esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Chitosan/Graphene Oxide Films for Biomedical Applications
### Chitosan/Graphene Oxide Hydrogels  
Hydrogels are cross-linked and three-dimensional structures that can absorb water and biological liquids without dissolving or losing their 3D network. They are able to absorb and hold high levels of exudate and can be separated from a wound's surface with minimal pain. [PERSON] et al. [44] prepared composite hydrogels from oxidized konjac glucomanan (OKGM) and carboxymethyl chitosan (CMCS) with different concentrations of graphene oxide (GO) as a nano-additive. Hydrogels have a 3D structure with a fast gelation time, good water retention capacity, and a high swelling ratio. The compressive modulus and strength were significantly enhanced by adding GO, which was due to the increased hydrogen bonding between the polymer chains and the GO. An in vitro cytotoxicity assay showed that the samples loaded with GO had better biocompatibility compared to those without GO, from which it was concluded that the prepared hydrogels could be considered as potential wound dressings. Another type of wound dressing was developed by loading polyhexamethylene guanidine (PHMG)-modified graphene oxide (mGO) into a polyvinyl alcohol/chitosan (PVA/CS) matrix [3]. The dressings had good mechanical properties, shorter swelling time, higher water vapor transmission rate (WVTR), faster cell proliferation, and greater antibacterial activity compared to those without mGO. The average wound closure time for the films loaded with mGO was 8.2 \(\pm\) 0.4 days, which was significantly shorter than the Vaseline gauze control and the hydrogels without mGO (about 14 days). An in vivo trial confirmed that this novel composite PVA/CS/mGO film had the ability to accelerate wound healing, and therefore showed promise in wound dressing applications. Layered nanocomposite hydrogel films containing chitosan and GO were prepared for use in biomedical applications by [PERSON] et al. [45]. The resultant hydrogels had superior mechanical properties and a pH-driven shape memory effect. Shape memory polymers are stimuli-responsive materials that can maintain a temporary deformed shape, but then return to their original permanent shape when exposed to external stimuli. In this study, the initial shapes of the CS and CS/GO hydrogels were straight strips. After 5 min of immersion in pH 3 solution, the films could be easily deformed and bent into a \"U\" shape by an external applied stress, and kept in that shape at pH 12 aqueous solution for 10 min. The deformed and bent films were then transferred back to pH 3 solution and, as can be seen from Figure 5, the CS/GO hydrogel reverted to its original shape in 9 min, whereas the CS hydrogel strip did not return to its original straight shape in more than 15 min. The shape fixity ratio of the hydrogels with 5 wt% GO was about 99% compared to 86% for the pure CS film. These results demonstrated that the prepared films could be combined with hydrogels for biomedical applications such as wound dressings. Several other studies involving the preparation of chitosan/GO hydrogels are listed in Table 1.
## 3 Simulation Results of Gaussian Beams
Recall the uniformity of optical intensity within the area of the receiving collimator, the total link loss in dB can be estimated by:  
\[\eta=10 log_{10}(\alpha\beta)=10 log_{10}\left(\frac{\left(\varphi_{c}/2\right)^ {2}}{R_{beam}^{2}}\frac{R_{core}^{2}}{\omega_{f}^{2}}\right), \tag{11}\]  
where \(R_{beam}\) is the beam radius at the receiving plane, and \(R_{core}\) is the core radius of the coupling optical fiber, substituting Equation (8) into Equation (11), we have:  
\[\eta=20 log_{10}\left(\frac{R_{core}}{f\theta}\right), \tag{12}\]  
where the unit of \(\theta\) is rad. With the objective of achieving a larger divergent angle \(\theta\), the total link loss can be alleviated by either reducing the focal length of the receiving collimator or increasing the radius of the fiber core coupled to the receiving collimator. Nevertheless, a smaller focal length generally implies a collimator with a reduced CA, thereby leading to an increased geometrical path loss. Instead of optimizing Equation (12), a more effective approach to mitigating the total link loss involves compensating for the focus shift and the selection of a receiving collimator featuring a larger CA. Then, the total link loss becomes:  
\[\eta=10 log_{10}\alpha=20 log_{10}\left(\frac{\varphi_{c}}{2z\theta}\right) \tag{13}\]  
In addition, the core radius of the coupling fiber should be larger than the practical beam waist of the receiving collimator. As a result, the fiber coupling loss is eliminated, and the achievable ROP is much increased.
