esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 4 Artificial Intelligence in Production, the Fermentation Process, and Bottling Quality Control
### Monitoring Fermentation Variables
#### 4.1.4 Integration of Multiple Variables  
The integration of multiple fermentation variables into a single AI system allows for a more holistic approach to fermentation management. AI platforms can simultaneously monitor and analyze temperature, sugar levels, and yeast activity, providing comprehensive insights into the fermentation process. This integrated approach enables more precise control and optimization, leading to higher quality and more consistent wine production.  
For instance, a study by [PERSON] et al. discusses the implementation of AI tools for modeling, predicting, and managing the fermentation process in white wine production. The authors used a multi-layer perceptron neural network integrated with genetic algorithms to predict the concentration of alcohol and the amount of substrate at different stages of fermentation. This approach enabled the researchers to automate the process, reducing the need for manual measurements and increasing the accuracy of predictions. The integration of variables such as temperature, time, initial substrate concentration, and biomass concentration into a single model significantly improved the predictive accuracy and control of the fermentation process [82].  
Another example is provided by [PERSON] et al., who used Support Vector Machines (SVM) to detect abnormal fermentations in wine production. By analyzing a large dataset of fermentation control variables and chemical markers, the SVM model was able to predict deviations early in the process. The integration of multiple variables, including density, YAN, brix, and acidity, allowed the system to identify potential issues and suggest corrective actions, thereby ensuring a more stable and controlled fermentation process [83].
## 2 Friction Phenomenon
### Sliding Friction
#### 2.2.1 Viscous Hydroplaning  
Viscous hydroplaning typically occurs on wet runways that have a smooth microtexture [24]. The microroughness has an amplitude ranging from 0.01 mm to 0.1 mm. The water film may have a thickness as little as 0.01 mm. The surface microtexture creates friction by providing a large number of sharp pointed projections that, when contacted by the tire tread, generate high but localized bearing pressures [25].  
The theory of viscous hydroplaning was studied as early as the 1960s [24], where the contact area between tire rubber and the surface asperities was described as a water film with three regions, referred to as the inlet, outlet, and central region, according to the degree of tire movement (Figure 3). Due to the viscous flow of the liquid between the tire and the asperity, the highest pressure (**p*) occurs in the central region. When the water pressure is equal to the pressure from the applied load, direct contact between asperity and rubber does not exist. In this case, if the water film thickness (h*) is greater than the size of the microasperities, a hydroplaning effect occurs. According to the calculations presented in [PERSON]'s study [24], friction decreases by 98% during hydroplaning.
## 5 Numerical Validation and Discussion
### Results in the IEEE 85-Node Test Feeder  
Table 4 presents best numerical results reached by the DCCBGA and the proposed NMA. It is worth mentioning that the BONMIN solvers do not converge to any solution for the IEEE 85-bus system.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Site (Node)** & **Size (kW)** & **\(A^{\text{cost}}\) (USD/Year)** & \(z_{1}\) (USD/Year)** & \(z_{2}\) (USD/Year) \\ \hline Bench. case & — & 4,8588,283.80 & 4,5888,283.80 & 0 \\ BONMIN & \{26, 27, 34\} & \{2400, 747.45, 1336.00\} & 3,355,261.86 & 2,788,935.98 & 566,325.88 \\ DCCBGA & \{11, 23, 25\} & \{1055.54, 1347.95, 2057.09\} & 3,354,711.16 & 2,791,274.19 & 563,436.97 \\ \hline NMA & \{10, 23, 24\} & \{994.25, 1409.42, 2056.85\} & 3,354,676.16 & 2,791,246.03 & 563,430.13 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimal locations and sizes provided by the GAMS and the proposed approach in the IEEE 34-bus system.  
Figure 6: Minimum and maximum voltage magnitudes in the IEEE 34-node test feeder when the NMA solution is implemented.  
