esa-sceva/satcom-chunk-collection huggingface dataset:
# PrECast: An Efficient Crypto-Free Solution for Broadcast-Based Attacks in IPv4 Networks
## 3 Mitigating the ARP-Poisoning Attack
### Algorithm-Based Solutions
Furthermore, to keep the ARP cache entries active, hosts have to send an ARP-request to every entry in the long-term table. This incurs unnecessary network overhead. * Since the neighbourhood density is not known to every host in the network, it is hard to set a threshold value for the number of good votes. * Even though the authors proposed a scheme to eliminate the overhead due to cryptographic tools, their proposed scheme incurs more traffic due to the voting mechanism in addition to the existing ARP broadcasting overhead. In general, any algorithm-based solutions to eliminate ARP-poison attacks has the following problems:  
* Most of the research proposals introduced some form of message authentication through the use of cryptography. They also used a centralized certificate authority to issue digital certificates. The use of any cryptographic tool at the client-side will introduce CPU latency due to computational requirements. Severe network latency is introduced if the cryptographic computations are done at the server-side. Thus, any scheme that uses cryptography is only suitable for a smaller network. * DDoS is a potential problem for any scheme that depends on a centralized service. * Redesigning or implementing a modified TCP/IP stack at the client-side is possible only in a smaller private network. For this type of network, a simpler alternative is to use a static ARP table that not only eliminates the ARP-poisoning problem, but also improves the performance in the absence of any ARP broadcasting. Implementing a modified TCP/IP stack is impossible in a network that uses the concept of BYOD. * There are few non-cryptographic techniques available in the literature to eliminate the ARP-poisoning attack; however, they use collaborative neighbour-based message authentication. In order for these protocols to work, the network has stringent conditions. One condition is to ensure that the number of well-behaving nodes exceeds the number of attackers. These conditions can easily be met in a private network, but are very difficult to ensure in a public network, Furthermore, in the absence of any incentive for message authentication (or voting) for other nodes, selfish nodes might restrain from voting in order to save their CPU and other network resources. Thus, a minimum threshold for message authentication may not be maintained.
## 3 Results
### Moment of Inertia and Rotational Energy  
Moment of inertia and rotational energy metrics (Table 2) demonstrated systematic adaptations across increasing difficulty levels, with significant changes in multiple kinematic variables during the takeoff phase. Moment of inertia parameters, including the maximum (F\({}_{3,36}\) = 10.2, \(p<0.001\), \(\eta_{\rm p}^{2}\) = 0.55), range (F\({}_{3,36}\) = 16.5, \(p<0.001\), \(\eta_{\rm p}^{2}\) = 0.66), and average (F\({}_{3,36}\) = 29, \(p<0.001\), \(\eta_{\rm p}^{2}\) = 0.71) moments, progressively increased with difficulty elevation. The range of moment of inertia exhibited the most substantial gain at 152.5%, with significant increments observed across multiple transitions (720-1080, 1080-1440, and 1440-1800). All the moment of inertia parameters showed large effect sizes, highlighting the considerable practical impact of difficulty level on body rotational adaptations during takeoff.  
Rotational energy variables similarly showed significant progressive increases, with maximum rotational energy (F\({}_{3,36}\) = 7.1, \(p<0.001\), \(\eta_{\rm p}^{2}\) = 0.37) gaining 139.2%, average rotational energy (F\({}_{3,36}\) = 22, \(p<0.001\), \(\eta_{\rm p}^{2}\) = 0.65) increasing by 176.6%, and departure moment rotation energy (F\({}_{3,36}\) = 10.1, \(p<0.001\), \(\eta_{\rm p}^{2}\) = 0.46) demonstrating a remarkable 310% total gain. The intermediate difficulty transitions, particularly between 1080 and 1440, were crucial, as most parameters exhibited significant adaptations during this phase. Notably, the moment of inertia to rotational energy ratio (F\({}_{3,36}\) = 10.1, \(p<0.001\), \(\eta_{\rm p}^{2}\) = 0.46) decreased significantly by 52.7%, highlighting the complex biomechanical adaptations occurring with increased difficulty levels. These rotational energy-related parameters also showed  
Figure 5: The maximal angular velocities at different difficulty levels during takeoff phases. (**A**) Upper trunk twist angular velocity. (**B**) Shoulder horizontal adduction angular velocity. (**C**) Shoulder horizontal adduction angular velocity. (**D**) Hip flexion angular velocity. (**E**) Knee flexion angular velocity. (**F**) Knee extension angular velocity. Error bars represent standard deviation (SD) and are displayed in the positive direction only. * indicates a significant difference compared to the previous difficulty level (\(p<0.05\)).  
