esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Low-Cost 3D Laser Scanning Technology
## 4 Problems of a Moving 2D LiDAR
### Problem I: Accuracy
#### 4.1.2 Problem 1.2--For Categories 1, 2, and 5: The Error Caused by the Motor
For the accuracy of the collected 3D point cloud, the synchronization between the data of the 2D LiDAR and the rotation angle of the motor shaft should be accurate. In the 3D scanning process of the prototype, for each sampling point of the 2D LiDAR, if the angular position of the motor shaft corresponding to this point can be accurately known, the synchronization error between the 2D LiDAR and the motor shaft angle can be completely eliminated. However, in actual situations, it is difficult to achieve. Therefore, the synchronization error is unavoidable. In [22; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98], the calibration of the synchronization between the 2D LiDAR and the motor shaft are done by using an angular position sensor (such as an encoder), while the cost of the sensor cannot be ignored. In [99], a more economical method has been proposed, and the cost of the angular position sensor can be avoided. For the prototypes of category (4) an irregularly rotating 2D LiDAR, the 2D LiDAR may not be rotated by a motor (for example, the rotation of the 2D LiDAR in [79] is driven by airflow), so it is not included in the categories of prototypes corresponding to Problem 1.4. Problem 1.4 will decrease the accuracy of the space 3D rigid body transformation between the 2D LiDAR coordinate frame \(L\) and the prototype coordinate frame \(P\) (which corresponds to Equation (3) in Section 3.6 of this paper), this will eventually decrease the accuracy of the 3D point cloud. 1.5 Problem 1.5--For Categories 1 to 6: The Error Caused by the Assembly Inaccuracy between a Moving 2D LiDAR and the Mobile Platform  
According to Equations (4) and (6) of Section 3.6 in this paper, it can be recognized that the relative position and attitude between a moving 2D LiDAR and the mobile platform should be accurately known, which depend on the mechanical assembly. Assembly inaccuracy between a moving 2D LiDAR and the mobile platform will eventually lead to the error of the 3D point cloud. In addition, in order to expand the scanning field of view and improve the performance on obstacle avoidance and 3D mapping, in some applications, multiple 2D LiDARs are mounted on a mobile platform, and the sampling points collected by multiple 2D LiDARs are converted to a global world coordinate frame. Therefore, errors of the space rigid body transformations between multiple 2D LiDAR coordinate frames also need to be noted. In [32; 33; 34; 35], calibration of the space rigid body transformations between the 2D LiDAR and the mobile platform, or between multiple 2D LiDARs are studied.
# Topology Optimization Based Parametric Design of Balloon Borne Telescope's Primary Mirror
## 4 Conclusions  
In this paper, the parametric design method of the primary mirror based on topology optimization is proposed. The topology optimization of the primary mirror was used as the basis for grouping lightweight ribs. The parametric design based on the compromise programming method can solve the contradictory multi-objective problem of primary mirror contradiction. This method can realize automated update iteration design, which greatly reduces the design cycles. Compared with the primary mirror structure designed based on traditional experience, the mass of the primary mirror designed by the proposed method was reduced from 33.8 kg to 30.9 kg, which meets the design requirement of less than 32.5 kg; the RMSx and RMSz were reduced by 14.3% and 10.5%, respectively; and the fundamental frequency was reduced by only 1.8%. The performance of the primary mirror met the requirements. The results show that the parametric design method based on topology optimization is effective for the design of the balloon-borne telescope's primary mirror. The parametric design method based on topology optimization provides an efficient technical mean and an engineering reference for the primary mirror of balloon-borne telescopes and space cameras in the future.  
It should be noted that although our parametric design of the primary mirror based on topology optimization achieved good results in this work, the actual mirror built with this method may not achieve the optimized performance due to manufacturing errors. Hence, subsequent research work will manufacture the primary mirror for further experimental verification.  
Project administration, W.Z. and X.W.; methodology, F.L. and H.Z.; writing--original draft, F.L.; writing--review and editing, W.L., G.L., and W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Strategic Priority Research Program of Chinese Academy of Sciences, grant number XDA17010205.  
