esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Having a smaller core size than that of standard specimen (\(\Phi\)100 \(\times\) 200) is a very useful and practical way to estimate the compressive strength of concrete without causing possibly serious damage to concrete members during the extraction process of the cores. It can also save on the in situ drilling time due to the high strength of concrete. Moreover, a smaller core is much more appropriate for high reinforced concrete members (such as nuclear power plant structures), in which the reinforcement space is relatively narrow. When the core is extracted from the concrete member, a correction factor should be examined as compared with the compressive strength of standard concrete specimens with a cylinder or cube type. The correction factor is mainly affected by varying the slenderness ratio (Length/Diameter). For a core size of 46 and 69 mm, the mixed strength decreased by around 70% as equal to 14 and 24 MPa with a maximum coarse aggregate of 22 mm at 28 days [24]. Another study found that the maximum aggregate size and the slenderness ratio increases as the core strength decreases [25]. Microcore strength tests were conducted on concrete with a compressive strength ranging from 20 to 50 MPa, using a core diameter of 28 mm and the strength reduction showed up to 79% [26]. For high strength concrete, one significant study reported that the core strength was about 95% of the total cylinder strength in concrete with 20 MPa compressive strength, whereas in 50 MPa concrete, the core strength was about 85% of the total cylinder strength [27]. For the special case of concrete with very high compressive strength, around 120 MPa, the discrepancy was between 72% and 85% of the standard cylinder specimen for cores with diameters between 25 and 100 mm [28]. In contrast with concrete cylinders, core strength tests can result in somewhat large variation in the prediction of the strength. In order to guarantee the applicability of the core strength test for testing high strength concrete, further investigation on correction factor is needed. In particular,comparing core specimens (including small-size core specimens) with the standard cylinder strength of the concrete should be investigated. This study proposes two methods for evaluating the compressive strength of HSC for the maintenance of NPP structures. One method is to use non-destructive techniques such as UPV and RH. The other is the partially destructive, concrete core strength test. The mix proportions of the HSC are designed to result in strengths of 40, 60, and 100 MPa, and three different core diameters are examined: 30, 50, and 100 mm. The compressive strength test of the concrete cylinder and core was carried out at 28 days and the correlation relationship between the tested and predicted strength using NDT was investigated. The estimation equation for the HSC cylinder was statistically obtained using a double linear regression method for better accuracy of prediction and superior validation. Furthermore, the strength correction factor between the cylinder and core (including small-size cores) specimen is examined.
## 2 TIA Design
### Input HPF  
An HPF before the TIA serves a dual purpose. First, it sinks the DC component term in (1), which can be large due to the powerful LO laser. Additionally, it can filter out the direct-detection term, which would otherwise be present, creating distortion in the IF signal at large received powers and limiting the receiver dynamic range. A trade-off exists for the cut-off frequency of this filter. Although a high enough cut-off is desired in order to filter out the direct-detection term, this would consume a significant fraction of the available PD-TIA bandwidth for high bitrate signals. The cut-off can then be chosen at a compromised value where sufficient filtering of the direct-detection term is achieved while keeping the maximum possible available bandwidth. A second order LC filter with 5 GHz cut-off frequency was implemented, whereas additional filtering is performed in the ED block depending on the target bitrate.  
In order to achieve this relatively low cut-off frequency, spiral inductors having a large inductance value are required, which in turn leads to large physical size and low self-resonance frequency (SRF). To provide a compact layout and extend the SRF, the inductors for the HPF were implemented as series inductors with opposite winding. Figure 3a shows a representation of such a dual-spiral inductor together with a typical single-spiral inductor design, both at the same scale. To compare the two approaches, an electromagnetic (EM) simulation for both designs was performed. The inductors are designed to have the same inductance value while keeping the same design parameters in both cases, that is, the same metal layer configuration, trace width, trace spacing, distance to the ground plane ring, and ground ring width. The plot in Figure 3b shows that a higher SRF can be achieved with the dual-spiral configuration for a given inductance value, while featuring a more compact layout.  
