esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Selection of Participants  
This preliminary retrospective study included 2158 patients who attended the Mo-Max (Oral Medicine and Maxillofacial Surgery) Department of Oral Science and Maxillofacial Surgery, Sapienza University of Rome, from 2019 to 2022. The Mo-Max Department is a task force comprising specialists in different medical disciplines such as oral medicine, prosthetics, maxillofacial surgery, oncology, radiotherapy, and histopathology, who collaborate to provide multidisciplinary group care to patients. The inclusion criteria encompassed adult patients (\(\geq\)18 years old) with complete medical records documenting their diagnosis, pharmacological treatments, and relevant clinical history. Patients were eligible for this study if they had a confirmed diagnosis of oral lichenoid lesions (OLLs) or were undergoing treatment with statins or satrans, regardless of the presence of OLLs. Only individuals with documented follow-up data over a minimum period of 6 months were considered. Exclusion criteria included patients with incomplete medical records, such as missing information on medication use, diagnosis, or clinical history.  
A total of 2158 patient records were analyzed. Of these, 1966 patients (91.1%) were excluded, as they did not meet the inclusion criteria, because they were not undergoing any drug therapy relevant to this study (i.e., neither statins nor satrans) (Tables 1 and 2). The remaining 192 patients (8.9%) were receiving pharmacological treatment, with 123 (5.7%) on statins and 69 (3.2%) on satrans (Table 2). Among the 2158 patients, 118 (5.5%) were diagnosed with oral lichenoid lesions (OLLs) (Table 3). Of these 118 OLL cases, 20 patients (16.9%) were on statin therapy, 11 (9.3%) were on satran therapy, and the remaining 87 patients (73.7%) were not undergoing any relevant pharmacological treatment (Table 4). Among these 118 OLL cases, subjects were predominantly female, with 75 women (63.4%) and 43 men, in line with literature reports.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Medicines**} \\ \hline  & & **Frequency** & **Percentage** & **Percentage** & **Cumulative** \\ \hline \multirow{3}{*}{Valid} & No drugs & 1966 & 91.1 & 91.1 & 91.1 \\ \cline{2-6}  & Drugs yes & 192 & 8.9 & 8.9 & 100.0 \\ \cline{1-1} \cline{2-6}  & Total & 2158 & 100.0 & 100.0 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: An explanatory table of the cases comparing whether patients were taking drugs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Drugs, Statins or Satrans** & & \\ \hline  & & **Frequency** & **Percentage** & **Valid** & **Permcentage** \\ \hline \multirow{3}{*}{Valid} & Other & 1966 & 91.1 & 91.1 & 91.1 \\ \cline{2-6}  & Statins & 123 & 5.7 & 5.7 & 96.8 \\ \cline{1-1} \cline{2-6}  & Sartans & 69 & 3.2 & 3.2 & 100.0 \\ \cline{1-1} \cline{2-6}  & Total & 2158 & 100.0 & 100.0 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: An explanatory table of cases comparing the total treatment with satrans, statins, or other drugs.
## 4 Application in the City of Dortmund
### System Dynamics Simulation Model
#### 4.1.1 Problem Identification  
E-commerce does not necessarily mean the absence of physical shops, but rather an evolution in the way retailers carry out orders. For this reason, e-commerce has led to an increase in innovative combinations of physical and digital solutions, resulting in different ways of preparing, distributing, and collecting orders from customers [48].  
Figure 4: The map of city of Dortmund with its codified districts.  
Figure 3: Schema of the integrated simulation-optimization approach.  
Examples include home delivery, collection points, and APLs. We focus on understanding APL systems as a UL delivery scheme in the context of e-commerce and evaluating its components over time.
# Weather-Aware Long-Range Traffic Forecast Using Multi-Module Deep Neural Network
## 4 Experimental Results and Discussion
### Experimental Results  
#### 4.2.1 Case I (Regular Day)  
Figure 8 plots the MAPE of each prediction model. The errors of CNN-only, \(k\)-nn, and ARIMA are unusually large during dawn and rush hour, respectively. In most cases, TW-DNN produces the best results, with only a smaller error (by 4% at most), compared to the others.  
Figure 7: Highway network with city names used in the experiments (the labels represent the name of the location).  
