esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Analysis  
### CR Spectrum Allocation  
In CR spectrum allocation, the gateway allocates free channels to secondary users by the allocation algorithm [11]. At present, the patterns of CR spectrum allocation can be divided into three categories [12], as shown in Figure 2.  
Different patterns of CR spectrum allocation techniques may be incorporated in the same network in practical applications to make the data transmission more efficient and reliable. At present, two CR spectrum allocation models have been primarily used: the model based on graph theory [13] and the model based on auction theory [14]. However, when CR is used in the smart grid, these two conventional models may not match because the smart grid communication system requires a high transmission rate and rapid time varying network environment to meet the huge amount of data that was generated during the power exchange and transmission. In this case, a new spectrum allocation model for use in a smart grid communication system is necessary [15]. Thus, this paper introduced a new CR spectrum allocation model based on non-cooperative game theory.
###### Abstract
To achieve both heterogeneous periodic data collection with high success ratio and unpredictable on-demand communication within a deadline over IWSNs, we propose a data traffic control scheme for polling-based communication in IWSNs and a scheduler which uniformly distributes network load over slots. Our proposed scheme incorporates multiple heterogeneous periodic data collection schedules in a single schedule. The proposed approach aims to balance network traffic of multiple applications as well as to provide high success data collection. In addition, the schedule gives all nodes fair opportunities to receive a polling query from a central node at a certain interval. It is important to generate a polling query to a node fairly, because the available bandwidth for downward traffic is less than that for upward. This mechanism also enables a center node to generate an unpredictable on-demand request to a node and receive a response within a deadline. The contribution of this paper is to propose a data traffic control scheme for polling-based communications and verify its performance from viewpoints of end-to-end communication success probability and balanced slot utilization. None of the conventional technologies for IWSNs focus on the problem that an unbalanced network bandwidth between uplink and downlink causes unexpected big latency of unpredictable on-demand communication or decrease of success probability for periodic data collection. Standard IWSN protocols like ISA100.11a typically have a scheduler for allocating network resources such as timeslots to all nodes. Since a network manager of IWSN protocols gathers information about network condition from all nodes, IWSN protocols normally require a large available bandwidth for upward traffic. In addition, the scheduler has to deliver the information to all nodes whenever a new node joins the network or a network topology changes. Therefore, it is difficult to support a polling-based unpredictable on-demand communication within deadline. In contrast,since our scheme only determines when to generate and transmit a packet at a root node for periodic data collection and unpredictable on-demand data collection. It does not need to adjust the schedule because the number of nodes does not change. The rest of the paper is structured as follows. We first describe assumptions and challenges in Section 2, and Section 3 presents an overview of related work. In Section 4 we propose the data traffic control scheme. Then, we evaluate uniformity and success ratio of collecting periodic data in Section 7. In Section 8, we discuss possibility of providing schedules when an application data traffic is high. Finally, Section 9 concludes the paper and describes future work.
## 4 Discussion
### Limitations  
Our study is subject to several important limitations that warrant consideration. The primary constraint lies in the utilization of patient data sourced from the MIMIC-III database, which reflects a specific patient demographic and healthcare context. Given that the data are predominantly sourced from large urban areas, the generalizability of our findings to ICU patients in smaller healthcare institutions remains uncertain. To enhance the robustness of our conclusions, future studies should strive to include data from diverse settings, including rural or smaller medical facilities.  
Furthermore, the absence of high-quality nursing records for a significant subset of patients poses a potential limitation, particularly in extrapolating the model's performance to other intensive care environments. Relying on doctor-recorded information during patient consultations, although valuable for disease and treatment research, introduces inherent challenges, such as common spelling errors and inaccuracies. These issues may compromise the quality of interpretation and subsequent predictions [34]. The lack of external validation for our prediction model is another notable limitation, impacting the overall reliability and credibility of the proposed model. Subsequent research endeavors should prioritize the inclusion of robust external validation procedures to fortify the standing of the model.  
In addition, it is crucial to note that our study exclusively focused on clinical data collected within the initial 24 h after ICU admission, along with a doctor's written summary. This temporal constraint hinders the comprehensive evaluation of changes occurring throughout the entire ICU admission period. A more exhaustive compilation of clinical records and test reports is essential for a thorough understanding and may exert a substantial influence on predicted outcomes. Researchers conducting future studies should remain cognizant of these limitations and actively address these challenges to propel advancements in the field.  
