esa-sceva/satcom-chunk-collection huggingface dataset:
# Doppler-Based Acoustic Gyrator
## 4 Conclusions  
In summary, we proposed a non-reciprocal acoustic phase shifter, providing different phase shifts for the forward and backward propagation of sound waves. To this end, we considered a simple acoustic waveguide that was filled with air. When the air within the waveguide was stationary, the signals travelling inside the waveguide experienced the same amount of phase shift regardless of the propagation direction. However, when a constant bias velocity was applied to the air, the structure became non-reciprocal and the sound waves travelling in opposite directions were subjected to different phase shifts. We further showed that, by changing the amount of bias velocity, one can tune the corresponding differential phase shift. Setting the differential phase shift to 180 degrees, we then realized an acoustic gyrator.  
In comparison with the proposals leveraging nonlinear effects to induce non-reciprocity for acoustic waves, the main advantage of our scheme is that it is a fully linear. While non-reciprocal acoustic devices based on non-linearities only work for certain (high) levels of input power, the proposed non-reciprocal acoustic phase shifters and the gyrator work properly regardless of the amount of input power. Such linear non-reciprocal devices may have potential applications to realize various miniaturized and efficient components including isolators, circulators, band pass filters, power controllers, and modulators to mention a few.  
[PERSON] carried out the research work under the supervision of [PERSON]. Both authors participated in writing the manuscript.  
This research was funded by Swiss National Science Foundation (SNSF) under Grant No. 172487.  
The authors declare no competing interests.
## 4 Analysis of Maze Generation Algorithms
### Maze Generation Time Analysis  
To analyze and compare the maze generation times of the implemented algorithms, the time taken to generate each of 100 mazes per algorithm was measured using a built-in software timer. Through data analysis, it was determined that [PERSON]'s algorithm and the Aldous-Broder algorithm stand out compared to the other methods (Figure 14). The time required to build the maze increased exponentially as the maze size increased. This impacts these algorithms most, as they need to visit every cell.  
Figure 14: Speed comparison of maze generation algorithms.  
When applying a solely random approach to choose direction in algorithms, no additional data structures can affect this selection. One benefit of this technique would be eliminating bias in the maze generation process. The bias becomes apparent when generating a specific quantity of mazes using each algorithm as evidenced by corridors oriented in particular directions and variations in the length and quantity of dead ends. An instance of a discernible prejudice is the Sidewinder algorithm, which consistently produces a pathway of interconnected cells that adhere to a single edge of the maze. For the Binary Tree maze, there are consistently two passageways. Other algorithms can be divided into two groups when observing the time ratio to generate the maze and its size (Figure 15).  
The Sidewinder and Binary Tree algorithms are the most efficient methods for achieving optimal results. This can be attributed to their comparatively uncomplicated execution and consecutive traversal of all the cells in the maze. The trade-off for achieving that level of speed is the previously mentioned bias. This bias is desirable in generating the starting levels with long straight corridors requiring simple solutions. Designing a visually captivating and intricate maze for advanced levels is crucial to enhancing the user's engagement and maintaining their attention. Additionally, the Binary Tree and Sidewinder algorithms cannot remove cells from the maze. These algorithms require a proper grid that is fully accessible. Some cells could reach a dead end and stop if they are missing. Consequently, it is impossible to use these algorithms to create game boards with visually appealing elements, such as water. To generate those game boards, algorithms such as the Hunt and Kill and Recursive backtracker algorithms were used. Their characteristics demonstrate a favorable balance between the requirement to produce visually appealing and complex mazes, minimal bias, and a reasonable generation time. Thus, they are highly suitable for designing harder levels of the game.
