esa-sceva/satcom-chunk-collection huggingface dataset:
# Knowledge Distillation Based on Fitting Ground-Truth Distribution of Images
## 4 Experiments and Analysis
### Experiment on Fitting the Ground-Truth Distributions of Features (LSH-T)
When the feature temperature is applied to the low feature outputs of pre-trained teachers, such as vgg13 and resnet32\(\times\)4, it addresses the issue of flat feature distribution and increases the discriminability of different features. This adjustment brings the teacher feature outputs closer to the ground-truth feature distribution of images, resulting in significant performance improvement for LSH. The highest improvement, relative to LSH, is observed in the distillation of ShuffleNetV1 using resnet32\(\times\)4, with an increase of 0.60% in classification accuracy. LSH-T performs far better than LSH in knowledge distillation where the teachers have low feature outputs. Table 7 demonstrates the distillation performance of LSH-T on the large-scale ImageNet-2012 dataset. Because the penultimate layer of feature output for the resnet34 teacher is in the normal range, we only set the feature temperature to 1.5, boosting the feature output by a small amount. It shows that both the Top-1 error rate and Top-5 error rate are lower than those of LSH, proving that our algorithm is equally effective for large-scale datasets. \begin{table}
\begin{tabular}{c c c c c c c c} \hline Teacher & WRN-40-2 & WRN-40-2 & resnet56 & resnet110 & resnet110 & resnet32\(\times\)4 & vgg13 \\ Student & WRN-16-2 & WRN-40-1 & resnet20 & resnet20 & resnet32 & resnet8\(\times\)4 & vgg8 \\ Teacher & 75.61 & 75.61 & 72.34 & 74.31 & 74.31 & 79.42 & 74.64 \\ Student & 73.26 & 71.98 & 69.06 & 69.06 & 71.14 & 72.50 & 70.36 \\ \hline KD [10] & 74.92 & 73.54 & 70.66 & 70.67 & 73.08 & 73.33 & 72.98 \\ FitNet [11] & 73.58 & 72.24 & 69.21 & 68.99 & 71.06 & 73.50 & 71.02 \\ AT [17] & 74.08 & 72.77 & 70.55 & 70.22 & 72.31 & 73.44 & 71.43 \\ SP [19] & 73.83 & 72.43 & 69.67 & 70.04 & 72.69 & 72.94 & 72.68 \\ AB [18] & 72.50 & 72.38 & 69.47 & 69.53 & 70.98 & 73.17 & 70.94 \\ FT [21] & 73.25 & 71.59 & 69.84 & 70.22 & 72.37 & 72.86 & 70.58 \\ FSP [20] & 72.91 & \(n/a\) & 69.65 & 70.11 & 71.89 & 72.62 & 70.23 \\ CRD [12] & 75.48 & 74.14 & 71.16 & 71.46 & 73.48 & 75.51 & 73.94 \\ CRD+KD [12] & 75.64 & 74.38 & 71.63 & 71.56 & 73.75 & 75.46 & 74.29 \\ SSKD [22] & 75.55 & 75.50 & 71.00 & 71.27 & 73.60 & 76.13 & 74.90 \\ LSH * [13] & 76.30 & 74.51 & 71.34 & 71.59 & 73.97 & 76.71 & 74.50 \\ LSH-T (ours) & 76.29 & 74.68 & 70.96 & 71.37 & 73.55 & 76.94 & 74.65 \\ \(\blacktriangle\) & \(-0.01\) & \(+0.17\) & \(-0.38\) & \(-0.22\) & \(-0.42\) & \(+0.23\) & \(+0.15\) \\ \hline \end{tabular}
\end{table} 
Table 5: Test accuracy (%) of the student network on CIFAR-100. The teacher and the student share similar architectures. We use * to denote methods we re-ran five times using author-provided codes. The results of our method were run five times. Red represents the best, while blue represents the second best. \(\blacktriangle\) indicates the performance improvement effect of LSH-T (ours) over LSH.
