esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Experimental Results
### Frequency Stability Estimation  
Frequency stability \(\sigma_{\text{f}}\) refers to the ratio of the amount of frequency variation (\(\Delta v\)) to the average frequency (\(\overline{v}\)) over a certain period of time when the laser is in continuous operation, i.e., \(\sigma_{\text{f}}\) = \(\Delta v\)/\(\overline{v}\)[30]. Obviously, the smaller the amount of variation \(\Delta v\) is, the smaller \(\sigma_{\text{f}}\) is, indicating better frequency stabilization. Measuring frequency stability requires lasers to beat frequency, and a simple estimation method is used here [29, 31]. We can see that through the frequency discrimination sensitivity \(k_{\text{f}}\), the relationship between error voltage and frequency can be obtained. Figure 13a shows the error signal recorded when the system is locked for 4.5 \(\times\) 10\({}^{4}\) s. After the system is locked, the laser frequency fluctuation \(\delta_{\text{f}}\) = \(\sigma_{\text{err}}\)/\(k_{\text{f}}\) can be estimated through the error voltage stability \(\sigma_{\text{err}}\), and then the frequency stability \(\sigma_{\text{f}}\) = \(\lambda\delta_{\text{f}}\)/\(c\), where \(\lambda\) is the wavelength and \(c\) is the vacuum speed of light. As shown in Figure 13b, we can see that when the 780 nm laser is locked, the stability of the error voltage is 5.77 \(\times\) 10\({}^{-6}\)@1 s. As the sampling time increases, the Allan variance decreases to 1.64 \(\times\) 10\({}^{-7}\)@1000 s. Then, we can deduce the laser frequency stability as 1.68 \(\times\) 10\({}^{-10}\)@1 s and 4.77 \(\times\) 10\({}^{-12}\)@1000 s.  
Figure 12: Measurement of (a) linewidth of SAS and (b) frequency discrimination sensitivity.  
Figure 13: (a) Variation and (b) Allan variance of the error signal when the frequency is locked.
## 5 Experimental Results and Evaluation  
In this section, the experimental configuration based on a proof-of-concept prototype implementation is described. Following that, we evaluate the performance of running SAUSA based on the numerical results, which is especially focused on the impact of the Blockchain on the system performance. In addition, a comparative evaluation of the previous works highlights the main contributions of SAUSA in terms of the lightweight Blockchain design, performance improvement, and security and privacy properties. Moreover, we analyze the security properties and discuss potential attacks.
# Enhancing the Recommendation of Learning Resources for Learners via an Advanced Knowledge Graph
## 3 Materials and Methods  
Now, we introduce the proposed LKGA model framework in detail. As shown in Figure 1, LKGA mainly consists of four components: firstly, the collaborative propagation layer, which propagates collaborative information through interactions between learners and learning resources; secondly, the knowledge graph propagation layer, which disseminates information through the knowledge graph; thirdly, the knowledge-aware embedding layer, which utilizes an attention mechanism to learn knowledge-based weights within the same set of entities and generates weighted representations of the entities; and finally, the prediction layer, which includes a novel aggregation method to aggregate representations of learners and learning resources from different propagation layers and outputs the predicted click-through rate.  
We assume a scenario of intelligent educational recommendation where there are a set of M learners \(\mathrm{L}=\{l_{1},l_{2},\cdots,l_{M}\}\) and a set of N learning resources \(S=\{s_{1},s_{2},\cdots,s_{N}\}\). Based on the implicit feedback of learners in their historical records, we can obtain a learner-resource interaction matrix, \(\mathrm{Y}=R_{M\times N}\), where \(y_{ls}=1\) indicates that the learner \(l\) has interacted with the learning resource \(s\), and otherwise, \(y_{ls}=0\). Additionally, we organize auxiliary information into a knowledge graph, \(G=\{(h,r,t)|h,t\in E,r\in R\}\), where each knowledge triplet, \((h,r,t)\), represents a relationship, \(r\), existing between the head entity \(h\) and the tail entity \(t\), with \(E\) and \(R\) being the sets of entities and relationships in the knowledge graph. Our goal is to learn a prediction function, \(\hat{y}_{ls}=F(l,s|\Theta,G)\), where \(\hat{y}_{ls}\) represents the predicted probability and \(\Theta\) denotes the model parameters of the function \(F\).  
