esa-sceva/satcom-chunk-collection huggingface dataset:
# AEF: Adaptive En-Route Filtering to Extend Network Lifetime in Wireless Sensor Networks
## 3 Proposed Work
### Route Setup  
Fixed path and energy-aware and dynamic routing in the proposed scheme are shown in Figure 2. The fixed path greedy routing considers only distance, while the proposed routing also caters for residual energy levels and the number of keys on candidate nodes. Following, the fitness evaluation function \(N_{f}\) is used to select the next forwarding node (i.e., next mode in the path) with the highest fitness value given in Equation (2).  
\[N_{f} =Energy_{r}\times\alpha+Dist_{s}\times\alpha+Keys_{n}\times((1- \beta)/2) \tag{2}\]  
where \(Dist_{s}\) is the distance of a node from the BS and \(Keys_{n}\) is the node key. The path creation is initiated at the BS when it needs to determine an event in an area of interest. This process is repeated to create a path from the BS to the source CH.  
Example of Energy-Aware Routing  
A shortest path or greedy routing used in GPSR [9] is shown in Figure 2a. A forwarding node \(N_{f}=v\) is selected among the neighbors of \(u\) which is the closest to the BS. In the proposed energy-aware dynamic en-route selection routing shown in Figure 2b, we use the fitness function presented in Equation (2). It considers residual energy, closest distance neighbor from the sink, and the number of keys on candidates nodes. A node with the leading value among candidate nodes is selected as the forwarding node, e.g., \(N_{f}=w\). The selection of node \(w\) in energy-aware routing is better than \(v\) in shortest path routing since the former considers the energy levels of a node and keys.
## 7 Discussion
### Policy and Future Research  
The analysis of STLF models not only affects the technical management of modern grid networks but also has policy implications. The formulation of accurate forecasts enables the formulation of more efficacious policy decisions pertaining to energy conversion, demand-side management, and investment in infrastructure. Moreover, the results of this analysis can inform future research in the development of models that are not only precise but also incorporate features such as adaptability and resilience against anomalies in data, which are crucial for the next generation of modern power grids and smart grids.
## 2 Materials and Methods  
### Study Area and Workflow  
The field experiment was carried out in White Nile State, Sudan, focusing on sugar cane, as this region stands out as one of the primary sugarcane cultivation areas in the country (Figure 1). Situated in the southern part of Sudan, the White Nile State spans latitudes 12.00 to 13.30 \({}^{\circ}\) N and longitudes 31.00 to 33.30 \({}^{\circ}\) E and is at an elevation of 384 m above sea level, covering an area of 39,701 square kilometres. Characterized by an arid to semi-arid climate, the state experiences varying annual rainfall levels ranging from 300 mm in the north to over 600 mm in the south. White Nile State was specifically chosen due to its status as a leading sugarcane producer in Sudan.  
The methodology of this research is depicted in Figure 2. The initial phase of the methodology involves the gathering of climate and crop data. Subsequently, three machine learning models (SVR, RF, and XGB) were utilized independently  
Figure 1: Geographical location of the study area and the meteorological stations.  
incorporating four remote sensing indices (EVI, NDVI, SAVI, and NDWI) to forecast the GWFP based on five scenarios that combine climate, crop data, and remote sensing indices.
## 4. Jitter Generation Method
The D flip-flop output side Q is pulled down to a low level or a high level depending on the data pattern level when the rising edge of jittery clock comes. For example, if the data pattern is at a high level when the rising edge of jittery clock comes, the Q side will be pulled down to a low level till next rising edge of jittery clock. In this way, jittery data pattern is synthesized because of jittery clock. The logical combination consists of a high-speed AND gate, a XOR gate, and three selectors, as shown in Figure 8, which are used to select the edge of jitter generation. It is necessary to decompose the input data pattern of the system into two channels that are separately delayed to generate jittery data pattern and select edge of jitter generation. The data pattern used to generate jittery data pattern and the data pattern used to select the edge of jitter generation are obtained by fanning out from the input data pattern, because the final rising or falling jittery data pattern is synthesized using logic  
Figure 3.0 The specific process of generating a random jitter data stream. Figure 3.1 Schematic of the principle of the jitter synthesis. operation of the jitter data pattern and the data pattern used to select the edge of jitter generation, as shown in Figure 3.2  
When the jitter on the rising edge of data pattern is to be generated, as shown by the solid line in Figure 8, selector#1 fans out two data patterns, which are used as input of the XOR gate and ADD gate, respectively, and selector#2 fans out jitter data pattern from the output of the D flip-flop as the other input of the XOR gate. The output of the XOR gate is the other input of the AND gate for generating jitter on the rising edge of data pattern. The timing diagram of jitter generation on the rising edge is shown in Figure 9, where the D Flip-flop represents the output of selector#2 and the pattern represents the two outputs of selector#1. When the jitter on the falling edge of data pattern is to be generated as shown by the dotted line in Figure 8, selector#1 fans out data pattern, which is used as input of the ADD gate directly, and selector#3 fans out jitter data pattern as the other input of the ADD gate for generating jitter on the falling edge of data pattern. The timing diagram is shown in Figure 10, where the D Flip-flop represents the output of selector#3 and the pattern represents the output of selector#1. Figure 3.3 Method of jitter generation on rising or falling edge. Figure 3.4 Time diagram of jitter generation on falling edge. Figure 3.5 Time diagram of jitter generation on rising edge.
