esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In summary, the novel method for determining the EO coefficients and their dispersion based on the photoelastic modulation is presented. The monochromator is selected to automatically scan the wavelength of the light source. The control of the PEM and the data processing are implemented in one FPGA. The DC and harmonic terms are extracted simultaneously by employing the digital phase-locked technology and then the EO coefficients are solved. The EO coefficient \(\gamma_{22}\) of a Y-cut LN crystal plate was measured under different applied DC voltages at visible wavelength light range of 0.42 to 0.8 \(\upmu\)m, after the phase retardation amplitude of the PEM was precisely calibrated. The experimental results demonstrate that the measurement sensitivity of the system is \(1.1\times 10^{-14}\) m/V, at sampling time of 198.9 ms. Fitting the measured results against the light wavelength, the dispersion of the EO coefficients with wavelength is obtained similar to the Cauchy dispersion formula \(\gamma_{22}=5.31\times 10^{-12}+\frac{4.071\times 10^{-13}}{\lambda^{2}}+ \frac{7.184\times 10^{-14}}{\lambda^{4}}\). Moreover, the fluctuations of the light source are eliminated, no mechanical adjustments are needed during testing, and the measurement sensitivity and speed are guaranteed. The method is suitable for studying the dispersion of the EO coefficients of crystal as well as of thin films and two-dimensional materials.  
[PERSON], [PERSON], and [PERSON] developed the theory; [PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministry of Science and Technology of the China, grant number 2013 DFR10150, National Natural Science Foundation of China, grant numbers 61505179, 61505180.  
The authors are grateful to [PERSON] and [PERSON] for fruitful discussions.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Methodology
#### 2.2.7 Estimation of Maize Yield in HARARE  
Yield data for Harare for the study period were not available in the Department of Agricultural Extension for the entire study period. A study by [35] in the context of small fields where maize was produced under diversified farming practices was used as a proxy for the variable agricultural practices and plot sizes similar to what is practiced by urban farmers in Harare metropolitan. Six cultivars were used, and three planting dates were selected to plant on a plot approximately 9 m \(\times\) 6 m, subdivided into smaller sub-plots. This layout was found to be similar to patterns observed in urban agriculture, also characterized by small farm areas [4], with different practices used even in farms that are next to each other. In view of this, maize yields obtained from the study by [35] over three farming seasons were used to calculate the average maize yield in tons per hectare for Harare. Using the mean and standard deviation, the best-case, worst-case, and average-case yields were derived. Assuming maize was the only crop under production in the study area in all periods, the total quantity of maize (in tons) produced for each year was modeled using Equation (3):  
\[\textit{Maize yield }(\textit{tonnes})=\textit{Cropland area}\times\textit{Estimated maize yield per hectare} \tag{3}\]  
where _Cropland Area_ is derived from supervised image classification and _Estimated maize yield per hectare_ is the worst yield, average, or best case yield per hectare estimation as determined from the mean yield per hectare \(\pm\) the standard deviation.
## 3 Results
### Correlation of VOCs and Species Detected in Sourdough and Dough Samples  
To understand the correlation among the detected VOCs and bacteria or yeasts isolated a Pearson's correlation index was calculated. The correlation plot is reported in Figure 8. Considering bacteria isolates, the major contributor to the aroma composition was attributed to _Fl. sanfranicscensis_, which was shown to be positively correlated (R\({}^{2}\geq\) 0.6; \(p\) < 0.05) with a wide range of VOCs belonging to four classes, such as aldehydes, esters, alcohols, and acids, as well as with a minor compound as limonene. Two other species, specifically _Leuc. citreum_ and _Lacp. plantarum_, contributed in a moderate way to the aroma production, resulting in each of them being positively correlated to four different compounds. In particular, _Leuc. citreum_ was correlated to acetaldehyde, ethyl butanoate, ethyl hexanoate, and acetoin (R\({}^{2}\geq\) 0.6; \(p\) < 0.05). In the case of _Lacp. plantarum_, the major contribution was related to 3,3-dimethyl hexane, ethyl butanoate, 2-butenal, and 1-butyl-2-propyl cyclopentane. Interestingly, _Furl. rossiae_ was shown to have a good correlation to ethyl alcohol production (R\({}^{2}\) = 0.83; \(p\) < 0.05). The other two species detected, _Lenl. parabuchtneri_ and _Leuc. mesenteroides_, were significantly correlated with just one compound, precisely 2-hexanol (R\({}^{2}\) = 0.87; \(p\) < 0.05) and isobutanol (R \({}^{2}\) = 0.71; \(p\) < 0.05), respectively. Regarding the yeasts detected, _K. humilis_ and _S. cerevisiae_, their contribution in terms of VOCs was shown to be, generally, very low. In particular, _S. cerevisiae_ was found to be positively correlated with isoamyl alcohol, ethyl hexanoate, phenethyl alcohol. No relevant correlation was highlighted for _K. humilis_.
