esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring the Role of IL-36 Cytokines as a New Target in Psoriatic Disease
## 1 Introduction
In addition, the mutation rates differ among pustular psoriasis phenotypes. In a study on 57 Chinese patients with pustular psoriasis, IL36 RN mutations were found in 75% of GPP patients and 94% of ACH patients [21]. On the contrary, [PERSON] and colleagues performed mutation analysis of the IL36 RN gene in 88 Japanese patients with PPP and identified three types of single base substitutions of IL36 RN; they were heterozygous and different from those found in European studies, and they were considered to be of no pathogenic relevance [22; 23]. Furthermore, other studies in patients with PPP have shown that the combined frequency of AP1S3 and IL36 RN mutations accounts for less than 10% of patients, suggesting not only that PPP is not clearly associated with IL36 RN but also that known genes account for only a minority of disease cases [17; 22]. Many advancements have been made in psoriasis treatment in this century. Identification of pro-inflammatory cytokines such as tumor necrosis factor (TNF)\(\alpha\), IL-17, and IL-23 as therapeutic targets allowed the development of several anti-cytokine monoclonal antibodies that revolutionized treatment of psoriasis [8]. Nevertheless, unmet needs remain, since some patients do not respond, stop responding over time, or suffer from side effects of these treatments. This especially applies to pustular psoriasis and PsA, where resistance to existing treatments and disease recurrence are commonplace. Therefore, novel therapeutic strategies are needed. The blockade of IL-36 family cytokines represents an interesting therapeutic target for psoriasis, pustular psoriasis, and PsA, among other diseases, and this will be the subject of this PubMed search strategy \"IL-36\" AND (\"psoriasis\" OR \"pustul\")-based review.
# Flickering-Free Distance-Independent Modulation Scheme for OCC
## 4 Modulation/Demodulation Scheme
Additionally, these correlation's values can be evaluated with an image's ROI containing two strips or more. Therefore, the theoretical distance range of the system depends on the emitter's projected size and the band's pixel size. These dimensions can be easily calculated using Equations (3), (5) and (6). Then the obtained values can be used for the frequency selection in the system design process. \[x=2\frac{N_{x}}{FOV_{x}}\arctan\left(\frac{d_{x}}{2D}\right) \tag{5}\]  
\[y=2\frac{N_{y}}{FOV_{y}}\arctan\left(\frac{d_{y}}{2D}\right) \tag{6}\]  
These equations estimate the 2D pixel projection (\(x\), \(y\)) in a picture of a real distance (\(d_{x}\), \(d_{y}\)) in function of the separation between the object and the camera (\(D\)), and the camera's FOV and resolution in pixels (\(N_{x,y}\)), as shown in Figure 9.  
Therefore, equations for determining the adequate frequencies for each implementation must be deduced. These equations are based on the \(\alpha\) parameter which relates \(f_{\text{SW}}\) and \(f_{\text{CAM}}\), called the frequency multiplier. This parameter has specific requirements related to the camera's characteristics, the emitter's size, and the distance range of the system. In the next subsection, the selection of this value will be explained.
# A Survey on the Application of WirelessHART for Industrial Process Monitoring and Control
## 3 Industrial Applications of WirelessHART  
This section gives a brief introduction to the WirelessHART protocol, its typical network structure, and different OSI layers usage. Furthermore, a detailed review of the application of WirelessHART for industrial process monitoring and control in both simulation and real-time environments will be discussed.
# Characterization of CYGNSS Ocean Surface Wind Speed Products
## 3 Results
### Triple Colocation Validation
#### 3.2.1 CYGNSS Global Wind Speed Data Products  
The results of the triple colocation analysis for wind speed observations away from storms is summarized in Table 1. Each row of the table considers a different combination of three wind speed products from the five that are available.  
The Root Mean Square Error (RMSE) entries are the standard deviations of the error in each of the three products, as estimated by the triple colocation analysis procedure. This results in three different estimates of the RMSE for the two CYGNSS wind speed products (FDS and NOAA) and five different estimates of the RMSE for the three non-CYGNSS products (ERA5, ASCAT, and GMI). Notably, one of the RMSE estimates for the non-CYGNSS products does not involve any CYGNSS data. Since the triple colocation method assumes that the errors in each of the three products are uncorrelated, there will  
Figure 10: Geographical distribution of FDS bias with respect to ERA5 (FDS—ERA5). Data are averaged over all of December 2023.  
be some inaccuracies in its estimates of the three RMSE values if here is in fact some correlation. The three non-CYGNSS products were chosen because they use different approaches to determining the wind speed--a reanalysis model, an active backscatter radar, and a passive microwave radiometer--so that their errors can be expected to behave differently and, in particular, their errors will have different correlations with CYGNSS errors and with one another. For this reason, consistency between the RMSE estimates derived using different triplets can be considered an indication that any residual error correlations between products that may be present did not have a significant impact on the RMSE values estimated.  
