esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Average End-to-End Latency  
Figure 12 provides the results of the average end-to-end latency. EQPD-MAC outperforms MPQ-MAC and RMP-MAC, achieving a reduced end-to-end latency of up to 76% and 61%, respectively. It can be seen that MPQ-MAC has the highest latency because the protocol requires nodes to wait longer for the \(T_{w}\) waiting timer to expire, resulting in increased latency. RMP-MAC also has a high latency because as the number of sensor nodes increases, the buffer limit is constantly exceeded, forcing the sensor nodes carrying the data packets to wait longer for the medium to become available. It can also be observed that the PMME-MAC protocol outperforms other protocols in terms of average end-to-end latency. When a sensor node sends a Tx-beacon with any priority, PMME-MAC stops the waiting timer and immediately begins transmitting, which decreases packet latencies but increases energy consumption for other sensor nodes. The sensor nodes in PMME-MAC, on the other hand, are unable to save power by going to sleep automatically when the channel is busy. Data packet latency in EQPD-MAC is around 24% more than in PMME-MAC, despite the fact that this is still within the permissible range. It is also important to note that EQPD-MAC can provide more than 30% improvements in energy consumption, so it does provide the best tradeoff between latency and energy.  
Figure 11: Average end-to-end latency of priority packets.
## 2 Materials and Methods
### Methods
#### 2.2.6 Optical Characterization  
The emission and excitation spectra were recorded at room temperature using a Fluorologg\({}^{30}\) Horiba Scientific (Model FL3-22) spectroscope, with a modular double grating excitation spectrometer (fitted with 1200 grooves-mm\({}^{-1}\) grating blazed at 330 nm) and a TRIAX 320 single emission monochromator (fitted with 1200 grooves-mL\({}^{-1}\) grating blazed at 500 nm, reciprocal linear density of 2.6 mm-mm\({}^{-1}\)). The excitation source was a 450 W Xe arc lamp. The emission spectra were corrected for detection and optical spectral response of the spectrofluorimeter and the excitation spectra were corrected for the spectral distribution of the lamp intensity using a photodiode reference detector. Emission decay curves were recorded at room temperature on a Fluorolog TCSPC spectrofluorometer (Horiba Scientific, Piscataway, NJ, USA) coupled to a TBX-04 photomultiplier tube module (950 V). The exciting source was a Horiba/Jobin-Yvon pulsed diode (NanoLED-390, peak at 388 nm). The absolute emission quantum yield values were measured at room temperature using a quantum yield measurement system C9920-02 from Hamamatsu. This system consists of a Xe arc lamp (150 W), a monochromator (bandwidth 2 nm), an integrating sphere made of Spectralon\({}^{\circledR}\) (99% reflectance for wavelengths within 350-1650 nm) with an inner diameter of 84 mm, and a multichannel detector (wavelength resolution \(<\) 2 nm). The emission quantum yield (\(\eta\)) is given by:  
\[\eta=\frac{N_{Emi}}{N_{Abs}}=\frac{\int_{\lambda_{1}}^{\lambda_{2}}\frac{ \lambda_{1}}{\hbar c}\Big{[}s_{em}^{sample}(\lambda)-I_{em}^{reference}( \lambda)\Big{]}d\lambda}{\int_{\lambda_{3}}^{\lambda_{4}}\frac{\lambda}{\hbar c }\Big{[}s_{ex}^{sample}(\lambda)-I_{ex}^{reference}(\lambda)\Big{]}d\lambda} \tag{1}\]  
where \(N_{Abs}\) and \(N_{Em}\) are the numbers of photons absorbed and emitted by a sample, respectively, \(h\) is [PERSON]'s constant, \(c\) is the velocity of light, \(I_{em}^{sample}\) and \(I_{em}^{reference}\) are theemission intensities the measured with and without a sample, respectively, in the emission spectra wavelength interval [\(\lambda_{1}\),\(\lambda_{2}\)] and \(I_{ex}^{sample}\) and \(I_{ex}^{reference}\) are the integrated intensities of the excitation radiation measured with and without a sample, respectively, in the excitation wavelength interval [\(\lambda_{3}\),\(\lambda_{4}\)] [29]. Three measurements were made for each sample, and the average values obtained are reported with an accuracy of 10% according to the manufacturer.
