esa-sceva/satcom-chunk-collection huggingface dataset:
# Interest Forwarding in Named Data Networking Using Reinforcement Learning
## 6 Results and Discussion
### Content Popularity  
In Figures 6 and 7, we compare the three forwarding strategies and show the impact of the content popularity model. The request distribution is uniform in the network, and we consider 5 different values of the skew parameter (\(\alpha=0.0,0.3,0.6,0.9,1.2\)), including \(\alpha=0\) which corresponds to equally popular items. The closer the skew parameter is to 0, the more ineffective in-network caching will be for the same catalogue size. Our observations confirm this because as most of the requests concentrate on a smaller proportion of the content catalogue, that is, as \(\alpha\) increases, all the forwarding strategies can hit more local caches. Also for \(\alpha=0.0\) and \(\alpha=0.3\), both ASF and NDNFS-RLRNN deliver almost identical performance with respect to the cache hit rate under the different caching policies. In fact, under the _Betw_ policy, ASF is slightly better than NDNFS-RLRNN for these values of \(\alpha\) in terms of the cache hit rate. However, as \(\alpha\) is increased from 0.6 to 1.2, NDNFS-RLRNN produces a performance improvement of between 27-43% compared with ASF.  
Finally, our results suggest that for ubiquitous caching policies like LCE and _ProbCache_, increasing the exploration in our proposed forwarding strategy, can deliver improved results with respect to exploiting in-network caching, at the cost of higher overhead as shown by the effect of increasing  
Figure 5: Network load performance of NDNFS-RLRNN (\(p=0.3\)), ASF and NRR for different cache size to catalogue size ratios, under: (**a**) LCE caching policy, (**b**) _Betw_ caching policy and (**c**) _ProbCache_ caching policy. The cache size of each CR is fixed at 10 data objects while we vary the catalogue size from 1000 to 5000 data objects. The cache size of each CR is fixed at 10 data objects. Content popularity is modelled according to the Mandelbrot-Zipf (MZipf) distribution with the settings: \(p=5.0,\alpha\in[0.6,1.2]\). The results reported are the average of 20 randomized simulation runs.  
the probing parameter in Figure 3. While the above statement is also true for the _Betw_ policy, interestingly, it is only under this policy that the NDNFS-RLRNN performs better than the NRR despite using significantly lower probing. Since _Betw_ mostly caches in specific nodes in the network, this demonstrates NDNFS-RLRNN's intelligence to search in the most worthwhile areas more efficiently than an approach that searches the entire network.  
Figure 6: Cache hit rate performance of NDNFS-RLRNN (\(p=0.3\)), ASF and NRR for different uniform content popularity distributions, under: (**a**) LCE caching policy, (**b**) _Betw_ caching policy and (**c**) _ProbCache_ caching policy. Content popularity is modelled according to the Mandelbrot-Zipf (MZipf) distribution with the settings: \(p=5.0,\alpha=0.0,0.3,0.6,0.91.2\). Each CR is equipped with a cache size of 0.4% of the content catalogue. The results reported are the average of 20 randomized simulation runs.
