esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adaptive Delay-Tolerant Routing Algorithm for Data Transmission in Opportunistic Social Networks
## 3 System Model Design
### Computation of Transmission Evaluation Value Based on CMFI  
It is well known that most routing algorithms in opportunistic networks tackle the issue of data transmission from the perspective of deterministic problems, which can be formalized as linear or nonlinear mathematical expression. However, the relationship between data transmission and two-factor (energy and cache) is actual a type of fuzzy uncertain mapping, while should be defined as the level of membership in a fuzzy system. In this section, we consider the issue as the membership relationship between data-transmission capability of nodes and their two-factor fuzzy input (the remaining cache and remaining energy). The Mamdani fuzzy inference system [13; 23] will be regarded as the model used in the proposed algorithm due to its high extensibility and applicability, and it consists of three components: the fuzzy component, the fuzzy inference component, and the De-Blurring component [13; 24; 25]. We introduce the rough process of each component in this section and the implement details of CMFI will be described in Appendix A.
# Considering Uncertainties of Key Performance Indicators in Wind Turbine Operation
## 4 Method and Data
### Data Resolution  
The effect of different data resolutions was assessed based on 1 Hz data from the WInD-Pool for a 5 MW offshore WT. The dataset comprises of 4,727,951 entries, or 55 days worth, of high-resolution operating data. Data of additional WT was available and used to validate the results. After filtering and merging, 98.5% of the data proved to be good quality, and data gaps were filled by linear interpolation to obtain a continuous and complete time series. This was necessary to investigate the effects of resolution changes without being affected by data gaps and resulting differently weighted data points.  
The 1 Hz data was then resampled to different resolutions up to hourly values. Special requirements were also considered, such as for vector functions to average angular values (e.g., wind direction) [30; 52]. This averaging process us d the 1 Hz data for each resolution so that sound statistics (min, max, std.) were available. All considered KPIs were then calculated for each resolution to assess sensitivities and uncertainties.
###### Abstract  
Lead (Pb) contamination was evaluated in 24 contaminated soils and sediments samples, representative of areas affected by mining, agricultural, commercial and residential activity, during the rainy and dry season. Pseudo-total concentration in soils (15.7-527.2 mg kg\({}^{-1}\)) and sediments (16.3-4273.3 mg kg\({}^{-1}\)) was determined and protocols were developed to analyze its chemical form, potential mobility and bioavailability. Five geochemical phases (exchangeable fraction, carbonate fraction, Fe/Mn oxide fraction, organic fraction and residual fraction) were obtained for the determination of Pb speciation and mobility using a modified Tessier sequential extraction procedure. The predominant fraction was the Fe/Mn oxide bound (both for soils and for sediments). However, their calculated mobility factors were 26% and 28%, respectively, representing significant risk to the environment. Geo accumulation values ranged from \(-\)0.7 to 4.4 (\"Unpolluted\"-\"Highly to extremely polluted\") for soils and from \(-\)0.6 to 7.4 (\"Unpolluted\"-\"Extremely polluted\") for sediments, suggesting the influence of human activity on the environment, mainly at sites located in the vicinity of the mine. Enrichment Factor values ranged from 2.1 to 87.8 for soils and from 2.5 to 698.7 for sediments (\"Moderated enrichment\"-\"Extremely high enrichment\"); values above 1.5 suggesting anthropogenic origin, thus representing a risk for biological organisms present in freshwater. In summary, these environmental indicators demonstrated than even in sites with low Pb concentration, contamination was observed, thus highlighting the need for continued monitoring due to the potential for significant public health risks.  
soil; sediment; mobility factor; geo accumulation index; enrichment factor; speciation; environmental indicators; risk; lead Article
# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 2 Routing on Wireless Sensor Networks  
The main goal of routing in WSN is to carry out data communication when trying at the same time to prolong the network lifetime and provide high quality of service during data delivery [12]. Based on the network structure, routing on WSN can be classified as data centric based routing, location based routing, group-based routing, or as hierarchical based routing.
## 4 Results and Discussion
### Evaluation of Conveyor Belt Misalignment Detection and Correction  
Due to the unmodifiable structure of the industrial BECS, all experiments were conducted using the actual conveyor belt system for the line detections and a simulated model to test the misalignment correction module. A top-mounted camera captured the conveyor's edge regions. OpenCV was employed for image processing, combining Canny edge detection and Hough line transformation to detect both left and right edge lines.  
