esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, we propose a novel routing protocol called MEDR tailored for DTMSNs. In MEDR, each sensor maintains two important parameters: the minimum expected delay (MED) and its valid period. According to the above parameters, MEDR can efficiently find out and utilize the temporary and local multiple-hop connected paths which are dynamically formed by moving nodes to improve the performance of data gathering. We evaluate the performance of MEDR, direct transmission, epidemic and FAD algorithms by extensive simulations. The experimental results show that our proposed MEDR protocol outperforms the other three approaches in terms of message delivery ratio and average delivery delay, and its transmission overhead is much less than that of FAD and epidemic routing.
## 2 Methodology
### Step 1: Identification of Research Studies  
Relevant studies were identified using three major search engines: Scopus, WoS and Google Scholar. The selection of the search engines was based on their comprehensive coverage and reputation in the academic community. Scopus and WoS are known for indexing high-quality, peer-reviewed journals, conference proceedings and other scholarly publications, ensuring the inclusion of rigorously reviewed research in our study. Google Scholar, on the other hand, complements traditional bibliographic databases by incorporating a broader range of sources. We found 260 studies, as shown in Figure 1. In Scopus, we found 49 studies (19%), in WoS 141 (54%) and in Google Scholar 70 (27%). The lower number of articles from Google Scholar is due to the fact that we used only peer-reviewed studies. In Google Scholar, the number of articles on these topics was 1450 when the \"reviewed articles\" filter was not used.  
The following generic keywords were used for identification:  
* Autonomous ships;
* Maritime education;
* MASS ships;
* Autonomous vessels;
* Maritime training.  
The choice of keywords in our study was based on a thorough consideration of the scope and objectives of our research. We wanted to identify and analyze relevant literature  
Figure 1: The information flow via the phases of the systematic literature review (Reported numbers valid as of 10 of October 2023).  
on the topics of \"autonomous ships\" and \"maritime education\", focusing particularly on the overlaps between these two fields. These keywords reflect the primary focus of our study and serve as the basis for our search strategy. We supplemented our primary keywords with related terms such as \"MASS ships\", \"autonomous vessels\" and \"maritime training\". With these additional keywords, we wanted to cast a wide net and capture all relevant literature that could not be explicitly captured by our primary keywords alone. The graphical analyses in Figures 2 and 3 demonstrate the value of the tool for recognizing the effective choice of the keywords \"autonomous ships\" and \"maritime education\" within the broader research landscape. By visually representing the semantic connections with the 30 most commonly used keywords, the VOS Viewer allows for a detailed examination of the keywords and overlaps that define the scientific discourse in these areas. This approach not only improves our understanding of the prevailing research topics but also helps to identify the potential areas of convergence or divergence within the overarching research framework.  
Figure 3: The link of the keyword “maritime education” with the 30 most used keywords in the research (VOS viewer).  
Figure 2: The link of the keyword “autonomous ships” with the 30 most used keywords in the research (VOS viewer).
## 3 Results and Discussion
### Comparative Study  
The maximum adsorption capacity and pH value for the removal of arsenic ions (As(V) and As(III)) were compared with previously studied adsorbents as tabulated in Table 2. According to Table 2, the adsorption capacities in the current study are comparable to the values reported in the literature indicating the affinity of the Fe\({}_{3}\)O\({}_{4}\)/CS/PEI nanocomposite for arsenic ions. However, the difference in the adsorbent efficiency between materials can be explained by the applied operating conditions and the variability of the interactions between pollutants and the material functional groups. Generally, strong interactions with the specific functional groups of the adsorbent surface permitted high adsorption capacity. Subsequently, the synthesized Fe\({}_{3}\)O\({}_{4}\)/CS/PEI nanocomposite can be considered as a promising nanosorbent for water treatment that must be investigated for the capturing of additional metals, dyes, and pharmaceuticals in the future. However, the complete view about the treatment study should be obtained by the determination of the thermodynamic parameters [60; 61; 62] of the adsorption process.