# Characteristic Length and Time Scales of the Highly Forward Scattering of Photons in Random Media
## 3 Results
### Length and Time Scales for the dEM to Be Valid in the Scattering Regime
From the results in the figures (a), (b), and (c), we evaluated the crossover lengths for the dE0, dE1, and dE2 as approximately \(10^{1}/\mu_{t}^{\prime}=10/\mu_{t}^{\prime}\), \(10^{0.6}/\mu_{t}^{\prime}\sim 4.0/\mu_{t}^{\prime}\), and \(10^{0.45}/\mu_{t}^{\prime}\sim 2.8/\mu_{t}^{\prime}\), respectively. These results mean that the scattering regime is classified into the three kinds of regimes by the two kinds of the crossover lengths for the dE1 and dE2. Here, we evaluated the crossover lengths for the dE0, dE1, and dE2 as a function of \(\mu_{t}^{\prime}\) for the purpose of the classifications of the scattering regime. As an alternative, we can consider the normalization of the SD distances by \(\mu_{t}^{M}=\mu_{s}^{M}+\mu_{a}\) because for the dEM, the inverse of \(\mu_{t}^{M}\) is the characteristic length of single scattering and absorption processes. Figure 8d shows \(E_{peak}^{M}\) for the dE0, dE1, and dE2 as a function of the SD distances by \(\mu_{t}^{M}\). The crossover lengths for the dEM with \(M=0\), 1, and 2 are within the regime of \(0.9\lesssim\log_{10}r\mu_{t}^{M}\lesssim 1.0\), less dependently on the expansion order \(M\). It is noted that the crossover length from the scattering and diffusive regimes depends on the expansion order \(M\) in the normalization by \(\mu_{t}^{M}\), so that we cannot evaluate the regime for the dEM to be valid uniquely in the normalization. The rough evaluation of the crossover length for the dEM as \(10/\mu_{t}^{M}\) means that after ten times scattering and absorption processes for the dEM, the \(M\)-th order dEA holds. Figure 9 shows the normalized peak times for the RTE, dE0, dE1, and dE2 as a function of the normalized SD distances by \(\mu_{t}^{\prime}\). From the figure and the results for the crossover lengths based on \(\mu_{t}^{\prime}\), we evaluated the crossover times for the dE0, dE1, and dE2 as approximately \(10^{1.6}/\mu_{t}^{\prime}v\sim 40/\mu_{t}^{\prime}v\), \(10^{0.8}/\mu_{t}^{\prime}v\sim 6.3/\mu_{t}^{\prime}v\), and \(10^{0.55}/\mu_{t}^{\prime}v\sim 3.5/\mu_{t}^{\prime}v\), respectively. Here, the evaluations of the crossover times are based on the \(t_{peak}\)-results for the RTE (dashed line in Figure 9 at the parameter set A) because the RTE is the most accurate photon transport model.
## 4 Experiments
### Results  
#### 4.2.1 Evaluating AV-BT  
**AVEL** in [6] adopts an audio-guided visual attention module (AGVA) which interacts between the audio and the visual encoder. It uses audio information to help the visual encoder focus on the sounding target to better filter out interference caused by the visual background. Apart from the AGVA module, the structures of our encoders are quite similar to those of AVEL. Particularly, the CNN backbone used for audio feature extraction is identical while the backbone for visual features has comparable capabilities. Even if AVEL is trained with full labels in an end-to-end manner, we still think it is reasonable to compare with it since the following research [12; 13; 19] directly adopted it to extract features.  
**Comparison with existing methods.** Analogous to the protocols in self-supervised image representation learning [23; 24; 25; 26; 27; 28], we directly train a classifier on top of the fixed representations produced by the encoders trained with AV-BT. Unlike using a single linear layer as the prediction head for single-modal representations, sequential audio-visual representations are much more complex, so we adopt a three-layer MLP (which has the same structure as the classification layer in Figure 4) to make predictions. The results are reported in Table 2, where **our AV-BT significantly outperforms the supervised method in both unimodal and multimodal cases**. Specifically, in the case where the CNN backbone used for audio feature extraction is identical, our method surpasses AVEL by 4% when using only audio data. This improvement in the unimodal case suggests that by maximizing the similarity of different modal features in the same latent space, features from each modality are enhanced to be more discriminative. An intuitive explanation is that by reducing the differences between different modal representations, both the visual and auditory properties of an object can be considered more collaboratively. For example, mandolins and guitars may be very close in appearance, but they are very different in sound. Combining both the visual and audio characteristics of the mandolin allows it to be better distinguished from the guitar. Moreover, the results also show that the improved separability of the unimodal feature also further enhances the performance in the multimodal case.
