esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Reinforcement Learning Algorithms for Path Planning Domain in Grid-like Environment
###### Abstract
The deep reinforcement learning approach has been chosen even though classical search techniques usually work satisfactorily for such pathfinding tasks since the results achieved by those algorithms often surpass the expectations, finding more feasible solutions than those designed by humans. Additionally, the search algorithms require precise values corresponding to the paths, based on which they can evaluate different scenarios. As the data available for the agent in such a scenario would be distance, traffic, and others, rather than throughput time and gas usage--both of which would have to be estimated--graph-search solutions would require additional means of doing so. On the contrary, the reinforcement learning algorithms can learn to estimate those values exceptionally well--as part of their learning process--often surpassing humans' possibilities and hard-coded algorithms, as was proven multiple times in the past [11; 12; 13]. Deep Reinforcement Learning (DRL) has become a powerful and very popular tool recently. The first and most popular area where DRL algorithms are used are games. Algorithms based on DRL were proved to beat human players in such games like chess, Go, shogi [11] or StarCraft II [14]. Additionally, DRL algorithms have been successfully applied in recommender systems [15], autonomous driving [16], healthcare [17], finance [18], robotics [19] and education [20]. The most troubling aspect of reinforcement learning, which is the requirement for substantial quantities of the data [8; 21], does not pose a problem in this scenario, as most of the delivery vehicles already have embedded GPS trackers. Thus, nearly every delivery company possesses real-life data about delivery targets and paths chosen by the drivers. Combined with the relatively accessible data (weather or roadblocks information), that can be utilised to construct an accurate model of the environment on which the agent can be trained. In this paper, we are focusing on evaluating whether the two previously mentioned novel algorithms might be a suitable solution to continuous optimisation problems, even when the environment possesses a high degree of aliased states--states in which two or more different actions are evaluated as yielding similar rewards--which increase the complexity and uncertainty of the stochastic learning process.
# Focus Estimation Methods for Use in Industrial SFF Imaging Systems
## 3 Results
### Comparison of Results for Measurements Obtained with Different Illuminators  
The last type of interference investigated is related to the geometric configuration of the illuminator-camera system. Changes in the position and type of illuminator cause glare, local saturation of the image, or uneven illumination of the scene. This section will present the results obtained using illuminators with different geometries and various diffusion levels.  
Figure 16 shows sample images of a captured metallic surface. Different effects are obtained depending on the used illuminator and how it is mounted with respect to the camera. A dedicated illuminator allows specific features of an object to be enhanced by changing the intensity of some regions of the image. The DL illuminator in Figure 16a produces a glare-free surface image. The HRL illuminator in Figure 16b enhances local surface defects in the form of scratches while maintaining uniform surface illumination. A BL illuminator placed at a low angle to the surface achieves similar scratch enhancement. However, the unevenness of the illumination of the scenes is noticeable. The centre of the image is significantly brighter than the edges of the image (Figure 16c).  
The AFE for each illuminator was calculated. The AFE values obtained by applying the maximum method for each of the considered operators were 0. The results obtained for the CoG method are shown in Figure 17. Applying the CoG method, the AFE value ranged  
Figure 16: Images of registered metal surface by using selected illuminators: (**a**) DL illuminator, (**b**) HRL illuminator, (**c**) BL illuminator.  
Figure 15: RMSE values as a function of contrast for both focus plane determination methods.  
up to 1. The exception is the SSTD operator, which achieved an AFE value of 10 for images recorded by using the CL illuminator.  
RMSE values varied depending on the illuminator and operator used (Figure 18). The worst results were obtained with the CL illuminator for determining the focus plane with the maximum function. RMSE values of around 40 were obtained for the DPT and DSL operators. The other operators achieved RMSE values of around 10. BL illuminator had similar values of around 10 for all operators. The other illuminators achieved better results. In their case, the operators with no fixed kernel size kept the RMSE value below 4.  
The CoG method halved the RMSE for illuminators CL and BL. For the other operators, the error values decreased slightly. Operators with no fixed kernel size obtained RMSE value below 3.
