esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Unified Architecture for Machine Learning in Future Networks
ML intent is a declarative description that can be used by the network operator to specify an ML application and the corresponding ML pipeline. The technology-specific implementation of the specified ML pipeline is then determined and realized by the MLFO in coordination with other management and orchestration functions in the management subsystem. An ML sandbox is an isolated domain that allows the hosting of separate ML pipelines to train, test, and evaluate them before deploying the pipelines in a live network. For training and testing, the ML sandbox can use data generated by a simulated underlay network and/or data collected from a live network. An ML sandbox may leverage ML techniques such as supervised learning to train and test ML models. The ML pipeline in the sandbox is also managed and monitored by the MLFO. Through having a sandbox subsystem, the ML architecture allows ML pipelines to adapt to the dynamic networking environment in future networks. The ITU-T ML architecture defines data handling reference points between the ML pipeline and the underlay network (also between the ML pipeline in the sandbox and the simulated underlay network). Using these reference points, any impact to the underlay networks is localized to the source of the data and the target of configurations (as a result of ML pipeline execution). The ML architecture also defines standard interfaces between each pair of subsystems, between the ML pipeline and the underlay network, between the ML sandbox and the simulated underlay networks. The SBI defined in the 5G SBA may be  
Figure 4: ITU-T unified architecture for ML in future networks. used for realizing the interfaces between subsystems, as well as the interfaces between ML pipeline nodes. The ITU-T ML architecture embraces the cloud-native network design principle and is essentially aligned with the 5G SBA. In this ML architecture, decomposition of an ML-based management application to a pipeline of logical nodes allows finer grained modularity of the management system, which may enhance the flexibility and agility of network and service management. Each pipeline node in the ML architecture may be realized as an NF service as defined in 5G SBA, which can be deployed independently as a VNF instance interacting with other nodes via the service-based interface. The ML architecture may be realized based on the NWDAF/MDAS functions in 5G networks; for example, an ML pipeline may have AMF as the SRC node, PCF as the SINK node, and the other nodes hosted on the NWDAF. The cloud-native design of the ML architecture allows the MFLO to compose ML pipeline nodes for constructing various ML applications that meet the diverse management requirements in future networks. The unified orchestration of ML pipeline nodes and network/compute functions in the ML architecture may greatly facilitate the seamless integration of ML techniques in future networks for supporting network-cloud/edge convergence.
## 6 Conclusions  
Based on the analysis of the provided article, several key conclusions can be drawn regarding the suitability of the investigated building for different utility functions and the associated capital investment required for improvement:  
* The proposed utility functions for the building include a nursery and kindergarten (Variant V1), shop (Variant V2), and office (Variant V3), in addition to the base residential function (Variant V0). These functions were chosen based on social and economic justifications within the housing estate.
* The suitability of the building for each utility function was assessed based on four criteria: thermal comfort (F1), building airtightness (F2), non-renewable primary energy indicator EP (F3), and vibrations (F4).
* The current state of the building was compared with the standards specified for each utility function across the four criteria. This comparison revealed variations in compliance, with some criteria meeting the required class, while others needing improvement.
* The level of capital investment needed to improve the building's condition for each utility function was estimated based on the assumption of increasing the class of each criterion by one point. The calculated costs varied for each variant, with Variant V2--shop requiring the least investment and Variant V1--nursery and kindergarten requiring the highest.
* The economic attractiveness of each utility function was assessed based on the relative level of capital investments required for building renovation. Variant V2--shop emerged as the economically beneficial option, while Variant V1--nursery and kindergarten required the highest investment, exceeding the costs of Variant V2 by six times.  
In summary, the analysis indicates that Variant V2--shop is the most economically viable option for future investments, while Variant V1--nursery and kindergarten requires significant capital expenditure. These findings provide valuable insights for decisionmakers regarding the optimal utilization of the building space within the housing estate.  
