esa-sceva/satcom-chunk-collection huggingface dataset:
# Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey
## 6 Conclusions  
In this paper, we provided a comprehensive review of recent works on EMF exposure, focusing on four main topics. First, we analyzed existing state-of-the-art survey papers and distinguished our work by categorizing it into five dimensions: 5G, resource allocation (RA), reconfigurable intelligent surfaces (RISs), EMF exposure, and EMF evaluation frameworks. Second, we conducted an in-depth discussion and comparison of 5G technologies, EMF evaluation frameworks, and metrics, exploring how critical enabling technologies--such as network densification, massive MIMO, and mmWave--affect EMF radiation exposure. Additionally, we extensively reviewed EMF-aware radio resource allocation techniques for 5G networks with and without the integration of RIS technologies. For the EMF exposure evaluation, we categorized approaches into geometric stochastic, deterministic, and experimental methods, highlighting state-of-the-art frameworks; their applicability, advantages, and limitations; practical examples; and the key metrics used to measure exposure. Each topic was examined to provide a detailed overview of current research and advancements. Finally, we identified major challenges and outlined open research directions related to EMF exposure management and resource allocation in 5G networks, offering insights for future exploration in this evolving field.  
Conceptualization, M.A.S. and H.S.L.; methodology, M.A.S.; validation, H.S.L. and K.S.D.; investigation, M.A.S.; resources, H.S.L. and K.S.D.; writing--original draft preparation, M.A.S.; writing--review and editing, H.S.L., K.S.D., K.A.A., C.C.Z. and S.F.C.; visualization, M.A.S. and K.A.A.; supervision, H.S.L., K.S.D., K.A.A., C.C.Z. and S.F.C.; project administration, H.S.L., K.S.D., C.C.Z. and S.F.C.; funding acquisition, H.S.L., C.C.Z. and S.F.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Ministry of Higher Education of Malaysia under the Fundamental Research Grant Scheme (FRGS) (Ref: FRGS/1/2020/ICT09/MMU/02/1). The APC was funded by Multimedia University.  
All data were contained in the main text.  
[PERSON] is employed by ZTE Malaysia. [PERSON] is employed by AXON Logic. [PERSON] is employed by MIMOS Berhad. The other authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.
# Psychoacoustic Approaches for Harmonic Music Mixing +
## 2 Consonance Models
### Roughness Model  
As stated above, the category of sensory consonance can be divided into three parts: roughness, tonalness and sharpness. While sharpness is closely connected to the timbral properties of musical audio [6], we do not attempt to model or modify this aspect, since it can be considered independent of the interaction of two pieces of music, which is the object of our investigation in this paper. [PERSON] and [PERSON] [11] discuss the strong relationship between roughness and tonalness as a sufficient reason to only analyze one of the two properties. The fact that roughness has been more extensively explored than tonalness and that most sensory consonance models build exclusively upon it motivates the use of roughness as our sole descriptor for sensory consonance in this work. For each of the partials of a spectrum, the roughness that is evoked by the co-occurrence with other partials is computed, then weighted by the dyads' amplitudes and, finally, summed for every sinusoid.  
The basic structure of this procedure is a modified version of [PERSON] and [PERSON]'s [12] roughness model for complex sonorities that builds on the roughness curve for pure tone sonorities proposed by [PERSON] and [PERSON] [13] (this approach also forms the basis of work by [PERSON] [14] and [PERSON] [15] on the analysis of consonance in tuning systems and musical performance, respectively). A function that approximates the graph estimated by [PERSON] and [PERSON] is proposed by [PERSON] [16]:  
\[g(y)=\left\{\begin{array}{ll}(\exp(1)\frac{y}{025}\exp(-\frac{y}{025}))^{2} &\quad y<1.2\\ 0&\quad\text{otherwise}\end{array}\right. \tag{1}\]  
where \(g(y)\) is the degree of roughness of a dyad and \(y\) the frequency interval between two partials (\(f_{i}\) and \(f_{j}\)) expressed in the critical bandwidth (CBW) of the mean frequency \(\bar{f}\), such that:  
\[y=\frac{|f_{j}-f_{i}|}{\text{CBW}(\bar{f})} \tag{2}\]  
and:  
\[\bar{f}=\frac{f_{i}+f_{j}}{2}. \tag{3}\]  
Since pitch perception is based on ratios, we substitute \(\text{CBW}(\bar{f})\) with [PERSON] and [PERSON]'s [17] equation for the equivalent rectangular bandwidth \(\text{ERB}(\bar{f})\) in Equation (2).  
