esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Passband Characteristics in Chebyshev Sharpened Comb Decimation Filters
## 2 Compensators: Applications and Design
### Design  
Usually, comb compensators are designed as finite impulse response (FIR) filters using various optimization methods to ensure that the cascade of the comb and the compensator provides as low as possible deviation in the passband of interest. We will adopt the denotations, as shown in the continuation.  
The comb filter of order \(K\) and the decimation factor \(M\) is denoted as follows:  
\[H(z,K,M)=\bigg{[}\frac{1-z^{-M}}{M(1-z^{-1})}\bigg{]}^{K}. \tag{1}\]  
The Chebyshev sharpened comb is denoted as \(G(z)\) and the compensator as \(C(z)\).  
Since the compensator works at a low rate, using the multirate identity [18; 19], the compensated filter \(Gc(z)\), which is a cascade of the Chebyshev sharpened comb and compensator is given as follows:  
\[Gc(z)=G(z)C(z^{M}). \tag{2}\]  
As the comb compensators introduced in [13; 14; 15] have sinusoidal magnitude characteristics while exhibiting high-quality compensation, we propose using the sinusoidal magnitude responses for the compensators proposed in this work to ensure the generalized approach and simplicity of the design. We consider narrowband and wideband designs, as described below.
## 2 Background
### Grayscale Statistical Feature-Based Object Detection Methods  
Grayscale statistical feature-based methods utilize a set of statistical parameters extracted from grayscale pixel intensity of images to recognize objects. Generally, grayscale statistical parameters are classified into two groups: gray-level co-occurrence matrix (GLCM) statistical parameters and gray-level histogram (GLH) statistical parameters.  
[PERSON] and [PERSON] [31] first introduced the GLCM, which described the frequency of one gray level appearing in a specified spatial linear relationship with another gray level within the area under investigation and originally proposed 14 GLCM statistical parameters to label the GLCM. From then on, these GLCM statistical parameters have been widely used in image recognition, such as detecting sea ice changing in images [32; 33; 34], classifying landscape images [35], and distinguishing computed tomography (CT) images of normal and abnormal tissues [36; 37]. However, it is difficult to understand the essential meanings of GLCM statistical parameters and to visibly check how these GLCM parameters can affect detection results since GLCM statistical parameters represent abstract characteristics of an object. Accordingly, GLH statistical parameters (mean, variance, integrated intensity, skewness, and kurtosis), representing the distribution of the pixel intensity of an image, have been developed and used in image recognition [38; 39; 40]. [PERSON] and [PERSON] [41] used GLH and GLCM statistical parameters respectively to classify normal and abnormal human brain CT images, and the results showed that the classification accuracy with GLH statistical parameters was higher than that with GLCM statistical parameters, proving GLH's superiority in image classification. This result was also supported by [PERSON] et al. [42]. In addition to medical use, [PERSON] et al. [43] classified traffic signs with GLH statistical parameters and got a high classification accuracy rate.  
The proposed method innovatively combines the GLH statistical parameters and shape features of structural elements to recognize columns and walls in images given that columns and walls have two distinguishing visual characteristics: (1) the gray levels of their surfaces are relatively homogeneous compared with their neighboring areas that contain various objects, and (2) their boundary edges are long vertical lines. Additionally, as some of GLH statistical parameters contribute little in representing specific objects, it is unnecessary to use all GLH parameters to detect objects. Thus, this study will select proper GLH parameters by considering both statistical meanings and visual meanings of these GLH parameters to decrease the computational cost for the detection of structural elements.
# System of Systems Lifecycle Management--A New Concept Based on Process Engineering Methodologies
## 5 Product Lifecycle Management
### Beginning of Life
#### 5.3.2 Design  
After the _Plan_ phase, the _Design_ phase follows. If the _Analysis_ brought about satisfying results, the product could be designed.  
* Research: this subphase deals with defining the precise product functional specification based on the input requirements, taking into account the related architectural considerations and, furthermore, examining the related development techniques.
* Development: when the _Research_ subphase is finished and the architectural concept is established, the development can start. In this subphase, the hardware and software components of the product are created.
## 4 Experiments and Results
### Experimental Environment and Parameters Design  
The experiments were conducted on a system running the Ubuntu 20.04.5 LTS operating system with CUDA version 11.2. The hardware configuration included an Intel(R) Xeon(R)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attributes** & **Training Set** & **Validation Set** & **Test Set** \\ \hline Number of Sample & 262,112 & 29,124 & 29,124 \\ Number of HS code & 227 & 227 & 227 \\ Number of declared elements & 167 & 156 & 158 \\ Number of chapters & 26 & 26 & 26 \\ Average sample length & 29 & 29 & 29 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical information on HSD datasets.  
