esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Wireless Monitoring and Control of a Grid-Connected Photovoltaic System
## 3 Description and Design of the Photovoltaic System
### Wireless Smart Photovoltaic System (WSPS)
#### 3.1.2 Boost DC/DC Converter  
The DC/DC converter used in the PV system is a boost converter. This device, responsible for power transmission, steps up the input voltage of the solar array. The boost converter is composed of an input capacitor with a value of 1000 \(\mu\)F, an inductor of 20 mH, a CSD19536 KCS MOSFET (Texas Instruments, Dallas, TX, USA) driven by an IR2112 (Ariat Technology Limited in Kowloon, Hong Kong, China), an MBR10200 (Taiwan Semiconductor, Mouser Espana) diode, and an output capacitor, \(C_{o}\), with a value of 4700 \(\mu\)F. The connection of the converter is shown in Figure 3, in which the electrical circuit is presented.  
The boost converter is responsible for regulating the voltage from the PV modules by means of a controller, in this case, the adaptive Lyapunov controller, adjusting the on and off switching duration by means of pulse-width modulation (PWM). The operation of the converter depends on the state of the switch. When it is on, the diode is in reverse polarity, and the inductor stores energy, whereas the load is isolated. When the switch is off, the load receives the energy from the inductor. The boost converter can work in two different modes, namely, the continuous conduction mode (CCM) (when the inductor current is always higher than zero) and the discontinuous conduction mode (when the inductor current is zero for a time period). In PV systems, the operation mode of the DC/DC converter is CCM.  
The equations that model a boost converter are (1) and (2).  
\[\frac{dV_{in}}{dt}=\frac{i_{in}-i_{L}}{C_{in}} \tag{1}\]  
Figure 3: Boost DC/DC converter topology.  
Figure 2: I–V and P–V curves in simulation and experimental tests.  
\[\frac{di_{L}}{dt}=\frac{V_{in}}{L}-V_{o}\gamma \tag{2}\]  
where \(\gamma\) is the control signal that can be rewritten as \(\gamma=\frac{1-D1}{D1}\) with \(D_{1}\) as the duty cycle, \(V_{in}\) is the boost converter input voltage, \(i_{in}\) is its input current, \(i_{L}\) is the inductor current, \(C_{in}\) is DC/DC converter input capacitor, and \(V_{o}\) is the boost converter's output voltage.  
In this study, the boost converter has been built to regulate the voltage from the PV system. The input voltage is 50 V, whereas the input current is 4 A. Thus, the maximum power transfer is 200 W. The output voltage is 120 V. The efficiency of the converter is 95%, and its output voltage ripple is 0.8%.
## Abbreviations  
AI--Artificial Intelligence; AFB--Adaptive-Beaconing-Based Fuzzy Logic; AODV--Ad hoc On-Demand Distance Vector (1999); ACOR--Admission-Control-enabled On-demand routing; DVRP--Distance Vector Routing Protocol; DYMO--Dynamic MANET On-Demand; DSR--Dynamic Source Routing; DSDV--Destination-Sequenced Distance Vector; E2 ED--End-To-End Delay; ETX--Expected Transmission Count; FANET--Flying Ad hoc Network; FQ-AGO--Fuzzy-Logic-Based Q-Learning Asymmetric Geographic Opportunistic; FIS--Fuzzy Inference System; GPSR--Greedy Perimeter Stateless Routing (2000); HSLs--Hazy Sighted Link State Routing Protocol (2003); LAR--Location Aided Routing; LDAB--Location PreDiction with Adaptive Beaconing; LEACH--Low-Energy Adaptive Clustering Hierarchy; LR--Link Reliability; MANET--Mobile Ad hoc Network; NRL--Normalized Routing Load; NS--Node Speed; OHND--One-Hop Neighbors' Density; FLDB--Fuzzy Logic Dynamic Beaconing; OLSR--Optimized Link State Routing; Optimal Beacon Interval Time (OBIT); OMNeT++--Objective Modular Network Testbed in C++; PDR--Packet-Delivery Ratio; RWP--Random Waypoint; STAR--Source Tree Adaptive Routing; UAV--Unmanned Aerial Vehicle; VANET--Vehicular Ad hoc Network; VADD--Vehicle-Assisted Data Delivery (2008), ZRP--Zone Routing Protocol.
