esa-sceva/satcom-chunk-collection huggingface dataset:
# Free Space Optical Communication: An Enabling Backhaul Technology for 6G Non-Terrestrial Networks
## 2 Fundamentals of FSO Signal Transmission  
The quality of a FSO signal is influenced by various factors, including geometrical loss, atmospheric attenuation, turbulence-induced fading, and losses due to pointing errors. Each of these will be elaborated in the following sub-sections.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Comparison Factors** & **RF** & **Optical** \\ \hline
**Bandwidth** & Hundreds of MHz to several GHz [63]. & Tens of GHz per wavelength. \\
**Interference** & Prone to interference from electromagnetic waves. & Immune to RF interference. \\ \hline
**Regulatory Requirements** & Requires licenses and compliance with regulatory guidelines. & Operates in the unregulated optical spectrum, eliminating the need for spectrum licenses and reducing regulatory constraints. \\ \hline
**Multipath Interference** & Subject to multipath interference due to reflections, diffractions, and scattering. & Highly directional laser beam, resulting in better signal integrity and reliability. \\ \hline
**Movement-Induced Issues** & Less susceptible to pointing errors and movement-induced issues. & Highly susceptible to pointing errors, which can impact signal stability, especially in adverse weather conditions or moving platforms. \\ \hline
**Security** & Vulnerable to eavesdropping and interception. & It offers higher security with narrower beams that are difficult to intercept. \\ \hline
**Latency** & Both RF and optical signals propagate through the Earth’s atmosphere at nearly the same speed, close to the speed of light in a vacuum [64]. \\ \hline
**Installation Complexity** & Relatively simpler installation. & Installation may be complex, requiring precise alignment and considerations for weather conditions. \\ \hline
**Cost** & Often lower initial setup costs. & May have higher initial setup costs due to specialized equipment, alignment requirements, and tracking systems. \\ \hline
**Scalability** & Easily scalable with additional equipment. & Scalability may be limited by atmospheric conditions and LoS requirements. \\ \hline
**Data Rate** & Typically, lower data rates compared to optical. & Offers higher data rates, especially for point-to-point communication. \\ \hline
**Reliability** & RF technology can be reliable in various environmental conditions. & Weather conditions, such as fog or rain, can affect reliability, leading to signal degradation. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of RF and FSO technologies for backhauling.
## 2 Materials and Methods
### Global Land Surface Satellite Datasets  
Leaf area index (LAI), albedo, LST, and gross primary productivity (GPP) from 1986 to 2016 were accessed from the Global Land Surface Satellite (GLASS) dataset ([[http://www.glass.umd.edu/](http://www.glass.umd.edu/)]([http://www.glass.umd.edu/](http://www.glass.umd.edu/)), accessed on 8 April 2023) [33, 34, 35, 36, 37]. All the parameter datasets feature a spatial resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) and were derived from advanced very high-resolution radiometer (AVHRR) sensor observations. The 8-day composite LAI and albedo data were annually aggregated by averaging the values within each calendar 8-day interval [38]. We averaged the 1-day LST composite data for each year. The GPP product,provided at an inherent annual resolution, was utilized directly without requiring further temporal aggregation.
# Authentication Securing Methods for Mobile Identity: Issues, Solutions and Challenges
## 5 Conclusions  
In this paper, we attempted to present the combination of multi-factor authentication that requires less user intervention. Due to security concerns, we introduced the asymmetric encryption protocol where the user's input itself is used as the encryption key. The PKI concept was used but without the requirement to engage certificate authority (CA), thus involving less cost.  
As mentioned earlier in this study, due to the uniqueness of fingerprints to identify the user and the accuracy of using GPS for location identification, fingerprint authentication and geo-location identification can be used to correctly authenticate the user, as these methods are unique and affordable to implement. We can see from all advertisements that mobile phones nowadays do have hardware support for both types of authentications. Having a dedicated server for authentication of mobile user identities will ease transaction validation and reduce the possible threats. The way mobile identity works is somewhat similar to a computer's single sign on (SSO) scheme, where the number of attempts by users to validate themselves is reduced because all authentications are performed through the server.  
