esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions  
The impact of Gaussian-shaped mountainous terrain on LIVs is evaluated through a comparative analysis with flat ground and cone-shaped terrains, three distinct lightning strike locations (SL1, SL2, and SL3, as shown in Figure 1), and two distribution line configurations (Configurations A and B) are considered. Additionally, the impacts of the height and width of the mountain are also analyzed.
# Quantum Implementation of AIM: Aiming for Low-Depth
## 2 Preliminaries
### NIST Post-Quantum Security  
To evaluate the security of a cipher against quantum attacks, NIST specifies security bounds for the cipher [10; 11]:  
* Level 1: Resource requirements for the attack are similar to those for breaking AES-128 (\(2^{170}\to 2^{157}\)).
* Level 3: Resource requirements for the attack are similar to those for breaking AES-192 (\(2^{233}\to 2^{221}\)).
* Level 5: Resource requirements for the attack are similar to those for breaking AES-256 (\(2^{298}\to 2^{285}\)).  
Based on the cost estimation of [PERSON]'s key search for AES variants in [PERSON] et al.'s work [16], NIST has calculated the quantum attack complexities for Levels 1, 3, and 5 (corresponding to AES variants) to \(2^{170}\), \(2^{233}\), and \(2^{298}\), respectively (total gates \(\times\) depth of [PERSON]'s search). One important point to note is that the attack complexity estimated by NIST in [10] is based on research results from PQCrypto'16 [16], and since then, quantum circuits for AES have been steadily optimized, leading to significant reductions in the cost of attacks in recent years [13; 14; 17; 18]. NIST acknowledges that the estimated attack complexity based on the levels is relative, considering the ongoing optimization of quantum circuits for AES (page 17 on [10]). Therefore, if an attack with reduced cost is proposed, the benchmark should be reconsidered.  
Recently, NIST adjusted the security bounds for AES [11; 19] based on the results presented in Eurocrypt'20 [14]. In [14], the quantum attack costs for AES-128, -192, and -256 were significantly reduced to \(2^{157}\), \(2^{221}\), and \(2^{285}\), respectively (which align with the costs specified in [11; 19]). However, there is an issue of underestimation of quantum resources for their implementations, particularly with respect to non-linear operations (like S-box).  
In [13], the authors analyzed estimation issue in [14] and reported corrected results. In addition to the corrections, they also presented depth-optimized quantum circuits for AES. It is worth noting that [13] has not yet undergone peer review, but the costs derived from their implementation currently represent the lowest estimates.  
Throughout this paper, we consider complexity estimates from NIST [11; 19] and the reduced estimates from [13] to assess the post-quantum security of AIM.
## 3 Results
### Response of CO\({}_{2}\) Fluxes to Temperature
#### 3.3.2 Critical Values of Temperature Effects on CO\({}_{2}\) Fluxes  
The soil and air temperatures were divided into eight and six intervals, respectively, to investigate CO\({}_{2}\) fluxes in different temperature ranges (Figure 11). The study showed that as the temperature increased, NEE tended to decrease and then increase, with a \"turning point\" at 25 \({}^{\circ}\)C (Figure 11a). When the soil temperature reaches 25 \({}^{\circ}\)C, it provides an optimal temperature for vegetation growth, and the stomata of vegetation roots open to efficiently absorb photosynthetic radiation and increase the photosynthetic absorption rate [16]. At this point, photosynthesis is greater than respiration in grassland ecosystems, leading to a relatively low concentration of CO\({}_{2}\) in the air and causing the grassland to generally act as a carbon sink. When the soil temperature drops to below 15 \({}^{\circ}\)C, vegetation is vulnerable to cold stress, and prolonged low temperatures are detrimental to crop growth and development, leading to reduced photosynthesis in the grassland ecosystem. This, in turn, weakens photosynthesis, causing CO\({}_{2}\) fluxes in the air to begin rebounding (Figure 11b). The trend of GPP is opposite to that of NEE, and the critical soil temperature for GPP is 25 \({}^{\circ}\)C (Figure 11c). The critical soil temperature for the effect on Reco is 10 \({}^{\circ}\)C.  
