esa-sceva/satcom-chunk-collection huggingface dataset:
# Challenges and Future Trends in the Treatment of Psoriasis [PERSON] and [PERSON]
## 2 Current Treatment Guidelines  
### Treatments for Mild Poriasis  
There is no consensus on the definitions of mild and moderate-to-severe psoriasis [11; 12]. Mild psoriasis is generally described as affecting less than 3% to 5% of the total body surface area. There are several treatment options available for mild psoriasis, including topical corticosteroids, vitamin D analogs, calcineurin inhibitors, keratudyltics, and targeted phototherapy [13; 14; 15]. The choice of treatment depends on factors such as the location and severity of the lesions, presence of comorbidities, and individual patient preferences.  
Topical corticosteroids are commonly used as the primary therapy for patients with mild or localized psoriasis. They work by reducing inflammation, inhibiting cell proliferation, and constricting blood vessels through the downregulation of inflammatory pathways. The selection of corticosteroid strength and formulation should be based on the location of the lesions to minimize adverse effects. Combined formulations of corticosteroids with vitamin D analogs or keratolytic agents, such as halobetasol propionate and taxarotene, are often more effective and have fewer side effects compared to using them individually [16; 17]. Additionally, they can also be used as proactive treatments applied twice a week when lesions show improvement [18].  
Topical vitamin D analogs function by inhibiting the proliferation of keratinocytes and promoting their differentiation. They can be applied liberally unless the patient has renal impairment. Adverse effects may include a burning sensation and irritation, but these usually decrease over time [19]. Topical calcineurin inhibitors like tacrolimus and pimecrolimus are used primarily for psoriatic lesions in facial and intertriginous areas by blocking T cell activation and inhibiting the synthesis of IL-2 and IFN-\(\gamma\). The main side effects of topical calcineurin inhibitors, similar to topical vitamin D analogs, are a burning sensation and skin irritation. These side effects can be more pronounced in areas with severe inflammation, and applying topical corticosteroids first can help reduce the likelihood of these side effects [20]. Topical keratudyltics, such as taxarotene [17] and salicylic acid, aid in the breakdown of thick scales on psoriasis plaques. Tazarotene, a retinoid, inhibits keratinocyte proliferation, while salicylic acid reduces scaling. Adjusting the concentration, formulation, or frequency of application or combining them with topical corticosteroids can help minimize adverse effects such as burning and irritation. Last, targeted phototherapy, such as excimer light therapy, utilizes specific wavelengths of light to treat localized plaque psoriasis. It has a low potential for carcinogenicity and can lead to significant improvement after approximately two months of treatment. Adverse effects may include a burning sensation and blistering, which are preventable with an appropriate treatment schedule [21].
## Materials and Methods  
### Study Area  
The samplings were carried out in three neighbouring Mediterranean brackish costal lakes located on the Tyrrhenian coast of central Italy (42\({}^{\circ}\)28'00\" North-12\({}^{\circ}\)51'00\" East): Lake Caprolace, Lake Fogliano and Lake Sabaudia (Figure 1). The three lakes respectively have a surface area of about 3 km\({}^{2}\), 4 km\({}^{2}\) and 3.9 km\({}^{2}\), and mean depths of 3 m, 2 m and 10 m. They are classified as non-tidal lagoons with a maximum tidal excursion of 0.21-0.23 m [44; 45; 46]. Salinity generally varies between 33.7 and 38.1 PSU in Caprolace, 29.9 and 39.2 PSU in Fogliano, and 28.8 and 33.7 PSU in Sabaudia. The annual average was \(36.3\pm 0.8\) PSU in Caprolace, \(35.3\pm 0.8\) PSU in Fogliano and \(31.7\pm 0.9\) PSU in Sabaudia in 2006-2010 [44]. Data are expressed as mean \(\pm\) standard error.  
The lakes are affected by various forms of anthropogenic disturbance related to organic and inorganic nitrogen inputs from urban treated sewage, livestock farming and agricultural activities, which are widespread in the surrounding areas [2; 3].  
Figure 1: Map of the sampling area. The map shows the costal lakes of Caprolace (LP), Fogliano (IP) and Sabaudia (HP) located on the Tyrrhenian coast of central Italy (42\({}^{\circ}\)28’00” North–12\({}^{\circ}\)51’00” East).  
