esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper presents a technique that can potentially help to determine the receptivity stage of the endometrium from histology images by automatically measuring the stromal nuclear changes. The presented technique is composed of an image segmentation model and the statistical evolution of segmented areas in hematoxylin and eosin (HE)-stained histology images. Three different endometrium receptivity stages, namely pre-receptive, post-receptive, and receptive, were compared. An ensemble-based AI model was proposed for histology image segmentation, which is based on individual UNet++, UNet, and ResNet34-UNet segmentation models. The performance of the ensemble-based technique was assessed using the Dice score and intersection over unit (IoU) values. In comparison to alternative segmentation architectures that were applied singly, the current ensemble-based method obtained higher Dice score (0.95) and IoU (0.90) values. The statistical comparison highlighted a noticeable difference in the number of nuclei and the size of the stroma tissue. The proposed technique demonstrated the positive potential for practical implementation for automatic endometrial tissue analysis.  
Keywords:image segmentation; endometrial biopsy; histology image +
Footnote †: journal: Applied Sciences
## 3 Results
### Methods Used for Quantifying Deviation Parameters  
Twenty-one studies [19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40] used the \"radiographic matching method\" to assess accuracy. This consists of superimposing the Standard Tessellation Language (STL) files generated in preoperative implant planning over Digital Imaging and Communications in Medicine (DICOM) files generated from postoperative CBCTs of the placed implants. Two studies [34; 41] used the \"digital registration method\", which consisted of superimposing the STL files used in preoperative implant planning over STL files generated by postoperative scanning of the placed implants.  
The programs used for measuring deviation parameters were: Mimics [19; 20; 22; 24; 25; 30; 32; 37; 38; 39; 40]; CodiagnostiX [21; 34; 35]; SimPlant [29; 31]; Nobel Guide(r) [27; 28; 33]; Procera (Nobel Biocare) [26]; Rhinoceros 4.036; 3 Shape Implant Studio [23]; Geomagic Studio [41] (Table 1).  
Deviation parameters were measured mainly at coronal and apical site levels, in mesiodistal, buccolingual, and apico-coronal directions. The measurements considered were linear, three-dimensional (3D), and angular deviations. These deviation parameters were presented as mean values and standard deviations (SD).
## 2 Proper Orthogonal Decomposition Theory
### Reconstruction of Wind Pressure Field  
Based on Equations (5) and (8), the mean square value of the fluctuating pressure can be obtained, which shows that the eigenvalue \(\lambda_{n}\) can describe the contribution of the eigenmode \(\{\varphi\}_{n}\) to the mean square value of the fluctuating wind pressure:  
\[\sigma_{h}^{2}(y)=\int_{0}^{+\infty}S_{h}(y,k_{1})\mathrm{d}k_{1} \tag{9}\]  
\[\sum_{i=1}^{N}\overline{P^{2}(x_{i},y_{i},t)}=\sum_{n=1}^{N}\sum_{m=1}^{N} \overline{a_{n}(t)a_{m}(t)}\{\varphi\}_{n}^{T}\{\varphi\}_{m}=\sum_{n=1}^{N} \lambda_{n} \tag{10}\]  
That is, the sum of the mean square values of the fluctuatingIf the first \(M\) order energy ratios can meet the accuracy requirements when \(i=M\), the first \(M\) eigenmodes can be used to reconstruct the wind pressure field:  
\[P(x,y,t)=\sum_{n=1}^{M}a_{n}(t)\varphi_{n}(x,y) \tag{12}\]  
According to the above-mentioned reconstruction of wind pressure field based on POD technology and through the idea of energy ratio of Equation (11), the wind pressure field can be well described by selecting a few order eigenmodes, which simplifies the analysis of fluctuating wind pressure. After the wind pressure field is reconstructed, the unknown point is interpolated by using the eigenmodes of the known position, and the wind pressure time histories of the unknown points can be predicted by Equation (12) [36; 37; 38].