To improve detection robustness under industrial conditions (such as varying lighting or machine-induced vibration), pre-processing techniques, including Gaussian blur and region of interest (ROI) cropping, were implemented.  
Figures 15 and 16 demonstrate the visual output of the misalignment detection algorithm under two actual conditions. Both left and right edges are successfully detected in the left image, and they are marked by green vertical lines with on-screen indicators \"Left Detected\" and \"Right Detected\".  
In contrast, the right image shows a misalignment event where the left edge of the conveyor is no longer visible within the frame. The system accurately detects this event and generates a red warning message, \"Left not detected\", which triggers the left-side motor for correction. This qualitative feedback loop is critical in maintaining conveyor centering without human intervention.  
To quantify the system's performance, four experimental conditions were defined:  
1. Normal Lighting (Stable Environment).
2. Low Lighting (Dim Factory Conditions).
3. Normal Lighting with Induced Vibration (Simulated Machine Activity).
4. Low Lighting with Vibration (Worst-Case Industrial Scenario).  
Despite minor degradations, the system maintained an average accuracy above 90% in all conditions, which is considered highly reliable for industrial operations. The error cases were primarily due to transient motion blur or temporary occlusion of the line markers, which were mitigated by frame-by-frame recovery logic.  
The image processing time per frame was measured at approximately 0.028 s, and the mechanical response time for activating the stepper motors and correcting the conveyor alignment ranged between 0.03 and 0.05 s depending on the severity of the belt displacement. The misalignment detection system demonstrated high sensitivity, rapid response time, and robust performance under real-world constraints.
# 5G/B5G Internet of Things MIMO Antenna Design
## 1 Introduction  
The exponential growth in data rates is an important aspect of modern research in mobile Internet of Things (IoT) communication. Substantial work has been done to improve the data rates and to fulfill ever-increasing requirements, such as 4G, 4G Long Term Evolution (LTE), and 5G [1, 2, 3]. However, data-hungry devices in IoT still need more and more data rates. Currently, 5G bands at Sub-6 GHz and mm-wave bands have been officially assigned by the Federal Communication Commission (FCC) to be used for 5G mobile communication to improve data rates [4]. Moreover, multiple-input multiple-out (MIMO) can further enhance data rates (channel capacity) by increasing the number of antennas [5, 6, 7, 8, 9].  
One possible IoT-based communication scenario is shown in Figure 1, which demonstrates that in IoT communication, microwave Sub-6 GHz and mm-wave bands will be combined to access high data rates in large geographical coverage areas [10, 11]. Therefore, in this work, the implementation of a Sub-6 and mm-wave MIMO antenna design is realized by utilizing the concept of a shared aperture antenna, as shown in Figure 1. The proposed design operates at the Sub-6 GHz band with wider antenna beamwidth and at the mm-wave band with a sharp directive antenna beam. The sharp directive beam with high gain is important in sending and receiving high-frequency signals at mm-wave bands. Additionally, high gain is required to mitigate high path loss at mm-wave bands [12, 13].  
