esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials And Methods
### Description of the INLAND Surface Model
#### 2.1.1 NEE Modeling  
The INLAND estimates the net ecosystem exchange of CO\({}_{2}\) (NEE) from the difference between heterotrophic respiration (\(R_{H}\)) and net primary ecosystem production (NPP). \[NEE=R_{H}-NPP \tag{1}\]  
Negative values of the NEE indicate that an ecosystem is assimilating carbon while positive values indicate the emission of carbon. Heterotrophic respiration occurs based on the decomposition of leaves, roots, and stems by bacteria and is a function of the temperature and moisture of soil and carbon mass in the litter. In other words, \(R_{H}\) denotes the emission of CO\({}_{2}\) (stored in lignin and structural reservoirs) to the atmosphere during the decomposition of organic matter. Based on equations proposed by [34; 35; 36], the model simulates carbon cycling occurring within the three reservoirs mentioned above (leaves, roots, and stems). To simulate these dynamics, it is necessary to introduce parameters associated with the carbon fraction of the litter layer stored in leaves (\(clitls\)), roots (\(clitrs\)), and branches and stems (\(clitrs\)) to the fraction stored in the soil (referring to humus physically or chemically protected, \(cosislop\), and unprotected, \(cosislon\), from decomposition), the decay rate of carbon reservoirs (\(kfactor\)) and the leaf biomass turnover time constant (\(tauleaf\), in years). The net primary productivity (NPP) of each functional plant type is calculated as a function of the gross photosynthesis rate, \(A_{g}\) (mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)), and from the respiration rate of the leaves, \(R_{leaf}\) (mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)), of fine roots, \(R_{root}\) (mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)) and of stems, \(stem\) (mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)) [29]  
\[NPP=\zeta\int(A_{g}-R_{leaf}-R_{root}-R_{stem})dt \tag{2}\]  
where \(\zeta=1-r_{growth}\) is a factor that attenuates NPP due to a carbon fraction lost as a result of plant development (i.e., the cost of building new tissues). The respiration rates of fine roots and stems are given by  
\[R_{stem}=r_{woodpy}\lambda_{sapproved}C_{stem}T_{stem} \tag{3}\]  
\[R_{root}=r_{rootpy}C_{root}T_{soil} \tag{4}\]  
where \(C_{stem}\) and \(C_{root}\) are carbon contained in woody and the fine roots, respectively, \(r_{woodpy}\) and \(r_{rootpy}\) are woody and fine root respiration coefficients (Kg y\({}^{-1}\)), and \(\lambda_{sapproved}\) is the sapwood fraction of the stem biomass. The \(T_{stem}\) and \(T_{soil}\) functions are the Arrenhius [37] temperature functions for stems and soil defined respectively as  
\[T_{stem}=\exp\left(stemae\left(\frac{1}{288.16}-\frac{1}{Tem_{stem}}\right)\right) \tag{5}\]  
\[T_{soil}=\exp\left(rootae\left(\frac{1}{288.16}-\frac{1}{Tem_{soil}}\right)\right) \tag{6}\]  
where \(stemae\) is the stress coefficient for the biomass of stems, \(rootae\) is the stress coefficient for biomass in the soil, \(Tem_{stem}\) and \(Tem_{soil}\) are the average temperatures of the upper canopy and root zone (\(K\)), respectively.
###### Abstract  
Critical Internet of Things (IoT) data in Fifth Generation Vehicular Ad Hoc Networks (5G VANETs) demands Ultra-Reliable Low-Latency Communication (URLLC) to support mission-critical vehicular applications such as autonomous driving and collision avoidance. Achieving the stringent Quality of Service (QoS) requirements for these applications remains a significant challenge. This paper proposes a novel framework integrating Software-Defined Networking (SDN) and Network Functions Virtualisation (NFV) as embedded functionalities in connected vehicles. A lightweight SDN Controller model, implemented via vehicle on-board computing resources, optimised QoS for communications between connected vehicles and the Next-Generation Node B (gNB), achieving a consistent packet delivery rate of 100%, compared to 81-96% for existing solutions leveraging SDN. Furthermore, a Software-Defined Wide-Area Network (SD-WAN) model deployed at the gNB enabled the efficient management of data, network, identity, and server access. Performance evaluations indicate that SDN and NFV are reliable and scalable technologies for virtualised and distributed 5G VANET infrastructures. Our SDN-based in-vehicle traffic classification model for dynamic resource allocation achieved 100% accuracy, outperforming existing Artificial Intelligence (AI)-based methods with 88-99% accuracy. In addition, a significant increase of 187% in flow rates over time highlights the framework's decreasing latency, adaptability, and scalability in supporting URLLC class guarantees for critical vehicular services.  
