esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Cryptography-Based Architecture for Secure Data Asset Sharing and Circulation Systems
## 3 Security Model
### Full Life Cycle Model  
At present, with regard to the definition of the whole life cycle model of data assets, most scholars simply consider the whole life cycle of the data, that is, only consider data acquisition, transmission, storage, use, sharing, destruction and so on. As a special asset, the whole life cycle of data sharing and circulation is determined by multiple dimensions and factors. Therefore, this paper proposes a whole life cycle model of data assets from three dimensions: the rights and interests subject, data life cycle and key links, as shown in Figure 2.  
The subject of rights and interests is involved in the process of the sharing and circulation of data assets. In the whole life cycle of data assets, four kinds of roles are mainly involved: producer, owner, user and manager. A producer refers to the provider of data, which can be a person, system, or equipment, etc. The owner refers to the organization or individual who owns or actually controls the data assets, and may transfer, lease, outsource the data assets, etc. A user refers to an organization or individual who uses the data, who can apply for the data, download data, analyze data, and so on. A manager refers to the organization or individual authorized by the owner to carry out data asset management,but who does not necessarily have ownership of data assets, such as data outsourcing service providers, cloud storage service companies and so on.  
The whole life cycle of data involved in the process of data asset sharing and circulation covers the whole life process of data asset production, storage, exchange, processing, return, destruction and so on.  
The key links involved in the process of data asset sharing and circulation mainly involve four typical scenarios: outsourcing storage, multi-party sharing, trusted operation and cross-domain return. Outsourcing storage refers to the practice of data asset owners entrusting all or part of the data to third-party service providers for storage, such as cloud storage services, AI training services, and so on. Multi-party sharing refers to the interaction of data between different data asset owners to achieve benefit sharing. Trusted operation means that the data asset has a complete record and deterministic execution result on data deletion, outgoing, copy and modification in the process of circulation. Cross-domain return refers to the return of data distributed in different regions to the data asset center or elsewhere through a transmission channel.
## 5 Linear Methods
### Economic Efficiency and Resource Allocation  
Linear programming (LP), a cornerstone of linear methods, is widely employed to optimize resource allocation in the food industry. From minimizing production costs to maximizing output efficiency, LP models enable manufacturers to make data-driven decisions. For instance, a study on bakery production demonstrated that LP could reduce costs by up to 15% by optimizing ingredient proportions and production schedules. Similarly, in dairy processing, linear optimization has been used to allocate raw milk effectively across multiple products, ensuring minimal waste and maximum profitability [44].  
Another key application of linear methods is in optimizing the use of agricultural resources. In regions with constrained land, water, and labor, LP models guide farmers in selecting crop combinations that maximize yields while adhering to resource limitations. For example, the application of LP in crop planning increased farm income by 20% while reducing water usage by 10%, demonstrating the dual economic and environmental benefits of linear methods [45].
## 1 Introduction  
Liver disease is a common medical problem that can be reversible due to the strong natural potential for liver regeneration [1]. Targeted signaling of regenerative stimuli in the body may be generally provided by cells of bone marrow origin, for example, by hematopoietic stem cells (HSCs) and mesenchymal stem cells (MSCs) [2; 3; 4]. In liver regeneration, HSCs and MSCs also exhibit a strong potential to launch regeneration [5].  
For the last two decades, numerous studies have regarded a beneficial impact of HSCs in liver regeneration, e.g., in liver grafting [6], although no direct mechanism of HSC action has been revealed [7]. Animal experiments and clinical trials have shown the high regenerative potential of HSCs in the liver [8; 9], with cholangiocarcinomaytes starting to proliferate from hepatic triads and to replace damaged or removed parenchyma. However, regeneration may be more challenging than simply a proliferation of cholangiocarcinomaytes. Thus, hepatocytes are known for their potential to experience hypertrophic changes in reaction to damage [10], and intracellular molecular chaperones can clear the debris and provide protein quality control in hepatocytes at the same time, provoking pronocogenic effects [11].  
The challenging regulation and probable adverse effects of HSCs in liver regeneration prevent a rapid development of HSC-related approach for liver regeneration therapy [11]. Therefore, the potential of HSCs in liver regeneration requires profound and comprehensive research, and a solution with fewer risks than intact HSCs should be found.  
