esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application of AI in Food Integrity
### AI and ML in Food Quality Control and Regulatory Compliance
[PERSON] et al. [55] utilized near-infrared hyperspectral imaging (900-1700 nm) in combination with partial least squares (PLS) regression to predict the nutritional/chemical composition of mutton (lamb meat). The constructed models demonstrated strong predictive ability with coefficients of determination (R\({}^{2}\)) ranging from 0.63 to 0.88. In a different study, [PERSON] et al. [56] demonstrated the capability of KNN modeling of near-infrared hyperspectral images for the detection and classification of mechanically-induced damage in 'Manila' mangos at different ripeness stages. A classification accuracy of up to 97.9% was achievable on the third-day post-damage using the KNN model. [PERSON] et al. [57] detected fecal contamination in apple skins using hyperspectral reflectance imaging techniques and various image processing algorithms, followed by PCA dimensional reduction. [PERSON] and colleagues [58] utilized a hyperspectral imaging system combined with various algorithms such as classification and regression trees (CART) and linear discriminant analysis (LDA) for early detection of rottenness caused by _Penicillium digitatum_ in mandarins. [PERSON] et al. [59] described a novel computer vision system utilizing an AI model based on the Mask region-based convolutional neural network (Mask-RCNN) architecture to detect tomato fruits and estimate their size and weight. Their trained model showed strong efficiency at identifying and distinguishing multiple occurrences of tomato fruit in intricate environmental conditions and the estimated dimensions by the model exhibited a promising correlation with the real dimensions.
## 4 Results
### Experimental Swaging  
For all the three heating regimes, the workpieces were experimentally swaged immediately after the induction heating procedures. Figure 8a depicts the final billets swaged at 700 \({}^{\circ}\)C, 900 \({}^{\circ}\)C, and 1100 \({}^{\circ}\)C. The figure clearly shows that the temperature of 700 \({}^{\circ}\)C was not sufficient for the heating of the bulk of the WNiCo workpiece, as the billet exhibited brittle cracking during swaging (insufficient plasticity). On the other hand, the swaging temperature of 1100 \({}^{\circ}\)C provided sufficient plasticity during  
Figure 7: Heating time- and temperature-dependences of the axial and surface workpiece regions during: simulated heating to 700 \({}^{\circ}\)C (**a**); simulated heating to 900 \({}^{\circ}\)C (**c**); simulated heating to 1100 \({}^{\circ}\)C (**d**); experimental heating to 900 \({}^{\circ}\)C (**e**). Temperature distribution throughout billet heated to 900 \({}^{\circ}\)C (**b**).  
swaging, but resulted in the oxidation of the swaged piece's surface. Swaging at 900 \({}^{\circ}\)C was the most favorable, as the final swaged piece exhibited a smooth and oxidation-free surface (Figure 8a).  
In order to verify the (industrial) applicability of the most favorable induction heating procedure, five workpieces were subjected to heating to 900 \({}^{\circ}\)C and subsequent swaging--the final swaged billets can be seen in Figure 8b. The figure shows the high quality of the swaged surfaces free of possible cracks or voids, and also free of oxidic scales. Nevertheless, to document (i.e., quantitatively characterize) the high surface quality of the swaged billets, a swaged piece pre-heated to 900 \({}^{\circ}\)C was subjected to 4K digital optical microscopy investigations of the surface. Prior to processing, the average roughness of a sintered piece was determined by the OM to be Ra = 1.6. Figure 8c shows the surface scan and roughness profile of a 900 \({}^{\circ}\)C swaged billet. As can be seen in the scan, swaging at 900 \({}^{\circ}\)C substantially decreased the average roughness to Ra = 0.46.