medium to large effect sizes, underscoring their practical significance in the adaptation to difficulty increases.
# Progress and Summary of Photodarkening in Rare Earth Doped Fiber
## 5 Experimental Setup of Photodarkening  
### Additional Loss Measuring Device at Characteristic Wavelengths  
The characteristic wavelength loss measuring device is to measure the loss of probe light with a specific wavelength in the process of PD. By coupling the pump light and probe light into the sample fiber at the same time, the change of probe light with time is recorded by a power meter. Because the pump light and probe light are mixed together, it is necessary to use a filter to extract the probe light [4; 7; 17; 20; 54]. To minimize the influence of probe light on the sample fiber, the probe light power needs to be far less than the pump light power. Consequently, it is not enough to extract probe light only by the filter, and the noise produced by the pump and ASE will disturb the measurement. Therefore, a combined PD and TB measurement of an LMA Yb-doped fiber is presented by [PERSON] et al. (Figure 8) [25].  
The device introduces a lock-in amplifier to extract probe light. To ensure the same degree of PD in the sample fiber and provide convenience for temperature control, the sample fiber needs to be short enough. Interestingly, PD has been shown a reversible process by applying thermal treatment [50; 92] or ultraviolet irradiation [2] to the Yb-doped fiber. Therefore, it is possible to repeat the experiment without replacing the optical fiber sample. This device is most commonly used in PD experiment [14; 22; 25; 28; 30; 33; 37; 73]. It cannot only measure the characteristic wavelength absorption, but also can be used in absorption spectrum measurement and refractive index measurement.
## 4 Discussion
### Lessons Learned at the 3 Deployment Locations  
#### 4.1.1 SkylImager at NREL  
The NREL microgrid was located at a specialized research facility, but every attempt was made to simulate conditions at a utility. The communications network was well established; there was abundant state-of-the-art ancillary equipment such as pyranometers and an on-site weather station, and the process of deployment went relatively smoothly. Still there were important lessons to be learned. The SkylImager was initially configured with a single Raspberry Pi 2, which proved insufficient for both acquiring images and processing them through the pipeline to produce irradiance forecasts. This problem was solved by adding an Odroid C1, but this made the design more complex and required bridging between the two SBC using a USB-internet connection. The plethora of operating systems for both the SBC and EMS servers provided still another challenge. There are differences in the way open source packages such as OpenCV and Mosquito install and operate on Raspbian, Ubuntu 14, and Ubuntu 16. In some cases, there are compiled binaries available and in others software must be compiled from source files. From a solar forecasting perspective, NREL was where the best, most complete data was acquired: over six months of daily images and ground truth pyranometer observations. It took researchers over a year to analyze the data and the process is ongoing.
