esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Photosynthetic Machinery Used by Cyanobacteria  
Cyanobacteria have evolved to adapt quickly and are able to efficiently capture light across a wide range of different wavelengths using a range of photosynthetic pigments including carotenoids, chlorophylls, and the tetrapyrrolic derivatives of chlorophylls. Cyanobacteria utilize photosynthetic machinery comprising three multi-subunit complexes, photosystems I and II (PSI, PSII) [39] plus the additional phycobilisomes (PBS) that attach to the cytoplasmic surface of thylakoid membranes and closely interact with PSII [40] (Figure 3A). The PBS comprise phycolipoproteins (PBPs) together with linker proteins. The PBPs are water soluble and brightly colored. The color arises from the attachment of a series of tetrapyrrole chromophores via a thioester linkage to a cysteine residue within the PBP [41]. The PBPs are subdivided into three groups relating to the energy they absorb and to the tetrapyrrole chromophore. The allophyocyanins (APCs) are chromophoric proteins that absorb low-energy light and make up much of the core of the PBS. At the base of the rods, next to the core, are the phycocyanins (PCs), which absorb intermediate-energy light, and at the far end of the rod are the phycoerythrins (PEs), which absorb high-energy light [42].  
Figure 3: _Cont._  
Marine and freshwater cyanobacteria, red algae, and cryptophytes all produce and use a subclass of the light-harvesting pigment phycoerythrin (PE) that is pink. This open-chain tetrapyrole pigment is called phycoerythrobilin (PEB) **1.** In addition to PE, marine cyanobacteria also use the pigment phycoerytholim (PUB), which is an isomer of PEB. The PE produced by freshwater cyanobacteria contains solely PEB, in contrast to their marine counterparts, which carry both PUB and PEB. Using PEs, within the PBS, cyanobacteria can absorb light between 450 and 700 nm at depths deeper than 200 m in the ocean where low light levels render chlorophyll ineffective.
# Research on Distributed Fault Diagnosis Model of Elevator Based on PCA-LSTM
###### Abstract  
A Distributed Elevator Fault Diagnosis System (DEFDS) is developed to tackle frequent malfunctions stemming from the widespread distribution and aging of elevator systems. Due to the complexity of elevator fault data and the subtlety of fault characteristics, traditional methods such as visual inspections and basic operational tests fall short in detecting early signs of mechanical wear and electrical issues. These conventional techniques often fail to recognize subtle fault characteristics, necessitating more advanced diagnostic tools. In response, this paper introduces a Principal Component Analysis-Long Short-Term Memory (PCA-LSTM) method for fault diagnosis. The distributed system decentralizes the fault diagnosis process to individual elevator units, utilizing PCA's feature selection capabilities in high-dimensional spaces to extract and reduce the dimensionality of fault features. Subsequently, the LSTM model is employed for fault prediction. Elevator models within the system exchange data to refine and optimize a global prediction model. The efficacy of this approach is substantiated through empirical validation with actual data, achieving an accuracy rate of 90% and thereby confirming the method's effectiveness in facilitating distributed elevator fault diagnosis.  
elevator; fault diagnosis; distributed system; PCA; LSTM Article Research on Distributed Fault Based on PCA-LSTM  
Chengming Chen, [PERSON], and [PERSON] (2020), 17, 250. [[https://doi.org/10.3390/a17060250](https://doi.org/10.3390/a17060250)]([https://doi.org/10.3390/a17060250](https://doi.org/10.3390/a17060250))
# The Need for Speed: A Fast Guessing Entropy Calculation for Deep Learning-Based SCA
## 5 Experimental Results
### Hyperparameter Tuning with Different Validation Metrics  
This section analyzes how the evaluated early stopping metrics perform with Bayesian optimization (BO) for the hyperparameter search [22]. For that, we consider the open-source **Bayesian Optimization** method provided in the _keras-tuner_[31] Python package. We run Bayesian Optimization for 100 searches with ASCAD datasets and the Hamming weight and identity leakage models. We repeat each search process five times for each different early stopping metric. The guessing entropy results without early stopping (\"all epochs\" labels in the figures from the previous section) are omitted because _keras-tuner_ inherently implements early stopping and, for this reason, it is not possible to select the best model by ignoring early stopping. The results reported in this section are extracted from the best-found model out of the five search attempts.  
