esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In the scope of this work, a mathematical model was performed in the Matlab/Simulink package of the discussed C3 PV module based on the \"Solar Cell\" component. The concentration of direct solar radiation was also considered in this model. Due to the lack of knowledge of the exact geometry of the Fresnel lenses used, test measurements were taken, in which the limit angle of deviation of the direction of direct radiation from the direction perpendicular to the module, effective focusing of the radiation, was determined to be about 5\({}^{\circ}\). Once this value is exceeded, it was assumed that radiation focusing does not occur and only partially scattered radiation reaches the cell. Based on the built model, sample simulation calculations were performed in the form of current-voltage characteristics of the C3 PV module for different angles of incidence of direct radiation on the module surface. An analysis of the performance of two 3.5 kW\({}_{\text{p}}\) CPV systems for different types of Fresnel lenses under actual operating conditions showed that a dirty lens causes a drop in performance of up to about 12% with a level of module alignment inaccuracy of less \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Name**} & **Focusing** & & **Type of** \\  & **Degree,** & **Type of Converter** & **Recommended** \\  & **[Suns]** & & **Tracking System** \\ \hline Low Concentrator & & & \\ Photovoltaic (LCPV) & 1.5–10 & modified crystalline cells & none or single-axis \\ Medium Concentrator & & & single-axis or \\ Photovoltaic (MCPV) & & crystalline or thin-film cells & dual-axis \\ High Concentrator & & & \\ Photovoltaic (HCPV) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification of concentric PV cells based on the degree of focusing of solar radiation, compiled based on [12]. than 1% [13]. Other studies have also shown that the impact of tracking inaccuracy on the power output of CPV modules is very significant. It has been shown that CPV needs a solar tracking accuracy of at least 0.5\({}^{\circ}\) to outperform other PV technologies [16]. The search for new solutions for CPV modules is also underway. A lens has been designed that is a combination of two linear Fresnel lenses placed perpendicular to each other and can collect and distribute direct sunlight in two dimensions. The simulation results show that the newly designed lens can achieve a high concentration ratio of 576 times [17]. The recent development of four-junction concentrator solar cells with record efficiency offers new opportunities to improve highly concentrated photovoltaic (HCPV) technology [18]. Investigations using an analytical GaInP/GaInAs/Ge three-junction solar cell model based on continuity and Poisson equations showed high agreement for an estimated efficiency of about 35% [19; 20].
# Frequency-Limited Model Reduction for Linear Positive Systems: A Successive Optimization Method
## 4 Simulation  
In the simulation part, a system \(\Sigma\) describing the compartmental network with two sub-systems is considered, and its state-space realization is given as follows:  
\[A=\begin{bmatrix}-1.5&0.6&1.0&0&0&0\\ 0.3&-1.9&0.2&0&0&0\\ 0.2&0.5&-2.7&1&0&0\\ 0&0&0.5&-3&0.6&0.5\\ 0&0&0&0.4&-1.6&0.3\\ 0&0&0&0.6&0.5&-1.6\end{bmatrix},B=\begin{bmatrix}1&0\\ 0&1\\ 0&0\\ 0&0\\ 0&0\\ 0&0\end{bmatrix}, \tag{24}\] \[C=\begin{bmatrix}\mathbf{I}_{2\times 2}&\mathbf{0}_{2\times 4} \end{bmatrix},D=\mathbf{0}_{2\times 2}.\]  
The example is drawn from [19]. The restricted frequency is taken as \([0,2]\text{rad}/\text{s}\). To develop a reduced-order model for approximating (24), we first adopt the initial condition as  
\[A_{r}^{0} =\begin{bmatrix}-1.9301&0.7093\\ 0.7188&-1.1613\end{bmatrix},B_{r}^{0}=\begin{bmatrix}0.0294&0.0562\\ 0.0847&0.0379\end{bmatrix}, \tag{25}\] \[C_{r}^{0} =\begin{bmatrix}1.8925&3.7318\\ 2.6822&1.2840\end{bmatrix},D_{r}^{0}=\begin{bmatrix}0.2669&0.0002\\ 0.0231&0.2462\end{bmatrix}.\]  
Using the initial model and applying Algorithm 1, we optimize the reduced-order model as  
\[A_{r} =\begin{bmatrix}-1.7544&0.3616\\ 0.5570&-1.2572\end{bmatrix},B_{r}=\begin{bmatrix}0.0004&0.1864\\ 0.1458&0.0001\end{bmatrix}, \tag{26}\] \[C_{r} =\begin{bmatrix}0.1540&5.9325\\ 4.8997&0.0754\end{bmatrix},D_{r}=\begin{bmatrix}0.0333&0.0001\\ 0.0042&0.0226\end{bmatrix}.\]  
with the optimal \(\gamma^{*}=0.0207\). By virtue of Lemma 2, it can be verified that the obtained reduced-order model (26) is positive. To validate the convergence property of the proposed algorithm, Figure 1 depicts the evolution of \(\gamma\) by configuring the maximum number of iterations \(1 Max=140\) and ignoring the tolerable bound \(\epsilon\). As shown in Figure 1, the proposed algorithm exhibits a monotonically decreasing trend in \(\gamma\) and converges to a fixed point eventually. Moreover, the result presents a significant improvement and provides a more exact approximation over the initial reduced-order model.  
