esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion
### Sustainability Index  
Figure 14 depicts the sustainability index (SI) parameter for the diesel system used with different fuel blends under four different loads. The engine's energy efficiency has a direct impact on the sustainability score [58]. As a result, the operational parameters' impacts under the same operating circumstances were comparable to their effects on the exergy efficiency. Since the environmental effect and the sustainability index of a process are mutually exclusive [62], and were examined for all fuel sources, the diesel engine sustainability index increased at 1500 revolutions per minute. Then, during the engine tests taken into consideration, the diesel engine's sustainability index varied from 1.27 to 1.46 [63]. When the results were calculated based on the fuel type, it became clear that the engine running on diesel fuel was more environmentally friendly than the engine running on biodiesel blends [64].
## 4 Research Methodology
### Server-Side Setup  
The server-side program for the smart access control system was developed in Java, as illustrated in Figure 6. The program utilizes the following libraries: info.picocli:picocli, javax.crypto, and java.net. After starting the server through the command line with the specified port parameter, the java.net.ServerSocket is used to listen on the designated port. Once a communication channel is established, the server connects to the client and begins exchanging data. During the connection, the D-H key exchange is used to generate the next key from the initial key. The messages received from the client (Arduino) are decrypted using Cipher.getInstance(\"AES/ECB/NoPadding\"). If the message is in RFID format, the system searches for the corresponding UID and triggers specific actions, such as sending HTTP requests or logging the access time into a database. After receiving a certain number of messages, the server re-initiates the D-H key exchange with the client to generate the next AES-128 decryption key.
## 4 Case Study  
A ship system is a mechanical system, such as an air system, lubrication-oil system, fuel-oil system, or cooling-water system. As described in this section, a main-engine fuel-oil supply system (FOSS), as shown in Figure 5a, was selected as the research object to verify the feasibility of the method. The function of the system is to provide suitable fuel for combustion. If the system failed unexpectedly, the ship propulsion system would shut down, and this would directly affect the safety of the ship.  
The system consists of four types of components. One component type is the pumps, consisting of the supply pump (SP) and circulating pump (CP), which provide pressure for the system to maintain the system pressure stability. Another type is the filters, consisting of the duplex filter (DF) and the auto filter (AF), which ensure the quality of the fuel and protect the equipment downstream. The third type is the heat exchanger (HE), which is mainly the atomization heater in the system used to ensure the proper viscosity of the fuel. The fourth type is the accessory components, including the pipelines, valves, oil tanks, etc.  
Figure 4: Determination of prediction starting point.  
Figure 5b illustrates the measurement parameters of the system, including: system working mode, pump inlet and outlet pressure, filter differential pressure, tank level, and fuel viscosity.
## 5 Topology and Overload Detection
### Overload Detection Results
First, we compared and reviewed two existing overload detection algorithms. The first is Fixed Threshold Alarm System (FTAS), which generates an alarm only when a specific CPU usage threshold (e.g., 85%) is exceeded, and has the simple but low sensitivity characteristic [26; 27]. The second is Trend-Based Utilization Detection (TUD), which analyzes the CPU change rate over time to detect a sudden increase. It is an algorithm with high sensitivity but potential for overreaction [28]. In order to objectively compare these two types of overload detection algorithms and benchmarking, we added two existing flash ADC structures: standard full-comparator flash ADC [29] and subranged Flash ADC [30]. Standard full-comparator flash ADC is fast, but power consumption and the number of comparators are very large, while subranged flash ADC reduces the number of comparators while maintaining speed (middle-segment structure), reducing the number of components by 30-40%. In the end, in order to objectively prove all of these, the analysis was conducted based on CPU usage, memory usage, execution delay time, number of ADC comparators (8b), ADC power (mW), hardware efficiency, etc. Table 5 presents a comprehensive benchmarking analysis comparing the proposed DCS architecture--integrating NFP, CFP, and AFP modules--with two representative legacy approaches across critical performance indicators, including CPU utilization, memory  
Figure 11: Simulation result on optimal utilization rate calculation overload detection system. footprint, computational latency, and hardware-level efficiency. The proposed system exhibits substantial advancements, achieving up to a 31.7% reduction in processor load and a 72.58% improvement in hardware efficiency, while also enhancing the granularity and sensitivity of overload detection mechanisms. These findings underscore the architectural superiority and applicability of the proposed solution for high-reliability, real-time control scenarios in resource-constrained industrial environments.
