esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Improving GPP Simulation Capability: The Ways Forward  
A reduction in model complexity and error accumulation should be a major consideration in improving GPP estimation. For example, the evaporative fraction parameter of the LUE-EF model involves more steps than the direct use of NDWI from satellite in the LUE-NDWI model, and consequently the risk of the error accumulation increases. The direct observation and continuous recording of NDWI as a remote sensing product is one of the main advantages of the LUE-NDWI model, which minimizes the risk of error propagation of the LUE-NDWI model at a regional to global scale. Therefore, the LUE-NDWI model is more practical and attractive in spatially-explicit simulations of GPP. In summary, the simple forms of GPP models and widely and readily available inputs, as compared with more complex global models, make them more practical for applications over large areas and better suited for attribution and uncertainty analysis [53; 63].  
Vastly different GPP products, as shown by the means, trends, and interannual variabilities of GPP, generated by the 24 models suggest our current ability in simulating global GPP is not encouraging (Figure 5a). For nearly 40 years of commitment to the global simulation of GPP, there does not seem to have a clear direction of improvement in GPP estimation as shown by the vast differences among GPP models in simulating global GPP. GPP model development is not explicitly directed, despite the constant emergence of new models. Our research indicates that both the structures of the models and input data are error prone. Therefore, it is necessary to optimize model structures as well as sufficient validation and calibration of the input data to improve GPP simulation.
# LoRa Mobile-To-Base-Station Channel Characterization in the Antarctic
## 2 Materials and Methods
### LoRa Modulation  
LoRa is based on a patented modulation technology [16] relying on chirp spread spectrum (CSS) modulation. As illustrated by the screen shot of the Anritsu MS2692A spectrum analyzer in Figure 6, the packet starts with 10 up sweeps, followed by 2.25 down sweeps. These down sweeps are the only down sweeps occurring in the transmission. The actual data transmission starts after the 12.25-sweeps-long preamble. Symbols are encoded into constant-rate frequency sweeps, with the information conveyed by a frequency jump. As this trace is generated with a spreading factor of 12, this means that every frequency jump seen in Figure 6 represents 12 bits of data.  
To improve the reliability of the transmission, the data are interleaved, spectrally whitened, and gray indexed. Moreover, forward error correction (FEC) is employed.
# A Review of Research on Diagnosability of Control Systems Based on Structural Analysis
## 4 Diagnosability Design Based on Structural Analysis
### Inherent Diagnosability Design
#### 4.1.1 Optimized Design of MSO Search Algorithm  
The key to structural decomposition is to extract the structurally overdetermined part. Only the structurally overdetermined part implies the ARR. Based on the structurally overdetermined part, the set of MSOs is obtained through further division to establish the sensitive relationship between the MSOs and the faults.  
[PERSON] took the reconfigurable battery system as the research object, established the structural model based on structural analysis, gave the sufficiency conditions of fault structure detectability and isolability, and transformed the fault diagnosis problem into the MSO search problem. In addition, based on the greedy algorithm, the traditional MSO search algorithm was optimized to simplify the computational complexity of MSO. And the superiority of the proposed MSO algorithm was simulated and verified [47]. [PERSON] aimed at the cumbersome process of obtaining the MSO set and a large amount of computation in the traditional structural analysis. [PERSON] designed the MSO optimization algorithm based on the structural equivalence classes, significantly reducing the number of iterations and ensuring the algorithm's timeliness [48]. [PERSON] addressed the problem that the number of MSOs showed exponential growth with increased system complexity. He designed a diagnosability index to measure the degree of diagnosability of the residual candidate set to reduce the number of residual candidate sets and maximize fault detectability and isolation [49]. [PERSON] analyzed and introduced the MSO algorithms, the CBMSOs algorithm, the SARR algorithm, and the PCC algorithm. He compared the merits and drawbacks of the four algorithms in finding the MSOs, analyzed the computational complexity of each algorithm, and used the dual-capacity water tank system; thus, he simulated and verified the performance of each algorithm's performance and complexity [50]. [PERSON] converted the fault diagnosis problem into an MSO problem based on the linear programming algorithm of binary integers that solves the MSO set and analyzes the fault detection and isolation [51].  
