esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimized MLP-CNN Model to Enhance Detecting DDoS Attacks in SDN Environment
## 1 Introduction
### Motivation  
In network security, the motivation behind this research is driven by the pressing need to combat the increasing menace of DDoS attacks. These attacks have evolved in frequency, sophistication, and ability to disrupt essential network services, leading to severe financial and reputational repercussions for organizations. This alarming trend in DDoS attacks demands an urgent response in the form of improved detection and mitigation mechanisms.  
One key area of concern is the vulnerability of networks operating under the paradigm of SDN. While SDN offers numerous advantages in centralized control and dynamic configurations, it also introduces novel vulnerabilities that make SDN environments attractive targets for DDoS attacks. Safeguarding SDN networks from such threats has become a vital concern, necessitating the development of advanced DDoS-detection solutions tailored to this unique network architecture.  
ML and DL techniques have emerged as potent tools for enhancing network security, given their capacity to process vast volumes of network traffic data and identify anomalous patterns. However, to fully harness the potential of these models, they require meticulous optimization, customization, and adaptation to the specific dynamics of SDN environments.  
Optimization plays a crucial role in the effectiveness of DDoS-detection systems. This entails fine tuning model hyperparameters, selecting relevant features, and refining the model architecture. In SDN, where network behavior can significantly differ from traditional networking environments, this customization and rigorous optimization are vital to achieving heightened accuracy and efficiency in DDoS detection.  
SDN networks introduce unique characteristics such as centralized control and dynamic reconfiguration. To effectively counter DDoS attacks in SDN, detection systems must be tailored to these distinctive attributes and demonstrate adaptability to evolving network conditions. Beyond accuracy, DDoS-detection models should offer interpretability, allowing network administrators to understand the rationale behind detection decisions. Furthermore, these systems must exhibit robustness in the face of rapidly evolving attack techniques and changes in network configurations.  
Developing an effective DDoS-detection solution for SDN requires a comprehensive evaluation within a realistic SDN environment. The rigorous assessment provides critical insights into the model's capacity to distinguish legitimate traffic from DDoS attacks, ultimately determining its overall efficacy.  
In light of these compelling motivations, this research embarks on developing an \"Optimized MLP-CNN Model\" explicitly designed to enhance the detection of DDoS attacks within SDN environments. By harnessing the capabilities of ML and DL and coupling them with meticulous optimization, this study aspires to furnish a robust, adaptable, and transparent solution that reinforces SDN networks against the mounting menace of DDoS attacks.
# Prototype of Augmented Reality Technology for Orthodontic Bracket Positioning: An In Vivo Study
## 2 Materials and Methods
### AR and Device Settings  
A wireless handpiece intra-oral camera (QOCA(r) Q-tube Wi-Fi Teeth Scope Pro, Quanta Computer Inc., Taoyuan City, Taiwan) was used, with the resolution and operation distance set to 640 \(\times\) 640 pixels and 3-12 mm, respectively. The operator can visualize the view of the patient's mouth and augmented information from the screen (Figure 2). Before starting bracket placement, all teeth were scanned, and the ideal bracket  
Figure 1: Protocol of the augmented reality–assisted bracket navigation system in the experiment. The protocol was divided into the computer engineering stage and the clinical stage, allowing convenient, precise, and concise communication with the engineer.  
position was analyzed using the intra-oral scanner (TRIOS 3 Basic, 3 Shape Dental Systems, Copenhagen, Denmark) and digital modelling software(Ortho Analyzer, 3 Shape Dental Systems, Copenhagen, Denmark). Pre-adjusted edgewise plastic brackets with an 0.018-inch slot (orthoEsther MB; Tomy) were bonded on the teeth. For bracket height measurement, the Boone gauge (G&H) was used in the control group (Figure 3). With an interval of 0.5 mm, the ruler allows the measurement of bracket height at 0.35, 0.40, 0.45, and 5.0 mm. The AR-assisted bracket navigation system was adopted in the experimental group. After bracket bonding, all teeth were scanned, and the final bracket
## 3 Results and Discussion
### Prediction Results of CNN-PSPA Models  
The CNN-PSPA models were also trained for predicting the site amplification factors of the Lower Hutt Valley. The CNN-PSPA models included six sub-models, described in Section 2.4.2. Unlike the trained CNN-FSPA model, which included the information for the testing samples, the trained CNN-PSPA models did not include the information for the testing samples. Since there was no information for test location at all in the training samples, the errors of the prediction results at test locations could be regarded as similar to those for the other unrecorded locations used for the reliability analysis of this type of model. In the following, the six CNN-PSPA models are used to discuss their prediction ability via error analysis. The errors of the amplification factors were also computed using Equation (4).
