esa-sceva/satcom-chunk-collection huggingface dataset:
# High Frequency Transformers for Solid-State Transformer Applications
## 4 Simulation Results
### Numerical Simulation Results
* Test 3--Power Bidirectionality: Consider that the system is supplying a purely resistive load with a power of 50 kVA in the direct power direction, and at instant \(t=1\) s, the power flow changes from the direct direction to the reverse direction, at which point the load is supplying the grid with a power of 50 kVA. The shift angle is only applied to the bridge on the secondary side of the HFT, operating the bridge on the primary side of the HFT with a zero angle. When the power flow switches  
Figure 11: Test 1: Temporal evolution of voltage, phase shift and power. from the forward to the reverse direction, the phase shift angle will have to take on a negative value, as verified in Figure 13.  
Figure 12 also shows the variation of the average instantaneous power at the transformer terminals, where it is possible to see the power bidirectionality. The efficiency verified when the power flow is in the direct direction is equal to the efficiency described in Test 1. When the power flow is in the reverse direction, the same efficiency value is verified, 99.5%, and in this instance, the value of the average instantaneous power in the secondary is higher than the value of the average power in the primary. This allows the conclusion that regardless of the power flow direction, for the same power value, the losses in the transformer will be approximately equal. The temporal evolutions of the voltage at the load terminals present the same temporal progression as described in Tests 1 and 2. Figure 12: Test 2: Temporal evolution of voltage, phase shift and power. Figure 13: Test 3: Temporal evolution of phase shift and power. The impact of the variation of the shift phase angle can also be seen in Figure 14, where the temporal evolution of the voltage at the HFT terminals is represented. When the power flow is in the forward direction, the secondary voltage leads the primary voltage. On the contrary, when the power flow is in the opposite direction, it is verified that the secondary voltage is laggs the primary voltage.
## 3 Optimal Algorithm for NMPC  
### Discretization of Kinematic Model Based on the Collocation Method  
The RK method is derived from the Taylor formula and uses a slope to express the differential. It is used to estimate the slopes of several points in the integration interval, and then to calculate a weighted average as the basis for the next point. This produces a higher accuracy numerical integration calculation method. In this study, we used a special form of second-order RK, the improved Euler method:  
\[\left\{\begin{array}{c}y_{i+1}=y_{i}+h\Big{(}\frac{1}{2}K_{1}+\frac{1}{2}K_ {2}\Big{)}\\ K_{1}=\ f(x_{i},\ y_{i})\\ K_{2}=f(x_{i}+h,\ \ y_{i}+hK_{1})\end{array}\right. \tag{9}\]  
The vehicle kinematics equation comprises expressions of the derivatives of state parameters, which satisfies the conditions of using a second-order RK. Using Equation (9), the vehicle yaw angle and coordinates in Equation (8) are discretized. Assuming that the discretization period is \(T\) and the predicted time domain is \(n\), we obtain  
\[\theta_{i+1}=\theta_{i}+\frac{Tv}{2L}(tan\delta_{i}+tan\delta_{i+1}) \tag{10}\]  
\[x_{i+1}=x_{i}+\frac{Tv}{2}(cos\theta_{i}+cos\theta_{i+1}) \tag{11}\]  
\[y_{i+1}=y_{i}+\frac{Tv}{2}(sin\theta_{i}+sin\theta_{i+1}) \tag{12}\]  
where \(i=0,1,\cdots,n-1\).  
The vehicle's initial yaw angle \(\theta_{0}\), initial wheel rotation angle \(\delta_{0}\), initial abscissa \(x_{0}\), current ordinate \(y_{0}\), and control input set \(\{\delta_{1},\cdots,\delta_{n-1},\delta_{n}\}\) are known. Substituting these parameters into Equation (10), the numerical solution sequence for the vehicle yaw angle \(\theta(t)\) at each node under the second-order RK method \(\{\theta_{1},\cdots,\theta_{n-1},\theta_{n}\}\) can be obtained. Substituting these parameters into Equations (11) and (12), a numerical solution sequence can also be obtained for the vehicle's abscissa \(x(t)\) and ordinate \(y(t)\).  
