esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Parking Block Segmentation from a Surveillance Camera Perspective
## 5 Conclusions and Recommendations  
The main focus of this paper was solving the parking block segmentation on a surveillance camera perspective problem by taking advantage of the satellite view of the same scene. The goal of this research was two-fold--firstly, to segment parking blocks on aerial images. Given the non-existence of a proper dataset to accomplish this specific task, we proposed _APKLOT_, a collection of 7000 parking block polygons that proved to be enough to achieve 50% IoU world wide; secondly, to translate these results to a surveillance camera perspective and measure the ensuing degradation. By testing on several CNNs we provide the groundwork to support the relationship between each hyperparameter, learning rate and duration. We demonstrated that using a simple architecture like the U-Net with skip connections is sufficient to segment simple shapes like the ones composing parking blocks, even though many are heavily occluded.  
For future work we recommend learning the homography automatically without relying on explicitly providing the camera projection parameters or the correspondence points. Then, our approach could be applied to any outdoor parking lot without human intervention. For the case of automating the correspondence points extraction, they can be learned using a metric learning approach as proposed in Reference [35].  
Conceptualization, N.H.-T., L.C. and M.G.-M.; methodology, N.H.-T. and L.C.; software, N.H.-T.; validation, N.H.-T., L.C. and M.G.-M.; formal analysis, N.H.-T. and L.C.; investigation, N.H.-T. and L.C.; resources, N.H.-T. and M.G.-M.; data curation, N.H.-T.; writing--original draft preparation, N.H.-T.; writing--review and editing, L.C. and M.G.-M.; visualization, N.H.-T.; supervision, M.G.-M. and N.H.-G.; project administration, L.C.; funding acquisition, N.H.-G. and M.G.-M. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{3}{c}{**Overall Accuracy**} & \multicolumn{3}{c}{**IoU**} & \multirow{2}{*}{
\begin{tabular}{c} \\ \end{tabular} } \\ \cline{2-2} \cline{5-10}  & **Mean** & & & & & & & & & & \\ \hline
0j-16b-0.011-0.00001r & 84.5 & 8.2 & 49.5 & 86.5 & 96.8 & 57.7 & 20.1 & 0.0 & 62.1 & 95.2 \\
0j-16b-0.011-0.00001r & 84.9 & 8.5 & 50.  
**Funding:** This research was funded by Tecnologico de Monterrey, grant number 470246. The APC was funded by Tecnologico de Monterrey.  
**Acknowledgments:** We wish to thank the _Instituto Tecnologico y de Estudios Superiores de Monterrey_ for financing the publication and providing the surveillance camera images. [PERSON] gratefully acknowledges the scholarship from CONACyT under grant number 470246 (CVU 622341) to pursue his postgraduate studies and making this research possible.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Demystifying DFT-Based Harmonic Phase Estimation, Transformation, and Synthesis
## 1 Introduction
### A Practical Approach to Harmonic Signal Processing
Or, if the waveform shape is to be preserved, they may remain unchanged, and only the changes in the synthesis affect the fundamental frequency parameters (\(\omega_{0}^{S}\), \(A_{0}^{S}\), \(\phi_{0}^{S}\)). In addition, the synthetic phase \(\phi_{0}^{S}\) may be decoupled from the original phase \(\phi_{0}\) since it can be easily synthesized using the value of \(\omega_{0}^{S}\).
## 4 Results and Discussion
### Exploratory Data Analysis
#### 4.1.2 Monthly Variability of Solar Irradiance  
Figures 8 and 9 depict the effect of aerosols and dust particles on solar irradiance during January-April of 2016 and 2017 over the AP using the RTM model. These months were selected because of high dust loading and frequent dust outbreaks during the winter and spring seasons over the AP. A monthly variability can be observed with higher solar irradiance values in the southern and southwestern parts of the AP during 2016 (Figure 8). The lower solar irradiance values observed in the eastern parts of the AP may be due to increased aerosol loading that can significantly affect solar irradiance near the Arabian Gulf (AG) and the Sea of Oman (SO) coastlines (\(\sim\)15% less) and central AP (\(\sim\)20% less). Similar trends of dust aerosols loading on solar irradiance can be observed during 2017 (Figure 9).  
