esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method Improves Speech Recognition with Contrastive Learning in Low-Resource Languages
## 3 Methods  
### Model  
The process of our method is shown in Figure 4. The original audio signal \(X\) is passed through \(encoder\) to extract features and generate latent speech representations \(Z=z_{1},...,z_{t}\) for \(t\) time-steps. Before \(Z\) is fed into the \(Transformer\) and \(Quantization\) modules, we randomly select starting time steps for the mask with a proportion of \(p\) across all time steps, and then mask the consecutive \(M\) time steps for the selected indices. During the masking operation, the latent speech representations \(Z\) at each time step can be considered as a starting time step, so the selected mask span may overlap. We put \(Z\) into \(Transformer\) and \(Quantization\) by masking operation to produce contextual vectors \(C=c_{1},...,c_{i}\) and quantized representation \(Q=q_{1},...,q_{i}\). The \(q_{i}\) of the corresponding step is used as a positive sample of the contextual vector \(c_{i}\), then \(K\) quantized vectors are randomly selected from \(Q\) as the negative sample \(\bar{Q}\) of \(c_{i}\). Put the unmasked \(Z\) into \(Transformer\) with no gradient calculation, then masking the output vectors with the same mask to produce support vectors \(S=s_{1},...,s_{i}\). \((S,\bar{Q})\) eliminates the false negative samples by the \(FNIE\) and generates a new set of \(K^{\prime}\) negative samples \(\bar{Q}^{\prime}\), which perform the calculation of the contrast loss.  
We hypothesize that the main difference between negative and false negative samples lies in the fact that false negative samples have a higher degree of similarity to positive samples, which can lead the model to learn in the wrong direction. To address this issue, we propose the FNIE method, which filters out false negative samples that exhibit significant similarities with positive samples.  
Figure 4: Illustration of the optimized model framework. Support Vectors and Negatives calculate the similarity and take the top-k similarity as false negative samples, which are deleted or absorbed as positive samples.
## 1 Introduction
An inappropriate variable selection strategy may not improve model performance and can lead to an unrobust model. Selecting too many spectral variables may leave interference and irrelevant regions in the model, reducing the effectiveness of simplification. Conversely, selecting too few spectral variables might remove those related to structural changes in the analyzed component, leading to inaccurate or unrobust models. Therefore, studying the impact of different variable selection strategies and the number of selected spectral variables on Raman-based model performance is meaningful. In this study, we aim to identify the most suitable variable selection strategy for Raman-based models in CHO cell culture processes, which can simplify the models, enhance their predictive capabilities, and promote a deeper understanding of the data. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Spectral Range** & **N** & **Analyzed Components** & **Literature** \\ \hline
800–1800 cm\({}^{-1}\) & 1001 & key biochemical values, VCD & [9] \\
500–1700 cm\({}^{-1}\) & 1201 & Gluc, Lac & [19] \\
400–1600 cm\({}^{-1}\) & 1201 & Gluc, Lac & [8] \\
400–1800 cm\({}^{-1}\) & 1401 & Gluc, Lac, titer & [20] \\
440–1860 cm\({}^{-1}\) & 1421 & VCD, Lac, NH\({}_{4}\)\({}^{+}\), amino acids & [17] \\
350–1775 cm\({}^{-1}\) & 1426 & Gluc, Lac, VCD & [21] \\
321–1890 cm\({}^{-1}\) & 1570 & Gluc, Lac, amino acids & [22] \\
350–1775 cm\({}^{-1}\), 2800–3000 cm\({}^{-1}\) & 1627 & Gluc, Lac & [23] \\
415–1800 cm\({}^{-1}\), 2800–3100 cm\({}^{-1}\) & 1687 & VCD, viability, cell diameter & [24] \\
300–1850 cm\({}^{-1}\), 2900–3200 cm\({}^{-1}\) & 1852 & Gluc, Lac, Glu, VCD & [25] \\
415–1850 cm\({}^{-1}\), 1860–2100 cm\({}^{-1}\) & 1978 & Gluc, Lac, NH\({}_{4}\)\({}^{+}\) & [16] \\
2800–3100 cm\({}^{-1}\) & 2601 & Gluc, phenylalanine & [7] \\
500–3100 cm\({}^{-1}\) & 2801 & Gluc, VCD, Lac, NH\({}_{4}\)\({}^{+}\) & [26] \\ \hline \hline \end{tabular} N represents the number of selected variables.  
