esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Spectral Amplitude Coding OCDMA System Utilizing a Single Photodiode Detection
## 3 Performance Analysis
### Analysis  
In this section, analytical expressions are presented for the mean optical power and noise in order to get the BER performance of the SAC-OCDMA system based on SPD with APDs. As mentioned earlier, EDW codes, characterized by unity cross-correlation, are utilized as the signature sequences for the proposed SAC-OCDMA system. For a weight of three (\(w\) = 3), the code length is as follows [21]:  
\[L=2k+\frac{4}{3}\bigg{[}\sin\bigg{(}\frac{k\pi}{3}\bigg{)}\bigg{]}^{2}\frac{8}{ 3}\bigg{[}\sin\bigg{(}\frac{(k+1)\pi}{3}\bigg{)}\bigg{]}^{2}+\frac{4}{3}\bigg{[} \sin\bigg{(}\frac{(k+2)\pi}{3}\bigg{)}\bigg{]}^{2}, \tag{1}\]  
where \(L\) is the code length and \(k\) is the number of users.  
The mean optical power reaching the photodetector when the desired user is active is as follows [20]:  
\[P_{User}=S\int\limits_{0}^{B}H_{E}(v)H_{D}(v)dv=S\frac{B_{0}}{L}\sum\limits_{i =1,\ E=D}^{L}C_{E}(i).C_{D}(i)=\ \frac{SB_{0}}{L}w=\frac{3 SB_{0}}{L}, \tag{2}\]  
where \(S\) is the received power spectral density level at the photodiode; \(B_{0}\) is the optical bandwidth partitioned into logical frequency bins of width \(B_{0}/L\); \(H_{E}(v)\) and \(H_{D}(v)\) are, respectively, the encoder and decoder transfer functions (i.e., they are spectral representations of the filters, both made of bins of a given spectral width that are fully transmissive or fully opaque); \(C_{\text{E}}(i)\) and \(C_{\text{D}}(i)\) denote the \(i\)th element of the encoder and decoder code words, respectively.  
For APDs, Equation (2) can be rewritten as  
\[P_{User}=\ \frac{3 SGB_{0}}{L} \tag{3}\]  
where \(G\) is the average internal gain of APDs.  
The PIN expression for unpolarized thermal light source is expressed as [20]  
\[\sigma_{PIN}{}^{2}=S^{2}B_{e}\int\limits_{0}^{B_{0}}[H_{E}(v)H_{D}(v)]^{2}dv=S^ {2}B_{e}\frac{B_{0}}{L}\sum\limits_{i=1,\ E=D}^{L}[C_{E}(i)]^{2}.[C_{D}(i)]^{2 }=\frac{S^{2}B_{e}B_{0}}{L}w\ =\frac{3S^{2}B_{e}B_{0}}{L} \tag{4}\]  
where \(B_{e}\) is the electrical bandwidth and is assumed to be equal to 0.75 \(\times\) RB, where RB is the bit rate.  
For APDs, Equation (4) can be rewritten as  
\[\sigma_{PIN}{}^{2}=\frac{3S^{2}GB_{e}B_{0}}{L}, \tag{5}\]  
The signal to noise ratio (SNR) is defined as [6]  
\[SNR=\frac{(P_{User})^{2}}{\sigma_{PIN}{}^{2}}=\ \frac{3 GB_{0}}{B_{e}L}, \tag{6}\]  
Based on the Gaussian distribution, the bit error rate (\(BER\)) is given by [6]  
\[BER=\frac{1}{2}erfc\bigg{(}\frac{\sqrt{SNR}}{2}\bigg{)}, \tag{7}\]  
where _erfc_ is the complementary error function.  
The parameters that were used in the simulation of the proposed system are given in Tables 2 and 3 [19; 20; 22].
## 5 Evaluation Results  
### Experimental Design  
In this section, we evaluate the DDPG-KRP algorithm performance with the network simulations with NS and D2D paradigms. In the simulations, we considered 1 BS and 7 network slices based on two categories of services (i.e., URLLC and video) with D2D communications. Table 2 shows each category of services with the specified number slice, UEs, And the D2D pair links are represented by a Poisson distribution. The simulations were performed using the following software and hardware configurations: MATLAB software version R2023b, 1.00 GHz Intel Core i5-1035G1 processor, and 16 GB RAM with no dedicated video card. The parameters for the mobile network scenario were similar to those used in [1; 9; 16]. Table 3 presents the full set of parameters for the simulations.  
