esa-sceva/satcom-chunk-collection huggingface dataset:
# Application Ranges of EPB Shield TBM in Weathered Granite Soil: A Laboratory Scale Study
## 5 Conclusions  
Soil conditioning methods applicable to the EPB shield TBM when the ground is composed of weathered granite soils were studied in this paper; the properties of the conditioned soils, i.e., workability (slump value), permeability, and compressibility, were evaluated in laboratory scale to find the optimum conditioning methods depending on soil water content. Application ranges of the EPB shield TBM in weathered granite soil were then proposed. The study conclusions are as follows.  
Controlling water content to within the allowable range (see Figure 5) is the most important factor for successful EPB shield TBM operation. Optimal (or at least allowable) water content increased with increasing fine particle content in the soil.  
It was found that if the water content of excavated soil is well controlled within the allowable range (see Figure 5) and the fine particle content finer than #200 sieve size is more than 1.2%, soil can be conditioned with foam only with an optimal FIR range of 22-67%. However, if water content exceeds the upper bound, water-absorbing polymer (solidification agent) should be added in addition to foam to change fluidic soils to plastic.  
Even with extremely coarse weathered granite soil (fine particle content less than 1.2%), the EPB shield TBM can still be adopted; however, polymer slurry (and additional water-absorbing polymer when digging out tunnels below the groundwater table) should be added with foam to change the cohesionless state of the soil to sticky or plastic.  
Application ranges of the EPB shield TBM of the weathered granite soil proposed in this study revealed that ranges were more expanded to the right (coarser) side compared with those proposed by [1]. The main reason for the expanded application range might be the particle-crushing effect of the weathered granite soil, which moves the particle-size gradation curve of the excavated soil to the left after soil conditioning.  
Conceptualization, T.-H.K. and I.-M.L.; Formal analysis, T.-H.K.; Funding acquisition, I.-M.L. and J.-J.P.; Investigation, T.-H.K., H.-Y.C. and Y.-M.R.; Methodology, T.-H.K.; Project administration, T.-H.K. and I.-M.L.; Writing--original draft, T.-H.K.; Writing--review and editing, I.-M.L. and Y.-M.R. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT of Korean government (MIST) (No. 2019R1C1C1008326) and Infrastructure and Transportation Technology Promotion Research Program funded by the Ministry of Land, Infrastructure and Transport of Korean government (18 SCIP-B066321-06, Development of Key Subsea Tunneling Technology).  
Not applicable.  
The authors declare no conflict of interest.
# Spatial Performance Indicators for Traffic Flow Prediction
## 4 Results  
In these experiments, we evaluated different traffic flow prediction methods using Global Moran's I, Getis-Ord General G, and a univariate analysis of APAM (mean, variance, skewness, and kurtosis) as the spatial KPIs. For Global Moran's I and Getis-Ord General G, the confidence level chosen is 90%, and the null hypothesis can be rejected if the \(p_{I}\)-value or the \(p_{G}\)-value is lower than 0.05.
## 3 Classification Based on Function
### Life-Detection Sensors  
Life-detection sensors are used to collect physiological, physical, and chemical information of trapped survivors to effectively identify their location immediately after a disaster [8]. Based on their principles and types of sensors used, life-detection technologies can be classified into acoustic life-detection techniques, optical detection techniques, radar life-detection techniques [9], and volatile organic compound (VOC) detection techniques [10; 11]. Acoustic life-detection technology is used to locate trapped individuals by detecting cries for help, movements, tapping, and even small chest fluctuations during breathing [12]. Passive sensors that receive trapped people's cries for help and knocking sounds have the advantage that rescue workers can hear these sounds and locate them if they are within the detectable range. However, the practical application of this technology requires sufficient experience from operators due to noisy sound at the earthquake site. Recent advancements in sensor technology have enabled the detection of the chest fluctuations of a trapped person during breathing by transmitting sound waves and analyzing reflected waves [13]. This approach has become more effective in recent years because acoustic signals can penetrate metal walls and detect stationary people through breathing movements alone without being disturbed by the remains of the victim [13; 14]. Optical detection technology includes visible light detection and infrared detection technology. The optical detection technology involves using a small camera equipped with
## Introduction  
Sexual pheromones are chemical signals that regulate the sexual behavior of insect species and which typically consist of characteristic components in specific proportions. These compounds are released into the air during sexual calling and can function at a distance, inducing orientation or provoking attraction responses and courtship in the opposite sex [1]. 2-Methyl-6-vinylpyrazine (Figure 1) is the main component of the _Toxotrypana curvicauda_ (Gerstaecker) sexual pheromone [2]. This fly is a major pest of the papaya fruit, and 2-methyl-6-vinylpyrazine has been used for management of this fly in Florida USA and Central America [1]. 2-methyl-6-vinylpyrazine has been reported in various products, as part of the Chinese liquor aroma pyrazine [3], in coffee [4], soy [5], maize flour [6] and wheat flour [7].  
