esa-sceva/satcom-chunk-collection huggingface dataset:
# A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA
## 5 Experimental Settings  
To define different, heterogeneous scenarios, we have considered four different aspects:  
* _Topology 1_ consists of using a laptop, iotgw1 in Figure 1, as a server/broker to control communication between clients IoTclient1 (a laptop) and Iotclient3 (a Raspberry Pi) on a Wi-Fi network (rf. to Figure 1).
- _Topology 2_ consists of using the device iotgw2 (a Raspberry Pi) as server/broker to control the communication between two clients.
* a fixed-size data packet of 7 KB, standing for an example of a small packet.
- a variable-size data packet randomly selected from an interval between 7 KB and 1000 KB (standing for an example of a large message).
* **Number of messages**. Three specific scenarios have been considered in terms of messages to be sent: 5, 500, and 5000 messages per unit of time (second).
* **Interarrival time (IAT)**. Three specific situations have been considered in terms of IAT, to respectively emulate examples of frequent, average, and not so frequent message exchange: 1 ms, 10 ms and 1000 ms.  
For all local experiments, each scenario has been run for the different evaluated protocols, namely CoAP, MQTT, and OPC UA. Each experiment has been repeated 10 times.  
Table 4 provides the different scenarios set for local experiments, derived from the combination of the three mentioned components.  
The scripts and raw results extracted for each protocol are available via GitLab ([[https://gitlab.com/iotlab_copelabs](https://gitlab.com/iotlab_copelabs)]([https://gitlab.com/iotlab_copelabs](https://gitlab.com/iotlab_copelabs)) (accessed on 15 May 2021)).  
Later, and in order to compare the behaviour of the selected protocols on a larger scale, the FIT-IoT platform has been used.The protocols evaluated on FIT-IoT were MQTT and CoAP, as the support for OPC UA in FIT-IoT is not out-of-the-box. We have tried to install the required libraries to run OPC UA. However, the quota provided to users was not sufficient to enable the installation of this communication protocol.  
The FIT-IoT scenarios are provided in Table 5, where _IAT_ corresponds to Interarrival Time in milliseconds; messages sent correspond to the number of messages sent per unit of time (seconds); number of senders and receivers cover examples of a small number of senders, medium (50), or large (94). For all of the experiments run in FIT-IoT, we considered a message size randomly selected from a uniform interval between 7 KB and 1000 KB.  
Therefore, for the experiments with the CoAP and MQTT protocols different network scenarios have been devised. Varying the number of nodes (senders and receivers), message sizes, number of messages sent, and the inter-arrival time. For all scenarios, one single broker for MQTT and one server for CoAP have been considered.  
Moreover, all of the experiments have been repeated 10 times, and the values provided in this report correspond to the _Average (AVG)_, _Standard Deviation (STDEV)_, _Maximum (MAX)_, _Minimum (MIN)_, _Median (MED)_, and _Confidence Interval (IC)_.
# Online Determination of Boron Isotope Ratio in Boron Trifluoride by Infrared Spectroscopy
## 2 Experimental Method  
### Instruments and Reagents  
The BF\({}_{3}\) was purchased from LiuFang Gas Corporation (Dalian, LN, China) with a stated purity of 99.95%. Small amounts of SiF\({}_{4}\) were present as an impurity in the BF\({}_{3}\) but these were ignored in this study. NIST SRM 951a isotopic reference material of boric acid (\({}^{10}\)B/\({}^{11}\)B is 0.2473 \(\pm\) 0.0002) was purchased from the National Institute of Standards and Technology (Gaithersburg, MD, USA). A gas cell (TianGuang Optical Instrument Co. Ltd., Tianjin, TJ, China) was equipped with the CaF\({}_{2}\) windows (\(\varphi\)40 \(\times\) 5 mm). The optical path length of this gas cell was 100 mm. An infrared spectrophotometer (Xintian Optical Analytical Instrument Co. Ltd., Tianjin, TJ, China) with an accuracy of transmittance lower than or equal to \(\pm\)0.2%.T. (TJ270-30A, Tianjin, China). The \({}^{10}\)B/\({}^{11}\)B ratio was measured as a reference by an ICP-MS X7 (Thermo Electron Corporation, Waltham, MA, USA) series mass spectrometer manufactured by the Thermo Electron Corporation.
