esa-sceva/satcom-chunk-collection huggingface dataset:
# Solving Task Scheduling Problems in Dew Computing via Deep Reinforcement Learning
## 5 Reinforcement Learning for Dew Computing
### Implementation Details and Challenges  
For the Dew environment simulator, we used the modified version of the DewSim simulator [21] proposed by [PERSON] et al. [7]. This version can handle both battery-dependant and non-battery-dependant devices. To simplify the process of training and testing deep RL methods, we also developed an OpenAI Gym environment [20]. This allows us to quickly and easily test different RL agents using existing implementations made for the OpenAI Gym library.  
Since EdgeDewSim is written in java and OpenAI Gym is written in python, we developed an interface that allowed us to control EdgeDewSim from OpenAI Gym. To do so, we added logic on top of EdgeDewSim to delegate the scheduling decision to an external agent, so that every time a job arrives, the simulator broadcasts the general state of the simulation and then listens for which device has to be selected, as shown in Figure 4. Then, we developed a new RL environment, following the guidelines from OpenAI Gym [20], that does three main things: (i) it establishes the connection with the simulator, (ii) it sends actions to the simulator, and (iii) it resets the environment to restart from an initial state. Further details about our implementation can be found in Appendix A.  
Figure 4: Sequence diagram of one job scheduling with an external environment.  
Figure 3: Training loop of the EdgeDewSim environment. The agent receives a job and the state of the devices. It then decides which device to assign the job to and sends that decision back to DewSim.  
In addition to implementing an efficient communication protocol between EdgeDewSim and OpenAI Gym, the main challenge toward using RL in Dew computing was to find a good set of hyperparameters for the agent. After careful fine tuning, we found a hyperparameter configuration that works well across all of our domains (as discussed below). However, we are not certain that those exact hyperparameters would work well in Dew environments that differ too much from the types of environments that we tested.
## 3 Results and Discussion
### Case-Study II: River Stretches  
The second case study presents detecting and measuring the Dora river stretches, which have widths that vary between few meters and tens of meters.  
The values of \(\alpha_{mean}\) for PRN 1 and PRN 32 from data set (1) are reported in Figure 10a, for one pass crossing the river course. These PRNs have been selected because they show an evident increment of \(\alpha_{mean}\) in the observed time window. Using the same approach followed for the previous case study, we superimpose the lines of specular points to an orthophotomap in Figure 10b. Again, the black points show the flight trajectory displayed at a rate of 1 Hz. The colored lines are the specular points displayed with a rate of 20 Hz. The values of \(\alpha_{mean}\) associated to these specular points are indicated by the colors of the lines. The reflected signals recorded from the river can be appreciated from Figure 10b, where the boundaries between land and water correlate well with the orthophotomap, particularly the reflections from the specular points associated to PRN1 (see the left part of Figure 10b). The width of the river where the specular points are red is approximately 30 m. However, differences in the reflected power can be noticed by observing \(\alpha_{mean}\) values for different satellites. Here, the values of \(\alpha_{mean}\) for PRN32 are lower compared the values associated to PRN1. This is reasonably due to the different features of the reflecting surface. While for PRN1 the line of specular points crosses a portion of the river with sharp transitions between land and water, the same ways as in case study 1 (i.e., the red portion in the figure), the line of specular points related to PRN32 falls on a portion of the river following an artificial dam. Here the roughness of the water is different than the other section of the river crossed by PRN1 mentioned above, with low water depth and stones outcropping on the river level.  
Figure 11 reports the reflections from another portion of the river. These were measured from data set (2). The width of this water channel is approximately 12 m. The sensor and the post-processing methodology are the same, so is the result (i.e., water channel detection): boundaries of the river stretch can be detected, and its width eventually estimated.  
Figure 11: Example of water channel width detection taken from data set (2).  
Figure 10: Example of river width detection taken from data set (1).
## 8 Limitations and Future Work  
The scores used to evaluate the two guides by NIST and BSI can also be used to evaluate other deployment guides or books for IPv6. Furthermore the BSI and NIST guides should be reevaluated when updated versions get published. For this purpose the list of relevant RFCs should be kept up to date as well. One could also investigate in future work if these guides offer misleading advice induced by governmental organizations that could lead to a weakening of security, such as in the case of the U.S. National Security Agency weakening a certain cryptographic standard issued by NIST [66].  
The importance ratings and the weights established in the survey are gathered for a general deployment scenario and by interviewing a limited group of experts. A larger survey, ideally repeated on a regular basis, could lead to more robust importance weightings of categories and also reflect changes of the audiences over time.  
In practice, there are various deployment scenarios with differing requirements. For example, stateless autoconfiguration might be interesting for infrastructures with many flexible workstations, but has minor relevance for an IPv6 deployment in a datacenter where often static configurations are preferred. Importance ratings and weights should be calculated for the different (and possibly individual) scenarios and applied for evaluation of the guides. Last but not least, the two guides should be also applied in a real IPv6 deployment to evaluate the actual usefulness in a controlled practical environment.
