esa-sceva/satcom-chunk-collection huggingface dataset:
# A Mathematical Model for Efficient and Fair Resource Assignment in Multipath Transport
## Appendix A Mathematical Annex
### General Constraints
Equation (A26) makes sure that the link identified to be congested is fully utilized i. e, the flow allocations uses the full capacity. If the link \((i,j)\in A\) is congested (\(y_{ij}^{st}=1\)) for the demand \([s,t]\in K\), the sum of allocated capacity to the flows sharing the link \((i,j)\in A\) is equal to the capacity of the link \((i,j)\in A\). It has already been concluded that the aggregated flow allocation on a link cannot be larger than the capacity of that link. Equation (A27) relates the two binary variables \(x_{ij}^{st}\) and \(y_{ij}^{st}\). It expresses that the link \((i,j)\in A\) can be on the path for the flow \([s,t]\in K\) even though the link \((i,j)\in A\) is not fully utilized. It also ensures that the identified bottleneck link \((i,j)\in A\) for the flow \([s,t]\in K\) is on the allocated path of the flow \([s,t]\in K\) i.e., \(y_{ij}^{st}\) can be 1 only when \(x_{ij}^{st}=1\):  
\[f_{ij}^{st}\leq cap_{ij}x_{ij}^{st}\qquad\forall(i,j)\in A,[s,t]\in K. \tag{12}\]4 (A25)  
\[f_{ij}^{st}\leq cap_{ij}x_{ij}^{st}\qquad\forall(i,j)\in A,[s,t]\in K. \tag{12}\]5 (A26)  
\[f_{ij}^{st}\leq cap_{ij}x_{ij}^{st}\qquad\forall(i,j)\in A,[s,t]\in K. \tag{12}\]6 (A27)  
**Congested links identifier constraints for multipath subflows:** If a flow splits into multiple subflows, each of them should be a part of at least one congested link, as it is the case for single-path flows. It can, however, happen that, on a given link, two or more subflows belong to the same flow. To identify the congested link for a subflow, consider a binary variable \(sf\_bneck_{ohij}^{st}\) which is 1 if the subflow \([o,h]\in A\) of the flow \([s,t]\in K\) is congested on link \((i,j)\in A\). Equation (A28) refers that the link \((i,j)\in A\) can be identified as congested link for the subflow \([o,h]\in A\) of the flow \([s,t]\in K\) only if the subflow \([o,h]\in A\) exists on that link. Equation (A29) makes sure that at least one congested link \((i,j)\in A\) for the subflow \([o,h]\in A\) of the flow \([s,t]\in K\). Equation (A30) implies that the subflow \([o,h]\in A\) of the flow \([s,t]\in K\) can not be congested on \((i,j)\in A\) if the flow \([s,t]\in K\) itself is not congested on that link i.e., \(sf\_bneck_{ohij}^{st}=0\) when \(y_{ij}^{st}=0\). Equation (A31) specifies that, if flow \([s,t]\) is bottlenecked on link \((i,j)\), then all subflows \([o,h]\) of flow \([s,t]\) are bottlenecked on \((i,j)\):  
\[f_{ij}^{st}\leq cap_{ij}x_{ij}^{st}\qquad\forall(i,j)\in A,[s,t]\in K. \tag{12}\]7 (A28)\[f_{ij}^{st}\leq cap_{ij}x_{ij}^{st}\qquad\forall(i,j)\in A,[s,t]\in K. \tag{12}\]8 \[f_{ij}^{st}\leq cap_{ij}x_{ij}^{st}\qquad\forall(i,j)\in A,[s,t]\in K. \tag{12}\]9  
\[\sum_{(i,j)\in A}x_{ij}^{st}\leq snd\_int^{st}\cdot rcv\_int^{st}\qquad\forall i \in V,[s,t]\in K, \tag{13}\]0  
After giving the general constraints of the linear programming model which are common to all fairness methods, the constraints specific for the particular fairness methods BSF, BFF and NFF are now discussed.
## 4 Grid Voltage Feedforward Control
### Traditional Current Loop Control  
As shown in Figure 8, the essence of conventional VSG control resides in an indirect approach that emulates the inertia and damping traits of synchronous generators to modulate the output current of grid-connected inverters. Nonetheless, when confronted with grid voltage sag accompanied by a certain degree of imbalance, achieving effective control over the output current of the grid-tied inverter becomes challenging, ultimately leading to the VSG's inability to perform LVRT. This section explores the inner workings of the current loop within the traditional VSG model, shedding light on the impact of unbalanced grid conditions on the VSG's output current. The current loop, extracted from Figure 2, is depicted below, where ig represents the grid-connected current.  