# Animal Pose Estimation Based on 3D Priors
## 2 Related Work  
### The 3D Human Pose Estimation Methods  
#### 2.1.1 Deep-Learning-Based Human Pose Estimation  
Thanks to deep learning and large-scale datasets, significant progress has been made in 3D human pose estimation. The existing deep-learning-based human pose estimation methods can be divided into the following categories:  
Full Supervision. Supervised approaches rely on large datasets that contain millions of images with corresponding 3D pose annotations. [PERSON] et al. [26] trained a neural network on 2D poses and corresponding 3D ground truth. Due to its simplicity, it can be quickly trained for a large number of epochs leading to high accuracy and serves as a baseline for many following approaches. Recently, Transformer [27] equipped with a global self-attention mechanism has also become popular in 3D pose estimation [28; 29]. However, the major downside of all supervised methods is that they do not generalize well to images with unseen poses.  
Weak Supervision. Weakly supervised approaches require only a small set of annotated 3D poses or unpaired 2D and 3D poses [30; 31; 32]. [PERSON] et al. [30] used a discriminator to represent the prior distribution of 3D poses. Meanwhile, in order to avoid the over-fitting problem that may be caused by using only the projection error, a camera network was also introduced to estimate the camera parameters. Based on [30], [PERSON] et al. [31] introduced a random variable to explicitly generate multiple 3D poses from a 2D input and select the optimal 3D pose from them. [PERSON] et al. [33] decomposed the observed 2D pose into a 3D pose and camera rotation using multi-view consistency constraints. Compared with completely supervised approaches, these weakly supervised methods generalize and transfer better to new domains. However, they still struggle with poses that are very different from the annotated training set.  
Unsupervised. [PERSON] et al. [34] proposed an unsupervised learning approach to monocular human pose estimation. They randomly projected an estimated 3D pose back to 2D. [PERSON] et al. [35] extended [34] with a cycle consistency loss that was computed by lifting the randomly projected 2D pose to 3D and inversing the previously defined random projection. [PERSON] et al. [36] further introduced a learnable scaling factor for the input 2D poses. [PERSON] et al. [37] improved their results by learning the camera distribution of the training set. The unsupervised methods do not generalize better than supervised ones, but they allow training models without 3D priors.
## 2 Approach
### Interpolation Strategy  
In this study, two interpolation strategies were applied in order to perform the data interpolation of 3D seismic data in the crossline direction. The first is an interpolation strategy using a 2D network, and the generated data and data extraction direction are shown in Figure 3. First, to train the model, data with a regular 50% missing trace were generated from the inline direction and 80 \(\times\) 80-sized patches were extracted, and this was used as the training data (Figure 3a). Next, to test the model, data with a regular 50% missing trace were generated from the crossline direction and 80 \(\times\) 80-sized patches were extracted, and this was used as the test data (Figure 3b). Finally, to predict data in the inline direction, the predicted data in the crossline direction, marked with a blue box in Figure 3c, were combined with the overall predicted data in the crossline direction, and the lost inline data prediction was performed (Figure 3c). In this study, a 2D CWGAN was used as the 2D network.  
The second strategy is an interpolation strategy using a 3D network, and the generated data and data extraction direction using this strategy are shown in Figure 4. To model training and testing, regularly 50% of the traces were decimated from the crossline direction, and an 80 \(\times\) 80 \(\times\) 80-sized cube was extracted to generate the data. In total, 70% of the extracted data were used as the training data (Figure 4a), and 30% were used as the test data (Figure 4b). In this strategy, crossline reconstruction was performed using the 3D U-Net and 3D CWGAN.  
Figure 3: Interpolation strategy applied to 2D cWGAN (the direction in which the data were extracted is indicated by orange lines, and the missing data are indicated by dotted lines). (**a**) Training data and data extraction direction of 2D cWGAN, (**b**) test data and data extraction direction of 2D cWGAN, and (**c**) data prediction method and label data for inline direction (the blue box represents an illustrative example of combining the predicted data in the crossline direction to perform data prediction in the inline direction).