\end{table} 
Table 1: The spectral regions used for Raman-based models in cell culture from the literature.
# Contextual Explanations for Decision Support in Predictive Maintenance
## 4 Experiments
### Discussion  
A comparison of the proposed explanation context for the Basic and Extended data representations, which are presented in Figures 3 and 4, shows considerable differences. The local context of the SHAP-based explanations for the Extended data representation (Figure 4B) is significantly less cohesive for each of the Cases compared to the results from the Basic data representation (Figure 3B). The attribute importance values, and the attribute ranking, fluctuate significantly and fail to form a consistent description indicating which attributes are important for the decision on the _alarm_ class. Moreover, the global context of the SHAP-based explanations presented in Figure 4C,D is significantly less informative when comparing to the results of the Basic data representation presented in Figure 3C,D. The attribute importance values presented on the plots do not show clear (or any) long-term changes. Therefore, the figures do not support conclusions about the current machine state.  
This implies that the proposed explanation context, designed to support the operator's understanding of the machine state, is significantly less helpful for the model based on Extended data representation, despite the application of explanation aggregation. Addressing this challenge could involve the use of an explanation by simplification approach, employing a surrogate model. In the case of the analysed task, the surrogate model is the model generated from the Basic data representation. Therefore, generating a local explanation for the alarm decision of the model based on Extended data representation should consist of the following steps:  
* Verification if the similar alarm was generated by the surrogate model on the Basic data representation;
* If yes, generation of a local explanation and its context for the decision of the surrogate model;
* Otherwise, generation of an aggregated local explanation for the decision of the model based on the Extended data representation and visualisation of the context for the recent alarm decision of surrogate model.  
This approach is reasonable because most of the alarms identified for the Extended data representation were identified as alarms for the Basic representation. Considering the time period until Failure 1, during which alarms were less frequent, almost 73% of outliers identified in the Extended data representation were identified in the Basic representation. If a surrogate model is used for the analysed data set, all alarms for which explanations are given in Figure 4 will receive explanations from Figure 3. This will significantly improve the comprehensibility of the model's decisions.
# Dynamic Packet Duplication for Industrial URLLC
## 2 Background
### 5G Multi-Connectivity Overview
In this paper, we propose the use of a ML predictor, which, based on channel conditions, predicts the E2E latency for URLLC devices in order to activate/deactivate duplication for a packet transmission.
