esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Future Work  
Several RF generation techniques were discussed, specifically regarding their usability for driving AOTFs in a space-based environment. The obtained results show a comparison between different RF generator techniques, all capable of fulfilling the imposed requirements. The proposed techniques all use Commercial-Off-The-Shelf available components, each having a space-grade counterpart available. This makes the practical conversion of the proposed techniques into a space-grade setup straight forward.  
A comparison is made between the different RF generation principles. Based on the description in the previous subsections, Table 4 gives an overview of the pros and cons of each generation technique.  
Figure 12: Principle of the DDS and DAC combination, repacked into one chip.  
Further practical testing can be carried out, especially for the RF generation techniques using DDS and DAC chip repackaging and ASIC design. This is part of future work. Eventually, Artificial Intelligence (AI)-based RF control can help to improve the applied RF signal towards the transducer of the AOTF. Hence, the DE of the AOTF can be increased. Additionally, using Gallium Nitride (GaN)-based drivers can help in miniaturung the RF generator design and improving efficiency.
## 3 Design of Hardware and Software for the Data Monitoring System
### Master Module to Wireless Module Communication  
The Master-Wireless communication starts with an _O_MRMd_ command (Figure 7) followed by a _O_MRDt_ command (as shown in _Wireless module communication Section)_ together with the _M_Dir_ and desired _Ch. PIN_R_ = 1 means the action is being performed. When the Wireless connected to the Master has the data from the sensors of the requested module, it sends an _akn_ to the Master module in order to indicate that the transmission has been successfully performed.  
After this, the Master sends an _O_MRDt_ command to the Wireless module to indicate the desired sensors with the _DP_ bytes. Once the Wireless module has those data inside the exit buffer, it sets _PIN_B_ to indicate the data are ready for a _RFrom_. This communication has a byte that indicates which kind of data is sent as header, for example, if the _DP_ includes the data from the last sensor, the header will  
Figure 8: Flowchart of wireless orders from Wireless module.  
indicate it. When the transmission has finished and the Master has all the data, it sends an _O_CCmd_ (Figure 6) command to release the Wireless module so it sets _PIN_R_ = 0 again.
###### Abstract  
The rapid development of Satellite Precipitation Products (SPPs) has heightened the need for a hydro-meteorological assessment of the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) products in different climate and geographical regions. Reliability of the IMERG early (IMERG_E), late (IMERG_L) and final (IMERG_F) run products in precipitation estimations was evaluated over the Kelantan River Basin, Malaysia from 12 March 2014 to 31 December 2016. The three IMERG products were then incorporated into a calibrated Soil and Water Assessment Tool (SWAT) model to assess their reliability in streamflow simulations. Overall, monthly precipitation variability is well captured by the three SPPs. The IMERG_F exhibited a smaller systematic bias (RB = 7.14%) compared to the IMERG_E (RB = \(-\)10.42%) and IMERG_L (RB = \(-\)17.92%) in daily precipitation measurement. All the three SPPs (NSE = 0.66\(\sim\)0.71 and R\({}^{2}\) = 0.73\(\sim\)0.75) performed comparably well as precipitation gauges (NSE = 0.74 and R\({}^{2}\) = 0.79) in the daily streamflow simulation. However, the IMERG_E and IMERG_L showed a significant underestimation of daily streamflow by 27.6% and 36.3%, respectively. The IMERG_E and IMERG_F performed satisfactory in streamflow simulation during the 2014-2015 flood period, with NSE and R\({}^{2}\) values of 0.5\(\sim\)0.51 and 0.62\(\sim\)0.65, respectively. With a better peak flow capture ability, the IMERG_F outperformed the near real-time products in cumulative streamflow measurement. The study has also shown that the point-to-pixel or pixel-to-pixel comparison schemes gave comparable conclusions. Future work should focus on the development of a standardized GPM hydro-meteorological assessment framework, so that a fair comparison among IMERG validation studies can be conducted.  
