esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in Research from Nanoparticle to Nano-Assembly: A Review
## 5 Conclusions  
SA processes are ubiquitously observed in the nature around us in living and nonliving systems. By and large, the SA of NMs is driven by the attainment of a globally ordered system from a less-ordered state, via certain principles that are locally operational and regulated in the micro-macro systems. The tendency to attain the energetic minimum state, which is also the most stable existence of any system, is facilitated by several aspects of SA, including the nano-components of interest, the interactions at the surface level between the considered NPs, the reversibility of the processes involved, and the nature and chemical functionality of the immediate nano-environment, as well as the flexibility to re-orient via controllable mobility. This review presents a comprehensive understanding of the different types of SA techniques that are available, where we initially introduce the classification and then elaborately discuss a few case studies.  
In summary, this review focuses on two major approaches of obtaining SA of NMs, including the stimuli-independent and dependent methods. While we detailed the solvent evaporation and gravitational sedimentation methods as stimuli-independent approaches, the stimuli-dependent ones were further classified as physical and chemical stimuli-based techniques. The light, electric-field, magnetic-field, and temperature-driven SA of NMs were discussed under the umbrella of physical stimuli-based approaches. The solvent, acid-base, metal ion, biomacromolecule, and gas-induced SA of NMs were captured and discussed in the domain of chemical stimuli-based SA. While we present a broad overview of the different techniques and mechanistic understanding of each of these categories, it is important to note that the successful incorporation of SA approaches in semiconductor devices as well as microelectromechanical (MEMS) technologies is expected to become a reality with the advent of novel nano-engineering protocols that are being developed and refined incessantly. The research highlights captured in this review are expected to serve as a guiding tool for researchers in the field of nanotechnology, especially catering to longsighted applications related to lab-on-a-chip, lab-on-molecule, and related applications associated with the internet-of-things (IoT). The comprehensive coverage of the key highlights from the existing techniques for SA would foster further fundamental research in this direction, suitable for applications in the intersecting fields of physics, chemistry, and life sciences.  
S.C. received financial support from Science and Engineering Research Board (SERB) under State University Research Excellence (SUR/2022/001424), DST-FIST (SR-FST-CS-I-2021-219(C)) and SRM University AP, Andhra Pradesh internal research grant (SRMAP/URG/E&PP/2022-23/014).  
**Conflicts of Interest:** The authors declare no competing financial interest.
# Round-Trip Time Ranging to Wi-Fi Access Points Beats GNSS Localization
## 6 Inverse Bayesian Grid for Finding AP Locations  
In localizing the UE, RTT ranges to several APs with known positions are measured. These ranges are then used either in least-squares multilateration or in a Bayesian grid method for estimating the position of the UE. Generally speaking, the accuracy of the estimated location is inversely proportional to the square root of the number of APs that respond. Naturally, the distribution of APs also affects accuracy. For example, there will not be sufficient constraint if most APs are located in more or less the same direction as seen from the UE. More generally, if the APs lie more or less along a straight line there will be a two-way ambiguity (related by reflection about that line). The dependence of accuracy on geometry is similar to the \"dilution of precision\" in GNSS parlance [52].  
Indoors, distributions for APs that are good for satisfactory Wi-Fi coverage tend to be useful for localization also. Such distributions tend to have roughly the same density throughout, avoid dead zones and avoid clustering of APs. (Although things tend not to work quite as well near corners of the convex hull of the AP locations, since constraints there are not available from all directions). By contrast, in outdoor localization, many APs may be arranged in clusters inside buildings and may lie more or less to one side of the UE.  
The localization process can be \"inverted\" and the positions of APs obtained from ranges measured from known positions of the UE. RTT ranging data for this purpose can be collected while walking around with the UE in hand. For such ranging data to be useful, there must be a way of independently locating the UE during this calibration step. This location information can be based on prominent landmarks, such as intersections of paths, corners of buildings, and so on (with a way of marking the times at which the UE passes them).  
