esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of CF-MMIMO Wireless Systems Using Dynamic Mode Decomposition
###### Abstract
The received signal at AP \(l\) during the pilot transmission is denoted as \(Y_{l}^{\text{pilot}}\in\mathbb{C}^{N\times\tau_{p}}\) and is given by  
\[Y_{l}^{\text{pilot}}=\sum_{i=1}^{K}\sqrt{\eta_{i}}h_{il}\phi_{t_{i}}^{T}+N_{l} \tag{1}\]  
where \(N_{l}\in\mathbb{C}^{N\times\tau_{p}}\) represents the receiver noise with i.i.d. elements distributed as \(\mathcal{N}_{\mathbb{C}}(0,\sigma_{0}^{2})\). The received uplink signal \(Y_{l}^{\text{pilot}}\) is used by AP \(l\) to estimate the channels \(\{h_{kl}:l\in\mathcal{M}_{k}\}\) for all the UE it serves. This estimation can be performed either directly at AP \(l\) or at the CPU. In the latter case, AP \(l\) acts as a relay, forwarding the received pilot signal \(Y_{l}^{\text{pilot}}\) to the CPU via the fronthaul link. The CPU can then compute all the channel estimates \(\{\hat{\mathbf{R}}_{kl}:k=1,\ldots,K,\,l\in\mathcal{M}_{k}\}\) using the received pilot signals \(\{\mathbf{Y}_{l}^{\text{pilot}}:l\in\mathcal{M}_{k}\}\). However, since the channel vectors are independent, computing the channel estimates separately at each AP \(l\) in the set \(\mathcal{M}_{k}\) does not affect optimality. The estimation results presented in this section apply to both approaches.
## 4 Results of Comparison of Proposed MVD Algorithm with Existing Algorithms
To keep the EI algorithm within the predefined limits, a greater number of points would be required, which would increase model complexity. Although the MVD algorithm may appear visually farther from the Ramberg-Osgood line for material 2, it is important to note that it guarantees all stress values calculated from strain values during finite element analysis will remain within the predefined limits. This level of reliability is not ensured with the EI and VM methods, which may exceed limits depending on specific combinations of parameters, making them overall less consistent. The MVD algorithm holds a distinct advantage by directly selecting points from the original Ramberg-Osgood curve, characterized by parameters \(K\) and \(n\), thereby eliminating the need for initial discretization. Furthermore, with the methodical approach of starting with only two initial points and systematically adding one at a time, the MVD algorithm exhibits fast computing times. On the other hand, due to its straightforward method of selecting equal intervals of points and calculating corresponding stress values, the EI algorithm exhibits faster performance than MVD on average. It is indeed possible that EI could achieve better \(SD_{\text{fin}}\) performance if given the same time as MVD. Despite this speed advantage, the results remain favorable for MVD due to conducting the simultaneous optimization. The EI algorithm might densely allocate points in areas already within the \(SD_{\text{lim}}\) measure, resulting in an unnecessarily high number of points, while the approach to  
Figure 9: Comparison of multilinear curves and their final stress deviations \(SD_{\text{fin}}\) for both observed materials generated with VW, MVD, and EI algorithms with \(SD_{\text{max}}=5\%\) and starting number of points for VW algorithm = 10,000.  
use fewer points is prioritized, as discussed in Section 1.2. In comparison to VW, which also performs optimization, the MVD algorithm exhibits significantly faster performance.
# Adaptive Bat Algorithm Optimization Strategy for Observation Matrix
## 4 Experimental Simulation
### Analysis and Selection of Transformation Basis
#### 4.3.1 Transform Base Setting  
In this section, the transformation bases involved are as follows:  
* Discrete Hartley transform matrix [68] (DHT),
* Discrete Fourier transform matrix [69] (FFT).  
Figure 7: Reconstruction error of each algorithm to signal-4 with different compression ratio: (**a**) Rate = 0.3; (**b**) Rate = 0.35; (**c**) Rate = 0.4; (**d**) Rate = 0.45; (**e**) Rate = 0.5.