# Improving Decodability of Polar Codes by Adding Noise
## 2 Related Works
### Conventional Perturbation Based Decoding Algorithms  
The concept of stochastic perturbation opens a new perspective where systems can benefit from adding artificial noise. In 1981, [PERSON] [37] found that the addition of suitable noise under certain conditions leads to an increase in a measure of the quality of signal transmission performance, which could be explained by the phenomenon of stochastic resonance [38; 39]. It can be shown that the performance of certain suboptimal detector may be improved by adding some white Gaussian noise [28], where the loss of detectability caused by lowering the signal-to-noise ratio (SNR) is offset by the increased sensitivity of the new noise.  
Based on the similar concept, a perturbed decoding algorithm (PA) was proposed for a concatenated CRC and convolutional code system [40]. The original signal is first decoded by the conventional Viterbi algorithm. If the CRC check fails, a perturbed received signal is created and then decoded by the inner Viterbi decoder. The perturbation by artificial noise injection is expected to increase the possibility that the transmitted codeword is obtained.  
In [30], a theoretical analysis was presented to gain further insight into stochastic resonance phenomenon, where the performance of a stochastic resonance enhanced detector was derived in terms of the probability of detection and the probability of false alarm. The theory behind PA was elaborated in [41]. The distribution of the numbers of perturbed decoding was derived for independent Gaussian perturbations. The dominant terms of the distribution indicate that the complexity of PA is highly dependent on the geometric structure of the error control code. If an ML decoder is employed as the inner decoder, the distribution for the numbers of perturbed decoding can be well approximated by a function of SNR, signal-to-perturbation-noise ratio (SPNR), and the two-centroid code spectrum, which provides theoretical support for related works.  
A BPL decoder that relied on artificial noise as a frame error rate (FER) or bit error rate (BER) performance booster in a subject of coding theory was presented in [30]. Artificially generated noises with different intensities are added to the received signal to avoid false convergence in a BP-based decoder. A dynamic perturbation decoding method for polar-CRC cascaded codes was proposed in [31]. Dynamic perturbation decoding can adjust the variance of the added perturbation noise according to the currently decoded codeword, so that the sequences obtained after each perturbation are as different as possible. Based on the analysis of FER and BER, a noise-assisted decoding algorithm for polar codes was proposed to improve the decoding performance [42]. The algorithm is realized by addingthe human-made noise, which is a post-compensation processing method for the existing algorithms. The disadvantage of the algorithm is that the noise power needs to be set artificially, and a large number of decoding attempts are performed without any direction, which greatly increases the decoding complexity.
