esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Non-Linear Diffusion Method
### Algorithm  
In this section, we propose a robust despeckling algorithm, Algorithm 1, for Synthetic Aperture Radar (SAR) images affected by speckle noise. The algorithm is a multi-step process designed to enhance image quality while preserving important details.  
```
Input: Speckle-noise SAR Image  Intermediary Steps Involved for Despeckling Step 1: Speckled SAR Image S as an input. Step 2: Obtained S\({}^{\prime\prime}\) after applying the log transformation on S. Step 3: Applying recursive Non-linear diffusion on S\({}^{\prime\prime}\). i. Initialize the coefficients f(\({}^{*}\)) and k. ii. Calculate the Perona-Malik diffusion using Equations (3) and (4). iii. for i = 1 to n, do \(\bullet\) Compute the general Non-linear diffusion using Equation (2). \(\bullet\) Store the value end for iv. The outcome image is A. Step 4: Applying DWT on A which decomposed into (A, H, V, D). Step 5: Further Apply the WGF on approximate part of (A) Two level decomposed images. Step 6: Apply IDWT on A, H, V, D. This results in IDWT. IDWT = IDWT (A, H, V, D). Step 7: Apply exponential transformation on IDWT, DI. Step 8: Applying method noise on speckled image S and filtered image DI. Step 9: FDI is the final despeckled SAR image.
```  
**Algorithm 1** **Proposed despeckling algorithm**
# Approaches to Parameter Estimation from Model Neurons and Biological Neurons
## 3 Biological Neurons: Estimating Parameters from Guessed Models
### Aggregate State Variables  
One effect of model error is to transform the minima of the cost function into deep valleys that correlate two or more parameters. The data misfit ellipsoid \(\delta c=ST\) (Figure 4a) subsequently becomes spiky. This can be seen in the spectrum of the principal axes calculated from the inverse covariance matrix of the parameters from a songbird neuron [8]. This spectrum, shown in Figure 5, has an exponential distribution with a few outliers at \(i=1-4\). Examination of the covariance matrix of the estimated parameters (inset)  
Figure 4: **(a)** Schematic of the cost function near the global minimum. The parameters (\(\overrightarrow{P}\)) estimated from different parameter guesses distribute on the ellipsoid surface \(\delta c=kT\). This ellipsoid effectively broadens the global minimum \(\overrightarrow{p}^{*}\) proportionally to the error variance. Actual parameters are approached by averaging \(\overrightarrow{P}\) over all initial guesses, since \(\overrightarrow{p}^{*}\approx\overrightarrow{P}\). **(b)** Activation thresholds (m) and inactivation thresholds (h) obtained by averaging over 84 values of random vector \(\overrightarrow{P}\). The average thresholds are shown for two interneurons, N1 and N2, of the songbird high vocal center. The ion channels are transient sodium (NaT), persistent sodium (NaP), potassium (K1), inactivating potassium (K3), muscarinic (K3), high threshold calcium (CaL), low threshold calcium (CaT), and hyperpolarization activated channel (HCN) [8].  
identifies the recovery time constants of the ion channels as the most loosely constrained of all the parameters. This is not surprising as they enter the argument of exponentials describing the kinetics of the gate variables. The covariance matrix also suggests that parameter correlations are greater within the subset of parameters of each ion channel.  
A strategy to extract biological information is to evaluate compound quantities that integrate correlations between more fundamental parameters. Ionic currents, or the amount of ionic charge transferred per action potential, may be reconstructed from parameter estimates of maximal ionic conductances, gate recovery times, and voltage thresholds. The reconstructed quantities often carry a much lower uncertainty than the estimates of model parameters. [PERSON] et al. (2018) have recently used this approach to predict the amount of charge transfer through individual ion channels modulated by inhibitor drugs. Data assimilation was able to predict the selectivity and potency of inhibitor drugs in good agreement with IC50 analysis, by estimating the amount of charge transferred across each ion channel before and after inhibition.
## 2 Materials and Methods
### Baseline Model Configuration and Validation  
The energy system of Tenerife (described in Section 1.2) was modelled in EnergyPLAN. The baseline model was configured according to 2022 data, the most recent and representative year with a complete available dataset [38]. The input data applied to the model are described as follows:  
* The aggregate demands for electricity (grid) and transport oil demand.
* The hourly distribution of electricity demand and the renewable energy production (WE and PV), extracted from data registered by the system operator [57].
