esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
### Comparison with Alternative Models  
Although LSTM networks have demonstrated robust performance in time series analyses for GPS spoofing detection, alternative architectures merit serious consideration, particularly in UAV systems, which often have tight constraints on energy, memory, and processing power. Classic machine learning methods, such as SVMs and RFs, have frequently been employed in anomaly and intrusion detection due to their comparatively lower computational overhead and straightforward implementation [41]. SVMs are known for their effectiveness in classifying high-dimensional data [42], although they may demand careful kernel selection and parameter tuning, which can be non-trivial when seeking to capture complex temporal dependencies. RFs offer generally strong generalization capabilities and interpretability [43], but their ability to handle long-term temporal patterns may be outperformed by deep architectures designed explicitly for sequence modeling.  
Beyond conventional approaches, modern deep learning architectures, such as Gated Recurrent Units (GRUs), Temporal Convolutional Networks (TCNs), and lightweight transformer variants, have gained prominence for tasks involving sequential data. GRUs possess a streamlined gating mechanism in comparison to LSTM; their reduced parameter count often translates into faster training and lower memory usage, rendering them attractive for resource-limited deployments like in UAVs [44]. TCNs utilize causal convolutions, thereby maintaining sequence order while affording substantial parallelization during the training stage [45]. Furthermore, simplified transformer architectures featuring fewer attention heads or aggressively pruned parameters can achieve competitive performance in sequence classification and anomaly detection while maintaining a smaller footprint [46].  
A direct empirical comparison with these alternatives could further support or refine our conclusions regarding LSTM-based defenses against GPS spoofing. Specifically, implementing baseline trials with SVMs, RFs, or even GRUs and TCNs would offer insights into their relative accuracies, inference times, and resource consumption. Although such detailed benchmarking lies outside the scope of the current study, it remains an important future direction for thoroughly assessing how different approaches balance detection efficacy with the practical demands of UAV systems. This comprehensive evaluation would help identify whether the proposed Genetic Algorithm-Optimized LSTM network is indeed the most appropriate solution or whether a more lightweight, yet sufficiently accurate, model would provide preferable trade-offs in certain UAV applications.
## 2 Materials and Methods
### 2.2. Cheminformatics Prediction
#### 2.2.2 Molecular Docking  
Preparation of the Compounds and Marottiline  
The preparation of the compounds **(5-7) _anti,_ **(5-7) _syn,_ **(5-7) _dec,_** and marotiline **9** for docking studies is carried out as follows; the compound structure was drawn using ChemDraw Ultra 12/ Chem14 Pro 12 program, the structure was checked and cleaned up through pressing structure icon. The ACS document 1996 setting was selected by pressing file icon. The structure was copied and pasted into Chem14 Pro 12 window in order to obtain the 14 structures of the compound and to pre-optimize the compound with its function MM force field then saved as MOL File (.mol) format and thus being prepared for docking.  
Preparation of the Protein Models  
The crystal structures of hNET homology models with access protein data bank (PDB) codes 4 xnx, 2 QJU, and 3 GWU were imported from the protein data bank (PDB; [[http://www.rcsb.org/pdb](http://www.rcsb.org/pdb)]([http://www.rcsb.org/pdb](http://www.rcsb.org/pdb)) (accessed on 15 April 2020)) into the Molegro Virtual Docker (MVD 2013.6.0.0 (win32)) program individually. The structure was prepared through the pressing icon, and the errors in the amino acids, which appeared as a warning, were corrected. The protein surface and binding cavity were identified, respectively. After resetting the view from the view icon, the ligand that already binds in the binding site of the protein was removed, and the other ligands were converted to cofactors. The protein was ready for docking.  
Docking of the Compounds into Protein Models  
The previously optimized **(5-7) _anti,_ **(5-7) _syn,_ **(5-7) _dec,_** and marotiline **9** were imported into the Molegro Virtual Docker (MVD 2013.6.0.0 (win32)) program containing pre-prepared protein. The docking wizard was selected, and then the program ran on a default setting. After finishing the run, the result file (in the left corner) was pressed, to open a new window containing poses of information and MolDock scores. The MolDock scores were recorded and the poses were visualized in Figures 1-3.  
