esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Recent research in underwater wireless sensor networks (UWSNs) has gained the attention of researchers in academia and industry for a number of applications. They include disaster and earthquake prediction, water quality and environment monitoring, leakage and mine detection, military surveillance and underwater navigation. However, the aquatic medium is associated with a number of limitations and challenges: long multipath delay, high interference and noise, harsh environment, low bandwidth and limited battery life of the sensor nodes. These challenges demand research techniques and strategies to be overcome in an efficient and effective fashion. The design of routing protocols for UWSNs is one of the promising solutions to cope with these challenges. This paper presents a survey of the routing protocols for UWSNs. For the ease of description, the addressed routing protocols are classified into two groups: localization-based and localization-free protocols. These groups are further subdivided according to the problems they address or the major parameters they consider during routing. Unlike the existing surveys, this survey considers only the latest and state-of-the-art routing protocols. In addition, every protocol is described in terms of its routing strategy and the problem it addresses and solves. The merit(s) of each protocol is (are) highlighted along with the cost. A description of the protocols in this fashion has a number of advantages for researchers, as compared to the existing surveys. Firstly, the description of the routing strategy of each protocol makes its routing operation easily understandable. Secondly, the demerit(s) of a protocol provides (provide) insight into overcoming its flaw(s) in future investigation. This, in turn, leads to the foundation of new protocols that are more intelligent, robust and efficient with respect to the desired parameters. Thirdly, a protocol can be selected for the appropriate application based on its described merit(s). Finally, open challenges and research directions are presented for future investigation.  
survey; routing; underwater wireless sensor networks (UWSNs); protocol; localization +
Footnote †: journal: Sensors  
0000-0002-4807-3800][PERSON]  
12
## 4 Conclusions  
In this study, the performance of a commercially available air cleaner was investigated numerically by using CADR and age of air parameters. Twelve different cases were considered to investigate the influence of air cleaner location (center location and wall-side location), particle diameters (1 \(\upmu\)m and 10 \(\upmu\)m), and blowing speed (2 m/s, 3 m/s, and 4 m/s) on the system performance. The velocity, temperature, and age of air distributions associated with the ACD in a room are shown. The main conclusions from the analyses presented in this study could be summarized as follows:  
* An increase in the air flow rate of the ACD causes more substantial recirculation inside the room. The age of air values decrease when the flow rates increase at both locations. Also, the center placement of the ACD shortens the air age due to a smaller recirculation zone and a more homogeneous air distribution.
* An increase in the air flow rate results in an increase in the CADR values of the ACD, meaning a better cleaning performance. As a result, an increase in the air flow rate has positive effects on both CADR and age of air parameters. The center placement of the ACD has higher CADR values compared to the wall-side placement.
* To improve the air quality at a certain location, both an increase in blowing speed and a change of device position should be considered. Another important point is that the increase in blowing speed increases the difference in air age values between the two positions. In other words, the air age values in the room become more dependent on the device position as the distance of the location from the device and the blowing speeds increase.
* By comparing the two performance methods, it is observed that the mean age of air method shows a recirculation structure of the air in the room. Therefore, the best position, which gives the smallest mean air age in the room, can be determined. Meanwhile, the CADR method gives the best collection efficiency considering the particle diameters and the flow rate. In addition to that, the decay rate of the particles and the required cleaning time interval for an air cleaning device can be determined using the CADR method. Hence, both methods are useful to determine the performance of an air cleaner device.  