HSC activity has already been shown to induce regenerative potential in various tissues, not only for intact HSCs [12; 13; 14] but also for apoptotic HSCs [15; 16; 17; 18; 19]. Many released elements of apoptotic HSCs, such as nanovesicles [19; 20], lipids [21], exosomes, microRNAs, and proteins [16; 17], may provide pro-regenerating changes.  
Moreover, the total RNA of HSCs can also enhance reparation [3; 22; 23; 24]. For instance, 23 miRNAs from bone marrow stem cells may participate in early liver regeneration with a reactivation of previously downregulated genes [24]. Not only do miRNAs exhibit regenerative potential [22; 24], but so do long non-coding RNAs (Lnc-RNA), short interfering RNAs (siRNA), and short nuclear RNA [25; 26; 27; 28; 29]. [PERSON] et al. reported total RNA from blood cells to launch regeneration [3]. In addition, total RNA may be preferable over protein-containing suspensions due to its lower immunogenicity.  
Apoptotic HSCs and total RNA from HSCs cannot survive for a long period of time. We consider they exhibit fewer risks for adverse effects than intact HSCs have due to the aforementioned indirect mechanism of pro-regenerative HSC potential.  
Our work aimed to compare the impact of intact HSCs (iHSCs), apoptotic HSCs (aHSCs), or total RNA derived from iHSCs on liver regeneration in rats after extended liver resection (ELR). We studied survival, biochemical, anatomical, and histological signs of liver damage and regeneration after the treatment.
## 3 Results
### Motion Trajectory Analysis
#### 3.1.1 The Coupling Analysis of Motion Trajectory and Radiation Power
After the electron and the laser pulse separate, the electron no longer experiences the laser pulse's influence and proceeds in linear motion. Through the analysis of this model, the general interaction process between the electron and the laser pulse can be summarized. The electron is influenced by the ponderomotive potential, which is directly proportional to the laser pulse amplitude and inversely proportional to the initial energy of the electron. The greater the initial energy of the electron, the less it is affected by the ponderomotive potential, and the less likely its motion state is to change. Conversely, the greater the laser pulse amplitude and the higher the laser pulse intensity, the stronger the confinement force exerted on the electron, resulting in a greater impact on the electron's motion. The direction of the force is influenced by the relative position of the electron and the laser pulse, and it always manifests as a repulsive effect on the electron, with the force directed from the laser pulse toward the electron being acted upon. The radial motion of the electron is influenced by the Rayleigh distribution of the laser pulse, exhibiting a Gaussian distribution in both the x and y directions. Since the interaction between the electron and the laser pulse does not occur at the coordinate origin, the radial motion is affected by the beam waist and chirp of the laser pulse. Consequently, the envelope of the acceleration experienced by the electron in the x and y directions exhibits an asymmetric double-peak distribution.
## 4 An Interoperable IoT Architecture for Precision Agriculture (RQ4)
### Sensors and Gateways  
The lowest level of the architecture coincides with the sensors positioned in agricultural fields at strategic points, to obtain a data sample that is as significant as possible for the uses and analyses that will be carried out subsequently. These sensors, as described earlier, can have different methods for collecting data and use different protocols for communication. The user is free to choose and add to the system the sensors they consider most suitable for the intended purpose. During installation, sensors are connected to the associated gateway based on their communication protocol. In the event that a sensor has an autonomous Internet connection, it is directly connected to the broker. Since limiting the types of protocols used would otherwise undermine the ultimate purpose of the system, a gateway is provided within the system for each type of communication protocol used by the sensors. There are numerous types of gateway available on the market that can be configured as desired to forward the received packets along a predefined channel, as we analysed in previous chapters. The gateway required for our system must be able to communicate with an MQTT broker [85]. The purpose of the gateway is to receive data from the connected sensors, identify which sensor the data came from, and publish it to the associated \"topic\". As mentioned earlier, there is a \"topic\" for each type of sensor present in the system. A gateway can publish to multiple \"topics\", as it can be connected to sensors from different manufacturers. It is not the gateway's responsibility to manage duplicate packets or the correct reception of information. These tasks are delegated to the network server, based on the level of QoS that is to be maintained within the system.
