esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Routing
### Routing Tree Maintenance  
In our data-aided routing protocol, the route maintenance of a node is triggered under the following events:  
(a) When a node's link quality to its parent gets worse or the link has failed.  
(b) When a node receives a pull beacon.  
After routing maintenance has been triggered, a node sends request beacons periodically to select a proper parent. If it finds a parent and the route cost to BS is significantly decreased (we choose 20% decrease as threshold), the node broadcasts this event using pull beacons because it may provide a better route for nearby nodes. On the other hand, if the new route cost is significantly increased (we choose 20% increase as threshold) or it cannot find a parent, the node also informs the event to its children using pull beacons so that they will adjust the network topology in time.  
The scene of route failure is demonstrated in Figure 5 (a). The link quality between node D and its parent B is estimated to be worse by transmitted data packets, and routing maintenance is triggered. As shown in Figure 5 (b), C is selected as its new parent.  
As shown in Figure 6 (a), when node C detects that node A is removed or failed, it can't find an available parent in its communication range. [PERSON] tells its children [PERSON] and F that the route is invalid by broadcasting pull beacons. Routing maintenance of E and F is triggered and a new routing tree is reconstructed as shown in Figure 6 (b).  
When a new node joins network, routing maintenance is triggered. As shown in Figure 7 (a), node F is a new node and its parent is BS. F broadcasts pull beacons. D receives the pull beacons and routing maintenance process is triggered. Eventually D chooses F as its new parent, as shown in Figure 7 (b).  
\begin{table}
\begin{tabular}{|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|} \hline \(i\) & **1** & **2** & **3** & **4** & **5** \\ \(C_{i}\) & 8 & 7 & 10 & 7 & 9 \\ \(H_{i}\) & 8 & 7.5 & 0 & 7 & 0 \\ \(T_{i}\) & 9 & 9 & 0 & 8 & 0 \\ Routing maintenance & No & No & Yes \(C_{3}>T_{2}\) & No & Yes \(C_{5}>T_{4}\) \\ \hline \end{tabular}
\end{table}
Table 2: Example of estimator.  
Figure 5: Declining of link quality triggers routing maintenance.  
If the change in link cost is significant (20%), D will inform its neighbors the cost change by pull beacons.
## 4 Discussion
### Impact of Think-Aloud on EMG Signal Integrity  
A component that should be considered in the interpretation of our results is the nature of the think aloud protocol. The flex controller system featured in game is responsible for directional control in the game environment. The target muscle group, the temporalis muscle, is responsible for the elevation, retraction, and side-to-side movement [15]. These actions are often also accomplished while speaking, leading to electrical signals that are received by the electromyographic electrodes that cause unintentional character movement. Many individuals within the study experienced difficulty completing the game while verbalizing their thoughts. This may have caused other thoughts and feedback from the game to be missing from their responses. As participants were encouraged to provide a steady stream of thought, this likely compounded any frustrations that they may experience. Though a retrospective think-aloud paradigm may provide benefits in some cases [45], the intention of this usability study was to receive comprehensive feedback for the eye-tracking interface. Retrospective think-aloud studies have been postulated and proven to minimize participant feedback because of a lapse in memory [46]. Retrospective think-aloud protocols make cause participants to be more biased in their feedback as participants can more easily conceal their thoughts [47]. This same scenario would likely not occur when our target population, individuals with ALS, is utilizing the gaming platform for training purposes. Future studies in the field of usability testing in alternative wheelchair systems may be able to modulate the methodology of this trial for more optimized results. Additionally, previous work in the field suggests that concurrent think-aloud may only slow down execution of task and its efficacy [45], supporting its validity in dual-task interference theory. Though concurrent think-aloud was ultimately our preferred methodology as, time on task was not a priority and the immediate verbalization of working memory prevented overload as per cognitive load theory, modification may provide useful. In specific, the addition of clear operational periods where participants can work in silence versus verbalization periods may prove useful. This technique may allow the benefits of both concurrent and retrospective think-aloud methods to come into play.
