esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Physiological Parameters of Sygurt
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Samples** & **pH** & **Acidity (\%)** & **Fat (\%)** & **Total Solid (\%)** & **Total Protein (\%)** \\ \hline S1 & 4.50 \(\pm\) 0.04 \({}^{\textbf{a}}\) & 0.89 \(\pm\) 0.05 \({}^{\textbf{a}}\) & 3.11 \(\pm\) 0.12 \({}^{\textbf{a}}\) & 16.48 \(\pm\) 0.13 \({}^{\textbf{b}}\) & 3.71 \(\pm\) 0.07 \({}^{\textbf{a}}\) \\ S2 & 4.49 \(\pm\) 0.07 \({}^{\textbf{a}}\) & 0.90 \(\pm\) 0.03 \({}^{\textbf{a}}\) & 3.10 \(\pm\) 0.09 \({}^{\textbf{a}}\) & 16.45 \(\pm\) 0.15 \({}^{\textbf{b}}\) & 3.49 \(\pm\) 0.04 \({}^{\textbf{d}}\) \\ S3 & 4.51 \(\pm\) 0.05 \({}^{\textbf{a}}\) & 0.88 \(\pm\) 0.07 \({}^{\textbf{a}}\) & 3.09 \(\pm\) 0.10 \({}^{\textbf{a}}\) & 16.57 \(\pm\) 0.12 \({}^{\textbf{ab}}\) & 3.55 \(\pm\) 0.06 \({}^{\textbf{cd}}\) \\ S4 & 4.50 \(\pm\) 0.02 \({}^{\textbf{a}}\) & 0.89 \(\pm\) 0.04 \({}^{\textbf{a}}\) & 3.07 \(\pm\) 0.11 \({}^{\textbf{a}}\) & 16.64 \(\pm\) 0.10 \({}^{\textbf{ab}}\) & 3.59 \(\pm\) 0.09 \({}^{\textbf{bc}}\) \\ S5 & 4.52 \(\pm\) 0.08 \({}^{\textbf{a}}\) & 0.87 \(\pm\) 0.08 \({}^{\textbf{a}}\) & 3.05 \(\pm\) 0.07 \({}^{\textbf{a}}\) & 16.83 \(\pm\) 0.18 \({}^{\textbf{a}}\) & 3.65 \(\pm\) 0.05 \({}^{\textbf{ab}}\) \\ \hline \hline \end{tabular}  
* S1—control plain yogurt; S2—probiotic yogurt with 0.5% _S. boulardii_; S3—symbiotic yogurt with 0.5% _S. boulardii_ + 1% inulin; S4—0.5% _S. boulardii_ + 1.5% inulin; S5—0.5% _S. boulardii_ + 2% inulin. Different letters superscripted denote statistical difference (\(p<0.05\)) within a column.  
\end{table} 
Table 1: Physiological parameters of yogurt samples before cold storage. Figure 1: Change of pH and acidity (**A**), syneresis (**B**), and fat content (**C**) of the yogurt samples during storage at 4 \({}^{\circ}\)C for 28 days: Control plain yogurt (S1), probiotic yogurt with 0.5% _S. boulardii_ (S2), symbiotic yogurt with 0.5% _S. boulardii_ + 1% inulin (S3), 0.5% _S. boulardii_ + 1.5% inulin (S4), 0.5% _S. boulardii_ + 2% inulin (S5). Different letters denote statistical differences (\(p<0.05\)).
