esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Applications of Microbial Fuel Cells in Sensors and Biosensors
## 1 Introduction
Biosensors can be also divided into several categories based on the transduction process, such as electrochemical, optical, piezoelectric, or thermal/calorimetric biosensors [37; 38; 39]. Biosensors have important applications in several fields, such as the food industry [40; 41], medicine [42], the marine sector [43], and others. Novel devices have recently been introduced as systems for DNA detection, which are capable of specific-sequencing with no external power, known as MFC-based DNA biosensors [44]. [PERSON] et al., with the help of Danesh Gostar Hamgam Ba Sanat Company (Babol, Iran), utilized a dual-chambered MFC system as a power supply to construct an analytical device for the monitoring and characterization of the DNA immobilization and hybridization events [45]. Specifically, the electrical power output of the MFC determined the hybridization of the DNA probe, and any discrimination of complementary DNA sequences, such as non-complementary sequences or one-base mismatched, was revealed. The experimental results using human serum as a sample, showed high sensitivity, significant stability, and acceptable reproducibility. Even more captivating, MFCs themselves can be employed as self-powered biosensors, thanks to their intrinsic sensitivity to biological and physico-chemical parameters. Monitoring microbial activity [46], biocorrosion by microbial biofilms [47], biochemical oxygen demand (BOD) [48], toxicants [27], and pH and temperature variations [26], are among the most interesting applications [10; 49]. Furthermore, MFC based (bio)sensors provide higher stability and sensitivity compared with conventional (bio)sensors [12]. Based on the above context, this review is divided in two differentiated sections. The first section is dedicated to identify the benefits and drawbacks of MFC integration as a power supply for sensors and biosensors, as well as to address the main factors influencing their power output performance, such as pH, temperature, and organic loading rate (OLR). In the second section, the use of MFCs as self-powered biosensors for toxicants and BOD is discussed.
## 5 Experiment Validation  
### Test Rig Instruction  
To validate the improvement and effectiveness of the proposed method, an artificial fault experiment of rolling element bearing is performed. As shown in Figure 10, the test rig is composed of three main parts: the electronic control system, data acquisition system, and the drivetrain components including the servo motor and gearbox. The data acquisition system contains a NI 9234 data acquisition card and a Dell laptop with the data acquisition program written by LabVIEW. The vibration signal of the artificial fault bearing was measured by an acceleration sensor with the sampling frequency of 12,800 Hz and the analyzed length of 40,960 points. The bearing types are separately 30,304 and 32,207 tapered roller bearing. As provided in Figure 11, the artificial defects of flaws and single pitting points by electrical discharge matching were separately set on a rolling element and outer raceway.  
The theoretical equations are introduced to calculate the defect characteristic frequency of bearings in the experimental process as follows:  
\[f_{bp}=\frac{D}{2d}[1-(\frac{d\cos\theta}{D})^{2}]f_{r} \tag{28}\]  
Figure 8: The filtered result of the simulated fault signal by the proposed method: (**a**) the time domain waveform; (**b**) the corresponding envelope spectrum.  
Figure 9: The envelope waveform of the filtered signal in time domain.  
\[f_{op}=\frac{Z}{2}(1-\frac{d}{D}\cos\theta)f_{r} \tag{29}\]  
where \(f_{r}\) represents the rotating frequency of shaft, \(\theta\) denotes the contract angle, \(D\) and \(d\) respectively denote the pitch diameter and rolling element diameter of the bearing. Then according to the bearing geometrical parameters displayed in Table 1, the fault characteristic frequency of the rolling element (\(f_{bp}\)) and outer-raceway (\(f_{op}\)) can be separately achieved.  
On the other hand, two assessment indicators are introduced for quantifying the performance of the proposed method. The first is the SNR considering the harmonic information about \(f_{p}\), expressed as follows [28]:  
\[SNR(f_{p})=10\log_{10}\frac{\sum\limits_{i=1}^{3}p(i\cdot f_{p})}{\sum\limits _{f=0}^{f/2}p(f)-\sum\limits_{i=1}^{3}p(i\cdot f_{p})} \tag{30}\]  
where \(p(f)\) is the amplitude value of envelope spectrum at frequency \(f\). The second is kurtosis, which is commonly used to measure signal impulsiveness in the field of rotating machinery fault diagnosis. Kurtosis is defined as  
\[kurtosis(x)=\frac{E\{x-u\}^{4}}{\sigma^{4}}-3 \tag{31}\]  
where \(u\) represents the average, \(\sigma\) represents the standard deviation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Pitch Diameter/D (mm)** & **Ball Diameter/d (mm)** & **Number of Roller (Z)** & **Contact Angle/\#** \\ \hline
32,207 & 53.31 & 9.52 & 17 & 14.04 \\
30,304 & 36.35 & 8.26 & 13 & 11.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structure parameters of rolling element bearings.  
