esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Illustrated Case Study
### Study Site  
The 274 km section of the I-40 used to test the methodology extends from Exit 259 (at the split with I-85 in Durham) to Exit 420 (Gordon Rd, at the eastern end of I-40 outside of Wilmington). This section contains 98 TMC segments, with an average length of 1.6 miles and a standard deviation of 2.05 miles. The first, median, and the third quartile values are 0.51, 0.67, and 1.56, respectively. The posted speed limit is either 65 or 70 mph. The data employed were for Tuesdays, Wednesdays, and Thursdays in April, May, September, and October of 2012 and 2013 (a total of 105 days). Aggregated 15-min TMC segment data were used to create the congestion contour plots. The crash data were for both directions. The crash dataset comprised 500 records (234 crashes westbound and 266 eastbound).
# Grading Algorithm for Orah Sorting Line Based on Improved ShuffleNet V2
## 3 Results and Discussion  
### Evaluation Metrics  
This study evaluates the model's performance using accuracy, precision, recall, and the F1 score. The computational complexity of the model is assessed based on the number of parameters and the volume of floating-point operations.  
Accuracy refers to the proportion of correctly predicted samples among all samples, as shown in Equation (9).  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{9}\]  
Precision is used to measure how many of the samples predicted as positive are truly positive. Its calculation formula is shown in Equation (10).  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{10}\]  
Recall is used to measure the proportion of actual positive samples that are correctly predicted as positive by the model. Its calculation formula is shown in Equation (11).  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{11}\]  
The F1 score is the harmonic mean of precision and recall, focusing on the balance between precision and recall. Its calculation formula is shown in Equation (12).  
\[\text{F1}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{12}\]  
where TP represents the number of actual positive samples correctly predicted as positive, TN represents the number of actual negative samples correctly predicted as negative, FP represents the number of actual negative samples incorrectly predicted as positive, and FN represents the number of actual positive samples incorrectly predicted as negative.
## 7 Modification Strategies for Sludge-Derived Biochar
### Thermal Activation and Pyrolysis Tuning  
The properties of biochar-based materials are significantly affected by the pyrolysis temperature. Higher temperatures (\(\geq\)600 \({}^{\circ}\)C) generally increase surface area, pore volume, and graphitic carbon content, at the expense of lower yield. Microwave pyrolysis at 800 \({}^{\circ}\)C increases SDB surface area from 5 to 214 m\({}^{2}\)/g, promoting graphitic domains for electron transfer [92]. Figure 3 graphically represents the presence of different types of functional groups at different temperatures. For SDB, increasing the temperature concentrates the catalytic active sites (by breaking down organic matter and exposing minerals). For instance, the PMS activation rate for sulfamethoxazole degradation increased by two times with the pyrolysis temperature from 500 \({}^{\circ}\)C to 700 \({}^{\circ}\)C [93]. Fast pyrolysis tends to produce smaller particles with more surface functional groups, while slow pyrolysis produces larger, more carbonized char. By adjusting these parameters, the pore structure and chemical composition of the SDB can be tuned to meet the needs of a particular application (adsorption vs. catalysis) [94]. The gas atmosphere during pyrolysis significantly impacts biochar properties: pyrolyzing sludge under CO\({}_{2}\) or steam can in situ activate it (etching pore structure), using an inert gas like N\({}_{2}\) prevents the oxidation of certain elements, preserving specific functional groups and chemical properties [95]. In summary, optimized thermal processing is usually the first step in producing high-quality SDB. It should be emphasized that higher temperatures (>700 \({}^{\circ}\)C) enhance aromaticity but reduce functional groups, necessitating balanced optimization.