Shared aperture or common aperture antennas have attracted considerable attention and interest recently. They have been proposed to operate at microwave Sub-6 GHz and mm-wave bands to satisfy the requirements of compact size, operating band, and directive radiation patterns at both bands [14, 15, 16, 17]. To date, few designs have been presented that can simultaneously satisfy those requirements. In this article, a simple method for designing
## 5 Results of the Study
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **Optimizer** & **test\_mse** & **test\_mse** & **test\_R\({}^{2}\)** & **Model** & **Optimizer** & **test\_t** & **test\_R\({}^{2}\)** \\ \hline  & adamax & 407 & 310 & 0.996 & & Adam & 346 & 255 & 0.9971 \\  & Adam & 409 & 295 & 0.996 & & Nadam & 376 & 275 & 0.9966 \\  & Nadam & 457 & 361 & 0.9949 & & adamax & 423 & 320 & 0.9956 \\  & RMSprop & 490 & 368 & 0.9941 & & RMSprop & 569 & 443 & 0.9923 \\  & SGDTrue & 788 & 604 & 0.9844 & & Bidirectional(LSTM(50, Alpha = 0.05)) & SGDTrue & 1044 & 781 & 0.9706 \\  & adagrad & 2147 & 1657 & 0.8749 & & model.add(Deasel(1)) & SGD & 1617 & 1272 & 0.9522 \\  & SGD & 1479 & 1148 & 0.9423 & & adagrad & 2356 & 1826 & 0.8345 \\  & adadelta & 3501 & 2709 & 0.1778 & & RMSprop & 4743 & 3693 & -1.188 \\  & Ftt & 4475 & 3706 & -1.1809 & & addelta & 4762 & 3745 & -2.6000 \\  & SGDFalse & 25,001 & 24,144 & -3.1014 & & Ftt & 5080 & 4236 & -3.9808 \\  & RMSprop & error & error & error & & SGDFalse & 36,371 & 35,825 & -14.097 \\ \hline  & Adam & 533 & 386 & 0.9928 & & Nadam & 584 & 422 & 0.9914 \\  & adamax & 551 & 395 & 0.9924 & & RMSprop & 625 & 495 & 0.9901 \\  & Nadam & 583 & 475 & 0.9914 & & Adam & 660 & 479 & 0.9889 \\  & RMSprop & 648 & 484 & 0.9893 & & RDspprop & 669 & 524 & 0.9882 \\  & RMSprop & 718 & 601 & 0.9868 & & adamax & 1082 & 839 & 0.97 \\  & SGDTrue & 942 & 742 & 0.9783 & & bidirectional(LSTM(50, activation = “sigmoid” & SGDTrue & 7147 & 6101 & -323.77 \\  & SGDTrue & 1796 & 1359 & 0.8969 & & adagrad & 6490 & 5150 & -19.000 \\  & adagrad & 4410 & 3545 & -10.0068 & & SGD & 6525 & 5222 & -20.221 \\  & adadelta & 5906 & 4933 & -14.449 & & Ftt & 6525 & 5189 & -3.10 \\  & Frtl & 5809 & 4667 & -25.009 & & addelta & 9916 & 7837 & -7.7048 \\  & SGDFalse & 32,292 & 31,633 & -7.1011 & & SGDFalse & 18,930 & 18,878 & -7.3997 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._data is divided into several subsets and the performance of the machine learning model is evaluated. Dividing the data into training and test data serves this purpose. We also tried to predict the next hour using the last 24 h of data for six columns. In fact, we created a separate dataset for each prediction and cross-validated it. We also used the sliding window method by shifting the data we use in each prediction by one hour and using the next 24 h of values.
## 3 Uncertainty Modeling Techniques
### Possibilistic Methods
#### 3.2.1 Possibility Theory  
Possibility theory is grounded in fuzzy logic, extending it to handle uncertainty and partial truth. It is characterized by possibility and necessity measures that express how feasible a particular event or condition is within a given context. This theory is applied extensively in decision-making processes, especially under conditions of uncertainty and vague information.  
Possibility theory has gained traction in power systems, particularly for handling uncertainty and imprecise information. In [36], possibility theory is employed alongside fuzzy numbers to tackle power flow problems that arise due to incomplete, inaccurate, or redundant data. This approach allows for the modeling of nested information--common in electrical networks due to varying levels of measurement accuracy--by formalizing the problem as an AC fuzzy power flow model. The method efficiently solves nonlinear fuzzy systems and demonstrates robustness and accuracy in benchmark simulations, making it well suited for real-world applications with uncertain data. In the domain of risk analysis, possibility theory is integrated with Failure Mode and Effects Analysis (FMEA) in [37] to improve the prioritization of failure modes. Traditional FMEA approaches struggle with overlapping risk membership functions, but by utilizing fuzzy similarity values and possibility theory, the method ensures that failure modes with similar risk levels are grouped more naturally without requiring arbitrary defuzzification. This integration enhances robustness and conformance in risk assessments, as demonstrated through case studies.  
