esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Polling Medium Access Control Protocol for Optic Wireless Networks
## 4 The Adaptive Polling Medium Access Control (APMAC) Protocol  
We propose the APMAC protocol for OWNs, that considers physical properties of OWNs and key network performance metrics such as waiting time, packet collision, utilization and fairness [43; 44; 48; 49]. The APMAC protocol operates in three phases: (i) association phase wherein VLNs establish connections with the VLAP (ii) data transmission phase in which data transmission between VLNs and VLAP occurs, and (iii) the dissociation phase in which connection between the communicating devices is terminated. For simplicity, we provide common abbreviations used in this study and their complete form in Table 2. Figure 2 presents the APMAC protocol considering association, data transmission and dissociation phases. Furthermore, Figure 3 shows the finite state machine of the VLAP and Figure 4 depicts the finite state machine of the VLNs considering association,  
Figure 1: The common indoor optical wireless network (OWN) scenario.  
data transmission and dissociation phases. The superframe is divided into collision avoidance slot (CAS) and non-contention slots. During CAS, VLNs contend for association. The mechanism of contention in this case is described by the control frame collision avoidance algorithm (CFCA) in Section 4.1. In the non-contention slots, VLNs communicate with the VLAP without contention.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Abbreviation** & **Complete Form** \\ \hline VLAP & Visible light access point \\ VLN & Visible light node \\ ADF & Availability data frame \\ ARF & Access request frame \\ AGF & Access grant frame \\ DAF & Data availability frame \\ DTF & Data transmission frame \\ FOV & Field of view \\ RTT & Round trip time \\ MTU & Maximum transmission unit \\ CTR & Connection termination request \\ CTA & Connection termination acknowledgment \\ ADFT & Availability data frame transmission state \\ ADFR & Availability data frame reception state \\ ARFT & Access request frame transmission state \\ ARFR & Access request frame reception state \\ AGFT & Access grant frame transmission state \\ AGFR & Access grant frame reception state \\ DAF & Data availability frame transmission state \\ DAF & Data availability frame reception state \\ DTFT & Data transmission frame recognition state \\ CTRT & Connection termination request transmission state \\ CTRR & Connection termination request reception state \\ CTAT & Connection termination acknowledgment Recognition state \\ \hline \hline \end{tabular}
\end{table}
Table 2: Abbreviations and their complete names.  
Figure 2: The adaptive polling medium access Control protocol.
## 3 Results
### Comparison of Previous Works  
Based on the AB dataset, the RMSE across 11 subjects of the proposed walking speed estimator is 0.051 \(\pm\) 0.016 m/s. Based on the PW dataset, the RMSE across six subjects is 0.036 \(\pm\) 0.021 m/s. The repeated-measures ANOVA (\(p\) < 0.05) is used to test the effects of walking speed on the absolute difference between estimated and actual speed estimation error.  
Previous works presented lots of methods to estimate walking speed. [PERSON] placed an IMU on the foot, and the RMSEs less than 0.05 m/s are obtained for tested speeds and inclines varying in the intervals [3,6] km/h and [5, +15]%, respectively [9]. [PERSON] used a single shank embedded IMU and achieve a mean RMSE of 0.09 m/s for a level walking mission [15]. With analysis of variance (ANOVA), [PERSON] computed the estimation errors at different speeds and slopes [16] and [PERSON] used machine learning models for both subject-dependent and subject-independent algorithms to estimate walking speed when the speed is lower than 0.9 m/s [11]. Table 1 reports walking speed estimation methods proposed in recent years, as well as the sensors used, number of subjects, statistical analysis methods and experimental results. Through comparison of the listed research works, it can be found that the proposed method in this paper has high accuracy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Authors** & **N Subject** & **Sensors** & **Method** & **Statistical Analysis** & **RMSE (m/s)** \\ \hline [PERSON] [9] & 5 AB & a foot IMU & Direct integration method & Standard & 0.05 \\ [PERSON] [16] & 9 TF & IMU installed on shank & Kinematic model & Not mentioned & 0.09 \\ [PERSON] [15] & 8 AB & IMU installed on shank & Direct integration method & ANOVA (\(p\) < 0.05) & 0.07 \\  & & combined with kinematic model & & ANOVA (\(p\) < 0.05) & 0.07 \\ [PERSON] [11] & 6 TF & IMU on the thigh & \begin{tabular}{c} Machine learning models including \\ LR, XGBoost and NN (<0.9 m/s) \\ \end{tabular} & \
\begin{tabular}{c} ANOVA (\(p\) < 0.05) \\ and Bonferroni \\ post-hoc correction \\ \end{tabular} &
\begin{tabular}{c} DEP: 0.014 \(\pm\) 0.001 \\ IND: 0.070 \(\pm\) 0.007 \\ AB: 0.051 \(\pm\) 0.016 \\ PW:0.036 \(\pm\) 0.021 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Related papers and experimental results of walking speed estimation methods.  
