esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 5 Discussion
### Comparison with Traditional Machine Learning Models  
In comparison to classical machine learning methods such as Random Forest (RF) and Gradient Boosting Decision Trees (GBDT), the proposed enhanced ResNet outperformed across all evaluation metrics. Traditional models rely on handcrafted features and cannot capture local spatial dependencies [73], making them less effective for high-resolution multispectral imagery. In contrast, convolutional neural networks (CNNs) are capable of automatically learning hierarchical spatial patterns, which is critical for modeling the structural and spectral variability of forest ecosystems [74]. The enhanced ResNet's strong generalization in heterogeneous subtropical forest environments--like the Liuchong River Basin--demonstrates the potential of deep learning approaches for operational forest carbon estimation. These results highlight the benefit of learning spatial and spectral features jointly in a hierarchical framework, which cannot be easily replicated by shallow learners or rule-based indices.
## 3 Characterisation
### Reference Materials and Batch Variability  
The development of characterisation methodologies and protocols, and hazard assessment assays and protocols requires an adequate body of reference NMs, which are available for comparison for a specific trait of the material (usually only one parameter such as chemical concentration for traditional chemicals). A certified standard is a sample that is certified for one parameter within a prescribed precision or accuracy. The EC's Joint Research Centre (JRC) Institute of Reference Materials and Measurements has developed a certified reference NM (silica) and the Institute of Consumer Health and Protections has assembled an archive of test nanomaterials, on which there is some agreement on some characterization measurements. However, in all cases to date, the characterization is in water or simple buffer rather that in the relevant biological or environmental media. The U.S. Department of Commerce's National Institute of Standards and Technology (NIST) is also trying to produce reference materials.  
It is difficult to see how the research community will make significant progress and reach consensus regarding the health and environmental impacts of NMs until NMs can be made reproducible from batch to batch, and/or we have a good understanding of the acceptable variability in specific NMs characteristics (e.g., size, size distribution, surface charge, surface impurities, surface chemistry, _etc._). This is information that is absolutely necessary _before_ cells or living organisms experience the NMs differently, in terms of uptake, localization, degradation/ bioaccumulation and impact.  
Of course the issue of batch-to-batch variability has a different relevance for different users. For example, academic researchers at least need to know exactly what is in the batch; industrial producers cannot scale up commercial activity until batch problems are resolved; and regulatory testers need a steady supply of the _same_ thing for routine testing. Clearly, adequate regulation of NMs for application will require a high degree of reproducibility between batches, so there is a driver to resolve this issue.  
**Recommendation 17**: More effort and urgency should be attached to the cooperative development of reference materials, useful both to nanomaterial developers and to those examining health, safety and environmental aspects [5].  
**Recommendation 18**: More cooperative effort on minimizing batch variability is needed. Researchers should report the batch number and state the measured impurities, which are usually related to the synthesis method.
## 2 Exploring SDN, NFV, Policies, ML, Network Slicing and Telecom Security
### Policy-Based Network Security
#### 2.4.1 Early Concepts of Security Policies  
The concept of network security policies is not new, as seen in the work of [PERSON] [55], where their importance is discussed, as well as one of the most important features when it comes to their implementation, their enforceability. Security policies (SPs) must be enforceable; otherwise, they are nothing more than guidelines for safe network environments. This work can outline what enforceable and non-enforceable security policies are; however, networks have advanced since then, and some of the limitations imposed by the technologies at the time did not allow the enforcement of some of the security aspects of these networks are now being surpassed as networks evolve [56].  
This was described relatively shortly after in the work of [57], which introduced how technological advancements allowed for more sophisticated implementations of network policies by introducing abstraction in the language used for policy definition.  
The introduction of high-level programming languages for network configurations can be seen in the work of [58], which developed Frenetic, a high-level language for OpenFlow networks. This work demonstrates how raising the level of abstraction in network programming benefits the management of complex configurations and the effective enforcement of security policies. Similarly, [59] introduced _PonderFlow_, another language designed for higher abstraction in network configuration, specifically for OpenFlow networks. _PonderFlow_ is an extension of _Ponder_, a declarative, object-oriented language for specifying management and security policies proposed by [60].
# Spot Market Cloud Orchestration Using Task-Based Redundancy and Dynamic Costing
## 5 Empirical Testing
### Experimental Design
#### 5.2.1 Experiment 1: Varying the Cost of Failure  
The objective of this experiment was to compare the behavior of the experimental and control algorithms in a market with a constant spot price of 1.00 but varying costs of failure from 1 to 100,000 per second (Figures 6 and 7). This scenario models the expected behavior of an enterprise cloud serving users for whom the majority of activity, and thus the highest impact of failure, occurs during business hours. The _SpotMarketProvider_ in this experiment varied the cost of failure according to the time of day, with the peak between 09:00 and 17:00.  
