esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This research study is drafted about intrusion detection in RPL-based resource-constrained smart industries. We have proposed a Fog-assisted DL-enabled intrusion detection framework (Cu-DNNGRU) to interrogate a diversified array of potential security threats in smart industries. The under-contention model is trained on the N-BaloT dataset, and its performance is evaluated on a reasonable spectrum of performance parameters equipped with accuracy, precision, recall, and F1-score. The proposed framework is then compared with several distinguished DL classifiers such as Cu-LSTMNN, Cu-BLSTM, and Cu-GRU for comprehensive performance analysis. The performance is further investigated along with some benchmarked DL algorithms from the literature. The systematic simulation results validate the effectivity of the proposed model with 99.39% accuracy, 99.09% precision, 98.89% recall, and 99.21% F1-score. The designed framework has overwhelmed existing competitive schemes with dominant performance towards efficient intrusion detection against less consumption of system resources. Finally, we aim to train the proposed model on different datasets and enhance its detection strengths in the future.  
Conceptualization, D.A.; methodology, D.A.; validation, D.A.; formal analysis, D.A. and H.W.; writing--original draft preparation, D.A.; writing--review and editing, D.A. and P.W.; visualization, H.W. and P.W.; supervision, H.W. and P.W. All authors have read and agreed to the published version of the manuscript.  
This research work is funded by the National Key R&D Program, The advanced research on new emergence and configuration technologies of Industrial IoT under Grant number: 2021 YFB3301000.  
Not applicable.  
The authors would like to thank [PERSON] for his guidance and support in completing this research work.  
The authors declare no conflict of interest associated with this research work.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Classifier** & **Dataset** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline _Proposed_ & Cu-DNNGRU & N-BaloT & 99.39\% & 99.09\% & 98.89\% & 99.21\% \\ \hline
[56] & BotStop & N-BaloT & 99.01\% & 99.02\% & 98.82\% & 98.09\% \\ \hline
[57] & ELBA-IoT & N-BaloT & 98.79\% & 98.90\% & 98.59\% & 98.39\% \\ \hline
[58] & XGB-RF & N-BaloT & 99.22\% & 98.99\% & 99.78\% & 99.09\% \\ \hline
[59] & CNN & N-BaloT & 99.16\% & 98.97\% & 99.63\% & 99.04\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the proposed model with recent threat detection approaches.
###### Abstract  
Various powders of graphene oxide (GO), GO with silver (GO-Ag) and zinc oxide (GO-ZnO) were obtained. The powders were silanized with (3-aminopropyl) triethoxysilane (APTES) aiming to be used, in a future stage, as additives in the hydraulic lime mortars composition. The powders were characterized by Fourier Transform Infrared Spectrometry (FTIR) and Scanning Electron Microscopy (SEM) before and after the silanization process. GO, GO-Ag, GO-Ag-APTES, GO-ZnO and GO-ZnO-APTES powders were also investigated by Thermogravimetric Analysis (TG/DTA) and Ultraviolet-Visible Spectroscopy (UV-Vis). Likewise, the antibacterial effect of powders against five bacterial strains was evaluated. The peaks associated to the functional groups from GO, GO-APTES, GO-Ag, GO-Ag-APTES, GO-ZnO and GO-ZnO-APTES powders were identified by FTIR analysis. The mass losses of powders, analyzed by TG/DTA were lower than those recorded for GO. By UV-VIS analysis, maxima corresponding to the electronic \(\pi\)-\(\pi\)\(\star\) and n-\(\pi\)\(\star\) transitions were recorded. SEM images highlighted the lamellar and layered structure of GO, but also the presence of Ag and Zn nanoparticles on the surface of graphene sheets. All these results confirm the presence of Ag/ZnO/APTES on the GO. The antibacterial effect evaluated by recording the diameter of the inhibition zone ranged between 12-22 mm.  
