esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work  
### Historical Handwriting Recognition  
The interest in historical handwriting analysis and recognition has increased substantially in the last decade [1]. Numerous research datasets have been created for different scripts and languages, including for example the English George Washington database [5], the Bangla CMATERdb1 database [6], the Spanish ESPOSALLES database [7], the Arabic VML-HD database [8], the Chinese CASIA-AHCDB [9], and the Swedish ARDIS database [10]. Additionally, several competitions have been held to compare state-of-the-art approaches to text line and word segmentation [11], handwritten keyword spotting [12], and handwriting recognition [13], to name just a few. The current state of the art is in large parts based on deep convolutional neural networks, with attention mechanisms playing an important role in recent architectures [14; 15]. Most methods for reading handwriting are segmentation-based, i.e., focusing on pre-segmented paragraphs, text lines, or words, and are trained with human-annotated handwriting samples, typically text line images together with their corresponding transcription.  
The creation of training samples for historical handwriting is a time-consuming and costly process. For modern handwriting, standard benchmark datasets such as the IAM database [16] have been obtained by asking writers to copy a given text by hand into special forms that facilitate the automatic extraction of handwriting images together with their corresponding transcription. For historical handwriting, however, time-consuming manual annotations of scanned manuscripts are needed. They typically consist of bounding boxes or polygons around text elements, together with their machine-readable transcription, which can often only be provided by experts in the case of ancient languages.
# Effect of Boundary Conditions on the Mechanical Behavior of the Geogrid-Soil Interface
## 3 Results and Discussion
### Variation Laws of Interfacial Shear Strength Indexes Subjected to Different Boundary Conditions  
The shear stress of the geogrid-soil interface could be calculated according to the Formula (2). The pullout test results under different boundary conditions were fitted, as presented in Figure 14. From Figure 14, it can be seen that the shear stress of geogrid-soil interface and normal stress showed a good linear relationship under different boundary conditions, and the correlation coefficients of the fitting were both 0.99.  
\[\tau_{g-s}=\frac{P_{\text{T}(\cdot)\&\text{P}(\cdot)}}{2 BL} \tag{2}\]  
where the \(\tau_{g-s}\) is the shear stress of the reinforced interface; \(B\) and \(L\) are the width and length of geogrid specimen in the test box, respectively.  
The equations of fitting, and shear strength characteristics of the geogrid-soil interface under four types of boundary conditions were presented in Figure 15 and Table 3, respectively. From Figure 15 and Table 3, the boundary conditions had a great influence on the apparent cohesion of the interface, but little influence on the apparent friction angle. Under T(R)&P(R), the apparent cohesion was the largest, which was 15.46 kPa. Then, the apparent cohesion under T(F)&P(R) was 8.56 kN. Last was T(R)&P(F) and T(F)&P(F), which were 2.34 and 0.68 kPa, respectively. In contrast to apparent cohesion, the variation range of the apparent friction angle under four types of boundary conditions was smaller, from 5.77 to 7.41\({}^{\circ}\). The reason was that the relative density of soil close to the positive boundary increased during the pullout test, then increasing the apparent cohesion mainly offered by passive bearing capacity. The pullout resistance, apparent cohesion under flexible boundary condition was less than that of the rigid boundary condition. As a result, the pullout resistance matching to the boundary condition was determined in the design of the reinforced soil structure, according to the results of T(R)&P(R) and _BRC_. Then, the corresponding interface strength parameters were calculated to ensure the safety and durability of the reinforced earth structure.  
Figure 14: The plot of normal loading versus shear stress and linear fitting of shear stress.
## 4 Experimental Results
### Performance Evaluation
#### 4.2.2 Connection Stability Analysis  
Connection stability plays a crucial role in maintaining seamless data transfer and drone coordination. The total number of connections established in each approach was 19 for PSO, 21 for GAs, and 20 for the hybrid approach, as can be seen in Figure 4. Even though GAs established a greater total number of connections, the hybrid approach achieved better connectivity, as shown by the shorter average connection distance of 1.59 km, as compared to 1.74 km for PSO and 1.69 km for GAs, as can be seen in Figure 5. The average distance connection metric reflects how efficiently drones are placed within the communication range. Lower average distances imply tighter formations with reduced latency and energy usage. The reduction in average connection distance shows that the hybrid approach places drones more optimally within communication range, reducing latency and improving network reliability.  
