esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on a Collaborative Control Strategy of an Urban Expressway Merging Bottleneck Area
## 4 An Illustrative Example
### Effect Evaluation
#### 4.3.2 Traffic Efficiency Improvement Analysis
It can be seen from Figure 22a that after the prediction model predicts that the traffic state in the merging area deteriorates, the control strategy intervenes in the traffic system, resulting in the first increase in the speed difference between the two merging areas and their upstream segments during the peak period, but at the same time, it also avoids the rapid deterioration of the traffic state at the merging area, prevents the decline of the traffic capacity at the merging bottleneck, and improves the uniformity of the traffic flow between adjacent segments during the peak period. It can be seen in Figure 22b and Table 14. Under the cooperative control state, the frequency distribution of the speed difference between adjacent segments is \(-6\) km/h\(\sim\)\(-4\) km/h and 4 km/h\(\sim\)6 km/h. The overall speed difference of the road segment is between \(-9.7\) km/h (a decrease of 41.85%) and 10.16 km/h, (a decrease of 30.79%) the range is 19.86 km/h (a decrease of 36.67%), and the standard deviation is 4.9042 (a decrease of 33.85%). Compared with the uncontrolled state, the range and standard deviation of the spatial speed difference under the cooperative control state are reduced, and the distribution of the speed difference is more concentrated. Figure 22: Spatial speed difference under coordinated control. (**a**) Spatial Speed Difference under coordinated control; (**b**) Spatial Speed Difference Distribution Histogram under coordinated control; (**c**) Comparison of average speed difference under uncontrolled and coordinated control. It can be seen that the time speed difference and spatial speed difference between the segments of the experimental segment are more concentrated than the uncontrolled state after cooperative control. The sudden change of the speed at the merging bottleneck is controlled. The operating state of the traffic flow near the merging bottleneck is no longer separated, and the spatial and temporal distribution uniformity of the speed is improved. The conflict risk caused by the frequent and large speed changes of the traffic flow is controlled, indicating that the cooperative control strategy improves the operation efficiency of the expressway and also plays a positive role in improving the safety of the traffic flow operation.
## 3 Deep Learning-Based Target Recognition Techniques
### Deep Learning-Based Target Segmentation Techniques
#### 3.2.1 Semantic Segmentation Techniques Based on Deep Learning
Although FCN has greatly promoted the development of image segmentation technology, two problems remain prominent: first of all, some information will be lost after the image is downsampled; second, the utilization of local and global features in the segmentation process is unbalanced. To address these problems, [PERSON] et al. [76] proposed a DeepLab semantic segmentation model to optimize the rough segmentation results with boundaries by constructing a CRF model. Subsequently, [PERSON] et al. successively proposed improved DeepLab models such as DeepLabv2X [77], DeepLabv3 [78], and DeepLabv3+ [79]. The DeepLabv2 model introduced Atrous Spatial Pyramid Pooling (ASPP) to achieve multi-scale segmentation. [PERSON] et al. [8] used the DeepLabV3 model to segment the RGB-D camera-acquired lithci images into three categories: background, fruit, and branches, and segmented and localized multiple lithci clusters in a complex environment, which provided a real-time harvesting solution for picking robots to technically support real-time picking. In addition, [PERSON] et al. [80] took a different approach to segmenting lychee images for recognition. The approach uses DeepLabV+ as the base model, avoids information loss by optimizing the feature extraction network, and enhances the computing speed by using migration learning, data enhancement, and coding and decoding structures. It is shown experimentally that the improved DeepLabV3+ semantic segmentation model has a better segmentation effect on lithci-growing branches with 76.5% MIoU. [PERSON] [81] proposed a SegNet semantic segmentation model, which offers more applications for picking robots. The operation is depicted in Figure 7. The SegNet model is improved for the upsampling process of the FCN model by storing Max-pooling indices (Max-Pooling indices) for recovering image information during the upsampling decoding before downsampling. [PERSON] et al. [82] developed a backbone and branch segmentation method using the Kinect V2 sensor and the SegNet semantic segmentation model. The accurate segmentation of picking points is crucial in the process of fruit picking by picking robots, but the SegNet semantic segmentation model is problematic in that the segmentation of picking points is not clear enough or might be wrong in the process of image segmentation.