## Appendix A  
All simulations of this study are implemented in Python programming language version 3.7.4 in Anaconda environment version 4.14.0. Jupyter Notebook version 6.0.1 is used as IDE. Below are given the important parts of the code.  
#PSO-estimated parameters of actual DC motor  
coefficient0 = 0.09365946  
coefficient1 = 0.00253256  
VaMax = 12 VaMin = -12  
#Function implementing DC motor dynamic behavior  
def DCMotorPlant(w_n_1, Va, disturbance):  
#Forward system model starts here.  
#Compare the Armature Voltage with Max/Min Value  
if Va > VaMax:  
Va = VaMax  
if Va < VaMin:  
Va = VaMin  
#Simulation of DC Motor  
dw_dt = -(coefficient0/coefficient1)*w_n_1 + (1/coefficient1)*(Va + disturbance)  
w = w_n_1 + deltaT*dw_dt  
return w  
#Function implementing PI controller  
def piController(wref_n, w_n_1, intError, prevError, PIcontrollerParameters):  
KP = PIcontrollerParameters[0]  
KI = PIcontrollerParameters[1]  
error = wref_n - w_n_1  
intError = intError + error*deltaT  
if intError > intErrormax:  
intError = intErrormin  
piOut = KP*error + KI*intError  
return piOut, intError, prevError  
#Function implementing single experiment performance  
#with given reference-disturbance pair  
def performExperiment(controllerType, PIDcontrollerParameters, ANNcontrollerParameters, reference, disturbance):  
tempReference = np.zeros((int(tmax/deltaT) + 1000))  
tempReference[0:int(tmax/deltaT)] = reference[:]  
inputToANNcontroller = np.zeros((numberOfStateVariables*pastAndCurrentWindow SizeInTermsOfSamples+numberOfReferenceVariables*totalWindowSizeInTermsOf Samples+1,1))  
intError = 0  
prevError = 0  
controllerOutput = 0  
n = 0  
w = np.zeros((int(tmax/deltaT)))  
for n1 in np.linspace(0, n1 max, num = n1 max-1):  
n = n + 1  
controllerOutput, intError, prevError = piController(tempReference[n-1], w[n-1], intError, prevError, PIcontrollerParameters)  
if n<=nPast + 1 or controllerType==\"PI':  
#piOut will be that of PI controller's output  
additiveTerm = 0  
else:  
#BUILD INPUT TO THE ANN CONTROLLER  
inputToANNController[0,0] = 1 #1  
inputToANN Controller[1:nPast+2,0] = w[n-nPast-2:n-1]  
inputToANNController[nPast+2:2*nPast+3,0] = tempReference[n-nPast-2:n-1]  
inputToANNController[2*nPast+3:2*nPast+3+nFuture,0] = tempReference[n-1:n+nFuture-1]  
#HERE THE ANN CONTROLLER  
additiveTerm = np.dot(ANNcontrollerParameters.transpose(), inputToANNController) controllerOutput = controllerOutput + additiveTerm w[n] = DCMotorPlant(w[n-1], controllerOutput, disturbance[n]) return w
## 3 Three-Tier Architecture of LS-WSNs
### Architecture Description  
Figure 3 presents our proposed heterogeneous LS-WSN architecture model. The peculiarity of the three-tier architecture that we propose compared to those already existing in the literature [33; 34], is that the first two levels of the architecture are built on a set of clusters with a well-defined communication strategy.
