esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Most existing studies employ broader geographic scopes or aggregated data, which may overlook local variations in transportation patterns and emission sources [19; 20]. Second, there is limited research on the integration of various sustainable transportation technologies and policy frameworks within a single metropolitan area. Previous studies often analyzed individual elements, such as EV adoption or ITS implementation, without considering how these elements interact within a comprehensive urban transportation system [21]. This piecemeal approach can result in suboptimal strategies that fail to maximize the potential benefits of combining different technologies and policies. Third, the existing literature frequently lacks a robust methodological framework for assessing the practical implications of sustainable transportation policies for urban planners and policymakers. Many studies provide theoretical models or simulations but do not connect these findings to actionable insights that can guide decision-making processes in real-world urban environments [22]. This study employs a high spatial resolution analysis to assess decarbonization potential. This approach was chosen to capture detailed variations in mobility patterns and emissions, which is crucial for identifying specific areas where decarbonization efforts can be most effective. Unlike broader analyses, high-resolution spatial analysis allows for more targeted and precise policy interventions.
## 5 Experimentation, Results, and Analysis
### Significance of Dynamic Context Modeling
#### 5.5.5 Real-World Applicability  
The real world often presents scenarios that are dynamic and ever-changing. Static models may fail to capture the nuances in such cases, making their applications limited. In contrast, a dynamic context-aware system like DCAT can be more reliable and versa  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Parameters (M)** & \
\begin{tabular}{c} **Inference Speed** \\ **(FPS)** \\ \end{tabular} &
\begin{tabular}{c} **GPU Memory** \\ **Usage (GB)** \\ \end{tabular} \\ \hline S2 VT & 36.5 & 32.5 & 2.7 \\ Up-Down Attention & 47.8 & 29.3 & 3.2 \\ Image Transformer & 62.3 & 21.4 & 4.9 \\ Oscar & 70.6 & 19.7 & 5.3 \\ AoANet & 68.9 & 22.6 & 4.8 \\ \hline \hline \end{tabular}
\end{table}
Table 19: Cont.  
tile, making it well suited for practical applications ranging from automated journalism to assistive technologies for the visually impaired.  
By incorporating dynamic context modeling into its architecture, DCAT addresses the limitations of traditional models and sets a new standard in the field of image captioning.
## 3 Mitochondrial Dysfunction in AAI-Induced Kidney Diseases
### Investigation Methods of Mitochondrial Function
#### 3.2.3 Mitochondrial Membrane Potential (MMP)  
The mitochondrial bioenergetic state is evaluated in living cells by the mitochondrial membrane potential (\(\Delta\psi\)) (MMP). These assays are used for semi-quantitative \(\Delta\psi\) analysis of fluorescent lipophilic cations (e.g., tetramethylhydamine methyl ester (TMRM), rhodamine 123 (Rhod123)), which enter the cells and accumulate in the mitochondrial matrix under normal conditions. Mitochondrial permeability transition pore (MPT), a protein complex formed in the inner mitochondrial membrane, appears under pathological conditions. An MPT pore opens when mitochondrial membranes become permeable, allowing molecules below 1500 Daltons molecular weight to enter, therefore AAI, which has 341 Da. In the literature, MMP was studied by staining the cells with a lipophilic cationic probe, 5,6,6-tetrachloro-1,1\({}^{\prime}\),3,3\({}^{\prime}\)-tetraethyl-benzimidazolyl-carboxamine iodide (JC-1). A decrease was observed in MMP in cells cultured with AAI for 12 h [2; 19; 20]. Another experiment monitored MMP with Rhod123 in isolated mitochondria preincubated with succinate, followed by AAI and Ca2+. Therefore, an accumulation of Rhod123 was observed in HK-2 cells when these were treated with the immunosuppressional cyclosporin A (CsA) and bongkrekic acid (BA) as MPT inhibitors [20]. When TMRM is used as a reagent, active mitochondria sequester it giving a fluorescent signal [22]. In cell cultures treated with AAI, an increased production of nitric oxide (NO) can be identified, leading to the production of reactive oxygen species (ROS), which cause mitochondrial dysfunction. In this assay, FCCP (ionophore uncoupler of oxidative phosphorylation) is used as a positive control because it eliminates MMP [22].
