esa-sceva/satcom-chunk-collection huggingface dataset:
# A Contribution of Shortest Paths Algorithms to the NetworkX Python Library
## 5 Experiments and Results
### Results of Algorithms Tests
#### 5.1.1 D*  
Figure 4 presents the optimal path found between Tirana and Helsinki using the D* Lie algorithm, along with the performance metrics compared to the benchmark of the A* algorithm.  
Figure 3: Optimal path identified by the A* algorithm (red line), used as a benchmark for result comparison with the other implemented algorithms.  
By analysing the result of the comparison run, we were able to conclude that the D* Lite is marginally slower than the A* while finding the same optimal path. However, the D* uses significantly more memory compared to A*. These results were somewhat expected since D* is designed for larger graphs with real-time graph changes, not static scenarios like the test case; its additional capabilities do not provide a clear advantage and may introduce overhead.
## 4 Discussion  
The results of this study demonstrate that pre-classifying gait patterns and integrating them with LSTM-RNN models significantly enhances the predictive power for temporally continuous data, such as the KAM. Specifically, the Multi-model Gait-based KAM Prediction System showed a marked improvement over the Single-model KAM Prediction System, achieving an average prediction error of 6.84 N \(\cdot\) m and a Mean Absolute Percentage Error of 8.47%, compared with the Single-model system's average prediction error of 8.82 N \(\cdot\) m and MAPE of 12.95%.  
The approach of pre-classifying gait patterns using IMU sensor data before predicting KAM proved effective in enhancing prediction accuracy. These findings underscore the potential of combining wearable technology with advanced machine learning techniques to develop cost-effective, efficient, and accurate monitoring systems for knee joint health. This method offers a practical alternative to traditional KAM measurement techniques, which require complex laboratory equipment and significant time and cost investments.  
However, this study has certain limitations that warrant attention. First, the sample size was relatively small, and the gait patterns were limited to five types, which may affect the generalizability of the findings. Future research should focus on expanding the dataset to include a broader range of participants and gait patterns, such as stair climbing and inclined walking, to improve the robustness and applicability of the models. Future research will also incorporate a broader range of gait patterns and environmental conditions to enhance model applicability. Additionally, further model architecture improvements will aim to reduce computational complexity and enable real-time KAM prediction, enhancing the system's practicality for real-world applications.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **Value** \\ \hline Single-model Avg. prediction error (N \(\cdot\) m) & 8.82 \\ Single-model MAPE (\%) & 12.95 \\ Multi-model Avg. prediction error (N \(\cdot\) m) & 6.84 \\ Multi-model MAPE (\%) & 8.47 \\ \hline \hline \end{tabular}
\end{table}
Table 6: KAM Prediction System Performance Evaluation Results.  
Advancements in this area are expected to contribute significantly to the prevention and treatment of knee osteoarthritis. By providing an accessible and cost-effective method for continuous monitoring of the knee joint load, this system has the potential to improve patient outcomes and reduce healthcare costs associated with osteoarthritis management.
## 3 Results  
### MSC Behavior in Collagen Gel Scaffold (In Vitro Study)  
Figure 1A shows the experimental schedule and method used in the study. The number of MSCs remaining in the scaffold at 21 days was significantly decreased compared with the number on other days (Figure 1B). During the first 14 days, MSCs retained their original characteristics, including abilities for self-renewal and multi-directional differentiation. As shown in Figure 1C, MSCs maintained expression of MSC positive markers (CD29, CD90, and CD105) without expressing negative marker (CD 45) could differentiate into adipocytes or osteoblasts based on oil red O or alizarin red S staining, respectively, and formed colonies from single cells.  
Figure 1: Mesenchymal stem cells (MSCs) state in collagen gel. (**A**) Experimental protocol for the in vitro study. MSCs isolated from wild-type C5/BL/6N mice were cultured in a collagen gel for 3 weeks.  
(**B**) Numbers of surviving MSCs in the scaffold. After the 3-week culture in collagen gel, numbers of cells were evaluated. MSCs could remain alive for at least 14 days. (**C**) Retention of MSC characteristics. After the 3-week culture in collagen gel, isolated cells were checked for the characteristics of MSCs, including self-renewal, multi-directional differentiation, and expression of MSC original markers (CD29, CD90, and CD105). (Top left) There were many CD29/CD90 double-positive cells for 14 days. (Top middle) There were many CD90/CD105 double-positive cells for 14 days. (Top right) There were no CD90/CD45 double-positive cells. (Bottom left) CFU-F of MSCs. MSCs cultured for 21 days generated fewer CFU-Fs compared with MSCs at other time points. (Bottom middle) Osteogenic differentiation of MSCs. MSCs cultured under osteogenic differentiation conditions for 4 weeks were stained with alizarin red S. (Bottom right) Adipogenic differentiation of MSCs. MSCs cultured under adipogenic differentiation conditions for 3 weeks were stained with oil red O. All data indicated that stemness may be retained for up to 14 days. * \(p<0.05\).
