esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Overview of HO Management in 5G UDSC Network
### 5G Small Cells Working Model  
The reference model of 5G is depicted in Figure 1 in which the AMF or Access & Mobility Management Function acts as a brain of the model. It performs different management roles such as connection, mobility, and registration management. Like AMF, SMF or Session Management Function is also important part of model, and it is responsible for controlling, monitoring session context, editing PDU, and linking to decouple data plane. Connection between DN and architecture is provided by the user plane function (UPF). UPF is also responsible for PDU session anchor point, managing quality of service as well as the routing and forwarding packets [52]. The other counterparts are represented in the figure below.
## 4 BP Neural Network and Pearson Correlation Analysis
### Model Setting
#### 4.1.3 Proposed Algorithm  
This paper introduces a BPNN algorithm comprising five components, with the procedures detailed in Figure 3.  
* First, the orthogonal experimental data were collected and divided into training and testing sets. Subsequently, normalization was performed to accurately capture the inherent characteristics of the data while eliminating scale restrictions [29];  
Figure 3: The proposed algorithm of BPNN.  
* Second, the number of input and output nodes was determined based on the criteria outlined in Section 4.1.1, with the activation functions of both the input and output layers set to tansy and purlin by default;
* Third, the Levenberg-Marquard algorithm was selected for training, establishing a neural network with parameters including 1000 training epochs, a 0.01 learning rate, and a \(10^{-5}\) minimal training goal error;
* Fourth, the number of hidden layers was chosen according to Equation (13), utilizing the specified training in the prepared neural network to calculate the mean square error. If this value falls below the specified training goal error, then the corresponding number of hidden layer nodes is confirmed as optimal;
* Fifth, confirming the BPNN model is achieved by incorporating all the relevant data and information above before commencing predictions.
# Nondestructive Monitoring Techniques for Crack Detection and Localization in RC Elements
## 9 DIC Strain Assessment  
A comparison between DIC results in terms of the strain field on the central span of the beam specimen and the FOS #2 measurements has also been presented in this section. Figure 15 (top) presents the strain field computed through DIC [22] at loading steps #4 (left) and #8 (right), while the FOS strains at the bottom side of the specimen are reported for the same loading steps at Figure 15 (bottom). Focusing on the 0.8 m central length of interest, the FOS #2 sensor highlights a reasonable constant tensile strain of about 1700 um/m at step #4 and 7000 um/m at step #8. These results have been computed considering a sampling interval and spatial resolution of 0.2 and 0.5 m, respectively, typically used for large real structures.  
The DIC strain field at the bottom side results in the range 1000-3500 um/m at step #4, and 4000-12,000 um/m at step #8. Therefore, a satisfactory comparison between the two adopted techniques can be verified.
# A Cooja-Based Tool for Coverage and Lifetime Evaluation in an In-Building Sensor Network+
## 4 Implementation
### Use Cases
#### 4.2.3 Correlation  
In WSNs, sensor nodes are often densely deployed to cover a monitoring area. This redundant deployment causes spatial correlation among the readings of nearby nodes. In this paper, we define reading correlation as spatial correlation, where the reading of a node may be predicted from the readings of its nearby neighbours with high confidence.  
We incorporate the concept of reading correlation using the inverse distance weighting interpolation method, such as used in [19]. The inverse distance weighting interpolation method is based on the close similarity principles: the shorter the distance between two things is, the more similar they are. In a building scenario, two nearby nodes may not have similar readings if they are in two different rooms. Therefore, to calculate the reading correlation of a sensor node in a room, we only take into account nearby nodes in the same room.  
