esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Rectifier Circuit for Piezoelectric Energy Harvesting from Human Motion
## 4 Results and Discussion  
### Shaker Test  
#### 4.1.1 Effect of Varying Input Voltage  
The rectified voltage through both H-Bridge and FBR circuits (stored in C1) over various input voltage and excitation frequency of 1 Hz and 2 Hz at a resistance of 910 k\(\Omega\), is shown in Figure 13a,b, respectively. It can be seen that when the input voltage increases, the rectified voltage through both circuits also increases accordingly. The trend of increase is more consistent in the case of H-Bridge when compared with the FBR circuit. More importantly, the H-Bridge circuit constantly produced higher rectified voltage when compared with the FBR circuit due to its low threshold voltage of MOSFETs.  
Another distinct reason for the H-Bridge circuit achieving higher voltage is that the resistance path of MOSFET, which is drain to source, can be controlled with the voltage applied to the gate terminal. When the input voltage on gate terminal is positive, say 1 V (Figure 13) with respect to the source, an electric field is established between the drain to the source (refer to Figure 8). Eventually, induced negative charges in P-substrate forms p-layer below the gate terminal, causing the p-layer to become an induced n-layer. These charges (i.e., electrons) form n\({}^{-}\) channel in between two n\({}^{+}\) regions (one n\({}^{+}\) region on drain, the other n\({}^{+}\) region on source), which cause the flow of current from drain to source.  
If the applied gate voltage is more positive, say 5 V (Figure 13), the induced n\({}^{-}\) channel becomes deeper and therefore, more current flows from drain to source.  
Figure 11: Cycling test.  
Figure 12: Experimental setup of a walking test on a treadmill.  
This shows that drain to source current is enhanced by the gradual increase of gate voltage. Therefore, when the applied voltage from PD is increasing from 1 to 5 V, the rectified voltage is also increasing accordingly. Another interesting observation is that, by comparing Figure 13a,b, the effect of excitation frequency has a negligible effect on the rectified voltage.  
Next, the output power is calculated by Equation (8), and the relationship with input voltage at different frequency is graphically shown in Figure 14. Regardless of excitation frequency and type of circuit, the output power generally increases with an increase in input voltage. Similarly, the output power produced by H-Bridge is considerably higher than the FBR circuit under all input conditions. In addition, it is found that slightly higher output power is achieved at higher frequency (i.e., 2 Hz) because the PD can carry more stable and higher current [43]. This observation is similar for both rectifier circuits.
## 2 FSO Communication System  
FSO communication systems are a vital part of modern telecommunication technologies, offering a LoS data transmission method through the atmosphere [16] and space telecommunications [17]. As illustrated in Figure 4, an FSO system primarily consists of three fundamental components: the transmitter (Tx-source), the channel, and the receiver (Rx-destination). Each of these components plays a critical role in ensuring the successful transmission of optical signals across free space. For instance, information waveforms are generated by a source and then modulated onto an optical carrier. This optical signal is transmitted through the atmosphere, or free-space, towards a remote destination. Along the way, the optical power emitted by the Tx is influenced by various factors before it reaches the Rx. As a result, the information received might not be an exact copy of the  
Figure 3: Outline of the article structure.  
originally transmitted information. At the Rx, the optical signal is collected and converted into an electrical current by a photodetector. The Rx then processes this electrical current to retrieve the originally transmitted information [1; 3]. So, the FSO communication system's efficiency and performance hinge on the seamless integration and alignment of these three components. The Tx initiates the communication by sending out the optical signal, the channel serves as the medium through which the signal travels, and the Rx completes the process by decoding the transmitted information at the destination. This section outlines the various processes involved in transmitting signals from the source to the destination in an FSO communication system.
Sensors_**2015**, _15_, 4273-4290; doi:10.3390/s150204273  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**An Accurate Link Correlation Estimator for Improving Wireless Protocol Performance**  
**[PERSON] 1, [PERSON] 2, [PERSON] 1,* and [PERSON] 1**  
1 College of Computer Science, Zhejiang University, #38, Zheda Road, Hangzhou 310027, China;  
E-Mails: [EMAIL_ADDRESS] (Z.Z.); [EMAIL_ADDRESS] (J.B.)  
