esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Anomaly Detection in Cold Chain Transportation Using IoT Technology
###### Abstract
Although real-time monitoring technologies on the cold chain will become more common in the coming years, several questions are raised by the imminent introduction of these new data monitoring sources, such as the placement of the sensors to take into account the entire load in the refrigeration equipment, the criteria for alert definition, and the methods for identifying and describing temperature abnormalities [21]. Regarding placement, numerous studies indicate that the temperature inside the refrigerated equipment is heterogeneous [11], and the current temperature control practice is complex because temperature measurements taken at one location may not represent what happens in the entire load. Additionally, only the ambient temperature is often monitored, while the product temperature may fluctuate, particularly in dynamic systems such as the cold chain [21]. The criteria for determining the alerts must also be well specified; a too lenient definition may result in undetected alerts and lead to food waste [21]. Conversely, too stringent criteria may result in false alerts and lead to food waste and high logistic costs [21]. Therefore, to create an effective alert system that reduces food waste, the total number of undetected and false alerts must be minimised [48]. Regarding the methods, to implement an efficient approach to detect temperature anomalies, a robust solution regarding the methodology used to determine the product temperature and the temperature distribution must be developed [49]. To our knowledge, there is still a lack of knowledge of potential anomalies, their characteristics such as occurrence, break level (gap between recommended and actual temperature) and their duration, especially for temperature anomalies caused by products remaining at low temperatures during deliveries. Hence, better characterising the temperature profile throughout the cold food chain is required to estimate and avoid food quality deterioration accurately. In this context, this study proposes a real-time monitoring system that uses Internet of Things technologies to gather data on the environmental conditions (temperature and humidity) of perishable food during transportation. The real-time monitoring system comprises temperature/humidity sensors, a cellular logger (which both acts as a sensor recording device and a gateway), a Big Data server, a web dashboard for monitoring, and an intelligent anomaly alerting system. Accelerometer data and GPS coordinates are used to indicate trip detection, which is utilised for both smart alerting and to increase the battery lifespan. The proposed system was applied in delivery vehicles for a Northern Irish food wholesaler. Integrating these technologies should ensure the quality and safety of the food products under analysis throughout the cold supply chain.
# The DLR FireBIRD Small Satellite Mission: Evaluation of Infrared Data for Wildfire Assessment
###### Abstract  
Wildfires significantly influence ecosystem patterns and processes on a global scale. In many cases, they pose a threat to human lives and property. Through greenhouse gas emissions, wildfires also directly contribute to climate change. The monitoring of such events and the analysis of acquired data is crucial for understanding wildfire and ecosystem interactions. The FireBIRD small satellite mission, operated by the German Aerospace Center (DLR), was specifically designed for the detection of wildfires. It features a higher spatial resolution than available with other Earth-observation systems. In addition to the detection of active fire locations, the system also allows the derivation of fire intensity by means of the Fire Radiative Power (FRP). This indicator can be used as a basis to derive the amount of emitted pollutant, which makes it valuable for climate studies. With the FireBIRD mission facing its end of life in 2021, this study retrospectively evaluates the performance of the system through an inter-comparison with data from two satellite missions of the National Aeronautics and Space Administration (NASA) and discusses the potential of such a system. The comparison is performed regarding both geometrical and radiometric aspects, the latter focusing on the FRP. This study uses and compares two different methods to derive the FRP from FireBIRD data. The data are analyzed regarding six major fire incidents in different regions of the world. The FireBIRD results are in accordance with the reference data, showing a geometrical overlapping rate of 83% and 84% regarding MODIS (Moderate-resolution Imaging Spectroradiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite) overdpasses in close temporal proximity. Furthermore, the results show a positive bias in FRP of about 11% compared to MODIS.  
