esa-sceva/satcom-chunk-collection huggingface dataset:
# The Emerging Energy Internet: Architecture, Benefits, Challenges, and Future Prospects
## 1 Introduction
### FREEDM Architecture with Multiple Levels
#### 2.1.4 Distributed Grid Intelligence (DGI) Level  
DGI is the core level of the FREEDM design and it governs the energy needs of local ER with the coordination of other FREEDM structures of the grid [47]. At the DGI level, energy management schemes are applied after the identification of loads and DESDs, and generation through DRERs. DGI will act as a common platform for all IEM and IFM nodes, through which a power balance is achieved in a time-sensitive manner. A multi-level ER communication is possible with a DGI platform [48]. For optimal load scheduling and grid stability management, DGI plays a key role in the shedding of unnecessary loads, turning on/off back-up generators, and discharging DESDs to serve the critical loads.  
Figure 3: Block diagram of a distribution-solid state transformer (SST).  
The DGI will coordinate with the IEM in order to change the load consumption, based on the different operating modes of the grid. If the disturbance of a grid (e.g., approaching a storm or peak-load hours) is anticipated, then the charging of DESDs is maximized and the power availability for low-priority loads is minimized. According to the alertness level by DGI, the excess power can also be shared with the other users of the FREEDM structure in the grid. In order to maximize effective power marketing, DGI will consider the business policies along with the operational requirements in decision making. With the DGI operating system, the number of IEM and IFM nodes can be increased or decreased as per the requirement, without any major change in the system. The DGI operating system is dumped in each node of IEM and IFM, making it a flat and distributed controlling architecture that is different from the centralized supervisory control and data acquisition (SCADA) conventional system. The coordination between multiple levels of control in different time scales is the main challenge for DGI software. Furthermore, DGI software should be able to tackle the challenges of interoperability when operating with devices manufactured by different vendors. This challenge can be addressed by adopting interoperable standards, like IEC 61850, DNP3, or openADR for DGI communication.
# Route Optimization for Open Vehicle Routing Problem (OVRP): A Mathematical and Solution Approach
## 3 Materials and Methods
### Problem Definition  
The open vehicle routing problem (OVRP) is classified as NP-Hard due to the exponential increase in computational time and cost as the number of clients or nodes increases. To handle this complexity, heuristic algorithms such as Lin-Kernighan (LKH), known for their effectiveness in generating feasible and good quality solutions in reasonable com  
Figure 2: Methodological proposal.  
putational times, are employed [28]. These algorithms not only reduce logistics costs, benefiting transportation companies [29], but also address problem-specific constraints, such as sub-path elimination constraints (MTZ), necessary to identify a Hamiltonian cycle, which involves visiting all nodes in a closed loop. In addition, the continuity of routes is ensured: if a vehicle enters a node, it must leave it. However, it is crucial to introduce constraints that interrupt this continuity to avoid erroneous solutions or excessively long processing times without finding a feasible solution. In summary, the goal is to use efficient heuristic algorithms to solve the OVRP, managing the complexities and constraints of the problem to optimize routes and reduce logistics costs.
# A Sustainability Analysis Based on the LCA-Energy-Carbon Emission Approach in the Building System
## 4 Results and Discussion  
### LCA-Emergy Analysis  
#### 4.1.1 Dominated Contributor  
The five stages of the whole life cycle of the building are studied and discussed. Firstly, the largest energy contribution is the building run phase because the running energy of 20 years is calculated in this paper (6.09 \(\times\) 10\({}^{20}\) sej). The secondary contributor is the energy in the stage of building materials (8.73 \(\times\) 10\({}^{19}\) sej), followed by the building construction stage (5.55 \(\times\) 10\({}^{19}\) sej), building demolition stage (1.12 \(\times\) 10\({}^{19}\) sej) and building renewal stage (1.42 \(\times\) 10\({}^{18}\) sej) in Figure 4.  
As the primary impact element, the building operation stage contains four types of inputs, which are Solar, Electricity, Heat and Water. Therein, electricity plays a major role from an energy point of view to analyze (98.3% of the entire operation's energy in the building).  
There are 19 categories of materials for the building materials stage (in Figure 3). Among them, steel, cement and brick are the key inputs, which account for 60.14%, 15.83% and 12.14% of the total building material energy.  
