esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Photocatalytic Reaction
#### 3.2.2 Effect of Initial pH Value  
Initial pH has been reported to exhibit significanteffects in photocatalytic applications [36]. The impact of initial pH on ethiofencarb degradation was studied and presented in Figure 6. The rates of the photodegradation of ethiofencarb were found to be strongly dependent on the pH of the solution. Ninety-eight percent of ethiofencarb was degraded within 24h at pH = 3, achieving the maximum reaction rate at this optimal pH. Increasing the pH value to 3, 5, 7, and 9 gradually decreased the photocatalytic activity of SnIn\({}_{4}\)S\({}_{8}\). The respective rate constants were found to be 0.1546 h\({}^{-1}\), 0.0982 h\({}^{-1}\), 0.0773 h\({}^{-1}\), and 0.0661 h\({}^{-1}\). This decrease in photocatalytic efficiency can be attributed to a Nernstian shift of the band edges to more negative values as the pH value increases, which reduces the oxidation potential of the positive holes [41; 42]. The slower kinetics can also be due to an increase in the concentration of hydroxyl groups on the surface at a higher pH value, which may decrease the conduction band electrons due to electron-hole recombination [43]. This latter model of faster hole-electron recombination at higher pH (9.5), has been previously reported [44]. Figure 6b presents the regression analyses based on the first-order reaction kinetics for the photodegradation of ethiofencarb. In C\({}_{0}\)/C = \(k_{\text{app}}t\) plots were used to calculate the rate constants (\(k_{\text{app}}\)), listed in Table 1 in addition to the linear regression coefficients. The determined values show that the appropriate first-order relationship appears to fit well.  
Figure 5: Effect of SnIn\({}_{4}\)S\({}_{8}\) dosage on the photocatalytic degradation rate of ethiofencarb. Experimental conditions: ethiofencarb concentration 10 mg L\({}^{-1}\); pH 5.  
Total organic carbon (TOC) was used to evaluate the mineralization ability of the SnIn4S8 photocatalyst for ethiefencarb degradation. The TOC removal rate was 42.2% after 24 h of the photocatalytic reaction under visible light. As could be seen, ethiefencarb degradation is much higher than TOC removal with SnIn4S8 photocatalyst. This great difference implies that the products of ethiefencarb oxidation remained generally at the intermediate product stage under the present experimental conditions.
## 2 Research Methodology  
### Overview of the Study Area and Data Sources  
#### Overview of the Study Area  
The Tumen River, originating from the eastern foot of the main peak of the Changbai Mountain Range and flowing northeastward into the Sea of Japan, is the boundary river between China and North Korea and Russia and North Korea. The Tumen River Basin (on the Chinese side), as shown in Figure 1, is located in the southeastern part of Jilin Province, China, within the Yanbian Korean Autonomous Prefecture, covering four counties, Yanji, Tumen, Longjing, and Hunchun, as well as parts of Hulong, Antu, and Wangqing counties. The geographical coordinates are 128\({}^{\circ}\)22\({}^{\prime}\)48\({}^{\prime\prime}\) E-131\({}^{\circ}\)18\({}^{\prime}\)33\({}^{\prime\prime}\) E, 41\({}^{\circ}\)59\({}^{\prime}\)49\({}^{\prime\prime}\) N-44\({}^{\circ}\)01\({}^{\prime}\)34\({}^{\prime\prime}\) N, with a total basin area of 22,690.70 km\({}^{2}\). The landscape here is dominated by low mountains and hills, with several river valley basins, and elevations ranging from 8 to 1689 m, higher in the south and north, and lower in the center and east (Figure 1). This area belongs to the temperate continental monsoon climate, which is warm and rainy in summer and cold and dry in winter. The average annual temperature in the region is between \(-1\)\({}^{\circ}\)C and 7 \({}^{\circ}\)C, with lower temperatures in the higher altitude areas and slightly higher temperatures in the river valleys and plains. The average annual precipitation is between 580 and 780 mm, with rainfall mostly concentrated in June, July, and August. The soils in the region are rich in types, including dark brown earth, albic soil, gray brown earth, and so on. Among the land resource types in the region, woodland dominates, followed by cultivated land, construction land, grassland, bare land, water bodies, and wetland. The forest cover in the region is about 85% and the overall ecological environment is good.  