# Development of Height Indicators using Omnidirectional Images and Global Appearance Descriptors
## 2 Omnidirectional Imaging and Global Appearance Descriptors
### Catadioptric Vision Sensors  
Catadioptric vision sensors consist of a conventional camera pointing towards a convex mirror with their axes aligned. In this work, a hyperbolic mirror will be considered and the axes of the mirror and the camera will be always parallel to the z-axis of the world reference system. The world information reflects onto the mirror and the camera captures this reflection, composing the omnidirectional image. Figure 1 shows the projection model of the catadioptric vision system, showing the World Reference System (WRS), the Camera Reference System (CRS), centered on the focal point of the hyperbolic mirror \(F^{\prime}\), and the Image Reference System (IRS). \(F\) is the focal point of the camera. The figure shows the projection of a world point \(P\) onto the mirror \(Q\) and from the mirror to the image plane \(m\). The image projected onto the image plane (plane of projection) is the omnidirectional view. The calibration of the catadioptric system provides us with a function \(f(a)\) that permits calculating, for each pixel in the image, the coordinates of the point of the mirror that has produced this pixel, respect the CRS [35]. \(a\) is the distance between the pixel considered and the center of the image, expressed in pixels. From the omnidirectional image, other projected versions of the visual information can be obtained, such as the cylindric projection (panoramic image), the orthographic projection (projection onto a plane) or the unit sphere projection [36]. Figure 2 shows (a) a sample omnidirectional image and three different projections obtained from it; (b) orthographic projection onto a plane parallel to the ground plane; (c) unit sphere projection and (d) cylindrical projection or panoramic view.
## 3 Results and Discussion
### Characterization of Cellulose Fibers
#### 3.1.3 Structural Analysis
However, it is worth noting that some studies have reported only two or three peaks [82; 83; 84; 85]. Utilizing the acquired data on hidden peaks, the cellulose crystallinity index was calculated. The results yielded values of 44.71%, 89.72%, and 82.41% for the untreated grass clippings, the alkali-treated sample, and the bleached sample, respectively. The observed increase in the crystallinity index for both the alkali-treated and bleached samples, relative to the untreated material, is hypothesized to be a consequence of the efficacious removal of amorphous components. This process ostensibly leads to an augmentation of \(\alpha\)-cellulose content within the material's composition.
# Optoelectronic Instrumentation and Measurement Strategies for Optical Chemical (Bio)Sensing
## 4 Design of Applications
### Fiber Optic Oxygen Sensor Based on Phosphorescence Lifetime Measurement
#### 4.2.3 Measurement System  
The proposed measurement scheme is shown in Figure 18[53]. To excite the chemical sensor requires a pulse of high-intensity light. We chose a UV LED with an emission peak close to that of the excitation of the chemical sensor.  
The phosphorescence emitted by the sensor is of low intensity. Specifically, the power of the light collected at the end of the fiber is less than 10 pW. Under these conditions, the best option is a photomultiplier tube (PMT), given its high sensitivity and good S/N ratio  
Figure 17: Spectrum of the Al-Ferron sensor in sol–gel support.  
Figure 18: Design of an optical oxygen sensor based on phosphorescence lifetime measurements.  
for low-light applications. It is possible to increase the sensitivity of the PMT by increasing the supply voltage, although other negative effects can occur, such as an increase in the dark current, which results in an offset voltage at the output. The output current of the PMT is converted into voltage in the first analog stage. Before making the measurement, it is necessary to introduce a certain delay to avoid the presence of residual luminescence. Once this delay is introduced, the curves in Figure 19a are obtained, which represent the attenuation of phosphorescence due to different concentrations of oxygen.  
To extract the lifetimes from these curves, we adjusted the curves first using simple exponentials and second using a double exponential. The latter setting is better but requires more processing. Once the lifetimes are known, the concentration of the analyte can be calculated using the well-known Stern-Volmer equation, which linearly relates lifetimes to oxygen concentrations:  
\[\frac{\tau_{0}}{\tau}=1+K_{SV}\left[O_{2}\right] \tag{19}\]  
where \(\tau_{0}\) and \(\tau\) are the lifetimes in the absence and presence of oxygen, respectively. \(K_{\text{SV}}\) is the Stern-Volmer constant, which is proportional to \(k_{d}\), the biomolecular quenching constant, and \(\tau_{0}\). Finally, Figure 19b shows the calibration curve according to the lifetime of the phosphorimeter using the Al-Ferron chemical sensor for low oxygen concentrations.
