esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Water Activity of Fruit Fibres and Pomace  
The water activity of the tested materials was measured to determine the baseline general properties. The results showed that the highest water activity, equal to 0.424, was characterized by chokeberry fibre. Apple and currant fibre had lower water activity,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Nutritional Value** & & & \\
**per 100 g** & & & \\ \hline
**Energy value** & 111 kJ/270 kcal & 1105 kJ/268 kcal & 1084 kJ/264 kcal \\
**Fat** & 4.5 g & 3.2 g & 5.0 g \\
**—saturated** & 0.4 g & 0.6 g & 1.1 g \\
**Carbohydrates** & 17.4 g & 24.0 g & 12.0 g \\
**—sugars** & 16.7 g & 13.7 g & 5.3 g \\
**Fibre** & 62.0 g & 60.0 g & 65.5 g \\
**Protein** & 9.0 g & 5.8 g & 10.5 g \\
**Salt** & 0.000 g & 0.001 g & 0.000 g \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nutritional values of chokeberry, apple and currant fibre [16; 17; 18].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sample** & **Water Content (g/g d.m.)** \\ \hline chokeberry fibre & 0.0550 \(\pm\) 0.0006 b \\ apple fibre & 0.0402 \(\pm\) 0.0012 a \\ currant fibre & 0.0381 \(\pm\) 0.0006 c \\ chokeberry pomace & 0.0798 \(\pm\) 0.0128 a \\ apple pomace & 0.0713 \(\pm\) 0.0009 e \\ currant pomace & 0.0768 \(\pm\) 0.0005 d \\ \hline \hline \end{tabular}
\end{table}
Table 2: The water content of the studied fibres and fruit pomace.  
0.341 and 0.324, respectively. The highest water activity among all types of pomace was possessed by currant pomace, which was 0.364. Apple pomace had a lower water activity of 0.032, while the lowest extrudate obtained from chokeberries was 0.321. The obtained water activity results are presented in Table 3.  
The results obtained for the water activity of the fibres allow for concluding that the water content of the material determines the water activity. The chokeberry fibre, having the highest water activity of 0.424, was also characterised by the highest water content water, which was 0.0550 g/g d.m. Next, in descending order, was apple fibre with a water activity of 0.341 and a water content of 0.040 g/g d.m. The lowest results were characterised by currant fibre, of which the water activity was 0.324 and water content 0.0381 g/g d.m. The water activity of each of the tested materials was below 0.6. This is a water activity value below which microbial growth is impossible. This means that the pomace and fibres of the selected fruits were microbiologically stable [21].
# Efficacy of Iopamidol for Sealing an Injured Thoracic Duct: Pilot Experiments in a Large Animal
## 1 Introduction  
Chylothorax can result from thoracic duct or large lymphatic vessel damage during pulmonary resection or cardiovascular surgery [1; 2; 3]. Current treatment options for chylothorax include conservative, surgical, and the more recent interventional radiological procedures [2; 3; 4; 5; 6; 7; 8; 9; 10; 11]. Percutaneous transabdominal thoracic duct embolization (TDE), an interventional radiological procedure, has been widely performed to promote early recovery from chylothorax [11]. Although TDE fails in 30% of patients because of thoracic duct variation [9; 11; 12; 13; 14], chylothorax has shown spontaneous healing immediately after lymphangiography alone, which may affect the healing of chylous leakage [11].  
Clinically, lymphangiography has been performed using lipiodol. Lipiodol-induced complications, including pulmonary embolization and cerebral embolization, have been reported [15; 16; 17]. To avoidthe serious adverse effects associated with lipiodol, we considered iopamidol (Fuji Pharma, Tokyo, Japan), an iodinated contrast agent used for angiography, to treat chylous leakage. As iopamidol is water-soluble and widely used as an angiographic contrast agent [18], it is expected to have considerably fewer adverse effects from embolization than lipiodol does. If iopamidol and lipiodol have the same therapeutic effect on chylous leakage, it would thus be safer to treat chylous leakage with iopamidol. We conducted animal experiments as a pilot study to confirm the therapeutic effect of both drugs on chylous leakage. We chose pigs as the experimental animal model because they are similar to humans in terms of body size, anatomical features, physiology, pathophysiological reactions, and diet consumption [12]. Pigs are often used to develop and improve biomedical procedures and medical equipment [2].  
