esa-sceva/satcom-chunk-collection huggingface dataset:
# Underwater Image Super-Resolution via Dual-aware Integrated Network
## 4 Experiments
### Experimental Evaluation on the ELIVP Dataset  
To further show the robustness and effectiveness of the proposed DAIN, we performed enhancement experiments on the EUVP dataset and compared our model with models including UGAN [54], UGAN-P [54], Funie-GAN [52], Funie-GAN-UP [52], Deep SESR [53], and Deep WaveNet [12]. EUVP consists of 11,435 pairs of underwater images for training and 515 pairs of samples for testing. They follow the standard procedures [54; 55] for optical/spatial image degradation and use manually labeled saliency mappings to create pairs of data.  
Table 3 displays the competitive performance of the DAIN proposed by us, attaining the highest values in terms of both PSNR and SSIM. Specifically, PSNR and SSIM are improved by at least 0.57 dB and 0.02, respectively. The DAIN is found to be slightly lower than Deep SESR in terms of UIQM, but the difference is only 0.03. The quantitative results of Funie-GAN-UP are far below our method. A visual comparison of the EUVP dataset is shown in Figure 4. Our DAIN produces much sharper image reconstruction. Most algorithms, including UGAN, Funie-GAN, and Deep SESR, ignore global dependency modeling, resulting in poor performance. By contrast, our DAIN adequately incorporates global dependency modeling whilst also considering local information, enabling our network to acquire more valuable information and enhance image quality.  
Figure 3: Visual comparisons between our DAIN and popular methods were conducted utilizing the UFO-120 dataset for scale factors of \(\times 2\), \(\times 3\), and \(\times 4\). Left to right: the original HR image, AMPCNet, Deep WaveNet, ESRGCNN, LatticeNet, RDLN, and the proposed DAIN.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Numbers** & **PSNR(dB)** & **SSIM** & **UIQM** \\ \hline UGAN [54] & 26.45 & 0.79 & 2.87 \\ UGAN-P [54] & 26.44 & 0.79 & 2.91 \\ Funie-GAN [52] & 26.16 & 0.78 & 2.95 \\ Funie-GAN-UP [52] & 25.16 & 0.78 & 2.91 \\ Deep SESR [53] & 27.03 & 0.80 & **3.06** \\ Deep WaveNet [12] & 28.56 & 0.83 & 3.02 \\ DAIN(ours) & **29.13** & **0.85** & 3.03 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative results of different methods in the EVUP dataset. Bold is the best performance.
# A Deadline-Aware Scheduling and Forwarding Scheme in Wireless Sensor Networks
###### Abstract  
Many applications in wireless sensor networks (WSNs) require energy consumption to be minimized and the data delivered to the sink within a specific delay. A usual solution for reducing energy consumption is duty cycling, in which nodes periodically switch between sleep and active states. By increasing the duty cycle interval, consumed energy can be reduced more. However, a large duty cycle interval causes a long end-to-end (E2E) packet delay. As a result, the requirement of a specific delay bound for packet delivery may not be satisfied. In this paper, we aim at maximizing the duty cycle while still guaranteeing that the packets arrive at the sink with the required probability, _i.e._, the required delay-constrained success ratio (DCSR) is achieved. In order to meet this objective, we propose a novel scheduling and forwarding scheme, namely the deadline-aware scheduling and forwarding (DASF) algorithm. In DASF, the E2E delay distribution with the given network model and parameters is estimated in order to determine the maximum duty cycle interval, with which the required DCSR is satisfied. Each node independently selects a wake-up time using the selected interval, and packets are forwarded to a node in the potential forwarding set, which is determined based on the distance between nodes and the sink. DASF does not require time synchronization between nodes, and a node does not need to maintain neighboring node information in advance. Simulation results show that the proposed scheme can satisfy a required delay-constrained success ratio and outperforms existing algorithms in terms of E2E delay and DCSR.  
delay bound; duty-cycled wireless sensor networks; routing protocol; geographic forwarding +
Footnote †: journal: Sensors
# A Secure Blockchain-Based Authentication and Key Agreement Scheme for 3 GPP 5G Networks
## 5 Security Evaluation  
In this section, we firstly justify the logical correctness of the 5 GSBA using Burrows-Abadi-Needham (BAN) logic. Then, we provide formal verification on the security of the 5 GSBA using the Scyther formal verification tool. Moreover, we present an extensive qualitative security analysis based on the discussion in Section 3.2 to show that the 5 GSBA is secure to fight against various malicious attacks.