## 2 Case Study
### Field Monitoring  
Field monitoring of the soft ground behaviour was performed for 177 days after the embankment construction. During the process of preloading, settlement markers were utilised in order to monitor the localised ground movements. In particular, two settlement plates were installed on the left and right sides of the embankment (SM1 and SM2) with a depth of 0.5m from the subsoil surface. The settlement plate was installed before the start of the construction of the embankment and was welded into the base of the steel riser pipes. The location of the position of the instruments and the arrangement of PVDs on the cross-section of the embankment is displayed in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer No.** & **Soil Strata** & \(\epsilon_{0}\) & \(G_{s}\) & \(c_{c}\) & \(p_{c}\) & \(k\) \\  & & & & & **(kPa)** & **(m/year)** \\ \hline
1 & Clay silt & 2.17 & 2.54 & 0.983 & 21 & 0.061 \\
2 & Silty clay & 2.33 & 2.54 & 1.02 & 18 & 0.074 \\
3 & Soft clay & 2.67 & 2.52 & 0.952 & 21 & 0.056 \\
4 & Silty clay & 3.00 & 2.52 & 1.07 & 17 & 0.052 \\
5 & Clay silt & 3.09 & 2.51 & 0.944 & 22 & 0.045 \\ \hline \hline \end{tabular} Note: \(\epsilon_{0}\) = initial void ratio; \(G_{s}\) = specific gravity; \(c_{c}\) = compression index; \(p_{c}\) = pre-consolidation stress; \(k\) = permeability.  
\end{table}
Table 1: Geotechnical properties.
## 4 Conclusions  
Microstructural investigations, together with the analysis of cavitation erosion characteristics performed on stress-relief-annealed INCONEL 625, yield the following conclusions:  
Figure 10: Micro-fractographic images of the degraded surface at varying cavitation exposure durations: (**a**) 15 min; (**b**) 60 min; (**c**,**d**) 165 min.  
* Initial stage: Vibratory cavitation erosion induces localized plastic deformations on the surface due to shock waves and microjets.
* Progression: Microjets create small pits, and shock waves produce slip lines. These pits develop along slip lines, precipitate-matrix boundaries, and twin boundaries, eventually coalescing into cracks and craters.
* The high values of ductility characteristics (A, Z), associated with favorable values of mechanical strength (Rm, Rp0.2), justify the slow and uniform degradation of the material.
* Sites of initiation: Annealing twin boundaries, slip lines, and precipitate-particle interfaces are the primary initiation sites for cavitation erosion.
* Microstructural analyses showed that slip bands formed as a result of the impact from microjets and shock waves generated by the repetitive implosion of cavitation bubbles serve as preferential sites for fatigue failure of the material.
* Material removal occurs along the slip bands, with microjets causing the formation of craters measuring a few micrometers, from which radial microcracks propagate, leading to fatigue fracture.  
Conceptualization, I.M., I.B., and I.D.U.; methodology, R.P., O.K., I.M., I.B., C.M.C., and I.D.U.; investigation, R.P., O.K., I.M., I.B., C.M.C., and I.D.U.; writing--original draft preparation, I.M., I.B., and I.D.U.; writing--review and editing, I.M., I.B., and I.D.U.; visualization, R.P., O.K., I.M., I.B., C.M.C., and I.D.U.; supervision, I.M. and I.B.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 4 Analysis of Examples
### Path Travel Time Reliability Analysis  
Table 5 shows the reliability analysis results under different fixed values of path travel time.  
It can be seen from the data in Table 5, in the case of a threshold to determine, before the highest path travel time reliability, when the threshold value is 6000, the seismic travel time reliability of road reached 98%, and with the seismic damage index increases, the road travel time is becoming more and more unreliable, when the threshold is 1800, seismic damage index of 0.9, the travel time reliability of this section is only 11%.