## 3 Results and Discussions
### Mass Intensity (MI) and Mass Productivity (MP)  
The results for _MI_ and _MP_ are shown in Figures 4 and 5, respectively. The process mass intensity is much higher when using ethanol, which means that the total mass used in the process is not recovered, and the mass of the final product is low. Despite this, the results revealed that the ideal _MI_ (1 kg/kg) was almost achieved by methanol; the _MI_, when using ethanol, was also close to unity. Therefore, both of these alcohols are green solutions or solvents to produce biodiesel, with methanol being better than ethanol. It should be noted that with higher alcohol ratios the mass productivity decreases for ethanol which mainly depends on the experimental _yields_ achieved in this study.  
Figure 4: Mass Intensity results for the simultaneous effect of MW/US in WCO transesterification. Effect of (**a**) different reaction time; (**b**) alcohol to oil molar ratio; (**c**) catalyst concentration and (**d**) sample (oil) volume.  
Figure 3: Atom efficiency results for the simultaneous effect of MW/US in WCO transesterification. Effect of (**a**) different reaction time; (**b**) alcohol to oil molar ratio; (**c**) catalyst concentration and (**d**) sample (oil) volume.
# Detecting Suspicious Texts Using Machine Learning Techniques
## 5 Experiments
### Evaluation Results
#### 5.2.2 Graphical Evaluation  
The ROC curve used as a measure of graphical evaluation as each class contains an equal number of texts. Figures 5-9 exhibit the ROC curve analysis of the BoW and the tf-idf feature extraction technique for F1, F2, F3, F4, and F5 features, respectively. For BoW with an F1 feature, logistic regression and random forest both provide the similar AUC values of 87.8%. SGD achieved 87.0% AUC, which increased by 2.3% by using the tf-idf FE technique. The AUC value of other algorithms also increased by employing the tf-idf feature extraction technique.  
With the F2 feature, LR obtained the maximum AUC value of 84.5%, while, for the F3 feature, SGD achieved the maximum value. In both cases, the tf-idf feature extraction technique was used. With tf-idf and the F4 feature, SGD beats others by getting a maximum AUC of 89.3%. The value of all the classifiers increased except the decision tree where its value decreased by 0.06%. Results with the F5 feature is quite similar to the F1 feature. However, LR outdoes SGD here by a margin of 0.02%. Critical analysis of results brings to the notation that the SGD classifier with the combination of unigram and bigram feature for the tf-idf feature extraction technique achieved the highest value for most of the evaluation parameters compared to others. The performance of the proposed classifier (SGD) was analyzed further by varying the number of training documents to get more insight.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifier** & **FE** & **Features** & **A (\%)** & **P (\%)** & **R (\%)** \\ \hline \multirow{8}{*}{SGD} & & F1 & 81.00 & 80.86 & 86.26 \\  & & F2 & 81.00 & 76.58 & 94.86 \\  & BoW & F3 & 78.21 & 72.31 & 98.58 \\  & & F4 & 81.28 & 83.79 & 82.28 \\  & & F5 & 78.57 & 81.30 & 79.84 \\ \cline{2-6}  & & F1 & 82.14 & 83.78 & 81.51 \\  & & F2 & 82.00 & 78.06 & 94.09 \\  & tf-idf & F3 & 78.92 & 73.54 & 97.04 \\  & & F4 & **84.57** & 82.09 & 92.42 \\  & & F5 & 83.92 & 81.04 & 93.53 \\ \hline \hline \end{tabular}
\end{table}
Table 7: _Cont._  
Figure 4: \(f_{1}\)-score comparison among different ML classifiers with F1, F2, F3, F4, and F5 features for the tf-idf FE technique.  
Figure 8: ROC curve analysis for the F4 feature where F4 represents a combination of unigram and bigram features.  
Figure 10: Effects of training set size on accuracy.  
Figure 9: ROC curve analysis for the F5 feature where F5 represents a combination of unigram, bigram, and trigram features.
