esa-sceva/satcom-chunk-collection huggingface dataset:
Sensors_**2011**, _11_, 5439-5468; doi:10.3390/s110505439  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Review_  
**A Survey on Multimedia-Based Cross-Layer Optimization in Visual Sensor Networks**  
**[PERSON] \({}^{1,2,\ast}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) DCA-CT-UFRN, Campus Universit ario, Lagoa Nova, Universidade Federal do Rio Grande do Norte, 59072-970 Natal RN, Brazil; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) DTEC-UEFS, Av Transnordestina, S/N, Novo Horizonte, Universidade Estadual de Feira de Santana, 44036-900 Feira de Santana BA, Brazil  
\({}^{\ast}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +55-84-8827-1120.  
_Received: 21 March 2011; in revised form: 13 April 2011 / Accepted: 15 April 2011 / Published: 18 May 2011_  
**Abstract:** Visual sensor networks (VSNs) comprised of battery-operated electronic devices endowed with low-resolution cameras have expanded the applicability of a series of monitoring applications. Those types of sensors are interconnected by _ad hoc_ error-prone wireless links, imposing stringent restrictions on available bandwidth, end-to-end delay and packet error rates. In such context, multimedia coding is required for data compression and error-resilience, also ensuring energy preservation over the path(s) toward the sink and improving the end-to-end perceptual quality of the received media. Cross-layer optimization may enhance the expected efficiency of VSNs applications, disrupting the conventional information flow of the protocol layers. When the inner characteristics of the multimedia coding techniques are exploited by cross-layer protocols and architectures, higher efficiency may be obtained in visual sensor networks. This paper surveys recent research on multimedia-based cross-layer optimization, presenting the proposed strategies and mechanisms for transmission rate adjustment, congestion control, multipath selection, energy preservation and error recovery. We note that many multimedia-based cross-layer optimization solutions have been proposed in recent years, each one bringing a wealth of contributions to visual sensor networks.  
**Keywords:** cross-layer optimization; visual sensor networks; wireless multimedia sensor networks; congestion control; error recovery
## 4 Discussion  
BUS images encompass speckle noise and other artifacts. Artifacts of BUS images are created via the characteristics of ultrasound themselves. They are categorized into four major classes: Degraded images, missing structures, mis-registered location, and falsely perceived objects [44]. Usually, artifacts may produce unnecessary clinical intervention. Such factors also reduce the efficacy of CADx framework that employs texture-based feature extraction. Some texture extraction techniques such as LBP and GLCM are very sensitive to the abovementioned factors. Therefore, only texture analysis does not accurately portray the lesions of BUS images. As shape-based features describes the properties of a lesion in a compact manner. In this paper, hybrid feature set is employed to produce more robust features to accurately characterize breast lesions and to enhance the performance of CADx system.  
Furthermore, ensemble classifiers enable a combination of individual classifiers, for example decision trees to boost the predictive capability. Decision trees are mostly suitable for the ensemble method since they are quick. Multiple learners are employed, weighted, and then merged in ensemble method to get a better result of classifier to individual classifiers. This method takes the 'wisdom of the crowd' idea in that individuals will pursue and evaluate numerous estimations before formulating a main decision. The most accepted case of selection for ensemble is boosting. It is a common technique for enhancing the operation of a weak learner for example, decision tree [43].  
In this paper, we concentrated on enhancing the efficacy of CADx system. In the research community, several techniques and methodologies have been proposed to isolate a benign lesion from malignant one accurately. A comparative analysis has been provided in Table 8 presenting the performance benchmark of the proposed system. [PERSON] et al. [45] proposed active contours without edges for segmentation, texture, and geometry analysis are performed for feature extraction and achieved 91.3% accuracy using SVM. [PERSON] et al. [46] employed fuzzy c-means clustering for segmentation, feature analysis was done using echogenicity and morphology, and accomplished 92.50% sensitivity for malignant lesion using the binary logistic regression technique. [PERSON] et al. [47] performed shape-based analysis and achieved 84.6% accuracy using ANN. The proposed CADx system  
Figure 8: Classification accuracy graph of OASBUD dataset.  
delineates better performance due to better segmentation, hybrid features extraction for the shape, and size of lesions and ensemble method of classification as compared to [45; 46; 47] in terms of accuracy and sensitivity.
