esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
While Open Platform Communication Unified Architecture (OPC UA) has become the de facto standard for industrial communication in the context of Industry 4.0, an abundance of scenarios still employ legacy communication protocols. In the course of research project InnoTop, a Software-Bus-Toolchain (SBT) was developed to meet this demand. InnoTop is a public funding programme provided by the Federal State Rhineland Palatinate and managed by the Investitions- und Strukturbank Rheinland-Pfalz (ISB). Its aim is to foster research and development in local SMEs. The contribution of this work consists of the development of an IoT builder framework that:  
* Allows for a quick implementation of business logic. That means the actual functionality in should be easy to set up without implementation of low-level functions. * Functionally separates data elements the IoT device interacts with, meaning the data can be defined and used independent from the low-level implementation. * Functionally separates the communication protocol from the business logic implementation. Figure 1: Development of IoT platforms according to IoT Analytics [19]. This framework allows for the quick creation of novel IoT devices and networks as well as integration of new IoT devices into existing environments, resulting in less time to release, less low-level implementation effort, higher flexibility in terms of decoupling hardware from function and unified date elements. In contrast to existing tools, the SBT considers programming environment for embedded devices and IoT platform alike, while reducing implementation effort for both. The implementation of abstraction layers allows for seamless transfer of application code between devices. Furthermore, these abstraction layers enable the integration into an IoT platform. Data is defined and standardised into data points that are described as Google Protocol Buffer elements. This standardisation provides fast creation and re-use of elements as well as a standardised interface to the communication layer. Additionally, any computation on the IoT platform, for which Node-RED is used, is performed based on these data points by functions specifically implemented in the course of the project. In the course of project SBT wrapper functions are implemented that provide standardised interfaces and enable exchange of data from physical sources, as well as hardware platform. The remainder of this work is structured as follows. A background is provided in Section 2. Requirements for an IoT builder set up are discussed in Section 3. The components developed in the course of this work are presented in Section 4. Implementation details are introduced in Section 5. The IoT builder environment is evaluated in Section 6. A conclusion is drawn in Section 7.
## 5 Results
### Exploratory Analysis
#### 5.1.1 Stationarity Test  
Similarly, the Augmented Dickey-Fuller test (ADF) [59] allows us to determine whether a time series is stationary or not. A stationary time series has the characteristic that its statistical properties remain constant over time. In the stationarity analysis using the ADF test, with an _alpha_ value of 95% as the confidence interval, the results are shown in Table 9.  
With the results obtained, it can be observed that the variable of interest, _kWh_, is non-stationary, indicating that the statistical properties of the data, such as the variance and mean, change over time. This result is expected due to the nature of electricity consumption data, where consumption trends or patterns are present given the same or similar consumers. It is also important to note that the observed increase or decrease can occur during certain seasons of the year. While there are techniques to transform data to achieve stationarity, they will not be applied, considering this characteristic to be important within the data. The results from the stationarity test guided us to delineate the methodologies and algorithms to be used in the development of the regressor models and outlier techniques.
# Large Eddy Simulation and Dynamic Mode Decomposition of Turbulent Mixing Layers
## 3 Results and Discussions
### Characteristics of the Turbulent Mixing Layer
#### 3.1.2 Influence of Inlet Disturbance on Turbulent Mixing Layer  
As mentioned above, in the study of disturbance influence, \(\alpha\) in the inlet disturbance is taken as 0, 0.0045, 0.045 and 0.45 respectively. If \(\alpha=0.0045\) corresponds to the reference disturbance, then \(\alpha=0\) corresponds to no disturbance, \(\alpha=0.045\) corresponds to 10 times disturbance, and \(\alpha=0.45\) to 100 times disturbance.  
Figure 4 shows the transient instantaneous vorticity contours of the last time of the 12 FTCs under different disturbances. It can be seen that the inlet disturbance significantly advances the flow direction of the mixing layer instability, and the influence on the instability increases with the increase of the inlet disturbance intensity.  