# Navigation in GEO, HEO, and Lunar Trajectory Using Multi-GNSS Sidelobe Signals
## 3 Results
### Navigation Performance
#### 3.3.3 Lunar Navigation Performance  
Considering the analysis of signal visibility in Section 3.2, all the three position schemes could not provide continuous position solutions with the threshold of 20 dB-Hz. Therefore, we only considered observations with a 15 dB-Hz threshold here. The differences of GREC SPP and nominal lunar trajectory are shown in Figure 18. We can see that very large positioning errors occurred near the end of the trajectory. RMSs in Table 11 show that the 3D position error of BDS-3/GPS combination decreased by 34.0% in contrast to BDS-3 only. This benefited from the significantly improved signal geometry. Positioning errors of the X and Y component were much larger than that of the Z component. A possible explanation for the smaller Z component was due to the high constraint in cross and along direction; the Z component was consistent with the cross direction [36]. When more systems were included, SPP accuracy only increased by 9.0% compared with the BDS-3/GPS combination. Figure 15 shows that GDOPs of GREC were only slightly smaller than that of the BDS-3/GPS combination. Therefore, a significant improvement in SPP accuracy could not be realized. The RMSs of code post-fit residuals were 5.65 m, 6.79 m, 6.99 m, and 6.86 m for BDS-3, GPS, BDS-3/GPS, and GREC combination, respectively. Among the observations, 42.3% of the received signals were below 20 dB-Hz and 84.4% were below 30 dB-Hz, resulting in higher code noise than the GEO and HEO spacecraft.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Position (m)** & **BDS-3** & **BDS-3/GPS** & **GREC** \\ \hline X & 7.4 & 6.9 & 6.1 \\ Y & 10.3 & 9.4 & 8.1 \\ Z & 19.4 & 18.2 & 15.5 \\
3D & 23.1 & 21.7 & 18.5 \\ \hline \end{tabular}
\end{table}
Table 10: RMS of Position with different system combinations.  
Figure 17: Differences between BDS SPP and the nominal HEO.
## 3 Results and Discussion
### Proton Conductivities (\(\sigma\)) and Hydrolytic Stability  
The proton conductivity of the sPEEK-based electrolyte membranes has been measured at 90 \({}^{\circ}\)C and at different humidity conditions, as shown in Figure 7. Nafion 212 commercial membrane was used as the benchmark and it is also reported in the graph for comparison. As expected, the conductivity of pristine sPEEK is lower than that of Nafion 212. Indeed, the ion conductive channels of sPEEK are narrower and less interconnected compared to the Nafion ones as a consequence of the lower microphase separation between the hydrophilic and hydrophobic domains. This obviously has a detrimental effect on its proton transport properties. However, the nanodispersion of a filler such as this PSLM material allows for a significant improvement of \(\sigma\) to be obtained under these operating conditions. In particular, the composite at 3 wt% filler loading achieves values not far from the benchmark. In addition, under low-hydration conditions (RH = 20-30%), an important prerequisite for the development of PEMFCs operating at temperatures above 100 \({}^{\circ}\)C and low humidity [40], the conductivity of the sPEEK-PSLM\({}_{3}\) membrane is very close to that of Nafion 212.  
Figure 6: Spin-lattice relaxation times (T\({}_{1}\)) as a function of the temperature (from 20 \({}^{\circ}\)C to 130 \({}^{\circ}\)C) of the water confined in sPEEK-based membranes.  
These results, together with the NMR outcomes seen above, prove that the dispersion of PSLM nanomaterials inside the sPEEK matrix creates an appropriate network that promotes effective proton transport by the more efficient Groththuss-type mechanism through the formation of highly connected proton paths.  
Finally, the long-term chemical stability of the sPEEK membranes was also evaluated. In this regard, the membranes were kept at 90 \({}^{\circ}\)C and 95% RH, and the decrease in proton conductivity was assumed as an indicator of membrane degradation. Figure 7b illustrates the relative variation of \(\sigma\) as a function of the treatment time (over 160 h) for two representative membranes; i.e., pristine sPEEK and sPEEK_PSLM\({}_{3}\). While pristine sPEEK undergoes a severe hydrolytic degradation (over 20% of conductivity loss after 160 h), the variation for the nanocomposite membranes was almost negligible, i.e., \(\sim\)2.3% after 160 h. Accordingly, the introduction of the organosilica platelets in the sPEEK matrix produced a remarkable increase in the chemical resistance of the PEM. We can hypothesize that the aqueous environment progressively decomposes the -SO\({}_{3}\)H groups, but the strong electrostatic interaction of these groups with the PSLM platelets prevents the polymer backbone from degradation.