## 5 Maintenance
### Tasks of Proactive Maintenance  
Industry 4.0 applications that usually involve IoT systems as well are not only the enablers of proactive maintenance; they also benefit from adopting it. This leads us to the concept of Cyber-physical systems that include numerous interconnected subsystems on a physical and a digital plane. Such a CPS collects data about the status and conditions of the subsystems or pieces of equipment which can be used for optimizing certain parameters of the system, thereby creating a feedback loop. This scheme can be also applied for  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Application** & **Typical Machine Learning Techniques** & **References** \\ \hline Visual quality inspection & CNN (Yolo, VGG, ResNet, DenseNet), Autoencoders & [104; 106; 107; 110; 111; 112; 114] \\ Anomaly detection & LSTM and PSO, kNN, SVM, PCA, XGBoost, Regressions, etc. & [117; 122; 125; 127; 129] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of applications of machine learning techniques in IIoT quality control.  
Figure 6: Difference between maintenance approaches in terms of condition and timemaintenance tasks in order to create an CPS/IoT-enabled proactive maintenance system. Such a system consists of several main areas that can be classified as follows [134; 135; 136]:  
**Fault detection**: -- Detecting malfunctions is a complex task which involves several data sources such as equipment monitoring sensors, environment monitoring sensors, telemetry data, etc., in order to be able to recognize failures. The most common data that are gathered by sensors are: vibration monitoring, sound or acoustic monitoring and oil-analysis or lubricant monitoring [137; 138].
**Diagnostics**: -- Diagnostic processes are at the core of prognostics and strategy planning as they provide an analysis of failures and hazards, thus enabling the creation of models. One of the main task of diagnostics is Root cause analysis, which is a framework for investigating hazards and systematically discovering the possible root causes [139; 140; 141].
**Prognostics**: -- The aim of prognostics is to estimate the future condition of equipment by modelling it based on the results of diagnostics. In most cases, the final goal of prognostics is to calculate the Remaining Useful Life (RUL) and Mean Time to Failure (MTTF). These factors play a key role in predicting and preventing possible future malfunctions and failures and help to schedule required maintenance tasks in time [142].  
As discussed, these areas of maintenance include tasks such as data analysis, pattern recognition, designing complex models of processes or object, and forecasting events (hazards and failures), which are areas where machine learning techniques traditionally outperform other methods and solutions. As the fields of maintenance, repair, and overhaul (MRO) do not have any strict regulations, state-of-the-art solutions can be constructed based on requirements and expectations, and they can also follow the already implemented solutions available in the literature or as an open-source project.
## 2 Materials and Methods
### Nonthermal Plasma Processes
#### 2.2.2 Plasma Process Kinetics  
Results obtained on pharmaceutical compounds removal by NTP show that, with respect to their degradation, percent removal ratios can be represented by the following equation:  
\[R=\left(1-\frac{c}{C_{0}}\right)\times 100 \tag{1}\]  
with \(c_{0}\) and \(c\) being the initial and final concentrations of a solute, respectively.  
Degradation efficiency (percent) is determined according to the energy yield (i.e., compound mass removed per unit energy spent) as:  
\[Y=\frac{C_{0}\cdot V\cdot R}{P.t}\times\frac{1}{100} \tag{2}\]  
where \(V\) is the solution volume (L), \(R\) the removal ratio (%), \(P\) the discharge power (kW) and \(t\) the HRT (h).  
The effects of operating parameters on NTP pollutant degradation performance have been investigated, showing that removal efficiency, intensity of electric field, shock waves and UV radiation generally increase with increasing peak voltage, with increased amounts of reactive species in both gas phase and aqueous solution. HRTs range usually around 8-30 min, depending on applied voltage and required efficiency.  
