esa-sceva/satcom-chunk-collection huggingface dataset:
# Texture Analysis is a Useful Tool to Assess the Complexity Profile of Microcirculatory Blood Flow
## 4 Discussion  
In this study, the acquired PPG signals and an artificial sine wave signal were decomposed with the WT into their respective components so that TE and CI could be calculated. The relative contributions of each of the components to the overall signal, expressed as amplitude ratios, are typical WT outputs but were not considered for this study as these results were previously published [4]. During leg lowering, perfusion decrease in the test foot is explained by a constriction of arterioles secondary to the increased venous distension when the foot is in a pendent posture, which constitutes the VAR [39]. The fast perfusion decreases in the control foot (not so pronounced as in the test foot) is thought to be due to a centrally mediated neurogenic reflex initiated to maintain the vascular homeostasis in the lower limb [4]. The sine wave signal scalogram showed TE values generally invariant regarding the component, which reflects the regularity of these signals. The highest TE values were recorded for both the highest (cardiac) and lowest (NOi) frequency components, meaning a richer texture was found in the corresponding ROIs, likely explained by the particular overlap of the spectral waves that define them. Contrarily, the PPG signal scalogram showed that the higher frequency (HF) components (cardiac, respiratory, myogenic) displayed higher TE values in either foot, meaning the corresponding ROIs are more texturally rich, i.e., more complex in comparison to the lower frequency components. This suggests that TE assessment of PPG scalograms is sensitive to the underlying phenomena that explain the physiological complexity of these signals. In our data, both TE and MSE analyses showed an overall decrease in the PPG signal and in the respective components' entropies during leg lowering in both the test and control feet. This result seems to indicate a bilateral decrease in the vascular adaptation capacity to the lower perfusion levels observed during leg lowering. This apparent decrease in adaptation capacity is further reinforced by the observation that several PPG components' entropies show significant differences between the recovery and baseline phases, suggesting that full recovery was not attained at the end of the protocol. In our view, these results indicate that that VAR creates a state of reduced perfusion that, while not compromising tissue viability, destabilizes the evoked regulatory mechanisms. When considering the test foot, MSE detected a significant decrease in the entropy of all components during VAR. TE, however, detected an entropy decrease in the cardiac,
# Intelligent Reflective Surfaces for Wireless Networks:
## 5 Practical Challenges, Open Issues and Future Research Lines
### Open Issues and Future Research Directions
#### 5.2.4 Wideband Systems  
Wideband IRS-aided systems have not received special attention. Most papers about IRSs assume narrow band model and these designs cannot be directly extended to the wideband scenario. The reason behind this claim is that the phase shifters in the IRS phase-shift matrix are frequency flat, and therefore, have to be jointly designed for all the subcarriers. Moreover, the large channel bandwidth leads to the beam squint effect, which in turn leads to more constrained designs. The work in [48] considers a wideband IRS-aided communication system under the beam squint effect. However, the work focuseson the channel acquisition problem and not on the IRS design for these systems. The design of IRS-aided wideband systems is certainly a challenging open issue suggested for future research.
# Atmospheric Effects on OFDM Wireless Links Operating in the Millimeter Wave Regime
## 2 Channel Model  
Besides modelling of the transmitter and receiver, analyzing the wireless link operating in the millimeter wave regime also requires accurate description of the medium. The transmitted signal \(E_{T}(t)\) is propagating in the medium, which can be treated as a linear system via its impulse response \(h(t)\). The received signal is the result of the convolution \(E_{R}(t)=E_{T}(t)*h(t)\). In the presence of Additive White Gaussian Noise (AWGN) \(n(t)\), the signal and noise obtained at the receiver's input and fed to the demodulator is the summation \(E_{in}(t)=E_{R}(t)+n(t)\). The link is illustrated in a block diagram in Figure 1.  
Figure 1: Block diagram of a wireless link operating in the millimeter wavelengths.  
