esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Although based on a reduced number of collected samples due to the pandemic event during the Antarctic campaign, this study confirms the pivotal role of Antarctica research in assessing the contributions and origins of small micro- and nanoparticles. In agreement with previous studies, micro- and nanoparticles related to anthropic activities were detected in snow samples collected in Terra Nova Bay, near active research stations. Considering that human activities in Antarctica are increasing, future expeditions should focus on the continuous monitoring of these materials in such remote areas to evaluate possible threats to the Antarctic environment and to better differentiate between local and long-range contamination so as to suggest actions for the preservation of the ecosystems.  
The comparison of the results obtained by using different analytical techniques remains a challenging issue due to the scarcity of harmonized methods in monitoring studies. Technological developments in innovative complementary analytical approaches, including sample treatments and their standardization, represent major requirements to be faced in the near future for the development of an integrated approach encompassing all the relevant metrological tools. Only then will it be possible to define useful protocols for routine laboratories and the scientific community, allowing for the real advancement of research and the effective environmental management of micro- and nanopollutants. Another important key point to be addressed in the near future is the development of high-throughput nano-analytical systems able to simultaneously provide information about the morphology, amount, and chemical composition of the nanoparticles. To this aim, the separation of plastic and non-plastic materials will play a pivotal role.  
Conceptualization, F.B., N.R. and L.N.; methodology, F.B., N.R., L.N. and M.M. ([PERSON]); formal analysis, F.B., N.R., L.N., M.M. ([PERSON]) and M.M. ([PERSON]); investigation, L.N., M.M. ([PERSON]), E.R. and M.P.; resources, F.B., L.N. and M.M. ([PERSON]); writing--original draft preparation, F.B., N.R., L.N. and E.R.; writing--review and editing, all authors; visualization, F.B., L.N. and M.C.; supervision, F.B. and L.N.; project administration, F.B.; funding acquisition, F.B. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the project \"Emerging Contaminants in Antarctic Snow: sources and TRAnsport (ECO AS:TRA)\", funded by the MIUR Programma Nazionale di Ricerca in Antartide (PNRA), grant PNRA18_00229.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
This work has benefited from the equipment and framework of the COMP-HUB and COMP-R Initiatives, funded by the \"Departments of Excellence\" program of the Italian Ministry for University and Research (MIUR, 2018-2022, and MUR, 2023-2027).  
The authors declare no conflicts of interest.
# Temporal Changes in Land Use, Vegetation, and Productivity in Southwest China
###### Abstract  
In recent decades, vegetation coverage and land use/land cover (LULC) have constantly changed, especially in southwest China. Therefore, it is necessary to conduct in-depth research into the temporal-spatial variation patterns of vegetation greening, LULC, and gross primary productivity (GPP). Here, we used remote sensing to analyze the spatial and temporal variation in the normalized difference vegetation index (NDVI) and GPP in the growing season under different LULCs in southwest China. Results showed: (1) From 2000-2019, the forest area in southwest China had increased by 2.1%, while the area of cropland and grassland had decreased by 3.2% and 5.5%, respectively. Furthermore, there are significant differences in spatial variation patterns. (2) NDVI and GPP in the growing season showed a general increasing trend (\(p<0.01\)); vegetation coverage is dominated by high coverage to highest coverage and medium coverage to high coverage transfer. (3) Under different LULCs, the migration directions of NDVI and GPP were different. The center of gravity migration of highest and medium coverage shifted to the southeast by 1.69\({}^{\circ}\) and to the northwest by 1.81\({}^{\circ}\), respectively. The results showed the ecosystem evolution and will help to guide the maintenance measure of ecosystem balance and sustainable development.  
southwest China; normalized difference vegetation index (NDVI); gross primary productivity (GPP); land use/land cover (LULC); center of gravity shift model +
Footnote †: journal: Journal of Applied Mechanics and Physics Letters A  
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Footnote †: journal: Journal of Applied Mechanics and Physics Letters A  
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Footnote 1: institutetext: School of Geography and Environmental Sciences, Guizhou Normal University, Guiyang 550000, China 1  
2
Footnote 2: institutetext: School of Karst Science, Guizhou Normal University, Guiyang 550000, China 2  
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Footnote 3: institutetext: Correspondence: [EMAIL_ADDRESS]
## 5 Results
### Identify the Best DFANN Complexity  
As an example, the DFANN developed specifically to assess the yearly heating energy demand intensity was used as an illustrative reference. In particular, the best DFANN complexity was identified on the basis of the following parameters:  
* Number of hidden layers (Min/Max): 2/6;
* Number of nodes per layer (Min/Max): 60/280;
* Maximum number of epochs: 1000, saving the best model developed along the epochs;
* Number of DFANNs concurring with the ensemble model: 10.  
