esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Biscuits are characterized by their popular sweet taste, but they have a poor nutritional profile due to their high sugar and saturated fat content, along with low fibre levels. Their sweetness primarily comes from sucrose, which not only determines the flavour but also performs several technological functions, making it difficult to replace in pastry products. Commercial sweeteners and soluble fibres designed for pastry products are available. Therefore, it is necessary to test the feasibility of using these ingredients in biscuit formulations and assess their impact on biscuit quality. Concurrently, the correlation analysis of dough rheological parameters, structure, and instrumental texture parameters with sensory characteristics will help identify which parameters are strongly correlated and can be used to predict biscuit quality. The purpose of this study was to investigate the dough rheological properties, structure, texture, and sensory characteristics of biscuits in which sucrose was replaced by the commercial sweeteners Tagatsese, malitol, and erythroid-stevia, with the addition of soluble fibres Nutriose(r) FB (wheat fibre) and PromOat 35 (oat fibre). At the same time, a correlation analysis was conducted between dough rheological parameters (stickiness, work of adhesion, dough strength) and biscuit quality parameters, such as water activity, water content, colour, texture (pore area, pore shape, pore elongation), and instrumental texture properties (hardness, brittleness, number of acoustic emission (AE) events, AE event energy), with sensory discrimination evaluated through a consumer test. The use of wheat and oat fibres in combination with sucrose resulted in biscuits with lower apparent density, increased porosity, and weaker texture (fracturability, hardness, number of AE events), yet they had better sensory properties compared to biscuits containing sucrose alone. Replacing sucrose with sweeteners combined with fibres led to a deterioration in the sensory quality of the biscuits and a significant change in the dough's rheological properties. Regardless of the type of sweetener, biscuits with wheat fibre were rated better than those with oat fibre. Of the tested sweeteners, only malitol combined with wheat fibre resulted in a sensory quality similar to that of sucrose biscuits. Correlation analysis of all measured biscuit quality parameters showed that only the number of AE events had a strong positive correlation with all tested sensory attributes. Porosity was only correlated with sensory crispness, and fracturability was correlated with sweetness, taste, and overall acceptability. Therefore, it appears that the number of AE events recorded at the time of breaking may be a reliable parameter for predicting biscuit quality.  
**Keywords:** sucrose; Tagatese; maltitol; erythritol-stevia; Nutriose; \(\beta\)-glucan; oat fibre; wheat fibre
## 2 Materials and Methods
### Tests and Datasets Creation
#### 2.3.4 Building of the Datasets for 3D Modelling  
The surveys performed for the construction of the datasets for 3D modelling were conducted with the Deeper Smart Sonar CHIRP+ implemented in the G > S > P Sys (as in Figure 1).  
The dataset for 3D modelling of artificial structures, in this case, a swimming pool with bottom variation, was constructed by surveying the pool used in the tests in Section 2.3.2. The dataset for the 3D modelling of the natural surfaces was constructed by a seabed survey in the site indicated in Section 2.3.2. The survey covered an area of about 3 hectares (about 2 beaches) with the most distant point from the coast surveyed at about 240 m (Figure 3a). For the survey, 16 transects orthogonal and 1 parallel transect to the coast were executed, and 1659 points were collected. The survey was conducted semi-automatically by \"creating\" the map in real-time by executing transects according to the areas of the map to be completed indicated by the Fish deeper--Fishing App. This survey mode can be compatible with cost-efficient science projects and citizen science projects, for example.  
The survey was conducted using a SUP board (Figure 3d-f) with a new floating platform connected to it (prototype 3--Figure 3b,c). The new low-cost platform had a trimaran structure for greater stability in navigation, and an \"F\" bracket was installed at the stern to planimetrically align the external GNSS antenna with the Sonar (Figure 3b-d--in green circle).  
In order to facilitate the exposition in Section 2.4, dedicated to 3D modelling, the file downloaded from the Deeper Smart Sonar CHIRP+ was called \"File Sonar\", the file containing the real-time positioning data in SPP of the SimpleRTK2B was called \"File SPP\" and the file containing the post-processed positioning data in PPK of the SimpleRTK2B was called \"File PPK\".  
