esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid FSK-FDM Scheme for Data Rate Enhancement in Dual-Function Radar and Communication
## 4 Receiver Design
### Radar Receiver  
Assume that the radar main beam contains \(M\) far-field targets. The vector form of the baseband signal received by the radar receiver is expressed as  
\[\mathbf{x}_{r}(t,\tau)=\sqrt{\frac{M_{T}}{L_{B}}}\sum_{m=1}^{M}\beta_{m}( \mathbf{a}^{T}(\theta_{m})\mathbf{s}(t,\tau))\mathbf{b}(\theta_{m})+\mathbf{e} _{r}(t,\tau)+\mathbf{n}_{r}(t,\tau) \tag{4}\]  
where \(\sqrt{\frac{M_{T}}{L_{B}}}\) is the received signal power, \(\beta_{m}\) is the reflection coefficient of the \(m\)th target, \(\mathbf{a}(\theta_{m})\) is the steering vector in the direction \(\theta_{m}\) from the dual-function transmitter, \(\mathbf{s}(t,\tau)\) is the base band signal, \(\mathbf{b}(\theta_{m})\) is the steering vector in the direction \(\theta_{m}\) at the receiver, \(\mathbf{e}_{r}(t,\tau)\) is the interference vector at the radar receiver, and \(\mathbf{n}_{r}(t,\tau)\) is the AWG noise vector zero mean with variance \(\sigma^{2}\mathbf{I}\) at the radar receiver.  
The reflection constant, \(\beta_{m}\), remains constant during each pulse but varies on a pulse to pulse basis, obeying the Swerling II model. Similarly, \(\mathbf{e}_{r}(t,\tau)\) is the interference vector that impinges on the receiver array from the side lobes. It is important to note that processing is performed directly on the receiver array \(M_{T}\times 1\) vector \(\mathbf{x}_{r}(t,\tau)\) without going into waveform diversity at this stage.
# A Comprehensive Literature Review on Hydrogen Tanks: Storage, Safety, and Structural Integrity
## 2 Hydrogen Tanks Classification
### Composite Tanks of Type IV  
Type IV tanks are similar to Type III tanks, except for the polymer-based liner [63]. Typically, High-Density Polyethylene (HDPE) is used with a thickness of about 5 mm. Alternative materials to HDPE are being studied to offer an increased hydrogen impermeability to reduce liner thickness [42]. Experimental tests demonstrated that PA66 (a type ofnylon) can reduce gas leakage through the liner by 92-95% concerning standard HDPE. The main problem is its cost. Indeed, using PA66 could potentially double the manufacturing cost of the whole tank [56]. The liner includes a metallic appendage (loss), needed as a connection element between the tank and the rest of the system (valves, conduits) [50]. The metallic boss is usually made of aluminum alloy 6061-T6, although alternative alloys with better mechanical properties are being evaluated [22]. Similarly to Type III tanks, the external coating of Type IV tanks is CFRP, utilizing the previously mentioned fiber types (T700). Some studies are investigating the possibility of applying other higher-performance, more expensive fiber types as an alternative. For instance, recent optimization studies for high-pressure hydrogen Type IV systems have shown that employing T720 and T800 fibers can reduce tank weight by 6.7% and 10.1%, respectively, concerning the standard T700 configuration. Since the polymer-based liner provides no structural contribution, the composite coating of Type IV tanks is generally thicker than that of Type III tanks (at the same internal pressure and dimensions) [59; 64].  
Another work introduces a framework combining Machine Learning (ML) with Finite Element (FE) analysis to optimize the design of type IV hydrogen storage vessels rated at 70 MPa. Due to their numerous thin-layer composites and complex dome shapes, the complexity of these vessels makes traditional numerical analysis time-consuming. That is why this approach merges detailed FE models with an Artificial Neural Network (ANN), addressing the irregular material distribution in both cylindrical and dome areas, optimizing the vessel design, and increasing burst pressure from 145 MPa to 157.74 MPa [53].