# An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels
###### Abstract  
Due to issues with sample quality, there is an increasing interest in deep learning models that can handle noisy labels. Currently, the optimal way to deal with noisy labels is by combining robust active and passive loss functions. However, the weighting parameters for these functions are typically determined manually or through a large number of experimental iterations, and even the weighting parameters change as the dataset and the noisy rate change. This can lead to suboptimal results and be time-consuming. Therefore, we propose an adaptively weighted method for the combined active passive loss (APL) in remote sensing image retrieval with noisy labels. First, two metrics are selected to measure the noisy samples: the ratio of the entropy to the standard deviation and the difference of the predicted probabilities. Then, an adaptive weighted learning network with a hidden layer is designed to dynamically learn the weighting parameters. The network takes the above two metrics as inputs and is trained concurrently with the feature extraction network in each batch, without significantly increasing the computational complexity. Extensive experiments demonstrate that our improved APL method outperforms the original manually weighted APL method and other state-of-the-art robust loss methods while saving the time on manual parameter selection.  
n  
aoisy labels; robust loss; remote sensing image retrieval; deep learning
## 1 Introduction
The present paper evaluates the results of a set of experiments on surface spills and floating marine waste identification based on random images as an initial stage of the development of a system for port water quality monitoring. After the methodological process (i.e., post-process) has been implemented, image sets have been obtained and analyzed to determine the amount and proportion of each image class that is required. In this sense, several computer vision techniques have been assessed, including Image Classification as the most promising one identified preliminarily. In order to evaluate the performance of the algorithm specifically for port environmental management applications, a novel performance index (the error index) has been proposed. The set of images has been conducted in the port of Palma de Majorca, which suffers important events of water quality degradation. The paper is organized as follows. Section 2 introduces the study area, the computer vision technique used, the spill and waste classification, the system layout, the images used, the algorithm training and the statistical reliability of the algorithms. Section 3 shows the results of the training processes and a comparison for different amounts of data available in terms of image set sizes and distribution. Section 4 presents a discussion on the design criteria for the system set-up and its further development. Finally, in Section 5, the conclusions of the study are summarized.
## 1 Introduction  
In previous generations of mobile systems and networks (2G/3G), a special emphasis has been placed in the radio domain on the development of distributed radio access network (D-RAN) architecture. It consisted in the fact that devices processing signals in the baseband (BB), intermediate-band (IF), and radio-frequency-band (RF) were located near to the mast with the antennas. The significant increase in the demand for signal processing, especially in the baseband, meant that we had to look for different solutions. Existing terminal devices, called base stations, can no longer be developed in the traditional way, because they would have to be supercomputers with high computing power in a moment (with high network traffic), and in other cases idling equipment (e.g., with a negligibly small nighttime traffic) that would have to be turned off to save energy without the use of their computational potential. The solution to this problem is to transfer computing functions into the network so that the computers performing these operations may be used for other purposes in the absence of mobile network traffic. This requires central control architecture of remote radio modules whose functions are as limited as their physical equipment allows.  
In the future Next Generation Radio Access Networks (NG-RANs) domain of the 5G systems, exactly centralized/cloud control technology (C-RAN) will be widely used with a large set of gNodeB (gNB) base stations that will also be working in distributed structures/architectures (not to be confused with D-RAN).
# Numerical Heat Transfer Simulation of Oil Shale Large-Size Downhole Heater
## 1 Introduction
Under similar mass flow rates and heat exchange quantities, the combined multi-shell heat exchanger exhibited a 13% lower average pressure drop compared to it [34; 35]. From 2012 to 2022, Jilin University conducted an in situ oil shale conversion pilot project at various depths in Nong'an and Fuyu City, Jilin Province. All these projects successfully yielded shale oil [36]. While the downhole heater plays a crucial role, the technical implementation process reveals many questions such as slow heating efficiency and limited heating volume. Previously studied downhole heaters had small, fixed shell dimensions constrained by the well diameter. Consequently, to enhance the efficiency of in situ oil shale conversion, this paper utilizes the Nong'an and Fuyu oil shale in situ pilot projects as an engineering foundation. It proposes the design of a large-size continuous helical baffle heater to offer technical insights for on-site construction.
