esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Deploying and effectively utilizing wireless sensor networks (WSNs) in underwater habitats remains a challenging task. In underwater wireless sensors networks (UWSNs), the availability of a continuous energy source for communicating with nodes is either very costly or is prohibited due to the marine life law enforcement agencies. So, in order to address this issue, we present a Q-learning-based approach to designing an energy-efficient medium access control (MAC) protocol for UWSNs through collision avoidance. The main goal is to prolong the network's lifespan by optimizing the communication methods, specifically focusing on improving the energy efficiency of the MAC protocols. Factors affecting the energy consumption in communication are adjustments to the interference ranges, i.e., changing frequencies repeatedly to obtain optimal communication; data packet retransmissions in case of a false acknowledgment; and data packet collision occurrences in the channel. Our chosen protocol stands out by enabling sensor (Rx) nodes to avoid collisions without needing extra communication or prior interference knowledge. According to the results obtained through simulations, our protocol may increase the network's performance in terms of network throughput by up to 23% when compared to benchmark protocols depending on the typical traffic load. It simultaneously decreases end-to-end latency, increases the packet delivery ratio (PDR), boosts channel usage, and lessens packet collisions by over 38%. All these gains result in minimizing the network's energy consumption, with a proportional gain.  
energy efficiency; low collision; MAC protocol; Q-learning; underwater wireless communication; underwater wireless sensor networks +
Footnote †: journal: Electronics Electronics  
02024
# Fast Ferroelectric Liquid Crystal Based Optical Switch: Simulation and Experiments
## 2 Experiments  
The experiments were conducted using the commercial smectic mixture KCFLC 10R from Kingston Chemicals Limited. The mesophases and transition temperatures while cooling down from isotropic phase were SmC* 62 \({}^{\circ}\)C, SmA* 97 \({}^{\circ}\)C, N* 107 \({}^{\circ}\)C Iso. The homogeneously aligned cells of thicknesses varying from 2 \(\upmu\)m to 25 \(\upmu\)m were used for the measurements. The cell thickness was estimated by transmittance measurements using Avaspec-2048 spectrometer (Avantes, Louisville, USA). The RN-1175 polymer (Nissan Chemicals, Tokyo, Japan) is used to obtain perfect homogeneous alignment. This thermo aligning polymer aligns nematic [30; 31; 32] and ferroelectric [33; 34; 35; 36] LC molecules homogenously in cells. The spin coated (3000 rpm, 30 s) Indium Tin Oxide (ITO) glass substrates were baked at 250 \({}^{\circ}\)C for an hour to generate planar alignment by evaporating excess solvent. The FLC cells were rubbed anti-parallel and sealed with epoxy on two sides.  
The molecular tilt angle (\(\theta\)) was measured by rotating a microscopic stage between \(+\theta\) and \(-\theta\) by the application of electric fields. The contrast ratio is the property, which determines the quality of a device, and was measured using stabilized He-Ne laser emitting at a wavelength (\(\uplambda\)) of 632.8 nm. The transmitted light intensity was captured by a photo detector and output signal was recorded on a dual-trace storage oscilloscope (Tektronix, Model TDS 2014). A square wave signal was applied to the sample at a driving frequency 110 Hz, which was obtained from a function generator (Agilent, Model 33120A). Figure 1 shows the schematic of the experimental set-up used for FLC physical parameter measurements.  
Figure 1: Schematic representation of the implemented electro-optic measurement technique.