# Unified Performance Analysis of Direct Air-to-Underwater RF/UWOC Systems
###### Abstract  
In general, wireless relays are used for connecting wireless communication links, but relays can bring security and deployment issues. This paper investigates the performance of air-to-water communication systems without relay nodes. The probability density function (PDF) of the RF link is obtained by unifying \(\alpha-\mu\), the extended generalized -\(\mathcal{K}\) (EGK) and Fisher Snedecor (\(\mathcal{F}\)) distributions of atmospheric turbulence. We obtained the joint PDF of the RF/UWOC system for atmospheric turbulence and ocean turbulence with nonzero mean single-sided pointing error as well as air-water interface under foggy conditions. Based on statistical results, exact expressions for outage probability (OP), bit error rate (BER), and channel capacity (CC) were derived. Finally, we validated the analytical expression using Monte Carlo simulation. The simulation results show that the fading parameters of the channel have a significant impact on the performance of a without-relay-assisted RF/UWOC system.  
atmospheric turbulence; air-to-water; H-fox function; fog: nonzero mean single-sided; Monte Carlo +
Footnote †: journal: _Photonics_  
0000-0002-0002-330][PERSON] 1  
1 School of Information Science and Engineering, The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Yanshan University, Qinhuangdao 066004, China; peijanghtunai.ysu.edu.cn (Q.P.); [EMAIL_ADDRESS] (F.L.); [EMAIL_ADDRESS] (C.K.); [EMAIL_ADDRESS] (J.L.)
# Optimal Estimation Retrieval of Aerosol Fine-Mode Fraction from Ground-Based Sky Light Measurements
## 5 Discussion
### Application Potential  
The algorithm proposed originally for ground-based intensity measurements is based on the optimal estimation inversion theory. Thus, it can be extended conveniently by adjusting the observation vector for other applications as long as the observations contain sufficient information. Polarimetry is one of the most prospective remote sensing approaches since it has a high sensitivity to aerosol [25]. SMAC also provides polarimetric measurements. Research on aerosol retrieval from the polarization data based on the proposed algorithm can be conducted. Furthermore, by introducing appropriate surface estimation methods, the algorithm can be further used for satellite platforms. Based on the feasibility of remote sensing FMF from ground-based single-viewing multi-spectral observation, a further inversion study for satellites can be carried out. The proposed algorithm is expected to be applied to aerosol remote sensing of SMAC on board the Chinese HJ-02 satellite in the future.
## 2 Literature Review and Hypothesis Development
### Theory of Planned Behavior
#### 2.1.3 Perceived Behavioral Control (PBC)  
PBC is a term that relates to the perceived ease or difficulty of engaging in an activity. It is formed by external and internal factors that either encourage or discourage a person from indulging in the action. As an instance, when customers adopt a new product, they establish the effort and resource needs [63]. Convenience factors such as price incentives, appliance availability, and the trustworthiness of energy efficiency labels all play a role in the context of EEHA [64]. [PERSON] et al. [6] evaluated the behavioral intention for electric vehicles by quantifying the PBC in relation to price and availability. The PBC was observed to have a favorable influence on the adoption intentions in this study. The PBC was described by [PERSON] et al. [65] as the perceived discomfort and financial benefits related to an energy conservation activity. [PERSON] et al. [6] also discovered a significant influence of the PBC on energy-saving behavior, resulting in the postulation of the following hypothesis:  
**Hypothesis 4**.: _The PBC is significantly and positively related to the intention of residents to purchase EEHA._  
Figure 1: Conceptual Framework [Source: Authors compilation].
## 2 Materials and Methods
### Data Sources  
The land-use datasets with a spatial resolution of 30 m for 1990, 2000, and 2014 were derived by interpreting the Landsat remote sensing images ([[https://www.usgs.gov](https://www.usgs.gov)]([https://www.usgs.gov](https://www.usgs.gov))), and this dataset was provided by Data Center for Resources and Environment Sciences, Chinese Academy of Sciences (RESDC, [[http://www.resdc.cn](http://www.resdc.cn)]([http://www.resdc.cn](http://www.resdc.cn))). These datasets use a three-level land-use classification theme. It covers 6 first-level land-use types and 25 s-level land-use types (Table 1). The digital elevation model (DEM) data with a spatial resolution of 30 m were obtained from the United States Geological Survey (USGS, [[https://www.usgs.gov](https://www.usgs.gov)]([https://www.usgs.gov](https://www.usgs.gov))). The data of precipitation and temperature were obtained from China'sMeteorological Data Network ([[http://data.cma.cn](http://data.cma.cn)]([http://data.cma.cn](http://data.cma.cn))) and the Hunan Provincial Statistical Yearbook. The administrative district data were derived from the national basic geo-databases. In addition, the dataset of reconstructed photosynthetically active radiation in China (1961-2014) [61] and soil classification data (Harmonized World Soil Database, HWSD) were used in our study.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**First Classification** & **Second Classification** & **First Classification** & **Second Classification** \\ \hline \multirow{3}{*}{Cultivated land} & Paddy field & \multirow{3}{*}{Forest land} & Woodland shrubland \\  & Dryland & & & Open forest land \\  & Dryland & & & Others \\ \hline \multirow{6}{*}{Grassland} & \multirow{6}{*}{High-cover grassland} & River \\  & & & Lake \\ \cline{1-1}  & High-cover grassland & & Reservoir pond \\ \cline{1-1}  & Medium-cover grassland & & & Permanent ice and snow \\ \cline{1-1}  & Low-cover grassland & & & Tidal flat \\ \cline{1-1}  & & & Beach land \\ \hline \multirow{6}{*}{Construction land} & \multirow{6}{*}{Urban land} & Sandy land \\ \cline{1-1}  & & & Gobi \\ \cline{1-1}  & & & Saline land marshland \\ \cline{1-1}  & & & Bare land \\ \cline{1-1}  & & & Bare rock ground \\ \cline{1-1}  & & & Others \\ \hline \hline \end{tabular}
\end{table}
Table 1: Land-use/land cover classification system.  
