esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Review Methodology
### Acknowledgment of Methodological Limitations  
While the review methodology employed was designed to be comprehensive and systematic, certain limitations must be acknowledged to provide a balanced perspective. The selection of databases, although extensive, may not have encompassed all the relevant studies, particularly those published in specialized or less accessible repositories. The exclusion of non-English publications introduced a language bias, potentially overlooking significant research conducted in other languages. Additionally, the focus on peer-reviewed and published studies may have resulted in the omission of valuable insights from grey literature, such as industry reports or unpublished research. The temporal constraint of including studies published only from 2015 onwards might have excluded foundational research that, while older, remains relevant to the topic. These limitations were considered in the interpretation of the review findings, and recommendations for future reviews include expanding database searches and incorporating multilingual sources to enhance comprehensiveness and mitigate potential biases.
# Identifying Mis-Configured Author Profiles on Google Scholar Using Deep Learning
## 8 Limitations  
In the paper, we introduce an approach to detect the mis-configured author profiles on Google Scholar. However, there are still some limitations of our work.  
Firstly, we believe that mis-configuration means wrongly claiming others' papers in the paper list. However, the author profile owner's missing papers, another scenario of mis-configuration, has not been taken into consideration. It is relatively difficult to detect the incompleteness of the paper list, and we might need to refer to third-party data sources.  
Secondly, while crawling data from Google Scholar, we select the research fields from the subdomains in \"Metric\" page. However, there are sub-sub-domains under these tags, and some computer science related fields are interdisciplinary. Some tags are not covered in this paper, such as Information Theory and IoT. We manually add some tags to the crawling list to expand the coverage of our dataset. However, there are still some tags being omitted, which leads to an incomplete set of computer science authors.  
Thirdly, owing to the limitation of labor force and time, we labeled 3000 scholars' author profiles. The labeled data may be biased on certain features.
# Partial Pre-Emphasis for Pluggable 400 G Short-Reach Coherent Systems
## 5 Design Trade-Offs  
An important aspect of 400 ZR system design is power consumption. Reducing the frequency response peaking of the pre-emphasis filter, i.e., relying more on the post-compensation of the channel ISI, increases the signal power out from DAC, thus lessening the required gain of the driver or EDFA.  
We summarize that partial pre-emphasis provides the following advantages for coherent system design at the expense of manageable performance penalty: (i) this leads to less signal distortion from the nonlinearity of the RF DA and modulator due to reduced PAPR. It also helps mitigate the effect of fiber nonlinearity during transmission [19]. However, this has minimal performance impact on the targeted short-reach transmission, i.e., 100 km specified for 400 ZR. (ii) It allows the transceiver to be more resilient to jitter induced by the PLL driving the DAC clocking. It is known that high-frequency components are more sensitive to jitter [20], therefore by performing less peaking (i.e., by reducing amplitudes of high-frequency components), smaller system SNR degradation is observed. As follows, the SNR degradation due to jitter (\(T_{jitter}\)) is calculated as a function of frequency components of the signal (\(Freq_{in}\))  
\[SNR_{jitter}\left[dBc\right]=\ -20*\log\Bigl{(}2*\pi*Freq_{in}*T_{jitter} \Bigr{)} \tag{2}\]  
(iii) It relaxes the \"Adjacent channel isolation\" per port specification for the channel Multiplex/DeMultiplex (CMD), since for point-to-point fixed grid systems (i.e., 75 GHz), partial pre-emphasis means that the inter channel interference will be mitigated due to less power leakage from adjacent channels. This also applies to gridless (or flexible-grid) coherent networks though not yet included in the 400 ZR standard. (iv) Moreover, the fidelity of clock phase error estimators, such as Gardner [21] and Godard [22], that depends on the clean energy at the low and high end of the signal frequency response improves since partial pre-emphasis reduces the leaked noise into the edges of neighboring channels [23].
# Designing a C-ITS Communication Infrastructure for Traffic Signal Priority of Public Transport
## Materials
### Objectives and Challenges  
The goal of the examination, analysis, and evaluation was to clarify and answer the following questions and use cases in order to provide a future-oriented conceptualization of public transport acceleration:  
* Which method can be used to determine the communication ranges that are necessary to send the minimum requirements of all currently defined registration points with C-ITS G5? (See Section 3.1).
* On which sections of the U5 track are public transport vehicles able to reduce delays? When exactly does this happen? (See Section 3.2).
* Which algorithm can be used to determine the C-ITS service that best suits the current state of traffic? (See Section 4).
