esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimentation and Analysis
### Qualitative Results
#### 4.2.3 Visualization of Results  
The model's performance was further validated on the KITTI and our self-collected datasets. Figures 7 and 8 show the qualitative results of these datasets. A consistent color-coding convention describes vehicle behavior: green for vehicles moving away, red for vehicles driving usually, and blue for vehicles changing lanes to the right. In Figure 7a,b show examples of vehicles accelerating away from their lane. Figure 7b,c depict a vehicle ahead changing lanes to the right, with Figure 7c showing the initial lane change and Figure 7d showing the vehicle completing the maneuver.  
In Figure 8a illustrates a car changing lanes, while Figure 8b shows a motorcycle accelerating. Figure 8c captures a car on the right side of the lane merging into the left lane, and Figure 8d depicts a vehicle on the right side of the road completing a lane change and preparing to change lanes again. These qualitative results further validate the classification and generalization capabilities of the proposed model on the self-collected dataset, even with fewer or no observed test vehicles.
## 4 Assessment Validation
### Quantitative Evaluation
#### 4.2.2 Results  
Assumption of Local Independence  
The results showed several item pairs with significant \(\chi^{2}_{LD}\) values, indicating local dependence. For these item pairs, removal of one item was considered based on the amount of dependence with other items, extreme proportion correct scores, or the presence of items with similar \(q\)-vectors (i.e, rows in the Q-matrix). In total, three items were removed from each assessment. The final assessments consist of 32 and 21 items, respectively, which are used in the subsequent analyses and can be found in the Supplementary Material.  
Q-Matrix Validation  
The stepwise Wald method suggested modifications for the \(q\)-vectors of 14 items of the first assessment (21 suggested entry changes) and for the \(q\)-vectors of two items of the second assessment (3 suggested entry changes). These suggestions were evaluated based on three aspects. First, the items and attributes were re-evaluated to assess whether the suggested modifications were theoretically defensible. Second, we inspected the results from the think-aloud study to determine the percentage of students who used the attributes under consideration when answering the items, thereby including empirical evidence for whether or not the items measure the attributes. Third, we compared the proportion of variance accounted for (PVAF) of relevant \(q\)-vectors to evaluate to what extent the attributes under consideration contribute to explaining variance in success probabilities. Only if suggestions were sensible from a theoretical perspective were modifications considered.  
In total, five Q-matrix entries were adjusted for five different items. These adjustments along with extensive argumentation can be found in Table 1 in Appendix A. Table 3 presents the final Q-matrices (along with reduced DCMs, which are discussed next). Both Q-matrices satisfy the conditions for generic identifiability from [40].  
reduce model fit compared with fitting the saturated LCDM to all items, \(\chi^{2}(17)=9.8\), \(p=0.91\) (assessment 1) and \(\chi^{2}(4)=5.1\), \(p=0.28\) (assessment 2). The reduced models are used in the subsequent analyses.  
The fit statistics to evaluate the absolute fit of the final model are shown in Table 4. The statistics indicate that the model fits adequately (for the \(RMSE_{2}\), the cutoff values 0.030 and 0.045 indicate excellent and good fit for the LCDM [56]; for the \(SRMSR\), values below 0.05 indicate acceptable model fit in DCMs, e.g., [57]). Further, classification accuracy and reliability are mostly high, as shown in Table 5. Detailed model results are included in the Supplementary Material.
## 3 System Model
### Workflow Execution Model  
The workflow execution model used in the proposed method is shown in Figure 1. The scientific workflow is described as DAG where the nodes denote individual executional tasks and the edges denote control and data constraints (i.e. the set of parameters) between the tasks. The output data in the form of files by one task is used as input data for another task. In the model used in this study, the scientific workflow is abstract, i.e. the workflow is unaware of the details of physical locations of the executables and the data. This model fits a number of workflow management systems (WMSs). IaaS providers allow WMSs to access to infinite pool of VMs for lease. For instance, Pegasus ([[https://pegasus.isi.edu/](https://pegasus.isi.edu/)]([https://pegasus.isi.edu/](https://pegasus.isi.edu/))) and Galaxy [23; 24]. An introduction of the main components of the workflow execution environment are given below, details of which may be found in [25].  
