esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Shoveling Experiments and Analysis
### Trajectory Planning  
Due to the different insertion depths, the shoveling trajectories are also different. The fuel consumption of shoveling operations with different shoveling trajectories has obvious differences. In this study, a variety of insertion depths are randomly selected, ranging from 600 mm to 1000 mm. Two types of materials are selected, including gravel and sand. Gravel is characterized by non-uniform, high-density, and large particles. At the same time, sand is characterized as uniform, low-density, and with small particles. Both above materials have high representativeness, and the relevant parameters of the two materials are shown in Table 1. Due to the different angles of repose of the two piles, their shoveling trajectories are also different, as shown in Figure 4.  
Figure 4: Range of shoveling depths. (**a**) Gravel pile; (**b**) Sand pile.  
Figure 3: Automatic shoveling platform and sensors. As shown above, 1 fifth wheel gauge, 2 flow sensor, 3 vehicle PC system, 4 fifth wheel gauge control module, 5 signal receiving system, 6 all data acquisition system, 7 lift cylinder displacement sensor, 8 shoveling weight acquisition system, 9 tilt cylinder displacement sensor.  
The shoveling area \(S\) consisting of the shoveling trajectory and the surface of the pile is shown in Figure 5. According to the geometric relationship in Figure 5, the shoveling area can be expressed as shown in Equation (1). However, the shoveling trajectory is not the same. However, the shoveling area composed of the shoveling trajectory and surface of the pile is always constant and equal to the bucket cross-section. When the length of \(l_{AB}\) is determined, both \(l_{BC}\) and \(l_{CD}\) can be expressed as a function of \(l_{AB}\) only. This is shown in Equation (2). When the shoveling area is held constant, different shoveling trajectories can be obtained by adjusting the length of \(l_{AB}\).  
\[S=\frac{V}{L}=\frac{1}{2}l_{AB}l_{BQ}+l_{BC}l_{BP} \tag{1}\]  
where \(V\) is the bucket volume, and \(L\) is the bucket length.  
\[\left\{\begin{array}{l}l_{BP}=l_{AB}\sin(\alpha)\\ l_{CD}=l_{BQ}=l_{AB}\tan(\alpha)\\ l_{BC}=\frac{S}{l_{BP}}-\frac{l_{BP}}{\sin(2\alpha)}\\ l_{BC}=l_{BC}\cos(\alpha)\end{array}\right. \tag{2}\]  
where \(l_{AB}\) is the insertion depth of the insertion phase, \(l_{BC}\) is the shoveling length of the scooping phase, \(l_{CD}\) is the shoveling length of the lifting phase, \(l_{BP}\) is the horizontal distance between the trajectory of the scooping phase and the surface of the pile, and \(l_{BC^{\prime}}\) is the wheel loader displacement of the scooping phase.
## 2 Model Approach
### Data Sources  
This paper's GPP and ET data were acquired utilizing the GEE platform PML_V2v0.1.7 data product ([[https://code.eartheengine.google.com/](https://code.eartheengine.google.com/)]([https://code.eartheengine.google.com/](https://code.eartheengine.google.com/)), accessed on 18 July 2021). Firstly, the PML_V2 image 1840 scenes passing over the Loess Plateau from 2001 to 2020 were acquired in the GEE platform using the filtering functions of time and space; the images were processed by cropping and removing the outliers, and the GPP, ET effects were fused for each year using the cumulative method. The PML-V2 model is based on the stomatal conductivity theory coupled with the total primary productivity process [22]. The parameters were determined and parameterized on 95 global eddy-related flux stations according to different vegetation types, and then extended globally based on land use data. To ensure the accuracy of the model simulation results, this thesis was validated using data from the FLUXNET2015 site ([[https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)]([https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)), accessed on 26 June 2022). Water-carbon coupling showed high accuracy in the study local area. NDVI data were obtained from the MOD13A2V6 product ([[https://code.earthegine.google.com/](https://code.earthegine.google.com/)]([https://code.earthegine.google.com/](https://code.earthegine.google.com/)), accessed on 26 June 2022). DEM data were obtained from the Shuttle Radar Topography Mission (SRTM). The SRTM elevation data were obtained from the Interferometric Synthetic Aperture Radar (InSAR) sensor, ([[http://srtm.csi.cgiar.org/](http://srtm.csi.cgiar.org/)]([http://srtm.csi.cgiar.org/](http://srtm.csi.cgiar.org/)), accessed on 20 June 2022). The data used in this study are shown in Table 1.  
