esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In addition, the generation process of \"forward (backward) block\" is limited to the job movements that do not incur additional electricity cost, which may cause some jobs to miss the optimum positions. Thus, by focusing on the jobs with short processing times, this paper proposes a more efficient greedy insertion algorithm with a multi-stage filtering mechanism (GIH-F) based on the continuous-time MILP model to address these issues. The proposed algorithm mainly consists of two stages: coarse granularity filtering and fine granularity filtering. In the coarse granularity filtering stage, all the possible positions are first divided into three levels (i.e., three layers) according to the price of electricity, corresponding to high-price, medium-price, and low-price layers. Then, all the jobs are sorted in non-increasing order of their electricity consumption rates and assigned to the layer with a lower price successively. Based on the concentration and diffusion strategy, once the layer to which a job belongs is determined, the hunting zone of possible positions of a job is concentrated in a certain layer. To find the optimal position, the job to be inserted can search for its position in a relatively large space in the selected layer. Then, considering processing times, electricity consumption rates of the jobs and electricity prices, the electricity cost with respect to each possible position can be compared using characteristic polynomials. Based on these, several judgment conditions can be set up in for a fine granularity filtering stage to determine the position of each job to be inserted. To summarize, the proposed algorithm can filter out impossible layers, and then judge each condition that belongs to the selected layer successively. Once the condition is satisfied, the job is just inserted into the corresponding position. Through coarse granularity filtering and fine granularity filtering, our algorithm does not have to traverse all possible positions, which leads to a great reduction in the time complexity. A real case study and two sets of randomly generated instances are used to test the performance of the proposed algorithm in this paper. The rest of this paper is organized as follows. Section 2 presents a description of the single machine scheduling problem under TOU electricity tariffs. In Section 3, an efficient GIH-F is developed. Next, a real case study and two sets of experimental tests are provided in Section 4. Finally, the conclusions and prospects are presented in Section 5.
# Blockchain and Smart Contracts for Digital Copyright Protection
## 3 Preliminary Considerations  
The research activity conducted over the last two decades has made it possible to indicate a list of common usability and security requirements that watermarking protocolsmust match in order to be suited for the current Internet [1]. The literature documents several examples of watermarking protocols that can match most of the requirements mentioned above. One example is the protocol described in [16], which is based on the \"buyer-friendly\" design approach [9]. Its peculiarity is the careful use of the TTP, whose role, due to the use of blockchain, can be confined to the initial phase of transactions between buyers and sellers. Such a solution makes the protocol provably secure without compromising its usability. However, TTPs are still considered some sort of \"big brother\", and watermarking protocols that exploit them are incompatible with privacy-oriented design specifics. Therefore, the only way to improve the protocol described in [16] is to eliminate the TTP.  
A possible approach to achieving the above mentioned goal involves exploiting two main facilities characterizing blockchains.  
The former is the capacity to record transactions in timestamped and cryptographically connected blocks saved in a public tamper-proof shared ledger so that anyone can verify their correctness. Such a facility makes TTPs superfluous for what concerns the need to establish mutual trust among unknown entities such as buyers and sellers.  
The latter is represented by smart contracts, which can be used to automatically deploy business logic for copyright when the terms of an agreement are reached between buyers and sellers. Smart contracts can execute preset and unchangeable actions when given events are triggered under specific conditions without control from TTPs.  
Both facilities can be used to implement the critical actions of watermarking protocols without resorting to TTPs, whereas the remaining actions can be implemented employing well-known primitives commonly used in previous and relevant examples of protocols [1]. Such an approach is just adopted to develop the watermarking protocol presented in this paper, which is primarily built on the primitives used in [16]. However, the protocol also exploits blockchain and smart contracts to avoid using a TTP and to match all the requirements reported in [1].
# Sediment Erosion Characteristics and Mechanism on Guide Vane End-Clearance of Hydro Turbine
## 5 Conclusions  
Erosion wear in the end-clearance of the turbine guide vane is a critical problem for hydropower stations, which must be investigated to reduce erosion damage and improve the operating efficiency of the turbine. In this study, the end-clearance flow of a guide vane with a large eccentric shaft was used as the research object, and numerical calculations with LES and DPM models were carried out to study the sediment erosion characteristics and mechanism in the guide vane end-clearance of a Francis turbine. The following can be concluded from this study:  
1. For a flow field with a 1 mm clearance, the number of particle trajectory per unit inlet area should be about 1, based on an independence test of the particle trajectory number.
