esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results on ML/DL-Based Modulation Recognition
### Overview of Modulation Recognition Techniques  
An overview of the ML/DL techniques, optimizers, and training examples for UWA communication systems is presented in Table 16. Since the same UWA modulation recognition system can recognize both single-carrier and multi-carrier modulations, we did not make separate tables for single- and multi-carrier systems. The descriptions of various parameters listed in this table were provided earlier in Section 3.1.  
It can be observed from Table 16 that various models such as Sequence Convolutional Network (SCNet), RNN, CNN, ResNet, SVM, and RL are applied across different studies. Optimizers include Adam, GD, and Momentum Stochastic Gradient Descent (MSGD), while some studies do not specify an optimizer. Training examples vary, with some studies using simulated data, others relying on measured data, and a few combining both approaches. Bellhop is a common simulation tool used in some cases. The diversity in models, optimization strategies, and training datasets highlights the evolving nature of modulation recognition techniques in underwater acoustic communication. To summarize, the range of models, optimizers, and training approaches in Table 16 reflects a shift toward data-driven modulation recognition methods, emphasizing both classification accuracy and adaptability across diverse underwater scenarios.
## 1 Introduction
It can be summarized from the reported literatures that the existing research mainly focuses on the frame structure or a single joint with regular symmetric columns, and the bidirectional stiffness is basically consistent when resisting seismic loads. However, the study of the spatial frame structure with special-shaped concrete-filled steel tubular columns has not been reported to the best knowledge of the authors. The spatial frame is more integral than the planar frame because of the addition of the floor diaphragms. The spatial frame structure of the special-shaped concrete-filled steel tubular column is more complicated than the frame structure of the regular column since the space utilization of the structure is enhanced. Moreover, the L-shaped concrete-filled steel tubular column is an asymmetrical structure, which will be subjected to torsion under seismic load at random directions. It is very complicated to define the interface and contact model in simulation. In the study of seismic problems, the nodes and bottoms of column of spatial frame structure with special-shaped concrete-filled steel tubular columns are weak parts and they are more susceptible to damage. The experimental investigation of seismic behavior of composite spatial frames with steel beams connecting to L-shaped CFST columns were conducted in the previous study by authors [59]. This study aims at extending the understanding of seismic damage of those composite structures by using nonlinear finite element analysis. Those experimental test models were adopted as physical model for the proposed numerical analysis. In this paper, a numerical model was built to simulate the behavior of composite spatial frames when applying a constant axial load on columns and fully-reversed lateral cyclic load on frames. Finite element analysis software ABAQUS was employed in this study. Six main components were considered in this analysis, i.e., the core concrete of L-shaped steel tubular columns, steel tubes, the interfacial contact between the concrete and steel tubes, steel beams and attachments characteristic between beams and columns, and the connection details between the columns and L-shaped inner diaphragm. A series of new experimental data was obtained by testing the behavior of the composite spatial frames under specific loads. This experimental test results were used to validate the numerical simulation model proposed in this paper. In the experimental study, each specimen consists of two-story single span steel beam (eight in total) and four L-shaped CFST columns. The effect of axial compressive load ratio and loading direction on the mechanical behavior of composite frames were experimentally investigated. The accuracy of the FEA model was estimated by comparing the numerical with the experimental results. It was found that the results from FEA model, which used the constitutive model of steel and core concrete proposed in this study conformed to those that were obtained by the experimental tests.
# Efficient Resource Management for Sum Capacity Maximization in 5G NOMA Systems
## 3 Resource Management With Fixed Power Control  
We can see in (5) of the optimization problem (P1) that the sub-channel allocation and power management are coupled with each other in terms of system sum capacity. We fist decouple the sub-channel assignment and power allocation and then employ the sub-optimal approaches to obtain the efficient solutions. In this section, we provide efficient sub-channel assignment under fixed power allocation. To reduce the SIC complexity and interference among NOMA users, we assume that a sub-channel can accommodate no more than two users. With that, the constraint (7) can be rewritten  
Figure 1: Downlink non-orthogonal multiple access (NOMA) system.  
