esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Network Protocol Syntax Extracting Method for Grammar-Based Fuzzing
## 3 Methods
### Packet Type Extraction Based on Basic Path Set  
In this step, the program structure and control flow of the Wireshark protocol dissector file are analyzed to get the basic path set of the whole file, then the basic path set is used to extract the packet types of the protocol. In contrast to traditional methods that focus on functions, methods, or data structures as the basic units of analysis, we center around the entire file as the fundamental unit to parse the basic path set. Each basic path represents a unique execution path in the protocol parsing process, encompassing various protocol parsing scenarios and packet structures. Therefore, each path in the basic path set is regarded as a parsing path of one packet type, and all constituent fields of the packet type can be extracted from the parsing path, thus all packet types and their corresponding field information can be obtained by parsing the whole basic path set. During the extraction process, we track the generation of the protocol tree along the parsing paths, allowing for the accurate extraction of attributes for each field, including offset, length, variable name, and byte order. Furthermore, these field attributes are further refined by associating them with extracted standard fields. The extraction process is described in detail in Section 4.
# Estimating Significant Wave Height from SAR with Long Integration Times
## 4 Validation of the Experimental Results With ECMWF and Sensitivity Analysis
### Sensitivity Analysis of Significant Wave Height Estimation  
As shown in Equation (19), the error of significant wave height estimation comes from the azimuth cutoff \(\lambda_{c}\), wavelength \(\lambda_{p}\) of ocean waves and propagation direction \(\phi\) of ocean waves. Then, the sensitivity analysis of significant wave height estimation will be carried out with the parameters referring to Section 3.1. The parameters are shown in Table 4. The sensitivity of the estimating significant wave height \(SWH\) to the parameter \(Z\) is defined as  
\[S_{Z}=\frac{\partial(SWH)}{\partial Z} \tag{25}\]  
where \(Z\in\big{[}\lambda_{c},\lambda_{p},\phi\big{]}\).
# Highly Accurate Deep Learning Models for Estimating Traffic Characteristics from Video Data
## 3 Methodology
### Zero-Inflated Logarithmic Link for Count Time-Series Model  
As discussed, traditional aggregated traffic flow data may not be suitable for modelling highly disaggregated traffic crashes. Therefore, the video analytics method developed in this paper could be employed in acquiring relevant data for such crash prediction models. Since we are interested in modelling traffic crashes that occur on a road segment over a time period (e.g., an hour, a day, a week, and so on), the statistical model should have the capability to control excess zeros as well as spatial and serial autocorrelation. Typical zero-inflated models can deal with excess zeros in crash prediction. However, such models cannot handle the serial correlation inherent in time-series crash data. A novel time series zero-inflated model with negative binomial assumption and a logarithmic link model has been developed to control both spatial and temporal structures in traffic crash analysis [11; 38]. The same method is applied in this study to demonstrate the highly disaggregated traffic flow data obtained by the mFairMOT. The model is briefly explained here.  
For a log-linear model with covariates where the parameters can be negative, the parameter space is taken to be:  
\[\Theta=\left\{\vartheta\ \in\ R^{a+b+c+1}:\ \beta_{0}>0,\ \Big{|}\beta_{1}\Big{|}, \ldots,\Big{|}\beta_{a}\Big{|},\ \Big{|}\alpha_{1}\Big{|},\ldots,\Big{|}\alpha_{b}\Big{|},\ \Big{|}\sum_{k=1}^{a}\beta_{k}+\sum_{k=1}^{b} \alpha_{l}\Big{|}<1\right\} \tag{14}\]  
Under negative binomial distribution, the quasi-maximum estimation for the dispersion parameter \(\varphi\) given regression parameter \(\Theta\) is expected to iterate until convergence [11; 38].  