The Algorithm 1 is designed according to the above calculation steps. The pseudo-code of the algorithm is as follows:
## 2 Materials and Methods
### Data Sources and Processing  
This study utilized the following data, as listed in Table 1: (1) LAI dataset, obtained from MOD15A2H, with a 500 m spatial resolution and 8-day temporal resolution. This study selected LAI from 2001 to 2020, used the mean value of LAI to extract monthly LAI values. (2) GPP dataset, obtained from MOD17A2H, with a 500 m spatial resolution and 8-day temporal resolution. This study selected GPP from 2001 to 2020, and used the sum value of GPP to extract monthly GPP values. (3) Land use/cover data was obtained from the International Geosphere Biosphere Program (IGBP) classification scheme of MCD12Q1 with a 500 m spatial resolution. We reclassified 17 land use types into six categories as follows: cultivated lands, construction lands, grasslands, forest lands, water bodies, and bare lands using Reclassify tool in ArcGIS 10.7. (4) Climatic data, including temperature (Temp), rainfall and solar radiation (Radia), were obtained from the Resource and Environment Science and Data Center. The spatial resolution resampling was 500 m. (5) The administrative boundary was obtained from the Geographical Information Monitoring Cloud Platform.  
Figure 1: Study area and its land cover types in 2020.
## 1 Introduction
### Contributions  
The article targets to increase the speed of the architecture while using less hardware resources, and it presents various contributions towards achieving this goal.  
* The proposed hybrid algorithm combines two different approaches, Montgomery ladder and Double and Add algorithm, to achieve unprecedented performance improvements. The Montgomery ladder algorithm is a commonly used method for scalar multiplication in elliptic curve cryptography, while the Double and Add algorithm is a simple and efficient method for point addition and doubling. By combining these two algorithms, the proposed hybrid algorithm is able to leverage the strengths of each approach and achieves better performance than either algorithm used alone.
* The proposed multiplication technique, based on radix-2 arithmetic, is a method for performing modular multiplication in a more efficient way. This technique splits the multiplication process into smaller subproblems and performs them using radix-2 arithmetic. By using this approach, the proposed algorithm is able to perform modular multiplication more efficiently and accurately, which is a critical operation in many cryptographic algorithms.
* The proposed two-stage pipelining technique is a method for increasing the throughput of the entire architecture by splitting the computation into smaller stages and processing them in parallel. This approach reduces the overall latency of the algorithm and allows for higher clock frequencies, resulting in faster computation times. By using this technique, the proposed algorithm is able to process more data in a shorter amount of time, making it more efficient and effective for real-world applications.
* A Finite State Machine (FSM) has been created to control the data path of a proposed architecture. This control path efficiently manages the operations of the data path.  
The structure of the article is described as follows: Section 2 provides the theoretical background pertaining to BEC. Section 3 describes the proposed hybrid algorithm that has been implemented in this article. Section 4 describes the proposed optimizations for performance improvement. Section 5 provides information on the proposed hardware architecture. The main findings of the article are discussed in Section 6. Finally, the conclusions are made in Section 7.
# An IoT Transfer Learning-Based Service for the Health Status Monitoring of Grapevine
## 2 Materials and Methods
### Transfer Learning
#### 2.2.3 Data Preprocessing and Hyperparameter Tuning  
Prior to their integration to the DL models, the data are subjected to preprocessing techniques. The first step is resizing the images into a specific range for each of the selected. MobileNet v2 and EfficientNet b0 have an expected input size of 224 \(\times\) 224 pixels. Specifically, MobileNet may have input images with a higher size than 32 \(\times\) 32, and higher resolutions lead to slower but better model performance. TensorFlow, the python library chosen for the implementation of the DL models in this study, requires an image input of 224 \(\times\) 224, so image preprocessing focused on this size would be appropriate for all the study cases of this research. Additionally, a normalisation to the range of [0, 1] is performed by dividing the pixel values by 255.  
Due to the fact that the classes of the grapevine subset of the Eden Library dataset is very imbalanced (Table 1), data augmentation on the training set was performed. Healthy and esca classes were reduced to 550 images each, and various data augmentation techniques were employed for the rest of the classes, using random combinations and values of rotation, shearing, zooming, and flipping of the images, as described in Table 2, until each class consisted of a representative number of images close to 550.  