Software-Defined Networking Network Functions Virtualisation; Ultra-Reliable Low-Latency Communications; edge computing; 5G Vehicular Ad Hoc Networks +
Footnote †: journal: Network
## 2 Background Knowledge  
### Streak Tube Imaging LiDAR  
The STIL workflow is depicted in Figure 1a. The laser is activated by the sequential pulse and is received by a streak tube image sensor. In this process, four subsystems generate data for reconstruction. The streak tube image detector records the time when echo photons come back, the scanning system provides the scanning angle using a grating ruler, the positioning and orientation system (POS) records the position and posture information at the same time, and the pulse sequential system triggers the three subsystems to start with a delay after the laser beam is emitted.  
The STIL works as an ALS, which is operated as shown in Figure 1b. It is located by the POS, which integrates a GPS and an inertial navigation system (INS). The POS calculates the GPS signals from satellites to its own position and obtains posture information from the INS.  
Figure 1: STIL working mechanism; (**a**) system compositions and workflow of STILs; (**b**) work status of an ALS; (**c**) laser line footprints.  
In addition, the scanning mirror swings back along the perpendicular direction of flight, and the laser footprint is formed on the ground, as shown in Figure 1c, where the green lines represent the laser streaks, and red indicates the scan trajectory. We designed different scanning speeds for forward and reverse directions and only used the forward scanning data. There are some overlapping areas between valid scans, which play an important role when checking the parameters.  
The POS, delay, and angle data can be calculated for reconstruction, but the streak image should be converted to distance and field angle. The working principle of the streak tube detector is shown in Figure 2.  
Currently, the impulse delay, incident time, incidence angle, scanning angle, and POS data may all be used to reconstruct the point cloud.  
However, there are inclinations among the scanner, the streak tube detector, and the POS during installation. Their installation mistakes make the point cloud reconstruction more challenging and less accurate.  
For low-altitude ALS, installation errors have a low impact on the inversion accuracy, such that a rough measurement is sufficient to ensure that the effect of systematic errors is smaller than the effect of random errors. Unfortunately, as the working height increases, the installation errors are amplified during the point cloud-inversion process. The placement parameters obtained from ground measurements cannot meet the accuracy requirements of point cloud reconstruction.  
Therefore, we had to check the system parameters with experimental flight data. These parameters are included in the coordinate transformation matrixes, under which the raw data are reconstructed into a point cloud. This is clearly a global nonlinear problem.
## 5 Deployment on LP-IoT Device  
The deployment of the ML-based models serves as the connection between the model development environment where the models are developed and the practical execution of the models in a real-world setting. During the deployment phase of our research, we employed Raspberry Pi 4 Model B as our LP-IoT device. The Raspberry Pi 4 B is a powerful single-board computer, well-suited for running highly complex ML-based algorithms due to its enhanced processing capabilities and increased memory compared to its previous versions. The device features a quad-core Broadcom BCM2711, with Cortex-A72 (ARM v8) 64-bit system SoC @ 1.5 GHz. The available RAM options include 2 GB, 4 GB, 6 GB, and 8 GB of LPDDR4-3200 SDRAM. We opted for the 8 GB version to fully leverage the microprocessor's capabilities. During the initial setup, we inserted a microSD card, linked peripheral devices, and configured fundamental parameters such as network connectivity. Subsequently, we installed the latest version of Python and proceeded to install Jupyter Notebook on Raspberry Pi 4 B. We executed the models by launching Jupyter in the browser and navigating to the location of the model scripts. The initial configuration and setup can be accessed through [31]. During the complexity analysis, we collected the complexity parameters, including CPU utilisation and memory consumption, for both RFR-based models during the training and testing stages. In addition, we also deployed the previously constructed ANN-based models to compare the complexity of the RFR model with the ANN-based approaches (as described in Section 6.3).
