esa-sceva/satcom-chunk-collection huggingface dataset:
# VDGA-Based Resistorless Mixed-Mode Universal Filter and Dual-Mode Quadrature Oscillator
## 2 Basic Concept of VDGA  
This study utilizes VDGAs as active building blocks in the design. The VDGA component features two voltage input terminals (p and n) characterized by high input impedance, three current output terminals (z+, z\(-\), and x) with high output impedance, and one voltage output terminal (w) exhibiting low output impedance. The differential input voltage, (\(v_{p}-v_{n}\)), is converted into current by the transconductance gain, \(g_{mA}\), which flows through the output terminal z+ (\(i_{z+}\)), while an inverted current flows via the terminal z\(-\) (\(i_{z-}\)). The voltage at terminal z+ (\(v_{z+}\)) is transformed into current at terminal x (\(i_{x}\)) by the transconductance gain \(g_{mB}\) and is simultaneously sent to terminal w (\(v_{w}\)) with a voltage gain \(\eta\). Figure 1 shows the VDGA representation and its CMOS realization with its ideal functionality expressed as follows:  
\[\begin{bmatrix}i_{z+}\\ i_{z-}\\ i_{x}\\ v_{w}\end{bmatrix}=\begin{bmatrix}g_{mA}&-g_{mA}&0\\ -g_{mA}&g_{mA}&0\\ 0&0&-g_{mB}\\ 0&0&\eta\end{bmatrix}\begin{bmatrix}v_{p}\\ v_{n}\\ v_{z+}\end{bmatrix}. \tag{1}\]  
Figure 1: Voltage differencing gain amplifier (VDGA). (**a**) Circuit representation; (**b**) CMOS realization.  
Here, \(g_{mk}\) (where \(k=A\), \(B\), \(C\)) and \(\eta\) are the transconductance gain and the transfer voltage gain of the VDGA, respectively. Generally, the \(g_{mk}\) value is electronically tunable via an external bias current, simplifying the design of circuit parameters. Electronic control becomes crucial when circuit design parameters are altered and the integrated circuit is constructed. According to the CMOS VDGA circuit in Figure 1b, the voltage gain \(\eta\) is equal to the ratio of \(g_{mB}\) to \(g_{mC}\) (or \(\eta=g_{mB}/g_{mC}\)).
# Comparative Study on Physicochemical and Nutritional Qualities of Kiwifruit Varieties
## 3 Results
### Principal Component Analysis (PCA)  
As seen in Table 2, the 11 quality indicators of the 14 kiwifruit varieties were subjected to principal component analysis and the Kaiser-Meyer-Olkin score was 0.545, demonstrating that these data were suitable for principal component analysis. Three principal components were obtained. The eigenvalues were greater than PCA 1. The total contribution was 79.98%, which represented most of the information of all indicators, in 14 kiwifruit varieties, and can be used as a comprehensive indicator for kiwifruit evaluation.  
In order to better explain the relationship between the quality indicators and the principal component factors, we rotated the extracted principal component factors, the magnitude of load reflected the importance of each variable in the principal component, and the analysis results are shown in Table 3. The first principal component was a combination of chlorophyll, carotenoids, flavonoids, lipid, organic acids, VC and pectin, which reflected the color characteristics and nutritional qualities of kiwifruits. The second principal component, including energy, dietary fiber and soluble sugars, reflected the nutritional quality and flavor of kiwifruits. The third component contained protein, which mainly reflected the rate of the fruit's catalytic metabolism. The results of the calculation of the scores and the overall ranking are shown in Table S1. The top four highest-ranking varieties among the 14 varieties of kiwifruit were 'Huate', 'MHTX', 'Jinkui' and 'Xuxiang', respectively. Therefore, we can focus our attention on 'MHTX' and 'Huate' and vigorously develop their nutritional value to provide processed kiwifruit products for consumers. Furthermore, 'Jinkui' and 'Xuxiang', as major commercial cultivars, could be vigorously developed to utilize their nutritional value for human health.
# Hyperparameter Optimization of Ensemble Models for Spam Email Detection
## 5 Conclusions  
This study evaluated and compared the performance of two ensemble models based on the random forest and extreme gradient boost ensemble algorithms. Baseline random forest and XGBoost spam detection models were developed based on the train/test split technique using the default parameters. The grid-search technique with 10-fold cross-validation was applied to search the hyperparameter space to determine the optimal hyperparameter values that optimized the performance of the random forest and XGBoost models. The performance of the baseline models was evaluated and compared with that of the tuned random forest and XGBoost models to examine the impact of hyperparameter tuning. The findings revealed that hyperparameter tuning improved the performance of the random forest and XGBoost models. The results also showed that the tuned XGBoost model outperformed the tuned random forest model for all metrics evaluated. The effectiveness of these ensemble models in spam email detection and classification was demonstrated.  