Figure 1: Evolution \(\gamma\) by the proposed algorithm.
## 4 Results
### Analysis of the Selected Studies
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Citation** & **Year** & **Type of Paper** & **Use Case Results** & **Livestock Production System** & **Application Domain** \\ \hline
[60] & 2023 & Journal Article & Yes & Dairy Cattle &
\begin{tabular}{c} Precision Livestock Farming \\ Production Management, \\ Animal Welfare \\ and Management \\ \end{tabular} \\ \hline
[61] & 2022 & Journal Article & Yes & Dairy Cattle &
\begin{tabular}{c} Intelligent Livestock \\ Monitoring, Animal Welfare \\ and Management \\ \end{tabular} \\ \hline
[62] & 2018 & Conference Paper & No & Swine &
\begin{tabular}{c} Animal Welfare \\ and Management \\ \end{tabular} \\ \hline
[63] & 2019 & Conference Paper & Yes & Swine &
\begin{tabular}{c} Real-Time Environmental and \\ Energy Control Animal Welfare \\ \end{tabular} \\ \hline
[64] & 2022 & Journal Article & No & All &
\begin{tabular}{c} Animal Welfare \\ and Management \\ \end{tabular} \\ \hline
[52] & 2021 & Journal Article & No & All &
\begin{tabular}{c} Animal Welfare \\ and Management \\ \end{tabular} \\ \hline
[65] & 2021 & Journal Article & No & All &
\begin{tabular}{c} Animal Welfare \\ and Management \\ \end{tabular} \\ \hline
[66] & 2022 & Conference Paper & No & All &
\begin{tabular}{c} Animal Welfare \\ and Management \\ \end{tabular} \\ \hline
[67] & 2023 & Journal Article & Yes & Swine &
\begin{tabular}{c} Real-Time Environmental and \\ Energy Control \\ \end{tabular} \\ \hline
[68] & 2022 & Conference Paper & No & Dairy Cattle &
\begin{tabular}{c} Real-Time Environmental \\ Control Pasture Management \\ \end{tabular} \\ \hline
[69] & 2021 & Journal Article & Yes & All & Decision-Making Supply Chain \\ \hline \end{tabular}
\end{table} 
Table 1: Overview of the recent DTs applications in livestock production systems.