# Pitch Channel Control of a REMUS AUV with Input Saturation and Coupling Disturbances
## 2 Model  
System models of vehicle often used in adaptive control applications can be separated into linear and nonlinear systems in terms of the form of plant models. However, linear plant system introduced in most adaptive control is derived from realistic vehicle via ignoring high-order or nonlinear terms in vehicle system. The six DOF nonlinear system model of REMUS 100 AUV supplied by Hydroid (a US subsidiary of Kongsberg Maritime and the manufacturer of marine robotics, Houston, TX, USA), obtained via combining theory and empirical data provides a more precise vehicle platform for improving the control of REMUS [30]. In this section, the six DOF general model and kinematics transformation of REMUS AUV are described in detail. To comprehensively evaluate our proposed control method, the hypothetical linear plant model of REMUS in pitch channel is then achieved for L1 AC controller.
## 5 Discussion
### Research Limitations and Future Prospects  
Despite the significant contributions of this study to the field of student academic performance prediction, several limitations remain. Firstly, this study has not fully considered factors that may influence academic performance, such as student personal characteristics, teaching methods, and family background. Future research could incorporate these factors to build a more comprehensive prediction model. Secondly, although multiple machine learning models were employed, the interpretability analysis of the models is not sufficiently in-depth. Future work could explore methods to enhance the interpretability of \"black box\" models. Regarding course relevance analysis, while strong correlations between courses were identified, the underlying causes were not explored in detail. Future research could examine how course content and teaching methods affect academic performance. In terms of model performance evaluation, although common evaluation metrics were used, the analysis of performance across different subgroups or under specific conditions was limited. Future studies could introduce more refined evaluation methods, such as fairness assessment and robustness testing. Finally, given that this study is based on cross-sectional analysis, lacking long-term tracking, future research could adopt longitudinal studies to track the dynamic changes in students' academic performance.
## 5 Discussion
As remote sensing technology advances, real-time atmospheric correction methods like the one proposed in this study will become increasingly essential for ensuring high-quality satellite imagery and reliable quantitative remote sensing products.
## 4 Overview of Wearable Interface for Providing Haptic Feedback
The SOA has to be greater than the signal latency since the authors employed numerous actuators to produce phantom feelings [88]. Accordingly, SOA \(\ \leq\ \text{Ts}\) demands that the signal duration Ts be less than 0.0698 s [84]. To obtain this, the authors in [84] used an RGB-D sensor to track the hand position and a computer to calculate the target solicitations. A sinusoidal signal with a 250 Hz frequency represents the source signal. Then, the driving signals are synthesized through two DAQ boards coupled with a custom piezo amplifier according to the actuators' target intensities. The mean Weber fraction for the developed haptic systems is depicted in Figure 12a. Specifically, it reports the outcomes of prior work in which a virtual item dimension assessment task was carried out using a mechanical feedback interface [89]. Figure 12b illustrates the experiment's outcomes related to personal realism evaluations, classified by the kind of haptic interface. Participants gave Type-B interfaces' rendering of the contact a considerably better realism rating than Type-A ones. The experimental results show that the proposed vibrotactile fingertip interface renders virtual objects with a perception comparable to that of a force feedback interface. Through this investigation, it has been shown that different technologies, sometimes even combined, can provide efficient and reliable haptic feedback for multipurpose applications, starting from biomedical to entertainment implementation. Therefore, the following table (Table 2) resumes the characteristics and technical specifications of all the devices analyzed in this paragraph. Figure 12: Average Weber fraction according to the haptic feedback type (**a**). The results concerning the haptic condition come from earlier research [89]. Mean subjective assessment of realism according to the haptic interface type (**b**) [83]. For both images, the standard errors are shown by the error bars. The single-star symbol (*) indicates that the observed significance level (_p_-value) of the test is lower than 0.05; in detail, it was equal to 0.013 for the test reported in (**b**).