The southeastern region of the AP, Figure 10, shows the highest solar irradiance during January-April compared to other regions. The figures also show low solar irradiance during March in the eastern region of the AP, which could be attributed to the high frequency of dust events during this time of the year.  
Figure 7: Model verification using experimental solar irradiance data collected in Dhahran (26.307\({}^{\circ}\) N, 50.146\({}^{\circ}\) E), Saudi Arabia during September 2005–November 2008.
# PrimaVera: Synergising Predictive Maintenance
## 2 State of the Art
### Prognostics  
The aim of prognostics is to develop accurate algorithms to predict the future failures of components and systems. The prognostics step follows the data processing step and quantifies relevant key performance indicators (KPI), such as the remaining useful life (RUL), time to first failure, availability and reliability. Although a lot of research has already been done in this field, still several major challenges remain. The first challenge is the gap between component and system level. Most of the methods available in literature predict failures on a component level, e.g., for bearings [43], rail [44] or vehicle tracks [45]. However, asset owners are interested in the availability and expected failure of the complete system [46]. As developing separate models for all components in a system still takes too much time and effort, solutions have to be found in either predicting system level failures from only a limited number of (critical) component models, or in speeding up the component model development process. In the former case, the selection of these critical components, especially for large and complex systems, is not trivial and requires attention. The second challenge is that many predictive models heavily depend on a large and complete set of failure data. As for well-maintained critical systems failures are by definition rare, such data sets are often not available. This means that data-driven models must be combined with domain knowledge or physics-based prognostic methods [47]. This relates to the third challenge: only a small number of experts possess detailed knowledge on the failure behaviour of components, which is also very application-specific. This makes it difficult to incorporate that knowledge in generic prognostics tools. Automation of the failure or root cause analysis would make this knowledge more accessible. The fourth challenge in prognostics is that actual application of the methods proposed in scientific literature in industrial practice appears to be rather limited [48]. The main reason is that companies struggle to determine which approach fits with their ambition and their data and knowledge maturity. The final challenge is human factor related: engineers are typically reluctant to adopt advice or predictions from 'black box' prognostic tools. Especially fully data-driven and AI-based methods are hard to comprehend. Adding explainability [49] to these kind of methods might assist in increasing trust in the predictions. To summarise, prognostic methods are still considered to have high potential in predictive maintenance, but wide application in industry is still hindered by both technical and organisational challenges.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 4 Proposed Elliptic Curve Signcryption Based Security Protocol for Smart Card (ECSSP-SC)
### Registration Phase
#### 4.2.1 Registration of the \(Svr\) with \(Rgc\)  
The steps performed for the registration of the \(SVR\) with \(RGC\) are given below. A pictorial representation of this process has also been shown in Figure 4.  
* The server \(SVR\) selects its identity \(I_{S}\). It is assumed that the identity \(I_{S}\) of the server is kept secret from all the parties except \(RGC\).
* \(SVR\) creates its public key as \(P_{S}=\ I_{S}G\) and communicates the message \(\{I_{S}\}\) to the \(RGC\).
* After reception of the message \(\{I_{S}\}\), the \(RGC\) chooses a key \(K_{RGC}\) and computes: \[v_{R}=Hash(K_{RGC})\] (15) \(RGC\) also generates the key: \[K_{RS}=(v_{R}\oplus I_{S}\oplus I_{R})\] (16) \(RGC\) sends the message \(\{K_{RS}\}\) to the \(SVR\).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Denotation** \\ \hline \(q,n\) & Two large prime numbers \\ \(F_{q}\) & Finite field over prime number \(q\) \\ \(E\) & Elliptic curve defined on \(F_{q}\) \\ \(A,\ B\) & Parameters for elliptic curve \(E\) \\ \(O\) & Point on infinity \\ \(G\) & Base point of \(E\) \\ \(USR\) & User having smart card \\ \(SVR\) & Server providing service \\ \(RGC\) & Registration center \\ \(v\) & Randomly selected integer by \(USR\) \\ \(I_{R}\) & Identity of \(RGC\) \\ \(I_{S}\) & Identity of \(SVR\) \\ \(I_{U}\) & Identity of \(USR\) \\ \(Hash\) & Hash computation \\ \(P_{S}\) & Server’s public key \\ \(PWD\) & Password of \(USR\) \\ \(T_{C}\) & Current timestamp \\ \(t\) & Expected delay \\ \hline \hline \end{tabular}
\end{table}
Table 2: Symbols and notations used in the proposed Elliptic Curve Signcryption based Security Protocol for Smart Cards (ECSSP-SC).