Previously this component was obtained using a 'traditional' extraction method, volatiles were extracted from the charcoal filter trap with dichloromethane and evaporated under vacuum [2]. The pyrazine was identified and synthesized using the method of [PERSON] using a Hoffman exhaustive methylation route in three sequential steps [2]: a) the production of 2-methyl-6-dimethylaminoethyl-pyrazine from 2,6-dimethylpyrazine, dimethylamine hydrochloride, and formaldehyde (under reflux); b) the conversion of the quaternary salt with methyl iodide (6-methylpyrazylethyltrimethylammonium iodide) and finally c) preparation of 2-methyl-6-vinylpyrazine with a total yield of 24%. Reflux heating took place over a total of 3.5 h; a stabilizing process of approximately 12 h was required for the production of the second intermediate. The purification and analysis of the intermediary products was necessary in this synthesis. Another reported synthesis of vinylpyrazines involves the chlorination of dimethylpyrazine to obtain the corresponding monocloromethyl isomer, and Wittig reaction after obtaining a phosphonium salt [8].  
Currently the application of solid phase microextraction (SPME) for volatile compounds analysis has been studied [9]. SPME is a simple, selective technique, which takes only a short time to complete and uses no solvent. It can be applied to flavor volatiles analysis [9] and volatiles released for live insects.  
The aim of this study was extract and identify 2-methyl-6-vinylpyrazine from calling male emissions and carry out a synthesis of this pyrazine by means of a 'one pot' reaction so as to achieve an improved yield, reduce the reaction time and avoid the purification and evaluation of intermediates which is required in the previously described procedure.
## 3 Results  
The following sections summarize the results of the studies that assessed FTIR against calibration gases or reference instruments. The sections present shortly the principle of operation of the reference instruments (details can be found elsewhere, e.g., [128; 129; 130]) and any advantages and disadvantages over FTIR. Then the results of the studies that have assessed FTIR are summarized. The details of the studies can be found in the Appendix A. Each table gives first the results with calibration gases. Then, the comparison of the FTIR and reference analyzers is divided into cases that both instruments were at the same location (dilution tunnel or tailpipe), or different (FTIR at tailpipe, reference at dilution tunnel). In the last case, the differences can be affected significantly by the uncertainty of the exhaust flow determination.  
To put the assessed values into perspective, Table 2 presents Euro 6/VI limits for light-duty vehicles and heavy-duty engines, an approximation of the mass emissions of 1 ppm concentration of various pollutants. Exhaust flows of 2 kg/km and 10 kg/kWh were assumed as the highest values for large engine displacement light-duty vehicles and heavy-duty engines [103]. The density ratios u of the relevant regulations were used.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pollutant** & **CO** & **NO\({}_{\mathbf{x}}\)** & **HC** & **NH\({}_{\mathbf{3}}\)** & **N\({}_{\mathbf{2}}\)O** \\ \hline Euro 6 limits (min of LD) [mg/km] & 500 & 60 & 100 & - & - \\
1 ppm uncertainty (LD) [mg/km] & 1.9 & 3.2 & 1.0 & 1.2 & 3.0 \\ Euro VI limits (min of HD) [mg/kWh] & 4000 & 460 & 660 & 10 ppm & - \\
1 ppm uncertainty (HD) [mg/kWh] & 9.7 & 15.9 & 4.8 & 5.9 & 15.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Current Euro 6/VI limits in the laboratory (min from compression ignition and positive ignition engines) for light-duty (LD) vehicles and heavy-duty (HD) engines. The uncertainty of 1 ppm is also expressed in mass considering exhaust flow of 2 kg/m\({}^{3}\) for LD and 10 kg/m\({}^{3}\) (HD) large engine displacement vehicles.
