esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Partially Grid-Connected Photovoltaic Microgrid Using IoT Technology
## 6 Conclusions  
This paper provides a proposed design and control method for a partially grid-connected PV microgrid. The control and architecture are designed to connect or detach non-critical loads between the microgrid and the utility grid. PV arrays, energy storage elements, inverters, solid-state transfer switches, smart-meter, and communication network of the microgrid were modeled, designed, and simulated using the Matlab/Simulink software. The LoRa communication network was used to design the IoT-based communication system between the smart meters and the microgrid controller. An IoT-enabled smart-meter with ZigBee connectivity has been built for comparison with the proposed LoRa end-node communication protocol requirements. For microgrid applications, appropriate configurations of LoRa physical layer devices were suggested to be with a bandwidth of 250 kHz, spreading factor of 7, and correction rate of 2/3.
# An Architecture for Reliable Transportation of Delicate Goods
## 4 The Main Scenario
### High Level Architecture  
The high number of operators that may be involved, their geographical dispersion, and their specific economic and technological contexts, imposes the use of general, universally available, technical solutions.  
Smartphones are, nowadays, ubiquitous devices on a global scale, with a broad range of prices and features. Mass production has made them affordable and feature-rich. In terms of business, their use requires low investment, either for those who would have to develop equivalent devices, or for those who would buy them. They feature an easy-to-use user interface, are highly portable, accessible and allow for interesting technical solutions in the scope of the addressed problem. Thus, almost all modern smartphones feature internet access, GPS, Bluetooth Low Energy, a camera and, as already mentioned, the possibility of providing an intuitive user experience. In the context of the work described in this paper, the authors took advantage of all these features to maximize the utility value of the solution.  
Considering the requirements and the above-mentioned smartphone features, BLE is an attractive technology to use in the development of our architecture. Bluetooth Low Energy is a technological evolution of classic Bluetooth (version 3 and earlier), developed according to requirements that adjusted very well to the needs of IoT, namely allowing communication with very low energy consumption and thus promoting something that is critical for all mobile platforms, which is autonomy [35; 36; 37; 38; 39]. Relying on BLE, smartphones and cloud computing, Figure 2 illustrates the high-level architectural view of the proposed solution.  
Figure 1: The transportation stages.
###### Abstract  
_Chlamydomonas reinhardtii_ is a unicellular green alga that can grow heterotrophically by using acetate as a carbon source. Carotenoids are natural pigments with biological activity and color, which have functions such as antioxidant, anti-inflammatory, vision protection, etc., and have high commercial value and prospects. We transformed _Chlamydomonas reinhardtii_ with the _BKT_ genes from _Phaffia rhodozyma_ (_PrBKT_) and Chlamydomonas reinhardtii (_CrBKT_) via plasmid vector, and screened out the stable transformed algal strains C18 and P1. Under the condition that the cell density of growth was not affected, the total carotenoid content of C18 and P1 was 2.13-fold and 2.20-fold higher than that of the WT, respectively. _CrBKT_ increased the levels of \(\beta\)-carotene and aastaxanthin by 1.84-fold and 1.21-fold, respectively, while _PrBKT_ increased them by 1.11-fold and 1.27-fold, respectively. Transcriptome and metabolome analysis of C18 and P1 showed that the overexpression of _CrBKT_ only up-regulated the transcription level of _BKT_ and _LCYE_ (the gene of lycopene e-cyclase). However, in P1, overexpression of _PrBKT_ also led to the up-regulation of _ZDS_ (the gene of \(\zeta\)-carotene desaturase) and _CHYB_ (the gene of \(\beta\)-carotene hydroxylase). Metabolome results showed that the relative content of canhaxanthin, an intermediate metabolite of aastanthin synthesis in C18 and P1, decreased. The overall results indicate that there is a structural difference between _CrBKT_ and _PrBKT_, and overexpression of _PrBKT_ in _Chlamydomonas reinhardtii_ seems to cause more genes in carotenoid pathway metabolism to be up-regulated.  