We present the following article in accordance with the ARRIVE reporting checklist.
# Motion Correction for Brain MRI Using Deep Learning and a Novel Hybrid Loss Function
## 6 Conclusions  
In conclusion, we developed a simulation framework and MC-Net for the correction of motion artifacts in brain MRI images. The MC-Net performed well on unseen data and images of different orientations with the same contrast (T1-weighted), but scans with other types of contrast will require additional optimizations. The high reader scores and evaluation metrics of the corrected images demonstrate the viability of MC-Net for correcting motion artifacts. Since this method is data-driven and independent of data acquisition or reconstruction and can be quickly performed, it may ultimately be suitable for routine clinical practice. In the future, we plan to improve the performance of MC-Net for other contrasts and may employ MC-Net using vision transformers (ViT) [21; 42].  
Conceptualization, T.E. and Z.W.; Methodology, L.Z., X.W. and Z.W.; Software, L.Z. and X.W.; Validation, L.C., E.H.H. and E.R.M.; Formal Analysis, M.R. and R.B; Investigation, Z.W. and T.E.; Resources, T.E.; Data Curation, X.W.; Writing--Original Draft Preparation, L.Z.; Writing--Review & Editing, T.E. and Z.W.; Visualization, L.Z. and X.W.; Supervision, T.E. and Z.W.; Project Administration, T.E.; Funding Acquisition, T.E., R.B. and Z.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by NIH grant 1R01 DA021146 (BRP), R01 AG060054, R01 AG070227, R01 EB031080-01A1, P41 EB029460-01A1, R01 AG081693, R01 EB031080, P41 EB029460, R21 AG080518, and 1 ULITR003098. M.R. and R.B. were supported in part by NSF grant DMS-2108900 and Simons Foundation Fellowship grant 818333.  
Restrictions apply to the datasets: The datasets presented in this article are not readily available because the research participants did not provide explicit consent for data sharing. Requests for accessing the datasets should be directed to [PERSON].  
The authors declare no conflicts of interest.
###### Abstract
This study is the first to integrate PF with RL to enhance distance estimation accuracy and reduce unnecessary handovers in NTN networks. PF provides more accurate position estimations for RL, while RL dynamically adjusts the handover strategy based on PF estimations, thereby improving decision accuracy and efficiency in NTN environments. Furthermore, the LBH approaches primarily rely on static thresholds for handover decisions, which lack adaptability to varying network conditions. Our approach, however, employs RL for adaptive adjustments, improving its ability to respond to changing NTN conditions and enhancing robustness in decision-making. Compared to conventional \(RSRP\)-based or static-threshold LBH approaches, LBH-PRL exhibits superior adaptability across diverse NTN network conditions, maintaining high handover efficiency while significantly reducing ping-pong handovers and increasing handover success rates. Thus, the primary innovation of this approach lies in the integration of PF and RL to develop an adaptive NTN handover strategy, where PF enhances RL learning efficiency by providing precise location estimations, ultimately leading to a dynamic and highly efficient NTN mobility management solution. To enhance the performance of NTN handovers, we propose the approach called location-based handover with particle filter and reinforcement learning (LBH-PRL). This approach utilizes a particle filter to estimate the distance between the UE and NTN satellites, followed by the application of reinforcement learning to determine the suitable hysteresis, _TTT_, and handover decisions for location-based handover (LBH) mechanisms in NTN networks. Through simulations, LBH-PRL demonstrates superior performance compared to \(RSRP\)-based handover and location-based handover approaches across key performance metrics, including the average number of handovers, average ping-pong rate, and handover failure rate. The remainder of this paper is organized as follows: Section 2 describes the handover issues in TN and NTN networks, Section 3 explains the machine learning-based NTN handover approach we proposed, Section 4 covers the simulation and performance evaluation, and Section 5 concludes the paper.
