esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 DGPS and FKP
### DGPS and Spatial Decorrelation
 \tag{2}\]  
As explained previously, the measurement of the user apart from the RS includes the spatial decorrelation errors. Hence, considering the decorrelation errors (\(\delta I,\delta T\)), the relationship between the common errors can be expressed as follows. \[ \begin{array}{l}I_{u}^{i}=I_{r}^{i}+\delta I^{i},\ T_{u}^{i}=T_{r}^{i}+ \delta T^{i}-r\Delta_{u}T_{\text{model}}^{i}\\ I_{u}^{i}=I_{r}^{j}+\delta I^{j},\ T_{u}^{i}=T_{r}^{j}+\delta T^{j}-r\Delta_{u }T_{\text{model}}^{j}\end{array}  \tag{3}\]  
where \(\delta I,\delta T\) are the components of the ionospheric and tropospheric delays, respectively, due to the spatial decorrelation between the user and the RS. In addition, the subscript \"model\" denotes the value that is calculated using a tropospheric model. Several errors can be appropriately determined using various models such as the Klobuchar model for ionospheric delay and a simple model for the tropospheric delay. In particular, the tropospheric delay can be approximated using the model because the tropospheric refraction arises from the predictable component [13]. Hence, assuming that the user and the RS use the tropospheric model to eliminate the tropospheric error in their  
Figure 1: DGPS concept and spatial decorrelation. measurements, the relationship of the tropospheric delay can be expressed using the above equation. Because the ephemeris error is less affected by the spatial decorrelation compared to the ionospheric and tropospheric delays, as listed in Table 1, it can be ignored in this mathematical analysis along with the noise. By substituting Equation (3) into Equation (2), we obtain the following equation. \[ \begin{array}{ll}{}_{u}\Delta_{r}{}^{i}\
abla^{j}\rho&=\overline{R}^{i}\cdot _{u}\Delta_{r}\overline{e}^{i}-\overline{R}^{j}\cdot_{u}\Delta_{r}\overline{e} ^{j}-\overline{R}_{u}\cdot\overline{e}^{i}_{u}+\overline{R}_{u}\cdot\overline {e}^{j}_{u}+\overline{R}_{u}\cdot\overline{e}^{j}_{u}-\overline{R}_{u}\cdot \overline{e}^{j}_{u}\\ &+\left(i^{\
abla j}\delta T-r\,\Delta_{u}{}^{i}\
abla^{j}\Gamma^{i}_{\text{ model}}\right)+\left(i^{\
abla j}\delta I\right)\end{array}  \tag{4}\]  
Finally, the errors due to the spatial decorrelation in the double-difference measurement are confirmed. Hence, it is reasonable to conclude that the position accuracy reduces because of the spatial decorrelation.
# Evaluation of Biochar and Composit Mixes as Substitutes to a Commercial Propagation Mix
## 2 Materials and Methods  
### Germination Substrate Selection and Characterization  
The BC used in this study was a byproduct of the fast pyrolysis of mixed hardwood (Proton Power, Inc., Lenior City, TN, USA). The nutrient analysis (N, P, K, Ca, Mg, S, B, Ca, Cu, Fe, Mn, Na,and Zn) of the BC was tested by the Texas A&M AgriLife Extension Service Soil, Water, and Forage Testing Laboratory in College Station, TX, USA and is shown in Table 1. The total N was determined spectrophotometrically by a combustion process [35] and the minerals (including P, K, Ca, Mg, S, B, Ca, Cu, Fe, Mn, Na, and Zn) were determined by inductively coupled plasma analysis of a nitric acid digest [36]. Two composets, including VC (Pachamama Earthworm Castings; Lady Bug Brand, Conroe, TX, USA) and CM (Back to Nature, Inc., Slaton, TX, USA), were chosen to mix with the BC. A commercial propagation mix (Propagation Mix; Sun Gro Inc., Agawam, MA, USA) was used as the control to germinate seeds. The pHs of the BC, VC, and CM were measured using a handheld pH-EC meter (HI 98129, Hanna Instruments, Woonsocket, RI, USA) and electrical conductivity (EC) was measured using the Bluelab Combo Meter (Bluelab Corporation Limited, Tauranga, New Zealand) according to the pour-through extraction method using the same amount of leachate for each test [37]. The pHs of the BC, VC, and CM were 11.18, 4.8, and 7.5, respectively. The ECs of the BC, VC, and CM were 2.0, 6.7, and 32.9 dS m\({}^{-1}\), respectively. The physical properties of the BC, VC, and CM were reported previously by [PERSON] et al. [26]. Physical properties, including the total porosity (the percent of pores in a substrate), container capacity (the maximum percent volume a substrate can hold water), air space (the percent of pores filled with air after drainage), and bulk density (the ratio of dry weight to the volume of the substrate) were determined using the North Carolina State University Horticultural Substrates Laboratory porometers [38]. Six replications were tested to determine the physical properties. The total porosities of the BC, VC, and CM were 84.7%, 75.0%, and 64.4%, respectively. The container capacities of the BC, VC, and CM were 60.3%, 72.2%, and 60.0%, respectively. The air spaces of the BC, VC, and CM were 24.4%, 2.8%, and 4.4%, respectively. The bulk densities of the BC, VC, and CM were 0.15, 0.38, and 0.62 g cm\({}^{-3}\), respectively.
