esa-sceva/satcom-chunk-collection huggingface dataset:
# MZM Optimization of PAM-4 Transmission in Data Center Interconnect
## 5 Conclusions  
An analog-optical pre-distortion of MZM operation is proposed. Two mechanisms of benefit are achieved by manipulating the MZM bias point. Optimal spacing between the four levels of the PAM-4 signal can be achieved according to the statistics and distribution of the various noise ingredients, particularly in multiplicative noise environments where ASE noise dominates. In addition, allowing operation of the MZM in its non-linear transmission regime enables increased OMA. In turn, the non-linear distortions are post-compensated by a non-linear equalizer, such as an MLSE. Experimental measurements reveal that utilizing the proposed optical pre-distortion scheme in PAM-4 transmission leads to OSNR sensitivity improvement of 5.3 dB at pre-FEC BER value of 1e-3.
## 3 Results and Discussion  
### Ozonation Treatment Efficacy  
Ozonation efficacy analysis was carried out within the scope of the work by [PERSON] et al. [18]. Complete degradation of the initial compounds was confirmed in all samples. However, several degradation by-products of these same compounds were detected [18; 22]: 2_A_-dihydroxybenzoic acid (2_A_-DHBA) and 3_A_-dihydroxybenzoic acid (3_A_-DHBA) for the MP effluent; 4-hydroxybenzoic acid (4-HBA) and 3_A_-DMBA for the PP effluent; Oxalic acid (AO) and Hydroquinone (HQ) for the PCT effluent; 3-amino-5-methylisoxazole (AMI) for the SMX effluent; 1-(2-benzaldehyde)-4-hydro-(1H,3H)-quinazolin-2-one (BQM) for the CBZ effluent; and 4-HBA, 2_A_-DHBA, 3_A_-DHBA, and 3_A_-DMBA for Mix 2. As the quoted authors discuss, this list represents the main by-products, and it may be incomplete since there are by-products that could not be identified in this analysis due to the low concentration and/or method limitations.
# Atomic Force Spectroscopy on Ionic Liquids
## 5 Structure of the Interface Layer of Ionic Liquids Revealed by AFS
### The Electrified Interface
These results show that the presence of rough surfaces introduces further complexity to the system, affecting the static structure of the ionic liquids as well as their dynamics. It should also be noted that although an ordered double layer structure at solid/liquid interfaces has been observed by AFS as well as by complementary methods on a large variety of ionic liquids, there seems to be no universal behavior. As a counterexample, [PERSON] et al. [104] reported on the interface between trihexyl(tetradecyl)phosphonium bis(mandelato)borate ([P\({}_{6,6,6,14}\)][BMB]) and different atomically flat surfaces, such as Au, mica, or silica. By investigating force-separation curves, no hints of the presence of a multilayer structure, but only a diffuse repulsion, were found. A possible explanation for this striking difference to conventional ionic liquids was suggested with regard to the molecular structure of [P\({}_{6,6,6,14}\)][BMB] having a large and flexible anion with delocalized charge and a compact and inflexible cation [104]. This combination could exclude a regular arrangement of the molecules at the interface. Furthermore, [PERSON] et al. [105] reported that even on popular imidazolium-based ionic liquids such as [EMIm][TFSI], no structured interface to mica is present if no water is present, as is discussed in detail below. This shows that the properties of ionic liquid/solid  
Figure 13: Atomic force spectroscopy (AFS) measurement of the [EMIm][TFSI]/HOPG interface (**a**) with unbiased surface, (**b**) at +1V, and (**c**) at -1V sample polarization; (**b,c**) additionally show the ion density of anions and cations derived by molecular dynamics at the interface, depending on the potential. Adapted with permission from [PERSON] et al. [101]. Copyright 2013, the American Chemical Society. interfaces are individual and must be investigated for every class of liquids, as well as for the specific boundary conditions, separately.