The atmospheric medium is characterized as a linear system with a transfer function \(H(jf)\) given in the frequency domain. In the time domain, the transmitted signal \(E_{T}(t)\) is presented as a carrier wave at a frequency \(f_{c}\) modulated by a wideband complex envelope \(\widetilde{A}_{in}(t)\)[8]:  
\[E_{T}(t)=\text{Re}\left\{\underbrace{[i(t)+jq(t)]}_{\widetilde{A}_{in}(t)} \cdot e^{j2\pi f_{c}t}\right\}=\frac{1}{2}\widetilde{A}_{in}(t)\cdot e^{+j2 \pi f_{c}t}+\frac{1}{2}\widetilde{A}_{in}{}^{*}(t)\cdot e^{-j2\pi f_{c}t} \tag{1}\]  
where \(\widetilde{A}_{in}(t)=i(t)+jq(t)\) is a complex envelope, given in terms of the In-phase \(i(t)\) and Quadrature \(q(t)\) baseband components. A Fourier transformation of Equation (1) is carried out to present the transmitted signal in the frequency domain:  
\[\begin{split} E_{T}(jf)=\mathcal{F}[E_{T}(t)]\equiv\int\limits_ {-\infty}^{+\infty}E_{T}(t)\cdot e^{-j2\pi ft}dt=\\ =\frac{1}{2}\widetilde{A}_{in}[j(f-f_{c})]+\frac{1}{2}\widetilde{ A}_{in}{}^{*}[-j(f+f_{c})]\end{split} \tag{2}\]  
where \(\widetilde{A}_{in}(jf)=\mathcal{F}\big{\{}\widetilde{A}_{in}(t)\big{\}}\). Using the transfer function \(H(jf)\) of the medium, the received signal can be now expressed in the frequency domain by:  
\[\begin{split} E_{R}(jf)=E_{T}(jf)\cdot H(jf)=\\ =\frac{1}{2}\widetilde{A}_{in}[j(f-f_{c})]\cdot H(jf)+\frac{1}{2 }\widetilde{A}_{in}{}^{*}[-j(f+f_{c})]\cdot H(jf)\end{split} \tag{3}\]  
Inverse Fourier transformation of Equation (3) results in the signal obtained at the receiver site:  
\[\begin{split} E_{R}(t)=\int\limits_{-\infty}^{+\infty}E_{R}(jf)e^ {+j2\pi ft}df=\\ =\text{Re}\left\{\underbrace{\left\{\int\limits_{-\infty}^{+ \infty}\widetilde{A}_{in}(jf)\cdot H[j(f+f_{c})]\cdot e^{+j2\pi ft}df\right\} \cdot e^{j2\pi f_{c}t}\right\}}_{\widetilde{A}_{out}(t)}\cdot e^{j2\pi f_{c}t} \right\}\end{split} \tag{4}\]  
The expression for the received complex envelope is identified in Equation (4) to be:  
\[\widetilde{A}_{out}(t)=\int\limits_{-\infty}^{+\infty}\widetilde{A}_{in}(jf) \cdot H[j(f+f_{c})]\cdot e^{+j2\pi ft}df \tag{5}\]
## 4 Results and Discussion
### Discussion  
In this paper, we tested the proposed HBC algorithm in a single channel coherent LR-PON, where the transmitted signal was distorted by the combination of SPM and the noise added by the receiver. As can be appreciated in the constellations of Figure 3c-k, the obtained symbols showed the characteristic spiral-like constellations that were prone to be clustered. The high distribution losses, which included the fibre and the splitting losses, however, made the constellations to have noise and the cluster borders to appear blurred. This was not the case of WDM systems, where the lack of  
Figure 5: BER in terms of the processed block size alongside with the efficiency of HBC for a power level of 7 mW at which optimum performance is achieved for HBC. For comparison purposes, the BER obtained using maximum likelihood is also included.  
splitters leads to higher received power and the subsequent reduction of the receiver noise impact. Indeed, for the configuration considered in this work, the launch optical power had to be increased well above the optimum power in order for the XPM and FWM to cause a distortion comparable to the excess splitting loss. In regards to dual polarization operation, the proposed algorithm could be modified to account for both polarizations simultaneously. This dimensionality increase, however, would lead to more complicated processing.  