## 4 Structure Parameters Evaluation
Furthermore, dispersion losses are related to the real part of the complex refractive index, \(n\), and materials can be chosen analyzing their optical properties and the functions values for its frequency/wavelength. Air, modeled by [PERSON]'s results, is chosen as the dielectric medium, because it is the most common dielectric medium and it is easy to characterize, allowing a wider range of application areas, without being too restrictive [40]. Figure 1: Topology of the simulated structure. Gold, modeled by the Rakic's Drude-Lorentz fitting, has its transition between Drude and interband regimes at approximately 2 eV\(\sim\)2.3 eV, i.e., 530 nm\(\sim\)600 nm, being used on the red and near-infrared regions [13, 14, 40]. Using aluminum, it is possible to obtain gain along all visible regions. Aluminum, also modeled by its Rakic's Drude-Lorentz fitting, has a larger negative real part of its dielectric function, and it is used for wavelengths between 400 nm and 650 nm. It is the result of an almost null dielectric function imaginary part until 500 nm. After that, its value is going to increase until a maximum located around 800 nm, making this material the worst to be used on the red and near-infrared region applications and the best one to create gain along all the visible region. Although the optical properties can allow us to select a metal. In alternative to others, it is also important to analyze the chemical stability of these metals, in order to know if the material will keep the same or if their chemical formula will change along time, leading to adjustments on their own optical properties and consequently, to the output spectrum. In this aspect, some passivation layers will be formed on aluminum, resulting in Al\({}_{2}\)O\({}_{3}\), whereas gold is known to have an excellent stability [13, 14, 40]. We chose quartz (SiO\({}_{2}\)) as the substrate, modeled by [PERSON]'s results, because it has almost no effect on the output spectrum, since its transition between Drude and interband regime is around 0.15 eV (8000 nm) [40]. Quartz complex dielectric function has a null imaginary part along all the simulated range, as well as a real part with small variations. Thus, the inclusion of this substrate will not create a new resonant peak in the simulated range. Furthermore, it is a hard material that allows high temperatures, suitable to guarantee some protection and robustness to the structure.
# Recommendation Algorithm Based on Heterogeneous Information Network and Attention Mechanism
## 3 Main Model  
The general framework of the proposed recommendation algorithm model (NMRec) in this paper is shown in Figure 3. First, a meta-path containing \(<\)_user, item, one-hop, and two-hop neighbor information of user and item\(>\)_ is utilized as input; then, convolution operations are introduced to generate potential interactions between meta-paths; after that, key interactions are captured and information is aggregated through the attention mechanism; finally, the model provides the final prediction.
## 4 Empirical Analysis
### Robustness Test  
In order to verify the robustness of the declared strategy calculated by the robust optimization model constructed in this paper, this section compares the expected profit of the system with the profit conducted by determining the actual output of the next day of the system, and analyzes the economic risks of the electric-gas interconnection system under different scenarios. Without considering the output uncertainty of the electric-gas interconnection system, the predicted value of the actual system output is simply set as the average value of the historical data. When \(\Gamma=0\), \(\widetilde{P_{\text{E,}I,k^{\prime}}}\), the predicted value of the actual output of the electric-gas interconnection system is equal to \(\widetilde{P_{\text{E,}I}^{\text{real}}}\), the midpoint of the confidence interval, and \(\widetilde{P_{\text{E,}I,k}^{\text{real}}}\) is the deterministic parameter. When considering the uncertainty of the system output, the robustness factor \(\Gamma>0\), \(\Gamma\) is set to 0, 6, 12 respectively, and the actual output of the next day is assumed to be \(\widetilde{P_{\text{E}}^{\text{real}}}=\widetilde{P_{\text{E,}I}^{\text{real}} }-0.5\widetilde{P_{\text{E,}I}^{\text{real}}}\), this section analyzes the impact of the robustness of the model on the profit calculation results.  