Keywords:wildfire assessment; FireBIRD; TET-1; BIROS; active fire; MODIS; VIIRS; fire radiative power +
Footnote †: journal: remote sensing  
_Article_  
The DLR FireBIRD Small Satellite Mission: Evaluation of Infrared Data for Wildfire Assessment
## 3 Results and Discussion
### Microstructure  
Sample S5 with 2.5 wt% nano-WC shows the optimum mechanical properties, so S5 and reference sample S0 are chose to be investigated. Figure 2 shows the fracture morphologies of matrix samples S0 and S5. In Figure 2a,b, the 0.5-6 \(\upmu\)m matrix grains can be differentiated according to their shape: polygon-shaped grains consisting of micron-WC particles [7; 8; 9; 29; 30; 31] and smooth round grains around, which are bonding phases bronze and Ni. As marked in Figure 2a, micro-WC particles are discerned from bronze phase grains by means of shape. The particle size of micro-WC in Figure 2 is from 0.5 to 6 \(\upmu\)m that is not consistent with initial micro-WC particle size (10 \(\upmu\)m). The ball milling process with WC balls is the reason of decrease in particle size. The ball milling machine (Focuroy, P400, Changsha, China) is not a high energy ball mill and its main function is to mix powders evenly. So the wear amount of WC balls during ball milling process is extreme small and can not influence experimental results. Elements W, Cu, Ni and Mn show clear signals and uniform elemental distributions in EDS element mappings in Figure 3. The acceleration voltage used in EDS mapping experiments was 25 kV which correlates with a higher depth of analysis. So the element W is not clearly separated from other metals because the depth of analysis is larger than one layer particles.  
WC nanoparticles are found on fracture surface of sample S5 with 2.5 wt% nano-WC addition (Figure 4). The wettability between WC and bronze is excellent [9], so WC nanoparticles are wrapped by bronze phase during sintering process.  
Figure 3: EDS element mappings of W, Cu and Ni for the fracture morphology of sample S5.  
Figure 2: The fracture morphologies of samples: (**a**) S0; (**b**) S5.  
SEM micrographs of impregnated diamond samples SD0 and SD1 are displayed in Figure 5. It shows that diamond grits are partially embedded in the matrix on fracture surfaces. As marked in Figure 5b,d, with the addition of 2.2 wt% nano-WC, the average width of the crack between diamond and matrix decreases from 4.1 \(\upmu\)m to 3.0 \(\upmu\)m. The smaller crack width means that the matrix holds diamond grits more firmly, which contributes positively to mechanical and tribological properties of impregnated diamond composites. The average width of the crack between diamond and matrix is shown in Table 3. The number of measured widths in the calculation of average is 15. With further increase of nano-WC addition, the influence on crack width is not significant. Some pores of SD1 found in Figure 5d have negative impact on relative density, which is consistent with the test results in Table 2. The pore existence states of sample SD2, SD3 and SD4 are similar to SD0.  
Figure 4: The fracture morphologies of sample: (**a,b**) S5.  
Figure 5: The fracture morphologies of samples: (**a,b**) SD0; (**c,d**) SD1.
# Prediction of Health Problems Using Deep Learning Images and Bio-Signals
## 3 Materials and Methods
### Prediction System of Health Status  
#### 3.2.1 Pulse Wave and HRV  
To determine health conditions, methods to measure and analyze bio-signals are commonly used. Bio-signals are signals of biological phenomenon in a human body measured in an invasive or non-invasive way by means of various types of sensors. Since biological tissues generating such signals are combined organically, they are quite complicated and sensitive to external environments. Thus, they may reflect abnormal conditions when measured. Among biological signals, signals that best reflect the biological state of a body are temperature, pulse waves, and electrocardiogram. Among these, pulse waves and electrocardiograms are used to measure HRV, which is information relevant to heart beats. HRV-based methods are designed to measure the state of a body simply in a non-invasive manner and provide scientific and reliable biological information [15].  
An electrocardiogram is used to measure the electric activity of a heart in a non-invasive manner by attaching 2 or more electrodes on the arms, legs, and chest. While it is possible to obtain various types of cardiac biological information on heart beats, irregular pulses, myocardial infarction, etc., it is difficult to measure in a crowded public place with  
Figure 3: Example of a thermal image dataset. (**a**) Fever symptom (37.5 \({}^{\circ}\)C or higher); (**b**) normal (below 37.0 \({}^{\circ}\)C).  
Figure 2: Network structure of faster R-CNN.  
many people [16]. Thus, pulse wave measuring methods to measure HRV on the basis of optical characteristics of skin are commonly used instead. PPG methods to measure pulse waves by means of a light source are used to extract information on heart beats by using an optimal detector and infrared ray or red/green light-emitting diodes. PPG-based heart beat measurement requires no separate electrodes attached to the skin and needs only a compact sensor on fingers or wrists to measure bio-signals. Thus, this method is convenient to be used in a public place. As shown in Figure 4, when light is emitted with finger surfaces facing each other, and with light-emitting diodes and light-receiving diodes attached on both upper and lower sides, it is possible to detect changes in the light permeability at the time (QRS complex) when blood passes through the peripheral blood vessel depending on the heart beats. Since the acquired pulse waves are small, signals obtained through an amplifying circuit are analyzed. As the obtained pulse waves are observed, it is possible to judge vascular diseases such as HRV, irregular pulse, and contraction.
