esa-sceva/satcom-chunk-collection huggingface dataset:
# Air Traffic Trajectory Operation Mode Mining Based on Clustering
## 5 Conclusions  
* In the cluster analysis, a combined model of OFAE + DPCA was proposed to make up for the problem that DTW is time-consuming and DBSCAN finds it difficult to distinguish clusters with large density differences in the traditional model, DTW + DBSCAN. The track compressed by OFAE can directly use the Euclidean distance measurement. DPCA can extract the density peak and directly classify the adjacent tracks. The combination of the two greatly saves time.
* In the process of solving the sparse autoencoder, the non-convex optimization objective was processed by convex relaxation, which was transformed into the optimization problem of L21-norm, and the ADMM algorithm was used for the step-by-step solution. Compared with the Lagrange solution with strong constraints, the ADMM solution allowed the model to deviate from the constraints to a certain extent while reducing the error function. When the deviation was limited, it was more conducive to the optimal solution.
* In future research, we will consider high-quality datasets obtained by classifying and identifying typical aircraft operating modes and establish a track prediction model to make the prediction results more targeted. In the face of abnormal mode data with few similar samples at the same time, it can also provide a comprehensive prediction based on similar patterns and improve the accuracy of track prediction.  
Conceptualization, X.T., Y.L. and K.C.; methodology, X.T.; software, K.C. and Y.L.; validation, X.T., Y.L. and K.C.; formal analysis, X.T.; investigation, X.T.; resources, Y.L.; data curation, X.T. and Y.L.; writing--original draft preparation, X.T. and K.C.; writing--review and editing, Y.L. and K.C.; visualization, X.T.; supervision, K.C.; project administration, X.T.; funding acquisition, X.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (61773202, 52072174), the Foundation for the National Key Laboratory of Science and Technology on Avionics System Integration (6142505180407), and the Civil Aviation Management Institute of China Key Foundation of General Aviation (CAMICKFJ)-2019-04).  
Not applicable.  
The authors declare no conflict of interest.
# Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer Model
## 2 Methodology
### Machine Learning-Based Model: Conformer: Convolution-Augmented Transformer Model
Notably, we find that incorporating an LSTM decoder has little effect on the accuracy for fault classification, although it is commonly used in Conformer models for ASR purposes [21].
# Organic Nanostructured Materials for Sustainable Application in Next Generation Solar Cells
## 4 Next Generation Applications of OPVs
### Organic Photovoltaics Spearheading Sustainable Agricultural Processes  
One of the fields where OPVs are used is in agriculture tailor-make greenhouses for environmental control of crops. Environmental control in greenhouses [63] is meant to achieve indoor temperatures, relative humidity, light and CO\({}_{2}\), which are as close as possible to optimal growth conditions for plants by using heating, cooling, ventilation, variable shading, and CO\({}_{2}\) enrichment and lighting systems [68]. A greenhouse structure covered with transparent materials can be made up of polymer (plastic), glass, or fiber-glass, which can allow the short waves of solar irradiation to enter the greenhouse [64]. Researchers have conducted studies on the use of polymer-based nanomaterial in trying to promote green chemistry, water and food security as well as dynamic and energy prediction modules to reduce energy consumption [66]. The impact of semi-transparent flexible and lightweight OPVs based on P3 HT: PCBM photoactive layer on pepper plant development was presented in a study by [PERSON] el al. They installed OPVs on the roof of a Mediterranean greenhouse, covering 22% of the total surface. The photosynthetic process of pepper plant vegetative development and fruit production was enabled by the construction of a sustainable eco-system in an OPV solar Mediterranean greenhouse.  