Figure 1: The LKGA model framework, which consists of four modules: the collaborative propagation layer, the knowledge graph propagation layer, the knowledge-aware embedding layer, and the prediction layer.
# Analysis of Mechanical Properties of Air-Ribbed Skeleton Membrane Structure
## 2 Single-Arch Membrane Structure Design Scheme  
When designing the membrane structure, it is necessary to consider the expansion time and aeration volume of the structure and to maximize the effective use of space within the given gas capacity. Therefore, the connecting rod between the air ribs in the traditional air frame membrane structure has been eliminated and vertical, single-arch air ribs are selected as the supporting structure. The fixed deployment time of the air-ribbed skeleton membrane structure established in this manuscript is 45 min by 10 people, and the deployment time of an ordinary frame tent is 60 min by 20 people. The modified structure reduces the folding time of the tent and requires fewer people.  
There are two types of arched air rib construction: vertical type and curved type. The vertical air frame membrane structure has a larger available space and is more suitable for complex terrain under the same conditions. Hence, the vertical type is chosen for the design. The membrane structure should be able to carry 30 people. According to the requirement of 2 square meters per person in an emergency house, the usable area of the membrane structure shall be 60 square meters. So, the expansion radius of the arched air rib is 4 m and the span is 8 m, shown in Figure 1. The distance between the two ends of the air rib is 18 m, and the distance between the two sides of the ground is 18 m. The section diameter of the air rib is 0.3 m, with a space of 2 m between air ribs. A total of 10 air ribs are arranged side by side to support the membrane structure.  
The air-ribbed membrane structure, which consists of tarpaulin and air ribs, is depicted in Figure 2. The air ribs and the outer tarpaulin are fixed by Velcro binding. External loads such as snow and wind are applied on the surface of the tarpaulin membrane directly; then, these loads are transmitted from the membrane surface to the air rib, which acts as the primary load-bearing component of the entire structure. To reinforce the overall construction, wind ropes are added on both sides of the air ribs and tarpaulin. The wind ropes are made of polyester core rope. On the end face, the wind ropes are fixed in a manner resembling the letter \"M\". The line between the connecting point on the end face of the tarpaulin and the original point is named line C. The angle between line C and the X axis is 45\({}^{\circ}\). For the side face, ten wind ropes are attached on each side of the tarpaulin in parallel and the height of the connected point is 2829 mm (4000/\(\surd\)2).
## 3 Results and Discussion
### Characterization of Cockleshell Powder
#### 3.1.2 Thermogravimetric Analysis (TGA) and DSC  
The main purpose of TGA and DSC analysis is to characterize materials by studying the variation in material mass as a function of time or temperature and the heat exchange between a sample and a reference. Figure 3 shows the TGA and DSC graphs.  
From Figure 3a, we can note the presence of three stages of mass loss: The first loss of 0.25% mass can be attributed to the evaporation of moisture present in the cockleshell powder [26]. The second loss (1.55%) is due to the dehydration of the water in the carbonate network due to the transformation of the aragonite phase (unstable phase) towards the calcite phase [31]. The third loss is a stage of rapid degradation of the mass up to a percentage of 43.51%, which represents the decomposition of calcium carbonate and the formation of calcium oxide with the release of carbon dioxide--a reaction that has already been recorded [32].  
A stabilization phase can also be observed: the weight of the sample remains almost stable from temperatures above 874.76 \({}^{\circ}\)C, indicating the complete formation of calcium oxide [26] with purity of 43.51%.  
Figure 3: The graphs: (**a**) thermogravimetric analysis (TGA) and (**b**) differential scanning calorimetry (DSC) of cockleshell powder.  
Figure 2: The curve of \(\Delta\)pH vs. pH\({}_{\text{i}}\) for determination of the pH\({}_{\text{pzc}}\) of CS powder.  
The DSC graph in Figure 3b shows a single endothermic peak at 898 \({}^{\circ}\)C, which represents the melting temperature of the material. The presence of a single peak shows that the cockleshell powder contains a single phase, giving an idea of the purity of the sample.