## 3 Results and Discussion
### Scenario III: Longitudinal Distance between Two Valves L = 4D (8 m)  
The comparison of flow field conditions in the areas between the butterfly valve and the Howell-Bunger valve for 4 m (2D) and 6 m (3D) showed that the hydraulic conditions, especially in terms of velocity profiles, turbulence kinetic energy, and vorticity, with increasing longitudinal distance, they were all improved. However, the unstable hydraulic conditions in the mentioned parameters in the areas close to the Howell-Bunger valve, they proved the necessity of modeling while increasing the distance between the butterfly valve and the Howell-Bunger valve. Subsequently, the numerical modeling of the flow field in the butterfly and Howell-Bunger valve areas was investigated based on 4D (8 m) distance conditions. The numerical model results regarding the velocity distribution in the areas of the butterfly valve and Howell-Bunger valve for conditions of 8 m at an opening of 100 valves, they were presented in Figure 10. As shown in Figure 10, the velocity profiles downstream of the butterfly valve they were fully developed, and stable hydraulic conditions were observed in the numerical model. Comparing the results between 3D and 4D modes, it was proven that the pipe length increase in this area can significantly improve the velocity distribution conditions. Subsequently, the positioning of a butterfly  
Figure 9: (**a**) sectional view of turbulent energy contours for the longitudinal distance between two valves = 3D; and (**b**) vorticity contours.  
value on the velocity profiles in the sections before the flow inlet, it can only slightly affect the Howell-Bunger valve.  
At this point it is that critical researchers to ensure the hydraulic performance of the areas between the butterfly valve and the Howell-Bunger valve, while considering the results of the turbulence kinetic energy for the distance of 8 m (4D), as depicted in Figure 11. The numerical model results stressed that the amount of turbulence kinetic energy in the lower part of the butterfly valve decreases sharply with increasing the distance between the butterfly valve and the intake valve, because the fully developed flow conditions can be well provided by increasing this distance. The distribution of perturbation energy confirmed the correct hydraulic operation of the flow in the pipe due to the proper longitudinal distance of the butterfly valve from the Howell-Bunger valve. For ease of comparability, the formation conditions of vorticity in the area between the butterfly valve and the Howell-Bunger valve were extracted from the numerical model, as shown in Figure 11. It was argued that the vorticity caused by the butterfly valve decreased in the section before the flow entering the Howell-Bunger valve.  
Figure 11: **(a)** sectional view of turbulent energy contours for the longitudinal distance between two valves = 4D; and **(b)** Vorticity contours.  
Figure 10: The Sectional view of velocity magnitude contours for the longitudinal distance between two valves = 4D.
## 2 Testing Conditions and Scheme
### Test Equipment  
The test setup primarily comprises precise triaxial loading and acoustic emission monitoring systems. As depicted in Figure 3, during the test, time synchronization between the loading system and the acoustic emission monitoring system is maintained, facilitating the analysis of the relationship between stress, strain, energy changes in the specimen, and the initiation and propagation of cracks. The experimental setup utilizes an existing triaxial test system of rock stress-seepage coupling, which includes a three-dimensional loading framework, an axial loading subsystem, a lateral loading subsystem, a servo control system, and a data acquisition system [19].  
Figure 3: Test system diagram: (**a**) triaxial test system; (**b**) PCI-2 acoustic emission monitoring system; (**c**) acoustic emission sensor layout diagram.  
Figure 2: Prepared red sandstone samples.  
The testing machine's maximum axial load capacity is 1600 kN, with a maximum indenter stroke of 400 mm; laterally, \(\sigma_{2}\) can exert a force up to 1000 kN with a ram stroke of 400 mm, while \(\sigma_{3}\) can exert up to 500 kN, also with a ram stroke of 400 mm. The primary control mode of the testing machine is load control, with a loading rate ranging from 0.01 kN/s to 100 kN/s and a control accuracy of \(\pm\)0.1%. Displacement control is also feasible, with the minimum loading rate set at 0.01 mm/s and the same control accuracy of \(\pm\)0.1%.  
A MISTRAS series PCI-2 acoustic emission monitoring and analysis system is used to collect acoustic emission event data during the precise triaxial loading of the sample. This system features high stability and accuracy in data monitoring and performs well even under complex background noise. It can instantly extract characteristic data parameters and process the waveform. The system's threshold is set at 40 dB, with a floating threshold of 6 dB, and the position of the first break marks the starting point of the waveform.