# Perceptual Quality of Audio-Visual Content with Common Video and Audio Degradations
## 9 Discussion and Comparison among Experiments
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Condition** & **st. dev.** & **\(t\)** & **df** & **\(\rho\)-Value** & **95\% CI** \\ \hline V-TC0 & 0.543 & \(-\)0.850 & 126 & 0.397 & [\(-\)0.272, 0.109] \\ V-TC1 & - & - & - & - & - \\
**V-TC2** & **0.454** & **4.912** & **168** & **0.000** & **[0.214, 0.502]** \\ V-TC3 & 0.622 & 1.280 & 168 & 0.202 & [\(-\)0.069, 0.325] \\ V-TC4 & - & - & - & - & - \\ V-TC5 & **0.650** & **4.708** & **162** & **0.000** & **[0.288, 0.704]** \\ V-TC0 & **0.376** & **2.533** & **128** & **0.013** & **[0.037, 0.299]** \\ V-TC6 & - & - & - & - & - \\
**V-TC7** & **0.477** & **5.919** & **164** & **0.000** & **[0.304, 0.608]** \\ V-TC8 & - & - & - & - & - \\ V-TC9 & - & - & - & - & - \\
**V-TC10** & **0.561** & **5.883** & **194** & **0.000** & **[0.340, 0.683]** \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Two-sample \(t\)-test analysis between equivalent conditions from Experiment 1 and Experiment 3 (st. dev. : Standard deviation, \(t\): Test statistic, df: Degrees of freedom). Conditions with statistically significant differences are highlighted.
# Augmented Lagrangian-Based Reinforcement Learning for Network Slicing in IIoT
## 2 System Model and Problem Formulation  
This section firstly presents the network model of network slicing and transmission rate model. Then, considering various requirements of different network slices, the con straints of different types of devices are developed. Finally, a constrained mixed integer nonlinear problem is formulated.
# An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels
## 4 Experiments and Analysis
### Comparison with Various SOTA Losses
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**The Weight of APL**} & \multicolumn{4}{c}{**The Type of APL**} \\ \cline{2-5}  & **NCE + RCE** & **NCE + MAE** & **NFL + RCE** & **NFL + MAE** \\ \hline \(\alpha=0.1,\ \beta=0.1\) & 90.81 \(\pm\) 0.66 & 90.75 \(\pm\) 1.36 & 91.10 \(\pm\) 0.62 & 90.05 \(\pm\) 0.18 2 \\ \(\alpha=0.1,\ \beta=1\) & 91.01 \(\pm\) 0.66 & 91.67 \(\pm\) 0.79 & 91.86 \(\pm\) 1.07 & 92.07 \(\pm\) 0.54 1 \\ \(\alpha=0.1,\ \beta=10\) & 92.33 \(\pm\) 0.67 & 90.70 \(\pm\) 0.42 & 91.52 \(\pm\) 1.10 & 90.87 \(\pm\) 0.59 \\ \(\alpha=0.1,\ \beta=100\) & 89.91 \(\pm\) 0.67 2 & 91.59 \(\pm\) 1.48 & 91.24 \(\pm\) 0.13 & 90.77 \(\pm\) 0.39 \\ \(\alpha=1,\ \beta=0.1\) & 92.56 \(\pm\) 0.31 1 & 91.34 \(\pm\) 2.49 & 91.39 \(\pm\) 0.13 & 91.15 \(\pm\) 1.94 \\ \(\alpha=1,\ \beta=1\) & 90.12 \(\pm\) 0.50 & 91.29 \(\pm\) 0.23 & 90.57 \(\pm\) 0.51 & 91.61 \(\pm\) 0.68 \\ \(\alpha=1,\ \beta=10\) & 91.67 \(\pm\) 0.93 & 91.76 \(\pm\) 1.31 1 & 90.00 \(\pm\) 0.71 2 & 91.33 \(\pm\) 1.08 \\ \(\alpha=1,\ \beta=100\) & 91.74 \(\pm\) 0.29 & 90.48 \(\pm\) 0.95 & 91.43 \(\pm\) 0.98 & 90.76 \(\pm\) 0.64 \\ \(\alpha=10,\ \beta=0.1\) & 91.10 \(\pm\) 1.67 & 88.73 \(\pm\) 1.84 2 & 90.88 \(\pm\) 2.83 & 90.23 \(\pm\) 0.26 \\ \(\alpha=10,\ \beta=1\) & 92.43 \(\pm\) 1.26 & 91.68 \(\pm\) 0.51 & 91.88 \(\pm\) 0.41 1 & 91.95 \(\pm\) 0.62 \\ \(\alpha=10,\ \beta=10\) & 91.23 \(\pm\) 1.19 & 91.64 \(\pm\) 1.04 & 91.82 \(\pm\) 0.74 & 90.42 \(\pm\) 1.41 \\ \(\alpha=10,\ \beta=100\) & 91.28 \(\pm\) 1.45 & 90.51 \(\pm\) 1.54 & 90.94 \(\pm\) 1.37 & 91.12 \(\pm\) 0.85 \\ \hline \hline \end{tabular}  
* * represents the highest retrieval precision, and superscripts 1 and 2 represent the highest and lowest retrieval performance of the 12 manually weighted combinations of \(\alpha\) NCE + \(\beta\) RCE, respectively.  