Figure 1: Structure of the **4 xnx** model complex with: (**a**) its ligand, (**b**) **6 _anti_** and reference drug marotiline, (**c**) pose of reference drug marotiline **9**, (**d**) pose of compound **6 _anti._
# Extraction of Dietary Fibers from Plant-Based Industry Waste: A Comprehensive Review
## 4 Dietary Fibers Extraction
### Cellulose Extraction
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Plant Resource** & **Reference** \\ \hline \multicolumn{3}{c}{**Barley bran**} \\ \hline Method & Enzyme-assisted extraction. & \\ \hline \multirow{3}{*}{Conditions} & \(\alpha\)-amylase—pH 6.5, 96 \({}^{\circ}\)C temp; glucoamylase—pH 4.5, 50 \({}^{\circ}\)C temp; protease—pH 7.5, 60 \({}^{\circ}\)C temp; pullulanase—4.5, 50 \({}^{\circ}\)C; two types of xylanase—pH 4.75, 50 \({}^{\circ}\)C temp. & [79] \\ \hline \multirow{3}{*}{Observations} & Results showed that the three-step purification using \(\alpha\)-amylase, protease, and xylanase for 4 h & \\  & increased the \(\beta\)-glucan content, and removed starch, protein, and pentosans. Also, the highest \(\beta\)-glucan purity (around 89%), as well as the lowest molecular weight, (2 \(\times\) 10\({}^{4}\) g/mol) were & \\  & achieved with this method. & \\ \hline \multicolumn{3}{c}{**Oat bran**} \\ \hline Method & Alkaline extraction. & \\ \hline \multirow{3}{*}{Conditions} & Endogenous enzyme inactivation and fat removal: water/ethanol (50:50 \(w\)/\(w\))—solvent; and & \\  & 80 \({}^{\circ}\)C temp. & \\ \cline{1-1}  & Extraction: pH 8.5 (NaOH/water, 1:40 \(w\)/\(w\)). & [80] \\ \cline{1-1}  & Deproteinization: at pH 4.5 and centrifugation. & \\ \cline{1-1}  & Enzymatic treatment: pancreatin, thermostable \(\alpha\)-amylase, and amyloglucidase. & \\ \hline \multirow{3}{*}{Observations} & The obtained \(\beta\)-glucan was successfully purified, and it can be concluded that a complex process, & \\ \cline{1-1}  & which includes different enzymes, is required for the removal of residuals from \(\beta\)-glucan. & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._index was 47.79%. To produce cellulose nanocrystals, acid hydrolysis and ultrasonication treatment were used and resulted in a higher crystallinity index (78%) than for \(\alpha\)-cellulose (69%) [86]. Preparation of cellulose nanofibrils from sugarcane bagasse was performed by [PERSON] et al. [87]. High-speed dispersion combined with ultrasonication, and preceded by pretreatments (including continuous steam explosion, dilute alkali-catalyzed hydrothermal treatment, and bleaching with hydrogen peroxide), was conducted. Continuous steam explosion pretreatment caused the violation of the middle lamella and the primary wall and also resulted in the defibration of the cellulose fibers. In the alkali treatment, dilute NaOH (0.4 wt%) solution heated at 200 \({}^{\circ}\)C removed hemicellulose and lignin. Bleaching with H\({}_{2}\)O\({}_{2}\) was the final step for cellulose production. The obtained cellulose nanofibrils possessed gel-like behavior in suspension and good thermal stability, with an environmentally friendly approach to preparation. Table 3 presents the studies conducted on cellulose extraction from various material sources.