## 3 UAV Operational Overview  
Each of the main sets emphasizes a function (or a set of functions) as its collaborative job with respect to other sets, which are together aimed at achieving the accurate, stable, and successful flight of a UAV [71]. The key to a general understanding of UAV technologies is to establish a relationship between the five main sets according to two parameters, as discussed in this review, including hierarchy [72] and integration [73]. Figure 1 presents an overview of the hierarchy and integration between the five sets.  
Additionally, a hierarchy exists between the composers of the same set. For instance, the commands from the autopilot system override the flight control and lead the navigation system [74]. The collision avoidance system utilizes data from the sensory subsystem as well as from the navigation system [75]. Higher prioritized hierarchies [76] (e.g., fail-safe) override and modify commands and data from other systems, where, for example, all sensory data would be instantaneously blocked to reduce computational efforts on the central processor and ensure that the UAV shuts down safely. As for hierarchies between the composers of different sets, the data processors on the first hand receive inputs from sensory subsystems, and sometimes from the control subsystem, for real-time processing [77]. The equipped computers on the UAV manage and coordinate tasks across all subsystems by executing flight control algorithms based on the processed data [78]. Data loggers, on the other hand, save different input data for further investigation during the same flight (i.e., metaheuristic analysis-based flight modification) or offline (when the UAV is not on a mission) [79]. The UAV's health and performance indices are captured by internal sensors, thus providing data to both the control and computing subsystems, where external sensors share environmental data with the corresponding computing subsystem for decision-making [80]. As shown in Figure 1, the control subsystem establishes real-time decision-making and executes flight control algorithms when integrated with the computing subsystems (i.e., based on the processed data output from sensors). In turn, the communication system, aside from facilitating interaction/integration among all subsystems, enables real-time command reception/execution when integrated with the control subsystem. From the different perspective of integration/hierarchies, a UAV's technology may implement mechanical redundancy systems [81] (i.e., to ensure reliability in the case of failures), encryption [82] (i.e., for secure data exchange/communication), and payload swing suppressor [83], which can act as standalone subsystems.
## 3 Methodology
### Motivations and Deployment Considerations
#### 3.2.3 Influence of Capacity and Speed on Network Signaling Overhead  
The capacity and speed of vehicles in VANETs directly impact network signaling overhead. High-speed vehicles generate increased signaling traffic due to frequent handoffs between network nodes. This overhead can degrade the overall network performance and hinder real-time communication. Understanding the relationship between capacity, speed, and signaling overhead is essential for optimizing network resources and minimizing delays in high-speed environments.
# Energy Demand Forecasting Scenarios for Buildings Using Six AI Models
###### Abstract  
Understanding and forecasting energy consumption patterns is crucial for improving energy efficiency and human well-being, especially in diverse infrastructures like Spain. This research addresses a significant gap in energy demand forecasting across three building types by comparing six machine learning algorithms: Artificial Neural Networks, Random Forest, XGBoost, Radial Basis Function Network, Autoencoder, and Decision Trees. The primary aim is to identify the most effective model for predicting energy consumption based on historical data, contributing to the relationship between energy systems and urban well-being. The study emphasizes challenges in energy use and advocates for sustainable management practices. By forecasting energy demand over the next three years using linear regression, it provides actionable insights for energy providers, enhancing resilience in urban environments impacted by climate change. The findings deepen our understanding of energy dynamics across various building types and promote a sustainable energy future. Stakeholders will receive targeted recommendations for aligning energy production with consumption trends while meeting environmental responsibilities. Model performance is rigorously evaluated using metrics like Squared Mean Root Percentage Error (RMSPE) and Coefficient of Determination (R\({}^{2}\)), ensuring robust analysis. Training times for models in the LUCIA building ranged from 2 to 19 s, with the Decision Tree model showing the shortest times, highlighting the need to balance computational efficiency with model performance.  
s +
Footnote †: journal: applied sciences  
Article
# Improving Air Pollution Prediction System through Multimodal Deep Learning Model Optimization
## 3 Dataset
### Numerical Dataset  
In our previous study [2], we constructed various datasets by merging the Korea Meteorological Administration (KMA) data providing information, such as temperature, precipitation, wind speed, and AirKorea data providing information on air pollutants.  
