esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Data  
#### 2.2.1 Land Use and Land Cover Data  
In order to quantify the temporal and spatial variations in land use and land cover in the BLA, we obtained the MCD12Q1 version 06 product from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on board the Terra and Aqua satellites [53]. This product was chosen because of its high accuracy over time and its public availability [41; 54; 55; 56; 57]. The algorithm that processes the product (V006 Global 500 m Land Cover Type Product) is based on supervised decision tree classifications of MODIS Terra and Aqua reflectance data and provides global land cover types at yearly intervals in 500 m of spatial resolution [53]. It contains five types of land cover classification, which describe surface properties, in sequence, derived from the International Geosphere-Biosphere Programme (IGBP), University of Maryland (UMD), Leaf Area Index (LAI), BIOME-Biogeochemical Cycles (BGC), and Plant Functional Types (PFT) classification schemes. The Type 1 classification scheme, from IGBP, was used (Table 1) between 2009 and 2019, as it is the most complete classification considering the purposes of this study [41; 53; 56]. To avoid mixing up atmospheric carbon estimates from land cover and use classes, we did not use the ENF, DNF, OS, BN, CS and NVM classes, since they were smaller than 1000 km\({}^{2}\) for all years [54]. The parameters of time and space for each type of land use and land cover were calculated using JavaScript language as input in the Google Earth Engine platform [58].
## 3 Results and Discussion
### Results of Photocatalytic Degradation under Different Conditions
#### 3.4.2 Effect of the Amount of Photocatalyst on the Treatment Effect  
Figure 5 shows the degradation results of 200 mL MB dye wastewater at an initial concentration of 20 mg/L after 2.5 h of photocatalytic treatment of 50, 100, 150, 200, and 250 mg composite photocatalyst. The initial pH value was about 7.78. As shown in Figure 5, with the increasing amount of composite photocatalyst, the degradation rate also increased, but when the amount of composite photocatalyst ranged from 250 to 300 mg, the degradation rate began to decline, so the optimal removal effect is achieved at 250 mg photocatalyst, and the degradation rate reaches 95%. When the amount of catalyst added achieved 250 mg, the degradation began to decline, because too much catalyst caused too many suspended solids in the wastewater, and the light translucency was very low.  
Figure 4: Degradation of methylene blue (MB) dye wastewater treated by photocatalyst containing different GO contents.
## 2 Mathematical Solution
### Analytical Model  
The arrival time \(\tau\) of the signal at position \((\xi,\vartheta,\eta)\) on the outer surface must be introduced to the displacement to analyze the observed AE signal. The arrival time of the P wave propagating with velocities \(c_{P}\) is given as follows:  
\[\tau_{P}=\frac{\sqrt{\xi^{2}+\eta^{2}}}{c_{P}},\]  
where \(\xi\) is given by Equation (6). The dominant displacement generated by the gas leak at a given time \(t\) can be given as follows:  
\[u_{\text{leak}}=P_{z}(t-\tau_{P})\Big{(}A_{0z}F_{dz}^{1}+F_{dz}^{4}\Big{)}e^{ -i\omega t}(d=\xi,\eta). \tag{50}\]  
The displacement \(u_{\text{leak}}\) in Equation (50) is associated with a single frequency. Considering that \(A_{0z}\) and \(F_{dz}^{4}\) are proportional to \(1/\omega^{2}\), Equation (50) can be rewritten for a multi-frequency wave as follows:  
\[u_{\text{leak}}(\omega_{1},\cdots,\omega_{n})=\sum\limits_{i=1}^{n}w_{\text{ emp},i}\frac{\omega_{i}^{2}}{\sum\limits_{j=1}^{n}\omega_{j}^{2}}u_{\text{ leak}}(\omega_{i}), \tag{51}\]  
where \(w_{\text{emp},i}\) is the observed weight of component \(i\). In this study, the relative value of the displacement is used to validate the amplitude of the observed AE using Equation (50).
# A Novel Dynamic Approach for Risk Analysis and Simulation Using Multi-Agents Model
## 2 Dynamic Modeling
### Random Number Models  
In the field of risk analysis, most of the work did not focus on studying the events and hence the probability of detecting events in a simulation is very rare.  
A possible solution is to conduct the experiment with a random generation of inputs. A computer is defined as a deterministic machine capable of carrying out instructions fed beforehand, represented as a program. Deterministic algorithms are used for the generation of random numbers (GRN); those numbers should resemble random even on large scales [16]. The best algorithms for GRN have been developed by mathematicians [17]. For reliability analysis, Monte Carlo Models, Markov models, and Agent-based models are on the top of random number models.
