esa-sceva/satcom-chunk-collection huggingface dataset:
# Characteristics of Deep Groundwater Flow and Temperature in the Tertiary Pohang Area, South Korea
## 2 Geological Setting  
Pohang belongs to the East Sea alkaline volcanic zone [18], occupying the central part of the Tertiary Pohang Basin. Granodiorite bedrock was formed as a result of the igneous activity at the end of the Paleozoic Era and is overlain by repeated layers of Cretaceous sandstone/mudstone (Figure 1[19]). Cretaceous lapilli tuff overlies the Cretaceous sedimentary rocks. Layers of Eocene crystal tuff and lithic crystal tuff are present on the Cretaceous rocks. In the middle Eocene, biotite granite intruded on the older rocks. Moreover, Miocene semiconsolidated sedimentary rocks (the Cheonbuk conglomerate, Hakrim Formation, Heunghae Formation, Idong Formation, Duho Formation, and Yeonam Formation) belonging to the Yeonil Group and composed of sandstone, shale, and mudstone were deposited along the east/southeast direction as a result of several marine transgressions and regressions, with a thickness greater than 1000 m [20; 21; 22; 23].  
The four deep boreholes were drilled vertically in the study area, at 1.8-2.8 km from the epicenter of the Mw 5.4 Pohang earthquake in 2017 (Figure 1). The borehole diameters decrease with depth. BH-1 has a depth of 1100 m, with a diameter of 76.2-101.6 mm; BH-2 has a depth of 1505 m, with a diameter of 1000 m.  
Figure 1: Geological map of the surrounding area of the deep boreholes (modified after [6; 7; 8]).  
of 88.9-203.2 mm; BH-3 has a depth of 950 m, with a diameter of 254-355.6 mm; and BH-4 has a depth of 2383 m, with a diameter of 203.2-254 mm. From top to bottom, the rock types of BH-1, BH-2, BH-3, and BH-4 consist of semiconsolidated mudstone, crystal tuff, lapilli tuff, sandstone/mudstone, rhyolite, andesitic breccia, dacite, and granodinite; these were identified using a drilling slurry. The thickness of the rock types is as follows: 200-370 m for the semiconsolidated mudstone, 73-240 m for the crystal and lapilli tuffs, 46-907 m for the mudstone/sandstone, 259-375 m for the rhyolite, and 834 m for the andesitic breccia [9; 24].  
The depth to the water is \(-\)60 m at BH-1 (\(-\)45 m, mean above sea level (masl)), \(-\)120 m at BH-2 (\(-\)107 m, masl), \(-\)120 m at BH-3 (\(-\)109 m, masl), and \(-\)110 m at BH-4 (\(-\)99 m, masl).
# Combined Use of LoRaWAN Medium Access Control Protocols for IoT Applications
## 3 LoRaWAN Background
In particular, Class A end-devices strongly favor UL communications, which are based on the communication needs of the end-device, while the transmission of DL messages is allowed only after the end-device has successfully sent an UL message, following the scheme shown in Figure 2.  
Once an UL transmission is completed, the end-device opens two receiving windows. The first window (RX1 in Figure 2) begins \(RX\_Delay1\) seconds (\(+/-20\) us) after the end of the UL transmission, while the second one (RX2) begins \(RX\_Delay2\) seconds (\(+/-20\) us) after the end of the UL transmission. The minimum duration of a receiving window is equal  
Figure 2: Receiving window timings for Class A end-devices. to the time required by the end-device's radio transceiver to recognize the preamble of the DL message. When a preamble is detected during a receiving window, the receiver remains active until the DL message is demodulated. If an end-device correctly demodulates a DL message during RX1 and it is the destination of the DL message, RX2 will not start. Note that the NS can send DL messages to specific end-devices at the start of each receiving window. After UL transmission, the end-device can only send a new message after receiving a DL message (in RX1 or RX2) or once RX2 has expired. Class B (Beaconing) end-devices support bidirectional communications with scheduled receiving windows. In fact, in addition to the Class A receiving windows, Class B end-devices open extra receiving windows at scheduled times. To implement this operational mode, the GWs periodically send a beacon to synchronize the end-devices. This way, the NS knows when the end-devices are listening. Class B end-devices realize a trade-off between energy consumption and latency of DL communication. Class C (Continuously listening) end-devices support bidirectional communications with maximal receiving windows. This class of end-devices provides almost permanently open receiving windows, except for when they are transmitting. Class C end-devices are typically mains powered, as they consume more energy than the Class A and Class B ones, and guarantee the lowest latency to DL communications. Since all LoRaWAN COTS devices must support Class A features, this paper explores the use of Class A end-devices. As shown in Figure 3, in a LoRaWAN network, the application payload is encapsulated in the FRMPayload field of a LoRaWAN frame (MACpayload in Figure 3). The MACpayload also contains a frame header (FHDR) and an optional FPort field. In turn, the physical payload consists of a single-octet MAC header (MHDR) followed by the MACpayload and ending with a 4-octet message integrity code (MIC). Figure 3 shows below each field the corresponding length in bytes. The maximum lengths of the FRMPayload (N) and MACpayload fields (M) are region-specific [36]. Note that for the LoRaWAN data messages (i.e., the ones with an nonempty FRMPayload field), the FPort field is mandatory. As a result, an overhead of 12 bytes is introduced in each LoRaWAN data message.
