esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Materials  
Anti-rabbit polyclonal anti-phospho-AKT (9271S), anti-phospho-ERK (9101S), anti-phospho-JNK (9251S), anti-phospho-p38 MAPK (9211S), anti-AKT (9272S), anti-ERK (9102S), anti-JNK (9258S), anti-p38 MAPK (9212S), and anti-p21 WAF1 (2947S) antibodies were obtained from Cell Signaling Technology (Danvers, MA, USA). Anti-rabbit polyclonal antibodies against MMP-9 (2395645) were obtained from Millipore Sigma (Burlington, MA, USA). Anti-mouse polyclonal antibodies against CDK2 (sc-163), CDK4 (sc-23896), cyclin D1 (sc-8396), cyclin E (sc-247), p27 KIP1 (sc-1641), p53 (sc-126), glyceraldehyde 3-phosphate dehydrogenase (GAPDH; sc-47724), HSP70 (sc-24), and SB203580 (sc-3533) were purchased from Santa Cruz Biotechnology (Dallas, TX, USA).
# Wearable-Based Intelligent Emotion Monitoring in Older Adults during Daily Life Activities
###### Abstract  
We present a system designed to monitor the well-being of older adults during their daily activities. To automatically detect and classify their emotional state, we collect physiological data through a wearable medical sensor. Ground truth data are obtained using a simple smartphone app that provides ecological momentary assessment (EMA), a method for repeatedly sampling people's current experiences in real time in their natural environments. We are making the resulting dataset publicly available as a benchmark for future comparisons and methods. We are evaluating two feature selection methods to improve classification performance and proposing a feature set that augments and contrasts domain expert knowledge based on time-analysis features. The results demonstrate an improvement in classification accuracy when using the proposed feature selection methods. Furthermore, the feature set we present is better suited for predicting emotional states in a leave-one-day-out experimental setup, as it identifies more patterns.  
activities for daily life (ADL); artificial intelligence; affective computing; machine learning; medical wearable; mental well-being; older adults; smart health +
Footnote †: journal:  
text, we refer indifferently to the emotional or affective state as a component of well-being, despite the differences between the terms as presented in [11].  
In this paper, we present an AI-based system (see Figure 1) to monitor the emotions of older adults during their daily life, with the goal of preserving their mental and emotional well-being. Our system is composed of the wearable watch-like Empatica E4 certified medical sensor [12] that collects several physiological and activity signals. Those signals are processed and converted into feature vectors that are classified into one of the affective concepts [13], which provide evidence of the mental well-being of the participants.  
To collect ground truth data from older adults to train the supervised classifier, we provided an app for the smartphone that includes an ecological momentary assessment (EMA) tool, allowing for quick and unobtrusive user input [14]. This tool simplifies the emotional state assessment of the participant by asking two simple questions related to levels of valence and arousal, each measured on a 5-point Likert scale.  
Our contributions with this work are as follows: (1) two feature selection methods whose results prove to work efficiently in discriminating different affective states; (2) a feature set that disregards domain expert knowledge during the feature engineering step; and (3) a thorough methodology evaluation using a collection of different classification methods in two experimental setups. Based on the experiments, we concluded future steps to improve healthcare systems based on bio-wearable sensors.
# An Under-Sampling Array Signal Processing Method Based on Improved Hadamard Matrix
## 2 Array Signal Processing Model Based on Compressive Sensing
### Array Received Signal and Its Sparsity  
#### 2.1.1 Narrowband Array Received Signal  
In this work, we assume there are N acoustic sensors the spacing is d in a uniform linear array (ULA) and K narrowband underwater sound sources or targets in the far field of the array, as in Figure 1. In addition, there is no mutual coupling between the array sensors, and the number of the sources or targets is less than the number of array sensors [27].  
According to the above assumptions, then the array received signal of the \(n\)th sensor can be written as:  
\[x_{n}(t)=\sum_{k=1}^{K}s_{k}(t)e^{-\beta 2\pi f_{0}}\frac{d(n-1)\cos\left(\theta_{k} \right)}{c}+v_{n}(t),\ \ n=1,2,\cdots,N, \tag{1}\]  
where \(s_{k}(t)\) is the signal emitted by the \(k\)th source; \(v_{n}(t)\) is the \(n\)th sensors receiving the environmental noise; \(f_{0}\) represents the narrowband frequency; \(c\) represents the sound speed; and the \(t=1,2,\cdots L\), where \(t\) represents the discrete sampling time.  
