esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Link Budget and Dual-Slope Path Loss Measurement Results  
The path loss is one of the most significant parameters in radio link design and provides a measure of channel quality. It is expressed as the average level of path loss in dB and varies as a function of the Tx and Rx separation distance. The path loss considers the propagation mechanisms present in the radio channel, such as free space, rejection, diffraction and scattering, the influence of the propagation environments (urban, suburban, rural, and highway), directional characteristics, antenna heights, and Tx and Rx separation  
Figure 4: Scenario 1 is shown with a red line, scenario 2 is shown with a green line, and scenario 3 is shown with a yellow line.  
Figure 5: Measurement scenarios, (**a**) urban high density, (**b**) urban low density, and (**c**) suburban. The Rx vehicle can be seen driving in front of the Tx vehicle.  
distance. In addition, the signal-to-noise ratio (SNR) is inversely related to the path loss; i.e., as the path loss increases, the SNR at the Rx decreases, and the coverage area is reduced.  
The average received power in log units (dBm) under free space propagation conditions, denoted by \(P_{Rx}(d)\), is given by the following:  
\[P_{Rx}(d)=P_{Tx}+G_{Tx}+G_{Rx}-10 log_{10}\left(\frac{4\pi d}{\lambda_{c}} \right)^{2}, \tag{1}\]  
where \(P_{Tx}\) is the transmitted power in dBm, \(d\) is the Tx/Rx separation distance; \(G_{Tx}\) and \(G_{Rx}\) are the transmit and receive antennas' gain expressed in decibels (dB), respectively; and \(\lambda_{c}\) is the wavelength associated with the carrier frequency \(f_{c}\). The last term in Equation (1) is the path loss for free space propagation conditions, \(PL_{FS}(d)\), expressed in dB.
## 5 Conclusions and Future Works  
### Conclusions  
We proposed the RTS/CTS scheme for IEEE 802.15.6 CSMA/CA based networks that enhance overall performance in the presence of hidden nodes. We considered different transmission power levels to extend the transmission range of nodes in case of CTS failure. By deriving analytical expressions, we analyzed the throughput and energy consumption of the proposed scheme for different priority classes and BER values and concluded that the RTS/CTS scheme is able to achieve best performance when compared with the basic IEEE 802.15.6 CSMA/CA protocol. The results presented in this paper may provide guidelines to select optimal parameters and protocols when deploying WBANs for different healthcare scenarios. The proposed scheme has the potential to solve exposed node problems by extending the transmission range of exposed nodes.
## 4 Proposed Secure MP-FrFT-OFDM Cryptosystem
### Authenticated Encryption Scheme  
The ciphering technique's steps can be summed up as follows:  
1. Read the image that was entered.
2. Convert the input image into binary format.
3. The first encryption step started with the diffusion process by Xoring, which converts the binary image data of 256 \(\times\) 256 \(\times\) 8 bits with the select random iterated chaotic sequence of 256 \(\times\) 256 \(\times\) 8 bits.
4. Apply convolutional coding for the diffusion 256 \(\times\) 256 \(\times\) 8 bits.
5. Apply QPSK mapping.
6. The second encryption step is the confusion process, applying the inverse MPFrFT based OFDM modulation based on the two secret fractional parameter vectors \(a=256\) bytes\((a_{1},a_{2},\cdots,a_{256})\) and \(b=256\) bytes \((b_{1},b_{2},\cdots,b_{256})\) as follows:  
\[a=0.9+\frac{1}{100}\sum_{i=1}^{i=256}a_{i} \tag{21}\]  
\[b=0.9+\frac{1}{100}\sum_{i=1}^{i=256}b_{i} \tag{22}\]