# Modelica-Based Energy Management of PEMFC Hybrid Power System of Vehicle
## 2 Mathematic Model of Hybrid Power System
### PEMFC Model  
The PEMFC system is a complex, multi-input, multi-output, and multi-time-scale nonlinear system [19]. It involves various dynamic processes, such as gas flow, electrochemical reactions, gas-liquid phase transitions of water vapor, and heat exchange between channels [20]. Generally, the regulation capability of the fuel cell stack itself is limited,  
Figure 1: Structure diagram of the proposed hybrid power system.  
particularly when there are significant changes in external environmental conditions. Therefore, the design and construction of corresponding auxiliary subsystems are fundamental to ensuring the stable operation of the PEMFC stack. As shown in Figure 2, hydrogen and oxygen flow through the anode and cathode, respectively, entering the PEMFC through the flow channels. After passing through the gas diffusion layer, they enter the catalyst layer where they gain and lose electrons. The electrons generated by the electrochemical reaction flow through the external circuit, forming an electric current. Simultaneously, the heat produced is absorbed by the exhaust gases and the fuel cell itself [21].  
The interconnections among subsystems introduce complex dynamic variations in critical physical parameters. For instance, the output voltage of the stack is influenced by factors such as stack temperature, relative humidity on the anode and cathode sides, gas pressure, and external current density [22]. Additionally, the internal humidity of the stack is closely tied to its operating temperature. To ensure the long-term stable operation of the PEMFC system, it is crucial to conduct in-depth research into the physical relationships among these subsystems [23]. Consequently, constructing a comprehensive dynamic model of the PEMFC system and analyzing its dynamic characteristics are fundamental tasks.
## 3 Principals of Routing Protocols and Their Breakdowns
### Geographical Information-Based Routing Protocols
#### 3.2.2 LDBR [46]  
The authors propose a lightweight depth-based routing protocol (LDBR) for UWSN. The LDBR protocol is a variant of the DBR protocol. The LDBR protocol, in contrast to the DBR protocol, provides a residual energy-based strategy to reduce energy usage. The packet will be sent toward the sink node if the node has a smaller depth and higher energy than the preceding node. The LDBR effectively transmits packets to the water surface while reducing energy usage to extend the network lifetime.
# Integration of Directional Antennas in an RSS Fingerprinting-Based Indoor Localization System
## 4 Deployment Cycle
### Planning Phase
#### 4.2.4 Simulation Tool  
Furthermore, related to the planning phase, the connectivity concept is an important figure of merit to take into account, as it is related to the visibility of the tag from the SRs. This is an important concept for the planning phase, since the localization engine needs RSS data to infer the tag location. Hence, the more SRs are connected to the tag, the more RSSs will be captured and, hence, the more accurate will be the location estimation.  
Connectivity is related to the probability of received packet (\(PRP\)), and statistically, it can be defined for any \(k\)-th SR as:  
\[PRP=\frac{n}{N} \tag{5}\]  
with \(n\) the number of received beacons measured by the \(k\)-th SR, over the total number of transmitted packets by the tag (\(N\)). Therefore, if an SR has not received any beacon from the tag (\(PRP=0\)), that SR will be considered as \"non-connected\" to that location, with a \(PRP=0\).  
This probability is a function of the RSS, as demonstrated in Figure 8, where the \(PRP\) calculated experimentally is shown. This graph shows the average percentage of received beacons when an RSS level is measured. As observed, a log-sigmoid function is able to approach the cloud of measurements. It should be noted that this probability of receiving a beacon is practically \(PRP=1\) when the measured RSS is greater than \(-80\) dBm. However, \(PRP\) falls down sharply till PRP = 0.5 for RSS = \(-86\) dBm and is almost null (\(PRP=0\)) when the RSS is below \(-90\) dBm. This is in line with our expectations, as below the sensitivity of the SR hardware (which is \(-90\) dBm), no measurements (or a very low number of them) should be obtained. Therefore, it can be derived that inorder to guarantee a _PRP_ > 90% in the whole scenario, the SRs must receive beacons with a minimum RSS of \(-\)83 dBm. These results reveal that the planning criteria can be laid down to meet a minimum _PRP_ to ensure that useful RSS features are provided to the localization server most of the time [27].