## 3 DC-DC Converters
### Cuk Converter  
Cuk converter is another basic DC-DC converter which is a series combination of boost and buck converters. This feature allows us to adjust the output voltage to greater or less than its supply voltage. However, this converter also has the same drawback as the buck-boost converter, which denotes its output polarity is always negative while operating in the CCM mode. The diode would be reverse-biased when the switch is turned on, and the inductors' current would increase; this means the power can be delivered to the output load. The diode would be forward-biased when the switch is off, and it leads to the charging of the coupling capacitor. Thus, the output voltage conversion ratio for the Cuk converter is as follows:  
\[\frac{V_{out}}{V_{in}}=\frac{D}{1-D} \tag{19}\]  
This topology can offer some benefits such as higher efficiency, less current ripple, and reduced switching losses over the buck-boost topology [44]. In order to provide a  
Figure 5: Circuit diagram of the multi-switch boost converter.  
positive output voltage, an extra diode and an extra switch can be added to the conventional topology, as shown in Figure 6[45]. The voltage conversion ratio would be the same as the inverting Cuk converter. This single-stage topology operates based on the charging and discharging of the capacitor located in the middle [46].
# Towards mmWave Altimetry for UAS: Exploring the Potential of 77 GHz Automotive Radars
## 5 Operational Requirements and Radar Specifications
### Maximum and Minimum Measurable Altitude
One important aspect to be apprised here is the role of the complex baseband architecture in absolving \(F_{s}\) from the Nyquist sampling criteria [65]. Essentially, the simultaneous sampling of I/Q components indirectly fulfils the Nyquist sampling criteria, but, since both components are being sampled in parallel, the minimum sampling requirement imposed by the Nyquist theorem to sample the incoming signal at twice the \(IF_{max}\) is reduced by half [66]. There are other benefits of using a complex baseband architecture that warrant an in-depth discussion in the signal-processing domain. With the number of range bins being \(N_{FFT}\), the expression for the maximum range can be given as  
\[R_{max}(N_{FFT},\Delta R)=N_{FFT}\Delta R. \tag{49}\]  
The above expression implies that, for a given \(N_{FFT}\), the maximum range decreases as the range resolution increases. This is an interesting phenomenon that results in the processing ability of the DSP being a critical bottleneck. Similar to the discussion on the range resolution, (16) and (31) assume that (49) does not limit the evaluated maximum range in the respective expressions. Essentially, if the hardware constraints governing the SNR and the \(IF_{max}\) is not a limitation to achieving a higher value of the maximum range, the processing prowess is still a constraint on simultaneously increasing the range and resolution. The upper bound for the maximum altitude in terms of (16), (31), and (49) can therefore be approximated as  
\[Alt_{max}\leq\text{Min}\ \{R_{max}(N_{FFT},\Delta R),R_{max}(SNR_{min}),\ R_{ max}(IF_{max},S)\}. \tag{50}\]
# Special Equipment Safety Supervision System Architecture Based on Blockchain Technology
## 4 Blockchain System Architecture for Safety Supervision of Special Equipment
### System Function
#### 4.3.1 Recording Safety Management Information  
Enterprises using special equipment need to conduct regular safety inspections and identify potential safety hazards, which is the most important part of their safety management. According to the laws and regulations, equipment users should conduct self-inspection at least once a month. With this function, users can record the safety management information representing their responsibilities in the digital ledger in time. Primary supervisors will participate in this work as a peer. In the supervision channel of some equipment that needs special attention, supervisory agencies will also be invited to participate in this work.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Inspection Items** & **Results** & **Remarks** \\ \hline Technical files & Pass/Fail & Describe the reason, if Fail \\ Operator certificates & Pass/Fail & Describe the reason, if Fail \\ Infrastructure & Pass/Fail & Describe the reason, if Fail \\ Cabs & Pass/Fail & Describe the reason, if Fail \\ Cages & Pass/Fail & Describe the reason, if Fail \\ Metal structures & Pass/Fail & Describe the reason, if Fail \\ Wire ropes and pulleys & Pass/Fail & Describe the reason, if Fail \\ Transmission systems & Pass/Fail & Describe the reason, if Fail \\ Electrical system & Pass/Fail & Describe the reason, if Fail \\ Safety system & Pass/Fail & Describe the reason, if Fail \\ Load tests & Pass/Fail & Describe the reason, if Fail \\ \hline \hline \end{tabular}
\end{table}
Table 3: The typical third-party inspection results of traction and positive drive lift.
## 4 Conclusions  
1. A graphic-analytical method was developed to predict the formation of textures in the form of microchannels with specified dimensions and controlled roughness using laser technology for processing metal surfaces. Using the diameter of the ablation crater in the calculations allows for the prediction of the texture in the form of microchannels with an accuracy of 10%.
2. It was experimentally proven that the texture configuration in the form of microchannels on the surfaces of X16 CrNi25-20 steel formed by nanosecond laser radiation makes it possible to significantly increase the resistance of surfaces to the adhesion of products left after the combustion of liquid and composite fuels.  