Figure 5a shows the vorticity thickness growth curve of turbulent mixing layer under different disturbances. It can be seen that with the increase of the disturbance, the turning position of the vorticity thickness growth curve is advanced, and the turning position of the vorticity thickness growth curve under 100 times disturbance is even ahead of the entrance. However, although the size of the disturbance significantly affects the turning position, it does not affect the growth rate of the turbulent mixing layer vorticity thickness after the turning point.  
Figure 4: Instantaneous vorticity contours with different forcing. (**a**) no forcing; (**b**) 1 forcing; (**c**) 10 forcing; (**d**) 100 forcing.  
Figure 3: Vorticity evolution in different instants.(**a**) 4.13 ms; (**b**) 4.14 ms; (**c**) 4.15 ms; (**d**) 4.16 ms; (**e**) 4.17 ms; (**f**) 4.18 ms; (**g**) 4.19 ms; (**h**) 4.2 ms.  
Figure 5b shows the variation curves of the velocity center line of the turbulent mixing layer under different disturbances. It can be seen that the greater the inlet disturbance, the greater the degree of the velocity center line deflecting to the low speed side. Under various disturbances, the turning position of velocity center line curve is always the same as that of vorticity thickness growth curve.  
Figure 6 shows the Reynolds shear stress profile of turbulent mixing layer under different disturbances. It can be seen from Figure 6a that when there is no disturbance, there is a big difference between the peak shear Reynolds stress at \(x=190\delta_{\omega}(0)\) and \(x=160\delta_{\omega}(0)\), and there is no self-similar state in the mixing layer; however, it can be seen from Figure 6b-d that when there is disturbance, the Reynolds shear stress profile downstream of the flow field is in close proximity, and the greater the disturbance is, the more forward the flow direction of self-similarity appears.
# Design and Application of a Smart Lighting System Based on Distributed Wireless Sensor Networks
## 4 Results
### Scenario 1: Metro Station
Comparison experiments were performed in Zones 1 and 2, which were symmetric and had the same number of LEDs. The smart lighting system was implemented in zone 1, while the normal lighting system was used in zone 2. 5 h during the periods of 3 days and 23 days, successively. The energy consumption and saving of the four subzones are presented in Figure 12 and Table 5. In the normal lighting system, subzone 2 consumed more energy than subzone 1, which was expected because subzone 2 had three more lights than subzone 1. However, in the smart lighting system, the energy consumption of subzone 2 was slightly lower than that of subzone 1, which indicated that the energy consumption under the proposed smart lighting system was independent of the number of lights. The average energy saving achieved using the proposed smart lighting system was about 45% compared to the normal lighting system.
## 3 Results  
The results are presented in Figures 2-5 with supplemental material as indicated. Figure 2 shows the complete GHG record for Howland Forest tower sites (US-Ho1,2,3) expressed in units of gC m\({}^{-2}\) y\({}^{-1}\) and as tCO\({}_{2}\) acre\({}^{-1}\) y\({}^{-1}\). Errors are graphed based on CARB-CAR relative to NEE results. Figure 3 provides the underlying annual R\({}_{\text{eco}}\), and GPP data plotted to illustrate the large variance of the R\({}_{\text{eco}}\)/GPP ratio across sites and years. Figure 4 shows the Howland GHG record, including soil chamber data for CH\({}_{4}\) and N\({}_{2}\)O. Figure 5 monetizes the data in Figure 4 based on data provided for each gas's reported social cost for a given year and discount rate.  
Figure 2: Yearly net ecosystem exchange (NEE) budgets (bars) and accumulated values (lines) for US-Ho1 (purple), US-Ho2 (green), US-Ho3 (brown) towers and for CARB (red) data in log-scale and in (**A**) grams of carbon per square meter and (**B**) tons of CO\({}_{2}\) per acre. (**C**) Absolute and percentage CARB yearly errors based on direct measurements from US-Ho1, in log-scale. Negative error values were made positive to include them in the log scale. A table of errors is provided in the Supplement Table S6.  