# Automated Docking System for LNG Loading Arm Based on Machine Vision and Multi-Sensor Fusion
## 2 Design of Docking Logic and Kinematic Analysis for LNG Loading Arm
### Decomposition of LNG Loading-Arm Docking Tasks
#### 2.1.2 Coarse Docking Phase  
The coarse docking phase aims to transition the loading arm from its initial position to a predefined range near the target flange. This phase focuses on efficient trajectory planning and control, which is achieved through the following tasks:  
* Target localization: The system employs a monocular camera combined with the YOLOv8 algorithm to detect the target flange and estimate its initial position using monocular image feature localization. Based on the target position, the system calculates the required joint angles for the loading arm to approach the flange efficiently.
* Motion angle calculation: Based on the target position, the system calculates the required joint angles for the loading arm to approach the flange efficiently.
* Posture adjustment: The system dynamically adjusts the arm's posture by calculating the angular deviation between the current and target positions.
* Range determination: The system uses angle sensors to assess whether the loading arm has entered the target range. If not, adjustments continue until coarse docking is completed.
## 3 Problem Statement
\begin{table}
\begin{tabular}{c c} \hline \hline
**Indices** & \\ \hline Sets & \\ \(N=\{1,2,\,\dots,\,n\}\) & Set of departments to be located \\ \(M=\{1,2,\,\dots,\,m\}\) & Set of fixed facilities \\ \(E=\{1,2,\,\dots,\,e\}\) & Set of elevators \\ \(F=\{1,2,\,\dots,\,f\}\) & Set of floors \\ \(R=\{1,2\}\) & Set of rows, 1 for the lower row and 2 for the upper row \\ Input variables: & \\ \(lr_{1},\,vr_{1}\) & The width and length of the lower row \\ \(lr_{2},\,vr_{2}\) & The width and length of the upper row \\ H & The height of each floor \\ \(c_{ij}\) & The patient flow between department \(i\) and \(j\) \\ \(r_{ij}\) & The closeness rating between department \(i\) and \(j\) \\ \(a_{i}\) & The area requirement of department \(i\) \\ \(l_{h}\) & The length of the hall \\ \(w_{c}\) & The width of the corridor \\ \(xe_{ij}ve_{i}\) & The x- and y- coordinates of elevator \(i\) \\ \(l\_fixed_{i}\) & The length of the fixed facility \(i\) \\ \(floor\_fixed_{i}\) & The located floor of the fixed facility \(i\) \\ \(row\_fixed_{i}\) & The located row of the fixed facility \(i\) \\ \(x\_fixed_{i}\) & The x- coordinate of the central point of the fixed facility \(i\) \\ \(n\) & The total number of departments \\ \(m\) & The total number of fixed facilities \\ \(e\) & The total number of elevators \\ \(f\) & The total number of floors \\ \(p\) & Penalty factor \\ \(M\) & A very large value \\ Decision variables: & \\ \(x_{i}\) & The x- coordinate of the central point of department \(i\) \\ \(l_{i}\) & The length of department \(i\) \\ \(w_{i}\) & The width of department \(i\) \\ \(d_{ij}\) & The total movement distance between department \(i\) and \(j\) \\ \(d_{ij}^{c}\) & The rectilinear distance between department \(i\) and \(j\) \\ \(b_{ij}\) & The adjacency factor between department \(i\) and \(j\) \\ \(d_{e_{i}}\) & The distance between department \(i\) and the nearest elevator \\ \(y_{ir}\) & A binary variable which is equal to one if department \(i\) is \\ \(y_{ir}\) & located in row \(r\) or is zero otherwise \\ \(lf_{i}\) & The floor in which department \(i\) is located \\ \(A_{ik}\) & A binary variable which is equal to one if department \(i\) is \\  & located in floor \(k\) or is zero otherwise \\  & A binary variable which is equal to one if department \(i\) and \(j\) \\ \(B_{ij}\) & are located on the same floor or is zero otherwise \\  & A binary variable which is equal to one if department \(i\) and \(j\) \\ \(F_{ij}\) & are located in the same row of same floor or is zero otherwise \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notations used in the formulation.
