esa-sceva/satcom-chunk-collection huggingface dataset:
# Directly Matching an MMIC Amplifier Integrated with MIMO Antenna through DNNs for Future Networks
## 1 Introduction
In the first phase, the antenna is modeled through the long short-term memory (LSTM)-based DNN, leading to a prediction of the load impedance, including real and imaginary parts, in a large bandwidth. Afterwards, the PA is modeled through LSTM-based DNN for optimizing the linearity performance of the system where it is optimized through the predicted optimal load impedances from the modeled antenna. For the proposed methodology, two electronic design automation (EDA) tools with a numerical analyzer (as MATLAB) are used where Microwave Studio (Dassault Systemes) and ADS are executed for designing antenna and PA, respectively. For sizing the existed components, the genetic algorithm (GA) is employed, and for providing the optimal hyperparameters, bayesian optimization (BO) is used. The contributions of this manuscript can be summarized as follows:  
* Modeling the antenna with LSTM-based DNN leading to estimate the real and imaginary parts of load impedances over the large bandwidth:
* Directly matching the antenna into the drain's optimal impedance:
* Modeling the PA with the LSTM-based DNN through multivariate Newton's method:
* Linearity improvement of the overall system by cointegration of antenna and amplifier as passive and active devices, respectively. This paper is organized as follows: Section 2 presents the proposed methodology for modeling antennas and amplifier, leading to enhanced linearity specification in the communication systems. Section 3 describes the practical implementation of the proposed method. Section 4 is devoted to providing the simulation results of optimized and modeled antenna with amplifier. Finally, Section 5 concludes this work.
# A Fault Analysis Method Based on Association Rule Mining for Distribution Terminal Unit
## 2 DTU Fault Analysis
### Configuration of DTU  
A picture and simplified configuration of a DTU are shown in Figure 2. It comprises 5 modules which are, respectively, the CPU module, the communication module, the sampling module, the control circuit module, and the power supply module. Among these, the CPU functions as the centralized controller, the communication module controls information interaction between the DTU and the main station, the control circuit module sends signals to breakers, and the power supply module provides electricity to other modules.
# A Survey of Security Vulnerability Analysis, Discovery, Detection, and Mitigation on IoT Devices
## 3 Vulnerability Analysis, Discovery, Detection, and Mitigation
### Research on Vulnerability Mitigation
#### 3.4.2 Access Control Method  
The access control method is to manage the IoT device's permission for the user side or the platform to restrict or stop the malicious behavior of attackers.  
**Technical requirements:** Scalability for the user side or the cloud side.  
[PERSON] et al.'s [107] first in-depth study of IoT security focused on a platform such as SmartThings. They found that great majority applications are overprivileged due to the capabilities being too coarse-grained, and devices used to communicate asynchronously with applications via events, which do not sufficiently protect events that carry sensitive information such as lock codes [107]. Many devices in the smart home are excessive permissions and ambiguous permissions management resulting in attacks on IoT devices and disclosure of privacy. Researchers at the University of Michigan have come up with a series of solutions to solve these problems. In 2016, they proposed _Flowfence_[108], a system based on data flow to protect privacy leakage. The application is divided into two components: (1) A set of Quarantined Modules that operate on sensitive data in sandboxes, and (2) Code that does not operate on sensitive data but orchestrates execution by chaining Quarantined Modules together via taint-tracked opaque handles--references to data that can only be dereferenced inside sandboxes. Then, in 2017, they implemented _ContextIoT_[109] based on context information, which can help usersimplement effective access control to prevent attackers from performing dangerous operations by identifying sensitive operation context identification and ensuring context integrity in runtime. Finally, _Tyche_[110] was proposed in 2018, a risk-based permission model for smart homes to solve the problem of excessive access permissions by building an Access Control Capabilities Lists (ACCLs) based on the source code level. _Smartauth_[111] and _FACT_[112] are also based on the ACCLs. However, they build the ACCLs in different ways. _Smartauth_ builds by documents that are identified by natural language processing (NLP) technology and APP source code, while _FACT_ builds during the phase of device development.