## 4 Experiment and Analyses
### Objective Evaluation
#### 4.2.1 Evaluation Using \(Q^{AB/F}\)  
The fusion metric, \(Q^{AB/F}\), which is widely used in the fused image evaluation [12; 24], can be extended to evaluate the image fused with three or more source images. This metric is mainly used to analyze the edge information of the fused images. The more edge information remaining, the larger the \(Q^{AB/F}\) value is. The extended version of \(Q^{AB/F}\) for multi-exposure fusion is defined as:  
\[Q^{AB/F}=\frac{\sum\limits_{i=1}^{N}\sum\limits_{x=1}^{H}\sum\limits_{y=1}^{W}w ^{I_{i}}(x,y)Q^{I_{i}I_{F}}(x,y)}{\sum\limits_{i=1}^{N}\sum\limits_{x=1}^{H} \sum\limits_{y=1}^{W}w^{I_{i}}(x,y)}, \tag{15}\]  
where \(I_{i}(x,y)\) denotes the fused image to be evaluated, \(Q^{I_{i}I_{F}}(x,y)=Q^{I_{i}I_{F}}_{S}(x,y)Q^{I_{i}I_{F}}_{a}(x,y)\), \(Q^{I_{i}I_{F}}_{S}(x,y)\) and \(Q^{I_{i}I_{F}}_{a}(x,y)\) are the edge intensity and orientation preservation values of the pixel at position \((x,y)\), respectively, and \(w^{I_{i}}(x,y)\) is the gradient magnitude of \(I_{i}(x,y)\), which is used as the weighting factor of \(Q^{I_{i}I_{F}}(x,y)\).  
The performance of the different methods on the 12 image sequences using \(Q^{AB/F}\) is listed in Table 1, in which the top two values are shown in bold. It can be seen from Table 1 that, in the fusion metric, \(Q^{AB/F}\), the performance of the proposed method and Liu15 was obviously superior to the others.
## 4 Conclusions  
This study developed a compact sewage treatment plant by applying both SBR and MBR processes and evaluated the performance of the device in terms of disposal, water quality and availability. The SBR process is suitable for small-scale sewage treatment, can treat a large quantity of waste water in a smaller site and has many other advantages, as compared to the conventional activated sludge process. On the other hand, the MBR process needs no sedimentation basin and shows high treatment efficiency. The findings of this study are summarized as follows.  
1. The COD\({}_{\text{Cr}}\) removal efficiencies were 90.33%, 94.67%, 83.33% and 94.67%. The average efficiency of over 90% indicated positive performance in treating organic matters.
2. The T-N removal efficiencies were 96.99%, 95.71%, 94.69% and 97.34%, of which the average exceeded 93%. However, further details of the mechanism need to be identified by investigating nitrogen removal issues such as DO aeration condition, stirring duration, ORP and NO\({}_{3}\).
3. The T-P removal efficiencies were 96.11%, 96.27%, 96.05% and 96.11%, of which the average exceeded 95%. The SBR and MBR process formed anaerobic and aerobic conditions without a separate coagulation process for removing phosphorus, thereby enabling easy phosphorus release and uptake.
4. As a result of comparison with MEPC. 227(64) regulation, the treated water's T-P concentration is unsatisfactory, but since the removal rate is satisfied, it will not be a problem, and in future, an additional study will be necessary for T-P removal.  
From the above results, it turned out that 70-50 min was the optimal condition achieving the highest removal efficiency. If a further study clarifies the water quality criteria of MEPC. 227(64) for _E. coli_ and chlorine and the details of mechanism, the proposed vessel sewage treatment plant will be more efficient  
Data curation, B.-G.J., M.-J.K. and J.-H.J.; Formal analysis, D.-Y.S.; Investigation, H.-J.J.; Project administration, Y.-I.C.; Resources, S.M.; Validation, N.-C.S.; Visualization, S.-C.L.; Writing--review & editing. J.-P.W. This study was conducted in 2017 under the support of the National Research Foundation of Korea financed by the government (Ministry of Science, ICT and Future Planning) (No. 2017R1A2B4011847). The authors declare no conflicts of interest.
