esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
We designed a structure of dual-coupled ridge waveguide in thin-film lithium-niobate-on-insulator (LNOI) and numerically studied the highly efficient, broadband, and flattened dispersive wave-enhanced supercontinuum generation in the mid-infrared region. By leveraging the mode coupling of the proposed dual-coupled waveguide structure, one of the supermodes, namely the anti-symmetric mode, can produce additional zero-dispersion wavelengths in the mid-infrared region, and consequently multiple normal dispersion regions for dispersive wave emission. Given the rich geometrical degrees of freedom powered by this dual-coupled LNOI waveguide structure, we can tailor the dispersion profile so that a well-established mode-locked fiber laser in the telecommunication band can serve as the pump. Thus, the whole system can potentially be fiber-to-chip integrated and packaged, enabling a compact, cost-effective, and low system-complexity platform. We numerically show that the broadband dispersive wave covering the wavelength range of 1.92-3.55 um (\(-\)20 dB level, near octave-spanning) with spectral flatness of 6.31 dB can be achieved using a 1550 nm, 190 p femtosecond pump seed. When the dual hump-shaped spectrum is obtained, the conversion efficiency of the mid-infrared dispersive wave can be up to 19.31%. The influence of the pumping conditions on the performance of mid-infrared dispersive wave generation was also studied. This work provides a competitive candidate for efficient, broadband, and flattened mid-infrared spectrum generation, which can find important applications in spectroscopy, metrology, and communication.  
m _Applied_  
Services
## 3 Underwater Routing Protocols
### Dbr-mac  
In the DBR-MAC protocol [63], low depth nodes that suffer from high data load are prioritized to access the channel. The angle, depth and overhead of the neighbor nodes are considered for the low depth nodes to make them access the channel with greater preference than the rest of the nodes. This leads to improvement in energy consumption, throughput and end-to-end delay. However, prioritizing the low depth nodes overburdens them, and they die in a rapid fashion. This creates energy holes in the network, which leads to packet loss in the subsequent operation time of the network.
# EEG Data Augmentation for Emotion Recognition with a Task-Driven GAN
## 3 The Proposed Method
### EEG Data Augmentation for Emotion Recognition  
#### 3.2.1 Structure for Multi-Channel EEG Feature Map  
As mentioned in Section 2.1, in this paper, we extract the two-dimensional feature map based on the spatial distribution of EEG channels and design the data augmentation network accordingly. In view of the superior performance of the EEG differential entropy features in the EEG-based emotion recognition tasks, we cited the preprocessing and feature extraction methods in [16] for EEG data to obtain the input of the proposed method. Firstly, the original EEG signal is decomposed into four frequency bands. Next, the differential  
Figure 1: The basic structure of GAN.  
entropy feature of EEG data from each frequency band is extracted and arranged into a one-dimensional vector. The feature vector is then mapped into a two-dimensional feature matrix according to the spatial distribution of EEG channels in the process of EEG acquisition. Finally, a feature cube of 4 \(\times\) 9 \(\times\) 9 is formed by stacking the two-dimensional feature maps of four frequency bands.  
For such a structure, we designed a generative adversarial network with classifier-assisted training. Table 1 shows the specific structures of the generator, discriminator, and classifier.
# Flexible Antennas for a Sub-6 GHz 5G Band:
## 4 Flexible Antennas for a Sub-6 GHz 5G Band
### Flexible Array Antennas for a Sub-6 GHz 5G Band
#### 4.2.2 Metamaterial-Based Flexible Antenna  
A flexible antenna array that is wideband and has a unidirectional radiation pattern is utilized in many medical applications in which the signal penetration can be focused on the targeted diagnosis region. One such wideband flexible array is proposed in [65] for wearable head imaging systems. The antenna comprises a meander line, a thick copper ground plane with 50 \(\Omega\) coaxial feed. The grounding of the cascaded meander line with the help of via introduces an additional inductance with which an enhanced bandwidth to 155% covering 0.45-3.6 GHz is achieved. A 16-element array is constructed to test to validate the results of the EM head imaging system. To investigate the bending performance, the antenna was bent using a multi-tissue model along different radii of r = 70, 60, 50, 40, 30, and 20 mm. It is observed that at the r = 70 mm, the array shows a similar antenna performance as that of a planar case. The radiation pattern over lower frequencies of 0.5 and 1 GHz shows a more unidirectional kind of pattern with a half power beam width of approximately 90\({}^{\circ}\). The SAR value for the operational bandwidth of 0.45 to 3.6 GHz is 0.2 W/kg for 10 g tissue which is under IEEE defined limit.  
