esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model
### Problem Formulation  
To enhance the efficiency of computational task offloading, we constructed a mathematical model tailored to minimize the total execution cost within the MEC framework. This mathematical model integrates the inherent complexities of the network's architecture, the communication resource in use, and the dynamics computation at play in MEC environments. Recognizing the finite nature of MEC server resources and the associated costs of server utilization, our model incorporates economic considerations. Specifically, due to MEC servers having limited resources, assuming that each strategy repeatedly selects an edge computing strategy will lead to a high cost. Therefore, we assigned very low rewards to successively select strategies for MEC as a main task processor; however, even if such a strategy emerges, it slowly disappears in the iterations.  
We used the following equation to express the cost consumption of the four task offloading strategies of the UE:  
\[C_{i}^{ue}=\begin{cases}C_{i}^{local}s_{i}\\ C_{i}^{t2d}s_{i}\\ C_{i}^{edge}s_{i}\\ C_{i}^{mig}s_{i}\end{cases}\forall i\in U_{i},s_{i}\in\{0,1,2,3\}. \tag{21}\]  
Since the aim of this study was to minimize the total cost of delay and energy consumption of UEs, the bandwidth resources of AP should be satisfied:  
\[\sum_{b\in B}M_{b}W_{sub,b}\leq W_{b}, \tag{22}\]  
where \(W_{b}\) represents the bandwidth of AP. \(C_{all}\) represents all UE devices, which can be calculated as  
\[C_{all}=\sum_{n=1}^{N}C_{i}^{ue},\forall i\in U_{i}. \tag{23}\]  
Under the constraints of maximum tolerable delay and energy consumption, the problem is expressed as  
\[\begin{array}{l}Min\text{C}_{\text{all}}\\ C1:s_{i}\in 0,1,2,3,\forall i\in UE_{i};\\ C2:t_{i}^{local}+t_{i,b}^{tran}+t_{i,b}^{edge}+t_{i,c}^{tran}+t_{i,c}^{t2d}+t_{i, b^{\prime}}^{mig}+t_{i,b^{\prime}}^{edge^{\prime}}<t_{i},\forall i\in U_{i};\\ C3:E_{i}^{local}+E_{i,b}^{tran}+E_{i,b}^{edge}+E_{i,c}^{tran}+E_{i,c}^{t2d}+E_{ i,b^{\prime}}^{mig}+E_{i,b^{\prime}}^{edge^{\prime}}<E_{i},\forall i\in U_{i};\\ C4:\sum_{n=1}^{N}C_{i}\leq C_{all},\forall i\in U_{i}.\end{array} \tag{24}\]  
Based on satisfying the QoS and QoE required by users, the solution of this problem can be expressed as solving the minimum total offloading cost \(C\) under the optimal offloading policy vector \(S\).
# Development of a Datalogger for Submarine Glider: Integration of Fault-Tolerant Software Layers
## 3 Results
### Datalogger Consumption  
To test the power consumption of the datalogger, the electrical current consumption was monitored for a write operation of the NAND flash memory chip in a flight mode operation. The power consumption of the datalogger during the information storage operation, and taking the data via serial at 115200 baud, is 1.6 mW. It means that if we use two batteries in series of 1.5 V and 3000 mAh each one, you would occupy the datalogger for more than 7 months.  
Regarding the consumption of microcontroller resources, the consumption of the microcontroller can be viewed in Table 6. The percentage of use of RAM and FLASH of the microcontroller is quite low. The resource still available can be used for other functions, for example, to restore values that are in flash and these are required to restore the datalogger to some original value, bad block addresses, wear leveling algorithms such as garbage collection, etc.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Means** & **Available** & **Used** & **Percentage of Use** \\ \hline RAM & 8192 & 3082 & 37\% \\ FLASH & 65,536 & 5440 & 8\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Consumption of the microcontroller memory.  
Figure 15: The decoding time increases if there is more probability of bit errors. If the read time increases, it is possible to predict the degradation of the NAND flash chip.
