esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Due to the complex ocean propagation environments, the underwater acoustic (UWA) multipath channel often exhibits block sparse time-varying features, and while dynamic compressed sensing (DCS) can mitigate the time-varying effects of the UWA channel, DCS-based algorithms have limited performance for the UWA channel with block sparsity. In this study, by formulating the UWA channel with blocks concatenation, a block sparse-based DCS approach (BS-CS) is proposed to explore the block and time-varying sparsity of UWA channel simultaneously. In detail, we firstly adopt a block sparse recovery algorithm, block orthogonal matching pursuit (BOMP), to compute the temporary estimate. Then, the CS approach is applied to compute the support additions, which are caused by the time-varying components of the UWA channel. Next, we use the selected support to perform the BOMP estimate, and obtain the estimated channel response. Finally, the numerical simulation and the sea experiment were carried out to verify the superior performance of the proposed BS-CS algorithm in the block sparse time-varying UWA channels.  
block sparse; time-varying; underwater acoustic communication; dynamic compressed sensing +
Footnote †: journal: Marine Science and Engineering
# Double Branch Attention Block for Discriminative Representation of Siamese Trackers
## 2 Related Work  
### Siamese Trackers  
In recent years, the trackers based on the Siamese framework have many advanced proposals. Full convolution trackers, such as SiamFC [9] and SiamDW [13], directly predict the target location through the similarity map and obtain a rough target bounding box with a fixed aspect ratio. Region proposal trackers, such as SiamRPN [12] and SiamRPN++ [20], feed the similarity map into the classification and regression branches and obtain a more accurate bounding box through the region proposal network. Compared with full convolution trackers, the application of the region proposal network makes trackers well fit the shape of the target and has fine effectiveness in dealing with the target deformation problem. Mask trackers, such as SiamMask [21], add a mask branch to the region proposal tracker to provide pixel-level refinement masks, which are more accurate than bounding boxes. However, they need a lot of extra semantic segmentation training data, and the increased computational cost has a negative impact on tracking efficiency.  
To achieve more accurate tracking effects at a low computational cost, we chose the region proposal tracker as the baseline tracker to carry the proposed DBA block. The baseline tracker consists of a feature extraction network and a region proposal network. The feature extraction network \(\phi\) extracts the feature information of the target \(z\) and the search input \(x\), respectively, and both of them share the same network parameters.  
The feature information \(\phi(x)\) and \(\phi(z)\) are cross-correlated to obtain a similarity measure \(g(x,\,z)\):  
\[g(x,\,z)=\phi(x)\star\phi(z) \tag{1}\]  
where \(\star\) represents a cross-correlation operator. The similarity measure \(g(*,\,*)\) expresses the similarity between the target and the proposal region of the search input, which reflects the possibility that the proposal region on the search input contains the real target. The similarity measure \(g(*,\,*)\) is fed into the Cls_head and Loc_head to generate dense classification responses \(cls\) and regression responses \(loc\).  
\[[cls,loc]=[\text{Cls\_head}(g(x,z)),\,\text{Loc\_head}(g(x,z))] \tag{2}\]  
The classification responses are responsible for the pixel classification to obtain the approximate location of the target, and regression responses are used to refine the size of the bounding box.
# Accurate Needle Localization in the Image Frames of Ultrasound Videos
## 2 The Proposed Method
### Phase I: Generating the QUS Image
#### 2.1.2 Generating the QUS Image  
The QUS image is obtained by analyzing the extracted features using an LDA classifier [43]. The LDA classifier is configured to classify the pixels of the ultrasound image into two classes: \"needle\" and \"background tissue\". In particular, the LDA classifier operates by transforming the features into a reduced-dimensional space that maximizes class separation and minimizes intra-class variance. During training, the LDA classifier calculates feature weights based on their effectiveness in distinguishing between the \"needle\" and \"background tissue\" classes. Therefore, the feature weights computed using the LDA classifier emphasize the features that maximize the differentiation between the needle pixels and background tissue pixels. The trained LDA classifier is then used to generate the QUS image by performing a weighted linear combination of the features. The procedure employed to train the LDA classifier is described in Section 3.2.  