## 3 Proposed Maturity Framework
### Structured Interviews with Experts  
1. What are the regulations that universities have to be compliant with?
2. Are universities in the UK obliged to have a security officer?
3. How do you conduct the DPIA and Risk Assessment? Do you do it in parallel or one after the other? Is data protection impact assessment under Risk Assessment?
4. Can you please briefly tell us the procedures you follow in order to be compliant with those regulations?
5. We have some categories that might not be applicable to universities, for example,'security for software development'--what is your opinion?
6. How are the roles and responsibilities between the DPO and the security officer split?
7. How do you actually merge security requirements and Data Protection requirements during the implementation of a new service?
8. What is the procedure that is followed when a security or data breach takes place?
9. What would be the added value of a cybersecurity assessment framework? What would you expect from such a model?
10. We have created an initial pool of sectors that our HCYMAF is going to investigate. Do you think that we may miss any important category?  
Before the final model is released to the HEIs, it should be validated through several pilot implementations. The model should preferably be used by organisations of different sizes and regardless of the activities they have, e.g., provide health studies. The first pilot has already been conducted, and the team at De Montfort University (DMU) in cooperation with the NCSC has already planned to run the other two cases in the next period. In the meantime, the DMU team has released the first version of the website which HEIs will use in order to perform self-assessments and receive the results in a graphical model and a gap analysis that showcases the cybersecurity sectors of HEI's IT systems that need immediate actions. Additionally, compliance reports will be produced automatically from the HCYMAF, giving the opportunity for the organisation to react fast and avoid penalties.  
Each HEI representative will need to register to the platform and then go through the guide. The process can be paused and continued at a later time since a lot of information and time are needed in order to conduct a full cybersecurity assessment. The results for each organisation are only visible to the organisation along with charts and reports that will help the security and data-protection-officers to take the appropriate measures. Aggregated results will be collected and used for analysis by the NCSC in order to prioritise future security plans.
## 3 Transduction Principles of MEMS AE Sensors
### Piezoresistive MEMS AE Sensors  
[PERSON] [17] demonstrated the piezoresistivity property of silicon, which has been used as a transduction mechanism for mechanical MEMS sensors since then. The change in resistivity is proportionate to stress as follows [18]:  
\[\frac{\Delta\rho_{ij}}{\rho}=\sum_{k,\ l}\pi_{ijkl}T_{kl}\ \ \ \ \ i,j,\ k,l=1,\ 2,\ 3 \tag{1}\]  
where \(\rho_{ij}\) is electrical resistance, \(\pi_{ijkl}\) is piezoresistivity coefficient, which depends on the crystal orientation and the dopant level, and \(T_{kl}\) is stress. The electrical resistance between two ends of a resistor is measured by  
\[R=\rho\frac{l}{A} \tag{2}\]  
where \(l\) is length, \(A\) is cross-sectional area [19]. The change in resistance is related to  
\[\frac{\Delta R}{R}=\frac{\Delta\rho}{\rho}+\frac{\Delta l}{l}-\frac{\Delta A}{A} \tag{3}\]  
Assuming that the resistor is exposed to uniaxial strain, Equation (3) becomes  
\[\frac{\Delta R}{R}=\varepsilon_{l}(1+2\upsilon)+\frac{\Delta\rho}{\rho} \tag{4}\]  
where \(\varepsilon_{l}\) is longitudinal strain, \(\upsilon\) is Poisson's ratio. The sensitivity is measured by the gauge factor (\(GF\)) as  
\[GF=\frac{\frac{\Delta R}{R}}{\varepsilon_{l}}=(1+2\upsilon)+\frac{\Delta\rho }{\rho\varepsilon_{l}} \tag{5}\]  
If the strain and the direction of current are both in longitudinal direction on \(<\)100\(>\) plane, then  
\[\frac{\Delta\rho}{\rho}=\pi_{11}T_{11}=\pi_{11}(E\varepsilon_{l}) \tag{6}\]  
where \(E\) is Young's Modulus of silicon. Here, \(\pi_{11}\) represents \(\pi_{1111}\). Combining Equations (5) and (6),  
\[GF=(1+2\upsilon)+\pi_{11}E \tag{7}\]  
The n-type dopant would result in higher gauge factor as \(\pi_{11}\) value is \(-102.2\times 10^{-11}\) Pa\({}^{-1}\), while it is \(6.6\times 10^{-11}\) Pa\({}^{-1}\) for the p-type dopant. However, the p-type silicon piezoresistor has higher conductivity than the n-type silicon piezoresistor, which reduces the effect of temperature to the output signal [20]. The temperature dependence of piezoresistivity coefficients influences the measurement [21]. Therefore, temperature compensation approaches should be applied [22].
