esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Antarctica is a remote and an isolated continent of large interest for the scientific community. Many research stations and remote sensors are scattered across the continent to conduct experiments related to different disciplines such as geology and physics that cannot be reproduced anywhere else on The Earth. The Research Group in Electromagnetism and Communications from LA SALLE, together with the Ebro Observatory (EO), both belonging to the Ramon Llull University, are partners of a research project for remote sensing and skywave digital communication from Antarctica. The project was born in 2003 with a double objective: transmit the data from remote sensors located at the Spanish Antarctic Station Juan Carlos I (referred to as SAS in the remainder) directly to the EO in Spain, and implement a long-haul oblique ionospheric sounder. Even though the SAS is only manned during the Austral summer, collection of scientific data is never stopped. While the station is left unmanned, the continuous set of data is stored in memory devices that will not be downloaded until the next Antarctic campaign. The information that has to be analyzed in almost real-time is transmitted to the EO in Spain through a satellite link. The skywave digital communication system is intended to transmit the information from the Antarctic sensors as a backup or even an alternative to the satellite. The oblique ionospheric sounder is a low power high frequency (HF) transceiver that monitors the ionospheric channel between the Antarctica and Spain. The sounder is fully configurable and can analyze different parameters from the ionospheric link at any frequency in the HF band. The oblique sounder is not the only sensor available at the SAS. In 1996 the EO deployed a geomagnetic observatory. The observatory is composed of two independent variometers, an Overhauser magnetometer deployed in dual axis Helmholtz coils in a \(\delta D/\delta I\) configuration, and a suspended tri-axial fluxgate magnetometer, along with sampling hardware and data logging software. Later, in 2004, a vertical-incidence low-power pulse-compressed ionosonde with a double delta loop antenna was also installed. The large amount of information gathered from the oblique sounding surveys is analyzed for both physics and communication purposes. One of our topics of research is to use the oblique sounding information to complement the ionospheric models obtained from the vertical sounding. From the communications point of view, the oblique sounding is used to obtain models of long-haul ionospheric channels. Parameters such as link availability, power delay profile and Doppler spread are analyzed and considered in the design of the physical layer of the skywave communication system. Currently, we have proposed two different physical layer solutions which are based on direct-sequence spread spectrum (DS-SS) signaling and orthogonal frequency division multiplexing (OFDM). Preliminary transmissions suggest that both solutions can achieve reliable low-power low-rate communications between Antarctica and Spain.
# Position Selection and Collision-Free Path Planning for Fruit Picking Robots
## 5 Experiment
### Experiment Result  
The experiment results of applying collision-free path planning using MPAPF to the manipulator are shown in Figure 21. In Figure 21a, the initial posture is depicted, and in b-d, the manipulator reaches the relatively easily accessible target objects without colliding with the shelf. In e-h, the manipulator approaches the target located behind the partition without colliding with the shelf or the partition. The path computation times were 4.9, 3.8, 4.8 and 45.2 s, respectively. Since the final target is located within the shelf and partition, the manipulator frequently remained closer than the threshold. The computation time was prolonged when approaching the final target due to the dense configuration of the point cloud. Each path consists of 42, 32, 42 and 78 path points. In experiment, the manipulator was operated with a 0.25 s interval between generated path points. The generated paths are shown in Figure 22. The paths to each target object are indicated in red, green, blue and cyan, respectively. As shown in Figure 22, none of the paths overlap with the shelf or partition. Joint angles are shown in Figure 23, where all the generated joint angles are within the joint limit range. The clearances in Figure 24 represent the distances between the positions of all the joints and the obstacles during manipulation, confirming that all parts of the manipulator did not approach closer than the set safety distance.  
Figure 21: The progress of the experiment: (**a**) initial pose; (**b–g**) approaching each target; (**h**) final pose.  
Figure 20: System diagram.  
Figure 23: Joint angles of the manipulator.  
Figure 22: Collision—free paths for each target.
## 4 Multi-Criteria Decision-Making Method for Emergency Communication Protocol (MCDM-ECP)  
In this section, a proposal of MCDM-ECP for hybrid DAWNs is presented for the selection of the next-hop node. This algorithm is formed by two algorithms: AHP, which evaluates the decision criteria for the selection of the objective node, and the TOPSIS algorithm, which evaluates opportunities for all of the neighbor nodes selected as the next-hop node, and organizes them from the most adequate to the least.
