esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Reinforcement Learning and Reward Shaping  
Reinforcement learning (RL) is a highly intelligent machine learning method. It does not rely on specific supervision, but purely on the learning strategy of starting from scratch by exploring the environment. RL has been proposed and created for some time, and more and more application domains see the key role of RL. However, most RL algorithms are still in their infancy and show very limited possibilities for real-world practical tasks. The high dimension of action space and state space and the difficulty of convergence hinder the development of RL. Represented by Deep Q-Learning (DQN), the combination of deep learning and RL has changed these. DQN uses a deep structure for value function approximation and designs two important techniques called the Experience Replay and the Target Network to make the learning more stable [24; 25]. Since then, the study in [26] proposed DDPG algorithm to handle continuous action spaces. In DDPG, the Experience Replay and the Target Network are preserved from DQN, but DDPG uses the actor-critic mechanism for simultaneously updating a policy network and a value network.  
RL is always time-consuming. In most practical situations, finding the optimal strategy through a lot of trial and error is unacceptable. In addition, it is important to evaluate the agent's behavior during exploration. Since the RL algorithm only updates the strategy according to the reward, the definition of the reward function is of great concern. Reward shaping provides a solution by shaping an additional reward that is often designed by human experts or extracted from human demonstrations. [PERSON] proposed an efficient potential-based reward shaping function and proved that it would not affect the convergence to the optimal policy [27; 28]. Long-term studies have been conducted in shaping with human advice or demonstrations [29; 30; 31].  
In this work, we designed a shaping reward inspired by experienced human tuning processes. In particular, the shaping term evaluates the tuning performance according to the changing trend of the S-parameters curve. Therefore, an instant reward will be obtained at each training step, and the reward will not be sparse anymore; thus, the learning is accelerated.  
As a whole, the method proposed in this work is also based on RL, which means we consider the tuning process rather than the filter model. Furthermore, we consider effective ways to improve the performance of RL in accelerating and transferring.
## 3 Physiological Signal Monitoring and WBSNs Overall System
### Wbsn  
Wireless personal area networks (WPANs) with intelligent physiological sensors allowing data acquisition and processing [29], and a wireless transmission system for disaster patient care have been proposed in [30]. Some of the existing challenges in a network with these features are: energy constraints, quality of service, sensor deployment, adaptability, real-time guarantee, network architecture, video transmission, among others [31]. A WBSN cannot have the same architecture as that of an environmental monitoring network, it should be different in order to maximize the life time of the network. Factors such as mobility of the patient, interference or noise, coexistence between communications protocols, features of vital signs, data rate, sampling, sensor duty cycle and security should be considered to develop an intelligent and adaptive WBSN for each scenario. There must be different strategies for sending data over a wireless network with reconfigurable topology. These strategies use parameters of sampling frequency, radio frequency, and data \"priorities\", because some parameters are more critical than others. However, wireless communications within a WBSN have a major constraint: energy efficiency. Some factors that counteract the optimal use of energy are:  
* Continuing and improper communication between devices.
* Housing resources and memory.
* Inefficient use of battery supply.
* Excessive traffic data and other types of information such as video and audio in real time.
* Interference and a large number of retransmissions.
* Inefficient network design that reduce power consumption of each node.
* Energy consumption of sensors according to the measured physiological parameter.
# On the Energy Efficiency of On-Off Keying Transmitters with Two Distinct Types of Batteries
## 4 Theoretical Analysis
### Comparison of the BECPB for Different Batteries  
To facilitate the BECPB comparison between the two batteries, the difference between the BECPBs is given by  
\[\Delta E_{\text{bat}}=E_{\text{bat,I}}-E_{\text{bat,M}} =\frac{1}{2B}\left[\frac{\frac{1}{2}\gamma t_{1}t_{2}-\eta P_{ \text{d}}}{(\eta-\gamma t_{1})(\eta-\frac{1}{2}\gamma t_{2})}+\frac{P_{\text{d }}}{\eta-\gamma P_{\text{d}}}\right] \tag{30}\] \[=\frac{1}{2B}\frac{-\gamma\eta P_{\text{d}}(t_{1}+\frac{1}{2}t_{2 })+\frac{1}{2}\gamma\eta t_{1}t_{2}+\gamma\eta P_{\text{d}}^{2}}{(\eta-\gamma P _{\text{d}})(\eta-\gamma t_{1})(\eta-\frac{1}{2}t_{2})}. \tag{31}\]  
where \(t_{1}=\xi Bd^{k}+P_{\text{a}}+P_{\text{d}}\), \(t_{2}=\xi Bd^{k}+P_{\text{a}}+2P_{\text{d}}\). Note that \(P_{\text{d}}<<1\), thus \(t_{1}\approx t_{2}\) and \(-\gamma\eta P_{\text{d}}(t_{1}+\frac{1}{2}t_{2})+\frac{1}{2}\gamma\eta t_{1}t _{2}+\gamma\eta P_{\text{d}}^{2}\approx-\frac{3}{2}\gamma\eta P_{\text{d}}t_{ 1}+\frac{1}{2}\gamma\eta t_{1}^{2}\), as a result, \(\Delta E_{\text{bat}}=0\) has two solutions, namely \(d_{1}=\left(\frac{2P_{\text{d}}-P_{\text{a}}}{\xi B}\right)^{-k}\approx 0\) and \(d_{2}=0\). Therefore when \(d>0\), \(\Delta E_{\text{bat}}>0\), which means the battery with IDC-based BUF corresponds to a higher BECPB than the battery with MDC-based BUF.