# On the Modeling of Automotive Security: A Survey of Methods and Perspectives
## 3 Automotive Security Models
### Security Modeling Methods for Automotive Industry
#### 3.1.1 Evita  
A security process is described in EVITA project and a security model proposed to analyze the risks of a vehicular IT security system in [84]. The threats are investigated and modeled from the dark-side scenarios. The security requirements are derived based on a set of use cases. Since the risk is determined by the probability of a successful attack and the damage effects caused by the attack, the proposed method took both of these two factors into account. It calculated the attack potential by using the Common Methodology for Information Technology Security Evaluation (CEM) [85]. The CEM method is used for the Common Criteria for Information Technology Security Evaluation (CC) [86]. The likelihood of mounting a successful attack depends on the parameters such as: the available information and the access to the target, the expertise and the tools of the attacks, and the elapsed time the attack takes. The attack paths to achieve attack objectives can be identified with attack trees. Moreover, the paper presented a method to compute the damage potential including four factors as: safety, finance, privacy, and operational performance for the automotive security domain. Among them,safety is a leading factor to determine the severity. The safety damage potential is valued according to automotive functional safety in [87]. In [88], the authors summarized a risk matrix based on the EVITA method. The risk matrix is designed by taking the example from the railway safety engineering to indicate the risk acceptance values, from negligible to unacceptable [89]. The paper also pointed out that the security threats and attack paths are identified from high-level security objectives, which are derived from the security questionnaires. This risk assessment approach can provide the automotive developers and the manufacturers with a systematic method to balance between costs and security risks and to make justifiable decisions effectively. The presented approach has been applied by some automotive manufacturers and been proven by a few projects. An automotive testing and evaluation methodology is proposed and validated for the case study of automotive Bluetooth interface [90]. The testing is carried out based on the EVITA threat model and the attack trees after the analysis. The main elements and process of the EVITA methodology are summarized and illustrated in Figure 3.
# Conceptual Design Evaluation Considering Confidence Based on Z-AHP-TOPSIS Method
## 3 Materials and Methods
### Fuzzy Linguistic Approach
#### 3.1.1 Fuzzy Set  
\(A\) fuzzy set is described by a membership function mapping the elements of a universe \(X\) to the unit interval [0, 1] and is defined below [6]:  
\[A\colon X\rightarrow[0,1], \tag{1}\]  
It can also be described as a set of ordered pairs of the form:  
\[A=\{(x,\mu_{A}(x)|\forall\in X,\mu_{A}(x)\in[0,\ 1])\}, \tag{2}\]  
where \(x\) is an element of \(X\) and \(\mu_{A}(x)\) denotes its corresponding degree.  
In conceptual design evaluation, information about incomplete evaluation is often expressed as the fuzzy number in a normal fuzzy set [36]. In many fuzzy number representations, trapezoidal fuzzy number (TpFN) and triangular fuzzy number (TFN) are often used because they are easy to calculate [37].  
A TpFN (\(a,b,c,d\)) is defined as follows:  
\[\mu(x)=\left\{\begin{array}{l}0,\ x<a\\ \frac{x-a}{b-a},\ a\leq x\leq b\\ 1,\ b\leq x\leq c\\ \frac{d-x}{d-c},\ c\leq x\leq d\\ 0,\ x>d\end{array}\right., \tag{3}\]  
A TFN (\(a,b,c\)) is defined as follows:  
\[\mu(x)=\left\{\begin{array}{l}0,\ x<a\\ \frac{x-a}{b-a},\ a\leq x\leq b\\ \frac{c-x}{c-b},\ b\leq x\leq c\\ 0,\ x>c\end{array}\right., \tag{4}\]
# Whole-Genome Alignment: Methods, Challenges, and Future Directions
## 3 Algorithmic Aspects of WGA Algorithms  
### Performance Characteristics  
The performance of whole-genome alignment (WGA) algorithms is often evaluated based on their speed, memory usage, scalability, and accuracy [44]. The speed at which an algorithm can process and align genomes is crucial in handling the ever-increasing size of genomic data. Memory usage determines the size of genomes or datasets that the algorithm can handle at once [45].  
Scalability in the context of WGA algorithms refers to the ability of an algorithm to handle increasing volumes of data without a proportional increase in computational resources. This is a critical characteristic, especially when dealing with larger and more complex genomes [46].  
The accuracy of WGA algorithms is usually assessed via comparing the generated alignment with a reference alignment or evaluating the biological relevance of the alignment. Accuracy is influenced by the algorithm's ability to correctly identify homologous regions, handle genomic rearrangements, and align divergent sequences [39; 47].  