As shown in Table 3, when \(\Gamma\) increases, the expected profit decreases, the profit deviation decreases gradually and the actual profit increases. When \(\Gamma=0\), i.e. without considering the uncertainty, if the actual output of the electric-gas interconnection system deviates greatly from the power generation plan, the system may lose more profits in the day-ahead bidding, and the profit deviation will disturb the normal operation of the electric-gas interconnection system. When \(\Gamma\) is 6 or 12, although the power deviation cannot be avoided and the profit deviation still exists, the profit deviation is obviously low and the actual profit is also significantly improved, which shows that considering the uncertainty in the optimization process can reduce the risk brought by the uncertainty factors to the profit. However, when considering the uncertainty of the system output, with the increase of the robustness factor \(\Gamma\), the expected profit obtained by robust optimization will be reduced, so the decision-maker needs to make a reasonable decision according to his own risk tolerance.  
Figure 17: Variation results of the coupling unit output power in scenario 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\Gamma\) = 0 & \(\Gamma\) = 6 & \(\Gamma\) = 12 \\ \hline Expected profit (EUR) & 12398 & 12285 & 12168 \\ Actual profit (EUR) & 6877.2 & 6995.8 & 7109.4 \\ Profit deviation (EUR) & \(-\)5520.80 & \(-\)5289.20 & \(-\)5058.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Actual profit and profit deviation of each scenario.
## 5 Numerical Experiments
### Analysis of Performance of DP, ar-DP, and PPO  
#### 5.3.1 Comparison Based on Computation Time  
We compare the computation times of PPO, DP, and ar-DP. Table 6 shows the computation times for Cases 1 to 7.  
In Cases 1 to 4, where price sensitivity was varied, ar-DP achieved an average computation time reduction of 558 s compared to DP. Additionally, PPO achieved an average  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Case 1** & **Case 2** & **Case 3** & **Case 4** & **Case 5** & **Case 6** & **Case 7** \\ \hline DP (s) & 714 & 729 & 636 & 668 & 597 & 638 & 574 \\ ar-DP (s) & 234 & 276 & 235 & 249 & 215 & 238 & 250 \\ PPO (s) & 163 & 160 & 155 & 160 & 161 & 154 & 160 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of computation times for DP, ar-DP, and PPO in seconds.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Price Sensitivity** & **Costs** \\  & \(\left(\textbf{{i}}_{1},\textbf{{i}}_{2},\textbf{{i}}_{3}\right)\) & \(\left(\textbf{{c}}_{h},\textbf{{c}}_{o},\textbf{{c}}_{l},\textbf{{c}}_{p}\right)\) \\ \hline Case 1 & (0.3, 0.5, 0.7) & (1, 3, 5, 7) \\ Case 2 & (0.03, 0.05, 0.07) & (1, 3, 5, 7) \\ Case 3 & (0.2, 0.5, 0.8) & (1, 3, 5, 7) \\ Case 4 & (0.4, 0.5, 0.6) & (1, 3, 5, 7) \\ Case 5 & (0.3, 0.5, 0.7) & (1, 3, 5, 10) \\ Case 6 & (0.3, 0.5, 0.7) & (1, 3, 7, 7) \\ Case 7 & (0.3, 0.5, 0.7) & (1, 3, 7, 10) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Case study parameter settings.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Learning rate & \(2.5\times 10^{-4}\) \\ Batch size & 73 \\ Number of epochs & 10 \\ Discount factor & 0.99 \\ GAE lambda & 0.95 \\ Clipping range & 0.2 \\ Entropy coefficient & 0.0 \\ Value function coefficient & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameter settings for PPO.  
reduction of 89 s compared to ar-DP. In Cases 1 and 5 to 7, where costs were varied, ar-DP achieved an average computation time reduction of 397 s compared to PPO. Moreover, PPO achieved an average reduction of 74 s compared to ar-DP.