Not applicable.  
The study did not report any data.  
The authors declare no conflict of interest.
# Improved Classification of Blockchain Transactions Using Feature Engineering and Ensemble Learning
## 3 Preliminaries  
Our primary work is mainly focused on classification of risky and non-risky blocks in the blockchain network using ensemble learning by training different classifiers on a dataset.  
Ensemble learning is an approach for solving a difficult problem that involves deliberately merging multiple machine learning models into a single predictive model. Ensemble methods, in general, are used to increase a model's overall performance accuracy by combining several separate models, also known as base learners, to predict the outcomes, in spite of using a single model. We utilize this strategy in our daily lives as well--for example, we seek the advice of several experts before making decisions, we read various product evaluations before purchasing a product, and a panel of judges confer among themselves to determine a winner. Ensemble learning is often divided into four categories:  
1. Boosting;
2. Stacking;
3. Bagging;
4. Cascading.  
We will discuss the first two techniques that help us in generating our classification model.
## 4 Discussion
### Design Challenges and Trade-Offs
#### 4.1.5 Financial Model Design  
To ensure financial sustainability and accessibility for diverse user groups, SPARK-IT has to use a multi-faceted financial model. Users can choose between the following:  
* _Flat Transaction Fee_: A small, fixed percentage fee on transactions between innovators and experts ensures predictable costs, ideal for occasional users.
* _Subscription Model_:
* _Basic Plans_: Provides low-cost access to essential features, allowing users to explore the platform without significant commitment.
* _Premium Plans_: Offer advanced features such as enhanced matchmaking algorithms, analytics, and priority support.
* _Organization Sponsorship_: Larger entities (e.g., universities or corporations) can spons platform licenses for their members. They can purchase SparkCoins and then redistribute them to their members as they see fit.
* _Pay-Per-Use Extra Features_: Specific value-added features, like a badge that proves the profile information has been verified, are available for a fee.  
Each of these design considerations requires a careful balance of trade-offs. Striking the right balance is essential for creating a platform that is both functional and scalable while maintaining the decentralized aspects and providing a user-friendly experience. The employed solutions and trade-offs must be iterated over time to optimize the system further as the platform grows.
# Particulate Matter Contamination of Bee Pollen in an Industrial Area of the Po Valley (Italy)
## 2 Materials and Methods  
### Sample Collection and Preparation  
In February 2017, a hive was placed in the peri-urban area of the city of Parma (44.834357, 10.353865), close to wheat and alfalfa crops and about 100 m from the A1 Milan-Naples highway. The highway exit and the Parma incinerator plant were located approximately 400 m from the hive (Figure 1).  
Figure 1: Area of investigation. Red dots represent the location of the hive with respect to the A1 highway and the incinerator plant [24].  
To avoid possible contamination of pollen caused by using pollen traps, analyses were carried out directly on pollen grains attached to the hindlegs of worker bees (Figure 2).  
Four sampling sessions were carried out monthly from May to August. During each session, ten forager bees carrying pollen grains were sampled while returning to their hives. Bees were immediately placed in soda-glass-capped vials (Chromacol Limited, Welwyn Garden City, UK) stored on ice to inactivate them and quickly transferred to the laboratory for sample preparation. Hind legs with pollen grains (total number of pollen grains analyzed in this study = 40) were dissected under a laminar flow hood to avoid external contamination. Two pairs of legs were mounted onto SEM stubs using double-adhesive carbon tape for SEM/EDX analysis. Stubs were carbon coated, as previously described [16].  
A few days after each sampling session, negative control bees carrying pollen grains were collected close to hives in a rural area located 10 km south of Parma (Northern Italy) near the bed of Parma creek and close to the foothills of the Apennine Mountains (44.690099, 10.336713). The control site was far from any known emitting source of PM (i.e., vehicular traffic, incinerators, agricultural lands).