# Integrating Decentralized Indoor Evacuation with Information Depositories in the Field
## Information Depositories  
### Awareness of the Indoor Environment  
The indoor space of a building can be represented by a navigable graph \(G(V,E)\), where \(V\) is a set of vertices \(v_{i}\) with a total count of \(\|V\|=m\), and \(E\) is a set of edges \(e_{j}\) with a total count of \(\|E\|=n\). Nodes represent locations, and edges represent a possibility to move directly between two locations. Moving agents (evacuees) can have partial or complete knowledge of this graph, depending on their individual familiarity with the environment. The partial or complete _awareness_ of the indoor space during evacuation consists of at least three dimensions:  
* The (partial or complete) original graph of the indoor environment. It represents the connectivity of the indoor environment before being affected by a disaster.
* The state of the original graph. States of nodes and edges track the changes caused by the disaster that evolves dynamically. This paper assumes two possible states of each edge or node: _unblocked_ and _blocked_. A _blocked_ edge is not passable for evacuees, while an _unblocked_ edge allows evacuees to pass through freely. A _blocked_ node can be represented by setting all its adjacent edges as _blocked_. In decentralized evacuation different evacuees can have different knowledge of the states of the graph.
* The time stamp of the state. It labels when that state of an edge or node has been lastly updated. Since the only sensors in decentralized evacuation are the (smartphones of the) evacuees,edges and nodes are only updated when encountered and found in a different state by an evacuee. Thus, these times are transaction times of individual sensors, not valid times of changes in the environment. Furthermore, in decentralized evacuation different sensor nodes can obtain the same information at different times.  
Awareness of the indoor environment can be represented by a set of triplets:  
\[\{(e,state_{e},t_{e})_{j}\},j\in\{1,2,\ldots,n\} \tag{1}\]  
where \(e\) denotes an edge of the graph, \(state_{e}\) denotes the state of that edge, and \(t_{e}\) denotes the time when that \(state_{e}\) has been obtained.  
Such awareness of the indoor environment is an enhanced graph for computing shortest evacuation paths while avoiding the known-to-be-blocked edges.
## 2 Related Work
Moreover, we observed that few papers have addressed diverse patterns for threat analysis while considering time, accuracy, and FPR. Several works have proposed hybrid models based on the CNN and LSTM to learn spatial and temporal data. However, LSTM is computationally complex and requires a long time for analysis [38]. The QRNN model is a type of RNN that allows for sequence modeling by implementing computation in parallel while maintaining the data's long- and short-term sequence dependencies [23]. We could not find a work that used the QRNN model to improve cyber threat classification time while demonstrating high accuracy. Thus, in this work, we propose a hybrid DL model for CTI for smart cities that addresses the abovementioned challenges and uses the QRNN model. The proposed hybrid model can improve threat classification accuracy and lower the FPR in a reasonable time. Therefore, it can predict different attacks to protect citizens' data and enhance the security of smart cities.
## 4 Case Study of Punjab OSM Data
### Results
#### 4.2.4 Route Navigability Assessment  
The model (Figure 5) used for the assessment of road navigability accepted two inputs: (1) routing network; (2) origin-destination points table. The origin-destination points dataset was prepared using the geocode plugin in QGIS, which uses Nominatim geocode services, to acquire the POIs for all districts of Punjab (India). The two inputs were given to the two components of the model, for finding the shortest OSM map distances (Figure 10) and direct distances between the points. In order to validate the results, a Python script was developed using the Google map distance matrix API to find the shortest routing distance between the same set of origin-destination points. Figure 11a,b present the plots of heuristic relative error used to measure the variation of shortest distances from direct distances obtained on OSM and Google maps, respectively. It was found that in the case of the district-to-district-based origin-destination matrix, nearly 69 routes had a distance more than 20% of the direct distances. Few longer routes were attributed to data preparation, as the neighboring state's network was not included. Therefore, particularly for the district Pathankot, the routes were 30% to  
Figure 9: Punjab OSM road network analysis over the years. (**a**) March 2008; (**b**) August 2012; (**c**) December 2016.  
40% longer approximately. As per the outcome of the model, it is concluded that the completeness of the primary roads was quite satisfactory, but other types of roads are less complete.  
