esa-sceva/satcom-chunk-collection huggingface dataset:
# An NUTSF at Sub-Region for Suppression of Torque Ripple in Switched Reluctance Motors
## 4 Proposed NUTSF Control Strategy  
### Cause of Higher Torque Ripple in Conventional TSF Control Strategy  
In order to reduce the torque ripple of the three-phase compound total torque, the actual torque of each phase is controlled according to the torque reference of each phase obtained by the TSF in the conventional TSF control strategy. However, the torque sharing function is united and fixed, so there is not good performance for torque ripple suppression at a wide speed in the conventional TSF control strategy.  
In the first half of the TpE, the change rate of the inductance of the incoming phase is low, and the actual torque produced by the excited current is very small, according to Formula (4), because the excited current is just beginning to rise here. If the torque reference assigned by the TSF to the incoming phase is too large, the actual torque will not be tracked easily, resulting in higher torque ripple.  
In the second half of the TpE, the change rate of the inductance of the incoming phase is high, which can produce enough excited torque to track the torque reference; the change rate of the inductance of the outgoing phase is high still and its torque is large afterward. Thus, a larger compound total torque is produced by both the incoming and outgoing phases in the second half of the TpE. If the torque reference allocated to the outgoing phase is large in the second half of the TpE, it will not only produce excessive actual torque, causing high torque ripple, but also make the outgoing phase difficult to demagnetize quickly, resulting in negative torque, which will affect the efficiency of the SRM.  
In order to minimize the torque ripple, a non-unity torque sharing function based on each sub-region is proposed. The improved exponential TSF is formulated by using the characteristics of the inductance and torque of the SRM in the TpE in this NUTSF control strategy. The shape of the TSF is automatically adjusted to accommodate different speeds by changing the th-power \(P1\) and \(P2\) of the TSF, therefore enhancing the torque tracking ability and reducing the torque ripple.
# A Sigfox Energy Consumption Model
###### Abstract  
Sigfox has become one of the main Low-Power Wide Area Network (LPWAN) technologies, as it has attracted the attention of the industry, academy and standards development organizations in recent years. Sigfox devices, such as sensors or actuators, are expected to run on limited energy sources; therefore, it is crucial to investigate the energy consumption of Sigfox. However, the literature has only focused on this topic to a very limited extent. This paper presents an analytical model that characterizes device current consumption, device lifetime and energy cost of data delivery with Sigfox. In order to capture a realistic behavior, the model has been derived from measurements carried out on a real Sigfox hardware module. The model allows quantifying the impact of relevant Sigfox parameters and mechanisms, as well as frame losses, on Sigfox device energy performance. Among others, evaluation results show that the considered Sigfox device, powered by a 2400 mAh battery, can achieve a theoretical lifetime of 1.5 or 2.5 years while sending one message every 10 min at 100 bit/s or 600 bit/s, respectively, and an asymptotic lifetime of 14.6 years as the message transmission rate decreases.  
Sigfox; energy; modeling; performance evaluation; Internet of Things; IoT; smart cities; LPWAN +
Footnote †: journal: Sensors
## 3 Results and Discussions
### Final Products Characterization
#### 3.2.7 Sensorial Analysis  
The final baked products manufactured with BW flour were more appreciated by panelists than those made with SF flour (Table 10). From the sensorial point of view, 20 BW1 and 10 SF1 buns were the most appreciated by the panelists, reaching a final hedonic score of 9.1 and 8.7 respectively. This is in agreement with [PERSON] et al. [78] who reported that the use of buckwheat flour in a range of 20-30% in bakery products manufacture reached the highest sensory acceptance. [PERSON] & [PERSON] [79] stated that breads produced with 10% and 20% buckwheat flour have been shown to have acceptable sensory qualities. The consumers' preference through the products with buckwheat flour could be justified by their better taste, structure and color, compared to those with SF. This is in line with [PERSON] at al. 2014, who reported that 12% SF addition could produce remarkable negative impact on the textural and sensorial properties of bread. Besides, the color of the products made with BW flour became less darker (Figure 1), compared with the buns manufactured with SF flour (Figure 2). On the other hand, the use of guar gum in the manufactured of baked products could have a positive influence on sensorial features such as taste and color [71, 78]. The positive influence of the guar gum on the final color of the bakery products is confirmed also by [PERSON] et al. [27] who reported that the polysaccharides for the guar gum could be involved in the Maillard reaction and influencing the intensity of the brown color. Due to the germination process, the amount of alpha-amylase increased favoring the hydrolysis of starch and enhancing the dextrin level during baking process. These enzymes in a higher amount could lead to sticky final baked products with a low satiety value, according to [PERSON] et al. [80]. Moreover, buckwheat sprouts have a bitter and astringent taste due to their high-level content of flavonoids, causing at high level percentages the product's consumer unacceptability [80].