## 5 Results
### Ideal Scenario (No Interference)  
In the considered real-world scenario, a desired signal is transmitted from ES2 via the victim satellite (S2: IS-1002 at 35\({}^{\circ}\) west) and received by ES4. In this first step, we consider an ideal scenario where no interference exists on the signal traveling between the ESs and satellite S2. We will consider a typical case of an ES transmitting with a power of 5 dBW, a QPSK modulated signal with a bandwidth B = 3 MHz, and an EIRP of 52.5 dBW during clear weather conditions. A typical ES has a figure of merit of 24 dB/K. The signal power levels, in this case, can be calculated using the end-to-end link budget equation presented previously. This begins with determining the uplink carrier-to-noise density ratio using Equations (9) and (10) and the S2 specifications. Using Equation (10) and (20) calculates the FSL:  
\[\text{FSL}=32.4+20\text{log}\left(14,\!000\right)+20\text{ log}\left(40,\!973 \right)=207.57\text{ dB} \tag{20}\]  
Using Equations (9) and (20), we get:  
\[\left[\frac{\text{C}}{\text{N}_{0}}\right]_{\text{up}}=47.5+5-207.573-\text{L} _{\text{ABS}}+5.6+228.6=79.12\text{ dB-Hz} \tag{21}\]  
It is well-established that the total atmospheric and ionospheric absorption loss \(L_{ABS}\) in the Ku-band can range from less than 1 dB under clear sky conditions to over 10 dB, and potentially exceed 20 dB during heavy rainstorms. Given that this study considers a practical scenario within Beirut territory, which experiences a significant number of clear sky days annually (estimated at approximately 200, while severe weather conditions are rare), it is important to evaluate the expected magnitude of \(\text{L}_{\text{ABS}}\) under such prevalent conditions. While some atmospheric and ionospheric absorption is inherent even under clear-sky conditions at Ku-band frequencies, this effect becomes more significant when considering the typically low elevation angles of signals transmitted from terrestrial stations to geostationary satellites, these losses are typically in the order of 0.5 dB. For the purpose of this initial analysis, and acknowledging that precipitation events would introduce considerably higher attenuation, we will proceed with the assumption that the clear sky \(\text{L}_{\text{ABS}}\) represents a relatively small contribution to the overall path loss. However, a more comprehensive analysis in future work could incorporate the statistical impact of varying weather conditions and elevation angles on \(\text{L}_{\text{ABS}}\). \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Satellite Specifications of IS-1002 at 35\({}^{\circ}\) West**} \\ \hline EIRP & Back-off & G/T \\
25.6 dBW & \(-4\) dB & 5.6 dB/K \\ Uplink parameters & & \\ Frequency & Antenna gain & HPA-dBW \\
14 GHz & 47.5 dB & 5 dBW \\ Downlink parameters & & \\ Frequency & Antenna gain & G/T \\
12 GHz & 46.2 dB & 22 dB/k \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Satellite uplink and downlink specifications.
# Enhanced Linearity in Intracranial Pressure Monitoring System Through Sample Isolation Bridge ROIC
## 4 Measurement Results
Research [40] demonstrates a commendable \(FoM_{SNDR}\) of 144.7 dB, associated with high power consumption. However, in this analysis, the peak SNDR surpasses that of [40] by 1.3 dB. Achieving a high-resolution circuit necessitates a more extensive focus on mitigating various forms of noise, including thermal noise and \(1/f\) noise. To specifically address \(1/f\) noise, it is essential to utilize transistors with significantly larger dimensions. However, utilizing these larger transistors leads to an increase in parasitic capacitors, which in turn slows down circuit performance; thus, higher currents become necessary to restore circuit speed. As a result, developing low-power designs proves to be challenging. Our methodology can effectively manage signal bandwidths considerably broader than those in [40], and the IA gain settings are found to be quite reasonable. The forest modulation values produced by the proposed ROIC circuit appear promising when high SNDR and signal bandwidth are both taken into account. The ROIC developed in [41] features a bandwidth comparable to that presented in this paper, achieving an SNDR of 50 dB and a \(FoM_{SNDR}\) of 137.8 dB with an IA gain of 178 (equivalent to 45 dB). In contrast, our design demonstrates superior SNDR and \(FoM_{SNDR}\) compared to those in [37; 40; 41; 42], even though it operates with a significantly reduced gain of 32.
