esa-sceva/satcom-chunk-collection huggingface dataset:
# NextGen ADS-B Software-Defined Reception with Enhanced Techniques
## 3 Methodology and Solutions
### ADS-B Error Detection and Correction Techniques  
There are three new methods for error identification and correction which are introduced in [1]. The first method deploys the current Sliding Window methodology, called \"Conservative\", in order to lower the rate of undetected errors. The second method, called \"Whole Message\", simulates how whole Mode A/C FRUIT (unwanted overlapping interference) may impact bit and confidence declarations. The third method, dubbed \"Brute Force\", does a constrained exhaustive search of all bit reversal sequences in search of low-confidence bits [26].  
By employing the \"Brute Force\" method of Cyclic Redundancy Check (CRC) based on the bit and confidence declarations outlined above, it is possible to discover and fix ADS-B data bits errors. Figure 5 is the flow of the detailed error correction procedure. Upon  
Figure 4: Enhanced Bit Detection and Confidence Declaration algorithm by using the multi-sample technique.  
receiving ADS-B data a CRC error detection can produce an error syndrome. If the error syndrome is equal to \"0\", the ADS-B message is accepted. If it is not \"0\", the algorithm checks the possibility of error correction by computing the length of the low confidence region and the number of its bits. Only when all the message's low confidence bits fall within a 24-bit frame and there are no more than 12 low confidence bits, the error correction algorithm will perform. In the first step, the conservative technique applies, if the error is corrected, the message will be modified accordingly and accepted, otherwise try every set of low confidence bits that is conceivable (if there are more than five low confidence bits, the received ADS-B data is discarded) and accept the set that is equivalent to the error syndrome.
# Empirical Performance Models of MAC Protocols for Cooperative Platooning Applications
## 4 Empirical Models
### Network Metrics In and Out of High Coherence  
In a scenario of heterogeneous platoon sizes, the network metrics follow the behaviour discussed earlier: despite more nodes being present in the system, platoon communications are not experiencing high coherence and the number of collisions and busy time ratio increase monotonically, as a result of more RF neighbours. The rate of collisions is shown in Figure 6(a) and busy time ratio in Figure 6(b). To model the rate of collisions, we propose an exponential model with formula \(y=\alpha\cdot e^{\beta\ x}\), where we force the y-intersect value to be 0. For busy time ratio, we found that a log-linear model \(y=\beta_{0}+\beta_{1}\ \ln(x)\) provides an appropriate fit. The corresponding parameter values are shown in Table 3, for all models. The mean square error (MSE) is used as the fitting quality evaluation metric. In most cases, alternative fitting curves were tested; we selected the ones providing inferior MSE.  
Figure 6: Number of radio-frequency (RF) neighbours vs. platoon size (default Tx power and used lanes).  
The homogeneous platoon size case is more complex since both CSMA/CA and PLEXE-slotted exhibit high coherence in their rounds, as discussed previously. For larger platoons, the reported number of neighbours decreases but the number of collisions increases substantially. This is shown in Figure 8a; e.g., refer to the CSMA/CA points in the range \(x=[59,65]\) and \(y=[100,140]\) (highlighted in the square in the plot), that correspond to platoon sizes 8 to 10. In the busy time ratio (Figure 8b), similar results are observable although at a relatively inferior scale. The pre-existing effects of increasing platoon size (higher frequency of hidden nodes, higher frequency of simultaneous transmissions) are aggravated by the high coherence between platoon rounds and degrade the channel performance, causing collisions to increase drastically while the number of neighbours decreases.  
This conclusion has an important consequence. In real-world conditions, the number of RF neighbours is easier to learn than the size of the platoons in our vicinity. However, as we observe that the number of neighbours above 60 is not monotonically increasing with respect to the platoon size, we cannot learn in that case whether the medium is in high coherence among rounds or not. RA-TDMAp is able to avoid the high coherence region by actually **promoting** decoherence (through its adaptive adjustment of round duration) for short periods until a steady state of coherent co-existence is achieved.
