esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Functionality and Application of Functional Ingredients from Jackfruit
### Applications
#### 4.2.2 Food Industry  
Regarding its applications in the food industry, some reports showed jackfruit and its derivatives (peel, seed, flour, chips, and wafers, among others) as functional foods thanks to their antioxidant proper, anti-cancer agents, and effect against the skin and vascular diseases [3]. Thanks to their properties, jackfruit bulbs can be used as ingredients for many foods and beverages such as jams, juices, concentrates, wines, and functional drinks, among others [50]. Further, their compounds can act as food flavoring or coloring agents [51]. Other studies report the combination of jackfruit pulp with other fruits like palmyra palm (_Borssus flabellifer_ L) and passion fruit (_Passiflora edulis_ [PERSON]) to make functional beverages with a high content of bioactive compounds [52]. In a study regarding the optimization of jackfruit nectar's processing conditions with a thermo-ultrasound, researchers found final nectar properties to be as follows: ascorbic acid content of 420 mg/L, total phenolics of 134 mg GAE/L, antiradical activity assayed with ABTS of 3.51 umol Trolox Equivalents (TE)/L and with DPPH of 75 umol TE/L, and residual activity of pectin methylesterase of 60% [53]. In addition, thermo-ultrasound proved to be effective as an alternative to pasteurization and generate good product quality [53].  
[PERSON] [54] reported that jackfruit seed flour could be used as a functional ingredient as it helps to reduce the concentration of gluten in baked goods. Thanks to their antimicrobial properties, nanoparticles generated from jackfruit seeds were proposed as agents against foodborne pathogens [3]. Reported research results on improving chocolate cream properties by incorporating jackfruit flour showed a significant increase in the content of polyphenols (127.00 mg/g), carotenoids (160.16 mg/g), and antioxidant activity IC50 (42.75 mg/mL), while positively affecting the product's sensory properties such as viscosity and color [55].  
Regarding jackfruit pectin, some possible uses in the food industry are as a gelling agent, emulsifier, stabilizer, and thickener agent in preparing jams, marmalades, and substitute for fats in various food formulations [19]. [PERSON] et al. [56] prepared snack bars using jackfruit seed flour and tested different formulations to compare their physicochemical, sensory, and nutritional changes during storage. Their results showed that the highest phenolic content in their formulations was 44.94 \(\pm\) 0.21 mg GAE/g, while the highest antioxidant activity was 59.34 \(\pm\) 0.26%, concluding that a jackfruit seed meal is a low-cost option for the preparation of value-added nutritional products [56].
# Early Diagnosis of Dementia from Clinical Data by Machine Learning Techniques
###### Abstract  
Dementia is the most prevalent degenerative disease in seniors in which progression can be prevented or delayed by early diagnosis. In this study, we proposed a two-layer model inspired by the method used in dementia support centers for the early diagnosis of dementia and using machine learning techniques. Data were collected from patients who received dementia screening from 2008 to 2013 at the Gangbuk-Gu center for dementia in the Republic of Korea. The data consisted of the patient's gender, age, education, the Mini-Mental State Examination in the Korean version of the CERAD Assessment Packet (MMSE-KC) for dementia screening test, and the Korean version of the Consortium to Establish a Registry for Alzheimer's Disease (CERAD-K) for the dementia precise test. In the proposed model, MMSE-KC data are initially classified into normal and abnormal. In the second stage, CERAD-K data are used to classify dementia and mild cognitive impairment. The performance of each algorithm is compared with that of Naive Bayes, Bayes Network, Begging, Logistic Regression, Random Forest, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) using Precision, Recall and F-measure. Comparing the F-measure values of normal, mild cognitive impairment (MCI), and dementia, the MLP was the highest in the F-measure values of normal with 0.97, while the SVM appear to be the highest in MCI and dementia with 0.739. Using the proposed early diagnosis model for dementia reduces the time and economic burden and can help simplify the diagnosis method for dementia.  