building materials; graphene oxide; FT-IR; TG; UV-Vis; SEM; antibacterial +
Footnote †: journal: Applied Sciences
## 7 Overview of Algorithms for Energy Optimization UAV-Based Cellular Networks
### Machine Learning Algorithms
#### 7.2.2 Unsupervised Learning (UL)  
UL algorithms work with a set of inputs. The input data set for training does not have a labeled output. For this reason, in UL, clustering, association, and pattern discovery are performed over existing data. It has a working mechanism that is different from SL. The purpose of UL is to enable the identification of patterns within the training datasets and categorize input objects according to the patterns defined by the system [159]. These algorithms are expected to develop specific outputs from unstructured inputs by looking for unexplored relationships between each instance or input object. UL algorithms can be classified into three main groups: clustering, association analysis, and dimensionality reduction. K-means, K-median, hierarchical clustering, and expectation-maximization are the most common examples of clustering category [160; 161]. APRIORI, Eclat and FP-Growth are examples of association analysis models [162]. Principal component anal  
Figure 7: Classification of machine learning algorithms within the scope of this work.  
ysis (PCA) and linear discriminant analysis (LDA) are examples of the dimensionality reduction category [163].
## 4 PFAC Calibration Results
### Jason-3 Cal/Val Results  
Jason-3 occupies a specific reference orbit for the multi-mission altimetry constellation. It flies on exactly the same ground-track as its predecessors TOPEX/Poseidon, Jason-1 and Jason-2, its repeat cycle is 10 days, and its measurements have provided accurate and consistent time-series for nearly three decades. In west Crete, the Gavdos Island is located under a crossover of Jason ground tracks of Pass No. 18 (descending) and Pass No. 109 (ascending) (Figure 22).  
Figure 21: The latest calibration results of Sentinel-3B for its ascending Pass No. 14 (upper) and No. 71 (lower), using sea-surface calibration.  
Figure 22: The sea-surface calibrating regions for Jason-3 along its Pass No. 109 and No. 018 over the PFAC Cal/Val facility. Sea-surface calibration is carried out at the Gavdos and at the RDK1 Cal/Val sites. The CDN1 transponder Cal/Val site has also been used for the calibration of Jason-3 Pass No. 18 (descending).
# Vector Magnetic Anomaly Detection via an Attention Mechanism Deep-Learning Model
## 2 Methods
### Data Preparation
#### 2.2.3 Data Set and Training  
We added the simulated background noise with gradual SNR (from \(-10\) dB to \(10\) dB) to the target signal to form the data set. Target signals greater than \(0.2\) in the normalized amplitude were marked as \(1\) to represent the presence of the target. The rest of the signals were marked as \(0\) to represent the absence of the target. We eventually generated a data set consisting of \(100\),\(000\) labeled examples. We randomly split the data into training (\(85\%\)) set, validation (\(5\%\)) set, and test (\(10\%\)) set. We used the Adam optimizer [22] with an initial learning rate of \(0.0003\). The batch size was set to be \(64\), and the whole training consisted of \(100\) epochs.
## 3 Analysis Results
### Internal Tides Networks  
In the wavelet power spectra (Figures 5 and 6), both diurnal internal tides (DIT) and semidiurnal internal tides (SIT) are found to be dominant. To investigate the dynamic aspects of internal tide modulation, we constructed daily-based networks corresponding to both internal tide periods, following the procedures outlined in the Network Construction section. We categorized the nodes into two subgroups representing the upper (circle) and lower (triangle) layers based on the results of wavelet spectral analysis. Then, using the Louvain algorithm, we examined the temporal evolution of network communities for both SIT and DIT networks.  
As illustrated in Figure 7, SIT networks exhibit two distinct characteristics: overall connectivity is low across all networks, and the modularity-based structure does not seem to vary significantly across both layers or over time. These observations indicate that SITs are incoherent among nodes, likely due to multiple SITs originating from external sources [44] and experiencing limited interactions within the eddy. If SITs were significantly interacting during the observation period, we would expect noticeable variability in different sub-domains. However, compared to DIT networks in Figure 8, there is no notable variation in network connectivity over time.  
Figure 8 reveals distinct behaviors in DIT networks compared to SIT networks (Figure 7). From Day 1 to Day 4, which represents the inner center, the lower layer (circle) displays a dense connectivity, while the upper layer (triangle) has a low connectivity. High connectivity signifies strong coherence among nodes, suggesting that the corresponding internal tides are not significantly modulated. Therefore, the considerably low connectivity in the upper layer indicates substantial modulation due to interactions between DITs and the mesoscale eddy.  
