esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Proposed Model
#### 2.2.4 Hyperparameters and Model Concept  
Another crucial aspect of the proposed model is the selection of hyperparameters. In deep learning models, hyperparameters are not learned during training but must be defined beforehand [31; 32]. These parameters are set externally to the model and play a pivotal role in shaping the architecture and training process of the neural network. Unlike model parameters such as weights and biases, which are automatically adjusted by the optimization algorithm during training, hyperparameters are predetermined and have a direct impact on the model's performance and behavior. The hyperparameters that were used to compile the proposed model are detailed in Table 2.  
The L1 and L2 regularizers were employed to apply penalties to the layer parameters, which are added to the loss function to prevent overfitting [33]. The chosen values, 1 \(\times\) 10\({}^{-5}\) and 1 \(\times\) 10\({}^{-4}\), respectively, are sufficiently low to avoid a sudden impact on the training process. The Adam optimizer was selected due to its computational efficiency as a stochastic gradient descent method, particularly suitable for problems with a large number of parameters and data [34]. The ReLU activation function was chosen to increase the sparsity of the network by converting negative values to zero [30]. Additionally, the network's learning rate was set to a low value, 1 \(\times\) 10\({}^{-4}\), to facilitate slower parameter updates and thereby aim for the best global minimum loss.  
For an initial evaluation of the proposed model for classifying 61 DGA families, the dataset was separated in the proportion of 70% for training and 30% for testing three times, with different random states, for 10% validation of the training set was selectively selected with the validation_split function. For each selection, the model was trained for 20 epochs, the best validation accuracy was saved using the callback function, and then the test set prediction was performed. The results obtained in this initial experiment are presented in Section 3.
## 4 Conclusions  
In this study, an SPME method for the extraction of aroma compounds from cooked sorghums was developed. Using this method, a total of 145 volatile compounds and 52 aroma-active compounds were detected in cooked glutinous and non-glutinous sorghum by GC\(\times\)GC-TOFMS and GC-O/MS respectively. These compounds were further identified through comparisons with reference/standard compounds. The aroma features and intensities of these compounds were evaluated by three well-trained panelists using olfactory detection. The aroma intensity of (\(E\))-2-heptenal, nonanal, (\(E\))-2-octenal, benzaldehyde, (\(E\))-2-undecenal, (\(E\), \(E\))-2,4-decdenal, \(\alpha\)-limonene, (-)-\(\alpha\)-Cedrene, thuiopense, \(\beta\)-elemene, \(\beta\)-damascenone, geranyl acetone, 2,3-octanedione, acetophenone, 2-pentylfuran, 2-acetylpyrrole and dimethyl disulfide, present significant differences between the two samples (\(p<0.05\)). (\(E\))-2-undecenal was undetected in glutinous sorghum, and (\(E\), \(E\))-2,4-decdenal, \(\alpha\)-limonene, thuiopense, and \(\beta\)-elemene were undetected in non-glutinous sorghum. Based on their aroma features, the compounds were grouped into eight general aroma categories, and the aroma intensities of each aroma group were totaled. The summed intensity of four aroma categories: sweet/fruity, green, volatile acids and citrus-like, present significant differences between glutinous and non-glutinous sorghum samples (\(p<0.05\)). Glutinous sorghum contained 12%, 20% and 21% more sweet/fruity, green and citrus-like total intensities, respectively than non-glutinous sorghum; and contained 14% less acid total intensity, emphasizing the aromatic differences between the two cooked sorghums. In addition, most of the compounds detected in the cooked sorghums were also detected in commercial Baijiu, indicating that the aroma compounds produced during sorghum distillation (cooking) have direct and significant influences on the final flavor quality of Baijiu.  
S.C., Conceptualization, Methodology, Validation, Analysis, Writing, Review & Editing, project administration; L.W., Supervision & Review; D.N., Validation, Analysis; L.L., Methodology, Analysis; H.W., Methodology, Validation, Analysis; Y.X., Supervision & Review. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (NO. 2018 YFC1604100), the China Postdoctoral Science Foundation (NO. 2018M631971), National First-class Discipline Program of Light Industry Technology and Engineering (LITE2018-12), and 111 Program of Introducing Talents (NO. 111-2-06).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
Samples of the compounds are not available from the authors.
