esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, a wall-climbing robot equipped with a rotating airflow suction unit was designed to clean wall surfaces. The internal fan of the suction unit, driven by a DC brushless motor, generates negative pressure to facilitate adhesion. This paper primarily investigates the impact of the number of fan blades, blade thickness, and adhesion leakage area on the adhesion effect, and presents the following conclusions:  
1. Compared to circular suction units, square suction units provide greater suction force for two main reasons: a larger adhesion area and reduced loss of the internal pressure gradient.
2. An increase in the number of blades improves the distribution of nonlinear velocity in the z-plane. Suction force increases significantly with the number of blades up to a certain point, after which further improvements are minimal. In practical applications, four or six blades are appropriate.  
Figure 19: Suction force under different leakage conditions (simulation data).  
Figure 18: Airflow in different leakage situations.  
As the number of blades increases, the thickness of the blades primarily affects the air volume in the rotating domain, influencing suction force; thinner blades are preferable.
4. This paper analyzes three dangerous working conditions that the wall-climbing cleaning robot may encounter, with edge leakage being the most dangerous, followed by corner leakage, and gap leakage having the least impact.
# Real-Time Indoor Positioning Based on BLE Beacons and Pedestrian Dead Reckoning for Smartphones
## 2 Related Work  
Over recent decades, various technologies such as WiFi, BLE, PDR, ultra-wideband (UWB), acoustic and ultrasonic wave, radio frequency identification (RFID), geomagnetic field, visible light communication (VLC), and combinations of these technologies have been used for indoor positioning on smartphones [4; 24; 25]. Although UWB and VLC can achieve high accuracy, they do not seem to receive as strong support from the community as BLE or WiFi [3]. In this section, we briefly review the advances in PDR positioning, BLE-based positioning, and fused positioning methods, which are mostly related to our work.
## 4 Discussions
### Research Highlights and Shortcomings  
In the past, when monitoring the temporal and spatial changes of ecological environment quality, based on the REEI index, Landsat satellite remote sensing images were mainly used, but the data quality presented certain limitations, while MODIS data products can offer a better supplement. This paper uses these two data sources to study the quality of the ecological environment and its temporal and spatial changes, which process can realize an analysis of the quality of the urban ecological environment more scientifically and quickly. At the same time, in order to analyze the degree and direction of the impact of human activities on the quality of the ecological environment from a quantitative and qualitative perspective, this paper uses POI data to generate a point density distribution map and conduct correlation analysis with RSEI and its constituent indicators. The calculation results met the significance test, which means that they meet the analysis requirements and have strong applicability.  
Of course, this study also has some shortcomings. There are many remote sensing data-processing tasks performed in this paper, which necessitate care and patience to complete the data preprocessing and computational analysis. In recent years, some scholars have proposed that the GEE platform can be used to access the database of the platform by writing simple programs to quickly process and analyze the geospatial data in the cloud, overcoming the low efficiency of traditional methods in processing remote sensing images [22; 46]. This approach also has certain limitations, such as requiring a certain level of programming knowledge and unimpeded network support. In addition, due to the limited data, this paper only uses the POI data in 2018 for correlation analysis, which cannot represent the situation with long-term series. Identifying a way to scientifically and efficiently monitor the regional ecological environment quality dynamically is the ultimate goal of this study. Exploring the potential factors affecting ecological environment changes [47] and digging into the mechanism of RSEI on ecological environment quality can provide a basis for ecological protection and urban construction planning in the study area. The convenient and rapid depiction of the ecological environment using the remote sensing ecological index has been verified, but the improvement of the relevant index and exploring the interrelationship between the index factors is still a research hotspot [48; 49], which can be further studied in the future.
# Soriasis: From Pathogenesis to Pharmacological and Nano-Technological-Based Therapeutics
## 5 Nanotechnological Approaches for Psoriasis
### Lipid-Based Nanoparticles
#### 5.2.3 Ethosomes  
Ethosomes are vesicular carriers based on ethanol, phospholipids, and water for topical application. Their composition permits good skin penetration, due to their softness and flexibility, caused by the high ethanol concentration. The proposed mechanism for penetration enhancement by ethosomes is based on the dual effect of ethanol on both the lipid bilayers in the stratum comeum and in the vesicle: ethanol enables fluidization of the lipids in the ethosomal structure on one side, along with changes in the arrangement of the lipids in the skin barrier on the other side. This allows the soft vesicles to penetrate the altered structure of the stratum comeum and release the activity in the deeper layers of the skin [143].  