We implemented the DDPG-KRP algorithm to solve the resource allocation problem in each network slice. The actor neural network was implemented with four fully connected hidden layers with 256 neurons each and the leaky ReLU activation function. Meanwhile, the critic neural network was utilized considering three fully connected hidden layers with 128 neurons each and the leaky ReLU activation function. The learning rates for both the actor and critic networks were 0.01. The batch size was 256. The replay buffer (\(R\)) size was 10,000. The discount factor for the cumulative reward was 0.90.  
The performance of the proposed DDPG-KRP algorithm was compared with those of two other DRL methods (i.e., DQN and DDQN) considering the same reward function proposed in Equation (16) and the action discretization using percentages and by also eliminating the undesirable actions for the DQN and DDQN algorithms. The neural network of the two methods was implemented with four fully connected hidden layers with 64 neurons in each and the leaky ReLU activation function. The hyperparameters were set with a 0.01 learning rate, 0.1 random action choice chance, 0.9 discount factor, 256 batch size, and 10,000 replay buffer size.  
The hyperparameters of the DDQN and DDPG were found by simple search until the smallest value was obtained for the loss function given by Equations (21) and (24), respectively. Notice that in this work, we used the same neural network for the DDQN and DQN, so they have the same network hyperparameter value.
# Applications of Pulsed Light Decontamination Technology in Food Processing: An Overview
## 5 Role of Pulsed Light Technology in Food Safety
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Food Products** & **Pulse Energy (J/cm\({}^{3}\))** & **Treatment Time (sq) \#** & **Logia Reductions** & **Reference** \\ \hline Fresh egg pasats & 1.75 & - & & 3.3 of \(S\). _entrica_ & [103] \\ \hline Whey & 1.1 & - & & 0.5 of \(L\). _innovation_ & [104] \\ \hline Fork and salmon & 30 & - & 0.96 (in pork) and 0.7 (in salmon) of aerobic microorganisms & [105] \\ \hline Cheese & 1.02–12.09 & - & & 3.74 \(\pm\) 0.86 of \(P_{innovation}\) \\  & & & 5.41 \(\pm\) 0.1 of \(E\). _coli_ ATCC 25,922 & [106] \\  & & & 3.37 \(\pm\) 0.2 of \(L\). _innovation_ & \\ \hline Strawberries & 63.2 & & & 2.4 of \(E\). coli OTS/H7 & \\  & & & 2.2 \(\sigma\) Salmonella & \\  & & & 1.8 of Murine norovirus (MNN-1) & [107] \\ \hline Rasberries & 53.9 & & & 4.5 of \(E\). coli OTS/H7 & \\  & & & 4.4 of Salmonella & \\  & & & 3.6 of Murine norovirus (MNN-1) & & \\ \hline Endive salad and & & & - & 2.34 of \(E\). _coli_ & \\  & & & 2.54 of \(L\). _innovation_ & \\  & & & 2.46 of natural microflora & \\ \hline Murg bean sproots & & & - & 1.91 of \(E\). _coli_ & \\  & & & 1.55 of \(L\). _innovation_ & \\ \hline Orange juice & & & & 0.3-0.8 of \(L\). _innovation_, \(E\). _coli_ and \(S\). Enteritidis & \\  & & & & Upto 0.5 of \(S\). _excessive_ & \\ \hline Strawberry juice & & & 0.3-0.8 of \(L\). _innovation_, \(E\). _coli_ and \(S\). Enteritidis & \\ \hline Apple juice & 71.6 & 60 s & & 1.6 of \(L\). _innovation_ & \\  & & & 2.1 of \(E\). _coli_ & \\  & & & & 2.4 of \(S\). Enteritidis & \\  & & & & Upto 1 of \(S\). _excessive_ & \\ \hline Goat milk & 10 & - & & 6 \(\sigma\). _coli_ & [110] \\ \hline  & 4 & & & 0.5-2.2 of initial microbial load & \\ \cline{2-6}  & & & & 1.85 of \(L\). _innovation_ & \\ Spinach & 0.8 & - & & 1.72 of \(E\). _coli_ & \\ \cline{2-6}  & & & & 2.6 of \(L\). _innovation_ & \\ \cline{2-6}  & & & & 2.3 of \(E\). _coli_ & \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._
## 2 Materials and Methods
### DSC Measurements  
The Q200 DSC (TA Instruments, Newcastle, DE, USA) was used to register melting profiles according to the procedure applied by [PERSON] et al. (2019) and [PERSON] et al. (2020). Next, 3-4 mg of each oil was weighted into aluminum pans, which were hermetically closed. The reference sample was an empty measuring pan. Measurements were performed under the following conditions: nitrogen flow rate of 50 mL/min at normal pressure, the melted samples were heated to 80 \({}^{\circ}\)C and held at this temperature for 10 min. Then, the samples were cooled to \(-\)80 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min and kept at this temperature for 30 min. The samples were heated to 80 \({}^{\circ}\)C at a rate of 15 \({}^{\circ}\)C/min.  
