esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Scheme and RF Transmission Algorithm
### Signal Decryption
(2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Predefined-time sliding mode attitude control for liquid-filled spacecraft with large amplitude sloshing. _Eur. J. Control_**2024**, _77_, 100970. [CrossRef]
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# On Coding and Decoding Reconfigurable Radiation Pattern Modulation Symbols
## 3 Results
### BER for AWGN Wireless Channel
Figure 5b shows the best performance when a minimum resolution of two is considered; this is due to the higher Euclidean distance between symbols obtained compared to the other cases. When \(\rho=2\) for Figure 5a,c, the BER results are equivalent. One may expect better performance when a small section of the space is measured with the same resolution as a vast section; however, the space limitation reduces the symbol diversity, lowering thus the distance between them. The proper selection of the resolution and the space measured need to be considered to improve the probability of error.
## 2 Preliminaries  
**Notation.** In the construction of the scheme, we will use a polynomial ring \(R=\mathbb{Z}[x]/(f(x))\), where \(f(x)\in\mathbb{Z}[x]\) is an irreducible polynomial with the degree \(d\). Let \(f(x)=x^{d}+1\). The elements in a polynomial ring \(R\) are represented in lowercase, and the polynomial vectors are represented in bold lowercase. For example, the coefficient of element \(a\in R\) is represented by \(a_{i}\); that is, \(a=\sum_{l=0}^{d-1}a_{i}x^{l}\). The infinity norm of polynomial \(a\)'s coefficient vector is defined as \(\left\|a\right\|\), and the \(l_{1}\)-Norm is defined as \(\left\|a\right\|_{1}\). The expansion factor of \(R\) is defined as \(\delta_{R}=max\big{\{}\frac{\left\|a\cdot b\right\|}{\left(\left\|a\cdot b \right\|\cdot\left\|b\right\|\right)}:a,b\in R\big{\}}\). When \(d\) is the power of 2, then \(\delta_{R}=d\). Let \(q>1\) be an integer. Let \(\mathbb{Z}_{q}\) denote the set of integers \(\left(\left.-q/2,q/2\right\rangle\right]\). For \(x\in\mathbb{Z}\), we use \(\left[x\right]_{q}\) to represent the unique integer in \(\mathbb{Z}_{q}\). For \(a\in R\), the notation \(\left[a\right]_{q}\) denotes the coefficient modulus \(q\) of polynomial \(a\). Here, \(\left[x\right]\) represents an integer set \(\left\{1,2,\ldots,x\right\}\); \(x\leftarrow\mathcal{D}\) denotesthe random uniform sampling of \(x\) from distribution \(\mathcal{D}\). All logarithms are based on 2. The tensor product of two vectors \(\mathbf{v},\mathbf{w}\) composed of polynomials with the same dimension is denoted as \(\mathbf{v}\otimes\mathbf{w}\), and the inner product of the two tensor products is denoted as:  
\[\langle\mathbf{v}\otimes\mathbf{w},\mathbf{x}\otimes\mathbf{y}\rangle=\langle\mathbf{v},\mathbf{x} \rangle\cdot\langle\mathbf{w},\mathbf{y}\rangle.\]  
BitDecomp \((\mathbf{x}\in\mathbb{Z}^{n})\) decomposes vector \(\mathbf{x}\) into its bit representation. Namely, \(\mathbf{x}=\sum\limits_{j=0}^{\lfloor\log q\rfloor}2^{j}\cdot\mathbf{u}_{j}\) with all \(\mathbf{u}_{j}\in\mathbb{Z}_{2}^{n}\) outputs \(\Big{(}\mathbf{u}_{0},\mathbf{u}_{1},\ldots,\mathbf{u}_{\lfloor\log q\rfloor}\Big{)}\in \mathbb{Z}_{2}^{n\cdot\lceil\log q\rceil}\).
## 4 Enterprise Network Functionality
### Data Flow and Communication Protocols  
Enterprise networks use transmission control protocol/internet protocol (TCP/IP) to transport data [61; 62]. These protocols organize data into packets and route them from their source to destination. Switching and routing devices utilize dedicated protocols such as open shortest path first (OSPF), border gateway protocol (BGP), and ethernet to determine the optimal path for every packet. The aim is to guarantee a dependable data flow throughout the network [63].
