esa-sceva/satcom-chunk-collection huggingface dataset:
# UAV Formation Shape Control via Decentralized Markov Decision Processes
## 4 UAV Motion Model  
The state of the \(i\)th UAV at time \(k\) is given by \(s_{k}^{i}=\left(x_{k}^{i},y_{k}^{i},z_{k}^{i},\phi_{k}^{i},\theta_{k}^{i},\psi _{k}^{i}\right)\), where \(\left(x_{k}^{i},y_{k}^{i},z_{k}^{i}\right)\) are position coordinates and \(\left[\phi_{k}^{i},\theta_{k}^{i},\psi_{k}^{i}\right]=\left[\textit{bank angle, pitch angle, heading angle}\right]\) are the Euler angles. The UAV motion dynamics are given by the following equations.  
\[u_{k+1} =T(-g\sin(\theta_{k})+r_{k}\varphi_{k}-q_{k}w_{k})+u_{k}+\mathcal{ W}_{k}^{u}\] \[v_{k+1} =T(g\sin(\phi_{k})\cos(\theta_{k})-r_{k}u_{k}+p_{k}w_{k})+v_{k}+ \mathcal{W}_{k}^{v}\] \[w_{k+1} =T\bigg{(}\frac{1}{m}(-F_{z})+g\cos(\phi_{k})\cos(\theta_{k})+q_{ k}u_{k}-p_{k}v_{k}\bigg{)}+w_{k}+\mathcal{W}_{k}^{w}\] \[p_{k+1} =T\bigg{(}\frac{1}{l_{xx}}(L+(I_{yy}-I_{zz})q_{k}r_{k})\bigg{)}+p _{k}+\mathcal{W}_{k}^{p}\] \[q_{k+1} =T\bigg{(}\frac{1}{l_{yy}}(M+(I_{zz}-I_{xx})p_{k}r_{k})\bigg{)}+q _{k}+\mathcal{W}_{k}^{q}\] \[r_{k+1} =T\bigg{(}\frac{1}{l_{zz}}(N+(I_{xx}-I_{yy})p_{k}q_{k})\bigg{)}+r _{k}+\mathcal{W}_{k}^{r}\] \[\phi_{k+1} =T(p_{k}+(q_{k}\sin\phi_{k}+r_{k}\cos\phi_{k})\tan\theta_{k})+ \phi_{k}+\mathcal{W}_{k}^{\phi}\] \[\theta_{k+1} =T(q_{k}\cos\phi_{k}-r_{k}\sin\phi_{k})+\theta_{k}+\mathcal{W}_{k }^{\phi}\] \[\psi_{k+1} =T((q_{k}\sin\phi_{k}+r_{k}\cos\phi_{k})\sec\theta_{k})+\psi_{k}+ \mathcal{W}_{k}^{\psi}\] \[x_{k+1} =T\Big{(}c_{\theta_{k}}c_{\phi_{k}}u^{b}+(-c_{\phi_{k}}s_{\phi_{k }}+s_{\phi_{k}}s_{\theta_{k}}c_{\phi_{k}})v^{b}+(s_{\phi_{k}}s_{\psi_{k}}+c_{ \phi_{k}}s_{\theta_{k}}c_{\phi_{k}})w^{b}\Big{)}+x_{k}+\mathcal{W}_{k}^{x}\]\[y_{k+1} =T\Big{(}c_{\theta_{k}}s_{\theta_{k}}u^{b}+(c_{\phi_{k}}c_{\phi_{k}}+s _{\phi_{k}}s_{\phi_{k}}s_{\phi_{k}})v^{b}+(-s_{\phi_{k}}c_{\psi_{k}}+c_{\phi_{k}}s _{\phi_{k}}s_{\psi_{k}})w^{b}\Big{)}+y_{k}+\mathcal{W}_{k}^{y}\] \[z_{k+1} =T\Big{(}-1*(-s_{\theta_{k}}u^{b}+s_{\phi_{k}}c_{\phi_{k}}v^{b}+c_{ \phi_{k}}c_{\theta_{k}}w^{b})\Big{)}+z_{k}+\mathcal{W}_{k}^{z}\]  
where, \(\mathcal{W}_{k}\) is a zero-mean Gaussian random variables, \([u_{k},v_{k},w_{k}]=[\)_longitudinal velocity_, _lateral velocity_, _normal velocity_] are the linear velocity, and \([p_{k},q_{k},r_{k}]=[\)_roll rate_, _pitch rate_, _yaw rate_] represent the angular velocity of the vehicle at time \(k\). \([F_{x},F_{y},F_{z}]\) are linear translation forces and \([L,M,N]\) are angular moments.
