esa-sceva/satcom-chunk-collection huggingface dataset:
# The Integration of Azure Sphere and Azure Cloud Services for Internet of Things
## 5 Experimental Results
### Cloud Layer  
In the cloud layer, all the device-to-cloud and cloud-to-device messages via Azure IoT Hub can be monitored by Device Explorer Twin. As shown in Figure 11, the data and time of the message, device name, device ID as well as sensor data are displayed in order within JSON format.  
Figure 11: Azure IoT Hub Data Monitor.  
Figure 10: UART Waveforms: (**a**) Transmit; (**b**) Receive.  
When the JSON packages arrive at the Azure IoT Hub, they will be processed in order. It takes time for the Azure IoT Hub to retrieve the messages and stream them to the Azure Stream Analytics. In order to evaluate the latency (milliseconds), all the messages from 2:00 am to 8:00 pm, 28 June 2019, are exported and analyzed. By the properties of \"EventEnqueuedUtcTime\" and \"EventProcessedUtcTime\", the latency of every message in Azure IoT Hub can be calculated. The average latency, the maximum latency and the minimum latency are depicted in Figure 12.  
According to the statistics in Figure 12, the minimum latency and the average latency are round ten milliseconds and hundred milliseconds, respectively. The maximum latency can reach 500 milliseconds and 650 milliseconds in 6:00 am-7:00 am and 3:00 pm-4:00 pm. Though they are relatively large compared with average latency, it is still acceptable for most of time-sensitive IoT applications.  
After real-time filtering by Azure Stream Analytics, the data is sent to Azure Table Storage. Azure Table Storage is used for saving data from sensors. The detail information of the table \"Azure Sphere MT3620\" is shown by Microsoft Azure Storage Explorer in Figure 13.  
In order to evaluate the performance of Azure Table Storage, the average end-to-end latency (AverageE2 ELatency) of successful requests and the average server latency (AverageServerLatency) are provided by Azure. AverageE2 ELatency includes the required processing time within Azure Table Storage to read the request, send the response and receive acknowledgement of the response. AverageServerLatency stands for the average latency used by Azure Table Storage to process a request, excluding failed requests. This value does not include the network latency which is included in AverageE2 ELatency. Latency evaluation for Azure Table Storage is shown in Figure 14.  
According to the curves in Figure 14, the values of AverageServerLatency and AverageE2 ELatency from June 28 to June 29 are 11.25 ms and 77.27 ms respectively, which means that the average entire latency is less than 100 ms. It is acceptable for most of IoT applications.  
Figure 12: Latency evaluation for Azure IoT Hub.
# An Accuracy-Aware Energy-Efficient Multipath Routing Algorithm for WSNs
## 3 Multi-Objective Optimization Model for EMRAR  
To design the EMRAR algorithm, we formulate the task of identifying a multipath set as a multi-objective optimization subject to constraints, which jointly optimize the objectives of reliability and energy consumption while adhering to the constraint of accuracy loss, which translates to reducing the number of intersection nodes. The construction of the optimization model is detailed as follows.  
The coding scheme will not be specified and analyzed as it falls outside the scope of the focus of this work.  
For ease of expression, the terms \"node\", \"sensor\", or \"sensor node\" will be interchangeably used to refer to a sensor node in the subsequent sections of this paper.  
Consider a wireless sensor network system with \(n\) sensor nodes randomly distributed in the monitoring area. Due to the limitation of the communication distance, all nodes send their collected data to the sink via a multihop mode. To facilitate our discussion, we establish the following definitions based on Graph Theory.  
* Weighted Undirected Graph \(G\): a WSN can be illustrated as a weighted undirected graph \(G=\{V,E\}\), where \(V=\{v_{1},v_{2},\ldots,v_{n}\}\) is the set of sensor nodes, and \(E=\{e_{1},e_{2},\ldots,e_{k}\}\) is the set of edges between two sensor nodes.
* Neighbor: Suppose the communication range of each sensor node is R. Let the distance between sensor nodes be \(i\)\(\in\)\(V\) and \(j\)\(\in\)\(V\) be \(d(i,j)\). If \(d(i,j)\)\(\leq\)\(R\), then nodes \(i\) and \(j\) are neighbors, and there is an edge between them. The neighbor relationship can be represented as \(i\)\(\in\)\(N(j)\) or \(j\)\(\in\)\(N(i)\), where \(N(i)\)\(\subset\)\(V\) and \(N(j)\)\(\subset\)\(V\) are the neighbor sets of \(i\) and \(j\), respectively.
