esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
For nifteRT, an autocontouring routine has been developed by implementing HPO with CMA-ES in a U-Net-based algorithm, and its contouring accuracy and speed were validated using in vivo MR images of 47 liver, prostate, and lung cancer patients. By optimizing six hyperparameters for each patient case, the algorithm achieved a DC, CD, and HD of 0.92 (0.04), 1.35 (1.03) mm, and 3.63 (2.17) mm, respectively, between the manual contours and autocontours averaged over all the patients. The developed routine allows for patient-specific HPO, leading to adequate autocontouring performance that may be comparable to that of human experts.  
Conceptualization, J.Y. and B.G.F.; methodology, G.H. and J.Y.; software, G.H. and J.Y.; validation, G.H. and J.Y.; formal analysis, G.H. and J.Y.; investigation, G.H., K.W., N.U., D.Y., J.W. and A.E.; resources, N.U. and J.Y.; data curation, G.H.; writing--original draft preparation, G.H.; writing--review and editing, G.H., K.W., N.U., D.Y., J.W., A.E., J.Y. and B.G.F.; visualization, G.H.; supervision, K.W., J.Y. and B.G.F.; project administration, J.Y.; funding acquisition, G.H. and J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Canadian Institutes of Health Research, under grant number 437221, and the Yau Family Foundation award.  
This study was approved by the Cancer Committee of the Health Research Ethics Board of Alberta (HREBA.CC-19-0158) and was conducted in accordance with the Declaration of Helsinki of 1975, as revised in 2013.  
Informed Consent Statement: Informed consent for participation was obtained from all subjects involved in this study.  
The data presented in this study are only available on request from the corresponding author due to ethical reasons.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Patient** & \begin{tabular}{c} **\# of** \\ **Convolutions** \\ **Range:** \\ \end{tabular} & \begin{tabular}{c} **Filter Size** \\ **1 or 2** \\ \end{tabular} & \begin{tabular}{c} **Feature Map \#** \\ **3 or 5** \\ \end{tabular} & \begin{tabular}{c} **\# of Poolings** \\ **[32, 128]** \\ \end{tabular} & \
\begin{tabular}{c} **Learning Rate** \\ **1 or 2** \\ \end{tabular} &
\begin{tabular}{c} **\# of Training** \\ **Image Pairs** \\ **[10, 30]** \\ \end{tabular} \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 1: Optimized hyperparameters using CMA-ES for liver (P1–11), prostate (sagittal: P12–22, coronal: P23–27, axial: P28–35), and lung cancer patients (P36–47).
# CARA: A Congestion-Aware Routing Algorithm for Wireless Sensor Networks
###### Abstract  
Congestion control is one of the key research topics in relation to the routing algorithms of wireless sensor networks (WSNs). In this paper, we propose a congestion-aware routing algorithm (CARA) for unlimited-lifetime wireless sensor networks by integrating the geographic distance and traffic load of sensor nodes. The algorithm takes alleviating congestion as the primary purpose and considers the traffic of the node itself and local network traffic. According to the geographic distance between nodes, CARA defines four decision parameters (node load factor, forward rate, cache remaining rate, and forward average cache remaining rate), selecting the best node as the next-hop through the multi-attribute decision-making method. Compared with the two existing algorithms for congestion control, our simulation results suggest that the CARA algorithm alleviates network congestion and meets reasonable network delay and energy consumption requirements.