## 3 Results and Discussion
### Optimization
Figure 13 shows the simulated \(z\) component of the electric field (perpendicular to the plane) for the configuration MET at the last frequency on the range, that is, 1.55 GHz. It is possible to note that the region of the receiving antenna was subjected to fields partially reflected on the floor, in accordance with the estimated and discussed behavior of the wave. Figure 13: The simulated \(z\) component of the electric field (\(E_{z}\)) at 1.55 GHz, when the configuration is MET. One can observe the difference between the superior and inferior halves of the open-space domain, due to the reflection on the floor. In this simulation, \(-0.37\leq E_{z}\leq 0.40\) V/m. For better contrast, the color range was set to \(\pm 0.15\). The in-plane coordinates \(x\) (horizontal) and \(y\) (vertical) are in meters. Using the values \(d\) in Table 1 to form \(\mathbf{b}\) in Equation (11), solving Equation (9), we get \(\mathbf{x}=[0.1447;1.1956;2.1084]\), \(v_{\mathrm{mut}}=0.6178\) and \(\varepsilon^{\prime}_{r}=2.6204\). Regarding the imaginary part, as shown in Equation (8), assuming \(\Delta A=c^{(\mathrm{MUT})}-c^{(\mathrm{AIR})}\), at the intermediate frequency 1 GHz (\(\lambda_{0}=0.3\) m), one obtains \(\varepsilon^{\prime\prime}_{r}=0.05\). This value is the same reported in [24], but it is higher than the reference [23], although it is still within the uncertainty levels reported there. Actually, the computational model used here assumed a lossless MUT and a nondispersive floor, which could contribute to this difference.
## 4 Experimental Results
### Improved UDN
#### 4.1.2 Comparison with Hand-Crafted Feature-Based Models  
The first experiment trained the improved UDN with set00-set05 of the Caltech dataset, and it tested all models on set06-set10. The second experiment trained the improved UDN with the INRIA dataset and tested all models on the ETH dataset [38]. As seen in Table 1, the improved UDN is superior to the other models based on hand-crafted features, which obtains the lowest LAMR. Hand-crafted feature models such as VJ(Viola and Jones) [39], HOG [8], HOG+LBP(Local Binary Pattern) [40], ChnFtrs(Integral Channel Features) [10], ACF(Aggregated Channel Features) [41] obtain worse performance on both datasets. Figure 9 visualizes some pedestrian detection results, (a) shows images with ground truth with labeled pedestrians, and (b-g) are detection results of different models. False positive samples are in the red box, which means that negative samples are mistakenly predicted as pedestrians. True positive samples are in the green box, which means that pedestrians are correctly predicted. False negative samples are in the purple box, which means that these pedestrians are not detected by the methods. As we can see, the improved UDN obtains more green boxes and less red boxes and purple boxes. It is more effective than purely hand-crafted models.
# Waveform Design for Space-Time Coded MIMO Systems with High Secrecy Protection +
## 3 Secrecy-Enhancing LD-QO-STBC Scheme
### The Design of Artificial Interference
 \tag{21}\]  
As shown above, \(\omega_{ij}\) has a non-static time-varying value that is dependent on \(\chi_{ij}\), as well as on \(h_{j}\). At the transmitter part, this AI contributes to reducing the difference of power allocations, while at the receiving end, it will be perfectly canceled out. Therefore, by using (21), (18) can be represented without any AI, as follows:  
