esa-sceva/satcom-chunk-collection huggingface dataset:
# Semantic Matching Based on Semantic Segmentation and Neighborhood Consensus
## 3 Approach
### Four-Dimensional Convolution  
The size of the 4D correlation map is \((h\times w)^{2}\), where \((h,w)\) represent height and width of the feature map. However, and the correlation number of correct matches is very small, only \((h\times w)\). This means that the great majority of the information in the correlation map corresponds to matching noise, in other words, the correlation map is easily affected by noise. Since the correlation map serves as the direct cue for semantic matching assignment, its accuracy directly determines the accuracy of the matching. As a result, it is necessary to optimize the correlations to reduce noise interference.  
There is a prior knowledge for filter correlations: a correct correlation has a coherent set of supporting correlations in the neighborhood. In other words, the matching continuity in the neighborhood of the image should be equivalently reflected in the correlation continuity in the correlation map. Therefore, we explore neighborhood consensus of the correlation space based on this prior knowledge. Here, we adopt a series of learnable convolution kernels to slide in the correlation space to constrain the contextual consistency, thereby correcting some outlier correlations.  
Specifically, since the space of the correlation map is four-dimensional, that is, combining two horizontal dimensions and two vertical dimensions of two feature maps, we use 4D convolution to process the correlation map as shown in Figure 4. It shows the convolution of \(C_{i,j,k,l}\)'s neighborhood, where \((i,j)\)\((k,l)\) are the coordinates of points \(p\) and \(q\) in the feature maps \(F_{A}\) and \(F_{B}\), respectively. Taking the width equal to 3 as an example, the 4D neighbors can be denoted as \(C_{i+\Delta i,j+\Delta j,k+\Delta k,l+\Delta l}\), where \(-1\leq\Delta i,\Delta k,\Delta k,\Delta l\leq 1\). Each 4D convolution kernel convolves this neighborhood to learn a specific local structure pattern. Its process can be regarded as a weighted average with a bias:  
\[C_{i,j,k,l}=\left(\sum_{\Delta i}\sum_{\Delta j}\sum_{\Delta k}\sum_{\Delta l}W _{\Delta i,\Delta j,\Delta k,\Delta l}C_{i+\Delta i,j+\Delta j,k+\Delta k,l+ \Delta l}\right)+b, \tag{5}\]  
where the weight \(W_{\Delta i,\Delta j,\Delta k,\Delta l}\) and the bias \(b\) are learnable parameters. Similar to 2D convolution, we use a series of 4D convolutions to capture more complex local structures to obtain more accurate correlations.
## 4 Results
### Discussion of Results and Limitations
Based on this, we are confident that the proposed scheme would produce even better results if a larger data set could be used, since it was observed that the reduction in the data set size reached around 12%, yet the metrics (RMSE and R\({}^{2}\)) also showed an increase in the network's performance. Regarding the limitations and requirements of the proposed storage cloud architecture and the proposed prefetch scheme, the prefetch scheme requires the user to perform at least five requests in order to predict the upcoming file. Unless these requests are performed, no prediction is made. This choice was made because after evaluating the prefetching scheme, it was discovered that the predictions were not accurate, and thus the process of prefetching a file and caching it would be not useful. Another limitation of the current architecture regards its inability to share the prefetched files with users other than the one for whom it was prefetched. As already described in Section 3.2.2, the files stored in the proposed storage cloud are also encrypted on the client side in order to ensure that the cloud provider cannot have access to the user's health data. Hence, the data were exclusively related to one patient. Thus, in a case where two healthcare professionals perform a request for the same type of health data but for a different patient, even if the data are prefetched for one healthcare professional, the other healthcare professional will not be able to utilize the prefetched files, since they regard a different patient. Another limitation of the proposed solution concerns what happens to data once they are prefetched. As presented in Section 3.2.6, if the data are requested by the user, they are transmitted to them and then dropped, while in the case where the prefetched data are not requested by the user, they are also removed from the Prefetch Engine Component in order to save resources. Considering the overall issues that occurred throughout this research, we targeted a list of obstacles related with the fact that the proposed storage solution and the integrated prefetching scheme were not yet tested by actual users and healthcare professionals. In fact, the data used for the evaluation were generated based on actual traffic. Moreover, another issue is related to the infrastructure used for the evaluation being self-hosted. It is within our intentions to also assess the proposed solution in cloud infrastructures as well in order to draw results from a production environment.
# Joint Task Offloading and Power Allocation for Satellite Edge Computing Networks
## 5 Performance Analysis
### Delay Performance with Different SSs and Computation Resources  
We choose different main frequencies from [300 MHz, 400 MHz, 500 MHz] and compute the delay with the different number of SSs.  
