esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Trading Power Consumption against QoS
### Proposed Mechanism  
In order to achieve the best possible performance in a network where sleep modes are available, we built a mechanism consisting of the following modules:  
* An enhanced neighbour-discovery mechanism for rapidly establishing whether a node's immediate neighbours are ON or OFF;
* A queue for storing packets at the previous hop if a node is suspended.  
As soon as a given node A identifies that a set S of its neighbours are unavailable, it creates a queue for each unavailable neighbour. Packets of the same next-hop are queued until this neighbour becomes available again. As soon as the next hop wakes up, A receives hello packets from this node and after re-establishing the connection the packets in the corresponding queues are released towards their next hop. Note that a node that has packets in such queues may enter sleep mode. We address this by using the \"suspend\" capability, with which memory is the only remaining function that consumes energy, so as to avoid losing the packets in the queues. At each node, the queuing mechanism includes the following steps (see Figure1):  
* When a packet is ready to be sent from a node, the state of the next hop is checked;
* If the next hop is ON then the packet is processed and sent out immediately;
* If the next hop is OFF the mechanism searches whether a storage queue exists for this hop.
* if there is no queue, a new queue is created for this missing hop and the packet is stored;
* if there is already a queue for this missing hop, the packet is simply placed at its end.  
An enhanced neighbour discovery mechanism runs concurrently in each node. It is implemented by exchanging frequent \"hello\" packets between neighbours, to enable fast identification when the neighbour becomes unavailable by entering the energy-saving sleep mode. This mechanism therefore is responsible for:  
* Sending frequent hello messages to inform the node's neighbours that it is ON;
* Keeping a state table of all its neighbours based on the hello messages it receives;
* Keeping the timestamp of the last hello message received from each neighbour;
* Removing a neighbour from the list if no hello messages have been received within a time-out interval;
* Re-adding a neighbour to the list when it wakes up from a sleep state;
* Processing the queue and sending out all the corresponding packets for a recently added neighbour;
* Informing the queuing mechanism which neighbours are ON (or not) based on the state tables of the neighbours.  
Intermediate nodes are controlled by an automated mechanism that decides when to suspend a node and when to turn it back ON again. Each node is controlled independently and SSH commands (pm-suspend) are sent to suspend the node, while wake-on-LAN messages are sent to wake the node from the suspended state [31].
## 4 Experiments and Results
### Segmentation Accuracy Evaluation
#### 4.2.1 Datasets and Implementation Details  
The BraTS Challenge 2019 [1, 48, 3] dataset contains 335 instances of data that is split into 200 training samples, 50 validation samples, and 85 testing samples. Experiments were conducted on a single Nvidia RTX 3090 GPU; the model was trained with cross-entropy loss using an Adam optimizer with a learning rate of \(1\times\mathrm{e}^{-5}\) for 300 epochs and employing a cosin decay learning rate scheduler and a batch size of 5.  