\end{table} 
Table 5: mAPs (%) (mean \(\pm\) standard deviation) on the UCMD dataset with 20% noise using 4 types of APLs. As shown in Table 5, the four APL combinations also have different weighting parameters to achieve optimality, and our method still outperforms their 12 manually weighted combinations. Additionally, our method outperforms the best manually weighted methods by 0.67-4.42% and other robust loss methods by 0.4-2.56%. It can be seen from Table 6 that our improved APL methods only yield a slight improvement by 0.4-0.98% over the GCE method. However, it is important to note that the GCE method requires manual determination of a hyperparameter in the (0, 1] interval, and unfortunately, the literature [14] does not provide a method for parameter selection. Compared to the ACE method, which also adaptively determines the hyperparameter, our improved APL methods show a more significant improvement of 1.85-2.43%. Moreover, our method is also superior to asymmetric loss functions such as AUL and AEL. The above results indicate that our weighted learning method is suitable for all types of APL methods and yields superior retrieval outcomes compared to other robust methods.
# A Quantitative Study of Axial Performance of Rockbolts with an Elastic-Debonding Model
## 4 Parameter Study and Discussion
### Rock Diameter
In this article, the installed length of  
Figure 3: Axial performance of rockbolts when the rock diameter varied: (**a**) force-deformation curves; (**b**) peak force variation trend. rockbolts was 2 m. Therefore, the installed length of rock anchors was diverse. The longer the installed length, the higher the tensile capacity of the bolting system. Higher tensile capacity of the bolting system led to the consequence that more cross-section area of rocks would be influenced by rockbolts. The second reason was that [PERSON] [26] tested bolts with relatively smaller diameters. In tests conducted by [PERSON] [26], cable bolts only had a small diameter of 15.2 mm. However, in this article, the tested rockbolt had a large diameter of 16 mm, which was larger compared with cable bolts tested by [PERSON] [26].
## 4 Integration of AKMA and the TLS 1.3 PSK Option for the AKMA Authentication Phase
### TLS 1.3 PSK Option as the \(\text{Ua}^{*}\) Protocol
At this point, in the case of the PSK-(EC)DE or 0-RTT modes, the UE executes the (EC)DE key exchange to obtain \(HS\) = HKDF-EXTRACT(\(x\cdot y\cdot G\), Derive-Secret(\(ES\), \"derived\", \"\")). Otherwise, the \(HS\) is computed through HKDF-EXTRACT(0, Derive-Secret(\(ES\), \"derived\", \"\")). Next, the UE decrypts the received [Finished] into the Server_Finished using the \(tk_{shs}\) and subsequently verifies the correctness of the Server_Finished with the \(fk_{s}\). If the verification is positive, the UE successfully authenticates the AF and the ServerHello sub-message. Based on this belief, it extracts the ServerHello extensions and the Application Data2 using the AEAD\({}_{dec}\) operation with the \(tk_{shs}\) and the \(tk_{sapp}\), respectively. Finally, the UE generates two protected values: [Finished] = AEAD\({}_{enc}\)(\(tk_{chs}\), AD3, Client_Finished) and [Application Data3] = AEAD\({}_{enc}\)(\(tk_{capp}\), AD3, Application Data3). It then composes the ClientFinish sub-message with these results and sends it to the AF within the Application Session Communication message. At this point, in the case of the 0-RTT mode, it is necessary to add the 'EndOfEarlyData' value to the ClientFinish sub-message to indicate that the \(tk_{cets}\) will no longer be used. Given the Application Session Communication message, the AF checks if the Client_Finished is valid using the \(fk_{c}\) after extracting that value through AEAD\({}_{dec}\)(\(tk_{chs}\), AD3, Client_Finished). In a positive case, the UE is authenticated to the AF. Moreover, it applies AEAD\({}_{dec}\)(\(tk_{capp}\), AD3, Application Data3) to extract the Application Data3. From this point, the UE and AF securely exchange their subsequent application data in the TLS 1.3 record protocol.
