esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper presents a methodology for energy management in a smart microgrid based on the efficiency of dispatchable generation sources and storage systems, with three different aims: elimination of power peaks; optimisation of the operation and performance of the microgrid; and reduction of energy consumption from the distribution network. The methodology is based on four steps: identification of elements of the microgrid, monitoring of the elements, characterization of the efficiency of the elements, and finally, microgrid energy management. A specific use case is shown at CEDER-CIEMAT (Centro para el Desarrollo de las Energias Renovables--Centro de Investi-gaciones Energeticas, Medioambientales y Tecnologicas), where consumption has been reduced during high tariff periods and power peaks have been eliminated, allowing an annual reduction of more than 25,000 KWh per year, which is equal to savings of more than 8500 \(\ell\). It also allows the power contracted from the distribution company by CEDER (135 kW) not to be exceeded, which avoids penalties in the electricity bill.  
smart microgrid (SMG); energy management system (EMS); efficiency; distributed storage system; dispatchable generation; distributed renewable generation sources +
Footnote †: journal: Applied Sciences
# Coupled Elastic-Plastic Damage Modeling of Rock Based on Irreversible Thermodynamics
## 2 Basic Framework of the Elastoplastic Damage Coupled Model
### Stress-Strain Relationship
According to the non-associated flow rule, plastic potential \(G^{P}(\sigma,\gamma^{P},\omega)\) and damage potential \(G^{\omega}(Y,\omega)\) are introduced to describe the flow of plastic strain and the evolution of plastic and damage variables:  
\[de^{p}=d\lambda^{p}\frac{\partial G^{p}}{\partial\sigma} \tag{27}\]  
\[d\gamma^{p}=d\lambda^{p}\frac{\partial G^{p}}{\partial R} \tag{28}\]  
\[d\omega=d\lambda^{\omega}\frac{\partial G^{\omega}}{\partial\sigma} \tag{29}\]  
where \(d\lambda^{P}\) and \(d\lambda^{\omega}\) are the increments of the plastic multiplier and damage multiplier, respectively. The yield criterion \(F^{P}\) and the plastic potential \(G^{P}\) do not include \(R\) and, thus, can be transformed into \(\gamma^{P}\). The plastic criterion \(F^{P}\) and damage criterion \(F^{\omega}\) determine the onset of plastic flow and damage evolution. According to the Kuhn-Tucker conditions, the loading-unloading conditions for plasticity and damage are represented as follows:  
\[F^{p}=0,\ d\lambda^{p}\geq 0 \tag{30}\]  
\[F^{\omega}=0,\ d\lambda^{\omega}\geq 0 \tag{31}\]  
During the inelastic loading process, variables \(\sigma\) and \(Y\) need to reside on the yield and damage surfaces, as per the consistency condition:  
\[dF^{p}=\frac{\partial F^{p}}{\partial\sigma}:d\sigma+\frac{\partial F^{p}}{ \partial\gamma^{p}}:d\gamma^{p}+\frac{\partial F^{p}}{\partial\omega}:d \omega=0 \tag{32}\]  
\[dF^{\omega}=\frac{\partial F^{\omega}}{\partial Y^{e}}:d\gamma^{e}+\frac{ \partial F^{\omega}}{\partial Y^{p}}:dY^{p}+\frac{\partial F^{\omega}}{ \partial\omega}:d\omega=0 \tag{33}\]  
The stress-strain relationship shown in Equation (24) lacks an expression for describing the elastic thermodynamic potential \(\phi^{e}\), which can be represented using [PERSON]'s law for elastic thermodynamic potential [85; 86]. \[\phi^{e}(\varepsilon^{e},\omega)=\frac{1}{2}\varepsilon^{e}:D:\varepsilon^{e} \tag{34}\]where \(D\) is the function of the damaged elastic stiffness matrix, which is a joint function of the damage variable \(\omega\) and the elastic stiffness tensor \(D_{0}\) in the undamaged state. The expression is:  
\[D=(1-\omega)D_{0} \tag{35}\]  
By substituting Equation (34) into Equation (24), the stress-strain relationship can be represented as:  
\[de^{p}=d\lambda^{p}\frac{\partial G^{p}}{\partial\sigma} \tag{27}\]0  
To better describe the nonlinear behavior of rocks, Equation (36) is written in incremental form:  
\[de^{p}=d\lambda^{p}\frac{\partial G^{p}}{\partial\sigma} \tag{27}\]1  
From Equation (37), it is evident that the stress increments arising from the elastic strain increments and the stress lost due to changes in elastic stiffness constitute the stress increment in the model.