## 3 Design of FWRMS
### Wrist and forearm Mechanism Design  
The wrist joint links the hand and the forearm together, the wrist formed by a series of eight carpal bones and a soft tissue wrapping them, basically the wrist important small joint anatomic planar structure. The wrist joint performs one DOF for wrist flexion/extension and one DOF for wrist radial/ulnar deviation [32; 36]. One DOF by the forearm joint is process supination/pronation (S/P), thanks to the elbow joint [37]. FWRMS approach provides a continuous passive motion to the wrist joint movement (F/E, R/U) and forearm (P/S) from the other side of a dual-sided actuator used to power these joint movements. The motor's torque causes the angular velocity to rotate with different ranges; these ROM are limited to the same functional ROM of targeted joint movement. Moreover, the motion can be started just from the zero position to ensure more safety.  
Furthermore, we designed special mechanical attachments to integrate and transmit these ROM to the desired joints movement. For each targeted joint movement, the attachments are designed to handle the hand palm as fastened to it. In the preliminary set-up, the mechanical handles must be individually and physically integrated with the actuator shaft; then, they either fix or replace another handle. This solution significantly reduces the complexity of the mechanical structure's entire space and cost. In general, the 3D mechanical structure of the wrist rehabilitation for wrist F/E attachment and mechanism is illustrated in Figure 4.  
Figure 4: 3D model structure and health object integration with the proposed wrist flexion/extension (F/E) attachment mechanism.  
For the wrist R/U handles, Figure 5 demonstrated the robot-human integration and the top and side view of 3D structure and real object movement of the rehabilitation robot mechanism, while Figure 6 illustrated the forearm P/S attachment mechanism.  
Like the design of finger therapy, the forearm should be configured to the robot in the wrist rehabilitation. Firstly, the end-users must be guided to position their forearm on the mechanical forearm support then tie it using woven belts fixed onto a chair. Then fasten their palms to the desired already motioned attachments only by replaceable Velcro straps. However, to get another therapy, the patients should sit on a seat, and the targeted joint and the device should be equal in height, then the patients should produce a grabbing capability to hold the end-effector of the proposed machine during therapy sessions.
# Smart Lighting Systems: State-of-the-Art in the Adoption of the EdgeML Computing Paradigm
## 3 Research Method
### Exclusion Criteria
#### 3.2.2 Identification of Citing Studies
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Ref.** & **Topic** & **Paper’s Keywords** & \\ \hline
[70] & Personalized energy-use behaviors in & Commercial buildings, IoT, Smartphone, \\  & commercial buildings. & Wi-Fi network, energy-use behavior, DL \\
[82] & Digitalization of highways. & Highway, DL, vulnerable road safety, smart city, IoT, vision node, renewable energy IoT; Smart lighting, Smart classroom, \\
[71] & Smart LSs for the learning context. & Environmental data-processing framework, \\  & & Learning context, LED lighting control \\  & & Daylight harvesting, energy-efficiency, \\  & & horticultural lighting, IoT, ML, NNs, \\
[72] & Horticultural LS. & Photosynthetically active \\  & & radiation measurement \\
[73] & Human activity recognition. & Smart lighting, Channel state information (CSI), HAR, EL \\  & & Smart lighting, activity recognition, IoT, \\
[77] & Activity recognition for smart lighting in & KNN, Naive Bayes, Hierarchical hidden \\  & & Markov model, PIR sensor \\  & & Smart lighting, Edge-cloud collaborated \\
[80] & Personalized service provision in a smart & learning, Edge intelligence, DRL, \\  & LS. & Personalized service provision \\
[83] & Smart LS that integrates dimming level & Smart lighting, light intensity, dimming \\  & with light intensity. & level, particle swarm optimization \\
[75] & Digital Twins of Smart Campus. & CPSs, Digital Twin, Urban IoT \\
[84] & Indoor automatic dimming system. & Smart lighting, daylight response dimming \\  & & system, PSO \\
[86] & Building automation. & IoT, smart lighting, building automation, \\  & & DL \\
[74] & Model Compression on Edge & Smart lighting, EC, NearCount, model \\  & Computing-Based Smart Lighting. & compression, product-of-sum \\ \hline \hline \end{tabular}
\end{table} 
Table 27: Data synthesis from primary studies. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Ref** & **Source** & **Year** & **Length** & **References** & **Citations** \\ \hline
[70] & Journal & 2020 & 16 & 159 & 33 \\
[82] & Journal & 2021 & 15 & 108 & 87 \\
[71] & Journal & 2020 & 26 & 54 & 22 \\
[72] & Journal & 2023 & 11 & 42 & 8 \\
[73] & Journal & 202
# Wireless Energy Harvesting with Cooperative Relaying under the Best Relay Selection Scheme
## 6 Conclusions  
In this paper, spectral and energy efficiency in AF-WEH relaying are investigated. We have formulated end-to-end SNR for WEH relaying and compared it with conventional CR over the BRS scheme to compute the SE over link adaptation networks for the optimal power-splitting ratio. The simulation results have shown that the WEH relaying performs better than conventional relaying especially at low SNR, which leads to optimum solutions for energy-constrained systems. Finally, the SE-EE trade-off metric is shown for both non-WEH and WEH relaying systems.  