Figure 1: Maps of Tumen River Basin, China.
# A Review of Image Reconstruction Algorithms for Diffuse Optical Tomography
###### Abstract  
Diffuse optical tomography (DOT) is a biomedical imaging modality that can reconstruct hemoglobin concentration and associated oxygen saturation by using detected light passing through a biological medium. Various clinical applications of DOT such as the diagnosis of breast cancer and functional brain imaging are expected. However, it has been difficult to obtain high spatial resolution and quantification accuracy with DOT because of diffusive light propagation in biological tissues with strong scattering and absorption. In recent years, various image reconstruction algorithms have been proposed to overcome these technical problems. Moreover, with progress in related technologies, such as artificial intelligence and supercomputers, the circumstances surrounding DOT image reconstruction have changed. To support the applications of DOT image reconstruction in clinics and new entries of related technologies in DOT, we review the recent efforts in image reconstruction of DOT from the viewpoint of (i) the forward calculation process, including the radiative transfer equation and its approximations to simulate light propagation with high precision, and (ii) the optimization process, including the use of sparsity regularization and prior information to improve the spatial resolution and quantification.  
diffuse optical tomography; image reconstruction; inverse problem +
Footnote †: journal: Medical Imaging  
1
## 3 Literature Review on Manipulator Design and Optimization Methods
### Topology Optimization and Detailed Analysis of Manipulator Loads
The operator relies on camera feedback that provides absolute information about the manipulator's end-effector position, and the manipulator typically moves at low speed. In the context of EOD tasks, high-speed maneuvers are neither required nor advisable. In fact, careless or imprecise movements could cause the detonation of the transported load. As a result, criteria favoring high speed or extreme structural stiffness are less relevant when optimizing portable robot manipulators for special tasks. In contrast, industrial manipulators are generally designed to maximize stiffness, with sufficiently high safety factors to prevent damage even in the event of a collision. Portable robots, however, are expected to lift relatively large masses compared to their own weight. This requirement necessitates lower safety factors in the structure. Consequently, accurately estimating loads becomes essential. Most references in the literature adopt the most severe identified load case for optimization. In some instances, equivalent loads are introduced to account for accelerations [27]. A more detailed analysis of how various manipulator configurations influence topology optimization can be found in the works of [PERSON] [21; 22; 23]. Nevertheless, these studies typically only consider loads resulting from gripping and moving objects, which makes sense for industrial manipulators where collisions are emergency scenarios. By contrast, portable robot manipulators for special tasks must account for all possible operating states and configurations, including those unusual from the perspective of classical industrial robots. Therefore, any optimization procedure should incorporate such atypical load conditions.
# Client-Aware Negotiation for Secure and Efficient Data Transmission
## 4 Result and Analysis
### Message Exchange  
The message exchange procedure of the CAN method based on renegotiation under two-way authentication was tested. The CAN procedure based on redirection is the same as the common redirection message exchange procedure, so it was not tested here. XiLinx Zynq UltraScale+ MPSoC ZCU104 was used as the server machine. The Firefox browser (Version 50.0.1) was used to log in, and the processor of the client machine was an Intel(R) Core(TM) i7-6700.  
As shown in Figure 5, if a client needs data transmission with the highest security strength, there is no need to renegotiate. After the handshake, the data transmission begins. The server hello message has the highest security strength cipher suite (Frame 8) using the symmetric encryption algorithm  
Figure 4: A flowchart of client-aware negotiation based on redirection.  
If a client needs data transmission without the highest security strength, renegotiation is needed. Figure 6 shows the whole message exchange procedure, which is divided into three parts: the first handshake, the server request for renegotiation, and the second handshake. The actual deployment of the secure transmission scheme occurs in the server hello message. In the server hello message of the first handshake, the cipher suite at the highest security strength is used (Frame 6). In the server hello message of the second handshake, the corresponding cipher suite is used by the client (Frame 17), after which, data transmission begins. In the second handshake, the port and IP do not change, which is the premise upon which this method can be used.