Figure 11: GMAP algorithm pseudo-code.
## 5 Applications
### Process Control  
Process control has evolved as one of the most important fields of application for LD based gas sensing. One interesting sub-category thereof is combustion control. By monitoring the concentrations of gases generated during combustion processes, such as CO, CO\({}_{2}\), O\({}_{2}\), NH\({}_{3}\), NOx and SO\({}_{4}\), precise information about the conditions inside the combustion chamber can be obtained. This information allows for a very efficient adjustment of the process conditions resulting in less pollutant emission and higher process efficiency. Usually such measurements are performed at some distance to the burning chamber, e.g., inside a chimney or an exhaust pipe. Consequently there is a time offset depending on how long the gases need from the burning chamber to the measurement setup.  
Diode laser based gas sensing however can be employed directly in the combustion chamber, hence enabling zero-offset measurements resulting in very fast and efficient process adjustment. Furthermore the gas measurement itself is very fast and selective due to the monomode emission of the DFB laser diodes [60,61]. Using information derived from the temperature dependent broadening of gas absorption lines, even the temperature in the combustion chamber may be monitored in-situ.  
Another example that should be mentioned is plasma control. DFB LD and DFB QCL have been shown to be very useful for the in-situ monitoring of industrial etch processes [43,62]. This enables not only the precise control of the gas composition in the reactor ensuring highly reproducible results, but also the detection of the successful removal of layers of the sample as the etching process proceeds and changes of the concentration of volatile etch products occur.
# Open Radio Access Networks For Smart IoT Systems: State of Art and Future Directions
## 4 Challenges and Future Research Directions
### Future Research Directions
#### 4.2.3 Explainable AI for IoT and O-RAN Integration  
One critical problem governing AI models and algorithms in AI-based O-RAN decision making requires transparency. This causes difficulties for network operators in identifying issues and root causes and further controlling the network with desired behaviors. Consequently, incorporating XAI into the O-RAN system management operations process to learn how AI/ML models and algorithms make decisions and find ways to validate them. Thus, to explain their outputs explicitly, running AI-based rApps/xAppsshould contain XAI approaches. This would enhance the accuracy and openness of outcomes made by these systems and the network operators optimize the functionalities of networks, leading to efficient network operations and services to IoT applications. For instance, rApps/xApps can be developed with large language model (LLM) capabilities to provide on-demand and real-time feedback to IoT systems.  
Developing a naturalistic AI solution for O-RAN will involve integrating natural language understanding and generation capabilities with O-RAN infrastructure to enhance network management, troubleshooting, and communication. This will make it easier for network administrators to interact with complex networking infrastructures using natural language and improve network operations' responsiveness and accuracy. Several factors shall be considered before bringing such solutions to fruition [104].
# Evaluation of HY-2 Series Satellites Mapping Capability on Mesoscale Eddies
## 3 Results
### Comparing OI Gridded Product  
We interpolated the altimetry satellite along ground tracks to gridded products by an OI analysis. Then, we tuned the parameters in the mapping procedure and obtained the best restoration of the gridded products.  
The comparison results of the SSH fields are shown in Figure 4. The differences between the reference groups and evaluation groups are (Figure 4a) 1.16 cm and (Figure 4b) 1.07 cm at high latitudes; (Figure 4d) 5.04 cm and (Figure 4e) 3.87 cm at middle latitudes; and (Figure 4g) 1.95 cm and (Figure 4h) 1.75 cm at low latitudes. This indicates that the evaluation groups have an overall improvement of 8%, 23% and 10% at high, middle and low latitudes due to the addition of HY-2 series satellites. Similarly, the RMSE values for the reference groups and evaluation groups are 1.52 cm and 1.40 cm at high latitudes, 8.16 cm and 6.24 cm at middle latitudes, and 2.57 cm and 2.31 cm at low latitudes, with a difference of approximately 0.12 cm, 1.92 cm and 0.26 cm, respectively. It is worth noting that the errors mainly occurred at the transition regions between cold eddies and warm eddies, and the increase in orbital coverage had a pronounced enhanced effect on areas with high SSH variability.  
