esa-sceva/satcom-chunk-collection huggingface dataset:
# Scene Text Recognition That Eliminates Background and Character Noise Interference
## 4 Experimental Results and Analyses
### Eliminate Background and Character Self-Noise Interference  
The ENBC network is trained on the text-train and text-valid datasets and tested on the text-test dataset. Table 1 lists the test results of the ENBC network, where P, R, andF are abbreviations for the evaluation metrics precision, recall, and F-score, respectively. IoU stands for intersection over union. IoU evaluates the overlap between the predicted foreground region and the ground truth label region, with IoU values ranging from 0 to 1, where higher values are better. The calculation formula is:  
\[IoU=\frac{TP}{TP+FP+FN} \tag{18}\]  
As shown in the table, the ENBC network achieved an F-score of 96.24% and an IoU value of 92.76% on the test set. This indicates that the ENBC network not only eliminates background noise in scene text images but also reduces the interference of character noise, thereby improving the accuracy and robustness of the recognition.  
The test results of the ENBC network on the text-test, IIIT5K, IC15, IC03-char, IC03-word, and CUTE80 datasets for removing background noise and character noise are displayed in Figure 10 below to confirm the network's generalization performance on more datasets. The first row of each dataset displays the input images, which display problems including character distortion, uneven illumination, object occlusion when there is background noise, and comparable background and character textures.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **P** & **R** & **F** & **IoU** \\ \hline Text-test & 93.91 & 98.69 & 96.24 & 92.76 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test results of the ENBC network on text-test (%).  
Figure 10: Partial sample test visualization results.
## 3 Results and Discussion
### FTIR Analysis  
FTIR was used to identify the functional groups on the membrane, photocatalyst, and photocatalytic membrane. The spectra are shown in Figure 3. The spectrum of CA membrane was characterized mainly by bands at 2940 and 2870 cm\({}^{-1}\) attributable to the asymmetric and the symmetric C-H stretching, respectively. Additionally, the band at 1745 cm\({}^{-1}\) was attributed to the carbonyl group of the ester functional group while the band at 1168 cm\({}^{-1}\) was spoken for by the asymmetric stretching of the ether groups and the band at 1215 cm\({}^{-1}\) was associated with the carboxylate C-O stretch.  
On the other hand, scrutinizing the FTIR spectrum of the photocatalyst, 1% CuO/TiO\(\varnothing\)GCN (19), evidence for TiO\({}_{2}\) nanoparticles and GCN was observed. For instance, the small band at 700 cm\({}^{-1}\) and the sharp decrease in transmission under 500 cm\({}^{-1}\) are attributable to the Ti \(-\)O\(-\)Ti stretching vibration. The presence of GCN was mainly signified by bands at 3100-3400 cm\({}^{-1}\) representing the N\(-\)H bond stretching vibration. The band at 1570 was associated with the C-N stretching while the bands at 1250, 1327, and 1420 cm\({}^{-1}\) were synonymous with the C-N stretching of the heterocyclic aromatic.  
The FTIR spectrum of the photocatalytic membrane was observed to be a sum of the two other spectra with a few discrepancies. For instance, the band associable with Ti\(-\)O\(-\)Ti at 700 cm\({}^{-1}\) was not on the spectrum of the photocatalytic membrane. The ester band of the membrane at 1745 cm\({}^{-1}\) appeared to be more pronounced and at a shorter wavelength, relative to how it was observed on the spectrum of the membrane alone.
## 4 Ambiguity Resolution
The residual histograms show symmetric distributions centered around zero cycles, particularly for NL ambiguities. The residuals of WL and NL ambiguities represent the disparities between the real values and the fixed values. The mean value, STD, and RMS of the ambiguity residuals are exhibited in Table 5. It presents the average residuals for both WL ambiguity and NL ambiguity, indicating that the mean residuals are near to zero. The RMS values of the residuals for WL ambiguity and NL ambiguity are 0.069 and 0.057 cycles for B1I/B3I, whereas those for B1C/B2a are slightly smaller at 0.066 and 0.055 cycles, respectively. Consequently, the B1C/B2a combination demonstrates superior performance in ambiguity fixing compared with B1I/B3I.
