esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
Considering music style, statistical differences were found with a large effect size for the total impacts (\(F\) = 532.57; \(p\) < 0.01; \(\omega_{p}\)\({}^{2}\) = 0.60), very low impacts (<2 g; \(F\) = 416.64; \(p\) < 0.01; \(\omega_{p}\)\({}^{2}\) = 0.54), low impacts (2-4 g; \(F\) = 295.92; \(p\) < 0.01; \(\omega_{p}\)\({}^{2}\) = 0.45), and moderate impacts (4-6 g; \(F\) = 118.24; \(p\) < 0.01; \(\omega_{p}\)\({}^{2}\) = 0.25); with a medium effect size for high impacts (6-8 g; \(F\) = 52.55; \(p\) < 0.01; \(\omega_{p}\)\({}^{2}\) = 0.13) and very high impacts (\(F\) = 24.48; \(p\) < 0.01; \(\omega_{p}\)\({}^{2}\) = 0.06); and with a small effect size for severe impacts (\(F\) = 11.14; \(p\) < 0.01; \(\omega_{p}\)\({}^{2}\) = 0.03). Cumbia and salsa obtained the highest total impacts and at very low (<2 g), low (2-4 g) and moderate (4-6 g) intensities (\(p\) < 0.01, \(d\) = 0.33-to-4.53, low to very high; cumbi = salsa > vallenato = merengue > bachata = chachacha > bolero > roots > kizomba). At high (6-8 g) and very high (8-10 g) intensities, salsa, cumbia, chachacha, and vallenato presented the highest impacts (\(p\) < 0.01, \(d\) = 0.73-to-1.18, moderate; salsa = cumbia = chachacha = vallenato > merengue > bachata > bolero > roots > kizomba). For severe impacts, the highest values were found for chachacha (\(p\) < 0.01, \(d\) = 0.22-to-0.73, low to moderate; chachacha > salsa = vallenato > cumbia > merengue > bachata > bolero = roots > kizomba). Finally, the interaction between the music style and body locations presented statistical differences with a large effect size for the total impacts, very low impacts, low impacts, moderate impacts, and high impacts (\(F\) = 12.78-to-28.14; \(p\) < 0.01; \(\omega_{p}\)\({}^{2}\) = 0.14-to-0.27); with a medium effect size for the very high impacts (\(F\) = 7.55; \(p\) < 0.01; \(\omega_{p}\)\({}^{2}\) = 0.08); and with a small effect size for severe impacts (\(F\) = 3.98; \(p\) < 0.01; \(\omega_{p}\)\({}^{2}\) = 0.04). The relationships between the total impacts and per intensity levels performed by the older people concerning the order of the songs throughout the dance lesson and the rhythm of the music (beats per minute) at each anatomical location are shown in Table 3. The analysis indicated that a faster music rhythm produced higher impacts with moderate correlations in the total impacts and at a very low intensity in the lower limb locations and at a low intensity in the ankles. In addition, small correlations were found in the knees at a low intensity and in the ankles at moderate, high, and very high intensities. No correlations were found between music rhythm and impacts in the upper limbs (scapulae and lower back). On the other hand, the order of the songs throughout the dance lesson produced greater impacts in the lower limb locations with small correlations in the total impacts and very low, low, and moderate impacts in the knees and ankles and in high and very high intensity impacts in the ankles.
## 4 Experimental Results
### Maximum Embedding Capacity

Table 4: PSNR and non-modified pixels of binary image hiding. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Payload** & **Proposed Scheme** & \multicolumn{4}{c}{**BRL-Scheme**[15]} & \multirow{2}{*}{**LSW**[18]} & \multirow{2}{*}{**Scheme**} \\ \cline{3-11}  & & \({images are shown as Figure 2, and the secret messages were generated from PRNG. For example, if we use the MGEMD scheme (when \(n=3\) and \(k=3\)) with non-compressed secret messages, then the group numbers are 87,381 (\(512\times 512\div 3\)) with the image size of \(512\times 512\). The embedding fixed secret capacity is 878,310 (87,381 \(\times(nk+1)=\) 87,831 \(\times 10\)) bits. However, according to our simulation results, the secret embedding capacity is 1,757,500 bits using our proposed scheme. That is to say, the embedding performance of the proposed scheme is \(200\%\) better than without using RLE. Furthermore, using the GMEMD method, we compare the embedding capacity with the compressed secret data or those that are not. The result is shown in Figure 7.