# Treatment of Aqueous Bromate by Superparamagnetic BiOCl-Mediated Advanced Reduction Process
## 2 Results and Discussion
### Transformation Intermediates of BrO\({}_{3}^{-}\)  
The product of BrO\({}_{3}^{-}\) (Br(+5)) reduction assumes several valence states (\(-\)1, 0, +1, +3). Hence, the formation of bromite (Br(+3)), HBrO/BrO\({}^{-}\) (Br(+1)), bromine (Br(0)), and Br\({}^{-}\) (Br(\(-\)1)) was monitored. The decrease in BrO\({}_{3}^{-}\) concentration was accompanied by the increase in Br\({}^{-}\) concentration (Figure 12a), and concentration change of BrO\({}_{3}^{-}\) linearly correlated with that of Br\({}^{-}\) (Figure 12b). Additionally, the mass balance of bromine suggests that Br\({}^{-}\) was the only detected bromine species during the degradation process. This result may be due to the reason that CO\({}_{2}^{\bullet-}\) possibly showed higher reactivity toward Br(+3) and Br(+1) than Br(+3) did (E\({}_{\mathrm{Br(0)/Br(-1)}}\) = 1.07 V, E\({}_{\mathrm{Br(+1)/Br(-1)}}\) = 0.77 V, E\({}_{\mathrm{Br(+5)/Br(-1)}}\) = 0.58 V, E\({}_{\mathrm{Br(+3)/Br(+1)}}\) = 1.52 V, [53; 54]). These results mean that all of the degraded BrO\({}_{3}^{-}\) transformed into harmless Br\({}^{-}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Electron Acceptors e** & **Concentration** & **Reaction Rate Constant with CO\({}_{2}^{\bullet-}\)** & **Scavenging Rate** \\ \hline BrO\({}_{3}^{-}\) & 6.6 \(\mu\)M & 1.4 \(\times\) 10\({}^{9}\) M\({}^{-1}\cdot\)s\({}^{-1}\) & 9.24 \(\times\) 10\({}^{3}\) s\({}^{-1}\) \\ NO\({}_{3}^{-}\) & 30–120 mg\(\cdot\)L\({}^{-1}\) & 6.5 \(\times\) 10\({}^{5}\) M\({}^{-1}\cdot\)s\({}^{-1}\)[52] & (0.3–1.3) \(\times\) 10\({}^{3}\) s\({}^{-1}\) \\ DO & 8.1 mg\(\cdot\)L\({}^{-1}\) & 2.4 \(\times\) 10\({}^{9}\) M\({}^{-1}\cdot\)s\({}^{-1}\)[52] & 6.0 \(\times\) 10\({}^{5}\) s\({}^{-1}\) \\ Fe\({}^{3+}\) & 1–4 \(\mu\)M & 5.0 \(\times\) 10\({}^{7}\) M\({}^{-1}\cdot\)s\({}^{-1}\)[52] & (0.1–2.0) \(\times\) 10\({}^{2}\) s\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Scavenging of CO\({}_{2}^{\bullet-}\) by different electron acceptors.  
Figure 11: Effects of common electron acceptors on the removal of bromate by UV/BiOCl-HAP\({}_{\mathrm{SM}}\)/ formate. Control blank (CB) conditions: [Formate]\({}_{0}\) = 4 mM; BiOCl-HAP\({}_{\mathrm{SM}}\) dosage 0.5 g\(\cdot\)L\({}^{-1}\), [BrO\({}_{3}^{-}\)]\({}_{0}\) = 6.63 \(\mu\)M, pH = 7.0 \(\pm\) 0.1, 25 \({}^{\circ}\)C, reaction time 15 min.
# High-Fidelity Surrogate Based Multi-Objective Optimization Algorithm
## 4 The Proposed Algorithm
### Surrogate Models
#### 4.2.2 Radial Basis Function (RBF)  
The interpolation of scatter data using radial basis functions has been shown to be highly accurate in high-dimensional problems. Radial basis function interpolation, Equation (7), is used to approximate the form.  
\[\hat{f}(x)=\sum_{n=1}^{N}\omega_{n}\varphi(x-x_{n}) \tag{7}\]  
where \(\varphi(x)\)=\(\varnothing(\|x\|)\) is a radial function. The positions \(x_{n},\ n=1\dots\ N\) are known as the _RBF_ centers.
