esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Heat Inactivation of UK114 and Data Analysis  
A UK114 solution (20 \(\mathrm{\SIUnitSymbolMicro M}\), 0.3 mg/mL) in physiological saline solution (0.9% NaCl) was divided into 120 \(\mathrm{\SIUnitSymbolMicro\mu L}\) aliquots in 500 \(\mathrm{\SIUnitSymbolMicro\mu L}\)-PCR tubes and incubated for different times at 95 \({}^{\circ}\)C (zero, 1, 2.5, 5, 7.5, 10, 16, and 22 h) in a Eppendorf 5331 MasterCycler Gradient Thermal Cycler (Eppendorf, Hamburg, Germany) with a hot lid to prevent evaporation. At different times, two aliquots was withdrawn, cooled, and briefly centrifuged to collect the sample at the bottom of the tube. After mixing, the sample was centrifuged again. No protein precipitation was detected. The CD spectra and the residual activity were measured on parallel samples.  
CD-derived data were analyzed using a two-state mechanism. Unfolding curves for the N \(\leftrightarrow\) D transition were normalized to the apparent fraction of the folded (F) using Equation (2)  
\[\mathrm{F}=(\mathrm{Y}-\mathrm{Y}_{\mathrm{D}})/(\mathrm{Y}_{\mathrm{N}}- \mathrm{Y}_{\mathrm{D}}) \tag{2}\]  
In the equation Y is the observed variable parameter (CD signal at 220 nm) and Y\({}_{\mathrm{N}}\) and Y\({}_{\mathrm{D}}\) are the values that are characteristic of the native and fully unfolded conformations, respectively. To determine the extent of UK114 inactivation, activity assays were carried out using l-Leu, LAAO, and different amounts of UK114 solution to ensure that the initial velocity values at any given time were calculated at a UK114 concentration where the relationship between initial velocity and UK114 concentration is linear. After normalization for the UK concentration used, the fraction of residual activity (F) was calculated by dividing the measured velocity by the initial reaction velocity of a sample that had not been heat-treated.  
CD and activity data were fitted together to Equation (3), describing a single exponential process, where F0 is expected to be equal to 1, \(k\) is the apparent rate constant for unfolding or inactivation, and \(t\) is the time in hours.  
\[\mathrm{F=F_{0}\cdot e^{-kt}} \tag{3}\]
# An Improved Channel Estimation Technique for IEEE 802.11p Standard in Vehicular Communications
## 5 Simulation Results and Analysis
### Computational Complexity  
Figure 12 shows the comparison of computation complexity in terms of the number of additions, as a function of the number of data symbols \(N_{d}\). Table 5 summarizes the comparison of the computation complexity of different scheme in terms of divisions, multiplications, additions, and subtractions.  
The higher Doppler shift causes stronger variation in the time domain as well. It is therefore essential to visualize how the value of \(f_{d}\) effects the channel conditions in time domain or along the length of data symbols. Figure 13 shows this effect for different values of \(f_{d}\) i.e., 1, 100, 200, 300, and 400 Hz.  
The impact of these values on the channel conditions is shown in Figure 13a-e. It is interesting to notice that at a very low value of \(f_{d}\) = 1 Hz, the channel remains almost invariant in the time axis. Then the variations become larger as the value of \(f_{d}\) increases.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme** & **Divisions** & **Multiplications** & **Additions** & **Subtractions** \\ \hline LS & 48\(N_{d}\) + 96 & - & 48 & - \\ DFT & 48\(N_{d}\) + 96 & - & 48 & - \\ STA & 104\(N_{d}\) + 104 & 148\(N_{d}\) - 2\(\beta N_{d}\) & 52 + 2(\(\beta\) + 1)(52 - 2\(\beta\))\(N_{d}\) & 48\(N_{d}\) \\ CDP & 192\(N_{d}\) + 48 & - & 48 & - \\ iCDP & 104\(N_{d}\) + 104 & 144\(N_{d}\) & 52+240\(N_{d}\) & 48\(N_{d}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Computation complexity comparison of the schemes.  
Figure 12: Computation complexity comparison of different schemes.