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 5 Power Optimization Strategies
### Designing an Energy-Conscious Algorithm  
At the heart of WMDs lies an algorithm which processes and analyzes data collected from health sensors, identifies patterns, provides predictive analysis, and offers personalized health insights. It can range from a simple set of instructions to complex AI algorithms commonly found in the reviewed literature. Examples include the infinite impulse response (IIR) filter [66], CNN [67], support vector machines (SVMs), multilayer perceptron (MLP) [67], random forest (RF), and artificial neural networks (ANNs). These algorithms consume energy primarily due to their computational tasks, such as data processing, real-time analysis, and communication. The complexity of computations increases with the algorithm's complexity, leading to higher energy consumption. Simplifying these computations and optimizing the algorithms can help reduce their energy usage.  
Wearable devices are often battery-powered and limited in computational resources, such as memory and processing power. As the complexity of computing algorithms in  
Figure 3: Categorization of identified power optimization strategies.  
creases, so do the demands on power consumption and computational resources. However, high-performance algorithms are essential for real-time monitoring and timely interventions in medical scenarios. The incorporation of AI in WMDs has further increased this complexity [68]. Therefore, it is necessary to optimize these complex algorithms for resource efficiency, power consumption, and performance before implementing them in WMDs. Optimized algorithms ensure efficient use of resources, allowing the device to perform its tasks without overloading the system. This optimization can significantly improve performance, resulting in faster data processing and quicker response times with an acceptable level of accuracy. Most importantly, optimized algorithms can reduce the number of computations required, thereby conserving energy and extending battery life. These benefits are particularly important for continuous health monitoring tasks which require real-time data processing over long periods. To address issues of resource consumption, memory, and power usage, various optimization techniques, such as approximate computing, parameter quantization, feature reduction, and signal format conversion, are employed to compress these models [69].
## 3 Experimental Results and Analysis
### Correlation Between Different Pollutants and Meteorological Factors  
Due to the influence of sensors at monitoring stations and the mutual impact between different pollutants and meteorological factors, there is a certain degree of correlation among various pollutants. As shown in Figure 6, the Pearson correlation coefficient [39] is used to measure the correlation between different variables, and it is presented intuitively in the form of a heatmap [40]. The horizontal and vertical axes in the figure list six pollutants and five meteorological factors monitored by the microstation, with the colored rectangles indicating the correlation between the pollutants on each axis. The closer the value in  
\begin{table}
\begin{tabular}{c c} \hline
**Feature** & **Unit** \\ \hline PM\({}_{2.5}\) & \(\mu\)g/m\({}^{3}\) \\ PM\({}_{10}\) & \(\mu\)g/m\({}^{3}\) \\ CO & mg/m\({}^{3}\) \\ NO\({}_{2}\) & \(\mu\)g/m\({}^{3}\) \\ SO\({}_{2}\) & \(\mu\)g/m\({}^{3}\) \\ O\({}_{3}\) & \(\mu\)g/m\({}^{3}\) \\ wind speed & m/s \\ pressure & Pa \\ rainfall & mm/m\({}^{2}\) \\ temperature & \({}^{\circ}\)C \\ humidity & rh\% \\ \hline \end{tabular}
\end{table}
Table 2: Feature samples.  
the rectangle is to 1, the stronger the positive correlation; conversely, values closer to \(-1\) indicate a stronger negative correlation.  
