esa-sceva/satcom-chunk-collection huggingface dataset:
# GNSS Timing Performance Assessment and Results Analysis
## 2 The Key Indicators and Assessment Method of GNSS Timing Performance
### Assessment Method of GNSS Time Transfer Accuracy  
According to BDS open service performance specification, timing accuracy is a specified indicator to assess BDS timing performance. Timing accuracy refers to the 95% probability level of the differences between BDT determined by BDS and real BDT [8], while the open service performance specification of both GPS and GLONASS specifies the  
Figure 1: Principle of GNSS timing.  
time transfer accuracy as the indicator of timing performance [7; 9]. Time transfer accuracy refers to the 95% probability level of the instantaneous time transfer error sequence between the estimated receiver time offset from a reference realization of UTC and the true offset over a specified time interval [17]. According to GNSS timing principle and the definition of performance indicators, the time transfer accuracy is considered as the main indicator of GNSS timing performance. It is difficult to obtain the real UTC. In fact, it is difficult to obtain the real offset of receiver time with respect to UTC. Therefore, the estimated receiver time offset, recorded as \(\text{t}_{\text{u}}\)-UTC\({}_{\text{GNSS}}\), refers to the offset between receiver time and UTC that GNSS broadcasts; the real receiver time offset, recorded as \(\text{t}_{\text{u}}\)-UTC\({}_{\text{true}}\), refers to the offset between receiver time and'real UTC/UTC(k)', and the time transfer error sequence is recorded as UTC\({}_{\text{GNSS}}\)-UTC\({}_{\text{true}}\).
## 4 Experiments
### Experimental Results  
All experiments were performed on a PC running Windows 10, equipped with an AMD Ryzen 9 5900X 12-Core Processor running at 3.70 GHz and 64 GB of RAM. This study aimed to classify the six datasets described in Figure 10 and Table 2 into structural elements and indoor clutter objects for removing indoor clutter objects in the point cloud. Figure 11 displays the ground truth and classification results of the structural elements and indoor clutter objects of the six datasets using the proposed method. The results of the ground truth-based accuracy, precision, recall, and F1 score are summarized in Table 3. The proposed method based on the six datasets achieved an average accuracy of 0.94, an average precision of 0.97, an average recall of 0.90, and an average F1 score of 0.94. Table 4 presents the classification results of the experimental data into structural elements and indoor clutter objects using the Auto-Classify Indoor function of commercial software. Table 5 presents the processing times of the proposed method for each dataset. The times were calculated except for the SOR filter step because it is manually performed. As observed in our experiments, the SOR filter step required a time of 1 to 2 s for its operation using commercialized software. Therefore, the times listed in Table 5 satisfactorily represent the time needed for the operation of the proposed method. Figure 12 presents a graph that compares the average performance of the proposed method and the Auto-Classify Indoor function of commercial software.  
Figure 11: Experimental results. (**a**) Parking lot, (**b**) basement, (**c**) apartment 1, (**d**) apartment 2, (**e**) lecture room 1, and (**f**) lecture room 2.
# Solar Filament Detection Based on an Improved Deep Learning Model
## 4 Method  
Based on the characteristics of solar filaments and the issues surrounding their detection, we designed a Hybrid CNN-Transformer detection model. The structure of the detection model proposed in this article is shown in Figure 7.  
As shown in Figure 7, the detection model proposed in this article can be divided into four parts: CNN Encoder, Transformer Encoder, CNN Decoder, and Res Path.  
The CNN Encoder uses three sets of Multi-scale Residual Blocks (MRB) to extract features from the input image. It down-samples the input feature map from its original size of (3, W, H) to (64, W/2, H/2), (256, W/4, H/4), and (512, W/8, H/8), obtaining feature maps that contain local detail information about solar filaments as extracted by CNN. Subsequently, by connecting the CNN Encoder and Transformer Encoder in series, the model leverages the powerful global receptive field of the Transformer to capture the global information of solar filaments. In the CNN Decoder, the Decoder Block is used to up-sample the image feature maps and restore their feature sizes, gradually increasing the image size from (512, W/8, H/8) to its original size. The res path implements skip connections, also mitigating the semantic gap between the feature maps obtained from the encoder and those from the decoder. Detailed introductions to each part of the network are provided below.
