esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
(**b**) TEM images demonstrating the diameter and internal structure of MSNs on the 200 nm scale-bar, demonstrating well-defined ordered long-range structure of inner mesopores and highly ordered porous channels. is expected to enhance its delivery, via both passive diffusion and active targeting with functionalized MSNs. There are two general methods by which drug molecules such as DOX are loaded onto functionalized MSNs systems: (i) pre-surface grafting [21; 22; 23; 24; 25; 26; 27] (Figure 2a) and (ii) post-surface grafting (Figure 2b) which is less frequently applied [28; 29]. For the pre-surface grafting method as illustrated in Figure 2a, native MSNs are immersed into a stirred solution of DOX, the DOX molecules then diffuse into the porous channels of the MSNs. The resulting DOX@MSNs (in which @ means _loaded_) are then grafted with PLH and TAM, forming DOX@MSN-PLH-TAM (Figure 2a). For the post-surface grafting method, fully functionalized MSN-PLH-TAM will be immersed into a stirred solution of DOX (Figure 2b). The resulting DOX@MSN-PLH-TAM particles are then collected by centrifugation. In this study, we are particularly interested in the loading of DOX into our novel MSN-based systems (Figure 1), using the unconventional post-surface grafting loading approach (Figure 2b). This is expected to avoid the leakage of DOX molecules which may occur during each step of surface functionalization with the pre-surface grafting method. Additionally, in this study, we focused on the in vitro release kinetics of our functionalized systems, attributed to the grafted PLH molecules. With the theory that PLH molecules which have a pKa -6.10, under physiological conditions (pH 7.4), will condense on the surface of MSNs. This will block the MSN pores and prevent the release of DOX [16]. Subsequently, as the PLH-gated nanosystems enter into tumours, the PLH molecules will become protonated by the acidic tumour environment (pH -5). The cationic PLH molecules, due to charge repulsion, are then expected to extend out from the surface of the MSNs, thus opening the pores to release DOX [16]. In addition to this, it was also hypothesized that the greater solubility of DOX molecules in acidic environments (pH 5) [30; 31] in combination with the introduction of PLH molecules onto MSNs should enhance the release of entrapped DOX molecules in acidic tumour microenvironments.
## 4 Results and Discussion
### Discussion  
In view of the results shown in this study in Sections 4.1 and 4.2, we could state that the proposed algorithm has some advantages and a few drawbacks with respect to standard feature extraction algorithms like SIFT [15] in the case of DEMs.  
First, our approach has demonstrated great potential for obtaining large sets of keypoints (homologous points) from two DEMs with low variance. In this sense, the proposed algorithm allows us to determine a more robust matching and transformation between two images.  
Second, we have proved that the algorithm is capable of matching two DEMs regardless of their differences in resolution and geographical areas. This has been achieved by the use of a two-step process: first, a general ICP alignment [22; 23], and then, a fine alignment by means of the keypoints. The final alignment allows us to use a variable-sized ring that can be used to analyze different levels of generalization for DEMs, as we have shown between DTM05 and DTM25.  
Third, the large set of keypoints determined by the proposed algorithm allows us to obtain a general transformation between DEMs to define a global quality assessment. In addition, we can analyze the local differences among matched keypoints to determine small changes between DEMs. This will allow us to detect changes in zones for multitemporal studies.  
Finally, the set of keypoints will be used in all the quality control assessment standards, like ASPRS [12], allowing us to fit the sample requirements of this standard. Moreover, the value of the Pearson correlation of each keypoint match can be used to weigh or filter the  
Figure 7: Examples of correlation coefficients greater than 0.99 (red colored zones) for the four tested zones. The DEMs were DTM05 compared with DSM05. The background is the orthophoto map of Spain (IGN of Spain) [24].  
points of the desired sample. Furthermore, keypoint matching can allow us to obtain a horizontal quality control assessment for DEMs.  
On the other hand, the algorithm has a few drawbacks. We highlight that the proposed methodology is time-consuming, especially in the fine matching procedure. Even using the improvements indicted in the methodology sections, it takes several hours to obtain the correlation in the worst cases (MDT05 vs. MDS05), using one threading processing under Python 3.10. For this reason, we have reimplemented using PyOpenCL 1.2 and tile overlapping in a i7-8550U CPU with an integrated GPU UHD 620. With this implementation, using the 24 computing units for GPU processing, the algorithm has been boosted and is able to be used to compare the two larger DEMs (MDS05 vs. MDT05) in a maximum of 300 s.  