## 4 WiFi 6/6E: IEEE 802.11 ax
### The 6 GHz Range  
The wireless bandwidth available in the 2.4 GHz and 5 GHz ranges is around 60 MHz and 500 MHz, respectively, and therefore requires other techniques, such as modulation, to increase the data rates. However, with the release of the 6 GHz band for unlicensed use, WiFi receives around 1200 MHz of fresh bandwidth. The 6 GHz networks are called WiFi 6E, and experience less interference from existing networks. The channelization of the WiFi in different bands is shown in Figure 4 for comparison.
## 2 Materials and Methods  
The American Petroleum Institute (API) brine was used as the simulated formation water, consisting of NaCl (80 g/L) and CaCl\({}_{2}\) (20 g/L) at a salinity of 100,000 mg/L. NaCl(99% purity) and CaCl\({}_{2}\) (93% purity) were purchased from Daejung Chemicals & Metals, Republic of Korea, and used without further purification. GPTMS and LUDOX(r) TMA colloidal SNPs were purchased from Sigma-Aldrich Korea (Seoul, Korea) and used without further purification. GPTMS was employed as a silane coupling agent, consisting of a trimethoxy group and epoxy, and primarily has an R-Si(OR\({}^{\prime}\))\({}_{3}\) structure for the surface modification of the nanoparticles. The SNPs were added to 34 wt.% silica suspension in H\({}_{2}\)O (pH 4-7), and each particle, with a nominal diameter of 20 nm, has an approximate surface area of 140 m\({}^{2}\)/g. Kerosene (for flotation tests) and crude oil (for core flooding tests) were utilized, and the properties of these oils are listed in Table 1. The rock samples were composed of Indiana limestone (CaCO\({}_{3}\), 97.07%) and Silurian dolomite (Ca(Mg)CO\({}_{3}\), 93.81%) purchased from Kocurek Industries, Inc. (Caldwell, TX, USA). For experimental purposes, the rock samples were prepared in powder form, sized between 44 and 74 microns (200 and 325 mesh), as detailed in Table 2. Furthermore, the core flooding experiments used core plugs obtained from rock samples collected from an operational field. The conventional rock properties of the carbonates used for nanofluid injection are listed in Table 3.
## 4 Testbed Design
### Multi-Node Container Orchestration  
Kubernetes is an open-source platform that streamlines the deployment, scaling, and management of containerized applications, relieving users from infrastructure concerns. It comprises two main components: the centralized control plane (Kubernetes master) and the decentralized data plane. The control plane consists of modular services like the K8s API, scheduler, and controller. The data plane is made up of distributed node agents called Kubelets [34], which act as local executors following commands from the master, such as starting or stopping containers. This separation allows users to define their desired application state, with Kubernetes striving to maintain that state. Figure 3 provides a high-level overview of our Kubernetes-enabled testing environment.  
A notable feature of Kubernetes is its ability to schedule the deployment and execution of containers across a node cluster using a scheduling algorithm. However, the default scheduler is designed to optimize node utilization and remain application-agnostic. For example, it may not consider data locality in scenarios like federated learning, where training data are distributed across edge devices or clusters. This can increase network traffic and latency due to unnecessary data movement. Moreover, federated learning requires frequent communication between edge devices/clusters and the central server for model updates. The default scheduler does not optimize scheduling decisions with regard to communication overhead, potentially increasing latency and impacting the training process. Additionally, each edge device or cluster in federated learning has its own resource constraints, such as limited computation power, memory, and network bandwidth.  
In our analysis, we examine the default policies of Kubernetes for federated learning applications. We specifically assess the performance of the default scheduler under two scenarios: one with user-defined constraints that cater to the application's specific require  
Figure 3: High-level overview of multi-host Kubernetes deployment.  
ments, and another without any user-defined constraints. Our analysis revolves around three key user-based constraints:  
* _Placement constraints:_ dictate the selection of nodes for scheduling FL clients, specifying which nodes should be chosen.
* _Resource constraints:_ determine the minimum and maximum amount of resources, such as CPU and memory, that an FL client can consume.
* _Timing constraints:_ define when a new training workflow will be instantiated.  