## 3 Model and Analysis
### Stochastic Taylor Expansion  
Since nonlinear Ito stochastic differential equations cannot be directly solved analytically, the stochastic Taylor expansion is applied using the Eulerian method for numerical simulation. Therefore, the Taylor expansions for the Planning and Designing Party (X), the Production and Construction Party (Y), the Operation and Maintenance Party (Z), and the Dismantling and Recycling Party (U) can be derived as Equations (25)-(28), respectively:  
\[x(t_{n+1})=x(t_{n})+h[(1+y\beta_{XY}+z\beta_{XZ}+u\beta_{XU})E_{X}-C_{X}+\Pi_{ X}+(1-y)N_{Y}+(1-z)N_{Z}+(1-u)N_{U}+G_{X}]x(t_{n}) \tag{25}\]  
\[+\Delta w_{n}\sigma x(t_{n})+R_{1}\]  
\[y(t_{n+1})=y(t_{n})+h[(1+x\beta_{YX}+z\beta_{YZ}+u\beta_{YU}-x\alpha_{YX})E_{ Y}-C_{Y}+\Pi_{Y}+G_{Y}+xN_{Y}]y(t_{n})+\Delta w_{n}\sigma z(t_{n})+R_{2} \tag{26}\]  
\[z(t_{n+1})=z(t_{n})+h[(1+x\beta_{ZX}+y\beta_{ZY}+u\beta_{ZU}-x\alpha_{ZX}-y \alpha_{ZY})E_{Z}-C_{Z}+\Pi_{Z}+G_{Z}+xN_{Z}]z(t_{n})+\Delta w_{n}\sigma z(t_{ n})+R_{3} \tag{27}\]  
\[u(t_{n+1})=u(t_{n})+h[(1+x\beta_{UX}+y\beta_{UY}+z\beta_{UZ}-x\alpha_{UX}-y \alpha_{UY}-z\alpha_{UZ})E_{U}-C_{U}+\Pi_{U}+G_{U}+xN_{U}]u(t_{n})+\Delta w_{n }\sigma u(t_{n})+R_{4} \tag{28}\]
# Ten Years of TerraSAR-X Operations
## 3 Evolution of SAR Product Portfolio  
In its ten years of operation, the TerraSAR-X ground segment extended and improved the SAR product portfolio continuously. The flexibility in commanding the versatile TerraSAR-X and TanDEM-X SAR instruments allowed for numerous radar experiments to test and demonstrate new SAR acquisition modes or mode variants, which are detailed in the following section. The most prominent extension was the operational release of Staring Spotlight (ST) and a six-beam wide ScanSAR mode in April 2013. But, not all evolutionary steps in the product development are related to innovative SAR mode design and instrument commanding, and not all of such experiments lead to new product releases. Since the TerraSAR-X mission is user driven and is not restricted with regards to the future product ordering to specific acquisition conditions, only those modes and product variants that fulfill the high product quality requirements on a global scale were selected, calibrated, and verified as operational products. Some mode variants like the experimental 300 MHz range bandwidth option for the high resolution spotlight were already added to the product portfolio right after the end of the commissioning phase because of their solid performance. Modes, like the experimental aperture switching ATI (ATIS) mode and TOPSAR, however, are limited due to the complexity in their commanding sequences to restricted acquisition scenarios, and hence are not included in the SAR product portfolio. All basic products as well as the operationally provided experimental products are generated by the TerraSAR-X Multi-mode SAR Processor (TMSP). Details are given in [2; 14]. As of 2018, the portfolio of basic [2] and experimental products [15] is characterized in Table 1.
# Reinforcement Learning for Options Trading
## 1 Introduction
To address the problems mentioned above, we put forward a framework to trade options using reinforcement learning. The underlying asset's trading data is utilized for training the RL model. Compared with the option contracts, the underlying assets have a more extended and continuous trading period, which is fit for RL training. To further augment the training dataset, we use candle data in different time intervals (a day, 60 min, 30 min, 15 min, and 5 min) to train the model, respectively. Different RL algorithms are used to test our methods, such as DQN, SAC, and proximal policy optimization (PPO). To relieve the volatility of options, we added a protective close strategy to the model. The strategy can prevent unbearable losses. The proposed model was tested on the actual market data for options trading. It can make reliable profits on various options contracts. Furthermore, we compared the models' performance training on different time interval datasets to find the proper time interval training datasets we should use. Our study has research significance and application meaning in options algorithmic trading. It has a reference value for other financial assets trading and training data augmentation problems. The main contributions of this paper are as follows:  
* We propose a framework to trade options using reinforcement learning. The model is trained on the underlying asset's trading data. Candle data in different time intervals (a day, 60 min, 30 min, 15 min, and 5 min) are utilized to augment the training datasets. By this means, adequate data is available for training the RL model, leading to a satisfactory result. * The protective close strategy is added to the RL model to prevent unbearable losses. Due to the high volatility of option prices, the strategy can protect the principal from suffering a massive drawdown. Moreover, the strategy could improve investment returns to some extent. * The experimental results showed that the proposed model, with the protective close strategy, can get decent returns, compared to the Buy and Hold strategy in options trading, which indicates that our model learns ways to make profit from underlying asset data and applies it to options trading. The remaining parts of this paper are organized as follows. Section 2 describes the background on reinforcement learning, options trading, and data augmentation. Section 3 introduces the options trading reinforcement learning (OTRL) framework. Section 4 provides experimental settings, results, and evaluation. Finally, Section 5 concludes this paper.