## 4 UAV Navigation and Control
### Path Planning and Trajectory Design
#### 4.2.4 Three-Dimensional Trajectory
Specifically, it involves processes such as linear interpolation and checking whether the interpolated points lie within the set of obstacles, \(\mathbb{P}_{\text{Obstacle}}\) Furthermore, the UAV's goal position is uncertain and depends on the movements of IoVs (ground vehicles). This means that the optimal UAV trajectory is dynamically affected by IoV movements, as described in OF2\({}_{2,3,4}\). As a result, the validation process requires checking an infinite number of potential (random) paths, which leads to the NP-hardness of the trajectory optimization problem in the context of wireless communication support for IoVs in future B5G and 6G networks. \(\square\)  
**Remark 11**. : _Lemma 1 highlights an open research question that has not been thoroughly investigated in the literature._
## 1 Introduction
The chemical gas detection experiments of the integrated system were carried out in the hovering and moving modes of the unmanned helicopter, respectively. The results show that the system can monitor, identify and warn of chemical gas in real time. The main contributions in this study are as follows:  
* We proposed to use an unmanned helicopter as the FTIR carrying platform. The payload and endurance advantages of unmanned helicopters reduce the weight and power consumption requirements of the spectrometer. To the best of our knowledge, this is the first attempt to integrate the FTIR with unmanned helicopters. * Aiming at the problem of removing the background and extracting the spectral characteristics of the gas of the spectrometer mounted on the unmanned helicopter, we proposed to collect multiple background spectra near the detection area to form a background spectral database, and use the algorithm based on robust principal component analysis to separate the low-rank background. The paper is organized as follows: Section 2 is divided into four parts: the theoretical model used in passive gas remote sensing; the gas detection algorithm based on RPCA; the interference module design; and the integration method of the FTIR system. Section 3 shows the details and results of the ground and flight gas detection experiments of the unmanned helicopter FTIR system. Sections 4 and 5 compare and analyze the experimental results and propose the improvement methods for future research.
## 3 Results
### Plant Model Results
SPECCA indexes, it can be seen that the value obtained with the SMR with MDEA CO\({}_{2}\) capture benchmark is substantially reduced when implementing GS-based CO\({}_{2}\) capture in the remaining models. In fact, when the efficiency of a specific plant is above the reference unabated SMR benchmark, the SPECCA index turns out to be negative. Indeed, this is a consequence of a negative energy penalty when integrating inherent CO\({}_{2}\) capture technology. If the SPECCA index is evaluated using equivalent efficiency values which do not account for the steam exports as a beneficial outcome from the plant (both for the specific plant with CO\({}_{2}\) capture and the reference plant), the SPECCA index becomes even smaller, as the associated CO\({}_{2}\) intensive steam generation in boilers is not considered. Lastly, it should be noted that due to the substantial mixing occurring in the GS clusters, the purity of the CO\({}_{2}\) stream obtained after cooling and water knockout resulted in >90 mol% of CO\({}_{2}\). It was assumed that the larger proportion of impurities (primarily N\({}_{2}\)) was acceptable for transport and storage conditions and that a higher compression duty resulting from these lower boiling point gases was considered acceptable. However, some sources recommend CO\({}_{2}\) purities >96 mol%, for which a purification unit may be required [56]. The overall power consumption will be minimally affected, with a marginal increase of specific emissions (due to purging in the purification unit), although the addition of cryogenic exchangers and vessels will lead to a higher overall plant cost [57].
## 3 Results
### Expansion Impacts on Vegetation in the Lanxi Urban Cluster  
During the study period, the expansion of built-up areas significantly negatively impacted vegetation in the Lanxi urban cluster, far outweighing any positive effects. Figure 5 represents the comparison between the positive and negative impact areas of major cities in the Lanxi urban cluster. Figure 5a,b show the regions and areas of positive impact from urban expansion in the Lanxi area, while Figure 5c,d represent the regions and areas of negative impact. From 2001 to 2021, the expansion of built-up areas resulted in a negative impact on vegetation covering 53.5 km\({}^{2}\), whereas a positive impact on vegetation covered 39.56 km\({}^{2}\). The negatively affected vegetation area was 1.35 times larger than the positively affected area.  
The assessment of impacts on various cities within the Lanxi urban cluster reveals a clear trend where, with the exception of Baiyin City and Haidong City, the negative impacts outweigh the positive ones. This indicates that most urban expansions have resulted in a loss of vegetation cover exceeding the areas where vegetation has been enhanced or protected. In Lanzhou, the area of vegetation negatively affected by built-up area expansion was 25.38 km\({}^{2}\), whereas the area of positively affected vegetation was 15.94 km\({}^{2}\). The area of negatively affected vegetation was approximately 1.5 times greater than the positively affected area. A similar situation was observed in Xining, where the negatively affected vegetation area was 5.56 km\({}^{2}\), while the positively affected area was 2.59 km\({}^{2}\). In Linxia Hui Autonomous Prefecture, the negatively affected area due to urban expansion was much larger than the positively affected area, with the former covering a significant portion of the expansion area, while the latter was smaller. Negative impacts are mainly due to the extensive conversion of natural habitats into urban areas, often driven by rapid urbanization and prioritization of development over environmental conservation. The relatively smaller areas of positive impact indicate limited green space preservation and a lack of effective integrated planning in urban environments.  