On average, the mean concentration of total nitrogen was 383.6 \(\pm\) 23.21 ug/L in Caprolace, 662.6 \(\pm\) 66.70 ug/L in Fogliano and 1006.1 \(\pm\) 49.97 ug/L in Sabaudia in 2006-2010. [PERSON] et al. [2] found the same trend in nitrate concentrations, with 12.2 \(\pm\) 2.9 ug/L, 42.4 \(\pm\) 61.3 ug/L and 91.9 \(\pm\) 70.24 ug/L in Caprolace, Fogliano and Sabaudia respectively in the same period.  
Lake Caprolace and Lake Fogliano (hereafter respectively LP and IP), characterised respectively by low and intermediate levels of eutrophication [3; 47], are Sites of Community Importance (SCIs) located within the Circeo National Park (Lazio).  
Lake Caprolace does not receive water inputs from the hinterland, while Lake Fogliano is affected by nutrient inputs from both the River Rio Martino and the livestock breeding activities practised in the surrounding areas. The annual concentration of _Chlorophyll a_ was generally lower in Caprolace (2.1 \(\pm\) 0.4 ug/L) than Fogliano (5.8 \(\pm\) 1.2 ug/L) in 2006-2010.  
Lake Sabaudia, the southernmost lake (hereafter HP), is affected by the highest anthropogenic pressure [3], mainly due to runoff from both the city of Sabaudia and cultivated fields in the surrounding areas as well as fishing and mussel farming. In this lake, freshwater inputs are present throughout the year.  
Annual algal biomass and _Chlorophyll a_ concentrations in this lake vary from 10.2 to 40.9 ug/L, with an average recorded value in 2006-2010 of 24.2 \(\pm\) 6.15 ug/L. Further details regarding the study area can be found in [PERSON] et al. [2] and [PERSON] et al. [3].
## 2 Materials and Methods
### Task Scheduling Algorithm
#### 2.2.2 Deep Reinforcement Learning  
Reinforcement learning (RL) [32], as shown in Figure 5, is a subfield of machine learning that enables intelligent agents to learn the optimal action policy by interacting with their environment. In RL (Figure 5) the agent obtains the state information \(S_{t}\) and reward \(R_{t}\) from the environment and selects an action \(A_{t}\) according to its policy. In response to the agent's action, the environment offers feedback \(R_{t+1}\), which is used to modify the agent's policy, allowing it to better adapt to the environment. Policy, reward, value, and environment are the four key components that the RL includes. Specifically, the policy describes the agent's behavior in response to a particular state, the reward defines immediate feedback, and the value defines the long-term gain.  
In RL, as opposed to supervised learning, there are no labeled training data. The agent can only acquire knowledge by interacting with the environment. This provides RL with a  
Figure 5: Reinforcement learning.  
substantial edge in numerous practical applications, such as autonomous driving, robot control, and game design.  
Deep reinforcement learning (DRL) combines RL and deep learning (DL) by employing deep neural networks (such as the convolutional neural networks, CNNs) to estimate the agent's policy or value functions, hence, allowing the learning of more sophisticated action policies. DRL algorithms can also be used to train agents to learn task scheduling policies, adaptively change the task scheduling strategies, optimize scheduling rewards, and provide a degree of flexibility and real-time responsiveness. Consequently, DRL algorithms are a potentially effective method for task scheduling.
## 2 Data Collection
### ConvGRU Algorithm Architecture
The transition through convolutional, ConvGRU, and deconvolutional layers (detailed in Figure 1) results in output matrices with specific filter numbers, heights, and widths, as indicated adjacent to the respective blocks in Figure 2.  
In this research, the many-to-one approach has been employed to relate input values to the model's output. Consequently, using 12 epochs of S4 observations, a future epoch is predicted, representing a near-real-time prediction. The rationale behind this decision stems from the fact that, despite the model's capability to handle more epochs (up to six), the results obtained from preliminary experiments and an examination of projects in the field of ionospheric scintillation prediction indicate a significant decrease in prediction accuracy with an increase in observational epochs. This decrease is evident to the extent that, in some studies, such as [35], it was observed that the predicted epochs exhibit a substantially reduced prediction accuracy. Even in instances where the intensity of geomagnetic disturbances is severe, the accuracy approaches nearly zero, rendering the predictive value practically negligible. With this historical perspective and insights gained from preliminary experiments, we have determined that employing the new method allows  
Figure 1: The encoder–forecaster structure employed in the proposed model. for a nearly instantaneous prediction of ionospheric scintillation within a broad region, such as South America, which, due to its proximity to the equatorial ionization anomaly (EIA), is highly susceptible to scintillation events. In this study, utilizing the ConvGRU method and incorporating two loss functions, along with the introduction of a custom-loss function, we aim to achieve a real-time prediction for S4 values.
