esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Principles  
### Focusing Delay Calculation  
The focusing delay time is related to the parameters of the transducer and the position of the focal point. The main parameters involved in the ultrasound transducer include subarray number and array spacing. The subarray number of elements \(m\) was 64 and the array spacing d was 1 mm. The position of focal point P was on the central axis of the subarray and the depth of the scanning was 180 mm. The known parameters were the ultrasound velocity \(c\) and the sampling rate \(f_{s}\), which were 1520 m/s and 40 MHz, respectively. The calculation of the focusing delay time \(\tau\) of the 64 channel linear array transducer is shown in Figure 5. However, the calculation only needed to consider channels 1 to 32, because the 64 channels were symmetrically related to the central axis of the subarray.  
The focusing delay time \(\tau_{j}(F,\beta_{j})\) of channel \(j\) can be calculated with the formula shown in Equation (1).  
\[\begin{array}{c}a_{j}=\left|\frac{64+1}{2}-j\right|\times d\ \ \ (j=1,2,\ldots,32)\\ \tau_{j}\big{(}F,\beta_{j}\big{)}=\left(\sqrt{{a_{j}}^{2}+F^{2}}-F\right)/c\\ \Delta F=c/2f_{s}=0.019\text{mm}\end{array} \tag{1}\]  
where the distance between array element \(j\) and the center axis is \(a_{j}\); the angle between the array element \(j\) and the center axis is \(\beta_{j}\); the focal length is \(F\); the ultrasound velocity is \(c\); the array spacing is \(d\); and the minimum distance between the two adjacent focal points is \(\Delta F\)[31; 32; 33].
## 4 Results
### Deep Displacement Data
#### 4.3.1 Rapid-Motion Stage  
This motion stage was recorded between February and early March 2016, with a duration of approximately one month, and consisted of two main events of movement. It was detected at all three monitored failure depths, albeit with some slight differences in terms of both magnitude and the timing of deformations (Figure 14).  
The total displacements measured by the FON2 and FON3 probes were comparable, showing two simultaneous positive gradients in cumulative displacement (Table 2 and Figure 14a). However, it should be noted that some interruptions in data acquisition occurred due to power outages. The longest lack of data affected the FON3 probe during the first part of the motion stage, with a duration of 14 days. Instead, the magnitude of deformations was lower at the landslide foot (i.e., FON4 probe) and roughly within the margin of accuracy of the sensor during the initial phase of the stage; here, only one small step in cumulative deep displacement occurred, which was, however, synchronous with the second one recorded by the probes within the FON2 and FON3 tubes. Some interesting outcomes were also noted in terms of daily displacement rate (Figure 14b and Table 2).  
Overall, the maximum rates of displacement recorded during the whole RM stage can be classified as \"very slow\" according to [63]. More specifically, during the first phase of motion, a sequence of short duration and small magnitude increases and decreases in displacement rate were generally recorded in FON2, while in FON3 a remarkable acceleration, outlined by an isolated peak in velocity, occurred (Figure 14b and Table 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Probe Id** & **Period** & **Daily Data Records** & **Total Displacement** & **Max. Displacement Rate (mm/Day)** \\ \hline FON2 & 6 February 2016–10 March 2016 & 31 & 5.4 & 0.9 \\ FON3 & 6 February 2016–10 March 2016 & 19 & 5.2 & 1.7 \\ FON4 & 6 February 2016–10 March 2016 & 28 & 1.9 & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the most important duration and mobility data regarding the rapid-motion (RM) stage.  
Figure 14: Average cumulative daily displacement (**a**) and daily displacement rate (**b**) plotted together with daily rainfall during the RM stage. In both panels, the main data gaps are indicated.  
The second phase of motion was characterized by a noticeable acceleration followed by a deceleration. Interestingly, this marked increase in displacement rates was simultaneous across the various sectors of the landslide, but did not appear to be directly triggered by the single severe rainstorms.
# Simulation and Design of Three 5G Antennas
## 5 Design of an Eight-Element MIMO Array Antenna for Mobile Terminals with a Metal Frame
### Fabrication and Measurement of the Eight-Element MIMO Array Antenna  
To validate the performance of the designed eight-element MIMO array antenna in real-world usage, the next step involves fabricating the antenna model for measurements. This will include measuring S parameters (reflection coefficients and isolation), radiation efficiency, radiation patterns, MIMO antenna correlation envelope coefficient (ECC), and channel capacity.