# Application of a Local Dynamic Model of Large Eddy Simulation to a Marine Propeller Wake
## 2 Numerical Experiments
### Governing Equations of Flow
Since then, LES simulation research was first carried out in schools Stanford-Torino and Grenoble, and then many researchers from around the world studied it. The calculation of the first LES simulation dates back to the 1960s, but the rigorous derivation of the governing equations for LES simulation in a general coordinate system was published in 1995. There are various ways of modeling the subgrid terms, and a lot of models existing. [PERSON] [20], a passionate researcher in LES research, clearly defines the various models currently used as two categories: functional model and structural model. After determining the approximation level, we can define the mathematical model of the physical system to be described. The so-called approximation level is to obtain the required accuracy on a fixed parameter system. In a macroscopic description, the corresponding scale is much larger than the mean free path of molecules, and the continuum paradigm is invoked. In this paper, we study the single-phase (water) incompressible viscous Newtonian fluid, assuming that the density of water is constant, without considering external body force, and without solving the energy equation. Under the framework of continuum mechanics, the most complete model is the general N-S equations, which uses state equations and empirical laws that describe the dissipation coefficient as other variables. Under afore mentioned assumptions, the general unsteady N-S equations can be rewritten in the physical space as  
\[\frac{\partial(\rho u_{i})}{\partial t}+(\rho u_{i}u_{j})_{,j}=\tau_{ij,j}+ \rho f_{i}^{b} \tag{2}\]1  
\[\frac{\partial(\rho u_{i})}{\partial t}+(\rho u_{i}u_{j})_{,j}=\tau_{ij,j}+ \rho f_{i}^{b} \tag{2}\]2  
where, \(p(=P/\rho)\) and \(v\) are the static pressure and the assumed constant kinematic viscosity respectively. They are the continuity and momentum equations with velocity field \(\mathbf{u}=(u_{1},u_{2},u_{3})\) expressed in a Cartesian coordinates system \(\mathbf{x}=(x_{1},x_{2},x_{3})\), which is the Eulerian velocity-pressure formulation. In order to achieve a well-posed problem, initial and boundary conditions must be added to the system later.
## 2 Materials and Methods
### Results of Sample ZrN A and Its Twin F  
This sample presents, unlike all the others, two important features: (i) the ZrN compound can easily be revealed already on the as-received sample (Figure 1a level one), and (ii) a little amount of oxygen is trapped in the whole thickness of the sample (Figure 2 O 1s for level two, but similar spectra are available for the subsequent levels). In Figure 1a, the nitride peak position for N 1s stands at 397.30 \(\pm\) 0.02 eV with a FWHM of 1.34 eV. After fit, Zr 3d showed instead two doublet-components, one centered at (179.5, 182.02) eV and associated to ZrN, the other at (181.99, 184.45) eV, representing the satellite's structure related to the zirconium nitride (3d\({}_{5/2}\), 3d\({}_{3/2}\) ) synthetic peaks (refer to Figure 1b). A perfect correspondence between the experimental \(\Delta\) = 2.43 \(\pm\) 0.02 eV and the literature value has been found for the ZrN compound, where \(\Delta\) represents the distance between 3d\({}_{3/2}\) and 3d\({}_{5/2}\) lines. The stoichiometry of the film at level two was calculated considering the ratio between N 1s total area and Zr 3d total area, but without the satellite's contribution. It resulted as being equal to 0.83 for level two, under stoichiometric in nitrogen when the N 1s peak position falls at 397.3 eV and 0.87 for level four when N 1s is peaked at 397.4 eV. Moreover, the Zr 3d of level four has a right-shift (as visible in Figure 1b right).  
Figure 1: In (**a**) N 1s surface signal (1) is compared with the undersurface correspondent spectra (2,3,4) in sample ZrN A; in (**b**) Zr 3d surface signal has been compared with the in-depth spectra for the same element at level 4, in ZrN A.  
A small amount of oxygen contamination [18], in this case, influences only the satellite contribution forming zirconium oxide. The peak fit with the LA (a,b,c) fit function has been pictured in Figure 3 (left and right) for level two of Zr 3d and N1s, respectively of sample ZrN A. Analogous situation was found analyzing the twin sample ZrN F.
# Wireless Sensor Network-Based Rockfall and Landslide Monitoring Systems: A Review
## 5 Conclusions  
Climate change and urbanization increase the risk of geohazards in the near future. For this reason, it is necessary to develop monitoring systems for hazard prevention and management. Historically, techniques of remote sensing have been used to observe deformation in geologic formations. However, these systems can be expensive. The development of wireless sensor network solutions opens up new possibilities for local monitoring at low cost.  
