esa-sceva/satcom-chunk-collection huggingface dataset:
# A Distributed Sensor System Based on Cloud-Edge-End Network for Industrial Internet of Things +
## 3 System Model and System Architecture
### Software System  
The software system plays a crucial role in processing, storing, and transmitting data. Improving the efficiency of data processing and transmission can significantly enhance the system's overall performance and reduce costs.  
The data processing module comprises several components, including an ultrasonic receiver, a signal conditioning circuit, an A/D converter, a processing unit, and memory.  
Figure 7: We have conducted field research on such scenarios as industrial robot fault monitoring, large mechanical arm actuator fault monitoring, distributed intelligent gearbox counting monitoring, online condition monitoring of steam turbine generator set, etc.  
The ultrasonic receiver converts mechanical waves with frequencies greater than 20 kHz into another energy signal, possessing characteristics such as A high frequency, short wavelength, small diffraction, and good directivity. The signal conditioning circuit is responsible for converting the electrical signal from the slave sensor into a signal that complies with the A/D converter's input range. The A/D converter, as shown in Figure 8, digitizes the analog signal. The data processing unit performs appropriate preprocessing of the collected industrial data, filtering out unnecessary and redundant information and sending valid information to the central computer [40].  
To convert the sound and acceleration analog data into digital data, we use an AD conversion module. The binary data are then sent to the buffer through the DMA pipeline, where the CPU processes and stores it in FLASH. Finally, the WiFi module is utilized to transfer data to the ESP32-S2 repeater and subsequently to the server for storage.
# A Dynamic Measurement System Based on Adaptive Clustering and Multi-Classifier
## 3 Materials and Methods
### Robust Detection of Dynamic Targets
#### 3.2.1 Input Processing  
The initial anchor box at the input stage plays a crucial role in the YOLO network, which recalculates the optimal anchor box values for different training sets during each session. However, these calculated values are not always ideal. In this study, we set the aspect ratio of the bounding box to approximately 1 and fixed three scales corresponding to the training datasets of 3 categories. This setting effectively restricts the adaptive adjustment method. When two objects are detected within the same grid, we employ a combination of clustering and pyramid techniques for selection, identifying small-scale targets through the shallow layers of the pyramid and large-scale targets through the deeper layers. Additionally, this study enhances the original YOLO padding function by utilizing negative samples such as the sky or white clouds for image-stitched-based filling, which reduces redundancy without compromising the detection of the refueling drogue.  
After front-end preprocessing, the three classifiers derived from clustering during sample training are used as decision reference values in this paper. These include the pre-selected box size range and the depth range of the inner and outer rings of the refueling drogue corresponding to the three classifiers. Under ideal conditions, the outer ring of the refueling drogue should be tangent to the respective preselected boxes in the binocular image. Thus, the horizontal axes \(\upmu\)L of the preselected box in the left perspective and \(\upmu_{r}\) of the preselected box in the right perspective can be used as disparity values to convert into depth information, as shown in Equation (2):  
\[\mu_{l}-\mu_{r}=\frac{BF}{Z} \tag{2}\]  
Figure 2: YOLOv5s architecture diagram.  
In the formula, \(B\) represents the baseline distance, \(F\) denotes the focal length, and \(Z\) stands for the depth value.
## Materials and Methods
### Analysis  
Data management were undertaken in Microsoft Excel 2016. All spatial analyses were carried out in ArcGIS Desktop 10.2.2 (Environmental Systems Research Institute, (ESRI) Redlands, CA, USA) and statistical analyses were undertaken in IBM SPSS for Windows, Version 22. Summary statistics were used to describe data from on the characteristics of the participants of different groups. [PERSON]'s alpha was used to quantify interrater reliability between self-report and medical record data. Subgroups were stratified as (1) rural/remote or metropolitan and (2) self-report or medical record. Missing spatial data was unable to be accounted as it relied upon adherence to the study protocol by participants. A summary of data points stratified by time provides is indicative of device adherence. Data accuracy in meters was collected, as was the source (GPS or Network). Stratification of available data were used to guide the final dataset used for analyses (data points with an accuracy of \(\leq\)20 m were retained for the spatial analysis). Statistical tests were not used to compare groups due to low overall participant numbers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Buffer Zone (meter)** & **Data Points (\(n\))** & **Data Accuracy (\%)** \\ \hline
5 & 18,096 & 43.1 \\
10 & 27,643 & 65.9 \\
20 & 34,097 & 81.3 \\
30 & 35,621 & 84.9 \\
40 & 36,555 & 87.2 \\
50 & 37,468 & 89.3 \\
100 & 39,157 & 93.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Buffer Zone accuracy.