# Salinity Inhibition in Thermophilic Anaerobic Digestion of Organic Waste
## 2 Materials and Methods
### Experimental Setup  
The experimental part of the study was conducted in two five-liter lab-scale anaerobic digesters operated at a constant temperature of 55 \({}^{\circ}\)C with a constant average organic loading rate (OLR) of 3.8 \(\pm\) 0.3 kg/m\({}^{3}\)/d of added VS, corresponding to 5.0 \(\pm\) 0.4 kg/m\({}^{3}\)/d of added tCOD. The hydraulic retention time (HRT) was 10 days. In the first digester--experiment, where salt addition was gradual, the pure substrate was started as a control for 24 days, a continuation of the full-scale digester operation where substrate and inoculum were collected. After 24 days, salt was added to a chloride concentration of 15 g/L (equivalent to approximately 21 g/L NaCl). Further, into the course of the experiment, the salt concentration (NaCl) was gradually increased stepwise by approximately 2.5 to 3.0 g/L. After reaching the desired chloride concentration (the targeted concentrations were 17.5, 20.0, 23.0, 25.0, and 28.0 g/L), the process was run for three HRTs. The objective was to test the anaerobic process during continuous--chronic exposure to high salt levels. In the second digester--experiment, where the salt shock was applied, the plain substrate was started as a control for 33 days, followed by shock loadings of salinity, with each desired concentration of salinity reached in 3 days. Shocks were introduced on the 34 th, 67 th, 100 th, 132 nd, 162 nd, 195 th, 235 th, 263 rd, 271 st, 278 th, and 285 th days of the experiment. The first shock was applied with 21 g/L NaCl (15 g/L of chloride), and later the peak concentrations were increased by 1.4 g/L. Each time after the shock, the chloride concentration was slowly decreased to approximately the initial value of 1 g/L (equivalent to 1.4 g NaCl) by simply omitting the salt addition. After 260 days of operation, the shock loads were applied consecutively every 7 days, and the chloride concentration was not allowed to decrease to the initial value in order to obtain an inhibition effect. In this case, the objective was to test the anaerobic process at high salinity shock loads (acute exposure), as when batches of very saline organic waste substrates were introduced. The salinity levels used in this study were selected based on available information from the relevant literature. These salt contents are found in various substrates originating from the food industry, e.g., in the production of instant soups. A practical example of such batches may be concentrated soup substrates in kitchen waste.
# Adaptive Virtual RSU Scheduling for Scalable Coverage under Bidirectional Vehicle Traffic Flow
## 4 RSU Allocation  
As the traffic condition is relatively independent for each region, the global traffic flow can be divided into different road segments. In this section, we will illustrate the RSU allocation in a road segment with bidirectional traffic flow.
## 2 Methods
### Data Sets and Processing Strategies  
To evaluate the impact of different constellation combinations on PPP performance, datasets from 143 MGEX stations were selected (shown in Figure 1) and used from 1 to 30 September 2022 (DOY 24 to 273). Signals from GPS, BDS-3, and Galileo can all be received by all stations. All data are downloaded through the GAMP-GOOD software, which is open-sourced at GitHub [38]. All PPP analysis was carried out using the open-source GAMP software [39], which is publicly available via the GPS Toolbox webpage. Static PPP and kinematic PPP modes are respectively used to evaluate the positioning performance of the double-frequency PPP model of six schemes, namely, GPS-only (G), BDS-3-only (C), Galileo-only (E), GC, GE, and CE combinations. Table 1 shows the PPP processing strategies in detail.
