esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Test Site Location--Plant Material--Treatments--Experimental Design  
A field experiment took place in a 5-hectare kiwifruit orchard located in Agrinio County, Western Greece, for two successive years (2018 and 2019). The orchard consisted of 'Hayward' cultivar vines, which were 15 years old and trained as a T-shape with planting distances of 2.0 m \(\times\) 4 m and a trunk height of 1.8 m. All the vines in the orchard received uniform cultural practices and inputs, including water, fertilizers, and phytosanitary products. The soil in the orchard was identified as loam, with a pH value of 7.25, 3.05% \(w/w\) CaCO\({}_{3}\) concentration, 1.76% \(w/w\) organic matter, and an electrical conductivity of 0.310 mS cm\({}^{-1}\) (based on soil analysis results provided by the farmer).  
Girdling treatments were conducted using a girdling cutter during the following periods: (a) early August (on 9 August 2018 and on 8 August 2019) (referred to as GA, August girdling), (b) early September (on 12 September 2018 and on 10 September 2019) (referred to as GS, September girdling), and (c) both in August and September (referred to as GAS). Girdling consisted of removing a stripe of approximately 5 mm ring of bark at the base of a producing cane (Figure 1). Special attention was given to completely removing the phloem tissue while minimizing any damage to the xylem tissue. Wounds were completely healed approximately one month after the treatments, as shown in Figure 1D. Each treatment was replicated four times, with four vines per replicate (i.e., 16 girdling applications per treatment). All treatments were applied on the same vines (on different canes) to reduce variability induced by the vine itself. Care was taken to choose canes with similar fruit numbers.
# An Adaptive Active Learning Method for Multiclass Imbalanced Data Streams with Concept Drift
## 3 The Proposed AdaAL-MID Algorithm
### Dynamic Label Budget Strategy in Concept Drift Scenarios  
When concept drift will occur in a data stream is uncertain, but a concept drift detector can effectively help identify it. It monitors the performance of base classifiers to infer changes in the data distribution and issues an alert when the performance of a base classifier is significantly affected, indicating potential concept drift. There are various choices for drift detectors, such as the drift detection method (DDM) [32] and adaptive windowing (ADWIN) [8]. In this paper, ADWIN was used as the drift detector, with two different thresholds set for ADWIN: one serves as a drift alarm to warn of impending concept drift, and the other acts as a drift confirmer to verify that concept drift has occurred in the data stream. Additionally, ADWIN is used as a trigger for ensemble updates. When ADWIN detects a change, a new classifier is trained to replace the worst-performing classifier in the ensemble. The initialization of the new classifier is performed on a sliding window that holds the most recent instances.  
This paper proposes a dynamic label budget strategy based on three stages (stable, warning, and confirmation). During the stable stage of the data stream, the label budget is consistently maintained at the default setting B. Subsequently, paired ADWINs running on each base classifier detect concept drift in the data stream. If at least one classifier reports a warning or confirms a drift, corresponding budget adjustments are made. The adjustment of the current label budget of the data stream is shown in Equation (1). When a warning is detected, the budget temporarily increases to 1.5B for \(\textit{size}_{w}\) instances following the warning flag \(\textit{t}_{warning}\). If no drift is detected afterward, the budget reverts to the default setting. When a drift is confirmed, the budget increases further to 2B and remains at this level for the subsequent \(\textit{size}_{d}\) instances to learn more instances and adapt to the new concept. Finally, the budget reverts to the default setting.  
\[budget=\left\{\begin{array}{ll}2\text{B},&\text{if detect drift \& $t_{drift}\leq t\leq t_{drift}+size_{d}$}\\ 1.5\text{B},&\text{if detect warning \& $t_{warning}\leq t\leq t_{warning}+size_{w}$}\\ \text{B},&\text{otherwise}\end{array}\right. \tag{1}\]  
This strategy allows the algorithm to temporarily increase the number of labeled instances during drift and then return to the original level, indirectly reducing the number of labeled instances during the stable phase. By flexibly allocating the labeling budget in the context of concept drift, the impact of concept drift on the classifier is reduced, and the classifier quickly adapts to new concepts. In this paper, the default settings are \(\textit{size}_{w}=50\) and \(\textit{size}_{d}=100\).