The proposed algorithm aimed to compensate the distortion in simple LR-PON systems without requiring high computational cost and a training sequence. In this sense, HBC can be considered as a trade-off between performance and complexity. Indeed, we can note that HBC outperformed the nonlinear phase rotation at the cost of higher complexity and latency. On the other hand, HBC presented a slightly worse tolerance to nonlinear distortion than other more sophisticated algorithms (for example, 2.5 dB of HBC vs. 3 dB of EM [37]), but without requiring initialization and the iterative process.  
Another point to be considered is the employed filters, in our simulations fourth order Bessel filters. These filters emulate the bandwidth limitation of both the transmitter electronics and PD response and remove part of the out-of-band noise. It is envisaged that the adoption of more sophisticated filters, Nyquist filtering in particular, could improve the performance, as they increase the SNR, thus making the clusters easier to discriminate.
# MTGNet: Multi-Agent End-to-End Motion Trajectory Prediction with Multimodal Panoramic Dynamic Graph
## 4 Experimental Results and Analysis
### Experimental Settings  
#### 4.1.1 Datasets  
We evaluated the experimental results on the Argoverse 2.0 Motion Forecasting Dataset. Compared with Argoverse 1.0, Argoverse 2.0 adds more scenes and annotation data, adopts a richer HD map format, provides more detailed map information, and supports more complex autonomous driving tasks. The Argoverse 2.0 dataset contains 250k 11 s scenes with a sampling rate of 10 Hz. For the training set and validation set, the first five seconds of each scene are used as model input, and the remaining six seconds are used as the basis for model prediction. For the test set, only the first five seconds are provided [47].
# Classification Models for Nitrogen Concentration in Hop Leaves Using Digital Image Processing
## 4 Discussion
No such model achieved a sensitivity higher than 60%, and the QDA model generated from set 2 achieved a mere 33,3% sensitivity. Thus, while LDA and QDA models based on mobile images could be useful for detecting higher nitrogen concentrations in the field, their lower sensitivity limits the detection of nitrogen deficiency. However, it is important to note that the image resolution of the mobile phone used in the study is very low, at only 1.2 MP. Most modern smartphones have higher-resolution cameras (e.g., iPhone 15 with 48 MP), with some recent high-end models reaching 200 MP (Samsung Galaxy S25 Ultra). These are much higher than the 34.8 MP scanner used in this study. Therefore, advancements in smartphone camera technology could significantly enhance the sensitivity and overall performance of these models in the future. In [PERSON] et al. 's study [46], LDA and QDA models were used to reduce the number of features in plant leaf images, followed by classifiers such as SVM, KNN, and logistic regression for classifying a specific image into a particular plant class. In this case, QDA performed better when applied to classes with different variances. In oil palm cultivation, by using leaf-scale and machine learning approaches, the performance of discriminant analysis models and SVM was compared in terms of defining nitrogen profiles [47]. Discriminant analysis identified a greater number of optimal spectral bands, and could more accurately discriminate nitrogen levels compared to the other model. However, SVM could achieve reasonable accuracy with fewer spectral bands, and when combined with leaf measurement, it presented the best discriminant function in the study. In this context, the performance of set 1 models is noteworthy. Despite containing only three variables, they exhibited an AUC equal to or superior to those set 2 models, which contained seven variables. Furthermore, the QDA model in set 1 achieved the highest AUC of all the models evaluated (0.85), indicating superior performance and greater classification power for the images. However, compared to previous studies in the literature focused on disease classification through leaf analysis, both the AUC and accuracy values were lower, with reported accuracy values of 94.35% and AUC values of 94.7% [48].
# Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets
###### Abstract  
Sentinel-1A and Landsat 8 images have been combined in this study to map rice fields in urban Shanghai, southeast China, during the 2015 growing season. Rice grown in paddies in this area is characterized by wide inter-field variability in addition to being fragmented by other land-uses. Improving rice classification accuracy requires the use of multi-source and multi-temporal high resolution data for operational purposes. In this regard, we first exploited the temporal backscatter of rice fields and background land-cover types at the vertical transmitted and vertical received (VV) and vertical transmitted and horizontal received (VH) polarizations of Sentinel-1A. We observed that the temporal backscatter of rice increased sharply at the early stages of growth, as opposed to the relatively uniform temporal backscatter of the other land-cover classes. However, the increase in rice backscatter is more sustained at the VH polarization, and two-class separability measures further indicated the superiority of VH over VV in discriminating rice fields. We have therefore combined the temporal VH images of Sentinel-1A with the normalized difference vegetation index (NDVI) and the modified normalized difference water index (MNDWI) derived from a single-date cloud-free Landsat 8 image. The integration of these optical indices with temporal backscatter eliminated all commission errors in the Rice class and increased overall accuracy by 5.3%, demonstrating the complimentary role of optical indices to microwave data in mapping rice fields in subtropical and urban landscapes such as Shanghai.  