According to their findings, semi-transparent OPV modules based on the P3 HT: PCBM photoactive layer with transparency of up to 19.4% in the Photosynthetically Active Radiation (PAR) region are suitable for utilizing a given land area for both agriculture and energy generation [69]. Specifically, the OPV shading effect on plant growth is found to be beneficial for pepper plant cultivation [70]. When the absorption range of such active layer nanomaterials is properly aligned with that of the plants, the scenario shifts from competition to synergism, as part of the energy provided by the photovoltaic system can be used to better control humidity and temperature levels inside greenhouses and polytunnels. Finally, such simple agrivoltaic devices might help to sustain development in dry rural regions by supplying clean energy to the surrounding populations, in addition to contributing to food production. Plants require as much light as possible for photosynthesis, thus the characteristics are primarily governed by this requirement. This necessitates excellent photovoltaic module transparency in the range of 1.77 to 3.55 eV, as this is the area of the sun spectrum where photosynthetic action occurs [71].
## 3 Results
The rate of ZnO reduction is significantly increased at a carbon content of 2.16%, compared to that at 0.85%; however, the difference at 10% ZnO is relatively small, at less than 0.8%/min. The difference between the experiments with C contents of 2.16% and 2.89% is even smaller (0.5%/min), the main reason being their very similar carbon concentrations. As the ZnO concentration in the slag is reduced, the reaction rates of all trials decrease linearly, and converge at lower concentrations of ZnO in the slag. At a residual content of 4% zinc oxide in the slag, the reaction rates of all tests are below 2%/min, and subsequently drop to zero. The most important aspect of the experiments presented in this paper is that the unwanted carbon input to the melt could be prevented. For this reason, in addition to the analyses of the slag, the C content in the metal bath was also determined after each test. The results and a trend line for each series of trials are shown in Figure 5. Similar to the highest reduction rates of ZnO taking place during the period of 0-6 min, the decrease in carbon concentration in the molten iron is also the greatest during this time. The rest of the process time shows constant C contents, indicating that an undesirable input of carbon to the metal bath could be prevented. The reduction of ZnO, which still occurs between 6 and 15 min, is not evident in the graphs. The large ratio of iron bath to slag means that \begin{table}
\begin{tabular}{c c c} \hline \hline
**C\({}_{0}\) in Metal** & **ZnO Concentration** & **Reaction Rate** \\
**[\%]** & **[\%]** & **[\%/min]** \\ \hline
0.85 & \([\text{ZnO}]=3.67391+6.83081\times\text{e}^{-0.26874\times t}\) & \(\text{r}=1.83571\times\text{e}^{-0.26874\times t}\) \\ \hline
2.16 & \([\text{ZnO}]=1.66117+8.86756\times\text{e}^{-0.30131\times t}\) & \(\text{r}=2.67188\times\text{e}^{-0.30131\times t}\) \\ \hline
2.89 & \([\text{ZnO}]=1.93761+8.45855\times\text{e}^{-0.3673\times t}\) & \(\text{r}=3.10683\times\text{e}^{-0.3673\times t}\) \\ \hline
4.15 & \([\text{ZnO}]=2.68455+7.89179\times\text{e}^{-98.022\times t}\) & \(\text{r}=7.73569\times\text{e}^{-98.022\times t}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Calculated changes in the concentration of zinc oxide in the slag, and the reaction rates. Figure 4: Comparison of reaction rates over the concentration range of 10% to 4%. the amount of carbon needed for the reduction of the remaining amounts of ZnO causes insignificant changes in the C concentration. The large metal/slag ratio was necessary in order to prevent limitation of the reduction process of ZnO by low amounts of C in the system. The decrease in carbon concentration in all test trials followed a similar temporal progression, and showed no unusual effects in the iron bath. In the tests carried out, the main reduction agent was carbon although, as described previously, zinc oxide can also be reduced by means of iron. This iron-reduced zinc oxide represents only a small proportion of the total mass of reduced zinc.
## 7 Evaluation
### Comparison of Performance  
Concave hull using KNN algorithm shows the best performance when detecting the boundaries and calculating the work area. However, it suffers from larger execution times as number of GPS points are increased as shown in Table 5. To overcome, this, we are using KNN with FLANN. FLANN using neighbourhood approximation and reduces the number of comparisons. As a result, the execution times of FLANN-based KNN is may folds less than the KNN mechanism, as elaborated in Table 5.
