esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Laboratory Experiments  
Laboratory experiments comprised two consecutive test phases, whose sample programs are listed in Table 1 (Phase 1) and Table 2 (Phase 2). All experiments were performed on the previously mentioned two mine soils (medium-grained sand and fine-grained silica sand), with three replicates each (n = 3), including an untreated control (C). Both phases comprised wind tunnel (cf. Section 2.3.2) and pocket penetrometer testing (cf. Section 2.3.3). Phase 1 investigated the effect of the biopolymer concentration on wind-induced soil loss and crust penetration resistance, testing seven different biopolymer concentrations at a fixed application rate of 0.5 L/m\({}^{2}\) (Table 1). Based on Phase 1's wind tunnel tests, the 'plateau concentration' was determined for each tested biopolymer-soil combination. In this study, the plateau concentration denotes the concentration, beyond which only a marginal reduction of the wind-induced soil loss occurs. These 'plateau concentrations' were subsequently used for Phase 2, which investigated the effect of the application rate on wind-induced soil loss and crust penetration resistance, testing five different application rates (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Biopolymer/ Control**} & \multicolumn{8}{c}{**Concentration (wt\%)**} & \multicolumn{8}{c}{**Application Rate**} \\  & \multicolumn{8}{c}{**Medium-Grained Sand and Fine-Grained Silica Sand**} & \multicolumn{8}{c}{**(L/m\({}^{2}\))**} \\ \hline C & 0.00 & & & & & & & 0.0 \\ XG \({}^{\text{a}}\) & 0.05 & 0.13 & 0.25 & 0.38 & 0.63 & 0.75 & & & 0.5 \\ CS & & 0.13 & 0.25 & 0.50 & 0.75 & 1.00 & 1.25 & 1.50 & 0.5 \\ CMC & & 0.13 & 0.25 & 0.50 & 0.75 & 1.00 & 1.25 & 1.50 & 0.5 \\ FBPC & & 0.13 & 0.25 & 0.50 & 0.75 & 1.00 & 1.25 & 1.50 & 0.5 \\ WP & & 0.13 & 0.25 & 0.50 & 0.75 & 1.00 & 1.25 & 1.50 & 0.5 \\ \hline \hline \end{tabular} Note. \({}^{\text{a}}\) = XG was tested at concentrations \(\leq\) 0.75 wt%, but at finer intervals, as higher concentrations yielded too viscous solutions to be sprayable.  
\end{table}
Table 1: Sample program of Phase 1, which investigated the effect of the biopolymer concentration on wind erosion resistance and crust penetration resistance.
# A Hybrid RF/FSO Transmission System Based on a Shared Transmitter
## 2 Principle
### Channel Models
#### 2.2.3 Hybrid RF/FSO Link  
According to the hybrid system architecture diagram, the link with higher SNR should be selected for data transmission in the system to optimize communication quality, so the total SNR of the system can be expressed as the maximum SNR of the two links, which is the hybrid SNR \(\gamma_{\rm hybrid}~{}=\max(\gamma_{\rm F},\gamma_{\rm R})\). This means that, when the signal attenuation is high on one link, the system can use the signal received on the other link, thus avoiding performance impairments.  
