esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Rheological Measurements  
Figure 6 shows the apparent viscosity of the emulsion phase for different shear rates and CMF doses. As observed with other emulsifiers, such as monoglycerides [2], an increase in shear rate decreased the \(\mu_{a}\) of the emulsion, following the Herschel-Bulkley model. This behavior of the emulsions was contrary to water or sunflower oil, both Newtonian fluids with viscosities of 0.001 and 0.1 Pa-s, respectively. The highest \(\mu_{a}\) was achieved in 50/50 O/W ratio with 0.75 wt% CMF (Figure 6b) and in 60/40 O/W ratio with 1.00 wt% CMF dose (Figure 6c). These results were according to the stability over time; the emulsions with the highest \(\mu_{a}\) had the maximum EI value and a uniform size distribution of emulsion droplets.  
Figure 5: Microscope images of emulsion phases and photographs of the graduated cylinder after 48 h with 0.75 wt% CMF using different O/W ratios: (**a**) 40/60, (**b**) 50/50, (**c**) 60/40, (**d**) 70/30.  
The relationship between the oil content in the emulsion phase (OCEP) and the \(\mu_{a}\) was also studied (Figure 7). The OCEP was calculated according to Equation (5) in which all CMF are in the emulsion phase, and the water and oil phases are formed only by water or oil, respectively.  
\[\text{OCEP (\%)}=\text{[Oil ratio (\%)--Oil phase (\%)] / [EI (\%)]\text{-}100} \tag{5}\]  
where the oil ratio represents the initial oil content to prepare the emulsion, and the oil phase represents the amount of oil on the top of the graduated cylinder after 48 h.  
Figure 6: Effect of CMF dose on apparent viscosity for emulsions with different O/W ratio: (**a**) 40/60, (**b**) 50/50, (**c**) 60/40, and (**d**) 70/30.  
Figure 7: Effect of oil content in the emulsion phase (OCEP) on apparent viscosity for different CMF doses using 1 s\({}^{-1}\) as shear stress.  
Figure 7 shows that the \(\mu_{\mathrm{a}}\) was quite similar for the same oil amount in the emulsion from 0.5 wt% CMF dose onward, showing there was no dependence on the CMF dose. The OCEP for the samples with the highest \(\mu_{\mathrm{a}}\) values for each CMF dose from 0.5 wt% onward was between 62% and 68%. However, when 0.25 wt% of CMF were added, the \(\mu_{\mathrm{a}}\) was lower than in the other doses.  
Table 2 summarizes the properties of the emulsions with the highest \(\mu_{\mathrm{a}}\) for each CMF dose. It can be seen that, from 0.5 wt% CMF dose onward, when the \(\mu_{\mathrm{a}}\) was maximum, the OCEP was around 65%, while the initial O/W ratio increased. In these cases, the amount of emulsion (EI) increased when the O/W ratio increased. In addition, the CMF required to enclose the oil drops was higher. Therefore, the characteristics of the final emulsions in the three cases were quite similar regarding oil, water, and CMF content, but the EI was higher or lower. The ideal emulsion would be an emulsion with around 65/35 O/W ratio and a CMF dose slightly higher than 1.00 wt% to obtain a high \(\mu_{\mathrm{a}}\) and reach a 100% EI value.
# An Extremely Close Vibration Frequency Signal Recognition Using Deep Neural Networks
## 3 The Proposed System
The final model, which embodies the detailed patterns and features acquired during training, is carefully preserved. This model, resulting from an extensive learning process, is now prepared for application in various frequencies of 2D vibration signal image classification tasks. \begin{table}
\begin{tabular}{c c} \hline Layer & Parameters \\ Number of neurons in the input layer & 256’256’3 \\ \hline Number of hidden layers & 3 \\ Number of neurons in each layer & Various-128:51 \\ \hline Activation function & ReLU \\ \hline Optimizer algorithm & Adam \\ Learning rate & Dynamic \\ \hline Batch size & 32 \\ \hline Number of epochs & 100 \\ \hline \end{tabular}
\end{table} 
Table 1: Parameters of the proposed DNN structure. The proposed method effectively utilizes the potential of pre-trained networks within a transfer learning framework, emphasizing the advantages of using established architectures as foundational elements for additional learning. This approach proves the practicality and flexibility of deep learning techniques in managing image-based datasets. It highlights the importance of fine-tuning to tailor pre-trained models to specific classification requirements. The capability of the proposed model to identify and categorize various frequencies of 2D vibration signal images across fifty-one categories illustrates the adaptability of DNN in extracting and learning from intricate patterns in visual data, setting the stage for novel applications in areas including automated 2D vibration signal image categorization and advanced image-based vibration recognition system.