# Dual-Branch Luminance-Chrominance Attention Network for Hydraulic Concrete Image Enhancement
## 3 Methods  
### Color Space  
As shown in Figure 2, we conducted a comparative analysis of hydraulic concrete images with varying luminance levels in both the RGB and YCbCr color spaces. The results indicate that hydraulic concrete images exhibit similar content characteristics across each channel in the RGB space, primarily due to the limited color richness of these images. In the YCbCr color space, the Y channel effectively reflects the overall luminance of the image, while the Cb and Cr channels preserve image detail information well, particularly in terms of defect details.  
Figure 2: Color space analysis of hydraulic concrete images.  
Given that the input images of hydraulic concrete are inherently in the RGB space, we incorporate a sophisticated color space conversion module at the initial stage of our model. This module meticulously transforms images from the RGB space to the YCbCr space, thereby isolating and acquiring the distinct information encapsulated within the three channels: Y (luminance), Cb, and Cr. Subsequently, these processed channels are dispatched to the backend modules for further elaboration and enhancement.  
Based on the BT.601 [40] specification, a color space transformation block (CSTB) was designed to transform RGB into YCbCr. The specific conversion method is outlined as follows:  
\[Y=0.299R+0.587G+0.114B \tag{1}\]  
\[Cb=-0.1687R-0.3313G+0.5B+128 \tag{2}\]  
\[Cr=0.5R-0.4187G-0.0813B+128 \tag{3}\]  
where \(Y\) represents the luminance channel and \(Cb\) and \(Cr\) represent the chrominance channels. \(R\), \(G\), and \(B\) represent the red, green, and blue channels, respectively, in the RGB color space of an image. Based on the distinct characteristics of \(Y\), \(Cb\), and \(Cr\), various processing methods have been specifically designed for luminance and chrominance handling, with the aim of obtaining high-quality images of hydraulic concrete.
## 2 Properties of the LBS Distribution
### Statistical Properties  
**Property 1**. : _If \(T\) follows standard logistic distribution, i.e., \(T\sim Logistic(0,1)\), then:_  
\[X=\frac{\beta}{4}\bigg{(}\alpha T+\sqrt{[\alpha T]^{2}+4}\bigg{)}^{2}\sim LBS (\alpha,\beta).\]  
Property 1 was used to calculate the LBS distribution's expected value, variance, skewness, and kurtosis. **Property 2**. : _If \(T\sim LBS(\alpha,\beta)\), then_  
_1. \(\frac{1}{\alpha}\bigg{(}\sqrt{\frac{X}{\beta}}-\sqrt{\frac{\beta}{X}}\bigg{)} \sim Logistic(0,1)\);_  
_2. \(cX\sim LBS(\alpha,c\beta)\);_  
_3. \(X^{-1}\sim LBS(\alpha,\beta^{-1})\)._  
**Property 3**. : _If \(T\sim LBS(\alpha,\beta)\), then, we can show that \(E(X)=\beta(\frac{1}{6}\alpha^{2}\pi^{2}+1)\), \(V(X)=\frac{(\alpha\pi\beta)^{2}}{3}(\frac{37}{60}\pi^{2}\alpha^{2}+1)\), \(\gamma_{1}(X)=\frac{30(1556^{\alpha}\tau^{6}+2944^{\alpha}\tau^{4}+3155^{ \alpha}\pi^{2}+210)^{2}}{49(7\alpha^{4}\tau^{4}+20\alpha^{2}\pi^{2}+30)^{3}}\) and \(\gamma_{2}(X)=\frac{30(8894^{\alpha}\tau^{8}+1240\alpha^{6}\tau^{6}+980\alpha^ {4}\tau^{4}+560\alpha^{2}\tau^{2}+210)}{7(7\alpha^{4}\tau^{4}+20\alpha^{2}\pi^ {2}+30)^{2}}\). We also can easily obtain \(E(X^{-1})=\beta^{-1}(\frac{1}{6}\alpha^{2}\pi^{2}+1)\) and \(V(X^{-1})=\frac{(\alpha\pi\beta^{-1})^{2}}{3}(\frac{37}{60}\pi^{2}\alpha^{2}+1)\) by using Property 2._  
**3. Inference for the LBS Distribution**  
This section examines the inference for the LBS distribution using two methods: MLE and MME. _3.1. Maximum Likelihood Estimation_  
The MLEs for LBS distribution parameters are discussed in this section. Let \((x_{1},x_{2},...,x_{n})\) denote an n-sized random sample having the LBS distribution with unknown parameter vector \(\zeta=(\alpha,\beta)^{T}\).
