esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Algorithm Implementation
#### 2.4.3 Model Structure  
To thoroughly examine the performance of different algorithms and identify the best model for the regional remote sensing of SD, we performed a series of experiments considering two main variables: spectral variables and different datasets of the time window between in situ data and satellite data. The spectral variables include five bands of OLI (443, 483, 561, 655, and 865 nm), which were considered to be the most sensitive and effective for the inversion of SD and have been demonstrated to retrieve SD in some inland waters [30; 48]. The spectral variables are divided into two groups: single band or band combination, which has five bands as candidates (group 1), and band combination with band ratio, which has 25 variables as candidates (group 2). Each group was trained and evaluated separately based on the time window datasets mentioned above (Table 2).
###### Abstract  
For highly demanding scenarios such as continuous bio-signal monitoring, transmitting excessive volumes of data wirelessly comprises one of the most critical challenges. This is due to the resource limitations posed by typical hardware and communication technologies. Driven by such shortcomings, this paper aims at addressing the respective deficiencies. The main axes of this work include (a) data compression, and (b) the presentation of a complete, efficient and practical hardware accelerator design able to be integrated in any Internet of Things (IoT) platform for addressing critical challenges of data compression. On one hand, the developed algorithm is presented and evaluated on software, exhibiting significant benefits compared to respective competition. On the other hand, the algorithm is fully implemented on hardware providing a further proof of concept regarding the implementation feasibility with respect to state-of-the art hardware design approaches. Finally, system-level performance benefits, regarding data transmission delay and energy saving, are highlighted, taking into consideration the characteristics of prominent IoT platforms. Concluding, this paper presents a holistic approach based on data compression that is able to drastically enhance an IoT platform's performance and tackle efficiently a notorious challenge of highly demanding IoT applications such as real-time bio-signal monitoring.  
IoT Wireless Sensor Network platforms; data compression; hardware accelerator; Wireless Sensor Networks; embedded systems; complete solution; experimental evaluation; hardware design; ultra-low power Article
# Machine Learning-Based Methods for Code Smell Detection: A Survey
## 4 Findings and Discussion
### Rationalize the Research Questions
#### 4.2.1 RQ1: Which ML Algorithms Are Used to Detect Code Smells?
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ML Algorithms** & **Number of Research Studies** & **\% of Research Studies** \\  & **(Out of 42)** & **(Out of 42)** \\ \hline Bayesian learning & 18 & 42.85714286 \\ Decision Tree & 27 & 64.28571429 \\ Ensemble learning & 21 & 50 \\ Miscellaneous & 12 & 28.57142857 \\ Neural network & 7 & 16.66666667 \\ Rule-Based Learning & 3 & 7.142857143 \\ SVM & 22 & 52.38095238 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Machine learning-based code smell detection techniques. prevalence of these ML algorithms in code smell detection, providing valuable insights for software engineers and researchers in the field. The ML algorithms, deep learning, J48, Naive Bayes, SVM, and Random Forest algorithms are employed. The number of research studies performed using these techniques is 15, 13, 12, 2, and 8, respectively. Figure 5: _Cont._  
[PERSON] et al. [3] applied Random Forest, Gradient Boosting Tree, Decision Tree, deep learning, SVM, Multilayer Perceptron, GA (Genetic Algorithm), Naive Bayes, and GA CFS (Correlation-based Feature Selection) over [PERSON] et al. [26]. Furthermore, 74 open-source systems were used; the highest and precision values are 96.43% and 98.18%, respectively, for the Random Forest algorithm. [PERSON] et al. [8] used the B-Random Forest, Random Forest, B-J48 UnPruned, B-J48 Pruned, and J-48 Unpruned approaches and found 95-98% accuracy. [PERSON] et al. [26] utilized J48, C4.5 Decision Tree, JRip, Naive Bayes, Random Forest, Sequential Minimal Optimization, LibSVM over Antipattern Scanner, iPlasma, Fluid Tool, Marinescu detection rule, PMD, and the Fluid Tool dataset and found 96-99% accuracy. [PERSON] et al. [27] proposed an SVM approach over ArgoUML  
Figure 5: Number of research studies using distinct classes of ML algorithms: (**a**) bar graph of Decision Trees; (**b**) bar graph of Ensemble Learmers; (**c**) bar graph of neural networks; (**d**) bar graph of Bayesian Learmers; (**e**) bar graph of Rule-Based Learning; (**f**) bar graph of Support Vector Machines; (**g**) bar graph of miscellaneous classes. v0.19.8 and Xerces v 2.7.0 datasets and found better results. [PERSON] et al. [28] developed a deep learning system for code smell detection. They also used K-nearest neighbors and cosine similarity machine learning algorithms for detailed comparison. [PERSON] et al. [29] proposed a CloudScent model, an open-source methodology for detecting code smells. The results demonstrate that the model can accurately detect eight code smells in the cloud.