# Depth Image Super Resolution Based on Edge-Guided Method
## 2 Proposed Method
### Edge-Guided Depth Interpolation
#### 2.2.1 Modified Joint Bilateral Filter  
After obtaining HR edge image \(\mathbf{E}_{h}\), HR depth image \(\mathbf{I}_{h}\) can be interpolated through a modified joint bilateral filter with the guidance of \(\mathbf{E}_{h}\). For each pixel \(p\) in the target HR depth image \(\mathbf{I}_{h}\), its value can be interpolated by a local neighborhood of LR image:  
\[\mathbf{I}_{h}(p)=\frac{1}{k_{p}}\underset{q\in N(p)}{\sum}\mathbf{I}_{l}(q \downarrow)\cdot f_{s}(\|\,p\downarrow-q\downarrow\|)\cdot f_{r}(\mathbf{E}_{h},p,q) \tag{8}\]  
where \(N(p)\) is an \(s\times s\) neighborhood window centered at pixel \(p\). \(p\downarrow\) and \(q\downarrow\) represent pixel coordinate corresponding to pixel \(p\) and pixel \(q\) in the LR depth image \(\mathbf{I}_{l}\), and only integer coordinate is considered. \(f_{s}(\cdot)\) is a Gaussian kernel with standard deviation \(\sigma\) and mean value 0, which is used to weight the correlation of different pixel in the neighborhood. \(k_{p}\) is a normalizing factor. \(f_{r}(\cdot)\) is a binary indicator, which determines whether or not two pixels are on the same side of the edge. The indicator is defined as:  
\[f_{r}(\mathbf{E}_{h},p,q)=\left\{\begin{array}{ll}1&\quad if\text{ {pixel }}p\text{ and {pixel }}q\text{ are at the same side of }\mathbf{E}_{h}\\ 0&\quad otherwise\end{array}\right. \tag{9}\]  
The concrete form of \(f_{r}(\cdot)\) can be created by discriminating the distribution of pixels \(p\) and \(q\).
## 3 Results & Discussion
### Factors Controlling Pore Cast Quality
#### 3.2.4 Optimum Impregnation Parameters  
When present in a pore cast, the various defects discussed above can lead to erroneous imaging results. Based on several experimental observations, we summarize the main attributes of the impregnation parameters as follows:  
1. The vacuum level should not exceed 80 mbar for amine-based epoxy systems to avoid excessive shrinkage defects. Our guideline is different than the suggested values of 1.33 mbar [20] and 0.067 mbar [24] for carbonates and tight rocks, respectively.
2. Pressure-assisted impregnation is essential to ensure complete pore invasion. Pessures of up to 65 bars were experimentally verified to have no adverse effects on both sample integrity (i.e., no cracks) and resin curing.
3. A degassing step prior to pressurized-impregnation is needed to remove any fluid traces and avoid bubble formation.
4. The benefits of adding viscosity modifiers to the epoxy system do not outweigh the risks, and hence a selection of epoxies that have adequate viscosity (i.e., below 0.5 Pa\(\cdot\)s) leads to a higher pore cast quality.  
Figure 12: Confocal images of two micromodels impregnated under a vacuum. Two compositions of resin and toluene were used: (**A**) epoxy mixed with 5% toluene and (**B**) epoxy mixed with 30% toluene. In model A, a phase boundary between toluene and epoxy was detected. Shrinkage defects, especially type 2A, were visibly more abundant in model B where the toluene fraction was higher.  
We followed these guidelines to fabricate high quality epoxy pore casts of microporous carbonate samples. Table 4 summarizes the impregnation parameters of Indiana limestone samples with the epoxy system described earlier.
