esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion and Comparison
### Differences with Related Methods  
The method by [PERSON] et al. [11] extracts features using a 1D CNN model and then passes them to the LSTM. Despite its good performance, the model complexity is high as compared to the proposed method. It takes an EEG signal trial of 60 s (7680 samples) as input and involves about \(\approx\) 61,184 learnable parameters, which are difficult to learn from a limited amount of available EEG data. The method by [PERSON] et al. [16] employs Siamese Neural Network (SNN) architecture to learn discriminative features and uses classifiers such as KNN, NB, RF, SVM, and XGboost (XGB). This method also takes EEG trials of 60 s and requires more computational time; it is not based on an end-to-end learning approach. Unlike this method, our method was based on an end-to-end 1D CNN model. It integrated the feature extraction and classification modules in a unified learning framework, and because of this, it learned discriminative features in a better way and results in better performance. In addition, our method employed EEG signal trials of 3 s, and as such, it is more efficient than the methods in [11; 12; 13; 16; 21; 27]. The complexities of the deep models used by [PERSON] et al. [14] and [PERSON] et al. [15] are very high, which are 5,048,898 and 138 million learnable parameters; they are difficult to train or fine-tune, avoiding overfitting using the available dataset. The methods of [14; 15; 16; 27] convert EEG signal trials (1D) into 2D images and then employ 2D CNN; that is why the models are  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Study** & **\# Channels** & **T. Length** & **Acc.** & **Sen.** & **Spe.** & **\# Parameters** \\ \hline [PERSON] et al. [11] & 16 & 60 s & 99.50 & - & - & \(\approx\)184.61 \\ \hline [PERSON] et al. [16] 2020 & 16 & 60 s & 95.00 & 98.00 & 92.00 & Not mentioned \\ \hline [PERSON] et al. [14] 2021 & 16 & 5 s & 94.89 & - & - & 5,048,898 \\ \hline [PERSON] et al. [15] 2021 & 16 & 5 s & 98.00 & - & - & 138 million \\ \hline [PERSON] et al. [21] 2023 & 16 & 60 s & 92.85 & 95.90 & 89.70 & Not mentioned \\ \hline [PERSON] et al. [12] 2022 & 16 & 60 s & 98.00 & 98.00 & 97.80 & Not mentioned \\ \hline [PERSON] et al. [13] 2022 & 16 & 60 s & 100.0 & - & - & Not mentioned \\ \hline [PERSON] et al. [27] 2023 & 16 & 60 s & 97.74 & 96.91 & 98.53 & Not mentioned \\ \hline
**Proposed method** & 16 & 3 s & 99.88 & 100.0 & 99.74 & 7358 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of the results of the proposed method with those of the state-of-the-art methods on public datasets [27].  
very complex. On the other hand, our method uses 1D CNN, and the design of the model is based on a pyramid approach, which further reduces the complexity of the model; the model contains only 7358 learnable parameters, which is a very small number compared to the models introduced in [11; 14; 15]; and it is easy to learn from the available data and avoid the overfitting problem. In addition, it needs very small storage space and is suitable for embedded systems.
# Investigation of CuTi Alloy for Applications as Non-Sparking Material
## 3 Results and Discussions  
Copper titanium (Cu-Ti) alloys have been investigated in the Cu-rich area, which is considered to be an ideal substitute for the widely used Cu-Be alloy for electronic components such as connectors and relay control elements [20] or anti-sickle elements. A considerable number of investigations have been conducted on how the Ti content and heat treatment conditions affect the electrical conductivity, thermal conductivity and mechanical properties [21]. There has been less research into CuTi intermetallic compounds, although pioneering studies have shown that these alloys can be used in high conductivity cables, grinding wheels and ship components [22], and only a few studies have been reported on the corrosion resistance of these alloys [20].
## 2 Experimental Procedure  
The ASW powder was provided by Ultromex Limited, Bromborough, UK. The bulk ASW powder was sieved into three particle size groups designated as Small (125-250 \(\upmu\)m), Medium (250-425 \(\upmu\)m) and Large (425-1000 \(\upmu\)m). The micrographs of the particles and the typical internal structure of the three ASW powders are shown in Figure 1. The ASW particles have irregular shapes with a porous structure and have a variable range of particle sizes. The mass-weighted mean diameters, average porosities and densities of the Small, Medium and Large powders are 207 \(\upmu\)m, 15% and 1.40 g/cm\({}^{3}\); 600 \(\upmu\)m, 20% and 1.25 g/cm\({}^{3}\); and 805 \(\upmu\)m, 30% and 1.15 g/cm\({}^{3}\), respectively. Each of the Small, Medium and Large samples was further split into two portions, where one portion was used as is, and the other portion was heated to 450 \({}^{\circ}\)C and air-cooled, to make a total of six different powder samples. The heated powders are designated with a suffix -H.  
