esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Calibrations of NDVI from Different Sensors  
Sixteen Landsat NDVI and 18 GF-1 NDVI data sets were calibrated to HJ NDVI data. After sensor calibration, the sensor differences among these three sensor data were significantly reduced.  
Table 4 shows the NDVI statistics of vegetation from H] and GF-1 data acquired on 19 August 2013 in Luntai, and Landsat and HJ data acquired on 7 October 2013 in Luntai. The mean and standard deviation (Stdev) value of vegetation NDVI of H], Landsat, and GF-1 WFV sensor showed a large difference before sensor adjustment. After calibration, the mean and Stdev value of those sensors were very close. Figure 2a shows the scatter plots between H] NDVI and Landsat NDVI and between H] NDVI and GF-1 WFV NDVI data before sensor calibrations. A large difference in their NDVI data is evident, where the samples were deviated from the 1:1 line. The difference between GF-1 WFV NDVI data and H] NDVI data is much smaller than the difference between Landsat and H] NDVI data. After sensor calibration (Figure 2b), the difference was significantly reduced, where most of the samples were distributed along the 1:1 lines.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Image Date**} & \multirow{2}{*}{**Sensor**} & \multicolumn{2}{c}{**NDVI of Vegetation**} \\ \cline{3-4}  & & **Mean** & **Standard Deviation (Stdev)** \\ \hline \multirow{3}{*}{7 October 2013} & H] & 0.3589 & 0.0923 \\  & GF & 0.4163 & 0.1083 \\  & Adjusted GF & 0.3587 & 0.0973 \\ \hline \multirow{3}{*}{19 August 2013} & H] & 0.5477 & 0.1646 \\  & Landsat & 0.6072 & 0.2732 \\ \cline{1-1}  & Adjusted Landsat & 0.5525 & 0.1657 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Normalized Different Vegetation Index (NDVI) statistics of vegetation of each sensors in Luntai.  
Figure 2: The scatter plot comparisons of Landsat Normalized Different Vegetation Index (NDVI) data, Gaofen satellite no. 1 wide field-of-view camera (GF-1 WFV) NDVI, and Huanjing satellite (HJ) NDVI data (**a**) before and (**b**) after sensor calibration.
## Appendix A The Estimated Results of the Bias Error for FOG-Based MWD  
The estimate of the Bias error derived by the linear EKF and nonlinear EKF were compared in Figure 11. The ACC bias error of \(X\)-axis, \(Y\)-axis and \(Z\)-axis is illustrated in Figure 11a-c, respectively. The FOG bias error of \(X\)-axis, \(Y\)-axis and \(Z\)-axis is illustrated in Figure 11d-f, respectively. It can be seen that the sensor bias errors estimated by the two filters all converge to a stable value. However, the bias errors of the ACCz estimated by the two EKF are different as well as that of the three FOGs. Among them, the bias errors of the three FOGs estimated by the nonlinear EKF are 0.99\({}^{\circ}\)/h, 0.58\({}^{\circ}\)/h and \(-1.66^{\circ}\)/h, while by the linear EKF is 1.21\({}^{\circ}\)/h, 0.80\({}^{\circ}\)/h and \(-2.08^{\circ}\)/h. The bias errors of the three ACCs estimated by the nonlinear EKF are \(-116.39\)\(\mu\)g, \(-1.74\)\(\mu\)g and 164.89 \(\mu\)g while they are 100.69 \(\mu\)g, 136.16 \(\mu\)g and 90.65 \(\mu\)g by the linear EKF. In Figure 11a,e, the estimation of the linear EKF has a large fluctuation. Most of the bias errors estimated by the linear EKF is a little bigger than that of the nonlinear EKF. The reason is that there is nonlinear error item in the sensors, but the linear EKF cannot recognize the nonlinear error and regards the nonlinear error as the bias error. Moreover, in the nonlinear error model (5) and (7), it can be seen that the nonlinear error item varies with the motion state of the sensor. It is obvious that it cannot be regarded as the constant bias error.
