esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Fluspect-B Model
#### 2.3.1 Pre-Processing  
After obtaining the eight different measurements in one sequence, \(R\), \(T\), and \(F\) were calculated. Pre-processing works with the initial data and derives the required data. In this process, we calculate \(\tau\), \(R_{a}\), \(T_{a}\), \(F_{u}\), and \(F_{d}\):  
\[\tau=\frac{I_{f}}{I}, \tag{1}\]  
\(\tau\) is the transmittance of a filter, which is defined as the fraction of the incident flux passing through that filter. It also represents the shading ability of the filter. A standard reflectance panel is used to  
Figure 2: Representation of the measurement sequence by FluoWat: (**A1**) unfiltered radiance; (**A2**) filtered radiance; (**B1**) leaf up; (**B2**) leaf up with filter; (**C1**) leaf down and (**C2**) leaf down with filter.  
estimate the incident (unfiltered, \(I\)) and filtered (\(I_{f}\)) incident radiation and the exact transmittance of the filter:  
\[R_{a}=\frac{E_{u}}{I}, \tag{2}\]  
\(R_{a}\) is the apparent reflectance, which is the ratio between the irradiating light and the face of the light that reflects upward; \(E_{u}\) is the upward light wave. The reflectance can be calculated by first calculating the apparent transmittance (\(T_{a}\)):  
\[T_{a}=\frac{E_{d}}{I}, \tag{3}\]  
\(T_{a}\) is the flux of the light passing through the leaves to the bottom, and \(E_{d}\) is the downward light wave:  
\[F_{u}=\frac{E_{uf}-E_{u}\tau}{1-\tau}, \tag{4}\]  
\[F_{d}=\frac{E_{df}-E_{d}\tau}{1-\tau}, \tag{5}\]  
where \(F_{u}\) and \(F_{d}\) are the backward and forward fluorescence on the illuminated side of the leaf [Wm\({}^{-2}\)um\({}^{-1}\)sr\({}^{-1}\)]; \(E_{uf}\) and \(E_{df}\) are the light waves emitted upward and downward under the filter conditions, respectively, in the 600-800 nm band. They only contain light irradiating from the leaves and are calculated from the radiance of the leaf using Equations (4) and (5)  
When the incident light is completely filtered and \(\tau\) is 0, \(E_{uf}\) is the measured ChlF; however, the current filters cannot completely filter the incident light at 650-800 nm. Therefore, \(E_{uf}\) includes the ChlF emitted by leaves and the reflected light of the unfiltered spectrum. The Formulas (4) and (5) are used to eliminate the light that is not removed by the filter.  
If we simply consider that all the light at 640-800 nm was removed by the filter, the measured ChlF \(F_{u}\) would be too large. Based on the apparent reflectance \(R_{a}\) (including the fluorescence contribution) and transmittance \(T_{a}\), the true \(R\) and \(T\) were calculated as follows:  
\[R=R_{a}\;for\;wl(650\;nm\;or\;wl)850\;nm, \tag{6}\]  
\[R=R_{a}-F_{u}\;for\;650\;nm<wl<850\;nm, \tag{7}\]  
\[T=T_{a}\;for\;wl(650\;nm\;or\;wl)850\;nm, \tag{8}\]  
\[T=T_{a}-F_{d}\;for\;650\;nm<wl<850\;nm, \tag{9}\]  
where \(wl\) is the wavelength. After pre-processing, the measured fluorescence can be obtained. With the reflectance and transmittance data, the model can simulate the fluorescence, which will be compared with the measured fluorescence. More details can be found in [PERSON] [49] and [PERSON] et al. [30].