This study reviewed the monitoring systems for landslides and rockfall events based on WSN. An analysis of the different monitoring systems was carried out, reporting implementation materials and methods, along with the functionality. Geographical and geotechnical details of the implementations were also reported. Some of the main characteristics of the reviewed research works were summarized.  
We can observe that the Internet of Things approach is the preferred choice for the more recent systems, as it allows flexibility. An analysis of the electronic components showed the different types of sensors used in different types of land movements, reporting a wide spectrum of sensor types for different applications. The LPWAN technologies were defined as the most frequent choice for radio communication, as they allow low-power performance by the sensor nodes. The majority of the systems present energy management techniques based on standby modes to achieve reduced current absorption from the battery.  
The current trends, achievements, and problems of the topic under investigation were discussed, along with future possibilities.  
Conceptualization, M.R. and M.S.; methodology, M.R., M.S. and V.S.; formal analysis, M.R., M.S., A.L., G.F. and V.S.; investigation, M.R., M.S. and V.S.; resources, M.R., M.S. and G.F.; data curation, M.R. and M.S.; writing--original draft preparation, M.R., M.S., A.L. and G.F.; writing--review and editing, M.R., M.S., A.L., G.F. and V.S.; visualization, M.R. and M.S.; supervision, V.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Analytical Framework of CP-Free Multiuser OFDM System for Coordinated Multi-Point at mmWave
## 2 System Model
### Signal Model
#### 2.3.2 With HPA
The received signals at the four users with HPA can be approximately written as follows:  
\[\widehat{Y}_{1}^{\prime\prime} =H_{\textit{eq1}}\check{X}_{1}+n_{11} \tag{29a}\] \[\widehat{Y}_{2}^{\prime\prime} =H_{\textit{eq2}}\check{X}_{2}+n_{22}\] (29b) \[\widehat{Y}_{3}^{\prime\prime} =H_{\textit{eq3}}\check{X}_{3}+n_{33}\] (29c) \[z_{k}(t)=A_{k}[\rho(t)]e^{\left|\rho(\varphi_{k}(t)+\varphi_{k}|\rho(t))\right|} \tag{26}\]0  
where \(n_{11}\), \(n_{22}\), \(n_{33}\), and \(n_{44}\) are the contaminating AWGN noises considered for user 1, user 2, user 3, and user 4. On applying the ZF technique, the detected transmitted signal can be represented as  
\[z_{k}(t)=A_{k}[\rho(t)]e^{\left|\rho(\varphi_{k}(t)+\varphi_{k}|\rho(t))\right|} \tag{26}\]1 \[z_{k}(t)=A_{k}[\rho(t)]e^{\left|\rho(\varphi_{k}(t)+\varphi_{k}|\rho(t))\right|} \tag{26}\]2 (30b) \[z_{k}(t)=A_{k}[\rho(t)]e^{\left|\rho(\varphi_{k}(t)+\varphi_{k}|\rho(t))\right|} \tag{26}\]3 (30c) \[z_{k}(t)=A_{k}[\rho(t)]e^{\left|\rho(\varphi_{k}(t)+\varphi_{k}|\rho(t))\right|} \tag{26}\]4  
The estimated signal models presented in Equation (30a) through Equation (30d) are approximated forms of real transmitted signals in the scenario of HPA implementation. However, by executing other steps to process further, eventually the transmitted signal for each user is retrieved.
## 5 Classification of PD Sources via DL Model  
Deep learning, particularly using convolutional neural networks (CNNs), shows promise in signal processing by autonomously extracting intricate features from time-frequency representations (TFRs) of partial discharge (PD) signals. CNNs efficiently classify these signals and identify various PD sources, despite challenges from noise and signal overlap. This study employs CNNs to categorize PD signals based on the number of PD sources, such as single, dual, or multiple, using TFRs as inputs. Due to their ability to extract spatial features from 2D TFRs, CNNs adeptly identify complex and overlapping patterns associated with multiple PD sources, applying convolution and pooling layers to disclose unique structures, as shown in Figure 39.  
