esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Solution Methodology
### Recursive Solution and DigSILENT Validation  
To validate the solution provided by GAMS software, the validation stage of the proposed solution methodology was implemented using the power flow method (successive approximation power flow method [33]) in the MATLAB programming environment, which was evaluated recursively until the convergence failed. An identical approach was adopted in DigSILENT software using the Newton-Raphson power flow method. The general flow diagram for the numerical validation in MATLAB and DigSILENT is depicted in Figure 3.  
The primary advantage of the proposed validation methodology using power flow approaches implemented in MATLAB and DigSILENT software corresponds to the possibility of storing the voltage profile for each simulation scenario to construct the nose curve for the most critical nodes.  
The power flow formula in the case of the recursive implementation of the successive approximation method is presented below:  
\[\mathbb{V}_{d}^{t+1}=\mathbb{V}_{dd}^{-1}\Big{[}\mathbf{diag}^{-1}\Big{(} \mathbb{V}_{d}^{t,\star}\Big{)}\left(\mathbb{S}_{dg}^{t,\star}-\mathbb{S}_{d} ^{t,\star}\right)-\mathbb{Y}_{ds}\mathbb{V}_{s}\Big{]}, \tag{10}\]  
where \(\mathbb{V}_{d}^{t}\) denotes the vector containing all the complex voltages in demand nodes, \(\mathbb{Y}_{dd}\) corresponds to a sub-matrix that relates demand nodes among them, \(\mathbb{S}_{dg}^{t,\star}\) indicates the vector containing all the complex power generation in dispersed generators, \(\mathbb{S}_{d}^{t,\star}\) represents the vector that contains all the complex power demands, \(\mathbb{Y}_{ds}\) corresponds to a rectangular sub-matrix that relates the demand and the slack nodes, and \(\mathbb{V}_{s}\) corresponds to the vector that contains all the voltages in the slack node. Note that \(t\) is the iterative counter, implying that, for \(t=0\), the initial voltage vector is assigned as \(\mathbb{V}_{d}^{0}=\mathbf{1}\angle 0^{\circ}\)[44].  
Note that the successive approximation power flow Formula (10) is evaluated until the convergence error is reached or the maximum number of iterations is fulfilled. The convergence criterion takes the following form:  
\[\max\Bigl{\{}\left|\mathbb{V}_{d}^{t+1}||-\left|\mathbb{V}_{d}^{t} \right|\Bigr{|}\Bigr{\}}\leq\varepsilon, \tag{11}\]  
where \(\varepsilon\) corresponds to the maximum convergence error, which was assigned, as recommended in Reference [44], as \(1\times 10^{-10}\).  
**Remark 3**.: _The most significant advantage of the successive approximation power flow method defined in (11) is that its convergence is ensured through the application of the Banach fixed-point theorem [33]. However, when the system approaches the voltage collapse point, the number of iterations increases, which can be used as a stopping criterion to finalize the search of the \(\lambda\)-coefficient, as explained in Figure 3._
# A Review of the Artificial Neural Network Models for Water Quality Prediction
## 3 Three Basic Model Structures in Water Quality Prediction
### Hybrid Architectures  
There is a growing tendency to use hybrid ANNs models, which play a huge role in modeling, for their ability to integrate with other conventional and more advanced modeling techniques [68], to create flexible and efficient models in recent years (see Figure 1). Hybrid models are divided into three categories, namely model-intensive, technique-intensive, and data-intensive [27]. The model-intensive approaches model the sub-components of the whole physical system and aggregate the overall response of each model. Relevant forms, such as LSTM-RNN [26] or FNN-WNN [69], are model-intensive methods. The core of the technique intensive methods is to develop a modeling framework that is able to take advantage of different technologies. Methods that combine ensemble approaches [32] or time series models that remove trends or periodicities like Autoregressive Integrated Moving Average-Radial Basis Function neural networks (ARIMA-RBFNNs) [70] or ARIMA-ANN [71] are technique-intensive methods. In this review, data-intensive approaches are to combine different technologies to preprocess the data. Wavelet analysis approaches such as WANN [72] can provide some useful information about the physical structure of the data. ANNs models the approximation and details component from the discrete wavelet transformation (see Figure 4). Dimensionality reduction methods such as PCA can reduce the dimension of the input data space to prevent redundancy [73]. Then, ANNs models some aggregative indices obtained by PCA (see Figure 4). Clustering methods [50] such as K-means-MLP [43] identify the data belonging to a particular class. Other data-intensive approaches include decomposition [5] and evolution-related [16] methods. ANN models the Intrinsic Model Function (IMF) obtained from the decomposition of complicated signals.
