esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### The Application of the CDHI in Rubber Plantations on Hainan Island  
Figure 3 shows the time series of the CDHI for rubber plantations in Hainan Island from January 2001 to December 2020. During this period, several severe CDHEs occurred in 2001, 2003-2005, 2010, 2015-2016, and 2020. Among these events, the CDHI values dropped to \(-2.11\), \(-2.41\), \(-2.10\), and \(-2.71\) in 2003, 2005, 2010, and 2020, respectively, indicating the occurrence of extreme CDHEs. The shaded areas in the figure corresponded to historical typical drought and heatwave events in Hainan Island, as recorded in the \"Yearbook of Meteorological Disasters in China\". These events covered the years 2003, 2004, 2005, 2010, 2015, 2016, and 2020. As shown in Figure S2, the frequency of CDHEs varied by month throughout the year. Mild events were most common in November and December, each accounting for 23%; moderate events mainly occurred in April and May, accounting for 15% and 14% respectively; severe events often happened in July and August, each accounting for 9%; while extreme events typically occurred in June, also accounting for 9%.
# A Cost-Aware Framework for QoS-Based and Energy-Efficient Scheduling in Cloud-Fog Computing
## 7 Evaluation
### Discussions on Obtained Results  
Findings of applying the cost-aware scheduling framework across the cloud-fog computing environment validates the performance and demonstrates the framework's effectiveness in mitigating cost \(\mathcal{C}\) of the QoS penalty and cost \(\epsilon\) of energy for formulated schedules at the cloud layer.
## 2 Related Works  
Elderly monitoring systems have significantly benefited from advancements in sensors, surveillance systems, and the Internet of Things (IoT), which facilitate real-time monitoring and enhanced connectivity. These technologies provide a detailed overview of an elderly individual's environment and health status [5]. Following this, developments in AI, specifically in person detection [6], pose estimation [7], and action recognition [8], have greatly improved the accuracy of these systems. These AI-driven techniques allow for precise identification and automated analysis of elderly movements, aiding in early detection of potential health risks.
# Reliability Modeling and Analysis of Multi-Degradation of Momentum Wheel Based on Copula Function
## 3 Establishing a Degenerate Model
### Failure Distribution  
According to the definition of degraded failure, the failure distribution of long-life products is the time distribution of degraded performance degradation to failure threshold \(l\) for the first time, i.e.,  
\[T=\inf\{t|X(t)\geq l,t\geq 0\} \tag{2}\]  
which is  
\[P\{T\geq t\}=P\{X(\tau)<l(0\leq\tau<t),X(t)\leq l,X(0)=0\} \tag{3}\]  
Let the distribution density of \(X(t)\) at time \(t\) be \(g_{x}(x,t)\).  
\[P(X(\tau)<l(0<\tau<t),X(t)\leq x)=\int_{-\infty}^{x}g_{x}(\xi,t)d\xi \tag{4}\]  
Let the probability density function of the product failure probability distribution be \(f(t)\), according to the definition:  
\[\begin{array}{ll}f(t)&=-\frac{\partial}{dt}P(T\geq t)\\ &=-\frac{\partial}{dt}P(X(\tau)<l\ (0\leq\tau<t),X(t)\leq l)\\ &=-\frac{\partial}{dt}\int_{-\infty}^{l}g_{x}(\xi,t)d\xi;\ t>0\end{array} \tag{5}\]  
Find \(g_{x}(x,t)\) in the formula to find \(f(t)\). When studying the distribution of the Wiener process at the time of first arrival [27], by defining an absorption column when the degradation amount reaches the failure threshold, the form of \(g_{x}(x,t)\) is given using the Kolmogorov forward equation. \(g_{x}(x,t)\) is  
\[g_{x}(x,t)=\frac{1}{\sigma\sqrt{2\pi t}}\left\{\exp\left[-\frac{\left(x-\mu t \right)^{2}}{2\sigma^{2}t}\right]-\exp\left[\frac{2\mu l}{\sigma^{2}}-\frac{ \left(x-2l-\mu t\right)^{2}}{2\sigma^{2}t}\right]\right\} \tag{6}\]  
Then,  
\[\begin{array}{ll}f(t)&=-\frac{d}{dt}\int_{-\infty}^{l}g_{x}(\xi,t)\\ &=-\frac{d}{dt}\left[\[PERSON]\left(\frac{l-\mu t}{\sigma\sqrt{t}}\right)-\exp \left(\frac{2\mu l}{\sigma^{2}}\right)\[PERSON]\left(\frac{-l-\mu t}{\sigma\sqrt{ t}}\right)\right]\\ &=\[PERSON]\left(\frac{\mu l-l}{\sigma\sqrt{t}}\right)+\exp\left(\frac{2\mu l}{ \sigma^{2}}\right)\[PERSON]\left(\frac{-l-\mu t}{\sigma\sqrt{t}}\right);\ t>0\end{array} \tag{7}\]  
where \(\Phi(x)=\frac{1}{\sqrt{2\pi}}\int_{-\infty}^{x}e^{-\frac{\xi^{2}}{2}}d\xi\) is the standard normal distribution function, and \(\Phi(x)\) is its density function.  