For a given vector of observations \(y=\{y_{1},\ldots,y_{n}\}^{T}\), the conditional quasi-log-likelihood function, up to a constant, can be expressed as:  
\[P(\theta)=\sum_{t=1}^{n}log\ p_{t}(y_{t};\theta)=\sum_{t=1}^{n}(y_{t}ln( \lambda_{t}(\theta))-\lambda_{t}(\theta)) \tag{15}\]  
where \(p_{t}(y_{t};\theta)\) refers to the probability density function and \(\lambda_{t}(\theta)\) refers to the conditional mean.  
The video detection technique extracts dynamic traffic parameters and integrates these motion parameters with a zero-inflated logit count time-series model to make predictions on traffic crashes.
# Coal Seam Thickness Prediction Based on Transition Probability of Structural Elements
## 2 RBF-Kriging for borehole Data Interpolation
### Implementation of RBF-Kriging  
The implementation of the proposed RBF-Kriging interpolation algorithm is summarized, as follows:  
* Step 1: For coal seam data \(\mathrm{D}=\left\{(l_{i},w_{i},z_{i})|l_{i},w_{i},z_{i}\in R^{+},0<i\leq n\right\}\), calculate the distance of from point \(x_{i}\) and point \(x_{j}\), \[d=\sqrt{\left(l_{i}-l_{j}\right)^{2}+\left(w_{i}-w_{j}\right)^{2}},0<i\leq j \leq n;\] (7) where \(l\) and \(w\) is plane coordinates of the location x
* Step 2: Calculate the variation function values between point pairs as, \[r(d)=\frac{1}{2}N(d)\sum\
olimits_{i,j=1}^{N(d)}\bigl{[}z(l_{i},w_{i})-z\bigl{(} l_{j},w_{j})\bigr{]}^{2},\] (8) and one-to-one correspondence with distance, where, \(d=\sqrt{\left(l_{i}-l_{j}\right)^{2}+\left(w_{i}-w_{j}\right)^{2}}\) and \(\mathrm{N}(d)\) is the number of experimental variation function corresponding to the lag distance \(d\), \(z(l_{i},w_{i})\) is the coal seam thickness at the point \(x_{i}\) that its plane coordinates is \((l_{i},w_{i})\);
* Step 3: Using RBF function to fit distance and variation function values and using the following function \[f(d,r)=\sum\
olimits_{p=1}^{P}\omega_{p}\varphi_{p}(d-d^{p}),\] (9)where \(\omega_{p}\) is the weight coefficient,\(\varphi_{p}(d-d^{p})\) is basis function and \(p\) is the number of basis function;
* Step 4: Calculate the variance \(\sigma\) of the distance between points and select the efficient data points \(x_{i}\) with \(d(x_{i},x_{0})\leq 3\sigma\) around the interpolation point \(x_{0}\).
* Step 5: According to the distance between the selected data point and the interpolation point, calculate the variation function values through \(f(d,r)\);
* Step 6: Calculate the interpolation weight vector \(\lambda\) using Equation (6); and,
* Step 7: Calculate interpolation values as \[\text{z}^{\prime}(l_{0},w_{0})=\sum\
olimits_{i=0}^{N}\lambda_{i}\text{z}(l_ {i},w_{i}),\;0\leq\sqrt{\left(l_{i}-l_{0}\right)^{2}+\left(w_{i}-w_{0}\right)^{ 2}}\leq 3\sigma,\] (10) where \(\text{z}^{\prime}(l_{0},w_{0})\) indicates the coal seam thickness at the interpolation point.  
Figure 1 shows the flow chart of the proposed algorithm.