# Nutritional Care Process in Hospitalized Patients with Obesity-Related Multimorbidity
## 9 Recommendations on the Use of Enteral Nutrition  
Enteral Nutrition (EN) is defined as the set of procedures used to deliver food to the gastrointestinal tract via a feeding tube. The most common indications for EN in hospitalized patients with ORM are established when [61]:  
* The oral route fails to meet more than 60% of daily energy needs for 3 to 7 days.
* PO cannot or should not be used (due to invasive mechanical ventilation, sedation, coma, oral/esophageal/laryngeal/maxillofacial injury, obstruction, or surgery, etc.).
* Severe dysphagia contraindicates oral route. In individuals with severe malnutrition or in the ICU, EN should be initiated within the first 24 to 48 h of admission with trophic feeding providing 10 to 15 mL/hour or 500 kcal per day.  
In conditions such as active gastrointestinal bleeding, shock, and low tissue perfusion with high doses of vasopressors or intoropes (greater than 0.3 ug/kg/min) or severe metabolic acidosis, it is recommended to delay initiation and start EN once hemodynamic stability is achieved or the acid-base imbalance is corrected [18].  
The required duration of EN will determine the type of tube to be used: for less than one month, temporary tubes such as nasogastric or nasoenteric tubes are recommended. Permanent tubes, such as gastrostomy or jejunumostomy, are indicated when EN is needed for longer than one month. In cases with a high risk of aspiration, nasojejunal tubes are preferred. During hospitalization, the use of commercial formulas is advised due to a lower incidence of reported infections. The selection of enteral formula types follows the same specifications as ONS and must be based on energy needs through macro and micronutrients [18; 19; 44] (Tables 2 and 3).
## 2 Materials and Methods  
### Wavelet Transform  
The fundamental method for the algorithm to work is wavelet transform, which decomposes the input signal into detailing coefficients that can be used for subsequent failure analysis and localization.  
The wavelet decomposition algorithm, often referred to as the [PERSON] algorithm [52] or fast wavelet transform, is an efficient method for decomposing a signal into multiple levelsof detail. This algorithm is the basis for many signal processing applications, including noise reduction, data compression, and feature analysis. The pseudocode provided as 'WaveletDecomposition' describes the scheme of [PERSON]'s algorithm for one-dimensional signals (Algorithm 1).  
```
0:signal -- input one-dimensional signal
0:wavelet -- wavelet type (e.g., 'db20')
0:level -- decomposition depth
0:Coefficient array \(coeffs=[cA_{\text{level}},cD_{\text{level}},\ldots,cD_{1}]\)
1:Initialize the list of coefficients \(coeffs\) as empty
2:current_signal \(\leftarrow\) signal
3:for\(k\gets 1\) to leveldo
4: Perform convolution of current_signal with the scaling filter (low-pass) from the selected wavelet \(\to cA_{k}\)
5: Perform convolution of current_signal with the high-pass filter from the selected wavelet \(\to cD_{k}\)
6: Perform a downsampling operation (select every second sample) for \(cA_{k}\) and \(cD_{k}\)
7:current_signal \(\gets cA_{k}\triangleright\) The signal at the next step is the approximating coefficients
8: Save \(cD_{k}\) to the internal buffer
9:endfor
10: Save current_signal (i.e., \(cA_{\text{level}}\)) to coeffs
11:Add all detailing coefficients \(cD_{k}\) to coeffs in order from last to first return coeffs
```  
**Algorithm 1**WaveletDecomposition (Mallat's Algorithm)
## 3 Results and Discussion  
The synthesis of the gelatin stage starts with the preparation of beef bones, washing, boiling (degreasing), followed by the demineralization process and extraction. The purpose of this stage is to obtain gelatin material in accordance with the Indonesian National Standard (SNI 01-3735-1995). At this stage, good operating conditions are sought so that the desired material is produced. These operating conditions include the selection of solvents used for soaking beef bones, namely acidic and alkaline solvents. Acid solvents used include HCl, H\({}_{2}\)SO\({}_{4}\), and CH\({}_{3}\)COOH, while the alkaline solvent used is NaOH. Salt solvents are also carried out, namely by using NaHCO\({}_{2}\). Other variables were the length of time for bone immersion, the concentration of the solvent used to soak the bones, and the extraction time of gelatin. After obtaining the material, characterization was carried out,namely physical tests, characterization of functional groups by FTIR, and morphological analysis by SEM-EDX.  
