esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Gateway-Based Energy Holes Avoidance Routing Protocol for WSN
## 2 Related Work
### Node Distribution Strategies  
Authors in [5] have given detailed guidelines to avoid energy holes in a uniform node distribution. They have analyzed that the energy holes problem cannot be completely removed from the network. Therefore, efficient mechanisms must be used to minimize the holes problem. Authors in [6] have used a non-uniform distribution of nodes near the sink to avoid energy holes near the sink. They assumed that if the node density is higher in the sink area than the outer part of the network, more nodes will be available to relay data of other nodes. When some nodes die, other nodes are still available for communication. This will increase the network lifetime as the number of data forwarding nodes increases near the sink area. Authors in [19] have used a transmission range-based energy consumption to establish uniform energy consumption among nodes. Node density in different regions of the area varies according to distance of nodes from the sink. Authors have computed the traffic load on sensor nodes and found that load is at a maximum in the innermost corona. As the distance increases from the outermost corona area to the inner area which is nearby the sink, node density also increases. In other words, the authors have used a non-uniform node distribution strategy. Our model follows a uniform node distribution strategy.
# High-Precision Pseudo-Noise Ranging Based on BOC Signal: Zero-Bias Mitigation Methods
## Appendix A  
When the baseband signal \(s(t)\) in each symbol period is inverted, the number of the composite waveform \(p(n)\) is \(N_{p}\) in the coherent integration time \(T_{coh}\). Since the initial phase of the received signal is the same as the local signal, the combined waveform \(p(n)\) is oddly symmetric with respect to the center point. When the composite waveform is discretized, the accumulated value \(\sum p(n)\) may be \(+1\), \(-1\), and \(0\). If the number of sampling points of the composite waveform in the coherent integration time \(T_{coh}\) is \(\overline{c}_{s}\big{(}=2d_{0}\tilde{F}_{s}/F_{p}\big{)}\), the number of composite waveforms in which the non-zero accumulated value is correlated with the distance between \(\overline{c}_{s}\) and the adjacent odd number. According to formula (15), the statistical value of the composite waveform whose accumulated value is non-zero is \(\tilde{N}_{p}\big{(}=F_{p}T_{coh}\cdot\big{|}\overline{c}_{s}-2\cdot round( \overline{c}_{s}/2)\big{|}\big{)}\). The number of waveforms whose accumulated value is \(+1\) or \(-1\) is \(\tilde{N}_{p}/2\), which is assumed to be an integer.  
For a pseudo-random spreading code sequence, the flipping of the chips is random. The composite waveform \(p(n)\) is generated at the position where the chip is flipped. The flip position of the chip exerts an influence on the integrated value of the composite signal during the coherent time. If the chip of the spreading sequence is flipped at the accumulated value of \(+1\), and the position of \(-1\) is not inverted, corresponding to the maximum zero-bias of the digital phase discriminator.  
However, the chip flip of the pseudo-random spreading code sequence occurs randomly, and the flipping probability is 1/2 and independent of each other. It is assumed that the chip flip is event \(\mathsf{X}(i),i\in\left[1,2,\cdots,\tilde{N}_{p}/2\right]\) at the position where the \(i\)-th composite waveform accumulated value is \(+1\), and the chip flip is turned to event \(\mathsf{Y}(j),j\in\left[1,2,\cdots,\tilde{N}_{p}/2\right]\) at the position where the \(j\)-th composite waveform accumulated value is \(-1\). The random variable \(\tilde{Z}\) is defined as follows:  
\[\mathbf{Z} =\mathsf{X}(1)+\mathsf{X}(2)+\cdots+\mathsf{X}\Big{(}\tilde{N}_{ p}/2\Big{)}-\mathsf{Y}(1)-\mathsf{Y}(2)-\cdots-\mathsf{Y}\Big{(}\tilde{N}_{p}/2 \Big{)}\]  
wherein, \(\mathsf{E}(\mathsf{X}(i))=1/2\), \(\mathsf{E}\Big{(}\mathsf{X}^{2}(i)\Big{)}=1/2\), \(\mathsf{E}(\mathsf{Y}(j))=1/2\), \(\mathsf{E}\Big{(}\mathsf{Y}^{2}(j)\Big{)}=1/2\), \(\mathsf{E}(\mathsf{X}(i)\mathsf{Y}(j))=0\).  