The values of the loss function applied to the training and testing samples of the DFANNs are exemplified in Table 6. Conversely, the distribution of the relative errors (y\({}_{\text{Pred}}\)-y\({}_{\text{True}}\))y\({}_{\text{True}}\) along the output bins is shown in Figure 2, with reference to the testing samples and to the following error intervals: error \(\leq\) 5%, 5% + error \(\leq\) 10%, 10% + error \(\leq\) 20%, 20% + error \(\leq\) 50% and 50% + error. Aside from that, the profile of the loss function value along the epochs is exemplified in Figure 3 and Table 7 (4-layer model, 280 nodes per layer), with separate references to the training and testing samples. As can be seen, the loss function curves calculated for the training and testing samples both converged to small values, therefore showing that no overfitting was taking place. Information about the input and output scaling was saved in pkl files, whereas the architecture parameters of each DFANN were saved in h5 files. Ultimately, information about the final ensemble model is represented in Figure 4 and Table 8, where the function loss values for the training and testing datasets along the epochs are shown as a whole for an ensemble model, consisting of 10 models of 4 layers and 280 nodes per layer. It is clear how the accuracy is much higher this way than in the case of a single model.  
Figure 1: Frequency of occurrence for values of yearly heating energy demand intensity.  
Figure 3: Profile of function loss values for the training and test datasets along epochs for a 4-layer model with 280 nodes per layer.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model ID [-]** & **Hidden Layers [-]** & **Nodes Per Layer [-]** & **Train Loss [-]** & **Test Loss [-]** & **Best Epoch [-]** & **Training Time [s]** \\ \hline
2
## 3 Results
### Classification of Evaluation Metrics for AIGC Models
#### 3.4.8 Evaluation Metrics for Speech and Audio Generation Techniques  
_RTF (Real-Time Factor)_: RTF, or real-time factor, is an evaluation metric used in speech synthesis tasks to measure the efficiency of the synthesis process. It quantifies the speed at which speech is synthesized in real-time. The Grad-TTS [152] model incorporates RTF as an evaluation metric to assess the efficiency and speed of the text-to-speech synthesis process.  
_PESQ (Perceptual Evaluation of Speech Quality)_: PESQ, or perceptual evaluation of speech quality, is an evaluation metric commonly used in speech enhancement tasks. It quantifies the perceived quality of enhanced speech signals by comparing them to the original, clean speech signals. The conditional generative adversarial networks (CGAN) [82] model with the Pix2 Pix framework utilizes PESQ as an evaluation metric to assess the improvement in speech quality achieved through the speech enhancement process, and also ProDiff [53] utilizes this metric to evaluate the generated speech. A higher PESQ score indicates a higher level of perceived speech quality and fidelity.  
_STOI (Short-Time Objective Intelligibility)_: STOI, or short-time objective intelligibility, is an evaluation metric used to measure the intelligibility of speech signals. It assesses the degree to which speech can be understood by comparing the enhanced speech signals to the original, clean speech signals. The CGAN [82] model with the Pix2 Pix framework also employs STOI as an evaluation metric to evaluate the improvement in speech intelligibility achieved through speech enhancement.  
_Error Rate:_ Error rate is an evaluation metric commonly used in speech synthesis tasks, specifically in the generation of speech from text. It measures the accuracy of the generated speech by comparing it to the desired or target speech. The feed-forward transformer (FFT) [42] model utilizes the error rate as an evaluation metric to assess the accuracy and fidelity of the synthesized speech.
## 2 Materials and Methods
Similarly, when the diameter of the fiber is 29 \(\upmu\)m, the effective refractive index of the LP\({}_{01}\) mode in the TMF is 1.457, at which time the LP\({}_{01}\) mode begins to leak into the cladding, and LP\({}_{01}\) mode coupling begins to occur in the coupling region of the STMC. When this happens, the purity of the LP\({}_{11}\) mode at port2 decreases. Therefore, in order to minimize LP\({}_{01}\) mode coupling in the coupling region and improve the output purity of the LP\({}_{11}\) mode, the diameter of the fiber cone should be between 29 \(\upmu\)m and 75 \(\upmu\)m.  
