esa-sceva/satcom-chunk-collection huggingface dataset:
# Flickering-Free Distance-Independent Modulation Scheme for OCC
## 6 Results
### Simulation's Results  
As a result of the simulation, 52,500 frames were obtained. Figure 11 illustrates two of the obtained binary images for \(\alpha=10\); \(f_{\mathrm{SW}}=300\) Hz and \(\alpha=50\); \(f_{\mathrm{SW}}=1500\) Hz, where we must highlight two things: there are at least 19 strip lines, and each band has at least 7 pixels. In fact, since the minimum \(\alpha\) value was set to 10, in all the frames at least 18 complete light bands were projected on the image. In the case of the bands' pixel width, with \(\alpha=70\), the frames showed values between four and five pixels, while lower frequency multipliers (\(\alpha<64\)) produced frames with bands of at least five pixels that can be easily extracted from longer distances. With this result, the proposed limits for the frequency multiplier have been validated.  
On the other hand, as can be seen in Figure 12, for all the selected values of the frequency multiplier \(\alpha\), the obtained PCC values of the consecutive frames for the selected frequencies corresponding to the data symbols were within the proposed ranges, without overlapping. In this way, the signals that correspond to the different symbols can be separated adequately. These results validate the calculated switching frequencies and therefore Equation (13). Nevertheless, this simulation was done without external interference, and for a short distance. For validating the modulation scheme, a field experimentation is needed. In this way, the impact that the environmental conditions, including external interference such as other illumination sources, the camera's frame rate fluctuations and changes in the distance could have over the modulation scheme can be tested.  
As an example of the system, a sample of the practical ROIs extracted from consecutive frames (obtained from the experimental videos) which represented the four possible transmitted symbols are presented in Figure 13. As can be observed, for all the switching frequencies the calculated PCC confirmed the proposed value ranges previously validated with the simulations. For example, in the case of the symbol \"10\" (\(f_{\text{SW}}=\alpha f_{\text{CAM}}=375\) Hz), the images are almost equal and the calculated PCC is greater than 0.7 (\(PCC=0.994>0.7\)). In the same way, for the symbol \"11\" (\(f_{\text{SW}}=(\alpha+1/6)f_{\text{CAM}}=379\) Hz), the light bands are slighted delayed and the calculated PCC is within the proposed range (\(0<PCC=0.535<0.6\)). Similarly, from the frames that represent the symbol \"01\" (\(f_{\text{SW}}=(\alpha+1/3)f_{\text{CAM}}=383.5\) Hz), the light bands are significantly delayed and therefore calculated PCC is within the proposed range (\(-0.6<PCC=-0.219<0\)). Finally, in the case of the symbol \"00\" (\(f_{\text{SW}}=(\alpha+1/2)f_{\text{CAM}}=387.5\) Hz), the obtained images are inverted and the calculated PCC is less than \(-0.7\) (\(PCC=-0.992<-0.7\)).  
Figure 12: Pearson Correlation Coefficient vs. switching frequency.
# Security Scheme Based on Parameter Hiding Technic for Mobile Communication in a Secure Cyber World
## 4 Security Analysis
### Mutual Authentication  
In the proposed security scheme, methods are being provided that UE, eNB, and MME can mutual authenticate at least twice.  
In the first challenge response process, UE and MME can mutual authenticate each other by generating and transmitting a series of bit strings through previously shared hash functions. Subsequently, MME can verify UE and eNB again using SSKUE and SSKeNB in restoring process of secret value, S.  
In the second challenge response process, UE can verify eNB because UE uses r0 bit sequences that are not used in the first challenge response and previously shared S-BOX. Additionally, eNB can verify UE through the challenge response process and verify MME as well because the authentication about r0 sequence is executed.
# Laboratory Evaluation and Finite Element Modeling of SBS and Basalt Fiber Modified Mixtures
## 3 Finite Element Modeling
### Simulation Results  
Simulations were performed for a total of 50,000 passes for each mixture type in ABAQUS. As shown in Figure 7, both the deformed zones and upheaval zones as a result of extensive rutting damage can be observed for the 7% SBS mixture. Von Mises stresses were determined under different depths of concentrated loading for both asphalt mixtures. The 7% SBS mixture shows higher stress concentrations when compared with the 3% SBS with 0.3% BF mixture.  