## 2 Materials and Methods
### Development of the Application
The key components include an initial \"lobby\" scene, where groups connect to the application and select their workrooms (Figure 1a), and dedicated \"room\" work scenes, where students incorporate their robotic system designs (Figure 1b-f). As depicted in Figure 1b, upon launching the application, each user enters the lobby, where they can connect and select their desired room. Once insidethe chosen room, users have the freedom to navigate the virtual environment naturally, by physically walking through the real space or utilizing the teleporting functionality, activated by performing a pinch gesture with the index finger and thumb of the left hand. This gesture triggers a blue target to appear, indicating the selected teleport destination. To confirm the teleportation, users simply need to separate their index finger and thumb. This intuitive and user-friendly feature enhances the immersive experience within the virtual environment. Furthermore, in a multi-user scenario within the room, the avatars of other users would be visible, as exemplified in Figure 1c. Notably, the application is designed to access Meta's proprietary credentials, resulting in the avatar displayed being identical to the user's selection in their Meta account, which is associated with their Meta Quest 2 headset. This integration ensures a personalized and consistent experience for each user, fostering a sense of individuality and familiarity within the shared virtual environment. Figure 1: _Cont._  
As previously stated, the application is designed to enable hands-only interaction with the virtual world, eliminating the need for external controllers. Users can manipulate virtual objects in the environment by using their hands, replicating real-world gestures, as illustrated in Figure 1d. Moreover, users have the unique ability to introduce new objects into the environment. Although this functionality was not required for the case study presented in this work, Figure 1e demonstrates how users can bring their own mobile phones into the virtual environment, to capture photos of their experience. To achieve this, users simply look at the palm of their left hand, prompting a green button symbolizing a mobile phone. Upon pressing the button, the virtual phone is introduced into the environment, visible to all present users, who can interact with and take it, if desired. To exit the virtual environment, users need to focus on the palm of their right hand and press the red exit button displayed in Figure 1f. This action brings up a menu exclusive to the user who wishes to leave, ensuring a confirmation step to avoid accidental exits. This thoughtful approach prevents unintended interactions and provides a user-friendly experience throughout the application.
## 4 HVAC Control
### Thermal Model
The ventilation heat load, \(\dot{Q}_{cent}\), is expressed in W and has the form indicated in (7):  
\[\dot{Q}_{cent}=\dot{m}_{cent}\cdot c_{p}\cdot\left(T_{ext}-T\right) \tag{7}\]  
where \(\dot{m}_{cent}\) is the mass flow rate of ventilation air required for the enclosure, expressed in kg/s; \(c_{p}\) is the specific heat of the ventilation air, expressed in J/(kg K); \(T\) is the temperature of the enclosure, expressed in K; and \(T_{ext}\) is the outdoor temperature, expressed in K. The flow rate \(\dot{m}_{cent}\) can be calculated as the product of the outdoor air density \(\rho_{ext}\), expressed in kg/m\({}^{3}\) by the ventilation flow rate \(\dot{V}_{cent}\) introduced into the enclosure, expressed in m\({}^{3}\)/s. This term \(\dot{m}_{cent}\) represents the mass flow rate of ventilation air required for the enclosure, expressed in kg/s. Therefore, this can be expressed as shown in (8):  
\[\dot{Q}_{cent}=\rho_{ext}\cdot\dot{V}_{cent}\cdot c_{p}\cdot\left(T_{ext}-T_{ int}\right) \tag{8}\]  
The \(\dot{E}_{g}\) term includes the thermal loads due to lighting \(\dot{Q}_{light}\), equipment \(\dot{Q}_{equip}\), and occupancy \(\dot{Q}_{ocup}\), which has a latent heat component in addition to a sensible heat component. \[\dot{E}_{g}=\dot{Q}_{light}+\dot{Q}_{equip}+\dot{Q}_{ocup} \tag{9}\]Finally, the thermal load introduced by the HVAC system present in the enclosure \(\dot{Q}_{clim}\) must be incorporated as shown in (9). By aggregating all the developments of the terms that have been made, Equation (1) can be reorganized in the following differential equation that determines the time evolution of the temperature:  
\[M\overset{.}{T}=A+B\ T+\dot{Q}_{clim} \tag{10}\]  
Equation (10) represents the thermal model to be solved. The terms \(A\) and \(B\) represent the grouping of terms obtained in the different Equations (3) to (8) that do not depend on \(T\) (term \(A\)) and those that multiply the variable \(T\) (term \(B\)), as well as those that represent all the actions on the enclosure outside the HVAC system to be controlled. In general terms, expressions (3) to (8), and, therefore, \(A\) and \(B\), are time-dependent functions, since they depend on the time of day and the thermal loads not associated with HVAC in each time phase. Finally, the term \(\dot{Q}_{clim}\) represents the action performed by the HVAC system and that will be the variable to be controlled to try to achieve the desired interior temperature \(T\) of an enclosure.