## 4 Stability Evaluation
### Result Analysis
#### 4.3.6 Tensile Zone  
Figure 17 shows the maximum principal stress nephograms of the salt cavern's surrounding rock under different internal pressures. A negative principal stress implies that the surrounding rock is under compressive stress, whereas a positive value indicates a tensile stress state. The figure shows that the maximum principal stress of the surrounding rock around the cavern under the four internal pressures is basically in a compressive stress state, without any tensile stress. As the internal pressure increases, the absolute value of the maximum principal stress of the surrounding rock at the cavern wall gradually increases. Under the same pressure, the maximum principal stress of the surrounding rock of the deep cavern is slightly less than that of the shallow cavern. The minimum absolute value of the maximum principal stress (\(-5.2\) MPa) is mainly located in the interlayer at the waist of the deep cavern, which indicates that the stability of the deep cavern is weaker than that of the shallow cavern. Moreover, the absolute values of the maximum principal stress of the surrounding rock of the salt cavern covered with residue are all relatively large. This observation indicates that the residue provides support to the surrounding rock of the cavern wall, thereby mitigating the deformation and damage of the surrounding rock.  
Figure 17: Distribution diagrams of maximum principal stress in surrounding rock under different internal pressures. (**a**) 10.5 MPa; (**b**) 12 MPa; (**c**) 25.5 MPa; (**d**) 29 MPa.
# Convolutional Neural Network-Based Travel Mode Recognition Based on Multiple Smartphone Sensors
###### Abstract  
Nowadays, large-scale human mobility has led to increasingly severe traffic congestion in cities, how to accurately identify people's travel mode has become particularly important for urban traffic planning and management. However, traditional methods are based on telephone interviews or questionnaires, which makes it difficult to obtain accurate and effective data. Nowadays, numbers of smartphones are equipped with various sensors, including accelerometers, gyroscopes, and GPS, providing a novel social sensing data source to detect people's travel modes. The fusion of multiple sensor data is a promising way for travel mode detection. However, how to use these sensor data to accurately detect travel mode is still a challenging task. In this paper, we presented a light-weight method for travel mode detection based on four types of smartphone sensor data collected from an accelerometer, gyroscope, magnetometer, and barometer, and a prototype application was developed. Then, a novel convolutional neural network (CNN) was designed to identify five representative travel modes (walk, bicycle, bus, car, and metro). We compared the overall performance of the proposed method via different hyperparameters, and the experimental results show that the F value of the proposed method reaches 97%, which verified the effectiveness of the proposed method for travel mode classification.  
traffic management; social sensing; travel mode; CNN; smartphone data +
Footnote †: journal: Applied sciences
## 4 Case Study
### Operations
#### Access QuadBox  
Periodically, each HSR terminal node, such as a DANH node or a RedBox node, sends an _HSR_Supervision_ frame over both its ports. Upon receiving the supervision frames, each access QuadBox learns and builds its MAC table, which contains the MAC addresses of all terminal nodes that belong to its DANH ring. An access QuadBox forwards a received unicast frame to its DANH ring if, and only if, its MAC table contains the MAC address of the destination node.  
The process of learning MAC addresses for trunk QuadBoxes is similar to that of Ethernet switches. Trunk QuadBoxes learn the MAC addresses of all terminal nodes in the network. By learning the MAC addresses, each trunk QuadBox builds its MAC table, which contains the MAC addresses of all terminal nodes associated with the node IDs of QuadBox pairs to which the terminal nodes connect. Based on the MAC table, a trunk QuadBox can determine the source and destination QuadBox pairs of a received unicast frame. When a trunk QuadBox receives a unicast frame, it maps the frame's source and destination addresses into source and destination QuadBox pairs, respectively, by looking up the MAC table. It then forwards the frame based on the source and destination QuadBox pairs by looking up the forwarding table.  
Figure 5: A sample high-availability seamless redundancy (HSR) connected-ring network.