## 2 User Terminal Specifications
### Antenna Specifications  
The gain of the UE antenna plays a decisive role in defining the achievable data rate according to the aforementioned link budget parameters but, at the same time, determines the complexity of the resulting terminal. Table 2 presents additional performance parameters that have to be considered in the UE antenna design. According to 3 GPP, the UE antenna should cover at least a bandwidth of \(\text{B}_{\text{op}}=100\) and 400 MHz at the n78- and n257-bands around their respective center frequencies f = 3.5 and f = 28 GHz [11]. In order to limit the polarization loss, axial ratios AR < 3 dB would be considered suitable [15]. Finally, the width and direction of the main beam, \(\Theta_{\text{UE}}\), must be considered appropriately so that the resulting field-of-view is covered with respect to spectral and angular masks and adequate gain. Our aim is to achieve \(\Theta_{\text{UE}}=\pm 30^{\circ}\) by considering \(\text{G}_{\text{UE}}=10\)... 15 dBi as attractive target values for efficient 5G automotive satellite communications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **Value** & **Unit** \\ \hline Orbital height & d & 400 & km \\ Center frequency & f & 3.5 & GHz \\
3-dB beamwidth of satellite & \(\Theta_{\text{sat}}\) & 2 & \({}^{\circ}\) \\ Radius of coverage area & R & 7 & km \\ Satellite antenna edge gain & \(\text{G}_{\text{sat}}\) & 38.3 & dBi \\ Free-space path loss & \(\alpha_{\text{FS}}\) & 156 & dB \\ UE output power & \(\text{P}_{\text{TX UE}}\) & 32 & dBm \\ UE antenna gain & \(\text{G}_{\text{UE}}\) & 10 & dBi \\ UE EIRP & \(\text{EIRP}_{\text{UE}}\) & 42 & dBm \\ Atmospheric loss & \(\alpha_{\text{atm}}\) & 3.5 & dB \\ Power level at satellite & \(\text{P}_{\text{Ksat}}\) & \(-117\) & dBm \\ Satellite noise figure & \(\text{N}_{\text{Sat}}\) & 3 & dB \\ Signal bandwidth & \(\text{B}_{\text{sig}}\) & 5 & MHz \\ SNR at demodulator & \(\text{SNR}_{\text{UL}}\) & 24.2 & dB \\ Spectral efficiency & SE & 4.15 & bits/s/Hz \\ Data rate & \(\text{DR}_{\text{UL}}\) & 21 & Mbit/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Link budget parameters for the n78-band in the uplink.
# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
## 5 Measurement Results and Discussion
### BER Measurements and Relation with the FI Path Loss Model  
In this section, the BER measured under each technology is compared with the previously designed FI path loss models. Hence, the received power estimated by each model is related to the empirical quality parameter measured at each point in order to predict the BER with a measure of power or RSSI. The FI model has been chosen because it is the best-fitting model.  
Figure 11 shows the SS-RSRQ parameter, 5G quality parameter, with distance. Figure 14 shows the BER value with distance in LoRa and Zigbee transmissions together with the SS-RSRQ parameter. It is observed how the transmission with Zigbee technology reaches a BER of 100% at 40 m in the NLOS-1 zone, while LoRa transmission has a BER of 0% at the same point. This is due, as already discussed in previous sections, to the fact that the transmission range of Zigbee technology is much lower than LoRa technology because Zigbee transmits with less power and has less sensitivity than LoRa. For this reason, Zigbee is limited to middle-range communications, while LoRa is for long-range communications.