\[\text{ERB}(\bar{f})=6.23(10^{-3}\bar{f})^{2}+93.39(10^{-3}\bar{f})+28.52 \tag{4}\]  
which [PERSON] [16] also cites as offering \"possible minor improvements.\" The roughness values \(g(y)\) for every dyad are then weighted by the dyad's amplitudes (\(M_{i}\) and \(M_{j}\)) to obtain a value of the overall roughness \(D\) of a complex sonority with \(N\) partials:  
\[D=\frac{\sum_{i=1}^{N}\sum_{j=i+1}^{N}M_{i}M_{j}g_{ij}}{\sum_{i=1}^{N}M_{i}^{2 }}. \tag{5}\]
## 1 Introduction
* Model Development: We propose a user satisfaction model based on response time, which comprehensively considers the timeliness of resource supply and scheduling efficiency. * Optimization of Dispatch Scheme: By integrating ice disaster trajectory predictions, the fast Newman algorithm is employed for regional partitioning. In addition, mobile energy and water storage vehicles are pre-deployed before the disaster to improve emergency response speed. * Resource Dispatch Optimization: A grouped scheduling strategy is adopted to reduce cross-regional resource flow and enhance resource utilization efficiency, while dispatch routes are dynamically adjusted based on real-time traffic network conditions to improve system stability and resilience. The effectiveness of the proposed resilience-assessment method and enhancement strategy is validated through case studies of different scales. To facilitate a comprehensive understanding of the proposed approach, Figure 1 depicts the scheduling framework. As illustrated in Figure 1, the scheduling framework highlights three key elements: the pre-disaster community grouping and resource pre-deployment process, the dynamic real-time dispatch stage, and the overarching objective of maximizing resource supply and user satisfaction. The remainder of this paper is organized as follows: Section 2 introduces the ice disaster scenario, develops the failure-rate model, and explains the community partitioning scheme based on the fast Newman algorithm; Section 3 formulates the integrated pre-disaster facility planning and spatiotemporal dispatch optimization; Section 4 presents comparative case studies on the IEEE-33-node system; finally, Section 5 concludes the paper, outlines current limitations, and discusses avenues for future research.
## 7 Summary and Conclusions  
This contribution presents a comprehensive experimental campaign including short- and long-term tests of bonded anchors for two different adhesive products. The obtained data are used to establish a relationship between load level and related time-to-failure of bonded anchors under sustained load. Using the author's data as well as literature data, the established linear regression model is compared to a number of alternatives. All models are assessed with regard to their predictive quality. Additionally, recommendations for testing and data analysis are derived. Finally, a new approach based on a sigmoid function is proposed for lifetime predictions of bonded anchors subject to sustained loads.  
Based on the results of the experimental investigation and the systematic analyses, the following conclusions are drawn:  
1. Experimental results clearly show the importance of the loading rate effect on strength. Possible loading rate inconsistencies between short and long-term tests would lead to inaccurate load level definitions and adversely affect TTF analyses.
2. The commonly assumed linear relationship between stress and logarithmic time-to-failure leads to overly conservative lifetime predictions and can neither represent the behavior of tests at high nor at low load levels.
3. A sigmoid regression model with horizontal asymptotes at high and low load levels can represent the actual shape of TTF data better without increasing the number of regression parameters.
4. A conservative choice of the lower asymptotic limit for sigmoid regression models, \(\kappa_{\infty}\), simplifies the analysis and ensures sufficiently but not overly conservative predictions.  
Short-term TTF data at very high load levels (in the concave region) can help to stabilize long-term predictions at low load levels (convex region). Data restricted to the intermediate linear domain around the inflection point contain only a limited value for TTF extrapolations.  
[PERSON], [PERSON], and [PERSON] conceived and designed the experiments. [PERSON] and [PERSON] performed the experiments on the bonded anchors under sustained load. [PERSON] and [PERSON] analyzed the data. [PERSON] wrote the paper with contributions from [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Christian Doppler Forschungsgesellschaft, Grant LICROFAST to [PERSON].  
The financial support by the Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology, and Development is gratefully acknowledged.  
The authors declare no conflict of interest. The industrial partners of project LICROFAST had a role in the design of the study. They had no role in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# A Reconfigurable Formation and Disjoint Hierarchical Routing for Reshapeable Bluetooth Networks
## 3 Mesh-Tree Topology Design  
A simple application of the reconfigurable mesh-tree is a network consisting of one designated root as a sink node with master or relay nodes in the mesh subnet, and several new roots, with their connected nodes, in the tree subnet. With the mesh subnet in a densely covered area, the path length of the network can be reduced to less than that of a ring topology, and the traffic load of the root node can also be mitigated to less than that of a tree topology.  