Gold 5218R (Intel, Santa Clara, CA, USA) CPU @ 2.10 GHz with 80 cores, 128 GB of RAM, and an NVIDIA GeForce RTX 3090 (NVIDIA, Santa Clara, CA, USA) GPU with 24 GB of video memory. The model was developed using the PyTorch (2.1.2 + cu118) framework. The values of the key hyperparameters used in the experiments are listed in Table 2.  
The common metrics used to evaluate the effectiveness of classification models are Precision (P), Recall (R), F1-score and Accuracy [25]. Given the varying number of samples across different HS code categories, we use the weighted averages of Precision, Recall, and F1-score (Weight-P, Weight-R, Weight-F1), along with Accuracy, as the primary evaluation metrics. These weighted metrics provide a more comprehensive assessment of the model's classification performance by accounting for category imbalances, which is calculated as shown in (19)-(22):  
\[\text{Weight-P} =\frac{\sum_{i=1}^{C}P_{i}\times w_{i}}{\sum_{l=1}^{C}w_{i}} \tag{19}\] \[\text{Weight-R} =\frac{\sum_{i=1}^{C}R_{i}\times w_{i}}{\sum_{i=1}^{C}w_{i}}\] (20) \[\text{Weight-F1} =\frac{\sum_{i=1}^{C}F1_{i}\times w_{i}}{\sum_{i=1}^{C}w_{i}}\] (21) \[\text{Accuracy} =\frac{TP+TN}{TP+TN+FP+FN} \tag{22}\]  
where \(w_{i}\) is the weight for class \(i\) (typically the number of samples in class \(i\)), and \(C\) is the total number of classes.
## 2 Materials and Methods
### Biosurfactant Effect on Removal of Some Pollutants from Synthetic Wastewater  
#### 2.6.1 Growth Curves in Contaminated and Uncontaminated Synthetic Wastewater  
The biosurfactant was obtained using a protocol described previously by [PERSON] et al. [31]. The cell-free broth obtained after filtering the supernatant was further used as a crude biosurfactant [32; 33].  
The growth curve of the yeast strains (_Rhodotorula mucilaginous_ CMGB-G1 and _R. mucilaginous_ CMGB 188--previously isolated from oil polluted environments and included in the CMGB collection of microbial strains) was determined in synthetic wastewater contaminated and uncontaminated with Pb\({}^{2+}\) and Cd\({}^{2+}\) ions, both in the presence or absence of a biosurfactant. This was achieved using 96-well plates containing 400 \(\mu\)L of sample inoculated with 1% of each yeast strain with an initial concentration of 2 McFarland (for _Sw_, _Pb_ and _Cd_ with P1, and P2, respectively) supplemented with 5% (_v/v_) biosurfactant (for _Bio Y_, _BioY_ + _Pb_, _Bio Y_ + _Cd_). The experiments were conducted for 24 h at a temperature of 28 \(\pm\) 1 \({}^{\circ}\)C (i.e., controlled conditions), and optical density values were recorded hour by hour at 570 nm using a Synergy HTX Multimode reader (BioTEK, Winooski, VT, USA). Optical density (OD) measurements were also used to determine the degree of yeast development during the 120 h of the experiment (i.e., experimental conditions).  
Synthetic wastewater was prepared by adapting the formulations outlined by [31; 34] comprising the following components: 200 mg/L D-glucose, 200 mg/L sucrose, 70 mg/L yeast extract, 66.73 mg/L (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\), 10.91 mg/L NH\({}_{4}\)Cl, 4.43 mg/L KH\({}_{2}\)PO\({}_{4}\), 21 mg/L MgSO\({}_{4}\).7H\({}_{2}\)O, 2.68 mg/L MnSO\({}_{4}\).H\({}_{2}\)O, 30 mg/L NaHCO\({}_{3}\), 19.74 mg/L CaCl\({}_{2}\), 0.14 mg/L FeCl\({}_{3}\)-6H\({}_{2}\)O, 330 mg/L NaNO\({}_{3}\), and 390 mg/L NaNO\({}_{2}\). For experiments regarding the removal efficiency of heavy metal ions, synthetic wastewater was contaminated with Pb\({}^{2+}\) (32 mg/L, (CH\({}_{3}\)COO)\({}_{2}\)Pb) and Cd\({}^{2+}\) (30 mg/L, Cd(NO\({}_{3}\))\({}_{2}\)) ions. The presented heavy metal ion concentrations were determined using ICP-MS analysis.  
