esa-sceva/satcom-chunk-collection huggingface dataset:
# Trusted Composition of Internet of Medical Things over Imperfect Networks
## 5 Structural Modeling Using AADL
Nurse_Station receives notification through Lost_Confirmation outgoing event data port if the response is not received within a specified time frame or does not match the notification. PCA_Pump models the PCA pump used to infuse drug into the patient. Because patients respond to opioids so differently, a steady basal rate of drug flow can be augmented by a patient-requested bolus by pressing the bolus request button. This puts the patient in-the-loop for their pain management. Too much opioid can cause respiratory depression, which may lead to brain damage or death. Nurse_Station system models the nursing station with human caregivers. It receives alarms and lost confirmation notifications on Patient_Alarm and Patient_Lost_Confirmation ports, respectively. Modeling the detailed functionality of the nursing station is beyond the scope of this study. When PCA_Pump receives Pause_Infusion(true) from the Coordinator, it reduces infusion to a KVO rate until it receives Pause_Infusion(false) to resume basal rate infusion. Every message received on Pause_Infusion is sent back on Pause_Infusion_Echo. As shown in Listing 1, the BLESS invariant << IS_SAFE() >> in the properties section of the SysIoT system type specifies the main safety property of the systems to be proved correct. Invariant IS_SAFE() expresses that in the event that the heart rate, SpO2, or respiration rate fall below the minimal threshold, the pump infusion will be adjusted to the KVO rate or will be halted. Section 6 provides further details on the assertions and Section 9 presents the proof details of this invariant. ```
systemSysIoTproperties BLESS::Invariant=>\"<<IS_SAFE()>>\"; endSysIoT;... <<IS_SAFE:((HEART_RATE<MINIMUM_HEART_RATE orSPO2<PROGRAMMED_SPO2 orRESPIRATION_RATE<MINIMUM_RESPIRATION_RATE) impliesPUP_RATE$()=KVO_RATE_property)orHAL() >>
```  
Listing 2 presents a partial specification of IoMT system implementation classifier. The subcomponents section of the SysIoT.impl (system implementation) defines contained subsystems, while the connections section specifies the appropriate connections between them. Connections are functional communication pathways allocated to physical components, which actually transmit messages by Actual_Connection_Binding properties. ```
systemimplementationSysIoMT.impl subcomponents pca_pump:systemPCA_Pump.impl; pulse_oximeter:systemPulse_Oximeter.impl; resp_monitor:systemRespiration_Monitor.impl; coordinator:systemCoordinator.impl; network:virtualbusNetwork_Bus; patient:abstractPatient; nurse_station:systemNurses_Station; connections rm_alarm:portresp_monitor.RM_Alarm->coordinator.RM_Alarm (Actual_Connection.Binding_>(reference(network))); rm_alarm_echo:portcoordinator.RM_Alarm_Echo->resp_monitor.RM_Alarm_Echo (Actual_Connection_Binding=>(reference(network))); -tubeandneedlebetweenpumpandpatient infusion_set:featurepca_pump.Infused_Drug->patient.Infused_Drug;... endSysIoMT.impl;}
```  
Listing 2: A partial specification of the IoMT system implementation.
# Voting-Based Contour-Aware Framework for Medical Image Segmentation
## 2 Method
### Loss Function  
We propose training the contour-aware network using the following cascaded loss function:  
\[L=L_{Seg}+\lambda L_{con}+\gamma L_{com} \tag{2}\]  
where \(L_{Seg}\) denotes the standard loss function to learn the semantics representations of the contour-aware network, \(L_{con}\) is used to supervise the boundary information of the contour module, \(L_{com}\) is used to correctly align the predicted semantic segmentation with the ground-truth semantic boundaries, and \(\lambda\) and \(\gamma\) are hyper-parameters.  
