esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview of Cube-Satellite Propulsion Technologies and Trends
## 2 Cold Gas Propulsion (CGP) Systems  
### Operating Principle  
A Cold Gas Propulsion System relies on the process of controlled ejection of compressed liquid or gaseous propellants to generate thrust. Due to the absence of a combustion process, a CGP system requires only one propellant (without an oxidizer), and hence can be designed with minimum complexity. The schematic of a typical CGP system is shown in Figure 1, and the main components include a propellant storage and a nozzle. The simpler design of a CGP system leads to a smaller system mass and lower power requirements for regulation purposes. However, these advantages come at the cost of a monotonically decreasing thrust profile over a period of time. The thrust produced is directly proportional to the pressure of the propellant inside the tank (propellant storage) and over the course of the mission, tank pressure decreases (due to propellant usage) resulting in a decrease of the maximum thrust that is generated by the system.  
Specific impulse (shown in Equation (5)) of a CGP system mainly depends on the exit-to-chamber-pressure (\(P_{e}/P_{c}\)) and characteristic velocity (\(C^{*}\)) [11]. The exit-to-chamber-pressure is related to the expansion of the propellant, while Poisson constant (\(\gamma\)) is the ratio of specific heats at constant pressure and constant volume. Characteristic velocity of a CGP system at any instant is a function of the velocity of propellant in Mach number [11]. Exit velocity (as shown in Equation (6)) is another important performance factor that not only depends on the exit-to-chamber-pressure, but also on the chamber temperature (\(T_{c}\)) [11]. The mathematical relations summarizing these relationships are described below:  
\[I_{sp}=\frac{\gamma C^{*}}{g_{o}}\sqrt{\frac{2}{\gamma-1}\left(\frac{2}{\gamma +1}\right)^{\frac{\gamma+1}{\gamma-1}}\left(1-\frac{P_{e}}{P_{c}}\right)^{ \frac{\gamma-1}{\gamma}}} \tag{5}\]  
Figure 1: Schematic of a Cold Gas Propulsion System.  
\[v_{e}=\sqrt{\frac{2\gamma T_{c}}{\gamma-1}\left(1-\frac{P_{e}}{P_{c}}\right)^{ \frac{\gamma-1}{2}}} \tag{6}\]
###### Abstract  
The time-dependent behavior and long-term stability of deep-buried tunnels in soft rocks have received lots of considerations in tunnel engineering and allied sciences. To better explore and deepen the engineering application of rock creep, extensive research studies are still needed, although fruitful outcomes have already been obtained in many related investigations. In this article, the Weilai Tunnel in China's Guangxi province is studied, taking its host rocks as the main research object. In fact, aiming at forecasting the time-varying deformation of this tunnel, a novel elasto-visco-plastic creep constitutive model with two variants is proposed, by exploiting the typical complex load-unload process of rock excavation. The model is well validated, and good agreements are found with the relevant experimental data. Moreover, the time-dependent deformation rules are properly established for the surrounding rocks, by designing two new closed-form solutions based on the proposed creep model and the Hoek-Brown criterion. To investigate the effects of the major creep parameters and the geological strength index (GSI) of the surrounding rocks on the time-dependent trend of the tunnel, an in-depth parametric study is carried out. It is shown that the convergence deformation of the surrounding rocks is remarkably influenced by the GSI and creep parameters. The convergence deformations calculated from the closed-form solutions conform well to the on-site monitoring data. In only 27 days after excavation, the creep deformation of the Weilai tunnel overtakes 400 mm, which is enormous. To guarantee the long-term stability of this tunnel, a robust support scheme and its long-term monitoring with appropriate remote sensors are strongly suggested.  
