esa-sceva/satcom-chunk-collection huggingface dataset:
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
###### Abstract  
Visible light communication (VLC) is a highly promising complement to conventional wireless communication for local-area networking in future 6G. However, the extra electro-optical and photoelectric conversions in VLC systems usually introduce exceeding complexity to communication channels, in particular severe nonlinearities. Artificial intelligence (AI) techniques are investigated to overcome the unique challenges in VLC, whereas considerable obstacles are found in practical VLC systems applied with intelligent learning approaches. In this paper, we present a comprehensive study of the intelligent physical and network layer technologies for AI-empowered intelligent VLC (IVLC). We first depict a full model of the visible light channel and discuss its main challenges. The advantages and disadvantages of machine learning in VLC are discussed and analyzed by simulation. We then present a detailed overview of advances in intelligent physical layers, including optimal coding, channel emulator, MIMO, channel equalization, and optimal decision. Finally, we envision the prospects of IVLC in both the intelligent physical and network layers. This article lays out a roadmap for developing machine learning-based intelligent visible light communication in 6G.  
visible light communication; artificial intelligence; machine learning; physical layer; network layer +
Footnote †: journal: _photonics_
## 3 Model Description
### Detailed Structure of TCReC
#### 3.2.2 Mask Method and Data Segmentation  
Prior to feeding the data to the encoder, TCReC applies a masking operation on the network traffic. The purpose of this mask operation is to enable the encoder to learn how to extract meaningful features from incomplete data during the training process. The masking process involves several steps. First, TCReC determines the number of packets in a flow and calculates the number of packets to be masked. Next, the packets are divided into two categories: masked and unmasked. The packets in the masked category are then masked, while the packets in the unmasked category remain unchanged. This approach allows TCReC to train the model to effectively handle and reconstruct data with missing or masked packets.  
During the mask operation, we incorporate a packet segmentation method. As shown in Figure 5, before applying the mask to the packets, we divide each packet into fixed-length blocks and mask these blocks. This block-based masking approach enables effective masking of the packets. Furthermore, these data blocks also affect the location encoding part, which will be discussed in detail in a subsequent section.  
Data segmentation is implemented to address the issue of ignoring detailed information when masking long segments of data, such as an entire packet or longer. Such masking can lead to a poor reconstruction effect. Conversely, if the data blocks are too short, it becomes challenging for the model to extract meaningful characteristics as they contain minimal information. Through experimentation, we have determined that a small data block strategy is effective, where a data block length of 16 bytes is used. This length strikes a balance between preserving essential information and facilitating the extraction of meaningful features.  
Figure 5: Illustration of mask method.
## 4 Experiment and Analysis  
In this section, to evaluate the predicted performance of ANFIS with an incremental tree structure based on CFCM clustering methods described in Section 3, experiments were conducted to predict computer performance using the computer hardware dataset. In this experiment, for the predictive performance of ANFIS using the grid-based rule generation method, which is a representative ANFIS, and ANFIS using the FCM clustering-based rule generation method, as well as ANFIS using the proposed method, the incremental tree structure-based CFCM clustering-based rule generation method is compared and analyzed.
## 4 Theoretical Framework
Taking Firm A's system service content as an example, the configuration and function of the software system will affect the refactoring process and the system's operating efficiency. Figure 3 shows the reconstruction process in Firm A after the introduction of S-D logic. The system was briefly classified and disassembled during the preliminary reconstruction of the structure, based on the relevance and functionality of its data tables. However, because of their binding services, many systems with decentralized operations could not achieve the desired performance. In monolithic architecture, if Functional System A fails to function, it will also affect Functional System B, causing the entire system to suspend service. Similarly, if decision makers can miniaturize the service system and decentralize it using the S-D logic concept, the risks of system failure will be reduced [28]. Furthermore, identifying the corresponding requirements and service process architecture while disassembling the system will help in the innovation and optimization of the service portfolio. Figure 3: Decomposition of service delivery. Figure 2: Hierarchical reliability assessment. In the system architecture theory, the introduction of the microservice system has the characteristics of high scalability, which enables making necessary changes when the market changes in the future [7]. From the standpoint of a partner company, more flexible services are easier to introduce into their systems. Taking the microservice system architecture as an example, the service application side can find the best architecture and import it through combination. Therefore, following the introduction of related concepts, Firm A will construct its microservices based on the specifications and requirements of the partner and customer after performing a matching analysis. The theoretical framework combining system construction, service quality assessment and cooperative innovation planning process guides the exploration of cocreated value. From a different perspective, adding a third party to the verification mechanism will make the system architecture more comprehensive [57; 58]. Cooperating companies can obtain corresponding knowledge from partners and create value together [59]. Finally, companies can discover opportunities to create value sustainably during mutual turmoil. This study framework (Figure 4) can be divided into three parts. A dynamic innovation framework can be derived by combining each block.
