esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Fluorescence and Lifetime Measurement  
In trivalent Pr\({}^{3+}\), excitation from the ground-state multiplet \({}^{3}\)H\({}_{4}\) into the multiplet \({}^{3}\)P\({}_{2}\) is followed by non-radiative decay into the metastable upper-level \({}^{3}\)P\({}_{0}\) from which most of the fluorescence lines originate. The fluorescence spectra for both \(\pi\) and \(\sigma\) polarization of sample Pr:YLF-3 in the visible spectral range are shown in Figure 5. Overall, the recorded fluorescence spectra were in good agreement with the spectra reported in the literature [10; 27]. However, the \(\pi\)-polarized fluorescence peak at 479 nm of the ridge waveguide and the bulk is lower by a factor of four in comparison to that in the literature, which could be caused by reabsorption. Other small differences compared to the results in the literature may be attributed to amplified spontaneous emission (ASE) due to the long propagation of the fluorescent light in the sample for both waveguide and bulk geometry.  
The recorded absolute fluorescence intensity in the waveguides was low for \(\pi\) polarization as a result of the high propagation losses for the TE modes in the ridge. Finally, no significant broadening of the fluorescence peaks was observed in the waveguides compared to the bulk, e.g., the peaks at 639.6 nm and 607 nm for \(\sigma\) polarization have FWHMs of  
Figure 5: Fluorescence spectra of bulk and ridge waveguide for (**a**) \(\pi\) polarization and (**b**) \(\sigma\) polarization, recorded from sample Pr:YLF-3 under excitation at wavelength of 444 nm.  
0.9 nm and 1.2 nm in the bulk and waveguide, respectively. The most significant relative difference in bulk and waveguide spectra is the strong enhancement in the \({}^{3}\)H\({}_{5}\) peak intensity. The corresponding transition \({}^{3}\)P\({}_{0}\)\(\rightarrow\)\({}^{3}\)H\({}_{5}\) is partially symmetry-forbidden according to the group theory for Pr\({}^{3+}\) on S\({}_{4}\) symmetry sites in YLF [28], and the increased peak height (especially for fluorescence within the waveguide) can tentatively be attributed to a reduced site symmetry after ion implantation, which is due to partial amorphization in the implanted surface area [4].  
From the measurements of fluorescence decay to \({}^{3}\)F\({}_{2}\) from the \({}^{3}\)P\({}_{0}\) level upon excitation into the \({}^{3}\)P\({}_{2}\) multiplet, lifetimes of 48 \(\upmu\)s and 50 \(\upmu\)s were obtained for the bulk and the waveguide, respectively. No significant change in the lifetime was observed, and the values are in accordance with the values in the literature [10]. Thus, from both the fluorescence and the lifetime measurement results, the conclusion that ion implantation has no negative effect on the spectroscopic properties of Pr:YLF could be drawn.
# Video Temporal Grounding with Multi-Model Collaborative Learning
## 3 Methodology
### Mutual Knowledge Transfer  
At the heart of the MMCL framework is the harnessing of inter-model knowledge exchange and cooperative optimization, capitalizing on each model's complementary capabilities to boost collective outcomes. Within this framework, pseudo-label generation and loss function design constitute the essential elements for enabling collaborative learning. Pseudo-label generation (detailed in Section 3.3) furnishes high-quality supervisory signals for cross-model knowledge migration, while the loss function employs a suitable optimization strategy to effectively merge single-model learning with inter-model collaboration. Accordingly, we construct the comprehensive loss function as a weighted amalgamation of supervision loss and transfer loss, satisfying both each model's need for accurate learning and the bidirectional knowledge exchange among multiple models. In the supervised learning stage, the supervision loss steers Models A and B to refine their performance using genuine labels as guidance. Formally, it is specified as follows:  
\[L_{\text{sup}}^{A}=\mathcal{L}(P^{A},GT),\quad L_{\text{sup}}^{B}=\mathcal{L}( P^{B},GT) \tag{17}\]  