## 3 Frequency Coordination Control Strategy at LCC Terminal
### Auxiliary Frequency Controller Design Based on VFF-RLS Algorithm
The control system block diagram is shown in Figure 3. With the addition of the correction section, the system's open-loop transfer function \(\phi_{0}(s)\), closed-loop function \(\phi(s)\), and closed-loop characteristic equation are as follows:  
\[\phi_{0}(s)=G(s), \tag{7}\]  
\[\Delta P=\Delta P_{\mathrm{G}}-\Delta P_{D}+\Delta P_{\mathrm{dc}}=(K+K_{ \mathrm{dc}})\Delta f. \tag{2}\]0  
\[\Delta P=\Delta P_{\mathrm{G}}-\Delta P_{D}+\Delta P_{\mathrm{dc}}=(K+K_{ \mathrm{dc}})\Delta f. \tag{2}\]1  
The root locus curve of the system after correction passes through the ideal dominant pole \(s_{d}=a\pm bi\), which satisfies the phase angle condition and magnitude condition:  
\[\left\{ \begin{array}{l}\angle G(s)G_{c}(s)\big{|}_{s=s_{d}}=\sum\limits_{i=1 }^{m}\angle(s+z_{i})-\sum\limits_{j=1}^{n}\angle(s+p_{i})=\pm 180^{\rm o}(2k+1)\\ |G(s)G_{c}(s)||_{s=s_{d}}=1\end{array} \right., \tag{10}\]  
where \(m\) and \(n\) represent the orders of the zeros and poles, while \(z\) and \(p\) represent the zeros and poles. \[G_{c}(s)=K\frac{s+T_{1}}{s+T_{2}}, \tag{11}\]  
where \(K\) is determined by the magnitude condition, and \(T_{1}\) and \(T_{2}\) are determined by the phase condition. Plugging the dominant pole \(s_{\rm d}\) into the open-loop system transfer function \(G(s)\) determines its phase deficiency and open-loop gain deficiency. From (10), the specific parameters of the correction section \(G_{c}(s)\) can be determined, obtaining the transfer function of the auxiliary frequency control. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Serial Number** & & **Steps and Instructions** \\ \hline
1 & Input Parameters & \(\theta_{0},P_{0},\lambda_{0},I_{k},U_{k}\) \\
2 & Update Gain Matrix & \(K_{k}=P_{k-1}\Phi_{k}^{T}\left[\lambda+\Phi_{k}P_{k-1}\Phi^{T}\right]^{-1}\) \\
3 & Update Parameters & \(\theta_{k}=\theta_{k-1}+K_{k}(y_{k}-\Phi_{k}\theta_{k-1})\) \\
4 & Update Covariance & \(P_{k}=\frac{1}{\left(I-K_{k}\Phi_{k}\right)P_{k-1}}\) \\
5 & Calculate Parameter Error & \(e=\frac{1}{\sigma}(\theta_{k})-y_{k}\) \\
6 & Update Forgetting Factor & \(\lambda(k)=\) \\
7 & Output Online Identified & \(\theta_{k}\) \\  & Parameters & \(\theta_{k}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Steps of the VFF-RLS algorithm. Figure 3: Diagram of the control system.
## 4 Experimental Tests
This mode, which is represented in Figure 9, is dominated by the compliance of J3, and shows some contribution of J2, owing to the inertial-cross coupling between the two joints. The second mode at 34.7 Hz is influenced by the structural compliance. In particular, structural compliance in the direction perpendicular to the axes J2 and J3 leads to an apparent torsion of the robot arm. The large damping ratio (4.4%) is in agreement with the contribution of structural deformation. The presence of structural modes in the range of frequencies of the modes dominated by joint compliance has been detected by other researchers [6]. Figure 10 deals with experimental results obtained in configuration Test 4 with lateral excitation (y direction). Both the direct point FRF and the CMIF highlight the presence of an important resonance peak beyond the 10 \(\div\) 40 Hz frequency band, which contains the previously identified modes. Modal analysis made it possible to identify a mode of vibration at 56.8 Hz with a 3.0% damping ratio, which is similar to the ones of the other joints. This mode, which is represented in Figure 10, shows a large contribution of the rotation about J4, which leads to lateral displacements of the points of the following links. Figure 8: Test 2, (a) direct point frequency response function (FRF) modulus, (b) phase, (c) complex mode indicator function (CMIF). Figure 10: Test 4, (**a**) direct point frequency response function (FRF) modulus, (**b**) phase, (**c**) complex mode indicator function (CMIF). Figure 9: Test 3, (**a**) direct point frequency response function (FRF) modulus, (**b**) phase, (**c**) complex mode indicator function (CMIF). Finally, Figure 11 shows experimental results obtained in configuration Test 5 with longitudinal excitation (x direction). At high frequency, there is a very important peak at about 170 Hz. Modal analysis showed that at 171.4 Hz there is a mode of vibration with large displacements localized in the last link and caused by the compliance of J5 (see Figure 11). The damping ratio of this mode is 2.8%.
