esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Energy Efficient Control of Induction Machines Using Anticipative Flux Templates
## 4 Template Based Solution
### Template Adaptation  
The template generation algorithm presented in the section above is only capable of processing individual speed ramps. In order to be used in a more general setup, where the machine is continuously accelerating and decelerating, the template generation must be combined with an adaptation algorithm.  
Figure 6: Flux template generation procedure.  
Figure 7: Flux trajectories for the motor specified in Table A1 for different torque steps.  
As displayed in Figure 8, the template adaptation algorithm will first delay the speed reference signal by the anticipative delay time \(\Delta t_{\text{a}}\) in order to achieve flux anticipation. Using the undelayed speed reference signal and a torque prediction model, the according optimal steady state flux is calculated. Due to the template generation procedure, the optimal steady state flux is automatically applied in steady state operation. If this optimal steady state flux changes significantly, a template generation is triggered, because a transient state has been detected. The template generation will then adapt to the new optimal steady state flux. If a template generation is triggered before the previous template trajectory was fully completed, the old template generation is aborted. A new template generation is started that meets two requirements: First, the final value of the new template generation matches the required optimal steady state flux. Second, the transition from one template generation to the next is continuous.  
The result of the template adaptation algorithm is shown in Figure 9. The upper plot displays the speed reference in real-time and the delayed signal, which is passed to the speed controller. In the middle plot, the required torque is displayed. The torque is estimated based on the speed reference using a linear friction model for simplicity together with 0.1 Nm of the static torque component starting from \(t=0.05\,\)s and a torque profile generation based on the a priori inertia \(J_{\text{M}}\) and \(J_{\text{L}}\). Based on the required torque, the optimal steady state flux is given in the bottom plot. Note that the slope of this trajectory continuously changes in contrast to the previous section. At time \(t=0.2\,\)s, it can be seen that the template adaptation algorithm adapts to the beginning of the torque ramp. The previous template was not yet finished, but the transition to the new template generation is smooth. The template reaches the desired steady state flux value during the ramp, although there is a changing slope at \(t=0.21\,\)s. The template adaptation algorithm is further able to adapt to the rising friction torque during the speed ramp, as can be seen in \(t\in[0.5\quad 0.7]\) s.  
Figure 8: Template adaptation scheme.
## 1 Introduction
### Challenges of MAC Protocol for IBSN  
One of the challenging objectives of wireless communication protocols for IBSN is to be ultra-low-power. Wireless communication in IBSN suffers from three main problems, which not only affect the power consumption of sensor nodes but also reliability and quality-of-service of wireless communication inside the body. These main problems are: (i) idle listening which occurs when the node listens for data-packets while no data-packets are being sent; (ii) overhearing, which occurs when the node is listening to data-packets which are not destined for it; and (iii) packet collision, which occurs when two nodes compete to transfer at the same time through the same channel. In general, MAC protocols are designed to reduce these problems by regulating the access to the wireless medium. In the case of IBSN, additional challenges are faced, for example signal attenuation due to the dynamic movements of the human body, very high attenuation of signals due to the conductive nature of the human tissues, and heterogeneous requirements of different devices that are present in the network. Apart from the traditional carrier sensing and time division methods, new technologies that enhance the MAC protocol design, such as WuR, are being currently researched.
# State-of-the-Art Internet of Things in Protected Agriculture
###### Abstract  
The Internet of Things (IoT) has tremendous success in health care, smart city, industrial production and so on. Protected agriculture is one of the fields which has broad application prospects of IoT. Protected agriculture is a mode of highly efficient development of modern agriculture that uses artificial techniques to change climatic factors such as temperature, to create environmental conditions suitable for the growth of animals and plants. This review aims to gain insight into the state-of-the-art of IoT applications in protected agriculture and to identify the system structure and key technologies. Therefore, we completed a systematic literature review of IoT research and deployments in protected agriculture over the past 10 years and evaluated the contributions made by different academicians and organizations. Selected references were clustered into three application domains corresponding to plant management, animal farming and food/agricultural product supply traceability. Furthermore, we discussed the challenges along with future research prospects, to help new researchers of this domain understand the current research progress of IoT in protected agriculture and to propose more novel and innovative ideas in the future.  