## 3 Results
### Hydroxapatite Enriched with Curcumin  
Curcumin (Cur), the active polyphenic compound derived from _Curcuma longa_, has garnered attention for its wide-ranging pharmacological effects. These include anti-inflammatory, antioxidant, hypolipidemic, anticoagulant, anti-angiogenic, and anticancer properties [86; 87]. By regulating key biological processes such as cytokine production, redox balance, and transcription factor activity, curcumin offers therapeutic potential in managing chronic conditions, including metabolic, autoimmune, and neoplastic disorders [87]. Moreover, its anti-cancer activity, particularly in inhibiting tumor cell proliferation, underscores its potential in oncology [88]. However, its clinical application is hindered by poor bioavailability, rapid metabolism, and fast systemic clearance. To address these challenges, nanoparticle-based delivery systems, such as nanogels, micelles, and lipid nanoparticles, have been developed to enhance curcumin's therapeutic potential [89]. In bone metabolism, curcumin's effects remain controversial. While its anti-osteoclastogenic properties suggest a role in regulating bone resorption, high concentrations have been shown to inhibit osteoblast proliferation and even induce osteoblast death under certain conditions [87]. Despite these limitations, combining curcumin with hydroxyapatite (HA) offers significant promise in treating bone-related disorders, particularly when engineered into nanostructured platforms. The combination of curcumin with HA has been achieved using various methods. [PERSON] et al. [89] synthesized hydroxyapatite-curcumin fibers via electrospinning, while [PERSON] et al. [87] utilized wet precipitation, followed by curcumin incubation in ethanol to enhance HA surface functionalization. The high porosity of HA surfaces is critical for effective curcumin attachment, making nanostructured HA ideal for targeted drug delivery platforms. These platforms have demonstrated potential for delivering curcumin to fight cancers like osteosarcoma, suppressing tumor growth while simultaneously promoting bone repair [88]. Additionally, curcumin-loaded HA scaffolds have shown promise in addressing oxidative stress-related complications in bone regeneration. In type 2 diabetes mellitus, elevated reactive oxygen species (ROS) impair mesenchymal stem cell (MSC) differentiation and delay osteogenesis. A study by [PERSON] and [PERSON] demonstrated that curcumin-loaded microspheres incorporated into fish collagen-HA scaffolds reduced ROS production, enhancing bone regeneration and vascularization in diabetic conditions [90]. This dual role of curcumin in suppressing oxidative stress and promoting bone repair highlights its therapeutic value. The challenges associated with doping HA have been presented in Table 3.  
The impact of different ions on the HA properties and structure have been presented in Table 4.
## 5 Results and Discussion
### On the Variability of Model Performance when Grouped by the Considered Scenarios
\begin{table}