# Multicriteria Optimization of Lower Limb Exoskeleton Mechanism
## Appendix A Kinematics of the Lower Limb Exoskeleton Mechanism  
The absolute coordinates of joints \(B\) and \(C\) are simply determined as follows:  
\[\xi_{B}=\xi_{A}+r_{AB}\cos\varphi \tag{1}\] \[\eta_{B}=\eta_{A}+r_{AB}\sin\varphi\] \[\xi_{C}=\xi_{B}+l_{BC}\cos\varphi_{BC}\] \[\eta_{C}=\eta_{B}+l_{BC}\sin\varphi_{BC}, \tag{2}\]  
where the angular position \(\varphi_{BC}=\angle(\xi_{B},\xi_{C})\) (Figure 1) of link \(BC\) is defined as  
\[\varphi_{BC}=\alpha_{BD}+\alpha_{DBC}, \tag{3}\]  
\[\alpha_{BD}=\angle(\overrightarrow{A}\xi_{B},\overrightarrow{B})=\text{atan 2}(\xi_{D}-\xi_{B},\eta_{D}-\eta_{B}), \tag{4}\]  
\[\alpha_{DBC}=\arccos\frac{|BD|^{2}+l_{BC}^{2}-l_{CD}^{2}}{2l_{BC}|BD|}, \tag{5}\]  
\[|BD|^{2}=(\xi_{D}-\xi_{B})^{2}+(\eta_{D}-\eta_{B})^{2}. \tag{6}\]The absolute coordinates of the joint \(E\) are  
\[\begin{split}\zeta_{E}&=\zeta_{B}+x_{E}\cos\varphi_{BC}- y_{E}\sin\varphi_{BC}\\ \eta_{E}&=\eta_{B}+x_{E}\sin\varphi_{BC}+y_{E}\cos \varphi_{BC},\end{split} \tag{10}\]  
where \(x_{E},y_{E}\) are local coordinates of joint \(E\) relative to \(Bxy\). Now, in the same way we analyze the dyad \(EFG\), and determine the absolute coordinates of joint \(F\):  
\[\begin{split}\zeta_{F}&=\zeta_{E}+l_{EF}\cos \varphi_{EF}\\ \eta_{F}&=\eta_{E}+l_{EF}\sin\varphi_{EF},\end{split} \tag{11}\]  
where \(\varphi_{EF}=\angle(\vec{A}_{E},\vec{E}\vec{F})\) is determined as:  
\[\varphi_{EF}=\alpha_{EG}+\alpha_{FEG} \tag{12}\]  
\[\alpha_{EG}=\angle(\vec{A}_{E}^{\frac{1}{3}},\vec{EG})=\text{atan2}(\xi_{G}- \xi_{E},\eta_{G}-\eta_{E}) \tag{13}\]  
\[\alpha_{FEG}=\arccos\frac{|EG|^{2}+l_{EF}^{2}-l_{FG}^{2}}{2l_{EF}|EG|} \tag{14}\]  
\[|EG|^{2}=(\xi_{G}-\xi_{E})^{2}+(\eta_{G}-\eta_{E})^{2}. \tag{15}\]  
The equations for the foot-center \(P\) are given in Section 2.
# A Non-WSSUS Channel Simulator for V2X Communication Systems
## 3 Multicarrier Data Frames of the DSRC and LTE-V2X Standards  
In this section, we present a brief overview of the IEEE 802.11p and LTE-V2X specifications for the physical layer of vehicular communication systems. In the case of the LTE-V2X, we review the characteristics of the PC5 interface, which is considered for V2V, V2I, and V2P applications. Specifically, the parameters of the multicarrier schemes for both sets of specifications, as well as their frame structures are presented for a typical 10 MHz bandwidth. Then, we describe a mathematical reference model of the received multicarrier data frame that is valid for both sets of specifications.
