esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 10 Routing and Energy Allocation in Rechargeable WSNs with Multiple Sources and Destinations
### Problem Formulation  
A formulation that maximizes the sum of general utilities of all sensor nodes was formulated as a convex NLP. A brief explanation for the formulation is given below:  
* Decision variables: All decision variables are continuous variables. There are three sets of decision variables, 1. \(w_{ij}\left(t\right)\) is the amount of data on the outgoing link \(\left(i_{j},j\right)\) for time slot \(t\), 2. \(x^{sj}\left(t\right)\) is the amount of data delivered from source \(f_{s}\) to the destination \(d_{s}\), 3. \(e_{s}\left(t\right)\) represents the amount of energy expended by a node \(s\) during slot \(t\).
* Objective function: is the sum of individual node utilities where each of these, \(U_{s}\left(\frac{1}{T}\sum_{i=1}^{T}x^{s}\left(t\right)\right)\), is a function of the amount of data delivered from source node \(f_{s}\) to destination node \(d_{s}\) in all  
Figure 15: Convergence of the ADMM consensus scheme versus the dual decomposition scheme represented by the flow conservation constraint violation [4].  
time slots over possibly multiple hops and multiple paths. Each utility function is assumed to be a continuous non-linear concave function. The time parameter in [5] is discrete.
* Constraint sets: There are three constraint sets, these are: 1. non-negative flow constraints on the flow variables \(w_{ij}\left(t\right)\geq 0\). 2. conservation of flow constraints that are linear constraints in the decision variables that represent the flow and amount of data delivered from a source node to a destination node, \(w_{ij}\left(t\right)\) and \(x^{s}\left(t\right)\), i.e., \(\sum_{\left(=1\right.}^{T}\sum_{i}w_{ij}^{d}-\sum_{t=1}^{T}\sum_{j}w_{ji}^{d} -\sum_{t=1}^{T}\sum_{5:f_{i}=i,d_{i}=d}x^{s}\left(t\right)\geq 0\). 3. The third ensures that the sum of flows emanating from a node \(i\) belongs to the set \(\Lambda_{i}\) of the different amounts of data in different time slots under a given replenishment profile vector \(\overrightarrow{r}_{i}\). For any data vector in \(\Lambda_{i}\), there exists an energy vector \(e_{n}\) that achieves that amount of data for a given modulation and coding scheme. The set \(\Lambda_{i}\) was proved to be convex in works earlier to [5].
## 3 Results
### Metallographic Analysis  
Figure 8 shows the metallographic structure of the three Al-Mg-Si alloy thin-walled profiles in the near-peak-aged state. The structures of the three profiles primarily comprise equiaxed grains, in which the 6 xxx-2(1.33 Mg/Si) profile has the largest grain size, the 6 xxx-1(1.6 Mg/Si) profile has the smallest grain size, and the 6 xxx-3(1.14 Mg/Si) profile has a grain size between the two. As shown in Table 3, the three profile types have average grain sizes of 63.6, 154.9, and 92.1 \(\upmu\)m, respectively. Compared to profile 6 xxx-1(1.6 Mg/Si), the average grain sizes of profiles 6 xxx-2(1.33 Mg/Si) and 6 xxx-3(1.14 Mg/Si) are higher by 143.5% and 44.8%, respectively. The aging process has no effect on the grain structure of the profile [29], from which it can be concluded that an increase in the total Mg + Si content increases the grain size.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Profile Number**} & \multirow{2}{*}{**Aging Process**} & \multirow{2}{*}{**Peak Load (KN)**} & **Total Energy** & **Effective Energy** & \multirow{2}{*}{**Cracked/Not Cracked**} \\  & & & **Absorption during** & **Absorption during** & **Cracked** \\  & & & **Crushing (KJ)** & **Crushing (KJ)** & **Crushing (KJ)** & \\ \hline \multirow{3}{*}{6 xxx-1(1.6 Mg/Si)} & 175 \({}^{\circ}\)C/10 h & 362.5 & 31.02 & 22.21 & Yes \\  & 175 \({}^{\circ}\)C/16 h & 402.75 & 30.9 & 16.55 & Yes \\  & 205 \({}^{\circ}\)C/6 h & 380.3 & 30.61 & 30.61 & No \\ \hline \multirow{3}{*}{6 xxx-2(1.33 Mg/Si)} & 175 \({}^{\circ}\)C/10 h & 425 & 18.93 & 9.55 & Yes \\  & 175 \({}^{\circ}\)C/16 h & 449.95 & 16.17 & 5.28 & Yes \\  & 205 \({}^{\circ}\)C/6 h & 397.75 & 21.88 & 21.88 & No \\ \hline \multirow{3}{*}{6 xxx-3(1.14 Mg/Si)} & 175 \({}^{\circ}\)C/10 h & 312.45 & 25.43 & 13.82 & Yes \\  & 175 \({}^{\circ}\)C/16 h & 383.2 & 27.26 & 10.87 & Yes \\ \cline{1-1}  & 205 \({}^{\circ}\)C/6 h & 358.85 & 28.19 & 28.19 & No \\ \hline \hline \end{tabular}
\end{table}
Table 2: Axial crushing properties of the three types of Al-Mg-Si alloy thin-walled profiles in different aging states.  