## 3. Freestanding Position-Based Routing Protocol
### Next-Hop Selection Algorithm  
#### 3.3.1 Location Prediction Algorithm  
When a vehicle needs to send or relay a packet, the location prediction algorithm must fill the gap between the last values stored in the one-hop neighbors table and the actual values of each neighbor. Thus, when a next-hop neighbor has to be selected, the LP algorithm is invoked before performing the selection. The LP algorithm implemented in FPBR works as follows:  
1. The number of entries in the one-hop neighbors table is updated as follows: if the age of the position information for one particular neighbor is older than 2 times the beacon rate, then this particular neighbor is deleted from the one-hop neighbors table.
2. After performing step 1, the position of all neighbors is updated in the one-hop neighbors table by means of Equation (2): \[P_{\textit{prospected}}=P_{\textit{current}}+(\hat{\vartheta}*dt+\hat{d}\overline {c}c*dt^{2})\] (2) where \(\hat{\vartheta}\) is the last stored velocity vector of the neighbor; \(\hat{d}\overline{c}c\) is the last stored acceleration vector of the neighbor; and \(dt\) is the information dwell time in the one-hop neighbors table.
3. The updated one-hop neighbors table is passed to the next-hop selection algorithm.
## 1 Introduction
### Theory and Algorithm
Comparing Equations (8) and (12), we can see that the composite parameter of the relative impedance and velocity perturbation (\(\sin^{2}\theta\frac{\partial p}{\eta_{0}}+\cos^{2}\theta\frac{\partial^{2}( \rho\rho)}{\partial\eta_{0}}\)) can be computed using the inverse scattering imaging condition (Equation (12)) after the proper preprocessing of source and receiver wavefields. To estimate the impedance perturbation, we must further separate the effects of impedance and velocity. Inspired by [PERSON] et al. [16], who proposed an exponential weighting function to select far-angle reflections for the purpose of tomographic inversion, we applied the following weighting function for Equation (12) to select near-angle reflections [15]:  
\[I_{small\ angle}=\int w\bigg{(}-\frac{1}{v^{2}}\frac{\partial p_{F}}{\partial t }\frac{\partial p_{B}}{\partial t}+\
abla p_{F}\cdot\
abla p_{B}\bigg{)}dt, \text{with }w=e^{-\frac{\alpha(-\frac{1}{v^{2}}\frac{\partial p_{F}}{\partial t} \frac{\partial p_{B}}{\partial t}-\
abla p_{F}\cdot\
abla p_{B})}{-\frac{2}{v^ {2}}\frac{\partial p_{F}}{\partial t}\frac{\partial p_{B}}{\partial t}}}\quad, \ \alpha>1 \tag{13}\]  
where the weighting term \(w\) is approximate to \(e^{-asin^{2}\theta}\) following Equation (11c). This weighting function equals to 1 when \(\theta=0^{\circ}\), and rapidly approaches 0 when \(\theta\) increases, which is designed to select small reflection angles. More details about this weighting function and the choice of \(\alpha\) are presented in the later section. Equation (8) shows that small-angle reflection produces an estimate of the impedance perturbation. Therefore, from Equations (8) and (13), we derived our proposed imaging condition for the relative impedance estimation from RTM:\[\frac{\delta(\rho v)}{\rho_{0}v_{0}}=-\iint 4v^{2}w\left(-\frac{1}{v^{2}}\frac{ \partial p_{F}^{\prime}}{\partial t}\frac{\partial p_{B}^{\prime}}{\partial t}+ \
abla p_{F}^{\prime}\cdot\
abla p_{B}^{\prime}\right)dt\mathrm{dx_{s}},\text{ with }w=e^{-\frac{\alpha(-\frac{1}{v^{2}}\frac{\partial p_{F}^{\prime}}{ \partial t}\frac{\partial p_{B}^{\prime}}{\partial t}-\
abla p_{F}^{\prime} \cdot\
abla p_{B}^{\prime})}{-\frac{2}{v^{2}}\frac{\partial p_{F}^{\prime}}{ \partial t}\frac{\partial p_{B}^{\prime}}{\partial t}} \tag{14}\]  
where \(p_{F}^{\prime}\) and \(p_{B}^{\prime}\) are forward and backward wavefields, respectively, scaled by \(1/\omega^{2}\) in the frequency domain. Figure 1 shows the workflow of our proposed method of estimating the relative impedance perturbation from RTM using the modified inverse scattering imaging condition. Compared with conventional RTM, the only difference is that we replaced the conventional cross-correlation imaging condition with our proposed imaging condition in Equation (14).