We used the beam propagation method (BPM) to creatively establish a conical three-dimensional waveguide model of the STMC. According to the cone simulation results shown in Figure 3, as the diameter of the TMF gradually decreases, the LP\({}_{11}\) mode gradually leaks from the core to the cladding for transmission. When the fiber diameter is stretched to 30 \(\upmu\)m and 31 \(\upmu\)m, the LP\({}_{11}\) mode has completely leaked out of the core and, at this time, the LP\({}_{01}\) mode is still bound in the core, only minimally leaking into the cladding. It can be seen that when the TMF fiber diameter is stretched to 30 \(\upmu\)m and 31 \(\upmu\)m, the separation of LP\({}_{01}\) and LP\({}_{11}\) modes can be effectively realized. Referring to the simulation results of the cone region, we used the BPM to establish a three-dimensional waveguide model of the coupling region of the STMC, and the simulation results are shown in Figure 4.  
According to the simulation results of the energy flow shown in Figure 4, regardless of whether the stretch is 30 \(\upmu\)m or 31 \(\upmu\)m, the energy exchange between the two fibers shows a periodic change law, and when the coupling length is chosen properly, the LP\({}_{11}\) mode in the first fiber can be almost completely coupled to the other fiber. In this paper, it is said that the optimal coupling length is the shortest distance transmitted when the LP\({}_{11}\) mode energy is coupled from the first fiber to the second fiber maximally. According to the simulation results shown in the figure, when the diameter of SMF-28e's coupling zone is 30 \(\upmu\)m, the optimal coupling length is about 4.5 cm and 5.5 cm for the first and second fibers, respectively. Finally, based on the above simulation results of the STMC, we used the fused tapered method to complete the STMC. The physical picture of the STMC is shown in Figure 5.  
Figure 3: LP\({}_{11}\) mode longitudinal energy flow simulation during TMF cone pulling, diameter (\(\upmu\)m) after TMF cone pulling: (**a**) 30 \(\upmu\)m; (**b**) 31 \(\upmu\)m.
## 3 Data-Processing Methodology
### Metrics Standardization  
The SAN software stack provides the option of standardizing the extracted metrics with respect to the person's baseline. Average HR and HRV values vary widely from person to person, and the instantaneous values for a given person depend on whether the individual is resting or is engaged in physical activity. We establish the baseline for each person by collecting the data over several days in this person's healthy state. The data are collected in the first-in, first-out buffer (Figure 5). Parameters of the baseline are used in the calculation of z-scores, according to the following formula:  
\[zX_{ij}(S)=\frac{X_{ij}(S)-\mu_{j}(S)}{\sigma_{j}(S)},\]  
where \(X_{ij}\) is the datapoint \(i\) of metric \(j\), \(S\) is the number of steps per minute, and \(\mu_{j}(S)\) and \(\sigma_{j}(S)\) are the mean and standard deviation of \(X_{ij}\), respectively, calculated from the baseline dataset that is matched to the \(S\) of the \(X_{ij}\) datapoint.  
The activity matching is determined according to the following rules using the current 5 min step count value and further limited using an average of the step counts for the previous 30 min (i.e., two-step match):  
* Data with step counts exceeding an UPPER_STEP threshold are considered equivalent;
* Data with step counts below a LOWER_STEP threshold are considered equivalent;  
\begin{table}
\begin{tabular}{l l l} \hline
**Metric** & **Unit** & **Description** \\ \hline \multicolumn{3}{l}{_Frequency Domain_} \\ TP & ms\({}^{2}\) & Total spectral power \\ VLF & & Log of very-low-frequency power (0-0.04 Hz) \\ LF & - & Log of low-frequency power (0.04–0.15 Hz) \\ HF & - & Log of high-frequency power (0.15–1.0 Hz) \\ Resp & Hz & Respiration rate \\ \multicolumn{3}{l}{_Nonlinear Domain_} \\ DFA & & Short- and long-term fluctuations in detrended fluctuation analysis \\ SD1 & - & Standard deviation of the points perpendicular to the line-of-identity in the Poincaré plot \\ SD2 & & Standard deviation along the line-of-identity in the Poincaré plot \\ SampEn & - & Sample entropy \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._* Data with step counts between the LOWER_STEP and UPPER_STEP thresholds are matched based on step counts within 30% of the current value. Lower limits on the minimum step match (e.g., \(\pm\)10 steps) and lower bound (e.g., greater than 0 steps) are applied.  