## 1 Introduction
These devices can be the standalone ubiquitous devices to support real-time decision making through the analysis base on bit data, and also can be the local servers which provide discovery, registration, and forwarding service to the devices and service clients in a specific network [16; 17]. However, in the IoT network, the constrained devices and local servers need to collaborate for providing better services to the clients. In this paper, we present a registration and discovery scheme for the IoT devices and WSPs which are deployed in different networks using different protocols. For the IoT devices, the IoTivity framework is applied, which is an open source implementation of the Open Communication Foundation (OCF) specification [18]. In the OCF network, devices use OCF messages to communicate, which are based on CoAP for the constrained IoT network. The functionalities supported by the RD server provide service of registration, discovery and request forwarding from the OCF network and WSPs from the Internet. The registration service is used by the client that registers the information of discoverable resources. The client can be a publishing client as well as a part of IoT device. An IoT device can hold the resources to provide sensing and actuating services in the OCF network. A WSP can be developed by an organization to provide services through the APIs to enable users to use the APIs and access services. To use the APIs in the OCF network by IoT clients, the information must be discoverable. The proposed RD server provides a consistent registration service for the RAML-based resource information. For the registration, a new data model is presented using the RAML definition. The RAML definition shall be pre-defined for the IoT device as well as the WSP and deployed on the client that can publish the RAML data to the RD server. A parser in the RD server interprets the RAMLdata and inserts them into the database (DB). In the OCF network, the IoT clients request the RD server to look up the registered information, then the resources shall be discovered from the OCF network and Internet. Once the resources are discovered, the IoT client can access the services. The RD server also provides the message forwarding service to the OCF client for accessing the HTTP-based services on the Internet. Figure 1 shows the network architecture that presents the interactions of entities for the registration, discovery, and accessing. The rest of the paper is structured as follows. Section 2 introduces related works. Section 3 introduces the proposed methodology of registration and discovery. Section 4 introduces the registration and discovery scenarios using the proposed elements in the OCF-based IoT network. Section 5 introduces the implementation results. Section 6 introduces the performance evaluation. Finally, we conclude this paper in Section 7.
# Weak Signal Processing Methods Based on Improved HHT and Filtering Techniques for Steel Wire Rope
## 1 Introduction
As the artificial intelligent techniques of machine learning deeply develops, many related studies have been reported regarding to the wire rope signal processing and quantitative defect inspection. For instance, [PERSON] et al. [27] proposed a discrimination method-based multi-array weak signal processing for wire rope, which not only improved the defect detection dimension and accuracy through binarizing local gray value of MFL data, but also realized the quantitative analysis of defect size. [PERSON] et al. [28] proposed a convolutional neural network-based hoisting wire rope inspection method, which could detect various faults such as the broken strand and twisted ropes automatically in real-time, and compared with k-nearest neighbor and an artificial neural network with back propagation. Moreover, other intelligent processing methods may also include the artificial neural network [29], support vector machine [30], AdaBoost classifier [31], and deep learning algorithms [32], which all improved the defect detection accuracy for steel wire rope. However, most of the signal denoising and processing methods such as the time domain filtering and frequency analysis only aim at the single environment, and some of the testing results were obtained even in the laboratory condition. As the long range or distance testing requirements are desperately needed, weak signal with low SNR processing is full of challenges and difficulty for all the signals are mixed with defect characterizations and strong noise features; worse still, some of their frequency bands are usually overlapped, which all make the common signal filtering methods invalid and unavailable in wire rope signal processing and defect recognition. To solve the above-mentioned challenges, a combined weak signal processing method based on improved Hilbert transform, signal differentiating, and filtering techniques are proposed. Starting with the experimental introduction and implementation, different wire rope signals with weak defect characterizations are analyzed and compared. Finally, the validity and feasibility, as well as the influence of the proposed methods, are demonstrated and studied in the perspective of time domain and short-time Fourier transform (STFT), which suggests that the proposed methods are full of potential in practical application for wire rope signal processing and defect inspection, especially in complex scenes.