## 4 Experimental Design and Result Analysis  
### Dataset and Experimental Environment  
The experimental data were collected from multiple traffic intersections in the main urban area of Guilin. After screening, we obtained a total of 2126 image data, each with dimensions of 960 \(\times\) 540 pixels. We used the roboflow platform for data annotation, and the dataset mainly includes three types of targets: electric bicycle riders (E_bicycle), helmet-wearing targets (Helmet), and non-helmet-wearing targets (No_helmet). The collected dataset covers various conditions such as location differences, lighting variations, and shooting angles. Among these, conditions of illumination are a vital factor affecting the performance of target detection. If the dataset only includes images under specific lighting conditions during training, the model may not be able to adapt to other lighting conditions. Similarly, the background complexity in different scenes is high, and there may be significant appearance differences between targets in the same category. Therefore, constructing a dataset that includes multiple lighting conditions and different scenes is crucial for enhancing the versatility of the model and improving its generalization ability. Figure 7 presents a number of images drawn from the dataset.  
To enrich the image data, we adopted data augmentation methods, applying operations such as flipping, cropping, adjusting brightness, adjusting saturation, and adding noise to the images. This enriches the variety of the data and boosts the model's generalization ability. By using this method, we can effectively expand the dataset, thereby enhancing the efficiency of network training. The enhanced dataset contains a total of 3827 images, which we have allocated in a ratio of 8:2 to form the training set and validation set, respectively.  
This experiment is performed on the Ubuntu 22.04 operating system, using a CPU model of E5-2687W and a GPU model of NVIDIA RTX-3060 with a memory capacity of 12 GB. The host memory is 24 GB. We choose Pytorch-2.1.0 as the deep learning framework, with Python edition 3.10 and CUDA version 12.1. The arrangements of the other training parameters are outlined in Table 2.  
Figure 6: Network structure diagram of C2f_EMA.  
Figure 7: Pictures of different scenes and lighting.
## 3 System Architecture
### Spatial Multiplexing Using Pixelated Shutter  
We describe the multiple access capability through the spatial multiplexing setup illustrated in Figure 1 (right). Consider two LEDs placed in space (at same height but separated along the horizontal) aiming to focus their signals (using the condenser lens) onto a photodetector (not shown in Figure 1 (right)) by passing through a 1 \(\times\) 2 LCD shutter pixel system, where the pixels are aligned next to each other along the horizontal axis. Let us consider that each pixel \(i\) is responsible for signals from corresponding LED \(i\). In this way, when the signals from the LEDs are beamed onto the photodiode, each pixel can selectively allow/disallow the signals provided the signals are independently identified (and differentiated) and the information on which signal (LED) should be selected is feedback to the shutter control unit (not shown in Figure 1 (right)).  
To ensure that the signals do not overlap onto a single shutter surface area, our design requires that each LED signal can be spatially separated onto independent shutter pixels. This depends on the size of the shutter, distance of communication and the spatial separation of the LEDs. Through lens equation [27] and using simple trigonometrical calculations, we derive that the minimum distance of separation between the LEDs must be \(h=dS_{1}/BFL\) where \(d\) is the distance between two shutter pixel centers (considering a square pixel it is the pixel side length), \(S_{1}\) is the distance from LED to the lens, and \(BFL\) is the back focal length which is the focal length of the lens. We also derive the minimum angle of separation between the LED beams as \(\alpha=2\arctan(\frac{h}{2S_{1}})\). These equations provide the designer the control of placing the LEDs in space so as to allow multiple access reception on the single photodiode receiver. We determine that the minimum horizontal separation between the LEDs has to be \(h=14.88\) cm and \(\alpha=51.2^{\circ}\) for the component values from our proof-of-concept prototype system (section 4), where \(S_{1}=15.5\) cm, \(S_{2}=8.2\) cm and \(BFL=3.75\) cm and \(d=3.6\) cm, radiation angle of the LED is \(50^{\circ}\), at a distance of 10 m the equivalent minimum separation distance would be close to 10 m, however, this distance can be reduced if the \(d\) were to be increased. By merely doubling the size (\(d\)) of the shutter, the required distance \(h\) now can be 5 m. This means that the selection of separation between LEDs in space and the size of the shutter involves a tradeoff. The tradeoff between \(h\) and \(d\) can also be adjusted by using LASER type emitters or LEDs which have smaller radiation angle.
