esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 STAR Algorithms to Convert Carrier Phase to Excess Phase and Bending Angle Profiles
### COSMIC-2 Excess Phase Calculation  
#### 2.4.1 Carrier Phase Model for Deriving the STAR COSMIC-2 Excess Phase  
We derive the RO carrier phase equation using the wave propagation theory (see Appendix A). The derived carrier phase model (also see [25; 42]) for retrieving the COSMIC-2 (and other RO missions) excess phase is  
\[\begin{array}{ll}L_{LG}^{F}(t^{R}(t^{\prime}))&=\Delta t^{R}(t^{\prime}) \cdot C+\delta^{R}(t^{\prime})\cdot C-\Delta t^{T}(t^{\prime\prime}-\tau)\cdot C -\delta^{T}(t^{\prime\prime}-\tau)\cdot C\\ &+\|\overset{\rightarrow}{x}(t)-\overset{\rightarrow}{x}(t-\tau)\|+\delta D \left(\overset{\rightarrow}{x}^{R}(t),\overset{\rightarrow}{x}^{T}(t-\tau) \right)+\Delta S+N+\in\end{array} \tag{1}\]  
where \(t\) is the GPS time defined on the geoid surface on the earth, \(L_{LG}^{F}(t^{R}(t^{\prime}))\) is the observed carrier phase at local receiver time \(t^{R}(t^{\prime})\), \(t^{\prime}\) is the proper time on the LEO orbit, \(\Delta t^{R}(t^{\prime})\) is the receiver (R) clock bias, \(\Delta t^{T}(t^{\prime}-\tau)\) is the GNSS transmitter (T) clock bias, \(t^{\prime\prime}-\tau\) is the transmitter's proper time at the signal transmitting, \(\delta^{T}\) is the time correction from the local observer on earth geoid to proper time on orbit, \(\overset{\rightarrow}{x}^{R}(t)\) is the receiver antenna position in ECI and \(\overset{\rightarrow}{x}^{T}(t-\tau)\) is the transmitter antenna position in ECI \(\delta D(\overset{\rightarrow}{x}^{R}(t),\overset{\rightarrow}{x}^{T}(t-\tau))\) is a gravitational delay, \(N\) represents the ambiguity, \(\Delta S\) represents the desired atmospheric delay (the excess phase), \(\in\) represents errors from the multi-path from the ambient environment of the receiver or other sources.  
The first two terms on the right represent the LEO clock error and corrections from local time to GPS time. The third and fourth terms are GNSS clock error and correction. The fifth and sixth terms are the range between GNSS/LEO and its correction due to gravitational delay, respectively. The eighth term is the phase ambiguity term. To estimate the excess phase \(\Delta S\), we must first estimate each term in Equation (1) for L1 and L2 and GPS and GLONASS, respectively. The excess phase is calculated by removing the contributions from all these terms.
# Improving Incident Response in Big Data Ecosystems by Using Blockchain Technologies
## 4 Incident Response for Big Data Ecosystems
### Preliminary Actions  
The main goal of this preliminary phase is to establish the objectives of the incident response plan, as well as its scope. To that end, there are two activities, which are summarized in Table 3.  
The first of these activities aims to define what each organization considers to be an event and what it considers an incident, i.e., to clarify the differences between them. In any IT system an event is normally any occurrence within normal behavior and anticipated in the requirements; for example, in a big data ecosystem an event would be any action carried out on the data, including its analysis, access or visualization. But it also includes the system errors that do not result in the operation's being halted. On the other hand, an incident can be defined as any malicious attack that has a nefarious purpose and which is against the security policies of the organization. For example, an illegal reading would be an event at first, but it is actually an incident. Obviously, incidents must be stored in the blockchain environment, so that they can be used as audit evidence in a legal process, or to carry out an investigation into the causes of the incidents. Moreover, events should be stored too, since they can be useful later gain a better understanding of what led to the incident; it may even occur that a \"normal\" event was not correctly tagged in the first place. Events are important in recovery from errors.  