###### Abstract  
The aim of this study was to analyze soybean proteins as sources of peptides likely to be bitter using fragmentomic and hybrid approaches involving in silico and in vitro studies. The bitterness of peptides (called parent peptides) was theoretically estimated based on the presence of bitter-tasting motifs, particularly those defined as bitter-tasting indicators. They were selected based on previously published multilinear stepwise regression results. Bioinformatic-assisted analyses covered the hydrolysis of five major soybean-originating protein sequences using bromelain, ficin, papain, and proteinase K. Verification of the results in experimental conditions included soy protein concentrate (SPC) hydrolysis, RP-HPLC (for monitoring the proteolysis), and identification of peptides using RP-HPLC-MS/MS. Discrepancies between in silico and in vitro results were observed when identifying parent peptide SPC hydrolysate samples. However, both analyses revealed that conglycinins were the most abundant sources of parent peptides likely to taste bitter. The compatibility percentage of the in silico and in vitro results was 3%. Nine parent peptides with the following sequences were identified in SPC hydrolysates: LSVISPK, DVLVIPLG, LIVILNG, NPFLFG, ISSTIV, PQMIIV, PFPSIL, DDFFL, and **FFE**ITPEK (indicators are in bold). The fragmentomic idea of research might provide a supportive method for predicting the bitterness of hydrolysates. However, this statement needs to be confirmed experimentally.  
bioinformatics; BIOPEP-UWM database; bitter-tasting peptides; hydrolysates; soybean proteins +
Footnote †: journal: Applied Sciences
# The 2014 Effusive Eruption at Stromboli: New Insights from In Situ and Remote-Sensing Measurements
## 2 Materials and Methods
### GBInSAR  
InSAR technique has been successfully used to catch slope deformation at active volcanoes [31; 32; 33], though Synthetic Aperture Radar (SAR) satellites are no longer able to follow the evolution of very fast slope deformation. Conversely, given their repeat time (minutes), GBInSAR can correctly measure very fast displacements, thus the InSAR technique is ideal for monitoring, surveillance, and early-warning applications [24; 34]. At Stromboli, a GBInSAR apparatus is working since 2003, located in a stable area N of the SdF (Figure 1b), and consisting of a transmitting and a receiving antenna moving along a 3 m long rail [10]. The GBInSAR, working in Ku-band, has also the great advantage of penetrating the dust clouds (abundant especially during the collapse events, see Figure 2), and working in every light and atmospheric condition. The GBInSAR measures ground displacement along the line of sight (LOS; Figure 1a,b), by computing via cross correlation the phase differences between the backscattered signals associated with two SAR images. Range and cross-range resolution are on average 2 \(\times\) 2 m, with a measured displacement precision lower than 1 mm [10; 11]. Due to the short-elapsed time (11 min) between two subsequent measurements, the interferometric displacements are usually smaller than half the wavelength (8.6 mm for the Ku-band), therefore unwrapping procedures are generally not necessary [10]. Moreover, unwrapping is a time-consuming process, avoiding the operational use of the GBInSAR during crisis management. For the Stromboli GBInSAR images, a coherence mask (threshold equal to 0.8) is set to reduce the noisy areas of the interferogram [11]. Displacement maps and time-series are obtained \"stacking\" the interferogram phase with a displacement measurement precision of 0.5 mm, obtained using 1-h averaged SAR images [35].
## 1 Introduction
To date, the ISCCP-H cloud climatology has not been evaluated against ground measurements. On the other hand, the CLARA-A2 cloud data set has been compared to other satellite (MODIS, PATMOS-x) and surface (SYNOP) products, but on a global scale. An intercomparison and evaluation of both CLARA-A2 and ISCCP-H cloud data sets is attempted here for the first time over Europe, which, as already stated, is a very interesting study region in terms of ongoing climate change and the ascertained role of clouds in this. Indeed, Europe is appropriate for such a study given its diverse geomorphology and geography, which along with the prevailing atmospheric circulation modes results in variable cloud patterns [15; 24; 25]. In this study, apart from their intercomparison, both CLARA-A2 and ISCCP-H are validated against the European Climate Assessment and Dataset (ECA&D) surface-based cloud dataset over Europe. It should be emphasized that the two utilized satellite data records have the advantage of providing long time series of cloud cover data and also offer a complete spatial coverage, which is of high importance when analyzing retrieved climate parameters and assessing regional averages and associated trends. It should be pointed out that both CLARA-A2 and ISCCP-H are recently produced data sets, derived by improved and state-of-the-art satellite algorithms, and therefore, an intercomparison between them, as well as a validation study with ground measurements is timely and important. The aim of our study is to identify differences between these two distinct new satellite data records and to validate them against ground-based measurements. This will help to further develop, correct or improve the algorithms utilized for the production of the two satellite data sets. The data records and the methods used in this study are presented in Section 2. Results of the comparison between the two satellite products as well as their validation against the ground measurements are described in Section 3. Section 4 provides the conclusions and a discussion about further research on the subject.