## 3 Results
### Size  
The spectral exponents of attenuation and backscatter increased slightly with height above the bed during the calm periods and during the passage of Maria and Ophelia. During the spring tides, the spectral exponent of attenuation increased with height above the bed, but the spectral exponent of backscatter decreased with height above the bed. Values of \(\gamma_{cp}\) were smaller during passage of the two storms than during the calm period and during spring tides. Values of \(\gamma_{bbp}\) were smallest during spring tides, even taking on negative values. Values were higher during the storms, and they were scattered during the calm period.  
Sauter diameter and the reciprocal of floc density generally were well correlated because they are based on similar measurements. Sauter diameter was smaller during passage of Ophelia, when it was -50 \(\upmu\)m, and during passage of Maria, when it was \(\sim\)100 \(\upmu\)m. During the spring tides, Sauter diameter was largest, with diameters \(\sim\)200 \(\upmu\)m. During the calm period, Sauter diameter was \(\sim\)150 \(\upmu\)m. During Maria, the spring tides, and the calm period, inverse densities were similar and equal to \(\sim\)20 \(\upmu\)m. Values were \(\sim\)10 \(\upmu\)m during the passage of Ophelia. During the passage of the two storms, inverse density and Sauter diameter values diverged near the bed, and during the spring tides, they diverged higher in the boundary layer.  
Size distributions from the LISST provide a more complete understanding of the vertical and temporal changes in size distribution. During the calm period and during spring tides, the size  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Maria** & **Spring Tide** & **Ophelia** & **Calm** \\ \hline \(b_{bp}(650)\) [m\({}^{-1}\)] & 0.11(0.06) & 0.04(0.01) & **0.30**(0.14) & 0.02(0.002) \\ \(c_{p}(650)\) [m\({}^{-1}\)] & 3.87(1.77) & 1.19(0.22) & **11.81**(6.51) & 1.06(0.09) \\ \(\gamma_{bbp}\) & **0.09**(0.02) & \(-distributions had well-defined modes at the upper end of the LISST range. The widths of the modes were relatively constant with height above the bed during the calm period, but they were wider nearer the bed during the spring tide period. During passage of Maria, distributions were unimodal, but they were more skewed to smaller sizes than during the calmer periods. During passage of Ophelia, curious bi-modal distributions emerged. One mode was centered on diameters of \(\sim\)100 \(\upmu\)m, and the other was located in the largest diameter class. This coarser mode was dominant farther away from the bed, and the finer mode grew larger nearer the bed.
# RHS-YOLOv8: A Lightweight Underwater Small Object Detection Algorithm Based on Improved YOLOv8
## 2 Methodology
### Small Object Detection Branch  
There are more difficulties in small object detection, resulting in many algorithms being much less effective in detecting small objects than large- and medium-sized objects. The main reasons affecting the performance of the algorithms are as follows: First, small objects have low resolution and insufficient information, resulting in fewer effective features extracted by the neural network. Second, small objects occupy a small area in the image and are susceptible to background interference, which requires high localization performance of the algorithm. Third, small objects are difficult to label and the training data are limited, which leads to poor model generalization ability. In the original YOLOv8, the pictures need to output the object-related information after converging the neck structure through the P3, P4 and P5 feature layers, and predict the object location afterwards. The sizes of these three layers of feature maps are 20 \(\times\) 20, 40 \(\times\) 40, and 80 \(\times\) 80 pixel points, respectively, but the information contained in the feature maps in the original model is not enough to support the adequate representation of the information of small objects. Therefore, we add the P2 detection head in the model, which is equivalent to four detection heads, so that the network can detect smaller objects. The P2 feature maps are located in the low feature layer, with the size of 160 \(\times\) 160 pixels which contains more information, has a better ability to transfer more shallow features to deeper features, and is conducive to enhancing the expressive ability of the multi-scale feature map extracted by the network, so that the network is better able to perform multi-scale object detection and describe the location of the small object. The structure of the YOLOv8 model with the addition of a small object detection branch is shown in Figure 6. Compared with the original YOLOv8 model, the amount of information obtained by this model increases, resulting in an increase in accuracy.