From a green communication perspective, our work combines different research trends like EH, EC, SE and EE over the SWIPT system. The significant finding from this paper indicates the potential of WEH technique, which can be applied in energy-constrained wireless CR systems and is expected to solve the problem of the conventional battery-operated relay in future 5G wireless networks, such as machine type communications and device-to-device communication. Wireless EH relaying can be employed in a scenario in which the machine type communications requests for device-to-device relays to forward the data to the machine type communications devices due to limited energy. In such a scenario, our proposed technique can significantly improve the EE compared to battery operated relaying. Multiple antenna system at the relay is expected to offer better performance than the single antenna relay employed in this work. As such, it can be the potential extension to our current works.  
Figure 10: The SE-EE relationship for non-EH and EH relaying, \(N=5\).  
Moreover, the effects of a different fading channel environment, such as Rician fading on the proposed system can also be considered for future works.  
**Author Contributions:** Formal analysis, A.A.; Methodology, A.A.; Software, A.A.; Supervision, R.N.; Writing--original draft, A.A.; Writing--review and editing, R.N. and M.I.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The authors would like to thank Centre of Research and Instrumentation (CRIM) and Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM) for their generosity in financial assistance.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Materials and Methods
### Model Formulation
#### 3.2.3 Feasibility Constraints  
The following constraints define the operational feasibility and logical structure of the inspection and rework planning. They ensure consistency between the adoption and activation of inspections, manage the allocation of time resources and number of inspections, and model the propagation of non-detected defects through the assembly process.  
Equation (6) ensures that an inspection methodology can be used (\(X_{s,p,h,k,t}\)) only if it has been installed at the corresponding assembly station (\(Y_{s,h}\)).  
\[X_{s,p,h,k,t}\leq Y_{s,h}\forall s\in S,p\in P,h\in H,k\in K,t\in T \tag{6}\]  
Equation (7) enforces consistency between the activation of an inspection task (\(X_{s,p,h,k,t}\)) and the number of inspections performed (\(N_{s,p,h,k,t}^{contr}\)). If the inspection activity is not activated, no inspections can be performed. Conversely, if the inspection is activated, the number of inspections is limited by the total demand for the corresponding product.  
\[N_{s,p,h,k,t}^{contr}\leq D_{p,t}\cdot X_{s,p,h,k,t}\ \forall s\in S,p\in P,h\in H,k \in K,t\in T \tag{7}\]  
Equation (8) ensures that the total inspection time plus the total rework time does not exceed the maximum allowable time for quality control activities in each period. The inspection and rework time are directly proportional to the number of inspections (\(N_{s,p,h,k,t}^{contr}\)).  
\[\sum_{s\in S}\sum_{h\in H}\sum_{k\in K}\sum_{t\in T}N_{s,p,h,k,t}^{contr}\cdot \left(q_{s,p,h,k}^{time}+rep_{s,p,k}^{time}\cdot def_{p,k}^{prob}\cdot det_{s, p,h,k}^{prob}\right)\leq q_{t}^{max}\ \forall t\in T \tag{8}\]  
Equation (9) defines the number of non-compliant products (\(NC_{p,k,t}^{prod}\)). The first term of the equation captures the expected number of defective units among those that are not subjected to inspection, based on the defect probability. The second term represents the expected number of defective units that are inspected but not detected, which depends on both the defect probability and the detection probability associated with the inspection process.  
\[NC_{p,k,t}^{prod}=\left(D_{p,t}-\sum_{s\in S}\sum_{h\in H}N_{s,p,h,k,t}^{contr }\right)\cdot def_{k,p}^{prob}+\sum_{s\in S}\sum_{h\in H}N_{s,p,h,k,t}^{contr }\cdot def_{p,k}^{prob}\cdot\left(1-det_{s,p,h,k}^{prob}\right)\forall p\in P,k\in K,t\in T \tag{9}\]  
Equation (10) ensures that inspection activities (\(X_{s,p,h,k,t}\)) are only assigned to stations where the control of the corresponding quality characteristics is technically feasible, according to the system's features.  
