esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Therefore, understanding the robot's geometry and workspace is crucial to avoid singularities, which can cause significant problems during operation. In this study, we consider a Delta robot equipped with stepper motors as inputs. Once the desired trajectory of the end effector is determined, inverse kinematics can be adopted to solve for the joint inputs analytically or numerically using optimization methods. Moreover, control algorithms can be implemented to further improve the reliability of the motion and the trajectory tracking accuracy of the Delta robot. The objective of this study is to develop a hybrid of data-driven and model-based sliding mode control algorithms instead of using the conventional optimal control to improve the trajectory tracking performance. The inverse kinematics of the Delta robot is analytically difficult to express in the control-affine form, which complicates the control design process. Therefore, to deal with this issue, we propose that neural networks are used to create a control-affine nonlinear input-output dynamic model of the Delta robot, considering the stepper motor angles and velocities as inputs. The neural network input-output model is trained with randomly sampled data in a sufficiently large workspace of the Delta robot. Furthermore, the model-based sliding mode control for trajectory tracking is designed based on the neural network model. The proposed neural networks make full use of the inverse kinematics while the sliding mode control delivers excellent performance in trajectory tracking for a Delta robot. We should point out that the proposed control-affine nonlinear dynamic model of the Delta robot is not derivable either from Lagrangian dynamics or inverse kinematics. It is completely data-driven and can be updated once new experimental data become available. This is also one of the contributions from our work. Section 2 presents the architecture of the Delta robot and its inverse kinematics. Section 3 presents the control-affine nonlinear dynamic model in terms of the neural networks. Section 4 presents the sliding mode control with the neural network model and extensive numerical simulation results to validate the proposed model and control design. Section 5 concludes this paper.
## 3 Related Research
### Summary  
In considering the related research, the studies identified are limited in terms of'real-time' disaster service response. An analysis of the studies clearly identifies the need for a rapid, and importantly, a planned and organised approach to enable effective and timely decisions to be made in _'real-time'_. However, all the research identified all approach disaster management and response from a different perspective.  
A survey of data management and analysis in disaster situations [16] has identified that disaster management has received increasing attention by computer scientists from multiple research disciplines. The focus of such research has been the drive to devise ways to manage and analyse the data produced in disaster management situations. A limitation in the current methods considered include: (a) they are applied in specific disaster scenarios with no optimal route identification or coordination with control services, (b) the methods have failed to consider or aggregate expert [human] sensibilities and preferences, weather influences, environmental phenomena, and real-time data sets.  
In this paper we attempt to address these limitation by introducing our novel approach which implements intelligent autonomous decision-support using hedge algebras integrated with _Kansei_ engineering evaluation. The proposed approach has been conceived to implement autonomous path planning and control of robotic systems applied in emergency scenarios.
# Grapevine Downy Mildew Warning System Based on NB-IoT and Energy Harvesting Technology
## 1 Introduction
### Contributions  
The contributions of this paper are as follows:  
* A new algorithm for the warning system and a model for the early prediction of downy mildew of grapevines (primary and secondary infections) in the vineyard use-case were developed and presented. In addition to temperature and relative humidity, it uses additional sensor data (hourly based data, wind speed, and day/night period) to make the model more accurate than traditional methods.
* A complete design and overview of the implementation of the NB-IoT and energy harvesting-based early warning system (called Winet) with a wireless sensor node for monitoring several environmental parameters are presented, including both hardware and software (cloud-based front-end, back-end, and mobile application). To the best of our knowledge, there has not been any published scientific research to date using the NB-IoT technology in downy mildew EWS implementation.
* The proposed model is compared with the alarms provided by the commercial Field-Climate system [29] that uses iMetos1 and iMetos3.3 data collection system, for the three particular vineyard locations of Vrsacki Vinogradi, Serbia, Rimski Sancevi, Serbia,and Trient, Switzerland, during the 2020 season. The results show a full correlation with iMetos(r) alarms in the case of severe infection warnings for all given locations. The correlation ranges between 0.4 and 1 for moderate and light infection alarms.
