esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Color Measurement  
To measure the color of the shade tab images, the L*, a*, and b* values were measured at the midpoint of each shade tab, where the point was intersected between two lines oriented in the inciso-apical and mesiodistal directions (Figure 3). Each measurement was repeated three times, and the mean values of L*, a*, and b* for each shade tab were calculated for both MC-DSG and SC-DSG.  
For the color measurement of all 29 VITA Linearguide 3D-Master shade tabs, the spectrophotometer (VITA Easyshade V, VITA Zahnfabrik) was used as the reference standard. During measurement, the device tip was positioned perpendicular to the middle third of each shade tab to ensure consistent readings. Before each session, the spectrophotometer was calibrated by placing the tip on the calibration block, following the manufacturer's guidelines. The CIE L*, a*, and b* values were then recorded for analysis. Three measurements were taken for each shade tab, and the mean L*, a*, and b* values for each shade tab were calculated [40].  
\begin{table}
\begin{tabular}{c c c} \hline
**Group** & **Standardized** & **Equipment** & **Manufactures** \\ \hline VITA EasyShade & & & VITA Zahnfabrik, \\ Advance V & Spectrophotometer & Bad Säckinogen, \\  & & Germany \\ \hline VITA Linearguide & & VITA Zahnfabrik, \\
3D-Master & & Bad Säckinogen, \\  & & Germany \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._The CIELAB coordinates (L*, a*, b*) were converted to the cylindrical CIELCH system to determine chroma (C*) and hue angle (H*). Chroma was calculated as C* = \(\surd\) (a*2 + b*2), and hue angle as H* = a tan2 (b*, a*), expressed in degrees from 0\({}^{\circ}\) to 360\({}^{\circ}\). These values were then used in the CIEDE2000 (\(\Delta\)E00) color difference formula, in line with the standard method recommended by the International Commission on Illumination (CIE) [26]. The color differences (\(\Delta\)E00) between both MC-DSG and SC-DSG and the spectrophotometer reference were calculated using the following equation:  
\[\Delta\mathrm{E}_{00}=\sqrt{\left(\frac{\Delta\mathrm{L}^{*}}{\mathrm{k}_{ \mathrm{L}}\mathrm{S}_{\mathrm{L}}}\right)^{2}+\left(\frac{\Delta\mathrm{C}^{* }}{\mathrm{k}_{\mathrm{C}}\mathrm{S}_{\mathrm{C}}}\right)^{2}+\left(\frac{ \Delta\mathrm{H}^{*}}{\mathrm{k}_{\mathrm{H}}\mathrm{S}_{\mathrm{H}}}\right)^ {2}}\]  
In this formula, \(\Delta\)L* represents the difference in lightness, \(\Delta\)C* indicates the chroma difference, and \(\Delta\)H* is the hue difference between the compared samples. The terms k\({}_{\mathrm{L}}\), k\({}_{\mathrm{C}}\), and k\({}_{\mathrm{H}}\) are parametric correction factors, which were each set to 1 as recommended for standard conditions. S\({}_{\mathrm{L}}\), S\({}_{\mathrm{C}}\), and S\({}_{\mathrm{H}}\) are weighting functions that adjust the influence of lightness, chroma, and hue on perceived color differences.
## 3 Results and Discussion
### Statistical Analysis  
Analysis of Variance for Kerf Taper Angle and Material Removal Rate  
Analysis of variance (ANOVA) was performed to validate kerf taper angle and material removal rate from the machining twelve profile, as given in Figure 8.  
The ANOVA results in Figure 8 denote that the percentage contribution of material thickness on kerf taper angle ranges from 69-91% with 5-18% for traverse speed. The kerf tapering results show the proportion of kerf top width to kerf bottom width. The variation between the top and bottom geometries denotes a higher kerf tapering. Kerf top or entry width is relatively higher than the exit width because the kinetic energy of abrasive particles is primarily at a high level and consistently decreases during the machining process [15]. An increase in material thickness denotes prolonged cutting operations, which continuously decrease the kinetic energy of abrasive particles, producing a higher taper angle.  
