esa-sceva/satcom-chunk-collection huggingface dataset:
# Assisted Migration Field Tests in Canada and Mexico: Lessons, Limitations, and Challenges
## 4 Lessons  
Pinus albicaulis Can Be Migrated North of Its Current Species Distribution in Canada within the Predicted Climatic Niche If Adequate Snowpack Is Present  
Germination and 9 or 10 year survival of seedlings varied greatly among sites. Establishment from seed was expected to be low relative to seedlings as seed germination is often poor for _Pinus albicaulis_, because seeds of this species are eaten by many rodents and birds. In 2017, 7.4% of all seeds produced seedlings that survived. Survival was highest at the northernmost site within the species range (Smithers, BC, Canada), at the southernmost site outside of the species range (Hazelton, BC, Canada), and surprisingly, at the northernmost site (Haines, BC, Canada), approximately 600 km north of the species' current distribution limit near the Yukon border. Germination rates varied with population but were more a function of environmental effects on seed maturation and condition at the time of harvest than genetic differences among populations. Early results in 2009 and 2010 showed that pre-treated seeds germinated faster and at higher rates than untreated seeds, but this advantage had disappeared by 2017. In the longer term, while most untreated seeds had germination delayed by a year while they completed maturation in the soil, a characteristic of _Pinus albicans_, they were better synchronized with site seasonality than treated seeds. Treated seeds germinated immediately following planting regardless of site conditions and suffered greater mortality post-germination. Snow cover and snow-melt timing played a key role in average seedling survival. Sites with little or no protective snow cover in winter and those with deep snowpacks that melted late in the year both had poorer survival than those on sites with continuous snowpacks in winter that melted in April or May.  
_Pinus albicans_ is an extremely slow-growing species. Average height of living seedlings after eight (untreated seeds) or nine years (treated seeds) was less than 20 cm, although individual seedlings varied greatly in height. Seedlings at the northernmost site (Haines) were approximately twice as tall as seedlings at all other sites, on average, indicating that latitude per se is not critical to the species' ecological niche, but rather, temperature and snowpack play critical roles. While this site is far north, it is not far from the Pacific Coast, and has intermediate snowfall (equivalent to 1020 mm of rain), relatively mild temperatures (0.4 \({}^{\circ}\)C mean annual temperature), and the warmest average summer temperature of all common garden sites [53]. These results collectively suggest that _Pinus albicans_ could be migrated to climatically favourable sites well north of its current species distribution. These are likely areas of colonization debt, i.e., post-glacial migration has been too slow to fill the available niche. If seedlings rather than seeds were used to nucleate conservation populations, the survival rate would likely be higher than the current study.
## 3 Experiments
### Curing and Test Regime
The reference electrode was made of copper/copper sulfate (Model 8-A, Farwest Corrosion Control Co., Denver, CO, USA) and two types of counter electrodes were used, namely stainless plates (35 \(\times\) 200 mm and 35 \(\times\) 100 mm) denoted by the external counter electrode and embedded mild steel in concrete denoted by the internal counter electrode. As shown in Figure 4, the two experimental setups were given depending on the types of the counter electrode. When using the internal counter electrode, the reference electrode was placed on the surface of the sample and then the measurements were carried out (refer to Figure 4a). On the other hand, for the experimental set-up with the external counter electrode, to improve the electrical connection between the counter/reference and the working electrodes, a wetted sponge was placed on the exposure face for 10 min before EIS measurements. When the measurement was conducted using stainless plates, the sponge was placed between the surface and the plates. In addition, a mass equal to 2 kg for the small stainless plate or 4 kg (2 kg + 2 kg) for the large stainless plate was placed on the plates on the small and long counter electrodes during the measurements to improve the extent of the contact at the electrode-sponge-concrete interface (refer to Figure 4b). As mentioned previously, sets of variables were used in this study to verify the concrete responses, as follows:  
1. The effect of contact solution to confirm the existence of an interfacial effect. * Mains tap water; * A saturated solution of calcium hydroxide (as calcium hydration causes typical hydration products to leach out easily in concrete); * Sodium chloride (0.55 M NaCl) solution (as the solution is the same as that used in the cyclic ponding regime); * A simulated cement pore solution comprising 0.1 M sodium hydroxide (NaOH) and 0.3 M potassium hydroxide (KOH) (as the alkali oxides in the cement clinker are highly soluble). 2. The effect of contact pressure to confirm the existence of an interfacial effect. * No pressure was applied on the external counter electrode; * A mass of 2 kg was placed on the center of the small counter electrode, or 4 kg (2 kg + 2 kg) were placed at the one-third and two-third loci of the long counter electrode to ensure uniform contact; * A mass of 1 kg was placed at the one-third point, and a mass of 2 kg was placed at the two-third points of the large counter electrode to simulate workmanship-induced errors caused by the nonuniformly distributed pressure. 3. The effect of counter electrode size to confirm the self-confinement of applied current to corroded steel. * Use of stainless-steel plates (lengths = 200 mm and 100 mm); * The effect of the counter electrode's position to confirm differences in high-frequency responses; * Internal and external counter electrodes. In summary, Figure 5 provides a diagrammatic representation of the experiment procedure. Figure 4: Experimental setups with internal and external counter electrodes.