2. The flow field at the front of the guide vane shaft was evenly distributed, and there was no obvious vortex structure, so there was basically no erosion wear. However, obvious vortex structures appeared on both sides of the shaft due to the disc shear effect. After fluid flowed through the shaft, the symmetrical von Karman vortex street was observed. Thus, regions where the vortex occurred had obvious erosion wear. When the turbulence integral scale, turbulent angle, and TKE all increase, this will lead to the more serious wear condition.
3. The flow around the airfoil has an influence on the clearance, so that the pressure characteristics of the airfoil surface develops around the clearance. Then, it affects the flow direction of the wake behind the clearance shaft. As a result, the small wing surface of the guide vane and the corresponding head cover showed serious erosion.
4. The energy in the low frequency plays a leading role in turbulent kinetic energy, and high frequency small-scale turbulence has little influence on the erosion wear.  
Writing--Original Draft Preparation, Y.C.; Writing--Review & Editing, R.L. and W.H.; Methodology, T.G.; Validation, M.S.; Data curation, S.W.  
This study was supported by the National Key R&D Program Projects of China (Grant no. 2018 YFB0606100), the National Natural Science Foundation of China (Grant no. 51669012, 51579125), and Science and Technology Planning Project of Gansu of China (Grant no. 18 CX5J021).  
The authors would like to acknowledge the support of the experimental data provided by Jiuquan San-yuan Hydropower Development Company (China).
# Eyelid Dynamics Characterization with 120 GHz mmW Radar
## 4 Experimental Validation  
The lens in front of the radar TRX_120_01 SoC produces a narrow spot about 3.5 cm in diameter at a radar distance of 1 m. The ranging radar's performance was validated with a small 2 cm side corner reflector controlled with a micrometric positioner. The theoretical parameters of the range resolution and phase/range sensitivity were confirmed experimentally. With a displacement in phase sensitivity of 3.4 \(\upmu\)m per degree, the capability to determine the range with submillimetric precision has been validated.  
The pattern of the lens antenna was measured using a static small corner surrounded by absorbing material as the reference target and scanning the radar antenna both in azimuth and elevation with a dual-axis angular positioner. Figure 7 shows the antenna pattern in Cartesian coordinates. The 3 dB angle is approximately \(\theta_{\text{beam,3 dB}}=2^{\circ}\) in the horizontal and vertical direction. The side lobes are suppressed to \(-33\) dB.
# Road Infrastructure Challenges Faced by Automated Driving: A Review
## 4 Identification of Challenges
### Interdisciplinarity  
The complexity of future mobility requires a wide range of expertise. Therefore, interdisciplinarity is essential when dealing with emerging technology. Yet, this is one aspect of the changes that is often neglected. The emergence of new technology has brought about challenges for the road authorities and industry to find people capable of dealing with this complexity. If we just look at AVs, we can see that this is a compound of mechanical engineering, electrical engineering and computer science that can no longer be separated, such as in the conventional development process. Moreover, the AV and ADAS are highly dependent on traffic and, still, on human drivers, which would need to be monitored during driving. Therefore, traffic sciences, psychology and even aspects of biotechnology are involved. This is, e.g., supported by the findings of the study in [96], where a system-theoretic process analysis was applied to improve existing road safety management. This shows the complex and interdisciplinary interactions required to maintain road safety. Without a doubt, this requires new expertise of the responsible persons. In addition, this study shows that feedback from human drivers on the perception of road, markings, and speed limitations is crucial in this process. When dealing with a fully AV in the future, this feedback to the road authorities will be required from AVs. So there are questions we could ask ourselves:  
* Do future trends in road infrastructure require interdisciplinarity?
* What expertises are expected from road authorities?  
Through this study, we saw that harmonisation, digitalisation and maintenance require a diversity of expertise. Regarding interdisciplinarity, we can take a global perspective,where the focus is on collaboration to define necessary policies, directives, regulations and standards that contribute to reliability and safety. Another perspective is more local, which affects a specific group of people such as academia or road authorities. Nevertheless, that is something that we can already see in academia and the projects that are established to support future mobility. To some extent, this can be applied to road authorities. Digitalisation may make some jobs redundant, but new opportunities will arise, especially in the field of big data and AI computing. For road authorities, one way to deal with this is through collaboration with vehicle manufacturers, telecommunication companies and others. However, integrating expertise in traffic and data analysis or communication is rather something that would be constantly needed, so there is a justified reason as to why they may want to deal with it internally.