## 3 Results
### Impact of Cropland Expansion on Surface Radiative Forcing  
Net shortwave radiation and longwave radiation changes caused by cropland expansion are shown in Figure 7. Net shortwave radiation due to cropland expansion declined, and the decrease was no more than 6 W/m\({}^{2}\). The smallest decrease was in semiarid Inner Mongolia, whereas the largest was in humid NC. There were large differences in the change of net shortwave radiation among the different types of cropland expansion. Deforestation caused a net shortwave decrease in all of the zones. It was mainly because of the increase of albedo. However, cropland expansion from grassland caused different changes of net shortwave radiation among the zones. In the humid regions, the net shortwave radiation due to land use change from grassland to cropland decreased, whereas, in arid and semiarid regions, it increased. The net longwave radiation changes due to cropland expansion showed great differences among the zones. In humid regions, it increased, whereas, in the arid and semiarid regions, it decreased. The longwave radiation was affected by both the emissivity and land surface temperature. Cropland expansion from deforestation caused the net longwave radiation to decrease in all zones. The net longwave radiation change of cropland expansion from grassland was different among the zones. Except for the arid NWC, net longwave radiation increased in the other three zones.  
The change of net radiation caused by cropland expansion is shown in Figure 8. Cropland expansion caused the net radiation to decrease in general over northern of China, and the decrease was no more than 7 W/m\({}^{2}\). Cropland expansion due to deforestation resulted in a net radiation decrease, and the decrease was from 6 to 17 W/m\({}^{2}\). This was mainly because deforestation caused both the net shortwave radiation and net longwave radiation to decrease. Unlike deforestation, cropland expansion from grassland caused a net radiation increase, and the range was from 4 to 9 W/m\({}^{2}\). In humid regions, the increase of the net longwave radiation was much more than the decline of the net shortwave radiation. In the arid and semiarid regions, the decrease of the longwave radiation was much less than the increase of the net shortwave radiation. Therefore, both in the humid regions and arid and semiarid regions, the net radiation due to cropland expansion from grassland all increased. The decrease of net radiation due to deforestation was larger than the increase of net radiation due to cropland expansion from grassland; therefore, the net radiation due to cropland expansion decreased.  
Figure 7: Changes of net shortwave radiation (**a**) and net longwave radiation (**b**) in different climate zones.