Figure 3: Yearly R\({}_{\rm eco}\) vs. gross primary productivity (GPP) for US-Ho1, US-Ho2 (blue points) and US-Ho3 (orange points). The regression lines (using all the data, solid red line; using only US-Ho3 data, solid orange line). The identity (dashed black line) and FLUXNET regression (dashed green line) were added as a reference. (**A**) All the years; (**B**) only years in the ranges (1050; 1400) and (1250; 1600) gC m\({}^{-2}\) y\({}^{-1}\) for R\({}_{\rm eco}\) and GPP, respectively.  
Figure 4: Yearly budgets (bars) and trends (lines) for CO\({}_{2}\) (**A**) and CH\({}_{4}\) (**B**) fluxes measured at the top of the tower, and soil CO\({}_{2}\) (**C**), CH\({}_{4}\) (**D**) and N\({}_{2}\)O (**E**) fluxes measured at chambers no. 2 (CO\({}_{2}\) and CH\({}_{4}\)) and no. 10 (N\({}_{2}\)O). Soil fluxes budgets from (**C**–**E**) correspond only to the period May–November, while the tower fluxes from (**A**,**B**) to the full year [38].
## 5 Discussion
### Limitation and Scope of the Project  
The data and results could be influenced by the number and types of fish and plants grown as well as the environmental conditions. The type of aquaponics used could be another influencing factor in optimal replenishment rate calculation. This project is carried out in a standard indoor coupled aquaponics system within a controlled environment. The results discussed above were generated with the data collected from a specific aquaponics system as described in the Section 3. This study was performed on a small-scale aquaponics system, and the application of the results of this research to medium-level or industrial-level farming needs adequate scaling in replenishment rate and other parameters according to the size of the system. The replenishment rate calculated in the project was sensitive to the aquaponics tank capacity used. This project used a 100 L water tank and optimal rate, and water quality was calculated accordingly. The experimentation was carried out in a controlled environment inside a closed room where artificial lighting was provided. A 19 L water replenishment system was used in this study and a different replenishment rate may result in different productivity rates. The results may have been impacted by the room environment such as humidity, room temperature and carbon dioxide levels. Artificial light operation timing, water recirculation timing, feed timing and feed quantity may have also influenced the results obtained. The results discussed are based on experimentation which was conducted for 91 days. The number of days may have influenced the productivity and impact of replenishment. That is, experimentation in an uncontrolled environment for a longer period than 91 days may cause variations in room temperature and humidity, which can potentially influence the water quality.  
This project opens a new arena of research in various aspects of smart aquaponics. Investigation on the effect of water replenishment on the plant's nutrients could be a potential research topic. Further analysis on the impact of the earthworms on different fish and plant types could be investigated. This research will be further explored with the growth-stage classification of the Swiss chords using machine vision and machine learning. Plant harvest time prediction is another prospective topic for investigation as a future step.