## 4 Simulation Results and Discussion
### Complexity Analysis  
Assume that \(K\) is the number of users in the system, and \(N\) refers to the number of subcarriers. In the root-finding method, the subcarrier and power allocation complexity are \(O(KN)\) and \(O(K)\)[21], while in the linear method, they are \(O(KN\text{log}_{2}N)\) and \(O(K)\)[22]. In the proposed method ACO-SPA, the ACO algorithm for subcarrier allocation utilizes several iterations with a number of ants to search for the optimal solution, which requires relatively higher complexity to achieve the high system sum rate and the strict fairness, with respect to the root-finding and linear methods. There is a tradeoff between computational complexity, and improvement of the sum rate and system fairness. The proposed method ACO-SPA achieves a sum rate higher than those of the root-finding and linear methods under the same conditions. Moreover, the proposed method ACO-SPA obtains a strict system fairness with the [PERSON]'s  
Figure 8: The normalized rate ratios per users.  
Figure 9: The [PERSON]’s fairness index of system _versus_ the number of users.  
fairness index identically equal to 1 while other two methods have different levels of relaxation of proportionality constraints. In a system with \(K\) users and \(N\) subcarriers, there are \(K^{N}\) possible subcarriers allocations. In terms of the optimal resource allocation, the computational complexity of the proposed ACO-SPA is significantly reduced. Assume the number of ants is \(N_{a}\), the maximum iteration is \(N_{i}\). Table 3 shows the comparison of the computational complexity of three methods.
# NDVI or PPI: A (Quick) Comparison for Vegetation Dynamics Monitoring in Mountainous Area
## 4 Discussion
### Contributions and Perspectives  
The primary contribution of this study is to explore the potential of PPI in alpine regions, characterized notably by seasonal snow cover and the abundance of coniferous forests. The results are promising, indicating that PPI offers a reliable opportunity for remote sensing-based phenological and productivity monitoring in these challenging environments. Furthermore, our study underscores the significance of the recently launched Sentinel-2 satellite constellation, which provides enhanced possibilities for more accurate studies with its 10 m resolution and 5-day revisit time, particularly in complex terrain such as mountainous areas. To advance this research field, we recommend (i) incorporating more detailed vegetation types, (ii) conducting additional comparisons with specific ground data tailored to mountain environments, and (iii) performing pixel-resolution analysis to better understand the impacts of topography in future investigations.
# IoTBystander: A Non-Intrusive Dual-Channel-Based Smart Home Security Monitoring Framework
## 8 Limitations and Ethical Considerations
### Bias in Detection  
As with any machine-learning-based system, there is the potential for bias in detection if the training data are not representative of the full diversity of devices and activities in real-world smart home environments. If the model is trained predominantly on data from a specific type of device or user behavior, it may perform poorly for underrepresented devices or activities. For example, events from less common devices and events that occur sporadically may be misclassified.  
To minimize the risk of biased detection, we will ensure that the training datasets used to train IoTBystander are as diverse and representative as possible. This includes incorporating data from a wide range of smart and non-smart devices, as well as ensuring that both typical and rare user behaviors are represented. In addition, the system can be periodically evaluated and updated with new data to capture emerging trends and scenarios. We also suggest integrating continuous learning mechanisms, where the system can learn from user feedback on detection accuracy and make adjustments over time to improve performance across all events.