Figure 4: Sea surface height (SSH) products for models, reference groups and evaluation groups at (**a–c**) high latitudes, (**d–f**) middle latitudes, and (**g–i**) low latitudes. Unit of SSH in cm.
## 3 Ground Landing Station Design
### Battery Storage System  
A horizontal circular battery array is used to replace the batteries. The proposed storage array is divided into two parts: a circular plate with battery slots and a mechanic base where the plate sits. The plate has six battery slots which consist of small slopes of 45\({}^{\circ}\) and 7 mm height, where the battery holders fit. The center and outermost parts of the slots are hollowed to let the battery swapping system work. Since one slot must always be empty to receive an incoming discharged battery, this means that the array can replace different batteries. The bottom side of the plate is a six-positioned carousel with a bearing guide, as seen in Figure 4.  
As described in Figure 4, the bottom part of the battery storage system consists of a circular 20 mm thick wall with three wheels on top of it. The wheels are evenly distributed on the wall, meaning they are located at 120\({}^{\circ}\) from each other. In the center of the base, a NEMA 32 stepper motor is located, which directly rotates the plate.
# Greenhouse Temperature Prediction Based on Time-Series Features and LightGBM
## 2 Related Work
LightGBM not only applies a series of environmental factors, such as humidity, air pressure, and outdoor temperature, to the prediction of the greenhouse temperature, but also comprehensively considers temporal and nonlinear changes in indoor and outdoor temperature. Adopting environmental factors and a time-series feature extraction method proposed in the literature [14], a greenhouse temperature prediction model is established to make a more accurate prediction of the greenhouse temperature and thus improve the quality and yield of crops and avoid unnecessary economic losses resulting from inaccurate prediction.
# An Improved Monte Carlo Reliability Analysis Method Based on BP Neural Network
## 3 Calculation Theory of Improved BP-MCS Method
### High-Precision BP Neural Network Prediction Model
For the first iteration, all the sample points with \(G^{\prime}(X_{i})\) close to 0 can be taken as the validation set and put into FEM to calculate the response, and the error between the predicted value and the real value can be compared. For subsequent iterations, the number of sample points in the validation set can be 15% of the number of training sets each time [24], and at this time, the validation set takes the sample points with large weight and \(G^{\prime}(X_{i})\) close to 0. If the BP model meets the required prediction accuracy, output the model. Otherwise, add the sample points from the validation set to the training set, return to step (3) to generate a new prediction set, and retrain the BP model. Repeat the above steps until the prediction accuracy meets the requirements. When training the BP model, its fitting and prediction accuracy can be evaluated using the determination coefficient (\(R^{2}\)), mean absolute error (\(MAE\)), and mean bias error (\(MBE\)). Higher model accuracy is indicated by \(R^{2}\) values approaching 1, with \(MAE\) and \(MBE\) values close to 0 [29]. Prior to using the BP model for prediction, it must demonstrate robust fitting capability on the training set to enhance prediction accuracy. Therefore, the BP model should satisfy the following criteria on the training set before proceeding to further accuracy testing: \(R^{2}>99\%\), \(MAE<0.1\) and \(MBE<0.01\). When verifying the model's accuracy on the validation set, the BP model must meet the following standards to ensure reliable failure probability calculations: \(R^{2}>98\%\), \(MAE<0.1\) and \(MBE<0.05\). \[R^{2}=1-\frac{\sum_{i=1}^{N}\left(G(X_{i})-G^{{}^{\prime}}(X_{i})\right)^{2}}{ \sum_{i=1}^{N}\left(G(X_{i})-\overline{G(X_{i})}\right)^{2}} \tag{8}\]  
\[MAE=\frac{1}{N}{\sum_{i=1}^{N}\left|G^{{}^{\prime}}(X_{i})-G(X_{i})\right|} \tag{9}\]  
\[MBE=\frac{1}{N}{\sum_{i=1}^{N}\left(G^{{}^{\prime}}(X_{i})-G(X_{i})\right)} \tag{10}\]