## 4 Simulation and Discussion
### Verification of Modified SBR-FBSSA Method  
In this subsection, the size of the cosine-canyon composite rough surface is set to \(L_{x}\times L_{y}=6\) m \(\times\) 9 m, \(l_{x}\times l_{y}=0.1\) m \(\times\) 0.1 m, the boundary lines located at \(y=\pm 3\) m, and the slope angles \(\alpha_{1}=\alpha_{3}=30^{\circ}\). The work frequency is set as 1 GHz, and the relative permittivity of the terrestrial surface is (8.79, 5.84) [43; 44]. The target, a 0.3 m \(\times\) 0.6 m \(\times\) 0.3 m flare, is 1.5 m above the cosine-canyon composite rough surface. The incident angle is \((\theta_{i},\varphi_{i})=(-30^{\circ},90^{\circ})\) and the observation angles are \((\theta_{s},\varphi_{s})=(-90^{\circ}\sim 90^{\circ},90^{\circ})\). The illustration of simulation geometry is shown in Figure 8. The bistatic radar cross section (RCS) results in HH- and VV-polarization are shown in Figure 9. Efficiency and accuracy results are listed in Table 1. The maximum error max\(\gamma\) and average error \(\langle\gamma\rangle\) with the total scattering angles \(N_{\theta_{s}}\) are taken into the forms as [30]  
\[\text{max}\gamma=\underset{\{\theta_{s}\}}{\text{max}}\Big{|}\gamma^{T}(\theta _{s})-\gamma^{R}(\theta_{s})\Big{|} \tag{24}\]  
\[\langle\gamma\rangle=\frac{1}{N_{\theta_{s}}}\sum_{\{\theta_{s}\}}\Big{|} \gamma^{T}(\theta_{s})-\gamma^{R}(\theta_{s})\Big{|} \tag{25}\]  
where \(\gamma^{R}\) denotes the computation results by MLFMA, which is often used as a standard algorithm, while \(\gamma^{T}\) denotes the SBR-TSM method or MSBR-FBSSA.  
Figure 8: The geometry of a flare above the cosine-canyon composite rough surface.  
Figure 7: Illustration of dimensions of the missile-like target.  
Based on Figure 9 and Table 1, the results calculated by SBR-TSM and MSBR-FBSSA methods are well consistent with the MLFMA's results in \(\theta_{s}=-45^{\circ}\sim 45^{\circ}\), while in \(\theta_{s}=-90^{\circ}\sim-45^{\circ}\) and \(45^{\circ}\sim 90^{\circ}\), the MSBR-FBSSA's results displayed the same variation tendency with the standard accurate numerical method MLFMA, while the results by the SBR-TSM method deviate in some angles. From the perspective of the maximum errors, the accuracy of MSBR-FBSSA is improved significantly. Considering the whole angular range and the complexity of the model, the average error can depict the accuracy of the methods in a more practical meaning. Obviously, from the perspective of the average error, our method possesses a better computational accuracy. What's more, considering the memory and time consumption, the algorithm presented in this article does not result in a significant increase in the computational burden. Above all, the efficiency and accuracy of the modified shooting and bouncing rays algorithm combined with the facet-based small slope approximation method developed in this article are verified.
# Automated Processing of fNIRS Data--A Visual Guide to the Pitfalls and Consequences
## 1 Introduction
### Statistical Evaluation of Task-Evoked Hemodynamics  
The general linear model (GLM) is widely used as a statistical method for modeling task-evoked activation in fMRI [35] and fNIRS [36] studies. The GLM allows for the estimation of task activation by explaining the recorded time series as the linear combination of several sources, namely the expected hemodynamic response plus a set of error terms [35]. GLM is often adopted for studying differences between groups or conditions because it is relatively simple to implement using multi-level analysis [28] and between-subject variation is averaged out, leaving significant group differences. However, at the subject level, significant variability in the shape and timing of an individual's hemodynamic response limits the accuracy of modeling activation with general linear regression [37].  
It has been accepted in the fMRI literature that applying derivatives of the hemodynamic basis function can improve GLM estimates by accounting for individual differences in the hemodynamic response [38; 39], yet the fNIRS community has only recently adopted the idea. Towards this idea of improving accuracy by capturing individual differences in the hemodynamic response, a recently developed method by [PERSON] et al. [40] attempts to do just that through the automated detection of various evoked hemodynamic shapes and characteristics. However, further research is warranted to determine this method's sensitivity and specificity to subject-specific evoked changes.  