### Contributions  
This paper presents three major points. In particular, a new method for creating base and detail layers based on the proposed structure-aware layer decomposition learning (SALDL) is introduced.  
First, to design the base layer, a new statistical distribution of the image difference between a blurred continuous-tone image and a blurred halftoned image with a Gaussian filter with a narrow output range is shown. Based on this observation, the base layer is reconstructed using a new GCM-based residual subnetwork that predicts the difference between the blurred continuous-tone image and the blurred halftoned image; this method differs completely from the existing PRL [23; 25], which uses an initial restored image from a DCNN for base layer generation.
* Second, the detail layer is generated based on structure-aware residual learning that predicts the difference image between the predicted base layer and the original image. To more effectively enhance image structures such as edges and textures, an image structure map predictor, which was introduced in a previous study [24], is incorporated into the residual detail layer learning, resulting in structure-enhancing learning. In addition, the predicted base layer is the low-pass-filtered version of the original image. Therefore, the proposed residual detail learning should be used to deblur the base layer, i.e., to remove the blurring of the base layer. This implies that the deblurring strategy is adopted in the proposed residual detail learning, unlike the existing PRL.
* Third, it is demonstrated that SALDL can be used to recover high-quality images from the predicted base layers whose quality is poor in terms of edge and texture representation. However, the existing PRL [23; 25] cannot yield satisfactory results from the same base layers. This reveals that the existing PRL is not suitable for low-quality base layers. By contrast, the proposed structure-aware residual learning method is more effective for describing image structures. To our best knowledge, this is the first study that performed the abovementioned comparison, and the experimental results confirmed the feasibility of the proposed SALDL as a new PRL for inverse halftoning that surpasses state-of-the-art methods such as PRL, U-net, and DCNN.
## 2 Materials and Methods
### Characterization of Cu/PPFC Nanocomposite Thin Films  
The nanocomposite structure of Cu/PPFC thin film was investigated by field-emission transmission electron microscopy (FE-TEM, Tecnai G2 F30 S-Twin, FEI Co., Hillsboro, OR, USA). Optical properties of the thin films were measured by UV-VIS spectroscopy (U-4100, Hitachi, Tokyo, Japan). Standard deviation of water contact angle, sheet resistance, optical transmittance, and b* (yellow index) were measured by five points at each sample. The measuring points were the center and four sides of the film. The surface energy of the nanocomposite thin film was measured using a contact angle analyzer (Phoenix 300 Touch, Surface Electro Optics, Suwon, Korea). The volume of water droplets was 2 \(\upmu\)L, and the images were automatically analyzed by the system. Structural characteristics  
Figure 1: (**a**) Sequential diagram of the fabrication of Cu/PTFE composite target. (**b**) Illustration of the sputtering process to deposit Cu/plasma polymer fluorocarbon (PPFC) nanocomposite thin films with radio frequency (RF), mid-range frequency (MF), and direct current (DC) power sources.  
of the Cu/PPFC nanocomposite thin films were analyzed by X-ray photoelectron spectroscopy (XPS, AXIS Nova, Kratos, Manchester, UK) and X-ray diffraction (XRD, SmartLab, Rigaku, Tokyo, Japan).
## 3 Results
### Study of Desorption  
The desorption cycle profile of phenol is shown in Figure 7. The curves of desorption from cycles 1 to 3 exhibited a comparable asymmetrical form along with a fast increase in concentration succeeded by a flatter decrease. It was observed that desorption cycles took 2 h, after which further desorption was insignificant. In the first cycle, the eluent's total volume at 2 h was 588 mL. The phenol ultimate concentration was acquired at 7, 6, and 4 min contact times and noted as 8282, 5861, and 5562 mg\(\cdot\)L\({}^{-1}\) for the first, second, and third cycle, respectively. In spite of the small volume of eluting solution, it contained a high concentration, which contributed to the processing simplicity, revival, and reuse of phenol.  
