esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Instrument Conditions  
#### 2.5.1 GC-MS  
Samples subjected to migration testing via liquid injection were analyzed using a 6890N gas chromatograph with a 5977D mass spectrometry detector (Agilent Technologies, Santa Clara, CA, USA). Separation was performed on an HP-5 MS capillary column (30 m \(\times\) 0.25 mm i.d. \(\times\) 0.25 \(\upmu\)m film thickness) obtained from Agilent Technologies (Madrid, Spain).  
A total of 1 \(\upmu\)L of migration extract was injected in the splitless mode. High-purity helium (99.99%) was used as the carrier gas at a constant flow rate of 1.0 mL/min. The GC oven temperature program was as follows: an initial temperature of 50 \({}^{\circ}\)C was held for 5 min, followed by an increase to 300 \({}^{\circ}\)C at a ramp rate of 10 \({}^{\circ}\)C/min. A solvent delay of 5 min was applied. Data acquisition was performed in the SCAN mode. Volatile compounds were identified by comparing the acquired mass spectra with those in the National Institute of Standards and Technology (NIST) Chemistry WebBook spectral library integrated into the instrument software. The minimum requirement of mass spectrum match to consider a volatile compound by NIST was 80%. The standard of the compounds was analyzed for confirmation and quantitation using calibration curves from an external standard compound in ethanol, based on the same time period and having a chemical structure similar or close to that of the obtained substance.
# Novel Scoring for Energy-Efficient Routing in Multi-Sensored Networks
## 3 The Proposed Mechanism
### Preliminary Formation for the Proposed Work
#### 3.1.3. Closed Neighbors  
Among the neighboring nodes, some nodes are relatively placed closer than other neighbors. Such nodes can be considered as closed neighbors. However, it is more appropriate to consider this distance based on the received signal strength indicator (RSSI) mechanism than the physical distance. An RSSI-based distance value is calculated by Equation (2) and is used to determine the set of neighbor nodes [42].  
\[DST_{i,j}=P_{i,j}^{-1}(d),\ \ \ P_{i,j}(d)=\frac{p_{i}\ G_{i}\ G_{j}\ \Lambda^{2}}{ \left(4\pi\right)^{2}d^{2}} \tag{2}\]  
where \(p_{i}\) denotes the transmission power, and \(G_{i}\) and \(G_{j}\) denote the antenna gains of nodes \(I\) and \(j\), respectively. Nodes \(i\) and \(j\) are the transmitter and receiver, respectively. \(\Lambda\) indicates the wavelength (meter) of the transmission signal. \(p_{i,j}\) is the receiving power at the node \(j\) when the inter-node distance is \(d\). In a constrained situation where the sensor nodes are deployed in a controlled environment, the Pythagoras two-dimensional distance formula can also be used for creating the set of CNs. Moreover, if nodes are deployed in irregular, unaligned, or not plane areas, then the same can be converted into the three-dimension distance formula.  
Each node keeps a separate set of closed neighbors (CNs) with their estimated location and energy information. If a node has a frequent number of CNs, then it means that the node has less opportunity to become a forwarder of other nodes. Nodes with a fewer number of CNs are vital and can perform more than others. The set of CNs can be calculated as follows:  
\[CN_{i}=\left\{j:DST_{i,j}\leq\ DST^{tr}\right\}\]  
\(DST_{tr}\) is a threshold value used to limit the succeeding nodes to being _CNs_ with a specific node. This value can be wisely defined by the consideration of the total number of nodes, nodes' placements, and the transmission range of the nodes. If the number of nodes is higher in a particular field, then we can assume that the nodes are more densely located. Similarly, the nodes with lower transmission power will make most of the nodes directly unreachable to each other. The value of \(DST^{tr}\) should be less than the maximum RSSI value in the network; otherwise, the parameters of the _CNs_ will have no or an erroneous impact on the mechanism. Moreover, if we use a very small \(DST_{tr}\) value, then it will return none or a smaller number of _CNs_. The proposed work uses the _CNs_ for the network optimization; therefore, the reasonable number of _CNs_ has a greater impact on the overall performance of our proposed work. In our work, for the experiments, we kept this value as half of the maximum possible RSSIs.
