esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Population Data  
The study included 274 pediatric patients comprising 96 female and 178 male patients aged 0 to 15 years. Table 1 presents the number of patients, body weight, effective diameter (Dw), and scan length categorized into five age groups. As the age increased, the median body weight (kg), effective diameter (cm), Dw (cm), and scan length (mm) increased. The median effective diameter was consistently less than the median Dw across all age groups. For the 0 to 6 months age group, the difference between the effective diameter and the Dw was not significant (\(p>0.05\)). However, for the age groups of 6 months to 3 years, 3 to 6 years, 6 to 12 years, and 12 to 15 years, the differences between the effective diameter and the Dw were significant (\(p<0.05\)).  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Age Group** & **No. of Patients** & **Body Weight (kg)** & **Effective Diameter (cm)** & **Dw (cm)** & **Scan Length (mm)** \\ \hline \multicolumn{5}{c}{**Median (IQR)**} \\ \hline
0 to 6 months & 26 & 5 (4.4, 6.8) & 12 (11.3, 12.5) & 12.3 (11.7, 13.1) & 149.2 (143.6, 153.7) \\
6 months to 3 years & 60 & 10.6 (9, 13) & 14.3 (13.8, 14.8) & 15.1 (14.6, 15.7) & 164.6 (160.1, 172.8) \\
3 to 6 years & 68 & 15 (13, 16.9) & 15 (14.7, 15.3) & 15.7 (15.4, 16.3) & 176.2 (168.2, 184.7) \\
6 to 12 years & 62 & 28.5 (20.2, 39.5) & 15.7 (15.3, 16.1) & 16.4 (15.9, 17) & 180.8 (175.8, 183.8) \\
12 to 15 years & 58 & 45 (39, 50) & 16 (15.7, 16.4) & 17.1 (16.6, 17.3) & 185.9 (177, 195.9) \\ \hline \end{tabular}
\end{table}
Table 1: Patient data categorized into five age groups for pediatric computed tomography head examinations.  
Figure 1: Positioning of the central slice of the scan length on the CT scanogram and contouring of the region of interest and anteroposterior (AP) and lateral dimensions in the axial cross-sectional CT image in head examinations of pediatric patients of different ages: (**A,B**): 2-month-old; (**C,D**): 1-year-old; (**E,F**): 5-year-old; (**G,H**): 10-year-old; (**I,J**): 12-year-old.  
The correlation between the Dw and the effective diameter was relatively weak, with an R-squared value of 0.31 and a significant difference (\(p<0.001\)).
# On the Link between Diesel Spray Asymmetry and Off-Axis Needle Displacement
## Materials and Methods
### Non-Dimensional Coefficients  
To allow the comparison among the holes, the results of the nozzle CFD model have been lumped in non-dimensional parameters that qualify the flow at each outlet section [45]. In the adopted scheme, Equation (16), the liquid-phase mass flow rate (\(m_{r}\)) passes through an effective area (\(A_{e}\)) with a uniform velocity (\(v_{e}\)), thus, vena contracta and cavitation affect the flow reducing the geometrical area (\(A_{g}\)) to the effective one (\(A_{e}\)). Considering an ideal flow, the theoretical velocity \(v_{t}\), Equation (17), is defined by means of a Bernoulli equation, assuming negligible velocity at the inlet section. The theoretical mass flow passing through the geometrical nozzle outlet section (\(A_{g}\)) is given by Equation (18). The first non-dimensional parameter is the discharge coefficient, \(C_{D}\) defined as follows (Equation (19)):  
\[m_{r}=\rho_{1}\ A_{e}v_{e}, \tag{16}\]  
\[v_{t}=\ \sqrt{\frac{2\big{(}P_{\text{inj}}-P_{\text{back}}\big{)}}{\rho_{1}}}, \tag{17}\]  
\[m_{t}=\rho_{1}\ A_{g}v_{t}, \tag{18}\]  
\[C_{D}=\frac{m_{r}}{m_{t}}. \tag{19}\]  
In order to isolate the area from the velocity contribute, the discharge coefficient is viewed as the product of two further non-dimensional parameters \(C_{V}\) and \(C_{A}\), Equation (20):  
\[C_{D}=C_{A}.\ C_{V}, \tag{20}\]  
\[C_{V}=\frac{v_{\text{eff}}}{v_{t}}, \tag{21}\]  
\[C_{A}=\frac{A_{\text{eff}}}{A_{g}}. \tag{22}\]
## 3 Results and Discussion
### Microbial Community Structure  
Among sampling sites, bacterial taxa (as relative abundance in %) in sediment samples varied greatly (Figure 2). At the phylum level, Proteobacteria, Bacteroidetes and Firmicutes were the dominant taxa. Proteobacteria cover a range from 33.1% (site 2643) to 56.3% (site 2651), Bacteroidetes a range from 10.5% (site 2635) to 17.2% (site 2648) and Firmicutes a range from 2% (site 2643) to 11.7% (site2635). At the class level, Gammaproteobacteria, Deltaaproteobacteria (identified taxa related to sulfate reduction [34]) and Alphaproteobacteria (identified taxa related to nitrate reduction [35]) were identified in all sampling points, showing a range from 31% to 54% at sites 2643 and 2651, respectively. Besides the Proteobacteria members, the association of sulphur reducers belonging to Bacteri and Clostridia comprised the main class consortium, covering ranges from 6% up to >16% (sites 2651 and 2652, respectively). At the family level, the abundance of taxa increased