Figure 8: Experimental results based on the PW dataset.
# Considering the Optimization Design of Urban Bus Network Scheduling
## 4 Example Analysis  
### Case Study Introduction  
#### 4.1.1 Data Introduction  
(1) Site and OD Data  
The case study in this paper focuses on five bus routes passing through the two stations (Station 98 to Station 101) with the highest repeat traffic in a certain area of Guangzhou City. The selected case includes a total of 134 stations in the network, with 102 stations in the road network. Among these, there are 38 stations in Haizhu District, 7 in Baiyun District, 13 in Liwan District, 7 in Yuexiu District, 13 in Tianhe District, and 24 in Panyu District. There are 18 stations that appear multiple times in the network, mainly located in Haizhu District and Liwan District. All five selected routes pass through the road segment between Station 98 and Station 101.  
The passenger demand between each station is based on the passenger card swiping data during the morning peak hours (7:00-9:00) of 25 April 2018, in Guangzhou City. There are a total of 10,302 OD pairs among the selected stations, with a total of 19,938 passengers.  
gers. Among the OD pairs with passenger demand exceeding 100 people, the departure stations are mainly concentrated in Haizhu District and Yuexiu District, while the destination stations are mainly concentrated in Tianhe District, Panyu District, and Haizhu District. Among these, the highest passenger demand is from Station 101 to Station 35, with 371 passengers.  
(2) Revised Network Data  
After refining the basic data, the next step is to further determine the dimensions of the selected bus network solution. Upon analyzing the basic data of the five selected bus routes in the optimized network case, it was found that the average distance between stations in each route is 668 m, with an average of 26.8 stations and an average route length of 17.32 km. Typically, urban bus route lengths are designed to be between 10 km and 30 km, with the number of stations set between 15 and 30. To meet the needs of passengers and operators, the network designers aim to keep the number of routes unchanged and try to maintain consistency in route length and number of stations with the original routes during the optimization process. Therefore, the dimensions of the bus network solution are determined as follows: 5 routes with 25 stations each.  
Based on the above introduction, the dimension of solutions in this network and scheduling optimization model is set to 130; 5 lines with 25 stops each, totaling 125 stops in the network; and 5 min intervals for each line. The adjacency matrix dimension in the actual-virtual public transport network is the sum of the road network stops and network stops, totaling 227.
# A Gesture-Based Teleoperation System for Compliant Robot Motion
## 4 Experiments
### Case Study of Peg-In-Hole Process
#### 4.2.2 Analysis  
In order to illustrate the effectiveness of the proposed system, as shown in Figure 14, an assembly system based on a haptic device was built for comparison. The haptic device is the Novint Falcon, which is a relatively inexpensive haptic device, and it was selected to offer force feedback while allowing the control of the end-effector with minimal effort [35]. It has only four buttons, which can only define four working modes; namely, coarse motion mode, fine motion mode, clutch mode and claw open/close gripper mode. In this experiment, two operators were used to perform 10 sets of experiments to accomplish the same assembly task.  
As shown in Figure 15, the gesture-based teleoperation system costs less time than the system based on the haptic device. In the stage of grasping the peg, since the center of the gripper needs to be aligned with the peg, the single-axis motion mode is crucial. Because the haptic device based system without the single-axis motion mode, it needs to adjust the position of the gripper many times, so it takes more time to adjust the position of the gripper. When moving the peg, the haptic device based  
Figure 14: The experimental platform based on haptic device. The haptic device is a Falcon haptic device which has three degrees of freedom.  
Figure 13: These two figures show changes of hand gestures, gripper states and contact forces.  
system needs to clutch more times than the gesture system because its working range is less than the leap motion. During putting the peg in the hole, while haptic-based systems can provide better force feedback, it needs more time to adjust the position because there is no single-axis mode.  