The second drawback that we detected is related to the necessity of a preprocessing alignment between the DEMs. However, the last procedure can be avoided in cases of well-defined DEMs, which include a transformation between the geographical and image coordinates. In this case, the fine matching can help determine the quality of the transformation.
# Evaluation of Radio Access Protocols for V2X in 6G Scenario-Based Models
## 2 Overview of V2X Protocols
### Nr-V2x  
Moreover, 3 GPP began introducing the concepts and preparations for NR-V2X in Release 15 in 2018. This release introduced improvements to the eMBB mode and increased speeds of IoT support. In 2020, Release 16 implemented the other two modes, mMTC and URLLC, using network slicing (splitting different use cases, making resource reservations).  
Furthermore, 5G NR-V2X was also introduced in 3 GPP Release 16, supporting connected and autonomous driving. Other topics presented in this release are NR-based access to unlicensed spectrum (NR-U), interference mitigation, satellite access in 5G mobility enhancements, integrated access and backhaul, satellite access in 5G, and mobile communications systems for railways. Release 17 was made available in 2022, whose focus was on making improvements to URLLC, network slicing, and the remote control of vehicles.  
In NR-V2X, two frequencies ranges (FRs) are available for transmissions: the sub-6 GHz band (FR1: 410 MHz-7.125 GHz) and the millimeter band (mm wave) (FR2: 24.25-52.6 GHz). Since the maximum bandwidth in FR1 is 100 MHz and, in FR2, it is 400 MHz, NR-V2X outperforms LTE-V2X since the maximum bandwidth in LTE-V2X is 20 MHz. Table 2 shows some of the main differences between LTE and 5G technologies.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **LTE** & **5G** \\ \hline Frequency band & Sub-6 GHz & FR1 (410 MHz to 7.125 GHz) \\  & & FR2 (24.25 GHz to 52.6 GHz) \\ \hline \multirow{4}{*}{Carrier bandwidth} & \multirow{4}{*}{Max: 20 MHz} & FR1: 5, 10, 15, 25, 30, 40, 50, 60, 80, 100 MHz \\  & & or Max: 100 MHz (at ¡6 GHz) \\ \cline{1-1}  & & FR2: 50, 100, 200, 400 MHz \\ \cline{1-1}  & & or Max: 1 GHz (at ¡6 GHz) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Feature comparison between LTE and 5G.
# Gas Turbine Off-Design Performance Adaption Based on Cluster Sampling
## 3 Results and Discussion
### Comparison of Random Sampling and Cluster Sampling  
In this section, two sampling methods, cluster sampling and random sampling, are used to select the sampling points for the subsequent performance adaption. The number of sampling data points was set to 10.  
Random sampling randomly generates 10 points from the dataset. Cluster sampling uses the k-means clustering algorithm and clusters the 146 data points into 10 clusters  
Figure 4: The measurements of the E-class gas turbine.  
based on the variables in Table 1. It produces 10 cluster centers and selects the points closest to the cluster centers as the sampling points. To compare the two sampling methods, the coverage of the total dataset and the coefficient of variation (\(CV\), representing the degree of dispersion) of each sampling set were calculated by Equations (13) and (14), respectively. The analysis results of the two sampling methods are shown in Figure 5:  
\[S=\frac{x_{max,sample\ set}-x_{min,sample\ set}}{x_{max,total\ data}-x_{min, total\ data}} \tag{13}\]  
\[CV=\frac{\sigma}{\mu} \tag{14}\]  
where \(x_{max,sample\ set}\) and \(x_{min,sample\ set}\) are the max and min values of the variables in Table 1 among the selected sample set, respectively; and \(x_{max,total\ data}\) and \(x_{min,total\ data}\) are the max and min values of the variables among the total dataset, respectively. \(\sigma\) is the standard deviation of the selected sample set, and \(\mu\) is the mean value.  
It can be seen that the cluster sampling method can collect data points with large differences as much as possible, which increases the data diversity and extends the coverage of the total dataset. However, using random sampling may lead to repeated sampling and missed sampling.