## 10 Discussion and Future Developments
### Tagging Tools  
An important issue encountered during the survey activities, which was underestimated during the phase of definition of the specifications of the tender, is the necessity for mobile mapping systems to include on-site positioning definition capabilities. It is essential that the surveyor, while in the field, can associate their own position with information indicating the specific area or section of the complex or building they are in. This data structuring is crucial for correctly organizing information within management databases, as performing this task during post-processing is extremely costly and often even impossible. To address this, effective tagging functions must be implemented, allowing the surveyor to easily input relevant information on-site during survey operations.
## 2 Theoretical Derivations
### Calculation of Prestress Loss, \(\sigma_{st,l}\)
#### 2.1.5 Prestress Loss Due to Crack Reduction and Closure, \(\sigma_{l4}\)  
In our experiment, due to the restraining effect of the prestressed steel strand, the cracks of the concrete core were reduced to some extent, or even closed. The reduction of the circumference of the pipe led to the prestress loss of the strands. Therefore, the change in the maximum width of the visible cracks in the concrete core can be utilized to estimate the prestress loss caused by crack reduction and closure.  
The change in the maximum width of the cracks corresponds to the change in the length of the prestressed steel strands (Figure 6). The prestressed steel strand is in the elastic phase, and the stress is proportional to its strain. Therefore, the prestress loss, \(\sigma_{l4}\), caused by the crack reduction and closure, can calculated according to the following equation.  
\[l_{i}=\frac{r_{st}}{D/2}w_{i} \tag{18}\]  
\[\frac{\sigma_{0}}{\sigma_{0-}\sigma_{l4}}=\frac{2\pi r_{st}+l_{1}}{2\pi r_{st }+l_{2}} \tag{19}\]  
where \(w_{i}\) is the maximum width of the visible cracks in the concrete core (m), \(i=1,2\) for the condition before and after the reinforcement, \(l_{i}\) is the length of prestressed strands corresponding to the maximum width of the cracks in the concrete core before and after the reinforcement (m), and \(D\) is the outer diameter of the concrete core (\(m\)).  
Therefore, the prestress loss due to the crack reduction and closure, \(\sigma_{l4}\), can be defined as Equation (20):  
\[\sigma_{l4}=\frac{\sigma_{0}(w_{1}-w_{2})}{\pi D+w_{1}} \tag{20}\]
# A Distributed Algorithm for UAV Cluster Task Assignment Based on Sensor Network and Mobile
## 5 Conclusions  
This article introduced a rational clustering method using information exchange to maximize the dense task processing in a clustered UAV network. The UAVs fetched and analyzed the information from the wireless sensor network regarding the slots, completion time, etc. Such an analysis was required in order to leverage the task acceptance rate and reduce pending tasks. In this process, state learning was deployed for switching between prolonged and reassigned tasks. The balancing factor was estimated as the reward of the state learning process for reducing reassignments and longevity across various slots  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Metrics** & **FDSA** & **CLA-TO** & **ETCTMA** & **RCM-IE** \\ \hline Task Processing Rate (/slot) & 45 & 85 & 121 & 164 \\ Completion Ratio & 79.57 & 84.86 & 89.84 & 95.058 \\ Reassignment (/slot) & 28 & 20 & 12 & 5 \\ Failure & 0.158 & 0.132 & 0.101 & 0.081 \\ Delay (s) & 2.24 & 1.77 & 1.37 & 0.527 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative analysis summary (clusters).  