## 3 Materials and Methods
### Synthesis of 4-antiyyrinyl-2-chloroacetamido-5-(4-sulfamoylphenylazo)thiazolo (6)  
A cold suspension of sulphanilamide (10 mmol, 1.70 g) in concentrated HCl (3 mL) was diazotized at 0-5 \({}^{\circ}\)C with a solution of NaNO\({}_{2}\) (0.70 g in 10 mL H\({}_{2}\)O) with stirring for 30 min. The diazonium solution was added dropwise to a mixture of chloroacetamide derivative **1** (10 mmol, 3.62 g) and sodium acetate (3 g) in 35 mL ethanol with stirring in an ice-bath for 1 h. The precipitate was filtered and recrystallized from EtOH/DMF mixture (5:1) to give the sulfamoylphenylazo-thiazole derivative **6**. Reddish-brown solid, yield = 74%, m.p. = 241-242 \({}^{\circ}\)C. IR (\(\overline{v}\)/cm\({}^{-1}\)): 3351, 3264 (NH\({}_{2}\) and N-H), 1697 cm\({}^{-1}\) (C=O). \({}^{1}\)H-NMR (DMSO-\(d_{6}\)): \(\delta\) 2.72 (s, 3H, CH\({}_{3}\)), 3.28 (s, 3H, CH\({}_{3}\)), 4.44 (s, 2H, CH\({}_{2}\)), 7.28-7.61 (m, 7H, Ar-H and NH\({}_{2}\)), 7.94-8.00 (dd, 4H, Ar-H), 12.92 ppm (s, 1H, NH). \({}^{13}\)C-NMR (DMSO-\(d_{6}\)): \(\delta\) 12.57, 35.16, 42.11, 108.81, 121.58, 124.41 (2C), 127.08 (2C), 129.21 (2C), 129.79 (2C), 132.25, 135.81, 140.27, 142.72, 147.47, 155.53, 161.21, 162.78, 165.31. Analysis for C\({}_{22}\)H\({}_{20}\)CIN\({}_{7}\)O\({}_{4}\)S\({}_{2}\) (545): Calcd.: C, 48.39; H, 3.69; N, 17.96%. Found: C, 48.27; H, 3.63; N, 17.85%.
# Use of Phase Change Materials for Food Applications--State of the Art in 2022
## 2 Food Applications of PCM
### Food Processing
#### 2.1.4 Solar Freezer/Pre-Cooler  
Cooling of harvested food, as fast as possible, including dropping core temperatures, increases the shelf life significantly and minimizes food wastage. This is especially critical for foods which have a high heat capacity, and spoil comparatively fast, such that technical solutions should be designed accordingly.  
A possible solution is a mobile solar-driven freezer/pre-cooler [18], shown in Figure 4, which runs totally off-grid, and is on wheels to be taken directly to the harvesting location. It allows cooling down the harvested products right after harvest, and also cooling them during the following transport (first mile transport).  
Figure 3: 1000 L bulk milk cooler with ice storage (source: Inficold).  
Another example is a solar powered cold storage facility, developed for fishermen, as stand-alone, off-grid, 100% solar-powered system. Two units with 20 m\({}^{3}\) are installed on remote islands in Indonesia [18]. They are designed to keep 1000 kg of fish at \(-2\)\({}^{\circ}\)C, in ambient temperatures of up to 40 \({}^{\circ}\)C, and have the capacity to cool down 200 kg of new, 'un-iced' fish per day (total cooling load of 30 kWh per day). A 6.4 kW solar array, backed up by a relatively small 10 kWh battery, runs the whole system. The system is equipped with PCM TES in the ceiling, capable to store 15 kWh cooling capacity at \(-4\)\({}^{\circ}\)C, which allows the compressor to do most of its 'work' during the day, when solar power is available.