Figure 11: The fault types of rolling bearing: (**a,c**) rolling element defect; (**b,d**) outer race defect.  
Figure 10: Test rig.
## 5 Experimental Evaluation
### Topology Formulation Mechanism Evaluation  
As described in Section 4, the NEURON protocol facilitates the creation of an overlay topology and consequently the deployment and provision of autonomic services over it. In order to show NEURON's suitability for this purpose, we compare the messages that are generated for the overlay topology formulation using DSR [40], _i.e._, another reactive routing protocol. In Figure 20, it is shown that the logarithmic behaviour of routing cost in DSR imposes extreme overhead to the network in comparison to NEURON. Furthermore, this overhead is much greater in dense networks.
## 3 System Design
### Acquisition in High-Sensitivity Mode
#### 3.4.3 Implementation
To accommodate this limitation, the receiver's sampling frequency is deliberately chosen to ensure that the computation of a 100 ms circular cross-correlation in the frequency domain results in an FFT size that is a multiple of 64\(k\) samples. Consistent with this, the sampling frequency is set to 15.728640 Mega samples per second (Msps), which provides sufficient capability to track GNSS signals in both the L1/E1 and L5/E5a bands [40]. As shown in Figure 3, the acquisition hardware accelerator incorporates a downsampling filter in the L1/E1 band, reducing the sampling frequency by a factor of 4 to 3.932160 Msps. Consequently, to compute the CAF using a CI time of 100 ms, FFTs/IFFTs with a transform size of 393,216 samples are required. This transform size is equivalent to 6\(\times\) 64\(k\) samples. To efficiently handle this calculation, the FPGA computes FFTs of \(393,216\) samples by combining six FFTs, each of 64\(k\) samples, following the approach depicted in Figure 3.0  
The LogiCORE FFT is configured to implement a 6-channel 64\(k\)-FFT on the incoming samples. An input sample selector cyclically distributes the incoming samples to the FFT channels in a sequential order (1 st sample to the 1 st channel, 2 nd sample to the 2 nd channel, and so forth. In the complex exponential combining block, a state machine combines the outputs of the 64\(k\)-FFT with the complex exponentials \(e^{-j2\pi\frac{k}{8}s}\), as illustrated in Equation (6). The complex exponentials are implemented using the CODIC algorithm. The LogiCORE FFT automatically applies a scaling factor (block exponent) to each channel to prevent overflow. The Apply Scaling Factors module in Figure 9 dynamically equalizes the various FFT channels by ensuring that the same scaling factor is uniformly applied to all of them. The scaling factor used is reported to the embedded processor. Finally, the 64\(k\)-FFTs, combined with the complex exponentials, are added to obtain the 393, 216-point FFT results. The 393, 216-point FFT and IFFT output samples are produced out of order. However, these samples are automatically reordered during the storage process to memory, minimizing latency. Figure 3.1 Large FFT/iFFT module implemented in the high-sensitivity acquisition.
# Information-Theoretic Analysis of a Family of Improper Discrete Constellations
## 6 Conclusions  
In this paper, we have proposed a family of improper constellations constructed by widely linear processing of proper \(M\)-QAM signals. We have shown that, when the coded channel symbols are uniformly drawn from these constellations, the achievable transmission rates are close to the ones dictated by the improper Gaussian capacity. In particular, except for a shaping loss of 1.53 dB encountered at high snr, there is no rate loss of the proposed improper discrete constellations with respect to the improper Shannon capacity. In practice, we can select the optimal phase at high snr, \(\phi^{*}(\kappa)\), obtained by minimizing the BER of the uncoded system without significant loss in performance at lower snr. Observe that this fact facilitates the practical implementation of these constellations since \(\phi^{*}(\kappa)\) does not depend on the snr and \(M\).  
These results are obtained by analyzing at the high- and low-snr regimes the family of mutual information, \(\{I(Y;V):V\in\mathcal{A}(\phi^{*}(\kappa,\text{snr},M),\kappa,M),\text{ for all }M\text{ and snr values}\}\), and by obtaining the shaping loss computed as the ratio of the normalized second moments of a multidimensional parallelogram and a multidimensional ellipsoid.  
We conclude that the proposed family of improper constellations is a good candidate to be used in communication systems when improper signaling is required over the AWGN channel. In fact, it behaves like the standard family of proper \(M\)-QAM constellations and can be viewed as its improper counterpart. In perspective, these results extend the analysis of improper signaling in interference-limited scenarios to non-Gaussian discrete constellations with equiprobable symbols.  