The purpose of the second activity is to define the different roles that will act in the incident response process. There are some typical roles in this type of activity, such as the incident response team, which aims to perform the tasks needed when carrying out this activity, including collecting evidence, studying incidents and reporting when certain types of incidents occur. Depending on the size of the organization, there will be one or more incident response teams, with their own respective focus on different parts of the ecosystem. Moreover, there are other types of common security roles that should play an important part in this process, such as the CISO (chief information security officer), the CTO (chief technology officer) and the CSO (chief security officer) who should be involved in the high-level decisions that are made, especially when the incident is severe. In addition, the rest of the stakeholders that interact with the big data ecosystem need to be involved in the incident response process, since they can be the first to detect that something is going wrong with the system. Typical stakeholders in a big data ecosystem are data scientists, big data architects, and data engineers.
# The Identification and Assessment of Rail Corrugation Based on Computer Vision
###### Abstract  
The identification and assessment of rail corrugation are two of the essential tasks of daily railway inspection to guarantee the safety of train operation and promote the development of an efficient maintenance strategy. In view of the requirements for automatic identification and smart decision-making, computer vision-based rail corrugation identification and assessment methods are presented in this paper. Firstly, an improved Spatial Pyramid Matching (SPM) model, integrating multi-features and locality-constrained linear coding (IMFLLC), is proposed for rail corrugation identification. After that, an innovative period estimation method for rail corrugation is proposed based on the frequency domain analysis of each column in the corrugation region. Finally, the severity of the rail corrugation is assessed with the help of the wear saliency calculation and fuzzy theory. The experiment results demonstrate that the proposed corrugation identification method achieves a higher precision rate and recall rate than those of traditional methods, reaching 99.67% and 98.34%, respectively. Besides, the validity and feasibility of the proposed methods for the rail corrugation period estimation and severity assessment are also investigated.  
Keywords:rail corrugation identification; period estimation; severity assessment; computer vision +
Footnote †: journal: Applied Sciences
## 5 Result and Discussion
### Algorithms' Performance Based on an Empirical Evaluation  
To validate the performance of the proposed strategies, a set of controlled experiments was created based on the original reference process model for the universities. The experiments focused on testing the performance of the strategies depending on both the log complexity and the model complexity.
# Post-Quantum Authentication in the MQTT Protocol
## 3 Results
### Results for KEM Authentication
#### 3.2.3 Time Taken  
To measure connection speed, the time elapsed for complete authentication was recorded for 100 handshakes in each protocol. The Python time library was used to write the difference between the clock time immediately before initiating the handshake and immediately after it was successfully completed. Results can be seen in Figure 9.  
Figure 8: Total bytes sent during connection for KEM-based and signature-based protocols.  
On average, the unauthenticated handshake took 0.4 ms, the signature-based authenticated handshake took 35 ms, and the KEM-based authenticated handshake took 10 ms. The KEM-based protocol was 71% faster than the signature-based protocol, but was still 22 times slower than the original MQTT protocol (i.e., without authentication).
## 2 Related Work
### Traditional Methods  
Historically, low-light image enhancement has been approached through techniques such as histogram equalization, gamma correction, and Retinex theory. Histogram equalization improves global contrast but tends to produce unnatural-looking results under non-uniform lighting conditions [18]. Retinex-based methods, inspired by the human visual system, decompose an image into reflectance and illumination components [6]. While such models, including Single-Scale and Multi-Scale Retinex [19], can enhance local contrast effectively, they often suffer from artifacts, noise amplification, and limited adaptability to different environments.  
Edge-preserving filters and image fusion techniques have also been explored [20]. However, most traditional approaches rely on handcrafted priors and lack the capacity to generalize to diverse and complex real-world lighting scenarios.
