esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated Photonics for IoT, RoF, and Distributed Fog-Cloud Computing: A Comprehensive Review
## 6 Key Enablers of High-Speed Networks Technology
### Integrating CMOS Photonics with Electronic Control Circuits  
Integrating CMOS photonics with electronic control circuits is pivotal for achieving compact, high-speed, and efficient optical systems. Flip-chip bonding is one prominent technique that enables direct electrical and thermal interfacing between electronic integrated circuits and photonic components fabricated using CMOS-compatible processes. This method facilitates the alignment and electrical connection of photonic and electronic chips through micro-bumps, significantly reducing parasitic capacitances and inductances,  
Figure 5: Energy-per-bit performance (pJ/bit) (red bars), total power-efficiency (blue bars), and PIC bandwidth (yellow bars) of photonic interconnects over time of PIC technologies. Figure taken from [50].  
and thus enhancing bandwidth and energy-efficiency [53]. Additionally, flip-chip integration supports higher-density interconnects and improves signal integrity compared to wire-bonding methods, enabling the scalable manufacturing of photonic-electronic modules suitable for Fog computing and high-performance data center applications [54]. Alternative approaches, such as monolithic integration, 3D stacking, and through-silicon vias (TSVs), are also gaining traction due to their potential to further minimize footprint, enhance system performance, and simplify assembly processes [49].  
A significant research opportunity lies in the heterogeneous integration of CMOS-compatible chips with other semiconductor platforms, such as silicon nitride (SiN), indium phosphide (InP), lithium niobate (LiNbO\({}_{3}\)), and III-V materials. While CMOS technology offers scalability, low-cost manufacturing, and mature electronics integration, it lacks the optical performance needed for certain photonic functions, such as on-chip lasing and application. Materials like InP support active photonic devices (e.g., lasers and amplifiers), SiN provides ultra-low-loss passive waveguides ideal for dense wavelength division multiplexing (DWDM), and LiNbO\({}_{3}\) enables high-speed, low-power electro-optic modulation. Research is needed to develop scalable packaging, bonding, and co-fabrication techniques that allow for seamless optical and electrical interfacing between these heterogeneous platforms. Co-integration strategies could enable the development of a new class of hybrid photonic-electronic systems for Fog computing, data centers, and quantum communication, combining the best properties of each material to overcome the current limitations in monolithic PIC designs.
# A Short-Term Wind Speed Forecasting Model Based on EMD/CEEMD and ARIMA-SVM Algorithms
## 4 Experiments and Results Analysis
### Analysis of Comparative Results
The possible causes of the results is that the single model cannot identify and update the model parameters in time, due to the sudden changes in the wind speed. In turn, it further verifies the feasibility and efficiency of the hybrid models. It is obvious that the decomposition technology has greatly improved the prediction accuracy, as seen by comparing the prediction results of EMD/CEEMD-ARIMA-SVM model with those of the ARIMA-SVM model. For dataset 1, the three evaluation indexes of MAE, MAPE, and RMSE for the EMD-ARIMA-SVM model are 1.027, 14.075, and 1.260, respectively, which are 43.6%, 45.6%, and 44.8% lower than those of the ARIMA-SVM model. Meanwhile, the indexes of MAE, MAPE, and RMSE for the CEEMD-ARIMA-SVM model are 0.664, 8.098, and 0.839, respectively, which are 63.5%, 68.7%, and 63.2% lower than those of the ARIMA-SVM model. Moreover, since a thoroughly decomposition of the original data is carried out by CEEMD, the prediction performance of the CEEMD-ARIMA-SVM is better than that of the EMD-ARIMA-SVM model. A similar conclusion can be found for the hybrid model applied to dataset 2 and dataset 3. Comparatively speaking, due to the greatest volatility of dataset 2, the evaluation indexes of the hybrid model CEEMD-ARIMA-SVM have the greatest reduction in effectiveness, reaching an astonishing level of a greater than 70% reduction. For relatively stationary dataset 3, the evaluation indexes of the hybrid model CEEMD-ARIMA-SVM are also reduced by nearly 50%. Generally speaking, the prediction performance of the EMD/CEEMD-ARIMA-SVM model is slightly better than the EMD/CEEMD-ARIMA model. However, the evaluation indexes have not improved significantly. The reason may be that the subseries is stationary and linear after the decomposition of EMD or CEEMD, and the prediction of the subseries by ARIMA has achieved good prediction results. The error subseries is so small that the error subseries prediction results of the SVM have little effect on the overall prediction results.
