esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Overall Stability of Insulated Cold Transfer pipes
### Overall Structural Stability of Long-Distance Insulated Cold Transmission Pipes
When the ground stress reaches 15 MPa, yielding starts to occur in the insulated cold transmission pipe, with the four corners of the pipe showing tension-p, which indicates tensile yielding has occurred at these locations. As the ground stress increases to 17.5 MPa (a value equal to the conventional ground stress magnitude at 700 m depth), the yielding failure of the pipe still occurs mainly at the corners, showing significant tensile yielding and a small amount of shear yielding. However, although there is a partial plastic zone in the insulated pipe at 15 MPa and 17.5 MPa, the plastic zone mainly occurs at the OCM and FCM filling materials, and the plastic zone is small, which does not affect the regular working operation of the insulated pipe. The insulated cold transmission pipe of the composite structure through the deep strata from the inside to the outside is composed of cold pipe, insulation-foamed cement paste layer, metal protection casing, solid pipe plugging cement layer outside casing, and borehole rock layer. The insulated cold transmission pipe, insulation cement layer, and casing are fixed through the solid pipe plugging cement layer outside the casing, forming the mechanical properties of the composite structure. Accordingly, the calculation simulation analysis of the overall strength of the cold pipe is reliable, which can ensure the safe and stable operation. The plastic damage distribution characteristics of the outer solid pipe material and the inner insulation layer under different ground stress conditions are shown in Figure 16. When the ground stress is less than or equal to 12 MPa, the material does not show plastic damage, and when it is beyond 12 MPa, the plasticity rate generally shows an increasing linear trend. Therefore, Equations (1) and (2) can be used to express the plasticity damage rate of the outer material and the inner insulation layer, respectively. \[PZ= \begin{cases}0&(\sigma\leq\ 12\ \text{MPa})\\ 2.34\sigma-28.03&(\sigma>\ 12\ \text{MPa})\end{cases}  \tag{1}\]  
\[PZ= \begin{cases}0&(\sigma\leq\ 12\ \text{MPa})\\ 6.52\sigma-78.28&(\sigma>\ 12\ \text{MPa})\end{cases}  \tag{2}\]  
where \(PZ\) is the plasticity damage rate, and \(\sigma\) is the ground stress level. The destruction of the material will also be affected by the size of the insulation pipe, geometry, material properties, and other factors. Equations (1) and (2) can be simplified as  
\[PZ= \begin{cases}0&(\sigma\leq\ 12\ \text{MPa})\\ a\cdot\sigma_{0}-78.28b&(\sigma>\ 12\ \text{MPa})\end{cases}  \tag{3}\]  
where \(\sigma_{0}\) is the critical stress level, and \(a\) and \(b\) are the physical parameters. Figure 15: Distribution of plastic zones in different parts of insulated cold pipe under different ground stress conditions.
# Target Localization via Integrated and Segregated Ranging Based on RSS and TOA Measurements
## 5 Simulation Results
Nevertheless, the result shows that the proposed heuristic approach is not perfect and has its vulnerabilities; hence, further improvements are welcomed in order to reach a more robust solution to the problem. Figure 3:0 RMSE versus \(N\) comparison for SR-WLS for different limits for \(\epsilon\) in the first considered scenario.