# Application of the Random Decrement Technique in Damage Detection under Moving Load
## 4 Results and Discussion
### Noise Effect  
The uncertainty imposed by the noise has not been discussed in this paper. However, it can be claimed that the results may not be highly affected by the incorporated noise. To further argue this claim, a closer look at the proposed method is addressed here. Based on the frequency content, two different cases can be assumed: (1) noise and structural response have the same frequency and (2) noise and structural response do not contain similar frequency content. In the first case, since the noise removal can distort the main response, it is not possible to attenuate the noise. However, in the second case, the incorporated noise can be efficiently reduced by using frequency filtering methods. The RD  
Figure 18: Normalized AI for six different damage scenarios obtained from laboratory models. (**a**,**c**,**e**) Cases N2D7-7, N2D5-7, and N2D3-7 of laboratory model; (**b**,**d**,**f**) cases N4D7-7, N4D5-7, and N4D3-7 of a laboratory model.  
technique can act as a de-noising technique. It means that the proposed method essentially attenuates the second category of noise. However, to deal with the first category of noise, the loading parameters such as velocity can be changed so as to get a structural response with frequency content far away from noise frequency contents. If it is not possible, the accuracy of proposed method would then be significantly affected.  
One way to detect the incorporated noise in laboratory tests is to separately record the acceleration data without loading. In this research, all the signals begin with a pre-loading segment that lasts for few seconds. This issue can be seen in Figures 10 and 11 of the paper, in which the main loading approximately starts after 5 s. Considering the frequency content of these pre-loading segments, it is found that the similarity between the frequency contents of noise and structural response is small enough to be ignored. It means that the majority of noises belong to the second category and thus could be removed by RD technique.
## 3 Results and Discussion
### Correlation Analyzing of Recorded Data  
The variations in air temperature, relative humidity, and atmospheric pressure and their influences on pollutant concentrations in the ambient air were analyzed using Spearman correlation annually. The results show that the CO concentrations had a weak negative correlation with atmospheric pressure, but a weak positive correlation with air temperature and air relative humidity, as shown in Table 3, was observed. Additionally, the dust concentrations in the air had a weak negative correlation with atmospheric pressure and air relative humidity, but a strong positive correlation (r = 0.723, \(p\leq\) 0.01) with air temperature, as shown in Table 3, was observed. However, the observed correlation coefficient ranges from 0.00 to 0.10, which means a negligible correlation; from 0.10 to 0.39, which means a weak correlation; from 0.40 to 0.69 means a moderate correlation; from 0.70 to 0.89 means a strong correlation, and from 0.90 to 1.00 means very strong correlation as reported by [51]. In a study by [PERSON] et al. [52], they recommended that there be a study to clarify the effect of changes in air temperature and humidity on the concentration of PM\({}_{2.5}\) dust particles. They used a GP2Y1010 AU0F sensor connected to an Arduino board to measure the concentration of dust particles in an indoor environment. They chose this sensor because of its low cost and its quality in measuring dust concentration. They compared their readings with the approved readings and found a strong correlation between the readings and the measurements for 15 min. Moreover, in the study of [PERSON] et al. [53], a strong positive correlation was reported between CO and PM\({}_{10}\) and PM\({}_{2.5}\). Additionally, they revealed that the changes in the meteorological parameters significantly affect the behavior of air pollutant parameters. Furthermore, during the winter and summer, there were significant correlations between particle matter and air relative humidity. These results add to our knowledge of how weather patterns affect air quality in agricultural settings and highlight the crucial role that meteorological factors play in shaping the dynamics of air pollution throughout the nation [54].
## Materials and Methods  
### Material and Equipment  
In view of the temperature of harvest time of frozen corn, the experiment was carried out at \(-\)5.0\(\rightarrow\)\(-\)10.0 \({}^{\circ}\)C in the Agricultural Experimental Base of Jilin University (N43\({}^{\circ}\)56\({}^{\prime}\)45.99\({}^{\prime}\), E125\({}^{\circ}\)14\({}^{\prime}\)52.44\({}^{\prime\prime}\)) in Changchun, Jilin province, China, in January 2020. The temperature was monitored by a digital thermometer (0.1 \({}^{\circ}\)C accuracy). Corn was collected after frost, and stored at \(-\)5.0 \({}^{\circ}\)C. The corn variety used in this experiment was Feitian 358, a native corn variety cultivated by the Dunhuang Seed Industry Co., Ltd in Wuhan, China. The average moisture content of frozen corn kernels was a 27.3% wet basis, measured using a grain moisture digital tester (0.1% accuracy).  