* In addition, the proposed model is compared with the latest mechanistic model [20]). The correlation ranges between 0.55 and 0.71 for the secondary alarms.  
The paper is organized as follows. A detailed system overview is provided in Section 2. The research results are presented in Section 3 while the discussion related to obtained results is in Section 4. Finally, the conclusions and future research directions are discussed in Section 5.
# Edge and Cloud Computing in Smart Cities
## 5 Challenges, Open Issues, and Future Research Directions
### Resource Allocation in Edge-Cloud Environments  
Efficient resource allocation in edge-cloud architectures is critical due to heterogeneous computational capacities, dynamic service demands, and network constraints. Unlike traditional cloud-based infrastructures, where centralized schedulers optimize resource distribution, multi-tier edge-cloud environments require decentralized and adaptive allocation mechanisms that can respond to real-time variations in workload and network conditions [176; 177].  
One of the main challenges is coordinating resource allocation across tiers. Since edge nodes have limited processing power and storage, prioritizing which tasks should be processed locally and which should be offloaded is non-trivial. Current strategies rely on heuristic-based scheduling, but RL and federated optimization are emerging as promising approaches. Additionally, the trade-off between latency and computational load must be optimized, as overloading edge nodes can lead to increased response times and energy consumption [178; 179; 180].  
Another key issue is interference in shared-resource environments. When multiple applications compete for edge resources, contention leads to degraded performance. Multi-tenant resource isolation, dynamic slicing, and QoS-aware workload allocation are necessary to ensure fair resource distribution. Moreover, in mobility-driven smart-city applications, handover-aware resource allocation is required to prevent service disruptions during user transitions between edge nodes [181; 182].  
The unpredictability of workloads in edge-cloud environments necessitates RL-based resource management strategies. Future research should focus on predictive workload balancing and decentralized task scheduling, which leverage AI models to dynamically adjust resources based on demand. FL approaches can further enhance allocation by enabling real-time adaptation while preserving privacy [183; 184; 185; 186].
## 3 Proposal Transmission Structure for an IP-Based Terrestrial Broadcasting System
### Proposal for an Internet Protocol-Based Terrestrial Broadcast Transmission Structure  
The transmission structure proposed in this paper uses a token bucket and leaky bucket. The DASH segment files are variable in size and must be transmitted within a limited time; thus, they have a burst characteristic. However, the broadcasting network transmits at a constant bit rate (CBR). Therefore, as illustrated in Figure 2, this paper proposes a structure that combines the token bucket and leaky bucket.  
A token bucket can accommodate a certain level of burst occurrence, and the leaky bucket can be transmitted at a CBR. Conceptually, multiple token buckets are used in consideration of multiple services (sessions). In addition, to efficiently use broadcast transmission resources, this paper proposes a structure that can serve by combining the best-effort traffic.  
The operation of the transmission structure proposed in this paper is as follows. The DASH segment file is input to each token bucket queue \(\mathit{TQ}_{1}\sim\mathit{TQ}_{N}\) for every unit time \(\rho\). Each token bucket can transmit data at a rate of as much as the token generation (service bit rate) \(\mathit{TC}_{1}\sim\mathit{TC}_{N}\) that generates tokens per unit time \(\rho\). The amount of data that each token bucket can transmit with the maximum burst is limited to the size of each token bucket \(\mathit{TB}_{1}\sim\mathit{TB}_{N}\). At this time, to avoid overflow, the total sum of \(\mathit{TC}_{1}\sim\mathit{TC}_{N}\) should be less than or equal to the service bit rate, \(\mathit{LC}\), of the leaky bucket, and the total sum of \(\mathit{TB}_{1}\sim\mathit{TB}_{N}\) should be less than or equal to the leaky bucket size \(\mathit{LB}\).  
The main cause of delay is when a DASH segment file larger than the token generation ratio is input to the queue. The delay increases in proportion to the volume of data remaining in the queue.  