1. Workflow submission: The user submits the workflow application to the WMS for scheduling of the workflow tasks. The WMS resides on the host machine which may be a user's laptop or a community resource.
2. Target execution environment: It can be a local machine, like the user's laptop, or a virtual environment such as the cloud or a remote physical cluster or a grid [26]. On the basis of the available resources, the WMS maps given abstract workflow into an executable workflow and execute them. Moreover, it monitors the execution and manages the input data, intermediate files and output files of the workflow tasks. An overview of the main components of the target execution environment are discussed below:Workflow mapper: The workflow mapper produces an executable workflow on the basis of the abstract workflow submitted by the user. It identifies the appropriate data and the software and hardware resources required for the execution. Moreover, the mapper can restructure the workflow for performance optimization.
2. Clustering engine: To reduce system overheads, one or more small tasks are clustered into single execution unit called job in WMS.
3. Workflow engine: The workflow engine submits the jobs defined by the workflow in order of their dependency constraints. Thereafter, the jobs are submitted to the local workflow scheduling queue.
4. Local workflow scheduler and local queue: The local workflow scheduler manages and supervises individual workflow jobs on local and remote resources. The elapsed time between the submission of a job to the job scheduler and its execution in a remote compute node (potentially on cloud) is denoted as the queue delay.
5. Remote execution engine: It manages the execution of clustered jobs on one or more remote compute nodes.
# Evaluating MPTCP Congestion Control Algorithms: Implications for Streaming in Open Internet
## 2 Materials and Methods
### Quality Measures
#### 2.1.2 Protocol Delay  
Protocol delay, denoted by \(T_{over}\), reflects the time a data packet waits in the buffer for stream reassembly. This delay starts from the moment the Master Controller receives the user data from the transmit buffer and ends when the acknowledgment for that data is received. Essentially, the protocol delay is equivalent to the delay on the slowest path and can be mathematically expressed as  
\[T_{over}(k)=\max_{i}T_{i}(k) \tag{2}\]  
where \(k\) denotes the sample index.  
To provide a finer evaluation of protocol delay, we introduce additional metrics that capture its average and maximum values over multiple experimental runs. They are useful for assessing the consistency and reliability of CC algorithm operation.  
The average protocol delay \(v^{r}_{av}\) is calculated as  
\[v^{r}_{av}=\frac{1}{K}\sum_{k=1}^{K}T_{over}(k) \tag{3}\]  
where \(K\) is the number of samples collected in a single experiment run \(r\).  
The maximum protocol delay \(v^{r}_{\max}\) for the same run is given by  
\[v^{r}_{\max}=\max_{k\in[1,K]}T_{over}(k) \tag{4}\]  
To obtain metrics for multiple runs, we introduce the average protocol delay \(v_{av}\) and the average maximum protocol delay \(v_{\max}\):  
\[v_{av}=\frac{1}{R}\sum_{r=1}^{R}v^{r}_{av} \tag{5}\]  
\[v_{\max}=\frac{1}{R}\sum_{r=1}^{R}v^{r}_{\max} \tag{6}\]  
where \(R\) is the total number of experiment runs.