# Pulsed Thermography Dataset for Training Deep Learning Models
## 2 Related Work
### Public Available Thermographic Dataset  
Infrared imaging and video datasets have been widely used in various applications, such as PTB-TIR [34] for pedestrian tracking and LLVIP [35] for low-light vision. However, most of the existing datasets are not designed specifically for NDE tasks. In a recent study, a PT dataset was introduced that contains composite material samples made of carbon and glass fiber-reinforced plastic [36]. However, this dataset is not publicly accessible and lacks annotations for machine learning applications. In [37], the authors describe the infrared measurement databases from Laval University, which include various materials such as composite materials, plexiglass, steel, aluminum, etc. The focus of this study is on defect segmentation in single infrared images and does not take into account the temporal information between thermal images. To the best of our knowledge, there is currently a lack of well-documented, publicly available PT datasets for defect segmentation that consider both spatial and temporal information within the thermal image sequences.
###### Abstract  
Imaging buried objects embedded within electrically large investigation domains can require a large number of measurement points. This is impractical if long data acquisition time cannot be tolerated or the system is conceived to work at some stand-off distance from the air/soil interface; for example, if it is mounted over some flying platform. In order to reduce the number of spatial measurements, here, we propose a method for detecting and localizing shallowly buried scattering targets from under-sampled far-field data. The method is based on a scattering model derived from the equivalence theorem for electromagnetic radiation. It exploits multi-frequency data and does not require that the transmitter and receivers are synchronized, making the source non-cooperative. To provide a benchmark against which spatial data have to be reduced, first, the number of required spatial measurements is examined by analyzing the properties of the relevant scattering operator. Then, since under-sampling data produces aliasing artifacts, frequency diversity (i.e., multi-frequency data) is exploited to mitigate those artifacts. In particular, single-frequency reconstructions are properly fused and a criterion for selecting the frequencies to be used is provided. Numerical examples show that the method allows for satisfactory target transverse localization with a number of measurements that are much less than the ones required by other methods commonly used in subsurface imaging.  
Keywords:electromagnetic inverse scattering; microwave imaging; non-cooperative sources; shallow subsurface prospecting +
Footnote †: journal: Applied Physics Letters
# Quantification of the Effective Detectable Period for Concrete Voids of CLP by Lock-In Thermography
## 5 Results and Discussion
### Comparative Analysis of Simulation and Experiment Results  
As can be seen in this study, simulation of LIT and confirmation of EDP can be useful means for detecting the concrete voids on the CLP backside and for setting the evaluation and criteria. In the case of simulation, as shown in Figures 14 and 15, the SNR of the void was higher than 30 dB in all cases, and it is expected that the detection of concrete voids on the CLP backside will be smooth. In the case of the experiment, as shown in Figures 16 and 17, the temperature and amplitude images showed irregular and low SNR regardless of the size of the void, and it was possible to secure an identifiable SNR only in the phase image. This is thought to be the effect of the uneven heat load of the halogen lamp. As can be seen in the simulation and phase images of an experiment, the SNR value of concrete voids has a large effect in proportion to the void area, but the effect by depth is very small.