# Optimization Environment Definition for Beam Steering Reflectarray Antenna Design
## 2 Antenna and Optimization Environment Description
### Optimization Environment
Solutions that violate these constraints are edited to fit them. Different approaches have been tested in this work for the feasibility function. As previously described, the Aperture Field Method is used to calculate the radiation pattern for each tested scan angle. For each of them, the two performance parameters (the radiation pattern error RE and the scanning direction error \(\Delta\theta_{s}\)) are calculated. The computation of the radiation pattern takes 1.73 s on Intel Core i7 for this antenna, which corresponds to 23.8 h for the entire optimization considering 50,000 objective function calls. The obtained performance parameters are combined by the cost function with a two-level scalarization procedure: the first level aims to obtain a single cost value for each scan angle, while the second level combines these costs to reduce the problem to a single objective one. The first level of scalarization is obtained by means of a single parameter (\(\lambda\)) that tunes the relative importance of the two performance indexed:  
\[c_{s}(\mathbf{d})=\mathrm{RE}_{s}(\mathbf{d})+\lambda\cdot\Delta\theta_{s}( \mathbf{d}) \tag{16}\]  
The second scalarization level combines the four obtained cost values: for each scan angle a different scalar value (\(\mu_{s}\)) is used to adjust the convergence of the procedure. This is very important for two reasons: firstly, solutions for larger scan angles are less effective than for small angles and secondly, the performance calculation procedure inside the Aperture Field Method penalizes errors closer to \(\theta=0\) more than the others. Thus, the final cost value is defined as follows:  
\[C(\mathbf{d})=\sum_{s=1}^{4}\mu_{s}\cdot c_{s}(\mathbf{d}) \tag{17}\]  
The final solution is assessed using a Finite Element Method, called full wave analysis: it cannot be introduced in the optimization loop because it requires many hours to compute the performance of a single antenna configuration.
## 6 Liposome Applications
### Food Applications  
Liposomes are emerging as a promising tool in the food industry due to their ability to encapsulate and protect sensitive ingredients, rendering them an attractive option for use in a variety of food products.  
The encapsulation of nutrients is a potential application of this technology. The encapsulation of vitamins, antioxidants and flavours by liposomes improves their stability and bioavailability. This application is particularly beneficial in dairy products, where liposomes can enhance the nutritional profile and extend the shelf life of the product.  
The utilisation of liposomes in fermentation processes is another potential application of this technology. The encapsulation of enzymes in liposomes has been demonstrated to markedly reduce fermentation times in cheese production, thereby enhancing productivity and product quality. For example, the use of liposome-encapsulated enzymes has been demonstrated to reduce the ripening time of cheddar cheese by up to 50%, resulting in significant economic benefits [89].  
Liposomes are also being investigated for their potential in food preservation, wherein antimicrobial agents are encapsulated. This approach has the potential to effectively control the growth of spoilage microorganisms, thereby ensuring food safety and extending shelf life.  
In the agricultural sector, liposome-encapsulated biocides, such as fungicides, herbicides, and pesticides, offer prolonged action with reduced environmental impact. Furthermore, liposomes can be designed to adhere to plant surfaces, ensuring longer retention and effectiveness.
## 2 Related Work
### Personalized Recommendation  
Personalized recommendation systems typically present users with options that match their preferences as they browse short videos, listen to music or shop online without a clear goal in mind, so the recommendations should be accurate, diverse, and novel. Traditional recommendation algorithms are mainly content-based [20], collaborative filtering [21] and hybrid recommendation [22]. Although they are widely used in the recommendation field, they suffer from the problem of not being able to build user models quickly and not being able to represent item features adequately due to the difficulty of performing adequate feature extraction. In particular, collaborative filtering also often faces the problems of data sparsity (low number of user rated items) and cold start (no rating data for new items or users).  
Deep learning brings new opportunities to solve these problems due to its powerful ability to extract user requirements features [2]. [PERSON] et al. [23] proposed a deep and wide model to successfully extract features from multiple sources of information such as user-item interaction matrix, attributes, and context. To extract dynamic information about user behavior sequences, [PERSON] et al. [24] developed a model called an Interval- and Duration-aware Long Short-Term Memory network to capture long-term and short-term user preferences.  
From the perspective of evolutionary optimization, personalized recommendation is a kind of dynamic optimization problems guided by user knowledge. The IEAs have been widely used in personalized recommendation problems to quickly track changes in user behaviors and habits to meet the individual needs [25; 26; 27]. An example is the combination of a Kano model and an interactive genetic algorithm proposed by [PERSON] et al. [26] for enhancing the effective user-interactive process of creating customer-oriented products and responding quickly to customer's needs.  