Conceptualization, M.K. and M.G.; methodology, M.K. and M.G.; software, M.K. and M.G.; validation, M.K. and M.G.; formal analysis, M.K. and M.G.; investigation, M.K. and M.G.; resources, M.K. and M.G.; data curation, M.K. and M.G.; writing--original draft preparation, M.K. and M.G.; writing--review and editing, M.K. and M.G.; visualization, M.K. and M.G.; supervision, M.K.; project administration, M.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
## 4 Numerical Study of SC Walls Perforation
### Model Setting  
The previously described ogival nose projectile was used as the penetrator. With an 800 mm length and width, the investigated concrete panels were attached with 3 mm to 11 mm thicknesses of the rear steel plate. With the same cross section, the concrete panel thicknesses were selected as 150 mm, 200 mm and 250 mm, respectively. For SC walls with 200-mm thick concrete panel, the striking velocities of the projectile were set at 550, 600, 650, 700 and 800 m/s. Furthermore, a striking velocity of 600 m/s was set for perforation simulations with 150 mm and 250 mm thick concrete panels.  
\begin{table}
\begin{tabular}{c c c} \hline \(V_{s}\) **(m/s)** & \(V_{r,e}\) **(m/s)** & \(V_{r,n}\) **(m/s)** \\ \hline
436 & 137 & 187 \\
482 & 207 & 238 \\
544 & 304 & 334 \\
651 & 451 & 474 \\ \hline \end{tabular}
\end{table}
Table 4: Comparison between experiment and simulation.  
Figure 8: Damaged contour of SC walls. (**a**) Rear view of SC walls; (**b**) section view of concrete panel; (**c**) numerical results of damaged steel plate; (**d**) numerical results of damaged concrete panel.  
For the sake of computational cost, the SC walls perforation models were developed as quarterly symmetric bodies with symmetric boundaries. Figure 9 shows the FE model in which the grids near the impact region were refined. For the 1/4 model of SC walls with a combination of 11 mm thickness steel plate and 250-mm thick concrete panel, the element numbers for the three parts are given in Table 5. These material models and their parameters are the same as in the validation model. An eroding algorithm was applied to all the interactions between the contact components.
# The Novel EfficientNet Architecture-Based System and Algorithm to Predict Complex Human Emotions
## 1 Introduction
Heart rate, skin conductance, and respiration are effective in determining a person's resting and active emotional states. These signals are also useful for assessing stress levels and for accurate emotion recognition. The autonomic nervous system plays an important role in regulating heart rates and cardiac outputs according to physiological and psychological stresses, which means that it is possible to acquire natural emotional states. Heart rate variability (HRV) is used to monitor the risk of emotional changes in real time in astronauts [11; 12]. Nowadays, ECG signals can be obtained from inexpensive, wearable, and mobile devices, making it possible to monitor without requiring any medical expertise. It is non-invasive and there is no need to visit a hospital or cardiologist [13]. For this reason, it was determined that HRV is the most suitable bio-signal for emotion recognition [14; 15; 16]. However, relying solely on one signal, such as HRV, may not capture the full complexity of human emotions, since emotional expressions can be influenced by many factors. Recognizing the limitations of single-signal approaches, this paper aims to address these challenges by proposing a sophisticated model through the integration of facial expression images and HRV data. By combining these two sources of information, our model aims to leverage the strengths of each approach. Facial expressions provide a direct and observable indicator of emotional states, capturing the nuances of human expressions. In contrast, HRV offers an internal physiological measure, reflecting the autonomic nervous system's activity in response to emotions. The contributions of this paper are summarized as follows:  
I. We have successfully trained CNN models for facial expression recognition using EfficientNet-B0, which optimizes the depth, width, and resolution of the network. II. Multi-Modal Data Integration: This study integrates both visual (facial expression images) and physiological (HRV) data to create a robust model. This comprehensive approach allows for a more detailed understanding of human emotions compared to traditional methods that rely on single data sources. III. We report state-of-the-art results on benchmark datasets. The rest of the paper is organized as follows. We present recent related work in Section 2. We describe the algorithms and data sources used in our study, including the data collection process, preprocessing techniques, and specific algorithms employed for emotion prediction, in Section 3. We present the experimental results analysis of our findings in Section 4. Finally, our conclusions in Section 5 summarize the main findings, with suggestions for future work.
