esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Sensitivity Displacement Sensor Using Few-Mode Optical Fibers and the Optical Vernier Effect
## 3 Experimental Setup  
A broadband fluorescence light source, operating in the 1500-1600 nm range, was produced using a 3 m erbium-doped fiber (EDF) (LIEKKI Er16-8/125), pumped via a wavelength division multiplexer (WDM) with a 980 nm pigtailed laser diode (LD). Following the EDF, the FMF structures were spliced (Figure 3). The SFS structure was composed of SMF28 and FMF fibers. The FMF employed was the FM SI-2-ULL (step index) from the manufacturer YOFC. The fiber specifications included a core diameter of 16 \(\upmu\)m, a cladding diameter of 125 \(\upmu\)m, and an operating wavelength range of 1450-1700 nm.  
At the outset of the experiment, both MZFIs, composed of FMF, displayed the typical interference patterns associated with the OVE, without requiring calibration. To facilitate  
Figure 3: Experimental setup for the in-cascade MZFIs SFS structure to obtain OVE.  
the experiment, the reference MZFI (\(MZFI_{R\ell}\)) was positioned in a straight configuration, while the sensing MZFI (\(MZFI_{S\ell m}\)) was placed inside a transducer with a calibrated 2 cm opening for curvature. As linear displacement occurred, the transducer gradually closed, inducing curvature in the optical fiber and subsequently causing spectral displacement.  
Five different concatenated MZFI tandems with varying lengths for \(L_{R\ell}\) and \(L_{Sen}\) were examined. The specific lengths of \(L_{R\ell}\) and \(L_{Sen}\), along with their respective FSRs and \(M\) factor, are summarized in Table 1. For each measurement, the Yokogawa AQ6370B optical spectrum analyzer (OSA) was configured with a resolution of 0.05 nm. The sensor fabrication process was as follows: First, the FMF sections were measured using the dimensions specified in Table 1. They were then cut with a high-precision optical fiber cleaver. The cut FMF sections were spliced with a Fujikura FSM-60S splicer between two SMFs. This process was carried out twice, as shown in Figure 3, to obtain the cascaded MZFIs. The \(MZFI_{R\ell}\) was fixedly placed in the work area, while the second \(MZFI_{Sen}\) was introduced into the TPU tubular structure to correlate the linear displacement with respect to the envelope displacement.  
In subsequent tests, three separate experiments were conducted to observe harmonic generation up to the fourth order, using the parameters listed in Table 2 below.
# Machine Learning Applications for Short Reach Optical Communication
## 3 Machine Learning for Short-Reach Optical Communication System Signal Processing
### Other Machine-Learning Based Signal Processing Applications for Short-Reach Communication Systems
#### 3.2.1 Auto-Encoder  
In addition to dealing with the impairments of individual components or sections in optical communication systems, the ML technology can also enable us to construct a communication system by jointly optimizing the transmitter, the receiver and the communication channel in a single end-to-end process. This provides a method to interpret the entire communication system as an auto-encoder [109]. The structure of a fully-connected neural network based auto-encoder is shown in Figure 11.  
The encode and decode sections are the two main parts of the auto-encoder. Encode is used to lower the input dimension, while decode is used to restore the reduced dimension code. The coding process keeps the primary features after dimension reduction, which can effectively reduce the transmission rate and improve the communication system's reliability [67].  
An auto-encoder for the IM/DD systems has been proposed and demonstrated based on the fully connected neural network and the bidirectional LSTM (BiLSTM) channel model [109]. The time domain optical signal characteristics are used, and various factors in the system are incorporated, including the nonlinearity, dispersion, attenuation, and optical-to-electrical square-law conversion. The BiLSTM, which consists of two LSTM  
Figure 11: Implementation of end-to-end system learning using the neural network-based auto-encoder.  
sequences in opposite directions, allows bidirectional information to be captured. The BiLSTM network consists of four layers: the input layer, the BiLSTM layer, the hidden layer (a fully connected layer), and the output layer. The input layer consists of 51 neurons for seven classes of optical signals at different transmission distances from 20 to 80 km. The BiLSTM layer has three sub-layers, with each layer containing 32 neurons (16 in each direction). The loss function for training is based on cross-entropy. Using the time domain data for training, the proposed auto-encoder successfully models the channel with variable fiber transmission distance. Simulation results show that the proposed auto-encoder model can achieve one order-of-magnitude lower BER than the traditional PAM-2 and PAM-4 systems with a simple FFNN equalizer at the fiber length of 80 km.