# Evaluation of BLE Star Network for Wireless Wearable Prosthesis/Orthosis Controller +
## 4 Results
### Latency
of 1.14 \(\pm\) 0.46 connection intervals.
# Virtual Grid-Based Routing for Query-Driven Wireless Sensor Networks
## 3 VGRQ: The Proposed Routing Mechanism
### Phases of VGRQ
#### 3.2.4 Query Transmission  
VGRQ is designed for a query-driven scenario, where the exchange of data is initiated only when the sink requires the data by sending a query in the sensor field. Thus, the mobile sink transmits queries within the sensor field. Similar to broadcasting the mobile sink's location, broadcasting the query is also not optimal in terms of energy. Thus, it is required to transmit the query in the sensor field in such a way that it reaches the intended AoI with less energy consumption and delay. The process of query propagation from the sink to the AoI is described below.  
_Step 1_: The sink sends _Query_ message to the closest CH mentioning AoI.  
_Step 2_: The CH, after obtaining the query, first identifies the location from where the sink requires data and then sends the query towards the intended CH along the chain.  
_Step 3_: The CH of the inquired area further sends a query to the sensor nodes belonging to the AoI. Two or more grid cells can be covered in an AoI. Figure 5 depicts query transmission in VGRQ.  
Figure 4: Updating mobile sink location in VGRQ. (**a**) Within same sector (**b**) In different sector.  
Figure 5: Query transmission in VGRQ.
# Clipping Noise Compensation with Neural Networks in OFDM Systems
## 3 Results
### Interpretation of NN Weight Matrices
We have tried to simultaneously use the time and frequency domain OFDM signals as the inputs to the NN, since the above interpretation suggests that they are both needed, yet, curiously enough, a better performance is not obtained from the training difficulties that are encountered due to the increased size of the NN.
## 3 Results
### FXS-FM vs. FXS-M  
The accuracy for the classification of FXS-M within the FXS-FM group is summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Classification Accuracy**} \\ \hline \hline \multirow{2}{*}{**Input Vector/ Classification Algorithm**} & \multicolumn{2}{c}{**S1**} & \multicolumn{2}{c}{**S2**} & \multicolumn{2}{c}{**S3**} \\ \cline{2-7}  & **Training \({}^{\text{a}}\)** & **Test \({}^{\text{b}}\)** & **Training \({}^{\text{a}}\)** & **Test \({}^{\text{b}}\)** & **Training \({}^{\text{a}}\)** & **Test \({}^{\text{b}}\)** \\ \hline Random Forest & 98.3\% & 98.3\% & 99.1\% & 98.3\% & 99.2\% & 96.7\% \\ CN2 Rule Induction & 96.2\% & 94.2\% & 97.4\% & 98.3\% & 95.8\% & 92.7\% \\ Neural Network & 95.7\% & 94.2\% & 99.1\% & 96.6\% & 97.9\% & 95.9\% \\ SVM & 97.9\% & 95.0\% & 100.0\% & 98.3\% & 97.9\% & 98.4\% \\ Decision Tree & 94.9\% & 97.5\% & 96.5\% & 98.3\% & 93.8\% & 90.2\% \\ k-NN & 95.3\% & 95.0\% & 100.0\% & 100.0\% & 96.3\% & 95.9\% \\ Naive Bayes & 88.9\% & 90.8\% & 99.1\% & 96.6\% & 77.5\% & 76.4\% \\ \hline \hline \end{tabular}  
* S1: sEMG parameters and co-contraction parameters. S2: kinematic parameters. 53: sEMG parameters, co-contraction parameters and kinematic parameters. \({}^{\text{a}}\) Classification accuracy on the training dataset. \({}^{\text{b}}\) Classification accuracy on the test dataset.  
\end{table}
Table 1: Accuracy of the seven supervised learning algorithms for the classification between FXS and CS.  
