esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Within this paper, the compaction characteristics of unbound and cement-bound mixtures for application as a pavement base course are investigated on two aggregate types using two laboratory compaction methods. The purpose of these tests was to compare the efficiency and difference in the compaction and strength characteristics achieved in the well-known Proctor test and, less commonly used in practice, the vibrating hammer test. The conclusions presented are drawn from the research conducted on a limited number of samples and, to reach general conclusions, more tests are to be conducted. Based on the laboratory research results, several conclusions can be drawn:  
* There is strong correlation between the compaction and strength characteristics obtained in laboratory conditions using the modified Proctor and vibrating hammer compaction methods for natural aggregate mixtures.
* A greater drop in the compaction characteristics for rubber mixtures and the use of the Proctor compaction method lead to the conclusion that the laboratory compaction method should be chosen carefully if alternative materials such as waste rubber are used.
* For higher cement content mixtures, a higher OMC difference depending on the laboratory compaction method used is observed and the laboratory compaction method should be chosen carefully, particularly if moisture-susceptible materials are used.  
* Comparing the compaction effects (MDD, OMC and density), it can be observed that approximately the same compaction energy was applied using both compaction methods. However, calculation of the compaction energy using a vibrating hammer is more difficult than for the Proctor test, and it should be investigated in more detail. Further research should provide an additional insight into the comparability of the vibrating hammer and standard Proctor tests, applying less compaction energy than the modified Proctor test used in this research.
* The choice of laboratory compaction method should be made carefully depending on the specific behaviour of materials used and the expected field conditions.  
Conceptualization, I.B. and M.Z.; methodology, I.B.; validation, I.B., M.G. and K.M.; investigation, I.B., M.G., K.M. and M.Z.; writing--original draft preparation, M.Z.; writing--review and editing, I.B., M.G. and K.M. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Croatian Science Foundation under the project UIP-2019-04-8195 Cement stabilized base courses with waste rubber for sustainable pavements--RubSuPave.  
Not applicable.  
The authors declare no conflict of interest.
# A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation
## 3 A Novel Adaptive Sparrow Search Algorithm  
The mutation strategy in SSA directly affects the exploration ability, convergence accuracy and speed. Therefore, strategy selection is very important for the performance of the SSA algorithm. Compared with other bionic algorithms, the SSA has better optimization performance in solving complex optimization problems, but it has the shortcomings of reduced population diversity and insufficient convergence accuracy. In order to overcome these shortcomings, the chaotic mapping strategy, adaptive weighting strategy and t-distribution mutation strategy are introduced in order to develop a novel adaptive sparrow search algorithm based on a multi-strategy fusion mechanism (CWTSSA) in this paper. In the proposed CWTSSA, the chaotic mapping strategy is employed to initialize the population in order to enhance the population diversity. The adaptive weighting strategy is applied to balance the capabilities of local mining and global exploration and improve the convergence speed. The adaptive t-distribution mutation operator is designed to improve the characteristic of global exploration and local exploration abilities, so as to avoid falling into the local optimum.
# Functional Connectome Fingerprinting Through Tucker Tensor Decomposition
## 2 Materials and Methods
### Tensor Notation  
We refer to multidimensional data structures as tensors. Mathematically, tensors can be described as objects that lie in \(\mathbb{R}^{I_{1}\times I_{2}\times\cdots\times I_{N}}\) and originate from the tensor product of \(N\) vector spaces. The number \(N\) of vector spaces from which a tensor originates defines the order (or mode) of the tensor. Throughout this work, we adopt the following notation: scalars are denoted by lower or upper case letters, e.g., \(x\) or \(X\), vectors are denoted by boldface lowercase letters, e.g., \(x\), matrices are denoted by boldface capital letters, e.g., \(X\), and high-order tensors are denoted by boldface, calligraphic letters, e.g., \(\mathbf{\mathcal{X}}\). Entries of a tensor \(\mathbf{\mathcal{X}}\in\mathbb{R}^{I_{1}\times I_{2}\times\cdots\times I_{N}}\) are denoted by lowercase letters with subscripts, e.g., \(x_{i_{1},i_{2},\ldots,i_{N}}\), with \(1\leq i_{n}\leq I_{n}\) for all \(1\leq n\leq N\). Tensor fibers are constructed by fixing every index of the tensor but one. Therefore, tensors have as many fibers as dimensions. For example, third-order tensors have column (mode-1), row (mode-2), and tube (mode-3) fibers, which are denoted by \(\mathbf{x}_{:,j,k}\), \(\mathbf{x}_{:,k}\), and \(\mathbf{x}_{ij,:}\), respectively, with colons denoting all the entries of a mode. A tensor slice is defined by fixing all entries from the tensor except two. We can define the slices \(\mathbf{X}_{i,:,}\), \(\mathbf{X}_{:,j,:,}\), and \(\mathbf{X}_{:,:,k}\) for a third-order tensor.  