## 2 Materials and Methods
### EC-Based GPP Estimation  
EC flux data processing and gap filling procedure were carried out according to the ChinaFLUX method [37; 50]. Here we only give a brief description. Data processing included the despiking to exclude extreme abnormal observations [50], coordinate rotation to eliminate anomalies resulting from tilt of the anemometer [51; 52], air density corrections to correct density fluctuations induced by temperature and water vapor (WPL corrections) [53; 54; 55], outlier rejection to filter data uncertainty (1 NEE1 > 3 mg CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)) [56; 57], and friction velocity threshold (u*) corrections to correct the underestimate flux in weak turbulence [50; 58].  
Due to sensor failures, unsuitable weather conditions, and data processing above, EC flux observational data generate discontinuous gaps [59]. We need to fill them in order to contrast with MODIS data products. Linear interpolation was used to fill the small gaps (less than 2 h). For the gapsmore than 2 h, two nonlinear empirical models were separately applied for daytime and nighttime data [50]. The daytime (Rn \(>\) 10 W m\({}^{-2}\)) CO\({}_{2}\) fluxes were estimated using the Michaelis-Menten equation [59; 60].  
\[NEE=-\frac{a\times Amax\times PAR}{a\times PAR+Amax}+Re \tag{7}\]  
where NEE (\(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)) and Re (\(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)) are the day time net ecosystem exchange and ecosystem respiration, respectively. _Pmax_ (\(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)) is the maximum ecosystem photosynthesis rate, and \(\alpha\) (\(\mu\)mol CO\({}_{2}\)\(\mu\)mol photons\({}^{-1}\)) is the apparent quantum yield and the maximum light use efficiency, which are taken as indicators of plant photosynthetic capacity [57; 60].  
The nighttime missing NEE data (Re) were filled with the exponential relationship between Re and soil temperature at 5 cm due to GPP is assumed to be zero during the night [61; 62].  
\[Re=a\times e^{(b\times T5)} \tag{8}\]  
where Re (\(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)) is nighttime ecosystem respiration. Ts5 (\({}^{\circ}\)C) is the soil temperature at 5 cm. \(a\) (The reference respiration when Ts5 = 0 \({}^{\circ}\)C) and \(b\) are the regression parameters.  
The gap-filled daytime NEE were partitioned into EC-based GPP (GPP_EC) as CO\({}_{2}\) assimilation and Re as CO\({}_{2}\) emission [57; 63].  
\[GPP\_EC=Re-NEE \tag{9}\]
## 4 Energy Consumption of Different Transport Schemes
### Energy Consumption per Unit Coverage
#### 4.2.1 Rural Area  
Figure 10a,b show the power consumption per square km plotted as a function of base station transmission power (Figure 10a) and cell size (Figure 10b) for the five fiber-wireless transport schemes investigated [41]. The power consumption per square km for the five transport schemes investigated follows the same trend, where it first reduces and then increases with transmitting power (increasing cell size). There is an optimal transmitting power (cell size) associate with each transport scheme to achieve the lowest energy consumption. The optimal transmitting power for baseband-over-fiber transport scheme is \(\sim\)40 dBm; for digitized RF/IF-over-fiber is \(\sim\)33 dBm; and for analog RF/IF-over-fiber is \(\sim\)22 dBm. Both results show that baseband-over-fiber transport scheme consumes the most power with regards to every square km. The power consumption per square km for the digitized and analog transport crosses at a transmitting power of \(\sim\)34 dBm, which indicates that analog RF/IF-over-fiber transport scheme is more energy-efficient when the base station transmitting power is \(<\)34 dBm and digitized RF/IF-over-fiber is more efficient for transmitting power \(>\)34 dBm. This can be attributed to the additional power required for digital signal processing for digitized transport which is regardless of the base station transmitting power. For current macro base station used in rural areas, the transmitting power is at least 40 dBm [45] and this is indicated by the red line in Figure 10a,b. Hence for the cell  
Figure 9: **(a)** Hexagonal cell coverage and the corresponding length measurement **(b)** Calculated cell side length _versus_ transmitting power for rural and urban areas (With kind permission from Springer Science and Business Media).  
size used in the rural area at present, the digitized RF/IF-over-fiber transport schemes are the most energy-efficient to service the rural area.