In this area, [PERSON] et al. prepared ethosomes as a delivering system for psoralen, obtaining a particle size ranging from 56.71 nm to 159.07 nm, showing the best entrapment efficiency ethosomes around 150 nm [144]. They studied the permeation of the drug compared to an ethanolic tincture of psoralen, observing a notable improvement (almost 7-times fold) in the deposition of the drug in rat skin. Conclusively, it was stated that the permeation and the penetration in the skin using ethosomes could decrease toxicity and increase the therapeutical effects in long-term therapy for skin disorders like psoriasis.
## 3 Results
### Coefficient of Variation of Carbon Fluxes  
The analysis of carbon fluxes of forest ecosystems in Yunnan Province showed that the coefficient of variation (CV) displayed a decreasing pattern from southwest to northeast (Figure 7). The CV of GPP fluctuated between 0 and 0.5, with the smallest CV (\(<\)0.1) in the forest ecosystems of the Xishuangbanna region and the largest interannual variability (\(>\)0.3) in the Qujing and Wenshan regions of eastern Yunnan. The CV of 0-0.1 accounted for 3.04% of the total area, that of 0.1-0.3 accounted for 84.76% of the total area, and 12.20% of the area had a CV larger than 0.3. Under the SSP2-4.5 and SSP5-8.5 scenarios, the interannual variation of 0.1-0.3 accounted for approximately 70%, and the area larger than 0.3 accounted for 23.69% and 23.03%, respectively.  
We discovered that the effects of different emission scenarios on ER were similar to those on the GPP pattern. The CV of ER was smaller in the south and northeast and higher in the northwest and southeast. The CV of ER in the SSP1-2.6 scenario was narrower than that in the other two scenarios, while the area with a CV greater than 0.3 was 16.13%, which was smaller than the values of 22.26% and 24.72% in the SSP2-4.5 and SSP5-8.5 scenarios, respectively. The area with a CV of 0.1-0.3 was 81.24%, which was higher than the values of 75.31% and 72.79% in the SSP2-4.5 and SSP5-8.5 scenarios, respectively.  
The spatial variation in the CV of NPP under different climate change scenarios was not substantial, with 41.91%, 40.21%, and 41.36% in the regions with CVs below 0.1 under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. The CV was less than 0.05 in the Pu'er and Xishuangbanna regions in the central parts, while those in the Kunming and Yuxi regions were greater than 0.2.  
The CV of NEP in the SSP1-2.6 and SSP5-8.5 scenarios were less than that in the SSP2-4.5 scenarios. The percentage of area with a CV less than 0.1 in the SSP1-2.6 and SSP5-8.5 scenarios was 51.76 and 51.07%, respectively, which was higher than that of SSP2-4.5 (42.91%). The percentage of area with a CV greater than 0.1 in the SSP1-2.6 and SSP5-8.5 scenarios was 48.24 and 48.93%, respectively, which was lower than the values of 56.03% for SSP2-4.5. The lower variability of forest carbon sequestration in the south than in the northwest and east is due to the lower interannual variability of temperature and precipitation in the south than in the north due to latitude and topography.  
Figure 6: Spatial distribution of annual mean carbon fluxes in Yunnan Province from 2020 to 2060 under different emission scenarios: (**a**) SSP1-2.6; (**b**) SSP2-4.5; and (**c**) SSP5-8.5.
# EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps
## 3 Materials and Methods
### System Overview  
The proposed EOR system achieves precise alignment between visual images and HD maps by integrating multiple components to enhance localization accuracy and robustness, as depicted in Figure 2. The framework consists of key stages: input, preprocessing, pose initialization, feature analysis, and pose refinement, each contributing to the system's overall effectiveness.  