The National Meteorological Science Data Center provided the meteorological information utilized in this paper ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)), accessed on 26 June 2022). Fifty-two meteorological stations, including precipitation and average temperature data, are involved within the Loess Plateau in seven provinces: Shanxi, Qinghai, Ningxia, Inner Mongolia, Henan, Gansu, and Shaanxi.
# A Novel Model for Accurate Daily Urban Gas Load Prediction Using Genetic Algorithms
## 3 Mathematic Model
### Solving of the MWP-DLP Model
#### 3.2.3 Solution Result  
The variation of the variable coefficient of the solving model is shown in Table 2. The comparison between the predicted value and the actual value for 100, 500, 700, and  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Date**} & **Daily Load** & **Average** & **Average Temperature** & **Cumulative** & **Wind** & **Solar** \\  & & **(10\({}^{3}\) m\({}^{3}\))** & **Temperature** & **of the Previous Day** & **Temperature** & **(Section)** & **Radiation** \\  & & & (\({}^{5}\)C) & (\({}^{5}\)C) & (\({}^{5}\)C) & & **(J/cm\({}^{2}\))** \\ \hline
1 & First year-11-21 & 310 & \(11.9\) & \(9.5\) & \(10.7\) & \(0.2\) & \(1664\) \\
2 & First year-11-22 & 245 & \(3.8\) & \(15.8\) & \(9.8\) & \(13\) & \(869\) \\
3 & First year-11-23 & 425 & \(-0.6\) & \(3.8\) & \(1.6\) & \(13\) & \(308\) \\
4 & First year-11-24 & 443 & \(1\) & \(-0.6\) & \(0.2\) & \(4.9\) & \(309\) \\
5 & First year-11-25 & 374 & \(5\) & \(1\) & \(3\) & \(0.2\) & \(581\) \\
6 & Second year-02-24 & 212 & 7.2 & \(6.3\) & \(6.75\) & \(0.2\) & \(664\) \\
7 & Second year -02-27 & 65 & 10.8 & \(9.6\) & \(10.2\) & \(0.2\) & \(1356\) \\
8 & Second year-02-28 & 64 & 11.1 & \(10.8\) & \(11\) & \(4.9\) & \(1414\) \\
9 & Second year-03-01 & 74 & 12.2 & \(11.1\) & \(11.7\) & \(18.3\) & \(1672\) \\ \hline \end{tabular}
\end{table}
Table 1: Basic data of Wuhan.  
1000 iterations is shown in Figure 5, and the comparison between the predicted value and the actual value for 4 iterations is shown in Figure 5. As can be seen from Table 2, as the number of iterations increased, the variance sum gradually decreased, and the predicted value was closer and closer to the actual value. After 1000 iterations, the variance sum trended toward 5381, and the iteration converged. It can be seen from Figure 5 that as the number of iterations increased, the smaller the deviation degree between the predicted value and the actual value became; the predicted value curve obtained by iterating 1000 times was the closest to the actual value, indicating that the better predicted value could be obtained by solving the MWP-DLP model using genetic algorithm. At the same time, it shows that MWP-DLP model can better predict the daily gas load in the city.  