GPM; IMERG; SWAT; Kelantan; Malaysia; precipitation; streamflow; hydrology; meteorology; satellite precipitation product +
Footnote †: journal: Remote Sensing, 10, 1011; doi:10.3390/rs10071011
## 5 Experimental Results and Analysis  
### Strain Distribution at the Top of the Pipeline  
The strain gauges at the top of the pipeline were arranged from left to right, and a uniform load was applied to the middle of the pipeline. The strain (\(\epsilon/-10^{-6}\)) evolution from A1 to A7 is shown in Figure 4. The positive strain represents tension, and the negative strain represents compression. Under the action of its own gravity and the applied load, the internal bending moment caused bending deformation and produced additional bending axial stress. The strain in A1 increased gradually with the increase in the load in the range of 480-2500 N, decreased gradually with the increase in the load in the range of 2500-6500 N, and reached the maximum at 2500 N (Figure 5a). The strain in A2 increased gradually with the increase in the load in the range of 480-3550 N, decreased gradually with the increase in the load in the range of 3550-6500 N, and reached the maximum when the applied load  
Figure 4: Pipeline strain data acquisition system and layout.  
reached 3550 N (Figure 5b). A1 and A2 were initially without load, so it was considered that the strain distribution at the top of the pipeline at the edge of the load increased first and then decreased. The strain at the A3 position at the end of the uniform bearing section of the pipeline gradually increased with an increase of the load in the range of 480-4470 N, and gradually decreased with the increase in the load in the range of 4470-6500 N and reached the maximum at the applied load of 4470 N (Figure 5c); the strain in A4 located in the center of the uniform bearing section increased gradually with the increase in the load in the range of 480-6500 N (Figure 5d). As the load increased, the position of the maximum strain gradually shifted to the center of the pipeline from position A1 to the center position A4.  
It can be seen from Figure 5e that the strain at the top of the entire pipeline was less than zero, indicating that the top of the pipeline was subjected to pressure under load. Through comparative analysis, it was found that only the strain in A4 in the center of the pipeline increased with the increase in the load under uniform loading, and the strain in the other positions increased first and then decreased with the increase in the load. The overall analysis showed that regardless of the uniform load, the strain at the top of the pipeline was symmetrical in the center in A4 after the load was applied, and the maximum strain was in the center of the pipeline.
## 2 Ultrasonic Pulse Velocity Test
### R-Wave Velocity (Vr) Measurement  
For the R-wave velocity measurements, the MASW (multi-channel analysis surface wave) associated with the induced mechanical waves passing through the concrete medium was measured by attaching a multi-sensor to the concrete surface, and generating and measuring the surface waves. The R-wave velocity was obtained by the phase velocity dispersion curve with high accuracy. A high signal to noise ratio (SNR) and good resolution were obtained by minimizing the noise using multiple sensors.  
In this study, a total of eight sensors were used, and the distance between each sensor was 10 cm, as shown in Figure 4. Experiments were conducted by two people. One person placed and initiated the MASW equipment on each line in Figure 2, and the other hit the concrete surface using an impact hammer and saved the collected data in a Microsoft Excel file.  
To obtain the surface wave velocity, the relationship between the velocity and frequency of the surface wave propagating in the target structure must first be clarified. In general, the relationship between the frequency and velocity of a wave is called a dispersion curve. To obtain the dispersion curve from the MASW test, data from multiple sensors is necessary, as shown in Figure 5. The surface wave velocity can be obtained by measuring the phase difference between each sensor. The obtained time-domain signal is represented as a phase velocity for various modes in the frequency domain, among which the A0 and S0 modes converged to the Rayleigh wave velocity.  
Figure 4: Test method for the multi-channel analysis surface wave (MASW). (**a**) Composition of test equipment; (**b**) Sensor placement for MASW test.  
Figure 5: Use of the dispersion curve to determine the R-wave velocity. (**a**) Time domain; (**b**) Dispersion curve.  
The relationships between the P-, S-, and R-wave velocities and the material properties are shown in Equations (1)-(3), respectively, assuming that the concrete was an elastic body. These equations can be used to estimate the elastic modulus, Poisson ratio, and density through the P-wave, S-wave, and R-wave velocities measured, using the methods described above. It was assumed that the density of concrete was 2400 kg/m\({}^{3}\) and the Poisson ratio was in the range of 0.2-0.3. The predicted elastic modulus and strength of the concrete were also similar to those measured by the ASTM C39 standard.  
\[V_{P}=\sqrt{\frac{E(1-\
u)}{(1+\
u)(1-2\
u)\rho}} \tag{1}\]  
\[V_{S}=\sqrt{\frac{E}{2(1+\
u)\rho}} \tag{2}\]  
\[V_{R}=\frac{0.87+1.12\
u}{1+\
u}V_{S} \tag{3}\]  
where, \(E\), \(\
u\), and \(\rho\) are the elastic modulus, Poisson ratio, and density, respectively.  