as \(\sum\limits_{j=1}^{K}\beta_{j,m}\leq 2,\ \forall m\). Further, we consider that the total number of sub-channel as \(M=K/2\). For the given power of BS, the problem of efficient sub-channel allocation can be then simplified as:  
\[\text{(P2)} \underset{\beta_{j,m}}{\text{maximize}}\bigg{\{}C_{\text{sum}}= \sum\limits_{j=1}^{K}\sum\limits_{m=1}^{M}\beta_{j,m}C_{j,m}\bigg{\}}\] (11) s.t. \[\sum\limits_{m=1}^{M}\beta_{j,m}\leq 1,\ \forall j, \tag{12}\] \[\sum\limits_{j=1}^{K}\beta_{j,m}\leq 2,\ \forall m,\] (13) \[C_{j,m}\geq C_{\text{min}},\ \forall j,\forall m. \tag{14}\]  
where constraint in (13) shows that a sub-channel can be assigned to no more than two users. Now we propose two algorithms for sub-channel allocation based on user channel condition and user minimum capacity requirement.
## 3 Results
### Anti-Biofilm Activity

Table 3: Minimum Inhibition Concentration (MIC) and Minimum Bactericidal Concentration (MBC) (mg/mL) of thymol, carvacrol, _p_-cymene, and \(\gamma\)-terpinene against pathogenic bacteria. Both the bacterial strains and the antimicrobial agents (EOs, main compounds, or WSTRTc extracts) significantly affected the MIC, MBC, MBIC, and MBEC values (\(p<0.05\)), and a strong interaction between the two factors was observed (\(p<0.05\)). In general, the strongest antibiofilm activity between the EOs tested was recorded by _S. thymbra_ followed by _T. capitatus_, while _O. hitrum_ had the least effect on biofilm inhibition (\(p<0.05\)) in all bacteria tested except _E. coli_ (\(p>0.05\) to _S. thymbra_) and _L, monocytogenes_ (\(p>0.05\) to _T. capitatus_). The EOs and their main compounds showed significantly (\(p<0.05\)) stronger antibiofilm activity against all bacteria tested than the WSTRTc extracts. _O. hitrum_ EO inhibited biofilm formation by _L. monocytogenes_, _S. Enteritidis_, and _P. mirabilis_ at higher concentrations compared to _E. coli_ (\(p<0.05\)). Thymol demonstrated significantly higher (\(p<0.05\)) anti-biofilm activity than the EOs in all bacteria tested, except for _S. thymbra_ against _L. monocytogenes_ where no significant differences (\(p>0.05\)) were noted. Carvacrol's biofilm inhibition activity was significantly higher (\(p<0.05\)) than that of _O. hitrum_ EO against _E. coli_ and _S. Enteritidis_, significantly lower (\(p<0.05\)) than _S. thymbra_ EO against _L. monocytogenes_, and significantly higher (\(p<0.05\)) against _P. mirabilis_ than _T. capitatus_ and _O. hitrum_ EOs. Likewise, \(\gamma\)-terpinene demonstrated stronger (\(p<0.05\)) antibiofilm activity against _E. coli_ and _S. Enteritidis_, as well as stronger (\(p<0.05\)) inhibition of biofilm formation than _T. capitatus_ EO against _S. Enteritidis_ and _E. coli_, than _O. hitrum_ EO against _E. coli_, _S. Enteritidis_, and _P. mirabilis_, but showed weaker (\(p<0.05\)) antibiofilm activity than _S. thymbra_ EO against _L. monocytogenes_ and _P. mirabilis_. Additionally, significantly lower (\(p<0.05\)) antibiofilm activity of \(\gamma\)-terpinene was noted against _L. monocytogenes_ compared to \(p\)-cymene and thymol and against _P. mirabilis_ compared to all compounds tested. Or note, the MBEC values of the EOs were significantly (\(p<0.05\)) higher than the corresponding MBIC values in all cases.
## 5 Conclusions  
A digital twin describes the virtual representation of a real process. This twin is constantly updated with real data and can thus control and adapt the real model. Designing suitable digital twins for path planning of autonomous robots or drones is often challenging due to the large number of different dynamic environments and multi-task and agent systems. Generating robust paths specifically on controlling a variety of robots and tasks is one of the most common problems in controlling autonomous systems. Although there are many different route planners, often only criteria such as the shortest distance are considered.  