Figure 4: Total connection count comparison.  
Figure 3: Coverage area comparison.
# A New Approach to Production Process Capability Assessment for Non-Normal Data
## 2 Indicators for Assessing the Quality Capability of the Process  
Process quality capability indicators are measures used to assess whether a process meets certain quality standards and is capable of producing products or services that meet customer requirements. These indicators allow an objective assessment of process capability and identification of potential areas for improvement [47; 48; 49].  
Testing the qualitative capability of a process involves determining how well a process meets the requirements for a particular characteristic. Quality indicators refer to the requirements set by the customer. These requirements usually relate to one specific feature [50; 51; 52].  
The basic indicators of process quality capability include the \(C_{p}\) and \(C_{pk}\) indicators. The \(C_{p}\) indicator is used to determine the spread of the process given the specification limits and how much the process is centered, i.e., how much the value of a feature is equal to the nominal capacity. It also implies the possibility of systematic errors in the production process [53; 54]. The \(C_{p}\) indicator is calculated as follows (1):  
\[C_{p}=\frac{USL-LSL}{6\sigma} \tag{1}\]  
where: _USL_--upper specification limit; _LSL_--lower specification limit; \(\sigma\)--standard deviation of sample.  
The \(C_{p}\) value helps to better understand the performance of the process. If it is more than 1.33, which corresponds to a percentage of incompatible elements of 63 parts per million (ppm), the process performance is satisfactory for a centered process. The quality conditions and corresponding \(C_{p}\) values are shown in Table 1[50; 54].  
The \(C_{pk}\) indicator is used to relate the variability of a process, showing how the process conforms to its specifications. \(C_{pk}\) is usually used to relate \"Natural tolerances (\(\pm 3\)\(\sigma\).\" with the specification limits. \(C_{pk}\) describes how well a process falls within specifications, relative to the process average [50; 55]. The \(C_{pk}\) indicator, which refers to the actual offset from the nominal value, is determined according to Formula (2):  
\[C_{pk}=\min\;(\frac{USL-\overline{x}}{3\sigma};\;\frac{\overline{x}-LSL}{3 \sigma}) \tag{2}\]  
where _USL_--upper specification limit; \(LSL\)--lower specification limit; \(\overline{x}\) and \(\sigma\)--mean value and standard deviation of sample. From among the calculated values, the lower value is selected. The desired value of the \(C_{pk}\) indicator to be able to speak of a qualitatively stable process, as in the case of the \(C_{p}\) indicator, is at least 1.33 (Table 1).
## 2 Related Work
### Source Location Privacy Protection in UASNs
In addition, existing research focuses on resisting passive attacks, and there are a lack of strategies for resisting active attacks, which necessitate continuous updates and improvements. This paper comprehensively considers privacy protection, delay, and energy issues and presents a game theory-based underwater source location privacy protection scheme to address the aforementioned problems.
# End-to-End Delay Model for Train Messaging over Public Land Mobile Networks
## 4 Proposed E2E Delay Model  
The events associated to G and Q states (discussed in the previous section) can be represented using the time diagram in Figure 6. The diagram reports the transmit and the corresponding receive times of each (single) message on two separate time axes.  
Indicating with \(R_{i}\) the receive time of the \(i\)-th message and \(T_{i}\) the corresponding transmit time, in the simple case in Figure 6, the inequality \(R_{i-1}>T_{i}\) is representative of buffering condition leading to transition in the Q state and causing (small or high) spikes in the E2E delay. Starting from the considerations in the previous section, the time scheme in Figure 6 is now used to derive the E2E delay model.
# Performance Evaluation of Carbon Black Nano-Particle Reinforced Asphalt Mixture
## 1 Introduction  
Poor flexible pavement performance at high temperatures is the major problem that is faced by the pavement industry in Pakistan. Asphalt pavements in the country fail prematurely because of mix rutting. Mix rutting is observed only in the high-temperature areas of the country. The lower softening point values of the locally produced unmodified binders make them susceptible to rutting during the summers. The other most commonly faced distress in the country is the moisture damage of the asphaltic pavements especially during the rainfall/monsoon season.  