# Numerical Phase-Field Model Validation for Dissolution of Minerals
## 6 Results and Discussion
### PF Validation against Analytical Solution for a Dissolution of Planar Mineral
As solid dissolution starts, the concentration of solution \(c_{\text{L}}\) at the diffusive solid-liquid interface rapidly reaches saturation concentration (i.e., \(c_{\text{Le}}\) equilibrium state), while the solute normalised concentration in the solid phase keeps constant at 1 (i.e., at initial concentration). The solutes form a diffusion layer at the thin solid-liquid interface and continue to enter (diffuse) into the bulk solution. This results in a gradual decrease in the width of the solid phase. The concentration of solutes in the solution is gradually increasing, and the increase of the concentration near the solid-liquid interface is particularly significant due to diffusion limited transport through the solution. In turn the concentration of solute smoothly decreases to zero from the interface zone to the right end of the solution. This confirms also that the dissolution is carried out in a system that is regarded as semi-infinite space, which is only controlled by a diffusion mechanism without any significant effects of the imposed boundary conditions (which deviate from the idealized semi-infinitive case). Figure 6 shows the movement of the interface over time. As the dissolution proceeds, the interface gradually moves toward the solid phase. It can be seen from the reduced width of the solid phase that the speed of dissolution starts faster and then slows down, again due to the limited diffusion process of the solute through the exposure solution.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Training Set**} & \multicolumn{6}{c}{**Test Set**} \\ \hline  & & **1** & **2** & **3** & **4** & **Accuracy (\%)** & **1** & **2** & **3** & **4** & **Accuracy (\%)** \\ \hline  & 1 & 2723 & 275 & 1 & 1 & 90.8 & 2367 & 289 & 5 & 3 & 88.9 \\  & 2 & 129 & 2840 & 20 & 11 & 94.7 & 163 & 2179 & 34 & 18 & 91.0 \\ SVM & 3 & 1 & 31 & 2450 & 518 & 81.7 & 5 & 44 & 1498 & 386 & 77.5 \\  & 4 & 1 & 20 & 570 & 2409 & 80.3 & 3 & 14 & 459 & 1440 & 75.2 \\  & total & & & & & & 86.9 & & & & & 84.0 \\ \hline  & 1 & 2836 & 164 & 0 & 0 & 94.5 & 2453 & 209 & 1 & 1 & 92.1 \\  & 2 & 141 & 2846 & 10 & 3 & 94.9 & 177 & 2179 & 29 & 9 & 91.0 \\ CNN & 3 & 0 & 8 & 2560 & 432 & 85.3 & 4 & 28 & 1584 & 317 & 81.9 \\  & 4 & 0 & 4 & 482 & 2514 & 83.8 & 0 & 8 & 372 & 1536 & 80.2 \\  & total & & & & & 89.6 & & & & & 87.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion matrices of SVM and CNN models using 3000 training samples of each rice seed variety in the Spectral range 2. SVM and CNN. When the number of training samples increased, the performances of the CNN models were superior to the corresponding SVM models. Deep learning methods could learn features automatically, and more samples would allow more potential feature combination to be explored. For models trained with less than 1500 samples, performance improved significantly with the number of training samples. For models trained with more than 1500 samples, performance improvements were not significant. In practical applications, models should be built to identify much more rice varieties. To achieve high model performance with a reasonable sample collection cost, it is better to keep a hold-out test set, and then gradually collect samples for training until test accuracy does not change significantly.