The manual annotation contains 4 classes: background (label 0), necrotic and non-enhancing tumor (label 1), peritumoral edema (label 2), and GD-enhancing tumor (label 4). The segmentation accuracy is assessed by a Dice score and Hausdorff distance (95%) metrics for enhancing the tumor region (ET, label 1), regions of the tumor core (TC, labels 1 and 4), and the whole tumor region (WT, labels 1,2 and 4).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Dice Score (\%) \(\uparrow\)**} \\ \hline
**Method** & **M = 10** & **M = 20** & **M = 40** & **M = 70** & **M = 110** & **M = 160** & **M = 200** \\ \hline Random & 54.35 & 74.74 & 80.62 & 83.31 & 85.10 & 86.50 & 87.15 \\ Rotation [46] & 53.51 & 76.52 & 82.54 & 84.90 & 85.31 & 87.04 & 87.43 \\ SimCLR [9] & 53.51 & 76.52 & 82.54 & 84.90 & 85.31 & 87.04 & 87.43 \\ LETCP & 59.43 & 76.83 & 83.52 & 85.38 & 86.45 & 87.69 & 88.14 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the proposed LETCP method with other baselines on BraTS 2021 [1, 2, 3]. \(\uparrow\) means higher is better.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Dice Score (\%) \(\uparrow\)**} \\ \hline
**Method** & **M = 10** & **M = 20** & **M = 40** & **M = 70** & **M = 110** & **M = 160** & **M = 200** \\ \hline Random & 54.35 & 74.74 & 80.62 & 83.31 & 85.10 & 86.50 & 87.15 \\ Rotation [46] & 53.51 & 76.52 & 82.54 & 84.90 & 85.31
## 3 Results and Discussion
### Biochemical Oxygen Demand (BOD\({}_{5}\)), Nitrogen, and Phosphorus  
Biochemical oxygen demand (BOD\({}_{5}\)), nitrogen, and phosphorus were determined in the system starting on day 28, when the plants had reached their maximum size. BOD\({}_{5}\) and different forms of inorganic nitrogen in the vermifilter feed and effluent, as well as their relative change due to treatment by vermifiltration, are shown in Table 3 for the best vermifiltration performance attained during the reported experimental period. Results for BOD\({}_{5}\), inorganic nitrogen, and total and dissolved phosphorus in the hydroponic treatment feed, effluent, and relative change are presented in Table 4 for the best hydroponic performance attained during the reported experimental period.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**EC (S/m)**} & \multicolumn{3}{c}{**pH**} \\ \cline{2-7}
**Time (Days)** & **VF Feed** & **VF Effluent/HP Feed** & **HP Effluent** & **VF Feed** & **VF Effluent/HP Feed** & **HP Effluent** \\ \hline
21 & 0.1610 & 0.3050 & 0.2928 & 6.72 & 7.34 & 7.65 \\
28 & 0.2237 & 0.2413 & 0.2776 & 7.38 & 7.60 & 7.77 \\
35 & 0.2475 & 0.2517 & 0.2989 & 7.02 & 7.61 & 7.77 \\
42 & 0.2901 & 0.2940 & 0.2642 & 6.52 & 7.25 & 7.69 \\
49 & 0.1591 & 0.1635 & 0.1605 & 6.87 & 7.71 & 7.71 \\ \hline \hline \end{tabular}
\end{table}
Table 2: EC and pH single readings in the Vermifiltration-hydroproponic system. VF: vermifilter; HP: hydroponic unit.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Feed** & **Effluent** & **RC (\%)** & _p-Value_ \\ \hline BOD\({}_{5}\) (mgO\({}_{2}\)/L) & 35.1 (\(\pm\)0.3) & 5.8 (\(\pm\)0.3) & \(-\)83 (\(\pm\)2) & \(<\)0.001 \\ NH\({}_{3}\)-N (mgN/L) & 8.9 (\(\pm\)0.5) & 0.075 (\(\pm\)0.004) & \(-\)99 (\(\pm\)7) & \(<\)0.001 \\ NO\({}_{2}\)-N (mgN/L) & 2.19 (\(\pm\)0.01) & 0.0307 (\(\pm\)0.0002) & \(-\)98.6 (\(\pm\)0.4) & \(<\)0.001 \\ NO\({}_{3}\)-N (mgN/L) & 173 (\(\pm\)1) & 192 (\(\pm\)1) & +11.0 (\(\pm\)0.8) & \(<\)0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Vermifiltration treatment feed, effluent, and relative change (RC) in water quality parameters. The results are presented as triplicate (\(n\) = 3) mean \(\pm\) standard error.
