esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 System Design
### Scaling  
The system might need to serve a considerable number of users, but this must not be confused with the actual needed functionality at scale. Undoubtedly, certain functionalities do not need the same amount of scaling capabilities. Features like login/register will not be as largely used as the actual document read/write operations. With these simple details, we can answer the first question: The system needs some parts of it to be more suitable for scaling than others.  
To answer the second question, we need to properly evaluate how system scaling works. Scaling, in simple terms, means the process of allocating more resources to better handle a task. Scaling can be of two types [37]:  
1. Vertical: where more hardware resources are provided to keep up with the load.
2. Horizontal: where more instances of the running process are provided to split the work among other workers.  
Vertical scaling is more expensive because it implies hardware changes. Moreover, it also imposes downtime since the system must be physically upgraded. Another drawback of vertical scaling is the price and hardware limitation.  
Horizontal scaling is more complex to achieve based on the tooling, it raises a series of issues on its own in terms of application architecture, but in the end, is the most efficient way of scaling for our system.
# Hydrothermal Carbonization of Spent Coffee Grounds
## 3 Results and Discussion
### Fuel Characteristics
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Temperature**} & \multicolumn{6}{c}{**Elemental Analysis (wt.\%, dry)**} & \multicolumn{6}{c}{**Proximate Analysis (wt.\%, dry)**} \\ \cline{2-10}  & **C** & **H** & **O** & **N** & **S** & **Others** & **VM** & **FC** & **Ash** \\ \hline
180 \({}^{\circ}\)C & 54.2 \(\pm\) 0.7 & 6.8 \(\pm\) 0.1 & 31.8 \(\pm\) 1.5 & 2.6 \(\pm\) 0.1 & 0 & 4.7 \(\pm\) 2.3 & 75.2 \(\pm\) 1.4 & 23.3 \(\pm\) 1.5 & 1.5 \(\pm\) 0.1 \\
190 \({}^{\circ}\)C & 56.1 \(\pm\) 2.0 & 6.7 \(\pm\) 0.1 & 29.5 \(\pm\) 0.9 & 2.7 \(\pm\) 0.2 & 0 & 4.9 \(\pm\) 1.3 & 73.4 \(\pm\) 3.3 & 24.9 \(\pm\) 2.7 & 1.7 \(\pm\) 0.6 \\
200 \({}^{\circ}\)C & 59.5 \(\pm\) 0.9 & 6.7 \(\pm\) 0.1 & 26.2 \(\pm\) 1.1 & 3.0 \(\pm\) 0.1 & 0 & 4.6 \(\pm\) 0.4 & 70.9 \(\pm\) 3.0 & 27.0 \(\pm\) 3.6 & 2.2 \(\pm\) 0.6 \\
210 \({}^{\circ}\)C & 61.0 \(\pm\) 1.3 & 6.6 \(\pm\) 0.1 & 24.7 \(\pm\) 0.8 & 3.0 \(\pm\) 0.2 & 0 & 4.7 \(\pm\) 1.3 & 69.1 \(\pm\) 3.0 & 29.1 \(\pm\) 3.1 & 1.8 \(\pm\) 0.1 \\
220 \({}^{\circ}\)C & 61.9 \(\pm\) 1.3 & 6.7 \(\pm\) 0.1 & 24.2 \(\pm\) 1.2 & 3.1 \(\pm\) 0.1 & 0 & 4.1 \(\pm\) 1.1 & 68.3 \(\pm\) 1.7 & 29.9 \(\pm\) 1.7 & 1.8 \(\pm\) 0.1 \\
230 \({}^{\circ}\)C & 63.3 \(\pm\) 1.4 & 6.7 \(\pm\) 0.1 & 22.4 \(\pm\) 0.6 & 3.2 \(\pm\) 0.1 & 0 & 4.4 \(\pm\) 0.7 & 67.2 \(\pm\) 4.5 & 31.0 \(\pm\) 4.2 & 1.7 \(\pm\) 0.3 \\
240 \({}^{\circ}\)C & 64.0 \(\pm\) 1.5 & 6.7 \(\pm\) 0.1 & 21.4 \(\pm\) 0.8 & 3.4 \(\pm\) 0.1 & 0 & 4.5 \(\pm\) 1.9 & 67.5 \(\pm\) 0.7 & 30.4 \(\pm\) 0.8 & 1.9 \(\pm\) 0.1 \\
250 \({}^{\circ}\)C & 65.6 \(\pm\) 0.7 & 6.6 \(\pm\) 0.1 & 19.2 \(\pm\) 0.3 & 3.6 \(\pm\) 0.1 & 0 & 5.0 \(\pm\) 1.0 & 69.8 \(\pm\) 1.4 & 28.3 \(\pm\) 1.3 & 1.9 \(\pm\) 0.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Elemental and proximate analysis of SCGs hydrothermally carbonized with different reaction temperatures (SGC: water = 1:4). Figure 6: Oxygen and volatile organic compound (VOC) contents of SCGs produced with different reaction temperatures and water contents. ratio of 1:1.5, and 0.8025 for carbon and 0.1466 for hydrogen at a ratio of 1:4. This indicates that the HHV of the reaction product is more affected by the carbon content than by the hydrogen content [22]. Figure 8: Van Krevelen diagram of raw material, coal, and hydrothermally carbonized SCGs produced with different reaction temperatures and water contents. Figure 7: O/C and H/C ratios of SCGs hydrothermally carbonized with different reaction temperatures and water contents.
