esa-sceva/satcom-chunk-collection huggingface dataset:
# Highway Microgrid Project Evaluation under Energy Transportation Integration
## 3 Highway Microgrid Comprehensive Evaluation Method
### The Calculation Method of Weight  
#### 3.1.1 Analytic Hierarchy Process  
AHP is a method to determine subjective weights. AHP digitizes and stratifies people's thinking process in complex decision-making problems after deep analysis of the nature of the problem, influencing factors, internal relationships, etc.  
AHP is a method of deciding subjective weights. Its basic steps are as follows [32]:  
Step 1. The judgment matrix of each level is built by the pairwise comparison method. The pairwise comparison method refers to the comparison of the relative importance between the two indicators when a certain indicator at the upper level is used as the criterion. The comparison scale \(a_{ij}\) was used to quantify the judgment results, as shown in Table 2. The judgment matrix \(A=(a_{ij})_{n\times n}\) can be set up. The judgment matrix is defined as follows: matrix A satisfies (1) \(a_{ij}>0\); (2) \(a_{ji}=1/a_{ij}\).  
Step 2. Figure out the maximum eigenvalue of matrix A and its homologous eigenvector.  
\[W_{i}^{1}=\frac{\overline{W_{i}^{1}}}{\sum\limits_{j=1}^{n}\overline{W_{j}^{1 }}},W^{1}=[W_{1}^{1},W_{2}^{1},\dots,W_{n}^{1}]^{T} \tag{18}\]  
\[\lambda_{\text{max}}=\sum\limits_{i=1}^{n}\frac{(AW^{1})_{i}}{nW_{i}^{1}} \tag{19}\]  
where \(W\) is the eigenvector of the judgment matrix; \(\lambda_{\text{max}}\) is the largest eigenvalue of the judgment matrix; \((AW)_{i}\) is the \(i\) element of the vector \(AW\).  
Step 3. The maximum eigenvalue method is applied to check the consistency of the judgment matrix. If the test passes, the eigenvector is the weight vector.  
\[CI=\frac{\lambda_{\text{max}}-n}{n-1} \tag{20}\]  
\[CR=\frac{CI}{RI} \tag{21}\]  
When \(CR\) is less than 0.1, it indicates that the judgment matrix has passed the consistency test, and the weight of the index can be obtained. Otherwise, it needs to modify the judgment matrix to make it consistent.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scale** & **Value Basis** \\ \hline
1 & Both indicators are equally important \\
3 & The former indicator is slightly more important than the latter indicator \\
5 & The former indicator is significantly more important than the latter indicator \\
7 & The former indicator is more important than the latter indicator \\
9 & The former indicator is extremely more important than the latter indicator \\
2, 4, 6, 8 & The importance of the two indicators lies between the above \\ \hline \hline \end{tabular}
\end{table}
Table 2: Scale definition of judgment matrix.
## 5 Discussion
In addition, it is important to mention that in previous reports using the 38-Cloud dataset, some images could not be segmented because, in certain snowy regions, the algorithm could not determine whether the image contained clouds or snow. To address this, methods such as VNDHR [54], a novel variational nighttime dehazing framework that uses hybrid regularization to improve perceptual visibility in hazy nighttime scenarios, could be used in such a study to test LSDS-HPC for improved results on Cloud-38. Finally, a major limitation of this study is the exclusive use of relatively small datasets, such as CIFAR-100, 38-Cloud, and MSLD, which do not exert significant pressure on the interconnect bandwidth in multi-GPU environments. This restricts the possibility of comprehensively evaluating the scalability and communication efficiency of LSDS-HPC infrastructure. For a more accurate performance assessment in scenarios requiring high communication intensity, it is advisable to incorporate large-scale datasets, such as ImageNet or high-resolution geospatial imagery. These datasets represent a greater demand for inter-GPU transfer and would allow the evaluation to approximate conditions typical of infrastructures listed in the TOP500, provided that the hardware employed is comparable to that used in this study.