# Characterization and Modelling of Potential Seaborne Disasters, in the ANA Region
## 4 Case Study: Exxon Valdez Oil Spill Disaster (1989, Alaska)
### Risk Modelling--Extension of the Models  
#### 4.2.1 Fault Tree Analysis (FTA), and Reliability Block Diagram (RBD)  
FTA and RBD are considered as root analysis techniques and have been applied to security and safety domains through the work of [44] [PERSON]. The FTA analysis presented in Figure 1 shows that the two main factors that caused the oil spill were the poor design of the vessel and the grounding itself.  
Note that usually the FTA method is used for component failures, as it is difficult to include human factors since they are difficult to quantify. However, here we tried to capture human factors in such analysis. The human factors are depicted as circles of 'H', 'T', 'J', and 'K' in the FTA in Figure 1, with an OR gate linking them to show their logical connection and hence a series structure in the equivalent RBD shown in the boxes in Figure 2.  
To describe the relationship between Figures 1 and 2, the letters in Figure 2 of the RBD model represent the same letters in Figure 1 that relate to the basic events (circles) in Figure 1. These basic events are sometimes also referred to as root causes. The mapping from the FTA (Figure 1) to the RBD (in Figure 2) is focused on showing the relationships between the basic events. This relationship is governed by the choice of the type of logic gates used in the FTA, where every OR gate in the FTA is represented as a'series relationship' in the RBD, and every AND gate is represented as a 'parallel structure' in the RBD. The more the configuration of the RBD has a series structure, the more vulnerable it is, as any failure of the basic events in a series structure means a failure of all other related events in series,  
Figure 1: The FTA for Exxon Valdez disaster.  
Figure 2: The equivalent RBD for Exxon Valdez disaster.  
whereas in the case where the prevailing structure is of a parallel nature, then this indicates a relatively safer configuration as more than one thing (basic event) needs to fail in order for the whole parallel structure to fail.  
Since the majority of series configuration of boxes in the RBD are related to human factors as depicted in Figure 2, this shows that such factors are the most vulnerable in the model and hence need more mitigation actions in terms of training, skills level upgrading, monitoring fatigue, redundancy, and backup systems.
## 3 Methodology
### Estimation of Individual Tree Features
#### 3.2.3 Stem Volume Estimation  
Single tree stem volume \(v\) [m\({}^{3}\)] is generally estimated based on easily measured field variables such as tree height and stem diameter. Often allometric equations are developed [29-31] e.g., using the diameter at breast height \(d_{1.3}\) [cm] as the only predictor:  
\[v=f(d_{1.3})=\beta_{0}d_{1.3}\,^{\beta_{1}}\,\text{or}\,\,\log(v)=\log(\, \beta_{0}\,)+\beta_{1}\log(\,d_{1.3}\,) \tag{2}\]  
or a two parameter model with the tree height \(h\) [m] as an additional predictor:  
\[v=f(d_{1.3},h)=\beta_{0}d_{1.3}\,^{\beta_{1}}h^{\beta_{2}}\,\text{or}\,\,\log (v)=\log(\,\beta_{0}\,)+\beta_{1}\log(\,d_{1.3}\,)+\beta_{2}\log(\,h) \tag{3}\]  
\(\beta_{0},\beta_{1},\beta_{2}\,=\) regression coefficients.  
Figure 6 demonstrates the very close relation between field-measured \(d_{1.3}\) and \(v\) for the 178 pine trees used in this study. In addition, a close relation can be observed for the field-measured tree height \(h_{field}\) and \(v\) as shown in Figure 7.  
Figure 5: Estimation of the crown base height \(cbh\) using the height distribution of laser reflections according to [7], Blue: height distribution function, Red: smoothed height distribution function, Green: second derivative of the smoothed height distribution function.  
The figures justify the use of general allometric equations for the estimation of single tree volume. Models based on allometric relations were also developed with ALS features for CHD approaches by others (e.g., [27, 28]) and furthermore by [32] to estimate basal area and stem volume for individual trees in a deciduous forest in California.  
