esa-sceva/satcom-chunk-collection huggingface dataset:
# Firmware Updates over the Air via LoRa: Unicast and Broadcast Combination for Boosting Update Speed
## 5 Simulation Tests and Results
### Comparison between the Update Methods Using Single-Run Simulations
#### 5.2.2 Broadcast + Unicast Method  
The firmware update of 10 nodes, with a maximum radius of 400 m, is simulated using the _broadcast + unicast_ method. The results presented in Figure 10a show the RSSI values seen by each node, i.e., during the broadcast phase (from 0 to 16,097 s), each frame appears 10 times, with the RSSI value corresponding to the receiving node. When this phase ends, a unicast one starts, in which the pending chunks of each node are completed (in this case, it takes 644 s to send 26 chunks to the nodes). As can be observed in Figure 10b, ACKs are sent by the nodes only in the unicast phase, in which the lost frames are sent individually.  
Figure 9: _Only unicast_ firmware update of 10 peers with a maximum radius of 2 km: (**a**) RSSI of the frames received by each node, containing the firmware chunks; and (**b**) RSSI of the ACK frames received by the gateway. The red line corresponds to the sensitivity of the receiver.  
The total time in this case is 16,775 s, i.e., 4.6 h. It represents 10.53% of the time required by the _only unicast_ algorithm (see Figure 8): the update time is roughly divided by the number of nodes (\(N\) = 10 in this case). It should also be noted that the broadcast stage is faster than the unicast update of a single node, which required 17,672 s on average, because, in this case, no ACKs are required.  
Next, another example illustrates what happens if the distance to the nodes is much higher (a maximum radius of 2 km). The results presented in Figure 11a show that, in this case, many frames arrive at the nodes below the sensitivity limit, so they are not received correctly. The broadcast stage lasts the same as that in the previous case: 16,097 s. But, in this example, the unicast stage is much longer: 120,316 s for the 10 nodes (see the ACKs Figure 11b).  
The total time in this case is roughly 136,413 s, i.e., less than 38 h. It represents 46.3% of the time required by the _only unicast_ method for the same distance (see Figure 9). The benefit is not so relevant in this case: a high number of chunks did not arrive correctly in the broadcast stage, so the unicast one is quite long. Nevertheless, it is shown that, in this case, a single initial broadcast round is able to halve the total update time.  
Figure 10: _Broadcast + unicast_ firmware update of 10 peers with a maximum radius of 400 m: (**a**) RSSI of the frames received by each node, containing the firmware chunks; and (**b**) RSSI of the ACK frames received by the gateway. The red line corresponds to the sensitivity of the receiver.
## 4 Fade Duration Experimental Results
Figure 8 shows these curves, in 5 min steps, for Spino d'Adda and Tampa, while Appendix B reports the data of the other sites. Figure 6: Median fade duration in process B (\(y\)-axis) versus the median fade duration in process A (\(x\)-axis) at the indicated sites. Spino d’Adda: continuous blue line; Gera Lario: continuous black line; Fucino: continuous red line; Madrid: continuous green line; Prague: continuous magenta line; Tampa: dashed red line; Norman: dashed magenta line; White Sands: dashed green line; Vancouver: dashed blue line. Figure 8: Annual cumulative number of outage occurrences at the threshold indicated in abscissa, 80 GHz, circular polarization, zenith paths at: (**a**) Spino d’Adda; (**b**) Tampa. The continuous lines are drawn at equal fade duration, in 5 min steps from \(\tau=5\) min to \(\tau=60\). The y–axis has different range. From Figure 8 and Appendix B, we can observe the following features:  
Figure 7: Average annual number of outage occurrences at the threshold indicated in abscissa—80 GHz, circular polarization, zenith paths–at the indicated sites. Spino d’Adda: continuous blue line; Gera Lario: continuous black line; Fucino: continuous red line; Madrid: continuous green line; Prague: continuous magenta line; Tampa: dashed red line; Norman: dashed magenta line; White Sands: dashed green line; Vancouver: dashed blue line. As threshold increases, the occurrences largely decrease for any \(D\), as physically expected. 2. As fade duration \(D\) increases, sharp peaks are clearly evident in many sites. 3. Peaks tend to occur at lower thresholds. 4. As fade duration \(D\) increases, curves tend to collapse, in agreement with the probability distributions \(P_{S,B}(D)\) shown in Figure 4a,b. For example, the curves regarding D = 50\(\sim\)60 min tend to coincide. As we have previously recalled, the two processes are not independent and this fact can be assessed by studying \(P_{S,B}(D)\) versus \(P_{S,A}(D)\), as we show in the next section by defining a useful parameter, the uniformity index.