# New Trends in Separation Techniques of Lithium Isotopes: A Review of Chemical Separation Methods
## 1 Introduction
### Challenges in Development of Separation Methods for Lithium Isotopes Using Electrochemical
#### 2.3.3. Methods Involving Resin-Supported Complexing Agents  
The ion-exchange chromatography can also be performed with resin-supported complexing agents. In the past, [2g,1,1] cryptand resins have been packed in a column (9 mm ID and 220 mm long) to separate the Li isotopes. Separation factors \(\alpha\) have been determined by [PERSON] et al. [42] for the partition of \({}^{6}\)Li and \({}^{7}\)Li between complexed ions with a cryptand [2g,2,1] resin, and lithium salts in methanol in a single-stage process. The study was conducted at different temperatures (0, 20 and 40 \({}^{\circ}\)C) and three Li halides (LiCl, LiBr, and LiI) were tested [42]. The separation factors \(\alpha\) were calculated as the isotope ratio \({}^{6}\)Li/\({}^{7}\)Li between the [2g,1,1] cryptand polymer and halides phase and decreased with temperature for all three Li halides, while the highest values were recorded at 0 \({}^{\circ}\)C: 1.045 \(\pm\) 0.002 (LiI), 1.045 \(\pm\) 0.003 (LiBr), and the highest, 1.047 \(\pm\) 0.004 (LiCl) [42]. More recently, as an innovative method, a novel lithium isotope separation polymer polysulfone (PSF)-graft-4\({}^{\prime}\)-aminobenzo-15-crown-5-ether (PSF-g-AB15C5) was synthesised through a nucleophilic substitution mechanism by [PERSON] et al. [43] from polysulfone (PSF) and 4\({}^{\prime}\)-aminobenzo-15-crown-5-ether (AB15C5) as starting materials for the synthesis (see Figure 4).  
Then the partition of lithium isotope was investigated during batch experiments of solid-liquid extraction using the above-mentioned polymer [43] and it was found that \({}^{6}\)Li, was enriched in the PSF-g-AB15C5 polymer. The effect of halide salts was also investigated, and in contrast with the findings of [PERSON] et al. [42], the order of the single stage separation factor obtained using different lithium salts in methanol decreased in the order  
Figure 4: Synthesis route of crown ether-grafted polymer PSF-g-AB15C5 (**A**). Lithium isotope separation mechanism using PSF-g-AB15C5 (**B**). Adapted from [43].  
LiCl! LiClO4! LiBr! LiCl. Additionally, different solvents were tested and at the same concentration of crown ether AB15C5 of 0.51 mmol/g PSF-g-AB15C5 immobilized on polymers, the separation factors increased as 1.010 \(\pm\) 0.002 (methanol)! 1.019 \(\pm\) 0.002 (ethanol)! 1.021 \(\pm\) 0.002 (propylene carbonate)! 1.027 \(\pm\) 0.003 (acetonitrile),!1.031 \(\pm\) 0.002 (ni-tromethane), according to the donicity of the solvents which represents a quantitative measure for the tendency of a solvent to donate electron pairs to acceptors [43]. With respect to crown ether concentration, the separation factor decreased from 1.015 \(\pm\) 0.002 to 1.003 \(\pm\) 0.001 with the decrease in AB15C5 concentration immobilized on polymer from 0.79 to 0.23 mmol/g in the CH\({}_{3}\)OH-LiCl/PSF-g-AB15C5 system [43].
###### Abstract  
The study used headspace solid-phase microextraction coupled with gas chromatography-time-of-flight mass spectrometry (HS-SPME-GC-TOF-MS) to analyze volatile compounds in leaves and fruits of _Amomum isako_, _Amomum pratsako_, and _Amomum kenigii_. The composition and aroma of distinct metabolites were analyzed using multivariate statistical methods. A total of 564 volatile compounds were identified from three species of the genus _Amomum_, which were further divided into nine categories: terpenoids, carboxylic acids, alcohols, hydrocarbons, aldehydes, ketones, phenols, ethers, and other compounds. Terpenoids and alcohols were the most abundant. The content and types of compounds vary in _A. tsaoko_, _A. paratsako_, and _A. kenigii_, so mixing or substituting them is not advisable. We selected 45 metabolites based on the criteria of the variable importance in projection values (VIP > 1.5) and one-way ANOVA (\(p\) < 0.05). The top 19 metabolites with the most significant VIP values were chosen. Interestingly, (\(Z\))-2-decenal was only found in _Amomum kenigii_, while nitroethane and nonanal were only present in cultivated _A. tsaoko_. Additionally, linalool, cineole, and (\(D\))-limonene were the main components affecting the aroma of three species of the genus _Amomum_. The volatile components identified in this study provide a theoretical basis for analyzing the unique flavor of _A. tsaoko_, _A. paratsako_, and _A. koenigii_.  