The CNN framework includes the following: 1. **Input Layer** : This processes 2D TFR images of dimension \(n\times m\), normalizing pixels to [0, 1]. 2. **Convolutional Layers**: This utilizes \(3\times 3\) kernels for feature extraction, employing ReLU for nonlinear activation. 3. **Pooling Layers**: These implement \(2\times 2\) max-pooling to reduce spatial dimensions, aiding in overfitting prevention. 4. **Inception Modules**: These engage in parallel convolutions with \(1\times 1\), \(3\times 3\), and \(5\times 5\) filters, capturing multi-scale features while \(1\times 1\) convolutions manage computational cost. 5. **Dropout Layers**: These regularly disable neurons (rate of 0.4) during training to mitigate overfitting. 6. **Fully Connected Layers**: These convert feature maps into vectors for classification and employ ReLU for hidden layers and softmax for outputs. 7. **Output Layer**: This produces class probabilities for single, double, and triple PD sources.  
The models were effectively trained using the Adam optimizer with a low initial learning rate for stable convergence. A stepwise learning rate decay was applied, reducing it by 0.1 every 20 epochs to enhance learning in later stages. ImageNet-pretrained weights were used initially for transfer learning benefits. Random horizontal flipping, a basic data augmentation method, was used to prevent overfitting. Table 6 summarizes all training hyperparameters.
## 5 Experiment
### Experimental Results and Analysis
#### 5.4.3 Time Granularity Analysis  
Time granularity refers to the time interval or duration covered by each data point during data collection or analysis. The size of temporal granularity significantly influences the capture of information, the complexity of models, and the accuracy of predictions. As shown in Figure 8, the impact of different time granularities on the prediction average metrics of the LGDMGCN model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **MAE (16 h)** & **RMSE (16 h)** \\ \hline LGDMGCN \(w/o\) At & 0.0483 & 0.2896 \\ LGDMGCN-Global & 0.0373 & 0.2957 \\ LGDMGCN-Local & 0.0395 & 0.2841 \\ LGDMGCN \(w/o\) Attn & 0.0410 & 0.2754 \\ LGDMGCN \(w/o\) Ext & 0.0378 & 0.2849 \\ LGDMGCN & 0.0371 & 0.2679 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiment results.  
It can be observed that the model performs best when the time granularity is 2 h. Results for 30 min and 1 h intervals are slightly inferior to the 2 h interval, while performance deteriorates the most with a 3 h interval. Generally, smaller temporal granularities can provide more temporal features, making the model more complex and detailed. This can help the model capture more patterns and regularities during training, thereby enhancing prediction accuracy. The time granularities of 30 min and 1 h are frequently adopted in research concerning shared bicycle demand prediction [1; 19]. However, our study focuses on the Chicago area during the COVID-19 pandemic, where some stations were closed due to minimal demand. These factors lead to sparse time series data compared to normal periods. In such cases, increasing the time granularity allows each data point to represent a longer period, reducing the proportion of missing data points. Although this approach may sacrifice some analytical precision, we are studying multi-step and relatively long-term forecasting, where some disregard for detailed information is acceptable. Therefore, the key lies in selecting the appropriate temporal granularity based on specific requirements and data characteristics to achieve the optimal balance.
# A Comparison of Faulty Antenna Detection Methodologies in Planar Array
## 4 Optimization Algorithms
### Pattern Search Optimization  
The pattern search optimization method uses a set of pattern vectors to choose points to search at each iteration [29]. At each step, the algorithm looks for a set of points, known as a mesh, that enhances the objective function. At each step, the algorithm polls the points in the current mesh to determine the values of their objective functions. The algorithm will terminate polling when it discovers a point with an objective function lower than the current point. This is referred to as a successful poll, and the next iteration will use it as the current point. A poll is considered unsuccessful if the algorithm is unable to identify a point that enhances the objective function. For the unsuccessful poll, the current point remains unchanged in the following poll. After a successful poll, the mesh size, \(\Delta\)m, is multiplied by 2, and if the poll is unsuccessful the \(\Delta\)m is reduced by 0.5.  
Then, the vector is added to the point with the best objective function value obtained previously. The optimization stops and returns the optimal failed location once it meets the termination conditions.  
An advantage of PS is it has a characteristic of global convergence, where it searches throughout the search range, which means it does not remain at a local minimum. PS can be easily implemented in mathematics and optimizations because it is developed with simple mathematic operations. PS consists of an array defined as a matrix to perform a restricted search [30]. The matrix is defined as mesh, and the current mesh is reduced by a set of survey constraints, which reduces the objective function [31]. The set of survey constraints ensures algorithm convergence. Figure 8 shows the pattern search optimization algorithm in a flowchart representation [32].