## 2 Background
### Test Problems
#### 2.4.2 Qap  
The formal definition of the QAP is as follows. We are given an \(n\) by \(n\) cost matrix \(C\), and an \(m\) by \(m\) distance matrix \(D\), such that \(m\geq n\). The NP-complete decision version of the problem [14] asks the question, for a given bound \(B\): Does there exist a one-to-one function \(f:\{0,1,\ldots,n-1\}\to\{0,1,\ldots,m-1\}\), such that:  
\[\sum_{i=0}^{n-1}\sum_{\begin{subarray}{c}j=0\\ j\
eq i\end{subarray}}^{n-1}C_{i,j}D_{f(i),f(j)}\leq B? \tag{7}\]  
The NP-hard optimization version of the QAP is to find the one-to-one function \(f:\{0,1,\ldots,n-1\}\to\{0,1,\ldots,m-1\}\) that minimizes  
\[\sum_{i=0}^{n-1}\sum_{\begin{subarray}{c}j=0\\ j\
eq i\end{subarray}}^{n-1}C_{i,j}D_{f(i),f(j)}. \tag{8}\]  
Most authors consider the case when \(n=m\). This problem is naturally represented with permutations. When \(n=m\), a solution (i.e., the one-to-one function \(f\)) is simply represented as a permutation of the integers \(\{0,1,\ldots,n-1\}\). In the more general case, a solution is the first \(n\) integers in a permutation of the integers \(\{0,1,\ldots,m-1\}\). The QAP is an especially challenging NP-hard optimization problem, where you most often find experimental studies utilizing what seems to be rather small instances (e.g., \(n<50\)). A wide variety of EA, metaheuristics, and heuristic approaches have been proposed for the QAP [49, 50, 51, 52, 53, 54, 55].
# An Overview of Lidar Imaging Systems for Autonomous Vehicles
## 2 Basics of Lidar Imaging
### Optical Phased Arrays
#### Summary  
Single-point measurement strategies used for lidar need imaging strategies to deliver the desired 3D maps of the surroundings of the vehicle. We divided them into scanners and detector arrays. A summary of the keypoints described is provided in Table 2.  
While mechanical scanners are now prevalent in the industry, the presence of moving elements supposes a threat to the duration and reliability of the unit in the long term, due to sustained shock and vibration conditions. MEMS scanners appear to be the alternative as they are lightweight, compact units with resonance frequencies well above those typical in a vehicle. They can scan large angles using adapted optical systems and use little power. However, they present problems related to the linearity of the motion and heat dissipation. OPAs would be the ideal solution, providing static beam steering, but they still present problems in the management of large power values, which set them currently in the research level. However, OPAs are a feasible alternative for the future in long-range, and currently they are commercialized in short and medium-range applications.  
On the detector array side, flash lidars provide a good solution without any moving elements, especially in the close and medium ranges, where they achieve comparable performance figures to scanning systems. Large spatial resolution using InGaAs detectors is, however, expensive; and the use of detectors in Geiger mode poses issues related to the presence of retroreflective signs which can temporarily blind the sensor. The AMCW approach, as described, is based on detector arrays but is not reliable enough in outdoor environments. Intermediate solutions combining scanning using cylindrical geometries (rotation around an axis) with line illuminators and linear detector arrays along a spinning axis have also been proposed, both using the scanning and the flash approaches.  
We do not want to finish this Section without mentioning an alternative way of classifying lidar sensors which by now should be obvious to the reader. We preferred an approach based on the components utilized for building the images, as it provides an easier connection with the coming Sections. However, an alternative classification of lidars based on how they illuminate the target would have also been possible, dividing them into those who illuminate the target point by point (scanners), as 1D lines (like the cylindrical approach just described in Section 2.2.3) or as a simultaneous 2D flow (flash imagers or TOF cameras). Such classification also enables us to include all the different families of lidar imaging systems. An equivalent classification based on the detection strategy (single detector, 1D array, 2D array) would also have been possible.
## 3 SSC and Equivalent White Noise Level
### Data Bits Misaligned by an Integer Number of Code Chips  
When the data bits modulated on the undesired and desired signals are aligned, i.e., \(\Delta\) is not zero, the situation is illustrated in Figure 5.  