# Coverage, Rate, and Last Hop Selection in Multi-Hop Communications in Highway Scenarios
## 4 Numerical Results
### Different Antenna Locations
The windshield location is a compromise between these two extremes, but the associated data rate is much smaller across the whole considered range of vehicle densities.
# Study of the Algorithm for Wind Shear Detection with Lidar Based on Shear Intensity Factor
## 2 Methods
### Framework of the Project  
This paper tested the feasibility of the improved algorithm by detecting wind shear in the simulated wind field. First, construct the three-dimensional simulated wind field under four weather conditions, then determine the location of lidar and slide path in the three-dimensional coordinate system of the simulated wind field. Then, simulate the lidar scanning the slide path, calculate the radial wind speed of the lidar at the slide path according to the elevation angle of the laser beam and further obtain the headwind profile. Next, calculate the wind shear intensity and position with the conventional and improved single ramp detection algorithms and double ramp detection algorithm. Finally, compare the calculated wind shear results of the three algorithms and analyze the advantages and disadvantages of each. The framework of this paper is displayed in Figure 5. It must be noted that the improved single ramp wind shear detection algorithm in this paper is Wind Shear Detection based on Severity Factor (WSDSF), while the conventional single ramp wind shear detection algorithm is Wind Shear Detection based on Headwind Profile (WSDHP).
## 2 The Mathematical Modeling and Simulation
In order to verify the accuracy of the mathematical model, according to the actual operation state and the actual situation of the measuring device, we design two sets of the angles \(\theta\), \(\theta\in[-2^{\circ},0^{\circ}]\)and \(\theta\in[0^{\circ},2^{\circ}]\) respectively, and generate the deflection angle according to the interval of \(0.5^{\circ}\). The simulation results are shown in Figure 2.  
It can be seen from Figure 2 that the maximum length error of the simulation results is less than \(3\) nm.
## 4 Results and Discussion
### Exploratory Factor Analysis (EFA)  
Exploratory factor analysis (with maximum likelihood estimation and varimax rotation) was conducted on unobservable factors (the items in Section 3 of the questionnaire) affecting public transport use during the pandemic. A four-factor solution was obtained based on eigenvalues > 1 criterion which explained about 64.438% of the total variance. The factor loadings are presented in Table 2. The items with loadings below 0.5 and those with high cross-loadings were removed. The sampling adequacy was satisfactory (Kaiser  
Figure 4: Distribution of responses for Section 2 of the questionnaire.  
Figure 3: Research methodology adopted for the study.  
Meyer-Olkin measure = 0.857); [PERSON]'s test of sphericity was significant (0.000); and the determinant of the matrix was 1.12 \(\times\) 10\({}^{-5}\).  
The first factor explained 26.397% of the total variance. Seven items representing attitudes loaded on the first factor, which explained 26.397% of the total variance and was consequently named as \"attitudes\". The second factor explained 19.189% of the total variance and six items loaded on this axis which represented \"awareness\" of the consequences. The third factor explained 9.928% of the total variance with two items loading on it representing the responsibility to keep public transport safe during the pandemic. Therefore, this factor was referred to as \"responsibility\". The fourth factor explained 8.924% of the total variance. Two items loaded on this factor which represented the \"difficulty\" in following precautionary guidelines while using public transport during the pandemic. [PERSON]'s alpha was adequate for all the factors i.e., 0.888, 0.871, 0.859 and 0.818, respectively suggesting that the items have high internal consistency.  
Three different models were developed treating the items in the Section 2 of the questionnaire as dependent variables and those in Sections 1 and 3 as explanatory variables (Figure 1).
# Combining Post Sentiments and User Participation for Extracting Public Stances from Twitter
## 4 Experiments
### Experiment on Sentiment Classification
#### 4.1.2 The Effects of Classifiers  
Next, the performance of sentiment classification using LSTM was compared with three different classifiers: Naive Bayes, ELM, and CNN, as shown in Table 2.  
