esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Hierarchical Classification  
This work uses a thematic hierarchical classifier tree and a thematic catalog in which each theme is associated with one or more numeric codes deduced from the hierarchical tree. The themes in the catalog were represented by words that can exist in the messages; specifically, in this work, we focused on English nouns appearing in WordNet [28]. For the system log analysis process, the words of each message were read, searched for in the catalog, and their thematic codes were extracted. The analysis of these hierarchical codes allowed obtaining a semantically equivalent but much more compact log.  
In the initial phase, a thematic classification tree is constructed, and a catalog of hierarchical codes is created with the themes and their relationships. Subsequently, an algorithm is developed that processes the original log to identify similar or duplicated themes, thereby compacting the input records while maintaining high semantic similarity.  
The steps of this algorithm are detailed below, whose novelty lies in the use of a catalog of hierarchical codes that reflect both their thematic specificity and their position within the higher hierarchy. Unlike traditional methods that simply reduce redundancy, our algorithm preserves the semantics of the logs using a hierarchical coding system based on natural language semantics. This ensures that essential information is not lost during the compaction process. Structuring and compacting logs more effectively significantly reduces fatigue from false alerts, enhancing the efficiency of the cyberattack and anomaly detection process.
## 3 The Proposed SensorAnt Protocol
### The Network Model  
A WSN can be designed as a connected graph _G(V, E)_, where \(V\) is the set of sensor nodes and \(E\) represents the set of communications links. Let 1 represents two sensor nodes which are directly connected while 0 represents two unconnected sensor nodes. Given a sensor node \(v\in\mathrm{V}\) with transmission range \(R\), if \(v\) is transmitting, all nodes within the carrier sensing of \(v\) will not be able to access the medium when the channel is busy. The transmitted packets from \(v\) can follow one of the possible paths in the graph _G(V, E)_ that connects \(v\) to the sink node. In the proposed model, a node can transmit data packets to any neighbor within a range. All sensor nodes are assumed static and are deployed randomly and with a finite battery power. All the sensor nodes have equal initial energy.
# Sphingolipidoses and Retinal Involvement: A Comprehensive Review
## 4 Subtypes of Sphingolipidoses
### Farber Disease
#### 4.5.2 Retinal Manifestations  
Fundus examination enables the identification of the cherry-red spot, which is the defining feature of this pathology [2]. SD-OCT enables the detection of a hallmark sign: hyperreflective bands localized to the GCL corresponding to ceramide deposition [35,122]. In murine models at 3 to 4 weeks of age, retinal development was reported as normal. However, alterations in the retinal layers were observed, with occasional hyperreflective spots in the vitreous, particularly near the optic nerve head [35]. At 8 to 9 weeks of age, a marked increase in hyperreflectivity, primarily along the RNFL and GCL, with hyperreflective specks, was observed in the vitreous body [35].  
Multimodal imaging and electrophysiology and post-mortem histology have been used to monitor the effectiveness of emerging therapies, such as enzyme replacement or gene therapies, in mouse models [1]. A study was conducted in a mouse model of Farber disease by inducing overexpression of the mutated ASAH1 gene via a recombinant adeno-associated virus (rAAV). The efficacy of this gene therapy was then evaluated by studying the progression of retinopathy. The results demonstrated a reduction in retinal ceramide accumulation, accompanied by a decrease in central retinal thickness and fundus hyper-reflectivity. These findings suggest that the retinal evaluation of sphingolipidoses through multimodal imaging could play a promising role in monitoring therapeutic responses [1]. Notably, the latest approved gene therapies, which rely on the transplantation of genetically modified stem cells overexpressing the deficient enzyme or the direct delivery of the mutated enzyme gene via a vector into a specific organ [2], rank among the most expensive treatments in the global pharmaceutical market [1]. Furthermore, substrate reduction therapy, designed to decrease sphingolipid synthesis, may inadvertently inhibit the production of crucial sphingolipid intermediates with essential biological functions, potentially leading to significant adverse effects [123]. Consequently, retinal imaging-based monitoring of therapeutic response is critical for optimizing drug administration, ensuring an optimal dose-efficacy balance, reducing the frequency of treatment and associated costs, and minimizing adverse effects.