The six ASW powders have different elemental compositions, as shown in Table 1. The compounds present in the ASW powders include alumina, iron oxide, magnesia, silica and aluminium. Most of the compounds are the same across the different samples, as the samples come from the same bulk ASW powder. The Small sample has additional cryolite, while the Medium sample has additional calcium fluoride and calcium oxide.  
The ASW syntactic foam (ASF) samples were fabricated by the pressure infiltration casting process [12; 13; 14; 15] using an aluminium 6082 alloy and the six ASW powders, to make a total of six different ASFs. The ASFs containing Small, Medium and Large powders are designated as S-ASF, M-ASF and L-ASF, while those containing the heat-treated powders are designated as SH-ASF, MH-ASF and LH-ASF, respectively. Several ASF samples were subjected to the T6 heat treatment [28] and are denoted with a -T6 suffix. For comparison purposes, an E-sphere syntactic foam (ESF) sample was also fabricated by the same procedure using the aluminium 6082 alloy and an E-sphere powder with a particle size range of 250-500 \(\upmu\)m, provided by Envirospheres Ltd. (Lindfield, Australia) [29]. The ASF and
## 3 Structuring of the Proposed Forecast Framework  
In this section, the production data from LinHuan Zhongli 1 # 330 MW circulating fluidized bed unit in China illustrates the prediction modeling steps. The boiler slime blending system was designed and manufactured by PUTZMEISTER, Germany. The coal slime blending method is a typical coal slime pumping system consisting of the coal slime silo, the bin bottom slide frame, the flushing water pressure pump, and four coal slime pumping pipelines. The coal slime guns are arranged at a position 2.6 m high from the air distribution board, two on each of the left and right side walls of the boiler, and arranged horizontally and symmetrically. The coal slime gun comprises the gun body, the ball valve, the gate valve, and the safety valve. This paper uses three datasets for prediction and comparison. Among them, dataset 1 and dataset 2 come both from the operational data from 18 to 20 June 2018. The difference is that dataset 1 is all the operational data from the unit during regular operation. Dataset 2 has fault data in the test set. Dataset 3 is the operating data of the unit between 12 September and 14 September 2015. All datasets have a sampling interval of 5 s and a prediction step of 6. The size of all datasets is 39,600. All data sets are divided into training, validation, and test sets according to the data segment lengths of 36,000, 1600, and 2000 in that order. The prediction task is shown as an example of left bed pressure prediction. The three datasets are described as follows:  
* Dataset 1: The entire dataset is in regular operation. The time range of the data set was from 18 June 2018, 0:21 to 20 June 2018, 7:21.
* Dataset 2: There was an inventory overturn accident in the test set. The time range of the data set was from 18 June 2018, 14:05, to 20 June 2018, 21:05.
* Dataset 3: The data in the dataset are normal operation data. The time range of the data set was from 12 September 2015, 0:21 to 14 September 2015, 7:21.  
The flow of the prediction method in this paper is shown in Figure 6. Among them, the variable screening method is described in Section 2.3. The hyperparameters of the model are adjusted according to the errors on the validation set. The program was compiled using Python, and the algorithm model used Pytorch and Scikit-learn framework. All experiments were carried out in the Python compiling environment using an Intel Core i9-10900K CPU and RTX3080 GPU machine. CUDA and cuDNN accelerate the algorithm model.  
Figure 6: Prediction flow diagram. The data are sequentially differenced and normalized. The variables are filtered by training set and validation set. Finally, the parameters of the model are trained by the training set. The hyperparameters of the model are selected by the validation set.