## 3 Case Study
### SKAB Dataset
#### 3.2.2 Experimental Setup  
The N-way K-shot experimental protocol was adopted for the fault diagnosis tasks within the SKAB dataset, in line with the standards of few-shot learning. For each classification task, N categories were randomly selected from the dataset, from which K examples were chosen to form the support set and Q examples to construct the query set. This sampling strategy resulted in the generation of 10,000 training tasks and 100 testing tasks, providing a comprehensive evaluation of the GOPML algorithm across a wide array of fault scenarios. The learning process was orchestrated across a two-tiered structure: the  
Figure 8: Visualization of a fault scenario in SKAB—simulation of fluid leaks and fluid additions.  
Figure 7: Front panel and composition of the water circulation, control and monitoring systems: 1—inverter; 2—a water pump; 3—emergency stop button; 4—electric motor; 5—compactRIO; 6, 7— solenoid valve; 8—a tank with water; 9—a mechanical lever for shaft misalignment. Not shown parts: vibration sensor; pressure meter; flow meter; thermocouple [34].  
task-specific inner layer and the meta-level outer layer. The learning rates were set at \(\alpha=0.01\) for task-level updates and \(\beta=0.001\) for meta-level updates. The initial phase consisted of five inner update steps for preliminary adaptation, followed by 10 fine-tuning steps to refine the model's performance.  
The base learner's architecture for the SKAB dataset was meticulously designed to capture temporal and feature dependencies crucial for accurate fault diagnosis. The choice of parameters for the SKAB dataset, such as the number and size of filters in the Conv1D layer, was specifically tailored to address the intricate fault patterns and operational variability characteristic of industrial processes. For instance, the larger filter size in Conv1D helped with capturing broader fault signatures, which was critical given the SKAB dataset's emphasis on complex and varied fault conditions. This particular setup ensured that the GOPML algorithm could effectively discern subtle differences in fault patterns, a key requirement for the accurate diagnosis in diverse industrial scenarios. Each module, detailed in Table 7, begins with a Conv1D layer equipped with 64 filters of size 5, followed by a ReLU activation function for non-linearity introduction and batch normalization to stabilize the learning process. Module 2 incorporates a Maxpool1D operation to reduce dimensionality and a dropout layer with a rate of 0.5 to mitigate overfitting. The final layer in the sequence was an LSTM with 100 units, capturing long-term dependencies within the time-series data. The structure of these modules, as outlined in Table 8, consists of alternating sequences of Module 1 and Module 2. The integration of an LSTM layer in the final step was particularly crucial for modeling the temporal sequences characteristic of the SKAB dataset's time-series data, ensuring a comprehensive understanding of fault patterns over time.
## 4 Problem Formulation
### Alternating Optimisation Technique  
In this subsection, a different sub-optimal algorithm will be developed based on alternating optimisation which is demonstrated in Algorithm 1. Let us assume \(\mathbf{w}=\sqrt{P}\bar{\mathbf{w}}\), where \(P\) denotes the transmit power at the AP and \(\bar{\mathbf{w}}\) is the transmit beamforming vector. From (P2), the optimised transmit power can be written as: \(P^{\bullet}=\frac{\gamma\sigma^{2}}{||(\mathbf{h}^{H}\mathbf{\Theta}\mathbf{G }+\mathbf{h}_{d}^{H})\bar{\mathbf{w}}||^{2}}\). And then the transmitted beamforming direction is optimised for given \(\theta\). Here, the \(n^{th}\) phase shift at the IRS can be expressed as:  
\[\theta_{n}^{*}=\phi_{0}-arg(\mathbf{h}_{n,r}^{H})-arg(\mathbf{g}_{n}^{H}\bar{ \mathbf{w}}), \tag{14}\]  
where \(\mathbf{g}_{n}^{H}\) denotes the \(n^{th}\) row vector of \(\mathbf{G}\), \(\mathbf{h}_{n,r}^{H}\) denotes the \(n^{th}\) element of \(\mathbf{h}_{r}^{H}\) and the function \(\arg(\cdot)\) is used to denote the phase (or argument) of a complex number.  