Here, \(P^{A}\) and \(P^{B}\) denote the predictions of Model A and Model B, respectively; \(GT\) stands for ground truth; and \(\mathcal{L}\) refers to the particular form of the loss function, which varies depending on the model category. The role of the supervision loss is to align \(P^{A}\) and \(P^{B}\) more tightly with \(GT\), providing direct supervised feedback to each model. In the knowledge transfer phase, the transfer loss drives collaborative learning between models by optimizing the pseudo-labels produced by the counterpart model, and leverages those pseudo-labels as indirect supervision signals to achieve cross-model knowledge transfer. Specifically, Model A's transfer loss \(L_{trans}^{A}\) uses the pseudo-labels \(PL^{B}\) produced by Model B, aligning them with A's predictions \(P^{A}\) to encourage Model A to learn B's predictive characteristics. Analogously, Model B's transfer loss \(L_{trans}^{B}\) imposes knowledge constraints on Model B via \(PL^{A}\), nudging its optimization objective toward Model A's predictions. The transfer loss is defined as follows:  
\[L_{\text{trans}}^{A}=\mathcal{L}(P^{A},PL^{B}),\quad L_{\text{trans}}^{B}= \mathcal{L}(P^{B},PL^{A}) \tag{18}\]Building upon the aforementioned supervision and transfer objectives, the MMCL framework accomplishes multi-model cooperative learning through a weighted synthesis of these losses. The aggregate loss function is given by the following:  
\[\text{LOSS}=L_{\text{sup}}^{A}+L_{\text{sup}}^{B}+\alpha L_{\text{trans}}^{A}+ \beta L_{\text{trans}}^{B} \tag{19}\]  
Here, \(\alpha\) and \(\beta\) serve as hyperparameters governing the balance between transfer losses. With this scheme, every model benefits from solitary supervision-driven optimization toward ground-truth labels and, in parallel, participates in inter-model knowledge exchange facilitated by the transfer losses.
# Impact of Heat Exchanger Effectiveness and EGR on Energy and Emission Performance of a CI Engine
###### Abstract  
This study explores the impact of intake air cooling intensity, defined by heat exchanger effectiveness (HEE) and exhaust gas recirculation (EGR), on the energy and environmental performance of a turbocharged compression ignition (CI) engine. Experimental investigations were conducted on a 1.9-litre CI engine operating at 2000 rpm under three brake mean effective pressure (BMEP) conditions (0.2, 0.4, and 0.6 MPa), which correspond to part-load engine operation. HEE was varied at 0%, 50%, and 100%, in both EGR-on and EGR-off modes. Additional numerical simulations were carried out using AVL BOOST software to analyze combustion dynamics, including engine operating cycle modeling to validate the accuracy of the combustion analysis. The results demonstrate that increasing HEE significantly improves cylinder filling and excess air ratio, leading to enhanced combustion efficiency and lower in-cylinder temperatures. This, in turn, reduces specific NOx emissions by approximately 40% with EGR and approximately 60% without EGR; however, under EGR-on conditions, the reduced combustion intensity leads to increased smoke and unburned hydrocarbon emissions--particularly at high cooling intensities. This effect is primarily associated with the engine control unit's (ECU) limitations on intake air mass flow to maintain the target EGR ratio. Integrated control of HEE and EGR systems improves engine performance and reduces emissions across varying conditions, while highlighting trade-offs that inform the refinement of air management strategies.  
compression ignition engine; heat exchanger effectiveness; exhaust gas recirculation; combustion efficiency; emission reduction
## 2 Sensor Structure and Design Steps  
The basic flat sensor structure [41], consisting of a two-port microstrip-fed rectangular patch, was printed on a 0.75 mm thick Roger RO3035 substrate with a dielectric constant \(\varepsilon_{r}\) = 3.5 and a loss tangent of tan\(\delta\) = 0.0015. The structure had overall dimensions of L \(\times\) W = 20 \(\times\) 28 mm\({}^{2}\) (Figure 1). The ground copper layer held a complementary split-ring resonator (CSRR). The conductors used in this design (patch and ground plane) consistedof 35 \(\upmu\)m thick copper layers. The final bended sensor configuration is presented in Figure 2. The novelty of this work lies in the investigation of a new bended-shape structure aiming at enhancing the performance of the microwave sensor. The geometric properties of our proposed sensor are given in Table 1.  
The sensitivity of the sensor was analyzed according to the S\({}_{21}\) presented results. The shift in the resonant frequency (\(\Delta\)fr = fr\({}_{-}\)0\(\%\) - fr\({}_{-}\)100\(\%\)) of S\({}_{21}\) was deduced. The percentage of relative frequency shift or Q factor (\(\Delta\)fr (3 dB)/fr\({}_{-}\)ref (\(\%\))) was also considered. Next, the sensitivity of the sensors was defined as the ratio of the resonant frequency change to the permittivity change (S = \(\Delta\)fr/\(\Delta\)cr = 1 (fr\({}_{-}\)0\(\%\) - fr\({}_{-}\)100\(\%\)) / \(\left|\left(\varepsilon\tau\_0\%-\varepsilon\tau\_100\%\right)\right|\)) [42; 43].  
