esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Parsing and Utilization of System Log Features in Log Analysis: A Survey
## 3 Feature Extraction and Utilization
### Digital Features
#### 3.1.5 Log Time Features  
Generally, in the process of converting log original events into log templates will separate the original statements into multiple parts. As shown in Figure 5, a parsed original HDFS log statement is divided into seven different parts: Date, Time, Pid, Level, Component, Template, and ParameterList. In addition to the log template repeatedly mentioned above, there is a large amount of information that has not been used, including time characteristics such as date and time.  
Most current sequence prediction methods are based on time-independent event sequences, such as predicting natural language sequences. However, for many real log events, the time span between events contains key sequence information. Time series are often event-dependent (a single time series has no practical significance). [PERSON] et al. [50] proposed two time encoding methods for this: (1) time masking of event embedding and (2) event-time joint embedding. Both methods can improve the accuracy of time series forecasting, which provides a precondition for applying time coding in log analysis.  
Log event features and log sequence features have the ability to describe the running status of the software system, but it is relatively difficult to find some other problems (such as performance detection [51]). The traditional method to detect performance anomalies is to perform intrusive detection when the system is running, which will affect the system performance. When the system has a performance problem, the time interval of some events in the system log will change significantly. Based on this, the researchers in [14] considered using the non-intrusive method of log exception detection to deal with the potential performance problems of the system. In order to capture time interval anomalies, the researchers [PERSON] et al. [18] calculated the time difference of two logs \(\Delta t_{i}=time_{i}-time_{i-1}(\Delta t_{1}=0)\) and then combined the time differences between all logs to obtain the time difference sequence \(\Delta T=[\Delta t_{1},\Delta t_{2},\dots,\Delta t_{k}]\). However, the time difference sequence is a one-dimensional time series and can only contain limited information. Therefore, [PERSON] et al. and [PERSON] et al. [52] used one-hot encoding and a custom linear layer to upgrade the feature dimension of the time difference sequence to make it serve downstream tasks. However, the logs generated by modern distributed systems are usually interwoven by multiple processes, which makes it challenging to effectively extract and utilize temporal features.
# Using Auto-ML on Synthetic Point Cloud Generation
## 3 Results and Discussion
Thus, the approach of using the Auto-ML system to find suitable training data is superior to the approach of creating a model based on the existing real data for the given task. The overall performance of the synthetically trained model suffers mainly from the poor prediction on Scene 8 (Figure 8). This is the only test scene with significant uneven terrain. Since uneven terrain is not taken into account in the synthetic data generator, the scene may be difficult for the network to predict because the training data did not contain this environmental property. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Test** & **Model Trained Synthetically** & **Model Trained with** \\
**Scene** & **Class Car IoU** & **Real Data** \\  & & **Class Car IoU** \\ \hline
1 & 0.983 & 0.566 \\
2 & 0.967 & 0.928 \\
3 & 0.973 & 0.803 \\
4 & 0.974 & 0.962 \\
5 & 0.934 & 0.915 \\
6 & 0.951 & 0.857 \\
7 & 0.745 & 0.917 \\
8 & 0.395 & 0.742 \\
9 & 0.933 & 0.585 \\
10 & 0.714 & 0.713 \\
11 & 0.993 & 0.984 \\
12 & 0.942 & 0.748 \\
13 & 0.924 & 0.797 \\ \hline Overall & **0.879** & **0.809** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Best performing synthetically trained model compared to the best performing real trained model. The model with the best performance per scene and the best overall performance is highlighted in green. The overall performance (in bold) is the average across all scenes. Figure 7: Visualization of the performance scatter
# The Design of a Long-Distance Signal Transmission System in a Bistatic RCS Testing System
###### Abstract  
In wireless communication and radar systems, long-distance signal transmission poses significant challenges that affect overall system performance. In this paper, we propose an innovative bistatic radar cross section (RCS) testing system designed to address these challenges, with a particular focus on its long-distance signal transmission capabilities. This system is capable of accurately measuring the RCS of a target and improving multipath channel modeling accuracy by using precise RCS values. The integrated upper computer software extracts amplitude and phase information from received echo signals, processes these data, and provides detailed outputs including the target's RCS, one-dimensional image, and two-dimensional image. The experimental results obtained prove that this system can not only achieve effective long-distance signal transmission but also substantially enhance the accuracy of RCS measurements, offering reliable support for multipath channel modeling. However, the conclusions drawn are preliminary and require further experimental validation to fully substantiate this system's performance. Future work will focus on improving system accuracy, minimizing the impact of environmental noise, and optimizing data-processing methods to enhance the efficiency of wireless communication and radar applications.  