In this case, the ACF of the incoming/undesired signal is:  
\[R_{11}(s-\Delta,t-\Delta)R_{22}(s,t)=\left\{\begin{array}{ll}\sum\limits_{p= 0}^{19}p_{T_{c}}(t-\Delta-(40,920k+2046p+v)T_{c})&0\leq s<\Delta\\ \sum\limits_{p=0}^{19}p_{T_{c}}(t-\Delta-(2046p+v)T_{c})&\Delta\leq s<T_{b} \end{array}\right. \tag{17}\]  
where \(k\) and \(v\) is set up using Equations (7), (9) and (10). As derived in Appendix B, assuming that the data bit misalignment is an integer multiple of the spreading code period \(T\), i.e., \(\Delta=KT\) for \(0\leq K<20\), where \(K\) is an integer, results in:  
\[SSC_{B1-I}=\frac{TT_{c}}{T_{b}}[\sin c(fT_{c})]^{2}\left[\frac{\sin^{2}(\pi fKT )+\sin^{2}(\pi f(20-K)T)}{\sin^{2}(\pi fT)}\right] \tag{18}\]  
which, for users near or at the Earth's horizon, could be approximated as:  
\[SSC_{B1-I}\simeq\frac{TT_{c}}{T_{b}}\left[\frac{\sin^{2}(\pi fKT)+\sin^{2}( \pi f(20-K)T)}{\sin^{2}(\pi fT)}\right] \tag{19}\]  
More generally, assuming that the data bit misalignment is an integer multiple of the spreading code chip width \(T_{c}\), i.e., \(\Delta=KT+CT_{c}\), where \(0\leq K<20\), \(0<C<2046\), \(K\) and \(C\) is an integer, yields:  
\[SSC_{B1-I}=\frac{2046-C}{2046}SSC_{B1-I,K}+\frac{C}{2046}SSC_{B1-I,K+1} \tag{20}\]  
where \(SSC_{B1-I,K}\) is the SSC in Equation (18) for any users or SSC in Equation (19) for users near or at the Earth's horizon, when \(\Delta=KT\).  
Figure 5: Undesired and desired signals misaligned by \(\Delta\).
# Digital Simulation and Analysis of Assembly-Deviation Prediction Based on Measurement Data
## 4 Discussion
An in-depth simulation study is necessary to investigate the thermal deformation of core machine parts. As the core machine operates under high-temperature and high-pressure conditions, accurate predictions of its thermal assembly are crucial. To obtain the deformation of parts at high temperatures, factors such as thermal expansion coefficient, elastic modulus, density and deformation curve corresponding to the high-temperature deformation of the material can be assigned in CAE software based on the working temperature distribution of the core machine. This study can provide a better understanding of the thermal behavior of core machine parts and inform the development of more reliable and accurate thermal assembly models.
## 3 Analysis of Horizontal Bearing Characteristics
### Soil Pressure Distribution Laws
It can be observed that with an increase in soil density, the soil pressure increments at the measurement points on the bucket foundation significantly increase, while those on the pile foundation decrease. It implies that as soil density rises, the bucket foundation predominantly shoulders the responsibility of resisting horizontal loads in the composite foundation. Figures 8 and 9 show the variations in soil pressure increments with depth at diverse measurement points for composite foundations with pile-bucket diameter ratios of d/D = 0.417 and 0.357, respectively, under loose sand conditions. A comparative analysis with Figure 5 reveals that as the diameter of the bucket foundation increases, the soil pressure increment at the 30 mm measurement point of the bucket foundation increases under the same load, while the soil pressure increment at the 200 mm measurement point of the pile foundation decreases. It indicates that under the same sand relative density conditions, increasing the diameter of the bucket foundation in the composite foundation has a significant effect on resisting horizontal loads. Figure 8: Soil pressure increment distribution of a loose sand pile-bucket diameter ratio of 0.417. (**a**) Front side of the foundation; (**b**) Back side of the foundation.