## 5 Conclusions
Nevertheless, after sunrise, once the fog had dissipated and transformed into low stratus, the satellite algorithm still continued to indicate fog--a typical false detection during the dissipation phase. Conversely, during the night of 20 January and the early morning of 21 January 2021, the algorithm misclassified widespread low-level clouds as fog, despite the absence of surface fog--representing a typical winter false detection case. To investigate the causes of over-detection and suggest improvements, we analyzed the ratio of hits and false alarms based on surface RH, AFTs (used to differentiate fog from clouds), and SZA. Most false detections occurred under RH! 90%, suggesting that post-processing based on surface RH thresholds could significantly reduce false alarms. Incorporating wind speed may further enhance performance for inland radiation fog events that frequently occur in autumn. In addition, improvements are needed in the 30-day minimum value composition of visible channel reflectance and the accuracy of AFTs, as proposed by [PERSON] et al. [31], through enhancing clear-sky brightness temperature fields from numerical models using dynamic bias correction. This study systematically assessed the overall detection performance of the GK2A_FDA and identified specific limitations and potential directions for improvement. The forthcoming companion paper (Part II) will present a post-processing framework that utilizes surface observations to enhance detection performance across the six sub-algorithms. Improved accuracy and reliability of GK2A_FDA across all regions and times are expected to support the practical application of fog products in hazardous weather forecasting, transportation safety, and aviation and maritime operations. **Author Contributions:** Conceptualization, H.-K.L. and M.-S.S.; methodology, M.-S.S. and H.-K.L. ; software, H.-K.L. ; validation, H.-K.L. ; formal analysis M.-S.S. and H.-K.L. ; investigation, H.-K.L. ; resources, H.-K.L. ; data curation, H.-K.L. ; writing--original draft preparation, H.-K.L. ; writing--review and editing, M.-S.S. and H.-K.L. ; visualization, H.-K.L. ; supervision, M.-S.S.; project administration, M.-S.S.; funding acquisition, M.-S.S. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was supported by the Specialized Graduate School Program for Confluence Analysis of Weather and Climate Data of the Korea Meteorological Institute (KMI), funded by the Korean government (KMA). **Data Availability Statement:** Data from GK2A/AMI level2 data, visibility data, and AWS/ASOS data are freely available from the Korea Meteorological Administration (KMA) API Hub ([[https://apihub.kma.go.kr/](https://apihub.kma.go.kr/)]([https://apihub.kma.go.kr/](https://apihub.kma.go.kr/))), accessed on 25 May 2025. **Acknowledgments:** The authors thank NMSC for providing the GK2A satellite data used in this study and the KMA for providing the ASOS/AWS station data. **Conflicts of Interest:** The authors declare no conflicts of interest.
# Near Infrared Illumination Optimization for Vein Detection: Hardware and Software Approaches
## 2 Methodology
### NIR Projection  
Different LEDs configurations were tested to compare their visualization performance depending on the parameter settings, which include LED wavelength, luminosity, and angle of illumination.
# Parameters Adjustment Optimization for Prepreg of Copper Clad Laminate Based on Virtual Metrology
## 5 Experimental Results
### VM Model Development  
Based on the collected data, 66.7% of data were randomly selected for training and the remaining 33.3% were saved for testing. According to the introduction in Section 3, \(\lambda\) and \(C\) were important parameters for LR and SVR, respectively. This research adopted 10-fold cross-validation to evaluate the trained VM model with different \(\lambda\) or \(C\). That means the training data was divided into 10 parts. For each \(\lambda\) or \(C\), one model was developed based on 9 parts, and validated by the remaining part. Following the same procedure, 10 models were developed and then validated. The level of \(\lambda\) or \(C\) with the lowest average mean absolute percentage error (MAPE) of the 10 validations was then adopted for training the VM model with all the training data.  
In this research, LR was trained based on \(\lambda\) between 0 to 2, and SVR was trained for \(C\) between 0 to 30. The training results of LR and SVR for the output value \"ratio of resin\" are illustrated in Figures 3 and 4. When \(\lambda\) = 0.1 and \(C\) = 21.1 the average MAPE of the corresponding trained LR and SVR were the lowest. Regarding LR+SVR, both \(\lambda\) and \(C\) needed to be optimized. A full factor experiment was constructed, and the results for output value \"ratio of resin\" are illustrated in Figure 5. In Figure 5, only the lowest average MAPE of each level of \(\lambda\) are shown. When \(\lambda\) = 0.05 the average MAPE was the lowest with \(C\) = 1.75. The final training and testing results are shown in Table 2.  
Figure 4: Training results of SVR with different C.  
Figure 3: Training results for lasso regression (LR) with different \(\lambda\).  
