esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dynamic Evolution Process of Underground Pipeline Collapse and Its Settlement Prediction
### PSO-LSTM Settlement Prediction Model
#### 3.4.1 LSTM structure  
LSTM is a variant of Recurrent Neural Network (RNN) that aims to solve the problems of gradient disappearance and gradient explosion in RNN, making it better able to capture and remember dependencies in long sequences [23; 24; 25].  
The main feature of LSTM is that it contains three gates and a cell state. These components work together to process sequence data. Here are the main components and functions of LSTM: The cell state is the internal memory unit in LSTM that can convey information throughout the sequence. Cell states can have information added or removed so that long-term dependencies are maintained. Input gates determine what information should be added to the cell state. It controls the importance of input information through the sigmoid activation function. The forget gate determines what information should be removed from the cell state. It controls the degree of forgetting of the cell state through the sigmoid activation function. The output gate determines which part of the cell state should be output to the next layer of the network, which is processed by a sigmoid activation function and a tanh activation function to generate the final output. Through the control of these gates, LSTM is able to effectively capture long-term dependencies as it can retain important information and discard unnecessary information, thus helping to avoid vanishing gradient or exploding gradient problems [26; 27]. The specific principles are as follows:  
1. Forgetting gate: The forgetting gate determines which information should be removed from the cell state. It controls the degree of forgetting of the cell state through the sigmoid activation function. \[f^{(t)}=\sigma\Big{(}W^{\text{\tiny{C}}}C^{(t)}+W^{th}h^{(t-1)}+b_{f}\Big{)}\] (10)
2. Input gate: The input gate determines which information should be added to the cell state. It controls the importance of input information through the sigmoid activation function. \[i^{(t)}=\sigma\Big{(}W^{\text{\tiny{iC}}}C^{(t)}+W^{ih}h^{(t-1)}+b_{i}\Big{)}\] (11) \[g^{(t)}=tanh\Big{(}W^{\text{\tiny{S}}}C^{(t)}+W^{\text{\tiny{gl}}}h^{(t-1)}+ b_{g}\Big{)}\] (12) \[S^{(t)}=g^{(t)}\times i^{(t)}+S^{(t-1)}\times f^{(t)}\] (13)
3. Output gate: The output gate determines which part of the cell state should be output to the next layer of the network and is processed by a sigmoid activation function and a tanh activation function to generate the final output. \[o^{(t)}=\sigma\Big{(}W^{\text{\tiny{oC}}}C^{(t)}+W^{\text{\tiny{obl}}}h^{(t-1 )}+b_{o}\Big{)}\] (14)  
Finally, combined with the output gate and the new internal state, the new hidden layer state \(h^{(t)}\) is output.  
\[h^{(t)}=tanhS^{(t)}\times o^{(t)} \tag{15}\]
## 4 The Proposed Method
### Improvement of FOA  
The fly distance range (_FR_) of FOA is a random value in the range of [\(-L\), \(L\)], which can be presented as \(FR\sim\) U(\(-L\), \(L\)), where \(L\) named as step size. The value of \(FR\) is distributed evenly among the value range. If the step size is big enough, the global search capability will be improved remarkably, while the convergence speed will be decreased obviously. Otherwise, the FOA easily gets stuck in local optimal, while has a high convergence speed.  
In order to balance the global search ability and convergence rate, the distribution function of FR is modified in this paper. The value of FR follows normal distribution, \(FR\sim\)N(\(0\), \(L^{2}\)). According to the characteristic of normal distribution, the probability of \(FR\in[-L,\ L]\) is about 68.27%, the probability of \(FR\in[-2L,\ 2L]\) is about 95.45% and the probability of \(FR\in[-3L,\ 3L]\) is about 99.73%. The probability density distribution of original fly distance range and the proposed one are presented in Figure 2. Most individuals fly towards the present best location, while more than 30% fruit flies continuing searching at a larger scale. Moreover, individuals flying towards the present optimum tend to a more concentrated region according to the probability distribution condition. Thus, the capacity of global searching and partial location are both enhanced. The flowchart of the IFOA is shown in Figure 3.
## 2 Materials and Methods  
### Feedstocks  
Sugar beet pulp (SBP) is a byproduct of sugar beet processing and is a type of lignocellulosic biomass which can be used for the production of bioethanol. SBP is the remainder of the cossettes after treatment with warm water to extract the sugar [45]. From one metric ton of sugar beet, 160 kg of sugar, 500 kg wet pulp, and 38 kg of molasses can be obtained [46]. In 2018, the harvest of sugar beets in the Northern Netherlands was 2,108,596 t [47; 48], meaning a total of 1,054,298 t SBP was available. SBP mainly consists of polysaccharides, such as cellulose (22-30%), hemicellulose (24-32%), lignin (1-2%), pectin (38-62%), and small amounts of fat, protein, and ash, which leads to a dry matter content of 75-85% [46]. Since the sugar beets are harvested from mid-September until the beginning of December, SBP is not vastly available. To solve this problem, SBP can be bought and stored at -20 \({}^{\circ}\)C, so the SBP can be continuously processed throughout the year [46].  