Figure 6 shows an example run of the control algorithm, while Figure 7 shows an example run of the experimental algorithm. In both figures, the redundancy of each of the microservices is shown with respect to the cost of catastrophic failure.  
The results from each of the immediate and delayed configurations of the orchestrator are given in Tables 1 and 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Orchestrator** & **Mean Running Cost** &
\begin{tabular}{c} **Mean Actual Cost of** \\ **Failures** \\ \end{tabular} & **Mean Total Cost** \\ \hline \multirow{2}{*}{**Exp. 1**} & Experimental & 0.94 & 0.06 & 1.00 \\ \cline{2-5}  & Control & 1.60 & 0.00 & 1.60 \\ \hline \multirow{2}{*}{**Exp 2.**} & Experimental & 13,438 & 3288 & 16,726 \\ \cline{2-5}  & Control & 53,590 & 0.00 & 53,590 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Aggregate results of experiments with immediate failure detection and remediation.  
Figure 6: Control algorithm behavior in Experiment 1 with immediate detection and remediation of failures.
# A Mobile Gait Training System Providing an Active Interaction
## 2 Proposed System
### Moving Part  
The moving part is responsible for moving the system and transmitting the training distance to the augmenting part. It keeps the distance between the robot and trainee maintained constantly based on a trainee's position and a robot's travel distance. The moving part uses a mobile robot and operates according to the procedure shown in Figure 4.  
In step (a), the distance between the system and the trainee is calculated. Both foot coordinates are received from the sensing part, and the average value of the data is calculated as the distance between the trainee and the system. In step (b), the direction of driving is determined based on the calculated distance. If the distance between the system and the trainee is less than 1 m, the system drives straight forward, or if the distance exceeds 1 m, it drives backward. In step (c), the system moves the robot. The robot keeps the 1 m distance by going straight and backward in the pre-programmed direction. Finally, in step (d), the gait distance of the trainee is calculated through the amount of change in the position of the robot. When the robot is automatically moved by the distance-keeping algorithm, the robot's self-position recognition function is used to accurately calculate the robot's position and travel distance. The self-position recognition function is a function of calculating the position of the robot itself through measurements such as the diameter of the wheels specified in the robot's specifications, RPM, and the number of wheels the motor has rotated. Through this function, the distance traveled by the robot is measured in real time. Since the robot automatically moves the distance as the trainee moves, the trainee's gait distance can be obtained through the amount of change in the robot's position.  
Through the above procedure, the moving part continuously updates training content according to the distance the trainee walked, enabling active gait training.
## 3 Attacks on HWMP
### External Attacks
#### 3.1.4 Impersonation Attacks (Loop)  
There are four address fields in the HWMP frames: transmitter, receiver, originator, and target. An attacker can impersonate other mesh STAs by changing the addresses in these fields. The impersonation attack can be used to create a loop [29] by manipulating the transmitter and receiver address fields. Figure 9 shows an example of a looping attack.  
S initiates a PREQ frame (as usual) until the PREQ frame reaches D. D will then reply to the request with a PREP frame. The malicious mesh STA M will impersonate A and B to confuse these STAs. A loop between A and B will be formed. The sequence of transmitted frames used by STA M to create a loop is as follows:  
D: D \(\rightarrow\) M: PREP[S - D]  
M: B \(\rightarrow\) A: PREP[S - D]  
M: A \(\rightarrow\) B: PREP[S - D]  
A: A \(\rightarrow\) S: PREP[S - D]  
B: B \(\rightarrow\) S: PREP[S - D]  
When S receives the PREP frame, it will choose either A or B as the next hop. At this point, A receives information that the next hop to reach D is B, while B receives information that the next hop to reach D is A. When S sends a data packet to D, it will transmit the packet to either A or B. Then, A and B will forward the packets to each other continuously. As a result, the data packet will never reach D _via_ M.
## 2 Study Area and Data Processing
### Discharge Data  
Figure 1 shows that five hydrological stations are situated in the basin. Discharge data at the five stations from 1 January 2014 to 31 December 2016 were obtained from DMH. These data were derived from water level observations using rating curves. The local hydrological stations routinely measure water level at 6:30, 12:30, and 18:30 UTC+06:30 every day. In the flooding season, water level is observed hourly when it reaches 100 cm below the dangerous stage. Thus, the obtained discharge time series vary in time intervals (1 h, 6 h, or 12 h), and the linear interpolation method was used to transform the discharge data into 3-h temporal intervals. These historical discharge data were adopted for calibrating hydrological models and evaluating flood simulation performance.