The search problem of MSO is the optimization problem. The current algorithms for solving the optimization problem are the particle swarm algorithm, the genetic algorithm, the ant colony algorithm, the neural network, and other intelligent optimization algorithms [52]. Adopting suitable optimization algorithms has a certain degree of influence on the speed and accuracy of the MSO search [53].
# On the Optimal Calculation of the Rice Coding Parameter
## 5 Multi-Parameter Rice Coding
### Multi-Parameter Coding and Decoding  
In the previous subsection, we defined different algorithms for the calculation of a set of Rice parameters that can be applied to partitions of the input data sequence. In this subsection, we propose a method for encoding the set of Rice parameters and the generated Rice codes of each sub-sequence within a single bit-stream without ambiguity.  
The proposed method encodes each sub-sequence separately by providing first a code for its Rice parameter, then the Rice encoded values in the sub-sequence, and finally, a terminating delimiter for the sub-sequence.  
In most applications, the Rice parameter is usually a relatively small value. In such cases, the code for the Rice parameter can be the corresponding Elias gamma code, or any other prefix non-parametrizable VLC code (refer to [33] for a list of some of them). In applications where the Rice parameter is usually large, the author suggests including instead a field of fixed bit-length and using the natural coding for the Rice parameter. In our numerical results in the upcoming Section 6, we will consider the later case and include an overhead of one single byte per sub-sequence for encoding the Rice parameter.  
As for the terminating delimiter, it should be recalled that in Section 1, an explanation of Rice coding and decoding for all integer values was given. It should be pointed out that, according to the definition of Rice coding presented here (from [33]), there could be two Rice codes for the input value zero (0), since zero is neither positive nor negative but still Rice coding utilises one bit for coding its sign. In this work, for convention, we propose the usage of negative zero for delimiting the termination of the Rice coding of a sub-sequence and the coding of the Rice parameter of the next sub-sequence. In this way, the delimiter will occupy \(r+2\) additional bits for each sub-sequence. In order to avoid confusion, an input sample with value zero should be always encoded assuming that it is positive.
# Gaze-Guided Control of an Autonomous Mobile Robot Using Type-2 Fuzzy Logic
## 5 Conclusions  
In this paper, we proposed a vision-based gaze guide mobile robot control. Application of gaze interaction, wearable, and mobile technology has mainly been conducted on controlling the movement of robots. Users were enabled to control the robot remotely and hands-free by using their eyes to specify the target where they want the robot to move to. A central processing unit executes data communication between the user and the robotic platform. This continuously monitors the state of the robot through visual feedback and send commands to control the motion of the robot. Our experiments include an overhead camera, an eye tracking device, a differential drive mobile robot, vision and IT2 FIS tools. This controller produces the required wheel velocity commands to drive the mobile robot to its destination along a smooth path. To achieve this requirement, our methodology incorporates two basic behaviors, map generation and go-to-goal behavior. Go-to-goal behavior, based on an IT2 FIS, is more smooth and uniform to progress handling data in uncertainties to produce a better performance. The algorithms are implemented in an indoor environment with the presence of obstacles. Furthermore, the IT2 FIS controller was strongly used to control a real-time mobile robot using exact gaze data obtained from human users using an eye tracking system. The differential drive mobile robot (EV3) was successfully commanded by the user's gaze. This method of interaction is available to most people, including those with disabilities and the elderly, who undermine motor ability. Thus, I would like to express that this technology needs to be developed in order to be able to be used in many fields. This system can also be an alternative or supplement to a conventional control interfacesuch as a mouse or joystick, etc. The results from the proposed technique have been illustrated via simulation and experiments. It is indicated that the intelligent vision-based gaze guided robot control (GGC) system is applicable and the IT2 FIS can successfully infer operator intent, modulate speed and direction accordingly. The experimental results obtained are very adequate and verify the efficacy of the proposed approach.  