The reconfigurable mesh-tree assumes that a set of Bluetooth nodes is randomly distributed in a specific geographical area, and not all nodes are necessarily within radio range. In order to construct a multi-hop scatternet, two additional assumptions are made: (1) Every node is aware of the number and the identity of its one-hop neighbors upon completing the boot procedure; (2) There is at least one path between any two nodes in the network.  
In contrast to our prior work [9], the size of the mesh-subnet is fixed and predefined by the constant \(k\) value regardless of the network size. However, the reconfigurable mesh-tree determines the variant \(k\) instead of the constant \(k\) layer in the mesh-subnet for the various network sizes.
## 4 Experiment
### Ablation Experiments  
This section presents a comprehensive analysis of the proposed MSSTTN model through extensive experiments conducted on the PEMS04 dataset. The aim is to verify the aforementioned improvement.  
For the temporal aspect, we design two ablation versions of MSSTTN:  
Figure 7: The average 60 min prediction results of (**a**) PEMS03, (**b**) PEMS04, and (**c**) PEMS08.  
1. MSSTTN-attn. This model removes the improved autoformer and replaces it with a self-attention-based transformer structure.
2. MSSTTN-single-_i_. This model extracts the trend-cyclical part by a single scale \(i\) that removes the multi-scale series decomposition. For the spatial aspect, we also design two ablation versions of MSSTTN: 1. MSSTTN-GCN. This model replaces the improved GWNN with a two-layer GCN. 2. MSSTTN-fixed. This model disables the scaling parameter learning so that the GWNN doesn't learn adaptive weights on graph signals of multi-scale frequencies.  
Table 6 shows the MAE, MAPE, and RMSE of 3, 6, 9, and 12 (15, 30, 45, and 60 min) prediction results of MSSTTN and the ablation models. Every hyperparameter setting in the unablated modules are the same as described in Section 4.2. From Table 6, we can conclude that:  
* From the spatial aspect, the MSSTTN-fixed outperforms the MSSTTN-GCN, demonstrating that for capturing the fixed spatial dependencies, the GWNN is naturally more appropriate than GCN, which aggregates the features inflexibly. Then, paying attention to the comparison of MSSTTN and MSSTTN-fixed, the overall performances of MSSTTN are all better. This validates that learning adaptive neighborhoods to enable graph signals passed in a multi-scale manner can improve the prediction precision.
* From the temporal aspect, MSSTTN-attn performs worst, verifying that the time series analysis and period-based operation can strengthen the temporal dependencies modeling, especially in the long-term. Comparing all the MSSTTN(single-i), we can observe that the appropriate long-term trend is more valuable than the short-term trend for capturing temporal dependencies. MSSTTN outperforms all the MSSTTN(single-i), demonstrating that each of the various scale trend-cyclical parts is helpful for time series analysis, so the multi-scale series decomposition we propose is an effective method. Through all the ablation experiments, we can easily understand the usefulness of all the improvements we have made.
## 3 System Model
### Definition of the 2D-DTMCs  
We propose a single 2D-DTMC per SN class to model the evolution of the state of nodes (active or inactive) and their queue lengths over time. More specifically, let \((i,m)\) be the state of an SN class, were \(i\) denotes the number of _DATA_ packets in the corresponding RN queue, \(0\leq i\leq Q\), and \(m\) denotes the number of active nodes of the class, different from the RN, \((m<N-1)\). Let \(P_{(i,m),(j,n)}\) denote the transition probability from state \((i,m)\) to state \((j,n)\).  
It is important to highlight that transition probabilities for each DTMC are substantially different from the ones defined in [23], as now they must take into account the fact that SNs might transmit multiple _DATA_ packets (a PF) in a single cycle. For convenience, these new transition probabilities are defined in [28]. Please refer to this document for a detailed description of their definition.  
We denote by \(R_{1,0}\) the fraction of cycles in which all C1 SNs are inactive and, therefore, there would be no PF transmissions from C1 SNs. Clearly, \(R_{1,0}\) is the fraction of cycles in which active C2 SNs can contend for medium access. \(R_{1,0}\) is the key coupling mechanism between the pair of DTMCs. The DTMC associated with C1 SNs is independent from the one associated with C2 SNs. Therefore, once the DTMC for C1 is solved, \(R_{1,0}\) can be determined and then, C2 DTMC can be solved.
## 2 Developed Methodology
### SVM-Based Classification Scheme  
Once a set of features was selected and measured, dimensions are reduced to three fundamental features. Applying the results of CCA, allows us to represent data in lower dimensions without compromising the meaning of the data. Figure 10a depicts further visualization of well-separated data in three-dimensional (3D) feature space. These geometric feature vectors, axis length ratio, orientation and length, are used as input to train an SVM [32]. This is motivated by the intuition that monitoring systems and analytical approaches take into account this geometrical information and make use of crack length and width as fundamental parameters for determining the severity of cracks. Moreover, in this case, these three parameters yield a high degree of variance, according to Figure 9. Reduction in computational complexity for analytical systems as outlined above, as well as a high degree of separability for these three parameters, make our crack detector methodology a practical tool. As one of the most widely used machine learning techniques, SVM [32] methodology performs classification by finding the maximum-margin hyperplane that separates two different categories with the best performance and maximizes the margin between the nearest data points and the hyperplane in different classes.  