## 4 Optimal Dispatch Model for BESS
### Constraints of the Optimal Dispatch Model  
#### 4.2.1 Operation Constraints of BESS  
Referring to [30], the operation constraints of BESS are established as follows. Equation (26) is the mutual exclusion constraint of charging and discharging states. Equations (27) and (28) are the charging and discharging power constraints. Equation (29) is the total power constraint. Equations (30) and (31) are the energy constraints.  
\[x^{\rm ES,\epsilon}_{k,t}+x^{\rm ES,f}_{k,t}\leq 1,\ \forall k\in N^{\rm E} \tag{26}\]  
\[0\leq P^{\rm ES,\epsilon}_{k,t}\leq x^{\rm ES,\epsilon}_{k,t}S^{\rm ES}_{k},\ \forall k\in N^{\rm E} \tag{27}\]  
\[0\leq P^{\rm ES,f}_{k,t}\leq x^{\rm ES,f}_{k,t}S^{\rm ES}_{k},\ \forall k\in N^{\rm E} \tag{28}\]  
\[P^{\rm ES}_{k,t}=P^{\rm ES,\epsilon}_{k,t}-P^{\rm ES,f}_{k,t},\ \forall k\in N^{\rm E} \tag{29}\]  
\[E^{\rm ES}_{k,t}=E^{\rm ES}_{k,t-1}+\eta^{\rm ES,\epsilon}P^{\rm ES,\epsilon}_ {k,t}\Delta t-\frac{P^{\rm ES,f}_{k,t}\Delta t}{\eta^{\rm ES,f}},\ \forall k\in N^{\rm E} \tag{30}\]  
\[0\leq E^{\rm ES}_{k,t}\leq E^{\rm ES}_{k},\ \forall k\in N^{\rm E} \tag{31}\]  
where \(x^{\rm ES,\epsilon}_{k,t}\) and \(x^{\rm ES,f}_{k,t}\) are the charging and discharging states of the BESS \(k\) at period \(t\), respectively; \(P^{\rm ES}_{k,t}\) is the net power of the BESS \(k\) at period \(t\); \(S^{\rm ES}_{k}\) and \(E^{\rm ES}_{k}\) are the power and capacity of the BESS \(k\), respectively; \(E^{\rm ES}_{k,t}\) is the capacity of the BESS \(k\) at period \(t\); \(\eta^{\rm ES,\epsilon}\) and \(\eta^{\rm ES,f}\) are the charging and discharging efficiencies of the BESS, respectively.
## 2 Materials and Methods
### Fire Inventory from NCAR (FINN)  
Satellite-derived data is from FINN version 2.5 [15; 16] in our study. FINN version 2.5 employed a spatial clustering algorithm to merge detected active fires since different active fires from satellites can correspond to a single fire event. Additionally, the clustering algorithm is utilized to combine fire detections from VIIRS and MODIS. FINN utilizes the combined outputs to estimate the burned area [16]. Since VIIRS has a higher resolution (375 m) than MODIS (1 km), FINN version 2.5 can detect fires with smaller burned areas [29]. This is particularly important for estimating emissions from prescribed burns, which are typical for low intensity and/or occur as understory burns. In this study, we extract all wildland fires from FINN version 2.5 that were detected in the southeastern United States (as defined in Figure S25) from 2013 to 2020. The burned area of prescribed fires is separated by a burn-type differentiation algorithm and compared with the burned area reported by the permits.
