esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Limitations and Challenges  
Despite its potential, the proposed framework is not without constraints. While it is focused on providing a high-level conceptual framework (roadmap for administrators, policymakers of various disciplines and technical developers), detailed implementation, and technical validation were not employed as it was deemed a topic for future research. Nevertheless, many challenges should be addressed either in design or during the implementation stages. Firstly, data quality stands as a key vulnerability. Malfunctioning sensors or inconsistent data feeds risk degrading the AI's predictive accuracy. A strong system of monitoring sensor health and detecting corrupted inputs is thus essential.  
Furthermore, while advanced models like deep learning can yield strong forecasting results, they introduce interpretability challenges, making it difficult for human administrators to fully grasp the rationale behind certain AI outputs [48]. This \"black box\" effect may erode stakeholder trust if not complemented by interpretable interfaces and opportunities for human overriding.  
Security remains another challenge. Increasing the number of IoT endpoints broadens the possible attack surface, necessitating stringent encryption, authentication, and network segmentation. Although intrusion detection systems can mitigate these threats, it is an ongoing race against evolving cyberattacks [9].  
In addition, sophisticated digital infrastructure is required, both in networking and edge or cloud computing. Data integration also poses significant hurdles: effectively merging operational data, academic data, and environmental sensor data demands careful design and collaboration among campus IT teams. Also, there are concerns about algorithmic transparency and ethical considerations around pervasive monitoring. Stakeholder trust is essential, and the framework's success depends on how administrators, faculty, students, and policymakers perceive and interact with automated decision-making.  
Finally, implementing AI-based solutions incurs short-term costs in hardware acquisition, staff training, and software integration. Without strategic planning and stable funding models, institutions may find it difficult to sustain these projects, especially when immediate returns on investment are not guaranteed [64].
# Methods of Atmospheric Coherence Length Measurement
## 4 Discussion  
The atmospheric coherence length reflects the optical wavefront distortion caused by atmospheric turbulence, which is able to predict the performance of FSO links operating under conditions of atmospheric turbulence. In this paper, the measurement methods associated with the atmospheric coherence length for the FSO links were reviewed. These include the temperature structure measurement method, the scintillation method, the radar measurement method, the differential imaging method, the wavefront structure function method, the slope difference method and the wavefront phase variance method.  
The temperature structure measurement method, laser scintillation method and radar measurement method calculate the atmospheric refractive index structure parameter by measuring the temperature, scintillation index and scattering cross section, and then retrieve the atmospheric coherence length. However, the temperature structure measurement method fails to measure in high altitude. The scintillometer is appropriate for the measurement in a limited range only due to the scintillation saturation effect. The radar measurement method is only suitable for the measurement of fixed-point targets. Furthermore, it is cumbersome to deploy and the cost is huge. The measurement of \(r_{0}\) based on the SHWS has drawbacks. The non-orthogonality of the polynomial derivatives introduces modal cross-coupling, which affects the variance. The wavefront phase variance method, which has a short exposure time and a large enough sample, can increase the accuracy of the calculation. It also has some improvements to address the limitation of the aperture size of the wavefront sensor.  
As for the measurements resulting from the methods described above, differences may exist due to intrinsic defects in the employed specific instruments. The temperature structure measurement method is highly reliant on the design of the Wheatstone bridge. The authors of [25] demonstrated that the scintillometer may provide larger turbulence strength than the DIMM. This might be attributed to the APD shot noise in the scintillometer receiver, which may be coupled with a measurement path. The radar measurement method depends on the calibration accuracy [35; 36]. Noise, aliasing and modal cross-coupling are deleterious effects introduced by the SHWS, which degrade the quality of the wavefront reconstruction and limit the accuracy of the wavefront structure function method, the slope difference method and the wavefront phase variance method [45].  
Conceptualization, M.L. and P.Z.; writing--original draft preparation, P.Z.; writing--review and editing, M.L. and J.H.; supervision, M.L.; project administration, M.L.; funding acquisition, M.L. and J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant 61805176, Grant 62001327).  
Not applicable.  
The authors declare no conflict of interest.