Our analysis aims to deliver insights into the impact of user-defined constraints and the potential for automatically inferring these constraints, rather than proposing a new scheduling algorithm.
## 3 Design Considerations of a VRLE
This information is mainly of three types: (i) instructions; (ii) help information; and (iii) conceptual information. The former refers to information that tells the user how to use the application (e.g., what options are available, what steps shall be followed, how to use the controls). The second refers to information that is shown when the user makes a mistake or when he/she asks for help when facing problems to continue using the application. The third case refers to information that clarifies concepts related to the experiment itself or concepts studied through the VRLE. Based on the development process outlined in Figures 3 and 4, we focus on the fact that before starting the development of a VRLE, two requirements must be met [1]: (i) the tool to be developed must improve the teaching-learning process; and (ii) the effort required to develop the tool must be justified, which will depend essentially on the advances in computation at that moment. Regarding this last point, it can be inferred that before starting the development of a VRLE it is very important to know the following data of the technology to be used: (i) availability in the market where the VRLE is to be developed; (ii) dissemination in universities and target students; and (iii) current price. For example, the development of a VRLE that requires a high-range HMD system would not be justified in a developing country because: (i) the necessary hardware cannot be acquired since it is often scarce or not available; (ii) the technology is not widespread in universities or among students in those countries; and (iii) the acquisition likelihood by universities or students in these countries is almost impossible due to its high cost. On the contrary, the development of such VRLE would be justified if it was designed to be used in a personal computer with medium or low computing capacity since these types of computers: (i) can be purchased in most cities; (ii) are currently widely spread in universities and among students; and (iii) are accessible and economical, and therefore their price would not stop their acquisition if necessary. Figure 4: A newer design process, including a step-by-step protocol, as described in this publication. Further analysis of the flow charts of Figures 3 and 4 reveals that all aspects related to the technical design of an application (e.g., determination of the level of realism, determination of the mode of interaction, selection of hardware and software) are closely related to what has been previously exposed. In this sense, we noticed a straightforward correlation: the higher the budget of the hardware, the higher the level of realism and interactivity the VRLE can offer, but the dissemination among end users will be lower. By contrast, the smaller the budget of the hardware, the greater the dissemination of the VRLE but the lower its realism and interactivity, thus becoming an \"undesirable\" tool for the student.
# Classification of Valvular Regurgitation Using Echocardiography
## 3 Methodology
### Proposed Methodology
#### 3.2.5 Classification  
Classification is carried out using Random Forest (RF). RF is preferred over other classifiers such as SVM as this paper involves multiclass classification, not binary classification where it is known to perform better [42, 43, 44]. It also gives us the probability of belonging to the class. It can also handle imbalanced data such as those relied upon in this work. It is robust to outliers [44]. RF classifier works well in small datasets and outperform other ensemble classifier as has the ability to tackle overfitting issues.  