_applied sciences_  
_Article_  
**Wear Prediction of Curved Switch Rail in High-Speed Turnout and Influence of Wheel and Rail Wear on Vehicle Dynamic Performance**  
[PERSON] 1, [PERSON] 1,2,3,*, [PERSON] 1, [PERSON] 1,2,3,* and [PERSON] 1  
1 School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China; [EMAIL_ADDRESS] (S.Y.); [EMAIL_ADDRESS] (H.M.); [EMAIL_ADDRESS] (Y.Z.) Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles, Beijing 100044, China  
3 Beijing Engineering Research Center of Monitoring for Construction Safety, Beijing 100044, China  
* Correspondence: [EMAIL_ADDRESS] (T.J.); [EMAIL_ADDRESS] (J.Z.); Tel.: +86-15801599683 (T.J.); +86-13611277353 (J.Z.)  
**Abstract:** A wear prediction model is built to research the wear of the curved switch rail in a high-speed turnout. The Archard wear model is used in the wear prediction model to analyze the profile evolution law. The non-Hertzian contact Kik-Piotrowski method based on virtual penetration is used as the contact algorithm for the Archard wear model. A dynamic model of the vehicle-curved switch rail system based on the predicted profiles of the curved switch rail and the measured wheel profiles with different stages is established. The effect of the wheel and curved switch rail profiles' wear on vehicle dynamic performance is analyzed. The results show that the wheel completely transitions from the stock rail to the curved switch rail between 35 and 50 mm head widths. As the head width of the curved switch rail increased, the position of the maximum wear depth gradually moved to the gauge shoulder. When the total passing weight of the train is 50 Mt, the 20 mm head width curved switch rail side wear reaches a maximum of 5.3 mm. The position in which the wheel transitions from the stock rail to the curved switch rail will be further away from the tip of the curved switch rail due to wheel-rail wear. Regarding the derailment coefficient, the wheel-rail vertical force and lateral force are both significantly impacted. However, they have little effect on the vertical and lateral acceleration of the vehicle. The wear of the wheels and rails has a higher impact on vehicle driving safety and a lower impact on vehicle driving stability.  
**Keywords:** high-speed turnout; curved switch rail; wear prediction; dynamic performance
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 6 Validation of the Proposed Protocol
### Routing Protocol Requirements Analysis  
While selecting the metrics for a routing protocol, the routing requirements of consistency, optimality, and loop-freeness must be met [74]. The routing metrics requirements are also emphasized by the IETF Roll Working group [7]. Authors in [26] validate their proposed approach for secure routing protocol as per the study of [74], which says that for a routing protocol to operate properly, it should fulfil the abovementioned three require ments. Additionally, a packet forwarding scheme and a path calculation algorithm are the two main elements of a routing protocol. Similarly, isotonicity and monotonicity properties are required to be satisfied by a routing metric [73; 74]. IETF ROLL [73] defines a composite metric as a routing metric that consists of basic and derived metrics imposing a purposeful mechanism or function.
# Challenges and Recommendations for Improved Identification of Low ILUC-Risk Agricultural Biomass
###### Abstract  
The \"low indirect land use change risk\" (\"low ILUC-risk\") concept was developed to assess whether crop-based biofuels would compete with other land uses and cause the expansion of agricultural land. At the core of low ILUC-risk is an \"additionality principle\", which requires that biofuel feedstock receive special treatment only if it is produced over and above the business-as-usual baseline. This paper examines and tests the European Commission's methodology for calculating the baseline for yield improvement projects, by applying it to publicly available Eurostat data at national and NUTS2 scales. We assess from a statistical perspective how variation in regional yield trends would lead to differences in the long-term outcomes of low ILUC-risk certification; we conclude that, as currently designed, the methodology would over-state the amount of additional production in some cases and could hence incentivise the diversion of crops from other uses into the biofuel sector. We introduce the terms \"tailwind additionally\", \"headwind additionally\", and \"additionally rather\" to characterise the phenomena which contribute to this outcome. Our results lead us to recommendations which may enhance both the attractiveness and the robustness of the low ILUC-risk system.  