## 3 System Model
### GSTBC-EFOGSM \(R=1/2\)  
In the case of the GSTBC-EFOGSM scheme with a coding ratio of R = \(1/2\) and time slot T = \(8\), the information bits were partitioned into four distinct groups, each of which was then mapped to a unique EFOGSM symbol, namely \(\mathbf{x}_{1}\), \(\mathbf{x}_{2}\), \(\mathbf{x}_{3}\) and \(\mathbf{x}_{4}\). Then, the transmitted signal was obtained by OSTBC coding, such as:  
\[\mathbf{S}_{4/8}=\begin{bmatrix}\mathbf{x}_{1}&\mathbf{x}_{2}&\mathbf{x}_{3} &\mathbf{x}_{4}&\mathbf{x}_{1}&\mathbf{\overline{x}}_{2}&\mathbf{\overline{x }}_{3}&\mathbf{\overline{x}}_{4}\\ \mathbf{x}_{2}&\mathbf{x}_{1}&\mathbf{x}_{4}&\mathbf{x}_{3}&\mathbf{x}_{2}& \mathbf{x}_{1}&\mathbf{x}_{4}&\mathbf{\overline{x}}_{3}\\ \mathbf{x}_{3}&\mathbf{x}_{4}&\mathbf{x}_{1}&\mathbf{x}_{2}&\mathbf{x}_{3}& \mathbf{\overline{x}}_{4}&\mathbf{x}_{1}&\mathbf{x}_{2}\\ \mathbf{x}_{4}&\mathbf{x}_{3}&\mathbf{x}_{2}&\mathbf{x}_{1}&\mathbf{x}_{4}& \mathbf{x}_{3}&\mathbf{\overline{x}}_{2}&\mathbf{x}_{1}\end{bmatrix}_{N_{t} \times 8} \tag{5}\]  
In the equation above, by using an overline notation, the complement of a signal \(\mathbf{x}_{k}\) is defined by \(\mathbf{\overline{x}}_{k}=[0,\ldots,0,\ P_{M}+M+1,S_{M/_{2}}+M+1,\ 0,\ldots,0]^{T},\ k=1,2,3,4\)[14].  
When the modulation signal is transmitted through the atmospheric channel, the electrical signal matrix \(\mathbf{Y}\in\mathbf{R}^{N_{t}\times T}\) received by the photodetector of the system can be written as follows:  
\[\mathbf{Y}=\omega P_{t}\mathbf{H}\mathbf{S}+\mathbf{n} \tag{6}\]  
In the equation, \(P_{t}\) denotes the transmit power, while \(\omega\) represents the detection efficiency of the photodetector. Additionally, the term \(\mathbf{n}\) represents \(Nr\times T\) the additive white Gaussian noise with zero mean and variance \(\sigma^{2}\). The channel gain matrix is represented by the symbol \(\mathbf{H}\in R^{Nr\times Nt}\) and can be expressed in the following manner:  
\[\mathbf{H}=\begin{bmatrix}h_{11}&h_{12}&h_{13}&\cdots&h_{1N_{t}}\\ h_{21}&h_{22}&h_{23}&\cdots&h_{2N_{t}}\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ h_{N_{t}1}&h_{N_{t}2}&h_{N_{t}3}&\cdots&h_{N_{t}N_{t}}\end{bmatrix} \tag{7}\]  
where the symbol \(h_{ij}\) represents the channel gain associated with the link between the \(j\)th laser and the \(i\)th photodetector, \(j=1,2,\cdots,N_{t}\), \(i=1,2,\cdots,N_{t}\).  