**Keywords:**_Chlamydomonas reinhardtii_; heterotrophy; overexpression; carotenoids; \(\beta\)-carotene; aastaxanthin; \(\beta\)-carotene ketolase; _BKT_
## 1 Introduction
### Three Types of JCAS Systems  
Within the realm of JCAS, three distinct types emerge: radar-centric design, communication-centric design, and joint design and communications [1; 3]. In the former two categories, where the primary system's integrity remains paramount, designers and researchers delve into identifying the supplementary function based on the signal forms of the main system. This often entails subtle adjustments and optimizations to both the system and signal to seamlessly accommodate this dual functionality. Conversely, the latter type offers a more flexible approach, enabling the design and refinement of the signal waveform and system structure without disrupting the communication or sensing processes [1; 3].
###### Abstract  
With the increasing demand for accurate and robust positioning solutions, the use of GNSS antenna arrays has gained significant attention. However, their development and testing are frequently constrained by the inflexibility of traditional hardware platforms, often requiring extensive reconfiguration throughout the development cycle. This paper presents a platform based on a system on chip to develop a highly flexible software-controlled system that is capable of directly sampling up to 16 antenna elements. Multibeam digital beamforming is implemented using the available FPGA resources and the resulting signal is reproduced by the integrated DAC and can be connected to any conventional single antenna GNSS receiver. This paper presents the architecture of the platform, detailing its components and capabilities. Our experimental results demonstrate that the system can phase shift every channel with errors of less than 0.5\({}^{\circ}\) and can reconfigure 4 simultaneous beams of a 16-antenna array at speeds of 1.2 kHz, and 20 beams at around 400 Hz. The average delay introduced by each channel of the system is around 381 ns with a maximum deviation of 1.05 ns. The delay was also measured for the implementation using 4 beams, which achieves a slightly bigger average delay of 384.6 ns while keeping the variation to 5 to 6 ns. This system is intended to be used as the backbone for the development of antenna arrays and beamforming algorithms. Given its flexibility, it is not necessary to develop new hardware between development iterations or even for different systems, as only the software layer needs to be modified. Consequently, it is possible to expedite the development stage before producing dedicated solutions for industrial applications.  
GNSS; SoC; FPGA; digital beamforming; antenna array +
Footnote †: journal: Applied Sciences
## 6 Conclusions  
In this study, the integrated PSO-BPNN-AdaBoost and PSO-BPNN-XGBoost models were developed for rockburst prediction with the imbalanced dataset. The key findings are concluded as follows:  
1. An original rockburst dataset consisting of 266 rockburst cases was constructed, and the characteristics of the dataset were analyzed by box-line plots. Although some outliers were discovered, they were retained to better depict the engineering status. Further, the two-dimensional distribution of the original dataset was analyzed by PCA and T-SNE, respectively. Based on the imbalance in the dataset, the SMOTE technique was employed to preprocess the original dataset by oversampling. Therefore, the rockburst dataset was balanced, and the SMOTE technology was used to oversample the raw data, which greatly improved the separability of the data set and emphasized the novelty of this study.
2. The hybrid PSO-BPNN-AdaBoost and PSO-BPNN-XGBoost models were developed for rockburst prediction, and they were evaluated with sixteen other machine learning models by using test data. Prediction results indicate that the developed models displayed high performance, with an accuracy of 0.851 for PSO-BPNN-XGBoost and 0.901 for PSO-BPNN-AdaBoost. The developed models also have significantly higher recall and F1 scores than other models. Then, rockburst cases from three different projects were used for model validation. The obtained results using the developed models are consistent with engineering records. Therefore, the reliability and superiority of the developed models are demonstrated.
3. Comparing the obtained results using the dataset preprocessed by SMOTE with those from the original dataset, the impacts of the SMOTE algorithm on the base learners were further discussed. The results show that the SMOTE algorithm can bias different models to varying degrees. However, significant performance improvement can be achieved by SMOTE when dealing with imbalanced datasets. Therefore, the data preprocessing is demonstrated to be valid and worthwhile. By analyzing the strengths and flaws of PSO, BP, AdaBoost, and XGBoost techniques, the advantages of developed models were examined.  