## 4 Current and Advanced Methods in BEMS
### Automation
#### 4.2.2 Smart Thermostat  
HVAC&R smart controlling management has become a major concern for both residential and commercial buildings. A smart thermostat is a device that learns user temperature preferences and is utilized in thermostatically regulated loads. It also makes things easier for customers by allowing remote access and communication with AMI based on price indications [98]. The smart features of programmable thermostats include sensing, machine learning, and a network connection. These thermostats are equipped with proximity and motion sensors, and their learning algorithm adapts to the user's past preferences at various times of the day. Various strategies have been investigated to sense the real-time occupancy/vacancy of HVAC zones in order to save energy without affecting occupant comforts, such as Radio Frequency Identification (RFID) [99], IR motion sensors [100], and programable thermostats [101]. However, the accuracy of detecting the occupant in a conditioned space is being questioned using a smart controller. To avoid this issue, the study in [29] describes a new wireless device platform and prototype development that combines an infrared (IR) and an optical (OP) camera to enable collaborative intelligence with minimal power consumption and improved accuracy. The system saves up to 26% of HVAC energy when compared to a programmed thermostat and schedule-based HVAC control.  
Furthermore, the availability of embedded intelligence in HVAC&R equipment might significantly reduce the cost of applying intelligent control systems, allowing for a much broader applicability. A cooling tower fan controller, for example, may have access to embedded performance data from each tower fan and chiller on the local network. Likewise, a chiller sequencing controller may obtain part-load information from individual chillers [88]. In addition, a compressor might have an embedded chip with a performance map and sensor inputs for suction and discharge conditions, allowing the map to be used to calculate refrigerant mass flow rate and compressor power consumption. Similarly, a condenser might contain a chip with a model, state variable readings, and a virtual refrigerant mass flow measurement to offer a virtual sensor for condenser airflow. The airflow through the condenser might therefore be used as a diagnostic signal for fouling or fan issues [93]. In addition, the spread of low-cost information for monitoring, diagnostics, and greater control can be enabled through embedded intelligence.
## 5 Conclusions  
Based on the LSTM neural network, this paper studies the very short-term prediction of the ship roll motion considering the wave elevation input. The influence of the wave data input on the prediction accuracy and the advance prediction duration is analyzed. Using the wave elevation and ship motion data, the feasibility of considering the wave elevation input to predict the ship roll motion is verified. The following conclusions are obtained.  
Compared with the prediction results solely based on the roll data input, the roll motion prediction considering the wave elevation input greatly improves both the prediction accuracy and the advance prediction duration;  
For single-wave data input, the LSTM model can mine the mapping relationship between the advance prediction duration and the distance of the wave height measurement points. The effective advance prediction time increases with the increase in the distance from the wave gauge to the ship;  
Compared with the prediction results of the single-wave data input, the prediction of the multiple-wave data input has better stability for different advance times. As the amount of data increases, the overall advance prediction duration of the LSTM model improves, and the model also has better robustness for larger advance prediction durations.  