Figure 8: Metallographic organization of the three Al-Mg-Si alloy thin-walled profiles in the near-peak-aged state: (**a**) Profile 6 xxx-1(1.6 Mg/Si); (**b**) Profile 6 xxx-2(1.33 Mg/Si); (**c**) Profile 6 xxx-3(1.14 Mg/Si).  
In addition, material ductility is inversely proportional to grain size, where the smaller the grain size, the more ductile the material [30; 31]. Therefore, the 6 xxx-1(1.6 Mg/Si) and 6 xxx-3(1.14 Mg/Si) profiles showed good crushing properties with only minor crack formations on the surface during compressive deformation, while the 6 xxx-2(1.33 Mg/Si) profile has a large grain size and poorer ductility, and hence it is more susceptible to cracking during the deformation process.
## 3 Results and Discussion
### Reasons behind the Failures of the Contemporary Approaches
#### 3.2.3 Sample with an Extremely Small Training Set  
The unavailability of large and diverse set of training samples is widely considered a major practical problem behind the unsuccessful implementation of the Custom Object CNN [33; 41; 42]. In addition, we could not find any public dataset similar to our study to enhance the sample set size. Large datasets are often needed to ensure that the deep CNN delivers the desired results. The success of deep learning methods [18; 25] is often found to entirely depend on the availability of a large and diverse set of training samples. Particularly in the present scenario, the size of the dataset is unsatisfactory; hence, the accuracy of the current Custom Object CNN, including the YOLO and RCNN models, was inferior to the proposed approach. We firmly believe that the accuracy can barely be improved in the absence of a large set of training samples. Similarly, the other deep learning implementation [18] will not be successful without a large set of training samples.  
This problem has been successfully countered using the proposed approach of analysing individual images based on pixel intensity. The proposed approach does not require a learning phase and hence can successfully and accurately be applied in the presence of a tiny number of grayscale image samples.
## 4 Conclusions  
Based on the results of the resistance of traditional vibrated, centrifuged and vibrocentrifuged concrete to sulfate attack and their analysis, comparison, and discussion, the following conclusions were formulated:  
1. As a result of 60 sulfate attack cycles, the maximum mass loss was 6.32% for vibrated concrete, 4.79% for centrifuged concrete, and 3.96% for vibrocentrifuged concrete. The mass loss of vibrocentrifuged concrete is the smallest in comparison with centrifuged (17% less) and vibrated concrete (37% less).
2. The compressive strength after 60 sulfate attack cycles decreased by 16.4% for vibrated concrete, 11.8% for centrifuged concrete, and 9.5% for vibrocentrifuged concrete. The loss of prismatic compressive strength for the same types of concrete was 17.5%, 12.7%, and 10.8%, respectively. The loss of cube and prism strength of vibrocentrifuged concrete is the smallest in comparison with centrifuged (20% and 18% less, respectively) and vibrated concrete (42% and 38% less, respectively).
3. Comparative analysis of the structure of concrete samples after 60 cycles of sulfate attack revealed less defectiveness of the structure of vibrocentrifuged concrete in comparison with samples of centrifuged and vibrated concrete.
4. Based on the results of assessing the sulfate attack resistance of concrete aged continuously for 180 days in a 5% sulfuric acid solution, the lowest sulfate attack rate and depth of destruction were recorded for vibrocentrifuged concrete. The sulfate attack rate of vibrocentrifuged concrete turned out to be 13% less than that of centrifuged concrete and 27% less than that of vibrated concrete. The depth of destruction of vibrocentrifuged concrete is 46% less than that of centrifuged concrete and 65% less than that of vibrated concrete.  
Vibrocentrifugation technology is proposed for implementation in structures operated in special conditions with an increased risk of sulfate attack in concrete. Prospects for further research are seen in the direction of studying the effect of other types of attack and the formulation methods analyzed in this work on the resistance of variotropic concretes.  