Conceptualization, S.L., W.L. and Y.C.; Methodology, P.L. and W.L.; Software, Y.C. and Q.D.; Validation, S.L. and W.L.; Formal analysis, P.L. and Y.C.; Investigation, S.L., P.L., W.L., Y.C. and Q.D.; Resources, S.L., P.L., W.L., Y.C. and Q.D.; Data curation, S.L. and P.L.; Writing--original draft, S.L. and P.L.; Writing--review and editing, W.L., Y.C. and Q.D.; Visualization, S.L. and P.L.; Supervision, W.L. and Y.C.; Project administration, S.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Engineering and Technology Research Center for Development & Utilization of Phosphate Resources Open Fund (NECP2023-04) and the National Natural Science Foundation of China (52204117).  
Not applicable.  
All data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.
## 3 Principles of Speed Estimation for Inter-Symbol Processing
### Communication Signals Segmented by Symbol  
Inter-symbol processing of signal segmentation is an important method in the reconnaissance of high-speed uniform radiation source targets. Since the radiation source does not have complex motion characteristics during motion, the processing can be simplified by dividing the received signal into several smaller time segments. The length of each small segment is the duration of a symbol. Specifically, the criterion for signal segmentation is that the length of each small segment is equal to the length of one symbol period.  
In this segmentation method, each \(S\) in Figure 2 represents a segmented signal of a code-element waveform, by which a high-speed moving target can be effectively scouted and analyzed. Because each segmented signal contains complete symbol information, signal processing and analysis can be performed more precisely. This method not only simplifies the processing but also improves the accuracy and efficiency of signal processing.  
The segmented signals are selected into \(M\) segments and aligned vertically to generate a two-dimensional signal matrix, where the horizontal length indicates the number of points sampled for each segment and the vertical length indicates the number of symbols. \(M\) one-dimensional segmented signals, \(S_{1}\), \(S_{2}\), \(S_{3}\), and \(S_{M}\), each segmented sampled signals with \(N\) points to form a two-dimensional matrix, where \(S_{1}\) denotes the segmented signal of the first symbol and \(S_{M}\) denotes the segmented signal of the \(M\)th symbol in order to obtain a more descriptive representation. Moreover, the two-dimensional matrix is represented by Equation (3).  
Figure 1: Schematic diagram of the speed estimation algorithm.  
\[Y=\begin{bmatrix}y_{1}(1)&y_{1}(2)&\cdots&y_{1}(N)\\ y_{2}(1)&y_{2}(2)&\cdots&y_{2}(N)\\ \vdots&\vdots&\ddots&\vdots\\ y_{M}(1)&y_{M}(2)&\cdots&y_{M}(N)\end{bmatrix} \tag{3}\]  
where each element \(y_{M}(N)\) of the two-dimensional matrix is one of the sampling points of a segmented signal after the balanced initial phase of Equation (2). Any row \(\big{[}\ y_{M}(1)\ \ y_{M}(2)\ \ \cdots\ \ y_{M}(N)\ \big{]}\) denotes the combination of \(N\) sampling points of \(M\) signals; any column \(\big{[}y_{1}(N)\ \ \ y_{2}(N)\ \ \cdots\ \ y_{M}(N)\big{]}^{T}\) denotes the set of \(N\) sampling points of \(M\) signals; and \(\bullet^{T}\) denotes the matrix transpose.
# Design of Wireless Sensor Network for Agricultural Greenhouse Based on Improved Zigbee Protocol
## 2 Design and Implementation of the Wireless Sensor Network  
### Topology Design of the Wireless Sensor Network  
In this research, the data required for crop monitoring were collected using various sensors commonly used in greenhouses and Zigbee in battery self-powered mode to form a wireless sensor network [15]. Due to the large number of Zigbee nodes required in actual production, a good network topology is particularly important for the fast communication between the network nodes. There are three types of Zigbee networks, namely, star topology, tree topology and WMN topology [16]. By comparing the advantages and disadvantages of various topologies, this research designed a WMN network topology; it uses direct data communication between routers, which can prevent the potential failure of the entire network system in the star model in case a problem with the coordinator is caused by a single link (Figure 1 shows the Zigbee wireless network topology).  
