esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Structure Overview and Modeling of a DDTV  
### Structures of a DDTV  
The hybrid powertrain system of a DDTV in this case can be classified as a series HEV system, and its schematic structure is shown in Figure 1. Two tracks are attached to the corresponding drive wheels, which are connected to half-drive-shafts through wheel side reducers. The half-drive-shafts are driven by two permanent magnet synchronous motors (PMSMs) coupled by a coupling mechanism, which could realize renewable power mechanical recirculation in small radius steering and reduce the demanded rated power of the PMSM [33]. Similar to the integrated transmission devices used in normal high-speed tracked vehicles, the coupling mechanism consists of several planetary gear sets for different driving situations, like zero radius steering or small radius steering; however, for normal driving situations, it acts just like a reducer that decouples the operating of dual motors. Thus, drive wheels on each side are separately driven by these motors when running and braking in normal use. There is also a hydraulic retarder and a mechanical brake attached to each half-drive-shaft for braking. The DDTV has two power sources, which are a combustion engine-generator set (CEGS) and an energy storage unit (comprised of battery packs and supercapacitors). The transmission control unit (TCU) is used to analyze the driver inputs and control the engine control unit (ECU), the generator control unit (GCU, which consist of a DSP and an AC/DC converter), the DC/DC converter, the motor control unit (MCU, which consist of a DSP and two DC/AC converters for dual PMSMs), and the braking system (including hydraulic retarders and hydraulic mechanical brakes). All these controllers are communicated through a high-speed CAN 2.0 network. This kind of structure provides convenience for coordinated control of motors, hydraulic retarders, and mechanical brakes when emergency braking occurs.
# Smart Disaster Response in Vehicular Tunnels: Technologies for Search and Rescue Applications
## 4 Experiments and Discussion
### Communication Experiments  
In the event of a fire or an accident, it is important that the data obtained from various sensors should correctly arrive at the control server through the wired or wireless networks. For this purpose, we defined the data delivery ratio (the number of packets successfully received against the total number of transmitted packets) and verified the performance. The experimental environment is based on the conceptual diagram in Figure 1, and the gathered data is transmitted to the server via WiFi network. WiFi uses IEEE 802.11 b/g/n/ac specifications and maintains a communication distance of 50 m. The experimental results are summarized in Table 3 based on the type of data and the data delivery ratio. Since the proposed system shows a delivery ratio of more than 97% in the wireless environment, it is considered that it can receive emergency signals accurately and successfully. Nevertheless, due to the characteristics of the wireless environment, reception errors may occur due to fading, distortion, packet collision, and so forth. However, if TCP/IP-based retransmission or error control is adopted, it can be overcome sufficiently.  
In addition to the data delivery ratio, we also analyzed the delay time and the server response time for each data transmission. Transmission delay time is the delay between sending a packet in the buffer of the sending node and receiving it successfully from the receiving node. The server response time is the elapsed time until the server interprets the data and approves the connection. The delay time for each data is shown in Table 4, the transmission time is close to real time. At most, it is about 500 ms. However, since the transmission of the image data takes about 1.8 s, the delay of the image data is longer than that of the other sensor data. This is because after the preprocessing of an image is finished, some software calculation time is required for it to be recognized as an image of fire. However, considering this, the accident can be propagated within 2 s. Therefore, the delay poses no great difficulty for actual rescue activities.
# Rheological Approaches of Wheat Flour Dough Enriched with Germinated Soybean and Lentil
## 2 Materials and Methods  
### Materials  
Refined wheat flour of the 650 type (harvest 2020) provided by the S.C. Dizing S.R.L. company (Brusturi, Neamt, Romania) was used. Germinated legume flours were obtained from lentil (_Lens culinaris Merr_) and soybean (_Glycine max_ L.) which were germinated for 4 days, lyophilized and milled before they were used in the wheat flour according to the method reported in our previous studies [12; 14].  
