esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensitivity Analysis for Microscopic Crowd Simulation
## 3 Results and Discussion
### Relation between Parameter and Quantity of Interest  
A starting point for a sensitivity analysis is to look at scatterplots between each input parameter and the quantity of interest, according to ([23], p. 113). Our quantity of interest is the crowd density in front of the opening, more precisely the maximum Voronoi density that is described in Section 2.2, measured in persons per square meter. We use the maximum of the Voronoi density over simulation time in order to obtain a scalar quantity of interest. Figure 5 shows the scatterplots. Please note that all parameters are varied at the same time. Each scatterplot shows a projection for the parameter that is shown on the horizontal axis.  
Figure 4: Effect of the uncertain parameters on the distribution of agents in front of and within the bottleneck. (**a**) Simulation where each parameter is set to its lower limit; (**b**) Simulation where each parameter is set to its upper limit.  
Figure 5: Scatterplot of input-output relation for each input parameter. We use the model evaluations from the derivative-based method. Each run is shown, that means 10 data points for each configuration. In total, 81 gradient samples are evaluated which leads to a total of 9720 runs.  
There is only one parameter with a clear correlation to the maximum Voronoi density: the strength of the need for personal space. As expected, a more pronounced need for personal space results in a lower density in the front of the bottleneck, where our measurement area is placed. In addition, we observe a slight trend for the bottleneck width. The density decreases with a wider opening. For the other three parameters, there is no clear trend visible, their responses are similar to the control parameter's response.  
In summary, the scatterplots indicate a strong impact of the need for personal space and a medium impact of the bottleneck width on the maximum Voronoi density. The other parameters do not seem to have a clear individual impact. However, interaction terms among them might have a strong impact. To identify higher order relations, we study the sensitivities in more detail with Sobol' indices and activity scores. Note that the choice of the quantity of interest has an impact on the strength of the correlation between input parameters and the simulation outcome. Therefore, it is important to choose a quantity of interest tailored to the research question of the study.
## 2 Materials and Methods
### Application of Physical Information to the Models  
After choosing the best architecture of each NN paradigm, we tested the effects of imposing physical constraints onto the models. This was performed via soft constraints [23], namely adding an error formula to drive the solution to mass conservation through the usual loss function. It was performed by calculating the mass flow imbalance (Equation (1))  
Figure 1: General layout of the tested 2D CNN + LSTM architecture.  
for each grid cell and taking the arithmetic mean of their absolute values for all of the computational domain. It should be further noted that the values at the boundaries are already in the datasets, which also gives physics information to the models. The aim of the mass conservation error evaluation is to insert physical coherence into the models, improving interpretability and accuracy.  
\[Mass\ imbalance=Mass\ flux_{out}-Mass\ flux_{in} \tag{1}\]  
where the overall mass flux can be defined as  
\[Mass\ flux_{out}-Mass\ flux_{in}=\oint_{boundaries}\rho\overset{\rightarrow}{V} \overset{\rightarrow}{A}\overset{\rightarrow}{A} \tag{2}\]  
Because Cartesian coordinates are being used, it is straightforward to solve the above integral only using the velocity components and the side lengths of each cell.  
The governing equations, in a non-dimensional form, for the 2D lid-driven problem are [31; 34]  
\[(\
abla\cdot\mathbf{u})=0 \tag{3}\]  
\[\frac{\partial\mathbf{u}}{\partial t}+(\mathbf{u}\cdot\
abla)\mathbf{u}=-\
abla\mathrm{P} +\frac{1}{Re}\Big{(}\
abla^{2}\mathbf{u}\Big{)} \tag{4}\]  
where \(\mathbf{u}=(u,v)\) are the velocities for the 2D case, and \(P\) and t are pressure and time, respectively. \(Re\) represents the Reynolds number, defined as a ratio between the product of velocity and lid length over the kinematic viscosity. For the boundary conditions, the velocities were set to zero over the lateral walls and to finite non-zero over the upper wall. For the domain walls, both epsilon and P were set as having zero gradient. Last, k was set to zero over the domain's walls.  