By using the inverse distance weighting interpolation method, the unknown measure at a position can be interpolated by means of a weighted average of the measures associated at the known points. It is achieved by the following function:  
\[\hat{V}_{i}^{t}=\frac{\sum\limits_{j=1,j\
eq i}^{n}\omega(i,j)V_{j}^{t}}{\sum \limits_{j=1,j\
eq i}^{n}\omega(i,j)} \tag{9}\]  
\(\hat{V}_{i}^{t}\) stands for the interpolated value of node \(i\) at time \(t\); node \(j\) belongs to the set \(Z\) of adjacent nodes; \(\omega(i,j)\) is the distance weight between node \(i\) and \(j\). If \(d(i,j)\) is the distance between node \(i\) and \(j\), the weight can be expressed as follows:  
\[\omega(i,j)=\frac{1}{d(i,j)^{m}} \tag{10}\]  
where \(m\) is a positive real number, called the power parameter. The absolute difference between the predicted value \(\hat{V}_{i}^{t}\) and real \(V_{i}^{t}\) is the error \(\epsilon\) called the redundancy of a node:  
\[\epsilon=|\hat{V}_{i}^{t}-V_{i}^{t}| \tag{11}\]  
By using the correlation method, sensors that have a high reading correlation can be considered as redundant and can be put to sleep. Nodes decide whether they are redundant or not based on \(\epsilon\). Sensors that have high reading correlations are sensors whose data reading can be predicted using the reading of nearby active sensors in the same room. With this method, we expect to improve the energy efficiency by putting more nodes to sleep.
## 2 Data Sources and Study Methods
### Evaluation Methods
#### 2.2.2 Coupling Coordination Degree Model Construction  
Coupling coordination theory aims to study whether the system and the internal elements of the system develop harmoniously in the process of mutual coupling, whether they develop from disorderly to orderly, and whether they develop from simple to complex to form a unified, organic whole. This theory has some key concepts, such as the degree of coupling (C) and the degree of coordination (\(D\)) [43]. In this study, the degree of interaction and mutual influence between the two systems is called the coupling coordination degree. It consists of three parts, namely, the classification system and discriminant standard of the degree of development (\(T\)), the degree of coupling (C), and the degree of coordination (\(D\)) [44; 45].  
\[T=\alpha u_{1}+\beta u_{2} \tag{1}\]  
In the equation, \(T\) represents the comprehensive evaluation index of tourism and urbanization to reflect their comprehensive benefits. \(\alpha\) and \(\beta\) represent undetermined weights. As two systems in different fields, tourism and urbanization influence and restrict each other in the development process. The coupling and coordination relationship between them is very obvious. Tourism plays a leading role in hospitality, entertainment, shopping, and other industries and creates many job opportunities. Meanwhile, urbanization affects the development of tourism and provides the economic support, tourist market, and infrastructure for the development of tourism. In the development process of urbanization, it will continue to improve the infrastructure, such as transportation, hotel facilities, cultural entertainment, shopping malls, and so on, which provides a broader platform for tourism development. Since tourism and urbanization complement each other, both \(\alpha\) and \(\beta\) are assigned a value of 0.5. \(u_{1}\) and \(u_{2}\) represent the tourism index and urbanization index, respectively.  
\[C=m\big{\{}(u_{1}\times u_{2}\cdots\times u_{m})/(u_{1}+u_{2}\cdots+u_{m})^{m} \big{\}}^{1/m} \tag{2}\]  
\[C=2\Big{\{}(u_{1}\times u_{2})/(u_{1}+u_{2})^{2}\Big{\}}^{1/2} \tag{3}\]  
\(C\) denotes the coupling degree of the two systems.  
\[D=\sqrt{C\times T} \tag{4}\]  
\(D\) is measured by integrating C and T to reflect the differences in coupling coordination levels.  
The coupling degree reflects the degree of interaction of the system, and the coordination degree reflects the coordination of the coupling degree. Referring to previous studies on the correlation levels of the coupling degree and the coordination degree, the coupling degree and the coordination degree of the two systems are divided into five levels, respectively, by using the method of a uniform distribution function (Table 2).
###### Abstract  
Reduced germination and early crop maturity due to soil compaction, nutrients stress, and low moisture are major constraints to achieve optimum crop yield, ultimately resulting in significant economic damages and food shortages. Biochar, having the potential to improve physical and chemical properties of soil, can also improve nutrients and moisture access to plants. In the present study, a growth room experiment was conducted to assess biochar influence on maize seed germination, early growth of seedlings, and its physiological attributes. Corn cob biochar (CCB) was mixed with soil at different rates (0.5%, 1%, 1.5%, 2%, 2.5%, and 3% \(w\)/\(w\)) before seed sowing. Results obtained showed that increasing CCB application rate have neutral to positive effects on seed germination and seedling growth of maize. Biochar addition at the rate of 1.5% (\(w\)/\(w\)) significantly increased shoot dry biomass (40%), root dry biomass (32%), total chlorophyll content (a and b) (55%), germination percentage (13%), seedling vigor (85%), and relative water content (RWC) (68%), in comparison to un-amended control treatment. In addition to this, it also improved germination rate (GR) by 3% as compared to control treatment, while causing a reduction in mean emergence time (MET). Moreover, application of biochar (3%) also resulted in enhancement of antioxidant enzyme activity, particularly superoxide dismutase (SOD) and catalase (CAT) by 13% and 17%, respectively. Conclusively, biochar application is an attractive approach to improve the initial phase of plant growth and provide better crop stand and essential sustainable high yields.  
