esa-sceva/satcom-chunk-collection huggingface dataset:
# Unbiased 3D Semantic Scene Graph Prediction in Point Cloud Using Deep Learning
## 3 Methods
### Unbiased Meta-Embedding  
In 3D scene graph prediction, a meta-embedding knowledge learning model [14] is used to learn the a priori knowledge obtained from the ground-truth one-hot code of the training set as common categories knowledge. It considers only the class-related labels without considering the visual perceptual information and then fuses the multi-source features and information in the final graph inference process. In training, we initially assume that there is a relationship between each entity object before organizing the objects and relationships into a graph structure, where the objects are represented as nodes and the predicate relationships are represented as edges. In terms of the network structure, a multi-layer perceptron (MLP) is used to raise the label vector to the same feature space as the point feature encoding vector. Then, an ENA-GNN network is used to enable the feature aggregation and message updates for each node and edge in the scene graph.  
As in Figure 2, the GT labels are used as network inputs, and the meta-embedding (ME) vector is first initialized, where \(ME\ \in\ R^{n\times C_{point}}\), \(n\) is the number of object categories or relational predicate categories, and \(C\) is the feature dimension. During the knowledge learning process, the meta-embedding vector is iteratively updated by continuously closing the gap between the embedding vector and the potential feature vectors. The deviation distance of the two vectors is used as a loss function and is back-propagated [40].  
However, the learned meta-embeddings as a priori knowledge are unclear and may interfere with the network model category prediction [14]. Based on the idea of unbiased training, an unbiased meta-embedding (UME) coding fusion approach is proposed to group the predicate relation categories in order of sample proportion. As seen in Figure 6, the categories are divided into three groups, and the detailed grouping strategy is referred to in Section 4.1.  
In KSGPN [14], it is known from the existing experiments that the embedding knowledge of category labels can guide the network to make more balanced predictions of relational categories. However, for the common relational categories, it may be noisy, thus affecting the accuracy of the common predicate relational categories (in Group 1). Therefore, for the common categories, we reduced the proportion of prior knowledge weights for the high-frequency samples (Group 1) in the relational inference process to avoid the formation of excessive irrelevant noise effects. For the samples with a relatively low frequency of occurrence (Groups 2 and 3), their prior knowledge encoding is retained to obtain unbiased meta-embedding encoding (UME).
## 4 Performance Analysis
### Simulation Performance Analysis
#### 4.1.1 Setting up a Simulation Model  
In this paper, a moving target detection model was used under a 50 Hz geomagnetic field. For this model, a ferromagnetic target 5 m long, 1.9 m wide, and 1.4 m high was placed in the model. The relative permeability was 1000. A simulated power frequency background field was constructed, which had a similar intensity to the measured one as well. An observation point was placed 5 m away from the target CPA, as shown in Figure 1:  
The target passed through the observation point at a speed of 5 km/h, resulting in the power frequency magnetic field disturbance signal as shown in Figure 2:  
Figure 1: Moving target detection model.  
Figure 2: Simulates the time\(-\)domain magnetic perturbation signal.
# Target Speaker Extraction by Fusing Voiceprint Features
## 2 Methodology  
### Overview of Tse-FV  
As shown in Figure 1, the workflow of Tse-FV consists of three stages: (1) _feature extraction_; (2) _feature fusion_; and (3) _target speech extraction_. In the feature extraction stage, voiceprint features, e.g., MFCCs and d-vectors, are extracted from the reference speech. In addition, magnitude spectrograms are extracted from the noisy audio. In the feature fusion stage, extracted voiceprint features and magnitude spectrograms are concatenated in three ways to construct fused features. In the speech extraction stage, speech separation models are adopted to generate masked magnitude spectrograms corresponding to the target speaker. In the end, the masked magnitude spectrograms are transformed to the clean speech of the target speaker using an inverse STFT. The three stages of the model are described in detail in the following.