Figure 8 demonstrates that each 100 MHz computing resource boost decreases processing delay by 16.62%. However, after a certain point, a further increase in bandwidth or satellite number does not significantly reduce the delay.  
Figure 8: Delay versus satellite number: simulation and ideal delay curves under different main frequency.  
Figure 7: Delay versus satellite number: simulation and ideal delay curves under different bandwidths.  
Figures 7 and 8 reveal a non-monotonic relationship between the onboard computing node scale and the delay under dual constraints of total system power and bandwidth. As edge computing satellites increase from initial numbers, system delay first decreases then increases.  
With fewer than four satellites, distributed parallel processing reduces delay. Beyond this threshold, constrained bandwidth linearly reduces per-satellite channel capacity, increasing transmission delay. Concurrently, total power constraints force reduced computing power per node, elevating processing delay. Numerical simulations show that exceeding four nodes creates a coupling effect between transmission and processing delays, causing inflection point and diminishing marginal returns. The optimal configuration is four satellites.
# A Study of Stray Neutron Field Measurements for the Neutron Scattering Instruments at CSNS
## 3 Experiments and Discussion
### Measurements  
Each sphere of the ERBSS acquires data in turn during the measurement. The \({}^{3}\)He counter and the neutron beam of BL06 were aligned horizontally. Depending on the experimental conditions, the duration of data acquisition by each sphere was between several minutes to around an hour, thus ensuring a statistical uncertainty of less than 3%. It was confirmed that the natural neutron background was negligible. The beam-on counting rates were thousands of times larger than when the neutron shutter was off. Figure 3 shows the measured count rates related to protons intensity, which was supplied by a beam monitor with a precision of \(\pm\)2%. As can be seen, because location A was closer to the neutron aperture and suffered less flux loss, its counting rates were higher than those for any condition at location B. The small-size ERBSS had relatively higher counting rates, suggesting that there was a large portion of low-energy neutrons at each location.  
The NADED took measurements at the same position as the ERBSS and for the same beam conditions. To eliminate statistical fluctuations, each measurement duration exceeded 20 min.
# Scanning Electron Microscopy Techniques in the Analysis of Gunshot Residues: A Literature Review
## 3 Results
### Applications of SEM in the Forensic Field  
SEM is an essential technique in forensic science for the detailed analysis of physical and biological evidence. Its forensic applications include determining the provenance and composition of materials and documenting microscopic details that can be crucial in criminal investigations. SEM is used for analyzing various types of evidence, including fibers, powders, explosives, and drug residues. Its ability to provide high-resolution, detailed images makes it invaluable for forensic analysis. One of the primary applications of SEM in forensic science is the analysis of textile fibers and dust residues collected at crime scenes. SEM's high resolution enables the identification of fiber morphology and composition, which can be crucial for comparison and correlation with specific sources [26]. SEM is also essential for analyzing dust particles and bullet residue. It allows for the identification of the chemical composition and morphology of these particles, providing clues about the specific types of ammunition and materials used in violent crimes [27]. The identification of damage to materials and fabrics, such as those caused by tears or abrasions, can also be assessed using SEM. High-resolution images can reveal the microscopic details of damage, which can help reconstruct the events and movements during a crime. SEM is also employed in the analysis of explosive residues found at crime scenes. It allows for the identification of the chemical components and microscopic structures of explosives, helping to link residues to specific types of explosive devices [28]. Another key forensic application of SEM is the analysis of seized pharmaceuticals, drugs, and chemicals. The technique provides details on the morphology and structure of drug crystals, which can be used to identify substances and determine their purity. Finally, the analysis of biological and blood residues remains an important forensic application of SEM, as it enables the examination of blood cell structure and tissue morphology. This application is useful for identifying and characterizing biological samples collected at crime scenes [29].
## 2 Materials and Methods
### Determination of Radical Scavenging Activity by DPPH Method  
DPPH (1,1\({}^{\prime}\)-diphenyl-2-picrylhydrazial-radical) was purchased from Sigma-Aldrich (St. Louis, MO, USA). This substance has a single electron on the nitrogen atom and its solution in methanol has an absorption maximum at \(\lambda\) = 517 nm. The mechanism of the DPPH method is based on the reaction between the test compound and DPPH-radical, wherein the potential free radical scavengers reduce DPPH-radical (violet solution) to a yellow colored 1,1\({}^{\prime}\)-diphenyl-2- (2,4,6-trinitrorhenyl) hydrazine by donating a hydrogen atom.  
