esa-sceva/satcom-chunk-collection huggingface dataset:
# UAV Formation Trajectory Planning Algorithms: A Review
## 4 Intelligent Algorithm
### Heuristic Algorithm
#### 4.1.8 Salp Swarm Algorithm  
The Salp Swarm Algorithm (SSA) is a new method to deduce and seek global optimization based on the swarming behavior of salps when navigating and foraging in the ocean. The SSA algorithm acts as an approximate global optimum by initializing a number of salps at random locations. Then, it calculates the fitness of each salp, finds the salp with the best fitness, assigns the position of the best salp to a variable as the source food to be chased by the salp chain, and uses the formula to update the fitness coefficient. For each dimension, the positions of the leading jumping body and the following jumping body are updated iteratively, and the search space determines the global optimal trajectory. Figure 18 is a Salp Swarm Algorithm (SSA) trajectory diagram.  
Figure 18: A SSA trajectory diagram (circles: obstacles).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Challenge** & **Optimization Criteria** & **Method** & **Dimension** \\ \hline D. [PERSON] et al. [83] & DTSCS & ST & CPIO & 2D \\ W.-y. [PERSON] and [PERSON] [84] & Trajectory & Iterative learning, social learning factor & MSIPO & 2D \\ H. [PERSON] et al. [85] & UAV clustering & Bayes’ formula, RTSM, B-spline curve & \(D^{2}PIO\) & 3D \\ B. [PERSON] et al. [86] & Trajectory & Cauchy mutation operator & ECM-PIO & 3D \\ Y. [PERSON] et al. [87] & Trajectory & triple mutation, timestamp segmentation, coordination costs & MGLPIO & 3D \\ J. [PERSON] et al. [88] & UAV clustering, coverage problem & Environment awareness, integer coding, discrete operations, mutation operators, natural selection & IPO & 2D \\ [PERSON] et al. [89] & UAV clustering & Probability graph model, pheromone graph, speed update, correction, elite generation & PIO & 2D \\ [PERSON] et al. [90] & UAV clustering & Probability graph model, rolling prediction, Bayes’ theorem & CPIO + APF & 2D \\ [PERSON]. [PERSON] et al. [91] & Trajectory & Offspring competition, collision detection & MSFOA & 2D/3D \\ [PERSON]. [PERSON] et al. [92] & Environment & Mission change and distance cost & ORPROA & 3D \\ [PERSON]. [PERSON] et al. [93] & Trajectory & TSS, greedy strategy, restart strategy and evolution strategy & NIFOA & 3D \\ [PERSON] et al. [94] & Time & Trajectory cost & IABC & 2D \\ [PERSON] et al. [95] & UAV clustering & Adaptive time coordination & ABC + A* & 3D \\ [PERSON]. [PERSON] et al. [96] & Trajectory & Fitness function, balanced search & AGA + IABC & 2D \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summaries the content of our review on PIO, FOA, and ABC.  
[PERSON] and [PERSON] [97] proposed a new Salp group algorithm (SSA), which uses multiple random operators to solve the problems of slow convergence and poor real-time performance of other heuristic algorithms in multi-UAV trajectory planning.
## 1 Introduction
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Type** & **Disadvantages** & **VCAFusion’s Novel Contributions** \\ \hline Traditional MSD & Sensitive to illumination variations. & Adaptive MSD with side window filtering. \\ Sparse Representation & Lose weak details in dark regions. & Retinex-based brightness adjustment. \\ Deep Learning & Require extensive night-image training. & Physics-inspired enhancement (no retraining needed). \\ Saliency Methods & Over-emphasize infrared thermal features. & Biologically-inspired saliency preservation. \\ Subspace Techniques & Poor edge preservation in low-light. & Hybrid MSD with edge-aware texture refinement. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The summary of existing fusion methods. Figure 1: An example of fusion results comparing our method (VCAFusion) with state-of-the-art methods. (**a**) Infrared image; (**b**) Visible image; (**c**) Fusion result from MSID [10]; (**d**) Fusion result from SeAFusion [17]; (**e**) Fusion result from the proposed VCAFusion method. The green and red boxes highlight the target regions in the magnified local areas. * A lighting correction and contrast enhancement approach to strengthen weak details while maintaining edge clarity. * A hybrid multi-scale decomposition (MSD) for better structure handling and a saliency-preserving strategy for improved detail integration. * Our method adapts to lighting variations and outperforms state-of-the-art methods in object highlighting and detail preservation (Figure 1e).