# A Charging and Discharging Data Privacy Protection Scheme for V2G Networks Based on Cloud-Fog-End
## 3 Implementation
### Aggregation Verification and Decryption  
After receiving the regional data reports \(T_{w}\) uploaded by \(FN_{w}\), the CSO first checks the validity of the timestamp \(TS_{w}\). Subsequently, the CSO performs aggregated verification on the \(m\) valid regional data reports \(T_{w}\) (\(w=1,2,\ldots,m\)) according to the following formula.  
\[\left\{\begin{array}{c}h_{2w}=H_{2}(ID_{FN_{w}},X_{c},Y_{c},\varphi_{w},TS_{ w})\\ P\sum\limits_{m=1}^{m}sig_{w}=\sum\limits_{m=1}^{m}Y_{w}+P_{pub}\sum\limits_{ n=1}^{m}h_{1w}+\sum\limits_{m=1}^{m}X_{w}h_{2w}\end{array}\right. \tag{5}\]  
After successful aggregated verification, the CSO proceeds to decrypt and verify the regional data reports \(T_{w}\) (\(w=1,2,\ldots,m\)) in sequence using the following formula, obtaining the complete charging and discharging data \(m_{i}\) uploaded by legitimate \(CP_{i}\).  
\[\left\{\begin{array}{c}U_{i}=(x_{c}+d_{c})R_{i}\\ h_{3i}=H_{3}(PID_{i},ID_{CP_{i}},X_{i},Y_{i},U_{i},R_{i})\\ m_{i}=C_{i}\bigoplus h_{3i}\end{array}\right. \tag{6}\]
## 6 Limitations of UAVs
### Computational and Storage Demands  
The high resolution and large volume of data generated by UAVs pose significant computational and storage challenges. Processing and analyzing large-scale UAV datasets demand substantial computational resources and advanced hardware, which may only be readily available to some researchers. For example, the dense and high-resolution images in datasets like UAVid and BioDrone require extensive processing power for effective utilization. Additionally, the storage of such vast amounts of data can be impractical for some institutions, hindering widespread access and collaboration.
###### Abstract  
The estimation of aboveground biomass (AGB), an important indicator of grassland production, is crucial for evaluating livestock carrying capacity, understanding the response and feedback to climate change, and achieving sustainable development. Most existing grassland AGB estimation studies were based on empirical methods, in which field measurements are indispensable, hindering their operational use. This study proposed a novel physically-based grassland AGB retrieval method through the inversion of PROSAIL model against MCD43A4 imagery. This method relies on the basic understanding that grassland is herbaceous, and therefore AGB can be represented as the product of leaf dry matter content (Cm) and leaf area index (LAI), i.e., AGB = Cm \(\times\) LAI. First, the PROSAIL model was parameterized according to the literature regarding grassland parameters retrieval, then Cm and LAI were retrieved using a lookup table (LUT) algorithm, finally, the retrieved Cm and LAI were multiplied to obtain the AGB. The method was assessed in Zoige Plateau, China. Results show that it could reproduce the reference AGB map, which is generated by upscaling the field measurements, in terms of magnitude (with RMSE and R-RMSE of 60.06 g\(\cdot\)m\({}^{-2}\) and 18.1%, respectively) and spatial distribution. The estimated AGB time series also agreed reasonably well with the expected temporal dynamic trends of the grassland in our study area. The greatest advantage of our method is its fully physical nature, i.e., no field measurement is needed. Our method has the potential for operational monitoring of grassland AGB at regional and even larger scales.  