The method of locating an AP is very similar to that used for localization of the UE in that a Bayesian grid of probabilities of possible AP position is updated using Bayes' rule based on a suitable observation model.
# Deep-cARe: Projection-Based Home Care Augmented Reality System with Deep Learning for Elderly
## 3 Material and Methods
### Deep-Care System Implementation
#### 3.1.3 PAR Module  
There are basic technologies to efficiently provide Deep-cARe system living support applications in the real environment. First, the space information is reconstructed in 3D through color and depth image. The optimal plane that can be easily and conveniently used by the elderly is extracted by analyzing the reconstructed 3D space. The optimal plane extracted through projection is provided for the information and application that are meaningful to the user.  
1. 3D space reconstruction To present dynamic information to the elderly, a 3D space should be constructed from the real space. To receive space information of an actual environment and reconstruct a 3D map, custom-made hardware, and map-construction technology based on a 3D point cloud are applied. A feature-matching method, which extracts features from the input images and calculates the pose by comparing them to the features of the previous frame, is applied. The 360\({}^{\circ}\) surrounding space information is obtained from an RGB-depth camera by setting the servo motor rotation of the pan direction to 10\({}^{\circ}\) units. Features are detected using the feature detection algorithm through the acquired color and depth input frames. Matching is then performed by creating  
Figure 3: Deep-cARe system software component module flow diagram.  
feature descriptors, and the FAST [25] and BRISK [26] algorithms are applied to increase computational speed. The final pose of the current frame is obtained by using the RANdom SAmple Consensus(RANSAC) algorithm [27] on the 3D world coordinate point value of the previous depth image and the two-dimensional (2D) matching points of the current color image. The point cloud information of the current frame is then rotated and translated according to 3D world coordinates. 3D space map reconstruction result as shown in Figure 4a.
2. Optimal plane selection In this step, the region that can be projected is extracted to recommend the optimal projection region in the reconstructed 3D map to the user. Depth segmentation is first performed using depth information of the reconstructed 3D map. The plane area is detected by executing the randomized Hough transform algorithm [28] based on the segmented areas. Initially, in the hypothesis step, a model that satisfies the sample data is created by randomly selecting three points at certain distances from the detected point. Subsequently, in the verification step, the plane model is evaluated after the inliers are extracted. If the number of inliers is greater than that of the existing plane, the plane of the largest area is detected by updating a new model. The final projection location is selected by minimizing the projection distortion using the normal vector of the maximum area. Meaningful plane information is extracted for projection from the 3D environment point cloud through this plane detection process. Final optimal planes are represented red boxes as shown in Figure 4b.
## 4 Discussion
### Limitations  
Although the PCWA-ResCNN model performs well in retrieving water quality parameters of the Yellow River in Ningxia, there are still some limitations to the present study. First of all, for the study area referenced herein, the sampling points were far from one another and the Yellow River flow rate was fast. Even if the prediction accuracy was high, it would be easy for the important characteristics of water quality between the sampling points to be lost, which would affect the overall evaluation of water quality. Due to the wide water areas such as lakes and reservoirs, the water flow is usually slow or static; therefore, the PCWA-ResCNN model has great application potential in these waters. Secondly, the currently available satellite data are limited, and remote sensing data with a high spatial resolution, high temporal resolution, and wide spectral range are relatively scarce [38,39]. The acquisition of remote sensing data usually requires a certain time interval, and the required high temporal resolution data may not be obtained in time, which affects the real-time and effective inversion of water quality parameters. The impact of climate and environmental factors (such as rainfall, temperature change, and pollution source change) is difficult to fully capture and quantify in the model, which may lead to differences in performance under different climatic conditions [40].  
In addition, the PCWA-ResCNN model has a high computational complexity and high resource requirements, which increases the difficulty of deployment and application, especially in resource-constrained environments [41]. The \"black box\" nature of deep learning models limits their interpretability, posing challenges to understanding the mechanisms of water quality change and developing management strategies [42]. Lastly, the adaptability of the model in different regions and time periods needs to be further verified. Differences in geographical and hydrological characteristics in different regions may lead to inconsistent model performance.
