esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The study covers publications from the last five years (2018 to 2024) and examines different bibliometric indicators, including trends in publication counts, key research domains, authorship patterns with a focus on collaboration trends, and contributions from various countries, including the recognition of the most active institutions, co-occurrence of keywords, and others, i.e., a bibliometric approach based on a wide WoS search in combination with OT topic can be seen as an exceptional feature of our review article. Still, it is worth mentioning that understanding of differences between OT and other related terms remains low even in academic publications and many definitions are used interchangeably. Due to that reason, we expanded our search from OT only to ICSs, which can be seen as the widest umbrella term, to avoid missing important, closely related publications (presented in more detail in Section 2). The analysis of the above-mentioned topics allows for highlighting future research directions and contributes to the advancement of OT security insurance. The following are considered the main contributions of this research:  
1. Systematic analysis of research trends: Through examining publication trends from 2018 to 2024, we offer a comprehensive overview of how research in OT security has developed, as well as tendencies in the number of published research, demonstrating the overall interest in the research area;
2. Collaboration trends: In order to analyze collaboration trends, patterns of collaboration among researchers, institutions, and nations are presented, as well an identification of the most active research groups is performed;3. Keyword trends: analysis of keyword trends acts as a supportive method while identifying the research trends, as well as interrelations of these trends;
4. Emerging and future research trends: identification of the latest and newly appearing research direction in the sphere of OT security, with a focus on tendencies in 2024 publication, which provide insights for future and the most perspective research directions and possible industrial innovations. This analysis reveals that the primary distinction of our research lies in the topic of OT security, which has not been previously analyzed with the help of the methodology presented in [23], which allows for a more nuanced understanding of emerging trends and gaps in the literature. Also, while the original methodology pays the most attention to identifying the trends and naming main research institutions, researchers, etc., we tried to determine the main problems derived from that information, such as a lack of collaboration and reasons for the domination of certain publication channels over others.
## 2 Materials and Methods
### Adsorption Experiments  
The batch adsorption study was carried out to determine the optimal operating conditions for the following effects: dose of bio-adsorbent (0.3-1.5 g); initial dye concentration (10-100 mg/L); contact time (0-60 min); and temperature (22-31-61 \({}^{\circ}\)C), and the pH of the solution (2-12) was adjusted by adding concentrations of (0.1-1 N) HCl or (0.1-1 N) NaOH. Adsorption kinetics were determined by contacting a 100 mL volume of Direct Red dye 81 at a concentration of 10 mg/L with a mass of 0.5 g of bio-adsorbent in ambient air, with a stirring speed of 300 rpm in a 60 min contact time. The samples were filtered by centrifugation (HETTICH ROTOFIX 32A, [PERSON], Tuttlingen, Germany) at 4000 rpm in an 8 min rotation time. The supernatants were analyzed by UV-visible spectroscopy (JASCO V-750) at a wavelength of 509.8 nm.  
The adsorption capacity (q\({}_{\rm e}\)) and the percentage of dye removal were calculated using the following equations [27]:  
\[\rm q_{e}=\frac{(C_{0}-C_{e})\ *\ V}{m} \tag{1}\]  
\[\rm R(\%)=\left(\frac{C_{0}-C_{t}}{C_{0}}\right)*100 \tag{2}\]  
where C\({}_{0}\) and C\({}_{\rm e}\) are the initial dye concentration (mg/L) and the equilibrium dye concentration (mg/L), respectively, V is the volume of the solution (L), and m is the mass of the adsorbent.
