esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Properties of Nanocellulose, Its Synthesis, and Potential in Biomedical Applications
## 2 Classification and Synthesis of Nanocellulose
### Cellulose Nanofiber (CNF)  
By-products of rice, corn, barley, banana, wheat, and sugar cane are used for CNF synthesis. Furthermore, wood and wood pulp are the most common sources of nanocellulose employed for manufacturing CNF [47].  
Mechanical treatment is the most common method for isolating CNF. However, other methods, such as enzymatic and chemical treatments, have also been documented [12; 21]. High-pressure homogenization, ultrasonic fiber delamination, and ball-mining are the preferred approaches to obtaining CNF. A dilute fiber-water mixture is supplied via a narrow, high-pressure outlet during the homogenization process. A significant change in pressure promotes fibrillation. Microfluidization is identical to homogenization, except there are no moving elements in the microfluidicizer, resulting in less blockage. The shear force decomposes the fibers in the interaction chamber, operating on the channel wall and collision current. To manufacture CNF, the fiber-water solution must be homogenized multiple times, which costs a lot of energy [12].  
In contrast to the homogenizer, the micro-fluidizer runs at a constant speed, while the homogenizer maintains a constant process mass. The threads are connected through a gap between the spinning and stationary discs during refining, performed using a disc filter [48]. The oxidation of cellulose using 2,2,6,6-tetramethylpiperidine-1-oxyl radical (TEMPO) is a well-known chemical pretreatment procedure for the production of CNF. Strong nitroxyl radical, TEMPO, has been found to oxidize alcohols to carboxylate groups on the surface of carbohydrates via aldehydes and is considered for its reaction rate and regioselectivity [49].  
Compared to CNC and BNC, CNF is a flexible and short fibril, with a length of 1-10 \(\upmu\)m and a diameter range within 20-100 nm, which varies depending on the defibrillation process and the cellulose sources. The crystallinity of CNF is substantially lower than that of BNC and CNC; however, CNF is made up of crystalline and amorphous domains. The Young's modulus of CNF is approximately 30 GPa, which is less than the BNC and CNC. CNF accounts for thixotropic and shear-thinning properties, although it is often unstable in solution [50].
## 4 Recurrent Neural Network Modeling
### Model Training and Evaluation  
The RNN models were implemented in Python using the Keras library and the TensorFlow platform. The LSTM model has an input layer (\(\mathrm{N}~{}\times 3\times 3\)), two hidden layers with 32 units each, and a fully connected layer, which outputs a predicted value using the rectified linear unit (ReLU) activation function, as shown in Figure 9(a). The LSTM unit can determine important features from an input sequence and keep such information in its memory. To train the model, experimental data for different step values were stacked together with feature scaling performed on the data. The model was trained with a batch size of 32 for 90 epochs, while 20% of the training set was used for validation. The learning rate was set to \(1\times 10^{-4}\) with a time step of three and the Adam optimizer algorithm employed. Similarly, the GRU model has an input layer, two hidden layers with 16 neurons each, and a fully connected layer with the ReLU activation function, as shown in Figure 9(b). The learning rate and batch size were \(1\times 10^{-4}\) and 24, while the model was trained for 20 epoch with the chosen Adam optimizer algorithm.  
Furthermore, the description of the two quantitative metrics used to evaluate the models' performance is stated below, with their corresponding values given in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **MAE (mm)** & **MSE (mm\({}^{2}\))** \\ \hline LSTM & 0.0825 & 0.0958 \\ GRU & 0.0906 & 0.0964 \\ \hline \hline \end{tabular}
\end{table}
Table 2: MAE and MSE for the LSTM and GRU models.  
Figure 10: A stacked 2-layer architecture for slave robot’s position prediction and compensation using (**a**) LSTM model and (**b**) GRU model.  