# Improved Scheduling Algorithm for Synchronous Data Flow Graphs on a Homogeneous Multi-Core Systems
## 1 Introduction
However, when an HSDFG has many endpoint instances, an additional instance with execution time 0 needs to be created to connect these endpoint instances. We have improved the method of calculating the level by using a reverse traversal algorithm that eliminates additional instances and calculate the longest path. Furthermore, we experimentally found that the scheduling policy in the PAPS algorithm does not perform well in some cases, without incorporating a core selection policy. Therefore, we use the short-job-first policy in the task-selection phase and the earliest-finish-time policy in the core selection phase. When evaluating the SDFG scheduling algorithm, test cases need to be implemented using specific data flow languages such as SteamIt [8] or COStream [9]. Using these data flow graph languages specific data flow procedures such as FFT can be implemented. If only these specific test cases are used, a comprehensive evaluation of the scheduling algorithm is not possible. This paper, therefore, designs the SDFG random generator, which  
Figure 1: Scheduling for HSDFG. can randomly generate SDFGs of a size that meets the actual demand size, as an input source for evaluating the scheduling algorithm. In summary, the main contributions of this paper are divided into three components and are the novelty that this paper aims to highlight. * Using data-driven thinking, the DAG computational model is extended to propose the SDAG model, which is applicable to the scheduling of dataflow programs under the current common processor architecture platforms;
* Improved the currently used SDFG scheduling algorithm to achieve better speedup;
* A new SDFG random generator was designed and used to create an SDFG dataset for evaluating the scheduling algorithm. The data are stored in matrix format, eliminating the need to create additional xml parsers and making it easier for researchers to use. The remaining parts of this paper are organized as follows. Sections 2 and 3 introduce the SDFG background and related work, respectively. Section 4 introduces the SDAG model and the LSEFT scheduling algorithm. Section 5 presents the SDFG random generator and experimental results. Finally, Section 6 concludes this paper.
# Measurement of Super-Pressure Balloon Deformation with Simplified Digital Image Correlation
## 3 DIC and SiDIC Experiments on a Rubber Balloon  
### Experimental Setups  
To verify the measurement accuracy of SiDIC, first, the measurement accuracy of DIC was determined using a random spray patterned rubber balloon as shown in Figure 3a. We used a rubber balloon with a maximum diameter of 20 cm and the size of the particles covering the balloon surface was 1-5 [mm] (1.5-12 [pixel]). Next, to verify the measurement accuracy of SiDIC, a balloon which not only has random spray patterns but is also covered by a net was used as shown in Figure 3b. This makes it possible to use both methods. The thickness of the net is 1 mm. We took pictures after the balloon surface dug into the net holes. In the SPB, it is known that the net and film deforms together in this state. Therefore, we assume it will deform together as well. To test whether DIC and SiDIC could be applied to the actual SPB, a rubber balloon covered only by a net, as shown in Figure 3c, was used.  