Pavement engineers are always in search of modifiers that could not only improve the rheological properties of the binders but could also concurrently enhance their adhesion capabilities. Asphalt mixtures have been modified in the past by carbonaceous materials, as they are believed to be intrinsically compatible with the asphalt mixture [1]. Among the carbonaceous materials, different researchers have utilized carbon black because of its easy availability. It has been learnt that high dosages of carbon black in asphalt can increase the rutting resistance and lessen the temperature susceptibility [2; 3; 4; 5; 6; 7; 8; 9; 10; 11]. It is also said that the performance of carbon black depends upon its structure,particle size and surface area [12; 13]. Nanotechnology is an emerging field and is extensively used by pavement engineers worldwide. Different types of carbon black nano-particles (CBNPs) have been manufactured and incorporated to modify asphalt properties [14; 15; 16] but, to the authors' best knowledge, the one made with natural sources has never been used by pavement engineers. The selected CBNPs come from a natural source and their effect on the rheology of the modified binder, bitumen-aggregate adhesion, and moisture susceptibility needs to be investigated.  
The pavement industry in Pakistan is still using penetration grading for bitumen selection while the rest of the world is fast adopting the latest performance grading (PG). Performance grading is based on the concept that the properties of the asphalt binder should be related to the conditions under which it is used. According to temperature zoning done by [PERSON] et al. [17], PG 70-10 would be sufficient for most parts of the country. Base asphalt binder generally used in the country has PG 58-22 which is softer than the required PG 70-10. As mentioned earlier, the problematic areas are only the high temperature areas of the country where, even in winters, temperatures hardly fall below 0 \({}^{\circ}\)C. Therefore, high PG values are of more concern and the main aim is to use a dosage of nano-material that would help achieve the 70 \({}^{\circ}\)C threshold.  
The paper aims to investigate the effect of CBNPs (150 nm) produced by perennial mountain trees' carbonization on the rheological properties of asphalt binder, aggregate and asphalt binder bond strength, and rutting resistance of asphalt.
# The EGNOS Augmentation in Maritime Navigation +
## 4 Results and Discussion
### Kinematic Test
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & & \multicolumn{3}{c}{**Horizontal**} & \multicolumn{3}{c}{**Vertical**} & \multicolumn{3}{c}{**3D Accuracy**} & \multirow{3}{*}{**Contin.**} & \multirow{3}{*}{**Signal**} \\ \cline{3-3} \cline{5-10}
**Device** & & & & \multicolumn{3}{c}{**Accuracy [m]**} & & & & & & \\ \cline{3-10}  & \multirow{-2}{*}{**Conf.**} & \multirow{-2}{*}{**Mean**} & **RMS** & **Max** & **Req. Imo 10 m (\%)** & \multirow{-2}{*}{**Mean**} & **RMS** & **Max** & **Mean** & **RMS** & **(\%)** & \\ \hline \multirow{3}{*}{u-blox} & GPS SPP & 0.88 & 0.92 & 1.80 & & 1.03 & 1.17 & 2.77 & 1.43 & 1.49 & \multirow{3}{*}{100} & \multirow{3}{*}{100} \\  & GPS + SBAS SPP & 0.68 & 0.72 & 1.89 & 100 & 0.50 & 0.61 & 1.99 & 0.90 & 0.94 & & \\ \cline{1-1}  & DGPS & 0.99 & 1.01 & 2.00 & & 0.52 & 0.67 & 3.35 & 1.18 & 1.22 & & \\ \hline \multirow{3}{*}{Xiaomi Mi 8} & GPS SPP & 2.30 & 2.70 & 16.31 & 99.9 & 3.40 & 4.43 & 28.31 & 4.42 & 5.19 & 100 & 100 \\ \cline{1-1}  & GPS + SBAS SPP & 2.42 & 3.06 & 27.66 & 99.0 & 3.67 & 5.09 & 43.17 & 4.71 & 5.94 & 100 & 97.8 \\ \cline{1-1}  & DGPS & 2.44 & 2.87 & 28.01 & 99.6 & 3.26 & 4.25 & 32.12 & 4.41 & 5.13 & 99.73 & 99.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary results of performance analysis conducted in terms of accuracy (using as figures of merit Root Mean Square (RMSE), mean and maximum errors) for both horizontal (H) and vertical (Up) components of position, tridimensional accuracy (using as figures of merit Root Mean Square (RMS) and mean errors), IMO parameter on absolute horizontal accuracy, availability and continuity (over 15 min) using data collected in kinematic mode by the u-blox receiver and the Xiaomi Mi 8 devices. Both figures confirm the results summarized in Table 3. In detail, Figure 11 shows the best performance obtained thanks to the use of EGNOS corrections, especially on vertical components of the position. Conversely, using data from the smartphone, the configurations perform similarly, as can be noted in Figure 12, with a smaller enhancement obtained using the GPS SPP setup, especially on a horizontal plane. However, the errors obtained using data from the smartphone are higher than the ones shown in Figure 11, which is related to the characteristic of smartphone measurements that are noisier than a usual mass market receiver, as is well known [14,20]. Figure 11: Horizontal and vertical positioning errors (in meters) as function of the time for GPS SPP (blue line), GPS + SBAS SPP (magenta line) and DGPS (orange line) configurations applied to data from u-blox receiver. Figure 12: Horizontal and vertical positioning errors (in meters) as function of the time for GPS SPP (blue line), GPS + SBAS SPP (magenta line) and DGPS (orange line) configurations applied to data from Xiaomi Mi 8.