At this stage, several weights were tested for the calculated errors of mass conservation, trying to optimize the PINNs results. The tested values were 0.0, 0.1, 1.0, 2.0 and 5.0.
## 5 Results and Discussion
### Explainable AI (XAI) Integration  
The transparency in the explainable AI tool builds trust and allows users to understand the reasoning behind the system's decisions. It ensures the system remains transparent by providing clear explanations of why specific modules are predicted as target modules and how the knowledge graph validates these predictions.  
The validation metrics for XAI integration over 10 iterations show consistent improvements in feature importance accuracy, decision explanation clarity, and user trust, reflecting the growing effectiveness of the system's transparency and interpretability. As shown in Figure 11, feature importance accuracy improved from 80% to 95%, demonstrating the system's ability to identify and prioritize key features contributing to predictions. Similarly, decision explanation clarity increased from 75% to 90%, highlighting the enhanced interpretability of the model's decisions and better alignment with user expectations. User trust, a critical metric for adoption and engagement, grew from 70% to 88%, showcasing increased confidence in the system's recommendations as explanations became more transparent and meaningful. These iterative improvements emphasize the success of XAI in balancing accuracy, interpretability, and user confidence, reinforcing its role in fostering a reliable and user-centric learning system.  
Figure 11: Integration of XAI performance metrics.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} GNSS & Global Navigation Satellite System \\ BDS & BeiDou Navigation Satellite System \\ GPS & Global Positioning System \\ SIS & Signal in space \\ DQA & Data quality analysis \\ SQA & Signal quality analysis \\ MQA & Measurement quality analysis \\ GLONASS & Global Navigation Satellite System \\ QZSS & Quasi-Zenith Satellite System \\ MEO & Medium Earth orbit \\ GEO & Geostationary Earth orbit \\ IGSO & Inclined geosynchronous orbit \\ CGCS2000 & Chinese Geodetic Coordinate System 2000 \\ WGS84 & World Geodetic System 1984 \\ BDT & BeiDou Navigation Satellite System Time \\ UTC & Universal Time \\ GPST & GPS Time \\ C/N\({}_{0}\) & Carrier-to-noise density ratio \\ RTK & Real-time kinematics \\ \end{tabular}
# Machine Learning Models for the Classification of Histopathological Images of Colorectal Cancer
## 2 Materials and Methods  
### Sample Description  
The dataset utilized in this study originates from research conducted by [PERSON] et al. [3], who acquired ten anonymized CRC tissue slides from the pathology archive at the University Medical Center Mannheim (Heidelberg University, Mannheim, Germany). The dataset was made publicly available by the authors for subsequent analyses of colon cancer images, and it consists of both low-grade and high-grade tumors without any additional filtering. Initially, the slides underwent digitization, followed by manual annotation and tessellation of contiguous tissue areas. This process resulted in the creation of 5000 non-overlapping tissue tiles, each measuring 150 \(\times\) 150 pixels (equivalent to 74 \(\upmu\)m \(\times\) 74 \(\upmu\)m). Consequently, the dataset encompasses texture features spanning various scales, ranging from individual cells to larger structures such as mucosal glands. For the analysis, eight types of tissue were selected, namely (1) tumor epithelium, (2) complex stroma, (3) immune cells (including immune cell conglomerates and sub-mucosal lymphoid follicles), (4) simple stroma (homogeneous composition, including tumor stroma, extra-tumoral stroma, and smooth muscle), (5) debris (including necrosis, hemorrhage, and mucus), (6) adipose tissue, (7) background/empty (no tissue), and (8) normal mucosal gland. In total, the dataset consisted of 5000 images, and all tissue types were equally represented (625 images for each of the eight tissue types). The images served as the training and testing dataset for the classification task outlined in the subsequent sections. Figure 1 depicts the first image from each class. The dataset was balanced and equally represented. Notably, the average intensity exhibited considerable variation among the tissue samples, reflecting the typical diversity observed in routine histopathological slides. A more detailed description of the tissue types can be found in [3]. The researchers ensured that each class encompassedboth bright and dark samples, thereby mitigating any potential bias in terms of average grayscale intensity [3].
