esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
#### 4.1.1 Porosities of Curved Wind Barriers  
Figure 9 illustrates the variations in aerodynamic load coefficients when the HST traveling at 300 km/h passes through curved wind barriers with three different porosities (10%, 30%, and 50%) in a crosswind of 15 m/s. Tables 1 and 2 present the amplitude of changes in the aerodynamic load coefficients and the amplitude of change rates of the aerodynamic load coefficients for the HST passes through curved wind barriers with different porosities. In these tables, \"Enter\" refers to the period from the nose tip of the head car entering until the nose tip of the tail car entering the wind barrier area, corresponding to the time interval of 0.990 s to 1.896 s. On the other hand, \"Exit\" represents the period from the nose tip of the head car exiting until the nose tip of the tail car exiting the wind barrier area, corresponding to the time interval of 2.790 s to 3.696 s.  
Based on Figure 9 coupled with Tables 1 and 2, it can be concluded that the aerodynamic load coefficients of each car of the train have a similar trend when entering and exiting the wind barrier area. These coefficients rapidly decrease upon entering the wind barrier area and sharply increase when exiting. The lower the porosity of the curved wind barriers, the greater the degree of the sudden change in the aerodynamic load of the HST. For example, when the head car of the HST enters a wind barrier with 50% porosity, the amplitude of the change in the lateral force coefficient is 0.0552, and the amplitude of the change rate is 0.3717. However, when the head car enters a wind barrier with a porosity of 10%, the corresponding values are 0.1067 and 0.8269, representing an increase of 93.30% and 122.46%, respectively. Compared with exiting the wind barrier area, the degree of the sudden change in the aerodynamic load of the HST entering the wind barrier area is greater. Furthermore, the head car exhibits the largest variations in aerodynamic load, while the tail car experiences the most intense fluctuations.
# Ibuprofen Removal by Aluminum-Modified Activated Carbon (AC@Al) Derived from Coconut Shells
## 3 Results and Discussion  
### Effect of Adsorbent Dosage  
Taking into account that the dosage of an adsorbent is one of the main parameters affecting the design and operation of an adsorption system, dosage was studied in our batch experiments to determine the ability of the tested material to remove Ibuprofen in aqueous solution, with an initial concentration of IBU 100 mg/L. Different concentrations of AC@Al from 0.2 to 1.0 g/L were used at specific acidic pH conditions [28], i.e., pH 3.0 \(\pm\) 0.1, at a constant temperature of 298 K for 24 h. As presented in Figure 1, with growing adsorbent dosage, the percentage of IBU removal augmented from 85% with 0.2 g/L to 100% with 1.0 g/L.  
Figure 1: Effect of AC@Al dose on IBU adsorption; IBU \(C_{0}\) = 100 mg/L, pH 3.0 \(\pm\) 0.1, \(T\) = 298 K, \(t\) = 24 h.
## 2 Materials and Methods
### Post-harvest Evaluation of Table Grapes under Citral Treatment
#### 2.5.4 Determination of Citral by GC-MS Analysis  
The determination of (E)- and (Z)-citral was carried out by a headspace solid-phase microextraction (HS-SPME) coupled to gas chromatography-mass spectrometry (GC-MS). Specifically, a GC-MS system composed by an Agilent 6890A GC (Agilent Technologies, Palo Alto, CA, USA) coupled to an Agilent 5973N inert MSD mass spectrometer equipped with Triple-Axis HED-EM detector was used. Two plastic trays were used for the citral analysis containing table grape samples and a plastic capsule impregnated with two citral concentrations (MIC and 4 MIC). Determinations were carried out at different sampling days (0, 1, 7, and 15). For vital extraction and desorption, a HS-SPME procedure was performed using a manual SPME sampler holder (Supelco, Belfonte, PA, USA). For analyte extraction, a SPME divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS, 1 cm fiber length) fiber was exposed to the plastic tray headspace for 30 min at 12 \({}^{\circ}\)C. After the extraction, headspace volatiles were thermally desorbed exposing the fiber into the Split/Splitless Injection Port of GC-MS system, which was equipped with a 0.75 mm i.d. Ultra-Inert Liner Straight and was kept at 250 \({}^{\circ}\)C for 5 min in splitless mode. The citral analysis was carried out by the GC-MS system equipped