esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Resolution Analysis with Visualization to Determine Network Attack Patterns
## 2 Previous Work
By decomposing input data until a pre-determined level, it can separate different levels of information (fine details vs. high level). Furthermore, prior literature indicates that MRA is computationally fast and appropriate for noise filtering and data reduction [30; 31]. In summary, numerous studies have been conducted to design effective network intrusion detection systems. However, it is important to note that one algorithm or approach cannot detect all existing or unknown attacks precisely due to the existence of anonymity in network traffic patterns. In the visualization community, researchers started to utilize various visualization techniques to address the limitations of traditional network intrusion detection analysis. Due to the importance of analyzing complex network traffic data, the community provides network traffic data as a part of the visualization challenge and motivates researchers to get involved in the visual analysis of real-world network traffic data. For example, the VAST 2012 challenge [32] provided the data from a financial institution to identify anomalies or problems visually and understand the health of a global corporate network. [PERSON] et al. [33] conducted a comprehensive review of existing network security visualization systems and classified them into five different use-case classes: host/server monitoring, internal/external monitoring, port activity, attack patterns, and routing behavior. While numerous network security visualization systems have been proposed, most systems focus only on how to represent collected log data or network events. To better understand network traffic patterns and detect network intrusions more accurately, visualization techniques should be integrated with computational and machine learning approaches. In the following sections, a detailed explanation regarding how we combine both computational approaches and visualization techniques is presented.
## 2 Materials and Methods
### Sensor Measurement Point  
The sensor measurement point is a critical component that houses the AS7265x multispectral sensor and is designed for accurate analysis of milk's chemical components. It consists of a tubing system through which the milk passes and is continuously analyzed. The tube is surrounded by a special plastic structure to protect the measurement process from external influences.  
This plastic structure is only large enough to accommodate the sensor and is made from a completely light-impermeable material. This feature prevents external light sources from affecting the sensor's measurements, thus enabling more stable and reliable data collection. The plastic structure contains a transparent glass section inside; this glass is strategically positioned to allow the sensor to project light onto the milk and detect the reflected light.  
This specially designed tubular measurement point provides the ideal environment necessary for conducting spectral analysis of milk. Spectral analysis uses the light's interaction with materials to measure their properties, often based on how light is absorbed, reflected, or transmitted at different wavelengths. The transparency of the glass allows the sensor to efficiently collect reflected light, while the surrounding light-impermeable plastic structure minimizes external light interference. This combination aids in the precise and accurate detection of milk components.
## 5 RNNs and LSTM  
Recurrent Neural Networks (RNNs) are regarded as a class of supervised learning procedures, and they can develop successive sequences for prediction and detection [27]. As shown in Figure 1, RNNs involve hidden layers composed of artificial neurons with feedback loops; therefore, they have dual inputs, i.e., the present and the recent previous response.  
In RNNs, hidden layers are capable to act as storage for the network at a specific time; this structure enables the RNNs to handle the preceding data for a prolonged period of time; additionally, RNNs can represent time dependencies for any sequence with a lower numeral of neurons. On the other hand, traditional RNN based on backpropagation through time (BTT) experiences slow learning and a vanishing gradient problem [28].  
Therefore, RNNs will not be the best candidate for signals that may be sent through fading channels that may disperse the signal and originate long-term dependencies among its samples [5]. Long short-term memory (LSTM) network, which is a one category of RNNs, is frequently used with sequences and time series data for categorization, where it can take advantage of time dependencies between sequences [5].  
LSTM network can develop knowledge among time steps of the data sequence and manage the long-term dependency process of time series data. Based on their underlying design, the LSTM network includes LSTM cells, and each cell contains a set of gates that are capable of saving and gaining access to data over extended periods of time and of counteracting the error from backpropagation [5; 28]. LSTM is able to receive a vector complex data, hence integrating the magnitude and phase parts of the received sequence concurrently. LSTM network can be considered as a proper selection to realize multiuser detection (MUD) and prediction when time series data are available [29].  
