esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion and Implication  
### Comparison of Methods  
The primary aim of this study was to evaluate the accuracy and reliability of two GNSS-based methods--Method 1 (vertical position minima) and Method 2 (vertical velocity minima)--for estimating per-step running parameters, including the \(SF\), \(SL\), and _running velocity_, during a 400 m sprint. By comparing these two methods with reference standards (3D video analysis and IMU data), we sought to determine which method provides more precise measurements, thereby optimizing the use of RTK GNSS technology in running performance analysis. Our results demonstrate that both GNSS-based methods could estimate the running parameters. However, Method 2 consistently outperformed Method 1 in \(SF\) and \(SL\) estimation across all evaluation criteria. For instance, in the calibrated camera sections, Method 2 exhibited lower \(RMSE\) values and higher ICCs compared with Method 1 for both the \(SF\) (0.205 Hz versus 0.291 Hz; ICC: 0.886 versus 0.813) and \(SL\) (0.143 m versus 0.190 m; ICC: 0.755 versus 0.615). These findings highlight the robustness of Method 2 for precise per-step analyses, particularly in capturing the \(SF\) and \(SL\) with greater accuracy. In contrast, for the running velocity, both methods exhibited comparable accuracy, with similar \(RMSE\) and ICC values. This comparable performance is likely due to the fact that the running velocity, being a product of the \(SF\) and \(SL\), reflects a combination of their respective errors, which may compensate for each other during calculations. The superior performance of Method 2 can be attributed to its alignment with key biomechanical events. By identifying the minima in the vertical velocity profile of the runner's head, Method 2 effectively captured the moments of IC. This approach is sensitive to the impact forces during ground contact, analogous to detecting the damping effect in the spring-mass model of running [21; 26]. Consequently, Method 2 provided more precise temporal markers for each step, leading to accurate \(SF\) and \(SL\) estimations. In contrast, Method 1 relied on detecting the minima in the vertical position of the head at the mid-stance phase. This method is potentially susceptible to left-right asymmetries in leg mechanics, which can cause inconsistencies in vertical displacement patterns [24; 25]. Such asymmetries may result in variations in mid-stance timing between the left and right legs, leading to _biases_ in \(SF\) and \(SL\) estimations. As a result, although the \(R\) values were similar between Methods 1 and 2, the presence of _bias_ in Method 1 led to lower ICC values, indicating reduced reliability. The influence of the track geometry also amplified these differences. Curved sections introduce biomechanical variations [31], such as centripetal
# Frequency Contour-Strip Method for Characterization of Damage in Structures under Noisy Conditions
## 3 Existing Frequency Contour-Line Method with Limitations in Portraying Damage
### Limitations in Frequency Contour-Line Method  
Despite the remarkable characteristic, the frequency contour-line method has substantial limitations in identification of a crack in a beam. The primary two limitations are clarified in the following.
## 3 Transactive DR Based on Flexibility Potential Evaluation
### Real-Time Demand-Response  
After receiving the DR task from the distribution system operator, the scheduling plan of each aggregator is optimized in order to satisfy real-time positive and negative response demands. Considering the lowest economic cost of DR services, the prosumer aggregators at each node of the distribution system need to meet the power flow constraints.  
Here, a linearized branch flow model is adopted to describe the power flow of each prosumer aggregator.  