Figure 10: Relationship between CO\({}_{2}\) fluxes and air temperature with different gradients. (**a**) The relationship between 2 m air temperature and NEE, (**b**) the relationship between 2 m air temperature and GPP, (**c**) the relationship between 10 m air temperature and NEE, and (**d**) the relationship between 10 m air temperature and GPP.  
NEE decreases gradually with increasing air temperature, and changes more smoothly when the air temperature is below 15 \({}^{\circ}\)C. However, it decreases more rapidly when the air temperature exceeds 15 \({}^{\circ}\)C (Figure 11b). Conversely, the trend of GPP is opposite to that of NEE, with GPP showing an increasing trend when the temperature exceeds 15 \({}^{\circ}\)C (Figure 11d). Nonetheless, Reco increases with temperature and does not demonstrate a critical value due to its unique geographical location with relatively low temperatures.
## 3 Electrically Small Planar Resonators: A Comparative Analysis and Circuit Models
### Topologies and Circuit Models of the Isolated Resonators
As shown in Figure 7(a), the OCSRR is an open particle with terminals for external connection (excitation) as indicated in the figure. From one terminal to the other there are two possible current paths: On one hand, the inductive path through the strip between the ring slots; on the other hand, the capacitive path through the slots. Therefore the OCSRR is an open parallel resonant tank. The capacitance of the particle is the capacitance of the CSRR, \(C_{c}\); the inductance is four times larger than the inductance of the CSRR. This means that the OCSRR is smaller than the CSRR by a factor of two. Indeed, the resonance frequency and electrical size of the OSRR and the OCSRR are identical (provided the same dimensions and substrate are considered). The complementary counterpart of the SIR has been called dumb-bell defected ground structure (DB-DGS). The reason is that this structure is dumb-bell shaped, and it is typically etched in the ground plane of microstrip lines, creating thus a defect, or pattern, in such a ground plane (Figure 7(b)). This structure behaves like a series connected parallel resonant tank, as reported in [28]. Thus, OCSRRs and DB-DGSs are of interest in applications where electrically small open parallel resonators are required. Many other electrically small planar resonant particles, such as multiconductor SRRs or spirals [29], Hilbert shaped resonators [30,31], electrically tunable resonators [32,33]_etc._, have been considered in the literature, but are out of the scope of this paper. Nevertheless, the authors recommend the books by [PERSON], [PERSON] \(\hat{\mathbf{n}}\) and [PERSON] [7] and by [PERSON] and [PERSON] [9] for an overview of the resonators considered in this paper and for other electrically small resonant particles. Figure 7: (**a**) Open complementary split ring resonator (OCSRR) topology and its equivalent circuit model. (**b**) Topology of a dumb-bell defected ground structure (DB-DGS) etched in a ground plane of a microstrip transmission line.
# An Abstraction Layer Exploiting Voice Assistant Technologies for Effective Human--Robot Interaction
## 4 The Proposed Use Case
### The Developed Applications
#### 4.1.6 Mr. Chess Application  
Mr. Chess is a Google Assistant action conceived to play chess with a user. This application demonstrates how standalone AC applications fit into our architecture. NAO acts like middleware between the user and the Google Assistant action. For this reason, the HRIC is not responsible for managing the interaction with humans, and the audio recorded by NAO's microphone is sent to Google Assistant, where it is elaborated and converted into text. The correct intent is found by the Dialogflow Agent, which recognizes a specified pattern.  
The Dialogflow Agent consists of two main components: intents and entities. They both manage and route user's requests. Intents are used to map users' input to responses, whereas entities are used to extract and understand useful data from intents inputs. The Mr. Chess application consists of three main intents:  
* _Default Welcome Intent_ is the default intent of the dialog. It starts when the user begins the conversation, and it first explains the rules of the game. At the first run, it asks for confirmation to extract the user's name from the Google account used in the callback function triggered by the intent. If it is a replay, the robot greets the user. Next, the intent checks if there is a pending active game, and the user is asked if either he/she wants to re-join it or if he/she wants to create a new one.