Table 7 shows the composition of the datasets for 3D modelling of the seabed (natural surface) and Table 8 shows the composition of the datasets for 3D modelling of the pool (artificial structure).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Device**} & \multirow{2}{*}{**Real Depth** **(m)**} & \multirow{2}{*}{**Water**} & **Temperature** & **Data** & \\  & & & (°C) & & **Sample (no.)** & **Main Data** \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Deeper Smart \\ Sonar CHIRP+ \\ \end{tabular} } & \multirow{2}{*}{0.9} & Fresh & 22.27 & 3700 & Depth \\  & & Salt–37.8\% & 20.90 & 3700 & Depth \\ \cline{2-6}  & \multirow{2}{*}{1.52} & Fresh & 22.27 & 3700 & Depth \\  & & Salt–37.8\% & 20.90 & 3700 & Depth \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Deeper Smart \\ Sonar Pro+ 2 \\ \end{tabular} } & \multirow{2}{*}{0.9} & Fresh & 22.27 & 3700 & Depth \\  & & Salt–37.8\% & 20.90 & 3700 & Depth \\ \hline \hline \end{tabular}
\end{table}
Table 6: Water depth datasets composition.  
Figure 3: (**a**) Survey area at sea; (**b**) preparation of instruments; (**c**) floating platform; (**d**) preparation of SUP for the survey; (**e**) floating platform connected to SUP; (**f**) survey at sea.
## 2 Materials and Methods
### Location and Scenario Information  
To support the evaluation of the physiological indicators listed, the proposed method also considered the collection of further information. In particular, it was necessary to record the position, as well as a range of contextual information useful for the characterization of the road environment, and of the situations and scenarios that may occur within it. This information was collected using a Global Positioning System (GPS) device and a video camera.  
The GPS was fundamental for the continuous spatial location of the data collected, and for the correlation between these and the black spots identified through the analysis of accidents. Several smartphone apps can fulfill this requirement and ensure instant car location data, including MotionXGPS (developed by Fullpower Technologies, Inc., Santa Cruz, CA, USA) and GeoTracker--GPS tracker (developed by [PERSON], Moscow, Russia). These applications allow saving files in.gpx format, manageable with Google Earth. For each point, longitude, latitude, altitude, speed, and time can be displayed.  
The use of a video camera was needed to effectively record the contextual features of the environment during the experimental driving tests, to identify events and situations that may indicate the potential causes of stress for the driver. During the field tests, different tools were used. In the first phase, a GoPro camera was used, positioned on the head of the driver (to record what he was actually seeing) or on the dashboard of the car. In the second phase, Camera Sunglasses (produced by Risest Technology Co., Fudian District, Shenzhen, China) were used.  
Further information could be collected through a recall test, carried out by the testers in both phases of the test (at the end of each driving test). The purpose of the questionnaire was to evaluate how stress was perceived by the user during the path, based on the perceptions declared by the testers. The recall test also allowed the collection of general information concerning the driving experience in the analyzed context.  
Similar to the approach adopted in the study by [PERSON] and [PERSON]. [24], two types of evaluation were requested in the recall test, shown below.  
Figure 3: (**a**) Bracelet for the measurement of the galvanic skin response (GSR) parameter; (**b**) MAXREFDES#73 smartphone application screenshot.  
1. The individual user was asked to remember the events considered most stressful during the test and was asked to give each one a score from 1 (no stress) to 5 (very high stress).
2. Participants were asked to list the causes of perceived stress from most stressful to least stressful.  
The results of the questionnaires were then compared with the data collected during the different tests.