###### Abstract  
To effectively decarbonize Europe's building stock, it is crucial to monitor the progress of energy consumption and the associated emissions. This study addresses the challenge by developing a national-scale urban building energy model (nUBEM) using artificial intelligence to predict non-renewable primary energy consumption and associated GHG emissions for residential buildings. Applied to the case study of Spain, the nUBEM leverages open data from energy performance certificates (EPCs), cadastral records, INSPIRE cadastre data, digital terrain models (DTM), and national statistics, all aligned with European directives, ensuring adaptability across EU member states with similar open data frameworks. Using the XGBoost machine learning algorithm, the model analyzes the physical and geometrical characteristics of residential buildings in Spain. Our findings indicate that the XGBoost algorithm outperforms other techniques estimating building-level energy consumption and emissions. The nUBEM offers granular information on energy performance building-by-building related to their physical and geometrical characteristics. The results achieved surpass those of previous studies, demonstrating the model's accuracy and potential impact. The nUBEM is a powerful tool for analyzing residential building stock and supporting data-driven decarbonization strategies. By providing reliable progress indicators for renovation policies, the methodology enhances compliance with EU directives and offers a scalable framework for monitoring decarbonization progress across Europe.  
Keywords:urban building energy model (UBEM); energy performance certificates (EPCs); machine learning; national building stock; data driven approaches; progress indicators; building energy efficiency; building carbon footprint; energy renovation policies +
Footnote †: journal: Applied sciences
# Unraveling the Impact of Class Imbalance on Deep-Learning Models for Medical Image Classification
## 3 Results and Analysis
### Results and Analysis of Layers Three and Four
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & & & & & & & & & \\ \hline keras & 50-300 & No\_bal & – & – & 250 & 300 & 1000 & 0.94 & 0.78 & 0.89 & 0.50 & 0.67 & 0.91 & 0.89 & 0.88 & 0.94 & 0.49 & 0.58 \\ keras & 50-300 & No\_bal & – & 300 & 100 & 100 & 1000 & 0.92 & 0.78 & 0.86 & 0.64 & 0.65 & 0.89 & 0.89 & 0.89 & 0.93 & 0.47 & 0.64 \\ keras & 50-300 & No\_bal & 200 & 200 & 150 & 250 & 1000 & 0.89 & 0.73 & 0.87 & 0.43 & 0.62 & 0.85 & 0.89 & 0.84 & 0.90 & 0.56 & 0.42 \\ \hline keras\_cnn & 50-300 & No\_bal & – & – & 100 & 150 & 1000 & 0.95 & 0.79 & 0.87 & 0.55 & 0.72 & 0.93 & 0.89 & 0.88 & 0.96 & 0.52 & 0.58 \\ keras\_cnn & 50-300 & No\_bal & – & 200 & 100 & 250 & 1000 & 0.95 & 0.78 & 0.87 & 0.57 & 0.69 & 0.93 & 0.89 & 0.88 & 0.96 & 0.51 & 0.56 \\ keras\_cnn & 50-300 & No\_bal & 300 & 100 & 50 & 50 & 1000 & 0.95 & 0.81 & 0.90 & 0.57 & 0.76 & 0.93 & 0.91 & 0.89 & 0.95 & 0.59 & 0.62 \\ \hline neuralnet & 50-300 & No\_bal & – & – & 300 & 50.00 & only\_200 & 0.85 & 0.60 & 0.85 & 0.31 & 0.68 & 0.65 & 0.90 & 0.83 & 0.68 & 0.68 & 0.37 \\ neuralnet & 50-300 & No\_bal & – & 150 & 250 & 50.00 & only\_100 & 0.78 & 0.66 & 0.82 & 0.30 & 0.66 & 0.76 & 0.86 & 0.84 & 0.83 & 0.42 & 0.42 \\ neuralnet & 50-300 & No\_bal & 50.00 & 100 & 200 & 50.00 & only\_100 & 0.62 & 0.62 & 0.82 & 0.30 & 0.73 & 0.72 & 0.88 & 0.81 & 0.79 & 0.57 & 0.24 \\ \hline rsnns & 50-300 & No\_bal & – & – & 150 & 150 & only\_200 & 0.69 & 0.70 & 0.77 & 0.35 & 0.57 & 0.85 & 0.83 & 0.87 & 0.92 & 0.12 & 0.58 \\ rsnns & 50-300 & No\_bal & – & 250 & 300 & 50 & only\_100 & 0.80 & 0.74 & 0.89 & 0.41 & 0.67 & 0.88 & 0.88 & 0.86 & 0.92 & 0.49 & 0.50 \\ rsnns & 50-300 & No\_bal & 300 & 250 & 300 & 50 & only\_100 & 0.77 & 0.75 & 0.83 & 0.41 & 0.67 & 0.90 & 0.85 & 0.89 & 0.94 & 0.29 & 0.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of the metrics obtained for 1000 training epochs with 2, 3, and 4 layers and tests in different R packages. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & & & & & & & & & & & \\ \hline keras & 50-300 & No\_bal & – & – & 250 & 300 & 3000 & 0.90 & 0.75 & 0.81 & 0.53 & 0.76 & 0.91 & 0.89 & 0.85 & 0.95 & 0.53 & 0.43 \\ keras & 50-300 & No\_bal & – & 300 & 100 & 100 & 3000 & 0.95 & 0.77 & 0.86 & 0.53 & 0.70 & 0.91 & 0.89 & 0.87 & 0.95 & 0.50 & 0.53 \\ keras & 50-300 & No\_bal & 200 & 200 & 200 & 150 & 250 & 3000 & 0.89 & 0.73 & 0.89 & 0.52 & 0.56 & 0.80 & 0.90 & 0.87 & 0.85 & 0.55 & 0.58 \\ keras\_cnn & 50-300 & No\_bal & – & – & 100 & 150 & 3000 & 0.96 & 0.80 & 0.87 & 0.59 & 0.77 &
## 2 Materials and Methods
### Data  
All subjects' MRIs were acquired using SIEMENS 3.0T Skyra (20-channel coil). The protocol includes 3D T1-weighted MRI, 3D/2D FLAIR MRI, and 3D SWI. Data of 76 patients consist of T1, T2, and FLAIR images. Data of 20 normal subjects include T2 and FLAIR images. For T1-weighted images, magnetization prepared rapid gradient echo (MPRAGE) sequence was used with repetition time (TR)/echo time (TE) = 2300 ms/2.98 ms; inversion time = 900; matrix size 256 \(\times\) 256 in-plane resolution = 1 \(\times\) 1 mm; slice thickness = 1 mm; number of slices = 192; and Grappa factor = 2. For T2-weighted images, an axial turbo spin-echo sequence was used with repetition time (TR)/echo time (TE) = 4450 ms/81 ms; flip angle = 150 degree; matrix size = 384 \(\times\) 384; in-plane resolution = 0.573 \(\times\) 0.573 mm; slice thickness = 5 mm; number of slices = 28. For FLAIR images, the 3D sequences were obtained using repetition time (TR)/echo time (TE) = 5000 ms/393 ms; inversion time = 1800 ms; matrix size = 256 \(\times\) 256; in-plane resolution = 1 \(\times\) 1 mm; slice thickness = 1 mm; and number of slices = 196. Parameters used to obtain the 2D FLAIR images include: repetition time (TR)/echo time (TE) = 9000 ms/95 ms; inversion time = 1800 ms; matrix size = 320 \(\times\) 320; in-plane resolution = 0.688 \(\times\) 0.688 mm; slice thickness = 5 mm; and number of slices = 28.