Further, it was witnessed that in Figure 11b, the roads' variation in Google data is more as compared to OSM data. Firstly, this was due to the lazy computation used to find the approximate closest node to the locations of interest, and instead of taking the fraction length of the street, whole lengths were taken. Secondly, Google optimizes for time (cost based on time), which also considers traffic conditions, whereas our algorithm was optimized for distance only. Lastly, nearly in the whole of the OSM dataset, the turn restriction attribute was missing.  
Figure 11: Dot plots of relative difference (if more than 20%) of the shortest distances and direct distances.  
**(a)** OSM routing distances; **(b)** Google routing distances.  
Figure 10: Shortest routes created by the [PERSON] algorithm [98].
## 3 Materials and Methods
### Data Preprocessing
#### 3.2.1 Imperfect Data  
The dataset may contain data that can be defined as defective for various reasons. These data are divided into various subcategories and can be processed using different solutions.  
* **Missing Data:** There are several approaches to address the issue of missing values in data preprocessing. The first option is usually to discard samples that may contain missing values. However, removing samples may introduce bias in the learning process or omit important information. The second option involves modeling the probability functions of the data and considers the mechanisms that cause missingness. This approach uses maximum likelihood procedures to approximate probabilistic models for filling missing values [39].
* **Noisy Data:** There are two main approaches in the data preprocessing literature for handling noisy data. The first is to correct the noise, especially if it affects the labeling of a sample. The second approach involves identifying and removing noisy examples from the training data [40].
### Fresh and Hardened State Tests  
The fresh properties were appraised with a slump test, while the compressive and splitting tensile strengths were measured to study the mechanical properties of all specimens. Moreover, durability properties were examined using freeze-thaw cycles, sodium sulphate attack, sulphuric acid attack, water absorption, and electrical resistivity. The test methods on fresh and hardened samples are summarized in Table 3. In this study, in order to investigate the effect of freeze-thaw cycles, 30 cycles were conducted. The specimens started the freeze-thaw cycles after 28 days of curing. Saturated specimens were placed at \(-20\)\({}^{\circ}\)C for 12 h and then at +4 \({}^{\circ}\)C for another 12 h to freeze and thaw. Regarding sodium sulphate attack, the 50 mm cubic specimens were prepared and placed in a tap water bath. After 28 days, three samples were moved to the bath containing 20% sodium sulphate solution, and three samples remained in the tap water bath. Finally, the compressive strength of all samples was compared at the age of 120 days. ASTM C267 [41] was also used to evaluate the compressive strength and weight loss of specimens in the presence of sulphuric acid (H\({}_{2}\)SO\({}_{4}\)). After 28 days of processing in the tap water bath, specimens were moved to a bath containing 25% sulphuric acid solution (\(w/w\)) and were subjected to sulphuric acid for 7 days.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**No**} & \multicolumn{1}{c}{**Name**} & \begin{tabular}{c} **Water** \\ **(g)** \\ \end{tabular} & \begin{tabular}{c} **Cement** \\ **(g)** \\ \end{tabular} & \begin{tabular}{c} **RM (g)** \\ \end{tabular} & \begin{tabular}{c} **GGBFS** \\ **(g)** \\ \end{tabular} & \begin{tabular}{c} **EAFD** \\ **(g)** \\ \end{tabular} & \begin{tabular}{c} **Sand (g)** \\ \end{tabular} & \
\begin{tabular}{c} **W/b** \\ **(mm)** \\ \end{tabular} &
\begin{tabular}{c} **Slump** \\ **(kg/m\({}^{3}\))** \\ \end{tabular} \\ \hline
1 & Ctrl & 1800 & 2820 & 0 & 0 & 0 & 10,563 & 0.64 & 150 & 2165 \\
2 & RGE5 & 1800 & 2679 & 47 & 47 & 47 & 10,563 & 0.64 & 150 & 2168 \\
3 & RGE10 & 1800 & 2538 & 94 & 94 & 94 & 10,563 & 0.64 & 145 & 2161 \\
4 & RGE15 & 1800 & 2397 & 141 & 141 & 141 & 10,563 & 0.64 & 140 & 2158 \\
5 & RGE20 & 1800 & 2256 & 188 & 188 & 188 & 10,563 & 0.64 & 135 & 2153 \\
6 & RGE30 & 1800 & 1974 & 282 & 282 & 282 & 10,563 & 0.64 & 130 & 2147 \\
7 & RGE40 & 1800 & 1692 & 376 & 376 & 376 & 10,563 & 0.64 & 115 & 2120 \\
8 & RGE50 & 1800 & 1410 & 470 & 470 & 470 & 10,563 & 0.64 & 105 & 2106 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mix proportions of all mortars and their properties.  