In this article, a shrinking-based method is presented to reduce the environment to a reasonable subset of paths. In particular, the robustness and implicit advantages of shrinkage for path generation are presented. The main advantage in contrast to the shortest path is the topological insurance of all alternative paths that are maximally far away from the boundary (centered). This centrality provides the advantage that the paths are predictable and sufficient for recalibration. The alternate paths also provide the ability to avoid a route if it is already occupied.  
In addition, digital twins can be derived directly from these paths that can be used to reference both the individual robot states and the paths. To this end, a path simplification was used to generate a graph from the digital image in which the individual task time is referred to the nod of the graph and the individual path time and path width is referred to the edge of the graph. This twin can now be used to control multiple autonomous robots in future works.  
The variation of map layouts as well as the placement of individual obstacles and the use of a 3D map demonstrate robust pathfinding that can handle multiple tasks simultaneously. Changes due to obstacles only affect the associated paths locally, leaving the other paths in the twin unaffected. Therefore, the control commands for the robots only need to be updated when new obstacles appear locally. If a robot is incorrectly positioned, it can be steered back to the nearest path via an anchor point shrinkage that is decoupled from the digital twin.  
In summary, this article shows the generation of a digital twin, enriched with process times as well as path widths, whose paths are constantly centered and which can react to a changing environment only with a local change in the path layout.  
Software, conceptualization, methodology, visualization, validation, investigation, resources, data curation writing--original draft preparation: M.D., writing--review, conceptualization and editing: H.V., P.S., S.G. and S.B.; project administration, funding question, supervision: S.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Effects of Corner Set--Backs on Wind Loads and Wind Induced Responses of Rectangular Tall Buildings
## 6 Conclusions  
In this paper, wind effects on rectangular high-rise buildings with various corner set-back rates are investigated through a wide range of tests on five models. The distributions of wind pressure coefficients, local wind force coefficients, base moment coefficients and  
Figure 17: The inertial wind loads. (**a**) Along-wind inertial wind loads. (**b**) Across-wind inertial wind loads.  
Figure 18: Along-wind equivalent static wind loads.  
the corresponding power spectrum are analyzed. Furthermore, correlation factors for wind loads reduction considering the effects of different corner set-back rates are discussed and the corresponding empirical formulas are proposed. The major findings of the paper are summarized below:  
1. The absolute values of the mean wind pressure coefficients vary more significantly on the side walls with corner set-back rate. In addition, the RMS wind pressure coefficients on the side walls of the model with a corner set-back rate of 5% are greater than those of the benchmark model.
2. The local mean and RMS drag coefficient are reduced by corner set-back modification. The maximum reduction of the mean drag wind force is found when the rate of corner set-back is 10%. The RMS local lift coefficients for the 5% corner set-back model are greater than those of the benchmark model in the middle height.
3. The RMS across-wind base moment coefficients with various corner set-back rates are less than the values specified in the design codes.
4. The peak value for power spectral spectra of the lift force coefficients is reduced by corner set-back treatment and the 20% corner set-back model has a gentle curve of power spectra densities than other corner set-back models. However, the variation of the corner set-backs has little effect on the power spectra of drag force coefficients.
5. The correlation factors considering different rates of corner set-backs on the wind loads are proposed in accordance with the outcomes of wind tunnel test. The correlation factors in along-wind are mostly smaller within 1.0, while correlation factors in across-wind are greater than 1.0.
6. Based on the analysis of a case study, the peak of along-wind and across-wind acceleration is basically the same when the building with a corner set-back rate of 5% and without corner modification. The maximum reduction of along-wind mean wind loads is found when the corner set-back rate of 10%, and the overall along-wind mean wind loads is decrease by about 50%. The maximum reduction of across-wind inertial wind loads is found when the corner set-back rate of 20%, and the across-wind inertial wind loads of the top building is decrease by about 67%.  