# Can Livestock Farming Benefit from Industry 4.0 Technology? Evidence from Recent Study
## 4 Results
### Dairy Farming
#### 4.4.2 State and Potential of Industry 4.0 Benefits
Still, in most stakeholder relationships with livestock farms, only a few data are exchanged, and only part of them by digital data transfer. More process data exchange will benefit not only the farm itself but also other stakeholders up to the consumer. Repeated unmodified production cycles in livestock farming are an ideal precondition for **Automated knowledge and learning**. Sensor data from the production process may be used for problem recognition and correction. Local results data such as milking results from the AMS or weight development in fattening may be used for automated learning as well as external data, for example, weight, quality, and health data from slaughtered carcasses. Today, knowledge about the processes is often generated by the farmer. Upcoming comprehensive and intelligent Farm Management Information Systems (FMIS), like Lely Horizon or BFN Fusion, will include algorithms for automated knowledge and learning in the future. **Collaboration competence** between farmers and other stakeholders not only needs digital process data exchange, as mentioned in the previous paragraphs. Another very important factor is the personal competence of the owners and working staff at farms and stakeholder companies. Industry 4.0 benefits may only be reached when people are well trained and motivated. Future FMIS will support increasing collaboration competence. As more process data become available digitally, they may contribute to **Productivity optimization**. Data like the weight of fatteners, audiovisual animal observation, slurry content, and the lung and liver health of carcasses may help to control and optimize the processes. This is essential for increasing sustainability including animal health and welfare as expected by society.
# Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks
## 3 Spatiotemporal Models for Continuous Emotion Recognition
### Pre-Training and Fine-Tuning of 2D CNNs  
Training CNNs on small datasets systematically leads to overfitting. To circumvent this issue, CNNs can be pre-trained or fine-tuned on datasets that are similar or not to the target task [40,41]. Well-known CNN architectures, such as AlexNet [63], VGG [43], and GoogleNet, form an important set of baselines for a large number of tasks; in particular, pre-training such networks on the ImageNet dataset constitutes a powerful tool for representation learning. However, recent FER studies have shown that VGG-Face architectures, which are trained on a huge dataset of face images, overwhelms architectures trained on ImageNet for FER applications [64]. Furthermore, [PERSON] and [PERSON] [1] showed that multi-stage fine-tuning can provide even better performance. Particularly, FER2013 [15], TFD [16] or, more recently, RAF-DB [65,66] datasets are good sources of additional data for FER tasks. [PERSON] et al. [67] and [PERSON] and [PERSON] [68] pursued interesting work on cross-dataset generalization tasks by switching in turn source and target FER datasets and evaluating the performance of FER models. [PERSON] and [PERSON] [68] showed that datasets are strongly biased, and they developed a novel architecture that can learn domain-invariant and discriminative features.  
