esa-sceva/satcom-chunk-collection huggingface dataset:
# An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System
###### Abstract
In this study, we intend to check the importance of these factors for foreign exchange rates of any country. This study also focuses on checking the impact of important global and local events on the foreign exchange rate of a country. For this purpose, some important local and global events are considered for each country and sentiment analysis is performed. The sentiment values are then used to train the deep learning model along with gold price index and crude oil prices. The paper has the following contributions:  
* Since time series models such as Auto regressive conditional heteroscedasticity (ARCH) and GARCH do not yield significant prediction results, we use a deep learning approach that also deals with nonlinear patterns of time series datasets. * The exchange rate is considered as a highly volatile variable and can be affected by any information floated in the market such as social media sentiments based on events at the macro level, therefore, we also consider investor sentiment while making exchange rate predictions which is a significant contribution to the existing body of knowledge. * We consider three different economies namely, Hong Kong (emerging), Pakistan (developing), and UK (developed) that belong to different regions which exhibit specific characteristics. Such a dataset has remained unexplored in the existing literature. The rest of the paper is organized as follows: Section 2 explains the related work, Section 3 presents the methodology, and Section 4 explains the results followed by the conclusion.
# Auditing Accessibility of Pavements and Points of Interest in Urban Areas: The 'Seek & Go' Tool +
## 8 Conclusions  
This research paper introduces the 'Seek & Go' application, which serves as an auditing tool designed to systematically gather georeferenced accessibility data for streets and POIs in urban conurbations. Its primary purpose is to supply the developed database with accessibility data, which, in turn, can be used to guide and inform disabled users, ultimately enhancing the mobility of citizens within the study areas.  
Potential areas for future research could involve an examination of policy-making processes at the municipal level, with a focus on incorporating the 'Seek & Go' mobile application into a comprehensive framework that can serve as a guide for stakeholders seeking to establish accessible-for-all services [60]. This approach can play a critical role in promoting sustainable urban communities, a priority identified in the United Nations' Sustainable Development Goal #11 [61].  
Conceptualization, C.A., D.A., A.M. and K.K.; methodology, D.A., A.M. and I.T.; software, I.T.; validation, C.A., D.A. and A.M.; data curation, C.A., D.A, N.T. and C.B.; writing--original draft preparation, C.A. and N.T.; writing--review and editing, D.A. and C.B.; visualization, C.A., N.T. and I.T.; supervision, C.A. and D.A.; project administration, C.A. and D.A.; funding acquisition, C.A., D.A., A.M. and K.K. All authors have read and agreed to the published version of the manuscript.  
This work has been co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call Research--Create--Innovate (project acronym: Seek & Go; project code: T1 EDK-00108).  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Results and Discussion
### Characteristic Observables of CoFe\({}_{2}\)O\({}_{4}\) Nanoparticles and Study Method
Figure 2: The variation of the position of maximum specific loss power (P\({}_{\text{sM}}\)) with the frequency of magnetic field (f) and diameter of CoFe\({}_{2}\)O\({}_{4}\) nanoparticles (D) in nanometers range. 4. The specific loss power for CoFe\({}_{2}\)O\({}_{4}\) nanoparticles is significantly lower than in the case of magnetite nanoparticles. However, if the power obtained under these conditions were sufficient to heat the nanoparticles to an optimum temperature of ~43 \({}^{\circ}\)C, and in a relatively short period of time (see Section 3.5), then the reduced power would not come as a disadvantage in the use of CoFe\({}_{2}\)O\({}_{4}\) nanoparticles in superparamagnetic hyperthermia for tumor therapy. In addition, we've shown a major advantage in point (ii), regarding intracellular therapy, which will increase the effectiveness of CoFe\({}_{2}\)O\({}_{4}\) nanoparticles in the hyperthermic destruction of tumor cells, much more efficiently from within. However, there is another important question that arises in these conditions, as follows: what is the appropriate magnetic field in which the formula of specific loss power in the linear approximation can be used? A detailed answer will be given in the next paragraph.