# A Python-Based Indoor Channel Model with Multi-Wavelength Propagation for Color Shift Keying
###### Abstract  
Color shift keying is a modulation scheme for visible light communication that uses fixtures with three or more narrow-spectral light-emitting diodes to transmit data while fulfilling the primary function of illumination. When this modulation is used indoors, the reflectivity of the walls strongly affects the inter-channel interference and illumination quality. In this paper we present an indoor channel model that takes into account multi-wavelength propagation. This model is available as an open-source Python package. The model calculates the inter-channel interference, illuminance, correlated color temperature, and color rendering index at the receiver position. The Python package includes a module for estimating the symbol error rate. To validate the model, we computed the received power at each color photodetector for four different indoor scenarios. The model demonstrated a color rendering index of less than 15 when using IEEE-based color shift keying and non-uniform illumination on a horizontal plane. The simulation determined the required luminous flux to achieve a symbol error rate of less than \(10^{-5}\) when the photodetector is at the center of the indoor space and vertically below the light source. To maintain a symbol error rate less than \(10^{-5}\), the luminous flux increases when the photodetector is displaced in a diagonal direction from the center of the plane.  
color shift keying; indoor channel model; recursive algorithm; lighting quality +
Footnote †: journal: _Photonics_  
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Footnote 0: _Email: [EMAIL_ADDRESS]
# Rapid-Detection Sensor for Rice Grain Moisture Based on NIR Spectroscopy
## 3 Results and Discussion
### Sensor Structure Design
#### 3.2.3 The Software Design  
The software system of moisture content determination of rice grain was programmed with a modular programming concept, mainly comprising a sensor probe control program, a rice grain moisture calculation program, an Ethernet control program, and a personal computer (PC) remote control program. More specifically, the UCOS (Micro-Controller Operating System) operating system was transplanted in this program, where the task functions of each function module were created through the main program of the system to realize functions such as sample measurement and data processing. Additionally, the task function module was divided into the following main sections: AD data acquisition module; Ethernet Transmission Control Protocol (TCP)/Internet Protocol (IP) data transmission module; model calculation module; Liquid Crystal Display (LCD) display module; function key selection module and FLASH memory data read-write module. Figure 6 presents the flowchart of this program.  
Based on Figure 6, the program initializes each function module first and creates the function module task through the operating system when powered on. Once the scan button is pressed, the program will jump to the corresponding task according to the specific key instruction. When the assigned task is completed, all the tasks except the key scan task are suspended, and the above steps will be performed again until the scan button is pressed again. Furthermore, in addition to remote data reading, the instrument supports real-time remote/local PC control functions, which are realized on the basis of communication protocols established by the PC host computer. In general, this program supports the main functions of data processing (data acquisition, AD sampling, model establishment,  
Figure 4: The block diagram of hardware design.  
Figure 5: The sensor probe and printed circuit board.  
data storage and display), data view and data transmission, which is in accordance with the hardware design.
# Pyramid Feature Attention Network for Speech Resampling Detection
## 4 Conclusions  
The application of deep learning methods in speech-resampling-tamper detection is a new task. Based on our previous work [19], this paper uses a feature pyramid network that integrates attention mechanism to extract the spectrogram features of original and tampered speech and classify them. The experimental results show that the proposed method can distinguish between the original speech and the resampled speech effectively under the condition of speech tampering with multiple resampling factors. Among them, the accuracy of resampling detection is significantly improved when the amplitude of tampering is small. In addition, in the test under different compression ratios, the proposed model shows relatively good robustness, and can still accurately detect and recognize the compressed speech. To enhance the robustness and generalizability of our findings, we plan to extend our experiments to more recent and larger datasets such as DidiSpeech [43] and AiShell-4 [44]. We also plan to make our resampled and compressed speech corpus publicly available for further validation. For future work, we will consider using other deep networks to further improve the accuracy of small-scale resampling-tamper detection, and estimating the resampling factor is also a part of our future plans.  
