esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Test Data Generation Using the Activity Diagram and Search-Based Technique
## 4 Experiment Description
### Experiment Planning
#### 4.2.2 Baseline Selection  
To answer our RQs, we selected the following two testing techniques for comparison with AutoTDGen:  
**DFAAD**: An experimental investigation of data flow annotated AD-based testing from our previous study [24]. In DFAAD, the flow of data is explicitly marked manually across the ADs of the SUT. We used DFAAD to investigate the feasibility and benefits of using ADs to derive test cases for data flow testing. The previous study converted the ADs into an intermediate test model, a data flow graph, and provided the test inputs manually. Therefore, we used DFAAD as a baseline for our RQs.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{_Systems_} & \multirow{2}{*}{_\#LOC_} & \multirow{2}{*}{_\#Classes_} & \multicolumn{4}{c}{**Branches**} & \multicolumn{4}{c}{**Statements**} & \multicolumn{4}{c}{**Mutants**} \\ \cline{3-10}  & & & **Min** & **Mean** & **Max** & **Min** & **Mean** & **Max** & **Min** & **Mean** & **Max** \\ \hline Cruise Control & 358 & 4 & 10 & 16.5 & 28 & 31 & 41.5 & 62 & 15 & 27.25 & 48 \\ Elevator & 581 & 8 & 0 & 17.5 & 72 & 8 & 45.75 & 152 & 2 & 30.9 & 111 \\ Coffee Maker & 393 & 4 & 16 & 26.7 & 48 & 42 & 52.7 & 72 & 24 & 39 & 68 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental subjects.  
**EvoSuite**: An open-source test suite generator for Java classes that uses an evolutionary approach based on a GA [20; 46]. EvoSuite has been evaluated in various open-source and industrial software systems. In academia, particularly in search-based testing, EvoSuite is considered to be a state-of-the-art testing tool.  
To investigate the possible benefits gained when the test data generation process is automated and test cases are optimized, DFAAD is a reasonable baseline for comparison. Since DFAAD is a manual model-based approach and closely related, it well confirms our need as a baseline. We found it difficult to choose other AD-based testing techniques from the literature as a baseline, because either they are poorly stated or follow a different approach that requires lots of effort to draw the ADs for larger subject systems. In addition, to test the claim that test automation should not come at the cost of losing test quality, we found EvoSuite to be another good baseline, because EvoSuite is a white box state-of-the-art test automation tool empowered with GA which also won the 2017 SBST tool contest [47]. There are also possibilities for selecting other techniques or tools as a baseline. However, there are also possibilities that these approaches may introduce confounding factors that cannot be controlled due to the usage of varying approach or the implemented methods, which may pose a threat to internal validity [44]. The motivation behind choosing the DFAAD and EvoSuite as the alternative approaches is to avoid possible confounding factors.
## 3 Classical Routing Protocols
### Modification of LEACH Based on CH Selection
#### 3.2.11 Leach-Mac  
In the LEACH protocol, an optimum value of CHs is pre-defined. However, the number varies randomly in a clustering algorithm. This randomness in LEACH is controlled in LEACH-MAC [85], which uses MAC layer information. The method tends to restrict the number of ADV messages of CH. When the selection of CHs starts, a variable _CHheard_ is initialized to 0 and is incremented by 1 when it receives an ADV message. After the threshold value, the node selects a random time between interval [0, _total_adv_time_], where _total_adv_time_ is the total time for CH transmission and reception given as  
\[total\_adv\_time=adv\_time\times(Num\_of\_clusters)\times 4+1) \tag{11}\]  
\[adv\_time=Transmission\_time\operatorname{of}(ControlPacket\_size\times 4) \tag{12}\]  
\[Transmission\_time=\frac{(Number\_of\_bytes\times 8)}{Bandwidth} \tag{13}\]  
If the selected time is \(R_{t}\), then the ADV sending time \(t_{adv\_CH}\) can be evaluated \(t_{adv\_CH}=\frac{R_{t}}{CurrentEnergy}\) so that nodes with higher energy could send ADV messages earlier than those with lower energy. Then, the node checks the variable _CHheard_, to know the number of ADV messages received. If the value is less than the optimal value of clusters formulated by Equation (1), it will send the CH ADV; otherwise, it remains a normal node. LEACH_MAC performs better than ALEACH and LEACH-DCS in terms of network lifetime and energy consumption.