## 3 Results
### Level of Patient Satisfaction
#### 3.2.4 Additional Non-Paid Services
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Measures** & **Not at All Satisfaction** & **Not Very Satisfied** & **Faily Satisfied** & **Satisfied** & **Very Satisfied** & **MS \(\pm\) SEM** \\  & **\# (\%)** & **\# (\%)** & **\# (\%)** & **\# (\%)** & **\# (\%)** & **\# (\%)** \\ \hline \hline Are you satisfied with dispensing time? & 415 & 199 & 305 & 133 & 36 & 2.24 \(\pm\) 0.03 \\  & (83.1) & (18.2) & (28.0) & (12.2) & (3.3) & 2.24 \(\pm\) 0.03 \\ \hline \hline Are you satisfied that the pharmacist/stiff consult and cooperate with the physician for the correct dispensing of nodes to you? & 268 & 205 & 337 & 286 & 22 & 2.99 \(\pm\) 0.03 \\ \hline Are you satisfied with dispensing pattern like care and proper labeling? & 191 & 71 & 429 & 165 & 232 & \(3.16\) \(\pm\) 0.04 \\ \hline Are you satisfied that instructions on your medications are easily readable? & 13 & 8 & 11 & 607 & 449 & \(4.35\) \(\pm\) 0.02 \\ \hline Do you afford waiting time? & 415 & 198 & 313 & 129 & 33 & \(2.23\) \(\pm\) 0.03 \\  & (83.1) & (18.2) & (28.7) & (11.8) & (3.0) & 2.23 \(\pm\) 0.03 \\ \hline Are you satisfied with attitude of counseling person? & 14 & 15 & 231 & 526 & 302 & \(3.99\) \(\pm\) 0.02 \\  & (1.3) & (1.3) & (21.2) & (48.3) & (27.7) & 3.99 \(\pm\) 0.02 \\ \hline \hline Are you satisfied with knowledge of counseling person? & 401 & 213 & 289 & 134 & 51 & \(2.28\) \(\pm\) 0.03 \\  & (96.8) & (19.5) & (26.5) & (12.3) & (4.6) & 2.28 \(\pm\) 0.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Patients’ satisfaction withthe dispensing and counseling practices (\(\pi\) = 1088). \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Measures** & **Not at All Satisfaction** & **Not Very Satisfied** & **Faily Satisfied** & **Satisfied** & **Very Satisfied** & **MS \(\pm\) SEM** \\  & **\# (\%)** & **\# (\%)** & **\# (\%)** & **\# (\%)** & **\# (\%)** & **\# (\%)** \\ \hline Are you satisfied with availability of medicines or health appliances you need? & 154 & 89 & 332 & 412 & 101 & \\  & (14.1) & (8.2) & (30.5) & (37.8) & (9.2) & 3.19 \(\pm\) 0.03 \\ \hline Are satisfied that you receive the medications from the pharmacy exactly according to the prescription or need? & 146 & 221 & 250 & 389 & 82 & \\  & (13.4) & (20.3) & (22.9) & (35.7) & (7.5) & 3.03 \(\pm\) 0.03 \\ \hline Are you satisfied with appropriate and safe storage of medicines in pharmacy shop or itsware-house? & 78 & 154 & 132 & 412 & 312 & 312 & 3.66 \(\pm\) 0.03 \\  & (7.1) & (14.1) & (12.1) & (37.8) & (28.6) & \\ \hline Are you satisfied with the quality of medicines? & 37 & 137 & 339 & 486 & 89 & 3.41 \(\pm\) 0.02 \\  & (3.4) & (12.5) & (31.1) & (44.6) & (8.2) & \\ \hline Are you satisfied with prices or discounts on medicines or health appliances you need? & 55 & 161 & 259 & 506 & 107 & \\  & (5.1) & (14.7) & (23.8) & (46.5) & (9.8) & 3.41 \(\pm\) 0.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Patients’ satisfaction about the medicine handling (\(\pi\) = 1088).
# The Interoperability of Learning Object Design, Search and Adaptation Processes in the Repositories
## 1 Introduction
Changes in educational processes require new competencies and skills of teachers for learning objects design, development, provision or management of the technology-based educational process [17; 18]. It is also related to the quality of the learning environment, e-learning objects (ELOs), repositories of open educational resources [19]. The authors [20] point out the pedagogical quality of learning resources. Many of these repositories follow a production model where the open educational resources (OER) are built by volunteer communities and do not have an effective quality control mechanism. Learning environments establish the successful implementation of the learning process. However, they do not ensure the effective design of the e-learning objects. The interaction between the environment for the design of learning objects repositories and the semantic web is needed [19]. A multifunctional platform is required for the design of different types of learning objects and to assure the effectiveness of the e-learning process implementation [21]. The existing developed multifunctional platforms of the e-learning objects do not [21]:  
1. Ensure the effective design of ELOs and adaptation;
2. Ensure the process of ELO design and adaptation in the multifunctional environment;
3. Have an automatic search of ELOs on the semantic web, which is directly targeted to the specific objects in repositories of open educational resources;
4. Allow the adaptation of the already developed ELOs by automatically assigning reusable objects. The object of this research is ELOs design and adaptation processes, multifunctional ELO environment that allows to effectively design new or adapt reusable ELOs. According to the literature review authors identify the objective of the research--to suggest to teachers an effective e-learning objects design, automatic search and adaptation processes in the multifunctional environment by developing a platform based on semantic technologies for e-learning objects design and adaptation. The scope of the present paper is to analyse the technological solutions related to the e-learning objects design and adaptation by attributing and reusing existing objects with similar content via the search on the semantic web.