Figure 3: Kinematic parameters used as input for the classification algorithms (S2) in FXS and CS subjects. * Statistically significant (\(p\) < 0.05).  
Overall, all the algorithms were generally able to classify gait characteristics from different input vectors. The average predictive accuracy for new data was 90.7% for S1 and 74.9% for S2. The best accuracy for S1 was achieved using CN2 rule induction (97.0%) while for S2 it was obtained using k-NN and neural network (84.8% for both algorithms). The worst accuracy for both vector sets was obtained using naive Bayes.  
In terms of input parameters, the best classification was obtained using S1 (sEMG and co-contraction parameters). This subset of parameters was then further investigated in order to verify the presence of statistically significant differences between FXS-M and FXS-FM. Statistically significant differences were observed in the position of the envelope peak in the left TA, GL, Bf and both the right TA and RF; the value of the envelope peak in the left TA and the right TA, GL and BF; the muscle duration in GL; the muscle activity onset and offset in both the left RF and BF; and the number of co-contractions in the left ankle joint and the right knee joint (see Figure 4).
# Copper Nanowires for Transparent Electrodes: Properties, Challenges and Applications
## 2 Electrical and Optical Properties of Cu NWs
The good performance is likely justified as the length distribution also includes structures as long as several hundred \(\upmu\)m, which may be responsible for the low \(Rs\) at such high \(T\). A similar finding was reported by [PERSON] et al., who obtained NWs with 17 \(\upmu\)m length and 17 nm diameter for a performance of 35 \(\Omega\)/sq at \(T\) = 90% and haze down to 1% at \(T\) = 94% (Figure 2) [31]. The authors used a silane as reducing agent for CuCl\({}_{2}\) in oleylamine and oleic acid. It has been shown that haze is related to NW diameter [32] so this is a further reason to try reducing NW diameter, where haze is to be minimized, beside improving \(T\). Another method for improving \(T\) was found to be purification [33; 34]. Here, NWs were purified by three cycles of centrifugation after adding \(n\)-hexane to the aqueous solution. NWs settled on the bottom of the hexane phase separating from other particles. \(T\) up to 97% were reported, associated to \(Rs\) = 103 \(\Omega\)/sq. [33]. Alternatively, a method to obtain high purity NWs was proposed by [PERSON] et al. by optimizing the relative concentrations of Cu precursor and capping agents [35]. A method for the production of NWs as long as 250 \(\upmu\)m was reported by [PERSON] et al. by using polyhydric alcohols in presence of glucose and capping agents like oleylamine and oleic acid [36]. The number of hydroxyl groups in such alcohols was also found to influence the NW diameter, with more hydroxyls decreasing diameter. Here films with \(Rs\) = 92 \(\Omega\)/sq at \(T\) = 91.5% were reported [36]. A synthetic process able to tune the diameters of Cu NWs was proposed by [PERSON] et al., who could change the diameter from 20 to 90 nm by changing the concentration of halide ions [37]. They reported \(Rs\) \(\sim\)60 \(\Omega\)/sq at \(T\) = 90%. Figure 1: SEM images from chemically produced Cu NW. (**a**) an low magnification entangled network of Cu NWs; (**b**) a high magnification view of individual Cu NWs. Adapted with permission from Ref. [18]. Copyright © 2021 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. The synthesis of curved Cu NWs was also reported. [PERSON] et al. deposited these curved structures, like those shown in Figure 3, onto glass and polyimide (PI) claiming that the curved structure improves the network properties [38,39]. However, no significant improvement in \(Rs\) and \(T\) was reported compared to other reports. [PERSON] et al. also reported the synthesis of curved Cu NWs and their deposition on PET and PDMS, and showed \(T>95\%\) associated with \(Rs\) = 110 \(\Omega\)/sq and haze < 2% [40]. In summary, this section provided an overview of the key electrical and optical properties related to transparent conductors, how they are affected by NW morphology and a brief account of the synthetic methods proposed to tune NW morphology in order to improve network performance.
# Post-Quantum Biometric Authentication Based on Homomorphic Encryption and Classic McEliece
## 3 Proposed Scheme Based on Homomorphic Encryption and Classic McEliece
### Security Analysis
#### 3.4.2 System Attacks  
Replay attacks, where an attacker tries to achieve a successful authentication using a previous string, are avoided by including the nonces.  