# Influence of Food Pigments and Thermal Aging on the Color Stability of Denture Base Resins
###### Abstract  
Color stability of acrylic resins is essential for preserving the aesthetic appearance of denture bases over time. This study explores how food pigments and thermal changes affect the color stability of commonly used denture base resins. Four acrylic resins were tested: three heat-cured acrylic resins with different characteristics (Zhermack(r)Villacryl H Plus V2, H Plus V4, and H Rapid FN V4) and one self-cured acrylic resin (Zhermack(r)Villacryl S V4). To simulate the oral environment, the resins underwent 1000 thermal cycles between 5 \({}^{\circ}\)C and 55 \({}^{\circ}\)C, followed by a 7-day immersion period in beverages such as coffee, red wine, a caramel-colored soft drink (cola), and distilled water (control), forming sixteen group of specimens (n = 5). Color changes (\(\Delta\)E) were measured using the VITA Easyshade V(r) spectrophotometer, following the CIEDE2000 standard. The findings revealed that thermal aging caused noticeable color changes in all resins (\(p<0.001\)). Red wine led to the most intense discoloration, followed by coffee. The caramel-colored soft drink caused moderate staining, while distilled water had a negligible effect. The type of polymerization did not affect the degree of discoloration, as no significant differences were found between the resins after exposure to beverages (\(p>0.05\)). Overall, this study highlights how both internal and external factors impact the appearance of acrylic resins. Thermal aging can accelerate polymer degradation, while pigments in beverages cause visible staining. Among the tested beverages, red wine proved to be the most aggressive due to its high pigment concentration and low pH. These findings emphasize the need for improved material formulations to enhance the longevity and aesthetic performance of dentures.  
acrylic resins; polymerization; food pigment staining; color stability; thermal aging; oral prosthetic materials 2025  
23
# Non-Orthogonal eMBB-URLLC Radio Access for Cloud Radio Access Networks with Analog Fronthauling
## 2 System Model
### Performance Metrics
#### 2.3.2 URLLC  
Differently from eMBB, URLLC service is mainly focused on low-latency and reliability aspects. Due to the short length of URLLC packets, in order to guarantee ultra reliable communications, we need to ensure that the error probability for each URLLC UE, denoted as \(\Pr[E_{U}]\), is bounded by a predefined value \(\epsilon_{U}\) (typically smaller than \(10^{-3}\)) as  
\[\Pr[E_{U}]\leq\epsilon_{U}. \tag{13}\]  
Concerning latency, we define the maximum access latency \(L_{U}\) as the maximum number of minislots that an URLLC UE has to wait before transmitting a packet. Finally, although rate enhancement is not one of the goals of URLLC service, it is still important to evaluate the per-UE URLLC rate that can be guaranteed while satisfying the aforementioned latency and reliability constraints. Similarly to (12), the per-UE URLLC rate is defined as  
\[R_{U}=\frac{\log_{2}(M_{U})}{n_{F}}, \tag{14}\]  
where \(M_{U}\) is the number of URLLC codewords in the codebook used by the URLLC UE for each information packet.
## 3 Results
### Identification of DEGs and HGsT10 and PPI Network Construction for ESCC Tumor and Normal Tissues  
Our present study has 28 ESCC tumor tissues and 28 normal esophageal tissues. Via GEO2R online tools, we extracted 250 DEGs from GSE161533. Results showed that a total of 113 DEGs were downregulated (logFC \(<\) 0), and 117 DEGs were upregulated genes (logFC \(>\) 0) in both tumor and normal tissues noted in Tables S1-S3 (Supplementary File S1). The extracted genes were constructed as a PPI network using the STRING 2.0 database, designed to predict gene-gene interactions. The STRING database was used to build a network model, and the genes with a score of \(\geq\)0.4 were chosen. The resulted network  
Figure 1: Dataset GSE161533 samples validation. (**A**) Volcano plot using limma for the normal and tumor samples shows the up and downregulated genes. (**B**) UMAP test to predict the samples clearer separation. (**C**) Boxplot of normalized expression values for the dataset GSE161533. The dots represent the median of each sample in the center of each box. Its variation among samples reflects the amount of data normalized, with a relatively straight line suggesting a reasonable level of normalization. Module preservation test with an independent dataset.  
consisted of 195 nodes and 292 edges and an average local clustering coefficient of 0.395. The constructed network was reconstructed with the CytoScape 3.8.2. and HGsT10 were identified using the CytoHubba module (Figure 2A). It used the Maximal Clique Centrality (MCC) algorithm, the most effective method of finding hub nodes. The top ten genes ranked with MCC scores were deliberated as hub genes in this annotation. The hub genes selected from the PPI network using the MCC algorithm of the CytoHubba plugin are shown in Figure 2B.  
