esa-sceva/satcom-chunk-collection huggingface dataset:
# Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications
## 5 Estimation of Tilted Global Irradiation (GTI) from Horizontal Global Irradiation (GHI)
### Methods  
A first forecasting method, a naive model, easy to implement and requiring no training step, i.e., no historical data set, was used as a reference model to compare it with more sophisticated models in terms of accuracy. It allowed us to see the improvement due to the use of the ANN forecaster.  
The persistence model, the simplest forecasting model, assumes that the future value is identical to the previous one (Equation (10)). The persistence forecast accuracy decreases significantly with the forecasting horizon [59].  
\[\hat{X}_{t+h}=X_{t}\text{ with X}=\text{GHI or DNI} \tag{10}\]  
The smart persistence (SP) is an improved version of the persistence one taking into account the diurnal solar cycle: The clear sky solar radiation profile over the day was used [41]:  
\[\hat{X}_{t+h}=X_{t}.\frac{X_{CSt+h}}{X_{CSt}}\text{ with X}=\text{GHI or DNI} \tag{12}\]  
\(X_{CSt}\) is GHI or DNI for a clear sky condition calculated at time t. This smart persistence model was applied in this paper and used mainly as a reference model.  
For the ANN model, as explained in Section 6.1, the clear sky index will be forecasted because it is a stationary series. Once the clear sky index is forecasted, the value of the forecasted solar irradiation, (\(GHI_{l+h}\), _or_\(DNI_{t+h}\)) is obtained by multiplying it by the calculated clear sky irradiation (\(GHI_{CSt+h}\) or \(DNI_{CSt+h}\)).  
Figure 9: Experimental and modelled solar irradiance curves in clear sky conditions (hour in true solar time) (Odeillo site), (**a**) GHI, April; (**b**) DNI, April; (**c**) GHI, September; (**d**) DNI, September.
# Large Language Models Meet Next-Generation Networking Technologies: A Review
## 2 Background
### From Natural Language to Network Configuration Language  
Natural language and network configuration language differ significantly in structure and complexity, presenting unique challenges in bridging the two for effective network operations. Natural language is inherently flexible, context-rich, and user-friendly, designed to accommodate human communication in a fluid and dynamic manner. In contrast,  
Figure 3: The timeline of the development of LLMs from 2018 to 2024 (June), showcasing key advancements and notable models. network configuration language is highly technical, consisting of non-standardized formats, symbols, and domain-specific terminologies. It encompasses a wide range of elements from high-level management policies to low-level technical specifics such as the command line interface and the network access control list. Traditional methods for translating natural language into network commands are limited to formalized approaches such as entity abstraction and template filling. These methods can only provide standardized translations, which are often insufficient for the nuanced and complex requirements of network operations. Network configuration language includes not only specialized nouns and protocols but also strict rules and mathematical constraints that govern its functionality. This complexity makes it challenging for non-experts to interact with network systems effectively using conventional translation methods. Existing modern network paradigms such as intent-based networking (IBD), software-defined networking (SDN), and goal-oriented (GO) communication for networking are essential techniques that leverage natural language for network operations. IBD allows users to specify their network requirements in natural language terms, which are then automatically translated into technical configurations. SDN separates the control plane from the data plane, enabling more flexible and dynamic management of network resources through high-level commands. GO communication focuses on achieving specific network objectives defined in natural language, streamlining the process of network configuration and management. By leveraging advanced techniques and frameworks, we can effectively interchange natural language and network configuration language, creating more intuitive and efficient network management systems. This approach enables even non-experts to perform complex network tasks with ease and confidence. In Table 2, we illustrate how natural language commands can be translated into specific network operations using various features and examples. By translating high-level natural language inputs into detailed network configuration tasks, we can simplify complex network management, making it accessible to users without deep technical expertise.
## 2 Materials and Methods
### Coral Canopy Assemblages  
Incorporating individual corals into a water column model would correspond to a single coral surrounded by an infinite extent of bare substrate, which is not a situation of practical interest for remote sensing. Propagation of reflectance through a plane-parallel water column model, such as HydroLight, necessitates constructing a \"pseudo plane-parallel\" canopy to characterize a horizontally invariant bottom boundary BRDF. To this aim, six assemblages of corals on flat substrate were constructed by randomly manually placing and rotating the individual low-resolution coral models in an area of 20 cm \(\times\) 20 cm (periodically repeating). Three assemblages were generated using corals from the shallow group, and three from the deep group. Viewed from above, the areal cover of the corals versus the substrate varied from 39% to 75%. Assemblages were named based on the group and percentage cover, specifically: S46, S58, S75, D39, D46, D74. There was no intention to specifically compare or construct any hypothesis concerning the shallow and deep groups, but it was considered prudent to be consistent and not to mix deep corals with shallow. The assemblages contained between 7 and 15 individual coral shapes, where some shapes appeared twice or three times in specific assemblages, but in different rotational positions. These randomly constructed canopies may not be strictly representative of a real canopy, but they do incorporate a mixture of typical coral morphologies and surface reflectances and enable an estimate of the optical properties of assemblages of such structures. In these models the total number of surface polygons, including substrate, varied from 35,000 to 68,000.
