esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion
### Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Classifiers** & **Accuracy** & **Classifiers** & **Accuracy** & **Classifiers** & **Accuracy** & **Classifiers** & **Accuracy** \\ \hline \multirow{2}{*}{**FS Approach**} & **Information** & \multirow{2}{*}{**\#**} & \multirow{2}{*}{**Gain Ratio**} & \multirow{2}{*}{**\#**} & **Intersection** & **\#** & **Union** & **\#** \\  & **Main** & & & & & & & \\ \hline \multirow{4}{*}{**F5**} & IG-ANN & 94.60 & GR-ANN & 93.86 & IMF-ANN & 92.80 & ANN UMF & 94.00 \\  & IG-C.4.5 & 99.54 & GR-C.4.5 & 99.10 & IMF-C.4.5 & 99.59 & C 4.5 UMF & 99.60 \\  & IG-Bagging & 99.48 & GR-Bagging & 98.95 & IMF-Bagging & 99.58 & Bagging UMF & 99.60 \\  & IG-kNN & 98.48 & GR-kNN & 98.43 & IMF-kNN & 98.76 & kNN UMK & 99.10 \\  & IG-Ensemble & 99.60 & GR-Ensemble & 99.14 & IMF-Ensemble & 99.70 & Ensemble UMF & 99.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracy results yielded by the five various ML with different FS approaches for multi-classification using the IoTID20 dataset. Figure 9: Confusion matrix yielded by the five various ML with different FS approaches for multi-classification of the IoTId20 dataset.
## 4 Results and Discussion
Lacking appropriate single frequency laser sources at 0.8 microns and 1.5 microns, we could not determine the intracavity losses of the NOPA directly with a conventional method. Instead, we evaluate the intracavity losses based on the relationships between the measured amplitude quadrature difference (phase quadrature sum) quantum correlation, and the corresponding ideal quantum correlation at the pump level of \(\frac{P}{P_{\text{sq}}}=0.6\). More precisely, we used the following beam splitter transformation equations, which modeled the effects of non-ideal efficiency:  
\[\frac{\eta_{\text{tot}}}{V_{Sq}^{0}}+1-\eta_{\text{tot}}=V_{Anti-sq} \tag{6}\]  
\[\eta_{\text{tot}}V_{Sq}^{0}+1-\eta_{\text{tot}}=V_{sq} \tag{7}\]  
where \(V_{Sq}^{0}\) was the ideal quantum correlation assuming 100% efficiency, and \(V_{sq}\) and \(V_{Anti-sq}\) were the measured quantum correlation and anti-correlation. Substituting the measured values of \(V_{sq}\) and \(V_{Anti-sq}\) into Equations (6) and (7), the total efficiency \(\eta_{\text{tot}}\) and the ideal quantum correlation \(V_{Sq}^{0}\) of the system could be derived. Starting from the total efficiency \(\eta_{\text{tot}}\), the intracavity loss \(L\) (both the linear loss and the nonlinear loss) could be easily derived. At this stage, by substituting the relevant parameters of \(\eta_{\text{tot}}=0.763\), \(\frac{P}{P_{\text{sq}}}=0.6\), and \(\frac{f}{f_{c}}=1/17\) into Equation (2), the theoretical entanglement level for the current improved system was calculated to be \(-5.97\) dB, which is in good agreement with the observed value.
## 4 Results and Discussion
### The Benefits of Local-Global Intensity Fitting  
A comparative experiment was performed to prove the effectiveness of the local-global intensity fusion in Section 3.1. We conducted different experiments, as shown in Table 2.  
In experiment A, the fitting intensity of the energy functional is the local intensity. In experiment B, the fitting intensity of the energy functional is the global intensity. Moreover, the energy functional with the fusion local-global intensity is shown in Experiment C. The contour extraction results of the experiments are shown in Figure 2.  
As shown in Figure 2, experiment A extracted the target contour in the intensity inhomogeneity region, but the result was greatly affected by the initial contour curve (blue circled area) and was sensitive to noise (green circled area). Moreover, the method of experiment A also extracted the contours of the non-boundary regions. Experiment B could extract the target contour in the intensity homogeneity region and was not disturbed by noise, but the target contour in the intensity inhomogeneity region could not be extracted. By comparing the segmentation results in Figure 2a-c, it can be seen that the fused energy functional (experiment C) can not only effectively eliminate the influence of the initial curve and noise interference but also effectively segment intensity-inhomogeneous regions.