# Adaptive Seasonality Anomaly Detection Algorithm under Intricate KPI Profiles
## 5 Conclusions  
In this work, we present _ASAD_, a seasonal adaptive KPI anomaly detection algorithm. To begin, _ASAD_ used a scaling Auto-Period algorithm to create a set of seasonal sub-sequences. Meanwhile, sub-sequence clustering is optimized using eBeats clustering algorithm and calendar-based correction method. Then, using the above clustering results, train several offline anomaly detection models. Finally, choose an appropriate offline model to detect KPI anomaly for online data based on the derived seasonal information. Experiments demonstrate that _ASAD_ can achieve seasonal adaptation effect and enhance overall KPI anomaly detection performance under intricate KPI profiles.  
Figure 11: Fragment alarm for anomaly detection.  
Figure 12: Experimental comparison to other algorithms.  
However, KPI time series data could contain multiple seasonal lengths and one abnormality does not necessarily indicate that the overall system is abnormal. Therefore, we are dedicated to study multiple seasonal lengths detection and correlation among many KPI anomalies. Future work would further improve KPI anomaly detection accuracy.  
Conceptualization, H.W., Y.Z. and L.C.; methodology, H.W. and F.L.; software, F.L.; validation, H.W. and Y.L.; formal analysis, H.W. and Y.Z.; investigation, H.W., Y.L. and F.L.; resources, H.W.; data curation, F.L.; writing--original draft preparation, H.W., Y.Z., Y.L. and F.L.; writing--review and editing, H.Z., B.X., M.X. and L.C.; visualization, F.L.; supervision, H.W., Q.W. and L.C.; project administration, L.C.; funding acquisition, L.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the Natural Science Foundation of China (No. 62072319), in part by the Science and Technology Department of Sichuan Province (No. 2022 YFG0041), in part by the Luzhou Science and Technology Innovation R&D Program (No. 2021 CDLZ-11) and in part by the Foundation of Science and Technology on Communication Security Laboratory (No. 6142103190415).  
Restrictions apply to the availability of these data. Data was obtained from [Sichuan University] and are available [[[http://dxpt.scu.edu.cn/](http://dxpt.scu.edu.cn/)]([http://dxpt.scu.edu.cn/](http://dxpt.scu.edu.cn/))] with the permission of [Sichuan University].  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Supporting the Management of Rolling Stock Maintenance with an Ontology-Based Virtual Depot
## 7 Short-Term Scheduling  
Step 4 concludes the steps of the method--the implementation and 'plugging in' of the computational processes proposed in Sections 6 within the SADT framework description, demonstrated here for the short-term scheduling process. Scheduling is the process of placing activities in a time horizon (with time units such as days), which includes the starting and ending times of the activities while honouring a set of temporal and resource constraints. Temporal constraints might be precedence (e.g., two activities can be executed simultaneously or the one before another) and deadline (which includes penalties if not satisfied). Resources constraints are the constraints related to human resources, equipment, and material resources. Resource constraints refer mainly to the quantity and available capacity of the resources required to perform a specific activity. Invariably, one may want to derive schedules which optimise one or more criteria such as to minimise the non-respected deadlines or to reduce the gap between a scheduled date and a due date for a specific activity [34]. This goal is usually motivated by the resulting costs when deadlines are not respected. This gap is known as the earliness/tardiness criteria with penalties [33].  
In some areas, such as preventive maintenance of rolling stock, earliness/tardiness criteria of the preventive maintenance interventions are critical. The PM maintenance intervention is preferably scheduled Just-In-Time (JIT), i.e., just in time for the due date, to avoid the earliness/tardiness costs. Rolling Stock preventive maintenance tasks are typically grouped into 'Exams', which comprise a range of inspections and light maintenance activities; these exams and heavy maintenance activities must be carried out on a predefined frequency based on either time or distance [33].  