\begin{tabular}{c l c c c c c c} \hline \hline \multirow{2}{*}{**Station**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**R2 Score**} \\ \cline{3-8}  & & **Mean** & **Std. Deviation** & **Mean** & **Std. Deviation** & **Mean** & **Std. Deviation** \\ \hline \multirow{3}{*}{BU04} & ST\_CNN\_v1 & 82.8 & 6.61 & 111.09 & 14.38 & 0.78 & 0.04 \\  & ST\_CNN\_v2 & 50.28 & 7.04 & 75.91 & 11.97 & 0.9 & 0.02 \\  & ST\_Dilated\_CNN & 69.98 & 7.3 & 87.57 & 9.8 & 0.86 & 0.02 \\ \hline \multirow{3}{*}{LE05} & ST\_CNN\_v1 & 60.06 & 2.78 & 87.39 & 1.65 & 0.87 & 0.05 \\  & ST\_CNN\_v2 & 44.22 & 1.95 & 68.4 & 3.12 & 0.92 & 0.02 \\  & ST\_Dilated\_CNN & 49.63 & 6.98 & 67.85 & 7.88 & 0.93 & 0 \\ \hline \multirow{3}{*}{P03} & ST\_CNN\_v1 & 58.49 & 7.07 & 89.56 & 13.16 & 0.87 & 0.02 \\  & ST\_CNN\_v2 & 46.71 & 7.24 & 72.12 & 12.06 & 0.92 & 0.01 \\  & ST\_Dilated\_CNN & 73.1 & 11.05 & 93.03 & 14.39 & 0.86 & 0.01 \\ \hline \multirow{3}{*}{P07} & ST\_CNN\_v1 & 83.86 & 11.25 & 113.29 & 15.07 & 0.76 & 0.03 \\  & ST\_CNN\_v2 & 60.02 & 13.27 & 86.8 & 20.44 & 0.86 & 0.02 \\  & ST\_Dilated\_CNN & 86.33 & 17.37 & 115.91 & 25.94 & 0.75 & 0.04 \\ \hline \multirow{3}{*}{SG01} & ST\_CNN\_v1 & 75.44 & 5.68 & 104.37 & 13.34 & 0.84 & 0.02 \\  & ST\_CNN\_v2 & 48.14 & 5.02 & 73.57 & 9.46 & 0.92 & 0.01 \\ \cline{1-1}  & ST\_Dilated\_CNN & 72.44 & 12.53 & 96.35 & 18.53 & 0.86 & 0.02 \\ \hline \multirow{3}{*}{SO01} & ST\_CNN\_v1 & 115.87 & 19.15 & 154.87 & 28.11 & 0.6 & 0.16 \\ \cline{1-1}  & ST\_CNN\_v2 & 88.24 & 25.99 & 134.97 & 42 & 0.69 & 0.16 \\ \cline{1-1}  & ST\_Dilated\_CNN & 56.39 & 9.33 & 77.56 & 10.91 & 0.9 & 0.01 \\ \hline \multirow{3}{*}{ZA02} & ST\_CNN\_v1 & 89.93 & 3.17 & 115.49 & 4.42 & 0.81 & 0.04 \\ \cline{1-1}  & ST\_CNN\_v2 & 71.25 & 3.32 & 93.49 & 8.60 & 0.86 & 0.01 \\ \cline{1-1}  & ST\_Dilated\_CNN & 58.47 & 6.42 & 74.22 & 8.81 & 0.92 & 0.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Mean and standard deviation values of the performance metrics obtained in Scenario 2.  
to show the worst performance at several stations, such as P07 and SO01, where it has the highest MAE and RMSE values, and the lowest R2 score values. In summary, these new experiments have shown that the ST_CNN_v2 and ST_Dilated_CNN models are consistent models, with the ST_CNN_v2 model being the best of the two, although the difference between them is not significant. As for the variability in the stations, the SO01 station is the one with the greatest variability, which is attributed to the fact that its neighbors are the furthest away, so the weight of the information they provide should be less. It is recommended to carry out more experimental repetitions to further validate these findings. The performance results analyzed in this section are also illustrated in the form of boxplots in Figure 10.
## 3 Methodology  
The proposed method (Figure 3) comprises a convolutional neural network (CNN), which is pre-trained for the task of HER2 scoring of tiles extracted from IHC stained slides. The pre-trained parameters are then transferred to the task of HER2 status prediction on H&E staining slide tiles, to provide the network with some knowledge of the tissue structures' appearance. Individual tile scores are then combined to obtain a single label for the respective slide. The data preprocessing methodology and the implemented networks are described below.