early diagnosis of dementia; machine learning; normal; mild cognitive impairment +
Footnote †: journal: Applied Sciences
## 2 Different Methods of Hydrogen Generation
### Latest Hydrogen Production Process Advances  
Almost 90% of all hydrogen that is produced today is from fossil fuel, called \"gray hydrogen.\" In order to utilize hydrogen as a clean fuel or energy vector, it is essential to clean up its production process. One approach to achieve this is through blue hydrogen, which is produced from natural gas via steam reforming. Regardless of the nature of the feed gas (such as methane or ethanol), the key factor is the production method, which generates carbon dioxide (CO\({}_{2}\)) emissions. To prevent these emissions from being released into the atmosphere, capture and storage of CO\({}_{2}\) technology should be employed. However, it is important to consider potential leaks along the supply chain. While this method can recover approximately 48% of CO\({}_{2}\) emissions, it falls short of fully addressing the issue, as it is estimated that only 90% of CO\({}_{2}\) emissions can be effectively reduced [34]. Blue hydrogen has been formally introduced into major economies' hydrogen programs. Figure 7 showcases the current and projected hydrogen production technologies, offering a visual representation of the various methods currently available and those anticipated for future development.  
The following advancements show green hydrogen-generating systems in Figure 8, which are still in the early stages of research for large-scale manufacturing and are exceedingly promising for fulfilling global decarbonization goals.
# Coupling Secret Sharing with Decentralized Server-Aided Encryption in Encrypted Deduplication
## 2 Background and Motivation
### Limitations  
Previous server-aided MLE implementations typically rely on a centralized key server that stores only a single secret key [2,4,5]. This design makes the key server a bottleneck in the encrypted deduplication storage system. Consequently, a tenant may prefer to deploy multiple key servers, each maintaining the same secret key, in order to scale up the system effectively. However, sharing a secret key among multiple key servers poses a serious security risk; if the secret key from one key server is compromised, it could lead to the breach of the entire system. Thus, decentralized server-aided MLE is proposed to benefit from deduplication while guaranteeing the scalability and confidentiality of the outsourced data.  
However, the cryptographic strategy (e.g., computational Diffie-Hellman (CDH) problem and blind signature) applied in the state-of-the-art decentralized server-aided MLE [8] incurs high additional computation overhead for encrypting a small chunk. For example, it only takes about 0.02 ms to compute the hash key of an 8 KB chunk, while the CDH and blind signature will take 8.41 ms and 92.45 ms, respectively [8]. Recall from Section 2.1 that deduplication storage systems usually configure 8 KB as the average chunk size. Therefore, the additional computation overhead will significantly degrade the deduplication efficiency.
## 1 Introduction
### A Motivational Example  
In order to gain a first intuitive insight about the potential energy-saving capability of the three-tiered networked computing platform of Figure 1, let us consider the toy example of Figure 2. It illustrates a \"wise\" strategy for allocating the tasks of an application DAG. The considered DAG is composed of eight tasks and nine (directed) edges. Each red-marked label \((a,b,c)\) on the nodes reports the (possibly, profiled) energies needed for the execution of the corresponding task at the Mobile, Fog, and Cloud, respectively. In the considered example, Nodes A and H are the input and output tasks of the overall DAG, respectively. Hence, by design, they are forced to be executed on the Mobile device (hence, the energies needed for their executions on the Fog and Cloud nodes are infinite; see the corresponding red-marked labels of Figure 2). Furthermore, in Figure 2, each blue-marked (resp., black-marked) label \((f,g)\) on the edges indicates the Mobile-to-Cloud and Cloud-to-Mobile (resp., Mobile-to-Fog and Fog-to-Mobile) energies needed for the inter-node transport of the data in the corresponding DAG edge. The computing energy \(\mathcal{E}_{\mathit{CMP}}\) and the networking energy \(\mathcal{E}_{\mathit{NET}}\) can be easily evaluated by summing the related elements in the red-marked, black marked, and blue-marked labels of Figure 2. These energies will be formally defined in Equations (5) and (6) of Section 3.2. The considered DAG being quite simple, it may be analyzed by hand. The analysis leads to the conclusion that the task allocation strategy that minimizes the total computing-plus-networking consumed energy \(\mathcal{E}_{\mathit{TOT}}\triangleq\mathcal{E}_{\mathit{CMP}}+\mathcal{E}_{ \mathit{NET}}\) allocates: (i) tasks \(\{A,H\}\) to the Mobile device (see the green-colored tasks); (ii) tasks \(\{C,G\}\) to the Fog (see the red-colored tasks); and (iii) tasks \(\{B,D,E,F\}\) to the Cloud (see the blue-colored tasks). In so doing, the total energy consumed by the optimal task allocation strategy equates: \(\mathcal{E}_{\mathit{TOT}}=32.0\) (Joule), with the computing energy \(\mathcal{E}_{\mathit{CMP}}=20.5\) (Joule) and the networking energy \(\mathcal{E}_{\mathit{NET}}=11.5\) (Joule). By contrast, the more direct (but sub-optimal) strategies that execute all tasks at the Mobile, Fog, and Cloud would require: \(\mathcal{E}_{\mathit{TOT}}=57.0,35.0\), and \(43.0\) (Joule), respectively. This confirms that a \"wise\" task allocation strategy that exploits _all_ the available Mobile-Fog-Cloudcomputing nodes may lead to energy saving, even in the presence of _non-negligible_ inter-node network energy consumption.