## 3 Results and Discussion  
### Numerical Validation  
To validate the numerical models employed, the response to blast events was compared with experimental data. This study encompasses two main validation aspects: (1) validating the modeling of blast loads and (2) validating the blast response of sandwich panels.
# Forward Fall Detection Using Inertial Data and Machine Learning
## 3 Development of the Fall Detection Models
### Training the LSTM Neural Network
In the training phase for the LSTM network, the outputs have a value of 0 for normal activity, meaning normal gait, an output value of 1 for damped forward fall events, and an output value of 2 for forward fall events without damping. Considering the data is composed of the raw normalized acceleration on the X and Y axes, the predicted values offer very good accuracy, as illustrated in Figure 13.  
The regression layer has one output that can have 3 continuous target values. Values near 0 are considered for a no-fall event, values from 0.5 to 1.5 for a forward damped fall event, and values from 1.5 to 2 for a forward undamped fall event. We converted the continuous class into discrete class labels by rounding the values to the nearest integers. Once the predictions were mapped to the nearest class label, we computed the confusion matrix and metrics, and the obtained results for the trained LSTM network were Accuracy: 84%, Precision: 96%, Recall: 98%, and F1 Score: 97%, which suggested that the model performed exceptionally well on the test dataset. For a better understanding of the network's performance, the confusion matrix was plotted, as shown in Figure 15. The confusion matrix considered all data, consisting of the 70% used for training, the 15% for validation, and the 15% for testing. The primary confusion matrix illustrated in Figure 15 reflects that the network was generally reliable in distinguishing between the classes. The network achieved a True Positive Rate (TPR) of 99.2%, with a False Negative Rate (FNR) of 0.8% indicating that Class 0 was detected accurately with very few instances misclassified into other classes. Class 1 had a TPR of 99.0%, showing good sensitivity by having the FNR at 1.0%, meaning the network rarely misclassifies Class 1. Class 2 showed a slightly lower performance, with a TPR of 89.7% and an FNR of 10.3%. While the network still performed reasonably well for Class 2, it occasionally confused it with Class 1, which is also a fall event.
## 3 Results and Discussion
#### 3.1.11. Fatty Acids/Amides  
In total, 16 fatty acids and one fatty acyl amide were annotated in peaks 83-99, equally distributed in both species (Table 1) and grouped in the major clusters in both negative and positive MNs, A and E, respectively (Figure 2 and Figure S1). This included mono-, di-, tri-, and tetrahydroylated fatty acids, as evidenced by the loss of water (H\({}_{2}\)O, 18 amu) and carboxylate (CO\({}_{2}\), 44 amu) moieties. Six saturated fatty acids were detected in peaks 83, 84, 86, 87, 98, and 99. For example, peak 86--detected in both species-- was identified as azelaic acid at [M-H]\({}^{-}\) 187.0975, C\({}_{9}\)H\({}_{15}\)O\({}_{4}\)\({}^{-}\) a dicarboxylic fatty acid, which showed fragment ions at \(m/z\) 169, C\({}_{9}\)H\({}_{13}\)O\({}^{-}\) [M-H-H\({}_{2}\)O], \(m/z\) 143, C\({}_{8}\)H\({}_{15}\)O\({}_{2}\)\({}^{-}\) [M-H-CO\({}_{2}\)], \(m/z\) 125, C\({}_{8}\)H\({}_{13}\)O\({}^{-}\) [M-H-H\({}_{2}\)O-CO\({}_{2}\)], \(m/z\) 97, C\({}_{6}\)H\({}_{9}\)O\({}^{-}\), \(m/z\) 80, \(m/z\) 71, \(m/z\) 69 C\({}_{4}\)H\({}_{5}\)O\({}^{-}\) and \(m/z\) 57, matching the literature [44]. Azelaic acid is incorporated into skin products for treatment of alopecia, as well as its reported cytotoxic and anti-inflammatory effects [44; 45]. This could account for the popular usage of cucumber in skin preparations [3]. In contrast, 10 unsaturated fatty acids were detected in peaks 85, 88, and 90-97 (Table 1). Peak 85 was identified as octadecemamide at [M+H]\({}^{+}\) 282.2796, C\({}_{18}\)H\({}_{36}\)NO\({}^{+}\) showed fragment at \(m/z\) 265 C\({}_{18}\)H\({}_{33}\)O\({}^{+}\) upon loss of ammonia (NH\({}_{3}\), 17 amu). The herein identified fatty acids matched reported literature on Cucurbitaceae seed oil contents [15].