The CSV file is read using the readcsv command from the panda library. The data is split into training and testing using the k-fold split command, and then this is fed to a random forest classifier that classifies the testing data into three classes 0, 1, and 2 (AR, MR, and TR, respectively).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Sl. No.** & **Name of Features** & **Mathematical Expression** \\ \hline
1 & Contrast & \(f_{1}=\sum_{i=1}^{N}\sum_{j=1}^{N}(i-j)^{2}P_{i,j}\) \\
2 & Dissimilarity & \(f_{2}=\sum_{i=1}^{N}\sum_{j=1}^{N}P_{i,j}|i-j|\) \\
3 & Energy & \(f_{3}=\sum_{i}\sum P_{i,j}^{2}\) \\
4 & Entropy & \(f_{4}=-\sum_{i=1}^{N}\sum_{j=1}^{N}P_{i,j}lgP_{i,j}\) \\
5 & Correlation & \(f_{5}=\sum_{i=1}^{N}\|\frac{(i-\mu_{i})(i-\mu_{i})}{\sqrt{\sigma_{i}^{2}} \sigma_{j}^{2}}\|\) \\
6 & Homogeneity & \(f_{6}=\sum_{i=1}^{N}\sum_{j=1}^{N}\frac{P_{i,j}}{1-P_{i,j}^{2}}\) \\
7 & Variance & \(f_{7}=\sum_{i=1}^{N}P_{i,j}(1-\mu^{2})\) \\
8 & Autocorrelation & \(f_{8}=\sum_{i=1}^{N}i,j!P_{i,j}\) \\
9 & Sum average & \(f_{9}=\sum_{i=2}^{2N}l.P_{x+y}l\) \\
10 & Sum entropy & \(f_{10}=-\sum_{i=2}^{2N}P_{x+y,\mu log}(P_{x+y,\mu})\) \\
11 & Sum variance & \(f_{11}=\sum_{i=2}^{2N}(l-f_{4})^{2}P_{x+y}l\) \\
12 & Difference entropy & \(f_{12}=-\sum_{i=0}^{N}P_{x-y}lP_{x+y}l\) \\
13 & Difference variance & \(f_{13}=\sum_{l=0}^{N}l^{2}P_{x+y}l\) \\
14 & Information measure of correlation 1 & \(f_{14}=\frac{\sum_{l=0}^{N}HXYX}{max(HX,HY)}\) \\
15 & Information measure of correlation 2 & \(f_{15}=\sqrt{(1-exp(-2(HXY2-HXY)))}\) \\
16 & Cluster Prominence & \(f_{16}=\sum_{i=1}^{N}\sum_{i=1}^{N}(i+j-\mu_{x}\mu_{y})^{4}P_{i,j}\) \\
17 & Cluster Shade & \(f_{17}=\sum_{i=1}^{N}\sum_{j=1}^{N}(i+j-\mu_{x}\mu_{y})^{3}P_{i,j}\) \\
18 & Inverse Difference Normalized & \(f_{18}=\sum_{i=1}^{N}\sum_{j=1}^{N}\frac{P_{i,j}}{1+\frac{1}{R}}\) \\
19 & Inverse Difference Moment Normalization & \(f_{19}=\sum_{i=1}^{N}\sum_{j=1}^{N}\frac{P_{i,j}}{1+\frac{1}{R^{2}}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Features extracted with their mathematical expressions.
## 3 Results
### BER Comparison of Watermarking Methods Based on Deep Learning  
The experimental results in Table 5 compare the BER values of retrieved watermarks under various quality factors for our method and recent deep-learning-based watermarking methods resistant to JPEG compression. The data are sourced from the experiments of [PERSON] et al. [17] and [PERSON] et al. [18]. [PERSON] et al. proposed the watermarking method of ReDMark, which uses three different types of network architecture models, including MT-Net, JT-Net, and GT-Net, during the training process. [PERSON] et al. confirmed in their experiments that, among the three different models, the two models of MT-Net and JT-Net have better robustness for JPEG compression. Therefore, we only compare the two models, MT-Net and JT-Net. In addition, [PERSON] et al. added a strength factor \(\alpha\) to the watermarking method of ReDMark to control the balance between robustness and imperceptibility. According to the experimental results of the paper, when the strength factor \(\alpha=1.0\), the imperceptibility of the watermark is poor, but the robustness is relatively good; however, when the strength factor \(\alpha=0.6\), the imperceptibility of the watermark will increase, while the robustness will decrease.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline Threshold & TP & FN & FP & TN & Accuracy & Precision & Recall & F1 Score \\ \hline
0.9 & 1598 & 2 & 5 & 13,339 & 0.999 & 0.997 & 0.999 & 0.998 \\
0.89 & 1598 & 2 & 7 & 13,337 & 0.999 & 0.996 & 0.999 & 0.997 \\
0.88 & 1599 & 1 & 8 & 13,336 & 0.999 & 0.995 & 0.999 & 0.997 \\
0.87 & 1599 & 1 & 9 & 13,335 & 0.999 & 0.994 & 0.999 & 0.997 \\
0.86 & 1600 & 0 & 10 & 13,334 & 0.999 & 0.994 & 1.000 & 0.997 \\
0.85 & 1600 & 0 & 10 & 13,334 & 0.999 & 0.994 & 1.000 & 0.997 \\
0.84 & 1600 & 0 & 14 & 13,330 & 0.999 & 0.991 & 1.000 & 0.996 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrix and accuracy indicators of model for different thresholds.  