Based on probabilistic theory, the channel statistical characteristics of the FSO and RF links are considered to be independent, and the hybrid CDF can be derived as [36, 37]  
\[\begin{split} F_{\gamma_{\rm hybrid}}(\gamma)&=P( \gamma_{\rm hybrid}\leq\gamma)=P(max(\gamma_{\rm F},\gamma_{\rm R})\leq\gamma) \\ &=P(\gamma_{\rm F}\leq\gamma)\cdot P(\gamma_{\rm R}\leq\gamma)\\ &=F_{\gamma_{\rm F}}(\gamma)\cdot F_{\gamma_{\rm R}}(\gamma)\leq \min(F_{\gamma_{\rm F}}(\gamma),F_{\gamma_{\rm R}}(\gamma)).\end{split} \tag{9}\]  
In a communication system, \(F(\gamma)\) can be interpreted as the probability that the SNR is below a certain threshold \(\gamma\), that is, the probability that a disruption occurs in the link. Based on the above equation, it is deduced that the outage probability of the converged system is lower than that of any single link, which indicates that the converged system will not be out of service as long as the signal quality of any one of the links is good enough, which improves the reliability of the system. According to the hybrid SNR \(\gamma_{\rm hybrid}~{}=\max(\gamma_{\rm F},\gamma_{\rm R})\), the hybrid system BER theoretical model can be expressed as  
\[P_{BER}^{hybrid}=P(\gamma_{F}\geq\gamma_{\rm R})\cdot P_{BER|F}(\gamma_{\rm F })+P(\gamma_{\rm R}>\gamma_{F})\cdot P_{BER|R}(\gamma_{\rm R}), \tag{10}\]  
so the hybrid BER can be expressed as  
\[\begin{split} P_{BER}^{hybrid}&=\int_{0}^{\infty} \Big{[}P_{BER|F}(x)\cdot f_{\gamma_{\rm F}}(x)\cdot F_{\gamma_{\rm R}}(x)+P_{ BER|R}(x)\cdot f_{\gamma_{\rm R}}(x)\cdot F_{\gamma_{\rm F}}(x)\Big{]}\,dx\\ &\leq\int_{0}^{\infty}\min\Big{(}P_{BER|F}(x),P_{BER|R}(x)\Big{)} \cdot\big{[}f_{\gamma_{\rm F}}(x)+f_{\gamma_{\rm R}}(x)\big{]}\,dx\\ &=\min\Big{(}E[P_{BER|F}],E[P_{BER|R}]\Big{)}.\end{split} \tag{11}\]  
Therefore, the BER expectation of the system is always less than or equal to the minimum expectation of the BER of the two links. This inequality indicates that the fusion system, by dynamically selecting the low-BER link, strictly outperforms any single link.
# Low-Power 8T SRAM Compute-in-Memory Macro for Edge AI Processors
## 4 Performance Comparison of Proposed Structures  
Figure 11a illustrates the traditional 8-transistor (8T) SRAM structure, which has been widely adopted in previous research and serves as a baseline for comparison [2]. This conventional 8T SRAM design has been extensively studied and optimized for general memory operations, such as read and write functions, but it often encounters limitations in terms of power efficiency, especially in applications requiring high-frequency operations or those constrained by energy consumption. In comparison, [PERSON] [3] introduced a novel low power 8T SRAM structure, depicted in Figure 11b, which focuses on addressing the challenges of high power consumption typically associated with traditional SRAM designs. [PERSON]'s design incorporates specific modifications aimed at reducing leakage currents and dynamic power consumption, making it a more power-efficient alternative, especially suitable for energy-constrained environments such as mobile devices and edge computing systems.  
Figure 10: Static body biasing schematic.  
In our study, we evaluated and compared the power consumption of these two existing SRAM structures--both the conventional 8T SRAM (Figure 11a) and [PERSON]'s low-power variant (Figure 11b)--with the newly proposed bit-cell structure designed as part of this research. The proposed bit-cell structure introduces innovative techniques to minimize power consumption during read and write operations while also enhancing performance in computational tasks, such as multiply-and-accumulate (MAC) operations. The comparison between these three architectures is conducted by analyzing their power consumption during both read and write cycles, with particular emphasis on the energy-saving capabilities of the proposed design. By quantifying the power efficiency of the proposed bit-cell in relation to the traditional and low-power 8T designs, this study aimed to demonstrate how architectural innovations can lead to significant improvements in power consumption, without compromising the stability or functionality of the SRAM cells. This comparative analysis not only highlights the improvements achieved by the proposed architecture but also provides insights into the trade-offs associated with each design. For instance, while [PERSON]'s low-power structure offers improvements in energy efficiency, it may introduce complexities or limitations in other aspects, such as area or operational speed. In contrast, the proposed bit-cell seeks to strike a balance between minimizing power consumption and ensuring high performance across various operational modes. By comparing the three designs, we aimed to provide a comprehensive understanding of the advantages and potential challenges associated with each structure, particularly in the context of compute-in-memory (CIM) applications, where power efficiency is paramount.