The system begins with pose initialization, where the 3D Iterative Closest Point (ICP) algorithm [20] estimates an initial camera pose relative to the HD map. Feature analysis follows, extracting SDF from both visual images and HD maps, including feature occupancy, feature Geometric Dilution of Precision (GDOP), and longitudinal difference. These factors provide insights into the spatial configuration of objects and predictive measures of localization accuracy, based on criteria from [19]. Concurrently, SSC maintains geometric relationships during the camera's perspective projection transformation.  
In the pose refinement stage, SDF and SSC collaboratively optimize the initial pose estimate, thereby enhancing the overall alignment accuracy. Sequential operations for pose refinement are elaborated in Figure 3. To ensure temporal consistency across frames, an Extended Kalman Filter (EKF) [21] integrates pose estimations, reducing uncertainties and enhancing the consistency and robustness across dynamic sequences.  
Collectively, the EOR system leverages ICP, SDF, SSC, and EKF to iteratively refine pose estimations, achieving reliable and precise localization. Figures 2 and 3 provide  
Figure 2: Framework of EOR.  
an overview and detailed breakdown of the EOR workflow, offering a comprehensive understanding of the methodology.
# LSANet: Lightweight Super Resolution via Large Separable Kernel Attention for Edge Remote Sensing
## 4 Implementation Details  
### Experimental Configuration  
In the experimental setup, the batch size is configured to 16, and the number of iterations is determined as 400. To improve data diversity and model generalization for remote sensing image super resolution, the following data augmentation strategies are employed: random horizontal/vertical flipping, 90\({}^{\circ}\)/180\({}^{\circ}\)/270\({}^{\circ}\) rotation, and pixel-wise Gaussian noise injection. For multi-scale training compatibility, batches are composed by randomly sampling images from \(\times 2\), \(\times 3\), and \(\times 4\) super-resolution tasks. The model is trained using the Adam optimizer (\(\beta_{1}=0.9\), \(\beta_{2}=0.99\)) with an L1 loss function for optimization. The initial learning rate is set to \(2\times 10^{-5}\), and dynamic regulation is implemented via MultiStepLR: the rate is decayed by 0.5 at 200 and 300 epochs. The experiment is conducted on an Ubuntu system with an RTX A5000 24 GB GPU. The operating environment includes Python 3.9, PyTorch 2.1.0, and CUDA 11.9.  
In the model parameter settings, the kernel sizes of the depthwise convolution and depthwise dilated convolution in the large separable kernel attention are set to 5 and 7, respectively. Following the configuration of the RFDN architecture, the number of residual large separable kernel feature distillation modules \(m\) and the intermediate feature channel count are set to 6 and 52, respectively. This configuration is chosen to maintain comparability in model depth and complexity, enabling a fair evaluation of the proposed enhancements.  
This paper uses PSNR [44] and SSIM [45] as the main evaluation metrics, where PSNR primarily measures the noise suppression capability of the remote sensing image reconstruction model, and SSIM evaluates its performance in preserving texture structures. The higher the values of these two metrics, the better the model's performance. It is noted that the reported PSNR and SSIM values are average results across multiple test datasets. Additionally, this study assesses the model's parameter count and computational complexity to further demonstrate its efficiency in terms of model scale and inference performance.
# Secure Physical Layer Network Coding versus Secure Network Coding +
## 4 Network with Three Source Nodes
### Secure NC  
As the first step, let us study the network with three sources under the framework of the secure NC. In Figure 4, every edge expresses a noiseless channel to transmit one element of \(\mathbb{F}_{q}\). Here, we consider the following two security requirements:  
* When [PERSON] eavesdrops only one edge among three edges (channels) between the intermediate nodes and the destination node, she obtains no information about each message.
* When [PERSON] eavesdrops only one intermediate (untrusted) node among three intermediate (untrusted) nodes, she obtains no information for each message. Here, no node colludes with another node.
## 5 Interface
### Mandatory Arguments  
For the minimal usage, the following five arguments are required:  
f handle to a function with the signature xdot = f(x,u,p,t) representing the system's vector-field and expecting the arguments: current state x, current input u, (current) parameter p and current time t.  
g handle to a function with the signature y = g(x,u,p,t) representing the system's output functional and expecting the arguments: current state x, current input u, (current) parameter p and current time t.  