# Bulk Solids Stacking Strategy of a Rectangular Ship Cabin
## 4 Experimental Verification and Analysis  
### Construction of Experimental Platform  
A rectangular single cabin model was used to perform scaled tests. The sequence of stacking locations is shown in Figure 9. In order to verify the applicability of the proposed stacking strategy, various bulk solids including resin particles, volcanic stone particles, corn, soybean and unhusked rice were selected as experimental materials. Through the stacking experiment, the contour curve of the stockpile was extracted and the three-dimensional model was established. The total mass and density of each material were determined as the calculation data. The optimal stacking model was calculated by the improved golden section method, and the stacking volume related to each stacking location was calculated. The test rig geometries and material parameters are shown in Table 1. The stacking locations of the first layer started from the middle of the bow, and the material was dropped at the four points of 0.2 m, 0.4 m, 0.6 m, and 0.8 m in sequence. The stacking locations of the second layer returned from the middle of the stern, and the material was dropped at the positions of 0.8 m, 0.6 m, 0.4 m, and 0.2 m. Then, the sequence moved to the side of the cabin 0.125 m away from the middle position and dropped materials at the four points of 0.2 m, 0.4 m, 0.6 m, and 0.8 m, and finally moved to the other side of the cabin 0.125 m away from the middle position and dropped materials at the four points of 0.8 m, 0.6 m, 0.4 m, and 0.2 m. The first layer of bedding materials accounted for 30% of the total, and the second layer accounted for 70% of the total.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multicolumn{1}{c}{**Parameter (Unit)**} & \multicolumn{4}{c}{**Value**} \\ \hline Materials & Resin particles & Volcanic rock & Corn & Soybean & Unhusked rice \\ Density (kg/m\({}^{3}\)) & 582 & 942 & 773 & 750 & 600 \\ Weight (kg) & 56.25 & 65 & 65 & 57.5 & 20 \\ Particle radius (m) & \(2\times 10^{-3}\) & \(3\times 10^{-3}\) & \(4\times 10^{-3}\) & \(2.5\times 10^{-3}\) & \(2\times 10^{-3}\) \\ Cabin size (m) & \(1\times 0.5\times 0.4\) & \(1\times 0.5\times 0.4\) & \(1\times 0.5\times 0.4\) & \(1\times 0.5\times 0.4\) & \(1\times 0.5\times 0.4\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Record of experimental parameters.  
Figure 8: The stacking model built by SolidWorks.  
The peak and valley heights of a pile are shown in Figure 10. The peak height was determined by subtracting the measured distance between the cabin top edge and the pile peak from the measured distance between the cabin top edge and the stockpile bottom. The valley height can be calculated in a similar way.
# Enhanced Three Layer Hybrid Clustering Mechanism for Energy Efficient Routing in IoT
###### Abstract  
Recently, different routing techniques were proposed for three layer clustering topology in Wireless Sensor Network (WSN) which outperform the basic two layer clustering hierarchy. The problem that remains in these approaches is the heavy control packet exchange between nodes after every round in order to choose efficient lower layer heads. Among these techniques is Hybrid Hierarchical Clustering Approach (HHCA), in which a distributed approach is proposed. According to HHCA, the upper layer heads are centrally selected by base station, while sensor nodes only have to select lower layer heads distributively. In this paper, enhanced three layer hybrid clustering mechanism is proposed that limits the exchange of control packets between nodes after every round for lower layer head selection. The energy of nodes are divided into levels upon which it is decided when nodes of a cluster need to enter into new cluster head selection phase. The proposed mechanism helps to limit control packet exchange between nodes to a large extent, at the same time keeping energy consumption between nodes balanced. Moreover, it is focused that higher layer heads are selected by base station in a manner that reduces backward transmission in the network as much as possible. Simulation results show that nodes in the proposed mechanism stay alive for a longer time as compared to other approaches, and it outperforms HHCA technique in network lifetime based on Half of the Nodes Alive (HNA) by 18 percent.  