# Enhancing PAPR and Throughput for DFT-s-OFDM System Using FTN and IOTA Filtering
## 3 Proposed IOTA-Based FTN-DFT-s-OFDM Waveform Design
### Receiver Design
\tag{12}\]  
Due to the cyclic structure of matrix **G**, singular value decomposition can be done as \(\textbf{G}=\textbf{Q}^{T}\textbf{\Lambda}\textbf{Q}^{*}\), with \(\textbf{Q}\in\textbf{C}^{N\times N}\) being the eigenmatrix of **G**, \(\boldsymbol{\Lambda}\) being a diagonal matrix and elements in the \(i-th\) row being the corresponding FFT coefficients. DFT vector can be used to derive the element in the \(l-th\) row and \(k-th\) column of **Q**. To reduce the computational complexity, the considered FDE algorithm aims to equalize the time-domain receiving signal in the transform-domain with the eigenmatrix \(\mathbf{Q}\), which is derived as  
\[\mathbf{\hat{y}}_{f}\simeq\mathbf{Q}^{*}\mathbf{\hat{y}}=\mathbf{\Lambda}\mathbf{ Q}^{*}\mathbf{x}+\mathbf{Q}^{*}\mathbf{n}=\mathbf{\Lambda}\mathbf{x}_{f}+ \mathbf{n}_{f}, \tag{13}\]  
where \(\mathbf{x}_{f}\) and \(\mathbf{n}_{f}\) refer to the transformed signal and noise vector, respectively. Then the equalization matrix \(\mathbf{W}\in\mathbb{C}^{N\times N}\) is derived based on \(\mathbf{\Lambda}\) according to the MMSE criteria, with \(\omega_{(i,i)}\) and \(\lambda_{(i,i)}\) the \(i-th\) rows of the diagonal matrix \(\mathbf{W}\) and \(\mathbf{\Lambda}\), respectively. Here, \(\omega_{(i,i)}\) can be expressed as  
\[\omega_{(i,i)}=\lambda_{(i,i)}^{*}/\left(\left|\lambda_{(i,i)}\right|^{2}+N_{0}\right) \tag{14}\]  
Finally, we get the estimated value \(\mathbf{\hat{x}}\) of the transmit symbol \(\mathbf{x}=\left[x_{1},x_{2},\ldots,x_{n}\right]^{\mathsf{T}}\) as  
\[\mathbf{\hat{x}}=\mathbf{Q}^{\mathsf{T}}\mathbf{W}\mathbf{\hat{y}}_{f}= \mathbf{Q}^{\mathsf{T}}\mathbf{W}\Big{(}\mathbf{\Lambda}\mathbf{x}_{f}+ \mathbf{n}_{f}\Big{)}. \tag{15}\]  
The computational complexity of the FDE receiver includes three parts: FFT complexity, weight calculation complexity and MMSE algorithm complexity. (13) uses FFT to transform the received signal into the frequency domain, with its complexity of order \(NlogN\). The calculation of the weight in (14) requires \(4N\) multiplications and the MMSE operation in (15) requires \(2N\) multiplications, whose complexity are both of order \(N\). Thus, the proposed receiver has the overall computational complexity of \(O(NlogN+N)\). The complexity of the FDE receiver is only related to the receiving block length \(N\), but not to the FTN acceleration factor \(\alpha\), filter type, or tap length. Therefore, the receiver complexity of the the IOTA-based scheme proposed in this paper is the same as that of the RRC-based NOW scheme. Compared with the time-domain equalization algorithm, whose complexity exponentially increases with tap length, the FDE receiving algorithm used in this paper has lower complexity, especially under high ISI. Until now, we finish the signal demodulation and successfully cancel the ISI. What is worth noticing is that FDE has relatively low complexity and can similarly achieve the detecting performance of optimal receiver.
# Optically Transparent Nano-Patterned Antennas: A Review and Future Directions
## 3 Summary of Various Transparent Antennas  
The \(R_{\text{s}}\)-\(T\) performance of TCFs is evaluated using a FoM consisting of \(\sigma_{\text{dc}}/\sigma_{\text{opt}}\), as expressed in Reference [38]. Table 1 summarizes the results of recently published transparent antennas featuring various TCFs. While graphene [4] and CNT [13] exhibit higher optical transmittances, the electrical sheet resistance remains excessively high for microwave applications. Compared to nano carbons and TCOs, metallic nanostructures feature smaller values of \(R_{\text{s}}\) and higher values of \(T\), resulting in enhanced \(\sigma_{\text{dc}}/\sigma_{\text{opt}}\). In the case of IZTO/Ag/IZTO [20], CuNW [30], and patterned metal grid [36; 37], \(\sigma_{\text{dc}}/\sigma_{\text{opt}}\) is remarkable, but the optical transmittance criteria (\(T>85\%\)) requires further improvement. However, the optical transmittance of the sample in Reference [38] (\(T=88\%\)) meets the aforementioned optical transmittance criteria (the FoM and \(R_{\text{s}}\) of the sample in Reference [38] are 887 and 3.22 \(\Omega\)/sq, respectively). In addition, not only the sheet resistance and optical transmittance, but also the optical degradation such as haze and moire effects should be considered when placed above substrates, such as active display panels, in future transparent antenna research.  