It should be noted that \(\mathbf{g}_{n}^{H}\)\(\bar{\mathbf{w}}\) is the combination of the transmitted beamforming direction and the AP-IRS channel. The fact that the derived phase, \(n\), is independent of the amplitude of \(\mathbf{h}_{n,r}\) is also significant to note. For fixed beamformimg direction \(\bar{\mathbf{w}}\), (P2) can be expressed as:  
\[\max_{\theta} |(\mathbf{h}_{r}^{H}\mathbf{\Theta}\mathbf{G}+\mathbf{h}_{d}^{H} )\bar{\mathbf{w}}|^{2}\] (15) s.t. \[0\leq\theta_{n}\leq 2\pi,n=1,...,N. \tag{16}\]  
The following steps are used for the optimisation of transmitted power.  
```
Initialise phase shift angle \(\theta=\theta_{1}\) and set staring iteration as \(t=1\). repeat  Solve problem (P1) for given \(\theta_{t}\), and calculate the optimised solution as \(W_{t}\).  Solve problem (P2) for given \(W_{t}\), and calculate the solution (using Gaussian randomisation) as \(\theta_{t+1}\).  Update \(t=t+1\). until The objective value decrease is lower than the given threshold, \(\epsilon\geq 10^{-5}\).
```  
**Algorithm 1** Alternating Optimisation Technique.
## 2 Material and Methods
### Financial Analysis  
In this section, the main economic indexes are presented. Before their calculation, it is important to define the capital cost of the whole installation (\(C_{0}\)), and the annual cash flow (\(CF\)). First of all, the capital cost is made up of the cost of the collectors, the TES tank, the ORC module, the PEM water electrolyzer, the hydrogen compressor, the hydrogen storage tank, and the heat exchangers (water preheater, and intercooler); therefore, the capital cost is calculated by the following equation:  
\[C_{0}=C_{PTC}+C_{tank}+C_{ORC}+C_{PEM}+C_{comp}+C_{hydrogen\_tank}+C_{HEX} \tag{32}\]  
In addition, the annual cash flow is defined by considering the inflows from the production of electricity and green hydrogen, which are described by the cost of electricity (\(K_{el}\)), and the cost of hydrogen (\(K_{hydrogen}\)), as well as the outflows from the operation and maintenance costs (\(K_{O\&M}\)); therefore, the following expression is made:  
\[CF=Y_{el,net}\cdot K_{el}+M_{hydrogen}\cdot K_{hydrogen}-K_{O\&M} \tag{33}\]  
(\(M_{hydrogen}\)) is the total annual hydrogen production in kg. Taking into account the above definitions, the four main economic indexes (simple payback period, payback period, net present value, and internal rate of return) are assumed. More specifically, the simple payback period (\(SPBP\)) is defined as:  
\[SPBP=\frac{C_{0}}{CF} \tag{34}\]  
The payback period (\(PBP\)) is defined as:  
\[PBP=\frac{\ln\left(\frac{CF}{CF-C_{0}\cdot i}\right)}{\ln(1+i)} \tag{35}\]  
The net present value (\(NPV\)) is defined as:  
\[NPV=-C_{0}+CF.\frac{\left(1+i\right)^{N}-1}{i\cdot\left(1+i\right)^{N}} \tag{36}\]  
The internal rate of return (\(IRR\)) is defined by the following non-linear expression:  
\[IRR=\frac{CF}{C_{0}}\cdot\left(1-\frac{1}{\left(1+IRR\right)^{N}}\right) \tag{37}\]  
The parameters of the financial study are presented in Table 3.
# Adaptive Learned Belief Propagation for Decoding Error-Correcting Codes
## Appendix A Protograph-Based QC-LDPC Codes
### Construction for the Multi-Edge Case  
The above construction can be extended to multi-edge PB QC-LDPC codes. Here, \(\mathrm{B}_{i,j}\), instead of a binary number, is a sequence of length \(D_{i,j}\) with entries in \(\mathbb{F}_{2}\). Likewise, \(p_{i,j}\) is a sequence of length \(D_{i,j}\) with entries \(p_{i,j}^{d}\in[-1{:}M-1]\), \(d\in[D_{i,j}]\). Then, \(\mathrm{H}_{i,j}=\sum_{d=1}^{D_{i,j}}p_{i,j}^{d}\). This code is represented by a Tanner graph where there are multiple edges of different types between the variable and the check nodes. We say this code has type \(D=\max_{i,j}D_{i,j}\geq 1\). A single-edge PB QC-LDPC code is type \(1\).  