## 1 Introduction
Most of the existing studies are focused on the stability of MTF and the penetration rate of CAVs while overlooking the impact of the distribution degree of CAVs on MTF. The instability of mixed traffic flow is mostly caused by the operation of human drivers. [PERSON] et al. [15] divides the driving style based on the cluster analysis method and constructs a car following model considering the driving style of the individual driver. Through experiments, [PERSON] et al. [16] determined the main parameters of drivers with different styles of car-following behavior and established a quantitative model of vehicle-to-vehicle interaction behavior considering driving styles. However, few studies have discussed the influence of driver style on the stability of MTF. Therefore, according to the analyses above, based on the IDM, besides considering the effect of the intelligent features of CAVs on traffic flow, this paper analyzes the influence of the response characteristics of human drivers on the stability of MTF and explores the influence of penetration rate and distribution degree of CAVs on MTF through numerical simulation. The rest of this paper is organized as follows: Section 2 presents an extended IDM applicable for RVs and CAVs, which lays a foundation for the study of MTF composed of CAVs and RVs. Section 3 firstly derives the criterion for the stability of the extended IDM, then analyses the traffic flow stability of different driver type, compares RV, ACC and CCAC traffic flow stability, and finally studies the relationship among the penetration rate of CAVs, equilibrium velocity and traffic stability in MTF. Section 4 compares the acceleration fluctuation of RV, ACC and CACC under two typical disturbances of braking and starting by numerical simulation at the microscopic scale, analyzes the influence of the penetration rate and distribution degree of CAVs and the driver style of RVs on MTF by numerical simulation at the macroscopic scale. Finally, Section 5 concludes this paper.
## 2 Materials and Methods
### Validation of Numerical Model
It can be seen that the present simulation obtains good agreement with the experimental data. Figure 3: Comparison of electric potential values with experimental data: (**a**) at x = 228.6 mm, (**b**) at x = 266.7 mm, and (**c**) at x = 304.8 mm. The present model is also validated against the experimental data of Ref. [9]. The experimental set-up had eight corona discharge wires. The operating voltages vary between 24 and 38 kV. Each corona wires' diameter (\(d_{w}\)) is 0.1 mm, the distance between the wire and the plate (\(S_{y}\)) is 120 mm, the distance between the wires (\(S_{x}\)) is 250 mm, the length (\(L\)) and height (\(H\)) of plates are 1200 mm and 150 mm, respectively. Particle density (\(\rho_{p}\)) and relative dielectric constant (\(\varepsilon_{p}\)) are 600 kg/m\({}^{3}\) and 2.5. The injected particles' diameter is 5 \(\upmu\)m. Gas velocity (\(U_{in}\)) is 0.4 m/s. Figure 5 shows the comparison of the present model computation of the collection efficiency values with the experimental data of Ref. [9]. It can be seen that the present simulation obtains good agreement with the experimental data.
# Adaptive Thresholding of CNN Features for Maize Leaf Disease Classification and Severity Estimation
## 3 Materials and Methods
### Proposed Methodology
#### 3.2.2 Transfer Learning  
Finally, transfer learning is applied to several pre-trained deep CNNs using the Keras and Tensorflow Python packages. The following parameters are used to train the network: base learning rate, 0.0001; momentum, 0.9; number of epochs, 500; and 80% training and 20% test samples. The PlantVillage maize leaf dataset had been augmented using several transforms (Section 3.1) to increase the size of the dataset to 46,896 images.
# Preassembly Cryogenic Drilling in Carbon Fiber Sandwich Sheets with Inner Foam Core
## 2 Materials and Methods
### Data Processing  
A design of experiments was carried out, in particular, an orthogonal design, L18 (\(2^{1}\times 3^{7}\)), defined by [PERSON], that allows the combination of a factor of two levels and seven factors of three levels, at most [32], in this case, a factor (FT) of two levels and two factors (N and F) of three levels for Fz, \(\Delta\)Di, and \(\Delta\)Do; their values can be seen in Table 1. As can be seen, the levels and values coincide with the thickness of the foam and the cutting conditions. These values were used throughout the experimental process. The foam thickness is also considered for \(\Delta\)Di because the chip exit can influence the final diameter. The mean analysis was selected because all are control factors and there are no noise factors, and its interpretation is more intuitive than the signal-to-noise ratio, but it is also the procedure that provides the best fit to the model. The process followed for its selection consisted of performing an analysis of the variance (ANOVA) and regression coefficients; the procedures that did not allow a good fit were rejected. Subsequently, within the nonsignificant factors, they were eliminated and hidden patterns were avoided, and a new ANOVA was taken into account; the software used, Statgraphics Plus 5.0 (Statgraphics Technologies, Inc., The Plains, VA, USA) [33], allows this as a basic statistical operation. Thus, the design was applied to the averages of the experimental values obtained and considering the assumption that smaller is better and nominal is better for diameter variation in the optimization process. ANOVA was performed to determine the significant factors at the 95% confidence level (\(p\)-value \(<0.05\)). Later, a third-degree regression model was defined [34]; this model was used because it allowed a higher determination coefficient, \(\mathbb{R}^{2}\), to be obtained, that is, a higher goodness of fit.  