Figure 16 shows the distribution of the step size in the teleoperation. It can be seen that the gesture-based system and the haptic device based system have a similar distributions: most of the step sizes were less than 1 mm, and some of them were smaller than 0.1 mm. A smaller step size means the position can be precisely adjusted, and even human hand motions are uncertain and noisy. It was noticed that the step size was related to several factors, such as the scaling coefficient and the human hand's velocity. The operator should choose fine motion mode and slow down their moving speed to improving accuracy and system safety.  
It can be seen from the above comparison that the gesture-based teleoperation system can perform high-precision operations due to good scalability; it is possible to select a suitable working mode to complete the task faster. Therefore, the proposed gestured based teleoperation system provides an alternative option for portable, precise and compliant teleoperation methods.  
Figure 16: The distribution of the step size in the teleoperation.  
Figure 15: Comparison of teleoperation time at each stage. D1 represents the gesture-based teleoperation system, and D2 denotes the system based on the haptic device.
# Evaluation of Orbital Drift Effect on Proba-V Surface Reflectances Time Series
## 4 Results
### Analysis of Estimated Drifts in TOC Products
#### 4.3.4 Statistical Analysis of Estimated Drifts for Near-Nadir Observations  
The same procedure used in the previous paragraph is repeated here, selecting only observations acquired under the viewing condition VZA < 10\({}^{\circ}\). This condition results in selecting only measurements acquired from the Proba-V nadir camera.  
The statistical distribution of estimated drift for TOC and BRDF-normalized near-nadir reflectances are presented in Figure 14. We can observe that noticeable drifts appear in TOC products, showing, to a large extent, the presence of negative biases for all bands, in particular for the SWIR, and all land cover classes, with more pronounced effects over forest, cropland and shrubland sites. This result is quantitatively consistent with the predicted negative changes of surface reflectances for the nadir camera over a forest site (see Figure 5 and Supplementary S.2).  
The biases observed in BRDF-normalized time series--see, in particular, a negative bias in the Blue and a positive one in the NIR band--can be ascribed to potential remaining real drifts in the measured reflectances, such as those caused by residual calibration drifts at Top-Of-Atmosphere (TOA) level. However, discussion on these temporal patterns is out of the scope of this paper, which is focused on quantifying the impact of the orbital decay under the assumption that the BRDF-normalized data provide the benchmark value, free of any spurious orbit-induced effect.  
Figure 14: The figure shows the boxplots of estimated drifts in the four spectral bands for near-nadir observations (VZA < 10\({}^{\circ}\)) aggregated per land cover class: Crop (CR), Tree (TR), Shrubland (SH), Grassland (GR), Sparse vegetation (SP), Bare soils (BA). Estimated drifts for TOC (red) and BRDF-normalized (blue) reflectances are presented for the bands: (**a**) Blue, (**b**) Red, (**c**) NIR, (**d**) SWIR. The parameters of the boxplot chart are those used and described in Figure 12.
## 2 Control Equations for Euler-Bernoulli Rotor
### Assembly of the Global Matrices  
By integrating the preceding equations, we can establish a novel finite element model in which both nonlinear forces are represented as external forces. This approach enables an intuitive analysis of how these nonlinear forces influence the rotor's response. In summary, building upon the free vibration governing equations of the Euler-Bernoulli beam and incorporating the squeeze film damper (SFD) oil film restoring force, lumped mass elements, and nonlinear support forces, the global matrix is derived by assembling the aforementioned matrices according to the degree of freedom (DOF) ordering, as follows:  
\[\begin{array}{l}\mathbf{M}\ddot{\mathbf{\delta}}+\mathbf{G}\dot{\mathbf{ \delta}}+\mathbf{K}\delta=\mathbf{F}_{sfd}+\mathbf{F}_{bnl}\\ \{\mathbf{M}\}=\sum\{\mathbf{M^{e}}\}+\sum\left\{\mathbf{M}_{lump}^{\mathbf{M }}\right\}\\ \{\mathbf{G}\}=\sum\{\mathbf{G^{e}}\}+\sum\left\{\mathbf{G}_{lump}^{\mathbf{e }}\right\}\\ \{\mathbf{K}\}=\sum\{\mathbf{K^{e}}\}+\sum\{\mathbf{K^{e}_{b}}\}\end{array} \tag{58}\]  