# A Method of Selecting Automated Driving Service Sections Considering Spatial Influence Factors
## 2 Data Analysis
### Status of Field Data  
The field data used in this study are collected by an automated driving vehicle sensor and transmitted to the roadside unit (RSU) through the communication terminal in vehicles. The data are then transmitted and stored to the Cooperative-Intelligent Transport Systems (C-ITS) center server in Seoul every second through commercial networks such as long-term evolution (LTE). The provided data are monthly and consist of 27 columns, including vehicle and location information and vehicle condition information. This study uses a driving mode change (ctrl control mode) related to the safety of automated driving, a vehicle ID (terminal ID) that can identify individual vehicles, and latitudinal and longitudinal coordinates (latitude, longitude) to identify where the driving mode change occurred, time (system time) to identify when the driving mode change occurred, and speed (spd) information that can be used to select the range for extracting multiple driving mode change points. Examples of the automated driving data columns used in the analysis are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Route No.** & **Route Length** & **Operation Time** & **Frequency/Day** \\ \hline Route A & 5.3 km & 09:30–12:00, 13:30–16:00 & 10 Times \\ Route B & 4.0 km & 08:00–17:00 & 16 Times \\ \hline \end{tabular}
\end{table}
Table 1: Operation status of automated driving paid services in Sangam, Seoul.  
Figure 1: The route status of automated driving paid services in Sangam. (**a**) Route A. (**b**) Route B.
## 5 Discussion
### Model Performance Across Demographics and Regions  
Geographic performance: The hybrid ARIMA-LSTM model yields significantly lower RMSE values across multiple provinces, with R2 values exceeding 0.80 in most cases, indicating strong predictive accuracy. In contrast, the Prophet model exhibits the highest error rates, often producing negative R2 values, suggesting poor model fit.  
Age group performance: The hybrid model provides the most accurate predictions for middle-aged and older adults (50+ years), achieving the lowest MAPE and RMSE values. In contrast, the Prophet model underperforms in most age categories.  
Gender performance: While the hybrid model excels in most categories, LSTM performs best in gender-based forecasting, particularly for male and female cancer incidence rates, as indicated by its superior MAE and RMSE values.  
The hybrid ARIMA-LSTM model's superior performance is attributed to its ability to capture both linear and nonlinear trends in cancer incidence data. ARIMA alone is well suited for handling linear trends but struggles with complex patterns, whereas LSTM excels in capturing long-term dependencies. By integrating these approaches, the hybrid model effectively mitigates their individual limitations, resulting in more accurate and reliable cancer incidence forecasts.
## 6 Results and Discussion
### Use Case 2: Limited Bilingual (Urdu-English) Data Available  
Table 4 shows the performance of the student models in the experiments corresponding to the second use case, in which only limited bilingual Urdu--English data were available. Notably, the student models referred to in this table were built using distilled data that contain sentence pairs from the original parallel data. As expected, the performance of these student models is significantly better than the students models reported in Section 6.1 (see Table 3).  
Figure 6: Student model performance vs. confidence threshold. The models corresponding to a confidence threshold of 0.0 represent SKD, while the models corresponding to the confidence thresholds of the first (0.1295), second (0.2175), and third (0.3802) quartiles represent CEKD.  
As can be seen from Table 4, SKD (SDD+OPD) outperformed the baseline model across all three metrics on both test sets. Interestingly, unlike the findings in Section 6.1, there are significant performance differences between the SKD model and the baseline across all evaluation metrics on both the IN22-Gen and FLORES-200 test sets. Similar trends can be seen in the performance differences between the three CEKD (FDD+OPD) models and baseline.  
We again refer to Figure 6 to discuss the impact of confidence thresholds on the performance of the student models. As above, the performance of the student models generally tends to improve as the threshold values increase, with only a few exceptions. In terms of the BLEU scores, the best-performing CEKD (FDD+OPD) model is the one trained with the threshold set to the second quartile. More specifically, compared to SKD, CEKD achieves an improvement of 0.61 BLEU points (corresponding to a 1.57% relative improvement) on IN22-Gen and 0.33 BLEU points (corresponding to a 0.85% relative improvement) on FLORES-22. We see very similar trends in performance with the chrF and COMET metrics. Note that no statistically significant differences in BLEU evaluation scores were found between SKD and CEKD.
# Analysis of AMI Communication Methods in Various Field Environments
## 5 Experimental Results  
In this section, according to the five types of field environment areas for the 18 sites with 48 MPs in operation, we conducted measurements of the PLC and wireless communication methods under AMI communication networks.