All combinations of the tree features listed in Table 5 were explored for volume estimation. First, each feature was tested as the only predictor for volume estimation. Then the features were combined with each other in multiple regressions. The allometric models are given in Table 7.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**No.** & **Model** \\ \hline
1. & \(\log(v)=\log(\beta_{0})+\beta_{1}\log(h)\) \\
2. & \(\log(v)=\log(\beta_{0})+\beta_{1}\log(cd)\) \\
3. & \(\log(v)=\log(\beta_{0})+\beta_{1}\log(cv)\) \\
4. & \(\log(v)=\log(\beta_{0})+\beta_{1}\log(h)+\beta_{2}\log(cd)\) \\
5. & \(\log(v)=\log(\beta_{0})+\beta_{1}\log(h)+\beta_{2}\log(cv)\) \\
6. & \(\log(v)=\log(\beta_{0})+\beta_{1}\log(cd)+\beta_{2}\log(cv)\) \\
7. & \(\log(v)=\log(\beta_{0})+\beta_{1}\log(h)+\beta_{2}\log(cd)+\beta_{3}\log(cv)\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Allometric models for stem volume estimation.  
Figure 6: Allometric relationship between field measured \(d_{1.3}\) and \(v\).  
Figure 7: Allometric relationship between field measured \(h_{field}\) and \(v\).
# Modeling and Analyzing Preemption-Based Service Prioritization in 5G Networks Slicing Framework
## 2 System Model
### Case Example of Two NSIs  
Consider the case example of two NSIs, i.e., \(\mathcal{S}=\{1,2\}\), instantiated at a common 5G BS. Clarify the Pre-emption scheme by supposing that with an arriving request bound to the via pre-emption admission path at the \(s^{\text{th}}\) NSI, i.e., \(s\in\{1,2\}\), a number  
\[u_{\hat{s}}^{(s,\mathbf{n})}=\left\lceil\frac{(\mathbf{n}+\mathbf{e}_{s})\cdot \mathbf{b}-C}{\mathbf{e}_{\hat{s}}\cdot\mathbf{b}}\right\rceil,\quad\hat{s} \in\{1,2\}\setminus\{s\}, \tag{3}\]  
of violators servicing at the \(s^{\text{th}}\) NSI undergo a service pre-emption or are discarded.  
As an example application, with an arriving request bound to the via pre-emption path at the 1 st NSI, exactly \(u_{2}^{(1,\mathbf{n})}\) violators servicing at the 2 nd NSI are discarded. Analogically, with an arriving request bound to the via pre-emption path at the 2 nd NSI, exactly \(u_{1}^{(2,\mathbf{n})}\) violators servicing at the 1 st NSI are discarded.  
Thus, improve the Pre-emption scheme for three NSIs in the following subsection.
## 4 Results and Discussion
### Wavelet Transform Results
#### 4.2.2 XWT and WTC Results
However, the color of the wavelet coherence spectrum indicates that the WTC value between precipitation and P1 (about 0.85) is significantly higher than that between reservoir water level and P1 (about 0.7), suggesting that the nonlinear displacement of point P2 is influenced by both precipitation and reservoir water level at a period of 1 year, with rainfall having a greater contribution. In Figure 13e,f, there is no apparent coherence between P3 and precipitation or reservoir water level in the 1-year period. Still, a coherence patch is found between the two images at the intersection of the horizontal axis of 2018 and the vertical axis of the 45-120-day period, which may be related to the large fluctuations in precipitation and reservoir water level in 2018.