The Q20 DSC equipped with a pressure chamber (PDSC--pressure differential scanning calorimeter) was used to analyze oxidative stability according to the procedure described by [PERSON] et al. (2020). Oil samples were oxidized in isothermal conditions at five different temperatures (100, 110, 120, 130, 140 \({}^{\circ}\)C) and under oxygen pressure of 1350-1400 kPa. Curves of heat flow versus oxidation time were obtained. From the curves, the time of reaching the maximum of the exotherm peak (induction time) was determined (2020).  
A graph of the logarithm of the induction time (\(\tau\)) versus the reciprocal temperature (in absolute scale) was plotted. Equation (5) was used to determine regression lines:  
\[\log\tau=a\ T^{-1}+b \tag{5}\]  
where a and b are adjustable coefficients.  
The activation energy of the oxidation process was calculated using the Ozawa-Flynn-Wall relationship Equation (6):  
\[\text{Ea}=2.19\ \times\ R\ \times\text{a} \tag{6}\]  
where R is the gas constant, and a is a coefficient from Equation (5).
# Physics-Based Noise Modeling and Deep Learning for Denoising Permanently Shadowed Lunar Images
###### Abstract  
The Narrow-Angle Cameras (NACs) onboard the Lunar Reconnaissance Orbiter Camera (LROC) capture lunar images that play a crucial role in current lunar exploration missions. Among these images, those of the Moon's permanently shadowed regions (PSRs) are highly noisy, obscuring the lunar topographic features within these areas. While significant advancements have been made in denoising techniques based on deep learning, the direct acquisition of paired clean and noisy images from the PSRs of the Moon is costly, making dataset acquisition expensive and hindering network training. To address this issue, we employ a physical noise model based on the imaging principles of the LROC NACs to generate noisy pairs of images for the Moon's PSRs, simulating realistic lunar imagery. Furthermore, inspired by the ideas of full-scale skip connections and self-attention models (Transformers), we propose a denoising method based on deep information convolutional neural networks. Using a dataset synthesized through the physical noise model, we conduct a comparative analysis between the proposed method and existing state-of-the-art denoising approaches. The experimental results demonstrate that the proposed method can effectively recover topographic features obscured by noise, achieving the highest quantitative metrics and superior visual results.  
lunar permanently shadowed regions; physical noise model; deep learning; full-scale skip connections; transformer; feature extraction +
Footnote †: journal: Applied sciences
## 1 Introduction
While some studies recently attempted to estimate LUE based on some indices such as the photochemical reflectance index (PRI) or the sun induced chlorophyll fluorescence (SIF) [19; 24], other studies considered the LUE term as a biome-specific constant [25], one which is usually dependent on seasonal changes [19; 26]. Besides LUE models, empirical models based on the relationship observed between VIs or the product VIs *PAR and GPP were also successfully frequently applied [9; 19; 27; 28]. In addition, since photosynthesis (GPP) and respiration (R\({}_{\text{eco}}\)) are generally positively correlated [29; 30; 31], reliable estimates of both NEE and GPP have been obtained from remote sensing products. Grasslands in the Slovenian Karst represent an important resource, as they are one of the first colonizers of shallow soils found in the area. In addition, they are important biodiversity hotspots, are used as extensive pastures by local populations, and contribute to sequestering C in soils [32]. With climate change, more uncertainty could arise regarding the provision of the aforementioned ecosystem goods and services. Thus, thematic digital maps reporting GPP and NEE estimates in the region might provide short-term information on the ecosystem's response to climate in an easy, cost-efficient way, with advantages for stakeholders engaged in grasslands productivity and sustainability. The objective of this study was to estimate NEE and GPP for a karst grassland in the Podgorski Kras plateau (Slovenia) by combining both EC and satellite data in order to provide a basis for large-scale monitoring of the C balance in the grasslands of this region. To that end, we aimed to:  
1. Evaluate the ability of different VIs retrieved from remote sensing platforms to represent GPP and NEE trends in a karst grassland;
2. Compare the performance of different models, integrating VIs in the estimation of GPP and NEE;
3. Apply obtained results to map NEE and GPP for the grassland area in the Podgorski Kras Plateau comparing the suitability of different remote platforms.