The results from BO for the ASCADf dataset are shown in Figure 5. The best results are obtained with FGE for both Hamming weight and Identity leakage models. In particular, for the Identity leakage model, as shown in Figure 5, the best-found model achieves GE equal to 1 with less than half of the attack traces needed for GEEA. In these experiments, mutual information provides less efficient results.  
Figure 6 provides BO results for the ASCADr dataset. For the Hamming leakage model, GEEA and FGE provide the best results. For the Identity leakage model, the results for FGE are superior, and only 60 attack traces are required for key byte recovery, while empirical GE requires 10\(\times\) more attack traces to succeed. Again, the mutual information metric delivers the worst results.  
Running hyperparameter tuning with Bayesian optimization for the CHES CTF dataset and the Hamming weight leakage model, the results obtained with FGE are significantly better than other validation metrics, as shown in Figure 7. We can see that FGE returns the best model that reaches GE equal to 1 in the attack phase with only 232 traces, while other metrics always require significantly more attack traces.  
Figure 5: GE results from best models found with BO with different early stopping metrics for the ASCADf dataset. (**a**) Hamming weight leakage model. (**b**) Identity leakage model.  
Figure 6: GE results from best models found with BO with different early stopping metrics for the ASCADr dataset. (**a**) Hamming weight leakage model. (**b**) Identity leakage model.
## 2 Data Acquisition and Preprocessing  
### Data Acquisition  
The data for this study were collected from a water quality parameter monitoring system at a water treatment company situated in Changchun, which is in Jilin Province in the northeastern part of China. The study sites and data sources are shown in Figure 2. This system utilizes sensors to sample and detect the water in the water treatment tank and upload the water quality parametric data to a water quality monitoring platform for monitoring and obtaining the water quality parameters, including the pH, DO, COD, among other relevant factors.  
Figure 1: Research plan chart.  
Figure 2: (**a**) Changchun’s geographical location and (**b**) on-site acquisition equipment.  
Among the common water quality parameters, the dissolved oxygen value is a fundamental indicator of the self-purification capacity of water. The recovery and stability of dissolved oxygen in a water body indicates its self-purification capacity. Concurrently, dissolved oxygen is a fundamental requisite for the survival of aquatic organisms. Consequently, the monitoring and forecasting of the dissolved oxygen value of a water body is of paramount importance for a water treatment plant to formulate water quality treatment measures [42]. In this paper, we employed 900 sets of acquired dissolved oxygen data to construct a forecasting model for water quality parameters. The sampling frequency of the data was one hour, and the data were subjected to analysis and forecasting. Furthermore, the acquired data underwent preprocessing to enhance their suitability for the establishment of forecasting models.
# You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection
## 1 Introduction
In this study, we present a novel neural network architecture inspired by the You Only Look Once (YOLO) algorithm [22]. YOLO gained attention in the Computer Vision community for object detection. It transformed bounding box prediction from a classification problem to a regression one. Using this approach, it obtained speedups of around 3\(\times\) without compromising accuracy. We present a system called You Only Hear Once (YOHO) that predicts the boundaries of acoustic classes through regression. YOLO has been adopted in the audio domain by visualising spectrograms as images. [PERSON] et al. [23] adopted YOLO for automatic bird song and syllable segmentation. [PERSON] et al. [24] presented a system called SpeechYOLO which treated audio fragments as objects. They adopted YOLO for keyword spotting tasks. [PERSON] et al. [25] investigated object detection techniques such as YOLO and CenterNet [26] for phoneme recognition. However, the novelty of the YOHO paradigm is that it converts frame-based classification into a regression problem by gradually reducing the temporal dimension through many convolutional layers. This makes the output of the network closer to human-readable labels, therefore reducing the need for post-processing. Separate neurons were used to detect the onset and offset of audio classes. We apply our system to audio segmentation and sound event detection tasks, where the literature has predominantly used frame-based classification. Furthermore, we present a multi-output system, which detects acoustic classes that can overlap with each other. We evaluate the YOHO algorithm for multiple audio event detection tasks. First, we explore music-speech detection in broadcast signals. We also compare our results with state-of-the-art algorithms on the Music Information Retrieval Evaluation eXchange (MIREX) competition dataset 2018 [27]. Second, we test our model on the TUT sound event detection dataset, which represents common sounds related to human presence and traffic. It was the dataset used in the Detection and Classification of Acoustic Scenes and Events (DCASE)competition 2017 [28]. Third, we evaluate our model on the Urban-SED dataset [9], which is a synthetic dataset for environmental audio. In all three cases, the YOHO algorithm performed better and faster than the CRNN. All the code associated with this project is available in this GitHub repository ([[https://github.com/satvik-venkatesh/you-only-hear-once](https://github.com/satvik-venkatesh/you-only-hear-once)]([https://github.com/satvik-venkatesh/you-only-hear-once](https://github.com/satvik-venkatesh/you-only-hear-once)), accessed on 2 March 2022).