The analysis indicates that when monitoring pollutant data with microstation sensors, PM\({}_{2.5}\) shows a positive correlation of 0.96 with PM\({}_{10}\) and a negative correlation of 0.38 with temperature. CO has a positive correlation of 0.46 with O\({}_{3}\) and a negative correlation of 0.4 with SO\({}_{2}\). The figure confirms that there is significant cross-interference among pollutants monitored by the microstation sensors, with data mutually impacting each other and substantially affecting monitoring accuracy. Additionally, meteorological factors also influence various pollutants. Therefore, when calibrating the data, it is essential to fully consider the interactions between different pollutants and the effects of weather to minimize their impact on data correction.
# Discrete-Time First-Order Plus Dead-Time Model-Reference Trade-off PID Control Design
## 4 Proportional-Integral-Derivative Parameter Design
### Optimization  
The controller parameters are optimized for the servo and regulation operations, respectively, subject to the established stability margin.  
As the optimization tool, the fmincon function in MathWorks MATLAB software was used, where the prescribed robust stability is set: \(M_{s}^{d}\in\{1.4,1.6,1.8,2.0\}\), and the optimal controller parameters are calculated based on the normalization parameters for the controlled plant: \(\tau_{0}\in\{0.3,\cdots,1.7\}\) and \(\tau_{a}\in\{0.01,\cdots,0.1\}\). As an example, the optimized controller parameters for \(M_{s}^{d}=1.4\) in the servo are shown in Figure 2.  
Figure 2: Relationships among optimized controller parameters and the normalized parameters \(\tau_{0}\) and \(\tau_{a}\) in servo design.
## 3 Comparison of Fetal Heart Rate Monitoring Techniques
### Photoplethysmography (PPG)  
Photoplethysmography (PPG) is used to measure the fetal heart rate, introduced in 1930. PPG is a non-invasive fetal monitoring procedure which uses changes in blood level volumes to measure FHR [51; 52; 53; 54]. The procedure involves the detection of FHR by transmitting a wavelength of 650-950 nm through the maternal abdomen, having a peak wavelength of 890 nm to penetrate the human tissue. The reflected light is analyzed to calculate the fetal heart rate. The example of a PPG signal comprising of both AC and DC components is shown in Figure 3[53].  
The AC component is the resultant of pulsatile changes in arterial blood volume, whereas the DC component corresponds to the tissues and muscles [53; 54; 55]. The AC component is helpful in determining the FHR as the arterial blood volume is synchronous with the heartbeat, but the signal obtained from the AC component is exceedingly small in range due to noises and artefacts [56; 57]. The major challenge in determining fetal heart rate through PPG is the extraction of signals from noisy transducer data, which is mainly  
Figure 3: PPG signal comprising of AC and DC components [53] (Reprinted with permission from ref [53] [PERSON]).  
Figure 2: The example of FECG embedded within the MECG signal [45] (Reprinted with permission from ref [45] [PERSON] (2012)).  
affected by acoustics, fetal and maternal contractions, fetal movements, and maternal breathing. The authors of [52] introduced a PPG system for fetal heart rate monitoring involving a bandpass filter (BFP) in the range of 710-740 Hz from the abdomen of the mother, a cut-off frequency of the reference signal kept at 15 Hz, and the signal is downsampled to 55 Hz, followed by a bandpass filter of 0.6-15 Hz to remove artifacts and unwanted noise in the received signal, deploying four phonogram sensors, as shown in Figure 4a, and the PPG sensors for experiment validation, shown in Figure 4b [52].  
The FECG signals were captured invasively using PPG electrodes, and the experiment procedure and signal processing successfully removed the noise and maternal artefacts from the FECG signal [52, 58].