## 1 Actinobacteria and Their Primary Metabolism as Basis for Secondary Metabolism Precursor Supply  
The Actinomycetota (Actinobacteria or Actinomycetes) are a phylum containing a group of terrestrial and aquatic Gram-positive bacteria with a high G + C content. This phylum includes bacteria that can be isolated from many different environments. Bacteria from this group are involved in the decomposition of organic materials like chitin and cellulose and play an important role in the nitrogen and carbon cycles as well as in organic matter turnover in nature. In comparison to fungi, Actinomycetes are much smaller and occupy many shared ecological niches, but also specific ones. They replenish the supply of nutrients in the soil that is a vital part of humus formation.  
Actinomycetes include following representative genera: _Actinomyces_, _Arthobacter_, _Corynebacterium_, _Frankia_, _Microoccus_, _Micromospora_, _Mycobacterium_, _Nocardia_, _Propioniibacterium_ and _Streptomyces_[1]. Some soil Actinobacteria, such as Franka, are endosymbionts of plants that are able to fix nitrogen in exchange for access to saccharides and other nutrients from the plant. Other species, such as some members of the genera _Mycobacterium_, _Corynebacterium_, _Nocardia_ and _Rhodococcus_, are human pathogens.  
More than two-thirds of all known antibiotics are synthesized by Actinobacteria from the genus _Streptomyces_[2], and there are many more unknown compounds produced by _Streptomyces_ spp. Recent studies showed that bacteria from this genus can synthesize more than 150,000 antimicrobial compounds that are currently unknown [3]. _Streptomyces_possess mechanisms to govern the metabolic pathways involved in production of secondary metabolites in response to nutrient availability and other external signals. Diverse substrates can induce the production of antibiotics, which may involve the activation of antibiotic-producing enzymes as well as the regulation of the biosynthetic activity of such enzymes. Nutrients like nitrogen-, carbon- and phosphate-containing compounds were reported to indirectly modulate antibiotic production by influencing the primary metabolism that provides precursor molecules for secondary metabolite biosynthesis [4]. The feedback and feedforward regulation coupled with the regulation of the nutrient supply have been shown to be mechanisms that could be applicable for the industrial production of secondary metabolites [5].
# Highly Sensitive Non-Enzymatic Detection of Glucose at MWCNT-CuBTC Composite Electrode
## 5 Conclusions  
The non-enzymatic direct determination of glucose using an MWCNT-CuBTC composite electrode by cyclic voltammetry, linear sweep voltammetry, differential pulse voltammetry, chronoamperometry, and multiple pulse amperometry in an alkaline sodium hydroxide solution as a supporting electrolyte has been achieved. Very good results from calibration plots of the anodic current versus glucose concentrations resulted from all electrochemical techniques tested, and corresponding analytical data regarding the sensitivities and the relative standard deviation, the lowest limit of detection, were obtained. The adsorption properties of the MWCNT-CuBTC composite electrode for glucose were exploited for the enhancement of electroanalytical performance for glucose detection by the elaboration of the preconcentration/voltammetric detection scheme, which allowed for an improvement of five times the sensitivity and the lowest limit of detection. By comparison with other copper-based electrodes, this electrode exhibited superiority in relation to its sensitivity for glucose detection, due to its better electrocatalytic performance for glucose oxidation. The association of the differential pulse voltammetric method with the standard addition method has been successfully used for fast analytical evaluation of pharmaceutical formulations which contain glucose (serum). The average glucose content in the serum, explored as real samples, was measured with this electrode in good accordance with those indicated by the suppliers. The MWCNT-CuBTC composite electrode's features, in relation with good electrochemical and electrocatalytic properties, good stability, reproducibility, and the long lifetime associated with the specific voltammetric and amperometric technique, make it very appropriate for real application in glucose detection and for further exploitation in the construction of new electrochemical sensors.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/23/8419/s1](http://www.mdpi.com/2076-3417/10/23/8419/s1)]([http://www.mdpi.com/2076-3417/10/23/8419/s1](http://www.mdpi.com/2076-3417/10/23/8419/s1)).  
