esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancement of the DRPE Encryption Algorithm for Phase CGHs by Random Pixel Shuffling
## 4 Results Discussion and Performance Analysis  
The generation of a 3D scene and its subsequent storage, encryption, and later retrieval through simulation or in the laboratory allow us to conduct the following general analysis. To ensure the robustness of an encryption algorithm, it must be capable of transforming the image into an unrecognizable form equivalent to random noise [43]. Therefore, the proposed procedure is robust to different types of attacks owing to the following:  
* CGH Storage: The 3D scene is stored within a CGH. Vulnerability to Statistical Analysis is avoided since methods such as intensity histograms cannot reveal patterns or facilitate partial image reconstruction. It is also resistant to partial transformations, as modifying a subset of pixels in the CGH does not compromise the integrity of the stored information.
* Pixel Shuffling: As will be shown next, pixel shuffling can be robust against brute force attacks when the number of possible pixel permutations is sufficiently large.
* Random Phase Modification: This adds an additional layer of complexity to the encryption, as it modifies the phase disturbance that each pixel of the CGH presents to the reconstruction wavefront. It also alters the phase histogram of the ciphertext, further complicating attacks using statistical tools.
## 2 Radio Communication Technologies
### The Main Characteristics of LPWAN Technologies
#### 2.2.3 Data Rate  
Speaking of LPWAN technologies, although the data rate (data transmission speed) is an important benchmark, it has to be considered alongside other benchmarks too. There are many modulation possibilities for a single LPWAN technology, so the amount of data that can be transmitted should be taken into account too. If the data transmission amount is limited, then the transmission speed actually remains in the background.  
Sigfox's data rate is fixed to 100 bps, with the limitation of being able to transfer 140 messages per day at maximum [10]. LoRaWAN's transmission speed ranges from 0.3 up to 27 kbps, depending on two conditions: the spreading factor and the bandwidth. RPMA has the highest data rate among the LPWAN technologies, but again the transmission speed in practice adapts to the characteristics of its channels [9]. NB-IoT has peak data rates of 26 kbps in the downlink and 66 kbps in the uplink, although in the extended coverage areas the speed is as low as a few kbps [19].  
Multiple experiments have been performed so far where the performance and data transmission speed of popular LPWAN technologies have been measured. In one study, four popular technologies, Sigfox, NB-IoT, LTE CatM1, and LoRa, were investigated in real environments. The experiment simulated use cases with high-speed end devices that move on roads or fly in the air. One of the final results implied that NB-IoT with the User Datagram Protocol (UDP) protocol for packet transmission could be used to follow the vehicles moving on highways and in heavy-traffic locations due to its broad coverage, as well as the 6% data loss rate and 11 s average delay [20].
## 4 IETF 6 LoWPAN Working Group (IPv6)
### Leveraging upon 6 LoWPAN to Realize the IoT
#### 4.3.2 6 LoWPAN over Non IEEE 802.15.4 Technologies  
The working group also adopted internet draft-ietf-6 lowpan-btle, which applies 6 LoWPAN technology to Bluetooth Low Energy. This draft is expected to be a companion of RFC 4944 by removing unnecessary features such as Mesh header and Fragmentation. This extension is the first draft that extends 6 LoWPAN to non-IEEE 802.15.4 networks. Draft-mariager-6 lowpan-v6 over-dect-ule, is another draft proposed to extend 6 LoWPAN to another non-IEEE 802.15.4 technologies. The draft proposes 6 LoWPAN technology for DECT ULE (_Digital Enhanced Cordless Technology Ultra Low Energy_). The last two drafts indicate that the working group is considering link-layer technologies other than 802.15.4 to use with 6 LoWPAN. As such, it is explored how 6 LoWPAN can operate over heterogeneous low-power technologies, in a similar way as how IP can operate over different underlying technologies.