The goal of this study was to develop a machine learning model that can predict the concrete strength more accurately using P-, S-, and R-wave velocities within reasonable ranges.
## 2 Theoretical Aspects
### Theoretical Background for Reduction of the Torsion for Structures  
Based on this analogy, an arbitrary storey, in plan, could have three degrees of freedom, two translations and one rotation. The two translations have the directions corresponding to the two axes whose origin is located in the CS and a rotation \(\varphi\)[16]. Each of these dynamic parameters correspond to a stiffness factor denoted with \(K_{1}\), \(K_{2}\) and \(K_{t}\), respectively. The inertia forces induced by acceleration caused by earthquakes always act in CM. These inertial forces are reduced to CS and generate a displacement corresponding to the minimum value of the three stiffness factors previously mentioned. To avoid unexpected behaviour of the structural elements between two floors, computation of the stiffness factors \(K_{1}\), \(K_{2}\) and \(K_{t}\) is required, respectively, along with their corresponding directions. It is very important to have translations along geometric directions because rotation is associated with torsion. According to this analogy, we are required to settle two important issues:  
1. [label=()]
2. calculation of the angle \(\theta\) between the first principal direction and the geometric determined at the point of pre-structural conformation;
3. reduction of the distance between CM and CS in both directions, in order to avoid torsion. The rigidity factors are maximally and minimally relative to the principal axes. The maximum value of the rigidity \(K_{1}\) corresponds to the axis 1-1, while the minimum value of the rigidity \(K_{2}\) corresponds to axis 2-2. According to this statement, \(K_{12}\) is cancelled when the centre of this system coincides with CS. To show the significance of these values, for example, if a force acts on the direction of 1-1 axis, then a translation takes place in direction 1-1. Thus, it is required to refer to these axes and to define the relative oblique rigidity for every structural element of the storey. The first step is to define the angle \(\beta_{k}\) with respect to the principal axes, whose expression is presented in Equation (17):  
\[\beta_{k}+\theta\ =\ \alpha_{k}\Rightarrow\beta_{k}\ =\ \alpha_{k}-\theta, \tag{17}\]  
where the angle \(\alpha_{k}\) shows the position of the principal axes 1k-1k corresponding to the element \(k\), and \(\theta\) is the angle between the arbitrary axis x-x and the principal axis 1-1. The angle \(\alpha_{k}\) is computed with local values, and \(\theta\) with global ones, so one may consider the contribution of all elements of the storey. The mathematical background is similar with the computation of the rigidity factors \(K_{kk}\), \(K_{kk}\), \(K_{xyk}\).
# Efficient Re-Parameterization Residual Attention Network for Nonhomogeneous Image Dehazing
## 4 Results
### Results of Ablation Studies
#### 4.2.1 Effect of MRAB Structure  
We first verify the effectiveness of different modules of the basic MRAB via ablation experiments on the NH-Haze (2021) dataset.  
The results are listed in the top rows of Table 2. In the S1 setting, the MRAB consists of only multi-branch convolutions and the LeakyReLU layer. On top of S1, we applied the other components to form different settings: S2 (apply the BN layer and Attention Module) and RRA-Net (apply the BN layer, attention module, and local residual connection). It can be seen that the inclusion of the BN layer and attention mechanism greatly improve the dehazing performance of the network, and the local residual connection is also beneficial.  
Figure 6: Qualitative comparison of dehazing results on an image with dense haze from the Dense-Haze dataset.
## 2 Background
### Test Case Optimization
#### 2.2.2 Test Case Selection  
A subset of test cases can be selected from changed parts of the software. The minimal number satisfies a specific objective function of the test suite; the suite is optimized using the selected cases. This is unlike test minimization; existing test cases are not modified, and only the required test cases are selected from the test suite \(T\). The test case set becomes a new test suite \(T^{\prime}\). For example, the branch coverage of each test case can be measured in \(T\), and test cases selected using an objective function to ensure 100 percent branch coverage; the minimum number of test cases in \(T\) required for this constitutes \(T^{\prime}\).