Figure 13: Delay.  
and clusters. The task allocation relies on network information such as communication range, cluster sustainability, and delay factor for admitting dense processes. The pending tasks were allocated using the last completion process within the cluster to reduce task reassignments. Using the RCM-IE distributed algorithm, UAVs could determine the sensor network's cluster task assignment rapidly and precisely. Shortest path detection in the sensor network could be performed with the help of the RCM-IE distributed algorithm, allowing UAVs to rapidly establish a cluster task assignment. RCM-IE can help to find the best ways for UAVs to talk to mobile information networks so that tasks can be assigned quickly and precisely. Depending on the responses from the previous state transitions, further slot allocations were performed. This was recurrent for the lesser balancing factor in preventing pending task density. The proposed RCM-IE maximized the task processing rate by 8.16% and the completion ratio by 10.3%. This method reduced reassignment, failure, and delay by 12.5%, 9.87%, and 11.79%, respectively.  
Methodology, J.Y.; Software, X.H.; Data curation, J.Y.; Writing--original draft, J.Y. and X.H.; Writing--review & editing, J.Y. and X.H. All authors have read and agreed to the published version of the manuscript.  
The authors received no financial support for the research, authorship, or publication of this article.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
## 6 Simulation Experiment  
We have run simulation experiment to investigate the feasibility and efficiency of our approach. Based on STK (3D display) and MATLAB (system performance calculation), we developed a simulation software for deployment, networking and system performance evaluation of UWSNs, which supports the random deployment and uniform deployment of sensors. The simulation parameters are set as follows: in a 12 \(\times\) 12 \(\times\) 4 km\({}^{3}\) of monitored space where the sensor nodes are deployed, in the beginning, every sensor node is randomly deployed on the surface of the water, it can carry out underwater and overwater communication, where the maximum underwater communication radius is \(r_{\text{b}}\) = 4 km and maximum \(d_{b}\) = 2 km, the communication bandwidth is \(b_{b}\) = 10 kbps, and other parameters are \(b^{\prime}{}_{b}\) = 5 Mbps, \(h_{b}\) = 100%, \(\epsilon_{b}\) = 10\({}^{3}\) kJ. Besides the sensor nodes, the network includes a master node whose parameters are \(v_{s}\) = 5 m/s and \(b_{s}\) = 40 kbps. The network also includes an UUV, whose parameters are \(v_{u}\) = 2 m/s, \(b_{s}\) = 20 kbps, \(d_{u}\) = 2 km, \(h_{u}\) = 100% and its lifetime is 7 \(\times\) 10\({}^{4}\) s (about 20 h) under normal working condition. At the same time, we assume that the velocity of the acoustic channel is 1500 m/s and the required maximum underwater bandwidth is \(B\) = 60 kbps.  
In the simulation experiment, when the sensors were deployed, the UWSN is generated according to CDNM, the master node and UUV are randomly deployed in the monitored space, and the detection accuracy is set at 1. In practice, when the detected precision is less than 1, we need more sensors to increase the probability of detecting the target. We assumed there were enough sensors and the monitored space was overcovered. In the network, it is supposed that a target randomly entered the monitored space, the closest sensor detected the target and transmitted its information (at 28 bytes per second) to the master node through the CDNM routing.
## 2 Data and Methods
### Data Collection
#### 2.1.4 Tropomi SIF  
The payload mounted on the Sentinal-5 Precursor, TROPOMI (TROPOspheric Monitoring Instrument), was launched on 13 October 2017. The wide swath width (~2600 km) makes the TROPOMI achieve a daily global coverage of almost 7 km \(\times\) 3.5 km spatial resolution at the nadir [42; 43]. The TROPOMI SIF (TROPOSIF) product has two versions:  
Figure 1: The location of the DM site. The red circle denotes the DM site.  
L2 and L2B. In the L2B TROPOSIF product, the far-red SIF was retrieved from two fitting windows, 743-758 nm and 735-758 nm, based on a data-driven method [44]. The 743-758 nm fitting window corresponds to an overcast sky which is derived using the observations with a cloud fraction below 0.8, and the 735-758 nm fitting window corresponds to a clear sky, which is derived using observations with a cloud fraction below 0.2. In this study, we chose the L2B TROPOSIF in the 743-758 nm fitting windows for data from 2018 to 2021 to extract the daily TROPOSIF values corresponding to flux sites in Section 2.1.3 ([[https://s5p-troposif.noveltis.fr/data-access/](https://s5p-troposif.noveltis.fr/data-access/)]([https://s5p-troposif.noveltis.fr/data-access/](https://s5p-troposif.noveltis.fr/data-access/)), accessed on 10 November 2022). We took the central longitude and latitude of TROPOSIF pixel as the center, and took the spatial buffer 0.01\({}^{\circ}\)\(\times\) 0.01\({}^{\circ}\) to extract the TROPOSIF values where flux tower sites resided in the buffer (0.01\({}^{\circ}\)\(\times\) 0.01\({}^{\circ}\)). Then, we eliminated negative SIF values, and screened out data with a cloud fraction of less than 0.2 for these extracted SIF data points.
