esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Antibacterial Activity of Mango By-Products
#### 3.2.3 Human Pathogenic Bacteria  
To compare the antibacterial activity of our extracts with other published studies, we used potentially human pathogenic bacteria that are generally tested in in vitro assays: _E. coli_, _K. pneumoniae_, _P. aeruginosa_ and _S. dysenteriae_. The agar diffusion assay showed the antibacterial properties of the extracts (from peel and seed) on all the species tested. _P. aeruginosa_ was the most susceptible species and, in general terms, the extracts obtained by process B showed a higher inhibitory capacity than those obtained by process A. Also, no differences were observed between cultivars.  
The serial dilution assays confirmed that all extracts had antibacterial properties, with MICs between 10 and 240 mg GAE/mL (Table 4). In the same way as in the agar diffusion assays, differences between species were detected; _P. aeruginosa_ was the most sensitive (MIC 10 mg GAE/mL) and _K. pneumoniae_ was the most resistant species (MIC 240 mg GAE/mL).  
No differences in the MIC values of peel extracts were observed among the extraction processes of the four bacterial species tested, except for _S. dysenteriae_, which showed a higher susceptibility to the extracts obtained with process B. Likewise, no differences in MIC values among cultivars (in peel and seed extracts) were observed, except in _P. aeruginosa_, where Keitt seed extracts had a lower MIC (10 mg GAE/mL) than Gomera-3 and Sensation (30 mg GAE/mL). The MBC results indicated that mango extracts had a bacteriostatic effect on the species tested (Table 4). Finally, [PERSON]'s analysis revealed a significant negative correlation coefficient between the MICs of _P. aeruginosa_ and the concentration (g/100 g dw) of flavonoids, gallates and gallotannins, and total phenolic compounds (r = \(-\)0.8216, \(p\) < 0.05). Likewise, a significant negative correlation was found between the MICs of _S. dysenteriae_ and the concentration of gallates and gallotannins (r = \(-\)0.7303, \(p\) < 0.05) (Table 2).
## 4 The Proposed Approach
### The Multi-Agent System (MAS) Proposed
They aid in the loading and unloading procedures, which are determined by the global agent's optimisation tactics. The system stays effective and responsive to demands in real time by dynamically managing each port's capacity and operating status to match the flow of trucks and containers. Interaction of the Two-Stage Optimisation (2 Stage-Opt) Model with the Multi-Agent System Simulation (MASS)  
The interaction between the 2 Stage-Opt model and the MASS framework is a crucial component in validating and operationalising the proposed container optimisation strategy. These models interact as shown in Figure 4. In the 2 Stage-Opt model, Stage 1 (Heuristic) begins by generating potential solutions for the container loading configurations. Each solution undergoes a feasibility check to determine whether it meets the space, weight, and other constraints defined by the model. In Stage 2, the 2 Stage-Opt model takes the feasible container groupings from Stage 1 and assigns them to trucks in a way that minimises the empty space in the containers. Once the optimisation model finds an ideal configuration for the container assignments, it produces a list detailing which containers should be loaded onto which trucks. This assignment list is then fed into the MASS, where it becomes the basis for simulating the operational dynamics of the logistics network. The MASS is based on the MAS proposed in Section 4.2 and uses the optimised configuration to initiate and manage interactions among various agents, such as trucks, containers, and nodes. The MASS framework plays the role of testing the validity of the 2 Stage-Opt solution. Upon initiation, the MASS begins by managing the flow of information between various agents, simulating the real-time interactions of trucks, containers, and nodes (such as ports and loading stations). When all the containers have been successfully shipped according to the optimised assignments, the MASS framework collects operational data that can later be analysed to assess the 2 Stage-Opt model's performance in real-world scenarios. Figure 3: UML diagram of the MAS’s behaviour.
