esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Identification of Stickwater Proteins  
The molecular weight of the proteins present in the stickwater was determined using a polyacrylamide SDS gel. The molecules present in the gel had molecular weights ranging from 6 to 200 kDa (Figure 2). Molecular weights of 200 kDa suggest the presence of the heavy chain of myosin, especially in those solutions treated with concentrations of 0.2 M and 2 M NaCl. These results are expected due to the concentration of muscle proteins in the effluent. During the fishmeal manufacturing process, one of the steps consists of pressing the fish, and an essential part of the muscle tissue is drawn into the pressing effluents [32]. Other proteins found in Alaska pollock SW and cod are troponin or tropomyosin, with a molecular weight between 32 and 34 kDa. However, in other studies, the proteins found with the highest concentration had molecular weights of 39, 60, 120, and 198 kDa, consistent with those found in this study [7].  
Finally, the presence of low-molecular-weight peptides (6 kDa) is considered a product of protein degradation and denaturation during thermal processing and pressing of fish [7; 30]. However, more research is needed to determine the origin of these molecules.  
Figure 2: Protein patterns of anchoveta stickwater with and without ultrasonic pulses. MW, molecular weight standard; SW, stickwater; MP, myofibrillar protein; SP, sarcoplasmic protein; StP, stromal protein.
# Improving Medical X-ray Report Generation by Using Knowledge Graph
## 4 Experiments
### Implementation Detail  
We use Densenet-121 as the core of our CNN model, and all images are resized to 256 \(\times\) 256. We use Transformer as the core of the text encoder. Both generators and interpreters are implemented based on Transformers and trained from scratch, all hyperparameters are selected based on the performance on the validation set, and the number of reporting encoder layers is set to 12. We train the classification and generation reports on the Open-I and MIMIC-CXR datasets using the Adam optimizer with an initial learning rate of \(3\times 10^{-4}\) and weight decay of \(1\times 10^{-2}\). For the interpreter, the Open-I dataset is trained using a learning rate of \(3\times 10^{-5}\), and the MIMIC-CXR dataset is trained using an Adam optimizer with a learning rate of \(3\times 10^{-6}\) and a weight decay of \(1\times 10^{-2}\). Wetrain the model with epochs of 50 for both Open-I and MIMIC-CXR datasets. We evaluate the proposed model on the validation set. Our experiments are trained in parallel on two RTX3090 sheets, and the experiment for each dataset is mainly divided into two parts. The first part is the training of the classifier and generator, and then the trained model is added to the interpreter for the second part of training. Each part is trained for 50 epochs in our experiments. The training time of the first and second parts is about 54 s and 59 s per round for the Open-I dataset, and about 34 min and 35 min per epoch for the MIMIC-CXR dataset, respectively.
# Training from Zero: Forecasting of Radio Frequency Machine Learning Data Quantity
## 3 Results and Discussion
The general understanding given in Figure 8 is that both NCE and LEEP will give a more realistic prediction for data quantity than Accuracy alone, while the prediction given by LogME can serve as an upper bound. Due to the log-linear regression, any deviation can result in orders of magnitude error in terms of either underestimation or overestimation, and since there are not enough data to acquire the metric that produces the best GoF regression, a midpoint estimate is recommended when only minimal data are available. The midpoint estimate seeks to balance the two extremes such that the estimate becomes \(\tilde{q}_{\text{MidPoint}}=0.5\cdot(\tilde{q}_{\text{NCE}}+\tilde{q}_{\text{ LogME}})\), such that the midpoint estimate averages the quantity estimates on the log scale rather than the linear. Returning to Table 6, an important note is understanding how long a sequential collection of data of this kind would take in order to accomplish; that is, for a collection that records at 10 kHz, these waveforms from three waveform groups {\(\Phi_{3}\),\(\Phi_{5}\),\(\Phi_{10}\)}, a collection of the number of observations implied, would require [1.72, 144.5, 33.4] years to acquire the target observations needed for the \(\Omega_{C}\) predictions and would require [0.989, 82.9, 19.1] terabytes of storage to store them in an uncompressed state. While this could be feasible if the collection was performed in a parallel rather than a sequential collection, the suggestion \begin{table}
\begin{tabular}{c c c} \hline \hline \(\Phi\) & \(\Omega_{C}\) & \(\Omega_{A}\) \\ \hline \(\Phi_{3}\) & \(88.5\times 10^{6}\) & \(3.47\times 10^{15}\) \\ \(\Phi_{5}\) & \(4.45\times 10^{9}\) & \(2.41\times 10^{21}\) \\ \(\Phi_{10}\) & \(514\times 10^{6}\) & \(1.57\times 10^{23}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Quantity estimates being taken as truth for the combinations of waveform groups, \(\Phi\), and training datasets, \(\Omega\). The augmented quantities are estimated using the LogME metric regression, while the captured quantities are estimated from either the NCE or LEEP metric based on the GoF in Table 4.  