## 3 Design and Implementation
### Software Architecture for P2P Massive System Deployment  
The open source P2P massive deployment system that is proposed in this work can fulfill the above-mentioned features and differs from existing solutions. The major features are summarized as follows. (1) Resolving the insufficient temporary storage issue: Traditionally, the file size of the P2P image data to be deployed should be smaller than the RAM size of the machine to be deployed. Otherwise, extra space for temporary storage will be required. The proposed solution can solve the problem. (2) Scalability: As the peers increase, the performance of the proposed system also increases. Thus, scalability can be achieved. (3) Compatibility: The proposed system can be fully compatible with our previous version of mass deployment software. Thus, existing image files can be deployed by the proposed BT-based solution. The features of our previous work [3], which include an open design, full automation, customization support, and the provision of a live system, can also be inherited.  
In this study, the massive deployment system was implemented in a P2P-based solution, where the BT protocol was introduced to distribute the data of the file system. The system architecture (see Figure 3), including the computer hardware, live OS, deployment system, and P2P mechanism for massive deployment, was proposed. Four modules are included in the deployment subsystem [3]: (1) partition table processing; (2) partition image processing; (3) pre-processing, and (4) post-processing modules. Two sub-modules, including the file system processing sub-module and the compressing and decompressing sub-module for the partition image, are included in the partition image processing module. When massive machines are connected from the network to the system, the bare-metal system provisioning can be achieved by using the BT data-sharing module, which includes the torrent server, torrent tracker, and peers (seeders and leechers). More explanations and information regarding these parts will be introduced in Section 3.2.  
Only one module from the deployment subsystem (refer to Figure 3)--namely, the partition image processing module--is directly related to the massive deployment module, because the major parts of the data being deployed are the partition images, which usually take a few to hundreds of GB. Therefore, a specific module is designed to enhance the performance of deployment. On the other hand, the information from the partition table is less than one KB. Furthermore, the pre-processing and post-processing modules do not use any data; instead, these two modules only execute some tasks before and after the partition is deployed. The file system blocks transferring (FSBT) data are massively distributed to all of the machines by the BT data-sharing module. Once the machine to be deployed receives the data, the machine's partition image processing module will write the FSBT data to the partition, and start sharing the FSBT data to other machines based on the BT protocol. Section 3.2 defines the details of massive deployment with the P2P protocol.
## 3 Results and Discussion  
### General Description of the Evolution of Vortices  
The experimental results of [PERSON] et al. [4] concluded that, for the centrifugal impeller operating under a quarter flow rate \(Q/Q_{n}\) = 0.25, the flow is unstable and stall cells emerge in the passages.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Mesh Number** & **H/m** \\ \hline Mesh 1 & 4,740,324 & 2.42 \\ Mesh 2 & 5,598,924 & 2.51 \\ Mesh 3 & 7,642,362 & 2.31 \\ Mesh 4 & 9,185,268 & 2.38 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mesh independence study.  
The distribution of streamlines in the impeller at successive moments is given in Figure 4 at the half-span cross section \(Z/b_{2}=0.50\). The notable two-passage phenomenon is observed in that the passages are stalled and unstalled in an alternative way, as denoted in the figure, where passages I, III, and V are stalled where severe flow separation and multiple stall cells are observed and where passages II, IV, and VI are unstalled where only small separated vortices occasionally emerge.  
For the stalled passage I, a large vortex appears on the suction surface of the leading edge of the blade, which blocks the upstream inflow, and its position hardly changes with the rotation of the impeller. The formation of this stall cell reduces the flow rate in this passage and consequently  
Figure 4: Evolution of streamlines with the rotation of the impeller at different moments within one cycle: the streamlines are colored by the velocity magnitude.  
induces an even larger stall cell on the pressure surface of the blade in the downstream part of the passage; the downstream stall cell sheds with the rotation of the impeller and moves in the downstream direction. For the unstalled passage II, a separated vortex is generated on the pressure surface of the blade near the outlet of the impeller. This vortex evolves in size with the rotating impeller but not the position and may vanish during part of the cycle; it almost does not affect the main flow in the passage. The evolution process of the separated vortices of different scales in other passages is quite similar to those reported in passages I and II.