The flours were analyzed according to the international ICC standard methods: ash content (ICC 104/1), moisture content (ICC 110/1), fat content (ICC 136), protein content (ICC 105/2) [30]. To be certain that the germinated soybean (SGF) and lentil (LGF) flour may be used in bread making, they were analyzed also from a microbiological point of view according to the following methods: molds and yeast according to SR ISO 7954:2001 [31], mycotoxins by using an ELISA kit (Prognosis Biotech, Larissa, Greece) and _Bacillus cereus_ according to the SR EN ISO 7932:2005 [32]. The wheat flour has been also analyzed for its wet gluten content and gluten deformation index according to SR 90:2007 method [33].  
Soveban germinated flour (SGF) at ratios of 5, 10, 15, and 20% and lentil germinated flour (LGF) at ratios of 2.5, 5, 7.5, and 15% were mixed with wheat flour and coded as SGF 5, SGF 10, SGF 15, SGF 20 and LGF 2.5, LGF 5, LGF 7.5 and LGF 10, respectively. Wheat flour without SGF or LGF was used as a control (C).
# Development and Operation of Track Condition Monitoring System Using In-Service Train
## 3 Track Condition Monitoring Results Using Vertical Vibration Acceleration
### Changes in Car-Body Vertical Acceleration over Time  
To investigate the long-term changes in the track condition for each target section, the maximum car-body acceleration measured in one trial was regarded as one data point and plotted. Figures 5 and 6 show the changes in vibration acceleration over time in the sections from 1.1 to 1.2 km and from 1.4 to 1.5 km.  
The change from October 2020 to July 2022 shows that the maximum value of the maximum car-body vertical acceleration has been increasing steadily, indicating that the track condition has been deteriorating. There is a period between January 2021 and June 2022 when measurements are not available due to vehicle inspections.  
The track beds were replenished and tamped down in July 2022, in the former section, primarily in the section from 1.110 to 1.145 km, where the measurements exceeded the target value of the track irregularity correction for riding comfort. However, for the section from 1.4 to 1.5 km, no track maintenance was performed, except in the section from 1.470 to 1.500 km.  
As shown in Figure 5, beginning from the time measurement commenced, the vibration acceleration gradually increased and then decreased after July 2022. Track maintenance was performed at this time, and its effects were confirmed.  
Figure 6 shows changes in the maximum vertical acceleration in the section between 1.4 and 1.5 km. There is no significant change in the maximum values of the vertical acceleration of the car body between October 2020 and July 2021. However, it can be seen that significant vertical acceleration of the car body has been observed since July 2021. This indicates that the track condition degraded rapidly.
## 4 Simulation Experiments and Results Analysis
### The Verification of Simulation Signals
Due to the nonlinear components coming from the spectrum hyperplasia of adjacent strong 16 QAM, the weak signal is interfered and its SINR is not large enough to directly carry on demodulation, thus its constellation is randomly distributed in Figure 5a. Meanwhile, in Figure 5b, benefiting from the mitigation method, the intermodulation in the polluted region is obviously depressed, finally obtaining a regularly-distributed constellation. This experiment illustrates that the proposed nonlinear distortion mitigation method can improve the ability of detecting weak signals, which is a crucial capacity of sensing performance. Since the constellations in Figure 7b,c seem to be nearly the same, their error vector magnitudes (EVMs) and bit error rates (BERs) are calculated to specifically explain the advantage of the best delay-based nonlinearity mitigation method. After normalization, the result is obtained and shown in Table 1. Compared to the results without the best delay, when the best delay condition is met, the EVM is lower and the BER is also better, as expected. As mentioned above, the major components distorting the weak 16 QAM belong to the first kind of intermodulation product. Since both the mitigation methods can effectively eliminate these nonlinearities and result in a considerable SFDR increase after nonlinearity elimination, it is rational that the EVM and BER achieved by the best delay-based approach do not have an additional increase in the order of magnitude. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Mitigation without the Best Delay** & **Mitigation with the Best Delay** \\ \hline EVM & 22.3\% & 15.1\% \\ BER & \(8.54\times 10^{-3}\) & \(3.12\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: EVM and BER of weak 16 QAM after nonlinearity mitigation with or without the best delay. Figure 7: The constellations of the 16 QAM signals in the polluted regions: **(a)** distorted weak 16 QAM; **(b)** weak 16 QAM after mitigation without delay; and **(c)** weak 16 QAM after mitigation at the best delay.
