esa-sceva/satcom-chunk-collection huggingface dataset:
# An In-Network Cooperative Storage Schema Based on Neighbor Offloading in a Programmable Data Plane
## 3 Proposed System Architecture
### In-Network Storage Service Node Structure  
According to the ID-based protocol described in Section 3.2, the intermediate router's in-network storage function is enhanced to take advantage of the power of the ID to support in-network storage. We designed its structure based on a programmable data plane. Figure 4 shows the inner structure of the enhanced network node. This can be an instance of a local computing model in ICN based on control and user plane separation. Currently the node structure is assumed to be deployed on every network node. The deployment is outside the scope of our paper and will be studied in other articles.  
The network intermediate nodes first need to ensure line-speed forwarding. In order not to affect the forwarding performance of the network intermediate nodes, the relevant status information collection, calculation, and maintenance are placed in the network storage processing element using local computing capacity. In addition, the calculated state value and decision result are inserted into the forwarding processing pipeline for ID-based protocol processing through the programming interface. The structure includes: the Identifying and Parsing packet, Decision making, Post-processing action, and Status information collection/state maintenance.  
* The \"Identifying and Parsing packet\" module needs to correctly identify our ID-based transport layer packet, parse packet fields, and obtain the storage service ID/data chunk ID, sequence, and preference;  
Figure 4: The extended network intermediate node structure.  
* The \"Decision making\" module executes one of the \"post-processing actions\" according to the state sets. If the state is set to \"forwarding to neighbor node\", the \"Decision making\" module executes the \"Forwarding to neighbor\" action; otherwise, the \"Storing locally\" action is invoked;
* The \"Post-processing actions\" module currently contains \"Storing locally\" and \"Forwarding to neighbor\". The \"Forwarding to neighbor\" action will change the destination IP of the data chunk packet to a new destination according to the rules from the computation result of the \"Status information collection/state maintenance\" module and then forward it. The \"Storing locally\" action will ACK the received packet, and then request the next segment and reassemble the data chunk from segments for storage in the local file system;
* The core module is \"Status information collection/state maintenance\". This module periodically collects neighbor status information and local status information, and parses packet information, then computes the state threshold and maps forwarding rules based on the ID. These results are set to shared memory that the forwarding pipeline can access according to the programmable interface. The time interval should be set at least as high as the maximum average RTT in the network [34].
# Opportunities for Machine Learning in District Heating
## 5 Gaps between Industrial District Heating Needs and Current Machine Learning Research
### Benefits of Demand Forecasting  
Accurate forecasting of the demand, however, only brings value to the extent that it can be used to improve (or optimise) network operations. The most commonly cited idea is reducing resource consumption through modulated production, based on predicted demand. Given that most networks tend to use a combination of different energy sources and fuels, from intermittent energy sources over CHPs, heat pumps to coal and oil boilers, forecasting has the potential to allow greater production flexibility. Renewable energy sources can be used whenever available, and fossil fuels only as the last resort. However, there still needs to be sufficient safety margins to guarantee that the heat is available on demand. In this context, there are clear diminishing returns related to precision of the forecasting, while imposing critical constraints on the robustness (i.e., significant under-estimations of the demand can render any given solution completely infeasible). These practical considerations are seldom addressed in the literature of ML as applied to the field of DH.  
The long lead times from production to the heat being available at a given point in the heat network, which can sometimes be several hours, pose another constraint for planning. District heating is, in this regard, quite different from many other domains, even ones superficially very similar, like electric grids. It has been shown that such long-term effects can be difficult to estimate purely from the primary side data. Instead, a promising direction is to investigate and perform peak shaving by modulating demand and reduce the use of more expensive or non-renewable fuels. The customer side demand could be explicitly lowered in periods where no cheap and sustainable energy is available, and increased when there is an abundance of it. This, in turn, requires integrating with the corresponding heating systems as well as indoor temperature sensors to guard against control actions jeopardising the indoor climate. While there are a number of commercial solutions available, the heterogeneous nature of buildings, and the range of possible objective functions, makes this difficult.  