The numerical results in Table 5 show that both algorithms present a very similar performance with respect to the final objective function value with a small difference of USD 48.02 per year of operation. In the case of the DCCBGA, 2594.03 kW of PV power are installed and the NMA installs 2598.44 KW of PV generation, i.e., a difference lower than 4.50 kW. However, the main difference lies in the nodes selected by each algorithm. These differences regarding nodes can be attributed to the fact that for the studied problem, the multi-period nature of the optimal power flow solution with PV sources can have multiple near-optimal solutions, i.e., different combinations of nodes with pretty similar objective function values.  
On the other hand, Figure 7 presents the maximum and minimum voltage values for all the nodes in the IEEE 85-bus system for the solution provided by the NMA. Note that in the periods of time between 2020 and 2021 when the demand was maximum and the PV generation was zero, the voltage profiles of the network had a minimum of 0.87131 pu, which is a normal operative condition for this system [27] due to the fact that the only source in this period of time is the slack source.  
Note that in the periods of time where the PV generation is prominent, i.e., 10 and 16 h, the voltage profile improves significantly, with respect to the benchmark voltage case; however, when no PV generation is available, the minimum voltage is equal to the benchmark case which is an expected behavior for this PV applications without batteries.
## 2 Related Works
### Conventional Zero-Trust Authentication Techniques  
An intelligent zero-trust architecture was constructed in [21] to achieve better information security in networks. However, the communication delay was not reduced. A convolutional neural network (CNN) and zero-trust security strategy were utilized in [22] for precise and timely recognition of false data injection attacks. However, the transmission delay was not reduced. An orbital zero-trust architecture (OZTA) was presented in [23] for LEO satellite networks.  
Zero-trust and edge intelligence (ZTEI)-empowered continuous authentication was carried out in [24] to attain better accuracy for user authentication. However, the false positive rate observed during the authentication process was higher. A Multi-Factor Authentication system was implemented in [25] with the support of Zero-Trust Network Access to achieve low computation and communication costs while maintaining a high level of security and reliability. A deep learning-based anomaly detection framework was designed in [26] for satellite telemetry with false anomalies. However, the existing framework features a high computational cost, over-fitting, and data privacy and security concerns. A Variance-Based Genetic Ensemble of Neural Networks was designed in [27] for increasing the efficiency of satellite anomaly detection. However, the false positive rate was higher.  
A zero-trust framework was presented in [28] by using Proof-of-Work and Mean Field Game theory to scale authentication in satellite-ground IoT scenarios. LSTM-based anomaly detection was designed in [29] to satellite telemetry, enabling real-time detection of unusual behaviors. A deep-learning model was intended in [30] that trains on synthetic telemetry faults to simulate and detect anomalies. A genetic-algorithm-optimized ensemble of neural networks was presented in [26] for satellite anomaly detection. Secure Optimization was performed in [30] for enhancing LEO link energy efficiency.  
On the contrary to state-of-the-art works, following are the key contributions:  
* Proposed MRFCO-ZTS is intended for fast adaptation to new threat patterns and Minimizes system load during detection/classification as where it utilizes Manta Ray Foraging Optimization for learning-based evolution of trust scores, using behaviors.
* Besides to that, proposed MRFCO-ZTS enforces Strict identity validation at every request, Dynamic trust re-evaluation based on behavior and past interaction, No implicit trust for previously authenticated satellite nodes, Trust decay over time in idle or suspicious nodes.
## 5 Implementation
### Communication  
Communication between machines and devices occurs following specific protocols. These protocols differ depending on the application area. Industrial applications employ certain established protocols, such as Modbus TCP/IP, Profinet, and OPC UA. In the automotive area, CAN is widely used. For home automation and IoT, MQTT, M-Bus, KNX, and Ethernet-based protocols are used for wired communication. In wireless use cases, ZigBee, LoRaWAN, and, more recently Thread as the protocol of Matter, are well-established. MQTT can be considered as the de facto standard for IoT applications. These protocols are provided by hardware drivers that correctly employ the hardware interfaces of a device, as well as by software stacks that implement the protocol to ease the integration. For the SBT framework, an interface between data and communication protocol was designed. Thus, the application specific code can hand over data to an abstracted communication interface, while the concrete implementation is device-specific. As a consequence, application-specific code can be re-used by changing the device and corresponding software stack. This layer of abstraction increases the usability and ease of use, while also providing benefits regarding the time to market. Most embedded hardware boards provide libraries with drivers for a given communication stack. The advantage of SBT was to implement and provide a layer of abstraction by creating a wrapper with interfaces that translates between the communication drivers and the data points. This allows for a seamless change of hardware platform, as the implemented wrappers provide the same interface for the application, i.e., the data points, while translating to all common hardware drivers.
