esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case study
### How Could the Implementation of the Proposed Architecture, QHAR, Support Self-Reconfiguration?  
To perform self-reconfiguration, three main actions must be performed autonomously [2; 31; 32; 33]:  
1. **Detection of the need for reconfiguration--**this is the preliminary phase of the actual reconfiguration process. With regard to faults, several advances were made in the implementation of self-diagnostic functions for certain faults [34; 35]. This generally concerns foreseen defaults [31]. For unforeseen defects or external disturbances, further progress is still needed [31]. QHAR could be used to help diagnose such unforeseen faults. Indeed, the proper functioning of an entity can be characterized by the values of the attributes/properties of its four possible dimensions (human, cyber, energy, physical), those of its three possible incoming and outgoing flows (data, physical, energy), and by the operations it is supposed to perform. The failure of any of these parameters might be a sign of failure of the entity in question. This failure may be noted by the entity itself, its local controller, or the central controller. The monitoring of all these parameters by the entity itself, its local controller, and/or the centralized controller can ensure that practically all potential defects can be detected. As far as external disturbances are concerned, they are detected by the centralized controller. In the case of a change in customer demand, for example, the centralized controller ascertains whether the current installation is capable of handling the new order. If it is not guaranteed, this may indicate the need to change the current configuration.
2. **Determination of the entities needed for the new configuration--**the need for reconfiguration detected earlier usually involves a change in the composition of the production system. This involves choosing the most appropriate entities for the current need. This choice may require decision support tools/methods and the definition of selection criteria. The first selection criteria are those relating to the operational capabilities and properties of the entities, where the operational capabilities and properties of each entity can be defined using the Q-Holon that represents it.
3. **Definition of a new layout--**this involves determining the location of the entities and the interconnections to be established between them. Thanks to the attributes/properties of the entities--in particular, the physical/spatial dimension--the best location can be defined for each entity. The interaction between entities can be characterized by the flows (material, data, and energy) they exchange. Thus, entities that exchange flows may be permanently or temporarily interconnected.
## 6 Closed-Loop Simulation
### Adaptability to Environment and Control System
It is worth noting that the final landing mass obtained by the open-loop trajectory planning is 31.752 t. When the aerodynamic drag is added, the final landing mass obtained by the closed-loop simulation is 32.616 t. The fuel consumption is reduced by nearly 1 t because aerodynamic drag has the positive effect of slowing the rocket's velocity, reducing the fuel consumption required for the landing.
## 5 Results of Analysis
### The Results of VDV Analysis
* Tramways show the lowest mean HPVI at about 0.57 with a relatively small standard deviation, suggesting less variation. * Buses have a mean HPVI of around 0.78, with variability indicated by the standard deviation. * Heavy trucks have only one measurement, showing the highest single HPVI value at 1.72. For VDV:  
* Metro trains show a higher mean VDV of 0.176 with a standard deviation of 0.055, indicating significant variability. * Tramways have the lowest mean VDV at approximately 0.059 with less variation. * Buses fall in the middle with a mean VDV of about 0.067. * Heavy trucks have a significantly higher VDV of 0.161 but as a single measurement, it is difficult to compare variability. These differences suggest that the type of vehicle has an impact on both HPVI and VDV measurements, with metro trains generally showing higher values and tramways showing lower values on average. Heavy trucks, though only represented by a single measurement, show notably high VDV. This analysis indicates that vehicle type does affect vibration impact measurements, with specific types of vehicles potentially causing more significant vibrations and discomfort.
## 5 Emerging Trends and Future Directions
### The Role of AI and Machine Learning in Advancing Data Analysis for IoT  
Artificial Intelligence (AI) and Machine Learning (ML) are playing a pivotal role in advancing data analysis, particularly in the context of IoT. In the maritime industry, they offer significant potential for automating and enhancing the analysis of data generated by IoT devices [83].  
* Automating Data Analysis: ML algorithms can automate the process of data analysis, reducing the need for human intervention. This is particularly valuable for handling the large volumes of data generated by maritime IoT systems. ML can quickly analyze this data, identifying patterns and trends that can inform decision-making [101].
* Predictive Analysis: AI and ML excel at predictive analysis, which involves using historical data to predict future events or trends. In the maritime context, this can be used for predictive maintenance, forecasting weather and sea conditions, predicting port congestion, and more. These predictive capabilities can enhance efficiency, safety, and strategic planning in maritime operations [25].