# Utilization of Quasi-Zenith Satellite System for Navigation of a Robot Combine Harvester
## 3 Results
### Performance Evaluation of Robot Combine Harvester
Figure 9: Logged positioning data against time from the first run controlled by (**a**) CLAS (**b**) MADOCA. Table 6 summarizes the results from the robot combine harvester controlled by MADOCA under dynamic conditions. The difference of the RMSE of the lateral deviation between the results from the robot combine harvester controlled by MADOCA under dynamic conditions. The difference of the RMSE of the lateral deviation between the MADOCA and the RTK decreases drastically in the second run. It keeps decreasing in the third run and it is almost constant until the fourth run. Unlike the CLAS, the MADOCA has time dependency phenomenon, according to the results from the static positioning experiments and the dynamic experiments while using the RTK as a controller. Although results from the static positioning experiments of the MADOCA positioning augmentation system suggest that it is necessary to wait 1 h after activation before using it, the results from the dynamic experiment suggest that it is necessary to wait 2 h. Therefore, the MADOCA should be activated 2 h before using it to control the robot combine harvester for a safe and efficient work. The average RMSE of the lateral deviation of the RTK or actual path, after the MADOCA converges to the RTK, is 0.086 m and 0.63 m for the first path and second path, respectively. Due to these RMSE values, the overlap between each path of the predesigned navigation map should be set to less than 0.09 while using the MADOCA to control the robot combine harvester. Please note that the RMSE values for the RTK in Tables 5 and 6 are different because the robot combine harvester navigated using either CLAS or MADOCA positioning augmentation systems. The RTK is only measuring the real position of the machine and it takes no part in the automatic navigation control. From Tables 5 and 6, it is not possible for the RMSE values of the RTK to be too similar for both experiments because that would mean that CLAS and MADOCA have almost the same accuracy.
## 3 Proposed Reversible Full Adder-Subtractor  
This section shows the RFAS circuit proposed in this study. To effectively implement the proposed circuit, the 3-input XOR gate proposed in paper [42] is modified to a 2-input XOR gate. In addition, we implement the logic diagram of reversible RFAS based on RQG introduced in Figure 4. For this purpose, the QCA implementation of effective FG and RQG circuits is necessary.  
Figure 7a implements the 2 \(\times\) 2 FG using the proposed 2-input XOR gate using QCA. It has two inputs, A and B, and two outputs, P and Q. The value input to A can be directly output as the value of P, and the value of Q is output by the XOR operation of A and B. Figure 7b shows the QCA layout of RQG using one majority gate, one rotated majority gate, and the proposed 2-input XOR gate. At the center of the circuit is a rotated majority gate consisting of three inputs, A, B, and C, which produces the first result, P. A has the value A' by the simple inverter located at the bottom, and the remaining inputs, B and C, meet at the majority gate located on the left side of the circuit to produce Q. At the same time, A and C produce the value of R by operating with the 2-input XOR gate located on the right side of the circuit.  
Figure 8 shows the QCA layout of the proposed multi-layered 4 \(\times\) 4 RFAS circuit consisting of three layers. The first layer, as shown in Figure 8b, faithfully implements the RQG circuit shown in Figure 7b. Two FGs are implemented in the third layer of Figure 8d. Figure 8c serves as a bridge connecting the first and third layers. In the first clock phase, the input value B of the third layer crosses the bridge of the second layer and comes down to the first layer. In the second phase, the FG located on the left side of the circuit of the third layer outputs B as inputs of B and D, and transmits it to the input value of the FG on the right side of the circuit. At this time, the value of the A\(\oplus\)C output from the first layer is transmitted to the input of the FG of the third layer through the bridge of the second layer, and Carry and Borrow are also output. Finally, in the third phase, Sum/Diff is output through the XOR operation on the two values input to FG.  
Figure 9 shows the simulation results of the proposed RFAS. In Figure 9, it is confirmed that Carry and Borrow are output on CLOCK1, the second clock phase, and Sum/Diff are output on CLOCK2, the third clock phase. In addition, the RFAS circuit outputs normally, as shown in Table 2, and the output polarization is very high, up to 0.992, and a stable output signal without noise is confirmed. The following metrics are defined for performance comparison. Cell count refers to the number of cells required for circuit design, area refers to the rectangular area required for circuit design, and delay refers to the clock cycle (1 clock cycle = 4 clock phases) until the first output of the circuit is produced.  
Figure 7: Proposed QCA implementation of reversible gates: (**a**) FG; (**b**) RQG.