## 2 Materials and Methods
### Evaluation  
The evaluation of the SL models was conducted to assess their performance and generalization capabilities comprehensively. To achieve this, commonly used classification metrics were employed, such as Accuracy, F1-score, Precision, Recall, Confusion Matrix  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Hyperparameter** & **Values** \\ \hline \multirow{3}{*}{SVM} & kernel & [linear, rbf, sigmoid] \\  & gamma & [0.001, 0.01, 0.1, 1, 10, 100] \\  & C & [0.1, 1, 10, 20, 50, 100] \\ \hline \multirow{3}{*}{RF} & n estimators & [500, 700, 1000, 2000, 3000] \\  & max depth & [5, 10, 20, 50, 100] \\ \cline{1-1}  & min samples leaf & [5, 10, 50] \\ \cline{1-1}  & min samples split & [5, 10] \\ \hline \multirow{3}{*}{MLP} & hidden layer sizes & [(50.), (100. ), (200. ), (50, 50, 50), (50, 100, 50), (100, 100, 100), (200, 200, 200) \\ \cline{1-1}  & alpha & [0.0001, 0.005, 0.01, 0.05, 0.1] \\ \cline{1-1}  & learning rate & [constant, adaptive] \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of hyperparameters to be optimized and their range of values.  
and Area under the receiver operating characteristic curve (AUC). The AUC metric was evaluated with a \"One vs. Rest\" approach. Graphics as Confusion Matrix and the Receiver operating characteristic (ROC) curve were evaluated as well.  
These evaluation metrics provide valuable insights into the strengths and weaknesses of each model, aiding in making informed decisions about their practical applicability. Additionally, they highlight potential areas for further refinement and improvement, contributing to the development of robust and effective predictive models.
## 3 Effect of Inhomogeneous Subgrade Support  
### Flexural Tensile Stress of Pavement Slab  
The relationship between the maximum flexural tensile stress of the pavement slab and the change rate of inhomogeneous subgrade support for different aircraft type are presented in Figures 3 and 4, respectively. The highest flexural tensile stress is taken as the flexural tensile stress of each condition in Group I and Group II.  
Figure 3 shows that the flexural tensile stress of the pavement slab increases with the change rate of inhomogeneous subgrade support. For instance, when the thickness of the pavement slab is 0.36 m and \(\beta\) is 85.7%, the flexural tensile stress of the pavement slab reaches 6.93 MPa, which has increased 34.9% for the condition of \(\beta\), which is 14.29% (1.51 MPa).  
Although increasing the thickness of the pavement could alleviate the effects of inhomogeneous settlement, the thicker pavement also requires more financial investment, and it may also cause some other problems. Thus, quantifying the influence of thickness change on the mechanical response benefit to design the airport rigid pavements. To satisfy the flexural stress of the pavement slab for the ultimate flexural stress condition, \(\beta\) should not exceed 38% (0.36 m) and 70% (0.40 m), respectively. On the basis of these findings, we conclude that the inhomogeneous settlement affects the flexural tensile stress of rigid pavements significantly. Therefore, for the A320, the value of \(\beta\) should not exceed 38% (0.36 m), 57.5% (0.38 m) and 70% (0.40 m), respectively.  
On the other hand, the wheel load type also affects the mechanical response of the pavement structure. For example, to ensure the safety under the ultimate flexural tensile stress condition, the value of \(\beta\) should be less than 57.5% (A320) and 33% (B737-800) when the thickness of the pavement slab is 0.38 m. In Figure 4c, when the thickness of the pavement slab is 0.38 m and 0.40 m, the values of \(\beta\) should not exceed 33% and 62%, indicating that the allowable change rate of the inhomogeneous subgrade support increases with the thickness of the pavement slab. The findings lead us to conclude that the effect of inhomogeneous subgrade support on the mechanical response of pavement structures should not be ignored in the design process of pavement structures.
