esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Modified Beer-Lambert Law  
The modified Beer-Lambert law is used to convert the acquired intrinsic reflection spectra into concentration changes. Optical functional brain maps are computed by assessing the oxy- and deoxy-genated hemoglobin concentration changes (\(\Delta C_{Hb\text{O}_{2}}\) and \(\Delta C_{Hb}\)) [9; 10; 43]. Using the near-infrared range, the oxidative state of cytochrome-c-oxidase concentration changes (\(\Delta C_{oxCCO}\)) can also be quantified [23]. The modified Beer-Lambert law can be expressed as a linear system [9]:  
\[\left[\begin{array}{c}\Delta A_{1}\\ \vdots\\ \Delta A_{N}\end{array}\right]=\left[\begin{array}{ccc}E_{1,Hb\text{O}_{2}} &E_{1,Hb}&E_{1,oxCCO}\\ \vdots&\vdots&\vdots\\ E_{N,Hb\text{O}_{2}}&E_{N,Hb}&E_{N,oxCCO}\end{array}\right]\times\left[ \begin{array}{c}\Delta C_{Hb\text{O}_{2}}\\ \Delta C_{Hb}\\ \Delta C_{oxCCO}\end{array}\right] \tag{6}\]  
with  
\[E_{k,n}=\int e_{n}(\lambda).D_{k}(\lambda).S(\lambda).L(\lambda).d\lambda. \tag{7}\]  
\(\Delta A_{k}\) is the absorbance changes measured by the spectral channel \(k\) of the camera (\(k\in[1;N]\) with \(N\in[3;25]\) for hyperspectral imaging and \(k\in[1;3]\) for RGB imaging):  
\[\Delta A_{k}=\log_{10}\left(\frac{R_{k}^{GM_{1}}}{R_{k}^{GM_{2}}}\right). \tag{8}\]  
\(R_{k}^{GM_{1}}\) is the intensity of the non-activated grey matter (see Section 2.4) acquired by the spectral channel \(k\) of the camera. \(R_{k}^{GM_{2}}\) is the intensity of the activated grey matter (see Section 2.4) acquired by the spectral channel \(k\) of the camera. The intensities were calculated with Equation (1) using the reflection spectra simulated by Monte Carlo simulations. \(\Delta C_{Hb}\) is the deoxygenated hemoglobin molar concentration changes (in mol\(\cdot\)L\({}^{-1}\)). \(\Delta C_{Hb\text{O}_{2}}\) is the oxygenated hemoglobin molar concentration changes (in mol\(\cdot\)L\({}^{-1}\)) and \(\Delta C_{oxCCO}\) the oxidative state of cytochrome-c-oxidase concentration changes (in mol\(\cdot\)L\({}^{-1}\)). \(\epsilon_{n}\) is the extinction coefficient of the chromophore \(n\) (in L\(\cdot\)mol\({}^{-1}\cdot\)cm\({}^{-1}\)). The spectral sensitivity of the channel \(k\) is represented by \(D_{k}(\lambda)\), and \(S(\lambda)\) is the normalized intensity spectrum of the light source. \(L(\lambda)\) is the wavelength dependent mean optical path length of the photons traveling in tissue estimated by Monte Carlo simulations (in cm); see Section 2.4. The concentration changes were obtained by matrix inversion once the matrix \(E\) was calculated.