# A Comprehensive Survey on the Societal Aspects of Smart Cities
## 1 Introduction
Additionally, most other reviews focus on the technical and technological aspects of a smart city, while our review aims to show how smart cities can impact society and the lives of their citizens. For example, a survey of IoT in smart cities delves into many of the same areas we explore (e.g., transportation, healthcare, and energy) but instead focuses on the technologies of a smart city, like sensors (e.g., chemical, bio, and motion) or network protocols (e.g., Bluetooth, Wi-Fi, and ZigBee), and how these technologies can be applied to those areas to implement smart solutions, but it does not explore how these technologies impact the society and its citizens where they are implemented [39]. It is quite easy to find many surveys of the same type in the literature, such as one that focuses on how smart technologies can be used to create floating cities [60], another that delves into how big data and data mining can be used on smart cites [57], and another into the algorithms used in smart city applications [61], with all of them focusing on the technological aspects with only a passing mention to the societal aspects. However, studies that focus on or mention in some detail the societal aspects of smart cities are much harder to find. Our paper focuses more on how these technologies affect society and on the current state of smart city research and how it is conducted. Furthermore, it needs to be stated that there is nothing wrong with the surveys, which focus on the technical aspects, and that our paper aims to cover an aspect of the research that is lacking in the current literature. The article is divided into six sections. Section two discusses the most accepted dimensions of a smart city, as shown in Figure 1, while section three explores different areas of smart city technologies, from architectural designs, standards, open data, internet of things, big data, artificial intelligence, and security, as show in Figure 2. In section four, various methods for ranking smart cities are discussed. Section five addresses the challenges that affect smart cities and what the future may hold for them according to current trends. Finally, section six concludes this paper by summarizing the presented information and key learning points. Figure 1: Smart city dimensions and applications.
# Multilevel Bipolar Electroforming-Free Resistive Switching Memory Based on Silicon Oxynitride
## 3 Results and Discussion
### Endurance and Retention Characteristics  
The DC endurance of the Au/Ni/SiO\({}_{\rm x}\)N\({}_{\rm y}\) (10 nm)/p\({}^{+}\)-Si memory device is shown in Figure 8. The devices were measured under a compliance current of 0.15 mA and a read voltage of \(-\)0.5 V.  
Under a positive voltage bias, the LRS state is controlled by the compliance current. To obtain the actual LRS current value, a negative bias voltage region was chosen to read values, as there is no compliance current during negative bias. The voltage for RESET1 can vary by \(-\)2 to \(-\)1 V, depending on differences in thickness and compliance current. Consequently, \(-\)0.5 V was selected as the read voltage, to ensure that the measured current was not affected by the compliance current.  
To assess the memory device, a total of 250 DC cyclic measurements were performed successively. To confirm the stability of each state, the first 120 cycles were performed while switching between HRS/ IRS, the next 60 cycles were performed between IRS/ LRS, and the last 70 switching cycles were performed between HRS/ LRS. The measured resistance ratios of the various states, R\({}_{\rm IRS}\)/R\({}_{\rm HRS}\), R\({}_{\rm LRS}\)/R\({}_{\rm IRS}\), and R\({}_{\rm IRS}\)/R\({}_{\rm HRS}\), were found to be larger than 10, 5, and 10\({}^{3}\), respectively.  
In addition, to check the pulse endurance property of the memory device, the SET voltage pulse (\(+\)6.0 V, 100 ms) and RESET voltage pulse (\(-\)6.8 V, 100 ms) were generated by a waveform generator, and the device state was monitored by using the read pulse (\(-\)0.5 V, 100 ms). The pulse endurance of up to 50 cycles for switching between the HRS and IRS was obtained on the SiO\({}_{\rm x}\)N\({}_{\rm y}\) device as shown  
Figure 8: DC endurance property of the Au/Ni/SiO\({}_{\rm x}\)N\({}_{\rm y}\)/p\({}^{+}\)-Si memory device.  
in Figure 9. The results show quite distinct HRS and IRS state currents as well as device operation at a low current level.  
The typical retention behavior of the device is shown in Figure 10 under a reading voltage of \(-0.5\) V. The device shows excellent retention characteristics as the resistance values in the HRS, IRS, and LRS show no degradation over \(1.8\times 10^{4}\) sec at 120 \({}^{\circ}\)C.
# A Survey of Security Vulnerability Analysis, Discovery, Detection, and Mitigation on IoT Devices
## 3 Vulnerability Analysis, Discovery, Detection, and Mitigation
### Research on Vulnerability Discovery
#### 3.2.2 Static Analysis Method  
The static analysis method can discover vulnerabilities in IoT devices without executing firmware. The process provides an understanding of the program code to find bugs. Thus, it is generally more scalable.  