IEAs have an inherent advantage in tracking changes in user preferences, but there is relatively little research on combining deep learning to model user preferences and consider the diversity and novelty of recommendations. Hence, how to use the user preference surrogate model built based on deep learning to further guide the evolution of the population and thus uncover more diverse and novel items becomes a problem that must be solved. Since there are hidden relationships between different preferences of the user, this problem can be solved if useful knowledge can be transferred from other preferences so that a certain preference can explore new items. Thus, a new IEA is explored to search for multiple preferences while also transferring knowledge between preferences.
# Copper Alloys Performance in High-Pressure and Low-Velocity Conditions Using a Custom Tribometer
## 2 Materials and Methods
### Design and Construction  
Figure 1 shows an overview of the external design, and Figure 2 details the main tribometer parts. Considering the high specific loads of interest, a design for applying loads using two pneumatic actuators with a combined load capacity of up to 784 N was chosen.  
Figure 1: Tribometer overall external design.  
Support for a \(\mathcal{O}6.00\) mm pin was developed to receive a \(\mathcal{O}1.5\) mm Type-J thermocouple. This thermocouple was chosen because its operating range is compatible with the expected temperature ranges for the tribological test and is associated with higher sensitivity and fast response. The support is mounted in a robust base with two axial ball bearings to significantly reduce the existing friction, whose force vector component could impair the friction force results. The concept designed for the support allows for a wide variety of different lengths for the pin, which can vary from 10 to up to 60 mm, and the pin diameter can be changed by replacing the pin holder.  
AISI 1045 steel disc with a diameter of 36 mm and a thickness of 5 mm was selected as a counter face. The maximum allowable deviation from parallelism is 0.01 mm. However, any other alloy can be used since it is not restricted. This material is chosen because of its widespread use as heavy equipment shafts. The dimensions of the disc and pin holder  
Figure 2: Main tribometer parts: (1) 4-wire load cell for normal load, (2) pneumatic actuator, (3) base, (4) axial ball bearing, (5) 4-wire load cell for friction force, (6) geared motor, (7) type J \(\mathcal{O}1.5\) mm thermocouple, (8) pin of test material, (9) steel disc.  
result in a 13 mm radius for the wear track. The disc support shaft is rotated by a geared motor with a maximum output of 207 rpm.  
Figure 3 details the communication system, with proper real-time recording of results. The \(\mathcal{O}\)1.5 mm Type-J thermocouple is linked with a digital temperature controller with a 4-20 mA analog output for pin temperature recording. To record real-time measurements of normal and friction force values, two aluminum single-point load cells were used, with dimensions of 149 \(\times\) 37 \(\times\) 24.5 mm, maximum capacity of 1982 N, and resolution of 0.0981 N, linked individually to a 4-20 mA analog converter.  
The three 4-20 mA signals are sent to a 4-channel analog-digital converter (ADC) with 16 bits of resolution, allowing a maximum of 860 samples per second. A frequency inverter controls the disc rotation, and the pneumatic actuator pressure is manually adjusted using a pneumatic regulator. The ADC is connected to an Arduino UNO, and programming was done to establish serial communication at an acquisition rate of 10 samples per second.  
Figure 3: Tribometer communication protocol.  
Arduino UNO is connected to a PC serial port, and a Python-based GUI accesses data via this communication protocol, allowing graphical monitoring of the test outcomes. Additionally, information from the serial port is saved in a.csv file for future analysis.
# Model for Cryogenic Flashing LNG Leak
## 3 Results
### Comparison with Established LNG Safety Guideline  
To gauge the adequacy of the proposed leak model to use for risk assessment, the gas dispersion result was compared with the LNG bunkering technical reference, TR56. This technical reference was produced by Enterprise Singapore and recognized by Singapore maritime authorities [30]. TR56 provides an LFL distance vs. stagnation pressure graph, shown in Figure 7, to advise how far flammable leak NG can disperse. The graph is based on a continuous release through a 25 mm-diameter hole. No further information on the surrounding geometry and wind condition was provided. Therefore, it is assumed the TR56 wind condition and geometry are identical to those in Section 3.1 leak scenario.  