The average of the three RMSE estimates for the CYGNSS FDS product is 1.35 m/s and the individual estimates are all within 3% of the average. Similarly, the average RMSE for the CYGNSS NOAA product is 0.98 m/s, with at most 5% deviation between the individual estimates. The lower RMSE for the NOAA product is consistent with earlier results and is believed to result from its use of trackwise correction of the L1 calibration and additional spatial averaging. As an aside, the triple collocation results also provide estimates of the RMSE performance for the three other products, with ASCAT and GMI quite similar at 0.45 and 0.47 m/s and ERA5 slightly higher at 0.74 m/s.
# The Design and Deployment of an End-To-End IoT Infrastructure for the Natural Environment
## 5 Deployment Experiences of an Environmental IoT
### Design of the Soil Node  
The basic enclosures were sufficient to exclude rain from the relay and soil nodes. There was some difficulty in excluding heavy rain from the hardware during soil node battery changes, which could be resolved with an external battery connected via a waterproof connector, avoiding the need to open the housing of the IoT node itself. Additionally, soil nodes near to the watercourse, in particular, were damaged by water ingress when submerged by flooding.
## 3 Results
### Experiment 2: Module Scale Research  
This experiment studies the performance comparison between the proposed method with different scales of classic YOLOv5 modules with Small, Medium, Large, and X large scales. Table 2 and Figure 10 show the test result.  
As shown in Figure 10, the best performing model is YOLOv5L with \(mAP_{0.5}\) = 98.56%, the YOLOv5 + ViT(5) \(mAP_{0.5}\) = 96.60%, which is 1.96% lower than YOLOv5L. However, the parameters of the proposed method in this paper are only 1.3 M, which is only 1.5% of the YOLOv5X. It can be seen from the training time and the average power consumption of the GPU that the proposed methods are more environmentally friendly and energy-saving. For example, the training process of YOLOv5 + ViT(4) lasts 55.6 min, and the average power consumption is 155 W while training a YOLOv5X model, the energy consumption is 7.8 times that of YOLOv5 + ViT(4). The method proposed in this paper is lightweight and low training power consumption and is especially suitable for deployed on satellites for onboard reasoning and online learning.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Precision (\%)** & **Recall (\%)** & \(mAP_{0.5}\) (\%) & \
\begin{tabular}{c} **Parameters** \\ **(Byte)** \\ \end{tabular} &
\begin{tabular}{c} **Training** \\ **Time(min)** \\ \end{tabular} & **Consumption (W)** & **FLOPs (G)** \\ \hline YOLOv5x & 98.30 & 95.24 & 98.05 & 86.2 M & 328.9 & 202.1 & 204.3 \\ YOLOv5l & 97.24 & 96.88 & 98.56 & 46.1 M & 197.0 & 192.2 & 107.8 \\ YOLOv5m & 97.41 & 96.33 & 98.50 & 20.9 M & 136.1 & 183.2 & 47.9 \\ YOLOv5s & 95.98 & 96.15 & 97.81 & 7.00 M & 72.6 & 174.5 & 15.9 \\ YOLOv5 + ViT(4) & 94.71 & 91.58 & 96.26 & 1.31 M & 55.6 & 155.7 & 1.1 \\ YOLOv5 + ViT(5) & 98.76 & 90.84 & 96.60 & 1.31 M & 46.7 & 151.9 & 1.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiment results of different scales.  
Figure 10: The **upper left** subgraph plots the Epoch-\(mAP\) curve; the **upper right** subgraph is the best \(mAP\) in 300 epochs; the **lower-left** subgraph plots the GPU power usage and training time curve; the **lower right** subgraph plots the GPU temperature during the training progress.