## 6 Practical Implications, Limitations and Future Research Lines  
For destinations with similar characteristics to those of the city of Popayan, that are starting the stage of developing their sector of tourism and count on cultural attractions of interest to visitors, it is fundamental to use a methodology of tourist movement analysis which allows them to manage the flows more efficiently, and also to optimize the carrying capacity of the resources; that way, they can achieve a more sustainable tourist activity. Additionally, this allows a better collection of information about the tourists they receive (number, profile and movements), which facilitates the definition of the marketing rules in the destination.  
One of the limitations of this study is the distribution of the samples, more specifically, the one regarding the tourists who used NFC, since due to the restrictions of the device, it represented a small number. Nevertheless, given the exploratory character of this research, it is considered big enough, as the proportion of the samples regarding foreign and national visitors is maintained, and the priority is the recorded movements. This is in agreement with the recommendations to assess technological systems using any interface novelty (in this case, the interaction with the mobile device), as proposed by [PERSON] (1993) [116], and [PERSON], [PERSON], & [PERSON] (2015) [117] for experimentation with this type of technologies.  
Another limitation of this study has to do with the number of participants in 2013, when just mobile telephones with GPS(Global Positioning System) were the chosen tool for data collection: not all the tourists were willing to allow all their movements during their visit to the destination to be tracked. This limited the scope of this study for this period.  
Any future research should test the advances in the use of the NFC technologies applied in tourism destinations not only cultural, in order to generalize these findings to a wider context. Also, it could be of interest to analyze the visitor's and other stakeholders perceived value of the application of new technologies from both points of view.  
**Author Contributions:** Conceptualization, L.F.-M.; Formal analysis, L.F.-M. and A.C.-A.; Investigation, A.C.-A.; Methodology, M.-F.B.-F., A.C.-A.; Resources, A.C.-A., M.-F.B.-F.; Supervision, L.F.-M. and A.M.-M.; Validation, L.F.-M. and A.M.-M.; Visualization, L.F.-M., M.-F.B.-F.; Writing - original draft, A.C.-A., L.F.-M. and M.-F.B.-F.; Writing - review and editing, A.C.-A., L.F.-M., A.M.-M. and M.-F.B.-F.  
**Funding:** This research was funded by [Universidad del Caucal], grant number [501100005682].  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Conclusions  
The determination of the best discriminating kinematic parameters for HOA and THA classification is a crucial step in the accurate diagnosis of the disease. DL is a promising methodology used in solving different classification problems, and it has been proved to be a reliable approach. In this research, a hybrid of LSTM and CNN is proposed to classify gaits as healthy, gaits with HOA, and gaits 6 months after THA. The LSTM model exploits temporal dependencies of the gait sequential data, while the CNN model is employed to detect local gait features. Specifically, two bi-directional LSTMs and six CNNs are stacked to form the network with a skipping structure to improve fitting. To evaluate the design and benchmarked with the literature, the discrimination between healthy and HOA gaits is tested. It is shown that the sagittal angles of the hip and knee are the top two most discriminating kinematic parameters with G-Mean scores of 97.8% and 97.6%, respectively. This is followed by the front angles of the knee and FPA with G-Mean scores of 96.5% and 94.9%, respectively. Overall, the performance of the selected kinematic parameters is above 80%, except for the sagittal angle of the FPA, which has a G-Mean score of 78.2%. The proposed model performs significantly better compared to SVM-based models [23] with a more than 10% higher margin, having similar results with image-based CNN models [24].  