Figure 1: Sketch of the study area.
## 4 Promising Neuropeptides Inhibitors for the Development of Effective Cancer Therapy
### Emerging Neuropeptides Inhibitors in Colorectal Cancer
#### 4.1.5 Neuropeptide Y  
Neuropeptide Y is secreted from tumor cells and acts through multiple receptors, especially the Y2 receptor (Y2R). It mediates proliferation and angiogenesis during cancer development. However, in some cases, it mediated chemoresistance by virtue of its Y5 receptor under various pathological conditions. Consequently, it was observed that altered expression of NPY and its receptors are directly correlated with poor clinical manifestations, low survival, and enhanced cell proliferation in different types of cancer, suggesting NPY as a remarkable drug target [131]. Overexpression of NPY activates the Y2 receptor on colonic endothelial cells and has a direct impact on angiogenesis and tumor growth following activation of the ERK/MAPK pathway in colon adenocarcinoma. However, it was demonstrated that the use of a specific Y2R antagonist inhibited the NPY-induced angiogenesis and orthotopic HT29 tumor growth in colon adenocarcinoma [132].
## 4 Simulation and Results
### Scheduling Performance Evaluation Metrics  
To validate the effectiveness of the proposed method, the following evaluation metrics are employed for performance assessment:  
1. Scheduling Success Ratio (SSR): \[SSR=N_{\textit{exc}}/N,\] (29) \[N_{\textit{exc}}\] represents the number of successfully scheduled tasks, and \(N\) denotes the total number of radar tasks requested to be scheduled. The SSR is defined as the proportion of successfully scheduled tasks.
2. Average time shift ratio (ASTR): \[\text{ATSR}=\frac{1}{N}\sum_{i=1}^{N}\frac{|t_{ei}-t_{si}|}{w_{i}},\] (30) \[w_{i}\] represents the time window length of task \(i\), \(t_{ei}\) is the expected execution time of task \(i\), and \(t_{si}\) is the actual execution time of task \(i\). The ATSR indicates the timeliness of task scheduling.
3. Hit value ratio (HVR): \[\text{HVR}=\frac{\sum_{i=1}^{N_{\textit{exc}}}P_{i}}{\sum_{j=1}^{N}P_{j}},\] (31) \[N_{\textit{exc}}\] represents the number of successfully scheduled tasks, \[P_{i}\] denotes the priority of each successfully scheduled task, \[N\] is the total number of radar tasks requested for scheduling, and \[P_{j}\] represents the priority of all tasks requested for scheduling. HVR quantifies the proportion of radar resources (such as time and energy) that successfully contribute to the high-value tasks such as detection, tracking, and engagement of targets.
# A Supervised Speech Enhancement Approach with Residual Noise Control for Voice Communication
## 5 Results and Analysis  
This paper uses four objective measurements to analyze the performance of proposed generalized loss, including noise attenuation (NA) [25], speech attenuation (SA) [25], PESQ [17], and SDR [39].
