esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Research of Photonic Reservoir Computing for Optical Channel Equalization
###### Abstract  
In this paper, photonic reservoir computing chip architectures for noise equalization in optical fiber communication channels are proposed. These architectures leverage optical computing instead of electrical computing to reduce computational pressure at the receiver and decrease processing latency. We examine the impact of factors such as the number of reservoir nodes, waveguide delay line length, and the number of input/output ports on equalization performance. We discuss the equalization ability of these architectures under various types of noise. After parameter optimization, the 36-node reservoir layout achieves a three-orders-of-magnitude reduction in bit error rate for 20 km OOK signals after equalization. Additionally, the chip architecture facilitates easy expansion of the all-optical readout layer, offering the possibility for further increasing the equalization speed.  
photonic chip; reservoir computing; optical channel equalizer; optical communication 2025
# Deep Learning for Short-Term Load Forecasting--Industrial Consumer Case Study
## 5 Discussion  
Suppliers want clients to use more energy rather than less, but the recent development of demand-side management, demand response, and smart grid [48,49] will shift the status quo from irresponsible behavior towards indispensable accurate predictions. According to [50], the industrial and commercial sector represents 63.46% of the world's total electricity consumption. The challenge is to anticipate the stochastic behavior of large consumers.  
Industrial load forecast plays an essential role in the cost of electricity, especially for a large consumer such as the one presented in this work. Load forecasting is important because planning for electricity supply is dependent on consumption forecasts. High imbalances generated between actual and forecast consumption will create a higher risk for all the participants in the power market. This work proposes a methodology for industrial load forecasting based on DL and AR. The result and analysis indicate that DL has a high order of stochasticity, and careful tuning of the parameters is needed. The challenge is to anticipate the stochastic behavior of large consumers. This work proves that deep neural networks can successfully forecast hourly industrial load. The obstacle to this approach is the lack of smart-metering and sensors for forecasting consumption in real-time. Without relevant data, efficient and replicable forecasting models do not represent a reliable investment for the private sector. The novelty of the work is the proposed framework applied for industrial load curves, the analysis of the best architecture, and the scalability of the deep neural networks using a simple complexity index. The study compared the forecast performance for seven methods and tested various combinations for forecast variables and lag structures. Our test sample results across 1608 hourly values (15 October-20 December 2019) indicate consistently that: (i) deep recurrent neural networks are suitable for industrial load consumption; and (ii) the best model implemented for is GRU. The work highlights that increasing the number of hidden layers and neurons in each layer (total parameters) can negatively impact the performance of the DL algorithms. Training a complex deep network, each layer can map exactly the relation between input and output,  
Figure 12: Comparison of the \(DL_{index}\) with the training time, number of epochs and MAPE.  
but the neural network will fail to generalize to new data needed for forecasting and later for electricity acquisition.
# Electrospinning of Chitosan for Antibacterial Applications--Current Trends
## 5 Electrospinning of Chitosan
### Discussions Regarding the Formation of Chitosan-Based Nanofibers for Defined Purposes  
The production of chitosan-based nanofibers is challenging, and variations in the different electrospinning parameters influencing the antibacterial properties of chitosan have to be taken into account, as discussed in Sections 4 and 5. Starting from the surface morphology of the nanofibers, the mechanical properties, the defined application for which the nanofibers are intended, and many other aspects must be considered and balanced. Little information can be found regarding the obstacles faced in achieving the desired fiber properties, such as size and diameter properties. During the electrospinning process, many parameters such as the concentration, solvent, viscosity, electrode-to-electrode distance, environmental conditions, and the electrospinning technology used (needle-based, needle-free, etc.) affect the final morphology and mechanical properties of the nanofibers. Obviously, the studies in the field of chitosan-based nanofiber production are supportive, although in order to produce defined nanofibers, experimental studies are required.  
In summary, it can be stated that an optimal solution for defined nanofibers with desired morphology and antibacterial properties is difficult because the variations are manifold. Therefore, a balance should be struck between different aspects to produce optimal nanofibers with defined properties.