# Distributed Uniform Streaming Framework:
This allows us to run each component on a designated device; the use of resources for the entire application can then be distributed along multiple devices. However, as the resource requirements are based on the amount of received events, such event-processing devices are prone to an overload of events, resulting in an overloaded device. However, these overloaded situations can be overcome if those application components are allowed to migrate to devices with more resources available. In this paper, we propose the Distributed Uniform Stream (DUST) Framework [5], which paves the road towards an elastic streaming platform. It optimizes the distribution of resources in a modern IoT environment. The core of the framework, called DUST Core, enables different application components to stream events between each other. As the components need to be able to migrate between devices, the underlying middleware configuration must be interchangeable; the Core makes abstraction of this towards the application components. Additionally, a distributed coordination system is introduced, which takes the optimization and distribution of the components over available resources into account. This results in a system of application components that can adapt to occurring changes in event-streaming rates in a flexible way. The paper is organized as follows. In Section 2, related work regarding this research is discussed. Section 3 elaborates on the proposed DUST Core and the Event-Streaming paradigm, while the coordinator system is described in Section 4. The conducted experiments and its results for different use cases are analyzed in Section 5. Section 6 elaborates on a real-world application architecture, created with the DUST Core. Finally, this paper is concluded, and future work is described in Section 7.
## 4 Results and Discussion  
### Performance  
Figures 2 and 3 show the performance metrics across the cross-validation rounds and for the independent hold-out test for each classification scheme, respectively. Tables 2 and 3 list the mean and standard deviation for each performance metric for cross-validation sets and hold-out test, respectively. The Logit Lasso algorithm is less affected by model overfitting as it can be noted from the smaller difference between training and test performance. Moreover, it is worth noting that among the various strategies, the SMOTE technique provides the best balance between sensitivity and specificity.  
Overall, the achieved performance compares favorably with that obtained in our previous work [34]. In particular, in this analysis, we also found the logistic regression model as the method with the most stable performance between the training and test sets. However, in contrast to our previous work, here we used different training sampling strategies that showed that SMOTE represents a promising method for this clinical challenge and that simply undersampling does not guarantee equally effective results.  
Other works addressed similar classification tasks, showing that the performance can be significantly improved by adding predictors extracted from imaging [35; 36], genetics [9] and nomograms of clinical and pathologic variables [37] with more complex nonlinear models such as deep neural networks [38]. In our analysis, we used simpler and therefore more interpretable models in combination with known sampling strategies in order to highlight different effects on performance. In future developments, we will expand the presented framework to investigate the impact of other features and predictive models.  
Figure 3: Performance metrics for the hold-out test for each classification scheme: violin plots of the distributions of the sensitivity values (**A**), specificity values (**B**), accuracy values (**C**), and AUC values (**D**).  
Figure 2: Performance metrics across the cross-validation rounds for each classification scheme: violin plots of the distributions of the sensitivity values (**A**), specificity values (**B**), accuracy values (**C**), and AUC values (**D**).
###### Abstract  
The recent expansion of global Lithium Ion Battery (LIBs) production has generated a significant stress on the lithium demand. One of the means to produce this element is its extraction from different aqueous sources (salars, geothermal water etc.). However, the presence of other mono- and divalent cations makes this extraction relatively complex. Herein, we propose lithium-sodium separation by an electrodialysis (ED) process using a Lithium Composite Membrane (LCM), whose effectiveness was previously demonstrated by a Diffusion Dialysis process (previous work). LCM performances in terms of lithium Recovery Ratio (RR(Li\({}^{+}\))) and Selectivity (S(Li/Na)) were investigated using different Li\({}^{+}\)/Na\({}^{+}\) reconstituted solutions and two ED cells: a two-compartment cell was chosen for its simplicity, and a four-compartment one was selected for its potential to isolate the redox reactions at the electrodes. We demonstrated that the four-compartment cell use was advantageous since it provided membrane protection from protons and gases generated by the electrodes but that membrane selectivity was negatively affected. The impact of the applied current density and the concentration ratio of Na\({}^{+}\) and Li\({}^{+}\) in the feed compartment ([Na\({}^{+}\)]\({}_{\mathrm{F}}\)/[Li\({}^{+}\)]\({}_{\mathrm{F}}\)) were tested using the four-compartment cell. We showed that increasing the current density led to an improvement of RR(Li\({}^{+}\)) but to a reduction in the LCM selectivity towards Li\({}^{+}\). Increasing the [Na\({}^{+}\)]\({}_{\mathrm{F}}\)/[Li\({}^{+}\)]\({}_{\mathrm{F}}\) ratios to 10 had a positive effect on the membrane performance. However, for high values of this ratio, both RR(Li\({}^{+}\)) and S(Li/Na) decreased. The optimal results were obtained at [Na\({}^{+}\)]\({}_{\mathrm{F}}\)/[Li\({}^{+}\)]\({}_{\mathrm{F}}\) near 10, where we succeeded in extracting more than 10% of the initial Li\({}^{+}\) concentration with a selectivity value around 112 after 4 h of ED experiment at 0.5 mA\(\cdot\)cm\({}^{-2}\). Thus, we can objectively estimate that the concept of this selective extraction of Li\({}^{+}\) from a mixture even when concentrated in Na\({}^{+}\) using an ED process was validated.  