## 3 Magnetic Characterization Instruments
### Faraday Rotation Magnetometers  
These devices are magnetometers based on the Faraday effect. More specifically, according to the Faraday effect, in some materials, when an external magnetic field is applied, linearly polarized light rotates. A Faraday rotation magnetometer has polarizers that can specify the angle of the rotation, which comes from a variation of the intensity. Afterward, knowing the angle of the rotation, the magnetic field can be determined and thus knowledge about the source can be obtained. In every Faraday rotation magnetometer, the light is polarized and passes through a special material, with the name Faraday crystal. For the light polarization to rotate, the applied magnetic field of the crystal should be in the direction of the light propagation. The schematic diagram that describes the structure of a Faraday rotation magnetometer is depicted in Figure 1. Observing this figure, it can be seen that the light source is depicted with the letter A, the collimator with the letter B, the polarizer with the letter C, the Faraday crystal with the letter D, the mirror coating with the letter E, the analyzer with the letter F, the objective with the letter G, and the camera with the letter H, while with numbers 1, 2, and 3 are indicating the beamsplitter, the photodiode, and the Helmholtz coils, respectively [85]. This device can provide rapid and non-destructive measurements, while its applications are various, like in magnetic stripe cards or banknotes [85].  
Some of the main drawbacks of this device are that it has limited use for quantitative measurements, the level of noise is considerably high, and the appearance of long-term displacements of the signal is a common phenomenon, thus there is a signal dependence from the time and the temperature. Moreover, the Faraday crystal has magnetic properties that can affect the quality of the measurements [85]. Some proposals for optimization of the characteristics of Faraday rotation magnetometers are presented in [86; 87]. More specifically, in [86], the Faraday rotation alkali atomic magnetometer is investigated and the reliance of the amplitude of the magnetic resonance signal on the probe light frequency detuning is studied. To increase the sensitivity of the device, the frequency of the probe should be detuned appropriately, to achieve a greater signal-to-noise ratio. In [87], the sensitivity of a Faraday rotation magnetometer is also investigated. in this article, it is proven that the device can obtain better sensitivity, the best rotation angle, and the lowest noise, if the frequency of the probe light is tuned to the edge of Doppler profile wings.
# A Computational Study of Polymer Solutions Flow Regimes during Oil Recovery from a Fractured Model
###### Abstract  
Increasing the efficiency of hydrocarbon field development is an important issue. One of the methods for increasing oil recovery is the injection of aqueous solutions of polymers. Although this method has been known and used for quite some time, further systematic research is needed to further improve its effectiveness. In this work, systematic computational studies of the features of oil displacement by aqueous polymer solutions from a naturally fractured structure were carried out. Direct numerical modeling of a two-phase immiscible flow in the process of displacing oil from a natural fracture structure using solutions of anionic polymers based on polyacrylamide was carried out. Aqueous solutions of three different polymers were considered, the concentrations of which varied from 0 to 0.1%, and the molecular weights were from 10 to 20 mln c.u. The rheological properties of polymers and their wetting characteristics have been previously studied in laboratory experiments. A distinctive feature of the polymers considered was the non-Newtonian nature of their aqueous solutions even at low concentrations. To take these processes into account, the computational technique has been extended to the case of non-Newtonian rheology for immiscible two-phase flow in one of the media. During numerical simulations, the effect of the concentration of polymers, their molecular weight, and charging density on the flow regimes in a fractured reservoir have been investigated systematically at various crude oil viscosities. It has been shown that the use of a 0.1% aqueous solution of polyacrylamide can increase the oil-recovery factor by 1.8 times. It has been established that, with an increase in the molecular weight and surface charge density of the polymer, the efficiency of its use for enhancing oil recovery increases. With an increase in the viscosity of the displaced oil, the effect of using the injection of the considered polymers also increases. The data obtained in this work can be used to further improve polymer-flooding technologies for oil fields.  
polymer flooding; carbonate reservoirs; fracturing; polyacrylamide solutions; numerical simulation; volume of fluid method Article
## 4 Simulation Results
As seen in Figure 6, it can be seen that the minimum harvested energy per energy vehicular receiver of the proposed robust beamforming design scheme decreases with the increase of the estimated channel error, \(\delta_{E}^{2}\), which confirms the motivation of the worse case robust optimization. Moreover, we learn intuitively that the proposed nonlinear EH beamforming design scheme performs considerably better than other beamforming design schemes but worse than the perfect case.