The \(\mathbf{S_{21}}\) parameter and Q factor of the proposed bended sensor, along with CST simulation results of mixing with different water-ethanol concentrations, are presented in Figure 3a,b, respectively. The final designed sensor exhibited a very high sensitivity and an acceptable quality factor compared to those reported in [41]. The frequency range was 400 MHz for a permittivity range of 65 [44] (see Appendix A). This enabled us to achieve  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline
**Parameters** & **W** & **L** & **Hs** & **W1** & **W2** & **W3** & **W4** & **L1** & **L2** & **L3** & **L4** & **L5** \\ Value (mm) & 28 & 20 & 0.75 & 6.2 & 3.6 & 2.94 & 1.6 & 9.2 & 8.54 & 6.2 & 1.6 & 2.26 \\ \hline
**Parameters** & **L6** & **d1** & **d2** & **d3** & **d4** & **d5** & **d6** & **a1** & **a2** & **a3** & **s** & \(\Theta\) \\ Value (mm) & 0.94 & 6.2 & 5.02 & 3.84 & 2.85 & 2.26 & 1.67 & 0.33 & 0.33 & 0.33 & 0.5 & 45\({}^{\circ}\) \\ \hline \end{tabular}
\end{table}
Table 1: Geometrical parameters of the proposed sensor structure.  
Figure 1: Geometric dimensions of the flat sensor structure [41]: (**a**) side view; (**b**) top view; (**c**) bottom view.  
Figure 2: 3D view of the proposed sensor: (**a**) upper side view; (**b**) inner side view; (**c**) final structure with embedded glass tube.  
a very high sensitivity of 6.15. Moreover, the sensor operates at a central frequency of 1.8 GHz.
# Ephemeral Node Identifiers for Enhanced Flow Privacy
## 4 Results
### Observed Flow Privacy  
This test was performed on the Testbed configuration as described in Section 3.3.1. The Testbed included commercial IPv6 routers, so there is an initial test to demonstrate that ILNP packets using eNIDs are treated as IPv6 packets. Additionally, a standard web server, nginx, was used with a standard command-line client, program, curl, as described in Section 3.3.1. The Testbed configuration allowed for controlled, repeatable measurements and traffic capture for assessing the \(P_{f}\) values for the _flow privacy metric_ requirement of Section 3.  
With reference to the other key evaluation requirements discussed in Section 3, this connectivity test demonstrated _Implementation in IPv6_, and also use of eNIDs for partially testing both _Communication across IPv6 networks_ and _Use in existing IPv6 applications_.  
Table 4 shows the final value of \(P_{f}\) for each client after the attacker observed all flows. In the SLAAC case, where a single static address was used, 864 flows were observed from the client, and all used the same source address. This results in \(f\) of 864 and \(c\) of 1, so \(P_{f}=\frac{1}{864}=0.001\) for all machines.  
With TA-SLAAC, there was a variation based on when exactly addresses happened to expire relative to the start of flows. This resulted in \(c\) of 28, 27, and 25 for the three clients, yielding \(P_{f}\) in the range 0.029 to 0.032. In all cases, TA-SLAAC provided better privacy than a static address with SLAAC, but were outperformed by eNIDs.  
With the use of eNIDs, \(c=f=864\), so \(P_{f}\) was always the maximal value of 1.  
Figure 8 shows how \(P_{f}\) changed as new flows were observed. eNIDs maintain an equal ratio of identifiers and flows, so \(P_{f}\) is always stable at 1. For SLAAC, each flow adds to the data that the attacker associates with the client, so \(P_{f}\) decreases, approaching zero. For TA-SLAAC, some flows cause \(P_{f}\) to drop (when flows are added to a cluster some time after it has been used), while others cause it to increase (when a fresh IID and IPv6 address is used). The flows causing a decrease in \(P_{f}\) reuse an identifier--\(c\) remains the same as before, but \(f\) increases--while the flows causing an increase in \(P_{f}\) do so by introducing a new identifier.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{3}{c}{**Client**} \\  & **A** & **B** & **C** \\
**Defence** & & & \\ \hline SLAAC & 0.001 & 0.001 & 0.001 \\ TA-SLAAC & 0.032 & 0.031 & 0.029 \\ eNIDs & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Observed values for \(P_{f}\) after all flows were complete (3 s.f. where required, details of test execution in Section 3.3.1). As expected, the final \(P_{f}\) values for SLAAC approach 0, and the values for eNIDs are stable at 1. TA-SLAAC falls between these bounds, with some variability between clients due to the precise timing of when new IID values were instantiated compared to the start of flows in the client-server communication.