However, the challenges and limitations of different policies and regulations, high-end device requirements, and the changeability of user information need to be addressed to make this authentication method more convenient, secure, and reliable for representing the user in the virtual world. Mobile identity is for more than simply providing access. Security is also an important consideration.  
Conceptualization, A.M. and M.K.A.A.; methodology, M.K.A.A. and Z.A.Z.; validation, M.K.A.-A., Z.A.Z. and A.M.; formal analysis, A.M., Z.A.Z.; investigation, Z.A.Z., A.M.; writing--original draft preparation, M.K.A.A. and A.M.; writing--review and editing, Z.A.Z.; visualization, M.K.A.A. and Z.A.Z.; supervision, Z.A.Z.; project administration, A.M.; funding acquisition, Z.A.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We are grateful to the editor and three anonymous reviewers for their valuable suggestions and comments, which significantly improved the quality of the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Analysis of Model Performance
### Analysis of Operation Conditions
#### 5.1.3 Analysis of Operation Using the IFEL Strategy  
As can be seen from Figure 8a, under the IFEL strategy, the power is mainly supplied by the photovoltaic, micro-gas turbine and power grid. During 0:00-2:00, the discharge of the battery supplements a small part of the electric load. From 7:00 to 16:00, the sunlight is relatively sufficient so that the photovoltaic power generation can produce more electric power. The generated power is mainly concentrated on the daytime, reducing the electricity purchase cost from the power grid. Obviously, it alleviates some of the electrical pressure from system. From Figure 8b, the heat power is mainly supplied by the micro-gas turbine. The output of waste heat recovery from the gas turbine varies with the fluctuation of load. The excess heat power is used for the AC cooling. From Figure 8c, the adsorption chiller bears more cold load than the electric chiller. The system adopts the IFEL strategy so that it initially ensures that it meets the demand of the electric load. Incidentally, redundant electric energy is generated for refrigeration.
# Denoising Diffusion Models on Model-Based Latent Space
## 4 Experiments and Evaluation
### Image Generation
#### 4.2.3 Categorical Reverse Process  
Comparatively, we evaluate the performance obtained with the formulation introduced in Section 3.2.2 for the definition of the reverse process. At training time, we leverage the objective function of Equation (5) with the differentiable decoding of \(\hat{x}_{0}\) derived in Equation (21). We found out that it is preferable to add a second term to the loss in order to condition the prediction of \(z_{0}\) codes based on those assigned by the VQ encoding, which are otherwise not explicitly in the derivation of \(\hat{x}_{0}\).  
The complete training objective used is  
\[\begin{split}\mathcal{L}_{cat}(\theta)=||x_{0}-\hat{x}_{0}^{ \theta}||_{2}^{2}+||\epsilon-\epsilon_{z}^{\theta}||_{2}^{2}\\ \epsilon_{z}^{\theta}=(a_{t}*z_{t}-\hat{z}_{0}^{\theta})/b_{t}, \end{split} \tag{24}\]  
where  
* \(\epsilon_{z}^{\theta}\) is the derivation for the predicted noise in latent space, obtained from the prediction for the uncorrupted image \(\hat{z}_{0}^{\theta}\) sampled from \(\hat{z}_{0}\) using the Gumbel-Softmax trick;
* \(a_{t}\) and \(b_{t}\) are time-dependent constants values that are derived from the detailed definition of the diffusion process in [1], the details of which we omit for simplicity;
* The \(\theta\) superscript is added to indicate values that are a function of model parameters.  
In this configuration, satisfactory results are obtained without resorting to the refiner model, as it can be appreciated in Figures 7 and 8. One interesting thing to study is the introduction of a temperature smoothing at inference time in the generative process (i.e., deriving \(z_{t-1}\) given \(z_{t}\)). Since \(z_{t-1}\) is obtained from the current estimate for \(\tilde{z}_{0}\), we can introduce a temperature factor \(\gamma\) in the sampling of \(\hat{z}_{0}\) from \(\hat{z}_{0}\)  
\[\hat{z}_{0}\sim softmax(\hat{z}_{0}/\gamma) \tag{25}\]  
The effect of the introduction of \(\gamma\) can be qualitatively appreciated in Figure 9. In particular, for \(\gamma\to 0\), the sampling operation corresponds to the \(argmax\) function and essentially defines the mode of the categorical distribution, while for larger values of \(\gamma\), the distribution shifts towards a uniform distribution.  