Finally, on the even more long-term perspective, accurate forecasting can provide a significant benefit for infrastructure planning. It amounts to using better-understood, and hence \"narrower\" safety margins. However, in the papers we have reviewed, there is no indication of any ML-driven analysis targeting these aspects.
## 2 Simulation Setup
### Validation of Grid Independence and Computational Accuracy  
In order to enhance computational efficiency while ensuring accuracy, grid independence validation was conducted. Figure 5 presents the relative variation in total drag of the airfoil under different levels of grid refinement. It can be observed that when the number of grid cells exceeds 100,000, the relative change in drag remains below 0.5%. Consequently, the grid with approximately 100,000 total nodes was ultimately chosen for the subsequent accuracy validation of simulation, with grid cell counts ranging from 800,000 to 1.2 million in cases where the airfoil featured grooves.  
Figure 4: Schematic representation of the computational domain grid and boundary conditions.  
Figure 5: Validation of grid independence.  
Figure 6 illustrates the lift coefficients of the airfoil at various angles of attack using the numerical methods mentioned above and compares them with the experimental results of the NACA2412 airfoil at low Reynolds numbers [45].  
It can be observed that within the range of 0 to 16 degrees of angle of attack, the computational results exhibit a notable agreement with the experimental data. Particularly noteworthy is the fact that the deviation in lift coefficient for cases with small angles of attack does not exceed 1%. This confirms that the numerical computation and grid partitioning methods employed in this study can exactly reflect the aerodynamic performance of the airfoil. Therefore, the simulation methods were adopted for subsequent research.
## 3 Software Cost Model Considering Operating Environment
### Results of Model Comparison
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**C\({}_{4}\)=100**} & \multicolumn{2}{c}{**C\({}_{4}\)=200**} & \multicolumn{2}{c}{**C\({}_{4}\)=300**} & \multicolumn{2}{c}{**C\({}_{4}\)=500**} \\  & **CT(T)** & **T\({}^{*}\)** & **CT(T)** & **T\({}^{*}\)** & **CT(T)** & **T\({}^{*}\)** & **CT(T)** & **T\({}^{*}\)** \\ \hline GO & 802.2589 & 19.6 & 803.8296 & 19.6 & 805.3418 & 19.7 & 808.2954 & 19.8 \\ DS & 552.2292 & 12.6 & 552.9287 & 12.6 & 553.6281 & 12.6 & 554.9656 & 12.7 \\ IS & 801.6840 & 19.6 & 803.2428 & 19.6 & 804.7607 & 19.7 & 806.2585 & 19.7 \\ YID & 1727.3457 & 8.6 & 2031.9155 & 10.2 & 2317.8014 & 11.0 & 2870.336 & 12.1 \\ P NZ & 1727.3457 & 8.6 & 2031.9155 & 10.2 & 2317.8014 & 11.0 & 2870.336 & 12.1 \\ PZ & 802.1319 & 19.6 & 803.7000 & 19.6 & 805.2135 & 19.7 & 808.1661 & 19.8 \\ TC & 1241.2487 & 29.9 & 1245.5003 & 30.0 & 1249.6314 & 30.2 & 1257.587 & 30.5 \\ Ray & 1652.3483 & 31.2 & 1667.7989 & 32.0 & 1682.3796 & 32.6 & 1709.535 & 33.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: ORTimes with minimum ETCs for all eight models in Case #4 on Dataset #1. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**C\({}_{1}\)=10**} & \multicolumn{2}{c}{**C\({}_{2}\)=5**} & \multicolumn{2}{c}{**C\({}_{1}\)=20, C\({}_{2}\)=10**} & \multicolumn{2}{c}{**C\({}_{1}\)=40, C\({}_{2}\)=20**} \\  & **CT(T)** & **T\({}^{*}\)** & **CT(T)** & **T\({}^{*}\)** & **CT(T)** & **T\({}^{*}\)** \\ \hline GO & 429.2488 & 21.5 & 805.3418 & 19.7 & 1502.2899 & 17.8 \\ DS & 289.1151 & 13.5 & 553.6281 & 12.6 & 1057.4527 & 11.7 \\ IS & 428.9210 & 21.5 & 804.7607 & 19.7 & 1501.2850 & 17.8 \\ YID & 2070.9625 & 12.5 & 2317.8014 & 11.0 & 2766.9606 & 9.5 \\ PNZ & 2070.9625 & 12.5 & 2317.8014 & 11.0 & 2766.9606 & 9.5 \\ PZ & 429.1771 & 21.5 & 805.2135 & 19.7 & 1502.0655 & 17.8 \\ TC & 692.6099 & 34.6 & 1249.6314 & 30.2 & 2218.1479 & 25.0 \\ Ray & 1039.9688 & 43.4 & 1682.3796 & 32.6 & 2454.5267 & 13.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: ORTimes and minimum ETCs for all eight models in Case #3 on Dataset #1. Figure 6: ORTime variation with minimum ETC for each model in Case #1 on Dataset #1.