# Log Volume Measurement and Counting Based on Improved Cascade Mask R-CNN and Deep SORT
## 3 Methods
### Evaluate Metrics  
To evaluate the detection accuracy of the object detection model, we employ two metrics: average precision (AP) and \(IOU_{mask}\). In object detection, we define TP as the count of true positives detected by the detector, meaning those detections that match the ground-truth boxes correctly (i.e., they have the correct classification and an IOU greater than a predefined threshold). FP represents the count of false positive detections, predictions with IOU values below the preset threshold, or multiple excessive detection boxes for the same ground truth. FN stands for the count of true positives that were not detected. Therefore, the precision and recall of log end objects can be calculated using Formulas (8) and (9).  
\[Precision=\frac{TP}{TP+FP} \tag{8}\]  
\[Recall=\frac{TP}{TP+FN} \tag{9}\]  
Precision and recall alone are insufficient for a comprehensive evaluation of detection quality. To address this limitation, we utilize average precision (AP) as a more robust metric. The AP is calculated by assessing precision and recall across various Intersection over Union (IOU) thresholds (e.g., 0.50, 0.55,..., 0.95, covering 10 thresholds). By plotting precision on the vertical axis and recall on the horizontal axis, we generate a precision-recall (P-R) curve. The area under this curve is then calculated to derive the AP value. Additionally, by categorizing objects by size, we can obtain different AP metrics, such as \(AP_{s}\) for small objects (less than 32 \(\times\) 32 pixels), \(AP_{m}\) for medium-sized objects (ranging from 32 \(\times\) 32 pixels to 96 \(\times\) 96 pixels), and \(AP_{l}\) for large objects (greater than 96 \(\times\) 96 pixels). This approach facilitates a more thorough evaluation of the model's detection performance across different object sizes.  
The AP metric provides a comprehensive assessment of the model's performance across various confidence thresholds. However, it does not directly account for the precise alignment of object boundaries. So we introduce the \(IOU_{mask}\) metric, which places a stronger emphasis on the exact alignment between the detection boundaries and the true object boundaries. Its calculation process is defined in Formula (10).  
\[IoU_{mask}=\frac{area(P)\cap area(G)}{area(P)\cup area(G)} \tag{10}\]  
where the \(P\) and \(G\) represent the areas of the log end output by the model and those manually annotated by humans, respectively. This formula calculates the ratio of the overlapping area between the two regions to the total area of both regions.
# Special Issue on Microgrids/Nanogrids Implementation, Planning, and Operation
## 1 Introduction  
Today's power system faces the challenges of environmental protection, increasing global demand for electricity, high-reliability requirements, clean energy, and planning restrictions. To move towards a green and smart electric power system, centralized generation facilities are being transformed into smaller and more distributed generations. As a result, the microgrid concept is emerging, where a microgrid can operate as a single controllable system and can be viewed as a group of distributed energy loads and resources, which can include many renewable energy sources and energy storage systems. Energy management of a large number of distributed energy resources is required for the reliable operation of the microgrid.  
Microgrids can allow better integration of distributed energy storage capacity and renewable energy sources into the power grid, therefore, increasing its efficiency and resilience to natural and man-caused disruptive events. In addition, microgrids and nanogrids are potential solutions for providing a better electrical service for both insufficiently supplied and remote areas. Microgrids networking with optimal energy management will lead to a sort of smart grid with numerous benefits such as reduced cost and enhanced reliability and resiliency [1]. As microgrids, nanogrids are an effective solution to promote renewable energy consumption and build a low-carbon and environmentally friendly power grid. They include small-scale renewable energy harvesters and fixed energy storage units typically installed in commercial and residential buildings.  
In this challenging context, the objective of this special session is to address and disseminate state-of-the-art research and development results on the implementation, planning, and operation of microgrids/nanogrids, where energy management is one of the core issues.