## 3 Choosing Optimal Parameters for SIPSO Algorithm  
The PSO algorithm can easily fall into the local optimum due to particle convergence. To solve this problem, a SIPSO algorithm is proposed based on structural dynamics and applied mathematics. As mentioned above, the parameters \(a\) and \(b\) of the SIPSO algorithm in Equation (5) have significant effects on its performance. In this subsection, three functions named the Rosenbrock criterion function, the Rastrigin criterion function, and the Griewank criterion function [56; 57] are used to choose the optimal parameters for the SIPSO algorithm. The three criterion functions are given as below:  
Rosenbrock criterion function [56],  
\[f(x)=\sum_{i=1}^{n}\biggl{(}100\Bigl{(}x_{i+1}-x_{i}^{2}\Bigr{)}^{2}+(x_{i}-1 )^{2}\biggr{)} \tag{15}\]  
Rastrigin criterion function [57],  
\[f(x)=\sum_{i=1}^{n}\Bigl{(}x_{i}^{2}-10\cos(2\pi x_{i})+10\Bigr{)} \tag{16}\]  
Griewank criterion function [57],  
\[f(x)=\frac{1}{4000}\sum_{i=1}^{n}x_{i}^{2}-\prod_{i=1}^{n}\cos(\frac{x_{i}}{ \sqrt{i}})+1 \tag{17}\]  
The initialization ranges of the Rosenbrock, Rastrigin, and Griewank functions are [\(-\)30,30], [\(-\)5.12, 5.12], and [\(-\)600, 600], respectively. The Rosenbrock function is an ill-conditioned quadratic function with one peak and the optimal value is 1. The Rastrigin function is a multimodal function with a large number of local extremum points and the optimal value is 0. The Griewank function is another multimodal function with the mutual influence of independent variables and the optimal value is also 0.
# Turning Video Resource Management into Cloud Computing
## 5 Conclusions  
With the volume of video resources is becoming larger and larger and data types are more various and complex, many difficulties have emerged in their management work. Fortunately, Hadoop poses new chances to resolve these problems. This paper proposes architecture to manage video resources based on Hadoop under a HDFS-based cloud-computing environment, and an experiment was done to test the new design. The system is able to ensure uniform video resource management and a high-performance processability by applying HDFS and Map/Reduce; thus, our system overcomes the difficulties related to emerging and merging policies in distributed video resource management service systems as well as to fault tolerance and load balancing management in large-scale distributed systems by obeying Hadoop policies. Based on the experimental results, we also suggest optimal Hadoop options for video resource management service systems in our cloud-based cluster servers. The study shows that using Hadoop to build a distributed storage system for video resources can enhance access speeds and save energy.  
This paper is specifically interested in architecture that deals with huge volumes of video resource management tasks, as most of the existing video resource management techniques do not consider parallel computing. The architecture should adapt to different users working on different platforms, and it should have a convenient, friendly, and simple user interface. The requirements of the architecture are portability, being service oriented, flexibility and easy extension. Users can easily use it through a web portal with a standard service-oriented interface. In the study, Map/Reduce could achieve process optimization by distributing tasks among available computing resources. In what follows, we will focus on the dynamic deployment of additional computer resources, as the means to handle seasonal load variations.  
This work is supported by the National Natural Science Foundation of China (No. 31400493), the Yunnan Provincial Research Foundation for Basic Application Research, China (Grant No. 2011 FB070), the Research Center of Kunming Forestry Information Engineering Technology (Grant No. 2015 FB04), the Natural Science Research Foundation of Education Bureau of Yunnan Province, China (Grant No. 2011Y282), and the Teaching Research Foundation of Southwest Forestry University (No.YB201120).  
[PERSON], [PERSON], and [PERSON] designed the concept of the article. [PERSON] and [PERSON] did the experiments and created the graphics. [PERSON] wrote the paper.  
The authors declare no conflicts of interest.  
Figure 7: The download time rate of videos decreases as the data size increases.
## 2 Problem Description  
A teleoperated robotic system is composed of three parts: (master, channel, and slave). The effects of tremors are concentrated in the master part (haptic device), which leads the slave part (slave robot) to generate noisy signals.  