# Understanding Microgrid Sustainability: A Systemic and Comprehensive Review
## 4 Information Field
### Information Elements  
The information layer has two elements: the information node and communication links. An information node is composed of a control part and a data part. These parts vary significantly depending on the microgrid, its information architecture, and how the information issues are handled. Communication links connect the different information nodes of the architecture together [136]. The literature on communication links divides them into different types of networks, namely Wide-Area Network (WAN), Field Area Network (FAN), Neighborhood Area Network (NAN), Building Area Network (BAN), Industrial Area Network (IAN), and Home Area Network (HAN) [78; 137].
## 2 Materials and Methods
### Bioavailability Index  
In vitro gastrointestinal digestion was performed according to the report conducted by [PERSON] et al. [34]. Lyophilized microcapsules (0.5 g) were combined with 0.5 mL of phosphate buffer (pH 7.4) and 1.0 mL of simulated saliva and shaken at 37 \({}^{\circ}\)C. Then, 6 M HCl was added to adjust to pH 3.0 and shaken again at 37 \({}^{\circ}\)C. After gastric digestion, the samples were subjected to intestinal digestion for 120 min by adding 1 M NaOH and adjusting to pH 7.0. The following enzymes were used during the entire digestion process: human salivary \(\alpha\)-amylase (EC 3.2.1.1, 2000 U/mL), porcine pepsin (EC 3.4.23.1, 2000 U/mL), porcine trypsin (EC 3.4.21.4, 100 U/mL), bovine chymotrypsin (EC 3.4.21.1, 25 U/mL), porcine pancreatic \(\alpha\)-amylase (EC 3.2.1.1, 200 U/mL), and porcine pancreatic lipase (EC 3.1.1.3, 2000 U/mL). Their activity after the entire process was stopped using MeOH (1:1 ratio) and centrifuged (10 min, 4 \({}^{\circ}\)C, 11,000\(\times\) g), and the obtained supernatant was lyophilized, and then the content of phenolic compounds was determined according to point 2.8. The bioavailability index (BI) of the phenolic compounds was calculated according to the equation below, where CF\({}_{\text{D}}\) is the content of phenolic compounds after in vitro digestion, while CF\({}_{\text{A}}\) is the content of phenolic compounds before digestion.  
\[\text{BI}(\%)=\frac{CF_{D}}{CF_{A}}\times 100\]
## 3 Study Area and Data
### Landslide Inventory  
A landslide inventory provides indispensable basic data for model building in LHA studies. We established an original landslide inventory based on high-resolution remote sensing images through indoor interpretation, and we further supplemented and improved the landslide inventory through field investigations. This landslide inventory was also published in China Scientific Data [43]. In the study area, we identified a total of 356 landslides that occurred from 1970 to 2024, and these were mainly rock landslides (Figure 4). According to the classification method proposed by [PERSON] (1978) [2], these landslides were mainly translational and rotational, complex, and topple slides. Additionally, most of these landslides were distributed in the V-shaped valleys along the KKH, especially between Aoyitake and Bulunkou (Figure 3c).  
Figure 4: Examples of past and recent landslides alongside the KKH: (**a–d**) the landslides distributed along the roads. The red dash line indicates the boundary of the landslide.  
Figure 3: Location map and landslide distribution of the study area: (**a**) the location of the study area in China; (**b**) the location of the study area in Xinjiang Province; (**c**) landslide distribution; (**d**) example 1 of the hydrological slope units; (**e**) example 2 of the hydrological slope units.  
In order to evaluate the adopted LHA models, we utilized GIS software (ArcGIS v10.4) and the MATLAB R2022a software to establish a training set and validation set based on the landslide inventory [5; 11; 41]. Firstly, using the hydrology tools in ArcGIS 10.4, we extracted the slope units as the evaluation units (Figure 3c-e), totaling 9456 slope units. Using DEM data with a 30 m resolution, we conducted depression filling and flow direction analysis to gather essential data for the creation of hydrological units. After calculating the flow and delineating the river network, we used the GIS software to automatically produce hydrological slope units that represented watershed and catchment line features. Any unreasonable unit boundaries were manually corrected. Next, we randomly selected non-landslide evaluation units of the same quantity as in the landslide evaluation units. Then, we mixed the landslide evaluation units with the non-landslide evaluation units. Finally, we used the MATLAB R2022a software to randomly select 70% of the mixed evaluation units as the training set for model building and the remaining 30% as the validation set for model evaluation.