## 3 Materials and Methods
### Data Processing  
Data processing consists of a set of techniques and steps applied to improve, organize, clean, and transform raw data to be analyzed. In addition, it helps to identify and correct errors, noise, and missing values in the data. Furthermore, data processing also addresses the integration and combination of data from different sources, allowing one to create a unified and coherent dataset.  
In this research, to carry out data processing, the rule 3-sigma (\(3-\sigma\)) was used to identify and remove outliers. This rule uses three times the standard deviation value from the mean (\(\mu\)) in a window of 25 samples, sliding this window through the series of measurements. The values \(\mu+(3\cdot\sigma)\) and \(\mu-(3\cdot\sigma)\) define the upper and lower limits of outliers detection [36].
# A Portable Device for the Measurement of Venous Pulse Wave Velocity
## 2 Materials and Methods
### Ultrasound Doppler Shift Signal  
The Doppler shift signal is generally provided by ultrasound machines as an audio signal output (e.g., from the headphone output). In the present study, a MyLab25 Gold (ESAOTE, Genova, Italy) equipped with a linear probe (LA523, ESAOTE, Genova, Italy) was employed. The probe was located transversally over the medial side of the arm, about 15 cm proximal to the elbow, with an angle of about 60 degrees to the basilic vein (BV). Using a pulsed-wave Doppler, it is possible to locate the sample volume precisely over the basilic vein. The Doppler shift is fed to the acquisition board (sampling frequency: 7.5 kHz). The acquisition of a 1 s epoch starts immediately after the trigger of the compressive stimulus. After completing the acquisition, the Doppler shift was processed in the time domain as follows. The root mean square envelope of the signal is extracted. The latter is smoothed through a moving average filter (sliding square window of 100 ms). The footprint was then identified as instant \(t_{1}\) at which the envelope reaches 5% of its baseline-to-peak amplitude. The latency (i.e., the pulse transit time) is then estimated as \(\Delta t=t_{1}-t_{0}\) (where \(t_{0}\) corresponds to the instant when the trigger is delivered, equal to zero in Figure 3), and the vPWV can lastly be calculated as \(\frac{\Delta x}{\Delta t}\).
## 2 Materials and Methods
### Polynomial Model for Weight Estimation  
In the current study, a polynomial model is proposed. This type of model fits the data to a non-linear model. The biggest problem is the adjustment of the coefficients and the proper determination of the grade. This research used a polynomial model based on the biometric variables of the sheep to estimate the weight. The expression proposed with these variables is shown in Equation (1), and the variables and their nomenclature are shown in Table 1.  
\[BWE=a_{1}HWK^{a_{8}}+a_{2}RHK^{a_{9}}+a_{3}BLK^{a_{10}}+a_{4}BDLK^{a_{11}}+a_{5 }BTLK^{a_{12}}+a_{6}GSCK^{a_{13}}+a_{7}ASCK^{a_{14}} \tag{1}\]  
The objective was to find the coefficients and exponents \(a_{n}\) that minimize the actual and estimated weights' error. The Root-Mean-Squared Error (RMSE) was used as a performance measure, defined by Equation (2). The solutions with a lower RMSE have a higher probability of hatching. A solution with an RMSE of 0 implies a prediction equal to the actual value. The RMSE magnifies errors of greater magnitude, but reduces those of lower magnitude. The errors expected in the estimates and experimental measurements are errors of low magnitude. Thus, the RMSE is an appropriate indicator for evaluating the algorithm's performance.  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(BWE-BW)^{2}}{n}} \tag{2}\]  
Metaheuristic search algorithms were used to estimate the 14 unknown coefficients. A significant advantage of using this type of algorithm is that it leaves the polynomial degree as an unknown. Given the complexity of the model, a logical option for flexibility and adaptability is metaheuristic algorithms. In this way, the metaheuristic algorithms analyze in each iteration the performance of the different degrees in each variable. For both algorithms, the same search limits were used. In the case of the coefficients, the search was limited between 0 and 5 and the coefficient values between 0 and 1.