Conceptualization, X.Z. and Y.Z.; methodology, X.Z. and Y.Z.; software, X.Z.; validation, X.Z.; formal analysis, Y.Z.; investigation, X.Z. and Y.Z.; resources, Y.Z.; data curation, X.Z. and Y.Z.; writing--original draft preparation, X.Z.; writing--review and editing, X.Z., Y.Z., Y.W., J.T., and S.X.; visualization, X.Z.; supervision, Y.Z.; project administration, X.Z. and Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant No. 62072057, in part by the Natural Science Foundation of Shanghai under Grant No. 17 ZR1411900, in part by the Opening Project of Shanghai Key Laboratory of Integrated Administration Technologies for Information Security under Grant No. AGK20190005 and in part by the Innovation Fund for Industry-University-Research of Chinese Universities under Grant No. 2021 ZYB01003.  
Not applicable.  
The dataset and code used in this study are available upon request from the corresponding author.  
The authors declare no conflicts of interest.
# Automatic Classification of Foot Thermograms Using Machine Learning Techniques
## 1 Introduction
[16], who proposed a machine learning-based scoring technique with feature selection, optimization techniques and learning classifiers to perform a binary classification of the thermograms of the feet into diabetic and nondiabetic classes. The same investigators also use the public dataset [15] to propose a machine-learning framework, based on a thermal change index, to classify the thermograms of diabetic individuals into five classes [17]. [PERSON] and [PERSON] [18] also used the dataset in [15] to propose the implementation of deep learning architectures under data augmentation methods, using the Fourier transform, to classify each foot thermogram separately as belonging to a diabetic or nondiabetic individual. Despite the great progress, there is still a need to develop techniques to classify thermal images that not only present a robust, accurate and detailed classification of the thermograms, but that are also practical enough to be used by health professionals in their clinical practices. With this in mind, with this work we aim to develop a functional methodology to analyze and classify a diversity of thermal changes in the plantar region of diabetic and healthy individuals; the public dataset [15] was used. Additionally, based on the experience acquired when working with the data from the aforementioned dataset and on the information collected during the literature review, a three-level scale was adopted to classify the feet of a DM individual into three categories. Thus, in this paper, two models, using different machine learning algorithms, for classifying thermographic images of the feet are proposed. In the first one, the foot thermograms are classified into four classes: healthy and three categories for diabetics. In the second, the classification process has two stages: Initially, each foot is classified as belonging to a DM or healthy individual. Then, if the foot is considered an individual DM foot, a classification refinement is made in a second stage. In order to extract the image descriptors used for foot classification, a cluster algorithm divides the thermogram into different regions. For each region, a set of regional descriptorsare calculated, being used as features to train and test both models. Five machine learning classification algorithms were evaluated in both models: logistic regression (LR), support vector machine (SVM) with two variants, and K-nearest neighbor (k-NN), with two variants. This paper is organized as follows: In Section 2 the used dataset is introduced and the proposed methodology is described. In Section 3 the obtained results are presented. Finally, Section 4 discusses the results and presents conclusions.