* _Make a Move_ manages most of the interaction between the action and the user. It is capable of understanding all the possible chess moves, which will be sent to the Chess REST APIs through an HTTP call. According to the answer of the REST APIs, the action will answer accordingly. If, for example, the move is invalid, the user will be informed. The game continues until either a stalemate or a checkmate occurs. The _Make a Move_ intent can be seen in Figure 2;
* _See Board_ is a helper whose purpose is to print the updated chessboard when the user wants. Note that this function works when the used robotic platform has a device to show output. As such, it does not work in our test-case.  
A new entity type, named _chess_coordinates_, was created. It specifies all the possible chess moves that can be found in users' utterances. An example of its definition is shown in Figure 3.
# A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks
###### Abstract  
Multi-Input Multi-Output (MIMO) can improve wireless network performance. Sensors are usually single-antenna devices due to the high hardware complexity and cost, so several sensors are used to form virtual MIMO array, which is a desirable approach to efficiently take advantage of MIMO gains. Also, in large Wireless Sensor Networks (WSNs), clustering can improve the network scalability, which is an effective topology control approach. The existing virtual MIMO-based clustering schemes do not either fully explore the benefits of MIMO or adaptively determine the clustering ranges. Also, clustering mechanism needs to be further improved to enhance the cluster structure life. In this paper, we propose an improved clustering scheme for virtual MIMO-based topology construction (ICV-MIMO), which can determine adaptively not only the inter-cluster transmission modes but also the clustering ranges. Through the rational division of cluster head function and the optimization of cluster head selection criteria and information exchange process, the ICV-MIMO scheme effectively reduces the network energy consumption and improves the lifetime of the cluster structure when compared with the existing typical virtual MIMO-based scheme. Moreover, the message overhead and time complexity are still in the same order of magnitude.  
WSNs; virtual MIMO; transmission mode; clustering range; hierarchical topology control +
Footnote †: journal: Sensors
# Blind Deconvolution with Scale Ambiguity
## 4 Discussion and Experimental Results
### Discussion
#### 4.1.4 Limitations  
The proposed method is based on an existing simple image prior. It may fail in some cases. Here we present one special case. As our method is under the uniform-blur assumption, if an image is blurred non-uniformly, like rotation blur, our method cannot restore the image effectively. Figure 7a shows one real example. As can be seen in Figure 7b, our method fails to generate desirable results.
# Execution Model to Reduce the Interference of Shared Memory in ARINC 653 Compliant Multicore RTOS
## 6 Conclusions  
In this work, the task execution model was applied to reduce the interference due to memory sharing in a multicore avionics system. In safety-critical systems, such as avionics systems, the system must be deterministic by reducing the effects of such interference. We applied the proposed execution model to COTS hardware and commercial RTOS. We proposed a multicore flow control method with pseudo-partition and message queues to implement the TDMA and AER execution models in VxWorks 653. Then, we showed that the execution model reduced interference due to the shared memory access between cores.  
We used the micro-benchmark to measure inter-core interference due to memory sharing in a multicore system. We measured the benchmark execution times, L1 and L2 cache misses, and the BIU requests in a quad-core architecture. We showed that the execution time variation increased when all cores accessed the memory and contention occurred. Based on this, when the TDMA model was applied, the non-determinism of the execution time was decreased by reducing the interference caused by shared memory. At this time, the L2 cache and the BIU requests decreased by about 60%. The AER model increased the execution time by about 20% due to the overhead for controlling the execution of processes running on each core. Memory queuing for the process' phase control of this model caused context switches, resulting in more cache misses than in TDMA. However, in the AER model, the number of cache misses was 50% less than when four cores concurrently accessed the shared memory. We also proposed a multi-TDMA, in which two cores run concurrently while the target hardware bus processes two coherency granules in one cycle. Furthermore, our experiments demonstrated that the TDMA and multi-TDMA models produce insignificant differences in the execution times and cache misses. In particular, multi-TDMA unlike AER does not require phase control. Therefore, the multi-TDMA model has a higher utilization than the TDMA model and a shorter execution time than the AER model.  
Conceptualization, S.P. and H.K.; methodology, S.P. and H.K.; software, S.P.; writing--original draft preparation, S.P.; writing--review and editing, S.P. and H.K.; supervision, H.K.; project administration, M.-Y.K. and H.-K.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Agency for Defense Development under the contract UD180002 ED.  