From Day 6 onwards, the connectivity behavior changes dramatically, with increased interconnections between the upper and lower layers. This overall dense connectivity  
Figure 8: Same as Figure 7 except for diurnal internal tide (DIT) networks.  
Figure 7: SIT (Semidiurnal Internal Tide) networks. The colors denote the communities.  
implies a noticeable reduction in the modulation of DITs. Additionally, the modularity-based community exhibits a distinct pattern--a clear separation between the upper and lower layers is visible in the inner center, while numerous communities emerge, linking nodes from both layers. In relation to the global wavelet spectra (GWS) behaviors depicted in Figure 5, these results demonstrate that internal tide modulations within the mesoscale eddy are primarily driven by interactions, such as subharmonics and cascading, between the diurnal internal tide (DIT) and the eddy. It is worth noting that our analysis findings are well consistent with the results of [PERSON] and [PERSON] [45], in which a traditional vertical-mode decomposition method was used to reach the same conclusion.
## Methodology
### Overview of MATSim  
This section first gives a brief overview of the general simulation approach used in MATSim and then introduces our self-developed multi-agent simulation tool. Here, we focus on the core of our simulation tool and how it differs from MATSim.  
MATSim is an activity-based, extendable, multi-agent simulation framework implemented in Java. It is a widely-used simulation tool that is designed to model a single day, the common unit for activity-based models. Because MATSim is activity based, every agent optimizes its daily activity chain in competition for space and time with all other agents. A MATSim run contains a configurable number of iterations, represented by the loop and detailed below [21]. A MATSim run starts with initial demand from the activity chain, road network, places of work and residences of a population within a study area. Every person that is considered an agent in MATSim possesses a fixed number of one-day plans, and each plan is composed of a daily activity chain. The plan of each agent is executed by mobsim and is given a score based on its performance by a scoring function. Based on the score that an agent's plan receives, the replanning module decides whether the plan must be modified in terms of its departure time, route, mode and/or destination [21]. The simulation output data can be used to analyze the results and to monitor the progress of the current simulation setup. Some of the files summarize a complete run, whereas others are created for a specific iteration only.  
The selection of the replanning fraction represents a related issue; 10% is a frequently-used heuristic value that works well in practice [23]. This fraction of person agents both improves the average population score and causes the system to relax as soon as possible by adjusting the travel plans. The iterative process is repeated until the average population score stabilizes. Note that there is no quantitative measure of when the system has \"relaxed\". Thus, it is difficult to determine the number of iterations needed for specific simulation scenarios; this number depends on the number of agents and the replanning strategy used.
# Effect of the Insulating Layer on the Properties of SMC Inductors
## 2 Soft Magnetic Composite Materials  
In the last three decades, powder metallurgy, together with other industrial environments, has involved innovative raw materials and products. This represented a starting point for the development of the brand-new concept of ferromagnetic materials (soft magnetic composites, or SMCs), presenting many advantages in sectors, such as those involving electrical machines [9]. The most evident advantage provided by SMC material concerns the possible directions of magnetic flux, as highlighted in Figure 1. The magnetic flux path has the same behavior in all directions (3D) for SMCs. Moreover, the eddy currents are only restricted to particle dimensions.  
SMC development started in the 1980s [13]. This consisted of electrically insulated iron powder particles with consequent isotropic magnetic behavior; the insulating material acts as a binder for the metal powder, assuming a dominant role in research activity. Therefore,  
Figure 1: The magnetic behavior difference between lamented steel and SMC materials: magnetic flux preferential directions and eddy-current paths.  
the coating process is crucial and may be prepared with different techniques [17]. SMCs' final magnetic and energetic properties depend on the volume ratio between the magnetic powder and insulating/binding material, so the insulating layer should be minimized around each ferromagnetic particle [18; 19; 20]. On the other hand, the layer's binding effect is inversely proportional to its thickness. Therefore, the research activities are oriented to optimize thinner and uniform binder layers, while maintaining good mechanical and dielectric properties.  
As SMCs still represent a tradeoff between electrical, magnetic, and mechanical aspects, the choice of the preferred properties depending on the specific final application [14; 21; 22] is a crucial point in the realization phase.  