## 3 Results  
### Initial Developmental Stage  
The Treemap graph in Figure 1 shows the mean value of the eight agronomic traits measured at the 3-4 leaf stage, before salinity stress imposition (NaCl application). In the commercial variety, the hydropriming treatment (T2) improved the mean values of most of the traits compared to no-priming, such as FWS (38%), FWR (11%), DWR (9.5%), SL (10.2%), BIO (6.2%), the hydropriming-biopriming treatment (T4), and the mean values of the traits RL (9%) and R/S (41.3%), while the biopriming treatment (T3) gave the highest mean value of the trait DWS. Also, the plants without any priming (no-priming treatment (T1)) performed well for most of the studied attributes. Statistically significant differences were found, in FWS, between the no-priming (T1) and hydropriming (T2) treatments. Regarding the local variety, the biopriming treatment (T3) improved the values of the following traits compared to no-priming: FWS(6.1%), DWS (7.7%), SL (5.4%) and BIO (9.5%), the hydropriming-biopriming treatment (T4) improved the following traits: FWR (14.5%), DWR (11.9%), root length (RL) and finally the hydropriming treatment (T2) improved the R/S ratio (64.6%). Statistically significant differences were found in DWS between hydropriming (T2) and biopriming (T3) treatments, and in RL between hydropriming (T2) and hydropriming/biopriming (T4) treatments, and between no-priming (T1) with hydropriming/biopriming (T4) treatments. In conclusion, the commercial variety seems to show higher mean values compared to the local variety in most agronomic traits in the early growth stages, mainly utilizing the hydropriming (T2) and hydropriming/biopriming (T4) treatments (Figures S1 and S2).
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 4 Datasets for Dense Video Captioning  
Dense video captioning is a task that involves generating natural language descriptions for multiple events occurring in a video, and it heavily relies on the availability of well-annotated datasets. This section provides an overview of commonly used datasets for dense video captioning, followed by detailed descriptions, characteristics, limitations, and the data preprocessing and augmentation techniques associated with each dataset. Table 1 presents a comparison of key attributes among various datasets, highlighting benchmark ones that have been utilized in recent video description research [1]. Additionally, it summarizes comparisons involving video caption datasets and 3D gaze-related datasets.
# Microbiological and Sensory Quality of Artisanal Sour Cream
## 3 Results and Discussion
### Sensory Analysis of Sour Cream Samples
#### 3.3.6 Flavour Properties  
Figure 5 illustrates the sensory characteristics related to the cream flavour, including the characteristic cream flavour, diacetyl flavour, off-flavours, and flavour persistence.  
A significant variability in flavour attributes was observed. The characteristic cream flavour received scores between 3 and 8, with a median of 5, whereas diacetyl flavour showed a slightly narrower range, from 2 to 6, with a median of 4. These flavours arise from biochemical processes occurring before, during, and after fermentation. Off-flavours were detected in a substantial number of samples, with scores ranging from 0 to 7 and a median of 3. These findings can be linked to poor hygiene practices during milking and production, which is confirmed by the results of the microbiological analysis. Exposure of milk to the barn environment and high yeast concentrations in the samples likely exacerbated the occurrence of off-flavours. Flavour persistence varied between 5 and 8. Aroma compounds in sour cream are product of chemical changes during processing, including lipolysis, glycolysis, and proteolysis [36]. Similar volatile compounds have been identified in other fermented dairy products, indicating comparable flavour profiles [42; 43]. Despite observed variability, the cream samples exhibited distinct characteristic aromas. The relationship between flavour, processing methods, and fat content critically shapes the sensory characteristics of sour cream and, ultimately, consumer preferences. A deep understanding of these dynamics enables manufacturers to better adapt their products to market demand and improve the quality of fermented dairy products.  
Figure 5: The flavour profile of sour cream.