###### Abstract  
Financial market prediction attracts immense interest among researchers nowadays due to rapid increase in the investments of financial markets in the last few decades. The stock market is one of the leading financial markets due to importance and interest of many stakeholders. With the development of machine learning techniques, the financial industry thrived with the enhancement of the forecasting ability. Probabilistic neural network (PNN) is a promising machine learning technique which can be used to forecast financial markets with a higher accuracy. A major limitation of PNN is the assumption of Gaussian distribution as the distribution of input variables which is violated with respect to financial data. The main objective of this study is to improve the standard PNN by incorporating a proper multivariate distribution as the joint distribution of input variables and addressing the multi-class imbalanced problem persisting in the directional prediction of the stock market. This model building process is illustrated and tested with daily close prices of three stock market indices: AORD, GSPC and ASPI and related financial market indices. Results proved that scaled t distribution with location, scale and shape parameters can be used as more suitable distribution for financial return series. Global optimization methods are more appropriate to estimate better parameters of multivariate distributions. The global optimization technique used in this study is capable of estimating parameters with considerably high dimensional multivariate distributions. The proposed PNN model, which considers multivariate scaled t distribution as the joint distribution of input variables, exhibits better performance than the standard PNN model. The ensemble technique: multi-class undersampling based bagging (MCUB) was introduced to handle class imbalanced problem in PNNs is capable enough to resolve multi-class imbalanced problem persisting in both standard and proposed PNNs. Final model proposed in the study with proposed PNN and proposed MCUB technique is competent in forecasting the direction of a given stock market index with higher accuracy, which helps stakeholders of stock markets make accurate decisions.  
Keywords:probabilistic neural network (PNN), multi-class undersampling based bagging (MCUB), stock market indices; multivariate distribution; global optimization; directional prediction +
Footnote †: journal: Applied Soft. 2019, 9, 5334; doi:10.3390/app9245334
# A Statistical Performance Analysis of Named Data Ultra Dense Networks
## 2 Motivation: Ultra-Dense Network
Furthermore, statistical modeling for ultra-dense networks can be utilized, which provides a broader view of the network topology and network characteristics. Statistical models can be practically utilized in network measurements, planning, management, and design requirements [23]. In summary, the statistical analysis provides meaningful information by summarizing the entire dataset [24]. In addition, it can be used for the prediction of dynamic network traffic, which may change as time goes by. In summary, statistical modeling or analysis can have significant success in the cost optimization and maintenance of networks and the planning and designing of different network parameters [25]. To the best of our knowledge, this paper is the first to present a statistical analysis of ultra-dense networks in bottleneck situations and to consider NDN as a network layer protocol. Figure 2: Example of the communication scenario in a stadium.
## 3 Controller Design
### Design of the Sliding Mode Controller for the Speed Loop via the FTSTRL  
An entire control block including fast-terminal super-twisting sliding mode control and an extended state disturbance observer is shown in Figure 1. This robust performance is achieved by continuously adjusting the control input to compensate for any disturbances and deviations from the desired speed. The speed loop operates by utilizing feedback from the actual motor speed, comparing it to the reference speed, and making real-time adjustments to the control signals. This dynamic adjustment process helps maintain consistent performance, minimizing the impact of external disturbances and parameter variations, and ensuring smooth and reliable operation of the system.  
The speed tracking error and sliding surface are same as in Equation (7). Therefore, based on Equation (11), the speed sliding mode control law can be obtained as follows:  
\[i_{q_{TFSTRL}}=\frac{1}{b}\int\limits_{0}^{t}\left[\dot{\omega}_{ref}+k_{1}|s|^ {\frac{1}{2}}\text{sgn}(s)+k_{2}\int\limits_{0}^{t}\text{sgn}(s)d\tau+k_{3}s^{ \frac{\delta}{\delta}}+k_{4}s-a\dot{\omega}_{m}+d\right]d\tau. \tag{12}\]  
Based on the CSTRL, the controllers used for the comparison reference are the same as in Equation (10). For the NSMRL:  
\[i_{q_{\text{\tiny{NSMRL}}}}=\frac{1}{b}\int\limits_{0}^{t}\Big{(}\dot{\omega}_ {ref}+\kappa|s|^{\rho}sgn(s)+\chi|s|^{5\cdot sgn(|s|-1)}s-a\dot{\omega}_{m}+d \Big{)}d\tau. \tag{13}\]  
**Theorem 1**.: _Configuring the controller as specified in Equation (12) ensures the stability of the motion system._  
Proof of Theorem 1.: The candidate Lyapunov function is selected as follows:  
\[V=\frac{1}{2}s^{2}. \tag{14}\]  
To ensure the system is asymptotically stable, it is sufficient to show that \(\dot{V}<0\) according to the Lyapunov stability theorem. This condition indicates that the Lyapunov function \(V\) is decreasing over time, leading to the conclusion that the system's state will converge to equilibrium. Consequently, the system will not exhibit unbounded behavior, ensuring reliable and stable operation under the given controller design.  