## 4 Expert Diagnostic System Design Based on Infrared Images
### Pilot Testing of the Expert System Using Infrared Thermography (IRT) Inputs  
The idea behind the design of the presented expert system is to evaluate its possibilities and its ability to correctly present diagnostic signals to the engine's operator or supervisory control system [64,65]. The designed rule base was therefore constructed to be very simple in order to evaluate the five pre-defined temperature states represented by hypotheses \(D\). Pilot tests were conducted to prove that the system works as intended. For real-world engine operation, it was only possible to test the states \(D_{0}\), D\({}_{1}\), \(D_{4}\), and \(D_{5}\). The resulting classifications for these tested situations are shown in Table 6. States \(D_{4}\) and \(D_{5}\) were artificially induced by quickly adding heating material on the surface of the engine's exhaust nozzle, which created anomalies in the temperature field of the engine. The condition of light overheating was achieved when the engine was running at a high speed setting, (i.e., at an operating speed of 50,000 rpm for around 20 s). The rules which were evaluated as true, and the way the diagnostic signal/hypothesis was obtained, are shown in Table 6. These results suggest that the diagnostic expert system is able to determine basic states represented by hypotheses \(D_{0}\), \(D_{1}\), \(D_{4}\), \(D_{5}\) and that the designed rules work.  
Additionally, another test was set up using the data from a single engine run, as shown in Figure 14. Normal and light overheating states were tested during standard operation of the engine, and are shown in Figure 20. It can be seen that the expert system selected the state \(D_{1}\) (light overheating) even though the speed of the engine and the corresponding temperatures had decreased. This was the correct selection, since the temperature of the casing takes more time to cool down, whereas the inner gas temperature as measured by internal sensors decreases immediately. This set of pilot tests showed that the inference engine works as intended and that the expert system is able to produce some sensible results in the evaluation of the technical state of the engine during normal conditions and to detect artificially induced structural defects. In order to prove the correctness of the setting of the rules \(R_{i}\), as well as the definition of intervals in these rules, a more systematic approach has to be taken. This will be designed in a follow-up study of the system.  
Figure 19: The inference network of the designed diagnostic expert system.  
Figure 20: The division of the engine’s operation into technical states \(D_{0}\) and \(D_{1}\).  
\begin{table}
\begin{tabular}{l} \hline \hline \(D_{0}\)—normal condition \\ \(R_{1}\): \(S_{1}=13,270\) AND \(S_{1,d}=20\) \\ \(R_{2}\): \(S_{2}=3628\) AND \(S_{2,d}=14\) \\ \(R_{3}\): \(S_{3}=734\), AND \(S_{3,d}=24\) \\ \(R_{1}\) AND \(R_{2}\) AND \(R_{3}=>D_{0}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Testing of the expert system for the evaluation of hypotheses \(D_{0}\), \(D_{1}\), \(D_{4}\), and \(D_{5}\).
# Research on Fault Diagnosis of Rotating Parts Based on Transformer Deep Learning Model
## 2 Materials and Methods
### Feature Extraction Module
#### 2.4.3 Feedforward Layer  
The multi-head self-attention layer is primarily designed to capture global dependencies in the input sequence. However, its dependence on linear transformations limits its capacity for representing non-linear features. To address this, a fully connected feedforward neural network layer is employed to perform non-linear transformations on feature vectors, aiming to learn more abstract fault features. This layer consists of two fully connected layers and a non-linear activation function. The output \(X\) from the above submodule is input into this layer, and the output is obtained through layer normalization and residual connection, denoted as \(X_{o4}\in\mathbb{R}^{M^{2}\times D_{embed}}\).  
\[X_{o4}=\text{LN}(\text{ReLU}\left(X_{o3}\cdot W_{1}^{F}\right)\cdot W_{2}^{F} )+X_{o3} \tag{15}\]  
where \(W_{1}^{F}\) and \(W_{2}^{F}\) represent the weight parameters of the two fully connected layers.