# Congestion Control and Traffic Differentiation for Heterogeneous 6 TiSCH Networks in IIoT
## 4 Multi-Queue Model and Priority-Based Transmission
### Mathematical Analysis  
In the following, we present a queuing analysis of the proposed multi-queue model where we mathematically formulate the average queue waiting time \(\overline{W_{Q_{i}}}\). The formulated \(\overline{W_{Q_{i}}}\) corresponds to an arbitrary packet in Q\({}_{i}\), which will be later referred as the tagged packet. The average queue waiting time of a packet is defined as the time elapsed from the instant the packet is received by the node until the instant it leaves the queue. Each queue in the proposed multi-queue model is represented by a finite-capacity queuing system with a finite storage of \(K\)[31]. The arrival rate to each queue is modeled based on the generation nature of the corresponding traffic. Since T\({}_{1}\) and T\({}_{2}\) are acyclic in nature, we model the packet arrivals to Q\({}_{1}\) and Q\({}_{2}\) as a Poisson process with parameters \(\alpha_{1}\) and \(\alpha_{2}\), respectively. T\({}_{3}\) traffic is cyclic; thus, packets arrive at Q\({}_{3}\) periodically with a rate \(\alpha_{3}\). In the TSCH schedule, the service time for all packets in each queue is deterministic and equals \(T_{c}\)[38], where \(T_{c}\) is the horizontal length of the TSCH schedule, i.e., the number of time slots per channel. Therefore, Q\({}_{1}\) and Q\({}_{2}\) represent an M/D/1/Kqueuing system, while Q\({}_{3}\) represents a D/D/1/Kqueuing system [39].  
According to our multi-priority model, packets from Q\({}_{1}\) are always transmitted first following the EDF scheduling policy, i.e., the packet with the shortest deadline will be transmitted first. We consider the general case that the packets in Q\({}_{1}\) and Q\({}_{2}\) may join the queue with different deadlines. For instance, considering two packets of the T\({}_{1}\) type in oil and gas industries, a packet that corresponds to a fire alarm may have a deadline that is different from that of a packet generated to alert about excessive pressure in a pipe. However, our proposed analysis can be simplified to a simple case of all packets within the same traffic category having the same deadline. The basic idea in the following mathematical formulations is to estimate the average number of higher priority packets with respect to an arbitrary tagged packet that would be transmitted ahead of it.
## 2 Problem Modeling and Analysis
### Reinforcement Learning-Based Global Path Planning Paradigm  
Global path planning involves converting raw environmental data into grid-based representations through SLAM algorithms, thereby transforming the problem into grid-optimized trajectory identification (Figure 3). This entails determining optimal routes between arbitrary start and goal positions within mapped environments. Conventional approaches often employ map-matching or trajectory-following techniques to localize robotic positions before initiating path optimization procedures.  
Figure 1: A schematic diagram of the electroencephalogram classification of the robot’s movement direction and different actions in the grid map, the ultimate goal of the robot’s movement is to reach the destination marked by the yellow star.  
Figure 2: Flowchart of the reward function for inverse reinforcement learning.  
Reinforcement learning algorithms demonstrate efficacy in simple grid-world scenarios through appropriately tuned reward/penalty mechanisms (e.g., step-count penalties, collision penalties, and goal-reach rewards). However, complex environments necessitate alternative strategies. Inverse reinforcement learning offers a viable solution by inferring latent reward structures from expert demonstrations, thereby enabling navigation policies that encapsulate human-like decision-making tendencies.
## 4 Analysis and Results
### Model Interpretation  
According to the model comparison results, the best customer churn prediction model was created from the random forest algorithm, which had a prediction accuracy of close to 90%. After that, to address with the black-box nature of machine learning algorithms, this paper illustrates the results of customer churn prediction models through two explainable AI tools, including LIME and SHAP.