* Grid technical restrictions, incorporated to the model as a minimum baseload. A value of 140 MW was applied based on previous history [57].
* Power-generation installed capacities and the average thermoelectric efficiency of the conventional powerplant [38].
* Heat/Cooling power demands were discarded as they are negligible compared to the rest of demands.  
Once the model of the energy system was configured, the next step comprised the validation phase. With this in mind, the years 2019-2022 were simulated, imposing the input parameters described above. The same normalised profiles for electrical demand and  
Figure 3: EnergyPLAN model structure. Definition of inputs/outputs. Adopted from [51].  
WE/PV generation (2022 data) were used across every test case, therefore only changing the aggregate demands and power capacities. The model was considered valid if the RE energy production and conventional generation/oil consumption were close to the data published.  
Table 4 includes the main results of the validation phase. The simulation outputs in terms of annual electricity demand (just for reference, as it was imposed), RE generation, and the conventional park performance are compared against published data. The same values for simulated WE generation are found in the years 2019-2021, as no new capacity was installed in those years and the same normalised generation profiles were used. The same reason applies to simulated PV generation across the four years (no changes in capacity).  
The low error values in predicting the annual renewable energy production, below 8.4%, evince that the annual energy source variability is not high, and, therefore, the same normalised profiles can be applied without introducing important errors to assess future scenarios. The acceptable estimation of RE generation ensure an accurate representation of the electrical energy generated and oil consumption of conventional generators, even in the event of a drop in demand for both 2020 and 2021 associated with the lockdown during the COVID-19 pandemic. The estimation errors are below 5% in terms of oil consumption with the sources related to the actual gen-sets warm-up/cool-down phases during the operation, as the model does not take this into account. Overall, the acceptable accuracy figures provided by the model confirm that it can be used with confidence to assess future scenarios of the island's energy system.
# An Overview of Biopolymers for Drug Delivery Applications
## 2 Types of Biopolymers Used in Drug Delivery Systems
### Proteins
#### 2.2.3 Collagen and Gelatin  
Collagen is a natural protein, richly present in animals. This biopolymer has been used in different DDS applications [103], mostly in oral drug delivery due to its biodegradability and biocompatibility. Also, collagen is non-antigenic, non-toxic, and presents synergism with bioactive compounds. Collagen has functional groups that can be quickly modified to obtain suitable properties for oral drug delivery. Moreover, it was demonstrated that collagen is the major component of some tumor microenvironments. This property is favorable in cancer therapy because the collagen compound is capable of infiltrating the tumor area to deliver anti-cancer agents [104].  
Gelatin is a natural biopolymer derived from collagen by acid/alkaline hydrolysis. Gelatin has suitable properties for the delivery of chemotherapeutics, and it is simple to cross-link with other compounds [105; 106]. The bioavailability of gelatin increases when it is modified to enhance drug encapsulation efficiency. Also, gelatin can be tailor modified to be appropriate for loading a desired drug. Thus, gelatin is excellent for the oral drug delivery of hydrophobic chemotherapeutics [107].  
The co-solvent method was used to create nanometric vesicles using a poly(styrene-b-ethylene oxide) block copolymer that included adapalene. The vesicles were combined with free adapalene and silver sulfadiazine and incorporated into collagen and gelatin matrices. The created material was demonstrated to function as a skin dressing that combined a longer and slower release of adapalene to promote skin healing with a gradual release of significant amounts of medicines within the initial hours of use (to stop the growth of the infection) [108]. In another study, ionic medication (ionized cromoglicate sodium and ipratropium bromide) release via inhalable dry powders including gelatin was investigated. It was demonstrated that the developed drug-loaded gelatin microspheres have excellent aerodynamic performances that are highly dispersible and biocompatible. Additionally, the materials' swelling profiles showed that, by preventing macrophage absorption, particle size can lengthen lung residence time. Therefore, because of its adjustable charge and swelling properties, gelatin may be a suitable and authorized excipient for pulmonary DDSs [109].