# An Intelligent Control Method for Servo Motor Based on Reinforcement Learning
## 3 Problem Description
### State Information of Servo Motor  
In this paper, we only consider three commonly used servo motors: the externally excited DC motor (ExtEx), series DC motor (Series), and three-phase permanent magnet synchronous motor (PMSM). The state information of the servo motor generally refers tosome physical quantities that can describe the actual running state of the motor, such as the angular velocity of the motor, the actual current, the actual voltage, and the motor torque.  
The state of an externally excited DC motor can be described by its angular velocity \(\omega\), torque \(T\), armature current \(i_{A}\), excitation circuit current \(i_{E}\), armature voltage \(u_{A}\), excitation circuit voltage \(u_{E}\), and service voltage \(u_{ser}\)[2], which is denoted as Equation (1).  
\[O_{ExtEx}=[\omega,T,i_{A},i_{E},u_{A},u_{E},u_{ser}] \tag{1}\]  
In the circuit construction of a series DC motor, the armature and the excitation circuit are connected in series, so their armature values are the same and can be denoted as \(i\). The input voltage is the sum of armature voltage and excitation circuit voltage, which can be denoted as \(u=u_{A}+u_{E}\)[2]. The state of the series DC motor is denoted as Equation (2).  
\[O_{Series}=[\omega,T,i,u,u_{ser}] \tag{2}\]  
A permanent magnet synchronous motor consists of three phases; each of them has a phase voltage and phase current, denoted as \(u_{a}\),\(u_{b}\),\(u_{c}\),\(i_{a}\),\(i_{b}\), and \(i_{c}\), respectively. Because the dynamic mathematical model of the PMSM in a three-dimensional static coordinate system is too complex, it is often converted to a DQ two-phase rotor coordinate system, where the inductance matrix will be simplified to a constant. In DQ coordinates, the voltage components in the \(d\) and \(q\) directions are denoted as \(u_{sd}\) and \(u_{sq}\), respectively. The current components in the \(d\) and \(q\) directions are denoted as \(i_{sd}\) and \(i_{sq}\), respectively. The rotor flux is \(\varepsilon\). Therefore, the state of the PMSM is denoted as Equation (3).  
\[O_{\text{PMSM}}=[\omega,T,i_{a},i_{b},i_{c},i_{sq},i_{sd},u_{a},u_{b},u_{c},u_{ sq},u_{sd},u_{ser},\varepsilon] \tag{3}\]
## 3 Proposed MIDE Algorithm
### Modified IDE-Based Detection Algorithm with Self-Update Damping
Hence, Equation (19) can be rewritten as:  
\[E\left(d^{1}\right)=2E\left(\sqrt{\sum_{i=1}^{N_{t}}\left(\Re\left(\mathfrak{K }^{1}\left(i,1\right)\right)\right)^{2}}\right)=2N_{t}\sqrt{\frac{\sum cand^{2} }{\sqrt{M}}} \tag{21}\]  
where _cand_ represents the candidate value set of \(\Re\left(\mathfrak{K}^{1}\left(i,1\right)\right)\) and \(M\) represents the modulation cardinality. For the ease of description, we still take the 16-QAM as an example. Then, _cand_ is denoted as the candidate values \(\left\{-3,-1,+1,+3\right\}\) and \(\sum cand^{2}=\left(-3\right)^{2}+\left(-1\right)^{2}+1^{2}+3^{2}\). \(M\) represents the modulation cardinality. Based on experience, the value of \(\alpha^{1}=0.8\) is set as 0.8. Then, substituting Equation (22) into Equation (18) yields the estimation of \(q\) as:  
\[q=\frac{E\left(d^{1}\right)}{4}=\frac{N_{t}}{2}\sqrt{\frac{\sum cand^{2}}{ \sqrt{M}}} \tag{22}\]  