After matching, we limit the size of the dataset in the first-in, first out buffer (Figure 5) to the most recent 1440 datapoints (equivalent to 5 days of matched data using a 5 min window) and calculate the mean and standard deviation of the metric for the baseline set. If the minimum amount of required data is present in the matched dataset, a standardized value is calculated. The hyperparameters of the calculations (e.g., the required numbers of datapoints) are configurable.
## 4 Experimental Results
### Comparision with Other Techniques
The proposed model has achieved more effective results when compared to the models used in [66] because the proposed method needs less data for training. Deep learning techniques require a considerable amount of training data to build an acceptable model that can identify the risk of malignancy at an early stage. In addition, the balance between the two classes is an important point that may affect the prediction of deep learning techniques. Training a model with imbalanced data may obtain high weight for one class that can reduce the quality of the sensitivity and specificity of the system. Table 11 shows that applying DenseNet and DenseNet-II on DDSM and BCDR datasets achieved better results \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \begin{tabular}{c} **Classification** \\ **Methods** \\ \end{tabular} & \begin{tabular}{c} **Dataset** \\ \end{tabular} & \begin{tabular}{c} **Total No.** \\ **of Images** \\ \end{tabular} & \begin{tabular}{c} **Classifier** \\ \end{tabular} & \begin{tabular}{c} **Feature** \\ \end{tabular} & \begin{tabular}{c} **Ac (\%)** \\ \end{tabular} & \begin{tabular}{c} **Sn (\%)** \\ \end{tabular} & \begin{tabular}{c} **Sp (\%)** \\ \end{tabular} & \begin{tabular}{c} **F-Measure (\%)** \\ \end{tabular} \\ \hline \multirow{4}{*}{[65]} & \multirow{4}{*}{INbreast} & \multirow{4}{*}{387} & \multirow{4}{*}{AdaBoost} & SIFT, GIST, HOG, LBP, & \multirow{4}{*}{87.93} & \multirow{4}{*}{57.20} & \multirow{4}{*}{97.73} & \multirow{4}{*}{-} \\  & & & VGG16, ResNet, and & & & & \\  & & & DenseNet (shape, & & & & \\  & & & & texture, and & & & \\  & & & & deep learning) & & & & \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Proposed \\ M-FD model \\ \end{tabular} } & \multirow{4}{*}{INbreast} & \multirow{4}{*}{200} & \multirow{4}{*}{ANN} & LBP & 82.5 & 83.33 & 80.64 & 86.79 \\  & & & & FD & 85.5 & 85 & 86.66 & 89.13 \\  & & & & M\_FD & 99 & 98.44 & 100 & 99.21 \\ \hline \multirow{4}{*}{[62]} & \multirow{4}{*}{BCDR} & \multirow{4}{*}{736} & \multirow{4}{*}{WC-SSA} & block-based discrete & \multirow{4}{*}{99.6} & \multirow{4}{*}{100} & \multirow{4}{*}{99.35} & \multirow{4}{*}{99.7} \\  & & & & wavelet & & & \\  & & & & packet transform & & & & \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Proposed \\ M-FD model \\ \end{tabular} } & \multirow{4}{*}{BCDR} & \multirow{4}{*}{736} & \multirow{4}{*}{ANN} & LBP & 80.43 & 82.79 & 77.12 & 83.17 \\  & & & FD & 81.93 & 84.79 & 78.09 & 84.29 \\ \cline{1-1}  & & & & M\_FD & 99.59 & 99.53 & 99.67 & 99.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance comparison with respect to INBreast and BCDR datasets. than on Mini-MIAS and INbreast datasets, when we applied the same models, because DDSM and BCDR datasets contain more images than Mini-MIAS and INbreast datasets.
# A Simple Approach to Dynamic Optimisation of Flexible Optical Networks with Practical Application
## 4 Related Algorithms  
Rapidly-varying transmission parameters, leading to changing values of \(m\), will require frequent (re-)optimisation of DRP-FON, and re-minimisation of \(C(t)\). Conventional linear and integer programming [21], 'branch-and-cut' [20], 'branch-and-price-and-cut' [22] methods, metaheuristics [14], or exhaustive search approaches [23] will be inappropriate. Not only is speed of the essence, but  
Figure 1: A flexible optical network (FON) with OEOs (relays) at all locations.  