\[X_{s,p,h,k,t}\leq A_{s,p,k}\ \forall s\in S,p\in P,h\in H,k\in K,t\in T \tag{10}\]  
Equation (11) limits each station to adopting (\(Y_{s,h}\)), at most, one inspection methodology.  
\[\sum_{h\in H}Y_{s,h}\leq 1\ \forall s\in S \tag{11}\]Equation (12) ensures that each quality characteristic is inspected (\(X_{s,p,b,k,t}\)) by, at most, one inspection methodology at one station, avoiding redundancy.  
\[\sum_{s\in S}\sum_{h\in H}X_{s,p,h,k,t}\leq 1\ \forall p\in P,k\in K,t\in T \tag{12}\]
## 4 Discussion  
Patients with dementia are more likely to injure themselves and others during episodes of aggression. The factors associated with these behaviors have been the subject of numerous studies. An important objective is to make caregivers less stressed by identifying the risks associated with these behaviors. Further investigation of the interaction between different cases of behaviors has been conducted by examining vocally and physically aggressive and non-aggressive behaviors in order to better understand the elements involved in agitation and aggressive behaviors in individuals with dementia. The results indicated that verbal aggressiveness was the most disruptive [53; 54].  
In this study, a machine-learning model that is capable of detecting and classifying aggressive and non-aggressive human behaviors according to audio data was developed, trained, and validated. This model is a multimodal analysis combining previous work, which will further enhance the capabilities of a proof-of-concept implementation and detect aggressive behavior in patients with dementia with higher accuracy and increased reliability. As in the previous work, limitations in acquiring clinical datasets from real dementia patients present an obstacle because of ethical concerns as well as patient consent. Collaborations with psychiatric institutions are mandatory to gather such data and demonstrate the applicability of the model under real-life conditions. A proof of concept provided the basis for the development of the model to simulate real-world scenarios, increasing the reliability of the model, preparing it for clinical applications, and allowing future model evaluations with clinical data [55; 56].  
The introduction of audio-based classifier predictions, combined with predictions from the visual classifier using a meta-classifier, enhanced model predictions by a factor of 3%. The visual prediction model in the final testing presented an **ROC-AUC score of 0.94**[18], with a **positive predictive value of 0.92** and a **negative predictive value of 0.74**. The final results, after evaluating the combined predictions using a meta-classifier, showcased an **ROC-AUC score of 0.96**, with a **positive predictive value of 0.87** and a **negative predictive value of 0.88**. These results suggest an **ROC-AUC score** improvement of **2.13%**. The **positive predictive value** slightly decreased by \(-5.75\)%.** A performance drop is anticipated because of the increased complexity of the information used, producing a realistic evaluation. A significant improvement in the **negative predictive value** by **18.92%** indicates that the model can more accurately predict negative occurrences. The late fusion of the two models led to a more precise and reliable application in predicting aggressive and non-aggressive episodes of behavior. Although these metrics show improvement, there are still some things to consider.
# Algorithm for Fatigue Crack Initiation Assessment Based on Industrial Photogrammetry
###### Abstract  
Industrial photogrammetry is a reliable method to achieve submillimeter accuracy when mapping 2D or 3D objects. In the field of fatigue testing of steel welded details, it can be used to find a new method of crack initiation assessment. Fatigue testing is an important method for determining and predicting the durability of structural details in service. The research presented in this paper is based on a computer vision algorithm developed using the open-source code OpenCV library and the Oriented FAST and Rotated BRIEF (ORB) method to provide a solution for the assessment of crack initiation. Within this research, a method for determining the crack initiation period using polynomial functions of a certain degree is developed. The developed algorithm fully automatically determines the test specimen displacement for all imagery and assesses the crack initialization period by polynomial interpolation with a percentage threshold. The algorithm shows us the best results based on a 26 th-degree polynomial with a deviation from the critical value of 5%. The validation of the algorithm was carried out using completely independently recorded data from the hydraulic press used for fatigue tests. The results of all test specimens show that the percentage accuracy of determination crack initiation period is between \(-0.04\%\) for test specimens S355-TA-AW-02 and S355-TA-HFMI-03 and \(-0.82\%\) for test specimen S355-TA-HFMI-03, with the mean of all results being \(0.39\%\).  