up to 631 families. The Proteobacteria members _Desulfobacteraceae_ (up to 13.5% at 2648) and _Rhodobacteraceae_ (up to 8.4% at 2651) were the most represented families across the analysed sediments (Figure 2). In association with _Spirocheataceae_, they carry out the main recycling and transformation of S-compounds [36; 37; 38; 39]. _Ectolithodospiraceae_ (up to 6.8% at 2649) represented the major community, which can utilize nitrogen and sulphur-compounds for anoxic metabolic pathways [40]. _Anaerolineaceae_, _Truepercaceae_ and _Marinilabiaceae_ are linked to the carbon cycle and the degradation of organic matter [41; 42; 43]. These families are able to recycle S-N-C under high salinity conditions [40; 44; 45], which may confer a turnover potential in sediments from Petrola Lake.  
In contrast, a minor group of families was present that, together, did not represent more than 14%; their presence may depend on EDC and land use distribution. Families such as _Alteromonasaceae_[46], with large genomes containing degradative genes, and halophilic _Halomonadaceae_ and _Halothiobacteraceae_, linked to N and S cycles [47; 48], contributed significantly in sectors of the lake with a direct influence of agriculture return flows (sites 2649 and 2652). Noticeably, these families were not found in the mining pond (2635). Due to the poor communication with the rest of the water mass, flooding occurs only during high stand level conditions. At 2635, an increase of between 2% and 10% in the abundance of the halophilic family _Halanarobiaceae_ was found to be closely linked with the recycling of several types of C-compound [49]. Importantly, sectors of the lake far from direct inputs of contaminants, such as 2643 and 2651, exhibited a high relative abundance of unknown families.
###### Abstract  
In this paper, a compact direction-finding system based on a deep neural network (DNN) with a single-patch multi-beam antenna is proposed. To achieve multiple beams, the patch is divided into four sectors by metal vias, and the pattern is tilted in the theta direction due to the coupled mode of the divided patch structure. This design allows a single-patch multi-beam antenna to generate eight beams using a combination of four excitation ports assigned to the four-divided sectors. This approach increases the amount of training data required for DNN-based direction finding without requiring multiple antennas, thus improving the accuracy of estimation probability. Furthermore, compared to our previous work, the parasitic elements are applied to improve the estimation probability by reducing the beamwidth of the antenna. The size of the antenna for the proposed direction-finding system is 0.444 \(\times\) 0.443 \(\times\) 0.008\(\lambda\) with a 97.7% estimation probability. The direction-finding performance has been validated and compared through the experiment to show higher accuracy with compactness than previously studied works.  
multi-beam antenna; patch; multi-port; direction finding; deep neural network Article
# Deep Learning Regressors of Surface Properties from Atomic Force Microscopy Nanoindentations
###### Abstract  
Atomic force microscopy (AFM) is a powerful technique to study the nanomechanical properties of a wide range of materials at the piconewton level. AFM force-indentation curves can be fitted with appropriate contact models, enabling the determination of material properties for a given sample. However, the analysis of large datasets comprising thousands of curves using conventional methods presents a time-intensive challenge. As a result, there is an increasing interest in exploring alternative methodologies, such as integrating machine learning (ML) models to streamline and improve the efficiency of this process. In this work, two data-driven regressors were tuned to predict the Young's modulus and adhesion energy from force-indentation curves of soft samples (Young's modulus up to 10 kPa). Both models were trained exclusively on synthetic data derived from the contact theories developed by [PERSON] as well as [PERSON] (JKR). The PyTorch library was employed to build and train the models; then, the key hyperparameters were refined by implementing the optimization framework Optuna. The first model was successfully tested with synthetic and experimental curves from AFM nanoindentations, and the second presented promising results on the synthetic data. Our work suggests that experimental data may not be essential for training data-driven models to predict surface properties from AFM nanoindentations. By delivering accurate predictions in a computationally efficient way, our regressors validate the potential of a deep learning approach in exploring AFM nanoindentations and motivate further development of similar strategies to overcome current limitations in AFM postprocessing.  