## 4 Controller Design
### RBFNN Adaptive Terminal Sliding Mode Fault Tolerant Controller  
In the urban rail ATO, the control method without fault-tolerant function is difficult to meet the requirements of high-precision control due to the limit value, delay and fault of many links such as an actuator, speed sensor, and interface equipment. On the one hand, the factors of comfort and the not infinite traction or brake force of the train lead to the input saturation problem; on the other hand, when the speed sensor detects the speed value of the urban rail train and transmits it to the on-board control unit, as well as the on-board software data preprocessing process, there is a time lag in the data transmitted to the on-board control unit. Furthermore, after a long-time high-speed operation of urban rail trains, the traction or brake actuator of the train will inevitably experience mechanical fatigue damage and accumulated wear due to the complicated work, which will lead to traction or brake failures of the train. This puts forward higher requirements for ATO; ATO systems must have adaptive fault tolerance to ensure the safe operation of urban rail trains [33,34].  
Considering the faults of the train traction or brake system and physical limitations, the actual control input of the train traction or brake system \(u_{c}\) is usually constrained by scalar function, upper and lower limits; that is, the relationship between \(u_{c}\) and designed control law \(u\) is as follows:  
\[u_{c}=\hat{\delta}sat(u)=\left\{\begin{array}{ll}\hat{\delta}u^{+}&u>u^{+}\\ \hat{\delta}u&-u^{-}\leq u\leq u^{+}\\ \hat{\delta}u^{-}&u<-u^{-}\end{array}\right. \tag{56}\]  
Design adaptive control law for scalar function  
\[\hat{\delta}=-\gamma\left[\frac{p}{q}e_{z}^{p/q-1}s-\varpi\hat{\delta}\right] \tag{57}\]  
where \(\gamma>0\) and \(\varpi>0\) are parameters to be designed, \(u^{+}\) and \(u^{-}\) represent the constant of the upper limit value of traction acceleration and the lower limit value of brake deceleration, respectively, and \(\hat{\delta}\) is an estimate of a time-varying scalar function \(\sigma(t)\). Scalar function \(\sigma(t)\) satisfies \(0\leq\sigma(t)\leq 1\), which represents the efficiency factor or health index of train traction or brake. \(\sigma(t)=1\) means that the train control mechanism can work normally. \(\sigma(t)=0\) means that the train control mechanism is completely invalid. \(0<\sigma(t)<1\) means that the train control mechanism is partially invalid. This paper assumes that \(0<\sigma(t)\leq 1\).
## 3 Results
### Reddyproc Interpolation
#### 3.1.2 Interpolation Precision  
The determination coefficient (R\({}^{2}\)), root mean square error (RMSE), and absolute mean error (MAE) were used to evaluate the interpolation accuracy of the Reddyproc algorithm. The determination coefficient of the CO\({}_{2}\) flux fitting obtained through the Reddyproc algorithm was 0.65 (Table 2). In addition, before the Reddyproc algorithm interpolated the missing values, it was necessary to detect the occasional spikes, namely, the outliers, in the half-hour flux data through the outlier detection technique, which usually does not directly affect the annual NEE. However, it affects the quality of the populated data set. Figure 3 shows the CO\({}_{2}\) flux data with the outliers not eliminated on the left, and there are obvious discrete values in some time periods with large deviations. Meanwhile, on the right, the outliers shown were detected and eliminated based on the outlier detection algorithm. After removing outliers, the quality of the CO\({}_{2}\) flux data was improved, and the dispersion was decreased, which indicated that the interpolation data was of a high precision and reliable quality.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Station Name** & **Pearson** & **R\({}^{2}\)** & **RMSE** & **MAE** \\ \hline Longbao & 0.80 & 0.65 & 1.91 & 1.34 \\ \hline \end{tabular} Pearson represents Pearson correlation coefficient, R\({}^{2}\) represents coefficient of determination, RMSE represents root mean square error, and MAE represents mean absolute error.  
\end{table}
Table 2: Interpolation accuracy of Reddyproc algorithm.
# Research on Joint Resource Allocation for Multibeam Satellite Based on Metaheuristic Algorithms
## 1 Introduction
The simulation results demonstrate that NDBC can effectively reduce the computational complexity while substantially improving the quality of the optimization solutions. We also find that the QPSO algorithm performs better than the GA, DE, and PSO algorithms on the JRA problem. We speculate that QPSO is more sensitive to search flexibility. The remainder of this paper is structured as follows. In Section 2, we present the link budget model and derive a formula for the beam data rate. In Section 3, we present the unused bandwidth allocation method, propose a non-dominated beam-coding method, and suggest a generic framework for applying the NDBC method to the metaheuristic algorithm. Finally, in Sections 4 and 5, we analyze the simulation results and conclude this paper, respectively.