## 2 Materials and Methods  
### Reagents and Antibodies  
Dexamethasone, DNCB, o-phthalaldehyde, and nitroblue tetrazolium (NBT) were purchased from Sigma-Aldrich (St. Louis, MO, USA). The following antibodies were used: rabbit antibodies against cleaved caspase-3 (Cas-3, #9661) and cleaved poly(ADP-ribose) polymerase (PARP; #9545) from Cell Signaling Technology (Danvers, MA, USA); rabbit antibodies against 4-hydroxynonanel (4-HNE; #ab46545) and matrix metalloproteinase (MMP)-9 (#ab38898) from Abcam (Cambridge, UK); rabbit antibody against nitrotyrosine (NT) from Millipore Corp. (#06-284, Billerica, CA, USA); rabbit antibodies against inducible nitric oxide synthase-2 (iNOS; #sc-651) and IL-1 (#sc-7884), and mouse antibody against TNF-\(\alpha\) (#sc-52746) from Santa Cruz Biotechnology (Santa Cruz, CA, USA); and goat antibodies against IFN-\(\gamma\) (#AF781) and IL-2 (#BAF1815) from R&D System (Minneapolis, MN, USA).
# Triaxial Compression Performance Research of Steel Slag Concrete on the Unified Strength Theory
## 3 Test Results and Discussion  
### Failure Modes  
Under the triaxial compression stage, the typical failure modes of SSC are shown in Figure 4, they were failure modes of the single-splitting, slant-shear, respectively. Generally speaking, the effect of replacement ratios on SSC had not to change the failure modes. However, SSC's brittleness was more significant with the increase of the SSA replacement ratio. The number of cracks on the specimen's surface increases with the SSA replacement ratio, which indicated that the SSC with a high replacement ratio was more seriously damaged. The failure mode of SSC was significantly affected by the stress ratio. When the stress ratio \(\sigma_{2}/\sigma_{3}\) was relatively small (e.g., \(\sigma_{2}:\sigma_{3}=0.25\):1), the failure mode that appeared in SSC was plate-splitting. Because the third principal stress \(\sigma_{3}\) was much greater than the others, the tensile strain appeared in the vertical direction of the surfaces of \(\sigma_{1}\) and \(\sigma_{2}\). However, due to the constraint of \(\sigma_{2}\), the tensile strain that appeared in the normal direction of the surfaces of \(\sigma_{2}\) was prevented. With the increase of the load, microcracks appeared on the surfaces of \(\sigma_{3}\) and \(\sigma_{2}\) and gradually extended throughout the specimens. The typical plate-splitting of SSC is shown in Figure 4a-c. With the increase of stress ratio \(\sigma_{2}/\sigma_{3}\), the tensile strain was prevented due to the constraint of \(\sigma_{1}\), and the slant-shear cracks occurred on the surface of \(\sigma_{2}\) due to the large shear stress \((\sigma_{3}-\sigma_{1})/2\). It can be found in Figure 4d-i. There are the single-shear and double-shear cracks on the surface of \(\sigma_{2}\), and the angles between slant-shear cracks and the direction \(\sigma_{3}\) are about \(20^{\circ}\)-\(30^{\circ}\). In addition, the stress ratio \(\sigma_{1}:\sigma_{3}\) also affects the failure mode of the specimen. When the stress ratio \(\sigma_{2}/\sigma_{3}\) was constant, the number of cracks generated by stress ratio \(\sigma_{1}:\sigma_{3}=0.1\):1 was less than that of by \(\sigma_{1}:\sigma_{3}=0.05\):1.
## 3 Results
### Rabbit Brain Imaging  
#### 3.2.1 Correlation Analysis and SVD-Filtering Results in RGB Channels  
Figure 9a,c,e shows the correlation analysis results of the spatiotemporal characteristics of RGB channels in the rabbit brain experiment. The correlation coefficient of the low-order singular value represents the similarity of the high-intensity tissue signal of the adjacent points, and the correlation coefficient of the high-order singular value represents the similarity of the blood flow signal and the low-intensity noise of the adjacent points. Figure 9b,d,f shows the comparison results of SVD filtering in each RGB channel. All the results were applied with the same low-order singular value cutoff threshold of 22.  