with a VF-WAXms (60 m \(\times\) 0.25 mm i.d., 0.25 um film thickness, Agilent Technologies) fused-silica capillary column and using programmed temperature mode. In particular, the temperature program started at 40 \({}^{\circ}\)C and held for 5 min, then raised at the rate of 0.033 \({}^{\circ}\)C/s to 140 \({}^{\circ}\)C, then raised at the rate of 0.083 \({}^{\circ}\)C /s to 170 \({}^{\circ}\)C, then raised at the rate of 0.833 \({}^{\circ}\)C/s to 230 \({}^{\circ}\)C, and held for 10 min. The helium flow was set to 0.016 \(\times\) 10\({}^{-3}\) L/s. The transfer line, ion source, and quadrupole temperatures were 280, 290, and 150 \({}^{\circ}\)C, respectively. Electron impact Ionization (EI+) mode with an electron energy of 70 eV was used and the mass spectra were recorded in the m/z range of 40-300 u. The total chromatographic run time was 72.2 min. The identification of the two isomeric forms of citral, (Z)-citral (retention time, tr = 53 min) and (E)-citral (tr = 56 min), was carried out by comparing the sample mass spectra with the spectra in the NIST/EPA/NIH Mass Spectral Database (National Institute of Standards and Technology, Version 2.0 f, 2008, Gaithersburg, MD, USA), using a match quality higher than 80. To confirm their identity, the linear retention index (LRI) was calculated for each volatile compound in relation to the retention times of C5-C29 n-alkanes and compared with those reported in the literature [35; 36]. The evaluation of the concentration of the (E)- and (Z)-citral was carried out normalizing the measured peak areas to their peak areas at the first sampling (t = 0), which was assumed to be equivalent to 100%.
# AI-Based Stroke Disease Prediction System Using Real-Time Electromyography Signals
## Related Works
### Stroke Prediction Using Machine Learning and Deep Learning
In an RNN structure, the current output results of cells in the neural network are affected by the previous calculation results,and it has an advantage in learning sequential data because it has memory information about the previous calculation results. Long Short-Term Memory (LSTM), a type of RNN, is a model that overcomes the structural shortcomings of the existing RNN and can solve the problem of calculating and decreasing values when error values are propagated to the neural network layer. This LSTM was first proposed by [PERSON] and [PERSON] in 1997 [PERSON] and [PERSON] in 1997 [PERSON] and [PERSON] (1997). Bloomberg Business Week in the United States described LSTM as AI that can be used and activated in various fields, including disease prediction and music composition. These LSTMs consist of a cell state, an input gate, a forget gate, and an output gate, and vector output values are generated at each gate through the sigmoid layer and the tanh layer. The cells of LSTM learn to recognize important inputs (input gate), learn to preserve them for a long period of state storage and defined time, and execute learning to extract them whenever necessary [PERSON] and [PERSON] (1997); [PERSON] and [PERSON] (1997). In a recent research trend related to LSTMs, studies have attempted to analyze the risk factors of EHRs (Electronic Healthcare Records) to predict LSTM-based cerebrovascular diseases [PERSON] and [PERSON] (1997). Specifically, by incorporating ICD-10 codes [PERSON] and [PERSON] (1997) and other potential risk factors patterns from EHRs, [PERSON] and [PERSON] (1997) confirmed that the LSTM algorithm is the most suitable for predictive analysis of any cerebrovascular disease or stroke. In another study, a methodology based on the LSTM model for predicting HDM (hemorrhagic transformation) in ischemic stroke was proposed by [PERSON] et al. [PERSON] et al. (2017). The LSTM network structure was designed using a combination of DWI (diffusion weighted images) and PWI (perfusion-weighted magnetic response images). A comparative analysis of 155 acute stroke patients collected from clinical trials showed 89.4 percent accuracy. While these studies have shown the potential of LSTM for the prediction of stroke, they have still used data such as EHR and images. In other words, there has been no research on the prediction and analysis of stroke using real-time bio-signals that are generated from everyday activities, such as walking and driving. Therefore, a new methodology for predicting stroke using real-time bio-signals is required as an alternative to the existing traditional methodology.