Figure 11: (**a**) Broadband optically transparent H-shaped slot antenna above the lab logo; (**b**) computed and measured VSWR of the broadband H-shaped slot antenna from 1.8 to 2.3 GHz [37]. (With permission from IEEE).
# Three-Dimensional Drone Cell Placement: Drone Placement for Optimal Coverage
## 4 Proposed Methods for Optimal DC Placement  
Three meta-heuristic algorithms have been implemented to address the DC placement problem, namely Particle Swarm Optimization, differential evolution, and Dispersive Flies Optimization. In the implemented algorithms, upon the first initialization, the population consisting of n number of agents (i.e., particles, chromosomes and flies) contains a random solution made up of x, y and z coordinates, where x and y are the latitudinal and longitudinal coordinates and z is the flying height of the DC. With each loop of the allotted time budget or termination criteria, the entire population moves within the search space, where the movement is dictated by each algorithm's inner working principle and updates the solution, i.e., the x, y and z coordinates. Throughout the loop for each of the search agents, a fitness function calculates the path loss experienced between the DC and nearby users and stores the number of users whose experienced path loss is less than the path loss threshold specified, i.e., the covered users. Once this calculation has been conducted for all the search agents in the population, the x, y, z location of the search agent having the most number of users covered is stored as the global best and the population (i.e., the swarm) is pushed towards the global best location. This process continues until all the users have been covered or until termination criteria are met. A brief overview of each algorithm's functioning is provided, along with their corresponding pseudo code.
# Estimating Interception Density in the BB84 Protocol: A Study with a Noisy Quantum Simulator
## 5 Intrusion Detection Method
### The Proposed Intrusion Detection Method  
To generalize Equation (11), the assumption on the channel noise should be released. For this aim, four different cases can be defined, depending on the presence of [PERSON] and noise. Each case has a different impact on the theoretical \(QBER\), as described in the following:  
1. **No system noise, no [PERSON]:**\(QBER=0\),
2. **System noise, no [PERSON]:** the \(QBER\) can be derived once the amount of random noise is determined,
3. **No system noise, [PERSON]:**\(QBER=0.25\,p\),
4. **System noise, [PERSON]:**\(QBER\geq 0.25\,p\).  
Among these alternative scenarios, the latter (with interception density \(p\)) is the most interesting one. Considering a noisy scenario and Equation (11), in the case where the parameter \(p\) is known, the information that could be derived from the \(QBER\) observation is that, if \(QBER<25\,p\%\), [PERSON] has not intercepted the exchanged bits. On the contrary, no useful information is available when \(QBER\geq 25\,p\%\) if no information on the noise is available. Of course, this is a probabilistic approach that may fail, as discussed below.  
The \(QBER\) can be estimated after establishing the number of bits used for testing the BB84 procedure (i.e., \(m\)), Therefore indicating with \(d\) the number of erroneous bits detected at [PERSON]'s side in the shared key:  
\[Q\bar{BER}=\frac{d}{m}. \tag{12}\]  
When considering the above analysis and Equation (11), the _missed detection_ (\(MD\)) event corresponds to the case that \(d<0.25 pm\), but [PERSON] is present, i.e., when the number of wrong bits in the shared key is less than the expected mean value.  
More specifically, when considering the corresponding integer value and, hence, setting a threshold equal to \(\lfloor 0.25 pm\rfloor-1\), the missed detection probability \(\mathbb{P}\{MD\}\) can be calculated as follows:  
\[\mathbb{P}\{MD\}=\sum_{i=0}^{\lfloor 0.25 pm\rfloor-1}\binom{m}{i}(0.25p)^{i} \left(1-0.25p\right)^{m-i}. \tag{13}\]It is worth noting that the above Equation does not consider the noise; consequently, the actual \(\mathbb{P}\{MD\}\) would be even smaller since the noise would increase the probability of a bit error on [PERSON]'s side. In other words, the proposed intrusion detection method incorporates errors on the bits due to system noise into [PERSON]'s intervention, thus following a conservative approach in evaluating the attack scenario.