Electrodialysis; lithium selective membrane; lithium-sodium separation; membrane selectivity; lithium recovery rate +
Footnote †: journal:
# EL-MTSA: Stock Prediction Model Based on Ensemble Learning and Multimodal Time Series Analysis
## 4 Experimental Results and Discussion
### Experimental Results and Analyses
#### 4.4.4 Comparative Analysis of Data Volume  
Through comparative experiments, it is observed that the four baseline models evaluated in this study tend to achieve higher accuracy when the data frequency is high, as illustrated in Figure 8. This is because higher-frequency data typically contains more information, enabling the models to capture richer patterns, and thus improve prediction accuracy. However, the EL-MTSA model demonstrates exceptional capabilities in processing sentiment data and capturing long-term temporal patterns, particularly in the 1-day and 15-min data configurations. By leveraging a mechanism akin to ensemble learning, the EL-MTSA model effectively integrates the strengths of different sampling frequencies and sentiment data features, combining the advantages of the baseline models to significantly enhance predictive performance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Dataset** & **Model** & **Frequency** & **MAE** & **SMAPE** & **RMSE** \\ \hline \multirow{5}{*}{SSE 500} & EL-MTSAs & 1 day & 109.1481 & 77.69253 & 4.588616 \\  & EL-MTSAs & 30 min & 99.29775 & 69.92445 & 4.142188 \\  & EL-MTSAs & 15 min & 92.28185 & 65.31663 & 3.872534 \\  & EL-MTSAs * & 1 day & 111.3266 & 81.13307 & 4.811939 \\  & EL-MTSAs * & 30 min & 103.9623 & 76.61262 & 4.531059 \\  & EL-MTSAs * & 15 min & 98.8652 & 72.78998 & 4.282745 \\ \hline \multirow{5}{*}{CSI A50} & EL-MTSAs & 1 day & 48.05319 & 34.09183 & 3.78931 \\  & EL-MTSAs & 30 min & 43.88325 & 31.81827 & 3.529929 \\ \cline{1-1}  & EL-MTSAs & 15 min & 39.15164 & 27.08353 & 3.00892 \\ \cline{1-1}  & EL-MTSAs * & 1 day & 58.76713 & 45.28799 & 5.016004 \\ \cline{1-1}  & EL-MTSAs * & 30 min & 46.90205 & 33.49926 & 3.709601 \\ \cline{1-1}  & EL-MTSAs * & 15 min & 44.21733 & 31.14188 & 3.452698 \\ \hline \end{tabular} The symbol *** denotes the ablation model.  