In recent years, because it is a portable and relatively inexpensive method for non-invasive functional neuroimaging, fNIRS has been increasingly taken up by new researchers interested in measuring brain activity and behavior. Further research and development into novel automated methods for detecting brain activity in real-world situations, such as the one developed by [PERSON] et al. [40], will accelerate new applications of fNIRS imaging. As the field grows, it is necessary for the fNIRS community to work towards accepted and standardized methodologies. In the  
Figure 3: Oscillations underlying fNIRS signals. An fNIRS signal is bandpass filtered to visualize specific frequencies bands that are known to arise from different physiological parameters (VLF—Very Low Frequency \(<\) 0.01 Hz; LF—Low Frequency 0.01–0.2 Hz; Respiration 0.2–0.6 Hz; and Cardiac 0.6–2.5 Hz).  
meantime, novice fNIRS researchers should remain critically aware of how processing steps can interact to alter their results. This manuscript offers a simple view into the effects of particular preprocessing steps, while evaluating common de-noising methods with respect to their impact on the sensitivity and specificity of detecting hemodynamic activation with general linear modeling, with and without derivatives.
###### Abstract  
Aiming at the problems of frequent identity switches (IDs) and trajectory interruption of multi-pedestrian tracking algorithms in dense scenes, this paper proposes a multi-pedestrian tracking algorithm based on an attention mechanism and dual data association. First, the FairMOT algorithm is used as a baseline to introduce the feature pyramid network in the CenterNet detection network and up-sampling the output multi-scale fused feature maps, effectively reducing the rate of missed detection of small-sized and obscured pedestrians. The improved channel attention mechanism module is embedded in the CenterNet's backbone network to improve detection accuracy. Then, a re-identification (ReID) branch is embedded in the head of the detection network, and the two sub-tasks of pedestrian detection and pedestrian apparent feature extraction are combined in a multi-task joint learning approach to output the pedestrian apparent feature vectors while detecting pedestrians, which improves the computational efficiency and localization accuracy of the algorithm. Finally, we propose a dual data association tracking model that tracks by associating almost every detection box instead of only the high-scoring ones. For low-scoring detection boxes, we utilize their similarities with trajectories to recover obscured pedestrians. The experiment using the MOT17 dataset shows that the tracking accuracy is improved by 0.6% compared with the baseline FairMOT algorithm, and the number of switches decreases from 3303 to 2056, which indicates that the proposed algorithm can effectively reduce the number of trajectory interruptions and identity switching.  
multi-pedestrian tracking; attention mechanism; dual data association; FairMOT
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 2 Materials  
### Study Area  
This study focuses on the Liuchong River Basin, located in Bijie City, in the northwestern part of Guizhou Province, China (Figure 1). The basin lies on the eastern edge of the Yunnan-Guizhou Plateau and is part of the Wujiang River system, which ultimately drains into the Yangtze River. The Liuchong River itself is a major tributary of the northern source of the Wujiang River, with a total length of 273 km and a drainage area of approximately 10,874 km\({}^{2}\). The river has a multi-year average discharge of 176 cubic meters per second. The study area is characterized by typical karst mountainous topography, with elevations ranging from approximately 1200 to over 2800 m. It experiences a subtropical monsoon climate with distinct wet and dry seasons, an annual average temperature of 12-17 \({}^{\circ}\)C, and total annual precipitation ranging from 900 to 1300 mm, with most rainfall concentrated between May and September.  
The diverse terrain and microclimatic variability support a wide array of forest types, including subtropical evergreen broadleaf forests, coniferous forests, and mixed forest ecosystems. In recent years, the Liuchong River Basin has seen large-scale ecological restoration and reforestation under national greening programs, resulting in a complex and heterogeneous forest structure. The area's rich vegetation, pronounced elevation gradients, and availability of ground-based forest inventory data make it a representative site for evaluating remote sensing-based methods for forest carbon stock estimation in mountainous environments.
# Control-Flow Integrity: Attacks and Protections
## 6 Analysis
### Overhead  
Overhead plays a significant part in CFI implementation, and, in most cases, helps determine whether a particular feature will be adopted for CFI implementation. Besides complexity, inefficiency, and weak features, most CFI techniques reported very high-performance overhead. Figure 3 presents the maximum performance overhead of 10 CFI techniques along with the number of discrete exploitations can be executed.  
