esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 AI/ML in the Standards and Industry
### International Telecommunication Union (ITU)  
The ITU-T study group (SG) 13 established the focus group (FG) on ML for future networks including 5G (FG-MLSG) in 2017, which was active between January 2018 and July 2020, and produced 10 technical specifications for the integration of ML into future networks [88]. The FG identified standardisation and research gaps but also specified data formats to be collected and processed for a variety of use cases. The use cases were grouped under five main categories, and for each of them, the requirements related to data collection, storage, and processing were determined. The identified use cases are relevant topics for further research work and are listed in Table 3.  
Figure 6: High-level reference architecture for a network management domain with closed-loop control and five domain functions as suggested by the ZSM committee. Domain-data analytics, e.g., for fault identification, are stored, and analytics about relevant past events are retrieved and enhance the performance of AI/ML tools residing under domain intelligence. Finally, the integration fabric is used as an interface for the exchange of information between different network management domains. Note that there are E2E services that consist of several domains, e.g., a service for E2E network slicing comprises core, transport, and RAN slice subnets spanning different management domains [87]. Another core contribution of the FG-ML5G is the recommendation for a high-level and interoperable architectural framework, which incorporates ML into future wireless networks [90]. In this architecture, a telecommunication network, e.g., 5G or IEEE 802.11, can become an underlay that provides data to the ML pipeline (or ML overlay). The latter collects the data through a standardised interface (the SRC) and passes them through a well-defined configuration, including data pre-processing and NNs associated with the subject use case. Finally, the ML overlay distributes the ML output to the corresponding SINR (another standardised interface), through which the output is applied to the underlay network. Apart from the SRC and SINR interfaces that interact with the NFs of the underlay network, the ML functionalities in the overlay are technology agnostic. Therefore, AI/ML algorithms and telecommunication technologies can evolve in parallel. The study in [91] utilises this architecture for ML-based association and handover in a WLAN. Another critical architectural component suggested by the FG-ML5G is the ML sandbox, where ML models are trained and tested before their live deployment in the underlay network [90]. In the sandbox, MNOs can compare the performance of various ML models and mitigate the risks associated with the adoption of ML techniques. Training ML models in the sandbox may use either real-network or simulation data, leveraging digital twins of
## 2 Materials and Methods
### Chemical Analysis of Feeds  
Throughout the study, samples from each feed type were collected and sent to the Chemical Analysis Laboratory of the Animal Nutrition Department, Faculty of Veterinary Medicine, Timisoara, Romania, for processing and analysis.  
The crude chemical composition was determined. The parameters analyzed for this purpose were the following: moisture (M%), dry matter (DM%), ether extract (EE%), crude protein (CP%), crude fiber (CF%) and nitrogen-free extract (NFE%). Regarding sample analysis, the protocols of standardized methods were performed considering the Association of Official Analytical Chemists [38], and adapted according to the recommendations of the equipment manufacturer (FOSS AnalyticsTM, Hillerod, Denmark). The samples were analyzed using two technical replicates. The obtained results and the reported conclusions represented the arithmetic mean of values for each sample.  
M% and DM% were determined by oven-drying samples at 105 \({}^{\circ}\)C using a BinderTMoven 09-11881 (Binder Inc.TM, Neckarslum, Germany). The EE% was determined using the Soxhlet method applied using a FOSSTM2055 analyzer (FOSSTM analytical solutions, Hillerod, Denmark). The Kjeldahl method was used to determine the CP% using FOSSTM8400-8420 analyzer according to the manufacturer's instructions. The standard method was used to determine crude fiber content using FOSSTM2010 analyzer (FOSSTM analytical solutions, Hillerod, Denmark), that automatically have the analysis method set. NFE% was calculated by subtracting the values of other chemical constituents determined by chemical analysis from 100, using the following formula:  
\[\text{NFE}\%=100-(\text{M}\%+\text{CP}\%+\text{EE}\%+\text{CF}\%).\]  
The nutritional value of feed used in the present study was calculated using following standardized Atwater formula, considering the Food and Agriculture Organization [39]:  
Metabolizable energy:  
\[\text{ME kcal}/\text{kg}=[(\text{CP}\%\times 4)+(\text{EE}\%\times 9)+( \text{CF}\%\times 4)+(\text{NFE}\%\times 4)]\times 10.\]
# Xanthone Derivatives in the Fight against Glioblastoma and Other Cancers
## 2 Antineoplastic Activity of Xanthone Derivatives
### Gartanin  
Gartanin (**6**, Figure 4) is an isoprenylated xanthone that can be found in the Mangosteen fruit. This compound has been tested, among others, on the **T98G glioma cell line** and showed cytotoxic activity in the **MTT** test at **10 \(\upmu\)M**. The compound inhibits proliferation, colony formation, and migration of glioma cells. This is probably due to cell cycle arrest at phase G1 and induction of autophagy. The decreased migration of malignant cells is probably caused by suppression of the MAPK signaling pathway. These results indicate that Gartanin is very promising regarding therapy of **gioblastoma**--both administered alone or as an additional drug in combination therapy with another, already established chemotherapeutic agent [57]. Gartanin also reduces the viability and proliferation of **prostate cancer 22 Rv1 and LNCaP cells** (in MTT test IC\({}_{50}\) equal to 14.9 \(\upmu\)M and 15.3 \(\upmu\)M, respectively) and induces apoptosis of these cells. The unique mechanism of action of this compound probably involves degradation of the androgen receptor--a target of prostate cancer [58].