###### Abstract  
Pulsed eddy current (PEC) is a widely utilized technology for the nondestructive inspection of industrial tubes and pipes due to its rapid and accurate results. To improve the longitudinal resolution of PEC, multiple transmitting coils (MTCs) are used to realize magnetic focusing. However, this approach is difficult to apply to narrow downhole environments because of the complex transmitting array and electrical circuits. To address this issue, we present a synthesized transmitting coil (STC) that combines MTCs into a single coil with multiple connected sections using different winding directions and number of turns to adjust the magnetic field distribution. A theoretical derivation was presented for the analysis and interpretation of the magnetic field, and a figure of merit (FoM) was constructed to optimize the STC parameters. Numerical simulations and experiments were performed to validate the proposed STC for downhole casing inspection, and the experimental results showed good agreement with the simulation results.  
magnetic focusing; synthesized transmitting coil; downhole; pulsed eddy current; casing inspection +
Footnote †: journal: Applied Sciences  
1 Shaanxi Key Laboratory of Measurement and Control Technology for Oil and Gas Wells, Xi'an Shiyou University, Xi'an 710065, China; [EMAIL_ADDRESS] (L.Y.);  
[EMAIL_ADDRESS] (C.L.);  
1882982927866163.com (Y.Z.)
## 2 Materials and Methods
### Datasets  
In practical application, the pass percentages of daily line loss rates in transformer regions are generally examined once a month in State Grid Corporation of China. In this case, the line loss rate dataset from July 2017 is utilized in the study, which is collected in daily intervals, in order to examine the pass percentage of line loss rates in the month. The pass percentage quota is especially important in July as it usually meets the peak load period of summer. The dataset is obtained from a total of 19,884 regions, which are located in Wuxi, Jiangsu Province, China. As a result, there are altogether 616,404 samples in this study, which satisfies the demand of big data analysis. Based on the dataset, about 80% of the samples (15,907 regions) are chosen as training ones and the others (3977 regions) are testing samples.
## 2 Inverse Design of Silicon Photonics with Iterative Optimization Algorithms
### Comparison of Iterative Optimization Algorithms for Silicon Photonics Design
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Device** & **Structure** & **Algorithms** &
\begin{tabular}{c} **Footprint** \\ **(\(\upmu\)m\({}^{2}\))** \\ \end{tabular} & **DOF** & **Response (dB)** & **Comments** \\ \hline PR [22] & Taper & PSO & \(15.3\times 1.5\) & 29 & S: 0.2 E: N/A & Easy to fabricate \\ PBS [24] & DC & PSO & \(5\times 1.5\) & 10 & S: 0.1, E: 0.5 & Good performance \\ Convertor [79] & Taper & PSO & \(18.6\times 2.8\) & 12 & S: 0.06, E: & Low DOF \\ Crossing [32] & SWG & GA & \(12.5\times 12.5\) & 3 & S: 0.64, E: 1.6 & Not compact \\ \hline PR [44] & QR-code & GA & \(4.2\times 0.96\) & 280 & S: 0.7, E: 2.5 & \\ PBS [40] & QR-code & DBS & \(2.4\times 2.4\) & 400 & S: N/A E: 0.9 & High DOF \\ Bend [37] & QR-code & DBS & \(3\times 3\) & 900 & S: 0.9, E: 1.5 & Compact \\ Diodes [43] & QR-code & DBS & \(3\times 3\) & 900 & S: 1.5, E: 2.1 & Not-hard to fabricate \\ MDM [34] & QR-code & DBS & \(2.4\times 3\) & 500 & S: 0.91, E:1 & Not very high \\ PS [38] & QR-code & DBS & \(2.72\times 2.72\) & 400 & S: 0.4, E: 0.7 & performance \\ Convertor [39] & QR-code & DBS & \(4\times 1.6\) & 320 & S: 1.4, E: 2 & \\ PR [33] & QR-code & DBS & \(5\times 1.2\) & 600 & S: 3, E: 4.3 & \\ \hline PBS [54] & Irregular & TO & \(1.4\times 1.4\) & 1225 & S: 0.6, E:0.82 & \\ MDM [48] & Irregular & TO & \(2.6\times 4.22\) & 11,429 & S: 1, E:1.2 & Very high DOF \\ Matrix [80] & Irregular & TO & \(4\times 4\) & 6400 & MSE: 0.0001 & Ultra-compact \\ Convertor [51] & Irregular & TO & \(4\times 1.5\) & 4382 & S: 0.08 & Very high performance \\ WDM [56] & Irregular & OF & \(2.8\times 2.8\) & N/A & S: 2, E: 2.4 & Hard to fabricate \\ \hline \hline \end{tabular}  
* Response (S) means the simulation results while response (E) signifies the experimental results.  