## 2 The IoV Scenario and Use Cases
### Use Cases  
3 GPP has defined six groups of requirements for V2X, five of them describing major V2X scenarios and an additional one devoted to general aspects transversal to the five major areas [22]. Different analysis of the use cases or slightly modified use cases can be found [31, 32, 33]. Each of these areas is in turn divided into a set of use cases and scenarios [23]. The six areas are: (i) general requirements; (ii) vehicles platooning; (iii) advanced driving; (iv) extended sensors; (v) remote driving; (vi) vehicle quality of service support. The first area is devoted to general requirements such as location accuracy, the type of messages that must be supported, or the connection density. As for the last area, i.e., vehicle quality of service support, it defines the monitoring of the quality of service of V2X services as a keypoint in the management of the network and the services themselves. 3 GPP defines this last area to make sure that real time data on the current quality of service is available for the network. In the following, the four remaining areas, from the second one to fourth one, are described and discussed focusing on the role AO and DML can play. In the following, the main VoI use cases are described as defined by 3 GPP in [22, 23].
## 2 Materials and Methods
### Subjective Experiments  
An additional set of subjective experiments was conducted to determine the SRTs (i.e., the SNR values required for 50% intelligibility) for various speaker-listener position configurations and occupancy modes. These experiments considered a total of 64 conditions, comprising 8 speaker-listener position configurations with 8 subject-occupancy modes for each (see Table 1).
## 3 Problem Solution Using Adaptive Learning Artificial Neural Network
### FANN Learning Phase  
The learning phase of FANN model is done using the learning Algorithm 1, which takes a set containing the reports collected from each ONU in the network during a single BBU processing time  
Figure 4: Adaptive learning method for training feedforward artificial neural network.  
cycle as inputs and returns a trained FANN model for that specific ONU. We assume that during the first BBU processing time cycle the OLT gives fixed grants to each ONU in the network as follows: \(G_{0}^{j}=\text{ BW /K}\), where BW is the total XGS-PON frame size in bytes and \(K\) is the number of ONUs in the network. This allows OLT to collect the initial set of ONU reports for the training process. After collecting these reports in an individual initial set associated with each ONU (i.e., \(\{R_{0}^{j}\), \(R_{1}^{j}\), \(R_{2}^{j}\)... \(R_{T-1}^{j}\}\), \(j=1\), \(2\), \(3\)... \(K\)), we run the learning algorithm 1 to train the FANN module by taking each ONU set of repots \(R_{t}^{j}\) ( \(t=0,\,1,\,2,\ldots T-1\)) as inputs and the last report collected from that ONU (i.e., \(R_{T}^{j}\)) as an output as illustrated in Figure 5a. We run the training process until we reach the desired training error. Then we save the network settings of the FANN module for each ONU so that it can to be used later in the prediction phase. (Note: we run the training algorithm every time we receive a new set of reports from ONUs, since the initial training set for each ONU is updated upon the arrival of a new report from ONU.)  
```
Input: A learning set containing \(j\)th ONU report \(R_{t}^{j}\), where \((t=0,1,2,\ldots T-1)\) FANN initial setting (e.g., number of layers, initial weights, activation function, learning rate, etc.) Output: A trained FANN model for \(j\)th ONU  Assume a generative FANN model for this ONU. For\(j=0\); \(j\) > the total number of ONU K, \(j\)++do While FANN does not converge to the desired error, do - Train the FANN model with \(R_{t}^{j}\) set as inputs and the report following the set \(R_{t}^{j}\) (i.e., \(R_{T}^{j}\)) as an output. Endwhile - Test trained FANN model on the testing set. Endfor - Return the trained FANN model for this ONU.
