esa-sceva/satcom-chunk-collection huggingface dataset:
# A Model-Free Online Learning Control for Attitude Tracking of Quadrotors
###### Abstract
Motivated by the need for an accurate, robust, and computation-saving attitude tracking controller for quadrotors, the present paper makes several improvements to the original OLC and applies it to a quaternion-based quadrotor attitude dynamical system. The main contributions of this paper are as follows. 1. A model-free online learning control (MFOLC) scheme is proposed to achieve the attitude tracking of quadrotors. The closed-loop attitude system is uniformly ultimately bounded (UUB) stable when the control torques and the external disturbances are bounded. 2. In contrast to previous studies on the robust attitude tracking control problem for quadrotors, the proposed controller is computationally inexpensive, and does not require accurate model parameters of quadrotors. Both simulation and real-world experiment show that our scheme can achieve attitude tracking in the presence of parameter uncertainties, external disturbances, and noisy angular velocity measurements. The rest of this paper is organized as follows. In Section 2, we introduce the quaternion-based mathematical model of a rigid quadrotor and a control problem statement. Section 3 presents the control law design and a stability analysis. A comparative simulation and a real-world experiment are given in Section 4. The conclusions and directions for future works are given in Section 5.
## 4 Network Planning and Optimization  
Radio network planning (RNP) is an essential task for wireless networks that mainly deals with the calculation of the number, location, and configuration of the radio network nodes. Fifth generation radio planning is now of utmost importance since not only does it require cost-optimized deployments capable of handling a variety of demand constraints, but also since it then affects the optimal placement of the core network elements, e.g., for achieving low latency values. The outline of the RNP process is shown in Figure 6. The RNP optimization algorithm has various parameters as inputs, including network positioning options (dense, very dense, ultra-dense deployment), capacity and coverage requirements, as well as system constraints (total bandwidth, maximum transmission power per MS/BS). Then, optimum dimensioning takes place, where specific metrics should be satisfied, such as minimum site acquisition cost, maximum throughput in the examined area, minimum total radiated energy, or optimization of handover zones [73]. Different inputs are required to solve the cell planning difficulty, depending on the objectives in focus and the phase of planning. Usually, the following inputs need to be known: traffic models, potential site locations, BS model, and propagation prediction models. Typical outputs, include but are not limited to: the optimal number of BSs, the best locations to install BSs, the types of BS optimal for each location, the configuration of parameters, such as antenna height, number of sectors and sector orientation, tilt, power, frequency reuse pattern, as well as capacity dimensioning, e.g., number of carriers or carrier components per sector.
# Revolutionizing Palm Dates Harvesting with Multirotor Flying Vehicles
## 2 Proposed Harvesting System  
The initial concept revolved around approaching the palm branch laterally and utilizing a laser module affixed to a multirotor flying vehicle (MRFV) for cutting. However, thorough investigations revealed the impracticality of this solution due to the laser module's insufficient power. As a result, the MRFV would need to get dangerously close to the branch to perform the cut, which posed significant obstacles.  
Subsequently, an alternative idea was proposed, involving a robot equipped with multiple articulating links to yield either a laser gun or a saw. This robot could be situated beneath the quadrotor's frame. However, upon assessing the dimensions of the robotic arm, it became evident that it would require a robust servo and lengthy links, rendering this proposal unfeasible. Additionally, the flexibility of an extended robotic arm presented further challenges.  
Ultimately, a decision was made to approach the branch from above and suspend the cutting tool (the saw) using a lengthy wire, hanging from an MRFV. The recommended system comprises a quadrotor flying vehicle with a winch mounted beneath it to support a suspended manipulator arm equipped with a saw mechanism, Figure 4. The quadrotor is equipped with an autopilot and a Raspberry Pi (RPI) companion computer linked to a depth camera. A mobile application has been developed for remote control of both the quadrotor's movements and the operation of the saw-equipped arm. Additionally, this application facilitates the viewing of live video feeds from cameras fixed on both the quadrotor's body and the robotic arm. The quadrotor transports the robotic arm to the date palm branch, and the saw mechanism is employed to execute the cut, with the branch securely held by an end effector attached to the arm.