# Influence of the Road Model on the Optimal Maneuver of a Racing Motorcycle
## 2 Road Modeling
### Acquiring and Preparing the Input Dataset  
Each point in the input dataset must be described by three coordinates expressed in the global, fixed inertial frame. The process of acquiring and preparing the input dataset can be divided into three steps.  
In the first step, track boundaries must be discretized. The points on the road edges can be located by GPS measurements, or by using computer programs that provide tools for exploring satellite images.  
The second step is to assign the elevation data to the previously specified points. Low-cost or no-cost methods of obtaining elevation data include ellipsoidal height measurements using GPS and datasets available on the Internet, known as digital elevation models. A DEM is a discrete representation of terrain elevations, along with an interpolation algorithm that allows the calculation of elevations at any point within the area for which the model was created. There are two types of DEMs based on the resolution of the data:  
1. Global DEMs covering large areas of the world, typically characterized by a resolution of 30 m or less;
2. Local DEMs provided by individual countries or regions, often with a high resolution of 5 m, 1 m, or even higher.  
There are many free global DEMs available, and their use is facilitated by online services that automatically assign elevations to user-provided sets of points with known geographic coordinates. The use of local DEMs requires more effort due to differences between datasets, in terms of the file formats and reference systems used. Another challenge is related to the selective coverage of the world map, or sometimes the need to purchase access to the database.  
The final step in the input data preparation process is to transform the geographic coordinates of the measured points into a rectangular coordinate system using a selected cartographic projection. In this step, the number of points on both road edges is standardized, the points on the track centerline are determined, and the arc length of the spine curve is estimated.
## 1 Introduction
Nonlinear dimensionality reduction methods, on the other hand, assume that data may contain nonlinear structures during the dimensionality reduction process, allowing for more accurate capture of complex relationships within the data structure. Among them, t-SNE is suitable for relatively small datasets, while the locality of LLE results in poor performance in preserving global structure [20]. AE are unsupervised neural network models aimed at learning data compression and are used to reconstruct the original input data [11; 21]. Flight parameters are extensive, and most parameters exhibit nonlinear relationships. AE models may be more suitable for reducing the dimensions of flight data  
Figure 1: Flowchart for F-35 Aircraft load Monitoring (The green box represents the recorded data). in this case, which is an innovative approach. The neural network prediction models after AE dimensionality reduction should be selected for strain prediction of an aircraft. The recurrent neural network (RNN) [22] and LSTM [23] are the mostly used models for this purpose. However, there has been less previous research to determine which model is more suitable for predicting strain in aircraft structures. In this paper, based on the recording data with a small fixed-wing unmanned aerial vehicle (UAV) sourced from the xx Institute of China, we identify the states of typical flight parameters, establish stiffness matrices between loads and strains through virtual ground calibration experiments, and develop a physical model to establish the mapping relationship between measured strain areas and critical strains. This provides necessary inputs and outputs to solve the mapping relationship between flight parameters and critical loads (and strains). The obtained data will serve as raw data for training AE model dimensionality reduction and deep learning neural network models, which will then be used to predict structural strains of new aircraft models and compare their accuracy. This study will proceed through five steps of strain prediction research, including dimensionality reduction of flight parameters, maneuver recognition based on flight parameters using MRF, wing virtual ground calibration testing, dimensionality reduction using AE neural networks, and strain prediction using LSTM. The research process is illustrated in Figure 2.