## 2 Design of a Structural Health Monitoring System for a Jacket Offshore Platform
### Assessment Subsystem
#### 2.3.3 Damage Identification Based on Strain Modal  
Damage identification is a very important link in the evaluation subsystem. It can identify whether local member damage occurs during the operation of offshore platform, and then locate and quantify the damage. Damage identification method based on strain modal has been employed and verified in previous research [31], and the strain modal can be obtained from strain monitoring, then, the damaged member can be located by determining the corresponding position of the mutation of the strain modal difference before and after the damage, and the magnitude of the amplitude can reflect the damage degree.
## 3 Research and Results
### Single Date Estimation Comparison  
In this subsection, we compare the performances of the wave kinematics-based DSPEB model (W-DSPEB), color-based DSPEB (C-DSPEB), and S2 Shares based on their single-date estimates. All test images date to 2018. Six images are collected for the tests in French Guiana, and four images for Gironde. Figure 8 shows an example single-date reconstruction over each of the test sites.  
As seen in Figure 8, we note that W-DSPEB outperforms S2 Shares on RMSE and correlation in the French Guiana site. While S2 Shares predicts shallow depths with high accuracy, W-DSPEB maintains a higher correlation in deeper waters. On the selected date in the Gironde site, we observe a scattered estimate from the W-DSPEB method which has an overall high correlation but is outperformed by S2 Shares in terms of RMSE.  
The performance of C-DSPEB varied greatly over different dates due to its sensitivity to background color, which we further discuss in the next section. The highest single-date RMSE of C-DSPEB based on the selected dates is 15.26, whereas the lowest single-date RMSE of C-DSPEB in French Guiana was 4.33. We note that this lower RMSE is similar to the performance of other deep learning color-based methods, notably [39], which has an RMSE of 3.03 in San Juan for a single date; Ref. [39] notes that the performance of the color-based deep learning model highly depends on water turbidity, which we observe here in the difference of prediction between different dates.  
We note that the training of both deep learning models C-DSPEB and W-DSPEB uses gradient estimates from mini-batches which can contain multiple dates together. The same point may therefore have multiple estimates attributed to it from different satellite images, and the gradient directions from these estimates will be averaged for the network update. We hypothesize that a training method focused on accuracy for a single date could improve the variability of estimates for the same date.  
Figure 7: Training of the W-DSPEB and C-DSPEB models, showing the MSE losses over the training and validation sets in French Guiana (**a**) and Gironde (**b**). Dashed lines represent performance on the validation set.
## 4 Results and Discussion
### Limitations  
Before concluding this article, it is important to mention some aspects that can influence the validity of the presented results.  
First, it is fair to say that the research design ensured large data variability, thereby diminishing way the relevance of individual psychological or physiological traits as well as conditions specific to certain seasons and classroom orientations. Indeed, the extended monitoring period not only allowed for gathering two years of outdoor and indoor environmental data, but also led to the collection of lesson attendance data for more than 20 different classes from 5 th to 12 th grade, with a total of more than 500 students. The fact that the four classrooms had different orientations (south, west, north) and were located on different floors of the building (intermediate and top floors) also contributed to data variability. However, it is also true that environmental and lesson attendance data can be influenced, among other things, by school architecture, students' and teachers' cultural traits, and Lisbon's climate. Therefore, the external validity of the study is limited to architectonic, cultural and climatic conditions typical of Southern European countries.  
Second, the research was designed to minimize unwanted residuals due to occupancy, especially those due to environmental variables. For this reason, great care was taken in the analysis of lesson attendance logs; in addition, a large number of outdoor environmental variables were included in the modeling, and we used sensors that were both accurate and precise. Nonetheless, it should be recognized that the measurement accuracy is lower for metabolic CO\({}_{2}\) production rate, and this variable (the only non-physical one) may be insufficient to characterize occupants' behavior.  