## 4 Proposed Multi-Channel Packet Scheduling and Forwarding  
IPT is beneficial to achieve higher throughout especially in one way packet forwarding. In order to fully exploit this feature, we newly introduce a multi-channel packet scheduling, assigning one channel to downlink traffic and other to uplink and downlink traffic. Here we assume two channels and RR-IPT is applied to channel 1 (Ch.1) for downlink traffic forwarding. Generally, uplink traffic is lower than downlink and it is also small enough for the system capacity. This paper proposes the mixed accommodation of up/downlink traffic in channel 2 (Ch.2) for effective channel utilization.  
The core node has packet buffers prepared for respective channels and a threshold is established for Ch.1's buffer. Downlink packets are preferentially assigned to Ch.1 and forwarded via RR-IPT. When the number of packets assigned to the Ch.1 buffer exceeds the predetermined threshold, thereafter newly originated packets are temporarily assigned to Ch.2. After the number of buffered packets for Ch.1 becomes less than the threshold, originated packets are assigned to Ch.1 again. Here assumes the threshold value as 100 packets and the number of downlink packets assigned to the Ch.2 buffer cannot exceed that of Ch.1 so as to avoid imbalance loading. Relay nodes utilize each channel independently; once the packet is assigned to the channel, it is forwarded to the destination node via the same channel (interface). This simple operation can eliminate overheads caused by the channel switching or dynamic CA per relay. This paper investigates following two types of packet forwarding schemes for Ch.2 where uplink and downlink traffic co-exist.
# Multiparameter Fusion Decision Routing Algorithm for Energy-Constrained Wireless Sensor Networks
## 1 Introduction
### Related Work
Then the adaptive flow control of DPCC takes an adapted rate, so that the data stream can adaptively select the path of transmission, which partially alleviates network congestion. In [32], the authors develop a queuing network model to observe the congestion. They used the principle of the hydraulic flow and proposed a Congestion Control-based Optimal Routing algorithm (CCOR). For computing node location and packet service rate, CCOR introduces two functions: link gradient and traffic radius. Finally, the path selection probability is assigned in terms of the link flow. In [33], by designing the pipeline model under the spatial relationship and network load, the Traffic-aware and Energy-efficient Routing (TER) algorithm sends the packet to the sink, at the same time meeting energy efficiency and congestion avoidance as much as possible. In [34], the authors developed an Active Queue Management (AQM) algorithm. The algorithm combines random early detection with a fuzzy proportional-integral controller for congestion detection. If congestion occurs, the fuzzy logic alters the rate at which the node's packets are sent and the length of a buffer queue. Different from the energy routing algorithm, which aims at extending the network lifetime, the congestion control routing algorithm provides a guarantee for the success of data transmission and helps to improve the performance of the data-centric WSN. It can be seen that for the routing design of WSNs, a large number of existing studies only consider one or several combinations of energy, distance, node queue, and other factors. It is undeniable that the algorithms proposed in this literature have indeed achieved some expected goals to some extent, but often, they degrade other aspects of network performance due to lack of consideration of other elements.
