esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Design and Methods
### Network Architecture Design  
To address the problems of feature space heterogeneity and probability distribution misalignment, we introduce the deep learning-enabled heterogeneous transfer learning framework, depicted in Figure 1.  
Due to the inherent complexity of real-world data, linear projections or shallow networks may be inadequate for capturing the intricate relationships within the data. Thus, we adopt a distinct approach by employing separate deep networks \(f_{\text{s}}(\cdot)\), \(f_{\text{t}}(\cdot)\) to facilitate the conversion of data from both the source and target domains into a shared feature space, leveraging the enhanced capacity for nonlinear feature transformation offered by deep neural networks.  
Then, the transformed labeled data stemming from both the source and target domains serve as input for the classification network \(f_{\text{c}}(\cdot)\). This ensures that the resulting model is robust and versatile, capable of effectively discerning patterns and making predictions on data originating from the target domain, a crucial requirement for successful transfer learning. Simultaneously, on the other side of this paradigm, the transformed data play a pivotal role in the calculation of the maximum mean discrepancy (MMD). This statistical metric serves as a vital tool for aligning the probability distributions across the two domains. By reducing distribution discrepancies, MMD facilitates the integration of unlabeled data from the target domain into the classification task, further enhancing the model's capability to make informed predictions based on this previously untapped data source. This dual-pronged approach capitalizes on the strengths of deep neural networks and statistical alignment techniques to optimize the utility of data from both domains in the context of the classification task.
# Can We Exploit Machine Learning to Predict Congestion over mmWave 5G Channels?
## 3 Scenarios and Dataset Generation  
In this section, we describe the process followed to obtain transport layer traces from communications over mmWave channels. In the literature we can find different approaches to obtain such traces. For instance, the authors in [PERSON] et al. (2019) create a 5G trace dataset from a major Irish operator. This dataset can be afterwards used to feed tools, such as Mahimahi Mahimahi (2019), to emulate end-to-end communication. However, this approach has two main limitations. First, the obtained traces could have been impacted by the particular congestion-control algorithm used by the transport layer protocol (quite likely TCP-Cubic). Hence, the metrics that could be extracted from such traces would not accurately reflect the most appropriate raw input to design a congestion-control algorithm. Besides that, as was mentioned before, existing studies point out the strong impact of the cellular protocol stack configuration over the congestion-control solution behavior. In this sense, real traces would hide such configuration, thus allowing less control over the analyzed scenario.  
As an alternative, we obtain the traces exploiting the ns-3 network simulator ([PERSON] et al., 2019), and in particular the mmWave module ([PERSON] et al., 2019), which has been used in the past to carry out end-to-end simulations ([PERSON] et al., 2019; [PERSON] et al., 2019). This module provides mmWave channel models, including those defined by the 3 GPP ([PERSON] et al., 2019), and novel ones proposed in the literature ([PERSON] et al., 2019). In addition, the module is equipped with a complex channel model ([PERSON] et al., 2019), which allows performing realistic simulations. On the other hand, it has been extended to allow dual connectivity ([PERSON] et al., 2019) and carrier aggregation ([PERSON] et al., 2019), which would allow us to analyze how the simultaneous use of different wireless channels might impact the performance of congestion-control solutions. In this work we focus on the analysis of different machine learning algorithms (and their potential) over a single mmWave channel, leaving multi-channel scenarios as an extension that we will tackle in our future work.  
In the following we describe the main characteristics of the analyzed scenarios, and we discuss the obtained parameters, which will be used in the following section to derive congestion metrics. In addition, we also study the impact that network scheduling policy may have over the transport layer performance.
## 3 Synchronization-Integrated Feedrate Scheduling  
Since the tool-tip position and tool orientation are fitted independently in different coordinates, the synchronization and feedrate scheduling are necessary for curve interpolation. In this section, synchronization-integrated feedrate scheduling method is presented to ensure that the tool-tip position motion and tool orientation motion are synchronized and smooth. Feedrate scheduling of the tool-tip position is conducted firstly to address the axial kinematic constraints. Then the tool orientation feedrate is obtained by feedrate deformation method to guarantee smooth rotary motion as well as to share the same motion time with the tool-tip position segment by segment. Thus, the synchronization and feedrate scheduling is implemented in one procedure.