Finally, although the developed path model takes into consideration both building physics and occupants' adaptive behavior, it is important to acknowledge that the excellent fit statistics of our trimmed model do not exclude alternative models which might produce similar or even better results [42].
# Optimization Strategies in Quantum Machine Learning: A Performance Analysis
## 3 Model Architecture
### Data encoding:  
Quantum data encoding is a crucial step in translating classical data into quantum states. Various encoding techniques, such as basis encoding, angle encoding, and amplitude encoding, are employed depending on the application and dataset characteristics [33].  
In this model, the Z feature map is used to encode classical data into a quantum state. A four-qubit quantum register, labeled 'q', is utilized, with operations applied to qubits 0, 1, 2, and 3. The encoding process involves the following sequence of operations for each qubit:  
1. Apply a Hadamard gate (H);
2. Apply a parameterized Phase gate (P) with a rotation angle defined as 2.0*x[i], where x[i] represents the classical input feature;
3. Apply another Hadamard gate (H);
4. Apply another parameterized Phase gate (P).  
This sequence (H-P-H-P) is repeated for all four qubits, creating a complex superposition state that captures non-linear relationships in the data. Figure 4 illustrates the detailed structure of the encoding circuit, which indicates the circuit construction of the Hadamard gate 'h', to create a superposition, and the Phase gate 'p' applied to all four  
Figure 3: Features’ contribution to each principal component.  
Figure 2: IG of the features.  
qubits in succession, from qubit 0 to qubit 3. In the end, four-dimensional classical data are encoded into a four-qubit quantum state using this specific pattern of gates.
## 3 Estimated Amplitude Standard Deviation  
Having presented the analytical expression used to obtain an estimate of the sinusoidal amplitude, \(\hat{A}\), from the acquired samples \(z_{i}\), one can proceed to determine the standard deviation of that estimate. To begin, one first determines the variance of the estimative of  
\[\begin{array}{c}\sigma_{A}^{2}\approx\left[\cos^{2}(\varphi)+\sin^{2}(\varphi) \right]\cdot\frac{A^{2}}{M}\left(1-e^{-\sigma_{\theta}^{2}}\right)+\\ -\left[\cos^{2}(\varphi)-\sin^{2}(\varphi)\right]\cdot\frac{A^{2}}{2M}\cos(2 \varphi)\left(e^{-\sigma_{\theta}^{2}}-e^{-2\sigma_{\theta}^{2}}\right)+\\ -2 cos(\varphi)\sin(\varphi)\cdot\frac{A^{2}}{2M}\sin(2\varphi)\left(e^{- \sigma_{\theta}^{2}}-e^{-2\sigma_{\theta}^{2}}\right).\end{array} \tag{23}\]  
Simplifying this leads to  
\[\begin{array}{c}\sigma_{A}^{2}\approx\frac{A^{2}}{M}\left(1-e^{-\sigma_{ \theta}^{2}}\right)+\\ -\cos(2\varphi)\cdot\frac{A^{2}}{2M}\cos(2\varphi)\left(e^{-\sigma_{\theta}^{ 2}}-e^{-2\sigma_{\theta}^{2}}\right)+\\ -\sin(2\varphi)\cdot\frac{A^{2}}{2M}\sin(2\varphi)\left(e^{-\sigma_{\theta}^{ 2}}-e^{-2\sigma_{\theta}^{2}}\right).\end{array} \tag{24}\]  
Combining the trigonometric terms leads to  
\[\sigma_{A}^{2}\approx\frac{A^{2}}{M}\left(1-e^{-\sigma_{\theta}^{2}}\right)- \frac{A^{2}}{2M}\left(e^{-\sigma_{\theta}^{2}}-e^{-2\sigma_{\theta}^{2}}\right). \tag{25}\]  
Further simplification results in  
\[\sigma_{A}^{2}\approx\frac{A^{2}}{2M}\left(2-3e^{-\sigma_{\theta}^{2}}+e^{-2 \sigma_{\theta}^{2}}\right). \tag{26}\]  
This is the main analytical result of this paper. Note that for small values of jitter and phase noise standard deviation, this expression can be reasonably well approximated by  
\[\sigma_{A}^{2}\Big{|}_{\sigma_{\theta}<1}\approx\frac{A^{2}}{2M}\sigma_{ \theta}^{2}. \tag{27}\]  
These derivations allow one to compute the standard deviation of the estimated sinusoidal amplitude given the actual amplitude, the number of samples, and the jitter/phase noise standard deviation.