To detect the transmitted signal, the receiver employed the ML decoding algorithm, which was equipped with complete information about the channel and ideal time synchronization. The criteria employed by the ML decoding algorithm can be expressed as follows:  
\[\mathbf{\hat{S}}=\operatorname*{argmin}_{\mathbf{S}\in\mathbb{C}}\|\mathbf{Y }\mathbf{-H}\mathbf{S}\|_{F}^{2} \tag{8}\]  
where \(\left\|\bullet\right\|_{F}\) denotes the F-norm and is the set of GSTBC-EFOGSM symbols.
# Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Fit Mines
## 3 Methods
### Experimental Setup for DNN Model Training
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}- \overline{y})^{2}} \tag{9}\]  
\[\text{MAPE}=\ \frac{1}{n}\sum_{i=1}^{n}\frac{\left|y_{i}-\hat{y}_{i}\right|}{y_{i}} \times 100\ (\%) \tag{10}\]  
T-significance test was performed to determine if the performance differences of DNN models for validation data are statistically significant. \(T\)-test calculates the \(p\)-value considering the mean and variance of MAPE differences of two DNN models and determines if null hypothesis is rejected or accepted. Null hypothesis means there is no predictive performance difference between two DNN models. If \(p\)-value is equal or less than significance level, null hypothesis is rejected. Equation (11) refers to the variance of MAPE differences of two DNN models, wherein \(k\) denotes the number of folds, \(r\) denotes the number of total rounds of cross-validation, \(x\) denotes MAPE differences for validation data of two DNN models, \(m\) denotes the mean of MAPE differences of two DNN models. Equation (12) describes corrected repeated k-fold cross-validation \(t\)-test [70]. Here, \(n_{1}\) denotes the number of training data, \(n_{2}\) denotes the number of validation data. T-value is converted into the \(p\)-value through the student's t-distribution considering the degree of freedom. Significance level was set to 0.05, and two-tailed test was conducted. \[\hat{\sigma}^{2}=\ \frac{1}{k\times r-1}\sum_{i=1}^{k}\sum_{j=1}^{r}\left(x_{ij}-m \right)^{2} \tag{11}\]  
\[\text{t}=\ \frac{\frac{1}{k\times r}\sum_{i=1}^{k}\sum_{j=1}^{r}x_{ij}}{\sqrt{ \left(\frac{1}{k\times r}+\frac{n_{2}}{n_{1}}\right)\hat{\sigma}^{2}}} \tag{12}\]  
Figure 8 depicts a flowchart of the algorithm developed in this study for DNN model training. This algorithm was developed as a Python script and implemented using TensorFlow (Google; Menlo Park, CA, USA)--an open-source deep learning module. The structure of the proposed DNN model was first configured, and subsequently, prediction error evaluation was performed with 5 \(\times\) 5-fold cross validation. After choosing the best hidden layer configuration of DNN model, final DNN model training was performed using whole training data. Optimal DNN model demonstrating the lowest prediction error was considered for subsequent utilization. One thousand iterations for DNN model \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Hidden Layer Configuration**} & \multicolumn{2}{c}{**No. of Hidden Layer**} & \multicolumn{2}{c}{**No. of Hidden Layer Nodes**} & \multicolumn{2}{c}{**No. of**} \\  & **From** & **To** & **Interval** & **From** & **To** & **Interval** & **Cases** \\ \cline{2-7}  & 3 & 5 & 1 & 30 & 50 & 10 & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hidden layer configuration for DNN model training. training was performed, and ReLU was used as the activation function. The Adam optimizer was considered as a means to employ the gradient-descent method.
# Cloud-Edge Collaborative Model Adaptation Based on Deep Q-Network and Transfer Feature Extraction
## 2 Preliminaries  
For edge object detection in multi-view satellite imagery scenarios, a cloud-edge collaborative model adaptation method driven by reinforcement learning is proposed. This method integrates a Faster Region-based Convolutional Neural Network (Faster R-CNN) [22], transfer learning-based feature extraction strategies, and a DQN-based decision mechanism to achieve dynamic optimization of the classification head in edge-side models.