## 5 Software Architecture
### Communications  
To optimize the time that the process is involved in the communication procedure, sockets over TCP are used for sending/receiving information to/from the server.  
As mentioned in Section 3, the gateway node can be used as a stand-alone sensor device that performs socket communication with the server through GPRS/SigFox/LTE networks or, in addition, used as an access point Wi-Fi server for reception data messages from sensor nodes.  
The information frames generated from the sensor nodes are sent directly to the database server if there is a recorded SSID available. Otherwise, the information is sent through the gateway. In this situation, the information registered by the nodes is carried out through an internal IP enabled by the gateway (192.168.4.1) common to all the slave nodes that communicate with the outside through it. In this case, the gateway node receives and coordinates the information from the slave nodes, synchronizing in each communication the \"time-synchronization\" slot and creating the necessary frames for the socket of the server that hosts the data.  
Table 1 shows the format of the data frames sent by the gateway node to the server once the information from four Slave nodes distributed and connected to the generated access point was received.  
After the data frames are sent and ACK from the server is received, the gateway node sends the timestamp in Unix epoch format and the geolocation coordinates using the engineering mode of the SIM800 to get detailed network information [53].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Node** & **Data Frame** \\ \hline Gateway & [GTW\({}_{1}\); Temp; Hum; \%CO\({}_{2}\); \%Lum; Vbatt; ppm C\({}_{2}\)H\({}_{4}\)] \\ Slave 1 & [SLV\({}_{1}\); Temp; Hum; \%Lum; Vbatt] \\ Slave 2 & [SLV\({}_{2}\); Temp; Hum; \%Lum; Vbatt] \\ Slave 3 & [SLV\({}_{3}\); Temp; Hum; \%Lum; Vbatt] \\ Slave 4 & [SLV\({}_{4}\); Temp; Hum; \%Lum; Vbatt] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Format of the data frames sent by the gateway node to the server.  
Figure 9: Execution diagram of the time-synchronizing procedure.
## 3 Results and Discussion
### Analysis of Field Tests Results
#### 3.2.3 Seed Groove Environmental Qualification Rate (SFEQ)  
The seed groove environmental qualification rate (SFEQ) reflects the contact effect between the seeds and soil in the seed bed, including two important indexes of the sowing depth and seed groove compaction. According to the criteria for determining the qualified seed groove environment in Section 2.5.1, the data in Figure 15 were extracted and calculated to obtain a comparison chart of the SFEQ under three regulation methods, as shown in Figure 16.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Plotting Parameters**} & \multirow{2}{*}{**Adjust Method**} & \multicolumn{3}{c}{**Vehicle Speed**} \\  & & \multicolumn{3}{c}{**/(km\(\cdot\)h\({}^{-1}\))**} \\ \cline{3-5}  & & **5–6** & **7–8** & **9–10** \\ \hline \multirow{2}{*}{Average sowing depth/mm} & Self-weight & 44.39 & 42.18 & 41.20 \\  & Mechanical & 49.52 & 46.87 & 45.03 \\  & Electro-hydraulic & 49.63 & 49.40 & 49.27 \\ \hline \multirow{2}{*}{Sowing depth qualification rate/\%} & Self-weight & 95.81 & 94.83 & 89.21 \\  & Mechanical & 98.54 & 98.23 & 96.72 \\  & Electro-hydraulic & 100.00 & 100.00 & 98.62 \\ \hline \multirow{2}{*}{Standard deviation of} & Self-weight & 5.23 & 6.72 & 6.91 \\  & Mechanical & 3.26 & 5.13 & 5.83 \\  & Electro-hydraulic & 3.80 & 2.97 & 3.17 \\ \hline \multirow{2}{*}{Coefficient of variation of sowing depth/\%} & Self-weight & 11.78 & 15.93 & 16.77 \\  & Mechanical & 6.58 & 10.95 & 12.95 \\ \hline \multirow{2}{*}{Theoretical variation of sowing depth/\%} & Electro-hydraulic & 7.66 & 6.01 & 6.43 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Field test results of sowing depth control.  
Figure 16: The seed groove environmental qualification rate (SFEQ).  