# NaviSoC: High-Accuracy Low-Power GNSS SoC with an Integrated Application Processor
## 2 SoC Overview and RF Architecture
### Radio Architecture for GNSS Signals  
A detailed block diagram of the GNSS radio is shown in Figure 2. It is divided into three main modules:  
* RF circuits that amplify and filter the GNSS signals received from an external passive antenna and pass them to the mixers,
* An LO (local oscillator) generation module implemented as an ADPLL (all-digital phase-locked loop),  
Figure 1: Block diagram of the CCNV1-A1 GNSS receiver system-on-chip (SoC).  
* IF (intermediate frequency) blocks (programmable gain amplifier (PGA), low pass filter (LPF), automatic gain control (AGC)) that process low-frequency I/Q (in-phase and quadrature) signals for 3-bit complex analog-to-digital converters (ADC).  
This approach allows complex processing to be moved to the digital domain, making the receiver more flexible. The GNSS AFE (analog front-end) was used to receive either any single GNSS signal or all GNSS signals centered around L1/E1 and L5/E5 bands simultaneously (see Figure 3).  
Figure 3: GNSS signals received and processed by the CCNV1-A1 chip: (**a**) L1/E1 band, (**b**) L5/E5 band.  
Figure 2: Block diagram of the dual-frequency GNSS AFE radio block.
# Process Optimization and Wear Performance of Plasma-Cladding Fe5 Coatings on Rotary Tillage Blades
###### Abstract  
Objective: This study was conducted to address the harsh working environment of agricultural machinery and improve the wear resistance of soil-contacting components such as rotary tiller blades, thereby extending their service life. Method: Plasma-cladding technology was employed to prepare an iron-based wear-resistant coating on the surface of rotary tiller blades. The following parameter combination was optimized using response surface methodology (RSM): a cladding current of 144A, a cladding speed of 23 mm/s, a powder feeding rate of 23 g/min, and a cladding distance of 12 mm. The microstructure morphology, phase composition, microhardness, and wear resistance of the wear-resistant cladding layer were investigated. Results: The results indicate that the interface of the cladding layer is clean and free from significant porosity or defects, exhibiting good metal-lurgical bonding with the substrate. The primary phases identified in the cladding layer include \(\alpha\)-Fe, Cr\({}_{7}\)C\({}_{3}\), Cr\({}_{2}\)Fe\({}_{14}\)C, and Cr-Ni-Fe-C solid solutions. The average hardness of the cladding layer is 1171 Hv0.5, approximately 2.9 times that of the substrate. In wet sand-rubber wheat weast under identical conditions, the weight loss of the cladding layer is only 1/21 that of 65 Mn steel, with minimal wear morphology. Field trials showed that the wear of the cladding layer rotary tiller blade was reduced by 24.5% compared with the unclad blade. The presence of the cladding layer significantly protected the integrity of the cutting edge, ensuring the functionality of the rotary tiller blade in cutting and throwing soil; thus, its original appearance was maintained even after prolonged wear. The findings of this study can provide a valuable reference for the enhancement of wear resistance for other soil-contacting components.  
rotary tiller blade; plasma cladding; wear; field trials 20251577  
1
## 3 Thermal Buckling Analysis and Optimization
### Thermal Buckling Optimization Model Setup
#### 3.2.2 Shape Optimization  
A parametric model as presented in Figure 7 was built for shape optimization. Three new parameters, a, b and c, were introduced for constructing the structure. It is assumed the boundaries of webs remain straight, and a, b and c were modified during optimization. The objective functions, critical load factors at two times were calculated by the finite element thermal buckling analysis. With the constraint on ITPS weight, the COBYLA (constrained optimization by linear approximation) was employed to establish an optimization solution.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & \(t_{w1}\)**/**mm** & \(t_{w2}\)**/**mm** & \(t_{w3}\)**/**mm** \\ \hline Range & 2.0–5.0 & 3.0–6.0 & 3.0–8.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ranges of variables for size optimization.  
Figure 7: Geometry for one cell of the shape optimization model.
