esa-sceva/satcom-chunk-collection huggingface dataset:
# A Crowdsensing Platform for Monitoring of Vehicular Emissions: A Smart City Perspective
## 5 Evaluation Fundamentals
### Research Questions  
The developed platform and performed evaluation were guided by the answer of two specific research questions, described as follows:  
1. What are the sensors supported by each vehicle? Although there is standardization of the OBD-II interface and related protocols, different vehicles models and brands may implement different sensors. This may be a problem for a generic and flexible platform. Therefore, in the evaluation performed: we wanted to check such expected differences.
2. What is the amount of CO2 emitted by the vehicles? This is in fact the motivation for this work, since there should be a low-cost and trustworthy way to measure the pollution in urban areas, and cars' emissions are the main source of pollution in modern cities. The evaluation will also be performed aiming to have information about it.  
Answering these questions, some smart-city initiatives may be designed based on the developed platform, in a more confident way. Such initiatives may not only be related to the exhibition of pollution data, but also considering the platform as a provider of input information for any system or tool that exploits such data in different ways (e.g., inferring traffic conditions or planning areas with better air quality to deploy bike tracks).
## 4 Discussion
Since machine learning algorithms attempt to dig out the implicit rules from a large amount of data, they can function as a tool to uncover a function, especially when this function is too complicated to be formally expressed. In this case, the input is sample data, and the output will be the expected result. Some existing and proven machine learning and neural networks methods have emerged to establish the estimation model based on the correlation selected features and retrieve soil moisture from SMOS data [66; 67]. Both machine learning and neural networks are of artificial intelligence. Machine learning and neural networks (aiming at more complex problems and big data) are methods of implementing artificial intelligence. Machine learning is a technique for data modeling. What is more profound is that it extracts the appropriate model from given data to explain and predict. Like some common statistical methods, machine learning is also a form of statistical learning method. A computer uses existing data to derive a model, and then uses the model to predict the result. We also used the latest published Random Forest method for accessing the variable importance as in the following figures. The results (Figures 16-18) obtained from the latest published Random Forest method [66] were similar to the case involving XGBoost. They also showed that SNR was the most sensitive variable among the input variables. The difference was that the values of importance differences between each variable from XGBoost were larger than the values from the Random Forest method. From the algorithm mechanism point of view, one reason could be that the Random Forest uses majority voting in the final output, while XGBoost accumulates all results from each step. Another reason may be that the Random Forest method is not sensitive to the optimize parameter, which is good for a beginner, and the XGBoost needs to spend time on the optimization work. Compared to the traditional statistics method, the machine learning algorithm is simpler and more flexible, and it is a good tool to find the underlying rules and value of data even from vast amounts of data. The pros of this study were to use the features of XGBoost method, which is a recently developed ensemble machine learning method good at the variable selection in data mining to examine the characterization of the input variables in the GNSS-R soil moisture retrieval. It showed a good correlation with the statistical analysis of ground-truth measurements. It is worthwhile before establishing models and can also help with understanding the underlying GNSS-R phenomena and interpreting the data.
# Effects of Nanoparticles on Algae: Adsorption, Distribution, Ecotoxicity and Fate
## 5 Factors Affecting Toxicity of NPs on Algae
### Surface Functional Groups and NP Types  
Different nanoparticles exhibit different levels of toxicity upon the growth of algae [26]. The properties of NPs, such as particle diameter, shape and surface morphology, affect the physical stability and the performance of NPs [4]. Many studies have strengthened the fact that the inherent properties of NPs, such as chemical composition, particle size, synthesis method, aggregation state, concentration and surface chemistry, may affect the toxicity of NPs on algae [3; 25; 90]. The toxicity of ZnO NPs and TiO\({}_{2}\) NPs are relatively stronger due to their good photosensitivity, in which the toxicity of ZnO NPs is stronger than TiO\({}_{2}\) NPs, resulting from the dissolution of zinc ions [25].  