The leaky bucket transmits data for the same size per unit time \(\rho\), and the delay of the leaky bucket is constant. If the leaky bucket has a remaining resource, the best-effort traffic can be transmitted using these resources to increase their efficiency.  
Figure 2: Proposal for the transmission structure for an internet protocol (IP)-based terrestrial digital broadcasting system. The token bucket can accommodate burst segment files, and the leaky bucket can be transmitted at a constant bit rate (CBR).
# Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects
## 2 Analysis of Modern-Day Smart Services
### Voice-Controlled Digital Assistants
IoT-enabled output devices are needed to render the results, e.g., smart speakers, a smartphone application, or compatible third-party devices such as smart light bulbs. To trigger these devices, a VDA backend has various adapters, e.g., an adapter to generate natural language to answer questions verbally or adapters by third-party vendors to control their IoT devices [66]. No matter which service is requested by a user, however, an important aspect of VDA is that the misinterpretation of his or her commands is minimal. If this is not achieved, a breach of immersion occurs and user acceptance decreases [67]. To this end, it is imperative that in the backend, models are trained based on many inputs from different users. Furthermore, the incoming requests must be thoroughly refined in order to be able to successfully interpret what is being said. In addition to monitoring the quality of the VDA, a feedback loop is required that involves human operators listening to selected recordings [68]. It is obvious that VDA also raise privacy concerns. The VDA providers are able to identify all of their users. Thus, they are able to link the recordings to the users. Taking this into account, the fact that VDA are able to wiretap users permanently is a particularly disturbing concern. Users do not know what data are actually captured and forwarded for processing. Even third parties can gain access to the data, e.g., for quality control or to provide an affiliated service. So, the privacy concerns regarding VDA are reasonable [69]. The inherent opportunities and privacy threats of voice-controlled digital assistants can be broken down as follows:  
Opportunities:  
* VDA allow voice-based control of smart devices, making them particularly helpful, e.g., for people with motor disorders. * The natural language approach of VDA reduces the technical hurdle for people who are less tech-savvy. * The knowledge that a VDA (theoretically) has at its disposal is almost unlimited. That is, VDA can be used to access required information quickly and easily in almost any situation in life. Privacy Threats:  
* Since a VDA waits for its specific keyword, it is never completely off. That is, all conversations are permanently recorded. * If a VDA is activated using the keyword and the voice recording is forwarded for processing, it is not possible for data subjects to trace who has access to it. * For third parties, a VDA is indistinguishable from conventional loudspeakers. Therefore, they are completely unaware that their conversations are also being recorded. Figure 3: Ecosystem and Architecture of a Voice-Controlled Digital Assistant.
# Damage Identification Method Using Additional Virtual Mass Based on Damage Sparsity
## 6 Conclusions  
In this study, an IOMP method combined with the additional virtual mass was developed to improve damage identification based on structural modal and minimize susceptibility to structural modal data, measurement point distribution, and noise, considering the initial condition of sparse structural damage. Through numerical simulation of a simply supported steel beam and the experimental verification of the steel frame model, the following conclusions are drawn:  
1. The IOMP method combined with the additional virtual mass method can effectively expand structural modal information to improve the accuracy of structural damage identification while constraining the optimization results to obtain sparse optimization results consistent with the actual local damage.
2. Compared to the Lasso regression model with the \(l_{1}\) norm and the ridge regression model with the \(l_{2}\) norm, the IOMP method selects independently damage substructures, which satisfies the initial condition that structural damage is sparse. In addition, the IOMP method does not need to select the regularization coefficient \(\lambda\), and thus, eliminates its direct influence on the optimization results to obtain more accurate damage identification results.
3. The IOMP method improves the deficiency of the OMP method in that the recognition result is a local optimal solution when combined with the additional virtual mass method, which integrates the recognition result better. Furthermore, it improves the logical defects of the OMP method effectively in predicting damage sparsity. The IOMP method is also accurate for selecting damaged substructures.  