Figure 6: (**a**) Steel mould used to produce cubic samples; (**b**) the strength-testing machine.
## 4 Adjustment of the TMC Simulations
### Number of Random Files  
The number of the random files generated for the TMC calculations can significantly affect final results and justifications. A special exercise was carried out to perform sensitivity analyses of the integral nuclear response (TBR) from the number of the random files for each isotope. The simulations were carried out with the HCPB DEMO geometry model described above and the TENDL-2017 nuclear data.  
The TENDL-2017 provides 300 random files for most of the target nuclides. This number may already be insufficient to find the general trend of the TMC results with high enough statistics. The procedures based on the use of the BAKED and SANDY codes were implemented to generate two sets of 1000 random files from original TENDL-2017 library. A sequence of the TMC calculations were performed to study this effect with the variated number of the files for each nuclide starting from 10 up to 1000. The results of the TBR calculations for 300 and 1000 random files for each nuclide are shown in Figure 3 both for the SANDY- and the BAKED-based methods.  
For the TMC, 10,000 iterations were chosen to obtain well-resolved TBR distributions. The probability distribution of the TBR integral response does not necessarily follow normal distribution. Being well fitted with Gaussian distributions, the results presented in Figure 3 provide the most probable TBR integral response. The results based on the SANDY calculations are slightly shifted to the lower TBR number compared to the BAKED-based ones and the difference does not exceed 0.2%. Due to the very low MCNP statistical uncertainty, the results shown in Figure 3 only reflect the uncertainty of the nuclear data used for the calculations.  
Shown in Figure 4 are the results of the sensitivity analyses for the TBR as the function of the random files number for each isotope. Generally, the TBR appeared to be bigger for the small numbers of the random files. The saturation of the results was achieved with the random files number >150. For further TMC calculations, 300 random data files were used for each nuclide.  
Figure 3: Distribution of the TBR with the random files obtained with BEKED and SANDY codes.
# Improving Smartphone GNSS Positioning Accuracy Using Inequality Constraints
## 2 Positioning Model and Inequality Constraints  
In this section, the positioning model on Android smartphones is introduced. Additionally, we describe the constraint matrices of three different applications.
# Long-Term Forecasting Using MAMTF: A Matrix Attention Model Based on the Time and Frequency Domains
## 4 Experiments
### Ablation Studies  
In the ablation study, we performed the model testing itself using two ablation studies. The first was to conduct an ablation study of the model architecture, as shown in Table 4. The second was to conduct an ablation study of the attention mechanism to test the superiority of the matrix attention mechanism by replacing different attention mechanisms into the model architecture, as shown in Table 5.  
We verify the advantage of our model structure by removing some of the structures in M-MAMFT, and the specific model structure ablation study is shown in Table 4. When only the time domain block is retained, the prediction performance of the three datasets shows a small decrease. When only the frequency domain block is retained, the prediction performance of the three datasets is poor. The effect of the feature reconstruction on the overall prediction performance of the model is verified by removing the real feature reconstruction part and the imaginary feature reconstruction part from the frequency domain block. When removing the real feature reconstruction part and the imaginary feature recon-struction part separately, the prediction results become progressively worse as the pre-diction length increases in the ETH2 dataset. In the Weather dataset, removing the re-al feature reconstruction part and the imaginary feature reconstruction part separately has a smaller effect on the prediction results. The overall prediction performance in the BCAT dataset is poorer when both the real and imaginary part operations are removed separately.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Metric**} & \multicolumn{4}{c}{**ETTh2**} & \multicolumn{4}{c}{**Weather**} & \multicolumn{4}{c}{**BCAT**} \\  & & **96** &  
In Table 5, we compare the prediction accuracy of the three datasets under the same model architecture with different attention mechanisms. We replace the matrix attention mechanism in the M-MAMFT model with several different attention mechanisms, such as FEA-f, to verify the superiority of matrix attention. Compared with the other attention mechanisms, the model with the matrix attention mechanism achieves the best prediction results, proving the effectiveness of our proposed matrix attention mechanism. Meanwhile, different attention mechanisms applied in our model architecture also surpass the prediction results of most of the models in Table 3, which also proves the advantage of our model architecture.