Grass straw (GS) is a byproduct of grass seed production and mostly consists of cellulose (40-50%), hemicellulose (25-35%) and lignin (15-25%) [49]. Due to the high content of cellulose and hemicellulose, GS is a promising feedstock for the production of bioethanol. Grass straw is available throughout the whole year if it is not frozen. In 2018, 933,020 hectares were occupied as grassland in the north of the Netherlands [50], which produced 3 metric tons of straw per hectare per year [51]. The composition of the feedstocks used in SuperPro Designer (Intelligien: New Jersey, USA) can be found in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component (Mass \%)** & **SBP** & **GS** \\ \hline Ash & 5.1 & 8.9 \\ Cellulose & 15.1 & 28.8 \\ Extractives & n.a. & 0.5 \\ Fats & n.a. & n.a. \\ Hemicellulose & 18.2 & 18.8 \\ Lignin & 0.9 & 13.4 \\ Pectin solids & 20.8 & n.a. \\ Proteins & 10.3 & 4.7 \\ Trash & n.a. & 0.5 \\ Water & 29.6 & 7.0 \\ Dry matter content (\%) & 70.4 & 92.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of the feedstocks.
# Recent Advances in Volatiles of Teas
## 3 Variation of Aromatic Volatiles between Tea Cultivars  
There are great differences in aromatic volatiles between tea cultivars. For Japanese green tea cultivars, cultivar \"Sayamakaori\" had greater amounts of nerolidol, _cis_-jasmone and indole, but less lanalool than cultivar \"Yabukida\" [42]. The _Camellia sinensis_ var. _assamica_ and its progenies, such as cultivars \"Shizu-Inzatsu 131\", \"Sofu\" and \"Fujikaori\", for Japanese green tea processing contain a specific volatile, methyl anthranilate [43]. The black tea made from var. _assamica_ had much higher ratios of lanalool, lanalool oxides and methylsalyclate to total volatiles, but lower ratios of geraniol and 2-phenylethanol to total volatiles than hybrids of var. _assamica_\(\times\) var. _sinensis_[44]. The differences in the volatile compounds between various types of tea depend on cultivar, growing environment and processing. Volatiles in fresh tea leaf are usually in the forms of glycosidically-bound precursors and are released by the action of hydrolases, such as \(\beta\)-primeverosidase, during tea processing [12; 13; 14; 15; 16; 17; 18; 19]. Researchers are now attempting to screen tea cultivars with characteristically nice aromas according to the levels of glycosidically-bound precursors and the activities of hydrolase enzymes.
# A Mobile Solution for Enhancing Tourist Safety in Warm and Humid Destinations
## 2 Literature Review
### Age and Gender Effects on Tourist Satisfaction, Engagement, and Loyalty in Mobile App Usage  
The integration of mobile applications has revolutionized the tourism industry, transforming how tourists plan, navigate, and engage with destinations [42]. Nevertheless, our understanding of how age and gender influence tourist satisfaction, engagement, and loyalty in relation to mobile app usage remains limited [19; 46]. Younger individuals exhibit higher levels of mobile app usage, seeking interactive and immersive experiences, while older tourists face barriers like technological unfamiliarity and privacy concerns [47].  
Gender differences exist, with females inclined towards social and communication-oriented apps and males leaning towards gaming or utility-based applications [48]. Tourist satisfaction, engagement, and loyalty play vital roles in shaping overall travel experiences [49]. Yet, limited research has specifically examined the relationship between mobile app usage and these outcomes, particularly considering age and gender as moderating factors. Further exploration is warranted to comprehensively understand how different age groups and genders respond to mobile app usage and its impact on their satisfaction, engagement, and loyalty toward tourist attractions.  
While direct studies on the relationship between SUS scores and tourist engagement and loyalty are limited, findings from related fields suggest that higher SUS scores enhance user satisfaction, trust, and loyalty [50]. Age and gender differences are observed, with younger users exhibiting higher levels of engagement and satisfaction and gender variations influencing user outcomes. However, further research is needed to investigate the combined effects of SUS scores, age, and gender on tourist engagement and loyalty within the tourism industry's mobile app usage context.