Mean Absolute Error: The mean absolute error indicates the average sum of the absolute difference between the actual slave robot position (\(y_{i}\)) and the predicted position (\(x_{i}\)), as given in Equation (3).  
\[\text{MAE}=\frac{1}{\text{N}}\ \sum_{i=1}^{\text{N}}\lvert y_{i}-x_{i}\rvert \tag{3}\]  
Mean Square Error: The mean square error is the average of the squared error values between the model's prediction and the slave robot response, represented by Equation (4).  
\[\text{MSE}=\frac{1}{\text{N}}\ \sum_{i=1}^{\text{N}}\left(y_{i}-\hat{y}\right)^{2} \tag{4}\]  
Based on the error values (in Table 2), the RNN models have comparative performance. The models' training, validation, and prediction performances are shown in Figure 11a-d. Figure 11 indicates that the validation loss for each model was less than 2% under each category, while the models aptly predicted the intended slave displacement for each set-value, i.e., 1-5 mm. The LSTM model has a longer training time compared with the GRU model, which utilized the least number of neurons and epochs. Thus, we suggest that either of the models could be deployed for position prediction and error compensation to eliminate motion lag between the master-slave platform during robot-assisted tool navigation.
# Contribution of Atmospheric Depositions to Inventory of Nutrients in the Coastal Waters of Crimea
## 2 Materials and Methods
### Chemical Analysis  
Atmospheric precipitation and seawater samples were analyzed in the laboratory of the Marine Hydrophysical Institute RAS (Sevastopol) for inorganic nitrogen, phosphorus, and silica concentrations using photometric analysis [28]. The ammonium concentration was determined photometrically with a spectrophotometer using a modified Sadgi-Solorzano method [29]. Sodium nitroprusside was used as a catalyst. In a phenol-hypochlorite reaction, the intensely colored blue compound indophenol was formed. In the laboratory, 10 mL of each sample was placed in plastic tubes. Then, 0.2 mL of phenol solution was added and (after mixing) 0.2 mL of hypochlorite solution was added. Samples and standards with added reagents were placed in a thermostat at 48-50 \({}^{\circ}\)C for 30 min. After cooling for 30 min in a dark place, the optical density of colored solutions was measured at a wavelength of 648 nm. Statistics of the results revealed that the minimum ammonia concentration determined by this method was 0.04 \(\upmu\)mol L\({}^{-1}\). The method error was \(\pm\)12%.  
An automatic Skalar San++ flow analyzer was used to determine concentrations of nitrate and nitrite. The automated procedure was based on the cadmium reduction method [28]; samples were buffered to pH 8.2 and pumped through a column containing granulated copper-cadmium to reduce nitrate to nitrite. Nitrite (originally present and reduced from nitrate) was determined by diazotizing with sulfanilamide and coupling with N-(1-naphthly)ethylene diamine dihydrochloride to form a highly colored azo dye, and its optical density was measured at 540 nm. The minimum concentration determined by this method was 0.07 \(\upmu\)mol L\({}^{-1}\). The method error was \(\pm\)10%.  
Inorganic phosphorus and silica concentrations were determined using a Unico spectrophotometer. The Murphy-Riley method was used to determine the inorganic phosphorus concentration [28]. The phosphorous-molybdenum complex was reduced by ascorbic acid in the presence of sulfuric acid and a catalyst, and its optical density was measured at 880 nm. The minimum concentration of phosphate determined by this method was 0.05 \(\upmu\)mol L\({}^{-1}\). The method error was \(\pm\)10%.  
The silica analysis was based on formation of a blue silicopolydenum complex [28]. Ascorbic acid was used as a reducing agent. To eliminate the interfering effect of orthophosphate, oxalic acid was added into the solution, preventing formation of a phosphorous-molybdenum complex. The minimum concentration of silica determined by this method was 0.05 \(\upmu\)mol L\({}^{-1}\). The method error was \(\pm\)10%.
## 3 Results and Discussion
### Interactive Luminescent Effect  
The conductive yarn, intertwined within the fabric's back layer, is connected to the electrical signal conversion module at both ends. The POF in the fabric is coupled with the LED light source in the circuit module. When the power is turned on, lightly pressing the sensing area on the fabric surface can change the luminescent color of the POF fabric. By long-pressing, the luminescent color can continue to change. Figure 11 illustrates the process of changing the luminescent color from red to green when the fabric surface is pressed. Video S1 demonstrates the dynamic interactive luminescent effect.  