According to Table 5, our method still has the best BER when QF = 10. When QF = 50, in terms of BER, our method is superior to the method of [PERSON] et al. and is not much different from the method of [PERSON] et al.; when QF = 70 and QF = 90, our method still has a BER of less than 0.009. It is worth noting that [PERSON] et al. did not discuss a QF of less than 50, while [PERSON] et al. did not discuss a QF of greater than 70. On the whole, the BER value of the method proposed by [PERSON] et al. and [PERSON] et al. will increase as the QF decreases, which means that the more information is lost after compression, the higher the error of the restored (retrieved) watermark. The reason is that the above two methods both increase the dimension of features through the network layer, such that the model can learn more parameters and improve robustness.  
Differently from the above two methods, our proposed architecture directly uses JPEG-compressed images for training and can train different models for different quality factors, and therefore has a low BER under any quality factor.
# The Potential of LEO Satellite-Based Opportunistic Navigation for High Dynamic Applications
## 4 Performance Characterization
### Effect of Error Sources
#### 4.2.3 Satellite Velocity Error  
A satellite velocity error distorts the predicted Doppler shift and as a result affects the navigation solution estimations. This error is added on top of the baseline scenario that contains thermal noise. Each satellite velocity coordinate has a constant error added to it. Its value is selected randomly using a Gaussian distribution with the standard deviation given in Table 5. This table defines two cases, an intermediate case in which we used an intermediate velocity error, and a worst case in which the tangent velocity error is increased to 3 m/s.  
The navigation state errors are illustrated in Figure 14. For the intermediate case of the satellite velocity error, the 3D position error is degraded by 273 m with regard to the noise only case. The degradation reaches 1.09 km for the worst case. The intermediate and worst satellite velocity errors degrade the user clock bias estimation by, respectively, several tens of and a few hundred milliseconds. The attitude, however, is slightly degraded in both the intermediate and worst cases (0.03\({}^{\circ}\) and 0.2\({}^{\circ}\), respectively).  
Figure 14: Influence of the satellite velocity error on (**a**) the position RMSE; (**b**) the attitude RMSE; (**c**) the receiver clock bias RMSE; (**d**) the receiver clock drift RMSE.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Radial** & **Tangent** & **Normal** \\ \hline Intermediate & 0.3 m/s & 1 m/s & 0.15 m/s \\ \hline Worst & 0.3 m/s & 3 m/s & 0.15 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Standard deviation of the satellite velocity error.
# A Multi-Objective Approach for Optimizing Virtual Machine Placement Using ILP and Tabu Search
## 3 Proposed Methods
### Multi-Objective Tabu Search
#### 3.2.2 Perturbation Procedure  
To generate a new solution from the current one, we define the following two perturbation procedures:  
* Swap perturbation: given a VM \(v_{i}\), where \(v_{i}\in V\), the following steps are conducted:
* Generate a random number \(k\), uniformly distributed in the interval [\(1..N\)].
* Swap VM \(v_{i}\) with VM \(v_{k}\), ensuring that \(v_{k}\) is a hosted VM. If \(v_{k}\) is not hosted, repeat the process by generating a new \(k\) until a valid swap can be performed.
* Migration perturbation
* Let \(P_{p}\), where \(P_{p}\in P\), be the PM currently hosting VM \(v_{i}\).
* Generate a random number \(q\), uniformly distributed in the interval [\(1..M\)], ensuring that \(q\
eq p\).
* Attempt to move VM \(v_{i}\) from \(P_{p}\) to \(P_{q}\). If the migration is not feasible due to resource constraints, fall back to the swap perturbation.  
The overall perturbation procedure is defined as follows:* Generate a random number \(i\), uniformly distributed in the interval [1..N], to select a VM \(v_{i}\). The two perturbations are executed, depending on whether VM \(v_{i}\) is hosted or rejected.
* If \(v_{i}\) is a rejected VM, perform a swap perturbation.
* If \(v_{i}\) is a hosted VM, execute a migration perturbation.