esa-sceva/satcom-chunk-collection huggingface dataset:
# Microscopic Experiments to Assess the Macroscopic Sweep Characteristics of Carbon Dioxide Flooding
## 2 Research Methods
### Experimental Methods  
The experimental steps for the glass etching object modeling of carbon dioxide microscopic waves and features were as follows:  
1. A model with specific pore structures and channels was created on a transparent glass plate using a hydrofluoric acid solution to simulate a real micro-pore network.
2. Simulation oil (a viscous liquid with dye mark) and simulation water (an aqueous solution with dye mark) were prepared according to the specific experimental conditions to be simulated.
3. The model was placed under a microscope or transparent observation equipment and connected to a fluid injection system to conduct different driving experiments, such as water flooding, carbon dioxide non-miscible flooding, and carbon dioxide miscible flooding, to simulate various extraction processes.
4. Using a microscopic visualization system, the initiation characteristics and dynamic evolution of residual oil in the blind-end pore space were observed and recorded through a microscope or a high-resolution camera under different alternation modes [17].  
The experimental steps for the macro-wave and characterization of carbon dioxide in a two-dimensional visible large-object model were as follows:  
1. All experimental equipment, including the injection pump, nitrogen cylinder, pressurization device, fluid intermediate container (containing N2 + CO2 mixture, formation water, crude oil), gas mass flow meter, and steam generator, were checked. The two-dimensional scaled physical model was prepared to simulate the environment of the underground reservoir.
2. The 2D visualized physical model was vacuumed and then saturated with simulated oil and placed at a constant temperature of 20 \({}^{\circ}\)C for 2 h to ensure full saturation. The vacuum pressure was 0.
3. Simulated formation water was injected at a rate of 50 \(\mu\)L/min until no further flow occurred in the 2D physical model.
4. During the experiment, parameters such as pressure, temperature, and flow rate were monitored and recorded, and the flow and ripple characteristics of the fluid in the porous medium were observed by the visualization equipment.
5. After the completion of the experiment, the collected data were analyzed to evaluate the macroscopic progression and coverage (wave and reach) of carbon dioxide in porous media and its impact on the displacement of oil and the change in the oil-gas interface [18]. In this context, \"wave\" refers to the movement of the injected carbon dioxide as it advances through the reservoir, forming a front that displaces the oil. \"Reach\" describes how far this wave extends and how effectively it sweeps through the reservoir, displacing oil from the rock matrix. Evaluating these characteristics helps to understand the efficiency of the recovery process and the extent to which the injected carbon dioxide can displace oil from the reservoir.
## 5 Discussion  
Virtual reality (VR) and digital environment technologies are still relatively new and rarely used in everyday situations. However, numerous studies have demonstrated their critical application in the business sector.  
This research has created a method for team collaboration and information processing using 3D and VR virtual environment tools in order to demonstrate the flexibility and significance of PLM systems using VR. Using VR to enhance team collaboration, engineers and designers in the field can continue to design and develop manufacturing procedures. Immersive 3D virtual environments can be used to transform previously established virtual environment processes. These environments are anticipated to improve human interaction and communication without requiring participants to be physically present. Instead of continuing to try to simulate interaction on virtual platforms, the experts can concentrate their research on new and distinctive opportunities in virtual environments with VR, as well as on enabling knowledge sharing and collaboration in education, services, meetings, sales and purchasing, and generally in all productive industries where more than one person can interact [36,37]. Based on real-world experience, the call for practical potential as it is presented in this paper could be effectively applied to any type of users or businesses, without incurring costs or risks from insufficient training.  
There are several benefits for stakeholders. The benefits for science consist of the possibility of simulating processes on the basis of a digital factory using VR, where it is possible to simulate selected business processes in order to prevent financial loss incurred by the company. Considering the benefits for practice, it is possible to apply a digital factory to companies in terms of the expansion or simplification of production in their enterprise. They can simulate individual processes and test the impact of conditions, energy load, ergonomics of employees, energy load of the enterprise and thus decide on the change or innovation of the process.  
Another benefit is the application of synergies between the processes that are present in the enterprise and those that are simulated in digital form, so that the results can be compared, processed, continuously improved on the basis of the processes and thus can intervene online in the detailed tasks and decisions in the selected process.  
