esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### LDPC Decoding Results  
For the LDPC decoding, the LDPC-GNN was evaluated using the same channel models as we utilized for the CNN-based OFDM receiver demodulation evaluation. The inputs for this GNN are the LLR outputs of the traditional receiver, with the LLRs presenting the log-ratio of the probability of the demodulated bit being a 1 versus a 0. LDPC rate matching is performed on the input to the model. The largest axis of the adjacency matrix is utilized for rate matching to trim padding bits before inference. Similar to the previous CNN model, a hard decision was made for the probability of 0.5 or larger being designating a 1. For evaluation of the LDPC model, the 16-QAM dataset was generated for the BP, Layered-BP, Normalized Min-Sum, and Offset Min-Sum decoding algorithms, the results of which can be seen in Figure 6.  
From these results, we can observe that the Min-Sum algorithms perform worse at SNRs between 5 and 10 dB compared to the Belief Propagation algorithms and our GNN models, but slightly outperform both in the SNR range of 12-25 dB. The algorithms are on par in the range above 25 dB. We can observe that the GNN operation closely matches the BP algorithms, and varying the parameter size of the model does not appear to impact the BER performance. This matches previously reported results, showing increased performance of BP algorithms up to 10 iterations, and improvement with increasing SNR [18]. Regarding training time, the larger 80 k parameter model converged faster than the 20 k model, but both produced these results after 2 epochs of training.  
Figure 4: CNN models—combined Rician results.
# Application of Date Palm Tree Branch-Based Activated Carbon for Aqueous Toxicity Reduction
## 4 Conclusions  
The results from the present investigation show that the application of high-specific-surface-area granular activated carbon (GAC) produced from an agricultural waste, i.e., date palm tree branches, can be successfully used for the removal of aqueous pollutants phenol, \(p\)-cresol, and copper. The adsorption equilibrium results from all pollutants seemed to follow the Langmuir adsorption isotherm model. The adsorption results following the Langmuir isotherm indicate a monolayer coverage along with bonding with specific surface sites. The maximum \(p\)-cresol adsorption capacity was noted at 83 mg/g. On the other hand, the maximum phenol adsorption capacity was determined to be 56.8 mg/g, which indicated that \(p\)-cresol adsorbs better on the GAC surface. However, copper showed a comparatively lower adsorption capacity of 9 mg/g. Also, the pH-dependent adsorption results for phenol/\(p\)-cresol and copper indicated to follow an anionic- and cationic-type  
Figure 6: (**a**) Langmuir adsorption isotherm parameter fitting for copper adsorption modeling. (**b**) Results of copper removal vs. pH (GAC: 3000 mg/L; initial copper concentration: 50 mg/L).  
adsorption behavior, respectively. Additionally, the adsorption kinetics data for phenol and \(p\)-cresol indicated a first-order rate. In summary, the results from the present study indicate that high-specific-surface-area granular activated carbon produced from date palm tree branches can be successfully used to remove phenol, \(p\)-cresol, and copper from the aqueous phase.  
M.V. and M.A.-S. contributed towards all aspects including conceptualization, experimental, and writing the manuscript. All authors have read and agreed to the published version of the manuscript.  
The authors are thankful to the Deanship of Research at KFUPM (FT161012) for their support.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors are thankful to the King Fahd University of Petroleum and Minerals (KFUPM) for providing the lab facilities and to the Deanship of Research at KFUPM for their support.  
The authors declare no conflicts of interest.
# HBIM: Background, Current Trends, and Future Prospects
## 5 Applications and Data Management  
Since the early emergence of HBIM, most scientific studies and publications have focused on four main areas: data collection methods and tools, 3D modeling, the establishment of procedures and workflows for infrastructure management, and, to a lesser extent, interaction with external software for additional analyses (mainly structural). Although these topics seem to cover a broad range, there is a noticeable lack of information on the actual application of the developed HBIM models in practice.  