The coefficient value of 1000 iterations in Table 2 was brought into Equation (6), and Equation (7), based on the daily gas load data of the city, was obtained.  
\[\begin{split} F_{2}&=79.6+20.6\times(1+0.021\times WS _{i})\cdot D_{i}\\ D_{i}&=\begin{cases}0&0.021\times SR_{i}+T_{eff,i}>18. 98\\ \frac{(0.021\times SR_{i}+T_{eff,i}-18.98)^{2}}{13.58}&12.19<\quad 0.021\times SR_{i}<18.98 \\ 15.59-0.021\times SR_{i}&-T_{eff,i}&0.021\times SR_{i}\leq 12.19\end{cases}\\ \\ T_{eff,i}=0.8672\times T_{AVG,i}+0.1328T_{AVG,i-1}\end{split} \tag{7}\]
## 3 Controller Design
### Fixed-Time Disturbance Observer Design
\tag{42}\]  
Taking the time derivative of Equation (42) and substituting Equation (28) result in the following error dynamics:  
\[\left\{ \begin{array}{l}\dot{e}_{1}{=}-\kappa_{1}\varphi_{1}\Big{(}\frac{ \varepsilon_{1}}{\varepsilon_{1}}\Big{)}+e_{2}\\ \hat{d}_{\alpha}{=}-\frac{\kappa_{2}}{\varepsilon}\varphi_{2}\Big{(}\frac{ \varepsilon_{1}}{\varepsilon_{1}}\Big{)}+\dot{d}_{\alpha}\end{array} \right. \tag{43}\]  
It follows from Assumption 1 and Lemma 3 that the estimation errors \(\left|e_{1}\right|\) and \(\left|e_{2}\right|\) will converge to the neighborhood of the origin in fixed time. Similarly, we can define the disturbance observer errors \(e_{3}=\omega_{z}-\hat{\omega}_{z}\) and \(e_{4}=d_{\omega_{z}}-\hat{d}_{\omega_{z}}\), and then fixed-time convergence of disturbance observer for the pitch rate channel can be proven with the same steps above. Suppose that the convergence times of \(\left|d_{\alpha}-\hat{d}_{\alpha}\right|\) and \(\left|d_{\omega_{z}}-\hat{d}_{\omega_{z}}\right|\) are bounded by \(T_{1}\) and \(T_{2}\), respectively, and the detailed derivation process can be found in reference [34]; thus, this paper will not elaborate on it further. The proof is completed. \(\square\)  
Using the disturbance estimations \(\hat{d}_{\alpha}\) and \(\hat{d}_{\omega_{z}}\) to compensate for control laws (31) and (37), the new control laws become  
\[\omega_{zc}{=}-\Big{(}f_{\alpha}-\dot{\alpha}_{c}+a_{1}\text{sig}(s_{1})^{r_{ 1}}+c_{1}\text{sig}(s_{1})^{r_{2}}+\hat{d}_{\alpha}+b_{1}\text{sign}(s_{1}) \Big{)} \tag{44}\]  
\[\delta=-1/g_{1}\cdot\Big{(}f_{\omega_{z}}-\dot{\omega}_{zd}+a_{2}\text{sig}(s_{ 2})^{r_{1}}+c_{2}\text{sig}(s_{2})^{r_{2}}+s_{1}+\hat{d}_{\omega_{z}}+b_{2} \text{sign}(s_{2})\Big{)} \tag{45}\]where \(b_{1}\) and \(b_{2}\) are the disturbance observer estimation bounds, satisfying \(\left|d_{\alpha}-\hat{d}_{\alpha}\right|\leq b_{1}\) and \(\left|d_{\omega_{z}}-\hat{d}_{\omega_{z}}\right|\leq b_{2}\).