# Energy Harvesting Hybrid Acoustic-Optical Underwater Wireless Sensor Networks Localization
## 4 Numerical Results  
In this section, we present numerous simulation results executed in MATLAB (R2016b, MathWorks, Inc., Natick, MA, USA) to investigate the performance of proposed AO-UWSNs localization technique. Two different scenarios are generated in a square area of \((10\times 10)\) m\({}^{2}\) for a various number of anchor nodes and sensor nodes. To generate different observations for each scenario, we randomly select \(m+n\) pair of noisy ranges \(\tilde{d}_{ij}\). Gaussian noise is added to each \(\tilde{d}_{ij}\), with mean \(d_{ij}\) and variance \(\sigma^{2}\). The first scenario consists of three anchor nodes (red squares) and five sensor nodes (green stars) as shown in Figure 2a. Four different observation views are generated for each scenario as shown in Figure 2b-e. It is clear from Figure 2f that the multiple observations result in smaller error function. The setup parameters for the first scenario and comparison of the error function are shown in Table 3.  
The second scenario consists of four anchor nodes and 20 sensor nodes randomly deployed in a square area of \((10\times 10)\) m\({}^{2}\) as shown in Figure 3a. Figure 3b-e shows the results of four different single observation views with different noise variance. It is clear from Figure 3f that the multiple observations result in a smaller error function. The setup parameters for the second scenario and comparison of the error function are shown in Table 4. The comparison for the two different scenarios between a single observation and multiple observations is shown in Figure 4, which clearly tells us that the proposed multiple observations approach results in a better approximation of the sensor nodes' configuration for a large noise variance. The final location estimation for both of the scenarios are  
Figure 2: (a) Actual locations of the nodes for \(m=3\) and \(n=5\); (b) Single observation at \(\sigma^{2}=1.77\) m; (c) Single observation at \(\sigma^{2}=0.5\) m; (d) Single observation at \(\sigma^{2}=0.17\) m; (e) Single observation at \(\sigma^{2}=0.05\) m; (f) Multiple observations.
###### Abstract  
In desert regions, aeolian sand is abundant, but it is not suitable to be used directly as the upper roadbed filler for highways. Generally, gravelly soil is mined around the desert as upper roadbed fill, resulting in high engineering expenses for road construction in the desert hinterland. Geocells have a significant reinforcing effect on aeolian sand. However, in the completed desert highway, the dynamic performance of geocell-reinforced aeolian sand as an upper layer of roadbed fill has not been studied. Using a field test method, the dynamic performance of geocell-reinforced aeolian sand as an upper roadbed fill is examined. The results show that the majority of the frequency distribution of road vibration is within 30 Hz. In the horizontal direction, the actual vibration amplitude decay on the side of geocell-reinforced aeolian sand is slower but smoother than on the side of gravelly soils. In vibration velocity, the work area depth of the geocell-reinforced aeolian sand side of the roadbed is less than that of the gravelly soil side. The maximum difference can reach 0.55 m. As far as vibration velocity is concerned, the 30 cm gravelly soils can be substituted with 15 cm geocell-reinforced aeolian sands as the upper roadbed. In summary, the dynamic attenuation characteristics of geocell-reinforced aeolian sand are superior to gravelly soils. The research results provide a reference for the design of the desert highway subgrade.  
moving wheel load test; geocell; aeolian sand; vibration velocity; roadbed work area
# Measurement and Modeling of Narrowband Channels for Ultrasonic Underwater Communications
## 4 Ultrasonic Fading Modeling for UAC
### Model Parameters Estimation  
The parameters of the \(\kappa\)-\(\mu\) shadowed distribution obtained by optimization for the different measured channels are presented in Table 3 (the Nelder-Mead numerical method, implemented in MATLAB routines, has been employed to calculate them). The error factor \(\epsilon\) used to quantify the goodness of fit between the CDF \(F_{\alpha^{2}}(\cdot)\) of the fading estimated from the measurements and the analytical CDF \(\hat{F}_{\alpha^{2}}(\cdot)\) calculated from the model with those parameters is given by:  
\[\epsilon=\max_{x}\left|\log_{10}\hat{F}_{\alpha^{2}}(x;\kappa,\mu,m)-\log_{10} F_{\alpha^{2}}(x)\right| \tag{1}\]  
with:  
\[\begin{array}{l}\hat{F}_{\alpha^{2}}\left(x;\kappa,\mu,m\right)=\frac{\mu^{ \mu-1}m^{m}(1+\kappa)^{\mu}}{\Gamma(\mu)(\mu\kappa+m)^{\mu}}x^{\mu}\\ \times\Phi_{2}\left(\mu-m,m;\mu+1;-\mu\left(1+\kappa\right)x,-\mu\left(1+ \kappa\right)\frac{m\kappa}{\mu\kappa+m}\right)\end{array} \tag{2}\]  
where \(\Gamma(\cdot)\) is the Gamma function and \(\Phi_{2}\) is the bivariate confluent hypergeometric function [22]. This error factor is a modified version of the well-known Kolmogorov-Smirnov (KS) statistic [23], but the logarithm of the CDF is employed instead of the CDF itself, to outweigh the fit in values close to zero (when fading is more severe). This region is of special interest in communication systems, since it determines some performance measures, like the outage probability or the bit error rate.  