The mean value of the random variable \(\mathbf{Z}\) is:  
\[E(\mathbf{Z}) =0\]  
The variance of the random variable \(\mathbf{Z}\) is:  
\[\mathrm{D}(\mathbf{Z})=\tilde{N}_{p}/4\]
# On the Impact of Mobility on Battery-Less RF Energy Harvesting System Performance
## 4 Simulations  
The goal of the simulation is to evaluate the performance of the proposed approach for a typical sensing application, where a mobile node roams within a large area from one charging station to another and opportunistically senses data. To mimic practical scenarios, where RF charging stations are placed along motion paths or its sojourn locations, the charging network is assumed to be sparse, i.e., the charging range is much smaller than the distance between charging stations, and at any moment of time, the node can harvest energy from one RF station only. Whenever a node reaches a charging station, it rests for a fixed amount of time and then moves towards a randomly selected charging station with a fixed velocity. A fraction of charging stations is selected as home stations where a node sojourns for a longer time. The sensor duty cycle is set to be equal to the amount of harvested energy, i.e., the sensor operates in energy-neutral mode [16].  
The proposed method has been evaluated using a custom simulator written in Python. The sensor node is implemented as an object with the following core methods: charge(), leak(), runTask(), setCapLarge(), setCapSmall(), getVoltage(), monitorColoc(), switchCap(). At every time step, the node moves towards a random waypoint, then charges, leaks, detects and tracks collocation and performsa task with available energy. The simulation parameters such as charging power, leakage and capacitor sizes have been initialised based on prototype measurements described in the following section.  
The performance is evaluated through measuring the total amount of usable harvested energy, sensing coverage measured by successful sensor activations and the number of served locations. The performance is compared with a baseline approach, where a fixed capacitor of either a small or large value is used. The simulation parameters are given in Table 1.
# Short-Term Power Load Prediction of VMD-LSTM Based on ISSA Optimization
## 5 Experiment and Analysis
### Parameter Settings  
In this study, the Variational Mode Decomposition (VMD) is configured with a penalty parameter of \(\alpha=1300\) to balance the bandwidth constraint strength and decomposition fidelity and a noise tolerance parameter of \(\tau=0\) to enforce noise-free decomposition. The number of decomposition modes \(K\) is adaptively determined via permutation entropy analysis, which quantifies the time-series complexity for an optimal decomposition. Other parameters follow the original VMD formulation: DC offset \(DC=0\), initial mode functions \(init=1\), and iteration convergence tolerance \(tol=10^{-7}\).  
To validate the proposed model's performance, comparative experiments were conducted using MLP, LSTM, and GRU models. The MLP adopted a basic configuration. The LSTM and GRU were configured with 64 units, respectively. All models shared common hyperparameters: a 0.2 dropout rate, MSE loss function, and Adam optimizer.  
To ensure a fair comparison and validate the superiority of the proposed Improved Sparrow Search Algorithm (ISSA), a unified parameter configuration is adopted for optimization algorithms (ISSA, SSA, SOA), including the following: the population size \(N=10\), iteration number \(T=30\), alert value \(f_{a}=0.6\) (triggering danger avoidance behavior), scout proportion \(\lambda=0.7\) (leading food source exploration), and danger-aware proportion \(p_{d}=0.2\) (modeling threat recognition). Optimization algorithms are primarily employed to optimize the hyperparameters of the LSTM model, specifically targeting three critical variables, the learning rate \(l_{r}\in[0.001,0.05]\), the number of hidden layer neurons \(L\in[10,500]\), and the training batch size \(N\in[100,1200]\), to identify their optimal configurations.
## 3 Model and Methodology
### Path Turn Constraint  
When UAVs are operating in low-altitude airspace over inhabited urban areas, all the turning angles should be subject to limitations of the UAVs' performances, which means that the planned path should be suitable enough to follow. For example, once a turning angle is out of the scope of UAVs' limitation, they cannot follow the path, which proves that it is not an optimal solution for the path optimization problem. According to this requirement, the path should be evaluated by using path turn constraint. Therefore, it needs to be quantized for a more accurate optimization. Figure 5 shows the quantization of the path turn constraint for a UAV path. As previously analyzed, the flying path is composed of several waypoints, and each of them can be denoted as \(WP_{i}\). The coordinate of a waypoint \(WP_{i}\) on a two-dimensional plane can be denoted as \((WP_{i}\_\_x,WP_{i}\_\_y)\).  