A different perspective is used in the analysis of THA gaits, with an in-depth investigation conducted on the misclassification results. This approach can provide a glimpse into the success or failure of THA surgery. It was noted that the highest proportion of errors came from the misclassification of THA. For the top four most discriminating parameters, healthy and HOA gaits resulted in 10-25% and 10-30% misclassifications, respectively. In a broader sense, these percentages signify kinematic outcomes returning to normal with healthy prediction, while functional impairments can still be present with HOA prediction for the true THA class. Hence, further examination was conducted on the misclassified gaits of subjects after THA using the best two kinematics, namely the sagittal angles of the hip and the knee. The subjects were tabulated according to healthy and HOA misclassification, and it was found that there was no cross-misclassification between the top kinematic parameters. This means that there are no common features between healthy and HOA gaits on these parameters, but there are some common features with the THA gait. Indeed, there is a class of THA gait that requires a rehabilitation effort to closely align with healthy features. Additionally, more than 50% of the misclassification results are common to both hip and knee sagittal angles, thus boosting confidence into the success or failure determination of THA operation. These findings are promising, and our novel DL  
Figure 16: HOA prediction of subjects after THA.  
methodology could be used in medical diagnostic systems. Finally, misclassifications are driving us to keep improving our methodology.
# A Survey of Graphical Page Object Detection with Deep Neural Networks
## 3 Methodologies
### Fully Convolutional Neural Networks  
Along with object detection algorithms, Fully Convolutional Neural Networks (FCNNs) [49] have been exploited to solve graphical page object detection in document images. The basic intuition behind FCNNs is assigning the label for each pixel present in an image. Figure 9 depicts the architecture of FCNNs and for further explanation, we refer our readers to [49]. [PERSON] et al. [35] posed the problem of tables and chart detection as a saliency detection problem. The authors propose that each class of page object can be referred to as a separate saliency category. To segment those categories (tables and charts), the system employs FCNNs where each pixel will be classified into tables, charts, or a background in a document image. The obtained saliency map is further propagated to the fully connected Conditional Random Field (CRF) [49], which smooths the system's output.
# Effects of Veneering Ceramic and Methods on Failure Load of Veneered Zirconia
###### Abstract  
Background: A variety of veneering options to zirconia frameworks are now available. The purpose of this study is to evaluate the effect of veneer materials, veneering methods, cement materials, and aging on the failure load of bilayered veneer zirconia. Material and methods: Zirconia bars (20 \(\times\) 4 \(\times\) 1 mm) were veneered to 2 mm total thickness (n = 10/group). Veneering method groups included: 1. Hand-layered feldsparathic porcelain (VM = Vita VM9, Vident) and fluorapatite glass-ceramic (CR = IPS e.max Ceram, IvoclarVivadent); 2. Pressed feldspathic porcelain (PM = Vita PM9, Vident) and fluorapatite glass-ceramic (ZF = IPS e.max ZirPress, IvoclarVivadent); 3. CAD-/CAM-milled feldspathic ceramic (TF = Vitablocs Triluxe Forte, Vident) and lithium-disilicate glass-ceramic (CAD = IPS e.max CAD, IvoclarVivadent). CAD/CAM veneers were either cemented with resin cements (P = Panavia21, KurarrayDental), (R = RelyX Ultimate, 3M ESPE), (M = Multilink Automix, Ivoclar Vivadendor) or fused with fusion glass-ceramic (C = CrystalConnect, IvoclarVivadent). A three-point bending test (15 mm span, zirconia on tension side) was performed on Instron universal testing machine (ISO 6872) recording load-to-failure (LTF) of first veneer cracks or catastrophic failure. For group VM, PM, TF-M, TF-C, CAD-M, CAD-C, ten more bars were prepared and aged with cyclic loading (100,000 cycles, 50% LTF) and thermocycling (2000 cycles) before testing. Data were analyzed by ANOVA, Tukey HSD post hoc tests, and \(t\)-test (\(\alpha\) = 0.05). Zirconia veneered with IPS e.max CAD by fusing had significantly higher failure load compared with zirconia veneered with other veneering materials (\(p\)\(\leq\) 0.05). For cemented veneers, the cement type had a significant effect on the failure load of the veneer zirconia specimens. Specimens cemented with Panavia 21 had a lower resistance to loading than other cements. The aging experiment revealed a significant difference in failure load between non-aged and aged bars in groups VM and PM, but not in the groups with CAD-/CAM-milled veneers. In conclusion, veneer materials, veneering methods, and cement materials have a significant effect on the failure load of bilayered veneer zirconia. CAD-/CAM-milled veneer zirconia is not susceptible to aging performed in this study.  