**Technical requirements:** The ability to fetch firmware of IoT devices.  
The analytical static analysis process is as follows: (1) Extract the firmware. (2) Reverse binary program in firmware. (3) Find the security problem by manual audit. In academia, researchers mainly explore automated static analysis methods to find vulnerabilities. In Table 2, we summarize the static analysis, including research targets, subdivided technology, and types of finding vulnerabilities. [PERSON] et al. [64] first analyze the firmware of embedded devices on a large scale and automatically. They automatically decompress and process firmware and use fuzzy hashes to match weak keys in firmware. _FIE_[65] based on _KLEE_[66] constructs the symbolic execution engine of embedded devices. It formulates memory specification, interrupts specification and chip specification to find out the problem of violating custom security specification in firmware. _Firmalice_[67] is also based on the symbolic execution method, which finds the authentication bypass vulnerability through the input determinism of the backdoor. For the taint analysis method, _SainT_[68] and _DTaint_[69] adopt static methods to discover vulnerability on the basis of APP or binary code of devices, respectively.
# Optimal Data Collection Time in LoRa Networks--A Time-Slotted Approach
## 3 The Problem of Bulk Data Collection
### Requirements  
In order to achieve bulk data collection, as the gateway cannot be available at all times, a mechanism to (almost) simultaneously wake up the nodes once the gateway becomes available must exist. A simple way to achieve that is to schedule the data collection process at predefined times. Apparently, due to the clock drift of the nodes, some of them will wake up earlier than others. Taking into account experimental data from our previous work [31], the maximum clock drift does not exceed 100 ppm, which is translated to a few seconds of time assuming that the data collection is executed once per day. However, having in mind that the data collection may take several hours to be completed (see experimental results in Section 6), the waking up waiting cost is negligible compared to the duration of the data collection. The gateway can initiate the data collection process after this short period of time. In the case of non-coordinated transmissions, such as in an Aloha approach, this can happen by simply sending an initialisation packet. However, if the transmissions are time-synchronised, the gateway needs to compute the schedule and send it to the nodes before starting the data collection. As we already mentioned, the computation and the transmission of the schedule are two critical operations that may considerably affect the reliability of the system as well as the data collection time. These two operations are the two main research subjects of the current paper.  
Moreover, since the gateway is expected to be online for multiple hours per day, new nodes can periodically wake up and register with the gateway. If the gateway is not present at that time, the node can return to sleep mode and wake up at a future time to try to register again. Since a gateway system consists of multiple transceivers and the registration takes place at a different frequency, registrations do not affect the data collection process. As a consequence, even if a joining node tries to register during the data collection phase, the latter would not be interrupted/interfered by the registration activity. Registrations can also be accelerated by considering a similar process to LoRaWAN's personalised activation (ABP). In this case, the gateway is aware of the new nodes while the corresponding DevAddr address as well as the security keys can be stored in the devices.