# Evaluation of a Reputation Management Technique for Autonomous Vehicles
## 3 System Design
### Assessment of Transaction Credibility and Aggregation  
On the local tier, neighboring vehicles are grouped as a cluster to be a blockchain subnetwork. Vehicles use their local sensors to make observations and predictions about their environment and broadcast this data to other vehicles within their cluster. Witness vehicles exist that can confirm or deny the vehicle's broadcasted data using their own  
Figure 1: The two-tier local and regional structure. First, three vehicles perceive the traffic light using their sensors, and the vehicle in the center communicates the information to the three surrounding vehicles. The two adjacent vehicles, who also perceived the traffic light, act as witnesses and may validate the data that the broadcaster vehicle sends. Then, a nearby RSU receives and appends the transaction to its own blockchain, which is available to other neighboring RSUs in its local area. The RSU generates a digest of the local blockchain to reflect the updated reputations of involved vehicles and directs the information up to the base station; from here, the digest is stored on the regional blockchain and circulated back down to other base stations and RSUs from non-local areas. Credit: Icons from draw.io and Wikimedia.  
local sensors and, subsequently, ascribe a reputation score--0 to denote a noncredible transaction and 1 to denote a credible one--to the given communication. All witness vehicles contribute a reputation score to the broadcasted communication, impacting its credibility by skewing its averaged reputation score, which is a floating-point number between 0 and 1. The sender and witness vehicles all sign the transaction with their private key, which is packaged along with the transaction contents into a block by the RSU.
# Pressure Vessel Design Problem Using Improved Gray Wolf Optimizer Based on Cauchy Distribution
## 3 The Proposed Algorithm
### Cauchy Initialization Strategy  
The GWO algorithm adopts a random method to initialize the population, and the evolution of the population is only guided by the better solution in the population, which makes the GWO algorithm easily fall into the local optimal value [34].  
In order to avoid the excessive concentration of initialized individuals and poor global search ability, combined with Cauchy random numbers [35], this paper proposes a Cauchy initialization strategy, as shown in Equation (10):  
\[\mathrm{X}=\mathrm{lb}+(\mathrm{ub}-\mathrm{lb})\cdot Cauchy(x_{0},\gamma) \tag{10}\]  
where \(\mathrm{lb}\) is the lower bound of the variable, \(\mathrm{ub}\) is the upper bound, and \( Cauchy(x_{0},\gamma)\) is the random number based on the Cauchy distribution.  
The distribution of the initial population in the search space plays a key and decisive role in the global search ability of the algorithm. Compared with the initial population distribution of the conventional random method, the introduction of the random numbers generated by the Cauchy distribution can improve the initial diversity of particles, and this diversity helps the algorithm avoid a premature concentration in the search space to obtain more exploration in the search process. The comparison between random initialization and Cauchy random initialization is shown in Figure 4.  
Figure 4 shows the scatter plots of the conventional random initialization and the Cauchy random value initialization proposed in this paper. It can be seen from the figure that the conventional random point selection may cause the initial individual distribution to be more concentrated, thus making the algorithm more likely to fall into the local optimum. Individuals are more likely to be created far from the center so they can cover most of the search space [36], which improves the diversity of the initial population and reduces the possibility of the algorithm falling into the local optimum.
# Comparative Technical-Economical Analysis of Transient Stability Improvements in a Power System
## 2 Description of Enhancement Solutions  
Many different techniques/solutions are known for improving transient stability margins in power systems. As described in the work of [3], these are divided into conventional and RES-based techniques. In this study, only conventional techniques for transient stability improvement are considered. Since no OPF study is performed, the re-dispatching technique is not considered.  
The improvement solutions considered in this study are preventive control of transient stability:  
* Reducing system reactances: This can be achieved by adding appropriate parallel transmission lines.
* And emergency transient stability control:
* Fast excitation system: it quickly controls the field-excitation circuit to improve the voltage at the generator terminals under transient conditions, thus reducing the oscillations in the electrical system;
* Variable shunt compensation: FACTS with the ability to regulate the voltage at selected points of the power system by varying reactive power can help improve transient stability by increasing the synchronization power flow between generators.  
Among FACTS, the following two solutions have been implemented:  
1. SVC (static var compensator): based on power electronic devices and passive elements. They allow fast voltage regulation at the interconnection nodes by injecting suitable reactive power. The effectiveness of this device depends on the installation position in the network;2 STATCOM (static synchronous compensator): consists of a voltage source converter, a shunt coupling transformer, and a DC source, usually a capacitor. This device is used to regulate the connection point voltage in a power system by injecting and absorbing reactive power into the system.