biochar; germination; seedling vigor; chlorophyll; germination rate; maize growth
# Numerical Simulation of Water Migration during Soil Freezing and Its Resulting Characterization
## 6 Discussion  
This study, while contributing valuable insights into permafrost water migration, is still in its nascent stages and has several areas for improvement. Firstly, our use of a single sample type has limited the generalizability of our findings. Future research will involve a multi-dimensional comparative analysis using different soil types to enhance the robustness and applicability of the model. Secondly, although our model builds upon the Harlan hydrodynamic model, the simulation of mass transfer (water) using the Van Genuchten infiltration model has not been entirely accurate due to unclear effects of ice blockage on water movement, and the introduction of the impedance factor introduces a degree of randomness to the parameters. Furthermore, the accuracy of the numerical simulation could be improved by deriving the Van Genuchten water infiltration parameters from Soil-Water Characteristic Curve (SWCC) experiments. This approach would align the simulation process of water migration more closely with actual conditions. Finally, our model simplifies the mechanical properties of permafrost, which is not merely a rigid body but a complex system. The reliance on a mechanical model based on linear elasticity may not fully capture the nuanced mechanical behavior of permafrost.  
Conceptualization, B.Z. and A.V.B.; methodology, B.Z.; software, B.Z.; validation, B.Z., L.I.E. and C.X.; formal analysis, B.Z.; investigation, B.Z. and L.I.E.; resources, A.V.B. and J.H.; data curation, B.Z., L.I.E. and C.X.; writing--original draft preparation, B.Z.; writing--review and editing, A.V.B.; visualization, A.V.B. and C.X.; supervision, A.V.B.; project administration, A.V.B.; funding acquisition, A.V.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest.
## 6 Discussions on Future Research Directions and Challenges
### The Influences of AI and ML on B5G Security and Privacy  
The advent of new network features, technologies and services in B5G networks will reveal potential security and privacy challenges. These challenges are not only from the perspective of the consumer but also from the viewpoints of vendors, network operators and service providers in terms of developing perfect deployment of service scenarios. Service providers should produce solutions to ensure the security, privacy and allocation of essential resources to their customers robustly and reliably in order to tackle these challenges. Enabling technologies, such as AI and ML, are integrated into 5G and B5G networks and are expected to meet the security and privacy requirements to a certain extent. However, the adoption of AI and ML technologies in wireless networks will raise concerns about privacy and security issues, such as those raised in [196; 197; 198; 199]. In order to address these problems in B5G networks, proactive privacy and security measures need to be developed. Such techniques will employ novel AI technologies and algorithms and subsequently, physical layer security may become one of the major areas of application for AI [200]. Due to the increase in data rates in B5G networks, the processing performance of such network elements will be further expanded, which will facilitate the strong combination of AI and future communication security design [201]. An ML-based autoencoder was proposed in [202] to reconstruct detection errors in order to discover irregular traffic data in future networks. ML-based PLS techniques, especially those regarding device authentication,were discussed in [203] to achieve a robust and highly reliable authentication for B5G networks. In [204], a big data analytics technique was applied to facilitate the rapid growth of B5G network data in specific scenarios. However, big data analytics can help to address abnormal user detection applications.