Then we discretize the potential space so that the source is sparse in spatial [28]. The space of interest is divided as P grid points, as in Figure 2, i.e., \(\theta\in\left[0^{\circ},180^{\circ}\right]\) is divided as \(\left\{\theta_{1},\cdots,\theta_{p},\cdots,\theta_{p}\right\}\) with \(P\gg K\), every point is viewed as a potential source. Therefore, the Equation (1) can be rewritten as:  
\[\mathbf{X}(t)=\mathbf{A}\cdot\mathbf{S}(t)+\mathbf{v}(t), \tag{2}\]  
where \(\mathbf{X}(t)\) is the signal data of size \(N\times L\) received by the ULA; \(\mathbf{v}(t)\) is the environmental noise data of size \(N\times L\) received by the ULA; \(\mathbf{S}(t)\) is \(K\) sparse underwater target echo signal amplitude information of size \(P\times L\); \(\mathbf{A}\) is the array manifold matrix of size \(N\times P\) under \(P\) gridding, i.e., \(\mathbf{A}=[a(\theta_{1}),a(\theta_{2}),\cdots,a(\theta_{p})]\), where \(a(\theta_{p})\) is the steer vector and it can be written as:  
\[\left(\theta_{p}\right)=[1,e^{-j(\frac{2\pi f}{c}\times d\times cos\theta_{p}) },\cdots,e^{-j(\frac{2\pi f}{c}\times(n-1)\times d\times cos\theta_{p})}]^{T},\ p=1,2,\cdots,P. \tag{3}\]  
As shown in Figure 2, the azimuth is divided into P grids by gridding, it is assumed that the P grids are sufficiently density, so that the real sources or targets can be viewed as being reasonably close to some of those grid points, where the solid circles represent the real targets, and the hollow circles represent the grid points without the real targets. The unknown vector \(\mathbf{S}\) represents the source amplitudes at all \(P\) grid points. The \(\mathbf{p}\)th entry of is equal to zero if there are no sources or targets at \(\theta_{p}\), otherwise, it is non-zero. Therefore, it can be considered that \(\mathbf{S}\) is sparse in spatial distribution.  
Figure 1: Received model of N-element uniform linear array.
## 5 Results and Discussions
### Influence of Aperture Parameters
Owing to the jet effect of the BCPBT, solid particles could be pumped to a higher position, which improved the lifting ability of the liquid to the solid particles. Consequently, the cloud height in the stirred tank with the BCPBT was higher than that with the PBT. Cloud height trends higher with an increase in aperture size, reaches a maximum value, and then drops gradually with further increases in the aperture size. The orifice flow velocity shows a dampening with the increase of the aperture size. If the aperture size is too small, the local eddy diffusion intensity is not enough. However, a slight velocity gradient is formed in the flow field when the aperture size is too large, thereby causing the shear stress of the impeller to weaken. The cloud height was the largest at the aperture ratio of 13%. With further increases of the aperture ratio, the cloud height slightly declined. An increase in the aperture ratio increased the number of apertures on the blade, and the jet flows were converted to concurrent flows, leading to a negative impact on the mixing process. However, the aperture ratio was too small to produce enough vortices for energy diffusion. From the above we can conclude that better solid-liquid suspension quality can be acquired when the aperture diameter is equal to 8 mm and the aperture ratio is equal to 13%. Figure 15: Influence of aperture parameters on local axial solid concentration profile. (**a**) Influence of aperture diameter, (**b**) influence of aperture ratio.
# A Complex Network Important Node Identification Based on the KPDN Method
## 4 Datasets
### Networks with Scale \(10^{4}\)-\(10^{5}\)  
(1) dimacs10-astro-ph. The dimacs10-astro-ph dataset from the DIMACS challenge is a co-authorship network in the field of astrophysics. This dataset contains 16,046 author nodes and 121,251 collaboration edges between them. It can be used to analyze the structural characteristics of academic collaboration networks, with the features of low network density but a high average clustering coefficient.  
(2) ca-Astroph. The ca-Astroph dataset is extracted from the arXiv e-print repository for the astrophysics collaboration network. The dataset scale is similar to dimacs10-astro-ph, containing 18,771 author nodes and 198,050 collaboration edges. This dataset is often used for academic collaboration network analysis, with a distinct core-periphery structure.  