artificial neural networks; contact mechanics; surface properties; nanoindentations +
Footnote †: journal: Applied Sciences
# Drilling Fluid and Cement Slurry Design for Naturally Fractured Reservoirs
## 5 Lost Circulation While Cementing through Naturally Fractured Reservoirs
### Cement Slurry and Set Cement Properties
#### 5.1.2 Cement Slurry Viscosity and Thickening Time  
The viscosity and thickening time must be optimized so that the slurry remains pumpable long enough to place the cement across the desired zones and achieve the top of cement (TOC). Additionally, the cement slurry should set as quickly as possible after pumping stops and minimize the wait on cement (WOC) time.
## 2 Materials and Methods  
### Study Area  
The Qianyingzi coal mining area in Suzhou, Anhui province of eastern China (Figure 1) was selected as the case study. Its geographical location is 116\({}^{\circ}\)20\({}^{\prime}\) E-116\({}^{\circ}\)57\({}^{\prime}\) E, 32\({}^{\circ}\)43\({}^{\prime}\) N-32\({}^{\circ}\)56\({}^{\prime}\) E with a north-south span of 10.8 km and an east-west span of 9 km, which results in a total area of about 97.2 km\({}^{2}\) within the mining boundary. Most importantly, as a part of the Huaibei plain of China, the mining area is a densely populated and important agricultural region, making it an important grain production base in China. The coal mine was built in the 1980s and has been mining for more than 40 years. A large depression appearedon the ground because of the long-term mining, directly leading to serious damage to the ground scene.  
The concerned mining face is named W3233 and is depicted in Figure 1c. Specifically, Figure 2 gives the actual position of it from underground view mode, which can be accessed and viewed from our 3D webGIS platform. The features of this working face are as follows: the strike length and dip length are about 3000 m and 360 m, respectively, and the average dip angle of the coal seam \(\alpha\) is 2\({}^{\circ}\). The mining depth \(H_{0}\) is approximately 460 m, and the average mining thickness \(m\) is 3.32 m. This mining working face was planned and started on 11 July 2022, and will be stopped on 15 October 2025. Table 1 presents the detailed prediction parameters related to this mining working field, comprehensively deduced from previous field observation data and used for surface deformation estimation. In Table 1, \(q\), \(\tan\beta\), and \(\theta_{0}\) stand for subsidence coefficient, the tangent of the main influence angle, and the mining transference angle, respectively, and (S\({}_{1}\), S\({}_{2}\), S\({}_{3}\), S\({}_{4}\)) is the left, right, upper, and lower deviation of the inflection point [1].
# Wireless Strain Gauge for Monitoring Bituminous Pavements
## 3 Installation of the Sensor in the Pavement Surface Layer  
Two core samples were taken at a distance of 2.5 m on the pavement. The first has a diameter of 108 mm and provides us with a core with a diameter of 96 mm. The second has a diameter of 97 mm and provides us with a core with a diameter of 90 mm. The latter is kept for laboratory measurements. This method is essential because it guarantees that the instrumented core has the same module as the rest of the pavement and, therefore, good continuity of the deformation field. The first is instrumented and then glued in the 97 mm diameter hole using epoxy glue. We chose epoxy glue, which has a tensile and flexural modulus near the grave bitumen layer one. Other technical constraints, like glass transition temperature, polymerization time, and positioning time, are considered. For the first laboratory test under hydraulic press, the test body is equipped with the following two gauges: one for longitudinal strain and another for transversal strain (Figure 4c). We chose a long strain gauge to measure the average strain of the test body. Their instrumentation involves several steps:  
* The surface of the test body where the gauges will be glued is grounded and a groove is made on the edge of the cylinder for the passage of the cables (Figure 4a);  
Figure 3: FFT expected signals.  
* A layer of hot bitumen is applied to fill the gaps. In order to perfect the surface condition, an epoxy resin is applied and then sanded (Figure 4b);
* The gauges are glued using a glue intended for this purpose (Figure 4c). At this point, the thermal compensation resistors and the Wheatstone bridge are wired;
* A PVC (polyvinyl chloride) strapping and a protective paste against humidity is affixed (Figure 4d).  