genus _Amomum_; volatile compounds; HS-SPME-GC-TOF-MS; differential metabolites 1
# 5G-AKA-FS: A 5G Authentication and Key Agreement Protocol for Forward Secrecy
## 1 Introduction
* Thanks to the Authentication and Key Management for Applications (AKMA) [18], the credentials generated through 5G primary authentication can be reused for application authentication in the 5G network environments; that is, the master session key negotiated during the 5G primary authentication is applied to derive an application key that allows a UE to authenticate itself to AKMA-based applications smoothly and efficiently. Evidently, the security robustness of the existing 5G primary authentications falls short of addressing the aforementioned concerns. As a result, it becomes imperative to bolster the security framework with robust public key-based measures and ensure the implementation of forward secrecy. Regarding EAP-AKA\({}^{\prime}\), there has been a standardization effort aimed at enhancing the protocol to incorporate support for forward secrecy (known as EAP-AKA\({}^{\prime}\)-FS) [19]. On the 5G-AKA side, there are existing works proposed to support unlinkability and forward secrecy, such as [3; 16; 17; 20; 21]. In particular, those 5G-AKA enhancements attempted to reuse the ECIES shared key, which is used to protect the UE's identifier in the initiation phase. However, in such an approach, if an adversarial security event happens in HN, forward secrecy is not guaranteed for the session key. In this paper, we propose a 5G-AKA-Forward Secrecy (5G-AKA-FS) protocol that supports forward secrecy and unlinkability together to solve the limitations of the existing studies and maximize the efficiency of authentication in 5G networks. The proposed protocol accomplishes forward secrecy and unlinkability by introducing an additional ECIES-based ephemeral key pair generation within HN. The main contributions of this paper are summarized as follows:  
* The 5G-AKA-FS protocol is designed to concurrently support forward secrecy and unlinkability. * We analyze the latest studies proposed to improve the vulnerabilities of 5G-AKA and provide a solid comparison of security attributes between our 5G-AKA-FS and the latest studies. * We conduct a rigorous security verification of the proposed protocol using formal verification (BAN Logic [22] and ProVerif [23]). * Performance evaluation is thoroughly carried out by measuring overhead in terms of computation and communication. The rest of the paper is organized as follows. Section 2 explores the existing enhancements of 5G-AKA, while Section 3 describes the preliminaries used in this paper. Moving on to Section 4, we present a detailed description of the proposed protocol, followed by the formal security analysis using BAN Logic and ProVerif in Section 5. Assessing performance, Section 6 carries out a comparative evaluation of security properties between the proposed and existing protocols, along with measured overhead. Finally, Section 7 provides the conclusion.
## 3 Experimental Results
### Characteristics of Fracture Propagation
#### 3.1.2 Analysis of Fracture Branches  
The fracture branches are a group of secondary fractures near the main fractures [41], and the observation of fracture branches is another crucial aspect to investigate the fracture propagation. Generally, fracture branches mainly occurred in the soft rocks (group B) and soft-hard rocks (group D). According to the observation of fracture branches, three types of fracture branches were found on the surfaces of specimens (Figure 7). Type-I fracture branches occurred on the surfaces of specimen B2 and B3, and they initiated from a main fracture at a low angle and then converged to the main fracture. Type-II fracture branches were most common in the results; they occurred on every specimen listed in Figure 7, and they connected two main fractures. Type-III fracture branches occurred on the surfaces of specimen D1-2 and D3-1, and they initiated from a main fracture connecting no fractures.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Group** & **Specimen NO.** & **Main Fracture Type** & **Fracture Branch Type** & **Typical Result** \\ \hline \multirow{6}{*}{A} & & Two type-A fractures cutting through & \multirow{2}{*}{None} & \multirow{2}{*}{\begin{tabular}{c} \\ \end{tabular} } \\  & A1 & the specimen completely & & None \\  & & None & None \\  & A2 & & None \\  & & Two type-A fractures cutting through & & \\  & A3 & the specimen completely & None \\  & & Four type-A fractures and & & \\  & B1 & three type-C fractures & & None \\  & & Four type-A fractures and & & \\  & B2 & two type-C fractures & Type-I and type-II & \\  & & Three type-A fractures and & & \\  & B3 & one type-C fracture & Type-I and type-II & \\  & C1-1 & None & None \\  & C1-2 & None & None \\ C & C2-1 & Type-B fracture & None & \\  & C2-2 & None & None \\  & C3-1 & Type-B fracture & None \\  & C3-2 & Type-B fracture & Type-I & \\  & D1-1 & \begin{tabular}{c} Three type-A fractures, one type-B \\ fracture and one type-C fracture \\ \end{tabular} & Type-II & \\  & D1-2 & \
\begin{tabular}{c} Three type-A fractures, and \\ one type-B fracture \\ Two type-A fractures, and \\ one type-B fracture \\ Two type-A fractures, and \\ one type-B fracture \\ Two type-A fractures, and \\ one type-B fracture \\ \end{tabular} & Type-II and type-III & \\ D & D2-2 &
\begin{tabular}{c} Two type-A fractures, and \\ one type-B fracture \\ Two type-A fractures, and \\ one type-B fracture \\ Four type-A fractures, one type-B \\ fracture and one type-C fracture \\ Three type-A fractures extended to the \\ middle, and one type-B fracture \\ \end{tabular} & Type-II & \\ \hline \end{tabular}
\end{table}
Table 4: The fracturing results of each specimen.