## 4 Spatial Cooperative Game Models and Their Applications in Terms of Locales
### The Scope of the Analyses  
The EU is one of the most extensive multinational coalitions and has the main objective of promoting its citizens' well-being and thus member states' welfare. Among EU members, some have higher weights than others. At the macro-multinational scale, spatial cooperative game theory models are used to determine (i) geographically critical players with key locations and important neighborhood connections in forming spatial coalitions, and (ii) economically critical players that provide significant economic returns to cooperating parties as a function of their size and scale.  
The following sub-sections have two different scenarios regarding EU countries. In the first one, the base GDP sizes are unequal (i.e., differentiated; see Table 9). In the second one, the base GDP sizes are assumed to be the same (Table 10). In both cases, the country base GDP size totals are the same, and their grand coalitions generate the same economic returns as per Equations (11) and (12).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Start**} & \multicolumn{3}{c}{**Models III and IV \({}^{*}\)**} \\  & & \multicolumn{3}{c}{**(Z = 39.04)**} & \multicolumn{3}{c}{**Normalized Values**} \\ \hline
**Code** & **Country** & **GDP (S4\({}_{i}\)-10\({}^{9}\))** & **X\({}_{Min}\)** & **X\({}_{Max}\)** & **X\({}_{Min}\)/GDP\({}_{p}\)** & **X\({}_{Max}\)/GDP\({}_{p}\)** \\ \hline AT & Austria & 159 & 159 & 533 & 1.00 & 3.40 \\ CZ & Czechia & 136 & 136 & 449 & 1.00 & 3.30 \\ DE & Germany & 1515 & 2738 & 6011 & 1.80 & 4.00 \\ HU & Hungary & 104 & 104 & 1147 & 1.00 & 1100 \\ IT & Italy & 836 & 1258 & 2912 & 1.50 & 3.50 \\ SK & Slovakia & 65 & 65 & 195 & 1.00 & 3.00 \\ SI & Slovenia & 26 & 26 & 54 & 1.00 & 2.10 \\ PL & Poland & 406 & 492 & 1581 & 1.20 & 3.90 \\ BE & Belgium & 191 & 191 & 645 & 1.00 & 3.40 \\ DK & Denmark & 104 & 104 & 1019 & 1.00 & 9.80 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Optimization results—Differentiated GDP.  
The following sub-sections compare and present the impacts of altered case assumptions, initially for in-scenario evaluations and then for intra-scenario evaluations.
# Resolution Aspects for Near-Field Projections
## 2 Materials and Methods
### Data Analysis  
The data analysis applied a logistic regression by using the function (Equation (2)) which was introduced by [PERSON] [41] and [PERSON] et al. [39]:  
\[P(x)=\gamma+(1-\gamma)\cdot\left(\frac{1}{1+\left(\frac{x}{\alpha}\right)^{- \beta}}\right) \tag{2}\]  
This logistic regression function was influenced by three parameters: \(\alpha\) specifies the stimulus (e.g., the image quality) at the 50% probability level within the range of possible solutions (quality level), the steepness of the function was described by \(\beta\) and the likelihood of correct answers by chance, such as mistyping, was considered by \(\gamma\). In both studies, the assumption of \(\gamma=0\) was applied to the analysis since the study design and the type of stimuli ensured no random or false answers regarding the subjectively perceived image quality. Therefore, a simplified logistic regression Equation (3) resulted in:  
\[P(x,\gamma=0)=\frac{1}{1+\left(\frac{x}{\alpha}\right)^{-\beta}} \tag{3}\]  
This simplified logistic regression function was fitted to the collected data of the presented stimuli and the corresponding responses from the forced choice method. The presented stimuli represent the different resolution levels projected on the street's surface and correspond to the \(x\)-data in the formula above. The \(P(x)\)-values are the related responses of the subjects to the perceived resolution levels. The values of \(\alpha\) and \(\beta\) were determined by using the Matlab curve fitting toolbox independently for every specific scenario in the applied testing condition. The input for finding the values of \(\alpha\) and \(\beta\) included the \(x\) and \(P(x)\) raw data to fit the logistic regression.  
Statistical analysis used a chi-square-test, which were calculated to evaluate a 5% significance level by comparison with the critical values of the chi-square distribution [42]. The chi-square-test was used to indicate and evaluate the dependencies for the perception regarding the influence of the ambient lighting, viewing distance influence and influence of displayed content.