aboveground biomass (AGB); grassland; PROSAIL; MCD43A4 2019111, 1597; doi:10.3390/rs11131597
# Efficient Gene Transfection by Electroporation--In Vitro and In Silico Study of Pulse Parameters
## 3 Results
### Effect of pDNA Concentration on GET, MFI, Cell Survival, and Overall GET
(**A**) C2C12 myoblasts, pulses: 25 \(\times\) 200 ns: 25 \(\times\) 200 ns, 10 Hz, 15.8 kV/cm; 100 \(\times\) 500 ns: 100 \(\times\) 500 ns, 10 Hz, 4.1 kV/cm; HF-BP: 2-2-2, 32p, 1000, 1 Hz, 1.25 kV/cm; 8 \(\times\) 100 μs: 8 \(\times\) 100 μs, 1 Hz, 1.25 kV/cm; and 8 \(\times\) 5 ms: 8 \(\times\) 5 ms; 8 \(\times\) 5 ms; 8 \(\times\) 5 ms; 1 Hz, 0.4 kV/cm; and (**B**) 1306 fibroblasts, pulses: 25 \(\times\) 200 ns: 25 \(\times\) 200 ns, 10 Hz, 12.8 kV/cm; 25 \(\times\) 500 ns: 25 \(\times\) 500 ns, 10 Hz, 6.5 kV/cm; HF-BP: 2-2-2, 32p, 100b, 1 Hz, 1 kV/cm; 8 \(\times\) 100 μs: 8 \(\times\) 100 μs, 1 Hz, 1.25 kV/cm; and 8 \(\times\) 5 ms: 8 \(\times\) 5 ms, 1 Hz, 0.6 kV/cm. 0 V represents control where cells were not exposed to electric pulses. Results are represented as an average of 3 repetitions. Statistical differences are given in the text. Bars represent standard deviation. pulse protocols, a plateau in the overall GET was observed between 100 \(\upmu\)g/mL and 500 \(\upmu\)g/mL pDNA concentrations, where the overall GET did not increase significantly with the increasing pDNA concentration. In contrast, with nanosecond pulse protocols, a significant increase in overall GET was observed between 250 \(\upmu\)g/mL and 500 \(\upmu\)g/mL pDNA concentration (Figure 5B). The importance of pDNA concentration is most noticeable with nanosecond pulses, where overall GET is less than a half in both cell lines already at pDNA concentration 250 \(\upmu\)g/mL. With the lowest pDNA concentration, 40 \(\upmu\)g/mL, overall GET in 1306 fibroblast was 3% and 5% for 200 and 500 ns pulses, respectively. In C2C12 myoblasts, the overall GET with 40, 80, and 100 \(\upmu\)g/mL pDNA concentrations was below 1% with both 200 ns and 500 ns pulses (Figure 5). Based on these results, a pDNA concentration of 500 \(\upmu\)g/mL was chosen to achieve robust overall GET.
## 3 Results and Discussion
### Analysis of the Results: Methodological Aspects  
The results presented above allow us to answer a question that is important from a practical point of view: what is the physical meaning of the coefficient \(\tau\) in Equations (11), (12), (22), etc.? Also, it is methodologically important to know how to calculate the coefficient \(\tau\)? To search for answers, we will use relations (22).  
Let's write: \(C_{water}^{(d.b.)}(t)=C_{water\_start}^{(d.b.)}\). Let \(\tau=t\). Then \(C_{water}^{(d.b.)}=C_{water\_start}^{(d.b.)}e^{-1}\approx C_{water\_start}^{(d.b. )}/2.718\) Consequently, the value \(\tau\) is equal to the drying time \(t\) at which the moisture content decreases by a factor \(e\approx 2.718\).  