## 4 Results and Discussions of Dynamic Characteristics
### Dynamic Characteristics Under Obstacle Crossing Condition  
For evaluating the obstacle crossing ability of the tracked vehicle, the vertical accelerations of the tracked vehicle and the symmetrical suspension under five obstacle crossing conditions (with high of 100 mm, 200 mm, 300 mm, 400 mm, and 500 mm) were studied and shown in Figure 17. From it, the vertical acceleration of the symmetrical suspension at the hinge points presents a similar tendency of the vehicle body at the mass center. Similarly, the vertical acceleration of the vehicle body is also much lower than that of the symmetrical suspensions, indicating the effectiveness of the symmetrical suspensions. When the vehicle body encounters an obstacle at \(t=2.5\) s, the vertical acceleration suddenly increases, followed by a violent vibration, and the intensity of vibration increases with the increase in obstacle height. Finally, the movement of the vehicle is smooth when it crosses the obstacle.  
Similarly, for evaluating the stability of the tracked vehicle under the obstacle crossing conditions, the corresponding dynamic deflections of the symmetrical suspensions were obtained and shown in Figure 18. When the vehicle body encounters an obstacle at \(t=2.5\) s, the dynamic deflections of the symmetrical suspension suddenly decrease, followed by a violent vibration, and the intensity of the vibration increases with the increase in obstacle height. Finally, the movement of the vehicle is smooth when it crosses the obstacle. For further analysis, the first two peak points in the curve were taken out for analysis, and the suspension deflection of valley evolution with the height of the obstacle can be illustrated as shown in Figure 19. The suspension deflections at the first two peak points decrease linearly with the increasing obstacle height.  
Figure 16: The maximum pitch angles of the tracked vehicle evolution with the climbing slope.  
Figure 17: The vertical acceleration of the vehicle body at the mass center and the symmetrical suspensions at the hinge point under the obstacle crossing conditions.  
Figure 18: The dynamic deflection of the symmetrical suspension under the obstacle crossing condition.  
Correspondingly, the vertical displacement of the vehicle at the mass center presents a similar tendency, which is shown in Figure 20. The area with background color in the figure is the area where the obstacle is located. The results show that when the vehicle body encounters an obstacle at \(t=2.5\) s, the centroidal displacement suddenly increases, followed by a violent vibration, and the intensity of vibration increases with the increase in obstacle height. Finally, the movement of the vehicle is smooth when it crosses the obstacle, which shows that the designed suspension system has a good ability to cross the obstacle.  
Figure 19: The suspension deflection of valley evolution with the height of the obstacle.  
Figure 20: Vertical displacement of vehicle at mass center.
# Tomographic Inversion Methods for Retrieving the
## 4 Application of the ERTA Approach to the Reconstruction of WV Fields
More specifically, with reference to Equations (4) and (5), \(n\) ranges from \(-\mathrm{N_{a}}\) to \(\mathrm{N_{a}}\) for what concerns the angular (Fourier) decomposition, while as regards Equations (14) and (16) I ranges from 0 to \(\mathrm{N_{r}}\) for the radial decomposition. In our implementation, \(\mathrm{N_{a}}\)=180 and \(\mathrm{N_{r}}\)=41. Figure 3b,c, respectively, show the ERTA reconstruction and the normalized error between the retrieved WV field and the original one of Figure 3a. These results refer to a constellation of 6 satellites (1 Tx-5 Rx). The reconstruction sampling grid has a spacing of 1\({}^{\circ}\) and 250 m in the angular and radial directions, respectively. For the generic position (_i,j_), where \(i\) and \(j\) indicate the indices of the discretized angular and radial positions, the normalized error is defined as  
\[e(i,j)=\frac{\hat{f}(i,j)-f(i,j)}{f(i,j)}\, \tag{17}\]  
where \(\hat{f}(i,j)\) denotes the reconstruction in (_i,j_) obtained from the inversion of the IWV. Note that high relative errors at the top are encountered due to the significantly low values of WV at the highest altitudes. Based on this first example, it can be stated that ERTA succeeds in retrieving the pattern of the unknown WV map, even though with a clear tendency to underestimating the actual WV content.