We defined our input training dataset as \(S=\{(X_{n},Y_{n},Z_{n}),n=1,\ldots,N\}\), where sample \(X_{n}=\left\{x_{j}^{n},j=1,\ldots,|X_{n}|\right\}\) represents the raw input images, \(Y_{n}=\left\{y_{j}^{n},j=1,\ldots,|Y_{n}|\right\}\), \(y_{j}^{n}\in\{0,\ldots,C\}\) represents the corresponding ground truth for image \(X_{n}\), and \(C\) is the segmentation class. \(Z_{n}=\left\{Z_{j}^{n},j=1,\ldots,|Z_{n}|\right\}\), \(Z_{j}^{n}\in\{0,1\}\) denotes the binary contour map generated by ground truth mask \(Y_{n}\). In the following formula, we omit \(n\) for ease of expression. To learn the semantic representations, we use cross entropy (CE) loss, which is calculated as follows:  
\[L_{Seg}(Y,P_{s})=-\sum_{i=1}^{C}y_{true,j}^{C}\log P_{s,j}^{C} \tag{3}\]  
where \(P_{s,j}^{C}\) is the semantic prediction of the contour-aware network at each pixel \(j\). We supervise shape information holistically using a class-balanced cross-entropy loss [43] to counteract the imbalance between edge and non-edge, which may be characterized as follows:  
\[L_{con}=-\beta\sum_{j\in\mathbb{Z}_{+}}\log P_{b}(z_{pred,j}=1|x;W)-(1-\beta) \sum_{j\in\mathbb{Z}_{-}}\log P_{b}(z_{pred,j}=0|x;W) \tag{4}\]  
where \(Z_{-}\) and \(Z_{+}\) are the edge and non-edge ground truth label sets. \(Z\) is ground truth binary edge map for raw training image \(X\). \(\beta=|Z_{-}|/|Z|\) and \(1-\beta=|Z_{+}|/|Z|\) denote the ratio of edge pixels to all pixels and the ratio of non-edge pixels to all pixels, respectively.  
\(W\) represents the parameters of the network, and \(P_{b}\left(z_{pred,j}\right)\) represents the probability of the predicted binary contour map of outlines of targets at pixel \(j\).  
We use the compatibility loss function inspired by [29; 31] to induce semantic segmentation to align with the ground-truth boundaries map, which could also be described as follows:  
\[L_{com}=\sum_{j\in Z_{-}}\left(\|\
abla\left(\operatorname{argmax}\left(P_{s,j }^{c}\right)\right)\|-\|\
abla y_{true,j}\|\right) \tag{5}\]  
We take the spatial derivative \(\
abla\) to convert the semantic prediction into contour predictions. Because argmax is not differentiable, we substitute the Gumbel SoftMax trick [44].
# A Comprehensive Hybrid Deep Learning Approach for Accurate Status Predicting of Hydropower Units
## 3 Methodology  
### Overview  
To address the complex dynamics of hydropower unit operation, this study introduces a novel hybrid deep learning model that integrates and analyzes time series data from multiple perspectives. The model combines advanced neural network architectures with tuna swarm optimization algorithm for enhanced parameter tuning, resulting in more accurate and reliable predictions of operational status.  
This section outlines the methodology used to deploy the proposed hybrid model, starting with an overview of its architectural design, followed by detailed explanations of each component. A workflow diagram is provided to illustrate the process flow and integration of the model components.
# Dual Clustering-Based Method for Geospatial Knowledge Graph Partitioning
## 3 Experiments
### Discussion  
The experiment results (Table 2 and Figure 10) show that GKGP-DC outperforms the comparison methods on spatial-related queries. The number of computational nodes traversed is at least three less for withinDistance and buffer queries and at least three less for kNN queries and intersect queries. In addition, the improvement is also speciously obvious in spatial-complex coupled queries. In all experimental results of spatial-graph coupled queries, the response time is reduced by approximately 76% at most (result of intersect-complex queries, Table 6 and Figure 14). The main reasons for this can be summarized as follows:  
(i) GKGP-DC preprocesses geographic nodes using density peak clustering, effectively grouping spatially proximate nodes together. This strategy significantly optimizes the execution efficiency of spatial queries. As for spatial-graph coupled queries, the results of spatial subqueries are confined to only a few partitions. This not only significantly reduces the assembly time of the sub-results, but also decreases the number of results generated by the factorized queries. Furthermore, the network transmission consumption is reduced. Overall, the partitions produced by GKGP-DC effectively reduce the total query response time for spatial-related queries.  
(ii) The preprocessing of geographic nodes may result in inferior partition quality compared to the results obtained by high-quality offline partitioning algorithms (such as Metis). However, GKGP-DC mitigates this issue by using the Leiden algorithm to identify graph structures. The graph clusters detected by Leiden are treated as the basic units for graph partitioning, which can preserve the structural characteristics of the graph. It is helpful for reducing the number of cross-partition edges. Therefore, the weighted graph produced after applying DPC and Leiden contains information on both spatial relationships and graph structures. The efficiency of both spatial queries and graph queries can benefit from this partitioning scheme.  
