esa-sceva/satcom-chunk-collection huggingface dataset:
# Routing Protocols Performance on 6 LoWPAN IoT Networks
## 2 Related Work
### Routing Protocol for Low-Power and Lossy (RPL) Networks  
The Internet Engineering Task Force (IETF) Routing over Low Power and Lossy (RoLL) Networks committee created RPL, a routing protocol designed for LLNs with devices with limited resources. RPL proactively optimizes routing for LLNs by applying a distance vector technique, meeting critical routing requirements over nodes with constrained resources [14; 15].  
A Destination Oriented Directed Acyclic Graph (DODAG) serves as the foundation for the protocol's topology [16]. Network topology setup creates a DODAG, which is uniquely recognized by a DODAG IP and RPL instance ID [17]. RPL networks directly control messages every node to identify the best parent sets to follow to reach the root, connecting with favored parents along the most effective route. Route path selection, which makes use of the ranking's idea, is crucial to RPL. In addition to identifying and preventing routing loops, ranks also let nodes distinguish between parents and siblings and allow them to keep lists of preferred parents and siblings for usage in the event of a link loss with the parent node [18]. RPL routing overhead increased in three scenarios: network topology change, additional information transmission, and speed movement of node increased [19]. Moreover, increased node mobility will result in higher energy consumption due to the need for frequent route rediscoveries [20].  
For the development and upkeep of descending routes, RPL provides two operating modes: storing mode and non-storing mode. Using Destination Advertisement Object (DAO) messages delivered in unicast to selected parents, each node in the storing mode saves routes published by all other nodes in its subtree. Only the DODAG root retains the routes that are announced by every node in the network when the mode is non-storing, meaning that intermediate parents do not store downstream routes [21].
###### Abstract
The baseline design of MIMPS-G500 mN is a standard 1U CubeSat size that can be expanded or clustered depending on the spacecraft size, required thrust level, and mission's \(\Delta V\) budget. One of the critical components in this propulsion system that required special attention and deep analysis was the feed and pressurization system. It was found that conventional systems such as stored gas or blow-down pressure-fed systems were introducing more limitations over time, especially due to the increasing performance requirements and the size and mass restrictions on the inert parts of CubeSats. Foreseeing that eventually, designers would face design-simplicity trade-offs in favor of performance, it was time to investigate, study, and analyze unconventional and more complex feed and pressurization systems for small-sized spacecraft. Thus, in the preliminary design of MIMPS-G500 mN, Figure 1 and Video S1, autogenous pressurization and micro electric pump feed (micro e-Pump feed cycle) were proposed using commercial off-the-shelf (COTS) components. Figure 1: MIMPS-G500 mN realistic render. In the following sections of this article, the three proposed green monopropellants, belonging to the Energetic Ionic Liquids (EILs) class, will be reviewed emphasizing their physical properties, performance and their development status. These three selected propellants were a result of a trade-off study that will be discussed in detail in Section 1.2. Furthermore, the feed and pressurization systems of the designed propulsion system will be discussed, and the basic concepts will be elaborated on. The rest of the article will discuss the system analysis, requirements identification, design methodology, and preliminary design process and the results will be numerically tabulated. Finally, a market survey on the state-of-the-art monopropellant propulsion systems for small-sized spacecraft, as well as commercial off-the-shelf green monopropellant thrusters will be presented, highlighting the main performance parameters and technical specifications of such systems and thrusters to serve as a reference for our proposed propulsion system MIMPS-G500 mN, as well as a reference for the readers of this manuscript. This article presents a more detailed analysis and results and extends the research work presented by the authors in the conference papers [7; 8; 9].
## 2 Materials and Methods
### Vegetative Indices Calculation  
The spectral characteristics of the leaves' reflectivity were measured with aid of the portable hyperspectral camera Specim IQ (Spectral Imaging Ltd., Oulu, Finland). The reflection spectra were obtained for each individual pixel of the image in the range of 400-1000 nm (spectral resolution 7 nm, 204 spectral bands, spatial sampling of 512 pixels). Based on them, the vegetative indices were calculated. As the light source for Specim IQ, four halogen lamps were used. They were installed symmetrically relative to the camera position.  