Generally, the shape of three-dimensionally deformed objects is measured using the three-dimensional DIC (3D-DIC). Since the planar limitation comes from the two-dimensional nature of the images shot by the camera, the solution is to use more than one camera. From images taken from two different angles of the same object, it is possible to estimate its 3D shape [13]. In this method, it is assumed that two-dimensional deformation measurement using DIC of each image taken from different angles is performed correctly. Therefore, in this research, we confirm whether the 2D deformation measurement of each image is done correctly. In this experiment, a digital camera is used to take an image of the balloon before and after deformation. Therefore, not the actual deformation of  
Figure 2: Coordinates of net intersections. (**a**) Reference image; (**b**) Deformed image.  
the surface of the balloon but the two-dimensional deformation amount on the image is measured. The unit deformation measured from the image corresponds to a pixel.
# PriSign, A Privacy-Preserving Single Sign-On System for Cloud Environments
## 7 Performance Analysis
### Experimental Analysis
For the user, the storage of the private key _usk_, public key _upk_, and credential _ucred_ takes 40 bytes, 128 bytes, and 256 bytes, respectively. For obtaining tickets, the storage of the presentation and ticket takes 1184 bytes and 336 bytes, respectively. For the sign-on protocol, the storage of the token _tok_ takes 1520 bytes. We note that all storage consumption values are byte scale, which is not a burden on the users or the server. In order to evaluate the performance in a real test, we compared the execution time with the system in [8; 9]. In order to fairly compare performance, we used the following settings during testing: (1) because the systems in [8; 9] do not support attribute-based fine-grained access control, we set \(q=0\) and \(I=0\) in the experiment; (2) because the systems in [8; 9] require a fixed number of designated verifiers while ours does not need this to be specified, we set \(J=1\) in the experiment; (3) the remaining parameters were set as \(n=5,t=3,k=5\). As shown in Figure 15, we compared the \(\text{\rm{ObtTkt.U}}\) algorithm in our system to the systems in [8; 9]. The computational overheads are 43.4 ms, 62.1 ms, and 65.5 ms, respectively, for our system and the systems in [8; 9]. Our system is 30% faster than the system in [8] and 33.8% faster than the system in [9]. As shown in Figure 16, we compared the \(\text{\rm{ObtTkt.l}}\) algorithm in our system with the systems in [8; 9]. The computational overheads are 36.9 ms, 37.2 ms, and 48.9 ms, respectively, for our system and the systems in [8; 9]. Our system performs as well as the system in [8], and is 24% faster than the system in [9]. Figure 15: Time comparison with Han19 [9] and Han18 [8] in \(\text{\rm{ObtTkt.U}}\). As shown in Figure 17, we compared the SignOn algorithm in our system with the systems in [8,9]. The computational overheads are 45.2 ms, 2.3 ms, and 4.5 ms, respectively, for our system and the systems in [8,9]. Our system is required to perform TIPFE encryption operations due to the need to support token-hiding, resulting in higher computing consumption than the systems in [8,9]. However, our computing costs are nonetheless on the second scale, and as such are suitable for deployment in real-world applications. As shown in Figure 18, we compared the Verify algorithm in our system with the systems in [8,9]. The computational overheads are 59.3 ms, 29.1 ms, and 38.7 ms, respectively, for our system and the systems in [8,9]. Although our system has a higher computational cost due to its support for token-hiding, the execution efficiency is nonetheless at the same level as the systems in [8,9], making it suitable for real-world applications. Figure 16: Time comparison with Han19 [9] and Han18 [8] in \(\mathtt{Ott}\)Tkt.l. Figure 17: Time comparison with Han19 [9] and Han18 [8] in SignOn.
###### Abstract  
This work investigates the fuel efficiency potential of Adaptive Cruise Control (ACC) systems, focusing on two optimization-based control approaches for internal combustion engine (ICE) vehicles. In particular, this study compares two model predictive control (MPC) designs. In the first approach, a strictly quadratic cost is adopted, and fuel consumption is indirectly minimized by adjusting the weights assigned to state tracking and control effort. In the second approach, a fuel consumption map is explicitly included in the MPC cost function, aiming to directly minimize it. Both approaches are compared to a globally optimal benchmark obtained with dynamic programming. Although these methods have been discussed in the literature, no systematic comparison of their relative performance has been conducted, which is the primary contribution of this article. The results demonstrate that, with proper tuning, the simpler quadratic approach can achieve comparable fuel savings to the approach with explicit fuel consumption minimization, with a maximum variation of 0.5%. These results imply that the first alternative is more suitable for online implementation, due to the more favorable characteristics of the associated optimization problem.  