As shown in Table 2, the performance of all three neural network-based classifiers are better than Naive Bayes for both [PERSON] and [PERSON] datasets. The best performance can be achieved for LSTM with a F1-measure of 72.97% and 71.71%, respectively, while CNN obtained a comparable F1-measure  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Bayes** & **ELM** & **CNN** & **LSTM** \\ \hline \multirow{3}{*}{[PERSON] dataset} & Precision & 68.74\% & 69.54\% & 73.03\% & 73.11\% \\  & Recall & 68.46\% & 69.43\% & 72.76\% & 72.82\% \\  & F-Measure & 68.59\% & 69.49\% & 72.89\% & 72.97\% \\  & Precision & 69.43\% & 69.97\% & 71.51\% & 71.74\% \\ [PERSON] dataset & Recall & 69.34\% & 69.96\% & 71.49\% & 71.67\% \\  & F-Measure & 69.39\% & 69.97\% & 71.50\% & 71.71\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance Comparison for Sentiment Classification.  
Figure 5: The effects of alpha on classification performance.  
of 72.89% and 71.50%, respectively. Since CNN parameters were not optimized, their performance are considered as comparable to those of LSTM.
## 2 Biosensor Application of Electrodeposited Nanoparticles
### Electrostatic Deposition  
In some studies, the gold nanoparticles are prepared first, and then electrodeposited onto the surface, with the applied potential promoting nanoparticle binding to the surface. For example, one study reported the solution phase preparation of gold nanoparticles (average size = 31.9 nm as determined by dynamic light scattering) by citrate reduction followed by their electrodeposition using a combination of chronopotentiometry and CV [38]. In the reported process, chronopotentiometry was used to vary the potential (versus Ag/AgCl) from 2.5 V to 0.2 V over 60 s followed by 90 cycles between \(-\)0.2 V and 0.8 V at 50 mV sec\({}^{-1}\). Scanning electron microscopy showed that the gold nanoparticles were distributed on the surface as mostly uniform areas, but there were also some non-uniform areas. The gold surfaces were then modified by SAMs of 11-mercaptoethanol acid, to which aptamers were conjugated using EDC/NHS coupling chemistry. The aptamers were specific to HCT116 colon cancer cells expressing carcinoembryonic antigen. A detection limit of six cancer cells mL\({}^{-1}\) was achieved by measuring the suppression of the redox current of the [Fe(CN)\({}_{6}\)]\({}^{3-/4-}\) redox probe upon cell binding, and this response was linear up to 25 cancer cells mL\({}^{-1}\).
# PPG and Bioimpedance-Based Wearable Applications in Heart Rate Monitoring--A Comprehensive Review
## 5 Heart Rate Monitoring  
The typical processing pipeline of wearable heart rate estimation includes three stages--pre-processing, heart rate calculation and validation against a known standard, as illustrated schematically in Figure 9.  
The pre-processing stage, for the most part, deals with signal filtering in order to, firstly, remove DC offset, slopes and wandering baselines and, secondly, remove noises in a specific frequency range using notch filters possibly followed by band-pass Butterworth filters. As reported in many sources, the presence of motion artifacts (MAs) in the measured signals is one of the most significant problems that may sabotage the efforts of heart rate estimation. Hence, specialized algorithms (see Section 5.1.3) are necessary to exclude the MAs from the measured signal. The cleaned signals are then commonly divided into overlapping frames, whose duration can either be fixed to a specific value or whose signal can be divided into individual cardiac cycles for further calculations [7].  
However, the ability to reduce the effect of MA at the hardware level should not be underestimated. Such solutions may rely on multi-channel optical sensors with multiple wavelengths [54] or with a sensor array [55]. In EBI-based cardiac-related data acquisition, the MA reduction solutions rely on electrode design optimization like the support structure advancement [56] or enhanced contact area materials like carbon fibres [57]. An appropriate approach is to measure the skin-electrode contact impedance concurrently to the interesting signal of EBI [58], besides the classical approach of utilizing the high input impedance instrumentation amplifiers [59].  
Figure 8: Features extracted from time derivatives of PPG signal—(**a**) the first-order derivative; (**b**) the second-order derivative [51].