## 1 Introduction
The authors remarked that while high upfront costs and standardization might become an issue, the implementation of this technology could provide a worthwhile, noninvasive, and rapid test that would enable better disease management and responses to community outbreaks [3]. The researchers also took on a different approach to collect VOC odor from patients in a less invasive fashion, that is, from the hands versus the breath. Numerous studies have been published on the availability of VOCs from the hands of individuals and the ability of these VOCs to differentiate between subjects [23; 24; 25]. Hand odor collection would allow for a more rapid screening of individuals for disease states versus breath analysis methodologies, which require large volumes of exhaled breath to be collected for disease state determination. The analysis of hand odor also proves less invasive than the nasopharyngeal swabbing incurred for various RT-qPCR and antigen diagnostic tests. Additionally, hand odor swabs can be treated as a \"clean sample\" requiring no sample cleanup or liquid extraction prior to analysis by analytical instrumentation. For these reasons, this research seeks to leverage the VOC detection of human hand odor using headspace solid phase microextraction (HS-SPME) sampling and gas chromatography-mass spectrometry (GC-MS) analysis to identify chemical biomarkers that may distinguish between SARS-CoV-2-positive and -negative individuals. The use of chemometric tools allows for efficient data manipulation and machine learning to develop sPLS-DA predictive models. This is the first in a set of papers that lays the groundwork for a non-invasive diagnostic approach for SARS-CoV-2. It is the belief of the researchers that the further expansion and successful implementation of this approach will also result in a framework for the development of rapid diagnostic tools for other diseases.
# Immobilization of Levocyetririzine on Mesoporous Silica for Antiallergenic Gel Formulation
## 2 Materials and Methods
### Infrared Spectroscopic (IR) Measurements  
Fourier transform infrared spectra with attenuated total reflectance (FTIR-ATR) were collected for the powder samples in the wavelength ranges of 4000 and 400 cm\({}^{-1}\) over 32 scans, with a resolution of 4 cm\({}^{-1}\). The measurements were conducted using a Tensor 27 (Bruker Optik GmbH, Leipzig, Germany) spectrophotometer fitted with a Platinum ATR unit A225. Air was used as the background medium during the measurements.
# Improvement of the Transglycosylation Efficiency of a
## 3 Results and Discussion  
### Screening of LnbB Variants  
Thirteen LnbB variants were designed and successfully expressed recombinantly in \(E\). coli BL21 (DE3) (Figure S3): N259T, D320E, D320T, W373F, W373H, W394F, W394H, G398R, \(\upalpha\)G398, Y419N, W465F, W465H, and D467N (Figures 2 and 3). Of these, D320E, W373F, W394F, and W465F have been studied elsewhere (2018; 2018), while the remaining nine variants are new and provide a foundation for comparing the effect of choosing different amino acid substitutions at N259, D320, W373, W394, Y419, W465, and D467. N259, Q190, and D467 essentially define subsite \(-2\) (the Gal binding site), D320 interacts with the GlcNAc residue on one side of subsite \(-1\), while Y419 and the three Trp residues are situated around subsite \(-1\) (Figure 3). G398 is approximately 13 A away from the ligand; this distance reduces to approximately 10 A if mutated to Arg in PyMOL (Figure S3).  
The transglycosylation abilities of each of the variants and the WT enzyme were screened using LNB-oxazoline as donor substrate, lactose as acceptor substrate, and the same enzyme dosage for all variants (1 \(\upmu\)M). The formation of LNT and isomers was monitored over 24 h (Table 1; Figure 4 and Figure S4). In all reactions, LNT was formed as the major product along with three different isomers (see Section 3.2). The different mutants showed maximum LNT yields ranging from 185 to 1285 \(\upmu\)M, whereas the maximum yield of total transglycosylation products (LNT plus isomers) ranged from 685 to 2388 \(\upmu\)M (Table 1). For WT LnbB, a maximum LNT yield of 495 \(\upmu\)M was measured after only 15 min of reaction. However, the formed product was quickly hydrolyzed by secondary hydrolysis, and after 24 h of reaction, only 56 \(\upmu\)M LNT remained (Table 1). The following sections examine the variants based on design strategy.