Figure 8 also shows the material removal rate obtained under variable conditions. The percentage contribution of material thickness on material removal rate ranges from 62-69%, with 27-36% for traverse speed. According to this statistical analysis, material thickness directly influences the measured output parameter in this case. Referring to ANOVA Tables A2 and A3 in the appendices, the obtained \(p\)-values are less than 0.05. Therefore, the impacts of material thickness and traverse speed are statistically significant.  
In AWJ cutting, machining is fundamentally executed by the cohering action produced through impact by a number of abrasive particles travelling at high velocity, towards a workpiece [14]. As a result, material removal rate and thickness are directly proportional, where it becomes possible to achieve higher MRR even when machining samples with increasing thickness.
# Performance of Dynamic Time Division Broadcast Protocol with Rateless Coding
## 5 Conclusions  
In this paper, we considered a two-way relaying channel with direct link and proposed the DTDBC-RC protocol to fully utilize the channel dynamics of the direct link by using rateless coding.  
Figure 8: The diversity-multiplexing trade-off (DMT) performance comparison of TDBC-IR, DTDBC-RC, and sub-DTDBC-RC.  
Figure 7: The expected rate comparison of TDBC-IR, DTDBC-RC, and sub-DTDBC-RC.  
We analyzed the performance (in terms of outage probability, expected rate, and DMT) of DTDBC-RC and presented a subslot implementation scheme (sub-DTDBC-RC) to derive the DMT of DTDBC-RC. Compared with the already existing TDBC-IR protocol, DTDBC-RC (or sub-DTDBC-RC) has better expected rate and DMT performance.  
[PERSON] and [PERSON] conceived of and designed the model, performed the theoretical analysis and simulation, analyzed the simulation result, and wrote the paper. [PERSON], [PERSON], and [PERSON] provided valuable suggestions for this paper and helped in simulation and writing. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China under Grant No. 61801492 and No. 61601490, and is also supported by research fund of National University of Defense Technology with contract No. ZK18-03-20.  
The authors declare no conflict of interest.
## 3 Our Contribution
### Systematic RLNC Based UWSN
#### 3.3.1 UWSN Network Model  
Figure 1 illustrates a three-dimensional (3D) UWSN model applied to examine the efficiency of the proposed SRLNC based UWSN routing protocol. The proposed network model contains multiple acoustic sensor nodes and multiple sink nodes. The sensor nodes possess similar architecture and are distributed randomly. The individual sink node can be assumed to have a radio frequency modern and an acoustic modern, and is aware of its 3D position and associated node information using location services and can save this for making routing decisions.  
Consider the sensor nodes being deployed across an underwater acoustic network in a Euclidean space \(\mathrm{D}\in\Re^{3}\). At time \(\mathrm{t}\), the network can be modelled as an undirected graph \(\mathrm{G}(\mathrm{t})=(\mathrm{V},\varepsilon(\mathrm{t}))\)[4], where \(\mathrm{V}\left\{\mathrm{n}_{\mathrm{i}}|1\leq\mathrm{i}\leq\mathrm{M}\right\}\) refers to the sensor nodes and \(\varepsilon(\mathrm{t})=\left\{\mathrm{e}_{\mathrm{ij}}|1\leq\mathrm{i},\ j \leq\mathrm{M},\mathrm{i}\
eq\mathrm{j}\right\}\) signifies a set of connecting links between the sensor nodes at time \(\mathrm{t}\). With \(\mathrm{\psi_{\mathrm{eij}}(t)\in\varepsilon(t)}\), nodes \(\mathrm{n_{i}}\) and \(\mathrm{n_{j}}\) are the neighbouring nodes at \(\mathrm{t}\) and can communicate with each other directly through an acoustic link over the applied channel. If \(\mathrm{\forall n_{i}\in V}\), the neighbouring nodes at time \(\mathrm{t}\) can be \(\mathrm{N_{i}(t)=\left\{\mathrm{n_{i}\in V}|\mathrm{\exists e_{ij}(t)\in \varepsilon(t)}\right\}}\). Here, all nodes are assumed to have equal transmitting power as well as radio range \(\mathrm{R}\).