## 4 Results and Discussion
### Experimental Setup
#### 4.1.4 Evaluation Metrics  
In this study, we adopt accuracy, precision, recall, F1-score as evaluation metrics, each reflecting the model's performance from different perspectives. Accuracy refers to the proportion of correctly predicted samples among the total number of samples, providing an intuitive reflection of the model's overall performance; however, it may lead to misleading conclusions in cases of class imbalance. Precision is the proportion of truly positive samples among those predicted as positive by the model, assessing the accuracy of the model's positive predictions. Recall, on the other hand, refers to the proportion of correctly predicted positive samples among all actual positive samples, reflecting the model's ability to identify positive instances. The F1-score is the harmonic mean of precision and recall, serving as a comprehensive measure of the model's classification performance, especially significant when addressing class imbalance issues. Finally, these evaluation metrics can be expressed by the following formulas:  
\[\text{Acc}=\frac{TP+TN}{TP+TN+FP+FN} \tag{8}\]  
\[\text{P}=\frac{TP}{TP+FP} \tag{9}\]  
\[\text{R}=\frac{TP}{TP+FN} \tag{10}\]  
\[\text{F1-score}=2\cdot\frac{P\cdot R}{P+R} \tag{11}\]  
Here, \(TP\) represents True Positives, \(TN\) represents True Negatives, \(FP\) represents False Positives, and \(FN\) represents False Negatives. By comprehensively applying these evaluation metrics, we can thoroughly assess the model's performance, considering both classification accuracy and model efficiency.
## 3 Results
### Students' Opinions towards the Four Teaching Tools--Multivariate Analysis
#### 3.2.1 In-Depth Analysis of Clusters Obtained for Classic Oral Presentations
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multicolumn{2}{c}{**General Idea: Classic Oral Presentations Help Best to Understand the Concepts Presented, Being a Clear Way of Explanation, Providing Essential Information, Stimulating Interest and Capturing Attention Best, Helping to Make Learning Easier, Providing the Most Correct Information and Therefore Being the Most Useful.**} \\ \hline \multicolumn{2}{c}{} & Cluster 1—agreement & Cluster 2—agreement & Cluster 3—agreement & \\  & to a very high extent & to a high and & to a low to & \\  & n (\%) & average extent & average extent & average extent & \\  & & n (\%) & n (\%) & & \\ \hline Sex & & & & 0.035 * \\ Male & 40 (19.1) & 54 (23.9) & 37 (31.9) & & \\  & Female & 169 (80.9) & 172 (76.1) & 79 (68.1) & & \\ Age group & & & & & \\  & 18–20 years & 129 (61.7) & 119 (52.7) & 49 (42.2) & & \\  & 21–24 years & 48 (23.0) & 68 (30.1) & 42 (36.2) & & \\  & over 25 years & 32 (15.3) & 39 (17.3) & 25 (21.6) & & \\ Level of openness to autonomous use of multimedia resources in learning: & & & & & \\  & 1—total disagreement & 16 (7.7) & 3 (1.3) & 1 (0.9) & & \\  & 2—partial disagreement & 53 (25.4) & 19 (8.4) & 11 (9.5) & & \\  & 3—neutral & 85 (40.7) & 97 (42.9) & 39 (33.6) & & \\  & 4—partial agreement & 30 (14.4) & 61 (27.0) & 25 (21.6) & & \\  & 5—total agreement & 25 (12.0) & 46 (20.4) & 40 (34.5) & & \\ Internet services mainly used for information (on a scale of 1 to 4): & & & & & 0.378 \\  & 1—most important & 71 (34.0) & 68 (30.1) & 41 (35.3) & & \\  & 2—important & 78 (37.3) & 87 (38.5) & 49 (42.2) & & \\  & 3—less important & 54 (25.8) & 64 (28.3) & 20 (17.2) & & \\  & 4—least important & 6 (2.9) & 7 (3.1) & 6 (5.2) & & \\ Internet services mainly used for communication (on a scale of 1 to 4): & & & & 0.000 ** \\  & 1—most important & 145 (69.4) & 136 (60.2) & 53 (45.7) & & \\  & 2—important & 50 (23.9) & 64 (28.3) & 40 (34.5) & & \\  & 3—less important & 10 (4.8) & 25 (11.1) & 23 (19.8) & & \\  & 4—least important & 4 (1.9) & 1 (0.4) & - & & \\ Internet services mainly used for entertainment (on a scale of 1 to 4): & & & & 0.542 \\  & 1—most important & 70 (33.5) & 68 (30.1) & 45 (38.8) & & \\  & 2—important & 71 (34.0) & 86 (38.1) & 33 (28.4) & & \\  & 3—less important & 53 (25.4) & 61 (27.0) & 30 (25.9) & & \\  & 4—least important & 15 (7.2) & 11 (4.9) & 8 (6.9) & & \\ Internet services mainly used for domestic facilities (on a scale of 1 to 4): & & & & 0.021 * \\  & 1—most important & 12 (5.7) & 12 (5.3) & 11 (9.5) & & \\  & 2—important & 32 (15.3) & 31 (13.7) & 26 (22.4) & & \\  & 3—less important & 66 (31.6) & 49 (21.7) & 28 (24.1) & & \\  & 4—least important & 99 (47.4) & 134 (59.3) & 51 (44.0) & & \\ \hline Total & & 209 (100.0) & 226 (100.0) & 116 (100.0) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative study of clusters identified for advantages of classic oral presentations.
# The Fruit Recognition and Evaluation Method Based on Multi-Model Collaboration
###### Abstract  
Precision agriculture technology based on computer vision is of great significance in fruit recognition and evaluation. In this study, we propose a fruit recognition and evaluation method based on multi-model collaboration. Firstly, the detection model was used to accurately locate and crop the fruit area, and then the cropped image was input into the classification module for detailed classification. Finally, the classification results were optimized by the feature matching network. In the method, the detection model was based on YOLOv8, and the model was improved by introducing a TripletAttention structure and an Attention Mechanism-Based Feature Fusion (AFM) structure. The improved YOLOv8 model improves the P, R, mAP50, and MAP50-95 indicators by 2.4%, 2.1%, 1%, and 1.3%, respectively, compared with the baseline model on only one generalized \"fruit\" label dataset. The classification model Swin Transformer used in this study has a classification accuracy of 92.6% on a dataset of 27 fruit categories, and the feature matching network based on cosine similarity can calibrate the classification results with low confidence. The experimental results show that the proposed method can be applied to the maturity assessment of apples and tomatoes, as well as to the non-destructive testing of apples.  
multi-model collaboration; fruit recognition and evaluation; improved YOLOv8 detection model; swin transformer classification model; feature matching network Article
# Multi-Robot Exploration Employing Harmonic Map Transformations
## 3 Methodology
### Harmonic Maps Transformation for Exploration  
To navigate within the explored region while avoiding static obstacles, a modified version of Harmonic Maps for Planar Navigation used in [22] is proposed. The aforementioned method maps the robots' workspace \(W\) into a punctured unit disk \(\mathcal{D}\). In contrast, our modified version of the transformation \(\mathcal{T}\) maps only the explored region \(\varepsilon\) into \(\mathcal{D}\) and has the following properties:  
1. Maps the explored region's outer boundary \(\partial\varepsilon\) onto the unit circle \(\partial\mathcal{D}\)
2. Maps each of the frontiers \(\partial\varepsilon_{f}\) of the outer boundary into a single point \(q_{f}\in\partial\mathcal{D}\)
3. Maps the inner obstacle boundaries \(\partial W_{j}\) that are within the explored region \(\varepsilon\) to a distinct point \(q_{j}\in int(\mathcal{D})\)
4. The transformation \(\mathcal{T}\) is a diffeomorphism for all \(p\in int(W)\).  
Figure 1: An example of a workspace with two obstacles. Three robots \(p_{1},\ p_{2},\ p_{3}\) are exploring the workspace. The colored areas correspond to the perceived part of the workspace so far.  