# A Mechanism for Reliable Mobility Management for Internet of Things Using CoAP
## 3 Reliable Message Transmission Algorithm and Analysis of Handover Delay of CoMP
We derive the average number of retransmission counts and average number of transmissions for a simple stop-and-wait mechanism of CoAP with packet-error rate \(A_{f}\). The average number of retransmission counts \(R\) for a CoAP message with \(f\) fragmented CoAP messages is derived as:  
\[R=\sum_{i=0}^{m-1}i\Big{(}1-A_{f}\Big{)}A_{f}^{i}+(m-1)A_{f}^{m} \tag{3}\]  
where \(m\) is maximum number of retransmissions with a typical value of 4 and \(I\) is the retransmission count. The average number of transmissions \(S\), including retransmission for a CoAP packet, is calculated as:  
\[S=\sum_{i=1}^{m}i\Big{(}1-A_{f}\Big{)}A_{f}^{i-1}+mA_{f}^{m} \tag{4}\]  
With a maximum \(m\) retransmissions, the probability of a CoAP messages not being transmitted is \(A_{f}^{m+1}\). The average retransmission delay \(D\) for transmitting a CoAP signaling message can be calculated as:  
\[D=\sum_{k=0}^{m-1}(1-A_{f})A_{f}^{k}\Big{\{}T_{r}+\Big{(}2^{k}-1\Big{)}T_{w} \Big{\}}+A_{f}^{m}(2^{m}-1)T_{w} \tag{5}\]  
where \(k\) is the retransmission count and the timeout \(T_{w}\) is the amount of time that the mCSN waits for an acknowledgement packet from a remote device. The roundtrip time \(T_{r}\) is the time from the start of the transmission until an ACK message is received. It is approximately equal to the end-to-end delay. The derivation of the above Equation (5) is included in the Appendix A. Using the average delay of a CoAP message derived above, we can calculate the total handover latency of CoMP. The handoff latency at the mCSN is the time interval during which an mCSN cannot send or receive any packets during a handover operation. The total handover latency consists of the link switching time (\(t_{L2}\)), which is caused by the L2 handover between the new-ER and mCSN, movement detection delay (\(t_{MD}\)) at the mCSN, IP configuration delay and duplicate address detection by the DHCP (\(t_{DHCP}\)), and location update latency (\(t_{BU}\)). \(t_{BU}\) is the sum of \(2t_{(mCSN,IMMS)}\) and \(2t_{(IMMS,CWC)}\), which are the transmission delay times of the PUT request/response messages for binding update between the mCSN and IMMS and between the IMMS and CWC, respectively. The total handover delay \(T_{CoMP}\) is then calculated as follows:  
\[T_{CoMP}=t_{L2}+t_{MD}+t_{DHCP}+2t_{(CSN,IMMS)}+2t_{(IMMS,CWC)} \tag{6}\]  
It is noted that \(t_{BU}\) increases as the retransmission count increases.
## 2 The Proposed Methodology  
### Materials  
A centralized database of high-resolution digital adult and pediatric images was created for this particular project, captured at Ear, Nose, and Throat (ENT) clinics and primary care settings at the Ohio State University (OSU) and Nationwide Children's Hospital (NCH) in Columbus, Ohio, USA in accordance with the OSU Institutional Review Board (IRB) approved protocol (the project identification codes: 2016H0011 and 2018H0395). A high definition (HD) video otoscope (JEDMED Horus+ HD Video Otoscope, St. Louis, MO, USA) was utilized to capture and record the video data. The video frames are of size 1440 by 1080 pixels and are recorded in MOV format.  