# Predicting Soccer Player Salaries with Both Traditional and Automated Machine Learning Approaches
## 3 Overview of Data and Preprocessing
### Data Preprocessing
#### 3.2.3 Introduction of New Features  
In addition to engineered features, we introduce the _league_weight_ feature to improve model accuracy by capturing league competitiveness. This feature combines two key factors:  
1. **Average Annual Salary Ranking** (\(S\)): The average player salary in million GBP, reflecting a league's financial strength and ability to attract top talent. Higher salaries often correlate with better resources and player performance [32].
2. **UEFA Coefficient** (\(U\)): Measures league strength based on club performance in European competitions over the past five seasons. Higher coefficients indicate more competitive leagues with better financial and institutional backing [38].  
We compute the _league_weight_ using a weighted normalization formula:  
\[\textit{league\_weight}\ =\alpha\cdot\frac{S}{\max(S)}+\beta\cdot\frac{U}{\max(U)} \tag{4}\]  
where \(\max(S)\) and \(\max(U)\) are the maximum salary ranking and UEFA coefficient values across leagues, respectively. The weights \(\alpha=0.6\) and \(\beta=0.4\) reflect the relative importance of salary and UEFA coefficient, with more emphasis on salary due to cases like France, where high wages exist despite lower UEFA rankings. We select a range of appropriate weight values based on our domain knowledge and test combinations of candidate weight values, determining the optimal set based on experimental results. Moreover, Table 5 presents the final results of the league weight calculation, derived from the average annual salary and UEFA coefficients for the top five leagues.
# A Single Motor-Driven Focusing Mechanism with Flexure Hinges for Small Satellite Optical Systems
## 2 Materials and Methods
### Requirements for a Novel Focusing Mechanism  
Given that the De-space significantly affects variations in MTF, the design of the focusing mechanism should ensure the restoration of optical alignment mostly by adjusting De-space, with limited adjustments to De-center and Tilt. As shown in Figure 2, by exploiting the relation between the De-space and MTF, the desired range of the De-space was defined to \(\pm\)10 \(\upmu\)m, and the desired De-center and the Tilt were limited to 8.2 \(\upmu\)m and 200 \(\upmu\)rad, respectively. Accordingly, due to the assigned ranges for the De-space, De-center, and Tilt (See Table 3), the resulting potential range of MTF is greater than 0.35, and a pixel loss can be minimized (less than 1).
# Lipreading Using Liquid State Machine with STDP-Tuning
## 5 Experimental Result
### Discussion  
#### 5.4.1 Confusion Matrix  
We observed the confusion matrix of the AVLetters dataset, as shown in Figure 7. We can find that the letters W, Y, Z, O, and A have a higher accuracy of correct recognition. However, the most common confusion is in (B, P), (C, D, T), and (Q, U) among these letter pairs, this is similar to the conclusion of [PERSON]. [PERSON] et al. (2018). The main reason for this phenomenon is that lipreading is performed through a single visual modality by distinguishing the visemes of these letters, but different letters may produce the same visemes. This is also the reason why it is so hard for lipreading experts to solve lipreading problems.  
Figure 6: Iterative Algebra and Accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline
**Layers** & **Number of Neurons** & **Accuracy (\%)** \\ \hline
4 & 237-128-93-28 & 50.93 \\
3 & 246-107-6 & 30.45 \\
2 & 173-55 & 55.32 \\
1 & 143 & 60.76 \\ \hline \end{tabular}
\end{table}
Table 3: Performance of different MLP structures on the AVLetters dataset.
# A Novel Protocol Using Captive Portals for FIDO2 Network Authentication +
## 1 Introduction
### The WebAuthn Protocol and Types of Credentials  
Both software-based passkeys and credentials generated in a security key (for simplicity, in this work we are also using the term _security key_ when we refer to a WebAuthn credential generated and managed by a security key) are involved in the two main ceremonies of the protocol: registration (or attestation) and authentication (or assertion).  
