esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems
## 3 Inform@Risk Monitoring System  
As stated before (Section 1.3), the geology of the project area is quite heterogeneous and complex, while at the same time a high landslide risk is present. With traditional measurement systems, monitoring this area is not possible, especially since it is an informal settlement where cost is one of the most important factors.  
Therefore, we developed a LEWS which bridges the gap between local and territorial systems. It offers comparably high spatial and temporal resolution while at the same time being able to monitor a whole neighbourhood where the exact failure location is unknown. On the other hand, it is capable of issuing reliable site specific early warnings without previously knowing where a landslide will develop. Additionally, the system should be low in cost and should be designed specifically for the use in urbanized low-income areas. Existing monitoring technology--including promising remote sensing techniques as, e.g., SB/GB D-InSAR, laser scanning and drone based photogrammetry--cannot achieve this, because they do not have the required temporal or spatial resolution, are not reliable enough during bad weather conditions, are to complex in operation or are too expensive.  
To meet these goals, the EWS requires a network of geosensors throughout the area, which in general are statistically distributed. The sensor density thereby is varied based on the landslide risk. This approach improves the benefit-cost ratio of the system, since in areas with low landslide risk, less sensors are distributed and therefore the density in high-risk areas can be increased. Hence, prior to planning the system, at least a qualitative risk assessment should be performed for a successful and effective system.  
In this chapter, the newly developed sensor systems are presented. First, we give an overview of the general layout of the EWS which comprises a combination of traditional landslide monitoring systems as well as new geosensors which use IoT technology. The overall system has been presented before in preliminary studies [63; 20; 64] but here we give a more detailed and in-depth description of the newly developed IoT devices (LoRa nodes).
# Infrared Imaging System with a Local Polarization Channel for Target Detection
## 2 Basic Theory  
For general passive polarization imaging, it is difficult to acquire high-resolution polarized images over a wide FOV. To address this issue, we developed an infrared imaging system with a local polarization channel to obtain infrared polarization information of the FOI without reducing the entire FOV. As shown in Figure 1, the system consists of three parts: the front imaging lens group; the middle micro-polarization group; and the back relay lens group. The micro-lens group, composed of micro-lens and micro-polarizer, was placed between the imaging lens and relay lens. In Figure 1, the entire target scene is shown in green, and the FOI is shown in red. The peripheral target scene is imaged at the image plane by the imaging lens and relay lens group, which comprise the peripheral imaging channel. The FOI is also imaged at the same image plane but by the imaging lens, micro-polarization group, and relay lens group, which comprise the local polarization channel. The micro-polarization group was placed near the intermediate image plane of the imaging lens group. The difference between the peripheral channel and the local polarization channel is whether the micro-polarization group is passed through by the light.  
In this design, the magnification of the local polarization channel, namely, the local magnification, is twice that of the peripheral imaging channel. The micro polarization group was rotated four times, and four images with the polarization characteristics of the FOI, namely, \(I_{0}\), \(I_{45}\), \(I_{90}\), and \(I_{135}\), were obtained. Based on these intensity measurements, three Stokes parameters were obtained [25; 26; 27], and the relationship was derived using the following equations:  
\[I=I_{0}+I_{90}/2 \tag{1}\]  
\[Q=I_{0}-I_{90} \tag{2}\]  
\[U=I_{45}-I_{135} \tag{3}\]  
where \(I\) is the total intensity of the light; \(Q\) is the difference between horizontal and vertical polarization, and \(U\) is the difference between linear \(45^{\circ}\) and \(135^{\circ}\) polarization. The angle  
Figure 1: Schematic of the infrared imaging system with a local polarization channel.  
of polarization (AOP), that is, the angle of the major axis of the polarization ellipse with respect to the x-axis, can be described as  
\[\text{AOP}=\frac{1}{2}\text{arctan}\frac{U}{Q} \tag{4}\]  
Thus, we can obtain the polarization information of the FOI. We can utilize a single optical system to obtain the infrared intensity image of the entire wide FOV and the local polarization image of the FOI in the same image plane.  
In addition, when the FOI changes or moves, we can shift the micro-polarization group over the x-y coordinate plane perpendicular to the optical axis to realize dynamic local polarization imaging.
# Influence of Atmospheric Pollutants and Weather Conditions on Agricultural Productivity in Italy
## 2 Materials and Methods
### Crops Data  
The cultivation analysis was conducted on various crops such as durum wheat, common wheat, and corn using data from the Italian National Statistical Institute (ISTAT) database [27]. As for the air quality, the analysis was conducted in Palermo, Bari, Ferrara, Padua, and Venice during the period 2012 to 2021.  