## 4 Prediction of Girder Design Strength for a Regression Algorithm
### Introduction of Regression Algorithms
#### 4.1.5 Gaussian Process Regression (GPR)  
Determining appropriate hyper-parameters for complex datasets is a difficult task. Accordingly, conventional regression algorithms determine parameters based on the prior experience of model characteristics and shapes. However, GPR has flexibility in relation to this. It is devoted to a broad family of functions based on covariance functions. This has an advantage in that the accuracy of the estimation is very high because it can provide an uncertainty estimate along with the prediction.
## 4 Simulation and Discussion
### Robustness Test
#### 4.3.4 Discussion  
Figure 4 runs a smaller number of times to achieve the fit. This is primarily due to the small number of GAVIN data, the characteristics of the data are relatively distinct, and the fitting point can be found quickly. Figure 6 does not have a fitting point, because DIP has a much larger number of non-essential proteins than essential proteins, with a ratio of 3:1, making it difficult to find corresponding characteristics. Figure 8 synthesizes the GAVIN and DIP data and hits the fit point after \(1.1\times 10^{6}\) times.  
When only training the GAVIN dataset, the overall accuracy is more than 80%; when only the DIP dataset is trained, the overall accuracy is more than 70%. After training with the DIP and GAVIN datasets, the overall accuracy is over 80%. No matter the difference in training datasets, the overall accuracy is relatively high, indicating that the IYEDNN algorithm has good generalization performance. The overall accuracy of Figure 5 is close to that of Figure 9, indicating that the GAVIN dataset has good universality and can better search for relevant features. Figures 4 and 6 also confirm this result. The false negative ratio of the three training models is low and the false positive ratio is relatively high. This is mainly due to the relatively large number of non-essential proteins in the GAVIN and DIP datasets, leading to more accurate identification of non-essential proteins by the model. Through the joint training of DIP and GAVIN data and the addition of missing gene data filling, the overall accuracy is improved and the essential protein recognition ability is enhanced.  
Figure 8: Training DIP+GAVIN data generation model.  
Figure 9: Training DIP+GAVIN data results test.  
It can be seen from Figures 5, 7 and 9 that after the IYEPDNN algorithm is trained on different datasets, there is little correlation between the overall accuracy, false negative ratio, false positive ratio, and correct recognition ratio of essential proteins and correct recognition ratio of non-essential proteins of each model. This indicates that the IYEPDNN algorithm has good robustness and can be applied to predict essential proteins in different scenarios.
## 2 The Microphone Model and Proposed Nested Microphone Array
### Microphone Signal Model  
The microphone signal modeling is an important part in the implementation of speech processing algorithms such as: speech enhancement, speaker tracking, speech recognition, etc. Two models are usually considered in this processing: ideal and real models [28]. In the ideal model, which is known as an open-space model, the received signal in a microphone place is a weakened and delayed version of the transmitted signal from the source location. The ideal model for microphone signals is expressed as:  
\[x_{m}[n]=\frac{1}{r_{m}}s[n-\tau_{m}]+v_{m}[n], \tag{1}\]  
where \(x_{m}[n]\) is the received signal in the \(m\)-th microphone, \(s[n]\) is the speech source signal (transmitted signal), \(r_{m}\) is the distance between source and \(m\)-th microphone, \(\tau_{m}\) is the time delay between source and \(m\)-th microphone, and \(v_{m}[n]\) is the additive noise in \(m\)-th microphone place. This model cannot show the real environments and close space conditions because the reverberation effect is discarded. Therefore, the real model is introduced for microphone signal simulations to provide the real environmental conditions for evaluating the speech enhancement algorithms. The real model simulates the microphone signal similar to the environmental conditions. The expression for real model is shown as:  
\[x_{m}[n]=s[n]*\gamma_{m}[r_{m},n]+v_{m}[n], \tag{2}\]  
where the source signal is convolved to the room impulse response to model the real environments. In this equation, \(\gamma_{m}[r_{m},n]\) is the impulse response between the source and \(m\)-th microphone, which contains the attenuation factor and whole reverberation effect in the real conditions, and * denotes to the convolution operator. The simulated signals are similar to real conditions by considering this mathematical real model.