## 2 Materials and Methods
### Pilot Test Procedures
#### 2.3.4 Microbiological Sampling  
In addition to the routine geochemical and contaminant sampling and analyses, samples were collected during the baseline recirculation testing, electron donor addition, and verification sampling phases of the pilot test for PFLA and Census qPCR analysis. Sampling of the microbiome was achieved by flowing the groundwater through a sterile filter (Bio-flo(r) samplers supplied by Microbial Insights) to collect the suspended microbes onto the filter. The total volume of groundwater passed through the filter was recorded and the filter was sealed and put on ice for delivery to the laboratory for analysis. The residues captured on the filter, including the microorganisms filtered from the groundwater, were backflushed in buffered sterile water solution for concentration via centrifuge. Extracts for MMM analyses were generated from these concentrated filtrate residues. Three samples were submitted to establish a baseline of the microbial consortia present in the hotspot and source zone areas of the shallow aquifer. A total of six groundwater samples were collected during the biorecirculation testing and 1 was collected during verification sampling period for the MMM analyses submitted to Microbial Insights. All samples were subjected to select MMMs to determine the presence of chlorinated hydrocarbon-degrading microorganisms and genes producing reductive dechlorination enzymes, the basic phylogeny of the microbiome, and their population density. Unlike traditional laboratory-based microcosm studies, these analyses were performed on fresh groundwater samples, without culturing or growth on selective medium.
# Energy Efficiency of a Decode-and-Forward Multiple-Relay Network with Rate Adaptive LDPC Codes +
## 5 Conclusions  
In this paper a DF-based two-hop multiple-relay network was studied as a solution to enhance IoT network performances with the limited node capabilities. Complex and computation intensive operations were carried out at relays. The scenario under consideration could increase the hardware and software complexity of the relays because relays have to implement the complex algorithms of code rate and transmit power adaptation. However, relays with some extra computing and storage capabilities could reduce the load of the base stations and central control nodes. In this work, we considered the transmit power allocation in different manners, at source and at relays. It was observed that optimized power allocation was the best method to enhance the BER performance under the constraint of energy efficiency.When there was code rate adaptation for the relay to destination link, a considerable gain could be attained without buffering the information at the intermediate relays. This method not only has many advantages over the technique with information buffering at the relays, but it also reduces the complexity of the overall system, helping the relays to consume less power and reduces the overall system delay. Future extensions of this work could include more sophisticated power allocation algorithm to further improve the performance with more practical constraints such as limited CSI feedback and energy balancing.  
**Author Contributions:** The contributions of the authors are as follows. [PERSON] did formal analysis, simulation, and original draft preparation. [PERSON] created the main ideas and validated analysis. [PERSON] did project supervision and theoretical analysis. [PERSON] crystallized mathematical framework and manuscript editing.  
**Funding:** The work of [PERSON] was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Korea Government (Grant number: NRF-2019R1F1A1047989).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 New Adaptable Strategy for RT and NRT troposphere Monitoring  
Optimizing the accuracy and the timeliness for the tropospheric products was the first motivation for developing a new adaptable processing strategy. The second goal aimed at effectively supporting various users by simultaneously combining RT and NRT analysis modes. The third goal was to retrieve all the advanced tropospheric parameters consistently when using a unique and flexible  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Solution Description** & \begin{tabular}{c} **BIAS (mm)** \\ **mean \(\pm\) sdev** \\ \end{tabular} & \
\begin{tabular}{c} **SDEV (mm)** \\ **mean \(\pm\) sdev** \\ \end{tabular} &
\begin{tabular}{c} **RMSE (mm)** \\ **mean \(\pm\) sdev** \\ \end{tabular} \\ \hline
**GOPQ—GPS+GLO** & \(1.8\pm 2.9\) & \(6.7\pm 1.2\) & \(7.5\pm 2.5\) \\
**GOPR—GPS** & \(2.0\pm 2.8\) & \(7.2\pm 1.0\) & \(7.9\pm 1.5\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary statistics over 18 stations from routine RT product using GPS and GPS + GLO data w.r.t. EUREF reprocessing.  
Figure 6: Monthly summary biases and standard deviations of real-time ZTDs over 18 stations.  
processing scheme and exploit optimally multi-GNSS data. Additionally, strategy targets all the advantages of PPP when using epoch-wise analysis supported by real-time precise products, e.g., by IGS RTS. The new strategy has been implemented in the G-Nut/Tefnut software designed for estimating ZTDs, horizontal tropospheric gradients and tropospheric slant path delays. Currently, the parameters are estimated using dual-frequency ionosphere-free linear combinations of pseudorange and carrier-phase observations.