## 4 Influence of Structural Parameters on Crossbeam Mode
### Sensitivity Analysis  
The sensitivity analysis of the crossbeam structure is carried out to determine the crucial parameters affecting the performance of the crossbeam, which can effectively aid an engineer to evaluate and prioritize the structural parameters of the crossbeam in the design. In this section, we choose the relative sensitivity analysis method. The four structural parameters analyzed in the previous section are selected as independent variables, and the first-order frequency and left modal node position are the dependent variables. By decreasing the independent variable \(x\) by 10%, the relative sensitivities to various structural parameters, \(S_{p}=\Delta y/\Delta x\), are computed, which are presented in Table 3 and Figure 9.  
Figure 8: The crossbeam mode versus the structural parameters in the end part: (**a**) mode versus the bending stiffness; (**b**) mode shape versus the bending stiffness; (**c**) mode versus the linear density; (**d**) mode shape versus the linear density.  
From the sensitivity analysis results, it can be known that the sensitivity of the various structural parameters to the modal performance of the crossbeam is different obviously. Both the bending stiffness in the middle and end part of the crossbeam have little effect on the mode shape, as shown in Figure 9. The main factor determining the mode shape of the crossbeam is the linear density. The bending stiffness in the middle part of the crossbeam has the greatest impact on the first-order frequency, followed by the linear density in the middle and end part. It is also obtained that the bending stiffness in the end part of the crossbeam has little influence on the crossbeam mode, so it can be considered as a secondary factor in the design.
# Driver Behavior Classification System Analysis Using Machine Learning Methods
## 4 Proposed Classification Methods
### Logistic Regression  
Since the late 1980s, the logistic regression algorithm has witnessed rising interest with increased utilization in research. It is well suited for applications whose main objective is to analyze categorical outcome variables. Logistic regression is usually used to obtain binary regression models where the output is either \"0\" or \"1\". It is a simple approach utilized when the desired outcome is binary [48].
# Data Fusion Based on Adaptive Interacting Multiple Model for GPS/INS Integrated Navigation System
## 4 Real Ship Experiment and Its Performance Analysis
### Simulation Results and Analysis  
For the sake of verify the performance of AIMM filter, this paper execute the simulation test which is given by  
\[\left\{\begin{array}{l}x_{m}=k_{1}x_{m-1}-k_{2}+\lambda_{m}^{1}\ \ \ \ \ if\ m\leq t/2\\ x_{m}=k_{3}x_{m-1}^{k}+x_{m-1}+\lambda_{m}^{2}\ \ \ \ \ otherwise\\ y_{m}=-4x_{m}^{(1/2)}+\rho_{m}\end{array}\right. \tag{22}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Gyroscope**} & \multicolumn{2}{c}{**Accelerometer**} \\ \hline Bias offset & \(\pm 20^{\circ}\)/h & Bias offset & \(\pm 50\) mg \\ Bias repeatability & \(<\pm 1^{\circ}\)/h & Bias repeatability & \(\pm 0.75\) mg \\ In run stability & \(1^{\circ}\)/h (\(1\sigma\)) & In run stability & \(0.25\) mg (\(1\sigma\)) \\ Scale factor & 1500 ppm & Scale factor & \(4000\) ppm \\ Angle random walk & \(0.0667^{\circ}/\sqrt{h}\) & Velocity random walk & \(55\) mg\({}^{\circ}/\sqrt{Hz}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: IMU specifications  
Figure 2: Sea trail trajectory in Dalian port. (**a**) Sea trail trajectory. (**b**) Experimental ship.  
in which \(t\) denotes the time interval, \(x_{m}\) and \(y_{m}\) denote state and measurement series, respectively. \(i=1,2\), \(\lambda_{m}^{i}\sim N\big{(}0,\sigma_{\lambda}^{2}\big{)}\)\(\sigma_{\lambda}=0.002\), and \(\rho_{m}\sim N\big{(}0,\sigma_{\rho}^{2}\big{)}\) indicate sequences with Gaussian white noises nonzero mean. \(k_{1}=0.986\), \(k_{2}=0.31\), \(k_{3}=0.001\), \(k_{4}=0.835\). \(x_{0}\)-\(N(20,\sigma_{\lambda})\).  