multipath channel; radar cross section; bistatic RCS; long-distance signal transmission; photoelectric conversion; Mach-Zehnder modulator
# A Novel MEMS Capacitor with a Side Wall for High Tuning Ranges
## 1 Introduction  
One of the recent trends in wireless communication devices is miniaturization and high precision using MEMS (Microelectromechanical Systems) technology, which can minimize the RF (Radio Frequency) device in accordance with integrating the digital circuits (e.g., planar and non-planar antenna, duplexer, bandpass and lowpass filter, power filter, power dividers, mixer, and amplifiers) of wireless communications [1]. Moreover, the wireless communication market is rapidly growing, and the demand for wireless communication terminals with lower power consumption and small sizes increased; IMT2000 (International Mobile Telecommunication 2000), which is represented by image communication, emerged as the next-generation industrial communication method. Therefore, the technical development of wireless communication devices should be performed as soon as possible in order to establish the technical basis for its own domestic market.  
Currently, the core passive component (e.g., inductor, capacitor) development technology of an RF system using MEMS, RF circuit, the integrated technology of developed passive components, and developed VCO (voltage-controlled oscillator) core RF circuit modules for RF circuits has become a focus of research [2, 3]. Moreover, the development of a MEMS capacitor that is compact and is a low operating voltage tunable-type device has been requested according to the RF communication technology. However, the specifications of the device were not still developed enough to meet the needs of such a demand at homeand abroad. Therefore, the development of capacitor passive elements is urgently needed for applications as the new MEMS technology.  
Studies on the MEMS capacitor with a high tuning range are active in overseas areas.  
A MEMS variable capacitor having two movable plates loaded with a nitride layer is proposed by [PERSON] and [PERSON] and [PERSON] [4]. Moreover, the electrostatically actuated varactor is based on a parallel-plate approach and is best suited for microwave/millimeter-wave applications by [PERSON] and [PERSON] [5]. A structure that extends the tuning range of MEMS capacitors by at least a factor of eight was proposed by [PERSON] and [PERSON] [6]. According to [PERSON], [PERSON] et al., they proposed a novel design of an electrostatic digitally controllable variable MEMS capacitor constructed using Cronos MUMPS technology and flipchip technology processing [7].  
There are many other studies. One of them proposed a simple flat-plate MEMS capacitor with four carrier beams ([PERSON] and [PERSON] [8]) but these papers were not performed at the level required by the optimization. In addition, the carrier beams were located in the same position in the air gap. Therefore, its tuning range is limited.  
In this paper, we introduce an optimized high tuning range MEMS capacitor with a side wall for supplementing these shortcomings.
# Post-Fire Vegetation Succession and Surface Energy Fluxes Derived from Remote Sensing
## 4 Discussion
### Relationship of MODIS Products to the GPP and NPP  
GPP can be directly estimated on a per-pixel basis from MODIS EVI data at a broad scale [31]. Nevertheless, the strong correlations between EVI and GPP/NPP in Figure 3 confirmed that the C-flux of post-fire vegetation succession could be estimated with fairly high accuracy by using the overall relationship. [PERSON]'s correlation coefficient (r) under different burn severities suggested that the relationships were not static and were affected by the burn severity. Our data results suggest that the EVI provides a simple but robust approach for estimating post-fire GPP and NPP and thus has similar potential for light-use-efficiency and data-driven models. The relationships between the LSWI-GPP and LSWI-NPP were stronger because the strong correlation between the EVI and LSWI supported this relationship. In addition, previous studies confirmed that the potential of this method for estimating terrestrial GPP is similar to that when using the MOD17 GPP and NPP datasets [54; 55]. No measured data were available in the fire study area, so this study could not easily conclude that the EVI had the potential to estimate terrestrial GPP during vegetation recovery.