Moreover, it is imperative to highlight that our study specifically targeted the prediction of mortality rates among heart-failure patients in the intensive care unit. Future research endeavors should extend their focus to encompass various diseases or conditions, allowing for a more nuanced exploration of predictive modeling across different patient populations [35; 36]. Investigating diverse patient groups is likely to reveal distinct patterns and factors influencing outcomes, contributing to a richer understanding of the complexities involved. This expansion in scope can pave the way for tailored predictive models that cater to the unique characteristics of specific medical conditions, thereby advancing the applicability and effectiveness of such models in clinical practice.
# Monitoring Water Area Dynamics in Kashgar (2003-2023) Using Multi-Source Remote Sensing Data
## 2 Materials and Methods
### Data
#### 2.2.2 Sentinel-2 Satellite Imagery  
The Sentinel-2 satellite constellation comprises two satellites, Sentinel-2A and Sentinel-2B, launched in 2015 and 2017, respectively. Both satellites exhibit a revisit period of 10 days, achieving a 5-day revisit period when operating in tandem. Equipped with a multispectral sensor covering the visible to shortwave infrared spectrum, Sentinel-2 provides varying spatial resolutions across bands. This study utilizes Sentinel-2 data with a 10 m spatial resolution for water body extraction. Leveraging its high temporal and spatial resolution,Sentinel-2 enables effective monitoring of subtle changes in water bodies, thereby enhancing water body extraction accuracy.
## 3 Experimental Results and Analyses
### Dataset Composition and Characteristic  
Our dataset consists of two parts, one of which is derived from aircraft structures, such as fuselage, wing, aircraft tail, and engine interior. The length of cracks ranges from 1 to more than 10 cm. About 580 images with cracks were collected by ourselves and servicing engineers which contains different cases of structural cracks in real aircraft from our partner aviation companies. The other is from the data of industrial equipment with similar cracks, as shown in Figure 6. The dataset contains 960 pictures in total. We use 800 samples as a training set and use 80 samples as a validation set for selecting a well-generalized performance model. Another 80 samples independent of the training and validation set are used as a test set.  
As shown in Figure 6, the cracks on the fuselage and wings of an aircraft are small and light in color, so the difference between the cracks and the background is small. Meanwhile, strong illumination also increases the difficulty of crack detection in these areas. It is more difficult to detect cracks because of the complex internal structure and background of the aircraft engine.  
Figure 6: Different industrial equipments with cracks which have background interferences (**a–d**), aircraft structures with cracks (**e–h**).
## 2 Study Area, Data and Methods
### Relative Importance Analysis  
In this study, temperature and precipitation were used to explore environmental impacts on vegetation growth asymmetries. Like the definition of vegetation asymmetry, the term AsyTemp (AsyPrcp) is defined to depict the difference in average temperature (summed precipitation) between the first and second half of the growing season (same period as for vegetation asymmetry), which is determined using the SOS, POP and EOS identified for phenological parameters. In Equation (7), Tau(Pau) and Tsp(Psp) represent temperature (precipitation) in green up (spring) and senescence (autumn) seasons, respectively.  
\[\begin{split} AsyTemp=Tau-Tsp\\ AsyPrcp=Pau-Psp\end{split} \tag{7}\]  
A relative importance (RI) approach is utilized to quantify their respective contribution to long-term changes of plant growth asymmetries in each grid cell. This method is expressed as a multiple linear regression (Equation (8)). There, AsyVeg, representing AsyV, AsyL and AsyR (dependent variable), is regressed against AsyTemp and AsyPrcp (independent variable). \(\varepsilon\) indicates drivers that are not mentioned but might contribute to variations in plant growth asymmetry. Relative importance was implemented using the'relaimpo' package in R [40], which is based on variance decomposition in multiple linear regression models.  
\[AsyVeg=\beta 0+\beta 1\times AsyTemp+\beta 2\times AsyPrcp+\varepsilon \tag{8}\]  
when the coefficient of determination (R\({}^{2}\)) for the above multiple regression was obtained, it is decomposed into non-negative contributions shared by each variable in the following formula (Img method in'relaimpo' R package [41]. The RIs of multiple variables sum to the total R\({}^{2}\) where \(x_{k}\) represents AsyTemp or AsyPrcp (\(k=1\), \(2\)), and \(p\) equals \(2\). The seqR\({}^{2}\) denotes the additional R\({}^{2}\) when adding the regressor \(x_{k}\) to a model with the regressors in set \(S\). Thus, LMG(\(x_{k}\)) is the RI for AsyTemp or AsyPrcp.  