## 3 Investigations Focusing on Flow Regime Characteristics
### Investigations with Single-Sized Spherical Particles  
#### 3.1.1 Experimental Materials and Methods  
Figure 11 shows the experimental visualization system for investigations focusing on flow regime characteristics. The main apparatus is a narrow quasi-2D viewing tank with a 1000 mm length (\(L\)) and 700 mm width (\(W\)). Recognizing that particle volume had no remarkable influence on DBF behavior [23], a 10 L particle volume was employed. As summarized in Table 1, when compared to the experiments focusing on the overall accumulated-bed characteristics, the experimental parameters cover the majority of ranges for the physical properties of debris (such as particle density and size) in actual severe accidents of SFRs. Furthermore, in addition to the particle density and size, the effects of additional various experimental parameters, including nozzle height (\(H_{\rm n}\) = 110-130 cm), nozzle size (\(d_{\rm n}\) = 10-30 mm), and water depth (\(H_{\rm w}\) = 0-60 cm), were studied. Additionally, the influence of the thickness (\(W_{\rm tank}\) = 30-60 mm) of the tank was also investigated.
# Weed Detection in Rice Fields Using Remote Sensing Technique: A Review
## 8 Conclusions  
Traditional practices are too time-consuming and require many human resources. Therefore, adapting automated practices into precision farming (PA) is the best practice to control weeds. Even though various platforms are available for data gathering, UAVs are the best for detecting weeds in paddy due to their availability, high-quality data delivery, and ease of handling. We had complete control over the data collection phase. The review proved that deep learning could convey high accuracy weed maps. However, this method requires a certain number of training data, resulting in massive agricultural databases. Therefore, to decide which algorithm best suits our research, we need to know what types of weeds we are dealing with by observing their types in paddy fields. It is not necessary to use a complicated algorithm to perform weed classification. Although some studies showed that deep learning might not be necessary when dealing with imagery, much simpler algorithms, such as OBIA, can perform adequate image analysis for detecting weeds in paddy fields. When comparing crops and weed types, both algorithms, ML and DL, had successfully generated a high accuracy map ranging from 85% to 99%, depending on the type of weeds and crops. Thus, we can expect the same accuracy in producing weed maps in paddy, regardless of the types of weeds present in the field. More research needs to be carried out, and this review has shown that improved weed management could optimize the usage of herbicides that should be applied on a site-specific basis. Not only did it increase yield production, but it also proved that this technology could control the spreading of weeds. It also effectively maximizes herbicide usage and decreases the budget required to purchase them.  
Conceptualization, R.R., N.N.C. and Y.A.; writing--original draft preparation, R.R.; writing--review and editing, R.R., N.N.C., Y.A., R.R., A.W., M.R.I., Z.B., W.F.F.I. and M.H.O.; visualization, R.R., Y.A. and F.R.; supervision, N.N.C.; funding acquisition, M.R.I. All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank the Ministry of Higher Education Malaysia for providing research funds from Long-term Research Grant Scheme (LRGS/1/2019/ UPM/01/2): Development of climate ready rice for sustaining food security in Malaysia (Vot no. 5545000).  
Not applicable.  
The authors wish to thank [PERSON] for the knowledge he shared in writing this manuscript.  
The authors have no conflict of interest to declare.