## 2 Related Works  
[PERSON] et al. [11] present a DL technique for managing this issue. The trained data, which has numerous images of malignant and benign cases, has been employed for training a deep CNN. Three training techniques were devised: a baseline technique in which the CNN structure can be well-trained from scratch, a TL method in which the pretrained VGG16 CNN structure can be additionally well-trained with the ultrasound imagery, and a finely tuned learning method in which the DL variables were optimally tuned to overcome overfitting. [PERSON] et al. [12] introduce a novel structure for classifying breast cancer (BC) lesions with an attention module in an adapted VGG16 structure. The implemented attention system will enhance the feature discrimination among the target and background lesions in ultrasound. The author devises a novel ensemble loss function, which can be a grouping of the logarithm and binary cross-entropy of hyperbolic cosine loss, to enhance the method discrepancy among labels and classified lesions.  
[PERSON] et al. [13] devised a channel attention module including multiscale grid average pooling (MSGRAP) for segmenting BC sections accurately in ultrasound imagery. The author establishes the efficiency of the channel attention elements by adding MSGRAP for semantic segmentation and advances a new semantic segmentation network including the presented attention modules for segmenting BC regions accurately in ultrasound images. Whereas a standard convolutional function does not use global spatial data on input and just uses small local data in a kernel belonging to a convolution filter, the presented attention component permits utilization of global as well as local spatial data. [PERSON] et al. [14] develop a technique called Domain Guided-CNN (DG-CNN) for incorporating margin data, a feature defined in the consensus for radiotherapyists for diagnosing tumors in breast ultrasound (BUS) imagery. In DG-CNN, attention maps which emphasize marginal regions of cancers were initially produced and combined through various techniques into the networks.  
[PERSON] et al. [15] attempted to advance an automated technique for distinguishing thyroid and breast lesions in ultrasound images with the help of deep CNNs (DCNN). To be specific, the author modeled a generic DCNN structure including TL and similar architectural variable settings for training methods for thyroid and BC (BNet and TNet) correspondingly, and to test the feasibility of these GA with an ultrasound image accumulated from medical practice. In [16], an RDAU-NET (Residual-Dilated-Attention-Gate-UNet) method was introduced and used for segmenting the cancers in BUS imagery. The method depends on the classical U-Net, but plain neural components were substituted by residual elements to enhance edge data and overcome the network performance deprivation issue linked with the deep network system. To raise the receptive domain and obtain more characteristic data, dilated convolutions can be utilized for processing the feature maps acquired from the encoder phases.
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 3 CPS Architecture and Applications
### CPS Conceptual Overview
#### 3.1.2 Sensing and Actuation System
The invasive sensor must, therefore, be robust to cope with the installed environment. It is a known fact that the sensor nodes are powered by battery, therefore, energy conservation is a key challenge that raises concern in the specification of sensor network for real-time pipe health monitoring, as illustrated in Figure 3[4]. As illustrated in Figure 3, depending on the application domain, the deployment of sensors strongly depends on the sensor type, the power requirements, data sampling rates, and the sensor coverage. For large-scale infrastructure, such as water distribution networks, sensor coverage is a key issue. Also, since continuous measurement of flow rate and pressure along a pipe is required for leakage and water quality analysis, these data need to be sampled continuously, thus, the sensors must offer a high data sampling rate. Also, sensors and the sensor nodes are battery powered, raising concern about the power constraint since the sensors are to run uninterrupted for a very long period. These requirements make the study of CPS sensing systems a hot topic in the research community. Studies on sensor coverage [38; 39; 40; 41] and improving the energy efficiency of sensor nodes [42; 43; 44; 45; 46; 47] may be found in literature. With the latest progress in sensor technology, mmWave sensors [48] have been developed. These sensors provide multidimensional sensing with precise measurement in both range and velocity and are capable of penetrating certain materials such as plastic and drywall with good resistance to environmental conditions such as rain, fog, dust, and snow [48]. Though the technology is still at its initial stage, it is expected to be a perfect fit for monitoring large-scale critical infrastructure such as water distribution networks.