Conceptualization, F.M. and E.I.S.; methodology, F.M. and G.G.; investigation, A.R. and S.M.; writing--original draft preparation, A.I., S.M., and A.B.; writing--review and editing, F.M., E.I.S., J.G., and G.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Funding for this study was provided by a grant from the Romanian Ministry of Research and Innovation, project number PN-III-P1-1.2-PCCDI-2017-0245/26 PCCDI/2018 (SUSTENVPRO), within PNCDI III, and by a grant from the Romanian Ministry of Education and Research, CNCS--UEFISCDI, project number PD 88/2020, project code PN-III-P1-1.1-PD-2019-0676, within PNCDI III.  
The authors declare no conflict of interest.
# Coverage and Rate Analysis for Location-Aware Cross-Tier Cooperation in Two-Tier HetNets
## 3 Analysis of the Coverage Probability and Average Rate  
This section is the main technical component. On the basis of the characteristics of the SINR for the typical user, we derive the coverage probability and average rate of the typical user for the proposed LA-CTC scheme. We begin our discussion with the distance distributions to the serving BS for the typical user.
# Analysis of Volatiles in Food Products
## 2 Isolation and Concentration as a First Step to the Analysis of Volatile Components
### Solvent-Assisted Flavor Evaporation (SAFE)  
The SAFE was established in the 1990s as a novel extraction technique, that admits to separate the volatile part from nonvolatile compounds [91]. SAFE is devoted to the extraction of the profile of aroma of foodstuffs to control aroma-active molecules and follows the molecular concept strategy. The key benefits of the SAFE technique are firstly inhibiting the formation of artefacts, secondly forbid the thermal degradation of nonstable volatiles' composition. The major disadvantage of this method is the time-consuming practice, mainly dealing with products with fats. Additionally, in contrast to SPME, SAFE needs a greater volume of samples for analysis. Though, the SAFE extraction is a very suitable extraction technique for sample preparation to GC-O analysis [92]. The technique of SPME could be too selective to capture the whole profile of aroma reported in the foodstuffs. Therefore, the SAFE methodology was mostly used to verify particular molecules responsible for the aroma of varied foodstuffs like orange juice, cocoa, products from cocoa, nuts, alcoholic beverages and also raw fruits, e.g., apricots [93; 94; 28; 95; 96; 97; 98; 99].  
SAFE is an important preliminary part of the molecular sensory concept, which allows separating volatile from non-volatile compounds to further analysis by GC-O and/or other GC instruments. The molecular sensory concept allowed to pointed out chemicals responsible for the off-flavor of stored orange juice prepared from concentrate [28], and to aroma reconstruction of apricots [99]. [PERSON] and [PERSON] [93] involved SAFE to prepare samples to further differentiate hazelnut cultivars, whereas [PERSON] et al. [95] pointed out differences in aroma composition between raw and fried Italian hazelnuts. Fractionation of aroma compounds from raw peanuts and panned fried peanut meal was also carried out using SAFE [90]. Moreover, [PERSON] et al. [96] and [PERSON] et al. [97]prepared samples by SAFE to determine aroma-active compounds of alcoholic beverages such as rums and beers, respectively, to distinguish the quality of the lower and higher-priced market-available products. Additionally, SAFE was used to prepare samples of Chinese alcohol based on sesame to prove their authenticity by determination of aroma profile [98].