# Combating Web Tracking: Analyzing Web Tracking Technologies for User Privacy
## 3 Countermeasures against Web Tracking
### Tracking Prevention
#### 3.2.4 Tracking Detection Using Machine Learning  
Advertising and tracking services hinder domain detection by using sophisticated tracking methods, such as distributing JavaScript code across multiple files, making it difficult for EasyList-based blocking tools to detect them. This has led to the need for machine learning techniques to be utilized in tracking detection, as suggested by recent studies [18; 43; 64].  
[PERSON] et al. [17; 45] developed AdGraph and FP-Inspector to identify and block tracking traces using machine learning. AdGraph uses a Chromium-based Web browser to extract HTML, network, and JavaScript data, generates graphs, and performs tracking detection and blocking with a random forest-trained identifier.  
However, [PERSON] et al. [18] developed WebGraph as an improvement of AdGraph. AdGraph is vulnerable to current hidden techniques, so the researchers proposed ML-based methods for advertising and tracking prevention. Unlike AdGraph's content detection, WebGraph uses action-based detection, which is more difficult to obfuscate. WebGraph builds graphs for actions and extracts flow features. The limitation of that work is that it only focused on a limited subset of browser APIs for advertising and tracking such as HTTP cookie headers, document.cookie, and window.localStorage, not considering other potential techniques.  
FP-Inspector uses OpenWPM to extract information through static and dynamic analysis and learn user identifiers based on their fingerprinting behaviors. The static analysis approach involves transforming the script into an Abstract Syntax Tree (AST), extracting features, and applying machine learning. Researchers also proposed a dynamic analysis method that captured and analyzed the code after it had been executed in an instrumented browser. This enabled the unpacking of a script that used techniques such as eval or function. Finally, FP-Inspector extracts JavaScript API features from the code, represented by the AST, which trackers can use for fingerprinting. A dynamic analysis helps to analyze elements that a static analysis cannot analyze such as obfuscated code. Machine learning models using decision trees can use extracted features for training the classifier.  
[PERSON] et al. [43] proposed a graph neural network (GNN)-based detection method. The movement of resources between the browser and the server can be expressed as a graph, and the domain name and URL become properties of nodes and edges. This allows them to graph multiple HTTP requests, not just individual webpages. The GNN can then learn both explicit and implicit features of Web tracking and advertising requests. Unlike existing AdGraph and WebGraph studies, which only use explicit features and train a random forest model for single HTTP requests, the GNN-based method has higher detection rates.