## 3 Numerical Experiments
### Imaging Results
#### 3.2.1 Cave Models
wave artifacts detected in model A-1, some interferences were found beneath the floors of the caves (Mark 1) due to the superposition of different S-waves. Compared with MVSS beamforming, in addition to the same interferences and artifacts in model A-1, the two floors overlap (Mark 1) in the Kirchhoff migration result (Figure 11c). This overlap appears again in the DAS beamforming result. This finding indicates that MVSS beamforming has a higher horizontal resolution than the other two methods. These imaging results further demonstrate that the imaging of these caves did not suffer from signal interference because the propagation paths of the waves reflected from the cave  
Figure 11: Model A-2. (**a**) True velocity model. (**b**) Imaging result of MVSS beamforming. (**c**) Imaging result of Kirchhoff migration. (**d**) Imaging result of DAS beamforming. Figure 10: Model A-1. (**a**) True velocity model. (**b**) Imaging result of MVSS beamforming. (**c**) Imaging result of Kirchhoff migration. (**d**) Imaging result of DAS beamforming. (**e**) Imaging result of RTM. boundaries do not overlap with each other. Besides, the S-wave artifacts appears in Kirchhoff migration and DAS beamforming (Mark 2). Compared with those in model A-1, the interferences in the Kirchhoff migration and DAS beamforming results in model A-2 are more intensive due to the more complicated structure (Mark 3). However, these interferences are still much weaker in the MVSS beamforming result, which means that MVSS beamforming exhibits better S-wave suppression. The imaging results of model A-3 are shown in Figure 12, in which two caves are aligned vertically in the middle of the area (a beaded cave model). The surface wave artifacts and S-wave interferences are similar to those in model A-1 and model A-2. In addition, there is some overlap between the roof of the shallower cave and the floor of the deeper cave (Mark 1), which means that the three methods have similar vertical resolutions in this cave model. Compared with the MVSS beamforming imaging result, the Kirchhoff migration imaging result suffers from more intensive and complicated interferences on both sides of the image caused by S-waves. These S-wave interferences are also relatively intensive in the DAS beamforming result.
# Performance Study and Efficiency Improvement of Ice Slurry Production by Scraped-Surface Method
###### Abstract  
In this study, the performance of ice slurry production by scraped-surface method was experimentally investigated. Temperature change characteristics, ice packing fraction (IPF) of ice slurry, power consumption of scraping system and coefficient of performance (COP) were measured by varying the concentration of sodium chloride solution, scraping speed, and solution flow rate. The effect of nanosilica on efficiency of ice slurry production was also studied. The results showed that scraping power consumption accounted for only a small proportion (about 5%) of the total power consumption of the system. An increase in the concentration of sodium chloride caused a decrease in the IPF and a decrease in the COP of the system. With the solution flow rate at 1.3 m\({}^{3}\)/h and scraping speed at 13 rpm, the maximum COP (2.43) was obtained. Furthermore, the addition of nanosilica had a significant effect on improving the system COP.  
ice slurry; performance; scraped-surface method; nanosilica +
Footnote †: journal: Applied Sciences
# Limitations and Performance Analysis of Spherical Sector Harmonics for Sound Field Processing
## 4 Limitations on Radial Extrapolation
### Interior Case
#### 4.2.1 Plane Wave
Figure 7: Extrapolation error \(\epsilon\) with different arriving directions of the plane wave: (**a**) extrapolation error with \(\theta_{M}=45^{\circ}\); (**b**) extrapolation error with \(\theta_{M}=75^{\circ}\); (**c**) extrapolation error with \(\theta_{M}=105^{\circ}\); (**d**) extrapolation error with \(\theta_{M}=135^{\circ}\).
# A Cross-Modal Hash Retrieval Method with Fused Triples
## 6 Conclusions  
In this paper, we propose a new end-to-end cross-modal hash retrieval method with fused triples called the across-modal hash retrieval method. Specifically, triple data selection is first performed on the dataset to filter out the information that is truly similar to the semantics of the tags by bringing similar samples closer together while pushing different samples further apart based on the tag information. Then, the text data are vectorized using the bag-of-words model. Multi-Layer Multivariate LSTM (Multi-LSTM) and Convolutional Neural Networks (CNN) extract features from text and image modalities. In order to fully exploit the semantic information of the data, we considered the semantic similarity relationships between and within modalities. For each sample, a positive sample of another modality and a negative sample of the same modality are selected to form a cross-modal triad to enhance the learning ability of the hash function in the network. In judging the similarity of data samples within the modality corresponding to that label, sample data with similar meanings can be generated. At the same time, we convert the vector codes obtained from triple data selection into binary hash codes to reduce the storage space, thus realizing cross-modal hash retrieval and inter-modal fast retrieval. Finally, we conduct experiments on three public datasets in cross-modal retrieval for the method proposed in this paper. The results show that the Tri-CMH method proposed in this paper has better results on regular datasets but has poor results on ultra-large datasets, which is further analyzed for underperformance. It is finally concluded that the method in this paper is more suitable for datasets with a sample size of 7000. It needs to be decomposed into multiple small-sample datasets when encountering larger datasets. Therefore, compared with other mainstream methods with obvious advantages, this paper's method can effectively retrieve  
Figure 5: Variation of mAP under different samples. (**a**) MIRFLICKR-25K data set; (**b**) NUS-WIDE dataset.  
semantic similarity information, improve inter-model and intra-modal training accuracy, and provide a reference for the new technology of cross-modal retrieval.  