2 Zhejiang Provincial Key Lab of Data Storage and Transmission Tech, School of Computer Science, Hangzhou Dianzi University, #1158, Baiyang Block#2, Hangzhou 310027, China;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel./Fax: +86-571-87953955.  
Academic Editor: [PERSON]  
_Received: 31 December 2014 / Accepted: 3 February 2015 / Published: 12 February 2015_  
**Abstract:** Wireless link correlation has shown significant impact on the performance of various sensor network protocols. Many works have been devoted to exploiting link correlation for protocol improvements. However, the effectiveness of these designs heavily relies on the accuracy of link correlation measurement. In this paper, we investigate state-of-the-art link correlation measurement and analyze the limitations of existing works. We then propose a novel lightweight and accurate link correlation estimation (LACE) approach based on the reasoning of link correlation formation. LACE combines both long-term and short-term link behaviors for link correlation estimation. We implement LACE as a stand-alone interface in TinyOS and incorporate it into both routing and flooding protocols. Simulation and testbed results show that LACE: (1) achieves more accurate and lightweight link correlation measurements than the state-of-the-art work; and (2) greatly improves the performance of protocols exploiting link correlation.  
**Keywords:** sensor networks; protocols; link correlation
# GNSS Performance Modelling and Augmentation for Urban Air Mobility +
## 3 GNSS Performance Characterization
### Error Modelling
#### 3.1.1 Ephemeris and Satellite Clock Errors  
The satellite clock bias depends on the stability of the atomic clocks on the satellites, the control segment monitoring network and the latency of the corrections. Although these clocks are highly stable, the clock correction fields in the navigation data message are sized such that the deviation between SV time and GPS time may be as large as 1 ms. The ground stations in the control segment determine and transmit clock correction parameters to the satellites for rebroadcast in the navigation message. These correction parameters are implemented by the receiver using the second-order polynomial [23]:  
\[dt^{p}=a_{f0}+a_{f1}(t-t_{\alpha})+a_{f2}(t-t_{\alpha})^{2}+\Delta t_{r} \tag{3}\]  
where \(a_{f0}\) is the clock bias; \(a_{f1}\) is the clock drift; \(a_{f2}\) is the frequency drift (aging); \(t_{\rm oc}\) is the clock data reference time; \(t\) is the current time epoch; \(\Delta t_{r}\) is the correction due to relativistic effects. This residual error is dependent on the Age Of Data (AOD), which is the time elapsed since the most recent control segment upload to a satellite. Residuals are the smallest following the upload, and then slowly degrade over time till the next upload.  
The ephemeris for each satellite is estimated by the control segment and transmitted to the satellite along with other navigation data parameters for re-transmission to the user receiver. The ephemeris corrections are derived from a curve fit of the prediction of each satellites position at the time of upload. The contribution of ephemeris error to the overall pseudorange error is due to the residual error that remains in the satellite position after the curve-fit. This residual error is split into three components: Along Track (ATK), Across Track (XTK) and Radial (RAD). The maximum error occurs when the satellite lies in the plane of the horizon and the line-of-sight (LOS) user-satellite lies on the geometric plane containing ATK. In general, the error can be expressed as a function of the three components, in the form [10]:  
\[\mathrm{ERR}=\mathrm{RADcos}\alpha+\mathrm{ATKsin}\alpha\;\cos\beta+\mathrm{ XTKsin}\alpha\;\sin\beta \tag{4}\]  
where \(\alpha\) is the angle between the LOS user-satellite and the satellite vertical and \(\beta\) is the angle between the ATK direction and the plane containing the LOS and the satellite vertical. The ATK and XTK are more difficult for the control segment to observe through its monitors on the Earth's surface, since these components do not project significantly onto the LOS(s) toward the Earth. The standard deviation of the pseudorange error model due to satellite clock and ephemeris inaccuracies is set to be equal to the satellite's User Range Accuracy (URA), which is a one-sigma estimate of the satellite's Signal-in-Space (SIS) User Range Error (URE).
# Effect of Tomato Juice and Different Drying Methods on Selected Properties of Courgette
## 4 Conclusions  
In all the materials tested, the water activity was <0.6, indicating the effective removal of water from the material and ensuring microbiological safety.  