industrial photogrammetry; fatigue test; as welded details; crack initiation; OpenCV; ORB
# Unsupervised Image Classification Based on Fully Fuzzy Voronoi Tessellation
## 3 Proposed FVT-FCM Algorithm
### Parameter Estimation  
According to Equation (21), there are four parameters to be solved, i.e., \(\mathbf{\mu}\), \(\mathbf{W}\), \(\mathbf{C}\), \(\mathbf{V}\). Due to the constraint conditions, \(\sum\limits_{j=1}^{m}\mu_{ij}=1,i=1,\ldots,n\) and \(\sum\limits_{l=1}^{k}w_{jl}=1,j=1,\ldots,m\) need to be satisfied for parameters \(\mathbf{\mu}\) and \(\mathbf{W}\). Thus, Lagrange function needs to be established, and then solved using the derivative method. \[ \begin{array}{rl}L(\mathbf{\mu},\mathbf{W},\mathbf{C},\mathbf{V})=&\sum\limits_{i=1}^{n}\sum \limits_{j=1}^{m}\mu_{ij}^{\lambda_{1}}d_{ij}^{2}+\eta\sum\limits_{j=1}^{m} \sum\limits_{l=1}^{k}w_{jl}^{\lambda_{2}}\sum\limits_{i=1}^{n}\mu_{ij}^{ \lambda_{1}}\phi_{il}^{2}+\\ &\sum\limits_{i=1}^{n}\gamma_{i}\left(\sum\limits_{j=1}^{m}\mu_{ij}-1\right)+ \sum\limits_{j=1}^{m}\rho_{j}\left(\sum\limits_{l=1}^{k}w_{jl}-1\right)\end{array}  \tag{25}\]  
where \(\gamma_{i}\) and \(\rho_{j}\) are Lagrange multipliers. To minimize \(L(\mathbf{\mu},\mathbf{W},\mathbf{C},\mathbf{V})\), it is necessary to make the partial derivatives of each parameter equal to 0,  
\[\frac{\partial L(\mathbf{\mu},\mathbf{W},\mathbf{C},\mathbf{V})}{\partial\mu_{ij}}=0,\ \frac{\partial L(\mathbf{\mu},\mathbf{W},\mathbf{C},\mathbf{V})}{\partial w_{jl}}=0\frac{ \partial L(\mathbf{\mu},\mathbf{W},\mathbf{C},\mathbf{V})}{\partial\mathbf{C}_{j}}=0,\ \frac{\partial L(\mathbf{\mu},\mathbf{W},\mathbf{C},\mathbf{V})}{ \partial\mathbf{V}_{l}}=0 \tag{26}\]  
(1) For parameter \(\mu_{ij}\),  
\[\frac{\partial L(\mathbf{\mu},\mathbf{W},\mathbf{C},\mathbf{V})}{\partial u_{ij}}=\lambda_{1} \mu_{ij}^{\lambda_{1}-1}d_{ij}^{2}+\eta\lambda_{1}\mu_{ij}^{\lambda_{1}-1}{ \sum\limits_{l=1}^{k}w_{jl}^{\lambda_{2}}\left(\mathbf{I}_{i}-\mathbf{V}_{l} \right)^{2}}+\gamma_{i}=0 \tag{27}\]  
Thus, we can obtain,  
\[\mu_{ij}=\frac{\frac{(-\gamma_{i})^{\frac{1}{\lambda_{1}-1}}}{ \left[\lambda_{1}\left(d_{ij}^{2}+\eta\sum\limits_{l=1}^{k}w_{jl}^{\lambda_{2 }}\left(\mathbf{I}_{i}-\mathbf{V}_{l}\right)^{2}\right)\right]^{\frac{1}{\lambda_{1}- 1}}}} \tag{28}\]  
According to \(\sum\limits_{j=1}^{m}\mu_{ij}=1,i=1,\ldots,n\), then,  
\[\sum\limits_{j=1}^{m}\frac{(-\gamma_{j})^{\frac{1}{\lambda_{1}-1}}}{\left[ \lambda_{1}\left(d_{ij}^{2}+\xi\sum\limits_{l=1}^{k}w_{jl}^{\lambda_{2}}(\mathbf{ I}_{i}-\mathbf{V}_{l})^{2}\right)\right]^{\frac{1}{\lambda_{1}-1}}}=1\rightarrow(-\gamma_{i})^{ \frac{1}{\lambda_{1}-1}}=\left[\sum\limits_{j=1}^{n}\left[\lambda_{1}\left(d_{ ij}^{2}+\eta\sum\limits_{l=1}^{k}w_{jl}^{\lambda_{2}}(\mathbf{I}_{i}-\mathbf{V}_{l})^{2} \right)\right]^{-\frac{1}{\lambda_{1}-1}}\right]^{-1}} \tag{29}\]  
Substitute Equation (29) into Equation (28).