Von Mises stresses were calculated for both mixture types and calculated at depths of 50 mm, 450 mm and 800 mm, as shown in Table 9. The highest stress concentrations are shown with the 7% SBS mixture, with a magnitude of 2.5% at 50 mm and 0.023 at 800 mm. However, the SBS 3% with 0.3% BF mixture shows the least amount of stress concentrations, with magnitudes of 2.31 at 50 mm and 0.019 at 800 mm.
## 2 Radio Communication Technologies
### Applications of LPWAN  
The coverage of the cell networks in rural areas is usually poor. Additionally, deploying WiFi or similar infrastructure may be expensive. Such terrains are challenging and the sensor nodes are scattered over large areas. LPWANs have evident applications in such situations, especially for industrial farming and agriculture applications. It is worth mentioning that today's most public gateways of LoRaWAN are installed in urban regions. In metropolitan environments, the LPWAN use cases vary from flood monitoring and tracking weather in weather stations to monitoring infrastructure and buildings, as well as in smart parking, smart metering and lightning, and even the management of waste. The first LPWAN introduced for IoT in 2009 was Sigfox, after which new LPWAN technologies have emerged. Weightless and Ingenu Random Phase Multiple Access (RPMA) are LPWAN technologies that take advantage of unlicensed radio bands for long-range transmission and low-power communication [9].
# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 3 A Brief Overview of 5G
### 5G Network Architecture in Brief  
The generic architecture for 5G is depicted by Figure 2. The main functional entities are the following.  
* User Equipment (UE)--is the actual end-device;
* (Radio) Access Network ((R)AN)--can include various technologies; the next-generation, 5G-specific radio controller and transceiver is the gNB;* User Plane Function (UPF)--provides packet routing, forwarding, and inspection; it is the anchor point for mobility management, and it is the user plane part of policy rule enforcement--among other roles;
* Data Network (DN)--e.g., operator services, Internet access or 3 rd party services;
* Authentication Server Function (AUSF)--supports authentication for 3 GPP access and untrusted non-3 GPP access;
* Access and Mobility Management Function (AMF)--is the termination point for non-access stratum ciphering and integrity protection, provides access authorization and authentication functions, manages mobility, registration, connections, reachability, and many more;
* Session Management Function (SMF) is responsible for various session-related control functions, including session establishment, modification, and release, including tunnel maintenance, among others;
* Network Slice Selection Function (NSSF)--selects the slice instances to serve the UE, and determines the related NSSAI (Network Slice Selection Assistance Information);
* Network Exposure Function (NEF)--provides exposure of capabilities and events, secure provisioning of information from external applications, translation of internal-external communication, and many more;
* Network Repository Function (NRF)--supports the service discovery function and maintains the Network Function profile;
* Policy Control Function (PCF)--supports unified policy framework to govern network behavior, provides policy rules and accesses policy information at UDR for relevant policy decisions;
* Unified Data Management (UDM)--is responsible for managing user data for various services, including access authorization, generation of authentication credentials, user identification handling, subscription management, SMS management, among others;
* Application Function (AF)--interacts with the core network in order to provide services such as application influence on traffic routing, or interactions with the policy framework for policy control.
## 5 Discussion
### Limitations  
This study used NMI and ND data collected from Japanese drivers in Japan. Therefore, the scenario components identified by the proposed method may reflect the characteristics of the traffic conditions and environment in Japan, as well as the driving characteristics of Japanese people. Thus, one limitation is that such components may not be directly applicable to other countries or regions. However, by applying the proposed method to the driving databases of different countries or regions, the critical environmental factors that must be considered in each country or region can be identified.  
Although the objective was to identify critical scenario components for ADSs, the method proposed in this study utilized a driving database driven by human drivers, not ADSs. We adopted this database because of the lack of accident and near-miss incident data on ADSs in urban environments. Therefore, not all of the identified components may be critical for ADSs. However, it can be assumed that there is a certain overlap between the behavior of human drivers and ADSs, and the proposed method can be effective to a certain extent, as shown in this paper.  