\[\dot{V}=s\dot{s} \tag{15}\] \[=s\Big{(}\dot{\omega}_{ref}-(a\dot{\omega}_{m}+b\dot{u}-\dot{d}) \Big{)}\enspace.\]  
Substituting Equation (12) into Equation (14), Equation (15) is given by  
Figure 1: Entire control block including fast-terminal super-twisting sliding mode control and extended state disturbance observer.  
\[\dot{V}=s\dot{s} \tag{16}\] \[=s\Bigg{(}\dot{\omega}_{ref}-(a\dot{\omega}_{m}+b\cdot\frac{1}{b} \Bigg{[}\dot{\omega}_{ref}+k_{1}|s|^{\frac{1}{2}}\text{sgn}(s)+k_{2}\int\limits_ {0}^{t}\text{sgn}(s)d\tau+k_{3}s^{\frac{4}{P}}+k_{4}s-a\dot{\omega}_{m}+\dot{d} \Bigg{]}-\dot{d})\Bigg{)}\] \[=s\Bigg{[}-k_{1}|s|^{\frac{1}{2}}\text{sgn}(s)-k_{2}\int\limits_ {0}^{t}\text{sgn}(s)d\tau-k_{3}s^{\frac{4}{P}}-k_{4}s\Bigg{]}\] \[\leq 0\]  
where \(s^{\frac{4}{P}}=|s|^{\frac{4}{P}}\text{sgn}(s)\)[29]. Therefore, Theorem 1 is proved completely.
## 2 Architecture of Dima System
### Hardware Layer  
The hardware layer contains hardware devices, their configurations, and network topology, which support the functions in the system layer. The main categories of DIMA devices are sensors, actuators, cables, switches, Core Processing Modules (CPMs), Remote Data Concentrators (RDCs), and Input/Output (I/O). The purpose of these devices is to provide the necessary resources (computing power, memory, data storage, etc.) to the system. Each device is characterized by its type (Central Processing Units (CPUs), network hardware, or peripherals), vendor, weight, volume, power, input ports, output ports and supported signal types. Such characteristics are important in designing the quality measures and constraints in the optimization problem. The details of the DIMA devices are as follows.