The use of all 24 features that are extracted from the ultrasound image to classify the pixels can lead to suboptimal performance. Therefore, the features are refined using the two-phase feature selection procedure described in [44]. In the first phase of this procedure, the minimum redundancy maximum relevance (mRMR) technique [43] is used to rank the features. The ranked features are used to generate 24 feature groups, in which the \(i\)-th group includes the top \(i\) ranked features. The LDA classifier is configured to utilize the features from each group to classify the pixels of the ultrasound images. The feature group that enabled the LDA classifier to achieve the highest classification accuracy is selected. In the second phase of the feature selection procedure, the features within the selected feature group are further refined using the backward elimination algorithm [45]. The goal of the backward elimination algorithm is to maximize the classification accuracy by retaining the important features and eliminating the irrelevant ones within the selected feature group. In this study, the features selected using the feature selection procedure include the speckle variations feature, the local phase feature, the sum average GLCM texture feature, and the sum variance GLCM texture feature. Figure 1g shows the QUS image that is generated by the LDA classifier using these four features.
# An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care
## 7 Outing and Visiting Detection
### Visiting Detection  
Visiting events are considered to be rare occasions where an increased sensor activity is reported, caused by the presence of visitors.  
The following features are extracted for a given time segment (between adjacent door activities): segment start time (\(T_{s}^{V}\)), and end time (\(T_{e}^{V}\)), as well as segment duration (\(D^{V}\)).  
Figure 12: Detected outing events of one patient.  
Furthermore, any signal overlap between sensor pairs \(O_{\{S_{i},S_{j}\}}\), where \(S_{i}\) and \(S_{j}\) indicate the \(i\)th and the \(j\)th sensor (\(1\leq\{i,j\}\leq N\), \(i\
eq j\), \(N\) is the total number of sensors deployed), as well as the overall signal overlap across all deployed sensors (\(O_{\{S_{1},S_{2},\dots,S_{N}\}}\)). As in Section 6.3.1, the start and end time are represented in the two-component cyclic manner. By doing so, each segment is represented in a \(6+\binom{N}{2}\)-dimensional feature space, where \(\binom{N}{2}\) is the number of sensor-pair combinations to compute the signal overlap.  
To extract the 'rare events', we conduct Isolation Forest [51] calculations on all candidate segments, represented in the feature space. Each segment is computed with an 'isolation score'. The lower the isolation score, the more likely the segment is to be 'normal'. We consider \(M_{IS}+2\times ST_{IS}\), where \(M_{IS}\) and \(ST_{IS}\) refer to the mean and standard deviation of the isolation scores, as threshold to decide whether the segment is a relatively rare occasion. Selected 'rare' events are then post-processed by removing those with sensor active levels lower than the median value of all segments. The objective is to disregard segments which are rare due to other reasons.  
Figure 13 depicts detected visiting events in a household, with an increased occurrence in December in comparison to other periods of the year, i.e., six visits were detected between the middle and the end of December 2019. Additionally, we can see that after 5 March 2020, there were barely any visiting events detected except for on 7 April and in the middle of June. Across the cohort, reduced visiting onsets were generally observed during lockdown times.
## 4 Experiments and Discussion
### Experimental Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Task**} & **Initialization** & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F1-Score**} & \multirow{2}{*}{**Accuracy**} \\  & **Mode** & & & & \\ \hline \multirow{4}{*}{Task 1} & LFCC & 0.819 & 0.826 & 0.820 & 0.812 \\  & MFCC & 0.834 & 0.838 & 0.835 & 0.831 \\ (fold 1) & FBank & 0.875 & 0.867 & 0.879 & 0.876 \\  & FEM-ATNN-trans & 0.926 & 0.939 & 0.931 & 0.926 \\  & LFCC & 0.934 & 0.932 & 0.932 & 0.929 \\ Task 1 & MFCC & 0.943 & 0.941 & 0.942 & 0.939 \\ (fold 2) & FBank & 0.971 & 0.971 & 0.971 & 0.970 \\  & FEM-ATNN-trans & 0.984 & 0.978 & 0.980 & 0.980 \\  & LFCC & 0.682 & 0.706 & 0.684 & 0.750 \\ Task 2 & MFCC & 0.742 & 0.740 & 0.739 & 0.792 \\  & FBank & 0.747 & 0.749 & 0.742 & 0.793 \\  & FEM-ATNN-trans & 0.824 & 0.846 & 0.833 & 0.878 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Recognition results of proposed method and methods based on mainstream primary features. The methods in Table 8 show studies in recent years, and our method shows competitive performance. On the one hand, most of the methods in Table 8 segment the recordings and randomly sample the adjacent segments as the train or test data, which is lower in classification difficulty than Task 1 used in this paper. On the other hand, some methods in the table adopt data augmentation technology, such as methods of No.7 and No.8, which both used SpecAugment. The FEM-ATNN-trans does not use data augmentation technology but still performs well.