## 2 Materials and Methods
### Antenna Design
However, circular and elliptical polarization can be achieved using different feed arrangements or slightly modifications made to the patch antennas. A circularly polarized antenna can mitigate the polarization mismatch loss, as discussed above in Section 1.2. Therefore, it is vital to design antenna with circular polarization characteristics for underground mine communication. Multiple studies [69; 70; 71; 72] have described several ways for achieving circular polarization for patch antennas, including feeding at two places, creating a slot in the patch, and reducing the opposite sides of a square patch. One of the simplest techniques for achieving circular polarization and a wide bandwidth of the antenna is to trim the opposite sides of the square patch. As a result, the above-mentioned rectangular patch antenna was reshaped into a square by equal its length and width. For optimal operation at 5.5 GHz, a parametric sweep was performed on the patch width (W1). Similarly, the rectangular patch's two opposite corners were trimmed and adjusted to obtain a center frequency of 5.5 GHz. Figure 7 illustrates the proposed square patch antenna concept with trimmed edges. Array antennas have various advantages over single-element antennas, including higher gain, increased directivity, and the ability to scan the antenna system's beam. To obtain the antenna's beam steering characteristics, an array of numerous elements must be built. As a result, a four-element linear array was designed to use for BM network for beam steering characteristics, as shown in Figure 8. The distance between elements has a significant impact on the performance of the antenna array. [PERSON] et al. [73] and [PERSON] et al. [74] investigated the influence of spacing on large antenna arrays. It was discovered that 0.5\(\lambda\) distance produces good directivity and reduces the side lobes of the radiation. As a result, the antenna array was constructed with 0.5\(\lambda\) separation between each unit. The entire length of the array (A_L) was 196.76 mm, with a gap (A_G) of 27.26 mm between the two elements. The copper ground plane completely covered the array's backside. Figure 6: Schematic representation of the single rectangular patch antenna with (**a**) front and (**b**) back view. After setup, the final designed antenna with 5.5 GHz frequency was analyzed and optimized to achieve maximum coverage and enhanced connectivity with respect to the underground mining environment.
## 3 Results and Discussion  
### Nutritional Characterization of N. oceanica, Standard and Supplemented Diets  
The nutritional composition of microalgae and diets comprised the evaluation of total protein, lipids, and ash, as well as the fatty acid profile (Table 1). _N. oceanica_ showed high protein (44.0 \(\pm\) 0.7%) and lipids (20.9 \(\pm\) 2.6%) content, being palmitoic (C16:1 n-7), palmit (16:0), and eicosapentaenoic (EPA, C20:5 n-3) the major fatty acids.  
The amount of EPA (21.40 \(\pm\) 0.64 ug/mg) attained for _N. oceanica_ biomass is in agreement with the value reported by [36]. Although the nutritional profile of _N. oceanica_ depends on the strain and growth conditions [37], these results are in line with other studies attesting that this microcalga is a rich source of lipids, particularly of EPA [38; 39; 40].  
Concerning feed composition, no statistically significant differences (\(p>\) 0.05) in the amounts of protein (from 15% to 17%), lipids (around 4%) and ash (around 11%) were observed for both diets. However, a distinct fatty acid profile was observed, noticing the presence of palmitoic (1.67 \(\pm\) 0.32) and EPA (0.95 \(\pm\) 0.21 ug/mg) fatty acids only in the supplemented feed. Linoleic (C18:2 n-6) and oleic (C18:1 n-9) were the major fatty acids, regardless of diet, with higher values in the standard (29.2 \(\pm\) 1.1 and 11.99 \(\pm\) 0.65 ug/mg, respectively). The amounts of palmitic acid and alfa-linolenic (C18:3 n-3) were similar in both feeds, whereas stearic (C18:0) acid was more abundant in the standard. In a broadly sense, the supplemented diet provides more 89 mg/100 g of n-3 PUFA in comparison with the standard.