# SNA: Sensitivity-Aware Neural Architecture Adaptation for Uniform Quantization
## 4 Experimental Results
### Ablation Study  
The experimental results used uniform quantization to simplify deployment and save time. Generally, mixed-precision quantization can achieve higher precision with the same size constraint compared to uniform quantization. To explore the effectiveness of SANA, we compared our results with ResNet-50-MP, which uses mixed-precision quantization with an average bitwidth of four. The results are shown in Table 2. As can be seen, using ResNet-50-SANA with W3A8 and a 1.155 multiplier can better improve the accuracy compared to ResNet-50-MP with 4 bit mixed-precision quantization and no channel splitting. Note that the models in Table 2 have the same model size. These results demonstrate that SANA can achieve better performance even compared to mixed-precision quantization, while having the extra benefits of being easier to deploy and faster to train.  
Another assumption we made is that larger models exhibit greater resilience to quantization, as compared to their smaller counterparts. This notion formed the basis of our approach to tackling the sensitivity issues observed in models such as ResNet-50 and EfficientNet-B2. This was empirically proven through the quantization results of various models. For instance, when comparing the full-precision accuracy with the channelwise W4A32 quantization accuracy on ImageNet [48], we observed that, for large models, the accuracy of quantized ResNet-50 (97.8 MB) and ResNet-101 (171.0 MB) dropped by 13.77% and 12.88%, while for small models such as SqueezeNet (0.5 MB) and DenseNet (33.0 MB), the accuracy drops were more intolerable, by 28.10% and 17.4%, respectively.
## 4 Methodology
### Arithmetic Block
#### 4.1.1 16-Bit Ripple Carry Full Adder  
A full adder is an adder that takes three inputs and outputs two results. The first two inputs are \(A\) and \(B\), while the third is an input labeled as \(C_{in}\) as indicated in Figure 2. The output carry is denoted by \(C_{out}\), while the normal output is denoted by \(S\), which stands for SUM. If \"Carry in\" is 0, the behavior of a full adder is identical to that of a half adder. However, if \"Carry in\" is 1, the sum's behavior is the inverse, and \"Carry out\" changes into an OR. As long as any of the inputs are 1, Carry out is 1. A full adder logic is designed to accept eight or more inputs to construct a byte-wide adder and cascade the carry bit from one adder to the next. Hence, this design can be used to create a 16-bit binary adder. We cascade 16 full adder blocks together, passing the Carry over from the first full adder to the second [21], from the second to the third, and so on. Bits of \(A[1]\) till \(A[16]\) and \(B[1]\) till \(B[16]\) are given simultaneously as a single bit to each one of the adders, and an initial \(C_{in}\) of 0 is given. A full adder operation takes place in each block, and Carry is passed along to the following full adder block. The adder blocks individually transmit out the sum of the bits one by one. Figures 3 and 4 show the circuit and waveform results from our full adder implementation.  
In Figure 3, we can observe four 4-bit ripple carry adders connected as a single ripple carry adder unit to generate a 16-bit output, along with the final carry output as an overhead. Figure 4 illustrates the simulated results of this circuit, with the first two waves being the two 16-bit inputs,'sel' describing the selector control signal, and 'out7971' as the 16-bit output followed by the overflow bit.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Selection Bits** & **Operation** & **Result** \\ \hline
000 & Addition & A + B \\
001 & Subtraction & A \(-\) B \\
010 & Multiplication & A \(\times\) B \\
011 & Division & A/B \\
100 & Bitwise AND & A\&B \\
101 & Bitwise OR & \(A\|B\) \\
110 & Inverter & A \\
111 & Base64 compression & Base64(A) \\ \hline \hline \end{tabular}
\end{table}
Table 2: ALCU and its operations.  
Figure 4: Waveform results of the addition module.  
Figure 3: Circuit for the full adder generated through Verilog.  
Figure 2: The 16-bit ripple carry full adder block diagram.
# Design of a Chaotic Trajectory Generator Algorithm for Mobile Robots
## 2 Chaotic Systems  
Chaos theory can be defined as the qualitative study of non periodic dynamical behavior shown by non-linear deterministic systems [12]. It can be observed in many natural engineering systems. Among the principal features of chaotic systems, we have the following:  
Figure 1: Predictable vs. unpredictable trajectories: (**a**) predictable; (**b**) unpredictable.  
* High sensibility to initial conditions;
* Deterministic with apparently random behavior;
* Non-periodic oscillations.  