The method described by [PERSON] et al. [23] was applied to measure the radical-scavenging potential of methanolic extracts from _A. calamus_, _C. vulgaris_, _L. minuta_ and _S. dimorphus_. In brief, 100 \(\upmu\)L of methanolic extract (1 mg/mL) was added to 3.9 mL of 100 \(\upmu\)M solution of DPPH in methanol. The absorption at 517 nm was measured 30 min later. Since the composition of the extracts is complex, the results for their radical-binding capacity were compared with those of Trolox (a water-soluble analogue of Vitamin E) and calculated using regression analysis from the linear dependence between the concentration of Trolox and the absorption at 517 nm on a Thermo Scientific Evolution 300 spectrophotometer. The results were expressed as mmol Trolox equivalent (TE) in 1 kg dm of the extract prepared. Each sample was analyzed in triplicate. The radical scavenging capacity was also calculated according to the following equation: DPPH radical scavenging (%) = [(A\({}_{0}-\)A)/A\({}_{0}\)] \(\times\) 100, in which A\({}_{0}\) and A are blank absorption and sample absorption, respectively. The values obtained are expressed as percentages in order to compare to the data published by other researchers.
###### Abstract  
This paper presents the fabrication of a fiber Bragg grating (FBG)-based Fabry-Perot (FP) structure (7 mm total length) in an adiabatic fiber taper, investigates its strain and temperature characteristics, and compares the sensing characteristics with a standard polyimide coated FBG sensor. Firstly, a simulation of the said structure is presented, followed by the fabrication of an adiabatic fiber taper having the outer diameter reduced to 70 \(\upmu\)m (core diameter to 4.7 \(\upmu\)m). Next, the sensing structure, composed of two identical uniform FBG spaced apart by a small gap, is directly inscribed point-by-point using infrared femtosecond laser (fs-laser) micromachining. Lastly, the strain and temperature behavior for a range up to 3400 \(\upmu\)e and 225 \({}^{\circ}\)C, respectively, are investigated for the fabricated sensor and the FBG, and compared. The fabricated sensor attains a higher strain sensitivity (2.32 pm/\(\upmu\)e) than the FBG (0.73 pm/\(\upmu\)e), while both the sensors experience similar sensitivity to temperature (8.85 pm/\({}^{\circ}\)C). The potential applications of such sensors include continuous health monitoring where precise strain detection is required.  
Fabry-Perot; microfiber sensor structure; femtosecond laser micromachining; direct writing; point-by-point fabrication; harsh environment fiber sensor; sensor for structural health monitoring
# A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans
## 1 Introduction
### Related Research in Breast Cancer Diagnosis Using Convolutional Neural Networks (CNNs)
In nuclear medicine, bone scintigraphy and PET/CT are the two most used methods for bone metastasis detection in breast cancer [61; 62; 63; 64]. Concerning the involvement and applicability of CNNs in diagnosis of metastatic breast cancer in bones from whole-body scan images, no previous works in CNNs exploration are reported. There are only a few research studies applying neural networks to deal with breast cancer bone metastasis classification in nuclear medicine. In what follows, the related papers of metastatic breast cancer diagnosis in bones using neural network methods are discussed.
## 8 Technical and Ethical Considerations for Effective Implementation of 5G Security
### Ethical Considerations  
Ethically, the implementation of 5G security faces multifaceted challenges. Data privacy stands as a paramount concern; ensuring that the advanced data collection capabilities of 5G networks do not compromise user privacy is critical. Algorithmic decision making, pivotal in machine learning-based security solutions, must be transparent and accountable to avoid issues like false positives in intrusion detection or algorithmic bias. Additionally, access to secure and robust 5G technology needs to be equitable to prevent economic and social stratification. This ethical mandate extends to environmental considerations, where the rapid obsolescence and replacement of hardware components should be managed responsibly to minimize electronic waste and environmental impact.
# N-Heterocyclic Carbene-Gold(I) Complexes Targeting Actin Polymerization
## 2 Materials and Methods
### Actin Polymerization/Depolymerization Assay  
The ability of the tested compounds to interfere with the actin polymerization and depolymerization reaction was measured using an actin Polymerization/Depolymerization Assay Kit purchased from Abcam, following the manufacturer's instructions. Both polymerization and depolymerization reactions occur in a 100 \(\mu\)L final volume, by using Labeled Rabbit Muscle actin reconstituted with the buffer G supplemented with 0.2 mM ATP and 0.5 mM DTT (1,4-dithiothreitol). For the polymerization assay, reconstituted actin was mixed with supplemented buffer G, and samples in a white 96-well plate, and then the polymerization, the reaction, was induced with the addition of the buffer P supplemented with 10 mM ATP. The solution was mixed and the data acquisition started. For the actin depolymerization assay, supplemented buffers P and G were mixed and incubated in a white 96-well plate at room temperature for one hour to polymerize the actin, protected from light. Then samples were added and the data acquisition started. Latrunculin A and cytochalasin B were use as control molecules at a concentration of 5 \(\mu\)M. For both assays, the assembly of actin filaments was determined by measuring the fluorescence (Ex/Em: 365/410 nm) in kinetic mode for 1 h at room temperature using a microplate reader. Images are representative of three independent experiments, each performed in triplicate.