3. The texture configuration in the form of microchannels on metal surfaces leads to decreasing the ignition, up to 25% for oil, up to 28% for emulsion, and up to 55% for slurry under conditions corresponding to the start-up of power-generating equipment. This is connected with the best spreading of fuel droplets over the heating surfaces modified by laser radiation and the largest increase in the surface area due to the developed roughness.
4. In practice, the heating surfaces textured in the form of microchannels contribute to the fuel combustion intensification under start-up conditions of power-generating equipment. In particular, the maximum size of the burnout region increases by more than 50%, and for composite fuels, the number of puffing initiations increases, which ultimately reduces the burnout time of the fuel droplets.
5. Under the nominal operating mode conditions of power-generating equipment, the heating surfaces textured in the form of microchannels makes it possible to reduce the ignition delay time of composite fuels, as well as liquid fuels widely used in practice, by up to 40%.
6. In the experiments reproducing the nominal operating mode, as well as the start-up of power-generating equipment, the best results according to the ignition (\(\tau_{d}\)) and combustion (\(\tau_{\text{burn}}\), \(N\), and \(D_{\text{max}}\)) characteristics of fuel droplets were demonstrated by the 2 AB texture. This is characterized by parallel microchannels that had a width of about 65 \(\upmu\)m with a distance between the axes of the microchannels equal to 195 \(\upmu\)m and a developed texture on the unprocessed region of the surface by laser radiation, but this was formed by crystallized metal melt.  
Conceptualization, D.G. and D.F.; methodology, E.O.; validation A.A.; investigation, S.B.; data curation, D.F.; writing--original draft preparation, E.O.; writing--review and editing, D.G.; visualization, A.A.; supervision, D.G.; funding acquisition, D.G. All authors have read and agreed to the published version of the manuscript.  
The study was supported by the program of the National Research Tomsk Polytechnic University (Priority-2030).  
The authors declare no conflict of interest.
# Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications
## 6 Structured Background Models
### Representation-based Models
The goal is to find the weight vector \(\underline{\alpha}_{CR}\) such that \(\left\|\underline{x}-\underline{D}\cdot\underline{\alpha}_{CR}\right\|_{2}^{2}\) is minimized under the constraint that \(\|\underline{\alpha}_{CR}\|_{2}^{2}\) is minimized, too. This can be formalized as:  
\[\underline{\hat{\alpha}}_{CR}=\underset{\underline{\alpha}_{CR}}{\text{argmin} }\left\|\underline{x}-\underline{D}\cdot\underline{\alpha}_{CR}\right\|_{2}^{2 }+\lambda\cdot\|\underline{\alpha}_{CR}\|_{2}^{2}, \tag{48}\]  
where \(\lambda\) is the regularization parameter that controls the penalty of the weight vector \(\underline{\alpha}_{CR}\)\(\ell_{2}\)-norm. Additionally, some authors [78; 161] suggested using a distance-weighted Tikhonov regularization besides the parameter \(\lambda\). The general detection statistic in collaborative representation ADs is the reconstruction error of the PUT. Equation (48) can also be expressed as [27]:  
\[\hat{\underline{\alpha}}_{CR}=\underset{\underline{\alpha}_{CR}}{\text{argmin} }\left[\underline{\alpha}_{CR}^{T}\cdot\left(\underline{D}^{T}\cdot\underline {D}+\lambda\cdot\underline{I}\right)\cdot\underline{\alpha}_{CR}-2\cdot \underline{\alpha}_{CR}^{T}\cdot\underline{D}^{T}\cdot\underline{x}\right]. \tag{49}\]  
Taking the derivative of (49) with respect to \(\underline{\alpha}_{CR}\) and setting it to zero returns [27]:  
\[\hat{\underline{\alpha}}_{CR}=\left(\underline{D}^{T}\cdot\underline{D}+ \lambda\cdot\underline{I}\right)^{-1}\cdot\underline{D}^{T}\cdot\underline{x}. \tag{50}\]  
By comparing (50) with the sparse representation model, it can be seen that collaboration-representation has a much lower computational cost as it offers the solution in a closed form. If the dictionary \(\underline{D}\) is determined locally, then expressions (47-50) should be replaced by the adaptive form \(\underline{\underline{D}}_{i}\). [PERSON] et al. [27] proposed the algorithm based on the concept that the background can be adequately represented by its spatial neighbors, but not by anomalies. They implemented a collaborative representation model, but for a detection statistic used a projection to a higher dimensional space and the use of the kernel trick. Low rank and collaborative representation AD (LRCRD) was presented in [162]. It divides the image into two parts: the background represented with the respective dictionary whose coefficients are constrained by low-rank and \(\ell_{2}\)-norm minimization, and the sparse anomaly part defined as the residual matrix constrained by \(\ell_{2,1}\)-norm minimization.