The expression for the grid-connected current in the aforementioned control block diagram is:  
\[i_{g}=\frac{Q_{PR}(s)K_{pvm}}{sL+R+Q_{PR}(s)K_{pvm}}i_{Lref}-G(s)v_{g} \tag{16}\]  
Figure 7: Control Block Diagram of Improved DSOGI-PLL Based on AHE.  
where, \(G\)(s) and \(i_{Lref}\) can be expressed as follows:  
\[G(s)=\frac{s^{2}LC+sCR+sCQ_{PR}(S)K_{pum}+1}{sL+R+Q_{PR}(S)K_{pum}} \tag{17}\]  
\[i_{Lref}=\frac{1}{sL+R}e-\frac{1}{sL+R}v_{g} \tag{18}\]  
In the equation, \(e\) represents the output voltage of the virtual synchronous machine control loop, which can be equivalently considered as the potential at the midpoint of the inverter's bridge arms. \(u_{g}\) represents the grid-side voltage. \(Q_{PR}\)(s) is the transfer function of the proportional resonant (PR) controller. \(K_{pum}\) is a normalization coefficient, which is set to 1 in the system [24].  
Based on Equations (16) and (18), it can be deduced that the grid-connected current of the VSG is influenced by both the equivalent potential e at the midpoint of the bridge arms and the grid voltage ug Under conditions of three-phase balanced and harmonic-free grid voltage, the grid voltage consists solely of positive sequence components. Consequently, the reference grid-connected current of the VSG also comprises only positive sequence components. However, when complex faults occur in the power grid, the grid-connected current will be affected and fail to meet the requirements.
## 2 Materials and Methods
### Methods
#### 2.2.2 Determination of Climate-Limited Area  
We used long-term monthly average climate data to develop scaling factors (0-1) (refer to the climatic limitation index) [24]. The temperature limitation index, radiation limitation index and water limitation index were calculated using the criteria proposed by [PERSON] et al. [24] as follows:  
\[iT=\left\{\begin{array}{rl}&1-\frac{T_{\min}-TM_{\min}}{TM_{\max}-TM_{\min}}, TM_{\min}<T_{\min}<TM_{\max}\\ &1,T_{\min}<TM_{\min}\\ &0,T_{\min}>TM_{\max}\end{array}\right. \tag{4}\]  
where \(iT\) is the temperature limitation index, \(T_{\min}\) is the daily minimum temperature, \(TM_{\min}\) and \(TM_{\max}\) are the thresholds of the daily minimum temperature, which were set as \(-5\)\({}^{\circ}\)C and \(5\)\({}^{\circ}\)C in this study, respectively.  
\[iR=\left\{\begin{array}{rl}&1-\frac{R_{\max}-RM_{\min}}{RM_{\max}-RM_{\min} },RM_{\min}<R_{mean}<RM_{\max}\\ &1,R_{mean}<RM_{\min}\\ &0,R_{mean}>RM_{\max}\end{array}\right. \tag{5}\]  
where \(iR\) is the radiation limitation index, \(R_{\max}\) is the daily mean PAR, \(RM_{\min}\) and \(RM_{\max}\) are the thresholds of the daily mean PAR, which were set as 75 W and 150 W, respectively. In addition, we used the ratio of precipitation to potential evapotranspiration (\(P/PET\)) as an indicator of water-limited conditions, as below:  
\[iW=\left\{\begin{array}{rl}1-\frac{P}{0.75^{iPET}},\frac{P}{PET}<0.75\\ 0,\frac{P}{PET}\geq 0.75\end{array}\right. \tag{6}\]  
The spatial patterns of the three climatic limitation indices are shown in Figure 1a. For classification, we define the pixels as the dominant temperature-limited area if: (1) \(iT\) is higher than \(iR\) and \(iW\), and (2) \(iT\) is larger than 0.25. Radiation-limited areas and water-limited areas were determined by the same criteria. We determined the pixels as having no climatic limitation where \(iT\), \(iR\) and \(iW\) are all lower than 0.25 (Figure 1b).
# Highly Sensitive Liquid M-Z Waveguide Sensor Based on Polymer Suspended Slot Waveguide Structure
## 2 Design of the Device Structure  
The overall structure is divided into Zone I (Y branch), Zone II (coupled zone), and Zone III (slot zone). The cross-sectional view of the device is shown in Figure 1a, and the longitudinal section of the slot region is shown in Figure 1b. The device is made of polymer and is surrounded by the test liquid. The device is a suspended structure with the following dimensions: w for the waveguide width on both sides of the slot, h for the slot height, f for the slot width, m for the holder height, n for the holder width, and j for the distance between the slot and the two holders. The device was supported by brackets on both sides of the waveguide and a slab, which formed a suspended structure and realized a buried structure inside the liquid. This construction increases the contact area between the waveguide and the liquid being monitored. Meanwhile, the slot structure is used to improve the interaction between the liquid sample and the optical wave evanescent field, hence, boosting the sensor's sensing accuracy. Based on the aforesaid structure, the device will not only be analyzed in terms of optical power ration, energy loss, and performance, but also be optimized in terms of device parameters, waveguide mode, and optical field.  