# Multi-Adjacent Camera-Based Dangerous Driving Trajectory Recognition for Ultra-Long Highways
## 5 Discussion  
In order to study the highway vehicle tracking technology, the model used in this paper performs well in terms of real-time, accuracy and robustness, and has achieved preliminary research results. However, when dealing with some scenarios with complex traffic conditions or a large monitoring span, the application of these models needs further in-depth research. Specifically, this paper proposes the following three aspects of improvement and perfection:  
1. The high-speed surveillance video data used in this paper is not perfect, especially the vehicle data for training the vehicle re-identification model in the study scenario is missing. This limits the combination of the deep appearance network for object tracking and the feature extraction network for vehicle re-identification, which increases the complexity of the model to a certain extent. Therefore, future work will further collect relevant data information to improve the model and improve its generalization performance.
2. Although the vehicle re-identification method based on attribute information does not need to consider external factors such as spatio-temporal information matching, and only needs to focus on the extraction of the attributes of the vehicle itself, the vehicle behavior detection based on trajectory rules separates the relationship between the vehicle and the surrounding environment and other vehicles when performing trajectory extraction analysis and vehicle abnormal behavior recognition. Therefore, in the subsequent research, the addition of spatio-temporal information of vehicles will be considered, and the deep learning method will be used to cluster and analyze the vehicle trajectory to comprehensively consider the driving behavior of vehicles.
3. In this paper, the overall task of highway vehicle tracking is divided into three sub-tasks: vehicle detection, vehicle tracking and vehicle re-identification, and the vehicle trajectory is processed according to the tracking results. The output of the former task is used as the input of the latter task, so the model construction and function implementation are slightly tedious. Therefore, in the subsequent research, we will try to build an integrated model, fuse several models, simplify the model structure, improve the convenience and efficiency of vehicle tracking, and truly realize the global tracking of vehicles on highways, and realize systematization and intelligence.
# Robust Feature Selection-Based Speech Emotion Classification Using Deep Transfer Learning
###### Abstract  
Speech Emotion Classification (SEC) relies heavily on the quality of feature extraction and selection from the speech signal. Improvement on this to enhance the classification of emotion had attracted significant attention from researchers. Many primitives and algorithmic solutions for efficient SEC with minimum cost have been proposed; however, the accuracy and performance of these methods have not yet attained a satisfactory point. In this work, we proposed a novel deep transfer learning approach with distinctive emotional rich feature selection techniques for speech emotion classification. We adopt mel-spectrogram extracted from speech signal as the input to our deep convolutional neural network for efficient feature extraction. We froze 19 layers of our pretrained convolutional neural network from re-training to increase efficiency and minimize computational cost. One flattened layer and two dense layers were used. A ReLu activation function was used at the last layer of our feature extraction segment. To prevent misclassification and reduce feature dimensionality, we employed the Neighborhood Component Analysis (NCA) feature selection algorithm for picking out the most relevant features before the actual classification of emotion. Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) classifiers were utilized at the topmost layer of our model. Two popular datasets for speech emotion classification tasks were used, which are: Berling Emotional Speech Database (EMO-DB), and Toronto English Speech Set (TESS), and a combination of EMO-DB with TESS was used in our experiment. We obtained a state-of-the-art result with an accuracy rate of 94.3%, 100% specificity on EMO-DB, and 97.2%, 99.80% on TESS datasets, respectively. The performance of our proposed method outperformed some recent work in SEC after assessment on the three datasets.  