# DDPG-Based UAV-RIS Framework for Optimizing Mobility in Future Wireless Communication Networks
## 4 Numerical Results and Analysis
### Scenario Setup  
Simulations were conducted using MATLAB R2022b on an X-64 processing system with an Intel(R) Core(TM) i7-10510U CPU running at 2.30 GHz and 12 GB RAM. The height, speed, carrier frequency, and transmit power of the UAV-RIS were chosen based on operational standards and 3 GPP recommendations. These selections aimed to ensure the relevance of the simulation results to urban scenarios. The urban environment was modeled with varying building area ratio (0.1 to 0.5), number of buildings (750/km\({}^{2}\) to 300/km\({}^{2}\)), and average building height (8 m to 50 m), impacting the signal blockage and LoS availability. The simulation considered different street widths, building distributions, and RIS configurations to reflect real-world challenges such as mobility and dynamic obstacles. The modified K-means algorithm incorporates both spatial proximity and SINR constraints. After initial spatial clustering based on Euclidean distance, SINR and achievable rate thresholds are applied within each cluster to maintain service quality. The algorithm iteratively refines cluster centroids until the cluster memberships stabilize with minimal changes, ensuring convergence. Table 2 summarizes the simulation parameters used in the study.
# Design and Control of a Battery Charger/Discharger Based on the Flyback Topology
## 3 Sliding-Mode Controller Design
### Reachability Conditions  
The reachability conditions evaluate the ability of the SMC to reach the desired surface starting from any operating condition. The concepts of the reachability analysis are:* If the system is operating under the surface \((\Psi<0)\), the switching function derivative must be positive \(\left(\frac{d\Psi}{dt}>0\right)\) to ensure that the system is able to reach the surface \((\Psi=0)\). This first concept is formalized as follows: \[\lim_{\Psi\to 0^{-}}\frac{d\Psi}{dt}>0\] (25)
* If the system is operating above the surface \((\Psi>0)\), the switching function derivative must be negative \(\left(\frac{d\Psi}{dt}<0\right)\) to ensure that the system is able to reach the surface \((\Psi=0)\). This second concept is formalized as follows: \[\lim_{\Psi\to 0^{+}}\frac{d\Psi}{dt}<0\] (26)  
However, the sign of the transversality condition defines the behavior of the switching function derivative, thus it is necessary to define the effective reachability conditions:  
* A positive transversality sign implies that positive changes on \(u\) (from 0 to 1) impose a positive switching function derivative. The formal description of this condition is \(\frac{d}{dt}\left(\frac{d\Psi}{dt}\right)>0\quad\Rightarrow\quad\frac{d\Psi} {dt}>0\) when \(u=1\).
* A negative transversality sign implies that negative changes on \(u\) (from 1 to 0) impose a positive switching function derivative. The formal description of this condition is \(\frac{d}{dt}\left(\frac{d\Psi}{dt}\right)<0\quad\Rightarrow\quad\frac{d\Psi} {dt}>0\) when \(u=0\).  
Taking into account that the design condition defined in (24) imposes positive transversality, the general reachability conditions (25) and (26) are rewritten as the effective conditions (27) and (28), respectively.  
\[\lim_{\Psi\to 0^{-}}\frac{d\Psi}{dt}\bigg{|}_{u=1} >0 \tag{27}\] \[\lim_{\Psi\to 0^{+}}\frac{d\Psi}{dt}\bigg{|}_{u=0} <0 \tag{28}\]  
The previous reachability conditions are analyzed by replacing the switching function derivative (22) into inequalities (27) and (28), obtaining the design expressions given in (29) and (30), respectively, where expressions (15) and (16) have also been used.  
\[-\frac{K_{\Psi}\cdot i_{dc}}{C_{dc}\cdot n}+\frac{K_{i}\cdot v_{b }}{L_{m}} >\frac{di_{dc}}{dt} \tag{29}\] \[\frac{v_{dc}}{n\cdot L_{q}}\cdot\left(\frac{K_{\Psi}\cdot i_{dc} \cdot L_{m}}{C_{dc}\cdot v_{b}}-K_{i}\right) <\frac{di_{dc}}{dt} \tag{30}\]  
In conclusion, the SMC fulfills the reachability conditions when expressions (29) and (30) are satisfied, thus those expressions must be considered in the calculation of the parameters.