The spectral characteristics of the leaves' reflectivity were measured on the 30 th day of the cultivation. After the obtained hyperspectral images processing, the following well-known indices for the plants state assessment were calculated: the photochemical reflection index PRI (often used as the stress indicator), the indices ARI and mARI (used to assess the anthocyanin content in plant leaves), the chlorophyll indices MND\({}_{705}\) and MND\({}_{750}\)/700 and the carotenoid index CRI\({}_{700}\)[28; 29]. The indices were calculated using the following formulas:  
\[\text{PRI}=((\text{R}_{531}-\text{R}_{570}))/((\text{R}_{531}+\text{R}_{570})) \tag{1}\]  
\[\text{ARI}=(1/\text{R}_{550})-(1/\text{R}_{700}) \tag{2}\]  
\[\text{mARI}=\text{R}_{850}\left((1/\text{R}_{550})-(1/\text{R}_{700})\right) \tag{3}\]  
\[\text{MND}_{705}=(\text{R}_{750}-\text{R}_{705})/(\text{R}_{750}+\text{R}_{70 5}-2\cdot\text{R}_{445}) \tag{4}\]  
\[\text{R}_{750/700}=\text{R}_{750}/\text{R}_{700} \tag{5}\]  
\[\text{CRI}_{700}=(1/\text{R}_{510})-(1/\text{R}_{700}) \tag{6}\]The peak sensitivity of the reflection index to the anthocyanins concentration is found in the band of 550 nm. The 700 nm band is the chlorophyll absorption band; it is used in connection with the bands overlap of the light absorption by the chlorophyll and the anthocyanins. The band of 850 nm for the mARI index was taken as the closest one sensitive to reflection from the near-infrared range.
# Effect of Post-Activation Potentiation on Weightlifting Performance and Endocrinological Responses
## 2 Method  
### Experimental Approach  
A randomised order design was used to look at the effect of supramaximal clean pulls on ground reaction force (GRF) and barbell kinetics and kinematics during a clean in 8 competitive weightlifters. In addition, the effect of supramaximal pulls on salivary testosterone and cortisol was also investigated. Subjects performed cleans on two separate occasions at 90% of their predetermined clean and jerk (CJ) 1 RM. Both the control and experimental sessions consisted of the subjects performing a self-selected warm up followed by a standardised weightlifting-specific warm up. Following this, the participants performed 3 cleans at ascending intensities from 50 to 80% 1 RM followed by 3 sets of cleans at 90%, interspersed with 1 min recovery between sets; this served as the control session. During the experimental session, a supramaximal clean pull was performed at 120% 1 RM, 3 min prior to the 90% 1 RM cleans. All cleans were performed on a dual force plate system synchronised with two 3-dimensional (3D) motion capture cameras to enable the collection of force-time data and barbell kinematics. The multiple repetitions (reps) conducted allowed for reliability statistics to be run on the kinetic and kinematic variables once the raw data were extracted and analysed. A saliva sample was taken at the beginning and end of each session to assess the subject's hormonal responses (testosterone, cortisol and T:C ratio). Each session was conducted at the same time of day to standardise the diurnal effects on hormonal status. A paired samples _t_-test was used to evaluate the differences between the control and experimental independent variables, with the magnitude of changes quantified using [PERSON]'s \(d\) effect sizes.
## 4 Numerical Simulation
### Sensitivity Analysis of Accessory Combination Usage Parameters
Table 7 also shows that in addition to the pitch response, the difference between the mean and maximum values of the surge, heave, and tension when changing the mass parameters of the clump weight is much smaller than other parameters. This indicates that when changing the mass parameter of the clump weight, whether it is used in combination with the buoy has little effect on the overall response, and the change in the mass parameter of the clump weight has a greater effect on the overall response of the wind turbine. Therefore, the clump weight mass has a greater influence on the mooring system compared to other parameters. When the clump weight mass parameters are changed the difference in lift rates for the floating wind turbine mooring system, whether the accessories are used alone or in combination, is small because the difference in the overall response of the two is small. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Response** & **Difference** & **Buoy Volume/\%** & **Buoy Position/\%** & **Mass of Clump Weight/\%** & **Clump Weight Location/\%** \\ \hline \multirow{3}{*}{Surge} & Mean & 7.08 & 0.672 & 0.439 & 1.674 \\  & Max & 0.055 & 0.237 & 0.038 & 0.104 \\ \multirow{3}{*}{Pitch} & Mean & 8.586 & 1.021 & 1.837 & 7.706 \\  & Max & 0.098 & 0.1632 & 0.473 & 0.0417 \\ \multirow{3}{*}{Heave} & Mean & 11.464 & 11.435 & 0.165 & 30.1 \\  & Max & 24.6 & 0.96 & 0.236 & 3.856 \\ \multirow{3}{*}{Tension} & Mean & 0.119 & 0.342 & 0.006 & 0.316 \\  & Max & 0.093 & 0.597 & 0.386 & 1.087 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The difference of the response lifting rate when the accessories are used separately and in combination. Figure 11: Comparison of improvement rate of mooring performance by changing mooring parameters. (**a**) Comparison of surge and pitch response optimization rates under different parameters. (**b**) Comparison of heave and tension response optimization rates under different parameters.
