esa-sceva/satcom-chunk-collection huggingface dataset:
# Removal of Chromium from Synthetic Wastewater Using Modified Maghemite Nanoparticles
## Materials and Methods
### Batch Adsorption Experiment and Chromium Analysis  
Batch experiments were conducted in 250 mL conical flasks (in duplicates) containing 20 mL of chromium solution of the desired concentration. Predetermined concentrations of adsorbents were added to these flasks. All the experiments were performed in an orbital shaker at room temperature (\(\sim\)35 \({}^{\circ}\)C) and 200 rpm. The pH of the solution was monitored using a digital pH meter (Orion 3-star, Thermo Electron Corporation, USA). Based on the results from preliminary kinetic studies, a stirring time of 80 min was selected as the equilibrium time. Upon completion of the experiments, the separation of the liquid phase from the spent adsorbent was achieved by filtration using Whatman 42 (2.5 \(\upmu\)m) filter paper. The chromium concentration in the filtrate was measured using an atomic absorption spectrometer (Varian SPECTRA A240, USA). The current was 7.0 mA, with the detection wavelength set at 357.9 nm and with a slit bandwidth of 0.2 nm, as recommended by the manufacturer. The percentage of chromium removal was calculated using Equation (1):  
\[RE\ (\%)=\frac{C_{0}-C_{e}}{C_{0}}\times 100 \tag{1}\]  
where \(RE\) is the chromium removal efficiency (\(\%\)), \(C_{0}\) and \(C_{e}\) are the initial and final or equilibrium concentrations in the solution (mg/L), respectively.  
Figure 1: (**a**) Fourier transform infrared spectrophotometer (FTIR) analysis for maghemite nanoparticles (NPs). (**b**) FTIR analysis for modified maghemite nanoparticles (MNPs).  
The adsorption equilibrium time was determined through preliminary experiments at an initial concentration of 20 mg/L, pH of 6.0, and at room temperature over a period of 120 min. The optimal contact time was found to be 80 min. Then, 10 mL of solution was taken in six conical flasks of 250 mL capacity and 0.05 g of MNPs were added to the solution. The flasks were taken out at regular intervals from the shaker, as stated above, and the solution was separated using the standard magnetic decantation procedure.
# A Layer-Wise Strategy for Indoor As-Built Modeling Using Point Clouds
## 4 Experiments
### Modeling Outputs  
Firstly, we utilized three terrestrial point clouds to test the proposed reconstruction method from 3D primitives. Table 2 lists the statistics of the reconstructed outputs and the parameters need to be adjusted in the reconstruction. Figure 20 contains the original point clouds and the reconstructed results. We also provided the reconstructed models rendered with a transparent view to display the  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Point Clouds** & **Apartment 1** & **Apartment 2** & **Office 1** & **Office 2** \\ \hline \# of points [million] & 5.0 & 4.5 & 10.1 & 52.2 \\ \# of stations & 15 & 14 & 32 & n/a \\ \# of rooms & 6 & 5 & 10 & 18 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The description and statistics of the input datasets.  
Figure 19: The process and results of building reconstruction: (**a**) The result of contour extraction (in layers), (**b**) the result of initial contour assembly, (**c,d**) the front view of building reconstruction overlapping with point clouds, and (**e,f**) the top view of reconstruction results. The colored point clouds in (**c,e**) represent the labels of segmentation, and the original point clouds are depicted in (**d,f**).  
inner spaces. In general, the proposed method can offer detailed 3D models including non-Manhattan structures, different heights of ceilings, and prominent structures.  
In the first and third datasets, all rooms have been reconstructed successfully. Nevertheless, one room in Apartment 2 was discarded in the reconstruction. The red circles in the middle column of Figure 20 denote the corresponding locations in the original point clouds and reconstructed models. The main reason behind this phenomenon is that this room was scanned incompletely. Furthermore, the noise filter aggravated the reduction of points, especially in the lower density area. Therefore, when the algorithm detected planar primitives, the number of supporting points in this room was insufficient.