Figure 6: Different bed depths effect on the adsorption’s curves of breakthrough onto RHAC (rate of flow of 10 mL/min; inlet phenol concentration of 1000 mg/L).  
Figure 7: Desorption cycle profile of phenol at a rate of flow of 8 mL/min & bed depth of 8.5 cm with 0.1 M NaOH as eluent.  
The regenerated RHAC was reused for further adsorption cycles. Figure 8 presents the curves of breakthrough produced for the cycles of adsorption. The breakthrough time (\(t_{b}\)) remained almost constant throughout the three cycles of sorption, which has noticeable significance for the feasible implementation of RHAC as phenol bio-sorbent. This is because RHAC entails persistent use of the bio-sorbent without an obvious lack of sorption process. Furthermore, no adsorbent swelling occurred, as specified by the constant column's bed depth [47]. The maximum uptake of phenol brought in a substantial decrease from 22.17 mg\(\cdot\)g\({}^{-1}\) to 16.71 mg\(\cdot\)g\({}^{-1}\) after the three cycles. The regeneration efficiency was 89.06% throughout the first cycle, then declined to 75.34% at the third cycle. The regeneration efficiency reduction occurred as some adsorbate ions were linked to the adsorbent via robust interconnection [47].
## 2 BERT Pre-Training Model
### Dynamic Fusion Mechanism  
In the dynamic fusion mechanism proposed in this paper, all layers in the BERT pre-training model are represented by using a multilayer perceptron (\(G_{\text{I}}(\cdot)\)). Let the source language sequence be \(x=(x_{1},x_{2},\ldots,x_{\text{I}})\) and the target language sequence be \(y=\left(y_{1},y_{2},\ldots,y_{\text{I}}\right)\), where I and J denote the lengths of x and y respectively.  
In the Transformer model, the encoder encodes the source language sequence as \(R_{\text{N}}^{E}\), which consists of a series of \(\left(r_{\text{N},1}^{E},r_{\text{N},2}^{E},\ldots,r_{\text{N},\text{I}}^{E}, \ldots,r_{\text{N},\text{I}}^{E}\right)\), where N is the depth of the encoder. \(R_{\text{N}}^{E}\) can be calculated by:  
\[\begin{split} H_{\text{N}}^{E}=\text{Att}\left(Q_{\text{N}}^{E},K_{\text{N}-1}^{E},V_{\text{N}-1}^{E}\right)\\ R_{\text{N}}^{E}=\text{LN}\left(H_{\text{N}}^{E}+\text{FNN} \left(R_{\text{N}-1}^{E}\right)\right)\end{split} \tag{2}\]  
In the BERT pre-training model, the source language sequence is encoded as \(R^{P}\), with \(R^{P}=\left(R_{1}^{P},\ldots,R_{1}^{P},\ldots,R_{L}^{P}\right)\), where L represents the number of layers of the pre-training model. The representation \(R_{1}^{P}\) of the l-th layer is given by the following multilayer perceptron:  
\[R_{\text{I}}^{T}=G_{\text{I}}\left(R_{\text{I}}^{P}\right), \tag{3}\]  
On the encoder end of the Transformer model, since the attention to information of each layer is different, specific contextual knowledge from all layer representations is obtained through the layer perception attention mechanism, and its calculation formula is as follows:  
\[e_{\text{I}}=\text{FNN}\left(\frac{1}{\prod_{i=1}^{1}r_{\text{I},i}^{T}}\frac {1}{\prod_{i=1}^{1}r_{\text{n},i}^{E}}\right) \tag{4}\]  
\[a_{\text{I}}=\frac{\exp(e_{\text{I}})}{\sum_{i=1}^{L}\exp(e_{\text{I}})} \tag{5}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline Mongolian text &
\begin{tabular}{c} _wheel\[C_{n}^{T}=\sum_{i=1}^{L}a_{i}R_{i}^{T} \tag{6}\]  