## 3 Results
From the statistical analysis of these ratios, which is shown in Figure 6, the following considerations can be drawn: (i) significantly higher amounts of \(\alpha\)-helix and -sheet structures (ALPHA and BETA ratios; \(p<0.05\)), as well as lower levels of random coil ones (RANDOM ratio; \(p<0.05\)), were found in **AY** compared to **AO** and **CY** compared to **CO** samples; moreover, a higher amount of folded structures was displayed in **Y** samples (in particular in **CY**) than in **O** ones (FOLDED/UNFOLDED; \(p<0.05\)); (ii) a higher quantity of \(\alpha\)-helix structures was found in **CY** samples compared to **AY** ones (ALPHA; \(p<0.05\)), while similar levels were observed in **AO** and **CO** (ALPHA; \(p>0.05\)); (iii) higher amounts of -sheet structures were found in **A** compared to **C** samples, and both in **Y** and **O** samples (BETA; \(p<0.05\)); (iv) higher amounts of random coil structures (RANDOM) were observed in **AO** and **CO** samples compared to **AY** and **CY** ones (RANDOM; \(p<0.05\)). Figure 5: Statistical analysis of the lipid component of **A** and **C** regions of **Young** and **Old** CC samples: \(\rm A_{CH}/A_{CH3}\) (degree of unsaturation in lipid alkyl chains), and \(\rm A_{CH2}/A_{CH3}\) (degree of saturation in lipid alkyl chains). Asterisks indicate statistically significant differences between **A** and **C** regions within the same group (\(\ast\ast\)\(p<0.01\); \(\ast\ast\)\(p<0.001\)). Different lowercase letters over histograms were used to indicate significant differences between **Y** and **O** groups (\(p<0.05\)).
# Loop Closure Detection Based on Multi-Scale Deep Feature Fusion
## 3 Loop Closure Detection Algorithm Based on Multi-Scale Deep Feature Fusion  
Different from traditional image classification, visual loop closure detection needs to determine whether the two moments are at the same location according to the similarity between the picture collected at the current time and the one taken earlier. Therefore, the algorithm in this paper has paired input, which corresponds to two branches in the algorithm as shown in Figure 1. The algorithm is divided into three layers: feature extraction, feature fusion and decision. The feature extraction layer extracts the deep feature of the input images. The feature fusion layer does multi-scale fusion and normalization of extracted features. The decision layer uses the fusion feature to detect the loop closure. The two branches of the algorithm are identical in the feature extraction layer and the feature fusion layer structure.
# Experiment and Analysis on Friction Damage and Energy Loss Characteristics of Potatoes
## 2 Materials and Methods
### Test Materials and Equipment
#### 2.2.2 Energy Loss Ratio  
During the process of the potato contacting the friction materials, the energy loss caused by friction inevitably changes the swing angle and height before and after potato friction, and this energy loss ratio was taken as an index to evaluate the energy loss due to friction. The energy loss of the single pendulum itself during a swing was ignored for its mass was much smaller than that of potato [23]. Figure 4 shows the position of the potato before and after contact with the friction material. The potato was released after lifting to height h1 at the beginning of the experiment; it then rubbed against the friction material in the lowest position and continued to swing forward to the highest position \(h_{2}\).  
From Figure 4, the energy loss before and after friction can be calculated using the following formula:  
\[\Delta E=mgh_{1}-mgh_{2} \tag{2}\]  
where \(\Delta E\) is the energy loss before and after friction (J), m is the potato mass (kg), \(h_{1}\) is the potato lift height at the start of the friction test (m), \(h_{2}\) is the potato swing height at end of the friction test (m), and g is gravity acceleration (m/s\({}^{2}\)).  
Figure 4: Positions of beginning and end of the potato friction test.  
Figure 3: Flow chart of photo processing of potato damage.  
According to the geometric relationship in Figure 4, further analysis shows that:  
\[h_{1}=O_{1}M-L\cos\theta_{0} \tag{3}\]  
where \(L\) is the distance from the pendulum center to the potato center (m), \(\theta_{0}\) is the potato lift swing angle at start of the friction test (\({}^{\circ}\)). Then \(h_{2}\) is calculated as:  
\[h_{2}=O_{1}M-L\cos\theta_{1} \tag{4}\]  
where \(\theta_{1}\) is the potato swing angle at the end of the friction test (\({}^{\circ}\)).  
Bringing (2) and (3) into (1) gives:  
\[\Delta E=mgL(\cos\theta_{1}-\cos\theta_{0}) \tag{5}\]  
In order to facilitate analyzing and comparing the energy loss of the potato during friction, the energy loss ratio of friction is defined as the ratio of energy loss before and after friction to the initial kinetic energy, and formula is obtained as below:  
\[\zeta=\frac{\Delta E}{mgh_{1}}=\frac{L}{h_{1}}(\cos\theta_{1}-\cos\theta_{0}) \times 100 \tag{6}\]  
where \(\zeta\) is the energy loss ratio (%).  