# The Current Development Status of Agricultural Machinery
## 2 Power System of Agricultural Machinery Chassis
### Electric Power System
[67] developed a dual-motor electric tractor capable of delivering a maximum output power of 13.9 kW and continuous operation for up to 4.5 h, meeting the requirements of orchard operations. To advance intelligent mechanized harvesting for small green vegetables, [PERSON] et al. [68] designed a self-propelled intelligent combine harvester capable of performing cutting, clamping, conveying, and collecting operations simultaneously. In summary, current research on electric powertrain systems for agricultural machinery primarily focuses on achieving high torque output, enhancing adaptability to complex operational conditions, and optimizing energy efficiency. These efforts are characterized by the technical feature of \"algorithm-driven hardware coordination.\" In China, the development of electric agricultural machinery remains at an early exploratory stage, with most studies currently centered on theoretical validation and prototype testing. Several critical challenges persist: (1) insufficient electromechanical coupling optimization in powertrain systems, resulting in low energy conversion efficiency; (2) a lack of endurance prediction models that can support continuous operation under complex working conditions; (3) inadequate chassis dynamics modeling, which limits terrain adaptability; and (4) immature multi-mechanism cooperative control algorithms that negatively impact operational precision. To address these issues, breakthroughs in core technologies must be pursued through a mechatronic-hydraulic-integrated design--such as terrain-motor torque mapping based on comprehensive chassis dynamics models--as well as adaptive control algorithms tailored for variable working conditions and decision-making frameworks leveraging multi-source information fusion. Guided by the strategic directions of \"intelligent electromechanical coupling, refined energy management, and precise scenario adaptation,\" these technological advancements aim to overcome the practical implementation barriers faced by electric agricultural machinery operating in hilly and mountainous regions.
## 2 Material and Methods
### LVO Definition  
For this study, the term intracranial LVO was classified into two types: (1) proximal anterior circulation occlusions involving the supraclinoid internal carotid artery (ICA) segment and the middle cerebral artery (MCA-M1); the MCA-M1 segment was defined as from its origin to its genu along the inferior aspect of the Sylvian fissure. The MCA beyond the origin of the anterior temporal artery or beyond early bifurcations was considered as an M1 continuation, provided the postbifurcating course preceded the genu; and (2) distal anterior circulation occlusions involving the MCA-M2. The M2 segments were defined as those immediately distal to the MCA bifurcation/trifurcation that ascend vertically within the Sylvian fissure.  
Proximal occlusions were further classified as limited to supraclinoid ICA, ICA extending to proximal part of MCA-M1, and limited to MCA-M1.
# Kinetics Study of Gas Pollutant Adsorption and Thermal Desorption on Silica Gel
## 2 Materials and Methods
### Experimental Apparatus and Procedure  
Figure 2 shows the experiment diagram for studying the adsorption and thermal desorption kinetics for silica gel. The adsorption and desorption performances of the loaded adsorbent particle were evaluated by using a TGA analyzer (SDTA851e, Mettler Toledo GmbH., Schwerzenbach,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Unit** & **Value** \\ \hline BET specific surface area & m\({}^{2}\)/g & 610 \\ Total pore volume & cm\({}^{3}\)/g & 0.36 \\ Average pore size & nm & 2.4 \\ Mean particle diameter & mm & 2.10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic physical properties of fine-pore silica gel.  
Switzerland) at flow rate of 100 sccm. The TGA analyzer can accurately measure the mass change of the adsorbent in these experiments. A low-temperature thermostat bath (DC-85 MC, Ningbo Tianheng Instrument Factory Co. Ltd., Ningbo, China) maintained an isothermal environment. Flow rates were controlled using mass flow controllers (MC-200 SCCM-D/5M-5 IN, MC-50 SCCM-D/5M-5 IN, Alicat Scientific, Inc., Tucson, AZ, USA). The inlet concentrations of carbon dioxide and methane were monitored by using an infrared gas analyzer (GXH-3011H, Institute of Beijing HUAYUN Analytical Instrument Co. Ltd., Beijing, China). Toluene gas was supplied from a gas bottle with 609 ppm toluene/nitrogen standard gas. Prior to an adsorption run, the silica gel was heated to a temperature of 393 K for at least two hours under a stream of carrier gas. This experimental pretreatment method can remove the impurities that might be adsorbed on the surface.  