## 2 Materials and Methods
### System Implementation
#### 2.2.2 Connection and Server  
The transmission of images from the smart glasses to our server is conducted using MQTT. MQTT is a standard for lightweight publish and subscribe systems, especially for constrained devices and with low- bandwidth as the device in our work. Using MQTT, a device can send and receive messages as a client. We handle the processing tasks in our server using Nodejs. We also use Node-RED, an open source solution developed by IBM, to link the operations between devices and the server.  
MQTT is a stable transmission method. However, in the case users lose their connection to the server, we also use local storage to keep the photos, and then upload to the server when connection is reestablished. The capacity of the local storage is about 16 GB, divided into 4 GB for operating system and 12 GB for data storage. An image taken from our device consumes about 130-220 KB. Thus, our device can keep over 90,000 photos.
## 4 Conclusions  
The heat transfer and flow characteristics in the equal, trapezoidal and circular-arc-shaped flow channels were studied experimentally in this paper, and the Reynolds number on the air side ranged from 1200 to 5100. The different circular radii \(R\) (\(R\) = 200 mm, \(R\) = 250 mm and \(R\) = 300 mm) of the circular-arc-shaped flow channel were analyzed. The core volume goodness factor was used to compare the equal cross-section flow channel, trapezoidal flow channel and circular-arc-shaped flow channel. This paper can be concluded as follows:  
* With the increase in the circular radius, both the \(j\) and \(f\) factors increase, and the highest overall heat transfer performance is obtained at \(R\) = 300 mm.
* Comparing the three kinds of flow channels by using the core volume goodness factor shows that the overall heat transfer performance ranks from the circular-arc-shaped flow channel, trapezoidal flow channel to equal cross-section flow channel from best to worst. The overall heat transfer performance of the circular-arc-shaped flow channel is 26.2% (maximum value) larger than that of the equal cross-section channel. The divergent structure has better thermal hydraulic performance than the tapered and straight structures.
* With the decrease in the inlet height (\(F_{h}\)), h in the flow channels increases gradually. The pressure drop in different flow channels increases with the increase in flow velocity in the form of a parabola.  
Figure 8: Comparison between the fitting values of the correlation expressions of factor \(j\) and factor \(f\) and the experimental data. (**a**) factor \(j_{r}\) (**b**) factor \(f\).  
* The empirical formulas for heat transfer and flow performance of different circular-arcs were fitted by multiple regression analysis and \(F\) significance test. The empirical formula of the \(j\) and \(f\) factors can achieve 90% accurate prediction and the error range is controlled within 10%. When processing experimental data, the maximum mean deviation and maximum absolute deviation of the \(j\) factor were 1.05% and 3.77%, respectively. The maximum mean deviation and maximum absolute deviation of the \(f\) factor were 2.8% and 4.91%, respectively.  