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
## 6 Security Analysis
### Informal Security Analysis
#### 6.1.3 Stolen Mobile Device Attack  
We assume that a \(MU_{a}\) can steal the mobile device of a legitimate user and extract the data \(\{A_{i},B_{i},P_{i}\}\) from the mobile device by utilizing a power analysis attack [20]. However, \(MU_{a}\) still cannot obtain a legitimate user's information because the parameters stored in the mobile device are masked using bitwise XOR operations and hash functions. Thus, the proposed scheme can defend against mobile device theft attacks.
## 4 Discussion
Consistent with these findings, we observed remarkable infiltration of various immune cells within the medial layer and adventitial layer of TRPV4-KO mice compared with that in WT, which can explain the augmented calcification in the aorta of TRPV4-KO mice after CaCl\({}_{2}\) injury. In response to CaCl\({}_{2}\) injury, it is known that the expression of proinflammatory cytokines will increase, leading to immune cell recruitment. The release of these cytokines recruits more immune cells for infiltration, which in turn promotes endothelial and medial layer damage where elastic fibers undergo disorganization and elongation, leading to the detachment of the media from the adventitia [46]. As a result, vessel elasticity and integrity will be lost and an aneurysm will form, as shown in the histological sections of CaCl\({}_{2}\) groups. However, the remarkable infiltration of immune cells and medial layer damage in TRPV4-KO mice compared to WT mice should be highlighted and necessitates further investigation of the possible underlying mechanism. Two previous studies suggested that the inhibition of TRPV4 reduces aortic inflammation and vascular remodeling, hence attenuating AAA formation [47; 48]. Those results conflict with the present study's findings showing a possible role of the TRPV4 channel in downregulating vascular calcification and AAA formation. Variations in animal models of aortic injury induction and AAA formation could be a possible reason for these conflicting results. More studies need to be conducted to explore the exact molecular mechanism behind the role of the TRPV4 channel in vascular calcification and AAA formation. The present study findings potentiate the therapeutic potential of TRPV4 activation in preventing AAA and vascular calcification. The present study has the following limitations: Although the animal model used in the present study may closely mimic human pathophysiology, this model may lack chronicity, which is a characteristic feature of the AAA formation. Another limitation of this study is the use of global knockout of TRPV4 rather than conditional knockout in VSMCs, with a possible effect of cells other than the cells of interest (VSMCs), which can interfere with the results, leading to less accuracy.
# Investigation on the Tip Positioning Accuracy of Cable-Driven Serpentine Manipulators
## 2 Kinematic Model and Position Accuracy Analysis
### Kinematic Modeling
#### 2.2.2 The Mapping between the Cable Length and the Joint Angle  
With the cable length vector defined as \(\mathbf{L}=\left[\begin{array}{cccc}L_{\mathrm{c1}\_{1}}&L_{\mathrm{c1}\_{2 }}&\ldots&L_{\mathrm{c\_i}\_{1}}&L_{\mathrm{c1}\_{2}}&\ldots&L_{\mathrm{c\_i }\_{1}}&L_{\mathrm{c\_i}\_{2}}\end{array}\right]^{T}\), we can get the mapping relationship between the cable length and the end pose when joint angles are given.  