# A Method to Construct an Indoor Air Pollution Monitoring System Based on a Wireless Sensor Network
## 3 Coverage Hole Detection and Healing
### Coverage Hole Detection Based on the VD  
As the sensors used in this work are embedded in public mobile phones, so these nodes move randomly, which is why the network is able to crack at any time (mobile phone is turned off, leaves the RoI, is disconnected, etc.). To define the coverage hole introduced by the off-grid sensor and its exact location, we will introduce the VD algorithm to provide each sensor with their own cell. In this way we can define with accuracy the coverage hole location.  
A VD is a partition of a space into small regions called the polygon of Voronoi based on closeness to points in a specific subset of the plane as each region contains one, and only one, site (point) and each point belongs to its region (cell) [27]. In this application, the VD devised the RoI into \(n\) cells where each sensor is the center of its own cell. To detect the coverage hole, we use the following theorems shown in Figure 1.
# A Lightweight Model for DDoS Attack Detection Using Machine Learning Techniques
## 1 Introduction
### Motivation  
Given the IoT devices' and systems network's limited resources, the attackers are not unsophisticated and have expanded themselves with a variety of inventive strategies to infiltrate them. There is typically a lot of traffic when there are billions of IoT devices. As a result, it could be difficult to distinguish between a DDoS attack and an ordinary traffic increase. DDoS identification intelligence is necessary to solve these problems. Both machine learning and deep learning techniques may play predictive roles across a range of various cases to fight against DDoS attacks on IoT infrastructure, giving organizations new insights and increasing the demand for performance tools and IT development. Deep learning and machine learning algorithms are among the best and most powerful methods to protect IoT networks from various attacks. One concern regarding this derives from the fact that DDoS assaults have become more frequent and attempt to target consumer-level IoT devices, as well as from the reality that consumers lack technological expertise or awareness of inherent weaknesses.  
A command server in Figure 1 contains multiple users in the internet world who access IoT devices on a day-to-day basis, which is all in turn connected with the target server. This gives the attacker an opportunity to attack the command server, which would indirectly affect the IoT devices that are connected to the network of bots. The creation of resistant solutions for IoT systems to identify attacks is one of the primary objectives for researchers to improve the rate of success in detecting dangerous threats against IoT architecture. To track and forbid unsuitable data flowing through the IoT network, security must be able to find unwanted and malicious traffic. The anomaly detection is done on edge devices, where it can be performed to analyze the data at the edge, allowing for real-time decision-making and reducing the need to transmit large amounts of data to a central server. This is often essential in IoT networks where bandwidth might be constrained. This performance is important, as performing detection at the edge saves bandwidth and reduces latency. Edge devices have constraints on computational power and memory, so efficient algorithms are needed and high performance must be achieved with minimal energy usage, especially in battery-powered devices. Quick detection is essential to respond to threats like DDoS attacks promptly and effective anomaly detection at the edge can provide early warnings of suspicious activities, enhancing overall network security. However, some ML models frequently misclassify the most hazardous traffic flows as a result of incorrect and subparfeature selection. The main objective is to undertake extensive research that can be applied to IoT fraud detection.
# Real-Time Water Level Monitoring Based on GNSS Dual-Antenna Attitude Measurement
## 2 Methodology
### Definition and Calculation of Attitude Angle  
#### 2.5.1 Definition of Attitude Angle  
The carrier attitude angle refers to the angular relationship between a carrier's own coordinate system (Body-Fixed coordinate system) and the Local Level coordinate system (LLS). It is typically represented by a set of attitude angles: heading, pitch, and roll. Since this study employs a single baseline measurement, only the heading and pitch angles of the carrier can be measured. Figure 3 shows the heading angle y as the angle between the projection of the carrier's main baseline vector on the LLS and the Y-axis of the LLS. The pitch angle p is the angle between the carrier's main baseline vector and the XOY plane of the LLS. The roll angle r is the angle between the X\({}_{\text{B}}\) axis along the Body-Fixed coordinate system and the XOY plane of the LLS.  
Figure 3: Definition of Heading, Pitch and Roll.