adaptive cruise control; model predictive control; dynamic programming; energy savings; safety +
Footnote †: journal: Applied Sciences
# Characteristic Analysis and Decision Model of Lane-Changing Game for Intelligent Connected Vehicles
## 5 Simulation Results and Analysis
### Analysis of Lane-Change Trajectory  
In the simulation scenarios, the center point of the intersection is taken as the origin of the coordinate axis, and the driving direction of the straight vehicle is taken as the positive direction of the \(x\)-axis. The lane-changing game scenario is obtained by filtering the vehicle trajectory, and the vehicle trajectory can intuitively reflect the interaction between vehicles and the driving status. In this paper, SUMO's built-in SL2015 lane-change model and the non-cooperative mixed strategy game model are used as comparison models. Among them, the SL2015 lane-change model is the default model for sub-lane scenarios, which can simulate the microscopic lane-change process of vehicles and reflect the interaction state between vehicles. The non-cooperative mixed strategy model is a decision model that maximizes its gain, and previous studies have shown that this model can better predict the decision outcome of the other player [31].  
Figure 6 presents the vehicle trajectory curves, the red line in the local zoomed-in figure is the LV trajectory and the black line is the RV trajectory. Figure 6a shows the SL2015 lane-changing model in SUMO, which is a non-game model reflecting the vehicle interaction in the situation where vehicles make decisions as independent individuals, and it can be seen in the figure that the lane-changing process of vehicles will have a large oscillating effect on the traffic flow, making the slope of the curve of the rear vehicle change more. Figure 6b is a non-cooperative mixed strategy game, although the model considers and predicts the other party's decision. The model is aimed at maximizing their interests, so that there is some conflict between the two parties' decisions, as indicated by the amplification curve, with the strategy of the LV lane change forcing the RV to slow down, and the slope of the RV's curve changes drastically. Figure 6c,d are the evolutionary game-based vehicle lane-change models proposed in this paper, with Figure 6c being the lane-changing and giving-way scenario, and Figure 6d being the no-lane-changing and no-giving-way scenario. From the dynamic evolution equations above, it is known that the evolutionary game lane-change model will converge on stable strategy combinations, while non-stable strategy combinations and saddle points are eliminated by the model as non-optimal decisions. The vehicle trajectory further indicates that the vehicle lane-change process of this model has less perturbation to the vehicles behind, and the stable strategy  
Figure 5: Evolutionary convergence of the proportion of lane-changing strategy under different remaining green time: (**a**) LV; (**b**) RV.  
separates the vehicle risk in time and space, which improves the feasibility of vehicle lane changes.
# Comprehensive Study of IoT Vulnerabilities and Countermeasures
## 2 Preliminary Materials
### Commonly Used Protocols
#### 2.7.18. Data Distribution Service (DDS)  
The Data Distribution Service (DDS) is a machine-to-machine protocol developed by the Object Management Group (OMG) that features decentralized nodes of clients throughout a system (nodes can identify themselves as subscribers or publishers through a localization server). DDS was created to overcome the disadvantages of centralized publish-subscribe architectures. Provides many quality-of-service parameters that allow users to control the behavior of the DDS system, such as improved scalability, increased reliability, reduced latency, bandwidth, and enhanced security (provides authentication, access control, confidentiality, and integrity to the information distribution) [55; 56].
# Review of Vibration-Based Structural Health Monitoring Using Deep Learning
###### Abstract  
With the rapid progress in the deep learning technology, it is being used for vibration-based structural health monitoring. When the vibration is used for extracting features for system diagnosis, it is important to correlate the measured signal to the current status of the structure. The measured vibration responses show large deviation in spectral and transient characteristics for systems to be monitored. Consequently, the diagnosis using vibration requires complete understanding of the extracted features to discard the influence of surrounding environments or unnecessary variations. The deep-learning-based algorithms are expected to find increasing application in these complex problems due to their flexibility and robustness. This review provides a summary of studies applying machine learning algorithms for fault monitoring. The vibration factors were used to categorize the studies. A brief interpretation of deep neural networks is provided to guide further applications in the structural vibration analysis.  