# Design of an Intelligent Vehicle Behavior Decision Algorithm Based on DGAIL
## 2 Methods
### Building Expert Samples
#### 2.4.1 Minimum Safe Distance for Vehicles  
The minimum safe distance of a vehicle directly affects the traffic efficiency and driving safety of the vehicle in a high-speed environment. If the minimum safe distance is set too large, it is beneficial to ensure the safety of the vehicle but will reduce the traffic efficiency of the road. Conversely, if the minimum safe distance is set too small, it can improve the traffic efficiency of the road but will increase the risk of rear-end collision. Therefore, this paper adopts the variable headway distance as the minimum safe distance of the vehicle, which considers not only the minimum critical distance of the collision between two vehicles but  
Figure 4: Discriminator of DGAIL algorithm structure diagram.  
also the change in the front and rear vehicle speeds and dynamically adjusts the distance in real time according to the vehicle speed. The formula for calculating the minimum safe distance D between two vehicles is expressed as follows:  
\[D=V_{1}\tau+(V_{1}-V_{2})T+L \tag{9}\]  
where \(T\) is the sampling period, \(V_{1}\) is the speed of the vehicle, \(V_{2}\) is the speed of the preceding vehicle, \(\tau\) is the headway, generally \(\tau\) = 0.5-1.5 s, and \(L\) is the minimum critical distance between the two vehicles before and after safely stopping, taking \(L\) = 2-5 m.
# Comparison of Bone Segmentation Software over Different Anatomical Parts
## 2 Methods  
### CT Scan Collection  
Medical images in Digital Imaging and Communications in Medicine (DICOM) format from three different subjects were taken from a previous work [25]. In detail, for each of them, a number of anatomical complexes of the upper and the lower limb, i.e., the shoulder, elbow, and wrist for the former, and the knee and ankle for the latter, were analyzed. These fifteen scans were from CT ('Brilliance 16-slice scanner', Philips Medical Systems; Best, The Netherlands), with matrix size 512 \(\times\) 512, voxel size 0.29 \(\times\) 0.29 \(\times\) 0.8 mm, layer thickness 0.5 mm, and field of view and data collection protocol set according to the specific anatomical complex to be analyzed. These technical parameters and the size of the anatomical complexes under analysis resulted in the following numbers of images, for each of the three subjects: 365, 256, and 321 for the shoulder; 353, 299, and 282 for the elbow; 239, 212, and 319 for the wrist; 204, 299, and 241 for the knee; 290, 481, and 315 for the ankle.
# From Mirrors to Free-Space Optical Communication--Historical Aspects in Data Transmission
## 8 Conclusions  
The need to improve data transfer drives the research and application of new technologies. Because approaches based on electric cables and optical fibers have many limitations, cable-free technology was developed. One of the representatives of wireless connections is Free Space Optics, which is based on the application of a laser for communication. Currently, FSO links operate using lasers with wavelengths of 800-900 nm and 1.5 \(\upmu\)m. Already with these parameters, they revolutionized data transfer. The selection of radiation sources should be done according to two main factors, i.e., the transmission properties of the atmosphere and eye safety. The maximum ranges of these links are determined by the properties of the transmitting and receiving systems, as well as by weather conditions. To increase the FSO's reliability, several solutions are employed, and among them are such systems as the automatic resumption of packet transmission, appropriate data stream transmission protocols, systems for targeting and stabilizing the position of the laser beam, or automatic gain control systems. Others include using systems to regenerate the shape of impulse waveforms, elements for optical systems heating, adaptive optics systems, and multibeam systems [115]. Still, the optimal structure of an FSO link depends on a compromise between the price, range, and data transfer rate concerning the intended applications. A practical hint to FSO link designers is that they must specify the requirements for the components of the individual link. This is extremely important to ensure the required transmission speed and the appropriate power of the optical signal. Other essential factors include the selection of an appropriate data stream transmission protocol in the optical channel, and the application of an automatic tracking procedure of the relative positions of the transmitting and receiving systems. A significant drawback of the available systems is the fact that they are sensitive to harsh weather conditions, and in particular to fog.  