The database took into account the quantity of crops produced per hectare and then calculated the yield per hectare to determine the actual amount of production (Tables 4-7).
## 3 Results and Discussion
### Characterization of Fried Mackerel Fillets
#### 3.3.4 Principal Component Analysis of Atlantic Mackerel Fillet Samples  
Aiming to exhibit an overall and comparative perspective on the nutritional composition and fatty acid profile of pre-coated fried fish fillets, a principal component analysis (PCA) was carried out (Figure 6).  
The first principal component (PC-1), accounting for 66.9% of the variability among samples, is positively correlated with PUFA, C18:2, and AI and negatively correlated with C18:1, MUFA, and n3/n6 ratio. The second principal component (PC-2), explaining 31.4% of the variability, is positively correlated with SFA and AI, while it is negatively correlated with \(\rm h/H\), C18:2 and PUFA. Conversely, TI, fat, moisture, and ash are the parameters that contribute the least to the variability among samples. As shown in Figure 6, the fried filter samples are divided into three distinct groups: (i) group I, which corresponds to the control samples, shows the highest values of SFA and AI, and the lowest levels of C18:1, and MUFA (ii) group II, corresponding to the Alg Pc samples, is characterized by the highest values of \(\rm h/H\), C18:2, and PUFA, and (iii) group III, comprising the Car Pc, Alg and Car samples, which are more similar to each other. This group is distinguished by the highest levels of C18:1, MUFA, and the n3/n6 ratio, along with the lowest values of C18:2. It is worth mentioning that coated fillets stand out as a healthier food product, with improved nutritional characteristics. In fact, the application of coatings in fish samples (Groups II and III) promoted a decrease in SFA and an increase in MUFA, thus leading to better AI and \(\rm h/H\) health lipid indices. The effect of _P. canaliculata_ extract was more evident in alginate-based coating, particularly in C18:1 and C18:2 contents and, consequently, in the n3/n6 ratio.  
Figure 6: Principal component analysis (PCA) of pre-coated fried mackerel fillets concerning: (i) total saturated fatty acids (SFA); (ii) monounsaturated fatty acids (MUFA); (iii) polyunsaturated fatty acids (PUFA); (iv) oleic acid (C18:1); (v) linoleic acid (C18:2); (vi) n-3/n-6 ratio; (vii) Hypocholesterolemic/hypercholesterolicine (h/H); (viii) atherogenic (AI); (xi) thrombogenic (TI) indices; (x) moisture; (xi) fat and (xii) ash. Control: uncoated fried fillets; Alg: fillets coated with alginate solution; Alg Pc: fillets coated with alginate solution supplemented with _P. canaliculata_; Car: fillets coated with carrageenanenan solution; Alg Pc: fillets coated with carrageenanenan solution supplemented with _P. canaliculata_. PC-1 and PC-2 explain, respectively, 66.9% and 31.4% of data variability.
###### Abstract  
Obesity is characterized by excessive fat accumulation and it may affect reproductive health by altering the sperm parameters, hormonal profiles, and gonadal functions. _Echinacea purpurea_ is a medicinal herb and is well-known for its anti-inflammatory and antioxidant activities. The purpose of this study was to examine the beneficial effects of _E. purpura_ ethanol (EPE) extract on the high-fat diet (HFD)-induced reproductive damage. The obese condition in male Sprague-Dawley rats was induced by feeding with a high-fat diet (45%) and later three different doses of EPE (93, 186, 465 mg/kg per day) were tested for 5 weeks. The results showed that the level of antioxidant enzymes was increased, whereas lipid peroxidation, blood glucose level, nitric oxide production, IL-6 level, and TNF-\(\alpha\) level were decreased in the presence of EPE extract. In addition to this, the sperm counts, motility, morphology, DNA damage, and mitochondrial membrane potential were also improved. Additionally, the protein expression of Peroxisome Proliferator-Activated Receptor Alpha (PPAR-\(\alpha\)), and Phospho-Adenosine Monophosphate-Activated Protein Kinase Alpha \(\frac{1}{2}\) (AMPK\(\alpha\)1/2) phosphorylation in liver tissue and Steriodogenic Acute Regulatory Protein (StAR) and 17B-Hydroxysteroid Dehydrogenase 3 (17B-HSD3) expressions in testes were increased. In conclusion, the administration of EPE extracts beneficially regulated the inflammation and reproductive damage in obese rats.  