During the building construction phase, there are six subsystems that need to be designed and analyzed, involving environmental inputs, water supply and sewage system treatment facilities, heating and cooling systems, electricity installations, telecommunications systems, the elevator system, etc. Their energy ratio is shown in Figure 5.  
Figure 4: Comparative analysis of each stage. (stage 1—building material production stage; stage 2—building construction stage; stage 3—building operation stage; stage 4—building renewal stage; stage 5—building demolition stage).  
Figure 6 demonstrates that water supply and sewage system treatment facilities are critical subsystems, accounting for roughly 73% of the total energy in the building construction stage, followed by environmental inputs (14%), electricity installations (7%), the telecommunications system (3%), heating and cooling systems (2%) and the elevator system (1%).  
In order to better update building types, there are three renewal strategies, respectively, and the specific details can be obtained in Table 1. The unit energy value reference can be found in the literature [46].  
Figure 5: The order of all kinds of materials.  
Figure 6: Six subsystems on the stage of building construction.  
According to Table 1, in the three kinds of updating strategies, energy accounted for 71.86%, 22.99% and 5.3%, respectively. However, for the building as a whole, they did not display the major roles.  
The stage of building demolition is distinguished in two ways. On the one hand, some materials will be recycled, such as glass, PVC, iron, diesel fuel, concrete, bricks and aluminum, which account for about 84% of the total demolition energy. On the other hand, approximately 16% of the entire energy will be lost because of the landfill style.
## 3 Results and Discussion
### Solid-State Studies of \(\gamma\)-CD-Phloroglucinol Inclusion Compound
#### 3.3.3 Thermal Analyses  
The thermograms of phloroglucinol, \(\gamma\)-CD, their 1:1 physical mixture and the inclusion compound \(\gamma\)-CD\(\cdot\)PG are represented in Figure 5. The thermogram of pure phloroglucinol shows an initial dehydration step of 15% and then no further mass loss until about 200 \({}^{\circ}\)C, the temperature that marks the onset of its decomposition. The decomposition step of phloroglucinol compound is absent in the thermograms of both the inclusion compound, \(\gamma\)-CD\(\cdot\)PG, and the physical mixture.  
While for \(\gamma\)-CD\(\cdot\)PG the absence of the guest decomposition step is expected because of the inclusion of the guest molecules in the channels of \(\gamma\)-CD, with subsequent slow or no release until the decomposition of the structure of the host, the feature is far less common in a physical mixture. This may be indicative of a strong affinity of this guest to the host, \(\gamma\)-CD, with possible in situ formation of an adduct over the course of thermogravimetric data acquisition.  
Phloroglucinol, \(\gamma\)-CD, their 1:1 physical mixture and the inclusion compound \(\gamma\)-CD\(\cdot\)PG were further studied using DTA. The results are represented in Figure 6.  
Pure phloroglucinol shows two endothermal peaks. The first one, centred at 60 \({}^{\circ}\)C, corresponds to the loss of hydration water molecules, and the second peak occurring at 217 \({}^{\circ}\)C, corresponds to its melting transition. The melting peak of phloroglucinol is absent from \(\gamma\)-CD\(\cdot\)PG, thus, confirming inclusion; it was also not observed in the trace of the physical mixture, similarly to the results obtained with TGA. This observation corroborates the TGA results and further confirms a strong host-to-guest affinity.  
Figure 5: Thermogravimetric traces of phloroglucinol (PG), \(\gamma\)-CD, their 1:1 physical mixture (1:1 mix) and inclusion compound \(\gamma\)-CD\(\cdot\)PG.
# Insider Threat Detection Based on Deep Clustering of Multi-Source Behavioral Events
## 4 Experiment
### Experimental Setup  
In the experiment, the proposed detection method was compared with the following existing insider threat detection methods.  
BAIT [29] proposed a semi-supervised classification method for detecting abnormal user behavior. This method defines 28-dimensional user behavior features, with 16 dimensions being basic features that calculate the frequency of user behavioral operations. The remaining 12 dimensions are combinations of these basic features, such as the ratio between one basic feature and another. The feature vector is then input into a support vector machine and Naive Bayes algorithms for classifying abnormal user behavior.  