Despite the growing popularity of HBIM, significant shortcomings have been identified that jeopardize their practical usefulness, and, in many cases, there is little evidence that the HBIM models developed during the last years are currently being used in practice ([PERSON], 2017). Several HBIM models were created without a clear and well-defined purpose, rendering them ineffective in addressing the practical needs that arise during the management of the building. Frequently, the required data are unavailable, either because it was not collected or was improperly stored, and, in some cases, the software used or the way the geometry is defined is incompatible with the current needs. In some other cases, HBIM models were created during a specific project with a limited scope (e.g., a specific restoration project), so they are abandoned after serving that function ([PERSON], 2017; [PERSON] et al., 2018).  
These issues could be addressed, or at least mitigated, by properly defining the objectives of the HBIM model from its initial conception, allowing for the selection of necessary data to fulfill its intended purpose, while at the same time developing models flexible enough to accommodate new data for potential future applications.
_Article_  
**Quantum Confinement Effect and Photoenhancement of Photoluminescence of PbS and PbS/MnS Quantum Dots**  
**[PERSON] 1, [PERSON] 1,2\({}^{\text{\textregistered}}\), [PERSON] 2,3, [PERSON] 4 and [PERSON] 1,***  
1 Department of Physics, Faculty of Science, Universiti Putra Malaysia, UPM Serdang,  
Selangor Darul Ehsan 43400, Malaysia; [EMAIL_ADDRESS] (M.S.Z.);  
[EMAIL_ADDRESS] (J.Y.C.L.)
2 Institute of Advanced Technology, Universiti Putra Malaysia, UPM Serdang,  
Selangor Darul Ehsan 43400, Malaysia; [EMAIL_ADDRESS]
3 Department of Chemistry, Faculty of Science, Universiti Putra Malaysia, UPM Serdang,  
Selangor Darul Ehsan 43400, Malaysia  
4 Institute of Microengineering and Nanoelectronics, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia; [EMAIL_ADDRESS]
* Correspondence: [PERSON]ESS]  
Received: 29 July 2020; Accepted: 15 August 2020; Published: 9 September 2020  
**Featured Application: Colloidal PbS quantum dots (QDs) that show a strong quantum confinement effect have potential towards photovoltaic application. Promising constant photoluminescence (PL) peak energies of PbS and PbS/MnS core shell QDs could be advantageous particularly for biological marker application.**  
**Abstract:** The quantum confinement effect and photoenhancement of photoluminescence (PL) of lead sulphide (PbS) quantum dots (QDs) and lead sulphide/manganese sulphide (PbS/MnS) core shell QDs capped with thiol ligands in aqueous solution were investigated. From PL results, the presence of MnS shells gives a strong confinement effect which translates to higher emission energy in PbS/MnS core shell QDs. Increasing MnS shell thickness from 0.3 to 1.5 monolayers (ML) causes a blueshift of PL peak energies as the charge carriers concentrated in the PbS core region. Enhancement of the PL intensity of colloidal PbS and PbS/MnS core shell QDs has been observed when the samples are illuminated above the band gap energy, under continuous irradiation for 40 min. Luminescence from PbS QDs and PbS/MnS core shell QDs can be strongly influenced by the interaction of water molecules and oxygen present in aqueous solution adsorbed on the QD surface. However, PbS/MnS core shell QDs with a shell thickness of 1.5 ML did not show a PL peak energy stability as it was redshifted after 25 min, probably due to wider size distribution of the QDs.  
**Keywords:** PbS quantum dots; core shell; photoluminescence; colloidal; photoenhancement
## 8 Conclusions  
An intuitive approach to model the steady state regime of the three DC/DC power converters' basic topologies (buck, boost and buck-boost) has been developed in this work. This approach is new because it is not present in the literature. Besides, the developed model is common (it is suitable for the three basic topologies), realistic (it includes the main non-idealities (losses) of the active and passive components of the three topologies), accurate (the behavior of the real converter and its model are very close) and practical (it demands no measurements and just needs to have the data provided by the manufacturers' data sheets of the converter components).  