Conceptualization, M.F.-C. and A.K.-K.; methodology, M.F.-C., A.K.-K., A.S. and P.S.; software, M.F.-C.; validation, M.F.-C., A.K.-K., A.S. and P.S.; formal analysis, M.F.-C. and A.S.; investigation, M.F.-C. and A.K.-K.; resources, M.F.-C., A.K.-K. and P.S.; data curation, M.F.-C., A.K.-K., A.S. and P.S.; writing--original draft preparation, M.F.-C., A.K.-K., A.S., D.O.P. and P.S.; writing--review and editing, M.F.-C., A.K.-K., A.S., D.O.P. and P.S.; visualization, M.F.-C.; supervision, M.F.-C. and A.K.-K.; project administration, M.F.-C. and A.K.-K.; funding acquisition, M.F.-C. and A.K.-K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Probiotics and Beneficial Microorganisms in Biopreservation of Plant-Based Foods and Beverages
## 4 Biopreservative Properties of Probiotics
### Fruits
#### 5.1.1 Melons  
Fresh-cut fruits such as melon can be contaminated during preparation (peeling, cutting, etc.) and processing. Specifically, the spoilage and pathogenic microorganisms may gain access to the nutrients inside the fruit and then multiply, causing deterioration [63]. Melons are highly susceptible to multistate foodborne outbreaks since they have a higher chance of contamination during growth, postharvest handling, packing, transportation, distribution, or final preparation. During the last three decades, several multistate outbreaks of salmonellosis and listeriosis have occurred due to the consumption of contaminated fresh-cut fruits [63, 64].  
[PERSON] et al. [62] reported that native microbial communities of the whole candaloupe inhibited attachment to find surfaces, as well as the survival and growth of _L. monocytogenes_ on cantaloupe surfaces and homogenized fresh-cut surfaces. Moreover, disinfection treatment was found to be detrimental to the native microbial communities, as the population of _L. monocytogenes_ was significantly higher in melons treated with either chlorine or ethanol than that given no disinfection treatment or washed with water [62].  
Riboflavin over-producing potential probiotic strains _Lactiplantibacillus plantarum_ B2 and _Limosilactobacillus fermentum_ (previously _Lactobacillus fermentum_) PBCC11.5 were reported as exhibiting satisfactory inhibitory effects against _L. monocytogenes_ on artificially contaminated melons. The viability of _Lb. plantarum_ B2 and _Lb. fermentum_ PBCC11.5 were \(3\times 10^{8}\) CFU/g and \(7.8\times 10^{7}\) CFU/g at the end of the refrigerated storage for 11 days (initial inoculation levels of the probiotic strains were \(1\times 10^{10}\) CFU/mL). The major technological and nutritional parameters of the fresh-cut cantaloupes were unaffected by probiotic enrichment. However, the addition of _Lb. plantarum_ B2 resulted in some undesirable sensory attributes after 11 days of storage. The riboflavin content increased approximately two-fold in probiotic cantaloupe. These results suggest that these two probiotic strains can enhance the safety of minimally processed melons due to their antagonistic effects on _L. monocytogenes_ of fruit origin [59].