\end{table} 
Table 1: Examples of silicon photonic devices designed using iterative optimization algorithms.
## 4 Discussion
### Uncertainties in the SIF-Based GPP Model
The chloroplast avoidance movements can lead to decreased light absorption and then influence the observed SIF signal, and further studies are needed. Additionally, leaf movements and leaf angle adjustments can also significantly affect the fractions of sunlit and shaded leaves, which should be considered in future research. The mismatches of the spatial and temporal scales between canopy SIF and EC-based GPP also lead to some uncertainties. The footprint of the EC tower covers 0.01 km\({}^{2}\) to 10 km\({}^{2}\) depending on the measurement height, wind speed and turbulent state of atmosphere [90], whereas the canopy SIF are instantaneously measured with a footprint less than one square meter. The availability, quality, and spatiotemporal coverage of SIF data are expected to increase drastically over the next few years. SIF measurements have been conducted at ecosystem, landscape, and global scales using field spectroradiometers, airborne instruments, and satellite platforms, respectively. The NASA's OCO-2 [43], ESA's Sentinel-5 Precursor (TROPOMI) [91] and ESA's Earth Explorer Fluorescence Explorer (FLEX) [92; 93] can provide SIF data at much higher spatial resolutions. In particular, the FLEX mission can provide auxiliary information such as NPQ and canopy temperature along with fluorescence information. These data would improve our estimation of the global carbon budget and our ability to track the health of terrestrial ecosystems. Additionally, the development of the SCOPE (Soil-Canopy Observation, Photosynthesis and Energy balance) model provides a theoretical way to model canopy SIF and photosynthesis by combing Soil-Vegetation-Atmosphere-transfer (SVAT) model with radiative transfer models [94]. Despite this promising scenario and the empirical evidence that SIF provides novel information to estimate photosynthesis, as found in our study, there are still many uncertainties in relating SIF and GPP, and further studies are needed.
## 6 Modified NPs, Particle Size and Their Effect  
Excessive use of nanotechnology resulted in large-scale manufacturing and utilization of engineered nanoparticles (ENPs) on an industrial level. In comparison with CNTs and Ag nanoparticles, natural nanoparticles exist in a massive amount in the environment. Since natural NPs are generated in an uncontrollable way, most of the research has related to characterization and focused on ENPs.  
A mixture of ENPs containing ZnO (20 nm), AgO (20 nm) and TiO\({}_{2}\) (30-40 nm) were compared with their bulk metal salts to evaluate their effects against non-spiked activated sludge (control) [100]. This study was conducted using three pilot treatment plants on a pilot scale. In comparison with the control plant, the specific oxygen uptake rate (SOUR), specific to microbes, increased 200% by introducing both nanoparticles and bulk metal mixtures. Selective damage was shown by scanning electron microscopy (SEM) on some microbial cells. Furthermore, due to the presence of NPs, flock size of activated sludge also reduced, but sludge volume index (SVI) remained the same. Various environmental factors such as a sludge volume index (SVI), natural organic matter, pH, light, etc., can affect the behavior and fate of NPs in the environment [101]. Bioavailable, chemical and physical properties of releasing NPs can be affected by various influences in nature.  
It is very important to assess the potential risk of nanomaterials and nanoparticles by scrutinizing the expected transformation and mobility, and their interaction with other materials [51]. The behavior and reactivity of the impact of NPs on terrestrial and aquatic media is determined by the shape and particle size [102]. For example, nanoparticles with a size less than 30 nm produced a more cytotoxic effect to _S. aureus_ and _Escherichia coli_[103] than an 8090 nm particle size [104]. This study proves that an AgO particle size greater 30 nm is unable to inhibit the microbial process. Size less than 5 nm in suspension is of particular interest due to its capability of nitrification inhibition in AS [105]. As shown for AgO, along with particle size, shape also plays a vital role and they can exist in rod, triangle or spherical shape. Out of these three distinct particle sizes, the truncated triangle form of silver oxide produced the strongest antibacterial effect on _Escherichia coli_ in both broth and agar cultures [106]. There is no evident conclusion of this observation from pure culture to complex wastewater because microbial cells' interaction with NPs can be attenuated or enhanced by wastewater components.