## 2 VLC System Model
### VLC Channel  
As explained in previous sections, although in this work, the mechanism implemented by CSK modulation is not used directly for data communication, its use takes advantage of the properties of light and its propagation. Thus, the methodology employed uses the power received to locate the target. To achieve this goal, various factors that affect the VLC channel must be considered. Consequently, the power received at the target can be accurately described as [41]  
\[P_{Ri}=P_{LOSi}+P_{Noise}, \tag{5}\]  
where \(P_{Ri}\) represents the power received from LED \(i\), \(P_{LOSi}\) denotes the power corresponding to the LOS component, and \(P_{Noise}\) represents the noise power. This study exclusively takes into account thermal and shot noise [42, 43]. Additionally, in this study, it is assumed that the system does not consider interferences from other luminaires. Furthermore, \(P_{LOS}\) can be expressed as  
\[P_{LOSi}=H_{LOSi}(0)P_{Ri}, \tag{6}\]  
where \(H_{LOSi}\) represents the channel gain for the LOS component and \(P_{Ri}\) denotes the transmitted power. \(H_{LOSi}\) can be mathematically described as [35]  
\[H_{LOSi}(0)=\begin{cases}\frac{A_{ri}(m_{i}+1)}{2\pi d_{i}^{2}}\cos^{m_{i}}{( \phi_{i})}\cos(\psi_{i})T_{s}(\psi_{i})g(\psi_{i}),&0\leq\psi_{i}\leq\Psi_{ci} \\ 0,&\psi_{i}>\Psi_{ci},\end{cases} \tag{7}\]  
where \(A_{ri}\) denotes the area over which the PD \(i\) receives light and \(m_{i}\) represents the Lambertian order defined as  
\[m_{i}=\frac{-\ln{2}}{\ln{(\cos{\phi_{1/2,i}})}}, \tag{8}\]  
where \(\phi_{1/2,i}\) represents the semi-angle at half the illuminance of an LED \(i\). It has previously been established that \(\phi_{i}=\psi_{i}\). Then, the application of Equations (6) and (7) results in the following expression:  
\[P_{LOSi}=\frac{A_{ri}(m_{i}+1)}{2\pi d_{i}^{2}}\cos^{m_{i}+1}{(\phi_{i})}T_{ si}(\psi_{i})g_{i}(\psi_{i})P_{Ri}. \tag{9}\]
## 1 Introduction
[25] found that when the water-cement ratio was 0.4 and 0.5, respectively, the peak and residual shear strength of grouted surrounding rock increased with the increasing normal stress. Taking the grouting reinforcement project of a large section cavern surrounding rock as the background, [PERSON] et al. [26] found a linear relationship between grouting volume and grouting pressure, hydraulic conductivity, and grouting time, and a square root relationship between slurry diffusion radius and grouting pressure, hydraulic conductivity, and grouting time through numerical simulations. On the basis of the tunnel crossing railway stations, [PERSON] et al. [27] used ANSYS software to simulate and analyze the grouting reinforcement effect of tunnel surrounding rock according to the equivalent continuous medium theory. The results show that grouting reinforcement has a good effect on reducing surface settlement and horizontal convergence. Through grouting experiments on fully weathered granite, [PERSON] et al. [28] studied the diffusion law of cement rule of different viscosity and the influence of grout viscosity on the reinforcement effect, and found that an increase of slurry viscosity strengthens compressive strength and shear strength. [PERSON] et al. [29] found that the tensile strength of rock cracks increases with increasing grouting viscosity,but the overall tensile strength was low. [PERSON] et al. [30] found that broken fine sandstone reinforced by compaction grouting slurry has obvious ductility, strong plasticity, and deformation resistance, and can remain stable in a large deformation range. [PERSON] et al. [31] took the compressive strength, deformation modulus and hydraulic conductivity of grouting reinforced rock mass as the evaluation index of grouting reinforcement effect, tested the grouting plus solid effect under different water-cement ratio and curing time, and found that slurry water-cement ratio has a significant impact on grouting reinforcement effect. [PERSON] et al. [32] found that grouting reinforcement can effectively suppress the stress concentration at the crack tip, improve the integrity of the specimen, avoid the formation of stress redistribution, and enhance the integrity of the specimen. Generally, the research on grouting reinforcement of surrounding rock has mostly studied the strength comparison and deformation analysis before and after grouting. However, only few studies have been conducted on the strength of grouted surrounding rock and how to determine grouting parameters of rock mass in engineering. With the land part of Haicang undersea tunnel as a background, laboratory experiments, literature analysis, and numerical simulation were performed to study the method and rationality of determining the grouting parameters of the surrounding rock of the ultra-shallow buried double-arch tunnel. The results can be referred by similar projects.