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 7 Case Study and Results
### Experimental Setup
#### 7.2.2 Data Generation  
* Temperature, vibration, tool wear, and operating hours recorded at 1-min intervals for 24 h.
* Simulates realistic wear and tear with fault probabilities increasing as tools degrade.
- Includes fault probability \(P(M_{i},J_{j},t)\) as the key output for optimization.
## 2 Materials and Methods  
### Materials and Chemicals  
Standards of PAEs investigated in this study, such as dimethyl phthalate (DMP; C\({}_{10}\)H\({}_{10}\)O\({}_{4}\); MW 194; first and second fragment _m_/\(\underline{\pi}\) ratio 163), diethyl phthalate (DEP; C\({}_{12}\)H\({}_{14}\)O\({}_{4}\); MW 222; _m_/\(\underline{\pi}\) 149 and 177), diisobutyl phthalate (DiBP; C\({}_{16}\)H\({}_{22}\)O\({}_{4}\); MW 278; _m_/\(\underline{\pi}\) 149 and 205); dibutyl phthalate (DBP; C\({}_{16}\)H\({}_{22}\)O\({}_{4}\); MW 278; _m_/\(\underline{\pi}\) 149 and 205), bis(2-ethylhexyl) phthalate (DEHP; C\({}_{24}\)H\({}_{38}\)O\({}_{4}\); MW 390; _m_/\(\underline{\pi}\) 149 and 261); di-n-octyl-phthalate (Dn\(\underline{\pi}\)OP); C\({}_{24}\)H\({}_{34}\)O\({}_{4}\); MW 391; _m_/\(\underline{\pi}\) 149 and 261), and bisphenol A (BPA;C\({}_{15}\)H\({}_{16}\)O\({}_{2}\); MW 228; _m/z_ 213 and 228), were obtained from Sigma-Aldrich (Milan, Italy). A mixture containing all the standards was prepared at an initial concentration of 200 \(\mu\)g mL\({}^{-1}\) and subsequently diluted up to 50 ng mL\({}^{-1}\). The solutions were stored in 2 mL under vials at -20 \({}^{\circ}\)C. _n_-Hexane, _n_-heptane, _iso_-octane, and benzene were of pesticide grade ([PERSON], Milan, Italy), whereas sodium chloride ([PERSON]) was of analytical reagent grade. Finally, 5 \(\mu\)L of anthracene (1 mg mL\({}^{-1}\)) (C\({}_{14}\)H\({}_{10}\); MW 178; LabService Analytica, Anzola Emilia, Bologna, Italy) were added as the internal standard (I.S.) to each sample before being processed.  
Regarding cross-contamination due to reagents (especially to minimize background contamination due to NaCl), materials, and laboratory equipment (e.g., glassware, tubing), which is still a fundamental problem in PAE analysis, all chemicals and instruments were subjected to severe cleaning procedures. Previous papers [58; 60] report all the details. In summary, the glassware was soaked and washed in acetone, dried at 140 \({}^{\circ}\)C for at least 4 h; NaCl was heated for 4 h at 140 \({}^{\circ}\)C and, after cooling, kept in a tightly sealed glass vial. For the PAE standard solutions (0.1 mg mL\({}^{-1}\) of each PAE), absolute ethanol was used; each solution was further diluted by ethanol to obtain solutions at different PAE concentrations for spiking the samples.
# Limitations of Using DAQ Cards for Absolute Vibration Measurement in Hydraulic Machines
## 4 DAQ Devices--Properties  
The term measurement card is understood as a device that converts an analog electrical signal into a digital form (quantization of the signal value), which is useful for a computer measurement system. The processed signal can be subject to further digital processing, analysis, recording, and visualization.  
The main element of the DAQ module is the A/D converter, which enables the conversion of an analog signal into a digital form. For this purpose, converters of various types (successive approximations, delta-sigma) are used, with different resolutions (usually between 12 and 24 bits), sampling frequencies, and measurement ranges.  