The authors declare no conflict of interest.
## 4 Training and Simulation
### Training Results  
#### 4.2.1 Reward Comparison  
Our study comprehensively evaluated the PER-MADDPG algorithm against existing MARL algorithms to verify its performance in active guidance control for IRW vehicles. The selected algorithms for reward analysis include the basic MADDPG and single-agent (SA)-based DDPG algorithms. We also introduce the multiagent proximal policy optimization (MAPPO), which is based on on-policy learning strategies, into the comparison. MAPPO, an extension of the proximal policy optimization (PPO) algorithm, employs a centralized value function method to optimize each agent's strategy; this method performs well in many cooperative Markov games and is often used as a benchmark for evaluating MARL performance [25; 26]. Like the MADDPG algorithm, MAPPO uses a combination of value function and policy gradient methods based on the CTDE framework for implementing collaborative controllers.  
To ensure the robustness of the evaluation, we used five random seeds to train the MARL agents. As the number of time steps in the training process increases, when the reward per episode becomes stable, the training of the MARL-based controller converges. We ensured uniformity in the main parameter settings across all algorithms for a fair comparison.  
Table 3 and Figure 7a display the differences in the performance of the reward values among the different algorithms. The training results highlight the superior performance of the PER-enhanced MADDPG algorithm, which achieved the highest reward values and demonstrated convergence after approximately 4.5 \(\times\) 10\({}^{5}\) training episodes. In contrast, the basic MADDPG algorithm lags in convergence speed and final reward achievement due to its inefficient use of high-value samples. The DDPG controller, limited by its high-dimensional observation requirements, failed to converge within the predetermined maximum number of episodes, with reward values significantly lower than those of both MADDPG and PER-MADDPG. Our analysis also indicates that when applying MARL algorithms to IRW guidance control, on-policy DRL algorithms lag in sample efficiency compared to off-policy algorithms. The MAPPO algorithm showed a decrease in performance when frequently reusing low-value samples, potentially leading to their final reward values falling into local optima.  
Figure 7: Reward comparison during training episodes. (**a**) Comparison of different DRL and MARL algorithms, (**b**) Comparison with different mini-batch sizes. The solid line shows the average return from five random initializations, and the shaded area indicates the standard deviation.  
The above comparison results demonstrate the performance of the PER-MADDPG algorithm, which can obtain higher reward values and exhibit faster convergence, demonstrating its high sampling efficiency.
# A Deep Analysis of Brain Tumor Detection from MR Images Using Deep Learning Networks
## 6 Conclusions and Future Works  
Early detection of brain tumors can play a significant role in preventing higher mortality rates globally. Due to the tumor's form, changing size, and structure, the correct detection of brain tumors is still highly challenging. Clinical diagnosis and therapy decision-making for brain tumor patients are greatly influenced by the classification of MR images. Early brain tumor identification using MR images and the tumor segmentation method appear promising. Nevertheless, there is still a long way to go before the tumor location can be precisely recognized and categorized. For the purposes of early brain tumor detection in our study, we used a variety of MRI brain tumor images. Deep learning models also have a significant impact on classification and detection. We proposed a CNN model for the early detection of brain tumors, where we obtained promising result using a large amount of MR images. We employed a variety of indicators to ensure the efficiency of the ML models during the evaluation process. In addition to the proposed model, we also took into account a few other ML models to assess our outcomes. Regarding the limitations of our research, as the CNN had several layers and the computer did not have a good GPU, the training process took a long time. If the dataset is large, such as having a thousand images, it would take more time to train. After improving our GPU system, we minimized the training time. Future work can be performed to better correctly identify brain cancers by using individual patient information gathered from any source.  
Conceptualization, M.I.M. and M.M.; methodology, M.I.M. and M.M.; software, M.I.M. and M.M.; validation, M.I.M. and M.M.; formal analysis, M.I.M.; investigation, M.M.; resources, A.A.; data curation, M.I.M.; writing--original draft preparation, M.I.M.; writing--review and editing, M.I.M. and M.M.; visualization, M.I.M. and M.M.; supervision, A.A.; project administration, A.A.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.  