(3) gotron-reuters. The gotron-reuters dataset built by [PERSON] et al. is a Reuters news text network containing 38,677 news article nodes. Edges are constructed between pairs of topically similar articles, with a total of 978,174 edges. This dataset can be used for text network analysis with high modularity.  
(4) prosper-loans. The prosper-loans dataset from the Prosper lending website contains 89,269 borrower nodes and 3,330,022 lending relationship edges between them. This is a typical financial transaction network dataset, often used for complex network analysis, with scale-free degree distribution.  
(5) facebook-wosn-links. The facebook-wosn-links dataset represents the linkage network between different Facebook pages, containing 63,731 page nodes and 817,035 inter-page linkage edges. This dataset can be used for social network analysis, with a high clustering coefficient and obvious community structure.  
As well as three artificial datasets: watts-strogatz [29], LFR [30], and barabasi-albert [31].
# Evaluation of _Aspergillus flavus_ Growth on Weathered HDPE Plastics Contaminated with Diesel Fuel
## 2 Materials and Methods
### Inoculation and Incubation  
The strain used in this study was _Aspergillus flavus_, selected for its recognized capacity to metabolize hydrocarbons and its tolerance to adverse environmental conditions [21]. This strain was obtained from the fungal bank of the Renewable Energies and Waste Laboratory (LERR-UC) at the Pontificia Universidad Catolica de Chile and maintained under refrigeration until experimental use. The genetic sequence of the strain employed in this study has been published in [22].  
The selection of incubation temperatures (20 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, and 30 \({}^{\circ}\)C) was based on the known thermal tolerance of _Aspergillus flavus_, which ranges approximately between 12 \({}^{\circ}\)C and 48 \({}^{\circ}\)C, with a general physiological optimum near 37 \({}^{\circ}\)C under laboratory conditions. Additionally, the production of secondary metabolites such as aflat is higher between 25 \({}^{\circ}\)C and 33 \({}^{\circ}\)C, with aflatoxin B1 (AFB1) peaking at 33 \({}^{\circ}\)C and AFB2 at temperatures around 25-30 \({}^{\circ}\)C, provided favorable water activity conditions exist [23]. The presence of aflat was not evaluated in this study.  
However, this study prioritized an environmental applicability approach. Considering that fungal-based biocleaning technologies could be implemented in industrial, agricultural, or exposed disposal environments, it was deemed essential to evaluate the efficacy of the fungus at temperatures compatible with ambient conditions, without the need for artificial thermal control. In this context, temperatures of 20 \({}^{\circ}\)C and 25 \({}^{\circ}\)C were included to represent real-world usage scenarios, allowing exploration of the potential of _A. flavus_ in sustainable and low-energy decontamination systems, even operating below its optimal toxin production range.  
Inoculation involved depositing a portion of active _A. flavus_ mycelium at the center of each plastic sample previously coated with PDA medium. The samples were placed in sterile glass Petri dishes and incubated in a forced convection incubator (Mermert IF55, Memmert GmbH, Schwabach, Germany), programmed at three experimental temperatures: 20 \(\pm\) 1 \({}^{\circ}\)C, 25 \(\pm\) 1 \({}^{\circ}\)C, and 30 \(\pm\) 1 \({}^{\circ}\)C. These conditions were selected based on the literature highlighting their influence on enzymatic activity and mycelial expansion in fungal bioremediation processes [8; 10]. The incubation period extended for 15 days, maintaining constant relative humidity and internal ventilation within the equipment.