## 5 Fall Detection Application
### Experimental Results
#### 5.2.1 Falling Experiment  
For the scenario of a person falling, we measured an individual's spectral signature using the radar, as shown in Figure 6a. In addition, we tracked the movement of the head using video processing. Figure 7 shows three frames from the video of the falling person; the red markings indicate the head movement tracking. Figure 8 shows the results of the two experiments described above.  
Figure 5: IF signals received by the radar for the evaluated movements of a test subject: (**a**) falling; (**b**) sitting; (**c**) walking; (**d**) falling while walking.  
It can be seen that the shape of the analyzed video signal fits the spectrogram from the radar. The inaccuracy of the slopes and the maximum point are attributed to the video camera's low frame rate. From the spectrogram, the duration of a person's fall was determined to be \(T_{1}=1.96\) s and the maximum frequency \(f_{max}=1.6\) kHz. Using Equation (5), and in the case that \(\alpha=0\), i.e., the person's motion is in the direction of the radar, it is possible to calculate the maximum speed during this motion \(v_{max}=2.55\) m/s. The time taken for the frequency to decrease back to \(f=0\) was \(T_{2}=0.425\) s; thus, the decreasing slope was \(m_{n}=3.764\) kHz/s. Note that, in reality, the expected slope should be steeper, because the subject in this experiment stopped himself before hitting the ground in order to avoid injury.  
Figure 6: Spectrogram of the micro-Doppler frequencies for the evaluated movements of a test subject: (a) falling; (b) sitting; (c) walking; (d) falling while walking.  
Figure 7: Process for the video analysis of a falling man, tracking head motion: (a) standing; (b) falling; (c) lying down.
## 2 Power Quality Disturbance Signals  
In this research, the PQD signals were generated following the IEEE-1159 standard. We focused on 13 types of PQD signals consisting of 10 single types, which are pure sine, sag, swell, interruption, harmonic, impulsive transient, oscillatory transient, flicker, notch, and spike, and 3 combined signals including sag with harmonic, swell with harmonic, and interruption with harmonic. All signals were randomly generated within their constraints at 50 Hz, 1 p.u. of amplitude, 10 cycles, 2000 sampling points at 10 kHz. The mathematic equations and parameter constraints of all signals are shown in Table 1.
## 1 Introduction
### Aim of the Study  
Owing to the increase in hub port calls of mega container ships, the marine traffic of Busan New Port and marine accidents that could occur during port berthing are emerging as worsening problems that require urgent solutions. Because knowledge of the increase in speed and automation of mega container ships is still lacking for port and port service workers, a comprehensive response system for unexpected marine accidents is also insufficient [6]. Ultimately, a method to dramatically reduce marine accidents should include an analysis of the causality system responsible for marine accidents involving mega container ships.  
The crane contact accident of the mega-container ship MV Milano Bridge at Busan New Port in 2020 was considered as a representative case in this study. The Functional resonance analysis method (FRAM), an appropriate technique for accident cause analysis and risk assessment, has been used to logically explore the complex organizational-technical, human-technical, and organizational-human relationships related to this accident [7]. This study aimed to enhance navigation safety and prevent marine accidents by proposing improvement plans. Additionally, this study seeks to contribute to the development of a systematic marine accident investigation and safety management system that focuses on identifying fundamental safety issues, rather than relying solely on reactive measures. The main contribution of this study lies in the utilization of FRAM to analyze the MV Milano Bridge accident, uncovering the intricate interplay of technical, human, and organizational factors involved. Using FRAM, this study establishes a foundation for the proactive analysis of system causes, reduction in human error, and mitigation of the risk of future marine accidents during pilotage and berthing operations. Notably, this study distinguishes itself by surpassing traditional accident analysis approaches that assume linear causality, as FRAM allows for a holistic interpretation of marine accidents by considering the interactions between system functions. Such an approach enhances the study's applicability in the maritime industry and highlights its potential for effective risk mitigation.  