## 3 Modelling and Optimization
### Leakage Current Minimization  
To reduce the leakage current, two switches, \(S_{5}\) and \(S_{6}\), are included in the circuit. When there is commutation from ON to OFF, the voltage across the parasitic capacitor changes from \(V_{DC}\) to 0 and vice versa. This sudden change in voltage results in leakage current. \(S_{5}\) and \(S_{6}\) are additional switches and are turned ON for most of the duration of a cycle. When the state of a switch changes from 0 to 1, the adjacent switch (\(S_{5}\)) has same state, and is triggered to turn off to include resistance (\(R_{1}\)) in the leakage current path for reducing the rate of the charging and discharging of the parasitic capacitor. Hence, the leakage current is reduced. Similary, the resistance (\(R_{2}\)) is included when \(S_{6}\) is triggered to turn off. The flows of leakage current are depicted in Figure 5a,b, respectively. At the instant when a DC source commutates from being disconnected to connected or vice versa, the surge in current is mitigated and the output waveforms are kept intact. The power loss cause by the addition of a resistor to the leakage current path is negligible, as the magnitude of the obtained leakage current is much less and a maximum of two switches are in conduction mode throughout the operation of HMLI. Also, the energy loss caused by the charging and discharging of a parasitic capacitor (\(C_{p}\)) for input voltage \(V_{dc}\) in a time period of \(T\) can be given as:  
\[P=\frac{C_{p}V_{DC}^{2}}{T} \tag{20}\]  
with \(C_{p}=100\) nF and \(V_{dc}=100\), power loss is less than 1 W.
# Improving 2-5 Qubit Quantum Phase Estimation Circuits Using Machine Learning
## 3 Results
### Quantum Experiment  
The [0.092, 0.58175, 0.181, 0.14525] 2-qubit datapoint from the IBMQ quantum computer Perth possessed a phase of 1/3 [20]. The traditional highest-peak method output a phase of 0.25, which had a 25% error when compared to the actual phase. The best 2-qubit model predicted a phase of 0.3394, yielding an error of 0.006.
## 3 Three-Dimensional Laser Scanning Technology and Application Scheme
### Internal and External Process of 3D Laser Scanning
#### 3.4.2 Internal Process  
Perform technical processing on the obtained tunnel 3D scanning point cloud data (Figure 9). The first step is to register the point cloud data obtained at the same measurement time, which is a necessary step to obtain a complete tunnel point cloud dataset. Each station will input the geodetic coordinate points of the station in the instrument before scanning to unify the coordinate values of the point cloud data. Since the known control points are used  
Figure 8: The excavation status of the monitored mileage.  
to set up the station, the coordinates of the measuring points are easy to obtain, and then the overall point cloud of the tunnel is obtained by splicing local point clouds. The second step is to filter the interfering point cloud data. During the monitoring process of the tunnel, there are construction machinery, tools and equipment, and staff in the scanning monitoring section, which will generate some interference points unrelated to the scanning section. The existence of these points will affect the monitoring and calculation of deformation. In order to ensure the accuracy of the monitoring results, the large-volume interference data in the point cloud will be manually filtered. For minor interference points, use the Trimble Business Center tunnel module to make the design data into a simple tunnel design model, and then import the actual scanned point cloud and filter the noise. This module can also compare with the design model and output the analysis report of over and under excavation of any section that the user can customize and edit. The final step is to extract the tunnel section coordinate data. Based on the statistical principle, the statistical average value of the point cloud data of a certain surface around the required analysis point is extracted, and the deformation value of the point is obtained after processing.
# Experimental Methods to Evaluate the Carbonation Degree in Concrete--State of the Art Review
## 1 Introduction
Carbonation of concrete is, thus, one of the main causes of the deterioration of reinforcement in the cement paste [13; 14; 15; 16; 17; 18; 19]. The significant effect of carbonation on concrete durability is varied, depending on the carbonation reaction during the carbonation process. Several methods for carbonation measurement are proposed based on different techniques. However, the two main methods identified are qualitative and quantitative methods. This paper aims to summarize both qualitative and quantitative methods to measure the carbonation degree of cementitious matrix, which is attracting attention currently for the enhancement of durability of concrete under the effects of carbonation weathering. The pH indicator, image and phase analysis, and CO\({}_{2}\) uptake techniques were evaluated. Several innovative measurements such as fiber optic systems, XRD, TGA-DSC, FTIR, SEM and ultrasonic phase were reviewed to investigate the diffusion of CO\({}_{2}\) inside the compositions and microstructure of composite.
