esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Effective SNR
Therefore, the system performance can be evaluated by \(SNR_{eff}\) as the ratio between the power of the useful signal and the effective noise power, expressed as  
\[SNR_{eff}=\frac{\alpha^{2}\sigma_{x}^{2}}{\sigma_{total}^{2}}. \tag{16}\]  
For \(\sigma_{x}^{2}=1\sigma_{w}^{2}=3.1486\ \times\ 10^{-4}\) and \(N=512\), the characteristic behavior of \(SNR_{eff}\) with different \(\gamma\) and \(\mathcal{Q}\) is depicted in Figure 1. It can be observed that the \(SNR_{eff}\) can achieve its maximum value regarding to the optimum clipping \(\gamma_{opt}\) for each \(\mathcal{Q}\), e.g., \(\gamma_{opt}=7\) dB for \(\mathcal{Q}=8\). Once the \(SNR_{eff}\) is computed, the BER performance of the DMT system can be easily evaluated with the help of the exact close-form expression of the QAM in AWGN channel [21]. As Figure 1 demonstrates, the \(SNR_{eff}\) degradation caused by the quantization is negligibly small when both \(\mathcal{Q}_{1}\) and \(\mathcal{Q}_{2}\) are greater than seven. In addition, the clipping effect dominates the system's \(SNR_{eff}\) as the \(\sigma_{w}^{2}\) and \(\gamma\) threshold is fixed. Therefore, if we perform an appropriate method for reducing the \(\xi_{\mathbf{C}}\) of the input \(C(n)\), then, a transmitted signal with a lower \(\underset{0\leq k\leq 2N-1}{\max}\Big{\{}\left\lvert x(n)\right\rvert^{2} \Big{\}}\) is obtained and the probability of the Y\({}_{d}\) exceeding a given \(\gamma\) within one symbol duration is reduced. This indicates that the \(\sigma_{clip}^{2}\) can be lowered accordingly. Motivated by this way, a novel DMT scheme is proposed in the followings to reduce the value of \(\xi_{\mathbf{C}}\), thus indirectly reducing the PAPR and improving the \(SNR_{eff}\).
## 3 Results and Discussion
### Cost and Environmental Impact Analyses
#### 3.2.2 Environmental Impacts  
Given the growing concerns regarding sustainability, recycling gasoline in the laboratory would not only reduce economic investments, but also entail a positive effect regarding environmental impacts. This study investigated the energy consumption and equivalent CO\({}_{2}\) emission of employing mineral powder to adsorb asphalt binder. According to the China Energy Statistical Yearbook 2013 [31], 1 L of gasoline could release 31.75 MJ energy and 2.21 kg equivalent CO\({}_{2}\), while its production would consume 4.54 MJ/L energy and emit 0.37 kg/L equivalent CO\({}_{2}\)[47]. On the other hand, the extraction of 1 kg limestone from a quarry would consume 80.00 kJ energy and emit 5.95 g equivalent CO\({}_{2}\)[48], and  
Figure 5: Adsorption mechanism for mineral powder: (**a**) adsorption; (**b**) adhesion; (**c**) sediment.  
the following crushing and grinding operations need 220.60 kJ/kg energy and emit 59.63 g equivalent CO\({}_{2}\)[49].  
Equations (2) and (3) are applied to integrate the saved energy and reduced equivalent CO\({}_{2}\). According to the data from referenced literatures, the removal of 1 kg of asphalt binder using mineral powder would cause 10.02 MJ energy consumption and 1.85 kg equivalent CO\({}_{2}\) emission, while dissolving asphalt binder by gasoline directly would result in 362.90 MJ energy consumption and 25.80 kg equivalent CO\({}_{2}\) emission.  
\[E=(E_{g}+\ E_{p})*v_{g}-E_{m}*m_{m} \tag{2}\]  
\[G=(G_{g}+\ G_{p})*v_{g}-G_{m}*m_{m} \tag{3}\]  
where, \(E\) and \(G\) represent the saved energy (MJ) and reduced equivalent CO\({}_{2}\) (kg), respectively; \(E_{g}\) and \(E_{p}\) stand for the energy consumption (MJ/L) generated by gasoline combustion and production, respectively; \(G_{g}\) and \(G_{p}\) are equivalent CO\({}_{2}\) (kg/L) generated by gasoline combustion and production, respectively; \(E_{m}\) and \(G_{m}\) mean the energy consumption (MJ/kg) and CO\({}_{2}\) emission (kg/kg) during manufacturing mineral powder, respectively; vg represents the volume of gasoline (l) and mm is the mass of mineral powder (kg).