Figure 1: RNN network architecture.
## 4 Conclusions  
Dimensional errors were identified and reduced by considering the geometrical parameters, e.g., the lines, curves, areas, and volumes of both the real and scanned objects. The three-dimensional objects were measured physically and virtually using CMM and SolidWorks, respectively. Volumetric errors were measured by comparing the 3D scanned volume and original volume from the CMM. Three-dimensional scanning factors were optimized with full factorial DOE, comprising four novel frameworks of 3D scanning for both simple and complex objects to minimize volumetric errors. Apart from the statistical errors (e.g., error PCRs), the volumetric scanning errors were found to be lower, considering the lower value of error range for complex geometry compared to objects with simple geometry. Concisely speaking, scanning errors for the complex sample were uniquely reduced by up to 0.1%, an extent which has never before been reported by earlier studies. The main contributions of this study are as follows:  
* A simple object has complexity in scanned data for all modes of 3D scanning in terms of insignificant factors and high values of severity indices (SIs) which cause high error PCRs. Therefore, simple objects should logically and decisively be modeled using CAD software, which can save time and money instead of proceeding with 3D scanning. * Medium detail watertightness and a simplicity of 70% were frequently concluded to be the optimal levels of watertightness and simplification in all modes of 3D scanning regardless of geometrical intricacies in simple and complex geometries. * With mode A of 3D scanning for complex geometry, only one 3D scanning factor was significant, i.e., watertightness. However, an un-watertight watertightness and a simplification of 70% were the optimal levels of watertightness and simplification, respectively, for complex geometry. This implies that mode A is recommended for scanning complex objects which have detailed scanning specifications, e.g., texture, smoothness, and all four levels of watertightness. * Irrespective of the sample geometry, error PCRs were lower for mode A and complex geometry. Hence, a combined effect of more than two levels of 3D scanning factors was expectedly significant in all modes of 3D scanning, helping us to justify the reason for which all modes of 3D scanning frequently delivered statistically insignificant factors except mode A for complex geometry. This is another way of verifying the suitability of mode A for the 3D scanning of complex objects. Future studies may be aimed at reducing the 3D scanning error using mode A of 3D scanning for those physical objects which have a moderate to high level of geometrical intricacy. In other words, simple objects will be excluded in favor of the inclusion of complex objects for 3D scanning. Scanned complex objects are also deemed essential in future studies because they are ideal candidates for additive manufacturing with the additional intention of saving unnecessary cost and time and achieving more precision and accuracy in terms of their geometrical dimensions and configurations.
# Chromosome Cluster Type Identification Using a Swin Transformer
## 1 Introduction  
Chromosomes contain the genetic information in a human cell [1]. Chromosome karyotype analysis facilitates the prenatal diagnosis of severe abnormalities or genetic disorders such as [PERSON] syndrome, [PERSON] syndrome, and Down syndrome [2, 3]. Given a grayscale image of a stained cell, the process of karyotype analysis is characterized by the segmentation of chromosome instances and the subsequent arrangement of the karyotypes according to their respective categories. Karyotype analysis is generally carried out by experienced medical practitioners. However, the manual analysis of karyotypes is tedious and highly time-consuming [4, 5, 6]. Furthermore, there can be inconsistencies in the process due to the required domain expertise or even due to the fatigue and over-burden of the medical practitioners. The above-mentioned limitations of the manual analysis of chromosome karyotype analysis motivate the need to design an automated decision support system for chromosome karyotype analysis [7, 8, 9]. An automated karyotype analysis model has two stages: _chromosome segmentation_ and _chromosome classification_. As shown in Figure 1, chromosome cluster type identification is a key step towards chromosome segmentation and classification [10, 11].  