# SENTINEL: A Secure and Efficient Authentication Framework for Unmanned Aerial Vehicles
## 7 Experiments
### Evaluation--Mutual Authentication and Key Agreement Protocol  
To evaluate the performance of the mutual authentication and key agreement protocol in SENTINEL, we measured the message size and the execution time of the entire procedure described in Figure 3. We also performed the same measurements on the \"TLS for IoT\" protocol with client authentication using an X.509 v3 certificate. For both systems, we conducted the same set of experiments 100 times and took the average. Table 3 summarizes the results. In addition, Figure 7(a) shows the distributions of the execution times for the mutual authentication and key agreement protocols, respectively, in SENTINEL and \"TLS for IoT.\"  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **SENTINEL** & **X.509** & **X.509 (gzip)** \\ \hline
**Size (bytes)** & 186 & 682 & 654 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation results of the size of certificates.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & & **SENTINEL** & **TLS for IoT** \\ \hline
**Time (ms)** & **Avg.** & 198.06 & 359.66 \\  & **Std.** & 39.25 & 50.80 \\ \hline
**Message size (bytes)** & **Send** & 426 & 708 \\  & **Receive** & 192 & 825 \\  & **Total** & 618 & 1533 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation results of mutual authentication and key agreement protocol.  
For SENTINEL, the average execution time was 198.06 milliseconds with the standard deviations of 39.25 milliseconds, and the total number of bytes sent and received was 618 bytes. For the \"TLS for IoT\" protocol with client authentication, on the other hand, the average execution time was 359.66 milliseconds with the standard deviations of 50.80 milliseconds, and the total number of bytes sent and received was 1,533 bytes. In comparison, the execution time of SENTINEL was 161.60 milliseconds faster than the \"TLS for IoT\" protocol on average. Furthermore, the total number of bytes sent and received in SENTINEL was 915 bytes smaller than the \"TLS for IoT\" protocol.
## 4 Implementation Method for Improving Accuracy  
### Error Analysis  
In practical applications, sample resolution and voltage ripple cause sampling errors, which affect the calculated result of insulation resistance. With the \(v_{11}\) of the \(M_{1}\) phase as an example, \(\Delta v_{11\_1}\), \(\Delta v_{11\_2}\), and \(\Delta v_{11\_3}\) are the sampling errors of \(v_{11\_1}\), \(v_{11\_2}\), and \(v_{11\_3}\), respectively. \(E_{1R}\) is the actual value of \(E_{1}\). \(E_{1C}\) is the measured value of \(E_{1}\). Considering the sampling error, the expression of \(E_{1R}\) and \(E_{1C}\) according to Equation (6) is shown as  
\[\left\{\begin{array}{l}E_{1R}=\frac{v_{11\_2}-v_{11\_3}}{v_{11\_1\_1\_1\_2}} \\ E_{1C}=\frac{(v_{11\_2}+\Delta v_{11\_2})-(v_{11\_3}+\Delta v_{11\_3})}{(v_{11 \_1}+\Delta v_{11\_1})-(v_{11\_2}+\Delta v_{11\_2})}\end{array}\right. \tag{10}\]  
\(\Delta E_{1}\) is the error of \(E_{1}\) and is defined as follows:  
\[\Delta E_{1}=E_{1C}-E_{1R} \tag{11}\]  
By substituting Equation (11) into Equation (10), \(\Delta E_{1}\) can be rewritten as  
\[\Delta E_{1}=\frac{(\Delta v_{11\_2}-\Delta v_{11\_3})-E_{1R}(\Delta v_{11\_1 \_1}-\Delta v_{11\_2})}{(v_{11\_1\_1}-v_{11\_2})+(\Delta v_{11\_1}-\Delta v_{11 \_2})} \tag{12}\]  
where \(\Delta v_{11\_1}\), \(\Delta v_{11\_2}\), and \(\Delta v_{11\_3}\) are uncontrollable components and \(E_{1R}\) is a fixed value. (\(v_{11\_1}\)-\(v_{11\_2}\)) is inversely proportional to \(\Delta E_{1}\). Similarly, \(v_{12}\), \(v_{21}\), and \(v_{22}\) can result in the same conclusion. In the application, the larger \(\Delta t_{11}\), \(\Delta t_{12}\), \(\Delta t_{21}\), and \(\Delta t_{22}\) are, the smaller the result error is. The larger the difference between the \(R_{a}\) and \(R_{b}\) is, the smaller the result error is. The higher the \(v_{\text{dc}}\) is, the smaller the result error is.