# The Progress in Treatment of \(\beta\)-Thalassemia and Research on Animal Models of the Disease
## 4 Animal Models of \(\beta\)-Thalassemia
### Cynomolgus Monkey Model  
The cynomolgus monkeys are a widely utilized non-human primate disease model. Its genetic and physiological similarities to humans establish it as an important research animal in the medical field. This model more accurately reflects the human phenotype of severe \(\beta\)-thalassemia compared to earlier mouse models. In severe \(\beta\)-thalassemia, the expression of \(\gamma\)-globin is significantly elevated to compensate for the excess \(\alpha\)-globin chains resulting from \(\beta\)-globin deficiency, thereby mitigating the imbalance. [PERSON] utilized CRISPR/Cas9 gene editing technology to knock out the HBB gene in macaques, successfully generating a model with 217 bp deletion in the coding region of this gene [55]. In the context of \(\beta\)-globin synthesis deficiency, this model exhibited a compensatory expression of \(\gamma\)-globin, with the levels surpassing those in neonatal control macaques, thereby paralleling the elevated fetal hemoglobin observed in patients with \(\beta\)-thalassemia. While the HBB gene knockout model developed in macaques demonstrates a rare genotype with specific deletions and primarily replicates the clinical manifestations and indicators of \(\beta\)-thalassemia at both the molecular and clinical levels, it retains significant clinical relevance. This model replicates many aspects of the \(\beta\)-thalassemia phenotype, particularly in Hbb[13] heterozygosity mice. It not only reproduces anemia and its related complications but also representsthe first instance of a mouse model exhibiting cardiac iron deposition [56]. Therefore, Hbbth3 heterozygous mice hold significant importance in iron overload research. Currently, the research on animal models of \(\beta\)-thalassemia primarily focuses on the aforementioned animal types, which exhibit a significant similarity in mimicking the pathogenesis of human \(\beta\)-thalassemia, thereby providing a valuable experimental foundation for a more comprehensive understanding and treatment of the disease.
# Optimized Weighted Nearest Neighbours Matching Algorithm for Control Group Selection
## 2 Related Work
Unfortunately, in many studies, control group selection is made by automatisms, and little attention is paid to the evaluation of the goodness of the control group. However, in the absence of this or in the case of bias in the control group, the results of the comparative analysis are questionable. The similarity of case and control groups can be evaluated from different aspects. On the one hand, the distributions of the covariates have to be similar in the treated and control groups. On the other hand, having treated and control groups with similar distributions on the baseline variables does not mean that individuals are also similar to each other. Therefore, the similarity of the paired individuals should also be evaluated. This second criterion is also not sufficient as an independent criterion either because the distributions may contain bias in this case. To measure the similarity of the distributions of the covariates, the standardized mean difference (SMD) [25] or goodness of fit tests can be used. For continuous variables, in the case of a normal distribution, the \(t\)-test [26], for general cases, the Kolmogorov-Smirnov [27; 28] test can be calculated. For nominal data, the chi-squared test [29], for ordinal data, the Mann-Whitney U test [30] can be applied. The main drawback of these tests is that they evaluate the similarity of the treated group and the control group on only a single covariate. Calculating the Distribution Dissimilarity Index (DDI) [31] opens another aspect to the evaluations, as it calculates the similarity of the covariates based on the differences in the frequencies of the histograms of the covariates. In biomedical studies, the Hansen-Bowers test [32] is applied for more complex evaluations. This measure allows the evaluation of the imbalance of all covariates simultaneously. As we mentioned before, the similarity of the distributions of the covariates does not mean that the matched individuals are also similar pairwise. The pairwise similarity of the matched objects can be evaluated by the Nearest Neighbour Index (NNI) and the Global Dissimilarity Index (GDI) [31]. The first measure only evaluates whether the paired individual is the closest individual from the candidate elements, while the second one considers the degree of the difference.
# Echelle Grating Spectroscopic Technology for High-Resolution and Broadband Spectral Measurement
## 3 Optical Layout
### Detector  
The two-dimensional spectrum of the cross-dispersion on the focal plane should be detected by detectors. Some early echelle spectrometers used the photomultiplier (PMT) [49; 50; 51] and photo-diode array (PDA) [52; 53]. Nowadays, area array charge-coupled devices (CCD) and Complementary Metal Oxide Semiconductors (CMOS) are widely used.  