deeplored buried tunnels; deep soft rocks; elasto-visco-plastic creep constitutive model; closed-form solutions; long-term stability; structural integrity; long-term monitoring Article
## 4 Results Analysis and Discussions  
### The Changes in Permeability  
To analyse the change law of the permeability of broken coal samples under cyclic loading and unloading conditions, the loss (\(D_{1}\)) of permeability of coal samples during the loading stage, the recovery (\(D_{2}\)) of permeability during the unloading stage and irreversible loss (\(D_{3}\)) of permeability after the one loading and unloading cycle were defined. These parameters are expressed as follows:  
\[D_{1}=K_{1}-K_{a} \tag{2}\]  
\[D_{2}={K_{1}}^{\prime}-{K^{\prime}}_{a} \tag{3}\]  
\[D_{3}=K_{1}-{K_{1}}^{\prime} \tag{4}\]  
where, \(K_{1}\) and \(K_{a}\) refer to the permeabilities (md) of coal samples at the first and the last stress points during loading while \({K_{a}}^{\prime}\) and \({K_{1}}^{\prime}\) denote the permeabilities (md) of coal samples at the first and the last stress points during unloading, respectively.  
By separately substituting the test results of permeability of broken coal samples under cyclic loading and unloading conditions into Equations (2), (3) and (4), the values of \(D_{1}\), \(D_{2}\) and \(D_{3}\) of coal samples were calculated, as given in Table 3 and Figure 8.  
As shown in Table 3 and Figure 8, during three loading and unloading cycles, the losses (\(D_{1}\)) of permeability of coal samples in the loading stage were 303.7, 79.8 and 36.8 md, while the recovery (\(D_{2}\)) of permeability in the unloading stage were 79.3, 41.6 and 15.7 md, respectively. Moreover, the irreversible losses (\(D_{3}\)) of permeability after each loading and unloading cycle were 224.4, 38.2 and 21.1 md, respectively. It can be seen that \(D_{1}\), \(D_{2}\) and \(D_{3}\) gradually decreased with increasing cyclic loading and unloading events. During the three loading and unloading cycles, the losses \(D_{1}\) (303.7, 79.8 and 36.8 md) of permeability in the loading stage were far larger than the recoveries \(D_{2}\) (79.3, 41.6 and 15.7 md) of permeability in the unloading stage in the same cycle. This indicated that during the same loading and unloading cycle, the loss (\(D_{1}\)) of permeability of coal samples was much greater than the recovery (\(D_{2}\)) of permeability in the unloading stage.
# Two-Dimensional Metal-Organic Frameworks and Their Derivative Electrocatalysts for Water Splitting
## 2 Fabrication Strategy of 2D MOF and Their Derivatives  
### Fabrication Strategy of 2D MOF  
The burgeoning demand for 2D MOFs with diverse properties has stimulated considerable advancements in their fabrication methods over the past years. Typically, the strategies for synthesizing 2D MOF nanosheets can be primarily bifurcated into two distinct paradigms: the top-down and bottom-up approaches (refer to Scheme 1). Top-down fabrication is achieved via the conversion of layered MOFs into nanosheets through chemical or mechanical exfoliation of corresponding bulk materials [89, 90, 91, 92]. In contrast, the bottom-up approach entails the direct formation of 2D MOF nanosheets through the strategic assembly of organic linkers and metal ions via ligand interactions [93, 94, 95, 96, 97]. This section aims to present an overview of these diverse fabrication methods, underlining their merits and inherent limitations.
# A Study on the Properties of Recycled Aggregate Concrete and Its Production Facilities
## 3 Development of the Technology to Improve the Recycled Fine Aggregate Quality
### Principle of Technology
#### 3.3.1 Absolute Dry Density  
The absolute dry density could affect the concrete strength if the aggregate density was lower than the Korean Standard requirement of 2.2 g/cm\({}^{3}\) or more. This test was implemented following the KS F 2504 \"Method to Test Density and Absorption Rate of Fine Aggregate.\" The absolute dry density was found to increase by an average of approximately 0.07 g/cm\({}^{3}\). The dry density changed from 2.25 \(\rightarrow\) 2.32 g/cm\({}^{3}\) in the first pass, to 2.25 \(\rightarrow\) 2.33 g/cm\({}^{3}\) in the second pass, and 2.28 \(\rightarrow\) 2.35 g/cm\({}^{3}\) in the third pass.
## 2 Problem Description
### The CVPP's Optimal Scheduling Problem and Assumptions  
This article proposes a two-stage scheduling model to maximize the CVPP's profit in the DA market and take full advantage of profit opportunities in the BE market while still ensuring the upward reserve capacity \(SR^{t}\) in the BC market (Figure 3). The uncertainty in RES and demand will be considered in this model.  