Conceptualization, W.L.; methodology, W.L.; software, W.L.; validation, W.L., Y.L., J.Y. and X.Z.; formal analysis, H.M., J.Y., X.Z. and X.X.; investigation, W.L., Y.L., J.Y. and J.W.; resources, X.X.; data curation, W.L.; writing--original draft, W.L.; Writing--review & editing, W.L., H.M. and Y.L.; visualization, W.L.; supervision, H.M.; project administration, H.M. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Liaoning Education Department Scientific Research Project (JZL202015404, LJKZ0625), and General project of Liaoning Provincial Department of Education (No. LJKZ0618).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
# Influence of Angiopoietin Treatment with Hypoxia and
## 4 Discussion
### Ang-1/2 and Its Effect on Metabolic Activity Per DNA in Normoxic & Hypoxic Conditions  
Out of all analyses, in cell metabolism relative to DNA amount, the difference between oxygen tension (\(\leq\) 5% vs. 21%) was in favor of hypoxia for every single sample tested in our three populations of interest, i.e., NP cells mixed, NP cells Tie2\({}^{-}\), and NP cells Tie2\({}^{+}\) populations. In our study, the samples in the hypoxic environment all achieved a higher metabolism, compared to the normoxic environment. This is in accordance with previous studies upon oxygen levels in NPC cultures, such as those of [PERSON] et al. [38], [PERSON] et al. [39], [PERSON] et al. [40], and [PERSON] et al. [31]. The authors demonstrated that hypoxia enhances NPCs phenotype, which results in greater ECM components production and, indeed, a higher metabolism.  
Additionally, as the NP cells mixed population and NP Tie2\({}^{-}\) cells population only differ in 1 to 5% of their cell population (only depletion of Tie2\({}^{+}\) cells), this could explain that stimulated samples showed comparable values in their metabolic activity [6]. In contrast, the NP cells Tie2\({}^{+}\) population responded in higher potency to the normoxic and hypoxic environments, compared to the two others population (i.e., mixed and Tie2\({}^{-}\)). On one hand, the upregulation of the cell's metabolism would therefore seem logical, as this population expresses the Tie2 receptor and thus can react to the stimulation (i.e., presence/absence of Ang) by its ligands Ang-1 and Ang-2. However, on the other hand, the stimulation effect remains in question, as the treated samples do not show any trend either in a dose- or protein-dependent manner.  
According to the findings of [PERSON] et al. [23], Ang-2 plays a role in suppressing cell adhesion, decreasing cell viability, and promoting NP cell apoptosis. Concerning the cell's metabolism, in our study, the findings for Ang-2 could not be confirmed, which supports the aforementioned lack of effect upon Ang-1 and Ang-2 stimulation. Thus, the more reliable and convincing explanation for the upregulation of fluorescence intensity (i.e., cell's metabolism) in the NP cells Tie2\({}^{+}\) population in normoxia and hypoxia, compared to the NP cells mixed and NP cells Tie2\({}^{+}\) populations, could potentially be that progenitor cells show a higher metabolism, compared to mature cells [41].  
Another aspect that has to be taken into consideration by comparing the findings of [PERSON] et al. [23] with our study is that the cells from our study were isolated from trauma discs and not from degenerated IVDs. Thus, the cells in [PERSON]'s study were already stimulated by Ang-1, due to potential blood vessel infiltration and inflammation in the degenerated disc as well as by Ang-2, as the Ang-2/Ang-1 ratio increases in degenerated discs for the same reason [37]. Whether such contradictory findings could be related to differences in IVD degeneration pathways between injuries (trauma), natural aging, or the degree of IVD degeneration is still unclear.
## 4 Results and Analysis of Spot Area Detection
### Calculation of Discharge Area Parameters in Ultraviolet Detection Images  
After grayscale conversion, image segmentation, and filtering, clear discharge area contours with smooth and non-adhesive boundaries were obtained in the ultraviolet detection images. At this point, the area of the light spot in the ultraviolet detection image can be calculated using a function. In this study, the light spot area is defined as the number of white pixels in the image after spot recognition, measured in pixels. By applying Equation (7), the number of white pixels in the image can be counted to obtain the area of the corona discharge light spot. In the equation, \(M\) and \(N\) represent the rows and columns of the processed RGB image matrix, respectively. \(B\left(x,y\right)\) represents the number of white pixels, where \(x\) and \(y\) are the horizontal and vertical coordinates of the pixels in the processed matrix.  
\[S=\sum_{i=1}^{M}\sum_{j=1}^{N}B(x,y) \tag{7}\]  
Figure 8: The image-processing process of ultraviolet detection images. (**a**) The original image; (**b**) grayscale processing; (**c**) image segmentation; (**d**) filtering.  