In order to prevent agglomeration and obtain the NPs with good dispersibility, the surface modification and functionalization of the NPs are often carried out by means of surface coating in the preparation process [90]. The change of the chemical properties of the NPs will affect the toxicity of NPs in the water environment. The study on the bioaccumulation kinetics of three different coated silver NPs showed that the coated NPs could lead to an increase in algal cell membrane permeabilitycompared with the bare Ag NPs, where the toxic effects of different coated Ag NPs on algae were also different [30]. It was proven that polymer-coated CuO NPs were more toxic to the green algae _Chlamydomonas reinhardtii_ than to that of the uncoated CuO NPs [65]. However, NPs can become less toxic after the coating process. The toxicity of TiO\({}_{2}\) coated with humic acid to algae weakened [91]. The Al-coated SiO\({}_{2}\) NPs have a tendency to aggregate with algal cells and thus become less toxic than uncoated SiO\({}_{2}\) NPs [23]. It may be deduced that the ecotoxicity of surface modified NPs is partly dependent on the properties of the coated function group or polymers.
## 4 Signal Models
### Spatial Signal Correlation Model for Satellite Systems  
In the received signal model, the final step involves introducing a signal model with correlation in satellite communication. It should be noted that correlation considerations are typically unnecessary in uplink transmission due to the lack of environmental influences and the characteristics of free space. However, for the downlink scenario, incorporating correlation becomes essential. This work considered a carrier frequency of 28 GHz, which falls within the Ka-band satellite communication. At such a high frequency, the nature of antenna systems is highly directional. Thus, a spatial signal correlation model that considers the antenna beam's directionality is necessary to model the wireless channel accurately. Therefore, the authorsutilized a spatial correlation model discussed in their previous work [14], which characterizes the channel by a correlation matrix that varies with the direction of transmission and reception. Thus, the channel is transformed to a spatially correlated Rician channel at the receiver for the downlink scenario. In directional spatial correlation models, the decorrelation distance value is typically small, often measured in meters or less.
# Could Acidified Environments Intensity Illicit Drug Effects on the Reproduction of Marine Mussets?
## 3 Results and Discussion
### Toxicity Assays  
#### 3.2.1 Fertilization Rate Assay  
Figure 2 shows results from fertilization assay under pH 8.3 with the different CC concentrations. The treatments did not present any significant difference (\(p<0.05\)) compared to N treatment considered negative control for this assay.  
Figure 3 shows the fertilization rate results for both acidification methodologies. The CO\({}_{2}\) acidification methodology at pH 7.5 with the highest CC concentration (50 mg L\({}^{-1}\) and 100 mg L\({}^{-1}\)) presented a significant decrease in the fertilization success of the mussel when compared with the control (N). In the case of the HCl acidification methodology, there was a significant decrease in the fertilization rate for all pH treatment and CC concentrations when compared to the control.  
Figure 3: _Perna perna_ fertilization rate results obtained after exposure to different concentrations of CC and acidification at different pH values 8.0, 7.5, 7.0 promoted by CO\({}_{2}\) and HCl supplies. Concentration 0 means acidified but with no CC. A statistically significant difference (\(p<0.05\)) compared with N (reconstituted seawater considered as negative control) is shown with “a”; and between CC concentrations in different acidified methods is shown with “b”.  
Figure 2: _Perna perna_ fertilization rate (in %) results obtained after exposure to different concentrations of CC (6.25, 12.5, 25, 50, and 100 mg L\({}^{-1}\)) without acidification at pH 8.3 considered the negative control. (N represents natural pH 8.3, with no acidification method applied).  
Agreeing with our results, ref [52] showed a reduction in the percentage of fertilized eggs of _C. gigas_ when the pH value was decreased by 0.7 from the original value compared to the other treatments. This might support the hypothesis proposed by [53], who affirmed that seawater acidification would affect the intracellular pH of sperm and alter sperm motility, fertilization, and embryo development. Similar species, such as the green-lipped mussel, have demonstrably increased respiration rates and reduction in growth and reproductive output at elevated \(p\)CO\({}_{2}\)[54].  
Regarding CC concentrations that the sperm was exposed to during a one-hour period, our results showed that even the highest concentration of CC (100 mg L\({}^{-1}\)) was not able to affect the sperm's ability to fertilize the oocyte. On the other hand, [31] presented the effects on the fertilization rate of _P. perma_ mussel already in the CC concentration of 1.25 mg L\({}^{-1}\).