Another limitation is that the scenario was limited to one in which an ego vehicle makes a right turn at a signalized intersection. Therefore, future research should apply the methodology used in this study to other scenarios and examine its usefulness.
## 3 Results  
The three tested groups showed different results of bacterial leakage, however, none of the groups prevented bacterial leakage completely. Table 1 shows the results of bacterial leakage for each group after exposure to _E. faecalis_ over a period of 60 days and the average duration (days) until bacterial leakage. The lowest number of leaked samples was detected in the MTA Fillapex (MF) group (30%), while the highest numbers of leaked samples were detected in the CeraSeal bio-ceramic sealer (CE) group (80%) and the AH Plus (CO) group (50%). No significant difference was detected among the study groups (\(p\) = 0.0609).  
Figure 1: Modified bacterial leakage test model.  
The results of the average number of days until bacterial leakage are demonstrated in the survival curves of the three tested groups (Figure 2). The MTA Fillapex (MF) group had an average of 47 days (SD = 20.9) and showed the longest time until bacterial leakage. The CeraSeal bio-ceramic sealer (CE) group showed the fewest number of days until bacterial leakage among all groups with an average of 25.3 days (SD = 20.3). The AH Plus (CO) group had an average score of 45 days (SD = 20.9).  
The SEM micrographs revealed the sealer gaps among the tested groups from a qualitative analysis of the bacterial leakage after 60 days. The MTA Fillapex bio-ceramic sealer (MF) with the single-cone technique had a better adaptation to the root canal wall as shown in Figure 3A-C. This was followed by the AH Plus sealer (CO) with the lateral condensation techniques shown in Figure 3G-I. Finally, the CeraSeal bio-ceramic sealer (CE) with the single-cone technique showed the least adaptation to the root canal wall at the sealer-dentin interface, as shown in Figure 3D-F.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Group** & **Percentage of Leaked Samples (\%)** & **Average Days (SD)** & _p-Value_ \\ \hline MTA Fillapex (MF) & 30\% & 47.0 (20.9) & \\ \hline CeraSeal sealer (CE) & 80\% & 25.9 (20.3) & 0.0609 \\ \hline AH Plus (CO) & 50\% & 45.2 (20.9) & \\ \hline \end{tabular}
\end{table}
Table 1: The results of bacterial leakage over a period of 60 days and the average duration (days) until bacterial leakage.  
Figure 2: (**A**) The survival curves of the 3 tested groups expressed in the number of incubation days before bacterial leakage. (**B**) A scattered dot plot for the average days till the bacterial leakage in each sample in all groups. The dotted line represents the mean of each group and error bars represent the SD (MF: MTA Fillapex, CE: CeraSeal bio-ceramic sealer, CO: AH Plus).
# An Overview on Push-Based Communication Models for Information-Centric Networking
###### Abstract  
Information-centric networking integrates by design a pull-based model which brings in advantages in terms of control as well as of in-network caching strategies. Currently, ICN main areas of action concern content distribution and IoT, both of which are environments that often require support for periodic and even-triggered data transmission. Such environments can benefit from push-based communication to achieve faster data forwarding. This paper provides an overview on the current push-based mechanisms that can be applied to information-centric paradigms, explaining the trade-off associated with the different approaches. Moreover, the paper provides design guidelines for integrating push communications in information-centric networking, having as example the application of this networking architecture in IoT environments.  
ICN; IoT; communication architectures and protocols +
Footnote †: journal:  
1
## 4 Modern Imaging Technologies
### CBCT: A Foundation for 3D Visualization  
The introduction of CBCT marked a transformative advance in dental imaging, providing high-resolution 3D reconstructions of maxillofacial anatomy. Unlike conventional radiographs, CBCT allows clinicians to evaluate structures from multiple angles, significantly improving diagnostic accuracy in fields such as implantable, orthodontics, endodontics, and maxillofacial pathology [9; 10]. Metrics such as spatial resolution and artifact suppression highlight the superiority of CBCT over conventional panoramic radiography. CBCT offers resolution thresholds ranging from approximately 75 to 200 \(\upmu\)m, compared to 300-500 \(\upmu\)m for 2D panoramic imaging. This enhanced resolution is particularly critical for detecting fine anatomical details such as accessory canals, root fractures, or cortical bone defects that may be missed on conventional radiographs. Additionally, CBCT systems employ sophisticated image reconstruction algorithms that minimize artifacts from overlapping structures or metal restorations, improving diagnostic confidence and reducing false positives [9; 10].  