## 4 Discussion
We assessed the impact of ComBat harmonization on the performance of RFs using two approaches; one using the slice thickness, and the second using the pixel spacing to group the scans. We have not provided any biologic covariates to the calculations of ComBat estimates, since the aim of radiomics studies is to investigate clinical correlations. We observed on average a decrease in the performance of RFs following ComBat harmonization in both of the approaches. The probability of obtaining a C-index of 0.70 or higher was also within chance level (<5%). When slice thickness was the batch used for ComBat harmonization, three intensity features were selected in 70% or more of the times. Two of these RFs were selected before and after harmonization. On the other hand, when pixel spacing was used as the batch, four intensity features were selected more than 70% of the times, one of which was selected before and after harmonization. In addition, \(t\)-test analyses showed that the performance of RFs before harmonization was significantly higher. This could be justified by a number of theories. First, the assumption of similar batch effects on the RFs is not met, which could affect the estimation of batch effects. Another possibility is that ComBat harmonization removed the biologic signals in the harmonization process. Regardless of the reason, there was no added value of performing ComBat harmonization in the reported manner in this dataset. This signifies the need for radiomics-specific harmonization methods. While we designed statistically thorough experiments, a number of limitations are to be acknowledged. First, there was a lack of data to perform reproducibility analyses and preselect reproducible RFs, as well as the lack of a more homogenous dataset to compare the performances. Second, the relatively low number of datapoints analyzed, as well as the different treatment regimens used could adversely affect the performance of the signatures developed, especially on the arterial subset of the data. More datapoints are needed to reach more concrete conclusions. Third, the data were collected from different centers, which could introduce more variations due to the patients' characteristics and center protocols. The small numbers of patients per center did not allow for adjusting for these potential variations. In addition, given that the scans were resampled before feature extraction, the impact of ComBat harmonization could be impacted. Lastly, other factors that are known to affect the concordance of RFs, such as intra- and inter-observer variabilities need to be assessed in future studies.
# Power Control Techniques for Interference Management-
## 2 Systematic Review Method
### Identification of the Relevant Literature
#### 2.2.2 Sources for Literature Search  
The search process entails a manual search of journal papers and conference proceedings from 2016 to 2023 to build our review of the recent literature. The authors in [29] emphasize the need to use more than one database for literature searches to maximize the likelihood of finding potentially relevant studies. However, no definite number of databases are recommended to provide a complete, objective, and unbiased relevant selection. This is due to the differences in coverage and indexing methods used [29]. Finally, we may use multiple databases for a literature review, not just one. This is because no single database can cover all the relevant and reliable sources for a topic and discipline. To broaden our scope, avoid bias, and enhance our research credibility, we used three databases in our search. Additionally, this enabled us to crosscheck and compare the results from the three databases to identify the most influential sources for our literature review. Suffice it to mention that there is no definite number of databases to use for literature searches. In this  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{3}{c}{**Refining Filters**} \\ \hline
**Scopus** & **Web of Science** & **IEEE Xplore** \\ \hline Open Access: All Open & Quick Filter: Open Access & Quick Filter: Open Access \\ Publication years: 2018–2023 & Publication years: 2018–2023 & Publication years: 2018–2023 \\ Document Type: Article & Document Types: Article & \\ Source Type: Journal & Web of Science Categories: & Source Type: Journal \\  & Telecommunication & Topics: 5G Mobile Communication; \\  & & Vitreless Channels; Telecommunication \\ Keywords: Power Control; Resource & & Topics: 5G Mobile Communication; \\ Management; Optimization; and Mobile & & Wireless Channels; Telecommunication \\ Telecommunication Systems & & power management \\ \hline \hline \end{tabular}
\end{table}
Table 2: Database search filters.  
research, we limited the search to three databases as too many can make our search too complex to analyze. In this study, three databases, shown in Table 3, were used to search for the literature. The databases were selected because of their usefulness across various disciplines, including electrical engineering.
## 4 Simulation Results
### Performance Evaluation
Regardless of the channel and SNR values, it can be concluded that the proposed synchronization method has a lower computational burden than the benchmark methods. Figure 5: Complexity and performance evaluation of the joint NR-PSS synchronization schemes: (**a**) computational complexity (**b**) detection performance.
# Software-Defined NB-IoT Uplink Framework--The Design, Implementation and Use Cases
## 5 Uplink Implementation  
The following section presents consecutive modules that cover all the vital operations within the whole NB-IoT uplink framework, including both NPUSCH channel procedures and demodulation reference signal (DMRS) signals generation, in accordance with the 3 GPP standards. Since the main concept is chunk-based, it is assumed that all transmitter blocks should be implemented and analyzed separately. However, holistic transmitter evaluation is inevitable as well. The modular architecture of the NB-IoT uplink procedures that fulfills the previously proposed approach is presented in Figure 2.  