## Materials and Methods
### Characterization of PolyHIPE Scaffolds  
The morphology of the polyHIPE scaffold was characterized with a JSM-6700 field-emission scanning electron microscope (SEM) (JEOL, Tokyo, Japan). The samples were mounted directly onto the SEM sample holder using double-sided sticking tape and were then sputter-coated with gold in vacuum prior to measurements. The average void size (D) was determined by statisticitcal calculation from the SEM images [39]. Mercury intrusion porosimetry was performed on a Micrometrics Autopore IV 9500 system (Micromeritis, Norcross, GA, USA) in order to determine the interconnected window diameter (d), total pore volume and porosity. Nitrogen adsorption/desorption measurements were performed on a Micrometrics Tristar II 3020 (Micromeritis, Norcross, GA, USA) gas sortometer with the sample maintained at -196 \({}^{\circ}\)C using liquid nitrogen. Samples were degassed at 80 \({}^{\circ}\)C for 10 h under vacuum prior to isotherm determination. The surface area was cumulated by the standard Brunauer-Emmet-Teller (BET) method with the nitrogen adsorption isotherms. Average pore size (<Dw>) was analyzed via the Barrett-Joyner-Halenda (BJH) method by utilizing nitrogen adsorption isotherms.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Formulation No.** & **St (mL)** & **DVB (mL)** & **Span80 (mL)** & **AIBN (g)** & **H\({}_{2}\)O (mL)** & **W/O a ** & **E/M b ** \\ \hline
1 & 5.00 & 0.80 & 2.90 & 0.25 & 34.8 & 80/20 & 50\% \\
2 & 5.00 & 0.80 & 2.90 & 0.25 & 49.3 & 85/15 & 50\% \\
3 & 5.00 & 0.80 & 2.90 & 0.25 & 78.3 & 90/10 & 50\% \\
4 & 5.00 & 0.80 & 2.30 & 0.25 & 72.9 & 90/10 & 40\% \\
5 & 5.00 & 0.80 & 1.70 & 0.25 & 68.0 & 90/10 & 30\% \\ \hline \hline \end{tabular}  
* W/O, the volume ratio of water phase to the oil phase. \({}^{\mathrm{b}}\) E/M, the volume ratio of emulsifier (Span 80) to the monomers.  
\end{table}
Table 1: Preparation parameters of the polyHIPE scaffolds.  
Figure 1: Synthesis pathway of the porous polyHIPE scaffolds.
# Dirichlet Matrix Factorization: A Reliable Classification-Based Recommender System
## 2 Proposed Model  
Let us assume that there are \(N\) users that may rate \(M\) different items. Rating means a user can assign a score to an item chosen from a discrete set of scores \(\mathcal{S}=\{s_{1},\ldots,s_{D}\}\). For instance, a rating consisting of giving one to five stars means \(\mathcal{S}=\{1,2,3,4,5\}\). The ratings that are known are represented by the ratings matrix \(R=(R_{u,i})\), where \(R_{u,i}=s\) if user \(u\in[1,N]\) has assigned the score \(s\in\mathcal{S}\) to item \(i\in[1,M]\) and \(R_{u,i}=\bullet\) if user \(u\) has not rated item \(i\). The matrix \(R\) is decomposed into \(D\) binary matrices \(R^{s_{1}},\ldots,R^{s_{D}}\), such that \(R^{s}_{u,i}=1\) if user \(u\) assigned the score \(s\) to item \(i\) (i.e., \(R_{u,i}=s\)), \(R^{s}_{u,i}=0\) if user \(u\) assigned other score than \(s\) to the item \(i\) (i.e., \(R_{u,i}\
eq s\)), and \(R^{s}_{u,i}=\bullet\) if the user \(u\) did not rate the item \(i\) (i.e., \(R_{u,i}=\bullet\)).  
As stated above, predicting those missing values of \(R^{s}\) is a classification problem, as \(R^{s}_{u,i}\) means whether user \(u\) thought that a rating of \(s\) is the best for item \(i\). The aim of our proposal is to model the random vector \((R^{s_{1}}_{u,i},\ldots,R^{s_{D}}_{u,i})\) as a Dirichlet random variable with parameters \(0\leq\rho^{s_{1}}_{u,i},\ldots,\rho^{s_{D}}_{u,i}\leq 1\). Therefore, \(R^{s}_{u,i}\) quantifies both the affinity of the user \(u\) for item \(i\) and the reliability of the prediction.  