Isolation Forest [30] was proposed as an unsupervised multi-source user behavior anomaly detection method. This method extracts 42-dimensional features from multiple sources of user behavior, including email usage features, email content features, login/logout features, application software usage features, and web browsing usage features. The average, weighted average, and weighted difference of the feature values for each day are calculated and concatenated as the feature vector. The Isolation Forest algorithm is then used to detect anomalies.  
Scenario-Based [31] was proposed as a semi-supervised multi-source user behavior anomaly detection method. Initially, 20-dimensional features are extracted from multiple sources of user behavior, such as login counts, file download counts, and email sending counts. A cost-sensitive approach is employed to undersample normal behavior samples to balance the number of normal and abnormal samples. Random Forest and Deep Autoencoder are then utilized to classify abnormal user behavior.  
In the experiment, the proposed method set the dimension of the embedding vector for event entities to 10. The recurrent neural network layer in the network was set as the Gate Recurrent Unit (GRU). The deep clustering algorithm was set to have 10 clusters. The length of the multi-source user behavior sequences was set to 8.
# End-to-End Light Field Image Compression with Multi-Domain Feature Learning
## 1 Introduction
In the MFLFIC-Net, the complete angle feature is obtained by exploring the large disparity angle information in the EPI domain. In addition, the correlation between the spatial feature and complete angle feature is explored to effectively reduce redundancy, thereby further improving the compression efficiency. The contributions are summarized as follows:  
1. A novel multi-domain feature learning-based light field image compression network is proposed to improve compression efficiency by effectively utilizing multi-domain features and their correlation to obtain a complete angle feature and reduce the redundancy among multi-domain features. 2. An EPI-based angle completion module is developed to obtain a complete angle feature by fully exploring the large disparity angle information contained in the EPI domain. 3. A spatial-angle joint transform module is proposed to reduce redundancy by modeling the intrinsic correlation between spatial and complete angle features. 4. Experimental results on the EPFL dataset demonstrate that MFLFIC-Net achieves superior performance on MS-SSIM and PSNR metrics compared to public state-of-the-art methods. The remaining sections of this paper are organized as follows. Section 2 briefly reviews the related works on light field image compression. Section 3 provides an introduction to the details of the proposed method. The experimental setup and experimental results are introduced in Section 4. Finally, the paper is summarized in Section 5.
# Position-Monitoring-Based Hybrid Routing Protocol for 3D UAV-Based Networks
## 3 Methods
### Selection of PHC
#### 3.3.2 Random PHC Selection  
The random method randomly selects a PHC node by utilizing the linear congruential generator (LCG) method, which considers the connections of each UAV with other UAVs in the network. LGS [45] is widely used due to its low memory allocation and very fast speed,  
Figure 2: Three-Dimensional UAV Mobility Model.  
particularly in computer hardware technology. Random PHC selection is the best choice, where UAVs have less resources. Random PHC can be calculated from Equation (13) for random LCG number generation:  
\[F(X_{i})=(a\times X_{i-1}+c)mod\ m,for\ i>0 \tag{13}\]  
where \(m\) is the modulus, \(a\) is a constant multiplier, \(c\) is a constant increment value, and \(X_{i}\) is a seed number.
## 3 Underwater Channel Characterization
### EM Propagation in Seawater
#### 3.4.1 Propagation Velocity of EM Waves in Seawater  
Propagation in such a conductive medium makes it difficult for EM waves to propagate. Therefore, the propagation velocity is expressed in Equation (7) [7]:  
\[c\approx\sqrt{\frac{4\pi f}{\mu\epsilon}} \tag{7}\]
## 3 The Self-Adaptive Mobile Clustering Algorithm (SAMCA)  
This section describes a dynamic distributed algorithm for organizing the mobile sensors using a timer and the characteristics of a network. The main assumptions are: (1) All sensors are homogeneous with the same transmission range; (2) Symmetric communication channel: all links between sensors are bidirectional; (3) Each sensor periodically broadcasts its local information to its neighbors. Note that there are no base stations to coordinate or supervise activities among sensors.