## 3 Obstacles in Oral Microbiome Studies  
### Sampling Issues  
One of the primary challenges in oral microbiome research is the unavailability of longitudinal sampling, which limits the ability to track microbial changes over time. Longitudinal studies are essential for understanding the dynamics of the oral microbiome and its role in the progression of oral diseases. Without repeated sampling, it is difficult to determine whether observed microbial changes are transient or persistent, and how they correlate with disease development or treatment outcomes [157,158]. Additionally, limited sample sizes reduce the statistical power and generalizability of findings. Small sample sizes can lead to overestimation or underestimation of microbial associations with disease states, making it challenging to draw robust conclusions [159]. The variability in the oral microbiome across different individuals necessitates larger sample sizes to capture the diversity and complexity of microbial communities accurately [159]. Studies with insufficient sample sizes may fail to detect significant differences or correlations, leading to inconclusive or misleading results [158]. The method of sample collection also poses significant limitations. The oral cavity has various niches, including saliva, tongue, buccal mucosa, dental  
Figure 1: Key factors influencing the oral microbiome (an original scheme based on current literature data).  
plaque, and subgingival areas, each harboring distinct microbial communities. Inconsistent sampling methods can lead to variability in the data, complicating comparisons across studies. Standardized protocols for sample collection, processing, and storage are crucial to ensure consistency and reliability in oral microbiome research [159, 160]. Moreover, the condition of the sample at the time of collection can affect the results. Factors such as the time of day, recent food intake, oral hygiene practices, and use of antimicrobial agents can influence the microbial composition [160]. These variables need to be controlled or accounted for in study designs and analyses of the results to minimize their impact on the findings. Another challenge is the potential contamination of samples. Oral microbiome studies are particularly susceptible to contamination from external sources, including laboratory environments and reagents used in sample processing. Rigorous controls and careful handling of samples are necessary to minimize contamination and ensure the accuracy of the results [159]. Technological limitations also play a role. While high-throughput sequencing technologies have revolutionized our ability to profile microbial communities, they come with their own set of challenges [157]. Sequencing depth, accuracy, and bioinformatic analyses can introduce biases and errors that affect the interpretation of dana [158]. The selection of sequencing platforms and analytical tools must be done with consideration of their limitations and potential impact on the study outcomes [159].
## 4 Methods for EIT Data Reduction and CNN Model Compression for Efficient Edge Deployment
### Reducing EIT Input Data Dimensionality  
In order to reduce the number of EIT measurements, our methodology presented in Figure 3 built on our previous study [38], which evaluated different dimensionality reduction algorithms. Genetic Algorithm (GA), PCA, Chi-squared tests, MRMR algorithm, and Laplacian scores were used to reduce the number of measurements from 40 to 20. In [38], it was shown that GA achieved the highest accuracy among the methods tested. However, in terms of computational time, GA was found to be extremely resource-intensive. The iterative nature of GA, involving the evaluation of multiple candidate solutions over generations, leads to high computational complexity, making it unsuitable for applications requiring real-time performance. In addition, GA can be sensitive to the choice of parameters such as population size, crossover rate, and mutation rate, which can require extensive tuning to achieve optimal results. Furthermore, the stochastic nature of the algorithm means that it does not always guarantee convergence to a globally optimal solution, especially in time-constrained applications. Due to these limitations, GA is not considered in this study as the goal is to develop a real-time system that can be implemented on edge devices where computational resources and power consumption are critical considerations. Support Vector Machine (SVM) classifiers were employed to classify EIT data both before and after the dimensionality reduction process. The SVM was selected due to its performance in high- and low-dimensional spaces, making it an appropriate choice for the feature sets of EIT data. Its ability to maximise the margin between classes ensures robust decision boundaries, reducing the risk of misclassification even with a reduced number of features.  