Similarly to EB, plasma generates reactive species (O\({}_{3}\), OH and H\({}_{2}\)O\({}_{2}\)) that degrade organic molecules. Since usually the discharge occurs in ambient air, O\({}_{3}\) is the main oxidant generated that is transferred to the liquid phase. Two mechanisms of O\({}_{3}\) reactions may occur: direct (oxidative) or indirect (conversion to -OH, which oxidizes organics); the latter is favored by high pH, while the former prevails in acidic conditions. Dissolved ozone in fact may cause slow -OH formation by H\({}_{2}\)O\({}_{2}\) decomposition at low pH, but this reaction is strongly accelerated at pH \(>\) 5.0. Direct ozone oxidation of micro-pollutants is quite efficient for those compounds containing functional groups (amino groups, aromatics, double bonds). As in EB processes, removal efficiency is higher in ultrapure and tap water and lower in complex solutions (e.g., landfill leachate) at equal HRTs. This may be not only due to the competition among solutes to consume the reactive species during the process (as seen for EB), but also to the generally higher conductivity of a complex solution, which makes it difficult to sustain strong electric fields and plasma channels, or produce reactive species due to more charged ions present [29].
# A Traceable Spectral Radiation Model of Radiation Thermometry
## 2 The Direct (Physical) Model of Radiation Thermometry
### Input Data of the Physical Model
#### 2.3.2 Atmospheric Transmission and Self-Emission  
The emitted and reflected radiation of the object \(M_{\text{obj}}(\lambda)\) travels through the atmosphere toward the radiation thermometer. During travel, the radiation is partially absorbed according to the spectral absorptivity of the air, while most of the radiation is transmitted according to the spectral transmissivity of the air \(\tau(\lambda)\). According to [PERSON]'s law (5), spectral absorptivity equals spectral emissivity, therefore atmospheric self-emission is present and can be considered as black body emission \(M_{\text{BB,atm}}(T,\lambda)\) (10) reduced by the spectral emissivity of the atmosphere \(\epsilon_{\text{atm}}(\lambda)\) (11).  
\[M_{\text{sen}}(\lambda)=M_{\text{obj}}(\lambda)\tau(\lambda)+M_{\text{BB,atm}} (\lambda)\epsilon_{\text{atm}}(\lambda) \tag{9}\]  
\[M_{\text{BB,atm}}(T,\lambda)=M_{\text{BB}}(T,\lambda) \tag{10}\]\[\varepsilon_{\text{atm}}(\lambda)=1-\tau(\lambda) \tag{11}\]  
The spectral transmissivity of the atmosphere is relatively high under usual indoor conditions, as already determined by the authors for the purpose of this study [28]. The spectral transmissivity of air is shown in Figure 4 for a 10 m atmospheric path; however, a 1 m measuring distance--along with 45% relative humidity, 1013.25 hPa atmospheric pressure and 25 \({}^{\circ}\)C atmospheric temperature--is used for calculations in this article.
# Collaborative Planning in Non-Hierarchical Networks--An Intelligent Negotiation-Based Framework
## 2 Collaborative Planning--Technical Background
### Coordination and Collaboration through Negotiation  
Several authors sustained that a decentralized planning approach with coordination among independent partners requires negotiation mechanisms [29; 30; 20].  
[PERSON], [PERSON], [PERSON], and [PERSON] [31] proposed an approach where they applied automated negotiations with companies or decision-making units represented as software agents as the mechanism to establish coordination in the supply chain. Negotiation is assumed as the process via which a group of agents communicate with one another to try and come to a mutually acceptable agreement on some matter.  
[PERSON] and [PERSON] [30] also presented multiagent systems as a solution to deal with the negotiation process for collaborative planning. According to the authors, an agent is an autonomous, goal-oriented software process that operates asynchronously, communicating and coordinating with other agents as needed. The state of coordination is achieved when the agents find a jointly acceptable point in the agreement space.  
Both models tackled the collaborative network coordination problem through the decomposition of the production planning model by a set of agents that represent each partner entity participating in the network. An iterative coordination scheme was proposed, defining the procedure for the exchange of the planning results, and these results were integrated as restrictions into the solving of the local models.  
Although the way in which the authors dealt with the problem of decentralized negotiation was innovative, these multiagent approaches continue to face a major obstacle: partners' unwillingness to share confidential information, critical to their own business processes. Therefore, a more practical approach is required to address to the problem of decentralized production planning negotiation among independent networked companies.