zirconia; CAD/CAM; crowns; prosthodontics; ceramics +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Physicochemical Properties
#### 3.1.4 Refractive Index  
The refractive index is one of the significant optical properties of materials used to check the purity of objects, identification and quantification of soluble compounds, etc. [50; 51]. The experimental refractive indexes of NADESs are presented in Figure 4 as a function of water content. The refractive index decreased with increasing water content. These results were expected since the mixture as a whole became less dense as the water content of the solution increased (Figure 2). The refractive index of NADES1 was similar to the data given in [52]. As far as we know from the available literature, the refractive indices for NADES2 and NADES3 and their water solutions were measured by us for the first time. The corresponding equations for the calculation of refractive indexes (y) as a function of water content (x) were calculated (Equations (8)-(10) for NADES1-NADES3, respectively). \[Y_{1}=-0.0012\times X+1.4523\ (R^{2}=0.998) \tag{8}\]  
\[Y_{2}=-0.0016\times X+1.4945\ (R^{2}=0.982) \tag{9}\]  
\[Y_{3}=-0.0012\times X+1.4633\ (R^{2}=0.996) \tag{10}\]  
The equations could be used for the control of water content in NADES for the industrial-scale application of relevant NADES. The other parameters closely associated with refractive indexes such as molar refraction, specific refraction, electronic polarization, and polarizability constant for studied NADESs were calculated according to equations presented in Section 2.3.3. The afore  
Figure 3: The relationship curves between pH and percentage of added water of the tested NADES at room temperature. NADES1—LA:ChCI (3:1); NADES2—LA: SL (1:3); NADES3—LA:GL:W (3:5:1). mentioned properties are useful in chemical, medical, biotechnical, and engineering fields. The data on electronic polarization are important for the understanding of forces between molecules and their behavior in solutions [37]. Since the electronic polarization (E) is a power state of the refractive index, it is expected that the dilution of NADES with water will lead to a decrease in electronic polarization. In our experiments, the values of E upon the dilution of NADES change insignificantly by no more than 10% (Table 1). We noted that the calculated specific refraction for all tested NADESs and their water solutions was not dependent on water content and was in narrow diapason (mean 0.205 \(\pm\) 0.011 cm\({}^{3}\)/g) for all tested samples (Table 1). Similar trends for specific refraction were observed for binary mixtures of the C60-tris-malonic derivative with water [25].
# Low-Latency Bit-Accurate Architecture for Configurable Precision Floating-Point Division
## 5 Results and Comparisons  
The proposed Predict-Correct iterative FP division architecture is synthesized with TSMC 90 nm standard cell library, using Synopsys Design Compiler. The implementation details are shown in Table 4. The FP division unit is synthesized with best achievable timing constraints, with constraint of max-area set to zero and global operating voltage of 0.9 V.  
In Table 4, two novel metrics are proposed. One is total time (latency \(\times\) period) and the other is efficient average time (latency \(\times\) period/efficient length). Define total time as the time needed for a division unit from inputting numbers to outputting results. Define efficient average time as the time needed for a division unit to process single bit of input numbers. Total time measures the computational speed of single division operation for a division unit. It finds its significance as division operation is not frequent in processors or co-processors, making the computational speed of single division operation important. Efficient average time measures the ability of a division unit to process high-precision input numbers, which makes it useful in large-scale high-precision applications.  
Figure 10: Latency of the proposed FP division for SP, DP, and QP inputs.  