# Applying Game Theory to Teach the PERT Scheduling Method
## 5 Analysis of Results
### Implications for Educators  
This research introduces new insights from a methodological perspective by providing a practical approach that applies Game Theory to teach a scheduling technique commonly used in engineering (PERT or Program Evaluation and Review Technique). Even though the research specifically focuses on civil engineering students, professors from other disciplines can modify the methodology presented here for their subjects. For instance, instead of using Game Theory to teach PERT, they might teach other scheduling techniques, such as CPM (Critical Path Method), Gantt charts, or Lines of Balance. The methodology can also be applied to various engineering courses involving decision-making (e.g., Structural Analysis, Computer Simulation, among many others). In other words, educators can retain the didactical tool of Game Theory while adapting the specific engineering topic to be taught, with special emphasis on those courses where decision-making plays a critical role.  
Regarding practical contributions, this study may serve as a valuable resource for engineering professors by (a) providing an introductory understanding of Game Theory and the elements of scheduling courses (e.g., PERT, CPM, Gantt, etc.) necessary for implementing challenging teaching methods that engage students; (b) facilitating the blending of Game Theory with PERT (or other courses where decision-making plays a role) in engineering education; and (c) including minor adjustments to the proposed methodology to accommodate students from different engineering disciplines or academic settings.  
In terms of how to scale the proposed approach for larger class sizes, some authors have raised recommendations. To scale a way of teaching without significantly increasing the course-related resources, [PERSON] and [PERSON] [99] suggest incorporating (a) an audience response system to make more effortless interactivity during live lectures and (b) peer review for the scalability of evaluations and to relieve professor workloads. Similarly, to improve the scalability of engineering courses, [PERSON] and [PERSON] [100] propose reducing the load on the main lectures by outsourcing much of the non-scalable tasks to teaching assistants. In this sense, all these recommendations may be incorporated into the present study if the proposed methodology is scaled to larger class sizes.  
In the end, this study offers a methodology to complement other teaching techniques, particularly for engineering courses requiring students to face decision-making processes. Utilizing Game Theory to teach a scheduling method like PERT may lead to a more engaging teaching environment compared to traditional engineering teaching techniques, ultimately enhancing academic performance by offering a teaching tool that manages engineering problems related to decision-making.
## 4 Feature Extraction and Control Methods
### Feature Extraction
#### 4.2.2 Cross-Power Spectrum Method  
The method is based on the principle of eliminating noise by a cross-correlation operation. The initial signal and a standard sinusoidal signal are cross-correlated, and the frequency of the sinusoidal signal is identical to the working frequency of the rotor system. The similar frequency component of the initial signal that is equal to the working frequency is strengthened, and the components of noise and other frequencies are effectively suppressed. Then, the amplitude as well as the phase of the same frequency signal are solved by using the cross-power spectral density function [24].  
The initial vibration signal and the standard sinusoidal signal are the same as Equations (20) and (21). The cross-correlation between these two signals is calculated by their convolution sum, so the cross-correlation function can be obtained by:  
\[C_{xy}(\tau)=E[x(t)-\mu_{x}]\cdot E\big{[}\big{(}y(t-\tau)-\mu_{y}\big{)}^{*} \big{]} \tag{26}\]  
where \(\mu_{x}\) and \(\mu_{y}\) are the mean values of these two signals.  
The cross-power spectral density function refers to the Fourier transform of the cross-correlation function, and it is expressed by:  
\[P_{xy}(w)=\int_{-\infty}^{\infty}C_{xy}(\tau)e^{-iw\tau}d\tau=\frac{\pi A_{0} }{2}e^{i\varphi_{0}}\delta(2\pi f_{0}-w)+\frac{\pi A_{0}}{2}e^{-j\varphi_{0}} \delta(2\pi f_{0}+w) \tag{27}\]  
where \(\delta\) is the Dirac function. When \(\omega\) is equal to \(2\pi f_{0}\), Equation (27) can be simplified to:  
\[P_{xy}(w_{0})=\frac{\pi A_{0}}{2}e^{i\varphi_{0}} \tag{28}\]  
Therefore, by computing the modulus and phase angle of the complex number, the amplitude \(A_{0}\) and phase \(\varphi_{0}\) of the unbalance vibration can be obtained.