# Optimisation of Synchronous Grouting Mix Ratio for Shield Tunnels
## 1 Introduction
These results show that the rheological model of the grouting material conforms to the Herschel-Bulkley (H-B) model and that the yield stress increases with time. Although researchers have conducted many related studies on the effects of the mix ratio or addition of new components on the rheological parameters and physical properties of slurries, there are few studies on the relationship between the rheological parameters and physical properties of slurries. Simultaneously, the grout segregation mechanism of the synchronous grouting slurry of shield tunnels and the anti-segregation performance have been rarely discussed [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18]. Therefore, the effects of each slurry component on the fluidity, free bleeding rate, and segregation rate of a mortar-truncated cone were investigated in this study. The relationships between the bleeding rate, segregation rate, and rheological properties of the slurry, and easy delamination, segregation, bleeding, and tube blocking of the slurry in practical engineering were established. The rheological index was obtained based on nonlinear fitting of the obtained rheological parameter data by investigating the rheological properties of the grouting slurry. A three-dimensional scatter plot was used to explore the relationship between the rheological parameters of the slurry, bleeding rate, and segregation rate. Combined with MATLAB, the mix proportion was optimised to produce a high-performance synchronous grouting slurry that can maintain stability over long distances and transportation times and prevent easy stratification, segregation, bleeding, and pipe blocking in a newly mixed grouting slurry of shield tunnels.
## 3 Results and Discussion
### Determination of Cell Sensitivity to NPs  
We assessed MTT reduction and LDH leakage as determinants of mitochondrial function and membrane damage, respectively. Compared with untreated cells, MTT results demonstrated a time- and concentration-dependent toxicity and a significant reduction in survival of MCF7 and HUVEC cells after exposure to either CoNiZn (Figure 4) or CoNiFe (Figure 5) NPs (\(p\) < 0.05). The percentages of viable HUVEC cells after 48 h exposure at concentrations 12.5, 25, 50, 100, 200, and 400 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL of CoNiZn NPs were 88%, 79%, 76%, 71%, 44%, and 24%, while the percentage of viable MCF7 cells treated with the same concentrations of CoNiZn NPs within the given time period was 80%, 71%, 47%, 40%, 38%, and 17% (Figure 4). Similarly, the percentages of viable MCF7 cells following 48 h treatment with concentrations 12.5, 25, 50, 100, 200, and 400 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL of CoNiFe NPs were 75, 71, 65, 48, 37, and 14%, while these percentages were 88%, 79%, 70, 52%, 35%, and 19% for HUVEC cells (Figure 5). IC\({}_{50}\) values for exposure of cells to CoNiZn NPs were 211.8 and 163.5 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL (after 24 and 48 h for HUVEC cells), and 89.24 and 64.06 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL (after 24 and 48 h for MCF7 cells), respectively. Furthermore, the IC\({}_{50}\) concentrations of CoNiFe NPs were 228.2 and 106.5 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL (after 24 and 48 h for HUVEC cells), and 124.3 and 82.71 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL (after 24 and 48 h for MCF7 cells). We observed that MCF7 cells were more susceptible to the prepared NPs, indicated by lower IC\({}_{50}\)s. It can be concluded that CoNiZn and CoNiFe NPs induced higher toxicity on cells derived from the human breast tissue and exerted less toxicity on normal human cells (IC\({}_{50}\) > 100 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL).  