## 2 Literature Review
### Financial Data Prediction Using LSTM in Finance
#### 2.1.2 Enhancing LSTM Forecasts with Attention Mechanisms  
The Attention mechanism in LSTM networks has been shown to significantly improve stock price prediction performance in the Hong Kong market [12]. Data from over 100 Hong Kong stocks were used for evaluation, and parameter tuning confirmed its effectiveness. The attention mechanism was also validated through a 'Long-Only' strategy, showing enhanced prediction accuracy. Overall, it notably enhances the performance of LSTM-based models in predicting stock movements.  
A comparison of several deep learning models for financial time-series prediction, including Multilayer Perceptron (MLP), one-dimensional Convolutional Neural Networks (1D CNNs), LSTM, and Attention networks, highlighted the benefits of a 60-day lookback period [11]. Attention networks achieved the highest hit ratio, while LSTM models excelled in handling sequential data. Weighted attention networks also enhanced visualization and analysis. The proposed model outperformed others, achieving a 0.76% hit ratio in KOSPI 200 trend prediction, underscoring the effectiveness of long-term sequential data for improved accuracy and interoperability.  
An Attention-LSTM model has been proposed for early warning of systemic risk in China's financial market, incorporating a network public opinion index from text mining [19]. Systemic risk arises when a financial institution's failure impacts the entire system, highlighting the need for a reliable warning model. The Attention-LSTM model enhances prediction accuracy with an Attention mechanism, outperforming other models in empirical analysis. The network public opinion index serves as a key indicator for forecasting market trends and risks, with potential for adaptation beyond China.  
A new time-series prediction model, LSTM-Attention-LSTM, has been introduced by combining LSTM and Attention mechanisms using an encoder-decoder structure to manage long input sequences effectively [20]. The experimental results demonstrate that this model achieves higher prediction accuracy than existing mainstream models, performing well in both short and long time steps. The effectiveness of the LSTM-Attention-LSTM model has been validated using five open-source datasets.
## 4 Proposed Online Method for Impedance Characterization of Electrical Machines
### Step 3--Search for dominant frequency components
#### Step 4--Impedance calculation  
To characterize the impedance behavior as a function of the excitation frequency, we must determine the corresponding impedance for each frequency \(f_{\mathit{prep}}\) contained in the vectors \(Q_{V}\) and \(Q_{I}\). The Ohm law is used to calculate the amplitude and the phase of this impedance, as described in Equations (10) and (11).  
\[|Z(f_{\mathit{prep}})|=\frac{|V_{\mathit{abc}}(f_{\mathit{prep}})|}{|I_{ \mathit{abc}}(f_{\mathit{prep}})|} \tag{10}\]  
\[\angle Z(f_{\mathit{prep}})=\angle V_{\mathit{abc}}(f_{\mathit{prep}})- \angle I_{\mathit{abc}}(f_{\mathit{prep}}) \tag{11}\]  
The raw impedance characteristics obtained independently from the two thresholding methods are shown in Figure 19. In particular, it can be seen that the point densities are not evenly distributed depending on the threshold used. On the one hand, around the so-called \"parallel\" resonances, i.e., when the inductive reactance is equal to the capacitive reactance, the current amplitude is minimal and its signal is in phase with the applied voltage. This results in a local impedance maximum and more points with the threshold at simple voltages. On the other hand, around the so-called \"series\" resonances, the applied AC voltage generates the maximum current amplitude. These two signals are then in phase. This results in a local impedance minimum and more points with the threshold at the currents.  
Also, as expected, the maximum frequency of these characteristics is equal to \(\frac{f_{\mathit{max}}}{2}\), i.e., around 15.6 MHz. Finally, these two profiles are particularly noisy at very high frequencies (over a dozen megahertz).
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### Forest AGB Maps Generated Using GIS-Based Methods  
#### 3.1.1 New IPCC Tier-1 Global Biomass Carbon Map for the Year 2000  
[PERSON] and [PERSON] [49] compiled a total of 124 carbon zones or regions with unique carbon stock values using the IPCC (International Panel on Climate Change) Tier-1 method [50]. Then, they incorporated these values with spatial datasets including land cover maps, ecoregions zones, continental regions, and frontier forest location maps, to generate a global biomass carbon map with a spatial resolution of 1 km. This dataset is the first globally consistent and spatially explicit estimate of vegetation biomass and carbon stocks, circa 2000. It has important implications for climate and environmental change studies, however, the methods employed in the generation of the dataset were not directly related to field measurements or validated with field data, and little is known about uncertainties in the map.