7. Add the cyclic prefix (CP) to the output of the secure MP-FrFT.
8. Send the encrypted image across an IoT channel to the recipient side.
## 4 Discussion
### Acoustic Parameters
#### 4.1.2 Edt  
As ISO 3382-1:2009 states 'The early decay time (EDT) shall be evaluated from the slope of the integrated impulse response curves (as the conventional reverberation time). The slope of the decay curve should be determined from the slope of the best-fit linear regression line of the initial 10 dB (between 0 dB and \(-\)10 dB) of the decay. The decay times should be calculated from the slope as  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Acoustic Parameters**} & \multicolumn{2}{c}{**Behringer**} & \multicolumn{2}{c}{**Genelec**} & \multicolumn{2}{c}{**Philips**} \\ \cline{2-7}  & **M.A.E.** & **S.D.** & **M.A.E.** & **S.D.** & **M.A.E.** & **S.D.** \\ \hline RT (s) & 0.059 & 0.049 & 0.072 & 0.059 & 0.123 & 0.139 \\ EDT (s) & 0.050 & 0.061 & 0.090 & 0.065 & 0.084 & 0.077 \\ C\({}_{80}\) (dB) & 1.563 & 0.946 & 1.159 & 0.967 & 1.459 & 1.090 \\ D\({}_{50}\) (\%) & 8.258 & 5.673 & 9.766 & 11.297 & 7.851 & 5.225 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mean Absolute Error (M.A.E.) and Standard Deviation (S.D.) of RT, E.D.T., C\({}_{80}\) and D\({}_{50}\) for measurements with different speakers compared with measurements with the dodecahedron speaker.  
the time required for 60 dB decay'. Results for EDT (Figures 7 and 9, Tables 1 and 2) present a mean absolute error compared with the measurements of the dodecahedron speaker of less than 0.1 s. One unanticipated finding of the results was that for the midsized room the mean absolute error was 0.17 s, significantly higher than the other spaces.  
The results for the EDT are satisfactory, but to a lesser extent, in relation to the results for the reverberation time. A possible explanation of the aforementioned result might be that the differences of the sound fields created between the dodecahedron speaker and the proposed method are more profound in the early stages of the impulse response measurements.
# A QoS Optimization Approach in Cognitive Body Area Networks for Healthcare Applications
## 4 Simulation Results
### Case B  
In this case, all the users (i.e., nodes) are moving. Since handovers are allowed from one zone to another, the users move freely and randomly across the entire region. Traffic load is also changing randomly as the nodes are entering or leaving a zone. The RL-CAA is a dynamic and robust algorithm which can confront the traffic change instantly, but it has a downside. More dynamics mean that the RL-CAA will undergo exploration of new channels as soon as the throughput goes below the throughput threshold. The RL-CAA is computationally complex and it has a learning curve before  
Figure 5: (**a**) Average BER for the SCA algorithm; (**b**) Average BER for the RL-CAA algorithm.  
Figure 6: (**a**) Average throughput for the SCA algorithm; (**b**) Average throughput for the RL-CAA algorithm.  
it can converge to new channel assignments. Hence, there is a tradeoff between the impact on the desired QoS and the RL-CAA execution overheads. We do not require too frequent execution of the RL-CAA, so we set a 5% margin in throughput requirements. As the gateway throughput falls 5% below the target throughput, the RL-CAA is executed by the gateway and the new channels are then assigned. Figure 7a,b show the average BER of the SCA and the RL-CAA, respectively.  
The RL-CAA is giving much consistent BER under 10% while SCA is fluctuating around 10%. The average throughput available at each gate for the SCA and the RL-CAA are shown in Figure 8a,b, respectively. The RL-CAA provides better distributed and higher throughput performance (reaches over 7 Mbps instantaneous throughput). The dissatisfaction probabilities, as experienced by the individual nodes, are given in Figure 9a,b for the SCA and RL-CAA, respectively.  
Figure 8: (**a**) Average throughput for the SCA algorithm; (**b**) Average throughput for the RL-CAA algorithm.  
Figure 7: (**a**) Average BER for the SCA algorithm; (**b**) Average BER for the RL-CAA algorithm.  