Nitrogen was the carrier gas in the following adsorption experiments of silica gel. The adsorption temperature was controlled at 298 K. Three adsorption experiments were separately carried out using carbon dioxide, methane, and toluene. The concentrations of the three gas pollutants were 1000 ppm, 1000 ppm, and 609 ppm, respectively.  
The thermal desorption experiments of toluene on the silica gel were conducted at three different temperatures of 313 K, 323 K, and 333 K, once the toluene adsorption reached saturation. In the thermal desorption experiments, the concentration of toluene was 609 ppm as before. The toluene concentration in the regeneration gas was employed to evaluate the feasibility of using the exhaust air regeneration.  
Figure 2: Experimental diagram and experimental apparatus; (**a**) schematic diagram of silica gel thermogravimetric analysis (TGA) experiments; (**b**) experimental apparatus.  
These temperatures were employed in order to evaluate the enthalpy of desorption, which can reveal the kinetics of thermal desorption.
# TSNetIQ: High-Resolution DOA Estimation of UAVs Using Microphone Arrays
## 4 Simulation
### Datasets Generation  
This paper simulated scenarios where the source signal arrives from various directions by rotating the microphone array. In each loop, a random rotation angle was set for the microphone array, which also represents the true angle of arrival \(\theta\) of the source signal. To reduce data volume, a 3 s segment of the sinusoidal signal was extracted as our audio data, stored in.npy files. Each data entry is an \(2M\times(f_{s}\times T)\) matrix. The angle of arrival \(\theta\) was used as the file name to facilitate data preprocessing during model training. This simulation generated 50,000 random datasets as the training set through a loop, and the distribution of the training set is shown in Figure 4. Our data was evenly distributed within the range from 0 to \(360^{\circ}\), and there was no situation when there were too many datasets in any direction. This experiment generated 10,000 random datasets as validation sets, as the true arrival angle \(\theta\) of our source signal has a resolution of \(0.1^{\circ}\).  
Figure 4: Arrival angle distribution of the training dataset.  
Figure 3: Diagram of the simulation scenario.
## 4 Optimization of ML Models and Results
### Model Performance  
GWO, GA, and PSO algorithms were used to optimize the parameters of the RFR, BR, and GBR. The R\({}^{2}\), R, RMSE, and MAE were used as the evaluation indicators for each regression model. To comprehensively evaluate the prediction performance of each model, a ranking method was adopted, i.e., the R\({}^{2}\), R, RMSE, and MAE of each model were sorted, with the best one achieving nine points. Finally, the total scores of each model were sorted.  
As shown in Figure 7, the rank score of GWO-GBR for the X-PPV, Y-PPV, and Z-PPV were 36, 34, and 36, respectively. The rank score of GA-GBR for the X-PPV, Y-PPV, and Z-PPV were 31, 31, and 32, respectively. The rank score of PSO-GBR for the X-PPV, Y-PPV, and Z-PPV were 29, 31, and 28, respectively. Thus, GWO-GBR, GA-GBR, and PSO-GBR were the first three models used to predict the performance of the X-PPV, Y-PPV, and Z-PPV.
## 3 Analysis of Numerical Stability and Accuracy
In the same time, if \(\rho_{\infty}\) is a constant, the decay rate of this algorithm turns out to be larger when \(\beta\) is smaller. As shown in Figures 2 and 3, the PE and AD are also related to \(\rho_{\infty}\) and \(\beta\). When \(\rho_{\infty}\) maintains a constant value, the trends of PE and \(\beta\) are positively correlated, while those of AD and \(\beta\) are opposite. Moreover, when \(\rho_{\infty}\) becomes larger, both AD and PE become smaller. The comparison of the GGW method and the proposed method is shown in Figure 4. When \(\Delta t/T\) is small (e.g., \(\Delta t/T\approx 10^{-2}\) in Figure 4), the spectral radius of the GGW method starts to reduce, while the spectral radius of the proposed method starts to reduce when \(\Delta t/T\) is larger (e.g., \(\Delta t/T\approx 10^{-1}\) in Figure 4). This illustrates that the proposed method improved the disadvantage of too fast numerical dissipation in the GGW method.