# Attention-Based Light Weight Deep Learning Models for Early Potato Disease Detection
## 4 Conclusions and Future Work  
The proposed work demonstrates that utilizing an attention-based approach can achieve similar accuracy with reduced model complexity. By embedding the attention concept in the deep learning model, the number of trainable parameters in the MobileNet model can be reduced by approximtaly 62%. Additionally, this approach maintains comparable precision, recall, and \(F_{1}\) score values with fewer parameters. The proposed technique can be extended to build state-of-the-art models with improved performance and fewer trainable parameters. The simplified models with fewer parameters enables the development of light weight, portable applications for devices with low computational power. For example, mobile applications can be developed to allow farmers to take a picture of a leaf and detect whether the plant is healthy or infected. This would be particularly useful for farmers who lack technical knowledge and cannot afford high-end computational devices. Similarly, light weight websites could be deployed to allow users to upload images of a leaves and distinguish between healthy and infected crops. The technique can be further enhanced by incorporating various other technologies such as quantum computation, deep learning architectures, vision-language models, etc.  
Methodology, A.S.R.; Formal analysis, A.S.R.; Investigation, S.S.K. and A.S.R.; Writing--original draft, A.S.R.; Writing--review & editing, S.S.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data presented in this study are openly available in reference number [2,22]. The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **With Attention** & **Without Attention** \\ \hline Precision & 89.62 & 90.03 \\ \hline Recall & 89.36 & 89.68 \\ \hline \(F_{1}\) Score & 89.42 & 89.71 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Summary table for VGG16.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **With Attention** & **Without Attention** \\ \hline DenseNet169 & 42 ms & 32 ms \\ \hline MobileNet & 34 ms & 22 ms \\ \hline XceptionNet & 32 ms & 21 ms \\ \hline VGG16 & 23 ms & 18 ms \\ \hline \hline \end{tabular}
\end{table}
Table 17: Comparison of the inference time with and without the attention module.
## 2 Adam-Bayesian-GPR-Based MC Reliability Calculation Procedure Framework
### Adam-Bayesian-GPR-Based MCS Reliability Analysis  
The whole calculation process of the Adam-Bayesian-GPR-based MCS reliability analysis method is depicted in a flowchart in Figure 1. The detailed steps are as follows.  
Step 1: Generate a dataset of geo-mechanical parameters using the LHS method.  
Step 2: Use the finite element method (FEM) such as ABAQUS 2020 to calculate the displacement of the tunnel at each monitoring point.  
Step 3: Establish the surrogate model by GPR training on the dataset:  
First, the original data of geo-mechanical parameters and displacements are preprocessed centrally and standardized to form the sample dataset;  
Second, select the appropriate kernel function based on the actual situation;  
Third, establish the model to map the nonlinear relationship between displacements and their corresponding geo-mechanical parameters;  
Finally, the hyperparameters of the GPR model are determined by the Adam algorithm and provide the optimal surrogate model. The Adam algorithm has become the mainstream method in the field of stochastic gradient optimization, with the advantages of easy implementation, low memory consumption, and high computational efficiency. It is very suitable for situations with large datasets and parameters. Therefore, the Adam algorithm is selected to tune the hyperparameters of the surrogate model established using GPR in this study.  