* Real-time Data Analysis: AI and ML algorithms can process and analyze data in real time, enabling immediate responses to changing conditions. This can be vital for applications such as navigation, safety monitoring, and incident response, where delays in decision-making can have serious consequences [102].
* Complex Pattern Recognition: AI and ML can identify complex patterns and relationships in data that might be missed by traditional analysis methods. This can provide deeper insights into maritime operations and facilitate more informed and strategic decision-making [103; 104; 105].
###### Abstract  
Face recognition has grown in popularity due to the ease with which most recognition systems can find and recognize human faces in images and videos. However, the accuracy of the face recognition system is critical in ascertaining the success of a person's identification. A lack of sufficiently large training datasets is one of the significant challenges that limit the accuracy of face recognition systems. Meanwhile, machine learning (ML) algorithms, particularly those used for image-based face recognition, require large training data samples to achieve a high degree of face recognition accuracy. Based on the above challenge, this research proposes a method for improving face recognition precision and accuracy by employing a hybrid approach of the Gabor filter and a stacked sparse autoencoders (SSAE) deep neural network. The face image datasets from Olivetti Research Laboratory (OLR) and the Extended Yale-B databases were used to evaluate the proposed hybrid model's performance. All face image datasets used in our experiments are grayscale image type with a resolution of 92 \(\times\) 112 for the OLR database and a resolution 192 \(\times\) 168 for the Extended Yale-B database. Our experimental results showed that the proposed method improved face recognition accuracy by approximately 100% for the two databases used at a significantly reduced feature extraction time compared to the current state-of-art face recognition methods for all test cases. The SSAE approach can explore large and complex datasets with minimal computation time. In addition, the algorithm minimizes the false acceptance rate and improves recognition accuracy. This implies that the proposed method is promising and has the potential to enhance the performance of face recognition systems.  
Gabor filter; face recognition; deep neural network; stacked sparse autoencoders; hybrid method +
Footnote †: journal: Applied Sciences  
1  
A Hybrid Method of Enhancing Accuracy of Facial Recognition System Using Gabor Filter and Stacked Sparse Autoencoders Deep Neural Network
## 7 Conclusions  
For thick LWSNs used in railway tunnel monitoring, we propose a self-organizing network method and an energy-balanced routing protocol. In the network's operation, anchor nodes play a role in node discovery and management. Regarding self-organization, we determine the positions of all nodes in the network by anchor node and establish adjacency relationships. Moreover, a timing mechanism is used to elect the optimal cluster heads to achieve unequal clustering. In the routing protocol, we select the next-hop cluster head node for intercluster data transmission based on various attributes of the cluster heads. Experimental results indicate that our method better balances network energy and extends lifespan compared with traditional energy-balanced routing protocols.  
However, this study still has limitations. Firstly, using RSSI for distance calculation in the topology discovery process is susceptible to noise interference, which reduces the localization accuracy. Increasing the number of anchor nodes at the edges of the monitoring area or increasing the number of localization attempts may improve accuracy to some extent. In addition, future research plans to adopt the D2 NN architecture proposed in the document in [31] for experiments, as it is more suitable for WSNs. Since this study is still in the simulation phase, issues such as hardware constraints and time synchronization are inevitable in practical applications, and addressing these will be one of the main focuses of our future research.  
Conceptualization, H.Y. and [PERSON]; methodology, [PERSON], [PERSON] and [PERSON]; software, [PERSON], [PERSON] and [PERSON]; validation, [PERSON], [PERSON] and [PERSON]; formal analysis, [PERSON], [PERSON], [PERSON] and [PERSON]; investigation, [PERSON], [PERSON] and [PERSON]; resources, [PERSON] and H.G.; data curation, [PERSON], [PERSON], [PERSON] and A.R.; writing--original draft preparation, [PERSON], [PERSON] and [PERSON]; writing--review and editing, [PERSON], [PERSON] and A.R.; visualization, [PERSON], [PERSON], [PERSON] and [PERSON]; supervision, [PERSON], [PERSON] and [PERSON]; project administration, [PERSON], [PERSON] and [PERSON]; funding acquisition, [PERSON], [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Natural Science Foundation Joint Fund of Liaoning Province China under Grant 2023-MSLH-248, Natural Science Foundation of Liaoning Province, China under Grant 2022-MS-438, Scientific Study Project for Institutes of Higher Learning, Ministry of Education, Liaoning Province under Grant LJKFZ20220184, Science and Technology Projects in Liaoning Province under Grant 2022 JH1/10800085.  