Regarding how our model estimates the parameters \(\rho^{s}_{u,i}\), it supposes that there exists some \(K\)-dimensional hidden user factors vector \(P^{s}_{u}=(P^{s}_{u,1},\ldots,P^{s}_{u,K})\in\mathbb{R}^{K}\) and hidden item factors vector \(Q^{s}_{i}=(Q^{s}_{i,1},\ldots,Q^{s}_{i,K})\in\mathbb{R}^{K}\) whose dot product is \(\rho^{s}_{u,i}\). This is consistent with other MF models in the literature. As \(P^{s}_{u}\). \(Q^{s}_{i}\) may return any real number the model uses a logistic-like smooth function \(\omega:\mathbb{R}\rightarrow[0,1]\) to normalize the result into \(0\leq\omega(P^{s}_{u}\cdot Q^{s}_{i})\leq 1\). In this way, we set \(\rho^{s}_{u,i}=\omega(P^{s}_{u}\cdot Q^{s}_{i})\) as a probability.  
Figure 1 exhibits the plate diagram of the model, which is composed of \(D\) different factorizations (one factorization for each possible score), each of which is composed of \(N\) variables \(P_{u}\) (latent factors of each user) and \(M\) variables \(Q_{i}\) (latent factors of each item). The hyperparameters of the model are two positive real values \(\sigma_{p},\sigma_{Q}>0\) (which will play the role of standard deviation of gaussian priors for regularization), an integer \(k>0\) (the number of hidden factors) and a smooth logistic-like function \(\omega\) used for normalization.
# Evaluating the Efficiency of Physical and Cryptographic Security Solutions for Quantum Immune IoT
## 5 Evaluation Approach for Quantum Immune Security
### Implementations for IoT Key Exchange Scenario
#### 5.2.1 Modeled IoT Platform  
The modeled platforms for simulation were Raspberry Pi2 and Pi3. Raspberry Pi2 contains a Broadcom BCM2836 chip, with a 900 MHz quad-core 32-bit ARM (Advanced RISC Machine CPU architecture designed by Arm Holdings) Cortex-A7 processor with 256 KB shared L2 cache, and 1 GB of RAM. Raspberry Pi3 contains a Broadcom BCM2837 chip with a 1.2 GHz 64-bit quad-core ARM Cortex-A53 processor, with 512 KB shared L2 cache, and 1 GB of RAM. Both have a separate graphical processing unit (GPU) that was not used in the simulations. All components required for modeling the execution platform for the use cases of this article could be instantiated from the ABSOLUT model library. Thus, the effort required for modeling the platform consisted of only making the connections between the components and setting the parameters of the models.
## 4 Experiments
### Initial Setup  
Before performing the experiments, the ESP32-DevKitC module was programmed with each of the selected configurations to verify the proper functioning of the generated certificates and the involved cipher suites. The ESP32 was configured for all the tests with Secure Hash Algorithm (SHA) and AES hardware acceleration enabled. NIST modulo \(p\) optimizations for ECC were used in all tests because they improve the performance on both cipher suites (during the ECDHE key exchange phase, ECC operations are performed). For RSA certificates with a key size of 1024 and 2048 bits, hardware acceleration for Multi-Precision-Integer (MPI) was used. For the 3072-bit RSA certificate, it was not possible to use MPI hardware acceleration: when such an acceleration was enabled, the ESP32 module rebooted as soon as the HTTPS connection was initialized. Considering that the ESP32 datasheet [54] states that the crypto engine supports up to 4096-bit operations for RSA, the problem seems to be software related. With the ECC certificates, no further issues were found.
# Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches
## 2 Security Challenges and Malware Attacks in the IoT  
Malware is a major security threat to the IoT, and detecting unknown malware is one of the key challenges for two reasons. First, the limitations of IoT devices, such as their low power retention capability and low computational processing capability, represent a significant challenge when aiming to apply security solutions. Second, introducing new ways to connect networks, such as cloud services, opens the door to many security attacks, such as malware attacks. Furthermore, connecting new devices that were not part of traditional networks via these new connection methods, such as smart sensors, makes applying security measures more complex. For these reasons, traditional malware detection mechanisms are not suitable for the IoT environment.  
In this section, we first present a brief background of IoT security challenges and limitations. This is followed by a study of existing methods for analyzing and detecting malware in general with a discussion of how they apply to IoT systems. Next, we examine IoT-related malware attacks, which have significantly increased in recent years and require immediate attention.