# A Study on the Impact of Low-Frequency Random Loading and Unloading on Silo Structures
## 2 Reinforced Concrete Silo Force Analysis
### Analysis of Silo Wall Pressure Monitoring
#### 2.2.2 Principles of Sensor Layout  
1. The placement of sensors directly affects the accuracy of the test data and the long-term performance of the sensors. It is essential to strictly follow standard procedures, perform inspections and calibration, and eliminate noise interference.
2. Before placing sensors, operating rules should be prepared, including instrument inspection, cable connection, burial methods, field observation and data compilation, etc., and instruments and cables need to be inspected. To ensure high measurement accuracy of the strain gauges, special treatment and multiple inspections are required.
3. For sensors, it is essential to align them as accurately as possible with the strain direction. Additionally, in order to prevent sensor displacement and angle changes during the construction process, it is important to make sure that the sensors are connected reliably to the measured components when they are deployed, so that the sensors can be retained for a long period of time to continuously monitor the structure's operating status.
4. For connecting wires, to prevent interference from external electromagnetic fields, all wires should be multi-stranded copper core shielded cables. During cable routing, the wires should maintain appropriate slack to prevent potential damage during construction. It is recommended to gather wires from the same control section in one place, assign a centralized number, and take centralized readings for easier protection and improved monitoring efficiency.
5. After the sensors are placed, they should be properly protected, and the cable number should be labeled to prevent rust, confusion, or loss. The cable length should not be changed arbitrarily. If it is necessary to change the cable length, observation values should be read and recorded before and after the length change.
6. The junction box should be kept dry, and the monitoring station can be equipped with dehumidification equipment as needed. Regular inspections of the monitoring system should be conducted.
# Congestion-Aware Geocast Routing in Vehicular Delay-Tolerant Networks
## 5 Performance Evaluation and Results  
The V-GRADIENT protocol was compared with different strategies described in Section 3. Most simulation parameters are as shown in Table 1, except the buffer sizes, which were increased to 20 MB on all nodes, so that network congestion was smaller. Three geocast groups were configured as shown in Table 3. Different message generators were deployed in each simulation simultaneously with the configuration shown in Table 4. Four different seed values are used for the movement model's pseudo-random number generator to reinforce the statistical significance of the results obtained. The corresponding 95% confidence intervals are presented in the graphs with the simulation results. In most cases, the confidence intervals are very small, which shows high confidence in the simulation results. A picture of the simulation scenario is presented in Figure 6, showing the Helsinki downtown area with 80 vehicle nodes and corresponding identifier numbers, small green circles depicting the nodes' wireless communication ranges and red, blue, and black circles depicting the ROI of active messages for geocast groups one, two, and three, respectively.  
Figure 5: Graph of \(F\!ood_{ntiq}\), from Equation (7), as a function of the initial Time-To-Live (TTL) and distinct penetration rates.
## 2 Materials and Methods
### Ray Launching Technique
Some restrictions apply when considering RL simulation techniques concerning near field possible discrepancies and uncertainties in the proximity of the transmitter location. The presented environmental RF-EMF assessment study considers only far field conditions and the influence of the user presence, density and distribution but does not include an in-body assessment, as RT techniques are not suitable for those purposes. Consequently, in order to prevent unreliable near field results, an adaptable/dynamic exclusion area of 5\(\lambda\) (\(\lambda\) as the wavelength of the propagating wave under consideration) is applied around the transmitter antenna, considering the different frequencies under analysis [44]. Furthermore, the in-house developed 3D-RL tool allows the possibility of applying hybrid techniques to decrease the computational cost given by the complexity of the environment under analysis. These hybrid techniques are the Neural Network (NN) module [31], the Diffusion Equation module [47] or the Collaborative Filtering module [48], which achieve accurate simulation results while the computational load decreases considerably. Figure 2 presents a flowchart of the enhanced 3D-RL simulation methodology for RF-EMF radiation exposure and regulation assessment, where the main steps of the algorithm, along with its main capabilities, are presented.