* Multipath Set \(MP(i,S)\): Suppose a source node \(i\)\(\in\)\(V\) can set up multipath to the sink \(S\)\(\in\)\(V\). Then, the set of all the paths from \(i\) to \(S\) can be represented as \(MP(i,S)\). \(|MP(i,S)|\) is the number of paths of \(MP(i,S)\). Each path in \(MP(i,S)\) can be represented as \(mp_{j}(i,S)\), \(j=1,\ldots,\)\(|MP(i,S)|\). \(\left|mp_{j}(i,S)\right|\) is the number of nodes in path \(mp_{j}(i,S)\).
# A Mobility Management Using Follow-Me Cloud-Cloudlet in Fog-Computing-Based RANs for Smart Cities
## 3 Preliminaries
### System Architecture
The new record of the BCE table is \(BCE\_table(UE\_id,prefix_{d},LMA_{d})\). After the IDMD receiving \(PBU\_message\), the IDMD sends a migration-request message to the FMCC. The migration-request message contains the ID of UE, the allocated prefix, the new allocated LMA, and the cache data in \(l_{d}\). After the FMCC obtaining the message, the FMCC concurrently finds the optimal transmission path between the new Data Center Gateway (DCG) of \(c_{d}\) and new allocated LMA of \(l_{d}\). When the FMCC determines the optimal path from \(c_{d}\) to \(l_{d}\), the FMCC sends out \(REQ\_message(UE\_id,prefix_{s},prefix_{d},TIM)\) to the new \(c_{d}\) to switch to the new path.
# Study of Discharge Characteristics on Ignition Performance via High-Speed Imaging in a CVCC
## 4 Conclusions  
This study investigated three types of ignition coils under different experimental conditions in a small CVCC, two types of ignition coils with the same discharge energy were analyzed in particular, and several interest points have been found in the combustion results. As such, a new visualization method using a high-speed infrared camera (FLIR X6900 sc) and a conventional high-speed camera (FASTCAM SA-X) simultaneously was pioneered in this research. This method can remove the interference of the preheat zone and detect the initial flame kernel more accurately. The combustion results can be explained by analyzing the images of simultaneous visualization. The key features identified in this study are summarized as below:  
1. It is confirmed that the high-speed infrared images can ignore the interference of the preheat zone caused by the discharge channel as the infrared images had hardly any brightness around the spark plug under the no fuel condition, while the discharge channel still formed during the ignition process;  
2. Increasing the mixture dilution ratio will decrease the speed of initial flame growth, which results in the decreasing of combustion speed and even increasing the ignition energy cannot reach the combustion speed of low diluted condition. While increasing the flow velocity may improve the combustion speed only if increasing the ignition energy correspondingly;  
3. Under the high diluted and low flow conditions, prolonging the discharge duration has more benefits to improve the ignition performance compared with the high discharge current at the same ignition energy. This is a result of the long discharge duration extending the heating time to the gas mixture, which can keep the initial flame growth speed and form the self-sustained flame by holding the initial flame around the spark plug;  
4. When comparing the same ignition energy coils under the enhanced flow condition, the discharge current has a significant impact on the ignition performance compared to the discharge duration. The enhanced flow stretches and wrinkles the initial flame kernel, and may prevent the initial flame from forming the self-sustain flame. High discharge current ensures the initial flame generation and propagation.  
Not applicable.  
The study did not report any data. This work was supported by JSPS KAKENHI Grant Number JP19K04233.  
The authors declare no conflict of interest.