# Automated Docking System for LNG Loading Arm Based on Machine Vision and Multi-Sensor Fusion
## 3 Design of a Vision-Based Deep Learning Loading-Arm Positioning System
### Monocular Camera Positioning Model
#### 3.3.3 Solve for Spatial Position from an Oblique Perspective  
In Figure 12(b), the imaging plane of the camera is parallel to the flange surface, but the field-of-view angle cannot completely cover the flange surface. To solve this problem, monocular camera B is installed at an angle to meet the shooting requirements, and the actual installation is shown in Figure 12(a). However, since the imaging plane of camera B is not parallel to the flange surface, there are limitations to using the positioning method in Section 3.3.2 directly. Therefore, the system converts the coordinate systems under the two installation conditions. Specifically, the image information in Figure 12(a) is mapped to the imaginary perspective in Figure 12(b) through perspective transformation. Assuming that the camera's photosensitive element is large enough and the field-of-view angle meets the requirements, after the coordinate system transformation, image information equivalent to the camera's perspective in Figure 12(b) can be obtained. The coordinate system transformation relationship is shown in Equation (28):  
\[\begin{bmatrix}X_{c}^{\prime}\\ Y_{c}^{\prime}\\ Z_{c}^{\prime}\end{bmatrix}=\begin{bmatrix}1&0&0\\ 0&\cos\theta&-\sin\theta\\ 0&\sin\theta&\cos\theta\end{bmatrix}\begin{bmatrix}X_{c}\\ Y_{c}\\ Z_{c}\end{bmatrix} \tag{28}\]  
where \(\theta\) represents the rotation angle of the camera, \((X_{c},Y_{c},\text{ and }Z_{c})\) are the coordinates in the camera coordinate system in Figure 12(a), and \((X_{c}^{\prime},Y_{c}^{\prime},\text{ and }Z_{c}^{\prime})\) are the coordinates in the camera coordinate system in Figure 12(b). Substituting Equation (28) into Equations (20) and (21) gives the conversion relationship from the coordinates of the pixels in the real image to the coordinates of the pixels in the virtual image, as shown in Equation (29):  
\[\left\{\begin{array}{l}u^{\prime}=(u-u_{0})/\left(\cos\theta+(v_{0}-v)\sin \theta/f_{y}\right)+u_{0}\\ v^{\prime}=f_{y}\big{(}(v-v_{0})\cos\theta+f_{y}\sin\theta\big{)}/\left(f_{y} \sin\theta-(v-v_{0})\sin\theta\right)+v_{0}\end{array}\right. \tag{29}\]where \((u\) and \(v)\) are the coordinates of a pixel in the real image, and \((u^{\prime}\) and \(v^{\prime})\) are the coordinates of a pixel in the virtual image after the transformation. As shown in Figure 14, plane a is the actual imaging plane, and plane b is the converted virtual imaging plane. In plane a, the green points indicate the contour points of the flange. After the coordinate transformation using Equation (29), these contour points are mapped to plane b. Subsequently, by fitting these contour points, a complete circular contour is generated, thereby extracting the key features (center and radius) of the flange, which satisfies the application conditions required in Equation (27).