In Figure 1, the haptic device (Touch X) has 6 degrees of freedom (DOFs). Its homogeneous transformation matrix between the \(i\)-th joint and the (\(i\)-1)-th joint generates deviation variables when the operator's hand with tremor touches the 6-th joint of Touch X. Firstly, the original joint angle \(\theta_{o}\) and the original link offset \(d_{o}\) of each joint change as \(\theta^{*}\) and \(d^{*}\), which can be described as  
\[\begin{split}&\theta_{i}^{*}=\theta_{oi}+\theta_{mi}+\Delta \theta_{i},i=1,2,\ldots,6\\ & d_{i}^{*}=d_{oi}+d_{mi}+\Delta d_{i},i=1,2,\ldots,6,\end{split} \tag{1}\]  
where \(\theta_{mi}\) and \(d_{mi}\) are the joint angle and the link offset generated by the operator, and \(\Delta\theta_{i}\) and \(\Delta d_{i}\) are the deviation joint angle and the deviation link offset caused by tremors. \(i\) represents i-th joint of the haptic device. With these disturbance variables, the homogeneous transformation matrix between adjacent joints \(i_{-1}^{i}T\), according to the modified D-H notation, can be rewritten as  
\[\begin{split}& i_{-1}T^{*}=\left[\begin{array}{cccc}c\theta_{i}^{*} &-s\theta_{i}^{*}&0&a_{i-1}\\ s\theta_{i}^{*}c\alpha_{i-1}&c\theta_{i}^{*}c\alpha_{i-1}&-s\alpha_{i-1}&-s \alpha_{i-1}d_{i}^{*}\\ s\theta_{i}^{*}s\alpha_{i-1}&c\theta_{i}^{*}s\alpha_{i-1}&c\alpha_{i-1}&c \alpha_{i-1}d_{i}^{*}\\ 0&0&0&1\end{array}\right],\\ & s=sin,c=cos,i=1,2,\ldots,6,\end{split} \tag{2}\]  
where \(\alpha\) is the kinematic link twist. The six matrixes \(i_{-1}^{i}T^{*}\) (\(i=1,2,\ldots,6\)) can be multiplied together as the pose matrix of the end effector \({}_{0}^{6}T^{*}\),  
\[\begin{split}&{}_{6}^{0}T^{*}=\left[\begin{array}{cccc}n_{1}^{* }&n_{x2}^{*}&n_{x3}^{*}&p_{x}^{*}\\ n_{y1}^{*}&n_{y2}^{*}&n_{y3}^{*}&p_{y}^{*}\\ n_{z1}^{*}&n_{z2}^{*}&n_{z3}^{*}&p_{z}^{*}\\ 0&0&0&1\end{array}\right],\end{split} \tag{3}\]  
where \(n_{xi}^{*}\), \(n_{yi}^{*}\), \(n_{zi}^{*}\) (\(i=1,2,3\)) are the rotational elements and \(p_{x}^{*}\), \(p_{y}^{*}\), \(p_{z}^{*}\) are the position vector.  
Influenced by tremors, the pose of the end effector change, which makes the slave robot deviates from its original track. The trajectory deviation over 100 \(\upmu\)m results in failure on the occasion of requiring precise manual positioning [31; 32; 33]. The tremor filter needs to be designed in the teleoperation system for the tremor compensation to eliminate tremors.
# Electric Field Distribution Induced by TMS: Differences Due to Anatomical Variation
## 1 Introduction
### Study Background and Objective  
The variability of electric fields among subjects because of their unique anatomical features has been a matter of interest for researchers working with the modeling of NIBS. [PERSON] et al. [31] studied the impact of inter-subject variation in transcranial direct current stimulation (tDCS) of the motor cortex, showing that CSF thickness and age are significant factors influencing an individual's EF distribution. [PERSON] et al. [32] investigated the variability of electric fields induced by transcranial temporal interference stimulation (tTIS) in the hippocampus, motor area, and thalamus, and emphasized the need for individualized stimulation montages. Moreover, [PERSON] et al. [33] showed how brain-scalp distance and CSF thickness influence the locality of a figure-of-eight coil and affect the intensity and spread of stimulation.  
Acknowledging the need for EF modeling in TMS applications, the idea behind the present study is to further investigate the impact of individual anatomy in the induced EF distribution in the brain, using a Hesed coil designed for deep TMS (dTMS), a version of TMS that allows the stimulation of deeper and larger brain structures, and to evaluate the implementation of personalized treatment planning in a real life TMS study.  
The anatomy of a patient may affect the EF distribution in two distinguishable ways, i.e., through anthropomorphic features of the head (e.g., its size and shape) and due to the tissue distribution inside the head. The contribution of these factors can be disentangledonly for coils that are not intended for focused stimulation, but rather have an impact on larger volumes of tissues, hence the choice of the Hesed coil in this study.