## 3 Experiment
### Data Processing  
The seismic data volume in the study area is well-defined and its full data information is shown in Figure 4, which was obtained from the Open Seismic Repository. The dataset consists of 1240 km\({}^{2}\) of time-migrated 3D seismic data, comprising 1676 main lines and 2197 tie lines located on the Gaojiapu block (Figure 4). The time range of the seismic data is 60 ms, the sampling rate in time dimension is 4 ms, and the sampling rate in spatial dimension is 25 ms.  
Before training the network, the most important step is data processing. The datasets were normalized to ensure the values varied within the interval 0 to 1, as shown in Formula (6):  
\[\overline{x}_{i}=\frac{x_{i}-min(x_{i})}{max(x_{i})-min(x_{i})} \tag{6}\]  
Figure 4: Three-dimensional seismic data in the study area.  
Figure 3: Seismic profile of the study area.  
where \(x_{i}\) is the original data of the i-th sample; \(\overline{x}_{i}\) is the data after \(x_{i}\) is normalized; \(max(x_{i})\) is the maximum value of the original data; and \(min(x_{i})\) is the minimum value of the original data.
# A Sea Horse Optimization-Based Approach for PEM Fuel Cell Model Parameter Estimation
###### Abstract  
This study aims to determine the model parameters of proton exchange membrane fuel cells (PEMFC) by employing the Sea Horse Optimization (SHO) algorithm, a novel metaheuristic approach inspired by natural behaviors. Although conventional algorithms in the literature have achieved considerable success in parametric modeling accuracy, many of them suffer from inherent drawbacks, such as premature convergence and entrapment in local minima. The SHO algorithm, with its adaptive and dynamic nature, is designed to overcome these limitations. To further evaluate its performance, a detailed parametric sensitivity analysis is conducted on SHO-specific control parameters. In this work, experimental polarization data from a Ballard Mark V PEMFC is used as a reference to estimate the equivalent circuit model parameters \(\epsilon_{1}\), \(\epsilon_{2}\), \(\epsilon_{3}\), \(\epsilon_{4}\), \(\beta\), \(\lambda\), \(R_{c}\). The SHO algorithm achieved a mean absolute error (MAE) of 0.001079 and a coefficient of determination (R\({}^{2}\)) of 0.999791, with a model-to-experiment fit ratio of 99.92%. Compared to similar studies reported in the literature, the results indicate that the SHO algorithm offers competitive performance. Moreover, the average convergence time is recorded as 1.74 s for 5000 iteration, highlighting the algorithm's rapid convergence and low computational cost. Overall, the SHO algorithm is demonstrated to be an efficient, robust, and promising alternative to conventional methods for parameter identification in PEMFC modeling.  
optimization techniques; PEMFC modeling; polarization curves; parameter extraction Article
## 7 Open Research Challenges in UWSNs
### Real-Time Support for UWSNs  
Critical applications such as natural disaster detection (e.g., earthquakes and tsunamis), oil spills, and territory surveillance require real-time support via an early warning to the authorities before executing necessary actions. However, in supporting the implementation of real-time UWSNs, researchers must consider and overcome the existing challenges.  
1. Transmission Range  
One of the unique features of the underwater environment is the ability of signal to be absorbed, based on the water depth level. The frequency should be reduced to minimize the effect of signal absorption. However, the range of transmission will become longer and, consequently, challenges arise concerning the probability of interruption and high data collisions.  