\[\begin{array}{l}F_{sfd}=\left[F_{x_{i}},F_{y}\right]^{T}\\ =\left\{\begin{array}{l}F_{x}=-F_{r}\cos\theta+F_{l}\sin\theta=\frac{\mu RL ^{3}}{C^{2}}\Big{(}I_{1}\dot{\varepsilon}+I_{2}\epsilon\dot{\theta}\Big{)} \frac{v}{e}-\frac{\mu RL^{3}}{C^{2}}\Big{(}I_{2}\dot{\varepsilon}+I_{3} \epsilon\dot{\theta}\Big{)}\frac{v}{e}\\ F_{y}=-F_{r}\sin\theta-F_{l}\cos\theta=\frac{\mu RL^{3}}{C^{2}}\Big{(}I_{1} \dot{\varepsilon}+I_{2}\epsilon\dot{\theta}\Big{)}\frac{v}{e}+\frac{\mu RL^{3 }}{C^{2}}\Big{(}I_{2}\dot{\varepsilon}+I_{3}\epsilon\dot{\theta}\Big{)}\frac{ v}{e}\\ \{F_{bnl}\}=\left\{F_{bnlx_{i}},F_{bnlx_{j}}\right\}^{T}\\ =\left\{\begin{array}{l}F_{bnlx_{i}}=k_{bnl}v^{3}\\ F_{bnlx_{j}}=k_{bnl}w^{3}\end{array}\right.\end{array} \tag{60}\]  
In this context, \(\mathbf{M}\), \(\mathbf{G}\), and \(\mathbf{K}\) represent the global mass matrix, the overall gyroscopic matrix, and the global stiffness matrix (which includes the linear component of the support force, excluding the SFD force), respectively; \(\mathbf{F}_{\text{sfd}}\) denotes the load array for the oil film force; and \(\mathbf{F}_{\text{bnl}}\) signifies the nonlinear component of the support force.
## 2 Related Works
### DL Approaches
#### 2.2.2 Segmentation DL Approaches  
[PERSON] [53] introduced three architectures to identify skin lesions: segmentation, structure segmentation, and diagnosis stages. First, the skin picture is segmented; then, the output of this stage is used as the input of the structure segmentation stage. Finally, the diagnosis stage links the outputs of the two previous steps and forecasts the skin lesion type. The generation of a labeled training database is the main challenge to creating a structure segmentation network in which each picture has an associated ground truth. This annotation is usually hard to obtain, as it demands a massive effort of the dermatologists to outline the segmentations manually.  
The study in [54] employed a DL segmentation model (U-Net) to create a segmented map of the lesion image, cluster sections of abnormal skin, and give the output to a classification network. Meanwhile, [PERSON] et al. [54] drew contours of each cluster with the output mask created by U-Net and applied a convex hull algorithm to crop each cluster. Each cluster was applied as input to the EfficientNet to predict the lesion type.  
[PERSON] et al. [55] presented an integrated framework that combines skin lesion boundary segmentation and classification steps. First, the study used DL full-resolution convolutional network (FrCN) to segment the skin lesion boundaries. Then, a CNN classifier (ResNet-50, Inception-v3, DenseNet-201, and InceptionResNet-v2) was used to classify the segmented skin lesions. The segmentation step is a crucial prerequisite for skin lesion diagnosis because it extracts principal features of different skin lesions.  
Table 1 presents the datasets of all DL methods mentioned in the related work section, including the number of images and classes. Three approaches test with the extensive databases (more than 10,000 images).  
Besides DL, other segmentation approaches achieved good results for different fields, such as surface defect detection and mineral separation. [PERSON] et al. [56] presented an automatic thresholding approach that improves [PERSON]'s method by applying an entropy weighting scheme and overcoming the weakness of [PERSON]'s technique in defect detection. [PERSON] et al. [57] presented an ore image segmentation algorithm using a histogram accumulation moment applied to multiscenario ore object recognition. Ore pictures in three separate scenarios are used to demonstrate the effectiveness and accuracy of the proposed approach. It is reasonable to inherit these ideas into bioimage fields.  
There are many methods for skin lesion classification in the literature review, including ML and DL frameworks. However, these methods suffer from one of the following drawbacks: (1) missing test results with big data, (2) having insufficiently good performance, and (3) heavy-weight model. In this study, we present a novel approach to overcome these limitations.