# Application of the Approximate Bayesian Computation Algorithm to Gamma-Ray Spectroscopy
## 5 Net Peak Area Extraction and ABC
### Abc
Note that the posterior is wider for the raw data; in this case, a short count time was simulated, resulting in 7000 counts over the 250 keV-wide region of interest, so smoothing has a large impact. In a separate set of \(10^{3}\) simulated test cases (the spectra in Figure 7 are test case number 1 of 1000), the ratio of the posterior standard deviation of the peak location distribution for smoothed spectra to that for raw spectra for peak location is approximately 0.45 (0.43/0.93 = 0.46), so the fact that the smoothed data lead to a narrower posterior for test case 1 is typical. For test case 1, the ratio of the standard deviation for the smoothed data to that for the raw data is 0.37/0.62 = 0.60. For area estimation, the ratio of the RMSE for the smoothed to that for the raw data is 1.41/1.84 = 0.77. Recall that the area is calculated as area \(=h_{1}\times\sigma_{1}\sqrt{2\pi}\) from Equation (1). Figure 6: The relative bias in model 1 and model 2. This observed tendency for the smoothed data to lead to a narrower posterior than the raw data do for peak location calls for attention to the \"is ABC calibrated\" question. Fortunately, ABC is shown here to again be well calibrated, with actual coverages of 0.89, 0.95, and 0.99, using the quantiles of the posterior to calculate the probability intervals having nominal coverages of 0.90, 0.95, and 0.99, respectively. Moreover, the average posterior standard deviation is [0.90 for the raw, 0.43 for the smoothed] while the observed RMSE is [0.90 for the raw data and 0.42 for the smoothed data], indicating excellent agreement with both the coverage and the RMSE calibration checks. Therefore, smoothing appears to improve peak location estimation, at least when the raw data arises from moderate or short count times leading to a modest number of total counts (as is typical in applications). Figure 8: ABC-based (Approximate Bayesian Computation) estimate of the posterior probability density function (pdf) for peak location using raw and smoothed data. Figure 7: Example of simulated raw and smooth spectra with a single true peak at 183.3 keV.
# VulPathsFinder: A Static Method for Finding Vulnerable Paths in PHP Applications Based on CPG
## 3 Background and Motivation
### Taint Analysis  
Taint analysis [34] is a technique that tracks and analyzes the flow of tainted information within a program. In vulnerability analysis, taint analysis is used to mark data of interest (_sources_, e.g., untrusted user-supplied data) as tainted data. Vulnerabilities can be discovered by tracking the flow of tainted data and examining whether it influences critical program operations (_sinks_). This transforms the problem of identifying program vulnerabilities into determining whether tainted information reaches sink points where critical operations are performed.  
Taint analysis typically involves the following components:  
* Identification of sources where tainted information is generated and marking the tainted data.
* Tracing and analyzing the propagation of tainted information through specific rules.
* Detection at sink points whether critical operations are affected by tainted information.  
Tainted information can propagate not only through data dependencies but also through control dependencies. We refer to information flow via data dependencies as explicit information flow and information flow via control dependencies as implicit information flow.  
* Data flow-based taint analysis treats taint analysis as data flow analysis focused on tainted data without considering implicit information flow. It involves tracking tainted information according to taint propagation rules or marking variables along the path of contamination and checking whether tainted information affects sensitive operations.
* Dependency-based taint analysis considers implicit information flow. The analysis process checks whether sensitive operations at sink points depend on operations that receive tainted information at source points based on dependencies between statements or instructions in the program.  
Static taint analysis systems [2; 4; 6; 7; 35] first parse the program code to obtain an intermediate representation. They perform auxiliary analyses such as control flow analysis to obtain control flow graphs, call graphs, and other necessary information. During the auxiliary analysis, the system can employ taint analysis rules to identify source points and sink points in the intermediate representation. Finally, the detection system utilizes static taint analysis based on the taint analysis rules to check for vulnerabilities of taint-related types in the program.
# ForecastNet Wind Power Prediction Based on Spatio-Temporal Distribution
## 2 Materials and Methods  
### Analysis and Processing of Wind Power Data  
#### 2.1.1 Wind Power Anomaly Data Processing  
Wind farm data mainly includes the spatial distribution of wind turbines, as well as dynamic background factors such as the temperature, weather, and internal state of turbines. Wind power data will inevitably have anomalies and missing information in the process of collection, transmission, processing, and storage. The existence of these anomalous data can reduce the efficiency of the model training and the accuracy of the prediction [35]. The anomalous data need to be handled without interfering too much with the original data.  
