esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Components Determination and Performance Evaluation of LS Pre-Maintenance Agent
## 3 Optimum Composition Ratios
### The Composition Ratio Based on Quick-Drying Test  
#### 3.2.1 The Proportion of Rock Asphalt and Reducing Agent  
Considering that the penetrant is expensive and produces a slightly irritating odor when it is heated, its hydrolysis reaction with water will affect road beauty, thus its dosage must be controlled. The initial penetrant proportion must be set as 20% of the four components. The mixtures were made according to different ratios and brushed on the road and penetration condition was observed. The results showed that when the rock asphalt and reducing agent mass was above 60%, the solution viscosity was too high. As shown in Figure 3, a thin film formed on the pavement, which hindered the penetration and volatilization process. The film edge tilted and fell off after solidifying, which means a poor maintenance effect and material waste. When the mass was less than 30% the solvent oil dosage was too high, resulting in an excessive volatilization rate that had a negative effect upon maintenance. In summary, the optimum ratio of the rock asphalt and reducing agent should be 30-60%.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Components** & \multicolumn{8}{c}{**Proportions**} \\ \hline Rock asphalt/g & 50 & 30 & 20 & 20 & 10 & 10 & 10 \\ Penetrant/g & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ Solvent oil/g & \textless{}50 & 50 & 100 & \textgreater{}100 & 100 & 200 & \textgreater{}200 \\ Precipitation/YES/NO & Yes & Yes & No & Yes & No & No & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Proportions and solubility results.  
Figure 3: Thin film on the pavement.
# Assessing the Impact of Varied Dark Chocolate Concentrations on Enamel and Dentine Microhardness
## 3 Results
### Dentin Microhardness  
Figure 3 presents a bar chart illustrating the average mean microhardness values for dentine, both initially and after immersing specimens in various concentrations of dark chocolate. The study groups consist of G1 (the control group) exposed to artificial saliva, which demonstrated a mean difference ranging from 51.31 to 60.95. In contrast, G2 (70% dark chocolate), G3 (85% dark chocolate), and G4 (100% dark chocolate) exhibited average mean differences ranging from 51.79 to 109.70, 51.81 to 124.10, and 51.53 to 140.20, respectively.  
In Table 2, the mean and standard deviation values, along with the standard deviation difference and the difference of mean denoted as DF, are presented. The results reveal a notable and gradual increase in the average microhardness values for dentine. This increase progressed from 57.91 \(\pm\) 8.332 to 72.29 \(\pm\) 2.752, ultimately reaching 88.67 \(\pm\) 8.602, corresponding to the escalating concentrations of dark chocolate (70%, 85%, and 100%). Significant and discernible differences were observed between the initial and final phases within each group, with a \(p\)-value \(<\) 0.05 indicating statistical significance.
## 7 Overview of Deep Learning in 5G Security
### Deep Learning Applications in 5G Security
#### 7.3.3 Deep Learning Models for Security Prediction  
One of the foremost challenges in maintaining the security of 5G networks lies in the ability to anticipate and respond to potential threats before they can cause significant harm. This requires the development of predictive models that can analyze past and present network data to identify potential future security issues. Deep learning, with its inherent capabilities in handling vast volumes of complex data, provides an efficient means to build such predictive models. Deep learning algorithms like LSTM and GRU, both variations of RNNs, have demonstrated significant promise in this context [127]. By virtue of their design, these algorithms can process temporal sequences of data characteristics, which is especially valuable when dealing with network data that evolve over time. By training these deep learning models on historical network data, including instances of past security breaches, it is possible to create a predictive model that can foresee potential security risks. These predictive models can detect early signs of unusual network behavior, helping security teams to address threats before they escalate into major security incidents proactively. For instance, LSTM and GRU can be trained to detect patterns indicative of DDoS attacks, one of the most common and damaging forms of cyberattack [151]. By identifying these patterns early, it is possible to take preventive measures and minimize the potential harm to the 5G network.