The values of \(L\) and \(h_{1}\) can be determined according to the test conditions, and \(\theta_{0}\) and \(\theta_{1}\) are determined by the angle measurement tool provided by the high-speed camera data processing software [23].
## 2 Biosynthesis and Key Regulatory Networks of Volatile Terpenoids
### Regulatory Determinants of Volatile terpenoid Biosynthesis
#### 2.2.2 Transcription Factors
), _CpMYC2_ and _CpbHLH13_ function as transcriptional activators, positively regulating the biosynthesis of linalool and \(\beta\)-caryophyllene by upregulating their biosynthetic gene clusters [74]. Transgenic overexpression of _LaMYC7_ in lavender (_Lavandula angustifolia_) significantly increased linalool and caryophyllene accumulation [75]. Similarly, overexpression of _AsMYC2_ in agarwood (_Aquilaria sinensis_) upregulated _TPS11_ and _TPS21_ expression, leading to enhanced sesquiterpene production through coordinated pathway activation [76]. These findings collectively establish MYC TFs as evolutionarily conserved regulators of terpenoid diversification. WRKY TFs orchestrate the production of all major classes of secondary metabolites--including terpen, phenylpropanoids, and alkaloids--by modulating the expression of genes encoding enzymes within their respective biosynthetic pathways [77; 78]. CrWRKY1 functions as a positive regulator of terpenoid indole alkaloid (TIA) biosynthesis in _Catharantthus roseus_[79]. _GaWRKY1_, a WRKY transcription factor, activates CAD1-A expression by binding to the W-box cis-element within its promoter, thereby regulating sesquiterpenoid biosynthesis in cotton (_Gossypium arboreum_) [80]. TF genes assemble into protein complexes that coordinately control the expression of enzymatic genes within terpenoid biosynthetic pathways [79; 81]. In freesia (_Freesia hybrida_), the FhMYC2 protein interacts with FhMYBB21 Ls to form a transcriptional repressor complex. This complex suppresses the expression of _FhTPS1_ by competitively inhibiting _FhMYB21 Ls_ from binding to the _FhTPS1_ promoter during floral development [82]. A jasmonate (JA)-responsive regulatory cascade was identified in _Curcuma wenyujin_, where _CwJAZ4/9_ proteins physically interact with _CwMYC2_ to negatively modulate JA-induced terpenoid biosynthesis (e.g., curcuminol, zederene and elemene). Transgenic overexpression of _CwJAZ4/9_ significantly reduced terpenoid accumulation in root hairs, while RNAi-mediated silencing enhanced JA-dependent terpenoid production, confirming their antagonistic regulatory roles [83].
## 3 Results and Discussion
### The Formation Mechanism and Discriminant Conditions of RSSF  
#### 3.2.1 The Formation Mechanism  
The average rainfall infiltration flow \(Q_{m}\) of the dam body is defined as Equation (14):  
\[Q_{m}=\frac{f\times s}{t} \tag{14}\]  
where \(Q_{m}\) is the total infiltration flow into the tailings pond, \(f\) is the rainfall intensity, \(s\) is the rainfall area, which is the product of the unit width of the tailings pond and the total length of the dam body, and \(t\) is the duration time.  
Corresponding to the rainfall in Taiyuan, \(f\) = 400 mm, \(t\) = 365 d, \(s\) = 538 m\({}^{2}\), according to Formula (14): it can be ascertained that \(Q_{m}\) = 6.82 \(\times\) 10\({}^{-6}\) m\({}^{3}\)/s. Using the same methods, we can obtain \(Q_{m}\) = 1.364 \(\times\) 10\({}^{-5}\) m\({}^{3}\)/s when \(f\) = 800 mm, and \(Q_{m}\) = 2.047 \(\times\) 10\({}^{-5}\) m\({}^{3}\)/s when \(f\) = 1200 mm.  
In the numerical analysis, the drainage flow through the initial dam at any time can be calculated, so the relationship curve between the drainage flow of the initial dam and the rainfall infiltration time can be drawn, as shown in Figure 9.  
