esa-sceva/satcom-chunk-collection huggingface dataset:
# Flying Free: A Research Overview of Deep Learning in Drone Navigation Autonomy
## 2 Approach
### Levels of Autonomy  
As a first step, we need to define the concept of autonomy for drones, with a view to recognising different levels of autonomous navigation. This paper identifies the emergent navigation features in current research against these levels. We apply the Six levels of autonomy standard published by the Society of Automation Engineers (SAE) International. Though the context of these levels was intended by SAE for autonomous ground vehicles, the logic can apply to any vehicle capable of autonomy [6]. The concept of autonomy for cars and drones is similar, implying a gradual removal of driver roles in the navigation of obstacles and path finding. This, progressing to fully independent autonomous navigation regardless of restrictions due to surface bound movement or obstacles. By examining the SAE levels of autonomy for cars, we note how each level is directly applicable to drones. This provides a useful line of analysis for our overview In Figure 2, we set out the functionality of drone navigation, mapped against these levels of autonomy. Autonomy starts at Level 1 with some features assisted, including GPS guidance, airspace detection and landing zone evaluation. These features are designed to provide automated support to a human operator. These features are already to be found in commercially available drones. Level 2 autonomous features are navigational operations that are specific and use case dependent, where an operator must monitor but not continuously control. In the context of drone operation this can include features where the drone is directed to navigate autonomously if possible, e.g., the \"follow me\" and \"track target\" navigational commands. Some of these features are available in premium commercial products. Level 3 features allow for autonomous navigation in certain identified environments where the pilot is prompted for engagement when needed. At level 4 the drone must navigate autonomously within most use cases without the need for human interaction. Level 5 autonomy implies Level 4 autonomy but in all possible use cases, environments and conditions and as such is considered a theoretical ideal that is outside the scope of this overview. Though this paper aims at evaluating the features of papers in the context of Level 4 autonomy, it was found that the bulk of the papers approached in the research pool involved Level 2 or 3 autonomy, with the most common project archetype involving DNN training for autonomous navigation in a specific environment.  
Figure 2: Level of autonomous drone navigation mapped by functional features.
## 4 Discussion
Furthermore, perfect absorption of the TM-polarized light at a different wavelength (\(\sim\)3.8 \(\upmu\)m) can be realized at the light incidence angle of 3.8 degrees. Figure 6 shows the reflection spectra at a normal incidence when adjusting the polarization angle. The polarization of the incident beam was changed from 0 degrees (TM-polarized) to 90 degrees (TE-polarized) by an interval of 2 degrees. We can see that the extinction of the reflection dip decreased with an increased polarization angle. This implies that the absorption rate at \(\lambda\) = 4.071 \(\upmu\)m reduces with an increased polarization angle. This is easy to understand, since the grating in our structure was a two-dimensional grating and was polarization sensitive. However, if the polarization angle was \(<\)18 degrees, the reflection dip still had a good intensity extinction (\(>\)10 dB). This shows that our structure had a good polarization tolerance. When our structure was used as a refractive index sensor, the simulated performance is illustrated in Figure 7. As the surrounding index increased, the resonance wavelength increased, and a quasi linear response was observed. We can obtain that the sensitivity \(S=\Delta\lambda/\Delta n\) was \(\sim\)3130 nm/RIU and the figure of merit \(\text{FOM}=S/\text{FWHM}\) was \(\sim\)103. Of particular note is that the resonance peak still existed in the case of a high surrounding index (\(>\)2.0), as shown in the inset of Figure 7. This reveals that, compared with those in the literature [17; 26; 27], the proposed sensor has the obvious advantage of still being effective in high-index surroundings. Figure 5: (**a**) Reflection as a function of the incident angle. Inset shows the incident plane, and \(\theta\) is the incident angle. The horizontal dashed lines marked by (I) and (II) are corresponding to incident angles of 2.4 degrees and 3.8 degrees, respectively. (**b**) Reflection spectra associated with the two cases (I) and (II) in (**a**). Figure 6: Transmission versus the polarization angle. Traverse-magnetic (TM) polarization corresponds to 0 degree. We also investigated the influence of the adhesion layer on the performance of our structure. Two kinds of materials, Cr and TiO\({}_{2}\), were taken into account for the adhesion layer. Figure 8 shows the simulated absorption spectra with different thicknesses of the adhesion layer. In the simulations, the optical parameters of Cr and TiO\({}_{2}\) from [32; 35] were used. The other structure parameters were kept unchanged. As shown in Figure 8a, as the thickness of the Cr layer increased, the absorption bandwidth increased slightly, whereas the absorption on resonance decreased significantly. As shown in Figure 8b, with the increased TiO\({}_{2}\) layer thickness, both the absorption bandwidth and the absorption on resonance were almost unchanged. These results suggest that the use of a TiO\({}_{2}\) adhesion layer would be advantageous for the fabrication of the proposed structure.