# Methodology for the Efficient Progressive Distribution and Visualization of 3D Building Objects
## 5 Discussion  
In this paper, we import 3D building models in CityGML data into a self-designed and MongoDB-driven middleware. The important improvement is the creation of a hierarchical tree design to store different detailed instances of the geometry of buildings. Therefore, we developed algorithms to extract different kinds of representations out of a CityGML building in both B-Rep and CSG.  
One of the advantages of CSG is that it can easily ensure that objects are \"solid\" or water-tight if all of the primitive shapes are water-tight. By comparison, when creating geometry based on boundary representations, additional topological data is required, or consistency checks must be performed to ensure that the given boundary description specifies a valid solid object.  
In this paper, the building is selected as the main city object to deal with. For other city objects in CityGML specifications, such as bridges, tunnels or city facilities, the proposed algorithm can also be applied with specific adjustment. Considering the features of the building, cross-section plane-based segmentation can be applied. However, different segment methods should be designed for different types of city objects. In the next study, road and city facilities, such as street lamps, will be studied for progressive CSG representation since these objects are widely distributed across the city. However, landmark city objects such as bridges, towers and some skyscrapers will be preserved in the LOD3 level considering their visual importance.  
A main limitation of the proposed building CSG generation method is that it mainly focuses on rectangular structures although a large proportion of total city models contain rectangular parts. But for non-rectangular structures, the CSG representation algorithm is required and the city model prototype set should be able to be extended automatically, according to different datasets.
## Materials and Methods
### Assessment of Flux Footprint  
To estimate the position and spatial extent of the area that is contributing to the measured carbon flux, a Lagrangian stochastic particle dispersion model was used [51]. In the Lagrangian formulation, the diffusion of scalar released from the surface is assumed to be statistically equivalent to the dispersion of an ensemble of particles that impact the ground within the surface area source and thereafter transport the scalar [52; 53]. The model uses stochastic backward trajectories of particles, which allows for the calculation of the footprint for a measurement point instead of an average over a sensor volume without a coordinate transformation that requires horizontal homogeneity of the flow [53]. Recently improved parameterisation of the model describes the flux footprint function's upwind extent and the crosswind spread of the footprint [51]. Data used in simulation are wind velocity, wind direction, measurement height, surface roughness, friction velocity, Monin-Obukhov length, and zero-plane displacement. Model is available as an online tool [54]. Flux footprint analysis showed that the fetch for the 90% contribution to measured flux before the elevation of the tower in 2011 was \(\sim\)350 m, and after the tower was elevated it was \(\sim\)500 m (Figure 2).  
In addition, the footprint analysis confirmed that the contribution to the measured fluxes arriving from areas outside of the forest (i.e., agricultural areas, highway, and the town of Jastrebarsko with distances from EC tower of 1.2 km W, 2.4 km NW, and 5 km NW, respectively) is likely negligible. All 24 circular plots with installed dendrometer bands on trees (see Section 2.5) lay within the footprint of EC tower.
## 4 Artificial Intelligence (AI)-Enabled 6G Networks
### Channel Estimation  
To fulfill the demanding requirements of smart city applications in terms of high data rate (Tbps), low latency (order of 0.1-1 ms), and high reliability (order of \(10^{-9}\)) [37], the 6G radio access will be enabled by emerging technologies such as Terahertz communication [38], visible-light communication, ultra-massive multiple-input multiple-output (MIMO) [39], and large intelligent surfaces [40]. These technologies will increase the complexity of the radio communication channels, making efficient channel estimation challenging using the traditional mathematical approaches. The wireless communication channel attenuates the phase shifts and attenuates and adds noise to the transmitted information. In this context, channel estimation can be defined as the process of estimating the characteristics of the communication channel to recover the transmitted information from the channel effect. To increase the performance and capacity of 6G communication, precise and real-time channel estimation becomes crucial. Recently, DL has gained wide attention for precise channel estimation. Figure 4 shows a DL-based channel estimation process where the signal is first transmitted along with some pilot (reference) signals. The  
Figure 3: Artificial Intelligence (AI)-enabled 6G networks.  
effects of the channel on the pilot signals are then extracted. The channel characteristics are then estimated by a DL method using the interpolated channel.  