Globally, this study considers using three different data sources for double transfer learning [69]: VGG-Face, ImageNet, and RAF-DB. There are already three architectures (VGG-11, VGG-16, and ResNet50) pre-trained on the first two datasets. However, such architectures had to be fully trained on the RAF-DB dataset. Several configurations were evaluated for training and fine-tuning different 2D CNN architectures on the RAF-DB dataset to determine how multi-stage fine-tuning can be well performed. The 2D CNN architectures were fine-tuned by freezing the weights of specific early layers while optimizing deeper ones. Since the three architectures have several convolutional blocks, the weights were frozen sequentially according to these blocks. The proposed architecture keeps convolutional blocks, while discriminant layers (i.e., fully connected layers) were replaced by a stack of two fully connected layers with 512 and 128 units, respectively, and an output layer with 7 units, since there are seven discrete emotion categories in the RAF-DB dataset: surprise, fear, disgust, happiness, sadness, anger, and neutral.
# Numerical Simulation of Flow Field of Submerged Angular Cavitation Nozzle
###### Abstract  
A model of a submerged angular cavitation nozzle is established, which consists of a contraction part, parallel middle part, and expansion part. Based on the CFD technique, a numerical simulation of the flow field of the submerged cavitation nozzle is carried out, in which a multiphase mixture model, cavitation model, and renormalization group (RNG) \(k\)-\(\epsilon\) turbulence model are applied. Considering the influence of mixture density on cavitation, the effects of the inlet contraction part, parallel middle part, and outlet expansion part on the velocity and vapor volume fraction are studied. The numerical simulation results show that the mixture density is essential in the cavitation jet. When the nozzle diameter \(d\) is fixed, the designed angular cavitation nozzle with contraction angle \(\alpha=13.5^{\circ}\), parallel middle part length \(Ld=3d\), expansion part length \(Le=4d\), and expansion angle \(\beta=60^{\circ}\) can effectively bring out cavitation. A cavitation cloud is produced near the rigid wall of the outlet expansion section and diffuses in a vortex ring shape. Optimizing the nozzle structure can improve the cavitation effect of the nozzle. The feasibility of this model is verified by relevant experimental data.  
cavitation jet; angular cavitation nozzle; submerged environment; numerical simulation +
Footnote †: journal: Physics Letters B
## 4 Discussion
### MCDA as Decision-Making Options for Computational Models
#### 4.2.3 MCDA Applicability for System Dynamics Models  
Decision-making in SD models is generally represented through the flows that connect stocks and are implemented in the forms of ordinary differential or partial differential equations [22]. As such, a decision criterion is represented within an equation in the form of a variable, with its coefficient representing the weighting. These coefficients can be constants established at initialization or change dynamically throughout execution. In SD platforms, these are commonly represented as stocks and auxiliary variables. MCDA techniques can be incorporated to handle situations where dynamic weightings are needed based on aggregate states of the SD simulation, different possible interpretations of auxiliary variables, or as a result of structural changes to the simulation.  
In SD models, decision environments are represented through the dynamic behavior of the system based on what is known about the state of the system using its variables and inventory levels [22]. These models have been used to identify, evaluate, and assist in making economic decisions for a variety of systems, such as for enhanced oil recovery operations [109] for industrial production and distribution systems [110], and for inventory logistics within healthcare systems [111]. The aggregate decision-making representations of these models can result in chaos due to the human decision-making behaviors of a significant minority within the model [85].  
MCDA has been utilized within SD models of health preparedness for pandemic influenza to evaluate mitigation strategies based on epidemiological parameters and policy makers' prioritizations [112]. The integration of MCDA with SD has been successful in representing multiple goals, objectives, and perspectives for community-based forest management [113]. A review of sustainable supply chain management identifies that the integration of SD with MCDA can help to address the identified scientific rigor shortcoming of neglected model validation and the disclosure of model equations [114]. Selecting suitable MCDA techniques based on Tables 16 and 17 requires considering the quantity of variables, any consensus among stakeholders, and the availability of empirical data to inform validation.
## 2 Materials and Methods
### Features
#### 2.1.3 Global Texture Features  
A large number of local features, through rational distribution and integration, become the global texture features of the work. The overall texture features can be reflected through the spatial frequency. The spatial frequency of a picture describes the frequency of changes in texture, details, structure, etc., at various spatial locations in the image.  