Figure 13: Slot based array antenna for sub-6 GHz 5G: (**a**) Antenna under testing; (**b**) S-parameter; (**c**) Radiation pattern along the E-plane and (**d**) H-plane. Reprinted from ref. [63].
# Activation Process of ONU in EPON/GPON/XG-PON/NG-PON2 Networks
## 5 Simulation Results
### Epon
Furthermore, our solution did not require a new PLOAM message or TC layer changes. Figure 8: Graph of activation time for XG-PON (**left**) and NG-PON2 (**right**) 32/64 units and 20-km ODN. Figure 9: Graph of activation time for XG-PON and NG-PON2 with a 1:128 split ratio and 20-km length of ODN (**left**) and XG-PON and NG-PON2 with a 1:256 split ratio and a 20-km length of ODN (**right**). Our optimization was based on current PLOAM messages and transmission convergence layer. There were no changes required. Visualization of the optimized GPON activation process is shown in Figure 11.
## 4 Methodology
### Proposed Hybrid Framework
#### 4.3.2 LSTM-Decoder  
The spatial feature maps generated by the MC-CNN-Encoder are temporally dependent on previous time steps. An LSTM-Decoder is designed to represent those temporal dependencies. It  
Figure 3: Overall architecture of the proposed hybrid deep learning framework. It has two main components: A multi-channel convolutional encoder (MC-CNN-Encoder) for spatial encoding and an LSTM-based decoder for temporal decoding.  
decodes the MC-CNN-Encoder output and reconstructs the sequence data using hidden states (feature maps). It is composed of an LSTM block and a fully connected neural network (FCNN) layer. The LSTM block comprises of two LSTM layers followed by a dropout layer. The output of the LSTM block is fed to the FCNN layer, where the output is mapped into the feature set we want to reconstruct.  
During the process of anomaly detection, the MC-CNN-Encoder learns a three-dimensional vector representation of the multivariate spatio-temporal input data. Then, the LSTM-Decoder uses the reshaped representation to extract temporal features and reconstruct the input data. The LSTM-block is trained to learn higher level temporal features of the input data, whereas the FCNN layer is trained to reconstruct the target univariate time series. In the LSTM block, two hidden layers are used with 64 and 16 hidden units each and followed by a dropout layer to prevent overfitting.  
For the final dense (FCNN) layer, the number of units is set to \(d^{\prime}\) and it equals to the number of univariate time series (features) that we want to reconstruct. The number of hidden units in the dense layer can be adjusted according to the problem context at hand. For an anomaly detection problem, we are only interested with the reconstruction of a subset of original spatio-temporal multivariate dataset and not the fully reconstructed version of it. The overall framework is trained to produce the target multivariate time series \(X=\{x_{1},\ \ldots,\ x_{T^{\prime}}\}\) of length \(T^{\prime}\) which is the size of the reconstruction window. The length of \(T^{\prime}\) can be equal to or smaller than the input window size \(T\) and should be tuned for each problem. Each sequence \(x_{i}\in\mathbb{R}^{d^{\prime}}\) is an \(d^{\prime}\)-dimensional vector where \(d^{\prime}\leq\ d\). The detailed architecture of the MC-CNN-Encoder and LSTM-Decoder are illustrated in Figure 4.  
Figure 4: The detailed architecture of the hybrid autoencoder framework: It has a Convolutional Neural Network (CNN)-based spatial encoder and a Long Short-Term Memory (LSTM)-Fully Connected Neural Network (FCNN)-based temporal decoder.
###### Abstract  
Despite many advantages, digital impressions, when compared to the conventional one, produce contrasting results and their complete substitution is still under debate. This comparative study aims to test a way to perform a clinical evaluation of digital impression Accuracy and Trueness with a gypsum check. After calculating the Trueness, Precision, and Accuracy of the digital impressions, a gypsum check was fabricated and screwed on implant abutments. The impression was not considered reliable if the gypsum check fractured during the insertion. The gypsum check test was correlated to a cut-off of 100 \(\upmu\)m Trueness. Mean Trueness was 151.19 \(\pm\) 37.23 \(\upmu\)m of the first optical impression and 125.47 \(\pm\) 41.90 \(\upmu\)m of the second optical impression. The Precision mean was 39.76 \(\pm\) 10.89 \(\upmu\)m. The mean Accuracy percentage was 98.69 \(\pm\) 0.29%. The gypsum checks fractured 10 times on 42 tests, and in any case, the Trueness value was above the 100 \(\upmu\)m cut off, with a \(p\) = 0.001. A gypsum check screwed onto an implant abutment could be considered a way to perform clinical measurement of Trueness, allowing the clinician to understand if the Trueness value is higher or lower than 100 \(\upmu\)m and reflecting the reliability of digital impressions.  