## 3 Research Methods and Data  
### Assessment of Water Conservation Function Based on InVEST Model  
The water production module of the InVEST model can be used to evaluate the depth of water conservation by integrating topographic factors, surface runoff differences and other factors. The model has the advantages of flexible parameter adjustment, spatial expression of evaluation results with multi-time, multi-scale, multi-scenario and multi-objective tradeoffs [30]. The specific formula is as follows:  
\[Water\ Retention=\min(1,\frac{249}{velocity})\times\min(1,\frac{0.9\times TI}{3}) \times\left(1,\frac{Ks}{300}\right)\times Y_{x} \tag{1}\]  
\[TI=\lg\left(\frac{Drainage\_Area}{Soil\_Depth\times Percent\_Slope}\right) \tag{2}\]  
In Equation (1), _Water Retention_ is the water conservation depth (mm), _Ks_ is the saturated hydraulic conductivity of soil (mm/d), _velocity_ is the velocity coefficient, _TI_ is the topographic index, which is a dimensionless parameter, and \(Y_{x}\) is the water production  
Figure 1: Overview of the study area.  
depth (mm). In Equation (2), _Drainage_Area_ is the number of grids in the catchment area, _Soil_Depth_ is soil depth (mm), _Percent_Slope_ is the percentage of the slope.  
\[Y_{x}=\left(1-\frac{AET_{x}}{P_{x}}\right)\times P_{x} \tag{3}\]  
\[\frac{AET_{x}}{P_{x}}=\frac{(1+\omega_{x}+R_{x})}{1+\omega_{x}\times R_{x}+1/R _{x}} \tag{4}\]  
\[\omega_{x}=Z\times\frac{AWC_{x}}{P_{x}} \tag{5}\]  
\[R_{x}=\frac{k_{x}\times ET_{0}}{P_{x}} \tag{6}\]  
\[AWC_{x}=\min(MaxSoilDepth_{x},RootDepth_{x})\times PAWC_{x} \tag{7}\]  
In Equation (3), \(AET_{x}\) is the grid's annual average actual evapotranspiration migration (mm), \(P_{x}\) is the grid's precipitation (mm). In Equation (4), \(R_{x}\) is the dryness index, which is a non-physical parameter of climatic soil attributes, \(\omega_{x}\) is the non-physical parameters of soil properties, \(Z\) is the seasonal constant, and its value range is between 1 and 10. According to the existing research, Yunnan belongs to the monsoon climate region, and the result is relatively scientific when \(Z\) is taken as 1 [31]. In Equation (5), \(AWC_{x}\) is the available water content of plants (mm). In Equation (6), \(k_{x}\) is the evapotranspiration coefficient of a vegetation type \(ET_{0}\) is the reference evapotranspiration (mm). In Equation (7), \(MaxSoilDepth_{x}\) is the maximum soil depth (mm), \(RootDepth_{x}\) is the root depth (mm), and \(PAWC_{x}\) is the available water for plants.
## 1 Introduction  
### Motivation  
Recently, the Unmanned Aerial Vehicle systems (UAVs) and/or Autonomous Underwater Vehicle systems (AUVs) have been involved in numerous applications, including both military and civilian applications such as defense, firefighting, pipeline inspections, exploration, photography, and monitoring. Researchers and engineers are showing great interest in UAVs and/or AUVs systems. This interest is mainly motivated by observable expansions of UAVs and/or AUVs applications, particularly now that UAVs are playing a significant role in combating the COVID-19 pandemic [1]. Due to the multiple advantages of UAV and/or AUV systems and their potential to perform complex tasks with high and reliable efficiency, multi-UAVs and AUVs are preferred to accomplish the assigned tasks more successfully and precisely compared to single-agent UAV and/or AUV systems. Numerous control algorithm designs have been reported in the literature to meet the application criteria either for a group of homogeneous or heterogeneous vehicle systems. Solutions are proposed for heterogeneous robotic agents to be allocated to corresponding tasks that impose specific execution requirements [2]. An optimal sequence of task-agent allocation to minimize the task allocation time or the consumption of the robots' energy for a team of heterogeneous mobile robotic agents is introduced in [3]. The formation containment control designs of multi-leader follower systems have been proposed and reported in [4; 5]; however, the influence of the information transmission between the agents was not considered in the design. Classical formation control designs of multi-agent systems with limited agents have been proposed by implanting controllers such as a state feedback controller, a Fuzzy Logic Control, an LQR controller, and a feedback linearization control technique [6; 7]. However, the presence of parameters' uncertainties and external disturbances may critically influence the performances of the previously mentioned controllers. DDS middleware has been proposed and designed to overcome the issue of the data loss between multiple UAVs, as well as the interoperability issue associated with heterogeneous systems. Therefore, application of the DDS middleware technique augmented with the flocking approach resulted in significant improvements in flexibility, reliability, and portability [8; 9].