# Performance Evaluation of CF-MMIMO Wireless Systems Using Dynamic Mode Decomposition
## 5 Downlink Transmission
### Non-Coherent Downlink Transmission  
This section explores the concept of non-coherent joint transmission in downlink CF-MIMO systems. This method enables each Access Point (AP) to transmit distinct data symbols to each User Equipment (UE), thereby reducing the phase synchronization requirements compared to coherent transmission. Utilizing Maximum Ratio precoding, the signal transmitted by AP \(l\) is expressed as  
\[x_{l}[n]=\sqrt{p_{d}}\sum_{i=1}^{K}\hat{h}_{il}[\lambda]\sqrt{\mu_{il}}q_{il}[ n], \tag{100}\]  
The signal-to-interference-plus-noise ratio (SINR) received by UE \(k\) at time \(n\) is detailed in Equation (101), where  
\[\text{SINR}_{m,k}[n]=\frac{\rho_{k}^{2}\sum_{l=1}^{L}\mu_{kl}| \text{tr}(Q_{kl})|^{2}}{p_{d}\sum_{i=1}^{L}\sum_{j=1}^{L}\mu_{il}\text{tr}(Q_{ il}R_{kl})+\rho_{k}^{2}p_{d}\sum_{i\in\Pi_{k}\backslash\{k\}}\sum_{j=1}^{L} \sqrt{\mu_{il}}\big{|}\text{tr}(\bar{Q}_{kil})\big{|}^{2}+\sigma_{d}^{2}}. \tag{101}\]  
In this equation, \(\rho_{k}\) represents the effective SINR for UE \(k\), while \(\sigma_{d}^{2}\) accounts for the noise variance.
## 2 Materials and Methods  
### Materials  
#### 2.1.1 Remote Sensing Data  
The GF-4 satellite (launched on 29 December 2015) has a geosynchronous orbit fixed at 105.6\({}^{\circ}\) E, an altitude of 36,000 km and a swath width of 400 km [27]. GF-4 can acquire remote sensing images in the extent of 2\({}^{\circ}\) S-68\({}^{\circ}\) N and 20\({}^{\circ}\) E-180\({}^{\circ}\) E. The GF-4 satellite is an important addition to the civil series of Chinese high-resolution earth observing satellites, as it provides both high spatial resolution (\(<\)100 m) and temporal (20 s) resolution visible, and near and medium wave infrared imagery with a pixel resolution of down to 50 m (Table 1).  
GF-4 images were obtained from ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/))). Land surface temperature (LST) products and surface reflectance products of MODIS were obtained from ([[https://ladsweb.modaps.eosdis.nasa.gov](https://ladsweb.modaps.eosdis.nasa.gov)]([https://ladsweb.modaps.eosdis.nasa.gov](https://ladsweb.modaps.eosdis.nasa.gov))). Digital elevation model (DEM) was obtained from ([[https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)]([https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/))). Considering that weather should be sunny or cloudless at the imaging time, the imaging time should be close enough, and the imaging area of two images should be overlapped as much as possible. The imaging time of MODIS and GF-4 were shown in Table 2. LST and DEM data were resized or resampled, using the nearest neighbor method, to either 50 or 250 m to match the spatial resolution of the GF-4 and MODIS imagery, respectively.
# Analysis of Total-Forms of Cyanotoxins Microcystins in Biological Matrices: A Methodological Review
## 2 Quantification of Total MC by the MMPB Method
### Pre-Treatment of Matrices  
In terms of pre-treatment, two approaches can be distinguished depending on the desired purpose: for tissues, enzymatic or chemical treatments are used to begin the digestion of the tissue and facilitate the oxidation of MCs, whereas, for cyanobacterial matrices, the objective will be to break the cells to release the toxins before filtering or pre-concentrating the sample. Concerning the enzymatic/chemical treatments, the first option consists of enzymatic digestion with trypsin, the objective of which is to affect the protein integrity of the matrix in order to facilitate the subsequent oxidation. The duration of the trypsin treatment can vary; early studies involving a trypsinization pre-treatment prior to Lemieux oxidation on freeze-dried _Lymnaea stagnalis_ tissues and serum suggested the addition of trypsin at a concentration of 0.5 mg/mL in a phosphate buffer for 2 h at 37 \({}^{\circ}\)C [28; 49]. Subsequently, a study on juvenile trout (_Oncorhynchus mykiss_) force-fed with MC-LR compared two trypsin contact times (2 and 18 h) and concluded that an 18-h trypsinization time yielded a quantity of MMPB around 20% higher than 2 h [29]. The other option is to use freeze/thaw cycles on the matrix in order to break the remaining cells and liberate the toxins before the addition of NaOH for tissue digestion (50 mM for 2 h; Roy-Lachapelle [62]). For the cyanobacterial matrix, an enzymatic or chemical pre-treatment is not useful; freeze/thaw steps or sonication can be used to release the toxins from the cells [63]. Other protocols involve preliminary steps to prepare the water or cyanobacterial samples, such as filtration (0.22 \(\upmu\)M nylon filters) or pre-concentration by SPE [63; 64].