**Example A2**.: _Consider \(\lambda=2\), \(\omega=3\),_  
\[\mathrm{B}=\begin{pmatrix}0&(1,1)&1\\ 1&0&(1,1,1)\end{pmatrix}\;and\;\;\mathrm{P}=\begin{pmatrix}-1&\begin{pmatrix}p _{1,2}^{1},p_{1,2}^{2}\end{pmatrix}&p_{1,3}\\ p_{2,1}&-1&\begin{pmatrix}p_{2,3}^{1},p_{2,3}^{2},p_{2,3}^{3}\end{pmatrix} \end{pmatrix}.\]  
_Then_  
\[\mathrm{H}=\begin{pmatrix}\mathrm{I}^{-1}&\mathrm{I}^{p_{1,2}}+ \mathrm{I}^{p_{1,2}^{2}}&\mathrm{I}^{p_{1,3}}\\ \mathrm{I}^{p_{2,1}}&\mathrm{I}^{-1}&\mathrm{I}^{p_{2,3}^{1}}+\mathrm{I}^{p_{ 2,3}^{2}}+\mathrm{I}^{p_{2,3}^{3}}\end{pmatrix}.\qed\]
# MEMS-Based Vibration Acquisition for Modal Parameter Identification of Substation Frame
## 2 Field Test of Vibration Data Acquisition with Portable Devices
### Overview of the Test Substation Frame  
The test substation frame is located in Baoji, Shaanxi Province, and represents the first of a new type of hybrid Gas-Insulated Substation. Due to the compact overall dimensions of this substation, conducting a dynamic response study on its substation frame holds significant engineering value, as shown in Figure 1a. Typically, substations consist of multiple rows of substation frames. To ensure the overall rigidity of the frame while maintaining the flexibility of the outgoing lines, the structural designers retained the central column of the first substation frame in the substation. Figure 1b illustrates the main dimensions of the test substation frame, which is a double-layer truss structure with left-right symmetry. The upper layer is the outgoing line truss, and the lower layer is the busbar truss. The length from the outermost column to the central column is 18 m, the height of the busbar truss is 15.7 m, and the height of the outgoing line truss is 26 m. Despite the relatively compact structure of this substation frame, a lightning protection wire has been installed to enhance its lightning resistance.
# Remote Sensing for Irrigation of Horticultural Crops
## 4 Approaches to Precision Irrigation
### Time of Measurements  
[PERSON] et al. (2017) found that the CWSI measured in the early afternoon hours was a good descriptor of water status of a pistachio orchard. [PERSON] et al. (2015) developed baseline equation to calculate eggplant CWSI reflecting water deficit and salinity stresses, and also studied the effect of growth environment (field and greenhouse) on eggplant CWSI values and its relationship with crop evapotranspiration and yield. CWSI values were significantly affected by water deficit and salinity, and rising levels of water salinity decreased the slope of the lower baseline in both crop conditions, along with a rise in the upper baseline.  
[PERSON] et al. (2018) found that the best time to obtain robust and more physiologically meaningful thermal data to assess grapevine water status was between 11:00 h and 14:00 h, independent of the variety.  
[PERSON] et al. [182] found differences in estimating CWSI at different times of day. The smallest difference was from 10:00 to 11:00 h and the highest from 12:00 to 13:00 h periods. In their study, they combined IR thermometry with some weather parameters to develop non-water-stressed and non-transpiring baselines for irrigated maize in of Colorado (USA).  