# Probabilistic Shaping for Finite Blocklengths: Distribution Matching and Sphere Shaping
## 2 Preliminaries  
### Notation and Definitions  
We use capital letters \(X\) to denote random variables, lower case letters \(x\) to specify their realizations. Random vectors of length \(n\) are indicated by \(X^{n}\) while their realizations are denoted by \(x^{n}\). Element-wise multiplication of \(x^{n}\) and \(y^{n}\) is shown by \(x^{n}y^{n}\). Calligraphic letters \(\mathcal{X}\) represent sets. We use \(\mathcal{X}\mathcal{Y}\) to denote \(\{xy:x\in\mathcal{X},y\in\mathcal{Y}\}\). The \(n\)-fold Cartesian product of \(\mathcal{X}\) with itself is denoted by \(\mathcal{X}^{n}\). Boldface capital letters \(\mathbf{P}\) specify matrices. Probability functions (density or mass) over \(\mathcal{X}\) are denoted by \(P_{X}(x)\). The probability density function of \(Y\) conditioned on \(X\) is indicated by \(P_{Y|X}(y|x)\).  
The discrete-time AWGN channel output is given at time \(i=1,2,\ldots,n\) by \(Y_{i}=X_{i}+Z_{i}\), where \(Z_{i}\) is the noise which is independent of the input \(X_{i}\), and drawn from a zero-mean Gaussian distribution with variance \(\sigma^{2}\). The noise \(Z_{i}\) is also independent over time \(i\). There is an average power constraint \(\mathbb{E}[X^{2}]\leq P\), where \(\mathbb{E}[\cdot]\) is the expectation operator. The SNR is \(\mathbb{E}[X^{2}]/\sigma^{2}\).  
The capacity of the AWGN channel is given by  
\[C=\frac{1}{2}\log_{2}(1+\text{SNR}), \tag{1}\]  
in bit/1-D. This capacity can be achieved as \(n\to\infty\) by employing a codebook (set of input sequences) in which all the codewords (input sequences) are generated with entries independent and identically distributed according to a zero-mean Gaussian with variance \(P\) (Chapter 9 in [79]). The corresponding random coding argument shows that channel input sequences, drawn from a Gaussian distribution, are likely to lie inside an \(n\)-dimensional ball of squared radius \(nP(1+\varepsilon)\) for any \(\varepsilon>0\), when \(n\to\infty\). This motivates to select the signal points from within an \(n\)-ball, or equivalently, to use an \(n\)-sphere as the signal space boundary, in order to achieve capacity. For a more detailed discussion on the asymptotic duality of Gaussian distributions and \(n\)-spherical signal spaces for large \(n\), we refer the reader to, e.g., (Section IV-B in [9]).
# Sustainable Plant-Based Diets and Food Allergies: A Scoping Review Inspired by EAT-Lancet
## 3 Results  
### Overview of Included Studies  
Following the database searches conducted in PubMed, Scopus, and Google Scholar, a total of 1462 records were initially retrieved. After the removal of 300 duplicates, 961 titles and abstracts were excluded based on irrelevance to this study's scope. Subsequently, 201 full-text articles were assessed for eligibility. Of these, 53 studies met the predefined inclusion criteria and were incorporated into the final synthesis, as illustrated in the PRISMA flow diagram (Figure 1). The included studies were published between 2010 and 2025 and encompassed diverse geographic settings, including Europe, North America, Asia, and international consortia. Study populations spanned from children and pregnant women to adult cohorts, with designs ranging from RCTs and observational studies to systematic reviews and preclinical investigations. Specifically, the final pool comprised 11 systematic reviews and meta-analyses, 6 randomized clinical trials, 17 observational cohort or cross-sectional studies, and 19 narrative reviews or experimental models (animal and in vitro). Integrative Role of Plant-Based Dietary Patterns in the Prevention and Management of Allergic Disorders  
The preventive and therapeutic effects of plant-based dietary models in allergic disease contexts, as reported in the selected studies, are summarized in Tables 3 and 4. These dietary patterns--including Mediterranean, vegetarian, and vegan models--emphasize the consumption of whole plant foods such as legumes, vegetables, whole grains, and fruits. A growing body of literature underscores how such patterns--particularly when they reduce rather than exclude animal products--enhance the intake of prebiotic fibers, polyphenols, and anti-inflammatory micronutrients. These components foster a eubiotic gut environment, reduce mucosal inflammation, and contribute to immune tolerance through the promotion of microbial short-chain fatty acid (SCFA) production, improved epithelial barrier integrity, and modulation of Th2/Th17-driven immune responses.