2. Link Reliability  
Link reliability is another vital element to achieve high delivery rates in real-time scenarios. The link reliability factor among the sensor nodes in the network can influence the delivery rates and consequently affect the transmission loss, which reduces the accumulated intensity of the waveform energy to propagate from source to destination. The presence of noise in the underwater environment can also cause a lost transmission, which consequently affects the reliability of data transmission. Furthermore, the reliability of connection may result in repeated data retransmission that increases the energy consumption of nodes and bandwidth usage. Therefore, a consideration of data transmission efficiency is crucial to avoid untrusted links.
## 4 Experimental Results and Analysis
### Model Configuration and Theory  
To better understand the performance differences among various models, we introduce the models used and their key parameter configurations. These models include BiLSTMModel, CNN, CNNLSTM, LSTM, CNNBiLSTMGRU, TransformerModel, LSTM-Transformer, CNNLSTMGRU, and MDFFE. The specific details are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
\begin{tabular}{c} Optimizer: [PERSON], \\ learning rate: 0.001, \\ epochs: 50, \\ batch size: 128, \\ and seed: 666. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Models and their key parameter configurations (detailing each model’s architecture, key parameters, and training methods for comparison and reproducibility).
# Analysis of Cryptographic Algorithms to Improve Cybersecurity in the Industrial Electrical Sector
## 5 Discussion
* AES is not ideal for older devices, as it would require software implementations that can be expensive, especially in AES-GCM. This type of encryption is particularly expensive when implemented in software. * ChaCha20-Poly1305 has been implemented for mobile devices by Google because it is a fast and secure stream cipher, adding it to TLS1.3. This means that if someone finds a flaw or vulnerability in AES-based cipher suites at some point in the future, it will give them a secure and fast option to fall back on. Therefore, the ChaCha20 stream cipher can come in to fill this situation [27; 33]. * AES has been more analyzed cryptographically due to its greater age, while ChaCha20 has a solid foundation available in an RFC standard it has been the subject of less cryptoanalysis since its creation. * ChaCha20 offers a similar level of security to AES. The software implementations of AES can be vulnerable to cache timing attacks, although this is less relevant due to the wide availability of hardware support. For post-quantum security, a 256-bit key is generally recommended, as 128-bit keys can leave open the possibility of batch or multi-target attacks, where multiple users on a system are attacked simultaneously [34; 35].
# Static and Moving Target Imaging Using Harmonic Radar
## 4 Synthetic Aperture Radar (SAR) Imaging
### SAR Imaging with Fixed 16-Channel Receiver  
#### 4.3.1 Experimental Setup  
In this section, we describe a fixed antenna array used to generate SAR images. The radar receiver was modified to incorporate a custom 16:1 switching network in order to collect data from a 16-antenna array. The antennas were spaced 7.6-cm (3-inch) apart, center to center, thereby achieving a 1.22-m (4-foot) aperture. The antenna aperture is shown in Figure 17.  
The custom 16:1 switching network was created from five 4:1 switches. One of the 4:1 switches fed the other four 4:1 switches. This provided 16 inputs and one output. The 16 inputs were connected to the antenna array and the one output was connected to the receiver of the radar. The switching network was controlled by a multichannel digital-to-analog converter (DAC).  
Figure 16: Explanation of Figure 15 features for: (**a**) Fundamental frequency; (**b**) Harmonic frequency.  
Figure 17: Photograph of the 16-antenna array used to collect SAR images.  
The fixed aperture SAR data collection was done in a wooden building located at the Adelphi Laboratory Center (ALC) location of the Army Research Laboratory. The floor of the building consisted of AstroTurf on top of sand, while the walls of the building were made of wood. For this data collection, the transmitter was placed in a metal box and covered with absorbing foam. This was done to reduce the harmonic leakage from the power amplifier and the cavity diplexer. The receiver was also shielded with metal and absorbing foam to reduce direct coupling onto the receiver.