The structure of the longitudinal axial threshing cylinder test device was shown in Figure 2. It was mainly composed of a power unit, feeding hopper, top cover, deflector, concave, threshing drum, frame and collection hopper. In order to clearly illustrate the structures, the top cover was set to be transparent. The threshing drum located in the threshing chamber consisted of a top cover and concave. Rasp bars and round head spikes were fixed onto the threshing drum surface circumferentially in a spiral shape. The concave surrounded the threshing drum at an angle of 180\({}^{\circ}\), and was bolted onto the frame. Its clearance was adjusted with the bolt mounting depth, and ranged from 30 mm to 50 mm. The concave diameter and area were 656 mm and 2.266 m\({}^{2}\), respectively. The diameter and length of the threshing drum were 408 mm and 2740 mm, respectively. The drum was powered with an electric motor rated at 55 kW and a variable frequency controller. Drum speed ranged from 200 rpm to 600 rpm, and was measured by an infrared velocimeter (0.01 rpm accuracy). The threshing drum inclined by 7\({}^{\circ}\) with the horizontal plane, the same as its tilt angle in a combine harvester. The corn ears feed rate ranged from 5 kg/s to 9 kg/s, according to the normal operating efficiency of a Chinese 5-row corn harvester. The maximum mass feed rate was determined in pre-tests and controlled by the feeding hopper opening size and conveyor speed. Samples were evenly distributed along the belt conveyor length and continuously fed into the threshing chamber.  
Figure 2: Longitudinal axial threshing cylinder test device.  
Corn ears were subjected to four kinds of mechanical actions by the threshing cylinder successively, including feeding, threshing, separating and expelling. The division of functional areas was shown in Figure 3. Firstly, corn ears were placed on the feeding hopper, and then fed into the threshing chamber and conveyed axially along with the threshing drum. Corn kernels were threshed from ears by the rasp bars, and fell into the collection hopper after being separated from corncobs by a round head spike. Finally, corncobs and leaves were then expelled from the outlets.
## 2 Materials and Methods
### Derivation of High-Resolution DSM from WV-2 Stereo-Pairs  
Commercial ortho-ready standard level-2A (OR2A) stereo WV-2 imagery was provided to authors through the DigitalGlobe Foundation [54]. A stereo-pair of WV-1 OR2A along-track panchromatic images acquired on 28 March 2017 was used for the derivation of a high-resolution digital surface model (DSM) of the study area. The creation of the DSM was performed with the _Geomatica OrthoEngine 2018_ software. OR2A imagery is the most suitable type of WV imagery for the creation of high-resolution DSMs [62]. Optical satellite modeling mathematical model was used for the correlation of image pixels with correct locations on the ground, where the correlation was based on vendor-provided and image-extracted rational polynomial coefficients (RPCs). WV image correlation was based on RPCs provided with WV OR2A stereo imagery and zero-order polynomial adjustment, which has been proven as a reliable method for DSM extraction [64; 62; 65]. Selected stereo pairs were related through automatic detection and collection of tie points (TP) within overlapping image areas. In total, the automatic collection resulted in 159 TPs collected across the overlapping stereo-pair imagery. Additionally, systematic compensation of RPC-induced errors and improvement of overall image exterior orientation was carried through a manual collection of ground control points (GCPs). In total, 8 ground control points (GCs) and 8 checkpoints (CP) were added manually throughout the study area (Figure 2). RMSE of the introduced GCPs, CPs, and TPs is given in Table 2.  