# Nodal Accuracy Improvement Technique for Linear Elements with Application to Adaptivity
## 5 Numerical Examples
### Elastic Plane Stress Problem  
Consider the following problem with the homogeneous Dirichlet boundary condition on all boundaries.  
\[\mathbf{Lu}\equiv\left(-\mathbf{S}^{\mathsf{T}}\mathbf{DS}\right)\mathbf{u}= \mathbf{f}\ -1\leq x\leq 1\ -1\leq y\leq 1 \tag{21}\]  
where \(\mathbf{u}\) contains two displacement components, \(u\) and \(v\), corresponding to the displacements in the \(x\) and \(y\) directions, respectively. \(\mathbf{S}\) is the strain matrix, \(\mathbf{D}\) the elastic matrix and \(\mathbf{f}\) is given in terms of the given solution \(\mathbf{u}\) as follows:  
\[\mathbf{S}=\begin{bmatrix}\frac{\partial}{\partial x}&0\\ 0&\frac{\partial}{\partial y}\\ \frac{\partial}{\partial y}&\frac{\partial}{\partial x}\end{bmatrix},\ \mathbf{D}=\frac{E}{1-v^{2}}\begin{bmatrix}1&v&0\\ v&1&0\\ 0&0&\frac{1-v}{2}\end{bmatrix},\ \mathbf{f}=\begin{Bmatrix}f_{x}\\ f_{y}\end{Bmatrix},\ \mathbf{u}=\begin{Bmatrix}u\\ v\end{Bmatrix}=\begin{Bmatrix}\cos\frac{3\pi x}{2}\cos\frac{3\pi y}{2}\\ \cos\frac{3\pi x}{2}\cos\frac{\pi y}{2}\end{Bmatrix} \tag{22}\]  
For brevity, set [PERSON]'s modulus of elasticity \(E=1\) and [PERSON]'s ratio \(\
u=0.3\). Errors of nodal displacements, \(e_{i,\max}^{h},\tilde{e}_{i,\max}^{h}\), and errors of interior displacements, \(e_{\max}^{h}\), \(e_{\max}^{*}\), \(\tilde{e}_{\max}^{*}\), are presented in Tables 2 and 3, from which similar conclusions to those in Section 5.1 can be obtained. The effective index for this example is shown in Figure 8 in two component directions, and it is clear that the effective index of the EEP does not converge to unity, whereas the one of the improved EEP converges to unity rapidly.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(m\)} & \multirow{2}{*}{\(N_{e}\)} & \multirow{2}{*}{\(\mathbf{FEM}\)} & \multicolumn{2}{c}{**Improved FEM**} & \multicolumn{2}{c}{**FEM**} & \multicolumn{2}{c}{**EEP**} & \multicolumn{2}{c}{**Improved EEP**} \\ \cline{3-10}  & & \(e_{\max}^{h}\) & \(\rho\) & \(\tilde{e}_{\max}^{h}\) & \(\rho\) & \(e_{\max}^{h}\) & \(\rho\) & \(e_{\max}^{*}\) & \(\rho\) & \(e_{\max}^{*}\) & \(\rho\) \\ \hline \multirow{4}{*}{\(1\)} & \(8\times 8\) & \(9.68\times 10^{-2}\) & - & \(1.12\times 10^{-2}\) & - & \(2.27\times 10^{-1}\) & - & \(1.30\times 10^{-1}\) & - & \(4.65\times 10^{-2}\) & - \\  & \(16\times 16\) & \(2.40\times 10^{-2}\) & 2.01 & \(7.20\times 10^{-4}\) & 3.96 & \(6.31\times 10^{-2}\) & 1.85 & \(2.62\times 10^{-2}\) & 2.31 & \(2.54\times 10^{-3}\) & 4.19 \\  & \(32\times 32\) & \(6.24\times 10^{-3}\) & 1.95 & \(4.58\times 10^{-5}\) & 3.98 & \(1.70\times 10^{-2}\) & 1.89 & \(6.31\times 10^{-3}\) & 2.05 & \(1.48\times 10^{-4}\) & 4.10 \\  & \(64\times 64\) & \(1.55\times 10^{-3}\) & 2.01 & \(2.91\times 10^{-6}\) & 3.98 & \(4.32\times 10^{-3}\) & 1.97 & \(1.56\times 10^{-3}\) & 2.01 & \(1.27\times 10^{-5}\) & 3.54 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Convergence orders of displacement component in \(x\) direction.  
Figure 7: Error distributions.
# Driver Fatigue Detection Based on Residual Channel Attention Network and Head Pose Estimation
## 4 Experiments and Results  
### Dataset  
This paper uses four datasets for training the RCAN and evaluating its performance. Table 2 shows all the datasets used in this study. The first dataset is CEW [31]. The authors of the CEW dataset collected 4846 eye images, which included 2384 open eye images and 2462 closed eye images. The size of these images is 24 \(\times\) 24. Therefore, the images need to be scaled to 56 \(\times\) 56 to adapt to the input size of the RCAN. This dataset has no image data of the mouth (open/close).  