Figure 6: Samples generated with continuous formulation (after refiner).  
Figure 7: Sampling steps (\(t=1000,900,\ldots,0\)) of the model trained with the categorical parameterization of the reverse process.
## 3 Results and Discussions
### Artificial Neural Network (ANN)
#### 3.4.4 Training Results and Visualization
This proposes that SCG may have a tendency to underfit the data, potentially due to its reliance on a gradient-based optimization process that converges more slowly. The MSE values indicate that the LM algorithm achieved the lowest error followed by BR and SCG with the highest error, as given in Table 4. In terms of the MAE, which measures the average magnitude of errors, the LM algorithm again outperformed the others with the lowest value, but BR followed closely, while SCG demonstrated the highest error. The results propose that LM and BR provide more accurate and stable predictions, whereas SCG performs a lower level of precision. The BR algorithm achieved the highest R\({}^{2}\) value (0.99915), indicating the best predictive performance among these algorithms. LM followed closely with an R\({}^{2}\) value of 0.99895, showing similar effectiveness. However, SCG, with an R\({}^{2}\) of 0.71337, exhibited the weakest correlation between the predicted and actual values, suggesting a limited ability to capture the underlying relationships within the data. In summary, the LM and BR algorithms show the highest performance, with LM showing the lowest prediction errors and BR providing the best generalization. SCG, while computationally efficient, lags in accuracy and precision, making it less suitable for high-accuracy TL glow curve modelling. Although the R\({}^{2}\) values for the BR and LM algorithms \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **MAE** & **MSE** & **R\({}^{2}\) Score** \\ \hline LM & 8.45 \(\times\) 10\({}^{-4}\) & 6.00 \(\times\) 10\({}^{-6}\) & 0.99895 \\ BR & 2.34 \(\times\) 10\({}^{-3}\) & 3.82 \(\times\) 10\({}^{-5}\) & 0.99915 \\ SCG & 1.45 \(\times\) 10\({}^{-2}\) & 6.78 \(\times\) 10\({}^{-4}\) & 0.71337 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance results of the different optimization algorithms. Figure 8: ANN simulation results for the SCG algorithm: (**a**) performance validation; (**b**) the linear regression analysis for the target and output. are very high (\(\geq\)0.998), overfitting was mitigated using stopping criteria, early stopping, regularization (particularly with BR), and stratified data partitioning. The BR algorithm especially demonstrated a well-balanced generalization performance, as validated by the close alignment of training and validation loss curves. The performance results of the optimization algorithms shown in Table 4 provide valuable information about their predictive capabilities and efficiency in luminescence glow curve modelling.
## 2 Problem Description
### Mathematical Model
Equation (10) represents constraints on the availability and capacity of hubs, preventing the allocation of more than the optimal capacity level to hubs and also preventing demand allocation to hubs in scenarios where the hub is unavailable. Equations (11) and (12) determine the coverage radius of each hub. Equation (13) indicates the permissible number of network changes at each level. Equation (14) identifies whether a hub has undergone changes during hub location or remained unchanged. Equations (15) and (16) ensure that the objective function value does not deviate by more than the allowable percentage from the optimal value in each scenario. The mathematical model not only considers traditional cost and demand coverage objectives but also incorporates environmental disruption scenarios. By modeling uncertainties such as service interruptions due to natural disasters or infrastructure failures, the proposed approach ensures robustness. Furthermore, a variable coverage radius is introduced, enabling adaptive allocation strategies for BTS hubs in response to fluctuating demand and operational constraints. The incorporation of green metrics, such as energy consumption and sustainability indices, further enhances the model's relevance to modern telecommunication challenges.
# Recognizing Urban Functional Zones by GF-7 Satellite Stereo Imagery and POI Data
## 4 Results and Discussion  
### Results  
#### 4.1.1 Experimental Setup  
The experiments were performed on a Windows Operating System, using CPU (Intel Core (TM) [EMAIL_ADDRESS] GHz), RAM (16 GB), and GPU (NVIDIA GeForce RTX 3060 12 GB). Experimental work for image feature extraction is performed using the Deep Learning Toolbox in MATLAB 2022b. The training data are randomly divided into training set and verification set according to the ratio of 4:1, and the test sets are used to verify the performance of the model. The learning rate of the pre-trained VGG16 in ST-CNN is \(3\times 10^{-5}\), and the learning rate of other layers is \(3\times 10^{-4}\). The adaptive moment estimation (Adam) algorithm is engaged in optimizing the model. The batch size is set as 32. The network with the pre-training model is trained for 20 batches. The network without the pre-training model is trained for 80 batches.  