Figure 1: Experimental tests: (**a**) drill bit after cryogenic machining; (**b**) diameter measurement by co-ordinate measuring machine.  
In addition, an optimization process was carried out to determine the cutting conditions that provide the best results. In the first stage, a single optimization step was performed. The optimization strategy was to minimize Fz and maintain the diameter differences (\(\Delta\)Di and \(\Delta\)Do) at 0.
# Semantic Features Based N-Best Rescoring Methods for Automatic Speech Recognition
## 6 Conclusions  
In this paper we have presented a new way to use semantic information to rescore the n-best hypotheses without neural network language models. Two kind of semantic features, namely LDA topic features and GloVe continuous word representations, are used and tested in this work. We have designed four sentence coordination scores from the two semantic features. Among the four scores, two are inspired by the LSM and LBL language model respectively, and the other two are first proposed in this paper. In addition, we have designed a fallibility weight to assist the computation of the sentence semantically coordination score by instructing the model to pay more attention to the words that may have more possible choice.  
Experiments show that a 10- or 20-dimension LDA based word probability score with fallibility weight reduces WER of Dev93 and Eval92 testsets by absolute values of 0.23% and 0.46% respectively. For word embedding features, the 50-dimension word-discourse probability score with fallibility and 300-dimension word-discourse probability score without fallibility make the most WER reductions for Dev93 and Eval92 testset respectively at 0.29% and 0.51%. Fallibility weight works well on most features for both LDA and word embedding based scores. Although it only reduced WER by a small scale, it strongly decreased the performance gap among features dimensions for both word-embedding and LDA based scores.  
As future work, we plan to explore higher-order embeddings which can represent more words rather than only one. On the other hand, the effect of the smoothing parameter \(\gamma\) will be explored in the future work. In addition, the fallibility weight can be used in other rescoring experiments.  
[PERSON] contributed to the idea of this paper, designed the algorithms and writed the original draft. [PERSON], [PERSON] was responsible for performing the experiments and the analysis. [PERSON] and [PERSON] reviewed and edited the paper. [PERSON] was in charge of supervision and project administration. This work is partially supported by the National Natural Science Foundation of China (Nos. 11590774, 11590770), the Key Science and Technology Project of the Xinjiang Uygur Autonomous Region (No. 2016A03007-1).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**LDA Scoretype** & **Word Embedding Score Type** & **Fallibility** & **Dev93 WER** & **Eval92 WER** \\ \hline baseline & - & - & 9.58 & 6.98 \\ LDAprob10 & word-pair30 & yes & 9.32 & 6.56 \\ LDAprob10 & word-discourse50 & yes & 9.30 & 6.54 \\ LDAtopsim40 & word-pair30 & yes & 9.32 & 6.52 \\ LDAtopsim40 & word-discourse50 & yes & 9.32 & 6.42 \\ \hline \hline \end{tabular}
\end{table}
Table 5: WER results of the combination of LDA-based scores and word embedding based scores.