# Opportunities for Machine Learning in District Heating
## 2 District Heating Networks
### Operational Challenges in Adapting District Heating Networks
#### 2.1.1 Heat Supply  
In this phase, heat energy is produced from CHP plants, boilers, geothermal, solar or recovery of surplus heat. In the production process, a number of control challenges need to be addressed:  
1. Unit commitment, e.g., choice of boilers to switch on. This relates to the sequence of production units to start or stop, where each one can also be controlled continuously within their own limits. In a CHP system, units could also include heat supply generated from solar cells whereby their output production is uncertain. Further, the amount of energy stored in the systems (thermal inertia) needs to be taken into consideration to fully understand the heat supply capacity. To be able to solve these problems in a good way, the heat load should be predicted, as well as the production for each of the supply units in a CHP system. Short-term heat load forecasting is therefore very important, since, without knowing how the heat demand of a network will evolve in the coming hours, it is impossible to select which units are the most efficient ones to switch on. In case large CHP units or heat pumps are present, which trade their power on the intra-day or reserve power markets, forecasting of the power price is also important.
2. Supply temperature levels and pressure heads. Apart from the control of the units to be switched, the control of supply temperature to the network, as well as the control of the pressure head in the network, can introduce inefficiencies into the DH system. Typically, these controls are static and rule-based. The set point of the supply temperature is, e.g., often determined based on the (moving average of the historic) outdoor temperature and a heating curve. The pressure head is often determined by measurements on critical locations in the network. These rule-based controllers guarantee the heat delivery to the costmers; however, they do not optimise towards  
Figure 1: Typically, the control of heat consumption and heat production in DH networks is achieved by means of 4 independent, non-interacting control actions. Loop #1 controls the heat consumed by the individual buildings, #2 controls the individual flow rate of the DH water being consumed. By aggregating individual building consumption, numbers #1 and #2) also determine the heat demand and flow rate in the entire network. At the production side, #3 controls the pressure levels in the network and #4 controls the supply temperature of the network to fulfill the heat demand and flow rates from the buildings [21].  
economical or ecological parameters. Rather than adopting a rule-based approach, a more dynamic control of the supply temperature and pressure head can help optimise setting temperature levels and controlling pressure heads.
# A Driving Behavior Planning and Trajectory Generation Method for Autonomous Electric Bus
###### Abstract  
A framework of path planning for autonomous electric bus is presented. ArcGIS platform is utilized for map-building and global path planning. Firstly, a high-precision map is built based on GPS in ArcGIS for global planning. Then the global optimal path is obtained by network analysis tool in ArcGIS. To facilitate local planning, WGS-84 coordinates in the map are converted to local coordinates. Secondly, a double-layer finite state machine (FSM) is devised to plan driving behavior under different driving scenarios, such as structured driving, lane changing, turning, and so on. Besides, local optimal trajectory is generated by cubic polynomial, which takes full account of the safety and kinetics of the electric bus. Finally, the simulation results show that the framework is reliable and feasible for driving behavior planning and trajectory generation. Furthermore, its validity is proven with an autonomous bus platform 12 m in length.  
autonomous electric bus; path planning; high-precision map building; trajectory generation 10.3390/610060051