\[\big{[}p_{ij}\big{]}=\left[\begin{array}{cc}0.9943&0.0057\\ 0&1.0000\end{array}\right] \tag{23}\]  
As shown in Figure 3, the state estimation information is displayed utilizing the IMM filter and AIMM filter. The improvement obtained on the basis of combining the adaptive systems can be seen in Figure 4 that describes the model probability of two types of filter modes. The upper image of Figure 4 indicates the result of IMM filter while another image denotes output of AIMM filter. As it can be seen in Figure 4, that the prevailing Model 1 of \(m_{p}\) take place by turn with Model 2 prior to the time of expectation (\(t=150\)). So Model 2 can be affected by estimated value of IMM filter. The situation is very similar after \(t=150\), and Model 1 remains predominant. Another image of Figure 4 indicates that there is a transition delay of Model 1 with regard to Model 2. It is mainly because of the existence of noise measuring data that makes it difficult to achieve the estimated state vector of every filter. Moreover, as shown in Figure 3, the state estimation generated by AIMM filter is superior to the IMM filter.  
Figure 4: Model probability of IMM and AIMM. (**a**) Model probability of IMM. (**b**) Model probability of AIMM.  
Figure 3: State estimation of IMM and AIMM.
# Space Efficiency in European High-Rise Timber Buildings
## 3 Results
### Space Efficiency
#### 3.3.4 Inter-Relationship of Space Efficiency and Structural Material  
Figure 9 depicts the relationship between spatial effectiveness and materials. The diagram features vertical columns to the right, showing the overall occurrence rate of instances classified by their individual building materials. Each green point represents the spatial effectiveness of structures linked to particular materials. Furthermore, a grey column is included to display the frequency of instances that utilize consistent building materials.  
Empirical data from multiple high-rise timber structures reveal that composite timber materials exhibit spatial efficiency levels ranging from 76% to 90%, while pure timber materials range from 70% to 90%. These figures translate to average spatial efficiencies of approximately 83% and 82%, respectively. The narrow variance of merely 1% between the average efficiencies of composite and pure timber materials signifies a substantial overlap in their performance metrics. This negligible difference in spatial efficiency highlights the comparable suitability of both material types for structural applications in high-rise constructions. The consistency in spatial efficiency metrics across diverse instances underscores the effectiveness of both composite and pure timber materials in meeting the structural demands of contemporary European architectural designs. Such findings are indicative of the robust versatility and efficacy of timber as a fundamental structural component, whether used in its pure form or in composite configurations.
## 3 Results
### Performance of CW Treating E. Globulus Leachate  
Table 2 shows the effluent quality of the CW treating _E. globulus_ leachate. BOD\({}_{5}\) removal was 93% and 82% in the planted and unplanted CW, respectively, at an OLR of 19.5 g COD/m\({}^{2}\)/day. The effluent pH during the operation was increased from 6.8 to 7.3.  
Figure 4 presents the behavior of the measured specific compounds. Aromatic compounds had a different behavior depending on the applied OLR. The removal efficiency of aromatic compounds (measured as UV\({}_{254\mathrm{nm}}\)) at the low OLR (14.1 g COD/m\({}^{2}\)/day) was 50% and 75% in the planted and unplanted CW, respectively, whereas at the OLR of 40 g COD/m\({}^{2}\)/day, the respective average reductions were 49% and 60%. The same behavior was found for lignin (measured as UV\({}_{272\mathrm{nm}}\) and UV\({}_{280\mathrm{nm}}\)). The efficiency at 14.1 g COD/m\({}^{2}\)/day ranged 50-70%, while at 40 g COD/m\({}^{2}\)/day it was 47% and 55% for the planted and unplanted CW, respectively. On the other hand, the efficiency of lignosulfonic acids (measured as UV\({}_{346\mathrm{nm}}\)) removal was 30-46% for the OLR of 14.1 g COD/m\({}^{2}\)/day and 30-42% for the higher ORL of 40 g COD/m\({}^{2}\)/day.  
Figure 3: Variations of (**a**) effluent chemical oxygen demand (COD) and (**b**) effluent total phenolic compounds concentration and respective removal rates in the planted and unplanted CW treating _P. radiata_ leachate.  