Conceptualization, M.G., and A.P.; writing--original draft preparation, M.G., A.P., M.O., K.M.; writing--review and editing A.P. and M.G.; visualization, M.O. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.  
Figure 7: Classification of FSO systems based on their coverage.
## 2 Materials and Methods
### System Operation  
The wearable device we designed primarily functions in a streaming mode, continuously capturing sensor data and wirelessly transmitting it to the processing unit of the RTBF system. To ensure smooth and efficient streaming, several tasks must be executed in a specific sequence:  
1. Sensor Data Acquisition--Collect readings from the three sensors integrated on Pololu's MinIMU-9 v5 board.
2. Data Formatting--Convert the raw sensor values into a format compatible with the UDP transport protocol.
3. Packet Assembly--Compile the formatted data into packets containing one or more sensor samples.
4. Data Transmission--Send the assembled packets to the processing unit via the Wi-Fi controller.  
Ensuring that sensor data acquisition occurs at uniform time intervals is critical for accurate data representation. We achieve this by embedding the sampling task within the timer interrupt routine of our periodic scheduler. This routine is triggered at precise times defined by \(n\cdot T_{s}\), where \(n\) is the sample index and \(T_{s}\) is the sampling period. This setup ensures a consistent sampling frequency of \(f_{s}=1/T_{s}\).  
The Data Formatting step involves converting the sensor readings and timestamps--originally represented as unsigned 16-bit or 32-bit integers--into ASCII-encoded decimal or hexadecimal values. This conversion is required by the UDP protocol used in the microcontroller's IP stack library. In practice, this step is often integrated with the packet assembly process.  
During Packet Assembly, the formatted sensor data are combined with control data and metadata, such as sensor identifiers, timestamps, and sample numbers. Each packet may contain multiple sensor samples collected over consecutive sampling intervals.  
The final step, Data Transmission, involves sending the assembled packet to the ATWINC1500 Wi-Fi controller through the SPI interface on the Feather M0 board, as illustrated in Figure 1. Depending on various parameters such as sampling frequency and I\({}^{2}\)C bus speed, the system operates in one of two modes: Single-Sample Mode--each streaming packet contains a single sensor sample. All tasks must be executed within the sampling period (\(T_{s}\)), as shown in Figure 4a. Multi-Sample Mode--Each streaming packet contains multiple sensor samples, usually spanning several sampling periods (e.g., S12 or F3 in Figure 4b). It can be observed that the formatting task is executed as soon as possible after each sensor sample is read. The assembly and transmission tasks are executed only after all expected sensor samples have been read and formatted. For example, in Figure 4b, samples 1 and 2 are assembled and sent together.
# Coordinated Control Strategy of CU-MTDC under Abnormal Conditions Considering Power Supply Security
## 4 Case Study
### Simulation under the Special Conditions of the Shared Control Mode  
In this case, the load condition is selected No.10 in Table 5. The active power of the \(Load_{18}\) and \(Load_{7}\) is increased by 180 MW and 90 MW, respectively. The maximum regulation capacity of the APC converters is \(\Delta P_{dc2 max}=298\) MW and \(\Delta P_{dcdmax}=122\) MW. The initial active power of converter 2, 4 and 5 is 500 MW, 200 MW and 220 MW, respectively. When \(t=0.2\) s, the output of the offshore wind farm is changed to zero. At \(t=0.5\) s, converter 1 is out of operation because of a serious converter fault. When \(t=0.7\) s, the AC system is involved in the control and the active power of the \(Load_{18}\) is decreased by 40 MW. Simulation results are shown in Figure 11.  
Figure 11: Simulation under the special condition of the shared control mode. (**a**) Active power of converters. (**b**) DC voltage of converters. (**c**) Active power of critical AC lines.  
As shown in Figure 11a,b, when \(t\) = 0.5 s, the master converter is out of operation for a long time because of a serious fault in the converter. Since the DC unbalanced power is 460 MW, shared control mode will be applied in both these two converters. At \(t\) = 0.58 s, the two converters reach their maximum regulation capacity limits, but the DC system still has unbalanced power that is not absorbed. At this time, a special condition of the shared control mode occurs. VSC\({}_{2}\) will continue absorbing the remaining unbalanced power, and VSC\({}_{4}\) maintain the maximum active power allowed. At \(t\) = 0.65 s, the DC unbalanced power is absorbed by both VSC\({}_{2}\) and VSC\({}_{4}\), and the active power is \(-\)168 MW and \(-\)78 MW. During the whole process, the DC voltage of all the converters is within the security range.  