The process of reshaping a tensor into a matrix is known as matricization. The mode-\(n\) matricization of a tensor \(\mathbf{\mathcal{X}}\in\mathbb{R}^{I_{1}\times I_{2}\times\cdots\times I_{N}}\) is given by the matrix \(\mathbf{X}_{(n)}\), and its columns correspond to the mode-\(n\) fibers of \(\mathbf{\mathcal{X}}\). The \(n\)_-mode_ matrix product between a tensor \(\mathbf{\mathcal{X}}\in\mathbb{R}^{I_{1}\times I_{2}\times\cdots\times I_{N}}\) and a matrix \(\mathbf{Y}\in\mathbb{R}^{I\
## 2 Laser Control of Ring Currents in a Molecule Having a Single Aromatic Ring
### Laser-Polarization Effects on Ring Currents within the Frozen-Nuclei Approximation
#### 2.2.3 Case of Degenerate Excited States
We present illustrative examples and the case of linear polarization (\(\beta=\pi/4\)) is the first one; Equation (5) is then rewritten as:  
\[\mathbf{e}=(\cos\delta)\mathbf{e}_{x}+(\sin\delta)\mathbf{e}_{y} \tag{24}\]  
and \(\delta\) determines the polarization direction. The Rabi frequencies \(\Omega_{X}\) and \(\Omega_{Y}\) are thus real-valued and their relative phase \(\theta\) takes either zero or \(\pi\). Henceforth, we denote the linear polarization vectors for \(\theta=0\) (\(e^{i\theta}=+1\)) and \(\pi\) (\(e^{i\theta}=-1\)) by \(\mathbf{e}^{(+)}\) and \(\mathbf{e}^{(-)}\), respectively. Do not confuse them with the spherical unit vectors \(\mathbf{e}_{+1}\) and \(\mathbf{e}_{-1}\). The values of \(\delta\) for \(\mathbf{e}^{(+)}\) and \(\mathbf{e}^{(-)}\) are \(\pi/4\) and \(-\pi/4\), respectively, from Equation (23). As in Equation (19), a linearly polarized laser pulse with \(\mathbf{e}^{(+)}\) [\(\mathbf{e}^{(-)}\)] creates an in-phase (out-of-phase) superposition \(\left|X\right\rangle+\left|Y\right\rangle(\left|X\right\rangle-\left|Y\right\rangle)\), which is an equal mixture of \(\left|+\right\rangle\) and \(\left|-\right\rangle\): \(P_{+}(t)=P_{-}(t)\) and thus \(l_{z}(t)=0\) for all \(t\). It is impossible to trigger ring currents in a degenerate system by a linearly polarized laser pulse. Next, for circular polarization, \(\beta=0\) (\(\pi/2\)) provides \(\mathbf{e}=e^{-i\delta}\mathbf{e}_{+1}\) (\(e^{i\delta}\mathbf{e}_{-1}\)) and \(\delta\) acts as an additional optical phase, which can be set to any value since Equation (19) tells us that the optical phase does not influence the superposition of the degenerate states. From the definition of \(\theta\) as an argument of \(\Omega_{Y}/\Omega_{X}\), we obtain \(\theta=\pm\left(\eta_{Y}-\eta_{X}\right)\) for \(\mathbf{e}_{\pm 1}\), where \(\eta_{X}\) (\(\eta_{Y}\)) is the angle of \(\mu_{X}\) (\(\mu_{Y}\)) with respect to the \(x\) axis; \(\theta=\pm\pi/2\) (\(e^{i\theta}=\pm i\)) in a degenerate system. The superposition states \(\left|U_{\pm}\right\rangle\) are therefore identical to the exact angular momentum eigenstates \(\left|\pm\right\rangle(\left|\mp\right\rangle)\) for left (right) circular polarization, while optical excitation occurs only to \(\left|U_{+}\right\rangle\). Ring current flows in the direction