## 3 Numerical Methods
### Validation Cases  
#### 3.3.1 Flow Past a Sphere  
The accuracy of the LS/IB method is first tested by performing the simulations of flows past a sphere. As suggested by the previous studies [27; 28; 29], when \(Re<300\), three different laminar flow regimes exist, and they are varied with the Reynolds number. These are the steady axisymmetric flow (\(Re<210\)), the steady nonaxisymmetric flow (\(210<Re<270\)), and the unsteady flow dominated by periodic vortex shedding (Re \(>280\)). Here we simulate six cases at \(Re=50\), \(100\), \(150\), \(200\), \(250\) and \(300\), and the Reynolds number is defined on the inlet velocity \(U_{\infty}\) and the sphere diameter \(d\), i.e., \(Re=U_{\infty}d/\
u\). Let \(x\), \(y\) and \(z\) represent the streamwise, vertical, and spanwise directions, respectively. The computational domain in and \(x\), \(y\) and \(z\) directions is \(30d\times 15d\times 15d\), and the grid number is \(384\times 160\times 160\). Near the sphere, the grid size is \(0.02d\times 0.02d\times 0.02d\). The flow is driven by a uniform inflow with \(u=U_{\infty}\) and \(v=w=0\) at the left boundary of the computational domain, and a convective boundary condition is used at the outlet. Periodic conditions are imposed in both the vertical and spanwise directions.  
For \(Re=50\), \(100\), \(150\) and \(200\), the flow past a sphere is steady and axisymmetric. As shown in Table 1, the drag coefficient \(C_{d}\) is varied with the Re number, and the results agree well with the data in references [27; 28; 29]. Besides, when the flow passes a sphere in streamwise direction, a steady separated bubble is formed behind the sphere. For \(Re=150\), the separation angle and the length of the recirculation bubble are shown in Figure 5. The topological structure is the same for other three cases, and the differences are displayed in Figure 6. At a different \(Re\) number, both the separation angle and the length of separation bubble are properly captured, and the results show good agreements with the data in references [27; 28; 29].  
At \(Re=250\), the flow behind the sphere loses its axial symmetry, indicated by the nonzero lift coefficient [30]. The flow becomes unsteady at \(Re=300\), and the vortices are alternatively shedding behind the sphere. The drag coefficient \(C_{d}\) and the lift coefficient \(C_{L}\) at \(Re=250\) and \(300\) are shown in Tables 2 and 3, respectively. At \(Re=300\), the Strouhal number of the shedding vortices is \(0.135\). All these results are in excellent agreement with the previous results [
## 2 Materials and Methods
### Data Collection, Experimental Places, and Weather Conditions  
For data collection, seven melons per treatment were recorded on the agronomic traits at 1-5 weeks after planting and harvestable fruits were assessed the fruit quality at 6 weeks after pollination. The agronomic traits were stem length, stem diameter (measured at the middle of stem), leaf width, leaf length (measured at the newest mature leaf), and leaf number (counted only mature leaves). The fruit traits were fruit weight, fruit height, fruit perimeter, rind thickness, pulp thickness, fruit cavity, pulp firmness, pulp color, total soluble solid (TSS), juice electrical conductivity (EC), juice pH, and juice titratable acidity (TA).  
The pulp's color was measured using a portable Hunterlab ColorFlex(r) EZ device (Hunter Assoc. Laboratory, Reston, VA, USA) in tristimulus mode, where \(L^{\star}\) stands for lightness and is measured on a scale of 0 to 100, \(a^{\star}\) for redness and greenness, and \(b^{\star}\) for yellownness and blueness. _Hue_ is referred to as a color's appearance parameters, while chroma (\(c^{\star}\)) corresponds to the saturation level of a color, with clear, bright, and brilliant being possible chroma values.  