An example of the model fit is presented in Figure 6, where the experimental CDF of the power gain measured in one of the channels is plotted _vs._ the analytical CDF of the modeled channel. The latter curve is the result of using the set of parameters in Table 3 and calculating the CDF according to Equation (2). The CDF obtained using the Rice model, the most common in UAC, is also shown in Figure 6. (For the Rice distribution, the so-called fading parameter \(K\) is used, which represent the ratio of the power in the LOS component to the power in the other multipath components [14].) The error factor obtained for the fit of this channel is 0.111, while for the \(\kappa\)-\(\mu\) shadowed, it is 0.026, considerably lower. As seen in Table 3, the fit of all of the measured channels with the two models exhibits a similar behavior, which supports the conclusion that the \(\kappa\)-\(\mu\) shadowed distribution is better to model underwater channels also in the ultrasonic band.
# Area of Operation Planning for Free-Floating Car Sharing Systems
## 3 Case Study for Vienna
### Mobility Demand Forecast
#### 3.2.1 The OEU Data Set  
The _OEU_ data set is based on the large-scale mobility survey \"Osterreich Unterweg\" that took place in 2013/2014 and contains data on the Austrian population's mobility behavior. The daily schedule of each participant was recorded, representing that person's mobility behavior for a single day. Each of these daily schedules consist of an alternating sequence of trips and activities. Each trip and activity includes data on their location and duration. Trips include information on the mode of transportation that was used, which can be walking, biking, driving, or using public transport. An activity is classified by its type, representing a person either being at home, working, shopping, running errands, educating themselves, or doing some kind of leisure activity.  
For the purpose of optimization, we used resampled data from the original data set only containing trips (and activities) in or near Vienna, resulting in over 1.6 million potential trips.  
We performed the following operations to obtain the final data:  
* Trips separated by an activity taking less than 30 min were merged. The underlying assumption is that customers would not end their rental of a shared car for such a short activity, because they would not risk another person taking the car they just used.  
* Trips separated by an _errand_ or _shopping_ activity were merged. Similar to the reasoning above, users would usually continue their trip after interrupting it with such an activity without their rented car being taken by another person.
* Trip sequences interrupted by activities outside of Vienna were merged, since it is not possible to end their rental if they are outside the AoO. Any trips before a person's first daily arrival in (or after their final departure from) Vienna (e.g., commuting trips by people living outside but working within the city) were dropped.
* Trips made on foot or by bike were dropped, as we expect that these trips would most likely not be replaced by trips with a shared car. The reason is that walking and biking do not cause additional costs, while car sharing is rather expensive. We assume that people using public transport might instead consider car sharing due to the added convenience factor.  
Only consecutive trips made by the same person were merged. The resulting trip set consists of approximately 800,000 trips that start and end in Vienna. Only trips originally made by car or public transport are included, as we assume that those would most likely be replaced by car sharing trips (in the latter case, e.g., to improve travel times or comfort). It places a higher emphasis on longer-duration trips, which are preferred by ELOOP's business strategy (and pricing model) for ecological reasons.
## 2 Mobile Social Networking
### Network Architectures for CAMSN Services  
In this section, we explain in further details the network architectures used in context-aware mobile social networking services.  
In client-server based CAMSN architecture, a client device might ask its application servers \"what devices are near location \(X\)?\" This same device may announce its location to its application servers. Most CAMSN services to date have client-server architectural design. These include FourSquare, BrightKite, Loopt, Google Latitude, and CenceMe [6; 7; 8; 9; 10].  
CAMSN services in peer-to-peer architecture poll or discover physically neighboring devices without discovering location information for those devices. MobiSN, which defines an ad-hoc routing protocol and profile matching functionality, is one example of a completely peer-to-peer CAMSN service [11].  