The manufacture of gelatin in this study uses beef thigh bone or femur, where the bone is more easily separated from the meat and the fat attached. Immersion treatment using NaOH solution so that the resulting gelatin is a basic type of gelatin. Immersion was carried out for 7 days, with the replacement of the solution every two days to prevent changes in concentration. The difference in concentration of NaOH solution greatly affects the bone structure of cows. The higher the NaOH concentration, the more porous and eroded beef bones were compared to a lower concentration where the beef bones were still intact and a little hard. Analysis to determine the characteristics of this gelatin is an analysis of yield, water content, ash content, pH, viscosity, and microbiological tests.  
The statistical analysis is shown in Table 1. Table 1 shows that the yield values range from 10.64% to 19.04%. Table 1 explains that soaking with different solvents and different extraction process times has significantly different effects (\(p\) < 0.05). The difference in extraction time, which is 4 h and 6 h, has a different effect on the resulting yield value.
# An Energy-Saving Forwarding Mechanism Based on Clustering for Opportunistic Networks
## 6 Performance Evaluation
### Tukey Test--Infocom Scenario
In Figure 8, in the cases where the minimum battery load limit for nodes to cooperate is greater than 25%, the overhead is less than 100 for all routing algorithms. On the other hand, when \(E\) = [0, 100], we found the overhead for the PRoPHET routing protocol to be about ten times higher than GPDMI and \(e\)GPDMI. The hypothesis for this result is that in PRoPHET, the forwarding probabilities take longer to adjust since Reality is a sparse scenario. Figure 7: Average delivery delay vs. battery ranges in Reality scenario. These results further demonstrate that \(e\)GPDMI mitigates network overhead, while stabilizing the message delivery probability in both scenarios.
## 1 Introduction
[7] investigated a hybrid LAES configuration for combined district cooling, district heating, and hot water and power supply in decentralised micro energy networks. Their study concluded that such a configuration can achieve an overall RTE between 52% and 76%. A LAES-assisted polygeneration system for micro-grids applications has also been proposed by [PERSON] et al. [14], [PERSON] et al. [15] focused on a combined, building-scale LAES and expansion system, while [PERSON] et al. [16] analysed exergetically an adiabatic LAES system using the Linde-Hampson liquefaction cycle. [PERSON] et al. [17] proposed the idea of improving LAES performance and viability by directly utilising the cold released at the discharge stage in a refrigerated warehouse, thereby reducing the power consumed by the warehouse refrigerating plant and enhancing the overall RTE of the LAES system. This technology paves the path for a considerable storage and subsequent use of energy from renewable energy sources (RES) along the food cold chain, thereby decarbonising the sector by enhancing the sustainability of both refrigeration equipment and power grids [17]. [PERSON] and [PERSON] [18], [PERSON] et al. [19], and [PERSON] et al. [20] also envisaged this approach as a beneficial option for future LAES implementation. Alongside refrigerated warehouses, food factories have significant potential for improving the RTE of a LAES system via tri-generation. The reason for this is that the technologies employed in food factories are energy-intensive and require both heating and refrigeration, along with electricity, at different stages of the production process. [PERSON] et al. [21] proposed several options for utilizing the heat recovered from a LAES system for various food processing technologies requiring steam and/or hot water. The specific energy consumption for manufacturing food products varies from 7 MJ/kg to 226 MJ/kg [22]. Food processing modes are energy-intensive and commonly employ electrical power or fossil-fuel boilers to heat steam or hot water, as well as to ensure refrigeration supply. Approximately 57% of the fossil fuel consumption in the food industry is spent to generate steam and hot water [23]. Hence, when powered by renewables, LAES systems are capable of decarbonising both power grids and production processes by simultaneously providing heat, cold and electricity. Whereas the concept of LAES-enabled tri-generation, which meets the energy demands of a food factory, was originally proposed by [PERSON] et al. [17] and [PERSON] et al. [21] in a mostly qualitative manner, this promising idea has not yet been pursued in more detail to explore quantitatively a particular technology for food processing and preservation. Consequently, the presented study aims to fill in the aforementioned knowledge gap by developing for the first time an innovative concept of enhancing LAES performance through its embedding in an integral tri-generation system supplying heat, cold and power to a food factory.