The layer perception attention mechanism can determine the importance of information in the pre-training model to the current layer, so as to obtain contextual knowledge that is more suitable for the current layer through \(C_{n}^{T}\). Similarly, the attention to information of each hidden state in the same layer is different from each other. Therefore, a context gating mechanism is adopted to control the fusion rate of each hidden state, that is, the representation \(c_{n,i}^{T}\) from \(C_{n}^{T}\) is fused into the corresponding state \(r_{n,i}^{E}\) of \(R_{n}^{E}\), and the calculation formulas are as follows:  
\[\gamma_{n,i}=\text{sigmoid}\left(FFN\left(r_{n,i}^{E}c_{n,i}^{T}\right)\right) \tag{7}\]  
\[\bar{r}_{n,i}^{E}=r_{n,i}^{E}+\gamma_{n,i}^{*}c_{n,i}^{T} \tag{8}\]  
The conceptual diagram of the dynamic fusion mechanism at the encoder end of the transformer model is shown in Figure 4:
# Traffic Collision Severity Modeling Using Multi-Level Multinomial Logistic Regression Model
## 5 Results and Discussion
### Multi-Level Multinomial Logistic Regression
Through careful driving, drivers also help to lower hazards for sensitive road users such as bicycles and pedestrians. Driver-independent components, on the other hand, are those governed by external conditions or infrastructure; they include traffic control measures, road design elements like medians and lane separations, environmental conditions like urban or rural settings, and animal involvement. By means of education and enforcement, differentiating these elements helps target driver behavior; by means of road design and policy measures, they help address systematic hazards. The equations for the Multi-Level Multinomial Logistic Regression model developed for the crash severity are presented below. In these equations, the PDO collision severity was used as the reference category. Thus, this would simplify the interpretation of the model coefficients. The odds ratios tell how the odds of each non-reference category change relative to the reference category. The reference category acts as a baseline, making it easier to interpret the impact of predictors on the probability of other outcomes by considering their deviations from the reference category. Fatality:  
\[Log\left(\frac{P(Y=Fatal)}{P(Y=PDO)}\right)=-10.4+1.85X_{1}-0.34X_{2}-0.38X_{3}+ 1.6X_{4}+0.25X_{5}+3.8X_{6}+4.5X_{7}-0.76X_{8}+9.2X_{9}-1.6X_{10}+2.8X_{11} \tag{3}\]  
Major Injury:  
\[Log\left(\frac{P(Y=Major_{Inj})}{P(Y=PDO)}\right)=-7.01+1.4X_{1}-0.69X_{2}-1.1 X_{3}+4.1X_{4}+4.4X_{5}+2.3X_{6}+3.6X_{7}-0.46X_{8}+7X_{9}+0.49X_{11} \tag{4}\]  
Minor Injury:  
\[Log\left(\frac{P(Y=Minor_{Inj})}{P(Y=PDO)}\right)=-0.7+0.62X_{1}-0.64X_{4}-0. 41X_{5}+1.2X_{6}+2.9X_{7}+5.7X_{9} \tag{5}\]  
The dataset comprises several variables related to traffic collisions and their characteristics in the above equations. The first three variables, \(X_{1}\), \(X_{2}\), and \(X_{3}\), represent different types of collisions: Head-On, Rear-End, and Sideswipe collisions, respectively. Traffic control measures are captured in \(X_{4}\) (Yes) and \(X_{5}\) (Other), indicating whether standard traffic control was present. The belting status of individuals involved in the collision is indicated by \(X_{6}\) (Yes), while the presence of a bicycle in the incident is denoted by \(X_{7}\) (Yes). Variables \(X_{8}\) (Yes) and \(X_{9}\) (Yes) account for the involvement of animals and pedestrians, respectively. The type of area where the collision occurred is specified by \(X_{10}\) (Urban), and \(X_{11}\) describes the roadway as a two-way divided road. This comprehensive set of variables aims to capture a detailed picture of traffic.