## 3 Results
### Variation in CO\({}_{2}\) Fluxes
#### 3.1.2 Daily and Monthly Variations of CO\({}_{2}\) Fluxes  
Figure 5 shows the daily and monthly variations in CO\({}_{2}\) fluxes during the growing season in the Middle Tien Shan grassland from April to September of 2018. NEE, GPP, and Reco all exhibit significant seasonal variation, with NEE and GPP showing opposite trends (Figure 6a). The maximum CO\({}_{2}\) uptake occurred on 17 June, at \(-\)9.73 g C m\({}^{-2}\) d\({}^{-1}\), and NEE showed CO\({}_{2}\) uptake from 15 May to 6 August, during which time the grassland ecosystem acted as a carbon sink. GPP varied from \(-\)2.48 to 13.18 g C \({}^{-2}\) d\({}^{-1}\), with a mean value of 4.26 g C m\({}^{-2}\) d\({}^{-1}\). Reco variation was relatively stable, ranging from 0.12 to 5.63 g C m\({}^{-2}\) d\({}^{-1}\), with a mean value of 2.46 g C m\({}^{-2}\) d\({}^{-1}\), which was significantly lower than GPP. In addition, Reco showed a slow upward trend in June and July, due to increased solar radiation and vigorous grass growth compared to other months, resulting in enhanced ecosystem respiration.  
Figure 5: **(a)** Diurnal variation in CO\({}_{2}\) flux from April to September of 2018, **(b)** diurnal variation in net ecosystem exchange (NEE) from April to September of 2018, **(c)** diurnal variation in gross primary productivity (GPP) from April to September of 2018, and **(d)** diurnal variation in ecosystem respiration (Reco) from April to September of 2018.  
The monthly variation in CO\({}_{2}\) fluxes in grassland ecosystems is shown in Figure 6b. The maximum monthly accumulation of NEE and GPP occurred in June, with \(-\)170.01 g C m\({}^{-2}\) mon\({}^{-1}\) and 261.27 g C m\({}^{-2}\) mon\({}^{-1}\), respectively, and the maximum monthly accumulation of Reco occurred in July, with 121.80 g C m\({}^{-2}\) mon\({}^{-1}\). During June and July, grassland ecosystems were in their peak growing season, showing CO\({}_{2}\) flux uptake, which was manifested as carbon sinks, while the rest of the months acted as carbon sources. April and September, the beginning and end of the growing season, respectively, were the months when photosynthesis and respiration were weaker in grass compared to other months. Therefore, the uptake or release of CO\({}_{2}\) fluxes was lowest throughout the growing season. The overall accumulation of NEE, GPP, and Reco during the growing season was \(-\)329.49 g C m\({}^{-2}\), 779.04 g C m\({}^{-2}\), and 449.55 g C m\({}^{-2}\), respectively.
## 1 Introduction
### Methodology for Testing Reinforced Concrete Structures  
Figure 1 depicts the typical deterioration process of reinforced concrete: A long-term or external force causes cracks to allow water intrusion; rust, then, starts to appear on the rebar, which pushes the concrete away. As a result, the bonding between the rebar and concrete weakens, and the number of cracks increases. The rebar eventually dissolves in the water, which decreases the diameter of the rebar rapidly. The bonding strength is different in each phase of the deterioration. When the reinforced concrete is healthy, the bonding between the rebar and the surrounding concrete is strong; however, when the diameter of the rebar becomes thinner due to corrosion, the bonding strength weakens.  
Ultrasonic guided wave technology [15; 16] is a form of propagation, in which the smallest dimension of the structure restricts the diffracted wave so that the energy can travel further; meaning it can propagate over long distances along longitudinal directions simultaneously. Various works that aimed to apply non-destructive testing and structural health monitoring have been reported [17]. In this study, we focus on the following point of view: When the bonding is strong, the ultrasonic wave propagated on the rebar can be modeled as an ultrasonic guided wave on a double-layer rod, where the first layer is the rod and the second layer is infinite concrete. In this case, the stress and displacement in the rod are constrained by the surrounding concrete, and the resulting ultrasonic waveform is quite complicated. When the bonding is weakened enough due to corrosion, the ultrasonic wave can be modeled as a single-layer cylindrical rod.  