Writing--original draft, Y.L.; Writing--review & editing, J.Y. and Y.Z. All authors have read and agreed to the published version of the manuscript. Full support for the work was provided by the National Natural Science Foundation of China (Project Nos. 51708207, 51878271, 51978593 and 51778554) and the Natural Science Foundation of Hunan Province (Project No. 2020)J5176).  
Not applicable.
# Comparing Monitoring Networks to Assess Urban Heat Islands in Smart Cities
## 6 Discussion of Results
### Concerning the Analysis of Measurement Data  
The individual quality control performed on each network revealed a high proportion of valid data across all three, supporting their initial and interchangeable use in UHI research. However, it is important to consider that WU network stations may have suffered an impact of sensor aging that has not been possible to identify. Sensor aging can degrade device performance and compromise data accuracy, particularly in long-term analyses or studies requiring high precision.  
In a more advanced quality control phase, where data from the three networks were cross-compared, notable differences emerged. The bespoke network displayed a highly uniform distribution of temperature records across various ranges. In contrast, the CWONs exhibited significant variability. This may be attributable to the absence of uniformity in CWON with regard to devices and their locations, which may lead to deficiencies in the interpretation of UHIs. The maximum difference in recorded amplitudes reaches approximately 9 \({}^{\circ}\)C, and discrepancies of up to 14 \({}^{\circ}\)C were observed between the hourly readings from different CWON AWSs, an implausible value for UHI conditions in any medium-sized city or for total uncertainty sensor.  
Further analysis at selected control points and zones revealed substantial inconsistencies in the local behavior of the two CWONs, both in weekly and monthly datasets. While the WU network demonstrated strong internal consistency and high correlation among its stations, the Nt network consistently reported higher values, especially in the upper temperature ranges, with differences exceeding 2 \({}^{\circ}\)C. This discrepancy may be attributed to the superior quality and calibration of the commercial devices used in the WU network.
## 1 Introduction
For these areas, some discriminative methods are proposed, such as a \"voting\" scheme, in which the area is marked as the category with the highest number of points [14]. Figure 2: (a) SMOTE algorithm random selection process; (b) SMOTE algorithm random generation process. In the literature [15], a discrimination method for such transition areas was proposed: for sample points in a transition area, the class to which the sample belongs can be determined from the distance between the sample and two adjacent hyperplanes. [PERSON] et al. concluded that in the transition area, the feature points belonging to the class are instead more distant than the hyperplanes of the class. This discriminative method, compared with the previous discriminative methods, no longer assigns the transition area to a certain class completely but divides it specifically according to the actual situation, which improves classification accuracy and precision to a certain extent. However, this method must discriminate each feature sample in the transition area in a targeted manner, and when the number of feature samples in the area is large or the number of sample classes is large, this method is not efficient. A one-time means is still needed to solve the problem. Based on the method proposed in the literature [15], this paper proposes a CSH-SVM improvement algorithm that no longer calculates the distance between each sample located in the transition interval and the adjacent hyperplane. The transition interval is partitioned with the existing hyperplane equation to form a new hyperplane equation. Combined with the SMOTE algorithm to process the unbalanced samples, the four conditions of tower icing, conductor galloping, tower base settlement, and water corrosion are finally classified and predicted, and preventive measures can be taken when the tower has mechanical failure precursors. This improved algorithm has a faster response speed for the classification prediction of new samples because it no longer calculates the distance between samples and hyperplane frequently. On the premise of reducing the amount of calculation, it has better classification efficacy, which can well solve the problem of fault prediction and classification in practical engineering applications, thus improving the safety and service life of monitoring objects as well as reducing the loss of personnel and property.