## 4 Discussion
### Limitations  
There were two potential limitations in this study. First, the sitting posture was not the same as that in real life, and the discomfort may be affected by the different sitting postures during the adjustment. Secondly, the sample size was not large enough to investigate the effects of physiological parameters, for example, gender, stature, weight, and BMI, on the discomfort, considering the significant individual differences in discomfort values of this study.
## 2 Experiments
### Statistical Analyses and Visualisation  
Pearson correlations calculation, Principal Component Analysis (PCA) and the visualisations of the results were performed in the R environment [65], packages _compositions_[66], _robCompositions_[67; 68], _FactoMineR_[69; 70]_corplot_[71] and _OpenAir_[72]; maps were created by _ESRI ArcGIS Desktop, version 10.5.0.6491_, using open source basemaps provided by ESRI (World Imagery [73], World Topographic Map [74]).  
Because the multivariate analysis has to be performed on complete datasets and zero values or fractions of detection limits are inappropriate [75], measurements under the detection limit were imputed using expectation-maximisation-based replacement by the ImpRZilr algorithm. Prior to the PCA, the data were transformed according to the Compositional data analysis (CoDa) principles [76; 77] using the centred log-ratio (clr) transformation, the approach that was proven to be appropriate recently [45; 78; 79]. Only the clr-transformed elemental concentration data were used for the construction of the model. The information on the season, the distance of a sampling site from the dominant source and its orientation to it (downwind/other), together with modelled PM\({}_{10}\) values for the individual sites, were added as supplementary variables. Original--untransformed--data were also projected on the PCA results to facilitate interpretation of the dimensions in terms of recorded element concentrations.  
For the individual seasons, the PCA on clr-transformed data was also performed, this time with no supplementary variables. On its results, Hierarchical Clustering on Principal Components was performed to reveal the similarities in sampling points.
## 4 Characteristics of the BDS Satellite Clock  
### Satellite Atomic Clock Model and Noise Characteristic  
Herein, the approximate model and fitting residuals of a satellite atomic clock are first analyzed. The fitting results of the model are sampled here to obtain the daily fitting residual, and the spectrum of the residual sequence is analyzed for the periodic characteristics of the noise of the atomic clock. Figure 6 lists the fitting residual data of three atomic clocks from different orbits, with the PRN of C01 (GEO), C16 (IGSO), and C25 (MEO) satellites; the corresponding spectrums are also given.  
In Figure 6, the RMS values of fitting residuals are 0.473 ns, 0.255 ns, and 0.116 ns respectively. As the longest-operating satellite clock, the overall fitting residuals for C01 are greater than the other two satellites, suggesting a higher noise level for C01 due to the long operating time and ageing components. In addition, the fitted residuals of the GEO and IGSO satellites are characterized by some cyclic variation with a period of about 51,840 s (180 days), while the MEO satellites do not have a significant long-term cyclic effect. This will be discussed in detail in Section 5.  
There are obvious periodic terms in the low-frequency region of the spectrum, related to the satellite orbit position and diurnal changes. Meanwhile, some periodic terms with small amplitude in the high-frequency part belong to the noise caused by other factors, with the amplitude interrelated with the type of atomic clock. After being converted, the cycle terms of GEO and IGSO satellite clocks are mainly 24 h, 12 h, and 8 h, and the periodic terms of MEO are mainly 12.921 h and 6.458 h. The main period terms of clock noise are close to their earth orbit operation period or 1/2 times the period. That is, the satellite atomic clock period term is slightly larger than the period of satellite orbit: 23.935 h for GEO/IGSO and 12.887 h for MEO satellite orbit. The reason is that the solar perturbation change caused by the orbit operation has a periodic impact on the operation of the satellite clock. In addition, J2 relativity is also an important reason for the periodic term [29]. The  
Figure 6: Residuals and spectrums of different satellite clocks.  
periodic terms of atomic clocks on different satellites in the same orbit are also different, which should be caused by other environmental noise.