When selecting a detector for the echelle spectrometer, we should consider some main parameters, such as the quantum efficiency, the image size, the effective pixels, and the pixel size. Within the wavelength range required, the higher the quantum efficiency is, the more sensitive the detector is. To capture the target spectrum in one shot, the image size ought to be big enough to cover all the monochromatic image spots that are to be measured [54]. For the primary dispersion direction, the height \(H\) of the detector should satisfy:  
\[\Delta l_{m}=\frac{f_{cam}\lambda_{m-ccn}}{d\cos\gamma\cos\theta_{B}}\leq H \tag{19}\]  
where \(\Delta l_{m}\) is the broadening of a single order on the image plane, \(f_{cam}\) is the focal length of the focusing optics, and \(\lambda_{m-ccn}\) is the central wavelength of the \(m\)-th order. For the direction perpendicular to the primary dispersion, the wavelength range is \(\lambda_{min}\)\(\sim\)\(\lambda_{max}\), and the width \(W_{d}\) of the detector should satisfy:  
\[f_{cam}(\chi_{max}-\chi_{min})\leq W_{d} \tag{20}\]  
where \(\chi_{min}\) and \(\chi_{max}\) are the dispersion angles at \(\lambda_{min}\) and \(\lambda_{max}\), respectively.  
The pixel size of the detector influences the system spectral resolution [55; 56]. No matter what kind of detector is selected, we must follow the Nyquist-Shannon sampling theorem. That is, each image spot should cover at least two pixels. For more reliability, we cover three pixels and obtain:  
\[\frac{\sin i+\sin\theta}{\lambda\cos\theta}f\cdot\Delta\lambda\geq 3p \tag{21}\]  
where \(\Delta\lambda\) is the spectral bandwidth of the slit image, and \(p\) is the pixel size of the detector. In the Littrow mode, the pixel size \(p\) should satisfy:  
\[p<\frac{2f\tan\theta_{B}}{3R} \tag{22}\]  
where \(R\) is the spectral resolution of the system, which is inversely proportional to the pixel size. There is a trade-off between the sensitivity of the detector and the spectral resolution.
## 1 Introduction
This effectively suppressed knock occurrence under high-load conditions due to the reduced effective compression ratio and lower peak in-cylinder temperature associated with LIVC [25]. As an alternative fuel, methane has environmental advantages because of its lower carbon content and higher hydrogen-to-carbon (H/C) ratio [26], leading to less harmful pollutants, including hydrocarbons and carbon monoxide [27]. CO emissions can be reduced by approximately 39.4%, even at wide-open throttle, and up to 93% for part-throttle conditions when raw biogas (a methane-rich fuel) is used in the spark ignition engine [28]. Regarding energy efficiency, natural gas also demonstrates promising potential. While methane might result in a minor reduction in indicated thermal efficiency (ITE), it offers advantages regarding lower BSFC and reduced heat losses [29]. The Atkinson cycle's unique thermodynamic process confers several advantages to gasoline engines, making it an attractive option for improving fuel economy and reducing environmental impact. The higher expansion ratio in the Atkinson cycle allows for greater energy extraction from the combustion gases before they are expelled, resulting in improved thermal efficiency, better fuel economy, and reduced CO\({}_{2}\) emissions [30; 31; 32; 33]. The delayedIVC also reduces pumping losses, further contributing to improved fuel economy [34]. The Atkinson cycle's lower peak combustion temperatures can lead to reduced nitrogen oxide (NOx) emissions, a major pollutant contributing to smog and acid rain [35], especially at higher engine loads [36]. The overall impact on emissions is generally positive, including with reductions in particulate matter [37]. In addition to the primary benefits, the Atkinson cycle combined with VVT can improve low-speed torque by altering the timing of intake valves to increase the compression ratio at lower speeds [38, 39]. The dynamic adjustment of the valves helps in achieving optimal valve overlap, which is crucial for improving volumetric efficiency (VE) and reducing pumping losses [40]. VVT can also enable cylinder deactivation strategies at light loads [41] and increase the efficiency of dual-fuel engines, operating on liquid fuel and natural gas [42, 43]. This paper evaluates the LIVC's effect on energy and ecological performance for an ICE operating on natural gas, utilizing numerical analysis and experimental tests. Section 2 outlines the methodology applied in the numerical modeling and experimental phases. Section 3 presents the results, including a relative assessment using energy efficiency and environmental impact criteria. The study's conclusions, together with recommendations for subsequent investigations, are outlined in Section 4.