\[y_{\text{bi}}=\sum\limits_{j=1}^{2}\left(\chi_{ij}h_{j}+\chi_{i(j+2)}h_{j+2} \right)+n_{\text{bi}}. \tag{22}\]  
In summary, the received signal at Bob during the four time slots can be represented as:  
\[\left[y_{\text{b1}}\ y_{\text{b2}}\ y_{\text{b3}}\ y_{\text{b4}}\right]^{T}= \left(\mathbf{X}+\mathbf{W}\right)\left[h_{1}\ h_{2}\ h_{3}\ h_{4}\right]^{T}+ \mathbf{n}_{b}^{T}, \tag{23}\]  
where \(\mathbf{W}\) is the AI matrix with \(\omega_{ij}\) as the element in the \(i\)-th row and \(j\)-th column. Then, the received signal after the interference cancelation can be equivalently represented as:  
\[\left[y_{\text{b1}}\ y_{\text{b2}}^{*}\ y_{\text{b3}}\ y_{\text{b4}}\right]^{T }=\mathbf{H}\left[s_{1}\ s_{2}\ s_{3}\ s_{4}\right]^{T}+\left[n_{\text{b1}}\ n_{\text{b2}}^{*}\ n_{\text{b3}}\ n_{\text{b4}}^{*}\right]^{T}, \tag{24}\]  
and the detection can be made exactly the same as in the conventional detection using (10). On the other hand, the received signal vector at Eve is expressed as follows:  
\[\left[y_{\text{e1}}\ y_{\text{e2}}\ y_{\text{e3}}\ y_{\text{e4}}\right]^{T}= \left(\mathbf{X}+\mathbf{W}\right)\left[g_{1}\ \ g_{2}\ g_{3}\ g_{4}\right]^{T}+\mathbf{n}_{e}^{T}, \tag{25}\]  
where \(g_{j}\) is the channel gain from the \(j\)-th transmit antenna from Alice to Eve. Because the added AI matrix \(\mathbf{W}\) has factors related to the channel gains of \(h_{j}\)s for Bob, it cannot be canceled out at Eve. That is, the received signal at Eve during the \(i\)-th time slot is expressed as follows:  
\[y_{\text{e}i}=\left\{ \begin{array}{ll}\sum\limits_{j=1}^{2}\left\{\left(\chi _{ij}+\frac{\chi_{ij(j+2)}h_{j+2}}{2}\right)g_{j}+\left(\chi_{i(j+2)}-\frac{ \chi_{ij(j+2)}h_{j}}{2}\right)g_{j+2}\right\}+n_{\text{e}i},&\text{if $\chi_{ij}=0$},\\ \sum\limits_{j=1}^{2}\left\{\left(\chi_{ij}-\frac{\chi_{ij}h_{j+2}}{2}\right)g _{j}+\left(\chi_{i(j+2)}+\frac{\chi_{ij}h_{j}}{2}\right)g_{j+2}\right\}+n_{ \text{e}i},&\text{if $\chi_{i(j+2)}=0$},\\ \sum\limits_{j=1}^{2}\left\{\chi_{ij}g_{j}+\chi_{i(j+2)}g_{j+2}\right\}+n_{ \text{e}i},&\text{if $\chi_{ij}\
eq 0,\chi_{i(j+2)}\
eq 0$}.\end{array} \right. \tag{26}\]  
Even though, there is the probability of not having AI, i.e., \(\chi_{ij}\
eq 0\) and \(\chi_{i(j+2)}\
eq 0\), and thus \(\omega_{ij}=0\), frequent uncanceled AI will cause serious performance degradation.
## 3 Results
### Predicting Stem Water Potential at Mevo Beitar Vineyard Using Single 'Global' Model  
Figure 10 shows the comparison between measured \(\Psi_{\text{stem}}\) averaged over the 60 plots at Mevo Beitar vineyard during the season of 2017 and predicted \(\Psi_{\text{stem}}\) using **MB-Reg, MB-Mult** and **Global-Mult**.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **Estimate 1** & \(\sigma\)2 ** & **t-Ratio 3** ** & **LogWorth** & \(p\)-Value 4 ** \\ \hline DOY & \(-0.0062\) & \(0.00045\) & \(-13.59\) & \(37.906\) & \(<\)0.0001 \\ \(\Delta\)NDVI & \(2.5164\) & \(0.37276\) & \(6.75\) & \(10.596\) & \(<\)0.0001 \\ SAVI avg & \(0.6570\) & \(0.09927\) & \(6.62\) & \(10.219\) & \(<\)0.0001 \\ NDVI max & \(-2.4509\) & \(0.43914\) & \(-5.58\) & \(7.508\) & \(<\)0.0001 \\ NDVI min & \(-1.0344\) & \(0.37159\) & \(-2.78\) & \(2.261\) & \(0.0055\) \\ Intercept & \(-0.3885\) & \(0.13803\) & \(-2.81\) & & \(0.0050\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Size effects (LogWorth) and estimates of the variables used in the ‘global’ multivariable regression model (i.e., the coefficients of the variables used in the linear model). The table was arranged from the variable with the highest to the lowest effect in the model.  