# Impact of Pore Geometry and Water Saturation on Gas Effective Diffusion Coefficient in Soil
## 4 Conclusions  
In this paper, two 3D images and simulations were used to evaluate the effect of saturation on pore structure and on the effective diffusion coefficient of soil. The simulations show that:  
1. The pore geometry including porosity, connectivity, pore size and interface strongly depend on water distribution and saturation, and the relationship between average pore diameter and gas saturation can be fitted into a power-law;
2. The present approach can be used to calculate the effective diffusion coefficient, but needs to know the pore structure;
3. The effective diffusion coefficient in the Knudsen region and in the continuum region approximates the effective Knudsen diffusion coefficient and the effective bulk diffusion coefficient, respectively. In the transition region, it can be calculated by the dusty-gas model under certain accuracy.  
Figure 11: Increase of the normalized effective diffusion coefficient with gas saturation follows a power law.  
Figure 10: The comparison between the normalized effective Knudsen diffusion coefficients calculated from pore-scale simulations with that estimated by Equation (7).  
* (4) Pore diameter is essential in calculating the effective Knudsen diffusion coefficient which increases with gas saturation following a power-law.  
[PERSON] and D.C. provided the experiments resources; [PERSON] conceived the methodology and software and wrote the original draft; [PERSON], [PERSON], [PERSON] and [PERSON] reviewed and edited the manuscript; datasets were prepared and visualized by [PERSON]; supervision, D.C.; funding acquisition, [PERSON] and Y.J.  
This research was funded by the National Natural Science Foundation of China (grant number 11602180, 41790432) and the Fundamental Research Funds for the Central Universities (grant numbers WUT: 2017 IVA064, WUT: 2017 IVB058). [PERSON] acknowledges support from CAS Pioneer Hundred Talents Program.  
The authors declare no conflicts of interest.
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
## 2 Fixed Wireless Access
### Trials  
Diverse FWA trials around the world have been implemented since 2016. AT&T, in collaboration with Ericsson and Intel, deployed its first 5G FWA, providing 4K video and VoIP calls using 15 GHz and 28 GHz spectrum [60]. The Terragraph project serves 100+ homes in 1 km\({}^{2}\) in Mikebuda, Hungary, providing coverage of 99.5% with top speeds of 1 Gbps for outdoor CPE and 605 Mbps for indoor ones. Nokia has achieved the highest throughput in Riyadh, Saudi Arabia in a high-density area [61]. In Romania, Orange connected 16 residential users to their 5G virtual packet core network using 26 GHz band equipment, showing data rates up to 1 Gbps for distances larger than 1 km [62]. Adtran has developed its Metnet 60G Mesh solution to extend gigabit connectivity to dense residential and business areas, using the IEEE 802.11 ad standard in the 60 GHz band to provide up to 4 Gbps per node in a self-organised network (SON) to improve redundancy and service. Diverse rural communities in North America have adopted this solution to implement low-cost gigabit connectivity [63; 64].
# Titanium Dioxide in Chromogenic Devices: Synthesis, Toxicological Issues, and Fabrication Methods
## 2 Synthetic Routes and Fabrication Techniques for TiO\({}_{2}\)
### Spray Pyrolysis  
Spray pyrolysis methods allow the formation of TiO\({}_{2}\) and TiO\({}_{2}\)-based thin films in a one-step process, without further purifications or more drying steps [13; 14; 15]. This method is based on the aerosol formation from precursors (metallic salts or a colloidal solution), which are heated in a furnace at high temperature, favoring formation of micro particles with a specific size after many steps of evaporation, drying, and annealing [91] (Figure 4). Spray pyrolysis is a very simple and cost-effective processing method that permits the acquisition of high-quality substrates or chemicals. The method is also suitable for depositing dense, porous, and multilayered films, making it a powerful tool in the glass industry [8] and in solar cell production [92].  
Recently, [PERSON] et al. [93] synthesized WO\({}_{3}\) and TiO\({}_{2}\) NPs by one-step flame spray pyrolysis, for the applications in EC devices using TIP as a titanium precursor and ammonium metatungstate hydrate ((NH\({}_{4}\))\({}_{6}\)H\({}_{2}\)W\({}_{12}\)O\({}_{40}\)\(\times\)H\({}_{2}\)O,) as a tungstate precursor. Annealing of up to 1000 \({}^{\circ}\)C for 2 h was used to improve the NPs crystallinity in order to use them in EC devices (2 wt%), and it showed high transmittance as well as a fast kinetics. [PERSON] et al. [94] obtained TiO\({}_{2}\) NPs directly from three organic precursors: TTIP, water-soluble titanium sources TC-300(r), and TC-400(r) using the low-pressure spray pyrolysis route. Dense films of TiO\({}_{2}\) were deposited on F-doped SnO\({}_{2}\) substrate by spray pyrolysis and were used for several applications such as photocatalysts, sensors, and solar cell fabrication [95].  