## 2 Method and Data Characteristics  
### Subsection Flow of the Study  
In this study, a representative urban area in Korea was selected as the target region. To develop a ground subsidence risk level prediction model based on machine learning, the historical information of ground subsidence, and attribute information of underground utility lines in the target region were used to build a dataset and then applied to the machine learning algorithm. The target region was divided into a grid with a total of 2391 squares of 500 m \(\times\) 500 m in size, using the ArcGIS program to predict risk level. Six types of underground utility lines included in each grid square were merged into a single type to extract the attribute information and density. The dataset was built using a method that calculated a risk level based on the number of ground subsidence occurrences in the grid using the historical ground subsidence information.  
The developed dataset was divided into training and test datasets at an 80:20 ratio to prevent overfitting of the model and to test the model. To mitigate the data imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied to the training data. This developed training dataset was applied to machine learning algorithms: RandomForest (RF), XGBoost (XGB), and LightGBM (LGBM) to check the model results by adjusting the hyperparameters that exhibited the optimal performance. Using the 20% test data, the model's performance was validated through the test indices of accuracy, F1-score, and area under the curve (AUC). Based on the test results, dataset types and machine learning models that exhibited optimal performance were selected, and the importance of the influencing factors was derived. Figure 1 shows the flow chart of the study.
## 1 Introduction
The main objective of these approaches is to determine the failure probability of a mechanical system under the influence of different uncertainties, such as loads or material properties, among others [33]. This can be achieved using approximation methods such as the First-Order Reliability (FORM) and Second-Order Reliability (SORM) methods or even simulation strategies such as the Monte Carlo Sampling (MCS) method [39]. The MCS is characterised by its simple and straightforward implementation to carry out a large number of simulations and to mathematically replicate the behaviour. Furthermore, when these simulation strategies are combined with one or more design objectives or cost functions, it is possible to achieve Reliability-Based Design Optimisations (RBDOs) [43]. While most previous works have focused on lower performance applications or partial replacement of aggregates, this work aimed to advance the understanding of the mechanical characterisation of structural concretes made from precast concrete rejects for the manufacture of structural elements such as beams. In addition, it incorporated advanced characterisation techniques and reliability-based design methods, adding a level of rigour and precision to the assessment of the uncertainties and variability inherent in recycled materials. In pursuit of this objective, a characterisation of the recycled aggregates was initially carried out in order to define an optimised concrete dosage. Then, tensile, compression and elasticity mechanical characterisation tests were carried out using the DIC technique to obtain their properties. Experimentally obtained full-field data from DIC were used along with robust design techniques to predict the behaviour of bending beams. Subsequently, prototypes of structural beams were manufactured using the same concrete. Finally, these beams were subjected to experimental bending tests to compare the results of these tests with the prediction made according to the properties obtained previously. Following this introduction, Section 2 describes the materials and methodology used in the tests of this research. In Section 3, the experimental results of the mechanical characterisation tests are shown, as well as a discussion of these results. An analysis of the validation tests performed on the manufactured beams is also presented. Finally, Section 4 draws the main conclusions on the suitability of the introduction of these recycled aggregates for the fabrication of structural elements and discusses future lines of research.
## 3 Proposed QoS_mPCD Scheme Procedure  
In this paper, we deploy mPCs to provide the best resources to high-priority users in assumed scenario. According to defined priorities, users can be separated during resource allocation procedures. The unplanned deployment of an mPC causes a severe user-association problem because of the mPC's mobile nature, and it also creates serious CCI [13]. This requires flexibility in the deployment of the mPC in order to overcome the CCI issues and also to ensure high-priority users can be served efficiently. Therefore, the proposed QoS_mPCD scheme is very suitable for dealing with said issues. The proposed procedure for deploying the mPC based on QoS priority access is shown in Algorithm 1.  