# Uncertainties and Perspectives on Forest Height Estimates by Sentinel-1 Interferometry
## 3 Results and Discussion
### Minimizing \(\sigma_{dh}\) through Simulated Scenarios
A focus point is that \(\sigma_{dh}\) remains at low values, while \(\gamma\) is higher than about 0.2, and can suddenly and significantly increase once this threshold is overcome. This suggests that a \(\gamma\) value of 0.2 can be somehow used to define significant/reliable measures. In general, when \(dh\) tends to HOA, \(\sigma_{dh}\) is higher, suggesting that small \(dh\) values are more accurate than large ones. Operationally speaking, since the authors approach is based  
Figure 6: SGRP (squared pixel size) vs. coherence. A \(\sigma_{dh}\) equal to 3 m was used during simulations obtained by varying \(\gamma\) and \(B\). Figure 7: Several scenarios of dh uncertainty according to expected \(\gamma_{FP}\) and \(dh\) at different \(B\) values. Isolines refer to the same \(\sigma_{dh}\) values. on height difference computation, expected _dh_ values (10-65 m) are always lower than HOA and result in a \(\sigma_{dh}\) < 5 m. Some examples are reported in Table 3.  
Similar results were found in [32, 54] by using ERS-1 data where phase unwrapping was adopted. Otherwise, in the proposed approach, similar accuracy was found avoiding phase unwrapping, resulting into a more robust and controllable forest height uncertainty estimation. Moreover, values of Table 3 prove that forest height estimates from S1 InSAR data can generate results with an accuracy comparable to those of traditional surveys, making this technique an effective tool for forest structural monitoring. Once more, this evidence highlights that an InSAR technique operated according to the above-mentioned optimization criteria can drive forest height estimates consistent with the ones found from ground surveys. Nevertheless, some limitations still persist concerning low coherence targets where InSAR fails to retrieve reliable forest height estimates [55]. It is worth stressing that these findings are based on simulations; therefore, future developments will be expected to sustain these deductions using real data and quantifying the actual (not the expected) errors in forest height retrieval.
## 3 System Model and Detail Design
### Case 2 with the Combination  
Case 2 with the combination scheme is determined by the amount of information currently being sent by the UE and received by the BS. Each time the UE retransmits, the BS evaluates whether it is more advantageous to forward the data through the cooperation node or retransmit them directly. Since the outcome of each transmission cannot be predicted in advance, it becomes challenging for the BS to preschedule resources. Moreover, allocating additional resources for intermediate operations to calculate case selection be comes necessary. This scheme can reduce the overall energy consumption to some extent. However, it may not optimize the system's latency to a significant degree. The pseudocode of Case 2 with the combination is shown as Algorithm 2.  
```
input : Threshold SNR \(\gamma_{\text{th}}\) and SNR of any two devices \(\gamma_{\text{UB},i}\)\(\gamma_{\text{UC},ij}\)\(\gamma_{\text{CB},j}\) \((i=1,2,\cdots,N_{\text{U}})(j=1,2,\cdots,N_{\text{C}})\) output : Overall transmission number \(T_{i}\) of \(i\)th UE \((i=1,2,\cdots,N_{\text{U}})\)
1 for\(i\gets 1\) to \(N_{\text{U}}\)do // calculate transmission number for UB link \(T_{\text{UB},j}\leftarrow(\gamma_{\text{th}},\gamma_{\text{UB},j})\); for\(j\gets 1\) to \(N_{\text{C}}\)do // calculate transmission number for UC link \(T_{\text{UC},ij}\leftarrow(\gamma_{\text{th}},\gamma_{\text{UC},ij})\); // calculate transmission number for CB link (only cooperation node transmits) \(T_{\text{CB},ij}\leftarrow(\gamma_{\text{th}},\gamma_{\text{UB},i},T_{\text{UC}, ij})/\gamma_{\text{CB},j}\); if\(\gamma_{\text{UB},j}<\gamma_{\text{CB},j}\)then \(T_{i}\gets T_{\text{UB},i}\) else \(T_{i}\gets T_{\text{UC},ij}+T_{\text{CB},ij}\);  end if  end for  end while