feature selection; speech emotion; classification; deep convolutional neural network; transfer learning +
Footnote †: journal: Applied Sciences  
12 August 2022  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# Shear Lag Effect on Box Steel Beams with Wide Curved Flanges
###### Abstract  
For wide flange box sections, conventional Euler-Bernoulli beam theory with maintaining the cross-section planarity may lead to underestimation of axial stresses. Axial stresses in cross-section flanges may have a non-uniform distribution due to shear pliability, decreasing in value from the flange-web junction to the middle area of the flange. This phenomenon leads to the introduction of an effective flange width with a uniform distribution of original maximum stress. Furthermore, the introduction of flange curvature makes it even more complex due to the varying lever arm of each flange part with respect to the neutral bending axis. Because of this, in some cases, it is hard to predict where the flange's highest normal stress value will appear. In this paper, the shear lag effect on wide curved box sections is analyzed through parametric numerical analysis using the FEA software Dlubal RFEM 5, together with visual programming performed in Rhino Grasshopper. This study investigates the interaction of the shear lag effect and plane section hypothesis, which can be simplistically represented as a reduction in the impact of shear lag and the activation of a larger part of the flange of a wide-flange beam in the structural system of a continuous beam. The results suggest that for higher flange curvature and higher width to length ratio, this effect is more prominent.  
shear lag effect; box cross-section; wide-flange beam; curved flange; steel bridges; grasshopper; parametric analysis +
Footnote †: journal: Applied
# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 5 Tooling in MLOps
### Tool Comparison
#### 5.2.20 Seldon Core  
Seldon core [78] also has a strong dependency on Kubernetes. It can be thought of as a wrapper around the model. It comes with multiple pre-packaged Inference Servers. It is a language-agnostic framework with support for many ML libraries. The tool allows the creation of more complex inference graphs where multiple model artifacts are containerized into separate reusable inference containers that are linked together.
## 5 Test Results and Interpretation
### AVT on a Typical Truck  
An AVT was performed on a truck that was adopted for the loading procedures and assumed to be representative for all (Figure 21). The identification aimed at investigating the truck fundamental frequencies, which are necessary to evaluate the possible bridge-truck dynamic interactions affecting the interpretation of the dynamic tests. Indeed, the trucks are usually modelled as added masses within FE models of bridges even if it is well-known that a dynamic interaction exists between the bridge and vehicles, leading to possible misunderstandings in the result interpretations, especially when the bridge and trucks have similar frequencies [25; 26].  
In this case, the coupled system shows the typical behavior of a tuned-mass system that is characterized by two response peaks of similar amplitudes that make the modal interpretation not easy.  
Based on the recorded accelerations on the truck, the power spectral densities (PSDs) for each of the three sensors are calculated and plotted in Figure 21a. In the PSD, which represents the frequency content of the measured accelerations, peaks highlight the presence of resonant frequencies of the system in the considered direction. In addition, the singular value decomposition (SVD) of the accelerometer recordings on the truck, and of the recording on the bridge that were made during the Dyn.1 test (unloaded bridge), are reported in Figure 21a for comparison. The transverse dynamics of the truck (represented with a dotted line in Figure 21a) is characterized by a resonant frequency of about 1.1 Hz,  
Figure 20: Acceleration time histories and relevant STFTs for the L1 and L2 sensors during the BLTs.  
while the vertical and longitudinal ones (represented with continuous and a dash-dot lines, respectively) by a frequency of about 1.7 Hz. Based on previously considerations and taking into account the fundamental frequency of the unloaded bridge (0.85 Hz), it can be concluded that the vertical dynamics of trucks is characterized by different resonances and truck-bridge interaction phenomena can be excluded for the first three bridge vibration modes.
## 2 Theoretical Background
However, some authors present DMMs in which the area of maturity assessment is strictly defined, for example, assessing maturity knowledge-intensive business processes [28], big data usage [29], digital information systems [26], but also logistics 4.0 [30; 31; 32]. Research findings in [33] indicate that most DMMs are built on the same principles as traditional CMMIs (Capability Maturity Model Integrations). The assessment of digital maturity areas most commonly concerns [34] strategy, leadership, customers, products, operations, culture, people, governance, and technology. Digital maturity models are used to benchmark and position companies in the served market and to determine digital transformation paths in such a way as to move from the current state to the desired level of maturity [22; 30]. However, it should be noted that the pace of digital transformation depends on the sector and the level of supply chain representation by the company. Research findings indicate that customer-oriented organizations operating in the B2C (business-to-customer) market are subject to a faster and stronger impact of digital transformation than an organization operating in the B2B (business-to-business) model [25]. Therefore,  
Figure 3: Characteristics of the maturity assessment model (based on [24; 25; 26]). this should be considered when conducting a digital maturity audit of each assessed organization.