## 4 Experiments
### Results
#### 4.3.2 Ablation Experiments
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Datasets** & **Indicators** & **1-Gram** & **2-Gram** & **3-Gram** & **1, 3-Gram** & **1, 2, 3-Gram** \\ \hline \multirow{4}{*}{Dictionary -based dataset} & Accuracy & 94.56\% & 94.72\% & 95.19\% & 95.24\% & 95.26\% \\ \cline{2-7}  & Precision & 94.48\% & 94.30\% & 95.26\% & 95.33\% & 94.94\% \\ \cline{2-7}  & Recall & 95.14\% & 95.67\% & 95.53\% & 95.56\% & 96.03\% \\ \cline{2-7}  & F1 & 94.81\% & 94.98\% & 95.40\% & 95.45\% & 95.48\% \\ \hline \multirow{4}{*}{Character -based dataset} & Accuracy & 98.45\% & 98.37\% & 98.01\% & 98.50\% & 98.51\% \\ \cline{2-7}  & Precision & 98.48\% & 98.43\% & 98.28\% & 98.50\% & 98.75\% \\ \cline{2-7}  & Recall & 98.38\% & 98.27\% & 97.68\% & 98.47\% & 98.22\% \\ \cline{2-7}  & F1 & 98.43\% & 98.35\% & 97.98\% & 98.48\% & 98.48\% \\ \hline \multirow{4}{*}{Full dataset} & Accuracy & 96.43\% & 96.39\% & 96.45\% & 96.62\% & 96.63\% \\ \cline{2-7}  & Precision & 96.62\% & 97.08\% & 96.75\% & 96.64\% & 97.10\% \\ \cline{2-7}  & Recall & 96.22\% & 95.65\% & 96.13\% & 96.60\% & 96.12\% \\ \cline{2-7}  & F1 & 96.42\% & 96.36\% & 96.44\% & 96.62\% & 96.61\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance of different n-gram sequence features. The results indicated that after adding statistical features to the word distribution features based on Wordninja, the indicators on the three datasets were improved, demonstrating that the statistical features can effectively improve the detection effect. Then, after adding 3-gram sequence features, the detection ability for the two kinds of AGDs improved, demonstrating the effectiveness of 3-gram sequence features. After that, we continued adding 1-gram sequence features, and the detection performance on the three datasets also significantly improved, so we also retained the 1-gram sequence features. Finally, after adding the 2-gram sequence features on this basis, the overall detection performance decreased due to the increase in the complexity of the model. The introduction of 2-grams led to a decrease in the effect of the model, so we did not consider the 2-gram sequence features in the final model. That is, Wordninja (Std + Mean) + 1,3-gram + statistical features were finally selected as our detection method.
## 4 Results and Discussion
### Simulated Results of High-Resolution SAR Imaging
However, the imaging result in Figure 7c,d reveals that all points in the range direction could be clearly distinguished, but the points in the azimuth direction cannot be clearly distinguished. Therefore, when the non-uniform sampling interval is set to 15 us, the imaging quality is consistent with the imaging result with Nyquist real-time sampling. When the non-uniform sampling interval is set to 28 us and 50 us, the azimuth imaging quality is obviously degraded.
## 1 Introduction
However, despite extensive research on mollusk shells, relatively few studies have shown a clear relationship between the element/Ca ratios obtained and SST [39; 56; 57; 58], with many reporting a low (or no) correlation between element/Ca ratios and SST in different mollusk species [59; 60; 61; 62; 63; 64]. This indicates significant species-specific variability in this regard, as well as divergence in how trace elements are incorporated into carbonate in different habitats [65]. Therefore, more analyses involving new shell taxa and species collected from coastal areas previously not considered are required to better understand trace element precipitation mechanisms and the wide-scale effectiveness of this approach as a past environmental proxy. In this study, we measured, for the first time, Mg/Ca ratios of limpet _Patella depressa_ Pennant, 1777 samples live-collected in northern Spain. This mollusk species was consumed in high numbers by Mesolithic humans during the Early Holocene period, along the Atlantic coast of Europe, representing an important percentage of all shell taxa recovered from shell middens excavated in northern Spain and France [66; 67]. Determining if Mg/Ca ratios derived from _P. depressa_ can be used as a suitable paleothermometer to reconstruct paleoclimate conditions and seasonality would have significant implications for future archaeological and paleoenvironmental investigations. Mg/Ca ratios were measured using laser-induced breakdown spectroscopy (LIBS), one of the most novel techniques for measuring element/Ca ratios on shells due to significantly easier sample preparation and lower measurement times [57; 58; 68; 69; 70]. A calibration-free LIBS (CF-LIBS) technique [71] was applied in order to estimate the molar concentrations of both chemical elements. This procedure was applied for the first time to marine mollusk shell biogenic carbonates. Although a recent isotopic investigation carried out on this species has shown that \(\xi^{18}\)O\({}_{\text{Shell}}\) derived from this species correctly reflects seasonal temperature variations year-round in northern Spain [72], LIBS-derived Mg/Ca ratio could offer significant improvements in relation to \(\delta^{18}\)O\({}_{\text{Shell}}\) as a SST recorder, allowing an increase in the number of shells measured for each stratigraphic unit, as well as the wider accuracy of archaeological conclusions. In order to determine if elemental series measured on _P. depressa_ are a suitable paleothermometer, Mg/Ca ratio series obtained were compared with stable oxygen isotope ratio profiles previously obtained from these same four marine mollusk shells [72], as well as with measured SST information at the place where mollusks grew.