## 3 Results
### Seasonality of MTVDI  
The spatial pattern of MTVDI differs greatly in different seasons (Figure 3), showing a clear transition of higher MTVDI pixels from north to south. From November to March, the drought mainly occurred in the northern Amazon tropical forests, which corresponds to the wet season in the southern hemisphere and dry season in the northern hemisphere. From the start of May to the end of September drought goes southward and then it releases from October to the end of the year. It is because during the period from March to September, the southern hemisphere gradually experiences dry season from north to south. It is notable that in the northeastern part of the study area encountered drought throughout the whole year.  
To better understand the spatial divergences of Amazon tropical forests, we use the K-means clustering analysis [78; 79] of MTVDI to group the study areas into six parts, named as NA1 (Northern Amazon 1), NA2 (Northern Amazon 2), CA (Central Amazon), WA (Western Amazon), SA1 (Southern Amazon 1), SA2 (Southern Amazon 2) (Figure 4). This result is similar to the seasonality cluster map identified from monthly climatology data of Ts, QSCAT, and reflectance of near-infrared band (NIR) for the Amazon tropical forests [79]. Results indicate the good ability of the MTVDI in reflecting the light and water differences across Amazon tropical forests.  
Figure 3: Spatial pattern of MTVDI seasonality over Amazonian tropical forests. The seasonality represents the mean values of each month from 2003 to 2010. Subplots (**a–I**) are monthly mean MTVDI from Januray to December, respectively.  
The seasonal variations of MTVDI over Amazonian tropical forests are presented in Figure 5. For all Amazonian tropical forests (black solid curve in Figure 5), the MTVDI increases in May at the end of the wet-season period and peaks around August during the dry-season period, and then drops down in November at the end of the dry season [54]. Among the six subregions, NA1 region shows annual higher MTVDI (annual mean MTVDI = 0.76), while SA1 region has the smallest MTVDI throughout the year (annual mean MTVDI = 0.58). In particular, the MTVDI in SA2, CA, WA, and NA1 regions show significantly seasonality, with an increase at the early stages of the dry season, a peak in August in the dry season and a drop during the wet season. However, the MTVDI in NA2 region shows the opposite pattern, where MTVDI increases at the start of November, but reaches its lowest point from June to October.  
Figure 4: The spatial distribution of (**a**) K-means clustering analysis map of MTVDI, (**b**) The seasonality cluster map identified from monthly data of land surface temperature (Ts), canopy backscatter from Quick Scatterometer (QSCAT), and reflectance of near-infrared (NIR).  
Figure 5: The seasonal variation of MTVDI over Amazonian tropical forests. The seasonality represents the mean values of each month from 2003 to 2010. NA1, NA2, CA, WA, SA1, and SA2 are the abbreviation of each subregion in Figure 4a. The gray shadings represent standard error of the mean (SEM).
# Dual-Energy CT Applications in Urological Diseases
## 5 Limitations  
DECT is a valuable technique, but it is associated with some limitations. DECT scanning has some requirements: high- and low-energy data should be acquired simultaneously or with a small interval, as a prolonged time gap (for example, related to patient movement, gastrointestinal peristalsis, or the flow of contrast material) results in a spatial mismatch between the two datasets, affecting the image quality. As dual-energy CT analysis is based on the contrast between the X-ray absorption of the two-energy data, the energy difference between the two-energy data should be large; a smaller energy difference instead results in a lower contrast-to-noise ratio. Another limitation associated with DECT and CT imaging, in general, is radiation exposure. Radiation exposure should not exceed 50 mSv per year or 100 mSv during a 5-year period, according to the International Commission on Radiological Protection standards. [88]: although a single CT scan would not exceed these thresholds, nephrolithiasis is frequently a lifelong condition that manifests early in life. The accumulation of scans results in a cumulative radiation exposure. A solution is represented by the application of a tin filter. Second-generation DECT scans were the first equipped with a tin filter in front of both X-ray tubes, which effectively absorbs primarily low-energy photons of limited diagnostic utility, resulting in a reduced radiation exposure to patients [89]. [PERSON] et al., for example, used a tin filter and a low-dose protocol and reported overall high image quality with a calculated mean overall effective dose of 3.34 \(\pm\) 1.84 mSv [90]. [PERSON] et al., also utilized a tin filter and calculated a mean overall effective dose of 3.38 \(\pm\) 1.93 mSv [38]. A study by [PERSON] et al., also supported the usage of the tin filter, demonstrating a 23% reduction in radiation exposure, a decrease in background noise, and improved image quality when compared to DECT without a tin filter [91].