Combined with laser-engraving technology, patterned luminescent effects can be achieved on the fabric surface. As shown in Figure 12, the laser-engraved \"sun\" and \"mountain\" pattern on the POF fabric surface emit bright light. Additionally, fabric interaction effects will be achieved by pressing the \"sun\" pattern, which is also the sensing area, as a button to control the luminescent color of the \"mountain\" pattern.  
Figure 11: Interactive luminescent effect.  
Figure 10: Measurement results of the luminescence value in different areas.
# Automatic Crack Classification by Exploiting Statistical Event Descriptors for Deep Learning
## 3 Framework for the Real-Time Classification of Acoustic Emission Data
### Analysis of Acoustic Emission Events Using Feature Extraction  
In order to envisage both realistic and large-scale applications, the classification process must be as fast and reliable as possible. Thus, each AE preprocessed waveform, \(\hat{s}_{q}(t)\), is turned into a compact representation through a set of \(\Gamma_{q}\) features, in both time and frequency domains. Figure 4a shows an example of time domain traces of three different reference signals \(\hat{s}_{q}(t)\) from tensile (red line), shear (blue line) and mixed-mode events (green line), and their Fourier spectra (Figure 4b), respectively. We have used both time and frequency information to calculate the instantaneous frequency, spectral entropy and spectral kurtosis as summarized in Table 1. For the sake of consistency, we will also generally refer later to these properties as event descriptors (EDs).
## 7 Bayesian Estimation
#### MCMC Method  
We use Metropolis Hastings procedure as:  
1. Set start values \(\psi^{(0)}\), \(\lambda^{(0)}\), and \(\theta^{(0)}\). Then, simulate sample of size \(n\) from E-WD \((\psi^{(0)},\lambda^{(0)},\theta^{(0)})\), next set \(l=1\).
2. Simulate \(\psi^{(*)}\), \(\lambda^{(*)}\), and \(\theta^{(*)}\). using the proposal distributions \(N(\psi^{(l-1)},\,V(\hat{\psi}))\), \(N(\lambda^{(l-1)},\,V(\hat{\lambda}))\), \(N(\theta^{(l-1)}\) and \(V(\hat{\theta}))\).
3. Calculate \(r=min\left(\frac{\pi^{*}(\psi^{(*)},\lambda^{(*)},\theta^{(*)})}{\pi^{*}(\psi ^{(l-1)},\lambda^{(l-1)},\theta^{(l-1)})},1\right)\).
4. Simulate \(U\) from Uniform\((0,\,1)\).  
5. If \(U<r\), then \(\left(\psi^{(l)},\lambda^{(l)},\theta^{(l)}\right)=\left(\psi^{(*)},\lambda^{(*)},\theta^{(*)}\right)\). If \(U\geq r\), then \(\left(\psi^{(l-1)},\lambda^{(l-1)},\theta^{(l-1)}\right)=\left(\psi^{(*)}, \lambda^{(*)},\theta^{(*)}\right)\).