A further significant advantage of SVM is its capacity to process non-linear data. By projecting the input data into a higher-dimensional space, SVM can effectively separate classes using a linear hyperplane, thereby overcoming the challenges posed by the non-linear relationships inherent in EIT measurements. The Chi-square test shows a superior performance for EIT measurements using the SVM classifier due to its statistical robustness in preserving the underlying data distribution and maintaining the signal-to-noise ratio. However, alternative methods showed limitations: PCA introduced significant information loss by prioritising variance, mRMR failed to capture complex inter-measurement relationships specific to EIT, and Laplacian scores demonstrated poor feature preservation in the non-linear EIT data domain. The Chi-square method offered a unique balance between statistical significance and feature preservation, making it the most suitable approach for maintaining measurement accuracy while reducing computational complexity in EIT reconstruction algorithms.
## 2 Finite Element Simulation
### Adjacent Box Girder  
One concrete girder with a cross-section consisting of three adjacent box girders in Fuzhou, China was chosen as the case study. The thickness of the Portland cement concrete overlay was 10 cm. The dimensions of the adjacent box girder cross-sections are illustrated in Figure 7. Eight temperature sensors were installed in the top flanges (1-T and 3-T), webs (1-W and 3-W), bottom flanges (1-B and 3-B), and cells (1-C and 3-C) of the adjacent box girders #1 and #3 during the construction process, as shown in Figure 7. Due to space limitations, the data from 24 July 2017 in summer, which is a sunny day with the highest ambient air temperature during the monitoring period, as well as high solar radiation and low wind speed, were selected for analysis. The meteorological data, including ambient air temperature, hourly global solar radiation, hourly diffuse solar radiation,  
Figure 5: FEM of the small box girder cross-sections: (**a**) FEM without air elements; (**b**) FEM with air elements.  
Figure 6: Comparison of different simulation methods for the temperature curves inside the cavities.  
and wind speed, were measured using a movable automatic meteorological station at the site with a measurement time interval of one hour.  
The midas FEA software was chosen to establish a FEM of the adjacent box girder cross-sections by using two-dimensional plane strain elements, as shown in Figure 8. The four simulation methods for the internal thermal boundary conditions summarized in Section 1 were considered in the FEM of the adjacent box girders. For the Measured Temperature Method, the measured hourly temperature curves inside the cell of the adjacent box girder (24 July 2017) obtained from temperature sensors 1-C and 3-C were chosen, as shown by the hollow points in Figure 9. For the Ambient Temperature Method, the measured hourly ambient air temperature curve (24 July 2017) was used, as shown by the solid line in Figure 9. For the Mean Temperature Method, the mean temperature (35.7 \({}^{\circ}\)C) over the previous two days (23 and 24 July 2017) was calculated, as shown by the dashed line in Figure 9. The mesh size of the FEMs using the Measured Temperature Method, Ambient Temperature Method, or Mean Temperature Method was set as 20 mm. The total numbers of nodes and elements were 4,459 and 4,211, respectively, as illustrated in Figure 8a. For the Air Element Method, the total numbers of nodes and elements for the FEM were 4954 and 4868, respectively, as illustrated in Figure 8b. Other detailed information was the same as the FEM of the box girder reported in Section 2.1.  
Figure 8: FEM of the adjacent box girder cross-sections: (**a**) FEM without air elements; (**b**) FEM with air elements.  
Figure 7: Layout of the adjacent box girder cross-sections (unit: cm).