Figure 1: (**a**) Schematic of the liquid sensor based on the polymer suspended slot waveguide. (**b**) Cross-section of the sensor showing the holder on both sides of the waveguide and the slab beneath the device which forms the suspended structure. w, h, f, m, n, and j are the widths of waveguides on both sides of the slot, the slot height, the slot width, the holder height, the holder width, and the distance between the slot and the two holders, respectively.
## 3 Development of SoS Metamodels and Ontologies
### OWL-Based Representation of SoS Metamodel  
The proposed SoS metamodel comprises elements classified into Abstract, Type, and Tuple. Therefore, the corresponding OWL terms for these element types must be specified in the ontology space, as shown in Table 1. The Protege ontology editing software (version 5.5.0) [45] models the example elements presented in the SoS metamodel, as shown in Figure 2.  
The Type is transformed into a class in ontology, as shown in Figure 2a, where an inheritance relationship links the \"Performer\" class and the \"OperationalPerformer\" class.  
\begin{table}
\begin{tabular}{c c c} \hline
**Metamodel Term** & **OWL Term** & **Example Elements in the SoS Metamodel** \\ \hline Type & Class & Capability, Mission, OperationalPerformer, OperationalActivity \\ Abstract & Class & Process, Performer \\ Tuple & Object property & Exhibits, isCapableToPerform, mapsToCapability, trace \\ \hline \end{tabular}
\end{table}
Table 1: ScS metamodel to OWL representation.  
Figure 1: A simplified version of the SoS metamodel.  
Figure 2: OWL representation. (**a**) OWL-based representation of the Type and Abstract, (**b**) OWL-based representation of the Tuple.  
Similarly, the Abstract is transformed into a class in ontology, exemplified by the \"Process\" and its sub-elements \"Function\" and \"OperationalActivity\". While \"Process\" is an abstract class and cannot be instantiated in practical cases, serving only for analysis and reasoning purposes. However, the sub-classes \"Function\" and \"OperationalActivity\" can be instantiated, representing these instances as individuals in the ontology.  
A Tuple means a relationship or flow between two elements and can be directly represented in OWL, which focuses on tuple relationships, such as \"A contains B\". Therefore, the Tuple in the metamodel is transformed into an object property in ontology, as illustrated in Figure 2b. For example, the relationship between \"Capability\" and \"Mission\" can be represented as an object property named \"trace\".  
To address the fact that the concept of an SoS is not explicitly represented in the SoS metamodel, an SoS class is added to the ontology using the \"EnterprisePhase\" stereotype from the UAF DMM. The interconnection between the SoS and its constituent CSs is established through the \"hasCS\" object property, while the \"hasMission\" object property is employed to depict the connection between the SoS and its mission.  
Based on the above analysis, Figure 3 shows the SoS ontologies modeled in Protege.
## 4 Simulations and Results Analysis
### Step Steering Maneuver
\(\lambda_{max}\) can be defined by various experts and automobile manufacturers, and it is set as 0.08 in this paper. Figure 18 depicts the sideslip angle response and the yaw rate response under disturbances. As shown in Figure 18, under the disturbances of the side wind in a 3-5 s period and the yaw rate sensor noise in a 7-8 s period, the sideslip angle and the yaw rate of the 4 WS vehicles with the H2/H\(\infty\) controller and the H\(\infty\) controller were not disturbed, while that of the vehicle equipped with the proportional controller showed large fluctuations, and the sensitivities \(\lambda_{sen}\) of the sideslip angle and the yaw rate to the disturbances were greater than 0.08. It was concluded that the H2/H\(\infty\) controller and the H\(\infty\) controller are more robust than the proportional controller. The yaw rate's settling time and the overshoot of the 4 WS vehicle with the H2/H\(\infty\) controller were lower than that of the H\(\infty\) controller, which shows that the H2/H\(\infty\) controller made the 4 WS vehicle more robust than the H\(\infty\) controller. Finally, the robust stability of the 4 WS control system was verified. Figure 19 shows the frequency response of the maximum singular value of perturbation block \(\Delta\) transfer function matrix, and \(\Delta\) reflects the mathematical modeling uncertainties. Figure 19 depicts the robust stability of the control system with respect to the modeling uncertainties. It can be seen that the maximum singular value of \(\Delta\) was less than 1 and that the H2/H\(\infty\) controller and the H\(\infty\) controller had great robust stability. Figure 17: The disturbances signals: (**a**) side wind; (**b**) yaw rate sensor noise.