6. Set \(l=l+1\).
7. Iterate Steps 2-6, M repetitions, and get \(\psi^{(l)},\lambda^{(l)}\) and \(\theta^{(l)}\) for \(l=1,\dots,\ M\).  
Suppose the squared error loss function, given by \(L_{SE}(\delta,\delta)=(\delta-\delta)^{2}\). By using the generated random samples from the M-H technique and for \(N\) is the nburn. Then, the Bayes estimator of \(\delta\) against the squared error loss function, is given by  
\[\delta_{SE}=E_{\delta}[\delta|\mathbf{x}]=\frac{1}{M-N}\sum_{l=N+1}^{M}\delta ^{(l)}. \tag{52}\]  
Next, suppose the LINEX (\(LE\)) loss function, given by  
\[L_{LE}\big{(}\delta,\delta\big{)}=\exp\big{[}\rho\big{(}\delta-\delta\big{)} \big{]}-\rho\big{(}\delta-\delta\big{)}-1,\quad\rho\
eq 0. \tag{53}\]  
The approximate Bayes estimate of \(\delta\) under \(LE\) loss function, is given by  
\[\hat{\delta}_{LE}=\frac{-1}{\rho}\log(E_{\delta}[\exp(-\rho\delta)|\mathbf{x} ])=\frac{-1}{\rho}\log\left(\frac{\sum_{l=N+1}^{M}exp\Big{(}-\rho\delta^{(l)} \Big{)}}{M-N}\right), \tag{54}\]  
Finally, suppose the general entropy (\(GE\)) loss function, given by  
\[L_{GE}\big{(}\delta,\delta\big{)}=\left(\frac{\delta}{\delta}\right)^{\varepsilon} -\varepsilon\log\left(\frac{\delta}{\delta}\right)-1. \tag{55}\]  
The approximate Bayes estimate of the parameters, given by  
\[\hat{\delta}_{GE}=\left(E_{\delta}\big{[}\delta^{-\varepsilon}\big{|}\mathbf{ x}\big{]}\right)^{\frac{-1}{\varepsilon}}=\left(\frac{1}{M-N}\sum_{l=N+1}^{M} \left(\delta^{(l)}\right)^{-\varepsilon}\right)^{\frac{-1}{\varepsilon}}, \tag{56}\]  
MCMC HPD credible interval Algorithm:  
1. Arrange \(\psi^{(*)},\lambda^{(*)}\) and \(\theta^{(*)}\) in rising values.
2. The lower bounds of \(\psi^{(*)},\lambda^{(*)}\) and \(\theta^{(*)}\) is in the rank \((M-N)*\varepsilon/2\).
3. The Upper bounds of \(\psi^{(*)},\lambda^{(*)}\) and \(\theta^{(*)}\) is in the rank \((M-N)*(1-\varepsilon/2)\).
4. Iterate the previous steps \(M\) times. Get the average value of the lower and upper bounds of \(\psi,\lambda\), and \(\theta\).
## 4 Experimental Results  
### Configuration of the Experimental System  
The configuration of our experimental system is shown in Figure 11, and the specifications of this system are as follows: (1) Personal computer: Calculates the coordinates of the handle and passes these coordinates to the robotic arm. (2) Robotic arm: DOBOT ARM 2.0, which has a repeatability of 0.2 mm. The robotic arm was controlled using Python 3.X code. (3) Brush pen: Two refillable brush pens with the same brush-spine length (8.5 mm) but different brush diameters (Figure 12). The brush pen on the top is Brush Pen #1, while the brush on the bottom is Brush Pen #2. (4) A4 paper: Ordinary printing paper. (5) Scanner: A 600 DPI scanner was used to scan the written characters, and the scans were then stored on a PC for analysis.  
Figure 11: Hardware architecture of the experimental system.
# Reverse Knowledge Distillation with Two Teachers for Industrial Defect Detection
## 4 Experiments
### Evaluation Protocol  
There are two evaluations, which are image-level evaluation and pixel-level evaluation. They both use the operating characteristic curve (ROC) and area under the curve (AUC) as the evaluation metric, called ROC-AUC. The ROC curve is a graph that shows the performance of a classification model across all thresholds. The two parameters that control this curve are the true positive rate (TPR) and the false positive rate (FPR). The equations are defined in (2) and (3).  
\[TPR =\frac{TP}{TP+FN} \tag{3}\] \[FPR =\frac{FP}{FP+TN} \tag{4}\]  
The AUC calculates the area under the ROC curve in two dimensions between (0,0) and (1,1). The network reports the ROCAU scores after being trained separately for each object class.  
For more accurate evaluation of anomaly localization performance, the per-region-overlap (PRO) metric is used. Unlike for pixel-by-pixel measurements, the PRO score treats anomalous regions of any size equally.
## 5 Performance Evaluation and Results Discussion
### Comparison in Delay  
SEP, LEACH, EESAA take 29%, 22.22% and 1423% more time in transmission when compared with ME-CBCCP, but with the increase in the number of nodes, genetic protocols, HCR and ERP took nearly 23 and 25.5 h to execute.  