## 3 Results and Discussion  
### Volatile Compounds in Non-Smoked Bacon Analyzed via GC-IMS and GC \(\times\) GC-TOFMS  
#### 3.1.1 GC-IMS Topographic Plots in Different Non-Smoked Bacon  
Figure 1 presents the 2D chromatogram and the fingerprints of volatile compounds in three non-smoked bacons. The drift time and retention time were represented by the X-axis and Y-axis in the 2D top view. Most of the signals appeared in the retention time between 200 s and 500 s and the drift time range of 1.0 to 1.5 riprel (reaction ion peak relative drift times). Figure 1a shows the reaction ion peak (RIP) as a red vertical line on the left, and the standardized drift time is 5.5 ms. Each colored point represents a compound, and the various hues are used to signify concentrations--red denoting more intensity, white denoting absence. The software extracted all volatile compounds from the spectra to develop a fingerprint for intuitively assessing the variations between aromas in the three samples. As shown in Figure 1b, 80 volatile compounds were separated and identified via GC-IMS. Detailed information on these volatiles is presented in Table 1. Each aroma compound was characterized by the retention index and drift time. Due to a limited library database, three substances could not be identified via GC-IMS. Therefore, there is still a need for further improvement in the compounds' characterization techniques of this method. The remaining 77 volatile compounds included 12 aldehydes, 16 alcohols, 12 ketones, 16 esters, 3 acids, 5 aromatic compounds, 4 alkanes, and 9 other substances. Some volatile compounds form multiple signals including monomers (M) and dimers (D). The formation of different signals from the same compound might be related to the concentration and half-life of the compounds in the drift tube [10]. These compounds had the same retention index, while the drift times were different. Seven volatiles were detected as monomers and dimers, containing 1-pentanol, 3-methyl-1-butanol, butanol, pentan-2-one, methyl acetate, undecane, and tetrahydrofuran. The peak intensities for about half of the aromas were not significantly different between the three samples. Alcohols and esters displayed a higher content in Yunnan Bacon, such as isoamyl acetate, ethyl acetate, endoborneol, and (Z)-3-nonen-1-ol. Aldehydes and acids were more abundant in Sichuan Bacon, including (E)-2-nonenal, butanal, and acetic acid. Overall, the type and quantity of aroma compounds in Yunnan Bacon were higher than those in the other two samples. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.
# Numerical Coupling between a FEM Code and the FVM Code OpenFOAM Using the MED Library
## 2 The Numerical Platform Environment
### FEM Code: FEMuS  
The computational environment of the numerical platform revolves around the in-house FEMuS multigrid finite element library, a C++ code that exploits various open-source libraries such as PETSc [17] for linear algebra and LibMesh [9] for creating and handling a mesh hierarchy. It contains multiple solvers for incompressible Navier-Stokes equations, convective and conductive heat transfer, turbulence, fluid-structure interaction, and multiphase flow [13]. One of the advantages of the FEMuS code is the easy implementation of new models by directly coding the constitutive equations to the FEM paradigm. This approach streamlines the process of integrating new formulations, increasing the versatility and adaptability of a simulation setup. In this framework, it is possible to efficiently model new physical phenomena such as the complex dynamics of turbulent flows in unconventional fluids such as liquid metals. Interested readers can find additional development of the numerical library in [13; 18; 19; 20; 21].  
In addition to its solving capabilities, for this specific work, the FEMuS library was extended to support the coupling with a MED-compatible C++ interface based on the SALOME platform.
###### Abstract  
A mechanically robust in-tube stainless steel microneedle solid phase microextraction (SPME) platform for dual electrochemical and chromatographic detection has been demonstrated. The SPME microneedle was fabricated by layer-by-layer (LBL) in-tube coating, consisting of carbon nanotube (CNT)/cellulose nanocrystal (CNC) film layered with an electrically conductive polyaniline (PANI) hydrogel layer (PANI@CNT/CNC SPME microneedle (MN)). The PANI@CNT/CNC SPME MN showed effective analysis of caffeine by GC-MS with an LOD of 26 mg/L and excellent precision across the dynamic range. Additionally, the PANI@CNT/CNC SPME MN demonstrated a 67% increase in sensitivity compared to a commercial SPME fiber, while being highly robust for repeated use without loss in performance. For electrochemical detection, the PANI@CNT/CNC SPME MN showed excellent performance for the detection of 3-caffeoylquinic acid (3-CQA). The dynamic range and limits of detection (LOD) for 3-CQA analysis were 75-448 mg/L and 11 mg/L, respectively. The PANI@CNT/CNC SPME MN was demonstrated to accurately determine the caffeine content and 3-CQA in tea samples and dark roast coffee, respectively. The PANI@CNT/CNC SPME MN was used for semiquantitative antioxidant determination and composition analysis in kiwi fruit using electrochemistry and SPME-coupled GC-MS, respectively.  