Conceptualization, S.A.S., E.M.S., A.N.B. and D.E.; methodology, S.A.S., E.M.S. and A.C.; software, S.A.S., E.M.S., A.N.B. and A.C.; validation, D.E., S.A.S., E.M.S. and A.N.B.; formal analysis, A.C., S.A.S. and E.M.S.; investigation, D.E., L.R.M., S.A.S., E.M.S., A.N.B. and B.M.; resources, B.M.; data curation, S.A.S., E.M.S. and A.C.; writing--original draft preparation, S.A.S., E.M.S. and A.N.B.; writing--review and editing, S.A.S., E.M.S. and A.N.B.; visualization, S.A.S., E.M.S. and A.N.B.; supervision, L.R.M. and B.M.; project administration, L.R.M. and B.M.; funding acquisition, A.N.B. and B.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The study did not report any data.  
**Acknowledgments:** The authors would like to acknowledge the administration of Don State Technical University for their resources and financial support.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Results and Discussion  
### Volatile Fraction Analysis  
Despite that, in the literature, there are several studies on _R. coriaria_ fruits concerning their non-volatile metabolites, the volatile composition has been less investigated. Recently, [PERSON] et al. reported the flavor profile results on sumac from Palestine, Jordan, and Egypt and its food products, analyzed via solid-phase microextraction (SPME) [15]. [PERSON] et al. identified over 120 constituents in the essential oil of _R. coriaria_ fruits, among which terpene hydrocarbons, oxygenated terpen, and aliphatic aldehydes were the most abundant [16]. The investigations of [PERSON] et al. on the aroma profile and essential oil composition of _R. coriaria_ fruits from four Sicilian sites of collection allowed the identification of 106 compounds by SPME analysis and 169 in the essential oils by GC/MS, and the main constituents were revealed as being \(p\)-anisaldehyde, (\(Z\))-2-heptenal, (\(E\))-2-decenal, \(\beta\)-caryophilene, and cembrene [3].  
The analysis carried out on the six samples led to the identification of 263 volatile compounds (Table S1), and to the best of our knowledge, this is the first time that the sumac flavor profile has been characterized so thoroughly. In particular, Sample 4 (99.47 \(\pm\) 10.66) was the most abundant in terms of volatile compounds, whereas Sample 3 (86.71 \(\pm\) 9.29) was the least abundant one. In terms of chemical classes, Sample 1 was the richest one in terpen hydrocarbons, whereas it turned out to be the poorest in furans (Figure 1).  
Specifically in the fresh fruits, among the terpen hydrocarbons, the most abundant compounds identified were \(\alpha\)-pinene, (\(E\))-caryophyllene, limonene, \(\gamma\)-muvurolene, \(\alpha\)-copaene, myrceene, \(p\)-cymene, \(\beta\)-pinene, \(\delta\)-cadinene, methyl acetate, \(\alpha\)-humulene, \(\gamma\)-terpinene, and \(\alpha\)-phellandrene, followed by acids (ethanoic, formic, isovaleric) and aldehydes (\(n\)-nonanal, \(n\)-hexanal, \(n\)-octanal, (\(E\))-2-heptenal). The sample harvested in the optimal ripening period featured a great relative area percentage for (\(E\))-\(\delta\)-Ocimene, (\(Z\))-\(\delta\)-Ocimene, _neo_-allo-ocimene, and (\(Z\))-3-hexenyl-2-methylbutanoate. Instead, a very little amount of the same compounds has been encountered in the sample harvested in October. As shown in  
Figure 1: Percentage abundance of the compounds classes present in the analyzed samples. For samples description refer to Section 3.1.  
Figure 2, the fruits dried in the stove (Sample 4) exhibited a remarkable quantity of ethanoic acid, (E)-2-heptenal, mycrene, \(n\)-nonanal, \(\alpha\)-copaene, (_E_)-caryophyllene, \(\alpha\)-humulene and cembrene with respect to those not subjected to the drying process (Sample 3).
# Sphingolipidoses and Retinal Involvement: A Comprehensive Review
## 3 Ophthalmological Examination: A Key Tool in the Diagnosis and Management of Sphingolipidoses
### OCT Angiography  
An advanced adaptation of OCT, known as OCTA, enables real-time imaging of retinal and choroidal vasculature, offering critical information on blood flow dynamics. Unlike traditional fluorescein angiography (FA), OCTA eliminates the need for exogenous contrast agents, instead utilizing motion contrast from light backscattering to map vascular structures [22]. In sphingolipidoses, particularly those characterized by retinal vascular alterations, such as FD or NPD, OCTA is a valuable tool for studying the vascular changes. It is primarily useful for assessing capillary density in the macular region, vessel tortuosity, detecting potential neovascularization and identifying areas of hypoperfusion [37; 38].  