In this paper, we adopted the features that showed the best performance in the previous study [2]. Our weather data collecting sites span seven regions of metropolitan cities (e.g., Busan and Incheon) and administrative regions (e.g., Gyeonggi-do, Chungcheongbukdo, and Gangwon-do). Both the satellite image and the numerical data are collected for 2020. The seven regions above metropolitan cities and administrative districts have multiple weather data collecting sites (e.g., 15 in Busan, 17 in Incheon, 7 in Chungcheongbuk-do, and 4 in Gangwon-do). Each site has five features (e.g., SO\({}_{2}\), NO\({}_{2}\), CO, O\({}_{3}\), and PM\({}_{10}\)). We selected sites in each region with a large population. The reason for the fine dust generation is the combustion of fossil fuels (e.g., coal and petroleum) due to electronic products, such as gas stoves and vacuum cleaners used at home, exhaust gas from cars, and smoke. The larger the population, the more factors cause fine dust. In addition, \"month\" was added as a feature to reflect the characteristics of the season, and the west sea wind direction of Incheon was included because the Korean Peninsula was affected by fine dust caused by the westerly wind.  
However, in the process of composing a new dataset by referring to the dataset used in the previous study [2], there were sites with no measuring stations from 2020. We excluded those sites in the new multimodal dataset for the year 2020, and the total number of features in the dataset in this study is 1030, and the number of sample is 8784.
## 4 Results Analysis and Discussion
### Feature Importance Comparison
In the Gradient Boosting Regression model, the adjusted \(R^{2}\) on the test set slightly decreases after removing B, DM, and SHIPTYPE features, but it improves  
Figure 11: MSE Comparison Diagrams: **(a)** Longitude Mean Squared Error; **(b)** Latitude Mean Squared Error; **(c)** SOG Mean Squared Error; **(d)** COG Mean Squared Error. when L is also removed, still showing good fit quality. For the Random Forest model, the adjusted \(R^{2}\) increases after removing some features, suggesting that excluding these features might help improve the model's performance. Overall, the performance of all models remains relatively stable across different feature sets, with the exclusion of specific features having a minor impact on the models' fit quality. The Random Forest Regressor, in particular, demonstrates better fitting performance after certain features are excluded.
## 1 Introduction
Recently, an NEX_LMS algorithm that uses the normalized signal as a reference has been proposed to improve the convergence rate for active sound quality control [18]. It improved the convergence rate of an ANC system using the Filtered-X LMS (FxLMS) algorithm, and achieved a large order-level reduction of the stationary disturbance of engine noise. An adaptive recursive algorithm has been proposed for the fast convergence of a controller in the ANC system of a short duct [19] for a non-stationary signal. In the present study, the optimized weights-FxLMS (OW-FxLMS) algorithm is proposed. Our goal is to control amplitude- and frequency-modulated noise, such as the order components of engine noise inside a car, with fast convergence. The performance of the algorithm is investigated with computer simulations and an experimental application in a car. The application purpose of this algorithm is to control the envelope of amplitude modulation signal and to generate the target sound with good sound quality inside of a car. The target sound is designed by the combination of the engine orders sound [20]. The sound quality of the target sound is determined by the sound quality index based on booming sound and modulation sound [21]. The booming sound and modulation sound were determined by the sound pressure level of the engine order sounds. Therefore, in order to design the amplitude of the target, the amplitude of the engine order sound should be controlled. The proposed optimized weights-FxLMS (OW-FxLMS) algorithm is suitable for this application. In Section 2, the novel OW-FxLMS algorithm is proposed. The utility of the proposed algorithm is validated in Section 3. The adaption for the proposed algorithm is explained in Section 4. The application of the algorithm to control of the interior noise is presented in Section 5. Finally, the conclusion is presented in Section 6.