The selection of the appropriate hyperparameters for model training is a critical topic that is addressed in this work. Hyperparameters may have a significant impact on how well the DL model works. In order to determine the best strategy that can be applied to the examined DL models, different methods can be selected, between including grid search, random search, and Bayesian optimization. In this work, the grid search method was employed and the hyperparameter values that were tested are shown in Table 3. The selection values were guided by a combination of empirical experience of the authors and insights drawn from relevant literature. While the specific values were chosen randomly to ensure a broad and unbiased exploration of the parameter space, they were informed by commonly used ranges and best practices as are documented in existing studies.  
In the output of the base models, the global average pooling technique was applied in order to prevent overfitting and improve the generalisation performance. Finally, the SoftMax activation function was used on the output layer and the ReLU activation function in the rest of the FC layer.
## 3 Results and Discussion
### Wettability and Surface Free Energy  
As it was discussed in the previous section, the study of solid-liquid interfacial properties is required to understand the tribological behavior in mixed and hydrodynamic lubrication regimes. Two of the most common interface properties are wettability and surface free energy, which are evaluated and calculated by contact angle measurements. Since, in this study, the lubricant that was used for the tests was PAO 8 oil, the contact angle measurements were carried out by using this oil as well. In short, the tests were performed by freely releasing an oil drop onto the surfaces of the coatings and checking how it spread over a certain period of time, which, in this study, was 0.06 s. Figure 4 shows the images taken at 0.06 s after the oil drop came into contact with the surfaces of the coating films and the values of the angles for one of the examples of each film contact angle measurement with PAO 8 oil.  
Figure 3: Stribeck curves for the lubricated contacts in the block-on-ring tests.  
A graph is presented with the measured contact angles values in Figure 5. Lower contact angle values mean that the lubricant tends to spread and adhere to the surface (better surface wettability), whereas higher contact angle values show the surface's tendency to repel the lubricant. On the other hand, surface free energy determines the strength of the interactions that the surface forms with the lubricant. Therefore, increasing the surface free energy of the coatings means the lubricant flows out more on the surface of the coatings and causes better tribological performance in the lubricated contacts. Observing Figure 5, it can be seen that the contact angles are much lower, and the surface energy is higher for Ne-DLC films compared to the Ar-DLC and CrN films. These results are in correlation with the Stribeck curves of the films since they also showed values of COF for the Ne-DLC films in the mixed and hydrodynamic lubrication regimes that were much lower than Ar-DLC and CrN, which means the lubricant had better spreading and adhesion to their surfaces, which caused them to have better performances in these regimes.
# Mitigation of Gaseous Emissions from Swine Manure with the Surficial Application of Biochars
## 3 Results
### Effectiveness of RO and HAP Biochar Treatment of Gaseous Emissions from Stored Swine Manure
In the case of RO treatment, there was a significant percent reduction (% \(R\)) that ranged from 19\(\sim\)39% for NH\({}_{3}\) in all three trials and the types of manure tested. H\({}_{2}\)S was mitigated (16\(\sim\)23% reduction), but not significantly for all trials. CH\({}_{4}\) was generated (\(-\)26 to \(-\)104%), but not significantly for all trials. CO\({}_{2}\) trended toward mitigation (0-25%), but not significantly for all trials. There was no significant effect on N\({}_{2}\)O (\(-\)1-7%). Overall, the phenolic VOCs had relatively high % R in most trials. Significant (66-78%) reductions for \(p\)-cresol were observed for all trials. The % \(R\) for skatole ranged from 29-72%, but not significantly for all trials. The % \(R\) for phenol and indole ranged from (\(-\)13-94%) & (\(-\)11-84%) but not significantly for all trials. The % \(R\) for isobutyric acid ranged from (9-26%) but not significantly for all trials. Finally, the odor was mitigated in all trials (17-30%) but not significantly for all trials. The RO treatment resulted in decreasing effectiveness (% \(R\)) for NH\({}_{3}\) mitigation, consistently for the first week, the first 2 weeks, the first 3 weeks, and the entire trial for all three