# The Relationship between Radon and Geology: Sources, Transport and Indoor Accumulation
## 4 Risk Assessment and Mitigation
### Techniques Used to Measure Radon Concentration in Rocks and Soils
#### 4.1.6 Uranium and Thorium Concentration Measurement Methods in Soil  
The measurement of uranium and thorium concentrations in soil is an essential task in the field of environmental radioactivity due to their long half-lives and potential health risks [181]. Various methods have been developed to perform this task, with gamma-ray spectrometry being one of the most widely used due to its non-destructive nature and ability to provide quick results. In this method, a high-purity germanium detector is commonly employed to measure the gamma radiation emitted by the decay of uranium and thorium isotopes present in the soil samples [182]. However, this method requires careful calibration and correction for self-absorption of gamma rays in the sample. For more precise measurements, particularly when lower concentrations are expected, moresensitive methods such as alpha spectrometry or mass spectrometry may be used. Alpha spectrometry is often applied to measure the alpha particles emitted during the decay of uranium and thorium, while mass spectrometry can provide highly accurate measurements by determining the ratio of different isotopes [183]. However, both methods require more complex sample preparation procedures, including dissolution of the sample and separation of the elements of interest.
# Lateral Stability of a Mobile Robot Utilizing an Active Adjustable Suspension
## 3 Active Suspension's Model
### Relationship between Input and Output  
In this section, we describe the input-output relationship of the active suspension. As shown in Figure 4, the aim of this mechanism is to achieve the relationship between input (\(L_{e}\)) and output (H). \(L_{e}\) is the linear actuator length, and H is the height of the COG. Thus:  
\[H(L_{e})=L_{0}*\cos(\
icefrac{{\alpha}}{{2}})+R+H_{2} \tag{8}\]  
where \(L_{0}\) is the length of the leg, \(\alpha\) is the included angle of two legs, and \(H_{2}\) is the vertical displacement between the hinged point of the leg and the COG.  
Hence, the entire equations to obtain the input-output relationship can be found in Appendix A [27], by substituting Equations (A2), (A4) and (A7) into Equation (8), the following deduction can be made:  
\[H(L_{e})=L_{0}\cos\!\left(w(\alpha_{1}(L_{e}))+\arcsin\!\left(\frac{l_{5}-l_{ 6}}{2l_{4}}\right)+\
icefrac{{\pi}}{{2}}-\beta_{4}-\
icefrac{{\pi}}{{2}} \right)+R+H_{2} \tag{9}\]  
Then, H is the function of independent variable (\(L_{e}\)):  
\[H=f(L_{e}) \tag{10}\]  
Therefore, the relationship between the input (linear actuator length) and the output (COG) of the active suspension is given by Equation (10). Each active suspension can actively adjust its COG.  
**4. Lateral Stability**  
Stability is important for locomotion systems; it is the essential prerequisite for the robot to travel on unstructured terrain. Posture control in an articulated ground vehicle is a possible way to enhance the locomotion performance [17]. The mobile robot proposed here can adjust its roll angle by using its active suspension.  
The purpose of this study is to propose a model that represents the relationship between the active input of a robot and its lateral stability. The kinematic model of the active suspension was established in Section 3. The relationship between the input, configuration, output, and COG of the active suspension is achieved. Therefore, we propose a mechanical model of a robot on a slope. A stability pyramid method is used to evaluate the robot's stability. We make several assumptions to simplify the problem [22]:  
1. The robot is equipped with four rigid wheels, driven independently.
2. The steering angle of each wheel is fixed at 0.
3. The robot travels along a flat side slope.
4. The traversing speed is low, and the motion of the robot remains in a steady state.
# Evaluation and Comparison of Spatial Clustering for Solar Irradiance Time Series
## 3 Characterization of Solar Irradiance Time Series
### Dynamic Parameters  
A fundamental task in these statistical analyses is to characterize the location and variability of a dataset [58]. In order to analyze and compare the dynamic behavior of irradiance time series, the use of the Hurst exponent (H) and forecastability coefficient (F) is proposed. H is related to fractal geometry at different scales (concept defined by [PERSON] [59]). The main idea states that fractals are objects that have a similar appearance when observed at different scales and have details that cannot be studied by Euclidean geometry. H was used in previous studies to characterize the variability of meteorological parameters [60; 61; 62]. The values of H for a time series with \(n\) components are in the range of 0 and 1 and are computed from the expected value (\(E[x]\)):  
\[E\left[\frac{R(n)}{S(n)}\right]= \propto n^{\text{H}}\text{ as }n\rightarrow\infty \tag{3}\]  
where \(R(n)\) is the range of the \(n\) cumulative deviations and \(S(n)\) is the sum of the first \(n\) standard deviations. According to the value of H, any time series can be classified into one of the three categories:  
* If H \(<\) 0.5, the series is anti-persistent. The closer the value is to 0, the stronger the mean-reversion process is. In practice, it means that a high value is followed by a low value, and vice versa.
