esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches
## 5 O-RAN Alliance Architecture
### O-Cui  
The is a logical node hosting the functions of RRC, SDAP, and PDCP protocols of the BS [58; 62]. In O-RAN architecture, O-CU can be further divided into O-CU-CP and O-CU-UP. O-CU-CP consists of the CP part of PDCP and RRC. Conversely, O-CU-UP covers the UP part of PDCP and SDAP. O-CU has a lot of interfaces which include the E1, E2, F1, NG, O1, X2, and Xn interfaces. The F1, NG, X2, and Xn interfaces can each be divided into the control and user interface. O-CU's architecture and its interfaces can be seen in Figure 13.  
E1, F1, NG, X2, and Xn are all interfaces defined by 3 GPP. O-RAN Alliance reuses the E1 specification defined by 3 GPP and adopts the E1 interface to bridge O-CU-CP and O-CU-UP. The F1 interfaces are used to connect O-CU to the O-DU. Inversely, the NG interfaces link O-CU to the 5 GC. X2 interfaces connect the O-CU to other eNB or en-gNB in EN-DC configuration. Lastly, Xn interfaces link the O-CU to other gNB or ng-eNB. Both X2 and Xn interfaces are adopted from 3 GPP with interoperability profile specifications added.
# Removal of Metals and Dyes in Water Using Low-Cost Agro-Industrial Waste Materials
## 4 Conclusions  
The high efficiency of the MWBC in the adsorption processes of methylene blue and Cr (VI) is presented, demonstrating that thermally treated biomaterials from lignocellulosic waste can replace commercial activated carbon. From SEM analysis, it was evidenced that the formation of tubular cavities and porous structure was caused by the basic hydrolysis with KOH. From adsorption tests, an adsorption capacity of 12.98 mg/g and 97.38% of efficiency was registered for MB at pH 10, while for Cr (VI), it was 11.35 mg/g and 85.15%  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Synthesis** & **Adsorption Capacity** & **References** \\  & **Parameters** & **(\% Efficiency)** & **References** \\ \hline \multirow{8}{*}{Commercial activated carbon} & Temperature: 200 \({}^{\circ}\)C & MB: 99 \(\pm\) 0.5 & \multirow{8}{*}{[56]} \\  & Time: 45 min & Pb (II): 99 \(\pm\) 0.5 & \\ \cline{1-1}  & Activator: KHO 1:1 & Cd (II): 99 \(\pm\) 0.4 & \\ \cline{1-1}  & & In 12 h & \\ \cline{1-1}  & Temperature: 550 \({}^{\circ}\)C & \multirow{2}{*}{} & \\ \cline{1-1}  & Time: 1 h & & \\ \cline{1-1}  & Activator: KOH (1 M & & \\ \cline{1-1}  & y 3 M) & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of MWBC with commercial biochar.  
Figure 12: Competitive adsorption of MB and chromium when (**A**) Cr (VI) is fixed and (**B**) MB is fixed.  
at pH 2. [PERSON]'s model adjusted the adsorption equilibrium data with R\({}^{2}>0.9\); then, it can be said that the process is controlled by chemisorption. The maximum adsorption capacities in the monolayer were 186.375 mg/g and 90.723 mg/g for Cr (VI) and MB, according to [PERSON]'s model. From the kinetic study, it can be said that the process occurs by chemisorption through electrostatic interaction and ionic interchange between adsorbate and adsorbent.  
Conceptualization, C.T.-T., A.V.-O. and R.O.-T.; data curation, R.O.-T.; formal analysis, C.T.-T. and A.V.-O.; methodology, C.T.-T.; project administration, C.T.-T. and A.V.-O.; resources, C.T.-T. and A.V.-O.; software, A.V.-O.; supervision, C.T.-T. and A.V.-O.; validation, C.T.-T. and R.O.-T.; visualization, R.O.-T.; writing--original draft, A.V.-O.; writing--review and editing, A.V.-O. and R.O.-T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data supporting this study's results are available upon request from the corresponding author.  
The authors thank the Universidad de Cartagena (Colombia) for the support in the development of this work regarding laboratory, software use, and time for their researchers.  
The authors declare no conflict of interest.