Figure 9 shows the predicted speeds of each model in colors; this is called a _heatmap_. Blue and yellow represent low and high speeds, respectively, while white only in the real data represents missing data. Both the link (location) and time are displayed in the vertical and horizontal axes, respectively. Careful observation of the real data in Figure 9a indicates that speeds are populated between 80 and 120 km/h in general. The real traffic data show two interesting phenomena:  
* The speeds at dawn (2:00-5:00) are recorded to be around 80 km/h, while those at daytime (6:00-18:00) are recorded to be around 100 km/h.
* There is severe congestion (i.e., speed lower than 20 km/h) in some links at around 8:00 and 18:00.  
TW-DNN data shows a close match to the real data, while CNN-only, \(k\)-nn, and ARIMA data do not.  
Figure 8: MAPE error of prediction models for Case I.  
Figure 9: Speed figure of (**a**) real data, (**b**) TW-DNN, (**c**) CNN-only, (**d**) \(k\)-nn, and (**e**) ARIMA for Case I.
# A Comprehensive Survey on Knowledge-Defined Networking
## 8 Discussion
### Benefits
#### 8.1.2 Higher Adaptability  
KDN has a more flexible network architecture that can easily adapt to changing network environments, which includes the ability to quickly add or remove network devices and services and the ability to scale network resources up or down as required. KDN can analyze vast amounts of data, identify patterns, and adapt network operations accordingly. One way of achieving adaptability is by automatically adjusting network resources based on knowledge generated by analyzing traffic patterns using machine learning. In such a case, for instance, if the knowledge plane detects very high traffic, the management plane can allocate more resources such as energy, bandwidth, etc. to make sure that the network operates swiftly without performance issues. After some time, when it detects low traffic, resource allocation can be dynamically reduced. Another example that can be obtained from fault management is the ability of KDN to adapt to link failures, where in such cases, the traffic can be rerouted in alternative paths, ensuring that the network operates with minimal interruptions. On the other hand, when new devices are introduced into the network, these devices can be automatically configured using the management plane, and network policies in the application plane can be adjusted to accommodate these devices. Furthermore, KDN can learn from data to generate knowledge or rules to adjust network policies, optimizing network performance over time. In this manner, it can dynamically adapt to network changes in real time.
## 2 OBS Networks
### IP over OBS Model  
Beyond reliable physical implementation of OBS networks, a number of very specific procedures are involved in the communication process. In computer-communication networks these procedures (protocols) are normally organized into hierarchical layers of general functionality that can be easily associated to well known reference models. An OBS network architecture can also be represented in layers as a set of protocols that provide services and exchange data. In fact, a layered architecture with well-defined interfaces between layers is crucial for the deployment of OBS networks, both in terms of their internal functioning and their interoperability with the other networks. As referred in a recent multilayered approach for OBS [19], in an IP over OBS hierarchy, the IP layer treats the OBS as its link layer, while the OBS operates on top of the optical layer. Therefore, as a data transport system, the  
Figure 1: OBS network (adapted from [18]).  
OBS network architecture implements the lower three layers of the reference model, namely physical, data link, and network. In order to understand the way OBS functions, it is important to have some understanding of what happens in this lower network layers.  
Figure 2 depicts a functional block diagram of the main functions that need to be executed by edge nodes and core nodes at the medium access control (MAC) layer and optical layer (physical). At the MAC layer, source and destination edge nodes perform the functions of burst assembly and disassembly, offset computation, CP generation, routing and wavelength assignment (RWA) and signaling. At the optical layer, intermediate core nodes are mainly responsible for scheduling and contention resolution of bursts in-transit. These MAC and optical layers offer a set of services that guarantee, for example, certain burst blocking probabilities to the client IP routers located in the layer above [20]. The \"quality\" of those services is, therefore, strongly dependent on the mechanisms implemented in these lower layers. Those mechanisms are represented with functional blocks in Figure 2 and will be described next.
## 6 AI-Assisted Wireless Localization Technology
### Resolving Spatiotemporal Asynchronization  
The problem of spatiotemporal asynchronization can occur in various situations. When the internal clocks of the anchor and device are different, the device that receives the signal cannot know the transmission start time of the signal. Hence, it causes an unknown time difference between the ToA measurement and leads to localization errors. To solve this problem, there is a method that uses a Lagrange programming neural network (LPNN) [35], which is a type of DNN. In this method, the LPNN uses a set of ToA measurements as input training data. At this time, the input includes the uncertainty of the transmission start time due to clock asynchronization. The LPNN can estimate the position of the terminal, the starting transmission time, and clock asynchronization simultaneously from the input, and based on this, the refined position of the device can be extracted.  