## 2 Transcriptional Regulation in Integrons
### The Role of Pc Promoters on Gene Cassette Transcription
#### 2.1.3 Pc Promoter in Class 3 Integrons  
The class 3 integon was the second RI/MI to have its Pc promoter characterized. The class 3 integon identified in a clinical _Serratia marcescens_ strain [54] was submitted to functional characterization to determine IntI3 activity and gene cassette array expression [55]. It was demonstrated that the expression of class 3 integon-containing gene cassettes was controlled by a common promoter, Pc3, which was located just inside the _intI3_ gene at a position equivalent to that found for Pc in class 1 integrons. The Pc3 (-35 TAGACA (17 bp) -10 TAGGCT) presented one and three polymorphisms in the -35 and -10 hexamers, respectively, relative to \(\sigma^{70}\) consensus promoter from _E. coli_[3]. The Pc3 strength was experimentally determined by measuring the streptomycin resistance conferred by the _addA2_ placed in the first position of the class 3 integon in a controlled system [55], and the results indicated that its strength was equivalent to that determined for PcW + P2 in class 1 integrons [2]. Another study concomitantly revealed the occurrence of a Pc3 variant in a class 3 integon recovered from a _Klebsiella pneumoniae_ strain [56], which harbored a gene cassette array distinct from that described in the first reported class 3 integon [54]. This Pc3 variant (-35 TAGACA (17 bp) -10 TAGGAT) differed from the canonical Pc3 by a C to A transversion in the -10 region, which might contribute to a differential transcription and, consequently, to variations in the antibiotic resistance phenotype.
# Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey
## 4 Types of Wireless Sensor Network  
In this section, the wireless sensor networks (WSNs) are classified into six types, including terrestrial, underground, underwater, multimedia, mobile, and wireless body area networks according to the environment. Therefore, each WSN type has its specific data-driven model mode that is based on data capture involving computation, analysis, and transmission method. The relationship between network application types and data-driven models, as in all land-dwelling applications, are known as the terrestrial type of WSN [34]. A fire in the forest is an example of an event-driven model. When fire occurs in a forest, sensor nodes sense and detect the specific data (event-driven model), collect the data, and send data to the sink node. Second, weather forecasting is a form of periodic or continuous (time-driven model), where sensor nodes capture the data continuously and send it to the sink node according to the time slot. Third, the query-driven model is mostly used in a healthcare environment where the operator creates the query for the specific requirement of the data in the healthcare process. Finally, hybrid data depend upon the environmental situation and condition of the scenarios. More than one data-driven model can be used in a specific application. For example, in an industrial environment, continuous data that are used for monitoring and observation are known as time-driven data, while during breakdown or some emergencies, these data are known as event-driven to resolve the issues or event by sensor nodes.  
Each group has its own various routing protocols, application, and related issues and constraints. Figure 2 shows the WSNs' various types, applications, architectures, data models constraints, and challenges.
# Comparative Study of Blockchain Hashing Algorithms with a Proposal for HashLEA
## 3 Background
### Hash Functions
#### 3.3.7 Ethash  
The Ethash algorithm was formed as a function to carry out the PoW of the Ethereum blockchain and some other cryptocurrencies. The Ethash algorithm combines aspects of the Keccak cryptographic function, a basis of SHA-3, in a unique way. It aims to be memory-hard--in other words, resistant to optimization by ASICs--while being very efficient on GPU-based mining. Concurrently, this should facilitate the implementation of optimized verification procedures that are both rapid and efficient. Ethash was specifically developed to deter the centralization of mining power and promote a more decentralized and egalitarian mining environment. The algorithm's resilience to ASICs is intrinsic to its memory-intensive nature, achieved through the utilization of a pseudo-random dataset initiated in accordance with the blockchain's length, thereby conferring variability. This dataset, designated as the Directed Acyclic Graph (DAG), undergoes regeneration approximately every 30,000 blocks, which is equivalent to approximately five days [63, 68].