## 4 Furrowing Depth Stability Control--Furrowing Depth Detection Technology  
With the gradual maturity of measurement and control technology, seeding machines are gradually developing in the direction of high speed and precision. In order to improve the stability of furrowing depth, the use of measurement and control technology to accurately detect the depth of furrowing has become an inevitable requirement to ensure the quality of seeding. At present, experts and scholars working in relevant fields have developed the measurement and control equipment for furrowing depth control and realized the precise adjustment of furrowing depth using the active profiling adjustment device. Trenching depth detection is widely used to detect the distance of the machine relative to the ground surface and, using mathematical modeling, to get the actual depth of furrowing. Scholars mainly adopt ultrasonic, optical ranging, pressure, and angle sensors for depth detection. By summarizing and analyzing, the commonly used sensors can be classified into contact, noncontact, and ingression. The main features, functions, and installation locations of these sensors are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Sensor Types** & **Installation Position** & **Main Features** & **Parts Pictures** & **Reference Author** \\ \hline Ultrasonic & In front of the work component & Noncontact detection; high accuracy; wide range of ranging; strong anti-interference ability & & [PERSON] et al. [64] \\ \hline Optical distance & In front of the work component & Noncontact detection; fast response time; high precision; insensitive to environmental disturbances & & [PERSON] et al. [65]; [PERSON] et al. [66]; [PERSON] et al. [67] \\ \hline Displacement & On rack & Noncontact detection; high measurement accuracy; small temperature coefficient; poor resolving power & & [PERSON] et al. [68]; [PERSON] et al. [64] \\ \hline Optical encoder & Swing-arm & Contact detection; high accuracy; high resolution; good stability & & [PERSON] et al. [69] \\ \hline Membrane pressure & Depth-limiting tire & Contact detection; simple structure; high sensitivity; fast response; wide measuring range; affected by temperature & [PERSON] et al. [70]; [PERSON] et al. [60]; [PERSON] et al. [71]; [PERSON] et al. [72] \\ \hline Force & Shaft pin; articulation point above & Contact detection; high accuracy; good stability; high sensitivity, fast response; good linearity & [PERSON] et al. [26]; [PERSON] et al. [73] \\ \hline Angle & Depth-limiting arm & Contact measurement without wear and tear; subject to electromagnetic interference, sensitive to ambient humidity & & [PERSON] et al. [26] \\ \hline Drying front & In front of the work component & Ingression detection; fast response time; high immunity to interference; lower accuracy; limited measurement range & & [PERSON] et al. [74] \\ \hline \end{tabular}
\end{table}
Table 4: Sensors commonly used for furrowing depth detection.
## 4 Discussion
### Limitation  
The limitations of this study are as follows:  
* Sample bias: The sample size was not sufficiently diverse. As closed-cabin personnel are mostly young males, there was a selection bias in the choice of participants. The experimental environment was set within a closed space, which may have affected the generalizability of the proposed method.
* Comparison with FaceReader: FaceReader was used as the control method. However, previous literature indicates that FaceReader's accuracy is somewhat limited for East Asian populations [50; 51; 52], which may affect the strength of the evidence provided in this study.
* Laboratory setting: The study was conducted in a laboratory environment rather than in a real closed cabin, such as a manned submersible. Real-life electromagnetic environments are more complex and may affect the effectiveness of the proposed method.
* Single modality: For convenience of deployment, this study only used respiration signals. According to other studies, employing multimodal signals such as EEG and skin conductance signals can significantly enhance the accuracy of emotion prediction [25; 53].
* Emotion stimulus material selection: In this manuscript, we utilize video as the emotion stimulus material; however, the literature [46] offers a more effective solution by using VR scenes to evoke the corresponding emotions. This method provides a more controlled and immersive environment for emotion elicitation and assessment, leading to applications in various domains such as mental health, marketing, and entertainment.  