# A Methodology to Model the Rain and Fog Effect on the Performance of Automotive LiDAR Sensors
## 4 Modeling of the Rain and Fog Effect in the Virtual LiDAR Sensor
### Beam Characteristics  
This work assumes a rectangular beam shape because the real LiDAR sensor used in this work has a rectangular beam shape; its near-field size can be described by the horizontal beam width \(w_{ho}\) and the vertical beam width \(w_{vo}\). With increasing range, the size of the beam also becomes larger in both directions. The divergence angles give this property with \(\theta_{div}\) as the horizontal and \(\varphi_{div}\) as the vertical divergence angle. For a given distance \(r>0\) distance from the sensor, the size of the beam is [12]:  
\[w_{h}(r)=w_{ho}+r\cdot 2 tan(\frac{\theta_{div}}{2}) \tag{18}\]and:  
\[w_{v}(r)=w_{v0}+r\cdot 2 tan\big{(}\frac{\varphi_{div}}{2}\big{)}. \tag{19}\]  
For the simulation of the effects of rain and fog, the volume of the beam between two given ranges, \(r_{0}\) and \(r_{1}\), is required (for instance, beam volume for range cells). As shown in Figure 5, it is trivial that the shape of the beam between \(r_{0}\) and \(r_{1}\) is a pyramidal frustum, the volume of which is given by:  
\[V=\frac{1}{3}h\cdot(A+\sqrt{(AB)}+B), \tag{20}\]  
where \(A\) is the area of the top of the frustum marked in yellow, \(B\) is the base of the frustum shown in green, and \(h\) is the height of the frustum. Applying this formula to the beam itself results in:  
\[V_{r_{0},r_{1}}=\frac{1}{3}(r_{1}-r_{0})\bigg{(}w_{h}(r_{0})w_{v}(r_{0})+\sqrt {w_{h}(r_{0})w_{v}(r_{0})w_{h}(r_{1})w_{v}(r_{1})}+w_{h}(r_{1})w_{v}(r_{1}) \bigg{)}. \tag{21}\]  
This equation can be used to determine the volume of a beam in the field of view (FoV) or calculate the beam volume for each range cell depending on the range resolution.
## 2 Materials and Methods
### Remote Sensing Data  
The boundary data for the Fen River Basin were sourced from the Center for Resource and Environmental Sciences and Data, Chinese Academy of Sciences (CAS). The data were structured using the GCS_Clarke_1866 geographic co-ordinate system.  
The corn distribution data for the Fen River Basin in 2020 encompassed the period 2016-2020. This dataset had a spatial resolution of 30 m and temporal resolution of 1 year.  
Normalized Difference Vegetation Index (NDVI) data were generated using the GEE platform, employing Sentinel 2 data for the computation of monthly NDVI values. The NDVI datasets had a spatial resolution of 500 m and were referenced to the WGS84 co-ordinate system.
## 3 UAV Flying Base Station Properties
### Operation on Battery  
Because the UAV flying base station is mobile and cannot afford wired connections hindering its mobility, a UAV flying base station operates on the battery energy resource to supply the electrical power supply. The UAV flying base station requires the battery energy for both its connectivity provision (i.e., it cannot support the connectivity to the user if it does not have electrical energy) and its mobility (move and re-position its location to improve the connectivity provision to the mobile user), as depicted in Figure 3. Because of the battery's finite energy amount, the flying base station operation requires regular re-charging when the battery is running low in energy. The re-charging phases interfere with the flying base station operations because of the fixed locations of the recharging stations, reducing the flying base station to stationary base station operations.  
**Security implications:** Such finite resources in battery energy and their direct impact on the connectivity provision lead to vulnerabilities against battery-draining DoS threats. In addition to the more traditional DoS channels focusing on networking bandwidth and processing resources, the battery energy provides a new channel for the DoS attacker to interfere with and disrupt the base station operations. Because the battery/energy resource is shared by both the base-station communication and the drone mobility, as depicted in Figure 3, there are greater DoS vulnerabilities than having either communication or mobility but not both. For example, the attacker manipulating the base station location can trigger greater power consumption for the signal transmission; requiring greater reliability and jamming resistance can incur greater bandwidth and thus power consumption, e.g., the code-division multiple access (CDMA) spread spectrum; and bogus injection messages can cause greater re-charging, disabling the optimal control of the mobility and location of the base station.
# Energy-Aware Forwarding Strategies for Delay Tolerant Network Routing Protocols
## 2 Related Work
### Delay Tolerant Networking (DTN) Routing Protocols
#### 2.1.1 Direct Delivery  
The Direct Delivery routing is a single-copy routing protocol [13] where nodes only carry one copy of the message for transmitting directly to the final destination without any relaying to additional nodes [6]. This strategy does not require any information about the network and permits the messages to be transmitted through one hop only when the source nodes are in contact with the destination. This simple protocol has minimum overhead ratio, but has low delivery ratio. It is typically used as a baseline for other protocols [9].