Internet of things; protected agriculture; integrated application; state-of-the-art +
Footnote †: journal: Sensors
## 5 Results and Discussion
### Comparison of Time-Space Complexity  
To gauge the effectiveness of the compared structures, a thorough evaluation was conducted, encompassing key performance indicators such as overall area (A), total processing time (T), and the area-time product (AT). This evaluation was carried out using the NanGate Open Cell Library, specifically designed for a cutting-edge 15 nm process technology and operating at a voltage of 0.8V. The assessment focused on the values of \(k=233\) and \(q=74\). To determine the area of each fundamental component (referred to as \(S\)), a comprehensive gate count was performed, utilizing the two-input NAND gate as the reference point for size comparison. The gate count provides an estimation of the area occupied by each component within the design. Additionally, the delay of the crucial logic components (II) was evaluated individually. The delay represents the time taken for a signal to propagate through a particular component. This assessment allows for understanding the performance characteristics of each component in terms of speed and latency.  
The assessment conducted for each component provides the following findings:  
1. Tri-state buffer: The area of a tri-state buffer (\(S_{tri}\)) is assessed to be 0.8, and the delay (\(\Pi_{tri}\)) is evaluated to be 7.9 ps.
2. Two-input AND gate: The area of a two-input AND gate (\(S_{A}\)) is calculated as 1.2, and the delay (\(\Pi_{A}\)) is estimated to be 11.3 ps.
3. Two-input XOR gate: The area of a two-input XOR gate (\(S_{X}\)) is determined to be 2.5, and the delay (\(\Pi_{X}\)) is evaluated to be 12.7 ps.
4. Two-to-one MUX: The area of a two-to-one MUX (\(S_{M}\)) is estimated to be 2.5, and the delay (\(\Pi_{M}\)) is calculated to be 12.4 ps.
5. D-Latch: The area of a D-Latch (\(S_{Latch}\)) is determined to be 2.8, and the delay (\(\Pi_{Latch}\)) is evaluated to be 16.6 ps.  
Based on the results presented in Table 2, it is evident that the proposed SISO systolic array multiplier architecture exhibits significant advantages over recently reported efficient bit-serial multiplier layouts, particularly in terms of area (A) and the area-time (AT) product. The average improvement in area is at least 16.4%, while the average improvement in the area-time product is at least 33.9%. These findings strongly support the suitability of the suggested architecture for IoT applications, where space complexity is a critical concern due to limited resources. By offering superior performance in terms of resource utilization, the SISO systolic array multiplier addresses the stringent resource constraints of IoT applications, making it an ideal choice for implementing cryptosystems in such scenarios.
# Privacy-Aware Access Protocols for MEC Applications in 5G +
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPP 3 rd Generation Partnership Project  
5G Fifth Generation  
AKA Authentication and Key Agreement  
AF Application Function  
DoS Denial of Service  
DTLS Datagram Transport Layer Security  
EST Enrollment over Secure Transport  
ETSI European Telecommunication Standards Institute  
GUTI Globally Unique Temporary Identifier  
IMSI International Mobile Subscriber Identity  
IoT Internet of Things  
MEC Multi-Access Edge Computing  
MNO Mobile Network Operator  
NFV Network Function Virtualization  
OR Onion Routing  
SDN Software-Defined Networking  
SUPI Subscription Permanent Identifier  
TCP Transmission Control Protocol  
TLS Transport Layer Security  
TMSI Temporary Mobile Subscriber Identity  
UDP User Datagram Protocol  
UE User Equipment  
UPF User Plane Function  
V2X Vehicle-to-Everything  
VPN Virtual Private Network
# Internet of Spacecraft for Multi-Planetary Defense and Prosperity
## 4 Planetary Defense: State-of-the-Art
### State of the Art Space Objects Detection  
#### 4.3.1 Detection Capability According to Object Dimension  
Prior to proposing any possible evolution plan for space object detection, it is noteworthy to review and conclude the performance of existing ones. The abovementioned Spaceguard survey activities involved several entities such as Lincoln Near-Earth Asteroid Research, Spacewatch, Near-Earth Asteroid Tracking (NEAT), Lowell Observatory Near-Earth-Object Search (LONEOS), Catalina Sky Survey, Campo Imperatore Near-Earth Object Survey (CINEOS), Japanese Spaceguard Association, Asiago-DLR Asteroid Survey (ADAS), and Near-Earth Object WISE. Thanks to the global joint efforts, most NEAs larger than 1 km in diameter (NEAs-km) have been surveyed and tracked. As of 2 May 2022, 877 NEAs-km have been discovered, which accounts for 95.3% of an estimated total of about 920 NEAs-km [142].  