The RL-CAA is giving much consistent BER under 10% while SCA is fluctuating around 10%. The average throughput available at each gate for the SCA and the RL-CAA are shown in Figure 8a,b, respectively. The RL-CAA provides better distributed and higher throughput performance (reaches over 7 Mbps instantaneous throughput).  
The dissatisfaction probabilities, as experienced by the individual nodes, are given in Figure 9a,b for the SCA and RL-CAA, respectively.The RL-CAA provides better QoS satisfaction as compared to the SCA. This can also be seen from Figure 9c, where the total dissatisfaction probabilities averaged over time for all nodes is presented for both algorithms. The RL-CAA provides, on average, a QoS satisfaction to individual nodes 97.2% of the time, while the SCA can only provide, on average, QoS satisfaction 79% of the time. These results consolidate the hypothesis that the proposed RL-CAA is highly effective in traffic changing environment and can accommodate handovers.
# High-Speed Visible Light Communications: Enabling Technologies and State of the Art
## 1 Introduction  
Visible Light Communication (VLC) is an emerging optical wireless data transmission approach that using visible lights as the signal carriers. The history of Visible Light Communication could be traced back to the 1880s, when [PERSON] developed a photophone by modulating sunlight. This invention even pre-dates the transmission of voice data by radio. Several other works using fluorescent lights as the light source for communication were demonstrated then with quite low data rates. According to the reports of [PERSON] et al., the data rate is only fast enough to refresh a 40-character display 2.5 times per second [1]. With the developments in man-made light sources, the traditional fluorescent lights and incandescent buls are gradually being replaced by light emitting diodes (LEDs). Due to the advantage of their lower costs, higher lifetime, low power consumption and high switching rate, LEDs have predominantly become the most favorable light source for VLC. In a LED-based VLC system, information is transmitted by modulating the intensity of LED at a rate much faster than the response time of the human eyes, which offers simultaneous illumination and high-speed data transmission. Therefore, it is regarded as an economic, secure, and ubiquitous data transmission solution, especially a perfect last-mile solution in the future [2].  
This review mainly focuses on the enabling technologies for high-speed visible light communication systems. It is organized as follows. Section 2 presents the inherent advantages and potential applications of VLC. Section 3 discusses detailed the technical approaches for the improvement of the data rate in a VLC system, including the novel transmitter architectures, optical filters, advanced modulation, and equalization technologies. Section 4 reviews the recent developments and the contributions by research groups involved in the field of VLC. Section 5 lists some issues that must be addressed by in-depth study in future research, and Section 6 presents important conclusions that are drawn from this survey as well as the prospects of the technology.
# Nonlinear Robust Adaptive Control of Universal Manipulators Based on Desired Trajectory
## 3 Controller Design
### Nominal Dynamics Feedforward  
Firstly, based on the results of parameters identification, combined with the properties of parameter linearization in Equation (3), the nominal dynamic model of the robot can be obtained:  
\[\hat{M}(q)\ddot{q}+\hat{C}\big{(}q,\dot{q}\big{)}\dot{q}+\hat{G}(q)+\hat{F} \big{(}\dot{q}\big{)}=Y_{I}\big{(}q,\dot{q},\dot{q},\ddot{q}\big{)}\theta_{l0}+Y _{f}\big{(}\dot{q}\big{)}\theta_{f0} \tag{10}\]  
where \(\theta_{l0}\) and \(\theta_{f0}\) are the inertia parameters of the links and joint friction coefficients obtained via the identification, respectively. From Assumptions 1 and 2, it can be seen that the above coefficients are uncertain, so the dynamic model obtained by the original identification of the parameters is called the nominal dynamic model.  