# Error Probability of a Coherent \(M\)-ary PSK FSO System Influenced by Phase Noise
## 3 Error Rate Analysis
### Representation of PDF of Signal Phase in Fourier Series Form
\tag{16}\]  
The PDF of phase \(\psi\) of the composite received signal is represented in the Fourier series form as  
\[f_{\psi}(\psi)=\frac{1}{2\pi}+\sum\limits_{n=1}^{\infty}b_{n}\cos(n\psi),\quad |\psi|\leq\pi, \tag{17}\]where the Fourier coefficient \(b_{n}\) depends on specific channel conditions. In our case, based on previous derivation, for the FSO channel influenced by the \(\mathcal{M}\) distributed atmospheric turbulence and pointing errors, the Fourier coefficient \(b_{n}\) is derived as  
\[b_{n}=\frac{\xi^{2}An}{4\pi}\sum_{k=1}^{\beta}a_{k}G_{3,4}^{4,1}\left(\frac{ \alpha\beta\kappa(g+\Omega^{\prime})}{(g\beta+\Omega^{\prime})\gamma}\left| \begin{smallmatrix}1-\frac{\eta}{2},1+\frac{\eta}{2},\xi^{2}+1\\ \xi^{2},\alpha,k,0\end{smallmatrix} \right.\right). \tag{18}\]  
For channel propagation conditions observed in [28; 29; 30], by applying quite the similar procedure, these Fourier series coefficients can be derived in the form  
\[b_{n}^{\text{GG}}=\frac{n}{2\pi\Gamma(\alpha)\Gamma(\beta)}G_{2,3}^{3,1}\left( \frac{\alpha\beta}{\bar{\gamma}}\left| \begin{smallmatrix}1-\frac{\eta}{2},1+ \frac{\eta}{2}\\ \alpha,\bar{\gamma},0\end{smallmatrix} \right.\right). \tag{19}\]
# Slip Estimation Model for Planetary Rover Using Gaussian Process Regression
## 2 Research Framework  
The research framework of this paper is shown in Figure 3. In general, it was consistent with our previous paper [22]; however, the variables considered in the study and the machine learning methods used were different. The specific research steps were as follows:  
1. Collect test data from tests conducted at a site equipped with a low-gravity simulation device and simulated soil. In this paper, we collected data on two kinds of simulated soil, while our previous paper [22] only used the data collected on JLU Mars-2. As such, we were able, in this work, to compare and discuss the influence of soil type on the applicability of slip estimation models.
2. Train slip estimation models with machine learning algorithms. In order to establish slip estimation models, considering the slip uncertainty, we used Gaussian process regression (GPR) to train models. GPR can predict not only slip values, but also their confidence intervals. GA-BP has also been used to train models to compare the performance of the two algorithms.
3. Evaluate the trained GPR models and GA-BP models on ground test data. In this paper, mean absolute error (MAE) and root mean square error (RMSE) were chosen as evaluation indices to evaluate the performance of the models.
4. Validate the trained models on telemetry data from Zhurong. We combined the path planning information and slip estimation information to correct the planned path, and then compared the end of the slip path with the actual final point.  
Figure 2: The routing path of Zhurong during its designed life.