Figure 2: Functional block diagram of the proposed TIA.
## 3 Redundant Sensors Configuration and Near-Vertical Algorithm
### Near-Vertical Well Attitude Measurement Method
The installation angle \(\alpha\) and \(\varepsilon\) are calibrated before calculating the attitude using the new forms \(inc^{\prime}\), \(tf^{\prime}\) and \(az^{\prime}\). The parameters \(K\), \(P\) and \(L\) are intermediate variables that prevent the final computation from being singular. In the following paper, we will validate the proposed method using the constructed test platform depicted in Figure 10.
# Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks
## 2 Methods
### Construction of the Micro-Capsule Networks  
To enhance the decoding efficiency of individual EEG samples, this study proposes a compact capsule network-based epilepsy recognition model. The framework integrates features of epileptic EEG signals with a dynamic routing mechanism, optimizing both signal characteristics and algorithmic adaptability.
## 2 MFC and Power Management System Specification
### MFC Electrical Equivalent Modeling  
The MFC can be electrically modeled as a voltage source with an internal resistance \(R_{MFC}\), open circuit voltage \(V_{MFC}\), and internal capacitance \(C_{MFC}\) as shown in Figure 1b. The value of \(R_{MFC}\) is composed of several different components (anode, cathode, membrane, and electrolyte resistances) [28; 29; 30]. The value of \(V_{MFC}\) is the MFC's thermodynamic voltage, which varies nonlinearly depending on the solution pH, temperature, and substrate concentration. Since MFCs produce a DC voltage, \(C_{MFC}\) is mostly ignored in similar works [16; 27]. However, due to the method of locating the Maximum Power Point (MPP) through the open circuit voltage of the MFC in the proposed PMS, \(C_{MFC}\) is important for the proper design of a Maximum Power Point Tracking (MPPT) algorithm; thus, it is included in our model. The equivalent circuit model of the MFC was used for the design of the PMS and the dynamic MPPT in this work.
## 4 Applications of Intelligent Acoustic Monitoring
### Environmental Monitoring and Biodiversity  
This subsection focuses on the analysis of environmental sounds and their use in assessing the condition of ecosystems when monitoring urban parks and biota [128].  
In [128], [PERSON] et al. conducted a pilot study using passive acoustic monitoring (PAM) and eco-acoustic indicators to assess the sound environment in Milan's Parco Nord urban park. They used a network of inexpensive sound recorders, distributed over approximately 20 hectares, to capture sounds in various locations and at different times of day. A set of seven eco-acoustic indicators, including ACI, ADI, BI, and AEI, was calculated from the recordings. Principal component analysis (PCA) and clustering were applied to distinguish areas with varying degrees of biotic activity and anthropogenic disturbance. The results indicated clear zones dominated by road and construction noise, as well as areas with richer biotic activity, which was also confirmed by field listening. The method proved effective and cheap, and the authors emphasised its potential as a diagnostic tool for monitoring biodiversity, detecting early effects of climate change, and supporting planning and conservation decisions. In the future, they plan to expand the study to other habitat types to evaluate the effectiveness of this methodology in different natural environments.  
Environmental acoustics is gaining importance as a low-cost and non-invasive tool in ecological research and urban greenery management.