## 7 Burst Detection Analysis
### Keyword Burst Detection
#### 7.1.3 Comparative Analysis of Keyword Trends: 2017-2023 vs. 2024  
Among the key trends discovered, 'operational technology' exhibits a burst rating of 3.32, ranking in 6 th place during the 2023-2024 timeframe. This is extremely important in the sense of this research since, as we could see from the previous analysis, more classical and narrow topics of ICSs and SCADA were dominating the landscape, and the detected burst of OT potentially shows the shift in the research direction and wider understanding of the area of the problem. One more important shift that can be noticed in keyword burst is an  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Rank** & **Keyword** & **Year** & **Strength** & **Begin** & **End** & **Visualization** \\ \hline
1 & integrated circuits & 2019 & 4.12 & 2021 & 2022 \\
2 & ics security & 2018 & 3.79 & 2018 & 2019 \\
3 & industrial control systems (ics) & 2018 & 3.72 & 2018 & 2020 \\
4 & process control & 2021 & 3.64 & 2021 & 2022 \\
5 & model & 2018 & 3.43 & 2022 & 2022 \\
6 & operational technology & 2019 & 3.32 & 2023 & 2024 \\
7 & networks & 2018 & 3.23 & 2022 & 2024 \\
8 & intrusion detection systems & 2020 & 3.2 & 2020 & 2022 \\
9 & cybersecurity & 2020 & 3.17 & 2020 & 2021 \\
10 & vulnerability analysis & 2018 & 3.07 & 2018 & 2018 \\
11 & cyber attacks & 2019 & 2.86 & 2021 & 2021 \\
12 & network & 2022 & 2.84 & 2022 & 2024 \\
13 & challenges & 2018 & 2.79 & 2023 & 2024 \\
14 & internet of things & 2019 & 2.7 & 2021 & 2021 \\
15 & framework & 2022 & 2.64 & 2022 & 2022 \\ \hline \end{tabular}
\end{table}
Table 13: Top 15 keywords with the strongest citation bursts. The Column Visualization illustrates the duration of citation bursts (red segments) from 2018 to 2024.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Rank** & **Keyword** & **Year** & **Strength** & **Begin** & **End** \\ \hline
1 & networks & 2023 & 1.26 & 2023 & 2024 \\
2 & challenges & 2023 & 1.26 & 2023 & 2024 \\ \hline \end{tabular}
\end{table}
Table 14: Top keywords with the strongest citation bursts in 2023–2024.  
increasing amount of more specific keywords, such as'model', 'framework', 'cyber attacks', and 'vulnerability detection'. They show that researchers switch from just defining the problem to attempting to systemize the research area ('framework','model') and deal with specific problems, such as attacks and vulnerabilities. It is a positive tendency, showing that research has reached the initial knowledge saturation level.  
Trends for 2023-2024 years only are not so easy to interpret at the moment. On one hand, there is a new 'challenges' keyword, while 'networks' have been such since 2022. The trend analysis needs to be repeated later after all data for the year 2024 is systemized in WoS.
# Validating a 5G-Enabled Neutral Host Framework in City-Wide Deployments
## 6 Validation of Use Cases
### KPIs and Measurement Methodology
#### 6.1.2 Data Plane Delay  
The time required to transfer a given piece of information between two nodes, measured from the moment it is transmitted by the source to the moment it is successfully received at the destination. This metric was evaluated by computing half of the round trip time experienced between a UE and a remote server.  
In particular, in the scope of the proposed framework, this performance was improved by enabling a more suitable allocation of remote servers closer to the end-user, i.e., at the edge to reduce network latency.
# Laser Technology in Photonic Applications for Space
## 2 Laser Devices
### Semiconductor Lasers  
Semiconductor lasers have been studied and used for years in space applications. Some of their main advantages include operation under direct current injection, which provides high electrical to optical power conversion efficiency, but also long lifetime and high output power. Although their divergence, beam quality, and intensity noise are not suitable for all applications, they are also the preferred pump element for solid-state lasers (SSLs) and fibre lasers since their wavelength can be slightly tuned to achieve optimum absorption of the laser media, making them the cornerstone for laser technologies used in space applications. Many materials are being used, such as GaAs, InGaAs, InP, and InGaAsP. The two most common types of semiconductor laser are the edge-emitting laser (EEL) and the vertical-cavity surface emitting laser (VCSEL).