## 5 Results and Discussion
### Current Issues and Controls Applied  
As with any methodology, there are some limitations and issues. Below the short descriptions of such issues are provided with comments on the controls that were or could be applied to minimize their influences on the quality of results:  
* One potential issue of the method proposed is that there is a small possibility that data are altered during the transformation processes. To resolve this issue, the data were visually compared during the heuristic phase from the input to the resulting files to verify the accuracy.
* Another weakness of the proposed method is that the output data will only be as reliable as the input data. That is to say that, if an inaccurate attack tree is converted to the expert system rules, then the expert system will not provide accurate information regarding any eventual risk assessments. There is, ultimately, not any way around this issue beyond double-checking that the attack trees are accurate, come from the reliable sources, and apply to the system in question. This will require that users of this method would have some knowledge of their systems and that they are receiving accurate attack tree information.
* Potentially, the greatest issue in this method lies in the extreme variability within XML. This has been mitigated somewhat by using the output from ATTop to standardize the expected input to ATES. However, the naming conventions of various tools being capable of significant differences for the same attributes (e.g., the attribute identifier \"duration\" referring to the length of time required for an attack could also be called \"time\" or even \"length\" by specific tools) means that the only solution is to code each tool's naming convention into the lister function.
# Gear Fault Diagnosis Based on Kurtosis Criterion VMD and SOM Neural Network
###### Abstract  
A gear fault diagnosis method based on kurtosis criterion variational mode decomposition (VMD) and self-organizing map (SOM) neural network is proposed. Firstly, the VMD algorithm is used to decompose the gear vibration signal, and the instantaneous frequency mean is calculated as the evaluation index, and the characteristic curve is drawn to screen out the most relevant intrinsic mode functions (IMFs) of the original vibration signal. Then, the number of VMD decompositions is determined, and the kurtosis value of IMFs are extracted to form the feature vectors. Then, the kurtosis value feature vectors of IMFs are normalized to form the kurtosis value normalized vectors. Finally, the normalized vectors of kurtosis value are input into SOM neural network to realize gear fault diagnosis. When the number of training times of SOM neural network is 100, the gear fault category is accurately classified by SOM neural network. The results show that when the training times of SOM neural network is 100 times, the gear fault diagnosis method, based on the kurtosis criterion VMD and SOM neural network is 100%, which indicates that the new method has a good effect on gear fault diagnosis.  
variational mode decomposition; kurtosis value; SOM neural network; instantaneous frequency mean; gear fault diagnosis Article
## 4 Results
### Multi-Source Data Fusion Experiment  
In this section, we aim to verify the effectiveness of the proposed multi-source data fusion method. Specifically, we utilize a Euclidean distance-based DTW algorithm to associate the visual trajectory of the target ship (generated by fusing data from a camera, GPS, and IMU) with the GPS trajectory in the E-AIS database. The dynamic and static information of both the own ship and the target ship is then jointly stored. Considering the crew's sailing habits and information needs, we constructed a simple, intuitive, and easy-to-understand human-computer interface, and constructed the AR navigation assistance results in the left camera image, so that the crews can understand the ship's navigation status more comprehensively and intuitively and improve the efficiency of acquiring and understanding the navigation information. Figure 21 visually demonstrates the AR navigation assistance results for the ship based on the perceptual data fusion framework.  
In Figure 21, the results of AR navigation assistance provide comprehensive information about the tracked ships, including static details (IMO, Name, Length, Breadth) and dynamic information (Lat, Lon, SOC, COG, HDG), as well as navigation information for the host ship (Lat, Lon, SOC, COG, HDG, etc.). The ship data fusion method proposed in this paper successfully and accurately matches the visual and GPS trajectories. The  
Figure 21: The AR navigation assistance effects of ships constructed at different timestamps in multiple scenes.  
dynamic information is updated in real time with the movement of the ship, allowing the ship AR navigation system to more accurately reflect the current status. The navigation assistance system helps the crew to improve the efficiency of information acquisition and understanding during navigation and also provides the crew with richer navigation assistance information, so as to improve the overall navigation experience.  