The coding of the samples used in this paper is shown in Table 1.
## 2 Related Work
### DV-Hop Error Analysis  
In DV-Hop, the coordinates of unknown nodes are estimated based on the number of hops and the beacon node coordinates, which often results in larger errors. There are three main factors contributing to these localization errors.  
1. Error in the hop value. Assume there are four neighboring nodes within the communication radius of a node. The hop value to each of the four neighboring nodes is assigned as 1, which is inconsistent with the actual situation. Therefore, there is a hop value error during the hop number acquisition stage [13].
2. Average hop distance error. The average hop distance to the nearest beacon node is used to calculate the distance between the unknown node and the beacon node. Since the path between the two beacon nodes is not a straight line, the average hop distance is often smaller than the actual value.
3. Coordinate estimation error. A certain amount of error inevitably arises during the node coordinate estimation stage in the distance calculation. As errors accumulate during the solution of the coordinate system equations, the final results deviate more significantly from the actual values [14].
# Rank and Wormhole Attack Detection Model for RPL-Based Internet of Things Using Machine Learning
The proposed model named MC-MLGBM leverages the light gradient boosting machine model to perform multiclass classification for attack detection where the main task is to classify the network traffic data into begin, RA, and WHA target classes. MC-MLGBM stands for multiclass machine-learning-based model leveraging the light gradient boosting machines for attack detection (MC-MLGBM). Moreover, a novel dataset is generated as part of this study due to the scarcity of publicly available datasets in the domain of RPL-based IoT. It includes benign, RA, and WHA traces where the attack data were collected through extensive network modeling and simulation utilizing various network models. The network models are exclusively designed and implemented for the generation of the required network traffic dataset. Furthermore, the mobility factor is addressed in this paper using the random waypoint mobility model to create a mobile RPL-based IoT network environment. The model performance is evaluated using standard evaluation parameters and metrics including accuracy, precision, and recall. To further validate the results for multiclass classification, three other multiclass-specific metrics are used. They are cross-entropy, Cohn's kappa, and Matthews Correlation Coefficient (MCC). Furthermore, the proposed model is compared with benchmark research and relevant classifiers. The experimental results present that the proposed model outperforms the benchmarks in terms of attack categorization, the number of attacks detected, mobility, and performance evaluation using ML parameters. The main contributions of the paper are summarized as follows:  
* A novel ML-based model is proposed for RPL-specific RA attack and SN-inherited WHA attack detection which is trained on a self-generated dataset. The parameters are optimized and characterized by high accuracy, a high detection rate, and high performance, which is assessed through standard ML evaluation metrics as well as multiclass classification evaluation metrics. * A novel dataset is generated that consists of both RPL-specific and SN-inherited attacks in the static and mobile state of IoT nodes. The dataset is produced to address the lack of recent datasets in the RPL-based IoT domain. * The light gradient boosting machine model is leveraged to perform multiclass classification for attack detection in RPL-based IoT. * An in-depth evaluation of the MC-MLGBM model is carried out based on different evaluation metrics in the training (for learning purposes) and testing phases. The remaining paper is organized as follows: Section 2 reviews the relevant existing literature, Section 3 presents the methodology and proposed model, Section 4 accounts the experimental results, and Section 5 discusses the conclusion, the limitations of this work, and future research direction.
# FluoC Spec 2--An Automated Field Spectroscopy System to Monitor Canopy Solar-Induced Fluorescence
###### Abstract  
Accurate estimation of terrestrial photosynthesis has broad scientific and societal impacts. Measurements of photosynthesis can be used to assess plant health, quantify crop yield, and determine the largest CO\({}_{2}\) flux in the carbon cycle. Long-term and continuous monitoring of vegetation optical properties can provide valuable information about plant physiology. Recent developments of the remote sensing of solar-induced chlorophyll fluorescence (SIF) and vegetation spectroscopy have shown promising results in using this information to quantify plant photosynthetic activities and stresses at the ecosystem scale. However, there are few automated systems that allow for unattended observations over months to years. Here we present FluoCSpec 2, an automated system for collecting irradiance and canopy radiance that has been deployed in various ecosystems in the past years. The instrument design, calibration, and tests are recorded in detail. We discuss the future directions of this field spectroscopy system. A network of SIF sensors, FluoNet, is established to measure the diurnal and seasonal variations of SIF in several ecosystems. Automated systems such as FluoCSpec 2 can provide unique information on ecosystem functioning and provide important support to the satellite remote sensing of canopy photosynthesis.  