# Explanation of Air Quality Data Using Takagi-Sugeno Fuzzy Inference System
## 5 Data Analysis
The first 10 values represent the linear terms (let us label them \(a_{1,1},a_{1,2},\ldots,a_{1,10}\)), and the 11 th value represents the absolute term (let us label it \(b_{1}\)) of the linear function. Then, the constant output for the first rule can be computed as  
\[c_{1}=a_{1,1}x_{1,1}+a_{1,2}x_{1,2}+\ldots+a_{1,10}x_{1,10}+b_{1}. \tag{7}\]  
We determined the output values for all 12 rules by using the same procedure. Then, based on the obtained values, we created a new Takagi-Sugeno FIS with constant outputs. We evaluated the generated FIS against our data and obtained an RMSE of 14.7281. The error of the created system was quite large, so we optimised the system again by using the ANFIS method. Through optimisation, we were able to reduce the error to a value of 9.9313, which is still a usable result compared with all the mentioned regression models (see Table 2). \begin{table}
\begin{tabular}{c c c} \hline \hline
**\#** & **Regression Methods Implemented in MATLAB Software** & **RMSE** \\ \hline
1 & Fine Tree & 11.400 \\
2 & Medium Tree & 10.717 \\
3 & Coarse Tree & 10.365 \\
4 & Linear Regression & 10.821 \\
5 & Interaction Linear Regression & 10.509 \\
6 & Robust Linear Regression & 10.891 \\
7 & Stepwise Linear Regression & 10.446 \\
8 & Linear SVM & 10.930 \\
9 & Quadratic SVM & 10.691 \\
10 & Cubic SVM & 69.599 \\
11 & Fine Gaussian SVM & 10.058 \\
12 & Medium Gaussian SVM & 9.9058 \\
13 & Coarse Gaussian SVM & 10.444 \\
14 & **Rational Quadratic GPR** & **9.2061** \\
15 & Squared Exponential GPR & 9.6948 \\
16 & **Matem 5/2 GPR** & **9.3543** \\
17 & **Exponential GPR** & **9.3088** \\
18 & Ensemble Boosted Trees & 9.9197 \\
19 & Ensemble Bagged Trees & 9.6739 \\ \hline \hline \multicolumn{3}{c}{**Proposed FIS Methods with Linear Outputs**} & **RMSE** \\ \hline
20 & **FIS with 12 rules** & **9.2817** \\
21 & **FIS with 28 rules** & **9.0016** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Used models. Figure 5: Abbreviated list of input and output variable parameters. From a mathematical point of view, as the output of the mentioned FIS, we obtained a nonlinear function that covers the entire input space. We are now able to assign to each tuple of inputs, as an output, one specific number that represents the value of \(PM_{10}\). From a scientific point of view, we have obtained a fuzzy rule base with which it is possible to present relationships between input and output variables by using human speech. We can create these rules as follows: We take the first rule and determine for which values of the individual input variables all Gaussian membership functions take the value 1 (parameter \(v\) of each Gaussian function from the first rule). Then, we assign an output value that is identical to the constant output value of the first FIS rule and is easily readable in MATLAB software. By using a similar principle with all rules, we obtain the rule base which has values as displayed in Table 3.
## 6 Underwater Object Localization Service Case Study Relying on Multi-Sensor Data Fusion
### Business Model  
Figures 9 and 10 illustrate how the elements and constraints (like UEC) found in the proposed meta-model and ArchiMO design tool can be used to define the business model at the time of creating each Smart Sensor (hydrophone). In the context of MO, the object localization system seeks to find and identify an object as it comes into the range of one or more sensors. Sensors are connected to Fusion Servers, which employ a distributed algorithm to determine the object's position based on the data collected from these sensors.  
In our approach, we concentrate on the communication among the several nodes that comprise the MO model. It is possible to add internal actions to the simulator's modules. Our goal is to demonstrate our capacity to model the MO scenario using the IMS core  
Figure 11: Consistency between business, application, and technology layers [2; 3].  
network and to produce simulation codes that can be used in NS-3 directly. This aids in assessing the design according to the networking concepts and the constraints that are defined in the meta-model (DSML).  
The various tasks and operations that must be carried out while localizing an object in an underwater environment are represented by the business model. Six Smart Sensors and three Fusion Servers are assumed to be part of the system's architecture. In the network design, both the fusion server and the Smart Sensor are regarded as end-user terminals even though they are crucial components of the information system. As a result, we decide to depict their roles in the business and application layers, leaving the technical layer to depict the network architecture and roles associated with IMS communication exchanges.  
Typically, each Smart Sensor finds the dolphin on its own and relays the information to the fusion server, which connects to the other sensors. After merging these inputs, the fusion server employs a localization algorithm to determine the dolphin's specific location underwater.  
The many tasks involved in the item localization process are represented by the business layer of ArchiMate (Figure 12). The dolphin is detected by the several Smart Sensors (1A, 2A, 3A, 1B, 2B, and C) when it passes across the detection region. DolphinDetection is the function that makes this detection possible. Subsequently, each DataFusion center determines the appropriate algorithm for object localization. The DataFusion centers perform this process (trilateration), after which the data are transmitted via the DataFusionTransmission function between the Data Fusion Servers for analysis and subsequent determination of the object's coordinates.