# EpiInfer: A Non-Markovian Method and System to Forecast Infection Rates in Epidemics
## 3 Materials and Methods
### ContinuousCalibrate: Systematic Search for \(p_{1}\) and \(p_{2}\)  
In the experiments with EpiPolicy generated data, \(inc\) is taken to be the inverse of \(\sigma\), which is the number of days to transition from the exposed state to the infected state. In experiments with real COVID-19 data, we used \(inc=7\) as a hyperparameter, as it is a common value for incubation periods and applies to COVID-19 which is our real data use case. If the incubation period was not known, the parameter sweep would be a three-level nested loop including \(inc\) as well as \(p_{1}\) and \(p_{2}\). We present the two-level nested loop case, where we know \(inc\).  
For a given number \(k\) of training days, the \(p_{1}\) and \(p_{2}\) are selected so that they minimize the RMSE between the predicted and real number of infected people from day \(k-1\) to \(k\) using _Epilnfer-core_. The search for \(p_{2}\) is conducted by iterating through a series of possible values, starting from 0.1 up to 1, with a step size of 0.1. For each of those values of \(p_{2}\), the search for the best corresponding \(p_{1}\) is carried out in a manner similar to binary search. The initial middle point for \(p_{1}\) is 0.5, and if this value causes the predicted data to be larger than the real data, choose \(p_{1}\) of 0.25. Otherwise, choose \(p_{1}\) to be 0.75. This process continues until the search is terminated. When the search ends, we will use the \(p_{1}\) that leads to the lowest RMSE on the training data. Algorithm 2 shows the pseudocode of this approach.  
```
1:functionsearch
2:for each \(p_{2}\) value between 0.1 and 1.0 in increments of 0.1 do
3:while doing binary search on \(p_{1}\)do
4: run Algorithm 1 (_Epilnfer-core_) based on current \(p_{1}\), \(p_{2}\) pair and \(inc\)
5: calculate RMSE for the last t training days
6: Update best \(p_{1}\), \(p_{2}\) if lowest RMSE seen so far
7: Update interval for \(p_{1}\) as described in Section 3.2
8:endwhile
9:endfor
10:return best \(p_{1}\), \(p_{2}\) pair
11:endfunction
```  
**Algorithm 2** Transition Parameter Inference Algorithm Using a Single Locale: For each subsequent training day, the goal is to find \(p_{1}\) and \(p_{2}\) values that minimize the Root Mean Squared Error (RMSE) of the number of infected people (in state \(I\)). \(inc\) is taken to be seven days because that is common in epidemics, but this could be changed.  
Section 7 shows that the accuracy as measured in terms of Root Mean Squared Error of Infection Rate estimates varies with \(p_{1}\), \(inc\), and \(p_{2}\) (in descending order of importance), sometimes significantly. The RMSE based on the parameter settings found by _ContinuousCalibrate_ turn out to be the best overall as shown in Appendix B. Thus, _ContinuousCalibrate_ is an important component of EpiInfer.