POI semantic information extraction, PCA dimension reduction, and RF, KNN, and SVM classifiers are all based on the Scikit-Learn library, and the programming language uses Python 3.7.
## 1 Introduction
### Salt Tolerance Mechanism of Halophytes: Role of Secondary Metabolites
#### 1.3.5. Regulation of ROS by Secondary Metabolites  
Reactive oxygen species (ROS) play a role in the signaling pathway, but their increased accumulation in cells triggers oxidative stress. Halophytes are equipped with powerful antioxidant systems with enzymatic and non-enzymatic components to regulate ROS in the cell. _Cakile martiima_ exhibited stimulation of amino acid biosynthesis and decreased sugar content and GABA to 400 mM NaCl stress at day 20 [39]. Three wild spp. of _Sueda_ synthesize betacyanin to regulate ROS and avoid oxidative stress [40]. Salinity also induces the production of various flavonoids and phenolic compounds in halophytes against ROS.  
[PERSON] et al. [41] reported that two halophytes, _Spergularia marina_ and _Glaux martiima_, inhabiting salt marshes showed an increased content of flavonoids (hesperetin, epicatechin, apigenin derivative, luteolin derivative) and protocatechuic acid, which were also correlated with increased antioxidant activity. [PERSON] et al. [42] reported an increased accumulation of phenolic acids, particularly hydroxycinnamic acids, and decreased accumulation of caffe, salicylic, and 4-coumaric acid in three _Brassica_ spp., upon short-term exposure to 200 mM NaCl stress. Myo-inositol, a derivative of inositol upregulated stress responsive genes in _Chenopodium quinaoa_, is related to antioxidant enzyme activities and increased accumulation of free amino acids and soluble sugars under salt stress [43].
# B5 GEMINI: AI-Driven Network Digital Twin
## 3 B5 gemini Network Digital Twin
### B5 gemini Infrastructure
#### 3.3.2 Deployment Module
resource \"vsphere_virtual_machine\" \"AMF\" { name = \"AMF_free5 Gc\" resource_pool_id = data.vsphere_resource_pool.pool.id datastore_id = data.vsphere_datastore.datastore.id wait_for_guest_net_timeout = 0 num_cpus = 2 memory = 4096 guest_id = data.vsphere_virtual_machine.template_free5 gc.guest_id folder = \"B5 GEMINI/dev\" network_interface { network_id = data.vsphere_network.network_5g.id adapter_type = \"vmxnet3\" } disk { label = \"disk0\" size = 25 datastore_id = data.vsphere_datastore.datastore.id } clone { template_build = data.vsphere_virtual_machine.template6_PP8.id customize { inux_options { host_name =\"AMF\" domain = \"B5 GEMINI\" } network_interface { ipv4_address = \"10.1.3.153\" ipv4_netmask = 24 } } } } ```  
Figure 3: Example of deployment module for a specific resource via Terraform. * Network Repository Function (NRF): Is a centralized repository for the service discovery broker for NFs. * Network Slice Selection Function (NSSF): Used by AMF to select network slice instances for particular services. * Policy Control Function (PCF): In charge of controlling QoS and control plane policy rules (slicing, roaming, mobility). It also provides the management of subscribers, applications, and network resources. * Session Management Function (SMF): Responsible for the interaction with the data plane and managing Protocol Data Unit (PDU) sessions. * Unified Data Management (UDM): Stores the authentication data and subscription information and manages the authentication repository. * Unified Data Repository (UDR): A database that stores and manages subscriber data, identities, and service configurations. * User Plane Function (UPF): Function that manages all the functionality related to the user plane, enabling data forwarding and packet processing. The different NFs are implemented separately as stand-alone processes that interact using the standard defined interfaces. This means that the NFs can be deployed separately using virtualization or container technology. Figure 5 shows an example of the distributed deployment, using overlay networks, of the NFs associated with the free5 GCore implemented in Docker.