# Hybrid NOMA Protocol with Relay Adaptive AF/DF Collaboration and Its Modeling Analysis in NB-IoT
## 2 System Model and Protocol Description
All the above cases are generated when the relay can successfully decode the required signals for both users based on the decode-and-forward (DF) collaboration, otherwise the amplify-and-forward (AF) approach will be used for the collaborative transmission of the decoded failed users, as shown in Figure 2e, which improves the relay utilization and reduces the requirement for the DF relay to successfully decode the superimposed signals. In the following, the protocol is developed in detail, and performance analysis is performed according to the five cases in Figure 2.
## 3 Proposed Methods
### Obtaining Images  
The free application SASPlanet [34] was used to obtain different images of the Mayan zone archaeological sites of Mexico and Central America. SASPlanet was designed for viewing and downloading high-resolution satellite images with the help of other servers such as Google Earth, Google Maps, Bing Maps, Nokia, Here, Yahoo, Yandex, OpenStreetMap, ESRI, and Navteq. The process followed to download the images was to identify the site of interest, select an area (see Figure 2a), choose the image quality, select the image format (in our work, we use the.tif format, however, any other format can be used), select the post-processing options, and save the georeferenced information (see Figure 2b). The image generated corresponds to the site of interest; the spectral range is from 380 to 700 (visiblelight), using three bands (RGB), with a spatial resolution of 15 m and a swath width at the nadir of 13.1 km. Figure 2 shows the process followed to obtain the images using SASPlanet.
## 3 Results
### Model Simulations of GPP, Re, and NEP under All Sky Conditions from 2003-2017
#### 3.1.5 Long-Term Variations of Annual Sums of GPP, Re, and NEP  
Under all sky conditions (i.e., realistic atmospheric conditions) from 2003-2017, the simulated and observed annual sums of GPP increased by 1.04% and 0.93%, respectively. Similarly, the estimated and observed annual sums of Re increased by 4.57% and 1.06%, respectively, whereas the computed and observed annual sums of NEP decreased by 0.18% and 0.01%, respectively. Considering NEE = \(-\)NEP, the calculated and observed annual sums of NEE increased by 0.18% and 0.01%, respectively, indicating that a carbon sink increased in the Qianyanzhou subtropical coniferous forest from 2003-2017. The above results manifested that a carbon sink increased in the Qianyanzhou subtropical coniferous forest from 1985 to 2017. Though annual sums of NEP were underestimated, similar long-term variations were found for the simulated and observed NEP. In addition, long-term variations of the simulations of GPP and Re were in agreement with their measurements.  
As for the long-term variations of driving factors of GPP, RE, and NEP, annual averages of PAR and air temperature in 2003-2017 decreased by 0.28% and 0.02% (corresponding to 0.06 \({}^{\circ}\)C), respectively, and annual mean water vapor pressure increased by 0.87% (Figure 10). Therefore, the increase in water supply (e.g., precipitation, soil moisture, vapour pressure  
Figure 9: Observed and calculated annual sums of NEP using the 2-factor EMGFP with coefficients at S/Q < 0.5, S/Q \(\geq\) 0.5, and their combinations and EMRe with error bars showing two times standard deviations of the observed NEPs (NEP\({}_{\mathrm{obs}}\), NEP\({}_{\mathrm{cal}}\), NEP\({}_{\mathrm{cal}}\), and NEP\({}_{\mathrm{cal}}\), respectively) from 2003–2017.  
deficit, and canopy water content) contributed to the increases in the calculated and observed annual sums of GPP and Re [40; 88; 89; 90]. It is known that many factors (light, T, RH, water, O\({}_{2}\), and CO\({}_{2}\) concentrations, etc.) control the processes of GPP, Re, and NEP, and the PAR energy controls the main processes and the interactions between GPP, Re, NEP, and their other driving factors (e.g., T, RH, and E) through different ways and mechanisms in the atmosphere, plants, and soil. PAR energy balance at the canopy level can capture and describe these multiple and dynamic interactions from the hourly to annual time scales, and some detailed mechanisms of PAR use are reported in papers [40; 41] and this study. Therefore, the positive and negative responses of GPP, Re, and NEP to their driving factors are easier and much more accurate to be determined using the PAR energy method than each single-direction specific process studying method.