# Understanding Consumer Acceptance for Blockchain-Based Digital Payment Systems in Bhutan
## 3 Theoretical Framework and Hypothesis Development  
### Theoretical Framework  
The conceptual diagram is proposed, as shown in Figure 1, based on the systematic review [35] carried out to identify the factors that influence the trust in and acceptance of blockchain adoption in digital payment systems. The paper also adopted UTAUT as the theoretical framework for understanding how blockchain acceptance will be driven by performance expectancy, effort expectancy, social influence, and facilitating conditions. UTAUT, a framework developed by [PERSON] et al. [53], is a powerful theory of the acceptance of technology based on constructs such as performance expectancy, effort expectancy, social influence and facilitation conditions. The systematic review suggested that UTAUT constructs could also be applicable since they have an impact on acceptance, while performance expectancy, effort expectancy, social influence, and facilitating conditions may be significant contributors to consumer acceptance. Those considerations could have significant implications for the implementation of blockchain in Bhutan's digital payments sector. This paper is focused solely on acceptance factors and takes performance expectancy, effort expectancy, social influence, and facilitating conditions as independent variables. The dependent factor of the model is whether or not the consumer is willing to accept the adoption of blockchain for digital payments. The acceptance in this paper represents the behavioral intention to adopt blockchain-based digital payment systems, reflecting consumers' willingness and readiness to use the technology. This paper analyzes these factors that influence consumer acceptance of blockchain adoption in Bhutan's digital payment systems. Through the UTAUT framework, this paper aims to determine whether these considerations, highlighted in the literature, resonate with Bhutan's unique cultural and regulatory context and explain the consumer's acceptance of blockchain adoption for digital payment systems. It will assess the fit of the UTAUT constructs in the context of Bhutan, emphasizing the role of performance expectancy, effort expectancy, social influence, and facilitating conditions in shaping acceptance. This study will investigate acceptance considerations in the adoption of blockchain in Bhutan, serving as a reference for future adoption research.  
The adoption of blockchain technology in digital payment systems is significantly influenced by acceptance [35; 89; 90; 91], which is shaped by factors such as performance expectancy, effort expectancy, social influence, and facilitating conditions [35]. This paper explores how these factors affect consumer acceptance in Bhutan's digital economy. The development of the following hypotheses is based on the systematic review [35] conducted to explore and validate the factors that influence acceptance of blockchain adoption in the digital payment system.
## Appendix B Correlator Output Variance Derivations  
We will develop analytical forms of correlator output variance in two situations: (i) data bits are aligned or (ii) data bits are misaligned by an integer number of primary code chips. The third situation where data bits are misaligned by an arbitrary fraction of a primary code chips is dealt with in Section 3.3 \"_Data Bits Misaligned by a Fraction of a Code Chip_\" of Section 3 \"SSC and Equivalent White Noise Level\". For simplicity, we use notation \(\lfloor a\rfloor\) to denote function floor (\(a\)).
## 3 System Model and Optimization Formulation
### Diverse QoS Constraints of Multiple Requests
#### 3.2.3 Latency Requirement  
Due to long communication links, satellite networks should not be expected to provide ultra-low delay guarantee within 1 ms, as 5G terrestrial facilities do. However, S-IoT should still provide multiple levels of delay guarantee for various delay-sensitive services. The latency requirement of services is denoted as \(T_{req,k}\) for terminal \(u_{k}\)'s service \(s_{k}\).  
Strictly speaking, different from the transmission rate and reliability requirements, the end-to-end delay of S-IoT data transmission is not only decided by a resource allocation strategy, but by the routing algorithm as well. The intuitive idea to satisfy end-to-end delay requirements is to guarantee each independent process. This paper, therefore, focuses on the latency guarantee of the uplink transmission process between the remote IoT terminal \(u_{k}\) and the satellite, which mainly consists of transmission latency and signal propagation latency. The uplink latency, denoted as \(T_{k}\), can be presented as:  
\[T_{k}=T_{P}+\left.L_{k}\right/C_{k} \tag{7}\]  
where \(L_{k}\) and \(C_{k}\) are the traffic data size and transmission rate of terminal \(u_{k}\), respectively. In addition, \(T_{p}\) is the propagation delay of signal transmitting. Decided by the distance between satellite and terminal \(u_{k}\), \(T_{p}\) can be approximately regarded as a constant value due to the high satellite orbit.  