We plan to address these issues in future studies. Previous studies have shown that the ability to maintain emotional stability has a significant impact on workplace performance. Therefore, further research is required to understand the patterns and distribution of emotional changes among personnel working in closed-cabin environments.  
Future research directions include the following.  
Diverse sample population: Include a broader range of participants, to improve the generalizability and robustness of the findings.  
Figure 15: Comparison between different methods of measurement on the dimensions of intrusiveness, system complexity, and deployment cost.  
Enhanced validation methods: Use more reliable algorithms that account for cultural and demographic variations, to strengthen the validation of the proposed method.  
Real-world testing: Conduct experiments in actual closed environments such as manned submersibles, to test the robustness of the method under real-world conditions.  
Multimodal signal integration: Incorporate additional physiological signals such as EEG and ECG signals to improve the accuracy and reliability of emotion recognition.  
To address these limitations, we aim to refine our method and enhance its applicability to various closed-cabin environments. Further investigation into the emotional stability of closed-cabin personnel during work will also contribute to improving their performance and safety.
# Drone Ground Impact Footprints with Importance Sampling: Estimation and Sensitivity Analysis +
## Appendix A UAV Model  
The aerodynamic force \(F_{a}\) in (4) and torque \(M_{a}\) in (5) can be expressed in the aerodynamic reference frame (related to the aerodynamic speed of the drone) as:  
\[F_{a}^{(w)} =q_{d}S\big{[}C_{X} C_{Y} C_{Z}\big{]}^{\top} \tag{11}\] \[M_{a}^{(w)} =q_{d}SL\big{[}C_{l} C_{m} C_{n}\big{]}^{\top} \tag{12}\]  
where \(q_{d}=\frac{1}{2}\rho V_{a}^{2}\) is the dynamic pressure, \(\rho\) the air density, \(V_{a}\) the airspeed, \(S\) the reference surface and \(L=diag(L_{a},L_{a},L_{a})\) a matrix with lateral and longitudinal reference lengths \(L_{a}\) (wingspan) and \(L_{o}\) (mean aerodynamic chord).  
In case of a non-zero wind speed vector \(V_{w}\), the airspeed is \(V_{a}=\|V-V_{w}\|\).  
A linearized aerodynamic model is used in this paper, where the lift (\(C_{L}\)), lateral (\(C_{Y}\)) and drag (\(C_{D}\)) coefficients are computed by  
\[C_{L} =C_{L_{0}}+C_{L_{a}}\alpha+C_{L_{a}}\dot{\alpha}+C_{L_{q}}\frac{q} {V_{a}}+C_{L_{a}}\delta_{e} \tag{13}\] \[C_{Y} =C_{Y_{\beta}}+C_{Y_{p}}\frac{p}{V_{a}}+C_{Y_{r}}\frac{r}{V_{a}}+ C_{Y_{\delta_{a}}}\delta_{a}+C_{Y_{\delta_{r}}}\delta_{r}\] \[C_{D} =C_{D_{0}}+C_{D_{C_{L}}}C_{L}+C_{D_{C_{L_{2}}}}C_{L}^{2}+C_{D_{ \delta_{e}}}\delta_{e}\]  
with \(\big{[}C_{X} C_{Y} C_{Z}\big{]}^{\top}=\big{[}-C_{D} C_{Y} -C_{L}\big{]}^{\top}\).  
Similarly, the aerodynamic coefficients regarding the torque are computed according to the following linearized model  
\[C_{l} =C_{l_{p}}\beta+\frac{L_{a}}{V_{a}}(C_{l_{p}}p+C_{l_{r}}r)+C_{l_ {a}}\delta_{a}+C_{l_{\delta_{r}}}\delta_{r} \tag{14}\] \[C_{m} =C_{m_{0}}+C_{m_{a}}\alpha+C_{m_{a}}\dot{\alpha}+\frac{L_{0}}{V_ {a}}C_{m_{q}}q+C_{m_{\delta_{e}}}\delta_{e}\] \[C_{n} =C_{n_{\beta}}\beta+\frac{L_{a}}{V_{a}}(C_{n_{p}}p+C_{n_{r}}r)+C_{ l_{a}}\delta_{a}+C_{l_{\delta_{r}}}\delta_{r}\]  
with \(\alpha\) the angle of attack, \(\beta\) the slideslip angle, \(\Omega=[p,q,r]^{T}\) the angular velocity vector between the NED and body frames and \((\delta_{a},\delta_{e},\delta_{r})\) the ailerons, elevators and rudder deflections.  