Hybrid architecture CAMSN services make use of some combination of client-server and peer-to-peer communications. A device on a hybrid service might discover proximity information (e.g. neighboring devices) via short-range communications, but use that information to look up user profiles on an authoritative application server. An advantage here is that the application server need not be concerned with location information. That is, an end-user device \(x\) will not ask the application server \"what devices are near my location, which is \(L\)?\" Rather, \(x\) would ask \"what profile data is available for device \(y\),\" where \(y\) is another device in the proximity of \(x\) (discovered via short-range communications). Examples of hybrid CAMSN services include WhozThat, Serendipity, and Aka-aki [5; 12; 13], all of which utilize both client-server (e.g. for user profile data retrieval) and peer-to-peer (e.g. for discovering proximity relations) network architectures.
## 2 Experiment  
### Deposition Process  
The LAM equipment is shown in Figure 3. The additive manufacturing device is mounted on the three-axis CNC machine (Shenyang Siasun Robot Automation Co., Ltd., Shenyang, China) which uses Simens 828D numerical control system (SIEMENS, Berlin, Germany) as shown in Figure 3a. The stroke of each axis is 1000 mm (\(X\)), 800 mm (\(Y\)) and 600 mm (\(Z\)), all of which are driven by servo motor (Shenyang Siasun Robot Automation Co., Ltd., Shenyang, China) and ball screw (Shenyang Siasun Robot Automation Co., Ltd., Shenyang, China).  
The additive manufacturing device consists of four functional modules: laser focusing module, vertical wire feeding module, shielding gas module and water-cooling module as shown in Figure 3b. In the laser focusing module, three laser beams are focused onto the wire at a certain angle (in this paper, the angle between the wire and the laser beam is 55\({}^{\circ}\)). In vertical wire feeding module, the wire is fed vertically to the substrate. The additive manufacturing device driven by the actuator moves along the arranged path, thus the components can be formed. Argon gas is injected around the feeding wire to prevent metal oxidation at high temperatures. The water cooling module provides cooling to the laser head to ensure long-term stable operation. The operating temperature of the laser head is 10 to 25 \({}^{\circ}\)C.  
Two components were fabricated in this experiment: one for the forming ability test and the other for the microstructural and mechanical properties tests. The shape of the component fabricated for the forming ability test consists of circles and polyline in all directions to test the forming ability and anisotropic forming problem of vertical wire feeding method. The tool-path of it is: first, scanning a circle with a diameter of 50 mm; second, scanning a twelve angle star which inscribed circle is 55 mm and circumcircle is 95 mm in diameter; third, scanning a circle with a diameter of 100 mm; finally, the additive manufacturing device is raised by 0.8 mm and repeat the first three steps.  
Figure 3: (**a**) The schematic of the laser additive manufacturing equipment; (**b**) The equipment of vertical wire feeding with an axisymmetric multi-laser source; (**c**) the additive manufacturing device of vertical wire feeding.  
For convenience, a cuboid block with the size of 81 mm \(\times\) 40 mm \(\times\) 40 mm was fabricated for the microstructure observation and mechanical properties tests. The tool-path of the cuboid block is shown in Figure 4.  
The components were fabricated on a Ti-6 Al-4V titanium alloy substrate with the size of 150 mm \(\times\) 150 mm \(\times\) 5 mm. The process parameters used of the two components in the deposition process are given in Table 1.
# Enhance Text-to-Text Transfer Transformer with Generated Questions for Thai Question Answering
## 2 Literature Review
### Data Augmentation Methods  
There are several research works in data augmentation for improving the performance in QA tasks. [PERSON] et al. [15] presented a cloze-style question generation method by extracting questions and answers using the document structure of English articles that mostly provides the summary of articles in the introduction. They used the BiDAF model as a QA model. This method was able to raise the EM and F1 evaluation scores by 0.32% and 0.11%, respectively.  
[PERSON] et al. [9] introduced a Question Generation pipeline with three Transformer based models inside. There are three steps of the pipeline including (1) answer generation, (2) question generation, and (3) question filtration. Answer generation is performed by a BERT model trained to select the candidate answer from a given context. Question generation is performed by a GPT-2 model [16] trained to create a proper question to a given context and answer. The last step, Question filtration, is performed by a BERT model trained with question answering objectives with human-labeled data. The researchers used this model to predict an answer from the generated question and context. If the answer from this model was equivalent to the answer from the answer generation step, they considered the generated question-answer pair to be an admissible sample. With this pipeline, they were able to generate more than 19 million question-answer pairs from Wikipedia articles, and used them to train the BERT model. The result achieved more than their baseline EM and F1 scores by 1.7% and 1.2%, respectively.  