In addition to the A/D converter, DAQ modules may contain auxiliary elements, e.g., filters, voltage dividers/amplifiers, current-to-voltage converters, and galvanic isolators.  
In the case of multichannel measurements, an appropriate number of A/D converters or multiplexers (switching elements that enable measurements to be performed in many channels using a single A/D converter) are used. In order to ensure signal invariance during sampling (signal quantization in the time domain) and to maintain measurement simultaneity in the case of multiplexed signals, sample-and-hold systems are sometimes used.  
The basic parameters of measurement cards include:  
* number of channels;
* resolution;
* sampling frequency;
* signal standard (type of signals);
* accuracy;
* measurement range;
* input type (single-ended, differential, pseudo-differential).
# Soft X-ray Microscopy Techniques for Medical and Biological Imaging at TwinMic--[PERSON]
## 3 Results
### Ptychography  
Figure 8 of this section shows expected imaging results using far-field ptychography [86] at TwinMic for biological studies. In particular, it shows a reconstruction of diffraction data acquired at 1.02 keV, achieving a resolution better than 50 nm for a sample area larger than 60 \(\times\) 60 \(\upmu\)m\({}^{2}\), where the specimen is a group of chemically fixed human mesothelial cells (Met5A) exposed to crocidolite fibres. The high quality of the obtained image allows us to better recognize the subcellular structures and to identify the nanofibres internalized by the cells. The spatial resolution achieved with this setup can be seen in Figure S1, showing the reconstruction of a tungsten Siemens Star test reference object with a 20 \(\upmu\)m external diameter. As can be seen from Figure S1, panel C, a spatial resolution better than 50 nm could be reached.  
Figure 8: Panel (**A**) shows the ptychographic phase reconstruction of chemically fixed mesothelial cells. This is an example of a large field of view (>60 \(\times\) 60 \(\upmu\)m\({}^{2}\)) examined with a high resolution (approx. 50 nm). Panel (**B**) and (**C**) show enlarged ROIs, as indicated in panel (**A**).  
The same type of scan could be subsequently performed across an energy range that covers a specific absorption edge, eventually allowing for XANES analysis.  
As ptychography is more complex and less diffused than older standard techniques such as STXM and full-field, such images are unique and of particular interest for the research community.
# Towards the Internet of Smart Trains:
## 5 The Rise of the Internet of Trains
#### Industrial IoT Developments in the Rail Industry
Another example is represented by the French SNCF [63], which is also using IIoT powered by IBM Watson's deep learning analytics platform and SigFox's IIoT network. These alliances are part of the company's 2020 strategy to become an industrial leader striving for operational excellence and optimum efficiency. SNCF has developed a prototype where data acquisition devices are fitted into the transmission system on a _Train a Grande Vitesse_ (TGV). Data are transmitted over GSM-R and can be accessed remotely at the train depot, enabling technicians to see how well the gearbox is performing. SNCF also uses Sigfox communications devices to measure the water level tank in TGV toilets, what speeds up the turnaround time when the train arrives at a depot. Besides, engineers can connect to running trains in real-time, enabling SNCF to figure out whether a component is likely to fail, which could lead to the train being taken out of service. The cloud enables SNCF to run distributed calculations, whose results can be reinjected into its train and rail maintenance processes.
## 4 Discussion
The proposed method had better results in terms of the accuracy, precision, AUC, and F1 score compared to when only the last four layers were fine-tuned (A2), a prior DL model on the same data set [8], and another deep learning approach for PD diagnosis [36], with approximately 10% of the run time of [8]. This likely reflects the relatively small data set, so that the other DL approaches could not fully capture the discrimination subspace given the data available. The transfer learning approach, adopted here, would appear to make discrimination tasks on smaller data sets more feasible. Finally, we note that the maximum values of a feature originated from different connections, different subjects, and different bands. This suggests that the features we found were not based on overfitting a small subset of subjects.