Figure 11: Loss graph for the CNN, ResNet-50, Inception V3, and VGG16.
## 4 Discussion
### NPP Response to the Meteorological Factors  
The correlation coefficient spatial distribution map (Figure 12) shows that precipitation was the main factor for grassland NPP in Inner Mongolia from 2000 to 2020, with which 97.06% of the total grassland showed a positive relationship. Most studies suggest that the precipitation is the most important meteorological factor which affected the change of grassland NPP in Inner Mongolia ([PERSON] et al., 2017; [PERSON] et al., 2017; [PERSON] et al., 2017), which is consistent with the conclusion of our research. The increase of precipitation over the last 20 years has contributed to the overall increase in grassland NPP. The grassland NPP shows a negative correlation with temperature in the water-deficient area while the relationship will turn into positive when the water is enough.  
Moreover, the correlation coefficients between precipitation and NPP in the north and south of the Greater Khingan Range have a certain difference of the strength. Combined with Figure 7, the changes of NPP in the different sides of the Greater Khingan Range are different significantly. For the two sides of the mountains, the differences in water and heat resources have a greater impact on grassland growth. In addition, as [PERSON] et al. (2017) summarized, human activities such as mining and reclamation had the most obvious impact on grassland NPP in west Xilingol during 2000-2018. More human activities can also weaken the response of grassland to meteorological factors, such as in the south side of the Greater Khingan Range.  
[PERSON] et al. [93] found that different grassland species had different responses to meteorological factors. Moreover, there is a time-lag in the response of vegetation to climatic factors [95], which is difficult to find on the annual scale. Thus, the changes of NPP in response to meteorological factors should be further analyzed on finer and longer time series data. In addition, the periodic law of meteorological factors should be taken into account, considering the high sensitivity of grassland to meteorological factors, especially for precipitation, which can avoid the inconsistent conclusion over a different study period.
## 2 Materials and Methods
### Gas Chromatography-Mass Spectrometry  
The targeted and untargeted analyses were performed using a GC-MS system (7890B-5977B, Agilent Technologies, Santa Clara, CA, USA) with a J&W HP-5 MS column (30 m \(\times\) 0.25 mm ID \(\times\) 0.25 \(\mu\)m, Agilent) and a carrier gas flow rate of 1.5 mL/min. The transfer line was set to 280 \({}^{\circ}\)C, connecting the column to an electron impact (EI\({}^{+}\)) source at 250 \({}^{\circ}\)C with 70 eV, and a single quadrupole mass analyzer was maintained at 120 \({}^{\circ}\)C.  
For the targeted analysis, the injector temperature was set to 245 \({}^{\circ}\)C using an ultra-inert double taper liner in splitless mode. The temperature program began at 100 \({}^{\circ}\)C, increased at 100 \({}^{\circ}\)C/min to 230 \({}^{\circ}\)C (held for 3.3 min), then ramped at 35 \({}^{\circ}\)C/min to a final temperature of 280 \({}^{\circ}\)C, maintained for 2 min. The total analysis time for this targeted method was 8 min. Initially, full scans were performed over a 50-300 \(m/z\) range for each hydrazone, allowing the identification of the most intense ions. Single ion monitoring mode was then used with the quantifier and qualifier ions summarized in Table S1 (Supplementary Material).  
For the untargeted analysis, the oven temperature started at 35 \({}^{\circ}\)C, held for 5 min, and increased at 5 \({}^{\circ}\)C/min to 280 \({}^{\circ}\)C (held for 10 min), resulting in a total run time of 64 min. The detector was turned off after 60 min. The injector was set to 270 \({}^{\circ}\)C using a straight ultra-inert liner (5190-4048) with a 0.755 mm inner diameter in splitless mode. Scan acquisition mode covered a 40-400 \(m/z\) range. Preliminary compound identification was performed using the NIST Mass Spectral Search Program (Version 2.2) by comparing experimental and reference mass spectra in the NIST/EPA/NIH Mass Spectral Library. Compound identification was based on matching fragmentation patterns and relative ion intensities, with only matches having a probabilistic score above 80% reported.