In this study, Gabor filters are utilized to extract the overall texture features of the image. By applying Gabor filters on different scales and orientations, the texture features of the entire image are obtained. The Gabor operator can acquire texture features of different frequencies and angles and can capture local spatial frequency and orientation information in the image:  
Complex:  
\[g(x,y;\lambda,\theta,\psi,\sigma,\gamma)=e^{-\frac{x^{2}+\gamma^{2}y^{2}}{2e^ {2}}}e^{i(2\pi\frac{x^{\prime}}{\lambda}+\psi)} \tag{6}\]  
Real:  
\[g_{real}(x,y;\lambda,\theta,\psi,\sigma,\gamma)=e^{-\frac{x^{2}+\gamma^{2}y^{ 2}}{2\
u^{2}}}cos(2\pi\frac{x^{\prime}}{\lambda}+\psi) \tag{7}\]Imaginary:  
\[g_{imag}(x,y;\lambda,\theta,\psi,\sigma,\gamma)=e^{-\frac{x^{\prime 2}+\gamma^{2}y^{ \prime 2}}{2\kappa^{2}}}sin(2\pi\frac{x^{\prime}}{\lambda}+\psi) \tag{8}\]  
where  
\[\begin{cases}x^{\prime}=xcos\theta+ysin\theta\\ y^{\prime}=-xsin\theta+ycos\theta\end{cases} \tag{9}\]  
Here, \(\lambda\) represents the wavelength, \(\theta\) represents the direction, \(\psi\) represents the phase offset, \(\gamma\) represents the aspect ratio of the space, and \(\sigma\) is the variance of the Gaussian filter. The mathematical expression for applying the Gabor filter to the image \(I(x,y)\) is a convolution operation:  
\[I_{g}(x,y)=I(x,y)*g(x,y;\lambda,\theta,\psi,\sigma,\gamma) \tag{10}\]  
Here, \(*\) represents the convolution operation, and \(I_{g}(x,y)\) is the filtered image. The parameters of the filter are set as follows: \(\psi=0,\sigma=2\pi,\gamma=0.5\), and different wavelengths \(\lambda\) and angles \(\theta\) are used, as shown in Figure 2. The mean, variance, and energy of the filtered image are calculated (refer to calculation Formulas (1), (2) and (5)).  
The mean value indicates the presence of obvious edges or textures in the original image at that wavelength and angle. The variance of the processed image reflects the degree of dispersion of the image gray level, that is, the complexity of the texture. The energy value of the processed image reflects the uniformity of the image texture.
# Fast Fractional-Order Terminal Sliding Mode Control for Seven-Axis Robot Manipulator
## 2 Preliminaries
### Fractional Calculus  
Fractional calculus defines the real number order for integral and derivative calculations. The Riemann-Liouville definition and the Caputo fractional derivative are adopted in this work.  
**Definition 1 [18].**_The \(\alpha\) th-order Riemann-Liouville integral of function is given as_  
\[D_{t_{0},t}^{-\alpha}f(t)=\frac{1}{\Gamma(a)}\int_{t_{0}}^{t}(t-\tau)^{\alpha-1 }f(t)d\tau,\;t>t_{0} \tag{6}\]  
_where \(\Gamma(\cdot)\) is the well-known Euler gamma function. \(t_{0}\) is the initial time. The Riemann-Liouville integral has the semigroup property of \(D_{t_{0},t}^{-\alpha}D_{t_{0},t}^{-\beta}=D_{t_{0},t}^{-\beta-\alpha},\;\forall \alpha,\beta>0\). The [PERSON] definition of the \(\alpha\in R^{+}\) th-order derivative is_  
\[{}_{RL}D_{t_{0},t}^{\alpha}f(t)=\frac{d^{m}}{dt^{m}}D_{t_{0},t}^{-(m-\alpha)}f( t)=\frac{1}{\Gamma(m-a)}\frac{d^{m}}{dt^{m}}\int_{t_{0}}^{t}(t-\tau)^{m- \alpha-1}f(t)d\tau,t>t_{0} \tag{7}\]  
_where \(m-1\leq\alpha<m,\;m\in Z^{+}\). For convenience, the Riemann-Liouville derivative is abbreviated as \(D_{t_{0},t}^{\alpha}\) in this article._  