LDH enzyme release assay is widely used in in vitro toxicological studies. Therefore, we measured LDH leakage in MCF7 and HUVEC cells exposed to 12.5, 25, 50, 100, 200, and 400 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL of the synthesized NPs for 48 h. Compared with untreated cells, exposure of HUVEC cells with 12.5 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL of CoNiZn and CoNiFe NPs did not enhance LDH leakage (\(p\) > 0.05). However, with increasing NP concentration, significant LDH leakage was observed in both cell lines as compared with untreated cells (\(p\) < 0.05) (Figure 6).  
Figure 4: Time- and concentration-dependent toxicity of CoNiZn NPs on Michigan Cancer Foundation-7 (MCF7) and human umbilical vascular endothelial cells (HUVEC) using MTT colorimetric assay following 24 and 48 h incubation. Data are expressed as mean \(\pm\) SD from one representative of three independent experiments. (**_p < 0.05_ compared with untreated cells).
# Public Key-Based Authentication and En-Route Filtering Scheme in Wireless Sensor Networks
## 1 Introduction
Additionally, with this mechanism, once a CH node refuses to send out the signed event report or sends out an event report with an illegitimate signature, a new CH node will substitute it and regenerate a signed event report. Third, the authors design an effective report forwarding protocol that can defend against selective forwarding attacks. Unless all upstream nodes (the neighbors closer to the BS) of the sender are compromised, this scheme ensures that legitimate reports will be forwarded to the BS with less communication and computation overhead. Fourth, to mitigate the impact of malicious nodes, the authors propose a trust value-based mechanism to identify and punish the malicious nodes in the network. When a node has any malicious behavior, its trust value will be reduced. When the trust value of a node falls below the predefined threshold, it will be isolated. Finally, by integrating the aforementioned mechanisms, the authors develop a public key-based authentication and en-route filtering scheme (PKAEF) to resist false data injection attacks, and report disruption attacks, and selective forwarding attacks. Detailed performance analysis and evaluation show that, in most cases, PKAEF outperforms PDF [19] and DSEDA [1] in terms of safety, filtering efficiency, and data availability. The rest of this paper is organized as follows. The related works are reviewed in Section 2. Section 3 introduces the system model and threat models. A public key-based authentication and en-route filtering scheme is proposed in Section 4 and then, Sections 5 and 6, analyses and evaluates of its performance in comparison to those schemes closest to that of this study's. Finally, the conclusion of this paper is in Section 7.
## 4 Proposed Detection Scheme
### Complexity Analysis  
In this subsection, the complexity of the presented methods is assessed in terms of complex floating point operation (flop). We count one complex addition as one flop and one complex multiplication as three flops, while one real and one complex magnitude operation are regarded as \(1/2\) and \(3/2\) flops, respectively [22]. Calculating \(\tilde{Y}_{l}(k+m)\) for \((2M+1)\) hypothesized valUE demands \(N_{p}+2M-1\) complex multiplications. To make a fair comparison, only the number of flops required to compute the real part of \(\Phi_{a}(m,n)\) and \(\Phi_{c}(n)\) is included in the complexity calculation. In Equation (7), calculating the quantity \(\Phi_{a}^{I}(m,n)\) requires \(3N_{p}/2-7/2\) flops. Thus, the total number of flops used to find \((\hat{v},\hat{n})\) in the HCJD scheme Equation (8) is \(3N_{p}+6M-3+(6M+3)(3N_{p}/2-7/2)\). Since the complex magnitude operation is based on both real and imaginary parts of \(\Phi_{b}(m)\) in the RCSD method, \(9N_{p}/2-15/2\) flops are needed to compute the cost function \(|\Phi_{b}(m)-1|\) so that Equation (11) obtains \(\hat{v}\) with \(3N_{p}+6M-3+(2M+1)(9N_{p}-15)/2\) flops. For computing \(\Phi_{c}^{I}(n)\), \(3(3N_{p}/2-7/2)\) flops are added. On the other hand, the proposed reduced-complexity joint detection (RCJD) scheme computes Equation (15) with \(3N_{p}/2-3\) flops for each IFO hypothesis. Since the quantity Equation (22) has been already computed during the IFO-matching stage in Equation (15), further flop is not needed. For \((2M+1)\) IFO trials, the RCJD method needs \(3N_{p}+6M-3+(4M+2)(3N_{p}/2-3)\) flops in total.