trials (Table 4). The % \(R\) for \(p\)-cresol was relatively steady. However, % \(R\) for several targeted gases (H\({}_{2}\)S, phenol, skatole, indole) were relatively steady in at least one of the trials. In the case of HAP treatment, the % \(R\) for NH\({}_{3}\) ranged from 4-21% but not significantly for all trials. H\({}_{2}\)S was mitigated (2-22% reduction), but not significantly for all trials. CH\({}_{4}\) was generated (\(-\)41 to \(-\)221%), but not significantly for all trials. CO\({}_{2}\) trended toward generation (\(-\)57-2%), but not significantly for all trials. There was no significant effect on N\({}_{2}\)O (\(-\)6-46%). Overall, the phenolic VOCs had relatively high % \(R\) in most trials. Significant (91-97%) reductions for \(p\)-cresol were observed for all trials. The % \(R\) for skatole ranged from 74-95% and was significant for all trials. The % \(R\) for phenol and indole ranged from (60-99%) & (29-94%) but not significantly for all trials. The % \(R\) for isobutyric acid ranged from (8-88%) but not significantly for all trials. Finally, the odor was generated in all trials (\(-\)12 to \(-\)59%) but not significantly for all trials. It is important to mention that the odor measurement does not capture odor character. The HAP treatment resulted in decreasing effectiveness (% \(R\)) for NH\({}_{3}\) mitigation, consistently for the first week, the first 2 weeks, the first 3 weeks, and the entire trial for all three trials (Table 4). The % \(R\) for \(p\)-cresol and skatole was relatively steady. However, % \(R\) for several targeted gases (H\({}_{2}\)S, phenol, indole) were relatively steady in at least one of the trials. CH\({}_{4}\) generation increased with the duration of most of the trials.
## 3 Proposed Crypto Processor Design
### Arithmetic Unit
Note that the multiplier produces output in \(2\times(m-1)\) bits; therefore, a polynomial reduction is essential to perform over \(2\times(m-1)\) bit polynomial to get the result back into \(m\) bit polynomial. The literature contains several polynomial reduction approaches; hence, we preferred modular reduction over \(GF(2^{233})\) recommended by NIST, which is shown in Algorithm 2. For 233 bit key length, the computational cost of the adder and multiplier units is one and 233 clock cycles, respectively. In Algorithm 1, the final two lines involve the computation of modular inversion. Numerous methods for modular inversion exist in the literature, but not all are suitable for hardware platforms due to varying mathematical structures [9]. In our design for an accelerator, we have utilized a square version of the Itoh-Tsuji algorithm [33], which requires squares equivalent to the field length followed by ten modular multiplications over the \(GF(2^{233})\) field. The modular inversion unit is not explicitly spotlighted in Figure 1, as the Itoh-Tsuji inversion algorithm leverages square and multiplication circuits commonly employed in PM architectures [12; 13]. As a result, this approach reduces both hardware costs and power consumption. As described, for a 233 bit key length, one square and multiplier circuit cost one and 233 clock cycles, respectively. Thus, implementing the Itoh-Tsuji algorithm for modular inversion over \(GF(2^{233})\) requires \(232\times 233\) clock cycles for square computations; this means that 232 square operations are needed, and each needs 233 clock cycles. On the other hand, \(10\times 233\) clock cycles are necessary for modular multiplication, which means that ten multiplications are required to implement inversion, and each needs 233 clock cycles. Thus, the total clock cycles for one modular inversion computation is 56,386 (54,056 for \(m-1\) square operations and 2330 for 10 multiplications). Figure 2: Modular adder over \(GF(2^{233})\).
# IEEE 802.11 ah: A Technology to Face the IoT Challenge
## 2 Challenges for IoT Applications and IEEE 802.11 ah
### Supporting a Large Number of IoT Devices  
IoT networks have the main characteristic of being formed by a large number of autonomous devices (typically ranging from hundreds to few thousands). This is because many of the applications are expected to operate over a large area. However, collisions occur frequently when a large number of devices try to communicate simultaneously. Excessive collisions result in reduced overall throughput in the network and thus, finding appropriate methods to reduce collisions is a challenge for the IoT. The IEEE 802.11 ah defines an optional new contention channel access mechanism called Restricted Access Window (RAW). This access method is designed to reduce collisions by improving the channel efficiency by dividing stations into different groups and restricting channel access only to a group at a particular time period.  