Regarding privacy, if the curiosity of the servers makes them exchange information, the authentication and database servers would only obtain the link between the protected biometric features and the index \(x^{U}\). The authentication and database servers know if the same or a different user is being authenticated. Anyway, the security of biometric data remains, since the Hamming weight of the protected features is greater than \(t\), so that they cannot be decoded. In addition, they do not know the user identifier \(ID^{U}\), so that they do not know which particular user is being authenticated.  
Figure 5: Verification phase of the distributed authentication protocol based on the Classic McEliece protection proposed.  
Similarity-based attacks [12] are possible if the distances between unprotected features are nearly the same as the distances between protected features. A search algorithm is applied to randomly generate first guesses, transform them into protected features, compute the distances with the protected features obtained, use the information to improve the probability of success with new guesses and repeat the process until a successful guess is reached. In order to carry out similarity-based attacks in the proposed scheme, the attacker should steal the client device to be able to generate the secret padding. In addition, the attacker should discover how the bits associated with the biometric data could be modified with the biometric sample provided \(S^{IV}\), which is difficult since the client device is assumed to be resistant to manipulations. In addition, the attacker should break the security of the authentication server to be able to decode, if possible, the distance between plaintexts. Even in that case, since the bits from biometric features can be permuted by the client device, the ordered vector \(b\) with the biometric features would be more difficult to extract. In this sense, we consider that our proposal shows enough robustness to similarity-based attacks.
## 2 Related Work
The algorithm distributes data points to clusters iteratively based on their closeness to cluster centers, and the centers are updated depending on the newly assigned points. An energy-efficient form of K-Means is constructed to reduce energy usage during the clustering process, particularly in resource-constrained contexts such as wireless sensor networks (WSNs). The proposed technique reduces communication distance between nodes while increasing network lifetime. However, in an unsecured and unlimited space environment, the proposed method is ineffective since it is vulnerable to intentional exploitation and may be damaging to sensor data. Further, Improved Chain-Based Clustering Hierarchical Routing (ICCHR) works by organizing CHs in a sequential chain-like structure [21]. Each CH act as in charge of a subset of clusters, with data transmitted down the chain until it reaches a sink or base station. This chaining strategy brings benefits such as lower overhead and more efficient data aggregation. From the simulated outcomes, the suggested ICCHR algorithm reduces the energy consumption ratio while spreading the load uniformly among sensor nodes. In the framework of Wireless Sensor Networks (WSN) for Internet of Things (IoT) applications, the study in [22] proposed Energy-Aware Cluster-based Routing (EACR-LEACH). The selection of Cluster Heads (CH) is an important part of the clustering process in this protocol. This protocol likely aims to select Cluster Heads that have sufficient energy, good connectivity with neighboring nodes, proximity to the sink, and possibly an even distribution of CH roles throughout the network by combining Remaining Energy, Number of Neighbors, Distance between Sensor Node and Sink, and Number of Times Node Acts as CH. The authors of [14] suggested a hierarchical routing technique for fog-assisted sensor networks. Regular sensor nodes and CHs just serve to forward data packets. Because fog nodes are superior in terms of storing, analyzing, and computation, they make all routing decisions. We examined the aforementioned protocols and found a number of shortcomings with Cluster Head (CH) selection and organization that fall short of the fundamental needs of the WSN-assisted IoT network. To ensure connection to the network and data transmission, robustness is necessary [25; 26]. However, the techniques outlined in this section might not be able to maintain network interaction because of the early demise of nodes close to BS. The objective of these routing protocols is energy optimization. They do not, however, consider additional WSN-assisted IoT features, such as scalability, load distribution, and other related ones. The goal of this research is to increase network lifetime by providing network load balancing and turning long-distance communications into shorter multi-hop distance communications.