# Enhanced Graph Autoencoder for Graph Anomaly Detection Using Subgraph Information
## 3 Proposed Method
### Subgraph Preprocessing Stage
Multi-view learning on graphs is an effective technique designed to incorporate multiple aspects of information and improve training while keeping GCN models shallow to prevent over-smoothing problems [42]. Methods commonly used to create additional views in GNN-based models involve constructing the diffusion matrix [43] and computing the node feature similarity matrix [44]. Assuming that anomalies--whether structural or contextual--tend to exhibit attribute patterns that differ significantly from their neighboring nodes, the model incorporates an additional node-subgraph similarity view as supplementary information for representation learning. This view aims to amplify the contrast by comparing node attributes with the mean attributes of their local subgraphs. While this operation is similar to the embedding enhancement process, it is computed using un-centralized subgraph features, capturing complementary information from the original attribute space. For each subgraph \(G^{k}\left(v_{i}\right)\) within the subgraph attribute matrix \(\mathbf{X}_{v_{i}}^{k}\), subgraph adjacency matrix \(\mathbf{A}_{V_{v_{i}}^{k}}\) and degree matrix \(\mathbf{D}_{V_{v_{i}}^{k}}\), the updated mean subgraph feature for node \(v_{i}\) is computed as follows:  
\[\mathbf{x}_{v_{i}}^{\prime}=MeanPooling\left(\mathbf{D}_{V_{v_{i}}^{k}}^{-1}\mathbf{A}_{ V_{v_{i}}^{k}}\,\,\mathbf{X}_{v_{i}}^{k}\right) \tag{3}\]The mean subgraph feature matrix is then denoted as \(\mathbf{X}^{\prime}\). Next, the cosine similarity between \(\mathbf{X}\) and \(\mathbf{X}^{\prime}\) is measured to generate the node-subgraph similarity matrix:  
\[\boldsymbol{S}^{sim}=\text{CosineSim}\left(\boldsymbol{X},\boldsymbol{X}^{ \prime}\right) \tag{4}\]
## 1 Introduction
A couple of methods have been proposed to deal with the resolution of wind vectors independently from SAR observations. [PERSON] et al. [10] take advantage of the wind streak features directly extracted from SAR images to approximate the wind direction for the inversion of the wind speed. However, not all SAR images are present with streaks [11]. In addition, the Doppler information is used as an extra variable in the wind inversion scheme [12]. The Doppler centroid anomaly (DCA) represents the difference in satellite-measured Doppler and the predicted Doppler associated with the instrument attitude. This DCA, with a series of corrections, corresponds to the geophysical sea surface motion. Such a principle was first demonstrated in [13], exhibiting a simple model relating Doppler velocity to surface wind and current velocity. The steps to correct the DCA to obtain the geophysical components are detailed in [14] and later applied over the Agulhas Current region [15]. [PERSON] et al. [16] analyzed the DCA data based on Radarsat-2 wide swath under tropical cyclones, which shows that DCA has positive and negative opposite values on both sides of the center of the tropical cyclone. Ever since then, over the past years, the potential of DCA to obtain wind and current velocity has gained growing attention [17; 18]. Taking advantage of Sentinel-1 products acquired over the coastal regions, we explore the capability of combining NRCS and DCA information to jointly retrieve sea surface wind field and surface current velocity in this study. The correction algorithm of DCA from Sentinel-1 level-2 products, including the scalloping effect, is detailed here. The rest of this paper is organized as follows. Section 2 gives the data and method to implement the overall processing flow, along with the GMFs included in this paper. Section 3 presents the processing chain and the inversion scheme as well as the validation of retrieved winds and current velocity. The Summary follows in Section 4.
# _Sci_
## 3 Results
### Comparisons of Radio Frequency and Optical Systems  
This paragraph describes comparisons of radio frequency and optimal systems [54]. Efforts to compare RF and optical systems are primarily focused on answering the question, \"Under what conditions should it be desirable to transition from RF to optical communications for high-capacity communications?\" The results of [54], depicted in Figure 3, show that each system provides advantages in particular regimes, and the design choice should be viewed with mission specificity in determining a course to follow. Optical systems have the advantage of extremely broad, unregulated bandwidth compared to radio frequency systems. At Ka-band frequencies of thirty-two or thirty-seven to thirty-eight gigahertz, bandwidth is typically five-hundred megahertz. For optical systems at 1.5 micrometers, the bandwidth may be one-thousand-times larger, allowing optical systems to carry substantially more information. For radio frequency systems to compete in a bandwidth-constrained environment, they must resort to bandwidth-efficient modulation for data rates above one gigabit per second, which is neither power-efficient nor mass-efficient for the transmitting terminal. Optical systems typically would have smaller received apertures and lower power efficient transmitters and receivers. A comparison of the burden to a spacecraft by both optical and radio frequency communications systems for data rates of ten, one-hundred, and one-thousand mega-bits per second and large distances is taken from [54]. For the comparisons drawn, the optical and RF ground terminals were selected to be similar in cost. The radio frequency system selected was composed of forty-five twelve-meter antennas, whereas the selected optical system was equivalent to a ten-meter optical telescope. Potential differences in availability were disregarded since the focus of the study was on spacecraft mass and power burden for high-rate mission data, under the assumption that essential communications will be provided by low-rate, high availability radio frequency. For both the radio frequency and optical systems, the required equivalent isotropic radiated power, for a given data rate and a given distance, was achieved by a design that realized the lowest possible communications subsystem mass (aperture plus power) consistent with achieving the lowest technology risk. A key conclusion of that study is that optical communications has great potential for high data rates and distances of 2.67 astronomical units and beyond, but requires research and development, and flight demonstrations to prove our technologies.