## 4 End to End Security Analysis on the Proposed IIoT Security Architecture
### Device Layer
#### 4.1.3 Media Access Control (MAC) and Internet Protocol (IP) Address
that consists of six parts: 3-bit format prefix (00,1 FP), 13-bit top-level aggregation identifier (TLA ID), 8-bit future use reservation (RES), 24-bit next-level aggregation identifier (NLA ID), 16-bit site-level aggregation identifier (SLA ID), and 64-bit interface identifier. The IP address focuses on network layer communication located at Layer 3 of the open system interconnection (OSI) model. The MAC address aims to transmit the data link layer or Layer 2 of the OSI model. Both IP address and MAC address can work together with address resolution protocol (ARP). MAC and IP addresses still suffer from numerous security threats and privacy issues, as summarised in Table 6. Whether the convention security mechanisms (e.g., cryptographic algorithm, firewall, intrusion detection and prevention system) and recent IPsec security mechanism (e.g., authentication header (AH) protocol, encapsulation security payload (ESP), etc.) are sufficient to protect their confidentiality, authentication, and integrity is still questionable with the recent widespread of advanced persistent threat (APT) attacks. Moreover, IPsec only serves as a security standard, and there exists an interoperability issue of implementing secure communication protocol across diverse manufacturers. For instance, HP does not support the Diffie-Hellman Group 1 key on Internet key exchange (IKE). \begin{table}
\begin{tabular}{p{42.7 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline
**Security Dimensions** & **Security Risks, Threats and Ongoing Challenges** & **Deployed Security Countermeasures** & **Ongoing Challenges and Future Woks** \\ \hline \multirow{7}{*}{Confidentiality and Privacy} & \multirow{7}{*}{MAC address is subjected to privacy risks of identity confidentiality and location confidentiality and used to track individuals’ spolocation & \multirow{7}{*}{\ \begin{tabular}{} \end{tabular}  } & RFC 4303 IP encapsulating security payload (ESP) protocol to provide message content confidentiality; however, with a limited traffic flow confidentiality [6] & \multirow{7}{*}{ \begin{tabular}{} \end{tabular}  } \\  & & & \\ \cline{1-1}  &
# Microbial Biopolymers: From Production to Environmental Applications--A Review
## 6 Applications of Microbial Biopolymers
### Microbial Biopolymers as Adsorbents
In another case, a sodium alginate/polyethyleneimine hydrogel was used for dye adsorption and demonstrated the excellent removal performance of methylene blue with a maximum absorption capacity of 400.0 mg/g. Within 30 min, approximately 99% of the dye was removed [270]. Furthermore, graphene oxide (GO) was encapsulated in a sodium alginate/polyvinyl alcohol compound. Used as an effective adsorbent for removing Cu\({}^{2+}\) and UO\({}_{2}\)\({}^{2+}\), the hydrogels showed a maximum absorption of 247.16 mg/g and 403.78 mg/g, respectively [271]. In another research, GO was used to formulate three-dimensional graphene oxide porous biopolymer gels, which foster the establishment of hydrogen bonding and hydrophobic interactions. This 3D network structure provides abundant active sites for the effective adsorption of cationic dyes and heavy metal ions. Another interesting technique was highlighted by [PERSON] et al. [272], which involved the use of aquatic sodium alginate for the gelation of heavy metal ions. In this study, alginate showed the fast gelation of Pb\({}^{2+}\), Cu\({}^{2+}\), and Cd\({}^{2+}\) within less than 10 min. The resulting gel-liquid separation from wastewater was achieved smoothly through gravity. Magnetic adsorbents are becoming increasingly favored in water treatment. Their rising popularity can be attributed to their capacity to enhance adsorption efficiency, minimize adsorbent wastage, and simplify separation through the use of an external magnetic field [273; 274]. [PERSON] et al. [275] developed magnetic cellulose, chitosan, alginate, and composite hydrogel beads. The bio-sorbent demonstrated strong potential for treating heavy metal-contaminated wastewater comprised of Ca and Fe ions and showcased enhanced thermal stability when compared to raw cellulose and chitosan. The mechanical resistance of some naturally derived microbial biopolymer-based membranes is poor. This can be overcome by embedding it with compatible nano biomaterials [276]. For instance, chitosan is an interesting biopolymer for this purpose due to its film-forming ability, biocompatibility, and favorable hydrophilicity [277]. [PERSON] et al. [277] investigated chitosan-based membranes for adsorptive studies. They suggested the concept of cross-linking chitosan with TiO\({}_{2}\) to create a self-regenerating dye adsorbent. Also, cross-linking with glutaraldehyde, glyoxal, and formaldehyde was suggested to improve its solubility and mechanical attributes.