## 6 Conclusions and Future Work  
### Conclusions  
This paper fills the research gap in analyzing seismic responses of suspended substation equipment by considering an \(\pm\)800 kV filter capacitor. It also proposes a vulnerability analysis method based on an adaptive boosting (AdaBoost) regressor for the filter capacitor. The AdaBoost regressors can consider the uncertainties of ground motions and multiple material parameters at the same time when deriving vulnerability curves. The efficiency and accuracy, compared with the traditional IDA, are validated by a case study on the filter capacitor. Specifically, the research conclusions are as follows:  
1. The suspended insulators of the filter capacitor do not meet the seismic design requirement due to the safety factor slightly less than 1.67 recommended by Chinese code. It is recommended to reduce the pre-tension force for increasing the safety factor while ensuring the minimum axial force is larger than 0, which will also avoid the pressure stress in the insulators.  
Figure 23: The influence of the pre-tension force on the seismic vulnerability produced by the AdaBoost regressor.  
2. The effects of increasing the pre-tension force is twofold: one is avoiding the pressure generation in the suspended insulators and reducing the maximum displacement of the filter capacitor, another is increasing the seismic failure risk, so increasing pre-tension force should be carefully considered and studied by a balance analysis of the multifaceted influence.
3. The established AdaBoost regressors accurately predict the vulnerability curves as IDA does, and also reduce the computation cost. The single-variable-input regressor has a lower maximum probability prediction error of 2.55% compared to the multi-variable-input regressor's error of 3.82%.
4. The failure risk of the filter capacitor will significantly increase as the pre-tension force increases from 20 kN, but the growth rate continues to fall. The increase in pre-tension force will increase the initial tension in the insulators so that the pressure could be reduced under a given earthquake. However, the tension increase in the insulators would make their stresses closer to the material strength, resulting in a higher failure risk. Due to the increased structural stiffness, the structural responses under a given earthquake will reduce, so the growth rate is gradually decreased.
## 3 Material and Methods  
### Material  
#### 3.1.1 Herbal Blends Preparation  
Two varieties of each herbal blend were selected for comparison. Except for the nut-based Egyptian dukkah (EDN), all of the tested blends were commercially available. The composition of each blend is listed below:  
Thyme-based Egyptian dukkah (EDT): thyme, peanut, chickpea, wheat, and lemon salt.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Tested Mixtures**} & **DPPH Assay** & **ABTS Assay** & **ORAC Assay** & **FRAP Assay** &
\begin{tabular}{c} **Ferrozine Iron Metal** \\ **Chelation Assay** \\ \end{tabular} \\ \cline{2-6}  & **uM T eq/mg** & **uM T eq/mg** & **uM T eq/mg** & **uM T eq/mg** & **uM EDTA eq/mg** \\ \hline EDT & 59.93 \(\pm\) 1.99 & 225.32 \(\pm\) 6.15 & 1577.86 \(\pm\) 50.14 & 145.26 \(\pm\) 4.32 & 51.69 \(\pm\) 5.33 \\ EDN & 26.92 \(\pm\) 0.75 & 125.15 \(\pm\) 5.92 & 1303.68 \(\pm\) 92.02 & 59.89 \(\pm\) 2.33 & 22.26 \(\pm\) 2.38 \\ SZS & 86.79 \(\pm\) 1.38 & 263.52 \(\pm\) 9.7 & 1138.11 \(\pm\) 83.03 & 144.81 \(\pm\) 7.95 & 19.30 \(\pm\) 0.52 \\ SZT & 104.10 \(\pm\) 1.68 & 337.26 \(\pm\) 13.84 & 1479.09 \(\pm\) 88.19 & 212.75 \(\pm\) 12.85 & 18.08 \(\pm\) 1.62 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Antioxidant potential of the tested herbal mixtures.  
Nut-based Egyptian dukkah (EDN): chickpea, coriander, sesame, mixture of nuts and apricot seeds, thyme (low percentage), and spices (cumin, salt). An equal proportion of chickpea, coriander, sesame, and a mixture of nuts and apricot seeds was utilized. The nut mixture itself was composed of equal proportions of hazelnuts, almonds, peanuts, walnuts, and apricot seeds. All ingredients were subjected to roasting at 60 \({}^{\circ}\)C for 1 h, with stirring before blending.  