## 3 Results and Discussion
### FTIR Spectra of Pellets
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Sum of Squares** & **df** & **Mean Square** & **F** & **Sig.** \\ \hline \multirow{3}{*}{Zn} & Between Groups & 498,363.018 & 3 & 166,121.006 & 1063.927 & 0.000 \\  & Within Groups & 2498.231 & 16 & 156.139 & & \\  & Total & 500,861.250 & 19 & & & \\ \hline \multirow{3}{*}{Cu} & Between Groups & 14,643.652 & 3 & 4881.217 & 92.952 & 0.000 \\  & Within Groups & 840.214 & 16 & 52.513 & & \\  & Total & 15,483.865 & 19 & & & \\ \hline \multirow{3}{*}{Pb} & Between Groups & 3586.758 & 3 & 1195.586 & 24.576 & 0.000 \\  & Within Groups & 778.374 & 16 & 48.648 & & \\  & Total & 4365.132 & 19 & & & \\ \hline \multirow{3}{*}{Cd} & Between Groups & 29.941 & 3 & 9.980 & 177.785 & 0.000 \\  & Within Groups & 0.898 & 16 & 0.056 & & \\ \cline{1-1}  & Total & 30.839 & 19 & & & \\ \hline \multirow{3}{*}{Cr} & Between Groups & 7004.878 & 3 & 2334.959 & 253.536 & 0.000 \\ \cline{1-1}  & Within Groups & 147.353 & 16 & 9.210 & & \\ \cline{1-1}  & Total & 7152.231 & 19 & & & \\ \hline \multirow{3}{*}{As} & Between Groups & 23.944 & 3 & 7.981 & 112.429 & 0.000 \\ \cline{1-1}  & Within Groups & 1.136 & 16 & 0.071 & & \\ \cline{1-1}  & Total & 25.080 & 19 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The ANOVA testing of some elemental compositions of samples. lulosic material [36], which is rubberwood in this case. After mixing with RDF, two new sharp peaks at around 2800-3000 cm\({}^{-1}\) can be seen. These peaks could correspond to the C-CH\({}_{3}\) groups [37], which may belong to the plastic waste in the RDF. The peaks, including those around 3000-3400 cm\({}^{-1}\), become less intense as the proportion of RDF increases, most likely due to the successful cross-linking during rubberwood and RDF co-pelletization, especially when RDF is added at a higher proportion.
## 6 Role of the PhenoCam Network in Supporting Forest Management in India
The Forest Survey of India (FSI) conducts satellite-based mapping and monitoring, producing a national forest area assessment report every two years. However, it is insufficient to meet the needs of managing Indian forests effectively. To address this limitation and enhance forest management strategies, the establishment of a long-term PhenoCam network across various Indian forests is crucial. By implementing PhenoCams, which provide continuous and accurate data on shifting phenology, healthindicators, biomass estimation, and gross primary productivity, researchers can gain a deeper understanding of the ecological impacts of climate change on different species. Moreover, phonologically informed forest management is crucial for arresting increasing forest fire events in Indian forests, which see surface fires starting with the accumulation of litter during EOS (senescence). The predictive modeling of meteorological variables crucial for defining EOS is the need of the hour. Improvements in the Dynamic Global Vegetation Models (DGVMs) and understanding the impact of climate change on forest ecosystems in India. Vegetative phenology holds significant importance in DGVMs. These models utilize information on plant biogeography and biogeochemical processes to predict ecosystem fluxes and climate fluctuations in the context of global change scenarios [91]. One crucial input for DGVMs is GPP), which is currently estimated using satellite data and still requires validation in the South Asian region. Recent studies have demonstrated the potential to estimate GPP [25] and LAI [86] using PhenoCam data. This indicates that PhenoCam technology can contribute to improving and strengthening satellite-derived GPP data, enhancing the accuracy of DGVM predictions, and making them more applicable to the unique conditions of the South Asian region. By integrating PhenoCam data into DGVMs, researchers can gain a better understanding of the impacts of climate change on forest ecosystems in India and refine management strategies accordingly.