## 4 Less Regular Connectivity Models
### Non-uniform Disk Radii  
If each node can adjust its transmission power independently, they have inhomogeneous transmission ranges. This model has also received considerable attention. [PERSON] and [PERSON] [51] showed that if inhomogeneous ranges are allowed, asymptotic connectivity is maintained if each node in a random network with \(n\) uniformly distributed nodes connects to \(\Theta(\log n)\) nearest neighbors. They further showed that the constant in the asymptotic relationship is greater than \(0.074\) and less than \(5.1774\), i.e., the network is almost surely disconnected if the asymptotic nodal degree \(k<0.074\log(n)\), and the network is almost surely connected if \(k>5.1774\log(n)\). The range of the constant \(k\) is further reduced to the interval \([0.3043,0.5139]\)[52]. The exact value of the critical constant remains an open problem.  
Having inhomogeneous transmission ranges causes the corresponding graph to be directed. This is not desirable from an engineering point of view, both because it complicates routing, and also because it bars the use of link-layer acknowledgements and retransmission. The solution taken by [PERSON] _et al._[53] is to consider connectivity induced by only the symmetric edges; that is, two nodes are connected only if their separation is less than the minimum of their transmission ranges. By applying [PERSON] and [PERSON]'s results, [PERSON] _et al._ developed a topology control mechanism that generates a network topology by connecting each node to its \(k\)-nearest neighbors. The value of \(k\) that can provide high probability of connectivity was numerically estimated to be around 9 for networks of 50-500 nodes. Other work that obtains the radio transmission ranges can be found in [54; 55; 56; 57].
# Modeling of Cotton Yield Estimation Based on Canopy Sun-Induced Chlorophyll Fluorescence
## 2 Materials and Methods
### Data Processing
#### 2.3.2 Correlation Analysis  
All 45 sets of data collected in each period were used for the application of Pearson correlation analysis. The correlation coefficient is an indicator of the closeness of the correlation between the corresponding variables. It is derived by the product-difference method, which is based on the deviation of two variables from their respective mean values, and the degree of correlation between the two variables is reflected by multiplying the two deviations. The formula for calculating the correlation coefficient is as follows: \(\text{x}_{\text{i}}\) and \(\text{y}_{\text{i}}\) denote the ith corresponding value of the two parameters, \(\overline{\text{x}}\) and \(\overline{\text{y}}\) denote the mean value of the two parameters, and n is the number of samples. The higher the value of the correlation coefficient of the samples, the higher the degree of similarity, and vice versa, the lower the degree of similarity.  
\[\text{r}=\frac{\sum_{\text{i}=1}^{\text{n}}\left(\text{x}_{\text{i}}-\overline {\text{x}}\right)\left(\text{y}_{\text{i}}-\overline{\text{y}}\right)}{\sqrt{ \sum_{\text{i}=1}^{\text{n}}\left(\text{x}_{\text{i}}-\overline{\text{x}} \right)^{2}}\sqrt{\sum_{\text{i}=1}^{\text{n}}\left(\text{y}_{\text{i}}- \overline{\text{y}}\right)^{2}}} \tag{6}\]
# A Novel Method for Detecting Advanced Persistent Threat Attack Based on Belief Rule Base
## 2 Problem Formulation  
In a word, the methods described in Section 1 are not effective enough to detect long-time multi-stage APT attacks. There are still three challenges:  
* **Small samples problem**, which makes parameter training of the model difficult to achieve the expected results. APTs reduce their chances of being detected by long and slow attack. Therefore, a defense system can only obtain a very small number of APT samples for a long time. The number of samples is usually not enough for training a data-driven model to achieve high detection accuracy.
* **Combining multiple information**, which is difficult for most existing methods. A variety of information exists in the process of APT attacks, such as expert experiences, domain knowledge, and quantitative data. In addition, there are various types of uncertainties in the exploitable information, such as the randomness of the moment of APT attack phase, the fuzziness between APT attacks and normal attacks, and the ignorance generated by zero-day attacks.
* **Interpretability of results**. If the model has no interpretability and cannot trace the reasons and process, the detection results will not be credible enough. A low-credibility model will lead to an increase in the false positive rate and false negative rate. On the other hand, the results of non-interpretation will affect the subsequent forensics of the APT attack.  