\[\text{LMG}(x_{k})=\frac{1}{p}\sum_{j=0}^{p-1}\left(\begin{array}{c}\sum_{S \subseteq\{x_{1},\ldots,x_{p}\}\backslash\{x_{k}\}}\frac{seqR^{2}(\{x_{k}\}|S) }{\left(\frac{p-1}{l}\right)}\\ n(S)=j\end{array}\right) \tag{9}\]
## 3 Case Study--Goldeneye CCS Site
### Approximated Predictive Mean and Uncertainty  
In Figure 8 we see that the accuracy around the leakage is high. We also observe areas with quite high uncertainty. Due to the nature of the deep-learning methods, it is difficult to get insight in why some regions are more uncertain than other. We can only expect the classifier to detect leaks if traces of the leak have reached that particular point. There may be several reasons why we see this behavior: The training and validation data may be too different from the test data. Time series data from the two classes may be too alike, making it very difficult to distinguish the classes, or the natural variability and a leak behave in some regions similar. The classifier will in these cases have a hard task, and in some cases misclassify or be uncertain about the prediction.  
Figure 9 shows kernel density estimates (KDE) of the 200 forward MC realizations for the three different locations in Figure 8. The yellow KDE is from the area south, in a region with high uncertainty. The distribution is almost uniform, making it very difficult to say if the time series arise from a leak situation. The blue KDE predicts that the time series arise from a leakage situation, with quite high confidence. This is reasonable, as the measurement is quite close to the leak locations. The cyan KDE  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenario** & **Prediction Probability** & **Standard Deviation** \\ \hline
0T & 90.15 & 115.48 \\
30T & 624.25 & 183.54 \\
300T & 633.58 & 161.82 \\
3000T & 773.77 & 153.94 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Approximation of area under the graph for the different scenarios for both prediction and standard deviation.  
Figure 7: 2D histogram of the mean prediction and standard deviation of all the time series in the test data set. The majority of the time series are predicted near 0 or 1 with low standard deviation. The color pallet have log-scale to visualize the time series that are classified with high standard deviation and low distinction in the prediction probability.  
shows relatively little spread, with a clear indication that this arise from a no-leak situation. Because of the distance from the leak location, this is reasonable, as traces from the leak might not reached this region of the area. One of the key concepts here has been to show how to model uncertainty in time series with modern deep-learning techniques. We therefore did not put a tremendous effort into optimization of hyper-parameters of the BCNN to improve the accuracy and decrease the uncertainty.
## 3 Results and Discussion
### Optical Absorption Spectra
This idea contradicts our results, where the LSPR stays at around 400 nm for all the experimental conditions. We believe that the red-shift reported by [PERSON] et al. [39] is more likely due to the agglomeration of Ag NPs, than to the increase in size (they reported an average size below 4 nm). [PERSON] et al. [17] also studied the effect of PVP concentration on the Ag NP formation under irradiation with nanosecond laser pulses. They reported an optical response similar to ours with the LSPR position fixed at around 400 nm, which reinforces our hypothesis. It should be noted that they had a lower concentration due to their experimental conditions: 10 min and 10 Hz (6 \(\times\) 10\({}^{3}\) laser pulses) and smaller particles because of the use of nanosecond laser pulses [16], but these differences should not affect the agglomeration behavior.
## 3 Results
### Effect of MLE on Plant Pigments
#### 3.3.5 Root Anthocyanin  
Figure 3D shows the effect of MLE application on anthocyanin content of wheat facing Cd stress. Results revealed that cadmium treatments of 500 uM and 1000 uM (under light) significantly increased the anthocyanin content of root by 61.4% and 68.6% as compared to control, while, on the other hand, application of 3% MLE along with combinations of 500 uM and 1000 uM cadmium solutions also increased the anthocyanin content by 66.38% and 71.4% compared to control.  
MLE 3% with both cadmium treatments even resulted in increased root anthocyanin compared to MLE 3% alone treatment (higher in shade compared to light).  
Figure 3: Effect of MLE application on plant pigments (**A**) chlorophyll a content (**B**) chlorophyll b content (**C**) anthocyanin of shoot (**D**) anthocyanin of root and (**E**) carotenoids content, with and without Cd stress under light and subdue light. Different letters above bars represents significant differences (\(p<0.05\)) at each measured point. Values represent mean \(\pm\) SE of three replicates of each treatment.