Keywords:photosynthesis; proximal remote sensing; spectroscopy; vegetation +
Footnote †: journal: Sensors
# Influence of Thoracolumbar Mobility on Running Performance: A Comparative Study
## 4 Discussion
### Thoracolumbar Mobility and Lumbar Region Shortness: Relationship with ROM  
In runners with a running history of the last six months, certain values such as nondominant LD elasticity, nondominant TL flexibility, modified Schober test results, and trunk flexion ROM were found to be higher compared to sedentary individuals. Furthermore, reduced sagittal plane hip motion during the swing phase and a smaller ankle plantar flexion angle during ground contact explained 79% of the variance in personal best performance. Slower middle-distance runners demonstrated initial ground contact with a more plantar flexed ankle and a forward-leaning trunk. The increased lumbar mobility (as per the modified Schober test) and trunk flexion ROM in the running for the last six months group may result from repetitive patterns associated with their running style. Previous research has also highlighted the role of thoracic mobility in improving performance [6].  
ROM, defined as the maximum distance a joint can move, is a critical parameter for musculoskeletal health. International guidelines emphasize the importance of regular exercise to preserve or restore physiological function [30; 31; 32]. Soft tissue flexibility is also recognized as a key determinant of ROM [33]. Additionally, connective tissue, particularly the deep fascia, affects ROM in two primary ways. First, it is intricately connected to the underlying muscles along its entire length, and changes in its mechanical properties (e.g., stiffness) can restrict muscle extensibility and, consequently, ROM [34]. Second, fascial tissues, which link muscles across joints, can directly limit ROM due to increased stiffness [35]. Our findings align with this, demonstrating a positive correlation between LD stiffness and TL flexibility.  
No significant differences in trunk rotation ROM were observed between groups, nor were there differences in trunk rotation flexibility across the groups.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{4}{c}{**Group**} \\ \cline{2-5}  & **RTY** & **RSM** & **Sedantary** & **\(p\)** \\  & **(\(n\) = 35)** & **(\(n\) = 35)** & **(\(n\) = 35)** & **(\(\chi\) \(\pm\) SD)** \\ \hline Lumbal extensor shortness & 20.74 \(\pm\) 12.56  & 13.71 \(\pm\) 4.26  \\ \hline D lateral flexion (ROM, \({}^{\circ}\)) & 18.31 \(\pm\) 2.44  & 16.00 \(\pm\) 2.70  \\ ND lateral flexion (ROM, \({}^{\circ}\)) & 18.33 \(\pm\) 4.22 & 19.00 \(\pm\) 6.42 & 16.80 \(\pm\) 4.38 & 0.186  \\ Flexion (ROM, \({}^{\circ}\)) & 85.69 \(\pm\) 28.86  & 76.26 \(\pm\) 31.39  & \textless{}0.001  \\ Extension (ROM, \({}^{\circ}\)) & 22.26 \(\pm\) 7.20  & 18.00 \(\pm\) 4.80  & 14.91 \(\pm\) 6.10  \\ \hline \hline \end{tabular}
\end{table}
Table 5: Trunk ROM and lumbar extensor shortness.
# A Study on the Electromagnetic-Temperature Coupled Analysis Method for In-Wheel Motors
## 3 Analysis of Coupling Factors
### Analysis of Coupling Factors
#### 3.2.2 Coupling Factors of the Temperature Field to the Electromagnetic Field
Figure 6 shows the curve of relative permeability with magnetic flux density, and the data from reference [22]. As shown in Figure 6, during the operation of the motor, the silicon steel material basically works at a higher magnetic flux density, over 1.4 T, the change in relative permeability with temperature is also very small. At the same time, the magnetic properties of 35A360 annular materials at different temperatures were studied by [PERSON] et al., of Okayama University, Japan. When it works in the low-temperature region below 500 \({}^{\circ}\)C, the permeability does not change obviously with temperature. When it works in the high-temperature region above 500 \({}^{\circ}\)C, the permeability decreases obviously with  
Figure 5: Permanent magnet (PM) remaining magnetism curve with temperature. Figure 6: The curve of relative permeability with magnetic flux density in silicon steel. the increase in temperature [23]. In summary, in the process of motor operation, silicon steel works in high magnetic flux density and the low-temperature working area, and its basic physical properties of permeability can be regarded as not changing with temperature. Through the analysis of the coupling factors mentioned above, it can be observed that the electromagnetic characteristics mainly influence the temperature distribution of the motor through the iron loss of the stator and rotor, copper loss of winding, and eddy current loss of the PM; among the influencing factors of the temperature on the electromagnetic characteristics, the resistivity of the stator material silicon steel and the winding material copper is the main factor. Therefore, in the subsequent electromagnetic-temperature coupling analysis, the coupling factors of the two fields mainly consider the strong coupling factors mentioned above.