Meanwhile, when there is a spatiotemporal asynchronization caused by the relative position changes of the devices, appropriate features are extracted and probabilistic data are obtained based on them to correct the location. One approach is to use a CNN [36] to learn the spatial-temporal-frequency characteristics of the multiple-input and multiple-output (MIMO) channel from CSI data. This method collects CSI data in MIMO-orthogonal frequency division multiplexing (OFDM) Wi-Fi systems and constructs a 3D amplitude and phase matrix. It then uses this as input for the CNN and learns amplitude and phase features as the data pass through 3D convolutional and pooling layers. Based on newly collected MIMO data, the output layer generates probabilistic classification results for location estimation.
# FSM-BC-BSP: Frequent Subgraph Mining Algorithm Based on BC-BSP
###### Abstract  
As graph models become increasingly prevalent in the processing of scientific data, the exploration of effective methods for the mining of meaningful patterns from large-scale graphs has garnered significant research attention. This paper delves into the complexity of frequent subgraph mining and proposes a frequent subgraph mining (FSM) algorithm. This FSM algorithm is developed within a distributed graph iterative system, designed for the Big Cloud (BC) environment of the China Mobile Corp., and is based on the bulk synchronous parallel (BSP) model, named FSM-BC-BSP. Its aim is to address the challenge of mining frequent subgraphs within a single, large graph. This study advocates for the incorporation of a message sending and receiving mechanism to facilitate data sharing across various stages of the frequent subgraph mining algorithm. Additionally, it suggests employing a standard coded subgraph and sending it to the same node for global support calculation on the large graph. The adoption of the rightmost path expansion strategy in generating candidate subgraphs helps to mitigate the occurrence of redundant subgraphs. The use of standard coding ensures the unique identification of subgraphs, thus eliminating the need for isomorphism calculations. Support calculation is executed using the Minimum Image (MNI) measurement method, aligning with the downward closure attribute. The experimental results demonstrate the robust performance of the FSM-BC-BSP algorithm across diverse input datasets and parameter configurations. Notably, the algorithm exhibits exceptional efficacy, particularly in scenarios with low support requirements, showcasing its superior performance under such conditions.  
BC-BSP; frequent subgraph; graph division; parallel algorithm +
Footnote †: journal: Applied sciences
## 4 Conclusions and Prospects  
This paper has proposed the MulSPIDeepONet architecture. This structure changes the direct mapping method between the variable function and the solution function in classical network models. Instead, our method uses an indirect mapping to find the solution function. This simplifies the solution process when faced with complex, highly nonlinear relationships, and accelerates the network learning process. By enhancing the close relationship between the independent variables and the solution function, the feature recognition ability of the model is dramatically improved at the physical level, which enhances the ability to solve PDE systems.  
The results of three example PDEs show that MulSPIDeepONet has stronger generalization ability than the classical PIDeepONet model. In the solution of PDEs, the prediction accuracy of MulSPIDeepONet is nearly three times greater than that of PIDeepONet, especially in the presence of large gradients and strong discontinuities. Therefore, the network structure proposed in this paper offers unique advantages, not only capturing complex physical phenomena but also handling regions with dramatic gradient changes. Moreover, it can accurately reflect the discontinuities within a system.  
Future work will focus on the following aspects. First, we will introduce additional indirect explicit constraint information into the transition subnetwork architecture, such as the explicit constraint information in the Green's function. Second, we will apply this  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Model** & **L2 Norm Error** & **MAE** \\ \hline \multirow{2}{*}{Radiu 1} & PIDeepONet & 0.00313 & 0.00125 \\  & MulSPIDeepONet & **0.00153** & **0.00064** \\ \hline \multirow{2}{*}{Radiu 2} & PIDeepONet & 0.00523 & 0.00122 \\  & MulSPIDeepONet & **0.00378** & **0.00083** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Test results for solving Eikonal equations. MulSPIDeepONet is superior to the baseline model in all indicators.  
model to more complex PDE scenarios, such as turbulent flows and ultra-high-speed flow field predictions, to verify the advantages of the proposed approach.  
Conceptualization, J.W., Investigation, J.W., Methodology, J.W., J.H., Q.W. and F.L., formal analysis, Z.C. and J.H., Supervision, Y.L. and F.L., Visualization, A.W., Writing--original draft, J.W., Writing--review and editing, J.W., Y.L., J.H. and F.L. All authors have read and agreed to the published version of the manuscript.  
