esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study aimed to assess the feasibility of end-of-season catch crop classification with Sentinel-2 time series, with specific attention given to the optimization of the model type and the input features. The best model-input combination was then tested in a real-world scenario for early-season catch crop classification in a separate target year, thereby testing its temporal transferability. In summary, RF displayed the highest adaptability to input features and often outperformed time series-specific classification methods, TSF, and 1D-CNN. This study demonstrated the benefits of using high temporal resolution time series, as well as a diverse range of input features for the catch crop classification task. When the models were applied for early-season classification in a separate target year, the models reached the F1-score threshold of 85% at least one month later than for the training years. Overall, the advantages of deep learning approaches (1D-CNN) could not be demonstrated, probably due to the limited amount of reference data available to train the models. However, very recent research into early-season crop classification and temporal transferability shows opportunities for the real-world application of deep learning classification models for catch crop classification.  
Conceptualization, K.V, S.H., E.B., and B.S.; methodology, K.V., S.H., and E.B.; software, K.V. and E.B.; validation, K.V.; formal analysis, K.V.; investigation, K.V.; data curation, K.V. and E.B.; writing--original draft preparation, K.V.; writing--review and editing, K.V., S.H., E.B., and B.S.; visualization, K.V.; supervision, S.H. and B.S.; project administration, S.H. and B.S.; funding acquisition, S.H. and B.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Research Foundation Flanders (FWO), grant number S006421N.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.  
**Acknowledgments:** A special thanks to the Vlaamse Landmaatschappji (Flemish Land Agency) and the Agency for Agriculture and Fisheries for their help regarding data collection. Also, thanks to [PERSON] for providing useful feedback regarding deep learning.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
###### Abstract  
Machine learning has been applied in continuous-variable quantum key distribution (CVQKD) systems to address the growing threat of quantum hacking attacks. However, the use of machine learning algorithms for detecting these attacks has uncovered a vulnerability to adversarial disturbances that can compromise security. By subtly perturbing the detection networks used in CVQKD, significant misclassifications can occur. To address this issue, we utilize an adversarial sample defense method based on non-negative matrix factorization (NMF), considering the nonlinearity and high-dimensional nature of CVQKD data. Specifically, we employ the Kernel Robust Manifold Non-negative Matrix Factorization (KRNMMF) algorithm to reconstruct input samples, reducing the impact of adversarial perturbations. Firstly, we extract attack features against CVQKD by considering the adversary known as Eve. Then, we design an Artificial Neural Network (ANN) detection model to identify these attacks. Next, we introduce adversarial perturbations into the data generated by Eve. Finally, we use the KRNMMF decomposition to extract features from CVQKD data and mitigate the influence of adversarial perturbations through reconstruction. Experimental results demonstrate that the application of KRNMMF can effectively defend against adversarial attacks to a certain extent. The accuracy of KRNMMF surpasses the commonly used Comdefend method by 32.2% and the JPEG method by 30.8%. Moreover, it exhibits an improvement of 20.8% compared to NMF and outperforms other NMF-related algorithms in terms of classification accuracy. Moreover, it can complement other defense strategies, thus enhancing the overall defensive capabilities of CVQKD systems.  