As shown in Figure 1, research questions and their details were derived to prepare systematic marine accident investigation and safety management measures.
## Appendix A C\({}_{4}\) Photosynthesis Equations for DCHM-V  
### Overview  
The DCHM-V models photosynthesis under a biochemical framework where it is assumed that the rate of carbon assimilation scales with the rates of key processes in the light and Calvin cycles. Photosynthesis is limited by either Rubisco activity or RuBP regeneration. Rubisco (ribulose-1,5-bisphosphate carboxylase/oxygenase) is the enzyme that catalyzes the reaction between RuBP (ribulose-1,5-bisphosphate) and atmospheric CO\({}_{2}\) beginning the process of carbon fixation and ultimately the conversion of CO\({}_{2}\) to sugars. C\({}_{3}\) photosynthesis is called as such because this first step, known as the Photosynthetic Carbon Reduction (PCR) cycle, results in a three-carbon compound, phosphoglycerate (3-PGA). However, Rubisco catalyzes two competing processes: (1) Carboxylation through which carbon is converted to sugars, and (2) oxygenation, where oxygen is added to RuBP creating phosphoglycolate which serves no metabolic purpose and is toxic at high concentrations [92]. Processing phosphoglycolate requires photorespiration which is energy demanding and results in a loss of 25-30% fixed carbon [93]. C\({}_{4}\) photosynthesis overcomes this limitation by spatially separating the sites of CO\({}_{2}\) fixation and assimilation, thus suppressing the oxygenase activity of RuBP. Reducing the need for photorespiration means a more efficient use of CO\({}_{2}\) for C\({}_{4}\) plants.  
C\({}_{4}\) plants accomplish more efficient carbon assimilation through the evolutionary adaptation of a bundle sheath cell. Unlike in C\({}_{3}\) photosynthesis where carbon fixation and assimilation both occur in the Mesophyll cell, in C\({}_{4}\) photosynthesis CO\({}_{2}\) is converted into a four-carbon organic acid (i.e., C\({}_{4}\)) through carboxylation of phosphoenolpyruvate (PEP) by PEP-carboxylate (PEPc). The C\({}_{4}\) compounds are then sent to the bundle sheath cells and decarboxylated to release CO\({}_{2}\). In the bundle sheath cells, Rubisco interacts with CO\({}_{2}\) and forms the 3-PGA as part of the PCR cycle. Thus, a biochemical model for C\({}_{4}\) photosynthesis must account for the limiting effects of both Rubisco and PEPc kinetics under light-saturated conditions.
# Evaluation of the Landsat-8 Albedo Product across the Circumpolar Domain
## 2 Materials and Methods
### Operational Blue-Sky Albedo
In cases where no valid AOD information could be retrieved, a universal climatology value of 0.2 is used. To further investigate the uncertainties resulting from the low resolution and possibly non-coincident AOD data, the full expression blue-sky albedo data is evaluated further using in situ AOD values at 550 nm from available Aerosol Robotic NETwork (AERONET) tower sites [41; 42; 43]. AERONET is a project established by NASA and PHOTONS (PHOTOstrie pour le Traitement Operationnel de Normalisation Satellitaire; Univ. of Lille 1, CNES, and CNRS-INSU) in collaboration with other national agencies and consists of a network of ground-based remote sensing aerosol towers to provide a continuous and accessible database for aerosol optical, microphysical and radiative properties for aerosol research and characterization, validation of satellite retrievals, and synergism with other databases. Its towers report measurements of atmospheric optical parameters with the CIMEL sun photometers every 15 minutes in the spectral range from 340 to 1020 nm. The Aerosol Optical Depth is calculated based on the spectral attenuation of a ray at each wavelength with the Beer-Lambert-Bouguer Law, which is based on the measurement of direct solar radiation with the aim of subsequent determination of atmospheric AOD and total content of certain gases [43]. This study used cloud-screened and quality-assured level 2.0 version 3.0 AOD product [44] at 500 nm. To compare blue-sky albedo calculated using the ground measured AOD to the MODIS derived AOD product at 550 nm, the AERONET AOD must be interpolated to 550 nm using the following:  
\[\tau_{550}=\tau_{500}\frac{550^{-\alpha}}{500}\]  