# Stable Analysis of Compressive Principal Component Pursuit
## 4 Proof of Theorem 1
### Estimate the third term of (5)  
Let \(\Lambda\) be a dual certificate obeying Lemma 1. Then, using triangle inequality, we have  
\[\|x_{0}+h\|_{2}\geq\|x_{0}+h^{\gamma^{\perp}}\|_{2}-\|h^{\gamma}\|_{2}\,. \tag{9}\]  
Combining with Lemma 3, we can obtain  
\[\|x_{0}+h^{\gamma^{\perp}}\|_{2} \geq \|x_{0}\|_{d}+\sum_{i=1}^{\tau}(\lambda_{i}-C_{\alpha}-\lambda_{ i}\beta)\|\mathcal{P}_{T_{i}^{\perp}}H_{i}\|_{(i)}\] \[\geq \|x_{0}\|_{d}+(1-C_{\alpha}\frac{1}{\min_{i}\lambda_{i}}-\beta) \sum_{i=1}^{\tau}\lambda_{i}\|\mathcal{P}_{T_{i}^{\perp}}H_{i}^{\Gamma^{\perp }}\|_{(i)}\] \[\geq \|x_{0}+h\|_{d}+(1-C_{\alpha}\frac{1}{\min_{i}\lambda_{i}}-\beta) \sum_{i=1}^{\tau}\lambda_{i}\|\mathcal{P}_{T_{i}^{\perp}}H_{i}^{\Gamma^{\perp }}\|_{(i)}\,,\]wherein, to get the third inequality, we used the fact \(\|\dot{x}\|_{d}\leq\|x_{0}\|_{d}\). For simplification, let  
\[C_{1}(\alpha,\beta)\triangleq\left(1-C_{\alpha}\frac{1}{\min_{i} \lambda_{i}}-\beta\right)>0\,.\]  
Therefore, we have  
\[\|x_{0}+h^{\gamma^{\perp}}\|_{2} \geq \|x_{0}+h\|_{d}+C_{1}(\alpha,\beta)\sum_{i=1}^{\tau}\lambda_{i}\| \mathcal{P}_{T_{i}^{\perp}}H_{i}^{\Gamma^{\perp}}\|_{(i)}\,.\]  
Combining with (7), we can obtain  
\[C_{1}(\alpha,\beta)\sum_{i=1}^{\tau}\lambda_{i}\|\mathcal{P}_{T_{ i}^{\perp}}H_{i}^{\Gamma^{\perp}}\|_{(i)}\leq\|h^{\gamma}\|_{2}\,.\]  
Then  
\[\sum_{i=1}^{\tau}\lambda_{i}\|\mathcal{P}_{T_{i}^{\perp}}H_{i}^{ \Gamma^{\perp}}\|_{(i)}\leq C_{2}(\alpha,\beta)\|h^{\gamma}\|_{2}\,, \tag{10}\]  
where \(C_{2}(\alpha,\beta)=1/C_{1}(\alpha,\beta)\). We will estimate the third term of (5). Using triangle inequality, we have  
\[\|\mathcal{P}_{T_{1}^{\perp}}\times\cdots\times\mathcal{P}_{T_{ \tau}^{\perp}}h^{\Gamma^{\perp}}\|_{2} \leq \sum_{i}\|\mathcal{P}_{T_{i}^{\perp}}H_{i}^{\Gamma^{\perp}}\|_{F}\] \[\leq \frac{1}{c(n,\tau)}\sum_{i=1}^{\tau}\lambda_{i}\|\mathcal{P}_{T_{ i}^{\perp}}H_{i}^{\Gamma^{\perp}}\|_{(i)}\] \[\leq \frac{C_{2}(\alpha,\beta)}{c(n,\tau)}\|h^{\gamma}\|_{2}\] \[\leq C(n,\tau,\alpha,\beta)\delta\,,\]  
where \(C(n,\tau,\alpha,\beta):=\frac{2C_{2}(\alpha,\beta)}{c(n,\tau)\sqrt{\tau}}\). The second inequality is set up by (6); the fourth inequality is obtained by (8); the last one is obtained by the fact \(\|h^{\gamma}\|_{2}\leq\frac{2\delta}{\sqrt{\tau}}\). Therefore, we can obtain  
\[\|\mathcal{P}_{T_{1}^{\perp}}\times\cdots\times\mathcal{P}_{T_{ \tau}^{\perp}}(h^{\Gamma^{\perp}})\|_{2}^{2}\leq C^{2}(n,\tau,\alpha,\beta) \delta^{2}\,, \tag{11}\]  
which implies that the third term of (5) can bound by \(C\delta\).