## 4 Experimental Result
### Result and Comparison  
The performance indicators for the results were expressed as F1-score (Equation (9)), Accuracy of each class, Overall Accuracy (OA) (Equation (10)), Average Accuracy (AA), and Kappa of all classes. The F1-score is the harmonic average of Precision (Equation (7)) and Recall (Equation (8)). Precision is the proportion of what the model classifies as true that is actually true. Recall is the proportion of what the model predicts as true among what is actually true. OA is the proportion of correct answers predicted correctly from the total data. AA is the average of the accuracy of each class. Kappa is a statistical measure for assessing the confidence of a match between a fixed number of appraisers when categorizing multiple items or classifying items. The measurement metric Kappa was calculated by weighting the measured accuracy. The last measure includes both diagonal and non-diagonal items in the confusion matrix and is a strong indicator of the degree of match.  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{7}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{8}\]  
\[\text{F1-score}=2*\frac{1}{1/\text{Precision}+1/\text{Recall}}=2*\frac{ \text{Precision}*\text{Recall}}{\text{Precision}+\text{Recall}} \tag{9}\]  
\[\text{OA}(\text{Overall Accuracy})=\frac{\text{TP}+\text{TN}}{\text{TP}+ \text{FN}+\text{FP}+\text{TN}}. \tag{10}\]  
TP is True Positive, FP is False Positive, FN is False Negative, and TN is True Negative.
# Developing a Generalized Regression Forecasting Network for the Prediction of Human Body Dimensions
## 4 Experimental Results and Discussion
### Experimental Setup
#### 4.1.2 Factor Analysis  
We used maximum orthogonal factor analysis variance to extract two principal factors from 13 observation items and eliminate multicollinearity among variables. The rotated component matrix is represented in Table 2, which lists the scores of each sample and factor. Based on the total variance explanation provided in Table 3, the height factor and perimeter (circumference) coefficient were found to represent most of the information on lower body size.  
To avoid large prediction errors caused by large differences in input data, both easy- and difficult-to-measure key human body parameters were normalized. To mitigate interference resulting from varying body shapes, the dataset was divided into distinct training and testing sets, with divisions based on variables, such as stature and waist girth. The training set underwent cross-verification \(k\) times to evaluate the results.
# Point Cloud Upsampling Algorithm: A Systematic Review
## 4 Deep Learning-Based Point Cloud Upsampling
### Supervised Upsampling
#### 4.1.2 Upsampling Component
Interpolation algorithms often result in some side effects, such as computational complexity, noise amplification, and blurred results. Therefore, the current trend is to replace interpolation-based methods with learnable upsampling layers. PUGeo-Net [15] achieves point cloud upsampling through a purely geometric sampling method. It represents the 3D surface as a 2D parameter space, and then samples from the parameter space, and finally uses the learned Jacobian matrix and normal displacement to remap the amplified 2D parameter samples to the 3D surface. Considering that computing and learning global parameters consume huge computational resources, the researchers simplified the approach to a local parameterization problem for each point. This method takes into account the geometric features of the input shape. However, their method requires additional supervision in the form of normals, which many point clouds do not have, such as those produced by LiDAR sensors.
## 5 Discussion
### Advantages and Disadvantages of the SVM Model  
Recently, considerable effort has been made toward the development of the LULC classification model by using remote-sensing images; to that end, multiple methods, such as the nearest neighbor method, decision tree model, artificial neural network model, and SVM model, have been used. The improved model used in this study offers several advantages: (1) It reduces the redundancy for object-based image analysis and is well suited for high-resolution remote-sensing images; (2) it can be used for feature selection and information extraction and offers the advantage of a higher reduction rate than that of other methods/models. In our future research, we plan to design and implement high-quality samples for achieving high-performance feature selection.
# A Novel Multipath Transmission Scheme for Information-Centric Networking
## 3 Design
### Protocol Stack  
In order to reduce deployment costs, it is reasonable to deploy the new ICN architecture on top of an existing IP network, so as to fully utilize the IP infrastructure. Figure 1 shows a practical ICN protocol stack, in which the ID layer is incrementally deployed on top of the IP layer, thereby extending the functions of the network layer. At the network layer, a protocol called the identifier protocol (IDP) [34] is running, which defines a set of regulations specifying how to operate on the NA according to the ID of the data packet, including adding, deleting, and modifying. Above the ID layer is a transport layer, on which we design a transport protocol [35], including the ICN packet format, transmission process, congestion control mechanism, and retransmission mechanism. The transport layer protocol can provide an efficient and reliable data transmission service, and it belongs to the scope of the transport layer.  