This study did not receive any external funding.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Flexible Simulation Platform for Generating Realistic Waveforms with Voltage Notches
## 4 Analysis of Key Parameter Dependencies, Limitations, and Impacts
### Impact of Key Parameters on Voltage Notch Waveforms
#### 4.2.5 Scenario 4: All Components  
In this scenario, all components are connected: the six-pulse rectifier, aggregated linear loads, and capacitor bank. The simulations examine the combined effects on voltage notch waveforms, considering both non-detuned and detuned capacitor banks.  
* \(\alpha\) varies from 30\({}^{\circ}\) to 85\({}^{\circ}\). Two configurations are considered: non-detuned and detuned capacitor banks.
- Fixed values from previous simulations are used. The corresponding \(R_{D}\) is calculated to maintain the full load operation of the rectifier. The target \(PF\) = 0.95 at the PCC is maintained by adjusting \(Q_{cb}\).
* Voltage and total current waveforms are heavily distorted due to the excitation of natural frequencies.
- The presence of linear loads slightly smooths the distortion compared to previous results without linear loads.
* Voltage and current waveforms, especially voltage notch waveforms, are smoother compared to non-detuned cases.
- The required \(Q_{cb}\) is higher than in previous scenarios to maintain the target \(PF\).
- The rectifier operates over a wider range of \(\alpha\) while maintaining the target \(PF\).  
The results indicate that integrating all components, including a detuned capacitor bank, results in smoother voltage and current waveforms and allows the rectifier to operate efficiently over a broader range of firing angles.  
Figure 12: Impact of \(\alpha\), with linear loads enabled, while maintaining a target \(PF\) at the PCC by adjusting \(Q_{cb}\) on voltage notch waveforms (**top**), input line currents of the rectifier (**middle**), and total line currents (**bottom**). (**a**) Non-detuned capacitor bank and (**b**) detuned capacitor bank.
# A Review of Recent Advances in Roll Stability Control in On-Road and Off-Road Vehicles
## 6 Comparative Analysis and Future Directions
### Future Directions  
In the future, technologies for roll monitoring, rollover warning, and stability control in both on-road and off-road vehicles should progress towards intelligent, data-driven, multi-sensor fusion, and collaborative control systems to enhance real-time performance, accuracy, and robustness. Despite significant progress, there remain numerous challenges in adapting to complex operating conditions, optimizing computational complexity, and coordinating multi-actuator control. Future research can be focused on the following areas:  
1. Future roll state monitoring systems will prioritize real-time, robust roll state sensing through advanced multi-sensor fusion (e.g., IMU, LiDAR, GNSS, vision) and adaptive filtering or nonlinear observers to improve accuracy under dynamic and noisy conditions.
2. Future systems will apply deep learning and reinforcement learning to build data-driven rollover prediction models, reducing reliance on complex dynamics while improving adaptability to extreme conditions. V2X integration will enable real-time cooperative risk assessment using shared vehicle and road data.
3. Control algorithms will increasingly utilize adaptive learning methods, such as deep reinforcement learning and neural networks, to dynamically adjust to diverse driving conditions. Hybrid modeling (physical + data-driven) will enhance robustness and applicability.
4. Future systems will evolve toward coordinated control of suspension, braking, and steering, improving stability in high-risk scenarios. Technologies like steer-by-wire and active chassis systems will support real-time structural adjustments.
5. To address computational challenges of high-precision controllers (e.g., MPC), research will focus on reduced-order modeling, control parameter tuning via reinforcement learning, and deployment via edge computing to ensure fast and efficient real-time performance.
6. For off-road environments, future work will enhance terrain perception through LiDAR, IMU, and vision fusion. Control strategies will include adaptive suspension, variable track width, and real-time center-of-gravity adjustments to maintain stability on uneven terrain.