# Light-Controlled Microbots in Biomedical Application: A Review
## 4 Aspects of Design
### Possible Workspace  
One of the most crucial aspects of light-activated microbots is where they may be used effectively. For example, the current technology of microbot manipulation using light is not sufficient enough to penetrate nontransparent media, which limits its workspace in deep locations such as inside a living body. One way to overcome the problem is to use larger wavelengths (e.g., NIR lights). The study showed that light with short wavelengths within the visible range has a penetration depth of 0.5-2.5 mm. In this case, the reason for less penetration is both absorption and scattering of the incident light. Depending on the wavelength of the visible light, 15-40% incident light is reflected. With increasing wavelength, the penetration becomes deeper and with 600-1600 nm wavelength range, absorption of light reduces and scattering overpowers the absorption. Using light from this wavelength range can result in a penetration depth of 8-10 mm [189]. Moreover, with increasing wavelength, the penetration of light increases to a certain point and then drops. The maximum penetration of tissue occurs in the wavelength region of 900-1100 nm. This  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Fabrication Technique** & **Applications** & **Resolution** & **Limitations** & **References** \\ \hline \multirow{3}{*}{Fused Deposition Modelling} & Transport, sensing, self-propelling vehicles & 100 \(\mu\)m & Limited dimensional accuracy Slower printing speeds & [181; 156; 182] \\ \multirow{3}{*}{SLS} & Scaffold base tissue engineering, drug delivery vehicles & 45 \(\mu\)m & Limited SLS materials, poor mechanical properties, low surface quality & [183; 184; 185] \\ \multirow{3}{*}{SLA} & Actuators, lab on a chip, microset tissue models, microbots & 3 \(\mu\)m & Expensive, printing larger prints is difficult & [174; 175; 176] \\ \multirow{3}{*}{DLW (or 2 PP/MPP)} & Microbots, microfluidic channels, drug delivery vehicles, scaffolds & 70–100 nm & Varying mechanical strength, material specificity & [186; 187; 12; 157; 188] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between different fabrication techniquesis one of the reasons why most optical tweezers operate at a wavelength around 1000 nm for biomedical applications, and also it reduces the chance of photodamage while ensuring stable trapping of the microparticles. Penetration also depends on the diameter of the light beam. A wide light beam results in greater penetration into the tissue [190]. However, penetration of this magnitude is not enough to perform in vivo operation of microbots inside the human body. Another study claimed that they were able to remove a blockage in the subdermal capillaries of living mice, which is an in vivo operation [191]. Although the possible workspace for light-actuated microbots is still limited, with the development of the technology, it is possible to perform the operation from in vitro to in vivo.
## 5 Data Collection  
The data collection procedure included a literature review and the identification of potential critical success factors. Eighteen non-financial factors were shortlisted. Then, the questionnaire designed to assess the impact and implementation of these factors in the construction industry was prepared. A total of 300 questionnaires were sent out to organizations, out of which 130 responses were received--that is a response rate of 43.3%. Approximately 40 responses were incomplete or had missing information about the company they worked for, which made it impossible to link the financial information. Hence, incomplete responses were excluded. Ninety percent of responses were used for this study, as presented in Figures 5 and 6. Out of the 90 responses, 72 responses were used for training and modeling purposes, and 18 responses were kept aside for validation purposes. The following results were obtained from the responses to the survey questionnaire. The total number of variables, including qualitative and quantitative, added up to 32 variables. Since the number of variables was very high, it was imperative to rank and determine the significant factors. To rank the factors, Regression Best Subset Analysis was carried out using Minitab (c) 17 and Analytic Hierarchy Process (AHP), followed by Hierarchical Fuzzy Expert System modeling using Fuzzy Logic Toolbox Matlab 2015. The model was tested and validated mathematically by Average Validity Percentage (AVP) and Average Invalidity Percentage (AIP).  
Figure 4: Financial critical success factors.  
\[Entropy=\sum_{i=1}^{n}\ln\frac{1}{p_{i}} \tag{1}\]  
where \(p_{i}\): Revenue share of the \(i\)th segment in total firm revenue, \(n\): Number of Market segments.
# Sediment Dynamics of the Neretva Channel (Croatia Coast) Inferred by Chemical and Physical Proxies
## 2 Study Area  
The NC (Figure 1) is a narrow, semi-enclosed basin located along the southernmost part of the Croatian coast. Oriented SE-NW, it is bounded to the NE by the Croatian coastline, to the NW by the open Adriatic Sea through the Korcula Channel, and to the SW by the Peljesac Peninsula. The NC is a microtidal, low-wave energy environment with river-dominated sedimentation processes [15; 17]. The river has an average annual water inflow of 332 m\({}^{3}\)/s with peaks in December and April, and with a long-term seasonal variability in terms of minimum and maximum monthly discharge from low to moderate, respectively [20]. The NR mouth, located \(\approx\)20 km to the east of the town of Metkovic, is characterized by a reclaimed alluvial plain and forms the largest depositional system in the southern Croatian coast, covering an area of \(\approx\)246 km\({}^{2}\).  