## 2 Related Work  
Despite numerous research efforts [4; 5; 6; 7; 8; 9], WSN routing remains to this day a fairly open issue. In this section we will only focus on more-oriented routing protocols that have been implemented on actual sensor network platforms. A number of cost-based routing protocols have been developed for modes using TinyOS [10]. MintRoute [1], MultiHopLQI [11], Collection Tree Protocol (CTP) [3], Routing Protocol for Low power and Lossy Networks (RPL) [12] and Arbutus [13] represent successive evolutions of a common cost-based paradigm defined in [1]. Link estimation is seen as an essential tool for the computation of reliability-oriented route selection metrics. Basically those routing protocolsaddress scenarios characterized by one sink (excepting CTP and Arbutus) and where nodes generate traffic periodically at a fixed rate. Routing protocols use gradient metrics to determine next hop while keeping some traffic in the network to maintain information about link quality. Routing of packets then is based on the selection of the best candidate next hop. MintRoute makes use of routing tables that keep the Expected Number of Transmissions (ETX) metric [14] of all of the neighbours of a node. In order to prevent tables from growing indefinitely, it makes use of a neighbourhood management policy [15].  
MultiHopLQI, does not use routing tables. A new parent in the routing tree is adopted if it advertises a lower cost than the current parent (_i.e._, next hop candidate node). The link metric is Link Quality Information (LQI) which is used additively to obtain the gradient of a given route. MultiHopLQI only keeps state for the best parent at a given time which drastically reduces memory usage and control overhead. CTP, uses ETX as link estimation. Routes are selected by according to the minimum gradient (_i.e._, sum of the costs of the route) and transmission deferrals are used in case of parent node congestion. The key feature of CTP is that it adjust control and state maintenance beaconing by means of the Trickle algorithm [16]. RPL improves CTP by enabling downstream communication (from the sink to the nodes). To the best of our knowledge, Arbutus is the unique protocol that pursuits load balancing. As MultiHopLQI, it does not keep routing tables but only the best candidate next hop for each node is kept. It builds a tree topology and beaconing is only carried out in a top down approach. Next hop estimation is calculated taking into account bottleneck information (which provide inherent load-balancing) and Channel State Information, LQI in IEEE802.15.4 protocol [17]. As authors outline in their work, even Arbutus performs as well as MultiHopLQI with a 30% energy savings, the calibration of the protocol is not trivial and depends of every scenario. Finally, our work pursuits the same objectives as Arbutus, but also simplifying the fine-tuning process of the network.
## 6 Conclusions  
An application of binary CPM is presented in this article to achieve the bandwidth and power efficiency improvements in the UWA channel. Based on SCFDE for CPM, and the output-memorized sequence of the equalizer, the PF-SCFDE for CPM is proposed. Compared with SCFDE, PF-SCFDE employs a prefilter to convert the information sequence output by the matched filter into an information sequence that contains forward information, thereby reducing the ISI caused by the backward information generated by the whitening filter. The LLR is used to estimate the forward information and make symbol detection, which can ultimately boost the receiver's performance. The simulation results indicate that the PF-SCFDE performance is superior to that of SCFDE when the channel  
Figure 11: Measured channel in a certain period.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SNR (dB)** & **Number of Frames** & **Error Bit of SCFDE** & **Error Bit of PF-SCFDE** \\ \hline
5–10 & 22 & 212 & 179 \\ \hline
10–15 & 52 & 58 & 42 \\ \hline
15–20 & 72 & 8 & 6 \\ \hline \(>\)20 & 64 & 0 & 0 \\ \hline Total & 210 & 278 & 227 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of error bits of received signal frames in different received SNR.  