# Femtosecond Laser Processing of Biodegradable Polymers
## 2 Femtosecond Laser Processing of Biodegradable Polymers  
Soon after the rise of research on femtosecond laser processing, pioneering studies on femtosecond laser ablation of polymers have been reported. Initial studies, including those using polymethyl methacrylate, were performed with femtosecond lasers oscillating at UV wavelength [5; 6], but, thanks to the high peak intensity of a femtosecond laser pulse, it is now widely known that polymers that show less linear optical absorption in visible to near-infrared wavelengths can be processed via nonlinear optical interaction by using femtosecond lasers [7; 8]. Precise three-dimensional (3D) structures can be fabricated, not only on the surface of a material, but also in its interior. Furthermore, the ultrashort interaction time of the femtosecond laser pulse and a material realizes much smaller HAZ, even for the processing of polymers that have low glass-transition and melting temperatures. These advantages are promising for processing biodegradable polymers. Table 1 lists publications on femtosecond laser processing of biodegradable polymers. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Year** & **Wavelength** & **Pulse Width** & **Material** & **Note** & **Ref.** \\ \hline
2000 & n/a & n/a & n/a & medical stents fabrication & [9] \\ \hline
2003 & mid IR & 15 us, 5 ps & Gelatin & surface ablation & [10] \\ \hline
2005 & 800, & 110 fs, & PCL & enhancement of wettability and hydrophobic nature & [11] \\ \hline
2005 & 800 nm, & 220 fs, & PCL, PGA & analysis of chemical structure and biodegradability & [12] \\ \hline
2005 & 193 nm & 35 ns & & cell attachment on patterned surface & [13] \\ \hline
2005 & 800 nm & 45 fs & collagen &
\begin{tabular}{l} cell attachment on \\ patterned surface \\ \end{tabular} & [14] \\ \hline
2007 & \begin{tabular}{l} 775 nm, \\ n/a \\ \end{tabular} & \
\begin{tabular}{l} 150 fs, \\ 120 ns \\ \end{tabular} & PCL, PET &
\begin{tabular}{l} laser ablation on \\ electrospun scaffolds \\ \end{tabular} & [15] \\ \hline
2007 & 800, 400, 266 nm, 1064, & \
\begin{tabular}{l} 90 fs, \\ 6 ns \\ \end{tabular} & gelatin &
\begin{tabular}{l} nano-foaming \\ \end{tabular} & [16] \\ \hline
2008 & 790 nm & 60 fs & gelatin, curcumin-doped & femtosecond laser ablation & [17] \\ \hline
2008 & 800, 400, 266 nm, & \
\begin{tabular}{l} 90 fs, \\ 25 ns \\ \end{tabular} & gelatin, collagen &
\begin{tabular}{l} Discussion on the mechanism of \\ nano-foaming \\ \end{tabular} & [18] \\ \hline
2010 & 800 nm & 30 fs & \
\begin{tabular}{l} gelatin, PVP, \\ PVP/chitosan \\ \end{tabular} &
\begin{tabular}{l} 3D internal modification \\ of biopolymers \\ \end{tabular} & [19] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Ultrashort pulse laser processing of biodegradable polymers.
# Data Quality Analysis and Improvement: A Case Study of a Bus Transportation System
## 5 Experimental Setup
### Experimental Data  
This study uses a real-world traffic dataset from a public bus transportation system in a Chinese city. The dataset was collected from July 2021 to February 2022. The dataset is 344.4 GB in size and contains a total of 544.48 million records. It contains 12 different tables covering route stops, vehicle scheduling, and GPS-related information for the bus transportation system. In addition, the specific table information is as follows: The \"QR_code\" table is 0.3 GB in size and contains 622,900 records; the \"Swipe\" table is 1.3 GB in size and contains 3,197,700 records; the \"IAO_station\" table is 4.1 GB in size and contains 10.62 million records; the \"Bluetooth\" table is 53.9 GB in size and contains 77.84 million records; the \"GPS\" table is 70.7 GB in size and contains 187.99 million records; and the \"Wi-Fi\" table is 214.1 GB in size and contains 264.0452 million records. In addition, the basic-static table is 19.82 MB in size and contains 157,100 records.