## 3 Result
### Experiment Results
#### 3.1.1 Eye-Tracker Results  
An eye-tracker recorded subjects' eye movements, capturing data like pupil data collection length and mean fixation duration. Pupil data acquisition duration consistently aligned with the video duration, serving as a metric for subjects' attentional focus (Figure 10).  
The first experiment had a longer duration than the second. Subjects' visual attention varied based on the experimental task, focusing on the overall environment in one and the guidance sign in the other. This highlights the dynamic nature of visual attention during the evaluation process, emphasizing the importance of an evaluation scheme that considers travelers' visual attention.  
The first experiment lasted longer than the second. Subjects' visual attention shifted between the overall environment and the guidance sign based on the task. This underscores the dynamic nature of visual attention during evaluation, highlighting the need for an evaluation scheme that considers travelers' visual focus.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Item** & **Variable** & \multicolumn{2}{c}{**Percentage (\%)**} & \multicolumn{2}{c}{**Item**} & \multicolumn{2}{c}{**Variable**} & \multicolumn{2}{c}{**Percentage (\%)**} \\  & & **Test1** & **Test2** & & & **Test1** & **Test2** \\ \hline \multirow{2}{*}{Gender} & Male & 80.77 & 70.97 & \multirow{2}{*}{Education} & Secondary and lower & 3.85 & 0 \\  & Female & 19.23 & 29.03 & & Post-secondary & 96.15 & 100 \\ \hline \multirow{4}{*}{Age} & \textless{}20 & 11.54 & 12.9 & \multirow{4}{*}{Transport related} & Yes & 57.69 & 61.29 \\  & 20–35 & 84.62 & 80.65 & & & & \\  & 35–50 & 0 & 3.33 & & No & 42.31 & 38.71 \\  & \(\geq\)50 & 3.85 & 3.33 & & & & \\ \hline \multirow{2}{*}{Frequency} & \textgreater{}6 & 0 & 0 & \multirow{2}{*}{Frequency} & 1–3 & 26.92 & 6.45 \\  & 3–6 & 7.69 & 0 & & 0 & 63.58 & 93.55 \\ \hline \multirow{4}{*}{Purpose} & Business & 7.69 & 0 & \multirow{4}{*}{Wayfinding mode} & Signage & 84.62 & 93.55 \\  & Traveling & 7.69 & 12.9 & & Navigation & 50 & 61.29 \\ \cline{1-1}  & Visiting & 11.54 & 6.45 & & Inquiry & 3.85 & 12.90 \\ \cline{1-1}  & Pickup & 3.85 & 3.23 & & Signage and navigation & 42.31 & 58.06 \\ \cline{1-1}  & Others & 73.08 & 77.42 & & Others & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic Attributes of Subjects.
# A Review of Industry 4.0 Manufacturing Process Security Risks
## 3 Identifying the Problem
### Types of Cyber-Physical Attacks
#### 3.2.5 Replay Attacks  
Even though authentication mechanisms can to a great extent prevent malicious commands from being executed by equipment that is targeted through cyber-attacks, an authenticated data packet can be retransmitted, but with modified data or instructions. Since the data packet appears to have a legitimate origin, it is still possible that such a packet can be altered so that it is transmitted to and processed by electronic equipment with no suspicion of malicious intent.  
Replay attacks can be avoided by incorporating a tracking mechanism, such as the use of a sequence number, to detect packets that have already been processed but have been retransmitted with possibly malicious commands and data. These packets could pass authenticity verification and execute the attack for which they were formulated.3.2.6. Side-Channel Attacks  
Side-channel attacks entail the collection of data due to information leakage by industrial equipment. For example, fluctuations in power usage due to processing data can leak out and provide attackers with valuable information on the inner workings of the system. More sophisticated attacks can be carried out by means of an in-depth monitoring of industrial manufacturing equipment, such as monitoring the positional characteristics of a robotic arm during production in order to formulate a near-precise reproduction of the manufacturing instructions sent to the robotic arms.  