Not applicable.  
Data availability Statement: Data are unavailable due to privacy.  
We, the undersigned, declare that this manuscript is original, has not been published before, and is not currently being considered for publication elsewhere, with no interest in pursuing publication elsewhere. This statement of interest is declared on behalf of all authors.
## 4 Experimental Results and Discussion
### Quantitative Metrics Comparison
#### 4.7.2 Conclusions  
The combination of C2f, Bot-Transformer, and GeLu modules, along with hyperparameter optimization, effectively demonstrates the improved performance of the YOLOv586 model in detecting hairiness in yarn. Each module contributed to different aspects of the performance, while hyperparameter optimization maximized these improvements. The  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{8}{c}{Metrics increases (\%)} \\ \hline Algorithm/ & \multicolumn{2}{c}{**mAP\_0.5:0.95**} & \multicolumn{2}{c}{**mAP\_0.5**} & \multicolumn{2}{c}{**F1-Score**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{1}{c}{**Accuracy**} \\ \hline YOLOv586 Default & N/A & N/A & N/A & N/A & N/A & N/A \\ \hline YOLOv586 + C2f & 1.18 & \(-\)2.70 & \(-\)0.53 & \(-\)0.34 & \(-\)0.72 & 0.15 \\ \hline YOLOv586 + Bot-transformer & 0.03 & \(-\)1.79 & 1.32 & 3.55 & \(-\)0.76 & \(-\)0.78 \\ \hline YOLOv586 + GeLu & 2.56 & \(-\)1.17 & 0.07 & 1.15 & \(-\)0.96 & \(-\)0.73 \\ \hline YOLOv586 + C2f & \multirow{2}{*}{1.33} & \(-\)1.27 & 1.29 & \(-\)0.11 & 2.69 & \(-\)1.17 \\ \cline{1-1} YOLOv586 + C2f + GeLu & 1.39 & \(-\)0.29 & 0.73 & 3.79 & \(-\)2.08 & 0.91 \\ \cline{1-1} YOLOv586 + Bot-transformer & \multirow{2}{*}{0.56} & \(-\)0.86 & 0.63 & 5.26 & \(-\)3.51 & 1.30 \\ \cline{1-1} \cline{6-6} YOLOv586 + Bot-transformer & \multirow{2}{*}{2.42} & \multirow{2}{*}{2.06} & \multirow{2}{*}{1.01} & \multirow{2}{*}{1.03} & \multirow{2}{*}{0.99} & \multirow{2}{*}{0.85} \\ \cline{1-1} \cline{6-6} + GeLu + C2f & & & & & & \\ \hline YOLOv586 + Hyper-parameters & 9.08 & 2.54 & 2.64 & 6.03 & \(-\)0.46 & 1.54 \\ \hline YOLOv586 Hairiness Improved & 11.55 & 5.435 & 3.71 & 7.36 & 0.39 & 1.11 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Metrics increases in percentage.  
final model YOLOv5s6 Hairiness Improved shows an enhanced capability in detecting and classifying loop and protruding fibers.
## 3 Results and Discussion
### CH\({}_{4}\) and CO\({}_{2}\) Emissions during Storage of Pig Slurry
Except for the second period of daily CO\({}_{2}\) emissions per VS, where a \(p\)-value of \(<\)0.05 was observed, there were no statistically significant differences between PFS and CTL in terms of daily CH\({}_{4}\) and CO\({}_{2}\) emissions per VS. Table 1 indicates that there were no significant differences in the concentrations of organic matter components, including TS, VS, and carbon, between the PFS and CTL. This result suggests that peat moss as a feed additive has no significant effect on the degradation of organic matter in pig slurry due to enhanced feed efficiency. Consequently, it can be surmised that the lower CH\({}_{4}\) emissions observed in PFS compared with CTL are attributable to the distinctive biochemical properties of HS rather than a reduction in organic matter. PFS has significantly higher levels of humic acid, fulvic acid, and humin than CTL, by a factor of 2.3, 1.8, and 1.1, respectively, which lends credence to this inference (Table 2). Prior research has documented that HS is critical in restraining methanogenesis or hydrolysis in anaerobic digestion [25; 26; 27; 28]. Although the daily emissions of CH\({}_{4}\) and CO\({}_{2}\) per VS were not significantly different between PFS and CTL, the present study found that the decomposition of degradable substances in the pig slurry of CTL occurred primarily during the second and third period, while the decomposition of recalcitrant substances occurred slowly after that, leading to a gradual decrease in the daily emissions of CH\({}_{4}\) and CO\({}_{2}\) over time. The cumulative emissions of CH\({}_{4}\) and CO\({}_{2}\) per VS were lower in the PFS than in the CTL, but no statistically significant differences were observed (\(p>0.05\)). The cumulative CH\({}_{4}\) emission of the CTL was 7.74 g\(\cdot\)kg\({}^{-1}\) VS, which was approximately 3.5 times lower than the CH\({}_{4}\) emission reported in a previous