The unique feature of the perceptual data fusion method based on ship ST-TF is that it can respond to possible emergencies such as communication or other equipment failures by displaying the visual latitude and longitude coordinates (as shown by the left ship in Scene 2 of Figure 21) without being associated with the GPS trajectory. Even in these emergencies, the system is still able to provide basic location information to the crew, providing critical data support for emergency response, search, and rescue operations. In addition, for ships not associated with a GPS track, displaying their visual latitude and longitude coordinates can effectively monitor and identify illegal ship operations. In terms of maritime surveillance, this feature can make up for the defects of communication equipment not configured according to regulations or maliciously shut down, and enhance the maritime surveillance capability, thereby strengthening the security of the entire maritime field.
# Design of a Hand-Launched Solar-Powered Unmanned Aerial Vehicle (UAV) System for Plateau
## 2 Conceptual Design Method
### Weight Estimation Model
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Unit** & **Description** \\ \hline \(C_{L}\) & 0.8 & - & Lift coefficient \\ \(C_{D}\) & 0.04 & - & Drag coefficient \\ \(I_{\max}\) & 950 & [W/m\({}^{2}\)] & Maximum irradiance \\ \(k_{temp}\) & 0.89 & & Temperature influence factor (5 \({}^{\circ}\)C) \\ \(k_{bat}\) & 290 & [Wh/kg] & Energy density of Li-ion battery \\ \(k_{sc}\) & 0.29 & [kg/m\({}^{2}\)] & Mass density of solar cells \\ \(k_{enc}\) & 0.13 & [kg/m\({}^{2}\)] & Mass density of encapsulation \\ \(k_{asppt}\) & 0.0042 & [kg/W] & Mass to power ratio of MPPT \\ \(k_{empty}\) & 0.008 & [kg/W] & Mass to power ratio of propulsion group \\ \(k_{cf}\) & 0.44/9.81 & [kg/m\({}^{2}\)] & Structural mass constant \\ \(m_{met}\) & 0.11 & [kg] & Mass of autopilot system \\ \(\eta_{bsc}\) & 0.8 & - & Efficiency of step-down converter \\ \(\eta_{bc}\) & 0.2 & - & Efficiency of solar cells \\ \(\eta_{cbr}\) & 0.9 & - & Efficiency of the curved solar panels \\ \(\eta_{dang}\) & 0.95 & - & Efficiency of battery charge \\ \(\eta_{ctrl}\) & 0.95 & - & Efficiency of motor controller \\ \(\eta_{dchang}\) & 0.95 & - & Efficiency of battery discharge \\ \(\eta_{part}\) & 0.9 & - & Efficiency of motor \\ \(\eta_{mpart}\) & 0.97 & - & Efficiency of MPPT \\ \(\eta_{ptr}\) & 0.85 & - & Efficiency of propeller \\ \(P_{ep}\) & 1.5 & [W] & Power of autopilot system \\ \(x_{1}\) & 3.1 & - & Airframe mass wingspan exponent \\ \(x_{2}\) & \(-\)0.25 & - & Airframe mass aspect ratio exponent \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The values of parameters correspond to the Mini-Phantom UAV design. Figure 5: Conceptual design progress of solar-powered UAV based on energy balance. The second part is the aerodynamic model, including the parameters that reflect the flight environment and aircraft lift and drag characteristics, which are mainly determined by the flight mission objectives. Mission parameters of Mini-Phantom UAV are shown in Table 2. In the aerodynamic model, the aircraft layout parameters are represented by aspect ratio and wingspan as input variables. The third part is the balance between the energy consumed by the aircraft and the solar energy captured, which connects the weight model with the aerodynamic model, forming a closed loop, through which the optimal aircraft layout can be obtained.
## 3 Estimation of Motion Parameters Based on the CCAE Method
### Implementation of the Proposed CCAE Method  
As described above, the velocity of the target can be estimated by the proposed CCAE method using only two echo signals. To estimate the acceleration or even higher order parameters, three or more echo signals are required. The flowchart of the proposed CCAE method is shown in Figure 3, including the following steps:  
1. The first UR or stretched signal is multiplied with the conjugate of the second UR or stretched signal. For acceleration estimation, the second echo signal is also multiplied with the third echo signal.