## 3 Instrument Characteristics and Tests
### Characteristics of Radiometer Sub-Systems  
#### 3.1.1 Frequency Transfer Function of the Microwave Assembly  
As already outlined in Section 2.1 and illustrated in the block diagram in Figure 2, the design of the MA and of the PDA comprise an LSB centered at 1,407.5 MHz and an USB centered at 1,419.5 MHz, both with 11 MHz bandwidth. This allows narrow band RFI occurring within the protected band 1,400 MHz to 1,427 MHz to be identified by monitoring differences between signatures at these two channels (frequency analyses).  
The measured frequency transfer functions of the two channels are shown in Figure 5a. The transfer function of the LSB (blue) is the result of the series connection of the band-pass filters BP1, BP2, and BP3a, whereas the transfer function of the USB (green) is determined by the characteristics of BP1, BP2 and BP3b. The critical attenuations at the limits of the protected band are -18.1 dB for the LSB and -17.99 dB for the USB, dropping off quickly to over -70 dB of attenuation within a few MHz.
# A Survey on Penetration Path Planning in Automated Penetration Testing
## 2 Overview  
### Automated Penetration Testing  
#### 2.1.1 Rule-Based Automated Penetration Testing  
PT requires testers to perform numerous repetitive processes. Initially, penetration testers developed automated tools such as Nessus and Metasploit to facilitate automated PT. The effectiveness of these tools largely depends on predefined penetration rules and attack payloads set by experts, enabling the automation of steps like vulnerability scanning and exploitation, which is considered as rule-based automated PT [14]. Rule-based automated PT has proven to be effective in handling specific penetration scenarios; however, it lacks flexibility and struggles to adapt to diverse and complex environments.
# Effective Blind Frequency Offset Estimation Scheme for BST-OFDM Based HDTV Broadcast Systems
## 1 Introduction
Since the number of available CPs is very small and some pilot information is not revealed until the TMCC information is decoded in the ISDB-T system, a direct application of the conventional joint LS estimation scheme [17; 18; 19] to the ISDB-T system gives rise to the loss in estimation accuracy. In particular, the numbers of CP and SP are changed according to which modulation is used in each segment. Therefore, it is of importance to make necessary changes to the conventional estimation strategy. For the purpose of accommodating the segment format, the estimation schemes [17; 18; 19] can be modified to utilize information-conveying TMCC control signals, which are present in an equal number regardless of a segment type, instead of the limited number of explicit pilots. In this paper, we propose an efficient joint estimation of CFO ad SFO in the BST-OFDM based HDTV broadcast system without relying on a priori knowledge of segment type. To end this, the proposed joint LS estimation of CFO and SFO uses information-bearing TMCC signals that are commonly present irrespective of a segment type. Using the repeated nature of TMCC signals to discard the phase ambiguity before decoding TMCC information, the proposed estimation scheme can be a good solution for reliable estimation of frequency offsets. We confirm via numerical simulations that the proposed joint estimation scheme has a comparable performance to the conventional estimation scheme in spite of using information-bearing TMCC signals in a blind manner. The remainder of this paper is organized as follows. A description of signal models adopted in this paper is presented in Section 2. In Section 3, the conventional joint LS estimation of CFO and SFO is discussed, resulting in some limitations when applied to the ISDB-T system. In Section 4, the effective joint LS estimation of CFO and SFO in the ISDB-T system using TMCC signals is suggested and its mean squared error (MSE) is derived. Section 5 presents the simulation results that verify the reliable operation of the proposed joint LS estimation scheme. Conclusions are drawn in Section 6.