**Acknowledgments:** The work of [PERSON] and [PERSON] has been partially supported by the Ministerio de Economia y Competitividad (MINECO) of Spain and Agencia Estatal de Investigacion (AEI)/(European Fund for Economic and Regional Development) FEDER funds of the E.U., under Grants TEC2013-47141-C4-R (RACHEL), TEC2016-75067-C4-4-R (CARMEN). The work of [PERSON] and [PERSON] was supported by the German Research Foundation (DFG) under Grants LA 4107/1-1 and SCHR 1384/6-1.  
**Author Contributions:** [PERSON] and [PERSON] conceived of the research question. [PERSON], [PERSON] and [PERSON] proved the main results. [PERSON], [PERSON], [PERSON] and [PERSON] wrote the paper. All authors have read and approved the final manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.
# Measurement and Prediction of Wind Fields at an Offshore Site by Scanning Doppler LiDAR and WRF
## 3 Results and Discussion  
The LiDAR measurements are presented and discussed in this section. Section 3.1 presents vertical wind profiles measured using DBS mode and compares those by mesoscale simulations. Section 3.2 validates the proposed method for velocity retrieval from volumetric scan. In Section 3.3, the effect of coastal terrain on the near-shore marine ABL is characterized. The flow field around the wind turbine is measured and compared with wake models in Section 3.4.
# Comprehensive Review of Storage Optimization Techniques in Blockchain Systems
## 2 Research Methodology and Related Work
### Related Work  
After comparing the existing review literature, our research makes a distinct contribution to the field of blockchain storage. For example, while Ref. [22] analyzes the potential security issues in decentralized storage, it lacks an in-depth exploration of storage redundancy and specific optimization strategies. Ref. [23] focuses on on-chain, off-chain, and cross-chain scalability but fails to adequately address the particular challenges in storage scenarios. Our research addresses this gap by concentrating on key issues in blockchain storage, such as storage redundancy, and explores various optimization techniques (e.g., data compression and deduplication), offering corresponding solutions based on practical application scenarios. Ref. [24] primarily discusses blockchain scalability from the perspectives of throughput and latency optimization but does not specifically analyze storage scalability. While Ref. [25] touches upon storage-related challenges, it does not delve deeply into optimization solutions. Ref. [26] provides a review of blockchain storage scalability, outlining the storage challenges currently faced by blockchain technologies. It discusses scaling solutions from three technical layers: Layer 0 scaling, on-chain scaling, and off-chain scaling. However, this review lacks a thorough examination of the practical difficulties faced by mainstream blockchain systems (such as Bitcoin and Ethereum) in terms of storage, as well as the bottlenecks encountered by these technologies. In contrast, our paper closely links storage scalability with practical technical solutions, providing concrete and actionable optimization methods. As such, our work holds stronger practical relevance in the context of blockchain storage optimization.  
Figure 2: Research process.  
Overall, while the existing literature primarily addresses blockchain systems from the perspectives of scalability, performance, or security, our research focuses on the specific challenges and optimization strategies related to blockchain storage, offering more viable technical solutions. This study fills an important gap in the current literature and provides new perspectives and methodologies for future research on blockchain storage optimization.
# Cloud Computing Based on Computational Characteristics for Disaster Monitoring
## 3 Cloud Computing Model for Disaster Monitoring
### Computational Characteristics and Classification of Disaster-Monitoring Models  
According to the classification of the disaster-monitoring models mentioned in the previous section, this section provides a detailed analysis and description of computational features for each type of algorithm.  
(1) Numerical Calculation  
This type of task is the simplest. Typical examples are contrast changes, gradation transformations, and vegetation index calculations. The computing model of this task is expressed as: \(F(x,y)=G[f(x,y)]\), where \(x\) and \(y\) represent the spatial position of the pixel, i.e., the row and column number, and \(f(x,y)\) represents the input data. For disaster-monitoring applications, the numerical calculation model could be the enhancement of the gray value of a pixel, in which \(f(x,y)\) represents the value of the pixel. It may also be a fusion of multi-source images, i.e., multi-phase or multi-channel data calculation, in which \(f(x,y)\) represents the multiple values of the multi-source images at this position. G represents  
This type of calculation is only performed using distributed parallelism between each neighborhood window. In this task, each pixel in the output image is related to the data in the neighborhood window at that pixel location in the input image.  
Frequency Domain Operation  
The frequency domain refers to the analysis of mathematical functions or signals with respect to frequency, rather than time [40]. A given function or signal can be transformed between the time domain and the frequency domain by transform. It is more conducive to finding relevant information from seemingly complex data. After analyzing this information, detailed features in the image can be extracted. The frequency domain calculation is usually divided into three steps. First, a time domain function \(f(t)\) is transformed into the frequency domain function \(F(s)\), denoted \(F(s)=G[f(t)]\). The frequency domain data is processed by the filter or the time-frequency window function. It is expressed as \(W(s)\) and finally converted into time domain data \(f(t)=G^{-1}[F(s)]\).  