```
1:For each BS\(j\in B\)
2:Initialization x \(\leftarrow\) 0
3:Total_RBs \(=\) 50 \(\leftarrow\) \(\left\{\begin{array}{ll}\text{First Schedule the best RBs,}&LTE-R\text{ user}\\ \text{Schedule the remaining RBs,}&non-LTE-R\text{ user}\\ \text{BS (K $\in$ 11)}\leftarrow\text{(BS[PS =3, $R=8])}\\ \end{array}\right.\)
4:parameters (\(i\), \(SINR\_threshold\), cellEdgeUE, cellCenterUE, LTE RUIE, PS UE, mPC UE, HII).
5:Step 1. Context Information Collection
6:Exchange the context information between UE and BS.
7:Count the number of cell-edge users based on SINR threshold.
8:for each UE \(u\in U\)do
9: Check \(SINR_{u,j}<SINR\_threshold_{u,j}\)
10:\(u_{edge}=u_{edge}+1\)
11:endfor
12:Users (PS-LTE UE, LTE-R UE, mPC UE) are classified based on priority.
13:Step 2. User Association and Resource Allocation
14:for each UE \(u\in U\)do
15:Estimate \(PL_{u,j}\) and select BS \(j\) that maximizes and associates user \(u\) with it.
16:Allocate maximum transmission power to users.
17:if\(SINR_{LTE-R,j}<SINR\_threshold_{LTE,R,j}\)
18: Use distance-based power control scheme for users to meet LTE-R UE SINR.
19:endif
20:Dynamically adjust RBs during the RBs allocation for mPC user.
21:endfor
22:Calculate the load on each BS \(j\).
23:Compute Throughput, Received SINR, and Interference on each UE \(u\), respectively.
24:\(i=i+1\)
25:Step 3. Application of SDN-based eICIC
26:while\(HII=0\)
27:for each \(M\in B\) in 1 st-tier do
28: Use optimum ABS ratio to accordingly transmit ABS.
29: Plan offloaded users' \(u\) in mPC during ABS.
30:endfor
31:endwhile
```  
**Algorithm 1** Proposed QoS priority-based mPC deployment scheme
## 6 Health Effects and Toxicity of GLY and GBHs
### Impacts on Reproduction, Hornones, and Teratogenicity  
It has been demonstrated that glyphosate and GBHs interfere with the human estrogen pathway [206]. Roundup inhibits the production of estrogen by the aromatase enzyme in the placental and embryonic cell lines of humans [212; 213]. For glyphosate, it has been proposed that the enzyme be inhibited competitively, an effect facilitated by adjuvants that increase glyphosate solubilization and activity in herbicide formulations [214].  
[PERSON] et al. [215] discovered the impact of GLY on estrogen receptors (ER) in breast cancer cell lines, most likely through an indirect mechanism because it is structurally incapable of binding \(\text{ER}_{\alpha}\). Because ER antagonists impede the glyphosate effect, [PERSON] et al. [160] theorized that glyphosate has estrogen-like properties interacting with the \(\text{ER}_{\alpha}\) and \(\text{ER}_{\beta}\). Furthermore, glyphosate may trigger up to 50% of the estrogen response and cause breast cancer cell proliferation. Upon exposure, both ERs were expressed after 6 h and just \(\text{ER}_{\alpha}\) after 24 h. In addition, endogenous estrogen had an antagonistic effect when it was present. As a result, GLY could disrupt estrogen pathways and cause endocrine disruption [206].  
Male offspring of rats that were given glyphosate exposure in an acceptable amount have lower testosterone production [158]. This endocrine imbalance influenced reproductive behaviour, resulting in changes in sexual choice and a long period in the females' first mount starting to reproduce [158]. [PERSON] et al. [216] reported that Roundup has been shown to suppress progesterone synthesis in human placental cells effectively, but only at greater doses compared to those that kill cells, implying that the progesterone impact is most likely due to cytotoxicity. GBHs also had an effect on human pregnancies, as shown by the association between GBH exposure and an increase in the frequency of premature births and miscarriages [217]. Several studies theorized that glyphosate cytotoxicity may be responsible for this behavior in light of its effects on embryonic, placental, and human cell lines [160; 218].  
GBHs have also been demonstrated to cause teratogenicity in fish (primarily in the form of heart abnormalities), mice, chickens, and rats, with brain and bone deformities [219; 220; 221]. According to [PERSON] et al. [222], GBH exposure before a child's birth has been associated with a higher prevalence of malformations such as Down's syndrome and cleft lip in humans. Then [PERSON] and [PERSON] [206] concluded that based on the evidence shown above, glyphosate and GBHs appear to have the ability to disrupt the system of reproduction at several stages, such as teratogenicity and hormonal pathway disruption.