## 4 Discussions
### Limitations and Future Trends  
NDVI can become saturated in areas of high-density green biomass, making it challenging to detect changes in regions with 100% vegetation coverage. Additionally, NDVI may not accurately reflect critical vegetation characteristics during seasonal peaks [48; 116; 117]. Furthermore, within the ZINNR, when wetlands transitioned to grasslands, the NDVI may exhibit an increasing trend, which is a false indication of effectiveness. This is a limitation of NDVI as a single assessing index. Nevertheless, we used a multi-factor collaborative framework to diminish or compensate for such deficiencies in this study. Other factors capturing changes in land use type and habitat fragmentation could reveal when apparent \"improvements\" in vegetation greenness may counter-intuitively signal deteriorating conditions in PAs. Our study underscores the importance of examining conservation outcomes through diverse lenses to obtain a comprehensive perspective. Therefore, in our future work, it will be necessary to corroborate the results obtained using NDVI with other data sources, such as high-resolution imagery and field survey data. To achieve more precise NDVI results and research conclusions, it is crucial to select appropriate observation periods to minimize the impact of seasonal changes in spectral signals on a temporal scale. High-resolution data can enhance the discernibility of various vegetation and ecological features within pixels at the spatial scale, partially compensating for the limitations of NDVI data in areas of high vegetation coverage. However, achieving high temporal and spatial resolutions simultaneously presents a challenge. Moreover, existing spectral indicators have limitations in accurately inverting vegetation feature parameters [117]. Therefore, we strongly recommend that future research employs advanced remote sensing methods, such as microwave remote sensing, to monitor and evaluate the vegetation dynamics in PAs more accurately and comprehensively.  
Apart from the types of PAs analyzed in this study, there are other important ecosystems such as wildlife and grasslands that merit attention. However, our current conservation effectiveness evaluation system does not encompass all types of PAs, presenting a challenge for future research. We aim to direct future research efforts towards addressing this limitation. To develop a comprehensive conservation effectiveness evaluation system, it is necessary to conduct extensive and in-depth studies on methodologies, indicators, and management techniques. This endeavor requires not only building upon existing research findings, but also collaborating with experts from various fields to establish a more comprehensive and scientifically rigorous evaluation framework and research paradigm. We are committed to achieving these objectives to provide increasingly accurate, comprehensive, and advanced scientific support for PA management. Furthermore, it is crucial to pay close attention to minimizing the spillover effects of PAs [118; 119; 120], an issue that will be a key focus of our future research endeavors.
# Damping Estimation of an Eight-Story Steel Building Equipped with Oil Dampers
## 2 Building Description and Its Structural Monitoring System
### Structural Monitoring System  
A structural monitoring system is installed in the building to monitor its seismic motions. The monitoring system consists of three two-directional acceleration sensors for measuring floor acceleration and four pairs of load cell and displacement transducer for recording the force and displacement responses of oil dampers. These accelerometers are placed near the central locations of the 1 st, 4 th, and 8 th floor plans, as shown in Figure 1b. They are utilized to measure the floor translational motions along with the two horizontal directions. Four oil dampers, as representatives of the two-type dampers, were selected to be monitored. Specifically, dampers installed on the 1 st and 8 th floors were chosen, with each floor having two monitored oil dampers along with its two principal directions.  
Figure 3 shows a typical set of measured seismic responses, including floor acceleration and force and displacement of oil dampers in the building translational direction. This data set was recorded during a large earthquake with a moment magnitude of 7.0, which happened on 26 May 2003. The force-displacement hysteretic curve of the oil dampers shows their damping and stiffness properties during this earthquake. The earthquake measurements obtained from this earthquake are utilized in this study to demonstrate the damping estimation process for the building.  
Figure 2: Damper configuration details.