## 4 Description of Different Deployed VPN Solutions
### IPsec Xanth Road Warriors Scenario  
Implementing a VPN service for remote clients using IKEv1/XAUTH with PSK is the least secure way to run IKE/IPsec. The reason is that everyone in the \"group\" has to know the PSK (also called secret). Even further authentication is required, such as a username and password, someone that knows the PSK can launch a man-in-the-middle attack pretending to be the VPN server. If the client connects to the rogue server, it will tell the attacker their username and password. On Android, this mode is called PSK XAUTH. On iOS/OS X this mode is confusingly called type \"IPSec\". Some other vendors call it \"Group PSK\". Supported clients are: all Apple iphones, ipads, Mac OS X, Android, Linux with NetworkManager or commandline and Windows Shrew client, or Cisco routers/firewalls. It is based on draft [19]. XAUTH also requires a username and password. The password can also contain a OTP such as Google Authenticator. Figure 7 shows the comparison of the average throughput and standard deviation in IKEv1 XAUTH Road Warriors scenario, wired (on the left) and wireless (on the right). As seen from the figure, OpenWRT as an Ikev1-XAUTH client turns out to be the best performing, closely followed by Windows 10, where, however, it is necessary to use a third-party application to allow the connection. As for the Wireless clients, the iPhones are the only compatible smartphones.
## 3 Results
### Genomic Prediction Using BLUP, GBLUP, and ssGBLUP Models  
Figure 2 presents the results of predicting seven traits using the pedigree BLUP, GBLUP, and ssGBLUP models. These results were obtained through 10-fold cross-validation, demonstrating the prediction accuracy and unbiasedness. The prediction accuracy based on the BLUP model was relatively low, ranging from 0.10 to 0.23. In contrast, the prediction accuracy based on the GBLUP model was moderate, ranging from 0.20 to 0.43. Compared to the BLUP model, the GBLUP model showed a significant improvement in prediction accuracy, with an average increase of 0.15 across the seven traits. Specifically, the average increases for growth traits, reproductive traits, and egg quality traits were 0.11, 0.14, and 0.21, respectively. Using SNP information, the prediction accuracy for the EWT improved the most, reaching an increase of 107.72%. The average prediction accuracy for the seven traits increased by 80.52%. The prediction unbiasedness of different models was close to 1. Compared to the BLUP model, the GBLUP model improved the overall prediction unbiasedness by 1.42% across the seven traits. Specifically, the prediction unbiasedness for growth traits, reproductive traits, and egg quality traits increased by an average of 0.09%, 2.26%, and 2.57%, respectively. The prediction unbiasedness for BSL decreased the most, by 1.97%, while EN25-44 WK showed the greatest improvement, with an increase  
Figure 1: Genetic improvements of related traits over generations based on the GBLUP model: (**a**–**c**) overall improvements; (**d**–**f**) improvements in male ducks; (**g**–**i**) improvements in female ducks. (**a**,**d**,**g**) Body weight at 6 weeks of age; (**b**,**e**,**h**) age at first egg; (**c**,**f**,**i**) egg number from 25 to 44 weeks of age. The \(X\)-axis represents generations, the \(Y\)-axis represents genomic estimated breeding values, and the vertical error bars represent the standard errors of the corresponding traits.  
of 5.34%. When combining the pedigree and SNP information, the prediction accuracy and unbiasedness based on the ssGBLUP model were relatively consistent with the results obtained from the GBLUP model.
## 3 Results and Discussion
### Training Setup  
The training setup details are provided in Table 2. The initial data we use is the 16-QAM signal generated from uniformly distributed random numbers following Gray mapping, and the length of both the input sequences and the label sequences is \(S=8192\). Before entering the model, the signal is upsampled and filtered by a shaping filter with a roll-off factor of 0.01. Due to the small amounts of trainable parameters, we can train the model even when the amount of training data is limited. We use 512 different sequences to generate labels and participate in training, with 87.5% of the sequences as the training set and 12.5% as the test set to train and save the best model. And we tested the trained model using a sequence with a length of 32,768. We believe that 1500 epochs are sufficient to generate the optimal model, and in actual testing, the model is often accurate enough at 500 epochs. It is worth noting that when the launch power is low, we use a smaller initial learning rate, \(\alpha_{0}\). As the launch power increases, \(\alpha_{0}\) should also be appropriately increased.