\[P_{mn}+p_{n,g}=\sum_{k\in\pi(n)}P_{nk}+p_{n,d} \tag{63}\]  
\[Q_{mn}+q_{n,g}=\sum_{k\in\pi(n)}Q_{nk}+q_{n,d} \tag{64}\]  
\[U_{n}=U_{m}-\frac{r_{mn}P_{mn}+x_{mn}Q_{mn}}{U_{0}} \tag{65}\]  
In addition, the power \(p_{n}\) provided by each aggregator should meet the range of evaluated flexibility:  
\[\sum_{j}P_{j,n,flex-}\leq p_{n}\leq\sum_{j}P_{j,n,flex+} \tag{66}\]  
The objective function of the scheduling plan is minimizing the total service cost:  
\[\min S=\sum_{n\in\pi(n)}p_{n}\cdot pr_{n,flex} \tag{67}\]  
After receiving the scheduling plan in (67), each aggregator performs real-time DR using a rolling horizon strategy. Each rolling decision solves the current time and the future time. The objective function of real-time scheduling is shown in (68):  
\[\min\mathrm{F}_{n}=\sum_{t\in\{\xi,\cdots\cdot T\}}\left((\mu_{\mathrm{buy},t} E_{buy,t,n}-\mu_{\mathrm{sell}}E_{sell,t,n})\Delta t+(E_{\mathrm{ch},t,n}+E_{ \mathrm{dis},t,n})\mu\cdot\Delta t\right)-\sum_{n\in\pi(n)}p_{n}\cdot pr_{n, flex} \tag{68}\]
## 4 Research Methodology
### Model Design
#### 4.2.2 Stacking Ensemble Learning  
In this section, we will discuss the proposed prediction model. As we can see from Algorithm 2, the main traffic dataset D is divided into sub datasets D = \(\{D_{1},D_{2},D_{3},....,D_{k}\}\) where K denotes the number of segments, and each segment's input parameters are total volume, average occupancy, and average delay. The output parameter of the segments is average speed. For each segment in (\(D_{k}\)), an RFR model will be utilized as a base regressor as demonstrated in Algorithm 1. In Step 1, the RFR models utilized the adjusted hyperparameter's values that are determined based on the RSCV technique to train the base RFR models. Due to the variance between traffic flow patterns of sub datasets, the hyperparameter's values for RFR models are different. In Step 2, meta regression model is developed. Stacking ensemble learning technique is used to build the DMLRF model, which combines the outputs of individual estimators (RFRs) and uses a meta model (DMLRF) to compute the final prediction where this model is trained by using cross-validated predictions of the base estimators [55]. This technique takes the output of RFR models as input of DMLRF model.  
```
Input: Split traffic dataset D into K subsets D = \(\{D_{1},D_{2},D_{3},...,D_{k}\}\), such that \(D_{i}\) is the data subset of Segment i. Combine segments between exits into J complex segments Output: A Distributed Machine Learning Random Forest (DMLRF) model for real-time traffic flow prediction.
1 Step 1: for i \(\gets\) 1 to K: (a) determine the hyperparameters' values using RSCV for data subset \(D_{i}\). (b) First level training: train random forest regression (RFR) model for data subset \(D_{i}\). End for
2 Step 2: for i \(\gets\) 1 to J Second level training: stack the output of individual estimators (RFR) and use a meta model (DMLRF) to compute the final prediction for Complex Segment i. End for
3 return DMLRF
```  
**Algorithm 2** DMLRF model creation
# Instance Segmentation and Number Counting of Grape Berry Images Based on Deep Learning
## 3 Results and Discussion
### Experimental Results  
#### 3.4.1 Preliminary Experimental Results  
The evaluation results of the example segmentation models are summarized in Table 2. The experiments showed that for the three model structures YOLACT, SOLO, and Mask R-CNN, the Mask R-CNN model achieved the highest segmentation accuracy. The Mask R-CNN model reached 88.08% and 82.04% for the \(AP_{0.50}\) and the \(AP_{0.75}\), respectively, 69.93%  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Optimizer** & **Rate** & **Decay** & **Momentum** & **Batch Size** \\ \hline YOLACT & SGD & \(1\times 10^{-2}\) & \(5\times 10^{-4}\) & 0.9 & 2 \\ SOLO & SGD & \(1\times 10^{-2}\) & \(1\times 10^{-4}\) & 0.9 & 2 \\ Mask R-CNN & SGD & \(1\times 10^{-3}\) & \(1\times 10^{-4}\) & 0.9 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration of instance segmentation model of grape berry.  
for the \(mAP\) and 85.98% for the \(mIoU\) on the test set, indicating that the test set of the segmentation mask had 85.98% of the regions matched with the real labels. In comparison, the Mask R-CNN model was 2.51% higher than the SOLO model in the \(mIoU\), 1.39% higher in the \(AP_{0.50}\), 1.77% higher in the \(AP_{0.75}\), and 2.68% higher in the \(mAP\). The Mask R-CNN model was 3.07% higher than the YOLACT model in the \(mIoU\), 2.94% higher in the \(AP_{0.50}\), 2.96% higher in the \(AP_{0.75}\), and 3.34% higher in the \(mAP\).  