# An Introduction of a Modular Framework for Securing 5G Networks and Beyond
## 3 Proposed Network Service Security (NSS) Framework
### Formal Verifications and Performance Evaluation of the Security Protocols
#### 3.4.2 Performance Evaluation Based on Analytical and Simulation Approaches
\begin{table}
\begin{tabular}{l l} \hline
**Primitive** & **Value** \\ \hline Symmetric key & 128 bits \\ \hline Asymmetric key & 256 bits \\ \hline SHA256 & 256 bits \\ \hline Token & 128 bits \\ \hline Nonce & 128 bits \\ \hline
5G IDs & 64 bits \\ \hline D2D IDs & 256 bits \\ \hline Nonce key & 256 bits \\ \hline D1 & 256 bits \\ \hline Strings & 32 bits \\ \hline MAC & 64 bits \\ \hline SQN & 48 bits \\ \hline Timestamp & 16 bits \\ \hline RES & 256 bits \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Cryptographic Primitive Size. \begin{table}
\begin{tabular}{l l} \hline
**Parameters** & **Values** \\ \hline Throughput & bits/ms \\ \hline Latency & ms \\ \hline \(m\) & messages primitive cost \\ \hline \(n\) & total sum of \(m\) \\ \hline \end{tabular}
\end{table} 
Table 7: Evaluation Metrics \begin{table}
\begin{tabular}{l l l} \hline
**Protocols** & **Computational Time (ms)** & **Total Time** \\ \hline \hline \multicolumn{3}{c}{**(ms)**} \\ \hline
5G Protocols & & \\ \hline SAP-AKA & \(T_{E}+6T_{Se}+T_{Ae}+6T_{Sd}+T_{Ad}+1T_{Ab}+13T_{KDF}\) & 439.2 \\ \multicolumn{3}{c}{\(+6T_{V}\)} \\ \hline NS-FId & \(T_{E}+6T_{Se}+4T_{Ae}+6T_{Sd}+4T_{Ad}+1T_{Ab}+2T_{KDF}\) & 396 \\ \multicolumn{3}{c}{\(+5T_{h}+2T_{n}+10T_{V}\)} \\ \hline DCCSS & \(T_{E}+8T_{Se}+8T_{Sd}+4T_{h}+2T_{n}+10T_{V}\) & 285 \\ \hline
5G-AKA & \(T_{E}+6T_{Se}+T_{Ae}+6T_{Sd}+T_{Ad}+1T_{Ab}+8T_{KDF}\) & 545.5 \\ \multicolumn{3}{c}{\(+2T_{h}+11T_{V}\)} \\ \hline
5G D2D Protocols & & \\ \hline DDSec & \(T_{E}+4T_{Se}+3T_{Ae}+4T_{Sd}+3T_{Ad}+2T_{K_{D2D}}+\) & 360.5 \\ \multicolumn{3}{c}{\(7T_{h}+1T_{h}+2T_{s}+7T_{V}\)} \\ \hline DDACap & \(T_{E}+5T_{Ae}+5T_{Ad}+2T_{K_{D2D}}+\) & 307.5 \\ \multicolumn{3}{c}{\(7T_{h}+1T_{s}+8T_{V}\)} \\ \hline \end{tabular}
\end{table} 
Table 8: Computational Cost of the Protocols.
# Integrated Sensing and Communication Using Random Padded OTFS with Reduced Interferences
## 7 Simulation Results
### Sinc Extrapolation-Based Data-to-Data Interference Reduction Only
Also, regular RP-OTFS outperforms ZP-OTFS in case of low SNRs, but for higher SNRs, interference compensation is needed for regular RP-OTFS to outperform ZP-OTFS. In case of lower SNRs, the dominating source of errors is noise, and since RP-OTFS has more active carriers, it is more efficient despite the bigger interference from the pilot zone. As the SNR increases, the interference becomes the dominating factor that determines the efficiency of the system. The interference compensation is not as efficient in the case of ZP-OTFS as it has less power in the pilot zone. One of the obvious solutions is to increase  
Figure 13: The efficiency of the data-to-data interference compensation in regular RP-OTFS without pilot repetition for different numbers of maximum reflections. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Description** \\ \hline Modulation & 4-QAM & Type of carrier modulation \\ \hline \(f_{c}\) & 70 GHz & Carrier frequency \\ \hline \(f_{s}\) & 100 MSPS & Sampling rate \\ \hline \(M\) & 32 & No of samples in delay direction \\ \hline \(N\) & 16 & No of samples in Doppler direction \\ \hline \(L\) & 8 & Number of pilot samples \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Values of simulation parameters. the power of the ZP-OTFS pilot to the level of the regular RP-OTFS pilot zone. However, this will lead to an extremely high peak-to-average ratio, which, in turn, will make the transmission in real systems extremely difficult.