Depending on a specific depot practice and regulations, maintenance intervals can include a tolerance, as shown in Figure 8, where a maintenance intervention might be performed before the due date or after. A cost is usually associated with early/late maintenance. Maximum maintenance intervals are effectively defined in law as a condition of operating the vehicle. Performing maintenance activities too soon is a waste of resources while performing them too late is penalised, as it means that the unit would not be available for passenger service whilst it is waiting for maintenance.
## 5 Structural Parameters Identification
The object value of the parameters identification procedure (Figure 13a) is convergent to the steady point with the optimization procedure proceeding to 200 generations;
2. The structural parameters, including: bending stiffness \(EI\),\(E_{s}I_{s}\), and sidewall stiffness \(k_{sr1}\), \(k_{sr2}\), \(k_{s\theta 1}\) and \(k_{s\theta 2}\), converge to the optimum solution as the optimization procedure proceeding 400 generations;
3. The steady object value of identification procedure utilizing the flexible ring on an elastic continuous sidewall ring tire model is compared with that utilizing the flexible beam on an elastic continuous sidewall beam model in Figure 13a. The compared identification procedure implies that the model prediction error using flexible ring on an elastic continuous sidewall ring tire model is less than that utilizing the flexible beam on an elastic continuous sidewall beam model resulting from taking the coupling feature between the radial and tangential deformation of flexible belt ring and continuous sidewall ring into consideration;
4. Table 2 illustrates the identified structural parameters respectively using flexible ring on an elastic continuous sidewall ring tire model and flexible beam on an elastic continuous sidewall beam tire model. The structural parameter bending stiffness of the continuous sidewall ring \(E_{s}I_{s}\), identified using the flexible ring on an elastic continuous sidewall ring tire model, is less and the structural parameters bending stiffness of flexible belt ring \(EI\), and sidewall spring stiffness \(k_{sr1}\), \(k_{sr2}\) are larger than that using the flexible beam on an elastic continuous sidewall beam tire model;
5. Figure 14 shows the compared modal resonance frequency of experimental modal resonance frequency (Figure 14b) and the analytical modal resonance frequency, respectively by the flexible ring on an elastic continuous sidewall ring tire model and flexible beam on an elastic continuous sidewall beam tire model; the compared result shows that the error between the analytical modal frequency with normal inflation pressure of the flexible ring on an elastic continuous sidewall ring tire model, and the experimental modal frequency is limited within 3% and less than that of the analytical modal frequency of the flexible beam on an elastic continuous sidewall beam tire model. Figure 14: Modal prediction with the identified structural parameters; (**a**) the compared modal resonance frequency between the experimental modal resonance frequency, predicted modal resonance frequency utilizing the flexible ring on an elastic continuous sidewall ring tire model and predicted modal resonance frequency utilizing flexible beam on an elastic continuous sidewall beam tire model; and (**b**) the compared modal prediction error between predicted modal resonance frequency utilizing the flexible ring on an elastic continuous sidewall ring tire model and predicted modal resonance frequency utilizing flexible beam on an elastic continuous sidewall beam tire model.
# A Vulnerability Scanning Method for Web Services in Embedded Firmware
###### Abstract  
As the Internet of Things (IoT) era arrives, the proliferation of IoT devices exposed to the Internet presents a significant challenge to device security. Firmware is software that operates within Internet of Things (IoT) devices, directly governing their behaviors and functionalities. Consequently, the security of firmware is critical to shielding IoT devices from potential threats. In order to enable users to operate a device intuitively, firmware commonly provides a web interface. Consequently, this interface frequently serves as the primary attack goal in Internet of Things (IoT) devices, rendering them susceptible to numerous cyber-attacks. Unfortunately, web services have complex data interactions and implicit dependencies, and it is not easy to balance efficiency and accuracy during the analysis process, leading to heavy overhead. This paper proposes a lightweight vulnerability scanning approach, WFinder, designed explicitly for embedded firmware web services to perform vulnerability checks on backend binary files in firmware. WFinder uses static analysis to focus on identifying vulnerabilities in boundary binary files related to web services in firmware. Initially, the approach identifies boundary binary files and external data entry points based on front-end and back-end associativity features. Subsequently, rules are formulated to filter hazardous functions to narrow the analysis targets. Finally, the method generates sensitive call paths from the external data input points to the hazardous functions and conducts a lightweight taint analysis along these paths to uncover potential vulnerabilities. We implemented a prototype of WFinder and evaluated it on the firmware of ten devices from five well-known manufacturers. We discovered thirteen potential vulnerabilities, eight of which were confirmed by the CNVD, and assigned them CNVD identification numbers. Compared with the most advanced tool, SATC, WFinder was more efficient at discovering more bugs on the test set. These results indicate that WFinder is effective at detecting bugs in embedded web services.  