```  
**Algorithm 1**Learning
## 2 Materials and Methods
### EVs Isolation and Identification  
After adherence, BMECs were cultured in an EVs-free medium (prepared by filtration through a 0.22 \(\upmu\)m filter and then centrifugation at 100,000\(\times\)\(g\) for 18 min) with and without icarini for 24 h. Then, the supernatant was collected to isolate EVs using the exosome isolation kit (MagCapture(tm) Exosome Isolation Kit PS, Wako, Japan). Each group of EVs was isolated from an equivalent number of BMECs and resuspended in PBS. The diameter distribution and volume of extracted EVs were analyzed using nanoparticle tracking analysis (NTA, Nanosight NS300, Malvern, UK) and the morphology was observed by transmission electron microscope (TEM; HT-7700, Hitachi, Tokyo, Japan). The EVs-carried proteins were assessed by Westernblot assay using primary antibodies to CD9 (Santa Cruz Biotechnology, Santa Cruz, CA, USA; dilution 1:1000) and CD81 (Santa Cruz; dilution 1:800).  
High-Throughput Sequencing Transcriptome and Quantitative Polymerase Chain Reaction (qPCR) of EVs-Carried miRNAs  
After extracting RNA from EVs by using TRIzol reagent, high-throughput sequencing of EVs-carried miRNAs was performed, according to standard procedures provided by Illumina. A small RNA sequencing library was prepared using the TruSeq Small RNA Sample Prep Kits (Illumina, San Diego, CA, USA). Then, the constructed library was sequenced using Illumina Hiseq 2000/2500 platform. miRNAs with lower expression (those with less than 10 copies in all samples) were first removed from the analysis. Then, differentially expressed genes between BMECs in the icarini-treatment and the non-treatment groups were screened using 1 log2(Fold change)! 1 and _p_-value \(\leq\) 0.05 as the threshold. Then, genes related to proliferation, angiogenesis and apoptosis of endothelial cells were selected for confirmation by using qPCR. The reverse transcription process was performed using the One-Step PrimeScript(r) miRNA cDNA Synthesis Kit (Takara, Shiga, Japan) and the amplification reaction was conducted using the SYBR(r) Premix Ex TaqMan II kit (Takara, Japan). The primer sequences of investigated miRNAs were: hsa-miR-1469, forward, 5'-GCGCAGCTGATAATGAA-3' and reverse, 5'-GTGCAGGGTCCGAGGT-3', hsa-miR-133a-5p, forward, 5'-GCCTCGGCCGGGCCG-3' and reverse, 5'-GTGCAGGGTCCGAGGT-3', hsa-miR-10b-5p, forward, 5'- GCTACCCTGTAGACCCAA-3' and reverse, 5'-GTGCAGGGTCCGAGGT-3'.
###### Abstract  
Data-driven methods have shown promising results in structural health monitoring (SHM) applications. However, most of these approaches rely on the ideal dataset assumption and do not account for missing data, which can significantly impact their real-world performance. Missing data is a frequently encountered issue in time series data, which hinders standardized data mining and downstream tasks such as damage identification and condition assessment. While imputation approaches based on spatiotemporal relations among monitoring data have been proposed to handle this issue, they do not provide additional helpful information for downstream tasks. This paper proposes a robust deep learning-based method that unifies missing data imputation and damage identification tasks into a single framework. The proposed approach is based on a long short-term memory (LSTM) structured autoencoder (AE) framework, and missing data is simulated using the dropout mechanism by randomly dropping the input channels. Reconstruction errors serve as the loss function and damage indicator. The proposed method is validated using the quasi-static response (cable tension) of a cable-stayed bridge released in the 1 st IPC-SHM, and results show that missing data imputation and damage identification can be effectively integrated into the proposed unified framework.  
structural health monitoring; missing data; damage identification; deep learning 2023