# A Heat Load Prediction Method for District Heating Systems Based on the AE-GWO-GRU Model
## 1 Introduction  
### Background  
District heating systems (DHSs) utilize local heat sources to distribute heat to buildings via heating pipes. Primarily, they serve to fulfill the heating requirements of residential, commercial, and industrial premises [1]. DHSs also offer benefits such as energy conservation and centralized management convenience. China's district heating industry has experienced rapid growth in recent years, witnessing significant expansions in heating coverage and capacity, along with increased adoption. As shown in Figure 1, the urban district heating area nearly doubled over a decade, with an average annual growth rate of 7.7%, increasing from 5.72 billion square meters in 2013 to 11.13 billion square meters in 2022 [2].  
The expansion of the district heating area continues unabated, inevitably leading to substantial energy consumption. Over the past decade, China's civil buildings have accounted for between 20% and 25% of the nation's total energy consumption. By the end of 2019, district heating in northern cities and towns consumed over 200 million tee of energy annually, constituting 21% of the energy utilized for building operations [3]. This underscores the significant potential for energy savings through district heating.  
Anhanging the operation of DHSs holds paramount importance in mitigating air pollution and reducing carbon dioxide emissions [4].  
As a pivotal technology for district heating, heat load prediction plays a crucial role in ensuring the safe, stable, economical, and environmentally friendly operation of the DHS [5]. By employing heat load prediction, the heat source can align with the heat supply according to the heat load demand to fulfill indoor thermal comfort requirements. The varying heat demands of heat users across different time periods coupled with the influence of transmission distance render heat load prediction challenging due to the characteristics of time variation, significant lag, and nonlinearity.
## Appendix A Mathematical Notation  
\begin{tabular}{l l} \(a\) & Semi-major axis of the elliptic orbit of the satellite \\ \(a_{\oplus}\) & Elipsoidal equatorial radius \\ \(c\) & Speed of light \\ \(\mathbf{C}\) & Covariance Matrix \\ \(\mathcal{D}\) & Domain of the objective function \\ \(e\) & Eccentricity of the elliptic orbit of the satellite \\ \(e_{\oplus}\) & Eccentricity of the Earth's ellipsoid \\ \(E\) & Emitter point \\ \(f\) & Frequency of the signal \\ \(\mathbf{F}\) & Matrix of Fisher \\ \(g\) & Nonlinear objective function \\ \(h\) & Height \\ \(\mathbf{H}\) & Geometric Design Matrix \\ \(i\) & Inclination of the orbit of the satellite \\ \(\mathbf{I}\) & Identity matrix \\ \(L\) & Total length of linear array of antennas \\ \end{tabular}
## 3 6G State-of-the-Art
### Evolution of 6G
#### 3.2.1 Mobile Broadband Reliable Low-Latency  
The distinction between eMBB and URLLC will become unsustainable for some applications, such as augmented reality, virtual reality, and wireless brain computing interfaces, because these applications require not just excellent reliability but also data throughput on the order of eMBB. As a result, a new service class called MBRLLC was created to enable 6G technology to achieve any desired performance with great dependability and minimal latency.
# Last-Mile Optimization Using Neural Networks
## 3 Proposed Method
### Evaluation Metrics  
To assess and compare the performance of distinct models, the following evaluation metrics were employed: accuracy, precision, recall, F1 score, and error rate. In these metrics, FN represents false negatives, FP denotes false positives, TN signifies true negatives, and TP corresponds to true positives.  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}}\times 100\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\times 100\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\times 100\]  
\[\text{F1}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}}\times 100=\frac{2\times\text{TP}}{2\times\text{TP}+\text{FP}+ \text{FN}}\times 100\]
_Applied_  
_sciences_  
_Article_  
A New Flow Entry Replacement Scheme Considering Traffic Characteristics in Software-Defined Networks  
**[PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]**  
School of Computer Science and Engineering, Kyonggi University, Gyeonggi-do 16227, Korea; [EMAIL_ADDRESS] (N.K.); [EMAIL_ADDRESS] (D.K.); [EMAIL_ADDRESS] (Y.J.); [EMAIL_ADDRESS] (C.L.)  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +82-31-249-9676  
Received: 17 April 2020; Accepted: 19 May 2020; Published: 22 May 2020  
**Featured Application: This scheme can be applied to network switches and network controllers in the Software-Defined Networks.**  
**Abstract:** The Software Defined Network (SDN) decouples control and data planes of the existing network architecture and a controller carries out centralized management and control of the network. This provides flexibility for the networks and enables efficient operation, depending on their condition. However, when the performance of switches comprising the SDN varies, any switches with low performance can cause flow table overflow and subsequent bottleneck with overhead concentrating in the controller. Besides, packet processing can be delayed, declining user QoS. The paper proposes a flow entry replacement scheme that takes into account the flow features to address the issue. The proposed Short Flow First (SFF) scheme discriminates the flow features based on the matching period of flow entry and determines a subject of the flow entry replacement with the features. For evaluation, the paper built the SDN environment using the Mininet emulator and compared the performance of the proposed SFF scheme with the representative flow entry replacement schemes. According to the results, the proposed scheme showed a good performance in the flow entry miss rate and packet delay due to failure of matching the flow entry.  