Initial condition sensibility indicates highly different temporal evolution on chaotic systems, despite very small differences in their initial conditions. This feature is desirable on patrolling robots, since it would cause the trajectory followed by the robot to be highly unpredictable. Chaotic systems are deterministic because, knowing the initial conditions and the equations of the system, their behavior can be determined. With this feature, we can say that a robot with a chaotic trajectory will be highly unpredictable for outside spectators, but known to the designer and any other observer that has the knowledge of the equations and the initial conditions used in its design.
# Real-Time Text-to-Cypher Query Generation with Large Language Models for Graph Databases
## 4 Concept  
As the existing literature attests, text-to-SQL systems have been thoroughly researched in the context of Natural Language Interfaces (NLIs) for databases. Shifting the focus to NoSQL databases, though, reveals more significant challenges due to their complexity. Query generation in graph databases is particularly challenging. Most of the literature focuses on SPARQL as a query language. Conversely, the Cypher Query Language (CQL), which is used to query graph databases in Neo4j, has been relatively less explored, both in terms of breadth and depth. To fill this research gap and develop an appropriate artifact for this topic, the Design Science Research Methodology (DSRM) as outlined by [PERSON] and [PERSON] [61] and further developed by [PERSON] et al. [62], was applied. This approach is particularly appropriate, as it allows a specific organizational problem to be addressed through the development of a meaningful IT artifact [63]. By applying DSRM, the research focuses on creating a solution that not only addresses the technical aspects of querying graph databases using CQL, but also considers the usability and practicality of the solution in organizational settings. The development of such an artifact, adapted to the complexities of NoSQL databases and Neo4j in particular, aims to make a significant contribution to the field by improving the capabilities and ease of use for practitioners and researchers alike.
###### Abstract  
Geopolymer concrete (GPC) is applied successfully in the construction of civil engineering structures. This outcome confirmed that GPC can be used as an alternative material to conventional ordinary Portland cement concrete (OPC). Recent investigations were attempted to incorporate recycled aggregates into GPC to reduce the use of natural materials such as stone and sand. However, traditional methodology used to predict compressive strength and to find out an optimum mix for GPC is yet to be formulated, especially in cases of GPC using by-products as aggregates. In this study, we propose novel hybrid artificial intelligence (AI) approaches, namely a particle swarm optimization (PSO)-based adaptive network-based fuzzy inference system (PSOANFIS) and a genetic algorithm (GA)-based adaptive network-based fuzzy inference system (GAANFIS) to predict the 28-day compressive strength of GPC containing 100% waste slag aggregates. To construct and validate these models, 21 different mixes with 210 specimens were casted and tested. Three input parameters were used to predict the tested compressive strength of GPC, i.e., the sodium solution (NaOH) concentration (varied from 10 to 14 M), the mass ratio of alkaline activation solution to fly ash (AAS/FA), ranging from 0.4 to 0.5, and the mass ratio of sodium silicate (Na\({}_{2}\)SiO\({}_{3}\)) to sodium hydroxide solution (SS/SH) which was varied from 2 to 3. The compressive strength of the fabricated GPC was used as output parameter for the prediction models. Validation of the models was done using several statistical criteria such as mean absolute error (MAE), root-mean-square error (RMSE), and correlation coefficient (\(R\)). The results show that the PSOANFIS and GAANFIS models have strong potential for predicting the 28-day compressive strength of GPC, but the PSOANFIS (MAE = 1.847 MPa, RMSE = 2.251 MPa, and \(R\) = 0.934) was slightly better than the GAANFIS (MAE = 2.115 MPa, RMSE = 2.531 MPa, and \(R\) = 0.927). This study will help in reducing the time and cost for the implementation of laboratory experiments in designing the optimum proportions of GPC.  