[PERSON] analyzed the original Mobile Robot motion control, visual servoing system and Gaze Guide control methods and applied Type-2 fuzzy logic for dynamic parameter adaptation applied for tracking trajectories, and contributed to performing the experiments and wrote the paper; [PERSON] reviewed the state of the art, analyzed the data and proposed the method; [PERSON] contributed to the discussion and analysis of the results.  
This research was funded by TUBITAK-BIDEB under grant 2214/A.  
The authors declare no conflict of interest.
# Path Loss Model for 3.5 GHz and 5.6 GHz Bands in Cascaded Tunnel Environments
## 2 Path Loss Model for Cascaded Tunnels in Far-Field Region of Propagation
### Calculation of Break Point  
Based on the waveguide model, radio propagation in tunnels can be modeled in the same way as the propagation of radio waves in a rectangular waveguide [34]. In the nearfield region, the electromagnetic field consists of multiple modes which interact  
Figure 1: Cascaded tunnel and antenna locations.  
and produce wide and rapid variations. In the far-field region, the fundamental mode determines the electromagnetic field [35]. Therefore, the break point (BP) is considered to divide the propagation region into a near-field region and a far-field region. This divide is determined by signal wavelength and the tunnel's dimensions [36].  
\[\text{BP}=\text{max}\left(\frac{w^{2}}{\lambda},\frac{h^{2}}{\lambda}\right) \tag{1}\]  
where \(w\) and \(h\) represent the width and height of the rectangular tunnel, and \(\lambda\) is the signal wavelength. At present, most subway tunnels are arch structured, and an arched tunnel can be equivalent to a rectangular tunnel [37].  
\[w=\sqrt{\frac{2s}{1+\cos\theta}\left[(\pi-\theta)r_{a}^{2}+h_{a}^{2}\tan\theta \right]} \tag{2}\]  
\[h\ =\sqrt{\frac{s(1+\cos\theta)}{2}\left[(\pi\ -\ \theta)r_{a}^{2}+h_{a}^{2}\tan \theta\right]} \tag{3}\]  
where \(r_{a}\) represents the radius of the arched tunnel, \(h_{a}\) represents the distance from the center of the tunnel to the bottom of the tunnel, \(s\) and \(\theta\) are the cross-sectional area ratio of a rectangular to a circular area, which can be calculated by the following expression.  
\[s=\sqrt[3]{\left(x_{1}^{0}\right)^{4}/16\pi} \tag{4}\]  
\[\theta=\arccos\left(\frac{h_{a}}{r_{a}}\right) \tag{5}\]  
where \(x_{1}^{0}\) is the first zero of the Bessel function.
# Challenges of Engineering Skillsets Essential for Driving Circularity of Smart Cities
## 2 State-of-the-Art Review
### Smart Cities  
#### 2.1.1 Internet of Things (IoT)  
The Internet of Things (IoT) is a technological practice, a system that has resulted from the evolution of conventional networks and enables quick and efficient decisions. It entails a large number of sensing devices that are connected to each other [9]. These devices share and analyze data and communicate through WIFI, Bluetooth, and other means to make an informed decision; in our case, the decisions involve the operation of a smart city that is sustainable, has low emissions, and enhances the quality of services for its citizens [9]. The idea is that there is minimal human interaction when decisions are made this way, which leaves out any risk of corruption or human error, ensuring that all decisions made are backed by data to ensure the best possible outcome with respect to a smart city's sustainable development goals.  
Although there are endless facts and data that are used by IoT technology in smart city decision-making, there are also drawbacks that must be considered. Ethical concerns have been raised about the General Data Protection Regulation (GDPR), security and data privacy breaches [10], and unauthorized access to connected devices; so, ethics play a big role when businesses decide to employ this technology in any capacity, with the aim of improving energy efficiency or reducing waste. Based on this, we included a key question in the questionnaire sent to industry experts: \"How are privacy and security concerns due to smart city technologies in your work addressed?\" Insights derived from this question will help improve our understanding of the precautions taken to mitigate and prevent these issues and will indicate other potential skills suggested by the experts, such as critical thinking, computing and software proficiency, and the indication of a growth mindset.  