where \(\tau_{550}\) is the target AOD at 550 nm, \(\tau_{500}\) is the measured AOD at 500 nm and \(\alpha\) is the ngstrom exponent calculated from the measured wavelengths between 440-675 nm and supplied in the AERONET level 2.0 dataset [45; 46; 47]. The ngstrom exponent is calculated as:  
\[\alpha=-\frac{\ln\left(\frac{\tau_{1}}{\tau_{2}}\right)}{\ln\left(\frac{ \lambda_{1}}{\lambda_{2}}\right)}\]where \(\tau_{1}\) and \(\tau_{2}\) are the aerosol optical depths at the wavelengths of \(\lambda_{1}\) and \(\lambda_{2}\), respectively. For the validation efforts presented here, the average of all quality screened AOD values within one hour of the Landsat overpass were used for the calculation of the full expression blue-sky albedo. Lastly, to examine the limits of an operational blue-sky product, a full-expression blue-sky albedo product with a constant AOD, that is, a climatology value of 0.2 is produced for comparison. The full expression blue-sky albedo derived from the three sources of AOD (BLUE_full(MODIS_ADD), BLUE_full(AERONET_AOD), and BLUE_full(Constant_AOD)) were then validated against the in situ tower measurements to determine the best practices and scalability of full expression blue-sky retrievals.
## 4 Measurement Results Analysis and Discussion  
Figure 6 shows the schedule of the construction progress. The shaft had not been excavated until 27 July 2015. On 16 August 2015, the shaft was excavated to 6.5 m; it took 21 days to achieve the design excavation depth. The measurements began on the first day of construction and lasted 64 days (monitoring was completed on 28 September 2015). The supporting structure was recycled, and the shaft was backfilled on 2 October 2015.  
To obtain the measurement data, the construction speed was deliberately slowed. The experience of field construction shows that the proposed technology avoids the steel bar binding and shotcrete needed for the traditional reinforced concrete support. The construction speed of the proposed technology can be increased by more than 1/3 compared with the traditional shotcrete approach under normal construction conditions.  
The dismantling and backfill construction of the shaft were completed in one day. The supporting structure can be reused after being dismantled and recycled. Compared with the traditional reinforced concrete supporting structure, the new supporting structure does not need to break the concrete when the shaft is backfilled. Thus, the new supporting structure is more convenient, environmentally friendly and recyclable.
## 4 Experimental Validation and Discussion  
### Experimental Data and Setup  
The bearing data used in this study were obtained from the Bearing Data Center of the Electrical Engineering Laboratory at the Case Western Reserve University (CWRU) [51; 52; 53]. Figure 6 shows the bearing test stand and its structural diagram. The test stand included a 2 horsepower motor, a torque transducer/encoder, a dynamometer, and control electronics. The motor bearings were seeded with single-point faults using electro-discharge machining (EDM). Faults with different severity degrees ranging from 0.007 to 0.040 inches in diameter were introduced separately at the inner race, outer race, and ball. The vibration data were collected using accelerometers, which were installed on the housing using magnetic bases and placed at a 12 o'clock position on both the drive end and fan end of the motor housing. The sampling frequency was set to 12 kHz.  
The 6205-2 RS JEM SKF deep groove ball bearings were chosen as study object and were seeded with faults of four different severity degrees (0.000, 0.007, 0.014, and 0.021 in) at the inner race, outer race, and ball. The vibration signals were collected under the condition of 0 horsepower (HP) load. Table 1 lists the test arrangement of the bearing fault severity. Twenty samples were chosen for each severity degree, with a total of 80 samples for one test. Each sample contained 1024 data points. In the following section, the results of Test 1 for each step are described in detail. At the end, the results of the other two tests are given, and the results of all the tests are compared.  
Figure 6: (**a**) Bearing test stand, and (**b**) its structural diagram.