Figure 10: Support vector machine (SVM)-based Classifier. (**a**) Three-dimensional (3D) feature space and (**b**) classification performance.  
The Simple SVM uses binary learners to train the model. However, this type of model tends to suffer from over-fitting. To solve this problem, cross-validation techniques are employed herein. In cross-validation techniques, a subset of data called \"validation set\" is used to test the classifier during the training stage. In an n-fold cross-validation technique (Figure 11), such as five-fold cross-validation [41], first, the training set is divided into \(n\) subsets equally. In order to ensure the proper learning, the mechanism is to test one subset using the model trained on the remaining n\(-\)1 subsets. This process is repeated \(n\) times so that each instance of the whole training set is validated once. The accuracy of the model is computed based on the average accuracy of \(n\) classifiers [41].  
In order to classify crack and architectural line features, a cross-validated SVM is applied to train a binary model, exploiting a three-dimensional feature space (Figure 10). In total, 80% and 20% of data are considered as training and test data, respectively. To ensure proper learning, five-fold cross-validation is performed. The classifier achieves an accuracy of 98.10%.
# Joint Delay-Doppler Estimation Performance in a Dual Source Context
###### Abstract
Regarding the contributions dealing with finite bandwidth signals it is common to resort to the narrowband signal assumption, that is, the impact of the Doppler effect on the baseband signal is neglected and only amounts to a time-varying frequency shift [20]. The derivation of such bounds are often based on the Slepian-Bangs formulas [21] or more general theoretical expressions for Gaussian observation models [22; 23; 24]. When considering a multiple source signal model the CRB derivation complexity increases significantly. Examples of those CRB expressions were derived for the case of extended targets [25] and in GNSS-R for altimetry purposes [26; 27; 28]. Despite these contributions, a dual source delay-Doppler estimation compact closed-form CRB expression for generic band-limited signals is not available in the literature, being an important missing point and a result of broad interest. In either the single or multiple source context, the problem can be formulated as a Conditional Signal Model (CSM) [16; 17; 18; 29; 30; 31; 32; 33]. Such model and the corresponding Conditional Maximum Likelihood Estimator (CMLE) lead to interesting properties: the CMLE performance is accurately described by the CRB in the asymptotic region of operation, i.e., it is efficient in the high signal-to-noise ratio (SNR) regime and/or the large sample regime [29; 34]. This makes the CMLE an interesting tool to validate CRB expressions, as done for instance in [16; 17; 18; 35]. Unfortunately, the main CMLEs drawback is their heavy computational load, which the practitioner usually wants to avoid, and therefore many solutions to reduce this load were proposed in the literature. A first approach consists in using sub-optimal techniques, where the multidimensional search is substituted with a simpler one-dimensional one, such as in the Capon [36] or MUSIC [37; 38] methods. Another approach is to reduce the computational burden by splitting the multidimensional optimization search in a number of recursive searches. The Alternating Projection algorithm [39] and the relaxed version of the CLEAN algorithm [40] are two well-known examples of this approach.
Therefore, the noise generated from the unconnected photodetector cannot affect the receiver. The switch control signal _EN_ causes the switch to be turned on. Depending on the ROI, it is predicted which photodetector will detect the return signal, so that the _EN_ signal is able to turn on the corresponding switch. The TCA amplifies and combines current signals generated using the photosensitive cells from incoming optical signals into one voltage signal for further processing. The switched 4-to-1 TCA is fabricated using a standard complementary metal-oxide-semiconductor (CMOS) 0.18 \(\upmu\)m technology. It provides 3.8 pA/\(\surd\)Hz average noise current spectral density with a bandwidth of 353 MHz and a transimpedance gain of 67.5 dB\(\Omega\). The core of the TCA consumes 17.8 mW of power from a 1.8 V supply. The core of the TCA occupies an active area of about 92 \(\upmu\)m \(\times\) 68 \(\upmu\)m and the die size including I/O pads is 1000 \(\upmu\)m \(\times\) 840 \(\upmu\)m.  
Figure 4: Operation example with the four proposed switched 4-to-1 TCAs and four multiple partitioned photodetectors. Figure 3: Proposed switched 4-to-1 transimpedance combining amplifier (TCA) with four partitioned photodetectors.