With smartphones constantly evolving into highly advanced and increasingly complex devices, the possibilities of extending the platforms on which side-channel attacks are performed using smartphones are on the increase. Studies [34; 35] are already in progress investigating the effect of smartphone-based side-channel attacks on 3D printers using the phone's built-in sensors. The focus of these studies has primarily been on acoustic and magnetic side-channel attacks [36; 37].  
The aforementioned attacks are by no means an exhaustive and complete list. Numerous different techniques exist that cyber attackers can use to attack cyber-physical systems. It can also be deduced that many of the known attacks found in the IT network domain can be equally threatening to devices in the OT network domain.
## 4 Result and Performance Assessment of Models
### R Curve  
The performance curve is the curve that shows whether the models follow the trend of the reference models. This curve gives an R-value (coefficient of correlation) calculated and given in the table (Table 1). Figure 4 shows the performance curve, and it can be seen that all the three models overlap each other and follow the same trend approximately. A slight deviation of data can be observed in the EmNN model, and it is clear from other criteria as well that EmNN did not perform that well.  
Figure 3: REC curve.  
Figure 2: Taylor diagram for (**a**) SOS–LSSVM, (**b**) EmNN, and (**c**) MARS method.
# Edge AI for Real-Time Anomaly Detection in Smart Homes
### Research Problem and Objectives  
Despite the advantages of Edge AI, developing efficient anomaly detection models suitable for resource-constrained edge devices remains challenging. Many existing models are computationally intensive and require substantial memory, making them impractical for deployment on typical smart home devices. Furthermore, the dynamic and heterogeneous nature of smart home environments complicates modelling normal behavior patterns, increasing the risk of false positives or negatives in anomaly detection.  
This research addresses these challenges by developing a lightweight, real-time anomaly detection framework optimized for edge deployment in smart homes. The specific objectives are as follows:  
1. Design an efficient anomaly detection architecture that leverages Edge AI to process data locally, reducing latency and preserving privacy.
2. Develop and implement lightweight machine learning models suitable for deployment on resource-constrained devices commonly found in smart homes.
3. Evaluate the proposed framework using simulated IoT data representative of typical smart home environments to assess its effectiveness in detecting anomalies.
# Electrically Small Water-Based Hemispherical Dielectric Resonator Antenna
## Appendix B
### Scattering Analysis of Water Sphere  
The solution to a plane wave incidence on a dielectric sphere is well known [29], and it was studied in a previous work [30]. The configuration is shown in Figure 11(a). The amplitude coefficients are [29]  
\[a_{n}=\frac{\varepsilon_{\text{r,w}}j_{n}(k_{\text{w}}r_{\text{w}})j_{n}{}^{ \prime}(k_{\text{w}}r_{\text{w}})-j_{  
The absorption, extinction, and scattering efficiencies were used to describe the response of the water sphere, and they are related by \(Q_{\mathrm{abs}}=Q_{\mathrm{ext}}-Q_{\mathrm{sca}}\), where  
\[Q_{\mathrm{sca}} =\frac{2}{\left(k_{0}r_{\mathrm{w}}\right)^{2}}\sum_{n=1}^{\infty }(2n+1)\Big{(}|a_{n}|^{2}+|b_{n}|^{2}\Big{)} \tag{10}\] \[Q_{\mathrm{ext}} =\frac{2}{\left(k_{0}r_{\mathrm{w}}\right)^{2}}\sum_{n=1}^{\infty }(2n+1)\mathrm{Re}\{a_{n}+b_{n}\}. \tag{11}\]  
The spectrum of the scattering and absorption efficiencies are shown in Figure 10(b) for a sphere with \(r_{\mathrm{w}}=55.18\) mm and a water temperature of 20 \({}^{\circ}\)C. Peaks at 300 and 430 MHz can be seen, caused by the magnetic dipole (\(b_{1}\)) and electric dipole (\(a_{1}\)) resonances, respectively. These were the resonances that we utilized in the antennas. We found that 32% (68%) of the extinct power was scattered (absorbed) at the magnetic dipole resonance frequency, which was very similar to the efficiency found for the magnetic dipole antenna.