firmware security; vulnerability discovery; web services; static analysis Article A Vulnerability Scanning Method for Web Services in Embedded Firmware [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]  
School of Cyberspace Security, Information Engineering University, Zhengzhou 450007, China; [EMAIL_ADDRESS] (X.M.); [EMAIL_ADDRESS] (C.Y.); [EMAIL_ADDRESS] (Q.W.); [EMAIL_ADDRESS] (Y.W.)  
* Correspondence: [EMAIL_ADDRESS]
## 3 Model Inversion and Application Case
### Model Inversion
However, the depth of the anomalous body in the inversion results appears to be shifted upward compared to the actual depth (see Figure 7b), which may be due to two factors: (1) the inversion model uses a block-continuous medium model, with a linear transition of physical properties from 2 m above the top of the target to 1 m above it or (2) the electrodes are deployed around the target body, resulting in a lack of geoelectric information at its top. Additionally, as seen in Figure 5a, the two anomalous bodies are closer to the electrodes in the X-direction and farther from the electrodes in the Y-direction, resulting in higher resolution in the X-direction for the \"\(\square\)\"-shaped electrode array compared to the Y-direction (the gradient change in the X-direction is greater than in the Y-direction). This indicates that the resolution is significantly related to the distance between the target body and the electrode array: the closer the distance, the better the detection results. By comparing and analyzing the inversion results of the \"U\"-shaped and \"L\"-shaped electrode arrays (see Figure 7c,d), it can be seen that both systems perform well in locating the low-resistivity body overall. However, there is a significant deviation in the position and shape of the high-resistivity body, which is only represented by a more diffuse high-resistivity feature. This is primarily due to the resistivity exploration method's sensitivity to low-resistivity bodies. Additionally, the low-resistivity anomalous body is closer to the electrode array, while the high-resistivity body is located in the observation blind zone of the electrode array.
## 4 Experimental Validation and Results Analysis
### Stray Light Simulation and Path Analysis
#### 4.3.2 Impact of Surface Roughness on Background Energy Levels  
To investigate the influence of optical surface fabrication errors on the system's SNR, a sensitivity analysis was conducted by scanning the mirror surface roughness in the range of 1-100 nm. As shown in Figure 10, the intensity of specular-scattered stray light increases significantly with surface roughness, exhibiting an exponential growth trend. When the surface roughness reaches 5 nm, the stray light power along the main path rises to 5.7 \(\times\)\(10^{-11}\) W (see Figure 11a), which is comparable to the communication signal power, resulting in the SNR dropping below 1:1 and making signal detection unfeasible. In contrast, when the roughness is controlled below 2 nm, the main-path stray light decreases to 7.96 \(\times\)\(10^{-12}\) W (see Figure 11b), achieving an SNR greater than 10:1 and meeting the system's interference suppression requirements. These results clearly demonstrate that the background light level is highly sensitive to the surface quality of critical mirrors. Therefore, it is recommended that the surface roughness of high-reflectivity components along the primary optical path be maintained below 2 nm to ensure reliable signal recognition.  
Figure 10: Stray radiation variation with roughness.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Stray Light Type** & **Energy Contribution (\%)** \\ \hline \hline
\begin{tabular}{c} Specular Scattering Stray Light \\ Transmissive Stray Light \\ \end{tabular} & 99.95 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main technical specifications of solar simulation module.