Numerical values of the aerodynamic coefficients and other UAV model parameters used in this paper are given in Tables 1 and 2 below.
# Static Osteogenesis versus Dynamic Osteogenesis:
## 1 Introduction  
It is generally accepted that bone matrix deposition depends on the secretory activity of osteoblasts arranged in monostratified laminae, where all secreting cells are synchronized by side-to-side and gap junctions [1, 2, 3] and are polarized towards the same direction, i.e., the osteogenic surface. Moreover, according to the classical view, it is considered that as osteoid seam secretion proceeds, the osteoblastic laminae move away from the osteogenic surface towards the vessels, and those osteoblasts selected to differentiate into osteocytes remain entrapped within the preposses matrix by the enlargement of their secretory territory (Figure 1) [4]. As a consequence, it has been clearly demonstrated that the rate of osteoblast movement is a function of the V/ST ratio (where V is the protoplasmic volume and ST is the secretory territory of the osteoblast). In other words, the osteoid compositional growth rate of each osteoblast depends on the proportion of its protoplasm engaged on a given bone surface [5]. In 1999, [PERSON] and coworkers [6] suggested the term \"dynamic osteogenesis\" to indicate this well-known type of bone formation (involving osteoblast movement) to distinguish it from another (until then) unknown type of bone deposition discovered for the first time by analyzing intramembranous ossification in detail. This new type of osteogenesis was named \"static osteogenesis\", because it is performed by stationary osteoblasts that transform into osteocytes at the same site where they differentiate without moving. Later, exhaustive structural and ultrastructural documentation on the morpho-functional differences between these two types of bone formation was provided [7].  
In the present review, the two types of osteogenesis, static (SO) and dynamic (DO) osteogenesis, are discussed not only in relation to their time of occurrence and the conditions in which they are performed, but also in relation to their respective morpho-functional significance in both physiological and pathological conditions.
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
## 6 Security Analysis
### Informal Security Analysis
#### 6.1.7 User Friendliness  
In our protocol, \(MU\) can easily change his/her own \(ID_{i}\) and \(PW_{i}\) without the assistance of the \(HA\). In particular, the proposed protocol allows the \(MU\) to change the original password \(PW_{i}\) in a short time. Because, the \(MU\) need not go through the entire login process, which saves the time as well as minimizes the computation complexity of the proposed scheme. Consequently, the proposed protocol is user-friendly.
# Integration of Geoscience Information for Disaster Resilience in Kuala Lumpur, Malaysia
###### Abstract  
Geoscience information supports strategic development planning for building disaster resilience in Kuala Lumpur, Malaysia, which is a city challenged by issues such as landslides, floods and unfavourable ground conditions. Aspects such as the subsurface setting and susceptibility to hazards offer insights to resolve risks that are expected to worsen with climate change. Geoscience data were collated from field investigation and other sources for spatial integration using geographic information system software. The information on engineering ground conditions and susceptibility to geohazards was then combined to demarcate zones that are suitable for urban development. This approach can be applied to other cities so that relevant geoscience information is integrated for planning and decision making in a changing climate. The findings reveal that 20% of the city has high suitability for development and is generally not prone to climate hazards. About 80% of the land area in Kuala Lumpur has medium to high ground constraint, and this includes around 25% of the city area that is susceptible to landslides and floods. In the worst-case scenario where no action is taken, communities and urban assets within these susceptible areas would be exposed and vulnerable to more landslides and floods due to climate change. Additional development should be limited in such areas, and where already developed, targeted hazard-specific measures can be taken to build resilience.  
geoscience; geohazard; susceptibility modelling; disaster resilience; disaster risk; sustainable development; land-use suitability; Kuala Lumpur; Malaysia 2023  
10.390/app13127004