As shown in Figure 11c, the active power of \(L_{5-7}\), \(L_{13-15}\) and \(L_{13-14}\) are 242.5 MW, 360.5 MW and 314.5 MW under the special condition of the shared control mode, respectively. The load ratio of \(L_{5-7}\) and \(L_{13-15}\) + \(L_{13-14}\) is 0.95 and 0.9642, respectively. Under this condition, the load ratio of \(L_{13-15}\) + \(L_{13-14}\) exceeds the allowable security range, and the AC system is involved in control. After the active power of the \(Load_{18}\) is decreased by 40 MW at \(t\) = 0.7 s, the active power of \(L_{13-15}\) and \(L_{13-14}\) are 353 MW and 311.5 MW, respectively. The final load ratio of \(L_{5-7}\) and \(L_{13-15}\) + \(L_{13-14}\) is 0.95 and 0.949, respectively.  
According to the simulation results above, a special condition of the shared control mode occurs when the total regulation capacity is insufficient. At this time, the VSC interconnected with a strong area will continue absorbing unbalanced power, and the others maintain the maximum power allowed. Since system security cannot be protected by the DC system alone, the AC system should be involved in the regulation. Through the coordination of the AC and DC system, the power supply security of the urban power grid is guaranteed.
# Split-Based Algorithm for Weighted Context-Free Grammar Induction
## 4 Results  
Our experiments were performed on Intel Core i7-7567U CPU, 3.5 GHz processor, with 32 GB RAM under Windows 10 operating system. Three methods, i.e., our proposal WGCS and two references methods: local search (LS) and ADIOS, were used to infer grammars for 28 benchmark datasets. A five-fold crossvalidation was performed on each set and the results were averaged. To evaluate the quality classification of the compared methods, we use the classification results stored in a confusion matrix. Four scores were defined as tp, fp, fn, and tn, representing the numbers of true positives (correctly recognised positive sentences), false positives (negatives recognised as positives), false negatives (positives recognised as negatives), and true negatives (correctly recognised negatives), respectively. Based on the values stored in the confusion matrix, we calculate the widely used Precision, Recall (Sensitivity), and combined metric F1-score. Precision is defined as \(P=tp/(tp+fp)\), Recall (Sensitivity) as \(R=tp/(tp+fn)\), and F1 as the harmonic mean of Precision and Sensitivity \(F1=2\cdot(P\cdot R/(P+R))\). Table 2 shows these results with respect to Precision (Pr), Recall (Rc), and F1 score. This table additionally shows the average production number and the average computation time obtained for each tested grammar by all methods. The average number of productions and the average computation time were calculated over five folds of the crossvalidation method used.  
To find out whether the observed differences are statistically significant, we follow the Wilcoxon-signed-rank test [30] for WGCS vs. LS and WGCS vs. ADIOS. In this test, the null hypothesis (\(H_{0}\)) states that the difference between the pairs follows a symmetric distribution around zero. The alternative hypothesis (\(H_{1}\)), on the other hand, states that the difference between the pairs does not follow a symmetric distribution around zero (i.e., the difference is not a coincidence). As can we see in Table 3, \(p\)-values are small enough (all below 0.025) to reject \(H_{0}\). Therefore, we can conclude that WGCS performs better than the two competing methods on prepared benchmarks, although it is a slower method than ADIOS.
# Fault Prediction for Rotating Mechanism of Satellite Based on SSA and Improved Informer
## 2 Background
Figure 2: Satellite payload connection and control relationship.
## 4 Suggestions for Mitigating the Barriers to the Use of Lidar for Control
### Use a Standardized Simulation Environment in the Early Design Phase  
When thinking about the optimization of lidars for control purposes, one should not be limited to the hardware device itself or the implemented software. In fact, there is still a huge potential for improving the preceding simulation process, more precisely the simulation tool chain. The better adjusted these techniques/tools are to the various subtasks of LAC, the more realistic and therefore more reliable the outcomes become. Consequently, establishing a kind of standardized LAC simulation environment would create added value. Furthermore, a modularized process with defined interfaces would ensure that every potential user is not limited to default features. One can optionally swap modules or extend them with needed functions. Wherever possible, new and useful developments should be shared within the community. The following modules are suggested as minimal requirements for a LAC simulation environment:  
1. **Pre-Processors:** Tools for generating input files for the most common aeroelastic simulation tools (e.g., Bladed, FAST). Furthermore, lidar vendors could share scripts to convert the output data of their specific devices to a defined data format that is compatible with the other modules' interfaces.  