In implantology, CBCT provides essential information on bone density, nerve pathways, and sinus anatomy, helping to ensure accurate and safe implant placement. In orthodontics, it facilitates the evaluation of impacted teeth and craniofacial anomalies. For endodontics, CBCT reveals hidden canals and periapical lesions that are often not seen on 2D images. It also plays a key role in the evaluation of the temporomandibular joint (TMI) by visualizing bony changes associated with conditions such as ankylosis, erosion, and osteoarthritis [11; 12]. In oral and maxillofacial surgery (Figure 3), CBCT supports comprehensive preoperative planning, intraoperative navigation, and postoperative evaluation of fractures, pathologies, and reconstructive procedures [13; 14].  
Figure 3: CBCT imaging of a 17-year-old patient presenting with a cystic lesion in the mandibular body. (**A**) Panoramic reconstruction showing the overall dentition and determining the full mesiodistal extent of the lesion, aiding in pre-surgical planning. (**B**) Sagittal view illustrating the lesion’s proximity to the tooth root and cortical borders. (**C**) Coronal view displaying buccalingual expansion and lesion involvement. (**D**) The 3D imaging provides a view of the lesion about the surrounding anatomical structures, facilitating planning of the surgical approach and risk assessment for nearby neurovascular elements. Image courtesy of [PERSON].  
Despite its advantages, CBCT is not without limitations. The technology involves higher costs and infrastructure demands compared to conventional radiography, limiting its adoption in resource-constrained settings. Radiation exposure, though reduced through low-dose protocols, remains a concern for routine imaging, especially in pediatric or follow-up cases. Operator training and standardization of image interpretation are also necessary to ensure consistent diagnostic outcomes [10].
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Sea Surface Temperature (SST)
#### 2.3.2 Microwave Radiometer  
The cloud penetrating capability of microwave radiometers enables them to provide SST data regardless of the cloud cover and atmospheric aerosols [98, 99, 153]. At long wavelengths, where \(hc\ll\lambda kT\), the spectral radiance can be formulated using the Rayleigh-Jean Law (Equation (4)) instead of Planck's Function [1]:  
\[T=\frac{\lambda^{4}}{2 kc}B_{\lambda}(T) \tag{4}\]  
where  
\[B_{\lambda}(T)=\varepsilon(\lambda)T \tag{5}\]  
in which \(T\) is the thermodynamic temperature; and \(\varepsilon\) denotes emissivity. When radiation passes through the atmosphere, some portions of it are absorbed, scattered, and emitted.  
Figure 6: SST maps at 01 UT on 2 May 2003 generated from (**a**) ASTER image with 90 m spatial resolution and (**b**) MODIS data with 1 km spatial resolution [100].  
Consequently, the measured BT by radiometers can be obtained based on the following equation [98]:  
\[B_{\lambda}(T)=\underbrace{\varepsilon(\lambda)T_{S}e^{-\tau}}_{\text{surface emission}}+\underbrace{T_{Up}}_{\text{atmospheric emission}}+\underbrace{(1-\varepsilon(\lambda))T_{Down}e^{-\tau}}_{\text{ reflected atmospheric emission}}+\underbrace{(1-\varepsilon(\lambda))T_{SP}e^{-\tau}}_{\text{reflected space emission}} \tag{6}\]  
where \(T_{S}\), \(T_{Up}\), \(T_{Down}\) and \(T_{SP}\) are the temperatures of the surface, upward atmosphere, downward atmosphere, and space, respectively. The RT models and statistical algorithms are typically employed for deriving SST from microwave radiometer measurements [98]. These models require environmental data (e.g., SST, atmospheric profiles, and wind speed/direction) and radiometer information (e.g., frequency, polarization, azimuth, and incidence angle) for modeling the TOA BTs [98]. The performance of such algorithms is dramatically reduced by the presence of instrument calibration errors and inaccurate environmental data [154]. However, the implementation of the statistical algorithms is much simpler, and calibration errors can be compensated in these methods [155]. Generally, the predicted SST by both techniques is negatively affected by variations in wind speed and foam coverage [153].  