Our dataset included 136 otoscope videos. First, we randomly chose 36 otoscope videos from our database, then an image analyst determined 764 images from the extracted frames of all the videos (Dataset 1). These images were selected to contain the eardrum in several different conditions such as blurriness, glare as much as possible. Two otolaryngologists ([PERSON] and [PERSON]) annotated the selected frames as a final step (Figure 2) and these frames were used to develop the semantic segmentation model. The second one (Dataset 2) containing 100 videos for an independent set was used for employing the stitching process along with a reader study to assess the efficiency of the proposed method. Table 1 shows the distribution of major diagnostic categories as reported by the expert physicians.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Abnormalities** & **Number of Occurrences** \\ \hline Effusion & 27 \\ Trypanosclerosis & 22 \\ Perforation & 15 \\ Retraction & 12 \\ Acute Otitis Media (AOM) & 11 \\ Cholesteatoma & 7 \\ Other Minor Cases & 6 \\ Total & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of Abnormalities in Dataset 2.  
Figure 1: Overview of our study structure: 36 otoscope videos were used for training a segmentation algorithm, which is then used to select appropriate frames for the 100 otoscope test videos. The composite videos from a control set without frame selection and SelectStitch method are then compared visually by three Ear, Nose, and Throat (ENT) physicians in two rounds of evaluation.  
An example frame for each of the six major abnormality categories from our dataset is depicted in Figure 3.
###### Abstract  
This research work implements an initial methodology for the assessment of Battery Energy Storage Systems (BESSs) based on Remaining Useful Lifetime (RUL), and its main contribution is the modeling and estimation of Health and Charge indicators through regression algorithms and binary classifiers during the battery's operation. Linear Regression, Ridge Regression, and Lasso Regression are the main algorithms for modeling the State of Health (SOH), while Decision Tree, Naive Bayes, and Logistic Regression are implemented as binary classifiers to estimate the charge and discharge during battery operation. Additional data science techniques are executed to provide feature selection, validation, and metrics of performance. The results show that binary classifiers achieve a remarkable accuracy, around 95% for charge and discharge predictions, which is supported by experimental battery measurements. Similarly, regression algorithms achieve accuracy results around 97% and provide a basis for determining the Remaining Useful Lifetime (RUL) according to the End-of-Life (EOL) criteria of a BESS.  
charge; discharge; battery; capacity; classifier; regression Article Modeling Battery Energy Storage Systems Based on Remaining Useful Lifetime through Regression Algorithms and Binary Classifiers. April 2023, 13, 7597. [[https://doi.org/10.3390/app1317597](https://doi.org/10.3390/app1317597)]([https://doi.org/10.3390/app1317597](https://doi.org/10.3390/app1317597))
## 2 Materials and Methods
### Preparation of Magnetic-Biochar/Alginate (MBA) Bead  
The literature contains various information indicating that sodium alginate is used in the synthesis of hydrogel beads and magnetic hydrogel beads. In physical crosslinking methods, divalent metal ions, particularly Ca\({}^{2+}\), are typically employed to create ionic interactions with the abundantly available anionic (-COO\({}^{-}\)) groups of alignates.  
The process of forming magnetic biochar/sodium alginate beads (MBA beads) consists of several sequential steps. In the first step, approximately 2.1 g of sodium alginate was mixed in 150 mL of distilled water for three hours. Subsequently, 3 g of magnetic biochar was added to this solution. Thorough mixing was ensured to achieve a homogeneous structure throughout the mixture. The resulting mixture was then dripped into a 4% (\(w/v\)) CaCl\({}_{2}\) solution using a 2.5 cc syringe, facilitating the formation of hydrogel beads that had a high degree of spherical shape and size uniformity. To allow for the beads' gelation reaction to complete, they were left in the CaCl\({}_{2}\) solution for two hours. In the final step, the beads were filtered out of the solution and rinsed with distilled water to remove any excess calcium and chloride ions. Finally, the beads were left to dry at room temperature. As a result, the obtained sample was labeled as BCA-Mag/AB and stored in a clean bottle for later use.