###### Abstract  
In this study, clove essential oil (CL), eugenol (EU), and cinnaldehyde (CI) were immobilized in starch sodium octenyl succinate (SSO) using an emulsification method. The main characteristics, stability, and biological properties of the prepared emulsions were established. Particle size analyses using dynamic light scattering showed that the smaller droplets were characteristic of emulsions containing CI (205-218 nm) and EU (181-236 nm), while the largest droplets were determined for CL emulsions (293-348 nm). Moreover, the highest antioxidant activity was determined for CL (79-83%) and EU (80-88%) emulsions, while CI emulsions showed the greatest antibacterial activity. The obtained emulsions were applied to the paper sheets and the bioactive properties of coated paper were studied. Evaluation of antioxidant properties revealed that high antioxidant activity reaching 76-92% and 87-91% was characteristic of coatings containing CL and EU, respectively. Meanwhile, coatings containing CI showed quite low antioxidant activity (4-9%) but demonstrated the greatest antimicrobial effect on Gram-negative and Gram-positive bacteria as well as yeasts. Hence, CL and EU emulsions could be used as effective natural antioxidants, whereas CI emulsions could be applied as an antimicrobial agent on cellulose-based substrates for a wide range of human health protection applications.  
clove oil; eugenol; cinnaldehyde; starch sodium octenyl succinate; emulsion; antioxidant; antimicrobial
## 3 Data Description and Model Evaluation Index
### Data Description
On the other hand, the differences between CHN_M and CHN_D compared to CHN_C mainly lie in the viewpoint, which are influenced by the camera's shooting angles. In CHN_M, the camera angle deviates more from the vertical direction of the road surface, incorporating a portion of the roadside scene into the images. Although CHN_D employs a top-down view, the drone's higher altitude above the ground allows for a broader coverage but results in a reduced resolution. Therefore, these two types of pavement images with different viewpoints can serve to validate the proposed model's ability to adapt to viewpoint differences. Moreover, since the CRDDC2022 dataset includes pavement images captured under varying external interferences, such as different weather and lighting, these data can be utilized to assess the model's generalizability to distribution biases. Given the variations in sample sizes and image resolutions across the datasets, we selected 2000 images from each of the five datasets within CRDDC2022 to ensure the diversity and comparability of the test set. These include various types of pavement distresses and images affected by external interferences, as illustrated in Table 2. Notably, since the CHN_M dataset contains only 1977 annotated images, all of them were included in the test set. Furthermore, to standardize the image resolution across both the training and test sets, images were cropped to resize them to a uniform resolution of 512 \(\times\) 512. At this image resolution, there is not much loss of detail information in the image, and the majority of pavement distress features in the resized image are preserved, which is sufficient for the model to identify distress targets. This resolution is suitable for images of different sizes from different datasets, allowing them to present a better display state for model detection. Finally, this resolution ensures that the pavement distress detection model can fully extract the features of the distress, which, to some extent, guarantees the accuracy of detection.