## 8 Commercialization Status of Stevia
It has been well received by many foods and beverage companies in Asia, the US, and Europe. Reb I molecule, produced by SweetGen, has recently obtained its no-objection certificate from FDA and GRAS and is ready to be commercially available in the near future (Reports, 2021). Recently, in 2020, Ingreedion acquired a 75% stake in PureCircle, which is offering a Reb M sweetener extracted directly from stevia leaves. Reb M is a newly identified best-tasting sweetener, and it holds high value in the market. In another collaboration, Cargill and DSM are working on the large-scale development of metabolic engineered production of RebM (brand name: EverSweet). EverSweet is environmentally friendly since the carbon footprint of its bioconversion is 60% lower than other sources, and it requires 70% less land area [108]. The global natural sweetener market is exponentially increasing and is estimated to reach USD 3.8 billion by 2025 with a CAGR of 8.8%. The production volume of stevia is expected to reach 72,670.9 tonnes by 2028 with a growth rate of 6.7% [109]. Stevia market is divided into three categories: extract powder, liquid, and leaves. The stevia extract powder generated the highest revenue in 2021. It is popular because of its ease of handling and accurate dosage efficiency compared to liquid extract, thus being a key contributor in the global market. Based on the application, the stevia market is classified into beverages, bakeries, confectioneries, dairy products, and dietary supplements. Recent developments show that in 2018, S&W Seed Company expanded its agreement with an international consumer company to produce non-GMO stevia crops by breeding and targeting novel superior quality traits [110]. Likewise, in 2021, Tate & Lyle and Codexis strengthened their partnership to increase the current production rate of commercially available stevia-based sweeteners DOLCIA PRIMA(r) Allulose and TASTEVA(r) M [111]. In April 2021, another major influencer, Archer-Daniels-Midland Company, expanded its R&D sector in Singapore and it is expected to develop next-level on-trend and nutritious products to meet the current demand for zero-calorie food and beverages in Asian provinces [112]. Figure 3 represents the summary of the different approaches and strategies utilized so far by the researchers to enhance SvGIs production and marketability.
# Assessment of ZTD Derived from COSMIC Occultation Data with ECWMF, Radiosondes, and GNSS
## 2 Materials and Methods
### ZTDs from Ground-Based GNSS-ZTD
For this reason, when performing data correction to obtain gnsZTD, we used temperature, humidity, and pressure at GPS receiver from ECH model as the meteorological observation that this station should provide. Meanwhile, the locations of the occultation events do not usually coincide with the locations of the IGS observation stations; we needed to perform a spatial matching (the horizontal resolution of 500 km and the vertical resolution of 5 km) to generate matched dataset between GNSS-ZTD and RO ZTD. The matched dataset was used in the following studies with a filter of a special horizontal or vertical resolution.
# On the Needs and Requirements Arising from Connected and Automated Driving
## 3 5 GCAR Use Cases
### See-Through  
The see-through use case belongs to the class of _cooperative perception_. Its goal is the exchange of data regarding detected objects and real-time video between nearby vehicles via wireless communication. Such information is particularly beneficial to facilitate safe overtaking maneuvers where a vehicle  
Figure 1: **(Left) Sketch of the lane merge use case with three vehicles. (Right) An example image of this use case recorded on a test track is shown. Here, the red car (Veh\({}_{1}\)) merges into the traffic on the main road.**  
requests the support of the vehicle in front of it (e.g., a truck), for the purpose of extending its local perception (e.g., detecting other vehicles from opposite direction that are not directly visible to the vehicle in the rear), as shown in Figure 2. The scene in front of the vehicle ahead is captured by a camera and transmitted to the rear vehicle to allow it to see through the front vehicle and bypass the occluded area. The see-through application requires an estimation of the relative position between the two vehicles referencing systems to relate their local perceptions to a common spatial reference in a consistent way. For this purpose, a sparse real scale 3D map is first computed from the front vehicle while using a fast stereo Visual Odometry (VO) approach, as described in [12]. Subsequently, the pose of the rear vehicle is estimated in this 3D map using two-dimensional (2D)-3D feature tracking. A dense disparity map is computed at the stereo vision system of the front vehicle in order to generate a synthetic image of the occluded area of the rear vehicle image. Based on that, the video data representing the region of interest is transmitted to the rear vehicle, which performs appropriate cropping of the received video and stitches it on the current view. The video data transmission is ended with the completion of the maneuver.