wireless sensor network; IoT; clustering; energy efficiency +
Footnote †: journal:  
_Article_  
Enhanced Three Layer Hybrid Clustering Mechanism for Energy Efficient Routing in IoT
# Artificial Neural Networks for Navigation Systems: A Review of Recent Research
## 4 Navigation Integration Enhanced by the ANN
### More Recent Development of NNs for Navigation System Designs  
For the past few years, one of the most well-liked trends in scientific study has been deep learning. A subset of machine learning algorithms called deep learning [94; 95; 96; 97; 98; 99] represents several levels of hierarchy and enables the construction of complex concepts from smaller ones. This section provides a review of the recent development of NNs for navigation system designs, including the following five categories: (1) nonlinear autoregressive exogenous neural network [90; 100; 101; 102; 103]; (2) long short-term memory (LSTM) NNs [91; 92; 104; 105; 106; 107]; (3) extreme learning machine (ELM) NNs [93; 108; 109; 103; 101]; (4) gated recurrent unit (GRU) NNs [88; 111; 112; 113; 114]; and (5) convolutional neural networks (CNNs) [115; 116; 117] with GRU.
## 4 Experimental Results
### Effectiveness of Side-Match Evaluation  
The performance of the proposed scheme is highly dependent on the side-match evaluation. To know the effectiveness of side-match evaluation for different images, the number of abnormal blocks detected in all test images are listed in Table 2. In our experiment, each \(512\times 512\) test image is divided into \(128\times 128=16,384\) blocks in total. In the worst case of 'Baboon' image, only 2.3% of total image blocks are detected to be abnormal. In the best case of 'Bridge' image, only one block is abnormal. These results ensure good performance of the proposed reversible data hiding scheme.
## 3 Results
### Field Trial  
To verify the reliability of the optimization results, the team conducted a field trial on 13 March 2023 at Xiaoji Green Garden Vegetable Cooperative in Yangzhou City, Jiangsu Province under optimal working parameters, as shown in Figure 7. The soil in the test field had a uniform texture, a flat surface, and a water content of 18.4%. Based on the actual situation in the field, the optimized manipulator operation parameters were adjusted for field verification testing. The output air pressure was set at 0.5 MPa, pulling angle at 70\({}^{\circ}\) and pulling speed at 8 cm/s. Three groups of uprooting tests were conducted, each consisting of 30 plants, and the test results were averaged. The results are shown in Table 10.  
Table 10 demonstrates a satisfactory agreement between the verification test value and the parameter optimization value, with a relative error of less than 5%. Therefore, it can be concluded that the parameter optimization model is reasonable. The optimal operating parameters for the clamping manipulator are a combination of an output air pressure of 0.5 MPa, a pulling angle of 70\({}^{\circ}\) and a pulling speed of 8 cm/s.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Project**} & **Rape Unrooted Removal Rate** & **Rape Removal Breakage Rate** & \multirow{2}{*}{**Pulling Force Y\({}_{3}\)/N**} \\  & \(\mathbf{Y_{1}}\)**\%** & \(\mathbf{Y_{2}}\)**\%** & \\ \hline Test value & 83.33 & 6.67 & 84.90 \\ Parameter optimization value & 80.14 & 6.44 & 83.02 \\ Relative error & 3.9 & 3.6 & 2.3 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Test scheme and results.  
Figure 7: Photos of field validation trials.
## 2 Channel Propagation Characteristics
### Characteristics of Mid-Band and High-Band Frequency Spectra
#### 2.1.3 Propagation Loss  
Propagation loss in the context of mid-band and high-band for 5G communication refers to the reduction in signal strength as the electromagnetic waves travel through the wireless medium [17; 18]. In the mid-band spectrum, propagation loss is primarily caused by atmospheric absorption, terrain, and foliage. As the frequency increases, the absorption of radio waves by the atmosphere also increases, leading to higher propagation loss. Additionally, the presence of obstacles such as buildings, trees, and hills can further attenuate the signal, reducing its strength and coverage [19]. This is illustrated in Figure 2.  