## 3 Results
#### 3.1.1 Determination of Semantic Axes or Semantic Space  
The set of adjectives was created using factor analysis based on the residents' ratings. The adjectives were made up of groups that showed a significant correlation to the users' responses. The factor analysis reduced the 34 expressions describing the affective impressions of the luminous environment into five uncorrelated axes. These semantic factors correspond to the group of affective assessment scales adjusted to the language used by the local residents to evaluate the PLE. These factors explained 75.13% of the variance of the original variables (Table 3). To identify the adjectives or terms that the respondents associated with the axes, the contribution of the original variables was analysed. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multicolumn{2}{c}{**Initial Eigenvalues**} & \multicolumn{2}{c}{**Rotation Sums of Squared Loadings**} \\ \cline{2-6}  & **Total** & **\% of Variance** & **Total** & **\% of Variance** & **Cumulative \%** \\ \hline
**1** & 18.26 & 53.71 & 11.67 & 34.28 & 34.28 \\ \hline
**2** & 2.30 & 6.78 & 5.02 & 14.76 & 49.04 \\ \hline
**3** & 2.11 & 6.21 & 4.34 & 1.77 & 61.81 \\ \hline
**4** & 1.77 & 5.21 & 2.62 & 7.70 & 69.51 \\ \hline
**5** & 1.10 & 3.22 & 1.91 & 5.62 & 75.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The total amount of variance explained. Table 4 shows the semantic axes, correlations with original terms, and percent variance explained. Table 4 presents the following semantic axes, the percentage of explained variance, and their correlations with the original terms:  
* 1 st axis: represents the _Expressive-interesting_ impressions of the PLE of the urban areas. The Kansei words that make the greatest contribution to this axis are as follows: \"expressive\", \"interesting\", \"colourful\", \"precious\", \"necessary\", \"cool\", \"clear\" and \"stimulating\". This axis explains 34.28% of the total variance. * 2 nd axis: it can be interpreted as being _Innovative-efficiency_. The terms grouped in this factor reflect the perceptions of the PLE as \"original\", \"reflecting\", \"magnificent\", \"joyful\", \"delicate\", \"monumental\", \"perfect\" and \"natural\". It explains 14.76% of the variance. * 3 rd axis: illustrates the perceptions of the PLE as being _Defined-sufficient_. Adjectives such as \"defined\", \"sufficient\", \"delicate\", \"safe\" and \"comfortable\" are very significant in this factor and have a negative correlation to the terms \"irritating\" and \"tatty\". It explains 12.77% of the total variance. * 4 th axis: represents the _Formal-uniform_ assessments of the PLE, featuring terms such as \"formal\", \"uniform\" and \"functional\". It explains 7.70% of the variance. * 5 th axis: defines the impression of the PLE as _Garing_. The adjectives that contribute to this group are \"glaring\" and \"brilliant\". This axis explains 5.62% of the variance.
# Speech Inpainting Based on Multi-Layer Long Short-Term Memory Networks
###### Abstract  
Audio inpainting plays an important role in addressing incomplete, damaged, or missing audio signals, contributing to improved quality of service and overall user experience in multimedia communications over the Internet and mobile networks. This paper presents an innovative solution for speech inpainting using Long Short-Term Memory (LSTM) networks, i.e., a restoring task where the missing parts of speech signals are recovered from the previous information in the time domain. The lost or corrupted speech signals are also referred to as gaps. We regard the speech inpainting task as a time-series prediction problem in this research work. To address this problem, we designed multi-layer LSTM networks and trained them on different speech datasets. Our study aims to investigate the inpainting performance of the proposed models on different datasets and with varying LSTM layers and explore the effect of multi-layer LSTM networks on the prediction of speech samples in terms of perceived audio quality. The inpainted speech quality is evaluated through the Mean Opinion Score (MOS) and a frequency analysis of the spectrogram. Our proposed multi-layer LSTM models are able to restore up to 1 s of gaps with high perceptual audio quality using the features captured from the time domain only. Specifically, for gap lengths under 500 ms, the MOS can reach up to 3-4, and for gap lengths ranging between 500 ms and 1 s, the MOS can reach up to 2-3. In the time domain, the proposed models can proficiently restore the envelope and trend of lost speech signals. In the frequency domain, the proposed models can restore spectrogram blocks with higher similarity to the original signals at frequencies less than 2.0 kHz and comparatively lower similarity at frequencies in the range of 2.0 kHz-8.0 kHz.  