[PERSON] et al. [41] proposed a Deep Neural Network (DNN)-based approach for channel estimation and symbol detection in an Orthogonal Frequency Division Multiplexing (OFDM) system. The DNN model is trained offline using OFDM samples generated using different information sequences under distinct channel conditions. The model is then used to recover the transmitted information without estimating the channel characteristics. [PERSON] et al. [42] proposed a Convolutional Neural Network (CNN)-based channel estimation framework for massive MIMO systems. The authors used one-dimensional convolution to shape the input data. Each convolutional block consists of a batch normalization layer to avoid gradient explosions [43] and a Rectified Linear Unit (Relu) activation function.
## 1 Introduction  
The fatigue failure beyond the traditional fatigue limit above \(10^{7}\) cycles is called very high cycle fatigue (VHCF) [1], ultra-high cycle fatigue [2], ultralong life fatigue [3], as well as very high fatigue life regime or gigacycle regime [4]. This fatigue regime is constantly gaining attention because of the efforts in lightweight construction and the resulting use of highly stressed components, as well as the higher traveling speed combined with the demand of high service life. Many components and parts such as automotive engines, rotors of helicopters, turbines, as well as wheels and axles of high-speed trains are subjected to cyclic loadings with cycles exceeding the traditional fatigue limit of \(10^{7}\) cycles by far [5; 6; 7]. To ensure safe design of those components, the characteristic fatigue mechanism in the VHCF-regime, which differs from the low cycle fatigue (LCF) and high cycle fatigue (HCF) must be understood. While internal crack initiation with the characteristic fish-eye morphology named by [PERSON] et al. [8] can be observed in the HCF-regime a unique change in microstructure in the direct vicinity of the crack-initiating defect is observed in VHCF-regime. This characteristic microstructural feature in the VHCF-regime was designated \"optically dark area\" (ODA) [9], \"granular bright facet\" (GBF) [10], \"rough granular area\" (RGA) [11], or \"fine granular area\" (FGA) [12] depending on the observation method. However, all four designations describe the identical fracture area that differs from the original structure. In several investigations [12; 13; 14; 15; 16] of the fracture surface area it could be shown by means of selected area electron diffraction (SAD) in the transmission electron microscope (TEM) that this area is a three-dimensional fine-grained layer, which is the reason why the term FGA has become established.  
However, the formation of this fine-grained layer is still not fully understood. For example, it is still unclear whether the formation of the FGA is a prerequisite or a consequence of the long crack-initiation period. Different experiments and observations led to different models and mechanisms to explain FGA-formation. A historical overview of the research topic is included in the recent review by [PERSON] and [PERSON] [17], while an overview of the effects of structural and operational factors influencing the VHCF behavior of steels is provided by the review of [PERSON] and [PERSON] [18].  
The main objective of this review is to provide an overview of the found characteristics concerning the FGA-formation process as well as the observed properties of the FGA. Furthermore, those characteristics are used to critically review the different postulated models and underlying mechanisms. The aim is to showcase models that can be considered refuted and to identify missing explanations for different observations concerning the possible models. This critical examination is supposed to lead to the identification of further necessary investigations or possible explanatory approaches.