## 4 Discussion
### Pathogen Features and Clinical Presentation  
Most dental abscesses are caused by the resident oral microbiota that enters normally sterile tissues. The major isolates are streptococcal and anaerobic bacteria, which are regarded as normal flora of the tooth and gingival crevice [63]. The microbiota specificity in odontogenic infections has been more clearly delineated with technological advances in sampling and anaerobic culture. [PERSON] et al. [46] reported that the most frequent species isolated in BAs are Gram-positive cocci (Streptococcus mutans, Streptococcus milha, Streptococcus intennedius) and Gram-negative rods (Aggregatibacter actinomycetemcomitans, Prevotella oris and Fusobacterium nucleatum). [PERSON] [64] reported that the most common pathogens involved in intracranial BAs were Streptococcus (42%), Fusobacteriae (14%), Aggregatibacter actinomycetemcomitans (8%) and Staphylococcus spp (8%). Similarly, we reported that the most common pathogens involved in the development of multiple BAs were Streptococcus viridans (with its different species), which was identified in 31.3% of cases, and Aggregatibacter actinomycetemcomitans (18.8% of cases), although Gram-positive and Gram-negative bacteria seem to be involved in equal part, as we found in this review. Anaerobic infection is favored in the brain due to the low oxygen tension of the interstitium and because the BA causes focal infarcts due to decreased oxygen supply [46]. Streptococcus intermediius, which is a member of the Streptococcus anginosus group, despite being a part of the normal microbiota, is one of the most common pathogens associated with brain and liver abscesses and thoracic empyema, increasing, as a result, the morbidity and mortality rates in affected patients [65]. Some oral bacteria such as Aggregatibacter actinomycetemcomitans is non-motile, facultative anaerobic, small Gram-negative coccobacillus that shows a predilection for the central nervous system when they produce systemic disease [46; 66]. However, it is difficult to isolate through culture due to its fastidious slow-growing nature, especially because it is often part of mixed infection with other bacteria [36; 67; 68]. As our review confirmed, BAs were often caused by poly-microorganisms (in 25% of cases) and for this reason it is difficult to identify accurate pathogens [31].  
In the case of a single BA, the presenting features and clinical neurological presentations depend on the size and intracranial location of BA, the virulence of the infecting agents, the immunologic status of the host, and the cerebral edema caused by the expanding intracranial mass lesion [28]. The classic triad of fever, headache and focal neurologic deficit is present in less than 50% of cases [28]. In the case of multiple BAs, the classic triad of fever, headache and focal neurologic deficit is present in more than 63% of cases. Similarly, mental status change and reduced consciousness is present in up to 37.5% of patients, as we reported, often accompanied by the acute onset of meningeal signs, suggesting either herniation or intraventricular rupture of the abscess.
# Fog vs. Cloud Computing: Should I Stay or Should I Go? +
## Modeling Platforms  
When working with sensors, it is common for users to need to transform the raw collected data into something else before storing the final values. For example, they may wish to standardize or clean the data (parsing), discard unwanted readings (filtering), or generate knowledge (predicting).  
With the task at hand, they will then need to decide where to execute it, and there are a few options to choose from: they can process the data on the same device that collected the values; they can send the data to other nearby devices, among which the task may be split; or they can send the data to be processed by more powerful and distant devices, such as cloud data centers.  
In this article, we discuss the first and third cases, with the second one being left to future work. Therefore, our main goal will be working toward the answer to the question \"is it worth processing a data stream on the device that collected it or should we send it somewhere else?\". In a way, looking for the answer to this question is akin to a search for cases where fog computing (performing computation close to the data source) is more profitable than cloud computing (sending data to be processed by faraway devices).  
For the purpose of this analysis, we consider that we are working with devices that are capable of executing custom code, connected to the Internet, and able to send packets to other devices. Furthermore, we assume that we are handling non-empty data streams with a limited size (i.e., the stream size is neither zero, nor infinite).