# A Voltage Multiplier Circuit Based Quadratic Boost Converter for Energy Storage Application
## 3 Loss and Non-Ideal Gain Analysis  
### Bifurcation of Losses  
The bifurcation of losses at 45 W is shown in Figure 5a. Switches and diodes constitute more than 60% of the total losses. Loss analysis is done in PLECS software by developing a converter's thermal model and putting the switching and conduction loss data in the look-up table from the datasheet. The converter's efficiency is 92.6% at 45 W and 12 V. From Figure 5b, as Vin increases, the converter's maximum efficiency improves substantially. This is because at higher input voltages, a small duty ratio is required to get the same amount of voltage gain, and hence conduction losses are significantly reduced. Higher input voltage leads to lower current in the circuit for the same output voltage, improving efficiency by reducing the conduction losses.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multirow{2}{*}{**Voltage Stress (Volt)**} & **Average Current** & **Average Current for** & \\  & & **During Their** & **the Complete** & **RMS Current (Amp)** \\  & & **Conduction (Amp)** & **Cycle (Amp)** & \\ \hline \multirow{2}{*}{\(S_{1}\)} & \(\frac{V_{\alpha}}{(1-D)}=\frac{V_{0}(1-D)}{(3-D^{2})}\) & \(\frac{V_{0}(1+D-D^{2})}{R(1-D)^{2}}\) & \(\frac{V_{0}(1+D-D^{2})}{R(1-D)^{2}}\) & \(\frac{V_{0}(1+D-D^{2})}{R(1-D)^{4}\sqrt{D}}\) \\ \hline \multirow{2}{*}{\(S_{2}\)} & \(\frac{V_{\alpha}}{(1-D)^{2}}=\frac{V_{0}}{(3-D^{2})}\) & \(\frac{V_{0}(1+D)}{RD(1-D)}\) & \(\frac{V_{0}(1+D)}{RD(1-D)}\) & \(\frac{V_{0}(1+D)}{R(1-D)\sqrt{D}}\) \\ \hline \multirow{2}{*}{\(D_{1}\)} & \(\frac{V_{\alpha}}{(1-D)}=\frac{V_{0}(1-D)}{(3-D^{2})}\) & \(\frac{2V_{0}}{R(1-D)^{2}}\) & \(\frac{2V_{0}}{R(1-D)}\) & \(\frac{2V_{0}}{R(1-D)^{3/2}}\) \\ \hline \multirow{2}{*}{\(D_{2}\)} & \(\frac{V_{\alpha}(2-D-D^{2})}{(1-D)^{2}}=\frac{V_{0}(2-D-D^{2})}{(3-D^{2})}\) & \(\frac{V_{0}}{R(1-D)}\) & \(\frac{V_{0}}{R}\) & \(\frac{V_{0}}{R\sqrt{1-D}}\) \\ \hline \multirow{2}{*}{\(D_{3}\)} & \(\frac{V_{\alpha}(2-D-D^{2})}{(1-D)^{2}}=\frac{V_{0}(2-D-D^{2})}{(3-D^{2})}\) & \(\frac{V_{0}}{RD}\) & \(\frac{V_{0}}{R}\) & \(\frac{V_{0}}{R\sqrt{D}}\) \\ \hline \multirow{2}{*}{\(D_{4}\)} & \(\frac{V_{\alpha}(2-D-D^{2})}{(1-D)^{2}}=\frac{V_{0}(2-D-D^{2})}{(3-D^{2})}\) & \(\frac{V_{0}}{R(1-D)}\) & \(\frac{V_{0}}{R}\) & \(\frac{V_{0}}{R\sqrt{1-D}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Voltage stress and current across the switches and diodes.  
Figure 5: _Cont._
## 5 Simulation Results
### Simulation Results
#### 5.2.6 Control Packet Overhead  
In the effective relay selection, the COASYM-MAC reduces the control packet overhead of the network as shown in Figure 15, and also immensely decreases the control packet transmission of the overall network. In ASYM-MAC, when the node performs a transition from the sender-initiated mode to receiver-initiated, it sends a burst preamble to seek the attention of the receiver and it happens every time when the sender wants to send data. Whereas in COASYM-MAC, the nodes wait for the beacon of a sibling to perform the cooperative communication. If it does not get a beacon from its sibling, then it waits for a neighbor beacon whose distance or hop count is lower than this node. Then, it sends data via this cooperative node and makes this node a parent permanently. Thus, it does not need to use a control packet for the next time. As shown in Figure 15, COASYM-MAC decreases control packet overhead by 48.26%, 46.84%, and 42.23% for 27 nodes, (Figure 15c), for 54 nodes (Figure 15b), and for 90 nodes (Figure 15c) respectively as compared to the ASYM-MAC protocol.  
Figure 14: System Throughput: (**a**) for 27 nodes; (**b**) for 54 nodes; and (**c**) for 90 nodes.
## 2 Materials and Methods  
The experiments were conducted at the Soft X-ray (SXR) endstation [16] of the LCLS. The X-ray pulse, which had an approximately Gaussian temporal envelope with a Full Width Half Maximum (FWHM) of 60 fs [17], was focused on 508 nm thick germanium foils, mounted on a silicon substrate. The targets were oriented at an angle of 45\({}^{\circ}\) with respect to the incident laser beam. The nominal energy of the laser was \(\sim\)2 mJ, reduced to 0.7 mJ after transmission through the beamline optics [18]. The photon energy of the XFEL pulse was varied between 1300 and 1600 eV in 100 eV steps, with a nominal bandwidth of 0.4% of the central photon energy in all cases. The spatial profile of the XFEL spot was measured using imprint measurements on PbI\({}_{2}\) targets mounted on a polymethyl methacrylate (PMMA) substrate [19]. The effective area of the laser spot was measured to be 28.5 um\({}^{2}\), resulting in a peak intensity of the XFEL of \(\sim\)\(5\times 10^{16}\) W cm\({}^{-2}\). The measured spot profile is shown in Figure 1b, where the \(y\)-axis indicates the fluence normalised to its peak, and the \(x\)-axis indicates the area enclosed by the corresponding iso-fluence profile, as detailed in [PERSON] et al. [10].  