The significant reductions in query response time and computational node usage show that GKGP-DC can optimize the performance of spatial queries and spatial-graph coupled queries. For a system requiring the distributed management of large-scale geospatial knowledge graphs, such as a geographic intelligent question-answering platform, GKGP-DC can improve system operation efficiency.  
Figure 14: Response time of intersect–graph coupled query (unit: seconds).
## 5 Conclusions  
In this paper, a joint satellite-transmitter and ground-receiver (JSG) digital pre-distortion (DPD) (JSG-DPD) technique is proposed to linearize a low Earth orbit (LEO) space-borne active phased array (APA). The method takes the approach of DPD coefficients extraction at the ground station, which saves a complete set of feedback loop at the satellite-transmitter side compared to the conventional DPD techniques. The issue of sidelobe information drowning in the additive white Gaussian noise (AWGN) is addressed by employing the superimposed training sequences (STS) method. The proposed method is experimentally verified to improve the adjacent channel power ratio (ACPR) by 4 dB, especially the error vector amplitude (EVM) greatly by 7.5%. In addition, the performance difference between the GMP model and the MP model is analytically compared. The proposed technique provides a feasible means for the linearization of space-borne power amplifiers (PAs) or APAs. In the future, we will address the problem of accurate synchronization of training sequences and seek opportunities for in-orbit validation in actual LEO satellite communication projects.  
Conceptualization, Q.C. and Z.W.; methodology, Q.C.; software, Q.C.; validation, Q.C.; formal analysis, Q.C. and M.S.; investigation, Q.C.; resources, Q.C.; data curation, Q.C.; writing--original draft preparation, Q.C.; writing--review and editing, Q.C., Z.W., G.F.P. and M.S.;  
Figure 14: Measured normalized PSD with different methods for different input power levels from VSG: (a) \(P_{in}\): \(-25\) dBm; (b) \(P_{in}\): \(-24\) dBm.  
visualization, Q.C.; supervision, Z.W., G.F.P. and M.S.M.; project administration, Z.W., G.F.P. and M.S.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by China Scholarship Council, grant number 202104910442.  
Not applicable.  
The authors declare no conflict of interest.
# Topology Inference and Link Parameter Estimation Based on End-to-End Measurements +
## 3 Topology Inference and Estimation of Link Parameters
### Reduction Update Formulas  
Contrary to the conventional greedy agglomerative approach that repeatedly joins the two overall \"closest\" nodes (in our context, the nodes with the overall largest LCA depth), the proposed NN chain-enhanced algorithm joins any pair of RNNs as it is found. Consequently, the order of the cluster merges might be different. However, provided that the reduction update formula satisfies two certain properties, the resulted hierarchy will be the same [17]. Such suitable reduction update schemes are:  
* the _single_ reduction update formula: \[\ell(u,k)=\min\{\ell(i,k),\ell(j,k)\}\] (11)
* the _complete_ reduction update formula: \[\ell(u,k)=\max\{\ell(i,k),\ell(j,k)\}\] (12)
* the _average_ reduction update formula: \[\ell(u,k)=\frac{1}{n_{i}+n_{j}}\big{[}n_{i}\cdot\ell(i,k)+n_{j}\cdot\ell(j,k) \big{]}\] (13)
* the _weighted_ reduction update formula: \[\ell(u,k)=\frac{1}{2}[\ell(i,k)+\ell(j,k)]\] (14)  
\(\forall k\in D,\ k\
eq u,i,j\), where \(i\) and \(j\) are the pair of RNNs, \(u\) is the newly created parent, and \(n_{i}\), \(n_{j}\), \(n_{k}\) are the sizes of clusters \(i\), \(j\) and \(k\), which in our context can be interpreted as the number of the descendants of the respective nodes. As can be seen, the single, complete, and weighted schemes are \"conservative\" in the sense that they are fixed and do not adapt to the discovered hierarchy, whereas the average scheme depends also on the number of offspring of the involved nodes, thus being better tailored to each particular case. In [13], the use of the weighted and complete update formulas has been examined. Here, we comparatively explore all of the aforementioned schemes, as well as the generic convex formula analyzed in Section 4.2.
# An Adaptable Train-to-Ground Communication Architecture Based on the 5G Technological Enabler SDN
## 4 Simulation Scenario  
This section details the simulation scenario that is configured to test the previously presented communication architecture and measure several performance indicators such as delay and data rate.