## 2 Unipolar Optical Digital Modulation Schemes and Spreading Codes
### Unipolar Optical Digital Modulation Schemes
The packet is a sequence of the pulses and the empty slots that is dedicated to a measurement point and generated by the modulation scheme. The SER \(P_{se}\) is optimum when the threshold factor \(k\) is 0.5. \[E_{RX}=E_{TX}\frac{\pi\tau_{o}\tau_{a}^{2}D_{R}^{2}\rho_{T}}{4R^{2}\theta_{R}} \tag{3}\]  
\[P_{se}=P_{0}P_{e0}+P_{1}P_{e1}=P_{0}Q\left(k\sqrt{\frac{E_{s}}{2N_{0}}}\right) +\left(1-P_{0}\right)Q\left(\left(1-k\right)\sqrt{\frac{E_{s}}{2N_{0}}}\right) \tag{4}\]  
\[P_{pe}=1-\left(1-P_{se}\right)^{\frac{N_{pkt}\bar{L}}{M}}\approx\frac{N_{pkt} \bar{L}}{M}P_{se} \tag{5}\] \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Modulation**} & \multirow{2}{*}{\begin{tabular}{c} **Number of Bits** \\ **per Block(\(M\)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Maximum** \\ **Number of** \\ **Number of** \\ **Pulses (\(N_{p}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Number of** \\ **Unique** \\ **Symbols (\(L\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Maximum** \\ **Number of** \\ **Time Slots** \\ **(\(L_{max}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Average** \\ **Symbol** \\ **Length (\(L\))** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Slot** \\ **Duration** \\ **(\(T_{s}\))** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Bandwidth** \\ **Requirements** \\ **(\(B_{req}\))** \\ \end{tabular} } \\ \hline NRZ-OOK & \(M\) & \(M\) & \(2^{M}\) & \(M\) & \(M\) & \(\frac{1}{K}\) & \(R_{s}\) \\ PPM & \(M\) & \(1\) & \(2^{M}\) & \(L_{PPM}\) & \(L_{PPM}\) & \(\frac{1}{K}\) & \(\frac{MR_{s}}{L_{PMT}}\) \\ DPPM & \(M\) & \(1\) & \(2^{M}\) & \(L_{DPPM}\) & \(\frac{L_{DPPM}+1}{2}\) & \(\frac{1}{K}\) & \(\frac{MR_{s}}{L_{DPPM}+1}\) \\ MPPM & \(\lfloor\log_{2}L\rfloor\) & \(w\) & \((\frac{n}{w})\) & \(n\) & \(\frac{1}{K}\) & \(\frac{MR_{s}}{n}\) \\ DPIM & \(M\) & \(1\) & \(2^{M}\) & \(L_{DPIM}\) & \(\frac{L_{DPPM}+1}{2}\) & \(\frac{1}{K}\) & \(\frac{MR_{s}}{L_{DPPM}+1}\) \\ DH-PIM & \(M\) & \(2\) & \(2^{M}\) & \(2^{M-1}+\alpha\) & \(\frac{2^{M-1}+2\alpha+1}{2}\) & \(\frac{1}{K}\) & \(\frac{MR_{s}}{2^{M}+1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of basic characteristics of digital pulse modulation techniques.
# Modeling DGNSS Pseudo-Range Correction Messages by Utilizing Satellite Repeat Time
## 4 PRC Modeling
### Limitation of PRC Modeling  
The PRC modeling introduced in this study is an estimation method using satellite repeat time and past data. This method has an advantage that the user can generate PRC corrections using the modeling parameters even in the case where the user does not receive the correction for a long time. However, there is a limitation in that modeling does not reflect the characteristics of an ever-changing atmosphere very well.  
Generally, the amount of signal delay due to the troposphere is about 2.3 m in the standard atmospheric condition [28]. Approximately 90% of the tropospheric delay is the dry or hydrostatic delay calculated as a function of surface pressure while the remaining 10% is the wet delay due to water vapor. Since the air pressure does not change very much day to day the dominant part of the tropospheric delay should have a very nice repeating pattern during several days.  
Figure 6: Differences between actual and predicted PRC values according to the elevation angle of (**a**) GPS satellites and (**b**) BeiDou satellites; (**c**,**d**) are the mean (bar) and standard deviation (error bar) of residuals for each elevation angle range; (**e**,**f**) show the relative error ratios.  
The ionospheric delay can change significantly for a short period of time due to the solar explosion, geomagnetic storms, and so on. In those cases, a modeling scheme based on the past data may not work as nicely as expected. In an environment where no PRC information can be received at all, however, it should be possible to obtain better positioning results by using estimated PRC because at least the tropospheric delay can be compensated. Also, a more reliable DGNSS service scenario can be devised by monitoring the ionospheric disturbance and issue a warning message.