Figure 2: The CVPP’s reserve provisioning scheme in the BC and BE markets.  
This model is outlined as follows:  
1. In the first stage, the CVPP defines its selling/buying power \(P_{sell}^{t}/P_{buy}^{t}\) on the operating day based on the day-head hourly forecasted RES power output and demand data. This trading strategy is submitted to the DA market, and changes are not allowed in real-time operations; otherwise, penalties will apply.
2. Closer to real-time, the CVPP receives the newest short-term forecast data of RES and demand. Normally, the very short-term forecasted results are quite accurate, so the CVPP can consider these values as the actual data. Hence, considering the BC contract's requirements, the CVPP decides its upward/downward reserve energy trading strategy in the BE market each quarter hour. Also, the operating parameters of each internal resource, especially the ESS, are determined to ensure compliance with trading strategy in the energy and reserve markets.  
It can be seen that the two-stage optimization model should be implemented before the DA market's gate closure time. At that time, RESs' available power output and customers' consumption are uncertain and unknown. In addition, the CVPP's operator cannot know whether the reserve capacity contracted in the BC market and upward/downward reserve energy expected to be submitted in the BE market are activated. As a result, the CVPP's operator should allow the following assumptions:  
* All contracted upward reserve capacity and non-contracted upward/downward reserve energy bids are selected and fully activated with the probability \(p_{act}\) assumed to be known in all quarter hours. To simplify the model, this article does not consider the inactivated or partially activated reserve scenarios.
* The very short-term forecasting results of RES and demand in the second stage are handled as the sum of the day-ahead hourly forecasting values and forecasting errors.
## 4 Materials and Methods
### Data Processing/Mining and Identification
#### 4.5.2 Retention Index  
Retention indices (RI) were calculated using 50 ng/L C8-C40 Alkanes calibration standard (Sigma-Aldrich, St. Louis, MO, USA). Liquid injection (1 \(\upmu\)L) in manual mode was used to improve the signal acquired. Acquired data was processed as indicated above and identified with NIST 17 library, then exported in library format. Agilent MassHunter WorkStation Library Editor 10.0 was used to activate only \"Compound name\", \"CAS#\", \"Retention Index\" and \"Retention Time\" columns, in that order, and saved in a CSV (comma-separated values) format to create the RT calibration file, which contains alkanes RI to be used in recursive analysis to calculate the RI of unknown components.
# Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System +
## 5 Algorithmic Design  
Compared with the original implementation [36], the ML pipeline is slightly tuned through experience with realistic experiments [43; 57; 58; 94]. It is now more adequate for handling different data input types, i.e., time series such as C/N0, AGC, and elevation and images of spectrograms.  
Since the framework focuses on the detection and classification of GNSS interference signals, the pipeline is designed following four steps:  
1. Pre-processing,
2. Critical snapshot detection,
3. Classification of interference types,
4. Post-processing based on the uncertainty of estimates.  
Section 5.1 first introduces the concept of our supervised ML pipeline before Sections 5.2 and 5.3 describe its pre-processing and ML-based detection and classification components. Section 5.4 describes the mechanic that ensures the reliability of the pipeline with uncertainty estimation.
# A Privacy-Preserving Scheme for a Traffic Accident Risk Level Prediction System
## 3 Preliminaries
### Homomorphic Encryption  
HE permits computation directly on encrypted data without deciphering it [30]. Generally, an HE scheme has four functions: _KeyGen_, _Enc_, _Dec_, and _Eval_[31]. First, the function _KeyGen_ generates the public and secret keys. The equation is presented as follows:  
\[(pk,sk)\leftarrow\textit{KeyGen}() \tag{1}\]  
where \(pk\) is the public key and \(sk\) is the secret key.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
The function \(Enc\) performs an encryption operation. The following equation represents the transformation from plaintext into ciphertext:  
\[c\gets Enc(pk,p) \tag{2}\]  
where \(c\) is a ciphertext, \(pk\) is the public key, and \(p\) is a plaintext.  
The function \(Dec\) performs a decryption operation. The following equation represents the transformation from ciphertext into plaintext:  
\[p\gets Dec(sk,c) \tag{3}\]  
where \(p\) is a plaintext, \(sk\) is the secret key, and \(c\) is a ciphertext.  