# Evaluation of Global Precipitation Products over Wabi Shebelle River Basin, Ethiopia
## 4 Result and Discussion
### Uncertainty Associated with a Pixel-To-To Point Method  
In addition to the spatiotemporal investigation, the significant effect of the position of the stations in a pixel on the evaluation of the global precipitation product was analyzed, as shown in Figure 1 (blue-colored grid box). Furthermore, attempts were made to compare a pixel of selected GPPs (PERSIANN_CDR) against reference data, using the spatial average of all existing station data versus individual gauge stations within a pixel. Findings show that the minimum RMSE was obtained for PERSIANN_CDR when comparing the spatial average over each gauge station in the blue-colored box, with an average value of 4.667, as presented in Table 5.  
resolution [44]. In addition, installing additional rain gauges is strongly recommended within the grid [25].
# Enhancing Flow Field Performance of a Small Circulating Water Channel Based on Porous Grid Plate
## 4 Discussions of the Fluid Field Characteristics  
The state of the fluid field with the rectification of the honeycomb and screens is shown in Figure 5. It can be observed that the honeycomb and the screen can effectively restrain the turbulence and improve the velocity uniformity of the fluid. The key parameters that affect the rectification performance of the rectification section are the thickness \(l\) and porosity \(q_{1}\) of the honeycomb, the porosity \(q_{2}\), the number of layers \(n\), and the spacing \(d\) of the screen. Therefore, the influences of the parameters of the rectification section on the flow characteristics of the test section have been studied. The single variable principle is adopted in the analyses--that is, when the influences of a single parameter are analyzed, other parameters remain unchanged. The initial values of the parameters are shown in Table 2. In order to obtain the accuracy results, the hexahedron element sizes were set less than 7 mm in the meshing process, and more than 300,000 elements were obtained. Before the calculations, we tested the mesh quality and found the minimum orthogonal quality was more than 0.2 and maximum ortho skew was less than 0.05, which could meet the requirements. The \(k\)-epsilon with standard wall functions were applied as the viscous model, and the Pressure-Implicit with Splitting of Operators (PISO) algorithm with neighbor correction were chosen for the calculations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameter** & **Initial Value** \\ \hline Honeycomb & Thickness \(l\) & 35 mm \\  & Porosity \(q_{1}\) & 0.6361 \\ \hline  & Porosity \(q_{2}\) & 0.6361 \\ Screen & Number of layers \(n\) & 3 \\  & Spacing \(d\) & 50 mm \\ \hline  & Pumping flow rate \(Q\) & 5 kg/s \\ \hline \hline \end{tabular}
\end{table}
Table 2: The initial values of the parameters.  
Figure 5: The state of the fluid field with the rectification of the honeycomb and screens.
## 4 Contact Force Calculation
### Contact Pair Detection  
In order to define the local reference frame shown in Figure 5, the point on the beam contacting with the block has to be determined. Let us assume that during the evaluation of the positions at the new time-step, the bodies overlap, as shown in Figure 7. The block vertex \(d\) is identified as the contact point on the block and forms one element in the contact pair. The contact point on the beam is chosen to be the point closest to vertex \(d\) in the considered configuration.  
This means that the point on the beam closest to the block vertex \(d\) has to be found. In order to do so, we need the distance function between the beam and block vertex \(d\) whose position is known.  