## 4 Discussion
In our model, we have found that the femoral tunnel (femoral footprint) in the AM portal, HTT, and TT techniques without LET is the place of the stress concentration, but the stress concentration shifts to the body graft for the HTT when LET is performed. Thus, the HTT combined with LET is suggested to be an acceptable surgical alternative both for the expected lower loads at the femoral tunnel contact and for hypothetical fatiguing loads because the distribution of the stress is shifted across the graft and not concentrated at the femoral tunnel contact, avoiding femoral tunnel windowing [10; 11]. Considering that the physiological aim of an ACLr is to restore the anatomical and physiological mechanical environment, our stress pattern for the HTT with LET is in accordance with recent findingsof healthy ACL modeling with higher femoral footprint stress concentrations during 0 to 30\({}^{\circ}\) of knee flexion [36] and combined loads (anterior translation, medial rotation, and valgus moment) with a joint flexed at 30\({}^{\circ}\)[11]. Unfortunately, the femoral footprint stress concentration is relevant to causing tunnel enlargement and graft wear following ACLr, causing graft lengthening and altered tibial stress contacts [1; 11]. Because of that, early interventions during the ligamentation stage for the AM and TT techniques with and without LET might predispose patients to tunnel enlargement and graft wear rather than HTT with LET. Undertaking the problem of biological graft healing with osteoinductive, angiogenic, or anti-inflammatory strategies has been recommended to promote tunnel mineralization and mid-substance graft ligamentation to mitigate higher and relatively low strains under repetitive loads concentrated at the femoral tunnel [37]. We acknowledge that our research is not without limitations. The primary limitation we recognize pertains to the inherent geometry of our ACLr graft model, with no higher knee constraint elements and parameters limited only to the validation model. Nonetheless, it is noteworthy that we have employed a comparable graft across three distinct techniques. On the other hand, the biological variability was controlled by simplifying the modeling to a healthy participant under a validated model. Also, our sample size is typical of finite element simulations [15]. Future directions should explore different anatomy graft and LET effects, different tissue parameters, and a tightened balance between the ACLr and LET to compare different kinds of LET on graft stress, pivoting movements [34], and in vivo knee-rotating movements following ACLr and LET to obtain strong clinical evidence.
# A Deep Learning Architecture For 3D Mapping Urban Landscapes
###### Abstract  
In this paper, an approach through a Deep Learning architecture for the three-dimensional reconstruction of outdoor environments in challenging terrain conditions is presented. The architecture proposed is configured as an Autoencoder. However, instead of the typical convolutional layers, some differences are proposed. The Encoder stage is set as a residual net with four residual blocks, which have been provided with the necessary knowledge to extract the feature maps from aerial images of outdoor environments. On the other hand, the Decoder stage is set as a Generative Adversarial Network (GAN) and called a GAN-Decoder. The proposed network architecture uses a sequence of the 2D aerial image as input. The Encoder stage works for the extraction of the vector of features that describe the input image, while the GAN-Decoder generates a point cloud based on the information obtained in the previous stage. By supplying a sequence of frames that a percentage of overlap between them, it is possible to determine the spatial location of each generated point. The experiments show that with this proposal it is possible to perform a 3D representation of an area flown over by a drone using the point cloud generated with a deep architecture that has a sequence of aerial 2D images as input. In comparison with other works, our proposed system is capable of performing three-dimensional reconstructions in challenging urban landscapes. Compared with the results obtained using commercial software, our proposal was able to generate reconstructions in less processing time, with less overlapping percentage between 2D images and is invariant to the type of flight path.  