## 5 Discussion
### Estimation Performance of the Proposed Method
As explained in Section 3.5, the participant was instructed to emit only a minimum muscle power to lift and keep the cylinder from falling. Even though such mechanisms minimize the possibility of human error, there is still a chance that a participant may have emitted a different muscle power, either stronger or weaker, within the same cylinder weight session. Despite the varying results in the data for several participants, the RF model demonstrated better performance in providing information on the muscles' contraction-relaxation state and the contraction force corresponding to finger conditions. Furthermore, the proposed method demonstrated great potential in addressing the challenge of monitoring muscle contraction force for home-based rehabilitation using an alternative modality, a stretch-sensor glove.
# Fuzzy Echo State Network-Based Fault Diagnosis of Remote-Controlled Robotic Arms
## 3 Deep Fuzzy Echo State Networks for Fault Classification
### A Fuzzy ESN
After determining the values of \(W_{1}\) and \(W_{2}\), the state of the reservoir is updated according to the following equation:  
\[x(t)=\tanh(W_{1}u(t)+W_{2}x(t-1)) \tag{10}\]  
where \(\tanh(\cdot)\) is the hyperbolic tangent activation function applied element-wise. Figure 2: The architecture of a single-layer fuzzy ESN. The predicted values of the output layer in the fuzzy ESN are calculated as follows:  
\[\hat{y}(t)=W_{3}x(t) \tag{11}\]  
where \(W_{3}\) are the output weights that should satisfy the following:  
\[c_{j}=\frac{\sum_{i=1}^{N}\eta_{ij}^{2}u(t)}{\sum_{i=1}^{N}\eta_{ij}^{2}} \tag{5}\]0  
where \(h(t)\) represents the output obtained after clustering, which can be calculated as follows:  
\[h(t)= \begin{cases}c_{j}&if\quad u(t)\in j-thcluster\\ 0&else\end{cases}  \tag{13}\]  
However, in practice, such \(W_{3}\) may not exist, and the predicted values \(\hat{y}(t)\) can only be approximated to \(h(t)\) through the continuous optimization of \(W_{3}\). The formulation of the optimization problem for the output weight is as follows:  
\[W_{3}=\underset{W_{3}}{\operatorname{argmin}}(\left\|W_{3}X-h\right\|_{2}^{2} +\lambda\left\|W_{3}\right\|_{2}^{2}) \tag{14}\]  
where \(\lambda\) is the regularization coefficient that introduces a penalty term to the optimization problem, balancing between reducing overfitting and fitting the training data well. Generally, the solution to this optimization problem is addressed using ridge regression with the pseudoinverse as follows:  
\[W_{3}=hX^{T}(XX^{T}+\lambda I)^{-1} \tag{15}\]  
where \(I\) denotes the identity matrix of the appropriate dimensions.
## 4 Time-Frequency-Space Resource Allocation Method in Sharing Network
### Smart Grid Technology
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Input Feature** & **Implication** \\ \hline \multirow{2}{*}{Service Cell} & SSB Beam ID & SSB Beam Index \\ \cline{2-3}  & SSB RSRP & SSB Reference Signal Received Power \\ \hline \multirow{2}{*}{Co-frequency Neighbor Cells} & SSB Beam ID & SSB Beam Index \\ \cline{2-3}  & SSB RSRP & SSB Reference Signal Received Power \\ \hline \multirow{2}{*}{Co-frequency Neighbor Cells} & SSB Beam ID & SSB Beam Index \\ \cline{2-3}  & SSB RSRP & SSB Reference Signal Received Power \\ \hline \multirow{2}{*}{Co-frequency Target Neighbor Cell} & SSB Beam ID & SSB Beam Index \\ \cline{2-3}  & SSB RSRP & SSB Reference Signal Received Power \\ \hline \multirow{2}{*}{Co-frequency Neighbor Cells} & RRC Connected User Count & \\ \cline{2-3}  & Pending Scheduling User Count & \\ \cline{2-3}  & Cell Average MCS & \\ \hline \multirow{2}{*}{Cell Resources} & Downlink Cell PRB Utilization Rate & \\ \cline{2-3}  & PDCCH CCE Utilization Rate & \\ \hline \multirow{2}{*}{Background Service} & Cell Last TTI Ratio & Representation of Background User \\ \cline{2-3}  & Service Proportion \\ \hline \multicolumn{2}{c}{**Output Feature**} & **Implication** \\ \hline Inter-Frequency Target Neighbor Cell & Inter-Frequency Spectral Efficiency & Spectral Efficiency \\ \hline \end{tabular}
\end{table} 
Table 5: Beam grid-neural network input feature and output feature. During the model prediction stage, the same feature vectors used during model training are inputted to realize the prediction of RSRP, spectrum efficiency, and beam ID information. Specifically, gNodeB utilizes A3 event measurement reports reported by users on the same frequency. The RSRP information within these reports is taken as the input for the smart grid model. This input is combined with other necessary feature information. By querying the RSRP model, spectrum efficiency model, and beam model for a particular inter-frequency point, it becomes possible to swiftly predict the RSRP, spectrum efficiency, and beam ID for a UE at that specific inter-frequency point's coverage location.