In our implementation, we have been able to put out a quotient portion with at least 29-bit in a single cycle for division, and a quotient portion with more bits using the correction mechanism between iterations in the same cycle. In addition, there is only one pre-computational cycle before the iterations, unpacking of dividend and divisor inputs and pre-configuration. Finally, no post-processing cycle for rounding after the iterations is needed.  
The total latency of the division unit consists of cycles of PRECONFIG, MANTISSA_DIVIDE and NORMALZE. Additionally, as the proposed division unit is iterative in nature, every next input can be applied soon after finite state machine (FSM) in MANTISSA_DIVIDE finishes the current processing. Thus, the Predict-Correct FP division unit will have a latency of 3, 4, and 6 cycles for SP, DP, and QP FP division computations.  
The Predict-Correct FP division unit with DP requires 1 more period than it with SP, whereas it with QP it needs 2 more cycles than with DP.  
It can be seen from Table 4 that the higher the precision of the proposed FP division unit, the more economical its latency and total time (latency \(\times\) period) in the implementation results. Furthermore, efficient average time (latency \(\times\) period/efficient length) also indicates that the proposed FP division architecture is of more value when used in more accurate computation.
# Spherical-Model-Based SLAM on Full-View Images for Indoor Environments
## 4 Method Overview  
Our method continuously builds and maintains a map, which describes the system's representation of the user's environment. The map consists of a collection of \(M\) point features located in a world coordinate frame \(W\). Each point feature represents a local, spherical-textured patch in the world. The \(j\)th point \(p_{j}\) on the map has a spherical descriptor \(d_{j}\), and its 3D world coordinate \(p_{jW}=(x_{jW},\ y_{jW},\ z_{jW})^{T}\) in coordinate frame \(W\).  
The map also maintains \(N\) keyframes, which are taken in various frame coordinates according to the keyframe insertion strategy. Each keyframe \(K\) contains a keyframe index \(i\), the rotation \(R_{iK}\) and translation \(T_{iK}\) between this frame coordinate and the world coordinate. Each keyframe also stores the frame image \(I_{iK}\), the index of map points \(V_{iK}\), which appear in the keyframe, and the map points' 2D projection \(P_{nVik}=(u_{nVik},\ v_{nVik})^{T}\).  
The flow of our full-view SLAM system is shown in Figure 4. Like other SLAM systems, our system also has two main parts: mapping and tracking. The tracking system receives full-view images and maintains an estimation of the camera pose. At every frame, the system performs two-stage coarse-to-fine tracking. After tracking, the system determines whether a keyframe should be added. Once a keyframe is added, a spherical epipolar search is conducted, followed by a spherical match to insert the new map. The flow of our full-view SLAM system is similar to many existing SLAM approaches. The difference is that these steps are handled based on the spherical model. Each step is now described in detail.
# Research on the Jiamusi Area's Shallow Groundwater Recharge Using Remote Sensing and the SWAT Model
## 4 Results
### Calculation of Groundwater Recharge in the Jianusi Area by the SWAT Model  
#### 4.2.1 Sub-Watershed Division and HRU Unit  
The sub-watershed division is a crucial component of the SWAT model's simulation of surface runoff. The optimal water logging area threshold (22,579.14 Ha) was adopted in this link based on the imported DEM topographic map and the actual river system vector map. The entire outlet of the Tongjiang Hydrological Station in Tongjiang City and the Fuyuan Hydrological Station in Fuyuan City was selected automatically by the SWAT model. Subsequently, the No. 1 drainage basin was split into 31 sub-basins, and the No. 2 drainage basin was separated into 9 sub-basins via artificial adjustment. Every HRU constitutes the smallest underlying surface unit and is unique in terms of land use, soil composition, and slope division. The number of sub-basins, land use, soil type, topographic slope, and reclassification threshold value all affect the number of HRUs. In this work, the minimum area ratio of soil type and slope reclassification was 10%, and the land use of the two basins was established accordingly. Ultimately, 98 HRUs were created from the No. 1 basin and 31 HRUs from the No. 2 basin.