**Definition 2 [8].**_The \(\alpha\in R^{+}\)th-order Caputo fractional derivative on the half axis \(R^{+}\) for a continuous function f(t) is defined as_  
\[{}_{C}D_{t_{0},t}^{\alpha}f(t)=D_{t_{0},t}^{-(m-\alpha)}\frac{d^{m}}{dt^{m}}( f(t))=\frac{1}{\Gamma(m-a)}\int_{t_{0}}^{t}\frac{f^{(m)}(\tau)}{(t-\tau)^{ \alpha-m+1}}d\tau,t>t_{0} \tag{8}\]  
_in which \(m-1\leq\alpha<m,\;m\in Z^{+}\)._  
**Property 1 [8].**_If the Riemann-Liouville derivative \(D_{t_{0},t}^{\beta}f(t)\;(m-1\leq\beta<m,m\in Z^{+})\) of a function \(f(t)\) is integrable, then_  
\[D_{t_{0},t}^{-\alpha}(D_{t_{0},t}^{\beta}f(t))=D_{t_{0},t}^{\beta-\alpha}f(t) -\sum_{j=1}^{n}\left[D_{t_{0},t}^{\beta-j}f(t)\right]_{t=t_{0}}\frac{(t-t_{0} )^{\alpha-j}}{\Gamma(1+\alpha-j)} \tag{9}\]  
**Property 2 [18; 19].**_The fractional-order Caputo fractional derivative has a relation to the Riemann-Liouville derivative._  
\[{}_{C}D_{0,t}^{\alpha}x(t)=D_{0,t}^{\alpha}(x(t)-\sum_{k=0}^{m-1}t^{k}x^{(k)} (0)/k!) \tag{10}\]
# Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland
## 3 Area of Study  
In 2017 in the U.S., there were an estimated 2.91 million km2 of grassland (Figure 1). The U.S. is the country with the second-largest area of grassland after China [54], sinking an estimated 13 teragrams of carbon (Tg C) annually [55]. According to [PERSON] et al. [12], grasslands in North America are projected to increase 18% in spatial extent by 2100.  
Grasslands show highly dynamic growth that can vary over time and be affected by external factors [15]. Grasslands respond quickly in greening and physiological activity in response to climate drivers, especially precipitation and water availability [13; 56] and, to a lesser extent, temperature [57]. Grassland productivity in the Great Plains is controlled by summer and spring precipitation [58].  
Our study site is located in Grand Forks County, one of 53 counties in the State of North Dakota, part of the Northern Great Plains region that shares the U.S. portion of the Red River Valley of the North with Minnesota (Figure 2). UND's Oakville is an important  
Figure 1: Grassland distribution in contiguous United States of America (Data source: [14]).  
area for tallgrass prairie research located 19 kilometers west of Grand Forks [59]. Oakville hosts one of the near-surface cameras of the PhenoCCam Network [60].  
The climate of Grand Forks County is classified as Snow climate (D) fully humid (f) with warm summer (b), according to the updated Koppen-Geger climate classification [62]. Grand Forks' coldest month is January, and the warmest is July. The county typically receives snow from October to May (Table 1). Snowfall is also highly variable among years but exhibits an average trend of increasing amounts (Figure 3).  
Figure 2: Area of study. Grand Forks County, North Dakota. Red areas are towns and cities; blue dot shows PhenoCCam at the Oakville location. The City of Grand Forks is the largest city in Grand Forks County, located on the eastern border with Minnesota. (Data Source: [61]).  
Other important weather events in Grand Forks County are blizzards, defined as storms with wind speeds equal to or higher than 15.6 m/s [65]. During a period of 39 years (1979-2018), 100 blizzards were reported at the Grand Forks National Weather Service Forecast Office [64]. In North Dakota, the probability of a blizzard occurring annually is more than 50%, or more than one event every other year [66]. In the Red River Valley, the probability per year increases to between 61.6% to 76.4%, the highest in the U.S. [67].