Thyme-based Syrian za'atar (SZT): sumac, thyme (high percentage), sesame, fennel, anise, coriander, chickpea (also called leblebi or qudamah), and spices (cumin, salt).  
Sumac-based Syrian za'atar (SZS): sumac (high percentage), thyme, sesame, fennel, anise, coriander, chickpea (also called leblebi or qudamah), and spices (cumin, salt).
# High-Quality and Reproducible Automatic Drum Transcription From Crowdsourced Data
## 4 Results
### Evaluation of Architectures  
Table 4 lists the four architectures we evaluated, specifically, a frame- or tatum-synchronous encoder, in combination with an RNN or self-attention decoder. In the following, we refer to the architecture consisting of the frame-synchronous encoder and RNN decoder as the \"reference\".  
On average, the frame-synchronous models (top two rows in Table 4) performed slightly better than the tatum-synchronous models (bottom two rows). This empirical finding contrasts with both our intuition and the results of [PERSON] et al. [5, 6], who observed that the tatum max-pooling layer always improved the F-measure achieved by the model. We hypothesize that the main reason behind the lower score for the tatum-synchronous models (compared to Ishizuka) lies in the lower quality of the tatum grid  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Architecture** & **ADTOF-RGW** & **ADTOF-YT** & **RBMA** & **ENST** & **MDB** \\ \hline \multirow{2}{*}{Frame} & RNN & **0.83** & **0.85** & **0.65** & 0.78 & **0.81** \\  & Self-att & **0.83** & **0.85** & 0.64 & **0.79** & 0.79 \\ \hline \multirow{2}{*}{Tatum} & RNN & 0.81 & 0.83 & 0.62 & 0.75 & **0.81** \\  & Self-att & 0.82 & 0.83 & 0.62 & **0.79** & 0.80 \\ \hline \hline \end{tabular} Values in bold are the best achieved on each dataset.  
\end{table}
Table 4: F-measure (between 0 and 1) for different architectures trained with all real-music datasets.  
used for pooling. In fact, we computed an F-measure between Madmom and ground-truth beats of 0.71, 0.62, and 0.84 for ADTOF-RGW, ADTOF-YT, and RBMA, respectively, while [PERSON] et al. reported much higher scores of 0.93 and 0.96 on their two datasets. The disagreement between the estimated and annotated beats shows that the tatums we employed were likely based on erroneous beat positions, and this hindered the performance of the model.  
By comparing the first two rows (frame-synchronous models) with one another, we conclude that the self-attention and RNN decoders obtained almost identical results. The same is true when comparing the third row with the fourth row (tatum-synchronous models). These results contrast with previous works, which tend to favor self-attention mechanisms over RNNs (see Table 1), especially in the presence of a large amount of training data, as in this study. An explanation is that the input sequence was short enough (4 s or 16 beats, \(T\) in Figure 2) to be fully modeled by an RNN, so no benefit was gained by employing the global sequence modeling capabilities of the self-attention mechanism.  
While a finer hyperparameter search could slightly change the values presented in Table 4, the differences we observed among the four architectures were such that no clear winner emerged. Since neither the tatum synchronicity nor the self-attention mechanisms significantly improved the results, in the next experiment we focused on the simpler frame-synchronous RNN.
## 3 Proposed Work  
This section presents our proposed work, the Zigbee network head choosing factor from the start evaluated by the signal-to-interference ratio of Zigbee nodes, congestion level, and survivability factor of nodes [8]. Zigbee nodes are clustered utilizing adaptive fuzzy c-means clustering dependent on the cluster head choosing factor. After that, data packets are encrypted by using the proposed DNA-based encryption algorithm. Finally, an optimized route is obtained by adaptive krill herd optimization. Figure 1 depicts the proposed algorithm's flow diagram.  
Our \"DNA-based encryption technique\" for enabling the secure routing for Zigbee-enabled IoT devices uses a \"DNA block cipher symmetric algorithm\". The \"DNA sequence\" generates a secret encryption key (SecKey). The two robust and optimized operations, \"substitution\" and \"transposition\", are applied to encrypt the data. This proposed technique is perfectly suitable for devices with limited \"computation resources\", such as Zigbee devices. Initially, \"the cluster head choosing factor\" is determined by the significant factors, including the \"signal-to-interference ratio\", \"congestion level\", and \"survivability factor\" of the Zigbee nodes [19; 20]. Based on this evaluation, \"network nodes\" are clustered using adaptive \"fuzzy c-means clustering\". Then, the DNA sequence encryption algorithm is used to send data securely.  