To solve the above challenges, a novel method for detecting an APT attack based on belief rule base is proposed in this paper. The BRB model is a new intelligent expert system with characteristics of both an expert system and data-driven model, which can be used to solve the decision problems of complex system [23; 24; 25; 26]. The proposed BRB-based APT detection model has the following features compared to traditional methods mentioned above:  
* The BRB-based APT detection model has an advantage in solving small sample modeling problem due to embedding of expert knowledge. Using expert knowledge can set reasonable initial parameters, and only a small number of samples are needed to complete the model training, which can address the small sample problem.
* The BRB-based APT detection model is a semi-quantitative method that can handle a wide range of uncertain information by using expert knowledge and quantitative data. Uncertain information contains more useful features of APT attack and can express more objective results.
* The BRB-based APT detection model is a white box model that has good interpretability of result. The rules of BRB are designed in accordance with human knowledge, which can help experts to make targeted defense strategies. The reasoning process of BRB is transparent. Any rules that have been activated are visible. Experts can trace how the APT attack occurred and how the results were derived.
###### Abstract  
Monitoring oil drift by integrating multi-source satellite imagery has been a relatively underexplored practice due to the limited time-sampling of datasets. However, this limitation has been mitigated by the emergence of new satellite constellations equipped with both Synthetic Aperture Radar (SAR) and optical sensors. In this manuscript, we take advantage of multi-temporal and multi-source satellite imagery, incorporating SAR (Sentinel-1 and ICEYE-X) and optical data (Sentinel-2/3 and Landsat-8/9), to provide insights into the spatio-temporal variations of oil spills. We also analyze the impact of met-ocean conditions on oil drift, focusing on two specific scenarios: marine floating oil slicks off the coast of Qatar and oil spills resulting from a shipwreck off the coast of Mauritius. By overlaying oils detected from various sources, we observe their short-term and long-term evolution. Our analysis highlights the finding that changes in oil structure and size are influenced by strong surface winds, while surface currents predominantly affect the spread of oil spills. Moreover, to detect oil slicks across different datasets, we propose an innovative unsupervised algorithm that combines a Bayesian approach used to detect oil and look-alike objects with an oil contours approach distinguishing oil from look-alikes. This algorithm can be applied to both SAR and optical data, and the results demonstrate its ability to accurately identify oil slicks, even in the presence of oil look-alikes and under varying met-ocean conditions.  
oil spill detection; oil drift; spatio-temporal oil spill variation; Sentinel-1/2/3; Landsat-8; ICEYE-X +
Footnote †: journal: remote sensing
## 4 Experiments
### Results--Unknown Assemblies  
As a final test, a more challenging unknown dataset was generated for the models. This was derived from the \"3D Assembly Repository\" of [PERSON] et al. [57] and consisted of several step assemblies, sorted into different product categories. Complex drawings were derived from these step assemblies, with sketches created for six assembly categories. Again, an example sketch for each category is displayed in Figure 9. The assemblies applied were hydraulic reduction, hydraulic rotor, double rotor turbine, wind turbine, differential and landing gear. In contrast to the first unknown dataset, considerably more symbols and many different variants were included in the individual sketches. The entire dataset consisted of 24 images and a detailed list of the occurring symbols is provided in Appendix B in Table 10.  
In general, the result scores, which are shown in Table 2, reveal that the recognition level drops in comparison to the first dataset. This is mainly due to the more complex drawings and increased variety of symbols. Furthermore, finer differences between symbols must be detected in this dataset, e.g., the level gear with and without hollow shaft. The findings from the first study are evident in this dataset as well, since the YOLO models performed better than the RCNN architecture and the SketchGraph 2 dataset produced the worst results for all models. The combination of Pillow data generation with the YOLO models also achieves good mAP50 values, although the YOLOv5 L provides better recogniti  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{10}{c}{**Unknown Gear Stages**} \\ \hline
**Dataset** & **SketchGraph 1** & **SketchGraph 2** & **SketchGraph 3** & **SketchGraph 4** & **Pillow 1** & **Pillow 2** \\ \hline
**Metric** & **mAP50** & **mAP** & **mAP50** & **mAP** & **mAP50** & **mAP** & **mAP50** & **mAP** & **mAP50** & **mAP** \\ \hline YOLOv5 S & 0.563 & 0.303 & 0.180 & 0.073 & **0.993** & **0.694** & 0.992 & 0.684 & 0.388 & 0.138 & 0.716 & 0.245 \\ YOLOv5 M & 0.805 & 0.455 & 0.076 & 0.033 & **0.994** & **0.689** & **0.994** & 0.685 & 0.625 & 0.253 & 0.868 & 0.309 \\ YOLOv5 L & 0.742 & 0.444 & 0.084 & 0.050 & 0.993 & **0.686** & **0.994** & **0.686** & 0.817 & 0.397 & 0.832 & 0.315 \\ MASK RCNN r50 & 0.149 & 0.056 & 0.080 & 0.031 & 0.741 & 0.389 & **0.834** & **0.440** & 0.439 & 0.268 & 0.235 & 0.154 \\ MASK RCNN r101 & 0.105 & 0.048 & 0.135 & 0.057 & **0.949** & **0.522** & 0.785 & 0.407 & 0.127 & 0.079 & 0.351 & 0.224 \\ Faster RCNN r50 & 0.286 & 0.117 & 0.241 & 0.110 & **0.736** & 0.526 & 0.757 & **0.535** & 0.042 & 0.013 & 0.197 & 0.105 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation metrics for all models and training datasets on unknown data with best results per model highlighted.  