## 3 Results and Discussion
### Thermal Analysis  
To evaluate the thermal stability of the MWNT-incorporated in situ polymers, a TGA was performed under a nitrogen atmosphere at a heating rate of 10 \({}^{\circ}\)C/min (Figure 3). With an increase in the MWNT content from 0 wt% to 2 wt%, the T\({}_{\text{d}}\) of the PS increased from 223 \({}^{\circ}\)C to 239 \({}^{\circ}\)C and 250 \({}^{\circ}\)C. A similar trend was observed in the PETE, where the T\({}_{\text{d}}\) values rose from 262 \({}^{\circ}\)C to 280 \({}^{\circ}\)C and 289 \({}^{\circ}\)C, demonstrating the positive effect of MWNT incorporation on thermal stability. This improvement in thermal stability can be attributed to the incorporation of the carbon-based MWNTs within the polymer matrices, which enhanced the thermal resistance of each in situ polymer sample. The DSC results indicated a slight increase in the T\({}_{\text{g}}\) of both the PS and PETE with an increasing MWNT content. For the PS, T\({}_{\text{g}}\) increased from 82 \({}^{\circ}\)C at 0 wt% MWNT to 90 \({}^{\circ}\)C and 91 \({}^{\circ}\)C at 1 wt% and 2 wt%, respectively. In the case of the PETE, the T\({}_{\text{g}}\) values were observed to increase from 83 \({}^{\circ}\)C to 88 \({}^{\circ}\)C and 91 \({}^{\circ}\)C as the MWNT content increased, displaying a trend similar to that of the PS. Additionally, T\({}_{\text{m}}\) values of 200 \({}^{\circ}\)C, 201 \({}^{\circ}\)C, and 222 \({}^{\circ}\)C were observed for the PETE samples containing 0 wt%, 1 wt%, and 2 wt% MWNTs, respectively. For the DSC measurements, a slow cooling rate of 1 \({}^{\circ}\)C/min was applied to promote annealing and enhance the crystallinity of the polymers. This condition was intended to maximize the crystalline regions during the cooling cycle and enable a clear observation of T\({}_{\text{m}}\) in the second heating cycle. Nevertheless, no T\({}_{\text{m}}\) was observed for the PS, which can be attributed to its inherently amorphous nature, in contrast to the PETE, which is generally considered a more crystalline polymer. As a result, distinct melting peaks were observed only for the PETE. These T\({}_{\text{m}}\) values also showed an increasing trend with a higher MWNT content. These results further support the positive influence of the MWNT incorporation on the thermal stability of the polymer materials (Figure 4) [20; 29; 30; 31].  
Figure 2: FE-TEM images of samples: (**a**) PS at 50,000\(\times\) magnification, (**b**) 2.0 wt% MWNT/PS at 50,000\(\times\), (**c**) PETE at 50,000\(\times\), (**d**) 2.0 wt% MWNT/PETE at 50,000\(\times\).
# Positioning Energy-Neutral Devices: Technological Status and Hybrid RF-Acoustic Experiments
## 3 Hybrid Acoustic-RF Approaches for Positioning Energy-Neutral Devices  
Unambiguously positioning a mobile device in 3D space requires the distance to at least four anchor nodes. These anchor nodes are distributed across the room and their locations are known by the positioning system (Figure 2). Particular challenges in positioning and tracking arise when the energy budget of the mobile device is very constrained. Clear insights into the mobile device's energy budget are therefore essential. In this section, we inspect the different components in a hybrid RF-acoustic positioning system and identify which key parameters affect the energy consumption. Based on this analysis, an optimized system design is presented. In the following subsections, we first focus on the setup options and signaling approaches for hybrid acoustic-RF positioning, and in the following, focus on tailored backscattering technology.
## 5 Optimization Control Strategies in Bems
### Uses of Optimization Algorithms in BEMS
#### 5.2.2 Artificial Bee Colony (ABC)  
ABC is an optimization algorithm based on honey bee foraging behavior [146]. In [147], a HEMS for household appliances was proposed by implementing DR schemes for residential consumers with facilitating renewable energy integration. This framework was solved based on an improved ABC algorithm. In [148], the authors proposed a new approach of ABC with Knowledge Base (ABC-KB) for the management of power and the occupant's preferred environment inside a residential building. ABC-KB uses less power than GA and PSO.