inherent to the eigenstate generated as noted in Section 2.1. The phase factor \(e^{i\theta}\) yielded by elliptical polarization is neither real nor purely imaginary regardless of whether \(\delta=\pi/4\) or \(-\pi/4\). The resultant behavior of \(\pi\) electrons is intermediate between those for linear and circular polarizations: The direction of ring currents is determined by the more populated of \(\left|+\right\rangle\) or \(\left|-\right\rangle\), while the magnitude of \(l_{z}(t_{f})\) is less than \(\hbar\) even for the \(\pi\)-pulse excitation.
## 3 Results  
Effect of Low Light Intensity on Pigments and Growth of H. pluvialis in the Green Stage on a TL-PSBR  
Algae in the biofilm were cultured at low light intensities from 20-100 umol photons m\({}^{-2}\cdot\)s\({}^{-1}\) to stimulate the growth of _H. pluvialis_ and were harvested after ten days to determine dry biomass productivity and pigment ratios.  
The results of dry biomass determination show that the higher the intensity of light, the higher the dry biomass productivity (Figure 2). The lowest dry biomass productivity was 2.6 g\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\) when illuminated with a light intensity of 20 umol photons m\({}^{-2}\cdot\)s\({}^{-1}\). At the light intensities of 80 and 100 umol photons m\({}^{-2}\cdot\)s\({}^{-1}\), the dry biomass productivities were 5.9 and 6.4 g\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\), respectively, but the difference was not statistically significant (\(p>0.05\), \(n=6\)). However, the dry biomass productivities at these two light intensities were significantly higher than the dry biomass productivities obtained when culturing algae at all lower light intensities (\(p<0.05\), \(n=6\)).  
The ratio of carotenoid/chlorophyll and cell morphology was used to determine the cell stage after harvest. The results clearly showed that the carotenoid/chlorophyll ratio increased gradually with an increment of the light intensity in the range of 20-100 \(\upmu\)mol photons m \({}^{-2}\).s \({}^{-1}\). Still, it never exceeded the value 1.0 in any case (Figure 3). The difference was statistically significant for this ratio at light intensities from 20 to 80 \(\upmu\)mol photons m\({}^{-2}\).s\({}^{-1}\) (\(p<0.05\), \(n=6\)).  
In all manipulations and after ten days, cells had no flagella and represented the palmella cell stage (Figure 4). Light intensities of 20, 40, 60, and 80 \(\upmu\)mol photons m\({}^{-2}\).s\({}^{-1}\) resulted in green cells in all cases. However, at the highest applied light intensity (100 \(\upmu\)mol photons m\({}^{-2}\).s\({}^{-1}\)), some palmella cells were noticed to accumulate some astaxanthin (Figure 4e). This result was consistent with those of the carotenoid/chlorophyll ratio and observing biofilm's surface after ten days of culturing.  
Figure 3: The ratio of carotenoids to chlorophylls in _H. pluvialis_ under different light intensities (in the range of 20–100 \(\upmu\)mol photons m\({}^{-2}\).s\({}^{-1}\)).  