## 1 Introduction
Liquefaction is a phenomenon that occurs when soil loses its strength and behaves like a liquid. This can cause structures to sink or collapse. The earthquakes caused significant damage to the port of Iskenderun, located in the Hatay province of Turkiye. The containers at the port were overturned, and a serious fire broke out. The fire was brought under control after three days of effort. The February 2023 earthquakes have once again highlighted the need for engineering measures to prevent liquefaction, especially in offshore structures to be built in seismic regions. The Petroleum Pipeline Corporation of Turkiye (BOTAS) plans to build an FSRU terminal in the Gulf of Saros (Figure 1). The project site is located near the Ganos Segment of the North Anatolian Fault Zone, one of the most seismic active regions on Earth. For this reason, a comprehensive geotechnical site investigation was carried out in the project area. The soil profile was identified by offshore drillings and the liquefaction potentials of marine sediments was determined. The foundation ground, which consists of liquefiable marine sediments, was improved by constructing jet grout columns on the seabed below the water level varying between 4 m and 18 m. The main purpose of the study is to investigate whether the jet columns are effectively constructed and to evaluate the quality of jet columns. Observational, mechanical, and geophysical methods were utilized to evaluate the quality of these columns. This study employs geophysical measurements in addition to mechanical boreholes and observations. The use of geophysical measurements for checking the jet grout performance is the novelty of the present study. The formation of jet columns was monitored step by step with geophysical measurements. Cores were then obtained from the constructed columns and their strength properties were evaluated. After jet grout improvement, the engineering fillings were constructed. The speed of quality control processes for assessment of the jet grout construction directly affects the overall construction time. Consequently, a fast procedure is proposed for the assessment of jet grout construction in offshore environments. This study is an interesting case study for offshore structures to be constructed in active seismic regions. The study also provides new geotechnical data for liquefaction from the Saros Gulf, Turkiye.
## 3 Undifferentiated Cells (Stem Cells)
### Fetal Stem Cells
#### 3.3.1. Ammiotic Fluid-Derived Stem Cells (AFSCs)
##### 3.3.2.1. General Information  
Human amniotic membrane (hAM) or amnion as the innermost layer of the fetal membranes is an avascular stromal matrix. This membrane has been widely used in tissue engineering based-approaches for healing different tissues including eyes [116], skin [117], teeth [118], and the cardiovascular system [119]. Additionally, the hAM is clinically used as a tissue with therapeutic possibilities for repairing different burns [120], leg ulcers [121], oral and maxillofacial disorders [122], and stroke [123].  
Histologically, the amniotic membrane is composed of five distinct layers including an epithelial cell monolayer, an acellular basement membrane layer, a compact layer, a mesenchymal cell layer, and a spongy layer [124]. HAM as a stem cell source benefits from several advantages such as a good yield of cells as well as not needing invasive or expensive procedures [125]. Moreover, the use of AM-derived stem cells for TE applications does not face ethical issues. Human amniotic epithelial cells (HAECs) and human amniotic mesenchymal stromal cells (HAMSCs) are the main stem cell types isolated and characterized from the hAM. It has been well documented that both of the cells possess stemness characteristics including the ability to self-renew [126], immunomododulation [127], and differentiation into cell lineages of different germ layers [128]. The isolation procedure of these cells is fairly easy and is performed via previously proposed methods [129; 130; 106; 131].  