# A Novel CDMA-Based Secret Message Multiplexing Scheme by Image Data Hiding +
## 3 The Proposed Secret Message Multiplexing Scheme
### PSNR Analysis  
Generally, the pixel values of original images can be regarded as randomly distributed; it is reasonable to suppose that after converted to a digit in the \((2N+1)\)-ary notational system by modulo operation, the occurrence of any number within the range of \([0,\ 2N]\) is equally likely. Likewise, to embed a \((2N+1)\)-ary CDMA codeword digit into a pixel, the value of modification is uniformly distributed between -\(N\) and \(N\). Theoretically, the mean-square-error (MSE) is given by  
\[\text{MSE}=\frac{1}{2N+1}\sum_{k=-N}^{k=N}k^{2}=\frac{N(N+1)}{3}. \tag{7}\]  
The peak-signal-to-noise-ratio (PSNR) for an 8-bit grayscale image is defined by  
\[\text{PSNR}=10\log_{10}\frac{255^{2}}{\text{MSE}}\ (\text{dB}). \tag{8}\]  
By applying the result in Equation (7) to Equation (8), the expected PSNR value of a marked image is given by  
\[\text{PSNR}=10\log_{10}\frac{3\times 255^{2}}{N(N+1)}\ (\text{dB}). \tag{9}\]  
Apparently, the number of users \(N\) dominates the mean-square-error and the PSNR value. A larger \(N\) means that more users are involved in the the process of CDMA multiplexing and longer spreading codes (or secret keys) are adopted to provide better data confidentiality for participants' secret. But inevitably, it also lowers the PSNR value and reduces the image quality. The expected PSNR values at different values of \(N\) are listed in Table 1.
# Simulation Study of Two Torque Optimization Methods for Direct Torque-Controlled Induction Motors
## 2 IM Torque Response Optimization
### Torque Optimization Method (2)  
Since optimization Method (1) depends somewhat on motor parameters, it is better to improve it by omitting the dependence on motor parameters. The torque rate relationship in Equation (12) will be re-derived and adapted to be parameter independent. The derivation is as follows:  
The IM torque equation may be written as:  
\[T=C_{1}Im\{\overline{\lambda_{s}}\overline{I}_{s}\} \tag{14}\]  
where (\(C_{1}\)) is a constant and (\(\overline{I}_{s}\)) is the stator current vector. Hence, the time rate differentiation of Equation (14) is:  
\[\frac{dT}{dt}=C_{1}\cdot Im\left\{\frac{d\overline{I}_{s}}{dt}\overline{ \lambda_{s}}+\overline{I}_{s}\frac{d\overline{\lambda_{s}}}{dt}\right\} \tag{15}\]  
But, if:  
\[\overline{\lambda}_{s}=\int_{0}^{t}(\mathbf{V}_{k}-R_{s}\overline{I}_{s})dt+ \overline{\lambda}_{o} \tag{16}\]  
then:  
\[\frac{dT}{dt}=C_{1}\cdot Im\left\{\frac{d\overline{I}_{s}}{dt}(\int_{0}^{t}( \mathbf{V}_{k}-R_{s}\overline{I}_{s})dt+\overline{\lambda}_{o})\ast+\overline{I}_ {s}(\mathbf{V}_{k}-R_{s}\overline{I}_{s})^{\ast}\right\} \tag{17}\]  
Substitute with \(t=0\) (the time of checking for the optimum voltage vector) to obtain the following:  
\[\frac{dT}{dt}=C_{1}\cdot Im\left\{\frac{d\overline{I}_{s}}{dt}\Big{|}_{t=0} \overline{\lambda_{o}}+\mathbf{V_{k}}\ \overline{I}_{s}\Big{|}_{t=0}\right\} \tag{18}\]  
The right part of Equation (18) has two added terms. The first term is constant
## 2 Satellite Sensor Data  
Several aspects have required the establishment of a hierarchy of processes that allow the generation of operational products (users level) and the development of processing algorithms characterised by its precision, autonomy and efficiency, namely: i) the different levels of processing involved to obtain the geophysics parameters; ii) the interest in multi-temporal and multi-sensorial  
Figure 1: Schematic procedure of the proposed automatic satellite imagery registration.  
studies; iii) the growing request of accuracy and temporal resolution in the satellite measurements and, iv) the technical complexity of the present remote sensing systems, some having a significant off-nadir viewing capability (_i.e._ NOAA-AVHRR and SeaStar-SeaWiFS).  
Specifically, oceanographic satellites images are subjected to different geometric distortions due to the Earth's curvature and rotation, the spacecraft's speed, altitude and attitude, and the scan skew. These distortions, if not properly accounted for, will prevent meaningful comparison among images. In this work, a Keplerian correction model, based on the satellites trajectories, have been previously used to compensate the systematic errors. The accuracy obtained from the application of these models is variable. Failures in the satellite's internal clock, inaccuracies in Keplerian orbital elements and lack of knowledge concerning the attitude angles, lead to that even the most complex models do not offer the desired accuracy of errors of less than one pixel [15]. Table 1 shows the main characteristics of sensors used in this work (altitude, orbit inclination, nadir resolution and viewing swath width).  