The displacement of the beam is given as  
\[v(x,t)=N_{v1}(x)\rho_{2}(t)+N_{v2}(x)\rho_{3}(t)+N_{v3}(x)\rho_{5}(t)+N_{v4}(x )\rho_{6}(t) \tag{59}\]  
Figure 7: Enlarged sketch of the block and beam in an overlapping configuration: \(v(x,t_{i})\)—beam deformed axis; \(b\)—point of contact on the beam; \(d\)—block vertex that impacts the beam; \(f(x)\)—distance between vertex \(d\) and beam axis.  
which, for the Euler-Bernoulli beam, is a third-degree polynomial. Labels \(\rho_{j}\) with \(j=1,\ldots,6\) are the local degrees of freedom of the beam element considered. Now, by denoting the \(x\) and \(y\) coordinates of vertex \(d\) as \(\left(d_{x},d_{y}\right)\) the distance function between the vertex \(d\) and the beam displacement curve is given as  
\[f(x)=\sqrt{\left(d_{x}-x\right)^{2}+\left(d_{y}-v(x,t_{i})\right)^{2}} \tag{60}\]  
where \(v(x,t_{i})\) is the beam displacement function at some instant \(t_{i}\). The distance function \(f(x)\) given in (60) will have its minimum value at the coordinate \(x\) that corresponds to the point on the beam that is nearest to vertex \(d\):  
\[\frac{\mathrm{d}f(x)}{\mathrm{d}x}=0, \tag{61}\]  
which gives  
\[v(x)\frac{\mathrm{d}v(x)}{\mathrm{d}x}-d_{y}\frac{\mathrm{d}v(x)}{\mathrm{d}x }+x-d_{x}=0. \tag{62}\]  
Equation (62) is a fifth-degree polynomial, and its roots are computed numerically using a Matlab built-in function. After finding the roots, the real values are inserted into the distance function \(f(x)\) and the root giving the minimal value of \(f(x)\) is chosen as the \(x\) coordinate of the contact point \(b\), denoted hereafter as \(b_{x}\), i.e., the coordinates of \(b\) are \(\left(b_{x},v(b_{x})\right)\). It is also required that the contact point lies between the beam nodes \(b_{x}\in[x_{1},x_{2}]\), where \(x_{1}\) and \(x_{2}\) are the \(x\) coordinates of the beam nodes. If this condition is not satisfied, then the contact search is performed on the corresponding neighboring element. Once the contact pair is determined, the local reference frame inclination angle \(\psi\) is evaluated as  
\[\psi=\tan^{-1}\left(\frac{\mathrm{d}v}{\mathrm{d}x}(b_{x})\right)+\frac{\pi}{ 2}. \tag{63}\]
## 5 Discussion
### Implications for Large-Scale Implementation  
While the DSSAT-GA framework presents a significant advancement in nitrogen management for cotton production, its large-scale implementation must be carefully considered in terms of both short-term economic viability and long-term environmental sustainability. The framework's capacity to reduce nitrogen inputs by 15-20% while maintaining yield stability offers immediate economic benefits through cost reduction and increased profitability. However, the true value of this approach extends beyond mere economic gains. From an environmental perspective, the reduction in nitrogen application directly translates to decreased risks of soil acidification and salinization, which are critical concerns in intensive agricultural systems. The mechanism-driven optimization inherent in DSSAT-GA allows for precise alignment of fertilizer application with crops, minimizing excess nitrogen that could otherwise leach into groundwater or contribute to greenhouse gas emissions. The integration of water-saving drip irrigation systems with this framework is essential to prevent potential tradeoffs between nitrogen use efficiency (NUE) improvements and irrigation-induced leaching. By combining these technologies, farmers can achieve dual objectives of resource conservation and economic viability. Furthermore, the framework's adaptability to different environmental conditions and crop varieties positions it as a valuable tool for sustainable intensification in diverse agroecosystems. However, realizing these benefits requires a long-term perspective. Monitoring soil health indicators over multiple growing seasons is necessary to fully quantify the extent of soil quality improvements and to ensure that the framework's positive environmental impacts are sustained over time. Additionally, lifecycle assessment (LCA) models could be employed to comprehensively evaluate the carbon footprint reductions associated with this method, providing a more complete picture of its environmental benefits. Through continued research and practical implementation, the DSSAT-GA framework has the potential to serve as a model for environmentally intelligent agricultural decision-making systems that balance productivity with ecological stewardship.