Figure 8: Quantity predictions based on a limited number of available data used to regress the estimate on the [Top Row: Capture \(\Omega_{C}\); Bottom Row: Augmented \(\Omega_{A}\)] datasets when being used to estimate across the [Left: \(\Phi_{3}\); Center: \(\Phi_{5}\); Right: \(\Phi_{10}\)] waveform space. The lines represent using {Accuracy: circle, NCE: square, LEEP: diamond, LogME: pentagram, Log Scale Midpoint of NCE and LogME: hexagram, Target: none} to predict data quantity needed, while Target is determined in Table 6.  
that should be taken rather than immediately starting a long-term collection is to instead improve the training routine and model architecture to instead allow for this procedure to produce a regression with a more significant slope than the approach used to produce these results.
## 4 Experiments
### Ablation Experiments
#### 4.5.1 Effectiveness of the Pretraining  
Before training, we first initialized the weight parameters using Res2 Net-50 [27] pretrained on the ImageNet [28] dataset, with the pretrained model provided by Res2 Net-50 [27]. To demonstrate the necessity of pretraining, the ablation experiment was conducted on the BCDD dataset. Moreover, the results are tabulated in Table 7, where \(\times\) indicates no pre-trained and \(\surd\) indicates pre-trained. From the visualization results in Figure 10, it is noticeable that the metrics were significantly lower without pretraining than with pretraining, based on which the necessity of pretraining is confirmed.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Model** & **Params.(M)** & **Flops(G)** & **Precision** & **Recall** & **F1** & **IoU** & **OA** \\ \hline
**FC-EF** & 1.29 & 2.92 & 75.97 & 70.80 & 73.29 & 57.85 & 87.83 \\
**FC-Siam-conc** & 1.93 & 4.55 & 76.41 & 76.17 & 76.29 & 61.67 & 88.83 \\
**FC-Siam-diff** & 1.75 & 3.99 & 88.05 & 55.29 & 67.92 & 51.43 & 87.68 \\
**CDNet** & 1.36 & 21.52 & 81.09 & 78.38 & 79.72 & 66.27 & 90.59 \\
**STANet** & 16.93 & 6.58 & 83.31 & 74.00 & 78.38 & 64.45 & 90.37 \\
**BiT** & 3.55 & 10.6 & 82.36 & 74.30 & 78.12 & 64.10 & 90.18 \\
**SNUNet(48)** & 28.34 & 97.87 & 79.04 & 76.71 & 77.86 & 63.75 & 89.71 \\
**ChangeFormer** & 267.90 & 129.27 & 84.99 & 70.93 & 77.33 & 63.04 & 90.19 \\ \hline
**DETDNet** & 56.33 & 18.08 & 83.05 & 79.29 & 81.13 & 68.25 & 91.30 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison results on the SYSU-CD dataset.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Pretrained** & **Precision** & **Recall** & **F1** & **IoU** & **OA** \\ \hline \(\times\) & 90.27 & 86.83 & 88.52 & 79.40 & 98.94 \\ \(\surd\) & 93.84 & 91.59 & 92.70 & 86.40 & 99.32 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The effect of pretraining.  
Figure 9: Visualization results of several CD methods on the SYSU-CD dataset. (**a**) T1 images. (**b**) T2 images. (**c**) Ground truth. (**d**) FC-EF. (**e**) FC-Siam-conc. (**f**) FC-Siam-diff. (**g**) CDNet. (**h**) STANet. (**i**) BIT-CD. (**j**) SNUNet. (**k**) ChangeFormer. (**l**) DETDNet.