## 2 Relay Node Deployment Problem for TT-WSN
### Energy Consumption Model  
The energy model used in this study mainly considers the most energy-intensive data transmission process while ignoring the energy consumption of the process of reception, processing, and perception [24].  
At time \(t>0(t\in\tau)\), the data packet \(P_{i}(t)\) sent by the sensor \(i\) includes the data packet forwarded by the sensor and the data packet generated by the sensor through perception. The sensor generates a data packet at every time unit.  
\[P_{i}(t)=1+\sum_{j\in\{S_{s}(t)-i\}}\kappa_{j,i}^{s}(t)\ \ t>0 \tag{1}\]  
Here, \(S_{s}(t)\) is the set of sensor coordinates with remaining power greater than 0, and \(S_{s}(t)\subseteq S_{s}\), \(S_{s}\) is the set of all sensor coordinates at the beginning. When time \(t>0\) and \(i\in S_{s}(t)\) is in the shortest path between \(j\in S_{s}(t)\) and the receiving node, \(\kappa_{j,i}^{s}(t)\) is equal to 1.  
When \(i>0\), the amount of energy consumed by the sensor \(i\) is provided as the equation below:  
\[EP_{i}(t)=P_{i}(t)\cdot amp\cdot k\cdot||i-\varsigma_{i}^{s}(t)||^{\alpha}\cdot\beta. \tag{2}\]  
Here, \(amp\) is the energy consumed by the power amplifier per bit (\(amp>0\)), \(k\) is the size of the data packet in bits, \(||\cdot||\) is the Euclidean distance between the two devices, \(\varsigma_{i}^{s}(t)\) is the next device on the shortest path, \(\alpha\) is the communication attenuation index (\(\alpha\in[2,4]\)), and \(\beta\) is the transmission quality parameter (\(\beta>0\)). This equation simulates the additional energy loss due to packet loss.  
The initial energy possessed by the sensor is _ie_. At time \(t\), the remaining energy of the sensor \(i\), \(EL_{i}(t)\) is expressed as follows:  
\[EL_{i}(t)=\left\{\begin{array}{cc}EL_{i}(t-1)-EP_{i}(t)&t>0\\ ie&t=0\end{array}\right. \tag{3}\]
# Compact Midwave Imaging System: Results from an Airborne Demonstration
## 4 Discussion
### Implications for Notional Spaceflight
#### 4.1.1 Accuracy
\right]=\left[ \begin{array}{c}tan(\alpha)\\ -tan(\alpha)\end{array} \right][h] \tag{10}\]  
\[\left[ \begin{array}{c}y_{f}-y_{n}\\ y_{a}-y_{n}\end{array} \right]=\left[ \begin{array}{c}(H/V)tan(\alpha)\\ -(H/V)tan(\alpha)\end{array} \right][v_{y}] \tag{11}\]  
The uncertainties in the retrieved zero-wind states \(\left(h,v_{y}\right)\) are therefore,  
\[\sigma_{h(v_{x}=0)}=\frac{\sigma}{\sqrt{2}tan(\alpha)}\, \tag{12}\]  
\[\sigma_{v_{y}}=\frac{V}{H}\frac{\sigma}{\sqrt{2}tan(\alpha)}. \tag{13}\]  
The asymptotic value for \(\sigma_{h}\) in Figure 22 turns out to be equal to \(\sigma_{h(v_{x}=0)}/\sqrt{2}\); therefore, the look-ahead/behind angle \(\alpha\) governs the height retrieval uncertainty in both the zero-wind and leader-follower configurations. Relating to the aircraft collections, consider Hold F2, where \(H\sim\) 13.85 km, \(V\sim\) 245 m/s, \(\alpha=\) 19\({}^{\circ}\), and the ground-sampling distance is -12.2 m AT and -22.8 m AT. The residual standard error from Figure 11 in the ground retrievals for Hold F2 was 20.2 m,  