# Research on an Equivalent Algorithm for Predicting Gas Content in Deep Coal Seams
## 3 The Gas Content of the Equivalent Layer  
### The Basic Definition  
In accordance with the definition of gas content, the gas content within the equivalent layer can be considered as the volume of gas contained in the equivalent coal and rock mass per unit mass. This encompasses two primary aspects: the fundamental gas content of the equivalent layer and the influence of geological factors on the gas content of the equivalent layer. Specifically, the fundamental gas content of the equivalent layer represents the baseline volume of gas present in the original coal and rock state, uninfluenced by geological structural factors. Additionally, the influence of geological factors on the gas content of the equivalent layer denotes the incremental value of gas content that arises due to the extent of geological structural impact within the equivalent layer.
## 2 Methodology
### Performance Metrics  
To assess the performance of the estimation results obtained from the methods (population-specific, patient-specific, and category-specific), the following performance metrics are employed:  
1. Mean Squared Error (MSE): MSE measures the average squared difference between the actual \(y_{i}\) and predicted \(\hat{y}_{i}\) values and is calculated as \[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}.\] (8)
2. Mean Absolute Error (MAE): MAE computes the average absolute difference between the predicted and actual values, providing a more interpretable measure: \[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}|.\] (9)
3. Coefficient of Determination (\(\mathbf{R^{2}}\)):\(R^{2}\) quantifies the proportion of the variance in the dependent variable that is predictable from the independent variable(s), and is given by \[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}-\hat{ y})^{2}},\] (10) where \(\hat{y}\) is the mean value of the observed values.  
The MSE and MAE provide insights into the accuracy of estimations, with lower values indicating better performance. \(R^{2}\) ranges from 0 to 1, where a higher \(R^{2}\) signifies a better fit of the model to the data. The results obtained from the three methods are compared in terms of estimation accuracy and computational efficiency. The analysis encompasses MSE, MAE, and \(R^{2}\) values to assess the effectiveness of each method. Additionally, consideration is given to the time consumed by each method in finding the optimal architecture, aiming to identify the most efficient method.
## 6 Conclusions and Future Work  
In this paper, we present CUBIA, a novel Wi-Fi tethering misbehavior detection mechanism that can accurately detect selfish behavior, e.g., manipulating CCA threshold, via AP-level collaboration in multi-AP network environments. We show that the benefits of MIM can be fully exploited and abused further by a selfish tethering node via CCA manipulation combined with its short link-distance. We also observe that the consequence of the selfish tethering behavior resembles that of the hidden node problem, but selfish tethering nodes tend to ignore the RTS/CTS mechanism. CUBIA employs a CUSUM algorithm for the online detection of abnormal network behavior and inject RTS/CTS frames to avoid mis-diagnosing the hidden node problem as a selfish tethering. Our simulation-based evaluation results show that CUBIA accurately distinguishes the selfish tethering behavior from other types of misbehavior including the hidden node problem.  
In the future, we would like to extend our work to pinpoint and localize the selfish node based on the cooperation among APs. It would also be interesting to study effective system-level follow-up actions against selfish tethering misbehavior, such as jamming-resilient dynamic channel re-assignment.  