Figure 5: The disturbed area and distribution of vegetation in the Lanxi urban cluster (km\({}^{2}\)). (**a**) Areas of each district/county positively affected, (**b**) Total area with positive impact in the region, (**c**) Areas of each district/county negatively affected, (**d**) Total area with negative impact in the region.
## Appendix A Network Architecture of the Proposed DDPM  
Figure 11 illustrates the network architecture adopted in this study. We implemented a code on the basis of one of the PyTorch implementations [27] of the pixel space DDPMs for two-dimensional images [3] so that it can handle three-dimensional images and run on eightNVIDIA A100 GPUs (NVIDIA Corporation, Santa Clara, CA, USA) with 40 GB of memory for each in a data-parallel manner. This code combines the attention mechanism [28], a linear attention mechanism, and U-Net [29] enabling local and global feature extraction and generation. All the Conv2D operations were converted into Conv3D operations. We adopted the SiLU activation function. The previous network of attention mechanisms was also converted into the equivalent three-dimensional network. We set the number of input image channels to 1. The depth of the three-dimensional U-Net in our code is 8. The Res-Net in different depths of the U-Net has the channel sizes of 4, 8, 16, 32, 64, 128, 256, and 512. For attention mechanisms, we set the dimension of each head to 8 and the number of heads to 4. For linear attention mechanisms, we set the dimension of each head to 8 and the number of heads to 2.
## 3 Materials and Methods  
This section provides a detailed description of the perceptual data fusion framework based on ship ST-TF, as shown in Figure 2. The framework includes ship detection and tracking, ship trajectory extraction based on binocular vision, and multi-source spatio-temporal data fusion. For the video data captured by the shipboard camera, the Bi-YOLO network is first used for ship target detection, and to address the ship occlusion problem in complex navigation environments, the more powerful OC-SORT algorithm is introduced to achieve stable target tracking. In the visual trajectory extraction stage, a visual localization model is constructed based on the principle of binocular imaging. This model is then integrated with the attitude information of the camera, enabling the shipborne terminal to perform absolute position estimation of ship targets. Subsequently, in the multi-source spatio-temporal data fusion, the visual trajectory is pre-processed to suppress the measurement noise, and then the DTW algorithm is used to match the visual trajectory with the GPS trajectory and align the spatio-temporal data points. After the trajectory matching, the data storage area is redesigned to jointly store the navigation information of the host ship and the target ship, thus improving the efficiency of the trajectory association. Finally, based on the results of multi-source perception data fusion, a ship AR navigation assistance system is developed, which improves the overall navigation experience of ship driving and enhances safety in the maritime field.
### Novelty and Contributions  
Existing literature such as [35; 36; 37; 38; 39] focused on the specific domain of study to assess the impact of the RUW on the energy, gas (crude oil), food, healthcare, and stock markets, respectively, whereas our work highlights the generalized societal emotions and views of the RUW. Moreover, by complementing prior studies such as [40; 41] we have chosen Twitter for this study to provide an automatic emotion classification framework by combining, NLP, TL, and ML altogether. Thus, this work demonstrates the novel approach of combining multiple above-mentioned state-of-the-art concepts (NLP, ML, and TL).  
The contributions of this work in the area of the RUW and social media analysis are four-fold:  
1. The proposed approach extends the sparse literature on the RUW by providing insights into societal emotions. This helps government officials and war analysts to understand the public overview of the war which further fosters action to draft appropriate guidelines and regulations related to the RUW.
2. Practically, the proposed framework can be utilized to automatize similar text classification-related tasks such as online spam and misinformation detection.
3. Noticeably, the research community is facing the challenge of a paucity of labeled datasets for the classification task and supervised ML task. The proposed framework has the potential to label any textual document-based data in terms of providing 27 distinct emotion categories for the classification task.