# Substrate-Integrated Waveguide Microwave Sensor for Water-in-Diesel Fuel Applications
## 7 Conclusions  
In this paper, a water-in-diesel sensor based on an SIW microwave applicator has been designed, constructed, and characterized. This characterization has confirmed the simulated performances in terms of bandwidth and impedance matching. Five water-contaminated fuel samples have been characterized in terms of dielectric constant frequency dispersion with a SPEAG DAK 3.5 probe. The same samples were detected with the designed SIW applicator allowing a water-in-diesel monitoring with a sensitivity of \(\Delta|S_{21}|/\Delta\rho=1.42\) mdB/ppm at a frequency of \(f=9.76\) GHz with a coefficient of determination \(R^{2}=0.94\). The minimum variation in water concentration with the proposed set-up is \(\Delta\rho=7\) ppm. The applicator is interesting because it is low-cost and low-profile, and it could be employed with a single frequency source and an MW power meter.  
Conceptualization, investigation, and methodology, A.M.L., V.V.F., V.P., O.L., M.C., A.D.N., F.A., F.P.; writing--original draft preparation, A.M.L., V.V.F.; writing--review and editing, A.M.L., V.V.F., V.P., F.P.; supervision, A.D.N., F.A., F.P.; All authors have read and agreed to the published version of the manuscript.  
The research received no external funding.  
Not applicable.  
This research has been partially developed within the projects POR FESRPSE 2014-2020 Innometwork \"Sinach-Integrated systems for mininvasive surgical navigation\"-n. BLNGWP7; PON R&I 2014-2020 \"New Satellites Generation components-NSG\"-Cod. Id. ARS01_01215 NSG; MIUR \"Agriculture Green & Digital-AGREED\", PNR 2015-2020, n. ARS01_00254; H2020-ICT-37-2020 \"Photonic Accurate and Portable Sensor Systems Exploiting Photo-Acoustic and Photo-Thermal Based Spectroscopy for Real-Time Outdoor Air Pollution Monitoring-PASSEPARTOUT\" n. 101016956.  
The authors declare no conflict of interest.
# An Early Investigation of the HHL Quantum Linear Solver for Scientific Applications
## 5 Scientific Applications and Evaluation
### Power Flow Problem in Power Grid
#### 5.1.3 Gate Counts and Depths of HHL Circuits.  
Because the circuits from later iterations are in a similar resource demand, we only look at the circuits in the first iteration. The depths and gate counts of HHL circuits are the same across N-R iterations when \(n_{c}\) is fixed. While HHL with \(n_{c}=5,6,7\) gives similar convergence speed and accuracy, the required resources to run the circuits exponentially increase as \(n_{c}\) increases based on Table 5. On the other hand, although gate fusion employed in NWQSim does not mitigate these exponential trends, it maintains a constant proportional performance across various HHL circuits: a \(79\%\) reduction in gate counts on all tested circuits regardless of the value of \(n_{c}\).  
Figure 3: Matrix expansion and error plot for the experiments of the power flow problem. (**a**) Expanding the coefficient matrices and the RHS vector in Equation (4) to fulfill the requirements of the HHL algorithm. (**b**) Two types of solution errors in each N-R iteration with various values of \(n_{c}\): the errors of the solution state (labeled “\(\left|x\right>\)”) in log base \(10\), \(\parallel\left|x\right>-\left|x\right>_{HHL}\parallel_{2}\), and the errors of the solutions (labeled “\(\vec{x}\)”) in log base \(10\), \(\parallel\vec{x}-\vec{x}_{HHL}\parallel_{2}\). The symbols \(A\), \(\bar{b}\), \(\vec{x}\), and \(\left|x\right>\) refer to the corresponding part in Equation (4). (**c**) The convergence performance under different values of \(n_{c}\). The \(y\)-axis is the convergence criteria, the infinity norms of \([\Delta\vec{P}^{T}\ \Delta\vec{Q}^{T}]^{T}\), in log base \(10\). The value at iteration \(0\) represents the norm from the initial guess, and the gray-shaded area is where the convergence criteria are satisfied.