deep learning; CNN; autoencoder; GAN; point cloud; 3D reconstruction; aerial images 2021
# Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5
###### Abstract
Quality of air and weather are inextricably linked with meteorological elements, such as air pressure, humidity, temperature, cloud coverage, wind speed, wind direction, and precipitation, having a significant impact on air quality forecasting. The latest artificial intelligence (AI) techniques are used for forecasting air quality. Moreover, due to increased computational power, many researchers have focused on deep learning techniques in various areas such as image analytics, video analytics, sequential modeling, and data analysis using data-driven models [5]. In various fields, artificial neural networks (ANNs) are also used for detection wherein the data used for analytics must be preprocessed in order to get faithful results [6; 7]. Raw data contains missing information and noise which may hamper the end results of any applied techniques. The research work presented in this paper focuses on a unique hybrid method named the MIA-LSTM method which uses iterative imputation to deal with unavailable values present in the data followed by an LSTM autoencoder to remove noise in the time series data and then predict the PM\({}_{2.5}\) concentration. The main contributions of this research paper are as follows:  
1. The use of an effective imputation method for handling missing information in the data by using an iterative method with an extra tree regressor as an estimator for finding replacements for missing fields in multivariate data. 2. Anomalies in the data are detected using an autoencoder that uses LSTM for encoding and decoding purposes where the threshold was set on the value of MAE for identifying the anomaly in the dataset
3. The proposed MIA-LSTM model that integrates a multivariate iterative imputation method and an autoencoder LSTM predicts PM\({}_{2.5}\) concentration with increased prediction accuracy by adding an extra LSTM layer in the last stage.
# Advancing Multi-UAV Inspection Dispatch Based on Bilevel Optimization and GA-NSGA-II
###### Abstract  
In multi-UAV collaborative power grid inspection, the system efficiency of existing methods is limited by the performance of both task assignment and path planning, which is critical in large-scale task scenarios, resulting in a huge computational cost and a high possibility to local optimality. To address these challenges, a bilevel optimization framework based on GA-NSGA-II and task segmentation is proposed to balance the total inspection distance and the distance standard deviation of UAVs, where the outer optimization employs the NSGA-II to assign task units to each UAV evenly, while the inner optimization deploys an adaptive genetic algorithm with an elite retention strategy to optimize the inspection direction and order in each task domain to obtain a Pareto-optimal solution set under constraints. To avoid the dimensionality disaster, the massive inspection points are combined into task units based on the UAV's endurance. In scenarios with 284 tower task points, the proposed algorithm has reduced the standard deviation of UAV flight distances by 41.91% to 84.63% and the longest flight distance by 29.41% to 43.98% compared to the GA-GA bilevel optimization. Against task-adaptive clustering optimization, it decreased the standard deviation by 18.25% to 94.93% and the longest flight distance by 15.97% to 37.33%. Applying it to 406 tower task points also confirmed the GA-NSGA-II bilevel optimization's effectiveness in minimizing the total inspection distance and balancing UAV workloads.  
bilevel optimization; task assignment; NSGA-II; genetic algorithm; elite retention Article
# Numerical and Experimental Investigations of a Micromixer with Chicane Mixing Geometry
###### Abstract  
A micromixer is a new type of chemical engineering equipment used to intensify the mixing process. This article provides details on flow regimes in microchannels with a complex geometry, such as with chicane mixing geometry. Experiments involving water, ink, and a micro digital camera have determined both the micromixer's initial mixing zone, and also the streamlines. Computational fluid dynamics (CFD) modelling helped identify the mechanism of stimulating effect, swirling and recirculation were identified as two special cases of the convective mixing process. To characterize the degree of mixing, a function of volume flow rate was proposed. A much higher degree of mixing in vortex flow compared to stratified flow was observed. The relationship between laminar flow and vortices shows a square-law dependence of pressure drop against the volume flow rate. The mixing cost and the mixing energy cost at Reynolds number of 50 are higher for the chicane micromixer than for micromixers without chicanes geometry.  