According to the Equation (10), the variables are all real-time signals measured by the sensors in the control process. and the following calculations are carried out using the desired trajectory to obtain the first term in the controller, i.e., the nominal dynamics feedforward term:  
\[\begin{array}{ll}u_{feedforward}&=\hat{M}(q_{d})\ddot{q_{d}}+\hat{C}\big{(} q_{d},\dot{q_{d}}\big{)}\dot{q_{d}}+\hat{G}(q_{d})+\hat{F}\big{(}\dot{q_{d}} \big{)}\\ &=Y_{I}\big{(}q_{d},\dot{q_{d}},\dot{q_{d}},\ddot{q_{d}}\big{)}\theta_{l0}+Y_{ f}\big{(}\dot{q_{d}}\big{)}\theta_{f0}\end{array} \tag{11}\]  
Figure 1: Block structure of nonlinear robust adaptive control based on desired trajectory.  
where \(q_{d},\dot{q}_{d},\ddot{q}_{d}\) represent the desired joint positions, velocities, and accelerations, respectively. In the following derivation, \(Y_{l}\big{(}q_{d},\dot{q}_{d},\dot{q}_{d},\ddot{q}_{d}\big{)}\) is denoted by \(Y_{ld}\), and \(Y_{f}\big{(}\dot{q}_{d}\big{)}\) is denoted by \(Y_{fd}\); similarly, \(Y_{l}\big{(}q,\dot{q},\dot{q},\ddot{q}\big{)}\) is denoted by \(Y_{l}\), and \(Y_{f}\big{(}\dot{q}\big{)}\) is denoted by \(Y_{f}\).
# Adaptive Trajectory Tracking Control for
## 2 Problem Formulation and Preliminaries
### Neural Network Minimum Parameter Learning Method  
The basic principle of neural network minimum learning parameter method is introduced in this subsection. In control engineering, the multi-layer neural network, represented by back propagation (BP) [29; 30] neural network and RBF neural network, are used to approximate nonlinear or unknown functions most widely [31]. Taking RBF neural network as an example, for any given continuous nonlinear function \(f(x)\) with \(f(0)~{}=~{}0\), it can be rewritten as  
\[f(x)~{}=~{}W^{T}h(x)+\varepsilon \tag{3}\]  
where \(W~{}=~{}[w_{1},w_{2},\ldots,w_{p}]^{T}\subset R^{p}\) is a weight vector; \(h(x)~{}=~{}[h_{1}(x),h_{2}(x),\ldots,h_{p}(x)]\) is Gaussian function; \(\varepsilon\) is the approximation error of the neural network and \(|\varepsilon|\leq\bar{\varepsilon}\), \(\bar{\varepsilon}>0\). \(p\) is the node number of neural network [32].  
However, multilayer neural network needs online estimation of weight vectors of neural network, which inevitably increases the computational load of the control algorithm, that is, the so-called \"curse of dimensionality\". In order to solve the above problem, in this paper, neural network minimum learning parameter method is used to approximate unknown function instead of RBF neural network. Compared with multi-layer neural network, neural network minimum learning parameter method can significantly reduce the computational burden of the controller. The essence of this method is that the proposed adaptive law does not depend on the number of neural network nodes by online estimation of the weight vector norm of the neural network. Specifically, define \(\phi~{}=~{}\left\|W\right\|^{2}\), and \(\phi\) is a normal number. \(\hat{\phi}\) is the estimated value of \(\phi\). Meanwhile, estimation error \(\hat{\phi}~{}=~{}\hat{\phi}-\phi\).
## 3 Model Description
### Motion Information Detection Channel
#### 3.1.3 Tm3 and Tm1 cells  
As illustrated in Figure 1b, Tm3 and Tm1 neurons are downstream neurons of the LMC, with each one particularly reacting to increments and decrements in brightness, respectively [60]. The Tm3 neuron is particularly sensitive to increases in brightness, while the Tm1 neuron responds significantly to decreases in brightness. In the motion information detection channel, Tm3 and Tm1 neurons act like biological half-wave rectifiers, rectifying the neural signals transmitted by the LMC, thereby enabling the parallel processing of different types of visual information. Let \(S^{+}(x,y,t)\) signify Tm3 neuron output, which is calculated by selecting the positive component of the LMC response \(L(x,y,t)\). The \(S^{+}(x,y,t)\) is expressed as  
\[S^{+}(x,y,t)=\big{[}L(x,y,t)\big{]}^{+}. \tag{6}\]  
In addition, findings suggest that the Tm1 neuron's output response lags behind that of the Tm3 neuron at the same spatial locations \((x,y)\) (see Figure 4a). Therefore, the Tm1 neuron response function \(S^{-}(x,y,t)\) is described as  
\[S^{-}(x,y,t)=\int[L(x,y,s)]^{-}\Gamma_{n_{2},T_{3}}(t-s)ds, \tag{7}\]  
where \([x]^{+}=\frac{|x|+x}{2}\), \([x]^{-}=\frac{|x|-x}{2}\).