# Advances, Progress, and Future Directions of Renewable Wind Energy in Brazil (2000-2025-2050)
## 3 Wind Farm Projects: Experiences in Brazil
### Offshore Wind Energy Projects--Future Facilities (2025-2050)  
As described above, offshore wind energy projects are expected to complement the capacity of existing onshore wind energy projects in Brazil. To this end, the plan is to complement the existing electrical transmission infrastructure, including transmission towers, onshore and offshore electrical substations, and power cables [27] (Figure 21). Brazil's future mega-electric infrastructure will supply energy to coastal and inland communities. According to Figure 22 below, the Brazilian regions with the greatest potential and offshore wind farm projects submitted for environmental assessment are (i) the Northeast region with the states of Rio Grande do Norte and Bahia; (ii) the Southeast region with the state of Rio de Janeiro; and (iii) the South region with the state of Rio Grande do Sul [27]. Figure 21: Electrical infrastructure projected in Brazil for onshore and offshore wind energy. Figure 22: Offshore wind projects in different regions of Brazil. In the following Table 14, the largest offshore wind farm projects with a process of Environmental Impact Assessment (EIA), which are in progress in 2025, are shown [33]:  
Considering Table 14 above, the project with the largest projected installed capacity is around 6500 MW. In most cases, the installed turbine capacity exceeds 10 MW, and the turbine hub heights are expected to be around 150 m.  
Offshore wind energy has positioned itself as one of the renewable energy sources with the greatest growth potential globally. Brazil, with its extensive coastline and favorable climatic conditions, is no exception. Analyzing hourly wind speed profiles and capacity factors in different regions of the country is essential for optimizing the design and location of future offshore wind farms [27]. Northeast Brazil is known for its high levels of solar irradiation and consistent winds, making it one of the most promising areas for wind energy onshore and offshore. Hourly wind speed profiles in this region show a tendency toward higher speeds during the afternoon and evening, with an annual average of 8 and 10 m/s in coastal areas and offshore platforms. These winds are driven by the trade winds, which are consistent throughout the year [27] (Figure 23).
# Time-Specified Adaptive Robust Control Framework for Managing Nonlinear System Uncertainties
## 2 Background and Related Work
### Comparison with Recent SMC Approaches  
Recent work on \"Improved Sliding Mode Tracking Control for Spacecraft Electromagnetic Separation Supporting On-orbit Assembly\" [50] has demonstrated significant advancements in SMC techniques for specific aerospace applications. This approach uses a boundary layer technique to reduce chattering and implements adaptive gains to handle uncertainties. While effective, it differs from our prescribed-time approach in several key aspects:  
Convergence guarantees: The improved SMC approach provides asymptotic stability, whereas our prescribed-time controller guarantees convergence at exactly the specified time.  
Parameter adaptation: Both approaches implement parameter adaptation, but our method specifically designs the adaptation law to ensure the prescribed-time convergence property is maintained.  
Post-convergence behavior: Our approach explicitly addresses system behavior after the prescribed time, ensuring continued stability without additional control redesign.  
Robustness implementation: While both approaches incorporate robust terms, our method combines saturation and hyperbolic tangent functions to simultaneously achieve chattering reduction and precise tracking.  
Application scope: The improved SMC is specifically optimized for spacecraft operations, whereas our approach provides a more general framework applicable to a broader class of nonlinear systems.
## 4 Security in 5G-Enabled D2D Communications Network
### Proposed D2D Security Solution  
After gaining access to network and services, the UE can request other UEs in proximity to share services following D2D communications process as specified in [13]. We propose a D2D level security solution to address the mentioned security issues to enable UEs access, cache and share content securely in different D2D communications scenarios of 5G such as roaming, non-roaming, in coverage and out of coverage shown in Figure 3 and explained as follows:  
1. Scenario A: Intra-operator, non-roaming and same cell, both UEs are in cellular coverage of gNB, same HN and subscribed to the same MNO.