## 1 Introduction
To overcome these challenges, this paper innovatively proposes a blockchain-based RSU cluster architecture and intra-cluster collaborative storage strategy for SIoV. Spectral clustering is employed to dynamically group RSUs into clusters, which ensures decentralized, adaptive aggregation of RSUs with limited storage resources into cohesive clusters, collaboratively maintaining blockchain integrity. Meanwhile, the collaborative storage strategy enables each RSU to prioritize and store blockchain data autonomously, which ensures low-cost, resource-efficient decisions that align with RSUs' computational constraints. Specifically, the main contributions of this paper are as follows:  
* This paper proposes an RSU cluster architecture for blockchain-enabled SIoV. In traditional blockchain systems, each node maintains a complete copy of the distributed ledger to achieve decentralized trust consensus. However, such a full replication model encounters significant resource constraints in SIoV scenarios. The collaborative storage scheme proposed in this paper leverages RSU clustering strategies and block allocation mechanisms to reduce the storage burden on individual nodes, ensuringthat each cluster can still deliver complete blockchain services and thus balancing decentralization with operational efficiency. * This paper introduces the RSU Access Preference-based Spectral Clustering Algorithm (RAPSCA) to optimize RSU grouping. Unlike traditional methods that rely solely on physical distance or communication cost, RAPSCA incorporates vehicle block access behaviors and storage availability, ensuring more efficient and rational resource allocation for collaborative storage. Additionally, to optimize intra-cluster block allocation, the Vehicle Service Priority-based Greedy Block Allocation Algorithm (VSPGBAA) is designed. This method reduces access latency and improves storage utilization by considering vehicle dwell time, block access probability, and RSU storage availability. * Extensive experiments evaluate the proposed methods in terms of storage hit rate, access cost, and runtime efficiency. Experimental results indicate that, compared to baseline algorithms, the proposed method achieves a 27.7% reduction in per-vehicle access costs for required blockchain blocks, and a 3.5-fold decrease in average algorithm execution time measured on identical hardware configurations.
## 2 Materials and Methods
### Performance Evaluation
To compare the classification performance obtained using our proposed CWD-based time-frequency features with the classification performance obtained using the features presented in the study by [PERSON] and [PERSON] [9], we applied the sliding window approach described in Section 2.4 to the EEG signals employed in the current study. After that, we computed the normalized PSD of the alpha and beta frequency bands for each EEG segment. The features extracted from the EEG segments associated with a particular window position are grouped to form a feature vector. Then, for each subject, we train a SVM classifier to classify the feature vectors into one of two pain-related classes, namely the no-pain class and pain class, using the cross-validation procedure described in Section 2.6. In this study, we compute the classification performance for each subject in terms of two standard evaluation metrics, namely the classification accuracy and the \(F_{1}-\)measure. These metrics are computed as follows [7; 35]. \[Accuracy=\frac{(tp+tn)}{(tp+tn+fp+fn)}\times 100\%, \tag{16}\]  
\[F_{1}-measure=2\times\frac{(PRE*REC)}{(PRE+REC)}\times 100\%, \tag{17}\]  
where \(tp\), \(tn\), \(fp\), and \(fn\) are the number of true positive, true negative, false positive, and false negative cases, respectively. Moreover, \(PRE=tp/(tp+fp)\) and \(REC=tp/(tp+fn)\) represent the precision and recall, respectively.
# Annotated-VocalSet: A Singing Voice Dataset
## 2 Steps to Generate the Dataset
### Checking Annotations Correctness  
After creating the annotations, several evaluations were used as listed below to check their correctness.  
* All the pitch contours and the events were plotted, similar to Figure 2, to double-check them manually. First, an unexpert but trained person checked and corrected the files.  
Then, another person with the same expertise double-checked them. Finally, an expert reviewed the annotations. As seen in Figure 2, it is possible to distinguish the played notes from a pitch contour. Therefore, when an event was not estimated correctly, the annotator could find and alter it.
2. A piece of software code was developed to check if the sequences of the onset, offset, and transition were correct. For example, an onset should be followed by an offset. In addition, the start and end of a transition should be between an offset and its consecutive onset. The list of the incorrect files was saved in a text file. Then, an expert corrected the erroneous files. These processes were repeated until the software code could not find any more errors.