Echinacea purpura; obesity; high-fat diet; oxidative stress; male fertility Article
## 4 Results
### Evaluation against KMA ASOS Stations
Group 2 showed high and low accuracy for the CNN model and operational algorithm, respectively, which was believed to be caused by the surrounding environment, due to the regional characteristics that greatly affected the stations. However, group 1 showed low accuracy regardless of the model; the in situ SSI measurements from these stations showed low quality over the testing period from 1 August 2021 to 31 January 2022.
## 2 Numerical Methods
### Approximated Polynomials and WMLS  
The WMLS-based solver developed in this study approximates the distributions of the physical quantities as a quadratic polynomial at calculation point \(i\) from those of surrounding points \(j\) within the effective radius of calculation point \(r_{0}\). The square of the residual between the approximated and actual values is minimized to find the coefficients of the polynomials, where the coefficients are spatial derivatives of the physical quantities. The approximate value of the physical quantity at a neighboring point \(j\) is expressed as Equation (4) using a second-order Taylor series expansion, where \(q\) is the physical quantity of a point of interest, for example, calculation point \(i\) with coordinates \(x_{i}\) and \(y_{i}\). \[q_{j}^{*}=q_{i}+\frac{\partial q^{*}}{\partial x}\Delta x_{j}+\frac{\partial q ^{*}}{\partial y}\Delta y_{j}+\frac{1}{2}\frac{\partial^{2}q^{*}}{\partial x ^{2}}\Delta x_{j}^{2}+\frac{\partial^{2}q^{*}}{\partial x\partial y}\Delta x_ {j}\Delta y_{j}+\frac{1}{2}\frac{\partial^{2}q^{*}}{\partial y^{2}}\Delta y_{ j}^{2}, \tag{4}\]  
where \(\Delta x_{j}=x_{j}-x_{i},\text{ and }\Delta y_{j}=y_{j}-y_{i}\). Additionally, the coefficients \(c_{1}\)-\(c_{5}\) are defined as  
\[c_{1}=\frac{\partial q^{*}}{\partial x},\text{ }c_{2}=\frac{\partial q^{*}}{ \partial y},\text{ }c_{3}=\frac{1}{2}\frac{\partial^{2}q^{*}}{\partial x^{2}}, \text{ }c_{4}=\frac{\partial^{2}q^{*}}{\partial x\partial y},\text{ }c_{5}=\frac{1}{2}\frac{ \partial^{2}q^{*}}{\partial y^{2}}. \tag{5}\]  
The sum of the squares of the residual between the physical quantity assumed by this approximate polynomial expression and the actual physical quantity is obtained at all surrounding points as shown in Equation (6). Equation (7) is the minimization condition of \(J\). After substituting Equation (4) into Equation (6) and differentiating with respect to \(c_{1}\)-\(c_{5}\), Equation (8) is derived.
## 3 Results
### Effect of MLE on Plant Biomass
#### 3.2.2 Shoot Dry Weight  
Figure 2B shows the effect of MLE application on shoot dry weight of wheat facing Cd stress. Results revealed that cadmium treatments of 500 uM and 1000 uM (under light) significantly reduced the shoot dry weight by 78.5% and 207% as compared to the control, while on the other hand, application of 3% MLE along with combinations of 500 uM and 1000 uM cadmium solutions increased the shoot dry weight by 7.23% and 0.6%.  
More interestingly, 3% MLE with both cadmium treatments even resulted in increased shoot dry weight compared to MLE 3% alone treatment under both light conditions (shade > light).  
Figure 2: Effect of MLE application on fresh biomass (**A**) shoot fresh weight (**B**) shoot dry weight (**C**) root fresh weight (**D**) root dry weight, with and without Cd stress under light and subdue light. Different letters above bars represents significant differences (\(p<0.05\)) at each measured point. Values represent mean \(\pm\) SE of three replicates of each treatment.