# Improving Decodability of Polar Codes by Adding Noise
## 1 Introduction
In [35], a post-processing technique was proposed to improve the performance of the SSC polar decoder in the 2D intersymbol interference (ISI) data storage system, namely the post-processing SSC (PP-SSC) decoding algorithm, in which the perturbation algorithm and the genetic algorithm (GA) successively generate perturbation vectors that accelerate the convergence of the decoder. Unfortunately, the secondary generation of perturbation noise by the GA is performed online, which greatly increases the delay and complexity of the decoder. In order to address these issues, this paper proposes several decoding algorithms for polar codes by applying the idea of adding noise, which generalizes the methods in our previous work [36]. However, more effective operations in the GA training process are used in this work. We first propose an online perturbed and directed neural-evolutionary (Online-PDNE) decoding algorithm. Then, a simplified version of Online-PDNE decoding algorithm, called the PDNE decoding algorithm, is proposed by using the genetic process in an offline manner. Finally, to further reduce the decoding complexity, we further propose a sole neural-evolutionary (SNE) decoding algorithm, which only invokes the pre-trained offline directed neuro-evolutionary noise and provides a considerable balance between the decoding performance and complexity. Simulation results suggest that our proposed decoding algorithms outperform the other conventional algorithms for decoding polar codes. In addition, the algorithms in this paper have more generality compared with the algorithm in [36]. The main contributions of this paper are summarized as follows:  
1. An online perturbed and directed neural-evolutionary (Online-PDNE) decoding algorithm is proposed, which makes polar codes have enhanced error correction ability. 2. To avoid the online training process, the PDNE decoding algorithm is proposed, in which perturbation noise and pre-trained offline directed neuro-evolutionary noise sequences are sequentially employed for re-decoding. 3. In order to further reduce the decoding complexity, the SNE decoding algorithm is further proposed, where only the pre-trained offline directed neuro-evolutionary noise by the GA algorithms is employed to improve the decoding performance. 4. The decoding algorithms proposed in this paper are more suitable for the scenarios where the channel quality gradually degrades, such as the storage channel. A good balance can be achieved in terms of the performance and complexity. The rest of the paper is organized as follows. Section 2 reviews the related work on polar decoding algorithms. In Section 3, the proposed decoding algorithms of polar codes are presented. Simulation results are provided in Section 4. Finally, Section 5 concludes the paper.
# Analysis of 5G and LTE Signals for Opportunistic Navigation and Time Holdover
## 4 Discussion
### Downlink Signal Availability and Power Levels  
One of the unexpected observations in this study was the large dynamic range of the reported received signal-to-noise ratio (SNR) reported by the STARE SDR over extended observation intervals, which did not seem to be accompanied by a change in the noise statistics of the measured delta range values. This observation implies that the downlink signals from the towers are dynamically adjusted during periods of light load, but not in a manner that the authors can conclusively identify from either the literature or observations. The literature supports a plurality of power saving options for 5G in general, though the vast majority of these are focused entirely on the power savings within UE devices [33; 34], which is reasonable as these tend to be battery powered. Even in cases where discussions of modification to the downlink signal structure are explicitly discussed in the context of energy savings, such as in [35], adjusting the base station multiple input multiple output (MIMO) configuration dynamically is typically for optimizations intended to reduce UE energy requirements. The 3 GPP standards do explicitly allow for a dynamic capacity scaling of a 5G gNodeB (gNB) when operating in environments where power supply is scarce or intermittently available, as per [36], but it is unclear if this can include adjustments to the PSS or SSS signal features that were used in this study, as these are essential for UE devices to detect and associate with the gNB or eNodeB (eNB). Some telecoms operators do pursue a power saving strategy involving the de-activation of an underutilized gNB that is co-located with an eNB when the utilization level of the cell does not require the throughput or other capabilities of the 5G radio, as per [PERSON] et al. [37].  
While these behaviors have impacts on the passive exploitation of these signals for either navigation or timing, the data presented in this paper did not suffer from the losses of lock caused by dynamic power reduction, nor a loss of tracking due to source deactivation, so the power saving did not appear to impact the results presented. The base stations could, in theory, reduce the power at which the PSS and SSS are transmitted. Given the information available, it is hard to tell if this is, in actuality, implemented in the towers available during the measurement campaign, but the authors believe that this is of no consequence in either case. Power saving could reduce the SNR and thus make acquisition more difficult or lead to a higher rate in the loss of lock; however, in a scenario where no loss of locks occur, such as in this paper, power saving should not have an appreciable effect on the results presented.