accuracy; intraoral scan; Trueness; digital impression; gypsum check; passive fit +
Footnote †: journal: Applied Sciences
# Beyond 5G Fronthaul Based on FSO Using Spread Spectrum Codes and Graphene Modulators
###### Abstract  
High data rate coverage, security, and energy efficiency will play a key role in the continued performance scaling of next-generation mobile systems. Dense, small mobile cells based on a novel network architecture are part of the answer. Motivated by the recent mounting interest in free-space optical (FSO) technologies, this paper addresses a novel mobile fronthaul network architecture based on FSO, spread spectrum codes, and graphene modulators for the creation of dense small cells. The network uses an energy-efficient graphene modulator to send data bits to be coded with spread codes for achieving higher security before their transmission to remote units via high-speed FSO transmitters. Analytical results show the new fronthaul mobile network can accommodate up to 32 remote antennas under error-free transmissions with forward error correction. Furthermore, the modulator is optimized to provide maximum efficiency in terms of energy consumption per bit. The optimization procedure is carried out by optimizing both the amount of graphene used on the ring resonator and the modulator's design. The optimized graphene modulator is used in the new fronthaul network and requires as low as 4.6 fJ/bit while enabling high-speed performance up to 42.6 GHz and remarkably using one-quarter of graphene only.  
fronthaul; FSO; 5G; 6G; graphene; modulator; mobile; security; energy; optics +
Footnote †: journal: Sensors
## 2 System Model  
We consider an uplink multiuser MIMO system, where a BS equipped with \(M\) antennas serves \(K\) users with a single antenna. The \(M\times 1\) received signal vector is expressed as  
\[\mathbf{y}=\sqrt{p_{u}}\mathbf{H}\mathbf{x}+\mathbf{n}.\]  
\(\mathbf{H}=\left[\mathbf{h}_{1},\cdots,\mathbf{h}_{K}\right]\) is the \(M\times K\) aggregated channel matrix, where the \(k\)th column \(\mathbf{h}_{k}\sim\mathcal{CN}\left(\mathbf{0},\mathbf{I}_{M}\right)\) represents the channel vector between the BS and user \(k\). \(\mathbf{x}=\left[x_{1},\cdots,x_{K}\right]^{T}\) is the \(K\times 1\) signal vector, where \(x_{k}\) is the transmit symbol for user \(k\) with \(\mathbb{E}\{\left|x_{k}\right|^{2}\}=1\). \(\mathbf{n}\sim\mathcal{CN}\left(\mathbf{0},\mathbf{I}_{M}\right)\) is the \(M\times 1\) additive white Gaussian noise vector with unit variance, and \(p_{u}\) is the uplink power. Using a linear receiver, the BS can obtain \(K\) streams for \(K\) users from the received signal \(\mathbf{y}\). Let \(\mathbf{G}\) be the \(M\times K\) matrix for the linear receiver. We consider two linear receivers, MF and ZF, i.e.,  
\[\mathbf{G}=\left\{\begin{array}{rl}\mathbf{H}&\text{for MF receiver}\\ \mathbf{H}\left(\mathbf{H}^{H}\mathbf{H}\right)^{-1}&\text{for ZF receiver.} \end{array}\right. \tag{1}\]  
Therefore, using the linear receiver, the \(k\)th stream for user \(k\) is expressed as:  
\[r_{k} =\mathbf{g}_{k}^{H}\mathbf{y}\] \[=\sqrt{p_{u}}\mathbf{g}_{k}^{H}\mathbf{h}_{k}x_{k}+\sqrt{p_{u}} \sum\
olimits_{j\
eq k}\mathbf{g}_{k}^{H}\mathbf{h}_{j}x_{j}+\mathbf{g}_{k}^{ H}\mathbf{n},\]  
where \(\mathbf{g}_{k}\) is the \(k\)th column of \(\mathbf{G}\), which represents the receiver vector for the \(k\)th user and \(\mathbf{h}_{j}\) is the channel between the BS and the \(j\)th user. Therefore, the received SINR for user \(k\) is expressed as:  
\[\gamma_{k}=\frac{p_{u}\left|\mathbf{w}_{k}^{H}\mathbf{h}_{k}\right|^{2}}{p_{u }\sum\
olimits_{j\
eq k}\left|\mathbf{w}_{k}^{H}\mathbf{h}_{j}\right|^{2}+1}, \tag{2}\]  
where \(\mathbf{w}_{k}=\mathbf{g}_{k}/\left\|\mathbf{g}_{k}\right\|\) is the normalized receive vector.