## 3 Result
### Topographic Parameters  
In addition to parent material, climate, anthropogenic activities, and time, topography plays a critical role in soil formation processes. Topographic variables are also essential indicators for terrain classification, among which surface curvature and slope are fundamental. The TWI and the SPI are commonly used to represent soil moisture status and the erosive potential of surface runoff, respectively, thereby emphasizing the influence of topography on the hydrological characteristics of the study area. In this study, elevation, slope, SPI, and TWI were employed to characterize the topographic attributes of the region.  
Based on the classification of China's basic landform types proposed by [PERSON] et al. [46], elevation in the study area was divided into three categories: 200-500 m (low-relief mountainous areas), 500-1000 m (medium-relief mountainous areas), and  
Figure 2: Spatial distribution maps of (**a**) soil total Se, (**b**) pH, and (**c**) SOM.  
1000-2500 m (high-relief mountainous areas). Similarly, slope was classified into three groups: less than 5\({}^{\circ}\) (gentle slopes), 5-15\({}^{\circ}\) (moderate slopes), and greater than 15\({}^{\circ}\) (steep slopes). As there are no standardized classification criteria for the TWI and SPI, the natural breaks (Jenks) classification method was applied. Figure 3 displays the spatial distribution maps of elevation, slope, SPI, and TWI across the study area.  
Figure 3 illustrates the overall topographic pattern of Zheng'an County, which can be divided into three major geomorphological regions: eastern, central, and western. The eastern and western regions are predominantly characterized by medium-relief mountainous terrain, featuring deeply incised river valleys and primarily unidirectionally inclined mountain slopes. In contrast, the central region mainly consists of low mountains and hills, with rivers forming \"U\"-shaped valleys, accompanied by well-developed floodplains and river terraces. The spatial distributions of slope, SPI, and TWI are complex and interwoven throughout the study area. As shown in Table 3, the average slope is 15.55\({}^{\circ}\), placing the region within the steep slope category, although areas of gentle slopes are also present. Variations in slope exert a significant influence on the region's hydrological conditions, and the spatial patterns of SPI and TWI are similarly affected by slope gradients.  
Figure 3: Maps showing the spatial distribution of (**a**) elevation, (**b**) slope, (**c**) Stream Power Index (SPI), and (**d**) Topographic Wetness Index (TWI).
## 3 Method of Generating Dummy Power Trace Data
### Assembly Instruction Extraction  
After executing cryptographic operations within the QEMU-based simulation environment, the next step involves extracting assembly-level instructions for each execution cycle. These instructions contain key details regarding the computational sequences being performed [39]. The extracted data includes memory addresses where instructions are stored, the types of instructions being executed, and the corresponding operand values manipulated during execution. Extracting this information is critical because each instruction contributes differently to power consumption, which is later analyzed in SCAs [30].  
To retrieve these instructions, GDB is utilized to monitor and capture executed commands in real-time. This enables precise tracking of how cryptographic algorithms operate at the machine level, helping to identify which instructions might be susceptible to power analysis. The debugging interface of GDB provides a structured format where assembly instructions, memory access patterns, and register manipulations are recorded.  
Table 5 presents an example of extracted assembly data obtained through GDB debugging. The extracted instructions include memory addresses, executed instruction types, and operand values stored in registers. This dataset serves as a representative example of how cryptographic operations are executed at the machine level and is crucial for power trace generation.  
The extracted assembly data is then used to generate power traces through the ELMO simulator. Each instruction contributes to a unique power consumption pattern based on factors such as the number of bits changed in a register or memory operation complexity. By mapping these execution patterns to power traces, it becomes possible to assess how DL models identify cryptographic operations and evaluate the vulnerability of existing countermeasures.
# Performance Model for Video Service in 5G Networks
## 3 Approaches
### Interference Coordination Approach
#### (1) Intra-cluster interference coordination  
Within each cluster, up to \(M_{max}\) UE can be scheduled simultaneously per time-frequency resource (per TTI per sub-band). A greedy algorithm has been used for intra-cluster scheduling. The algorithm starts by adding a randomly selected UE, UEs are added according to:  
IC-P1: Increased network utility defined in (9).  
IC-P2: Increased number of UE scheduled satisfying the constraint defined in (14).  