## 1 Introduction  
The molecular landscape of colorectal cancer (CRC) is a testament to the complexity of cancer biology, showcasing the genetic interactions and expressions that underpin the disease's progression and response to treatment. Central to this landscape are three key markers of this study: Arylsulfatase B (ARSB), TP53, and Maspin, each contributing uniquely to the oncogenic process. ARSB is a lysosomal enzyme involved in the degradation of glycosaminoglycans, molecules pivotal for cell signaling and tissue organization. Studies have shown that ARSB plays a significant role in modulating the tumor microenvironment, influencing CRC aggressiveness and patient prognosis. The decreased expression of ARSBhas been correlated with enhanced tumor growth, invasion, and metastasis [1]. ARSB is proven to play role in CRC progression, suggesting that it could serve as a marker for CRC aggressiveness and a potential therapeutic target [2]. TP53, known for its critical function in cell cycle regulation, apoptosis, and genomic stability, is one of the most extensively studied genes in cancer biology. Mutations in TP53 are prevalent in CRC, leading to a loss of function and contributing to tumor development, progression, and resistance to chemotherapy. The nature and location of TP53 mutations are determinant factors in the clinical outcomes of CRC patients. A comprehensive review discusses the multifaceted roles of TP53 mutations in CRC, underscoring their impact on disease prognosis and treatment strategies [3; 4; 5]. Maspin, a serine protease inhibitor with multifaceted roles in cancer biology, is implicated in tumor suppression, apoptosis regulation, and the inhibition of metastasis and angiogenesis. Its expression in CRC has been associated with tumor differentiation, invasion, and patient survival, suggesting its potential utility as a prognostic marker and therapeutic target [6; 7; 8]. The investigation of Maspin, TP53, and ARSB in CRC not only provides a deeper understanding of the molecular mechanisms driving the disease, but also highlights the potential of these markers in improving the diagnosis, prognostication, and development of personalized therapeutic strategies.  
The aim of this study was to investigate the possible role of Maspin, TP53 and ARSB in CRC progression, and to highlight the prognostic importance of their dual or triple expression.
## 2 Circuit Design and Analysis
### Noise Reduction with Large Substrate Resistance  
Because the receiver sensitivity is determined by the thermal noise floor at input, the noise figure (NF) of the receiver, and the signal-to-noise ratio (SNR) requirement at the detector and NF of the receiver is dominated by the first stages of the receiver [20], a noise reduction technique with substrate resistor is applied to decrease the noise power of LNA.  
As shown in Figure 5a, \(R_{B}\) is employed in an N-type Metal-Oxide-Semiconductor (NMOS) device to ameliorate the noise performance of the proposed CMOS LNA. The structure of a NMOS with \(R_{B}\) is shown in Figure 5b. The NF can be derived as [21]  
\[\text{NF}=\text{NF}_{\text{min}}+\frac{G_{n}}{R_{s}}\times\left|Z_{s}-R_{opt}-jX _{opt}\right|^{2} \tag{9}\]  
\(G_{n}\) and \(\text{NF}_{\text{min}}\) can be written as  
\[G_{n}=\frac{K_{g}\omega^{2}C_{S^{n}}^{2}}{g_{m}}\times\left(1+\frac{R_{d}}{R_{ ds}^{\prime}\parallel R_{sub}}\right) \tag{10}\]  
and  
\[\text{NF}_{\text{min}}=1+2G_{n}\big{(}R_{g}+R_{s}+R_{opt}\big{)} \tag{11}\]  
Figure 4: Principle of the input matching technique. (**a**) [PERSON] chart when the switch is off, (**b**) [PERSON] chart when the switch is on.  