This research's findings demonstrate practical use for virtual reality and digital factories in various contexts. Therefore, it is crucial to advance with new practical work in this field such that it can continue to demonstrate empirical certainty regarding both the degree of acceptability and the application in real-world situations, with the knowledge that there is momentum for technological advancement in its application.
## 2 Methods
### Customization
#### 2.2.2 Continuous Feedback Loop and Deployment Strategy in JAVIS  
A continuous feedback loop is integral to the efficient development and deployment of customization requests within the JAVIS system. This loop ensures that all data within the internal network remains secure and confined while JAVIS is updated externally to leverage the latest libraries and tools. Upon completion of updates, a single Docker command transforms JAVIS into a production-ready state, allowing it to be seamlessly reintroduced into the internal network. Throughout this process, critical databases, including user data, analytics, and others, remain intact due to their volume mapping with Docker Compose, as illustrated in (Figure 1). This procedure ensures that data cannot go out due to network security, and new updates to the system are allowed to ensure improvement and customization.
## 3 Results and Discussion
### Effect of Pomelo Cultivars on Color and Viscosity of Juices
Among these pomelo fruit juices, HPGT obtained the highest TP (156.75 mg GAE L\({}^{-1}\)), TF (208.37 mg RE L\({}^{-1}\)), and ascorbic acid (0.58 g L\({}^{-1}\)) contents, while the lowest concentrations of TP, TF and ascorbic acid were all found in the LP Guanyiyu juice (LPGU). The TP and TF content of the HPGT juice were 2.28- and 5.07-fold higher than those of LPGU juices, respectively. This finding is similar to the previous study on pineapple juice [45]. The cultivars' diversity resulted in different levels of TP, TF and ascorbic acid in pomelo juices, and these three bioactive substances were generally higher in HP juices than LP juices. Figure 3: Effects of cultivars on viscosity of pomelo juices. (**A**) LP juice; (**B**) HP juice.
## 3 Results
### Scaling and Reliability
#### 3.4.1 Scaling Mode: Scaling and Reliability at Scale  
To evaluate JAVIS's scalability and reliability under higher user loads, a container utilizing the scaling mode was deployed on a legacy server running Ubuntu 18.04, equipped with four NVIDIA A6000 GPUs, each possessing 50 GB of memory. Despite the A6000's older and comparatively slower architecture relative to contemporary accelerators such as the A100 or H100, the container delivered robust performance. This outcome underscores JAVIS's ability to efficiently produce results even on smaller and less advanced server configurations.
# Using Low Salinity Waterflooding to Improve Oil Recovery in Naturally Fractured Reservoirs
## 3 Setup of Numerical Models
### Intermediate Scale Modelling  
An intermediate scale model was constructed to simulate the low salinity waterflooding using the same properties of the fine-scale model. The dimensions of the model are 200 ft \(\times\) 200 ft \(\times\) 60 ft with a gridblock resolution of 40 ft \(\times\) 40 ft \(\times\) 12 ft, and a total number of 250 cells for both matrix and fractures. Moreover, average matrix properties were used to identify the impact of low salinity injection water on oil recovery without heterogeneity effect. Appropriate fluid properties and relative permeability sets of Upper Qamchuqa formation were used to initialise the simulation model. The matrix block dimension in this model is the same as the dimension of the fine-scale model of the previous section (i.e., 5 ft \(\times\) 8 ft \(\times\) 12 ft) to allow for performance comparison.  
Two recovery cases were simulated to evaluate the matrix recovery. The first model was designed to mimic the water advancement in fractures due to their high permeability leaving the matrix oil trapped behind, as shown in Figure 6A. In this case, it has been assumed that fractures were flooded with low salinity brine, while the matrix saturated with oil and connate formation water. Then, a fluid exchange process occurs under static conditions between the matrix and fractures due to capillary, gravity, and viscous forces. In the second case, the model comprises an injector and a producer located at the opposite corners of the grid, as illustrated in Figure 6B. In this case, fractures were assumed to be fully saturated with oil, while the matrix blocks saturated with oil and connate water. The low salinity brine was injected at a variable rate to maintain a constant bottom hole pressure, and the producer was opened to flow from the first layer only. The same prediction scenario of 10 years of the fine-scale model was adapted for simulating the above-discussed cases. The intermediate scale models have two objectives: (1) validate the simulation outcome of the single block model by comparing their results. (2) illustrate the differences between the injection scenario and the flooded scenario of the low salinity brine, which cannot be performed using the illustrated single block model in Figure 5B.
