esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effect on Specific Energy
#### 3.4.2 Effect of Spacing on Specific Energy  
Spacing generally has a more dominant effect on specific energy compared to penetration depth [12]. If the spacing is too close, the specific energy is significantly high, and the cutting is inefficient because the rock is overcrushed. In addition, tool wear is high owing to the high friction between the rock and the tool. If the spacing is too wide, the specific energy is significantly high again, and the cutting is inefficient because cuts do not occur in the relieved mode where tensile fractures from adjacent cuts reach to form a rock chip. Furthermore, a ridge that might result in shock loads causing serious failure of the cutting tool or a stop of the machine is generated. The minimum specific energy is obtained using the optimal \(s/p\) ratio. This represents the most efficient cutting conditions, largest rock chip, and minimum tool wear [14; 41].  
The relationships between the \(s/p\) ratio and specific energy obtained in this study are shown in Figure 18. In the rock specimen of USC 50 Mpa, the optimal \(s/p\) was 2.94 for cutting with a penetration depth of 3 mm, and 3.11 and 3.14 for penetration depths of 6 mm and 9 mm, respectively (Table 9). The optimal \(s/p\) ratio at which the specific energy was minimized was almost the same, regardless of the penetration depth. For the other rock specimens, the optimal \(s/p\) value was between 2 and 4, regardless of the penetration depth. This was consistent with several studies in which the optimal \(s/p\) ratio of the pick cutter of the roadheader ranged from 2 to 5 [13; 14; 19; 27]. Therefore, it can be expected that the cutting tool of the pre-cutting is similar to that of the pick cutter. The details of the specific energy according to spacing are listed in Table 8. The optimal \(s/p\) ratio and the minimum specific energy are presented in Table 9.  
\(p\): penetration depth, UCS: uniaxial compressive strength, Opt. \(s/p\): optimum spacing to penetration depth ratio, Min. \(SE\): minimum specific energy.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} \(p\) \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{\(s\)} & \multirow{2}{*}{\(s/p\)} & \multicolumn{4}{c}{**UCS (MPa)**} \\ \cline{3-6}  & & & & 20 & 30 & 40 & 50 \\ \cline{3-6}  & & & & & \multicolumn{4}{c}{\(SE\) (MJ/m3)} \\ \hline  & 32 & 5.33 & 39.4 & 46.3 & 69.9 & 74.8 \\
9 & 8 & 0.89 & 31.9 & 33.4 & 46.5 & 71.1 \\  & 16 & 1.78 & 20.6 & 23.5 & 29.7 & 40.4 \\  & 24 & 2.67 & 16.9 & 18.0 & 23.2 & 46.3 \\  & 32 & 3.56 & 17.2
## 5 Comparative Analysis of Sensitive Factors  
In the actual construction process, some parameters will change due to the influence of various circumstances, which will lead to the deviation from the design stage of the construction, and eventually may lead to a certain deviation between the linear shape of the completed bridge and the theoretical situation. Therefore, the primary problem of construction control is how to adjust the parameters so that the parameters are closer to the actual value to ensure that the stress condition and linear change in the bridge are as close as possible to the theoretical value, and the key variables can be identified. Through sensitivity analysis, the degree of influence of uncertainty on bridge deformation can be assessed and corresponding measures can be taken to reduce the risk caused by uncertainty.  
Based on the abovementioned parametric analysis, the effects of five parameters (i.e., the internal prestress, bulk density of the main beam, pipeline friction coefficient, pipeline deviation coefficient, and external stress) on the deflection of the main beam could be analyzed more intuitively, as given in Table 1. As shown in Table 1, the deflection of the bridge is most sensitive to the bulk density, successively the external prestress, internal prestress, pipe friction coefficient, and pipeline deviation coefficient. More attention should be paid to the control of the bulk density and internal and external prestresses of the main beam of the north approach bridge of Nanjing Xinshengwei Yangtze River Bridge during the construction process.  
It should be also noted that simplified models are often based on several assumptions and simplifications, which can lead to differences from the actual situation. Therefore, the results of sensitivity analysis may be limited by these assumptions and may not fully accurately reflect the behavior of real systems. Sensitivity analysis requires the selection of parameters to be analyzed, which involves subjective judgment. If the parameters selected are incomplete or inaccurate, the understanding of the system behavior may be incomplete or inaccurate.
# Method for Improving the Reliability of SRAM-Based PUF Using Convolution Operation
## 3 The Proposed Scheme
### The Lightweight Scheme  
Many IoT devices do not have a large number of SRAM cells. It is better to generate ID numbers of the same length with fewer SRAM cells, so we propose a lightweight scheme. The standard matrix is filled by self-filling and the step size of the convolution is half of the length of the convolution kernel. In the lightweight scheme, the standard matrix is \(a\times a\), the convolution kernel is \(b\times b\) and the length of self-filling is equal to the step size of the convolution. The size of the output result is \(\left(\frac{a}{b/2}\right)\times\left(\frac{a}{b/2}\right)\). If \(a=m/4\) and \(b=n/2\), the lightweight scheme uses one-sixteenth of the hardware resources above to generate the same length ID number.  