Materials for radio-frequency applications, such as EMI absorbers, are made of finer powders with a thick insulating layer, while an SMC for an electrical machine shows thicker powder grains and late saturating, with good large-cycle behavior and the thinnest coating layer.  
Generally speaking, the insulating layer can be organic or inorganic, but hybrid solutions are also found sometimes [17].  
Figure 2 shows the ferromagnetic particles with different types of insulating coatings.  
In the present work, some specific layers have been selected to explore--in the first approach--their application in inductance sectors. The coating weight percentage is maintained at a high rate to reach the abovementioned properties for medium-high frequency applications.
## 3 Custom Interior-Point Method for Solving FLPRC
### Barrier Subproblem  
The main idea of the interior-point method is casting the inequality constraints into the objective function as  
\[\begin{array}{ll}\text{minimize}&\mathbf{c}^{T}\mathbf{v}\\ \text{subject to}&A\mathbf{v}\leq\mathbf{b}\end{array}\quad\to\quad\text{ minimize}\quad\mathbf{c}^{T}\mathbf{v}+\frac{1}{t}\phi(\mathbf{v}) \tag{27}\]  
where \(t\) is the scaling parameter, and \(\phi(\mathbf{v})\) is defined as  
\[\phi(\mathbf{v},\lambda) = \sum_{i=1}^{n_{\lambda}}\phi_{i}(\mathbf{v}) \tag{28}\] \[\phi_{i}(\mathbf{v},\lambda) = \left\{\begin{array}{l}-log(b_{i}-\mathbf{a}_{i}^{T}\mathbf{v}) \\ \infty.\end{array}\right. \tag{29}\]  
The problem is called a _barrier subproblem_ and the function \(\phi\) is called a _barrier function_. This function is convex and smooth, and, thus, the barrier subproblem becomes convex if the original problem is convex. The first derivative of this function is  
\[\
abla\phi(\mathbf{v},\lambda) = \sum_{i=1}^{n_{\lambda}}\frac{\mathbf{a}_{i}}{b_{i}-\mathbf{a}_{ i}^{T}\mathbf{v}} \tag{30}\]  
We define \(\mathbf{z}\) with elements \(z_{i}=1/(b_{i}-\mathbf{a}_{i}^{T}\mathbf{v})\), and we have  
\[\
abla\phi(\mathbf{v},\lambda)=A^{T}\mathbf{z}\]  
The optimality condition for the _barrier subproblem_ is  
\[\
abla\big{(}\mathbf{c}^{T}\mathbf{v}+\frac{1}{t}\phi(\mathbf{v})\big{)}= \mathbf{c}+\frac{1}{t}A^{T}\mathbf{z}=\mathbf{0}. \tag{31}\]  
Comparing this with the gradient of Lagrangian (26a),  
\[\
abla_{x}L(\mathbf{v},\lambda)=\mathbf{c}+A^{T}\lambda=\mathbf{0}\]we have  
\[\lambda=\frac{1}{t}\mathbf{z}=\frac{1}{t(\mathbf{b}-A\mathbf{v})}. \tag{32}\]  
Substituting this into the _complementary slackness_ (26b) and eliminating linear inequalities (26c) and (26d), we have the modified KKT condition:  
\[\mathbf{c}+A^{T}\lambda=\mathbf{0} \tag{33}\] \[\mathbf{diag}(\lambda)(\mathbf{b}-A\mathbf{v})=(1/t)\mathbf{1}\]  
The modified KKT conditions (33) satisfy the following two properties:  
* These conditions are far easier to solve than the KKT condition (26)
* As \(t\) increases, these condition approaches the KKT condition (26).  
Combining these two properties, the FLPRC is solved to optimality. The modified KKT conditions (33) are solved by the Newton method, which is described in detail in the next section.