Vacuum impregnation had a significant effect on the properties of the courgette, increasing dry matter content and density and decreasing AW, which promotes better shelf life and microbiological safety. The courgette after VI was characterized by lower drying shrinkage. In terms of color, impregnation with tomato juice increases the intensity of red, positively affecting the visual aspects of the courgette.  
Dried material obtained using the freeze-drying method are characterized by increased brightness of the courgette, which is important for its visual appeal. In addition, the dried material is characterized by the lowest water activity, contributing to better product shelf life and microbiological stability. In addition, dried material obtained using this method are characterized by higher dry matter content and lower density. In addition, freeze drying minimizes shrinkage of the material, helping to preserve the original structure and shape of the courgette. Its effect on the high gelling index is also significant, indicating better gelling properties of the courgette after rehydration, which is important in the context of its various industrial applications.  
The logistic model best describes the kinetics of courgette drying, with high R2 values, low RMSE and \(\chi^{2}\), suggesting its usefulness in optimizing drying processes.  
Increasing the temperature in the convection drying process accelerates the evaporation of water, shortens the drying time, increases the dry weight, and reduces the water activity, density, VGI, and shrinkage of the courgette.
_applied sciences_  
_Article_  
Comparative Analysis of Solar Radiation Forecasting Techniques in Zacatecas, Mexico  
**[PERSON] \({}^{\ddagger}\), [PERSON] \({}^{\ast,\dagger}\), [PERSON] \({}^{\ast}\), [PERSON] \({}^{\circled}\), [PERSON] \({}^{\circled}\), [PERSON] \({}^{\circled}\), [PERSON], [PERSON] \({}^{\circled}\) and [PERSON] \({}^{\circled}\)  
Laboratorio de Sistemas Inteligentes de Vision Artificial, Poggrado en Ingenieria y Tecnologia Aplicada, Universidad Autonoma de Zacatecas, Zacatecas 98000, Mexico; [EMAIL_ADDRESS].com (M.I.E.-L.); [EMAIL_ADDRESS] (C.A.O.-O.); [EMAIL_ADDRESS] (H.A.G.-O.); [EMAIL_ADDRESS] (G.D.-F)  
\({}^{\ast}\) Correspondence: [PERSON]_ADDRESS] (L.O.S.-S.); [PERSON]_ADDRESS] (C.L.C.-M.)  
\({}^{\ddagger}\) These authors contributed equally to this work.  
**Featured Application: Accurate solar radiation forecasting is essential for optimizing solar energy systems in Zacatecas, Mexico. Our comparative analysis identifies the most reliable methods for the region, enhancing solar power plant operations, energy grid management, agricultural planning, and climate studies. Implementing the best techniques can provide significant economic, environmental, and social benefits for Zacatecas and similar regions.**  
This work explores the prediction of daily Global Horizontal Irradiance (GHI) patterns in the region of Zacatecas, Mexico, using a diverse range of predictive models, encompassing traditional regressors and advanced neural networks like Evolutionary Neural Architecture Search (ENAS), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Meta's Prophet. This work addressing a notable gap in regional research, and aims to democratize access to accurate solar radiation forecasting methodologies. The evaluations carried out using the time series data obtained by Comision Nacional del Aguua (Conagua) covering the period from 2015 to 2018 reveal different performances of the model in different sky conditions, showcasing strengths in forecasting clear and partially cloudy days while encountering challenges with cloudy conditions. Overall, correlation coefficients (r) ranged between 0.55 and 0.72, with Root Mean Square Error % (RMSE %) values spanning from 20.05% to 20.54%, indicating moderate to good predictive accuracy. This study underscores the need for longer datasets to bolster future predictive capabilities. By democratizing access to these predictive tools, this research facilitates informed decision-making in renewable energy planning and sustainable development strategies tailored to the unique environmental dynamics of the region of Zacatecas and comparable regions.  
**Keywords:** solar radiation forecasting; artificial neural networks; Zacatecas; predictions solar patterns; convolutional neural networks
## 4 Measurement and Analysis of PD
### The Single PDs Occurring in the Positive Half-Period  
Due to the polarity effect of the needle-to-plate defect, insulation breakdown frequently occurs in the positive half-period. Therefore, it is an indispensable capacity for an UHF antenna sensor to detect single PDs occurring in this half-period. In this paper, single PDs (2 ms) are sampled and analyzed under different voltages, with a sampling frequency of 10 GHz, in order to further assess the PD detection capability of the built-in sensor. Figures 17 and 18 show the single PDs detected by the built-in antenna sensor and the external antenna sensor under different voltages, respectively.  