The failure distribution function for the degeneration model is  
\[F(t)=\[PERSON]\left(\frac{\mu t-l}{\sigma\sqrt{t}}\right)+\exp\left(\frac{2\mu l}{ \sigma^{2}}\right)\[PERSON]\left(\frac{-l-\mu t}{\sigma\sqrt{t}}\right);\ t>0 \tag{8}\]  
The unknown parameters of the failure distribution function are the drift parameter \(\mu\) and the variance coefficient \(\sigma\). The degradation failure threshold \(l\) can generally be given based on engineering experience combined with the failure mechanism of long-life products. Now, we only need to find the estimated value of unknown parameters to get the product's failure distribution function, and then obtain more reliability information.
# Visualization Assisted Approach to Anomaly and Attack Detection in Water Treatment Systems
## 2 Related Works
The problem arises when analyzing and monitoring the system behavior where an analyst or operator needs to review tens of parameters, and the application of advanced visualization techniques could increase the efficiency of their work. Only a few visual analytics solutions aimed to assist in the monitoring of complex object behavior and anomaly detection are suggested. [PERSON] et al. [18] explored the capabilities of pixel based techniques with different layouts and line charts to detect patterns and anomalies in energy consumption. In [19], the authors adopted matrix-based and RadViz visualizations to analyze anomalies in heating, ventilation and conditioning data. In [20], the authors applied another multidimensional data projection technique, namely multidimensional scaling, to analyze streaming data from multiple sensors. To be able to apply it, the authors evaluated the pairwise similarity in data streams from sensors and used it to map sensors on the plane. Such projection allowed the authors to represent sensors' functioning as a trajectory of points on the plane, and anomalies in their behavior could be detected as anomalies in their trajectories. In [21], the authors proposed an interactive visual analytics system for equipment line monitoring that supports not only smart parameter monitoring but also machine learning model inspection and updates. The latter is achieved by evaluating the characteristics of the training set and current data. To visualize streams of sensor data, the authors use either line charts with the time axis or pixel-based visualization to cope with large volumes of data. Let us note the main peculiarities that distinguish this work from the analyzed existing results in the field:  
* Focus on the subject area of water treatment. In particular, when defining the classes of attacks, the authors considered the nature of the viscosity and fluidity of water, which determines some inertia and often a gradual change in the accumulation and transfer of water, and as a result, sensor readings in the generated time sequences. * Building and configuring a set of classifiers as attack and anomaly detection tools based on machine learning methods specific to the problem being solved and the list of critical attacks generated. * Application of the visual models as means of visual exploration of the system parameters, and monitoring of the system state. The peculiarity of this component lies in the computation and visualization of the metric that characterizes the changes in the system at some given moment of time. * Combination of the proposed visual analysis and machine learning methods, and presentation of the visual-based express evaluation of the system to make decisions on more detailed manual and/or machine-assisted checking of the system state, firstly, and visual validation of the constructed machine learning classifiers by means of visual analysis and exploration of the system parameters as well as the system state to check the effectiveness of these classifiers, secondly.