## 4 Results and Discussion
### Statistics of SAR-Derived Parameters
#### 4.1.1 SAR NRCS, Incidence Angle and SSW Relative Direction  
Table 1 collects helpful statistics at various ROI dimensions. In particular, mA_ROIi and sA_ROIi represent respectively the mean and the standard deviation of the generic SAR-derived input parameter A, which is, in turn, the NRCS, the incidence angle and the MS LG-Mod SSW relative direction, evaluated within the i-th ROI. The abbreviations min, max, mean and std stand for minimum, maximum, mean and standard deviation values  
Figure 2: The scheme of the proposed methodology for the estimation of Synthetic Aperture Radar (SAR) Sea Surface Wind (SSW) speed and its uncertainty, and the Multi-Scale (MS) Local Gradient-Modified (LG-Mod) processing flowchart.  
computed considering all the analysed N_ROI cells. For the actual S-1 dataset, tabled statistics suggest that:  
1. The MS LG-Mod SSW relative direction, expressed in the range [0\({}^{\circ}\), 360\({}^{\circ}\)], is on average about 184\({}^{\circ}\), with a small variation (growing) of standard deviation depending on the examined ROI dimension. This means that the visual selection of the S-1 regions characterised by clearly visible wind rows yielded to this prevailing wind direction. This fact confirms that, according to [44], wind rows detection depends strongly on the azimuth angle, with patterns detection rates at crosswind (\(\varphi\) = 90\({}^{\circ}\)/270\({}^{\circ}\)) being much lower (i.e., 3-10 times) than for upwind (\(\varphi\) = 0\({}^{\circ}\)) or downwind (\(\varphi\) = 180\({}^{\circ}\)). As regards the uncertainty associated with the SSW relative direction (by the MS LG-Mod), both the precision and the accuracy are decreasing (improving) with an increasing ROI dimension, ranging from 12.08\({}^{\circ}\) (at 5 km) to 4.16\({}^{\circ}\) (at 15 km) and from 5.56\({}^{\circ}\) (at 5 km) to 1.52\({}^{\circ}\) (at 15 km), respectively. As expected, a greater ROI size allows a better directional estimation of the wind, due to a higher number of usable points for the estimation within such dimension ROI [40].
2. The incidence angle is quite stable around the average value of about 37.3\({}^{\circ}\), with a small standard deviation which is basically independent from the ROI dimension. Hence, wind rows are most commonly observed at higher incidence angles in the available dataset, thus confirming again [44]. The uncertainty associated with the incidence angle increases (worsens) with an increasing ROI dimension in terms of both mean and standard deviation, ranging from 0.09\({}^{\circ}\) (at 5 km) to 0.27\({}^{\circ}\) (at 15 km) and from 0.01\({}^{\circ}\) (at 5 km) to 0.02\({}^{\circ}\) (at 15 km), respectively.
3. The NRCS is about 0.1022 with a standard deviation of about 0.0526, considering the three different ROIs dimensions adopted for processing. The uncertainty of the NRCS slightly increases (worsens) with an increasing ROI dimension in terms of mean and standard deviation, varying from 0.0056 (at 5 km) to 0.0063 (at 15 km), and from 0.0029 (at 5 km) to 0.0035 (at 15 km), respectively.
###### Abstract  
Recently, there is increasing interest in identifying benign and malignant nodules by using ultrasound diagnostic technology, which should be helpful to release patients with benign thyroid nodules from suffering unnecessary needle biopsy. In this work, fractal analysis was investigated for the capability of differentiating benign from malignant thyroid nodules during ultrasonography. The B-mode images for 57 patients with suspicious thyroid nodules were captured, followed by ultrasound-guided needle aspiration. The region of interests (ROIs) were subsequently selected from ultrasound images. Then, fractal analysis was performed to extract fractal texture features, and derive the fractal dimension of ROI. The results showed that a significant difference was observed between benign and malignant thyroid nodules (\(p\) < 0.05), by comparing this parameter based on independent nonparametric Mann-Whitney U test. It suggested that fractal dimension is a helpful feature for distinguishing thyroid nodules. In addition, fractal analysis of the B-mode image can provide a reliable reference for tissue typing in ultrasound diagnosis.  
Keywords: thyroid nodules; fractal dimension; B-mode image +
Footnote †: journal: Medical Imaging