## 5 Decentralization of VANETs
### Decentralized Storage (DS)  
Decentralized storage technologies like IPFS [70], Arweave [54], and Filecoin [71] are being used in VANETs to improve data availability, security, and scalability. IPFS is a peer-to-peer network that allows for the storage and sharing of data across multiple nodes, ensuring redundancy, high availability, and expedited access. Arweave, on the other hand, uses blockchain technology to store data in perpetuity with a single payment, making it an ideal solution for archival purposes. Filecoin aims to transform cloud storage into an algorithmic marketplace using a native token for storage space buying and selling [26].  
They also provide more efficient and cost-effective data storage and access solutions compared with conventional centralized cloud storage services. For scenarios requiring prolonged data retention, systems like Arweave are essential, ensuring data accessibility for future analysis. This integration represents a step towards resolving VANETs' intrinsic challenges and redefining data management paradigms [28]. Moreover, decentralized systems like IPFS, Arweave, and Filecoin introduce robust security measures, including encryption and hash-based addressing. These measures are instrumental in safeguarding sensitive data transmitted across VANETs, thereby bolstering the privacy and integrity of V2V and V2I communications.
# Space Object and Background Polarization Models and Detectability Analysis
###### Abstract  
During the daytime, the commonly used optical intensity imaging techniques for spacecraft detection usually lead to poor detection due to strong background noise. Since some materials with low reflectivity in the visible band have higher reflectivity in the near-infrared band and less sky background noise, this paper carries out a study of near-infrared polarization detection techniques. In this paper, firstly, the parameters in the pBRDF model of common materials are inverted and found to be improved compared with the literature, and secondly, a complex spherical projection is proposed to represent the polarization degree and polarization angle information of the sky background, and this projection has less aberration than the direct projection. Finally, from the above, the polarization detection contrast range and the original intensity contrast range are calculated, and it is found that the maximum of the former is higher than the latter in some angles.  
micro polarizer imaging; polarization bidirectional reflection distribution function; ground-based optical detection; complex spherical mapping +
Footnote †: journal: Medical Imaging  
**Clustering:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Space Object and Background Polarization Models and Detectability Analysis. _Appl. Sci._**2022**, 12, 10714. [[https://doi.org/10.3390/app122110714](https://doi.org/10.3390/app122110714)]([https://doi.org/10.3390/app122110714](https://doi.org/10.3390/app122110714))  
Academic  
Only at night can visible light intensity be detected, and during the day it is challenging to separate target information from noise. In order for the device to detect the target during the day, the target information needs to be extracted from the daytime noise by the polarization method in infrared band. This study develops a polarization bidirectional reflection distribution function while taking the ing function into consideration. It then validates the model's viability using parameter inversion, obtains the contrast between intensity imaging and polarization imaging through a model simulation of polarization angle and polarization degree, and validates the benefits of the polarization imaging technique for space targets.
## 4 Particle Swarm Optimization and Experimental Verification
### Experimental Verification  
The particle swarm algorithm was utilized to optimize the response surface polynomial derived from GRA. The fitness function iteration curve and the final particle state are depicted in Figure 8a,b. The analysis outcomes reveal that the algorithm converges rapidly, initiating convergence at the 11 th iteration, with the predicted optimal correlation degree being 0.89363, corresponding to the optimized parameters: a friction coefficient of 0.3, an initial temperature of 1250 \({}^{\circ}\)C, and a downward pressing speed of 7.5 mm/s. Following this, simulations were executed using the predicted optimal process parameters, and a comparison of the initial process parameters versus the best process parameters is presented in Table 8. The findings indicate that the forming load is 361 MN, the equivalent stress is 29.9 MPa, the damage value is 0.560, and the GRD is 0.9014. In comparison to the pre-optimization state, the forming load has been reduced by 86 MN, the equivalent stress has been diminished by 2.5 MPa, the deformation is more uniform, and the damage value has been decreased by 0.188, thereby validating the efficacy of the optimized process parameters, which provides a theoretical basis for reducing forging costs, improving the uniformity of forging deformation, and reducing scrap rates in actual production.