# Estimation of the Block Adjustment Error in UAV Photogrammetric Flights in Flat Areas
## 3 Results
### Photogrammetric Flight Results
#### 3.2.2 Block 2 Results  
Block 2 covers an approximate area of 19 ha. The GCPs continued to be distributed at the four corners of the block; points 3A and 4A were the same as in Block 1 (Figure 4), and points 5A and 6A were the same as in Block 3. Figure 9 shows the results of the RMSE for the GCPs and ChPs in the four scenarios studied. The GSD was 6.22 cm for Scenario A, 5.15 cm for Scenario B, 5.48 cm for Scenario C, and 4.26 cm for Scenario D.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Scenario** & **Type** & **Point** & **East** & **North** & **Altitude** & **RMSE** & **Mean RMSE** \\ \hline \multirow{4}{*}{A} & \multirow{4}{*}{GCP} & 1 & 4.9 & 2.9 & \(-\)0.2 & 5.7 & \multirow{4}{*}{5.4} \\  & & 2 & \(-\)0.9 & \(-\)4.2 & 0.2 & 4.3 & \\  & & 3 & \(-\)1.5 & 6.5 & 0.2 & 6 & \\  & & 4 & \(-\)2.5 & \(-\)5.1 & \(-\)0.2 & 5.7 & \\ \cline{2-8}  & \multirow{2}{*}{ChP} & 18 & 7.5 & 15.1 & 5.5 & 17.7 & \multirow{2}{*}{11.4} \\  & & 19 & \(-\)2.9 & \(-\)3.7 & \(-\)1.8 & 5.1 & \\ \hline \multirow{4}{*}{B} & \multirow{4}{*}{GCP} & 1 & 5.1 & 2.1 & \(-\)0.1 & 5.5 & \multirow{4}{*}{5.2} \\  & & 2 & \(-\)1.7 & \(-\)3.9 & 0.03 & 4.3 & \\ \cline{1-1}  & & 3 & \(-\)0.4 & 5.9 & 0.07 & 5.9 & \\ \cline{1-1}  & & 4 & \(-\)3 & \(-\)4.1 & \(-\)0.1 & 5.1 & \\ \cline{1-1} \cline{2-8}  & \multirow{2}{*}{ChP} & 18 & 9.9 & 12.3 & 0.7 & 15.8 & \\ \cline{1-1}  & & 19 & \(-\)5.3 & \(-\)2.7 & \(-\)1.6 & 6.2 & \\ \hline \multirow{4}{*}{C} & \multirow{4}{*}{GCP} & 1 & 3.5 & 1.8 & \(-\)0.1 & 3.9 & \multirow{4}{*}{3.6} \\  & & 2 & \(-\)1.1 & \(-\)2.9 & 0.05 & 3.1 & \\ \cline{1-1}  & & 3 & \(-\)0.7 & 3.9 & 0.08 & 3.9 & \\ \cline{1-1}  & & 4 & \(-\)1.7 & \(-\)2.8 & \(-\)0.08 & 3.3 & \\ \cline{1-1} \cline{2-8}  & \multirow{2}{*}{ChP} & 18 & 7 & 13.7 & 4.2 & 16 & \\ \cline{1-1}  & & 19 & \(-\)6.2 & \(-\)2.5 & 1.1 & 6.8 & \\ \hline \multirow{4}{*}{D} & \multirow{4}{*}{GCP} & 1 & 5.5 & 2.8 & 0.1 & 6.1 & \multirow{4}{*}{5.3} \\  & & 2 & \(-\)1.9 & \(-\)4.4 & \(-\)0.1 & 4.7 & \\ \cline{1-1}  & & 3 & \(-\)1.3 & 5.6 & \(-\)0.1 & 5.7 & \\ \cline{1-1}  & & 4 & \(-\)2.3 & \(-\)3.9 & 0.1 & 4.5 & \\ \cline{1-1} \cline{2-8}  & \multirow{2}{*}{ChP} & 18 & 8.1 & 12.6 & 1.2 & 14.9 & \multirow{4}{*}{14.2} \\ \cline{1-1}  & & 19 & \(-\)13.2 & 0.3 & \(-\)2.1 & 13.4 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mean RMSE of the GCP and ChP for the four Block 1 scenarios (in cm).  