## 3 Application Examples
### Calculation of Fracture Parameters  
#### 3.1.1 Fracture Density  
Calculating fracture parameters is essential for assessing the effectiveness and permeability of fractures. By selecting four wells within the study area that have not undergone hydraulic fracturing, fracture parameters were analyzed and statistically processed using electrical imaging data. The distribution differences of fracture parameters between each well are significant. The analysis results are shown in Figure 2, and the fracture density of each well is mainly distributed in 1-5/m, with an average value of 1.3-4.5/m. Additionally, statistical analysis is performed on the fracture angles of four wells, which are illustrated in Figure 3. The fractures predominantly fall into the medium to high-angle range, varying from 40\({}^{\circ}\)to 80\({}^{\circ}\), and account for over 75% of the total quantity of fractures.
# Enhanced Multistream Fast TCP: Rapid Bandwidth Utilization after Fast-Recovery Phase
## 3 Enhanced Multistream Fast TCP (EMFast TCP)
### Congestion Control after Fast-Recovery Algorithm (CCaFR)
This function calculates congestion window after every round-trip-time (RTT). This is the normal behavior of the protocol.
## 4 Results
### Complementary Analysis: TabNet Attention vs. SHAP and LIME Rankings  
To complement the LIME-based explanations shown in Figure 8, we performed a comparative analysis between the top features highlighted by SHAP and LIME and the native attention scores produced by TabNet across decision steps. Table 11 summarizes the comparison for the ten most frequently appearing features in the instance-level explanations.  
Figure 8: LIME-based explanations for three randomly selected instances.  
The analysis reveals a partial alignment between the features emphasized by TabNet's native attention mechanism and those prioritized by SHAP and LIME. Notably, tcprtt, dbytes, and sbytes are consistently ranked among the top features across all three interpretability sources. This consistency reinforces their relevance in detecting anomalous traffic patterns.  
On the other hand, while TabNet assigns substantial attention to features such as ct_state_ttl and sttl, these features are ranked lower in SHAP and LIME outputs, suggesting that TabNet may leverage more distributed feature importance across its decision steps. This distributed nature of attention may support robustness, but may also reduce traceability compared to post hoc methods.  
These findings reinforce the value of combining intrinsic interpretability mechanisms, such as TabNet's native attention masks, with post hoc explainability techniques like SHAP and LIME to obtain a more comprehensive understanding of model behavior. This multi-perspective approach allows for the validation of feature relevance both during and after inference, contributing to a more robust and transparent interpretation process. In forensic applications, where transparency, traceability, and evidentiary reliability are essential, the convergence of different explainability strategies can significantly enhance expert validation and strengthen the legal defensibility of AI-driven decisions.  
XGBoost exhibited a significant drop in performance from validation (97.8%) to test data (85.91%), indicating overfitting. We verified that the data partitioning preserved temporal integrity and avoided leakage, suggesting that inadequate regularization was the likely cause. Future work will address this by adjusting regularization parameters and incorporating generalization-focused model selection, which is essential for forensic reliability.
## 1 Introduction  
In mimicking the structural and natural complexity of living tissue, current technology such as _in vitro_ 3D spheroid cell culture models is evolving compared to the 2D cell culture model. _In vivo_ cell growth and cell signaling are highly dependent on the extracellular matrix (ECM) and the interaction produced by different kinds of cells. _In vitro_ 3D cell culture models aid in the study of molecular level tissue function by employing co-culture models and developing drugs for the cancer model in mitigating animal usage for drug testing. In oxygen and nutrients, the gradient can be closely mimicked by 3D cell culture techniques compared to 2D cell culture techniques [1]. However, 3D _in vitro_ spheroid models seriously suffer from image acquisition and standalone image processing algorithms. As a result, user intervention during analysis might lead to heavier biases, thereby leading to erroneous results.  