## 3 UC Program Considering Multiple Generation Types
### Constraints for Generation Units
#### 3.2.9 Line Transmission Power Constraints  
The line transmission power constraints are expressed as:  
\[\sum_{\text{g}\in\Omega^{i}_{G}}P_{g,t}+\sum_{h\in\Omega^{i}_{H}}P_{h,t}+P^{ \text{z}}_{i,t}-L_{i,t}-P^{c}_{i,t}+P^{d}_{i,t}=\sum_{j\in\Omega^{i}_{t}}P_{ij,t} \tag{29}\]  
\[P_{ij,t}=\frac{\theta_{i,t}-\theta_{j,t}}{x_{ij}} \tag{30}\]\[-P_{ij,t}^{\max}\leq P_{ij,t}\leq P_{ij,t}^{\max} \tag{31}\]  
where \(\theta_{i,t}\) denotes bus \(i\) voltage angle at time \(t\), and \(x_{ij}\) denotes the branch reactance between bus \(i\) and bus \(j\). \(P_{ij,t}^{\max}\) represents the maximum active power flow limits for branch \(ij\). \(\Omega_{\rm{C}}^{i}\) represents all thermal generation units at bus \(i\), \(\Omega_{H}^{i}\) represents all nuclear power units at bus \(i\), and \(\Omega_{\ell}^{i}\) represents all buses linked to bus \(i\).  
Introducing the probability constraint, the line transmission capacity constraint is the joint opportunity constraint when the line power is regarded as a random variable.  
\[FC_{i,t}=(a_{i}(P_{i}^{\min})^{2}+b_{i}P_{i}^{\min}+c_{i})u_{i,t}+\sum_{k}s_{i} ^{k}p_{i,t}^{k} \tag{32}\]  
Equation (32) is transformed into a deterministic constraint as follows:  
\[\begin{split} P_{ij,t}&\leq P_{ij,t}^{\max}-F_{ \Delta P_{ij,t}}^{-1}(\beta_{1})\\ P_{ij,t}&\geq P_{ij,t}^{\min}+F_{\Delta P_{ij,t}}^{- 1}(\beta_{2})\end{split} \tag{33}\]  
where \(\beta_{1}\) and \(\beta_{2}\) are the confidence level.
# Simulation of Cross-Correlated Random Fields for Transversely Anisotropic Soil Slope by Copulas
## 4 Simulation of CCRF from Copula Aspect
### Gaussian Copula-Based CCRF  
In the previous section, a framework of copula-based standard uniform CCRF has been established. As soil parameters obeying Gaussian copula, the simulation approach of Gaussian Copula-based CCRF is displayed in Algorithm 2.  
(1) Define the independent standard normal random fields of \(x\) and \(y\) as \(\mathbf{H}^{IAG}=\left[\mathbf{H}_{x}{}^{IAG},\mathbf{H}_{y}{}^{LAG}\right]^{\mathrm{T}}\).  
(2) Perform Cholesky decomposition on the correlation coefficient matrix \(\theta\) composed of Gaussian copula parameter \(\theta\) to obtain the lower triangular matrix \(\mathbf{L}_{0}\).  
(3) Let \(\mathbf{H}^{CAG}=\mathbf{L}_{0}\mathbf{H}^{TAG}\), then the cross-correlation standard normal random fields  
\(\mathbf{H}^{CAG}=\left[\mathbf{H}_{x}{}^{CAG},\mathbf{H}_{y}{}^{CAG}\right]^{\mathrm{T}}\) can be obtained.  
(4) Let \(\mathbf{H}^{CAU}=\Phi(\mathbf{H}^{CAG})\), where \(\Phi(\cdot)\) is the CDF of the standard normal distribution, and the cross-correlation standard uniform random fields of \(x\) and \(y\) can be obtained  
\(\mathbf{H}^{CAU}=\left[\mathbf{H}_{x}{}^{CAU},\mathbf{H}_{y}{}^{CAU}\right]^{\mathrm{T}}\).  
(5) Define \(F_{x}{}^{-1}(\cdot)\) and \(F_{y}{}^{-1}(\cdot)\) as the inverse functions of the CDF of \(x\) and \(y\), respectively. Perform isoprobabilistic transformation on \(\mathbf{H}^{CAU}\) to obtain the cross-correlation non-normal random fields of \(x\) and \(y\)\(\mathbf{H}^{CAN}=\left[\mathbf{H}_{x}{}^{CAN},\mathbf{H}_{y}{}^{CAN}\right]^{\mathrm{T}}= \left[\mathbf{F}_{x}{}^{-1}(\mathbf{H}_{x}{}^{CAU}),\,F_{y}{}^{-1}(\mathbf{H}_{y}{}^{CAU}) \right]^{\mathrm{T}}\).  