## 3 Analytical and Experimental Results
### Mechanical Characterization
The excellent mechanical behavior in the longitudinal direction is due to the excellent mechanical properties of the continuous fibers themselves. In the transverse direction, the observed properties are those of the (poorer) matrix material. The values provided by the manufacturer [51] for the CF/PA6 and pure Nylon filaments are respectively 540 MPa and 50 MPa for the flexural strength, and 51 GPa and 1.4 GPa for the flexural modulus. These values were obtained using a method similar to the test standard ASTM D790. In order to quantify the variability, Figures 11 and 12 show the mean value and standard deviation of the determined mechanical properties (flexural modulus, flexural strength, maximum strain) for each set of tested specimens. The obtained mechanical properties for the different sets differ noticeably in terms of mean value and less in terms of standard deviation. In the longitudinal direction, the flexural modulus ranges from \(36.5\pm 3.3\) GPa to 40.2 \(\pm\) 3.1 GPa. Likewise, the flexural strength ranges from 515.7 \(\pm\) 47.7 MPa to 569.2 \(\pm\) 64.8 MPa. In comparison, the flexural strength differs more in the transverse direction and ranges from 37.9 \(\pm\) 8.0 MPa to 43.9 \(\pm\) 8.7 MPa. However, the mean values for the flexural modulus and maximum strain differ less between the different sets, for the transverse direction. Table 3 shows the calculated statistical quantities of the measured mechanical properties to quantify the level of variability for all test specimens. The coefficient of variation (c.o.v.) is the ratio of the standard deviation to the mean value. It allows easy comparison of the degree of variation of mechanical properties. In general, the c.o.v. in the transverse direction is at least twice as large as in the longitudinal direction. The lowest c.o.v. is observed for the maximum strain. It is 4% in the longitudinal direction, corresponding to a moderate level of variability, and 13.4% in the transverse direction, corresponding to a high level of variability. A moderate to high level of variability occurs for flexural modulus and flexural strength, with a c.o.v. of about 10% in the longitudinal and 20% in the transverse direction, respectively.
## 3 Results and Discussions
### Surface Element Composition  
Energy-dispersive X-ray spectroscopy (EDX) is a valuable technique for identifying the elemental composition of the surface of activated carbon [38]. The elemental components regarding mass percentage and the atomic percentage of Raw-RGL and AC-RGL are shown in the EDX spectra of Figure 3A,B. The spectra for both adsorbents show two main components, which are carbon (C) and oxygen (O), which is a characteristic of plant-based materials [44]. Chloride (Cl) is from the coating during sample preparation for SEM analysis. There are other minor peaks corresponding to potassium (K-0.34%) and calcium (Ca-0.37%) on Raw RGL and K (0.56%) and Ca (1.9%) on AC-RGL. Additional peaks for Si (0.64%) and Mg (0.72%) are observed in AC-RGL. The different element ratios are shown in Tables 1 and 2. The higher C and O content is noticed in AC-RGL because of the carbonization and activation of the Raw-RGL. The results produced using EDX agreed with the functional group obtained using (FTIR).  
\begin{table}
\begin{tabular}{c c c} \hline
**Element** & **Mass (\%)** & **Atom (\%)** \\ \hline C & 62.99 & 70.12 \\ \hline O & 34.90 & 29.17 \\ \hline K & 1.08 & 0.37 \\ \hline Ca & 1.02 & 0.34 \\ \hline Total & 100.00 & 100.00 \\ \hline \end{tabular}
\end{table}
Table 1: The surface element analysis of Raw-RGL adsorbent.  
Figure 3: EDX graph of (**A**) Raw-RGL and (**B**) AC-RGL adsorbents.
# Robust Full-Screen Wavelength Calibration Algorithm
###### Abstract
At the central pixels, we use the Levenberg-Marquardt algorithm to correct the function relationship between wavelength and feedback value and obtain a sinusoidal function. 2. We creatively propose converting the feedback values of the non-central pixels into its corresponding feedback values at the central pixels, and then calculate the wavelength using the formula in the previous step. Based on the linear relationship between wavelengths and feedback values, the Huber regression is employed to fit a robust model to better overcome the inherent instrument errors and operation mistakes. 3. By integrating the formulas obtained in step 1 and step 2, we obtain a wavelength calibration formula that can accurately calibrate all pixels. Experiments show that the novel calibration method has good continuity and high wavelength calibration accuracy. The rest of this study is organized as follows. Section 2 mainly describes the structure and related principle of the scanning, double-layer, secondary diffraction, linear-array CCD spectrometer. Section 3 describes the detailed calibration algorithm. The performance of the algorithm is verified by some experiments in Section 4. Finally, Section 5 contains the conclusions of the study.