The developed model transforms a complex system with strong non-idealities in the form of distributed parameters, into a simple and intuitive scheme of concentrated parameters, which accurately reflect the actual behavior of the converters components.  
The quality of the developed model has been checked by two independent lines. The first has been to probe how the relationships developed in this work can lead to the approximations commonly used in literature. The second has demonstrated how the behavior of the developed model matches the behavior of an actual converter in any of the studied basic structures. Moreover, the developed model has been subjected to a battery of comparatives for the three types of basic converters regarding the conventional model used until now in the literature. The results are summarized in Table 1 and the conclusion can be that the quality of the developed model is excellent and it far exceeds the conventional model.  
As a future work that in fact it is already running, authors of this manuscript are working to try to extend the approach presented in this paper to other DC/DC converters topologies.  
[PERSON] and [PERSON] conceived and designed the model; [PERSON] performed the experiments; [PERSON] analyzed and wrote the paper.  
The authors declare no conflict of interest.
# Improvements in Rock Mass Description for Stope Design by Geophysical and Geochemical Methods
## 6 Photogrammetric Scanning for Rapid Geometrical Characterization  
Structure-from-motion-multi-view stereo (SfM-MVS) photogrammetry is an effective technique for digitizing underground spaces for remote and rapid rock mass mapping. However, the conventional image acquisition process can be laborious and time-consuming. This can be problematic, especially if there is a need for fast access to geotechnical data to be applicable in the stope design process, as identified in the industry survey. Therefore, a method for rapid photogrammetric digitization of tunnels and stopes for rock mass mapping was developed. It provides the orientation of rock mass features such as discontinuities and their statistical properties within a reasonable time frame.
# A Novel Cooperative Opportunistic Routing Scheme for Underwater Sensor Networks
## 3 Opportunistic Void-Avoidance Routing Protocol
### Routing Algorithm  
In the OVAR routing algorithm, we select a forwarding set based on two metrics: packet delivery probability and packet advancement. In this section, we first explain how the packet delivery probability can be estimated from receiving beacons. We then specify how packet advancement is modelled in our routing algorithm.  
The OVAR routing algorithm is divided into three phases. First, an adjacency graph is constructed at every node, and using a heuristic, a clique sub-graph with the maximum expected packet advancement (EPA) is created to ensure that hidden nodes are removed from the forwarding set and the chance of successful delivery is increased. Second, the number of forwarding nodes in the  
Figure 2: Example in which node \(R_{i}\) is forwarding a packet.  
forwarding set is adjusted to make a trade-off between reliability and energy consumption. Finally, the holding time is calculated at each candidate node before forwarding the packet. In order to illustrate the protocol, we consider a local OVAR scenario like the one presented in Figure 2.
# Text-Mining-Based Non-Face-to-Face Counseling Data Classification and Management System
## 3 Proposed Scheme
### System Process  
Figure 2 below shows the overall process of the system proposed in this paper.  
The overall process of the proposed system is composed of six main stages, each focusing on processing and analyzing counseling data to ultimately provide counselors with suitable information for the client.  
1. Data Collection: In the first stage, the counseling data entered by the client are transmitted to the system server via the client server.  
Figure 2: Process of the proposed system.  
Data Purification: In the second stage, the counseling data undergo a purification process to eliminate unnecessary elements. During this stage, the data are tokenized into individual words, and stopword removal is applied to delete unimportant words. This leaves only the core text data required for analysis. The tokenized data are then processed using the Word Embedding technique to semantically vectorize each word into a Word2 Vec model.
3. TF-IDF Weight Calculation: In the third stage, the purified data are used to calculate the weight of each word using TF-IDF. TF-IDF evaluates the significance of words, assigning lower weights to frequently occurring but semantically insignificant words. The TF-IDF weighted values are multiplied by the Word Embedding vectors to emphasize the meanings of important words.
4. Analysis and Classification: In the fourth stage, the data, now with completed weight calculations, undergo analysis and classification. During this process, similar words are clustered, and the resulting values help identify the primary topic of the counseling data.