## 3 Electromagnetic Modeling of Balise for Underwater Debris  
When the balise uplink FSK signal penetrates conductive media, it generates an electric field that suppresses changes in the magnetic field. Due to the conductivity of water, eddy currents are produced, resulting in additional power loss. Moreover, as the uplink FSK signal frequency is 4.234 MHz, the eddy current loss in water media can no longer be disregarded [27]. Consequently, the calculation and analysis of eddy current loss become crucial.
## 4 Materials and Methods
### RNA Immunoprecipitation Assay  
Cells were fixed with 1% formaldehyde in a PBS buffer for 10 min at room temperature, and cross-linking was quenched with 0.125 M glycine for 5 min. Then, cell pellets were resuspended in the lysis buffer (50 mM Tris, 100 mM NaCl, 0.5% NP40, pH 8.0) containing a complete protease inhibitor cocktail (B14001, Bimake, Houston, TX, USA) and RNase inhibitor (R0102, Beyotime, Shanghai, China). Cell extracts were sonicated for 5 min with a sonicator (JY92-IIN, SCIENTZ, Ningbo, China), and a 100 \(\upmu\)L sample of the supernatant was saved as the input. The remaining cell lysates were incubated with protein A/G magnetic beads (B23202, Bimake) conjugated with an antibody against normal rabbit IgG or MCPIP1 (ab97910, Abcam, Cambridge, UK) at 4 \({}^{\circ}\)C overnight. After washing at least three times with a lysis buffer, the immune complexes were mixed with TRIzol reagent (ThermoFisher Scientific, Waltham, MA, USA) for RNA extraction according to the manufacturer's instructions. RT-qPCR was applied to measure the RNAs enriched by protein.
###### Abstract  
Regular citizens equipped with smart devices are being increasingly used as \"sensors\" by Smart Cities applications. Using contacts among users, data in the form of messages is obtained and shared. Contact-based messaging applications are based on establishing a short-range communication directly between mobile devices, and on storing the messages in these devices for subsequent delivery to cloud-based services. An effective way to increase the number of messages that can be shared is to increase the contact duration. We thus introduce the _Friendly-Sharing_ diffusion approach, where, during a contact, the users are aware of the time needed to interchange the messages stored in their buffers, and they can thus decide to wait more time in order to increase the message sharing probability. The performance of this approach is anyway closely related to the size of the buffer in the device. We therefore compare various policies either for the message selection at forwarding times and for message dropping when the buffer is full. We evaluate our proposal with a modified version of the Opportunistic Networking Environment (ONE) simulator and using real human mobility traces.  
smart cities; human sensors; opportunistic networks; enabling communication technology +
Footnote †: journal: Sensors
# Galileo Single Point Positioning Assessment Including FOC Satellites in Eccentric Orbits
## 1 Introduction
The above mentioned analysis was carried out on 20 days of 1-Hz observations collected by a receiver placed in YEL2 IGS (International GNSS service) station located in Yellowknife Geophysical Observatory, Northwest Territories, Canada. This station located at high latitude also allowed us to test the accuracy of the Galileo system, as ESA declares that the Galileo navigation signals will provide good coverage even at latitudes \begin{table}
\begin{tabular}{c c c c c} \hline
**Satellite** & **Semi-Major Axis (km)** & **Eccentricity** & **Inclination (deg)** & **Orbital Period (h)** \\ \hline IOV and FOC & 29,599.8 & 0.000 & 56.0 & 14.08 \\ E14 and E18 & 27,977.6 & 0.162 & 49.850 & 12.97 \\ \hline \end{tabular}
\end{table} 
Table 1: Galileo satellite orbital parameters. IOV, In-Orbit Validation; FOC, Full Operational Capability. Figure 1: Ground tracks of the Milena (E14), Doresa (E18), E1, and E2 Galileo satellites depicted in blue, red, yellow, and green, respectively, on DOY 52 2018. In the figure, for better readability, only four satellites are shown. up to 75 degrees north [15]. To achieve this goal, the specifications defined in the Galileo Signal in Space Interface Control Document (ICD) were used to develop a suitable software tool in the MATLAB(r) environment. The tool is able to compute satellites' position from both the broadcast and precise ephemerides, to assess GDOP (Geometric Dilution Of Precision), PDOP (Position Dilution Of Precision), HDOP (Horizontal Dilution Of Precision), and VDOP (Vertical Dilution Of Precision) values and to compute single-point positioning on all Galileo frequencies.