However, for NEAs with absolute magnitude \(17.75<H<22.75\) (corresponding to a diameter larger than 100 m but smaller than 1 km), 12,058 have been surveyed as of 1 May 2022, which on average makes up only 15.8% of the estimated total (\((7\pm 2)\times 10^{4}\)) [142]. Based on the current detection pace, as shown in Figure 14, it might take decades to locate all these NEAs. Moreover, an even higher proportion of asteroids smaller than 100 m is  
Figure 15: Plot of orbits of known potentially hazardous asteroids with sizes over 140 meters as of early 2013 [138].  
not yet located, which could take more resources and time to accomplish. This can also be interpreted that the current detection capability for asteroids of some specific dimension is still relatively limited, which could weaken the success rate of early warning and slow the reaction effort and mitigation deployment when a real threat appears.
# Applying Topological Information for Routing Commercial Vehicles Around Traffic Congestion
## 3 Data Collection
### Data for Cost-Benefit-Analysis  
The developed model quantified the resulting benefits by comparing diversion scenarios to scenarios without diversion. This study used the total travel time along the alternative route, taken from the output of the developed model, to compute the reduction in delay  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Data Description** & **Data Source** \\ \hline Streets Dataset & Street database including street shapefile, one-way restriction, U-turn, prohibited street, toll road, hierarchy, and shape-length & FDOT, Unified Base \\ Traffic Dataset & Historical traffic data and live traffic data & Repository, HERE \\ School Zones & Private and public school information including school address, zip code, and county & Florida Geographic Data Library \\ Hospital Locations & Hospital facility information, address, zip code, and county & Florida Geographic Data Library \\ National Bridge Inventory & Data on more than 600,000 bridges in the US; includes information about design load, vertical clearance, and efficiency ranks & Bureau of Transportation Statistics \\ Interstate Exits & The dataset contains roadway ID, exit number, county, and district & FDOT Transportation Data and Analytics Office \\ \hline \hline \end{tabular}
\end{table}
Table 2: Network dataset description and sources.  
due to the diversion. Moreover, as reducing delays can also decrease fuel consumption, both delay reduction and fuel consumption savings were converted to monetary values using conversion factors obtained from the U.S. Census Bureau.
# The New CAP Theorem on Blockchain Consensus Systems
## 4 The New CAP Theorem
### Scalability and Performance Implications in Large-Scale Systems  
The theorem has direct implications for Blockchain scalability and performance. In decentralized networks, increasing node involvement in the agreement process, i.e., higher \(W\), improves Consensus (C) but inherently hurts Autonomy (A) and entropic Performance (P). This explains why large-scale fully permissionless systems like Bitcoin experience high confirmation times and energy costs, while hybrid or permissioned consensus systems such as Ethereum PoS and Hyperledger Fabric introduce optimizations at the expense of decentralization as discussed in Section 5.2.  
Transaction Finality: As \(W\) increases, the time required to finalize transactions also increases in the generic case. Bitcoin's block time, which is dynamically adjusted to 10 min, ensures high consensus at the cost of throughput and entropic performance, while Ethereum's PoS finality mechanism speeds up transactions and reduces decentralization.  