micromixer; convective mixing; stratified flow; vortex flow; initial mixing zone; mixing index +
Footnote †: journal: Applied Physics Letters
# The Contribution of Near-Surface Geophysics for the Site Characterization of Seismological Stations
## 2 Methodology
### Seismic Method
#### 2.1.2 Processing
Then, for each of the selected windows, a Fast Fourier Transform is applied to each component to obtain the respective spectra. The processing includes the application of a 5% Tukey taper window and a Konno-Ohmachi [54] smoothing window, with a 15% width on a logarithmic scale. The geometric mean of the two horizontal components is calculated to obtain an average spectrum, as recommended by the SESAME guidelines [52]. Finally, the HVSR is calculated for each individual window. From the distribution of spectral ratios for each selected window, the average spectral ratio and standard deviation at each frequency are calculated. _GEOPSY_ automatically determines a band containing the dominant peak frequency, with the maximum corresponding ground amplification factor. The determined dominant frequency peak is practically the fundamental resonant frequency, \(f_{0}\), and its associated amplification factor, \(A_{0}\). An example of the microtremor processing with _GEOPSY_ at station MDRA is presented in Figure 2. It is noted that tests using the squared average of the horizontal components usually resulted in slightly higher amplification factor values. To avoid cases where the ambient noise conditions on a specific day were biased due to a local noise source or lack of energy in certain frequency bands, the HVSR measurements at each site were repeated for recordings of different days. The results were then evaluated on a day-specific level but also collectively, also taking into account possible changes in a station's instrumentation. Moreover, the variations of the fundamental frequency for the same station may be due to the nature of the acquired data. We also focus on secondary peak frequencies of the HVSR, to distinguish the possible fundamental frequency, as well as other peaks likely associated with different geological layers.
## 3 System Design Results
### Visible Light Band Subsystem Design  
A detector with a pixel size of 10 \(\upmu\)m is selected for the visible light band. According to calculations, the focal length of the system is 5000 mm, and the field of view is 1\({}^{\circ}\)\(\times\) 0.2\({}^{\circ}\). The system design parameters are shown in Table 1.  
The schematic diagram of the visible light band subsystem is shown in Figure 3. The system adopts a coaxial biased field design, consisting of three quadric mirrors and a folding mirror. The surfaces of the primary mirror and the third mirror are ellipsoid, the surface of the secondary mirror is hyperboloid, and the aperture diaphragm is located on the primary mirror. The angle between the incident light in the central field of view and the horizontal direction is 0.9\({}^{\circ}\), and the Alpha tilt of the folding mirror is 6\({}^{\circ}\). The intermediate image plane is located behind the main mirror, which is a non-ideal image. Placing a stray light stop at the intermediate image position can reduce the influence of stray light on the system. The folding mirror is located near the middle image plane, the size is small, and its direction has little effect on the image quality.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameter** & **Focal Length** & **Pupil Diameter** & **Pixel Size** & **FOV (\({}^{\circ}\))** & **System F\#** & **Wavelength** \\  & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** \\ \hline Value & 5000 & 500 & \(10\times 10\) & 1 \(\times\) 0.2 & 10 & 450–900 \\ \hline \end{tabular}
\end{table}
Table 1: Visible light band optical system parameters.  
We select nine points on the image plane A (\(-0.5^{\circ}\), \(1.1^{\circ}\)), B (\(-0.5^{\circ}\), \(1^{\circ}\)), C (\(-0.5^{\circ}\), \(0.9^{\circ}\), D (\(0^{\circ}\), \(1.1^{\circ}\)), E (\(0^{\circ}\), \(1^{\circ}\)), F (\(0^{\circ}\), \(0.9^{\circ}\)), G (\(0.5^{\circ}\),\(1.1^{\circ}\)), H (\(0.5^{\circ}\), \(1^{\circ}\)), I (\(0.5^{\circ}\), \(0.9^{\circ}\)) as the field of view points for evaluating the panchromatic imaging performance of the system. The image quality evaluation of the system is shown in Figure 4. It can be seen from the figure that the MTF curve of the system is close to the diffraction limit, and the average MTF value at 50 lp/mm is higher than 0.45; from the image point diagram of the system, it can be seen that the image points are all smaller than Airy disk; from the field curvature and distortion curve, it can be seen that the distortion is less than 0.5%. The image quality is good and meets the expected requirements.  
Figure 4: Image quality evaluation of visible light subsystem (**a**,**b**) System MTF curve, (**c**) System image point diagram, (**d**) System field curvature and distortion.  
Figure 3: Schematic diagram of visible light band subsystem design.
## 4 Results and Validation
### Selected Results
#### 4.2.2 Poco da Cruz, Reservoir
The new approach clearly shows the capability of reconstructing data gaps, especially in the period between 2004 and 2012 where more data gaps occur. In this period, the averaged fill area is 6.25 km\({}^{2}\) which is about 11% of the AOI. The cross-validation for Poco da Cruz Reservoir clearly shows the problematic of the function selection for the computation of the relationship between surface area and water levels. The linear and polynomial fitting by using again 10% of available points show clear differences as shown in Figure 18. The linear fit yields an RMS of 3.53 km\({}^{2}\) which is 6.07% of the maximum area. By using the polynomial fit, the resulting RMS strongly decreases to 1.10 km\({}^{2}\) which is only 1.90% of the maximum area. The selection of the fitting function has a strong impact on the resulting RMS as the Poco da Cruz Reservoir shows a clear polynomial dependency between surface areas and water levels.