# Grouped Contrastive Learning of Self-Supervised Sentence Representation
## 4 Experiments
### Semantic Textual Similarity Task  
The goal of the semantic textual similarity task (STS) is to evaluate the similarity between two sentences by directly computing the cosine distance [36]. Then, the cosine distance correlates with a labeled similarity score (from 0 to 5) by Pearson or Spearman correlations to obtain a matching score. In this way, the matching score can reflect the semantic similarity between two sentences. We train our self-supervised GCLSR model with pre-trained word2 vec on \(10^{4}\) sampled sentences randomly drawn from English Wikipedia [21]. The stop epochs are 20, and the best checkpoint on validation datasets is used for testing. Finally, we use the SentEval toolkit [36] to measure our proposed method on 7 STS tasks, i.e., STS 2012-2016 [37; 38; 39; 40; 41], STS Benchmark [42], and SICK-Relatedness [43]. In these datasets, sentence pairs are from news articles, news conversations, forum discussions, headlines, and image and video descriptions. Following [16], (a) we employ Spearman correlation as the only metric to evaluate the quality of sentence representation in STS tasks. Ref. [16] argues that Spearman correlation better suits the needs of evaluation; (b) no additional networks are applied on top of sentence representation. Put differently, we directly calculate the Spearman correlation using cosine similarities; (c) given that STS data of every year include several sub-datasets, we concatenate all sub-datasets to calculate the Spearman correlation. The operation \"concatenate\", incorporating different subsets, is more proper compared with other methods in practical applications.  
The results of the evaluation are shown in Table 2. We observe that all variations we proposed work well and are better than word2 vec embeddings (on average). Specifically, we improve the average Spearman correlation from 44.16 to 66.75 compared with word2 vec embeddings (on average), the pre-trained language model BERT (from 56.57 to 66.75), RoBERT (from 56.57 to 66.75), and CLEAR (from 61.8 to 66.75). Furthermore, our proposed data augmentation PWVA outperforms the compared methods of EDA and back-translation on STStasks using the proposed contrastive learning framework GCLSR\({}_{base}\). The application of self-attention (GCLSR\({}_{base}\)+PWVA+self-att.) and grouping (GCLSR\({}_{base}\)+PWVA+self-att.+GP) can also improve the performance. The experimental results show that our proposed method GCLSR\({}_{base}\)+PWVA+self-att.+GP can also achieve a better result compared with GCLSR\({}_{base}\)+EDA+self-att.+GP and with GCLSR\({}_{base}\)+TransL.+self-att.+GP. In addition, we note a significant distinction wherein strong performance in the STS tasks does not inherently translate into improved results in the transfer tasks. Consequently, it is prudent to primarily consider the outcomes from the STS evaluations for the purpose of comparison.
# Energy Harvesting for TDS-OFDM in NOMA-Based Underwater Communication Systems
## 6 Experimental Results and Discussion  
In this section, we will deal with the simulations carried out on MATLAB to evaluate the performance of the proposed TDS-OFDM-NOMA compared with the conventional CP-OFDM-NOMA in terms of BER and the system throughput. Moreover, in this section, we will evaluate the performance of the proposed energy harvesting system for the TDS-OFDM scheme over the long UWA tap delay channel. The sink node generates \(10^{6}\) random information bits and transmits to far and near users over the time-varying UWA channel. The simulation parameters of this paper are shown in Table 1. The underwater path loss is obtained by using Equation (16). In this paper, we use a Gaussian assumption geometric description for the underwater acoustic channel model to evaluate the proposed TDS-OFDM-NOMA scheme [29]. This underwater acoustic model reflects the physical features of the acoustic propagation and the predictable random channel disparities. The far and near users are dropped randomly in the simulation area. Based on the distances between the users and the sink nodes, the underwater acoustic channels are recognized. The underwater acoustic channel parameters for near and far users are listed in Table 2, and the time development of the extent of the baseband impulse response for rapid underwater acoustic channels of the near and far user is shown in Figure 6a,b, respectively. In Figure 6, the distance between the transmitter and receiver is 50 m and 100 m for far and near users, respectively.