The coordinator, router, and terminal are interconnected in layers, which expand the monitoring area and the establishment and later maintenance of the network. The devices communicate with one another in the Zigbee wireless network, and their own addresses are unique. According to the IEEE802.15.4 standard [17], the address of the devices consists of a 64-bit physical address and a 16-bit network address. However, it is worth noting that the physical address of all Zigbee devices is standardized at 64 bits, with nothing required to be carried out to the physical address or the Zigbee network structure; thus, the allocation of the network addresses is indeed crucial in the Zigbee network. The distribution of Zigbee network addresses is determined by the relationship between parent nodes and child nodeswithin the network. The parent node in the lower layer can establish connections with child nodes. On the other hand, child nodes can only connect to the parent nodes in the upper layer. \(L_{m}\), \(C_{m}\) and \(R_{m}\) are three essential values in the Zigbee network, which, respectively, represent the Zigbee network level, the number of child nodes that can be connected to the parent nodes and the number of routers in the network [18; 19]. The address interval can be calculated. The address interval can be calculated according to Equation (1):  
\[\text{Cskip}(d)=\left\{\begin{aligned} & 1+C_{m}\times(L_{m}-d-1)& R_{m}=1\\ &\left(1+C_{m}-R_{m}-C_{m}\times R_{m}^{L_{m}-d-1}\right)/(1-R_{m} )\end{aligned}\right. \tag{1}\]  
In a Zigbee wireless network, the network topology works and the network address of each device can be calculated, as shown in Figure 2.  
Determining the network address of each device is of great importance for wireless sensor networks. If a single device is abnormal or faulty, the network address can quickly determine the faulty node, which can greatly facilitate the operation of wireless sensor networks.
## 4 Results
### Experimental Setup  
To summarize what is described in the previous sections (Sections 4.2 and 4.3) and make explicit the techniques applied, the steps performed are described below:  
* Raw data acquisition of the signal;
* Creating the dataset through preprocessing;
* Applying PCA to the preprocessed data (\(k\) is determined based on the best model);
* The ML algorithm performs the prediction using the data described in the previous step.  
Figure 4, summarises the splitting operations performed during the experiments.  
The first operation was to perform dataset splitting of 80/20. The test set partition was kept aside and was not used in the analysis. The train set partition was used for a k-fold cross validation analysis [44].  
In k-fold cross-validation, we randomly split the training dataset into k folds without replacement, where k-1 folds were used for the model training, and one fold was used for performance evaluation. This procedure was repeated k times to obtain k models and performance estimates. We then calculated the average performances of the models based on the different independent folds to obtain a performance estimate that is less sensitive to the training data's sub-partitioning than the holdout method. Typically, we use k-fold cross-validation for model tuning, finding the optimal hyperparameter values that yield a satisfying generalization performance. Once we have found satisfactory hyperparameter values, we can retrain the model on the complete training set and obtain a final performance estimate using the independent test set. The rationale behind fitting a model to the whole  
Figure 4: Representation of data splitting approach adopted in the experiments.  
training dataset after k-fold cross-validation is that providing more training samples to a learning algorithm usually results in a more accurate and robust model.  
In particular, in our work, the training dataset was divided into five folds, and during the 5 iterations, four folds were used for training, and one fold was used as the test set for the model evaluation. The estimated performances \(E_{i}\) (for example, classification accuracy or error) for each fold were then used to calculate the estimated average performance \(E\) of the model. Regarded k-fold cross validation, hyperparameter tuning analysis was carried out on each proposed classifier under study (Section 3.7), to identify the best hyperparameters. For each PCA value, five best models were identified. Each of these were compared with the identified best models of the other PCAs. The absolute best model, and the corresponding k-value (PCA), were then identified.  
Figure 5 below, shows the approach previously described:
# A Reduced-Order Model for the Vibration Analysis of Mistuned Blade-Disc-Shaft Assembly
## 3 Reduced-Order Modeling
### Model Order Reduction via Cyclic Symmetry Analysis
Then, \(\mathbf{\Phi}_{t}\) can be explicitly expressed as  
\[\mathbf{\Phi}_{t}=(\mathbf{E}\otimes\mathbf{I}_{N_{s}})\underset{h=1,2,\cdots N }{\mathrm{Bdiag}}\left(\overline{\mathbf{\Phi}}_{h}^{s  
where the subscripts of \"1\", \"2\" and \"3\" in the above matrices represent the terms in the corresponding block-circulated matrices in Equation (A10) shown in Appendix A.  