Figures 11 and 12 visualize the results of the fruit segmentation on the test set of the YOLACT, SOLO, and Mask R-CNN models, respectively. To better observe the segmentation results, each fruit region was randomly filled with different colors. From the segmentation results, the three network models could detect most grape berries; however, there were differences in the segmentation details. YOLACT could essentially detect the fruit region; however, the edge of the fruit segmentation was not smooth enough, and there were many redundant boxes. The SOLO model better located each berry area, and achieved better segmentation for complete grape berries; however, the quality of the edge segmentation of the overlapping berries was not precise and complete, and there was a problem of the missed detection of individual berries. Mask R-CNN was better than the other two models in the overall edge details of fruit segmentation, but there was also the problem of the missed detection of individual fruit. Therefore, this research chose the Mask R-CNN model as the preliminary model for grape berry segmentation, and further improved the Mask R-CNN model through the linear weighting idea in Soft-NMS.
## 3 Results and Discussion
### Mechanical Properties
The variations in \(n\) and \(K\) with temperature are shown in Figure 6. It is apparent that the value of \(n\) plateaued within the temperature range of 300-400 \({}^{\circ}\)C, with a peak occurring specifically at 300 \({}^{\circ}\)C. Similarly, the value of \(K\) also exhibited a peak at 300 \({}^{\circ}\)C. As the temperature increased beyond 400 \({}^{\circ}\)C, both \(n\) and \(K\) underwent a notable decline. Specifically, as indicated in Table 1, when the temperature increased to 700 and 800 \({}^{\circ}\)C, the value of \(n\) decreased to a negative value, indicating that the softening mechanism has completely dominated the deformation of the FeCrAl alloy. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Temperature (\({}^{\circ}\)C)** & \(\sigma_{\mathbf{Y}}\) **(MPa)** & \(\sigma_{\mathbf{TS}}\) **(MPa)** & **EL (\%)** & **RA (\%)** & \(n\)** & \(K\) \\ \hline
200 & 485.0 (4.2) & 592.0 (26.8) & 16.7 (2.9) & 57.4 (6.0) & 0.117 (0.015) & 842.1 (63.7) \\
300 & 482.1 (28.3) & 616.5 (20.5) & 15.7 (2.6) & 52.1 (3.4) & 0.140 (0.013) & 935.4 (6.09) \\
400 & 446.2 (12.7) & 581.3 (10.9) & 16.6 (0.6) & 52.9 (2.9) & 0.137 (0.011) & 874.8 (30.7) \\
500 & 426.0 (38.1) & 531.0 (43.8) & 18.1 (0.6) & 59.7 (7.0) & 0.099 (0.009) & 777.2 (21.6) \\
600 & 403.3 (8.5) & 439.5 (21.9) & 20.1 (2.3) & 73.9 (5.9) & 0.050 (0.010) & 554.5 (21.3) \\
700 & 202.0 (17.0) & 182.0 (25.4) & 28.2 (7.1) & 85.1 (5.4) & \(-\)0.021 (0.002) & 181.8 (4.4) \\
800 & 94.5 (23.3) & 95.0 (22.6) & 52.0 (8.0) & 93.2 (3.0) & \(-\)0.025 (0.008) & 81.5 (17.1) \\ \hline \hline \end{tabular}  
* \({}^{1}\)\(\sigma_{Y}\), \(\sigma_{\mathbf{TS}}\), EL, RA, \(n\), and \(K\) indicate the yield strength, tensile strength, elongation, reduction in area, strain-hardening exponent, and strain intensity coefficient, respectively. \({}^{2}\) The number in parenthesis indicates the standard deviation.  
\end{table} 
Table 1: Statistical results of tensile properties of FeCrAl alloy at high temperatures. Figure 5: Variations in (**a**) ultimate tensile strength and yield strength, and (**b**) elongation and reduction in area with test temperature. Figure 6: Variation in strain hardening exponent with temperature at the strain rate of \(10^{-3}\) s\({}^{-1}\).
## 4 Results and Discussion
### Indication of Received Signal Strength (RSSI) vs. Signal-to-Noise Ratio (SNR)
#### 4.1.1 Network Performance for Different SF Configurations  
The averages of RSSI and SNR per SF configuration in the LoRaWAN network are shown in the Figure 27.  