# Efficient and Safe Strategies for Intersection Management: A Review
## 3 Efficiency
### Jam Detection and Congestion Avoidance
#### 3.1.1 Jam Detection
The widespread use of car GPSs ensures that we can get traffic data and detect blocking information. Using low-cost GPS equipment to monitor traffic conditions in real-time, the paper [29] proposes a new real-time traffic evaluation coupling method, which can successfully match more than 98% of GPS data with service routes. The traffic network evaluation result shows that the method has reasonable practicability and can obtain more accurate vehicle information, so that we can timely use the algorithm to avoid congestion, thus alleviating traffic flow. With the development of smartphones, the use of GPS on smartphones has become more widespread. Using the collective intelligence recorded by smartphones, a sensor data platform is proposed for sensing, detecting, and visualizing road surface roughness and driving behavior [30]. The authors collected real driving records from different roads in Hyderabad and India, comparing the significant differences in visual conditions in terms of road conditions and driving behavior. The sensor is selected as the detection tool in this paper, due to the low judgment accuracy of the current image processing technology and the fact that GPS loading is likely to reveal the user's location privacy, thus causing malicious attack.
_Article_  
Application of the DC Offset Cancellation Method  
and S Transform to Gerbox Fault Diagnosis  
[PERSON] 1,*, [PERSON] 1, [PERSON] 2 and [PERSON] 1  
1 The management department of Mechanical Engineering College, Shijiazhuang 050003, China; [EMAIL_ADDRESS] (J.Z.); [EMAIL_ADDRESS] (L.W.)
2 Public Enterprise Elektroprivreda BiH, Coal Mines Kreka, Tuzla 75000, Bosnia and Herzegovina; [EMAIL_ADDRESS] or [EMAIL_ADDRESS]
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-186-0329-2514  
Academic Editor: [PERSON]  
Received: 31 December 2016; Accepted: 9 February 2017; Published: 20 February 2017  
In this paper, the direct current (DC) offset cancellation and S transform-based diagnosis method is verified using three case studies. For DC offset cancellation, correlated kurtosis (CK) is used instead of the cross-correlation coefficient in order to determine the optimal iteration number. Compared to the cross-correlation coefficient, CK enhances the DC offset cancellation ability enormously because of its excellent periodic impulse signal detection ability. Here, it has been proven experimentally that it can effectively diagnose the implanted bearing fault. However, the proposed method is less effective in the case of simultaneously present bearing and gear faults, especially for extremely weak bearing faults. In this circumstance, the iteration number of DC offset cancellation is determined directly by the high-speed shaft gear mesh frequency order. For the planetary gearbox, the application of the proposed method differs from the fixed-axis gearbox, because of its complex structure. For those small fault frequency parts, such as planet gear and ring gear, the DC offset cancellation's ability is less effective than for the fixed-axis gearbox. In these studies, the S transform is used to display the time-frequency characteristics of the DC offset cancellation processed results; the performances are evaluated, and the discussions are given. The fault information can be more easily observed in the time-frequency contour than the frequency domain.  