## 1 Introduction
In this method, they considered not only the effects of solar wind and geomagnetic activities but also the magnetic field perturbations caused by plasma depletion, field-aligned currents, etc., which may represent an objective approach to obtain space magnetic field perturbations related to earthquakes. In February 2018, the China Seismo-Electromagnetic Satellite (CSES) was successfully launched, providing a new space platform for seismic ionospheric research and seismic electromagnetic monitoring systems [22; 23]. In this study, we developed a polarization method for satellite data to analyze and study the anomalous characteristics of the ionosphere associated with Ms \(>\) 5.0 earthquakes in Northeast Asia with data retrieved from the high-precision magnetometer (HPM) payload onboard the CSES.
## 3 Numerical Examples  
In this section, three numerical examples are presented to verify the effectiveness of the proposed multiscale design for the Voronoi-GSCS. All the examples were based on a square design domain with \(L=40\), as shown in Figures 5a and 6a,b. In the first benchmark example, a uniform distributed heating with \(T=0.025\) was set in the design domain and \(T=0\) was imposed in the left middle edge of the domain. A gradient distributed heating, \(T=0.01\)-\(0.05\), was set in the design domain and \(T=0\) was imposed in the left middle edge of the domain in the second example. In the third example, a uniform distributed heating, \(T=0.025\), was set in the design domain and \(T=0\) was imposed in the four corners of the domain. The predefined material volume limit was \(0.4\). The lower and upper limit of the relative density was \(0.1\) and \(0.9\), respectively. All the quantities were dimensionless in the examples.  
Figure 5: Results of the uniform distributed heating (I): **(a)** the numerical model; **(b)** the relative density distribution; **(c)** the mesh size function; **(d)** the skeleton of the Voronoi-GSCS; **(e)** the signed distance function of the skeleton; **(f)** the reconstructed Voronoi-GSCS.  
Figure 6: Numerical example model: **(a)** gradient distributed heating; **(b)** uniform distributed heating (II).
###### Abstract  
Watermarking is a technique used to address issues related to the widespread use of the internet, such as copyright protection, tamper localization, and authentication. However, most watermarking approaches negatively affect the quality of the original image. In this research, we propose an optimized image watermarking approach that utilizes the dual-tree complex wavelet transform and particle swarm optimization algorithm. Our approach focuses on maintaining the highest possible quality of the watermarked image by minimizing any noticeable changes. During the embedding phase, we break down the original image using a technique called dual-tree complex wavelet transform (DTCWT) and then use particle swarm optimization (PSO) to choose specific coefficients. We embed the bits of a binary logo into the least significant bits of these selected coefficients, creating the watermarked image. To extract the watermark, we reverse the embedding process by first decomposing both versions of the input image using DTCWT and extracting the same coefficients to retrieve those corresponding bits (watermark). In our experiments, we used a common dataset from watermarking research to demonstrate the functionality against various watermarked copies and peak signal-to-noise ratio (PSNR) and normalized cross-correlation (NCC) metrics. The PSNR is a measure of how well the watermarked image maintains its original quality, and the NCC reflects how accurately the watermark can be extracted. Our method gives mean PSNR and NCC of 80.50% and 92.51%, respectively.  
watermarking; dual-tree complex wavelet transform; particle swarm optimization algorithm; imperceptibility 2020  
Aticle
## 2 Parallelisation of DEM
### GPU Parallelisation of \"Calculate Contact Forces\"
#### 2.2.2 Advanced Scheme  
The advanced scheme can be divided into two phases: neighbour searching and exact contact detection. The neighbour searching phase roughly identifies the potential neighbours of each particle and constructs a potential contact pair list, while the exact contact detection phase accurately calculates the overlaps between particles through naive detection. The efficiency of the contact detection schemes is mainly dependent on the neighbour searching phase by excluding the unconnected particles from the potential contact pair list. Particularly, the computational domain is divided into a grid of cells (Figure 2a), each of which accommodates a number of particles. An individual particle may be in contact with another one sharing a common cell or in neighbouring cells. Then the computational cost of contact detection is proportional to the average number of particles in the neighbour cells \(n_{\text{c}}\). In Algorithm 2, the total number of manipulations is \(O(Nn_{\text{c}})\), which is much less than in the naive algorithm.  