X-ray spectroscopy was used to diagnose the plasma. The spectra of the emission at 30\({}^{\circ}\) with respect to the target normal were measured with a flat-crystal Bragg spectrometer which used a Beryl (\(10\bar{1}0\)) crystal (\(2d=15.96\) A) for diffracting the X-rays before they were recorded by a Princeton Instruments CCD camera. The angular set-up of the experiment was designed so that the spectrometer was sensitive to X-rays between 1100 and 1600 eV, to observe the emission from L-shell transitions. A schematic drawing of the experimental set-up is shown in Figure 1a.  
Figure 1: (**a**) Schematic drawing of the experimental set-up. The X-ray free electron laser (XFEL) beam impinges upon the Ge foil at a 45\({}^{\circ}\) angle. The X-rays emitted around 30\({}^{\circ}\) with respect to (w.r.t.) the target normal are then diffracted by a beryl crystal before reaching the CCD camera. (**b**) Fluence scan of the laser spot. The laser fluence is normalised to its peak, and the \(x\)-axis shows the area enclosed by the isol fluence profiles, as detailed in [PERSON] et al. [10].
## 6 Cryptography
### Algorithms for Images
#### 6.2.3 Hardware-Implemented Cryptographic Algorithms
[128] developed a time-frequency domain encryption technique based on multi-chaotics for physical layer security and a chaotic map (CM) for peak-to-average-power ratio (PAPR) reduction. They experimentally demonstrated its feasibility in an Orthogonal Frequency Division Multiplexing (OFDM)-Passive Optical Network (PON) system. The proposed scheme is based on the Lozi and logistic maps and can generate chaotic sequences to scramble the subcarrier in both the time and frequency domains to enhance physical layer security. In the experiment, an 8.9 GB/s encrypted OFDM signal was securely transmitted over 100 km of standard single-mode fiber. The Lozi map was used to generate phase rotation sequences for frequency domain encryption and the logistic map to generate index sequences for time-domain interleaving. The combination of these two maps can enhance the key space and the security of an OFDM-PON system. In frequency domain encryption, the chaotic sequences are generated by a Lozi map (88) with \(a=1.7\), \(b=0.5\). To generate phase rotation sequences for the frequency domain, \(\{x_{i}\}\) and \(\{y_{i}\}\) are postprocessed to be \(\{Rx_{i}\}\) and \(\{ly_{i}\}\), as described by  
\[\left\{ \begin{array}{l}R_{x_{i}}=sign(mod(fix(x_{i}\times 10^{14})\cdot 256)-1 28),\\ I_{y_{i}}=sign(mod(fix(y_{i}\times 10^{14})\cdot 256)-128),\end{array} \right. \tag{90}\]  
where the function fix(\(x\)) returns the nearest integer rounded to zero. Then, the following phase rotation sequence \(\{p_{i}\}\) is obtained:  
\[p_{i}=R_{x_{i}}+jly_{i},\quad 1\leq i\leq N. \tag{91}\]  
Here, \(N\) is the number of OFDM subcarriers. After constellation mapping, the input sequence becomes a matrix of \(X=\left[X_{0},X_{1},\ldots X_{n-1}\right]^{T}\), where \(X_{k}\) represents the corresponding subcarrier data. In a transmitter, the input sequence \(X\) is expanded to \(K\) identical sequences. According to the different initial conditions of the Lozi map, different phase rotation sequences \(\{p_{n}\}\) are obtained. After calculating the PAPR values of the encrypted OFDM signal, the minimum PAPR signal is transmitted, then chaotic time domain interleaving is performed. Additionally, the logistic map is used to generate a permuted index sequence. The experimental results showed that the proposed method can effectively prevent illegal users from eavesdropping. Moreover, the proposed scheme has low complexity, low cost, and is easy to implement.
## 1 Introduction
This system can directly transfer signals to a PC to process a substantial amount of data, which significantly reduced the time required for the acquisition and reconstruction of echo signals.