It will be interesting to compare the XGBoost model and deep-learning models for spam detection in a future study in a bid to gain further insight into the development of efficient and effective spam email detection systems.  
It is important to note that the distribution of classes in the dataset used in this study is complementarily balanced. The behavior of these models will be different with a significantly imbalanced dataset. Future studies will look at the performance of these models on an imbalanced dataset.  
Conceptualization, T.O.O.; methodology, T.O.O. and D.O.O.; software, T.O.O.; validation, T.O.O. and D.O.O.; formal analysis, T.O.O. and D.O.O.; investigation, T.O.O.; resources, T.O.O. and D.O.O.; data curation T.O.O. and D.O.O.; writing--original draft preparation, T.O.O. and D.O.O.; writing--review and editing, T.O.O. and D.O.O.; visualization, T.O.O. and D.O.O.; supervision, T.O.O.; project administration, T.O.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are openly available in GITHUB at [[https://github.com/[PERSON]/enron_spam_data](https://github.com/[PERSON]/enron_spam_data)]([https://github.com/[PERSON]/enron_spam_data](https://github.com/[PERSON]/enron_spam_data)) (accessed on 17 August 2022). Application for consent to use the dataset is not required.  
The authors declare no conflict of interest.
# Towards a Single Integrative Metric on the Dynamics of Social-Environmental Systems
They concluded these indices had limited explanatory power, and thus were not useful, if not altogether misleading, for policy development and evaluation. More recently, [PERSON] (2020) pointed out deficiencies in ecological indicators and derived a Sustainability Development Index (SDI), which is comprised of HDI, total \(\text{CO}_{2}\) emissions at the national level, and the material footprint of countries [13]. While this is an important step forward, the use of \(\text{CO}_{2}\) emissions in the SDI calculation only considers energy consumption and does not include emissions arising from ecosystem processes. Thus, while a plethora of indices exist, none were able to reliably represent all three SES pillars in a way that would contribute to impact assessments or policy [33; 34; 35]. In seeking a new, integrated metric on SES (IMoSES), we applied the following criteria: (1) quantitative metrics for each of the three sustainability pillars (social, economic, and ecological systems), and (2) annually available data to facilitate comparisons through time. Ecosystem production (e.g., NPP), GDP and POP are among the most widely recognized and applied measurements in ecosystem science, macroeconomics, and the social sciences, respectively (Table 1). IMoSES is an integration of GDP, POP, and NPP in the context of land area (A, km\({}^{2}\)) with critical resource input (e.g., evapotranspiration) as the regulator (i.e., the denominator in Equations (3)-(5)). We calculate IMoSES across the Asian Drylands Belt (ADB) as a proof of concept to demonstrate its use and interpretation in understanding SES dynamics. The ADB region is used because of (1) data availability after QA/QC in our labs and (2) the dramatic shifts in recent decades in social, economic, and ecological systems. Countries in the ADB include the Newly Independent States (NIS) formed by the breakup of the Soviet Union. Many of them have experienced periods of armed conflict and/or profound social changes that have initiated a series of economic changes and shifts in livelihoods. Physically, the region has also experienced higher-than-global average warming and reduction in water vapor influx [36], more frequent climatic extremes such as severe winters/_dzuds_[37], heatwaves [38], and large-scale dust storms [39].
## 6 Evaluation, Benefits, and Limitations of GA-Based Routing  
GA-based routing offers several advantages in the context of routing for EnFVs. Firstly, GAs are highly effective in addressing complex optimization problems with multiple objectives, allowing for the simultaneous consideration of various critical factors.  
A comprehensive quantitative performance evaluation of GA-based routing was conducted in [16] for optimizing route selection within the EnFVs paradigm. This evaluation treated energy consumption and transmission rates as a MOO problem, and GA was employed to select the best optimal solution from the Pareto front. The assessment was carried out using real New York City traffic trace data, where it achieved an impressive 90% packet delivery ratio with GA. In contrast, three state-of-the-art solutions yielded packet delivery ratios ranging from 50% to 70%. It is worth noting that the optimal route chosen by GA incurred a slightly longer connection time, approximately 4-5 s, compared to all existing solutions. This outcome is particularly significant considering the high mobility of the flying vehicles involved, which travel at speeds of 10-50 m per second. A connectivity advantage of more than five seconds represents a substantial gain in EnFV routing performance. However, it is crucial to acknowledge that genetic algorithm-based routing comes with its share of limitations and challenges. Notably, the management of trade-offs between multiple objectives demands careful consideration since optimizing one objective may necessitate compromises in others.  