The isolated forest algorithm quickly separates the outliers and vacancies in the data from the normal data. Based on the length of the data path to score the data to determine the degree of abnormality, the higher the score, the higher the degree of abnormality of the data. By setting a certain proportion of abnormal data, the algorithm can quickly distinguish between abnormal and normal data, and the abnormal score of the isolated forest algorithm is calculated as shown in Equation (1).  
\[S(x)=2^{-\frac{E(h(x))}{c(x)}}, \tag{1}\]  
where \(S(x)\) is the outlier score of samples \(x\), with a value range of [0,1]. The larger the value, the more likely the sample is to be labeled as an outlier. \(h(x)\) is the path length of the sample in the tree, \(h(x)=\ln(x)+\zeta\), and \(\zeta\) is Euler's constant. \(E(h(x))\) is the mean value of the path length of the sample \(x\) in the tree. \(c(x)\) is the average search path length of the binary tree constituted by a dataset containing \(x\) samples.  
The Lagrange interpolation method is used to repair the abnormal data detected by the isolated forest, as shown in Equation (2).  
\[L(x)=\sum_{i=0}^{n}y_{i}\prod_{j=0,j\
eq i}\frac{x-x_{i}}{x_{i}-x_{j}}, \tag{2}\]  
where \(x_{j}\) corresponds to wind speed and \(y_{j}\) corresponds to wind power. Figure 1 shows the wind speed-power plot for the detection of anomalous data. Figure 1a shows the results before detection and repair, and Figure 1b shows the results after detection and repair. The orange dots are the anomalous and repaired data, respectively, and the blue dots are the normal data.
## 1 Introduction
### Analysis of Virtual-Physical Integration in Various Stages
The proportion of information technology in the above system characteristics continues to increase, deepening the transition from physical to virtual. The integrated technological concepts evolve from 3D modeling, VR, and AR to digital twins, parallel systems, and CPS, progressively enhancing the degree of virtual integration and supporting the industrial system through trial and error, deduction, simulation, and computation enabled by information technology. Three-dimensional modeling is the foundational technology for realizing VR, which, along with VR and AR technologies, collectively realizes digital twins [29]. Digital twins can extend into parallel systems, and when combined with control, they form programmable CPS. The higher the degree of virtual-physical integration, the more intelligent the system characteristics supported. For instance, digital twin technology can support offline simulation and display, immersive training and simulation, real-time simulation and monitoring, and production process simulation and validation. Parallel systems further enable reverse control, and the highest degree of virtual-physical integration in CPS can provide a complete human-machine integration and collaboration platform. As the degree of virtual-physical integration increases, user participation evolves from on-site operations in the real world to on-site operations via virtual interfaces, and ultimately to virtual operations by virtual humans in a virtual world.
## 5 Simulation Results
### Baseline  
Let us introduce the in-line interference mitigation technique employed by DUCA. In this scheme, an exclusion angle \(\alpha_{0}\) is delineated as the most significant intersection of \(\alpha_{U}\) and \(\alpha_{D}\). The interference avoidance rule of DUCA is explicitly outlined by  
\[\left\{\begin{array}{ll}\text{can not be accessed},&\theta_{k_{i}}\leq\theta_{0} \\ \text{both links can be accessed},&\theta_{0}<\theta_{k_{i}}\leq\theta_{\text{max}} \end{array}\right.. \tag{37}\]  
The satellite association rule for DUCA is segmented into two scenarios, as follows: Case 1: Failed access. The condition of exclusion angle for Case 1 is the same with (33). Case 2: Successful access for both links,  
\[\left\{\begin{array}{ll}\text{min}\big{\{}\theta_{L_{0}},\theta_{H_{0}} \big{\}}<\theta_{0}^{U};\text{max}\big{\{}\theta_{L_{0}},\theta_{H_{0}}\big{\}}> \theta_{0}^{D}\big{\}}\cup\\ \text{min}\big{\{}\theta_{L_{0}},\theta_{H_{0}}\big{\}}>\theta_{0}^{D}\big{\}}. \tag{38}\]