CVQKD; adversarial defense; non-negative matrix factorization; machine learning +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods  
### Sampling  
In total, 110 Italian honey samples, collected in 2019, were purchased from different specialized dealers and supermarkets. From Apulia, a region particularly suited to honey production, samples were supplied by beekeepers belonging to the association UNAAPI, a confederation of beekeepers present throughout the region. Specifically, samples of honey made from sweet cherry tree (_Prunus avium_ L.), thymme (_Thymus vulgaris_ L.), almond tree (_Prunus amygdala_ L.), eucalyptus (_Eucalyptus canaludensis_ L.), coriander (_Coriandrum Satvium_ L.), cornflower (_Centaurea cyanus_ L.), thistle (_Sillybum marianum_ L.), acacia (_Robinia pseudoacacia_ L.), citrus, honeydew and multifloreal honey were collected. Details of honey samples analyzed are reported in Table 1. The identification of the honey sample's floral origin was assessed by palynological analyses (CRA-API Bologna), and confirmed, only for Apulian honey samples, by sensorial analyses performed by a specialized expert assessor selected from the professional trained members belonging to the Italian Register of Experts in the Sensory Analysis of Honey.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Floral Origin** & **Geographical Origin** & **Number of Samples** \\ \hline Sweet Cherry & Apulia & 10 \\ \hline \multirow{3}{*}{Thyme} & Apulia & 5 \\  & Sardinia & 3 \\  & Sicily & 2 \\ \hline \multirow{3}{*}{Almond} & Apulia & 6 \\  & Sicily & 4 \\ \hline \multirow{3}{*}{Eucalyptus} & Apulia & 3 \\  & Calabria & 2 \\  & Lazio & 2 \\  & Sardinia & 1 \\  & Sicily & 1 \\  & Tuscany & 1 \\ \hline \multirow{3}{*}{Coriander} & Abruzzo & 2 \\  & Apulia & 4 \\  & Emilia Romagna & 2 \\  & Marche & 2 \\ \hline \multirow{3}{*}{Cornflower} & Apulia & 4 \\  & Lazio & 2 \\  & Marche & 2 \\  & Tuscany & 2 \\ \hline \multirow{3}{*}{Thistle} & Apulia & 2 \\  & Calabria & 4 \\  & Sicily & 4 \\ \hline \multirow{3}{*}{Acacia} & Sardinia & 2 \\  & Abruzzo & 1 \\  & Apulia & 2 \\ \cline{1-1}  & Campania & 1 \\ \cline{1-1}  & Piedmont & 1 \\ \cline{1-1}  & Tuscany & 2 \\ \cline{1-1}  & Veneto & 1 \\ \hline \multirow{3}{*}{Citrus} & Apulia & 4 \\ \cline{1-1}  & Basilicata & 2 \\ \cline{1-1}  & Calabria & 2 \\ \cline{1-1}  & Sicily & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geographical origin of Italian honey.  
For each type of honey analyzed, 10 samples were collected and stored in a dark place at room temperature until analysis.  
Before assessing the MGO content, some physicochemical properties of the samples (aw and pH) and the composition criteria reported in Council Directive 2001/110/EC [39] relating to honey (hydroxymethylfurfural (HMF), electrical conductivity, moisture content, diastase) were characterized.
# Going Deeper into OSNR Estimation with CNN
## 2 Principle of Proposed Scheme
### Inception Architecture  
Given that sparsely connected architecture avoids overfitting without utilizing high computation efficiency on dense matrix multiplication, the Inception module aims at improving hardware computation speed when keeping the sparse structure of a network. The Inception block is a combination of variously sized kernels by concatenating several parallel branches of convolutional layers. An example structure is depicted in Figure 2.  
In addition, the Ref. [30] introduces \(1\times 1\) convolution for dimension reduction to alleviate the computation complexity of expensive \(3\times 3\) and \(5\times 5\) convolution. The following ReLU also provides a model with extra nonlinearity.  
After various modifications made on Inception-v1 in [37], the latest work of [38] incorporates residual connections, which is argued to be necessary for deep networks to improve training speed empirically [39]. Furthermore, though Inception-v4 without residual connections can achieve similar performance with similar expensive Inception-ResNet-v2, the Ref. [38] admits that residual connections do accelerate training significantly. Secondly, residual connections are able to solve the degradation problem where training errors increase with a deeper network, and accuracy gets saturated or even degrades rapidly. The shortcut in ResNet endues the network with identity mapping, which is proved to be the best choice [40].