HE schemes also have the function \(Eval\). This function evaluates a circuit (e.g., a machine learning model). The equation is presented as follows:  
\[Eval(pk,C,c0,\dots,cn) \tag{4}\]  
where \(pk\) is the public key, \(C\) is a circuit, and \(c0,\dots,cn=Enc(pk,p0,\dots,pn)\).  
As a result, the following equation establishes that the result of circuit \(C\) on plaintext is equal to performing the \(Enc\), \(Eval\), and \(Dec\) functions:  
\[Dec(sk,Eval(pk,C,Enc(pk,p0,\dots,pn)))=C(p0,\dots,pn) \tag{5}\]  
Depending on the type and number (depth) of operations, HE schemes can be classified as Partially Homomorphic Encryption, Somewhat Homomorphic Encryption (SHE), and Fully Homomorphic Encryption. The following is a short description of the schemes mentioned above.
# Force-Based Self-Organizing MANET/FANET with a UAV Swarm
## 2 Mobile Ad Hoc Networks (MANETs)
### Target MANET-Applications and -Scenarios  
We briefly discuss three applications. In all of these, the drone-based MANET is connected to the ground at its entry (base) and exit points (terminals). For the remainder of this article, we consider only flat landscapes and treat the problem as a 2D problem.  
We argue that this approximation is valid if the vertical separation between drones is orders of magnitudes smaller than the target distance between them in the horizontal plane (e.g., tens of meters vs. kilometers) and the environment is not obstructed by tall structures (e.g., maritime or coastal region, flat plain, river delta, etc.).
## 3 Results
### Implementation of Attention Mechanisms  
Since US images of the prostate often contain artifacts, methods to focus attention on the most relevant regions are desired. Hard attention can be achieved at the image level by defining a Region of Interest (ROI), which directs the relevant feature space.  
Figure 4: Illustration of different 2D slice directions (grey). (**a**) The prostate (blue) radially sliced from a 3D TRUS acquisition around its axis of rotation (red); (**b**) sagittal prostate slices; (**c**) transverse prostate slices; (**d**) coronal prostate slices.  
[PERSON] et al. (2021) [29] inherently obtained the ROIs of the prostate using a probability map of the organ's center of mass, which was then used to form a bounding box corresponding to the ROI. In contrast, PROST-Net, proposed by [PERSON] et al. (2022) [33], used a Region Proposal Network to define the ROI. When evaluated, PROST-Net segmented 2D TRUS images with a DSC of 0.87, while [PERSON] et al. [29] achieved a DSC of 0.93.  
Alternatively, soft attention can be achieved by introducing channel/spatial attention modules to the DL architecture. This allows the network to adaptively recalibrate the weights of each channel or region in the feature space. [PERSON] et al. (2019) [37] were pioneers in introducing soft attention for prostate segmentation on TRUS, integrating an attention module that leverages spatial information to select more discriminative features. Although the proposed DL architecture achieved a DSC of 0.90, the contribution of the attention module was not quantitatively evaluated, nor was the network tested on separate test data. Following this work, [PERSON] et al. (2022) [36] retrained their architecture based on 802 training samples and achieved a DSC of 0.92, proving the robustness of their network on unseen data.  
Similarly, [PERSON] et al. (2022) [36] incorporated Coordinate Attention Blocks (CAM) to increase segmentation performance. CAM aims to perform channel attention while preserving positional information. Ultimately, this method presented similar DSC outcomes compared to [PERSON] et al. (2019) [37] when evaluated on the same test data.  
In the proposed ADC-Net by [PERSON] et al. (2023) [31], a spatial-based attention module was introduced to enhance the network's ability to learn useful features, improving the DSC and Jaccard index by 3% and 4%, respectively.
## 4 Results
### Results of the Evaluation of Geological Factors
#### 4.1.4 Sensitive Geological Conditions  
(1) Distance to Fault Zones  
In terms of geological structure, Nantong City is situated within the Lower Yangzi Fracture Block. The tectonic movement in the area is characterized by differential elevation changes. There are two significant fracture belts: the east-west Bencha river fault zone, and  
Figure 11: Suitability distribution based on water content of water-bearing rock groups.  