## 2 Approach and Improvements
### Attribute Reduction  
While feature extraction addresses detection performance in terms of good FPR and TPR performance, another target was to maximize throughput. The number of attributes that have to be processed has significant influence on classification time and hence limits throughput. Therefore, we reduce the number of attributes as follows: For every attribute, we compute the attribute weights \(w_{\rm valid}\) for valid and \(w_{\rm invalid}\) for invalid messages over the whole training data set. The attribute weights are defined as:  
\[w_{\rm valid,i}=\frac{1}{N_{\rm valid}}\sum_{j=1}^{N_{\rm valid}}x_{j,i}\qquad \text{and}\qquad w_{\rm invalid,i}=\frac{1}{N_{\rm invalid}}\sum_{j=1}^{N_{\rm invalid }}x_{j,i}\]  
where \(x_{j,i}\) denotes the number of occurrences of attribute \(i\) in message \(j\). To decide whether an attribute \(i\) is significant or not, we calculate the difference \(\Delta\sigma_{i}\) between the attribute weights of valid and invalid messages:  
\[\Delta\sigma_{i}=|w_{\rm valid,i}-w_{\rm invalid,i}|\]  
If \(\Delta\sigma_{i}\) is below a predefined threshold \(\epsilon\), attribute \(i\) is removed from the feature vector. Figure 3 illustrates the attribute reduction. In this example, attributes 3, 7 and 11 are not significant for a distinction between valid and invalid messages. The difference \(\Delta\sigma_{i}\) for these attributes is below the threshold \(\epsilon\) and we thus remove the attributes from the feature vector.
## 5 Evaluation Results and Discussion
### Illustration of Optimized TPs in Urban Network  
Finally, we illustrate the results of the optimization of TPs in the exemplary urban network WRO-17, with traffic demands between 1 and 2 wavelengths per a site, WDM capacity \(W=8\), and the Flex-O network deployment scenario applied. Topology WRO-17, shown in Figure 10a, represents remote sites (denoted by circles) placed in proximity of a subset of real antenna locations (denoted by triangles) in the center of the city of Wroclaw. The links connecting the remote nodes and the hub site (denoted by a hexagonal) are driven along streets, and their lengths reflect real physical distances.  
Figure 10b shows the optimal routing of TPs (each in a different color) with traffic aggregation at intermediate nodes and assignment of wavelengths. OADM-1 and OADM-2 are used to aggregate 1 and 2 wavelengths, respectively, at the intermediate sites. The optimized use of OADMs allows to decrease the total number of TPs from 17 TPs necessary in the Flex-D/Fix-D scenarios to 4 TPs in Flex-O, in which the paths aggregate traffic from up to 5-6 remote sites. In this example, the network deployment cost can be reduced from about $18,000 (in Flex-D) to about $7000 (in Flex-O), which leads to the cost savings of about 60%. The much lower cost values in network WRO-17, when compared to previously studied networks, are related to the lower overall length of the optical fiber used, which is a dominant cost element as shown in Section 5.3. Finally, we can see that the use of wavelengths is unrepeatable on particular TPs, which validates the correctness of the wavelength assignment constraints in the MILP model.  
Figure 10: **(a)** WRO-17 network topology and **(b)** optimal configuration of TPs in WRO-17 assuming traffic load \(\rho=1.5\) and WDM capacity \(W=8\).  
Figure 9: Comparison of particular cost elements in different network deployment scenarios assuming different traffic loads (\(\rho\)) and WDM system capacities (\(W\)) in network MESH-38.
# Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data
###### Abstract  
Peatlands play an important role in the global carbon cycle as they contain a large soil carbon stock. However, current climate change could potentially shift peatlands from being carbon sinks to carbon sources. Remote sensing methods provide an opportunity to monitor carbon dioxide (CO2) exchange in peatland ecosystems at large scales under these changing conditions. In this study, we developed empirical models of the CO2 balance (net ecosystem exchange, NEE), gross primary production (GPP), and ecosystem respiration (ER) that could be used for upscaling CO2 fluxes with remotely sensed data. Two to three years of eddy covariance (EC) data from five peatlands in Sweden and Finland were compared to modelled NEE, GPP and ER based on vegetation indices from 10 m resolution Sentinel-2 MSI and land surface temperature from 1 km resolution MODIS data. To ensure a precise match between the EC data and the Sentinel-2 observations, a footprint model was applied to derive footprint-weighted daily means of the vegetation indices. Average model parameters for all sites were acquired with a leave-one-out-cross-validation procedure. Both the GPP and the ER models gave high agreement with the EC-derived fluxes (R2 = 0.70 and 0.56, NRMSE = 14% and 15%, respectively). The performance of the NEE model was weaker (average R2 = 0.36 and NRMSE = 13%). Our findings demonstrate that using optical and thermal satellite sensor data is a feasible method for upscaling the GPP and ER of northern boreal peatlands, although further studies are needed to investigate the sources of the unexplained spatial and temporal variation of the CO2 fluxes.  