# MixFormer: A Self-Attentive Convolutional Network for 3D Mesh Object Recognition
## 4 Results and Discussion
### Semantic Segmentation Experiments  
To validate the feature learning effect of the proposed MixFormer in this paper, we used 370 models from SCAPE, FAUST, MIT, and Adobe Fuse as training data and 18 models from the human category in the SHREC07 dataset as test data. All models were segmented by [PERSON] [49] et al. into eight categories of labels: head, torso, thigh, forearm, hand thigh, calf, and foot.  
We combined the main module of MixFormer with the U-Net network to use the acquired model features for downstream tasks, such as semantic segmentation. Limited by the memory size of the graphics card, we first downsampled the model to 1024 local surfaces during the training process, and then predicted the human model. The semantic segmentation effect is shown in Figure 5.  
From the segmentation visualization results, it can be seen that the proposed MixFormer has good feature extraction ability and the ability to establish inter-feature dependencies, and can clearly delineate all parts of the human body and achieve good segmentation results. On the sampled model, mIoU is 0.849. However, some errors do exist at the joint demarcation line, which may be due to the fact that the marker of the model is not particularly fine and the joint demarcation line is difficult to define from the semantic point of view.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy/\%** \\ \hline PointNet++ [47] & 87.9 \\ PCT [20] & 92.4 \\ \hline MeshNet [16] & 88.4 \\ MeshWalker [48] & 90.5 \\ SubdivNet [46] & 91.5 \\ Ours & 93.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification accuracy of different methods on Manifold40.
# A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n
## 3 Cotton Boll Disease Detection Algorithm and Improvement  
### Selection of the Basic Detection Model  
The YOLO (You Only Look Once) series is a representative single-stage object detection algorithm that regards object detection as a regression problem. It directly predicts object bounding boxes and classes from images. The primary advantage of this method lies in its high speed, enabling real-time object detection, which has led to its widespread application in the field of computer vision. To select the most suitable YOLO algorithm, five commonly used versions--YOLOv5s, YOLOv7, YOLOv8n, YOLOv9, and YOLOv11n--were compared. The results are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Model Name** & **Precision/\%** & **Recall/\%** & **[EMAIL_ADDRESS]/\%** & **FLOPs/G** & **Parameters/M** \\ \hline YOLOv5s & 87.3 & 79.2 & 82.60 & 15.8 & 7.2 \\ YOLOv7 & 86.8 & 78.3 & 81.3 & 105.1 & 37 \\ YOLOv8n & 86.2 & 81.5 & 83 & 28.7 & 11.8 \\ YOLOv9t & 87.5 & 80.3 & 80.7 & 6.4 & 1.7 \\ YOLOv11n & 90.1 & 81.9 & 83.3 & 6.4 & 2.5 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of the detection effect of different models.  
Figure 4: Sample data enhancement: (**a**) original, (**b**) color, (**c**) mirroring, (**d**) random rotation, (**e**) adding noise, (**f**) random masking, (**g**) scaling, (**h**) contrast adjustment, and (**i**) random cropping.  
As shown in Table 1, YOLOv11n achieved the highest [EMAIL_ADDRESS] of 83.3% and the highest precision of 90.1%, surpassing all other models in both metrics. In contrast, YOLOv9t recorded the lowest [EMAIL_ADDRESS] of 80.7%. Furthermore, YOLOv11n attained a recall rate of 81.9%, which is also the highest among the models, demonstrating its strong capability in disease detection. Moreover, YOLOv11n exhibits significantly fewer parameters and lower computational complexity than YOLOv5s, YOLOv7, and YOLOv8n. Despite YOLOv11n having 0.8M more parameters than YOLOv9t, YOLOv11n achieves a 2.6 percentage point higher [EMAIL_ADDRESS]. Therefore, considering its overall performance superiority, YOLOv11n was selected as the baseline network for this study, outperforming the other compared YOLO models.