# Detecting Cyber Threats in UWF-ZeekDataFall22 Using K-Means Clustering in the Big Data Environment
## 6 Limitations of This Study  
While this study presents strong results using K-means clustering for cyber threat detection, several practical considerations should be noted when applying these findings to real-world environments:  
* Artificial Class Balance: The UWF-ZeekDataFall22 dataset used in this study contains a roughly 50/50 split between attack and normal data. This balanced distribution was designed to facilitate consistent metric evaluation and highlight the effects of different labeling strategies. However, in real-world network environments, attack data typically represents only a small fraction of overall traffic. As a result, further investigation is needed to validate the effectiveness of this approach in highly imbalanced scenarios where the rarity of attacks can impact both clustering and evaluation.
* Adaptive Thresholding: The use of 1, 25%, and 50% thresholds to label clusters as attack clusters reflects a range of detection sensitivities. While this provides useful comparisons between aggressive and conservative labeling strategies, these thresholds were arbitrarily chosen, based on some initial experimental results. An adaptive method based on cluster composition, risk levels, or known attack tactics may improve accuracy and generalizability. Further research is needed to develop adaptive thresholding mechanisms.
* Assumption of Cluster Geometry: The K-means algorithm assumes that clusters are spherical and separable by Euclidean distance. This assumption may not hold in complex network traffic where attack behaviors can form irregular or overlapping patterns. Alternative approaches such as DBSCAN, hierarchical clustering, or hybrid techniques could be explored to address this limitation.
## 3 Results
### Analysis of the Influencing Factors  
Anhui Province was divided into five natural regions based on topography and eight major soil types to analyze seven natural factors influencing WUE: the temperature, net radiation, precipitation, LAI, LST, GPP, and ET. Figure 18 shows that precipitation is the key factor for WUE in the Huaibei Plain and southern Anhui's hills, ET dominates in the river plains and Jianghuai hills, and net radiation is crucial in the Dabie Mountains. The factors affecting WUE vary across regions, with their overall importance ranked as precipitation > ET > GPP > net radiation > temperature > LAI > LST. Specifically, precipitation mainly influences moisture, yellow-brown, and cinnamon soils; ET affects southern paddy, blood paddy, and krasnozem soils; and GPP impacts chrysolite and brown soils in the Huaibei Plain and Dabie Mountains.  
In summary, precipitation is the main factor influencing the WUE in moist and chrysolite soils; ET is the main influencing factor for southern and blood paddy soil, and GPP is the main factor influencing the WUE in brown soil regions [62]. These findings indicate that WUE is influenced by various natural factors, resulting in the different WUE characteristics and patterns observed.  
Figure 17: Multiple fractal spectra of eight soil types in Anhui Province: (a) moist soil, (b) cinnamon soil, (c) red soils, (d) chrysolite, (e) yellow-brown soils, (f) southern paddy soils, (g) blood paddy soils (h), brown soils.