Conceptualization, H.S.; methodology, H.S.; software, S.F.; validation, S.F.; formal analysis, D.Q.; writing--original draft preparation, D.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, Grant Numbers 51806114 and 51874187.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# A Survey on Formal Verification and Validation Techniques for Internet of Things
## 4 Formal Methods
* **Reusability:** Formal methods can improve the reusability of software components by providing a clear and precise specification of their behavior. This means that software components can be reused in different contexts without introducing errors or defects. Formal methods can also help to ensure that reused components behave correctly in all contexts. * **Standardization:** Formal methods can provide a standardized approach to software development and verification. This means that software systems can be developed and verified using a common set of techniques and tools, which can improve interoperability and reduce the risk of errors or compatibility issues. * **Confidence:** Formal methods can provide developers and stakeholders with confidence in the correctness and reliability of the software system. This can increase trust in the software system and reduce the risk of negative consequences, such as system failures or safety hazards. Table 4 summarizes the advantages of utilizing formal methods in software engineering, including abstraction, rigorous analysis, early defect discovery, correctness guarantees, reliability, and reusability. \begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**Advantage** & **Description** \\ \hline \multirow{4}{*}{Abstraction} & Formal methods provide a way to represent complex software systems in a simplified and abstract manner, which can help to reduce the complexity of the system and make it easier to reason about. \\ \hline \multirow{4}{*}{Rigorous Analysis} & Formal methods provide a way to analyze software systems rigorously and to prove that they meet their specifications. This can help to ensure that the system behaves correctly and that it meets the needs of its stakeholders. \\ \cline{2-3}  & Formal methods can help detect errors and defects early in the development process, which can make them easier and less expensive to fix. \\ \hline \multirow{4}{*}{Correctness Guarantees} & Formal methods can provide guarantees that a software system is correct and meets its specifications. This can help to increase the confidence in the system and reduce the risk of errors or defects. \\ \cline{2-3}  & Formal methods can provide guarantees that a software system is correct and meets its specifications. This can help to increase the confidence in the system and reduce the risk of errors or defects. \\ \cline{2-3}  & Formal methods can provide guarantees that a software system is correct and meets its specifications. This can help to increase the confidence in the system and reduce the risk of errors or defects. \\ \cline{2-3}  & Formal methods can provide guarantees that a software system is correct and meets its specifications. This can help to increase the confidence in the system and reduce the risk of errors or defects. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Advantages of formal methods in software engineering.
# Design of a Technique for Accelerating the WSN Convergence Process
## 3 Theoretical Principles Used in Research
### Ad Hoc On-Demand Distance Vector Protocol (AODV)  
AODV is a reactive routing protocol in which links are established between nodes only when needed, and the protocol initiates a search whenever a source node (S) needs to reach an unknown network [61]. The line is kept alive as long as resource S needs it, or as long as the multicast node group exists. Links between nodes are always loop-free thanks to sequence numbers. A sequence number is created by the destination node for each piece of route information that is sent from the source node. When it is possible to choose between two paths, the path with the higher sequence number is selected. Nodes maintain the next hop information in their routing table. Four types of Hello messages are used in this protocol, Route Request (RREQ), Route Reply (RREP), Route Error (RERR) [62; 63].  
Hello messages are used to monitor the state of the link to a known neighbor and are periodically broadcast to the neighbor. If the neighbor is not able to receive the Hello message, the line between them is evaluated as nonfunctional. If the source S is looking for a route to an unknown destination (D), it sends a so-called RREQ. In each node between S and D, information about the link to node S is stored. In case this new node is found, or the node already has a link to resource S, it starts generating RREP. The RREP is sent unicast hop-by-hop back along the route to the source S. After the source S receives the RREP, it has a functional route to the destination D. If S receives multiple RREP responses, i.e., multiple routes exit between S and D, S chooses the shortest one according to the hop count.  
When data flow between S and D, each node stores time data for each route. If a route has not been used for a certain period, the node cannot know whether it is operational and removes that route from its routing table. Subsequently, it sends RERR in the direction of node S. On the line to S, each node deletes the record of the currently nonfunctional route. After S receives the RERR, it is unable to send data to D and restarts the route search by sending an RREQ if necessary. The principle is shown in Figures 2 and 3.
# Research Progress of Gas Sensor Based on Graphene and Its Derivatives: A Review
## Gas Sensor
### Graphene Gas Sensors with Different Working Principles
#### 2.2.1 Resistive Gas Sensor  
Most gas sensors reported so far are operated in a resistive mode. The gas concentration is analyzed by direct test sensor with the change of resistance under the interaction with the detection gas [14]. Its structure is shown in Figure 1. The substrate is made of insulating material such as ceramic or silicon dioxide, and the graphene material or various graphene composite materials is coated or grown on the surface of the substrate as gas-sensing materials. The electrodes are drawn at both ends of the gas-sensitive material. When the detected gas and gas-sensitive materials in contact, gas molecules adsorbed on the surface of gas-sensitive materials resulting in resistance changes. The gas can be measured qualitatively and quantitatively according to the change of resistance [15].  
Figure 1: Resistive gas sensor structure.