esa-sceva/satcom-chunk-collection huggingface dataset:
# Federated Learning for Maritime Environments: Use Cases, Experimental Results, and Open Issues
## 4 FL-Aided Fuel Consumption Prediction  
This section presents a practical implementation of FL, exploiting a dataset provided by a commercial maritime enterprise. The goal of the presented experimental results is to quantify the degree to which an FL-based decentralized scheme is superior against benchmark approaches, namely against a centralized, a transfer learning, and an ensemble learning scheme. Specifically, a multi-variate regression scheme is considered to model and predict the on-road fuel consumption of large cargo ships. Given that fuel consumption is proportional to the ship's consumed Main Engine Power (MEP), the latter was used as the target variable. Since an accurate fuel consumption prediction can allow the adoption of a proactive policy and timely corrective actions towards reducing both the ship's operational costs and environmental footprint, MEP prediction is considered a crucial optimization task in maritime environments. Coupled with the previous point, proactive adjustment of fuel management acts beneficially for both the ship operator (since it saves fuel) and the environmental footprint (reduces pollution), perfectly fitting with the timely concerns faced by the whole maritime era, namely the \"Fit-for-55\" concept.  
A deep learning solution using fully connected Artificial Neural Networks (ANNs) is adopted to estimate the MEP based on multiple ship- and weather-related features as model predictors. Given that the relationship between predictors and the target variable is not known a priori, ANNs were selected as the regression models because of their ability to accurately estimate both linear and non-linear complex functions.  
Noteworthily, the fuel consumption prediction paradigm comprises an indicative practical use case for comparative purposes. Without a loss of generality, other objective-specific use cases could be also considered (e.g., weather forecasting, wind pattern predictions, prediction of the vessel propulsion engine condition, just-in-time arrival modeling, etc.) for deploying the FL model and comparing it with other baselines. The following simulation setups were conducted in Python 3.8 using the Sci-kit learn and Tensorflow (version 2.4) libraries. The training phase of all algorithms ran on a PC (CPU i7-8700; 3.2 GHz; RAM 8 GB; no GPU usage).
## 3 Results and Discussion
### Hierarchical Cluster Analysis (HCA) and Volatile Profiles Development
#### 3.3.6 Changes in the Concentration Profiles of Hydrocarbons and Terpene Hydrocarbons  
Ten hydrocarbon compounds were identified in among the nine samples, as shown in Figures 3 and 4. Nevertheless, only one alkane, nonyl-cyclopropane (0.03 \(\mathrm{\SIUnitSymbolMicro g}\)/g) was detected in final product S-240, which supports previous reports in the literature [4; 6; 10].  
Most of the hydrocarbons were found in the initial clarified juice sample S-0, their intensity attenuated gradually thereafter with some fluctuations, among which the minty scented \(\beta\)-phellandrene (0.32 \(\upmu\)g/g) was the only monoterpene hydrocarbon detected in the S-0. At the initial stage, 3-ethyl-2-methyl-1,3-hexadiene had the highest concentration (2.29 \(\upmu\)g/g) and went down to zero at S-90. However, the hydrocarbons identified in fresh cane juice were not classified as an aroma component as they have high odor threshold values [13; 14].  
D-limonene was the only terpene hydrocarbon that was detected throughout the heating and dehydration process, except in S-180, and was present in the final product (S-240, 0.2 \(\upmu\)g/g), presenting a floral-like odor contribution to the NCS aroma.
###### Abstract  
The World Meteorological Organization (WMO) recommends that the most recent 30-year period, i.e., 1991-2020, be used to compute the climate normals of geophysical variables. A unique aspect of this recent 30-year period is that the satellite-based observations of many different essential climate variables are available during this period, thus opening up new possibilities to provide a robust, global basis for the 30-year reference period in order to allow climate-monitoring and climate change studies. Here, using the satellite-based climate data record of cloud and radiation properties, CLARA-A3, for the month of January between 1981 and 2020, we illustrate the difference between the climate normal, as defined by guidelines from WMO on calculations of 30 yr climate normals, and climatology. It is shown that this difference is strongly dependent on the climate variable in question. We discuss the impacts of the nature and availability of satellite observations, variable definition, retrieval algorithm and programmatic configuration. It is shown that the satellite-based climate data records show enormous promise in providing a climate normal for the recent 30-year period (1991-2020) globally. We finally argue that the holistic perspectives from the global satellite community should be increasingly considered while formulating the future WMO guidelines on computing climate normals.  
climate normal; climatology; satellite remote sensing; clouds and radiation; climate change; climate data records; climate monitoring; essential climate variables; climate anomalies; WMO
## 4 Results
### Hydrological Assessment  
The statistical metrics validation of the IMERG products as shown in Figure 5 indicate that they have great potential for hydrological modelling in the KRB. Generally, the IMERG_F performed the best among the evaluated products in daily streamflow simulation, as indicated by the high NSE (0.71) and R\({}^{2}\) (0.73) values and a low RB (\(-\)5.3%) value. Meanwhile, the IMERG_E (NSE = 0.7 and R\({}^{2}\) = 0.75) showed a slightly better performance than the IMERG_L (NSE = 0.66 and R\({}^{2}\) = 0.75). However, both IMERG_E and IMERG_L dramatically underestimated the SWAT daily streamflow by 27.6% and 36.3%, respectively. Therefore, a systematic bias correction scheme will be required before applying the near real-time IMERG products in any operational hydrology applications in this basin. Figure 4: Calibration (1 January 2011–31 December 2013) and validation (12 March 2014–31 December 2016) of the SWAT-simulated daily streamflow with observed streamflow at the Jambatan Guillermard.