ecosystem respiration (ER); footprint analysis; gross primary production (GPP); net ecosystem exchange (NEE); peatland; Sentinel-2; upscaling +
Footnote †: journal: remote sensing  
0000-0002-4607-2238][PERSON]  
0000-0002-0002-3381][PERSON]  
0000-0002-4607-2381][PERSON]  
0000-0002-4607-2381][PERSON]  
0000-0002-4607-2381][PERSON]
# Combined Optimization of Maintenance Works and Crews in Railway Networks
## 5 Results
This variation from the implemented use case in this paper arises from the different combined pairs of tasks, which directly influence the track possession cost. Since more pairs of tasks cannot be simultaneously performed on the same link in this scenario, tasks are scheduled in other weeks, resulting in increased costs. The route cancellation cost, which has seen a slight reduction, is significantly influenced by the duration of projects taken at a different price compared to the base model. Additionally, the manner in which specific tasks are optimally performed also contributes to this cost. The model's sensitivity to cost is crucial and highly relevant for railway operators. Simultaneously, the cost of work crews for executing the tasks is EUR 63,740, and this specific cost is weighted at 1 EUR per kilometer using the parameter in the \(W_{lq}\) model. To sum up, maintenance tasks that included work crews in the objective function during the solution process resulted in varying execution times. This observation highlights that the model required the simultaneous optimization and scheduling of maintenance tasks and work crews to achieve the best outcomes. Comparing our approach with additional studies, [PERSON] et al. [29] presented a model designed to schedule track maintenance activities in the short term, once both the train schedule and maintenance plans have been established. The model's primary aim is to arrange the maintenance tasks and allocate maintenance crews to them, all while striving to minimize the overall maintenance expenses. Furthemore, the formulation permitted the assignment of various crews to the same task at various moments and took into account the specific time needed for setting up and concluding each instance of the activity. The model was resolved through the implementation of a two-stage heuristic approach. In the first stage, a feasible solution was generated, and in the second stage, simulated annealing was employed. Our study further extends the aforementioned study, also including long-term maintenance activities (projects). On the other hand, [PERSON] et al. [35] focused on optimizing the maintenance schedule for both railway operators and maintenance contractors considering hindrance and flexibility for both stakeholders. They formulated the problem as a multi-objective optimization problem (MOOP) with the first objective function minimizing the cost caused by hindrance for the train operators, and the second objective function maximizing the amount of scheduled slots in the maintenance schedule by maximizing the value of the maintenance slots above the maintenance demand. However, our research extends this study further by assigning a set of work crews to the maintenance tasks and minimizing their total costs.
## 6 Simulation Results
### Performance Analysis
#### 6.2.1 Performance Comparison of Convergence  
Firstly, in order to find the learning rate that makes the proposed model converge best, we conducted experiments at three different learning rates. As shown in Figure 3, when the learning rate was equal to \(10^{-4}\), the convergence rate of the agent was slow, and the total reward value did not reach the optimal value. To make the convergence speed up, we increased the learning rate to \(10^{-3}\), which made the convergence faster and the total reward higher. When the learning rate increased to \(10^{-2}\), it was easy for the agent to converge to a local optimum, resulting in poor convergence results. Therefore, in the training of the agents, the learning rate was set to \(10^{-3}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of trains & 2 \\ Number of wayside nodes & 7 \\ Bandwidth & 10 MHz \\ Max buffer size & 250 kb \\ Average packet size & 25 kb \\ SNR threshold & 31 \\ Gaussian noise power spectral density & \(-174\) dBm/Hz \\ Weight of latency, \(\omega_{1}\) & 0.5 \\ Weight of channel throughput, \(\omega_{2}\) & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Communication system parameters.  
The goal of agent training is to better avoid outage events and reduce packet loss rate. As shown in Figure 4, the probability of outage events decreased dramatically in the first 1000 episodes, indicating that the agent learned to select next-hop relays within communication range. At the same time, the probability of network congestion gradually decreased during the training process, which illustrates that the agent successfully avoided congested nodes. The simulation results show that the MADDQN algorithm could effectively avoid outage and congestion events, ensuring the quality of transmission.