## 5 Discussion
### Dependence of Image-segmentation Models on Image Resolution  
Choosing original images with the proper resolution is vital for ensuring operational efficiency and accuracy. We therefore verified the reliability of the proposed method for various image resolutions. The lower-resolution images were resized to four different resolutions by scaling down the original image resolution to obtain images with resolutions ranging from \(1024\times 1024\) pixels to \(32\times 32\) pixels.  
Figure 6 shows the segmentation accuracy for different image resolutions. The curve shows that the resolution of the input image strongly affects the image-segmentation accuracy for both the HSV and EDA methods, which we attribute to their pixel-based characteristics. The color characteristics, or the grey level, change completely when the image resolution changes. No such abnormal fluctuation appears in the results of the other three ML-based or DL-based methods, because their accuracy depends only on the size of the training set, and not on the resolution of the input images. Thus, cameras with lower resolution, such as GoPro or mobile phone cameras, could be adapted to furnish the data.
_Viruses_**2013**, \(5\), 619-653; doi:10.3390/v5020619  
_Article_  
**Phage Lambda P Protein: Trans-Activation, Inhibition Phenotypes and their Suppression**  
**[PERSON] *, [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]**  
Department of Microbiology and Immunology, College of Medicine, University of Saskatchewan, Saskatoon, S7N 5E5 Canada; E-Mails: [EMAIL_ADDRESS] (S.H.); [EMAIL_ADDRESS] (C.E.); [EMAIL_ADDRESS] (M.A.H.); [EMAIL_ADDRESS] (K.M.); [EMAIL_ADDRESS] (W.W.); [EMAIL_ADDRESS] (C.H.)  
* Author to whom correspondence should be addressed; [EMAIL_ADDRESS]; Tel.: +1-306-966-4307; Fax: +1-306-966-4298.  
_Received: 4 December 2012; in revised form: 21 January 2013 / Accepted: 29 January 2013 / Published: 6 February 2013_  
**Abstract:** The initiation of bacteriophage \(\lambda\) replication depends upon interactions between the _ori\(\lambda\)_. DNA site, phage proteins O and P, and _E. coli_ host replication proteins. P exhibits a high affinity for DnaB, the major replicative helicase for unwinding double stranded DNA. The concept of P-lethality relates to the hypothesis that P can sequester DnaB and in turn prevent cellular replication initiation from _oriC_. Alternatively, it was suggested that P-lethality does not involve an interaction between P and DnaB, but is targeted to DnaA. P-lethality is assessed by examining host cells for transformation by ColE1-type plasmids that can express P, and the absence of transformants is attributed to a lethal effect of P expression. The plasmid we employed enabled conditional expression of \(P\), where under permissive conditions, cells were efficiently transformed. We observed that ColE1 replication and plasmid establishment upon transformation is extremely sensitive to P, and distinguish this effect from P-lethality directed to cells. We show that alleles of _dnaB_ protect the variant cells from \(P\) expression. P-dependent cellular filamentation arose in _ArccA_ or _lexA_[Ind] cells, defective for SOS induction. Replication propagation and restart could represent additional targets for P interference of _E. coli_ replication, beyond the _oriC_-dependent initiation step.  
**Keywords:**_E. coli_ DnaB replicative helicase; bacteriophage lambda (\(\lambda\)) replication initiation protein P; allelic alterations of _dnaB_ and P; ColE1 plasmid curing and replication inhibition; cellular filamentation
## 3 Case Study
### Analysis of Influenced OD Passenger Flow  
When an urban rail transit system is disrupted, many passengers are forced to change their travel routes, as described in Section 3.2. Moreover, there are many travelers who may have to change their original station and destination station. This means that the origin-destination (OD) passenger flow may be affected by the disruption. Based on the identification of abnormal stations and the estimation of influence intensity in Sections 3.2 and 3.3, the OD passenger flow at three stations representative of different station types were analyzed in detail: (1) disrupted station: Bajiao Amusement Park station; (2) ordinary station affected by the disruption: Babaoshan station; (3) transfer station affected by the disruption: Military Museum station.