## 4 Results and Discussion
### Flow Behaviors
Figure 10 shows the average dimensionless velocity of air flow into windward side (\(U_{\rm w}\)) of each row of heat sink array in the in-line arrangement formed of different heat sinks when \(Re=500\). As the flow resistance of passages between pin fins of heat sink is higher than the lateral by-pass gap between heat sinks, the air flow leaks into the by-pass gap continuously after it enters the first row of heat sink array. The leakage flow of upstream heat sink is higher than downstream heat sink. The heat sink with higher flow resistance (_i.e._, heat sink with lower porosity or larger number of pin fins) has less air inflow and more leakage flow. The simulation results also indicate that the influence of current relative longitudinal distance (\({X_{L}}^{*}\)) between heat sinks on the air flow entering the heat sink and the leakage flow can be neglected. However, when the relative transverse distance (\({X_{T}}^{*}\)) between heat sinks is large, the air flow entering the heat sink is low, and the leakage flow is high, thus reducing the forced convection heat transfer capability of heat sink array. Figure 11 shows the dimensionless pressure drop when the air flow at \(Re=500\) passes through different heat sink arrays with different \(X_{T}\)\({}^{*}\) and \(X_{L}\)\({}^{*}\) values. The result indicates that changing \(X_{L}\)\({}^{*}\) did not affect the dimensionless pressure drop, but increasing \(X_{T}\)\({}^{*}\) would reduce dimensionless pressure  
Figure 10: Dimensionless velocities of fluid flow through windward sides of various heat sinks: (**a**) Sample 1; (**b**) Sample 2; (**c**) Sample 3; (**d**) Sample 4; and (**e**) Sample 5.  
drop significantly. Because an increasing \({X_{T}}^{*}\) increases the air flow passing by the heat sink and reduces the air flow through the heat sink, the overall dimensionless pressure drop decreases. In addition, the array composed of heat sinks with higher flow resistance (_i.e._, heat sink with lower porosity or larger number of pin fins) has a larger dimensionless pressure drop.
## 2 TDLAS-WMS Detection Theory
### Direct Absorption Spectroscopy  
The Beer-Lambert law indicates that, when a beam of infrared light is transmitted through the C\({}_{2}\)H\({}_{2}\) molecule, a specific wavelength of light is absorbed by the C\({}_{2}\)H\({}_{2}\) molecule. The selective absorption of C\({}_{2}\)H\({}_{2}\) molecules causes the attenuation of light energy, and the energy of light attenuation is proportional to the number of molecules of C\({}_{2}\)H\({}_{2}\)[20; 21]. The light intensity after absorption is obtained as follows.  
\[I_{\rm t}=I_{0}e^{[-\alpha(v)C]} \tag{1}\]  
where \(I_{0}\) is the initial emitting light intensity, \(L\) is the optical path length, \(C\) is the measured gas concentration, and \(\alpha(v)\) is the absorption line shape function. At a standard atmospheric pressure, we use the Lorentz line shape function to describe the gas absorption coefficient, as follows.  
\[\alpha(v)=\frac{\alpha_{0}}{\left(\frac{v-v_{0}}{\Delta v}\right)^{2}+1} \tag{2}\]  
where \(\alpha_{0}\) is the absorption coefficient at the center of the gas absorption line, \(v_{cm}\) is the center frequency of the gas absorption peak, and \(\Delta v\) is the half-width at half-peak of the absorption line.
# SRv6-Based Edge Service Continuity in 5G Mobile Networks +
## 2 Background on SRv6 Network Programming
### SRv6 Network Programming  
SRv6 Network Programming is a framework that allows the encoding of a sequence of instructions in an IPv6 packet. Each instruction is identified by an SID and is executed on the node that has instantiated that SID. Those instructions can range from simple packet forwarding to more complex functions [23]. Functions are called behaviors in the SRv6 Network Programming domain.  