According to the MCC scores, the top ten highest-scoring genes, namely matrix metallopeptidase 9 (MMP9), matrix metallopeptidase 3 (MMP3), matrix metallopeptidase 1 (MMP1), C-X-C motif chemokine ligand 8 (CXCL8), secreted phosphoprotein 1 (SPP1), urokinase plasminogen activator (PLAU), matrix metallopeptidase 13 (MMP13), collagen type I alpha 1 chain (COL1A1), collagen type I alpha two chains (COL1A2), and plasminogen activator, urokinase receptor (PLAUR), were identified as the hub genes.
## 4 Results  
### Multi-Temporal InSAR Results  
As a result of multi-temporal InSAR analysis, two main products are produced; (1) mean deformation velocity maps in line-of-sight (LOS) direction, and (2) time-series of displacements for measurement points that are provided in the following subsections.
# Secure Physical Layer Network Coding versus Secure Network Coding +
## 4 Network with Three Source Nodes
### Secure PLNC  
#### 4.2.1 Use of Secure CAF  
Now, we assume the following assumption:  
**(A3)**: The channels over the pairs \((e_{1},e_{6})\), \((e_{2},e_{4})\), and \((e_{3},e_{5})\) are Gaussian MACs as in (2).  
That is, the eavesdropper is supposed to access only one of the information symbols at the intermediate (untrusted) nodes, which corresponds to Case 2 of Section 4.1. Then, using secure CAF [21], we construct our protocol.  
As the first step, we discuss the case when \(q\) is not a power of 2. In the Gaussian MAC \((e_{1},e_{6})\), we employ secure CAF so that the node \(I_{2}\) obtains the information symbol \(M_{1}\oplus M_{3}\). Similarly, \(I_{1}\) and \(I_{3}\) obtain the information symbol \(M_{2}\oplus M_{3}\) and \(M_{1}\oplus M_{2}\), respectively. Hence, the information symbols at every intermediate (untrusted) node are independent of the messages \(M_{1}\), \(M_{2}\), and \(M_{3}\). In the next step, the intermediates (untrusted) nodes \(I_{1}\), \(I_{2}\), and \(I_{3}\) transmit their received information symbols \(M_{1}^{\prime}\), \(M_{2}^{\prime}\), and \(M_{3}^{\prime}\) to the destination node \(D\) via the Gaussian MACs with three input signals. Then, applying separate decoding, the destination node \(D\) recovers the information symbols \(M_{1}^{\prime}\), \(M_{2}^{\prime}\), and \(M_{3}^{\prime}\). Using the method presented in Section 4.1.1, the destination node \(D\) obtains the original information symbols \(M_{1}\), \(M_{2}\), and \(M_{3}\).  
When \(q\geq 4\) is a power of 2, to apply the method given in Section 4.1.1, the node \(I_{2}\) needs to obtain the information \(M_{1}\oplus cM_{3}\). This task for \(I_{2}\) can be implemented by a secure CAF with a two-dimensional vector over the finite field \(\mathbb{F}_{2}\) by the prior conversion from \(M_{3}\) to \(eM_{3}\) at the node \(S_{3}\) before use of the Gaussian MAC \((e_{1},e_{6})\). The same method is applied to the Gaussian MACs \((e_{2},e_{4})\) and \((e_{3},e_{5})\). The remaining part of this protocol can be performed in the same way as the above.  
In the above way, the framework of the secure PLNC enables us to implement the secure code for an attack on an intermediate (untrusted) node by using secure CAF. That is, this code guarantees Security (S4). This protocol requires no additional random variable, unlike the protocol presented in Section 4.1.2.