## Security Analysis in Spatial and Frequency Domains  
In this session, to evaluate variations of security characteristics from the host images to dual chaotic watermarked images, both spatial domain and frequency domain analyses were conducted. Firstly NPCR (Number of Pixel Change Rate) and UACI (Unified Average Changing Intensity) were selected as quantitative metrics in the spatial domain. In each case, variations in dual chaotic watermarked image blocks were analyzed. Without loss of generality, Case 3 was selected for quantitative analysis in the spatial domain. Secondly the discrete entropy and relative entropy analyses in the frequency domain were also conducted. The primary color components of red, green and blue in three selected Cases (1, 2, 3) were processed individually. Similar conclusions were made even though quantitative analyses were implemented in another domain.
# Deep Learning Methods for Space Situational Awareness
## 2 Related Works
### Non-AI-Based Methods
The event camera's resolution is relatively low due to the high pixel size. * Low Fill Factor: the fill factor of event cameras is usually small, which means that a lot of pixel area is useless. In Table 1, we summarize the methods, their characteristics, and the bibliographic references. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method Name** & **Description** & **Reference Number** \\ \hline \multirow{8}{*}{Background subtraction method} & This paper introduces background subtraction methods and reports a comparison of the most promising cutting-edge algorithms. & [83] \\ \cline{2-3}  & This work provides a specific perspective view on background subtraction for detecting moving objects, as a building block of many computer vision applications, being the first relevant step for subsequent activity recognition, classification and analysis. & [84] \\ \cline{2-3}  & This article introduces a background subtraction algorithm for detecting fast moving objects. In particular, the algorithm proved effective in detecting change in global illumination, static foreground objects, camouflaged objects, ghosts and dynamic background compared to seven other cutting-edge methods. & [85] \\ \hline \multirow{4}{*}{Optical flow analysis method} & This article presents an experiment to verify the accuracy of the optical flow method (i.e., the apparent movement of individual pixels in the image plane). & [86] \\  & The accuracy of the technique is evaluated for different amplitudes of sub-pixel vibration displacement. & [87] \\ \hline \multirow{4}{*}{Track-before-detect method} & This paper addresses the detection and monitoring of weak and maneuvering targets using the MF-TBD (Multi-Frame Track-Before-Detect) method. & [87] \\ \cline{2-3}  & This work considers the underwater tracking of an unknown and time-varying number of targets, for example acoustic emitters, using passive array sonar systems & [88] \\ \hline \multirow{4}{*}{Frame difference method} & This article introduces the problem of moving target detection in video sequences. An improved frame difference target detection algorithm is proposed on the basis of the self-updating medium background model. & [89] \\ \cline{2-3}  & A motion detection application with the frame difference method on a surveillance cameras CCTV (Close-Circuit Television) is discussed. & [90] \\ \cline{1-1} \cline{2-3}  & This article introduces the problem of moving target detection in video sequences. An improved frame difference target detection algorithm is proposed on the basis of the self-updating medium background model. & [91] \\ \cline{1-1} \cline{2-3}  & A motion detection application with the frame difference method on a surveillance cameras CCTV (Close-Circuit Television) is discussed. & [90] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Non-AI-based methods for SSA space debris detection.
# Efficient Learning of Healthcare Data from IoT Devices by Edge Convolution Neural Networks +
## 6 Conclusions  
Smart medical treatment can provide timely and effective services for people's health. In this article, we present a hybrid smart medical architecture based on edge and cloud computing. This architecture can not only reduce diagnosis delay and transmission overhead, but also protect data privacy. Furthermore, we design an effective deep learning model for ECG inference which can be deployed and run on edge smart devices. This diagnosis model achieves a good balance between diagnosis accuracy and resource cost. In addition, we propose a data enhancement method for ECG based on DCGAN, which expands ECG data volume and data categories. The experimental results show that, through data enhancement, not only the overall accuracy of the diagnosis model is improved, but also the diagnosis category is expanded.  