Figure 1: Study area and sampling locations. Shaded relief image derived from bathymetric data with sun illumination from NW, 45\({}^{\circ}\) over the horizon and no vertical exaggeration (grid resolution 10 m). Elevation and bathymetry data from EMODnet gridded data (www.emodnet.eu/bathymetry). Filled red and pink circles indicate box corer and grab samples locations, respectively. Bathymetry contour interval is 10 m. Red box in the inset indicates the study area.  
The NR, despite high annual precipitation in the catchment area, has a low stream density because part of its water is collected into karstic aquifers [20]. Furthermore, five hydropower plants in the Neretva catchment impound a total area of 36 km\({}^{2}\), storing a 1.1 km\({}^{3}\) water reserve [21]. As a consequence, the present-day delta is not associated with the large volumes of deposits that other river systems along the western Adriatic coast are. Finally, water regime in the lower course of the NR is complicated by the interaction with seawater ingression. For instance, deepening of the NR riverbed may cause higher salinity of both water and soil due to a drop in the ground water level [17; 22]. Salt water constantly penetrates into underground waterways, leading to the salinity of the soil, particularly during the dry season when the river flow is reduced [23]. Hence, the natural environment of the lower river course may be strongly threatened in terms of chemical, physical and biological changes [17], especially where human activities cause pressure, such as road construction sites, urbanization, hunting and mining [22; 24; 25]. Moreover, the NR is moderately affected by untreated municipal and industrial (metallurgy and other lighter activities) wastewaters.
## 1 Introduction
In the classification of audio data, as CNN cannot process sound directly [30], the sound of digital signals is often converted into spectrogram images [31] by a Short-time Fourier transform (STFT) or a wavelet transform. In particular, the STFT is a low-complexity time-frequency method capable of analyzing non-stationary signals which has a low computational burden [32]. However, the dimension of the spectrogram after STFT is relatively high, resulting in a large amount of subsequent CNN calculation, which increases the complexity of CNN learning. Furthermore, a nonlinear transformation can be applied to the frequency axis after the STFT process, to obtain a Mel spectrogram with lower dimensions, by compressing the frequency range [33]. This makes it easier for the CNN to extract and process specific features. This paper proposes a non-destructive detection method for pipeline ponding by referring to a pipeline structure characteristic detection method which combines the percussive detection method and a CNN. During detection, a constant energy spring impact hammer is first used to impact the pipeline under different ponding volumes to generate sound, and the collected acoustic signals are converted into the Mel spectrogram. Then, the CNN is used to perform a two-dimensional convolution operation on the Mel spectrogram and the convolution kernel matrix, and realize the identification of pipelines with different ponding volumes according to the output matrix. The rest of this paper is organized as follows: Section 2 introduces the principle of percussion-based pipeline ponding detection using CNN and network model evaluation metrics; Section 3 introduces the experimental equipment and experimental procedures; Section 4 presents the experimental results and comparative analysis with other recognition models; Section 5 summarizes the advantages and disadvantages of the method proposed in this paper.
## 2 Materials and Methods
### Operating Conditions  
All anode compartments were inoculated with activated return sludge (6000 mg-VSS/L) obtained from a municipal wastewater treatment plant (WWTP) (Busan, Korea). The sludge was circulated for 24 h. Subsequently, the three MFCs using synthetic WW were operated individually (phase I) for 40 days. And then the three MFC were connected hydraulically from S-MFC to L-MFC in series for 160 days; phase II for forward flow (ascending connection) and phase III for reverse flow (descending connection) (Figure 2). Power generation was also tested in phases II and III according to the electrode connection method (in series and parallel). All MFCs were operated at the same flow rate (38.4 mL/h) and room temperature (\(24\pm 7.5\)\({}^{\circ}\)C). A synthetic WW contains glucose, 0.5 g/L (500 mg-COD/L); K\({}_{2}\)HPO\({}_{4}\), 0.035 g/L; NaHCO\({}_{3}\), 0.6 g/L; NH\({}_{4}\)Cl, 0.5 g/L; NaCl, 0.04 g/L; MgSO\({}_{4}\)7H\({}_{2}\)O, 0.01 g/L; KCl, 0.02 g/L; CaCl\({}_{2}\)2H\({}_{2}\)O, 0.001 g/L and yeast extract, 0.005 g/L.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**MFC**} & **Total Anode Size 1** & **Inter-SEA Distance 2** & **Volume** & **AVR 3** \\  & **(cm2)** & **(cm)** & **(mL)** & **(m2/m3)** \\ \hline S-MFC & 96 & 0.4 & 19.2 & 500 \\ M-MFC & 96 & 0.8 & 38.4 & 250 \\ L-MFC & 96 & 1.6 & 76.8 & 125 \\ \hline \hline \end{tabular}  
* The sum of each anode size in two SEAs. 2 The thickness of anode chamber. 3 The ratio of total anode area for volume (AVR = [total anode size (m2)/reactor volume (m3)].  