The following sections briefly describe the data processing steps to generate Sea Surface Temperature (SST) and Ocean Colour imagery, using AVHRR/3, SeaWiFS, MODIS and ATSR data, which are conveniently used in various fields, such as oceanography, meteorology or fishery.
## 2 System Modeling
### Mechanical System  
The motion dynamics of the inertial load are obtained by applying [PERSON]'s second law as  
\[J\ddot{\theta}=\tau_{L}-\tau_{f}(\dot{\theta})-\tau_{d} \tag{1}\]  
Figure 1: The structure of the studied servo valve-controlled electro-hydraulic system.  
where \(J\) and \(\theta\) are the moment of inertia and the angular position of the load, respectively. \(\tau_{L}\), \(\tau_{f}\), and \(\tau_{d}\) are the load torque of the hydraulic actuator, frictional torque, and disturbances caused by modeling errors, unmodeled dynamics, and external load, respectively.  
The load torque generated by the pressure difference between chambers of the actuator is determined by [36]  
\[\tau_{L}=P_{L}D_{m} \tag{2}\]  
where \(P_{L}=P_{1}-P_{2}\) is the load pressure with \(P_{1}\) and \(P_{2}\) representing the pressures of the two actuator chambers and \(D_{m}\) denotes the radian displacement of the actuator.  
Due to the high stiffness of EHSSs, the frictional torque can be modeled without considering its dynamics as follows [16]:  
\[\tau_{f}=B\dot{\theta}+\left(\tau_{c0}+\tau_{s0}\exp\left(-\frac{|\dot{\theta }|}{\omega_{s}}\right)\right)\text{sgn}(\dot{\theta}) \tag{3}\]  
where \(B\) is the viscous friction coefficient, \(\tau_{c0}\) and \(\tau_{s0}\) are the parameters for Coulomb and Stribeck frictions, \(\text{sgn}(\bullet)\) and \(\exp(\bullet)\) represent the standard signum and exponential functions, respectively, and \(\omega_{s}\) is Stribeck velocity. Consequently, the frictional torque in (3) can be re-arranged as  
\[\begin{split}&\tau_{f}=\tau_{smf}+\Delta\tau_{f}\\ &\tau_{smf}=B\dot{\theta}+\left(\tau_{c0}+\tau_{s0}\exp\left(- \frac{|\dot{\theta}|}{\omega_{s}}\right)\right)\tanh(c_{f}\dot{\theta})\end{split} \tag{4}\]  
where \(\tau_{smf}\) represents the smoothened term of frictional torque, \(\tanh(\bullet)\) is the hyperbolic tangent function, \(c_{f}\) is a positive constant, and \(\Delta\tau_{f}\) denotes the approximation error caused by replacing the \(\text{sgn}(\bullet)\) function by the \(\tanh(\bullet)\) function.  
Combining (1), (2) and (4), one obtains  
\[\ddot{\theta}=\frac{D_{m}}{J}P_{L}-\frac{\tau_{smf}}{J}-\frac{\Delta\tau_{f}+ \tau_{d}}{J} \tag{5}\]
# Influence of Roll Diameter on Material Deformation and Properties during Wire Flat Rolling
## 3 Results
### Damage Value with Roll Diameter  
The damage value of the wire was analyzed during the process to evaluate the formability with the roll diameter. In this study, the normalized Cockcroft and Latham fracture criterion [34; 35] was applied as follows:  
\[D=\int_{0}^{\varepsilon_{f}}\frac{\sigma^{\star}}{\overline{\sigma}}d\overline {\varepsilon} \tag{10}\]  
where \(\sigma^{\star}\) is the maximum principal tensile stress and \(\overline{\sigma}\) is the von Mises equivalent stress. Figure 11a compares the contour of the damage value in the wire with the roll diameter. The distribution of the damage value varied with the roll diameter. The free surface region tended to have the peak damage value during wire flat rolling, which is consistent with previous results using hardening material by [PERSON] et al. [36] and [PERSON] et al. [12]. However, the center region exhibited the maximum damage value as the roll diameter increased, as shown in Figure 11b,c.  
Figure 10: Variations in (**a**) \(\alpha\) and (**b**) \(L\) with the roll diameter during wire flat rolling.