# Potential of Deep Learning Methods for Deep Level Particle Characterization in Crystallization
## 4 Results
### Comparison of Mask-RCNN and Classical Approach  
For direct comparison to the classical method of [PERSON] et al. (2015), the plain Mask-RCNN approach and the classical method are both applied to the two-class task and evaluated with respect to the COCO-metrics. The Mask-RCNN is trained directly and without any modifications on the two-class training dataset, i.e., the original dataset reduced to sc and ag. In effect, this is equal to the Mask-RCNN of the M2_1 approach detecting only sc and ag. Figure 8 shows the comparison between Mask-RCNN and the classical pipeline, either one with a threshold of \(d_{eq}=50.8\)\(\upmu\)m applied. It can be seen that Mask-RCNN outperforms the classical pipeline in overall detection accuracy and in per-class accuracy. As shown in Figure 8a, the Mask-RCNN approach wins by a significant margin in either category.  
Figure 6: Comparison of different size thresholds for Mask-RCNN M3_2 PP.  
In both methods, overall accuracy increases with increasing object size, as shown by size-dependent values \(AP_{\{s,m,l\}}\) in Figure 8b, indicating that both detection and discrimination accuracy decrease with decreasing particle size. The detection of agglomerates yields higher AP than single crystal for both methods, with Mask-RCNN being significantly more accurate. For small particles, \(AP_{s}\) scores 30.4 for Mask-RCNN and only 8.1 for the classical approach. The gap in accuracy gets smaller for medium-sized particles, with \(AP_{m}=66.5\) for Mask-RCNN vs. \(AP_{m}=39.1\) for the classical approach, and more so for large particles  
Figure 8: Comparison of results for Mask-RCNN and classical approach. (**a**) Default AP, (**b**) Size-dependent AP.  
Figure 7: Visualization of the three-class task using M3_2 PP. (**a**) Single crystals (purple) and agglomerates (green) detected from Figure 1, (**b**) Primary crystals in agglomerates (random color for each) detected from Figure 1, (**c**) Exemplary agglomerates: Selected segmented agglomerates (random color for each primary crystal in agglomerate) at the top and the corresponding cropped original agglomerates at the bottom, (**d**) Insufficiently detected contours of primary crystals in agglomerates.  
(Mask-RCNN: \(AP_{l}=84.5\), classical: \(AP_{l}=76.3\)). Even though accuracy is dependent on object size for the Mask-RCNN approach as well as the classical one, and though the differences in accuracy become smaller with increasing object size, in summary, the Mask-RCNN approach gives an enormously large improvement over the former method, especially for small- and medium-sized particles.
## 2 Materials and Methods
### Deep Learning Models  
#### 2.5.1 Rnn  
Instead of having a feed-forward network (from input to output), an RNN also takes into account past output \(h_{(t-1)}\) and thus is connected to the future input \(x_{(t+1)}\). The neuron is connected to itself when not considering time \(t\), as shown in Figure 3 on the left, which is the same structure unrolled through time on the right, where it is more apparent that output \(h_{0}\) is not only the output of time 0, but is part of the operation of time 1. This process continues until frame \(t\), and thus an ANN can process sequences via the inclusion of past output into the next operation.  
A 1-layer RNN would be used with a fixed set of standard hyper-parameters to test the model's performance without requiring great training times: 25 epochs, 128 batch size, and 5 sequence size. Additionally, the number of neurons was adjusted via trial and error, considering the training and validation loss graph across epochs, as the chart tells if the model is under-fitting or over-fitting the training dataset. The number of neurons changed in powers of 2 according to \(f(x)=2^{x}\), while adjusting \(x\) and keeping it always as a power of 2, as it helps the process. It finally reached 8 neurons per layer, where the model neither over-fitted the training dataset nor under-fitted it.  
The recurrent activation function used was sigmoid, as shown in Equation (11), and the final activation function was tanh. This functions are useful when explaining both LSTM cell and GRU cell in Sections 2.5.2 and 2.5.3, respectively.  
\[\sigma(x)=\frac{1}{1+e^{-x}} \tag{11}\]
## 3 Results
### Full LOFA Scenario at 300 kW/m\({}^{2}\) with Calculated Sensitivities  
The analyses performed adopting a sensitivity matrix calculated starting from a steady-state condition were sufficient to demonstrate the capabilities of the MELCOR 1.8.6 code. However, these analyses left two open questions:  
* Too-small uncertainty ranges for the outlet coolant temperatures;
* The lack of back-flows in C3 scenarios.  