## 6 RL Training Settings  
DDQN is based on optimal action-value function, \(Q^{*}(s,a)\), which is defined as the maximum of the expected cumulative discounted rewards by following any policy \(\pi\), after taking action \(a\) in state \(s\)[2], given by:  
\[Q^{*}(s,a)=\max_{\pi}\mathbb{E}\Big{[}R_{i+1}+\gamma R_{i+2}+\gamma^{2}R_{i+3} +\ldots\Bigm{|}S_{i}=s,A_{i}=a;\pi\Big{]} \tag{8}\]  
With \(Q^{*}\) being known, an optimal policy \(\pi^{*}\) is to greedily select the action with the highest value in any state.  
To accommodate the single-ring RISC problem, our improved DDQN, termed Double Deep Dynamic Q-network (D3 ynQN), has training settings different from the original ones in two aspects. First, we propose a new TD algorithm to accommodate the time-varying environment. Second, we introduce an operation to augment state transition samples. Algorithm 1 exhibits the pseudo-code of D3 ynQN training process.  
```
1 Initialize the behavior network with random weights \(\theta\);
2 Initialize the target network with weights \(\theta^{-}=\theta\);
3 Set the exploration probability \(\epsilon=1.0\);
4 Set the time step \(i=1\);
5 Set the state and action number \(j=1\);
6 // see the hyper-parameters in Table 2
7 whilehave not selected enough actionsdo
8 Sample the vehicle volumes, turning ratios, and pedestrian volumes;
9 Execute a simulation run to the moment of initial state;
10 whilenot reach the end of simulation perioddo
11 Select the \(j^{\text{th}}\) action \(A_{i}^{j}\) randomly with probability \(\epsilon\), otherwise \(A_{i}^{j}=\text{argmax}_{i}Q(S_{i}^{j},a;\theta)\);
12 Execute the current simulation run to time step (\(i\)+\(d\));
13 Get the (\(j\)+1)th state \(S_{i+j}^{j+1}\).
14 Collect the cumulative discounted rewards \(RS^{j+1}\) between time steps \(i\) and (\(i\)+\(d\)) by Equation (11);
15 Perform the data augmentation on transition \((S_{i}^{j},A_{i^{\prime}}^{j},RS^{j+1},S_{i+d}^{j+1})\);
16 Store the augmented transitions in replay buffer;
17 Sample a mini-batch of transitions \((S_{i}^{j},A_{i^{\prime}}^{j},RS^{j+1},S_{i+D}^{j+1})\) randomly from the replay buffer;
18 Compute the mean TD error \(L^{l}\) for the mini-batch by Equation (15);
19 Perform a SGD step on \(L^{l}\) with respect to \(\theta\);
20 Reset \(\theta^{-}=\theta\) every a certain number of SGD steps;
21 Anneal \(\epsilon\) if it is greater than the final value;
22 Set \(i=i+d^{j},j=j+1\);
23 endwhile
24 Stop the current simulation run;
25 endwhile
```  
**Algorithm 1**Training process of D3 ynQN
## 3 GNSS Data Collection with Android-Based Smartphones
### Stochastic Model Settings
Table 2), allowing us to also take into account the repeatability of the Galileo satellites. Finally, we remark that the common low-noise amplifier (LNA) noise between the receivers is mostly cancelled out in the zero-baseline setup [30]. We can see in Table 2 that the code STDs increase by a factor of five when going from using external antennas in the short-baseline setup, to using internal antennas. For instance, we can see that the GP4 code STD increases from about 1.2 m when using external antennas up to approximately 6.0 m when internal antennas are used, whereas the corresponding S20 code STD increases from about 0.5 m to 2.3 m, respectively. The corresponding GP4 phase STD increases from 2 mm to 4 mm, whereas the S20 phase STD remains the same at 3 mm when going from using external to internal antennas. This increase in code STDs implies that the internal antennas are indeed very sensitive to multipath effects and that the code observations are more affected [31]. We can also see in Table 2 that the phase and code STDs are significantly improved in the zero-baseline setup, since here we can expect any multipath effects to be significantly reduced.