# Retrofitting a Building's Envelope: Sustainability Performance of ETICS with ICB or EPS
## 4 E-C2C Method
### Environmental Performance  
The quantification of the environmental performance of the 3E-C2C method follows the LCA standardised method [24] and the principles included in European standards [22; 25; 26]. The CML 2001 baseline Environmental Impact Assessment Method (EIAM) and corresponding environmental impact categories were used in LCA results calculation. Regarding the quality of background data: The LCA databases used (Ecoinvent and ELCD) were updated within the last 10 years, and all selected datasets imply a European average technology or a specific European country. The environmental performance of each life-cycle stage is defined by:  
* Product Stage (A1-A3): The LCA data of the manufacture of each construction material or product started with the corresponding Life-Cycle Inventory (LCI), mainly based on updated site-specific data from Portuguese plants, thus proving its temporal, geographical, and technological representativeness. The composition considered for each material used in ETICS with ICB and EPS was based on one of the Portuguese producers [27]. A detailed inventory analysis is not included in this paper but was already provided for both insulation materials in two other paper of the authors [1; 23].
* Construction Process Stage (A4-A5): The renovation operation corresponds to the installation of the product in the building, including: Removal of the old render and paint and corresponding transportation to waste processing and disposal, and external rendering, and insulation of the external wall with ETICS with EPS or ICB of variable thickness.
* Use stage--maintenance, repair, and replacement (B2-B4): The environmental impacts of the materials for maintenance, repair, and replacement operations during the study period, including the corresponding waste flows. The impacts do not include other impacts from this operation due to their variable and unpredictable nature.
* Use stage--energy performance (B6): The 3E-C2C approach determines the energy performance from the estimation of the heating and cooling energy needs during a building's operation calculated by the simplified assessment method described in Portuguese national regulations [28; 29; 30]. These needs are, in the 3E-C2C method, divided by the area of the external wall being studied to provide a value associated to the declared unit used, and allow the estimation of their environmental impacts. This value and the environmental impacts are estimated considering a residential heating and cooling model using an updated Portuguese electricity mix [31].
* End-of-life stage (C): The 3E-C2C considers the transport of the discarded product as part of the waste processing, including the transport of waste (C2), the waste processing (C3) and the waste disposal, and the physical pretreatment and management of the disposal site (C4). The environmental impacts of demolition (C1) were not considered, as they are similar for all the alternatives.
# Research on Soil Pesticide Residue Detection Using an Electronic Nose Based on Hybrid Models
## 3 Results and Analysis
### Results of the Supervised Identification of Pesticide Types  
The results of identifying the pesticide residue types are shown in Table 4, which shows the effectiveness of using KNN, RF, and an SVM in the pesticide residue pollution type detection module. From the table, it can be seen that different feature extraction methods combined with different classifiers have a significant impact on the recognition results, which are the same as the traditional research methods of the electronic nose [35]. In specific detection applications, the selection of different feature extraction methods and classifiers is the key to optimizing the performance of electronic nose systems. The recognition rates of four feature extraction methods, _FT_, _MAX_, _Mean_, and _WT_, combined with three pattern recognition methods, KNN, RF, and an SVM, demonstrate that the electronic nose can fully recognize the types of organophosphorus, organosulfur, and pyrethroid pesticides, providing the accurate basis for subsequent contaminated soil remediation work. Using _MAX_ or _Mean_ as the feature extraction method and an SVM as the supervised learning classifier achieves the highest pesticide type recognition rate of 99.27%.  