In the proposed model, the turning angle is defined as the deflection between two sequential waypoints, such as \(WP_{i}\) and \(WP_{i+1}\) in Figure 5. In order to obtain a preferred UAV flight path that is as close to the straight line connecting the starting point with the destination point as possible, a deflection angle between the line connecting two sequential waypoints and the horizontal line passing the previous waypoint is used as the turning angle. In Figure 5, \(a_{i}\) is the defined turning angle between two neighboring waypoints, \(WP_{i}\) and \(WP_{i+1}\), which is assumed to be positive all the time. Its value can be calculated by using the 2D coordinates of two sequential waypoints, as expressed in Equation (3). In this equation, the turning angle \(a_{i}\) is denoted by the arctangent and would be kept positive from the beginning to the end in the proposed model. \((WP_{i}\_\_x,WP_{i}\_\_y)\) and \((WP_{i+1}\_\_x,WP_{i+1}\_\_y)\) are the two coordinates of waypoints \(WP_{i}\) and \(WP_{i+1}\), respectively. The accurate turning angle at each waypoint on a path can be calculated by using Equation (3).  
\[\alpha_{i}=\arctan\left|\frac{WP_{i+1}\_y-WP_{i}\_y}{WP_{i+1}\_x-WP_{i}\_x}\right| \tag{3}\]  
Once the turning angle at every waypoint is obtained by using Equation (3), the total path turn constraint can then be quantized with Equation (4), which is denoted as \(J_{T}\). In this equation, \(N\) is the number of waypoints on a UAV path, which is determined by the path-planning algorithm.  
\[J_{T}=\sum_{i=1}^{N}\alpha_{i} \tag{4}\]
# A Compact Instrument for Temperature-Programming-Assisted Capillary-Nanoliquid Chromatography
## 3 Results
### Comparison of the Developed System with a Commercial Miniaturized LC Instrument  
To compare the performance of the developed system with that of a commercial miniaturized LC instrument, the contribution of the extra-column volume (ECV) to band broadening was measured using the linear regression method. Specifically, the extra-column variance (\(\sigma^{2}\)ec) was determined by injecting a solution containing uracil, naphthalene, phenanthrene, anthracene, and pyrene at a 5 \(\mu\)L/min flow rate. The mobile phase, composed of ACN/H2O (70:30 \(v/v\)), was optimized to yield retention factors (k) between 1 and 5 for the analytes.  
Figure 7: Chromatograms obtained using both the commercial microcell (Shimadzu) and the capillary cell developed in this work. The analysis was performed using a lab-made C-18 250 \(\mu\)m i.d \(\times\)150 mm packed column and a flow rate of 3.0 \(\mu\)L/min, with an injection volume of 60 nL. The elution order of the analytes was as follows: (1) naphthalene, (2) phenanthrene, and (3) anthracene. The concentration of each analyte was 30 mg/L.  
The detection system was configured with the shortest response time (0.2 s) and the highest acquisition rate allowed (12.5 Hz) to minimize external influences on the measurements. Using the data obtained, a plot of column variance (\(\sigma^{2}\)col) versus the retention volumes (VR\({}^{2}\)) of the PAH peaks was constructed (Figure 8). The intercept of the linear regression line from these plots corresponds to \(\sigma^{2}\)ec. At the same time, the slope is inversely related to column efficiency (1/N).  
The calculated \(\sigma^{2}\)ec values were 0.0044 \(\upmu\)L\({}^{2}\) for the homemade system and 0.0362 \(\upmu\)L\({}^{2}\) for the commercial instrument. These values correspond to ECVs of 0.27 \(\upmu\)L and 0.76 \(\upmu\)L, respectively. Additionally, the intrinsic efficiencies of the columns were determined to be 17,943 and 11,087 theoretical plates, respectively.  
The developed system maintained an extra-column variance contribution within the commonly accepted threshold of 10% of the total peak variance, indicating competitive performance in preserving column efficiency compared to a commercial miniaturized LC instrument.
