topic modeling solutions.
## 2 Experimental Section
### Quantum Fireworks Algorithm
#### 2.1.2 Quantum Evolutionary Algorithm  
The development of quantum mechanics impels quantum computing to be increasingly applied in various fields. In quantum computing, the expression of a quantum state is a quantum bit, and usually quantum information is expressed by using the 0 and 1 binary method. The basic quantum states are \"0\" state and \"1\" state. In addition, the state can be an arbitrary linear superposition state between \"0\" and \"1\". That is to say, the two states can exist at the same time, which challenges the classic bit expression method in classical mechanics to a large extent [17]. The superposition state of quantum state can be presented as shown in Equation (6).  
\[|\psi>=\alpha\,|0>+\beta\,|1>\ \,\ |\alpha|^{2}+|\beta|^{2}=1 \tag{6}\]  
where \(|0>\) and \(|1>\) are the two quantum states \(\alpha\) and \(\beta\) are the probability amplitudes. \(|\alpha|^{2}\) represents the probability at a quantum state of \(|0>\) and \(|\beta|^{2}\) represents the probability at a quantum state of \(|1>\).  
In QFA, the updating proceeds by quantum rotating gate, and the adjustment is:  
\[\left(\begin{array}{c}\alpha_{i}^{\prime}\\ \beta_{i}^{\prime}\end{array}\right)=\left(\begin{array}{cc}\cos(\theta)&- \sin(\theta)\\ \sin(\theta)&\cos(\theta)\end{array}\right)\left(\begin{array}{c}\alpha_{i} \\ \beta_{i}\end{array}\right) \tag{7}\]  
in which set \(U=\left(\begin{array}{cc}\cos(\theta)&-\sin(\theta)\\ \sin(\theta)&\cos(\theta)\end{array}\right)\), \(U\) is quantum rotating gate, \(\theta\) is quantum rotating angle, and \(\theta=\arctan(\alpha/\beta)\).  
A quantum evolutionary algorithm is proposed based on a probabilistic search, as the conception of qubits and quantum superposition means a quantum evolutionary algorithm has many advantages, such as better population diversity, strong global search capability, especially great robustness, and the possibility of combining with other algorithms.