The closer the RSSI is to zero, the better the signal intensity, in this sense it is possible to observe in the Figure 27a that the larger SF configurations (10 to 12) have an average signal intensity between \(-110\) and \(-103\) dBm having a mm better signal intensity compared to the smaller SF configurations (7 to 9) having an average between \(-112\) and \(-110\) dBm. The difference between the settings is not significant, however, when it comes to signal in relation to noise, even with a better RSSI, the larger SFs are more affected by interference in relation to barriers as can be seen in the Figure 27b. The smaller SFs have an average  
Figure 26: Boxplot chart.  
Figure 27: Average RSSI and SNR per SF LoRaWAN. (**a**) RSSI. (**b**) SNR.  
between \(3\) and \(-5\) dBm of SNR, while the larger configurations have from \(-5\) to \(-15\), which means that considering \(-7\) an ideal SNR, the larger configurations are significantly affected by the barriers and this is due to the greater distances that these configurations allow to achieve precisely because of the RSSI they operate.  
In the LoRaMESH scenario, Figure 28 the SF configurations have the same performance as the LoRaWAN in terms of both RSSI and SNR, however the difference between the SFs is less significant compared. In terms of RSSI, Figure 27a, the LoRaMESH operates with an average between \(-98\) and \(-118\) dBm, when it comes to SNR the variation is between \(12.6\) and \(11.1\) dBm. In this case, the variation between the SF settings show operating equally for both LoRaWAN and LoRaMESH, having better RSSI values and worse SNR values, Figure 27b, for larger SFs and the opposite for smaller SFs, however, it is worth noting that the LoRa technology that operates with LoRaMESH has a better average RSSI compared to the LoRaWAN, reaching differences of up to \(20\) dBm better, when the SNR is considered, the LoRaMESH is able to operate with a higher value, even if it is using larger SF. That is, it is possible to conclude that the LoRaMESH has less signal interference in relation to possible noises, but this only happens because the technology itself works with smaller distances like \(600\) m maximum. LoRaWAN operates with distances of up to \(2.35\) km and this is a relevant factor for these results.
# Lossless and High-Throughput Congestion Control in Satellite-Based Cloud Platforms +
## 4 Implementation  
Our CCLF approach focuses on preventing packet loss, reducing flow completion time, improving bandwidth utilization, and enhancing network throughput. Notably, Interest packets are smaller in size than Data packets. The network reserves a portion of its available bandwidth specifically for forwarding Interest packets in all directions. Therefore, this study concentrates on congestion control and traffic scheduling for Data packet forwarding, excluding the scheduling of Interest packets.  
To evaluate the performance of our proposed CCLF method, we implemented it in an NS-3-based NDN simulator, namely ndnSIM [39]. This simulator includes the NFD prototype [40] and the ndn-cxx library [41]. The ndn-cxx is a C++ library for implementing NDN primitives, while NFD is a network forwarding daemon that implements the logic for packet forwarding and processing.
## 2 Materials and Methods
### Image-Based Fire Heat Release Rate Prediction Methods
#### 2.1.2 YOLO Segmentation-Model-Based HRR Prediction  
To provide a baseline for comparison with the proposed YOLO-YCbCr-based HRR prediction method, a deep learning-based approach using the YOLOv8 segmentation model (YOLO-seg) [33] was also implemented. Except for the segmentation component, the overall HRR prediction process (flame size extraction and HRR estimation) follows the same procedure as described for the YOLO-YCbCr-based HRR prediction model. As illustrated in Figure 3, the architecture of the YOLO-seg model includes three primary components, namely, backbone, neck, and output, each designed to optimize real-time object segmentation performance. The backbone is responsible for extracting hierarchical features using 3 \(\times\) 3 convolution layers, C2f modules, and a spatial pyramid pooling fast (SPPF) module. This architecture replaces the C3 module used in previous YOLO architectures with a C2f structure, improving gradient propagation and reducing computational overhead, thereby enhancing the training efficiency. The SPPF module maintains its role  
Figure 2: Procedure of image-based fire heat release rate estimation.  
in aggregating multiscale spatial information, enabling the model to effectively capture objects of varying sizes. The extracted features are further processed in the neck through concatenation operations and upsampling, reinforcing multiscale feature fusion. Unlike earlier YOLO models, the YOLO-seg model reduces the number of \(1\times 1\) convolution layers in the feature fusion process, enabling a more direct integration of multiscale features. This approach not only reduces unnecessary computations but also better preserves spatial information. Finally, in the output stage, the model introduces a mask head to predict object boundaries at the pixel level, enabling detailed segmentation. Through these architectural enhancements, the YOLO-seg model maintains high segmentation accuracy with the high computational efficiency of YOLO-based models, which makes it suitable for real-time video analysis applications that require simultaneous object detection and segmentation.