**Algorithm 2** Simulation algorithm of Gaussian copula-based CCRF
## 7 Addons and Extensions
### Calculation of Maximum Speed for Radiant Exposure  
Let \(P\) be the radiant power of one germicide tube. Note that radiation is possible only in the horizontal direction. Irradiance \(E_{e}\) (radiant flux received by a surface per unit area) from one tube is based on the cylinder surface \(S\):  
\[E_{e}=\frac{P}{S}, \tag{9}\]  
\[S=2\pi r(r+h). \tag{10}\]  
where \(h\) is the tube height (m) and \(r\) is the distance (m) between the germicide tube and a nearby point that must be exposed to UVC (a red point in Figure 13).  
The longer the point is exposed to given irradiation, the more viruses will be killed. This is given by radiant exposure \(H_{e}\) (J/m\({}^{2}\)). The value of radiant exposure \(H_{e}\) = 3500 \(\upmu\)Ws/cm\({}^{2}\) is needed to kill 99% of the covid viruses [34]. Note that speed of the moving robot must be adjusted to ensure desired radiant exposure.  
In Figure 13, the red point is a place along the trajectory that needs to be disinfected, purple circles are two germicide tubes, and \(l\) is the distance traveled by the robot during which a point to be disinfected is exposed to UVC:  
\[l=2d\tan 45^{\circ}. \tag{11}\]  
The time \(t_{dr}\) needed to reach value \(H_{e}\) = 3500 \(\upmu\)Ws/cm\({}^{2}\) is calculated by  
\[t_{dr}=\frac{P_{t}}{P}, \tag{12}\]  
where \(P_{t}\) is the the value of the equivalent power needed to ensure 3500 \(\upmu\)Ws/cm\({}^{2}\), and \(P\) is a function of distance \(d\). Table 4 shows examples of maximum speed \(v_{max}\)  
\[v_{max}=\frac{l}{t_{dr}}, \tag{13}\]  
Figure 13: The geometry of robot movement during disinfection.  
of the robot for effective disinfection. The maximum speed for each segment of robot trajectory is calculated in order to ensure desired radiant exposure of given surfaces along path. It can be concluded that predefined trajectories of Phollower with this add-on will ensure safe and effective disinfection. However, the proposed germicide add-on has one disadvantage. Germicide tubes are placed vertically, and therefore floors are less exposed to UV+ radiation. The highest point of radiation is at the height of an average adult's shoulder. Disinfection of surfaces and objects in reachable distance of a human's hand is a primary objective here, as these surfaces can be tapped or touched by hands and infected by viruses. This disadvantage can be solved by using one more horizontally placed tube in the front part of Phollower. This will be offered as another add-on functionality.  
The power consumption of UV+ add-on is 300 W, which reduces the total operating time of the robot by around 40%. A full charge of the battery takes 75 min and enables several disinfections per night. However, the UV+ add-on operates with the same battery as the remaining part of the robot to not increase production costs. Moreover, if the UV+ add-on was powered by its own separate battery and both batteries were charged form the same charger, a balance circuit would have been required to charge unevenly discharged batteries.