\[L_{c\_1,1}=\sum_{k=1}^{i}\Big{(}\big{\|}\mathrm{B}_{i-1,\mathrm{L}}A_{i,1} \big{\|}+\mathrm{B}\Big{)}, \tag{4}\]  
\[L_{c\_2,2}=\sum_{k=1}^{i}\Big{(}\big{\|}\mathrm{B}_{i-1,\mathrm{2}}A_{i,2} \big{\|}+\mathrm{B}\Big{)}, \tag{5}\]  
where, \(L_{c\_1}\) represents the length of the cable_disk(i)_1; \(\mathrm{B}_{i-1,\mathrm{1}}A_{i,1}\) represents the length between disk_i-1 and disk_i of the cable_disk(i)_1 (i.e., the distance between the hole \(\mathrm{B}_{i-1,\mathrm{1}}\) and the hole \(\mathrm{A}_{i,1}\), shown in Figure 2); \(L_{c\_2}\) represents the length of the cable_disk(i)_2; and \(\mathrm{B}_{i-1,\mathrm{2}}A_{i,2}\) represents the length between disk_i-1 and disk_i of the cable_disk(i)_2 (i.e., the distance between the hole \(\mathrm{B}_{i-1,\mathrm{2}}\) and the hole \(\mathrm{A}_{i,2}\)).  
Given a joint angle vector of the manipulator, the length of each driving cable can be calculated by Equations (4) and (5). However, the same cable lengths may correspond to different joint angle vectors, that is, the mapping from cable lengths to joint angles was a one-to-many relationship.
# Characterisation of Dust Particles Deposited on Photovoltaic Panels in the United Arab Emirates
## 1 Introduction
Characterisation techniques, including particle size analysis, XRF, XRD, and SEM, were employed. The cleanness index changes rate (CICR) showed a strong negative correlation with the dust accumulation rate (DAR). Calcium dominated the elemental composition, with calcite, dolomite, and quartz as the primary minerals. These data provide essential insights for understanding and mitigating the impact of dust on PV performance in arid climates. The research in [17] investigates PV power losses due to soiling in Lahore, Pakistan, considering variable tilt angles and manual vs. automatic cleaning methods. The soiling rate for 30\({}^{\circ}\) tilted panels was consistently around 0.8% per day, one of the highest reported in South Asian and Gulf regions. Dust accumulation rates were recorded at per day for panels fixed at 30\({}^{\circ}\) tilt. Dust composition, analysed through SEM, XRD, and EDX, revealed high carbon and quartz contents attributed to air pollutants. Another study in [18] investigates the impact of different dust components on the performance of PV cells in Sohar, Oman. The collected dust, primarily consisting of sand (65%), cement (5.25%), gypsum (4.94%), ash (4.92%), and other components, was analysed. The study finds that while most dust components have limited effects on the current of the PV cell, they noticeably influence the cell voltage. The decrease in power generation is particularly evident with ash, cement, and new garments, with sand having the most significant impact. Iron powder and red sand were found to have the least effect on the PV cell's performance. To address the adverse effects of dust on the efficiency of PV systems, the present study extensively examined dust particles across five distinct sites in the UAE. The primary objective was to comprehend the composition and variations among the dust types prevalent in the region. The findings from this investigation are crucial for the development of targeted and efficient cleaning strategies, as well as the design of specific soiling mitigation technologies tailored to the unique climatic conditions of the UAE. By understanding the characteristics of dust in this environment, this research aims to minimise power losses in PV systems, providing valuable insights for the optimisation of solar energy generation in the region.
# Automatic Era Identification in Classical Arabic Poetry
## 3 Dataset and Methods
### Methods  
In this study, we consider two classification tasks aiming to automatically assign to each poem in the collection a label that denotes the poet's name or the era in which it was composed. We take a supervised learning approach, where a classifier is first provided with labeled examples, i.e., poems for which the poet and era are known. To generalize beyond a specific poem, we represent each poem in terms of features, encoding properties of interest that are related to word usage, syntax, and semantics (topics). In addition, we encode as features a set of known relevant literary phenomena, automatically detecting and denoting the presence of such phenomena in each poem. Given the small size of our dataset, we applied a 10-fold cross-validation (CV) process (Using 10-fold CV, the classifier is trained on 90% of the labeled examples and evaluated on the remaining 10%, where each example is included in the evaluation set exactly once). Assuming that the poems by the same poet or from the same era have common characteristics, the classifier is expected to attribute a new (unlabeled) poem to the most relevant group based on its feature representations. Our poems are of different lengths. In the present study, the whole poem is used in order to check its relevance to a certain period. Looking at parts of poems, i.e., a group of verses that describe a minor topic extracted from the whole poem, or even a single verse, may be an interesting idea for future research. Our experiments using different classifiers and feature sets shed light on the predictive value of these features. Importantly, in the learning and evaluation process, the classifiers only see the poems for which authorship is undisputed ('original'), in order to avoid noise in the form of possibly incorrect labels. Having set the learning parameter choices to their best-performing values according to the CV results, we apply and examine the classification predictions for those poems whose authorship is dubious.  