The Internet of Things is an 'architecture', a layered model format carefully planned by architects, with specific capabilities involving technical expertise, as shown in Figure 1. The main aim of this research is to identify the engineering capabilities and transferable skills needed to implement CE in smart cities in a way that maximizes its potential, and it has already been suggested that architects should begin to work on a new IoT framework that can optimize functionality, capabilities, and security. However, this must be performed in a way that does not cause fragmentation (disconnection of some parts of the Internet of Things from others) [11].  
Studying the capabilities of industry experts may indicate the skills required to implement a revised IoT architecture that can maximize data analysis and outputs.
## 3 Results and Discussion
### Model Establishment and Significance Analysis  
As shown in Table 7, a regression fitting analysis of the experimental results was conducted using Design-Expert 8.0.6 software. The quadratic polynomial regression models obtained are as follows:Y1 = 13.92 + 0.035A + 0.18B + 0.28C - 0.17D - 0.12 AB - 0.43 AC - 0.19 AD + 0.28 BC - 0.18 BD - 0.08 CD - 0.24A2 + 0.3B2 - 2.13C2 - 1.77D2; Y2 = 78.86 + 0.81A + 1.10B + 3.88C - 4.01D - 1.07 AB - 0.5 AC + 1.81 AD + 6.54 BC + 1.27 BD + 2.38 CD + 2.12A2 + 5.22B2 - 8.95C2 - 7.91D2. Analysis of Y1 (TSS/TA Ratio): The analysis of the quadratic regression model for Y1 indicated significance after optimisation, with a non-significant lack-of-fit (\(p\) > 0.05), \(R^{2}\) = 0.9841, and \(R^{2}\)Adi = 0.9681, indicating good predictive capability. According to the \(p\)-values, the linear term C, interaction term AC, and quadratic terms B2, C2, and D2 have significant effects on the TSS/TA ratio of the juice (\(p\) < 0.01). Additionally, the linear terms B and D and the quadratic term A2 significantly affected the TSS/TA ratio (\(p\) < 0.05). The influence of each factor on the TSS/TA ratio was in the order: C (sterilisation temperature) > D (sterilisation time) > B (ultrasonication time) > A (homogenisation time). Through optimisation, the parameters yielding the maximum TSS/TA ratio are homogenisation time of 2.73 min, ultrasonication time of 2.94 min, sterilisation temperature of 102.06 \({}^{\circ}\)C, and sterilisation time of 85.69 s, resulting in a predicted TSS/TA ratio of 14.4105 and an actual value of 14.3. Analysis of Y2 (Sensory Evaluation Score): The analysis of the quadratic regression model Y2 indicated that the optimised model F was 61.74, which was highly significant with \(p\) < 0.0001, and the lack-of-fit term was not significant (\(p\) > 0.05). The correlation coefficients \(R^{2}\) = 0.9861 and \(R^{2}\)Adi = 0.9721 suggest that the predicted values of the model closely reflect the actual values. Based on the \(p\)-values, the linear terms C and D, interaction terms BC and CD, and quadratic terms A2, B2, C2, and D2 had significant effects on the sensory evaluation of the juice (\(p\) < 0.01). The linear term B and interaction term AD
## 4 Proposed Method for Physical Layer Performance Estimation
### Proposed Power Estimation Algorithm
* _Step 8_: from the estimated position of the blinds nodes and making use of the most appropriate channel model \(F_{jk}^{B}\) (this is selected again on the basis of the ambient environment), the transmission power of node \(k\) seen by the blind node \(j\) is computed.