With machining, the bottom of the drilling core is removed and a void appears between the bottom of the hole and the instrumented core. This is simply filled with sand. To ensure the correct alignment, a line parallel to the longitudinal gauge axis is drawn on the top of the test body. During gluing, the core is placed so that the top line is aligned with the pavement direction. After the installation of the core in the pavement layer, a groove is made between the two core holes. This will receive the cables of the classic gauge system (Wheatstone bridge) used for comparison with our wireless sensor. The remaining hole receives a PVC box containing the sockets for connecting the instrumented core to the electronics. This box is waterproof and is flush with the surface of the road. Figure 5 shows a schematic of the test setup. During measurements, the center axis of the MLS10 twinning is positioned at 550 mm from the measuring instrument using a system of marks to ensure the alignment of the longitudinal axis of the gauge with that of the wheels.  
Figure 4: Preparation of the test core. (**a**): Machined test core; (**b**): test core before gauges gluing; (**c**): two perpendicular gauges; (**d**): finished test core.
## 4 Simulation Model and Results
### Model Description of Grid Connected HMLI  
The PV system leakage current standards are provided according to grid safety and regulation. Hence, it is necessary to check leakage current minimization in grid-connected mode. The necessary parameter values are provided in Table 4. The dynamic simulation results are presented here, in which the current is increased at 5 s and decreased at 5.1 s to show the fast dynamic response of the system in grid-connected mode. The grid connection control is implemented using PLL, which provides a phase angle for the current. The provided phase is used with a sine function and a constant magnitude to generate the current reference. Figure 10a shows output voltage levels of grid-connected HMLI. For the current reference of 6 A, HMLI generates seven levels as there is no requirement for two levels (133 V, \(-\)133 V) for low current generation. For the current reference of 7 A, HMLI outputs nine voltage levels (\(-\)133.33 V, \(-\)100 V, \(-\)66.66 V, \(-\)33.33 V, 0 V, 33.33 V, 66.66 V, 100 V, and 133 V).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Inductive load & 2 mH \\ Resistive load & 15 \(\Omega\) \\ DC-Link capacitor & 1000 \(\upmu\)F \\ Sampling frequency & 20 kHz \\ DC voltage & 100 V \\ Grid voltage & 110 V (peak) \\ Capacitor voltage & 33.33 V \\ Reference load current (peak) & 7 A \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters for grid-connected HMLI.  
Figure 9: Dynamic results of HMLI: (**a**) load current tracking when decreasing the reference current, and (**b**) load current tracking when increasing reference current.  
When the current reference is again decreased to 6 A, the HMLI output voltage generation decreases to seven levels. Similar charging and discharging instances in capacitor voltage can be observed in Figure 10b. The capacitor is provided with charging and discharging vectors in less time as the sampling frequency interval is increased. Figure 10c,d show grid current and grid voltage respectively. Grid current is in accordance with the reference provided and grid voltage does not change because of proper inverter operation.  
Figure 10: Simulation results of HMLI: (**a**) grid voltage dynamics of HMLI, (**b**) capacitor voltage balancing, (**c**) Grid voltage, and (**d**) grid current.  
Figure 11a depicts the CMV of the HMLI. Figure 11b shows the RMS value of the leakage current, measured around 19 mA, and peak to peak it is also less than 300 mA. Both the RMS value and peak-to-peak value are under the regulatory norms. Thus, the simulation results presented here are in accordance with the theoretical expectation.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 7 Performance Analysis of the Proposed ECSSP-SC
### Comparison of Resistance Capability against Attacks  
Section 6 has shown that the proposed mutual authentication protocol possesses the capability to counter replay attacks, user impersonation, server impersonation, insider attacks, known key attacks, password guessing attacks, de-synchronization attacks, and MITM attacks. A comparative analysis of the attack resistance ability of the ECSSP-SC and other protocols was performed and is shown in Table 6.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Protocol**} & \multicolumn{3}{c}{**No. of ECPM and Modular Exponentiation Operations Performed**} \\ \cline{2-7}  & \multicolumn{2}{c}{**USR**} & \multicolumn{2}{c}{SVR} & \multicolumn{2}{c}{**Total**} \\ \cline{2-7}  & \(P_{M}\) & \(M_{E}\) & \(P_{M}\) & \(M_{E}\) & \(P_{M}\) & \(M_{E}\) \\ \hline [PERSON] et al. [7] & 2 & 0 & 2 & 0 & 4 & 0 \\ [PERSON] [11] & 0 & 2 & 0 & 4 & 0 & 6 \\ [PERSON] et al. [22] & 0 & 2 & 0 & 1 & 0 & 3 \\ [PERSON] et al. [27] & 3 & 0 & 3 & 0 & 6 & 0 \\ [PERSON] et al. [29] & 2 & 0 & 2 & 0 & 4 & 0 \\ [PERSON] et al. [47] & 3 & 0 & 2 & 0 & 5 & 0 \\ [PERSON] et al. [49] & 2 & 0 & 2 & 0 & 4 & 0 \\ [PERSON] et al. [55] & 3 & 0 & 3 & 0 & 6 & 0 \\ [PERSON] et al. [61] & 3 & 0 & 3 & 0 & 6 & 0 \\
**ECSSP-SC** & 2 & 0 & 2 & 0 & 4 & 0 \\ \hline \hline \end{tabular} P\({}_{M}\)—elliptic curve point multiplication, M\({}_{E}\)—modular exponentiation.  