## 5 Simulation  
### Simulation Environment  
To validate the effectiveness of the proposed algorithm, we implement and evaluate TCEB with the typical clustering-based algorithms LEACH [7], EDCS [8], and GR-CTC [10] under almost the same parameters setting in Matlab R2013a and Atarraya [30]. As we discussed before, all nodes are randomly deployed in a square of 100 m\(\times\)100 m, and the sink is located at the center of the area, i.e., (50, 50). More detailed parameters which we used in the simulation are given in Table 1. Particularly, the weighted factors, such as \(\beta\), \(\gamma\), and \(\omega\), are taken from the results of hundreds of experiments.  
During the simulation, we mainly make two kinds of testing experiments. One is for self-comparison which is to observe the efficiency of TCEB under different node numbers and energy factors. The other is for comparison with typical clustering-based algorithms to find whether TCEB has preferable merits. To eliminate the randomness of the experimental results, all of the tests are performed at least 100 times to get the average value.
## 4 Discussion
### Limitations and Future Research Lines  
It is essential to acknowledge this study's limitations to provide context for our findings and guide future research. Primarily, the sample size for our investigation was determined by convenience, reflecting the challenges inherent in recruiting participants fitting our specific study criteria. This reliance on a convenience sample poses limitations, including potential biases and the difficulty in generalizing our findings to a broader population. Such a sampling approach was necessitated by logistical constraints and the niche nature of the study population. Moreover, we included this sampling methodology as a limitation within this study to maintain transparency and provide a comprehensive understanding of the context within which our results should be interpreted. Future investigations should aim to utilize larger and more diverse sample sizes to enhance the generalizability of their findings. Despite these limitations, this study offers valuable insights into the multifaceted effects of acute physical fatigue induced by HIIT, highlighting areas for further exploration in the nuances of these effects across different populations and sports contexts.  
Building upon the insights and limitations identified in this study, future research should focus on expanding the understanding of acute physical fatigue's effects across a broader spectrum of populations and athletic disciplines. Investigations could include a wider variety of training modalities and participant demographics to enhance the generalizability of findings. Additionally, longitudinal studies are needed to explore the long-term impacts of HIIT on physiological and psychological aspects of fatigue, recovery, and performance. Incorporating advanced technologies for real-time monitoring and analysis can also provide deeper insights into the immediate and cumulative effects of training on athlete health and performance. Finally, future research to engage larger and more diverse cohorts is necessary in order to enhance the external validity of our findings.
## 2 Preliminaries and Related Work
### Related Work  
Several surveys have been conducted in the literature illustrating the federated learning concept and its challenges. For instance, the article in [1] provides a comprehensive survey of federated learning for the IoT. The authors cover state-of-the-art federated learning, the federated learning role for IoT services and applications, and federated learning challenges.  
In [37], [PERSON] et al. provide a comprehensive survey of the use of federated learning in mobile edge networks. Their work covers the concept and background of federatedlearning, federated learning challenges, security, privacy issues, and federated learning applications in mobile edge networks.  
Similarly, in [38], [PERSON] et al. explained the FL concept and system components, providing a taxonomy for different federated learning aspects. In [22], the work covers federated learning concepts and applications. Some works cover challenges along with the concept of federated learning and its applications [32; 39]. In [33], [PERSON] et al. provided a survey focused on the non-IID data problem challenge in FL. The authors in [40] provide a review article on federated learning in smart cities; they illustrate the advantages and disadvantages of implementing federated learning in smart cities. The article focuses on privacy and security issues of federated learning in smart cities. The articles in [41; 42; 43] focus on reviewing blockchain in federated learning, while the article in [44] focuses on FL-enabled 6G technology, specifying its requirements, applications, and current challenges. In [45], [PERSON] et al. focus on the concept of vertical federated learning and its challenges. The study in [17] provides a systematic mapping study highlighting why federated learning has been used and different machine learning pipelines used for federated learning, while a systematic mapping study focused on energy-constrained IoT devices [18].  