Sentinel-1A; Landsat 8 OLI; subtropics; urban agriculture; smallholder; paddy rice 1
# Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks
###### Abstract  
This paper considers interference management and capacity improvement for Internet of Things (IoT) oriented two-tier networks by exploiting cognition between network tiers with interference alignment (IA). More specifically, we target our efforts on the next generation two-tier networks, where a tier of femtocell serving multiple IoT devices shares the licensed spectrum with a tier of pre-existing macrocell via a cognitive radio. Aiming to manage the cross-tier interference caused by cognitive spectrum sharing as well as ensure an optimal capacity of the femtocell, two novel self-organizing cognitive IA schemes are proposed. First, we propose an interference nulling based cognitive IA scheme. In such a scheme, both co-tier and cross-tier interferences are aligned into the orthogonal subspace at each IoT receiver, which means all the interference can be perfectly eliminated without causing any performance degradation on the macrocell. However, it is known that the interference nulling based IA algorithm achieves its optimum only in high signal to noise ratio (SNR) scenarios, where the noise power is negligible. Consequently, when the imposed interference-free constraint on the femtocell can be relaxed, we also present a partial cognitive IA scheme that further enhances the network performance under a low and intermediate SNR. Additionally, the feasibility conditions and capacity analyses of the proposed schemes are provided. Both theoretical and numerical results demonstrate that the proposed cognitive IA schemes outperform the traditional orthogonal precoding methods in terms of network capacity, while preserving for macrocell users the desired quality of service.  
internet of things; interference alignment; heterogeneous networks; cognitive radio +
Footnote †: journal: Sensors
# Noninvasive Deep Learning Analysis for Smith-Magenis Syndrome Classification
## 2 State of the Art
### Clinical Application of Deep Learning Through the Analysis of Dysarthria
This study [82] focused on speech recognition for patients with hyperkinetic dysarthria disorder (RHDD) using a CTBNet model that combines convolutional neural networks (CNNs) and bidirectional gated recurrent units (Bi-GRUs) with an attention-CTC decoder to handle the temporal aspects of dysarthric speech. In [83], the authors provided a comparative analysis of pretrained deep learning models for detecting dysarthric speech using the scalogram. Dysarthria and SMS are two distinct conditions, although both can affect communication. Dysarthria is a motor speech disorder caused by weakness, paralysis, or lack of coordination in the muscles used for speech. In contrast, SMS is a genetic disorder, which impacts physical, intellectual, and behavioral development. As mentioned before, the previously cited authors have worked on speech problems using databases of individuals with dysarthria speech, mostly in English, from multiple patients and conditions. The novelty of this work lies in the dataset involving individuals with Spanish SMS. Some of them work at the level of word or character instead of discourse, i.e., speech to text techniques. In addition, while previous research has centered on transforming audio into visual representations, i.e., voicegrams or scalograms, this research work directly processes audio signals in their raw format, placing it within the domain of sound processing. Furthermore, some studies use CNNs, a method also employed in the authors' proposal. However, in this case, the authors additionally propose the use of audio synthetic data instead of synthetic visual data, given the specific characteristics of the study population.
# Multi-Scale Aggregation Stereo Matching Network Based on Dense Grouping Atrous Convolution
## 2 Related Work and Methods
### Methods
#### 2.2.5 Loss Function  
DenseGASPP, we propose, is trained end-to-end and supervised by the ground truth disparity. However, for the KITTI dataset, the high sparsity of the disparity ground truth may not actively promote our learning process. Inspired by the knowledge reported in [30], we used the prediction results of the pre-trained stereo model as the pseudo ground truth  
Figure 4: Schematic diagram of the multi-scale cost aggregation module.  
supervision. Specifically, we used a pre-trained model to predict the disparity maps on the training set and used the prediction results as a pseudo label. In other words, there is no real ground truth disparity in the pixels.  