It is important to note that an uplink transmission may be processed based on the two different variants, i.e., format 1 or format 2, corresponding to the data and control information transmission, respectively. Each format determines applied channel coding procedures, whereas the further modules are uniform for both variants. All blocks presented in Figure 2, including the NPUSCH and DMRS paths, are described within the next subsections.
# Minimizing the In-Cloud Bandwidth for On-Demand Reactive and Proactive Streaming Applications
## 4 Optimizing Cloud-Based Streaming Internal Bandwidth: Case of Popular Video
### Approximating SASST by SB  
When the system is in a stationary regime, the storage node opens a new CS each \(n^{*}\) slots of time. Thus, we can see the CS streaming channels as portrayed in Figure 7. If we consider each \(n^{*}\) in a given CS as a video segment and we name it \(segment_{i}\) where \(i\in\{1,2,3,\ldots,N\}\) (we assume here that \(\frac{D}{n^{*}}=N\)), then we obtain the same broadcasting schema as SB [6,18]. Each channel broadcasts one instance of the video shifted by the size of one segment from the previous and the next adjacent channels. This means that one instance of the entire video is broadcast across all channels in each segment duration period. Hence, any SN can download the entire video if it connects to all the channels at the same time after one time slot. Using SB, any SN could download the entire video by connecting to only one channel during the CS duration to download the video segments one by one.  
Figure 6: Percentage of \(n^{*}\) as a function of the total video size \(D\) for \(n^{*}\) even and odd, respectively. (**a**) \(n^{*}\) even; (**b**) \(n^{*}\) odd.  
The question is how to minimize the number of broadcasting channels while allowing the SN to download the video without missing any segment. The key scheduling answer to this question is to broadcast each segment \(i\) at least once on any of the channels each \(i\) time slots. By time slot, we mean one segment duration (i.e., equal to \(n^{*}\) slots). This property is inherited from the playback continuity property, where a video player must find segments \(i\) available for playback immediately after playing the \(i-1\) previous segments. In other words, segment \(i\) should be downloaded during the playback period of the previous segments or should be immediately available on one of the channels for playback. Figure 6(b) shows an optimal scheduling of the 17 segments initially broadcast by SASST. As portrayed in Figure 6(a), the streaming bandwidth drops from 17 to 4 broadcasting channels for the same video.  
This scheduling problem has been proven to be NP-hard in [50]. Nevertheless, for a fixed video broadcasting time horizon, we can afford to solve the scheduling problem through an exact method using a linear program formulation. Through a fixed time horizon, we mean that we know for how long we will keep streaming (broadcasting) the video. The  
Figure 7: From proactive SASST to optimal scheduling of 17 segments. (a) SASST as SB; (b) Optimal Scheduling with 17 segments.  
optimization problem formulation is presented in the next subsection. For the case of an unknown time horizon, we propose in Section 4.3 a different formulation of the problem to preserve a reasonable solving cost with regard to the formulation of the fixed time horizon described in Section 4.2. NOPP is based on the two proposed versions of optimal schedules (known and unknown time horizons).
# A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications
## 4 Hybrid Positioning Systems  
An hybrid positioning system by definition is a system that combines two or more systems in order to enhance the performance offered by these systems individually. Currently, there are myriads of hybrid positioning systems in the literature that combine the different IPS reviewed so far.  
A complete classification of hybrid positioning systems is not feasible due to the large amount of possible combinations of IPS that can form an hybrid system. Therefore, in this section we will only review the most interesting systems for mass market applications. Table 2 and Table 1 present a comparison between the IPS methods reviewed in the previous sections including the main characteristics of each method. Furthermore, the main problems in the scalability of the systems are included. For example, angle and time based methods require additional hardware and therefore the cost of the system increases. Moreover they cannot be applied to a wireless network if they are not synchronized with the nodes of the network. Therefore, those systems are not the best option for mass market applications. Similarly, fingerprinting methods require a laborious calibration campaign for every indoor environment and thus they are also not valid for mass market applications. The rest of the systems, that is, RSS, proximity, PDR and SLAM, are valid for mass market applications but the combination of the RSS with the PDR is the most interesting one as it provides accurate results with low monetary cost and low/medium computational cost. In particular, we will focus on the following three groups:  
* **RSS-IMU hybrid systems:** here we include the methods that combine inertial measurements with RSS measurements either by using a propagation model or a fingerprinting approach.
* **Map hybrid systems:** here we embrace the methods that in addition to the RSS and/or IMU measurements also use the map of the building to enhance the performance of an IPS.
* **Smartphone hybrid systems:** here we include those RSS-IMU and Map hybrid systems that have been specifically designed for smartphones.