The second dataset is DROZY [32]. The DROZY dataset includes 36 video sequences in which volunteers are in the state of drowsiness. We transformed these video sequences into 6210 images as a dataset that includes the eye and mouth states.  
The third dataset is YawDD [33]. The authors of the YawDD dataset collected a total of 351 video sequences that simulated various characteristics of fatigue driving. These video sequences contain the real-time states of eyes and the mouth. Therefore, we transformed these video sequences into images frame by frame and collected 5510 eye state images and 4925 mouth state images. Then we annotated the obtained dataset. The eye state dataset has 3009 open eye images and 2501 closed eye images. The mouth state dataset has 2874 open mouth images and 2051 closed mouth images.  
The last dataset is a self-built dataset named simulated driver fatigue (SDF). We gathered 20 volunteers to simulate fatigue driving in a real driving environment. Each person simulated three driving fatigue states: yawning, blinking frequently, and closing the eyes for a long time. The SDF dataset obtained 18,209 annotated eye images and 9772 annotated mouth images by clipping the video frames in the dataset. In addition, SDF contains 10 one-min videos simulating the change of the driver's head posture. The video annotated the frame when the Euler angle of the driver's head deflected too much and annotated it as over-angle. This study selects the over-angle according to the SDF dataset. Figure 5 shows examples of all datasets this study used.
# Designed on 0.18 um CMOS Process Small Size Broadband Millimeter Wave Chip Antenna
## 2 Materials and Methods  
Figure 1 is a top view of the chip antenna proposed in this article, designed using a 0.18 \(\upmu\)m CMOS process, which shown as Figure 1. This antenna composed of two triangular patches and a 1 \(\times\) 2 microstrip line power divider. We used a GSG feed structure at the feed line, which is connected to Metal\(\_\)1. For grounding, adding a circular groove design at the grounding point can achieve better impedance matching. The design of the trenching on the triangular patch in the picture is to comply with the limitations of the design rules. Figure 2 shows the 0.18 \(\upmu\)m CMOS process stack architecture, with a silicon dioxide layer thickness of 10.34 \(\upmu\)m, and a dielectric coefficient of 4. The silicon substrate thickness is 543.5 \(\upmu\)m and the dielectric coefficient is 11.9. The triangular patch antenna and microstrip line power divider are implemented using a sixth metal layer.  
Figure 1: The top view of proposed chip antenna.  
Figure 3 and the geometric diagram of the chip antenna proposed in this article. Table 1 shows the dimensions of the geometric parameters of each part of the chip antenna. The reflection coefficient, gain, and efficiency of the chip antenna are simulated, shown in Figures 4-6, respectively. The minimum reflection coefficient of chip antenna simulated at 64 GHz, with bandwidth of 57.6-93.8 GHz at the \(-10\) dB standard. The maximum gain and efficiency are \(-0.4\) dBi and 40.9%, respectively, at 64 GHz.  
Figure 3: Proposed chip antenna geometry.  
Figure 2: The antenna stack architecture of proposed chip antenna.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value (\(\mu\)m)** & **Parameter** & **Value (\(\mu\)m)** \\ \hline W & 1200 & L & 1200 \\ W\({}_{1}\) & 450 & L\({}_{1}\) & 50 \\ W\({}_{2}\) & 200 & L\({}_{2}\) & 400 \\ W\({}_{3}\) & 10 & L\({}_{3}\) & 350 \\ W\({}_{4}\) & 12 & L\({}_{4}\) & 15 \\ W\({}_{5}\) & 110 & L\({}_{5}\) & 20 \\ W\({}_{6}\) & 40 & L\({}_{6}\) & 20 \\ W\({}_{7}\) & 20 & L\({}_{7}\) & 140 \\ W\({}_{8}\) & 10 & L\({}_{8}\) & 75 \\ W\({}_{9}\) & 50 & L\({}_{9}\) & 45 \\ W\({}_{10}\) & 70 & L\({}_{10}\) & 90 \\ W\({}_{11}\) & 250 & L\({}_{11}\) & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the proposed chip antenna.  
Figure 4: Simulated S11 of the antenna.  
Figure 5: Simulated antenna gain.