# Prediction of Walnut Deterioration Using Kernel Oxidative Stability
## 3 Results and Discussion
### Relations between Kernel OSI and Quality of Kernel and Extracted Oil during Storage
Rancimat method has been widely used in research and industry for oil analysis and strong correlations were previously described between chemical composition and OSI of the samples [26]. In a previous study, no significant correlation was found between shelf life at room temperature and Rancimat induction time; the authors discouraged the use of this method to predict shelf life for olive oil [25]. However, the authors based their conclusions on the differences between oxidation changes at room temperature compared to accelerated oxidations changes in the samples from the Rancimat. Nowadays, OSI is commonly used as a part of prediction models to evaluate oils shelf life, not a direct comparison to actual storage shelf life [8]. Oil extraction from the walnut kernels has an impact on oil characteristics that may influence oil oxidation which give misleading information on the susceptibility of kernels to oxidation [27]. Besides oil extraction conditions, the susceptibility of iodine (used for PV analysis) to oxidation in the presence of oxygen can be a further source of variability when analyzing a sample after lipid extraction [28]. In addition, peroxide values do not always give a reliable measure of the oxidation, since peroxides can further degrade in advanced oxidation, so simultaneous measurement of secondary products is appropriate [29]. Therefore, the development of a methodology that predict walnut deterioration using the kernels without prior oil extraction would simplify sample evaluation and decrease unpredictable variations on the sampling process. In addition, Rancimat oxidative stability, contrary to chromatographic methods and sensory analysis, does not require long or meticulous experimental work, an experienced analytical chemist or lengthy the data processing. Knowing that oxidation processes may be different when using an accelerated method, in this study we focus on establish a relation between the OSI obtained by Rancimat method on kernels and quality parameters of each sample analyzed. Most correlations between oxidative parameters and OSI  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Operational** & **Storage** & **Moisture** & **Fat** & **PV** & **K322** & **K268** & **Hexanal** & **Oil OSI** & **Rancidity** \\ \hline
110 \({}^{\circ}\)C and & 0 & \(-0.772\) \({}^{\ast\ast}\) & \(-0.378\) & \(-0.604\)\({}^{\ast\ast}\) & \(-0.372\) & \(-0.533\)\({}^{\ast\ast}\) & \(-0.355\) & 0.643 \({}^{\ast  
during storage had a coefficient higher than 0.5 and were significant (\(p<0.001\)). Therefore, the results from this study could lead to an application of accelerated oxidation and establishment of acceptable value for walnut kernel stability and shelf life for the industry.
## 3 Grassmann MTS Clustering  
Since (dis)similarity measurement and learning are the key components of MTS clustering, the problem of MTS clustering is revisited as a learning problem in the Grassmann manifold using linear dynamic modeling. Moreover, depending on the characteristics of the adjusted clustering algorithm, the learning problem can be boiled down to a point-to-point matching problem through geodesic distances. Broadly speaking, the MTS clustering on the Grassmann manifold can be streamlined and grouped into three categories as the _intrinsic approach_, _extrinsic approach_, and _semi-intrinsic approach_. In the following subsections, each of the categories will be detailed.
## 4 Discussion
### Analysis of the Evaluated Metrics for Each Method
#### 4.1.1 Standard Deviation  
Standard deviation reflects the variability of the delivered power. The results show that the V2 GSUN method exhibits significantly higher standard deviation compared to V2 GSmooth and V2 GGlide, particularly at low SoC levels (<20%). This highlights the superior performance of V2 GSmooth and V2 GGlide in reducing oscillations, regardless of the system's charge state, making them more effective for ensuring stable power delivery.  
Figure 10: Performance metrics comparison for power smoothing methods as a function of SoC.
# A Fully Integrated Low-Power Multi-Mode RF Receiver for BDS-3/GPS
## 4 Receiver Design
### RF Front-End
In Figure 3, the signals of ACTBA are transmitted to Gm through capacitors \(C_{5}\) and \(C_{6}\) with the same value of 2.216 pF; Gm, mixer, and Tia make up GMT. A passive switching mixer is selected due to its high linear characteristics and low noise performance [23]. A self-biased inverter structure and current reuse technology [7] are used in the Gm circuit to improve the gain while reducing power. The Tia is similar to that in [8]. The Gm and Tia can provide a certain gain to relieve the gain pressure of other RF stages. In the traditional blocking scheme, the blocking capacitors are located at the input of the CBPF. These capacitors are advanced to the output of Gm, described as capacitors \(C_{9}\) and \(C_{10}\), with the same value of 2.216 pF in Figure 3. The frequency is higher than that at the input of the CBPF, so the capacitors are smaller and fewer, conserving space. The simulation shows that for the RF front end, the cascade gain is 52.3 dB and the cascade NF is about 1.5 dB. There are magnitude and phase imbalances in the differential output of ACTBA. However, because of the structure of the mixer, the input mainly focuses on the difference between the positive and negative signals. Therefore, even if the ACTBA outputs have imbalances, the differential output of the IF will have good balance.