UE is added till the criteria are broken or maximum number of users per cluster is reached. In this way, UEs are partitioned into groups, and the groups are assigned different time-frequency resources. Scheduled UE will not be scheduled to the next time-frequency resource in the same intra-cluster coordination period (ICCP) (ICCP is the number of TTIs within which all UEs are scheduled once). The Algorithm 1 is shown in the following pseudo-code.
## 5 Conclusions  
In this paper, a double-branch neural network with SAM adaptive filter modules is proposed to estimate DOA in the presence of colored noise. Specifically, the filter module is used to adaptively filter the frequency components of colored noise while retaining useful information. In addition, CNN and LSTM are integrated to simultaneously mine the temporal and spatial information of the signal data. Simulation experiments show-cased the remarkable performance of the proposed model. At an SNR of \(-5\) dB, our network achieves an accuracy rate of 85%, with an RMSE of only 2.03\({}^{\circ}\). These results demonstrate a significant improvement over existing algorithms, particularly in challenging scenarios characterized by low SNR and limited snapshot counts. The proposed network exhibits superior capabilities in suppressing colored noise and enhancing DOA estimation performance.  
Conceptualization, W.X; formal analysis, M.L.; methodology, supervision, S.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
**Data Availability Statement:** Relevant information and codes are available from the corresponding author if required.  
The authors would like to thank the referees for their useful suggestions which have significantly improved the paper.  
Authors [PERSON] and [PERSON] were employed by the company Zhenjiang Jizhi Ship Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
Figure 11: Performance of DOA estimation under specific angle with SNR being \(-5\) dB and the number of snapshots being T = 200. (**a**) Prediction results of the first DOA. (**b**) Prediction results of the second DOA.
## 3 Methodology
### The Proposed Multimodal Hidden Markov Model (MHMM)
#### 3.2.2 Statistical Modeling of Emission Probabilities  
A critical design choice in an MHMM for continuous data is the selection of appropriate probability distributions for the emission probabilities \(b_{j}(O_{t})\). For this exposition we use surgery proficiency as the use-case and use statistical properties and benchmarks from surgical literature, particularly the work of [PERSON] et al. (2025) [24], the following distributions were chosen:  
* Task Completion Time (TCT)-Lognormal Distribution: TCT data are positive, skewed right, and bounded at zero, with a long tail due to errors or hesitation. The lognormal distribution is suitable for such data [66].
* Heart Rate Variability (HRV)-Normal (Gaussian) Distribution: HRV, as a physiological measure, clusters symmetrically around a mean for a given cognitive state, making the normal distribution appropriate [67].
* NASA Task Load Index (TLX)-Normal (Gaussian) Distribution: Subjective TLX scores are assumed to follow a normal distribution around a mean characteristic of a proficiency level [37].  
The use of these specific distributions ensures that the MHMM accurately captures the statistical properties of each data stream, thereby improving the fidelity of the emission probability calculations and the overall accuracy of proficiency state inference, creating distinct multimodal signatures for each proficiency state for the surgery use-case. More generally, the specific distributions and their parameters will depend on the application in terms of the tasks, multi-modal measurements, and of course the human operators and associated proficiency levels.
# Carbon Dots with Up-Conversion Luminescence as pH Nanosensor
## 2 Materials, Their Characterization, and Research Methods
### Spectrophotometry of Aqueous Solutions of CDs  
The optical extinction spectra of aqueous solutions of CD-poly, CD-COOH, and CD-OH were obtained on a Shimadzu UV-1800 spectrophotometer in the wavelength range of 190-1100 nm with a resolution of 1 nm. The informative part of the obtained spectra is shown in Figure 3.  
As can be seen from the presented data, the extinction spectra of aqueous solutions of CDs have a number of features. In all optical extinction spectra for the studied aqueous solutions, an intense peak is observed in the short-wavelength region, the right shoulder of the band of which extends up to ~250-300 nm. According to the literature data [41], the peak of CDs in this region is due to \(\pi\)-\(\pi^{*}\) transitions in aromatic sp\({}^{2}\) carbons. In the extinction spectra of CDs with polyfunctional and carboxylated surfaces, a peak is observed in the  
Figure 3: Optical extinction spectra of aqueous solutions for the studied CDs. pH = 7.  
region of 275 nm, corresponding to n-\(\pi^{\star}\) transitions of C=O [42] bonds and corresponding to n-\(\pi^{\star}\) transitions of C=O, C-N, or C-OH bonds in sp\({}^{3}\)-hybridized domains associated with carboxyl (-COOH) or amine (-NH\({}_{2}\)) groups on the CDs' surface [43]. In the optical extinction spectra of CD-OH, these peaks are absent.