Figure 5: (**a**) Section view of wind speed distribution in the vertical upward leak; (**b**) section view of the vertical upward leak showing LFL distance of 16.72 m.  
Figure 6: (**a**) Section view of the tailwind leak showing LFL distance of 49.83 m; (**b**) section view of the headwind leak showing LFL distance of 32.53 m.  
For an 8 bar 25 mm leak, the worst LFL distance was 60 m, roughly 10 m further than the proposed model result. The LFL distance for a 25 mm hole was expected to be further than a 9 mm hole since the leak area is bigger. However, considering that the cross-sectional area of the 25 mm hole was approximately 7.7 times that of a 9 mm hole, the difference of 10 m LFL distance seems to have been underestimated. One explanation for the minor LFL distance difference could be that a portion of the 25 mm leaked LNG did not vaporize immediately and form a rainout. This means that not all the leak LNG contributed to the immediate gas dispersion. In the case of the 9 mm leak, the SPARCLING experiment showed that no rainout was formed for this leak size. Therefore, the NG dispersion rate of the 25 mm hole will be greater than that of the 9 mm hole but not necessarily proportional to their cross-sectional area difference. At first glance, the proposed leak model prediction is probable.
## 3 Results and Discussion
### Computational Overhead  
In computational overhead, the execution time for generating the key, encrypting and decrypting the information, and the time taken for the break-glass key process is compared with the existing lightweight break-glass access (LIBAC) and multi-authority MCP-ABE-BG, as shown in the following figures.
# A Digital Twin-Based Approach for Detecting Cyber-Physical Attacks in ICS Using Knowledge Discovery
## 5 Methodology
### Information Gathering
#### 5.1.2 Data Collection  
Device logs are collected from the identified registers within the SCADA system, which is configured to operate in a cyclic acquisition mode. This configuration logs data at regular intervals, providing detailed monitoring of the system's state changes and capturing all relevant activities, even those not directly observable as events.  
Each log entry is timestamped and includes the current values of all identified registers. These entries capture the state of the system at each time point, providing a detailed view of the system's operation.  
Figure 4: Recorded values over a six-hour time interval for different registers of the mixer PLC.
## 3 Results and Discussion
### Selected Sugars in Banana Peel Hydrolysates Before and After Yeast Cultivation
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Cellobiose** & **Glucose** & **Xylose** & **Arabinose** \\ \hline
**Sample** & & \multicolumn{3}{c}{**Concentration [g/L]**} \\ \hline PT & 0.02 \(\pm\) 0.001 & 5.23 \(\pm\) 0.194 & 5.56 \(\pm\) 0.298 & 0.73 \(\pm\) 0.071 \\ ME & 0.32 \(\pm\) 0.008 & 8.08 \(\pm\) 0.439 & 8.8 \(\pm\) 0.753 & 1.55 \(\pm\) 0.123 \\ PTE & 0.29 \(\pm\) 0.026 & 9.27 \(\pm\) 0.327 & 9.24 \(\pm\) 0.703 & 1.93 \(\pm\) 0.154 \\ UE & 0.31 \(\pm\) 0.028 & 8.55 \(\pm\) 0.81 & 8.8 \(\pm\) 0.373 & 1.68 \(\pm\) 0.095 \\ \hline \end{tabular}
\end{table} 
Table 3: Concentration of selected sugars before yeast cultivation in banana peel hydrolysate. into the results we presented. It would be interesting to develop this research to determine the optimal microwave dose for treating banana peels before enzymatic hydrolysis. After reviewing the literature [39; 40; 42; 45], we decided to adopt only one variant of BP ultrasonic treatment for this initial research. The parameters we applied translated into the results we obtained. In our opinion, determining the optimal dose of ultrasound for treating banana peels before enzymatic hydrolysis will be one of the directions of our further research. The possibility of replacing non-ecological pressure-thermal treatment, which additionally requires pressure equipment, using microwaves or ultrasound, is promising for assessing the potential of banana peels as a raw material for producing microbial protein. Table 4 displays the sugar concentrations in the effluent following yeast cultivation. The results (Table 4) show that the yeast culture effluent, compared to the hydrolysates before the culture, contained only residual concentrations of cellobiose and glucose (the concentration of which was, in many samples, below the detection limit). The concentrations of xylose and arabines were also reduced by several dozen percent to below 0.63 g/L for xylose and below 0.28 g/L for arabines. The results suggest that the yeast strains used were characterised by their ability to assimilate glucose, cellobiose, xylose and arabines, which is confirmed by the data presented in the literature [38; 62; 63; 64]. The reduction in cellobiose concentration may also result from its enzymatic breakdown into glucose by cellobiase present as a side activity, not declared on the label, in the enzyme preparations used [65].