It can be seen that there is a 7% mean difference between the mechanical and electro-hydraulic regulation in the SFEQ. Combining the two sowing depth qualification rates confirms that although mechanical regulation can achieve the ideal sowing depth, excessive downforce will inevitably cause excessive soil compaction. Comparing the pass rate of the self-weight-adjusted seed groove environment, it can be found that the self-weight downforce of the single unit cannot meet the downforce requirement of the no-till soil environment. In the case of setting the sowing depth of 50 mm, the best rate of the SFEQ of the electro-hydraulic regulation was 98.6% with the maximum error of 2.4, the mechanical regulation was 91.6% with the maximum error of 4.29, and the self-weight regulation was 88.3% with the maximum error of 2.8. It confirmed that the control of soil compaction by electro-hydraulic regulation was more stable and reliable than mechanical and self-weight regulation, which further demonstrated the importance of electro-hydraulic regulation in high-speed no-till operation. Electro-hydraulic regulation in high-speed no-till operation is superior.
## 5 Theoretical Analyses of Hard Projectile Perforation on SC Walls
### Stages of SC Walls Perforation  
Based on the SC wall penetration resistance mechanism, a five-stage semiempirical analytical model was developed, i.e., projectile nose part penetration in concrete (c cratering), stable penetration (tunneling), shear plugging, plate petaling with concrete plugging and plate petaling only, as depicted in Figure 1.  
For stage 1 and 2, the projectile penetrates the front concrete panel with different contact areas. Hence, a deep penetration model [32; 33] is utilized to analyze the first and second perforation stages. For penetration in the concrete panel, the normal stress \(\sigma_{n,c}\) acting on the projectile nose can be expressed as \(\sigma_{n,c}=Sf^{\prime}_{c}+\rho v^{2}\) where \(f^{\prime}_{c}=41\) MPa, \(\rho=2240\) kg/m\({}^{3}\). The static resistance term in the normal direction \(Sf^{\prime}_{c}=82.6\times\left(f^{\prime}_{c}/10^{6}\right)^{-0.544}\times f ^{\prime}_{c}=449.17\) MPa and the dynamic term \(\rho v^{2}\) change with actual projectile velocity.  
During stage 3, the projectile passes through the concrete fragments until it hits the rear steel plate. For thicker rear plates, the pulverized concrete pieces in the rear crater have more support and provide more penetration resistance to the projectile. Related to its thickness, a certain deformation happens to the rear steel plate. According to previous literature [14; 28; 42; 43], it is assumed that the rear crater depth of the pulverized concrete near rear surface follows the relationship \(H_{p,r}=2.5d_{p}\). With reference to [44; 45], the penetration model with spherical cavity expansion analysis can be modified to describe the penetration response in pulverized concrete. Due to the decreasing static resistance (fragile material), the normal penetration resistant stress \(\sigma^{\prime}_{n,c}\) can be expressed as:  
\[\sigma^{\prime}_{n,c}=(0.1+0.3\times\frac{h_{t}}{d_{p}})Sf^{\prime}_{c}+\rho v ^{2} \tag{18}\]  
For stages 4 and 5, the projectile penetrates both the pulverized concrete as well as the rear steel plate. Considerable deformation occurs to the rear steel plate with destructive form. The process of a projectile perforating a thin steel plate is complex; therefore, the energy method is used to analyze rear steel plate perforation. The mean penetration resistant force is noted as \(F_{mean}\), and the length is assumed to be \(d=h_{t}+1.5h\) according to the simulation results. For steel 1006 material, \(\sigma_{y}=350\) MPa, \(\sigma_{\rm u}=500\) MPa, \(p=0.36\) and \(R=35\) J/cm\({}^{2}\).  
As the projectile perforates the SC wall with a 200-mm thick concrete panel and a 3-mm thick steel plate, the resistant force acting on the projectile nose with a 700 m/s striking velocity is given in Figure 15, where five different stages of SC walls perforation are depicted in different colors for better visualization.