## 4 Results and Discussion
### Discussion of This Paper
#### 4.3.2 Estimation of Rail Weld Based on ABA Signal Using Time-Frequency Techniques  
We note that the maximum acceleration of the axle box (202 m/s\({}^{2}\)) in Figure 5a is not the peak excited by weld RW1, but is the wave crest (148 m/s\({}^{2}\)) appearing after 11.15 s. In other words, considering merely the maximum amplitude of the ABA in the time domain, the degradation might be overestimated. As the rail weld can excite high frequency vibrations in ABA, which can be found through S transform, we can identify the true location of the rail weld by time-frequency techniques and get corresponding amplitude, as shown in Figure 5. Further, we can estimate the rail weld through a safety limitation (220 m/s\({}^{2}\)) proposed in Section 4.2. However, a question we should pay more attention to is whether short wavelength irregularity in a rail weld can inspire a higher contact force and increase more rapidly than that of longer wavelengths [9]. Therefore, there is a need to identify the geometries of rail welds.  
By parametric analysis in Section 4.1, it can be found that the weld length and depth determine the vibration frequency and amplitude of ABA, respectively. Based on these characteristics, we present an approach for obtaining rail weld geometry from ABA, expressed in Figure 15. Step 1: develop a database of ABA and rail weld geometries through the FE model established in Section 3.1; Step 2: analyze the measured ABA in time and frequency domains using a time-frequency technique, and identify the wavelength and wave depth of the rail weld from the database established in step 1; finally, make a decision whether the rail weld should be maintained. Taking RW1 as an example, it may be 0.1-0.2 m in length as it can excite frequencies of 350 Hz and 1150 Hz shown in Figures 5b and 10b. Further, the true amplitude of ABA by RW1 is 148 m/s\({}^{2}\), and we can judge that the wave depth should be about 0.2 mm according to Figure 11a.
# Research on Fleet Size of Demand Response Shuttle Bus Based on Minimum Cost Method
## 4 Numerical Simulation
### Case Illustration: Application Scenario in a City Fringe of Heilongjiang  
To demonstrate the practical applicability of the proposed model, a case scenario based on an urban fringe area in Heilongjiang Province is introduced. This region exhibits sparse travel demand and limited conventional bus coverage, especially in suburban residential areas. The average demand density in this area is approximately 15 persons/h/km\({}^{2}\), mainly during morning and evening peak hours. The primary goal is to connect three residential communities to a major transit hub with trunk bus or metro services. Based on data from local transportation planning agencies, the proposed model is applied to determine optimal fleet size and departure frequency.  
Simulation results show that the connector-type DRC can reduce total system cost by approximately 20% compared to fixed-route buses while maintaining passenger transfer waiting times below 0.7 h. Although no actual deployment has yet occurred, the operational  
Figure 4: Trends in optimal fleet size under different scenarios.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Q**} & \multicolumn{2}{c}{**k = 10**} & \multicolumn{2}{c}{**k = 20**} & \multicolumn{2}{c}{**k = 30**} & \multicolumn{2}{c}{**k = 40**} \\ \cline{2-9}  & **B** & **minC** & **B** & **minC** & **B** & **minC** & **B** & **minC** \\ \hline
10 & 0.96 & 408.68 & 0.89 & 409.18 & 0.84 & 410.39 & 0.80 & 411.98 \\
20 & 1.92 & 916.42 & 1.79 & 937.76 & 1.69 & 958.73 & 1.60 & 978.00 \\
30 & 2.88 & 1620.98 & 2.69 & 1677.04 & 2.53 & 1730.93 & 2.39 & 1779.64 \\
40 & 3.83 & 2522.29 & 3.58 & 2626.96 & 3.37 & 2726.92 & 3.19 & 2816.86 \\
50 & 4.79 & 3620.34 & 4.47 & 3787.51 & 4.21 & 3946.70 & 3.99 & 4089.65 \\ \hline \end{tabular}
\end{table}
Table 9: Analysis of key impact factor results.  
parameters used are derived from real planning data, confirming the model's practical relevance and feasibility.