## 3 Results and Discussion
### Spatial Distributions of Continuous Error Metrics
However, three Runs of IMERG V6 generally show a smaller magnitude of CV by 50-90% as compared to the rain-gauge-based product. IMERG-F V5 appears to be marginally better than IMERG-F V6 in terms of CV. Moreover, CV depicted  
Figure 3: Spatial distributions of bias (mm day\({}^{-1}\)) in the IMERG estimates as compared to the rain-gauge-based dataset over India for the GPM (2014–2017) and pre-GPM (2010–2013) periods. by IMERG-F V6 during the pre-GPM period, is closer to observations as compared to the GPM period. The spatial distributions of RMSE and the systematic error component for IMERG products as compared to the rain-gauge-based dataset are presented in Figure 6. As the sum of systematic and random error components is always 100%, the random error component can be determined by visualizing the systematic error component. Therefore, the random error component has not been shown in the figure. In general, the magnitude of RMSE in IMERG estimates is larger over the higher mean rainfall regions and vice versa. However, IMERG-E and IMERG-L show higher RMSE than the IMERG-F product. Although IMERG-F V6 does not show any notable improvement in RMSE than IMERG-F V5, the magnitude of RMSE is notably smaller in IMERG-F V6 during the GPM period (2014-2017) than the pre-GPM period (2010-2013). Larger systematic errors in IMERG products can be seen primarily over the orographic regions of the west coast, northeast, and northern India. Near-real-time IMERG products have the smallest systematic error as compared to the IMERG-F product. However, IMERG-F V6 has a larger systematic error than IMERG-F V5. Additionally, the systematic error is smaller in IMERG-F V6 across the country except for the southeast peninsular India during the pre-GPM period than the GPM period. This analysis suggests that the IMERG-F V6 product needs a suitable bias correction before its integration in any specific application, particularly for the GPM period. However, it would be interesting to examine the sources of errors in IMERG products using sub-daily scale precipitation analysis. Few studies over the US region showed that passive microwave sensors provide the most skillful estimates to IMERG, whereas infrared estimations have rather poor skill [60; 61; 62]. Figure 4: Spatial distributions of topography (m), mean monsoon rainfall (mm day\({}^{-1}\)) from the IMD rain-gauge-based data, and bias (mm day\({}^{-1}\)) in IMERG-F version 6 estimates for 2014–2017 over west coast of India. Figure 5: Spatial distributions of coefficient of variations (%) in daily monsoon rainfall across India from the IMD rain-gauge-based data and IMERG estimates for the GPM (2014–2017) and pre-GPM (2010–2013) periods. Figure 6: Spatial distributions of root mean square error (mm day\({}^{-1}\)) and contribution of systematic error component (%) to the total error in IMERG estimates as compared to the rain-gauge-based data over India for the GPM (2014–2017) and pre-GPM (2010–2013) periods.
## 2 Optimal Control Model for PMCU Path Planning
### Boundary Conditions  
The state variables constraints mainly refer to the constraints imposed on each state variable during PMCU pushing, which can be expressed as:  
\[\left\{\begin{array}{l}x_{min}\leq x_{i}(t)\leq x_{max}\\ y_{min}\leq y_{i}(t)\leq y_{max}\\ v_{min}\leq v_{i}(t)\leq v_{max}\\ \theta_{min}\leq\theta_{i}(t)\leq\theta_{max}\end{array}\right. \tag{7}\]  
Figure 2: A kinematic model of a PMCU.  
In Equation (7), \(x_{min}\), \(x_{max}\), \(y_{min}\), \(y_{max}\), etc., are the constraints of each state variable during PMCU pushing. In addition to path state variables constraints, PMCU pushing is also constrained by the initial state and the end state. The initial state of the PMCU pushing into the deck via the port side opening can be expressed as:  
\[\left\{\begin{array}{l}x_{i}(t_{0})=x_{i0}\\ y_{i}(t_{0})=y_{i0}\\ v_{i}(t_{0})=0\\ \theta_{i}(t_{0})=0\\ \end{array}\right. \tag{8}\]  
\(x_{i0},y_{i0}\), etc., in Equation (8) are the initial state constraints for the \(i\)th PMCU. The endpoint of PMCU pushing is the target installation location of the different cabins and must satisfy the required pose constraints for the installation, which can be expressed as:  
\[\left\{\begin{array}{l}x_{i}\Big{(}t_{f}\Big{)}=x_{if}\\ y_{i}\Big{(}t_{f}\Big{)}=y_{if}\\ v_{i}\Big{(}t_{f}\Big{)}=0\\ \theta_{i}\Big{(}t_{f}\Big{)}=\theta_{if}\\ \end{array}\right. \tag{9}\]  
\(x_{if}\), \(y_{if}\) etc., in Equation (9) are the end state constraints for the \(i\)th PMCU. In addition, the boundary conditions of the control variables are shown in Equation (10).  
\[\left\{\begin{array}{l}u_{1 min}\leq u_{i1}(t)\leq u_{1 max}\\ u_{2 min}\leq u_{i2}(t)\leq u_{2 max}\\ \end{array}\right. \tag{10}\]
## 4 Conclusions  
This research was aimed to provide information about the effects of various sand types as well as different macro-textures on the abrasion resistance of RCCP by simulating the traffic abrasion. Results, enumerated below, are useful because enough RCCP surface abrasion resistance will help maintain proper skid resistance during its service life and enables to use of this type of pavement without an asphalt overlay in high-speed roads:  
In general, since different-size/-shape aggregate combinations had proper interactions that led to better mechanical performance, mixtures made with different sand compositions had higher density and flexural/compressive strength than those made with only one type of sand (except silica mixtures that had the highest density).  