* If H \(=\) 0.5, the series is totally random.
* If H \(>\) 0.5, the series is persistent. The closer the value is to 1, the stronger the trend.  
There are several methods to calculate H. We used rescaled range analysis, average wavelet coefficient, and periodogram regression detailed in References [63; 64]. The different techniques are close in terms of results, with a difference of less than 10%. Therefore, we did not know which one to use, so we decided to average the results obtained with the three methods.  
The \(F\) coefficient describes how a model trajectory diverges from a true system trajectory. It is bounded between 0% and 100% and provides insight into the extent to which solar radiation time series can be predicted. This parameter can be estimated from a Monte Carlo method to determine \(RMSE_{max}\) (the maximum error related to a site where the GHI randomly oscillates between 0 and \(GHI_{CS}\)) and the RMSE of the smart persistence predictor (\(RMSE_{p}\)) [65]. \(F\) can be calculated with Equation (1):  
\[F\approx 100\%\ \times\left(1-RMSE_{p}\ /\ RMSE_{max}\right) \tag{4}\]  
The results for all the stations are given in Appendix A Table A1. For more details concerning this metric, interested readers can refer to Reference [66].
## 4 Results
### Geodetic Data (GNSS and DInSAR) Analysis
#### 4.5.1 GNSS Analysis for the Period 2003-2020  
The up component of the GNSS dataset (2003-2020) clearly showed that the PACA permanent station was relatively stable, with little vertical deformation (Figure 9a) and a low rate of subsidence experienced (about 1 mm/yr), which can be at the limit of significance for the accepted accuracies for this methodology. For a more accurate determination of the vertical ground motion, a first order linear trend was estimated from the CGNSS data. The results clearly depicted a periodic trend (Figure 9a), which is a well-known feature of the continuous GNSS time series [73]. Indeed, long-period noise in GNSS position time-series data is ubiquitous and often the most limiting factor preventing proper detection of subtle ground deformation in the geodetic signals. The continental hydrology loading was proven to be the largest contributor to surface deformation at seasonal and even longer timescales [74]. We estimated the elastic ground displacement, due to the hydrological loading over the Sarno plain, using the MERRA2 (modern-era retrospective analysis for research and applications) [75] and GLDAS (global land data assimilation system) [76] hydrological models. Computation of the ground displacement at the selected sites used in this study was made available at the EOST loading service [77]. The output grid of these models has, for MERRA2, a spatial resolution of 0.5\({}^{\circ}\)\(\times\) 0.625\({}^{\circ}\) latitude by longitude and an hourly temporal resolution, while GLDAS has a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) and a three-hour temporal resolution. As the Sarno plain covers an area of 15 \(\times\) 8 km, a single grid cell of the MERRA2 or GLDAS models covers the entire area.  
The annual phase and the amplitude of 5-7 mm of elastic displacements, provided by both MERRA2 and GLDAS models, match the de-trended geodetic time series of the PACA CGNSS station. The first-order linear fit preliminarily applied to the CGNSS time series indicated a mean geodetic velocity of -1.28 \(\pm\) 0.03 mm/yr (Figure 9a). After periodic trend removal with the hydrology model, a more reliable mean subsidence rate was obtained (Figure 9b). Regardless, even after the removal of the periodic trend, GNSS data provided evidence of stability or very low subsidence (<1.5 mm/yr) for Palma Campania.