# Fractional-Order Closed-Loop Model Reference Adaptive Control for Anesthesia
## 6 Simulations
### Identification  
To assess the variability among patients, 30 patient models (taken from different studies: patients 1-10 [82], patients 11-20 [36], patients 21-30 [83]) were used to emphasize the variability among the population. Figure 4 shows the responses of these models to a step input. Table 1 shows the pharmacodynamic and the biometric characteristics of the 30 patients.  
As can be observed from Table 1, the parameters of the PK/PD models showed significant variation, depending on age, weight, height, and gender, causing substantial variation in the step response.  
To verify the ability of the proposed FOMs to capture the dynamics of the PK/PD model, a simulation was carried out with the three structures proposed.  
We used a nominal patient to carry out this simulation. Figure 5 shows the identification scheme, and Figure 6 shows the model's outputs and the identification error. We can observe that the three fractional-order structures proposed can capture the step response of the PK/PD model. In Figure 7, the evolution of the model's parameters is shown.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Patient** & **Age** & **Height** & **Weight** & **Gender** & \(\mathbf{EC_{50}}\) & \(\mathbf{\gamma}\) \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 1: Patients’ pharmacodynamic parameters and biometric features.  
Figure 4: Patient’s response to a step input.  
Figure 5: Identification scheme.  
Figure 6: Identification, bispectral index (BIS) output of the PK/PD model and the proposed fractional-order model (FOMs).  
It can be seen that the input-output behavior of the PK/PD model was well captured by the proposed model structures.  
It is worth noting that the objective of this study was the design of an adaptive control scheme, not to model a particular system, and, for adaptive control, in general, only a model structure is needed. Therefore, with the identification scheme and the simulation presented, it is shown that the proposed models were able to capture the input-output behavior of the presented example. Moreover, it was shown that the simple fractional-order models (13-15) are able to capture the responses of the patients, as illustrated in Figure 4.
# Poisoning Attacks against Communication and Computing Task Classification and Detection Techniques
## 3 System Model  
### System Architecture  
The considered edge-computing network is an edge-computing network that consists of diverse servers (with diverse computing and storage capabilities) and diverse IoT devices (generating diverse communication and computing requests). The network architecture is presented in Figure 1.  
In this work, the 2C tasks (requests) are represented by three features: (1) the required end-to-end latency in milliseconds, (2) the required reliability (in terms of the packet error rate (PER)), and (3) the computational complexity in floating-point operations \(\times 10^{9}\) (GFLOPs). In general, other features might be considered for 2C task classification (although some may translate to the ones given above, such as the task complexion speed). Here, we selected the ones that were most relevant for 2C tasks in 5G and 6G services.  
The idea was to classify the tasks to delegate each task to the appropriate server or virtual machine (VM). The allocation might follow several scenarios, but here, for simplicity, every available server or VM and the associated communication link was considered to  
Figure 1: System architecture.  
serve a given requirement (latency, reliability, or task complexity) either with a high or low attribute. Hence, eight servers with associated communication links (called 2C links for simplicity) were possible to consider, which represented eight 2C links, as presented in Table 2. The links' features are represented by a single binary digit, with zero being for low-quality service and one being for high-quality service. The decision about the state of each 2C link's feature \(x_{i}^{n}\), \(n\in\{1,2,3\}\) was obtained as follows:  
\[{x_{i}}^{n}=\begin{cases}1(\text{high})&{x_{i}}^{n}>\alpha_{i}^{n}\\ 0(\text{low})&\text{otherwise}\end{cases} \tag{1}\]  
where \(\alpha_{i}^{n}\) is the middle point of the \(n\)th feature's interval and \(i\) is the 2C link ID (\(i\in 1,2,\ldots 8\)).
## 2 Materials and Methods  
### Materials  
This research was conducted in a test area within the Sauipe River basin (Figure 3), located in northeastern Brazil, characterized by the presence of the Atlantic Forest, although fragmented. It covers an area of approximately 23,198.48 hectares, located between the UTM coordinates 564362.36 and 8659814.07, in the DATUM WGS84 system, zone 24S. The basin spans the municipalities of Entre Rios, Aracas, and Alagoinhas, with most of it situated in the latter. The local landscape consists of a mosaic of vegetation types, such as Cerrado, Semideciduous Seasonal Forest, and Dense Ombrophilous Forest, in addition to transitional areas with varying canopy openings. The region is surrounded by an agricultural matrix and eucalyptus cultivation areas [29]). The Sauipe River, with an approximate length of 40 km, flows through the municipalities of Alagoinhas, Aracas, and Entre Rios. In many sections, the river is dry and lacks riparian vegetation, indicating degradation of the riparian environment. Its main tributary is the Encantado Creek, along with several smaller streams.  