In ICN, the chunk is the basic data unit for transmission and caching, which is bound to an addressable globally unique name. Most of the existing switches or routers are two-layer or three-layer structures; therefore, the ICN packet can be normally routed and forwarded on the existing network. In order to achieve smooth evolution, we consider the deployment of hybrid networking, where the entire network consists of IP routers and ICN routers. The ICN router has a complete ICN protocol stack, as well as computing and storage capabilities. In addition, we apply the concept of late binding in ICN [36], which means that the NA of the packets can be modified according to the policy generated by the local computing module during the forwarding process. Here are some typical examples: In the chunk retrieval scenario, the ICN routers can select the optimal replica by modifying the destination NA of the request packets according to the NAs of content providers obtained by querying the NRS via a chunk ID [37]. In mobility scenarios, the mobile device, whose NA is changed, can re-register its NA and ID relationship with the NRS; then, the ICN routers can modify the destination NA of data packets according to the NA obtained by querying the NRS via mobile device ID, so as to ensure transmission continuity [38].
# Visual Detection of Surface Defects Based on Self-Feature Comparison in Robot 3-D Printing
## 3 Evaluation of Contour Similarity Based on Empirical Formula
Figure 7: The contours of the projected image (**a**) and the image captured (**b**) in the experiment. in an image captured by the platform and n represents the contour number of an image projected by a theoretical model. A represents the pixel statistics values of the contour area. According to formula 1, we can choose different threshold and weight coefficients to adapt to different detection environments and requirements. In this experiment, \(\beta_{n}\), (\(\alpha_{1}\)\(\alpha_{2}\)\(\alpha_{3}\)) and l are set to \(\{1\}\), \(\{0.4,\ 0.3,0.3\}\) and 80, respectively. Thus, the maximum value of SOCP is 1, and the closer the value is to 1, the higher the similarity is. The similarity of contours parameters calculated in Figure 7 is in Table 3 (the size of the picture is \(1626\times 1236\) pixels.) In our previous work [19], the morphological operator with 5 pixels by 5 pixels was adopted and the size of contour filtering was set as 75 pixels. We kept the same experimental parameters as previous work. In this experiment, the minimum defect size that our system can detect is 8.5 pixels by 8.5 pixels and the image size of our system acquiring is 1626 pixels by 1236 pixels. As seen from the Table 3, the red font means that the similarity reaches a certain threshold. In other words, contours 1, 2, and 3 are the self-feature contours a, b, and c, respectively, while contours 4, 5, and 6 are defects contours. It is obvious that the detection results are in good agreement with Figure 7. When there is an extra theoretical contour, it means that some features are not printed out. And when the experimental contour is redundant, some surface defects may have occurred. As long as the theoretical contours correspond to the experimental contours one by one, there are no defects in the picture. Self-feature extraction of the model itself based on the location relation of the point cloud could distinguish the self-feature of the model and defects in the manufacturing process. The similarity formula which takes the location, shape, and size of contours into consideration can effectively evaluate the similarity between the real contours and theoretical feature contours, reducing the missing detection rate. The method we proposed in this work can detect defects that are generated in the process of FDM and distinguish the self-features of the objects from real defects, reducing the model's missing rate. And it has a great application potential in real-time quality inspection in FDM, especially for models with great gradients.
## 2 System Architecture
### CNN and LSTM Models  
Thermal conformance has recently become a crucial index by which users evaluate home systems. In this study, the collected sensor data were used to train and validate the temperature prediction model established using deep learning techniques [11]. CNNs are specialized neural networks designed for processing structured grid data, such as images. They use convolutional layers to detect features and are highly effective at handling spatial data with minimal preprocessing. However, CNNs are computationally intensive and require large amounts of labeled data for training. They are best suited for applications involving spatial data, such as image processing. The first model used for temperature prediction was a CNN comprising a convolution layer, pooling layer, and fully connected layer [12]. In the convolution layer, the convolution operation convolves the original data with multiple randomly generated filters to extract feature values from the data. After convolution is complete, an activation function is introduced to perform a fitting operation on the data before sending it into the pooling layer. In the pooling layer, the computational  
Figure 4: Schematic of KNN algorithm.  
load is reduced by averaging the original data to retain crucial features or by executing a maximization process to obtain eigenvalues of dimensionality reduction. Max pooling is primarily used, serving as a good anti-noise function. In the fully connected layer, the output from the max pooling operation is flattened before being input into subsequent parts of the neural network. Figure 5 presents the system architecture of the CNN used in this study.  
LSTMs are a type of RNN specifically designed to learn long-term dependencies and retain information across longer sequences. They effectively address the vanishing gradient problem and excel at capturing long-term dependencies in sequential data. However, LSTMs are more complex than standard RNNs, are computationally intensive, and require careful tuning of hyperparameters. They are ideal for processing sequential data that requires handling long-term dependencies. The second model used for temperature prediction was the LSTM network, which is a special recurrent neural network [13; 14]. Figure 6 shows the adopted LSTM model. The LSTM network can learn and retain memory tasks over a longer duration compared with traditional recurrent neural networks. It can solve the problem of gradient explosion and disappearance during the long-term memory training process. Figure 6 illustrates the LSTM network used in this study. The