In spite of these limitations, genetic algorithms remain an invaluable tool for multi-objective routing in electric and flying vehicle systems. The following sub-sections delve into the benefits, limitations, and trade-offs associated with GA-based routing in more detail.
# A Reliability Engineering Approach for Managing Risks in CubeSats
## 3 Materials and Methods  
The first step of an FMECA consists in structuring the problem, which means to build a model for representing the system at hand. Traditionally, these models rely on a number of assumptions, including the calculation of parameters whose numerical values are assumed to be known. Dealing with the FMECA, the evaluation of a quantitative analysis requires basic failure rate data. However, in case of a university CubeSat, it appears difficult to obtain these estimations, due to the very limited, even null, knowledge of the flight configuration items reliability parameters. Therefore, when the components features are not commercially available on a detailed technical datasheet, the estimation has to rely on similar items, or on experts' judgments. The proposed method integrates the traditional FMECA formulation [28] and available standards (GPR 7120.4A; MIL-STD-1629), by suggesting a 5-steps procedure, detailed in the following paragraphs (Sections 3.1-3.5).  
As a preliminary step, it is necessary to determine the functional level breakdown structure for the analysis. In particular, this preliminary phase is necessary to locate the reliability study in a specific design phase.  
Based on the benefits of performing an FMECA early in the design phase, this paper shows the application of a so-called functional FMECA [29]: an FMECA performed and documented for proposals and trade studies, aimed at evaluating and providing support for the design architecture, in line with a Preliminary Design Review (PDR, [30]).  
Starting from the outcomes of the functional FMECA, the method is identically applicable as an interface FMECA [29], in order to extend the analysis at the piece-part level, as the detailed design unfolds during further stages of the PDR and, possibly, even during the Critical Design Review (CDR) timeframe. Since design solutions are based upon flight heritage, detailed design data during the PDR timeframe shall be made available to complete the FMECA analysis for evaluating the most suitable design candidates during trade studies. Note that the proposed approach has to be conceived as an iterative process, in order to allow a back and forth analysis (from part to system levels, and vice versa) which also ensures that the detailed design still maintains the intended system architecture, in line with both design and reliability requirements.
## 3 Metabolic Responses to Different Antibiotic Classes
### Anatomical Niches and Local Metabolism: The Role of Biofilms  
Anatomical niches characterized by poor perfusion, altered pH, or hypoxic conditions--such as cardiac valves, fibrotic lungs, and urinary tracts--provide microenvironments conducive to biofilm formation, wherein microbial communities are encapsulated in a self-produced extracellular matrix [118].  
These environmental conditions promote bacterial adhesion, matrix component production, and microbial persistence, contributing to antimicrobial resistance and chronic infection development [119].  
Within biofilms, bacterial cells exhibit significant physiological heterogeneity, marked by reduced respiratory activity and a predominance of fermentative metabolic pathways [120; 121].  
Specific metabolic pathways are activated, including those of glycogen, acetate, and glycerol--adaptations that support survival under environmental stress. In parallel, there is an accumulation of extracellular polymeric substances (EPS) and redox-active proteins, which enhance cellular protection and structural stability of the biofilm [120; 121].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ecological Effect** & **Mechanism** & **Pathogen Adaptation Strategy** & **References** \\ \hline \multirow{2}{*}{Microbiota Disruption} & Loss of commensal competition and niche availability & Clonal expansion of metabolically & [113; 114; 115] \\ \cline{3-4}  & Lysis of microbiota releases & Use of host-derived substrates to & \\ \multirow{2}{*}{Metabolite Release} & substrates (succinate, & activate virulence and persistence & [116; 117] \\  & sialate, ethanolamine) & Biofilm formation and recurrent & \\ \hline \multirow{2}{*}{Dysbiosis-Driven Colonization} & Depletion of colonization & Infection by _C. difficile_, & [113; 114; 115; 116; 117] \\  & resistance factors & _K. pneumoniae_, _E. facecium_ & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ecological and metabolic impact of antibiotics on the gut microbiota.  
These conditions promote persistent and stratified metabolic rewiring, with differentiated subpopulations exhibiting high levels of tolerance [122].  
Moreover, the biofilm extracellular matrix functions as a physical barrier, impeding antibiotic penetration and protecting bacterial cells from antimicrobial agents [122].