## High-Throughput Mass Spectrometry Applied to Structural Genomics  
**[PERSON]**\({}^{\dagger}\)**, **[PERSON]**\({}^{\dagger}\)**, **[PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]**\({}^{\star}\)  
Structural Genomics Consortium, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Oxford OX3 7 DQ, UK  
\({}^{\dagger}\) These authors contributed equally to this work.  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: nicola.burgess-brown(@sgc.ox.ac.uk; Tel.: +44-1865-617750; Fax: +44-1865-617575.  
External Editor: [PERSON]  
_Received: 1 July 2014; in revised form: 3 September 2014 / Accepted: 11 September 2014 / Published: 9 October 2014_  
**Abstract:** Mass spectrometry (MS) remains under-utilized for the analysis of expressed proteins because it is inaccessible to the non-specialist, and sample-turnaround from service labs is slow. Here, we describe 3.5 min Liquid-Chromotography (LC)-MS and 16 min LC-MSMS methods which are tailored to validation and characterization of recombinant proteins in a high throughput structural biology pipeline. We illustrate the type and scope of MS data typically obtained from a 96-well expression and purification test for both soluble and integral membrane proteins (IMPs), and describe their utility in the selection of constructs for scale-up structural work, leading to cost and efficiency savings. We propose that value of MS data lies in how quickly it becomes available and that this can fundamentally change the way in which it is used.  
**Keywords:** high-throughput; mass spectrometry; protein expression; structural genomics; integral membrane protein (IMP); post-translational modification (PTM); intact mass
## 2 Literature Review
### Cybersecurity Risks of SCADA  
Cybersecurity remains a key concern as SCADA systems adopt new capabilities. Recent research has discussed approaches like platform diversity to reduce the cyber risks of Internet-connected systems [18]. Studies have also evaluated tailored encryption schemes and adaptive authentication methods for resource-constrained SCADA [19]. As attacks grow more sophisticated, research continuously works to enhance the resiliency of modernized SCADA infrastructure against both current and emerging threats.  
SCADA connectivity to corporate environments also introduces cyber risks if not protected. Most legacy SCADA devices lack modern safeguards, leaving them vulnerable to attacks that could manipulate, disable, or destroy physical assets and processes. Potential attack pathways include HMIs, data historians, communication channels, programmable logic controllers (PLCs), remote terminal units (RTUs), input/output (I/O) servers, and sensors. By exploiting these components, unauthorized users could disrupt operations through intentional faults or failures [20].  
Documented attacks directly targeting SCADA systems include Stuxnet, Havex, Black-Energy2, CRASHOVERIDE, and TRISIS/TRITON [21]. Their goals ranged from surveillance and data theft to process disruption to equipment destruction. For example, Stuxnet specifically targeted uranium enrichment centrifuges in Iran and manipulated their operations to cause mechanical failures over time. These incidents highlight the serious cyber-physical risks that industrial operators face as SCADA environments become more interconnected with IT systems and remote access.
# The Effect of Land Cover on the Nectar Collection by Honeybee Colonies in Urban and Rural Areas
## 3 Results  
### Effect of Location and Type of Environment on Bee Foraging  
The annual nectar gains, hive weight losses, and the number of forage days in the locations under study are presented in Figures 1 and 2. The highest annual nectar gain (127.5 kg) was recorded at the urban apiary in Warsaw, while the lowest (33.9 kg) was observed at the rural site near Cracow. The total hive weight loss, resulting from the consumption of stores and nectar evaporation, was negative in all locations, both urban and rural. The greatest losses were recorded in Rural Tychy (\(-25.0\) kg) and Warsaw (rural: \(-20.9\) kg; urban: \(-20.6\) kg).  
Despite considerable variability between individual apiaries, the average annual nectar gains were similar in urban and rural environments. The mean annual hive weight gain in urban apiaries was 72.9 kg, compared to 64.5 kg in rural sites; however, this difference was not statistically significant (t = 0.455, df = 8, \(p\) = 0.66) (Table 2). Furthermore, annual hive weight losses did not differ significantly between environments, averaging 18.5 kg in urban areas and 17.7 kg in rural areas (t = 0.332, df = 8, \(p\) = 0.74). A significant difference was found in the number of forage days, defined as days with positive hive weight gain. Urban apiaries experienced a significantly longer active foraging period, with a mean of 67.4 days per season, compared to only 55.9 days in rural apiaries (difference = 11.5 days; t = \(-2.665\), df = 8, N1 = 5, N2 = 5, \(p\) = 0.02) (Table 2). This statistically significant disparity suggests that bees in urban environments had access to forage resources over a longer period than those in agricultural landscapes.  