2. The FFT operation is performed on the multiplication results for energy accumulation.
3. Estimate the frequencies of the above FFT results.
4. The acceleration and velocity are calculated using the estimated frequencies according to Equations (23) and (24).  
Although the ACCF method [24; 25] also adopts the conjugate multiplication, it is basically different from the proposed CCAE method. In the ACCF method, first, pulse compression is applied to the two adjacent UR signals by the FFT operation, and then, a conjugate multiplication is performed with the pulse compression results. Then, for velocity estimation, the correlation result is transformed into the time domain by an IFFT operation. If we only estimate the velocity of the target by using two echo signals, the ACCF method requires the FFT (or IFFT) operation three times, while the proposed method only requires once. Besides, the ACCF method can be applied to only the UR signals, while the proposed CCAE method can be applied to both UR and stretched signals.
## 4 The Calculation of Reliability Based on Fuzzy Mathematics
### A Simplified Method for the Calculation of Reliability  
The performance function of SRHPC frame structures in the process of reliability calculation (Equation (17)) is a highly nonlinear implicit function of the design variable, so, if Equation (17) is used directly to calculate the reliability, there will be a large amount of computation and not easy to converge. Therefore, in this paper, the reliability calculation of the SRHPC frame structure was simplified and the performance function at different performance levels was changed into an explicit form. Finally, the form of the changed function is shown in Equation (35):  
\[f\left(u_{0},u_{p}\right)=u_{0}-u_{p}, \tag{35}\]  
where \(u_{p}\) is the explicit form of the random variable of seismic effect.  
After such a simplification, only the standard values and probability characteristics of random variables \(u_{0}\) and \(u_{p}\) could be obtained for calculating the reliability degree of SRHPC structure; the main steps are as follows:  
(1) Obtain the probability statistic characteristics of the structural resistance term and the seismic effect term, which mainly includes the distribution pattern, the mean value, and the variable coefficient. This paper assumes the resistance term and seismic effect term expressed in the inter-story drift form and their distribution type is lognormal distribution [29; 30].  
Calculate the standard values of the SRHPC frame structural seismic effect according to the fuzzy seismic response spectrum, then obtain the average values of the earthquake effect according to Step (1). The standard values of the SRHPC frame structural resistance term are the quantitative values of the performance index, which were obtained in Section 2.3.1 and shown in Table 3. Next, the mean value of the resistance item can be obtained by Step (1).
3. After obtaining the types of probability distribution, average and the variation coefficient of the resistance and seismic effect items, an explicit performance function was established, as shown in Equation (35). The fuzzy reliability degree of the frame structure under different performance levels can be calculated according to the improved Monte Carlo simulation method mentioned in Section 4.
# An Algorithm to Retrieve Precipitable Water Vapor from Sentinel-2 Data
## Materials and Methods
### Theoretical Basis of PWV Inversion  
Solar radiation passes through the atmosphere and is either partially absorbed or scattered by the atmosphere before reaching a satellite sensor. For a homogeneous Lambertian surface, if the proximity effect is not considered, the radiance received by a satellite sensor includes mainly the direct reflection of the surface and the solar radiation scattered by the atmosphere (path radiance) [25; 42; 43], which can be expressed by Equation (1).  
\[L_{Sensor}(\lambda)=L_{Sun}(\lambda)\tau(\lambda)\rho(\lambda)+L_{path}(\lambda) \tag{1}\]  
where \(\lambda\) is the wavelength, \(L_{Sensor}(\lambda)\) is the total radiance that reached the sensor, \(L_{Sun}(\lambda)\) is the above atmosphere solar radiance, \(\tau(\lambda)\) is the total transmittance, \(\rho(\lambda)\) is the ground reflectance, and \(L_{path}(\lambda)\) is the path radiance.  
Atmospheric transmittance is affected by gas components, such as carbon monoxide, carbon dioxide, ozone, methane, and water vapor. The amounts of carbon monoxide, carbon dioxide, and other gases are relatively fixed, while water vapor content changes significantly and is the main factor affecting transmittance. The relationship between atmospheric transmittance and water vapor content can be constructed, and PWV can be estimated through the transmittance calculated by remote sensing images. So, Equation (1) can be written as:  
\[\tau(\lambda)=\frac{L_{Sensor}(\lambda)-L_{path}(\lambda)}{L_{ground}(\lambda)} \tag{2}\]  
where \(L_{ground}=L_{Sun}(\lambda)\rho(\lambda)\) is the total ground reflected radiance.  