# Generic Methodology for Field Calibration of Nacelle-Based Wind Lidars
## 1 Introduction  
### Profiling Lidars for Power Performance  
The rapid increase in wind turbines' size has created a need for developing new power performance assessment procedures. The effects of wind speed and direction variations--called shear and veer respectively--over the rotor swept area on power curves can no longer be neglected [1]. Measuring the wind at one point, e.g., hub height, has consequently become insufficient.  
Light detection and ranging (lidar) is a remote sensing technology addressing this challenge. Its multiple applications have found their way into the wind energy market. Ground-based lidars are presently being used to measure wind profiles [2]. They offer a practical and accurate solution for measuring wind over the entire rotor disk. Even though two-beam nacelle-based lidars completinghorizontal wind speed measurements are unable to estimate the wind shear, they show promising capabilities to assess power performance [3] and obviate the erection of expensive meteorology masts, especially offshore.  
A new generation of commercially developed profiling nacelle lidars combine the benefits of both technologies. A wind profiling nacelle lidar probes the wind at multiple heights and distances upstream of a turbine -- or downstream for wake measurements -- from its nacelle. The commercially available technology ranges from scanning to multi-beam lidar systems, e.g., the Zephir Dual-Mode (ZDM) and the 5-beam Avent Demonstrator (5B) lidars (Figure 1, see details on the measurement principles of both systems in [4,5]).
## 3 Results and Discussions  
### Failure Mode and Deformation  
In several OSC and VESC tests, the distorted shape of the stone columns was captured by filling the stone column and the region of the loading plate with plaster of Paris paste. In HRSC experiments, however, filling with plaster of Paris paste was not possible due to the presence of horizontal sheets. As a result, after the test, the soft soil surrounding the column was meticulously sliced vertically to detect failure and deformation. The bulging failure mode was dominant in all the single stone column testing. Bulging failure happened at a depth of D to 2.5 D from the top of the stone column according to the findings in the case of OSC, which can be seen in Figure 4a. The result of this study is comparable to those made by [PERSON] and [PERSON] (2013) and [PERSON] et al. (2014). The rigidity of a stone column rises when it is enclosed because it provides greater protection from bulging by mobilising hoop stresses within the geosynthetic material. In the case of VESCs, just beneath the encasement length, bulging is readily discernible for L\({}_{\rm r}\) = 0.25 L, as shown in Figure 4b(iv). This bulging depth and the position of the bulge can be found as similar to that of the OSC case (Figure 4a). Bulging was minimal for the column with a 50% encasement length (L\({}_{\rm r}\) = 0.5 L). Only a minor lateral distortion was seen underneath the encased zone (Figure 4b(iii)). A similar observation was noticed for a 75% encasement length, i.e., L\({}_{\rm r}\) = 0.75 L (Figure 4b(ii)). The 100% encased column (L\({}_{\rm r}\) = L) failed the punching test because there was no bulging along its whole length (Figure 4b(i)). A similar deformation pattern was observed by previous researchers (2013, 2013).  
In the case of HRSCs, when horizontal strips were provided throughout the length of the column at equal intervals (i.e.,100 mm), the column collapsed due to localised swelling at around 1.5 D to 2.5 D, which was similar to that of OSC (Figure 4c(i)). This indicates the inadequacy of the spacing between the horizontal layers of geotextiles. Figure 4b(ii) shows subtle bulging after the reinforcement was conducted for the top half of the column (0.5 L from column head) at the intersection of the reinforced and unreinforced sections of the column length. When the reinforcement was performed for the bottom half (0.5 L from centre to foot), the bulging failure was similar to that of an unreinforced case.