# 6D Pose Estimation of Objects: Recent Technologies and Challenges
## 3 Non-Learning-Based Approaches
### 3D-Information-Based Approaches
#### 3.2.1 Matching-Based Approaches  
The aim of matching-based approaches is to search for the most similar template in the dataset and return the 6D pose of the template. Because the 3D original data is always too large, processing these data directly can be computationally expensive. Therefore, many preprocessing methods have been proposed to reduce the complexity of the task. [PERSON] et al. [70] proposed two methods for solving this problem. One was to use a 2D/2.5D object detector for scene point clouds. YOLO was used to segment the scene point cloud with 2D bounding boxes due to their lower time consumption. The other preprocessing method was to extract the keypoints in the template point clouds. Points with more information, such as the points on edges, were preserved and points on surfaces were removed in order to compress the point cloud. [PERSON] et al. [71] combined PCOF-MOD (multimodal PCOF), balanced pose tree (BPT), and optimum memory rearrangement into 6D pose estimation to optimize data storage structure and lookup speed. To improve the accuracy of matching, [PERSON] [72] et al. proposed a novel multi-task template matching (MTTM) framework that finds the nearest template of a target object from an image while predicting segmentation masks and a pose transformation between the template and a detected object in the scene using the same feature map of the object region. In [73], research was carried out on tracking and control for micro-electro-mechanical system (MEMS) microassembly. The correlation between the real-time 3D vision tracking method and the control law based on 3D vision was demonstrated, and a pose-based visual servoing approach was used to to enable a precise regulation toward zero of 3D error.
## 3 Experimental Results and Analysis  
### Datasets  
To evaluate the effectiveness of our proposed method, we used two datasets for validation, both sourced from the WHU Building Dataset [35], available at [[http://gpcv.whu.edu.cn/data/building_dataset.html](http://gpcv.whu.edu.cn/data/building_dataset.html)]([http://gpcv.whu.edu.cn/data/building_dataset.html](http://gpcv.whu.edu.cn/data/building_dataset.html)) (accessed on 2 March 2023). To differentiate between them, we have named them the Aerial Imagery Dataset (AIDS) and the Building Change Detection Dataset (WBDS). Both datasets consist of large-area images and corresponding building labels in Shapefile format. During the process of use, we convert the vector data in Shapefile format into a raster binary image that has the same resolution as the image, with buildings represented in white and the background in black. Due to GPU memory constraints, we were unable to process these large-area images directly. Therefore, we cropped the area images to produce relatively large-format images (e.g., 2048 \(\times\) 2048). The size of the cropped large-format image is 2048 \(\times\) 2048, which is eight times larger than the common image size of 256 \(\times\) 256. For large-format labels, the cropping processing is exactly the same as the original image. Prior to cropping, the datasets were divided into training, validation, and test sets in a 6:2:2 ratio.  
**AIDS Dataset**: Collected in New Zealand, the AIDS dataset comprises more than 22,000 individual buildings. The original area image is a large image that measures 1,560,159 pixels \(\times\) 517,909 pixels with a spatial resolution of 0.075 m. Cropping the entire area image resulted in 12,940 large-format images with sizes of 2048 \(\times\) 2048. The dataset was divided into 7764 training sets, 2588 validation sets, and 2588 test sets according to the 6:2:2 ratio.  
**WBDS Dataset**: Covering a substantial portion of Christchurch, New Zealand, the WBDS dataset includes bi-temporal images from 2012 and 2016, each accompanied by corresponding building label data. The original image size is 32,507 pixels \(\times\) 15,345 pixels with a spatial resolution of 0.2 m. After cropping, 630 large-format images with sizes of 2048 \(\times\) 2048 were generated. The dataset was divided into 378 training sets, 126 validation sets, and 126 test sets in a 6:2:2 ratio.