# Breast Geometry Characterization of Young American Females Using 3D Image Analysis
## 3 Result and Discussion
### Breast Placement Analysis
Although the angle of the breast base did not seem to play a critical role in breast placement (F = 0.083, \(p\) = 0.920), it was found that the average chest plane leaned backward for about 5-6\({}^{\circ}\) when standing straight (Table 8). The breast placement angles against body axes were large at the outer breast edge in cluster 2 and small at the lower and inner edges in cluster 3. The upper angle showed the most distinction between clusters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Placement Clusters and Cluster Averages**} & \multicolumn{2}{c}{**Scheffe’s**} \\ \cline{2-5}  & **Cluster 3 \({}^{\text{a}}\)** & **Cluster 1 \({}^{\text{b}}\)** & **Cluster 2 \({}^{\text{c}}\)** & **Post-Hoc** \\ \cline{2-5}  & **(n = 56)** & **(n = 68)** & **(n = 32)** & **Analysis** \\ \hline Mid-shoulder to BP (\%) & **53.3071** & **56.4885** & **61.0803** & a \textless{} b \textless{} c \\ Shoulder-neck to BP (\%) & **62.9277** & **66.4513** & **71.1484** & a \textless{} b \textless{} c \\ Shoulder tip to BP (\%) & **56.7086** & **60.6434** & **65.0497** & a \textless{} b \textless{} c \\ Front neck to BP (\%) & **57.6098** & **60.9834** & **64.7953** & a \textless{} b \textless{} c \\ \hline Breast depth (mm) & **53.3380** & **63.1685** & **74.0087** & a \textless{} b \textless{} c \\ Body depth (mm) & **150.9157** & **162.9482** & **179.4178** & a \textless{} b \textless{} c \\ Breast–body depth ratio (\%) & **35.3807** & **38.8312** & **41.2641** & a \textless{} b \textless{} c \\ Breast span half (mm) & **93.5386** & **99.9116** & **111.0519** & a \textless{} b \textless{} c \\ \hline BP angle (\({}^{\circ}\)) & 23.8400 & 23.0169 & **36.9816** & b, a \textless{} c \\ BP-body angle (\({}^{\circ}\)) & 38.6362 & 37.8900 & 38.2584 & a, b, c \\ Breast base Y angle (\({}^{\circ}\)) & 5.9063 & 5.6385 & 5.6125 & a, b, c \\ Upper breast Y angle (\({}^{\circ}\)) & **32.6212** & **35.4322** & **38.5525** & a \textless{} b \textless{} c \\ Lower breast Y angle (\({}^{\circ}\)) & **34.9413** & 39.2759 & 41.4175 & a \textless{} b, c \\ Inner breast X angle (\({}^{\circ}\)) & **17.1580** & 19.9809 & 20.8213 & a \textless{} b, c \\ Outer breast X angle (\({}^{\circ}\)) & 61.0893 & 61.9385 & **65.3587** & a, b \textless{} c \\ \hline \hline \end{tabular} The superscripts \({}^{\text{a}}\), \({}^{\text{b}}\), and \({}^{\text{c}}\) indicate each placement cluster denoted in the far-right column.  
\end{table} 
Table 8: Geometric characterization of the placement clusters.
## 3 Case Study Calculation
### Longitudinal Unevenness IRI  
Longitudinal unevenness analysis is based on long-term measurements conducted through the road inventory. Long-term pavement performance monitoring has been carried out since 1998 across a road network exceeding 18,000 km in length. These data were used to derive polynomial degradation functions for semi-rigid pavements on arterial roads, specifically validated for Central European climate conditions (Table 5).  
Measurements were obtained using the PROFILOGARPH GE system, whose outputs enable mathematical derivation of both longitudinal and transverse roughness functions.  
Figure 8: Evaluation of track depth in relation to the number of passes with different functions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Moisture Classification** & **Semi-Arid** \\ \hline Thornthwaite moisture index & \(-\)36 \\ Duration of dry season (months) & 7.8 \\ Mean monthly precipitation (mm) & 64.1 \\ Temperature classification & \\ \hline Temperature Classification & Temperate—cool \\ \hline Mean temperature (\({}^{\circ}\)C) & 8.8 \\ Avg. temperature range (\({}^{\circ}\)C) & 20 \\ Days T \textgreater{}32 \textasci{}C (days) & 12.1 \\ Freeze index (C-days) & 332 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Climate zone in which the presented deterioration functions apply.  
Figure 7: Longitudinal evaluation of track depth measured by the Bibus scanner (IRI).  
Longitudinal roughness is quantified through the international roughness index (IRI), calculated according to Equation (4) [64]:  
\[\text{IRI}=\frac{1}{N-1}*\sum_{i=2}^{N}T_{i} \tag{4}\]  
where _Ti_ is the arithmetical average value of _Ti_ ordinates, and \(N\) is the number of measurements.  
The degradation function derived from long-term IRI measurements, showing the relationship between IRI values and design axle loads (DALs), is presented in Equation (5):  
\[\begin{array}{c}IRI=-1.140*3+1.497*3-0.833\text{x}+0.448\\ R^{2}=0.955\end{array} \tag{5}\]
# Free Space Optical Communication Networking Technology Based on a Laser Relay Station
## 5 Conclusions  
In this paper, we use PWM modulation technology combined with the data routing and forwarding capabilities of laser relay stations to realize free-space optical communication networking, and use high-speed ADC to demodulate the received signal, which increases the stability of the free-space optical communication system. A series of two-dimensional image transmission experiments and bit error rate test experiments are carried out on the system. The experimental results show that the system is more flexible than the point-to-point laser communication system, and the communication quality is higher. At the same time, the system realizes point-to-multipoint free-space optical communication without adjusting the transmission angle, we believe that the system proposed in this paper can provide an effective solution for free-space optical communication networking technology.  