Because there may be some unstable cells that are repeatedly calculated, the convolution is computed with a step size of \(b/2\). The redundancy coefficient of the lightweight scheme is larger than the one-layer convolution scheme, according to Formula 3. A large redundancy coefficient can ensure 100% reliability of the lightweight scheme. However, the discrimination ability and inter-chip HD of the lightweight scheme may not be as good as for the one-layer convolution scheme. Since cells with initial values of 0 and 1 have the same probability of being affected by aging, use of the convolution operation can offset the effect of aging on reliability.
## 4 Experiment
### Experimental Results and Analysis
#### 4.2.4 Further Analysis Based on Experimental Results  
The SVM model is integrated according to the grammatical features of Chinese to obtain rich relational features between entity pairs and then trained and tested by the SVM classifier. However, as the number of entity relation types increases and the objects to be processed are complex entity relations, the optimal boundaries of various relation classes are often difficult to determine, resulting in low performance of SVM. The PA-LSTM model adds a location-aware attention mechanism to the LSTM network. The results of this paper's model are considerably better than those of the PA-LSTM model. This is because although the PA-LSTM model learns the global location of entities in the sequence, it does not pay attention to the rich dependency features in the sentences. The GCN model learns the dependency information and adds the shortest-path pruning strategy. However, it is experimentally demonstrated that this pruning method may ignore important information. The new pruning strategy proposed in this model can well avoid the over-pruning problem. The AGGCN model takes the dependency tree as input directly and uses a multi-head attention mechanism to focus on relevant substructures in the dependency tree. However, in the field of Chinese medicine, sentences are usually long, and the relations between entities are complex. Taking the dependency tree as input directly brings useless information. In addition, constructing multiple attention-weighted graphs through dependency relations also increases the computational complexity. According to Table 4, our proposed BAGCN model has better results on the Chinese medical relation extraction task. Compared with previous work, we combine sequence information and dependency relations to learn sentence information. A  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(\mathbf{p}\) & **R** & **F1** \\ \hline SVM & 59.37 & 54.18 & 55.83 \\ PA-LSTM & 61.28 & 58.57 & 59.79 \\ GCN & 63.15 & 58.23 & 60.24 \\ AGGCN & 68.48 & 63.14 & 65.35 \\ BAGCN (Pruning) & 74.28 & 65.75 & 67.29 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results with baseline models (unit: %).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(\mathbf{p}\) & **R** & **F1** \\ \hline B+G (Local) & 63.04 & 60.28 & 60.44 \\ B+G (SPT) & 65.27 & 60.7 & 61.18 \\ B+G (Local+SPT) & 64.39 & 61.88 & 62.17 \\ B+A+G (Local) & 66.95 & 64.30 & 64.91 \\ B+A+G (SPT) & 67.93 & 62.57 & 63.83 \\ B+A+G (Local+SPT) & 74.28 & 65.75 & 67.29 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of ablation experiments with different models (unit: %).  
new pruning strategy is also adopted to effectively utilize relevant information and ignore irrelevant information in the dependency tree. In addition, a multi-head attention mechanism is added to capture the global information of the sentences. In this way, the model improves the performance of relation classification of Chinese medical entities.
# Adaptive Pre/Post-Compensation of Cascade Filters in Coherent Optical Transponders
## 2 Sources of Filtering in Optical Links  
With fixed-grid switching elements, wavelength selective switches (WSSs) and channel mux/demux (CMD), being built with different technologies, as well with diverse manufacturing variations and a large number of suppliers [17]. Therefore, the predictability of the frequency response and center frequency shift of the concatenation of all the filtering effects is a challenge. In [17], the distribution of center frequency offset varies from the range of (\(-1\), \(+1\)) to (\(-4\), \(+4\)) GHz. As presented in [18], each WSS port can be modeled as super-Gaussian and is specified by two parameters: (1) 3 dB frequency width (\(\sigma_{sg}\)) and (2) order (\(n\)):  
\[S_{sg}(f)=\frac{1}{\sigma_{sg}\sqrt{2\pi}}\exp{[-(\frac{f^{2}}{2\sigma_{sg}^{ 2}})}^{n}]. \tag{1}\]  
While in [19], the characterization of 96 CMD instances built with arrayed-waveguide gratings (AWG) technology, showed a variation of \(+\)/\(-5\) GHz around channel nominal frequency. As well, the insertion loss per port varies and alters the shape of the propagated waveform. For our simulations and laboratory experiments, we got the frequency response of the aggregation of 18 commercial 50 GHz fixed-grid WSSs and one set of worst-case Multiplexer (Mux)/Demultiplexer (Demux) of a 50 GHz fixed grid CMD. The responses, showed in Figure 1b, include some ripples in the passband. The phase response was ignored for two reasons: one is that the concatenation of filters causes a reduction in the phase ripple variance (based on our observation and, that can be explained with the law of large numbers), second is that the adaptive filter at the receiver can correct for phase disturbance with no penalty.