###### Abstract  
With the advent of the \"Internet of Things\" (IoT), insurers are increasingly leveraging remote sensor technology in the development of novel insurance products and risk management programs. For example, Hartford Steam Boiler's (HSB) IoT freeze loss program uses IoT temperature sensors to monitor indoor temperatures in locations at high risk of water-pipe burst (freeze loss) with the goal of reducing insurances losses via real-time monitoring of the temperature data streams. In the event these monitoring systems detect a potentially risky temperature environment, an alert is sent to the end-insured (business manager, tenant, maintenance staff, etc.), prompting them to take remedial action by raising temperatures. In the event that an alert is sent and freeze loss occurs, the firm is not liable for any damages incurred by the event. For the program to be effective, there must be a reliable method of verifying if customers took appropriate corrective action after receiving an alert. Due to the program's scale, direct follow up via text or phone calls is not possible for every alert event. In addition, direct feedback from customers is not necessarily reliable. In this paper, we propose the use of a non-linear, auto-regressive time series model, coupled with the time series intervention analysis method known as _causal impact_, to directly evaluate whether or not a customer took action directly from IoT temperature streams. Our method offers several distinct advantages over other methods as it is (a) readily scalable with continued program growth, (b) entirely automated, and (c) inherently less biased than human labelers or direct customer response. We demonstrate the efficacy of our method using a sample of actual freeze alert events from the freeze loss program.  
Gaussian process regression; Student-\(t\) process; impact of interventions; IoT sensor data; time series analysis; variational inference Article
## 3 Results and Discussion
### Method Validations
#### 3.3.1 LOD, LOQ, and Linearity of the Calibration Curve

Table 3: LOD, LOQ, and linearity of the first 40 pesticides using GC–MS/MS. LOD, LOQ, and linearity of the other 83 pesticides are listed in Table S3. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Pesticides** & \
\begin{tabular}{c} **Limit of Detection** \\ **(\(\mu\) kg\({}^{-1}\))** \\ \end{tabular} &
\begin{tabular}{c} **Limit of Quantification** \\ **(\(\mu\) kg\({}^{-1}\))** \\ \end{tabular} & **Linearity (\(r^{2}\))** \\ \hline
1 & Abametcin B1 & 3 & 10 & 0.9999 \\
2 & Acephate & 3 & 10 & 0.1000 \\
3 & Acetamiprid & 3 & 10 & 0.9933 \\
4 & Aldicarb & 3 & 10 & 0.9904 \\
5 & Amisulbrom & 3 & 10 & 0.9979 \\
6 & Azimsulfuron & 3 & 10 & 0.9974 \\
7 & Azimphos-methyl & 3 & 10 & 0.9985 \\
8 & Azoxystrobin & 3 & 10 & 0.9918 \\
9 & Bendiocarb & 3 & 10 & 0.9982 \\
10 & Bensulfuron-methyl & 3 & 10 & 0.9978 \\
11 & Benthiavalicarb-isopropyl & 3 & 10 & 0.9969 \\
12 & Benzobicyclo & 3 & 10 & 0.9932 \\
13 & Benzoximate & 3 & 10 & 0.9963 \\
14 & Bitertanol & 3 & 10 & 0.9995 \\
15 & Boscalid & 3 & 10 & 0.9971 \\
16 & Bromacil & 3 & 10 & 0.9965 \\
17 & Buprofezin & 3 & 10 & 0.9911 \\
18 & Cadusafos & 3 & 10 & 0.9974 \\
19 & Cafenstrole & 3 & 10 & 0.9904 \\
20 & Carbaryl & 3 & 10 & 0.9905 \\
21 & Carbendazim & 3 & 10 & 0.9977 \\
22 & Carbofuran & 3 & 10 & 0.9935 \\
23 & Carboxin & 3 & 10 & 0.9913 \\
24 & Carfentrazone-ethyl & 3 & 10 & 0.9901 \\
25 & Carpropamide & 3 & 10 & 0.9917 \\
26 & Chlorpyrifos & 3 & 10 & 0.9991  
\end{table} 
Table 4: LOD, LOQ, and linearity for the first 40 tested pesticides using LC–MS/MS. LOD, LOQ, and linearity for other 179 pesticides are listed in Table S4. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Pesticides** & \
\begin{tabular}{c} **Limit of Detection** \\ **(\(\mu\) kg\({}^{-1}\))** \\ \end{tabular} &
\begin{tabular}{c} **Limit of Quantification** \\ **(\(\mu\) kg\({}^{-1}\))** \\ \end{tabular} & **Linearity (\(r^{2}\))** \\ \hline
18 & Carbophenothion & 3 & 10 & 0.9998 \\
19 & Chlorantraniliprole & 3 & 10 & 0.9991 \\
20 & Chlordane-cis & 3 & 10 & 0.9997 \\
21 & Chlordane-trans & 3 & 10 & 0.9999 \\
22 & Chlorfenapyr & 3 & 10 & 0.9993 \\
23 & Chlorfenwinphos & 3 & 10 & 0.9999 \\