# Occlusion-Robust Multi-Target Tracking and Segmentation Framework with Mask Enhancement
## 4 Experiments
### Experiment Results
In Figure 4a, for horizontally moving pedestrians under static camera, most of the mask IDs remain unchanged after mutual occlusion. In Figure 4b, the movement of the camera and pedestrians causes complete and partial occlusions jointly, resulting in fierce bounding box variations and mask variations within the detections, but our method still keeps the occluded targets' mask IDs correctly. Specifically, in Figure 4a, three targets under trees mutually occlude each other, yet our method maintains their mask IDs without failure. The tracker only loses track when one target crosses the camera view, causing prolonged full occlusion. This aligns with the quantitative results where our method reduces FN by 5.9% compared to ReMOTS_. Notably, even when severe occlusions occur between targets, our method successfully maintains the mask ID throughout the frame sequences. Figure 4b further highlights this capability: the green-masked target (initially heavily occluded by the red-masked one) is accurately segmented and tracked despite drastic mask deformation during occlusion. The ID remains stable after the red-masked target moves away, and the segmentation becomes more precise. Similarly, the yellow-masked target (initially non-occluded) retains the correct mask ID when later obscured by the red-masked person, proving our method's adaptability to dynamic occlusion severity. The performance data and visualization results shown above propose that MHT framework can associate masks across frames and achieve target tracking with segmentation, as well as that it is more adaptive to severe occluded scenarios.
# Energy Harvesting Hybrid Acoustic-Optical Underwater Wireless Sensor Networks Localization
## 2 System Model and Proposed Technique
### System Model  
Consider an AO-UWSN which consists of \(m\) anchor nodes and \(n\) sensor nodes embedded on the ocean floor or suspending sensors. Assuming that \(m<n\) and anchor node locations are known apriori, a fully connected network is considered where each sensor node is able to communicate with close and distant neighbors using optical and acoustic channels, respectively. Every sensor node shares its neighborhood information with the surface buoy. The proposed technique consists of three major steps:  
* **Step 1:** The sensor nodes sweep the neighboring region using the optical channel and find the ranges to its neighbors.
* **Step 2:** Nodes which are not within the reach of an optical channel are communicated by using the acoustic channel and computes the acoustic ranges.
* **Step 3:** The surface buoy fuses the optical and acoustic ranges to compute the pairwise estimated distance matrix and applies a weighted multiple observation dimensionality reduction to find out the location of each sensor node.
## 3 Results and Discussion
### Quantification of Isoflavone Components  
Numerous studies have confirmed that soybean isoflavenes are effective cancer-preventive agents for lowering risks of various cancers [31]. Soybean isoflavenes mainly exist in two forms: aglycon (genistein, daidzein and glycitein) and different types of glucosides (genistin, daidzin and glycitein). The aglycon form is the most biologically active isoflavone for mammal metabolism [6]. In the present study, six kinds of isoflavenes were compared between different treatments (Table 2). Overall, chitol oligosaccharidesacharide treatments induced statistically significant changes in their contents. Regardless of the molecular weight, all the promoting effects of chitol oligosaccharidesacharide were dependent on its concentration. Treatment with an amount of 0.01% chitol oligosaccharidesacharide increased the content of these six isoflavenes in the soybean sprouts extract significantly. Among these elicitation treatments, 0.01% of 1 kDa chitol oligosaccharidesacharide was the most effective one. The highest content of glycitein was 0.251 mg/g dry weight, which was about 20.92-fold compared with the control (0.012 mg/g dry weight) when the seeds were treated with 0.01% of 1 kDa chitol oligosaccharidesacharide. After treatment with 0.01% of 2 kDa and 3 kDa chitol oligosaccharidesacharide, the concentration of glycitein increased to about 18.33-fold and 19.67-fold in comparison with that of the control, respectively. There were also significant differences in the concentrations of genistein, daidzein, genistin, daidzin, and glycitin between the control and 0.01% of 1 kDa chitol oligosaccharidesacharide treatments, and the contents of them increased by 16%, 6%, 62%, 90%, and 68%, respectively.  
The observed impact of chitol oligosaccharidesacharide on isoflavone concentrations was in accordance with previous studies, which reported that seed and foliar treatments of soybean plants with chitosan could bring about a significant increase in the concentration of daidzein, genistein, glycitein, as well as total isoflavone in soybean when compared with untreated control plants [32]. Furthermore, it is known that the biosynthesis of isoflavone is submetabolic, via the malonate and phenylpropanol pathways [33]. [PERSON] and [PERSON] have reported that various biotic and abiotic stresses could stimulate the phenylpropanol metabolism [34]. As a result, it is made clear that chitol oligosaccharidesacharide has the eliciting activity in inducing the stress responses and secondary metabolism of soybean sprouts, and can be used as a harmless alternative to produce high quality soybean sprouts with a good market potential [35].