# A Lightweight AI-Based Approach for Drone Jamming Detection
## 4 System Model
### Evaluation Metrics  
The performance of the model was evaluated using the state-of-the-art evaluation metrics for ML classification algorithms. The metrics are all based on the number of true positives, false positives, true negatives and false negatives. The chosen evaluation metrics are the following:  
* **Accuracy:** the ratio of correctly classified data points to the total number of data points. \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\] (1)
* **Precision:** the ratio of correctly classified anomalies to the total number of data points classified as anomalies. \[Precision=\frac{TP}{TP+FP}\] (2)
* **Recall:** the ratio of correctly classified anomalies to the total number of anomalies. \[Recall=\frac{TP}{TP+FN}\] (3)
* **F1 score:** the harmonic mean of the precision and recall. \[F1=2\times\frac{Precision\times Recall}{Precision+Recall}\] (4)  
The combination of these metrics gives us concrete insight into the performance of the model.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 2 Metal Sulfide Thin Films
### Nickel-Doped Indium Sulfate Films  
The In2S3 Ni films were deposited onto indium tin oxide (ITO)-coated glass by using indium (III) acetate (In(C2H3O2)3), nickel (II) chloride (NiCl2), and sodium thiosulfate under 75 deposition cycles [59]. The sulfur/(In+Ni) ratio (0.79 to 0.4), band gap (1.99 to 1.61 eV), and crystallinity of the sample reduced with the increasing nickel (Ni) concentration (4% to 6%).
## 2 Materials and Method of Measurement  
A nonlinear polymer has been selected for experimental analysis. Cellulose was discovered in 1838 by [PERSON], and it became the most common organic polymer [31]. Cellulose is a polymeric carbohydrate with repeating hydroxy groups of \(\beta\)-1,4 linked D-glucose units of six-carbon monosaccharides; thus, its chemical formula reads (C\({}_{6}\)H\({}_{10}\)O\({}_{5}\))\({}_{n}\). These hundreds to thousands of repeating units form linear chains. In order to produce individual cellulose microfibrils from the pulp, 2,2,6,6-tetramethylpiperidine 1-oxyl (known as TEMPO)-mediated oxidation was followed by a mechanical treatment such that the lengths of microfibrils were 1-2 \(\upmu\)m and the widths of microfibrils were 3-4 nm, corresponding to a parallel bundle of 5-7 cellulose chains. Sonication and ultracentrifugation were subsequently used for removing large aggregates. The progress was observed under an optical microscope, and the Brunauer-Emmett-Teller (BET) surface area was determined as 34.3 m\({}^{2}\)/g. The original suspension of nanofibrillated cellulose in pure water had a concentration of 0.125 wt %, and by using evaporation, the suspension of 0.74 wt % was prepared and used for the experiments.  
All preparations and measurements were performed by the group of Prof. [PERSON] at the Biomedical Laboratory, Malmo University, and the dataset is published in [32]. A standard rotational viscometer, namely the Bohlin CVO rheometer from Malvern Instruments, with a cone-plate configuration, was selected with a steel cone of 40 mm in diameter and a 4\({}^{\circ}\) cone angle, with the truncated apex at 150 \(\mu\)m to eliminate any contact between the cone and plate. After calibrating and placing the sample limited to the circumference, the cone and probe were covered with a plastic lid to avoid evaporation. All experiments were performed at 20 \({}^{\circ}\)C. With the same sample, a standard amplitude sweep and a frequency sweep were performed by controlling the shear stress; see Figure 1 for the results. A harmonic excitation was achieved by steering the shear stress with a given frequency and amplitude. For the amplitude sweep, the frequency was fixed at 1 Hz, and the amplitude was varied from 5-40 Pa. Analogously, for the frequency sweep, the amplitude was kept constant at 40 Pa, and the frequency was varied from 0.1-40 Hz. Each experiment was repeated two times, and all data are shown in Figure 1 by means of the so-called storage and loss moduli.  
The represented experimental data are an example to present the strategy used in this work. Because of the highly-nonlinear character of the material, especially the frequency sweep has noise between 20-25 Hz. Furthermore, the precision of the amplitude sweep is low. However, we emphasize that the methodology explained in the next sections is not susceptible to experimental inaccuracies and performs well even under high repeatability errors.