In the high-band spectrum, propagation loss becomes more significant due to the short-wavelength characteristics of these frequencies. Millimeter wave (mm-wave) signals have high propagation loss when they encounter obstacles like walls, buildings, and even rain or foliage. Moreover, they experience rapid signal attenuation over long distances due to their high absorption by atmospheric gases [15; 20]. This has historically been a significant weakness in mm-wave technology, but with the employment of beamforming techniques, advanced antenna systems, and small-cell deployments, this issue may be mitigated in both mid-band and high-band frequencies. These techniques aim to direct andamplify the signal towards the intended receiver and compensate for the loss experienced during propagation, improving coverage and overall network performance.
## 3 Results and Discussions
Peaks due to lattice oxygen at 528.28 eV, oxygen vacancies at 530.8 eV, M = O at 529.4 eV, and OH adsorbed on the surface at 533.1 eV were observed [25; 26; 28]. The ratio of oxygen vacancy in O 1 s spectra was calculated by Equation (2):  
\[(\text{Oxygen vacancy})(\%)=\frac{\text{Area}_{\text{Aoxygen vacancy}}}{\text{ Area}_{\text{Lattice oxygen}}+\text{Area}_{\text{Oxygen vacancy}}+\text{Area}_{\text{M=O bond}}+\text{Area}_{\text{OH}}} \tag{2}\]  
Comparing the pristine and thermal stress--applied specimens, the oxygen vacancies in PBCCu0 decreased from 57% to 43%, and PBCCu0.2 and PBCCu0.5 also decreased by approximately 1% and 3%, respectively; therefore, the electrochemical catalytic capacity is expected to decrease accordingly. In the case of the M = O peak, the ratio of the M = O peak doubled in all compositions after thermal stress application. This result is attributed to the formation of the BaO secondary phase, which is confirmed by the crystal structure analysis in Figure 1b. As shown in Figure 5a, PBCCux showed a conductivity of up to 1200 S/cm. However, after applying thermal stress, as shown in Figure 5b, the electrical conductivity was 11-17 S/cm, and the conductivity gradually increased as the temperature increased. Figure 4: O 1 s core level spectra (black solid line) together with the fit results (colored area) for PBCCux: (**a**) Pristine and (**b**) After applying thermal stress for 100 h on 1050 \({}^{\circ}\)C.  
Figure 5c shows the electrochemical impedance spectra of PBCCux measured using a symmetric cell. The polarization resistance value was calculated through the distance between the intersection point and \(Z^{\prime}\) when \(Z^{\prime\prime}\) was 0. The polarization resistance of PBCCux was 0.91 \(\Omega\) cm\({}^{2}\) (PBCCu0), 0.53 \(\Omega\) cm\({}^{2}\) (PBCCu0.2) and 0.26 \(\Omega\) cm\({}^{2}\) (PBCCu0.5). As the amount of Cu is increased, the polarization resistance gradually decreases, and PBCCu0.5 is approximately 71.43% smaller than PBCCu0. This result indicates that Cu doping contributed to the reduction of polarization resistance by inducing an increase in oxygen vacancies and an improvement in the interaction with adsorbed oxygen [32]. Figure 5d shows the polarization resistance after applying thermal stress. Before and after thermal stress application, the polarization resistance gradually decreased as the content of Cu increased, and the polarization resistance increased up to 244.5 \(\Omega\) cm\({}^{2}\). The degradation of PBCCux by thermal stress is induced by the formation of BaO.
# Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion
## 4 Data Expansion: How to Get Enough Data?
### Combination with Classical Models
It is proved that the proposed scheme can be useful for expanding the training dataset even when there is no measured data at a new frequency. Figure 6: Expansion of training data with the usage of classical model. In addition, we selected 6 seats from each row and added these 30 measured samples at 5.8 GHz to the training dataset, together with the data at two known frequencies and those generated by the log-distance model. The hyperparameters were the same as those when no measured data at 5.8 GHz were involved in the training dataset. Then, the predicted path loss values at all 110 seats are shown in Figure 8. The remaining 80 samples in the test dataset are employed for evaluation. The RMSEs of BPNN, SVR, and RF are 1.37 dB, 1.51 dB, and 1.72 dB. It means that the performance of these machine-learning-based models can be further improved if partial measured data at a new frequency have been obtained and utilized for training [35]. This result shows that the prediction accuracy of the model is related to the number of samples. The proposed scheme can effectively expand the training set so as to obtain more samples that reflect the propagation laws at the new frequency. Figure 8: Prediction performance of different models on the 110 samples at 5.8 GHz. For the samples at 5.8 GHz, 30 estimated samples and 30 measured samples participate in the training process. Figure 7: Prediction performance of different models on the 110 samples at 5.8 GHz. For the samples at 5.8 GHz, only 30 estimated samples participate in the training process. When a new frequency band is adopted in a wireless communication system, it is hard to collect enough data in a short time. With historical data at known frequencies or only a few data at the new frequency, large deviations in the prediction results are likely to happen due to the bias of the training dataset. Therefore, it is beneficial to provide more data at the new frequency to help find the propagation laws and to improve the prediction accuracy. It has been shown that using the classical models to generate partial channel data is an efficient solution for reducing the prediction inaccuracy caused by data bias. Based on the above results, it can be concluded that this data-expansion method can provide new ideas for quick and efficient path loss prediction at new frequencies. The method may be also helpful to reduce the cost and improve the efficiency of wireless communication planning and deployment.
## 7 Open Issues and Opportunities for Future Research
### Management Architecture for Cross-Domain Service Provisioning  
Cross-domain management for end-to-end service provisioning in future networks with network-cloud/edge convergence is a particularly challenging problem. The design of a scalable structure for end-to-end service management across multiple autonomous domains is a key research topic from an architectural perspective. One possible development direction in this area is to follow the hierarchical structure defined in the current ZSM framework. Such a hierarchical structure requires an appropriate level of abstraction in terms of data/information and management capabilities between the two levels (individual MDs at the lower level and the E2 EMD at the higher level) in order to achieve a scalable management architecture, which has not been fully specified by the ZSM standard, thus offering a topic for future work.  
Another research direction for addressing the cross-domain challenge is to explore alternative management architectures. Converged network-cloud/edge service provisioning across different service providers (e.g., network operators, edge/cloud service providers, and Internet service providers) makes a single end-to-end management domain unrealistic. Therefore, alternative structures for cross-domain end-to-end management should be explored. For example, peer-to-peer structures with federation between individual management domains and hybrid structures that combine federative and hierarchical management all deserve thorough evaluations in terms of their effectiveness and scalability for supporting intelligent management in future networks.
# Formation Mechanism and Control Technology of an Excavation Damage Zone in Tunnel-Surrounding Rock
## 6 Recommendations and Limitations  
This study explored the damaging effect of the use of electronic detonators and nonelectric detonators on the protected surrounding rock. The monitoring and calculation results of damage range and rock crushing degree show that a nonelectric detonator has a positive effect on the protection of surrounding rock. Its application in practical engineering can be embodied in the following two aspects: Firstly, in terms of the loose range of surrounding rock, the thickness of the loose circle of surrounding rock caused by the electronic detonator is 1 m less than that of the nonelectric detonator on average, so the length of the anchors can be shortened appropriately when setting the primary support. In terms of the degree of rock fragmentation, the maximum acoustic velocity attenuation caused by an electronic detonator is only half of that of a nonelectric detonator. Therefore, the amount of shotcrete can be reduced, and the spacing of steel arches can be appropriately increased when setting the primary support. This study provided a reference for the optimization design of primary support parameters of the tunnel after blasting, which is helpful in reducing construction costs.  
However, the current testing methods cannot accurately divide the failure zone, the crack zone, and the plastic zone, so the size of the loose circle in the test is not uniform. Although specifications [29] provide blasting damage standards, a rock mass is judged to be damaged if the change rate of the rock mass wave velocity before and after blasting is greater than 10%. In some cases, the surrounding rock in the loose circle fractured but still retains high stability. In addition, the influence of initial surrounding rock fissures is not excluded in this study. Therefore, further classification criteria of surrounding rock loose circles and the influence of initial surrounding rock fissures will be considered in future studies.