Conceptualization, X.T.; Y.S.; methodology, X.T.; software, X.T.; validation, X.T.; Y.S.; formal analysis, X.T.; investigation, X.T.; resources, X.T.; data curation, Y.S.; writing--original draft preparation, X.T.; writing--review and editing, X.T.; visualization, X.T.; supervision, Y.S.; project administration, X.T.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is unavailable due to privacy.  
The authors declare no conflict of interest.
## 3 Simulation Results
### Case 1: Active Power Flow
In addition, for unbalanced loaded SST, results presented in Figure 16 show that presented SST model drew a balanced three phase current from the grid with a harmonic content of 2.15% only for each phase current. The voltage and current response of SST-VSC at DC -link \(V_{DC,\,m\text{s}}\) with unbalanced loading is given in Figure 8:2
# Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV)
## 2 Background
### Review of Existing ML and DL Security Approaches in IoV
#### 2.5.2 Deep Learning-Based IDS
[58] introduced an RNN-LSTM-based intrusion detection system for robotic vehicles, utilizing deep learning to capture the temporal patterns of cyberattacks. To address the high computational demands, a cloud-based offloading framework was implemented. The study also explored the trade-off between offloading and detection latency. While machine learning and deep learning approaches have made significant strides in addressing security challenges in the Internet of Vehicles, they still face substantial limitations, such as high computational costs, latency issues, scalability challenges, and the risk of high false positive rates. These constraints highlight the growing need for Generative AI-based IDS, which present more flexible, resource-efficient, and adaptive solutions capable of addressing the dynamic and increasingly sophisticated security threats faced by IoV networks. By leveraging generative models, IDS systems can enhance threat detection accuracy and adapt in real time to evolving attack strategies.
# Development of Potato Mass Estimation System Based on Deep Learning
###### Abstract  
Potato is one of the world's four major food crops as an important resource cultivated in about 150 countries. As precision agriculture has recently attracted increasing attention for its role in improving productivity, interest in yield monitoring is also increasing. Yield monitoring is a precision agriculture technology, and it can help farmhouse business management in the future by contributing to variable fertilization and supply and demand control. The present study was carried out to develop and evaluate a system that uses machine vision and deep learning technologies to estimate potato mass to monitor potato yield. The system performs object classification using the YOLOv5 algorithm to sort out potatoes among various foreign substances, object tracking using the DeepSORT algorithm to track the sorted potatoes, and volume calculation using the lengths of the major axis and minor axis of the tracked potatoes. The results of analyzing the function of the developed yield monitoring system showed an object detection rate of 95.2% and a weight measurement error of 9%, indicating that the computation load must be reduced through algorithm optimization to improve the accuracy and that error correction needs to be performed based on the potato position within the view angle.  
potato; yield monitoring; precision agriculture; YOLOv5
## 3 Results
### Fracture Toughness Measurements for Different Fluid Compositions  
A total of 22 Obourg Chalk samples were tested with different pore fluids using the SCB method described in Section 2.4. In addition to air and water, three different brines were used as pore fluids, keeping the ionic strength constant at 0.59 mol/L. Similar to the UCS measurements reported in Section 3.1.1, the salinity of the monovalent NaCl brine was 35 g/L. For the Ciply Chalk, due to the lack of material, only six samples were tested, three under dry conditions, and three with distilled water as pore fluid. The average values and standard deviations for the fracture toughness KIC in mode I are given in Table 5. A strong water weakening effect is again observed with a wet-to-dry ratio WDR(KIC) = 0.65 for Obourg Chalk and 0.60 for Ciply Chalk. Concerning the results for the tested brines, surprisingly, sodium chloride has the strongest weakening effect (for the UCS no difference was found with water), calcium chloride and magnesium chloride have also a larger weakening effect than water. All in all, the KIC measurements for the brine-saturated samples are in poor agreement with our results for UCS. This will need to be further investigated.