Recently, the use of HAMSCs together with a scaffold for tissue engineering strategies has grown with interest [132]. HAMSCs were first introduced by [PERSON] et al. as cells with the potential of osteogenic and adipogenic differentiation [93]. According to the International Society for Cellular Therapy, these cells are formally named amniotic membrane-human mesenchymal stromal cells (AM-hMSCs) [133]. In a cell culture condition, AM-hMSCs display a spindle-like shape morphology and are able to attach to tissue culture plastic [134]. In addition to human, the presence of these cells has been reported in other mammals such as cat [135], horse [136], and bovine [137].
# Necessary Morphological Patches Extraction for Automatic Micro-Expression Recognition
###### Abstract  
Micro expressions are usually subtle and brief facial expressions that humans use to hide their true emotional states. In recent years, micro-expression recognition has attracted wide attention in the fields of psychology, mass media, and computer vision. The shortest micro expression lasts only \(1/25\,\mathrm{s}\). Furthermore, different from macro-expressions, micro-expressions have considerable low intensity and inadequate contraction of the facial muscles. Based on these characteristics, automatic micro-expression detection and recognition are great challenges in the field of computer vision. In this paper, we propose a novel automatic facial expression recognition framework based on necessary morphological patches (NMPs) to better detect and identify micro expressions. Micro expression is a subconscious facial muscle response. It is not controlled by the rational thought of the brain. Therefore, it calls on a few facial muscles and has local properties. NMPs are the facial regions that must be involved when a micro expression occurs. NMPs were screened based on weighting the facial active patches instead of the holistic utilization of the entire facial area. Firstly, we manually define the active facial patches according to the facial landmark coordinates and the facial action coding system (FACS). Secondly, we use a LBP-TOP descriptor to extract features in these patches and the Entropy-Weight method to select NMP. Finally, we obtain the weighted LBP-TOP features of these NMP. We test on two recent publicly available datasets: CASME II and SMIC database that provided sufficient samples. Compared with many recent state-of-the-art approaches, our method achieves more promising recognition results.  
micro-expression; local movement; necessary morphological patches (NMPs); local binary pattern; feature selection Article
# Best Practices Evidenced for Software Development Based on DevOps and Scrum: A Literature Review
## 3 Results
### Empirical Evidence for Scrum and DevOps Benefits  
While the previous sections identified key factors and practices for successful Scrum and DevOps implementation, this section examines the empirical evidence supporting these claims, drawing upon the case studies, experimental validations, and quantitative data reported in the reviewed literature.  
Several studies found in the review provide real-world examples of the benefits of adopting Scrum and DevOps. The authors of [46] present a case study examining the productivity gains of DevOps adoption by an IT team. While the abstract does not detail specific metrics, the study suggests a positive correlation between DevOps implementation and team productivity. Similarly, the authors of [49] offer a theory, model, and case studyof DevOps adoption, providing a more in-depth analysis of the factors contributing to successful implementation.  
Additionally, the impact of specific DevOps practices is explored by the authors of [94], who characterize the influence of continuous integration based on empirical results from over 250 open-source and proprietary projects. This large-scale study provides valuable insights into the benefits of continuous integration on software quality and delivery speed. In addition, the authors of [100] further investigate this topic, presenting a large-scale empirical study on the impact of continuous integration on other software development practices. Their findings highlight the interconnectedness of various DevOps practices and the synergistic effects of their combined implementation.  
On the other hand, the authors of [118] examine the shift-left testing approach in DevOps. While the abstract focuses on the benefits, challenges, and best practices, the study likely contains empirical data related to the impact of shift-left testing on software quality and defect reduction. Additionally, the authors of [112] select DevOps' best test practices that are significantly related to those presented in this study, which provides a solid foundation and validation of the selection of DevOps practices.  
However, it is important to acknowledge that the existing empirical evidence has some limitations. The focus of the study in [46] is the impact of DevOps alone. In addition, there is a lack of specific data on the implementation of DevOps in VSEs. Therefore, future research should focus on conducting rigorous quantitative studies in VSEs to address these gaps and provide more conclusive evidence of the benefits of Scrum and DevOps in these organizations.