ME-CBCCP performs computations within and outside the cluster level so when we compared it with MODELACH and T-DEEC it takes 20% and 7% more time than both, but this is tolerable due to the better gain in terms of parameters, network lifetime and scalability as can be noticed from Figure 7. The total time taken in transmission in ME-CBBCP is 537 s, but SEP and LEACH take 1700 s and 3400 s, respectively, to execute.  
Figure 6: Network lifetime comparison (1500 nodes, with the help of dead nodes in 300 m\({}^{2}\)).  
Figure 7: Transmission delay (I) (1000 nodes, 200 m\({}^{2}\)).  
MODLEACH takes 275 s, T-DEEC takes 464 s and EESAA takes 8000 s in execution (Figure 8). In Figure 9, the results of transmission delay are shown in a large network and with the number of nodes. Results show that ME-CBCCP is performing better than other protocols. It is proved by these results that traditional protocols for WSNs cannot be applied to the IoT framework directly and they need to be modified.
## 4 Assessment and Analysis of LeGNSS Precise Positioning
### Statistical Analysis of Convergence Time on a Global Scale
The convergence time of 5 s sampling interval is almost the same as that of 10 s. The averaged convergence time of 1 s sampling interval is the shortest, which are all smaller than 5.0 min, with the improvement of 44.3%, 41.5%, and 46.4% compared to those of the 30 s sampling rate for east, north, and up components, respectively. Figure 10: Histogram of convergence time for sampling intervals of 30, 10, 5, and 1 s (from top to bottom) G/C/L PPP. The dashed line means the convergence time of 95% of the world’s stations are smaller than the corresponding time.
## 3 Results
### Selection of \(A_{VNDVI}\) Sensitive Band  
If \(\mathrm{A_{VNDVI}}\) contains too many non-sensitive bands, the sensitivity of parameters to LAI will be reduced, thus affecting its inversion accuracy and reliability. The trends between near-infrared, red,  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & **NDVI** & **GNDVI** & **BNDVI** & **GRNDVI** & **GRNDVI** & **RBNDVI** & **A\(\mathrm{{}_{\mathrm{ADVI}}}\)** & **A\(\mathrm{{}_{\mathrm{ENDVI}}}\)** & **A\(\mathrm{{}_{\mathrm{GSRNDVI}}}\)** & **A\(\mathrm{{}_{\mathrm{GSRNDVI}}}\)** & **A\(\mathrm{{}_{\mathrm{GSRNDVI}}}\)** & **A\(\mathrm{{}_{\mathrm{GSRNDVI}}}\)** \\ \hline NDVI & 1.0000 & 0.9417 & 0.7065 & 0.9813 & 0.9083 & 0.9494 & 0.9951 & 0.9360 & 0.7345 & 0.9708 & 0.9365 & 0.9597 \\ GNDVI & 0.9417 & 1.0000 & 0.8663 & 0.9881 & 0.9779 & 0.9716 & 0.9639 & 0.9932 & 0.8616 & 0.9916 & 0.9884 & 0.9938 \\ BNDVI & 0.7065 & 0.8663 & 1.0000 & 0.7932 & 0.9037 & 0.8310 & 0.7536 & 0.8786 & 0.9331 & 0.8217 & 0.8516 & 0.8977 \\ GRNDVI & 0.9813 & 0.9881 & 0.7932 & 1.0000 & 0.9450 & 0.9734 & 0.9910 & 0.9796 & 0.8100 & 0.9958 & 0.9633 & 0.9851 \\ GRNDVI & 0.9083 & 0.9779 & 0.9037 & 0.9450 & 1.0000 & 0.9694 & 0.9119 & 0.9760 & 0.9421 & 0.9569 & 0.9993 & 0 green, and blue bands, and A\({}_{\rm VNDVI}\) are shown in Figure 1. The sensitive bands can be derived from the calculation formulas of the sensitive function (3) and A\({}_{\rm VNDVI}\) shown in Table 1: NIR--820\(\sim\)840 nm and 860-900 nm, Red--680-690 nm, Green--520-550 nm and 560-580 nm, and Blue--490-520 nm.