# LEO-Augmented GNSS Based on Communication Navigation Integrated Signal
## 4 Comprehensive Comparison
### Comparison of Methods 2 and 3  
The signal for method 2 is also composed of multiple unit bands. Therefore, it also holds the advantage of CN0 margin. As the receiver does not have to deal with the signal data in real-time for burst signal, it should satisfy the accuracy requirement when CN0 is 40 dBHz, \(T_{\textit{col}}\) is 90 ms, and \(\tau_{b}\) is 0.1. The result of \(\sigma\) is 0.0075, which is calculated from Equation (6) based on the parameters set above. Therefore, the code frequency should be 0.226 MHz, which means that the number of occupied unit bands would be 11 (0.4482 MHz).  
When the occupied navigation frame is 1 out of 10 frames, the capacity loss is 0.44%. The capacity loss of method 3 in 3.2 is 3.17%, which is about 7.27 times of method 2.  
It can be concluded that method 2 causes really low communication capacity loss. But to satisfy the accuracy threshold, the receiver has to increase \(T_{\textit{col}}\) and decrease \(\tau_{b}\).
# Detecting Cyber Threats in UWF-ZeekDataFall22 Using K-Means Clustering in the Big Data Environment
## 5 K-Means Clustering Results and Discussion
### Finding the Ideal Number of Clusters
#### 5.4.2 Second Method: Clusters with at Least 25% Attack Data as Attack Clusters  
From the previous set of experiments, specifically Table 6, since the best results were obtained with a seed of 200 for clusters with at least 25% attack data, the seed of 200 was used for the following set of experiments. And, looking back at Table 3 and Figure 2, the best results were obtained using cluster sizes around 300, hence cluster sizes between 100 and 400 were tested for this experimentation.  
Analysis of the Second Method:  
* Performance Trends Across Cluster Counts
* Increasing the number of clusters improved detection accuracy, with precision and recall exceeding 98.7% beyond 200 clusters.
* In terms of computational time, although the lower number of clusters (250 clusters or less) had lower computational times and the higher number of clusters (above 250 clusters) had higher computational times, as in the First Method, there was no definite pattern.
* At 325 clusters, precision and recall reached 98.77% and 98.74% respectively, with no major changes after that, making 325 the optimal choice in terms of performance, but computational time was actually rather high for 325 clusters.
* Best Performing Cluster Count
* At 400 clusters, no additional gains were observed, reinforcing that 325 clusters were the ideal selection. Beyond this point, the model showed diminishing improvements, indicating that further increases in cluster numbers would not show significant performance benefits. Though computational time was high at 325 clusters, since there was no definite pattern in computational time, we are ignoring the aspect of computational time here.  
* A seed of 200 with 325 clusters will produce the best set of results for clusters with at least 25% attack data. The best results are bolded in Table 9.