## 2 WBAN Applications for Health Monitoring
\begin{table}
\begin{tabular}{c c c} \hline \hline  & & Aiding Professional and Amature \\  & & Athletic Training [17] \\  & & Wearable Health Monitoring [18] \\  & & Asthma Monitoring [18] \\  & & Sleep Staging or Monitoring [19] \\ Medical & & Fall Detection [20] \\  & & Assessing Soldier Fatigue and Battle \\  & & Readiness [21] \\  & & Patient Monitoring [22] \\  & & Telemedicine Systems [23] \\ \cline{2-3}  & & Cardiovascular Diseases [25] \\  & Implantable WBAN [24] & Diabetes Control [26] \\  & & Cancer Detection [27] \\ \hline  & & Video steaming \\  & & Data file transfer \\  & & 3D video \\  & & Sports \\ \cline{2-3} Non-Medical & & Life-threatening conditions monitoring: \\  & Emergency (non-medical) [13] & firefighters, soldiers, deep-sea \\  & & explorers, and space explorers. \\ \cline{2-3}  & Entertainment Applications [17] & Games \\  & & Social networking \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Some typical applications of WBANs. With the widespread application of WBANs in health monitoring, technical requirements need to be made specific to the types of disease and different application scenarios. We must not only have different requirements for sensor nodes and topology, but also transmission rate and real-time performance. Table 3 describes some typical technical requirements of BAN applications. Three aspects, \"power consumption\", \"quality of service (QoS)\" and \"safety for the human body\" have significant differences in the BAN standards compared to 802 other standards [52]. Table 4 shows the desired ranges matching the proposed requirements of WBAN standards. To sum up, in this chapter, we introduced some typical application scenarios of WBAN, especially summarizing various diseases monitored by using WBANs. Obviously, each disease requires selecting appropriate sensors, and WBAN technology has its own technical requirements for various diseases and application scenarios. Therefore, the WBAN is a heterogeneous system with its special technical requirements, proprietary standards and challenges compared to the traditional wireless sensor networks. \begin{table}
\begin{tabular}{c c c c} \hline \hline
## 2 Methodology
### Mesh-Independent and Numerical Methods Validation  
Here, the result of a 1:30 scaled moving model experiment [53] where the train with a parabolic wedge nose traveling through the tunnel at 360 km/h is chosen to verify the accuracy of the mesh strategy and numerical method. As presented in Figure 6, the nose is a parabolic wedge shape. In the full-scale model, the streamlined nose of the train is 6 m long, and the train's width and height are both 3 m. Figure 7 illustrates the distribution of the tunnel/train model and the arrangement of measurement points. The tunnel model is a square-section tunnel with a width of 11 m and a height of 7.5 m. The distance between the train and the tunnel wall is 1.5 m. Measurement point P1 is placed 38 m from the tunnel entrance to capture the initial compression wave. The movement of the train inside the tunnel is achieved through overset mesh method, with numerical methods and grid strategies consistent with those described in Sections 2.3 and 2.4.  
To conduct a mesh independence study, three sets of meshes with consistent strategies but different sizes are developed. The coarse, medium, and fine meshes have 2.49, 3.96, and 6.90 million cells, respectively. Table 1 lists the key meshing parameters for the three sets of meshes. The size and layout of the medium-density mesh are consistent with those described in Section 2.3. Figure 8 compares the time histories of the pressure coefficient and pressure coefficient gradient at point P1 calculated by the three sets of meshes. It also compares the time history of the pressure coefficient gradient with the experimental results. The initial compression waveforms captured by the three sets show consistent trends, with differences in amplitude being less than 1.40%. In terms of the pressure coefficient  
Figure 6: The train model used in moving model experiments: (**a**) side view; (**b**) top-view; (**c**) three-dimensional view.  
gradient, the computational results from the three mesh sets are generally consistent with the experimental trends, and the time of obtaining the maximum pressure coefficient gradient is also relatively close. However, there are significant differences in the maximum pressure coefficient gradient values. Compared to the experimental results, the relative errors for the coarse, medium, and fine meshes are 13.43%, 5.65%, and 4.95%, respectively. In summary, considering both computational resources and accuracy, the medium mesh strategy is chosen for the study of the 400 km/h train passing through the tunnel scenario.