# Rare Earth Elements Uptake by Synthetic Polymeric and Cellulose-Based Materials: A Review
###### Abstract  
Contemporary industrial processes and the application of new technologies have increased the demand for rare earth elements (REEs). REEs are critical components for many applications related to semiconductors, luminescent molecules, catalysts, batteries, and so forth. REEs refer to a group of 17 elements that have similar chemical properties. REE mining has increased considerably in the last decade and is starting an REE supply crisis. Recently, the viability of secondary REE sources, such as mining wastewaters and acid mine drainage (AMD), has been considered. A strategy to recover REEs from secondary water-related sources is through the usage of adsorbents and ion exchange materials in preconcentration steps due to their presence in low concentrations. In the search for more sustainable processes, the evaluation of synthetic polymers and natural source materials, such as cellulose-based materials, for REE capture from secondary sources should be considered. In this review, the chemistry, sources, extraction, uses, and environmental impact of REEs are briefly described to finally focus on the study of different adsorption/ion exchange materials and their performance in capturing REEs from water sources, moving from commercially available ion exchange resins to cellulose-based materials.  
rare earth elements; lanthanides; cellulose; functional polymers; adsorption; ion exchange +
Footnote †: journal: Polymers  
lesser efficiency of non-REE materials under development for the same applications [5]. Due to their importance, finding new sources of REEs is also critical. Despite their abundance, REEs' distribution in the earth's crust is dispersed, and rarely accumulates to form mineral ores and deposits, mainly because REE cations could substitute other ions in the crystal lattices of minerals [3; 4]. Currently, REEs are mined using extraction and concentration steps directly from rare-earth-containing ores [3; 4]. Looking for new sources of REEs, interest in secondary and residual deposits that contain REEs is growing. Mining residues and rock weathering, such as mining wastewaters, acid mine drainage (AMD), and regolith, are listed as potential sources of REEs, and research is under development to extract and optimize REE recovery [4; 5]. In this context and considering that REEs in mining wastewaters and AMD are present in low amounts, adsorbent and ion exchange materials could be useful as part of preconcentration and/or purification processes, which makes the evaluation of those types of materials for REE capture from secondary sources interesting. This review explores fundamental aspects of REEs to understand the origin and characteristics of secondary sources and explore options for REE recovery from those sources using commercial ion exchange resins and synthetic polymeric materials to finally focus on cellulose-based materials with the aim of considering cellulosic materials as a viable and sustainable alternative to commercial resins and synthetic materials.