Considering the impact of different feature extraction methods on pesticide type recognition, the average recognition rates of _FT_, _MAX_, _Mean_, and _WT_ in the three classifiers were calculated to be 96.68%, 98.13%, 98.37%, and 98.54%, respectively. _WT_ achieved the highest average recognition rate, indicating its effectiveness in accurately identifying soil pesticide types. In evaluating the impact of different supervised learning classifiers on pesticide type recognition, the average recognition rates of KNN, RF, and the SVM using different features were found to be 98.03%, 97.48%, and 98.28%, respectively. The SVM displayed the highest average recognition rate, possibly due to its advantages in solving nonlinear problems and handling small sample problems [36].
## 4 The Proposed MRMR-SATIDS System
### Training and Testing of SATIDS  
Figure 7 and Algorithm 1 summarize different phases of the proposed MRMR-SATIDS. To accurately detect attack type, the following steps are followed:  
1. Preparation of the dataset: For continuous features, remove NaN and infinity values from the data using the mean of the appropriate column. In addition, the IP address and time stake label are removed.  
Figure 5: Modified DL-based LSTM network.  
Figure 6: Proposed algorithm parameters (accuracy vs. dropout layer probability).  
The two datasets contain raw data. If this raw data is input into our proposed system, slow convergence will occur. Normalization strongly influences the performance of a neural network. With unnormalized data, numerical ranges of features may vary strongly. The data (\(x\)) can be normalized by subtracting the mean (\(\mu\)) of each feature and dividing by the standard deviation (\(\sigma\)). This way, each feature has a mean of 0 and a standard deviation of 1. This results in faster convergence.  
\[x\ :=\frac{x-\mu}{\sigma}\]  
2 Feature selection process: Extract the important features of the dataset using the MRMR-FS and then modify the dataset.  
3 The dataset is divided into 70% for training and 30% for testing.  
4 Training process: SATIDS network based on LSTM network is trained using the modified training part of the dataset. The data is separated from the type of data. Each line of data enters the network separately.  
5 After training, the network is ready to identify the category of data (normal or malicious).  
6 When the tested part enters the trained model, it is categorized as normal traffic or a type of attack.  
7 The network performance is analyzed by comparing the system output to the testing result.  
**OUTPUT** traffic type (normal/anomaly) and determine the attack type  
**INPUT** ToN-IoT/InSDN datasets  
Normalize dataset  
Remove IP address and time stack from the dataset  
Select the more effective features and eliminate the less effective ones using MRMR-FS  
Divide the dataset into 70% for training and 30% for testing  
Train the proposed IDS using the training dataset which contains traffic data and label  
**For** each data line  
train network with each traffic record and consider the traffic label  
**END FOR**  
Insert the testing dataset to the trained IDS  
Classify traffic (normal or a type of attack)  
Calculate IDS performance  
Figure 7: Block diagram of the proposed system.
# Real-Time Motion Detection Network Based on Single Linear Bottleneck and Pooling Compensation
## 3 Methods  
Motivated semantic segmentation-based methods [9, 11, 13, 33] in motion detection tasks typically use lightweight operators [22, 23, 24, 37], and here we propose a real-time motion detection network based on single-linear bottleneck and pooling compensation, MDNet-LBPC.  
The overview of our framework is illustrated in Figure 1. In the first stage (blue), the encoder takes an RGB image as the input, and outputs multi-scale spatial feature maps. We replace the most computationally expensive CNN block with our single linear bottleneck operator to reduce the computational cost. In the second stage (green), the decoder uses the coarse probability maps and intermediate spatial feature maps from the first stage to further refine predictions through our proposed pooling compensation mechanism. At the same time, the lower left corner shows the relationship between the input and output in a single linear bottleneck operator. A single linear bottleneck block looks like a residual block, where each block contains one input, then a bottleneck, and finally an extension. However, the bottleneck actually contains all the necessary information, and the extension layer is only the implementation of the nonlinear transformation of the adjoint tensor. Therefore, we can directly use shortcuts between the bottlenecks.  
We will elaborate the single linear bottleneck operator in MDNet-LBPC in Section 3.1, and the polling compensation mechanisms in MDNet-LBPC in Section 3.2. In Section 3.3, We will analyze the computational cost of our model. In Section 3.4, we will provide more implementation details.