Thus, the latency requirement can be written as \(T_{k}\leqslant T_{req,k}\), where \(T_{req,k}\) is the maximum tolerable uplink latency of the service \(s_{k}\) provided by terminal \(u_{k}\).
# Code Obfuscation: A Comprehensive Approach to Detection, Classification, and Ethical Challenges
## 6 Working Process
### Feature Importance Analysis (GB)  
In order to analyze the importance of various features used in our models, Table 4 represents the importance of each feature as determined by the Gradient Boosting Model.  
The table highlights the features most influential in the model's ability to classify obfuscated and non-obfuscated files. Below is a breakdown of their significance:  
**String Entropy (0.3547213)**  
String entropy emerged as the most important feature, indicating that the randomness of string literals strongly correlated with obfuscation. Higher entropy suggests heavily obfuscated code designed to be less readable.  
**Standard Deviation of Token Length (0.2837422)**  
This is the second most significant feature, highlighting that variability in token lengths is a key marker of obfuscation. Obfuscated code typically shows greater irregularity in token lengths due to complex and random-looking identifiers;  
**Overall Code Entropy (0.2372359)**  
Overall entropy is also a critical factor, as higher values indicate the increased complexity and unpredictability of obfuscated code;  
**Unique Token Ratio (0.0615993)**  
Although less influential than the top three, a higher unique token ratio plays a role in detecting obfuscated code, as obfuscation often introduces a wide variety of distinct tokens;  
**Average Token Length (0.0211782)**  
This feature has relatively low importance, suggesting that the average token length is less indicative of obfuscation compared to variability in token lengths;  
**Comment Density (0.0170135)**  
Comment density has a minimal contribution. While obfuscation can remove or alter comments, this alone is not a strong indicator of obfuscation;  
**Keyword Count (0.0166855)**  
The frequency of specific keywords shows low importance, indicating that the use of control structures is not significantly different between obfuscated and non-obfuscated code;  
**Median Token Length (0.0078241)**  
Similar to average token length, this feature is less significant, underscoring that variability and complexity in token lengths are more indicative of obfuscation.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Feature** & **Importance** \\ \hline String Entropy & 0.3547213 \\ Std Token Length & 0.2837422 \\ Entropy & 0.2372359 \\ Unique Token Ratio & 0.0615993 \\ Avg Token Length & 0.0211782 \\ Comment Density & 0.0170135 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Feature Importance Scores from Gradient Boosting Model.
## 4 Discussion and Analysis of the Results
### Performance Comparison  
As depicted in Figure 7 and Table 5, the performance of the proposed model is improved. Compared to the original paper, the proposed model achieved better recognition performance with a smaller number of epochs. However, the proposed model took a longer time for training. This is due to the nature of an end-to-end learning approach [42] that incorporates multiple and diverse network layers in a single unified framework. Therefore, the proposed model can be assessed and  
Figure 6: Sample mis-recognized text-line images. (**a**) Printed text-line images. (**b**) Synthetically generated text-line images. The small green and red rectangles are used to mark the substituted and deleted characters, respectively, while the blue color represents inserted characters.  
training time may be further improved by following some other concepts like decomposition [43] or the divide-and-conquer approach.  
The comparisons among the proposed approach and others' attempts done on the ADOCR Database [9] are listen in Table 5, and the performance of the proposed model shows better results on all three of the test datasets.  
All results are reported as character error rates (CERs) as results of insertion, deletion, and substitution of characters in the predicted output. This can be efficiently computed using Equation (5).