Through the above process, the obtained images from the experiment were processed, and the following conclusions were drawn. For the 5 mm diameter conductor, when the gain of the UV imager was set to 100% and the observation distance was set to 2 m, the variation of the spot area with the voltage was observed. The relationship between the spot area and the voltage for the 5 mm conductor is depicted in Figure 9.  
The voltage and its corresponding UV spot area were fitted using the least squares fitting algorithm, and the obtained function relationship is shown in Equation (8). The calculated fitting coefficient for the function relationship is 0.9735, indicating a good fitting result. From the fitting results, it can be observed that the relationship between the spot area and the conductor voltage follows a quadratic function.  
\[U=-1.5769\times 10^{-8}\times S^{2}+5.5232\times 10^{-4}\times S+50.4771 \tag{8}\]  
In the equation, \(U\) represents the applied voltage on the conductor, the unit is kV, and \(S\) represents the spot area.
## 1 Introduction
In the proposed method, FMT modulation is exploited to modulate information symbols onto separate subcarriers for parallel transmission, and TR technique, as well as adaptive equalization, is adopted at the receiver to suppress ISI and noise. The performance of the proposed method is assessed through simulation and real data from a trial in an experimental pool with the frequency band of 7.5-15.5 kHz. Performance indicators of the proposed method are compared with that of the TR acoustic communication using SC modulation with the same spectral efficiency, and results show the validity of the proposed method. The contribution of this paper consists of three dimensions: (1) aiming at the issue that the large ISI span in the high data rate TR acoustic communication using SC modulation decreases the performance of the TR process and increases the computational complexity of equalization for post-processing, the TR acoustic communication method using FMT modulation is proposed; (2) the system model of the proposed method including the transmit structure and the receive structure are presented, and verified using theory analysis; and (3) through simulation and experiment, the performance of the proposed method is assessed and compared with that of the TR acoustic communication using SC modulation, and results confirm that the proposed method improves the performance of the TR process and reduces the computational complexity of adaptive equalization for post-process. The paper is organized as follows. Section 2 presents the system model of the proposed method including the transmit structure and the receive structure, and analyzes the system model in theory. Section 3 assesses the performance of the proposed method through simulation and real data from a trial in an experimental pool, and compares the performance of the proposed method with that of the TR acoustic communication using SC modulation. Finally, concluding remarks are given in Section 4.
# Empirical Approach to Defect Detection Probability by Acoustic Emission Testing
## 3 POD Estimation
### Defect Detection Probability Calculation  
AE signal parameters modeling described in the previous section makes it possible to implement an algorithm for calculating POD as a detailed numerical experiment, including modeling AE signals about variations in the values of a priori unknown defect parameters.  
The initial data are the steel grade, wall thickness, parameters of liquid media, maximum value of the applied mechanical load, frequency range, and characteristics of AE sensor. Based on specified initial parameters, considering the different locations of the defect, the calculation of realistic models of AE signals has been carried out. The coordinate of the defect varies from 0 to \(L\) with a step \(\Delta x\), where \(L\) is the distance between the sensors that form the location sensor array. Models are calculated for two AE sensors that form the linear location. Each of the models is formed for the same source parameters \(N^{\prime}_{\Sigma}\) and \(A^{\prime}_{i}\) (\(i\)\(\epsilon\)[1, \(N^{\prime}_{\Sigma}\)]) with the distance from the defect \(x\) and \(L\)-\(x\), respectively.  
Since the formation of a location cluster of a certain density is considered as a criterion for defect detection, a location procedure is carried out to check the detection condition, followed by an assessment of the formed location cluster density.  
In accordance with regulatory documentation, the location error is about 5% of the distance between the sensors \(L\). Therefore, the density of the location cluster is determined over the interval length \(\Delta L=x\pm 0.025L\) as \(\rho_{clust}=\frac{N^{clust}}{\Delta L}\), where \(N^{clust}\) is the number of indications in the location interval. The flowchart of this algorithm is shown in Figure 6.  
A defect is detected if the location cluster has reached the critical density \(\rho_{clust}^{crit}\). Otherwise, a type-II error occurs. Since the simulation is based on the probabilistic model of  
Figure 6: Flowchart of the algorithm for POD assessment.  
the AE source, to obtain a reliable POD, it makes sense to repeat the calculation procedure \(\sim\)10\({}^{4}\)-10\({}^{6}\) times. POD at a certain location is determined as the fraction of the total number of repetitions \(N\), at which the detection condition is satisfied. For the case of an arbitrary and a priori unknown location of the defect, POD is determined as  
\[p=\frac{1}{L}\int_{0}^{L}p(x)dx, \tag{2}\]