## 2 Radiative Processes
### The Fermi Model  
To determine the transitions between a vibrational level and another level of a bound state, it is necessary to perform a calculation of the transition probability.  
Consider a system withunperturbed Hamiltonian \(H_{0}\) Eigen values \(E_{n}\) and Eigen states \(|\psi_{n}\rangle\). The transition probability of a bound vibrational state \(|\psi_{n}\rangle\) towards a continuum state \(|\psi_{m}\rangle\), under the effect of a disturbance W (external field), is given by [PERSON]'s Golden Rule:  
\[p_{nm}(t)=\frac{2\pi}{\hbar}|W_{nm}|^{2}\rho(E_{m}=E_{n})t \tag{1}\]  
where \(\rho(E)\) is the density of states in the continuum such that \(\rho(E)\)dE is the number of states in an energy interval dE, and \(W_{nm}=\langle\psi_{n}|W|\psi_{m}\rangle\) is the matrix element of the disturbance W.  
The probability of transition per unit time is written:  
\[\frac{dp_{nm}(t)}{dt}=\frac{2\pi}{\hbar}|W_{nm}|^{2}\rho(E_{m}=E_{n}) \tag{2}\]  
The transition probability makes sense when the perturbation is applied for a sufficient interval of time (\(t>\succ\frac{2\pi\hbar}{\Delta E}\)) for the system to feel the disturbance.
## 2 Preliminaries on Mittag-Leffler Functions, Their Properties and Available Fractional Operators
### The Fractional Order in Caputo-Fabrizio and Atangana-Baleanu Derivatives  
Caputo-Fabrizio Fractional Operator: The Fractional Parameter  
In the definition of the Caputo-Fabrizio operator, the stretched time \((t-s)\) is multiplied by a dimensional factor \(\lambda=\alpha/(1-\alpha)\) which should have a dimension \(s^{-1}\) while actually, it should be dimensionless because physically \(\alpha\) is a dimensionless parameter. By a nondimesionalization of the exponential function with the help of characteristic time of the relaxation process \(t_{0}\), namely  
\[\exp\frac{(t-s)}{\tau}=\exp\frac{(t/t_{0}\ -s/t_{0}\ )}{\tau/t_{0}}=\exp\frac{( \bar{t}-\bar{s})}{\bar{\tau}} \tag{61}\]  
where \(\tau\) is the relaxation time and in the context used earlier \(\tau=1/\lambda\) in the definition (58) (see also the explanations about the construction of (59) and has a dimension \(1/time\).  
This nondimesionalization does not change the meaning of the exponential relaxation function but avoids any doubts about the definition of the fractional order \(\alpha\) as [63].  