24 & Chlorluazuron & 3 & 10 & 0.9997 \\
25 & Chlorobenzilate & 3 & 10 & 0.9999 \\
26 & Chlorpropham & 3 & 10 & 0.9993 \\
27 & Chlorpyrifos-methyl & 3 & 10 & 0.9996 \\
28 & Cyfluhrin & 3 & 10 & 0.9999 \\
29 & Cyhalothrin & 3 & 10 & 0.9990 \\
30 & Cypermethrin & 3 & 10 & 0.9998 \\
31 & Cyprodanil & 3 & 10 & 0.9996 \\
32 & DDD-p,p\({}^{\prime}\) & 3 & 10 & 0.9764 \\
33 & DDE-p,p\({}^{\prime}\) & 3 & 10 & 0.9999 \\
34 & DDT-o,p\({}^{\prime}\) & 3 & 10 & 0.9904 \\
35 & DDT-p,p\({}^{\prime}\) & 3 & 10 & 0.9998 \\
36 & Deltamethrin (Tralomethin deg.) & 3 & 10 & 0.9994 \\
37 & Diclorop-methyl & 3 & 10 & 0.9999 \\
38 & Dicloran & 3 & 10 & 0.9992 \\
39 & Dicofol & 3 & 10 & 0.9996 \\
40 & Dieldrin & 3 & 10 & 0.9995 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._
# A Robust Multi-Watermarking Algorithm for Medical Images Based on DTCWT-DCT and Henon Map
## 2 The Fundamental Theory
### Henon Map  
A henon map is a two-dimensional nonlinear discrete chaotic map [25]. It is sensitive to initial values, has unpredictability, and is ergodic, which is very similar to image encryption [26]. Thus, it is often used in the study of image encryption algorithms. It is defined as follows:  
\[\left\{\begin{array}{l}x_{n+1}=1-ax^{2}{}_{n}+y_{n}\\ y_{n+1}=bx_{n}\end{array}\right. \tag{2}\]  
where, \(n\) represents the number of iterations, \(x,y\) represent the iteration values [27]. \(a,b\) are two control parameters, \(a\in(0,1.4)\), \(0.2<b\leq 0.314\). Under their control, the henon mapping system can be in a chaotic state, which \(a=1.4\), \(b=0.3\). Figures 4 and 5 show images of the non-attractive area and the two attractive domains displayed by the henon map under different parameters. In our proposed algorithm, we take the value of \(a\) as \(1.399\) and the value of \(b\) as \(0.314\).  
Figure 4: The non-attractive region, color indicates the number of times the iteration has escaped.  
Figure 3: Shift invariance of 2D DTCWT: (**a**) Input image; (**b**) Reconstructed images.
## 4 Insights into Bacterial-Algal Biomass in Different Operational Configurations
### Continuous Flow Reactors (CFRs)  
Currently, CFRs are the most widely used for the treatment of domestic effluents using microalgae-bacteria aggregate [50; 81]. Continuous flow reactors have lower installation and operating costs, as well as simpler operation [56]. When applied to systems with microalgae-bacteria aggregation, they require greater control of the feed to ensure the structural integrity of the biomass in its stratified form [121].  
Furthermore, some studies have achieved success in promoting a consortium between algae and bacteria in continuous flow reactors (Table 2). [PERSON] et al. (2017) [51] compared bacterial AGS and algal-bacterial AGS in CFR with recirculation. The results showed better performance in the reactor with algae, achieving COD, TN, and TP removals of 100%, 98%, and 64%, respectively. Additionally, the CRF system exhibited greater energy efficiency compared to the SBR with algal-bacterial consortium used in the study by [PERSON] et al. (2015) [86]. Another important operational parameter is the HRT. High HRTs result in higher costs, possibly due to the lower amount of biomass in suspension inside the reactors. In an algal-bacterial CFR, it was possible to establish an HRT of 6 h, treating low-concentration effluent (COD: 300 mg/L; N-NH4\({}^{+}\): 100 mg/L; P-PO4: 10 mg/L). In cases where CFRs require an internal sedimentation zone, the selection of flow must be in line with the sedimentation speed of the particles, i.e., only biomass with little sedimentation will be washed out of the reactor into the final effluent [122].  
Another critical aspect in CFR is the amount of dissolved oxygen (DO) injected into the system, which must facilitate the growth of heterotrophic biomass and the expected aerobic process, such as organic matter degradation and nitrification. Conversely, the production of photosynthetic oxygen is linked to the applied light intensity, with higher light intensity favoring the formation of microalgae flocs and bacteria with good sedimentability [123; 124]. Therefore, parameters such as SRT, DO, and light intensity need to be well defined when planning CFR systems.