low ILUC-risk; indirect land use change; biofuel feedstock; additionally; Renewable Energy Directive methodology; yield baseline; agricultural practices; statistics; Eurostat data +
Footnote †: journal: Medical  
To get around this issue, the Commission has developed two concepts to identify which feedstocks are likely to be associated with ILUC emissions [7; 8]. First, feedstocks which are associated with large-scale expansion into high-carbon land are designated \"high ILUC-risk\"; biofuels made from these feedstocks are to be phased out of EU renewable energy targets. Second, \"low ILUC-risk\" feedstocks are those produced through methods which cause no additional demand for land. Specifically [9] (emphasis added):  
_\"Biofuels, bidiquids or biomass fuels should be considered low ILUC-risk only if the feedstock used for their production is cultivated as a result of the application of duly verifiable measures to increase productivity beyond the increases which would be already achieved in a business-as-usual scenario'._
## 8 Conclusions  
This article provides a comprehensive overview of space situational awareness systems and orbit determination methods. The escalating presence of space debris has underscored the critical importance of space situational awareness systems, prompting major spacefaring nations to invest in diverse space target surveillance systems. To enhance space situational awareness capabilities, these nations are increasingly directing their efforts towards the development of space-based optical platforms.  
Addressing the core challenge of orbit determination for space-based optical space situational awareness platforms, scholars have proposed theoretical frameworks, establishing a three-stage system comprising orbit association, initial orbit determination and precise orbit determination. However, the evolution of huge space constellations and the rapid proliferation of space debris impose heightened requirements on the construction of space situational awareness systems and the efficacy of associated methods. Numerous theoretical and technical challenges persist in achieving precise orbit determination for space targets.  
Anticipating future demands for enhanced space situational awareness, further in-depth research into space target orbit determination methods is imperative and will serve as the theoretical bedrock for ensuring the secure operation of spacecraft.  
Conceptualization, resources and writing--original draft preparation, Z.Z.; writing--review and editing, G.Z.; resources and writing--original draft preparation, C.L.; supervision, J.C. and W.C.; revision, X.N. and Z.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Youth Innovation Promotion Association of CAS (2022410), Photon Plan of XIOPM (E45542Z1), The National Natural Science Foundation of China (62303378) and The Foundation of Shanghai Astronautics Science and Technology Innovation (SAST2022-114).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 2 Methodology
### Developing an Empirical Determination of Wettability from Core and NMR T\({}_{2}\)LM Data  
A correlation between Amott-USBM test results and NMR T\({}_{2}\)LM from the same well depth was used to develop the empirical correlation between laboratory wettability data and field wettability data. This technique is sought to work for both clastic and carbonate reservoirs and oil and gas fields to determine in situ wettability conditions. A combined Amott-USBM wettability data from the five (5) core samples were used to establish a relationship with NMR T\({}_{2}\)LM. The depths of these cores were matched with the NMR T\({}_{2}\)LM log from the same reservoir. The depth matching enabled a cross-plot of the Amott USBM wettability results and NMR T\({}_{2}\)LM. An exponential function was used for this research because, regardless of how low the T\({}_{2}\)LM will be, in a hydrocarbon-filled reservoir interval, there will never be the existence of 100% water; hence Amott-USBM wettability  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample ID**} & \multirow{2}{*}{**Depth m**} & \multirow{2}{*}{**K\({}_{\text{air}}\) md**} & \multirow{2}{*}{**Helium Porosity**} & \multicolumn{2}{c}{**Amott Wettability**} & \multirow{2}{*}{**Oil Drive**} & \multirow{2}{*}{**Water Ara**} & \multirow{2}{*}{**USBM**} & \multirow{2}{*}{**Wettability**} \\  & & & & **Index to** & & & & & \\  & & & **PV** & **Water** & **Oil** & & & & \\ \hline
8 & 1271.60 & 4490 & 0.308 & 0.313 & 0.00 & 0.77 & 0.25 & 0.479 & Water wet \\
9 & 1271.90 & 3130 & 0.318 & 0.158 & 0.00 & 0.75 & 0.27 & 0.443 & Water wet \\
10 & 1272.21 & 2677 & 0.297 & 0.143 & 0.00 & 1.79 & 0.32 & 0.750 & Water wet \\
15 & 1273.70 & 2611 & 0.277 & 0.116 & 0.00 & 2.18 & 0.42 & 0.719 & Water wet \\
62 & 1290.75 & 2540 & 0.249 & 0.513 & 0.00 & 1.33 & 0.35 & 0.582 & Water wet \\ \hline \hline \end{tabular}
\end{table}
Table 2: Amott-USBM wettability measurements of core samples used in correlating core to log wettability.  