## 3 The ALIEN Architecture
### Model Description
#### 3.4.3 Latent Information Digestion and Prediction Layer (PREDICTION)  
The latent information digestion and prediction layer (PREDICTION) receives the outputs of the first two layers, i.e., \(\widehat{\mathbf{\mathcal{F}}}^{u,v_{c}}\), \(\mathbf{\mathcal{F}}^{u,v_{c}}\) and \(\mathcal{I}_{u}^{v_{c}}\), and generates \(\mathbf{\mathcal{P}}_{u}^{v_{c}}\), i.e., \(u\)'s preference vector for \(v_{c}\):  
\[\mathbf{\mathcal{P}}_{u}^{v_{c}}=[\widehat{\mathbf{\mathcal{F}}}^{u,v_{c}};\mathbf{ \mathcal{F}}^{u,v_{c}};\mathcal{I}_{u}^{v_{c}}]. \tag{20}\]  
Next, \(\mathbf{\mathcal{P}}_{u}^{v_{c}}\) is sent through a fully-connected layer, to calculate the probability of \(u\) clicking \(v_{c}\):  
\[prob(\mathbf{\mathcal{P}}_{u}^{v_{c}})=\mathrm{MLP}^{L^{\prime}}(\mathbf{\mathcal{P}}_ {u}^{v_{c}}), \tag{21}\]  
Finally, \(v_{c}\)'s prediction score \(\widehat{y}\) for \(u\) is:  
\[\widehat{y}=\mathrm{sigmoid}(prob(\mathbf{\mathcal{P}}_{u}^{v_{c}}))=\mathrm{ sigmoid}(\mathrm{MLP}^{L^{\prime}}(\mathbf{\mathcal{P}}_{u}^{v_{c}})), \tag{22}\]  
where \(\widehat{y}\in\{0,1\}\) and \(\mathrm{sigmoid}(\mathrm{x})=\frac{1}{1+exp(-x)}\).  
Figure 3: The working procedure of \(\mathbf{w}_{ci}\).
# Analyzing and Controlling Inter-Head Diversity in Multi-Head Attention
## 6 Experiments on Controlling Inter-Head Similarity Methods
### Analysis on Controlling Inter-Head Diversity  
We report the performances of our suggested methods in Table 4 and the controlled inter-head similarity with our suggested methods in Table 5. We also plot SVCCA coefficient curves in Figure 4.  
With Drophead, all models show increased inter-head similarity compared to the baseline. As \(\gamma\) increases to 0.0 to 0.5, inter-head similarity indeed increases to 0.397 to 0.709, indicating that Drophead affects inter-head similarity by reducing the number of _effective_ heads as desired. We observe this clear tendency by comparing SVCCA coefficient curves (a) in Figure 4 to (b) in Figure 3. The curve of 8\(H\)512_d with \(\gamma=0.3\) is very similar to that of 4\(H\)256_d, and as the rate increases \(\gamma=0.5\), the curve becomes similar to that of the model with fewer heads 2\(H\)128_d.  
In addition, as opposed to the expectation of [PERSON] et al. [7] have expected, we find that the orthogonality loss does not diversify inter-head similarity. For +ORTHO and +HSIC, every model shows average disagreement score [7] as 0.999, which implies that two vectors from different heads are orthogonal. However, instead of diversifying, the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & _Dim/Head_ & **SVCCA** & **CKA** \\ \hline
2\(H\)64\(d\) & 32 & 0.793 & 0.553 \\
2\(H\)128\(d\) & 64 & 0.712 & 0.488 \\
2\(H\)512\(d\) & 256 & 0.559 & 0.344 \\ \hline
4\(H\)128\(d\) & 32 & 0.504 & 0.277 \\
4\(H\)256\(d\) & 64 & 0.541 & 0.309  
orthogonality loss slightly increases inter-head similarity measured in both SVCCA (from 0.397 to 0.420) and CKA (from 0.199 to 0.366). Nevertheless, the model only with the orthogonality loss performs better than a baseline as it records 34.03 BLEU score (+ortho ONLY in Table 4). We suspect that the performance improvements are caused by steep rises of top-few SVCCA coefficients. The affects of the orthogonality loss on top-few SVCCA coefficients are depicted in (b) and (d) in Figure 4 (as comparing curves of _baseline_, _ortho 0.1, ortho 1.0, and ortho 10.0_). The orthogonality regularizer makes the heads similar to each other in a prime direction while sustaining other directions diverse, hence it makes the model robust to both general features and rare features.  
Lastly, we observe that HSIC regularizer directly enforces each head to be diverse as shown in both Table 5 and (c) in Figure 4. While the other two methods increase inter-head similarity, HSIC regularizer is the only method to diversify inter-head similarity without modifying a model's architecture. Although _increasing number of heads_\(H\) also diversify inter-head similarity, it has a critical downside that architectural modification must be accompanied.