# A Review of Oil Spill Remote Sensing
## 2 Detection and Mapping of Oil on a Water Surface
### Near-Infrared (NIR)  
Near infrared (NIR) bands with wavelengths of 0.75-1.4 \(\upmu\)m, have only a short time record in oil spills. NIR bands on the MODIS and MERIS satellites and the airborne AVIRIS were introduced and with it some research begun on oil spill applications. Oil spill remote sensing from these platforms was carried out during the Deepwater Horizon spill [17; 18]. Two groups of researchers employed NIR to estimate oil thickness using the fact that reflection in the NIR varies somewhat with oil thickness [19; 20; 21]. [PERSON] et al. [20] studied AVIRIS NIR data from the Deepwater Horizon spill to define the geometries of slicks. An important finding of this study is that a minimal resolution is needed to display oil slicks accurately. It was found that, when 50% oil sheen fractional coverage was required, a 30 m resolution sensor was required.  
The near-IR has also been used for other mapping purposes. One group delineated residual marsh oiling in Barataria Bay, Louisiana, after the Deepwater Horizon oil spill by using AVIRIS data [22; 23]. Comparing the accuracy of the interpreted AVIRIS data over Barataria Bay resulted in an 87.5-93.3% accuracy compared to ground truth data. Shoreline oiling was found to extend 10.5 m into the marsh and showed a reduced oiling with increasing separation from the shoreline. Others have found some spectral reflectance spectra due to an emulsion as opposed to un-emulsified oil [24]. Further research on the use of near-IR is needed to extend its application to oil spills.
## 3 Results  
### Spectral Analysis  
This research collected the spectral signals of fertilizer samples in powder form. The specific process was as follows. The fertilizer powder was spread in a Petri dish with a diameter of 75 mm and the surface was smoothed with a glass plate, the probe detection cavity was connected at the end of the three-in-one optical fiber, and the probe detection cavity was placed on the flat surface of the fertilizer powder for measurement. The light source and spectrometers were turned on, and after the temperature of the light source and spectrometers stabilized, the spectral signals were collected. The whole experiment process was carried out under shaded conditions. Combining the band coverage of the light source and the performance of the spectral signals in different bands, the spectral band with large noise was removed, and finally the spectral signals of 550-950 nm in the Vis spectrometer, and 1050-1640 nm in the NIR spectrometer were intercepted, as the original spectrum for subsequent analysis. The spectral curves of one sample group is shown in Figure 3.  
Although spectra were collected by two different portable spectrometers, the spectral curves of the two spectrometers had relatively good continuity. In the original spectral curves, different fertilizers show obvious differences in the visible light band and the near-infrared band. Since the content of each nutrient element in the samples collected in this experiment had a large span, the spectral morphology between different fertilizer types also showed obvious difference. After obtaining the original spectra, the S-G smoothing and SNV methods were used to preprocess the spectra, and the final spectral curves are shown in Figure 4.
## 3 System Model
### Network Model  
As shown in Figure 1, a remote area beyond the reach of terrestrial BSs is considered in this paper. There is an LEO satellite 1 in space, called the local satellite, around which there are \(M-1\) neighboring satellites, i.e., LEO satellite \(j\), \(j\in\mathcal{M}\), where \(\mathcal{M}=\{2,3,\ldots,M\}\). All satellites in the same orbit move in the same circular direction. We define a satellite within the communication range of the local satellite at the moment as a neighboring satellite. These LEO satellites are all deployed with MEC servers. \(N\) IoT devices are randomly distributed in the ground area covered by the local satellite, and the set composed of them is denoted by \(\mathcal{N}\). These IoT devices are equipped with satellite antennas and can communicate with the local satellite directly. Each IoT device requests an indivisible task; for instance, IoT device \(i\) requests task \(i\), denoted by \((c_{i},a_{i})\), where \(c_{i}\) is the computation intensity, i.e., computing resources required for computing one bit of task \(i\) (cycles per bit), and \(a_{i}\) is the data size of task \(i\) (bits). Different IoT devices have different computing capabilities. MEC servers have more computing resources than IoT devices, but the computing resources of MEC servers are still limited. Task generation mechanisms may be periodic generation, random generation, and event-triggered generation. The problem we study is to build the corresponding task model for the set of tasks generated by the IoT devices in the network at a certain moment. We assume that each IoT device has only one task at the moment and perform computation offloading according to the task model. Based on the offloading decision, the local computing portion of the task of each IoT device is computed by the IoT device itself. The satellite allocates its computing resources to the offloading portion of the tasks of the IoT devices and concurrently computes the tasks offloaded by the IoT devices. There are three strategies to complete task \(i\), \(i\in\mathcal{N}\). IoT device \(i\) computes task \(i\) locally, i.e., local computing. IoT device \(i\) offloads task \(i\) to the local satellite, and task \(i\) is computed by the local satellite's MEC server, i.e., local satellite edge computing. IoT device \(i\) offloads task \(i\) to the local satellite, then task \(i\) is migrated to neighboring satellite \(j\), \(j\in\mathcal{M}\) and computed by the neighboring satellite's MEC server, i.e., neighboring satellite edge computing.