The transmittance ratio between the water vapor absorption channel and the atmospheric window channel is:  
\[\frac{\tau(\lambda_{1})}{\tau(\lambda_{2})}=\frac{L_{Sensor}(\lambda_{1})-L_{ path}(\lambda_{1})}{L_{ground}(\lambda_{1})}\times\frac{L_{ground}(\lambda_{2})}{L_{Sensor}( \lambda_{2})-L_{path}(\lambda_{2})} \tag{3}\]  
where \(\lambda_{1}\) is the water vapor absorption channel, and \(\lambda_{2}\) is the atmospheric window channel.  
Previous studies have shown that in the range of 865-1240 nm, surface reflectance is almost the same or linear with the wavelength [44]. Assuming that the reflectance and total ground reflected radiance of each channel are roughly equal, and that the transmittance of the atmospheric window channel is approximately equal to 1, Equation (3) can be simplified as follows:  
\[\tau(\lambda_{1})=\frac{L_{Sensor}(\lambda_{1})-L_{path}(\lambda_{1})}{L_{ Sensor}(\lambda_{2})-L_{path}(\lambda_{2})} \tag{4}\]  
Therefore, in this study, MODTRAN 5 was used to simulate the atmospheric radiative transfer process and obtain the path radiance, and the relationship between transmittance and water vapor was fitted to build the PWV estimation model. The spatial distribution map of PWV was obtained based on the radiance ratio of the water vapor absorption channel to that of the atmospheric window channel of Sentinel-2.
## 3 Results and Analyses
### The Quality Evaluation of Photoacoustic Image
The larger PSNR value indicates the better quality of the reconstructed image and the smaller error. SSIM evaluates the similarity between the reconstructed image and the original image from the perspectives of brightness, contrast, and structure. Its value range is [0, 1]. When the SSIM value is closer to 1, the quality of the reconstructed image is better. As shown in Table 4, the PSNR and SSIM values of the photoacoustic image obtained by the proposed method are both higher than those obtained by the other two methods. The quality evaluation data of photoacoustic images show that the photoacoustic image obtained based on the proposed method has fewer errors and higher fidelity. The photoacoustic image qualitative and quantitative evaluation results show that the photoacoustic image obtained based on the proposed method has higher quality and fidelity. This proves that the optimal sound speed field obtained by the proposed method is more accurate than that estimated by the other two methods, which is helpful for improving the performance of PAI.
# Human-AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM Era +
## 2 Background and Related Works
### RSA Services for People with VI  
RSA service is an emerging navigational aid for PVI [41]. The implementation of various RSA services differs in three key areas. (i) The communication medium between users and remote sighted assistants. Earlier prototypes used audio [8], images [2; 12], one-way video using wearable digital cameras [9; 42], or webcams [9], whereas recent ones use two-way video chat using smartphones [6; 7; 10; 43]. (ii) The instruction form. RSA services are based on texts [44], synthetic speech [8], natural conversation [6; 7; 10], or vibrotactile feedback [11; 45]). (iii) Localization techniques, for example, via GPS sensor, crowdsourcing images or videos [2; 19; 46; 47], fusing sensors [19], or using CV as discussed in the next subsection.  
Researchers have studied crowdsourced and paid RSA services. For crowdsourced RSA services (e.g., TapTapSee [5], BeMyEyes [6]), researchers concluded that they are feasible to tackle navigation challenges for PVI [48; 49]. However, potential issues in crowdsourced RSA services include the following: (i) users trust too much in subjective information provided by crowdworkers, and (ii) crowdworkers are not available at times [50]. Compared with crowdsourced RSA services, [PERSON] et al. [51] and [PERSON] et al. [1] reported that assistants of paid RSA services (e.g., Aira [7]) are trained in communication terminology and etiquette, ensuring that they do not provide subjective information. Furthermore, they are always available.