This work was supported in part by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT) (No. NRF-2020R1A2C1013308) and the Gachon University research fund of 2019 (Grant No. GCU-2019-0776).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AP & Access Point \\ CCA & Clear channel assessment \\ CST & Carrier sensing threshold \\ CUSUM & cumulative sum \\ FER & frame error rate \\ MIM & Message-In-Message \\ SIR & signal to interference ratio \\ SoI & the Signal of Interest \\ WLAN & Wireless LAN \\ \end{tabular}  
\(\gamma_{m}\) & _SIR threshold_ for modulation scheme \(m\) & \(\beta_{m}\) & _MIM threshold_ for modulation scheme \(m\) & \(\mathcal{T}_{FER}\) & target frame error rate &
## 3 Results and Discussion
### Flame Retardancy Tests

Table 3: Flammability results for low density polyethylene (LDPE) with a given amount of added FR. The second group of sulfenamides was based on bis(2,2,6,6-tetramethyl piperidinyl) that are less volatile than group I sulfenamides. The samples self-extinguished even before flame removal (Table 1). The recorded damage lengths above 55 mm were mainly caused by polymer melting. The thermal stabilities were dependent on the linker moiety X and increased in the following order: IIb < IIa < IIc. The compound IIc was chosen as the most promising candidate in this series due to its excellent flame retardant efficacy, high thermal stability, excellent processing behavior and clarity of the produced polypropylene film. The third group of sulfenamides consisted of polymeric sulfenamide structures. Tentative structures for the reaction products of Uvinul(r) 5050 H and Sabo(r)stab UV 94 are shown in Figure 4. Similar thermal stabilities and anti-flame properties were recorded for both polymeric compounds IIIa and IIIb (Table 1). The result demonstrates that polymeric sulfenamides are also able to effectively impart flame retardancy to polypropylene films. enhancement of polystyrene as earlier described in the literature [14; 39; 40]. When thiyl and amine radicals are generated by thermal cleavage, they can abstract hydrogen from polystyrene backbone, form intermediate reaction products where sulfur species (e.g., polysulfides) are attached to polystyrene and take part in various kind of chain transfer and oligomerization reactions. Sulfur catalyzed recombination of flame radicals can also occur. In order to study amine moieties with a very high charge dissipation capacity, carbazole based sulfenamide derivatives of group V were synthesized. The thermal stability of compounds Va and Vb was expected to be high due to planarity of N. Both compounds exhibited excellent flame retardant performance at a loading of 1 wt % without any flame dripping. Compound Vb had slightly higher thermal stability and showed better flame retardancy than compound Va. Both compounds also passed the criteria for UL94 VTM-2. The sixth group of sulfenamides contained imide and imidazole moieties. In the imide and imidazole derivatives, the N atom is next to electron withdrawing carbonyl groups. In theory, this type of sulfenamides should have high thermal stabilities and excellent free-radical quenching capabilities. It was found that both VIa and VIb could effectively improve the flame retardancy of polypropylene films.
## 3 Model Validation and Simulation Details
### Boundary Conditions  
Boundary conditions are necessary for solving the control equation. The boundary conditions for the steady-state simulations are provided in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Materials**} & **Density** & **Specific Heat** & **Thermal Conductivity** \\  & **(kg/m3)** & **Capacity (J/(kg\(\cdot\)K))** & **(W/(m\(\cdot\)K))** \\ \hline Water & 998.2 & 4182 & 0.6 \\ Water & 0.5542 & 2014 & 0.0261 \\ vapor & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical property parameters of coolant.  
Figure 5: Vapor phase volume fraction distribution under water supply conditions of (**a**) 1 and (**b**) 0.2 m/s.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Density** & \multicolumn{2}{c}{**Specific Heat Capacity**} & \multicolumn{2}{c}{**Thermal Conductivity**} \\  & **(kg/m3)** & **(J/(kg\(\cdot\)K))** & & **(W/(m\(\cdot\)K))** \\ \cline{3-5}  & & **Temperature (\({}^{\circ}\)C)** & **Value** & **Temperature (\({}^{\circ}\)C)** & **Value** \\ \hline \multirow{8}{*}{**GH4099**} & & 20 & 543 & 100 & 10.47 \\  & & 100 & 552 & 200 & 12.56 \\  & & 200 & 562 & 300 & 14.24 \\  & & 300 & 572 & 400 & 15.91 \\  & & 400 & 582 &  
Aiming to conduct the transient phase-change cooling performance during the coolant injection, the transient boundary conditions for heat flux are also provided as shown in Figure 6.  
To set appropriate thermal boundary conditions, preliminary calculations and fitting were performed on the original data. Different heat flux density boundary conditions were applied to the top and body of the rudder shaft, with monitoring points set at the top of the rudder shaft, the upper through-hole, and the lower through-hole. The heat boundary conditions that most closely matched the original data were fitted. The fitting results are shown in the figure below, in Figure 6b. The highest temperature at the top of the rudder shaft is slightly higher than the initial condition, with a maximum error of 4.9%.