# IOF-Tracker: A Two-Stage Multiple Targets Tracking Method Using Spatial-Temporal Fusion Algorithm
## 3 Experimental Results and Analysis  
### Dataset Section  
This experiment was conducted on the two most popular datasets in the field of multi-target tracking under unconstrained environments for pedestrian detection and tracking: MOT17 [17] and MOT20 [18]. MOT17 contains video sequences captured by both static and moving cameras. In contrast, MOT20 includes crowded scenes and has added new video sequences and more complex scenarios, significantly enhancing the test for target tracking algorithms. Both datasets contain training and test sets but no validation set.
# Strength and Compressibility of Ammonia-Soda Residue from the Solvay Sodium Plant
## 3 Results and Discussion
### Drained Triaxial Tests
#### 3.3.4 Stiffness  
The small-strain stiffness can be assessed by the tangential shear modulus (G\({}_{\text{sec}}\)) calculated using the relation between the deviator stress and deviator strain as follows:  
\[G_{sec}=\frac{\delta q}{3\delta\varepsilon_{\text{s}}} \tag{4}\]  
where \(\delta_{\text{q}}\) is the increment in deviator stress and \(\varepsilon_{\text{s}}\) is the increment in deviator strain.  
The degradation of G\({}_{\text{sec}}\) with the deviator strain for different confining pressures is presented in Figure 8a. The stiffness of the ASR increases with the confining pressure. This trend is corroborated by the reported dependence of the normalised shear modulus and confining pressure [57; 58]. The shape of the degradation curves varies slightly for the ASRs tested (Figure 8a). The typical S-curves were found in the most conducted tests. The samples of ASR I show the more gradual degradation of stiffness compared with the samples from ASR II. All tests, except the sample from ASR II under confining pressure of 500 kPa, indicate a threshold yield strain, which separates the plateau of the initial constant stiffness from the start of substantial stiffness degradation. Figure 8b illustrates the variation of stiffness with the deviator stress for the considered confining pressures. The stiffness of ASR reduced with the deviator stress.  
Figure 8: Small-strain stiffness of ASR: (**a**) deviator strain \(\varepsilon_{\text{s}}\) vs. tangential shear modulus G\({}_{\text{sec}}\), (**b**) deviator stress q vs. tangential shear modulus G\({}_{\text{sec}}\).
## 3 The Location-Based Interactive Model
### Periodic Sensing Model  
In the current IoT-cloud model [3], sensors periodically report sensing data to the cloud without awareness of location and demand of mobile users. The advantage of this model is that sensing data can be updated to the cloud periodically so that sensing data are highly available to users.  
However, we argue that this approach has the following limitations: (1) sensors report sensing data periodically without knowledge about demand of users, so a high volume of data may be redundant; (2) waste storage in cloud; (3) high energy consumption required by sensors; and (4) sensing data are collected with a predefined fixed QoS metrics only (i.e., delay, sensing rate). The type of model may be suitable for applications having fixed QoS requirements only while in practical applications and users may have different requirements. In many applications, sensing data are requested on demand in the context of users. When there is no demand from users, continuous sensing report is obviously unnecessary and inefficient for resource-constrained sensors.
# Accelerated Testing and Reliability of FDM-Based Structural Electronics
###### Abstract  
With the development of the miniaturization of electronic systems, heat dissipation from components has become an increasing challenge. Structural electronics represent a new approach to this problem. Instead of downsizing all the elements, in this idea, electronic parts of the device are embedded into its mechanical construction. This approach has many advantages, but the reliability of systems constructed this way has not been extensively studied so far. In this work, circuits consisting of silver ink conductive traces were printed on FDM polymer substrates, with or without 0 T resistors, and were subjected to accelerated aging testing. The samples were divided into three groups, and for each of them, the mean time to failure was calculated, which for the best group was 8000 h. This paper also presents the mechanism that led to the failure of these systems, as well as actions that will lead to the elimination of this phenomenon.  
structural electronics; reliability; additive manufacturing; 3D printing 2022