Figure 8: Bar plots of the R- and B-channel mean intensities of the bovine blood solution in ROI with concentrations ranging from 0% to 100%. Error bars are plotted from standard deviation of signal intensities of bovine blood solution in ROI.  
Figure 9: Cont.  
As can be seen from Figure 9a, the correlation analysis results of \(\mathrm{G}H_{2}\) convolution output only show a high correlation in the lower order, which means that the signals in the R channel are mainly composed of tissue motion and noise. After removing the tissue motion signals by low-order threshold SVD clutter filtering, the majority of the remaining power Doppler results were mainly contributed by noise (Figure 9d). Conversely, one can see that the correlation analysis results for the B channel essentially contain more blood flow signals (Figure 9c). Therefore, the power Doppler results obtained under the same low-order threshold SVD filtering also showed more details of the blood flow (Figure 9f). The result of the G channel can be considered as the result of conventional low-order SVD filtering (Figure 9e).  
Due to the attenuation of an ultrasound in biological tissue, the lower half of the power Doppler image is usually noise-saturated, which results in a particularly low CNR. To suppress the influence of noise, a noise subtraction method to obtain the final Doppler image by subtracting the noise signal of \(\mathrm{G}H_{2}\) from the blood flow signal of \(\mathrm{G}H_{8}\) was proposed (step 5 in Section 2). Since these two signals are obtained with the same ultrasonic sequence, SVD processing, ROI and time period, the noise subtraction process provides a more uniform result of power Doppler imaging. Its superiority to conventional low- and high-order SVD filtering will be demonstrated in Figure 10.  
Figure 9: Correlation analysis results of the spatiotemporal characteristics of R-channel data (**a**), G-channel data (**c**) and B-channel data (**e**). Comparison results of power Doppler image of the R-channel (**b**), G-channel (**d**) and B-channel (**f**), with the same low-order singular value cutoff threshold of 22. Both (**b**), (**d**) and (**f**) are displayed under the 50-dB dynamic range.
# Geometric Similarity Measurement Method for Micro Scene Generalization
## 2 Methodology
### Direction Similarity
#### 2.2.1 Direction Voronoi Graph Model  
The direction Voronoi graph model considers all sides of the directional relationships between two targets, and uses a set of multiple directions to describe the directional relationships.  
To simplify the discussion, the two object groups in Figure 9 are used as an example. The specific calculation process is divided into the following five steps:  
1. Construct a point set of the two object groups.
2. Generate the Voronoi diagram. If the three vertices of a triangle belong to the same object group, it is called the first type of triangle; otherwise, it is called the second type of triangle (Figure 9).
3. Delete the first type of triangles, and the remaining triangles form a visible triangulation network (Figure 9). The area covered by the triangulation network is the visible area between the targets.
4. Connect the midpoints of the triangle waists to generate the Voronoi diagram of the direction (Figure 9).
5. Calculate the azimuth angle of the normal of each Voronoi side, and use the eight-direction system to classify the azimuths within the same direction into one category. Then, calculate the percentage of the length of the Voronoi side in each main direction. As such, the precise directional relationship between group targets can be obtained.
## 2 Description of the Problem
### Objective Functions  
OPF problems normally include one or several objective functions (\(F\)). Function \(F\), in this study, calculates the overall fuel cost of thermal power plants (\(Fcost\)) and is formulated in terms of the output power generation (\(P_{Gi}\)) as follows:  
\[\min\;Fcost(x,y)=\sum_{i=1}^{NG}\Bigl{(}\alpha_{i}+b_{i}P_{Gi}+c_{i}P_{Gi}^{2 }\Bigr{)} \tag{16}\]  
In this equation, \(a_{i}\), \(b_{i}\) and \(c_{i}\) show the cost coefficients of the \(i\)th unit.  