## 3 Data Analysis
### Relationships between GNSS-R Observables and Soil Moisture
This difference could be explained by the fact that the GLORI measurements were corrected for cross-polarization effects, thus removing most of the residual LHCP signal contributions from the RHCP reflected antenna. This limitation could be due to the high noise level in the PR data, coming from low values of \(\Gamma_{RR}\). For this reason, it seems difficult to consider this observable in the analysis of soil moisture effect. Concerning the dependence on the elevation angle for the two observables (reflectivity and SNR) we observed a sensitivity slightly higher for high elevation angles in the case of a low LAI class with sensitivity of reflectivity to soil moisture; for example, equal to 45.7 dB/(m\({}^{3}\)/m\({}^{3}\)) for 70-90\({}^{\circ}\) and 40 dB/(m\({}^{3}\)/m\({}^{3}\)) for 50-70\({}^{\circ}\), and approximately the same for SNR (equal to 39 dB/(m\({}^{3}\)/m\({}^{3}\)) for 70-90\({}^{\circ}\) and to 38.9 dB/(m\({}^{3}\)/m\({}^{3}\)) for 50-70\({}^{\circ}\)). This sensitivity is attributed to the vegetation's influence and its high attenuation of GNSS-R signals at lower elevation angles. Table 2 summarizes all the results of the GNSS-R observable sensitivities to soil moisture for the different configurations discussed above. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**50–70\({}^{\circ}\)**} & \multicolumn{4}{c}{**70–90\({}^{\circ}\)**} \\ \cline{2-9}  & \multicolumn{2}{c}{**LAI < 1**} & \multicolumn{2}{c}{**LAI > 1**} & \multicolumn{2}{c}{**LAI < 1**} & \multicolumn{2}{c}{**LAI > 1**} \\ \cline{2-9}  & \multicolumn{1}{c}{**S**} & \multicolumn{1}{c}{**R\({}^{2}\)**} & \multicolumn{1}{c}{**S**} & \multicolumn{1}{c}{**R\({}^{2}\)**} & \multicolumn{1}{c}{**S**} & \multicolumn{1}{c}{**R\({}^{2}\)**} & \multicolumn{1}{c}{**S**} & \multicolumn{1}{c}{**R\({}^{2}\)**} \\ \cline{2-9}  & \multicolumn{1}{c}{**dB/(m\({}^{3}\)/m\({}^{3}\))**} & \multicolumn{1}{c}{**R\({}^{2}\)**} & \multicolumn{1}{c}{**dB/(m\({}^{3}\)/m\({}^{3}\))**} & \multicolumn{1}{c}{**dB/(m\({}^{3}\)/m\({}^{3}\))**} & \multicolumn{1}{c}{**dB/(m\({}^{3}\)/m\({}^{3}\))**} & \multicolumn{1}{c}{**R\({}^{2}\)**} \\ \hline \(\Gamma_{RL}\) & 40 & 0.62 & 22.2 & 0.08 & 45.7 & 0.54 & 28.4 & 0.14 \\ SNR & 38.9 & 0.64 & 25.4 & 0.20 & 39 & 0.41 & 24.1 & 0.10 \\ PR & 30 & 0.17 & 44 & 0.20 & 14.4 & 0.10 & \(-\)3.5 & 0.01 \\ \
# Evaluation on Radiometric Capability of Chinese Optical Satellite Sensors
## 2 Materials and Methods  
### Test Site  
A site approximately of 30 km \(\times\) 30 km located within the Badain Jaran Desert is adopted. It locates in central Inner Mongolia of Northern China (Figure 1). The site is selected for the following three reasons [12; 13]: First, the area is temporally, spatially and radiationally stable in brightness,spatial homogeneity, altimetric and bidirectional effects, seasonal variation, and long-term stability [14]. Second, sand is the primary surface material in this area, provides a high-reflectance surface. Third, the site has a typical continental arid climate, and the atmosphere is always dry and clean (low aerosol).