## 4 The ST-LRMA Based Data Collection Method
 \tag{14}\]  
By the method of Lagrange multipliers, the constrained optimization problem Equation (14) can be revised as the following unconstrained model:  
\[(\mathbf{L}^{*},\mathbf{R}^{*})=\operatorname*{argmin}\limits_{\mathbf{L}, \mathbf{R}}\|\mathbf{L}\|_{F}^{2}+\|\mathbf{R}^{T}\|_{F}^{2}+\lambda_{1}\| \mathbf{L}\mathbf{R}^{T}\mathbf{D}\|_{F}^{2}+\lambda_{2}\|\mathbf{S}\mathbf{ L}\mathbf{R}^{T}\|_{F}^{2}+\lambda_{3}\|\mathcal{A}(\mathbf{L}\mathbf{R}^{T})- \mathbf{M}\|_{F}^{2}. \tag{15}\]  
where \(\|\mathcal{A}(\mathbf{L}\mathbf{R}^{T})-\mathbf{M}\|_{F}^{2}\) represents the reconstruction error at the sampling subset \(\mathbf{M}\) with an adjusting weight \(\lambda_{3}\). This is the final ST-LRMA model for data collection. To solve problem Equation (15), we adopt an alternative iteration algorithm for \(\mathbf{L}\) and \(\mathbf{R}\). Firstly, \(\mathbf{L}\) is initialized randomly. Then optimize \(\mathbf{R}\) by a linear least square method with fixing \(\mathbf{L}\). Note that the operator \(\mathcal{A}(\cdot)\) confines the equation group onto the subset of the valid entries in \(\mathbf{M}\), so we use a subset of the equations in the linear least square aggression instead of the complete equation group. After \(\mathbf{R}\) has been updated, we fix \(\mathbf{R}\) and alternatively optimize \(\mathbf{L}\) with respective to the new objective function. Repeat the above alternative iteration procedure until meeting the convergence condition or exceeding the maximal iteration number.
## 4 Discussion
### Addressing Existing Framework Limitations  
Most open-source tools available for LLM deployment are designed for online environments, rendering them incompatible with hospital settings where offline operations are mandatory due to privacy concerns. Additionally, current hospital-deployable systems often address privacy but lack comprehensive capabilities such as conversation management, analytics, and seamless deployment across varying scales. Frameworks like RayLLM, while supporting scaling, have limited functionalities, are outdated, and fail to integrate critical elements like user-friendly interfaces, database connectivity, and feedback loops. JAVIS overcomes these limitations with its modular and hospital-centric architecture, offering a complete solution that is scalable, compliant, and customizable.  
Comparative Analysis with Existing Frameworks  
As part of our evaluation of JAVIS within hospital environments, we compared its core features against alternative LLM deployment solutions (see Table 11). These solutions include RayLLM, OpenLLM, and H2O LLM Studio, each of which exhibits certain limitations when considered for clinical use. In particular, OpenLLM and H2O LLM Studio rely heavily on external cloud setups, limiting their feasibility in strictly offline or privacy-sensitive settings. RayLLM partially addresses these concerns by enabling internal network deployment but lacks modern container maintenance and user interface support. In contrast, JAVIS not only maintains complete on-premises control over data and workloads but also integrates multi-LLM/VLM support and real-time monitoring, ensuring both scalability and compliance with regulations akin to HIPAA. This holistic approach, summarized in Table 11, positions JAVIS as the superior choice for healthcare institutions that require robust, private, and customizable AI solutions.
# Analysis of the Mode Shapes of Kaplan Runners
## 3 Natural Frequencies and Mode Shapes of Simplified Structures
### Influence of the Blade Number on the Mode Shapes
#### 3.3.2 Diametrical Mode Shapes of Bladed Disks
A torsional vibration with two torsional axes on each blade is associated with this orientation (See Table 7, second row, \"Bladed Disk\" column). As is shown in Table 8, for a bladed disk, with Z = 6, vibrating at the mode (6,0), the global mode shapes are similar to the previous case. In general, when the number of nodal diameters is equal to the number of blades (m = Z) torsional global mode shapes of the bladed disk are generated. In contrast to the case of the modes (m = Z/2, 0), in this case, every blade vibrates in-phase with respect to the next blade. It is important to remark that in a bladed disk with an even blade number, global mode shapes that show a uniform deformation distribution of the blades are generated for every m = (k*Z)/2 nodal diameters, with k = 1,2,3... In Tables 9 and 10, bladed disks with an odd number of blades are shown (Z = 5 and Z = 7). In both cases, the number of nodal diameters is equal to the number of blades (m = Z). Then, the same torsional global modes described before are present in these cases. Hence, for a bladed disk with an odd blade number, the diametrical global mode shapes that show a uniform deformation distribution of the blades is generated for m = k*Z nodal diameters, with k = 1, 2, 3... Finally, since the non-uniform deformation distribution mode shapes are also present in Kaplan runners, the cases when the nodal diameter distribution is not compatible with the bladed disks geometry are also studied. In Tables 11 and 12, the modes (2,0) and (3,0), are shown for every bladed disk, respectively. It is possible to see that the nodal diameters of the modes are redistributed, in order to match the structure geometry with the mode shape. In this kind of mode shapes, not only different amplitude of the blades with respect to the others exists, but also different mode shapes of the blade can be seen in the bladed disk. For instance, in Table 11 columns Z = 6 and Z = 7, bending and torsion mode shapes of the blade coexist in the mode (2,0) of the bladed disk. Due to the blade number influence, the modes (2,0) and (3,0) are present in every bladed disk as a global mode shape but with a non-uniform behaviour of the blades, with the exception of the bladed disk with Z = 4 and Z = 6, respectively. The detail of the transition of the mode shapes from the disk to the different bladed disks, can be seen in Appendix B, in Tables 13 and 14. It may be useful to the reader to have a better comprehension of the nodal diameters distribution in Tables 11 and 12.