# A Review on Millimeter-Wave Hybrid Beamforming for Wireless Intelligent Transport Systems
## 2 Literature Review
** & **Algorithm/Method** & **Year** & **Reference** & **Performance Metrics** & **Characteristics** & **Limitations** & **Performance Evaluations** \\ \hline \multirow{4}{*}{1} & Environment- & & & The research introduces an & Especially in scenarios with & The proposed environment-aware hybrid beamforming technique \\  & & & & environment-aware hybrid & changing propagation & hybrid beamforming technique \\  & Environment- & & & pseudorming technique for & conditions of dynamic user & based on CKM demonstrates \\  & & & & height Spectral efficiency & mmWave massive MIMO & locations, there is a necessity for & significant improvements in \\  & & & & & & further research to optimize the & effective communication rates, \\  & & & & & & CGM generation process and & even under moderate user \\  & & & & & reduce real-time training & address potential inaccuracies or & location errors, compared to \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline \multirow{4}{*}{2} & Hybrid & & & The research presents a novel & sum-power and practical & superior performance over \\  & beamforming & & & hybrid beamforming design & in the design require validation & half-duplex systems with limited \\  & design to maximize & 2022 & & & for millimeter-wave & under different network & optimized WSR considering \\  & the weighted sum & & & full-duplex (FD) systems that & configurations and channel & practical hardware impairments, \\  & rate (WSR) & & & maximizes the WSR. & conditions to assess the impact & point power constraints, and an \\  & & & & & on system performance and & optimal power allocation scheme. \\ \hline \multirow{4}{*}{3} & Full-duplex & & & & The joint optimization & \\  & integrated sensing & & & & analog/digital (\(\Lambda\)/D) & The research demonstrates & accurate radar target estimation, \\  & & & & & beamformers and & high DL communication rates, \\  & and & & & & & to maximize Downlink (DL) & and precise sensing of multiple \\  & communication & & & & communication rates and & transmission rates and \\  & & & & power & frequency. & improve radar target sensing & framework for \(\Lambda\)/D beamforming \\  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**S. No. ** & **Algorithm/Method** & **Year** & **Reference** & **Performance Metrics** & **Characteristics** & **Limitations** & **Performance Evaluations** \\ \hline  & IRS-aided & & & & & The research demonstrates that the proposed joint design of IRS reduction matrices and hybrid beamforming architectures for mmWave MIMO \\  & HNWave & & & & & \\  & multiple-input & & & & & \\  & HNO systems. & & & & & \\  & HNO systems. & & & & & \\  & HNO systems. & &
# Experimental Study on Phase Scintillation of Optical Transmission in Atmospheric Turbulence
## 3 Experimental Measurements
### Wavefront Phase Evaluation Metrics
As the distance increases, the _PV_ and _RMS_ values of the wavefront under different weather conditions also increase, indicating that the distortion becomes more severe with distance. Figure 8: _PV_ values of wavefront for samples from 3 links. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Maximum _PV_ Value of the** & **Minimum _PV_ Value of the** & **Mean _PV_ Value of the** \\  & **Wavefront (\(\mu\)m)** & **Wavefront (\(\mu\)m)** & **Wavefront (\(\mu\)m)** \\ \hline T1 & 161.94 & 34.14 & 57.23 \\ T2 & 71.76 & 49.78 & 61.82 \\ T3 & 58.91 & 43.11 & 52.85 \\ T4 & 59.35 & 52.34 & 53.84 \\ T5 & 85.62 & 46.18 & 70.03 \\ T6 & 87.69 & 62.46 & 68.11 \\ T7 & 112.99 & 67.87 & 88.11 \\ T8 & 181.54 & 77.02 & 129.41 \\ T9 & 75.45 & 55.38 & 74.19 \\ T10 & 88.42 & 60.32 & 73.46 \\ T11 & 91.25 & 60.05 & 72.09 \\ T12 & 76.29 & 62.38 & 66.15 \\ T13 & 73.68 & 61.05 & 65.52 \\ T14 & 65.07 & 54.34 & 56.56 \\ T15 & 51.62 & 46.80 & 49.39 \\ T16 & 92.63 & 65.74 & 72.15 \\ T17 & 89.57 & 71.86 & 74.15 \\ T18 & 124.77 & 111.83 & 117.41 \\ T19 & 117.75 & 78.79 & 105.85 \\ T20 & 652.67 & 56.45 & 96.47 \\ T21 & 305.04 & 44.56 & 92.72 \\ T22 & 375.99 & 50.75 & 82.70 \\ T23 & 288.09 & 56.07 & 81.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical distribution of _PV_ values of the wavefront for measured samples. Figure 9: _RMS_ values of wavefront for samples from 3 links. This pattern is also observed in links 2 and 3. From Figures 8a and 9a, it is observed that, under heavy rain conditions, the measured samples T7 and T8 show abrupt changes in the \(PV\) and \(RMS\) values at around 420 and 800 sampling points. The \(PV\) values increase from 90 \(\upmu\)m and 100 \(\upmu\)m to 110 \(\upmu\)m and 150 \(\upmu\)m, respectively, and the \(RMS\) values increase from 23 \(\upmu\)m and 25 \(\upmu\)m to 27 \(\upmu\)m and 40 \(\upmu\)m. The causes of this phenomenon are as follows. Raindrops can cause significant turbulence in the air, disrupting the uniformity of the light wavefront and leading to higher values. In heavy rain, this effect is more pronounced due to the larger density and size of the raindrops. Dense raindrops can cause multiple absorption and scattering events, inducing and amplifying the phase shift of the light signal, leading to higher values and more distorted wavefronts. The intensity of heavy rain frequently fluctuates, causing rapid changes in the refractive index along the optical path, resulting in abrupt changes in the wavefront. In summary, atmospheric turbulence, scattering, absorption, and dynamic changes in the intensity of heavy rain collectively affect the light wavefront, leading to significant abrupt changes in its values. These factors interact and complexly interfere with signal transmission in optical wireless communication systems, exacerbating wavefront distortion.