## 6 Design Considerations for LPWAN Networks
### Traffic Requirements  
Support for the varying nature of devices and traffic with differing attributes of bandwidth, latency, size of packets, and so on drive a range of design considerations. Minimally, LPWAN technology needs to provide basic admission and user traffic management depending upon its network access architecture. Some LPWAN technologies may send data without having to undertake any elaborate admission activity and data bearer setup. On the other hand, another technology may decide to go through protocol-based admission control and data bearer setup. Basic admission and user data transmission is particularly relevant to applications where the traffic is homogeneous and limited, and all devices are of the same type, for example, meters only. However, for heterogeneous devices and co-existence of varying types of devices in the same LPWAN network, these capabilities need to be enhanced. Admission control mechanisms may need to have the capabilities of handling congestion, selectively blocking off admission for new traffic requests, and being able to handle admission on a priority basis. In some applications, multiple levels of device traffic priority management may be needed with different attributes of guaranteed and nonguaranteed bit rates for device data bearers.  
For applications requiring mobility, the use of a control plane to carry selected data traffic may be invoked in order to handle overload traffic conditions. For minimizing the processing load on the devices, several capabilities, for example, support for handovers, need to be implemented primarily in the access station instead of in the device.
## 2 System and Channel Models
### Pointing Error  
In maritime environments, both Tx and Rx are mobile platforms; therefore, the performance is mainly affected by the pointing error, especially the jitter that continues to occur even after beam alignment. In particular, as shown in Figure 1, the movements of the mobile platform in maritime environments cause pointing errors that can be classified as rolling, yawing, and pitching. Rolling is a movement wherein the left and right sides of the mobile platform swing up and down, respectively, and yawing is a movement wherein the mobile platform swings left and right around the keel point. Pitching is a movement in which the front and rear of the mobile platform swing up and down, respectively. Yawing and pitching cause displacement only in the \(x\) or \(y\) axes, whereas rolling causes displacement in both the \(x\) and \(y\) axes. Therefore, the effect of yawing and pitching can be modeled with the single-sided pointing error model, whereas the effect of rolling can be modeled with the double-sided pointing error model [16]. In general, the mobile platform in maritime environments is affected by more than two of the three main factors mentioned above: the pointing error can be modeled using a double-sided error model [16].  
Based on [2; 16], we assume that the beamwidth of a Gaussian beam is \(\omega_{z}\) and the aperture radius is \(a\). Then, at distance \(l\), we can approximate the fraction of the collected power as  
\[I_{p}(s;l)\approx A_{0}\exp\left(-\frac{2s^{2}}{\omega_{zq}}\right), \tag{5}\]  
where, as shown in Figure 1, \(s\) is the radial displacement between the centers of the beam and the detector, \(A_{0}=[\text{erf}(v)]^{2}\) is the fraction of the collected power at \(s=0\) when the ratio between the beamwidth and aperture radius is \(v=\sqrt{\frac{\sigma^{2}\tau}{2\omega_{z}z}}\), and \({\omega_{zq}}^{2}=\omega_{z}^{2}\frac{\sqrt{A_{0}\pi}}{2v\exp\left(-v^{2} \right)}\) is the equivalent beamwidth, where \(\text{erf}(x)=\frac{2}{\sqrt{\pi}}\int_{0}^{x}e^{-l^{2}}dt\) is the error function. When \(\omega_{z}>6a\), the approximation expression of \(I_{p}\) is valid, which is also valid for typical FSO communication systems [2]. Pointing error models can be classified according to boresight and jitter. In this study, the effect of jitter that continues to occur even after beam alignment is considered; in particular, a simple situation in which jitter occurs equally is considered (i.e., zero boresight and identical jitters). Therefore, we assume that our pointing error model follows a Rayleigh distribution [8] and the corresponding PDF of \(I_{p}\) can be expressed as  
\[f_{I_{p}}(I_{p})=\frac{\xi^{2}}{A_{0}\xi^{2}}I_{p}\xi^{2}-1,\quad\text{ for }\ 0\leq I_{p}\leq A_{0}, \tag{6}\]  
where \(\xi=\frac{\omega_{zq}}{2\sigma}\) is the ratio of the equivalent beam radius at the receiver to the standard deviation of the pointing error displacement at the receiver.