# Calibration Method of Orthogonally Splitting Imaging Pose Sensor Based on General Imaging Model
## 3 Results
### Parameter Experiment  
According to the calibration procedure proposed in Section 2.2.4, two different radial basis functions and different shape parameters are used to perform the interpolation. The calibration results are applied to the fitting calculation respectively for error evaluation of the calibration data and the test  
Figure 7: Test data set (**a**) is the world coordinate of the test dataset. (**b**) is the image coordinate of the test dataset.  
data. The error evaluation is represented by the distance from the target point in the world coordinate system to the fitted Plucker line. The error evaluation is shown in Figure 8.  
As can be seen from Figure 8, for the orthogonally splitting imaging pose sensor, when the MQ function is used as the radial basis function, the RMS of the calibration data set is similar to the RMS of the test data set. And when the Gaussian function is used as the radial basis function, the calibration result has a large difference between the calibration data set and the test data set. Therefore, the calibration accuracy of the MQ function is higher than the one of the Gaussian function.  
From the experimental data, the shape parameters with the minimum difference between RMS of the test data set and RMS of the calibration data set are selected to calibrate the orthogonally splitting imaging pose sensor.  
Figure 8: Analysis of different radial basis function interpolation and fitting accuracy with different shape parameters. (**a**) is the root mean square (RMS) calculated by using different shape parameters with different Multi-Quadric function (MQ function). (**b**) is the RMS calculated by using different shape parameters with different Gaussian function.
###### Abstract  
Landscape management requires spatially interpolated data, whose outcomes are strictly related to models and geostatistical parameters adopted. This paper aimed to implement and compare different spatial interpolation algorithms, both geostatistical and deterministic, of rainfall data in New Zealand. The spatial interpolation techniques used to produce finer-scale monthly rainfall maps were inverse distance weighting (IDW), ordinary kriging (OK), kriging with external drift (KED), and ordinary cokriging (COX). Their performance was assessed by the cross-validation and visual examination of the produced maps. The results of the cross-validation clearly evidenced the usefulness of kriging in the spatial interpolation of rainfall data, with geostatistical methods outperforming IDW. Results from the application of different algorithms provided some insights in terms of strengths and weaknesses and the applicability of the deterministic and geostatistical methods to monthly rainfall. Based on the RMSE values, the KED showed the highest values only in April, whereas COK was the most accurate interpolator for the other 11 months. By contrast, considering the MAE, the KED showed the highest values in April, May, June and July, while the highest values have been detected for the COK in the other months. According to these results, COK has been identified as the best method for interpolating rainfall distribution in New Zealand for almost all months. Moreover, the cross-validation highlights how the COK was the interpolator with the best least bias and scatter in the cross-validation test, with the smallest errors.  
monthly rainfall; spatial analysis; inverse distance weighting (IDW); kriging; New Zealand; landscape management Article
## 5 Conclusions  
In this paper, we present a cost-effective digital baseband architecture for wireless VR applications. Unlike the previous architectures that adopt the soft-decision and iterative ECCs associated with the complex interleaving scheme, to overcome the practical burst-aware channel condition, the proposed architecture uses only simple hard-decision BCH codes as the component ECCs and introduces the block-level interleaving architecture that minimizes the hardware costs. The optimized parallel encoder and decoder architectures of the proposed interleaved-BCH ECC allow the energy-efficient baseband processing, reducing the overall energy consumption of wireless VR systems significantly. The prototype baseband system utilizes two identical baseband processing lanes for supporting the QPSK modulation with the existing 60 GHz RF circuits, achieving a data rate of more than 12 Gbps, which is sufficient to transfer the uncompressed FHD video streams.  
Conceptualization, S.H. and Y.L.; methodology, S.H. and S.M.; software, S.H. and S.M.; validation, S.H., I-Y.O. and Y.L.; formal analysis, S.H.; investigation, S.H. and D.K.; resources, S.H., I-Y.O. and Y.L.; data curation, Y.L.; writing-original draft preparation, S.H. and Y.L.; writing-review and editing, D.K. and Y.L.; visualization, S.H.; supervision, Y.L.; project administration, Y.L.; funding acquisition, Y.L.  
This research was supported by the National Research Foundation (NRF) grant funded by the Korea government (MSIT) (2018R1A-1025679); by the Ministry of Science and ICT, Korea, under the ITRC support program (IITP-2019-2016-0-00309); by the Sports Promotion Fund of Seoul Olympic Sports Promotion Foundation from Ministry of Culture, Sports and Tourism; and by the IC Design Education Center (IDEC).  
The author declares no conflict of interest.