The experimental pipeline designed and implemented for the study (illustrated in Figure 1) includes data pre-processing, feature extraction, and then classification. Each step is described in detail below.
# Dynamic Performance of Soil-Tunnel System under Transverse Sinusoidal Excitations
## 2 Test Design  
### Prototype of a Site  
The stratigraphic parameters of prototype site in Shanghai are shown in Table 1. To simplify the experimental study, a uniform ground is assumed with an equivalent depth of 42.0 m. The average unit weight is 18.59 kN/m\({}^{3}\), taken as a homogeneous soil. The equivalent shear wave velocity of the ground is 187.45 m/s in the way of weighted average method. Therefore, the fundamental frequency of the site is 1.116 Hz (0.896 s) by means of analytical formula for uniform ground.  
The prototype of the assembled lining is from a road tunnel (as in Figure 1) to be constructed in the site. It consists of 10 segments manufactured with strength grade C60 reinforced concrete, including 7 standard-segments, 2 adjacent segments, and a key-block. The external diameter of the tunnel is 14.0 m. The thickness of a segment is 0.6 m, with width of 2.0 m. During assemblage of a lining ring, any two segments are fastened with 2 bolts of strength grade 6.8 (the yield strength of it is 480 MPa).
## 6 Results of Multidimensional Identification
### Algorithm Execution Time  
For measuring the algorithm's execution time, 20 tests were conducted, and the results are shown in Table 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test** & \(Q_{m}\)**Error** & \(T_{s}\)**Error** & \(x_{B}\)**Error** & \(x_{D}\)**Error** \\ \hline Least squares method & 0.227 & 0.68 & 0.1449 & 0.1584 \\ Traditional T-S with 9 rules & 0.0033 & 0.0065 & 0.0945 & 0.0893 \\ Case (a) & 0.0024 & 0.0065 & 0.0056 & \(9.1762\times 10^{-4}\) \\ Case (b) & 0.0027 & 0.0067 & 0.0058 & \(9.5558\times 10^{-4}\) \\ Case (c) & 0.0029 & 0.0069 & 0.0078 & \(0.0012\) \\ Case (d) & 0.0026 & 0.0065 & 0.0052 & \(9.7571\times 10^{-4}\) \\ Case (e) & 0.0025 & 0.0060 & 0.0044 & \(8.9471\times 10^{-4}\) \\ Case (f) & 0.0024 & 0.0057 & 0.0039 & \(8.0794\times 10^{-4}\) \\ Case (g) & 0.0024 & 0.0058 & 0.0036 & \(7.9984\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Identification errors.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Multivariable Thermal Mixing Process** & \multicolumn{2}{c}{**Binary Distillation Column**} \\ \hline
**Test** & **Traditional T-S [\(\mu\)s]** & **Multidimensional T-S [\(\mu\)s]** & **Traditional T-S [\(\mu\)s]** \\ \hline
1 & 0.526674 & 0.340448 & 0.683674 & 0.482748 \\
2 & 0.481436 & 0.453689 & 0.636436 & 0.597189 \\
3 & 0  
From the obtained results in this test, it can be observed that there is an average execution time for the traditional T-S identification algorithm applied to the multivariable thermal mixing process of 0.514 \(\upmu\)s, and for the multidimensional identification, there is an average time of 0.426 \(\upmu\)s. For the case of the binary distillation column, the average execution time of the traditional T-S identification algorithm is 0.665 \(\upmu\)s, and for the multidimensional identification, the average time is 0.5732 \(\upmu\)s. Therefore, it is considered that the execution of the identification algorithm with multidimensional membership functions takes less time than the execution of the identification algorithm with monodimensional membership functions.
# Dynamic and Static Investigation of Ground Heat Exchangers Equipped with Internal and External Fins
## 3 Numerical Setup  
### Mesh Generation  
The mesh generation is performed based on both structured and unstructured grids. After defining the boundaries and edges of different parts of the geometry as lines or curves, a two-dimensional grid is created in Cartesian coordinates and then this 2D grid is extended in the direction of depth. The cells are equally or exponentially distributed in different parts of the geometry. For this purpose, a high number of cells are considered for places with steep temperature slope, for example, near the borehole and pipe walls. For the areas where the temperature slope is low, the size of the cells is increased. Figure 3 shows the computational mesh of the simple U-tube GHE. The upper and lower surfaces around the borehole are made up of 600 cells, which have bigger sizes in the middle part. The borehole is divided vertically into 70 layers with different distances. The curved area of the U-tube at the bottom of the pipe requires finer and different mesh from the other domains of the geometry due to the complexity of the geometry. Around the curved edges, using unstructured meshes is more practical.  