# Functionalization and Modification of Bitumen by Silica Nanoparticles
## 3 Results and Discussion
### Functionalization of SiO\({}_{2}\) Nanoparticles.
The transition from the viscoelastic to the liquid mode ends when the modulus G' is no longer detected and, therefore, the loss tangent tan \(\delta\) diverges at approximately the same temperature values [20]. The loss tangent tan \(\delta\) increases with increasing temperature, which indicates a decrease in the consistency of the material and the predominant liquid-like behavior increases with increasing temperature. It is noteworthy that a hardening effect marked by a shift of the sol-transition temperature higher than 5 \({}^{\circ}\)C is rather significant for bitumen. Similar rheological behaviors were receded for well-known additives such as polyphosphoric acid (PPA) [21; 22] but environmental contamination issues have to be taken into account when using such additives. To this regard, FSNPs are far more eco-friendly additives. In this context, the recently published work of [PERSON] et al. [23] also shows the performances of diversely functionalized **SNPs** with long alkyl chains embedded with silane end groups to improve anti-aging and low temperature properties. Although these **FSNPs** require multi-step synthesis and were used in 4% weight ratio, they also show the importance of functionalized nanoparticles to promote their application in road paving with enhanced performance and sustainability. Considering the micellar method usually adopted for bitumen description, organic molecules have been proven to stabilize clusters of a wide variety of inorganic nanoparticles [24; 25]. Therefore, in this work the stabilization of nanoparticles is carried out using the principle of altering the nanoparticle Surface free energy [26; 27], driven to the extreme with chemical functionalization of nanoparticles. However, in the complex framework of the micellar model, it can be argued that another nanoparticle stabilization mechanism is concurrently present, i.e., the competitive interactions between the organic moiety of the capping agent and the aphiphilic resins pre-existent in the bitumen. In fact, it has been recently highlighted that specific interactions between surfactants and organic molecules can give peculiar self-assembly processes [28] with consequences in dynamical behaviour and transport properties [29; 30]. Therefore, the final result must be seen as the delicate summing up of different contributions coming from nanoparticles, from the capping agent's nature, and their interactions with the complex amphiphilic molecules of the bitumen, all embedding the apolar matrix.
# Multivariate Regression Analysis for Identifying Key Drivers of Harmful Algal Bloom in Lake Erie
## 3 Results and Discussions
### Multivariate Regression Model Performance
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & & **df** & \
\begin{tabular}{l} **SS—Sum** \\ **of Square** \\ \end{tabular} &
\begin{tabular}{l} **MS—Mean** \\ **Squares** \\ \end{tabular} & **F-Ratio** & \(p\)**-Value** \\ \hline
**Model 1** & & & & & & \\  & Regression & 5 & 0.989 & \(1.98\times 10^{-1}\) & 3250.46 & \textless{}1 \(\times\) 10\({}^{-4}\) \\  & Residual & 453 & 0.028 & \(6.09\times 10^{-5}\) & & \\  & Total & 458 & 1.017 & & & \\
**Model 2** & & & & & & \\  & Regression & 5 & 2.598 & \(5.20\times 10^{-1}\) & 2067.33 & \textless{}1 \(\times\) 10\({}^{-4}\) \\  & Residual & 453 & 0.114 & \(2.51\times 10^{-4}\) & & \\  & Total & 458 & 2.712 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: ANOVA table for statistical significance. Table 6 are different from 0, and most of them are less than 0.05 (except TSS for model 1 and Chl-a for model 2), indicating that these variables have a meaningful effect on target variables. The standard error (SE) values in the table measure the variability of the coefficients. Although the _p_-value for TSS in Model 1 and for Chl-a in Model 2 exceeds the 0.05 threshold, the variable was retained due to its ecological significance as a proxy for nutrient-bound particulates that influence bloom formation. Smaller SE values indicate that the estimates are more precise. In Model 1, all independent variables have small SEs, suggesting stable estimates. In Model 2, although most parameters have small SEs, POC and PON have slightly larger SEs, indicating more variability in their estimates. To verify the assumptions of linear regression, we conducted visual diagnostic methods for both residual normality and homoscedasticity. These included Q-Q plots for assessing the distribution of residuals and residuals vs. fitted value plots for detecting potential heteroscedasticity. Visual diagnostics are commonly accepted in applied environmental modeling as effective tools for evaluating model adequacy, particularly when dealing with complex or large datasets where slight departures from assumptions may not substantially impact model interpretation [61; 65; 66]. This approach is especially prevalent in environmental sciences, where residual patterns often reflect natural variability, measurement noise, or sampling limitations rather than systemic modeling flaws. Prior studies in water quality modeling, hydrology, and ecological regression have recommended visual inspection as a practical and informative method for validating model assumptions [67; 68]. In this study, the Q-Q plots for both models demonstrated approximate normality, while the residuals were symmetrically distributed around zero with no clear patterns, supporting the assumption of constant variance. These results reinforce the robustness of our multivariate regression models under real-world environmental data conditions.