5. Code Assignment: In the fifth stage, important words within each cluster are grouped, and the resulting values are output based on their order in each group. A code value is then derived by comparing the clustered data with a predefined word dictionary. This code is compared with the classification codes stored in the database, and a final code is assigned to the counseling data. The newly assigned code and the corresponding data are stored in the database for future analysis.
6. Result Provision: In the final stage, data with the same code value as the newly assigned code are retrieved from the database and presented to the counselor. This completes the system's overall process, allowing the counselor to use the provided information to offer appropriate counseling solutions to the client.
# Indoor Localization Using Semi-Supervised Manifold Alignment with Dimension Expansion
## 2 Related Work
Our goal is to obtain the transformation matrices with respect to the augmented physical coordinates, as well as the corresponding RSS measurements, which form the common manifold, and then achieve the localization of the target based on merely the known transformation relations. In addition, the localization performance under different noise perturbation conditions is analyzed, and meanwhile, the generalization of the objective function to the scenario with multiple types of measurements used for localization is also discussed.
## 7 Results
### Evaluation Framework
#### 7.5.2 Anonymity Evaluation  
A privacy-aware framework ensures anonymity and unlinkability between clients and transactions. Evaluating anonymity involves retrieving crucial information during user interaction and chaincode execution. The client identity library built into the chaincode aids in analyzing disclosed identity attributes in various interactions. The library provides a way to access private validator information using the transport-layer security protocol for encryption. Blockchains are typically assigned public keys that conceal confidential data.  
Since Hyperledger Fabric offers a large configuration space, various parameters should be configured for anonymity. Moreover, a blockchain network's performance should be measured by various static variables, including the endorsement policy and the numbers of orderers, peers, and organizations. At least one organizational peer should sign each transaction proposal to prevent unnecessary interactions between endorsing parties. Two organizations (org1 and org2) were established with different technical constraints and levels of network complexity. The organization comprised three validating peers and four orderers.
# Coronary Centerline Extraction from CCTA Using 3D-UNet
## 4 Experimental Results and Discussion  
### Coronary Dataset  
To test the implementation of the proposed neural network, we chose the dataset from the Rotterdam Coronary Artery Algorithm Evaluation Framework [30]. This is a public benchmark to evaluate algorithms for the task of centerline extraction from CCTA data. The training dataset is publicly available, but nonanonymous registering is required. A standardized quantitative method for evaluating the centerline extraction algorithms is also provided [31].  
The dataset is split into a training set and a testing set. The training set consists of 8 volumetric images and the test set consists of 24 volumetric images. All the images have four target vessel descriptions with each containing four reference points (described later). The training set images additionally contain the ground truth in the form of a reference file for the target vessels. This makes it challenging for supervised learning algorithms since many of the vessels are not reported as part of the ground truth. The size and resolution for each training volume are described in Table 1.  
The images are packed in two files. One is a \".mhd\" ASCII human-readable header file, which describes volume dimensions (e.g., 512 \(\times\) 512 \(\times\) 304), the offset, and element spacing (voxel size in real world). The other is a \".raw\" file, which contains the voxel (volume pixel) intensities, expressed in Hounsfield units (HU), following the formula intensity = HU + 1024, with a voxel value of zero corresponding to \(-\)1024 HU. The format is Insight Toolkit (ITK) compatible.  
The ground truth reference files contain a list of the points that are on the centerline of the vessel. The points are described as x,y,z real-world coordinates, the radius of the vessel at that point, and interobserver variability at that position.  
There are three categories for extraction: fully automated, minimal user interaction, and interactive extraction [32].  
For the fully automated method, two points of reference are provided--one inside the distal part of the vessel, which uniquely identifies the vessel to be tracked, and one at a short distance to the starting point of the centerline.  