## 3 Materials
### Glass Fibre Reinforced Polymer (GFRP) Connectors  
The composite action between the TRRPC facings of the element was enhanced by incorporating GFRP truss-like connectors. The connectors were fabricated using pultruded bars made of E-glass fibers impregnated with an epoxy resin. The bars, having a nominal diameter of ca. 6.1 mm, were reinforced by a bundle of E-glass fibers to form helical ribs on the bar's surface. In a half-cured state, the bars were bent into a zig-zag shape, followed by final curing. Two configurations were studied in this project, denoted as single (S) and double (D); see Figure 4a. A double connector is composed of two single connectors mirrored with respect to the longitudinal direction and fastened at intersecting points using plastic tie straps. The connector performance in an element was previously investigated via modelling and testing on a component level [22; 24]. As a result, the diagonal bars were observed to be primarily loaded by axial tensile and compressive forces, as illustrated in Figure 4b.  
The incorporation of connectors in thin facings is generally challenging, as it may prove difficult to enable load transfer without causing local pull-out failure at the connector. Accordingly, it was of key importance to further understand the properties on both material and component levels. Tensile, compression and pull-out tests were therefore performed; for details, refer to [22]. Table 2 provides the experimental results for the given GFRP connectors.  
The critical buckling load in compression was experimentally quantified for different buckling lengths, based on the length of connector diagonals (inclination of 45\({}^{\circ}\)) in elements with different facing distances, i.e., dependent of the FC insulation thickness. In this study, the TRRPC facing distance was set to 150 mm which corresponds to a buckling length of approximately 212 mm.  
The pull-out capacity of the connectors was determined from small-scale tests. Pull-out tests were conducted on connector segments cast in TRRPC panels (\(50\times 400\times 400\) mm) with an embedment length of 10 mm. To simulate the actual loading of the connector in a facing (see Figure 4b), loading was applied axially along the connector at a 45\({}^{\circ}\) angle from the surface of the facing. The test parameters presented here were established based on a parametric study conducted in this project to initially evaluate the effect of embedment depths and connector types, see [24].
## 4 Conclusions  
This work presented a low-profile slotted patch antenna, with a compact size, for use in biomedical implants. The proposed antenna resonates at 915 MHz in the ISM band. The antenna is made of Rogers RT/Duroid 5880 material ( \(\varepsilon_{\rm r}\) = 2.2, tan \(\delta\) = 0.0009) and has the overall dimensions 7 mm \(\times\) 7 mm \(\times\) 0.254 mm. At 915 MHz, the antenna's observed gain is \(-\)28 dB, while the measured bandwidth is 220 MHz inside the skin-mimicking-gel material. The SAR values inside skin tissue were 8.22 W/kg. The biocompatibility and safety considerations in medical application design were also investigated. The stated antenna is compact enough to be implanted in biomedical applications. Considering the overall performance, the stated design is an ideal candidate for the targeted medical application.  
Conceptualization, S.A., B.M., K.N.P., S.H., A.G., M.A. and M.D.; methodology, S.A., K.N.P., M.L., A.J.A.-G., A.G., M.A. and M.D.; software, S.A. and B.M.; validation, S.A., B.M., K.N.P., S.H., M.L., A.J.A.-G., A.G., M.A. and M.D.; formal analysis, S.A., B.M., S.H., M.L., M.A. and M.D.; investigation, S.A., S.H., A.J.A.-G., A.G., M.A. and M.D.; resources, S.A., B.M., K.N.P., S.H., M.L., A.J.A.-G. and A.G.; data curation, S.A., B.M., K.N.P., S.H., M.L., A.J.A.-G. and A.G.; writing--original draft preparation, S.A.; writing--review and editing, S.A., B.M., K.N.P., S.H., M.L., A.J.A.-G., A.G., M.A. and M.D.; visualization, S.A., B.M., A.G., M.A. and M.D.; supervision, A.G., M.A. and M.D.; project administration, S.A., A.G., M.A. and M.D.; funding acquisition, M.A. and M.D. All authors have read and agreed to the published version of the manuscript.  
This project received funding from Universidad Carlos III de Madrid and the European Union's Horizon 2020 research and innovation program, under the Marie Sklodowska-Curie Grant 801538. It also received partial funding from the Researchers Supporting Project number (RSP-2021/399), King Saud University, Riyadh, Saudi Arabia.  
All data have been included within the manuscript.  
The authors appreciate financial support from Universidad Carlos III de Madrid and the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska Curie Grant 801538. In addition, the partial support from the Researchers Supporting Project number (RSP-2021/399), King Saud University, Riyadh, Saudi Arabia, is acknowledged.  
The authors declare that they have no conflict of interest.