```
1: For i = 0 to N
2: Check Xi
3: Add Particle i into particle list of Cell c
4: End
5: For i = 0 to N
6: Check Xi, then know:
7: Neighbour cells of Particle i
8: For particles in neighbour cells
9: \"Naive\" detection
10: End
11: End
```  
**Algorithm 2** Neighbour cell scheme.  
To evaluate the distribution of the workload across the procedures, a sequential CPU simulation of a triaxial test was performed with a total of 3920 particles (the first scenario in Table 1, which will be detailed later). As the naive algorithm is used, more than 99.6% of the computational effort is allocated to the contact detection operations. With the neighbour cell contact detection scheme adopted, the computational efficiency is promoted around 44 times. The establishment of the contact pair list takes about 11.6% of the total runtime, while the essential contact force calculation accounts for 69.2%. The \"Update kinematic state of particles\" operations occupy 19.2% of the total computational efforts. Trivial runtime is allocated to other manipulations, such as the movement of wall and save old state variables.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Number of** \\ **Particles** \\ \end{tabular} } & \multirow{2}{*}{\(D/d_{\text{50}}\)} & \multirow{2}{*}{
\begin{tabular}{c} **CPU Sequential** \\ **(10 Threads)** \\ \end{tabular} } & \multicolumn{2}{c}{**CPU Parallel**} & \multicolumn{2}{c}{**GPU Parallel**} \\  & & & **(10 Threads)** & & **(Double-Precision)** \\ \hline \multicolumn{2}{c}{**Runtime (s)**} & \multicolumn{1}{c}{**Runtime (s)**} & \multicolumn{1}{c}{**Speedup**} & \multicolumn{1}{c}{**Runtime (s)**} & \multicolumn{1}{c}{**Speedup**} \\ \hline
3920 & 25 & 618 & 144 & 4.3 & 19 & 33 \\ \hline
7840 & 31.5 & 2248 & 500 & 4.5 & 49 & 46 \\ \hline
15,680 & 40 & 6072 & 1265 & 4.8 & 76 & 80 \\ \hline
31,360 & 50 & 13,047 & 2558 & 5.1 & 150 & 87 \\ \hline
100,880 & 72 & 40,37
# Bayesian Model Infers Drug Repurposing Candidates for Treatment of COVID-19
## 4 Discussion
CHEK1 activation is specifically controlled by ATR through phosphorylation, forming the ATR-CHEK1 pathway. This pathway is involved in a broad spectrum of DNA abnormalities including inhibition of DNA replication, UV-induced DNA damage, interstrand DNA crosslinking and virus infection [38]. The inferred association between the viral titer and the expression level of CHEK1 in the host cell suggests that SARS-CoV-2 could use the ATR-CHEK1 pathway for its replication. Indeed, several studies have reported that activation of the ATR-CHEK1 pathway occurs during viral DNA synthesis in various viruses including coronavirus [39], hepatitis B virus [40], human papillomavirus [41], parvovirus B19 [42]. Furthermore, utilization of CHEK1 inhibitors has been successfully used to reduce viral replication. For example, the CHEK1 inhibitor MK-8776 reduced viral DNA amplification by 90-99% in HPV infected cells [43]. Similarly, the CHEK1 inhibitor UCN-01 has been reported to decrease hepatitis B virus DNA yield of HL7/02 cells by 80 to 90%, without significantly affecting cell survival [40]. The identification of a relationship between CHEK1 expression and a high SARS-CoV-2 titer associated with the literature on CHEK1 inhibitors and viral replication, supports the investigation of these molecules as a potential therapeutic modality for the treatment of COVID-19. Due to the known functions of CHEK1 in cancer through the regulation of the DNA damage response and cell cycle checkpoints, numerous CHEK1 or CHEK1/CHEK2 inhibitors have been developed over the past two decades or are currently in clinical development, mainly for oncology applications. Evaluating the repurposing of these drugs for the treatment of COVID-19 presents an opportunity to rapidly bring therapeutic solutions to patients currently affected by this disease. The drug signature for diclofenac was identified influencing SARS-CoV-2 viral titer to both CHEK1 and MAOB. Diclofenac is used for pain treatment and belongs to a class of non-steroidal anti-inflammatory drugs (NSAIDs). The ability of thiazoltiones and ibuprofen to increase ACE2 expression was reported as a potential susceptibility factor for SARS-CoV-2 infection in patients with diabetes and hypertension. However, the World Health Organization (WHO) and the European Medicines Agency (EMA) provided follow up guidance on the lack of scientific evidence supporting role NSAIDs such as ibuprofen in SARS-CoV-2 infection. The identification of diclofenac drug signature and the association of CHEK1 and MAO-B with the viral titer network provide additional insights into an indirect influence of these agents in the viral infectivity. The targets identified in this study supporting underlying mechanism(s) of SARS-CoV-2 infectivity are consistent with targets/therapeutic profiles identified using physical protein interaction map [44].