# A General Framework Based on Machine Learning for
## 2 Background
### Benchmark Instances  
For this investigation, we considered a total of 400 binary CSP instances--each constraint involves no more than two variables--split into four sets as follows. Sets BQWH15 and BQWH18 contain 200 satisfiable (there is, at least, one solution) balanced quasi-group instances with holes. Set EH185 contains 100 unsatisfiable (we can confirm that there is no solution at all) 3-SAT instances that have been converted into CSP instances using the dual method as described in [54]. The last set, GEOM, contains 100 kind-of-random instances in which a constraint between two variables can only exist if the distance between the two variables involved is less than \(\sqrt{2}\). The GEOM generation model assumes that random coordinates are assigned to the variables in order to estimate the distance. GEOM contains both satisfiable and unsatisfiable instances with 50 variables each and a uniform domain size of 20. At this point, we must highlight that there are other instances available at the repository but that these ones were selected because they comprise a variety of representative patterned, quasi-random, and structured CSP instances that are publicly available at [[http://www.cril.univ-artois.fr/~lecoutre/benchmarks](http://www.cril.univ-artois.fr/~lecoutre/benchmarks)]([http://www.cril.univ-artois.fr/~lecoutre/benchmarks](http://www.cril.univ-artois.fr/~lecoutre/benchmarks)) (accessed on 16 March 2021).  
To deepen the characterization of these instances, we provide an overview of the distribution of the features described in Section 2.2 for all 400 CSP instances considered for this work, grouped by set. We can observe from Figure 2 that, in general, these features produce a very narrow range of values inside each set. This suggests that the instancesin each set are similar to each other when analyzing each feature in isolation. However, the values for these features vary from set to set, which allows the ML methods to grasp some information to discriminate among instances and to associate them to a suitable solving method.
# Performance Evaluation of UAV-Enabled LoRa Networks for Disaster Management Applications
## 2 Network Setup and Architecture
### Base Station  
In its most minimal configuration, the BS contains the same WiFi equipment of a UAV, runs the same MANET routing protocol and integrates a LoRaWAN network server that is used to manage the LoRaWAN network, as given in Figure 4.  
Figure 3: System Stack of UAV.  
Figure 2: System stack of Ground Nodes (GNs).  
It is considered that all UAVs fly at the same altitude in a 2D plane. A three-dimensional configuration of UAVs is still allowed in the developed system, but the benefits in terms of extended coverage to GNs and collision avoidance are not investigated as part of this work. This means that even if UAVs have different altitudes, the mobility algorithm behaves as if they are on the same plane. An altitude optimization procedure may be introduced seamlessly on top of the system at a later time. Moreover, all UAVs can move with constant speed in any direction. Drones are complex systems that require complex controllers to manage speed, acceleration, stability and heading along the six-axis. This work focuses on the performance of the networks and on the ability of the UAVs mobility algorithm to improve the coverage of GNs. UAVs also have access to the received power of neighboring UAVs and GNs or can estimate it based on their position. Routing messages can be used to collect the received power, which is necessary to determine the link budget of the links with neighboring UAVs. The received power of GNs can be collected when an uplink message is received. Eventually, the received power can be estimated by resorting to propagation models and the positions of the nodes.
## 2 Methodology
### Prototype Characterization  
Due to the differing properties of the materials used to manufacture the alternator cores, a test was conducted to determine the inductance of the alternator for each type of material used. In similar studies, equivalent methodologies have been utilized to excite the core of a copper wire coil and thereby determine its inductance [43]. In our case, a resonant LC circuit was employed, the operating principle of which is based on the energy being alternately absorbed between a capacitor and a coil. Equation (1) shows the relationship between the parameters of an LC circuit  
\[f=1/2\cdot\pi_{\mathrm{v}}\mathrm{\sqrt{L}\cdot C} \tag{1}\]  
where \(f\) is the resonance frequency of the circuit, \(\mathrm{L}\) the inductance of the coil, and \(\mathrm{C}\) the capacitance of the capacitor.  
The test conducted involves connecting the alternator coil with a capacitor of known capacity and monitoring the discharge curve for the different prototypes using an oscilloscope. By analyzing the response signal of the LC circuit and rearranging Equation (1), the inductance can be calculated using Equation (2).  
\[\mathrm{L}=\mathrm{C}/(2\cdot\pi\cdot f)^{2} \tag{2}\]  
In addition to characterizing the inductance of the alternator, the damping of each cycle can also be analyzed, which refers to the losses per period of a damped system by examining the losses that occur in each cycle in the LC circuit. This damping is indicative of the energy losses that will occur in the alternator's core. Therefore, the greater the damping,  
Figure 3: Magnetic field lines in (**a**) central position, and (**b**) lateral position of the prototype designed and manufactured for this work.  
Figure 2: CAD design of the alternator prototype.  
the greater the losses produced in the alternator and the lower its performance. For the execution of the tests, a 10-pF capacitor charged to a voltage of 15 V was used.