To calculate the coefficient \(\tau\), it is necessary to carry out a test drying and build a plot of the dependence \(C_{water}^{(d.b.)}(t)\). The point on the plot for which \(C_{water\_start}^{(d.b.)}\approx C_{water\_start}^{(d.b.)}/2.718\) corresponds to \(t=\tau\).  
If we use formula (28a), then the coefficient \(\tau\) is calculated in the same way, but in this case the test drying is carried out only at a temperature of \(T=T_{\_}{\mathit{r}\mathit{r}\mathit{f}}\), i.e., for \(\phi=1\) in Equation (23). This is how the value \(\tau=140\) min was found for the above modeling results (Figures 7 and 8).  
The presented method for determining coefficient \(\tau\) is recommended for calculations on wet and dry basis. The transition from one basis to another is carried out according to Equation (7).  
With regard to calculations on a wet and dry basis, it is methodologically important to note the following. Figures 2 and 3 show that calculations results on a wet and dry basis are practically equivalent at low moisture. However, as moisture increases, the equivalence of the calculated data on a wet and dry basis is lost (Figures 4 and 5). Taking this circumstance into account, it is possible to formulate restrictions on the field of application of the calculation formulas, for which, however, further experimental and theoretical studies are required. From a methodological point of view, it is important to note that in this work a phenomenological approach was used to modeling changes in the moisture content of a capillary-porous material in a thin layer. This approach is used in applied sciences, for example, [28; 29]. As is known, phenomenological models take into account only the observed (external) properties of objects and do not take into account the internal mechanisms of phenomena, for example, the change in the moisture content of a capillary-porous material is studied without detailed analysis of moisture transfer. A detailed consideration of the transfer of mass and heat would lead to a model that belongs to a different class [8; 25]. Analysis of models that do not belong to the class of phenomenological models is beyond the scope of our work.
# Damage Evaluation in Tempered Vacuum Glazing via Multivariate Statistical Methods
## 1 Introduction  
Vacuum glazing is a high-quality product that possesses transparency, heat preservation, and sound insulation. Compared to insulating glass, vacuum glazing lasts longer and ensures the better heat transfer at a more compact design [1, 2, 3]. The manufacturing process of vacuum glazing is complex and needs two pieces of washed glass. First, support pillars are arranged in a specific manner on one piece of glass coated with an edge sealing solder. Another piece of glass is heat sealed and the space between both sheets of glass is afterwards evacuated with a vacuum pump [4, 5, 6]. However, low bend strength and impact toughness, as well as angular fragmentation after breaking, make vacuum glazing uncomfortable for safe use because of the risk of personal injury.  
The mechanical strength and impact toughness can be enhanced by exposing one layer of tempered glass to the compressive surface stress and another layer to the interior tensile stress. Unlike the common glass, the bend strength of tempered glass can be increased by 3-5 times and the impact strength is enhanced by 5-10 times [7]. Meanwhile, the long-term medium-high temperature may cause the stress attenuation effect, and the glass performance will be degraded during the manufacturing process, which may affect the safe use of the product [8, 9]. The common damage-inducing factors of TVG are surface stress, vacant pillars, sealing quality, and micro cracks. However, their industry-scale assessment with respect to product quality and eligibility is still a challenge [10, 11, 12, 13].  
In view of the complex quality parameters of TVG, one can mention a study of [PERSON] et al. [14] who performed the grey relational analysis to study the module vacuum glass without, however, reporting the specific quality evaluation and the classification model. In that regard, this paper aims to evaluate the TVG performance by applying the multivariate statistical methods such as principal component analysis (PCA) and hierarchical component analysis (HCA). Both of these approaches are widely applied in the quality assessment related to food, medicine, wine, and other fields [15, 16, 17]. Attention ispaid to the development of the reliable evaluation model that would provide the references to product quality.