# A System Architecture of a Fusion System for Multiple LiDARs Image Processing
## 4 Implementation and Evaluation  
### Experimental Environment  
The fusion system is connected to up to four LiDAR sensors through Ethernet. It exports the processed data to the external system through wired or wireless communication. A high-resolution LiDAR sensor requires a lot of resources for data processing because the amount of data is very large. We consider a software architecture of the fusion system using the lightening of the algorithm in the embedded system environment. The hardware platform for the proposed system is manufactured using NXP's LS1028A dual-core general-purpose process. Five Ethernet ports are configured to connect multiple LiDAR sensors. It also includes wireless interfaces such as WiFi and LTE to communicate with external systems. The LiDAR sensors are made by Carnavicom Co., Ltd. and the sensors employ 16 channels. Each LiDAR sensor has a wide field of view which has up to 145\({}^{\circ}\), 18,560 points per frame, and a sampling rate of 30 Hz. LiDAR sensors and the fusion system communicate with UDP based on port and IP. The fusion system includes functions such as collection, parsing, conversion, and formatting of data. Figure 6 shows an embedded hardware and block diagram of the fusion system for the experiment.  
For the evaluation, the response time is measured by varying up to four LiDAR sensors connected to the fusion system. This response time refers to the process life cycle of the clients and the server in the fusion system for multiple LiDAR sensors. The response time of the client process means the sum of initialization time, waiting time, operation time, and transmission time to the server. The response time of the server process means the time taken to receive data from the clients and export the data to the external system. As many client threads are created as the number of connected LiDAR sensors. These threads are allocated to core1, and the server exclusively allocates core2. The proposed system architecture is mounted on the fusion system. We implemented GUI to evaluate the point cloud and response time of the LiDAR sensor.  
In Figure 7, the top figures show the connection of LiDAR sensors and the proposed fusion system, the bottom figures show the GUI to evaluate the point cloud of the LiDAR sensors and the overall block diagram of the proposed fusion system for multiple LiDAR sensors.  
Figure 6: Hardware platform of the fusion system.
## 2 Methods
### Robust Regression with Odometer Aiding
 \right]_{1\times 8} \tag{30}\]  
\[\delta\mathbf{z}_{3d}=\left[\Delta d_{3d}-r_{3d}\right]_{1\times 1}=\mathbf{H}_{ WLS}\delta\mathbf{\hat{x}}_{WLS}+\eta_{\Delta d} \tag{31}\]  
with  
\[r_{3d}=\left(\Delta r_{x}^{e}\right)^{2}+\left(\Delta r_{y}^{e}\right)^{2}+ \left(\Delta r_{z}^{e}\right)^{2}\]  
where \(\mathbf{H}_{WLS}\), \(\delta\mathbf{\hat{x}}_{WLS}\), \(\eta_{\Delta d}\), and \(r_{3d}\) are the design matrix of averaged displacement, error state, including position and receiver clock bias for WLS filter, combined errors comprising sliding, heading change and noise, and initial distance derived from the change of initial rover position between consecutive epochs; \(\Delta r_{x}^{e}\), \(\Delta r_{y}^{e}\), and \(\Delta r_{z}^{e}\) denote the difference between the initial position vectors in the three directions of the e-frame. The measurement model in the WLS filter is similar to the design in EKF, as mentioned previously, and is not differentiated here. The benefits of odometer aiding for robust regression, include the improvement in observability of geometry, which combines the ground-based (odometer) and space-based (GNSS) sources, and the enhanced connection of relative-position change in addition to the TDCP measurements. Those contributions enhance WLS filter to find more accurate results; therefore, a priori weighting indicators in (27) will be more reliable to implement in EKF estimation. The flowchart depicting the combination of the proposed methods in this study is shown in Figure 3. The data flow from process 1 to process 6 aims to determine the condition of combining TDCP measurements to update EKF (process 6) according to the result of receiving carrier-phase measurements (process 3) and cycle slip detection (process 4). After odometer measurements are introduced, the priority is the detection of stationary motion (process 8), since the strong intervention from zero velocity constraint updates EKF without any GNSS measurements (process 9 and 13) and fix the position output (process 14), ending the whole process flow for the current epoch. While the vehicle is not stationary, odometer-aided robust regression process based on WLS will be conducted to obtain weighting scales (process 11), and the corresponding GNSS measurements, odometer-aided velocity constraint (based on the resultant speed, process 10), and lateral velocity constraint (process 12) will be used to update EKF accordingly (process 13). After the position and velocity states have been estimated, and the heading (derived from the velocity states) does not change significantly (process 15), odometer-based FDE in (23) will be activated (process 16). Once an unsatisfactory estimation is detected, the odometer-based DR solution will bridge the gap during the estimation (process 17). Otherwise, MAF will be activated (process 18) and produce the averaged heading for other processes (process 12, 15, and 17) for the next epoch.