s +
Footnote †: journal: Sensors  
olid phase microextraction (SPME); dual electrochemical and GC-MS detection; modified stainless steel microneedle (MN)
###### Abstract  
This work focuses on improving the fluorescence intensity of cerium oxide (ceria) nanoparticles (NPs) through added plasmonic nanostructures. Ceria nanoparticles are fluorescent nanostructures which can emit visible fluorescence emissions under violet excitation. Here, we investigated different added plasmonic nanostructures, such as gold nanoparticles (Au NPs) and Cadmium sulfide/selenide quantum dots (CdS/CdSe QDs), to check the enhancement of fluorescence intensity emissions caused by ceria NPs. Different plasmonic resonances of both aforementioned nanostructures have been selected to develop optical coupling with both fluorescence excitation and emission wavelengths of ceria. In addition, different additions whether in-situ or post-synthesis have been investigated. We found that in-situ Au NPs of a 530 nm plasmonic resonance wavelength provides the highest fluorescence emissions of ceria NPs compared to other embedded plasmonic structures. In addition to the optical coupling between plasmonic resonance of Au with the visible emissions fluorescence spectrum of ceria nanoparticles, the 530 nm in-situ Au NPs were found to reduce the bandgap of ceria NPs. We suggest that the formation of more tri-valent cerium ions traps energy levels along with more associated oxygen vacancies, which is responsible for increasing the fluorescence visible emissions intensity caused by ceria. As an application, the gold-ceria NPs is shown to optically detect the varied concentration of iron tiny particles in aqueous medium based on a fluorescence quenching mechanism. This work is promising in different applications such as biomarkers, cancer treatments, and environmental pollution monitoring.  
ceria nanoparticles; gold nanoparticles; quantum dots; fluorescence +
Footnote †: journal: Applied Physics Letters
# Data Chunks Placement Optimization for Hybrid Storage Systems
## 3 The Model
### Optimal Uploading Criteria  
The optimal uploading criteria is formulated by the following steps:  
* The chunks transfer from the public node to the receiver node is represented by a binary matrix \(\mathsf{X}\), where the elements are \(\mathsf{x_{ijg}}\)\(=\{0,1\}\) being transferred from node \(i\) to substorage \(g\) of node \(j\).
* The overall size of the chunks, which were transferred to receiver nodes, should not exceed the storage size. Namely, the data reliability criteria of the QoS standard should not be violated.
# Advanced Noncoherent Detection in Massive MIMO Systems via Digital Beamspace Preprocessing
## 3 Numerical Results  
Numerical simulations are conducted for an \(N_{\text{u}}=3\) user scenario and a BS employing a uniform linear array of \(N_{\text{rx}}=128\) directional patch antennas [15] with inter-element distance \(d_{\text{a}}=\lambda/2\). The FoV of the BS, where the incoming signals can be processed from, is assumed to be \(\Phi=\pi/2\). The users equipped with single omni-directional antennas are located under a given angle \(\phi_{u}\) in the far-field of the BS as illustrated in Figure 3. In the following, users 1 and 3 are positioned at a fixed angle of \(\phi_{1}=-30^{\circ}\) and \(\phi_{3}=30^{\circ}\), respectively.  
A propagation channel is considered, where both LOS and multi-path propagation are taken into account and its respective power ratio is set according to (3). The multi-path components of each user, which are in total \(N_{p}=3\), impinge at the BS having an angular spread with standard deviation \(\sigma_{\text{s}}\) from the nominal angle \(\phi_{u}\). The respective channel matrices from the cluster-based channel model are normalized in such a way that \(||\mathbf{H}||_{\text{F}}^{2}=N_{\text{u}}\). This power control ensures that the total induced sum power at the BS is equal to the number of users and the difference in channel attenuation between the users remain. If the total receive power of the users is equal, the total SNR per user reads \(1/\sigma_{\text{n}}^{2}\), which is used for setting the noise power \(\sigma_{\text{n}}^{2}\) at each antenna branch.  
The performance measure is the symbol error rate (SER), which is evaluated for noncoherent detection using DFDD/nDFE with (and without) digital beamspace preprocessing. To this end, transmit symbols from a quaternary DPSK constellation and a transmission burst of length \(N_{\text{bl}}=201\) are considered. The SER is averaged over \(N_{\text{ch}}=50,\!000\) channel realizations acquired from the adapted COST 2100 channel model.  