3. In the process of combining the extracted notes with the scores as discussed in Section 2.4, if the number of extracted notes was not equal to the number of notes in the ground truth, the automatic tool listed the incorrect files to be investigated by the user. These steps were repeated until no error was reported by the tool.
4. Finally, with a piece of code, the information in all the files, including the header in the \"extended 4\" directory, were combined to have all the information in one CSV file. This file is available in the Annotated-VocalSet root directory and named \"all-files.csv\". Then rows were sorted based on the column, named Shifted_F0--Nominal_F0. The values in this column were expected to be between \(-1\) and \(1\). Therefore, all the records that did not belong to this range were investigated manually to fix the inaccurate ones. Therefore, after this test, most of the notes were within the expected range, and this paper's authors became convinced that the out-of-range values were not errors due to the erroneous actions of the authors but were due to errors in the notes produced by the singers.  
After these checks, the correctness of the annotations satisfied the authors.
# Low-Complexity Hybrid Optical OFDM with High Spectrum Efficiency for Dimming Compatible VLC System
## 5 Conclusions  
In this contribution, a novel DCHO-OFDM scheme was conceived for simultaneously achieving the dual functionality of data transmission and illumination in VLC. To achieve high spectrum efficiency, the bipolar unclipped ACO-OFDM signal was elaborately combined with the unipolar down-clipped PAM-DMT signal during the OFF state of PWM signal, while it was jointly transmitted with the upper-clipped PAM-DMT signal during the ON state. For the purpose of dimming control, a time-varying bias was applied to convert the superposed bipolar signals to unipolar ones, so that the PWM signal can be invoked without introducing interference. According to our simulations, a wide dimming range was obtained in DCHO-OFDM, with a high spectral efficiency and less fluctuation.  
Figure 8: The achievable spectrum efficiency as a function of dimming level \(\eta\), under a noise power of \(-8\) dBm.  
Figure 7: The achievable spectrum efficiency as a function of dimming level \(\eta\), under a noise power of \(-2\) dBm.  
As the interference generated by the proposed scheme is orthogonal to the transmitted information, the demodulation of DCHO-OFDM can be easily executed upon employing a standard OFDM receiver, which may allow the proposed scheme to further cooperate with the DCO-OFDM scheme, fulfilling the dynamic communication and illumination requirements of the VLC system.  
Conceptualization, S.F.; Methodology, S.F.; Software, S.F.; Validation, H.F., Y.Z. and B.L.; Formal Analysis, S.F.; Investigation, H.F.; Resources, S.F.; Data Curation, S.F.; Writing-original Draft Preparation, S.F.; Writing-review & Editing, B.L.; Visualization, S.F.; Supervision, B.L.; Project Administration, H.F.; Funding Acquisition, B.L.  
This research was supported in part by the National Natural Science Foundation of China under Grant 61571108, in part by the open research fund of National Mobile Communications Research Laboratory, Southeast University (No. 2019D18), in part by the Research Center of Optical Communications Engineering and Technology, Jiangsu Province (ZXF201902), and in part by the Fundamental Research Funds through the Central Universities under Grant JUSRP11919, in part by the Wuxi Science and Technology Development Fund (No. H20191001).  
The authors declare no conflict of interest.
## Data and Methods
### Object Detection  
StreetScouting platform is based on a couple of state-of-the-art object detection approaches in order to detect objects that are of a particular point of interest. More specifically, the Cascade R-CNN [33], and the RetinaFace [34] architectures are utilized. Cascade R-CNN is a multi-stage object detection architecture that consists of a sequence of detectors trained with increasing IoU thresholds, to be sequentially more selective against close false positives. RetinaFace is a robust single-stage face detector, which performs pixel-wise face localization on various scales of faces by taking advantage of extra supervision by facial landmark annotations, in combination with self-supervised multi-task learning, and is shown to achieve great results in the task of face detection. In this section, we describe the object detection models that are used from the platform.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Class** & **Annotated Objects** \\ \hline trash bins & 324 \\ recycling bins & 220 \\ trees & 2480 \\ street lights & 880 \\ shops & 1324 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Total number of annotated objects per class for the _GRUBLES objects_ dataset.