\end{table}
Table 4: _Cont._  
Figure 8: _Cont._
## 4 Simulations
### Design Method
Specifically, Figure 16 predicts an approximated deviation of 0.25, 0.5, 1, and 2, when a sinusoidal disturbance with magnitude 0.01, 0.02, 0.04, and 0.08, respectively, appears in the system. This prediction matched with the simulation results presented in Figure 17; see the zoom-in of the figure. When the amplitude of the sinusoidal disturbance is fixed, one can obtain curves similar to those presented in Figure 16 for several frequency values. Figure 18 presents the mentioned curves, when the sinusoidal disturbance has an amplitude of 0.01 and different frequency values. Then, whenever the amplitude and frequency are known, the curves in Figure 18 can be used to tune the control magnitude in accordance with the desired force error. Figure 16: Curves of steady-state error \(e_{\rm ss}\) in terms of control gain \(K_{p}\) and sinusoidal amplitude. Figure 17: Simulation of the force control system: sinusoidal disturbance with variable amplitude. Figure 19 presents the results of the simulation of the force control system with the sinusoidal disturbance of amplitude of 0.01 and different values of frequency. One can see that the simulation results matched with the predicted force error of the design curves in Figure 18. For a control magnitude of one, i.e., \(K_{GM}=1\), the predicted force errors are 0.5, 1, 3.5, and 10 [N], when a disturbance with frequency 0.628, 1.25, 2.5, and 5 rad/s, respectively, emerges. These force errors are similar to the ones obtained in the simulation presented in Figure 19; see the zoom-in. Note that the gain margin of 6.3 (see Figure 11) must be considered during the selection of the control magnitude \(K_{GM}\) in the curves presented in Figures 16 and 18.
## 2 Materials and Methods
### Data Collection and Preprocessing
#### 2.2.2 Data to Calculate Carbon Use Efficiency  
The carbon use efficiency (CUE) of terrestrial ecosystems is defined as the ratio of net primary production (NPP) to gross primary production (GPP) [37; 38], a critical metric for measuring carbon transformation, depicting the capability of ecosystems to transfer carbon from the atmosphere to terrestrial biomass [39].  
This study utilized GPP data from the Global Land Surface Satellite (GLASS) data products. The GLASS-GPP algorithm originates from the EC-LUE model [40], incorporating the impacts of several significant environmental variables such as atmospheric carbon dioxide concentration, radiation components, and vapor pressure deficit (VPD), which can effectively replicate spatial, seasonal, and annual variations, especially enhancing the model's ability to represent annual variations in GPP [41]. The data are available on an annual basis with a resolution of 500 m.  
Meanwhile, the NPP data were derived from the MODIS NPP product (MOD17A3 HGF) version 6.1, which offers NPP data with a 500 m resolution. The annual net primary production is calculated by summing all 8-day net photosynthesis (PSN) products (MOD17A2H) of a specified year. The PSN values are determined by the difference between the gross primary production (GPP) and maintenance respiration (MR), presented as annual data with a resolution of 500 m.  
It is important to add that all data were processed to have a spatial resolution of 500 m, facilitating better calculations.
###### Abstract
[PERSON] et al. [20] compared the results for tool wear estimation using vibration and audio signals with LSTM and bidirectional LSTM. In particular, the input vector for the networks was composed of features extracted by a scalogram generated by a Morlet transform and a generative network, as a data augmentation method. The literature review about the state-of-the-art methods for tool condition monitoring, with particular reference to the machining of composite materials, shows that the employment of automatic feature extraction techniques, which allow the complete automation of the tool wear estimation process, has not been fully explored. As regards the employment of recurrent neural networks, some applications have been reported in the literature to estimate tool wear in the machining of metals, but very few applications have been proposed in the machining of composite materials. Moreover, most of the proposed applications generally employ manual feature extraction methods, which, on the one hand, require a strong knowledge of the problem by the developer to extract useful information, and, on the other hand, imply some simplification hypotheses, such as considering the data Gaussian distributed or linearly related. To tackle this issue, the specific contribution of the present work is related to the development of a new integrated method for tool wear prediction in drilling based on the employment of autoencoders for automatic feature extraction and gated recurrent unit (GRU) memory-based recurrent neural networks for tool wear prediction. This method can exploit the potential of both models for tool wear prediction and represents an advancement compared to the literature, allowing the full automation of both feature extraction and tool wear prediction tasks. The proposed method is developed in this work to realize tool condition monitoring in the drilling of CFRP/CFRP stacks for aeronautical assembly based on force, torque, vibration, and acoustic emission sensor signals. Different models are developed and assessed, also by comparing the results obtained with the proposed method with those achieved using other neural network approaches, such as traditional feedforward neural networks, and considerations are made of the influence that memory-based hyperparameters have on tool wear estimation performance.