# Effects of Corner Set--Backs on Wind Loads and Wind Induced Responses of Rectangular Tall Buildings
###### Abstract  
In order to investigate the effects of corner set-backs on wind loads and wind-induced responses of rectangular high-rise buildings, pressure measurements were carried out on a benchmark model (CARRC) and four models with different rates (5%, 10%, 15%, 20%) of corner set-backs in a boundary layer wind tunnel. The test results show that the corner set-backs contribute to reducing along-wind loads of the rectangular high-rise building models, and the maximum reduction happens at 10% corner set-back. The across-wind loads decrease as the rate of corner set-back is increasing and the maximum reduction emerges at 20% corner set-back. The RMS accelerations at the top of models also decrease with the increasing of rate of corner set-back in along-wind and across-wind. Through the fitting of test results, empirical formulas for the correlation factors of base moment coefficients of rectangular high-rise buildings with different rates of corner set-back are put forward. Moreover, the correlation factors for the power spectrum densities of base moments are listed at typical frequencies corresponding to the practical tall buildings. The outputs of this paper aim to serve as references for wind-resistant design of similar buildings in strong wind region.  
rectangular high-rise building; corner set-back; wind tunnel test; wind loads; wind-induced responses; correlation factor +
Footnote †: journal: Applied Sciences  
1
## 1 Introduction
DPCs produce numerous soluble factors, such as vascular endothelial cell growth factor (VEGF), insulin-like growth factor-1 (IGF-1), hepatocyte growth factor, transforming growth factor-\(\beta\)1 (TGF-\(\beta\)1), and others [19], that are essential for the development and maintenance of hair follicles. It has been reported that VEGF and IGF-1 are essential growth factors for DPCs and biomarkers for hair growth [20; 21]. VEGF is a growth factor that regulates hair morphogenesis and hair growth by promoting angiogenesis, which restores the oxygen supply to tissues. IGF-1 signaling has been identified as a crucial mitogenic and morphogenetic regulator in the biology of hair follicles, influencing follicular proliferation, tissue remodeling, and the hair growth cycle. In contrast, dysfunctional DPCs that produce pro-inflammatory and chemo-attractive cytokines can result in keratinocyte damage in the hair matrix, and, consequently, hair growth disorders [22]. It is believed that the interaction between the epidermal and dermal components of the hair follicle is essential for maintaining hair growth [23; 24]. Consequently, ORS keratinocyte cultures have been used in secondary in vitro screening models to examine hair growth [25]. Phytochemicals are compounds derived from different parts of plants, including the roots, leaves, flowers, and seeds. Phytochemicals contain polyphenols as active ingredients. They can be classified into flavonoids (catechins, isoflav, proanthocyanidins, and anthocyanins) or non-flavonoids (phenolic acids, benzoic acids, stilbene, and resveratrol). They have been widely studied due to their potent antioxidant activity, which can protect skin from ROS-induced skin inflammation, photoaging, and photocarcinogenesis [26]. Altogether, the ROS-mediated activation of the immune system in the DPCs has been implicated in the pathogenesis of hair loss, suggesting that phytochemicals in essential oils could prevent ROS-mediated hair loss in humans [27]. This study expects the phytochemicals in essential oils to promote hair growth through the ROS-scavenging activity of DPC.
# Study on Vertical Load-Carrying Capacity of Post-Grouting Bored Piles
## 1 Introduction  
At present, pile foundation is the main form of foundation in engineering construction. Bored pile is one of the more widely used pile types, and its vertical bearing capacity is mainly from pile side frictional resistance and pile tip resistance. However, due to the influence of stratigraphic conditions and piling technique, bored pile inevitably produces pile side mud skin and pile tip sediment, so the vertical bearing capacity of the pile foundation cannot be fully developed [1; 2].  