###### Abstract  
Sixth-generation (6G) wireless networks demand a more efficient implementation of non-orthogonal multiple access (NOMA) schemes for severe multipath fading environments to serve multiple users. Using non-orthogonal multiple access (NOMA) schemes in IoT 6G networks is a promising solution to allow multiple users to share the same spectral and temporal resource, increasing spectral efficiency and improving the network's capacity. In this work, we have evaluated the performance of a novel progressive pattern interleaver (PPI) employed to distinguish the users in interleaved division multiple access (IDMA) schemes, suggested by 3 GPP guidelines as a NOMA scheme, with two multi-carrier modulation schemes known as single-carrier frequency-division multiple access (SC-FDMA) and orthogonal frequency-division multiplexing (OFDM), resulting in SC-FDMA-IDMA and OFDM-IDMA schemes. Both schemes are multi-carrier schemes with orthogonal sub-carriers to deal against inter-symbol interference (ISI) and orthogonal interleavers for the simultaneous access of multiple users. It has been suggested through simulation outcomes that PPI performance is adequate with SC-FDMA-IDMA and OFDM-IDMA schemes in terms of bit error rate (BER) under multipath channel conditions. Moreover, regarding bandwidth requirement and the implementation complexity of the transmitted interleaver structure, PPI is superior to the conventional random interleaver (RI).  
6G; IoT; NOMA; SC-FDMA; OFDM; IDMA; multiple access; ISI; multi-carrier modulation; multipath channel +
Footnote †: journal: _Sensors_  
_Sensors_**2024**, 24, 3648. [[https://doi.org/10.3390/s24113648](https://doi.org/10.3390/s24113648)]([https://doi.org/10.3390/s24113648](https://doi.org/10.3390/s24113648))
## 6 Discussion
The defined incentives mechanism is incentive compatible, meaning that the truthful report of preferences is the optimal strategy for each EV driver. For this mechanism to work, there is a strong assumption that the EV drivers behave as strategic agents. However, it is not always true, and people often tend to do irrational decisions from an analyst's point of view. But many studies in behavioral economics assumed this is a result of not having full information [34]. Therefore, we work with a hypothesis that by repeating action (charging at the station), EV drivers can learn their optimal strategy and so start to behave as strategic agents. However, it is also necessary to test this hypothesis in future work. A similar mechanism was proposed in [30]. However, the control policy in that study is defined for a longer period, so the participants cannot change (connect or disconnect from the charging station) or change their preferences during this period. To prevent this unrealistic scenario and create an algorithm more suitable for a real operation, our manuscript assumes an online control mechanism and so controls the charging and defines incentives at each time step. The proposed mechanisms are tested in three simulation scenarios to demonstrate the presented properties of the system. The results from the first two simulation scenarios show that declaring true preferences is an optimal strategy for all participants. The incentive mechanism is even advantageous for the charging station operator. The first scenario also confirms that the mechanism satisfies the efficient exit condition, meaning that participants irrelevant to the mechanism do not receive any payment. The third scenario presents a more realistic operation and confirms the operation of the proposed mechanism in a more real-life situation at the charging stations. The results from the simulation scenarios also show that the accuracy of the renewable energy source output prediction can affect the utilization of the renewable energy source for charging EVs. Therefore, one of the possible ways for future research is to improve PV power plant output predictions or find a balance between the accuracy of power prediction and the time steps of the control algorithm. Nevertheless, random behavior of EV drivers in regards to connecting to charging stations can also affect the utilization of the renewable energy source, and the overloading of power lines or unexpected peak demand can occur in the main grid. To prevent this effect on the main grid, the storage system could be designed for the charging station. So, the next focus of future research can be on the optimal storage sizing that can compensate for these unexpected events while meeting the technical and economical limits of the system.
## 6 Experiment
### Results in the Black-Box Setting
#### 6.3.3 Effectiveness Under Defensive Mechanisms  
We also conducted experiments in a black-box setting to explore whether poisoning attack methods could evade detection by the RDMA mechanism. The experimental results are displayed in Table 7.  
By fixing the number of generated fake users at 30 and varying the number of spy users, we observed that when the number of spy users was small, the decline in hit rates for the three attack methods was not significant. However, when the number of spy users reached 70, the hit rate sharply decreased. This suggests that as the number of spy users increases, the fake user data generated by the DRLAttack strategy becomes more generic and easily detectable by defense mechanisms, significantly impacting the effectiveness of the final attack.