# Proxy-Based Adaptive Transmission of MP-QUIC in Internet-of-Things Environment
###### Abstract  
With the growth of Internet of Things (IoT) services and applications, the efficient transmission of IoT data has been crucially required. The IETF has recently developed the QUIC protocol for UDP-based multiplexed and secure transport. The Multipath QUIC (MP-QUIC) is also being discussed as an extension of QUIC in the multipath network environment. In this paper, we propose a proxy-based adaptive MP-QUIC transmission for throughput enhancement in the IoT environment. In the proposed scheme, a proxy device is employed between IoT clients and IoT server to aggregate the traffics of many clients in the access network. The proxy will transport a large among of traffics to the server, adaptively to the network conditions, by using multiple paths in the backbone network. For this purpose, the proxy device employs a path manager to monitor the current network conditions and a connection manager to manage the MP-QUIC connections with the IoT server over the backbone network with multiple paths. For effective MP-QUIC transmission, the proxy will transmit the prioritized packets to the server using the best path with the lowest round-trip time (RTT), whereas the non-prioritized packets are delivered over the other paths for traffic load balancing in the network. From the testbed experimentations with the MP-QUIC implementation and ns-3 simulation modules, we see that the proposed scheme can outperform the normal QUIC (using a single path) and the existing MP-QUIC scheme (using the round-robin policy) in terms of response delay and total transmission delay. Such performance gaps tend to increase as the link delays and packet loss rates get larger in the network.  
Keywords:QUIC; MP-QUIC; IoT; proxy; path manager; connection manager +
Footnote †: journal: Electronics Electronics
# Portable Smart Spectrometer Integrated with Blockchain and Big Data Technology
## 4 Big Data Technologies
### File Processing of Spectral Datasets  
The HDFS is designed to store large files, and the typical size of its file blocks is 64 MB or 128 MB. If it is used to store small files, on the one hand, the storage space of the blocks is wasted. On the other hand, each file must maintain a certain amount of metadata, which will result in inefficient storage [26]. The collected spectral data has the characteristics of high data dimension and small single files. If no additional processing is performed, the management efficiency will be low. Therefore, small spectral files should be combined in a certain format in advance of storing the spectral data.  
Different models of portable smart spectrometers have different spectral ranges and resolutions, so they output different data formats. However, for the same spectrometers, spectral files have the same spectral range and resolution. When merging spectral files, it is only necessary to retain the spectral range once, thus a large amount of information redundancy can be eliminated. Table 1 shows an example of the spectral file format. \"P\" represents the item type of spectral measurement. For example, alcohol concentration detection and sugar concentration detection for wine are two different item types. \"P\" can also represent a combination of multiple item types, such as the combination of alcohol concentration detection, sugar concentration detection and the authenticity of wine. \"m\" represents the case of the mth measurement. \"H\" represents the hash value of the data. The hash value can be used as a unique label for the data and its use can be seen in Section 5. \"\(\uplambda_{1}\)\" to \"\(\uplambda_{n}\)\" represents the range of wavelengths. \"\(\uplambda_{n}\)\" represents the nth spectral component of \"P.\" \"T\" represents the temperature value  
Figure 8: File structure of the big data management system.  
at the time of spectral measurement. \"TS\" represents the timestamp and \"ID\" represents the identity of the spectrometer, equivalent to the system ID in the blockchain system.  
With the help of big data analysis technology and spectral analysis technology, the recorded spectral data can be used to analyze the relationship between various detection results. For example, the relationship between the alcohol concentration and the authenticity of a certain wine. Moreover, the recorded spectral data can also be used to build spectral analysis models. For example, the content of a chemical constituent in the wine can be obtained by spectral analysis according to the spectral data, and then based on a large number of analysis results, a spectral analysis model for detecting the content of this chemical constituent in the wine can be built.
## 3 Results  
### Assessment of CFAPAR for Cropland Productivity  
To evaluate how well CFAPAR represents cropland productivity, it was necessary to investigate its relationship with cropland productivity. Figure 2 shows a reasonably good linear correlation between crop NPP (\(\mathrm{NPP_{CAR}}\)) and CFAPAR at the CAR level (\(\mathrm{CFAPAR_{CAR}}\)) on the Canadian Prairies. The correlation coefficient (\(r\)) is 0.60 (\(p\) < 0.01, \(n\) = 513). The result indicated that CFAPAR could be considered as a reasonable surrogate of cropland productivity, in agreement with some other studies [4; 58].