The TSS was assessed by a brix refractometer (Sinotech (Thailand) Co., Ltd. (Bangkok, Thailand), model RHB-18 ATC) and the results were expressed as \({}^{\circ}\)Brix.  
Melon juice's EC and pH were assessed using EC and pH meters (Hanna Instruments, Inc. (Smithfield, RI, USA), edge(r) model HI2030).  
Juice extract was titrated to pH 8.1 with 0.1 M NaOH to determine the TA, and the percentage of citric acid in the samples was then measured [14].  
The fruit quality of melon was analyzed at The Center for Scientific and Technological Equipment, Walailak University. The experiment was conducted during April to June 2023. During the experiment, rainfall varied from 0.6 to 143.8 mm, and relative humidity varied from 77.6 to 87.6%, with June observing the highest levels of both rainfall and humidity and May having the highest relative humidity. The temperature ranged between 23.7 and 37.7 \({}^{\circ}\)C, with April as the hottest month. The weather data was collected from the greenhouse's weather station.
## 4 Empirical Insights from Sectorial and Regional Studies
### AI-Driven Bank Digitalization and SME Financing in China  
[PERSON] et al. [116] investigate how AI and big data are transforming SME financing in China through digital bank models. Their study presents a theoretical model that compares three types of financial institutions: traditional large banks, regional banks, and internet banks. The findings suggest that internet banks--leveraging AI-driven risk assessment and digital loan technologies--are better positioned to meet the financing needs of digitally mature SMEs. Meanwhile, regional banks retain advantages for SMEs with low digital footprints, due to their relational lending capabilities and access to internal business information.  
This case highlights how the effectiveness of AI applications in the financial sector depends on technological readiness (TOE: Technological), SME digital maturity (TOE: Organizational), and the institutional banking environment (TOE: Environmental). Although the paper does not offer firm-level performance metrics, it underscores the importance of strategic digital alignment between SMEs and financial institutions to unlock AI's potential to improve credit access and reduce financing restrictions. The findings reinforce the challenge of aligning external financial ecosystems with the adoption capacity of SMEs, especially in rapidly digitizing economies.
## 6 Simulations and Testbed Experiments
### Simulation Settings  
To verify our approaches, we consider a network consisting of 200 stationary nodes. Each node has a radio range of 250 m. The nodes are randomly and uniformly distributed in a square area with side length of 2,000 m. The sink node is located at the center of the area. We keep locations of all nodes unchanged.  
With reference to the IRIS note module produced for commercial purposes by Crossbow [27], we set the energy consumption model as 53 mW, 70 mW, 48 mW and 0.033 mW to correspond to the Receive, Transmit, Idle and Sleep states, respectively.  
The data communication rate is set as 250 kbps. The application layer of each node is set to generate 100 bytes data simultaneously every 20 seconds. The wake-up time is set every 10 seconds and the scheduling update time is set every 60 seconds.  
We use NS2 as simulation environment. The MAC layer adopts the RTS/CTS based CSMA/CA protocol.  
In a WSN with dense nodes and synchronous data sampling, serious collisions and energy consumption exist. If the data lossless is not guaranteed, the packet loss rate must be considered as a performance metric. Because the objective of our CDSAs is to reduce energy consumption, we assume packets are lossless. Thus, the sender must re-transmit the data packet if it fails to transmit the packet.  
To evaluate our approaches, three groups of experiments are carried out. The first group of experiments adopts the non-scheduling approach, in which only CSMA/CA protocol is applied. This group is used as a benchmark for our two CDSAs. The second group adopts the O-CDSA and the third group employs the T-CDSA. In each group we perform 20 independent experiments, respectively. Each experiment iterates 40 rounds in the program.  
The static route is formed at the RD stage. Initially, the sink node broadcasts a route discovery packet (RDP). This RDP is then received by the nodes around the sink node. After a random delay in each node receiving the packet, the node continues broadcasting the RDP. Each node regards the sender of the packet received first as its next-hop and ignores the other RDPs received. The RDP is broadcasted until all the nodes in the network receive it. The static route is formed eventually at the end of the RD stage. The first experiment in each group uses the same seed number at the RD stage, and the rest of experiments use different seed number. Therefore, the static routes in the rest of experiments are different from each other except that the topologies of the first experiment in each group are identical.