## 2 Literature Review
### Comparison of Holochain with Other DLTs in IoT Networks
One significant limitation is the lack of established standards for Holochain, which complicates its integration with enterprise-grade systems. The authors in [21] systematically compare Ethereum and Hyperledger Fabric, emphasizing their use in attribute-based access control within IoT environments. The study highlights that both platforms benefit from robust ecosystems and established standards, like Ethereum's ERC tokens and Hyperledger Fabric's modular architecture, which provide versatility for various blockchain applications. Holochain lacks similar widely accepted protocols, which poses challenges for developers and enterprises looking for interoperability and consistency across platforms. Since it diverges from traditional blockchain paradigms, integrating Holochain with existing systems, especially those reliant on global consensus mechanisms, presents significant complexities and costs. Holochain's agent-centric approach offers promising solutions for decentralized data ownership but raises concerns about data consistency and security in adversarial environments due to the absence of a common ledger. As the authors in [22] discussed, in IoT networks, the lack of a unified ledger can be exploited by adversarial agents to create conflicting data states, increasing the risk of inconsistencies and undermining trust. They highlight that without a centralized mechanism to ensure coherence, managing distributed agents in hostile conditions requires robust trust management and validation strategies to mitigate risks effectively. This underscores the need for enhanced frameworks to maintain data integrity in decentralized systems like Holochain. These concerns are compounded by the novelty of the Holochain framework, which is still in the process of gaining widespread adoption and developer support. The relatively small community and lack of established standards make it harder to build trust among enterprises and developers, hindering its scalability in large-scale IoT deployments compared to more established DLTs like IOTA, which has been specifically designed with IoT in mind. IOTA's Tangle framework has already been successfully employed in real-world IoT applications, such as Bosch's IoT devices and the +CityxChange smart city initiative, where it facilitates secure P2P energy transactions without intermediaries [23]. This demonstrates IOTA's readiness for practical use, with a focus on scalability, energy efficiency, and data integrity. Holochain, while promising in terms of decentralized data ownership, remains less proven in such large-scale applications, especially in scenarios requiring high levels of consistency and security across distributed systems.
# Reducing Emissions Using Artificial Intelligence in the Energy Sector: A Scoping Review
## 3 Results
### AI Applications in Energy Generation for Emission Reduction
The authors modeled the cogeneration process with Engineering Equation Solver software and used the model to create a dataset. A hyperparameter-optimized Support Vector Regression (SVR) model was fitted to the dataset, and a Multi-Objective Grasshopper Optimization Algorithm was used to find optimal parameters for the system. The authors tested different optimization targets with double- and triple-optimization experiments. The optimal scenario prioritized exergy efficiency, total product cost rate, and CO\({}_{2}\) emissions, achieving an exergy efficiency of 45.6%, a total product cost rate of 2.716 $/GJ, and a freshwater consumption of 30.26 kg/s. [PERSON] et al. [54] used ML methods to predict the lower heating value of different fuel blends. The authors prepared fuel blends where good quality coal was used as a base, and 10%, 20%, 30%, 40%, and 50% concentrations of lower quality coal, rice husk, and sugarcane bagasse were mixed into the blend. The authors measured the characterized properties of all the mixes and of each of the ingredients in full concentration. The measured properties were moisture content, ash content, volatile matter, sulfur content, fixed carbon content, hydrogen content, and lower heating value. The authors used Ridge Regressor, Nystrom Kernel SVM Regressor, and Linear Regressor to model the fuel blend lower heating value based on the characterized properties. The authors discovered that Ridge Regressor was the best performing model and reported R\({}^{2}\) values of 0.993, 0.979, and 0.983 for validation, cross-validation, and holdout experiments, respectively. The authors claimed that the model can be used to suggest the most suitable fuel blend where the percentage of the coal is replaced with biomass, thus reducing carbon footprint. [PERSON] et al. [55] proposed an approach to make smart grids more sustainable and reliable by effectively integrating renewable energy sources with conventional power generation methods. The authors used deep learning-based methods to model a microgrid with integrated wind and solar cell power generation. The optimal response of the model was solved using the Multi-Objective Ant Colony Optimization (MOACO) method. The authors claimed that the developed microgrid model can be used to forecast demand and supply, schedule power delivery according to demand, and provide actionable insights of the operation of the smart grid system. The test results were evaluated across various scenarios to optimize operating costs using the MOACO. The authors showed that simulations of demand response based on the developed models reduce solar and wind power operating costs.