## 3 System Model and Problem Statement  
### System Model  
In this paper, we consider an underwater node collaboration network comprising \(N\) sensor nodes. As shown in Figure 1, nodes are divided into several clusters depending on the physical proximity of the nodes. The nodes within a cluster communicate with each other to accomplish cooperative tasks. Each node has a unique identity. As shown in Figure 2, the network topology is mesh-type. The transmission range is the entire cluster. Any two nodes can communicate with each other via a shared acoustic channel. We assume the message is backlogged, i.e., a source node always has messages to send to other nodes in the cluster. Each node is equipped with only one transceiver. Thus, only one message can be successfully received by a node at a time. If more than one message simultaneously reaches a node, no messages can be received successfully. This is because these messages are conflict. Except for the source node and the destination node, the message is regarded as interference. In addition, we assume that the node is half-duplex, so the node is incapable of simultaneously sending and receiving messages. The network carries only unicast messages, i.e., each message has a single destination node.  
We use the TDMA technique. In the time domain, the network channel is divided into times slots of the same length. Nodes independently use single time slots to transmit or receive messages. After the message reaches the destination node, the destination node returns an Acknowledge Character (ACK) packet containing the message reception. All messages are of equal length and all ACK packets are of equal length. Moreover, each time slot corresponds to the time duration for a message transmission plus the time duration for an ACK packet transmission.  
Figure 1: The underwater node collaboration networks.  
Figure 2: Mesh-type network topology.  
Like most scheduling-based MAC protocols, we use the time slot as the unit of propagation delay between nodes, as shown in Equation (1),  
\[d_{ij}=\frac{|v_{i}-v_{j}|}{v\times\eta}, \tag{1}\]  
where \(i\) and \(j\) denote the nodes in the network. \(v_{i}\) and \(v_{j}\) are the position vectors of node \(i\) and node \(j\), respectively. \(v\) is the propagation speed of the sound in the water. \(\eta\) denotes the length of a time slot. The propagation delay between node \(i\) and node \(j\) is \(d_{ij}\). It takes \(d_{ij}\) time slots to transmit messages between node \(i\) and node \(j\).  
\[G=\max_{i,j}d_{ij}, \tag{2}\]  
where \(G\) is the maximum propagation delay and indicates the network range. The unit of \(d_{ij}\) and \(G\) is time slot.  
We assume that the propagation delay from node \(i\) to node \(j\) is equal to the propagation delay from node \(j\) to node \(i\), i.e., \(d_{ij}=d_{ji}\). Due to the non-negligible propagation delay, node \(i\) sends a message in slot 1, but it does not reach node \(j\) until time slot \(t+d_{ij}\). When the message arrives at node \(j\), node \(j\) returns an ACK packet with the message reception status. As the ACK packet is very short, we assume that ACK packets do not conflict.
# Image Quality for Near-Eye Display Based on Holographic Waveguides
## 4 Discussion
It would be better to maximize the diffraction efficiency of the in-coupling DOE and to incrementally increase the output diffraction efficiency to increase the level of brightness uniformity in the image. However, in the case of a fully analog waveguide, it is necessary to match the development parameters for all diffraction gratings in one process, so the relief depth is a compromise. 4. When greater depth is provided, along with the diffraction efficiency growth, the scattering on the structure also increases. Since the light beam in the waveguide has multifold interactions with the hologram grating, with an increase in the diffraction efficiency, the eye begins to notice a significant brightness unevenness during pupil replication. Scattering losses are also increased. Therefore, when looking for a compromise between the level of brightness and its non-uniformity, more attention should be paid to the second parameter. The presence of chromatism is a significant undesirable effect in terms of the ergonomics of AR display, especially for a binocular model, where two frames for the left and right eyes with different chromatic aberrations need to be combined. This effect cannot be completely eliminated with a coupler based on diffraction gratings, since DOEs are dispersion elements and, on the contrary, divide the incident light by spectrum. Furthermore, the light exiting the optical engine and entering the waveguides by means of DOEs is separated by angle and by spectrum and connects again at the exit toward the observer's eye. If some of the light in one holographic waveguide attenuates more than in the other two (due to recording errors or non-optimal diffraction efficiency), the subtractive color superposition image on the output will differ from the input one due to different optical losses for different spectral components.