## 1 Introduction
Therefore, our study's significance is based on four complementary aspects: (a) the importance of identifying parameters that characterize the subtle changes occurring in the early stages of CAN (i.e., the subclinical phase, where the disease is present but symptoms are not yet evident or are very mild), enabling the initiation of preventive treatment to control symptoms and potentially slow or reverse the disease progression [4; 5]; (b) a novel application of the EMD method; to the best of our knowledge, the EMD method has not been applied to analyze HRV signals of DM patients with CAN at different levels of severity; (c) proposing the utilization of HRV indices as a replacement for CARTs in diagnosing CAN, offering the advantages of time-effectiveness, patient-independence, and providing quantitative measurements; and (d) contributing to ongoing research on the relevance of nonlinear HRV analysis approaches in investigating the neural control of the cardiovascular system in the presence of CAN. Therefore, our research aims to investigate the relevance of the EMD-based features extracted from HRV signals to differentiate between progression levels of cardiovascular autonomic neuropathy among type 2 DM patients (i.e., no CAN, subclinical CAN, and established CAN). The paper is organized as follows: Section 2 delineates the methodology, which includes: (i) a description of participants and the inclusion criteria, (ii) the data acquisition process and data pre-processing, (iii) the EMD method and feature extraction, and (iv) the statistical analysis. Sections 3 and 4 present the results and discussion, respectively. Finally, Section 5 concludes the paper.
# Modalflow: Cross-Origin Flow Data Visualization for Urban Mobility
## 3 Design Requirements
### Considerations on Cross-Origin Dataset Comparison  
Pervasive data as a secondary data source has its own qualities that sets it apart from primary source data. Here, we try to name and account for the aspects that make difficult the comparison between these sources, in the case of mobility flow data.  
_Trip closure._ The definition of one trip does not share its origin in self-reported and predicted sources. For quantitative methodologies, what defines a trip as a unit is not part of any of these dimensions but a discursive element: the motivation of the sampled person (e.g., go to work, buy food, go for a walk, return home, etc.). Because tracking does not deal with persons but with devices, a trip is not a given entity, but it must be inferred from the data itself, usually from finding stay patterns that then become origin-destination features.  
_Representativeness._ Surveys count with a relatively limited sample size, so sampling must be carefully orchestrated in order to achieve representativeness, the basic condition that guarantees itsusefulness as a tool for urban planning. The sample is based on the most recent census or projection of the census, which is the sampling universe, and then each point amplified based on expansion factors determined by statisticians. Because representativeness can only be achieved up to a certain level of granularity, observed patterns that are beyond that level should be considered to be an artifact (i.e., a value introduced artificially by the measuring tool). Mobile phone network data, although with a much higher level of sampling, may not count with this desirable quality.  
_Sampling density._ The number of samples can vary by orders of magnitude between surveys and network data. Can sampling differences impair comparison? This enormous distribution difference can have an important effect when visualizing at finer granularity.  
_Dynamic behaviour._ The temporal dimension is not equally accounted for by different sources. Travel surveys have an abstract temporality: a model is produced for a 'general day', which is then extended into laboral day and weekend behaviour for summer/winter seasons. It does not capture dynamic behaviour, as network data are able to.  
_Modality._ From a transportation analysis perspective, modality cannot be overlooked. However, it is a hard dimension for pervasive data, as it usually has to be inferred. Probably due to this, visualization literature is usually focused on single mode (or absence of modality) analysis tasks.
## 3 Results  
Endermologie was initially developed to standardize and enhance the effectiveness of treatment for burns, scars, and muscle regeneration. The mobilization of tissues was found to improve fibrosis reduction, edema resorption, adhesion breakdown, tissue elasticity, oxygenation, and trophism. Studies have also highlighted its role in the treatment of hypertrophic burn scars [19].  
Endermologie has applications in peripheral lymphedema and secondary arm lymphedema, as well as cancer-related lymphedema treatment, edema management, and lymphatic drainage [13; 15; 22; 28; 38]. Findings suggest that endermologie not only reduces edema but also improves quality of life in individuals with lymphatic dysfunction.  
Authors conducted studies assessing the effects of endermologie on radiation-induced skin fibrosis. The results indicated that mechanical stimulation through this technique improves skin elasticity and reduces fibrotic changes, providing a promising, non-invasive approach for managing post-radiotherapy complications. Moreover, the therapy has been applied for mastectomy scars and skin trophic improvement during breast reconstruction with lipofilling [1; 20; 21; 31], further demonstrating its benefits in post-cancer recovery and tissue regeneration. Authors emphasized its role as a non-invasive approach for tissue rehabilitation by influencing post-traumatic, postoperative, and post-implantation recovery.  
Endermologie is widely recognized as a non-invasive method for body contouring and cellulite reduction. Vacuum massage has been shown to alter the structural and physiological properties of the skin, leading to improved skin firmness and elasticity. Additionally, it has demonstrated benefits in lipid mobilization, body fat reduction, and cellulite management [24; 26; 29; 30; 35; 37]. The technique has also been explored for its effectiveness in lipodystrophy [9] and panniculinis/lipoatrophy [23; 25; 27], broadening its range of applications in metabolic and adipose tissue disorders.  