## 2 System Model and Problem Formulation
### Problem Formulation  
According to (2) and (3), the data rate of the \(k\)-th DL UE can be expressed as  
\[R_{\text{D},k}=\log_{2}\left(1+\frac{\left|\sqrt{\xi_{\text{t}}}\mathbf{h}_{ \text{D},k}^{H}\mathbf{\Phi}_{2}\mathbf{G}\mathbf{\Phi}_{1}\mathbf{H}_{\text{ TB}}\mathbf{f}_{k}\right|^{2}}{\sum\limits_{i=1,i\
eq k}^{K_{\text{D}}} \left|\sqrt{\xi_{\text{t}}}\mathbf{h}_{\text{D},k}^{H}\mathbf{\Phi}_{2} \mathbf{G}\mathbf{\Phi}_{1}\mathbf{H}_{\text{TB}}\mathbf{f}_{i}\right|^{2}+ \sigma_{\text{D}}^{2}}\right). \tag{11}\]  
Similarly, the UL data rate of the \(l\)-th UL UE is given by  
\[R_{\text{U},l}=\log_{2}\left|\mathbf{I}+\frac{P_{\text{U}}\mathbf{\hat{H}} \mathbf{h}_{\text{U},j}\mathbf{h}_{\text{U},j}^{H}\mathbf{\hat{H}}^{H}}{ \sum\limits_{j=1,j\
eq l}^{K_{\text{U}}}P_{\text{U}}\mathbf{\hat{H}}\mathbf{h }_{\text{U},j}\mathbf{h}_{\text{U},j}^{H}\mathbf{\hat{H}}^{H}+\sum\limits_{i= 1}^{K_{\text{D}}}\mathbf{H}_{\text{SI}}\mathbf{f}_{i}\mathbf{f}_{i}^{H} \mathbf{H}_{\text{SI}}^{H}+\sigma_{\text{U}}^{2}\mathbf{I}}\right|, \tag{12}\]  
where \(\mathbf{\hat{H}}=\sqrt{\xi_{\text{t}}}\mathbf{H}_{\text{BB}}^{H}\mathbf{\Phi}_ {1}\mathbf{G}^{H}\mathbf{\Phi}_{2}\), \(\mathbf{H}_{\text{SI}}=\mathbf{H}_{\text{TR}}+\sqrt{\xi_{\text{r}}}\mathbf{H }_{\text{BR}}^{H}\mathbf{\Phi}_{1}\mathbf{H}_{\text{TB}}\).  
In this paper, we jointly optimize the beamforming design at the BS and BIOS to maximize the SR of UL and DL while effectively managing SIC. It is worth noting that excessive residual SI will cause the saturation of ADC, preventing it from functioning properly. However, most of the existing works are based on perfect SIC. To tackle this issue, we take into account the hardware limitations and then impose a constraint to maintain the residual SI power below a threshold, which is different from other papers. Specifically, the problem can be formulated as follows:  
\[\max_{\mathbf{\Phi}_{1},\mathbf{\Phi}_{2},\mathbf{F}} \sum\limits_{l=1}^{K_{\text{U}}}R_{\text{U},l}+\sum\limits_{k=1}^{K _{\text{D}}}R_{\text{D},k}\] (13a) s.t. \[\left\|\mathbf{H}_{\text{SI}}\mathbf{F}\right\|_{F}^{2}/N_{\text{ R}}\leq\gamma, \tag{13b}\] \[\left|\mathbf{\Phi}_{1}\right|_{ii}=\left|\mathbf{\Phi}_{2} \right|_{jj}=1,\forall i,j,\] (13c) \[\text{Tr}\Big{(}\mathbf{F}\mathbf{F}^{H}\Big{)}\leq P_{\text{s}}. \tag{13d}\]  
Constraint (13b) maintains the residual SI power below a threshold to achieve efficient SIC. Constraints (13c) and (13d) result from the constant modulus constraint of the BIOS scattering elements and the power limitation at the BS, respectively.
# Impact of HPC and Automated CFD Simulation Processes on Virtual Product Development--A Case Study
## 2 Materials and Methods
### The Case: Elefant Racing  
In this section, we present the object of our study, the formula student team Elefant Racing (University of Bayreuth, Germany), in more detail. Formula student racing is a competition for engineering students. Every year, the teams develop a new racing car and compete against each other. Our institute supports students by providing infrastructure and knowledge. To be successful, a team must engage in research, manufacturing and testing, in addition to designing the racecar.  