The Zigbee network is made up of a set of nodes called \(\text{N}_{\text{O}_{1}}\), \(\text{N}_{\text{2}}\), \(\text{N}_{\text{O}_{3}}\)... \(\text{N}_{\text{O}_{i}}\)] and assumes the \"source\" as \(\text{N}_{\text{O}_{1}}\) and the \"destination\" as \(\text{N}_{\text{O}_{i}}\). The proposed study groups network nodes based on three efficient parameters. The suggested design process is thoroughly covered in the sections that follow.  
Figure 1: The proposed framework for DNA-based Encryption in Zigbee Network.  
A. Three Factors Used to Select Nodes: Three factors affect clustering nodes and are covered in this section: the links are the \"signal-to-interference and noise ratio\", the \"path's survivability factor\", and the \"congestion level at the next-hop node\".
## 2 Related Work
### CTDE and Value Decomposition Method  
The CTDE framework addresses the challenges of exponential growth of joint action space within MASs and reliability allocation in decentralized MADRL by using individual observations as inputs to independent deployment strategies [24]. One of the most common is the AC algorithm [25; 26], wherein the strategy \(\pi_{\theta}=\{\pi_{\theta_{1}},\ldots,\pi_{\theta_{i}},\ldots,\pi_{\theta_{n }}\}\) of n actors is updated by maximizing the action value function of the critic network. The policy gradient update is as follows:  
\[\
abla_{\theta}J(\pi_{\theta_{1}},\cdots,\pi_{\theta_{n}})=E\Big{[}\
abla_{ \theta}\pi_{\theta}(\tau)\
abla_{a}Q_{tot}(\tau,a,s)|_{a=\pi_{\theta}(s)}\Big{]}, \tag{1}\]  
where \(Q_{tot}\) signifies the approximate centralized \(Q\)-value function of all critics at the time of the joint policy \(\pi_{\theta}\).  
The Individual Global Maximum Principle proves to be an effective approach for achieving decentralized execution in CTDE [27]. It ensures consistency between local greedy behavior and global greedy behavior, as depicted in Formula (2), where \(Q_{i}\) denotes the \(Q\)-value of a single agent.  
\[\begin{array}{rl}\arg\,\underset{a}{\max}\,Q_{tot}(s,a)=&(\arg\,\underset{a _{1}}{\max}\,Q_{1}(s,a_{1}),\\ &\cdots,\\ &\arg\,\underset{a_{n}}{\max}\,Q_{n}(s,a_{n}))\end{array} \tag{2}\]  
Many methods have been proposed to solve the limitation of \(Q\)-value estimation bias in value decomposition methods based on this calculation principle, such as Randomized Entity-wise Factorization for Imagined Learning (REFIL) [28], weighted qmix [29], value function factorization with dynamic weighting (DW-QMIX) [30], a Residual Q Functionbased approach (ResQ) [31] and QMIX Algorithm Activated by General Softmax Operator (SQIX) [32], etc. While excelling in multi-agent environments with discrete actions, these algorithms cannot be directly applied to environments with continuous action spaces.
## 3 Results  
The key technical risks related to providing the MISTiC Winds observation capability relate to providing accurate, precise hyper-spectral radiometry within the limited resources of a micro-satellite, with instrumentation based on technology suitable for the LEO space environment. Several steps have been taken under the effort reported here to reduce the technology risks associated with the MISTiC instrument payload. These include the fabrication of a flight-like or brass-board infrared imaging spectrometer optical assembly-including the reduction to practice of the innovative elements in its optical design, key performance tests of the spectrometer, and the high-energy proton total-dose testing of the selected infrared focal plane array detector. Key results from this instrument risk reduction work, as well as some instrument hardware adaptations needed for the airborne demonstration, are reported in Appendix A. In addition to the instrument hardware risks, the hyperspectral method for vertically resolved wind observation itself has had only limited demonstration prior to this work. To further reduce risks associated with the MISTiC Winds observation method, airborne observations using an instrument incorporating this spectrometer, and flown aboard a NASA ER2 were performed, with comparisons to independent observations of the wind, temperature, and moisture fields. Example results for these tests are provided below.