## 1 Introduction
Based on the experiences of various stakeholders who recently experienced large-scale disaster events in Indonesia, there are five CDR domains, namely social, economic, institutional, infrastructural, and environmental domains [26]. Such domains were implemented to quantitatively assess CDR on flood disaster in a district, or _kecamatan_ in Bahasa Indonesia, in the municipality of Bojonegoro of the Eastern Java province [27]. Another research focused on the assessment of social domain of CDR in post-disaster area [28]. Unfortunately, there is a lack of information on CDR development in disaster-prone area. Moreover, there is no explanation yet on the difficulties to relocate people to a safer area, such as in the municipality of Banda Aceh of the Aceh province after the 2004 Indian Ocean Tsunami [29], as well as in the municipality of Pandeglang of the Banten province after the 2018 Sunda Strait Tsunami and in the municipality of Sukabumi of the Western Java province after the 2018 landslide [28]. One of the objectives of the research highlighted in this paper was to develop community resilience to disaster in Indonesia. The research considered the Japanese experience on developing CDR in Indonesia. This research also put in mind special social, economic, institutional, infrastructural, and environmental characteristics of Indonesia. Most importantly, the research focused on the development of CDR in a disaster-prone area to explore the possibility to develop a resilient community that has not experienced a large-scale disaster. It is expected that the community will be adequately resilient in case of the occurrence of various scale of disaster. Furthermore, the mentioned research was not intended to assess the degree of community resilience in Indonesia. Rather, the research focussed on closing the gaps on CDR development on each subcategory. Furthermore, this paper highlights the completeness of data concerning the existing CDR condition. To initiate the development of CDR in a disaster-prone area without any experience on a large-scale disaster occurrence, the Lembang Fault area was chosen as the pilot. Extending approximately 29 km in the west-east direction to the north of the municipality of city of Bandung, the Lembang Fault area is a geological hazard-prone area. Between 2010 and 2017, 13 earthquakes with magnitude between 0.4 and 3.3 Mw occurred in the area [30]. With the annual fault movement of 6 mm [31], Lembang Fault activity may produce an earthquake with a magnitude between 6.5 and 7.0 Mw, and a repetition time between 170 and 670 years [32]. Development of CDR in the Lembang Fault area is considerably important as the hazard may affect 8 million people in the municipality of the city of  
Figure 1: The number of natural hazards, including flood, tornado, landslide, earthquake, forest and land fire, tidal and abrasion and volcanic eruption, as well as their fatalities in Indonesia between 2010 and 2020 [15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25]. Bandung, the municipality of Western Bandung, the municipality of Bandung, and the municipality of the city of Cimahi [33].
## 5 Discussion
### Evolution of Ore-Forming Fluids  
The migration and enrichment of trace elements in fluids during the formation of ore deposits are influenced by physical and chemical factors such as temperature, pressure, and oxygen fugacity [52]. The ratio of Co to Ni in pyrite is commonly utilized to assess the formation temperature of the minerals [53]. Generally, pyrite associated with volcanic activity exhibits Co/Ni ratios between 5 and 50. Hydrothermal pyrite, formed at temperatures above approximately 200 \({}^{\circ}\)C, displays Co/Ni values between 1.15 and 5. Pyriteformed through sedimentation under low-temperature conditions typically has Co/Ni ratios of less than 1 [54]. The Co/Ni ratios of Jiaojia pyrite during different mineralization stages are shown in Figure 8. Stage I had Co/Ni values ranging from 0.50 to 1.47 with an average of 1.19. Stage II showed Co/Ni values ranging from 0.27 to 1.69 with an average of 1.01. Stage III exhibited Co/Ni values ranging from 0.58 to 294.89 with an average of 24.95. These data suggest that Py I and II were formed by hydrothermal fluids at higher temperatures than Py III.  
In Stage III, the Co/Ni ratios exhibited an anomaly, as shown in Figure 5, with Co showing an abnormality. During the late-stage mineralization of the Jiaojia gold deposit, immiscibility of the fluids likely occurred as the increase in the Co content in Stage III can be attributed to the immiscibility of the sulfide fluids [55]. As the temperature decreases, the immiscible sulfide liquid (ISL) transforms into a monosulfide solid solution (MSS) and residual liquid (REL). The partition coefficient of Co between MSS and REL was greater than 1, indicating that Co easily entered the MSS during differentiation [56]. The homogenization temperature of various inclusions in the main mineralization stage of the Jiaojia gold deposit mainly ranged from 200 to 360 \({}^{\circ}\)C [57].  
The Cu/Au ratio in pyrite also reflects the oxidizing conditions of fluids [45]. When the Cu/Au ratio is approximately 1, the process occurs exclusively in oxidized environments. However, when the Cu/Au ratio exceeds 1, Cu directly replaces Au, indicating a reducing environment [45; 58]. The Cu/Au ratios in the Jiaja pyrite in all mineralization stages were greater than 1, indicating a relatively low oxygen fugacity of the ore-forming fluids.  
In conclusion, the ore-forming fluid of the Jiaojia gold deposit was situated within a medium- to low-temperature reducing environment, with temperatures gradually decreasing from ore stages I to III.
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 5 Clustering Process Optimization
### Data Aggregation Phase
#### 5.3.1 Tree-Based Data Aggregation  
These schemes are used for distributed data aggregation, and rely on some network data aggregator nodes; this ensures that the data paths of the SNs contain these data aggregator nodes [60]. The protocols in this category are used to construct an energy-aware data aggregation tree [61].