# Decentralized Documentation of Maritime Traffic Incidents to Support Conflict Resolution
## 3 Related Work
### Further Approaches  
In the automotive sector, the Event Data Recorder (EDR) is currently used to record incident sensor data [32]. Triggered by a crash, these record low-bandwidth data including car and engine speeds, brake status, and accelerations [33]. The data is stored in non-volatile memory within the control unit. These systems are permanently integrated into cars and operate on proprietary interfaces [34], but are themselves probably not adequately protected against manipulation of the collected sensor values, and measures against subsequent overwriting are currently not described [35].  
Some works deal with fishery logbooks, i.e., logbooks in which the catches of fishermen can be documented. However, there is no focus on the manipulation of the data, as these are concerned with the architecture of the data exchange [36] or the analysis of these data [37].  
A low-threshold approach to analyzing maritime sailing behavior and traffic situations is to look at AIS data. This data is publicly receivable and can be purchased afterwards from service providers but lacks a fine-grained resolution. Vessel Traffic Services [38] use this data (along with other sensors such as radar, cameras, etc.) to provide live assistance and instructions [39]. Since this data is sent publicly at regular intervals, the data received in this way can be used as additional anchor points for a possible conflict resolution.  
In summary, the maritime domain still lacks a method for trusted, automatic data recording of multiple sensors without compromising data sovereignty and privacy. The blockchain-based approaches shown are promising but still miss some practicality regarding transaction speed, scaling, and compartmentalization. Some of these shortcomings are solved by using classical approaches based on cryptographic signatures, but these lack the distributed characteristics of the blockchain. As a result, we are not aware of any approach that sufficiently addresses the properties of non-repudiation, decentralization, and performance simultaneously.
# Effectiveness of Laser-Assisted Teeth Bleaching: A Systematic Review
## 3 Results
### General Characteristics of the Included Studies
#### 3.2.1 Dide Laser-Assisted Bleaching  
Authors most often chose to use a diode laser for bleaching in their studies. This procedure appeared in as many as 29 papers [2; 3; 4; 5; 7; 8; 9; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54]. In 11 of them, the in vitro whitening involved human teeth [34; 35; 37; 40; 42; 45; 47; 50; 51; 52; 55]. It was observed that when using 35% HP, the best whitening effects were achieved with a laser power of 1.5 W, regardless of whether the wavelength was 810, 940, or 980 nm [37; 55; 56]. For higher and lower power values, the results were not as significant [37; 55; 56]. When using CP as a bleaching agent, both [PERSON] et al. [35] and [PERSON] et al. [46] proved that the effect was proportional to the CP concentration and exposure time. This relationship did not occur in the case of HP and bovine teeth [38; 48]. The studies also included two publications in which the whitening agent was activated in the pulp chamber with a diode laser, and, in both cases, a more favorable effect was obtained at a shorter wavelength [33; 43]. It was much more difficult to find relationships in the studies conducted in patients [2; 5; 7; 41; 44; 49; 54; 57; 58]. In these cases, very different wavelengths, concentrations of preparations, and methods for assessing the effects were used. For example, [PERSON] et al. [54] used an 810 nm diode laser to activate materials containing 35% HP, 38% HP, and 15% CP. After exposing each material for 3 min, he concluded that they were equally effective [54]. However, effectiveness cannot be conclusively determined for whitening dental materials where the \(\Delta\)E typically reaches a value less than 1, indicating that the color difference is not noticeable [36; 35]. The exception is a study conducted by [PERSON] and [PERSON] [4], who whitened a composite restorative material (Filtek(tm), 3 MESPE, Dental Product, St. Paul, MN, USA) with 40% HP activated by a 940 nm diode laser, achieving \(\Delta\)E values of 5-10, which are comparable to those observed in enamel whitening [4]. Additionally, some publications included studies using hybrid light. [PERSON] et al. [3] reported that irradiation with an 808 nm diode laser combined with blue and violet LEDs increased whitening efficiency. In contrast, [PERSON] et al. [9], who added a violet LED to an 810 nm diode laser, found that the effect was no different from using 35% HP without activation [9].