# Two-Stage Beamforming Technique for GNSS Applications +
## 2 The Proposed Technique  
In this section, we present the beamforming strategy adopted during the acquisition process. Figure 1 shows a schematic of the proposed technique. As can be seen, the proposed technique utilizes a two-stage procedure as follows:  
**The first stage** exploits the acquired complex symbols (i.e., the complex IQ symbols that are associated with the k-th satellite codephase and doppler frequency for every antenna element). During this stage, the SAGE algorithm is used to estimate the AoA and the covariance \(\mathbf{R}_{\mathbf{xx}}\) matrices of the direct and reflected GNSS signals; for details regarding the array signal processing with the GNSS signals, the reader is referred to [3]. The SAGE algorithm employs a maximum likelihood estimator to jointly estimate the parameters of the signals received [14]. It can estimate the AoA and the covariance \(\mathbf{R}_{\mathbf{xx}}\) matrices of highly correlated signals based on a few received signal samples. This capability allows the SAGE algorithm to decompose the received signal into its constituents (i.e., direct and reflected signals). This capability is extremely important in urban environments, due to the presence of the intensive multipath effect [11].  
**The second stage**, on the other hand, exploits the MVDR algorithm to produce the weight vector that steers the main beam towards the satellite's direction and the nulls towards the multipath effect [13]. The MVDR algorithm utilizes the AoA of the direct signal and the covariance matrix that is associated with the reflected signals \(\mathbf{R}_{\mathbf{xx}}^{\mathbf{m}}\) to estimate the k-th weight vector that is associated with the k-th satellite. By exploiting \(\mathbf{R}_{\mathbf{xx}}^{\mathbf{m}}\), which is provided by the SAGE algorithm, the MVDR beamformer will place nulls only in front of the reflected signals. Furthermore, the covariance matrix of the reflected signals is now independent of the direct signal. Hence, the MVDR can estimate the weight vector correctly.  
It is worth noting that GNSS antennas are usually of RHCP type, and the reflected signals will probably have LHCP characteristics. Therefore, the reflected signal will be received with a low signal level. Accordingly, the AoA of the direct path (that is produced by the SAGE algorithm and exploited by the MVDR beamformer) is associated with the signal that has the highest power.  
Finally, the estimated weight vector will be passed to the tracking correlators to provide the beamforming effect during the tracking process.
## 1 Introduction
In this work, a novel methodology for prediction of earthquakes using feature engineering and a deep-learning-based technique is proposed. First, we collected the data for three regions: California, Chile, and Hindukush. The data collection was followed by data cleaning and pre-processing. New features were calculated based on the various seismic laws (such as the Gutenberg-Richter law). The features included the seismic rate of changes, foreshock frequencies, the release of seismic energy, the total time of recurrence, the maximum/minimum relevance, and redundancy. These features were extracted and used as input for our deep learning model. Afterwards, a deep-neural-network-based architecture was proposed, which was evaluated against standard benchmark algorithms, using accuracy, precision, recall, and F1-score. Unlike previous works, we conducted Out of Sample testing, to validate the generality of the proposed technique. Out of Sample testing means that the model is trained on one dataset, but is evaluated on a different dataset. A better performance on an Out of Sample test reflects that the model is generalized, and can be used for datasets other than the one on which it was trained. The results showed that the proposed deep neural network was more accurate than the other machine learning approaches. This research will aid risk and uncertainty mitigation, for better decision-making regarding earthquake prediction, in various ways. The rest of this paper is organized in the following manner: Section 2 presents the proposed methodology, including the dataset, feature engineering, the proposed deep neural network architecture, the benchmark algorithms, and the evaluation metrics; the results are presented and discussed in Section 3; Section 4 concludes the work.
## 4 Results and Analysis
### The Combustion Fit Results of Engine Running with Generator Conditions  
After obtaining the Seiliger parameters fitting results of one engine operating point, the overall measurements are calculated. Figure 11 shows the trend of Seiliger parameters \(a\), \(b\), \(c\) and \(n_{exp}\) as functions of effective power \(P_{e}\) when engine running at 900 r/min and 1000 r/min respectively.  