Furthermore, the teams must also deal with marketing, finance and management issues. Therefore, Elefant Racing can also be regarded as a small company. Considering the team's usually very small financial resources, cost efficiency is probably a much more significant factor than in actual companies. Thus, Elefant Racing is an ideal candidate to study the benefits of automated HPC-based development processes.  
Each season, the formula student team designs a new racecar from scratch. Up to now, the students have carried out their simulations and optimizations on conventional workstations with the known limitations about computational power and capacity. In the context of our research, we opened our HPC infrastructure to the team, provided methodological knowledge and conducted the implementation. This was the first time that the team was able to perform a CFD simulation of the entire racecar. Previously, only specific parts had been investigated aerodynamically, therefore HPC opened up new possibilities in terms of holistic optimization. Their development workflow was similar to [18]. Since our institute supported and guided Elefant Racing for several years, the team's efficiency increase through HPC could be quantified by comparing historic key performance indicators (KPIs) with today's: \"drag\" and \"downforce\". Drag is the force that a surrounding fluid exerts on an object in motion. The smaller the drag, the more efficient the movement of the racecar. In contrast, downforce is a vertical force in the racecar acting perpendicular to the road in addition to the racecar's gravitational force. A high downforce is generally desirable because it leads to higher friction between the tire and the road, thus enabling higher cornering speeds. Therefore, the team's objective is always to minimize drag while maximizing downforce.
## 3 Results and Discussion
### Principal Component Analysis  
The RVA profiles were analysed using PCA to investigate underlying relationships in the data set and better interpret the patterns in the pasting curves. Figure 2 shows the PCA (panel A, score plot; panel B, loadings) from the analysis of the flour and powder samples (CPF, CKP, and FxF) and the corresponding blend samples analysed using the RVA. It was observed that the first two principal components (PCs) explained 99.3% of the total variance. For PCA models, a Q\({}^{2}\) value can be used to interpret the predictiveness of a component, and a Q\({}^{2}\) value close to that of R\({}^{2}\) indicates good predictability. The PCA model had an R\({}^{2}\) and Q\({}^{2}\) of both 0.993, which indicates excellent model reliability and that the model can be applied to interpret the effect that CKP powder has on the pasting properties of the FxF and CPF flour blends.  
The PCA scores reveal the patterns in the samples associated with the increase in CKP powder in the blends (Figure 2A). The first PC (89%) is associated with the increase in CPF flour in the blend, while PC2 (10%) is associated with the increase in FxF flour in the mix. The highest loadings in PC1 can be observed around 12 min, associated with FV, whereas the highest loadings in PC2 were observed around 3 min and 11 min, both associated with PV and FV, respectively. Because the highest loading was observed at approximately 12 min, the score plot was colour-scaled by the viscosity at 12.5 min (associated with FV) to assist in the visual interpretation of the model. The colour scaling indicates that the highest FV is observed within the FxF and CPF flour blends, and the lowest FV is observed within the CPF flour and CKP powder blends. Therefore, CKP powder is a suitable addition to FxF flour to modulate FV and may be added to CPF blendsto modulate PV (Figure 1). This information is highly valuable for the bulk transfer of feed ingredients during processing, where it is important to account for viscosity changes while feed ingredients are pumped through pipes, mixed, and subject to either heating or cooling. Overall, Figure 2 demonstrates that by using a PCA score plot, one can easily and visually assess the pasting properties of a high number of flour blends fortified with insect powder, interpret techno-functional properties, and make informed decisions for processing applications.
## 3 Results and Discussion
### Characterization of BC-Curcumin Membrane
#### 3.2.2 Gas Permeability Test  
From Table 1, the water vapor transmission rate (WVTR) of BC film can be seen to have slightly reduced from about 597 to 434-506 g/m\({}^{2}\)/day by the absorption of curcumin at concentrations of 0.2-1.0% (\(w/v\)). The WVTR values of the BCC films were found to be similar to those of commercial films such as Ultec(r) and Bioclusive(r) [34]. This investigation revealed that the WVTR of BCC films falls within the acceptable range for a skin dressing. Generally, the evaporative water loss rate for normal skin is 204.0 \(\pm\) 115.2 g/m\({}^{2}\)/day, while that for injured skin is 5138 \(\pm\) 202 g/m\({}^{2}\)/day. The WVTR value for dressings has been recommended to be within the range of 70-9400 g/m\({}^{2}\)/day to control loss of fluid from the injured skin, and maintain the condition of the skin [35,36].  