During registration, a credential is created and linked with a user account. This involves storing the created public key and the credential identifier in the authentication server. During authentication, the user can verify their identity using the previously registered credential. Specifically, they use the private key to sign a challenge, which is verified by the authentication server. This challenge-response mechanism of public-key cryptography ensures the authenticity and integrity of the authentication operation.  
A FIDO authenticator generates an RSA or ECDSA key pair, forming a FIDO credential in a secure environment. In the case of hardware security keys, this is an independent cryptographic chip. Other devices may implement Trusted Execution Environments (TEEs) to implement secure cryptographic capabilities. A secure environment protects the private key in a secure storage to ensure its confidentiality and allow to perform all operations without the private key leaving the authenticator. During authentication, the challenge and other options enter the secure environment, where they are signed with the private key. After the operation, only the signature leaves the authenticator in the form of a response.  
As said, a FIDO authenticator can generate these credentials during the registration ceremony. If the credential is stored into the hardware device memory, then the credential is known as a resident (or discoverable) credential. If the credential does not use the security key memory, then it is known as a non-resident (or non-discoverable) credential. For a user, the type of credential is almost transparent. However, from the development point of view, each type of credential involves a different registration and authentication flow. Also, not all security keys and operating systems support resident credentials. In this paper, we developed support for both types of credentials to increase the compatibility of the system.
## 3 Materials and Methods
### Airborne Remote Sensing of Study Area and Fields
#### 3.1.2 UAV Remote Sensing  
At Stoneville, Mississippi, in 2012, USDA-ARS scientists began to develop UAVs for remote sensing of RGB, multispectral, thermal, and hyperspectral cameras and sensors for various applications, including crop herbicide damage assessment, herbicide-resistant weed identification, crop yield estimation, and crop phenotyping, for crop field monitoring [16; 30].  
At Brooksville, Mississippi, USDA-ARS scientists began to conduct UAV remote sensing of a soybean field in 2017 and continued in 2018 and 2019. For this study, UAV multispectral images were acquired using a Parrot Sequoia multispectral camera (Parrot, Paris, France) with a broadband RGB sensor and narrowband red, green, red-edge, and NIR sensors flown with an eBee senseFly fixed-wing UAV (senseFly, Cheseaux-sur-Lausanne, Switzerland).  
At Pontotoc, Mississippi, USDA-ARS scientists similarly began to conduct UAV remote sensing of crop fields in 2017 and continued in 2018 and 2019, with most flyovers in 2018. For the studies, UAV images were also acquired using the Parrot Sequoia camera flown with the eBee senseFly fixed-wing UAV.
## Abbreviations  
* ANN & Artificial neural network ARIMA & Auto-regressive integrated moving average BFOA & Bacterial foraging optimization algorithm CDD & Cooling degree days CV-RMSE & Cross-validation root mean square error DNN & Deep neural network EMS & Energy management system GDP & Gross domestic product HDD & Heating degree days IBFOA & Improved bacterial foraging optimization algorithm LF & Load forecasting LSSVM & Least square support vector machine LSTM & Long short-term memory LTLF & Long term load forecasting MAE & Mean absolute error MAPE & Mean absolute percentage error MCO & Movement control order ML & Machine learning MSE & Mean square error MTLF & Medium term load forecasting NRMSE & Normalized root mean square error PCC & Pearson's correlation coefficient R\({}^{2}\) & Determination coefficient RE & Renewable energy RMSE & Root mean square error SARIMA & Seasonal auto-regressive integrated moving average STLF & Short-term load forecasting SVM & Support vector machine VSTLF & Very short-term load forecasting
# Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
## 3 Proposed Hybrid Graph Embedding and Adaptive Convolution Networks
### Adaptive Convolutional Network
#### 3.5.1 Dynamic Kernel Generation  
In this stage, the weights of convolutional kernels are adjusted dynamically according to the input features at a given time step.  
\[\text{K}_{\text{t}}=\text{f}_{\text{adapt}}(\text{F}_{\text{t}}) \tag{10}\]  
In Equation (10), \(\text{K}_{\text{t}}\) represents adaptive convolution kernel weights for time step t. \(\text{f}_{\text{adapt}}\) represents a function (typically implemented as a neural network) that dynamically generates kernel weights, and \(\text{F}_{\text{t}}\) represents input features extracted at time t. Dimension: \(\text{K}_{\text{t}}\in\mathbb{R}^{k\times k}\), where \(k\) is the kernel size. Encoding spatial or temporal information requires extracting input properties from data at a certain time, such as frames in a sensor data series. A convolution kernel is often generated by a dynamic function with the help of a small neural network trained to convert feature inputs into kernel weights. After that, the network processes the input features using the kernel, which enables it to dynamically specialize its convolution operation according to the data's content. Because the kernel changes depending on the input characteristics, the network can better deal with variances in the input data, leading to content-aware processing. Furthermore, dynamic kernels improve generalizability by decreasing sensitivity to noise and environmental factors.