Given the set of observations \(\mathbf{y}^{*}\) in the test sample dataset, Adam is used to learn the  
Step 4: The posterior probability of geo-mechanical parameters is calculated using the Bayesian inference theory. The mean and variance of the predicted value of geo-mechanical parameters are obtained by Formula (4).  
Step 5: Random sampling using the LHS method is carried out based on the mean and variance of geo-mechanical parameters obtained in Step 4. Then, the displacements of the tunnel are calculated by the optimal ABGPR-based surrogate model.  
Step 6: Using the Monte Carlo simulation (MCS) reliability analysis method, we calculate the failure probability of tunnel primary support structure using Formula (13).  
\[P_{\text{f}}\approx\frac{N_{\text{f}}}{N} \tag{13}\]  
where \(P_{\text{f}}\) is the failure probability of the tunnel primary support structure; \(N_{\text{f}}\) is the number of \(g^{j}_{\text{Tunnel}}(\mathbf{X})=u_{\text{max}}-u_{j}(\mathbf{X})<0\) in all \(g^{j}_{\text{Tunnel}}(\mathbf{X})(j=1,2,\cdots,N)\); \(N\) is the total number of random samples.  
In this study, our numerical simulation did not take into account dynamic loads and weakening of the rock mass due to the presence of water.  
Figure 1: Calculation procedure of ABGPR–MCS reliability analysis.
## 3 Methodology
### Feature Selection
#### 3.2.6 Minimal-Redundancy-Maximal-Relevance  
Minimal-redundancy-maximal-relevance (mRMR) is a filter-based feature selection method that aims to prioritize features that exhibit a high correlation with the target variable (relevance) while simultaneously minimizing redundancy among selected features. This ensures that the selected features provide unique information, enhancing the model's ability to discriminate between different classes [44]. For continuous features, relevance  
Figure 6: Top 100 features selected using FFS based on the SGD algorithm.  
Figure 7: Absolute coefficient values of the top 100 features selected using SVM-RFE.  
is typically calculated using the F-statistic, which assesses the variance between groups, while redundancy is measured using the Pearson correlation coefficient, quantifying the linear relationship between pairs of features. This dual focus allows mRMR to effectively evaluate the importance of features in relation to both the target variable and each other [45]. Two common objective functions are Mutual Information Difference (MID) and Mutual Information Quotient (MIQ), representing the difference and quotient of relevance and redundancy, respectively [46].  
In this work, mRMR was implemented using mrmr-selection 0.2.8 from Python Package Index (PyPI).  
Figure 8 shows the rankings of the top 100 features selected by the mRMR method, where features are ranked based on their relevance to the target variable and their redundancy with respect to other selected features. Features on the left have higher ranks, indicating greater importance. The mRMR method ensures that the selected features provide complementary and unique information by balancing relevance and redundancy.
## 3 Materials and Methods
### Classification Methods
#### 3.4.4 Logistic Regression  
A straightforward parametric statistical method, logistic regression is regarded as the industry standard in the credit scoring domain. It is employed to resolve regression issues and binary classification problems (default and non-default in this work) [5]. Finding the logarithm of the ratio of two probability outcomes of interest is the aim of the LR model [58].  
The LR model for the independent variable \(p\) can be written as (Equation (5)).  
\[P(Y=1)=\frac{1}{1+e^{-(\beta_{0}+\beta_{1}X_{1}+\beta_{2}X_{2}+\cdots+\beta_{ p}X_{p})}} \tag{5}\]where \(P(Y=1)\) is the probability and \(\beta 0,\ \beta 1,\ \dots,\ \beta p\) represent the regression coefficients. There is a linear model hidden within the logistic regression model. The natural logarithm of the ratio of \(P(Y=1)\) to \((1-P(Y=1))\) gives a linear model in \(Xi\) (Equation (6)).  
\[g(x)=\ln\left(\frac{P(Y=1)}{1-P(Y=1)}\right)-\beta_{0}+\beta_{1}X_{1}+\beta_{2 }X_{2}+\dots+\beta_{p}X_{p} \tag{6}\]  
\(g(x)\) has many of the desirable properties of a linear regression model. Independent variables can be a combination of continuous and categorical variables.
# New Framework for Human Activity Recognition for Wearable Gait Rehabilitation Systems
## 3 Signal Processing Techniques
### Cepstral Analysis for HAR  
The concept of cepstrum has been utilized in various applications such as speech recognition, gear/machine diagnostics, and echo detection/removal [37].  