Figure 2: Final biomass and biomass growth rate (average of biomass increase per day over ten days) of _Haematococcus pluvialis_ under different light intensities (in the range of 20–100 \(\upmu\)mol photons m\({}^{-2}\).s\({}^{-1}\)).  
Consequently, the light intensity of about 80 umol photons m\({}^{-2}\).s\({}^{-1}\) was found to be the most suitable for the growth of _H. pluvialis_ and still maintained cells at the green vegetative stage in biofilm.
# Analysis of Pre-Driver and Last-Stage Power--Ground-Induced Jitter at Different PVT Corners
## 4 PSIJ Sensitivity Study of Two-Stage Driver
### Frequency Sensitivity of PSIJ Transfer Function
This dependency is explored by the second experiment shown in Table 7, where the last-stage frequency of the applied sinusoidal voltage is swept for a different sinusoidal frequency applied at the pre-driver stage  
As the frequency of the pre-driver supply variations is close the sensitive frequency bandwidth of the \(S_{p}(f)\), the total jitter increases, as shown in Figure 12. Hence, the total jitter at a lower frequency depends on the phase difference between \(V_{DD}\) and \(V_{DDQ}\) supply voltage waveforms. Moreover, the total jitter is dominated by the pre-driver's circuit jitter, as the supply voltage frequency of the last stage increases. Therefore, the PSIJ sensitivity can be conceptually assumed to be a superposition of the pre-driver and last-stage PSIJ. \[S_{IO}(f)\cong S_{P}(f_{P})+S_{L}(f_{L})-J_{IO} \tag{8}\]  
where \(J_{IO}\) is the jitter induced by the full driver's input-output timing distortion while the circuit is powered by DC PSV at the SS-40 \({}^{\circ}\)C corner. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**PSJ TF** & **Supplies** & \(V_{DD}\) & \(V_{DDQ}\) \\ \hline IO buffer & \(\alpha_{L}\) (V) & \(f_{L}\) (MHz) & \(a_{L}\) (V) & \(f_{L}\) (MHz) \\ \hline \hline \end{tabular}
\begin{tabular}{c c c} \hline \hline
**PSJ TF** & **Supplies** & \(V_{DD}\) & **\(V_{DDQ}\)** \\ \hline \hline
**IO buffer** & \(0.16\) & \([10,\,11\times 10^{3}]\) & \(0.16\) & \([57,\,11\times 10^{3}]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experimental settings of the frequency sensitivity of the PSIJ transfer function of the driver’s IO stages of results shown in Figure 11.  
Figure 10: Output eye diagram due to last-stage PGSV variation with \(a_{L}=160\) mV at \(10\) MHz (black), \(123\) MHz (blue), and \(1\) GHz (red). Figure 11: Output eye diagram due to distinct pre-driver and last-stage PSV variations with \(a_{L}=a_{p}=160\) mV, \(f_{P}=123\) MHz, while \(f_{L}=10\) GHz (black), \(f_{L}=123\) MHz (blue), and \(f_{L}=10\) MHz (green). The superposition of the jitter frequency sensitivity TF can be valid if the supply voltage variation is low enough to approximate the nonlinear I-V and C-V functions of the transistor as linear behavior of the applied input voltage and supply voltage variations. The PSIJ of the pre-driver and last stage can be superposed to lead to the total output jitter if the PGSV (i.e., \(V_{DD}\) or \(V_{DDQ}\)) is composed by two sinusoidal frequency noises under small amplitude variations. The linear superposition of the jitter TF of the pre-driver and last stage can hold under small signal variations. The next section explores how the superposition formula is only valid for small signal analysis.
# A Cross-Medium Uplink Communication Model Merging Acoustic and Millimeter Waves
## 3 Communication
### Coding Experiment
#### 3.1.1 Band Pass Filter Design  
To mitigate the interference caused by additive noise during the demodulation process, a filter was designed to suppress the noise. In this study, a 7 th order Butterworth bandpass filter was employed due to its smooth frequency response and absence of phase distortion. The filter was configured with high and low cut-off frequencies set at 300 Hz and 100 Hz, respectively. This allows for the precise analysis of wave characteristics and accurate extraction of phase information from water surface fluctuations. The filtering results are presented below.  