# FL-LoRaMAC: A Novel Framework for Enabling On-Device Learning for LoRa-Based IoT Applications
## 4 Proposed Framework: FL-LoRaMAC
### Network Joining  
In FL-LoRaMAC, all devices taking part in the training need to join the LoRa network first. The joining procedure is similar to that of the legacy LoRaWAN protocol as mentioned in [22] with some additional steps and information in join messages.  
Whenever a device is powered ON, it sends a join request \(\mathcal{J}\_\)Req' to the network server via an LoRa transceiver. Once the \(\mathcal{J}\_\)Req is sent, the device waits for the join response \(\mathcal{J}\_\)Res'. If the end device receives the \(\mathcal{J}\_\)Res, it configures itself according to the \(\mathcal{J}\_\)Res.  
On the network server side, a timer termed as the join request timer starts periodically. If the \(\mathcal{J}\_\)Req from the end device is received before this timer is expired, the network server prepares a \(\mathcal{J}\_\)Res. The network server has access to the information regarding the AI model architecture; we termed this information as 'MODEL_INFO'. It also contains information regarding the downlink channels and spreading factor on which the global model parameters will be broadcasted (DL_INFO), the information about the data fragments that will be sent (FRAG_INFO), the information about how frequently the device should perform the listening, and for the duration (LISEN_INFO). The network server will also send MODEL_INFO, DL_INFO, FRAG_INFO, and LISTEN_INFO along with the legacy LoRaWAN\(\mathcal{J}\_\)Res. All this information will be used by the end devices to configure themselves to communicate gradient updates as described in Section 4.2. Once the timer is expired, no \(\mathcal{J}\_\)Res for the end devices will be sent.
## 3 The Mediator-Wrapper Architecture
### On the Roles of Mediator-Wrapper Components  
Considering the previously mentioned roles and interactions of wrappers and mediators in the MW architecture, it can be determined what kind of properties these components should have, or to which rules they should adhere. In a general sense, any system component should satisfy the following conditions [39]:  
* It can be used by other software elements, its \"clients\".
* It possesses an official usage description, which is sufficient for a client author to use it.
* It is not tied to any fixed set of clients.  
This set of conditions can be expanded to determine what conditions a wrapper or mediator should specifically meet. For wrappers in a data source integration system, by following the example of [2; 3] (in their case the GARLIC system), the following rules (goals) are set:  
* The start-up cost to write a specific wrapper should be small. The wrapper itself can be constructed quickly with little need for prior knowledge of the data source integration system internal structure. There is a basic service upon which a specific wrapper is built upon.  
Figure 2: MW architecture with layered mediators [28].  
* Wrappers should be able to evolve. Incremental upgrades to the wrapper should be possible.
* Wrappers should be modular and independent. Wrappers for new data sources can be integrated into the existing data source integration system without disturbing user applications, and other wrappers or components.
* Wrappers should be participants in query planning. The wrapper may use whatever knowledge it has about a repository's query and specialized search facilities to dynamically determine how much of a query the repository is capable of handling.  
The wrapper component type is succinctly defined in Definition 1.  
Definition 1: The wrapper is a component that allows uniform access to a data source by wrapping the data source in terms of schema, queries, and data.  
For mediators in a data source integration system, following the ideas of [PERSON] [26], the following rules are set:  
* Structuring mediators into hierarchies should not lead to problems.
* Mediators should drive transformations. Mediators are there to accommodate the need for data and metadata restructuring. Queries are also affected by this restructuring.  
The mediator component type is succinctly defined in Definition 2.  
Definition 2: The mediator is a component used to manage transformations involving multiple unified schemas, the data they represent, and queries used to acquire the data.  
The rules for mediators and wrappers imply that they are each architectural quanta. An architectural quantum is defined by [PERSON] et al. [25] as an independently deployable component with high functional cohesion. This surprising compatibility with modern architectural ideas is the main reason why we consider the MW architecture a relevant research topic even today.