## 3 DNN-Based Synchronization and the Proposed Receiver
### Full Receiver in Baseband
Assuming that the inverse time offset is \(\{\hat{\tau}^{\prime}(nT_{\text{BB}})\}_{n}\), the time transformation in the resampling compensation described as \(\big{\{}(nT_{\text{BB}},nT_{\text{BB}}-\hat{\tau}^{\prime}(nT_{\text{BB}}) )\big{\}}_{n}\) is the inverse function of that with the estimation \(\big{\{}(mT_{\text{BB}},mT_{\text{BB}}-\hat{\tau}(mT_{\text{BB}}))\big{\}}_{m}\). Therefore, the inverse timing offset can be obtained as follows:  
\[\hat{\tau}^{\prime}(nT_{\text{BB}})=nT_{\text{BB}}-\text{Linear}\Big{\{} \big{\{}(mT_{\text{BB}}-\hat{\tau}(mT_{\text{BB}}),mT_{\text{BB}})\big{\}}_{ m};nT_{\text{BB}}\big{\}}. \tag{22}\]  
It is further used for resampling by Farrow filter and phase rotation, written as:  
\[r^{\prime}(nT_{\text{BB}})=\text{Farrow}\Big{\{}\big{\{}(mT_{\text{BB}},r[m] )\big{\}}_{m};\ nT_{\text{BB}}-\hat{\tau}^{\prime}(nT_{\text{BB}})\Big{\}}e^{ -\hat{\jmath}2\pi f_{c}\hat{\tau}^{\prime}(nT_{\text{BB}})}, \tag{23}\]  
where the first item on the right side is the Farrow filter's output with the delay adjustment \(\hat{\tau}^{\prime}(nT_{\text{BB}})\). As shown in Figure 3, the Farrow filter consists of \(N_{\text{F}}+1\) component filters \(\{f_{k}(n)\}_{k=0}^{N_{\text{F}}}\) and the output of Farrow filtering is the polynomial function of \(\hat{\tau}^{\prime}(nT_{\text{BB}})\) and the filter array outputs. As the sample clock is well recovered, the resampled waveform is directly decimated into the symbol rate for the following equalization. The resampled waveform \(r^{\prime}(nT_{\text{Sym}})\) is assumed to be well compensated for in the motion effects, and the residual CIR is time-invariant. As shown in Figure 2, the CIR is obtained as \(\left\{\hat{h}(nT_{\text{Sym}})\right\}\) for each frame based on least-squared (LS) channel estimation, and then the zero-forced equalization is carried out to obtain the output symbols \(\left\{\hat{x}(nT_{\text{Sym}})\right\}\).
# Free Space Optical Communication Networking Technology Based on a Laser Relay Station
## 1 Introduction
The DPPM mode has a high-power utilization ratio and frequency band utilization ratio, and a low channel occupation ratio, but its modulation and demodulation are difficult to achieve due to its great difficulty. The PAM mode requires a large transmission power, a complex modulation and demodulation circuit, and a high bit error rate. At the same time, in the above-mentioned modulation methods, it is necessary to ensure that the clocks of the transmitting system and the receiving system are strictly aligned, or data transmission may be misaligned, which may lead to information exchange failure between the two information systems [26; 27; 28; 29; 30; 31]. In order to realize point-to-multipoint free-space optical communication on the ground and improve the quality of information transmission, based on the full analysis of the advantages and disadvantages of the above modulation methods, this paper proposes a realization method of free-space optical communication networking technology based on pulse width modulation (PWM). The binary data transmitted by each bit is characterized by sending a pulse signal with a fixed period and an unfixed pulse width into the space. The pulse signal with a narrow pulse width represents datum 1, and the pulse signal with a wide pulse width represents datum 0. At the same time, the receiver samples the signal received by the detector at a fixed frequency through high-speed ADC. In each part of the data modulation period, the width of the modulation pulse is determined by the number of times the ADC samples to a high level. According to the different data frame codes demodulated by the relay system, the data are sent to targets in different directions to achieve point-to-multipoint free-space optical communication. The modulation and demodulation processes are implemented by FPGA [32; 33; 34; 35; 36].