## 1 Introduction
In this study, DGs and CBs have been placed in optimal locations with the optimal capacities separately and together in IEEE 33-bus and 69-bus systems with the AOA algorithm, and the optimum power factor of each DG is determined with the proposed algorithm. The main contributions of this paper are summarized as follows:  
* The placement and capacity of each DG and CB have been successfully determined by minimizing total power losses and voltage deviation with AOA. * DGs and CBs are placed in the distribution networks both separately and together under different scenarios. * The optimal power factor, which is an important parameter of DG and is not examined adequately in the literature, has been determined with AOA in addition to the optimal location and capacity. * The proposed algorithm has been implemented in the IEEE 33-bus and IEEE 69-bus systems in various scenarios and the obtained results are compared with the results of other well-known optimization methods.
# Pointing Accuracy of an Operational Polarimetric Weather Radar
## 8 Backlash  
The solar boxscans can also be used to assess the backlash between the bull gear and the absolute encoder, and secondly between the bull gear and the motor of the gear drive system. For a given angle source, this is quantified by computing the pointing bias when either the antenna is moving in clockwise (cw) or counter clockwise direction (ccw). In doing so we effectively use only half of the sample size of each boxscan. The azimuth bias results from this re-sampled data are shown in Figure 12. At a given azimuth position, the difference between the azimuth bias estimate using cw and ccw data is interpreted to be the backlash. The difference is quasi constant (about 0.01\({}^{\circ}\)) for bias estimates using absolute encoder angles. The differences between cw and ccw azimuth bias estimates are larger and more dependent on the actual azimuth position if the motor angles are used to tag the data. The difference between the cw and ccw azimuth bias has a magnitude up to 0.05\({}^{\circ}\). Compared to a target value of 0.04\({}^{\circ}\) this result is still considered to be acceptable. Note, some backlash is needed, otherwise the risk of a failure due to a tightly adjusted gear-drive system will increase due to the increase of mechanical stress. The adjustment of the backlash is a tedious task and requires experience.  
Figure 11: Azimuth bias determined from the motor encoder (“udp”, data from 9 July 2018) and the absolute encoder (operational angle source, data from 8 July 2018). Shown is also the difference between the two biases and the difference of the two angle sources based on the backlash maintenance scan.
# The Integration of Earthwork Design Review and Planning Using UAV-Based Point Cloud and BIM
## 2 Literature Review
### Integration of Point Cloud and BIM  
The integration of different sensors with UAV technology increases its application. Most of the application was focused to increase UAV mapping, detection of affected areas after a disaster, detection of the ground object, etc. UAV was integrated with the geological map to increase geomatics application in geology [22]. A machine learning algorithm was applied in integration with high-resolution imagery from the UAV to detect damage and undamaged roofs after a disaster [23]. These detection algorithms and sensor technology with UAVs help in finding information on terrain that can be used for different purposes. The literature shows that the UAV has automated the process of modeling and reconstruction of facilities in integration with other sensors and detection algorithm based on artificial intelligence and machine learning. However, the integration of UAVs and BIM for earthwork design review and earthwork planning has not been discussed by researchers in detail. The application of UAVs and BIM in earthwork is limited to capturing terrain information and getting a point cloud. These point cloud data are used for further estimation of earthwork. Point cloud from the laser scanner and UAV was integrated by [5] to develop a hybrid scanning method to increase the accuracy of the terrain. Such studies help in increasing the accuracy of the terrain which improves estimates of cut and fill. However, UAV and BIM integration for earthwork planning are still not studied by researchers. Earthwork begins at the start of a construction project and its characteristics are different than other major activities that come after. Delay in this stage could affect the rest of the major activities and therefore the decision taken by the construction managers in earthwork planning is more important and has a greater impact on the overall project performance. To make the earthwork planning more efficient, a new design pipeline based on advanced techniques should be adopted. Integration of earthwork design review and planning may increase the efficiency of earthwork operation.
# Experimental Design of a Mobile Landing Platform to Assist Aerial Surveys in Fluvial Environments
## 4 Modeling and Control
### Dynamic Modeling  
The application of model-based design approaches to controller prototyping allows for quick deployment and debugging of control strategies before implementing them in the real platform. For the Strider V1.0 a dynamic model has been obtained and can be used to evaluate more complex control policies that can be implemented in the future.  
There exist many different approaches to surface vessel dynamic modeling. In [21] a wide array of different models for the description of the vessel motion are covered.  