# Mass Estimation-Based Path Tracking Control for Autonomous Commercial Vehicles
## 3 Path-Tracking Controller
### Robust Mass Estimation for Commercial Vehicles  
#### 3.1.1 Vehicle Longitudinal Dynamics Model  
Characterizing the vehicle as a rigid body establishes the foundation for formulating its longitudinal dynamics model. By applying the principles of [PERSON]'s second law, this model (Figure 4) effectively encapsulates the vehicle's motion characteristics across a range of operating conditions [16].  
During the vehicle's movement, it is subject to various forces, which can be represented by the following formula:  
\[m\dot{V}_{x}=F_{T}-F_{j}-F_{w}-F_{i} \tag{10}\]  
Figure 3: Path-tracking controller architecture.  
Considering that the slope of the road is typically small, the following assumptions can be made: when the slope angle \(\theta\) is small, the approximations \(sin\alpha\approx tan\alpha\approx\alpha\) and \(cos\alpha\approx 1\) may be applied. Based on these assumptions, Equation (1) can be simplified to the following [17]:  
\[\dot{V}_{x}=\frac{T_{qt}i_{0}i_{x}\eta}{m\tau}-gf-g\alpha-\frac{C_{d}A_{\theta} V_{x}^{2}}{2m} \tag{11}\]
## 2 Methodology
### Machine Learning Models
#### 2.3.4 Neural Network  
A neural network, which is by nature different from the above algorithms, was then utilized. A neural network uses interconnected neurons in a layered structure that resembles the brain.  
To capture the complex, nonlinear relationships inherent in the oil reservoir data, this study utilizes neural networks (Figure 6). Unlike linear models or tree-based methods, ANNs excel at discerning the subtle patterns and interactions among multiple variables.  
Figure 5: Stacking regressor architecture.  
Figure 6: Neural network architecture for the black oil model.  
Our model was constructed with five layers, including four fully connected (Dense) layers and one output layer. The ReLU activation function was used. The neural network was trained on input data that include various parameters related to oil wells, such as the depth of the well, its geological characteristics, characteristics of the oil reservoir, etc. After training, the neural network can predict the oil recovery factor based on the new input data.  
Through a training process that minimizes the mean squared error between the predicted and actual values, the network learns to accurately forecast the oil recovery. This training utilizes the Adam optimizer, an efficient algorithm for adjusting the network's internal parameters to best fit the data.  
Training regression models can take significant computational time, as shown in the Results and Discussion Section. To speed up this process, parallel computing is used.  
The current implementation of our neural network is static, focusing on prediction of the oil recovery factors based on the snapshot data of the reservoir's characteristics. We recognize the dynamic nature of oil reservoirs and suggest that future research could explore dynamic neural network models that can incorporate temporal changes and production process data to better reflect the evolving conditions of oil reservoirs.
## 4 Opportunities and Challenges
### Interoperability with Smart Buildings and Intelligent Transportation Systems  
Technological developments made at the grid edge, in the form of smart buildings, EVs, and intelligent transportation systems, evolve these components from passive to active participants in the system. Figure 2 summarizes the interaction of smart buildings and intelligent transportation systems with a power system. This presents numerous opportunities to enhance the efficiency and robustness of the grid, as components work harmoniously in the context of smart cities.  
Grid-interactive buildings are capable of adapting to time-of-use electricity pricing and responding to external weather conditions using prediction control strategies on resources, such as heating, ventilation, and air conditioning (HVAC) and storage systems [92]. Accompanied by the development of AI techniques, such prediction control strategies become more efficient, sustainable, and robust. Specifically, DRL has shown considerable effectiveness in overcoming several challenges posed in this domain, such as dealing with varying building environments [93]. Furthermore, the deep penetration of DERs converts users to prosumers and introduces unforeseen complexities through a bi-directional interactive system between the prosumers and the grid in a building. Building energy management systems (BEMSs) seek to improve energy efficiency and conservation. The field of AI brings novel and efficient approaches to the development of smart BEMSs in areas such as pattern prediction of energy usage, load profiling, detection of electricity anomaly/theft, and load profiling [94; 95].  