Preoperative and postoperative applications of endermologie have been reported to be beneficial in liposuction procedures. Additionally, its role in postoperative ileus following colectomy has been explored, indicating potential benefits in the treatment of pain and accelerating gastrointestinal recovery [36].  
Endermologie has demonstrated therapeutic potential in fibromyalgia management [32], where it aids in pain reduction and tissue relaxation. It has also been investigated as a treatment for [PERSON]'s disease [33], morphea [34], and other conditions affecting connective tissue health.
## Materials and Methods
### Electrochemical Characterization  
Membrane potentials (\(\Delta\Phi_{\text{mbr}}\)) and equilibrium electrical potential differences between the 2 NaCl solutions of different concentrations (Cf = 0.01 M and Cv ranging between 0.002 M and 0.1 M) at both membrane sides were determined by measurements performed in the dead-end test cell already indicated (Supplementary Information, Figure S2), with the 2 Ag/AgCl reversible electrodes (to Cl\({}^{-}\) ion) connected to a digital voltmeter (Yokohama 7552, 1 G\(\Omega\) input resistance) and magnetic stirrers placed in the bottom of each cell working at a stirring rate of 540 rpm (to minimize concentration polarization at the membrane surfaces [37]). Because measured values (\(\Delta\)E) included two different contributions, electrode potential (\(\Delta\Phi_{\text{elect}}\)) and membrane potentials (\(\Delta\Phi_{\text{mbr}}\)), the latter values were obtained by subtracting from each \(\Delta\)E value the corresponding \(\Delta\Phi_{\text{elect}}\) (which depended on electrolyte concentration, \(\Delta\Phi_{\text{elect}}\) = (RT/zF)ln(Cv/Ct), where R and F are gas and Faraday constants, respectively, while T represents the temperature of the system); hence: \(\Delta\Phi_{\text{mbr}}\) = \(\Delta\)E - \(\Delta\Phi_{\text{elect}}\)[38]. Measurements were performed at standard pH (5.8 \(\pm\) 0.3) and room temperature (25 \(\pm\) 2) \({}^{\circ}\)C.  
Electrochemical impedance spectroscopy (EIS) is an alternating current (a.c.) technique used for electrical characterization of membranes in \"working condition\", that is, in contact with electrolyte solutions. EIS allows, under certain conditions, the estimation of different contributions: membrane, electrolyte solution, and membrane/solution interface, by using equivalent circuits as models [39; 40]. The impedance (Z) is a complex number, with real (Zreal) and imaginary (Zimg) parts (Z = Zreal + j Zimg) that are related to both the transport of charge across the membrane and charge storage, through its electrical resistance (R) and capacitance (C) [39]. The analysis of the impedance data was performed by using the Nyquist plot in the complex plane (\(-\)Zimg versus Zreal) [39]. EIS measurements were carried out with the membranes in the electrochemical test cell by connecting the electrodes to a Frequency Response Analyzer (FRA, Solartron 1260, Hamshire, UK) using a maximum voltage of 0.01 V and an interval of frequency ranging between 1 Hz and 10\({}^{7}\) Hz, in open circuit mode. The ZView 2 data analysis program (Scribner, Southern Pines, NC, USA) was used for determination of electrical parameters, and EIS data were corrected by the influence of connecting cables and other parasite capacitances.
## 1 Introduction
Our previous study found that ozone pretreatment reduced the hydrophobicity and enhanced the negative surface charge of alkali lignin, thereby facilitating the enzymatic hydrolysis of cellulose [16]. However, the effect of changes in the phenolic hydroxyl content of ozone-pretreated lignin on the adsorption of cellulase has not been investigated in our previous studies. Despite extensive research conducted on the removal of lignin using ozone and promoting the enzymatic hydrolysis of cellulose, there is a lack of studies elucidating the underlying mechanisms that enhance enzymatic hydrolysis efficiency by examining factors influencing non-productive adsorption between MWL and cellulase resulting from ozone treatment. The aim of this study was to examine the impact of ozone pretreatment on the physical structure and chemical properties of MWL, as well as its effect on the non-productive adsorption of cellulase. This was accomplished by utilizing \({}^{31}\)P-NMR, HSQC-NMR, FTIR, and GPC to elucidate the structural characteristics of MWL. Moreover, Langmuir adsorption isotherms, hydrophobicity, and zeta potentials were employed to characterize the adsorption of cellulase onto lignin. This will enhance our understanding of the interaction between lignin and cellulase and ultimately improve biotechnological processes.