Figure 9: Results of predicted (using the ‘global’ multivariable regression model) vs. observed \(\Psi_{\text{stem}}\) in the 82 vineyards along the 2017 growing (dashed line indicates 1:1 slope).  
**Global-Mult** is shown to reproduce seasonal changes in observed \(\Psi_{\text{stem}}\) at Mevo Beitar with a fairly high accuracy (R\({}^{2}\) = 0.84; RMSE = 0.12; bias = 0.07, Figure 10a,c), at least as good as the site-specific models of **MB-Reg** (R\({}^{2}\) = 0.84; RMSE = 0.11; bias = \(-\)0.02, Figure 10a,b) and **MB-Mult** (Figure 10a). Thus, **Global-Mult** appears to present a useful tool for seasonal (weekly) predictions of \(\Psi_{\text{stem}}\) in Mediterranean vineyards.
# An Effective Wide-Bandwidth Channel Access in Next-Generation WLAN-Based V2X Communications
## 2 Background
### Adjacent Channel Interference Problem in V2X Communications  
For the vehicular environment, there have been a number of prior studies [15,16] on adjacent-channel interference problems. Adjacent channel interference is caused by a power leakage of signals transmitted in neighboring channels, which is nonnegligible. Because of the adjacent-channel interference problem, transmission in nearby channels causes a degradation of the signal quality and an increased channel access delay. Therefore, to reduce negative impacts on the signal quality of the frame transmission on adjacent 10-MHz channels, authors in reference [17] suggested schemes to alleviate such channel interference caused by transmission in adjacent channels.  
The previously described adjacent-channel interference problem should be taken into account for the channel access rule of wide-bandwidth transmission. In the wide-bandwidth channel access procedure in IEEE 802.11n or IEEE 802.11 ac, when a busy channel is detected in the secondary channel by ED sensing, the station may transmit frames using only the primary channel. This is called a fallback operation. However, in V2X communication, a fallback operation causes an adjacent-channel interference problem because it transmits frames using adjacent channels even though a station senses transmission on the neighboring channel. The fallback operation causes more severe adjacent-channel interference by ignoring the transmissions in nearby channels. Therefore, in the proposed channel-access method for 20-MHz transmission, it is suggested not to allow the transmission of a 10-MHz fallback operation in case of a busy channel indicated in a secondary channel, as in reference [18].
## 2 Experimental Section
### GC-FID and GC-MS Analysis  
GC-FID analyses were performed using a PerkinElmer (Waltham, MA, USA) AutoSystem XL GC apparatus equipped with a FID system and a fused-silica capillary column (30 m \(\times\) 0.25 mm, film thickness 1 \(\upmu\)m) coated with Rtx-1 (PDMS). The oven temperature was programmed from 60 to 230 \({}^{\circ}\)C at 2 \({}^{\circ}\)C/min and then held isothermally at 230 \({}^{\circ}\)C for 35 min. The injector and detector temperatures were maintained at 280 \({}^{\circ}\)C. The samples were injected with an SPME inlet liner (0.75 mm i.d.; Supelco) using hydrogen as the carrier gas (1 mL/min). The retention indices of the compounds were determined relative to the retention times of a series of \(n\)-alkanes (C\({}_{5}\)-C\({}_{30}\)) with linearinterpolation. The relative concentrations of components were calculated from the GC peak areas without using correction factors. Samples were also analyzed with a PerkinElmer TurboMass detector (quadrupole), coupled to a GC PerkinElmer AutoSystem XL, equipped with a fused-silica Rtx-1 capillary column. The ion source temperature was 150 \({}^{\circ}\)C, and the ionization energy was 70 eV. Electronic ionisation (EI) mass spectra were acquired over the mass range of 35-350 Da (scan time 1 s). Other GC conditions were the same as described for the GC-FID analysis. Identification of the components was based on: (1) the comparison of their GC retention indices (RI) on a nonpolar column, determined relative to the retention time of a series of _n_-alkanes with linear interpolation to the retention times of authentic compounds or data with the laboratory's library; (2) the comparison of the RI and spectra with commercial mass spectra libraries [27, 28].