# A Trusted Lightweight Communication Strategy for Flying Named Data Networking
## 6 Challenges in FNDN Security
### Access Control  
Access control is one of the most important requirements in FNDN. When consumers send interest for data, it should be checked whether the consumer has access rights for that content or not. Another important aspect is the entity who is responsible to validate the access rights, whether it should be the publisher or any intermediate node? In wired networks, usually intermediate routers are responsible for such verifications; however, in mobile networks such as FNDN, this security aspect needs in-depth investigation.
## 2 A New Drainage Method for the Tailings Pond
### Slotted Drainage Pipes  
The slotted drainage pipes, as shown in Figure 3a, use a polyethylene (PE) material and are designed with a combination of holes and slots [29]. The outer diameter of the pipe and the wall thickness of the pipe are 75 mm and 6 mm, respectively. Figure 3b shows 12 permeable slots that are evenly distributed along the longitudinal wall of the pipe. Holes with 8-mm diameter are designed in a spiral shape with a spacing of 150-200 mm on the bottom of the seepage slots. Braided screens of stainless steel wire are used in the filter layer of the drainage system. According to the principle of drainage and depressurization of the filter layer, the pore diameter of the stainless steel wire is matched with the composition of the tailing particles, as shown in Table 1.  
Figure 1: A satellite image of the Xigou tailings pond.  
Figure 2: The layout of the drainage pipes. (**a**) Traditional approach. (**b**) A new drainage method combining a horizontal pipe with an upward bending slotted pipe.  
With this arrangement, seepage that previously went through the holes is replaced by seepage through the slotted surfaces, which is beneficial for increasing the seepage area of the drainage pipe in the tailings dam. Through rough calculations, the use of the proposed new drainage structure can increase the seepage capability to approximately 20 times more than that of the traditional drainage structure.  
Traditional drainage pipes (Figure 4) are widely used in practice in China. The diameter of a traditional drainage pipe is 100 mm, and 8 holes with diameters of 10 mm are located in a section of the pipe. The seepage area of a single hole is equal to 3.14 \(\times\) (5 mm) = 78.5 mm\({}^{2}\). The values of the contact area (CA) and the total seepage area (SA) of a 1 m pipe are equal to 3140 cm\({}^{2}\) and 61.80 cm\({}^{2}\), respectively. Therefore, the seepage ratio (SR) is (62.80/3140) \(\times\) 100% = 2%. However, for the slotted drainage pipe, the SA value of a permeable slot is equal to 1000 mm \(\times\) 10 mm = 100 cm\({}^{2}\). There are 12 permeable slots distributed along the longitudinal wall of a pipe, and the total SA value is 1200 cm\({}^{2}\). Therefore, the SR value is 38.2%. By comparing the values of the SRs, it is clear that the new drainage pipe can raise the seepage capability by approximately a factor of 20.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline Tailings \(d_{0.075}\)/\% & 95 & 90 & 80 & 70 & 50 & 40 & 30 & 20 & 10 & 5 \\ \hline Filtering Net Mesh Numbers & 120 & 120 & 100 & 100 & 80 & 80 & 60 & 60 & 60 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The aperture of the stainless steel filter.  
Figure 4: The structure of a traditional drainage pipe.  
Figure 3: The structure of a slotted drainage pipe. (**a**) Pipe structure. (**b**) Pipe section. (**c**) Pipes are clad with the screen as a filter layer.