# A Survey of the Effects of Vehicle Configuration on Urban Air Mobility +
## 2 Aerial Urban Vehicle Configurations and Concepts
### Horizontal Take-Off and Landing (HTOL)
#### 2.1.2 Morphing-Wing and Flying Car (Roadable Aircraft)
The most common failures are motor and propulsion failure, as well as actuator failure. Additionally, flying cars are vulnerable to wind due to their large body or fuselage. To function both as a conventional car and an airplane, flying cars should have a light and sturdy chassis and body to prevent crashes from small impacts on the ground and sky [31; 32].
## 2 Data and Methodology
### Data
#### 2.1.2 Reanalysis Data  
The reanalysis data used in this study came from the ERA5 hourly data (ERA5 from now on) provided by the European Center for Medium-Range Weather Forecasts. It has a temporal resolution of one hour and a spatial one of 0.1\({}^{\circ}\), and it contains values of multiple variables for each hour and grid point [21].  
The ERA5 data used in this study covers 10 years, from 2010 to 2019, both included. The 9 grid points closest to the anemometer were selected. As seen in Figure 2, they form a 3 \(\times\) 3 square around the town of Eibar, being the one in the center the closest to the anemometer.  
From all available variables, the 10-m and 100-m components of wind were used. The wind speed module and direction were calculated from these components, so the ERA5 data would be similar to the anemometer's.  
Figure 2: Location of the studied building (**red**) and ERA5 grid points (**blue**). The grid points are numbered according to their proximity to the anemometer.
# Design of a Fixed IF Down-Conversion Double-Balanced Mixer for UHF Band Applications
## 4 Simulation and Experimental Results
### Mixer Layout Design and EM Simulation  
Figures 21 and 22 present the simulated and, respectively, validated prototype of the down-conversion double-balanced differential mixer.  
The planar mixer board measures roughly 1.650\({}^{\prime\prime}\)\(\times\) 1.000\({}^{\prime\prime}\)\(\times\) 0.140\({}^{\prime\prime}\) overall and includes the local oscillator balun (0.800\({}^{\prime\prime}\)\(\times\) 0.800\({}^{\prime\prime}\)\(\times\) 0.084\({}^{\prime\prime}\)). It is fabricated on a six-layer (each layer is Rogers 4003 1 oz Cu 8 mil height substrate) board. The top layer consists of the first half of the circuit's symmetry and local oscillator balun, while the bottom layer consists of the other half of the circuit's symmetry and IF transformer. The DC bias line, common ground, and signal connection of the LO and RF signal run in the intermediate layers.  
Initially, the two-layer board was used for implementing the mixer design presented in Figure 2. Transistors on the board were not isolated from one another, which caused problems with cross-talk between LO and RF signals. Active devices were arranged symmetrically to address this layout problem (the layout optimization is visible in Figure 21). To reduce the LO-RF feedthrough, the layout minimized cross-overs of the LO and RF signal lines wherever feasible.  
Figure 21: Optimized final EMcosim layout of the novel double-balanced mixer: (**a**) top view of the PCB and (**b**) bottom view of the PCB.  
Figure 20: Extrapolation of IP3 from fundamental and third harmonic tone power.
# Carbon Dioxide Uptake by Brazilian Cement-Based Materials
###### Abstract  
The worldwide cement industry plays an important role in addressing the climate change challenge. Brazil's cement industry currently has 91 cement plants with an installed production capacity of 94 million tons per year and has started to calculate the net CO\({}_{2}\) emissions to achieve a carbon-neutral cement sector by 2050. Accordingly, the carbon dioxide uptake due to mortar and concrete carbonation is subtracted from the carbon dioxide emitted by the chemical reaction for the calcination of lime, i.e., the calcination process performed during clinker production. Nowadays, the Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas (GHG) Inventories to report the GHG emissions do not include any calculation procedure to consider the mortar and concrete carbonation. However, the Intergovernmental Panel on Climate Change (IPCC)'s Sixth Assessment Report (AR6) recognizes the physico-chemical process known as carbonation. Brazilian net carbon dioxide emissions of cements produced from 1990 to 2019 are estimated considering the carbon dioxide uptake during the service-life and end-of-life and secondary usage stages (Tier 1). This is a fundamental scientific and technological novelty that changes the current approach to estimate the carbon dioxide emissions due to the Portland cement clinker production. Even considering the relative novelty of this approach, it should be promoted in the future and included in the national inventory report (NIR). The carbon dioxide uptake by mortar and concrete carbonation for 30 years is about 140 million tons. Within this thirty-year period about 483 million tons have been released due to the calcination process.  