Figure 5a shows the COD removal from the _E. globulus_ leachate in the CW units at OLR ranging from 10 to 40 g COD/m\({}^{2}\)/day. Different removal rates were found for the planted and unplanted CW. The planted CW showed 60% BOD\({}_{5}\) removal with an average effluent concentration of 80 mg/L. On the other hand, at the higher OLR of 40 g COD/m\({}^{2}\)/day the BOD\({}_{5}\) removal efficiency was 55% and the effluent concentration was 420 mg/L. However, the unplanted CW showed 70% BOD\({}_{5}\) removal at an OLR of 14 g COD/m\({}^{2}\)/day (with an effluent BOD\({}_{5}\) concentration of 200 mg/L), and at the higher 40 g COD/m\({}^{2}\)/day, the BOD\({}_{5}\) removal was 65% (with effluent BOD\({}_{5}\) concentration of 330 mg/L).  
Figure 5b presents the removal rates of the total phenolics compounds for the different OLR applied to the CW units. The planted CW showed removal efficiencies up to 55% at an OLR of 14 g COD/m\({}^{2}\)/day with effluent concentration of 1000 mg/L. At the higher OLR of 40 g COD/m\({}^{2}\)/day,  
Figure 4: Removal efficiency of **(a)** aromatic compounds; **(b)** lignin-derived compounds; and **(c)** lignocellulosic acids contained in _E. globulus_ leachates treated in the planted and unplanted CW.  
the removal efficiency was 55% and the effluent concentration was 5700 mg/L. On the other hand, the removal efficiency in the unplanted CW was 70% at the OLR of 14 g COD/m\({}^{2}\)/day (with 2200 mg/L of total phenolic compounds in the effluent) and 65% at the OLR of 40 g COD/m\({}^{2}\)/day (with 4500 mg/L of the total phenolic compounds in the effluent).
## 3 Aip Array Platform Applications
### Channel Sounding Results  
The channel sounding measurements results are shown in Figure 14a,b for without and with the metal plate scenario, respectively. The AOD and AOA of the LOS path between Tx and Rx are the same, which is \(18.4^{\circ}\) calculated according to Figure 13. It can be observed in Figure 14a that the highest peak appears when both the Tx AiP and Rx AiP steering a beam in around \(18^{\circ}\), demonstrating that the AiPs based channel sounder is able to accurately detect the LOS propagation path in this channel.  
The power angle delay profile (PADP) obtained by the channel sounder for the channel without and with the metal plate when the Tx steering a beam to direction \(17.9^{\circ}\) is shown in Figure 15a. It is known that path measured is the LOS path with AoA and delay are around \(18^{\circ}\) and \(12.6\) ns respectively. Because of the system delay of the AiPs, the delay is larger than the calculated value of \(10.05\) ns. Similarly, when the Rx AiP beam-steering angle is \(17.0^{\circ}\), the measured PADP presented in Figure 15b. The AoA and delay of the measured LOS path are around \(18^{\circ}\) and \(12.6\) ns respectively as well. The combination of Figure 15a,b can explain that in Figure 14a, even if there is no propagation path in the vertical and horizontal direction of the LOS path, there is still a certain amount of power lumps measured due to AiPs beam-steering pattern side lobes.  
Figure 14: (**a**) Without the metal plate and (**b**) with the metal plate results of the channel sounding.  
Further, when the metal plate is placed to create a reflection path between the Tx and Rx, as shown in Figure 13. The calculation shows that the AoD anf AoA of the reflection path is \(45^{\circ}\) and \(-45^{\circ}\) respectively. The measurement result is given in Figure 14. In comparison to Figure 14, the channel sounder detects the LOS path as well as the reflection path. It can be seen that the AoA of the reflection path is around \(35^{\circ}\), which is different from the calculated value of \(45^{\circ}\). This is caused by position error of the metal plate and the directional beam patterns of both AiPs. In addition, when the beam-steering angle of the Tx AiP is \(47.5^{\circ}\) (the closest angle to \(45^{\circ}\) at which the Tx AiP performs beam-steering), the measured PADP for the channel is displayed in Figure 16. The measured AoA of the LOS path and the reflection path are around \(20^{\circ}\) and \(-45^{\circ}\) respectively, both close to the calculated values. The measured delay for the reflection path is \(14.67\) ns, which is \(1.33\) ns larger than the calculated one. The delay error is caused by the position errors of the Tx, the Rx and the metal plate, and the system delay of the AiPs.