# DANet: A Semantic Segmentation Network for Remote Sensing of Roads Based on Dual-ASPP Structure
## 2 Materials and Methods
### Loss Function and Evaluation Metrics
#### 2.4.2 Evaluation Metrics  
Evaluation metrics such as _Intersection over Union_ (_IoU_), _Recall_ (_Re_), and _F1-score_ are used to validate the network structure performance.  
The _Intersection over Union_ (_IoU_) method is used to calculate segmentation accuracy by avoiding the impact of background pixels. This results in a more precise representation of the segmentation outcome. The calculation formula for _Road IoU_ used in semantic segmentation of remote sensing images is shown in Equation (7):  
\[Road\ IoU=\frac{TP}{TP+FP+FN} \tag{7}\]  
Figure 6: Schematic diagram of TP, TN, FP, FN.  
_Re_ is a metric used to assess the accuracy of a model's segmentation performance by measuring the correct detection of the target area and its location. A higher value of _Re_ indicates better performance. Equation (8) provides the formula for calculating _Re_.  
\[Re=\frac{TP}{TP+FN} \tag{8}\]  
The _F1-score_, also known as the balanced F-score, is a metric that combines Precision (Equation (9)) and _Recall_. It is defined as the harmonic mean of Precision and _Recall_. The _F1-score_ can range from 0 to 1, where a value of 1 indicates the best model output and a value of 0 indicates the worst model output. Equation (10) represents the calculation of the _F1-score_.  
\[Precision=\frac{TP}{TP+FP} \tag{9}\]  
\[F1\text{-}Score=\frac{2}{\frac{1}{Precision}+\frac{1}{Recall}}=2\times\frac{ Precision\times Recall}{Precision+Recall} \tag{10}\]
# Research into Robust Federated Learning Methods Driven by Heterogeneity Awareness
## 3 Heterogeneity-Aware Federated Optimization Framework
### Overall Framework of Heterogeneity-Aware Federated Optimization  
#### 3.1.1 Modeling  
In this part, we model the problem of federated optimization under non-IID data conditions. The goal is to quantify and incorporate the heterogeneity across clients into the federated learning framework. Our model consists of three main components, as illustrated in Figure 2.  
(A) A Global Feature Extractor: This component models the local data distributions across clients and extracts consistent embeddings for each client.  
(B) A Latent Information Extractor: We estimate the distributional characteristics (mean and covariance) of the local feature space to quantify the heterogeneity among clients.  
(C) A Heterogeneity-Weighted Aggregation Strategy: The heterogeneity metric from each client influences the weight assigned during aggregation, allowing the global model to focus more on stable clients.  
The integrated model supports both local training and global aggregation to ensure robustness under heterogeneous conditions.
## 3 Uncertainty Propagation Approach of Constellation Phase
### Hypothesis Testing of Monte Carlo Simulations Results  
The overall terminal phase quantities obtained from the Monte Carlo simulation are denoted as \(X_{t}\). The Gaussian distribution remains normal after propagation by the linear or linearized dynamic equations [29], while the two-body motion plays a dominant role in the orbital dynamics, and the \(J_{2}\) regimens and the atmospheric drag regimens are small quantities. Thus, the terminal quantities can be considered to obey the same Gaussian distribution \(X_{t}\sim N(\mu_{t},\sigma_{t})\). In order to test whether the mean \(\mu_{t}\) of the relative phase distribution is zero, a statistical hypothesis test is required [30]. Two mutually opposing hypotheses are made for \(X_{t}\)  
\[H_{0}:\mu_{t}=0,\,H_{1}:\mu_{t}\
eq 0. \tag{19}\]  
The test statistic distinguishing the first hypothesis from the alternative hypothesis is \(z\) value and is given by  
\[z=\frac{E(\Delta\phi)-0}{\sigma(\Delta\phi)/\sqrt{N}}. \tag{20}\]  
The significance level \(\alpha\) is given and the standard Gaussian distribution table is queried to determine the rejection domain. If \(|z|<z_{\alpha/2}\) is satisfied, the hypothesis \(H_{0}\) is accepted (the opposing hypothesis \(H_{1}\) is rejected), i.e., it is considered that, ideally, two co-plane satellites will not diverge or cluster under perturbations; if \(|z|\geq z_{\alpha/2}\), the hypothesis \(H_{1}\) is accepted (the hypothesis \(H_{0}\) is rejected), i.e., it is considered that ideally the non-uncertainty case, the relative phase will also have drifted.
# Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation
## 2 Materials and Methods
### Traditional Image Processing Algorithms Description
#### 2.5.3 Weighted Iterative Threshold Approach  
This method is an iterative way to compute the optimal threshold for a given image. At each step, the threshold is computed using the following formula:  
\[T=\frac{(1+\delta)\times\mu_{1}+(1-\delta)\times\mu_{0}}{2} \tag{4}\]  
where \(\mu_{1}\) and \(\mu_{0}\) are, respectively, the average gray level values of foreground and background pixels. With this new threshold, new sets of foreground and background pixels can be computed with the following mean values, and the process repeats with the computation of a new threshold. When the difference between two successive thresholds is lower than a certain tolerance \(\Delta\), the process stops. This algorithm is dependent on a scalar parameter \(\delta\) which can vary in the range \([-1.0,+1.0]\).
# Experimental Study on Properties of Ultrasonic Coupling Agent with Graphene in NDT
## 2 Principle and Performance Index of an Ultrasonic Coupling Agent
### Performance Evaluation Index  
In this work, the performance of the composite coupling agent was evaluated by analyzing peak-to-peak value, attenuation coefficient, and energy value of the received signal in the time domain. Peak-to-peak value refers to the difference value between the highest and lowest value of a signal in the same period. Peak-to-peak values \(V_{p}\) are expressed by the following formula:  
\[V_{p}=V_{\text{max}}-V_{\text{min}} \tag{4}\]  
\(V_{\text{max}}\) and \(V_{\text{min}}\) denote the maximum and minimum amplitude values of the first echo received in the time domain, respectively.  
The attenuation of ultrasonic wave is more sensitive to material properties [17]. The phenomenon of energy loss will occur when sound waves propagate through a medium or across media's boundaries [18]. In addition, the ultrasonic wave velocity is directly linked to the mechanical properties of an elastic medium: Young's modulus (\(E\)), Poisson's ratio (\(v\)), and density (\(\rho\)) [19]. When the propagation medium changes, it will cause energy loss and attenuation. The main attenuation of ultrasonic wave originates from absorption and scattering, and depends on the properties and thickness of the medium [20]. The ultrasonic attenuation coefficient of single-layer steel can be expressed as:  
\[\alpha=-\frac{10}{d(n-m)}\text{lg}\frac{P_{n}}{P_{m}}(\text{dB/mm}) \tag{5}\]  
where \(d\) value is the thickness of the steel plate; \(P_{n}\) and \(P_{m}\) denote echo sound pressure amplitude at the \(m\) times and \(n\) times, respectively. In this paper, the amplitude of the first and second echo was selected to calculate \(P_{n}\) and \(P_{m}\), namely \(n=1\) and \(m=2\).  
In this study, the energy value \(E\) is used to indicate the amount of energy passing through the specimen. The energy calculation formula [13] of the signal can be expressed as:  
\[E=\int_{0}^{t}A^{2}dt \tag{6}\]  
where \(A\) is the peak value of the primary echo signal at time \(t\).  
In the time domain diagram, the amplitude square of the ultrasonic primary echo signal is integrated, and the obtained integral value is taken as the energy value of the ultrasonic primary echo signal.  
In addition, the pulse method is used to measure ultrasonic wave velocity in the coupling agent. Ultrasonic sound velocity can be obtained by the following formula:  
\[C=\frac{L}{\Delta t} \tag{7}\]  
where \(\Delta t\) is the time between the excitation signal and the received signal observed on the oscilloscope, \(L\) is the thickness of the coupling agent. In this work, \(L=20\) cm.  
According to Equation (3), the acoustic impedance of the composite coupler was calculated. The acoustic impedance of the coupling agent was obtained when graphenewas added to the coupling agent or not; the influence of graphene on the performance of the coupling agent was easily obtained.