The simulation results in Section 4 show that the proposed MIDE algorithm can improve the BER performance significantly compared to the IDE-based algorithm, which employs the fixed damping factor. The procedure of the proposed MIDE detection algorithm is illustrated in Algorithm 1. ```
1 Input: 1) \(\mathbf{H}\), the channel matrix
2) \(\mathbf{y}\), the received signal matrix
3) \(K\), the number of iterations
4 Output: the detected signal \(\mathbf{\hat{x}}\leftarrow\mathbf{\hat{x}}^{K}\)
5 Preprocessing
6:\(\mathbf{D}\leftarrow\left[diag\left(\mathbf{H}^{H}\mathbf{H}\right)\right]^{-1}\);
7:\(\mathbf{W}\leftarrow\mathbf{D}\mathbf{H}^{H}\);
8:\(q\leftarrow\frac{N_{t}}{2}\sqrt{\frac{\sum{cond^{2}}}{\sqrt{M}}}\);
9 Initialization
10:\(\mathbf{\hat{x}}_{d}^{0}=\mathbf{0}\); Iterative process (repeat K times)
11:for\(k=1\)-\(K\) (iteration times) do
12:\(\mathbf{z}^{k}\leftarrow\mathbf{\hat{x}}_{d}^{k-1}+\mathbf{W}\left(\mathbf{y}- \mathbf{H}\mathbf{\hat{x}}_{d}^{k-1}\right)\)
13:\(\mathbf{\hat{x}}^{k}\)\(\leftarrow\)\(\Pi_{\Omega}\left(\mathbf{z}^{k}\right)\)
14:\(\mathbf{r}^{k}\leftarrow\mathbf{\hat{x}}_{d}^{k-1}-\mathbf{\hat{x}}^{k}\)
15:\(d^{k}\leftarrow\sqrt{\sum\limits_{i=1}^{N_{t}}\left(\Re\left(\mathbf{r}^{k} \left(i,1\right)\right)\right)^{2}+\left(\Im\left(\mathbf{r}^{k}\left(i,1 \right)\right)\right)^{2}}\)
16:\(\mathbf{\alpha}^{k}\leftarrow\frac{d^{k}}{d^{k}+q}\);
17:\(\mathbf{\hat{x}}_{d}^{k}\leftarrow\left(1-\mathbf{\alpha}^{k}\right)\mathbf{ \hat{x}}_{d}^{k-1}+\mathbf{\alpha}^{k}\mathbf{\hat{x}}^{k}\);
18:endfor
```  
**Algorithm 1** The pseudocode of the MIDE detection algorithm.
## 5 Conclusions  
In this study, a new MCR-UWSN technique is derived to accomplish an energy-efficient performance in UWSN. The MCR-UWSN technique incorporates a two stage process, namely CEPOC-based cluster construction and MHR-GOA-based routing. The CEPOC technique has derived a fitness function involving distinct input parameters for CH selection and cluster construction. Moreover, the MHR-GOA technique is proposed to optimally derive the routes for multi-hop communication. The MCR-UWSN method has the superior LND of 1187 rounds, whereas the LEACH, LEACH-ANT, CUWSN, EOCA, and ACOCR methods have a minimal LND of 710, 906, 989, 1021, and 1187 rounds, respectively. In order to prove an enhanced performance of the MCR-UWSN technique, a series of simulation processes take place, and the results are examined under different dimensions. The simulation results guaranteed an enhanced energy-efficient performance of the MCR-UWSN technique over the existing techniques. In the future, data aggregation and underwater object tracking techniques can be designed for UWSN.  
Conceptualization: N.S. & P.M.; Methodology: N.S. & P.M.; Validation: N.S. & Y.A.; Formal Analysis: S.A. & N.S.; Investigation: P.M. & N.S.; Resources: N.S.; Data Curation: P.M.; Writing original draft preparation: P.M.; Writing review and editing: N.S. & Y.A.; Visualization: O.I.K.; Supervision: N.S. & Y.A.; Project Administration: N.S. & Y.A.; Funding Acquisition: S.A. All authors have read and agreed to the published version of the manuscript.  
This research is funded by Taif University, TURSP-2020/313.  
Not Applicable.  
The study did not report any data.  
We deeply acknowledge Taif University for supporting this Research through Taif University Researchers Supporting Project Number (TURSP-2020/313), Taif University, Taif, Saudi Arabia.  
The authors declare no conflict of interest.