# Which Local Search Operator Works Best for the Open-Loop TSP?
## 2 Local Search
### Link Swap  
Solving the open-loop variant of TSP allows us to introduce a new operator that is not possible with the closed-loop problem. _Link swap_ is analogous to relocate, hence we consider one link, AB, and swap it into a new location in the tour, see Figure 7. However, to keep the new solution as a valid TSP tour, we are limited to three choices: replace AB by AT\({}_{2}\), BT\({}_{1}\), or T\({}_{1}\)T\({}_{2}\). As a result, one or both of the nodes A and B will become a new terminal node.  
In the O-Mopsi example in Figure 7, we can see that a single link swap operation improves the solution by reducing the tour length up to 137 m.  
We have \(N\)-1 choices for a link to be removed, and for each of them, we have three new alternatives. Thus, the size of the neighborhood is 3*(\(N\)-1) = O(\(N\)). Link swap is a special case of 3-opt and relocate operator, but as the size of the neighborhood is linear, it is a faster operation than both 3-opt and relocate operator.
# Study of Noise Effect of Slag Storage Technology on Surrounding Environment
## 2 Materials and Methods  
### Materials  
#### 2.1.1 Study Area Description: Metallurgical Plant  
The present paper deals with the impact of the operation of a metallurgical plant on the acoustic situation of the protected areas in its surroundings. The study mainly focuses on the noise generated during the discharge of the slag pots, which is the dominant source of disturbance in the vicinity of the protected areas of the metallurgical plant. The purpose of this paper is to determine the impact of the operation mainly on the protected areas with regards to the requirements of the Decree of the Ministry of Health of the Slovak Republic No. 549/2007 Coll. and to find out whether the permissible noise levels are exceeded.  
In accordance with Decree No. 549/2007 Coll. of the Ministry of Health of the Slovak Republic, the surroundings of the metallurgical plant under consideration can be classified as category II (residential zones), which means that the permissible values of the determining variables of noise are 50 dB in the reference time interval day and evening and 45 dB in the reference time interval night. The operation of the slag landfill is continuous. The process equipment (grinding and slag sorting) is operated as required. Deskulling of the slag pots is carried out in summer at the reference time interval day and evening (6 am-10 pm) and, in winter, also at the reference time interval night (10 pm-6 am). Traffic noise associated with the operation of the metallurgical plant is not subject to assessment--car and rail traffic routes are located away from the assessment area and, due to their distance from the residential area, do not have any impact on the assessed protected area [13; 14].  
The dominant sources of noise in the operation of the metallurgical plant include noise from the deskulling of slag pots, handling of ladle cars, unloading of slag from slag pots, and other technological activities--transport, cooling, crushing, and sorting of slag.  
A view of the metallurgical plant under consideration and the situation in its vicinity is shown in Figure 1.
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
## 4 Proposed Image Compression and Encryption Algorithm
### Compression with Confusion-Diffusion through Kd-Tree, Wavelet Tree and Huffman Coding
#### 4.2.2 Diffusion and Compression with Wavelet Tree  
Further compression along with confusion-diffusion was achieved via a modified wavelet tree by reordering L-array elements. We convert the L-array bits to symbols (2-bit symbols) and rearrange these symbols via the wavelet tree to achieve additional compression and diffusion.