The climate in the region ranges from humid to subhumid, with an average annual temperature of 24.7 \({}^{\circ}\)C and precipitation ranging from 1200 to 1800 mm, with lower rainfall values found in the eastern portion of the basin [28]. The predominant soils in the Sauipe River basin are Latossolos and Argissolos. Latossolos are deep, well-drained soils with low natural fertility due to intense leaching, a typical feature of humid climates. Argissolos, on the other hand, have a leached surface horizon and a clay-rich subsoil, making them low in fertility and prone to erosion [28]. The altitude in the basin ranges from 150 m to sea level, with the Reconcavo and Tucano sedimentary basins being predominant, alongside smaller tertiary deposits. The geomorphological formation of the region is characterized by the coastal plateaus, derived from continental sediments from the Tertiary Period, composed of sandy, sandy-clayey sediments, and pebbles, ranging from unconsolidated to slightly consolidated [30]. The terrain is highly dissected, with strongly undulating hills and convex, tabular hilltops [29].  
The following materials were necessary for this research: Landsat 08 image dated 16 October 2020, obtained from USGS 2021, with WGS 84 projection system, zone 24 South. ALOS PALSAR L-band image, dated 19 November 2009, was also used. We also utilized meteorological data from the Alagoinhas station provided by [31]; the data pertain to the annual water balance for a soil storage capacity typical of oxisols, which predominate in the studied watershed.  
Figure 3: Location of the Sauipe River basin on the northern coast of Baha, Brazil.
# Low-Complexity Pupil Tracking for Sunglasses-Wearing Faces for Glasses-Free 3D HUDs
## 1 Introduction
(**a**) 3D crosstalk occurs when the error associated with the eye-tracking accuracy exceeds the 3D crosstalk margin. (**b**) 3D rendered results corresponding to high-accuracy eye tracking with smaller error than the 3D crosstalk margin. The 3D crosstalk margins of the AR 3D HUD prototype are 12 mm, representing the eye-tracking precision threshold. The purpose of this study was to realize pupil center localization for drivers wearing sunglasses. Our proposed system consists of face detection, eye-nose shape keypoint alignment, a tracker checker, and tracking mode switching. For scenarios such as the user wearing sunglasses, corresponding shape aligners are applied using an image classification approach for pupil center localization. Occlusion, which can be exacerbated by sunlight reflection, makes pupil localization quite challenging in such scenarios. To tackle this problem, we use nonoccluded areas to infer the pupil center. The contributions of our method can be summarized by the following points:  
* We propose a pupil center localization system applicable to both bare faces and sunglasses-wearing faces. We classified facial images into these two categories and performed pupil tracking accordingly. * For sunglasses-wearing faces, we inferred the eye position behind the sunglasses by applying a supervised regression method to the non-occluded areas.
## 5 Results
### Detector Model Results  
A summary of the experimental results is displayed in Table 4, showing the DCGANs + YOLOv8 + TTA model performing the best, achieving a mAP on the test data of 0.810. YOLOv5 and YOLOv7 achieved mAPs of 0.621 and 0.643, respectively, while YOLOv8 trained on the original data only achieved a mAP of 0.680. As shown in Table 4, the precision and recall of YOLOv8 were 0.928 and 0.891, respectively, which were superior to that of YOLOv7 (0.911 and 0.83) and YOLOv5 (0.890 and 0.804), indicating that the newer YOLO architectures tend to have more true positive detections and fewer false positive detections. When comparing the inference speed of the different YOLO architectures, YOLOv8 and YOLOv7 had approximately the same speed and were 3% faster than YOLOv5 but yielded a 13% improvement in the F1 score when comparing YOLOv5 to YOLOv8.  