## 2 Related Works
The authors developed methods that optimize the system's transmission delay and highlight the impact of the image encoding rate and size of the neural network. Their results were based on a real-time object recognition application. They showed that the image level compression as well as their neural network size are key parameters that affect object recognition accuracy and also latency from one end to the other. A mobile augmented reality for real-time object detection for was introduced by [PERSON] et al. [31]. This system can achieve accurate object detection for commodity AR/MR with a frame rate of 60 fps. The authors of this work demonstrated that using low-latency offloading techniques can effectively decrease the offloading latency and the amount of bandwidth consumed. The authors prototyped an E2E system that is implemented and used on mobile devices. The results showed that the system after using smart offloading techniques and fast object detection methods to sustain the detection accuracy can reduce the false detection rate by 27-38% and increase the detection rate by 20-34%. [PERSON] et al. [32] presented an enhanced object detection system for an edge-based Internet of Vehicles (IoV) system, aiming to reduce latency. They proposed a new method for object detection based on a video key-frame, which can achieve a compression ratio of 60% with an error rate of only 10%. The proposed method can also accelerate the processing of frames in the edge IoV system. Finally, [PERSON] et al. [33] presented an analytic deep-learning performance evaluation using the YOLO object detector in the NVIDIA Jetson platform. The authors used general deep learning frameworks like TensorRT and TensorFlow-Lite on embedded and mobile devices to get an analytic performance evaluation. The evaluation was performed on the NVIDIA Jetson AGX Xavier platform employing CPU and GPU utilisation, latency, energy consumption, and accuracy. The evaluation model was the YOLOv4 object detector using COCO, PASCAL, and VOC Datasets. The findings indicated that the implementation of a deep learning model on a mobile device using the TensorFlow-Lite framework is the best solution. On the contrary, when implementing a deep learning model on an embedded system with a built-in Tensor core the most effective framework is the TensorRT framework. All in all, the studied literature provides evidence that the processing of the computing workloads is flowing from the cloud to the edge and that AI workload in edge computing architectures is a rapidly emerging research field. Moreover, a great effort has been made by the research community in designing optimization algorithms to improve the performance of edge computing systems. The most common solution in task offloading approaches is that the workload is transferred to local servers which are making the computation. Our work is, as far as we know, the first study that explores the problem of executing the workloads by splitting the task from servers to end devices.
## 2 Detection Algorithms
### Learned Thresholded Subspace Projection (LTSP)  
#### 2.6.1 Motivation and Overview  
Conventional peak detection techniques suffer from degraded performance in the presence of strong noise and oscillator-induced spectral distortion. While FFT-based detection is simple and fast, it is highly sensitive to spectral leakage and noise. Subspace-based techniques such as full SVD or MUSIC can provide enhanced robustness, but at the cost of significantly increased computational complexity and sensitivity to model order selection.  
To address this trade-off, the proposed Learned Thresholded Subspace Projection (LTSP) method combines signal subspace projection and low-rank reconstruction to enhance signal quality prior to peak detection. The goal of LTSP is to improve detection reliability under moderate SNR and phase noise conditions while maintaining compatibility with real-time implementation platforms such as FPGAs and embedded DSPs.
## 4 Finite Element Modelling of an EG Rotor-Bearing System
### Global Equation of Motion and Solution Procedure to Compute Eigenfrequencies  
The element matrices of a cracked and uncracked EG rotor, steel disc and linear isotropic bearings are assembled to form the global equation of motion of a cracked and uncracked EG rotor-bearing system.  
\[[M]\big{\{}\ddot{q}\big{\}}+[[C]-\Omega[G]]\big{\{}\dot{q}\big{\}}+[K]\{q\}=\{Q\} \tag{37}\]  
where \(\{Q\}\) is the global excitation vector, \(\{q\}\) is the nodal displacement vector, \([G]\) is the global gyroscopic matrix, \([M]\) is the global mass matrix, \([K]\) is the global stiffness matrix, \([C]\) is the global damping matrix, \(\Omega\) is the rotor speed of the system. Natural frequencies of an EG rotor-bearing system with and without crack are determined by representing Equation (37) as  
\[A\dot{p}+Bp=0 \tag{38}\]  
\[A=\begin{bmatrix}0&[M]\\ [M]&-\Omega[G]\end{bmatrix},\ B=\begin{bmatrix}-[M]&0\\ 0&[K]\end{bmatrix},\ p=\begin{B}\dot{q}\\ q\end{B}\]  
The solution for Equation (38) can be assumed as  
\[p=p_{o}e^{\lambda t} \tag{39}\]  
On substituting Equation (39) in Equation (38), the eigenvalue problem is can be expressed as  
\[\Big{(}B^{-1}A+\lambda I\Big{)}p_{o}=0 \tag{40}\]  
The eigenvalues are of the form  
\[\lambda_{n}(\Omega)=\xi_{n}(\Omega)\ \pm\ i\omega_{n}(\Omega) \tag{41}\]  
The inverse of the imaginary part gives the whirl frequency, and at \(\Omega=0\), the inverse of the imaginary part gives natural frequency in \(\
icefrac{{rad}}{{f_{s}}}\).