# Detection of Forest Tree Losses in Cote d'Ivoire Using Drone Aerial Images
## 4 Discussion
### Advantages and Methodological Limitations  
In terms of the choice of drone, the small size of the drone used (19.9 cm \(\times\) 8.3 cm \(\times\) 8.3 cm) allowed us to transport and launch it from any location in the forest while avoiding trees. Compared with other acquisition platforms, such as satellites or airplanes, the drone used allowed us to acquire images at a higher resolution with a lower operating cost [43]. Although the drone can be affected by cloud cover or fog like other optical sensors, it offered us the flexibility to avoid these. However, the main limitation with the drone used is the size of the area to be covered. Indeed, although, in most cases, the forest areas to be covered easily exceed 1 km\({}^{2}\) (i.e., 100 ha), it should be noted that the flight capacity of a multirotor drone rarely exceeds 1 to 2 h of flight. This flight endurance being the greatest limiting factor for forestry use, we must accept that the use of a drone will remain a tool for analysis at the local scale only [16].  
In terms of tree crown delineation, this method could be tedious due to manual delineation. Given the number of trees in a forest, this is probably not a viable solution for a national-scale forest preservation project.  
In terms of geometric correction of the digital surface models, we propose frame-to-frame georeferencing to reduce errors. The accuracy of the photogrammetric data could have been improved by using ground control points [41].
# Heat Stress Affects H3K9 me3 Level at Human Alpha Satellite DNA Repeats
## 1 Introduction
No change in the level of H3K4 me2/3 is detected either at heterochromatic or euchromatic alpha satellite repeats. We also followed the dynamics of expression of genes containing alpha repeats within introns as well as of the genes closest to the intergenic alpha repeats, upon heat stress. Expression of the genes is downregulated, being negatively correlated with H3K9 me3 levels at the associated alpha repeats. Further studies are necessary in order to reveal the possible contributions of H3K9 me3 enriched alpha repeats, in particular those located within introns, to the silencing of their associated genes.
## 2 Data and Preliminary Process  
### Data Collection  
For data-based predictive models, it is important to acquire sufficient and high-quality data. For a high-accuracy prediction model, high-quality training data must be collected from the processing stage. In this paper, a university laboratory was selected as a target building to develop a performance prediction model for an air-cooled heat pump system. The data required to develop the heat pump performance prediction model was collected through the laboratory. In the laboratory, HVAC (Heating, Ventilation and Air Conditioning) test equipment is established, an air-cooled heat pump is installed as a heat source system, a single duct VAV (Variable Air Volume) system is established as an air conditioner, and the indoor environment is controlled through terminal unit control. The laboratory's air-cooled heat pump system is installed as shown in Figure 1. The system configuration of the laboratory is shown in Table 1, and the specifications of the heat pump system are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{2}{c}{**System**} & \multicolumn{2}{c}{**Number of System**} \\ \cline{2-4}  & & AHU & 1 \\ \cline{3-4}  & & VAV terminal unit with reheat coil & 2 \\ \cline{3-4}  & Heat source system & Air-cooled heat pump systems & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: System configuration.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{2}{c}{**Rated Capacity (kW)**} & \multicolumn{2}{c}{**Power Consumption (kW)**} \\ \cline{3-5}  & & **Cooling** & **Heating** & **Cooling** & **Heating** \\ \hline Heat source system & Indoor unit & 4.64 & 5.2.0 & 0.01 & 0.01 \\ \cline{3-5}  & Outdoor unit & 4.64 & 5.12 & 30.5 & 29.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: System specification.  
In the target building, a BAS (Building Automation System) for system control is established, enabling automatic control and monitoring of the heat pump system. Sensors such as temperature and pressure were installed in the indoor and outdoor units of the heat pump to collect the operational data required to develop the heat pump system performance prediction model. Figure 2 shows the diagram of the heat pump system and the location of the sensor installation. The operation data of the heat pump system was measured at 1 min intervals for each item, and data such as inlet/outlet temperature on the heat source side, inlet/outlet temperature on the load side, heat pump power consumption, outdoor temperature, and indoor temperature were collected.