2. Scenario B: Intra-operator, non-roaming and different cells, both in HN coverage, subscribed and served by the same operator but in different cells.
3. Scenario C: Inter-operators and roaming, one UE is in HN, while the other UE is roaming in visited network, and both users subscribe to different operators.  
Scenario D: Inter-operators and out of coverage, both UEs are out of coverage and can share content without involving their HNs. It also applies to emergency situations. Both UEs subscribed to different operators.  
The D2D security solution consists of DDSec and DDACap protocols, which intend to provide authentication and authorization to access, cache and share data between two UEs in different D2D scenarios. Additionally, a UE will be able to transfer its access permissions and abilities to another UE. DDSec is intended for network-assisted D2D communications, while DDACap is intended for non-network-assisted direct D2D communications.  
The \(UE_{i}\) is free to choose any publicly known information as its ID such as public key or IP address based on IBE scheme [51]; this can be used together with another form of ID such as the Generic Public Subscription Identifier (GPSI) from primary authentication [1; 50] and external ID (EID) and federated ID (FID) from secondary authentication and authorization procedures [38; 39], respectively. This eliminates the need for distributing keys as in the traditional AKA or Public Key Infrastructure (PKI) and use of Subscriber's Permanent Identifier (SUPI) outside the HIN as specified in 5G standard [1]. For integrity, privacy and uniqueness, the UE uses a pseudonym ID in D2D communications calculated from the UE's preferred choice of ID, \(hash(x(ID))\), where \(x\) is the public key of UE, randomly generated using hash function \(hash(PK_{UE_{i}}(ID))\)[40]. The relation between UE and data is determined by pseudonym IDs of the UE and the data which become the logical address \(nAdd\) of the UE with the data. The data's unique ID \(D1\) is generated by hashing the data name \(hash(URI)\). The \(UE_{i}\) would first hash both the UE's pseudonym ID and \(D1\) as \(hash(UEID,D1)\) and then publish it as its logical address. The keys and IDs are generated using ECIES [52] according to 5G standard [1].
# Toward a Comprehensive Understanding and Evaluation of the Sustainability of E-Health Solutions
## 6 E-Health Sustainability Assessment Method
### Procedures of Sustainability Assessment  
The designed sustainability assessment method in this research was found to be aligned with the work of [PERSON] et al. [49]. The e-health sustainability model is comparable to the reference model of the TCMMI framework, in which the quality attributes in their work correspond with the sustainability aspects, and the product quality dimensions correspond with sustainability dimensions. Hence, we designed the sustainability assessment method to be aligned with previously published work (TCMMI) [49], and it is also aligned with the ISO/IEC 15504 standard (Guidance on performing an assessment) [66]. The sustainability assessment method propagates the scores of each level of the sustainability model to produce the overall sustainability level of the e-health solution. All assessment method procedures and steps are explained in the following subsections. Figure 6 presents the main phases of sustainability assessment procedures.
# Survey of Cybersecurity Governance, Threats, and Countermeasures for the Power Grid +
## 4 Cybersecurity Threats in Energy System OT Networks  
Cybersecurity threats in Energy System OT Networks may be categorized by the security services targeted by the attack. From a risk perspective, it is also particularly useful to order these services by priority. In OT networks, systems not only govern critical operational processes, such as manufacturing machinery, power generators and distributions systems, but may also be responsible for the safety of workers or, in the case of smart grids, perform time critical tasks to prevent cascading failures and facilitate a key service on which national security is built. A failure in the power grid not only puts people's lives at risk, but can have disastrous impact on everything from transportation to financial services, defense, and more. Such critical OT operations require quick system responses to perform as intended. The low latency requirements of these systems would therefore rely on constant availability. Even a momentary outage could cause a safety critical system to fail to respond within a defined time - with potentially fatal consequences. For this reason, OT Networks rank availability typically as their highest priority, followed by integrity and confidentiality. Therefore, we categorize attack impacts in terms of security services in the following order of priority: Availability, Integrity, and Confidentiality.  