RMS delay spread is low. A sea trial demonstrated the ability of the PF-SCFDE to weaken multipath effects, which makes it a suitable communication scheme for shallow water acoustic channels.  
Conceptualization, R.H. and N.J.; methodology, R.H.; software, Z.G. and D.X.; validation, N.J. and J.H.; formal analysis, [PERSON]; investigation, [PERSON]; resources, S.G.; data curation, N.J.; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON], N.J. and J.H.; visualization, D.X.; supervision, [PERSON]; project administration, S.G.; funding acquisition, N.J. and J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Youth Innovation Promotion Association, Chinese Academy of Sciences (no. 2018028, no. 2019025).  
The authors declare no conflict of interest.
# Highly Doped Phosphate Glass Fibers for Compact Lasers and Amplifiers: A Review
## 2 Rare-Earth Active Ions for Phosphate Fiber Lasers
The most common laser transition in Erbium is that from the \({}^{4}\)I\({}_{13/2}\) manifold to the ground-state \({}^{4}\)I\({}_{15/2}\) (see Figure 3a), centered at around 1.55 \(\upmu\)m. This transition forms a three-levels system for which a high pump power is required to obtain a population inversion and thus reach the threshold. The most common pumping arrangement is based on the transition \({}^{4}\)I\({}_{15/2}\rightarrow{}^{4}\)I\({}_{11/2}\) with a wavelength of around 980 nm, although in-band pumping (\({}^{4}\)I\({}_{15/2}\rightarrow{}^{4}\)I\({}_{13/2}\), e.g., at 1.45 \(\upmu\)m) is also possible [28; 29]. This transition is widely exploited in Er\({}^{3+}\)-doped fiber amplifiers (EDFAs). In order to increase lasers and amplifiers efficiency, Erbium-doped fibers and amplifiers are often co-doped with Ytterbium, which exhibits larger absorption cross-section and broad absorption band (from \(\sim\)850 to \(\sim\)1070 nm). In this scheme Ytterbium, which acts as a sensitizer, strongly absorbs pump photons, and then resonantly transfers its energy to Erbium that acts as activator. Figure 3: **(a)** Typical energy level diagram of Er\({}^{3+}\) ions in phosphate glasses; **(b)** Common absorption and emission spectra of Er\({}^{3+}\) ions in phosphate glasses (adapted from [12]). Figure 2: **(a)** Typical energy level diagram of Nd\({}^{3+}\) ions in phosphate glasses; **(b)** Typical absorption and emission spectra of Nd\({}^{3+}\) ions in phosphate glasses (adapted from [27]). Phosphate glass is considered an excellent host for an Yb\({}^{3+}\)-Er\({}^{3+}\) co-doped system in virtue of its high emission cross-section, low accumulative energy transfer rates and low backward energy transfer [30]. In fact, in comparison to silicate glasses (\(\sim\)1100 cm\({}^{-1}\)), the larger phonon energy in the phosphate host (\(\sim\)1200 cm\({}^{-1}\)) increases the transition probability for \({}^{4}\)I\({}_{11/2}\)\(\rightarrow\)\({}^{4}\)I\({}_{13/2}\) relaxation [31], which prevents the back energy transfer from Er\({}^{3+}\) to Yb\({}^{3+}\). Furthermore, thanks to the large spectral overlap between the Yb\({}^{3+}\) emission spectrum (\({}^{2}\)F\({}_{5/2}\)\(\rightarrow\)\({}^{2}\)F\({}_{7/2}\)) and Er\({}^{3+}\) absorption spectrum (\({}^{4}\)I\({}_{15/2}\)\(\rightarrow\)\({}^{4}\)I\({}_{11/2}\)), the energy transfer efficiency from Yb\({}^{3+}\) to Er\({}^{3+}\) in phosphate glasses can reach 95% [11]. Typical absorption and emission cross-sections of Erbium ions in phosphate glasses, together with lifetime of the \({}^{4}\)I\({}_{13/2}\) level, are reported in Table 1.