The modified transformation is visualized in Figure 2. Since no closed-form solutions exist for non-trivial domains of the aforementioned transformation, the inner and outer boundaries are subdivided into a sufficient number of linear elements in order to numerically approximate \(\mathcal{T}\) with \(\tilde{\mathcal{T}}\). Thus, the following transformation is given  
\[\tilde{\mathcal{T}}(p)=\left[\tilde{u}(p),\tilde{v}(p)\right]^{T}=q,p\in\varepsilon,q\in\mathcal{D}, \tag{2}\]  
with \(\tilde{u}(p),\tilde{v}(p)\) being harmonic functions with respect to \(p\), by approximating an ideal harmonic map \(\mathcal{T}\).  
The HMT for Exploration is particularly useful for navigating inside the explored region while avoiding the static obstacles, since each inner obstacle is mapped to a single point. Thus, in the transformed space \(\mathcal{D}\), a line that connects the mapped position to the desired destination may intersect with inner obstacles with zero probability. Furthermore, since frontiers are mapped to points, navigating towards them becomes trivial, as shown in later sections.
# Saturation Based Nonlinear FOPD Motion Control Algorithm Design for Autonomous Underwater Vehicle
## 1 Introduction
However, the studies of this kind of controller are still limited. Further research of the stability and dynamic control performance of FOPID type controllers used on AUVs needs more exploration. In this paper, a saturation based nonlinear fractional-order PD (FOPD) controller is proposed for the motion control system of AUVs. Compared with traditional PID type controller, the proposed controller can achieve better dynamic performance and robustness. A saturation limitation is also added to the FOPD controller to adapt to the nonlinear of the control system. In addition, the proposed controller reserves the advantages of traditional PID type controller, such as simple structure, easy tuning and easy implementation. In the simulation examples, different objective function weight pairs are presented to satisfy different kinds of control requirements. Finally, the outperformed dynamic control performance of AUV yaw and depth systems is shown by the set-point regulation and trajectory tracking examples. The rest of this paper is organized as follows. Section 2 presents the modelling of AUV motion control system. Section 3 gives the preliminaries of fractional calculus. The controller design and stability analysis process are presented in Section 4. Section 5 shows the simulation examples of AUV yaw and depth control with the proposed controller. Finally, conclusions are drawn in Section 6.
# Channel Estimation and Iterative Decoding for Underwater Acoustic OTFS Communication Systems
## 3 Underwater Acoustic OTFS System Model  
### OTFS Modulation  
We assume that one UWA OTFS frame has \(M\) subcarriers and \(N\) symbols. Then, the bandwidth, signal duration, and symbol number are \(M\Delta f\), \(NT\), and \(MN\), respectively, where \(\Delta f\) is the subcarrier spacing and \(T=1/\Delta f\) is the symbol duration.  
We place OTFS symbols \(x(q,l)\) into DD grids (size: \(M\times N\)) with delay resolution \(1/(M\Delta f)\) and Doppler resolution \(1/(NT)\), where \(q=0,1,\ldots,N-1\) and \(l=0,1,\ldots,M-1\). After inverse symplectic finite Fourier transform (ISFFT), OTFS symbols \(x(q,l)\) in the DD domain are transformed into \(X(n,m)\) in the time-frequency domain:  
\[X(n,m)=\frac{1}{\sqrt{MN}}\sum_{q=0}^{N-1}\sum_{l=0}^{M-1}x(q,l)e^{2\pi(\frac{ nq}{N}-\frac{m!}{M})}. \tag{1}\]Then, \(X(n,m)\) is transformed into the passband time domain signal \(\tilde{s}(t)\) after Heisenberg transformation and up-conversion:  
\[\tilde{s}(t)=s(t)e^{i2\pi f,t}=\frac{1}{\sqrt{M}}\sum\limits_{n=0}^{N-1}\sum \limits_{m=0}^{M-1}X(n,m)\tilde{g}(t-nT)e^{i2\pi m\Delta f(t-nT)}e^{i2\pi f_{c} t}, \tag{2}\]  
where \(g(t)\) is the rectangular window function and \(f_{c}\) is the carrier frequency.