When combined with DCGANs, the precision and recall continue to improve. TheYOLOv8 model achieved an F1 score of 0.91 at a confidence level of 0.617 for all classes, while the DCGANs + YOLOv8 + TTA model resulted in an improved F1 score of 0.96 at a lower confidence level of 0.334. The precision and recall of the DCGANs + YOLOv8 + TTA model were also greater than that of YOLOv8 alone (0.928 and 0.891) and the DCGANs + YOLOv8 model (0.945 and 0.893). When combining DCGANs and TTA with YOLOv8, the synthetic data and augmentation data helped the model generalize better over diverse scenarios, making it more accurate at lower confidence levels. By lowering the confidence threshold, the model can detect more nuanced or difficult-to-detect objects, thus increasing the overall recall. DCGANs and TTA provide more robust and diverse training data where the model can afford to lower its confidence threshold while still improving accuracy. In contrast, traditional models like YOLOv5 and YOLOv7, though improved upon by their iterations, still fall short compared to YOLOv8. When extended to incorporate DCGANs, these models see improvements, particularly in recall and precision, but do not achieve the same high standards set by the latest enhancements in YOLOv8. While the DCGANs + YOLOv8 + TTA model was more accurate than only using DCGANs or only running a YOLOv8 model, the processing speed was greatly reduced (155 fps to 92 fps). However, given the sensitive nature of this application and the high cost of false positives associated with traffic enforcement, the approach remains viable despite the greater computational power required.
## 2 Materials and Methods
### Improved Unsupervised Homography Estimation Model  
During the stamping process, the opening and closing of the mold causes random vibrations to the metal sheet, and the complexity of the on-site environment also changes. These factors lead to random shifts in the sheet in the real-time collected images. Therefore, when estimating the homography matrix for image registration, the model is required to have scale and rotation invariance comparable to traditional methods, as well as robustness to lighting changes and noise. The unsupervised homography estimation method mentioned earlier has shown improved performance under lighting changes compared to the supervised method in [14], but it still lacks resistance to deformations and position changes in real environments, making it unsuitable for the scenarios discussed in this paper. Additionally, current supervised and unsupervised homography estimation models are trained on synthetic datasets, which are far removed from real-world conditions.  
To address the aforementioned issues, this paper proposes an improved unsupervised homography estimation model that introduces a special deformable convolution module. This module expands the receptive field while enhancing the model's ability to learn rotational invariance during homography estimation. A GN normalization layer is added after each convolution to improve the model's stability when training with large-resolutionimages in small batches. A multi-scale image pyramid is constructed from the input image, allowing for homography estimation at different scales and improving the model's ability to learn scale invariance. Additionally, a real dataset of stamping process images collected with an industrial camera is created. The dataset is augmented by randomly adding various disturbances, lighting conditions, contrast, and blur levels to enhance the model's ability to estimate homography for sheet metal shifts in a vibrating stamping environment.
# Traffic Noise Prediction Applying Multivariate Bi-Directional Recurrent Neural Network
## 3 Results and Discussion
### Model Hyperparameters Tuning  
Different hyperparameters were also tuned during the whole model training process. Compared to conventional fully connected ANN, the RNN model has much more hyperparameters to tune. The final setting of the tuned hyperparameters can be found in Table 6.  
A GRU model with three layers was finalized. Each layer has 50, 100 and 1 neuron, respectively. The learning rate determines the step size at each iteration when moving toward the minimum of loss function during the optimization process [45]. Therefore, it controls the speed at which the model learns. The learning rate was set to 0.0001 here. The batch size controls the number of training samples that are used for weights update at each iteration, which was finally fixed at 256. Adam was chosen as the optimization algorithm, which is an advanced method for stochastic optimization [46]. Dropout regularization was employed with a dropout rate of 0.4, in order to efficiently mitigate overfitting [47]. The number of epochs was not predefined. Instead, the duration of the training was configured by applying Keras EarlyStopping API, which enables us to compare the model performance between training data and validation data [44]. During the training process, the loss of training data and validation data will both sink along with the epochs. The training data loss will keep decreasing, as long as the training continues. However, the validation data loss will start to increase, when the model is overfitting. With the help of Keras EarlyStopping API, the model stops training, when the validation loss does not reduce any more, which helps to avoid model overfitting (Figure 18).  
As shown in Figure 18, at Epoch 143, the model has stopped training, because validation data loss does not reduce any more.