# Measurement of Head-Related Transfer Functions: A Review
## 5 Single-Loudspeaker-Based Fast HRTF Measurement Methods
Instead of using inertial sensors [142; 166; 168], VR/AR/MR headsets [120; 166; 168; 169], and cameras [170], some researchers proposed to acoustically track head movements based on the recorded ears signals and the knowledge of speaker positions [150], or by analyzing recorded signals with an additional microphone array [171]. The mobile systems described above show the potential for the fast acquisition of individual 2D HRTFs with only a few measurement devices. There are still some challenges to be considered, e.g., the synchronization between the orientation data and microphone signals, uncontrollable head-above-torso orientations (HATOs) during the measurement, influences of the variable rotational speeds, and the HMD on the accuracy of HRTFs [120; 167; 172; 173]. As mentioned in Section 3, except for several special designs such as the setup \(\rm M_{I}\) in Figure 8, multi-loudspeaker systems are commonly designed for measuring HRTFs with a fixed distance. With a single loudspeaker, it is a time-consuming task for measuring HRTF datasets with a dense spatial resolution and various distances. Recently, [PERSON] et al. [120] developed a MR-based mobile measurement system (see setup \(\rm S_{K}\) in Figure 9) for continuously measuring distance-dependent multi-directional HRTFs (3D HRTFs). The MR device is used to detect the head orientation, the source-listener distance,and display movement pattern and measurement points to subjects. Unlike 2D HRTF measurements, the subject is not only asked to rotate her/his head but also to move towards or away from the loudspeaker. If the current source-listener distance is one of the desired measurement distances (an appropriate tolerance of the distance should be defined), some virtual 2D points representing measurement directions are visible through the HMD, and the subject should rotate her/his head to cover these measurement directions. At the same time, HRIRs are adaptively calculated using the NLMS method in real-time, and the quality (ESA) of each estimated HRTF is provided to the subject. If the current distance is not one of the desired distances, the measurement points (virtual 2D points) are not visible through the HMD. [PERSON] et al. [120] illustrated that the acoustical influence of the MR device (Microsoft HoloLens) on the binaural cues are overall small, but for some lateral directions the distortions are slightly larger than JNDs. There are still several open issues that should be addressed especially for measuring near-field HRTFs, e.g., the property of the sound source, the accuracy of the estimated distances. Nevertheless, this method provides a novel solution towards fast measuring 3D individual HRTFs with a single loudspeaker.
## 3 Materials and Methods
### Accuracy Assessment  
A systematic accuracy assessment was performed on the six maps that resulted from the classification workflows described in Section 3.3. The assessment was performed on the set of 1136 plots that were kept for the validation of the models, which were not used in the calibration process nor in the training of the models. As for the training, only the central part of the plot (3 \(\times\) 3 pixel) was considered in order to take into account any shifts between the S1 and S2 and Google Earth images that were used in the response design.  
Little guidance is available on how to handle sample units that are pixel blocks and not single pixels [58]). Ref. [59] recommends not to use the mode of the pixel block as it does not correspond to the map that is given to the user and [53] advises that the rules for defining an agreement between the reference and the map classification need to be clearly stated. In our case, we chose to compare the different workflows in a similar way despite the two different training approaches that were used (\"pixel\" versus \"window\"). We defined the population map (i.e., the per-class areas of agreement and the areas of each class-specific type of disagreement) as the difference map that was created by overlaying the single class from the reference sample onto the output map [59]. The specific value of each pixel of the output maps was kept and not averaged for the size of the sample unit. The overall accuracy (OA), the producer accuracy (PA) and the user accuracy (UA) were the selected metrics that were employed in order to compare the performance of each machine learning classifier in each classification workflow. A visual assessment was also performed in order to identify the classifier that best captured the landscape.  
Class 21 (grass and single trees) was designed to give us information regarding whether there were single trees in the 5 \(\times\) 5 plot. Due to the specificity of this non-homogeneous class, it could not be used in an accuracy assessment that compared the population of the 9 pixels of the reference map and the classification map. The window-based classification models were therefore evaluated with class 21 considered as class 32 (grass). In addition, a specific evaluation of the 98 samples of class 21 was conducted for the three classification workflows. The different maps were assessed in terms of the presence or absence of tree cover and the presence of class 21 in the 5 \(\times\) 5 plots identified visually as 1-10% tree cover.