Figure 8: Example sketches for different gear stages from the first unknown test dataset.  
YOLO derivatives. The highest accuracy is provided by the YOLOv5 L with SketchGraph 3 dataset, resulting in a mAP50 of 0.906 and mAP of 0.562.
## 6 M2M Application Protocols
### Evaluation and Comparison
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **CoAP** & **MQTT** \\ \hline
**Communications Model** & Request-Response, or Pub-Sub & Pub-Sub \\
**RESTful** & Yes & No \\
**Transport Layer Protocol** & Preferably UDP; TCP can be used & Preferably TCP; UDP can be used (MQTT-S) \\
**Header** & 4 Bytes & 2 Bytes \\
**Number of message types** & 4 & 16 \\
**Messaging** & Asynchronous and Synchronous & Asynchronous \\
**Application Reliability** & 2 Levels & 3 Levels \\
**Security** & IPSEC or DTLS & Not defined in standard \\
**Intermediaries** & Yes & Yes (MQTT-S) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between main features of Constrained Application Protocol (CoAP) and Message Queuing Telemetry Transport (MQTT). discipline [51]. CoAP's non-persistent publication discipline gives priority to sending new publications while MQTT-S attempts to retransmit old ones. CoAP abd MQTT allow messages to be sent and received asynchronously, but only CoAP supports synchronous messaging. Finally, only CoAP provides a request-response protocol compliant with REST concepts. Both CoAP and MQTT-S support the use of UDP, intermediary nodes, gateways, to perform requests and responses/relay messages on behalf of other nodes, do caching, aggregation, _etc._ So, further studies, deployments and field trials need to be conducted to assess their performance, especially in constrained devices and networks, and when a large number of devices is present. MQTT does not provide service discovery. The performance of CoAP service discovery is discussed in [74]. In summary, CoAP discovery protocols show better performance in terms of overhead than DNS-based discovery protocols, since it was designed aiming at resource efficiency for constrained devices and networks. Furthermore, based on measurements and functionalities, the authors claim that CoAP's resource discovery allows a more efficient and richer set of mechanisms to perform lookups than DNS-based protocols.
## 4 Discussion
### Expected Yield Prediction Accuracy during the Nitrogen Application Period  
A high yield prediction accuracy (mean NRMSE was 14%) was achieved when assimilating a synthetic ESA Sentinel-2 LAI time-series during the recommended growth stages for N fertilization only. Although noise was added to this synthetic time-series to characterise the Sentinel-2 LAI retrieval uncertainty, the assumption of clear-sky observations available every five days was perhaps optimistic due to the frequent cloud cover of the study region. Depending on the crop growth stage, the recommended winter wheat N management dates for the United Kingdom are often for several weeks between March and April [46]. We would, therefore, anticipate some useable optical EO data within this period. The number of LAI observations can also be supplemented with data from Unmanned Aerial Vehicle platforms [15] or exploring the information content of Radar data [51]. Nonetheless, our results demonstrate that if LAI observations were available during this management period, around five months in advance of harvest, then CARDAMOM-DALEC-Crop MDF could generate timely yield forecasts that could be used to adjust N management. When supporting such management decisions, the lack of information on future weather conditions is, however, a significant source of uncertainty in model yield forecasts that needs to be further explored [5].