# Design and Research of Thermoelectric Generator Simulation System for Boiler Flue Gas Waste Heat
###### Abstract  
One of the significant factors contributing to high energy consumption is the unutilized waste heat from flue gas in industrial boilers. Thermoelectric generator (TEG) technology can directly convert thermal energy into electrical energy, and has been gradually applied in the field of waste heat recovery due to its simple and reliable structure, environmental protection, and other advantages. In this paper, a thermoelectric generator simulation system of boiler flue gas waste heat is proposed. The experimental platform is designed by simulating the flue gas waste heat temperature condition of boiler, and the structure of cold and module and hot end module is optimized. During the experiment, the fixed temperature difference was set at 120 \({}^{\circ}\)C (hot end:150 \({}^{\circ}\)C-cold end: 30 \({}^{\circ}\)C). An analysis is conducted on the volt-ampere characteristics and output power of the TEG module. The output characteristics of the TEG system are analyzed under the conditions of variable load, constant load, different pump speed, different heat dissipation modes, and series and parallel connection method. The results show that the experimental platform can instantaneously and accurately test the output parameters of the TEG system, and ensure the intended design requirements. When the ratio of the load resistance to the internal resistance of the TEG module is approximately 1-15, the output power of the system reaches its maximum. In order to optimize the output power of the TEG system, a power prediction-based adaptive variable step size maximum power point tracking (MPPT) algorithm is introduced. Additionally, a corresponding mathematical model is formulated. Simulations demonstrate that the time of the improved algorithm to reach the stable maximum power point is 1.54 s faster than that of the traditional algorithm. The improved MPPT algorithm satisfies the criteria for speed and accuracy, diminishes superfluous energy waste, and enhances the overall system efficiency. The research results have certain guiding significance for the design and application of subsequent TEG system.  
TEG; structure optimization; characteristic analysis; improved MPPT algorithm +
Footnote †: journal: Applied Sciences  
1 Faculty of Innovation Engineering, Macau University of Science and Technology, Macau 999078, China  
2 School of Mechanical and Electrical Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China  
3 School of Computer Science, Guangdong University of Education, Guangzhou 510010, China  
* Correspondence: [EMAIL_ADDRESS]
## 5 Conclusions  
Multi-angle tower-based optical spectra data were observed with EC flux data during the period from April 2014 to March 2015 for the purpose of studying the ability of PRI in estimating the LUE of a sub-tropical planted mixed forest stand of Southern China. The following conclusions could be drawn from this study:  
1. With the increase of VPD, Ta and PAR, the daily scale PRI, and LUE showed a downward trend, and the response of LUE to changes in these bioclimatic factors were more sensitive than PRI. The correlation of PRI and LUE at half-hour scale was the most significantly affected by PAR, followed by temperature in the wet season. The correlation of PRI and LUE at a half-hour scale was mostly affected by PAR, followed by VPD in the dry season.  
2. During the study period, there was a significant linear relationship between LUE and PRI on both the half-hour and daily scale. There was a significant difference in correlation between PRI and LUE throughout the growing season. The correlation was the strongest in November, 2014 (R\({}^{2}\) = 0.91), and the weakest in July, 2014 (R\({}^{2}\) = 0.24).  
3. For subtropical forests, the spatial difference of the canopy is small, and has weak effects on LUE.  
In addition to these findings, some shortcomings in our research deserve some attention. For example, the measurements of some pigments (e.g., xanthophyll and chlorophyll) can provide more useful information to further analyze seasonal changes in subtropical mixed forests. In our subsequent work, it will be necessary to pay attention to the influence of the vegetation canopy structure on LUE.  
**Author Contributions:** For research articles with several authors, a short paragraph specifying their individual contributions must be provided. [PERSON]. ([PERSON]) and [PERSON] conceived and designed the research; [PERSON]. ([PERSON]) conducted the experiments, wrote manuscript, and data analysis; [PERSON], J.C., and M.A. contributed significantly to the research method and the manuscript revision. [PERSON]. ([PERSON]) made an important contribution to lidar data processing. L.Z., L.S., G.Z. and Z.M., devoted to data pre-processing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the National Key Research and Development Program of China (2016 YFA0600202 and 2017 YFC0503803), the National Natural Science Foundation of China (41571192 and 41871342).  