# No Free Lunch--Characterizing the Performance of 6 TiSCH When Using Different Physical Layers
## 6 Experimental Results
### End-to-End Reliability  
We call end-to-end reliability the portion of User Datagram Protocol (UDP) datagrams of each mode that reach the root, and use the counter in the datagrams to compute it. Table 3 shows PDR statistics over the last 15 min of the experiments, computed for all motes in the network. We expect close to 100% PDR in all cases (commercial TSCH implementation offer >99.999% PDR [30]).  
Besides possible bugs in our implementation, packet drop might be happening because of queue overflow. As shown in Section 6.1, a lower link budget leads to higher DAG rank, and more relaying, which increases the likelihood of filling up the packet buffer and dropping packets. Figure 7 show the Cumulative Density Function of the queue occupancy values reported by the motes, in the same period corresponding to Table 3. Even though Table 2 correctly indicates the buffer can hold up to 15 packets, 5 additional buffer entries are reserved for control frames such as Enhanced Beacons and acknowledgements. When the number of occupied entries in the buffer exceeds 15, more data packets cannot be admitted into the buffer and are dropped. Motes running O-QPSK 2.4 GHz and OFDM 868 MHz drop packets 3% and 1% of the time, respectively. FSK 868 MHz does not suffer buffer overflow, as the network is much shallower.  
Figure 6: Contention and slow neighbor discovery cause the nodes’ rank to be artificially high at the beginning of network formation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Min** & **Average** & **Median** & **Max** & **StDev** \\ \hline FSK 868 MHz & 100.0\% & 100.0\% & 100.0\% & 100.0\% & 0.0\% \\ OFDM 868 MHz & 93.8\% & 99.8\% & 100.0\% & 100.0\% & 0.9\% \\ O-QPSK 2.4 GHz & 73.3\% & 98.1\% & 100.0\% & 100.0\% & 6.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: End-to-end Packet Delivery Ratio (PDR) statistics over all motes in the network, computed over the last 15 min of the experiments.
## 3 Metabolic Responses to Different Antibiotic Classes
### Antimetabolite Antibiotics: Direct Effect and Biosynthetic Compensation  
Antimetabolites, such as sulfonamides, trimethoprim, and fosfomycin, exert their antibacterial activity by interfering with biosynthetic pathways essential for bacterial survival. Specifically, sulfonamides and trimethoprim act by sequentially inhibiting enzymes in the folic acid synthesis pathway: the former block dihydroproteate synthase, while the latter inhibits dihydrofolate reductase, thereby impairing the production of thymidine and purine nucleotides required for DNA synthesis [64]. Fosfomycin, by contrast, inhibits an early and critical step in bacterial cell wall biosynthesis by irreversibly blocking the enzyme MurA (UDP-N-acetylglucosamine enolpyruvyl transferase), thus preventing the formation of muramyl precursors and resulting in rapid bacterial lysis [65]. The inhibitory effect of trimethoprim on the folate pathway can be partially circumvented by _E. coli_ through the activation of adaptive metabolic circuits, including the uptake of exogenous purine bases and the modulation of de novo nucleotide synthesis [66]. This condition triggers adaptive transcriptional responses, including derepression of purine biosynthesis genes mediated by reduced activity of the PurR repressor, and indirectly, activation of the stringent response via RelA, leading to the synthesis of (p)ppGpp. The combined effect of these mechanisms promotes the expression of alternative biosynthetic pathways aimed at restoring nucleotide homeostasis [67]. In _Escherichia coli_, inhibition of peptidoglycan synthesis by fosfomycin may induce adaptive metabolic responses, including modulation of the glucosamine-6-phosphate metabolism and activation of the pentose phosphate pathway, in order to support the biosynthesis of cellular precursors and the production of NADPH [68]. Table 3 provides a comprehensive synthesis of antibiotic-specific metabolic rewiring strategies that modulate bacterial survival and stress tolerance (Table 3). Metabolic Pathways Involved in Rewiring: Glycolysis, TCA, Oxidative Stress, Amino Acid Metabolism, and Membrane Biosynthesis  
Bacterial adaptive responses to antibiotics extend beyond growth modulation and entail a profound reprogramming of central metabolic circuits. This section examines the major biochemical nodes subjected to rewiring, highlighting their roles in tolerance, persistence, and the evolution of resistance.