# Conceptual Framework for Future WSN-MAC Protocol to Achieve Energy Consumption Enhancement
## 3 State-of-the-Art Approaches for MAC-Protocols Optimization
### General Approaches
The first approach is priority-based, involving picking a node as the cluster's head that has a large number of packets in the queue and the most energy among the virtual cluster's neighboring nodes. The second algorithm employs a regression technique to estimate a node's duty cycle based on the traffic conditions and residual energy. Only a few nodes are involved in the packet transfer. As a result, the number of overloaded packets at intermediate nodes is reduced. In [37], the authors present a centralized mechanism to schedule Time Slotted Channel Hopping (TSCH) time-slots with the optimal usage of resources. The suggested approach relies on full sub-graphs formed from the topology's collision matrix. Sub-links graphs can be scheduled in the same time-slot but on distinct channels at the same time. The suggested technique is performed using the Personal Area Network Coordinator (PANC), which is supposed to know the topology completely. The TSCH MAC has also been used to develop a Markov model for estimating transmission time and energy usage during frame transmission. The suggested method outperforms previous analogous systems in terms of performance. The beacon slot collision problem was proposed in the work [38] based on presenting a non-conflicting beacon scheduling system based on association order. Distributed multi-channel Deterministic and Synchronous Multichannel Extension--Guaranteed Time Slots (DSME-GTSs) scheduling was also developed in this work; this allocates DSME-GTSs across multiple channels optimally. The objective is to minimize the number of time-slots used while maximizing the usage of available channels. The performance of the suggestedmechanisms is investigated in terms of energy efficiency, transmission overhead, scheduling efficiency, throughput, and latency through simulations and is proven to outperform previous systems. Table 1 lists a summary of the general approaches proposed for enhancing the performance of the MAC protocol when used for WSN.
###### Abstract  
Amorphous Ta-Zr-Si-B-C and Ta-Zr-Si-B-N coatings were deposited by magnetron sputtering using a ceramic target in Ar + C\({}_{2}\)H\({}_{4}\) and Ar + N\({}_{2}\) reaction media. Thermal stability under in situ TEM heating, vacuum annealing and nanoindentation, as well as the effect of heating on structural-phase transformations in coatings and their mechanical characteristics, have been studied. The results showed that the heating of amorphous Ta-Zr-Si-B-C and Ta-Zr-Si-B-N coatings in a TEM column leads to precipitation h-TaSi\({}_{2}\) and c-TaC crystalline phases at a temperature of 600 \({}^{\circ}\)C and h-TaSi\({}_{3}\) and c-TaN at a temperature of 1000 \({}^{\circ}\)C, respectively. Similar structural-phase transformations were revealed as a result of vacuum annealing. An increase in temperature from 20 to 1000 \({}^{\circ}\)C led to a decrease in the hardness of the carbon-containing coating from 21 to 16 GPa as a result of stress relaxation. For the Ta-Zr-Si-B-N coating at a temperature of 1000 \({}^{\circ}\)C, an increase in hardness was observed from 18 to 27 GPa, which is associated with the crystallization of the coating, with the formation of a nanocomposite structure with h-TaSi\({}_{3}\) and c-TaN crystallites of 6-10 nm in size and an a-Si(Zr,B)N amorphous region.  
reactive magnetron sputtering; amorphous and nanocomposite coating; TaSi\({}_{2}\); TaN; TaC; hardness; in situ TEM heating; thermal stability
## 2 History of Electrospinning
### Electrospun Nanoparticles
In addition, the fabrication of bioactive particles via electrospinning methods and the subsequent use of these treated nanoparticles in the incorporation of the biocompatible polymer matrix like PCL has been intensively explored for the preparation of electrospun mats with outstanding properties that condone the usability in the biomedical technology specifically for tissue regeneration due to the refined porosity with analogous average diameters found in human tissues. However, PCL as the matrix has limited applicability in biomedical practices because it cannot solely used to regenerate bone tissues due to the minimal stiffness, hydrophobicity and marginal bioactivity. Nonetheless, the use of bioactive materials together with PCL for the preparation of the electrospun mats has received great attention for novel development of membranes with superb cell proliferation and wound dressing [31; 32; 33; 34]. For example, [PERSON] and co-workers [35] investigated the use of extracted aloe vera chitosan nanofibrous materials and the subsequent fabrication using electrospinning method in the application of skin recovery and wound healing. It was discovered that the electrospun membranes promote the rapid cell proliferation and migration. Furthermore, these electrospun mats have antimicrobial activity, which is very vital for wound dressing or healing. The utilization of bioactive ceramic materials such as TiO\({}_{2}\) and Na\({}_{2}\)Ti\({}_{6}\)O\({}_{13}\) in the electrospinning method for the preparation of PCL nanocomposites membranes with plausible bioactivity from which the bone tissue regeneration can be established is an area of interest in medical society. Due to their abundance, insignificant toxicity and stable chemical structure, ceramic particles can be treated by the electrospinning technique to allow the formation of the membranes with viable stiffness and bioactive characteristics for cell proliferation and excellent production of bone structures [34; 36].