Different microwave radiometers have been launched and employed for SST estimation. AMSR2, AMSR-E, SMMR, Windsat, GPM Microwave Imager (GMI), and TRMM Microwave Imager (TMI) are well-known microwave radiometers for SST estimation. The Remote Sensing Systems (RSS) organization generates the SST products provided on a daily, 3-days, weekly, and monthly basis using the TMI, AMSR-E, WindSat, AMSR2, and GMI datasets.
# Smart System for the Optimization of Logistics Performance of the Pruning Biomass Value Chain
## 2 Biomass Supply Chain and Its Actors
### Actors in the Pruning Biomass Supply Chain and Their Role in the Smart System  
For the pruning supply chain, four actors of the biomass supply chain were considered:  
**Producers:** Farmers are primary producers of biomass. They produce, harvest, process (chipping, baling) and sell biomass. An account for access to the Information platform could be created for each farmer participating in the system, so that they can put all the necessary information regarding their products.  
**Traders:** The biomass traders buy, store, and sell the biomass. Therefore, traders who own storage and fulfil quality control criteria could be registered and get an ID number from system to gain access to the platform for information sharing.  
**Transporters:** Biomass transporters or logistics operators are those who handle biomass procurement, source identification, and a proper way of delivering to end users with the desired characteristics. These logistics operators can play complex roles by interacting with different actors.  
**Consumers:** Consumers are end-users who can order biomass product needed. They can have their own account to access the Information platform perform online order and receive information. They specify the quality and quantity of biomass required at power plant. They also determine and control the state of deliveries. Consumers can also follow information flow continuity regarding their orders and deliveries.
# AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection
## 2 Introduction to Related Technologies
### Blockchain
#### 2.1.2 Consensus Algorithm
However, the delegate selection mechanism may result in power centralization, and there is a risk of collusion among delegates, which can weaken decentralization. As Algorithm 1 shows, the DPoS consensus mechanism operates through four main phases. Initially, all stakeholders (\(S\)) vote to select a small group of delegates (\(D\)), typically much fewer in number than the stakeholders, who are entrusted with the task of block production. During each round, delegates take turns to collect transactions, produce blocks, sign them, and broadcast them to the network. Each received block is validated by other nodes to ensure integrity and correctness. If any delegate misbehaves, such as missing blocks or producing invalid data, stakeholders have the right to revoke their delegation through a new voting process. This approach ensures efficient, scalable, and democratic block validation while significantly reducing the number of nodes involved in the consensus process compared to traditional proof-of-work mechanisms. (4) PBFT  
PBFT achieves consensus through multiple rounds of voting among a limited number of nodes, making it suitable for high-trust private blockchain environments such as Hyperledger Fabric. Its main advantages are low latency and high throughput, making it particularly well suited for financial blockchain applications. PBFT does not involve mining or computational races. As a result, its energy consumption is extremely low, typically less than 0.0001 TWh annually, especially in enterprise deployments (IBM Blockchain Whitepaper, 2022). However, PBFT lacks economic incentives and relies on the honesty of participating nodes. If a significant number of malicious nodes are present, the consensus process may be compromised. The PBFT consensus process is illustrated in Algorithm 2. Initially, the system defines \(N=3f+1\) nodes to tolerate up to \(f\) faulty nodes. The primary node is selected based on the current view number. The consensus proceeds through several phases:  
1. Client Request: A client initiates the process by signing and sending a transaction request to the primary node. 2. Pre-Prepare Phase: Upon verifying the client's request, the primary node broadcasts a PRE-PREPARE message containing a digest of the request. 3. Prepare Phase: Replica nodes validate the PRE-PREPARE message and broadcast PREPARE messages to all other nodes. 4. Commit Phase: When a node receives \(2f\) matching PREPARE messages, it broadcasts a COMMIT message. 5. Reply Phase: After collecting \(2f+1\) matching COMMIT messages, the node executes the operation and sends a reply to the client. 6. View Change: If a timeout occurs or the primary is suspected to be faulty, nodes initiate a VIEW-CHANGE protocol to select a new primary node. This multi-phase voting ensures consensus integrity even in the presence of faulty or malicious participants.