## 1 Introduction
To overcome this problem, some researchers spent effort to design more compact and portable instruments. For example, [PERSON] [15] proposed a software receiver over a Nano-ITX single board computer combined with two radio frequency (RF) front-ends featuring a common clock, connected to a universal serial bus (USB) bridge for high-speed data transfer. In [16, 17], a prototype of an FPGA-based real-time GPS reflectometer is presented, which computes the full two-dimensional delay Doppler maps every 1 ms and performs coherent and incoherent averaging. Other remarkable examples are the designs of Starlab: Oceanpal(r)[12] and the SAMGNSS reflectometer [18, 19] are two instruments that they developed. While the former collects the LHCP reflected GNSS signals from the sea surface, the latter enables the reception of both polarization components of the reflected signal for soil moisture retrieval [13, 18, 19, 20, 21]. This paper presents the design and prototyping of a GNSS-based bistatic radar for small UAVs to be employed in environmental monitoring campaigns, for the water content classification of land and for the detection of water surfaces. Section 2 introduces the major requirements that guided the first phase of the design: among all, the light weight and the reduced size of the sensor, as well as the need for a GNSS antenna able to receive the LHCP and RHCP components of the reflected signals over two separate channels. Section 3 provides an overview of the hardware architecture, with a functional block diagram and the layout of the components, which were integrated into a case with an airfoil shape. It also explains some details of the software running on the microprocessor that controls the overall system, developed under a software radio paradigm to introduce flexibility. The remarkable hardware feature consists of the capability to simultaneously collect both polarizations' data streams, synchronized with the same clock. This makes the proposed sensor different with respect to the reflectometer presented in [18], which, on the contrary, switches among the reflected RHCP and LHCP RF signals and, therefore, processes them in a sequential way. Although the focus of this work is not on the reflectometry data post-processing, but on the GNSS sensor design and implementation, Section 4 briefly discusses the background of such a discipline to give better evidence of the overall process that starts from GNSS measurements and ends with moisture-related information. Thus, Section 5 proceeds with the description of some results obtained in the field with a small aircraft. Finally, Section 6 concludes the paper with some open issues and the expected developments and exploitation of this work.
## 4 Results and Discussion
### Construction of the Training Dataset  
Through hierarchical clustering, each verb entry has been assigned a corresponding situation type label. However, based on previous research, it is necessary to filter the data rather than retain all of it. [PERSON] [6; 7] attached situation types to the senses of verbs and retained each sense along with its situation type label. Nevertheless, the prediction accuracy of situation types using selected linguistic feature vectors was only 68.44%, and even with embedding vectors, the accuracy reached merely 72.05%. These experimental results indicate that the methods previously used to construct training datasets require improvement.  
We attempted to predict situation types by retaining only the primary senses of verbs, based on the following rationale: first, primary senses are used more frequently and can better reflect the core meaning of verbs. By associating each verb's primary sense with its corresponding situation type, more distinct feature vectors can be formed. Second, focusing on primary senses can make the dataset more concise, thereby enhancing the specificity and accuracy of the classification model. After manually selecting the primary senses, the retained data included a total of 1133 verbs, comprising 823 activity verbs, 136 state verbs and 174 transition verbs.  
We employed both supervised and unsupervised methods to extract verb vectors for constructing the situation type datasets. The supervised approach included linguistic feature vectors extracted from the CCL corpus and the Sinica corpus. The unsupervised approach involved embedding models, specifically Word2 vec, FastText, BERT and ChatGPT vectors.  
The following Table 4 shows the parameter configurations for training Word2 vec and FastText embeddings on the CCL corpus:  
Both the Word2 Vec and FastText models described above were trained using the Gensim library (v4.3.3) in Python (3.12.1). Additionally, we used the pre-trained language model BERT-wwm to construct 768-dimensional feature vectors for each verb. Finally, we extracted word vectors from ChatGPT (GPT-4 version), with each verb vector comprising 1536 dimensions.  
Ultimately, we obtained two linguistic feature vector matrices and four embedding vectors matrices. These will be used in subsequent grouped experiments for predicting verb situation types.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Model** & **Word2 vec** & **FastText** \\ \hline Algorithm & Skip-gram & CBOW \\ Vector Size & 500 & 500 \\ Window Size & 5 & 5 \\ Minimum Word Count & 1 & 1 \\ Negative Sampling & 5 & 5 \\ Subsampling of Frequent Words & \(10^{-5}\) & \(10^{-5}\) \\ Learning Rate & 0.025 & 0.025 \\ Epochs & 5 & 5 \\ Random Seed & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter settings for different word embedding models.