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 6 Proposed Protocol
### Mobile Device Registration Phase  
The mobile device chooses an identity (\(ID_{M}\)), password (\(PW_{M}\)), and random number (\(r_{M}\)). Afterward, the mobile device computes \(HPW_{M}=h(ID_{M}\parallel PW_{M}\parallel r_{M})\), \(HID_{M}=h(ID_{M}\parallel HPW_{M}\parallel r_{M})\) and sends \(\{HID_{M}\}\) to the GCS.  
The GCS checks \(HID_{M}\) in the record, chooses a random number (\(r_{2}\)), and computes \(K_{M}=h(HID_{M}\parallel k\parallel r_{2})\). Next, the GCS stores \(\{HID_{M},r_{2}\}\) and sends \(\{h(\cdot),K_{M},PID_{D}\}\) to the mobile device.  
The mobile device computes \(F_{M}=h(h(HID_{M}\parallel r_{M})\oplus h(HPW_{M}\parallel r_{M}))\), \(K_{M}^{*}=K_{M}\oplus F_{M}\) and \(G_{M}=h(F_{M}\parallel K_{M}\parallel r_{M})\) and stores \(\{PID_{D}^{*},K_{M}^{*},G_{M},r_{M},h(\cdot)\}\). The mobile device registration phase is presented in Figure 3.  
Figure 3: Proposed mobile device registration phase.  
Figure 2: Proposed drone registration phase.
# Soil Texture Estimation Using Radar and Optical Data from Sentinel-1 and Sentinel-2
## 2 Materials and Methods  
### Study Site  
The study site is located on the Kairouan Plain which covers a surface area in the eastern portion of the Tunisian Atlas (centered on 9\({}^{\circ}\)53'57''E and 35\({}^{\circ}\)4'51''N). This area has a semi-arid climate and mean annual precipitation of 300 mm [30]. The landscape is mainly flat. The principal land cover categories are agricultural fields, urban areas, salt flats, water bodies and some low-lying mountains located in the West. The economy of the Kairouan Plain is based on the agricultural sector, in which the dominant crops are mainly olive groves and winter crops [12]. Due to severely limited water resources in the region (annual rainfall of 300 mm and a high rate of evaporation), the management of these resources is essential.  
The soils found on the study site are formed mainly by alluvial deposits, with clay and coarse sand formations, and are characterized by a high spatial variability of their texture. The main soil units are poor soils, complex soils (several soil units), calcimagnetic soils and isohumic soils [26]. In this context, the extreme floods of 1969 led to strong spatial heterogeneities in terms of soil texture, as they deposited sandy to sand loamy materials in the upstream and downstream sectors. They also carried large quantities of alluviala which, together with the natural deposit mechanism, changed the spatial distribution of these deposits [31]. Indeed, the thrust of the deposit along the major bed of the wadi, when it overflows, leads to a distribution with the coarsest components remaining upstream whereas the finest alluvia are carried downstream.  
On the other hand, the introduction of crop systems and fields has led to cyclical evolutions of the surface conditions and land use over the time. Tillage and farming operations can thus modify the physical characteristics of the soil, resulting in changes in its surface texture.
## 3 Results and Discussion
### Application of the Optimized Auxetic Sandwich Panel for Armored Fighting Vehicle Protection
#### 3.4.2 Simulation Results
Additionally, the layered arrangement of the auxetic structure in the direction of the blast allows it to progressively absorb impact energy from the outermost to the innermost layers, thereby mitigating the direct effects of blast loads on occupants or OSP. However, a significant challenge in utilizing ASPs as protective structures lies in their manufacturing process. For large-scale production, auxetic cores with basic configurations, such as REH, DAH, and SH, can be manufactured using sheet-folding methods. However, this method requires more effort and time [70] compared to the manufacturing of conventional honeycomb or aluminum foam, leading to higher production costs. Furthermore, ensuring effective bonding between layers presents another challenge in the ASP manufacturing process, as inadequate bonding can compromise the structural integrity and blast-resistance performance of the panels. Future research should focus on addressing the manufacturing and cost-related challenges associated with the large-scale application of ASPs as protective structures. Figure 22: Dynamic responses of (**a**) displacement and (**b**) acceleration on the occupant side of the AFV for different structure types.