Conceptualization, Q.Z. and J.H.; methodology, D.X. and H.W.; software, D.X. and J.H.; validation, Q.Z. and L.J.; formal analysis, Q.Z. and D.X.; resources, J.H.; writing--original draft preparation, Q.Z. and D.X.; writing--review and editing, J.H. and L.J.; visualization, H.W.; supervision, Q.Z.; funding acquisition, J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (NSFC) (51878118), of the Educational Department of Liaoning Province (LJKZ0031), of the Liaoning Provincial Natural Science Foundation of China (20180551205), of the Fundamental Research Funds for the Central Universities (DUT19 LK11), and of the National Science Centre, Poland (project 2018/31/B/ST8/03152).  
Not applicable.  
Figure 12: Contrast with sparsity in frame Experiment.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.
## 3 Background
### Approximate Q-Value Functions  
As mentioned above, ICE uses \(\hat{V}(\varphi^{t})=V^{0..t-1}(\varphi^{t})+H^{t..h-1}(\varphi^{t})\) to guide the search and \(H^{t..h-1}(\varphi^{t})\) is a heuristically estimated value using approximate Q-value functions. There are three well-known approximate Q-value functions, QMDP, QPOMDP and Q\({}_{\text{BG}}\), which are all approximations for the optimal Q-value function Q\({}^{*}\). Although the optimal policy can be extracted from Q\({}^{*}\), it is costly and impractical to compute.  
[PERSON] and [PERSON] [25] used QMDP to solve Dec-POMDP that is approximated as an underlying MDP. This underlying MDP is defined by a single agent which takes joint actions but has the same state set, transition function, and reward function as original Dec-POMDPs. It assumes that the state can be fully observed and the single agent gains more information than Dec-POMDPs do.  
QPOMDP[25] is another approximation for Q\({}^{*}\). In this case, Dec-POMDPs are treated as underlying POMDPs defined by a single agent which takes joint actions and receives joint observations but has the same state set, transition function, observation function, and reward function. It assumes that underlying POMDPs are settings of centralized control, which allow for better coordination. This means that the single agent knows the joint action-observation history while the agents in Dec-POMDPs can only gain local information such as individual actions and observations.  
Q\({}_{\text{BG}}\) is proposed by [PERSON] et al. [4], which assumes that the agents know the joint action-observation history \(\overset{\rightarrow}{\theta}^{t-1}\) and the joint action \(\overset{\rightarrow}{a}^{t-1}\) for the previous time step \(t-1\). This assumption defines a Bayesian games (BGs) for each (\(\overset{\rightarrow}{\theta}^{t-1}\),\(\alpha\)) pair. The actions taken by agents can be solved by constructing BGs.  
QMDP, QPOMDP and Q\({}_{\text{BG}}\) are all proved to be upper bounds of Q\({}^{*}\)[4]. These approximate Q-value functions form a hierarchy of upper bounds to Q\({}^{*}\):  
\[\text{Q}^{*}<\text{Q}_{\text{BG}}<\text{Q}_{\text{POMDP}}<\text{Q}_{\text{MDP}} \tag{4}\]  
This means that all these approximate Q-value functions are admissible heuristic for Dec-POMDPs.  
Apart from the Q-value functions mentioned above, [PERSON] et al. [18] proposed a factored Q-value function as a heuristic payoff function for factored Dec-POMDP. The precondition of a decomposition is that the Q-value function of every joint policy is factored. Each local component of this kind of Q-value function depends on a subset of state factors and the action-observation histories and actions of a subset of agents.
## 3 Induction Coils  
### Coils Type  
Induction coils have advantages such as low cost and endurance, so they are widely used in wire rope defect detection. Additionally, induction coils have many other forms, including coils made of enameled wire, flexible printed coils (FPC), and integrated inductance. And the three different kinds of coils shown in Figure 11 are tested in the next experiment.  
To begin with, for the integrated inductance, due to its sensitivity being relatively low, they won't introduce too much noise, and it is usually applied in detection at a state of small lift-off distance. Thus, the signal of the defect at a large lift-off distance gets restrained by its low sensitivity.  