# Dynamic Routing in Flying Ad-Hoc Networks Using Topology-Based Routing Protocols
## 3 Simulation Setup and Performance Metrics
### Performance Metrics
#### 3.2.3 Load (bits/s)  
Network load is the total number of packets transferred from the upper layer to the MAC layer. FANETs routing traffic are affected by the high network load, and by increasing the collisions of the control packets, hence slowing down the packet delivery ratio in the channel. The exploited bandwidth, processing time, and buffer availability at intermediate nodes results in the network load.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Area Dimensions & 1 km \(\times\) 1 km, 2 km \(\times\) 2 km \\ Altitude of UAVs & 40 m \\ Number of UAVs & 30, 100 \\ Directional Gain & 10 dBi \\ Frequency & 24 GHz \\ Physical Characteristics & IEEE 802.11 g \\ Data Rates & 54 Mbps \\ Packet Interval (s) & Exponential (1) \\ Packet Size (byte) & 1024 \\ Simulation Time & 200 s, 1200 s \\ Node Type & Mobile \\ Mobility Model & Random Way point \\ Speed of UAVs & 25 m/s, 60 m/s \\ Reception Power Threshold & -95 dBm \\ Transmission Power & 0.005 W \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.
# A New SEPIC-Based DC-DC Converter with Coupled Inductors Suitable for High Step-Up Applications
## 1 Introduction
The new SEPIC-based converter introduced in this paper employs coupled inductors and exhibits a static conversion ratio that is higher than the classical one. In Section 2, the development of the novel proposed converter and the dc analysis are presented. The theoretical waveforms, the peak-to-peak ripples, the semiconductor current and voltage stresses, the CCM operation conditions and the theoretical comparative study to other step-down/step-up topologies are presented in Section 3. The next section provides a design example. The simulation and the experimental results developed are presented in Section 5. In Section 6, the suitability and feasibility of the proposed SEPIC converter in a photovoltaic application is proven. The perturb and observe (P&O) method will be implemented as a MPPT algorithm, and the whole PV system will be validated by both simulations and experiments. The last section is dedicated to some discussions.
## 2 Materials and Methods
### Preprocessing of Multi-Platform Laser Point Cloud  
Based on the previous analysis, it is known that the TLS system is more accurate and the HLS system is more flexible, so they are chosen to capture the spatial information of the underground mine. However, the TLS system adopts a self-defined coordinate inside the instrument, and the coordinate origin is located in the center of the laser scanner. As a result, the multi-station point clouds acquired by the TLS system use different coordinate systems, and it is necessary to lay out and extract sphere targets to align the multi-station TLS point clouds. At the same time, the HLS point cloud is solved by data processing software.  
The huge amount of point cloud data inevitably contains redundant and noisy points, which not only increases the point cloud operation time but also seriously affects the registration accuracy. In our study, considering the scanning density of a 3D laser scanner decreases with the increase in distance, to acquire the uniform point cloud characterizing an underground mine, the uniform sampling method based on spatial distance is used for point cloud downsampling.  
(1) Calculate the boundary value of the whole object according to the coordinates of the point cloud, where the max and min values are defined as \(p_{\text{max}}=\{x_{\text{max}},y_{\text{max}},z_{\text{max}}\}\) and \(p_{\text{min}}=\{x_{\text{min}},y_{\text{min}},z_{\text{min}}\}\), respectively.  
(2) According to the scanning scenes, the voxel size \(r\) for different point cloud models is set. The voxel size determines the sampling speed and the point cloud density.  
(3) The number of voxels \(n_{1},n_{2},n_{3}\) along the coordinate axes \(X,Y,Z\) of the point cloud model is calculated using Equation (1):  
\[\{n_{1},n_{2},n_{3}\}=\left\{\left\lfloor\frac{x_{\text{max}}}{r}\right\rfloor- \left\lfloor\frac{x_{\text{min}}}{r}\right\rfloor+1,\,\left\lfloor\frac{y_{ \text{max}}}{r}\right\rfloor-\left\lfloor\frac{y_{\text{min}}}{r}\right\rfloor+ 1,\,\left\lfloor\frac{z_{\text{max}}}{r}\right\rfloor-\left\lfloor\frac{z_{ \text{min}}}{r}\right\rfloor+1\right\} \tag{1}\]  
(4) The voxel index to which each point \(p(x,y,z)\) belongs is calculated by Equation (2), and then the points with the same value are put into the same voxel grid.  
(2)  
Figure 1: Flowchart of multi-platform point cloud registration.  
(5) For each voxel grid of the point cloud model, the distance \(d\) between each point in the voxel and the center of the voxel can be acquired. In our study, the point with the smallest distance is retained, i.e., the point closest to the center of the voxel is retained to represent the voxel. It is characterized by a uniform distribution of sampling points, which does not move the point cloud and is more accurate.