## 6 Discussion  
Based on the results of measurement by the ultrasound pulse method, there were observable changes in the velocity of the ultrasound wave passing through the specimens after the previous blast load, i.e., there was an increase in the speed of the ultrasound waves. This phenomenon was not expected at the beginning of the experiment, but from the previous evaluation, it is clear that these changes are not negligible because of the high number of measuring points, where the increase in the velocity was demonstrated. The graphical evaluation of destructive laboratory tests shows a gradual decrease in strength after reaching the maximum load-bearing capacity, which affirms high stiffness of the material and its quality. Only for the specimen A2_3, was there a decrease in the velocity of the ultrasound wave after blast loading in all measuring points, i.e., there was no strengthening of SFRC. At the same time, from the evaluation of the decrease of the force depending on the increasing deflection, a faster decrease of the residual force after reaching the maximum bearing capacity is visible. It is possible to deduce a partial conclusion that there was a blast loading failure, and simultaneously the formation of cracks in the internal structure of the concrete slab was confirmed by NDT. After subsequent static load, and reaching F\({}_{\text{max}}\), cracks opened and steel fibers were activated, which due to their hooked ends were not torn out from concrete but still were able to bear tensile stress. This result was expected for all three testing specimens, but was not proven.  
The obtained measurement results show that in the case of SFRC slabs evaluated using the NDT ultrasound pulse method, after dynamic blast load, the cement matrix was strengthened in combination with a possible change in the orientation of steel fibers. As the SFRC recipe had a relatively high concentration of fibers, which is more often used for the production and assembly of military protective structures, rather than for civil engineering buildings, there could also be clusters of fibers, and this would subsequently affect the velocity of passage of ultrasound.  
Another possible aspect explaining the increase in the velocity of ultrasound waves is, of course, the quality control of the production technology of the testing specimens themselves. This can be verified using other NDT methods such as radiography and computed tomography, which were not used in the initial phase of the experiment due to financial demands.  
With regard to the designated concentration of steel fibers in concrete mixture, it is also possible to argue about the significance of the suggested dimensions of the specimens. The specimens with 60 mm of thickness can also affect the result of the ultrasound measurement with respect to the wavelength and the setup of the ultrasound device itself. In the subsequent phases of the experiment, further tests will be planned regarding the aspects described above, as they may have significantly influenced the obtained results.
# Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
## 6 Conclusions
### Findings  
This review summarizes the findings of various approaches developed for multiple applications as follows:  
* The wavelet decomposition method predicted wind speed and power accurately and effectively using improved time series, chaotic time series, and grey models [74; 81]. The false nearest neighbor analysis method forecast the chaotic behavior of the wind-wave characteristics, including wave period and height [109].
* Hilbert-Huang transform and Hurst analysis is a proper choice to forecast the multiscale chaotic characteristics of wind power [108]. In contrast, ensemble EMD and full parameters continued fraction is appropriate for predicting wind power's nonlinear chaotic time series [119].
* The empirical dynamic model presented in [165] forecast the wind speed for various height levels. At the same time, the fractal dimensional-based self-adaptive model for wind speed predicted atmospheric motion and fractal features [152].
* The approaches such as the ordinary least square method [28], recurrent predictor neural network [36], hybrid Elman-NARX neural network [78], and embedding theorem-repetitive fuzzy [21] forecast the sunspot number (chaotic time series) effectively. In all these cases, the sunspot data were collected from the world data center for Belgium's sunspot index.
* The combination of chaos theory and techniques, such as ensemble EMD and CNN-SSA, effectively forecast the PV system's output power under certain conditions, such as rainy, heavy cloudy, lightly cloudy, and sunny conditions [155; 158]. The data were collected from the St Lucia campus PV station, Australia, in all these cases.
* The integration of the BPNN with GA, SA algorithms [115], parallel chaos [118], wavelet decomposition-based methods [120; 157] was successfully used to forecast the deregulated power system's short-term electrical energy demand. These methods help in proper economic power dispatching with an enhanced demand response that assists in efficient spot price-fixing in the deregulated power market.
* The regression analysis models using ANN and chaotic nonlinear dynamic [73] and coupled quantity-pattern similarity [18] were validated to predict the stream water temperature and monthly precipitation.
* The minimum phase space-based EKF method was used to forecast the blind equalization in power line communication systems to overcome channel noise [106].  
* The response surface-based Bayesian inference [149] and PCE-based hybrid MCMC [163] approaches were used to predict the generator's dynamic parameters, such as inertia, exciter gains, damping ratio, and droop.
* The independent component analysis method in [94] adequately estimated the amplitude and frequency of power systems' highly distorted signals to avoid the ferroresonance effect.
* The Markov chain switching regime model enhanced the precision accuracy and is helpful for wind power forecasting during scheduling and planning [144].
## 5 Experimental Results
### Obstacle Avoidance Flight Experiments
It served as essential ground-truth data, particularly in the absence of an onboard localization system, and confirmed that the drone adjusted its trajectory and heading based on the proposed collision avoidance algorithm.