[PERSON] and [PERSON] [163] reported that clouds (thin cirrus), often present during the best two-hour-time for CWSI monitoring, rapidly caused changes in levels of solar radiation and, as a consequence, an effect on \(\mathrm{T_{nws}}\). They also provided a detailed description of physiological variations within a canopy; particular attention was given to rapid changes of leaf temperature in response to environmental fluctuations (e.g., air turbulence, changing radiation). For this reason, they proposed the adoption of a 2-h-averaged CWSI value based on 15-min average measured values (canopy temperature, air temperature, solar radiation, relative humidity, and wind speed), which provided a sound daily CWSI value under variable climatic conditions. The results appeared to be consistent even when comparing two grape cultivars, characterized by different canopy temperature in response to climatic conditions and irrigation conditions.  
[PERSON] et al. [172] found a good estimate for the calculated daily mean CWSI of well-watered leaves between 10:00 and 16:00 h. [PERSON] et al. [84], using airborne thermal data of an olive orchard, found the best results from 07:30 to 09:30; however, at 12:30 h there was an overestimate of canopy conductance values. [PERSON] et al. [202] found that the correlation between CWSI and \(\mathrm{\Psi_{L}}\) had different \(R^{2}\) values at different hours of measurements (0.46 at 09:30 and 0.71 at 12:30), suggesting that the latter was the more favorable time for obtaining thermal images that were linked with \(\mathrm{\Psi_{L}}\) values.  
In [PERSON] et al. [145], CWSI was measured from 13:00 to 15:30 coinciding with the times at which the non-stressed baseline was obtained.
## 5 Conclusions  
In this paper, the rain effects on the performance of a full-duplex E-band link is investigated based on measurement campaign done in Malaysia. The CDF of rain attenuation at 73.5- and 83.5-GHz E-band links were calculated for longer (1.8 km) and shorter (300 m) LOS paths. The maximum rain attenuation for full-duplex links are 40.1 dB and 25 dB for 1.8 km and 300 m at the rain rates of 108 and 193 mm/h, respectively. The rain rates above 108 and 193 mm/h cause an outage of full-duplex links with outage probabilities are \(2.9\times 10^{-4}\) and \(6\times 10^{-5}\) for longer and shorter links, respectively. The throughput degradation for full-duplex link with rain rate was presented. The E-band link with low rain rate below 5 mm/h along 1.8 km and 10 mm/h along 300 m can support up to 4.5 Gbps for long link and 2.8 Gbps for short link; while for heavy rain above 80 mm/h (at 1.8 km distance) and 110 mm/h (at 300 m distance), the throughput degrades to 283.2 Mbps. Applying these findings would  
Figure 8: Variation of throughput with rain rate at full-duplex wireless back-haul E-band link along 300 m.  
Figure 7: Variation of throughput with rain rate at full-duplex wireless back-haul E-band link along 1.8 km.  
help to enhance the architecture and service of full-duplex wireless back-haul E-band connections that will be formed at other related sites as well as in other tropical regions.  
Conceptualization, A.A.-S. and M.H.A.; data curation, A.A.-S. and M.C.; formal analysis, A.A.-S., M.M., M.C., M.H.A., and T.A.R.; funding acquisition, A.A.-S., M.C., and M.H.A.; investigation, A.A.-S., M.M., M.C., M.H.A., and T.A.R.; methodology, A.A.-S. and M.M.M.; project administration, A.A.-S., M.C., and M.H.A.; resources, A.A.-S., M.M., and M.H.A.; software, A.A.-S. and M.M.M.; supervision, M.C. and M.H.A.; validation, M.M., M.C., M.H.A., and T.A.R.; visualization, M.M., M.C., and T.A.R.; writing--review and editing, M.M., M.C., and T.A.R. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Universiti Teknologi Malaysia (UTM) under HICOE Grant RJ130000.7851.4J413, and by MOHE through FRGS-UTM Grant RJ130000.7851.5F177. It was also supported by Manu Lab, NTNU, Gjovik.  
The authors declare no conflict of interest.