```  
**Algorithm 2**Case 2 with combination.
# Comparative Analysis of the Methods for Fiber Bragg Structures Spectrum Modeling
## 3 Results and Discussion
### Amendment to Layer Sweep Method for Phase-Shifted FBG Modeling  
In order to eliminate the spectral response asymmetry of the phase-shifted FBG modeled using the Layer Sweep method, it is necessary to ensure the symmetry of the partitioned refractive index profile of the FBG relative to the center of the phase shift. For that, the authors introduce an additional condition to the choice of the partition interval, according to which each FBG period must contain only integer and odd number of partition points. The simulation results obtained using the proposed approach are presented in Figure 11, where the value of \(\Delta z\) = (1/7) \(\times\)\(\Lambda\) is taken as the partition interval.  
The spectrum obtained using the LS method shown in Figure 11a closely replicates the spectrum derived from the OptiGrating software despite rather coarse partition of the refractive index profile depicted in Figure 11b. In the considered case, the RMSD\({}_{\text{LS}}\) = 0.0151, which is significantly lower even than the deviation demonstrated at \(\Delta z\) = 0.03 \(\times\)\(\Lambda\) (see Figure 9). The RMSD values for various partition intervals corresponding to the introduced condition are shown in Figure 12.  
Figure 11: Modeling results of the \(\pi\)-phase-shifted FBG using the Layer Sweep method with the partition interval of \(\Delta z\) = (1/7) \(\times\)\(\Lambda\): (**a**) resulting spectra: of LS method (red solid line), OptiGrating spectrum (blue dotted line); (**b**) and refractive index profile of the modeled grating.  
The data points can be approximated by the fourth-order polynomial (blue line in Figure 12) with the following coefficients: \(-27.929\), \(9.476\), \(-0.2491\), \(3.734\times 10^{-3}\), \(3.667\times 10^{-3}\) and the norm of residuals equal to \(2.123\times 10^{-5}\). Hence, the RMSD of the phase-shifted FBG spectrum modeled using the LS method with integer number of partition points in the FBG period decrease monotonically with the reduction of the partition interval. With the vanishing partition interval, the RMSD\({}_{\rm LS}\) of the phase-shifted FBG model converges to the value slightly lower than the RMSD\({}_{\rm TM}\) similarly to the homogeneous FBG modeling.
## 5 Conclusions  
In this paper, the authors have proposed an UWB pulse position modulation with an additional time-shift technique for positioning applications in nondestructive environments of the UWB systems. The proposed method is utilized to determine the distances and the position of a buried object in heterogeneous environments. The proposed technique's quality is assessed based on the calculation errors compared to the actual values and comparison with the conventional modulation methods. Combining with the Levenberg-Marquardt Fletcher algorithm to determine the buried object location when the propagation environment is unknown, the efficiency of the proposed technique is better than the PPM-UWB system with the same model. Evaluating different UWB systems based on the calculation of errors provides reliable information for choosing modulation schemes with suitable parameters in the design of UWB systems for positioning applications, especially in nondestructive environments.  
However, in this work, only one buried object location is determined; multiple buried object estimation algorithm will be researched in our future works. Additionally, the research on parameters of the buried object, such as shape, size, permittivity and so on, is left for the future work.  