\[\frac{1-\alpha}{\alpha}=\frac{\tau}{t_{0}}\Rightarrow\alpha=\frac{1}{1+\tau/t _{0}} \tag{62}\]  
The Atangana-Baleanu derivative of Caputo sense (ABC) with \(B(\alpha)=1\) can be rescaled as [64]  
\[\begin{split}^{ABC}D_{\alpha+}^{\alpha}f(t)=\frac{1}{1-\alpha} \int\limits_{0}^{z}\frac{df(\bar{s})}{ds}E_{\alpha}\Big{[}-\Big{(}\frac{\bar{ t}-\bar{s}}{\tau}\Big{)}^{\alpha}\Big{]}d\bar{s},\\ \frac{1-\alpha}{\alpha}=\Big{(}\frac{\tau}{t_{0}}\Big{)}^{\alpha} =(\tau)^{\alpha},\quad\bar{t}=\frac{t}{t_{0}},\quad z=s/t_{0}\end{split} \tag{63}\]  
In detail, we have \[{}^{ABC}D_{a+}^{a}f(t)=\frac{1}{1-\alpha}\int\limits_{0}^{\frac{2}{2}}\frac{df(s)}{ ds}\Bigg{\{}\sum\limits_{k=0}^{\infty}\frac{1}{\Gamma(\alpha k+1)}\left[-\left(\frac{ f-s}{\tau}\right)^{\alpha}\right]^{k}\Bigg{\}}ds \tag{64}\]  
Therefore, the argument of the Mittag-Leffler kernel \(E_{a}(-Z)\) is \(Z=[\alpha/(1-\alpha)](t-s)^{\alpha}\) and consequently we get [64]  
\[\left(\frac{1-\alpha}{\alpha}\right)^{k}=\left(\frac{\tau}{t_{0}}\right)^{k} \Rightarrow\alpha=\frac{1}{1+(\tau/t_{0}\ )} \tag{65}\]  
The results are the same as established for the Caputo-Fabrizio operator [64; 65; 66].  
Hence, the fractional order \(\alpha\) introduced through \(\lambda=\alpha/(1-\alpha)\) in the generalized formulation of the derivatives has a physical meaning and is strongly related to the characteristic time scales of the relaxation process modeled. In this context, the case with the Caputo-Fabrizio operator is more physically clear due to the large background of exponentially decaying processes.
## 4 Results
### Lithology Classification Feature Extraction
Transitional facies clusters exhibit moderate DT values and high TQA fluctuations, indicative of the heterogeneous nature of sand- limestone interbeds in a tidal flat environment. The variation in TQA is further amplified by cementation differences resulting from periodic exposure. To address potential discrepancies between statistical clustering and geological reality, the lithology assignments of each cluster were rigorously validated against physical measurements. The lithology corresponding to each cluster was calibrated by integrating LWD logging parameters with high-resolution borehole imaging data (Figure 14). For example, cluster 7, which is statistically dominated by coarse sand (74.1%), exhibited clear gravel laminations and high-angle cross-bedding in imaging logs, confirming its interpretation as a high-energy fluvial deposit. Conversely, cluster 6, containing 44.8% clay shale and 27.1% limestone, displayed alternating thin-bedded shale and mcritic limestone layers in imaging data, aligning with its transitional facies classification. Such cross-validation ensures that the UMAP + K-means-derived clusters not only reflect statistical similarity but also correspond to measurable geological features, thereby mitigating the risk of overinterpreting purely algorithmic patterns. The 15 cluster labels generated by K-means clustering were incorporated into the lithology classification model as categorical features. Along with the original logging features and UMAP dimensionality-reduced features, these labels formed a multimodal input space. The cluster labels were transformed into categorical features using One-Hot  
Figure 13: Distribution of parametric features across clusters: (**a**) feature distribution of DT for each cluster and (**b**) feature distribution of TQA for each cluster. encoding, thereby capturing the topological correlations and sedimentary facies information of the samples within the low-dimensional manifold space. By leveraging unsupervised clustering, hidden geological structure patterns were extracted, addressing the strong reliance of traditional supervised learning on artificial labels. This approach enhances the model's generalization ability, particularly in small sample scenarios. This paper introduces a feature engineering framework that combines physical interpretability with data-driven generalization, facilitating the intelligent recognition of highly heterogeneous thin reservoirs.