## 3 Results and Discussions
### Data Set Building
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Sample number & 110 in total, 48 from diabetic subjects, 62 from nondiabetic subjects \\ \hline Sample size for PA signal acquisition & Approximately 15 mm diameter \\  & 5–6 mm thickness \\ \hline Microscope image & Olympus IX-81 fluorescent microscope, captured as multiple \\  & TIFF-gray-scale images \\ \hline Image] & Version 1.42, National Institutes of Health and University of \\  & Wisconsin, Madison, WI, USA \\ \hline Optical Parametric Oscillator & OPO, Vibrant 532 I, Opotek, Carlsbad, CA, USA \\ \hline Nd:YAG pulsed laser & Brilliant B, Quantel, Bozeman, MT, USA \\ \hline Laser beam & 1210-nm wavelength, 1.5-cm diameter, 10-m] pulse energy, \\  & 5-ns pulse duration \\ \hline Needle hydrophone & 5 cm, 0.5 mm\({}^{2}\), NIH Resource Center for Medical Ultrasonic \\  & Transducer Technology, University of Southern California, United \\  & States, detection bandwidth of 52.5 MHz was centered at 35 MHz \\ \hline Preamplifier & 18 dB ZFL-1000+, Mini-Circuits, Brooklyn, NY, USA \\ \hline Amplifier & 40 dB, 5072 PR, Parametrics, Waltham, MA, USA \\ \hline Oscilloscope & TDS 540, Tektronix, Beaverton, OR, USA \\ \hline Sampling rate & 250 MS/s \\ \hline \end{tabular}
\end{table} 
Table 1: Experiment parameter setting. Figure 3: Data augmentation. (**a**) Raw signal was divided into three parts based on time. (**b**) Signal from (**a**) with noise and its power spectrum in log scale before and after normalization. domain by making magnitudes of all frequency components divided by their sum, which can be defined as  
\[P_{norm}(k)=\frac{P(k)}{\sum\limits_{i=0}^{N-1}P(i)},k=0,1,\ldots,N-1, \tag{4}\]  
where \(P(k)\) is the power spectrum of the PA signal and \(P_{norm}(k)\) is the power spectrum after normalization. One example of power spectrum in log scale are shown in Figure 3b. In this study on human adipose tissue specimens, we focused on the spectral band of 0.5 to 24.5 MHz. The minimum value of 0.5 MHz was chosen to remove the low frequency noise below 0.5 MHz which was mainly caused by the laser illumination on the surface of the hydrophone. The maximum value of 24.5 MHz was chosen because the power spectrum before normalization reached the noise level (\(-40\) dB) at this frequency. We aimed to test the validity and potential of deep learning method for adipocyte size evaluation based on the dataset.
###### Abstract  
Evaluating the brittleness of rocks or rock masses is a fundamental problem in geotechnical engineering. This study proposed a new index that expresses brittleness as the rate of damage development in rock. The brittleness index was derived from statistical damage theory. It depends on the four material parameters, i.e., the peak strain, peak strength, Poisson's ratio and elastic modulus. The validity of the proposed brittleness index was confirmed through two case studies, including triaxial compression test results for coals subjected to varying confining pressures and for sandstones at various temperatures. Uniaxial compression experiments were then performed on rock-like materials to examine the effects of model size and joint dip angle on rock brittleness using the proposed brittleness index. Results show that the brittleness of the jointed specimens varies in a complex pattern with the model size and joint dip angle. Generally, the brittleness index initially reduces and then grows with the increasing joint dip angle, and larger specimens tend to be more brittle. Furthermore, large specimens containing horizontal or vertical joints are particularly susceptible to brittle damage. The proposed brittleness index has merits such as a clear physical meaning and simple expression, making it a valuable tool for evaluating rock brittleness.  
brittleness index; damage mechanics; statistical damage theory; size effect; joint dip angle