The limitations of OCTA are comparable to those of SD-OCT. Furthermore, imaging with FA enable the detection of vascular integrity abnormalities, identifiable as dye-leakage from the vessels, that OCTA is unable to visualize [31].
###### Abstract  
Smart home devices, also known as IoT devices, provide significant convenience; however, they also present opportunities for attackers to jeopardize homeowners' security and privacy. Securing these IoT devices is a formidable challenge because of their limited computational resources. Machine learning-based intrusion detection systems (IDSs) have been implemented on the edge and the cloud; however, IDSs have not been embedded in IoT devices. To address this, we propose a novel machine learning-based two-layered IDS for smart home IoT devices, enhancing accuracy and computational efficiency. The first layer of the proposed IDS is deployed on a microcontroller-based smart thermostat, which uploads the data to a website hosted on a cloud server. The second layer of the IDS is deployed on the cloud side for classification of attacks. The proposed IDS can detect the threats with an accuracy of 99.50% at cloud level (multiclassification). For real-time testing, we implemented the Raspberry Pi 4-based adversary to generate a dataset for man-in-the-middle (MITM) and denial of service (DoS) attacks on smart thermostats. The results show that the XGBoost-based IDS detects MITM and DoS attacks in 3.51 ms on a smart thermostat with an accuracy of 97.59%.  
intrusion detection system; embedded machine learning; TinyML; internet of things; machine learning; cloud computing; edge machine learning +
Footnote †: journal:  
Article
## 4 Conclusions  
This study investigates to develop a novel machine learning method can be used to estimate the fresh and hardened characteristics of SCC. Four models, including GMDH, MPMR, ENN and hybrid ANN-PSO algorithms, are evaluated and compared to detect the slump flow and 28-day compressive strength of SCC. Laboratory data obtained from [PERSON] et al. [4] was used in this study. The data comprises FA, SF, and LP as part of the cement by mass in the total powder content. In addition, the impact of admixture contents is investigated for studying the effect of powder content of admixture, which would decrease the admixture cost of the SCC. Results of the model's performance demonstrated that the developed ENN model was significantly trained, tested and validated for estimating the slump flow and compressive strength of SCC. In addition, it can be used as a machine learning technique for estimating the accurate slump flow and compressive strength values of SCC. The performance of MPMR was successfully trained to estimate the slump flow and compressive strength of SCC, but its performance with a low amount of an available data of SCC is shown to be lower than other models. The sensitivity analysis of input variables for estimating slump flow and compressive strength of SCC through ENN model shows that the impact of fly ash for detecting slump flow and compressive strength is 12.37% and 35.6%, respectively. Thus, the sensitivity of the powder content of fly ash is has a significant impact on the estimated SCC characteristics.  
[PERSON] and [PERSON] conceived and designed the research. [PERSON] and [PERSON] collected and analyzed the data for modeling. [PERSON] designed and performed the algorithms. [PERSON] wrote the manuscript and evaluated the results. [PERSON] and [PERSON] revised the final form of the manuscript. [PERSON], [PERSON], [PERSON] and [PERSON] reviewed and edited the manuscript. All authors have read and agreed to the publish version of the manuscript.  
This research received no external funding.  
This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 20 NANO-B156177-01).  
The authors declare they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.  
Figure 9: Sensitivity index of input variables for estimating S and CS of SCC.
## 2 Data
### Satellite Data  
The SMOS SM retrieval algorithm is based on the minimization of a cost function whose main component is the sum of the squared weighted differences between measured and modeled brightness temperatures (\(T_{B}\)). The target accuracy of the global SMOS L2 SM maps is 0.04 m\({}^{3}\)/m\({}^{3}\)[17]. The SMOS BEC Level 3 (L3) SM v.1 products acquired over the Iberian Peninsula are freely distributed at BEC [47]. These products were obtained by quality-filtering and re-gridding from the operational ESA SMOS L2 v.5.51 products. Grid points affected by radio frequency interference (RFI) and/or SM with Data Quality Index (DQX, defined as the error standard deviation) greater than 0.07 m\({}^{3}\)/m\({}^{3}\) are discarded. A DQX-inverse weighted average is applied to bin the data from the Icoshedral Snyder Equal Area (ISEA 4H9) grid to the global cylindrical 25-km Equal-Area Scalable Earth (EASE ML) grid. More details are available in [41]. A comprehensive validation of SMOS L2 and L3 SM products using two complementary small-scale and large-scale in situ networks (REMEDHUS and Inforiego, respectively) and a surface water balance model can be found in [48].  