**Acknowledgments:** We further acknowledge [PERSON] (Nanjing University, China) and [PERSON] (Peking University, China) for their valuable discussions on data processing.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# GNSS Timing Performance Assessment and Results Analysis
## 2 The Key Indicators and Assessment Method of GNSS Timing Performance
### Assessment Method of GNSS Time Transfer Accuracy
#### 2.2.1 Assessment Principle of GNSS Time Transfer Accuracy  
The assessment principle of GNSS time transfer accuracy which takes UTC as the reference timescale and UTC(NTSC) as the intermediate timescale is shown in Figure 2. Firstly, receiver time is synchronized to UTC(NTSC). Secondly, GNSS time transfer error sequences are obtained as follows: (1) the time offset UTC(NTSC)-UTC\({}_{\text{GNSS}}\) is estimated by data processing which follows the GNSS timing principle described in Section 2.1; (2) the real-time offset UTC(NTSC)-UTC\({}_{\text{true}}\) is calculated using offset UTC-UTC(k) from BIPM; (3) then, the offset UTC(NTSC)-UTC\({}_{\text{GNSS}}\) is converted to UTC\({}_{\text{GNSS}}\)-UTC\({}_{\text{true}}\). Finally, the GNSS time transfer accuracy can be obtained by calculating the 95% probability level of GNSS time transfer error sequence. For BDS, GPS, GLONASS, and Galileo, UTC\({}_{\text{true}}\) refers to UTC, UTC(USNO), UTC(SU), and UTC, respectively. UTC\({}_{\text{GNSS}}\)-UTC\({}_{\text{true}}\) offsets of BDS, GPS, GLONASS and Galileo are calculated according to Equations (2)-(5).  
\[\text{UTC}_{\text{BDS}}\text{-UTC}=-[\text{UTC(NTSC)-UTC}_{\text{BDS}}]_{\text {Mon}}-[\text{UTC-UTC(NTSC)}]_{\text{BIPM}}, \tag{2}\]  
\[\text{UTC}_{\text{GPS}}\text{-UTC(USNO)}=-[\text{UTC(NTSC)-UTC}_{\text{GPS}}]_{ \text{Mon}}-[\text{UTC-UTC(NTSC)}]_{\text{BIPM}}+[\text{UTC-UTC(USNO)}]_{\text{ BIPM}}, \tag{3}\]  
\[\text{UTC}_{\text{GLONASS}}\text{-UTC(SU)}=-[\text{UTC(NTSC)-UTC}_{\text{ GLONASS}}]_{\text{Mon}}-[\text{UTC-UTC(NTSC)}]_{\text{BIPM}}+[\text{UTC-UTC(SU)}]_{\text{ BIPM}}, \tag{4}\]  
\[\text{UTC}_{\text{ Galileo}}\text{-UTC}=-[\text{UTC(NTSC)-UTC}_{\text{ Galileo}}]_{\text{Mon}}-[\text{UTC-UTC(NTSC)}]_{\text{BIPM}}. \tag{5}\]
## 4 Discussion and Conclusions
### Impact on Training Speed and Computational Efficiency  
An analysis of the impact of the additional channel on the training process showed that using probability maps did not significantly increase the training time. In the case of DeepLabV3+ and ENet, training even accelerated, which can be explained by faster gradient stabilization due to the additional information from the probability maps.  
For certain models, the loss function was minimized more quickly when four channels were used.
## 2 Background and Related Work
### Background
#### 2.1.1 V2X Standards
Figure 4 shows the structure of Part I and Part II of BSM defined in the SAE J2735 standard. BSM has a very important role in road safety, but location errors often occur because they are GPS-based. To solve this problem, a method of collecting and correcting BSM by RSUs to obtain accurate location information of nearby vehicles has also been proposed [21]. A method to increase the positioning accuracy is also proposed [22]. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Message** & **Description** \\ \hline
**BSM** & Basic Safety Message & Vehicle V2X safety information \\
**SPaT** & Signal Phase and Timing & Traffic signal and timing information \\
**MAP** & Map Data & Intersection and road lane information \\
**TIM** & Traveler Information Message & For sending advisory and road sign information \\
**RSA** & Road Side Alert & For the alerting of nearby hazards information \\
**PVD** & Probe Vehicle Data & For collecting a vehicle’s traveling information \\
**TAM** & Toll Advertisement Message & V2X-based fee collection data with SAE J3217 [20] \\
**SDSM** & Sensor Data Sharing Message & Reserved for future use \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The main message set of SAE J2735. Figure 3: Comparison of ITS stacks by V2X protocol. WAVE uses U.S. standards, and ETSI ITS-G5 uses European standards. In C-V2X, the lower-level layer uses 3 GPP specifications, and the higher-level layer can apply U.S. and European standards.
