esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
In the RSM technique, responses are optimized by changing different variables [26]. It shows a relationship between measured responses and various input variables. RSM is helpful to optimize, design, develop, or improve any procedure where responses are affected by various factors [27]. There are six steps of the RSM method, (1) to declare the input variables and the required output responses, (2) To adopt an experimental design plan, (3) to perform regression analysis with the quadratic model of RSM, (4) to find the variables that significantly affect the output responses by carried out statistical analysis of variance (ANOVA) for the independent input variables, (5) to determine the situation of the quadratic model of RSM and decide whether the model of RSM needs screening variables or not, (6) to optimize, conduct confirmation experiment and verify the predicted performance characteristics. Many studies have been carried out in processing industries to optimize process conditions such as extraction of oil, protein, phenolic compounds, pigments, polysaccharides using RSM [28].
## 4 Experiment Overview
### Communication Protocol  
In all our experiments, each sensor executed the same protocol, which we implemented in Contiki OS 2.7 [34]. For a deployment using \(N\) sensors, all nodes were initialized with identifiers ranging from 1 to \(N\). To avoid internal interferences, nodes communicated using a round-robin algorithm. When it was a node's turn to send messages, it broadcasted \(M\) messages at a frequency of one message per 500 ms. This upper layer synchronization allowed us to disable the carrier sense mechanism and practically use a transparent MAC protocol.  
The transmitted messages contain the identifier of the sender, a sequence number, the transmission power used by the message sender and a dummy application payload. Upon message reception, a node stores in its external flash memory all the information found in the message, as well as information on the RSSI and the link quality indicator (LQI) provided by the receiver radio module. This communication protocol is described by Algorithms 1-3. Algorithm 1 was executed by each sensor at initialization, while Algorithms 2 and 3 were callbacks called for messages transmission and reception respectively.  
Figure 3: Sensors deployment.  
```
0:\(N\): Number of sensors, \(F\): Communication frequency, \(P\): Transmission power, \(\varphi\): Transmission period, \(i\): Sensor rank
1:ConfigureCommunicationFrequency(F)
2:ConfigureTransmissionPower(P)
3:\(TxStartDate\leftarrow\) GetTxStartDate(\(i\), \(N\), \(\varphi\))
4:SetTimer(\(TxStartDate\), TxCallBack)
```  
**Algorithm 1** Sensor initialization--called at sensor initialization.  
```
0:\(PayLoadSizeList\): The list of different payload sizes, \(P\): Transmission power
1:sequenceNumber \(\leftarrow\) 0
2:for plsize in \(PayLoadSizeList\)do
3: SendBroadCastMessage(plsize, sequenceNumber, P)
4: SetTimer(Now+\(\varphi\), TxCallBack)
5: sequenceNumber \(\leftarrow\) sequenceNumber + 1
6:endfor
```  
**Algorithm 2** TxCallBack--called when a sensor wants to send messages.  
```
0:\(RcvMessage\): The new message received
1:RSSI \(\leftarrow\) GetRSSI(\(RcvMessage\))
2:LQI \(\leftarrow\) GetLQI(\(RcvMessage\))
3:SenderID \(\leftarrow\) GetSender(\(RcvMessage\))
4:sequenceNumber \(\leftarrow\) GetSequenceNumber(\(RcvMessage\))
5:PayLoadSize \(\leftarrow\) GetPayLoadSize(\(RcvMessage\))
6:P \(\leftarrow\) GetTransmissionPower(\(RcvMessage\))
7:SaveDataToExternalMemory(RSSI, LQI, SenderID, sequenceNumber, PayLoadSize, P)
```  
**Algorithm 3** RxCallBack--called each time a message is received.
## 2 Types of Nanomaterials Used in Cosmetic Formulations
### Metal Nanoparticles  
Metal nanoparticles are non-specific anti-bacterial agents of wide spectrum, that have been proposed as an alternative to classical antibiotics to overcome bacterial resistance [98]. The most common types of metal nanoparticles are based on silver, zinc oxide, gold, or copper. Their interest as ingredient of cosmetic formulations is attributed to their antifungal and antibacterial properties and can be used as preservatives. Besides, these particles are reported to be biocompatible, non-toxic, and chemically inert, and of high physicochemical stability [99]. Silver has been used as an antimicrobial agent for wound healing, and there are several available dressings containing AgNO\({}_{3}\)[4; 5].  