# Localization of a BLE Device Based on Single-Device RSSI and DOA Measurements
## 8 Conclusions and Further Work  
In this paper, we presented an alternate approach to finding the location of a BLE beacon for applications that involve finding missing items. An Arduino BLE 33 sense board was developed as an Eddystone BLE beacon and a scanner application was developed on an iOS device to find the beacon. By utilizing the RSS information, which is a part of the advertising packet transmitted by a beacon, and the inbuilt magnetometer and GPS of an iOS device, we were able to design an algorithm that effectively navigates the user towards the Beacon in multipath fading environments. Through experimental results, we successfully demonstrated the accuracy of the designed algorithm and the method of measurement in different layouts of a home. Our results show that, from 13 different measurements in finding a beacon using the proposed algorithm and measurement method, the estimated DOA was within 60\({}^{\circ}\) of the actual direction 92.3% of the time and within 30\({}^{\circ}\) 77% of the time. Additionally, the results presented in this paper can be easily reproduced with other scanner devices like Android smartphones and different BLE beacons like iBeacons or Alt-Beacons.  
Although a home environment can be a good testing ground to show the effects of multipath fading, our research did not include testing in an outdoor environment or with many Bluetooth signal sources (like a crowded mall) where these effects are amplified. This research did not evaluate the battery consumption for the beacon and the scanner device (i.e., a smartphone) while capturing raw data. This evaluation can be part of an extension of this work. The newer version of Bluetooth (i.e., version 5.4) introduced additional features such as periodic advertising with response (PAwR) and encryption of advertising data, which allows for secure bidirectional communication between the beacon and the scanner. In our research, we tested with Bluetooth v5.0, which only supports uni-directional advertising. Adapting and testing our proposed algorithm and measurement method with the newer Bluetooth v5.4 by utilizing PAwR can be a good extension of this research. This research can also be extended into the field of home and warehouse robotics.  
Conceptualization, H.K. and X.L.; methodology, H.K. and X.L.; software, H.K. and X.L.; validation, H.K., Y.C., V.C. and X.L.; formal analysis, H.K., Y.C., V.C. and X.L.; investigation, H.K. and X.L.; resources, X.L.; data curation, H.K. and X.L.; writing--original draft preparation, H.K. and X.L.; writing--review and editing, H.K., Y.C., V.C. and X.L.; visualization, H.K. and X.L.; supervision, X.L.; project administration, X.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 18: Histogram of the difference between the estimated DOA and actual direction of the BLE beacon.  
Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 4 Proposed Architecture
### Gateway Layer  
This layer was designed to manage the device groups deployed in different buildings to ensure the security and scalability of the framework. Once the new device was mounted, its corresponding gateway could update the container manager in the next layer (Fog layer) to update its discovery service, thus allowing other containers to be updated.  
The gateway layer ensured that all containers were running efficiently. The decisions with light computation needs were also done at this level, as this Pi board had constrained resources.
###### Abstract  
To reveal the influence mechanism of mining speed on roof fracture-type rockburst, the Brazilian split technique combined with acoustic emission monitoring technology was employed to study the effects of loading rates on the tensile failure characteristics and acoustic emission parameters of coal series sandstone. The linear relationship between the tensile strength of the samples and the change rate of tensile stress was determined. The mining speed was introduced into the mechanical model of initial and cyclic fracture of the hard roof, and the quantitative relationship between the maximum rate of change of tensile stress within the hard roof and the mining speed was derived. Based on this, a computational model for the bending elastic energy of the hard roof during initial and cyclic fractures, considering the mining speed, was established. The main findings are as follows: As the loading rate increases, the distribution range of acoustic emission energy in sandstone Brazilian split samples before failure widens, with a significant rise in acoustic emission ring-down counts and energy at failure. At lower loading rates, acoustic emission events primarily occur near sample failure, whereas at higher rates, they mostly happen in the early loading stage. The higher the mining speed, the less opportunity there is for internal micro-fractures to develop and expand before the hard roof fractures, which macroscopically results in increased tensile strength and a larger amount of energy released at the moment of fracture. Bending elastic energy rises approximately linearly with mining speed, and the thicker the hard roof, the more sensitive the bending elastic energy is to changes in mining speed. This effect is even more pronounced during cyclic fractures. Optimizing mining speed is crucial for preventing roof fracture-type rockbursts, especially in mining workfaces with thick and hard roofs.  
hard roof; Brazilian splitting test; tensile strength; bending elastic energy; mining speed Article Study on the Tensile Failure Characteristics and Energy Calculation Model of Coal Seam Hard Roof Considering the Mining Speed. _Appl. Sci._**2024**, _14_, 5734. [[https://doi.org/10.3390/app14135734](https://doi.org/10.3390/app14135734)]([https://doi.org/10.3390/app14135734](https://doi.org/10.3390/app14135734))