## 2 Materials and Methods
### Separation and Digestion Methods  
Each sample weight was 5 g of dry-weight stabilized sewage sludge. The samples were each converted to 100 g for comparative analysis. First, a density separation was performed using a saturated solution of zinc chloride (ZnCl\({}_{2}\)), with 100 mL of saturated solution per 5 g of residue. The samples were placed in a stirrer and mixed for 30 min at 140 rpm. After mixing, the samples were left for 24 h to achieve density separation. The top layer was poured onto a filter and washed with deionized water. The filters were allowed to dry under natural conditions. The solid phase separation process was repeated twice to recover the most significant possible fraction of MPs. The next step was to subject the sample to a digestion process. For this purpose, a 15% hydrogen peroxide solution was prepared. The samples dried on the filter were washed with the prepared solution and transferred to a beaker. As in the case of density separation, 100 mL of hydrogen peroxide solution per 5 g of stabilized sewage sludge was used. The digestion process took 48 h. After this operation, the residue was filtered and flooded with 15% hydrogen peroxide  
\begin{table}
\begin{tabular}{l c} \hline
**Component** & **Content/Determined Value** \\ \hline Total moisture, wt \% (\% m/m) & 78.2 \(\pm\) 2.3 \\ Dry matter, wt \% (\% m/m) & 21.8 \(\pm\) 2.3 \\ Organic substance, wt \% DM & 64.7 \(\pm\) 12.8 \\ Nitrogen, wt \% DM & 5.3 \(\pm\) 1.6 \\ Phosphorus, wt \% DM & 2.7 \(\pm\) 0.2 \\ Magnesium, wt \% DM & 0.6 \(\pm\) 0.1 \\ Calcium, wt \% DM & 2.6 \(\pm\) 0.5 \\ Copper, wt \% (mg/kg) & 321.2 \(\pm\) 68.0 \\ Cadmium, wt \% (mg/kg) & 0.7 \(\pm\) 0.2 \\ Lead, wt \% (mg/kg) & 20.6 \(\pm\) 7.9 \\ Zinc, wt \% (mg/kg) & 674.1 \(\pm\) 66.2 \\ \hline Chrome, wt \% (mg/kg) & 24.9 \(\pm\) 3.8 \\ \hline Mercury, wt \% (mg/kg) & 0.3 \(\pm\) 0.1 \\ \hline \end{tabular}
\end{table}
Table 1: Characterization of stabilized sewage sludge.  
solution for 48 h. The samples were quantitatively transferred to a filter and washed with deionized water, allowing for the removal of the unnecessary sample matrix. Then, the MPs analysis process was performed.  
Only glass and metal laboratory equipment was used during the analysis. The samples were tightly protected against the migration of MPs from the environment. Only outerwear made of non-synthetic materials was worn by the researchers. A blank test was performed without an environmental sample, which showed no evidence of the presence of microplastics.
## 8 Analysis of Results
As previously anticipated, the clarity index depends on the sound energy arriving within 80 ms (for music) and the reflections arriving in the following decay instants. The ratio determines that the optimal value for clarity is 0 dB, with some tolerance that can range from -2 to +2 dB [23]. Since clarity depends on the location occupied in the audience, the spatial distribution maps are the best representation, as shown in Figure 7, that are related to 1 kHz. The graphs in Figure 7 show the plots of the simulated results related to music clarity. In particular, Figure 7a,b show that the C80 values are highly above the upper range limit set by criteria, to be more than 10 dB with scenario A and around 9-10 dB with the type B shell, with no particular difference between 0% and 100% room occupation. With the installation of the type B shell, related to scenario C, the C80 values were significantly lowered and brought to the optimal range, to be between 0 dB and 3 dB in unoccupied conditions and around 2-3 dB when fully occupied, as shown in Figure 7c. Similar to clarity, the strength (G) also depended on the position of the listener in the seating area [24]. For outdoor spaces, G can have also negative values (below zero) since per definition G equals 0 dB at 10 m from the sound source in free field conditions. For indoors, G can be between 5 dB to 10 dB, mainly based on the interior design and the volume of the room. The graphs summarized in Figure 8 show the simulated results at 1 kHz related to all the considered scenarios. The simulated results related to strength are shown in the graphs of Figure 8. Figure 8a shows that G values are equal to 0 dB at the first row of seats, and to assume negative values up to the last row, meaning that the reflections given by the only flo  
Figure 8: Spatial distribution of strength (G) at 1 kHz, with and without audience: project A (**a**), project B (**b**), and project C (**c**). make the space suitable for good listening conditions [25]. A slightly improved situation was found with scenario B, with the addition of the acrylic panels at the perimeter of the stage and the suspended ones above the stage. Figure 8b shows that the G values range between 4 dB, closer to the stage, and 0 dB found at the last rows of seats. If the extension of stalls on a second level would be in place, this scenario is not sufficient to provide a good listening condition. Figure 8c shows that the G values with the new acoustic shell is found between 10 dB at the front line of the stage to 4 dB at the last rows of seats. If it is considered that the new acoustic shell is designed with the sides open for access reasons, as well as at the back over the staircase, the overall result is very good for a semi-enclosed space, in line with the expectations of this paper.