The first issue is probably linked to the use of a sensitivity matrix coming from steady-state conditions, while the second one is probably linked to the assumptions made for the coolant inlet pressure through Channels 1 and 5. To prove the correctness of these two explanations, the Full LOFA scenario at 300 kW/m\({}^{2}\) has been re-investigated calculating the sensitivity matrix adopting the brute force method. The set of parameters considered in this analysis was also extended to include the inlet coolant pressure in Channels 1 and 5.  
The outlet coolant temperatures from the channels under heat load are shown in Figure 12, while Figure 13 shows the results achieved from the two sensitivity runs performed considering the inlet pressure from Channel 1 as parameter. The results achieved demonstrate that the small uncertainty ranges shown in Figures 7, 9 and 11 are mostly linked with the use of a sensitivity matrix obtained from a steady-state calculation.  
However, the uncertainty ranges obtained in MELCOR 1.8.6 are smaller than those obtained in RELAP5-3D [4], but this is not surprising considering that RELAP5-3D offers more options and models to reproduce the thermal-hydraulics of the system.  
Instead, the behavior of two sensitivity runs performed considering the inlet pressure from Channel 1 as parameters show that a small variation of this parameter can completely change the outcome of the calculations (non-linear variation in the surrounding of the nominal parameter value). This supports the same conclusion drawn in [4] that linked the inconsistent behavior of the temperature in the C3 scenarios with the user's assumption made and not with the code.
## 3 Results and Discussion
### BLUF Modules in Association with the Effector Domains
#### 3.3.6 PRK Superfamily  
The PRK family represents a group of three types of P-loop containing kinases, i.e., phosphoribulokinase [105], uridine kinases [106], and pantothenate kinases (CoaA) [107]. This family is named after one of its members, i.e., phosphoribulokinase (PRK), which drives the phosphoryl transfer from Mg-ATP to ribulose 5-phosphate to form ribulose 1, 5-bisphosphate (RuBP) during the pentose phosphate pathway [105]. In _E. coli_, the _udk_ gene encodes the pyrimidine salvage enzyme uridine kinase, causing the phosphorylation of uridine/cytidine into UMP/CMP using GTP as the phosphate donor [106]. Pantothenate kinase controls the rate-limiting step in the coenzyme A (CoA) biosynthesis pathway [107]. The _CoA_A gene is transcribed to produce the 1.1 kb transcript, which is further translatedinto two protein products of 36.4 and 35.4 kDa, respectively [107]. The resultant proteins encoded by the _coaA_ gene, showed a difference of eight amino acids at the N-terminus. The _E. coli_ strains bearing multiple copies of the _coaA_ gene showed a higher activity of the pantotheanate kinase [107].
# A New Effective Narrowband Active Noise Control System for Accommodating Frequency Mismatch
## 2 Materials and Methods
### The Limitations of Conventional NANC System  
If the frequencies of the reference signal have no discrepancy with the original signal, as shown in Figure 2a, the mean square error (MSE) value gradually decreases and tends to be stable in the end. However, as shown in Figure 2b, even if a 1% FM appears in the NANC system, the system performance will sharply deteriorate, the MSE curve will oscillate periodically, and the noise suppression performance of the traditional NANC system is worse.
# A Lightweight AI-Based Approach for Drone Jamming Detection
## 5 Model Performance
### Parameters Tuning Phase
#### 5.1.1 n_estimators Tuning  
The first parameter that we decided to tune was the _n_estimators_ parameter. The tuning values are shown in Table 5.  
The effect on the evaluation metrics of the model is shown in Figure 6.  
Figure 5: Input signal in the case of constant jamming. The normal traffic signal is concatenated with the jamming signal.  
Figure 6: Impact of the numbers of estimators (n_estimators) on several performance metrics (accuracy, precision, recall, F1 score), where a linear increasing trend is pointed out for all the metrics.  
\begin{table}
\begin{tabular}{c c} \hline n\_estimators & Values \\ \hline Minimum & 1 \\ Maximum & 50 \\ Step & 1 \\ \hline \end{tabular}
\end{table}
Table 5: n_estimators tuning values.  