Methodology, C.D.; software, C.D.; validation, C.L.; formal analysis, Z.W.; investigation, Z.G.; resources, Z.L.; data curation, J.S.; writing--original draft preparation, C.D.; writing--review and editing, M.T.; supervision, J.S.; project administration, M.T.; funding acquisition, J.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grants No. 62090053, No. 62090054 and No. 61934003; The Major Scientific and Technological Program of Jilin Province under Grant No. 20200501007 GX; The Project of Industrial Technology Research and Development of Jilin Provincial Development and Reform Commission under Grant No. 2020C019-2 and the Program for JLU Science and Technology Innovative Research Team under Grant No. 2021 TD-39.  
Not applicable.  
The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Structures of FSO** & **Multi-Destination** & **Response Time** & \multicolumn{2}{c}{**Frame Synchronization**} & **Mechanical Alignment** & **Refs** \\
**Communication System** & **Data Transfer** & **Response Time** & \multicolumn{2}{c}{**Frame Synchronization**} & **Mechanical Alignment** & **Refs** \\ \hline Rotating double prism & Yes & 1.5 s & Yes & Yes & [3] \\ magnetometer sensors & Yes & \textless{}5 s & — & Yes & [7] \\ VIPA-based 2D optical & Yes & — & Yes & No & [11] \\ beam-steering technique & & & & & \\ Point-to-Point System & No & 28.87 ns & No & No & [34] \\ wavelength-division & Yes & — & Yes & No & [46] \\ multiplexing & & & & & \\ Relay system & Yes & 11.957 us & No & No & Our work \\ \hline \end{tabular}
\end{table}
Table 3: Performance comparisons of the reported laser communication system.  
**Acknowledgments:** We are grateful to the anonymous reviewers for constructive comments that improved the quality of the work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Proposed PBFT Architecture and Model
### Network Architecture  
The proposed PBFT method is designed to perform location-based clustering for nodes participating in the network and dynamically adjust the hierarchical structure of the blockchain network. Figure 2 illustrates an example of the blockchain network structure proposed in this paper. In Figure 2, 224 nodes participating in the network are evenly distributed in each cluster. Each cluster is divided and managed by 56 nodes. Each cluster has three hierarchical layers, with one CPN managing each cluster. Furthermore, each group comprises one GPN and three NNs, representing a blockchain network structure. The maximum number of child groups is 2.  
A distinctive feature of the proposed PBFT method is that it dynamically changes the network hierarchy and group structure, considering factors such as the number of nodes participating in the network and latencies. Therefore, the maximum number of child nodes and the hierarchical structure of the network can vary based on the estimated consensus time estimation model in Section 3.3.2.  
The location-based clustering can be applied to the proposed PBFT method in various ways. For example, in the proposed PBFT method, clustering can be applied based on countries or cities because it is easy to determine the location of nodes participating in consensus within a private blockchain system. The proposed PBFT method utilizes these characteristics to divide and manage all nodes into multiple clusters to minimize communication delay during the consensus process. This can bring benefits in terms of consensus time. Detailed results on comparing consensus time are described in the experimental results.
## 3 Methodology  
The multivariate time-series data can be formalized as \(X=\{X_{1},X_{2},\ldots,X_{T}\}\in\mathbb{R}^{T\times N}\). Each element \(X_{i}\) represents the value of \(N\) variables at timestamp \(i\). In addition, the constants \(T\) and \(N\) denote the dimension of time steps and variates, respectively. Time-series forecasting intends to analyze the latent relationship of the observation and then predict the next \(L\) time steps, i.e., \(Y=\{X_{T+1},X_{T+2},\ldots,X_{T+L}\}\in\mathbb{R}^{L\times N}\). TSNN-ESTD focuses on improving the long-term dependence capture ability of traditional time-series prediction models, so as to optimize the long-term prediction performance.  