# Application of Polymers as a Tool in Crystallization--A Review
## 2 Basic Research
### Discussion and Conclusions
#### 3.2.6 Discussion and Conclusions  
Crystallization is one of the most important unit operations in the quality control of pharmaceutical products. Specific molecular (i.e., polymorphism) and particulate (i.e., particle size and crystal habit) properties of pharmaceuticals can be obtained by their controlled crystallization. Moreover, these molecular and particulate properties govern the manufacturability, stability and biopharmaceutical performance of the drugs. Current methods for controlling critical crystal size and morphology are inefficient and often lead to undesirable solid states such as metastable polymorphs or amorphous forms. To avoid this, an approach for producing crystals of a poorly water-soluble pharmaceutical compound embedded in a polymer matrix is proposed. Polymer-induced crystallization creates opportunities for convenient pharmaceuticals engineering, allowing consistent improvements in bioavailability and processability. Furthermore, the technique of polymer-directed crystallization seems to be promising for the improving properties of drug crystals. The researchers indicated different ways to rationally design the geometry of heterogeneous surfaces to enhance the nucleation of small-molecule organics. The crystallization of active pharmaceutical ingredients can be carried out using supramolecular gel media. As a result, control over the crystal properties like size, morphology, and polymorphism was gained. Moreover, it has been shown that the utilization of polymers offers additional advantages such as drug solubilization, crystallization inhibition and improvement in the release patterns of drugs.
# Guava Disease Detection Using Deep Convolutional Neural Networks: A Case Study of Guava Plants
## 4 Results and Discussion
### Evaluation Measure
#### 4.1.3 F1 Score  
The F1 score is an essential measure, as it is calculated for both the essential measures of recall and precision. Recall is also known as sensitivity and is the measure to detect positive class predictions among true positives and false negatives. For the F1 score, we need to calculate the sensitivity as follows:  
\[Recall\ (Sensitivity)=\frac{TP}{(TP+FN)} \tag{12}\]  
The second measure, precision, is also separately calculated and used in the F1 score measurement. Precision is calculated to obtain a truly predicted class over true and false positives. It is represented as:  
\[Precision=\frac{TP}{(TP+FP)} \tag{13}\]  
After obtaining the precision and recall, the F1 score can be calculated. After having _TP_ over _TP_ and _FN_ by recall and by obtaining _TP_ over _TP_ and _FP_, we can obtain more precise measurements for true-positive predictions. The final F1 score can be calculated as:  
\[F1-score=2\times\frac{Precision*Recall}{Recall+Precision} \tag{14}\]  
Therefore, the F1 score equation can be defined as two multiplied by the ratio of the product and summation of precision and recall.
## 4 Results and Discussion
Aerodynamic noise prediction performance is detailed in Figure 6 and Table 2. At the lower Reynolds number (Re \(\approx\) 78,000), 250 decomposition modes outperform a 300-mode configuration, confirming VMD's adaptability to flow energy variations. Within short-term predictions of 50 steps, both cases achieve R\({}^{2}\) above 0.98 and low RMSE. For prediction within 150 steps, all cases maintain R\({}^{2}\) above 0.95 with RMSE below 3.96 dB, confirming temporal stability under varying flow conditions. The model maintains high accuracy at Re \(\approx\) 78,000 and 104,000, validating robust generalization capacity for flow energy variations and turbulence characteristics in practical aerodynamic systems. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time Steps** **Ahead** & **Model** & **RMSE** & **R\({}^{2}\)** \\ \hline
50 & ESN & 5.1514 & \(-\)0.5559 \\  & EMD-ESN & 4.6260 & 0.6395 \\  & CEEMDAN-ESN & 11.1393 & \(-\)1.0905 \\  & VMD-ESN & 1.0524 & 0.9813 \\ \hline
150 & ESN & 15.1145 & \(-\)30.7351 \\  & EMD-ESN & 11.0795 & 0.0497 \\  & CEEMDAN-ESN & 9.3568 & 0.3223 \\  & VMD-ESN & 3.3225 & 0.9145 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Prediction performance comparison of different models. Figure 6: Prediction results of the VMD-ESN model at an inflow velocity of (**a**) 60 m/s (Re = 78,000) and (**b**) 80 m/s (Re = 104,000).