## 3 Robust Extended Kalman Filter
### RAIM Algorithm
\right]^{T} \tag{48}\]  
The norm squared of \(\delta X_{EN}\) (errors in easting and northing) assuming this specific choice of \(e\) is computed as:  
\[\left\|\delta X_{EN}\right\|^{2}=e^{T}\tilde{M}_{0}^{T}\tilde{M}_{0}e=\left(m _{1i}^{2}+m_{2i}^{2}\right)\beta^{2} \tag{49}\]  
From Equation (43), \(\hat{e}=Se\) is recalled. Then \(S^{T}S=S\) and the diagonal entry \((i,i)\) of \(S\) is denoted \(s_{ii}\) and we have:  
\[\left\|\hat{e}\right\|^{2}=\hat{e}^{T}\hat{e}=e^{T}S^{T}Se=\left|s_{ii}\right| \beta^{2} \tag{50}\]  
Combining Equations (49) and (50) gives:  
\[\left\|\delta X_{EN}\right\|^{2}=\frac{m_{1i}^{2}+m_{2i}^{2}}{\left|s_{ii} \right|}\|\hat{e}\|^{2} \tag{51}\]  
Or  
\[\left\|\delta X_{EN}\right\|=\sqrt{\frac{m_{1i}^{2}+m_{2i}^{2}}{\left|s_{ii} \right|}}\|\hat{e}\|=\alpha_{i}\|\hat{e}\| \tag{52}\]  
The obtained equation is a linear function with a straight line through the origin and a slope \(\alpha_{i}\). The slope \(\alpha_{i}\) of the failure mode axis related to observation \(i\) is computed for all \(i=1,\ \ldots,\ n\) as:\[\alpha_{i}=\sqrt{\frac{m_{1i}^{2}+m_{2i}^{2}}{|s_{ii}|}} \tag{53}\]  
The mode axis with the largest \(\alpha_{i}\) is denoted \(\alpha_{max}\) and the HPL is defined as:  
\[HPL=\alpha_{\max}\sigma_{0} \tag{54}\]  
where \(\sigma_{0}\) is calculated following Equation (45).
## 2 Provision of Energy and Macronutrients in Pediatric Patients on PN
### Glucose  
D-glucose (dextrose) is the main source of calories in PN, but also accounts for a major part of the osmolarity in PN solutions. All body cells and organs utilize glucose, and for the brain, renal medulla, and erythrocytes, glucose is an obligatory energy substrate [17].  
Factors guiding glucose intake with PN include age, disease status, nutritional status, and the concomitant provision of other macronutrients (lipids and amino acids). The rate of glucose delivery should not exceed the maximum rate of glucose oxidation. Studies have shown a maximal oxidation rate of about 12 mg/kg body weight (BW) per min (17.2 g/kg BW per day) in term infants after surgery or infants on long-term PN [17; 18; 19].  
The higher the glucose delivery rate, the higher the rate of hyperinsulin, favoring a costly lipid synthesis from glucose (lipogenesis) with subsequent lipid deposition in adipose tissue and liver. Moreover, glucose overfeeding induces hyperglycemia and increases CO2 production and minute ventilation [17]. In critically ill children with insulin resistance and beta-cell dysfunction, risk of hyperglycemia is a particular concern [17; 20] and represents an independent risk factor for morbidity and mortality [21; 22].
## 3 Results
### Results of Analytical Methods
#### 3.3.3 Results of Measurement of Water-Soluble Solid Content, pH Value, and Colour
In summary, the colour-stabilising effect of the extracts utilised in the treatments was more pronounced in the Bonita (white-fleshed) variety. [PERSON] et al. [45] investigated the alterations in the colour coordinates of freshly cut potato slices when applying anti-browning agents. Their findings indicated that increasing the concentration of ascorbic acid and citric acid resulted in higher L* and a* values, and very high concentrations of these acids led to the whitening of the samples. In the present study, a similar effect was observed for the Covington (dark-orange-fleshed) variety, where ascorbic acid and citric acid increased the colour coordinate values, particularly the a* values. In contrast, for the Bonita variety, which is characterised by white flesh, a modest rise in L* values was recorded, accompanied by a pronounced increase in a* values. This was reflected in the changes observed in the \(\Delta\)E* values and the browning index (BI). By inhibiting the enzymatic browning reaction on the skin, the reaction on the peripheral and central parts of sweet potato slices was decreased by acidic electrolyzed water. The browning index (BI) of fresh-cut sweet potato slices ranged from 1.95 to 2.81, showing similar results with the effects of apple pomace extract (AP) and ascorbic acid (AA) used in the present study [40]. A study investigated the effect of quercetin treatment on the colour quality and phenolic content of fresh-cut potatoes. The results showed that quercetin played an important role in preventing enzymatic browning, extending shelf life, and maintaining the quality of fresh-cut potatoes, but ascorbic acid (AA) was more effective based on the browning index (BI) values [46]. Four browning-susceptible cultivars and three browning-resistant cultivars were investigated to understand the browning process of potato tubers. The browning index (BI) values ranged from \(-\)2.5 to 1.8 in the case of the browning-resistant varieties, while the values were higher (2-12) in the case of the browning-susceptible potatoes. The total polyphenol content was lower (76-90 mgGE/100 g) for the browning-resistant varieties, while it was higher (68-121 mgGE/100 g) for the susceptible varieties [47].