Figure 2 displays our concept of the detection method, based on an ultrasonic guided wave and applied laser technologies, for the corrosion of the reinforced concrete structure of the Fukushima Daiichi NPP. In our concept, an ultrasonic guided wave is excited by irradiating the rebar with a pulsed-laser, after uncovering the concrete. The ultrasonic waveform, which changes depending on the degree of deterioration, is measured at another measurement point.  
Figure 1: Typical corrosion process of reinforced concrete.
# Robust Symbol Timing Synchronization for Initial Access under LEO Satellite Channel
## 4 Numerical Results  
### Simulation Condition  
When a LEO channel is considered, the additional guard time should be secured for initial access and synchronization maintenance. Thus, we assume that the maximum HGT is rather large. In addition, for higher symbol rate support by 10 Mbaud (i.e., Mega symbol per second), the increase in HGT is inevitable in terms of symbol length. On the contrary, the Doppler can be larger when taking into account low symbol rate support by 64 Kbaud (i.e., Kilo symbol per second) and 128 Kbaud. In [12], the HGT length is set to 14 symbols in a Traffic Burst (TB). Here, we take into account all worst cases summarized in Table 1 according to LEO channel characteristics as illustrated in Section 2.1. For LB bursts in the RCS2 system, they are set to a maximum of \(+/-200\) symbols that correspond to 20 \(\upmu\)s. For simplicity, we assume that nonlinearity components such as SSPA, Adjacent Channel Interference (ACI), and phase noise condition are negligible because they are not explicitly related to fine STR operation in terms of functionality. From a system point of view, nonlinearity can significantly influence the system's performance unless its requirements are defined. This paper includes the consideration that the received signal can be impaired by nonlinear channel implicitly because the channel model is built in low SNR and high CFO conditions. Consequently, the received signal distortions by inter/intra channel interferences incur an extra SNR loss in the demodulator. Therefore, we expect that the STR performance results related to the proposed scheme remain consistent under such an environment. This paper includes the consideration that the received signal can be impaired by nonlinear channel implicitly because the channel model is built in low SNR and high CFO conditions. Consequently, the received signal distortions by inter/intra channel interferences incur an extra SNR loss in the demodulator. Therefore, we expect that the STR performance results related to the proposed scheme remain consistent in such an environment. As addressed in Section 2.1, each burst has a frequency and timing offset selected from a uniform distribution over the entire uncertainty range by maximum.  
In the case of waveform ID, we selected representative formats, as summarized in Table 2. For the other Modulation and Code rate (MODCOD) pair related to waveform ID, it is expected that the O&M algorithm without using known (i.e., preamble, pilot, and postamble) symbols can be better than the proposed one intuitively because the ratio of known symbols length in a burst can be reduced if the demodulator can work at higher SNR condition. Consequentially, the estimation accuracy of the proposed one is deteriorated due to the limitation of available known symbols.
# Algorithm for Generating S-Boxes with Prescribed Differential Properties
## 3 New Algorithm for Generating S-Boxes with Prescribed Differential Properties
### General Idea of the Algorithm  
The general idea of our randomized search algorithm is presented as Algorithm 2. We store a partially determined S-box using lists \(X,Y\). In each step, we choose a random new point \(a\) from the remaining unassigned points from \(\mathbb{Z}_{2}^{n}\) and determine a random coordinate \(S(a)=b\). Then, we check the partial DDT; if it satisfies the conditions, we extend lists \(X,Y\) and continue with the algorithm. When we have assigned a value to each point from \(\mathbb{Z}_{2}^{n}\), we return a finished S-box with a DDT that satisfies the criteria.  
In general, Algorithm 2 is not guaranteed to find an S-box; a partial S-box given by sets \(X,Y\) may be constructed with a correct partial DDT in such a way that no successor fulfills the DTT criteria. Thus, we need to introduce an additional parameter, \(limit\), that can be used to restrict the search to a fixed number of maximum retries.  
```
0:\(n>0\), S-box input size.
0:\(m>0\), S-box output size.
0:\(satisfies\_conditions()\), a function that returns true if partial DDT satisfies criteria.