Another optimization function is _Ploss_ so that active power loss of the power system is minimized:  
\[\min\;Ploss(x,y)=\sum_{i=1}^{NTL}\sum_{\begin{subarray}{c}j=1\\ j\
eq i\end{subarray}}^{NTL}G_{ij}V_{i}^{2}+B_{ij}V_{j}^{2}-2V_{i}V_{j}\cos \delta_{ij} \tag{17}\]  
The third optimization function attempts to minimize voltage deviation (\(VD\)) to bring safety to the equipment and provide high-quality services to the customers [55]:  
\[\min\;VD(x,y)=\sum_{i=1}^{NL}\Bigl{|}V_{i}-V_{i}^{ref}\Bigr{|} \tag{18}\]  
Here, \(Vi\) is the voltage magnitude of bus \(i\), whereas \(V_{i}^{ref}\) expresses the reference voltage magnitude of bus \(i\), generally set at one p.u.  
Traditional power plants generally require fossil fuel to rotate the turbine and generator shaft, thus, producing the output power. In this process, much pollution is emitted, which needs to be addressed. Equation (19) formulates the minimization of nitrogen oxide (\(NO_{x}\)) and sulfur oxide (\(SO_{x}\)) gases emission levels [56]:  
\[\min\;Emission(x,y)=\sum_{i=1}^{NG}(\alpha_{i}+\beta_{i}P_{Gi}+\gamma_{i}P_{Gi} ^{2}+\xi_{i}exp(\theta_{i}P_{Gi})) \tag{19}\]  
where, \(\alpha_{i}\) (ton/h), \(\beta_{i}\) (ton/h MW), \(\gamma_{i}\) (ton/h MW2), \(\xi_{i}\) (ton/h) and \(\theta_{i}\) (1/MW) are emission coefficients of the \(i\)th power plant.  
To consider the violation of constraints, a penalty function as follows is added to the main objective function:  
\[\begin{array}{l}J=\sum\limits_{i=1}^{NG}F_{i}\left(\;P_{Gi}\right)\;+\; \lambda_{P}\left(\;P_{Gi}-P_{Gi}^{\text{lim}}\;\right)^{2}\;+\;\lambda_{V} \sum\limits_{i=1}^{NL}\left(\;VL_{i}-VL_{i}^{\text{lim}}\right)^{2}\\ +\;\lambda_{Q}\sum\limits_{i=1}^{NG}\left(\;Q_{Gi}-Q_{Gi}^{\text{lim}}\; \right)^{2}+\;\lambda_{S}\sum\limits_{i=1}^{NTL}\left(\;S_{i}-S_{i}^{\text{ lim}}\right)^{2}\end{array} \tag{20}\]  
Here, \(\lambda_{P}\), \(\lambda_{V}\), \(\lambda_{Q}\) and \(\lambda_{S}\) denote penalty factors; and \(x^{lim}\) represents an auxiliary variable defined as follows:  
\[x^{\text{lim}}=\left\{\begin{array}{l}x\quad x^{\text{min}}\;\leq\;x\;\leq\;x ^{\text{max}}\\ x^{\text{max}};\quad x>x^{\text{max}}\\ x^{\text{min}};\quad x<x^{\text{min}}\end{array}\right. \tag{21}\]
## 3 Results
### Aviation Technical Support as a Service
By leveraging a distributed network of expertise, the industry can collectively advance aircraft health management practices, share insights, and drive continuous improvement. Figure 9: Ecosystem of HMGT based on ATSaaS concept. However, the successful implementation of the ATSaaS model requires careful consideration and effective collaboration between airlines and specialized service providers. The standardization of data formats, protocols, and interfaces is essential to ensure seamless integration and data exchange among various stakeholders. Additionally, robust cyber-security measures and data protection protocols must be in place to safeguard sensitive aircraft health data and maintain operational integrity. The Aviation Technical Support as a Service concept represents a significant evolution in aircraft health management, offering a distributed and specialized model that leverages the expertise of specialized organizations. By outsourcing specific functions to these organizations, airlines can benefit from improved expertise, cost savings, flexibility, and agility. While the implementation of the ATSaaS model presents challenges, it holds the potential to drive innovation, collaboration, and continuous improvement in the aircraft maintenance and health management domain. The next evolution in aviation health management moves towards a more interconnected, intelligent, and collaborative system. This system not only optimizes maintenance and improves aircraft availability but also leverages the collective expertise and data for industry-wide advancements, driving innovation and maintaining high safety standards. Figure 10 illustrates the progressive evolution of ATSaaS, showcasing a tiered approach to aviation health management. A three-tiered cloud-based architecture of HMGT with ATSaaS approach can be proposed.