# A Systematic Review of Cybersecurity Risks in Higher Education
## 8 Vulnerabilities in HE
### Technical Vulnerabilities in HE
#### 8.2.1 Bring Your Own Device (BYOD)  
_Bring your own device_ is the standard for HE, where both students and employees connect their privately-owned devices to the network [50]. BYOD problems occur when the network has an inappropriate security architecture, and poorly implemented access control mechanisms. Typically, trust and authorization in the network will be lacking, meaning that when a person's device is authenticated on the network, it obtains access to most or all of the resources without adequate security mechanisms in place. Security zones are either weak or non-existent, and BYODs roam the network. [PERSON] and [PERSON] [50] discuss the implications of BYOD in HE: Protection against devices that are infected with viruses, primarily through downloading unauthorized content and accessing malicious websites and bringing the compromised device inside the network.  
FireEye [5] describes a few of the implications, such as no patch-level awareness for the devices in the network; devices can enter the network being unpatched for months or years, and administrators cannot force patching. Both FireEye, and [PERSON] and [PERSON] argue that in most cases, central IT cannot take responsibility for the security of personal BYODs. Poor network oversight leads to the problem that campus IT has no way of knowing which devices should and should not be connected to the network. Furthermore, FireEye writes that these issues _\"add up to massive endpoint environments with little or no control over device-level security\"_[5]. They point to the lack of device registrations and basic protection mechanisms which means that anyone can connect to the university network. Problems with BYOD are enhanced technical support, network overload and security issues.
# Machine Learning-Assisted Adaptive Modulation for Optimized Drone-User Communication in B5G
## 1 Introduction
### Related Work
Unlike supervised or unsupervised algorithms, RL schemes learn by interacting with the environment. In [27], the authors first proposed the possibility of applying RL in achieving AM at a given Signal-to-Noise Ratio (SNR). In [28], the authors propose an RL-based algorithm for link adaptation in orthogonal frequency division multiplexing (OFDM). In a similar work, AM is considered in a 5G environment in [29]. Unlike in [28; 29], the authors propose a Neural Network-based extension of an RL scheme called Deep RL to achieve AM in the heterogeneous cognitive network in [30].
# Evaluation of IEEE802.15.4g for Environmental Observations
## 4 Analysis
### Urban Canyon Scenario  
High data rates with high PDR values are reached up to 540 m away from the TX node. The maximum length of the radio link is at 779 m, with important losses at 602 m and 742 m due to obstacles and the topography of the scenario.
## 3 DL Models for Ocean Studies
### Vit
Unlike CNNs, which require regularly gridded input data, GNNs can flexibly represent observations as graphs, where nodes correspond to observation points and edges incorporate spatial proximity or learned relationships [93]. This flexibility makes GNNs well suited for irregular spatial data processing, and they have been successfully applied to various ocean-related tasks, such as modeling large-scale ocean circulation and forecasting SST or MHWs [94; 95]. For instance, researchers have modeled SST as spatiotemporal graphs [96], where each node represents an observation point defined by a specific latitude and longitude. The node features consist of historical SST time series and other auxiliary variables, such as wind speed or atmospheric pressure, capturing the temporal evolution of the local environment. The edges encode spatial relationships between nodes, such as geographical proximity or physical connectivity. This graph-based network allows the model to leverage the structural information of a graph, which is beneficial for capturing spatial dependencies and modeling regional interactions. In practice, researchers often combine multiple architectures to harness their complementary capabilities; for example, coupling a CNN with an RNN to extract spatial patterns and temporal dependencies simultaneously [97] or incorporating Transformers alongside CNNs for hybrid local-global feature extraction. Such integrated approaches can provide more robust and accurate models to tackle complex ocean and climate change challenges.