# Rapidly Deployable IoT Architecture with Data Security: Implementation and Experimental Evaluation
## 4 Application Implementation
### Fall Detection for Elderly  
Fall detection saves the lives of many elderly and disabled people by informing relatives and emergency services. IoT system can greatly improve the health and living condition of elderly people [3]. In this variant of the LiB IoT system as in Figure 10, ADXL345 3-axis accelerometer was used to detect changes in motion to indicate a fall. It is an ultra-low power portable sensor which can also detect free fall. It can communicate both via SPI and I2C and in this case, SPI was used to integrate this sensor with MSP432 32-bit ARM microcontroller [66]. The microcontroller with low-power mode and internal real time clock was used to wake up the device during deep sleep stages and synchronize communication. MSP432 has an AES hardware accelerator, which provides security while the data is being transmitted to the LoRa device [67] without affecting the performance of the firmware [8].  
As in Table 3, the majority of current is consumed by the LoRa radio RFM95W while transmitting or receiving. When a fall was detected, the accelerometer sensed a change in the X, Y, Z direction and sent it to the microcontroller, which in turn woke the LoRa radio, and transmitted the information to the internet gateway, which further sent a tweet via PubNub [68]. By including a low power GPS, this system could also send location information to assist caretaker or relatives as necessary. The portability of this LiB system allows it to be strapped to the arm without any discomfort.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Active Current (mA)** & **Sleep Current (mA)** \\ \hline
8-bit MCU & 0.2 & 0.06 \\ CC2540 BLE & 30 & 0.3 \\ BME680 & 0.1 & 0.01 \\ LDR & 0.4 & 0.4 \\
**Total** & 30.7 & 0.77 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Current consumption in indoor environmental monitoring system.  
Figure 10: System Architecture for fall detection.  
This is a sensitive application and the safety of the patient depends on the authentic triggering of the circuit. If the data is not encrypted, attackers can cause false alarm or disable alarms causing a detrimental effect.
# A Dataset of Pulsed Thermography for Automated Defect Depth Estimation
## 3 Data Preparation and Pulsed Thermography (PT)  
The material used for the specimens in this study was PVC. This is consistent with our previous research [18]. This set included an additional 19 specimens featuring flat rectangular indentations at the bottom. Figure 1 illustrates all potential defect locations within the dataset. The experimental setup remained identical to that described in our earlier work, with a schematic representation provided in Figure 2.  
During the experiment, an infrared camera (FLIR SC5000) was employed to capture infrared images. Two high-power lamps, each with a power output of 4 KJ, were used to heat the specimens. The operation of the camera and the lamps was coordinated through a computer-controlled unit, ensuring precise timing for triggering events in predefined intervals. Initially, a signal is sent to activate the camera to start recording. After a certain time interval, a second trigger signal is sent to activates the flash. The camera, with a resolution of 320 \(\times\) 256, records infrared video at a frequency of 10 Hz for 181 s. This process generated a dataset with dimensions of 256 \(\times\) 320 \(\times\) 1810 for each measurement.  
Figure 1: CAD image for all the possible defects’ locations, sizes, and depths.  
Figure 2: Experiment set up of PT inspection.
## 3 Numerical Models
### Boundary Conditions and Mesh Convergence Studies  
In this numerical simulation, air and water were utilized as the first and second phases, respectively. The numerical model described above was solved using ANSYS Fluent software developed by America, and the surface tension coefficient between the two media remained constant at 0.072 N/m. The turbulence effect was resolved via the model. A standard wall function was set to solve the boundary layer of the experimental jumper. The velocity input and pressure outlet were specified, and the wall was given a non-slip wall boundary condition.  
The momentum equation was solved using the QUICK format, the pressure equation was solved using the PRESTO scheme, and the relaxation factor was set at 0.7 for the pressure and 0.3 for the momentum. To ensure the stability of the simulations, the CFL (Courant-Friedrichs-Lewy) number was kept below 1 during the simulations. As described in Figure 6, structured meshes generated using the ICEM tool were used to discretize the computational domain of the fluid.  
A mesh convergence study was performed to determine the optimal number of grids. Generally, a larger number of grids leads to more accurate simulation results while significantly increasing the computing cost. An appropriate number of meshes should be selected to balance the computational accuracy and resources. Three different meshes with grids numbers of M\({}_{1}\): 663,022, M\({}_{2}\): 883,268, and M\({}_{3}\): 1,109,088 were used to carry out the simulations under the working condition of a mixing velocity of 2 m/s and a gas content of 50% as the representative case. The maximum pressure was monitored at P1, and the time-averaged value of the pressure difference between P1 and P2 was used to evaluate the grid resolutions. The results using the three meshes are compared in Figure 7. The relative discrepancy among the results of the three grids was within 5%. Considering the computational accuracy, cost, and efficiency, the mesh with a total grid number of 883,268 was chosen for the simulations.