## 3 Results and Discussion
### Kinetics Studies
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Parameters** & \multicolumn{4}{c}{**MB**} & \multicolumn{4}{c}{**MG**} \\ \hline C\({}_{\mathrm{o}}\) (mg/L) & 5 & 15 & 20 & 25 & 10 & 20 & 35 & 50 \\ \(\mathrm{q}_{\mathrm{e(cal)}}\) (mg/g) & 4.898 & 8.680 & 9.901 & 9.713 & 9.478 & 19.564 & 34.420 & 42.900 \\ \(\mathrm{q}_{\mathrm{e(cal)}}\) (mg/g) & 4.903 \(\pm\) 0.011 & 8.688 \(\pm\) 0.091 & 9.560 \(\pm\) 0.140 & 9.731 \(\pm\) 0.117 & 9.503 \(\pm\) 0.135 & 19.633 \(\pm\) 0.153 & 34.480 \(\pm\) 0.547 & 43.480 \(\pm\) 0.646 \\ k\({}_{2}\) (g/mg.min) & 0.198 \(\pm\) 0.009 & 0.054 \(\pm\) 0.007 & 0.034 \(\pm\) 0.002 & 0.030 \(\pm\) 0.002 & 0.072 \(\pm\) 0.022 & 0.054 \(\pm\) 0.013 & 0.015 \(\pm\) 0.004 & 0.007 \(\pm\) 0.001 \\ R\({}^{2}\) & 0.999 & 0.989 & 0.989 & 0.991 & 0.991 & 0.997 & 0.990 & 0.992 \\ ARE (\%) & 0.10 & 0.09 & 0.62 & 0.18 & 0.26 & 0.35 & 0.17 & 1.35 \\ SD & 0.031 & 0.226 & 0.246 & 0.215 & 0.263 & 0.307 & 0.977 & 0.985 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters for second-order kinetic model for removal of MB and MG dyes by 4 AZW at 25 \({}^{\circ}\)C using nonlinearized model.
# Personalized Tour Itinerary Recommendation Algorithm Based on Tourist Comprehensive Satisfaction
## 3 Problem Definition and Modeling
### Comprehensive Satisfaction of Tour Itinerary
#### 3.2.5 Comprehensive Satisfaction Score  
After considering the aforementioned factors, we define the comprehensive satisfaction score (\(CSS\)) for the tour itinerary, represented by the following equation:  
\[CSS(I)=TUS(I)*ISASA(I)*FS(I)+R_{div1}(I)*\alpha_{1}+R_{div2}(I)*\alpha_{2} \tag{7}\]  
Comprehensive satisfaction considers the time utilization rate, scenic spot attractiveness, itinerary feasibility, and diversity of scenic categories. Considering that not every user has needs related to the categories of scenic spots, we add the diversity reward of the scenic spot categories as additional terms to the \(CSS\). Decision variables \(\alpha_{1}\) and \(\alpha_{2}\) are initially set to 0 by default but can be set to 0 or 1 based on user selection, but both cannot be set to 1 simultaneously.
## 4 Influence of Dataset Features
### Correlation of GC-Ratios  
Our experiments show that the GC ratio does not significantly affect the performance of the two algorithms (Spearman test, \(p>0.5\) for both algorithms). However, the time consumption of both algorithms has a negative correlation with the ratio of the number of distinct k-mers and the total number of k-mers in the dataset ([PERSON]'s correlation, KMC3: r = \(-0.487\), \(p=0.006\); CHTKC: r = \(-0.555\), \(p=0.001\)). The larger the ratio, the more degraded algorithm performance is, and sequencing quality and dataset size are more easily affected (Figure 4). The above results show that we should optimize the k-mer counting algorithm according to the number of distinct k-mers and the total k-mers in the dataset. GC content cannot be used as an index to evaluate the algorithm's robustness.