## 6 Conclusions  
To address the challenges associated with the limited labeled training samples and high computational complexity for HSI classification, this study proposes an end-to-end dual-branch network structure, HDCDF. Instead of full 3D convolutions, we use mixed 2D and 3D depth-wise separable convolutions, offering computational efficiency. The input of the HDCDF framework is achieved after a series of data preprocessing tasks, including the PCA strategy to reduce dimensionality and rotation with equal probability to increase the number of trainable samples. Based on hybrid depth-wise separable convolutions, 2D-DCAttention module, and hybrid residual blocks, we design two branches to simultaneously capture joint spectral-spatial features and introduce some strategies to prevent overfitting involving early stopping and a dropout layer. To accelerate the counter-propagation and convergence, Mish is introduced as the activation function, taking full advantage of the spectral-spatial correlation of HSI. Experimental results demonstrate the validity and generalization of the HDCDF. In detail, the overall accuracy of the three widely used datasets is as high as 95.73%, 96.5%, and 97.84%, respectively, in cases with limited training samples.  
A prospective avenue for our research involves extending the framework's application to forthcoming hyperspectral datasets, beyond just the existing public datasets. The main focus is still on creating models that can make the most out of a small amount of training data and maintain lower computational costs.  
Conceptualization, H.D. and Q.L.; methodology, Y.Y. and H.D.; software, Y.Y.; validation, H.D.; resources, H.D. and Q.L.; data curation, Y.Y.; writing--original draft, Y.Y.; writing--review and editing, Y.Y. and H.D.; visualization, Y.Y.; supervision, H.D. and Q.L.; funding acquisition, H.D and Q.L. All authors have read and agreed to the published version of the manuscript.  
Figure 19: Results of different activation functions.  
**Funding:** This research was funded by the Tianjin Science and Technology Planning Project, grant number 23 YDTPJC00350.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: The IP, UP, and SV datasets can be obtained from www.ehu.eus/ ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes (accessed on 23 December 2024).  
**Conflicts of Interest**: The authors declare no conflicts of interest.
## 2 Related Work
They used the elliptic curve cryptography technique and the enhanced LEACH protocol under the parameters of dead nodes, network lifespan, and energy consumption. [PERSON] et al. [41] proposed the lightweight encryption and OBT-based authentication technique (LEOBAT) based on lightweight cryptography to authenticate the required device in the IoT network using a unique ID. By doing so, the IoT network can be secured from various attacks. This technique is also compared with existing techniques, such as DES and blowfish, to claim efficient and fast authentication. [PERSON] et al. [42] introduced the intrusion detection model to secure the communication of IoT networks against different sinkhole attacks. The proposed model provided exceptional results, with a detection rate of 95%. [PERSON] et al. [43], considering the end-to-end route, developed a cluster-based algorithm for wireless mesh networks connected with the IoT to reduce the end-to-end delay and enhance the quality of the internet. [PERSON] et al. [44]proposed a mutual authentication and key agreement scheme to increase the security of the smart manufacturing industry against different attacks. The proposed scheme uses bitwise XOR operations with physical unclonable function (PUF), along with the elliptic curve cryptography (ECC) and cryptographic hash functions. Their work showed better results compared to the state-of-the-art schemes. [PERSON] et al. [45] proposed a scheme that addresses the opacity problem of distributed estimation. The scheme employs the final-state opaque (FSO) technique to find opacity-enhancing distributed estimation while maintaining high accuracy. Moreover, it proposed consensus-based distributed estimation algorithms based on the bias-adding and state decomposition methods to provide the FSO in the presence of crafty intruders and malicious nodes, respectively. [PERSON] et al. [46] devised a distributed optimization algorithm using homomorphic encryption. In this algorithm, two parties compute the objective value without showing their respective data. The algorithm also highlights the problem of input and output inference for a class of quadratic joint functions. So far, different studies have been performed to enhance the efficiency and security of IoT-enabled systems by utilizing appropriate techniques to tackle different network layer attacks. Table 1 presents a comparison of different eminent-existing related works.