[PERSON] et al. produced cosmetic emulsions of silver (mean size 8-140 nm) and gold (mean size 13-99 nm) nanoparticles, and demonstrated their fungicidal activity [100]. The authors also showed that the skin permeability of silver and gold nanoparticles was dependent on their concentration in the cosmetic emulsions. A pectin-based silver nanocomposite film has been described for the transdermal delivery of donepezil [101]. The developed nanocomposite induced \(<\)9% hemolysis in sheep erythrocytes, demonstrating to be non-toxic and biocompatible and with improved antimicrobial activity against _Staphylococcus aureus_ and _Escherichia coli_.  
Cosmetic products manufactured by L'Oreal with gold nanoparticles are already on the market [102], with the purpose of enhancing the firmness and elasticity of skin and delaying the aging process [103].
# A Study of MANET Routing Protocols in Heterogeneous Networks: A Review and Performance Comparison
###### Abstract  
Mobile ad hoc networks (MANETs) are becoming a popular networking technology as they can easily be set up and provide communication support on the go. These networks can be used in application areas, such as battlefields and disaster relief operations, where infrastructure networks are not available. Like media access control protocols, MANET routing protocols can also play an important role in determining network capacity and system performance. Research on the impact of heterogeneous nodes in terms of MANET performance is required for proper deployment of such systems. While MANET routing protocols have been studied and reported extensively in the networking literature, the performance of heterogeneous nodes/devices in terms of system performance has not been fully explored yet. The main objective of this paper is to review and compare the performance of four selected MANET routing protocols (AODV, OLSR, BATMAN and DYMO) in a heterogeneous MANET setting. We consider three different types of nodes in the MANET routing performance study, namely PDAs (fixed nodes with no mobility), laptops (low-mobility nodes) and mobile phones (high-mobility nodes). We measure the QoS metrics, such as the end-to-end delays, throughput, and packet delivery ratios, using the OMNeT++-network simulator. The findings reported in this paper provide some insights into MANET routing performance issues and challenges that can help network researchers and engineers to contribute further toward developing next-generation wireless networks capable of operating under heterogeneous networking constraints.  
heterogeneous; MANET; AODV; OLSR; BATMAN; DYMO; heterogeneous networks 2025 14 872
## 3 Software and Image Processing
Several techniques like (i) manual selection, (ii) predeterminate single wavelength, (iii) principal component analysis (PCA) and (iv) cluster analysis can be used in order to create a \"mask\" capable of isolating the ROI [14] as reported in Figure 5.  
Figure 4: Schematic representation of a hyperspectral image processing analysis. Finally, spectra may need to be preprocessed to reduce the noise associated with the data, minimize artifacts, and amplify the information. In principle, well-known spectral preprocessing methods applied to spectroscopy are also used in hyperspectral imaging. However, preprocessing techniques must be handled with care, taking into account the possible loss of some information, resulting in a decrease in spatial and/or spectral resolution, and the final goal of the analysis. Figure 6 graphically summarizes the most common mathematical pretreatments performed on NIR spectra labeled according to the percentage of moisture content (MC %) of the samples (Figure 6a shows the raw spectra). Many smoothing methods (e.g., Gaussian filter, moving average, median filter, and Savitzky-Golay (SG) smoothing) are used as a filter to reduce spectral noise. Additive and multiplicative effects are very common in optical data, influencing the global intensity (typically arising from unwanted light scattering) and/or producing baseline vertical shifts (offsets). For these reasons, treatments like standard normal variate (SNV, Figure 6b) transform or the multiplicative scatter correction (MSC Figure 6c) are generally applied. Derivation methods (usually first and second derivative, Figure 6d) are also used to enhance the resolution and minimize the spectra offset and drifts. Finally, different column-wise normalization and/or scaling treatments (like mean centering, autoscaling, range scaling, Pareto scaling, etc.) become fundamentals to homogenize the data to perform a correct explorative and modeling phase (Figure 6e). Since the variance values depend on the scale of the variables, it becomes difficult to compare and impossible to combine information from variables of different nature, unless properly normalized [18; 19; 20]. Moreover, different combinations of these methods applied simultaneously can also be used for signal processing [21]. Figure 5: Depiction of three different methodologies (k\(-\)means analysis, wavelength selection, principal component analysis) for background. Reprinted with permission from Ref. [17]. 2019, Elsevier.