# Integrated Photonics for IoT, RoF, and Distributed Fog-Cloud Computing: A Comprehensive Review
## 9 Summary of Benefits  
Integrated photonics offers a transformative set of advantages for both Fog and Cloud networks, enabling next-generation distributed computing architectures. In Fog computing, PICs provide ultra-low-latency, compact form factors, and energy-efficient data-processing--ideal for real-time applications at the network edge, such as in autonomous vehicles, industrial IoT, and smart infrastructure. In Cloud data centers, integrated photonics supports ultra-high bandwidth, scalable interconnects, and CPO, reducing power consumption and enabling denser compute fabrics for AI and data-intensive workloads. Across both layers, PICs facilitate dynamic traffic management, increased spectral efficiency, and extended transmission distances through wavelength division multiplexing (WDM) and coherent optics. Furthermore, their compatibility with optical computing, neuromorphic photonics, and quantum technologies positions integrated photonics as a foundational technology for secure, high-performance, and future-proof Fog-Cloud ecosystems.
# Evaluation of the Consistency of Three GRACE Gap-Filling Data
## 6 Conclusions
2020013045 and ZY20215159), the Key Project of Science and Technology Research for Universities of Hebei Province (grant no. ZD20200407), the Hebei Key Laboratory of Earthquake Disaster Prevention and Risk Assessment (grant no. FZ213109), and the Beijing Key Laboratory of Urban Spatial Information Engineering (grant no. 20220102). The SSA data are available at [[https://doi.org/10.18419/darus-807](https://doi.org/10.18419/darus-807)]([https://doi.org/10.18419/darus-807](https://doi.org/10.18419/darus-807)). The IGG data are available at [[https://doi.org/10.22000/357](https://doi.org/10.22000/357)]([https://doi.org/10.22000/357](https://doi.org/10.22000/357)). The QF and Swarm data are available at the International Center for Global Earth Models ([[http://icgem.gfz-potsdam.de](http://icgem.gfz-potsdam.de)]([http://icgem.gfz-potsdam.de](http://icgem.gfz-potsdam.de))). The altimetry data are available at CSIRO ([[http://www.cmar.csiro.au/sealvel/sl_data_cmar.html](http://www.cmar.csiro.au/sealvel/sl_data_cmar.html)]([http://www.cmar.csiro.au/sealvel/sl_data_cmar.html](http://www.cmar.csiro.au/sealvel/sl_data_cmar.html))). The Argo data are available at [[https://argo.ucsd.edu/data/argo-data-products/](https://argo.ucsd.edu/data/argo-data-products/)]([https://argo.ucsd.edu/data/argo-data-products/](https://argo.ucsd.edu/data/argo-data-products/)). The precipitation data are obtained from Climate Data Store ([[https://cds.climate.copernicus.eu/#](https://cds.climate.copernicus.eu/#)]([https://cds.climate.copernicus.eu/#](https://cds.climate.copernicus.eu/#))!/home). The climate-driven water storage changes model (GRACE-REC) is available for download at the website [[https://figshare.com/articles/dataset/GRACE-REC_A_reconstruction_of_climate-driven_water_storage_changes_over_the_last_century/7670849](https://figshare.com/articles/dataset/GRACE-REC_A_reconstruction_of_climate-driven_water_storage_changes_over_the_last_century/7670849)]([https://figshare.com/articles/dataset/GRACE-REC_A_reconstruction_of_climate-driven_water_storage_changes_over_the_last_century/7670849](https://figshare.com/articles/dataset/GRACE-REC_A_reconstruction_of_climate-driven_water_storage_changes_over_the_last_century/7670849)). We are grateful to Locher and Kusche for providing IGG data. We thank the International Centre for Global Earth Models for providing QF data. Thanks to CSIRO for providing sea level height data for \"Combined TOPEX/Poseidon, Jason-1, Jason-2/OSTM and now Jason-3 sea level fields - several versions\". Special thanks to the Commonwealth Scientific and Industrial Research Organization for providing altimetry data. Furthermore, thanks to the Scripps Institution of Oceanography for providing the monthly gridded Argo data. The authors declare no conflict of interest.