# A Review on LiFi Network Research: Open Issues, Applications and Future Directions
## 7 Comparison between LiFi and other OWC/RF Technologies
\begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**METRICS** & **FSO** & **VLC** & **OCC** & **LiFi** \\ \hline Indoor/outdoor & Outdoor & Indoor/outdoor & Indoor/outdoor & Indoor/outdoor \\ Outdoor/indoor & Outdoor & Indoor & Outdoor & Indoor \\ stability & LOS/NLOS & LOS only & LOS/limited NLOS & LOS only & LOS/NLOS \\ Topology & P2P & P2P, limited P2 MP & P2P & P2P, P2 MP, MP2P, MP2 MP \\ Mobility support & No & Possible & No & Yes \\ NLOS support & No & No & No & Yes \\ Transmitter & LD & LD/LED & LED & LD/LED \\ Receiver & PD & PD/Cam & Cam/IR & PD/Solar cell \\ Comm. Distance/range & \textgreater{}10,000 km & 20 m & 60 m & 10 m \\ Multi-user access & No & Yes & No & Yes \\ Interference & Yes & Yes & No & Yes \\ Outdoor vulnerable & Yes & Yes & Yes & Yes \\ (Fog/rain) & One direction & One direction & One direction & Bi-directional \\ Comm. direction & IR/VL/UV & VL & IR/VL & IR/VL/UV \\ Cell size/service area & Small & Medium/small & Small & Ultra-small/small \\ Data rate & High speed 1–40 Gbps & LED = \textasci{}10 Gb & The achievable data rates & LED = \textasci{}10 Gb \\ Security & High & LD = \textasci{}100 Gb & are low\textasci{}55 Mbps & LD = \textasci{}100 Gb \\  & & High & High & High \\  & & & Applications on existing & \\  & & & smartphones, such as: & \\  & & 1. Indoor & 1. Indoor & 2. Outdoor \\  & provide ultra-high & 3. Vehicles & & ing/navigation, & 3. Underwater \\ Applications & speed backhaul & 4. Outdoor & 2. asset tracking and & 4. Vehicles \\  & connections within a & 5. IoT & 5. IoT & 5. IoT \\  & data center & 6. M2M & 4. V2V & 6. D2D \\  & & & 5. I2V & 7. M2M \\
5G support & Yes & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 4: LiFi and other OWC comparison [19; 188; 189; 190; 191; 192; 193].
# Research on the Self-Drilling Anchor Pull-Out Test Model and the Stability of an Anchored Slope
## 4 Self-Drilling Anchor Grouting Slope Simulation Analysis  
### Project Overview  
This section of the model is based on the reconstruction project of an abandoned open-pit mine in the Jinpu New Area of Dalian City, as shown in Figure 20. The mining area was originally a hilly landscape, with an elevation ranging from 80 to 180 m and a relative height difference of 50 to 100 m. The area slopes from north to south with a natural gradient of 15 to 25\({}^{\circ}\), locally reaching over 30\({}^{\circ}\). Long-term mining has resulted in multi-level artificial slopes with heights ranging from 30 to 50 m, forming a stepped structure. The slopes are predominantly concave and linear, with some irregular cross-sections. The slope angles range from 35\({}^{\circ}\) to 60\({}^{\circ}\), and the rock mass is weathered and fractured due to geological and weathering processes, with loose rock fragments accumulating and evidence of sliding and collapse. The stratigraphy includes a surface layer of gravelly sandy soil, 4 to 8 m thick, with loose structure and high permeability; a highly weathered dolomitic limestone layer,  
Figure 19: Soil stress contour map.  
10 to 15 m thick, with a fragmented structure and well-developed joints and fractures; and a weakly weathered dolomitic limestone layer, with a relatively intact structure but with gently inclined structural planes.
# An Adaptive Neuro-Fuzzy Propagation Model for LoRaWAN
## 3 Proposed Model Description
### Model Input  
A relatively similar set of inputs that are defined in the COST231 model was considered, however, three additional inputs were added based on our knowledge of propagation and common sense. In addition, three of the COST231 inputs (base station height, mobile station height and their height difference) were combined into one. Many of these modeling inputs were acquired from Google maps to further facilitate the modeling. The only output of the system is the received signal strength indicator (\(RSSI\)). These inputs are explained as follows:  
1. [label=()]
2. spreading factors (\(s_{m}\)) of LoRa's CSS modulation (\(7\leq s_{m}\leq\)12);
3. height difference (\(\Delta h=h_{bs}-h_{ms}\)) between the base station (\(h_{bs}\)) and mobile station (\(h_{ms}\)), where \(h_{ms}\) is the altitude of earth at the location of measurement;
4. free space path loss (\(l_{fs}=20\log\frac{\lambda}{4\pi d_{los}}\)) to include the effect of frequency, \(\lambda\) is the wavelength of transmission and \(d_{los}\) is the LoS distance (regardless of obstruction) between transceivers;
5. clutter ratio (\(c_{los}\)) in the LoS, total number of buildings and structures in LoS, regardless of their heights;
6. acute angle between the LoS and the axis of the road (\(\phi\));
7. relative width of the street (\(w_{s}\));
8. \(d_{w}\) defined as the length of LoS that is on the water divided by the \(d_{los}\);  
where \(h_{bs},h_{ms},\phi,w_{s}\) and \(d_{w}\) are acquired from Google map images and, therefore, may have some inaccuracies. For certain scenarios, some of these parameters may not be very important, or may not exist, and therefore, would not apply at all.