# Investigating the Performance of FixMatch for COVID-19 Detection in Chest X-rays
## 2 Literature Review
### COVID-19 Detection Using Deep Learning
The authors used a stacked ensemble method and used two pretrained models, namely VGG19 and DenseNet121. The final classification was performed by using a Scalar Vector Model (SVM). The authors also created their own dataset by extracting images from five different sources, including Kaggle and GitHub repositories. The final dataset consisted of 798 COVID-19 images, and this amount is larger than the number of images present in the previously reviewed works above. Similarly, [PERSON] et al. [8] created their own dataset. Their dataset consisted of two different sets, with the first split into three categories, namely COVID-19, Pneumonia and normal, and the second split into COVID-19, Bacterial Pneumonia, Viral Pneumonia, Lung Opacity not Pneumonia and normal. This is the first dataset to split the groups into five to further distinguish between different forms of Pneumonia. For training, the authors used 404 images of each class to have a balanced set, along with 12 augmented versions of each image. The authors set aside 100 images for validation and 207 for testing. They used Argmax to get the final prediction after getting probabilities of each class from the three model within the ensemble model. For this paper, ResNet-50, Inception-V3 and DenseNet-161 were used. The authors were able to achieve an accuracy of 75.23% for the three-class dataset and 81.0% for the five-class dataset. [PERSON] et al. [42] proposed the use of the Xception model, along with a Choquet Fuzzy ensemble scoring method with a balanced Kaggle dataset, and was able to achieve an accuracy of 99.57%. [PERSON] et al. [43] proposed a voting-based ensemble that consisted of the top five models from five different scenarios. These five scenarios were introduced to remove the effect of unbalanced data. Each scenario was applied on eleven different transfer learned models, such as XceptionNet, VGG and Resnet. The input to the models was lung segments from the CXRs, and the five scenarios were weighted loss, image augmentation, undersampling, oversampling and hybrid sampling. They tested their ensemble network on a combination dataset of Kaggle and GitHub and achieved a highest accuracy of 99.23%, using an ensemble of XceptionNet, MobileNetv2, DenseNet201, InceptionResNetV2 and NasNetMobile with image augmentation. Table 3 below summarizes the work reviewed for the above section.
## 4 Conclusions  
Long-term forecasting of electrical generation in Kuwait was investigated using five models to predict the electrical generation until 2030. Different statistical performance metrics were employed to assess the accuracy, generalization, and robustness of the models. The Holt-Winters model's triple seasonality outperformed the other models in terms of the MAPE, RMSE, CVRMSE, MAE, and R\({}^{2}\). According to the different performance indicators, the double seasonality of the Holt-Winters model was the second-best performing model with superior accuracy. The Prophet model, with multiple regressors, has shown comparable performance to the Holt-Winters double seasonality model. In contrast, the least performing models were the single regressor Prophet model followed by the single seasonality Holt-Winters model. The estimated annual electrical generation for the year 2030 of the Holt-Winters models' triple and single seasonality models were 92,535,555 and 102,262,507 kWh, respectively. The generation of electricity in Kuwait during the upcoming ten years was then presented and discussed. The results reveal that the higher electrical generation period occurs between May and September of the upcoming two years. The five models' long-term electrical generation forecast was then used to estimate the fuel's total cost to provide an overview for policymakers to support the execution of planning decisions.  