As we can see from the graph, performance increases as the number of estimators increase, converging to a stable value. Figure 6(a),b show that the time required both for training and classification increases linearly with the number of estimators. This means that choosing an appropriate number of estimators is crucial, as it can greatly affect the model performance.  
We accordingly selected n_estimators = 15, as it provided a good balance between model performance and computational resources required. The model metrics keep slowly improving until 45 estimators, but the performance increase is minimal, making it not worth such a large increase in training and classification time, especially considering the resource-constrained nature of the scenario under consideration.
## 4 Results
### Establishing the EASY System Characteristics
#### 4.3.1 Potential Users  
Four major user groups of EASY were envisioned. The major end-users were landholders who would use EASY as a farm management tool. In addition to accessing spatial information and datasets, to this group EASY would provide access to textual documents (e.g., extension notes, relevant research articles and technical reports) concerning improving farm productivity and environmental performance. This capability could be developed by cross-referencing existing online knowledge repositories such as VRO and value-adding to existing knowledge hubs. Landholders may also use purpose-built topic packages and view spatial data and modelling results relevant to their own farm context. Importantly, landholders could also contribute aggregated information to data repositories relevant to farm management activities and regionally-based or industry-based environmental monitoring.  
Extension officers and agricultural consultants were another major end-user identified. Given that the key role of agricultural extension activities is the provision of information to landholders, EASYwould be an ideal way to disseminate information that landholders can then apply to their own farm context. Another useful role for EASY is as a way to collect information about on-ground activities that would allow extension staff to better understand/monitor the uptake of new land management practices and standards.  
The third major user group identified was regional environmental managers (such as CMA staff) and agricultural program managers who could use the topic package functionality extensively. Topic packages (or additional modules related to specific NRM or agricultural topics) could be developed to package specific information and to analyse and report on data collected in EASY. Importantly, this information would include what has been input by landholders, extension officers, and agricultural consultants with over-arching privacy/access arrangements.  
Finally, researchers could use EASY to deliver research outcomes and materials to landholders, extension officers and agricultural consultants. EASY would provide a platform where models and research outcomes could be documented, published and accessed by extension officers and agricultural consultants to run \"what if?\" scenarios to examine potential land management practices. EASY would therefore be a vehicle by which research results contribute to practical real-world outcomes.  
Striving to meet these different needs was seen by the EASY project team as a way to enhance uptake. As noted, much work in agricultural DSSs is focussed on a single user-group or application. Using a system-based approach to DSS design was seen as the best way of developing a system that could meet the needs of multiple users by virtue of having an inherently flexible and expandable structure.
# Fully Leveraging Deep Learning Methods for Constructing Retinal Fundus Photomatages
## 2 Methods
### Frame Sorting
#### 2.2.1 Vessel Segmentation and Frame Preprocessing  
Unique landmarks are vital for image registration, for which vessels are an important and reliable source. However, often in fundus images, the vessels have very low contrast and they are unclear, especially for those in the peripheral that mostly contains thin vessels. Thus, we propose a preprocessing method to enhance the contrast that is based on vessel segmentation.  
We apply the SSANet that was proposed by [PERSON] et al. [19], which incorporates a layer for scale-space approximation to better deal with vessels of various widths, in order to generate a pixelwise vessel probability map for each frame. We then construct a binary mask from the map through thresholding, which is then used as a stencil for enhancing the frame contrast. Specifically, we simply increase pixel values by 30% for non-vessel pixels, while maintaining the values of vessel pixels. Because vessel pixels consistently have lower intensities, this enhances the contrast. Figure 4 depicts a visualization of this process. We note that we train the SSANet on 554 images that were sampled from our private dataset described in Section 3.1, with ground truth being generated by the method of [PERSON] et al. [15, 16], based on the registered vessels of corresponding fluorescein angiography (FA) images, with minor manual corrections.  
Figure 3: A visual summary of the frame integration pipeline, including a two-step rigid and non-rigid registration method adapted from [15], together with image blending.