Next, for the flexible printed coils, its sensitivity is a little bit higher than integrated inductance, because it can detect serious defects such as the irregular joining of wires or untwining of wires in strands. Nonetheless, a single broken wire signal cannot be distinguished from the sampling.  
Figure 8: (**a**) Single broken wire; (**b**) Binding single broken wire; (**c**) two broken wires.  
Figure 10: (**a**) 3D model sensor head; (**b**) Real sensor head.  
Figure 9: (**a**) 20 mm lift-off distance signal with the 24 mm diameter wire rope; (**b**) 25 mm lift-off distance signal with the 24 mm diameter wire rope.  
At last, the coils made of enameled wire have a high sensitivity leading to overwhelmed noise in the useful signal. Three different kinds of signals showed in Figure 12, where ID denotes induction, FPC denotes flexible printed coils, and Coil means coil made of enameled wire  
According to the experiments above, all three different kinds of coils cannot get the distinguishable signal. Therefore, it is challenging but meaningful to find one kind of suitable sensor in a wire rope detecting system. The results also demonstrated that the Coil has the best sensitivity and could be used for large lift-off distance detecting.
## 1 Introduction
### Contributions  
As mentioned previously, the usage of meta-learning methods for model training is a proven idea to solve the problem of poor performance of AMC tasks under the few-shot condition to some extent. But the existing methods are mainly concentrated in the field of image classification, and the effect is not satisfactory when directly applied to the task of classification of modulated signals. For the characteristics of modulated signals, the existing meta-learning methods need to be appropriately modified and optimized to extract more effective prior knowledge. Targeting the above problems, this paper combines the method of image-based modulated signals with the meta-learning method, which provides a new way for the AMC task under few-shot conditions. The contributions of this paper are summarized in the following four points:  
* We propose a novel approach that combines time-series signal visualization with meta-learning to tackle the small sample problem. We transform communication signals into images and employ a metric-based meta-learning method for feature extraction and classification.
* In the signal representation stage, we employ Singular Spectrum Analysis (SSA) to reduce noise and eliminate redundant information in the signals. Subsequently, the signal sequences are transformed into two-dimensional images. This method enhances the exploration of signal content through signal decomposition and reconstruction. In contrast to traditional sequential signal processing methods that only extract features between adjacent time steps, this approach can capture the correlations between any two time points.
* In the classification stage, we adopt a metric-based relation network. The feature embedding module converts samples into high-dimensional feature representations. Then, the relation metric module measures the distances between samples. Ultimately, this approach achieves AMC under the small sample condition.
* We conduct simulations on the publicly available RadioML.2018.01a dataset to validate the advantages of the proposed method. Compared to the direct application of traditional machine learning methods for AMC, the method proposed in this paper attains higher recognition accuracy while employing a smaller number of samples. Furthermore, it demonstrates superior recognition capability when contrasted with the conventional approach of representing sequences.
# Design and Control of an Ultra-Low-Cost Logistic Delivery Fixed-Wing UAV
## 4 Flight Control System  
### Flight Control System Overview  
At the center of the system, as shown in the Figure 29, is the flight controller, which is connected to the battery and GPS. The battery provides power, and the GPS module is used for positioning and navigation. The flight controller controls several parts of the UAV, including the left and right ailerons, the rudder, and the hanging tail, through several micro-servo motors. These servomotors are responsible for adjusting the position of the respective components to change the direction of the flight and attitude of the UAV. Two electronic speed controllers (ESCs) are connected to the rear left and rear right motors. The ESCs control the speed of the motors, which, in turn, control the thrust and flight speed of the UAV. An NUC is connected to the flight controller for processing more complex data or performing advanced flight tasks. An image recognition module is also connected to the battery.  
Figure 28: Simulated drop point of 500 mL water bottle thrown multiple times. (The deeper the red dot, the closer the distance to the target, the blue represents the middle of 3–5 m, and the green represents exactly 5 m).  
Figure 29: UAV structure schematic.