Recent methods for chromosome cluster type identification [10,11] exploit deep models based on convolutional neural networks (CNNs). However, CNNs are known to exhibit inductive bias and fail to capture long-range dependencies [12]. Recently, transformers have been introduced to circumvent the aforementioned shortcomings of CNNs [12]. Motivated by the success of transformers in image processing applications [13, 14, 15, 16], we introduce chromosome cluster transformer (CCT)--a swin transformer [17]-based hierarchical transformer model that leverages self-attention to identify cluster type in chromosome images. To summarize, this paper makes the following research contributions:  
* We address the limitation of state-of-the-art CNN-based chromosome cluster type identification methods in capturing long-range dependencies. Towards this end, we propose the chromosome cluster transformer (CCT), which successfully captures the global context required for the successful identification of chromosome cluster types.
* To the best of our knowledge, this is the first work in the domain of chromosome cluster type identification that utilizes a transformer model.
* To circumvent the limited availability of training data for cluster type identification, the proposed CCT exploits a transfer learning approach.
* The proposed CCT outperforms the state-of-the-art traditional vision transformer in chromosome cluster type identification.
* Furthermore, the proposed CCT outperforms the existing state-of-the-art chromosome cluster type identification methods.
* Additionally, to provide insights on the improved performance, we visualize the activation maps obtained using Gradient Attention Rollout [18].
# Augmented EMTCNN: A Fast and Accurate Facial Landmark Detection Network +
## 4 Experiment
### Accuracy of Landmark Point Extraction  
In this experiment, we compare the landmark point extraction accuracy of three models--augmented EMTCNN, EMTCNN, and Dlib--using the Helen dataset, which was not present in the training set. These images have 68 predefined facial landmark points as the ground truth. Hence, the accuracy can be compared quantitatively using the total distance between the ground truth points and landmark points extracted by each model. The mean normalized distance (MND) can be computed using Equation (2):  
\[MND=\left(\begin{array}{cc}\frac{1}{68}\sum_{i=1}^{68}\frac{\sqrt{\left(x_{ i}-\hat{x}_{i}\right)^{2}}}{W}+\frac{\sqrt{\left(y_{i}-\hat{y}_{i}\right)^{2}}}{H} \end{array}\right)\times 100. \tag{2}\]  
Table 3 compares the mean normalized distances of various methods used to extract the 68 facial landmark points, including EMTCNN and Augmented EMTCNN. Since Dlib was trained with the -300-W dataset, it was excluded from the comparison for 300-W. As we can see from the table, the mean normalized distance for Augmented EMTCNN was improved compared to that for EMTCNN.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Mean Normalized Distance**} \\ \cline{2-3}  & **Helen** & **300-W** \\ \hline DRMF (Discriminative Response Map Fitting) [52] & 6.70 & 9.22 \\ \hline RCPR (Robust Cascaded Pose Regression) [53] & 5.93 & 8.35 \\ \hline ESR (Explicit Shape Regression) [54] & 5.70 & 7.58 \\ \hline CFAN (Coarse-to-Fine Auto-encoder Networks) [55] & 5.53 & 7.69 \\ \hline SDM (Supervised Descent Method) [56] & 5.50 & 7.50 \\ \hline CFSS (Coarse-to-Fine Shape Searching) [57] & 4.63 & 5.76 \\ \hline TCDCN (Tasks-Constrained Deep Convolutional Network) [58] & 4.60 & **5.54** \\ \hline Dlib [37] & **4.47** & - \\ \hline EMTCNN & 5.66 & 6.63 \\ \hline Augmented EMTCNN & 4.65 & 5.59 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of mean normalized distances.  
For the ground truths shown in Figure 6(a), the actual facial landmark points extracted using the Dlib, EMTCNN, and Augmented EMTCNN models are shown in Figure 6(b)-d, respectively. From the figures, we can see that each model performs slightly differently depending on the input image, even though their overall detection results are acceptable. Figure 8 shows more examples of facial landmark detection performed by our Augmented EMTCNN model.  
Figure 8: Facial landmark points extracted by our Augmented EMTCNN model.  
Figure 7: Comparison of facial landmark extraction results.