Figure 8: Applying EEMD on gastrocnemius muscles.  
Figure 7: Denoised vastus medialis (channel 1) and gastrocnemius (channel 2) muscles.  
This analysis involves calculating the power spectrum of the logarithm of the power spectrum, with the aim of identifying echoes in seismic signals. In this manuscript, we propose the employment of the autocepstrum analysis (ACA) as a robust feature extraction approach for lower limb activity detection. Autocepstrum analysis was used for the detection of spread spectrum signals in low signal-to-noise ratio (SNR) environments [38]. The literature suggests using CA to take advantage of the discriminative activity information found in acceleration signals for HAR, which is based on homomorphic analysis. Information regarding whole-body dynamics can be separated out and converted into a compact representation known as cepstral coefficients via homomorphic analysis [39; 40].
# Inversion and Analysis of the Initial Ground Stress Field of the Deep-Buried Tunnel Area
###### Abstract  
The detailed analysis of the initial ground stress distribution law is an important work for the safety of tunnel construction and operation. Especially, the high ground stress phenomenon in the deep-buried tunnel area is common, which has a great impact on the tunnel construction. Based on the on-site measured ground stress data, the analysis of the initial ground stress field by numerical simulation and multiple linear regression is mainly described in this study. Following the comparison and selection of three coefficient estimation methods for the regression equation, the best regression method is selected for inversion and verification. The distribution characteristic of the initial ground stress at different buried depths of the tunnel line is obtained. The inversion results of the initial ground stress in a tunnel area, in China, show that the lateral pressure coefficient gradually decreases with the buried depth increasing, while the overall lateral pressure coefficient is in the range of 1.0-2.0, showing a more significant horizontal tectonic. At the area where the tunnel passes through the fault, a small amount of horizontal tectonic stress is released. The ratio of horizontal principal stress to vertical principal stress is smaller than that on both sides, which is different from the distribution characteristic of lateral pressure coefficient without the impact of fault. It shows that faults have a great influence on ground stress. The lateral pressure coefficient in the area near the fault must be determined according to the on-site measured results.  
Keywords:initial ground stress inversion; numerical simulation; multiple linear regression; deep-buried tunnel; lateral pressure coefficient +
Footnote †: journal: Applied
## 2 Materials and Methods
### Fabrication of PDMS Microwell Mouds and Microtissue Formation  
Concave PDMS (polydimethylsiloxane) microwells were employed to generate self-assembled microtissues using lower numbers of cells than traditional pellet cultures or alginate bead models using a similar approach as described previously [30]. The microwell layout was designed using SolidWorks software (Solid Solutions Management Ltd, Dublin, Ireland). Mouds were designed with 100 microwells (10 \(\times\) 10 array) containing 500 um hemispherical concave microwells, with 1390 um centre-centre distance and a depth of 1750 um (Figure 1). A Formlabs Form 2 printer (Formlabs GmbH, Berlin, Germany) was used to 3D print the clear resin master stamps. After printing, the master stamps were washed in isopropyl alcohol for 20 min to remove excess residue and subsequently cured under an ultraviolet lamp (4 W, Uvitec, Cambridge, UK) for 1 h. 3D printed stamps were inserted into 12-well plates containing approximately 1 mL of PDMS (Sylgard\({}^{\text{\textregistered}}\) 184, Sigma-Aldrich, Arklow, Ireland), degassed in a vacuum oven and cured at 80 \({}^{\circ}\)C for 4 h. After cooling, the master stamps were removed carefully with the use of a tweezers. The PDMS microwell moulds formed were washed with ethanol and phosphate-buffered saline (PBS, pH 7.4), sterilised by UV-irradiation for 3 h and allowed to dry overnight in a laminar hood. The morphology and the diameter of the PDMS microwells formed were characterised by scanning electron microscopy (SEM) (SEM, SUPRA 35 V P, Carl Zeiss, Oberkochen, Germany). For microtissue formation, the moulds were placed in 12-well plates and seeded with different densities of NP and NC cells (1000, 2500, and 5000 cells/microwell or 100,000, 2500,000, 5000 cells/mould) and centrifuged at 300\(\times\)\(g\) for 3 min to initiate microtissue formation. The plates were then cultured in a humidified atmosphere at 37 \({}^{\circ}\)C and 5 %O\({}_{2}\) in 1 mL of media supplemented with or without growth factors as described below. Preliminary experiments investigated the formation of microtissues with less than 1000 cells/microtissue. However, these were found to be inconsistent and lacked cohesion and integrity, making them unsuitable for further experiments.