# A Person Re-Identification Method Based on Multi-Branch Feature Fusion
## 3 Multi-Branch Feature Fusion Network
### Saliency Guidance Local Branch  
The saliency guidance local branch is used to deal with the occlusion in the person re-ID. We apply data augmentation to the input images of this branch to generate a sufficient number of occluded samples for learning. The main idea of the Anti-erasure Federated Block Network (AFBN) proposed in this paper is to utilize two adversarial classifiers driven by feature erasure to find complementary object regions. The architecture of AFBN is depicted in Figure 5.  
Figure 4: Hybrid Attention Module.  
The AFBN uses the OSNet of [8] as the backbone feature extraction network, and the feature maps extracted from the backbone network are input into parallel classification branches, each consisting of block networks. In order to avoid block overlapping and invalidation of the same region after erasure, the number of blocks in the two branches is different.  
We propose Algorithm 1 for AFBN that identifies distinctive regions on the feature map generated by Classifier A by applying a predefined threshold. This identification process guides the zero-value replacement of the input features in Classifier B within the identified regions. Following this, a global average pooling layer and softmax layer are connected for classification. Ultimately, an integrated feature map is obtained by combining the complementary feature maps generated by the two branches.  
The algorithm of AFBN is shown as follows.  
```
Input: Training data \(I=\{(I_{i},y_{i})\}_{i=1}^{N}\), threshold \(\delta\)
1:while training does not converge do
2: \(S\gets f(\theta_{0},I_{i})\)\(\triangleright\) Extract initial features \(S_{0}\)
3: \(M^{A}\gets f(\theta_{A},S,y_{i})\)\(\triangleright\) Extract features \(M^{A}\)
4: \(R=M^{A}>\delta\):\(\triangleright\) Obtain high discriminative regions \(R\)
5: \(S\gets erase(S,R)\)\(\triangleright\) Obtain erased features \(\overline{S_{0}}\)
6: \(M^{B}\gets f\big{(}\theta_{B},\overline{S},y_{i})\)\(\triangleright\) Extract features \(M^{B}\)
7: \(\bar{M}_{\textit{fuse}}=\max\Big{(}\overline{M^{A}},\overline{M^{B}}\Big{)}\)\(\triangleright\) Obtain fusion feature \(\bar{M}_{\textit{fuse}}\)
8: endwhile Output:\(\bar{M}_{\textit{fuse}}\)
```  
**Algorithm 1**AFBN  
\(I=\{(I_{i},y_{i})\}_{i=1}^{N}\) represents the training image set, where \(y_{i}\) is the label of the image \(I_{i}\), and \(N\) represents the number of images. The input image \(I_{i}\) is first transformed by the backbone network \(f(\theta_{0})\) into a spatial feature map \(S\in R^{H_{1}\times W_{1}\times K}\) with \(K\) channels and \(H_{1}\times W_{1}\) resolution. \(\theta\) represents the learnable parameters of the neural network. \(f(\theta_{A})\) represents Classifier A, which can generate an object feature map \(M^{A}\in R^{H_{2}\times W_{2}}\) of size \(H_{2}\times W_{2}\), and \(M^{A}\) is used to highlight the discriminative feature region of the target.  
Figure 5: Structure of AFBN.
## 4 Sound-to-Image Translator
### Overview  
The training process of the S2I translator is depicted in Figure 1. Initially, an audio autoencoder is trained using log-mel spectrograms computed from 1 s audio segments. Then, the frozen encoder is used to extract the audio embeddings that will be forwarded to the generator. During the training phase, the generator will try to fool the discriminator, which is trained once for every five updates of the generator. At this stage, the discriminator will receive balanced batches of real and synthetic images, along with their corresponding target scores, for modeling visual feature extraction. An aural-visual coherence check is performed by concatenating the source-audio embedding with the input of the discriminator's last layer, effectively merging aural and visual modalities. This enables the discriminator to jointly assess both the realness of the generated images and their semantic coherence with the corresponding audio, and output what we call a realness-and-coherence score (RC-score).