## 2 Related Works
### Weed Segmentation  
Research on weed detection has been conducted in recent years [13; 14; 15; 16; 17]. Most of these studies use UAVs to obtain data. [PERSON] et al. developed a dataset based on aerial images captured from corn, wheat, and soybean fields and applied semantic segmentation to detect weeds, achieving an intersection over union (IoU) of 0.9291 [13]. [PERSON] et al. constructed a dataset using aerial images of paddy fields and performed three-class semantic segmentation to classify weeds, rice plants, and the background, with a mean IoU (mIoU) of 0.618 [14]. The dataset used in their study is publicly available and can be used for further research. Building on this, [PERSON] et al. performed training and inference using multiple NN models on the same dataset and achieved a higher mIoU of 0.781 with their proposed MSFCA-Net [17]. In addition to the paddy field dataset, their experiments included the soybean, sugar beet, and carrot datasets. The paddy field dataset yielded the lowest segmentation accuracy, suggesting that weed detection in paddy fields is particularly challenging. As described above, numerous studies have reported semantic segmentation for weed detection. However, most of these studies have utilized datasets based on aerial imagery. As discussed in Section 1, these approaches do not resolve the issue of weeds being hidden beneath rice plants and not being visible in the captured images in the first place. To address this limitation, we proposed an alternative imaging approach using an aquatic drone equipped with a forward-facing camera to achieve an mIoU of 0.676 [18].
## 2 Probabilistic Approach for Cracked Concrete under Carbonation
### Carbonation Model of Cracked Concrete  
For concrete structures with cracks, CO\({}_{2}\) can ingress into concrete through both the crack zone and sound zone. The total flux of CO\({}_{2}\) equals the sum of CO\({}_{2}\) flux in the sound and crack zones. The total flux of CO\({}_{2}\) can be determined as follows:  
\[D_{cr}\omega+D(l-\omega)=D_{eq}l \tag{6}\]  
where \(D_{cr}\) is the CO\({}_{2}\) diffusion in the crack zone (\(D_{cr}\) relates to crack geometry characteristics, concrete materials properties and saturation degree of cracked zone); \(\omega\) is the crack width; \(l\) is the total length of the concrete structural element; and \(D_{eq}\) is the equivalent diffusion coefficient of concrete.  
Compared to the total length of concrete, the crack width is very small and the value of \((l-\omega)\) is approximately equal to that of \(l\). The equivalent diffusion coefficient of concrete can be determined as follows:  
\[D_{eq}=\left[D_{cr}\omega+D(l-\omega)\right]/l\approx D_{cr}\omega/l+D=\left[ 1+\frac{D_{cr}}{D}\frac{\omega}{l}\right]D \tag{7}\]  
In Equation (7), the value of \(\frac{D_{cr}}{D}\) is the ratio of CO\({}_{2}\) diffusivity in the cracked and sound zones. With the increase of crack width \(\omega\) or decrease of crack interspace \(l\), the equivalent diffusion coefficient of concrete increases.  
Combining Equation (7) with Equation (4), the carbonation depth of cracked concrete can be estimated as follows:  
\[x_{c}=\sqrt{\frac{2D_{eq}[CO_{2}]_{0}t}{[CH]+3[CSH]}}=\sqrt{\frac{2[CO_{2}]_{0}t }{[CH]+3[CSH]}}\sqrt{\left[1+\frac{D_{cr}}{D}\frac{\omega}{l}\right]D} \tag{8}\]  
Climate change, such as the increases in CO\({}_{2}\) concentration and environmental temperature, will accelerate carbonation of concrete. Equation (8) does not consider the time dependent CO\({}_{2}\) concentration and environmental temperature. By using the difference method, Equation (8) can be re-written as follows:  
\[dx_{c}=CR\frac{1}{2\sqrt{t}}dt \tag{9}\]  
\[CR=\sqrt{\frac{2[CO_{2}(t)]_{0}D_{eq}(t)}{[CH]+3[CSH]}} \tag{10}\]  
where \(dx_{c}\) is the increase of carbonation depth in time increment \(dt\); and \(CR\) is the carbonation rate parameter.  
With the increase in environmental temperature, CO\({}_{2}\) diffusivity increases. [PERSON] et al. [5] proposed that the dependence of CO\({}_{2}\) diffusivity on temperature can be described using Arrhenius's Law as follows:  
\[D(T)=D_{ref}\exp\left[\beta\left(\frac{1}{T_{ref}}-\frac{1}{T}\right)\right] \tag{11}\]  
where \(D_{ref}\) is the CO\({}_{2}\) diffusivity at the reference temperature; \(\beta\) is the activity energy of CO\({}_{2}\) (\(\beta\) = 4300); and \(T_{ref}\) (298 K) is the reference temperature.