Seiliger parameter \(a\) indicates the premixed combustion phase and a large value of \(a\) is associated with more premixed combustion. At each engine speed, the value of \(a\) increases at low load and reaches a maximum, then decreases when going to higher load. As to the effect of engine speed on the  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Seiliger**} & \multicolumn{2}{c}{**Cylinder 1**} & \multicolumn{2}{c}{**Cylinder 2**} & \multicolumn{2}{c}{**Cylinder 3**} & \multicolumn{2}{c}{**Cylinder 4**} & \multicolumn{2}{c}{**Cylinder (average)**} \\ \cline{2-10}  & \multirow{2}{*}{**Value**} & **Heat Input** & \multirow{2}{*}{**Value**} & **Heat Input** & \multirow{2}{*}{**Value**} & **Heat Input** & \multirow{2}{*}{**Value**} & **Heat Input** & \multirow{2}{*}{**Value**} & **Heat Input** \\  & & **Ratio** & & & & & & & **Ratio** & **Ratio** \\ \hline a & 1.311 & 20.83\% & 1.349 & 23.05\% & 1.329 & 21.04\% & 1.338 & 22.44\% & 1.331 & 22.11\% \\ b & 1.344 & 41.92\% & 1.330 & 40.92\% & 1.347 & 41.07\% & 1.330 & 40.68\% & 1.338 & 41.85\% \\ c & 1.375 & 12.77\% & 1.516 & 16.74\% & **1.127** & **4.64\%** & 1.427 & 14.24\% & 1.505 & 16.53\% \\ n\({}_{exp}\) & 1.180 & 24.49\% & 1.198 & 19.29\% & **1.151** & **33.25\%** & 1.185 & 22.65\% & 1.197 & 19.51\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of advanced Seiliger process fit in 4 cylinders.  
Figure 11: Seiliger parameters at engine constant speed: (**a**) 900 r/min; (**b**) 1000 r/min.  
Seiliger parameter \(a\), it can be observed that for a certain load point parameter \(a\) is lower for a higher engine speed. At a certain engine speed, \(b\) goes up with increasing load. The engine speed seems to have less effect on \(b\), i.e., there is hardly any differences for different engine speeds at the same load.  
Seiliger parameter \(c\) represents the isothermal combustion stage, to some extent, the large value causing late engine combustion. The value of \(c\) goes up with the engine power increasing, which means, for this engine, when the engine is running at higher load, the later combustion occurs more frequently. Seiliger parameter \(n_{exp}\) indicates the very late combustion phase during expansion. Due to the polytrophic expansion exponent, the value of \(n_{exp}\) varies in a relatively small range (around 1.2 to 1.35) and decreased with engine power going up, representing less heat input during the very late combustion stage.
# Benefits of Auditory Training with an Open-Set Sentences-in-Babble-Noise
## 3 Results
### Training and Conservation Effects: Objective Tools  
Following the null results of spontaneous learning and two months of training, the scores of each test in T1 and T2 were averaged into a single pre-training score, and the scores of each test in T3 and T4 were averaged into a single post-training score. These scores were compared to those obtained in the conservation session (T5). A two-way repeated measures MANOVA was carried out on speech perception measured by the four speech perception tests, with test session (pre-training, post-training, conservation) as a within-subjects variable (Figure 2). A main effect for Session was observed (F(8,6) = 5.091, \(p\) = 0.031, \(\mathrm{\SIUnitSymbolMicro n}^{2}p\) = 0.872)). A two-way repeated-measures univariate ANOVA for each task showed a main effect for the test session in sentences comprehension (HeBio) with 4 TBN (F(2,26) = 11.59, \(p\) < 0.001, \(\mathrm{\SIUnitSymbolMicro n}^{2}p\) = 0.47)), and with SSN (F(2,26) = 16.37, \(p\) < 0.001, \(\mathrm{\SIUnitSymbolMicro n}^{2}p\) = 0.56)), and words comprehension (CVC) in quiet (F(2,26) = 3.8, \(p\) = 0.036, \(\mathrm{\SIUnitSymbolMicro n}^{2}p\) = 0.23)), but not in digits identification (DIN test) (F(2,26) = 1.59, \(p\) > 0.05, \(\mathrm{\SIUnitSymbolMicro n}^{2}p\) = 0.11)). Post hoc Least Significant Difference (LSD) tests showed differences between pre-training and post-training for sentences comprehension (HeBio) with 4 TBN (LSD = 2.539, SE = 0.635, \(p\) = 0.002) and SSN (LSD = 5.557, SD = 1.333, \(p\) = 0.001), and words comprehension (CVC) in quiet (LSD = 5.893, SE = 1.901, \(p\) = 0.008), but no differences between post-training and conservation (HeBio sentences with 4 TBN: LSD = 0.800, SE = 0.620, \(p\) = 0.219; HeBio sentences with SSN: LSD = 0.750, SE = 0.422, \(p\) = 0.099; CVC words in quiet: LSD = 0.536, SE = 2.636, \(p\) = 0.842).