A molten salt-assisted pyrolysis process was achieved to obtain TiO\({}_{2}\) nanowires [96] from TiCl\({}_{4}\)-ethyl acetate and Na\({}_{2}\)S-ethyl acetate. Anatase TiO\({}_{2}\) nanowires were obtained after calcination at 820 \({}^{\circ}\)C while rutile TiO\({}_{2}\) nanowires were obtained at a higher temperature (970 \({}^{\circ}\)C).
# Energy-Aware Wireless Sensor Networks for Smart Buildings: A Review
## 8 Proposed Framework to Design Energy-Aware WSN for SEEB  
This paper conveyed that energy optimization in WSN nodes can be achieved at different levels, such as the hardware design, the operating system choice, the data acquisition technique, and the networking technology. The next figure illustrates the proposed framework for designing a WSN inside a smart building.  
Figure 3 illustrates how the deployment environment affects the choice of the WSN node's components. First, the deployment environment directly affects the network topology because of the interference and connectivity issues indoors. For example, opting for a full mesh network topology, where obstacles might not allow a node to be connected with multiple nodes, will not be a good solution. As a result, the topology choice will impact the node's role in the network (end node, router node, or cluster-head). Second, the wireless sensor network goes for a specific routing protocol and a medium access control protocol depending on the network topology. The network topology also affects the data acquisition technique at the application layer. Hence, the adopted operating system must support the selected network protocol, data link protocol, and the data acquisition technique.  
Moreover, the selected hardware configuration must support the operating system. The battery choice is also based on power capacity and the deployment environment, as each battery type is dedicated to a specific deployment environment. For example, lithium polymer cannot be used in an environment that might contact water or high humidity.
## 4 Discussion: Mechanistic Studies of the CL Detection System  
### CL Spectra  
The mechanism of this CL reaction system were investigated by the CL spectra (Figure 5) obtained from an F-7000 fluorescence spectrophotometer, where (a) luminol-Ni(IV)-TEB; (b) luminol-Ni(IV)-SAL; (c) luminol-Ni(IV); (d,e) Ni(IV)-SAL/TEB. It can be seen that all curve charts have same absorption peak at approximately 425 nm, which implies that all these CL reactions shared a common emitting species derived from the aminphthalate spectrum belong to CL species of luminol [33]. The CL  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Spiked (\(\times 10^{-8}\) M)**} & \multicolumn{2}{c}{**Found (\(\times 10^{-8}\) M)**} & \multicolumn{2}{c}{**Recovery (\%, n = 3)**} & \multicolumn{2}{c}{**RSD (\%, n = 3)**} \\ \cline{2-9}  & **SAL** & **TEB** & **SAL** & **TEB** & **SAL** & **TEB** & **SAL** & **TEB** \\ \hline \multirow{3}{*}{**Urine**} & 1.0 & 1.0 & 1.05 & 1.08 & 105.0 & 108.0 & 1.8 & 2.0 \\  & 10.0 & 5.0 & 10.28 & 4.86 & 102.8 & 97.2 & 1.7 & 2.1 \\  & 40.0 & 8.0 & 38.03 & 8.33 & 95.1 & 104.1 & 1.7 & 1.8 \\ \hline \multirow{3}{*}{**Feed**} & 1.0 & 1.0 & 1.03 & 1.05 & 103.0 & 105.0 & 2.0 & 2.1 \\  & 10.0 & 5.0 & 9.75 & 4.75 & 97.5 & 95.0 & 1.9 & 2.3 \\ \cline{1-1}  & 40.0 & 8.0 & 39.36 & 7.82 & 98.4 & 97.8 & 1.8 & 2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Recoveries obtained for SAL and TEB in Urine and Feed samples.  
intensities of luminol-Ni(IV) could also significantly enhance with the TEB or SAL addition, and TEB was more sensitive to this system than SAL. This result is consistent with experimental data.