## 1 Introduction
In particular, rice fields can be differentiated from other crops due to the presence of standing water during crop transplanting. Based on limited ground-based data, phenology-based classification can detect plant growth stages based on their vegetative cover changes (i.e., [22; 40]). Most studies that use the phenology-based method are based on spectral indices and classify such indices using supervised or manual classification. There is still a lack of studies that employ unsupervised classification to differentiate spectral signatures into phenological classes. The Google Earth Engine platform (GEE, www.eartheengine.google.com, accessed on 9 April 2022) is one of the most promising developments for earth science data access and analysis [41], making the processing of multi-temporal satellite data more streamlined. The GEE provides a variety of raster data sets such as Sentinel-1 and -2, Landsat 4, 5, 7, and 8, MODIS, etc. In addition, the new datasets are periodically ingested. Furthermore, the GEE offers an interactive platform for geospatial and machine learning algorithms. Several studies have utilized the GEE platform for rice field mapping [22; 29; 30; 34; 35; 40]. In a recent study, NESEA-Rice10 [40] produced a rice field extent map covering Southeast Asia (including Malaysia) and North-East Asia. The map was a new achievement because it covered a large area with high resolution (10 m). However, the map still has not been validated in tropical regions. Furthermore, NESEA-Rice10 only provides static information on where rice is grown. Based on the research gap identified in the literature, this study introduced a phenology-based approach to map rice field extent and the cropping calendar in Peninsular Malaysia, covering over 13 million ha. This was achieved using an integration of Sentinel-1 and 2 time series data using unsupervised classification in the GEE platform. The outcomes of this study will provide an efficient framework for mapping rice fields and their growth stages, which will be valuable in tackling food security issues and mitigating methane gas emissions from rice cultivation.
## 1 Introduction
The target performance criteria for the developed CLSM are summarized in Table 1. In particular, the water volume increased and the materials (i.e., binder and byproduct) volume was minimized in order to facilitate the placement of the CLSM. To achieve the foregoing, a series of laboratory tests were conducted to measure the flowability, density, bleeding, and compressive strength of the new CLSM in accordance with test standards [35; 36; 37; 38]. A sensitivity analysis was performed to compare the flowability, bleeding, and compressive strength of the new CLSM according to the water-material ratio \begin{table}
\begin{tabular}{c c c} \hline \hline
**References** & **CLSM Application** & **Criteria and Requirements to Be Fulfilled** \\ \hline \multirow{8}{*}{ACI 229R-13} & General backfilling & High degree of flowability (\(>\)200 mm spread) \\  & (void filling, filling abandoned & Setting time and early strength are not critically essential \\  & underground structures, etc.) & Twenty-eight-day compressive strength should be less \\  & & than 0.5 MPa \\ \cline{2-3}  & Excavatable backfilling & High degree of flowability \\  & (underground water, sewer and & Less subsidence and quick setting time \\  & storm drainage pipelines, & Easy to re-excavate—manually or mechanically \\  & roadway trenches, etc.) & Twenty-eight-day compressive strength should be less \\  & & than 2.1 MPa \\ \hline \multirow{8}{*}{This study (Target performance)} & \multirow{4}{*}{General and excavatable backfilling (void filling)} & Considerably high degree of flowability (\(>\)400 mm spread) \\  & & Less subsidence (\(<\)5\% bleeding) \\ \cline{1-1}  & & Quick setting time (\(>\)0.1 MPa at 4 h) \\ \cline{1-1}  & & Easy to re-excavate—manually \\ \cline{1-1}  & & Twenty-eight-day compressive strength \(\leq\) 1.0 MPa \\ \hline \hline \end{tabular}
\end{table} 
Table 1: General criteria and requirements for CLSM application and target performance in this study. (W/M). Finally, the performance of the newly developed CLSM was compared with that of predevelopped CLSMs based on soils and bottom ash collected in South Korea.