# Measurement and Analysis of RSS Using Bluetooth Mesh Network for Localization Applications
## 4 Bluetooth Mesh Network for Experimental Studies of RSS Measurements
### Design of Communication Protocols for Channel-Aware Multi-Device RSS Measurements
It is also used to detect packet losses, which is necessary when tuning network parameters to achieve reliable networking performance.
# A Deep Learning Approach for Automatic Hate Speech Detection in the Saudi Twittersphere
## 4 Experiment Setup
### Neural Network Models
#### 4.2.1 Feature Representation  
The first layer of all proposed architectures is an embedding layer that maps each tweet, represented as a sequence of integer indexes, to a 300-dimension vector space using a pretrained word2 vec model [38]. We trained our own word2 vec model using the Continuous Bag of Words (CBOW) training algorithm. We used a subset of our collected data as training collection. The collection had a total of 17.6 million tweets and 536,000 tokens. Before training, all tweets were preprocessed following the same preprocessing steps described earlier. As for the model's hyperparameters, we selected a window of size 3, since the length of tweets is generally small. We set the vector dimensions to 300 and the other hyper-parameters to their defaults. To prepare the tweets for the embedding layer, we started by creating a vocabulary index based on word frequency where every unique word in our dataset was assigned a unique integer value (0 was reserved for padding). Then, we converted each tweet to a sequence of integer indexes and padded them to ensure that all sequences had the length of the longest tweet in the dataset. Then the tweets' sequences were fed into an embedding layer that mapped word indexes to a pretrained word embeddings. The output of the embedding layer was an (\(m\times 300\)) tweet matrix, where \(m\) is the length of the longest tweet in the dataset.
# Machine Learning-Based Solutions for Handover Decisions in Non-Terrestrial Networks
## 3 Proposed Method
### NTN Simulator and Data Description
#### 3.1.1 LEO-Satellite Data Collection  
We have gathered details about UE and LEO satellites. As seen in Figure 4, we selected the latitude, longitude, and altitude (LLA) data from the start (0 s) and end (120 s = 2 min) of the simulation for the LEO satellite. The speed of the LEO-Satellite was 7.56 km/s, whereas the altitude was 300 km.
# A Review of Physical Layer Security Techniques for Internet of Things: Challenges and Solutions
###### Abstract
Unlike the traditional cryptographic approaches, physical layer security (PLS) takes advantage of the intrinsic characteristics of wireless channels, such as noise, fading, and interference, to boost the signal reception at the legitimate receiver and degrade the received signal quality at the eavesdropper, and realizes the keyless secure transmission via signal design and signal processing [9; 10]. Compared with the cryptographic approaches, PLS techniques have the following advantages. First, PLS schemes do not rely on the encryption/decription operations, thus overcoming the difficulty in the distribution and management of secret keys in large-scale heterogeneous IoT. Second, PLS techniques can fully exploit the characteristics of wireless channels to realize adaptive signal design and resource allocation, thus providing flexible security-level configurations and QoS guarantee. Third, PLS techniques only need to complete the relatively simple signal processing algorithms, which incurs less overhead compared to the encryption-based method does. Based on the above discussions, it can be found that PLS is a promising solution to secure the future IoT. Although the research of PLS techniques has reached fruitful outcomes, it is still challenging to develop the PLS solutions for IoT applications. In particular, IoT has four unique features: low-cost, wide-range coverage, massive connections, and diversified services. How to design PLS strategies that well match these four features remains an open problem. In this article, we would like to present a comprehensive review of the physical layer security techniques for Internet of Things. The primary goal of this article is to provide an avenue for people trying to educate themselves in the state of the art in this area. The scope of this article mainly includes the introduction of the basic principle of physical layer security, the representative PLS techniques, the unique challenges faced by PLS protocol design, and the emerging IoT-oriented PLS solutions. The remainder of this article is organized as follows. In Section 2, the information-theoretical fundamentals of physical layer security is briefly introduced, followed by the literature survey of major achievements in PLS research. In Section 3, the challenges of PLS protocol design for IoT are discussed. Section 4 highlights some emerging PLS techniques that are well suited to the unique features of IoT. Finally, we conclude the paper and point out several further directions in IoT-oriented PLS research in Section 5.