## 4 Results
### Statistical Error Propagation in QoE considering Correlation between PLR and Jitter
Finding the ratio of slopes of Equation (4) and Equation (7) reveals that the slope of the QoE (jitter) model in Equation (7) is \(\approx\)50 times greater than the slope of QoE (jitter) model in Equation (4). Even with a significant increase in \(\partial QoE/\partial jitter\) for Equation (7) no significant increase in propagated CI width of QoE was seen in Figures 4 and 5. This is because of \(\partial QoE/\partial PLR\)\(\approx\)\(\partial QoE/\partial jitter\) for both cases. Thus, for the VoD application modelled in this work using an exponential function the propagated statistical errors do not depend on jitter. Figure 4: Propagated uncertainty in QoE vs. PLP at jitter = 1 ms with correlation using jitter model [14]. Figure 5: Propagated uncertainty in QoE vs. PLP at jitter = 5 ms with correlation using jitter model [14]. Although, there is around 10-40% correlation observed in all cases, in none of the cases the propagated confidence interval width of QoE changes significantly as a function of correlation. This can be explained using Equation (5) for both jitter models, where we can see that \(\partial QoE/\partial PLR\) is squared and as described before the magnitude of \(\partial QoE/\partial PLR\) is significantly higher than \(\partial QoE/\partial jitter\) and hence the propagation of uncertainty for VoD QoE is mainly dependent upon the rapid change in QoE with PLR (about the operating point) rather than much slower change in QoE with jitter or correlation about the chosen operating points. These results are a critical contribution to the process of QoE assessment, but are limited to the QoE evaluation of VoD applications modelled. The proposed technique can be used to model other multimedia applications, perhaps also as modelled using different mathematical functions i.e., power-law or polynomial. In addition, the results presented here consider QoE as a function of PLR and jitter separately. The analysis of correlation shows that they have a weak tendency to appear together and, in that case, the propagated errors in QoE will vary from the presented results in this work. For VoD applications modelled using an exponential function, we observed that the propagated statistical error show insignificant dependence on jitter, but in cases when jitter appears with PLR, we would expect different results for propagated errors.
## 4 Materials and Methods
### Equipment Used
#### 4.1.1 Sensor Nodes  
The sensors used for the experiments were Oracle Sun Small Programmable Object Technology (SunSPOT) devices. The SunSPOT platform provides a framework for rapid prototyping of WSAN applications using J2 ME CLDC API v1.1.  
SunSPOT devices are modular, so their hardware capabilities can be extended by the addition of extra boards. The typical SunSPOT sensor configuration is composed of two boards: the main board and the sensor board. To operate as a standalone, all SunSPOT sensors are equipped with a 3.7-V battery with a nominal capacity of 770 mAh. The main board also provides wireless communication capabilities through a CC2420 module using an inverted-F antenna. This enables the SunSPOT devices to communicate with other 802.15.4-capable devices that use the 2.4-GHz band.  
These sensor devices can be programmed using a restricted Java subset for a specific Java Virtual Machine, known as Squawk [19]. This micro edition virtual machine for embedded systems and small devices was designed to have a small footprint with minimal external dependences [26; 27].  
To ease the programming of applications for the Internet of Things, the SunSPOT platform also includes its own set of extra libraries to be run on top of Squawk. The SunSPOT software development kit (SDK) gives access to the communications and to the resources on the sensor board. The last stable version of the SunSPOT SDK was released in November 2010 as Version 6.0 and codenamed \"Yellow\". This version has some issues when trying to check the available capacity of the battery, returning random values. This issue was solved in the last developers version of the SDK, released in May 2011 as Version 7.0 and codenamed \"Teal\". We have updated the sensor nodes used in the experiments to this last version so we can make use of the correct information about the available battery capacity.  
The sink node is just like any other SunSPOT device without the sensor board and the battery. It allows a computer to interact with the sensors. We have developed a remote control application that allows us to control the execution of the experiments and to collect the received information from the deployed sensors. The sensors themselves also store information related to the operation of the  
Figure 8: Equipment used in the experiments. (**a**) Computer and standalone sensor nodes; (**b**) deployed sensor node.  
self-adaptive system, the battery status and their node degree in order to compare the results from the experiments.