## 1 Introduction
This study found a similar pattern of temporal activation for the NH participants and post-lingual CI participants with good speech perception, both showing strong cortical responses to natural and vocoded speech (no significant differences between these two stimuli), and smaller activation to scrambled speech and environmental sounds. On the other hand, CI participants with poor speech perception had similarly large areas of cortical activation for all four stimulus types. Prelingual CI users, whose language systems were deprived during language development may activate different processes when listening to degraded speech, compared with post-lingual CI users or NH listeners who have well-functioning language systems [37]. Hence, conclusions regarding cortical activation to speech in prelingual CI adults, compared to NH adults who listen to vocoded and non-vocoded stimuli, are difficult to draw based on previous studies. Furthermore, none of these studies have tested cortical activation in response to speech in background noise. Given that CI users typically struggle with understanding speech amid background noise, a complex task that involves an interplay of sensory and cognitive processes [51], one cannot assume that a similar pattern of activation will be demonstrated in quiet and noisy conditions. In our current study, we aimed to investigate the pattern of brain activation involved in speech perception in quiet and background noise conditions in three groups of participants: prelingual CI adults, NH adults who listened to spectrally degraded (vocoded) stimuli, and NH adults who listened to natural stimuli. To examine the possible relationships between behavioral SIN perception and cortical activity to speech, speech reception thresholds in noise (SRTn) were also measured in all participants.
# Four-Port MIMO Antenna System for 5G n79 Band RF Devices
## 3 Results and Discussion
### Radiation Patterns  
The purpose of the proposed MIMO antenna was to provide pattern diversity characteristics. Figure 6 shows the radiation patterns of the proposed MIMO antenna at two principal planes of theta 90 and phi 90. The simulated and measured results are in close agreement with each other. Also, the radiation patterns of all four radiating elements are shown so that the diversity characteristics achieved can be clear.  
In Figure 6a, theta 90 patterns of Ant1 and Ant3 are shown. The maximum lobe direction for Ant 1 is at 280 while that for Ant 3 is at its opposite, at 208\({}^{\circ}\). Similarly, in Figure 6b, the Ant2 maximum lobe direction is between 90 and 120\({}^{\circ}\), while Ant4 is opposite that of Ant2. In Figure 6c,d, the radiation characteristics at Phi 90 are shown. Ant1 gives a maximum lobe at 300 with an angular width of 250, while Ant2 exhibits wide beam width characteristics of approximately 60\({}^{\circ}\). For Ant3 and Ant4, the same radiation mechanism is observed with a 180\({}^{\circ}\) shift. In this way we can see that the proposed design radiates in all directions with pattern diversity characteristics.  
Figure 5: Measured S-parameters of proposed antenna: (**a**) reflection coefficient, and (**b**) ports Isolation.  
Figure 6: _Cont._
## 2 Literature Review
### Proportional Hazards Model  
To enhance predictive maintenance strategies effectively, it is essential to optimize decision-making. Integrating economic aspects into CBM is crucial for this purpose, with PHM playing a key role in achieving this objective [2]. This statistical model utilizes historical data to develop a function that considers both the age and condition factors of the asset when calculating its conditional hazard level [10].  
In various industrial sectors, including mining, gastronomy, logistics, and finance, the successful implementation of PHM has been observed. Within the field of maintenance, numerous studies have used PHM to enhance CBM policies. In Ref. [11], a two-level CBM policy for ship pumps was optimized under competing risks, incorporating stochastic maintenance quality, with PHM employed to model the deterioration of the pump system. In Ref. [12], a CBM policy featuring dynamic thresholds and diverse maintenance actions for periodically inspected systems was introduced. PHM was used to model the failure time, considering both minor and catastrophic failures. Finally, in Ref. [13], a hybrid repairreplacement policy integrating PHM with a stochastically increasing Markovian covariate process was introduced.  
The literature demonstrates the importance and advantages of integrating PHM with CBM for effective intervention policies. However, challenges related to obtaining parameters for the PHM model in real-world multi-covariate scenarios have not been fully explored. Therefore, this paper aimed to integrate PHM with CBM in complex industrial settings, augmenting them with machine learning tools to streamline reliability calculations and policy generation.