The \(\alpha\)-cut method is one of the possibilistic methods, which can be used to calculate the possibility distribution of the possibilistic output variable (\(\overset{\sim}{Y}\)) of uncertain input variables (\(\overset{\sim}{X}\)). \(\overset{\sim}{Y}\) can be represented in the form of a multi-variable function as follows:  
\[\overset{\sim}{Y}=h\left(\overset{\sim}{X}_{1},\overset{\sim}{X}_{2},\ldots, \overset{\sim}{X}_{N}\right) \tag{41}\]  
When the possibility distributions of the uncertain input variables \(\overset{\sim}{X}\) are known, the possibility distribution of \(\overset{\sim}{Y}\) can be determined using the \(\alpha\)-cut method. For a given input variable, the \(\alpha\)-cut of \(\overset{\sim}{X}\) is determined as follows:  
\[A^{\alpha}=\left\{x\in U\mid\pi_{\overset{\sim}{X}}(x)\ \geq\ \alpha,\ 0\ \leq\ \alpha\ \leq\ 1\right\} \tag{42}\]  
\[A^{\alpha}=\left[A^{\alpha},\overline{A}^{\alpha}\right] \tag{43}\]  
Here, \(U\) represents the universe of discourse for \(\overset{\sim}{X}\), or the range of its possible values, and \(\underline{A}^{\alpha}\) and \(\overline{A}^{\alpha}\) denote the lower and upper bounds of \(A^{\alpha}\), respectively. In Figure 5, a typical \(\alpha\)-cut for a trapezoidal membership function is illustrated.
## 2 Background and Pseudo-Doppler Principles  
### Real and Virtual Doppler Effect  
Because most of the analyses involve circular geometry, it is useful to proceed in those terms. Accordingly, visualize an antenna element at position \"\(n\)\" on a circular locus having radius \(R\) as in Figure 1, with a plane wave incident on it from a point source \"P\" at distance \(L\) from the center of the circle, in the far field. At the antenna element the phase of the incident plane wave relative to that at the source is given by  
\[\varphi_{n}=\frac{2\pi}{\lambda}(L-R\cos(\theta-\theta_{n})\sin\phi) \tag{1}\]  
Next, imagine that the antenna is moving along the circular locus with tangential velocity, \(v\), in the direction of the colored arrow. With the radius remaining constant at \(R\), this velocity involves only the change in azimuth angle \(\theta\) with time, as \(v=Rd\theta/dt\). The corresponding change in phase at the antenna is derived by applying the chain rule as  
\[\frac{d\varphi_{n}}{dt}=\frac{2\pi v}{\lambda}(\sin(\theta-\theta_{n})\sin\phi) \tag{2}\]  
where \(f_{Doppler}=v/\lambda\) is recognized as the Doppler shift frequency due to the tangential motion at velocity \(v\). This frequency is imposed upon the signal received at the antenna and if its angular position varies uniformly in time as \(\theta(t)\), it results in a sinusoidal frequency modulation of the received signal with a deviation equal to \(f_{Doppler}\) (when elevation angle is \(\varphi=\pi/2\)) and phase corresponding to the azimuth direction of arrival of the plane wave. This is a first hint that spatial information about a received signal can be determined in frequency domain. This phenomenon is sometimes used in radio azimuth direction-finding applications.  
The motion of the antenna does not have to be real--it can be emulated using several antennas spaced at intervals of \(d\) around the circular locus and switching their outputs to the analysis receiver at intervals of \(\tau=d\)/\(v\). It then appears to the analysis receiver that it is sampling the output of one antenna moving around the locus with velocity \(v\) at intervals of \(\tau\), because it observes the same Doppler shift, which is actually a pseudo-Doppler shift as there is no real motion involved.  
In the present application any actual motion of the source or the receiving antennas will be assumed to be 0, and the source of the OAM signals will be positioned at elevation angle \(\varphi=0\) to keep the analysis simple. The reference phase can be taken at the center of the circle in the far field by setting \(L=0\). The only relevant phase shifts will then be relative phases between two or more antenna elements on the same locus.
# Long-Distance Shield Tunnelling Performance Prediction Based on Informer
## 5 Conclusions  
The LSPP model proposed in this study is designed to provide an accurate prediction of the performance of the shield tunnelling over long distances. The model draws on the structural advantages of the Informer model in long-range sequence prediction and combines the specific context of engineering practice, which provides sound theoretical support and practical guidance for construction decisions.  
With the help of the construction data of the interval of Nanjing Line 5, Wenjing Road Station-Chalukou Station, the performance of the proposed LSPP is compared with that of the LSTM and GRU models for the prediction of the penetration. The conclusions can be summarized as follows: First, LSPP can achieve accurate prediction of shield performance over long distances. The performance indexes are excellent when the length of the prediction sequence reaches six rings, where R\({}^{2}\) reaches 0.82 and the RMSE is only 2.13 mm/rev. Second, compared with the LSTM and GRU models, the proposed LSPP model shows the smallest decrease in performance and the best prediction accuracy with the increase in prediction sequences. Finally, LSPP has better adaptation to different formations in the case selected for this study.  