Distinct CFI techniques use different platforms to measure execution, performance, and space overhead. However, we narrow our focus only towards the maximum performance overhead. Both CFI, KCofi causes overhead of 45% and 27%. Both CFI techniques can also be exploited by 2 different attack vectors. Kernel CFI causes the highest overhead among all the software-based CFI techniques. We show that Kernel CFI can be exploited by only 1 attack vector. Although bin-CFI consists of a considerable amount of overhead but 2 different attacking techniques can bypass this CFI technique. Hardware-based enforcement, CCFI, comprises 52% overhead raising the question if hardware implementation is cost-effective beside software implementation. CCFI can be exploited by more than one attacking technique, and its effectiveness has also been experimented by various research groups. CFI enforcement with such amount of overhead is not accepted and must receive a denial.  
\begin{table}
\begin{tabular}{l|l|l|l|l|l|l|l} \hline \hline
**CFI Tech.** & **Code Reuse** & \begin{tabular}{l} **Code-** \\ **Injection** \\ \end{tabular} & **Disclosure** & **Return2 user** & **Replay** & \
\begin{tabular}{l} **Gadget** \\ **Synthesis** \\ \end{tabular} &
\begin{tabular}{l} **Loop** \\ **Attack** \\ \end{tabular} & **Jujustsu** \\ \hline CFI1 & [70] & & & & & & \\ CCFI & [75; 76] & & & & & \\ binCFI & [44; 47; 79] & & & & [53] & \\ CCFIR & [60; 79; 80] & & [50.81] & & [53] & [82] & \\ HW CFI & [83] & & & & & \\ PICFI & [71] & & & & & \\ KCofi & [70] & & [70] & & & \\ Kernel CFI & [64], [72] & & & & & \\ IFCC & & & & & & \\ CFB & & [73] & & & & \\ SafeDispatch & [47; 50] & & & & & \\ C-Guard & [84] & [74] & & & & \\ RAP & & & & & & \\ O-CFI & [85] & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: State of the art of the attacks bypassing CFI.  
However, O-CFI another hardware-based implementation comprises only 4.7% overhead and can be bypassed by only 1 exploitation type. Compiler implemented CFI approaches, such as PICFI, IFCC, SAFEDISPATCH comprise very low overhead too and also be bypassed by 1 exploitation. CCFIR also consists of very low overhead, 8.6%. However, this technique can be exploited by 4 different attacking techniques such as Code reuse attack, Disclosure attack, Gadget Synthesis, and Loop attack. Our research indicates that CCFIR has also been experimented by the most number of research groups to show that it is vulnerable and can be bypassed by different attack vectors.
# Numerical Simulation on the Leakage-Induced Collapse of Segmental Tunnels
## 4 Tunnel Collapse Process Analysis
### Strata Response
#### 4.1.3 Strata Displacement  
To analyze the displacement of the strata after the leakage at the tunnel waist, four layers of monitoring points were arranged every 100 mm above the tunnel at a burial depth of 0 mm (top of the tunnel) to 400 mm (strata surface). The distribution of the displacement in different stages of seepage erosion is shown in Figure 11, in which the monitoring points falling to height 0 indicate that the points rushed into the tunnel. The following can be seen from the figure:  
1. After the leakage, the layer at a height of 0 mm (i.e., the top of the tunnel) started to subside. The layers at other heights above the tunnel remained stable, as shown in Figure 11.
2. After the formation of the initial soil cave, the particles at 0 mm above the tunnel were lost, and the stratum at the height of 100 mm subsided slightly, while the stratum above 100 mm remained stable, as shown in Figure 11.
3. After the formation of the secondary soil cave, the subsidence range of the stratum expanded. The stratum at the height of 300 mm started to subside, and the stratum at the height of 400 mm remained stable, as shown in Figure 11.
4. After the formation of the third soil cave, the strata above the tunnel at the height of 0 mm\(\sim\)300 mm all settled downward visibly, but there was only a slight settlement on the strata surface, as shown in Figure 11.
5. After the destabilization of the third soil arch, the strata at a height of 400 mm, i.e., the strata surface, started to subside downwards, as shown in Figure 11.  
It can be seen from the displacement patterns of the strata at different depths above the tunnel that, although soil loss occurred after the tunnel leakage, the strata above the soil cave remained temporarily stable. When the soil cave loses stability, the upper soil will fall suddenly and generate impact loads to the tunnel.  
Figure 10: Contact force chain: (**a**) Initial contact stress; (**b**) Soil arching effect after leakage; (**c**) Reappearance of the soil arch after destabilization; (**d**) Further upward development of the soil arch; (**e**) Soil arch destabilization.