# A Short-Term Photovoltaic Power Prediction Model Based on the Gradient Boost Decision Tree
## 4 Case Studies and Simulation Results
### Monthly Average Accuracy Comparison  
The results based on the one-day data are to show the performance intuitively, but are not convincing enough due to the randomness existing in solar generation. In order to analyze the accuracy of the two algorithms further, we chose the data in April (spring), July (summer), October (autumn), and January (winter) for testing. The comparison results of nRMSE and MAPE are shown in Table 1.  
As shown in Table 1, the prediction accuracies of the SVM algorithm and ARMA algorithm in different seasons are not as stable as that of the GBDT algorithm, especially for the accuracy measured in MAPE. The reason is that the prediction errors of the SVM algorithm and ARMA algorithm are large when the output power is small. On the contrary, the discrete piecewise function of the GBDT algorithm can fit well the non-continuous function relations, so it can avoid this kind of problem, resulting in more stable performance in different power conditions.
# Optical Hyperspectral Image Cryptosystem Based on Affine Transform and Fractional Fourier Transform
## 2 The Hyperspectral Encryption Scheme
### The Encryption Scheme  
The flowchart of the intact encryption algorithm is displayed in Figure 1. As shown in the Figure 1, the original hyperspectral image was separated into single bands and then each possible pair of bands was combined by employing the affine transform. Subsequently, the scrambled data were converted in FrFT domain. Finally, the parameters \(\alpha\) in FrFT generated in the encryption scheme were calculated by DCT. The output result of DCT serves as the key of the cryptosystem. In the encryption approach, two bands are combined into one complex function and then transformed in the FrFT domains. Therefore, the final encrypted data obtained in the output plane has only 50% bands of the original hyperspectral image. For instance, the original hyperspectral image having 100 bands is encrypted, and the corresponding encrypted data has 50 bands. Accordingly, the storage space of the encrypted data is reduced.  
Figure 1: The flowchart of the encryption algorithm. SLM: spatial light module; CCD: charge-coupled device; DCT: discrete cosine transform; FrFT: fractional Fourier transform.  
The encryption system can be carried out by an electro-optical hybrid setup depicted in Figure 1. The FrFT is achieved in an optical system and the affine transform and DCT are calculated in computer. Furthermore, the spatial light module (SLM) and charge-coupled device (CCD) are considered and utilized to accomplish data communication between the computer and optical system.
## 3 Artificial Audio Effect Technologies
### Digital Signal Processing
The auto-tune effect processor developed by [PERSON]'s company Antares since the late 1990s was originally designed to apply subtle corrections to voice recordings while still keeping a natural timbre and intonation in order to lower costs by eliminating the need for manual corrections or retakes. Soon, however, music producers started to use the effect in such a way that it altered the vocal recordings, deliberately making them sound heavily processed and unnatural. Cher's 1998 song \"Believe\" is considered the first mainstream song that features the typical synthetic vocal sound characterised by perfect pitch and unnatural instantaneous pitch changes introduced by the exaggerated auto-tune effect. [PERSON] himself stated that he \"never figured anyone in their right mind would want to do that\" [131]. An example for a purely digital effect is the _bitcrusher_, a distortion effect that transforms the sound by reducing the bit-depth and sample rate of the input signal. The output sound is characterised by a decreased bandwidth and added quantisation noise. Furthermore, computer systems allow the organisation of a vast number of grains in granulation-based effects [132] and make it possible to apply reverberation by convolution, where a signal is convolved with an impulse response to recreate the acoustic characteristics of a real or artificial space. This technique arguably producesthe most realistic room simulation; however, standard convolution lacks the ability to control the produced sound or to interpolate between a set of given impulses [133]. It was [PERSON] [99] who proposed the first digital simulation of reverberation. However, it was not until 1977 that the first completely digital reverberator has been made available commercially [134]. Around the same time, the first approach for capturing and reapplying a convolution reverberation was developed [133]. Today, artificial and convolution reverberation is a standard tools in audio production, as technology moves further from hardware and closer to software solutions. Modern digital reverb units may be some combination of artificial and convolution techniques or may even emulate vintage analogue solutions. Convolution is also used to emulate other linear systems, such as the tonality of guitar cabinets. A review of techniques for the digital emulation of tube-based amplifiers have can be found in References [135; 136].