## 3 Results and Discussion  
### Heat Stability and pH Stability of GE  
GE is usually applied as material in food processing, so adjusting the temperature and pH of the surrounding environment is common. On the other hand, high temperature and strong acid or alkaline conditions may cause degradation of \(\beta\)-glucan. Therefore, it is necessary to investigate the stability of GE to heat and pH [14]. Firstly, the yield of GE was measured as 6.99 \(\pm\) 0.23%, and the \(\beta\)-glucan content of GE was 62.63 \(\pm\) 1.54% according to the regression equation, which was  
\[\text{Y}=3.279X+0.002 \tag{6}\]  
where Y was the OD\({}_{500}\), and X was the \(\beta\)-glucan content (mg/mL). Heat stability of the GE was systematically evaluated by wet heat in the water at 100 \({}^{\circ}\)C and dry heat in the oven between 140 \({}^{\circ}\)C and 220 \({}^{\circ}\)C. As shown in Table 1, \(\beta\)-glucan content of GE almost kept constant from 100 \({}^{\circ}\)C (wet heat) to 200 \({}^{\circ}\)C (dry heat) for 6 h. When the temperature climbed to 220 \({}^{\circ}\)C, the content reduced to 98.21 \(\pm\) 1.83% but even this decline was not significant (_p_ > 0.05). Thus, GE exhibited excellent heat stability under boiling and high-bake. The pH of natural GE solution was measured as 6.6. The results of pH stability of the GE are also shown in Table 1. The content of \(\beta\)-glucan was basically unchanged at neutral and alkaline conditions of pH 7-11, but it decreased dramatically to 92.96 \(\pm\) 2.83% as the pH was lowered to 5 (_p_ < 0.05). Furthermore, the strong acid condition of pH 3 generated a lower \(\beta\)-glucan content of 70.45 \(\pm\) 3.78%. Therefore, the GE had better pH stability at neutral or alkaline condition than acid one, which demonstrated the GE is more suitable to be applied to weak acidic, neutral, or alkaline food.
# Efficient Communication Model for a Smart Parking System with Multiple Data Consumers
## 4 Efficient Communication Model for an SPS with Multiple Data Consumers
### Concise Data Format for Data Exchange  
The occupancy data can be expressed in binary as they are two-state data, which are either occupied or not occupied. Hence, a single byte can represent eight parking slots and an 80-byte IP payload can effectively contain occupancy data for 640 parking slots. The occupancy data can be multiplexed at the data servers and is used for exchanging collated data between servers. The same is sent to data consumers. The concise string is then broken down back to occupancy data in the consumer node. The BR uses the compressed occupancy string in the HTTP data exchanged with the web browser. This short data exchange format reduces the load time of the web page.  
The algorithm presented in Algorithm 1 takes the occupancy data array and converts the same to a single consolidated string. The index of the array is mapped to the position of the parking lot and is subsequently filled with either zero or one. The data collators fill the respective slots in the data array and convert the data to a concise string. This string is eight times compressed and can hold over 600 occupancy data in a single IP payload of IEEE 802.15.4 mesh.  