## 4 Experimental Results and Analysis
### Robustness
The robustness can be attributed to two key factors: the utilization of ConvNeXt combined with LK-PAN and the effective training strategy for image identifiers. Beyond the numerical results, it is essential to address overfitting when evaluating the effectiveness of a neural network. Overfitting occurs when a network memorizes the training data instead of learning the target function. In the context of zero-watermarking, overfitting could lead to a network that memorizes images under various attacks instead of extracting robust features. However, ZWNet effectively avoids overfitting. This is primarily due to the strict separation of training and testing images. During the training stage, ZWNet has not been exposed to the four test images shown in Figure 9, preventing it from memorizing these images based on prior knowledge. Therefore, it is evident that ZWNet has successfully learned to extract robust image features rather than merely memorizing them.
## 2 Materials and Methods
### Correlation Analysis and Geographic Detector  
Pearson correlation analysis was performed to assess the relationship between flux-measured GPP and different remote sensing indices [47]. Geo-detectors are spatial statistical tools used to detect the influence of multiple independent or joint effects of various independent variables on a dependent variable across different spatial units [48]. Specifically, they assess whether the combined effect of independent variables X1 and X2 enhances or diminishes the explanatory power on the dependent variable Y, or if their impacts on Y are independent. The assessment method involves first calculating the q values of factors X1 and X2 individually and jointly on Y and then comparing these q values. The larger the q value is, the stronger the explanatory power of the factor. This study employs geo-detectors to analyze the capacity of five remote sensing indices to predict rubber forest GPP.
# Wavelet Time-Frequency Analysis on Bridge Resonance in Train-Track-Bridge Interactive System
## 3 Time-Frequency Interaction Analysis on the Train-Track-Bridge System
### Time-Frequency Analysis from Excitation to Bridge Response
#### 3.3.2 On the Resonantly Transitional Bridge  
In order to study the time-frequency evolutionary characteristics on the resonantly transitional bridge from excitation to response, the speed conditions of 175, 210 and 550 and 680 km/h were selected, and the corresponding first-order loading frequencies are 1.94, 2.33, 6.11 and 7.56 Hz, respectively, according to the division in Figure 6 of Section 3.2. The CWT scalograms of mid-span bridge excitation and vertical acceleration response are shown in Figure 8 and it can be seen that:  
1. For the excitation scalogram in Figure 8a,c,e,g, the time-frequency band distributive regulations are almost identical to Section 3.3.1. The frequency band circled in white is defined as the quasi-resonance-triggering band because the frequency components within this band are close to the bridge natural frequency (6.5 Hz).
2. For the response scalogram in Figure 8b,d,f,h, the triggered quasi-resonance bands completely dominate the entire response because they are close to the bridge natural frequency. Simultaneously, the sorting of scalogram values in descending order is listed as (f) > (h) >> (b) > (d).
3. In general, the bridge's resonantly transitional response is stationary while the train is crossing the bridge. The quasi-resonance-triggering band is included within each order of loading frequency and in this case, the triggered quasi-resonance band will dominate the bridge response. Typically, the closer the quasi-resonance-triggering band is to the natural frequency of the bridge, the more the bridge resonant response will be intensified. Therefore, reasonably controlling train speed can crucially help to keep the bridge away from intense quasi-resonant response.
## 5 Discussion
### The Importance of the Results  
These spatial ability measuring tests are part of the curriculum of engineering studies, and some universities even have these tests as part of their entrance exams. Since these tests are quite important and the world is transitioning into a digital age, a virtual reality-based version was created as part of this study. Although our participants had \"infinite\" minutes to complete these spatial ability measuring tests, when paper-based tests are done,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Factors** & **Pairs** & **Triplets** & **Quartets** & **Quinets** \\ \hline undefined FoV & 11 * & 34 * & 76 * & 44 * \\
45\({}^{\circ}\) FoV & 8 & 27 & 57 & 36 \\
60\({}^{\circ}\) FoV & 9 & 17 & 22 & 11 \\
75\({}^{\circ}\) FoV & 5 & 19 & 25 & 10 \\
90\({}^{\circ}\) FoV & 6 & 14 & 18 & 10 \\ \hline \(-\)45\({}^{\circ}\) ROT & 4 * & 6 & 8 & 5 \\ \(-\)30\({}^{\circ}\) ROT & 2 & 4 & 9 & 6 \\ \(-\)15\({}^{\circ}\) ROT & 0 & 5 & 15 & 9 \\
0\({}^{\circ}\) ROT & 3 & 19 * & 36 * & 24 * \\
15\({}^{\circ}\) ROT & 3 & 6 & 15 & 9 \\
30\({}^{\circ}\) ROT & 3 & 9 & 13 & 7 \\
45\({}^{\circ}\) ROT & 3 & 7 & 12 & 7 \\ \hline DD & 8 & 30 & 66 & 52 \\ GVR & 17 * & 69 * & 99 * & 59 * \\ \hline
1.5:1 CR & 2 & 14 & 30 & 14 \\
3:1 CR & 8 & 21 & 32 & 12 \\
7:1 CR & 13 * & 58 * & 93 * & 54 * \\
14:1 CR & 6 & 16 & 26 & 14 \\
21:1 CR & 8 & 29 & 34 & 17 \\ \hline SH OFF & 2 & 38 & 68 & 65 \\ SH ON & 8 * & 49 * & 97 * & 67 * \\ \hline \hline \end{tabular}
\end{table}
Table 6: Number of occurrences of each factor in significant combinations. The largest occurrence per factor is denoted by *.  