carbon neutrality; climate change; carbon dioxide uptake; carbon dioxide capture and utilization; mitigation; negative-CO\({}_{2}\) concrete; Brazilian cement industry
## 4 Proposed Framework
### Layer 2--Feature Engineering  
The second layer, or feature engineering, takes its name because it is where raw data from system calls is processed into features (characteristics) that will compose the datasets of input to the machine learning model. According to [13; 19], data preparation and features extraction are decisive factors and therefore need more attention when compared to choosing the best detection model. Thus, a critical part of a project involving machine learning is defining a good set of features data that will be used for training the [35] model. According to [16], the selection of features is useful to decrease the computational difficulty, eliminate data redundancy, improve the detection rate of machine learning techniques, simplify the data and reduce false alarms. Thus, it can be said that the selection of features in framework begins with determining which filters will be used in Sysdig to capture attributes of system calls.  
Logstash is the tool used to consume Redis data and filter and process raw data from system calls [33]. Through Logstash, it is possible to manipulate and treat each attribute that makes up a system call, converting strings into numerical values, discarding unnecessary information and adding other information if necessary. To avoid an additional processing load on cluster Kubernetes, Logstash was configured on a host dedicated to this service. If necessary, multiple instances of Logstash can be configured, allowing the scalability of this service and greater capacity in processing the data flow. After data processing by Logstash, each event corresponding to an invoked system call is indexed as a document in Elasticsearch in indexes that can be created dynamically according to the number of documents indexed or at predefined time intervals. Indexes in Elasticsearch are the data structures that contain the datasets with the system calls and their features already encoded in numerical format, suitable for the processing done by the machine  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Filter** & **Description** \\ \hline evt.num & Incremental event number. \\ \hline evt.time.iso8601 & Event timestamp in iso8601 format (including nano seconds and timezone). \\ \hline proc.name & Name of the process that generated the event. \\ \hline evt.dir & Event direction (‘\(>\)’ for input events and ‘\(<\)’ for output events). \\ \hline evt.category & Event category (e.g., ‘file’ for file opening and closing operations, \\  & ‘net’ for network operations, etc.). \\ \hline syscall.type & Name of the invoked system call. \\ \hline evt.latency & Delta between an exit event and the correspondent enter event, in nanoseconds. \\ \hline evt.rawres & Event return value as a number. \\ \hline evt.info & Returns the arguments as the evt.args field and higher level information. \\ \hline \hline \end{tabular}
\end{table}
Table 2: System calls attributes collected by Sysdig.  
learning algorithms [33]. Just as communication between Logstash and Redis requires authentication, so does the integration between Logstash and Elasticsearch.
# Covert Channel Based on Quasi-Orthogonal Coding
## 2 Radio Physical Layer Steganography
### Quasi-Orthogonality  
We speak of orthogonality when the inner product of the \(x_{1}(t)\) and \(x_{2}(t)\) signals is zero, according to the formula:  
\[\int_{-\infty}^{\infty}x_{1}(t)\cdot x_{2}^{*}(t)dt=0 \tag{6}\]  
The correlation of two time-limited signals defined over the time interval \(0<t<\tau\) is defined as:  
\[r(t)=\int_{-\infty}^{\infty}x_{1}(\rho)\cdot x_{2}^{*}(\rho-t)d\rho, \tag{7}\]  
where \(\rho\) is a dummy variable.  
For two orthogonal signals for each \(t\) shift, a zero correlation is obtained if these signals are disjoint in frequency domain. Hence, separated band-limited signals in frequency are the main method of obtaining orthogonal signals. For signals that occupy the same frequency band, there is no possibility of zero cross-correlation for any \(t\) time shift.  
Quasi-orthogonality [21] refers to signals (waveforms) that exhibit low cross-correlation. Two waveforms are quasi-orthogonal if:  
\[\left|\int_{-\infty}^{\infty}x_{1}(\rho)\cdot x_{2}^{*}(\rho-t)d\rho\right|<\varepsilon \tag{8}\]  
where \(\varepsilon\ll 1\) for any \(t\), and \(x_{1}\) and \(x_{2}\) are normalized to a unit energy value.