## 3 Results and Discussion
### Combined Method and Performance
%RSD with and without internal standard (IS) refers to 20 \(\mathrm{\SIUnitSymbolMicro g}\)/kg concentration, extracted five times. The method was also tested for other currently used pesticides, namely, boscalid, diflufenican, epoxiconazole, metalaxyl, and terbutylazine, but was found lacking in terms of precision (R2 = 0.899-0.94; RSD as high as 40% for 20 ug/kg extractions; n = 5). In the future, an isotopically labeled internal standard which better mimics the chemical properties of these compounds should be used to mitigate such problems. Further optimization of the method in order to achieve better concentration factors from the immersion-SPME extraction would be desirable. In terms of the GC-MS/MS determination, it was possible to isolate every analytes' signal, and thus, a combined method involving more compounds could be viable. A chromatogram from a spiked sample can be seen in Figure 4.  
The limit of quantification obtained for glyphosate was significantly lower than other published methods (e.g., Ref. [3], LoQ of 50 ug/kg), likely due to the low extraction solvent volume used and high concentration factor generated by drying the aqueous extract, which nonetheless is a difficult and time-consuming process, often avoided. It is possible that a different soil, particularly of finer texture, might require a larger extraction solvent volume to obtain an adequate recovery for glyphosate, which would compromise the detection limit, unless an even larger concentration is performed. In terms of the other analytes, detection and quantification limits are within normal ranges for most multiresidue methods, but not significantly better [6].
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 5 Publicly Available Datasets for Medical Video Analysis
### ASU-Mayo Clinic Colonoscopy Video Dataset  
The ASU-Mayo clinic is the largest colonoscopy video dataset [11]. The training set comprises 20 colonoscopy videos, of which ten videos show polyps, and the other 10 show normal physiology. Furthermore, the test set comprises 18 unlabeled colonoscopy videos to assess the performance of the applied algorithm. The resolution of the videos is 688 \(\times\) 550 and contains varying amounts of frames ranging from 324 to 1925. The videos are stored in.wmv format.
## 5 Tactile Internet Application Requirements
### Discussion on Application Requirements
In 5G, interoperability is supported through standardized protocols, such as 3 GPP's 5G NR, network slicing, and cloud-native architectures. 6G plans to advance interoperability further by leveraging AI-driven network orchestration, blockchain-based authentication, and dynamic spectrum sharing to integrate terrestrial, satellite, and underwater communication systems.
# Smart Grid Data Management in a Heterogeneous Environment with a Hybrid Load Forecasting Model
## 6 Results and Discussion (Case Study 2)
### Case Study 2: Data Analytics (Load Forecasting)  
Due to the lack of sufficient time-series information in Case Study 1, for data analytics of load forecasting, we have obtained another dataset from the Iraqi Ministry of Electricity. The dataset contained the power load specific to the Baghdad Governate for 12 months of 2019 (1 January 2019 to 31 December 2019) and distributed according to a timestamp in a matrix of 24 h per day. The summary of the dataset is shown in Table 6.
## 2 Pre-Cryptographic Security Technologies
### Level Security Models as Early Data Protection Mechanisms  
_Multi-level security (MLS),_ invented in the 1970s, implements one out of two security goals, either confidentiality or integrity. Its name is derived from various levels of privileges needed by subjects to access data. MLS is a long-established and well-researched security concept that was developed for public and military administration and their confidentiality needs. Its basic assumption is that there are security levels from \"public\" to \"top secret\" that are assigned to documents or data sets. Access to documents is only allowed for users, programs or systems that have at least the same or higher security clearance than the object that is being accessed. Such classifications exist in many areas of government. There are several security models that specify MLS. The most important examples are Bell-LaPadula [3], Biba [4] and Lomac [5]. MLS is most concerned with managing access to data, and with protecting its integrity. It was clearly developed with large, centralized data processing in mind. Concerning privacy, MLS has a particular challenge where data has varying levels of sensitivity, which may even change over time.