In aquatic environments, the noise primarily manifests as a superposition of 1/f noise and white noise. Under the environmental conditions of our study, the attenuation rate stood at \(-\)8.07 dB/decade, highlighting that the noise power predominantly resided in the low-frequency range. Given the severe attenuation of high-frequency acoustic waves in water and the relatively proximate transmission distance in our setup, it was impractical to indefinitely escalate the frequency of the acoustic waves in the communication system solely to circumvent low-frequency noise. Tailored to the carrier frequency range employed in our experimental system, we endeavored to elevate the low cut-off frequency of the filter without compromising the carrier, while retaining the narrowness of the transition band, leading to the successful design of the filter in question.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\xi\) & \(\epsilon\) & \(\eta\) \\ \hline
1–1.56 & 10\({}^{-14}\)–10\({}^{-15}\) & 10\({}^{-10}\)–10\({}^{-9}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The value range of correction parameters.  
As depicted in Figure 7, the resulting filtered time domain image exhibits a smoother and clearer waveform. This effective noise reduction technique significantly diminished the influence of noise and interference. The filtering process played a crucial role in subsequent tasks, such as the waveform analysis and phase extraction of the water surface, thereby greatly enhancing the overall communication performance of the system.
## 2 Materials and Methods  
### Conditions of Plant Growth  
The present study took place in the Apulia region, southern Italy, specifically on a PV farm situated in the province of Lecce at coordinates Y: 40.306792\({}^{\circ}\) and X: 18.021823\({}^{\circ}\) (WGS84). The experimental area comprised an open-field cultivation space, allowing us to expose the crop to two distinct lighting exposures: one under full sunlight and the other in the shadow of the PV panels.  
The PV panels were oriented with solar exposure toward the South-North and did not present the typical height from the ground commonly associated with agrivoltaic systems designed for machine-assisted cultivation. However, such an unconventional setup was chosen for an initial assessment of the diverse sun exposures that tomato plants undergo during their growth beneath the PV panels.  
Tomato (_Solanum Lycopersium_ L.) certified seeds of the yellow date cultivar Dolly, used in this study, were sourced from the Botanical Garden of the University of Salento in Lecce, Italy. After soaking in running tap water for 2-3 h, the seeds were planted individually in 5 L plastic pots, all filled with the same commercial soil to ensure similar initial soil moisture and nutrient levels. The germination process took place in a controlled growth chamber, maintaining conditions at 22 \({}^{\circ}\)C temperature, 60% humidity, 25 \(\upmu\)E light intensity, and a photoperiod of 16 h light and 8 h darkness. Following germination, a total of 24 tomato seedlings were transplanted into pots at the end of June 2023. Out of these, 12 seedlings were placed in plots exposed to full sun, while the remaining 12 were grown in shaded plots beneath the PV panels.  
In each plot, the plants were further divided into two groups, each consisting of 6 tomato plants. These groups were subjected to different watering regimes. The watering process involved manually pouring 1000 mL of water every 2 days for each plant, using a graduated cylinder (Table 1).  
This equated to a total of 35 L for the high-water group, supplied either in the light (HW-control) or in the shade (HW-PV panel). Similarly, the low-water group received 500 mL of water applied to each plant every 2 days using a graduated cylinder, resulting in a total of 17.5 L for plants in the light (LW-control) or shade (LW-PV panel) (Figure 1). After 75 days from sowing, tomato fruits within the full ripening stage, identified by their bright and uniformly yellow-orange color upon visual assessment, were harvestedfrom the 6 plants within each different water supply group for yield determination and subsequent analyses.