For the minimal user interaction, only one point is allowed but can be chosen from more options, which include the two points described before, a starting point of a centerline, an endpoint of a centerline, and a user-defined point. The first four points are provided with the downloadable data. A vessel tree can be obtained if one starts from the starting point of the centerline. If that is the case, other reference points can be used.  
For the interactive extraction, any given number of reference points can be used, even user-defined points by manual clicking.
###### Abstract
Evaluating the relationship between the climate and WUE in forested wetlands may offer new knowledge relative to ecosystem responses to climate variability and expected future extreme conditions. Besides maximizing economic returns in managed forests, the role of forest management in maintaining forest carbon stocks and the maximization of water use efficiency must be emphasized in forest planning because these actions will help mitigate climate change [26]. Age-related trends may also be a primary source of spatial variability in carbon and water fluxes [27; 28; 29; 30] across the landscape and, thus, WUE. Researchers often find it difficult to obtain long-term ecological observations. Therefore, some studies use sites of different age but sharing similar attributes (chronosequence) to examine how an ecosystem behaves after a disturbance [31; 32]. It is important that a chronosequence follows the same growth trajectory, similar species composition, and low frequency/severity of major disturbances [33]. Recognizing its limitations, the eddy covariance flux methodology has advantages for estimating GPP [31] and ET [34; 35; 36] at the ecosystem level, such as a large footprint and continuous monitoring of C and water fluxes, that integrate environmental and eco-physiological variation over relevant scales. With the aim of better understanding the WUE (GPP/ET) of converted forested wetlands in North Carolina, we compared the eddy covariance flux estimates of two adjacent loblolly pine plantations. One is a young pine plantation (2-8 years old) and the other a mature pine plantation (15-28 years old), which comprised a chronosequence in the current study. These flux stations are registered under the AmeriFlux/FLUXNET network [37; 38; 39; 40; 41; 42; 43; 44]. In this study, we aimed to (1) determine the inter-annual and seasonal trends in GPP, ET, and WUE and (2) evaluate the impact of age and climate, to include the 2007/2008 drought, on WUE and its components. We hypothesized that (1) a mature pine plantation will have a higher GPP and ET than young pine; (2) there is a strong dependence of WUE on age; (3) there would be an enhancement in WUE with drought events; (4) strong coupling of ET and GPP would exist even with drought. Our results may integrate WUE controls in simulating functional adjustments of forested wetland ecosystems to future fast growing commercial plantations in response to changing environmental conditions.
###### Abstract  
The relentless growth of wireless networks coupled with the burgeoning demand for dynamic resource allocation has spurred research into innovative solutions. This paper presents an evaluation of Cloud-based Dynamic Network Scaling and Slicing (CDNSS) as a promising approach to meet the evolving demands of wireless networks. By leveraging cloud infrastructure and slicing techniques, CDNSS offers the flexibility to dynamically scale resources and allocate network slices tailored to diverse service requirements. The evaluation encompasses the performance of CDNSS in terms of scalability, resource utilisation and Quality of Service (QoS) provisioning. Through extensive simulations and analyses, the efficacy of CDNSS in addressing the challenges of resource management and service differentiation in wireless networks is demonstrated. The findings underscore the potential of CDNSS as a pivotal technology to enhance the efficiency and adaptability of wireless network architectures in the era of dynamic connectivity demands.  
5G radio access network; network slicing; physical resource management; QoS; bandwidth; numerology; SDN; NFV-MANO; OpenDaylight +
Footnote †: Copyright © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## Methods and Data
### Data Preprocessing Steps  
Data pre-processing is a necessary task before starting data analysis. The pre-processing includes several steps as follows:  
* Step 1: The dataset needs to be cleaned before applying the algorithm; in this step, we process the missing values and fill them with zero.
* Step 2: Sort the date in ascending order starting with 1 April 2020.
* Step 3: Extract day, month, and year from the date column for analysis.
* Step 4: Split weekend days from weekdays and apply pivoting on the indicator column to get case type in different columns, then fill missing values with zero.
* Step 5: Create and prepare a new dataset for each case daily and cumulatively for Saudi Arabia.