Scalability: Large-scale systems must carefully balance \(W\) to maintain their efficiency. Empirical studies of Ethereum and Hyperledger Fabric show that consensus participation thresholds significantly impact overall network performance [17; 18].  
Figure 7: Three-dimensional plot of the entropic Performance (efficiency) of a dynamic system of N nodes for \(N=1\):100, \(W=0.1\):\(N\)\(-\)1, \(E=1\), and \(M=1000\) bit. Maximum efficiency at low witness participation is illustrated.  
Network overhead: In peer networks, higher \(W\) results in non-linear data traffic increment, leading to congestion and increased latencies. The exponential relationship of \(W\) to the latency is recorded [8].  
In the next section, the findings and the broader implications of the _new CAP theorem_ are discussed. The practical ways in which the large-scale Blockchain systems balance (C), (A), and (P) are also analyzed through empirical data benchmarking.
# Comparative Analysis of 5G Mobile Communication Network Architectures
## 8 Conclusions and Future Work  
In this study, three communication network architectures were analyzed for their response capability under normal and disruptive data traffic conditions. The current CCC architecture, the MEC architecture, and the newly proposed HCC architecture were modeled using a Python-based simulation software. The behaviors of these architectures were assessed using actual data traffic patterns obtained from the field. The results were used to quantify the advantages and disadvantages of each architecture, and they demonstrated the potential benefit of the new HCC architecture. Insights gained from the case study were discussed, along with how these insights can be used to benefit the actual deployment of the HCC architecture in the field.  
There is scope to further analyze the HCC architecture. The architecture can be assessed in terms of other key performance indices, such as the latency, cost, utilization, and profit, which are critical performance criteria for 5G network architectures. Additionally, for the HCC architecture, determining the optimal allocation between the central and edge clouds is an important research topic. What are some other realistic data traffic scenarios that need to be considered? How can computing resources be optimally allocated among the central cloud and edge clouds based on possible scenarios? Is there a \"golden allocation ratio\" that can include the majority of possible scenarios? Is there any variation of the HCC architecture that can further enhance its performance? Will there be any performance improvement by integrating machine learning algorithms into offloading decision framework? All of these questions present valuable opportunities for future research.  
Conceptualization, W.L. and E.S.S.; Methodology, W.L., E.S.S, W.Y.K. and H.H.; Software, W.L.; Validation, W.L., E.S.S., W.Y.K. and H.H.; Formal analysis, W.L.; Investigation, W.L. and E.S.S.; Resources, E.S.S.; Data curation, W.L.; Writing--original draft preparation, W.L. and E.S.S.; Writing--review and editing, W.L. and E.S.S.; Visualization, W.L. and E.S.S.; Supervision, E.S.S., W.Y.K. and H.H.; Project administration, E.S.S.; Funding acquisition, E.S.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2016R1D1A1A09916273).  
The authors declare no conflict of interest.
# An Effective Simulation Scheme for Predicting the Aerodynamic Heat of a Scramjet-Propelled Vehicle
## 3 Validation Tests
### Transonic Stage
#### 3.1.2 Numerical Method  
At the transonic stage, the pressure-based solver was used with a coupled algorithm [25]. The second-order upwind interpolation scheme was selected for the density [26]. The gradients were computed according to the least-squares cell-based method. For preventing spurious oscillations, a gradient limiter was adopted, which used the Minnod function [17] to limit and clip the reconstructed solution overshoots and undershoots on the cell faces. The second-order scheme was used for interpolating the pressure values at the faces, which reconstructed the face pressure using a central differencing scheme. The validation parameters of the convergence numerical results were as shown in Table 3.