## 2 Materials and Methods
### Image Scan  
All PET imaging scans (FBB, FMM: 90 min after the injection of 8 mCi, FPN: 60 min after the injection of 10 mCi) were performed [36; 37; 38; 39] using a Biograph 40 mCT Flow PET/CT scanner (Siemens Healthcare, Knoxville, TN, USA). A helical CT scan was performed with a rotational time of 0.5 s at 120 kVp and 100 mA without an intravenous contrast agent. The PET scan was followed by a CT scan and acquired for 10 min in the 3D mode. All images were acquired from the vertex to the base of the skull. Image reconstruction was performed with a point spread function (PSF) and time-of-flight (TOF) algorithm with a 400 \(\times\) 400 matrix, voxel size of 1.02 \(\times\) 1.02 \(\times\) 1.50 mm\({}^{3}\), five iterations, and 21 subsets. MRI scans were acquired using a 3.0T GE Discovery MR750 scanner at a coronal T1 weighted sequence, voxel size 0.41 \(\times\) 0.41 \(\times\) 1.50 mm\({}^{3}\), FOV = 21 cm, TR = 9.5 ms, TE = 4.0 ms. MRI was converted into a surface image from a voxel image using FreeSurfer's reconstruction.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & & \multicolumn{3}{c}{**Number**} & \multicolumn{1}{c}{**Age (Mean \(\pm\) SD)**} \\  & & **Total** & **M:F \({}^{*}\)** & **70\(\geq\):70\(<\)** & ** \\ \hline FBB & Negative & 12 & 6:6 & 6:6 & 62.42 \(\pm\) 10.21 \\  & Positive & 24 & 12:12 & 12:12 & 67.25 \(\pm\) 6.63 \\ FMM & Negative & 40 & 19:21 & 24:16 & 71.78 \(\pm\) 8.37 \\  & Positive & 38 & 20:18 & 22:16 & 71.89 \(\pm\) 7.52 \\ FPN & Negative & 33 & 7:16 & 8:15 & 67.57 \(\pm\) 7.27 \\  & Positive & 42 & 14:28 & 30:12 & 73.67 \(\pm\) 6.67 \\ \hline \hline \end{tabular} \({}^{*}\) M:F, Male–Female; \({}^{**}\)70\(\geq\):70\(<\), over 70: under 70.  
\end{table}
Table 1: Distribution of the number, sex, and age of patients with FBB, FMM, and FPN PET images used in this study.
# High-Dimensional Data Analysis Using Parameter Free Algorithm Data Point Positioning Analysis
## 6 Results and Discussion
### Comparison with Four Popular Methods
#### 6.2.5 Performed on Other Methods  
In this study, we propose a novel clustering algorithm and compare it with four popular clustering methods: DBSCAN, affinity propagation, Mean Shift, and K-means. These methods are widely used by researchers to compare their algorithms. Additionally, we implemented four state-of-the-art algorithms: evolutionary clustering algorithm star (ECA*) [58; 59], fuzzy K-means [60], junction based on object similarity (JBOS) [61], and K-means++ [62]. However, these algorithms have some drawbacks, such as the need for manual parameter selection and instability in producing different cluster points each time they are run. The fuzzy K-means method may also result in a large number of missing data points for dataset 1, although it achieves the maximum silhouette coefficient value for dataset 2. The number of clusters and silhouette coefficients are shown in Table 3. JBOS, ECA*, and K-means++ methods provide comparatively lower silhouette coefficient values for both datasets. The main advantage of using our proposed algorithm is that it is completely parameter-selection-free, eliminating the need for prior knowledge about the datasets.
# Electrospinning of Chitosan for Antibacterial Applications--Current Trends
## 7 Application Fields of Electrospun Chitosan Nanofibers and Chitosan Derivatives
### Water Filtration
#### 7.1.2 Electrospun Fibers from Chitosan-Polymer Mixtures for Water Filtration
According to the literature, hydrophobic membrane surfaces have a greater fouling propensity; therefore, hydrophilic membranes are less prone to develop biofouling because they are believed to strongly adsorb a layer of water molecules, preventing bacteria and other foulants from colonizing on their surfaces [163]. Due to the antibacterial potential of chitosan nanofibers, they have been applied to textile-based microfilters to reduce the pore size and to filter microorganisms from air and water based on size exclusion. A blended solution of chitosan-polyethylene oxide (PEO) was electrospun on a non-woven polypropylene substrate and the nanofibrous filter resulted in _E. coli_ reduction after 6 h of contact [164].