# The Integration of Azure Sphere and Azure Cloud Services for Internet of Things
## 4 System Implementation
### Software
#### 4.2.4 Software for ZigBee Wireless Sensor Network  
There are four official protocol options for end users to choose from Texas Instruments for CC2530, i.e., SimpliciTI Compliant Protocol Stack, RF4 CE Compliant Protocol Stack, IEEE802.15.4 Medium Access control (MAC) software stack and Z-Stack [47]. In this article, Z-Stack is used for wireless sensor nodes management and data transmission.  
The program running in the CC2530 ZigBee node with Z-Stack executes as follows. After the initialization process, the Operating System Layer executes the main loop in which the task list will be accessed and checked in order. As soon as the condition is met, the specified task will be carried out immediately. For ZigBee Coordinators in this article, there are two main tasks, which are UART message handler task and ZigBee wireless network incoming package handler task. On the one hand, the UART message handler task deals with the incoming data from MT3620 and then relays the  
Figure 5: The application manifest file for the project of MT3620 as gateway.  
Figure 6: The flowchart of the application.  
message to ZigBee End Device via Z-Stack protocol message. On the other hand, the wireless network incoming package handler task is responsible for processing with the Z-Stack protocol message from ZigBee End Device and transmitting the valid data to MT3620 via UART interface. For ZigBee End Device equipped with different kind of sensors, there are two main tasks as well. Firstly, reading data from sensors and transmitting message to ZigBee Coordinator are completed in the Data Send Handler event within a user defined interval. Secondly, the downlink messages from ZigBee Coordinator through Z-Stack protocol are handled in the Message Process event.  
The payload of the uplink message is shown in Figure 7. The payload is prefixed with one-byte Sensor ID, which is closely followed by two-byte temperature data, two-byte humidity data, two-byte light data, two-byte gas data and one-byte PIR sensor data.
# Ad-BBR: Enhancing Round-Trip Time Fairness and Transmission Stability in TCP-BBR
## 3 Overview of BBR
### Motivation  
#### 3.2.1 A Discussion on the Phenomenon of Data Over-Injection and the Issue of RTT Unfairness  
One of the original design goals of the BBR algorithm is to operate the link's data transmission state at [PERSON]'s optimal operating point. This ensures the maximum delivery rate and the minimum delay time (RTprop) while avoiding queue formation and minimizing latency. However, since BDP values differ for flows with varying RTTs, each flow achieves a different optimal operating point on the link. The long RTT flow will occupy a larger portion of the bottleneck bandwidth and buffer due to its higher RTT value. As the RTprop increases, the short RTT flow enters the CWND's bounded phase, allowing the long RTT flow to further seize bandwidth and buffer resources, depriving the short RTT flow of these resources.  
Although the BBR congestion control algorithm dynamically adjusts the sending rate by calculating the BDP to proactively avoid excessive data influx, potential overestimation of the bottleneck bandwidth (BtlBw) and underestimation of the RTprop can occur. To compensate for this, BBR sets the CWND value slightly higher than the calculated BDP to buffer ACK delays and queuing within the link. However, this results in an instantaneous rate exceeding the network capacity, especially when multiple streams share the link. Their synchronized probing of bandwidth causes collective data over-injection. Regardless of whether the RTT streams are long or short, the CWND values tend to be slightly larger than the ideal values, leading to a sending rate that surpasses the link's capacity and results in queues shared across all streams.  
As shown in Figure 3, when multiple streams with different RTT values compete for the same bottleneck link, the original BBR algorithm operates slightly to the right of the predicted ideal optimal points A and B, reaching the actual operating points C and D.  
This results in the total transmission rate exceeding the available bottleneck bandwidth. Over time, this leads to the formation of a persistent queue on the bottleneck link, causing queue-induced delays. Since this persistent queue is shared by all existing streams, long RTT streams, which occupy more bandwidth and buffer space and have higher sending rates, push short RTT streams into a vicious cycle. As fairness continuously deteriorates, long RTT streams eventually become limited by the CWND, but by this point, short RTT streams are left with minimal bandwidth. This situation is highly detrimental to the current scenario, where long RTT streams are prevalent and short RTT streams frequently appear in 5G and Wi-Fi networks.  
Additionally, because BBR is not sensitive to packet loss events, when the bottleneck buffer is small, data over-injection leads to queuing, resulting in a higher retransmission rate on the current link.