###### Abstract  
Optimizing in vitro shoot regeneration conditions in wheat is one of the important steps in successful micropropagation and gene transformation. Various factors such as genotypes, explants, and phytohormones affect in vitro regeneration of wheat, hindering the ability to tailor genotype-independent protocols. Novel computational approaches such as artificial neural networks (ANNs) can facilitate modeling and predicting outcomes of tissue culture experiments and thereby reduce large experimental treatments and combinations. In this study, generalized regression neural network (GRNN) were used to model and forecast in vitro shoot regeneration outcomes of wheat on the basis of 10 factors including genotypes, explants, and different concentrations of 6-benzylaminopurine (BAP), kinetin (Kin), 2,4-dichlorophenoxyayacetic acid (2,4-D), indole-3-acetic acid (IAA), indole-3-butyric acid (IBA), 1-naphthaleneacetic acid (NAA), zeatin, and CuSO\({}_{4}\). In addition, GRNN was linked to a genetic algorithm (GA) to identify an optimized solution for maximum shoot regeneration. Results indicated that GRNN could accurately predict the shoot regeneration frequency in the validation set with a coefficient determination of 0.78. Sensitivity analysis demonstrated that shoot regeneration frequency was more sensitive to variables in the order of 2,4-D \(>\) explant \(>\) genotype \(<\) zeatin \(<\) NAA. Results of this study suggest that GRNN-GA can be used as a tool, besides experimental approaches, to develop and optimize in vitro genotype-independent regeneration protocols.  
Keywords:plant tissue culture; in vitro regeneration; artificial intelligence model; optimization algorithm; genotype-independent +
Footnote †: journal: Applied
## 5 Experimental Results and Evaluation
### Prototype Implementation  
We used the Python language to implement a proof-of-concept prototype including client and server applications and microservices. A micro-framework called Flask [28] was used to develop RESTful APIs for the applications and microservices. We used standard python library cryptography [29] to develop all security primitives, such as the digital signature, symmetric cryptography (Fernet), and hash function (SHA-256). Solidity [30] was used for smart contracts' implementation and testing, and all SCs were deployed on a private Ethereum test network.  
The experimental infrastructure worked under a physical local area network (LAN) environment and included a cloud server and several desktops and Raspberry Pi (Rpi boards). Figure 3 shows the experimental setup for our prototype's validation. A desktop emulated the private server, which stored the point clouds data managed by the data owner. To evaluate the impact of the hardware platforms on the data user side, both the Rpi and desktops were used to simulate a user client that requests data access. The private Ethereum network consisted of six miners, which are deployed on the cloud server as six containers separately, and each containerized miner was assigned one CPU core, while the other microservice containers that were deployed on the desktops and RPis worked in light-node mode without mining blocks. All participants used Go-Ethereum [31] as the client application to interact with the smart contracts on the private Ethereum network. Regarding the Swarm-based DDS, we built a private Swarm test network consisting of five desktops as the service sites. Table 1 describes the devices that were used to build the experimental testbed.
# Improved SE-ResNet Acoustic-Vibration Fusion for Rolling Bearing Composite Fault Diagnosis
## 2 Methods
### Low-Rank Multimodal Fusion  
The feature fusion of multiple modalities can obtain the correlation and complementary information between different modalities. The low-rank multimodal fusion method, introduced by [PERSON] and his colleagues, is the multimodal fusion approach utilized in this paper [25]. The details are as follows.  