compressive strength; geopolymer concrete; adaptive network-based fuzzy inference system; artificial intelligence
## 3 Results
### Results of the Training and Prediction  
Figure 4 shows the results of the training and loss for four applied models of CNNs: EfficientNet, VGG16, ResNet50, and DenseNet121. Although the results through VGG16 were not trained well, others showed good performance with nearly 80% accuracy. Our dataset includes 2 classed: smoker and non-smoker with 220 images per one class. We used rate (60/20/20) to split our dataset: 132 images in train set, 44 images in test set and validation set. Because the number of human subjects was small, the function of ImageNetaGenerator Class in Keras was applied for increasing the number of images. It is well-known that CNN is only relevant when they are trained with a huge amount of data. In order to make the most of our few training examples, we can augment them via several random transformations. This helps to prevent overfitting and improves the generalization of the model. In Keras, this can be done by the class keras.preprocessing.image.ImageDataGenerator. The total number of images was 2600: 80% was used for the training and validation, and the left 20% was for the testing. We implemented experiment on four CNNs models: VGG, ResNet, DenseNet and EfficientNet with our dataset. And the result was showed on Tables 3 and 4.  
Figure 4: Results of training (**a**) and loss (**b**) for four models of CNNs: EfficientNet (blue color plot), VGG16 (orange color plot), ResNet50 (green color plot), and DenseNet121 (red color plot).  
Figure 3: An example of the capillary image of nailfold (**a**) and tongue (**b**) between smokers and non-smokers.  
Table 3 represents the trainable and non-trainable parameters of four CNN models: EfficientNet-B1, VGG16, Resnet50 and DenseNet121. These results show that the total number of parameters of EfficientNet-B1 is the least of the four models, and is equal to the one fourth of the number of parameters of ResNet and half of VGG16's. However, EfficientNet-B1 is the most effective, thus EfficientNet-B1 is considered to be the good choice in our data.  
Table 4 represents the results of prediction for four different algorithms of CNNs: EfficientNet, VGG16, ResNet50 and DenseNet121. It is shown that the results of prediction for EfficientNet indicates the best performance among four different CNNs.  
Compared to the result of algorithms with handcrafted feature extraction such as SSIM and SIFT, it was found that most of CNN algorithms without handcrafted feature extraction worked better for the image processing of capillaries, although VGG16 showed the failure.
# Effect of Wing Corrugation on the Aerodynamic Efficiency of Two-Dimensional Flapping Wings
## 3 Effect of Wing Corrugation on the Two-Dimensional Flapping Wing
### Discussion  
The two-dimensional CFD analysis showed that at \(r=0.375R\), the wing corrugations placed on the entire wing do not help to improve the aerodynamic performance, which experiences relatively higher AoAs during the translational stage of (55-63)\({}^{\circ}\). On the other hand, at \(r=0.375R\), the presence of the corrugation at the leading edge is beneficial in enhancing the lift coefficient and gliding ratio of the two-dimensional flapping wing. However, for the two-dimensional flapping wing at \(r=0.75R\), where the AoAs during the translational stages are relatively lower at (37-47)\({}^{\circ}\), all corrugated wings have lower lift coefficients and gliding ratios, compared to those of the non-corrugated wing.  
Furthermore, the effect of the size of the leading-edge corrugation was also examined. At \(r=0.375R\), the LE3 wing with the largest leading-edge corrugation performed the best among the leading-edge corrugated wings, with a 3.6% increase in lift coefficient and a 7.9% increase in gliding ratio, compared to those of the S wing. Meanwhile, the LE1, which has the smallest leading-edge corrugation, has the lowest performance among the three leading-edge corrugated wings, with a 1.5% increase in lift coefficient and a 2.7% increase in gliding ratio, compared to those of the S wing. However, at \(r=0.75R\), the wing with the largest corrugation (LE3) has the worse aerodynamic performance,  
Figure 12: Flow fields and swirling strength around the five wings at \(r=0.75R\).  
with a 6.2% reduction in lift coefficient and a 2.4% reduction in gliding ratio, compared to those of the S wing. Meanwhile, the aerodynamic performance of the LE1 wing is almost the same as that of the S wing. This investigation shows that the corrugations in the leading edge of the wing are beneficial in enhancing the aerodynamic performance of the flapping wing that operates at relatively high angles of attack during the translational stages (at \(r=0.375R\)). However, for the flapping wing that operates at relatively low angles of attack during the translational stages (at \(r=0.75R\)), the corrugations are detrimental to the aerodynamic performance. Therefore, the wing with the large leading-edge wing corrugations is recommended only for the inboard wing, where, in the KUBeetle case, the AoA is high.