study [12]. The discrepancy between our experimental results and those reported in other studies is likely attributable to differences in the characteristics of the pig slurry and variations in temperature conditions. The notable variation in CH\({}_{4}\) emissions observed during a specific period in the control group is attributed to the intermittent release of CH\({}_{4}\) through ebullition [42]. Furthermore, the high variability of gas concentrations, including CO\({}_{2}\) and CH\({}_{4}\), in livestock manure slurry and their tendency to reach maximum values are well-established phenomena in laboratory-scale studies [42; 43]. Based on the daily and cumulative emissions of CH\({}_{4}\) and CO\({}_{2}\) per VS, the present study suggests that incorporating 3.0% peat moss into conventional pig feed may be an effective way to reduce CH\({}_{4}\) and CO\({}_{2}\) emissions. This finding can be explained by the improved feed efficiency that leads to a lower organic matter content in pig slurry, ultimately reducing the emissions of CH\({}_{4}\) and CO\({}_{2}\)[20].
# Compensating Uncertainties in Force Sensing for Robotic-Assisted Palpation
## 1 Introduction
Additionally, static experiments are performed to obtain the stiffness of the soft tissue, in which the measured force is not affected by the inertial force since the force sensor stays still during the experiments. From the experiment results, it can be found that after the inertial force compensation, the calculated stiffness of the soft tissue is improved to better describe the real mechanical property of the soft tissue during dynamic robotic palpation tasks. The remaining of this paper is organized as follows. Section 2 comprehensively presents the procedure of robotic-assisted palpation, while also performing a quantitatively stiffness assessment and noting the force measurement uncertainty. A force compensation framework is also presented. In Section 3, the neural-network-based approach is utilized to model the inertial force of the robotic palpation, and the modeled inertial force can be canceled during the palpation tasks to get an improved force measurement. Experiment results in Section 4 firstly presents the force measurement uncertainties caused by the probe during dynamic robotic operation, then the force compensation framework is used with the neural-network-based modelling, and the obtained results verify that the proposed approach can efficiently remove the effect of inertial force and thus get an improved measurement result in which [PERSON]'s modulus is close to the static experiment results (actual stiffness of soft tissue defined in this paper). The discussion and conclusion are provided in Section 5 and 6 respectively.
# Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models
## 6 Results
### Comparison and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Aggregation**} & \multicolumn{2}{c}{**Ref. Model**} & \multirow{2}{*}{**Best Model**} & \multirow{2}{*}{**IIMP (\%)**} \\  & & \multicolumn{2}{c}{_baseline_s1_} & & & \\ \cline{3-6}  & & \multicolumn{1}{c}{**CV(RMSE) (\%)**} & \multicolumn{1}{c}{_CV(RMSE)_} & \multicolumn{1}{c}{_RMSE_} & \multicolumn{1}{c}{_nRMSE_} \\  & & \multicolumn{1}{c}{**CV(RMSE) (\%)**} & \multicolumn{1}{c}{_(kW)_} & \multicolumn{1}{c}{_(\%)_} & \\ \hline \multirow{2}{*}{\(ge1\)} & 15 min & 104.7 & 80.2 & 22.5 & 9.4 & 23 \\ \cline{2-6}  & 1 h & 101.3 & 77.4 & 21.7 & 8.8 & 24 \\ \hline \multirow{2}{*}{\(ge2\)} & 15 min & 51.5 & 28.7 & 28.6 & 6.5 & 44 \\ \cline{2-6}  & 1 h & 50.4 & 27.1 & 27.0 & 6.2 & 46 \\ \hline \multirow{2}{*}{\(ut\)} & 15 min & 89.7 & 69.9 & 18.2 & 14.2 & 22 \\ \cline{2-6}  & 1 h & 89.0 & 67.3 & 17.6 & 13.8 & 24 \\ \hline \multirow{2}{*}{\(lc\)} & 15 min & 36.2 & 14.8 & 19.6 & 4.7 & 59 \\ \cline{2-6}  & 1 h & 35.7 & 13.7 & 18.2 & 4.3 & 62 \\ \hline \multirow{2}{*}{\(pv\)} & 15 min & 96.6 & 65.1 & 7.2 & 9.0 & 33 \\ \cline{2-6}  & 1 h & 91.5 & 60.4 & 6.6 & 8.3 & 34 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Achieved forecasting improvement. Figure 8: \(Cont.