The collected GCPs and TPs were used for bundle adjustment and the creation of an epipolar image, which is required for the final extraction of the DSM. The last step of the DSM creation covered automated DSM extraction, which was based on the semi-global matching (SGM) method, with a minimal pixel sampling interval and low smoothing level.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Point Type** & \(\mathbf{N_{o}}\) & **RMSE X (m)** & **RMSE Y (m)** & **RMSE Z (m)** & **MEAN RMSE (m)** \\ \hline GCP & 8 & 0.403 & 0.684 & 0.484 & 0.524 \\ CP & 8 & 0.398 & 0.751 & 0.749 & 0.633 \\ TP & 159 & 0.063 & 0.016 & 0.013 & 0.031 \\ \hline \hline \end{tabular}
\end{table}
Table 2: RMSE reported for GCPs, CPs and TPs used for the creation of the WV-1-derived DSM of study area.  
As a result of the applied processing, the high-resolution (1 m) digital surface model of the study area was created (WV-DSM).
## 4 AI Agents: A Primer for Data Annotation
### Role of AI Agents in Data Annotation  
AI agents introduce autonomous decision-making and goal-driven behavior with minimal supervision. Unlike conventional agents that operate within predefined rules and fixed tasks, AI agents demonstrate genuine agency through independent action and the purposeful pursuit of complex objectives [45]. In the context of data annotation, this evolution addresses major limitations such as inconsistent labeling across annotators, limited scalability with growing datasets, and declining quality over time as requirements change. Whereas LLMs focus on executing specific tasks, AI agents manage end-to-end workflows through multi-agent collaboration, dynamic task decomposition, and persistent memory [63]. Key advancements of AI agents for data annotation include the following:  
* Collaborative Workflows: Specialized agents coordinate tasks through role assignment, where retriever agents collect relevant examples, labeling agents apply consistent annotation schemas, and validator agents perform quality checks. This structure enables scalable annotation pipelines that maintain consistency across large datasets.
* Adaptive Quality Control: Systems use confidence scoring to identify uncertain annotations requiring human review, anomaly detection to flag inconsistent labeling patterns, and cross-validation mechanisms in which multiple agents annotate the same samples independently to resolve disagreements and improve reliability.
* Context-Aware Adaptation: Agents adjust annotation strategies based on data characteristics, learn from feedback to refine guidelines, adapt to evolving annotation schemas, and handle domain shifts without requiring complete model retraining.  
* Consensus Management: Multi-agent systems detect annotation conflicts, orchestrate consensus-building among human annotators, and maintain annotation histories to track changes in interpretation of ambiguous cases.
* Multimodal Integration: Advanced systems coordinate annotation across text, image, and audio modalities through specialized agents that preserve semantic consistency, resolve cross-modal dependencies, and ensure coherent labeling in unified annotation projects.
# Drilling Fluid and Cement Slurry Design for Naturally Fractured Reservoirs
## 5 Lost Circulation While Cementing through Naturally Fractured Reservoirs
### Cement Slurry and Set Cement Properties  
The importance of cement slurry design is especially pronounced in situations of placing a cement slurry in fractured zones or zones of low pore pressure, because then one can expect a loss of the cement slurry in the surrounding rocks. Since each well is unique according to its characteristics, which include pore pressure, fracture gradient, bottomhole temperature, and bottomhole pressure, physical properties of the formation, wellbore deviation, annular space clearance, and other parameters, the properties of the cement slurry should be appropriately optimized according to the requirements of each well.  
The properties of a cement slurry such as density, viscosity, and thickening time are the most important parameters and should be considered when designing a successful primary cementing operation.
## Appendix B Detailed Expressions for the EKF, Matrices, and Coefficient Used in This Paper
### The Detailed Expression of the Coordinate Transformation Matrix \(C_{b}^{e}\)  
The coordinate transformation matrix from the body frame to the Earth frame is shown in Equation (19):  
\[C_{b}^{e}=\left(\begin{array}{ccc}-\sin L_{b}\cos\lambda_{b}&-\sin\lambda_{b }&-\cos L_{b}\cos\lambda_{b}\\ -\sin L_{b}\sin\lambda_{b}&\cos\lambda_{b}&-\cos L_{b}\sin\lambda_{b}\\ \cos L_{b}&0&-\sin L_{b}\end{array}\right) \tag{19}\]  
where \(L_{b}\) is the geodetic latitude of the user and \(\lambda_{b}\) is the geodetic longitude of the user.