4. Methodologically, this work emphasizes using byte-level BPE encoding and Emorberta for extracting the emotions from the text to further classify. This will open the doors for existing researchers to implement state-of-the-art revolutionary encoding and emotion analysis techniques.
# On the Rollout of Network Slicing in Carrier Networks: A Technology Radar
## 5 CN Domain  
In this domain, the technology radar captures information from two different dimensions: functionality and add-on features. The _functionality_ dimension deals with the discussion on how to use CN functions for the construction of different network slices, scouting different deployment options for these slices depending on the isolation and business requirements of hosted services. On the other hand, the _add-on features_ dimension refers to the set of value-added solutions that complement and extend baseline slice functionality. The network operator can optionally make use of these solutions to either (i) provision enriched services to the customer, i.e., new revenue streams; or (ii) streamline internal network operation, i.e., OPEX savings.
# An Interpretable Deep Learning Approach for Detecting Marine Heatwaves Patterns
## 2 Methods  
In this paper, we present the framework of predicting marine heatwaves, as depicted in Figure 1. We establish a nonlinear predictive relationship between oceanic meteorological factors and sea surface temperature (SST) using an LSTM model. The temporal importance of meteorological input variables is explained using the Expected Gradients (EG) method. Then, the types of heatwave events are revealed through cluster analysis. Additionally, we investigate the memory mechanism of the LSTM network in simulating different types of heatwave events using the Additive Decomposition (AD) method. The specific workflow is as follows:  
We first utilize the LSTM model to establish a nonlinear predictive relationship between oceanic meteorological factors and SST within the target marine heatwave region. To predict the occurrence of heatwave events, we select daily sea surface temperature, sea surface pressure, 10m zonal wind speed, and 10m meridional wind speed as input variables, as they are closely related to the formation and development of marine heatwaves.  
To explain the temporal importance of the meteorological input variables (sea surface pressure, 10m zonal wind, and 10m meridional wind) in the model's prediction of marine heatwaves, we apply a state-of-the-art interpretability technique called Expected Gradients (EG) [26]. By employing the EG method, we obtain importance scores for sea surface pressure, 10m zonal wind, and 10m meridional wind for each target heatwave event.  
By interpreting these feature importance scores, we can reveal the mechanisms of heatwave events. For this purpose, cluster analysis is employed in this study to group the predictive results of marine heatwave events based on similar patterns of feature importance, with different clusters potentially associated with different heatwave event mechanisms.  
To investigate the different behaviors of the LSTM network's memory mechanism in simulating different types of heatwave events, we employ another interpretability technique called Additive Decomposition (AD) [27]. In contrast to the EG method, the focus of the AD method is to study the detailed evolution of signals within the hidden units of the LSTM network that influence predictions.  
The experimental setup in this study incorporated the following hardware and environmental configurations. The hardware consisted of a CPU, the Intel Xeon Gold 6130; graphics cards, with two NVIDIA GeForce RTX 2080 Ti, each having 11 GB of video memory; system memory with a capacity of 251 GB DDR4; and 3.6 TB of hard disk storage. The software environment was based on the Ubuntu 16.04 LTS operating system, using Python 3.7.0 as the programming language. Dependency libraries included the deep learning library TensorFlow 1.13.1 and the interpretability toolkit Shap 0.42.1, with CUDA version 11.3 installed. The model prediction evaluation metrics were MSE (Mean Squared Error) and RMSE (Root Mean Squared Error). The operating principles and steps of each module are introduced in the following sections.
## 4 Numerical Modeling
### Soil Parameters  
The soil parameters were selected from the soil investigation report (Table 1). Some of the parameters for the analysis were not directly available in the report; correlations available in the literature based on the SPT tests have been used. The soil stress-strain relationship has been modeled by applying the Hardening soil model [17]. The rock material has been modeled with the Mohr-Coulomb model. For the concrete piles and raft, a linear elastic material set has been applied using the concrete weight and its stiffness. For the stability analysis conducted in SLOPE/W 2021 software, all the soil, rock, and concrete properties have been modeled with the available Mohr-Coulomb model.  
The rockfill material was modeled with the Mohr-Coulomb model with unit weight as 22 kN/m\({}^{3}\), elasticity modulus as 80 MPa, Poisson ratio as 0.25, angle of internal friction as 40\({}^{\circ}\), and cohesion value as 5 kPa.