###### Abstract  
Acoustic waves are attenuated by fish schools as they propagate through the ocean. The attenuation by fish schools is not currently considered in fishery acoustics and sonar applications, especially at mid-frequency bands. In this study, fish school attenuation experiments were conducted with a number of individual fish in situ in a net cage at mid-frequency bands (3-7 kHz). The target fish species was the Japanese horse mackerel (_Trachurus japonicus_), which typically forms fish schools in the coastal ocean of northeastern Asia. The attenuated acoustic waves were measured for the cases of non-net, only net (0), 100, 200, 300, 400, and 500 individual horse mackerel in the net cage. Results showed that the acoustic signal attenuation increased with the number of horse mackerels. The mean and maximum attenuation coefficients were approximately 6.0-15.4 dB/m and 6.5-21.8 dB/m for all frequencies, respectively. The measured attenuation coefficients were compared with the ones from previous studies to propose new regression models with normalized extinction cross-sections of weight and length of fish. This study confirmed that the fish school attenuation could not be ignored and compensated at mid-frequencies in the ocean. These results would be useful for fishery acoustics, especially in the development of scientific echo-sounder, and naval applications of sonar operations and analysis.  
sound attenuation; fish schools; mid-frequency; in situ measurement; Japanese horse mackerel +
Footnote †: journal: Applied Sciences
## 2 Chemical Synthesis of Nucleoside Di- and Triphosphates
### Synthesis of Nucleoside Di- and Triphosphates via Dichlorophosphate Intermediate  
[PERSON] and co-workers described another method for the synthesis of the P-O-P bond. They used the [PERSON]'s protocol [70], the reaction of unprotected nucleosides with POCI\({}_{3}\) in trimethyl phosphate as a solvent, to generate reactive dichlorophosphate **12** intermediate. After adding tetraalkylammonium phosphate salts and hydrolysis in triethylammonium bicarbonate (TEAB) buffer (pH 7.5), various nucleoside di- or triphosphates could be obtained (Scheme 9) [71]. [PERSON]'s approach was recently optimized using modern techniques to control the progress of the reaction and the application of liquid chromatography to isolate the products (NTPs, 51-74% yields) [72].  
Interestingly, nucleoside 5\({}^{\prime}\)-O-dichlorophosphates of type **12** were prepared previously using tetrachloropyrophosphate (P\({}_{2}\)O\({}_{3}\)Cl\({}_{4}\)) as a phosphorylating agent; however, they were not converted into pyrophosphates, but hydrolyzed to nucleoside 5\({}^{\prime}\)-monophosphates [73; 74].  
The main advantage of [PERSON]'s approach lies in the use of unprotected nucleosides as substrates. Unfortunately, this method, due to a strong activation of the phosphorus center in intermediate **12**, required difficult-to-control conditions to give satisfactory results [72]. In order to overcome these drawbacks, [PERSON] and co-workers proposed using PPN pyrophosphate (Figure 1), which indeed remedied some of these problems [68]. Finally,  
Figure 1: Tris(bis(triphenylphosphorarylidene)ammonium) pyrophosphate (PPN).  
replacing POCI\({}_{3}\) with PSCI\({}_{3}\) in the first stage of the synthesis allowed them to obtain various \(\alpha\)-thiotriphosphate analogs of NTPs (dAZTP\(\alpha\)S, dGTP\(\alpha\)S, UTP\(\alpha\)S, dTTTP\(\alpha\)S) in 26-60% yields [75; 76].  
In order to obtain analogs of P-CH\({}_{2}\)-P diphosphates, [PERSON] et al. [77] used the phosphorylation of nucleosides under [PERSON]'s conditions with methylenebis (phosphonic dichloride) **13** bearing a methylene bridge between two phosphorus centers (Scheme 10). Compound **13** was found to be more reactive than POCI\({}_{3}\), apparently due to the presence of the -CH\({}_{2}\)- group in the bridging position, which could not donate electrons by back donation and thus increased the electrophilicity of the phosphorus center. After the generation of trichlorophosphate **14** and its hydrolysis in TEAB buffer (pH 7.5), analogs of nucleoside diphosphates (P-CH\({}_{2}\)-P), which were difficult to access in any other way, were obtained in good yields (approx. 70%) [77].