As shown in Figure 1, the two-stage integration strategy was adopted to correct the deviation of long-term prediction with the help of short-term prediction. First, the vanilla encoder-only forecaster iTransformer was utilized to achieve the preliminary prediction. Furthermore, we introduced the encoder-decoder architecture to optimize the original version for both short-term and long-term prediction. Subsequently, we explored the ensemble strategy of the three benchmark predictors to obtain the final prediction.  
Figure 1: TSNN-ESTD framework. (Left part: Base time-series forecaster network structure. Middle part: Three individual time-series forecasters with encoder-only and encoder–decoder architecture. Right part: Ensemble strategy for bias mitigating).  
TSNN-ESTD retains the iTransformer encoder (which uses self-attention and inverted embeddings to capture global dependencies) and replaces the MLP projection in the decoder with an LSTM-based decoder. The LSTM decoder complements the iTransformer encoder by addressing the MLP's inability to model sequential dependencies during generation, ensuring coherent and stable predictions while leveraging the encoder's global insights. This hybrid approach balances global context and local temporal coherence, particularly beneficial for complex time-series forecasting tasks.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 6 TWT in Wi-Fi 6
### Target Wake Time (TWT) Mechanism in Wi-Fi 6
#### 6.2.4 TWT Operation Modes  
There are multiple modes for TWT agreements, which we exemplify in the following subsections.  
Implicit vs. Explicit Mode  
The service periods (SPs) in this agreement can be implicit or explicit, which are also called periodic or aperiodic. In the implicit agreement, the successor SPs apply the same parameters as the first SP [5]. Explicit agreements require a new set of TWT parameters for the next SP.  
Announced vs. Unannounced Mode  
In announced mode, a station indicates to the access point that it is ready to receive buffered data. Only then does the access point send data to the station. Conversely, in unannounced mode, the access point assumes that the corresponding stations are awake in the SPs and immediately sends data to them. Announced mode is more efficient for saving energy when a station has a very small amount of frames to receive.  
Trigger-Enabled vs. Non-Trigger-Enabled Mode  
In trigger-enabled mode, the access point sends a TF at the beginning of an SP and schedules the transmissions for the stations participating in this TWT session. Thus, it limits the number of collisions. In non-trigger-enabled mode, the stations decide when to start the transmission without any negotiation with the access point.
# Virtual Grid-Based Routing for Query-Driven Wireless Sensor Networks
## 3 VGRQ: The Proposed Routing Mechanism
### Phases of VGRQ
#### 3.2.5 Data Transmission  
Sensor nodes, upon receiving the query from their respective CH, check whether they belong to the AoI or not. If they belong to the AoI, then they sense the data and send it to their respective CHs. The CHs further perform fusion on the received data and transmit it towards the mobile sink along the chain, as shown in Figure 6. By employing this data transmission mechanism, the need to establish explicit routes for the sensor nodes is mitigated, resulting in energy conservation.
# Constitutive Characteristics of Rock Damage under Freeze-Thaw Cycles
## 6 Conclusions  
The stress-strain curves of rocks with various FTC strengths were obtained by dynamic compression tests under different loading rates and confining pressure. The study found:  
(1) Through different FTC tests, the physical properties of rock samples change significantly. The FTC has a great influence on rock parameters, including saturation density, porosity and P-wave velocity. With the increase in FTC frequency, the mineral particles on the surface of the sample peel off, and the internal particles soften, resulting in the decrease in saturated mass, the expansion of pore structure, and the increase in mass loss rate. At the same time, the damage accumulation reduces the wave velocity.  
(2) With the increase in the number of FTCs, the change in rock damage value gradually flattens, the final strain value increases, and the cracks increase. With the increase in FTCs, the peak strain value of rock damage change rate increases, indicating that the ductile failure characteristics are more and more significant.  
(3) Although this study involves four FTC conditions, the results may be limited. In the future, by increasing the number of FTCs and confining pressure conditions, the mechanical properties of rocks under the coupling of freeze-thaw and confining pressure can be more comprehensively understood, and the universality of test results can be improved.  
Conceptualization, Y.L., Z.W., H.C. and T.W.; methodology, Y.L.; software, Y.L.; validation, Z.W.; writing-original draft preparation, T.W. and Z.W.; writing--review and editing, T.W. and H.C.; project administration, H.C. and H.C.; funding acquisition, T.W. and H.C. All authors have read and agreed to the published version of the manuscript.  
This study was sponsored by the National Natural Science Foundation of China (Grant No. 52308393) and the State Key Laboratory of Precision Blasting and Hubei Key Laboratory of Blasting Engineering, Jianghan University (Grant No. PBSKL2023B2).  
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.