## 4 Security and Privacy of IoNT
### Security and Privacy Attacks
#### 4.4.1 Disruption Attacks  
The following describes disruption attacks that target the IoNT:  
* Flooding/denial-of-service (DoS) attack: It is possible to overwhelm the availability of potential IoNT nodes by delivering bogus messages [38]. The successful execution of a DoS attack by the attacker in this scenario has the potential to impede the availability of services provided by an IoNT network [40]. An aggressive variant of a denial-of-service attack known as a distributed denial of service, or DDOS, is characterized by the use of a greater number of compromised nodes to flood the system. This makes it more difficult to identify the origin of the attack. A wide variety of DDOS attacks can be launched by attackers by utilizing automated methods such as botnets [36; 37; 38; 39; 40].
## 3 Results and Discussion
### Strength of Glue Joints in Sets Versus Wood Wettability  
When comparing the results of the wood wettability (evaluated by contact angles in the \"initial\" state \(\theta_{0}\) and in the \"equilibrium\" state \(\theta_{\rm e}\)) and the strength of glued joints (evaluated separately for the first group of test sets \"Treated spruce and Untreated spruce\" and the second group of test sets \"Treated spruce and Treated spruce\"), no evident links occurred between strength and wettability (Figure 6). This result can also be seen from the highest adhesion strength of the control set created from two untreated specimens of spruce wood and PUR glue (Table 2). On the contrary, the untreated spruce wood specimens did not show always the highest wettability, i.e., they did not have always the lowest contact angles \(\theta_{0}\) and \(\theta_{\rm e}\)--see a significant decrease of \(\theta_{0}\) in presence of H\({}_{3}\)BO\({}_{3}\) and (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) (Table 1, Figure 6).  
The change in the contact angles of spruce wood specimens could be caused by changes in the chemical composition of the surface zone of the wood due to the selected preservatives and the filling of the lumens and intercellular spaces of the surface zone of the wood with them. Besides these factors, the wettability of the wood surface is significantly affected by its roughness [68; 69; 70]. The roughness of wood can be changed by using various chemical substances, which also can cause degradation of the lignin-polysaccharide structure of wood cells. Thus, due to the changes in roughness and molecular structure of wood, subsequently, the wettability of wood surfaces can be changed as well. Simultaneously it is necessary to mention the fact that the contact angle values can be affected also by the heterogeneous structure and different densities of wood in various parts of a specimen,  
Figure 6: Comparison of the percentage change in the strength of glued joints (\(\Delta\sigma\)) for the first and second group of test sets and the percentage changes in the contact angles (\(\Delta^{\circ}\)) for the spruce wood specimens treated with the 2% solutions of inorganic preservatives.  
in connection with the specific width of growth rings and the portion of earlywood and latewood [71; 72].  
From microscopic analyses of the \"wood--PUR\" interphases (Figure 5), it can be concluded that the reduced strength of the glued joints of test sets created from specimens containing inorganic preservatives was mainly caused by changes in the penetration of the PUR glue into the wood structure and the creation of thinner joint layers. This could be due to the irregular penetration of the PUR glue into the wood structure pretreated with preservatives and the unevenness of the glued joint as a result of the formation of a larger number of air bubbles in the cured PUR glue layer.