Despite the excellent performance of the LSPP, due to the complexity of the actual construction conditions and the large fluctuation of the real penetration, the model's prediction accuracy still needs to be improved in some extreme cases. In this study, we only focused on a single tunnel project, and the adaptability of LSPP under different geological conditions lacks extensive validation. Future research can improve the applicability and prediction accuracy of the model by increasing the data samples under different tunnels and strata, integrating more construction variables, and describing the actual construction conditions from multiple dimensions, so that it can better meet the needs of actual projects.  
Conceptualization, M.H. and P.C.; Methodology, M.H. and P.C.; Software, P.C.; Validation, M.H.; Formal analysis, M.H. and P.C.; Investigation, M.H. and P.C.; Resources, M.H. and P.C.; Data curation, P.C.; Writing--original draft, P.C.; Writing--review & editing, M.H. and P.C.; Visualization, M.H. and P.C.; Supervision, M.H.; Project administration, M.H.; Funding acquisition, M.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data in the case study are not publicly available due to the confidentiality requirement of the project.  
The authors declare no conflict of interest.
# A New Parameter-Free Slope Unit Division Method That Integrates Terrain Factors
## 5 Conclusions  
A new slope unit definition and extraction method (TFPF-SU) for regional landslide analysis is proposed. On the basis of the principle of consistency between the slope direction and curvature, a slope unit is defined as a continuous and uniform closed area with no abrupt change in slope direction. To assess the accuracy and efficiency of the developed TFPF-SU method, Dongchuan District, Yunnan Province, China, was selected as the research area, and the proposed method was compared with a commonly used hydrological analysis method. The following conclusions can be drawn from this study:  
(1) A new method for extracting slope units (TFPF-SU) was proposed by integrating terrain parameters such as slope, aspect, and curvature. This method dynamically selects seed points and uses region growing based on terrain similarity, combined with a stack-structured recursive marking process. This approach makes the slope unit subdivision more flexible and detailed, ensuring unit continuity and completeness.  
(2) Through qualitative and quantitative analyses, the effectiveness of the TFPF-SU method was validated, and a detailed comparative study with the hydrological analysis method was conducted. The results show that the TFPF-SU method outperforms the hydrological method in terms of structural rationality and terrain authenticity of the slope units. Compared to the hydrological method, the slope units generated by TFPF-SU are more uniform and better suited for landslide analysis scenarios.  
Conceptualization, J.F.; data curation, P.L. and K.L.; formal analysis, P.L. and Y.D.; funding acquisition, J.F. and Y.Z.; investigation, Y.D.; methodology, P.L.; project administration, J.F.; resources, P.L., K.L. and Y.Z.; software, P.L. and Y.D.; supervision, J.F.; validation, P.L.; visualization, K.L.; writing--original draft, P.L. and J.F.; writing--review and editing, P.L., J.F. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 42171413); a grant from State Key Laboratory of Resources and Environmental Information System; and the National Key Research and Development Program (Grant No. 2021 XJKK0303).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author (The data are not publicly available due to restrictions by the data collector and provider).  
We would like to thank the China Aero Geophysical Survey and Remote Sensing Center for Natural Resources for providing remote sensing images and DEM data of the Dongchuan District. We also thank the editors and reviewers for their constructive comments and suggestions.  
The authors declare no conflicts of interest.
# Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
## 4 Design Issues in Gait, Balance and RoM Wearable Systems
### Sensor-to-Segment Alignment  
After sensor locations are selected, another problem is known as sensor-to-segment alignment. Sensor-to-segment alignment is known as the orientation of each sensor relative to the assigned segment previously selected. A study indicated that the position of the sensor relative to the segment is usually far less important for obtaining valid segment orientations than the sensor-to-segment alignment [85; 86]. Calibration procedures to address this problem have been proposed, such as static pose calibration, requiring the user to take on specific poses, functional calibration, requiring the user to perform movements around defined axes, and technical calibration, requiring manual alignment with respect to the bone structure [85; 87; 88; 89]. These procedures still have potentially large human-induced errors and researchers have started to study ways to integrate machine learning and deep learning techniques to help improve inaccuracies [45; 46; 51; 62; 70].