## 3 Communication Characteristics of a QADA Receiver
### Noise  
Two sources of noise are considered: shot noise and thermal noise. Therefore, the total noise is the sum of both. The shot noise is modelled as Gaussian noise. The spectral density of the shot noise of each quadrant is given by  
\[N_{0}=2 qRp_{n}A_{k}\Delta\lambda \tag{7}\]  
where \(q\) is the electron charge, \(R\) is the PD responsivity, \(p_{n}\) is the spectral irradiance, \(A_{k}\) is the area of each quadrant in the PD, and \(\Delta\lambda\) is the optical bandwidth. The area of one quadrant in a square PD is given by \(A_{k}=L_{\text{PD}}{}^{2}\). The spectral irradiance depends on the level of illuminance and the spectral characteristics of the light source. Typically, work on VLP or VLC uses a spectral irradiance of \(p_{n}=6\,\upmu W/\left(\text{cm}^{2}\cdot\text{nm}\right)\), which represents a worst-case scenario when the receiver is close to a window in a daylight environment [35]. Instead, in this paper, in order to provide more realistic results, the spectral density of the background light is \(p_{n}=0.29\,\upmu W/\left(\text{cm}^{2}\cdot\text{nm}\right)\). This represents a case of the spectral irradiance caused by LED luminaires that produces typical level of illumination. More details on how this value is derived can be found in Appendix B.  
The thermal noise spectral density is calculated based on the noise equivalent power (NEP), which is the incident light level required to generate a current equal to the thermal noise current [36]. NEP is used by the manufacturers of PDs to express the level of noise and can be found in most datasheets [37]. The spectral density of the thermal noise is given by  
\[N_{\text{Thermal}}=\left(\text{NEP}\left(\lambda_{0}\right)\times R\left( \lambda_{0}\right)\right)^{2} \tag{8}\]  
where \(\lambda_{0}\) is the wavelength at which the NEP is measured. The value of NEP used in this paper is \(\text{NEP}=1.4\times 10^{-14}\,\upmu W/\sqrt{\text{Hz}}\), which is taken from the datasheet of the quadrant PD (Hamamatsu S5980 [38]). This is the PD used in the experiment in Section 6.  
Figure 4: Relation between the FOV-C of the QADA receiver and the aperture height.  
Figure 3: Difference between the FOV-C and the FOV-P of the QADA receiver.
# Biofilm Lifestyle in Recurrent Urinary Tract Infections
## 6 Strategies for Eradication of Biofilms
### Enzymatic Degradation of Biofilms  
Using enzymes for biodegradation of biofilm polymer matrix is an attractive strategy to counteract mature biofilms. The biofilm matrix is formed basically from polysaccharides, proteins and DNA; this DNA is necessary for the initial attachment and aggregation of biofilm matrix and required for biofilm formation and regulation [155; 156]. DNase is a degrading enzyme that can break down the phosphodiester linkage of DNA embedded in biofilm matrix resulting in effective biofilm [157]. Indeed, the antibiofilm activity of the DNase enzyme was repeatedly studied and showed significant biofilm inhibition against both Gram-negative and Gram-positive bacteria [158]. As another example, alginate lyase is an enzyme obtained from a marine bacterium that has a potent antibiofilm activity against _P. aeruginosa_ and _E. coli_[159; 160]. Additionally, DNase and alginate lyase effectively disintegrate the biofilm of _E. faecalis_ and _E. facecium_[161]. Other examples include glycosidases and proteases which are degrading enzymes that are produced by bacterial cells themselves. Such enzymes can be used for hydrolytic degradation of biofilm matrix material and, hence, dismantling the enclosed bacterial community of their protective polymer coat. Some proteases such as proteinase K, serratioopeptidase, carboxypeptidase, \(\alpha\)-amylase and others have also been screened for their anti-biofilm activities and showed significant biofilm eradication ability [162; 163; 164].