As can be seen from Figure 9, the drainage flow of the initial dam grows with the increase in rainfall infiltration time. The drainage flow rate of the dam in the first 20 years is less than the average rainfall infiltration flow, which indicates that the water storage of the tailings in the dry-stack tailings pond is increasing, resulting in the continuous uplift of the saturation line in the dam. The larger the difference between the drainage flow and average rainfall infiltration flow, the faster the increase in the water storage in the dry-stack tailings pond and, furthermore, the faster the rise in the saturation line, which is consistent with the results about the evolution law of the saturation line in Figures 5-7. Evidently, this reveals the reason for the rapid rise of the saturation line in the early stage of rainfall infiltration. With the time continuing, when the drainage flow of the initial dam is roughly equal to the average rainfall infiltration flow, the water storage in the tailings pond is no longer increased. Correspondingly, the saturation line is no longer uplifted, and the seepage field of the tailings pond is in a stable state. Due to the inhomogeneity of rainfall within a year, the seepage field of tailings pond will be in a fluctuating state, and its fluctuation characteristics are related to the rainfall curve. There are similar rules in Figure 9,c.  
Figure 9: The variation curve of initial dam drainage flow with rainfall time: (**a**) Taiyuan (the annual rainfall of 400 mm); (**b**) Zhengzhou (the annual rainfall of 800 mm); (**c**) Guiyang (the annual rainfall of 1200 mm).
###### Abstract  
Pose estimation of heavy construction equipment is the key technology for real-time safety monitoring in road construction sites where heavy equipment and workers on foot collaborate in proximity. Ultra-wideband (UWB) radios hold great promise among various sensing technologies for providing accurate object localization in indoor and outdoor environments. However, in a road construction environment with heavy vehicles and equipment, the performance of UWB radios drastically declines because of blockages in the transmission signal between the transmitter and receiver causing Non-Line of Sight (NLOS) situations. To address this deficiency, our study presents a real-time pose estimating system called ViPER+ that can overcome NLOS situations and accurately determine the boundary of heavy construction equipment with multiple UWB tags attached to the surface of the equipment. To remove the impact of NLOS signals, we introduced an input correction method prior to localization to correct the input of the localization algorithm. Evaluation of ViPER+ in a real construction environment indicates that embedding NLOS detection technique in UWB-based pose estimation resulted in 40% improvements in location accuracy and 25% improvement in update rate compared to its previous implementation (ViPER).  
safety; location tracking; construction equipment; UWB; NLOS; LOS +
Footnote †: journal: Applied  
1
# Game-Theoretic Power and Rate Control in IEEE 802.11\(p\) Wireless Networks
## 4 Numerical Results
### Discussion  
In this paper, we have introduced a utility function as the product of the rate and the PRR, which depend on the transmission power. This is essentially different from [17], where a bits/joule approach is employed for the description of the utility function. In our case, we exploit the different PRR descriptions on the PHY or MAC layer, Equations (1) and (17), depending on the SINR range. Thus, for SINR between 0 and 8, (17) performs better by maximizing the utility function.  
The equilibrium that is best in our paper is when the rate and PRR is maximum, using the maximum transmission power due to duty cycling, which diminishes or stabilises interference. On the other hand, the maximum transmission power of the work of [PERSON] et al. [17] results in an equilibrium which is not the best, since it raises interference between the nodes, which in turn decreases the utility function. Essentially, [PERSON] et al. focus on energy-efficient optimisation, which takes into account a thorough interference model. The approach we are proposing optimises the transmission power and the rate with respect to no or stable interference, which we ensure by assigning time slots to each transmission, thus resembling to a Time Division Multiple Access (TDMA) framework. One drawback of this approach is the scalability due to vehicle density, which however may be solved assuming the locality of the vehicles.  
In this work, solid mathematical procedures that operate nicely in simulation have been proposed. Several Nash equilibrium properties have been derived for our problem formulation. The utility function has been transformed to concave in order to make it solvable in polynomial time and avoid practical implications such as the time of execution in [17]. However, further tests are required to put into our algorithm to check for run time as well as scalability performance. The main difference is the fact that we use a duty cycling method to allow maximum transmission power utilisation compared to the competitor, which uses a thorough queuing theory model and a different utility function. Note that the best equilibrium of [17] is the one where the transmission power is not the maximum due to interference.  
A drawback of this work is that it does not take into account a thorough queuing model to the optimisation. Taking into account the delay relationship with the rate and PRR requires a thorough M/G/1 model to be lumped into our optimisation model. Certainly, this work can be extended with such a model, but it is beyond the scope of this paper.