\end{table}
Table 4: Number of operations in different smart card security protocols.  
Figure 10: Comparison of the communication cost of smart card security protocols.  
Figure 9: Comparison of the computational time of smart card security protocols.
# Quantum-Secure Coherent Optical Networking for Advanced Infrastructures in Industry 4.0
## 4 Vulnerability Analysis
### OTU4 Frame Structure Security Analysis  
By synthesizing management plane documentation, overhead field definitions, and real-world exploit data [32], this study identified direct vulnerabilities in OTU4 frames. Specifically, overhead byte manipulation targeting the Trail Trace Identifier (TTI) or Bit Interleaved Parity (BIP-8) fields can create covert channels or corrupt data integrity. Triangulation confirmed that multiple sources had reported anomalies in TTI usage, often dismissed as simple configuration errors [33]. An iterative risk assessment [34] revealed that frame synchronization vulnerabilities--for instance, misaligning the Frame Alignment Signal (FAS)--could produce cascading errors with minimal attacker effort. This not only jeopardizes user data but also impedes forward error correction, undermining high-availability systems in the financial and healthcare sectors.  
The OTU4 framing structure carries payload and management information as an integrated entity as a bridge to data and the control plane. We point out vulnerabilities in specific fields (novel) such as the Trail Trace Identifier (TTI) and Bit Interleaved Parity (BIP-8) that attackers can use to insert or modify data without being detected.  
By precisely timing their manipulation of the FAS field, attackers can exploit frame synchronization vulnerabilities targeting these overhead bytes. An attacker can modify the FAS pattern (F6F6F6282828 in hexadecimal) such that the receiver loses its ability to correctly align frame boundaries. This misalignment leads to cascaded errors during the interpretation of the subsequent fields and, ultimately, during payload demarcation. For instance, a strategically wrong byte alignment misalignment by only one byte offset may cause the receiver to make wrong assumptions about payload boundaries, in turn resulting in the error multiplication of the forward error correction (FEC) decoding process. Error detection mechanisms (like BIP-8 or the BTL Authentication) may thus fail to discover the manipulated frame boundaries, leading to subsequent undetected data corruption in high-reliability application fields like financial transactions or healthcare data transport.  
Figure 5: DSP chain vulnerable components.  
Misalignment is especially injurious during burst-mode transmission where multiple frames are corrupted before the receiver resynchronizes.  
Figure 6 provides a detailed visualization of the OTU4 frame structure, highlighting the following critical fields: a Frame Alignment Signal (FAS), a Trail Trace Identifier (TTI), and the use of Bit Interleaved Parity (BIP 8). The diagram explains how each field could be exploited, as well as the vulnerabilities generated by frame boundary manipulation and the cascading effect of misalignment on data integrity.
# The Role of Extracellular Matrix (ECM) Adhesion Motifs in Functionalised Hydrogels
### Hydrogels as Sophisticated, Biomimetic Materials
#### 1.2.1 Synthetic Hydrogels  
As the classification suggests, synthetic hydrogels are chemically synthesised and are thus manufactured via highly reproducible methods [14]. They also have the advantage of having \"tuneable\" properties. For example, the mechanical properties of hydrogels such as stiffness and/or crosslinking can be specifically altered or tuned to mimic the various ECM microenvironments of different tissues. Mechanical properties such as these also have a profound effect on the mechanotransduction of cells within a hydrogel [8], contributing to the conversion of physical cues into biochemical signals, leading to enhanced cell behaviours such as spreading and migration [9]. Molecular cues can also be incorporated within the chemical formula, such as cell adhesion motifs, to improve cell adhesion performance. The challenges associated with synthetic hydrogels should also be considered, where there is a possibility that the biomaterial itself or any by-products produced may have a toxic effect, resulting in a loss of biocompatibility.