## 4 Experimental Study  
### Experimental Setup  
In order to verify the optimization results of PIPR's end face, an experimental setup was designed, as shown in Figure 23. The experimental system includes a hydraulic pump, relive valve, flowmeter, pressure sensor, and data acquisition system. During the experiment, the hydraulic pump was first opened to inject the water into the pipeline, and the pressure data were collected when the pipeline was filled with a smoothly flowing medium. Due to the limited space, a similarity experiment was performed on the pipeline with a 50 mm diameter, and the structural parameters of PIPR were proportionally reduced. To study the flow field characteristics at different locations, seven observation points were set on the pipeline, namely A-G in Figure 23. With the increase in the plugging process, the in-pipe flow field changes more dramatically. Therefore, the observation points are not evenly distributed, and they are more concentrated at the position of complete plugging. To observe the in-pipe flow field more clearly, a transparent pipeline of organic glass was used to capture the motion trajectory of the particles through PIV technology. The PIV system included high-speed cameras, a telecentric lens, and a laser generator. A detailed description of experimental equipment is shown in Table 8.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **s/cm** & **_h/cm_** & **_r/cm_** & \(S_{p}\) & \(\frac{\overline{\Delta\textbf{r}}}{\Delta\textbf{r}}\)**/Pa/m** \\ \hline IWOA & 18.76 & 4.25 & 7.17 & 0.27 & 25,135.38 \\ WOA & 14 & 2 & 8 & 0.33 & 27,164.62 \\ PSO & 24 & 2 & 8 & 0.59 & 26,144.70 \\ GA & 14.08 & 2.02 & 7.83 & 0.33 & 27,221.86 \\ Response surface method & 14.09 & 2.02 & 7.19 & 0.32 & 27,391.37 \\ Before optimization & 24 & 12 & 8 & 0.01 & 33,565.43 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The optimization results of different methods.  
Figure 22: The values of fitness function of IWOA and WOA.  
To validate the advantages of PIPR's end face optimized by IWOA, the improved PIPR model before optimization and the original PIPR model were used for comparison. Based on the numerical simulation and optimization results in Section 3, the end face optimized by IWOA is elliptical. Before optimization, the improved PIPR model had a parabolic-shaped end face, and the original PIPR model had a footstep-shaped end face, as shown in Figure 1. The PIPRs with three different end faces were used in the plugging experiment, respectively. The vibration characteristics and pressure fluctuation of the flow field were compared for three PIPR's end faces.
# A Lightweight Model for DDoS Attack Detection Using Machine Learning Techniques
## 1 Introduction
### DDoS Attacks  
DDoS assaults directed toward IoT systems can seriously damage IoT devices [1]. According to the most current estimate, there will be up to 35 billion connected IoT devices by 2025, with roughly 50% unprotected and vulnerable to most security attacks, including DDoS attacks.  
In 2017, the IoT Reaper, also referred to as IoTroop, was a botnet that exploited IoT device vulnerabilities to form a large botnet [2]. It targeted devices like IP cameras, routers, and network-attached storage (NAS) devices, infecting them. While its motives were uncertain, the Reaper botnet had the capability to execute significant DDoS attacks. Luckily, security researchers intervened and stopped its growth by gaining control of its command-and-control infrastructure.  
In 2018, Satori, also called Okiru, emerged as a variant of the Mirai botnet. It focused on exploiting vulnerabilities in IoT devices, particularly Huawei routers [3]. Satori took advantage of a remote code execution vulnerability found in the routers' management interface to enlist them into the botnet. Once compromised, these devices became potential sources for launching DDoS attacks and carrying out other harmful actions.