Such issues can be overcome by different automated computer-aided design (CAD) tasks such as segmentation, i.e., the extraction of the region of interest from the images. In the literature, several studies showed multiple usages of automatic segmentation approaches for variable cellular types [2, 3]. [PERSON] et al. [4] stated that the segmentation of cells through automation is an essential step in image cytometry and histometry. [PERSON] et al. [5] proposed a novel non-linear segmentation model to discriminate and quantify living or dead cells. [PERSON] and [PERSON] [6] focused on digital pathology and microscopy image analysis, and extensively reviewed the techniques. Considerable progress was achieved in the past, but algorithms still suffer from biasing and need to boost their accuracy and robustness, consume less time, and self act against the upcoming applications. Color-based segmentation using traditional clustering algorithms, on the other hand, is relatively easy, and complexity is lesser when compared to that of segmentation techniques [7]. It is likewise more relevant for biomedical image segmentation, as the count of clusters is known beforehand. However, traditional clustering techniques suffer from various issues such as being trapped in local optima, and having sensitivity to initial cluster centers and boundary-level constraints [8]. In recent advances, many nature-inspired algorithms arose to solve these clustering problems. The hybridization of nature-inspired algorithms with each other and with traditional clustering techniques was described by [PERSON] et al. [9], [PERSON] et al. [10], and [PERSON] et al. [11] to resolve clustering issues. However, all of these clustering algorithms need much parameter initialization (Table S1), increasing their complexity and manual intervention. Moreover, incorrect parameter initialization affects the end outcome. To skip the overhead of parameter settings, a nature-inspired algorithm called the teacher learning-based optimization (TLBO) algorithm [12] was proposed.
# Traffic Aware Scheduler for Time-Slotted Channel-Hopping-Based IPv6 Wireless Sensor Networks
## 5 Materials and Methods  
### Evaluation Environment and Placement of Nodes  
The proposed scheduler is implemented in Contiki-NG [17] and is evaluated in the Cooja simulator [3]. Two placements of nodes, illustrated in Figure 8, are considered. In the first one, Figure 8a, the sink node is placed at the top of the grid, and the sending nodes are positioned below. This placement of nodes facilitates the creation of a RPL topology where the children of the root have different subtree sizes. In the second placement, Figure 8b, the sink node is situated in the middle of the grid and the sending nodes around. This placement of nodes results in a RPL topology in which the root's children have a similar subtree size. A total of 21 nodes (1 sink and 20 sender nodes) were used in all placements. The transmission and interference ranges are equal (50 m) and are identical for all the nodes. The size of the grid is 200 m \(\times\) 200 m.  
Figure 7: Summary of the allocation and deallocation phases for the root and its children. (**a**) The algorithm for allocating and deallocating Tx slots for the root’s children. (**b**) The algorithm for allocating and deallocating Rx slots for the root.
###### Abstract  
This research addresses the efficient integration and sizing of flexible alternating current transmission systems (FACTS) in electrical distribution networks via a convex optimization approach. The exact mixed-integer nonlinear programming (MINLP) model associated with FACTS siting and sizing aims for the minimization of the expected annual operating costs of the network (i.e., energy losses and FACTS purchasing costs). The constraints of this problem include power equilibrium equalities, voltage regulation bounds, and device capacities, among others. Due to the power equilibrium constraints per node and period, the MINLP model is a non-convex optimization problem. To transform the exact MINLP model into a mixed-integer convex one, the approximation of the product between two variables in the complex domain is relaxed through its hyperbolic equivalent, which generates a set of convex cones. The main advantage of the proposed mixed-integer convex model is that it ensures the global optimum of the problem, even when considering objective multiplexes. Numerical simulations in the IEEE 33-, 69-, and 85-bus grids demonstrate the effectiveness and robustness of FACTS integration via the proposed convex approach in comparison with the exact solution of the MINLP model in the GAMS software as well as with combinatorial optimization algorithms (i.e., the black widow optimizer and the vortex search algorithm). All simulations were carried out in MATLAB with Yalmip optimization and the Gurobi and Mosek solvers. The simulation results show that, for a fixed operation of the FACTS devices (i.e., a VAR compensator) during the day, the annual operating costs are reduced by 12.63%, 13.97%, and 26.53% for the IEEE 33-, 69-, and 85-bus test systems, respectively, while for the operation variable, the reductions are by 14.24%, 15.79%, and 30.31%, respectively.  
mixed-integer convex model; optimal integration; global optimum; flexible alternating current transmission system +
Footnote †: journal: _Algorithms_  
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