## 2 Overview of EV and PV Systems
### Electric Vehicles  
According to the definition of [PERSON] et al. [PERSON] et al. (2018), a vehicle that is powered by electrical means whilst using one or more motors for motion is called an EV. Although the classification of EVs encompass hybrid electric vehicles (HEVs), battery electric vehicles (BEVs), PHEVs, and extended range electric vehicles (EEVs) ([PERSON] and [PERSON], 2010; [PERSON] et al., 2018), this paper is only focused on the BEVs and PHEVs. BEVs are equipped with a fully electric drivetrain, which is powered by rechargeable batteries. On the other hand, PHEVs have a hybrid vehicle drivetrain, which uses a combination of an internal combustion engine (ICE) and electrical power, similar to a HEV, although its rechargeable battery can be charged by plugging it into an external power supply, unlike HEVs ([PERSON] and [PERSON], 2010; [PERSON] et al., 2018). EV chargers can be classified as on-board or off-board and conductive or inductive. Based on the place where the battery charger is installed, it can be classified as on-board or off-board. In this way, an on-board battery charger is installed inside the EV, while off-board chargers are placed outside the EV ([PERSON] and [PERSON], 2010; [PERSON] et al., 2018). Battery chargers can also be classified as conductive or inductive, depending on the mode of power transfer from the power source to EV ([PERSON] and [PERSON], 2010). In this way, power flow between the PV/grid and the EV battery is delivered using plugs and sockets when utilizing conductive chargers ([PERSON] and [PERSON], 2010; [PERSON] et al., 2018). On the other hand, the transfer of power using resonance via a wireless medium is allowed by the inductive chargers [PERSON] et al. (2018). However, the inductive EV chargers are not considered in this paper. Moreover, there are different standards available that regulate conductive charging operations for EVs/PHEVs with many classifications and rules regarding charging topologies, communication and connectors ([PERSON] and [PERSON], 2010). The Society of Automotive Engineers' (SAE) SAE J1772 standard is one of the most accepted standards for EV/PHEV charging power converters ([PERSON] and [PERSON], 2010; [PERSON] et al., 2018). As stated in the SAE J1772 standard, three AC charging power levels are designed for on-board chargers, whereas three DC charging power levels are dedicated for off-board chargers ([PERSON] and [PERSON], 2010; [PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018), as shown in Table 1.  
The AC Level 1 charging is defined for slow charging operations. It is intended to be used for single-phase AC sources in common household circuits or charging stations with power levels that peak at 1.92 kW ([PERSON] and [PERSON], 2010; [PERSON] et al., 2018). The AC charging level 2 and 3 refers to semi-fast charging and fast charging, respectively. However, this paper is only focused on DC charging systems.
# A Review on the Prospects of Mobile Manipulators for Smart Maintenance of Railway Track
## 2 Current Technologies for Railway Track Maintenance  
Railway asset maintenance systems range in size from carriages to hand-held devices. These can be stationary or movable. Fixed maintenance assets are usually positioned along the railway infrastructure network at crucial locations. They record information, such as passing train information, and monitor track components. On the other hand, movable devices are designed to run along the railway track in order to perform specific tasks at a certain speed. In some cases, the movable platform can be airborne, such as an unmanned aerial vehicle (UAV) or a helicopter. Table 1 shows the advantages and disadvantages of several devices for use in railway track maintenance. These devices are equipped with various types of sensors for navigation, monitoring, surveying, and inspection. Common inspection methods used so far include visual inspection, digital image correlation (DIC) [20] and thermal cameras [21] for rail component inspection, laser vision sensors [22] for rail wear and track geometry assessments, laser distance meters [23] for rail corrugation, ultrasonic sensors [24] for surface and internal defect determination, light detection and ranging (LiDAR) [25] for track clearance and vegetation detection, eddy currents [26] for internal rail defect inspection, electromagnetic acoustic transducers (EMAT) [27] for surface and internal defect analysis, ground-penetrating radar (GPR) [28] for ballast fouling and moisture assessment, interferometric synthetic aperture radar (InSAR) [29] for track settlement, etc.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Main Tasks** & **Advantages** & **Disadvantages** \\ \hline Push trolley & \begin{tabular}{c} Track inspection, \\ carry material \\ \end{tabular} & \begin{tabular}{c} Smaller in size, \\ easy to transport \\ \end{tabular} & \begin{tabular}{c} Slow speed, \\ human-operated \\ \end{tabular} \\ \hline Road-Rail Vehicle (RRV) & \
\begin{tabular}{c} Repair, transport material, \\ inspection \\ \end{tabular} & High speed, easy to transport & Costly, human-operated \\ \hline Specialist trains & Repair and inspection of track &
\begin{tabular}{c} High speed and payload, \\ multiple measurements \\ \end{tabular} & Very costly to operate \\ \hline Train-borne system & Track inspection, track repair & Moderate cost, easy to install & Limited validation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Type of track maintenance devices and systems used in railway.
# Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review
## 6 Conclusions  
Next-generation (5G) network are going to play a significant role in smart health-care and IoT applications. Smart health-care and IoT applications have a vital role in the 5G network from functionality and economic point of view. In this paper, we highlighted various applications with diverse perspectives and compared short and long-range communication technologies in terms of range, frequency, power usage, and data rate for smart health-care. Furthermore, four different scenarios are considered according to different requirements for 5G network (i.e., enhanced mobile broadband, low-latency and high-reliability communications, massive machine-type communications, and Internet access for wireless regional area networks), and presented different technology trends for the achievement of these requirements in 5G network and are thoroughly discussed. Finally, we presented the open issues and future research directions related to smart health-care in the 5G network. This provides the opportunities for researchers looking towards starting research within the field of 5G based smart health-care using different approaches, such as machine learning, scheduling, routing, handover, and clustering. There remains an expansive sum of future work due to unique characteristicsof smart health-care, and this paper has laid a strong foundation to inspire researchers to investigate the open issues raised in this topic, as mentioned in Table 5.  
**Author Contributions:** All the authors contributed to the work and wrote the article. A.A. proposed the idea, designs, and writing, M.T., M.A.S., K.I.A., A.M. and A.N. suggested directions for the detailed designs as well as coordinated the work. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Sunway University PGR Support.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Stock Price Prediction Using a Frequency Decomposition Based GRU Transformer Neural Network
## 2 Related Work
### Attention Based Models  
[PERSON] et al. [43] incorporates multi input LSTM to attention based neural networks. In this model, there is a multi input LSTM and a attention layer, followed by several ReLU layers to get the final prediction. The multi input consists of a main stream and 3 auxiliary feature groups in order to avoid dimension expansion problems. It is showed that in the dataset of CSI-300 index with a time range of 4 years, there is a significant improvement compared to several LSTM or CNN based methods. [PERSON] et al. [44] develops a transformer-based model with 2 input layers, 3 transformers layers, 1 pooling layer, followed by 2 dropout layers and 2 dense layers, which is used to do the forecasting of Dhaka Stock Exchange(DSE), including daily, weekly and monthly stock prices, with a promising results. [PERSON] et al. [45] proposes CapTE (Capsule network based on Transformer Encoder) model to do the stock movements prediction. The transformer encoder is utilized to filter the deep notable features of text in social media. Combined with the stock data, it will be passed into 2 CNN and 2 dense layers to get the final prediction. It shows significant results when applying to a dataset including 47 stocks with a time range of 10 years. [PERSON] and [PERSON] [29] combines the aforementioned DeepLOB with an encoder-decoder structure to do the prediction. The encoder part is a DeepLOB model, through which the stock data is transformed into a vector, followed by a decoder to get the final predictions. Experimental shows that it outperforms DeepLOB and other time-series related models when applied to FI-2010 dataset, especially when the prediction time horizon is long. [PERSON] and [PERSON] [46] integrates transformer with CNN layers to do the stock movement prediction. The structure of the model consists of 2 multi head layers, 1 transformer and 2 CNN layers. In the experiment, it is showed that this model is superior to several CNN or RNN based models under the dataset of the S& P 500 index.  
Compared to other papers above, in this paper, FDG-Trans intends to combine GRU and transformer, which can take advantage of the fast-speed GRU, and capture the semantic signals along different timestamps. In addition, the decomposition of the frequency technique has been added to our model. After the decomposition, the fluctuation of the price will be largely reduced, which leads to more stable training data for the training process of the neural network, thus improves the prediction performance.