## 2 Background
### Circadian Hygiene in Healthcare Settings
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Metric** & **Year** & **Calculation Method** & **Architectural Application** & **Evaluation Methods/Threshold** & **Ref.** \\ \hline
**Melatonin Suppression Index** & 2001 of melatonin in response to a light source to a reference light source &
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} **Calculation Method** & **Architectural Application** & **Methods/Threshold** & **Methods/Threshold** & **[33; 34; 35]** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: An overview on lighting metrics. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Metric** & **Year** & **Calculation Method** & **Architectural Application** & **Evaluation Methods/Threshold** & **Ref. ** \\ \hline
**Circadian Stimulus** & 2010 & Considering spectral distribution of light source and human eye’s sensitivity at different wavelengths & LEED and WELL Building standards Architectural organizations: IALD, IES & Measured on a scale from 0.1 to 0.7, with higher values representing greater circadian impact. Threshold of 0.3 to 0.4 for maintaining entrainment and promoting healthy circadian rhythms in daytime environments. & [13; 36; 37] \\
**Circadian Effectiveness Factor** & 2011 & Taking into account both the circadian impact of a light source and the energy efficiency of the light source. Calculating by taking the ratio of the circadian action of a light source to the amount of light energy produced by the light source. & [35; 38] \\
**Equivalent Melanopic Lux (EML)** & 2013 & Considering melanopsin sensitivity of the human eye by measuring biological influences of light on humans & WELL Building Standard requirement & Measured 1.2 m above floor, assumed to be eye-level for a desk-based worker. At least 200 EML to ensure occupants’ circadian rhythm is adequately supported. & Measured 1.2 m above floor, assumed to be eye-level for a desk-based worker. At least 200 EML to ensure occupants’ circadian rhythm is adequately supported. & Measured to be eye-level for a desk-based worker. At least 200 EML to ensure occupants’ circadian rhythm is adequately supported. & [17; 43; 35; 41; 42] \\
**Circadian Getency Spectrum (CPS)** & 2013 & Calculated by taking the ratio of the circadian action of a light source to the circadian action of a reference light source, typically a 3000 K warm-white LED & Allowing for comparing different lighting systems & Compared with broad-spectrum light (white light), with a peak at 612 nm, the CPS model is more effective in suppressing melatonin levels and regulating circadian timing. & [17; 43; 44] \\
**C circadian Potency Spectrum (CPS)** & 2013 & Consistent results of melatonin levels induced by the light stimulus for extended exposure time to eyes compared to the MSF model. & Allowing for comparing different lighting systems & Compared with broad-spectrum light (white light), with a peak at 612 nm, the CPS model is more effective in suppressing melatonin levels and regulating circadian timing.
# Geochemical Analysis of a Multi-Layer Hydrocarbon Reservoir in the Wuerhe Area, Junggar Basin
## 1 Introduction  
A Permian-Triassic normal crude oils reservoir (normal crude oils reservoir include light oils reservoir and medium reservoir oils) has been found, and a Jurassic heavy oil reservoir and a bitumen mine have been discovered in the Wuerhe area of the Junggar Basin [1]. Heavy oil is an important resource for the development of world economy. In the northwest margin of the Junggar Basin, the proven accumulated heavy oil geological reserves are nearly \(3\times 10^{8}\) t, accounting for 97% of the total proven accumulated heavy oil geological reserves in the basin [2]. The main causes of heavy oil and bitumen in the Junggar Basin are the decomposition, consumption, and oxidation of hydrocarbon components in crude oil by biodegradation, water washing, and oxidation [2, 3]. Most scholars use 3D static modeling to study multi-layer oil reservoirs by sedimentology and well log-based petrophysics [4, 5, 6]. They are usually not clear about the causes of multi-layer oil reservoir evaluation, because they lack a geochemical mechanism [7]. This study analyses the physicochemical properties of crude oil and geological evolution process. It reveals the oil alteration process and the formation process of light oil-heavy oil-oil sand and bitumen multilayers. This research also provides theoretical guidance for the exploration and development of oil reservoirs.
## 4 Results and Discussions
### Gas Content Characteristics  
The gas content of the coal seam is the basis of CBM geological evaluation and determines the resource potential of CBM development [40]. The gas contents, referring to CBM contents in this study, of coal seams in the Dacun Block vary from 0.11 to 35.68 cm\({}^{3}\)/g, with an average of 17.27 cm\({}^{3}\)/g.  