## 5 Results
### Experiment A: Leader Selection
In the light of the previous observations, hereafter, attention is paid to the message content and to the communication state in order to understand the mechanics of the agents interactions. Figure 9a shows the swarm spatial configuration, while Figure 9b displays the communication state of each robot at each time step. Black bars indicate that the corresponding agent in the ordinate axis is in send mode and blank spaces mean relay mode of the robot. Note that there is a clear correlation between the communication mode of the robots and their LED action. Specifically, most of the time that robots are in send mode, the LED claiming leadership is turned on (see Figure 4). Additionally, Figure 9c displays the message sent or relayed at each time step. The legend shows the possible symbols and the corresponding 2-dimensional vector. Provided that Figure 9b,c are analyzed jointly, the following communication mechanics can be assumed. * If an agent claims leadership, its communication state settles as send state. On the contrary, non leader robots enter into relay mode. * Agents in send mode mostly emit symbol 15, corresponding to message \((1,1)^{\top}\). * The message sent by the potential leaders is spread around the entire swarm, resembling a wave-like propagation. In fact, Figure 9a clarify that the agents sending symbol 15 more frequently are those with less number of hops to the leader (node 7). * Messages between symbols 0 and 15 are essentially transient messages caused during the rise time of the motor neuron firing rate. This implies that only symbols 0 and 15 are actually relevant for the communication, and, therefore, there is no need to include more than 2 symbols in the communication of this experiment. The resulting communication emerged as a result of evolution in the leader selection task is essentially an abstract communication in which the environmental context is not communicative. More precisely, it is a signaling based communication. Figure 8: Percentage of simulation time with a single leader elected when communication stimulus are inhibited and in normal conditions. Stimulus inhibition is performed by replacing with zeros the corresponding input stimulus at neural level. Inhibition of the variables is performed separately and one by one.
## 7 Results and Discussion
Additionally, the forwarding mechanism disregards the link quality and unconsidered the void areas before making routing decisions. Our proposed routing scheme ensures the selection efficient path selection and hence surpasses the remaining routing protocols even at the lesser node densities. Figure 7c illustrates the comparison of our proposed routing protocol with some existing routing protocols in terms of end-to-end-delay. The DBR and EEDBR routing protocols suffer from the issue of void regions where no nodes are available to route the packet to the destination. This problem is significantly higher, especially in lesser node density networks, and causes longer end-to-end delay. The EE-LCHR and IBAS exhibit almost similar end-to-end-delay, which is the least among all the compared routing protocols. The end-to-end-delay experienced by our proposed routing protocol is significantly shorter than DBR and EEDBR; however, it is slightly longer than EE-LCHR and IBAS. Our proposed strategy is focused on transferring the data to the cluster head, having better link quality. In dynamic environments, the link stability swings rapidly, which leads to packet transmission to the nearest cluster head. This brings in a higher number of hop counts. Although the reliability increases, and energy is preserved, the end-to-end delay increases.
## 4 Discussion
The mapped drainage pipe locations are in a line, thereby indicating the presence and direction of a buried agricultural drain line. always, severely limit radar signal penetration into the subsurface, thereby making GPR drainage pipe detection difficult [4; 5]. Transient shallow hydrologic conditions also have a substantial impact. Soils that are not too dry or too wet are best for GPR drainage pipe detection, especially good are moist soils surrounding air-filled drainage pipes, which is a common situation occurring a day or more after a large rainfall event [5]. The integration of GPR with RTK/GNSS can therefore be a valuable tool for determining drainage pipe patterns in agricultural fields. The subsurface drainage system information obtained by GPR-RTK/GNSS could be very useful for retrofitting a subsurface drainage system for subirrigation or to improve soil water removal efficiency, with both scenarios requiring installation of new drain lines in between pre-existing drain lines [12; 13; 14], which in turn cannot be accomplished without knowing where the preexisting drain lines are located. Knowledge of drain line patterns obtained via GPR-RTK/GNSS could also be used for repairing part of a subsurface drainage system that is not functioning properly. Finding drainage pipes with GPR-RTK/GNSS is certainly an improvement over tile probe and trench excavation methods, because GPR-RTK/GNSS field data collection, compared to these other two methods, is much faster, is better at determining drain line directional trends, and causes no pipe damage. Consequently, in areas where GPR is capable of detecting buried drainage pipes, GPR-RTK/GNSS can supplant tile probe and trench excavation methods for mapping drain line patterns.