This is in contrast to IT networks, where confidentiality and data integrity would be of higher priority than a momentary lapse of availability. In IT networks, data is the important commodity. Hence, protecting that data is more important than a temporary lapse in availability. The focus of IT networks are aimed at an organization's day to day operation, such as the the storage of information or completion of automated processes. Provided that the integrity and confidentiality of the systems are maintained, short outages  
Figure 3: Typical Power SCADA Architecture.  
will have little impact on an organization. Most tasks that would fail due to outages can be completed once systems become available again.  
In some cases, an attack may impact more than one security service. In such cases, the attack will only be described in the higher priority service category but the other services that are potentially impacted will be mentioned as well. For instance, although a FDIA is primarily an attack against data integrity, it may potentially impact availability [13]. Since availability is the higher priority category, it will be described in the Availability Threats subsection and not in the Integrity Threats subsection.
# Shifting from Conventional to Organic Filter Media in Wastewater Biofiltration Treatment: A Review
## 3 Filter Media Selection on the Efficiency of Biofililtration  
Filter media selection plays an important role in the biofilter as it is one of the main components besides the backwash system and aeration process. Moreover, the oxygen-substrate transfer rate and hydraulic characteristics are affected by the filter media selection [32]. Granular activated carbon (GAC) [53], quartz sand [54], and anthracite [55] are the frequently used conventional filter media in biofilter systems. In recent years, there is a rising interest among researchers in developing and utilizing organic or waste materials as the filter media in the biofiltration treatment process. According to [PERSON] et al. [56], several countries, such as the United States and Canada, have applied organic materials in a real-scale decentralized biofiltration treatment system due to several advantages including low operational and construction costs, low maintenance, and does not require a highly-skilled operator to operate the system. Moreover, the utilization of agricultural waste materials as biofilter media will also reduce solid waste residues throughout the world.
## 3 System Design
### Neighbor Discovery Protocol  
As mentioned in the Introduction section, the node number of neighbors could be usually obtained from the built-in routing protocol. Thus, no extra components are needed for discovering the node neighbors, encouraging this approach to be readily embedded in existing WSAN solutions.  
However, that is not the case for the deployment platform that is to be analyzed in this paper. The SunSPOT library provides two routing protocols: _Ad hoc_ On-demand Distance Vector (AODV) [18] and Link Quality Routing Protocol (LQRP) [19]. LQRP is indeed a modified version of AODV, where the next hop in a route is selected by the link quality indicator (LQI) instead of the number of hops. Both of these protocols keep a neighbor list. However, its information is only updated on two occasions:  
* Whenever the validity of an entry expires.
* Whenever a new route request is performed.  
This is not enough for the purpose of the self-adaptive controller described so far, as the information will not be updated unless a new route discovery is requested.  
Therefore, a neighbor discovery protocol has been carried out. This does not replace the built-in routing protocol in the SunSPOT platform. Indeed, both protocols run in parallel.  
The neighbor discovery protocol in question is quite simple, conceived just for testing the adaptability of the controller. In any case, there is a whole area of study focused on the exploration of neighbor discovery protocols for wireless sensor networks, either passive, active or both [20; 21; 22; 23].  
The accomplished neighbor discovery protocol is an active three-way one, as shown in Figure 6. The node that wants to update its neighbor list starts the handshake by broadcasting a NEIGHBOR  
Figure 5: Activity diagram for the component _PowerScalingController_.  
REQUEST message. The nodes that receive the message issue a unicast NEIGHBOR RESPONSE message. Upon receiving this message, the originating node knows that it is able to reach the responding one and receive the messages, so it can be considered as a neighbor and, thus, added to the list. It then also issues a unicast NEIGHBOR ACK message to acknowledge the other node as a neighbor. At this point, the second node knows that it has been able to reach the originating one, as well as getting its messages. Therefore, it can also be added to its own neighbor list.  
The class diagram for the neighbor discovery protocol is illustrated in Figure 7.  
Figure 6: Sequence diagram for the neighbor discovery protocol.  
Figure 7: Class diagram for the neighbor discovery protocol.