If g = 1, the identity output functional \(g(t,x(t),u(t),\theta)=x(t)\) is assumed.  
* three component vector \(\mathtt{s}=\) [M,N,Q] setting the dimensions of the input \(M\ :=\ \dim(u(t))\), state \(N\ :=\ \dim(x(t))\) and output \(Q\ :=\ \dim(y(t))\).
* two component vector \(\mathtt{t}=\) [h,T] specifying the time-step width \(h\) and time horizon \(T\).
* character selecting the empirical Gramian type; for details see Section 5.2.
## 2 Development of the Methods  
Babajee [7] has recently improved the \(3^{rd}HM\) method to get a fourth order method for single equation  
\[\left\{\begin{array}{l}y_{k}=x_{k}-\frac{2}{3}\frac{f(x_{k})}{f^{\prime}(x_{ k})}\\ x_{k+1}=x_{k}-\frac{1}{2}\left[\frac{f(x_{k})}{f^{\prime}(x_{k})}+\frac{f(x_{k})}{f^{ \prime}(y_{k})}\right]\ \left[1-\frac{1}{4}\left(\frac{f^{\prime}(y_{k})}{f^{\prime}(x_{k})}-1 \right)+\frac{1}{2}\left(\frac{f^{\prime}(y_{k})}{f^{\prime}(x_{k})}-1\right)^ {2}\right]\end{array}\right.\]  
This method is one of the member in the family of higher order multi-point iterative methods based on power mean for solving single nonlinear equation by [PERSON] _et al._[9].  
We next extend the above idea to the multivariate case. For the method given in Equation (2), we propose an improved fourth order Harmonic Mean [PERSON]'s method (\(4^{th}HM\)) for solving systems of nonlinear equations as follows:  
\[\begin{array}{l}x^{(k+1)}=G_{4^{th}HM}(x^{(k)})=x^{(k)}-H_{1}(x^{(k)})A(x^{( k)})F(x^{(k)})\\ H_{1}(x^{(k)})=I-\frac{1}{4}(\tau(x^{(k)})-I)+\frac{1}{2}(\tau(x^{(k)})-I)^{2},\quad\tau(x^{(k)})=[F^{\prime}(x^{(k)})]^{-1}F^{\prime}(y(x^{(k)}))\\ A(x^{(k)})=\frac{1}{2}\left([F^{\prime}(x^{(k)})]^{-1}+[F^{\prime}(y(x^{(k)}))] ^{-1}\right),\quad y(x^{(k)})=x^{(k)}-\frac{2}{3}u(x^{(k)})\end{array} \tag{6}\]  
where \(I\) is the \(n\times n\) identity matrix. We further improve the \(4^{th}HM\) method by additional function evaluations to get a multi-step version called \((2r+4)^{th}\)_HM_ method given by  
\[\begin{array}{l}x^{(k+1)}=G_{(2r+4)^{th}HM}(x^{(k)})=\mu_{r}(x^{(k)})\\ \mu_{j}(x^{(k)})=\mu_{j-1}(x^{(k)})-H_{2}(x^{(k)})A(x^{(k)})F(\mu_{j-1}(x^{(k) }))\\ H_{2}(x^{(k)})=2I-\tau(x^{(k)}),\quad j=1,2,...,r,\,r\geq 1\\ \mu_{0}(x^{(k)})=G_{4^{th}HM}(x^{(k)})\end{array} \tag{7}\]  
Note that this multi-step version has order \(2r+4\), where \(r\) is a positive integer and \(r\geq 0\). The case \(r=0\) is the \(4^{th}HM\) method.
## 3 Proposed Architecture Overview
### Assumptions  
This paper's contributions make the assumptions as follows:  
* _Tagged Data_: It is assumed that publishers create a limited number of _alphanumeric_ tags accurately for each topic they publish. This framework relies on these tags to identify potential substitutes.
* _Periodic Data_: The data streams are periodic, that is, they are expected to send data at regular intervals. Periodicity assists in identifying delays in publishing intervals by establishing the expected timing for publication data and identifying deviations from these intervals.