## 7 Conclusions and Future Work  
The presented method allowed for an autonomous annotation of almost 700 min of raw footage; moreover, the distillation and augmentation techniques inside the self-learning loop significantly increased the detection rate, while also preventing the increase of false positives and false negatives. Additionally, multiple underwater environments and conditions, including obscured scenarios with the suspension of sand, were used and tested, proving its versatility. Furthermore, the obtained results against the ground-truth annotations of unseen images provided an exceptional instant detection mean average precision, even in smaller models aimed at edge deployment.  
Future work is presented in Figure 7. It consists of the implementation of an advanced and more state-of-the-art tracking method, which will allow for more precise detection, either inside the self-supervising loop or when performing distillation while generating a  
**Author Contributions**: Conceptualization, R.J.M.V.; Data curation, R.J.M.V. and I.E.O.; Formal analysis, R.J.M.V.; Funding acquisition, J.S.; Investigation, R.J.M.V., J.P.S. and A.B.; Methodology, R.J.M.V.; Project administration, J.S.; Resources, L.B.; Software, R.J.M.V.; Supervision, J.S. and J.M.F.R.; Validation, R.J.M.V.; Visualization, R.J.M.V.; Writing--original draft, R.J.M.V.; Writing--review & editing, R.J.M.V., J.M.F.R. and L.B. All authors have read and agreed to the published version of the manuscript.  
**Funding**: This work was financed by the project KTSealDrones for aerial and aquatic vehicles knowledge and technology transfer for the transboundary development of marine sciences and fishery -- reference 0622_KTTSEADRONES_5_E--financed by the European Regional Development Fund (ERDF), POCTEP--Interreg VA Spain-Portugal, a program for cross-border cooperation. Also by the PO SEUR program (Operational Program for Sustainability and Efficient Use of Resources; Portugal 2020 Strategy) as the main financial support of the MARSW project (POSEUR-03-2215-FC-000046), and Portuguese national funds from FCT--Foundation for Science and Technology through projects UIDB/04326/2020, UIDP/04326/2020, UIDB/50009/2020 and LA/P/0101/2020. [PERSON] was supported by a doctoral grant from FCT (UI/BD/151307/2021).  
**Institutional Review Board Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.  
**Acknowledgments**: CCMAR--Centro de Ciencias do Mar--for their support, availability, and access to their data and knowledge. This work was supported by Portuguese Foundation for Science and Technology (FCT), LARSyS FCT-project UIDB/50009/2020, and CCMAR FCT-project UIDB/04326/2020.  
**Conflicts of Interest**: The authors declare no conflict of interest.
# A Systematic Analysis of Narrowband IoT Quality of Service
###### Abstract  
Narrowband-IoT (NB-IoT) is part of a novel group of access technologies referred to as Low-Power Wide Area Networks (LPWANs), which provide energy-efficient and long-range network access to IoT devices. Although NB-IoT Release 13 has been deployed by Mobile Network Operators (MNO), detailed Quality of Service (QoS) evaluations in public networks are still rare. In this paper, systematic physical layer measurements are conducted, and the application layer performance is verified. Special consideration is given to the influence of the radio parameters on the application layer QoS. Additionally, NB-IoT is discussed in the context of typical smart metering use cases. The results indicate that NB-IoT meets most theoretical Third Generation Partnership Project (3 GPP) design goals in a commercial deployment. NB-IoT provides a wide coverage by using signal repetitions, which improve the receiver sensitivity, but simultaneously increase the system latency. The maximum data rates are consistent over a wide range of coverage situations. Overall, NB-IoT is a reliable and flexible LPWAN technology for sensor applications even under challenging radio conditions. Four smart metering transmission categories are analyzed, and NB-IoT is verified to be appropriate for applications that are not latency sensitive.  
narrowband IoT; quality of service; performance; mMTC; LPWAN; 5G