## 2 Proposed Solution
### People Detection and Tracking  
This section discusses the proposed framework's people detection and tracking models. The people in the scene were detected using the \(F_{p}\) detection model and then tracked over different frames using the \(F_{p}\) tracking model. The detection model used for this purpose provides a bounding box for the person's position, whilst the tracking model assigns each person a unique ID and tracks them through time.  
The detection model provides a time-varying vector containing information on people's spatial location. It is defined as \(I_{p}(t)=\{bb_{p1}(t),bb_{p2}(t),\ldots,bb_{pk}(t),\ldots,bb_{pn}(t)\}\), where \(n\) is the number of bounding boxes and \(bb_{pk}(t)=(u,v,r,h,c_{p})\) is a five-tuple that represents the bounding box representing a person at time \(t\). In \(bb_{pk}(t)\), variables \(u\) and \(v\) represent the two-dimensional coordinates of the bounding box's center, \(r\) represents the bounding box's aspect ratio, \(h\) represents the bounding box's height, and \(c_{p}\) represents the detection's confidence level, as shown in Figure 2. The tracker assigns an ID and updates bounding box information based on previous and current data. The output of the tracker is defined as \(\overline{J_{p}}(t)=\{bb_{ip1}(t),bb_{ip2}(t),\ldots,bb_{pk}(t),\ldots,bb_{ipn }(t)\}\), where \(bb_{ipk}(t)=(u,v,r,h,c_{p},i)\) is a six-tuple representing updated bounding box information with assigned ID, \(i\), for \(k\)th person.  
Figure 1: A high-level overview of the proposed system.  
Given its robustness and real-time prediction capabilities, the YOLO network [48] for people detection (\(F_{p}\)) and the DeepSORT algorithm [18] for tracking (\(F_{p}\)) were used in this paper. DeepSORT by itself handles minor occlusions for people by the Kalman filtering mechanism. We implement another level of interpolation to handle the missed detections on top of this. Given an image, the YOLO network predicts the bounding boxes of many predefined object classes that are present in a scene. Following that, the output is created by applying non-max suppression [49] and filtering the bounding boxes belonging to people. The DeepSORT algorithm then assigns indices, \(\overline{j_{p}}\), to these detected bounding boxes using the Mahalanobis distance and the cosine similarity of the deep appearance descriptors. The publicly available weights trained using the COCO dataset [50] were used to initialize the weights of the YOLO model, whereas the weights trained using the MOT dataset [51] were used to initialize the DeepSORT model.
## 2 System Reliability Evaluation Methods
### System Reliability Evaluation Method Based on the Modified AHP
#### 2.2.1 Establishment of a Hierarchical Mode  
System reliability is closely related to the reliability of a single component. Figure 2 shows the hierarchical model.
# User Utility Maximization in Narrowband Internet of Things for Prioritized Healthcare Applications
## 5 Performance Evaluation
### Simulation Environment  
Table 3 represents the simulation parameters and their values following 3 GPP standards [34], where the cellular layout is considered as a hexagonal grid using three sectors per site with an inter-site distance of 500 m along with a frequency band of 900 MHz considering a 180 kHz channel bandwidth. The system-level performance is developed by an NS-3-based LTE environment, where multiple UEs are connected with multiple eNBs, supporting up to 10 to 100 eNBs and 100 to 1000 UEs with good channelization specifying the time slot. The various NB-IoT devices installed in a typical hospital environment, including the OPD, Ward, Emergency, Cabin, and Operation Theater, have been considered as data generation sources in the simulation environment. Here, we consider three types of events, namely, critical, relevant, and normal. We also consider the occurrence of an event and its type in a certain NB-IoT UE using uniform random distribution. Therefore, in a given scenario, the number of events varies from 0 to the total number of UEs intended for communication, each generating 3 to 15 packets/sec randomly. The data ranges and payload information sizes produced from different sensors are adapted from [11] and resemble a practical healthcare application environment. The RadiobearerStatCalculator is used as a simulation trace file to obtain a view of different parameters. The experimental data are analyzed for uplink users, and the average results from 30 experimental runs are plotted in the graph data points. The events happen randomly in the network at different places, which is considered over the simulation period of 1000 s.