Figure 2: Hops and transmission limits.  
optimisation may also have to be performed on network devices with limited processing power. Instead, we base our approach on approximation algorithms for related problems in Section 2[13] and, in particular, the 'ADD' algorithm[16] for optimisation of relay placement in wireless networks (without the connected core network requirement).
# Trends and Opportunities in the Dairy Industry: A2 Milk and Processing Methods
## 5 Milk Processing
### Non-Thermal Methods
#### 5.2.4 Ionizing Radiation (e-Beam, Gamma)  
The electron beam emitter (e-beam) directs the electron beam toward the targeted sample, which leads to the interaction of electrons with food. It leads to the inactivation of micro-organisms through both direct and post-mediated mechanisms. Direct radiation includes ionizing radiation that damages DNA and consequently inhibits cell division. In addition, it generates free radicals in the interior of microbial cells, which cause DNA strand breaks and physical damage, leading to cell death. The indirect action of e-beam causes the formation of reactive hydroxyl radicals. Their accumulation leads to lysis of the cell. Products subjected to such treatment can change their sensory properties, the perception of which (negative/positive) depends on the evaluator [109; 110].  
Gamma radiation works on the same principle, contributing to the indirect and direct inactivation of micro-organisms and viruses [111]. Like e-beam, it can be used at lower doses (0.25-2.25 kGy) to increase the quality of vegetables and fruits without affecting ripening speed or loss of firmness. Higher doses up to 30 kGy are used to extend the shelf life of animal products and disinfect surfaces and packaging [86].  
Milk can also be exposed to electromagnetic radiation. This process must be carried out for a short time, which allows it to maintain higher nutritional values than in traditional processing. In many European countries, the irradiation could be applied at 3 kGy to casein up to 30 kGy to dried milk products [86]. [PERSON] and his team (2023) recommend using e-beam treatment at 5-10 kGy to preserve cow's milk and 20 kGy to obtain a functional product with health-promoting properties. Lower doses for dairy products appear less effective, as demonstrated by the [PERSON] team (2017), which could not extend the shelf life of butter made from sheep's milk to which a dose of 1 kGy of gamma radiation was applied [86; 112].
## 4 Experiment
### Wind Speed Measurement Experiment
In addition, according to the error bars at the approximate points, it can be seen that compared with the first-order HSCW, the difference between the wind speed inversion results of the second-order HSCW at the approximate point position and the commercial CDWL inversion results is relatively small. In this paper, 500 sets of wind speed inversion results obtained from the measurements of four independent apertures at a distance of 110 m (a total of 2000 data points) are selected for linear analysis. The results of this analysis are presented in Figure 16. The horizontal coordinates in this figure represent the wind speed inversion results of the commercial CDWL at the 110-meter-point location, while the vertical coordinates represent the wind speed inversion results of the CDWL prototype. As can be clearly discerned from Figure 16, the inversion results processed by utilizing the second-order HSCW are more closely aligned with the commercial CDWL inversion results, exhibiting a higher degree of fit. Figure 17 shows the RMSE of wind speed inversion for four approximate points after adding different window functions. At the same distance, after determining the wind speed inversion results of the commercial CDWL as the true values during wind speed measurement, we used 500 sets of experimental data from each of the four line of sight directions for error analysis. In the figure, the horizontal axis represents the distance, and the vertical axis represents the average RMSE of 2000 sets of data. The average RMSE of the four approximate points for calculating the wind speed results of the CDWL prototype using the RW, first-order HSCW, and second-order HSCW inversion are 0.449 m/s, 0.266 m/s, and 0.184 m/s, respectively. Figure 16: Linear fitting results of wind speed.
## 2 Materials and Methods
### Methods
#### 2.3.4 Trend Analysis  
The Mann Kendall (MK) test is a non-parametric method recommended by the World Meteorological Organization for time series trend analysis and has been widely applied in research [70; 71]. This method does not require data to follow a normal distribution and is less sensitive to the influence of outliers, making it particularly suitable for analyzing long-term trends. It has been extensively used by scholars to examine trend changes in variables such as rainfall and temperature. In this study, the MK test was applied to analyze trends in raster data, while simple linear regression analysis was employed to evaluate trends in interannual data.