The reduced-order mistuning matrix \(\mathbf{\widetilde{K}}_{\delta}\) is also generated via the coordinate transformation in Equation (3), giving  
\[\mathbf{\widetilde{K}}_{\delta}=(\mathbf{\Phi}_{t})^{\mathrm{T}}\mathbf{K}_{ \delta}\mathbf{\Phi}_{t}. \tag{11}\]  
In most available investigations, only the mistuning in blades was taken into account. This may partly have been because the blade tends to be more flexible and less structurally coupled than the disc. Actually, mistuning in discs is also inevitable due to manufacturing tolerances and deteriorations during operation. The mistuning in discs may not play that significant a role as blades in the vibration of bladed discs. Nevertheless, it may be non-negligible for blade-disc-shaft assemblies, as the flexibilities in the shaft may lead to significant coupling motions between the shaft and disc. Therefore, the mistuning in blades and discs is considered and dealt with independently in this paper. Then, \(\mathbf{\widetilde{K}}_{\delta}\) can be further expanded, without loss in accuracy, into blade and disc parts as  
\[\mathbf{\widetilde{K}}_{\delta}=\left(\mathbf{\Phi}_{t}^{b}\right)^{\mathrm{ T}}\mathbf{K}_{\delta}^{b}\mathbf{\Phi}_{t}^{b}+\left(\mathbf{\Phi}_{t}^{d} \right)^{\mathrm{T}}\mathbf{K}_{\delta}^{d}\mathbf{\Phi}_{t}^{d}=\mathbf{ \widetilde{K}}_{\delta}^{b}+\mathbf{\widetilde{K}}_{\delta}^{d}, \tag{12}\]  
where \(\mathbf{\Phi}_{t}^{b}\) and \(\mathbf{\Phi}_{t}^{d}\) denote the mode matrices corresponding to the blade and the disc parts, respectively, and \(\mathbf{K}_{\delta}^{b}\) and \(\mathbf{K}_{\delta}^{d}\) represent the corresponding mistuning matrices of the two parts. Despite the simple expressions, \(\mathbf{\widetilde{K}}_{\delta}^{b}\) and \(\mathbf{\widetilde{K}}_{\delta}^{d}\) are difficult to determine, as the mistuning matrices \(\mathbf{K}_{\delta}^{b}\) and \(\mathbf{K}_{\delta}^{d}\) of a realistic bladed disc are highly random and difficult to measure. For this reason, frequency mistuning models were more often employed in available investigations for mistuning quantization. In this paper, such a model is also used. Detailed deductions on obtaining the reduced-order mistuning matrices \(\mathbf{\widetilde{K}}_{\delta}^{b}\) and \(\mathbf{\widetilde{K}}_{\delta}^{d}\) are presented in the sections below.
## 2 Instruments and Methods
### Measurements of the Atmosphere Optical Depth by the \"Sky Dip\" Method
#### 2.2.1 Basics of the \"Sky Dip\" Method
Three measurement modes are possible. In the first one, the measurements are performed at two zenith angles and at the angle near the horizon as a reference (see Section 2.2.1). In the second mode, the data at 5 zenith angles are acquired. The angle near the horizon is not mandatory. Last, the absolute measurements of the sky brightness are possible. As mentioned above, the frequency responses of the channels, especially in the 3 mm band, are not well-defined. This can create problems with the interpretation of the measurement results. In [35], an attempt is made to determine the effective frequencies of the channels by a comparison of the experimental values of the coefficients in Equation (1) with the values obtained from the model calculations for a range of frequencies. The experimental values were obtained using independent measurements of the PWV amount. As a result, it was found that the effective frequencies of the 3 mm and 2 mm channels are 79.7 and 134 GHz, respectively. The effective frequency of the 3 mm channel is lower than the specified frequency range of the 3 mm amplifier. It shows that the contribution of the molecular oxygen absorption band near 60 GHz is significant in this channel and should be taken into account. Two pieces of such a radiometer have been manufactured; however, the second one lacks the 2 mm channel. At the same time it has a better constrained frequency response of the 3 mm channel.