Figure 10: Suitability distribution based on water system density.  
the northwest-facing fault zone spanning Jianghai Town to Langshan Town and Yangtze River Town. The primary influence of these fracture zones extends from Haian Town to Chengdong Town, and further to Yuanzhuang Town, Hekou Town, and Yangkou Town in Nantong City and its southwest regions, including Wuyao Town, Jiuhua Town, and Langshan town Street, rendering them unsuitable for development. As the distance from the fracture zones increases, their impact decreases, gradually enhancing the suitability for underground space development. Consequently, areas completely outside the influence of the fracture zones are deemed suitable for development, as depicted in Figure 12. Since the Neoproterozoic era, the terrestrial neotectonic movement in Nantong City has been characterized by differential uplift and, except for the M5.0 magnitude earthquake near Wolf Mountain in 1615, there has been no significant seismic activity causing serious damage in over 400 years. Thus, the development of underground space in Nantong City is less affected by the presence of fault zones.  
(2) Susceptibility of Geological Hazards  
Nantong City is situated in the Yangtze River Delta, where rivers interact with the ocean. It experiences a pronounced monsoon climate with abundant rainfall. The geological conditions are complex, leading to various types of geological hazards, primarily including collapse, landslides, ground subsidence, and soil liquefaction. According to the geological hazard prevention and control plan for Nantong City, landslide and collapse risks are mainly concentrated in the Wushan areas of Chongchuan District. There are varying degrees of ground subsidence across the city, with funnel-shaped subsidence prevalent in coastal areas near Bancha Town, Yangkou Town, Changsha Town in Rudong County, and nearby coastal areas such as Haifu Town and Donghai Town in Qidong City, with a subsidence rate of less than 20 mm per year. Based on the susceptibility of geological hazards, the suitability of underground space development is categorized into four levels, as shown in Figure 13.  
Figure 12: Suitability distribution based on fault zones distribution.
# Improvements of the xAAL Home Automation System
## 3 The xAAL Devices Behavior and xAAL Schemas
### Inheritance of Schemas  
There is a notion of inheritance between schemas. A schema can extend an existing schema. xAAL defines the first three levels of this genealogy:  
1. A basic generic schema, named basic.basic, common to all existing devices in the world, that everyone has to implement. (See Section 4.)
2. A basic schema for every class (e.g., _lamp.basic, thermometer.basic, switch.basic_). Such basic schemas inherit from the generic schema, and extend it by defining basic functionalities shared by all device variants of the corresponding class (e.g., lamps basically can do on/off).
3. Advanced schemas for more complex devices, by extending the basic schema of the corresponding class, and by defining new functionalities (e.g., lamps basically do on/off, but some more sophisticated lamps are dimmable, others offer RGB control). All variants of a class must inherit the basic schema of the class (e.g., lamp.dimmer extends lamp.basic which extends basic.basic).
4. Thereafter, manufacturers of home automation equipment will naturally define their own schemas among their products range. However, manufacturers must not define their own schemas as level 1 schemas (the generic schema is the only one), nor as level 2 schemas (basic class schemas). Schemas from manufacturers are necessarily extensions of schemas from level 2 or higher. While naming such schemas, manufacturers may use the second word (the variant name) of the pointed pair as their own discretion. This is their responsibility to choose a name that may not conflict with existing ones. However, the schema name should refer above all of the functionality of the device and means to interact with it; the name should also give an idea of the nature of the device.  
Definition of the Extension Process:  
Let's consider a first schema that is extended by a second schema. The later express differences with the former. The extension process produces a new schema. Extending a schema has the following meaning:  
* The latter schema may introduce new _attributes_, _methods_, _notifications_, and associated _data types_. The result of the _extend_ operator is a new schema that contains all _attributes_, _methods_, _notifications_, _data types_ of the former schema, plus the new ones introduced by the latter schema.
* The latter schema may overload the definition of some existing objects, that is to say _attributes_, _methods_, _notifications_, and _data types_. An overloading means that such an object has the same name but a different definition. There is no attempt to merge the old and the new object definition.  
The resulting schema contains all new objects (_attributes, methods, notifications, data types_) plus old ones that have not been overloaded.