## 5 Simulation Results
### Experiment with Multipath  
In this section, we simulate the Rayleigh channel for multipath, which is suggested by the DVB-T standard [1] for portable reception. Since the method presented in this paper relies on the LOS, we add the LOS signal in this model. The related power, delay and phase values can be found in [1]. In order to simplify the simulation, we delete the multipath signals, which have a time delay lager than 15 OFDM  
Figure 14: _Cont._samples with respect to the LOS signal, corresponding to a 492 m separation in 2 K mode, based on the fact that long delay multipaths can be filtered out on the basis of the initial information.  
The correlation with the Rayleigh multipath channel is shown in Figure 15. The Hata propagation loss model is also introduced. The trajectory estimation error is shown in Figure 16. From this error plot, we see that the position error is comparable with the errors in the absence of multipath. This is probably due to the fact that the LOS is present with the strongest signal power. There are four emitters visible, which can also improve the performance in difficult environments for 2-D positioning.  
Figure 16: The position estimation errors with multipath channel.  
Figure 15: Acquisition results with multipath channel.
# Data-Driven Network Anomaly Detection with Cyber Attack and Defense Visualization
## 3 Background
### Threat Description
#### 3.3.2 TCP-SYN Flood Attack  
This is a denial-of-service (DoS) attack to exhaust resources and render a targeted host unresponsive during the standard TCP three-way handshake. The attack involves repeatedly sending SYN packets to all target host ports, causing the target host to try to respond with an SYN-ACK packet from each open port. The result is that legitimate clients are denied service because of an overflow in the host's connection tables.
# End-to-End Light Field Image Compression with Multi-Domain Feature Learning
## 5 Conclusions  
This paper proposes a novel multi-domain feature learning-based light field image compression network. In particular, an EPI-based angle completion module is developed to obtain a complete angle feature by fully exploring the large disparity angle information contained in the EPI domain. In addition, a spatial-angle joint transform module is proposed to reduce redundancy in features by modeling the intrinsic correlation between spatial and complete angle features. Experimental results demonstrate that MFLFIC-Net achieves superior performance on PSNR and MS-SSIM metrics compared to public state-of-the-art methods. In future work, it will be worthwhile to research the optimization of the algorithm structure and explore more lightweight networks for lower computational complexity, such as simpler and more effective feature extraction methods. At the same time, it is necessary to study possible trade-offs, such as maintaining the stability and accuracy of compression  
Figure 6: Ablation study on the EPFL dataset.  
performance while reducing network complexity. In addition, there is a need to balance the relationship between compression ratio and image quality to ensure that complexity is reduced while still maintaining sufficiently high compression efficiency.  