## 3 Flexible Substrate Antenna Design
### Antenna Design  
Once the comprehensive examination and verification of the properties of the selected materials are completed, the design and the shape selections for the antenna remain crucial. According to different specific applications and frequency requirements, various designs have been previously published for flexible microstrip antennas, their specific applications, and frequency requirements including aperture coupled antennas [22; 23; 24], planar inverted-F antennas [25; 26], monopole and dipole antennas [27; 28; 29], various alphabetic shaped, E-shaped, F-shaped, and H-shaped antennas [14; 30; 31; 32], and antennas based on the CPW feed [33; 34; 35]. These are all microstrip antennas and fabricated on flexible substrates.  
The basic microstrip patch antennas are mostly used in wireless applications due to their simple shapes, light weight, and small size, and because they support linear and circular polarization, are capable of dual and triple frequency band operations, have low fabrication and manufacturing costs, and are easy to fabricate and integrate with IoT [11; 36; 37; 38; 39; 40; 41; 42]. Being extremely compatible with flexible polymer-based antennas, a microstrip patch antenna design was selected for flexibility tests.  
Although there are some limitations, such as a narrow bandwidth as a function of the thickness of the substrate material at higher frequencies above 5 GHz, these do not impact our target purposes, including the testing of the bending capabilities of polymers on the radiation characteristics of the antenna. There are, however, also some disadvantages, such as low gain and efficiency, and surface wave excitation [43]. Unfortunately, this surface wave excitation results in power loss as the power is scattered when the antenna bends, thus causing degradation of the signal strength. The low efficiency of the microstrip patch antennas is due to the high \(Q\) factor, resulting in these losses in relation to the antenna [44].  
Amongst the various shapes possible for microstrip patch antennas, the E-shape is simpler to design and optimize, and was selected herein. The gap between the feedline and the patch \(G_{pf}\) (see Figure 5) provides a wider bandwidth compared to a simple patch antenna. Therefore, to determine the flexible polymer-based antenna behaviours for different applications, nine E-shaped microstrip antennas were designed to operate in three frequency ranges: 2.2-2.5 GHz, 2.5-5.0 GHz and 5.0-30 GHz.  
To corroborate the impacts of bending, an E-shaped microstrip patch antenna was initially designed on the PET substrate for the operating frequency of 2.45 GHz. Two more antennas with the same shape and characteristics were then made on the Teflon and PVC substrates.  
Figure 4: Polymer film sheets: (**a**) transparent PET, (**b**) PTFE Teflon, (**c**) transparent PVC.
# A Compact Size Antenna for Extended UWB with WLAN Notch Band Stub
## 3 Results and Discussions  
### Hardware Prototype and Measurement Setup  
To verify simulated results, the hardware prototype of the proposed compact antenna was fabricated using a commercially available substrate. Then, the return loss of the proposed work was measured using Vector Network Analyzer (VNA), while far-field parameters, including gain and radiation pattern, were measured using an electromagnetically isolated RF anechoic chamber. Figure 4 depicts the fabricated prototype along with the far-field measurement setup.
## 4 Numerical Results and Discussion
### Comparison and Validation
As far as the effect of total numbers of layers \(N_{L}\) is concerned, we present the main results in Figure 5a-f, for different reinforcement patterns, where we increase the weight fractions \(g^{*}_{\textit{GPL}}\) (%) from 0.2 up to 1.2, by steps of 0.2. According to the plots in Figure 5, the natural frequency seems to remain almost unaffected by the numbers of layers, for a reinforcement distribution of type 1 and 4, whereas it varies significantly with \(N_{L}\) for Pattern 2 and 3. More specifically, a symmetric distribution of type 2 or 3 yields a monotonic decrease or increase of the natural frequency, respectively, up to a threshold value. Figure 3: First natural frequency (GHz) of the composite curved nanobeam for different nonlocal parameters and reinforcement patterns; \(N_{L}\ =\ 12,g^{*}_{\textit{GPL}}\ =\ 1\%,k_{1}\ =\ k_{2}\ =\ 0\). As the last systematic investigation, we analyze the effect of the reinforcement geometry, here estimated in a dimensionless form, \(\frac{l_{CPL}}{h_{CPL}}\), for different patterns. The numerical results are shown in Figure 6a,b for an opening angle of the nanostructure \(\theta_{0}\ =\ 1\) rad and \(\theta_{0}\ =\ 2\) rad, respectively, with a clear monotone increase of the natural frequency with the dimensionless length of the reinforcement, \(\frac{l_{CPL}}{h_{CPL}}\). Figure 4: Effect of the GPL weight fraction and opening angle \(\theta_{0}\) on the \(1^{\text{st}}\) natural frequency (GHz) of the composite curved nanobeam; \(N_{L}\ =\ 12\), \(\mu\ =\ 1\ nm,k_{1}\ =\ k_{2}\ =\ 0\). Figure 5: Variation of the first natural frequency (GHz) of the composite curved nanobeams with the total number of layers and different patterns; \(\theta_{0}\ =1\)_Rad_, \(\mu\ =1\)_nm_, \(k_{1}\ =\ k_{2}\ =\ 0\).