# A Dynamic Framework for Community-Facility Siting with Inter-Community Competition
## 5 Results
### Comparison Between BOMSDM and the Realistic Solution  
The results of the realistic solution and optimized solutions of BOMSDM with the same number of facilities are illustrated in Figure 4. It shows that the realistic solution allocates facilities to 60 communities, with an SDGC of 0.226 and an SSR of 0.283, indicating relatively good equity (though not reaching the optimal level below 0.2) but poor efficiency.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Iteration**} & \multicolumn{3}{c}{**Service Distribution Gini Coefficient**} & \multicolumn{3}{c}{**Service Supply Rate**} & \multicolumn{3}{c}{**Community Allocation Rate**} \\ \cline{3-11}  & & & **Mean** & **Median** & **Minimum** & **Mean** & **Median** & **Maximum** & **Mean** & **Median** & **Maximum** \\ \hline \multirow{3}{*}{Scenario I with p\({}^{\text{z}}=72\)} & BOMSDM/ & 500 & 0.027 & 0.025 & 0.002 & 0.390 & 0.392 & 0.405 & 0.911 & 0.911 & 0.911 \\  & Model II & 1000 & 0.008 & 0.009 & 0.001 & 0.412 & 0.412 & 0.415 & 0.609 & 0.614 & 0.620 \\ \hline \multirow{3}{*}{Scenario II with p\({}^{\text{z}}=79\)} & BOMSDM & 300 & 0.047 & 0.049 & 0.036 & 0.418 & 0.418 & 0.419 & 1.000 & 1.000 & 1.000 \\  & Model I & 1500 & 0.020 & 0.023 & 0.001 & 0.428 & 0.429 & 0.432 & 0.722 & 0.722 & 0.734 \\ \cline{1-1}  & Model II & 500 & 0.008 & 0.007 & 0.001 & 0.438 & 0.440 & 0.448 & 0.635 & 0.633 & 0.671 \\ \cline{1-1}  & Model III & 1000 & 0.022 & 0.024 & 0.013 & 0.412 & 0.416 & 0.426 & 1.000 & 1.000 & 1.000 \\ \hline \multirow{3}{*}{Scenario III with p\({}^{\text{z}}=119\)} & BOMSDM & 1000 & 0.009 & 0.010 & 0.000 & 0.591 & 0.595 & 0.597 & 1.000 & 1.000 & 1.000 \\ \cline{1-1}  & Model I & 1500 & 0.006 & 0.006 & 0.000 & 0.562 & 0.567 & 0.571 & 0.822 & 0.823 & 0.835 \\ \cline{1-1}  & Model II & 1000 & 0.016 & 0.014 & 0.002 & 0.590 & 0.590 & 0.595 & 0.807 & 0.810 & 0.823 \\ \cline{1-1}  & Model III & 1500 & 0.012 & 0.011 & 0.002 & 0.503 & 0.505 & 0.510 & 1.000 & 1.000 & 1.000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: A comparison between the result of the BOMSDM and Models I, II, and III.  
Setting the total number of facilities to 72 aligns with the realistic number of recycling stations, where the BOMSDM produces a total of 29 Pareto optimal solutions (Table S1). Figure 4 shows that in all solutions, facilities are evenly distributed among 72 different communities, and the SDGCs and SSRs are significantly improved compared to the realistic solution. All solutions achieve SDGCs below 0.2, with a mean value of 0.027, representing an 88.1% enhancement over the realistic solution. The average SSR is 0.390, which is a 37.8% improvement over the realistic solution. The highest SSR recorded is 0.405, representing a 43.1% increase compared to the realistic solution.
## 3 Results and Discussion
### Characterization of Nanoparticles of Garlic Extract by TEM  
In order to confirm the nature of the nanoparticles from the ultrasonicated garlic extract, TEM was used and it revealed that the garlic nanoparticles consisted of random sized particles, a few rods, and a few spherical particles (Figure 6), which were randomly dispersed, and had small sizes (less than 50 nm). The antibacterial property of garlic extract could be attributed to its bioactive particles' small size and morphology since particle size and surface area influence the interaction between chemical compounds and biological systems. Reduction in the size of bioactive particles leads to an increased surface area coming in contact with microorganisms, enhancing their interaction and leading to antibacterial activities [54], this is in line with other studies that have been done on the antibacterial properties of nanoparticles and have indicated that small and formless particles are the most effective particles to inhibit the growth of bacteria [55; 56]. Various writings have documented how nanoparticles act and highlighted that they inhibit bacterial growth by anchoring to and penetrating the bacterial cell wall. When the reach the inside of the bacteria, they start modulating cellular signaling by dephosphorylating putative key peptide substrates on tyrosine residues leading to the inhibition of bacteria growth [57].  