Conceptualization, K.Y. and Y.L.; methodology, K.Y.; software, K.Y. and G.L.; investigation, D.J. and B.Z.; resources, Y.L.; data curation, G.L.; writing--original draft preparation, K.Y.; writing--review and editing, K.Y., Y.L., G.L., D.J. and B.Z.; supervision, Y.L., D.J. and B.Z.; project administration, Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Science and Technology Major Project of Tibetan Autonomous Region of China under Grant XZ202201 ZD0006G03.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 6 Lightweight IDS for IoT Devices  
Contrasting IoT devices with general-purpose computers, IoT devices are generally limited in storage and computation power, whereas in conventional IT systems, administrators deploy IDS agents to network entities with high computing and storage capabilities [101]. Though IDS can play an effective role in securing IoT systems, the resource-constrained nature of IoT does not allow hosting the conventional resource-intensive IDS solutions [102]. Considering the development of IDS for IoT poses a significant security challenge, researchers delineate various characteristics of IoT networks attributable to constraints in memory capacity, processing power, and battery energy capacity [103]. The computational limitations of IoT devices impose restrictions on the design of IDS to be lightweight and efficient, ensuring the security of the network against potential cyber-attacks while using resources efficiently.  
According to [104], lightweight systems generally aim to save energy and reduce computational resources. The study in [105] described a system as being light if it has reduced energy consumption. Hence, a lightweight system can be generally described as one that can simply perform its needed computation with a limited amount of resources. Conversely, authors in [106] described a lightweight IDS as a small module that consumes a minimum amount of memory resources enough to be used as a permanent element of the Wireless Sensor Network (WSN) security infrastructure. In [107], a lightweight IDS refers to a security mechanism that requires the minimum amount of computational power, storage, and energy resources while achieving the desired security goals.  
In this section, we present critical insight into state-of-the-art techniques for obtaining lightweight IDS as suitable security solutions for the protection of the IoT ecosystem. To provide an extensive review of the underlying study, an approach is devised for the selection of relevant articles directly addressing the underlying system or a part of it. In the devised approach, search queries are run over the online portals as earlier described in Section 2. The search results yield that research in this domain has gained attention since 2019 and onwards, and based on the obtained results, all journal articles and conference papers published between the years 2019 and 2024 are selected for the said study. It is observed that two techniques are commonly adopted in literature for achieving lightweight IDS for IoT systems: feature selection techniques, including the emerging ensemble feature selection as the prominent ML-based approaches, and secondly, collaborative techniques.
# Editorial: Special Issue on Data Compression Algorithms and their Applications
## 1 Introduction  
Data compression is classic research area in computer science focusing on the efficient storage and communication of data. Data compression is ubiquitous throughout science and engineering and essentially any data of non-trivial size is stored or communicated in compressed form on any modern computer system. With rapid advances in data collection in areas such as e-commerce, astronomy, climatology, bioinformatics, and particle physics, the need for efficient data compression is stronger than ever.
# A Cooperative MAC Protocol for a M2M Heterogeneous Area Network
###### Abstract  
With the increasing demand of Machine to Machine (M2M) communications and Internet of Things (IoT) services it is necessary to develop a new network architecture and protocols to support cost effective, distributed computing systems. Generally, M2M and IoT applications serve a large number of intelligent devices, such as sensors and actuators, which are distributed over large geographical areas. To deploy M2M communication and IoT sensor nodes in a cost-effective manner over a large geographical area, it is necessary to develop a new network architecture that is cost effective, as well as energy efficient. This paper presents an IEEE 802.11 and IEEE 802.15.4 standards-based heterogeneous network architecture to support M2M communication services over a wide geographical area. For the proposed heterogeneous network, we developed a new cooperative Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) medium access control (MAC) protocol to transmit packets using a shared channel in the 2.4 GHz ISM band. One of the key problems of the IEEE 802.11/802.15.4 heterogeneous network in a dense networking environment is the coexistence problem in which the two protocols interfere with each other causing performance degradation. This paper introduces a cooperative MAC protocol that utilizes a new signaling technique known as the Blank Burst (BB) to avoid the coexistence problem. The proposed MAC protocol improves the network QoS of M2M area networks. The developed network architecture offers significant energy efficiency, and operational expenditure (OPEX) and capital expenditure (CAPEX) advantages over 3G/4G cellular standards-based wide area networks.  
heterogeneous network; IEEE 802.11; IEEE 802.15.4; 6 LoWPAN; M2M communication; low power network +
Footnote †: journal: Sensors. Actuator Netw. **2016**, \(5\), 12; doi:10.3390/jsan5030012