## 3 D-MSR Protocol Stack Architecture
### Upper Data Link Sub-Layer (Resource Reservation Layer)  
To enable the initial communication between two neighbor nodes (that can be used by the routing layer), these nodes should agree on the same link (timeslot and channel offset). Furthermore, based on the traffic that passes through this edge, more links need to be reserved to enable real-time end-to-end connections. In the centralized approach, the network manager schedules the initial communication resources as well as the required resources for further communications and fills in the data link layer communication tables in each network device based on those schedules. However, TSCH does not describe any distributed mechanism, by which either the initial communication links for neighbor nodes or more communication resources, for real-time end-to-end communications, can be allocated. For this reason, we define an upper data link sub-layer (resource reservation layer) on top of the data link layer, to configure the data link layer communication tables and to schedule the communications between neighbors.  
Two modules are defined in the upper data link sub-layer: _neighbor connection manager_ and the D-SAR module. The _neighbor connection manager_ allows the TSCH MAC protocol to be glued onto the higher layer (routing layer), besides providing initial neighbor nodes communications. The D-SAR module reserves communication resources along the path in different phases of the network operation to enable real-time end-to-end connection either for management traffic purposes or to sensor/process data traffic. As is shown in Figure 5(b), _neighbor connection manager_ and D-SAR modules configure the data link layer communication tables (the link table and superframe table), to allocate or release the communication resources. The remainder of this section focuses on the _neighbor connection manager_ and D-SAR module respectively.
## 2 Methodology
### Data Gathering and Search Strategy  
In this paper, we use the WoS repository as the main source for top-tier scientific publications. For this research, the query strings in the WoS were crafted as four separate subrequests:  
1. \"((\"operational technology\" OR OT) OR (\"industrial control system\" OR ICS)) AND threat AND vulnerab* AND (security OR countermeasure*);
2. \"((\"operational technology\" OR OT) OR (\"industrial control system\" OR ICS)) AND protocol AND vulnerab* AND (security OR countermeasure*);
3. \"((\"operational technology\" OR OT) OR (\"industrial control system\" OR ICS)) AND ((intrusion OR attack OR malware) AND detection);
4. \"((\"operational technology\" OR OT) OR (\"industrial control system\" OR ICS)) AND security AND ((\"machine learning\" OR ML) OR (\"deep learning\" OR DL)) AND dataset'.  
Keyword cleaning and synonym merging were not performed. The goal was to minimize the risk of excluding research publications from the search due to mismatched keywords in the requests and to obtain overlapping results of keyword synonyms for further analysis of the authors' understanding of the problem area and the terminology/keywords in use.  
Requests cover the period from 2018 to 2024 and include the most relevant keywords. The search was split into 4 sub-requests in order to tune the request as much as possible on one hand (in each sub-request) and to cover all topics defined as important (by creating several specialized sub-requests). The idea of creating a single request was rejected since it could potentially remove some important articles from the results due to high request complexity. Later, a joint list of publications was created, and duplicates and no appropriate publications included due to wrong keywords defined by the authors were removed. Review and removal were performed manually in order to minimize the risk of mistakes.  
The search parameters were set to encompass 'full record and cited references' to guarantee a thorough collection of relevant documents. Regarding inclusion criteria, the primary categories of publications incorporated were articles, review articles, and conference proceedings papers, while other types of publications, such as letters, book chapters, or abstracts, were removed from the search. The search was conducted in October 2024, resulting in 1133 results without filtering. After removing inappropriate and duplicating papers, 1124 were left, of which 583 were classified as proceeding papers, 517 as articles, and 32 as review articles.
# Context-Based Energy Disaggregation in Smart Homes
## 5 Experimental Results
### Data Analysis and Pre-Processing  
As a preliminary step, we have evaluated Tracebase [10], which is the dataset that we adopted in this work, and performed few preprocessing operations on the data. Tracebase, which has been introduced in Section 3, is a public, password-protected dataset. It consists of real power consumption traces of a range of electric appliances that have been collected in more than ten households and office spaces. The trace collection script, described by [PERSON] _et al_. [10] (2012), has been configured to gather one sample per second; furthermore, every sample is stored with its timestamp. However, because of the topology of the data collection network and the encountered delays, the authors stated that traces may also show a higher or lower frequency; this physical characteristic forced us to perform an accurate data analysis and a pre-processing phase that are described below. Moreover, this dataset is conceived to perform the appliance classification, thus it provides reliable power consumption traces as they all have been detected with a dedicated smart plug. Therefore, it does not include an aggregate consumption signal. In this work, we set up a synthetic aggregate power trace consumption that is composed of a sample-sum of a selected subset of the available traces. Indeed, Tracebase includes up to 1270 monitoring traces of 122 devices of 31 different appliances types, but we used a subset made by 423 traces of 43 devices belonging to 6 types (Table 4), by selecting those devices that presented a major number of traces and less holes in the monitoring interval.  