Figure 5: \(Cont.\)From (10) and (11), it can be observed that the increased resistance of the equivalent substrate resistor \(R_{\text{sub}}\) diminishes \(G_{n}\), which in turn reduces the minimum noise figure NF\({}_{\text{min}}\). Furthermore, the NF can also be reduced by reducing \(G_{n}\) and NF\({}_{\text{min}}\) in (9) [22]. Figure 6 presents the simulated noise factor \(F\) contributed by all MOSFET devices with and without \(R_{B}\). Note that the simulated \(V_{SB}\) approaches zero, so the body-effect transconductance can be neglected [23].  
A larger \(Z_{sub}\) by increasing the value of \(R_{B}\) results in the reduction of the noise factor. The noise factor \(F\) contributed by the MOS device is shown in Figure 6, and a considerable noise power reduction in the MOS device can be demonstrated by the additional resistance \(R_{B}\). A maximum of 32% noise reduction can be achieved without requiring additional chip area and dc power because the size of the 8 k\(\Omega\) High Resistance Implant (HRI) resistor is only 2 um \(\times\) 15 um [22]. Figure 7 shows the simulation results of the noise figure with and without the additional resistance \(R_{B}\). A decrease of 0.71/0.67/0.64 dB noise figure was achieved at 2.5/3.5/5.2 GHz due to the usage of the larger resistance \(R_{B}\) = 8 k\(\Omega\).  
Figure 5: Additional larger substrate resistance \(R_{B}\). (**a**) A NMOS device with a larger substrate resistance \(R_{B}\) = 8 k\(\Omega\), (**b**) structure of a NMOS with \(R_{B}\), (**c**) noise equivalent circuit of a NMOS device with \(R_{B}\).  
Figure 6: Simulated RF MOSFET noise factor \(F\) (NF = 10 log\({}_{10}\) (1 + F)) and noise reduction with and without \(R_{B}\).
# Named Entity Recognition Method Based on Multi-Feature Fusion
## 3 Research Design
### Stage 2: Named Entity Extraction Model
#### 3.3.4 CRF Layer  
The multi-head attention mechanism can effectively fuse features to a large extent and independently predict the optimal probability label for each word, but it cannot solve the dependency and rationality problems between adjacent labels. Therefore, this paper introduces the CRF layer as the output layer of the model. CRF (Conditional Random Field) is a probabilistic graph model commonly used in sequence labeling tasks. Its basic idea is to optimize the accuracy of sequence prediction by jointly modeling input features and output label sequences and considering the dependency between labels. Its scoring formula is as follows (13):  
\[\mathrm{s}(x,y)=\sum_{i=1}^{n}L_{y(i-1),y_{i}}+\sum_{i=1}^{n}P_{i,y_{i}} \tag{13}\]  
where \(L\) is the transfer matrix, \(L_{y(i-1),y_{i}}\) represents the transfer score from the label sequence \(\mathrm{y}(i-1)\) to \(\mathrm{y}_{i}\), and \(P_{i,y_{i}}\) represents the probability that the \(i\)-th character of the input sequence \(\mathrm{x}\) is labeled as \(\mathrm{y}_{i}\). The conditional probability distribution is as follows (14):  
\[\mathrm{P}(\mathrm{y}|\mathrm{s})=\frac{e^{s(x,y)}}{\sumWhen training CRF, the maximum likelihood method is often used to maximize the probability \(y^{*}\) of the correct label sequence, as shown in Formula (15):  
\[\ln P(y^{*}|s)=s(x,y^{*})-\ln(\sum_{\vec{y}\in y_{x}}e^{s(x,\vec{y})}) \tag{15}\]  
Finally, the global optimal result is output through Formula (16):  
\[y^{*}=\operatorname*{argmax}_{\vec{y}\in y_{x}}(x,\vec{y}) \tag{16}\]  
CRF focuses on finding the optimal label sequence given an input. Its advantage is that it can flexibly combine multiple input features and consider the relationship between labels by optimizing the global score of the label sequence. In NER, two unrelated entity labels often do not appear together; CRF can improve the accuracy of annotation by learning these constraints [26].