## 4 SRF-IoT Design
### Identifying Malicious Nodes  
In case a node starts attacking the network using blackhole and rank attacks, SRF-IDS will immediately notice this behaviour in the packet forwarding metric. A packet is then sent to the monitored network so that nodes will assign a low trust for these neighbours. The algorithm used by SRF-IDS detectors to detect the various routing attacks is shown in Figure 3. The procedure starts by enabling a promiscuous mode in SRF-IDS detectors to start sniffing network traffic. Once a packet is captured, its packet type is checked and proper thresholds are used to determine if the network is under DIS flooding attack. Specifically, if the packet interval is above the predefined \(threshold_{DIS}\), then the SRF-IDS detector alerts the network administrator for a possible DIS flooding attack, the node is reported to the SRF-IDS root for blacklisting, and the SRF-IDS detector continues packet sniffing. If the packet interval is at normal levels, the packet is checked if it needs to be forwarded by the received node. This is done by checking the destination IP (\(dstIP\)) field to be different with the next hop IP (\(next\_hopIP\)). In the case where \(dstIP\) is equal to the received node IP, the packet is discarded and the procedure restarts. Otherwise, the \(dstIP\) is stored in a table for further checks. SRF-IDS detectors continue to sniff packets in order to decide if the neighbouring node actually forwards the packets. This is translated into the following condition: if the captured packet source IP (\(srcIP\)) is equal to \(next\_hopIP\) then it means the neighbour (next hop) node transmitted the packet. The next check is to validate the destination node. If the stored packet IP (\(stored\_pkt\_dstIP\)) is equal to the new destination IP (\(dstIP\)) then the packet is forwarded correctly, and the node's packet counter is increased by one point. In case \(srcIP\) equals \(next\_hopIP\) but the \(dstIP\) is not expected, it means the packet is not forwarded, and the SRF-IDS flags the device as a possible malicious node. The last step is to discard the packet and continue capturing network packets for other nodes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Values** & **Explanation** & **Actions** \\ \hline \(\geq\)0 and \(\leq\)25 & No Trust & Avoid node \\ \hline \(\geq\)26 and \(\leq\)50 & Low Trust & Select only if no other option exist \\ \hline \(\geq\)51 and \(\leq\)75 & Medium Trust & Select only if other nodes are below this rank \\ \hline \(\geq\)76 and \(\leq\)86 & High Trust & Best candidate parent \\ \hline \(\geq\)87 and \(\leq\)100 & Full Trust & Ideal parent, select without comparisons \\ \hline \hline \end{tabular}
\end{table}
Table 2: Trust scale.  
Figure 3: SRF-IDS detectors operation flow chart.
## 2 Principle  
### Power Fading with Differential Driven Mode  
We first start from the conventional IM transmitter operating at differential driven mode, as shown in Figure 1a. For the sake of simplicity, we use \(s(t)\) to represent the electrical signal normalized by \(V_{\pi}\), then the output optical field \(E_{1}(t)\) of MZM is  
\[E_{1}(t)=e^{js(t)+j\varphi}+e^{-js(t)-j\varphi}\approx 2\cos\varphi+j\Big{(}e^{ j\varphi}-e^{-j\varphi}\Big{)}s(t)=C-2\sin\varphi s(t) \tag{1}\]  
where \(\varphi\) represents the bias induced constant phase shift and \(C=2\cos\varphi\) is tge real-valued optical carrier. \(e^{x}\approx 1+x\) is utilized for first-order approximation throughout Section 2. After fiber transmission and square-law detection of PD, the output photocurrent \(I_{1}(t)\) can be calculated as  
\[\begin{array}{l}I_{1}(t)=\left|E_{1}(t)\otimes h(t)\right|^{2}=\left|C-2 \sin\varphi s(t)\otimes h(t)\right|^{2}\\ =C^{2}-2\sin\varphi CS(t)\otimes h(t)-2\sin\varphi CS^{*}(t)\otimes h^{*}(t)+ \ldots\\ \approx C^{2}-2\sin\varphi CS(t)\otimes[h(t)+h^{*}(t)]\end{array} \tag{2}\]  
where \(h(t)\) is the impulse response of fiber dispersion in the time domain and the conjugate symmetry of real-valued signal \(s(t)\) is employed. To focus on the impact of dispersion on linear terms, the second-order signal-signal beating interference (SSBI) is ignored in Equation (2). With the help of Fourier transform, the convolution in the time domain can be turned into multiplication in the frequency domain.  
\[I_{1}(f)=C^{2}\delta(f)-2\sin\varphi CS(f)\cdot[h(f)+h^{*}(f)]=C^{2}\delta(f)- 2\sin\varphi CS(f)\cdot H_{1}(f) \tag{3}\]  
were \(h(f)=\exp\left(j\pi\lambda^{2}DLf^{2}/c\right)\) is the frequency domain transfer function of dispersion [6], which is a pure phase distortion. Finally, the overall transfer function is obtained as  
\[H_{1}(f)=h(f)+h^{*}(f)=2\cos\left(\pi\lambda^{2}DLf^{2}/c\right) \tag{4}\]  
According to Equation (4), the phase to amplitude conversion occurs after the conjugated superposition and those frequencies satisfying \(\pi\lambda^{2}DLf^{2}/c=(1/2+n)\pi\) would suffer from power fading [8]. Moreover, it should be noted that the frequency notches are independent of bias in this case.