## 2 Data and Methods
### Flux Data  
Carbon and water fluxes were measured with eddy covariance systems, which consist of an open-path CO2/H2O gas analyzer (model LI-7500, Licor Inc., Lincoln, NE, USA) and a 3D sonic anemometer/thermometer (model CSAT3, Campbell Scientific Inc., Logan, UT, USA) [23]. These flux data were originally sampled at 10 Hz, and the values averaged over 30 minutes were obtained from the flux stations. At these eight sites, GPP and ET were aggregated to an eight-day step from half-hourly CO2 and H2O flux data measured by open-path eddy covariance techniques. Outlier data were rejected, and unreasonable nighttime values were excluded according to friction velocity. Gaps in half-hourly CO2 and water flux data were filled using a nonlinear regression algorithm [24]. GPP was estimated from the observed Net Ecosystem Exchange (NEE) and total ecosystem respiration (RE), which is the sum of nighttime and daytime ecosystem respiration. The daytime ecosystem respiration was calculated from the relationship between nighttime ecosystem respiration and soil temperature and moisture. Detailed steps in processing eddy covariance datasets were described in [PERSON] _et al._[25] and [PERSON] _et al._[10].
# Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time
## 4 Results
### Performance Assessment  
In this section, we evaluate the seven correction methods in reproducing the precipitation characteristics by comparing corrected GPM and CMPA for 2017, and discuss their advantages and disadvantages.
# Maximum Transmit Power for UE in an LTE Small Cell Uplink
## 3 Network and Propagation Model
### HetNet Deployment with Small Cells near Macro Cell eNodeB
#### 3.3.3 Received SIR  
The received SIR for user \(u_{i}\) can be obtained by dividing Equation (6) for small cells by Equation (22) as:  
\[SIR_{\text{rx},u_{i}}=\frac{p_{\text{rx\_small},u_{i}}}{I_{\text{HetNet}}} \tag{23}\]
## 3 Results
### Image Registration Scenario  
We compared the performance of our method with patch-based adaptations of (i) sum of absolute differences and (ii) mutual information. For a given set of corresponding landmarks in two images, we computed the similarity of patches extracted at the landmark positions in the source image with all positions in a \(64\times 64\) window around the corresponding landmark in the target image. The score was calculated as the distance between the position with the best similarity value and the actual landmark position in the target image. This score was averaged over all landmarks for eight different image pairs coming from the ANHIR 2019 challenge dataset. Each image pair had between 60 and 90 landmarks, 676 in total.  
In Figure 6 boxplots of differences between the best matching position and the hand-marked landmarked are shown for each of the considered metrics. Summaries of these distances are given in Table 1. Only in two cases were the differences between any of the new metric and the mutual information statistically significant (\(p<0.05\) for Wilcoxon rank sum test): in one case (03_S2_S5), MI was superior to the two proposed distances, while in the second case (05_S2_S6), MI was inferior. Thus, the two new distances seem to perform similarly to MI, while outperforming the raw pixel-based metric (SAD). At the same time, the stability of the new metric seems to be slightly better than the other two, as shown by the lower standard deviation of the corresponding errors (Table 1).  
We also explored the applicability of the novel feature extraction method and similarity metric for two different image registration algorithms. We developed a deformable image registration method based on ITK, akin to the methods presented in [35]. The second approach matches ORB key-points extracted using OpenCV using our similarity metric. Neither of the approaches yielded viable results, as can be seen in Figure 7. Both methods are open for further development.