* _Manual data stream grouping_: To establish quality, the proposed methods rely on sensor associations based on their own proximity and the proximity of the observed environment. It is assumed that this association is performed manually as part of the topic registration, and a group has a minimum of three topics. Under normal circumstances, the blockchain can keep track of and confirm this relationship continuously.  
Figure 2: A detailed overview of the smart broker.  
For this paper, we consider that there is a fixed window of time \(w_{ij}\), for which the time series in a group would follow a relationship (direct or inverse) at a degree of \(v_{ij}\). This is a common feature in many IoT application data that track a periodic system. In this paper, a weather data set is used that exhibits this property [53]. However, in other practical applications, the windows themselves could vary over a period of time, and the expected relationship scores would also be different for those time periods. Such possibilities are not the focus of this paper, where we emphasize the smart broker's structure and ability to detect quality and recommend substitutions.  
* _Fixed Location_: The IoT devices are assumed to be in fixed positions. These positions may be geographic or referential, such as in a building or factory. In other words, they are non-mobile by nature. Any change in the location needs to be updated in the blockchain and the pairings.
## 4 V2H and V2G
### Prospects
#### 4.4.2 Integration with Retired EV Batteries  
The inherent property of batteries is that the capacity will gradually decrease over time and cycles. Previous research has demonstrated that when the remaining storage capacity of onboard batteries is less than 70-80% of its designated storage capacity, it will be obsolete for the inability to sustain the duration and mobility of EV driving [79]. However, the usage of secondary batteries as stationary storage for smart grid will undoubtedly extend the life cycle of onboard batteries and enhance resource utilization. Nissan, in collaboration with Enel and Eaton, is conducting a project at its regional office in France to integrate secondary batteries into a V2G system, consisting of 100 bidirectional chargers for V2G following CHAdeMO protocol and an energy storage subsystem combining more than 60 \"second life\" batteries with solar power generation [80]. The 1 MWh of energy stored in this system combined with the 1-2 MWh of energy stored in the EVs at nearby lots provides an adequate energy storage capacity for the appliances. The project demonstrates a solution for integrating the secondary battery with the V2G system and utilizing its residual storage capacity.
## Materials and Methods
### Otsu Thresholding-Based Region Growing Segmentation (OTRGS)  
There are several automated and semiautomated methods for extracting ROI. Automated ROI extraction is generally based on the idea of image segmentation, but there is no single technique for ideal segmentation [25]. On the other hand, semiautomated techniques are based on expert opinion, but human-based extraction has some limitations. To solve this problem, Otsu thresholding-based region growing segmentation (OTRGS) was employed on the liver cancer fused image dataset. Liver CT and MR images were divided into four equal regions. Groups of neighboring seeds were used for the formulation of an identifiable region. These seeds were in the shape of an irregular polygon with a variable radius from the center of an image, and that ensured a maximum chance of grouping seeds that belong to the same region. It also allowed any possible size, dimension, and shape to be considered as a region of interest (ROIs). At the postprocessing stage, the Otsu thresholding-based segmentation was employed on improved segmented regions. Otsu thresholding [26] involves iteration through all possible threshold values. It calculated the measure of spread for the pixel level on each side of the thresholding; the pixel either falls in the foreground or background.  
Let \(\omega_{\beta}\), \(\theta_{\beta}\), and \(\sigma_{\beta}^{2}\) be the weight, mean, and variance for the background, respectively. Meanwhile, \(\omega_{\phi}\), \(\theta_{\phi}\), and \(\sigma_{\phi}^{2}\) are the weight, mean, and variance for the foreground, respectively, i.e., \(\beta\) = background and \(\phi\) = foreground.  
The variance within class is denoted by \(\sigma_{\gamma_{1}}^{2}\), and between-class variance is denoted by \(\sigma_{\gamma_{2}}^{2}\), and they are defined as under Equations (5) and (6), respectively.  
\[\sigma_{\gamma_{1}}^{2}=\omega_{\beta}\sigma_{\beta}^{2}+\omega_{\phi}\sigma_ {\phi}^{2} \tag{5}\]  
and  
\[\sigma_{\gamma_{2}}^{2}=\omega_{\beta}\omega_{\phi}\big{(}\theta_{\beta}- \theta_{\phi}\big{)}^{2} \tag{6}\]