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 3 Proposed Methods
### Region Filling
#### 3.4.5 Inner Layer Filling  
Having obtained an outer layer of coverage circles along the \(x\) and \(y\) axes, we now concentrate on the inner layers. We introduce the concept of _TwoNeighbours_ and _ThreeNeighbours_. _TwoNeighbours_ is the procedure by which we find the center and radius of a circle based on its two neighbours, as shown in Figure 10(a), where we find \((x,y)\) and \(r\) based on the parameters of its neighbouring two circles. The working of _TwoNeighbours_ is similar to that of the algorithms for filling circles along the \(x\) and \(y\) axes. We choose a point \((x_{2},y_{2})\) at a distance \(d_{1}.r+r\) from \((d_{1}.x,d_{1}.y)\) and \(d_{2}.r+r\) from \((d_{2}.x,d_{2}.y)\). Because the value of \(r\) is initially unknown, it is chosen as the average of \(d_{1}.r\) and \(d_{2}.r\). We find the center of the new circle \((x,y)\) at an angle \(\theta\) along the line between \((x_{1},y_{1})\) and \((x_{2},y_{2})\) at a distance \(r\) from \((x_{1},y_{1})\). We continue to change the values of \((x_{2},y_{2})\) and \((x,y)\) (a similar situation as in algorithm _FillAlongXAxis_) until we do not obtain a new point for \((x,y)\). The detailed algorithm is shown in Algorithm 3. The concept of _ThreeNeighbours_ is simpler than that of _TwoNeighbours_, as we have a specific point \((x,y)\), as shown in Figure 11b; here, \((x_{1},y_{1})\) is a point at a distance \(r\) from both \((x_{1},y_{1})\) and \((x_{2},y_{2})\). The value of \(r\) is the average radii of all three neighbouring circles. We find each circle in the inner layers based on the _TwoNeighbours_ and _ThreeNeighbours_ concepts. Wherever possible, we use _ThreeNeighbours_, as it is easier to process. The detailed algorithm used for inner \(x\)-direction filling is provided in Algorithm 4 (Filling along the \(y\)-axis follows a similar procedure). Figure 12 shows the output after filling the inner layers in both the \(x\) and \(y\) directions. Figure 11: Principles behind: (**a**) _TwoNeighbours_ and (**b**) _ThreeNeighbours_. Figure 12: The output after filling an inner layer in both the \(x\) and \(y\) directions. The areas marked in grey show the wastage of coverage.