## 2 Materials and Methods  
### Clayey Soil  
The clayey soil obtained from Shanghai, China was used in this study. Tested by the Mastersizer 2000 particle size analyzer (Malvern, UK), the particle gradation curve of the clayey soil was obtained and is shown in Figure 1. The soil is made up of sand, silt, and clay with 14.9%, 74.1%, and 11.0% weight content, respectively. The soil properties are liquid limit \(w_{\rm L}=38.5\%\), plastic limit \(w_{\rm P}=24.3\%\), optimum moisture content OMC = 21.9%, specific gravity \(G_{\rm s}=2.7\), \(D_{50}=0.03\) mm, and pH = 7.2. As per ASTM D2487, the clayey soil was therefore classified as lean clay (CL).  
X-ray fluorescence spectrometer (XRF) is an instrument that can perform rapid, simultaneous determination of multiple elements. Under the excitation of X-rays, the inner electrons of the atoms of the detected element undergo energy level transitions and emit secondary X-rays (i.e., X-fluorescence). In this study, the X-fluorescence spectrometer ARL ADVANTX IntelliPower TM 4200 (ThermoFisher Company, Waltham, MA, USA) was used to test the soil chemical elements and composition. The XRF results indicated that the most abundant elements are Si, O, Fe, Ca, Al, Mg, K, and Na. Compounds such as SiO\({}_{2}\) (58.68%), Al\({}_{2}\)O\({}_{3}\) (13.51%), CaO (7.47%), and Fe\({}_{2}\)O\({}_{3}\) (7.44%) have the highest concentrations.
## 6 Traditional and DL Approaches for Object Detection in AVs
### DL Approaches for Object Detection in AVs
#### 6.2.2 DL Approaches for Pedestrian Detection
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Ref.** & **Year** & **Technique** & **Weather Classes** & **Accuracy** \\ \hline
[12] & 2022 & SqueezeNet, ResNet-50, EfficientNet-b0 & cloudy, rainy, snowy, sandy, shine, and sunrise & 98.48\% 97.78\% 96.05\% \\ \hline
[137] & 2020 & YOLOv3 & night & 93.66\% \\ \hline
[140] & 2022 & YOLOv5 & Fog, rain, snow, sand & 94.7\% \\ \hline
[130] & 2023 & CNN & Cloudy, Fog, Rain, Shine Sunrise & 98\% \\ \hline
[131] & 2022 & ResNet, GoogleNet, SqueezeNet & Fog, Low light, sun glare & 100\% 65.50\% 90.0\% \\ \hline
[133] & 2021 & Faster R-CNN & Rain, Fog, Snow, Sand & 95.16\% \\ \hline
[134] & 2020 & SSD & Sunny, overcast, Night, rain & 92.18\% \\ \hline
[141] & 2022 & YOLOv5 & night & 96.75\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Vehicle detection previous work with DL. stereo-vision technology with two cameras. The integration of data from various sensors, such as radar or LIDAR, is proposed to enhance autonomous driving performance. The research also advocates for the application of DL methods to refine feature extraction and categorization, achieving an AP of 89.85% and a mAP of 90% on the CVC-09 and LSFIR databases. In the realm of nighttime pedestrian recognition, Ref. [150] introduces an enhanced Faster R-CNN architecture, which delivers significant improvements, particularly for distant pedestrians. The study in [151] tackles the issue of optical camera-based pedestrian recognition under adverse weather conditions by employing a cascaded classification technique that incorporates both local and global features, resulting in increased detection rates. The effectiveness of this strategy is demonstrated in challenging weather conditions such as snow or rain. The use of YOLO-v5 in [152] introduces a novel multispectral method for pedestrian detection, combining independent subnetworks for RGB and IR images to create an effective detector. The method's performance on open databases, with high mAP scores and IoU of 0.5 across various datasets, is encouraging. and another study [153] evaluates YOLOv7, Faster R-CNN, SSD, and HoG for both vehicle and pedestrian detection in different weather conditions. YOLOv7's superior performance in terms of accuracy, precision, recall, and mAP underscores the critical role of weather conditions in selecting appropriate DL methods for autonomous driving.