## 2 Related Works
### Subdomain Adaptation  
Maximum mean discrepancy (MMD) is widely used to evaluate the distribution difference between \(D_{s}\) and \(D_{t}\)[24]. However, using MMD to align the global distribution ignores the relationship between the source and the target domain's relevant subdomains, losing each subclass's fine-grained information. As such, it usually causes data confusion between both domains. Therefore, this paper introduced LMMD to align the distribution between the relevant subdomains. LMMD is expressed as:  
\[d_{\mathcal{H}}(P,Q)\stackrel{{\triangle}}{{=}}\mathbb{E}_{c} \|\mathbb{E}_{p(c)}\left[\phi(x^{s})-\mathbb{E}_{Q(c)}[\phi(x^{t})]\right]\|_{ \mathcal{H}}^{2} \tag{6}\]  
where \(x_{s}\) and \(x_{t}\) represent the sample instances in \(D_{s}\) and \(D_{t}\), respectively, \(P\) and \(Q\) are the distributions followed by these domains, \(\|\cdot\|_{\mathcal{H}}\) is the regenerating kernel Hilbert space (RKHS), \(\phi(\cdot)\) is the mapping function, and \(\mathbb{E}(\cdot)\) represents the mathematical expectation of the subclass. This paper introduces the concept of weights, which can be simplified as follows:  
\[\left\{\begin{array}{c}\hat{d}_{\mathcal{H}}(P,Q)=\frac{1}{c}\sum\limits_{c =1}^{C}\left\|\sum\limits_{x_{i}\in D_{s}}\omega_{i}^{sc}\phi(x_{i}^{s})-\sum \limits_{x_{j}\in D_{t}}\omega_{j}^{tc}\phi(x_{j}^{t})\right\|^{2}\\ \omega_{i}^{t}=\frac{y_{i}}{\sum_{(x_{j},y_{j})\in D_{t}}y_{j}}\end{array}\right. \tag{7}\]where \(\omega_{i}^{sc}\) and \(\omega_{j}^{tc}\) are the weights of \(x_{i}^{s}\) and \(x_{j}^{t}\) belonging to subclass \(c\), respectively. In this study, one-hot coding was used to calculate the weight \(\omega\) of each sample belonging to the class. Further, \(y_{ic}\) is the \(c\)-th element of the source-domain label vector \(y_{i}\), representing the probability that the sample belongs to class \(c\). For target domain samples without labels, the pseudo-label \(y_{j}^{t}\) output by the SoftMax was used to calculate the weight \(\omega_{i}^{tc}\) of sample \(x_{i}^{t}\) belonging to class \(c\). Finally, the SPDSAN will generate activation functions in \(l\) layer, namely \(\left\{z_{i}^{sl}\right\}_{i=1}^{n_{s}}\) and \(\left\{z_{j}^{tl}\right\}_{j=1}^{n_{t}}\) to achieve the deep network adaptation. Therefore, the subdomain adaptation function is:  
\[\begin{array}{c}\hat{d}_{\mathcal{H}}(P,Q)=\frac{1}{c}\sum\limits_{c=1}^{C} \sum\limits_{i=1}^{n_{s}}\sum\limits_{j=1}^{n_{s}}\omega_{i}^{sc}\omega_{j}^{ sc}\mathcal{K}(z_{i}^{sl},z_{j}^{sl})\\ \hskip 14.226378 pt+\sum\limits_{i=1}^{n_{t}}\sum\limits_{j=1}^{n_{t}}\omega_{ i}^{tc}\omega_{j}^{tc}\mathcal{K}(z_{i}^{tl},z_{j}^{tl})\\ \hskip 14.226378 pt-2\sum\limits_{i=1}^{n_{t}}\sum\limits_{j=1}^{n_{t}}\omega_{ i}^{sc}\omega_{j}^{sc}\mathcal{K}(z_{i}^{sl},z_{j}^{tl})]\end{array} \tag{8}\]  
where \(z^{l}\) denotes the \(l\)th (\(l\in L=\{1,2,\cdots,|L|\}\)) layer activation.