Further work can be carried out from the following aspects. (1) The representation of ECG's medical morphological features in data still needs to be further explored. Some heart diseases can be judged by a simple and single ECG waveform, but the waveforms of many heart diseases often show complex and diverse morphological characteristics. This requires researchers to have a deeper understanding of these data to design more excellent diagnostic model. (2) Although data enhancement technology has effectively improved the diagnostic accuracy of some ECG categories, most of the ECG category data are still vacant. This requires further strengthening of cross-professional cooperation, especially cooperation with hospitals with massive data sources. (3) The model needs to consider individual differences. The current ECG diagnosis is mostly based on publicly available datasets and does not fully consider individual differences. To further improve the practical application effect of ECG automatic diagnosis model, it is necessary to update the diagnosis model according to the characteristics of each individual.  
Conceptualization, J.Y. and Y.H.; formal analysis, B.F. and Y.H.; funding acquisition, Y.H. and B.F.; investigation, B.F. and Y.H.; methodology, Y.H. and J.Y.; Project administration, R.L.; resources, B.F. and Y.H.; software, Y.H.; validation, B.F. and Y.H.; writing--original draft, B.F. and Y.H.; and writing--review and editing, Y.H., R.L. and R.J. All authors have read and agreed to the published version of the manuscript.  
The research was supported by the National Natural Science Foundation of China under Grant 61932010.  
Figure 15: Comparison of experimental results before and after data enhancement.
# A Compact Low-Power LoRa IoT Sensor Node with Extended Dynamic Range for Channel Measurements
## 3 Hardware Verification Setups
### Outdoor Measurement Setup
#### 3.1.2 Receivers  
The first receiver (RX1) was placed in an office environment at the eleventh floor of a modern building located at the edge of the city of Ghent. The distance from this building to the transmitter is approximately 4.0 km. The line-of-sight (LoS) path includes several urban features such as densely packed houses, a sizable park, a large conference center, a university campus, large apartment buildings, a canal and multiple large roads. The receiver was placed indoors, allowing it to be powered by a power supply. It was connected to similar monopole antennas as used at the transmit side. Yet, the 868 MHz antenna was attached to the outside of the building in order to avoid the signal attenuation by the building's highly insulating windows, which block the 868 MHz signals in such a way that they could not even be received. Interestingly, 434 MHz signals propagate through these windows considerably better. A second receiver (RX2) was placed indoors, on the fourth floor of a building located closer to the transmitter. For this receiver, the distance to the transmitter equals 2.4 km. At this time, however, the path to the transmitter is non-line-of-sight (NLoS) and includes densely packed houses, a large railway station and a university campus. The hardware setup is equal to the one used for the other receiver (RX1). All of the receivers discussed in this paper store both the received packet numbers as well as the SNR measurements and attenuation settings corresponding to these packets in their flash memory.
# Characteristics of Removal of Lead, Cadmium and Chromium from Soil Using Biosorbent and Biochar
## 2 Materials and Methods
### Biomass Pyrolysis  
The pyrolysis of aspen wood sawdust was carried out in an electric tubular furnace with a tube diameter of 100 mm (Strohlein Instruments, Ofen 85, Germany). Argon (Ar) was used as the inert gas, with a flow rate maintained at 12 dm\({}^{3}\)/min. In the first stage, the furnace was purged with argon for 60 min to remove air. The pyrolysis temperature was maintained at 500 \({}^{\circ}\)C. To obtain a sufficient quantity of material for the studies, three experiments were conducted, each with samples weighing approximately 20 g. These samples were then transferred to the central part of the reactor for a three-hour pyrolysis process. After completing the pyrolysis cycle, the device was left to cool down with a continuous flow of inert gas. The obtained biochar (BC) samples were weighed, averaged, and separated into coarser and finer fractions for subsequent use in the studies. The average yield of the pyrolysis process was 24.5%.