Table 2 shows the performance of the six developed VM models. It was found that using LR+SVR and taking second order terms into consideration had the best performance in training and testing results for both output variables. This research then adopted the two developed VM models for optimizing the adjustment of input variables.
# Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data
## 4 Discussion  
### Reasons for GPP Underestimation in Croplands  
Estimated GLASS GPP and the MODIS GPP product underestimated and poorly represented FLUXNET GPP, especially in croplands. Estimated GLASS GPP and MODIS GPP were much lower than FLUXNET GPP within growing seasons, but estimated GLASS GPP and MODIS GPP were higher than FLUXNET GPP in non-growing seasons. One of the reasons for these results may be that the potential LUE for crops shows no difference between C3 and C4 species in the algorithm. The C4 crop species have a larger photosynthetic capacity compared to the C3 species; thus, it is necessary  
Figure 12: Validation of estimated GLASS NPP and MODIS NPP against BigFoot NPP: **(a)** validation of estimated GLASS NPP against BigFoot NPP; **(b)** validation of MODIS NPP against BigFoot NPP. \(x\) is the average of BigFoot NPP, the years being averaged are shown in Table 1; \(y\) is the average of NPP in 2004, 2008 and 2012.  
to have accurate species-distribution potentials for LUE products in order to determine regional and global estimates of GPP [48]. Second, the height of the eddy covariance instruments, related to the spatial representativeness of the observed carbon flux data, is usually low in croplands, such as BE-Lon and US-Ne3 (about 3.5 m). Scale mismatch exists between ground observed footprint size and satellite-derived footprint size. Thirdly, the problem of mixed pixels may also have an influence on the accuracy of estimated GLASS GPP. Some other types of land cover (such as bare land, grassland and artificial land) may exist in a MODIS 1 km cropland pixel, which may lead to overestimation of FPAR and LAI in the non-growing season, and underestimation of FPAR and LAI in the growing season [49; 50]. This might also be the reason for the overestimation of GPP in the non-crop-growth season and underestimation in the crop growth season.
# A Scheme for Controlled Cyclic Asymmetric Remote State Preparation in Noisy Environment
## 2 Four-Party Controlled Cyclic Asymmetrical RSP Protocol of Sequentially Increasing Qubits States
### Experimental Realization in IBM QE  
The implementation of a particular protocol is made solely in view of the number of qubits the device supports. It provides free access through a cloud-based web-interface called IBM Quantum Experience (IBM QE), which allows researchers to design, test and run their experiments. Based on the high level of control that can be reached on the IBM digital quantum computer, we perform a proof-of-principle experimental realization of the four-party controlled asymmetric RSP scheme in IBM quantum computer. Randomness intrinsic in quantum mechanics is shown with the help of measuring an equal super position state in the computational basis for single run. The experiment is repeated for a different number of shots in the device to get the probability of obtaining each output state accurately. The single-qubit quantum gates like Identity gate(I), Pauli gates (X, Y, Z), Hardman gate (H), and phase gates (S, \(S^{\dagger}\), T, \(T^{\dagger}\)) are available in the IBM quantum experience toolbox. These gates can be inserted anywhere in the circuit utilizing a graphical user interface which allows click, drag, and drop method. In the Four-party controlled cyclic asymmetrical RSP protocol of sequentially increasing qubits states, we assume that the measurement basis are \(\ket{\Phi^{+}}_{g},\ket{\Phi^{+}}_{5},\ket{\Phi^{+}}_{1},\ket{\Psi^{+}}_{d}\), \(\ket{\Psi^{+}}_{b^{\prime\prime}},\ket{\Psi^{+}}_{c^{\prime\prime}}\), and the coefficient of measurement basis is \(a_{0},a_{1},b_{0},b_{1},c_{0},c_{1}=\frac{1}{\sqrt{2}}\). Owing to the q-sphere simulation is only available for circuits using less than 6 qubits, we suppose that the phase coefficient of measurement basis is \(\theta_{0},\theta_{1},\phi_{0},\phi_{1},\gamma_{0},\gamma_{1}=0\). We implement controlled Z gate on qubits 2, 6, and 8, and controlled X gate on qubits 3, 4, 6, 7, 8 to achieve the above scheme. The circuit diagram constructed is shown in Figure 4:  