## 3 Numerical Analysis
### Effect of Air Gap  
Among all the antenna dimensions, except for the air gap, \(g\), and cavity sidewall height, \(H_{sw}\), the rest are directly related to the physical dimensions of the two radiators. Although the air gap does not affect the physical dimensions of any antenna components, it determines the spacing between the ground plane and the open prism/feedlines. We performed a parametric study on the air gap to understand its effects on the antenna performance. In our studies, the air gap varied from 10 mm to 16 mm with a 1 mm increment. All other antenna dimensions remained constant, with \(L_{g}\) = 165 mm, \(H_{sw}\) = 80 mm, \(s\) = 3 mm, \(l\) = 53 mm, \(w\) = 30 mm, \(l^{\prime}\) = 1 mm, and \(w^{\prime}\) = 3.6 mm.  
The differential reflection coefficient results for different air gaps are given in Figure 5a. It shows that the lower frequency shifts down and the upper frequency shifts up as the air gap increases from 10 mm to 16 mm, which leads to an 8.8% increase in the impedance bandwidth (from 51.7% to 60.5%). However, a further increase in the air gap may raise certain sections of the reflection coefficient (i.e., around 1.2 GHz and 1.8 GHz) above \(-\)10 dB and lead to a smaller impedance bandwidth. By observing the real part of their input impedances, we can see that the number of peaks in the real part of these curves within the operating frequency bands remains the same (i.e., 2) for all cases. Figure 5b shows the peak diagonal plane cross-polarization of the antennas with different air gaps. Here, we only show the cases \(g\) = 10 mm and \(g\) = 16 mm because the rest follow the same trend. As can be seen, the effect of the air gap on the cross-polarization is negligible at frequencies \(\leq\) 1.45 GHz. Above 1.45 GHz, an increase in the air gap leads to a slightly higher cross-polarization ratio. Thus, a smaller cross-polarization bandwidth. In this paper, the cross-polarization bandwidth is a measure of the frequency range that meets the design requirement of X-pol \(\leq-\)25 dB.  
Figure 4: The octagonal cavity-backed antennas with and without the open prism: (**a**) Differential reflection coefficient; (**b**) Differential input impedance.  
As can be seen, at this stage, the impedance bandwidth (i.e., for S\({}_{\text{dd11}}\leq-10\) dB) is much wider than the cross-polarization bandwidth (i.e., for X-pol \(\leq-25\) dB). For instance, the -impedance bandwidth for the case \(g=11\) mm is 54.3%, while its cross-polarization bandwidth is 34.9%.
## 2 Materials and Methods
### Methods
#### 2.2.5 Qualitative-Quantitative Comparisons  
All of the above-mentioned variables measured via sensors installed in situ and via remote sensing were finally qualitatively and, where feasible, quantitatively compared. Firstly, in situ and remotely sensed pairs of water levels and temperatures and displacements are compared. Then, the temporal behaviour of the forcing factors and resulting displacements are analysed for both in situ and remotely sensed triplets (i.e., two forcing factors and one displacement). In particular, their behaviour is evaluated to quantify the times at which peaks and/or plateaus occur, besides analysing their range of variability.
# DSP-Based 40 GB/s Lane Rate Next-Generation Access Networks +
## 1 Introduction
This work extends our previous work in [11; 12] via contributing a fair performance comparison between all three schemes by considering symbol-spaced equalization, including both linear and nonlinear equalizers. A low complex maximum likelihood estimation equalization has also been considered for the first time. This work distinguishes from previous work [4; 5; 6] where comparison between different modulation formats is made by using DSP with similar complexities, and with the same feedforward equalizer (FFE)/decision feedback equalizer (DFE) tap count and maximum likelihood sequence estimation (MLSE) state count, although Duobinary schemes can use a smaller number of MLSE states. An analysis and comparison of the complexity of all equalization strategies are also undertaken in the present contribution. Results show that the choice of proper modulation format and the DSP configuration depends on the transmission distances of interest.