The MODIS LST v.5 products from Terra (MOD11A1) and Aqua (MYD11A1) satellites are provided by the U.S. Land Processes Distributed Active Archive Center (LP DAAC) [49]. They have 1 km of spatial resolution and a nominal accuracy of 1 \({}^{\circ}\)C under clear sky conditions. Higher error (from 4 \({}^{\circ}\)C to 10 \({}^{\circ}\)C) could be expected in presence of clouds and heavy aerosols.  
Figure 1 also displays maps of 1-km MODIS LST Aqua day (top right) and 25-km SMOS BEC L3 SM (bottom right) over the Iberian Peninsula, both time-averaged during the study period. Note the different size of MODIS and SMOS pixels.
## 2 Materials and Methods
### DNA Extraction and PCR Amplification  
Total microbial genomic DNA was extracted from the BRW samples using the E.Z.N.A.\({}^{\text{\textregistered}}\)soil DNA Kit (Omega Bio-tek, Norcross, GA, USA) according to the manufacturer's instructions. The hypervariable region V3-V4 of the bacterial 16S rRNA gene was amplified with primer pairs 338F (5\({}^{\prime}\)-ACTCCTACGGGGGGCAGGCAG-3\({}^{\prime}\)) and reverse primer 806R (5\({}^{\prime}\)-GGACTACHVGGGWTCTAAT-3\({}^{\prime}\)), and the ITS1 region of the fungi was amplified with the forward primer ITS1F (5\({}^{\prime}\)-CTTGGTCATTTAGAGGAATGA-3\({}^{\prime}\)) and reverse primer (5\({}^{\prime}\)-GCTGCGTCTTTCCATCG-3\({}^{\prime}\)) by an ABI GeneAmp\({}^{\text{\textregistered}}\) 9700 PCR thermocyler (ABI, Foster City, CA, USA). The polymerase chain reaction (PCR) amplification cycling conditions were established by referring to [PERSON] et al. [13]. The PCR product was extracted from 2% agarose gel, purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA), and quantified using Quantus\({}^{\text{\textregistered}}\) Fluorometer (Promega, Madison, WI, USA).
# Cooperative Friendly Jamming Techniques for Drone-Based Mobile Secure Zone
## 5 Experiment
### Simulation
#### 5.1.3 Results of Simulation  
The source node transmitted 100,000 SignalSymbol data, and the BER was measured for the destination node and the eavesdroppers. The average BER was obtained by repeating the simulation 1000 times. In every case, the average BER of the destination node was 0, which means that all data sent by the source node were received. Figure 5 shows a graph that applies the average BER results of the eavesdroppers to the ILR metrics defined in Section 3.  
In Figure 4a, the ILR of every eavesdropper is 1. Therefore, if the devices have no mobility and the friendly-jamming technique is not used, the eavesdropping probability of the eavesdroppers is high.  
In Figure 4b, the characteristics of [PERSON]'s Friendly UAV Jamming model (Fri-UJ) [19] are included. Fri-UJ reduces the eavesdropping probability by using multiple UAVs as jammers to transmit friendly jamming signals in a mobile communication network environment. However, unlike CFJ-DMZ, the mobility of the source node is not considered. At this time, the ILR of eve2 and eve5, which are within the cooperative jamming signal range of the drones, is 0.754 and 0.960, respectively. The ILR of the eavesdroppers outside the cooperative jamming signal range of the drones is 1. Therefore, if the friendly jamming technique is used without the mobility of the devices, the eavesdropping probability of eavesdroppers is high.  
In Figure 4c, the ILR of every eavesdropper is 1. As the distance between the source and destination nodes is close, the signal strength of the source node is reduced. However, it does not have a significant impact on the communication quality of the eavesdroppers, because the simulation environment is a free space. Therefore, if the devices have mobility but the friendly jamming technique is not used, the eavesdropping probability of the eavesdroppers is high.  
In Figure 4d, which is the case of the CFJ-DMZ model, the ILR of eve2 and eve5, which are within the cooperative jamming signal range of the drones, is 0.024 and 0.014, respectively. Furthermore, the ILR of the eavesdroppers outside the cooperative jamming signal range of the drones is close to zero, and the average ILR of all eavesdroppers is 0.03. Therefore, when the mobility of the devices and the friendly-jamming technique are used together, the communication quality of the eavesdropper decreases. Compared to Figure 4b that includes the characteristics of Fri-UJ, the average ILR is reduced by 92%, and compared to Figure 4a,c, the average ILR is reduced by 97%, facilitating secure communication in the secure zone.  
Figure 5: Simulation results: conventional schemes (**a**–**c**) and proposed scheme (**d**).