## 3 Results
### Verification of Scheduler Operation
The lower trace (D4) represents sensor reading tasks at sampling periods, the middle trace (D1) shows packet assembly times, and the upper trace (D3) indicates packet transmission times. The sampling time is 10 ms, as indicated by two vertical dotted lines. Figure 6: Oscilloscope traces of one packet sending episode at a 1750 Hz sampling frequency with 26 sensor samples per packet. The lower trace (D1) represents 26 sensor sampling periods, the middle trace (D3) shows packet assembly time, and the upper trace (D4) indicates packet transmission time. Two vertical dotted lines measure the packet assembly time of 2.3 ms.
# Defining the Ideal Phenological Stage for Estimating Corn Yield Using Multispectral Images
## 5 Conclusions
#### Limitations and Future Perspectives  
A number of studies have been conducted to estimate corn yield using multispectral images. Similar to other studies presented in the literature, this study is limited to local prediction models, given that the data used are restricted to a planted area of 21.56 ha. The challenges in implementing the models in areas neighboring the study area require indexing of more yield data from other agricultural regions, that is, corn growing areas subject to different management techniques and environmental conditions.  
From an economic standpoint, there is also a need to apply prediction models to multispectral sensors with better spatial resolutions. This is because the prediction models generated via images with average spatial resolution provide only general information of the real yield observed in loco. Corn is one of the most valuable agricultural crops in the world, and thus, the more specific the information, the more justifiable is the implementation of this technology for crop management.  
**Author Contributions:** Conceptualization, C.A.M.d.A.J. and G.D.M.; methodology, C.A.M.d.A.J.; validation, G.D.M.; J.V.M.B., D.J.M. and G.O.O.; formal analysis, G.D.M.; J.V.M.B., D.J.M. and G.O.O.; investigation, L.C.M.X.; resources, C.A.M.d.A.J.; data curation, L.C.M.X.; writing--original draft preparation, C.A.M.d.A.J.; writing--review and editing, L.C.M.X.; visualization, L.C.M.X.; supervision, G.D.M., J.V.M.B., D.J.M. and G.O.O.; project administration, G.D.M.; funding acquisition, G.d.O. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research was financed by the company Lallemand Plant Care, through a partnership with the Federal University of Uberlandia, Brazil for the promotion of student grants for the development of research.  
**Data Availability Statement:** The datasets generated during and/or analyzed during the present study are not publicly available since, despite being anonymized, they portray a single farm, so publicly sharing the data can be experienced as sensitive for the case study participants. Datasets are available from the corresponding author on reasonable request.  
**Acknowledgments:** We thank the Universidade Federal de Uberlandia for the assistance and training provided to the authors. The company Lallemand Plant Care for the support and supply of the data used in this research.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
Compressed natural gas (CNG) refueling stations operate under highly dynamic thermodynamic conditions, requiring accurate modeling and optimization to ensure efficient performance. In this study, a dynamic simulation model of a CNG station was developed using MATLAB-SIMULINK, including detailed subsystems for multi-stage compression, cascade storage, and vehicle tank filling. Real gas effects were incorporated to improve prediction accuracy of the pressure, temperature, and mass flow rate variations during fast filling. The model was validated against experimental data, showing good agreement in both pressure rise and flow rate evolution. A two-stage multi-objective optimization approach was applied using Taguchi experimental design and gray relational analysis (GRA). In the first stage, storage pressures were optimized to maximize the number of vehicles filled and gas mass delivered, while minimizing compressor-specific work. The second stage focused on optimizing the volume distribution among the low, medium, and high-pressure tanks. The combined optimization led to a 12.33% reduction in compressor-specific energy consumption with minimal change in refueling throughput. These results highlight the critical influence of pressure levels and volume ratios in cascade storage systems on station performance. The presented methodology provides a systematic framework for the analysis and optimization of transient operating conditions in CNG infrastructure.  
CNG refueling station; dynamic modeling; fast filling process; multi-objective optimization; Taguchi method; gray relational analysis +
Footnote †: journal: Applied Sciences