The system configuration and the parameterization of both propagation channel and noncoherent detection are summarized in Table 1.  
Figure 3: User and base station (BS) arrangement to evaluate the noncoherent massive MIMO system with digital beamspace preprocessing.
## 4 Results
### AD Definition as a Data Preprocessing Method (Substudy 1)
#### 4.1.1 Outlier Detection
This is mainly due to the completely different mechanism that is employed by this method, where the molecules identified as X-outliers are the most isolated ones from the clusters that are formed in the tSNE 2D space, in contrast to tree-ensembles that are employed by _iForest_ and _RF confidence_. The effects of both X-outliers and Model-outliers on the model performance are further investigated in the following parts. Concerning y-outliers, it is seen in Figure 2(a)-c that, when _RF confidence_ is employed, these are mostly located in the areas with high anomaly scores, outside the cluster with a high density of points. Accordingly, their elimination can be carried out via the elimination of X-outliers in this case. Figure 2(d), where the _RF confidence_ plot is reproduced, coupled with a color scale indicating the enthalpy values of the molecules, seems to confirm that a high percentage of both X- and y-outliers is displayed by molecules of lower enthalpy values. This observation, in conjunction with the distribution of enthalpy values in the dataset (cf. the first article of the series [15]), corroborates the hypothesis of poor representation of these molecules in the dataset (e.g., very large molecules). In this case, the elimination of these points is questionable. In fact, points from poorly represented domains may be useful to a model whose applicability will need to include such molecules. In the case of entropy, the elimination of y-outliers via the one of X-outliers does not seem feasible, as shown in Figure 4, where a non negligible amount of y-outliers is contained in the area with low _RF confidence_ anomaly scores, within the cluster with a high density of points. This could be related to the eventual existence of property cliffs, namely molecules that have similar descriptor values but very different property values, thus posing significant problems for QSPR/QSAR modeling studies, as these rely on the similarity principle [62]. The existence of such issues in the dataset is also evidence of the limitation of the descriptor-based molecular representation approach in these studies. Finally, looking at Figures 3 and 4, it seems that, for _RF confidence_, the regions with the lowest anomaly scores seem to contain less molecules with high absolute standardized residuals, which are located in a less dense region with higher anomaly scores. This effect is particularly visible for entropy. All these elements highlight a possible relation between _RF confidence_ anomaly score and high prediction errors, which could be exploited during ML model deployment to new species to assess the reliability of the predictions. In other words, the aim is to investigate whether a new species with a low _RF confidence_ anomaly score is more likely to provide more reliable model predictions and the opposite. Figure 4: X-outlier detection on the preprocessed data with _RF confidence_ for **entropy**.
## 2 Related Work
### Crosstalk Noise  
Crosstalk noise is an unavoidable characteristic of photonic devices. [PERSON] et al. proposed a method for selecting the optimal crossing angle of the waveguide to reduce the crosstalk [17]. [PERSON] et al. proposed multimode-interference (MMI)-based waveguide crossing instead of plain waveguide crossing for reducing signal power loss [18]. They designed a compact structure to minimize the crosstalk at the device level.  
Despite these efforts, leakage signals that act as crosstalk cannot be ignored due to their accumulation. Especially, crosstalk noise should be considered as network size increases. In a network-level, [PERSON] et al. analysed crosstalk noise and worst-case SNR through a formal approach in a mesh-based ONoC [19]. According to their research, crosstalk noise causes severe performance degradation and limits the scalability of the HONoC architectures.  
Studies on crosstalk noise have been conducted with respect to router architectures, network topologies and traffic patterns [20; 21; 22]. On the other hand, there are few studies regarding crosstalk noise when integrating the SOA. The SOA can significantly reduce insertion loss; however, it can also increase crosstalk noise. In this regard, we analyse the correlation between the SOA, insertion loss and crosstalk noise and then propose a methodology to efficiently compensate insertion loss and minimize crosstalk noise.