# Astronomical Investigation to Verify the Calendar Theory of the Nasca Lines
## 2 The Sun Worship in Pre-Columbian Cultures on the Current Territory of Peru
### The Sun Worship in the Pre-Iaca Cultures
#### 2.3.2 Casma/Sechin  
One of the oldest pre-Columbian cultures, the Casma/Sechin culture (about 3500-200 BC), refers to a large concentration of archaeological sites in the north coast of Peru, about 350 km north of the present-day capital Lima. The sites are situated in the Casma-Sechin River Basin, close to the Pacific coast.  
Figure 5: Digital models of the well-known objects in Machu Picchu: (**a**) Intihuatana, often referred to as an astronomical observatory, (**b**) “Altar”, ritual stone.  
Figure 6: The face of the main pyramid in Caral is oriented to the sunrise at December solstice and the sunset at June solstice (source: Bing Maps © 2010 Microsoft Corporation and its data suppliers).  
One of the archaeological sites of the Casma/Sechin culture is a complex that includes four main local sites: Sechin Bajo, Taukachi-Konkan, Sechin Alto and the Temple of Sechin Mountain (Templo de Cerro Sechin). The most recent investigations at the site of Sechin Bajo resulted in the discovery of a sequence of three building complexes built between 3500 and 1500 BC [11]. That means that the Casma/Sechin culture is now considered the oldest civilization of the Americas. The Sechin Alto complex (1800-900 BC) is the largest structure of the Casma/Sechin Valley and it consists of several constructions. Slightly northwest of the site of Sechin Alto, the Taukachi-Konkan (approximately 2100-1000 BC) site can be found. According to [PERSON] [9; 12], the average orientation of all three complexes is 65.3\({}^{\circ}\), close to the sunrise at the June solstice.  
The ceremonial center Chankillo is located about 10 km southeast of Sechin and it is dated to around 300 BC [13]. The site consists of multiple structures, plazas and courtyards, spread over an area of approximately 4 km\({}^{2}\). The most impressive part of this site is a fortified temple on top of a hill. Towards the east of the temple is a large area with several buildings, a huge plaza and another very impressive structure called the Thirteen Towers. It runs north to south along a low nearby ridge.  
Both structures, the Temple and the Thirteen Towers, form an artificial \"toothed\" horizon when viewed from the ground. According to [PERSON] and [PERSON] [13], one can observe the rising and setting Sun over the course of the year from two observation points to the west and east of the towers (Figure 7). The Chankillo towers thus provide evidence of early solar horizon observations and of the existence of sophisticated sun cults.
## 4 Results and Discussion
### Time Step Independence Verification  
When calculating transient states, it is crucial to set the time step appropriately because the accuracy of the simulation results depends on it. In this study, time steps of 0.01 s, 0.05 s, and 0.1 s were used to investigate the effect of different time steps on the convergence of the calculation results. Figure 9 shows the change in temperature for non-dimensional time. Notably, the temperature recorded with a time step of 0.05 s deviated by 0.30% and 0.09% from that obtained with 0.01 s and 0.1 s, respectively. Balancing the need for computational efficiency with the requirement for accuracy, we opted to utilize a time step of 0.05 s for our simulations. This choice ensured that our results were both accurate and computationally efficient, enabling us to conduct reliable transient-state simulations.  
Figure 8: Grid independence verification (graph of temperature variation for non-dimensional at the reference point).  
Figure 9: Time-step independence validation.
## 2 Experimental Setup and Results
The frequency of the RF signal depends on the central frequency of the high-Q BPF; we can use different central frequencies of the high-Q BPF for different applications [24]. To further analyze the performance of the system, the bit error rate versus different optical received power was measured. Error-free is achieved while the optical received power is larger than \(-\)16.5 dBm. Figure 2.5 Output power stability for the selected wavelengths. Figure 2.6 The architecture of the self-start multi-wavelength laser for RoF transmission.