The oxygen transmission rates (OTR) of BC and BCC films are shown in Table 1. As the curcumin content increases, the OTR decreases. Furthermore, the OTR of BCC film was found to be significantly lower than the WVTR. This discrepancy is likely due to the incorporation of curcumin into the spaces between the BC microfibrils, resulting in a denser structure. Because oxygen molecules are larger than water molecules, the denser structure could cause a significant reduction in the diffusivity of oxygen through the film. The low OTR values could be due to the measurement at 0% RH. However, for clinical use, the OTR of the moist film might be significantly enhanced. A high relative humidity will cause the film to swell, resulting in an increase of OTR.
# Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network
## 1 Introduction
### Discussion of Existing Studies  
Based on the literature review, extensive efforts have been made by international organizations and the relevant authorities to maintain the safety level of the NSR. Many studies on the NSR have been conducted, and safety issues for shipping operations that move through Arctic waters are still the main concerns globally due to the unavailability and uncertainty of information associated with navigation along the NSR. Improving the capacity to safeguard various ships transiting Arctic waters, especially the NSR, is a great challenge for the commercial operation of the NSR. The lack of safeguard capacity evaluations considering the safety of shipping operations along the NSR is a current gap in the literature. To address this gap, the present study proposes a comprehensive assessment model involving a fuzzy BN and information entropy to quantitatively measure the resilience of the NSR.  
The present study mainly aims to pioneer the establishment of an integrated evaluation model based on fuzzy BN information entropy theory to measure the resilience of the NSR  
Figure 1: Resilience description associated with system performance and state transition [28].  
safeguard system and investigate the influence of contributing factors on system resilience. Under the framework of resilience, the disruptions to the NSR safeguard system were first identified by a literature review and expert elicitation. Then, resilience decomposition was performed to identify the contributing factors for system resilience, which were mapped into the BN structure together with the factors related to disruptions as the parent nodes in the BN. Experts with navigation experience or academic experience associated with the NSR were employed as consultants to quantify variables by using fuzzy theory. Finally, we ran the developed BN with GeNIe 2.3 software and generated conclusions. The main features of the present study are summarized as follows:  
* A system resilience analysis framework is explored to improve the safeguard capacity of the NSR in mitigatingvarious disruptions that affect safe transit along the NSR;
* A methodology is established to conduct a resilience assessment of the NSR based on the integration of fuzzy theory, a BN, and information entropy theory;
* The proposed fuzzy BN-based model can effectively cope with the uncertainty and unavailability of information associated with Arctic waters;
* The conduction of different types of analyses, such as forward and backward propagation, sensitivity, and information entropy analyses, helps us obtain a complete understanding of NSR resilience.
# Using Machine Learning to Predict Indoor Acoustic Indicators of Multi-Functional Activity Centers
## 4 Results and Discussions
### Machine Learning Model Results
#### 4.3.2 Comparison with Traditional Estimation Formulas  
For the prediction of reverberation time, we used three estimation formulas ([PERSON]'s, [PERSON]'s, and [PERSON]'s method) to compare with the ML model. For the 800 spaces generated in this study, the reverberation time obtained by Odeon simulation was taken as the \(x\)-axis, the scatter plot is shown in Figure 21, and the _RMSE_ is shown in Table 11. The figure clearly shows that the predictive ability of the three traditional formulas is poor, and reverberation time would be underestimated in most spaces. In contrast, the GBDT model constructed in this study has quite a high accuracy.  
Figure 21: Scatter plot of reverberation time.  
Figure 20: _JND_ of each model with testing set (outliers not shown).  
Comparing the _JND_ distributions (Figure 22), we found that the predictive ability of the ML model is much higher than that of the traditional formula. The data of the GBDT model concentrate near 0, and in almost all the data, _JND_ falls within the range of \(\pm 1\). However, the _JND_ of the traditional estimation formula is approximately in the range of \(\pm 15\), and most of the data tends to be negative, suggesting that the RT of the traditional estimation formula is likely to be underestimated within the scope of this study.