The effectiveness of LLLT in reducing pain caused by the orthodontic treatment has been frequently studied over the last few years [15]. It has been demonstrated that LLLT has analgesic and anti-inflammatory properties due to the fact that it increases local blood flow, reduces levels of prostaglandin E2, and suppresses collagenase [16; 17]. As recent studies have shown, LLLT therapy uses visible light (generally red) or almost infrared light generated by a laser or a diode (LED-light emitting diode). The light spectrum is narrow with a wavelength of 600 to 1000 nm. The intensity is approximately 1 to 20 J/cm\({}^{2}\)[14]. The anti-inflammatory effect of LLLT functions through nonthermal photobiological mechanisms in which the laser light is absorbed by chromophores (functional groups graphed in a molecule's structure responsible for its color), thus modulating physiopathological processes. Pain arises as a result of the activation of harmful nerve terminations in the epidermis or mucosa. At the level of these somatosensitive nerves, harmful stimuli such as heat, mechanical forces, and also chemical stimuli related to the inflammatory process are turned into action potential. The latter are then transmitted to the central nervous system. By blocking these nerves, and this transmission by default, the painful sensation will be reduced. The specific action of the LLLT is either to suppress harmful receptors or to block the neuronal response to harmful stimuli and the generated action potential [18; 19]. In order to analyze the degree of pain that patients report during an orthodontic treatment, a series of studies have been conducted (including questionnaires and telephone interviews). However, due to the small number of patients included in the studies and different results and methodology, there is still no clear proof of the positive effects of LLLT [20]. As pain is the most common complaint and reason for quitting the orthodontic treatment, in this study we aim to evaluate the influence of laser therapy on pain during the orthodontic therapy. The null hypothesis states that there is no statistically significant difference between the VAS score values of HC and HL immediately after bonding the orthodontic appliance, after the first, second, and third activation.
# Quantum-Secure Coherent Optical Networking for Advanced Infrastructures in Industry 4.0
## 2 State of the Art
### Research Gaps and Opportunities  
Although critical gaps exist, significant advancements in securing optical transport networks also exist, particularly in an environment where MACsec and OTU4 framing coexist. MACsec implementations today fail to secure OTU4-specific vulnerabilities such as control field manipulations, creating a disjointed security architecture. Additionally, QKD remains a robust quantum-safe form of encryption, but the integration with legacy protocols remains far from complete [28].  
For emerging technologies such as NFV and SDN, although there are promising results, their use in optical networks is hindered by scalability and interoperability with existing systems. Adaptive security mechanisms must be highly integrated with real-time monitoring to keep pace with changing threats [29].  
The fundamental tension between quantum-safe security requirements and the performance demands of high-speed optical networks has not yet been fully addressed in current research. Especially missing is the integration of post-quantum cryptography with OTU4 framing and its latency and throughput implications. Additionally, although individual security solutions have been suggested in these areas, an all-encompassing security framework addressing quantum and classical threats simultaneously while satisfying network performance metrics is mainly missing.
## 4 Discussion
### Limitations and Further Research  
The developed method's key limitations are its dependence on the control object's model parameters (such as the static gain, time constants, and nonlinear components) and precise identification, which can reduce the modelling and control accuracy in the event of errors in these parameters [64; 65]. The method also requires careful adjustment of the controller's adaptation parameters and optimization coefficients, which complicates its implementation in practical conditions [66]. Nonlinear components in the control object model may not be applicable to systems with more complex dynamics, and the dependence on the training data (noise, sensor errors) quality can significantly worsen the results [67; 68; 69]. High computational complexity, requirements for the neural network architecture, and the need for complex optimization of the stopping criteria also limit the method's applicability, especially in actual conditions with limited computing resources [70]. In addition, there is an overfitting risk when using small or nonuniform datasets, and modelling systems with considerable delays may be inaccurate. The method showed high efficiency for a specific engine type (TV3-117), but its generalizability to other kinds of control systems requires additional adaptation. The algorithm's implementation in real systems requires powerful computing resources [71; 72; 73], which may be problematic for existing systems with limited power. A roadmap for future research to address the developed method's limitations is presented in Table 9.