# Research on a Strategy for Prediction Methods of Submarine Self-Propulsion Hydrodynamic Performance
## 2 Numerical Simulation Basics
### Governing Equations  
The governing equations are the unsteady, three-dimensional, incompressible flow continuity equation and the RANS equations. The continuity equation is  
\[\frac{\partial U_{i}}{\partial x_{i}}=0 \tag{1}\]  
The velocity \(U_{i}\) can be decomposed into a mean velocity component and a fluctuating velocity component:  
\[U_{i}=\overline{U_{i}}+u_{i}^{\prime} \tag{2}\]  
The momentum equation can be expressed as follows:  
\[\frac{\partial(\rho U_{i})}{\partial t}+\frac{\partial(\rho U_{i}U_{j})}{ \partial x_{j}}=-\frac{\partial p}{\partial x_{i}}+\frac{\partial}{\partial x _{j}}\left[\mu(\frac{\partial U_{i}}{\partial x_{j}}+\frac{\partial U_{j}}{ \partial x_{i}})\right]-\frac{\partial\rho\overline{u^{\prime}_{i}u^{\prime} }}{\partial x_{j}}+S_{j} \tag{3}\]  
In this study, when conducting numerical simulations of the submarine self-propulsion hydrodynamic performance using the steady MRF method, the first term was neglected. In the momentum equation, \(U_{i}\) represents the mean velocity; \(u_{i}^{\prime}\) denotes the turbulent velocity component in the Cartesian coordinate \(i\)-direction; \(p\) represents the mean pressure; \(\rho\) denotes fluid density; \(\mu\) represents the dynamic viscosity coefficient; \(S_{j}\) represents the source-term generalized momentum; \(\rho\overline{u^{\prime}_{i}u^{\prime}_{j}}\) represents the Reynolds stress term.
## 4 A RAMI-Inspired Reference Architecture
### Data Security  
Privacy is a strong requirement that cross-cuts all the layers. Since private and sensitive data may be handled, both raw data coming from the shop floor and those elaborated along the path must be secured. In that respect, procedures to protect data at rest (e.g., data anonymization, data encryption) as well as data in transit (e.g., secure communication channels) must be enforced.
## 4 Experiments and Results
### Results  
Table 1 shows the named entity recognition experimental results for each dataset, with baseline results from FewNER [13], FIT [7], LPNER [8], and SpanNER [14] cited from their original publications, while BERT-LSTM-CRF, Biaffine, and our LDE models were directly implemented by us. To provide context for comparison, we include several state  
of-the-art few-shot nested NER approaches: FewNER [13], a meta-learning based model that separates task-independent and task-specific components; FIT [7], which employs a three-stage process (focusing, bridging, and prompting) to handle nested entities without source domain data; LPNER [8], a label-prompt method that integrates prompt learning with prototype networks in a single round; and SpanNER [14], which decomposes NER into span detection and class inference using natural language descriptions. In experiments with nested named entity recognition data (GENIA, ACE 2004, ACE 2005), when trained on the full dataset, both biaffine and LDE models showed higher performance compared to the BERT-LSTM-CRF model using nested tags, though the performance difference between biaffine and LDE models was minimal. However, in 5-shot, 10-shot, and 20-shot experiments, the LDE model demonstrated higher performance than the biaffine model, indicating that the proposed LDE model is effective in few-shot environments. Notably, while existing few-shot named entity recognition methods like FewNER [13], FIT [7], and LPNER [8] required complex methods dividing tasks into multiple stages to handle nested NER, the LDE model achieved similar or better performance than existing research in few-shot environments by simply modifying the classification layer, demonstrating the effectiveness of the label description approach for few-shot nested NER. However, in extreme one-shot environments, the LDE model showed lower performance compared to
# Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing
## 4 Personalized Privacy Protection
### Experimental Settings  
In our experiments, we utilized two widely used real-world datasets, Gowalla and Yelp, as the core foundations. Gowalla, a social network dataset, provides genuine location check-in data from users of the Gowalla mobile application, recording their activities at various global locations, including timestamps and user profiles. Within our model, we consider the users in Gowalla as workers, each check-in point being a previously accepted and executed task.  
On the other hand, Yelp offers an extensive compilation of business- and user-related information sourced from the Yelp website. It encompasses intricate business data such as geographical coordinates, operating hours and user ratings, alongside user-generatedreviews. With data points comprising 15,583 restaurants, 70,817 users and 335,022 reviews, we regarded restaurant locations as tasks and user reviews as accepted tasks.  
Our experiments were conducted on a computer equipped with an Intel i7 CPU and an NVIDIA 3090 GPU. Each experiment was repeated multiple times to derive the average value as the final result.