## 2 Materials and Methods
### Attenuated Total Reflection Spectroscopy  
The infrared absorption spectra of the zirconia powder, the polymers and the compounds were measured by attenuated total reflection spectroscopy (ATR). ATR spectroscopy was performed using a Vertex 70 spectrometer (Bruker, Ettlingen, Germany) at room temperature. A total of 128 scans were accumulated with a resolution of 4 cm\({}^{-1}\).
## 3 Results and Discussion
### Mean and Total EF and Photochemical Ozone Creation Index, POCI  
Table 1 shows the mean EF (\(n=20\)) for every analyzed terpene, the total EF considering all terpenes, their respective POCI and the C flux for _P. \(\times\) acerifolia_ and _S. molle_. Table S3 shows the transformation procedure from EF to carbon flux of terpenes emitted by _Platanus_\(\times\)_acerifolia_ and _Schinus molle_. The total EF of _P. \(\times\) acerifolia_ was dominated by isoprene, which makes 99.4% while for POCI isoprene makes about 99.6%. The correlations (Pearson-\(r\)) between EF and environmental variables were significant between relative humidity and \(\alpha\)-terpinene and \(\alpha\)-humulene compounds with \(r=0.646\) and \(0.635\), respectively.  
The contribution of isoprene to the total EF of _S. molle_ was 86.3% with a lower amount of monoterpenes, being higher compared to _P. \(\times\) acerifolia_. The POCI of _S. molle_ was dominated by isoprene, which makes this compound the main ozone precursor for this species. However, the POCI of _S. molle_ is 6.9 times smaller than the one for _P. \(\times\) acerifolia_.
## 3 Weighted Least Square Support Vector Regression Model
### Improvement of LSSVM  
(1) Horizontal weighted input vectors  
The mode of multi-inputs and single output is always emerging in the icing forecasting task. The value of the input vector \(x_{i}\) is distributed along with the time and the influence of the actual value of the icing thickness at different times and can be reflected by the weighted processing. Therefore, the input vector can be weighted according to the following formula:  
\[\hat{x}_{i}=x_{ki}\cdot\delta(1-\delta)^{n-i},\quad k=1,2,\cdots,l \tag{12}\]  
where \(\hat{x}_{i}\) is the weighted vector; \(x_{ki}\) is the original vector; \(k\) is the dimension number of input vectors; \(\delta\) is a constant.  
(2) Vertical weighted training data set  
The predicted value of ice forecasting is not only related to the elements in the input vector, but also with the sample group. This correlation is reflected as: near distance samples have a great influence on the prediction, but the distant samples have little effect on the forecasting. So it is necessary to reduce the impact of near distance samples on the prediction model by using different membership values of the current icing thickness. The membership values can be calculated by using the linear membership \(\mu_{i}\), and the equation is as follows:  
\[\mu_{i}=\beta+i(1-\beta)/N,\quad 0\leq\mu_{i}\leq 1 \tag{13}\]  
where \(\mu_{i}\) is the value of membership; \(\beta\) is a constant between 0 and 1; \(i=1,2,\cdots,N\). The input sample set can be transformed into:  
\[T=\left\{\begin{array}{cccc}(x_{1},y_{1},\mu_{1})&(x_{2},y_{2},\mu_{2})& \cdots&(x_{N},y_{N},\mu_{N})\end{array}\right\} \tag{14}\]  
The determination value of \(\beta\) directly impacts the performance of LSSVM, thus the value of \(\beta\) can be obtained by calculating the gray correlation coefficient. The gray correlation coefficient is calculated as follows:  
\[r(x_{0}(k),x_{i}(k))=\frac{\Delta_{ki}(\min)+\rho\Delta_{ki}(\max)}{\Delta_{ki} +\rho\Delta_{ki}(\max)} \tag{15}\]  
\[\Delta_{ki}=|x_{0}(k)-x_{i}(k)|\quad\rho\in[0,1] \tag{16}\]  
\[\beta_{i}=\sum_{k=1}^{N}r(x_{0}(k),x_{i}(k)) \tag{17}\]  
Considering the multi-inputs and single output of icing forecasting, this paper will let \(x_{0}=Y\), \(Y=\{y_{1},y_{2},\cdots,y_{N}\}\).