speech signal processing; speech inpainting; audio inpainting; long short-term memory; deep learning 2024 16 63 10.3390/616020063  
1
###### Abstract  
We measured dynamics of solar-induced chlorophyll fluorescence at telluric oxygen absorption bands O\({}_{2}\)A and O\({}_{2}\)B in evergreen spruce and deciduous beech forests. Seasonal variations in fluorescence emissions were compared with NDVI. Daily changes in fluorescence emissions were compared with canopy shadow fraction (\(\alpha_{S}\)) dynamics, which showed impact of branch and leaf positions on detected fluorescence signals based on comparison with canopy height model. Absorbed photosynthetically active radiation (APAR) was recognized as a large determinant of fluorescence changes within the O\({}_{2}\)A band (SIFA), with R\({}^{2}\) > 0.68. Fluorescence within the O\({}_{2}\)B band was more directly linked to NDVI. Although, the seasonal dynamics of fluorescence within the O\({}_{2}\)B band (SIFB) were similar to SIFA in the spruce forest. In the beech forest, SIFB showed different seasonal dynamics as compared with SIFA in the spruce forest showed a relationship to gross primary productivity (GPP), with R\({}^{2}\) = 0.48, and a relationship of R\({}^{2}\) = 0.37 was estimated for the SIFA-GPP connection in the beech forest. SIFB was better linked to seasonal GPP in the beech forest, but with a negative slope in the relationship with R\({}^{2}\) = 0.61. We have shown that measurements of passive fluorescence signals at telluric oxygen absorption bands can contribute to understanding to photosynthesis processes in forest canopies.  
3-FLD; fluorescence; GPP; LiDAR; NDVI; shadow fraction Article
# A Novel Cryptography-Based Architecture for Secure Data Asset Sharing and Circulation Systems
## 4 Security Technical Framework
### Security Execution Layer  
Cross-convergence of multiple technologies: In the process of data cleaning and secure storage, clear text cleaning, dense state weight removal, fragmentation storage and other technologies are used to preprocess the file based on data sensitivity, and to improve the dynamic efficiency under different security levels to achieve the balance and optimization of security efficiency. In the precise determination of data assets, attribute-based access control, continuous trust evaluation, hybrid encryption and other technologies are adopted to reduce the correlation between ciphertext and complex policies, improve the accuracy of ownership boundary division, and realize the dynamic binding of attribute permissions. In the trusted management of data operation, the trusted supervision chain of data operation is constructed by using the technology of intelligent contract and blockchain, through reliable contract rule design and efficient contract rule optimization. In the process of efficient and secure data return, anonymous communication and multi-path dispersion technology are adopted, and a high-security protocol cluster is designed to realize the multi-dimensional security protection of traffic characteristics, connection relationship, network topology, information content and user identity in the process of data return transmission. At the same time, the software of secure routing and covert channel construction technology, for rapid deployment, resources on-demand reconstruction of the transmission channel to achieve anti-traceability and tracking of data efficient and secure return.
## 3 Setup and Measurement Campaign  
The outdoor GNSS measurements were taken on two consecutive days in September 2022, on 19 September 2022 (day-of-year 262; from now on DOY 262) and on 20 September 2022 (DOY 263) at the same site (Figure 5). The reason for the two-day experiments with multiple repetitions of the jamming measurements was redundancy, but also, we believed that, after the first day, something might have gone wrong with the observation reception of the Samsung S20 smartphone. It is well known that smartphones often have problems with system settings when collecting GNSS data, which can also affect the loss of observations. Therefore, specifically for this jamming experiment, where observation loss was commonplace, we tried to ensure that all the smartphones were in observation receiving mode during the measurements and that the app was always open during the experiments. However, despite constantly monitoring what was happening in the Geo++ application, we could not be sure that the storage of observations was working smoothly. The Geo++ RINEX Logger app version 2.1 was used on all the smartphones to store the raw observations.  
Figure 4: Test site in Crnotice, Slovenia: (**a**) testing professional geodetic receivers in 2019; and (**b**) simultaneously testing smartphones and geodetic receivers in 2022.