For the disparity prediction \(\hat{d_{i}}\), \(i=1,2,3\), bilinear up-sampling was first performed to the original resolution. The corresponding loss function is defined as:  
\[L_{i}=\sum_{p}V(p)\cdot\mathcal{L}\Big{(}\widetilde{d_{i}}(p),D_{gt}(p)\Big{)} +(1-V(p))\cdot\mathcal{L}\Big{(}\widetilde{d_{i}}(p),D_{pseudo}(p)\Big{)}, \tag{5}\]  
where \(V(p)\) is a binary mask to denote whether the ground truth disparity for pixel \(p\) is available, \(\mathcal{L}\) is the smooth \(L_{1}\) loss [9], \(D_{gt}\) is the ground truth disparity, \(D_{pseudo}\) is the pseudo ground truth, and \(i\) represents the sequence of scales.  
The final loss function is the combination of all disparity predicted losses:  
\[L=\sum_{i=1}^{N}\lambda_{i}\cdot L_{i} \tag{6}\]  
where \(\lambda_{i}\) is a scalar for balancing the different terms.
# Collision Avoidance Adaptive Data Rate Algorithm for LoRaWAN
###### Abstract  
Long-Range Wide-Area Network (LoRaWAN) technology offers efficient connectivity for numerous end devices over a wide coverage area in the Internet of Things (IoT) network, enabling the exchange of data over the Internet between even the most minor Internet-connected devices and systems. One of LoRaWAN's hallmark features is the Adaptive Data Rate (ADR) algorithm. ADR is a resource allocation function which dynamically adjusts the network's data rate, airtime, and energy dissipation to optimise its performance. The allocation of spreading factors plays a critical function in defining the throughput of the end device and its robustness to interference. However, in practical deployments, LoRaWAN networks experience considerable interference, severely affecting the packet delivery ratio, energy utilisation, and general network performance. To address this, we present a novel ADR framework, SSFIR-ADR, which utilises randomised spreading factor allocation to minimise energy consumption and packet collisions while maintaining optimal network performance. We implement a LoRa network composed of a single gateway that connects loads of end nodes to a network server. In terms of energy use, packet delivery rate, and interference rate (IR), our simulation implementation does better than LoRaWAN's legacy ADR scheme for a range of application data intervals.  
adaptive data rate; collision; interference; Internet of Things; LoRa; LoRaWAN; spreading factor +
Footnote †: journal:  
002 2024 16 380 1
# Autonomous Agent Navigation Model Based on Artificial Potential Fields Assisted by Heuristics
## 3 Proposed Approach
### Partial Observability  
Partial observability is a condition such that only part of the environment can be observed at a time. This is the case for any mobile robot since sensors do not have infinite range. Even in systems that rely on a Global Positioning System (GPS) or another global source of information [37], details of such information are limited. As an example, let us consider a group of agents in a 2D environment. A GPS can deliver information about global position but not about the details of the terrain, such as elevations or obstacles like trees. For the latter, sensors with enough range and resolution are required.  
If a global source of information is available, it can be combined with local information like that provided by the perception system of the agent. However, this approach must rely entirely on the connection with the global source. If the connection is lost, then the navigation problem becomes more complicated. Assuming that the agents must be able to navigate by their own means, an approach to navigate under partial-observability conditions and no previous knowledge of the environment is proposed herein.  
Path-planning problems require knowledge of the environment in order to calculate a suitable path. Thus, if there is no previous knowledge, a path can not be computed a priori. Hence, the remaining option is to calculate the path as the agent moves through the environment while avoiding obstacles and possible dangers.  
An approach that makes it possible to combine path and trajectory planning as part of the control system is the Artificial Potential Fields approach. In this work, a novel approach toward the potential function is used to reduce the probabilities of local minima along with heuristics to encourage exploration and implicitly avoid local minimum conditions.