Figure 16: **(a)** PDs with frequency reference under 6.0 kV; **(b)** The enlarged view of PDs occurring in the positive half-period; **(c)** The enlarged view of PDs occurring in the negative half-period.  
Figure 17 indicates that the amplitude of PDs in the positive half-period are significantly larger than that of PDs in the negative half-period under the same voltage conditions, and the magnitude of the PD amplitude increase is more obvious under the same conditions. Compared with the single PDs detected by the external antenna as shown in Figure 18, not only do PDs detected by the built-in sensor possess a larger amplitude under the same voltage conditions, but the amplitude variation of PDs is less obvious.  
Figure 17: Single PDs detected by the built-in antenna under different voltages.  
Figure 18: Single PDs detected by the external antenna under different voltages.  
PDs is also more evident for the same voltage difference. These experimental results also show that the built-in antenna sensor possesses high detection sensitivity and strong anti-interference ability, and the external antenna sensor presents poor PD detection capacity because of the electromagnetic shielding effect of the all-metal casing and the interference from the electromagnetic environment.  
In general, parameters used to characterize the strength of PD in high-voltage switchgear, namely the severity of the internal insulation defects, are closely related to the times and amplitudes of PDs. They all can be detected through the built-in antenna sensor presented in this paper. The experimental results show that the built-in antenna sensor has great PD detection capacity, and it has more advantages in high-voltage switchgear PD detection than the external sensor.
## 2 Materials and Methods
### Measurement of Vine Growth and Yield Parameters  
In the year 2019, one week before the harvest, the area of primary and lateral leaves of ten shoots per replicate was measured by using a portable leaf area meter (Yaxin-1242, Beijing, China). The length and the diameter (the third internode from the base) of ten shoots per replicate were measured. At harvest, ten randomly sampled bunches per replicate were weighed to calculate the average bunch weight, and the number of the bunches was counted on ten vines for each replicate. The yield at harvest was monitored by weighing vine bunch weight. During winter pruning, canes from five vines per replicate were pruned and weighed.
# Condition-Based Monitoring and Maintenance: State of the Art Review
## 2 Literature Review
### Markov Chain  
In general, failure is random in nature, and thus it is quite challenging to model. In this context, one of the most frequently used techniques is to employ the Markov chain (MC) method, which is even effective in modelling failure where such indications are not clear in the historical dataset. It is often used in different applications where greater uncertainties are generally expected. In [47], a semi-Markov decision process (SMDP) was developed for the maintenance policy optimization of condition-based preventive maintenance problems, demonstrating that the deterioration rate at each failure stage was the same, thus suggesting an optimal policy was a sort of dynamic threshold-type scheme. In [48], a MC-based algorithm was proposed for a deteriorating system for simultaneous optimization of parameters of condition-based preventive maintenance with an accumulated deterioration random failure. In a similar study, Hontelez et al. [49] developed optimum CBM policies using a discrete Markov decision method. In [50], a MC-based model using relevant condition predictor was proposed for reliability prediction for systems under condition-based maintenance.
# Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder
## 3 Experimental Result
### Experimental Environment  
We conducted experiments on Cityscapes' foggy dataset [32; 33; 34], which is widely used for other research and shows meaningful results on real world dataset. It provides a collection of synthetic foggy images and transmission maps (ground-truth material). Using foggy dataset and rain streaks [10] with (5), we collected 18,006 training images, 2871 validation images, and 9189 test images. In addition, using models pretrained on generated Cityscapes rain dataset, 24 real world road images were tested which includes haze and rain streak effects. FHRR-Net and other comparison methods were trained and tested on same dataset condition. After each convolutional layer in the encoder-decoder network, batch normalization [35] and ReLu [36] layer are used. Adam optimizer method [37] was used to train the network with batch size 128, momentum to 0.9. Initial learning rate is set to 0.01 for 100K iterations and 0.001 for 100K iterations to 200K iterations and terminate the training at 200K iterations. All experiments are conducted using NVIDIA Geforce GTX 1080 with Tensorflow library.