Although the accuracy of the calculations increases with the number of cells, the higher computation time should also be considered. As a result, by examining the grid independency test, it is possible to obtain a suitable and optimal mesh with the necessary accuracy to achieve the final results and to avoid increasing computation time. Grid independency of simple and finned U-tube GHE has been investigated by varying the number of cells from coarse mesh to finer mesh. Table 2 shows the outlet fluid temperature in the simple U-tube GHE under the steady-state condition for different grid cells. A total cell number of \(330\times 10^{3}\) is used for smooth U-tube GHE since no temperature change is observed after increasing the number of cells. Grid independency for finned U-tube GHEs is presented in Table 3. Due to the small dimensions of the fins used on the inner and outer surfaces of the pipes, the numerical models require a finer grid size than a simple pipe. The number of cells specified in GHEs with external and internal fins is \(500\times 10^{3}\) and \(420\times 10^{3}\), respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Mesh 1** & **Mesh 2** & **Mesh 3** \\ \hline Elements number (\(\times 10^{3}\)) & 174 & 330 & 500 \\ Fluid outlet temperature (\({}^{\circ}\)C) & 24.86 & 24.85 & 24.85 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Grid-independency test of smooth U-tube GHE.  
Figure 2: Cross-sections of smooth and finned U-tubes: (**a**) simple U-tube; (**b**) U-tube with outer fins; (**c**) U-tube with inner fins.
# Compression of GNSS Data with the Aim of Speeding up Communication to Autonomous Vehicles
## Appendix C  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Symbols**} & \multirow{2}{*}{**Frequency**} & **Huffman** & \multirow{2}{*}{**Num of Bits**} & **Space Savings (Bits)** & **Saved Bits** & **Freq \(\times\)Num** & **Entropy** \\ \cline{5-8}  & & **Coding** & & & & & **of Bits** & **Entropy** \\ \hline
0 & 193,868 & 010 & 3 & 193,868 \(\times\) 8 - 193,868 \(\times\) 3 - 969,340 & 969,340 & 581,604 & 0.3401972807 \\ \hline
0 & 128,388 & 1011 & 4 & 128,388 \(\times\) 8 - 128,388 \(\times\) 4 = 513,552 & 513,552 & 513,552 & 0.2663735204 \\ \hline
0 & 97,393 & 0110 & 4 & 97,293 \(\times\) 8 - 97,393 \(\times\) 4 - 389,572 & 389,572 & 389,572 & 0.2229590946 \\ \hline
1 & 91,188 & 0010 & 4 & 91,188 \(\times\) 8 - 91,188 \(\times\) 4 - 364,752 & 364,752 & 364,752 & 0.2131418223 \\ \hline
0 & 82,150 & 11110 & 5 & 82,150 \(\times\) 8 - 82,150 \(\times\) 5 = 246,450 & 246,450 & 410,750 & 0.1989196096 \\ \hline
1 & 81,703 & 11101 & 5 & 81,703 \(\times\) 8 - 81,703 \(\times\) 5 = 245,109 & 245,109 & 408,515 & 0.1981833350 \\ \hline
0 & 74,786 & 11010 & 5 & 74,786 \(\times\) 8 - 74,786 \(\times\) 5 = 224,358 & 224,388 & 373,930 & 0.1865413311 \\ \hline
2 & 61,997 & 10011 & 5 & 61,997 \(\times\) 8 - 61,997 \(\times\) 5 = 185,991 & 185,991 & 309,985 & 0.1636685171 \\ \hline
5 & 52,276 & 10000 & 5 & 52,276 \(\times\) 8 - 52,276 \(\times\) 5 = 156,828 & 156,828 & 261,380 & 0.1449276078 \\ \hline
3 & 49,158 & 01110 & 5 & 49,158 \(\times\) 8 - 49,158 \(\times\) 5 = 147,474 & 147,474 & 245,750 & 0.136305132 \\ \hline
3 & 41,594 & 111110 & 6 & 41,594 \(\times\) 8 - 41,594 \(\times\) 6 = 83,188 & 83,188 & 249,564 & 0.1226949237 \\ \hline
4 & 38,094 & 111000 & 6 & 38,094 \(\times\) 8 - 38,094 \(\times\) 6 = 76,188 & 76,188 & 225,564 & 0.1149702278 \\ \hline
1 & 35,174 & 110010 & 6 & 35,174 \(\times\) 8 - 35,174 \(\times\) 6 = 70,348 & 70,348 & 211,044 & 0.108335318 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fixed character encoding and decoding map used in this study.