\)  
It can be appreciated how the error of the baseline is usually greater than the error of the best model, with the only exception in the given examples being day number two of Figure 8b. On this day, it can be appreciated how the best baseline model occasionally achieved a better result than the best model. However, these kinds of occurrences are expectable in the environment under study. The consumption and generation of the campus have low levels of aggregation, which produce behaviors that are difficult to predict. For this reason, on some specific days it may happen that the best selected model is outperformed by another one, which occurred on the indicated day. Despite these infrequent events, the \(CV(RMSE)\) (which should be taken as criterion for model selection) show from a global point of view the goodness of each model, as seen in Tables 5 and 6. Figure 8: Actual power vs. forecasting (baseline and models) with an aggregation of 15 min. “Bl err” (baseline error) indicates the \(CV(RMSE)\) for the best baseline model. “Mod err” (model error) indicates the \(CV(RMSE)\) for the best model. (**a**) \(geI\); (**b**) \(ge2\); (**c**) \(ut\); (**d**) \(lc\); (**e**) \(pv\).
# Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network
## 3 Constructing the Framework for CNN and LSTM Assisted GNSS/INS Navigation System
### Constructing the Framework for CNN- and LSTM-Assisted GNSS/INS Navigation System  
Because the fusion navigation data is a series of time series, there is this obvious spatiotemporal correlation between them. Therefore, a CNN and LSTM are combined to extract more high-dimensional features from the long-term dataset using the CNN and are combined with LSTM to synthesize the series of high-dimensional features for time series prediction.  
The combination of a CNN and LSTM [41; 42; 43] forms a powerful deep learning architecture, leveraging the CNN's feature extraction capabilities and LSTM's ability to handle sequence data and capture long-term dependencies. The key steps in constructing a CNN-LSTM-based network model are feature extraction in the CNN layer and model training in the LSTM layer.  
Advantages of this combination include the following:  
* Powerful Feature Extraction: The CNN automatically extracts useful features from raw data, reducing the need for manual feature engineering.
* Handling Sequence Data: LSTM can process variable-length sequence data and capture long-term dependencies, which is crucial for many practical applications.  
Flexibility: The combination of a CNN and LSTM can be adjusted and optimized based on the specific requirements of the task, such as adjusting the number of CNN layers, convolutional kernel sizes, and LSTM hidden units.  
Based on the characteristics of GNSS/INS navigation systems data, the specific steps to construct the corresponding neural network architecture are as follows:
# Optimized Classification of Intelligent Reflecting Surface (IRS)-Enabled GEO Satellite Signals
## 1 Introduction
### Organization of Article  
The remainder of this article is arranged as follows. Section 2 presents a detailed literature review of the existing techniques. The proposed IRS-enabled architecture is derived in Section 3, and the problem is formulated for the GFN-based modulation classification. The performance evaluation of the proposed problem is reported and discussed in Section 4, where the graphical and numerical analyses are presented. Finally, the proposed problem is concluded in the last section, i.e., Section 5.
## 3 Approach
### Programming Interface
#### 3.4.2 Graphs  
Any interesting application will consist of multiple blocks connected together. In order to control multiple blocks at once, they can be assigned to a Graph, allowing them to be spawned, joined and managed together. This joint management of blocks also allows the framework to coordinate when one block experiences errors, the others can either continue running, pause or shut down.  
Adding blocks to a graph is very easy (see Figure 1(b)) and involves simply defining the block inside of the graph's context (i.e., define them indented under the _with_ statement).  
Figure 1: Code examples illustrating the usage and extensibility of REIP SDK in video capture and processing application. (a) Block implementation for a machine learning model. It consists of an _init_, \(\mathit{process}(\dots)\) and \(\mathit{finish}\) methods. The _process_ method takes frames from multiple cameras and chooses one to perform object detection on it. (b) Graph definition showing object detection on a video stream with the detections saved in JSON files and uploaded to an API endpoint.