Conceptualization, N.T.H. and P.T.H.; methodology, N.T.H. and N.L.C.; software, N.T.H.; validation, N.T.H. and P.T.H.; formal analysis, P.T.H. and N.L.C.; investigation, N.T.H.; resources, N.T.H.; data curation, P.T.H.; writing--original draft preparation, N.T.H.; writing--review and editing, N.T.H. and N.L.C.; visualization, N.L.C.; supervision, P.T.H.; project administration, P.T.H. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter/Notation** & \(\mathbf{d_{1}}\) & \(\mathbf{d_{2}}\) & \(\mathbf{x_{T}}\) & \(\mathbf{\varepsilon_{1}}\) & \(\varepsilon_{2}\) \\ \hline True value [m] & 0.63 & 0.42 & 1.6 & 3.5 & 4.5 \\ By UWB-OOK [m] & 0.9418 & 0.6623 & 2.1362 & 4.3216 & 5.7261 \\ By UWB-PPM [m] & 0.7923 & 0.5742 & 1.9316 & 2.7109 & 3.3590 \\ By UWB- & & & & & \\ PPM-ATS [m] & 0.6523 & 0.3899 & 1.7107 & 3.2829 & 4.8167 \\ Error of UWB-OOK & 0.3118 & 0.2423 & 0.5362 & 0.8216 & 1.2261 \\ Error of UWB-PPM & 0.1623 & 0.1542 & 0.3316 & 0.7891 & 1.141 \\ Error of & & & & & \\ UWB-PPM-ATS & 0.0223 & 0.0301 & 0.1107 & 0.2171 & 0.3167 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of the estimated parameters.  
Figure 15: The true and estimated locations of buried object using the conventional UWB-PPM and the proposed UWB-PPM-ATS.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare that there is no conflict of interest regarding the publication of this paper
## 6 Thematic Taxonomy of UWSNs
### Routing Protocol  
Routing protocol becomes a crucial design task in a network layer to provide various requirements for acoustic communication to identify and sustain the network routes. In the past, as well as the present, many protocols have been proposed and explored to enhance network performance for underwater sensor networks. The authors have analyzed the recent study of routing protocols in UWSNs and identified that the principal objective of most routing protocols is energy efficiency (see Table 5). The primary challenge of operating underwater sensor nodes is to maintain the limited amount of energy to continue operation.  
Acoustic underwater communications consume a larger value of energy compared to the terrestrial radio frequency. Those sensors located which are one-hop away from a static sink would suffer from a severe exhaustion of their battery power, which may cause energy holes. It may result in possible network disconnection and consequently preventing messages from achieving the sink. The unique characteristics of underwater environment should be taken into consideration through the operation of the time-varying channel in designing routing protocol. Most of the early work on routing layer has concentrated on compromising with great delays while producing energy efficient communication but has not included important propagation factors such as frequency-dependant attenuation, bottom surface reflections and Doppler effect, which highly affect energy consumption through both power and rate.  
On top of that, the current routing protocols also emphasize the use of adaptive routing, opportunistic, cross-layer, cooperative, and artificial intelligence-related routing protocol to satisfy different requirements of UWSNs (see Table 6). Naturally, the underwater environment is harsh, unreliable, and sparse. In consequence, these unstable states expose UWSNs to natural fragmentation due to the mobility of sensors, thereby affecting the reliability of data transmission from source to destination. Therefore, the designs and techniques of routing protocol are required to deal with these issues.
## 2 Methods
### CFD Method
#### 2.1.1 Geometric Model  
Before numerical simulation, a geometric model was created in SOLIDWORKS (2024), followed by importing it into ICEM (v.19.0) for structured meshing, The mesh type was a hexahedral unstructured mesh. Subsequently, the simulation analysis was conducted using ANSYS Fluent software (2024 R1) to validate the accuracy and reliability of the simulation. The three-dimensional computational domain is shown in Figure 1. The model is accurately built based on the real dimensions of a deep-sea mining vehicle's walking track teeth, and a previous simulation study showed that the jet velocity begins to decline rapidly at around 0.31 m for a 30 mm diameter linear nozzle. To handle the jet's core efficacy region, the simulation analysis used a distance of 0.16 m between the nozzle injection point and the root of the tooth and the dimensions of the nozzle flow field measuring 1.8 m \(\times\) 0.3 m \(\times\) 0.3 m (length \(\times\) width \(\times\) height). This precise parameter selection was intended to maximize cleaning efficiency while optimizing energy consumption during the cleaning procedure.