## Appendix B Correlation Operation  
Using the local spread spectrum code sequence and the received signal after sampling \(r(k)\) to perform correlation operations, the despreading data are obtained:  
\[\begin{array}{ll}y_{l}&=\frac{1}{N}\sum\limits_{k=lN}^{(l+1)N-1}c^{*}(k)r(k )\\ &=\frac{A(l+1)N-1}{N}c^{*}(k)\sum\limits_{p=-\infty}^{\infty}d_{p}\sum \limits_{m=-\infty}^{\infty}c_{m}w(mT_{c}-pT)h(kT_{c}-mT_{c}-\tau T_{c})e^{i(2 \pi fkT_{c}+\varphi)}\\ &\quad+\frac{1}{N}\sum\limits_{k=lN}^{(l+1)N-1}c^{*}(k)n(k)\\ &=S_{l}+\frac{1}{N}\sum\limits_{k=lN}^{(l+1)N-1}c^{*}(k)n(k)\end{array} \tag{5}\]where \(m=k-q,q\) is the delay, since the spread spectrum code is an aperiodic long code, \(0\leq q<lN\). \(S_{l}\) in (A5) can be expressed as  
\[\begin{array}{ll}S_{l}(q)&=\frac{A}{N}\sum\limits_{k=lN}^{(l+1)N-1}c^{*}(k) \sum\limits_{p=-\infty}^{\infty}d_{p}\sum\limits_{m=-\infty}^{\infty}c_{m}w( mT_{c}-pT)h(kT_{c}-mT_{c}-\tau T_{c})e^{i(2\pi fkT_{c}+\varphi)}\\ &=\frac{A}{N}\sum\limits_{k=lN}^{(l+1)N-1}c^{*}(k)\sum\limits_{p=-\infty}^{ \infty}d_{p}\sum\limits_{q=-\infty}^{\infty}c_{k-q}w(kT_{c}-qT_{c}-pT)h(qT_{c}- \tau T_{c})e^{i(2\pi fkT_{c}+\varphi)}\end{array}\] (A6)  
When \(q=0\) in (A6), the corresponding signal component is  
\[\begin{array}{ll}S_{l}(q=0)&=\frac{A}{N}h(-\tau T_{c})\sum\limits_{k=lN}^{( l+1)N-1}c^{*}(k)c(k)\sum\limits_{p=l}d_{l}\sum\limits_{m=k}w(kT_{c}-lT)e^{i(2\pi fkT_{c} +\varphi)}\\ &=\frac{A}{N}h(-\tau T_{c})d_{l}\sum\limits_{k=lN}^{(l+1)N-1}c^{*}(k)c(k)e^{i (2\pi fkT_{c}+\varphi)}\\ &\approx\frac{A}{NT_{c}}h(-\tau T_{c})d_{l}\int_{lN\tau_{c}}^{(l+1)N \tau_{c}}e^{i(2\pi f+\varphi)}dt\\ &=Ad_{l}h(-\tau T_{c})\frac{\sin(\pi fT)}{\pi fT}e^{i\varphi_{l}}\end{array}\] (A7)  
Its average power is expressed as  
\[p_{s}=E\Big{[}\big{(}S_{m,q=0}\big{)}\big{(}S_{m,q=0}\big{)}^{*}\Big{]}\approx A ^{2}h^{2}(-\tau T_{c})\bigg{[}\frac{\sin(\pi fT)}{\pi fT}\bigg{]}^{2}\] (A8)  
\(q\
eq 0\) corresponds to the ISI caused by the channel band limiting effect (time domain trailing effect). If only the ISI of two adjacent symbols is considered, \(|q|<N\) can be assumed. \(p_{l1}\) is the interference power when \(\mathrm{q}>0\), while \(p_{l2}\) is the interference power when \(q<0\). The total interference power can be expressed as  
\[p_{l}=p_{l1}+p_{l2}=\frac{A^{2}}{N}\Bigg{[}\sum\limits_{q=1}^{N-1}h^{2}(qT_{c }-\tau T_{c})+\sum\limits_{q=-N+1}^{-1}h^{2}(qT_{c}-\tau T_{c})\Bigg{]}\] (A9)  
In summary, the signal after despreading can be approximated as  
\[\begin{array}{ll}Y_{l}(q)=\frac{1}{N}\sum\limits_{k=lN}^{(l+1)N-1}c^{*}(k) r(k-q)\approx Ad_{l}h(-\tau T_{c})\frac{\sin(\pi fT)}{\pi fT}e^{i\varphi_{l}}+ \frac{1}{N}\sum\limits_{k=lN}^{(l+1)N-1}c^{*}(k)n(k)+n_{2,l}(q)\\ =Be^{i\varphi_{l}}+n_{1,l}(q)+n_{2,l}(q)\\ \end{array}\] (A10)  
where \(B=Ah(-\tau T_{c})\sin c(fT)\), \(n\sim N(0,\sigma_{n}^{2})\), and \(n_{1,l}\sim N(0,\sigma_{n}^{2}/N)\). \(n_{2,l}\) is the noise caused by ISI. The simulation results show that when \(N>32\), \(|fT|<1/4\) and \(|\tau T_{c}|<1/4\), the interference power of (A8) is at least 20 dB lower than the signal power of (A9), which is negligible in the context of engineering.