Figure 22: Leader–follower retrieval uncertainties as a function of separation between the satellites. The simplified model includes only random errors. The practical accuracies achieved using the MISR A cameras and GOES are shown as dashed lines independent of the time separation between MISR and GOES acquisitions [4]. In practice, the contributing errors are more nuanced and include, for example, the effect of cloud shape changes that grow with \(T\). Good results with operational satellites winds are achieved with \(T=\) 1, 5, and 10 min that are characteristic of GOES-R MESO, CONUS, and Full Disk repetition rates. As \(T\) grows to 15 min and beyond, cloud shape changes begin to affect matching accuracy and diminish the number of high-quality matches. implying a residual geolocation error of 9.8 m AT or 0.8 sampling distances. The residual standard wind velocity error in the Hold F2 ground retrievals was 0.27 m/s, implying a residual geolocation error of 7.4 m XT or 0.3 sampling distances. These implied geolocation errors are in family with the geolocation errors in pixels assumed for a spacecraft. If the standard geolocation error were 10 m in each axis in the aircraft flights, then the covariance matrix from the retrieval process would provide an estimate of the standard errors for the retrieved states that can be compared with Equation (8). This comparison is provided in Table 5 to confirm that the simplified model provides a good estimate of retrieval accuracy and therefore is useful for mission design purposes.
## 3 Theoretical Background
### Compressive Sensing (CS)
Using the above solution, the reconstruction of \(x\) can be achieved using following equation:  
\[x=\Big{(}VS^{+}U^{T}\Big  
Considering these characteristics of OMP, the orthogonalization of the dominant columns group of the sensing matrix for speech and noise signals can be achieved as follows:  
1. Let \(x_{s}\) and \(x_{n}\) be the sparse representation of speech and noise signals, respectively.
2. If the sensing matrix is denoted by \(\phi\), then the compressed speech and noise signals can be obtained by \(y_{s}=\phi x_{s}\) and \(y_{n}=\phi x_{n}\), respectively.
3. The OMP algorithm can be used to recover the enhanced speech signal \(x_{s}^{\prime}\) from \(y_{s}^{\prime}\). The OMP finds the dominant columns, their contributions, and the reduced sensing matrix with only dominant columns (denoted by \(C_{iter}\), \(\lambda_{iter}\), and \(\phi_{iter}^{dominant}\), respectively, in Algorithm 2) to estimate the enhanced speech signal while leaving the residue noise component columns intact.
4. If \(V_{s}\) and \(V_{n}\) are the reduced sensing matrix columns' contributions to the speech and noise signals obtained through OMP, respectively, then the orthogonalization of dominant columns group of the sensing matrix can be achieved by optimizing the columns of the sensing matrix, such that: \[f_{obj}=minimize\left(\sum_{i=1}^{N_{s}}\sum_{j=1}^{N_{n}}V_{s}(:,i)\cdot V_{ n}(:,j)\right)\] (17) where \(N_{s}\) and \(N_{n}\) are the number of columns in matrix \(V_{s}\) and \(V_{n}\), respectively. The optimization is performed to minimize the \(f_{obj}\) value to its maximum extent or up to a certain termination threshold.