## 4 System Components
### Networking System  
The networking system builds a communication graph for data to be exchanged between drones, as well as between drones and a ground infrastructure. The networking system should guarantee the closed loop by driving the data flow between sensing, decision-making and actuation. This is achieved by considering that those three elements (i.e., sensing, decision-making and actuation) may reside in different drones, providing a cooperative sensing. For instance, a drone may sense a new obstacle that does not interfere with its own position, but it may have an impact in the overall swarm. This information is made available to another drone able to compute an action, which may be executed by a set of other drones that are in the way of such obstacle.  
In a drone swarm, communication is not only imperative for disseminating observations, tasks and control information, but can also assist in coordinating the drones more effectively and safely. The communication demands vary significantly in different applications. However, it is particularly a challenging task to provide robust networking, due to the energy limitation of drones and to external factors such as wireless shadowing and intermittent available links. The sub-sections below, are dedicated to analyzing the networking requirements and constraints as well as deliberations on fundamentals and pivotal properties that should characterize the networking system.
# Novel Sparse-Coded Ambient Backscatter Communication for Massive IoT Connectivity
## 1 Introduction  
### Backgrounds  
Massive connectivity is a key component of future Internet of things (IoT) where radio frequency (RF) devices continuously interact with humans' daily routine. The vision includes smart home gadgets connected to Wi-Fi (Wireless Fidelity) [1; 2], real-time interactive applications such as Bluetooth exchanging video/audio data with a smart phone [3], and inventory management in a huge warehouse [4]. In these applications, RF devices should be designed in low power for prolonging their lifetime and small-form factor for cost savings. Unfortunately, it is reported that most of the power in RF devices are consumed by oscillators, which generate sinusoidal carrier signals for modulation of data. The high power consumption for this digital-to-analog converter (DAC) hinders connectivity for numerous IoT applications with energy-constrained RF devices [5; 6; 7]. Hence, alternative modulation without the oscillator is considered to be a promising solution for fulfilling IoT requirements.  
Ambient backscatter communication (AmBC) is an innovative solution for batteryless IoT, since it replaces power-hungry oscillator with passive load impedances connected to RF antenna [5; 6; 7]. RF tags, kinds of RF devices composed of low-power micro-controller and load impedances, can reuse ambient signals such as television (TV) [5; 7] and Wi-Fi [1; 2; 6] for sending their data. Instead of generating RF sinusoidal signals, RF tags simply reflect incoming ambient signals and modulate their data byswitching load impedances. These load impedances implemented by passive circuit components, resistors and capacitors can constitute two-dimensional signal constellation space spanned by real and imaginary axes of reflection coefficients [8; 9]. Only a few \(\mu\)W of power is required for AmBC, which is in sharp contrast to traditional active RF radios demanding few tens of mW. In addition, ultra-low power consumption of backscatter tags can sustain with ambient RF energy harvesting (EH). Ambient RF signals can be recycled not only for EH but also for information transmission over RF signals by controlling reflection coefficients at RF tags. Therefore, AmBC has strong advantages in terms of energy efficiency and installation cost by reducing the size of RF hardware.
## 4 Findings and Discussion
### Explainable Frameworks in XAI
#### 4.3.3 What Is LORE and What Is Its Applicability to IDSs for ICVs?  
For a given black box case, LOcal Rule-based Explanations (LORE) introduced by [PERSON] et al. [109] generates an interpretable prediction. On a dense set of synthetic examples, a decision tree is utilized to train the local interpretable predictor. A local explanation that consists of a single-choice rule and a number of counterfactual rules for the reversed decision can be extracted from the decision tree. This framework is local-based, post hoc, and model-agnostic [128]. The originators of the LORE in [109] based their work on a local interpretable predictor, a decision tree. The decision tree allows for the extraction of a local explanation, which consists of a single choice rule and a collection of counterfactual rules for the reversed decision [128]. The identified challenge that LORE's applicability to XAI for ICV could be the need for human comprehensibility of the explanations provided by LORE [109]. To achieve a flexible framework for LORE interpretability, [PERSON] et al. [129] proposed the use of k-optimal associations, known as Local Rule-based Model Interpretability with k-optimal associations (LoRMIkA). LoRMIkA provides a flexible way to obtain predictive rules needed for explanations. The argument here is that the most predictive rules are not necessarily the rules that provide the best explanations. Since the ICV situation is expectedly dynamic, LoTMIkA shows a promising adoption owing to its ability to provide multiple rules capable of explaining predictions in various scenarios.