0:\(limit\), the maximum number of tries. \(X\leftarrow[],Y\leftarrow[]\) \(counter\gets 0\) while\(len(X)<2^{n}\)do if\(counter>limit\)then return\(\varnothing\) endif \(a\leftarrow\)\(\in_{R}\)\(\mathbf{F}_{2}^{n}\setminus X\) \(b\leftarrow\)\(\in_{R}\)\(\mathbf{F}_{2}^{2^{n}}\) {for bijective S-box use: \(b\leftarrow\)\(\in_{R}\)\(\mathbf{F}_{2}^{m}\setminus Y\)} \(PDDT\leftarrow\)\(partial\_ddt(X+[a],Y+[b])\) if\(satisfies\_conditions(PDDT)\)then \(X.append(a)\) \(Y.append(b)\) endif \(counter\gets counter+1\) endwhile return\(Sbox(X,Y)\)
```  
**Algorithm 2** Randomized algorithm to construct S-boxes with prescribed differential table  
Note that the presented methods can be used to generate both general S-boxes (any vectorial Boolean function) and bijective S-boxes,. When generating bijective S-boxes, it is necessary to set the same S-box input and output size (\(m=n\)) and restrict the selection of the \(y\) values to ensure that no \(y\) value is repeated. The appropriate modification is marked in the formal descriptions of Algorithms 2 and 3 in the comments.
# A Reconfigurable Framework for Hybrid Quantum-Classical Computing
## 3 Proposed Framework
### Quantum Machine Learning Task
#### 3.1.4 Training: Gradient Computation and Loss Function
The kernel takes original_loss, perturbed_loss, the current weight \(weights\_local[i]\), \(learning\_rate\), and epsilon as inputs and outputs the calculated \(grads[idx]\) and the updated weight \(updated\_weights[idx]\). This process iteratively adjusts weights, optimized for parallel execution and high throughput, to minimize the loss function, effectively accelerating the quantum circuit's parameter optimization. Finally, the output data block transfers the updated weights, final loss, and gradients back to the host memory. This pipeline-based approach, with targeted HLS optimizations and explicit interface definitions, efficiently utilizes the FPGA's resources, significantly accelerating the weight optimization process for quantum circuit training.
## 2 Materials and Methods
### Hardness of Decoding Disambiguation Information in Absence of Payload
On the other hand, the amount of revealed knowledge about the \(n\) bit long input by the disambiguation information is \(n\big{(}\frac{2}{d}-\frac{4}{d\cdot 2^{d}}\big{)}\) bits. The advantage gained by keeping \(W\) secret should accommodate the loss by making disambiguation information public. This simply yields the following equation. \[\log(2^{d}!) \geq n\cdot\Big{(}\frac{2}{d}-\frac{4}{d\cdot 2^{d}}\Big{)} \tag{20}\] \[\frac{\ln(2^{d}!)}{\ln 2} \approx\frac{2^{d}\cdot\ln 2^{d}-2^{d}}{\ln 2} \geq n\cdot\frac{2}{d}\] (21) \[\frac{2^{d}\cdot d\cdot\ln 2-2^{d}}{\ln 2} \geq n\cdot\frac{2}{d}\] (22) \[2^{d}(d-1.44) \geq n\cdot\frac{2}{d}\] (23) \[d\cdot 2^{d}\cdot\Big{(}\frac{d-1.44}{2}\Big{)} \geq n \tag{24}\]  
Due to Lemma 6, the choice of \(d\) creates an upper bound on the size of the input data that will be subject to the proposed non-UD coding scheme. On the other hand, it would be appropriate to select \(d\) such that the input size is at least \(d\cdot 2^{d}\) bits to confirm with the computations in the size arguments of the payload and the disambiguation information, which assumed all possible \(2^{d}\) symbols are uniformly i.i.d. on the input. The minimum and maximum block sizes defined by the \(d\) parameter are listed in Table 4 considering these facts. Therefore, given an input bit string \(\mathcal{A}\), if it is preferred to keep payload secret and disambiguation information plain, achieving the non-UD coding in blocks of the any preferred size in between these values is appropriate in practice. The value of \(d\) plays a crucial role both in the security and in the to-be-encrypted data size. It is good to choose large \(d\) for better security with less (even negligible when \(d>8\)) overhead. On the other hand, the payload size is inversely proportional with \(d\), and thus, the reduction in the data volume to be encrypted decreases when \(d\) increases.