Combinations of different types of siliceous and calcareous sands not only economized the concrete mixture but also improved its abrasion resistance and also skid resistance. The higher was the fracture rate of the sand containing more manufactured proportions, the more was the abrasion resistance; this did not apply to cases where only one sand type was used (except manufactured silica sand). In the case of skid resistance, the effect of different sand types on BPN is not statistically significant, but the combination of different sand types is useful when only the average values of BPN are considered.  
In general, different macro-textures showed suitable durability against abrasion and had almost the same performance regarding the abraded mass; almost all macro-textures with minimum MTD = 0.8 mm met this criterion even after abrasion. Stamping macro-textures had the highest MTD, and seeding ones had the lowest.  
The results show that abrasion reduces the skid resistance of the RCCP samples by about 10%. Statistical studies showed that the skid resistance is completely dependent on the type of macro-texture so that the brooming macro-textures show the highest, and the seeding ones show the lowest skid resistance before and after abrasion.  
Regardless of the results of skid resistance of brooming macro macro-textures, a significant relationship can be established between the MTDs of the other macro-textures and BPN. A trade-off between technical and economic conditions concludes that roller compacted concrete pavement with big stamping and one-way brooming macro-textures made of a combination of manufactured calcium and natural silica fine aggregate (Ca50 CSi50N) will have suitable performance in terms of abrasion and skid resistance as well as mechanical performance.  
Conceptualization, Data curation, Formal analysis, Funding acquisition, M.A.; Investigation, M.A. and G.L.; Methodology, Project administration, Resources, M.A.; Writing--original draft, M.A.; Writing--review and editing, G.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Shahid Rajaee Teacher Training University under contract number 18027.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Computational Cases
### Progressive Damage Analysis of Slopes Under Seismic Loading  
The distribution of tensile damage regions in the model under dynamic loading at different moments is presented in Figure 9. The figure shows that at t = 0.04 s when the dynamic load was applied to the bottom of the model, only individual units at the boundary of the rear edge of the slope top exhibited tensile damage. As the seismic wave propagated upward, the area of tensile damage at the slope top gradually expanded, and the depth of the area was approximately 0.2 m, as shown in Figure 9b. At t = 2.04 s, the area of tensile damage was concentrated near the foot of the slope and within a depth range of 0.2 to 0.5 m below the slope surface. At t = 3.40 s, the area of tensile damage was primarily concentrated near the top of the slope at a depth of about 1.7 m. The distribution of tensile damage in the model at other moments is shown in Figure 9e,f. Although the area of damage in the model differed, the depth of tensile damage concentrated near the top of the slope remained consistent.  
Figure 10 shows that, under seismic loading, the area of tensile damage in the model was concentrated mainly at the depth range of 1.7 m below the top surface of the slope. At the initial stage of seismic loading (t = 0.04 s), the shear damage area in the model was mainly concentrated near the foot of the slope, in the depths of the foundation, and at the boundary position of the leading edge of the foot of the slope. At t = 0.40 s and 1.84 s, the shear damage area near the foot of the slope gradually extended upward. Although the area was increasing, it had not yet connected with the tension-shear damage demarcation  
Figure 9: The evolution pattern of tensile damage region in the model under dynamic loads: (**a**) t = 0.04 s; (**b**) t = 0.40 s; (**c**) t = 2.04 s; (**d**) t = 3.40 s; (**e**) t = 3.80 s; (**f**) t = 4.40 s.  
line. At t = 2.24 s, the shear damage area near the foot of the slope extended upward and connected with the tensile damage area. At this time, the tensile-shear damage area was also connected.