planetary gearbox; fault diagnosis; predictive maintenance; deterministic component cancellation; S transform
## 2 Research Status of Automatic Docking of Fluid Loading and Offloading Arms  
Scholars and enterprises at home and abroad have carried out research related to the docking of fluid pipes, such as [PERSON]'s automatic docking hose-type aerial refueling [4], which installs laser or infrared receiving devices at the oil pipe nozzles and refueling ports connected to an orientation controller. The electronic control system for automatic docking of marine loading and offloading arms invented by the No716 Research Institute of China Shipbuilding Industry Corporation uses a combination of various sensors and adopts the dual positioning mode to realize the automatic docking of marine loading and offloading arms [5]. Lianyungang Oceanic Fluid Handling Equipment Co., Ltd. has published a patent for an automatic docking system suitable for terminal oil handling arms and ship flanges [6]. It is mainly monitored in real time by a CCD camera to achieve precise docking of connectors and target flanges. The patent [7] of automatic alignment fluid loading and offloading arm and its control system applied by Lianyungang Hong Ze Machinery Equipment Co., Ltd. discloses a loading and offloading arm structure and automatic alignment system on a tank car. It can realize automatic identification and servo control of the loading and offloading arm and can also realize the remote operation of the fluid loading and offloading arm through human-computer interaction. From the research results disclosed above, it can be seen that, in the application cases in aviation, shipping, and land transportation, the focus is on the realization of the docking automation process, and the working efficiency of the automatic docking system is not high.  
An automatic docking system for a small crane tube used in the lower part of a tank car has not been studied in the literature. The purpose of this project is to replace the manual operation with a system whose efficiency is higher than or at least the same as that of manual operation. The diameter of the blue port is much smaller than that of the marine loading and offloading arm interface and the tank port of the tank car, so its relative positioning accuracy needs to be high.
## 7 Conclusions and Future Direction  
In this paper, we proposed ASAART for wireless sensor networks to address the limitations of the SSR and SHR protocols. We integrated autonomous self-awareness and adaptive features into the SHR protocol to make it more resilient to faults and errors and energy efficiency for efficient and reliable routing of sensor data in wireless sensor networks. We achieved this by combining both continuous and prioritized slotted back-off delay, and multiple randomize function techniques to obtain local and global network state information for faster routing path formation and speedy convergence to the minimum routing path. We proposed a route repair technique for reliable transmission of sensor data in the presence of permanent and transient node failure rates, and efficient adaptation to simultaneous network topology changes. We conducted extensive simulations under five different scenarios. The simulation results showed that the ASAART technique performed better in terms of high resiliency to errors and failure and better routing performance and energy consumption compared with the SSR and SHR protocols in the presence of transient and permanent node failure rates and in a highly congested, faulty, and scalable sensor network. The proposed approach is energy efficient because it consumes less energy compared to the SSR and SHR protocols. Future research will address the issues of node mobility. In this case, we will integrate both local and global self-awareness and adaptive features for efficient and reliable cost table information update.
## 3 Results and Discussion
### Stability Test Results and Analysis of Sowing Transverse Grain Spacing  
Figure 7 depicts that as the disturbance signal increases, the lateral sowing grain distance tends to increase along with an increase in dispersion degree regardless of whether the machine uses the position compensation function. However, when the dynamic compensation function is implemented, the coefficient of variation of the sowing transverse grain spacing is reduced by an average of 9.16% compared to when it is not used. Under the three working conditions, NY/T 1628-2008 \"Operation Quality of Corn No-Tillage Planter\" requires that the maximum deviation of the row spacing within the same sowing range should not exceed 4 cm, meaning that the transverse grain spacing of sowing seeds should all fall within a range of 2 cm, while the row spacing deviation will not exceed 4 cm when more than two rows are used. The use of the dynamic compensation function satisfies these requirements.  
During testing, the servo electric push rod's range was limited to 100 mm, resulting in a large overall structure with installation limitations. However, in real-world field environments, the offset of the seed discharge pipe outlet is often less than 100 mm. Thus, reducing the measuring range of the servo electric push rod can result in a smaller overall structure that can be installed on seeding machines to achieve multi-row sowing.  
While adding a device to the existing sowing machine will inevitably increase costs, it can maintain optimal plant and row spacing, thereby providing better growth conditions for corn seeds and increasing production. In further research and planning, a balance between economic and social benefits and cost should be sought.