As stated by [PERSON] _et al._[10], Tracebase presents several detection inhomogeneities; a daily power trace can, in fact, show more than one sample per second or a lack of data for some seconds. Hence, we processed all the daily power trace by normalizing each one with 86,400 samples (number of seconds in a day). We have performed the zero-padding or the average operations on the missing/surplus samples and put the obtained values in a normalized trace. These operations have become reasonable after the data analysis. For instance, when a device results in being disconnected to the electric network (OFF state), the meter has obviously gathered a zero-consuming trace; thus, we have zero-padded the missing samples and reduced in an only zero value the surplus samples gathered at the same second. Moreover, when the device is active (ON state) we have evaluated the samples immediately before and after the missing one/ones and performed an average operation of them and then filled the missing value with the obtained result. An analogous operation has been performed in the case when there was more than a sample per second.
## 4 WT/PV ES Optimization Decision
### Stage 1: WT/PV Siting and Sizing
#### 4.2.1 Objective Function  
The objective function of the upper optimal programming is determined by:  
\[F=\min(F_{1},F_{2}). \tag{26}\]  
1. Power-flow voltage stability \(F_{1}\)  
Reasonable WT/PV connection will improve the voltage distribution of the entire distribution network and enhance the power-flow stability margin of the distribution network. The goal is to maximize the power-flow voltage stability indicator \(L\) within a day, as follows:  
\[F_{1}=\max\{L_{1}\quad L_{2}\quad\ldots\quad L_{24}\}. \tag{27}\]  
2. Total investment cost \(F_{2}\)  
Considering the initial investment budget of WT/PV and offshore construction, \(F_{2}\) is defined as the total cost of offshore WT/PV investment and construction; that is:  
\[F_{2}=C_{1}P_{W}+C_{2}P_{PV}, \tag{28}\]  
Figure 4: Hierarchical planning process.  
where \(C_{1}\) and \(C_{2}\) are the per-capacity purchase and construction costs of WT and PV units, respectively, and \(P_{W}\) and \(P_{PV}\) are the installed capacities of WT and PV units, respectively.
# Turbofan Engine Health Prediction Model Based on ESO-BP Neural Network
## 4 Simulation Experiments and Result Analysis
### Comparative Experiments  
To evaluate the prediction accuracy and stability of ESO-BP, we compared its performance with that of convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), and BP neural networks trained using two different functions. The CNN, RNN, and LSTM models employed in this study used the Adam optimizer [41] for training. BP(SCG) and ESO-BP(SCG) were trained using the scaled conjugate gradient (SCG) backpropagation algorithm [42]. BP(LM) and ESO-BP(LM) were trained using the Levenberg-Marquardt (LM) backpropagation algorithm [43].  
Table 5 displays the averages (Avg) and standard deviations (Std) of the evaluation metrics after 100 model training turns. The results indicate that ESO-BP consistently outperformed mainstream models in all the evaluation metrics, proving the effectiveness of the ESO-BP neural network model in predicting the RHI of turbofan engines.  
The results demonstrate that ESO-BP(LM) attained the highest prediction stability, showcasing the smallest standard deviation across all evaluation metrics. This suggests that combining the LM algorithm with improved initial values assists neural networks in avoiding local optima, leading to increased stability. In terms of prediction accuracy, ESO-BP(LM) outperformed in FD001, FD002, and FD004, showcasing a superior predictive capability across various operational conditions. The R\({}^{2}\) and RMSE of ESO-BP(SCG) on FD003 were slightly better than those of ESO-BP(LM), indicating that predicting the RHI of turbofan engines under multi-fault conditions poses a particular challenge. ESO improved the performance of BP neural networks with both SCG and LM, indicating that using ESO to initialize BP neural networks is a beneficial approach. The BP neural networks in the following study were trained with the LM algorithm for a better performance, according to the results in Table 5.  
Figure 11: Comparison of predicted and real degradation curves of four engines in FD001 test set.  
To illustrate the deviation between the predicted and real RHI values for each operating cycle of all test set engines in FD001, the real RHI value serves as the horizontal axis, and the predicted RHI value serves as the vertical axis in Figure 12. The deviation between predicted and real values for the different models is shown in Figure 12. The majority of the scatter points from the four models align closely with the reference line. This figure illustrates that the ESO-BP(LM) neural network model exhibits fewer anomalous scatter points and achieves the best prediction results. Figure 13 shows that the predicted RHI degradation curves of the turbofan engines using the proposed method are closer to the real degradation curves compared to other models.