The set of vectors representing the \(M\) modal coded messages is \(\left\{Z_{m}\right\}_{m=1}^{M}\). The tensor is represented by extracting the outer product of multiple modal features from the input. Prior to the computation of the outer product, the vectors are appended to the rear of each modal feature. This is done to model the interactions between subsets of the modalities. The unimodal input tensor is  
\[Z=\otimes_{m=1}^{M}Z_{m},Z_{m}\in R^{d_{m}}, \tag{7}\]  
where \(\otimes_{m=1}^{M}\) is the outer product of the tensor. \(Z_{m}\) represents the input representation resulting from the vector splicing operation. The input tensor is forwarded through the linear layer to produce a vector, as follows:  
\[h=g(Z;W,b)=W\times Z+b, \tag{8}\]  
where \(W\) is the weight tensor and \(b\) is the offset. Since \(Z\) is a tensor of order \(M\), \(W\) is a tensor of order \(M+1\). The dimension is \(d_{1}\times d_{2}\times\ldots\times d_{m}\times d_{h}\). This method requires the creation of a high-dimensional tensor. Its dimension is \(\prod_{m=1}^{M}d_{m}\). The number of parameters to be learned by \(W\) will then show exponential growth. Low-rank multimodal fusion decomposes the weight tensor into a modality-specific set of low-rank factors. This allows the high-dimensional tensor to be obtained non-explicitly and \(h\) to be computed. Let the weight tensor \(W\) be a set of \(d_{h}\) order-\(M\) tensors. For an order-\(M\) tensor \(\tilde{W}_{k}\in R^{d_{1}\times d_{2}\times\ldots\times d_{m}},k=1,2,\ldots,d _{h}\), there exists an exact form of vector decomposition:  
\[\tilde{W}_{k}=\sum_{i=1}^{R}\otimes_{m=1}^{M}w_{m,k}^{(i)},w_{m,k}^{(i)}\in R ^{d_{m}}, \tag{9}\]  
where \(R\), which makes the decomposition valid and minimal, is the rank of the tensor. Give this value of \(R\) to \(r\). \(r\) decomposition factors are used to reconstruct a low-rank version of \(\tilde{W}_{k}\). The new weight tensor is  
\[W=\sum_{i=1}^{r}\otimes_{m=1}^{M}w_{m}^{(i)}\,, \tag{10}\]  
After decomposing \(Z\) into \(\{Z_{m}\}_{m=1}^{M}\), \(h\) can be derived as  
\[h=\left(\sum_{i=1}^{r}\otimes_{m=1}^{M}w_{m}^{(i)}\right)\times Z=\bigwedge_{ m=1}^{M}\left[\sum_{i=1}^{r}w_{m}^{(i)}\times Z_{m}\right], \tag{11}\]  
where \(\Lambda_{m=1}^{M}[\cdot]\) is the product of tensors. \(w_{m}^{(i)}\) is the \(i\)-th low-rank decomposition factor of mode \(M\). The low-rank multimodal fusion structure is shown in Figure 1.  
Figure 1: Low-rank multimodal fusion.
## 4 Meta-Heuristic Algorithms
### Constraint Handling in Evolutionary Optimization Problems  
Section 5 details the application of the aforementioned evolutionary algorithms to solve model (12), specifically addressing the constraints \(\sum_{i=1}^{n}\varepsilon_{i}=1\) and \(\varepsilon_{i}\geq 0\). Consequently, this section elucidates the methodology by which these algorithms accommodate such constraints. Both NSGA2 and SPEA2, as general-purpose multi-objective evolutionary algorithms, inherently lack specific internal mechanisms for directly handling constraints within their foundational formulations. Consequently, their application to constrained optimization problems necessitates reliance on external constraint management techniques. For the portfolio optimization problem, which is characterized by simplex feasibility constraints--namely, the non-negativity of individual weights (\(\varepsilon_{i}\geq 0\)) and the requirement that the sum of all weights equals one (\(\sum_{i=1}^{n}\varepsilon_{i}=1\))--we implemented a two-pronged repair-based approach, as follows:  
* **Non-Negativity of Weights (\(\varepsilon_{i}\geq 0\)):** This constraint was enforced through a repair mechanism applied during the population generation process. Specifically, any portfolio weight that became negative as a result of genetic operators (crossover and mutation) was immediately set to zero. This procedure guarantees that all candidate solutions consistently adhere to the non-negativity requirement.
* **Sum of Weights Equals One (\(\sum_{i=1}^{n}\epsilon_{i}=1\)):** This critical constraint was meticulously managed by a dedicated normalization operator. Following the application of crossover and mutation, which can generate offspring with a sum of weights not equal to one, a normalization step was performed. In this step, the sum of all generated weights (which had already undergone the non-negativity repair) was computed, and subsequently, each individual weight was divided by this calculated sum. This iterative process ensures that the sum of weights for every portfolio precisely equals one in every generation. This repair operation is applied immediately after offspring production in each generation, thereby guaranteeing that every individual evaluated and propagated to the subsequent generation constitutes a valid portfolio conforming to the simplex feasibility constraints.