# A Noncontact Method for Calibrating the Angle and Position of the Composite Module Array
## 2 Calibration System  
The composite module array is composed of a number of composite modules. Each composite module provides different geometric information through a plane and rotating paraboloid. The self-developed angle sensor is based on the principle of optical self-collimation. Figure 1 shows the calibration system, including the machine tool spindle, angle sensor, composite module array, and laser interferometer.  
[PERSON] [11] introduced the measurement mechanism of using the angle sensor and the composite module to realize the four degrees of freedom information of the moving spindle, including the 2-D angle and 2-D displacement. Using this mechanism, the machine tool spindle drives the angle sensor to scan the composite module array. Through the image processing of the spot on the angle sensor, the relative 2-D position information between the composite module can be obtained from the rotating paraboloid, and the relative 2-D angle information between the composite module can be obtained from the plane. The laser interferometer is used to obtain the relevant parameters during the movement of the spindle.
## 3 Results
### Validity Analysis  
Hair [73] suggested checking convergent and discriminant validity. Convergent validity is seen in the average variance extracted (AVE) value. Table 5 shows that all AVE values exceed 0.5, indicating that all constructs are acceptable and useful for further analysis.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Construct** & **Items** & **Factor Loading** & **CR** & **AVE** & **Cronbach Alpha** \\ \hline \multirow{3}{*}{PU} & PU1 & 0.86 & 0.94 & 0.84 & 0.875 \\ \cline{2-6}  & PU2 & 0.91 & & & \\ \cline{2-6}  & PU3 & 0.88 & & & \\ \hline \multirow{3}{*}{PEU} & PEU1 & 0.86 & 0.93 & 0.81 & 0.853 \\ \cline{2-6}  & PEU2 & 0.81 & & & \\ \cline{2-6}  & PEU3 & 0.80 & & & \\ \hline \multirow{3}{*}{SN} & SN1 & 0.79 & 0.94 & 0.88 & 0.705 \\ \cline{2-6}  & SN2 & 0.83 & & & \\ \hline \multirow{3}{*}{FC} & FC1 & 0.75 & 0.94 & 0.84 & 0.764 \\ \cline{2-6}  & FC2 & 0.79 & & & \\ \cline{2-6}  & FC3 & 0.87 & & & \\ \hline \multirow{3}{*}{PLAY} & PLAY1 & 0.93 & & & \\ \cline{2-6}  & & & 0.92 & 0.85 & 0.886 \\ \cline{2-6}  & PLAY3 & 0.90 & & & \\ \hline \multirow{3}{*}{CL} & CL1 & 0.87 & & & \\ \cline{2-6}  & CL2 & 0.94 & 0.94 & 0.83 & 0.853 \\ \cline{2-6}  & CL3 & 0.90 & & & \\ \hline \multirow{3}{*}{ATT} & ATT1 & 0.89 & 0.95 & 0.87 & 0.857 \\ \cline{2-6}  & ATT2 & 0.91 & & & \\ \cline{2-6}  & ATT3 & 0.90 & & & \\ \hline \multirow{3}{*}{ACC} & ACC1 & 0.97 & & & \\ \cline{2-6}  & ACC2 & 0.95 & 0.93 & 0.81 & 0.889 \\ \cline{2-6}  & ACC3 & 0.87 & & & \\ \hline \multirow{3}{*}{SAT} & SAT1 & 0.91 & & & \\ \cline{2-6}  & SAT2 & 0.83 & 0.90 & 0.76 & 0.887 \\ \cline{2-6}  & SAT3 & 0.87 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Measurement construct validity with the factor loading, CR, and AVE.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **PU** & **CL** & **PLAY** & **FC** & **SN** & **PEU** & **ATT** & **ACC** & **SAT** \\ \hline PU & **0.310** & & & & & & \\ \hline CL & 0.235 & **0.346** & & & & & \\ \hline PLAY & 0.275 & 0.313 & **0.374** & & & & \\ \hline FC & 0.242 & 0.258 & 0.295 & **0.320** & & & \\ \hline SN & 0.222 & 0.259 & 0.269 & 0.249 & **0.304** & & \\ \hline PEU & 0.264 & 0.257 & 0.314 & 0.272 & 0.288 & **0.344** & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Discriminant validity result and root of average variance extracted.  
The discriminant validity was analyzed by assessing the square root of AVE for all variables. Table 5 shows that the AVE value is greater than the inter-construct correlations, implying that discriminant validity is accepted [77].