## 3 Results
### High-Pass Filter  
We tested our high-pass filter algorithm based on the manual cone counts. A sigma radius of 1.5 uM yielded close resemblance with the average human grader counts in healthy participants. The high-pass filtered automated counts revealed a median of 154 cones (range: 133-194) at 3 degrees, 123 cones (range: 109-132) at 6 degrees and 115 cones (range: 98-154) at 9 degrees. We calculated a good ICC of 0.865 (range: 0.755-0.926), and the Bland-Altman analysis (Figure 2D) revealed no significant difference (\(p\) = 0.137, one sample \(T\)-test) between average manual counts and computer-assisted high-pass filter counts in our healthy subjects. We also excluded the presence of proportional bias via a linear regression analysis (\(p\) = 0.185). In 6 out of 45 counts, the difference between manual and  
Figure 2: Bland–Altman plots of intergrader differences between healthy participants and CACD patients (**A**), intragrader differences in healthy subjects (**B**), intragrader differences in CACD patients (**C**) and differences between human graders and automated, high-pass filtered counts (**D**).  
computer-assisted counts was larger than the computed limits of agreement. In our CACD patient group, a high-pass filter sigma radius of 1.5 \(\upmu\)M resulted in a mean overestimation of 52.82 cones (standard deviation: 19.37) of computer-assisted cone counts versus human graders and we found a corresponding poor ICC of 0.467 (range: 0.075-0.765). Increasing the Gaussian sigma radius via trial-and-error to 2.5 \(\upmu\)M resulted in a higher level of agreement (Figure 2D). We identified a good ICC of 0.816 (0.462-0.938) when using a sigma radius of 2.5 \(\upmu\)M, and the Bland-Altman analysis showed no statistically significant difference (\(p\) = 0.085, one sample \(T\)-test) in average manual counts and computer-assisted high-pass filtered counts in CACD patients. We used a linear regression analysis to exclude the presence of proportional bias (\(p\) = 0.435). In 2 out of 15 counts, the difference between manual and computer-assisted counts was larger than the computed limits of agreement. At 3, 6 and 9 degrees, we found a median count of 136 (range: 117-149), 135 (range: 110-167) and 131 (range: 108-155) cones, respectively. Although the cone counts of patients and healthy subjects partially appeared similar, in CACD patients, there was no progressive decrease in cone count with increasing eccentricity, as was characteristic in our healthy participants (Figure 4).  
Figure 3: Example of the appearance of the cone mosaic on high magnification module imaging in a CACD patient (**A**). The green cross marks the location of the fovea. The red lines indicate corresponding retinal eccentricities. The yellow squares represent selected ROIs, depicted magnified at approximately 3 degrees (**B**), 6 degrees (**C**) and 9 degrees (**D**) from the fovea. An imaging artifact is marked by the asterisk. Scale bar: 20 \(\upmu\)M.
# Applications of Raman Microscopy/Spectroscopy-Based Techniques to Plant Disease Diagnosis
## 4 Raman Spectroscopy--Principles of Raman Spectroscopy  
Light scattering occurs in a molecule when a photon interacting with a coherent molecular vibration of the medium collides with a vibrationally hot state. If this process occurs without energy loss, the act of scattering changes the direction of the photon and the polarizability of the coherent mode of the medium. The degree of the polarizability change is related to the eternally scattered photon, originating from an electronic excited state and a molecular vibrational state [44]. The inelastic scattering rules for the final state imply that the energy of the scattered photon is directly correlated with the energy of the accessible vibrational states in the medium. Hence, the scattered Raman photon has an energy that deviates as a Raman shift from the energy of the incident photon [44; 45].  
Raman spectroscopy is a powerful tool for probing levels of biological organization from molecules to whole cells without extrinsic staining. The molecular specificity of Raman spectra facilitates the quantification and spatial identification of functional groups, such as proteins and lipids or carotenoids [37]. For example, tomato carotenoids exhibit two strong Raman peaks in spectral regions of 1100-1200 and 1400-1600 cm\({}^{-1}\) due to the stretching vibrations of C-C bonds [39]. Quantitative and label-free spatially resolved measurements of lipid and protein contents are particularly important for understanding cell biology because of their physiological demands. Additionally, Raman microspectroscopy is compatible with a variety of live-cell time-resolved measurements. Compatibility with life and depth-resolved imaging are particularly advantageous for studying the cellular dynamics and functions underlying complex (dys)regulation mechanisms and the spatial distribution of molecular species in the environment of living cells [37; 46].