\end{table}
Table 1: Configuration of small (S-), medium (M-), and large (L-) microbial fuel cells (MFCs) used in a stack system.  
Figure 1: The schematic diagram of microbial fuel cell in this study.
# Seasonal Variations of Carbon Dioxide, Water Vapor and Energy Fluxes in Tropical Indian Mangroves
## 3 Flux Calculations and Corrections
### Secondary Data Processing
#### 3.2.4 Friction Velocity Correction  
Though the eddy covariance method is perhaps the best and most direct method for measuring mass and energy exchanges for terrestrial ecosystems, during the night it becomes complicated and uncertain because of the low turbulence and relatively stable air stratification. The standard method to process nighttime CO\({}_{2}\) flux data is to primarily assess the quality of the nighttime flux data using the friction velocity threshold, then to establish statistical models using reliable nighttime CO\({}_{2}\) flux data with meteorological data. Finally, we correct the incorrect data using the established model [35].  
Since low friction velocity (u*) corresponds to calm nights, the u* threshold is used to identify the low turbulent flux data and the reliable flux data as described by [PERSON] _et al._[36]. This method is the statistically based, site-independent determination of the lower u* threshold. The CO\({}_{2}\) flux data is divided into six temperature classes with equal sample size. Each temperature class is subdivided into 20 u* classes. The u* threshold for each temperature class was defined as the u* class where the nighttime flux reached more than 95% of the average flux within the higher u* classes. In order to account for seasonal variation in vegetation, the final u* threshold was calculated in three-month subsets as the median of the six temperature classes. The obtained u* thresholds for three-month subset data were 0.1, 0.13, 0.12 and 0.08, respectively. If the u* threshold was unable to be determined, a default value of 0.2 m\(\cdot\) s\({}^{-1}\) was used. The low turbulent flux data were subsequently removed, which amounts to 12% of the total data.
## 2 Materials and Methods
### Demographic, Medical, and Laboratory Data  
Demographic and medical information, including age; gender; body weight and height for body mass index; and personal history including smoking, alcohol and lifestyle, medication history, operation history, especially on the gastrointestinal system and comorbid conditions, were obtained from the study patients' medical records and interviews by questionnaire. Biochemical measurements using standardized methods were performed in the central laboratory of Kaohsiung Medical University Hospital. Liver function profile, including serum aspartate aminotransferase (GOT) and alanine aminotransferase (GPT); thyroid function, including Thyroid Stimulating Hormone (TSH), free T4, T3, thyroglobulin, thyroglobulin and microsomal antibody, parathyroid hormone (PTH), and total calcium; kidney function, including serum creatinine, and urine total protein and creatinine; and sex hormones, including follicular stimulating hormone (FSH), estradiol and testosterone, cortisol, and IGF-1 were measured. Each patient's blood samples were obtained for laboratory tests, including albumin level for GNRI calculation, which is an original nutritional risk index for older subjects, with a formula of GNRI = (14.89 \(\times\) albumin (g/dL)) + (41.7 \(\times\) (body weight/ideal body weight)). General laboratory and specific osteoporosis evaluation markers, including bone resorption marker Carboxy-Terminal Collagen Crosslinks (CTX), Bone Alkaline Phosphatase (BALP), and vitamin D were measured.