# Power Consumption Modeling of Discontinuous Reception for Cellular Machine Type Communications
## 7 Conclusions  
In this paper, we proposed a semi-Markov chain model for analyzing the DRX mechanism in cellular MTC networks. The proposed analytical model was verified through simulation results. The performance metrics, such as the power-saving factor and wake-up Latency, were modeled with high precision and can be used for further research in the area of power saving for MTC communications, e.g., in Reference [31].  
We further investigated the influence of various DRX-related parameters on the performance of the power-saving factor and wake-up latency. Our results showed that the packet inter-arrival time and the tail time influenced the \(P_{S}\) factor the most. The \(P_{S}\) factor was significantly increased and decreased for the greater values of packet inter-arrival time and the tail time, respectively. Moreover, the on duration and long cycle timers notably influenced the \(P_{S}\) factor. On the other hand, the wake-up latency is maximum-influenced by the inter-arrival time and the on duration timer. However, the wake-up latency significantly decreases when the tail time increases.  
In the future, the proposed semi-Markov model can also be adapted for modeling of the battery life time of a device. Consequently, the operating time of a device can be estimated with high precision.  
Funding acquisition, A.F.; Methodology, Y.M.; Project administration, A.F.; Validation, Y.M. and L.Z.; Writing--original draft, Y.M.; Writing--review and editing, L.Z. and A.F.  
This research was funded by the European Union programme Erasmus Mundus FUSION (Featured eUrope and South asla mObility Network) Strand 1, Lot 11, Grant Agreement Reference number 2013-2541/001-011 EM Action 2 partnerships, and the APC was funded by the University of Bremen.  
We thank the International Graduate School for Dynamics in Logistics (IGS), doctoral training group of Log_Dynamics_, University of Bremen, Germany, for the financial support of this work.  
The authors declare no conflict of interest.  
Figure 10: Performance analysis of the DRX mechanism for a different number of \(N\). Other parameter settings include \(\frac{1}{\lambda}=60\) s, \(T_{l}=20\) ms, \(T_{ON}=40\) ms, \(T_{SC}=0.64\) s, \(T_{LC}=1.28\) s, \(T_{RC}=260\) ms, and \(T_{tail}=11.576\) s.
# Predicting Output Power for Nearshore Wave Energy Harvesting
###### Abstract  
Energy harvested from a Wave Energy Converter (WEC) varies greatly with the location of its installation. Determining an optimal location that can result in maximum output power is therefore critical. In this paper, we present a novel approach to predicting the output power of a nearshore WEC by characterizing ocean waves using floating buoys. We monitored the movement of the buoys using an Arduino-based data collection module, including a gyro-accelerometer sensor and a wireless transceiver. The collected data were utilized to train and test prediction models. The models were developed using machine learning algorithms: SVM, RF and ANN. The results of the experiments showed that measurements from the data collection module can yield a reliable predictor of output power. Furthermore, we found that the predictors work better when the regressors are combined with a classifier. The accuracy of the proposed prediction model suggests that it could be extremely useful in both locating optimal placement for wave energy harvesting plants and designing the shape of the buoys used by them.  
renewable energy; machine learning; wave energy converter; regression Article
## 4 Experimental Setup
### Research Questions  
To evaluate the proposed approach, we design two research questions (RQs) to conduct the evaluation. In RQ1, we compare the effectiveness of machine learning models (including deep learning models) that could be embedded in our approach; In RQ2, we evaluate the ability of test case prioritization.  
* **RQ1. Can we find a better ranking model to prioritize test cases for CAD software?** To save the time cost of defect detection, developers want to find out defects within a limited time. That is, developers need to automatically prioritize test cases before executing them. In this paper, we design RQ1 to explore the effectiveness of machine learning models in the model of test case prioritization.  
* **RQ2. How effective is the proposed approach in test case prioritization in testing CAD software?** In CAD software, a single test case may cover multiple functions, and a single function may be covered by multiple test cases. Therefore, it is difficult for developers to determine which test case should be executed first. We design RQ2 to evaluate the ability of test case prioritization in testing CAD software.