## 1 Introduction
### Overview of DNA
#### 1.1.3 Complementary Rules  
DNA exists in a twisted ladder-like structure in humans, where the DNA bases A, G, C, and T are paired. These bases are adenine (A), guanine (G), cytosine (C), and thymine (T). An A in one strand will pair with a T in the other strand in two DNA helix strings, based on the complementary mapping rules in Table 2. Similarly, a G in one strand will pair with a C in the other [19]. This rule is known as Chargaff's base-pairing rule, which must be followed to keep both the strands chemically connected. This is why both the strands are always complementary to each other. According to the complementary rules, a single strand of DNA is converted to a double-stranded DNA molecule. The single strand of DNA is considered a parent strand, while the second strand is called a daughter strand.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & \\ \hline \multirow{11}{*}{**TCGU**} & & & UUU & Phe & UCU & Ser & UAU & Tyr & UGU & Cys & U \\  & & UUC & Phe & UCC & Ser & UAC & Tyr & UGC & Cys & C \\  & & UUA & Leu & UCA & Ser & UAA & Stop & UGA & Stop & A \\  & & UUG & Leu & UCG & Ser & UAG & Stop & UGG & Trp & G \\  & & CUU & Leu & CCU & Pro & CAU & His & CGU & Arg & U \\  & & CUC & Leu & CCC & Pro & CAC & His & CGC & Arg & C \\  & & CUA & Leu & CCA & Pro & CAA & Gln & CGA & Arg & A \\  & & CUG & Leu & CCG & Pro & CAG & Gln & CGG & Arg & G \\  & & AUU & Ile & ACU & Thr & AAU & Asn & AGU & Ser & U \\  & & AUC & Ile & ACC & Thr & AAC & Asn & AGC & Ser & C \\  & & AUA & Ile & ACA & Thr & AAA & Lys & AGA & Arg & A \\  & & AUG & Met & ACG & Thr & AAG & Lys & AGG & Arg & G \\  & & GUU & Val & GCU & Ala & GAU & Asp & GGU & Gly & U \\  & & GUC & Val & GCC & Ala & GAC & Asp & GGC & Gly & C \\  & & GUA & Val & GCA & Ala & GAA & Glu & GGA & Gly & A \\  & & GUG & Val & GCG & Ala & GAG & Glu & GAG & Gly & G \\ \hline \hline \end{tabular}
\end{table}
Table 1: Formation of mRNA codons and their relevant protein representations [9].
# Joint Radar-Communication Optimization of Distributed Airborne Radar for AOA Localization
## 3 JRCO Algorithm
### Sensing of Neighboring UAV States and Networking  
The function of this module was to sense the state of neighboring \(M_{i}\) UAVs based on the selected \(\omega_{i}^{t}\) when \(i\), \(j\), and \(t\) were all determined. The information sensed by the \(i\)-th UAV included the \(\hat{\mathbf{r}}_{i}^{t}\) and \(\hat{\mathbf{C}}_{\theta j}^{t}\) of \(M_{i}\) UAVs around it. To construct the objective function for the current \(i\)-th UAV, the \(i\)-th UAV also needed to estimate the target location \(\hat{\mathbf{u}}\) based on the surrounding \(M_{i}\) UAVs. Therefore, the \(i\)-th UAV also needed to measure the AOA with neighboring \(M_{i}\) UAVs, and the measurement model is given in Equation (8). The estimation method for the target location is provided in Section 3.4.
# Estimation of BER from Error Vector Magnitude for Optical Coherent Systems
## 5 Conclusions  
The estimation of BER from EVM analysis has been investigated for QPSK, 16-QAM, and 64-QAM with carrier phase recovery and differential decoding. The laser phase noise was compensated using the feedforward blind phase search algorithm which is suitable for real-time implementation. It has been demonstrated that the relationship between EVM and BER for data-aided reception can also be applied to nondata-aided reception with a correction factor for different values of \(\Delta\
u\cdot T_{s}\). The calibrated BER has also been shown to reliably monitor the performance near the target BER for the different modulation formats.  
This work was funded by the EMRP project IND51 MORSE and EMPIR project 14 IND13 PhotInd. The EMRP was supported by the EMRP participating countries within EURAMET and the European Union. The EMPIR programme has been co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation programme.  
**Conflicts of Interest:** The author declares no conflict of interest.  
Figure 4: Performance of estimated and calibrated BER from EVM analysis for: (**a**) quadrature phase shift keying (QPSK) (\(\Delta\
u\cdot T_{s}=10^{-4}\)); (**b**) 16-QAM (\(\Delta\
u\cdot T_{s}=10^{-4}\)); (**c**) 64-QAM (\(\Delta\
u\cdot T_{s}=10^{-5}\)). The actual BER is based on error counting.