## 5 Results and Discussions
We only accepted corresponding VIF (variance inflation factor) values below 5 and, additionally, an average less than 2.5, while a worrying collinearity is usually considered for VIF > 10 [68]. Consequently, we have dropped twelve variables with high intercorrelation, and responsible for causing inflated estimations and inaccurate variances [69] (p. 1). After eliminating these problematic variables in a specific order (_avg_HSG_, _avg_BG_, _age_, _d_edu_, _m_edu_, _agreeable_, _consci_, _lab_success_source_, _parental_seq_lvl_, _interpers_trust_lvl_, _p_cl_asum_resp&child_future_, _priv_), no signs of multicollinearity have been detected, and this is proof of a reliable binary logistic regression. Moreover, this cleaned set of variables also increased the percentage of complete records (at least 95%). Tables 2-4 correspond to three regional modelsdeveloped considering the answers in each of the three university centres from Iasi, Cluj-Napoca, and Sibiu. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**Logit Regression**} & \multicolumn{2}{c}{**Poisson Regression with Robust Standard Errors**} \\ \cline{2-6}  & **(a)** & **(b)** & **(c)** & **(d)** & **(e)** & **(f)** \\ \hline \multirow{2}{*}{_clar\_eq\_migrWEur\_pers\_ul\_recog_} & 2.245 *** & 2.239 *** & 2.232 *** & 1.499 *** & 1.493 *** & 1.446 *** \\  & (0.162) & (0.163) & (0.169) & (0.103) & (0.104) & (0.106) \\ \hline \multirow{2}{*}{_urban_} & \(-\)0.101 & \(-\)0.156 & & \(-\)0.0616 & \(-\)0.0991 \\  & (0.167) & (0.171) & & (0.100) & (0.100) \\ \hline \multirow{2}{*}{_income\_lvl_} & \(-\)0.0092 & \(-\)0.0346 & & \(-\)0.00574 & \(-\)0.0208 \\  & (0.051) & (0.052) & & (0.033) & (0.032) \\ \hline \multirow{2}{*}{_no\_of\_sibl_} & \(-\)0.0506 & \(-\)0.0172 & & \(-\)0.0327 & \(-\)0.0113 \\  & (0.077) & (0.077) & & (0.047) & (0.046) \\ \hline \multirow{2}{*}{_both\_p\_gone\_abrad_} & 0.103 & 0.182 & & 0.0587 & 0.138 \\  & (0.242) & (0.242) & (0.147) & (0.150) \\ \hline \multirow{2}{*}{_only\_m\_gone\_abrad_} & \(-\)0.079 & \(-\)0.0756 & & \(-\)0.0445 & \(-\)0.0208 \\  & (0.262) & (0.267) & (0.157) & (0.156) \\ \hline \multirow{2}{*}{_only\_d\_gone\_abrad_} & 0.00939 & 0.0705 & & 0.00672 & 0.0368 \\  & (0.212) & (0.219) & (0.122) & (0.124) \\ \hline \multirow{2}{*}{_open_} & \(-\)0.18 & & \(-\)0.106 \\  & (0.167) & & (0.098) \\ \hline \multirow{2}{*}{_inclined\_to\_exact\_sci_} & \(-\)0.216 & & & \(-\)0.13 \\  & (0.167) & & (0.101) \\ \hline \multirow{2}{*}{_high\_believe\_in\_[PERSON]_} & \(-\)0.231 & & & \(-\)0.126 \\  & (0.201) & & (0.120) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Raw coefficients (Logit vs. Poisson) of the model based on responses from FEAA, UAIC (Iasi). Figure 2: The core (top influence) of the general model as result of using data mining tools: neural networks-based algorithm in Microsoft Excel DM and DMX queries in Microsoft SSAS.
## 3 Results  
### Annual Publication Trend  
Figure 3 presents the annual publication trend in the LBAs research between 2011 and 2021. The number of publications considered the literature published up to January 2022. Therefore, publications in 2021 are fully counted.  
Although there is some fluctuation in data, the LBAs research has witnessed growth over the 11 years. The increase between 2011 and 2018 is related to the higher participation of Chinese and North American scholars in LBAs research. China, Canada, and the United States were responsible for about 47% of the selected publications between 2011 and 2018. This trend has strengthened over the years, and the collaboration between European, Asian, and North American countries is mainly responsible for the high number of publications between 2019 and 2021.  
The interest of scholars in LBAs research is a consequence of the current economic scene. With the enhancement of the bioeconomy, the global lignin market has been expanding [37]. North American and European companies have been increasing their investments to transform lignin into a more attractive raw material for the market through new lignin-based products [13]. In addition, there is a growing demand for the lignin market in emerging Asian economies such as China, Japan, India, and Indonesia [3].