Burial depth is one of the important factors affecting the gas content distribution of the coal seam, and its control over the gas content is mainly affected by the temperature and pressure conditions [8]. The influence of temperature and pressure conditions on the adsorbability and even gas content of coal seam presents two different effects [41]. On one hand (positive effect), the adsorption capacity of coals is enhanced with the increase in formation pressure, resulting in the increase in adsorbed gas content; on the other hand (negative effect), the free energy of CBM increases and the adsorption capacity of coal weakens with the increase in formation temperature, leading to the decrease in adsorbed gas content. When the two effects are similar or equal under a specific burial depth, there will be an inflection point in the relationship between adsorbed gas content and burial depth, which is the critical depth. When the depth is greater than the critical depth, the adsorbed gas content of the coal seam gradually decreases. The total gas content usually displays a similar law with the increase in depth as well. In the study area, the total gas  
Figure 3: The scatter plots between the (**a**) CH\({}_{4}\), (**b**) N\({}_{2}\), (**c**) CO\({}_{2}\), (**d**) H\({}_{2}\), and (**e**) C\({}_{2+}\) concentrations and the depth of the coal seam in the Dacun Block.  
content presents an increasing and then decreasing trend as the depth rises (Figure 4a), and the inflection point (critical depth) is around 700 m.  
The greater dispersion that exists between burial depth and total gas content is because other factors, such as coal rank, coal composition, coal quality, depositional condition, and structural deformation, may also influence the gas content [22; 42]. For example, the moisture contents in coals present a negative relationship with the gas content of coal seams in the study area (Figure 4b). The geological structure also has an obvious influence on the gas content. Taking C\({}_{17}\) and C\({}_{25}\) coal seams as examples, the CBM content of coal seams both show an increasing trend from the two limbs to the hinge zone of the Dacun syn  
Figure 4: The relationship between the (**a**) burial depth, (**b**) moisture contents, and gas content of coal seams in the Dacun Block.  
Figure 5: The gas content isoline of coal seams C\({}_{17}\) (**a**) and C\({}_{25}\) (**b**) in the Dacun Block.
## 3 Results  
### Identification and Characterization of FaAKR23 in Cultivated Strawberry  
In our previous work, we identified that the transcript level of _FaAKR23_ was the highest in receptacles and that its expression levels increased by 142-fold from the green fruit stage to the red fruit stage, followed by the _FaAKR24_, which is the homologous gene of _FaGalUR_[33]. In this study, we further investigate the function of _FaAKR23_. To analyze the evolutionary relationship among these reported AKR members in _Arabidopsis thaliana_, _Oryza sativa L., _Prunus persica_, and _Fragaria \(\times\) ananassa_[32; 38; 39; 40], a maximum likelihood (ML) tree was constructed using MEGA 7. The results showed that all AKRs were divided into five subgroups, and FaAKR23, FaAKR24, AtA6 PR1, and AtA6 PR2 were located in the same group, implying a partly similar function among them (Figure 1A). Furthermore, the protein sequence alignment of AtA6 PR1, AtA6 PR2, FaAKR23, and FaAKR24 suggest that these four proteins are harboring the conserved motif of the Aldo-ket red domain (Figure 1B); another study has indicated that AtA6 PR1 and AtA6 PR2 are cytosolically localized and respond differentially to cold and salt stresses [40].  
To investigate the potential function of _FaAKR23_, we firstly examined the _FaAKR23_ transcription levels in different tissues of octopolid strawberry _F. \(\times\) ananassa_ cv. 'Sweet Charlie' (Figure 2A). As shown in Figure 2B, _FaAKR23_ transcript levels were present at much lower levels in leaves and flowers than in fruits, and its expression increased extremely during fruit development until ripening; this implies that _FaAKR23_ might be involved in strawberry fruit ripening and quality formation. In addition, the protein structure predicted by SWISS-MODEL showed that the FaAKR23 protein folds into a TIM barrel structure, which is highly conserved during evolution [41] (Figure 2C).  
Figure 1: Phylogenetic analysis and multiple sequence alignment of AKR proteins. (**A**) Phylogenetic analysis of AKRs. At, _Arabidopsis thaliana_; Fa, _Fragaria \(\times\) ananasa_; Os, _Oryza sativa.L_; Pp, _Prunus persica_. The full-length protein sequences are provided in Table S1; (**B**) multiple sequence alignment was performed using the protein sequences of FaAKR23, FaAKR24, AtA6 PR1, and AtA6 PR2; the Aldo-ket red domain is labeled by a gray line.