## 1 Introduction
As for the versatile ML algorithms, artificial neural network (ANN), support vector regression (SVR), Gaussian process regression (GPR) and random forest (RF) are reportedly effective for crop LAI retrieval [25; 26; 27; 28]. However, gradient boosting regression tree (GBRT), a highly robust ML algorithm for a wide range of applications, is capable of achieving high levels of accuracy for regression problems [29; 30; 31] and to our knowledge, has not been investigated for LAI retrieval. Further studies should be conducted to assess the performance of the GBRT model in crop LAI retrievals. Many studies have been dedicated to crop LAI retrieval using MLRAs. However, there are a limited number of academic studies involving comparisons of different MLRAs for crop LAI retrievals using remote sensing. Apparently, none of these studies have focused on multispectral remote sensing data, and none of these studies have evaluated the different impact factor together, to conduct a comprehensive comparison. In addition, the validation of global LAI products are important procedures to ensure the application of the products in a wide range of fields [32]. Regional high-resolution LAI maps can be used as a reference LAI map to validate the global LAI products, which calls for efficient, robust and accurate algorithms for LAI retrieval. The objective of this study is to compare the performance of five advanced MLRAs (ANN, SVR, GPR, RF and GBRT) for cotton LAI retrieval in a relatively comprehensive manner. We conducted the study over the entire growth period of cotton using Sentinel-2 spectral bands and corresponding ground data. Specifically, the following research questions are addressed:  
(1) Which of the five MLRAs perform best with regard to model accuracy? (2) Which is the fastest model during the training and validation processes? (3) How does the number of training sample size influence the performance of the five MLRAs? (4) How does the number of spectral bands influence the performance of the five MLRAs? (5) Which is the best model in consideration of model accuracy, computational efficiency, sensitivity to training sample size, and sensitivity to spectral bands together? (6) How accurate are the global LAI products in Northwest China?
## 3 Case Studies: Application to Continuous and Lumped-Parameters Systems
### Case Study 1: Coupling of Continuous Substructures  
This case study examines a dynamic structure constrained at both ends with fixed-fixed boundary conditions and composed of three flexible substructures. The overall system receptance matrix is determined using the RCFBS approach, with results compared against the numerical FEA and the modal method. Each subsystem is modeled as a beam element, facilitating a straightforward analytical derivation of global stiffness and mass matrices. This approach ensures consistency in the generalized coordinates and DoFs across different methods.
## 3 Main Concerns in AM Quality Control
Studies have shown that the powders with spherical shapes are much preferred to obtain uniform distribution and packing of the powders because of the advanced flowability and spreading characteristics [29]. In addition to powder shape, the powder chemical composition can also affect the quality of the AM parts. For some alloys, vaporization of some elements from powder due to highly localized heat input can be problematic. Poor quality of the powders originating from the chemical composition of the powder [31], powder size distribution (PSD) [32], moisture content, pore size and shape [33], surface area, microstructure, thermal properties, density, flowability, and morphology can lead to undesired impurities, precipitations and other microstructural deficiencies such as cracks. Furthermore, the powders' humidity content is significant in the overall quality of the manufacturing process. Powders may become wet during the handling and storage stages or due to a poor powder-making process. During the manufacturing process, the water content of the powders is decomposed into oxygen and hydrogen atoms after exposure to beam power. As a result of the high cooling rate, the atomized water components cannot escape from the melt and are trapped inside the material during the solidification process. Porosities in original powders were created during the \"powder making process\", mostly remain inside the finished material even after the re-melt process during the additive manufacturing process. Sometimes, post-processing is required to control the humidity content of the powders to reduce the initiation of the porosities. Moreover, there is optimal particle size distribution for each additive manufacturing technique. Therefore, powder size distribution is another important parameter for evaluating the quality of the feedstocks. Because of all these challenges, at the moment, pre-alloyed powders for additive manufacturing applications are available only for limited material grades. However, recent studies attempt to increase the range of the powder types and applicable material grades. The continuous advances in powder production and usage make the qualification process challenging.