Conceptualization, A.I.A., A.M., and A.S.A.; methodology, A.M.A. and A.I.A.; software, A.M.A.; validation, A.M.A., A.I.A., A.S.A., M.R.A., and S.N.A.; formal analysis, A.M.A., A.I.A., A.S.A., and F.M.A.; investigation, A.M.A., A.I.A., and A.S.A.; resources, A.M.A., A.I.A., A.S.A., M.R.A., S.N.A., and F.M.A.; data curation, A.I.A., A.M.A., A.S.A., and F.M.A.; writing--original draft preparation, A.I.A., A.M.A., and A.S.A.; writing--review and editing, A.I.A., A.M.A., A.S.A., M.R.A., and S.N.A.; visualization, A.I.A., A.M.A., and A.S.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare
## 4 Test on Beams
### Failure Tests on the Retrofitted Beams
#### 4.3.2 Retrofitted Beam Failure Mechanism and Damage
The beam DB2-R behaved similarly to DB1-R (Figure 7a,b): shear failure started near the H hinge support with a first wide diagonal crack at about a quarter of the beam span and a second diagonal crack near the H support (Figure 8b). The shear failure of DB2-R occurred at about the same load value measured for the beam DB1-R, but the failure mode was different because the anchorages of the new bottom longitudinal rebars were extended over the supports of the beam DB2-R. This longer rebar anchorage anchored better the new rebars with respect to the one in the beam DB1-R. The new bottom reinforcement gave a better contribution to resist the force due to the shear at support and therefore the shear cracks moved toward the beam midspan (the crack located at about a quarter of the beam span from the support H). The experimental behavior of this beam can be described along the load versus displacement curve shown in Figure 7b. Along the initial branch of the curve, the beam DB2-R presented about 14% greater stiffness than the DB2 beam before retrofitting based on the midspan deflection: the mean deflection values for F = 72 kN were 0.92 mm and 1.04 mm for the beam DB2-R and DB2 (point 1, Figure 7b). When the load is 300 kN, the CFRP U-strips started to debond locally (point 2, Figure 7b). The same phenomenon was observed also by [PERSON] et al. 2005 [PERSON] et al. reinforcement reduced the transverse steel contribution because plastic force distribution was limited along the transverse steel and the shear cracks angle was about 45\({}^{\circ}\): the number of the stirrups that gave a contribution to the shear resistance was modest. However, the CFRP reinforcement reduced the width of the shear cracks improving the aggregate interlock and contrasted the cover spalling improving the dowel effect of the longitudinal rebars.
# An Accurate Jacobian Matrix with Exact Zoeppritz for Elastic Moduli of Dry Rock
## 5 Numerical Examples
### Water-Oil Interface  
A water-oil model is built to further check the efficiency of this algorithm. When seismic waves penetrate from the water-saturated layer to the oil-saturated layer, the partial derivatives of reflectivities \(R_{pp}\) and \(R_{ps}\) with respect to the elastic moduli \(K_{d1}\), \(K_{d2}\), \(\mu_{d1}\) and \(\mu_{d2}\) of dry rock become real numbers rather than complex numbers.  
Figure 6 shows the partial derivatives of reflectivity \(R_{pp}\) with respect to the elastic moduli \(K_{d1}\), \(K_{d2}\), \(\mu_{d1}\) and \(\mu_{d2}\), respectively. Different from Figures 2 and 3, there is no singular or zero point at the critical angle. Due to the substitution of pore fluids, the curves of partial derivatives of \(R_{pp}\) with respect to the elastic moduli are asymmetric. Comparing with other curves, the variation of partial derivative of \(R_{pp}\) with respect to \(\mu_{d2}\) is much smaller (Figure 6a). The enlarged details can be observed in Figure 6b by zooming in the curve.  
Figure 7 shows the partial derivatives of the reflectivity \(R_{ps}\) with respect to the elastic moduli \(K_{d1}\), \(K_{d2}\), \(\mu_{d1}\) and \(\mu_{d2}\), respectively. Similar to Figure 6, these curves are asymmetric. The partial derivative of \(R_{pp}\) with respect \(\mu_{d1}\) is much larger than others. The enlarged partial derivatives of \(R_{pp}\), with respect to \(K_{d1}\), \(K_{d2}\) and \(\mu_{d2}\), are shown in Figure 7b.  
Figure 5: The partial derivatives of reflectivity \(R_{ps}\) with respect to the shear moduli \(\mu_{d1}\) (**a**) and \(\mu_{d2}\) (**b**) of the dry rock (oil–water interface). Solid lines: the real values of partial derivatives of \(R_{ps}\) with respect to \(\mu_{d1}\) and \(\mu_{d2}\), respectively; Dashed lines: the imaginary values of partial derivatives of \(R_{ps}\) with respect to \(\mu_{d1}\) and \(\mu_{d2}\), respectively.  
Figure 6: The partial derivatives of the reflectivity \(R_{pp}\) with respect to the elastic moduli \(K_{d1}\), \(K_{d2}\), \(\mu_{d1}\) and \(\mu_{d2}\) of the dry rock, respectively (water–oil interface). (**a**): The partial derivatives of \(R_{pp}\) with respect to \(K_{d1}\), \(K_{d2}\), \(\mu_{d1}\) and \(\mu_{d2}\), respectively; (**b**): The zoomed in curve of \(R_{pp}\) with respect to \(\mu_{d2}\).