## 2 Problem Statement
The frequency \(\bar{\omega}_{j}=j\bar{\omega}\) is associated with the wavelength \(\lambda_{j}\) by the known ratio: \(\bar{\omega}_{j}=2\pi c/\lambda_{j}\), \(j=1,\,2,\,3\). Parameter \(\bar{k}_{1}\) is a dimensional wave-number of the LFW. In turn, \(\Delta_{21}\bar{k}\) and \(\Delta_{31}\bar{k}\) are dimensional phases mismatching between the IFW or the HFW and the LFW, respectively: \(\Delta_{j1}\bar{k}=\bar{k}_{j}-\bar{j}\bar{k}_{1}\), \(j=2,\,3\). Parameters \(\bar{k}_{2},\bar{k}_{3}\) are the wavenumbers at the frequencies \(\bar{\omega}_{2},\bar{\omega}_{3}\), respectively. Variables \(\bar{A}_{j}\) are the complex envelopes of the wave packets measured in physical units. \(A_{0}\) is a normalization value, which is chosen as the squared root of the LFW and HFW intensities sum at their pulse centers. In Table 1, we present the physical parameters at various frequencies in a dependence of the wavelength of LFW (4, 10, and 24 \(\,\)\(\,\)\(\,\)\(\,\)\(\,\)\(\,\)\(\,\)\(\,\)\(\,\)\(\,\)\(\,\)\(\,\)\(\,\)\(\,\,\)\(\,\)\(\,\)\(\,\,\)\(\,\)\(  
Below, we also neglect the group velocity mismatching: \(v_{21}=v_{31}=0\) to clarify the SOD influence on the frequency conversion efficiency. This imposes certain restrictions on the crystal length and the pulse durations, which should be large enough to avoid the running away from each other at the output face of the crystal. If the pulse duration is about 1 ps, then such a length can achieve a value about a few dozens of centimeters. If the pulse duration is about 100 fs, then the group velocity mismatching does not essentially influence the conversion efficiency, until ten centimeters of the optical pulse propagation distance. It only changes the pulse shapes by making them asymmetric.
# ORC Optimal Design through Clusterization for Waste Heat Recovery in Anaerobic Digestion Plants
## 2 Case Study  
### Reference Layout  
This work refers to an existing anaerobic digestion plant located near Pisa (central Italy), whose configuration is presented in Figure 1.  
The plant is based on two anaerobic digesesters with a total capacity of 4600 m\({}^{3}\). The plant is operated for the co-digestion of municipal sewage from the wastewater treatment plant and the organic waste from the municipality collection (referred to as sludge). The nominal plant capacity is 10.8 t/h of sludge. Digesters operate at a constant temperature of 37 \({}^{\circ}\)C (mesophilic digestion), producing biogas with a final methane concentration of 65%. The biogas is desulfated, stored in a gasometer, and then used to power a Capstone CR600 microturbine [29], designed to work with biogas, consisting of three modules of 200 KWel each, for a total of 600 KWel.  
The produced electrical power meets the internal plant need. The turbine exhaust gases are sent to a heat exchanger, where they transfer heat to an intermediate water circuit that heats the incoming sewage. The thermal energy in excess is dissipated by diverting part of the mass flow before the heat exchanger. Before entering the heat exchanger, a part of sludge inside the reactors is recirculated and mixed with incoming sewage to avoid overheating. The recirculation ratio, defined as the ratio between the mass flow rate of sludge recirculated and the mass flow rate of sludge entering the plant, was around 23:1. The high recirculation ratio allowed for the sludge temperature in the heating system to be kept near 37 \({}^{\circ}\)C without damaging the digestion substrate.  
The plant behavior was simulated in a previous study through a hybrid steady-state and dynamic model [11]. From the model, operating data on a fifteen-minute basis for a period of one year are available, comprehending, among the others:  
* Exhaust gases temperature.
* Exhaust gases composition, assumed to be constant over time (0.038% CO\({}_{2}\), 0.020% H\({}_{2}\)O, 0.190% O\({}_{2}\), and 0.752% N\({}_{2}\)).
* Thermal power required by incoming sewage to keep the optimal temperature in the reactors.  
Historical series of climate data for the definition of ambient conditions were obtained from [30]. Figure 2 reports the histograms of plant operating conditions. As it can be noted, there are some preferential operating conditions, especially concerning the heat flow rate required by the digester \(Q_{dig}\) and the mass flow rate of turbine exhaust gas \(\dot{m}_{f}\). On the other hand, the turbine exhaust temperature \(T_{f}\) resulted in having a more limited variation between its maximum and minimum values. \(\dot{Q}_{dig}\), \(\dot{m}_{f}\), and \(T_{f}\) strongly impact ORC operation. As these quantities significantly vary during the year, a design approach that allows the mitigation of ORC part-load is further justified.