## 4 Service-Oriented Approach for Dynamic Network Services
### Dynamic Service Chaining of Virtual Network Functions
For this reason a \"Concrete Service Chain\" should include references to dynamic information on the status of the service instance and its constituent elements, _i.e._, monitoring information related to individual VNF instances and links connecting them as well as derived monitoring information at the chain level (e.g., end-to-end delay). At runtime, if one or more VNF instances are no more available or QoS degrades below a given threshold, the service selection task can be rerun to perform service substitution. Service delivery consists in provisioning delivery paths throughout the selected chain of VNFs thereby accomplishing the required VNF associations. It concerns the use of forwarding functions provided by the underlying network infrastructure to guarantee the proper connectivity among VNF instances specified in the \"Concrete Service Chain\". The service delivery task is realized through the enforcement of data flow forwarding rules able to address the connectivity requirements among the selected VNF instances. Here SDN plays a key role since it offers the capability to programmatically enforce traffic forwarding rules across network nodes on per-flow or per-tenant basis. Such capability can be exploited to selectively deliver service data through the dynamically established sequence of VNF instances thereby accomplishing the deployment of a network service chain [42; 43]. Service Placement consists in the allocation of virtual resources executing VNFs and/or re-arrangement of running VNFs (e.g., migration) while optimizing a specified cost function (e.g., energy consumption, network congestions). Indeed, the VNF instances that take part to a service chain may be deployed in different locations and be provided by different parties. This task runs in the background with respect to the above-mentioned tasks. Indeed, the orchestration assumes that the VNF instances are available at the moment of the service deployment as well as throughout the delivery phase. In order to cope with dynamic application requirements (e.g., user mobility, QoS, or security requirements) or unexpected events (e.g., load congestions, failures), NSPs implement proper resource re-arrangement strategies to deploy VNFs according to the new context while guaranteeing the continuity of the service chain.
## 8 Cooperative Model-Predictive Control Methodology  
This section introduces a unified MPC strategy to maintain a docking approach for the long range and a finer docking maneuver in the short range by accommodating a nonlinear and a linear MPC designed with edge weight information and task prioritization. This section is divided into four subsections, where non-linear MPC (NMPC), linear MPC (LMPC), cooperative task prioritization, and implementation of the control strategy are described in Sections 8.1, 8.2, 8.3, and 8.4, respectively.  
The introduced method can be applied on all of the agents as the formulation only considers local neighbor information; therefore, formulations will be provided for the agent denoted as \(i\), which is defined by the states \(\mathbf{x}_{i}(t)\in\mathbb{R}^{n_{x}}\), inputs \(\mathbf{u}_{i}(t)\in\mathbb{R}^{n_{x}}\), and outputs \(\mathbf{y}_{i}(t)\in\mathbb{R}^{n_{y}}\). Given the states and inputs, the non-linear dynamics of agent \(i\) are given in the following form:  
\[\dot{\mathbf{x}}_{i}(t) = \mathbf{f}_{i}(\mathbf{x}_{i}(t),\ \mathbf{u}_{i}(t)) \tag{62}\] \[\mathbf{y}_{i} = \mathbf{h}_{i}(\mathbf{x}_{i}(t),\ \mathbf{u}_{i}(t))\]Equation (63) provides the discrete linear representation of the agents' dynamics. This is obtained by linearizing the current state and input, which is denoted as \((\mathbf{x}_{i,c},\ \mathbf{u}_{i,c})\) and then by converting the continuous time system in a discrete system using Euler discretization (see [46]).  
\[\mathbf{\Delta}\mathbf{x}_{i,k+1} = A_{i}\mathbf{\Delta}\mathbf{x}_{i,k}+\mathbf{B}_{i}\mathbf{\Delta}\mathbf{u}_{i,k} \tag{63}\] \[\mathbf{\Delta}\mathbf{y}_{i,k} = C_{i}\mathbf{\Delta}\mathbf{x}_{i,k}+\mathbf{D}_{i}\mathbf{\Delta}\mathbf{u}_{i,k}\]  
where \(k\) is the current sample such that \(\mathbf{\Delta}\mathbf{x}_{i,k}\ =\ \mathbf{x}_{i,k}-\mathbf{x}_{i,c}\), and \(\mathbf{\Delta}\mathbf{u}_{i,k}\ =\ \mathbf{u}_{i,k}-\mathbf{u}_{i,c}\). In this work, we assumed that full state information is shared among the agents. Therefore, matrices \(\mathbf{C}_{i}\) and \(\mathbf{D}_{i}\) are assumed to be \(\mathbf{I}\) and \(\mathbf{0}\), respectively, with compatible sizes.