# A Mathematical Study on a Fractional-Order SEIR Mpox Model: Analysis and Vaccination Influence
## 4 Stability Analysis
### The Invariant Region  
This subsection intends to study the non-negativity of the solution of model (19). For instance, the solution of model (19) will be validated in accordance with the positive values of the initial conditions. For this purpose, we suppose for all non-negative values of \(t\) that the domain of the desired solution \(\psi\) has the following form:  
\[\psi=\bigg{\{}(S(t),E(t),I(t),R(t))\in\mathbb{R}_{+}^{4}:\ S(t)+E(t)+I(t)+R(t) \leq\frac{\Lambda}{\mu}\bigg{\}}, \tag{20}\]  
where \(S(t),E(t),I(t),R(t)\geq 0\). One could observe that all model parameters have non-negative values (i.e., \(\sigma,\
u,\mu,\alpha,\gamma,\beta,\Lambda,\geq 0\)). Now, in order to show the non-negativity of the solution of model (19), we recall the following two results, which would pave the way to achieve our first target.  
**Lemma 1** ([12; 13]).: _(Generalized Mean Value Theorem). Suppose that \(\Theta(t)\in C[a_{1},a_{2}]\) and \({}^{C}D_{*}^{\rho}\Theta(t)\in C(a_{1},a_{2}]\), where \(0<\rho\leq 1\), then we have:_  
\[\Theta(t)=\Theta(a_{1})+\frac{1}{\Gamma(\rho)}\,{}^{C}D_{*}^{\rho}\Theta( \zeta)(t-a_{1})^{\rho}, \tag{21}\]  
_where \(0\leq\zeta\leq t\), \(\forall t\in(a_{1},a_{2}]\)._  
**Remark 1** ([12; 13]).: _Suppose that \(\Theta(t)\in C[0,\ a_{2}]\) and \({}^{C}D_{*}^{\rho}\Theta(t)\in C(0,\ a_{2}]\), where \(0<\rho\leq 1\). In view of the above Lemma, one can conclude that, if \({}^{C}D_{*}^{\rho}\Theta(t)\geq 0\), \(\forall t\in(0,a_{2}]\), then \(\Theta(t)\) will be a non-decreasing vector-valued function, while if \({}^{C}D_{*}^{\rho}\Theta(t)\leq 0\), \(\forall t\in(0,a_{2}]\), then such a function will be a non-increasing one._  
**Theorem 2**.: _There exists a unique solution \(\Theta(t)=[S(t),E(t),I(t),R(t)]^{T}\) of system (19), subject to the initial condition \(\Theta(0)=[S(0),E(0),I(0),R(0)]^{T}\in\mathbb{R}_{+}^{4}\), and this solution will remain in \(\mathbb{R}_{+}^{4}\)._  
Proof.: Tracking the necessary results deduced by the author in [13] leads us to identify, for definite, a unique solution \(\Theta(t)\) on \((0,\infty)\) for system (19), subject to the initial condition \(\Theta(0)\). On the other hand, in order to show that the domain \(\mathbb{R}_{+}^{4}\) is indeed the positively invariant region, we have to observe the following assertions:  
\[{}^{C}D_{*}^{\rho}S(t)|_{S=0} =(\Lambda-\beta S(t)I(t)-(\
u+\mu)S(t))|_{S=0}=\Lambda\geq 0, \tag{22}\] \[{}^{C}D_{*}^{\rho}E(t)|_{E=0} =(\beta S(t)I(t)-(\mu+\gamma)E(t))|_{E=0}=\beta S(t)I(t)\geq 0,\] \[{}^{C}D_{*}^{\rho}I(t)|_{I=0} =(\gamma E(t)-(\mu+\sigma+\alpha)I(t))|_{I=0}=\gamma E(t)\geq 0,\] \[{}^{C}D_{*}^{\rho}R(t)|_{R=0} =(\
u S(t)+\alpha I(t)-\mu R(t))|_{R=0}=\
u S(t)+\alpha I(t)\geq 0.\]  
This validates that the solution of system (19) is non-negative over the domain given in (20).