The output state of the four-party controlled cyclic asymmetrical RSP protocol can be obtained by performing a measurement operator at the end of the lines, which in turn gives the average probability of each output state. From Figure 5, it shows the state of qubits 1,5,9,10,11,12,13 are \(\ket{0}\). In the ideal situation, the mean probabilities for the output state of qubits 2, 3, 4, 6, 7, 8 are 0.125, the output state is \(\ket{\varphi}_{234678}=\frac{1}{2\sqrt{2}}(\ket{000}+\ket{111})\otimes(\ket{00}+ \ket{11})\otimes(\ket{0}+\ket{1})\otimes(\ket{0}+\ket{1})\), and the result after running ten times in Jupyter Notebook (Figure 5b) is more stable than the result after running once
# Enhancing Social Skills in Autism Students with Augmented-Reality Picturebooks
## 3 Methodology  
### Research Design  
This study aims to understand the effectiveness of the intervention of the picturebooks combined with an augmented-reality program on the \"Interpersonal skills\" of students with ASD in an Elementary school resource class, including immediate results and maintaining effectiveness. Secondly, single-subject experimental research is a research method that is suitable for groups or individuals, especially if the researcher focuses on the behavior of a specific individual. And by manipulating independent variables, controlling extraneous variables, and replicating experimental effects, it is considered to have good internal validity [46; 47; 48]. Therefore, this study adopts the multiple-probes design across subjects of the single-subject experimental research method to understand the interpersonal interaction performance of three students with ASD, and the study participants recorded at least three baseline period data before entering the intervention period. When the baseline data of the first participant showed a stable state, the intervention period was entered, and the other two study participants remained in the baseline period and collected data on a tentative basis. After the intervention data of the first participant improved steadily, the intervention of the second participant was carried out until the intervention of the third participant, so as to prevent participants from being continuously exposed to the baseline period without intervention, which will cause their resentment and boredom [49; 50; 51]. The experimental design was divided into three stages: baseline period, intervention period, and maintenance period. The baseline period adopted the original picturebook teaching; during the interventional period, the picturebooks combined with an augmented-reality course were involved; during the maintenance period, the original picturebook teaching course was resumed, and four assessments were conducted one week after the end of the course.
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 2 Literature Review
### Communication  
Communication management is essential among vehicles and network edges, and among the distributed edges themselves. The focus on the provision of communication management systems in the context of a MEC environment is still limited. In [9], the authors proposed a publish-subscribe semantic-based MEC architecture to enable interactions at the semantic level among geo-distributed  
Figure 1: The VEC Architecture.  
edge nodes. It assumes that a publisher issues a message with a topic, and defines the class of an event pattern to which a subscribing edge node can subscribe. Among receiving edges, only interested nodes (subscribers) accept the message, and then cooperate in responding to it. A proposed domain ontology is utilized to define related concepts and relationships among them. In addition, an event-driven architecture based on active rules is used to exchange semantic resources among edges. Specifically, an active rule defines a set of actions to take in response to a specific semantic event based on the ontology. It uses a well-defined formalization based on a structure of event-condition-action rules. An event matcher captures semantic events by evaluating resources with a set of active rules, and a condition evaluator checks if a specific action can be taken upon event detection. A sequence of actions is managed by an action scheduler, and semantic events are forwarded by a knowledge propagator to interested nodes or to the cloud.  
A considerable research is focusing on load balancing in an edge-based vehicular network. In [5], the authors aim at achieving optimal load balancing, by utilizing constrained optimization particle swarm optimization algorithm. When a task is transferred from end users to the edge, it is divided into several subtasks. A subtask may be transferred to the fog node or cloud node for calculation. The node which distributes a task will also execute part of it. Results are finally merged and sent back to the end users. The problem is modeled as minimizing the maximum latency of subtasks over the network, and simulation results prove the efficiency of the proposed algorithm in latency reduction.