deadlines are included. Since virtual reality-based versions of these tests are appearing in the modern age, these tests may be taken in virtual environments. This would mean that deadlines should be included in this variant as well.  
As virtual environments are made up of various components, the perceptions of users can be influenced. According to the results, the optimal test deadline can vary among various virtual environments, depending on their various factors: the FoV, camera rotation, contrast ratio, the existence of shadows, and even the display devices themselves can significantly affect completion times. Thus, when these tests are taken inside virtual environments, the optimal test completion deadline has to be carefully selected by the test organization committees.  
Since no virtual environment is made up of one factor, six were investigated in this study. Out of six, five have significant effects on completion times. According to the results, while certain pairs of factors significantly interact with each other, the test completion times are significantly increased when factor quintets are investigated. In the end, it can be concluded that test completion times are significantly increased by these factors in virtual environments. Naturally, every increase depends on the parameters of virtual environments, but in the worst case, 7-10.5 min should be added to the test deadlines when these tests are taken inside virtual environments.
## 5 Numerical Modeling
### Model and Material Properties
The interface elements are used to simulate the interaction between soil and structure, and the interface elements are comprised of a series of the triangular elements contained three nodes, as is shown in Figure 24. The normal and shear constitutive equation of interfaces elements are as follows [40]:  
\[F_{n}^{(t+\Delta t\ )}\ =\ k_{n}\mu_{n}A+\sigma_{n}A \tag{6}\]  
\[F_{si}^{(t+\Delta t)}\ =\ F_{si}^{(t)}+k_{s}\Delta\mu_{si}^{(t+0.5\Delta t)}A+ \sigma_{si}A \tag{7}\]  
where, \(F_{n}^{(t+\Delta t)}\) is the normal at time \((t+\Delta t)\); \(F_{si}^{(t+\Delta t)}\) is the shear force vector at time \((t+\Delta t)\); \(\mu_{n}\) is the absolute normal penetration of the interface node into the target face; \(\mu_{si}\) is the incremental relative shear displacement vector; \(\sigma_{n}\) is the additional normal stress added due to interface stress initialization; \(\sigma_{si}\) is the additional shear stress added due to interface stress initialization; \(k_{s}\) is shear stiffness, \(k_{n}\) is normal stiffness, and \(A\) is representative area associated with the interface node. The absolute normal penetration of interface surfaces and contact nodes and shear relative velocity are calculated in every step. Subsequently, they are substituted into the constitutive equations of interface elements to obtain normal stress vector and shear stress vector. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Soil**} & \multirow{2}{*}{**Thickness (m)**} & **Internal** & \multirow{2}{*}{**Cohesion \(c\)**} & **Poisson** & **Deformation** & **Density** \\  & & **Friction** & **(kPa)** & **Ratio** & **(MPa)** & **(kg-m\({}^{-3}\))** \\ \hline backfill soil & 3.4 & 10 & 1 & 0.28 & 16 & 1800 \\ gravel sand & 6.0 & 37.0 & 1 & 0.26 & 33.0 & 2000 \\ round gravel & 11.0 & 36.7 & 1 & 0.25 & 30.9 & 2050 \\ medium coarse sand & 10.0 & 34.0 & 1 & 0.29 & 19.0 & 1980 \\ \multirow{2}{*}{boulder clay} & non-penetrating layer & 29.3 & 1 & 0.30 & 23.0 & 2000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Soil stratigraphy and properties at the site. Figure 24: Interface element. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Material**} & \(\mathbf{v}\) & **\(\boldsymbol{E\) (GPa)**} & \(\boldsymbol{\gamma\) **(kN\(\cdot\)m\({}^{-3}\))**} \\ \hline primary lining & 0.2 & 20 & 24 \\ foot-lock bolt & 0.25 & 72 & 25 \\ top longitudinal beam & 0.2 & 32.5 & 24 \\ bottom longitudinal beam & 0.2 & 32.5 & 24 \\ cylindrical steel column & 0.25 & 69 & 38 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Material parameters in the numerical model.