For the Strider V1.0 model, a Maneuvering Theory approach was used. It considers the vessel's movement at a constant positive speed or with small variations. Only surge, sway and yaw of the  
Figure 8: Main mechanical elements for propulsion and direction: (**a**) 3-blade propeller; (**b**) Rudder; (**c**) steel transmission shaft; (**d**) and waterproof servo for rudder control.  
vessel are considered due to its assumption of calm waters and low swell. Besides this, we will make the following assumptions due to the symmetry and small size of platform:  
1. The body-fixed frame axes coincide with the principal axes of inertia of the body
2. The moments of inertia are constant.
3. Body symmetry with respect to the center of mass is assumed.
4. The body-fixed frame origin \(O_{0}\) is coincident with the center of mass.
5. Restricted, calm, and still-water bodies are assumed. This implies that no currents or waves affect the motion of the ship.
6. Heave, roll and pitch motions are neglected due to a zero-frequency wave excitation assumption.
7. Surge motion is decoupled from sway and yaw motion due to the symmetry of the vessel hulls.
8. Added mass effects on the hulls are neglected since only steady motion will be considered.  
If we deem the previous points and the symmetry of the boat fixed axes, we obtain the following expression, derived from the general motion equation for a rigid solid considering the made assumptions. A more detailed description of the dynamic model development and the components of the force and torque vectors can be found in [20]:  
\[\begin{bmatrix}m_{v}\mathbf{I_{3\times 3}}&\mathbf{0_{3\times 3}}\\ \mathbf{0_{3\times 3}}&\mathbf{I}\end{bmatrix}\begin{bmatrix}\mathbf{\chi}^{B}\\ \dot{\mathbf{\omega}}^{B}\end{bmatrix}+\begin{bmatrix}\mathbf{\omega}^{B}\times(m_{v} \mathbf{V}^{B})\\ \mathbf{\omega}^{B}\times(I\mathbf{\omega}^{B})\end{bmatrix}=\begin{bmatrix}\mathbf{F }^{B}\\ \mathbf{\tau}^{B}\end{bmatrix} \tag{14}\]  
where \(m_{v}\)\([Kg]\) is the mass of vessel and \(I\)\([N\)\(m\)\(s^{2}]\) is the diagonal inertia matrix with respect to the body frame. If we apply the assumptions under Maneuvering Theory, the obtained expression from the simplified above matrix equation is:  
\[\begin{bmatrix}m_{v}&0&0\\ 0&m_{v}&0\\ 0&0&I_{z}\end{bmatrix}\begin{bmatrix}\dot{u}\\ \dot{v}\\ \dot{t}\end{bmatrix}+\begin{bmatrix}0&-m_{v}r&0\\ m_{v}r&0&0\\ 0&0&0\end{bmatrix}\begin{bmatrix}u\\ v\\ r\end{bmatrix}=\begin{bmatrix}X\\ Y\\ N\end{bmatrix} \tag{15}\]
## 2 Numerical Simulation Study of Thermal Storage Tanks
### Mathematical Model  
To ensure accurate calculations and simplify the process of analyzing the complex fluid flow and heat exchange during the experiment, the water tank numerical model was based on several assumptions.  
The heat storage tank was adiabatic to the outside world. There is no heat exchange with the external environment.  
The inlet structure walls were adiabatic, indicating that there was no heat transfer when water flowed from the inlet device into the tank.  
The density and dynamic viscosity vary with temperature.  
The continuity equation is as follows:  
\[\frac{\partial\rho}{\partial t}+\rho\
abla\cdot V\ =\ 0 \tag{1}\]  
The momentum equation is as follows:  
\[\rho\frac{\partial u}{\partial t}+(\rho u\cdot\
abla)u\ =\ -\
abla p+\
abla\cdot \tau-\rho\beta(T-T_{ref})g \tag{2}\]  
The energy equation is as follows:  
\[\rho c_{p}\frac{\partial T}{\partial t}+\rho c_{p}u\cdot\
abla T\ =\ \
abla\cdot(k \
abla T) \tag{3}\]  
where \(\rho\) is the density of water, kg/m\({}^{3}\); \(t\) is the time, s; \(V\) is the fluid partial velocity in the three directions of x, y, z, m/s; \(u\) is the fluid velocity, m/s; \(p\) is the pressure, Pa; \(\beta\) is the thermal expansion coefficient, \(1/\)K; \(c_{p}\) is the specific heat capacity of the constant pressure, J/(kg\(\cdot\)K); \(k\) is the heat transfer coefficient of the fluid, W/(m\({}^{2}\cdot\)K).