Figure 2: The annual nectar gain, hive weight loss, and number of foraging days were measured across a range of urban and rural apiary locations.  
Kendall's tau correlation coefficient between nectar gain (measured in this study using a hive scale assigned to a single colony) and the average honey yield per colony (\(n=20\) pairs; data from two years) was 0.53, which was statistically significant. In turn, the correlation between nectar gain and honey yield of the same individually monitored colony was substantially higher--0.86--and statistically significant. The honey yield data were obtained from previous research [15].
## 5 Conclusions  
This paper presents a novel coordinated control strategy to address the deterioration of vehicle smoothness and drivability during the clutch slipping process. The strategy was combined with the motor control and engine control, taking both torque disturbances and model uncertainty into account. Based on the GP-SMC method, a suitable controller for the motor was designed to reduce torque interruption. The dynamic differential equations of the vehicle were constructed by means of the sliding surface with the Lagrange function, and solutions with an accuracy of \(O\) (\(h^{4}\)) were obtained by using the Runge-Kutta method. Meanwhile, an adaptive PI controller was constructed to adjust the engine speed to reduce the speed difference of the clutch during the slipping process. To verify the effectiveness of the proposed coordinated control strategy, a control model was formulated in the MATLAB/Simulink environment, and HIL simulations were carried out. The results demonstrate that the expected smoothness and drivability of a parallel HEV could be obtained. The vehicle jerk under the proposed coordinated control was limited to 4 m/s\({}^{3}\), while the value without coordinated control was 18.81 m/s\({}^{3}\). The clutch frictional losses were also effectively reduced, which will extend the clutch lifespan. Although the complexity of the proposed algorithm increased, it was still acceptable. Hence, the proposed coordinated control strategy helps to improve the performance of parallel HEVs and promote their application in the automotive industry. For future work, the system delay and parameter sensitivity studies will be involved to improve the robustness and performance of the coordinated control strategy. Conceptualization, S.X. and X.T. ; methodology, C.W. and Y.Q. ; validation, X.L., J.Z. and T.H. ; writing--original draft preparation, S.X., X.T. and X.S. ; supervision, X.T. and X.S. All authors have read and agreed to the published version of the manuscript. This work was supported by the Foundation for the Jiangsu Key Laboratory of Traffic and Transportation Security (No. TTS2021-02), the sponsored project of Jiangsu province Six Talent Peaks (grant no. XNYQC-002) and the Key R & D projects of Huai'an (grant no. HAG201912). Figure 13: Vehicle jerks for the three cases. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Control Strategy** & **Clutch Frictional Loss (kJ)** & **Maximum Vehicle Jerk (m/s\({}^{3}\))** & **Maximum Vehicle Velocity Error (\%)** \\ \hline Case I & 6.074 & 18.81 & 7.37 \\ Case II & 4.983 & 8.65 & 3.95 \\ Case III & 1.897 & 4.03 & 1.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison for the three cases. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 2 Evolution of C-RAN Architecture, and Its Types, Advantages, and Challenges
### Challenges in C-RAN
#### 2.6.2 Fog RAN  
F-RAN is a promising paradigm that enhances the C-RAN architecture by allowing the RRHs to be equipped with local caches for storing frequently requested contents and signal processing capabilities. F-RAN takes the benefit of both fog computing and C-RAN, which extends the traditional cloud computing to the network edge. The main idea is to ensure that all traffic is not offloaded directly from the centralized cloud server, while some local traffic should be delivered from the caching of adjacent RRHs or smart UEs [35]. This collaborative processing minimizes the overload of the constrained fronthaul links as well as alleviates the queuing and transmission delay. The optimal design of an F-RAN requires edge caching, fronthaul, and wireless transmission to be jointly optimized to leverage the edge processing and virtualization. The authors in [38] presented an information-theoretic model for F-RAN, aiming at characterizing the key tradeoff between the system performance in terms of delivery latency and resources available for fronthaul, caching, and wireless transmission. In the F-RAN architecture, UEs access F-RAN adaptively, and the transmission mode is selected based on UEs movement speed, distance, location, QoS requirements, processing and caching capabilities. More details on four different transmission modes in F-RAN and also the comparison on the characteristics of the three promising architectures (C-RAN, H-CRAN, and F-RAN) are described in [35].