Figure 8: Orthophotograph of Block 1 created after the photogrammetric process; background image from Google Satellite orthophotograph. The GCPs are shown distributed at the four corners of Block 1 for the four scenarios (points 1, 2, 3, and 4); points 18 and 19 were left as ChPs.  
Figure 9: Distribution of the mean RMSE of the GCPs and ChPs of the four blocks for the scenarios studied (for A, B, C & D, vid. Section 2.3 and Figure 7).
# Anomaly Detection for Hydraulic Power Units--A Case Study
## 2 Materials and Methods
### Algorithms Deployment
#### 2.3.1 Anomaly Detection  
The goal of deployment is to perform calculations hourly to provide information about anomalies. The developed approach involves a reference curve described by three parameters per characteristic and an error threshold per characteristic. Such a set of  
Figure 7: Cloud ecosystem for deploying algorithms into production.  
parameters enables us to check whether, in the monitored time period, the anomaly has appeared. Such a functionality could be implemented with Azure Functions.  
Azure Functions are part of the Azure Function App, which is an environment for Azure Functions execution. This environment has its own environment variables that can be accessed by Azure Functions inside it. The parameters required to work out the anomaly detection hypothesis are:  
* _a, b, c_--coefficients for Equation (16). Two sets of parameters, because of two fitted characteristics;
* Interquartile Range (IQR) criterion mentioned in Section 2.2.3. Two parameters, one per each characteristics.  
In total, the Function App had to store eight parameters, which is a complete set to run Azure Function for anomaly detection. All these parameters are declared as Function App environment variables.  
The parameters of the algorithm could be accessed from other sources such as Azure BLOB Storage or Table Storage. For simplicity of implementation, it was decided to place them inside the Function App. In this way, it is easy to change them on the spot without modification of the Azure Function code, which is important from a long-term maintenance perspective.  
Finally, the algorithm should deliver value to the end user. For fulfilling this requirement, after developing hypotheses, feedback to the IoTHub in the form of a JSON message is provided. The message contains information on whether an anomaly has occurred, with specifications on from which characteristics the anomaly has emerged. Moreover, the \"intensity\" of the anomaly is sent to IoTHub. The message after processing is put with its content into Azure Table Storage. Then, MOLOS.CLOUD web SCADA is able to extract data from Table Storage and show alarms about anomalies if necessary. In the end, the end user is aware of occurring anomalies.
# Towards Adaptive Multipath Managing:
## 2 Problem Statement
The resulting receiver buffer blocking in MPTCP, with a great number of out-of-order data (see Figure 5) held in the constrained receiver buffer, prevented the efficient use of Path A for data transmission and resulted in serious throughput performance degradations. From the above experimental results, we can observe that the MPTCP could achieve lower application level performance even than using a single path when it simultaneously exploited multiple paths that had huge path quality differences. When a path experienced network failure or multiple paths had huge quality differences, the receiver buffer blocking may become an inevitable phenomenon in MPTCP. Unfortunately, the standard MPTCP path management mechanism was very simple; it used all the available paths in the MPTCP connection for concurrent multipath data transmission, without considering that wireless network latency and link failure tended to occur frequently due to mobility, signal fading, and wireless interference. To remedy this situation, this paper proposes a lightweight multipath management mechanism to help MPTCP know not only when, but also how to prevent the use of an underperforming or failure path in multipath transmission. This approach could possibly alleviate the receiver buffer blocking and optimize the throughput performance of MPTCP. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Application traffic type & FTP traffic \\ Number of paths & 2 \\ Access link bandwidth & 100 Mb \\ Access link delay & 1 microsecond \\ Bottleneck link bandwidth & 10 Mb \\ Bottleneck link delay & 50 ms \\ The link queue type & Droptail \\ The link queue limit & 500 packets \\ Window size on each path & 100 \\ Packet loss on each path & 1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The parameters used in the simulations. Figure 2: A sample dual dumbbell simulation topology. Figure 4: The congestion window comparison. Figure 5: The out-of-order data comparison. Figure 3: The throughput comparison.