## 4 Results and Discussion  
### Network Topology Analysis  
The constructed Space L model of the Chongqing metro network contains 256 nodes and 288 edges. Table 4 presents its main topological features. The average node degree is 2.25. The average shortest path length is 15.4, indicating that the commuting distance for passengers within the Chongqing rail transit system is approximately 15.4 stations.
## 2 Materials and Methods
### Study Data
#### 2.2.2 Meteorological Data  
The meteorological data used in this study were derived from ERA5 meteorological reanalysis data released by the European Center for Medium\(-\)Range Weather Forecasts (ECWMF), including monthly products of precipitation (P, mm), air temperature (T, \({}^{\circ}\)C), solar radiation (SR, MJ\(\cdot\)m\({}^{-2}\)), and wind speed at 10 m height (U, m\(\cdot\)s\({}^{-1}\)). The spatial resolution of the data is 0.1\({}^{\circ}\). In addition, the specific humidity (SH, Kg\(\cdot\)kg\({}^{-1}\)) data used in this study were obtained from the Global Land Data Assimilation System (GLDAS\(-\)2.1) with a spatial resolution of 0.25\({}^{\circ}\), all meteorological data above ranged from 2001 to 2020, and their spatial resolution was resampled to 500 m by using the nearest-neighbor interpolation method. The spatialized meteorological data were employed to perform correlation analysis and multiple linear regression analysis with ET data to investigate climate driving types of ET in the YRB.
## 5 Model Verification and Comparative Analysis
### Limitations  
The case collection and the prototype platform implementation have a certain degree of limitation. In order to trace the causes of medical disputes and better demonstrate the problems arising from doctor-patient communication, we processed the audio of doctor-patient dialogues in local hospitals by collecting them and converting them into text utilizing manual translation. Although this can more intuitively show the problems in doctor-patient communication, the need to track the patient's entire consultation process makes the collection difficult and time-consuming, and ultimately, the number of collected cases is small. Secondly, we designed the DIKWP artificial awareness system based on the scheme of this paper. However, due to the insufficient number of collected samples, the system needs further validation.
## 3 Results and Discussions
### Proximate Composition for Biscuits with RFSF  
The proximate composition of the biscuits with RFSF (B0, B10, B25, B40) is summarized in Table 4 Incorporation of the RFSF in biscuits has a significant influence on the major components (\(p<0.05\)) and a decrease of the moisture content was observed while fat, ash, protein and fiber content of composite flour mixes increased. RFSF had a significant (\(p<0.05\)) effect on the moisture content of the biscuits compared to the control with a decrease in moisture content from 7.34% to 4.92%. Similar patterns were previously noticed by [PERSON] and [PERSON] (2018) and [PERSON] et al. (2019) who found that as the flaxseed flour concentration increased in the blend, the moisture content of cookies decreased. These results may be due to low moisture content (1.64%) of RFSF. Moreover, a high amount of fat (42.50%) was recorded in RFSF, which also contributed to the hydration of the dough, thus reducing the moisture of the biscuits. Furthermore, the low moisture content of the biscuits allows a longer preservation period, due to minimal microbial or chemical activity.  
Ash content increased from 1.23% (control biscuits) to 2.60% (sample B40). This might be due to the higher mineral content of flaxseed flour (S  
lignans, flaxseed contains high amounts of phenolic compounds, such as ferulic acid, syrginic acid, cinnamic acid, vanillic acid, \(p\)-coumaric acid and gallic acid [64]. However, although flaxseed enriches the material basis for exerting its antioxidant activity, previous studies have observed differences in the quality characteristics of flaxseed varieties from different regions of the world, indicating some geographical and varietal specificity [64]. [PERSON] et al. [64] reported that the total phenolic contents in studied flaxseed varieties ranged from 109.93 mg GAE/100 g to 246.88 mg GAE/100 g, and the DPPH values ranged from 32.56 mg TE/100 g to 46.22 mg TE/100 g. Even if the roasting process of flaxseeds could produce a slight decrease in the antioxidant activity, the baking process resulted in a significant increase in antioxidative activity due to the Maillard pigments recognized as having a high antioxidative capacity [59].