To conclude, the first method cannot be used with Thai articles because the Thai article structure is more ambiguous than English. It cannot simply extract the answers and questions by using heuristic rules. Given this limitation, using a deep learning model to extract answers and questions is a more appropriate method to synthesize the data.
## 3 Method
### The Framework of CoReaAgents  
#### 3.2.1 Plan Agent: A Precise Task Planner  
There are two categories of static and dynamic planning in the current task reasoning strategies. Static planning lacks the ability to flexibly execute tasks and cannot adjust its approach in a timely manner if problems arise during the execution process.Dynamic planning lacks an overview understanding of the task and could deviate from the task target when performing complex tasks. Thus, we propose the Plan Agent in the CoReaAgents framework, which can provide a blueprint for the execution of the task, controlling the general direction for the task's accomplishment. Specifically, the primary responsibilities of the Plan Agent include tool filtering and task planning.  
**Tool filtering:** The Plan Agent filters the retrieved tools to discard irrelevant and redundant information. It accomplishes this by understanding the description, the parameters and the role of each tool, which ensures the selections are highly relevant to a specific task. This process can significantly avoid the negative impact of irrelevant information on task planning.  
**Task planning with CCoT:** Inspired by [PERSON] [64], we propose CCoT (Code and Comments of Thoughts), which accomplishes task planning with codes and comments. As shown in Figure 3, CCoT comprises two main parts: the first part involves analytical natural language task descriptions given as comment parts, and the second part contains the actions needed to invoke the tool as code segments. This not only ensures the continuity of LLMs in carrying out logical planning but also enables LLMs to maintain consistency between the thoughts involved in task planning and the behavior involved in tool invocation. Utilizing this method, the Plan Agent develops a comprehensive planning strategy by decomposing complex tasks into manageable subtasks that exhibit specific dependency relationships and an established execution order.  
Figure 3: Task planning with the proposed CCoT by the Plan Agent. It takes natural language task descriptions as comment parts (noted as blue words ) and designs the actions required to invoke the tool as code segments (noted as red words). This not only ensures the continuity of LLMs in carrying out logical planning, but also enables LLMs to maintain consistency between the thoughts involved in task planning and the behavior involved in tool invocation.  
**Communication with the Tool Agent and the Reflect Agent.** The Plan Agent acts as a precise task planner, taking into account the global aspects with a prospective approach to the overall planning of the task (details in Section 3.3.2). It plans an execution strategy that is coordinated with the plan for reflection of the Reflect Agent and the tool augmentation task of the Tool Agent to facilitate dynamic adjustments of the action path.
## 4 Experimental Analysis and Discussion
### Analysis of Experimental Results
Each sine and cosine patterns for different noisy speckle fringes have different denoising thresholds. The sine-cosine transform wavelet threshold denoising method achieves denoising adaptively according to the different denoising thresholds. Figure 13: (**a**,**d**,**g**,**j**) are shearography–interferometric phase fringe patterns and speckle–interferometric phase fringe patterns, respectively; (**b**,**e**,**h**,**k**) are filtered by sine–cosine transform wavelet threshold filtering method when the decomposition level is five; (**c**,**f**,**i**,**l**) are obtained by the sine–cosine transform average denoising method. To show more details of the sine-cosine transform (SCT) wavelet threshold denoising method, we calculate the denoising thresholds of each sine and cosine patterns after the sine-cosine transform, as shown in Table 1.  
The denoising effect can be evaluated with the indices of code execution time, peak signal-to-noise ratio, and structure similarity [26]. Peak signal-to-noise ratio (PSNR) reflects the detail retention ability of a pattern, and the higher the PSNR, the better the detail retention of the pattern. Structural similarity (SSIM) is a full-reference pattern quality evaluation index that measures pattern similarity from brightness, contrast, and structure. When the execution time is similar, the SSI of the sine-cosine transform wavelet threshold method is lower than that of the sine-cosine transform average method, as shown in Table 2. Meanwhile, the PSNR and SSIM for the two methods are almost identical. The experimental results show that the sine-cosine transform wavelet threshold denoising method with five-level decomposition is better than the sine-cosine transform average denoising method.