## 4 Results and Discussion
### Effect of Initial Vertical Effective Stress and Loading Frequency  
Figure 8 shows the relationship between the loading frequency and the CRR at 15 cycles (CRR\({}_{15}\)). For a given loading frequency and density state, the CRR\({}_{15}\) value decreased when the initial vertical effective stress increased. A comparison of Figure 8a,b reveals that the cyclic resistance of dense sand decreased more significantly than that of loose sand. This is because the dilative behavior of dense sand is inhibited under a high initial vertical effective stress. [PERSON] and [PERSON] [19] emphasized that an increase in the confining stress had a small effect on the dilatancy of loose sand but caused a progressive reduction in the dilatancy or enhanced contraction of dense sand.  
The CRR\({}_{15}\) values increased with the loading frequency, regardless of the initial vertical effective stress and relative density. Figure 9 presents the percentage increase in the cyclic resistance at 0.1, 0.5, and 1 Hz relative to the cyclic resistance at 0.05 Hz. For loose sand at \(\sigma_{\mathrm{v0}}{}^{\prime}=100\) kPa, an increase in the loading frequency from 0.05 Hz to 0.1, 0.5, and 1 Hz led to the cyclic resistance increasing by 3.7%, 7.9%, and 12%, respectively. For dense sand at \(\sigma_{\mathrm{v0}}{}^{\prime}=100\) kPa, an increase in the loading frequency  
Figure 7: Comparison of cyclic resistance curves (**a**) \(f=0.05\) Hz, (**b**) \(f=0.1\) Hz and (**c**) \(f=0.5\) Hz at different density states for \(\sigma_{\mathrm{v0}}{}^{\prime}=200\) kPa.  
from 0.05 Hz to 0.1 and 0.5 Hz led to the cyclic resistance increasing by 2.9% and 19%, respectively. Similar results can be seen for \(\sigma_{\rm v0}\)\({}^{\prime}\) = 200 kPa. [PERSON] and [PERSON] [11] reported that the cyclic resistance of loose sand (D\({}_{\rm r}\) = 30%) increased by 8.9% when the frequency was increased from 0.05 to 1 Hz in a cyclic triaxial test. Furthermore, in cyclic triaxial tests conducted by [PERSON] and [PERSON] [13] under a confining stress of 100 kPa, the relative increase in the liquefaction resistance at 12 cycles was 33.3% for loose sand (D\({}_{\rm r}\) = 28%) and 15.4% for dense sand (D\({}_{\rm r}\) = 70%) when the loading frequency varied from 0.05 to 1 Hz.
# Remote Control of a Mobile Robot for Indoor Patrol
###### Abstract  
This study applies smartphone, Bluetooth, and Wi-Fi wireless network to control a wheeled mobile robot (WMR) remotely. The first part of this study demonstrates that the WMR can be controlled manually by a smartphone. The smartphone can remotely control the WMR for forward, backward, left-turn, and right-turn operations. The second part of this article presents object tracking. The WMR can follow a moving object through the use of image processing for object tracking and distance detection. In the third part, infrared sensor and fuzzy system algorithms are integrated into the control scheme. Through wall-following and obstacle-avoidance control, the WMR can successfully perform indoor patrol.  
fuzzy control; obstacle avoidance; wireless communications; image processing Article
## 3 Results and Discussion
### The Effectiveness of Removing Ammonium Nitrogen from Wastewater
The use of immobilized microalgae resulted in an efficiency of ammonium nitrogen removal at a level of 21-60%, and the use of microalgae in the form of a suspension was lower. It was at a level of 10-52% (Figure 5A,B). Moreover, the application of _Chlorella vulgaris_ (Figure 5A) helped to achieve better a maximum removal efficiency of N-NH\({}_{4}\)+--60% for sample 10 CvA than after the addition of _Scenedesmus armatus_--44% for sample 5 ScB (Figure 5B).