# On the Use of Transformer-Based Models for Intent Detection Using Clustering Algorithms
## 4 Results and Discussion
### Pooling Methods  
This experiment evaluates the effect of using different pooling methods for sentence embedding when feeding a K-Means clustering. The V-measure scores of all six models are shown in Table 2. The best layer reports the highest score found in each model. By examining the table, it is possible to determine that BERT, RoBERTa, and GPT-2 are the best overall performers. Even though RoBERTa's authors reported better performance for downstream tasks through eliminating the NSP and training on further data, in this analysis, this model offers no substantial improvements over BERT, which was the best performer using mean-pooling. Unexpectedly, GPT-2 achieves the best performance when max-pooling is used. This model is remarkable for text generation but is not known for its word and sentence embeddings for clustering or information retrieval tasks. Most models perform the best in the initial layers, except for RoBERTa and ALBERT. ALBERT was the only model that achieved better results using its last layer, possibly due to its new pre-training task (the SOP).  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Best** & **Mean-** & **Max-** & **Min-** & **Pooling Con-** & **Pooling Con-** & **Model** & **Best Layer** & **Mean-** \\  & **Layer** & **Pooling** & **Pooling** & **Pooling** & **catenation** & **Model** & **Best Layer** & **Pooling** \\ \hline Word2 Vec & - & 74.28 & - & - & - & Word2 Vec & - & 74.28 \\ GloVe & - & 72.86 & - & - & - & GloVe & & 72.86 \\ ELMo & - & 34.70 & - & - & - & ELMo & & 34.70 \\ BERT & 1 & **45.43** & 34.73 & 26.57 & 31.43 & BERT & 1 & 45.43 \\ RoBERTa & 11 & **40.22** & 23.97 & 21.90 & 24.45 & RoBERTa & 11 & 40.22 \\ GPT-2 & 1 & 24.22 & **43.18** & 41.00 & 39.22 & GPT-2 & 1 & 24.22 \\ XLNet & 1 & **23.03** & 19.27 & 20.41 & 21.28 & XLNet & 1 & 23.03 \\ ALBERT & 12 & **29.43** & 13.87 & 16.75 & 16.97 & ALBERT & 12 & 29.43 \\ ELECTRA & 2 & **39.29** & 28.04 & 27.22 & 29.22 & ELECTRA & 2 & 39.29 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean V-measure scores (in %) of the examined models for sentence embedding.  
Figure 2: Sequence of followed-up queries and the conclusions reached after each experiment, indicating in which subsection the evaluation was performed.  
Mean-pooling was identified as the best choice for sentence representation. GPT-2 was the only outlier, where min-pooling and max-pooling methods were superior to mean-pooling. It was observed that concatenating pooling methods did not lead to better performance, even though additional information was provided by this method. Unsurprisingly, the baselines show superior performance, namely Word2 Vec and GloVe. These two embeddings have been designed explicitly for sentence representation, while ELMo and transformer-based models have not. ELMo and transformers perform downstream tasks well, but a good sentence representation is not implied.
# Data Fusion Analysis and Synthesis Framework for Improving Disaster Situation Awareness
## 6 Objectives and the Effects of Data Fusion
### Quality Objectives of Data Fusion  
This section delineates the calculation of the individual effectiveness, cost, and timing values of the data sources that are introduced in the previous section.  
In the Cambridge dictionary [59], effectiveness is defined as \"achieving the result that you want\". In our context, the effectiveness value is considered from the perspective of the following three objectives:  
* **Objective 1:** The accuracy of estimating the effects of disasters is represented as a probabilistic variable. This value must be sufficiently high for a given purpose;
* **Objective 2:** The accuracy of estimating the horizontal coordinates of a (living) person after a disaster as a probabilistic variable. This value must be sufficiently high for a given purpose;
* **Objective 3:** The accuracy of estimating the vertical coordinates of a (living) person after a disaster as a probabilistic variable; this value must be sufficiently high for a given purpose.  
For each data source, the effectiveness values can be taken from the related product catalogs and/or computed by experimentation. The effectiveness values must be computed per objective and assumed to be between 0 and 1, where 1 is the maximum possible effectiveness.  
For each objective, different data sources can be selected and used. In case of adoption of multiple data sources for a given objective, the total effectiveness value is computed according to the data fusion formulas, which are given in Section 6.2.  
The cost and timing values are considered by the objectives 4 and 5:* **Objective 4:** The estimated cost value of a data fusion per element which is represented as a probabilistic distribution function. This value must not exceed the budgeting constraints;
* **Objective 5:** The estimated timing value of a data fusion per element which is represented as a probabilistic distribution function. This value must not exceed the deadline constraints.  
In Appendix A, three tables are defined that contain the estimated parameters for the effectiveness, cost, and timing values of the prospective data sources.