To the best of our knowledge, our article is the first article that provides a systematic mapping study for federated learning challenges. This work aims to identify the most used techniques to overcome the non-IID data challenge in FL and the most used techniques to provide communication efficiency in federated learning. Furthermore, we studied the works that solve both problems in their work. Also, we provide information about the widely used local models and datasets as these are essential in any federated learning approach. Furthermore, the study also highlights the publication venues, types, and years of these articles.
## 2 Navigation and Localization
### Geophysical Navigation  
To avoid the problem of INS drifts and the cost of infrastructure for underwater acoustic systems, geophysical navigation (GN) is a favorable alternative. These approaches match the sensors  
Figure 8: Range-only/Single-beacon positioning of a fixed target from a moving vehicle.  
measurements with geophysical parameters such as bathymetry, magnetic field, and gravitational anomaly contained in a map. Navigation technology based on GN can correct the INS error over a long time [54], without the need to bring the AUV to the surface. The navigation algorithm estimates navigation errors, which are sent to the vehicle navigation system to correct its position. By providing continuous corrections, this method allows the vehicle to maintain required position accuracy without the need for external sensors, such a GPS. The main limitations of GN is the need for a map available prior the mission, and the computational complexity of searching for a correlation within the map and the sensor estimations. In the other hand, the key advantage of GN over other technologies is the large operating range when in use. Given a map, GN provides bounded localization error with accuracies dependent on the DR navigation, the map resolution, and the sensitivity of the geophysical parameter to change vehicle state [55].  
GN matching algorithms are classified in two different broad categories: batch methods and sequential methods [26]. The main algorithms for those methods have been TERCOM and Iterated Closest Contour Point (ICCP) [56] for batch methods; SITAN, Beijing university of aeronautics and astronautics Inertial Terrain-Aided Navigation (BITAN) and BITAN-II [25; 30; 57] for sequential methods. TERCOM correlates active range sensor observations with a digitized elevation database of terrain. Meanwhile, the essence of SITAN is the acquisition mode and tracking mode, which are basically a state-estimation problem based on an Extended Kalman Filter (EKF) after the non-linear system state equation and observed equation are linearized. Particle Filter (PF) and Bayesian estimators are also algorithms used in sequential methods.
# On the Redundancy in the Rank of Neural Network Parameters and Its Controllability
## 4 Proposed Method
### Comparison with Low-Rank Approximation  
This method can be seen as a form of low-rank approximation. However, there are a few key improvements in this method. First, this process is _dynamic_, in the sense that the desired rank of the matrix does not need to be predefined. As shown in the experiments section, this leads to different sizes in each layer, allowing the model to have more trainable parameters where needed and to remove parameters that do not affect performance. Similar observations has been made in the automatic structured pruning literature [36; 39; 40]. Second, in the factorized form \(\mathbf{PW^{\prime}Q}\), only \(\mathbf{W^{\prime}}\) is trainable, while \(\mathbf{P}\) and \(\mathbf{Q}\) are nontrainable, sparse matrices with only \(m\) and \(n\) nonzero elements, respectively. This always shrinks the size of the gradient-based parameter search space, which makes the optimization process more efficient and effective. On the contrary, in the case of factorizations where all factorized matrices are trainable, the number of free parameters increases unless \(m^{\prime}\) and \(n^{\prime}\) becomes small enough to satisfy \(mm^{\prime}+m^{\prime}n^{\prime}+n^{\prime}n<mn\). Third, while most previous low-rank approximation methods focus on factorizing the parameter matrices after the task-specific training is finished, our method is most effective when its applied simultaneously with the training, since it is beneficial to the optimization process. Last but not least, the matrix reduction is a deterministic process that does not require an iterative method.