According to the literature [3], post-grouting reinforcement technology was first used in 1958 by Venezuela in the construction of the Maracaibo bridge using grouting tube to reinforce the pile in the bridge foundations, and was tested and found to have significantly improved the pile-bearing capacity. [PERSON] et al. [4] conducted static load tests on bored pile and post-grouting bored pile in a trial piling project for the Sutong Yangtze River Bridge. The results showed that the ultimate bearing capacity of the pile after grouting increased by 1.48 to 2.0 times and the total pile tip resistance increased by 2.46 to 7.21 times. It is believed that the slurry increases the strength of the soil at the pile tip and part of the soil around the pile by permeating and compacting the soil layer, thus increasing the ultimate bearing capacity of the pile. [PERSON] et al. [5] carried out static load tests on three piles of a supertral project and investigated the load-bearing characteristics of large-diameter, extralong bored pile using numerical analysis. The ultimate load-carrying capacity of pile with pile tip grouting was found to increase by almost 50% compared to ungrouted pile, and the ultimate load-carrying capacity of piles with composite pile tip-pile side grouting was found to roughly double. [PERSON] et al. [6] established a method for calculating the bearing capacity of post-grouting pile applicable to different grouting types by analyzing a large number of post-grouting static load test data.  
Many other scholars have also summarized many ultimate bearing-capacity calculation formulae for post-grouting pile in their research, mainly based on the calculationformulae for the bearing capacity of ordinary pile with the introduction of corresponding enhancement factors for consideration according to the different grouting methods [7]. This method is an empirical formula based on actual measurements under specific conditions, and its applicability is somewhat limited by various conditions in the project, such as the difference of grouting amount. This paper analyzes the load-transfer law of bored pile in conjunction with actual engineering and the effect on the load transfer of pile after the use of reinforcement methods, such as pile tip grouting, pile side grouting, and composite grouting. In the engineering design stage, the difference in geological conditions and the amount of grouting provides the corresponding theoretical basis for the prediction of the ultimate pile-bearing capacity.
## 4 Security Proofs
### Ticket Swinding Resistance  
In our scheme, a mobile user has to show \(T=(F_{1}(m^{*}),\delta^{*},\sigma^{*},\gamma,s^{*})\) for authentication. In this subsection, we will prove that none can successfully pass authentication via an eavesdropped \(T\). We call this Ticket Swinding Resistance. In order to prove this, we first introduce the communication model and some definitions as follows. The Communication Model. We briefly describe the communication model [21; 22] of our distributed environment. Oracle \(\Pi_{MS_{i},V_{j}}^{u}\) models that a mobile user \(MS_{i}\) performs the anonymous authentication protocol of Section 3.4 with the entity \(V_{j}\) in the \(u\)th session of \(MS_{i}\). Oracle \(\Pi_{V_{j},MS_{i}}^{v}\) models that a system entity \(V_{j}\) performs the protocol with the mobile user \(MS_{i}\) in the \(v\)th session of \(V_{j}\). An adversary \(E\) is a probabilistic polynomial-time Turing machine that is allowed to make the following queries. * \(Execute(\Pi_{MS_{i},V_{j}}^{u},\Pi_{V_{j},MS_{i}}^{v})\): This query models all kinds of passive attacks. \(MS_{i}\) and \(V_{j}\) will carry out the protocol of Section 3.4 and the adversary \(E\) can eavesdrop all messages transmitted between \(MS_{i}\) and \(V_{j}\). Figure 9: The oracles in the proof of Tamper Resistance. * \(\mathit{Send}(\Pi^{u}_{\mathit{MS}_{i},V_{i}^{\prime}},\ \mathcal{M})\) or \(\mathit{Send}(\Pi^{v}_{V_{j},\mathit{MS}_{i}^{\prime}},\ \mathcal{M})\): This query models all kinds of active attacks. The adversary \(E\) can send any message \(\mathcal{M}\) to \(\Pi^{u}_{\mathit{MS}_{i},V_{j}}\) or \(\Pi^{v}_{V_{j},\mathit{MS}_{i}}\) which will give responses to \(E\) according to the protocol of Section 3.4.