# A Security-Oriented Data-Sharing Scheme Based on Blockchain
## 4 Experiment and Analysis
### Analysis of On-Chain Data Security  
In this scheme, the security of the data owner \(u_{i}\) sharing his own data \(d_{i}^{m}\) on the blockchain may be ensured using this technique. First, blockchain technology has the characteristics of decentralization, openness and transparency, and non-tampering, which are related to the principle of blockchain. Therefore, the characteristics of blockchain technology can be utilized to ensure the security of shared data \(d_{i}^{m}\). However, since the data on the blockchain are visible to everyone and have the nature of openness and transparency, it may raise the risk of data leakage. Therefore, the proposed scheme in this paper improves the security of data on the blockchain using a lightweight on-chain/off-chain collaborative data security storage scheme based on ECC and ECDHE.  
The ECC algorithm provides comparable or higher levels of security using smaller keys and can support faster cryptographic operations. On the other hand, the ECDHE key negotiation algorithm has forward security, and the negotiated shared key is time sensitive. When the user gets the encrypted message, the shared key \(S\) must be decrypted to get information about the shared data \(d_{i}^{m}\). The security of the shared key \(S\) can be guaranteed in the encryption process. According to the principle of the discrete logarithm, in the process of calculating the public key \(P=dG\), it is easy to calculate the public key \(P\) by knowing theprivate key \(d\), but it is difficult to calculate the private key \(d\) by knowing the public key \(P\) and the base point parameter \(G\). Therefore, even if a malicious party hijacks the private key \(d\) of a communication process, it cannot compute the shared key \(S\) and thus cannot decrypt the ciphertext \(C\) and ultimately cannot steal any information from the shared data \(d_{i}^{m}\), which enhances the security of the data-sharing scheme.  
The hash value derived from IPFS is encrypted before blockchain storage, protecting it from unauthorized network exposure. Moreover, NFTs serve as digital proof of ownership, enhancing security through their unique non-replicability, tamper-resistance, and indivisibility, which effectively represent unique data assets on the blockchain. The encryption information of the shared data \(d_{i}^{m}\) is written into the NFT, which improves the security of the shared data on the blockchain. Furthermore, the proposed scheme integrates a tracking mechanism into the NFT structure, encoding data-sharing records within the NFT itself. This capability enables the retrieval of the data's circulation history on the blockchain, including transaction timestamps and the identities of participating users in each instance, as illustrated in Figure 11. This feature strengthens the security of data stored on the blockchain.
# Communication-Efficient Vertical Federated Learning
## 4 Experimental Setup
### Aggregated Model Training  
After latent representations are generated from all the clients, they are aggregated by the guest client (C1) for final model training. The aggregated data are then divided into three groups: training (70%), validation (15%), and testing (15%). The _Adult Income_, _Hear Disease_, and _Wine Quality_ datasets were used for a binary classification task. The _Logistic Regression_ model was used to train the final model with these datasets. On the other hand, the _Rice MSC dataset_ was used for a multiclass classification task, and the _Support Vector Classifier_ (_SVC_) model was used to train the final model with this dataset.
# A Deep Reinforcement Learning Approach for Active SLAM
## Appendix A Proof of the Monotonicity Property of the Robot's Pose Uncertainty  
### Representation of the State  
The goal of a SLAM framework is to simultaneously reconstruct both the 3D trajectory of the system and a sufficiently accurate model of the navigation area, from the observations provided by the available sensors. Following a reduced-order notation of [40], the state of the system at time \(k\) is given by the tuple:  
\[x_{k}=(R_{k},\ p_{k}) \tag{1}\]  
where \(R_{k}\in\text{SO}(3)\) is the rotation matrix and \(p_{k}\in\mathbb{R}^{3}\) is the translation vector.