Figure 5: FTIR spectrum of nanoparticles from garlic extract.
## 1 Introduction
The objective of this study was to bioprint hiPSC-derived neural tissues that matured into MNs in combination with a novel fibrin-based bioink using the RX1 tm bioprinter in rapid manner. Here, we analyze the composition of 30-day cultures of bioprinted hiPSC-derived NPCs exposed to the small molecules, CHIR99021 (CHIR), SB431542 (SB), LDN-193189 (LDN), purmorphamine (Puro), and retinoic acid (RA), to promote MN differentiation. Previous studies have demonstrated that the small molecules, SB431542 and DMH1, induce dual-SMAD inhibition, a requirement for neural pattering in stem cells [24; 25]. The molecule, LDN-193189, is also a BMP inhibitor and can be used instead of DMH1 [24]. Moreover, the small molecule, CHIR99021, activates the Wnt pathway, enhancing neural patterning of pluripotent stem cells [25]. In addition, the combination of RA and puro promote pluripotent stem cell differentiation into motor neurons [25; 26; 27]. Cylindrical constructs were printed using the RX1 tm bioprinter with seven layers and a 1 cm diameter in a process that took under 5 min to generate four constructs. Cell viability, immunocyt (ICC), and flow cytometry studies were performed to analyze the bioprinted constructs and their differentiation into MNs.
# Biomimetic Origami: Planar Single-Vertex Multi-Crease Mechanism Design and Optimization
###### Abstract  
Space exploration and satellite communication demand lightweight, large-scale, and highly deployable structures. Inspired by the folding mechanism of drilled lizards and origami mechanisms, this study explores a deployable structure based on the single-vertex multi-crease origami (SVMCO) concept. The design focuses on crease distribution optimization to enhance deployment efficiency. A mathematical model analyzes the relationship between sector angles of three types of facets and structural performances, providing guidelines for achieving optimal deployment. Drawing from the rib patterns of drilled lizards, a rib support system for thick-panel mechanisms was designed and verified through a physical prototype. The structure achieves smooth-surface deployment with fewer supports, offering a lightweight and efficient solution for deployable systems.  
biomimetic design; origami mechanisms; single-vertex multi-crease origami; deployable structures; origami crease optimization Article
# Traceability of the Micro Scale Pipe Viscometer for Traceable Calibration of Dynamic Viscosity
## 2 Materials and Methods
### Calibration of All Sensors Measuring Physical Quantities
#### 2.2.1 Piston Prover  
The piston prover generates the volume flowrate, which is traceable to time and length [15]. The inner diameter of conventional glass syringes ILS from WICOM International GmbH or homemade stainless steel pistons are calibrated by the length laboratory at various positions and an average inner diameter with corresponding uncertainty is determined [16; 17]. The position of the plunger of the piston is measured using the linear measuring system LMS-LIMES80-130 of the high-precision linear stage LIMES 80-130-Hi00 from OWIS GmbH (Staufen im Breisgau, Germany), which is calibrated using interferometry to obtain traceability. The linear measuring system sends pulses that are counted using an FPGA and a timestamp of the FPGA is recorded in order to form a pair of values that are the position and the corresponding timestamp. The real-time speed is then determined using a linear fit of the position as a function of time. The counter clock on the FPGA is calibrated and thus the timestamps are traceable, obtaining the speed of the piston and the calculated volume flowrate traceable to length and time.  
Figure 1: Schematic setup of the pipe viscometer with temperature (T) and pressure (P) sensors upstream and downstream of the microtube.  
Figure 2: The micro pipe viscometer at METAS: (**A**) piston prover, (**B**) temperature sensors, (**C**) pressure sensors and (**D**) the glass microtube with an inner diameter of 0.13 mm and a length of 200 mm.
# Target Identification via Multi-View Multi-Task Joint Sparse Representation
## 4 Multiple Feature Extraction Methods for Target Identification
### Local Binary Pattern (LBP) Feature Extraction Method  
LBP is a feature extraction method for local texture features in an image. Its essence is to describe the relationship between pixels in an image and pixels in its neighborhood. LBP has the advantages of gray constancy and rotation constancy. The algorithm for extracting LBP features from the ship database is as follows:  
Step 1: Divide the manually drawn object of interest box into 16 \(\times\) 16 cells;  
Step 2: Compare each pixel of the ship sample with the gray value of the surrounding eight pixels, and convert the binary results into decimal numbers;  
Step 3: Calculate the LBP value of each pixel in each cell, count the frequency of LBP value of each pixel, and obtain the histogram;  
Step 4: Splice the statistical histograms of each cell into separate feature vectors.  