## 3 Proposed Method
### Overview of The Proposed Method  
Contrastive learning has achieved great success in the field of unsupervised learning. In this paper, we determined the mapping function between haze and haze-free images by combining GAN and contrastive learning. As shown in Figure 1, the network structure proposed in this paper consists of a generator and a discriminator, where the generator consists of three parts: an encoding module, a residual module, and a decoding module. The feature fusion module based on the multi-scale skip connection proposed in this paper is used in the generator, aiming to recover the haze image into a clear and haze-free image, while the discriminator uses the now widely used PatchGAN [52], aiming to distinguish the generated clear and haze-free image from the original haze image. Given a haze image, we firstly adopted the encoding module to encode the input haze image into a low-resolution feature map, then used the residual module to extract more complex and deeper features from the low-resolution feature map and simultaneously remove the haze, and finally used the decoding module to output the final restored image, in which the residual module is essential because it not only extracts deeper features and improves the expression of characteristics, but it also stabilizes the training process by mitigating the problem of vanishing gradient and exploding gradient.
## 4 Natural Antioxidants and Their Role in Cosmetics
### Algae-Derived Antioxidants
#### 4.10.3 Chlorophyceae (Green Algae)  
Chlorophytes are a division of algae rich in a variety of substances beneficial to the skin and to the whole human body. They contain large amounts of plant pigments, vitamins, and macro- and microelements possessing antioxidant properties and for this reason are often used as components of anti-aging preparations and cosmetics for mature skincare. The species most often used in cosmetic products include _Ulva lactuca_, _Caulerpa lentillifera_, and _Chlorella_ sp. [195].  
Freshwater unicellular green microalga _Haematococcus pluvialis_ is the richest source of natural astaxanthin, the carotenoid pigment (3,3\({}^{\prime}\)-dihydroxy-8-carotene-4,4\({}^{\prime}\)-dione), and is now cultivated at industrial scale. Astaxanthin has antioxidant, UV-light protection, anti-inflammatory, and other properties and has important applications in the nutraceuticals, cosmetics, food, and aquaculture industries. Also, the compound will likely be one of the highest-value microalgal ingredients in the future [73; 197].  
The single-celled algae _Chlamydomonas nivalis_ produces phenolic compounds, free proline, astaxanthin, and provides antioxidant protection factor in response to UVA and UVC light. This species is widely used in cosmetics, known as snow algae, and possesses anti-aging properties [198].  
Prasiolin (MAA) is a new UV sunscreen compound found in the terrestrial green macroalga _Prasiola calophylla_[189].  
Some other species of green algae and their bioactive antioxidant and anti-aging compounds that are promising for applications in cosmeceuticals are _Bryopsis plumose_ (polysaccharide), _Chaetomorpha antennaia_ (fucoxanthin), _Chlamydomonas hedleyi_ (MAA), _Chlorella_ (sporopolenin, MAA, lutein), _Dunialella salina_ (\(\beta\)-carotene), _Entromorpha_ sp. (polysaccharide), _Gayralia oxysperma_ (fucoxanthin), and _Ulva_ sp. (polysaccharide, phenol, flavonoid, sulfated polysaccharide). A special product called Dermochlorella [PERSON], St. Malo, France, it is an anti-wrinkle agent and is derived from _Chlorella vulgaris_ extract [192].
# Deep Learning of GNSS Acquisition
## 3 Deep Learning Method for GNSS Acquisition
### Data-Driven, Physics-Based Signal Acquisition
An NN consists of an input layer, one or more hidden layers, and an output layer, as well as predefined activation functions that connect adjacent layers. Each layer has a specific weight, which is usually determined with backpropagation during a training process that involves large amounts of data with known labels [49; 50]. The network design process is important in order to achieve high accuracy while keeping the network complexity within feasible bounds. Some aspects are effective in designing the network, such as the number of layers, number of neurons, and type of optimizer. In this work, we considered the pretrained VGG16 neural network model as a baseline, where some heuristic modifications to adapt those hyperparameters to the problem at hand were implemented after several trials. The use of VGG16 is common in image processing tasks, which resembles the type of classification challenges the proposed algorithm needs to tackle. More automated approaches to select the NN architecture can be considered in future works, such as the use of Bayesian Optimization [51]. Additional details on the NN structure are provided in Section 4.