EVs bridge between power networks and transportation systems and augment the grid with the capability to store and deliver electricity in a variety of locations, therefore, optimizing energy efficiency. According to [96], California currently has the greatest number of EVs nationwide, i.e., more than half a million by 2022. This represents a significant potential to make the power system more efficient. Specifically, through a collaborative communication infrastructure, EV owners are able to optimize their charging behavior based on multi-dimensional data, such as traffic data, charging prices, commuting time, locations of EV charging stations, etc. In return, power grids enable the dispatch of servable EVs as DERs for voltage regulation [97]. Furthermore, advances in technology have enabled not only vehicle-to-grid (V2G) interactions but also vehicle-to-vehicle and vehicle-to-home interactions. This enables a more flexible allocation of resources that can be utilized to meet real-time demand. Therefore, it is essential that the development of technologies efficiently model the behaviors of these technologies and their interaction with the grid.
# Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning
## 7 Results and Discussion
### Throughput Performance
#### 7.3.3 UE Satisfaction Rate  
Finally, Figure 11 shows the UE satisfaction rate for both algorithms. It refers to the percentage of time that the data rate requirement for each UE is met. The result clearly shows that a larger number of UEs are satisfied by the proposed algorithm is compared to the 3 GPP scheme. If we consider UEs who are satisfied at least 50% of the time with their uplink and downlink data rates, then the proposed algorithm has outperformed the 3 GPP scheme by approximately 40%.  
Figure 10: Average UE throughput for (**a**) downlink and (**b**) uplink.
# Resilient Green Cellular IoT for Landslide Monitoring Using Voice Channels
## 1 Introduction
### Challenges  
We faced many challenges for real-time communications among the C-IoT nodes deployed on this site, and some of the challenges are given below:  
1. The nodes in the site use solar power for functioning during the daytime and at the same time harvest the energy. The energy harvesting is used to power the node at night. Being a hilly area with heavy rains, there is always a lack of sunlight. To keep the energy consumption _low_, we choose ultra-low-power nodes with only a few KB RAM. _Resource constraints_ on the nodes prevent them from running popular security protocols, as mentioned in References [10; 11], including the most recent one [12]. Moreover, existing AI and machine learning algorithms requiring more memory for packet anomaly detection in case of DoS attacks cannot be used on these nodes. Hence, the solution must work on a low memory hardware.
2. The nodes in the large deployment site may use _heterogeneous carriers_ to connect to their cellular networks. Though there are some solutions [13] using deep convolutional neural networks (CNN) for detecting DoS attacks, there are no inbuilt security mechanisms against DoS attacks in the cellular hardware. In addition, the end-users may be unaware of security solutions adopted by different carriers. Hence, the solution requires to be both carrier and cellular hardware agnostic in reducing DoS attacks.
3. The C-IoT nodes are connected with various sensors, such as moisture, pore pressure, and movement sensors, and they collaboratively _communicate with each other in real-time_ to track complex phenomena, such as landslides. Two prevalent cloud-based node interaction models in the IoT domain are: Request-Response and Publish-Subscribe [14]. 1. In _Request-Response_ (denoted by R/R), the client node sends a message to the message-broker on the cloud. The receiver node connects to the broker periodically to check for messages and fetches them. The drawback of this method is that periodical polling with the broker results in non-real time communication between the nodes.  
Figure 1: Landslide deployment site in a hilly terrain.  
* In the _Publish-Subscribe_ (denoted by P/S) mechanism, the client IoT node sends a message to the message-broker on the cloud. The receiver nodes subscribe to the message-broker and listen for incoming messages on an open port in the public network. The message-broker broadcasts these messages to the subscribers when it receives the message. This mechanism provides real-time communication between two nodes but makes the nodes vulnerable to DoS attacks in public networks due to continuous listening for incoming messages.