Image analysis usually uses Laplace transform, \(Z\)-transform, Wavelet transform, and Fourier transform to filter the frequency domain. These transforms can be interpreted as capturing a form of frequency, and hence the transform domain is referred to as a frequency domain. This type of calculation is only performed for distributed parallelism between each neighborhood window.
## 5 Simulation Analysis
It compares the boom valve-control system's control performance and energy-saving characteristics with those of the variable-speed pump-control and compound pump-control systems. The traditional valve-control boom hydraulic cylinder, the variable-speed pump-control boom hydraulic cylinder, and the compound pump-control hydraulic cylinder can all make the boom run smoothly and drive the working device to complete the digging action, which further proves the rationality of the parameter-matching design of the pump-control system. The displacement-control error of the hydraulic cylinder of the valve-controlled boom is the smallest, and the response speed is the fastest. As shown in Figure 15, the expected displacement of 0.801 m can be reached within 12.02 s. The response speed and error of the variable-speed pump-control arm are the second best; 12.07 s can achieve a  
Figure 16: Comparison of the boom displacement of the three control modes. Figure 15: Control model of the composite pump-control system. displacement of 0.805 m compared with the response time and the displacement error of the valve-control system, which are increased by 0.05 s and 4 mm, respectively. Compared with the valve-control system, the response time is increased by 0.07 s and the displacement error is increased by 6 mm for the displacement of the composite pump-control arm, which reaches 0.795 m in 12.09 s.  
As illustrated in Figure 17, in the driving system of the electric excavator's working device, the composite pump-controlled hydraulic cylinder system's energy consumption is reduced by about 18.9% compared with the variable-speed pump-controlled hydraulic cylinder system and by about 39% compared with the traditional valve-controlled hydraulic system.
## 3 LoRa and Localization Algorithms
### Localization Algorithm
#### (a) Range-free localization algorithms
A comparative analysis of fully range-based and hybrid range-based algorithms is shown in Table 2. Fully range-based algorithms are used to determine the distances or angles between nodes to allow an unknown node to be identified easily, whereas hybrid range-based algorithms use various distance and angle measuring methods. Different parameters, such as node density, range, scalability, and reliability of algorithms, are shown in Table 2. These exploit geometry to improve hybrid AOA/TDOA-based localization (EATL). Fusion of RSSI and TDOA measurements from the wireless sensor network provides robust and accurate indoor localization (FRTL). Hybrid range-based algorithms perform better than fully range-based algorithms. Table 2 shows that hybrid range-based algorithms perform better in range combinations using TOA, TDOA, and RSSI. Scalability and accuracy of the fully range-based algorithms are comparatively lower than those of hybrid range-based algorithms. To design a reliable and scalable sensor network architecture for smart monitoring, we proposed an architecture in which data nodes are deployed on agricultural land and agricultural field node localization is used to establish proper sensor coordination. For the deployment of data nodes to establish suitable communication among sensors, i.e., temperature, humidity, rainfall, altitude, pressure, and fire sensors in open-field lands, hybrid range-based localization techniques are preferred. Data nodes transfer information to the gateway and from the gateway to the cloud platform. A data logger was designed in which all of the sensor data can be stored. For the storage of data, both the local server and cloud server are preferred. The user on a remote device can visualize the data. From remote devices, via the Internet a farmer or any user can operate motors, sprinklers, and blowers connected to farming land.
## 4 Experimental Results
### Dataset  
The evaluation of integrating the proposed loss into different CNN models was conducted on three prominent FBP benchmarks, encompassing both restricted (SCUT-FBP and SCUT-FBP5500) and unconstrained (MEBeauty) environments. More details on the datasets and evaluation process can be found in the subsequent sections.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer Name** & **Kernels** & **Size** & **Stride** \\ \hline Input & - & \(128\times 128\times 3\) & - \\ Convolutional-1 (BN + ReLU) & 64 & \(11\times 11\) & 1 \\ Max pooling & & \(2\times 2\) & 2 \\ Convolutional-2 (BN + ReLU) & 64 & \(9\times 9\) & 1 \\ Max pooling & & \(2\times 2\) & 2 \\ Convolutional-3 (BN + ReLU) & 32 & \(7\times 7\) & 1 \\ Max pooling & & \(2\times 2\) & 2 \\ Convolutional-4 (BN + ReLU) & 16 & \(5\times 5\) & 1 \\ Max pooling & & \(2\times 2\) & 2 \\ Convolutional-5 (BN + ReLU) & 12 & \(3\times 3\) & 1 \\ Max pooling & & \(2\times 2\) & 2 \\ Convolutional-6 (BN + ReLU) & 8 & \(3\times 3\) & 1 \\ Average pooling & & \(2\times 2\) & 1 \\ Fully Connected + Dropout & & & \\ The proposed ensemble loss with response & & & \\ Regression & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: The configurations of the proposed FIAC-Net-regression-based for FBP.