The LBP's texture feature of the ship is presented in Figure 6, and the histogram of the ship is shown in Figure 7.  
Figure 5: HOG feature extraction image of the ship.  
Figure 6: LBP texture image of the ship.  
In Figure 6, the principle of LBP is to compare the gray value of a pixel in the ship image with the gray value of the pixel around it, and then convert the comparison results into binary mode. From Figure 6 it can be seen that after LBP feature extraction the remarkable ship texture feature map is displayed, and it has the characteristics of gray scale constancy and rotation constancy. From Figure 7, most of the energy is distributed in 59 histograms with high probability. Taking into account the compression of ship eigenvectors, it is very reasonable for us to use these 59 histograms to describe the ship texture in this experiment.
# An Energy-Efficient and Secure Routing Protocol for Intrusion Avoidance in IoT-Based WSN
## 5 Network Model and Discussion
### Numerical Results of ESR Protocol  
In this section, we evaluated the numerical results of ESR protocol in the comparison of LEACH-ER, LEACH-MAC and CCBRP. The numerical results of ESR protocol were measured with other solutions under different network topologies. The network topologies were varied based on a different number of nodes from 100 to 500. To establish the dynamic network topologies, all the nodes were distributed randomly over the sensor field. The comparison among protocols was performed with respect to network lifetime, average end-to-end delay, packet delivery ratio, average communication cost, network overhead and the frequency of route re-discoveries parameters. Moreover, error bars based on varying standard deviations were used to indicate error or uncertainty in the measurement of experimental results.
## 5 Improved DSR Using Phonological Features in Vocabulary-
### Results
#### 5.4.1 Qualitative Evaluation of PFs  
As introduced before, PFs are used as an intermediate representation to achieve phone recognition. If the PFs of a speech frame are accurately inferred, the dot product of the corresponding phone for this frame will be the largest among the phone vocabulary, leading to successful recognition.  
To illustrate how well PFs are inferred and utilized during phone recognition, we plot the PFs of a canonical and a dysarthric speech sample extracted from the PF models trained with canonical and dysarthric data, respectively. The chosen canonical speech sample is from the validation set of a canonical speaker _CF02_ of **UASPEECH**, while the dysarthric speech sample is from the test set of a dysarthric speaker _M10_ of **UASPEECH**. Both samples contain the word _'line'_, which has phonetic transcription _'[l, a, j, n]'_ (via _Epitran_), and the phone sequences are accurately recognized using the corresponding PFs model.  
Figure 4 shows plots of the extracted PFs from the canonical (Figure 4b) and dysarthric (Figure 4c) speech samples, alongside the standard PFs for reference (Figure 4a). In the plots, the \(x\)-axis represents each PF, while the \(y\)-axis is the phone sequence.  
Figure 4: Plots of standard and extracted phonological features of the phone sequence _’[l, a, j, n]’_. (**a**) Standard PFs of the phone sequence; (**b**) extracted PFs of a canonical speech sample inferred by a PFs model trained with canonical data; (**c**) extracted PFs of a dysarthric speech sample inferred by a PFs model trained with dysarthric data.  
The plots show that the extracted PFs match the ideal values for both types of speech, with smaller absolute values for dysarthric speech. This less confident estimation reflects the poor pronunciation in dysarthric speech.  
Most misestimations happen when a zero value is expected, but this will not lead to inaccurate recognition as explained above. Non-zero PF elements can also be extracted incorrectly. The PF'sg' (spread glottis) should be '\(-\)1' for all four phones but are often estimated as positive in both canonical and dysarthric examples. These wrong estimations will also not lead to a higher dot product for other phones as these PFs are always '\(-\)1' across the whole phone vocabulary. Another mistake in the dysarthric example is the '_round_' PF of phone '_l_' being extracted as positive instead of negative. Despite this, phone recognition remains accurate as no other phones in the vocabulary have the same PFs as '_l_' but differ in the '_round_' PF. Such conditions illustrate that while accurate estimation of the entire 25-dimensional PF vector is expected for phone recognition, certain inaccuracies do not necessarily lead to errors. This robustness may contribute to the **PFs** method's ability to mitigate overfitting.