**Keywords:** flow entry replacement; SDN; switch; controller; multimedia; network
# Spider U-Net: Incorporating Inter-Slice Connectivity Using LSTM for 3D Blood Vessel Segmentation
## 5 Discussion
### Anisotropy of the Dataset  
3D medical images vary in slice thickness and inter-slice spacing according to the manufacturer, machine, and reconstruction method. In the volumetric view, this characteristic means that each 3D volume has a diverse voxel size along the z-axis and means the entire dataset is anisotropic. There is some evidence to suggest that 3D U-Net performance tends to deteriorate when trained with an anisotropic dataset [17; 18]. We also conducted an ablation study with our in-house isotropic dataset by collecting MRA volumes with the same manufacturer and image acquisition settings. The average DSC of 2D U-Net, 3D U-Net, FCN-RNN, and Spider U-Net was 0.696, 0.697, 0.725, and 0.737, respectively. In this experiment, the performance of 3D U-Net was not inferior to 2D U-Net. This shows that 3D U-Net has difficulty when trained with an anisotropic dataset. On the other hand, Spider U-Net, which relies on LSTM when extracting z-axis information, demonstrates robustness in terms of anisotropy by showing consistent results on every dataset. As a result of this characteristic, an additional reconstruction stage to match up voxel size was not required.
# An Improved PSO Algorithm and Its Application in GNSS Ambiguity Resolution
###### Abstract  
In this study, we proposed a novel method for global navigation satellite system (GNSS) ambiguity resolution (AR). The proposed method utilizes an improved particle swarm optimization (IPSO) algorithm to obtain the GNSS integer ambiguity with the double differenced (DD) float resolution and its corresponding covariance matrix. First, we introduced population maturity to the standard PSO (SPSO) algorithm for the adaptive adjustment of inertia weight. Next, to improve the global convergence and robustness of the SPSO algorithm, we adopted population classification and constructed a Gauss mutation for the particle evolution process of the optimal population. Then, we applied the IPSO algorithm in the field of GNSS AR, called IPSO-AR. Finally, we evaluated the performance of the IPSO-AR algorithm under different DD ambiguity float resolutions with various dimensions and precisions. Numerical results showed that compared with the SPSO-AR algorithm, the IPSO-AR algorithm has a superior correct rate, but low efficiency. Under the appropriate parameter settings, the efficiency of the IPSO-AR algorithm is mainly dependent on the dimensions of DD ambiguity, whereas the correct rate of the IPSO-AR algorithm is mainly dependent on the precision of DD ambiguity. The proposed IPSO-AR algorithm has potential applications under the conditions of few visible satellites or constrained baseline length.  
g 2018 8 990 2018 13 June 2018 16 June 2018
# Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation
## 2 Materials and Methods
### Traditional Image Processing Algorithms Description
#### 2.5.4 Local Threshold Approach Based on Rectangular Areas (LTA)  
The algorithm used is from MATLAB. It scans the whole image and computes a local threshold. The used size of the window is given by the following formula:  
\[Size=2\times floor\left(\frac{Imagesize}{16}\right)+1 \tag{5}\]  
Over this window, a Bradley's mean [39] is computed. By comparison between a pixel's energy value and this local mean, the classification of the pixel as background or foreground occurs. This process is carried out using a scalar parameter called sensitivity (S). It varies in the range \([0,1]\) and indicates sensitivity towards thresholding more pixels as foreground.