# Experimental Approaches in Delineating mTOR Signaling
## 4 Identification and Characterization of mTOR Signaling Pathway Components
### Biochemical Approaches to Identify mTOR Signaling Pathway Components
#### 4.1.17 GATOR2 as a Sestrin 2 Downstream Target Was Identified through Sestrin 2 Pulldowns  
[PERSON] et al. also identified Sestrins as GATOR2 binding partners but through a different route [54]. It was previously reported that as a stress-responsive gene, Sestrin 2 inhibited mTOR1 signaling through the AMPK/TSC signaling axis [55; 56]. However, when Sestrin 2 and S6K were cottransfected into immortalized _AMPK\(\alpha\)-/-_ MEFs, Sestrin 2 was able to suppress mTOR1-mediated S6K phosphorylation even in the absence of AMPK\(\alpha\). Further, Sestrin 2-mediated inhibition of S6K phosphorylation in HEK293T cells could be partially released by the AMPK inhibitor compound C. These data suggested that in addition to the Sestrin 2/AMPK/TSC signaling, there was another yet unknown parallel signaling playing a role in Sestrin 2-governed mTOR1 suppression. To identify additional Sestrin 2 downstream targets in inhibiting mTOR1, [PERSON] et al. performed a tandem affinity purification from human mammary epithelial MCF10A cells stably expressing SBP (Streptavidin-Binding Peptide)-Flag-Sestrin 2 and identified GATOR2 proteins including Mios, WDR59, and WDR24 as Sestrin 2 binding partners. In addition, [PERSON] et al. identified GATOR2 as Sestrin 2 binding partners by a similar tandem purification approach using Sestrin 2 as the bait [57].  
1.18. SLC38A9 as a Lysosomal Arginine Sensor for mTOR1 Activation Was Identified by Flag-GATOR2 and RagB-IPs  
Identification of v-ATPases as a required factor governing mTOR1 activation through an \"inside-out\" mechanism by sensing lysosomal amino acid concentrations [58] suggests the existence of possible amino acid sensor(s) on lysosome. In brief, 30 million of HEK293T cells stably expressing individual GATOR2 components or Rags, including Flag-tagged p18, p14, HBXIP, c7 orf59, and RagB, were used for Flag-immunoprecipitations under nonheated conditions and eluted by Flag peptides (Flag-METAP2 was used as a negative control) [59]. A lysosome membrane amino acid transporter SLC38A9 was a common hit in mass spectrometry analyses and shown to play a role in sensing lysosomal arginine sufficiency in activating mTOR1 [59]. Interestingly, in a follow-up study, [PERSON] et al. found that essential amino acids were accumulated in lysosome in SLC38A9-deficient cells and unraveled an unexpected function of SLC38A9 in regulating amino acid homeostasis through transporting essential amino acids out of lysosome in an arginine-dependent manner [60]. Recently, the molecular mechanism for SLC38A9-mediated mTOR1 activation was revealed by identification of SLC38A9 as a GEF for RagA. Specifically, bacterially purified SLC38A9N (aa 1-119) proteins were observed comrigated with Rag GTPases in vitro in a gel filtration experiment, and a stoichiometric amount of SLC38A9N proteins were found to inhibit the incorporation of radioactively labeled nucleotides into RagA in vitro by increasing the GDP off-rate [50].
## 2 Basic Principle of SAR Imaging Simulation
### SAR Echo Signal Simulation  
In the process of ocean surface echo signal simulation, there are two key problems to be solved. One problem is the calculation of the ocean surface backscattering coefficient, and the other problem is the generation of echo signals. Among these problems, the calculation of the backscattering coefficient is the basis of the echo signals.
# P-Fuzz: A Parallel Grey-Box Fuzzing Framework
## 3 Methodology
### Races and Exceptions Handling Strategies
#### 3.2.2 Case 2: Several Clients Update the Bitmap in the Database Together  
We store the bitmap as a record in the database for sharing the coverage information. A data race happens as shown in Figure 3. Elements in the bitmap with \"1\" or \"0\" represent the edge uncovered or covered. The figure reveals that two clients update their new bitmaps (Figure 3b,c). If we do not control the merging process, to merge the bitmap of client1 and then client2, some valuable information will get lost as in Figure 3d.  
To alleviate this situation, we start a service in the server to manage the merging operation. The service builds a queue to store the bitmap temporarily. When bitmaps from different clients come up to the database, they are enqueued according to the time order. The database merges these enqueued bitmaps by \"AND(&)\" operations one by one so that the bitmap maintains all the necessary information.