Figure 3: NMR T\({}_{2}\) multimodal pore size distribution of core samples at 100% water saturation.  
index will never reach 0. This mathematical model will form the basis for determining the wettability from NMR T\({}_{2}\)LM. In classifying the wettability condition, an integration of Amott-USBM proposed for this study is illustrated in Figure 4. The proposed sub-categories of oil-wet and water-wet will help capture the different strengths of wettability existing in the reservoir. Wireline and core data from a sandstone oil and gas reservoir in Australia were used to test and validate the hypothesis. These data included Gamma ray (GR), Density (DEN), Neutron (NEU), Total porosity (POR), Resistivity (RES_DEP, RES_MED, RES_SLW), water saturation (Sw), and permeability (PERM) logs. The second part of this research involves applying machine learning techniques in predicting NMR T\({}_{2}\)LM in the field using wireline logs. The developed methodology and empirical equation were then applied to the predicted T\({}_{2}\)LM to predict the wettability state.
## 3 Methodology  
In this research, we introduce an innovative model, denoted as LSTM-Attention-BiTCN, which is designed to enhance the precision and efficacy of identifying potential suicide ideation from user interactions on social networks. This model harnessed the textual data from Reddit, analyzing posts as reflections of the emotional and cognitive states of its users. It integrated state of the-art deep learning architectures, including LSTM and BiTCN, to dissect and comprehend the intricate patterns in the data. Our methodology encompasses multiple phases: data aggregation, preprocessing, embedding of words, model training, and an assessment of performance.  
As illustrated in Figure 1, the initial step involved preprocessing Reddit posts by eliminating any extraneous elements that may noise the analysis. Subsequently, the cleaned input was fed into the embedding phase to distill and encapsulate the salient attributes of the textual data, thus refining the input for enhanced model accuracy. The LSTM-Attention-BiTCN classifier embarked on its analytic process by assigning the curated word sets to their specific vectors through the embedding layer. The LSTM component of the model was adept at unraveling and retaining word dependencies of considerable temporal extent. This LSTM-processed information was then channeled through a series of BiTCN layers designed to unravel extended temporal patterns embedded within the texts. An attention layer, following the BiTCN layers, served to selectively emphasize significant textual elements by assigning adaptive weights, thus sharpening the focus on crucial signals indicative of suicidal intent. After that, two pooling layers (average pooling and max pooling) processed the information separately, eventually merging their outputs to form a cohesive vector that entered the classification sequence. Finally, the classification process employed \"ReLu\" and \"Sigmoid\" activation functions within two dense layers to distill the probability of suicidal content within each post. To mitigate the risk of overfitting to the training dataset, two strategically placed dropout layers were integrated: one succeeding the embedding layer and another preceding the final dense layer.
## 3 Methods
### Network Planning and Genetic Algorithm
#### 3.4.1 WHIPP Network Planning Approach  
In the WHIPP tool, the time-consuming path loss calculation requires using a heuristic approach for finding the optimal network deployment. The algorithm is based on successively adding new access points (APs) until the required coverage is achieved, followed by checking if any two closely spaced APs can be replaced by one AP. We refer the reader to [12] for more information. The required coverage can be predefined per room by the user and determines the maximal allowable PL between any given AP and receiver location.
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Screening Models
#### 3.1.10 RT1D  
RT1D is a comprehensive solution developed in Visual Basic, designed to operate directly within an Excel spreadsheet. It excels in simulating biochemical and geochemical reactive transport scenarios, making it particularly well suited for laboratory experiments [409]. Notably, RT1D stands out for its capability to tackle advanced biogeochemical challenges, including rate-limited sorption, bioaugmentation, microbial transport, denitrification, and sequential batch reactor dynamics. This model's sophistication and versatility set it apart from other screening tools.
## 2 Research Status on Anhydrous Hydrazine Fuel
### Research Status on UDMH Fuel
Adding HEH, which has a lower vapor pressure and higher boiling point, to UDMH can significantly reduce its hazardous properties. [PERSON] et al. [109] discussed the limitations of using low-resolution mass spectra for identifying UDMH transformation products. It presented a study that employed machine learning-based prediction of mass spectra and retention indices, in combination with GC-MS and HRMS, to improve identification confidence. The study proposed the most confident structures for 24 compounds, including 11 compounds in the NIST database and 13 new compounds. Six new compounds were not previously described as UDMH transformation products. Machine learning methods were used to select the most confident structures based on RI and mass spectra prediction. Critical Assessment of UMDH Literature  
This section presents a comprehensive overview of the research status on UDMH fuel, encompassing oxidation mechanisms, degradation products, detection methods, and potential applications. However, more in-depth analyses of the experimental evidence supporting each theory would significantly enhance our understanding of UDMH oxidation. It is imperative to critically evaluate and compare the different theories, carefully considering the strengths and weaknesses of each, to identify the most plausible mechanisms. To provide a comprehensive understanding, it is essential to conduct a comparative analysis of the sensitivity, selectivity, and applicability of different detection techniques. By critically analyzing the environmental risks and discussing potential mitigation strategies, including their effectiveness and feasibility, we can ensure the safe and responsible application of UDMH fuel.