###### Abstract  
The evolution of 3 GPP non-terrestrial networks (NTNs) is enabling new avenues for broadband connectivity via satellite, especially within the scope of 5G. The parallel rise in satellite mega-constellations has further fueled efforts toward ubiquitous global Internet access. This convergence has fostered collaboration between mobile network operators and satellite providers, allowing the former to leverage mature space infrastructure and the latter to integrate with terrestrial mobile standards. However, integrating these technologies presents significant architectural challenges. This study investigates 5G NTN architectures using satellite mega-constellations, focusing on transparent architectures where Starlink is employed to relay the backhaul, midhaul, and new radio (NR) links. The performance of these architectures is assessed through a testbed utilizing OpenAirInterface (OAI) and Open5 GS, which collects key user-experience metrics such as round-trip time (RTT) and jitter when pinging the User Plane Function (UPF) in the 5G core (5 GC). Results show that backhaul and midhaul relays maintain delays of 50-60 ms, while NR relays incur delays exceeding one second due to traffic overload introduced by the RFSimulator tool, which is indispensable to transmit the NR signal over Starlink. These findings suggest that while transparent architectures provide valuable insights and utility, regenerative architectures are essential for addressing current time issues and fully realizing the capabilities of space-based broadband services.  
NTN; 5G; mega-constellation; transparent; OpenAirInterface; Starlink 2025
## LSTMCaps Autoencoder Network  
### Long Short-Term Memory Network  
The LSTM network, proposed initially in 1997 by [PERSON] and [PERSON] [17] but popularised recently by its widespread usage in commercial environments, is a popular iteration of the RNN that can overcome the vanishing gradient issue and allows for the learning of long-term dependency. The vanishing/exploding gradient problem commonly occurs in RNN architectures when training, using the backpropagation through time algorithm due to the depth of the unrolled RNN as well as the shared weights across the RNN cells. The calculated derivatives during training are prone to exponentially increasing or decreasing as the calculation progresses through the network. This results in either a lack of change or extreme changes to the RNN weight values, which in the very worst cases means that the model stops training completely in the case of a vanishing gradient, or trains erratically and fails to converge to a minimal error.  
The LSTM architecture addresses this by using a specialised architecture that integrates \"gates\" to the architecture to allow the cell state to forget values and replace values, then decide which values to output and send to the next cell. The forget gate uses a sigmoid layer on the input \(x_{t}\) and previous hidden cell state \(h_{t-1}\) to output a vector \(f_{t}\) that determines which irrelevant data from the previous cell state, \(C_{t-1}\), to remove from the current cell state.  
\[f_{t}=\sigma\Big{(}W_{f}\times[h_{t-1},x_{t}]+\ b_{f}\Big{)} \tag{1}\]  
The input gate determines the information from the candidate values that is stored in the cell state by using a sigmoid layer on the input \(x_{t}\) and previous hidden cell state \(h_{t-1}\) to decide which values to update, \(h_{t}\), and a tanh layer to create a vector of candidate values, \(\widetilde{C}_{t}\), to add to the cell state; the Hadamard product of which is used as the values to be added to the new cell state.  
\[i_{t}=\sigma(W_{i}\times[h_{t-1},x_{t}]+\ b_{i}) \tag{2}\]  
\[\widetilde{C}_{t}=\tanh\big{(}W_{C}\times[h_{t-1},x_{t}]+b_{C}\big{)} \tag{3}\]  
\[C_{t}=f_{t}\times C_{t-1}+i_{t}\times\widetilde{C}_{t} \tag{4}\]  
The output gate determines which part of the previous hidden cell state \(h_{t-1}\) to output to the next cell by applying a sigmoid function on the input \(x_{t}\) and previous hidden cell state \(h_{t-1}\), then calculating the Hadamard product between the tanh of the current cell state \(C_{t}\) and the output of the sigmoid gate \(o_{t}\).  
\[o_{t}=\sigma(W_{o}\times[h_{t-1},x_{t}]+\ b_{o}) \tag{5}\]  
\[h_{t}=o_{t}\times\tanh\big{(}C_{t}\big{)} \tag{6}\]  
A visualisation of this architecture is illustrated in Figure 1.  
Figure 1: Visualisation of the LSTM cell [17].