Legacy IEEE 802.11 supports up to 2007 associated stations per Access Point (AP), due to the limited number of available Association IDentifiers (AID) that can be assigned to each associated station. In order to increase the number of supported stations by AP, IEEE 802.11 ah utilizes a novel hierarchical AID structure. The new AID consists of 13 bits and thus the number of supported stations increases to \(2^{13}-1\) (8191). AID structure consists of four hierarchical levels (i.e., page, block, sub-block, and station's index in sub-block). IEEE 802.11 ah employs the aforementioned structure to group stations based on similar characteristics (e.g., traffic pattern, location, battery level, etc.).
## 4 Real-Time Prediction  
### Data Pre-Processing  
The surface displacement data of the Baige landslide was recorded by the FDMS starting on 31 October 2018. On late 3 November, the second landslide occurred. To find the precursor feature, we used the displacement data obtained by Beidou receivers from 31 October to 6 November to train the KF-FFT-SVM model. Figure 11 shows the raw data of BD1, BD2, BD3, and BD4. The Beidou receiver recorded the three-dimensional (X, Y, Z) displacements of the landslide, while, for simplicity, only the horizontal (X) displacement data were used here. The raw displacement data were obtained at an interval of 10 min. From Figure 11, there were no displacements within 30 min for the majority of the time. Therefore, we sampled the raw data at intervals of 30 min. The sampling result is shown in Figure 12, and this was used to train the KF-FFT-SVM model instead of the raw data.  
Figure 11: Raw displacement data.  
Figure 12: Sampling of the raw displacement data.
# Computer Modelling of Heliostat Fields by Ray-Tracing Techniques: Simulating the Sun
## 1 Introduction  
Our only neighbor star, the Sun, has always inspired human communities, from religion to art, due to its role in providing us with light, heat and ultimately life, but also as a link to the mysteries of the universe. However, only in the last century we started actively developing technologies to collect the massive amount of energy provided by the Sun. At the same time, the computer revolution allowed us to perform accurate astronomical calculations and simulate physical events using modelling techniques such as computer ray tracing.  
Concentrated solar power (CSP) systems have been developed to achieve high fluxes of Sun radiation, at the expense of requiring accurate tracking systems to follow the Sun's position during the day [1]. Solar tracking can be achieved using fundamental astronomical data from an almanac [2] or lookup tables [3], even considering detailed perturbations of the Earth's orbit around the Sun. However, modern CSP facilities are expected to process large amounts of data, coming from multiple sensors, on real-time, so these astronomicalmodels and algorithms must be implemented directly into the software controlling the CSP operation.  
The literature describing the calculation of the Sun's position is extensive, with varying implementations, differing in accuracy and output information. Important works over the last 50 years include [3; 4; 5; 6; 7; 8; 9; 10], based on simplified formulas from [2], and more accurate formulations such as [11; 12; 13; 14], the last two derived from [15]. Detailed analyses of these algorithms, regarding their history, implementation details and accuracy can be found in [11; 12]. A tool for quantitative analysis of some of these algorithms is available in [16]. The work reported in [17] is focused on improving the solar position algorithm's speed. Accuracy of the algorithm [3] was also improved by introducing new expressions for the atmospheric refraction [18], right ascension [19], and for correcting the discontinuities in solar azimuth estimated by the code at the moments of sunrise and sunset [20].  
With this work other researchers are encouraged to write their own software, to increase knowledge, control, flexibility, efficiency, accuracy and error detection. Step-by-step detailed models to calculate the Sun's position are introduced first, followed by mathematical techniques to generate random rays emulating the Sun's features. Two very different locations are suggested, well-known for their heliostat fields [21], integrated in the EU network of solar research facilities, to calculate realistic Sun astronomical results. The data obtained for these locations are then reported and discussed. The goal is to optimize the performance of heliostat fields at these two locations, as reported in subsequent publications.
# Wavelet-Based, Blur-Aware Decoupled Network for Video Deblurring
## 5 Ablation Study  
To gain deeper insights into the working mechanism of our proposed method and validate the effectiveness of core components, we conducted systematic ablation studies. Our network design is based on a key concept: decomposing the deblurring task into two subtasks through wavelet transform. The first subtask focuses on structure restoration, handled by the multi-scale progressive fusion (MSPF) module. The second subtask addresses detail enhancement, managed by the blur-aware detail enhancement (BADE) module.