## 5 Experimental Evaluation
### Non-IID Data Distribution Method  
The non-IID Data distribution was designed to distribute a dataset into subsets across multiple clients. This was achieved by first sorting the dataset based on labels, then dividing it into shards, and finally distributing these shards across clients. The method ensured that each client received a specific number of shards, but the data within those shards were not uniformly distributed across classes.  
1. Sorting by labels: Given a dataset \(D\) with \(n\) samples, each associated with a label from a set \(L\), the dataset is sorted on the basis of these labels. This results in a sequence \(D^{\prime}\) where samples with the same labels are grouped together.
2. Shard creation: The sorted dataset \(D^{\prime}\) is divided into \(N\) shards, each containing S samples. Thus, each shard \(Shard_{i}\) is defined as: \[\text{Shard}_{i}=\Big{\{}d_{(i-1)S+1},d_{(i-1)S+2},\ldots,d_{iS}\Big{\}}.\] (24)
3. Data allocation to clients: For each client \(c\), a specific number \(X\) of shards are randomly selected without replacement from the set of all shards. The union of samples from these selected shards forms the non-IID dataset for client \(c\). Mathematically, the dataset \(D_{c}\) for client \(c\) is: \[D_{c}=\bigcup_{k=1}^{X}\ \text{Shard}_{i_{k}},\] (25) where \(i_{1},i_{2},\ldots,i_{X}\) are the indices of the shards selected for client \(c\).  
This study considered three non-IID data distribution scenarios such as low (20%), medium (50%) and high (80%) to evaluate the performance of the UA-FedRL model. The relevant parameters used to create scenarios can be seen in Table 3.
## 2 Effects of Nicotinamide Riboside on Several Organs and Systems
### Nicotinamide Riboside and the Urinary System  
In the U.S. hospitals, about 3-10% of the adults suffer from acute kidney injury (AKI). NAD\({}^{+}\) supplementation, by oral administration of NAM, NA, NR, dihydronicotinamide riboside (reduced NR, NRH) and NMN, and SIRT activation may be useful in the prophylaxis and treatment of AKI, if some recent studies on MT metabolism are developed [109; 110; 111; 112; 113]. For example, in an experimental model of newborn (one day) mice from mothers fed a low protein diet, NRS restored the SIRT3 expression, by the induction of PGC-1\(\alpha\), improved MT and cellular protection against OXS, increasing the density of nephrons, renal capillaries, and glomerular podocytes [114].  
The effect of four NRPT doses (NR/PT--1.25 g/50 mg, 2.5 g/100 mg, 3.75 g/150 mg, 4 g/200 mg), administered twice a day for two days, was tested in a randomized, double-blind, PLA-controlled study in 24 hospitalized AKI patients. Serum NAD\({}^{+}\) levels were increased by NRPT treatment. Safety was assessed by various assays, such as estimated glomerular filtration rate (eGFR), creatinine levels, electrolyte levels, hepatic function parameters, and blood count; these all remained unchanged. Minor gastrointestinal (GI) side effects were reported only by three patients. NRPT supplementation up to 1 g/200 mg twice a day was reported as being safe and well tolerated in hospitalized AKI patients [110].  
In another recent preclinical study, in rats, NR prophylactic supplementation did not mitigate tubular impairment and induction of profibrotic genes neither in bilateral I-R injury-induced long-term AKI nor in chronic kidney disease (CKD) experimental model, even though an increased NAD\({}^{+}\) blood levels were evidenced [115]. However, [PERSON] et al. (2023), in a study in male mice with cisplatin (CPT)- or I-R-induced AKI, suggested that NR and NMN supplementation restored NAD\({}^{+}\) serum levels and improved MT metabolism and kidney functionality [116].  
A randomized PLA-controlled, double-blind, crossover trial was conducted for six weeks to determine the impact of NR (1 g/day) and coenzyme Q10 (CoQ10, 1.2 g/day) on exercise tolerance and metabolic profile in 25 CKD patients with eGFR of \(<\)60 mL/min/1.73 m\({}^{2}\). Compared with PLA, only systemic MT metabolism and lipid profile (e.g., TG, free fatty acids, ceramides) were improved by NR and CoQ10 co-administration, but not aerobic capacity (VO\({}_{2}\) peak) or total work efficiency [117].