2. **Wind Field Generator:** In the context of LAC, wind fields should be generated differently for realistic simulation. Several types of wind events (e.g., extreme operating gusts) have to be included in turbulent wind fields while ensuring acceptable computational effort [50].
3. **Lidar Simulator:** The lidar should be modeled with its most important properties, e.g., number of beams, scanning angles, number and spacing of range gates, update/measurement rate, range weighting function, probe length, etc.
4. **Wind Field Reconstruction:** Tools and algorithms for reconstruction of rotor effective wind characteristics from LOS data. A common data format with a defined interface is preferable for immediate application of new lidar devices.
5. **Controller Compilation Framework:** Here, the feedback as well as the feedforward controllers and all their subfeatures are included. It is not mandatory to use Matlab/Simulink (The MathWorks, Inc., Natick, MA, USA) like many control engineers, but it is beneficial when source code can be compiled to a dynamic link library (DLL) so the controllers can be shared within the community.
6. **Post-Processing:** A wide variety of tools for statistical evaluation and plotting of simulation results.  
It is intended that, within one module class, there are several versions that can vary in complexity quite significantly. Depending on the stakeholder's needs, one can combine the submodules to create user-specific simulation environments. Wherever necessary, modules should also be executable independently to allow focusing on a specific problem.
# Incentive Price-Based Demand Response in Active Distribution Grids
## 5 Simulation Studies  
In this section, the simulation studies done for a numerical evaluation of the proposed IDR method formulated in Section 4 are presented. A simplified model of a LV distribution grid from Askov, Denmark with 17 nodes connected with residential customers, PVs and BESS is used for the case studies done in this paper. The schematic diagram of this LV grid is shown in Figure 3. The cable parameters and the sizes of PV systems and BESS connected at each node of this grid can be found in [13]. As of today, the customers connected to this grid have no BESS systems. The PV systems are operated with unity pf and there are no voltage problems experienced in this grid. More PVs as well as BESS alongside some of the PVs are considered for the simulation studies in this paper based on a future operational scenario of this grid. The penetration level of PVs calculated from the ratio of the rated PV generation to the rated load power is around 95% and the total ratings of all BESS is 62% of the LV substation transformer rating. The battery energy capacity is set to be 20 kWh for all the BESS. The purpose of the above addition is to evaluate the grid for potential over-voltage issues without and with the proposed demand response control of the BESS.  
A typical daily profile of PV power generation and load consumption with 10 min time resolution is shown in Figure 4. It is to be noted that the load active and reactive power profiles and the solar irradiance profile used in this work are provided by the DSO, Norlys. All the case studies done in this paper are done using the Optimization Toolbox(tm) of Matlab/Simulink(tm) on an Intel(r) Core-i7 2.59-GHz personal computer.
## 3 Results  
### Comparison of the Spatial Patterns of the HTC and DISS Indices  
The compatibility of the DISS and HTC indices is shown in spatial form in Figure 14. Two dates representing various patterns of drought conditions were selected: 29 May 2015 and 20 June 2018. The DISS images (Figure 14a,b) and the HTC images (Figure 14c,d) reveal a similar distribution of drought conditions on the territory of Poland, represented by red and orange colors. For instance, on 29 May 2015, the drought conditions, represented by the drought and drying DISS class, appeared in western and central Poland--that is, in the NUTS 2 PL61 and PL41 regions. The rest of Poland remained under average or good moisture conditions. The HTC images correspond to these conditions, presenting low HTC values (less than 0.9) in the same region. The other situation occurred on 20 June 2018, when the majority of Poland (excluding the southern part) was affected by serious drought (Figure 14b). A similar pattern is reflected by the HTC index (Figure 14d), with low HTC values (below 0.9) occurring across a substantial part of Poland.