## 3 Numerical Scheme
### Energy Analysis
#### 3.5.2 Horizontal Polarisation  
On the other hand, the product of Equation (54a) by \(h\left(\delta_{t}.\mathbf{u}^{n}\right)^{\mathsf{T}}\), and that of Equation (57a) by \(\left(\delta_{t}.\mathbf{u}_{FB}^{n}\right)^{\mathsf{T}}\), yield a numerical power balance for the horizontal polarisation:  
\[\delta_{t-}\mathrm{H}_{u}^{n}=\mathrm{P}_{u}^{n}-\mathrm{Q}_{u}^{n} \tag{62}\]  
where the numerical energy \(\mathrm{H}_{u}^{n}\) is defined as:\[\mathrm{H}_{u}^{n} =\mathrm{H}_{u,s}^{n}+\mathrm{H}_{u,FB}^{n} \tag{63a}\] \[\mathrm{H}_{u,s}^{n} =\frac{\mathrm{p}h}{2}|\delta_{t+}\mathbf{u}^{n}|^{2}+\frac{Th}{2} (\mathbf{D}_{x-}\mathbf{u}^{n})^{\mathsf{T}}\mathbf{D}_{x-}\mathbf{u}^{n+1}+ \frac{El_{0}h}{2}(\mathbf{D}_{xx}\mathbf{u}^{n})^{\mathsf{T}}\mathbf{D}_{xx} \mathbf{u}^{n+1}\] \[\quad-\frac{\lambda_{2}\mathrm{p}kh}{4}|\delta_{t+}\mathbf{D}_{x -}\mathbf{u}^{n}|^{2}\] (63b) \[\mathrm{H}_{u,FB}^{n} =\frac{1}{2}\left(\mathbf{M}_{FB}\delta_{t+}\mathbf{u}_{FB}^{n} \right)^{\mathsf{T}}\delta_{t+}\mathbf{u}_{FB}^{n}+\frac{1}{2}\mathrm{u}_{t+} \left(\left(\mathbf{K}_{FB}\mathbf{u}_{FB}^{n}\right)^{\mathsf{T}}\mathbf{u}_{ FB}^{n}\right) \tag{63c}\]  
The power \(\mathrm{P}_{u}^{n}\) brought in or out by the player excitation is:  
\[\mathrm{P}_{u}^{n}=h\left(\left(\mathrm{u}_{t}\mathbf{u}^{n}\right)^{\mathsf{ T}}\left(\delta_{t}\mathbf{J}^{n}\right)\right)\mathbf{f}_{\mathbf{u}}^{n}+ \left(\delta_{t}\mathbf{u}_{FB}^{n}\right)^{\mathsf{T}}\mathbf{f}_{\mathbf{ext },FB}^{n} \tag{64}\]  
The power \(\mathrm{Q}_{u}^{n}\geqslant 0\) dissipated by friction and damping is:  
\[\mathrm{Q}_{u}^{n} =\mathrm{Q}_{u,s}^{n}+\mathrm{Q}_{\varphi}^{n}+\mathrm{Q}_{u,FB}^ {n} \tag{65a}\] \[\mathrm{Q}_{u,s}^{n} =\lambda_{1}\mathrm{p}h|\delta_{t}\mathbf{u}^{n}|^{2}+\lambda_{2} \mathrm{p}h|\delta_{t}\mathbf{D}_{x-}\mathbf{u}^{n}|^{2}\] (65b) \[\mathrm{Q}_{\varphi}^{n} =\left(\mathbf{h}\odot\mathbf{v}_{\mathrm{rel}}^{n}\right)^{ \mathsf{T}}\mathbf{f}_{\mathbf{u}}^{n}\] (65c) \[\mathrm{Q}_{u,FB}^{n} =\left(\mathbf{\lambda}_{FB}\delta_{t}\mathbf{u}_{FB}\right)^{ \mathsf{T}}\delta_{t}\mathbf{u}_{FB} \tag{65d}\]
# RSSI-Based MAC-Layer Spoofing Detection: Deep Learning Approach +
## 5 Experiments and Results
### Model Switching at Runtime  
In Section 4.2 we have explained the need for a bi-modal LSTM autoencoder classifier, and we have proposed a fully automated and adaptive approach to switching between the two train models/classifiers at runtime. Using the collected real-world datasets we have put this idea to test by continuously monitoring the reconstruction error of the two train models at runtime. As depicted in Figure 13, at night when the RSSI stream had relatively lower volatility, the night model (the blue line) resulted in low reconstruction error while the day model (the orange line) resulted in high reconstruction error--as to be expected. However, at the point in time when the volatility was about to pick up, we can observe a sudden jump in the night model's reconstruction error accompanied by significant improvement in the day model's reconstruction error, ultimately resulting in a crossover between the two error lines (orange and blue). This is a clear indication that the night model could be retired, and the day model could be activated for detection. Clearly, this demonstrates the viability of the crossover indicator to facilitate an automated and adaptive switching schedule between the two trained LSTM autoencoder models.  
Figure 11: Comparison of ‘Normal Classification’ of our novel detection method with two other [13, 23] state-of-the-art approaches proposed in the literature.  
Figure 12: Comparison of ‘Spoofed Classification’ of our novel detection method with two other [13, 23] state-of-the-art approaches proposed in the literature.  
Figure 13: Tracking reconstruction error of two trained models during an entire day. The crossover point between the two reconstruction error lines (orange and blue) coincide with increase in volatility of RSS stream (the red line)= a clear indicator to be used to switch between trained models.