# Uniform Linear Antenna Array Beamsteering Based on Phase-Locked Loops
## 5 Measurement on ULA  
A \(10\) m \(\times\) 5 m \(\times\) 5 m anechoic chamber was used for the experimental measurements of radiation patterns [16]. The linear array was used as the transmitter as shown in Figure 5a. A common 10 MHz reference was fed to all PLLs, the dual channel IF receiver, and LO  
Figure 6: Block diagram for a 5-element ULA set-up.  
Figure 7: PLL boards testing before integration with the ULA.  
signal generator (as seen in Figure 8). One channel of the IF receiver is fed with the output of one PLL via a directional coupler (to reduce the power level and remove any DC) mixed with the LO signal and the second channel comes from the receiving horn in the far field mixed with the LO. The radiation pattern measurement set-up in the anechoic chamber with all the hardware to test is shown in Figure 9. In the next section, we describe how we phase and power-align each PLL in the far field using the anechoic chamber acquisition system (Figure 8).  
Figure 8: Radiation pattern measurement set-up for 5-element ULA in anechoic chamber representation.  
Figure 9: Radiation pattern measurement set-up for 5-element ULA in anechoic chamber.
## 4 Experimental Results and Analyses  
### Establishment of a Logistics Express Impact Indicator System  
The logistics industry occupies an important position in modern urban development, and its industry development has a profound impact on the regional economy. In this paper, on the basis of reviewing the literature, 18 indicators are selected as the logistics express demand impact indicator system alternative. In selecting the indicators, there are the following principles:  
1. The selection of indicators should reflect the development trend of the regional logistics industry as much as possible.
2. Select indicators that have a greater impact on the regional logistics and express delivery industry.
3. Ensure the reliability and authenticity of the sources of indicators.  
In this paper, the grey correlation model is used to analyze the 18 indicators and the regional logistics express volume. The grey correlation index of 18 indicators for regional logistics express volume was derived. The 14 indicators with grey correlation coefficients above 0.75 were selected for the next step, as shown in the Table 1.
## 3 DA-Based Turbo Equalization
### Interference Cancellation  
In the process of interference cancellation, the residual CCI in the signal \(r^{\alpha,m}(n)\) must first be estimated. Without considering the error of channel estimation, the estimated CCI can be given by  
\[\hat{I}_{cci}^{\alpha,m}(n)=\sum_{l=-\infty}^{+\infty}\sum_{\gamma=1,\gamma \
eq\alpha}^{2}q^{(\alpha,\gamma),(m,m)}(l,n)\widehat{s}^{\gamma,m}(l) \tag{11}\]  
where \(q^{(\alpha,\gamma),(m,m)}(l,n)\) is the composite channel response given by Equation (9), \(\widehat{s}^{\gamma,m}(n)\) represents the decision value of the symbol \(s^{\gamma,m}(n)\). In the first iteration, \(\widehat{s}^{\gamma,m}(n)\) is set to zero. In the following iteration, \(\widehat{s}^{\gamma,m}(n)\) is computed as \(\widehat{s}^{\gamma,m}(n)=\text{argmin}_{s\in\mathcal{S}}|s-\sum_{s^{\prime} \in\mathcal{S}}s^{\prime}p_{\text{in}}(s^{\gamma,m}(n)=s^{\prime})|\), where \(s\) denotes a complex value belonging to the \(2^{\text{Q}}\)-ary symbol alphabet \(\mathcal{S}\) and \(p_{\text{in}}[s^{\gamma,m}(n)=s]\) refers to the input a priori probability that \(s^{\gamma,m}(n)=s\).  
After removing the estimated CCI from \(r^{\alpha,m}(n)\), the output signal of interference cancellation is  
\[\begin{array}{ll}z^{\alpha,m}(n)&=q^{(\alpha,\alpha),(m,m)}(n,n)s^{\alpha,m }(n)+\sum_{l=-\infty,l\
eq n}^{+\infty}q^{(\alpha,\alpha),(m,m)}(l,n)s^{\alpha,m}(l)+\eta^{\alpha,m}(n)\\ &+\sum_{l=-\infty}^{+\infty}\sum_{\gamma=1,\gamma\
eq\alpha}^{2}q^{(\alpha, \gamma),(m,m)}(l,n)s^{\gamma,m}(l)-\sum_{l=-\infty}^{+\infty}\sum_{\gamma=1, \gamma\
eq\alpha}^{2}q^{(\alpha,\gamma),(m,m)}(l,n)\widehat{s}^{\gamma,m}(l) \end{array} \tag{12}\]  
Figure 4: Block diagram for DA-based turbo equalization.