## 4 Experiments and Discussions
### Image Acquisition Experiment
#### 4.2.1 Image Acquisition by the System  
Before collecting the fabric image, it was necessary to calibrate the speed of \(x\), \(y\), \(z\), \(a\), \(b\) axes of the system. Additionally, the fabric surface should be kept as flat as possible in the acquisition process, so as to obtain high-definition images. Five hundred images were collected for each class of fabric. Part of the image collected by the system is shown in Figure 12.
# A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research
## 3 Position Estimation Systems
### Dead-Reckoning (DR) and Inertial Positioning  
DR is the process of estimating the position and heading of a vehicle based on previous position measurements. The simplest position estimates are made using rotary sensors (encoders) fixed to the steering wheel and the wheels of the vehicle [27], a technique known as odometry. It is not capable of quantifying slippages or lateral movements of the vehicle, and for this reason is complemented with Inertial Measurement Units (IMUs) which combine accelerometers, gyroscopes and magnetometers [28]. The incorporation of these sensors allows the previously mentioned errors to be corrected and the sampling speed of the measurement system to be increased. However, they introduce errors due to the measurement of first and second order variables [29]. IMUs alone do not provide a global vehicle position, so they are usually accompanied by a GNSS system.  
The current positioning systems for vehicles are hybrid systems that merge data from different sources such as odometry, IMUs, GNSS, LiDARs, RADARs and cameras to obtain a reliable position and heading with tolerable error.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **GPS** & **GLONASS** & **GALILEO** & **BEIDOU** \\ \hline Satellites & 24 & 24 & 30 & 30 + 5\({}^{\star}\) \\ Precision & 7.8 m, civil & 7.4 m, civil & 1.0 m, civil & 10 m, civil \\  & 5.9 m, military & 4.5 m, military & 0.01 m, advantage & 0.1 m, military \\ Coverage & Global & Global & Global & Chinese \\ Period & 11 h 58 m & 11 h 15m & 14 h & 12h 53m \\ height & 26,650 Km & 19,100 Km & 23,222 Km & 21,150 Km \\ Owner & EEUU & Russia & European Union & China \\ \hline \hline \end{tabular} \({}^{\star}\) Geostationary Satellite.  
\end{table}
Table 2: Most commonly used global positioning systems.
## 2 Related Work
### Methods Based on Recurrent Neural Network (RNN)  
With the rapid development of deep learning technology, many session recommendation algorithms based on deep learning have been born. The recurrent neural network (RNN) is a mainstream deep learning model for modeling sequential data [8]. [PERSON] et al. [9] introduce RNN into the recommendation algorithm, regard the interaction of a session clicking a series of items as a sequence, and input the sequence into the model to predict the next clicked item. [PERSON] et al. [10] propose a neural attention recommendation model (NARM), which combines an attention mechanism with a multi-layer gated recurrent unit (GRU) encoder, which is a variant of RNN, in order to capture more representative session embeddings. The model uses the bilinear matching scheme based on the unified session embedding to calculate the recommendation score of each candidate item and then achieve better recommendation performance. The downside is that a session may contain multiple user selections and even noise, so they do not generate all the correct dependencies. Existing session-based recommendation methods mainly focus on the current session, ignoring collaboration information in historical sessions, which are sessions generated by other users and reflect similar user intentions to the current session. In response to this problem, [PERSON] et al. [11] propose a collaborative session-based recommendation approach with parallel memory modules (CSRM), which uses a memory network to encode the current session, taking the information of the current session and neighbor sessions into account based on session suggestions to better predict the intent of the current session. One problem with session-based recommendations is how to generate accurate recommendations at the beginning of a session before knowing the user's current interests at the start of the session. [PERSON] et al. [12] transformed the short-session problem into a few-shot learning (FSL) problem, explicitly designing a local module based on GRU encoding and a global module considering similar historical sessions, and achieved state-of-the-art performance.