# Estimation of Optimal Speed Limits for Urban Roads Using Traffic Information Big Data
## 3 Data Collection and Model Equation Development
### Development of Adjustment Factor Estimation Criteria
#### 3.4.3 The Adjustment Factor (fPL)  
Pertaining to the Level of Parking  
The adjustment coefficient for parking density is calculated in the same way as the road functional class, with the adjustment factor (fPL) expressed as:  
\[(67.38\text{ km}\times 1.00)/80\text{ km}/\text{h}=0.84,\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variables** & **R2** & \
\begin{tabular}{c} **Degree of** \\ **Freedom** \\ \end{tabular} & **F-Value** & **\(p\)-Value** & **Constant** &
\begin{tabular}{c} **Regression** \\ **Coefficient** \\ \end{tabular} \\ \hline Road function & 0.240 & 35 & 10.71 & 0.002 & 61.10 & \(-\)5.11 \\ Median strip & 0.384 & 35 & 21.18 & 0.000 & 49.98 & 14.68 \\ Degree of parking & 0.177 & 35 & 7.33 & 0.011 & 59.91 & \(-\)6.31 \\ Number of accesses & 0.278 & 35 & 13.07 & 0.001 & 58.63 & \(-\)0.36 \\ Total number of traffic breaks & 0.555 & 35 & 42.35 & 0.000 & 62.53 & \(-\)2.13 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Regression analysis results for selected variables.  
Figure 14: Adjustment factor development (**a**) and standardization for the existence (**b**) of the median strip.  
Figure 13: Adjustment factor development (**a**) and standardization for the existence (**b**) of road function.  
\[\mathrm{F_{PPLj}}=1.00-0.16\times(\mathrm{V_{PPLj}}-1)/2, \tag{7}\]  
where \(\mathrm{V_{PL}}=1\) if the level of parking is almost zero, 2 if it is low, and 3 if it is high.  
The estimation process is expressed in a graph, as shown in Figure 15.
# Improving Congestion Control of TCP for Constrained IoT Networks
###### Abstract  
For smooth integration with middleboxes on the Internet, TCP (Transmission Control Protocol) is favorably considered as a transport-layer protocol for IoT (Internet of Things) networks. In constrained networks, TCP tends to perform well with a small window size. For example, the uIP (micro IP) TCP/IP stack sets the TCP window size to one segment by default. In such a case, managing the retransmission timer is a primary approach to congestion control. In this paper, we examine the congestion control mechanism of TCP in the uIP stack using grid topology networks. In the preliminary test using the Cooja network simulator, the results show that the original uIP TCP causes lots of retransmissions when a radio duty cycling mechanism such as ContikiMAC is used. One main reason is that, once retransmission is deemed to be necessary, the original uIP TCP sets the retransmission timer based on the fixed RTO (retransmission timeout) before performing a retransmission. Since ContikiMAC may cause large RTT (round-trip time) variations due to the hidden terminal problem, the retransmission timer based on the fixed RTO value may cause lots of retransmissions. To address the problem, we propose a new scheme for managing the retransmission timer which adopts the notion of weak RTT estimation of CoCoA, exponential backoffs with variable limits, and dithering. Simulation results show that our proposed scheme reduces retransmissions while enhancing throughput and fairness when an RDC (radio duty cycling) mechanism is used.  
TCP; IoT network; congestion control; retransmission timer +
Footnote †: journal: Sensors
# Privacy-Preserving Transfer Learning Framework for Kidney Disease Detection
## 3 Material and Methods
### Dataset  
The dataset obtained via the Picture Archiving and Communication System from several hospitals in Dhaka, Bangladesh, was employed in this study. Patients with kidney tumors, cysts, stones, and normal tissue were included. Following the established procedure, whole abdomens and urinary tracts were scanned, and both coronal and axial slices from both contrast and non-contrast scans were used. Next, the Dicom images were carefully chosen individually based on the diagnosis. From these images, a Dicom image stack was created by the data providers, which included the specific area of interest for each radiologic finding. Then the information and metadata of each patient were retrieved from the Dicom images and converted to a lossless jpg image format. Following the conversion process, a radiologist and a medical technician reviewed every image finding to verify the truthfulness of the data [40].  
The dataset obtained consists of 12,510 distinct CT images. We selected a subset of 12,400 images, allocating 3709 to cysts, 5031 to normal tissue, 1377 to stones, and 2283 to tumors [40]. Figure 9 shows CT kidney images of normal, cyst, stone, and tumor. Normal kidneys are characterized by a smooth cortex and medulla. Kidney cysts appear on CT images as round or oval-shaped lesions, usually dark in color and with sharp edges. Kidney stones appear as dense masses, usually bright white on CT images. The size and location of the stone determines the severity of symptoms. Kidney tumors may appear different in density from normal kidney tissue. The size, shape, and location of the tumor are also important.