# Investigation of Laser Polishing of Four Selective Laser Melting Alloy Samples
## 2 Materials and Methods
### Morphology Observation by 3D Optical Profiler  
Roughness measuring methods include laser reflectivity, contact stylus tracing, tactile profile measurement, focus variation, fringe projection technique and confocal laser scanning microscope [31; 32]. [PERSON] et al. presented a novel method based on convolutional neural networks (CNN) for making intelligent surface roughness identifications and achieved high-precision surface roughness estimation [33]. [PERSON] et al. introduced a SRAS system capable of detecting surface ultrasound waves on the rough-surface of an as-deposited SLM sample [34]. In this work, the surface morphologies of the samples before and after laser polishing were observed using a 3D optical profiler (RTEC Up Dual-Mode, Rtec Instruments, San Jose, CA, USA). It was used to scan the nine areas to get nine sets of data, which were then processed using the Gwyddion free and open-source software, which is a modular programme for scanning probe microscopy (SPM) data visualization and analysis. [PERSON] et al. expressed the line roughness of the samples in terms of its arithmetic mean height (\(R_{\text{a}}\)) [35]. \(S_{\text{a}}\) is the extension of \(R_{\text{a}}\) to a surface. It expresses, as an absolute value, the difference in  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Area Index** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline Power (W) & 100 & 100 & 200 & 200 & No & 300 & 300 & 400 & 400 \\ Feed rate (m/s) & 0.5 & 1.0 & 0.5 & 1.0 & No & 0.5 & 1.0 & 0.5 & 1.0 \\ \hline \end{tabular}
\end{table}
Table 2: Laser-polishing parameters for eight areas.  
Figure 3: Initial and polished surfaces: (**a**) Ti6 Al4V, (**b**) AlSi10 Mg, (**c**) 316L and (**d**) IN718.  
height of each point compared to the arithmetical mean of the surface. It is defined by ISO standard (ISO 25178-2:2012). The expressions of \(R_{\rm a}\) and \(S_{\rm a}\) are as follows:  
\[R_{\rm a}=\frac{1}{l}\int_{0}^{l}\Bigl{|}y(x)\Bigr{|}dx \tag{2}\]  
\[S_{\rm a}=\frac{1}{A}\iint\limits_{A}\bigl{|}Z(x,\ y)\bigr{|}dxdy \tag{3}\]  
where \(l\) is the length of the surface profile, \(y(x)\) is the deviation of the surface profile at a point \(x\) from the mean height over the profile, \(A\) implies that the integration is performed over the area of measurement and \(Z(x,\ y)\) is the function representing the height of the surface relative to the best fitting plane, cylinder or sphere.
## 3 Results and Discussion  
### Nutritional Profile  
The chemical composition (moisture, crude protein, oil, and ash) is presented in Table 1. The moisture content of freshly collected WOF was 62.08%, which was within the range of 62-65% reported by [PERSON] et al. [42], studying ninety-eight Kalamata olive fruit samples collected from different geographical regions in Greece. Regarding crude protein content, the present results (5.22% dw) were similar to those reported by [PERSON] et al. [22], studying eight Moroccan wild olive populations. Regarding to the oil content of WOF (21.44%), our results were found to be in the range of 12.25-31.81% for Moroccan WOF [22],but higher compared to those of Tunisian oleaster cultivars (15.04%) [43], or Moroccan wild olives (5.0-7.7%) [44]. With regard to the ash content of the studied WOF (2.61%), our findings were in accordance with those of [PERSON] et al. [45], but lower than those reported by [PERSON] et al. [43], measuring a mean value of 14.75% for two Tunisian oleaster samples. The variation in chemical composition of WOF samples is probably due to genetic and environmental factors and geographical variations [46]. Fatty acid composition is an important quality parameter and authenticity indicator of olive oils, which is closely related to stability, nutritional, and cosmetic properties [22]. The studied WOF oil showed a fatty acid composition (Table 1) within the limits established by the IOC 2019 [47], except for linolenic acid and lignoerc acid. The total monosaturated fatty acid content level of the studied oleaster oil was the major subclass of fatty acids, comprising 72.80% of the total fatty acids quantified, followed by saturated fatty acids (16.76%) and polyunsaturated fatty acids (10.81%). Oleic acid was the predominant monounsaturated fatty acid in the oleaster oil composition, constituting 71.55% of the total fatty acid content, with values within the range (64.7-76.15) found by [PERSON] et al. [7], comparing four Algerian oleaster oils. Linoleic acid was the predominant polyunsaturated fatty acid in the studied WOF oil (8.26%), followed by linolenic acid (1.38%), which exceeds the 1% limit established by the IOC, 2019 [47]. Similar to our results, [PERSON] et al. [7] and [PERSON] et al. [23] found greater levels of linolenic acid in oleaster oils. Palmitic acid was the main saturated fatty acid present in oleaster oil (12.79%), followed by stearic acid (2.56%) and palmitoleic acid (1.00%). It has been reported that oleaster oils contain higher levels of stearic acid than virgin olive oils, so it could be considered a marker of varietal characterization [23]. In addition, oil from WOF also showed lower levels of myristic, heptadecanoic, arachidonic, eicosenoic,heneicosanoic, behenic, and lignoerci acids. Similar fatty acid profiles were also reported