health monitoring; vibration; deep neural network +
Footnote †: journal: Applied Sciences
# Structure, Biology, and Therapeutic Application of Toxin-Antitoxin Systems in Pathogenic Bacteria
## 3 TA Modules from Pathogenic Bacteria
### Mycobacterium Tuberculosis  
_M. tuberculosis_ is neither Gram-positive nor Gram-negative and instead classified as an acid-fast bacterium. This bacterium lies dormant inside macrophages in the human respiratory system and has the potential to cause tuberculosis [134]. _M. tuberculosis_ H37 Rv possesses a total of 79 chromosomal TA systems, which can be categorized into 68 well-known and 11 putative TA systems [3]. There are six well-described type II TA families, including VapBC (50 systems), MazEF (10 systems), RelBE (two systems), HigBA (two systems), ParDE (two systems), and YefM/YoeB (one system). Among these, some VapBC systems have been biochemically well studied to date. For example, fluorescence experiments using fluorescent-labelled RNA substrates of unknown sequence revealed that two VapC toxins, Rv0624 and Rv0627, have ribonuclease activities that are dependent on Mg\({}^{2+}\)/Mn\({}^{2+}\) and Mg\({}^{2+}\) ions, respectively. Rv0624 can also degrade tRNAMet, though its activity is relatively low. The VapC2 toxin (Rv0301) has a ribonuclease activity that cleaves single-stranded viral MS2 RNA [18]. However, the catalytic mechanism involving divalent ions is not well characterized.
# Reliability Prediction of Mixed-Signal Module Based on Multi-Stress Field Failure Mechanisms
## 1 Introduction
Second, electro-thermal-mechanical multi-field dynamic coupling is implemented, with the finite element method and [PERSON]'s constitutive model being used to quantify the feedback effect of thermal stress on solder joint fatigue, including power supply fluctuations caused by increased contact resistance. Finally, component-level failure mechanism modeling is developed for aerospace applications. The dynamic degradation-failure bimodal prediction models are established to address NBTI effects and solder joint fatigue, overcoming the limitations of traditional static analysis methods.
# The Marine Bacterial Genus _Euzebya_ Is Distributed Worldwide in Terrestrial Environments: A Review
## 6 Culture Media for the Isolation of _Euzeyba_ in Terrestrial Environments  
From 90 papers reporting the occurrence of _Euzeyba_ in different environments (Tables 1-3), only 13 described the isolation of bacteria, but only three marine species of _Euzeyba_ were successfully isolated on marine culture medium with 1-2% of sodium chloride, pH near 7, and at a temperature of 25 \({}^{\circ}\)C [14; 15; 16].  
The terrestrial environments where _Euzeyba_ have been found are characterized by haloalkaliphilic conditions, high pH (9-10), and high to moderate salt contents. The availability of water in these ecosystems is widely variable, from dry conditions to 100% relative humidity, which suggests the great adaptability of this genus. In addition, the range of mean temperatures of these environments is highly variable, from \(-30\)\({}^{\circ}\)C (winter in McMurdo Dry Valleys) to \(>\)40 \({}^{\circ}\)C in deserts, with large daily temperature fluctuations in each location.  
The culture media used by different authors contained a wide array of carbon and nitrogen sources (peptone, tryptone, starch, tyrosine, glycerol, asparagine, sodium ca-sineate, malt extract, humic acid, glucose, oatmeal, etc.), mainly used for the isolation of _Actinomycetota_. At the same time, the media rarely contained high concentrations of salts (sodium or calcium), and the pH was not adjusted to the alkalinity ranges where terrestrial _Euzeyba_ and other related bacteria are abundant. None of these attempts were able to isolate strains of _Euzeyba_, _Nitriliruptor_, _Rubrobacter_, _Soliruprobacter_, _Gaiella_, _Halomonas_, etc., which clearly indicates that the culture media used failed to reproduce the ecological conditions where these bacteria succeed.  
As a matter of fact, _Rubrobacter_ strains were isolated from biodeteriorated Roman tombs and a church with abundant efflorescences using media developed for halobacteria (DSMZ media 372, 1018, 1350) [125]. Three of these strains represented a new species, _Rubrobacter bracarensis_, that grew on tryptone soya agar (TSA) with concentrations of 30% NaCl, with an optimum at 3-10%, in contrast to other described species of _Rubrobacter_, which did not grow at concentrations above 5-6% NaCl [126]. This proves that culture media mimicking the original ecological conditions can provide novel, not yet cultured strains.