Input is \(ODA\), Occupancy data array Output is \(occupancy\_str\), Concised occupancy data as string for\(i=0\) to \(no\_of\_parking\_slots-1\)do for\(j=0\) to \(7\)do shifted\(\_bit=ODA[i]\)\(<<j\) combined\(\_byte=combined\_byte\mid\) shifted\(\_bit\) endfor \(byte\_str=int\_to\_char(combined\_byte)\) concatenate \(byte\_str\) with \(occupancy\_str\) endfor ```
**Algorithm 1** Convert occupancy data to a concise string.  
The algorithm presented in Algorithm 2 takes the consolidated string and converts it back to occupancy data.
``` Input is \(occupancy\_str\), Concised occupancy data as array of characters Output is \(ODA\), Occupancy data array \(bit\_mask[]\) is [128, 64, 32, 16, 8, 4, 2, 1] for\(i=0\) to \(no\_of\_parking\_slots-1\)do \(int\_value=char\_to\_int(occupancy\_str[i])\) for\(j=0\) to \(7\)do \(occupancy\_bit=bit\_mask[j]\) & \(int\_value\) if\(occupancy\_bit>0\)then \(occupancy\_bit=1\) else \(occupancy\_bit=0\) endif \(ODA[i]=occupancy\_bit\) endfor endfor ```  
**Algorithm 2** Convert the concise string back to occupancy data.  
All these decisions are expected to play a role in establishing efficient communication between all the concerned entities of the SPS system.
# Multi-Omics Approaches to Study Molecular Mechanisms in _Camanabis sativa_
###### Abstract
The diploid genome consisting of nine autosomes and a pair of sex chromosomes [24; 25], is highly heterozygous, and contains many repetitive elements (-70%) [5; 26; 27]. Despite the presence of distinct sex chromosomes, some external factors, such as a shorter photoperiod, a lower temperature, and the application of chemicals, such as ethylene inhibitors, on leaves, can enhance pollen production in female flowers, resulting in 'feminised seeds' [28]. This technique has been often used in cannabis breeding to generate target populations to investigate key phytochemical and qualitative traits. The presence of many lipophilic cannabinoids and terpenoids in cannabis, along with numerous other primary and secondary metabolites, adds complexity to metabolic analyses [20]. Therefore, a detailed analysis at both the chemical and molecular level is extremely challenging, requiring the use of innovative and advanced approaches. NGS technologies have revolutionized plant biology and have been applied widely to non-model systems with relatively low costs. This is presently facilitating the application of combined approaches, based on the relationship between genomic, transcriptomic and metabolomic methods, exploiting the inter-related data sets [29]. Multi-omics has already been used successfully to identify correlations between different biological components and metabolic pathways in several other crops [30]. These approaches have facilitated a comprehensive genetic and metabolic mapping of cannabis, allowing an accurate characterisation and further exploration of: (i) the relationships between sub-species, (ii) the relationship \begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviations** & **Definition** \\ \hline SPE & Solid Phase Extraction \\ SPME & Solid Phase Micro Extraction \\ SVs & Structural Variants \\ THC & Tetrahydrocannabinol \\ THCA & Acidic Precursors of Tetrahydrocannabinol \\ WGCNA & Weighted Gene Co-expression Network Analysis \\ WGS & Whole Genome Shotgun Sequencing \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._between genotypes and phenotypes, and (iii) novel metabolites/biomarkers in cannabis breeding programs. In this work, we review the available genomic resources, and recent genomic, transcriptomic and metabolomic tools for improving cannabis knowledge, with a focus on multi-omics approaches. With the term'multi-omics', we refer to any approach applying the principles of different -omics sciences to analyse a given issue, not necessary based on an initial integration of different omics data sets. The integration and concurrent analysis, which allows a more comprehensive and powerful interpretation of multi-omics data, is complex and requires advanced bioinformatic pipelines [31]. However, discoveries have been made through more simplistic analysis of each-omic dataset individually and then comparing the results to look for correlations. Research perspectives are examined for both these strategies, and suggestions made for possible future studies.