An SRv6 Service SID is a 128-bit address structured as LOC:FUNC:ARG. The number of bits composing each part are not pre-established; the only constraint is that the total length cannot exceed 128. LOC identifies the locator, i.e., the endpoint node, and it is the routable part. FUNC is an opaque identifier of the behavior locally instantiated by the node identified by LOC. It is bounded to the SID. Finally, the ARG field contains the behavior's arguments. The SID structure is usually defined by the SRv6 domain provider, which establishes the number of bits to be used for each part and assigns the locator to each node. Then, each node or a central entity can assign the bits related to the function and argument parts. In a distributed scenario, each SID must be advertised together with a codepoint, mapping the SID to a specific function. Codepoints and their meaning must be known by all the SRv6-enabled devices within the domain. In a centralized scenario, the SIDs will be configured and installed by the central controller, which also instructs the source node to enter a segment list.  
In [23], a set of standard behaviors is defined; however, SRv6 Network Programming has the flexibility to support the association of any function with an SID, as shown in [24; 25; 26]. In what follows, we give an overview of the standard behaviors exploited in this work. In Section 2.2.1, we describe H.Encaps.Red, which is used to tunnel the packet through the 5 GC, encapsulating it into an outer IPv6 header along with an additional SRv6 sub-header.  
In Section 2.2.2, we illustrate H.Insert, which, instead, adds an SRv6 sub-header to the original IPv6 packet. Finally, in Section 2.2.3, we detail the End behavior, applied by each SRv6 endpoint.
# Effects of Cement-Mineral Filler on Asphalt Mixture Performance under Different Aging Procedures
## 4 Conclusions  
To explore the influence of long-term road performance and mechanical properties of asphalt mixtures, this study analyzed different cement-mineral filler combinations through a series of asphalt mixture laboratory tests. Cement was selected as it can effectively improve the water stability performance of asphalt mixtures compared with mineral powder. Further, a modified asphalt mixture (AC-20) was selected as the test object for the laboratory tests. The optimal cement-mineral filler combination ratio was also recommended.  
1. Cement can improve both the high-temperature performance and water stability of asphalt mixtures; however, when the proportion of cement is continuously increased, it will adversely affect the performance of asphalt mixtures after long-term aging.
2. Considering the various requirements for long-term road performance of asphalt mixtures for three aging conditions, this study indicated that the ratio of \(\mathrm{M_{PC}}\)/\(\mathrm{M_{MP}}\) must not exceed 2:2. Further, the ratio must be determined after a comprehensive consideration of the mixing ratio design of the asphalt mixture and performance requirements.
3. The results of the low-temperature bending and splitting tensile tests confirm that excessive addition of cement filler will embrittle the modified asphalt mixture during long-term aging, resulting in a decrease in tensile properties. Among these properties, the bending stiffness modulus of the long-term aging test piece in the low-temperature bending test was particularly high and increased significantly with an increasing \(\mathrm{M_{PC}}\)/\(\mathrm{M_{MP}}\) ratio.
4. The specific influence of the filler on the performance of the asphalt mixture should not be directly compared with the test results of the asphalt cement. The asphalt mixture should be tested as the test object. The mechanism by which the filler influences the performance of the asphalt mixture should be further studied from the perspective of microscopic and molecular dynamics.  
Conceptualization; writing--review and editing; visualization; project administration; experiment, Z.F.; conceptualization; experiment guidance, X.W.; investigation and software, Z.Z.; check and partial test, Y.Z.  
This research received no external funding.  
The authors declare no conflict of interest.
# Online Identification of Shearer Cutting State Using Infrared Thermal Images of Cutting Unit
## 3 Shearer Cutting Unit Tracking in Infrared Thermal Images  
In order to achieve the online identification of a shearer cutting state, the position of the cutting unit in the infrared video must be tracked in real time so that cutting-temperature-characteristic information can be accurately extracted. In this paper, on the basis of the location detection of the cutting part, a space-time context algorithm was utilized to achieve cutting unit tracking.
## 2 Epitaxial Growth on InP-on-Insulator Substrate
### Regrowth of InP  
The epitaxial growth of InP on an InP-on-insulator substrate is an important process because it enables the fabrication of a BH on a SiO\({}_{2}\)/Si substrate, which makes it possible to integrate low-power-consumption-membrane optical devices on a Si substrate. Figure 5a-c shows the process flow of fabricating a BH. First, an MQW-DH was grown on standard InP substrate and transferred to thermally oxidized Si substrate by using a direct bonding technique. Next, a SiO\({}_{2}\) layer was deposited using plasma-enhanced chemical vapor deposition (p-CVD). An \(\sim\)800-nm-wide SiO\({}_{2}\) mask was formed using lithography and reactive ion etching (RIE). The MQW-DH was then etched by dry- and selective wet-etching, which leaves a 50-nm-thick InP layer except for under the SiO\({}_{2}\) mask (mesa-stripe) [59; 61; 62]. After that, undoped InP was grown on the InP membrane, except for the SiO\({}_{2}\) mask, to fabricate the InGaAsP/InP BH. The epitaxial growth temperature was 610 \({}^{\circ}\)C and the pressure was 4.0 kPa. The growth rate was \(\sim\)0.4 \(\upmu\)m/h. Figure 5d shows a cross-sectional scanning electron microscope (SEM) image of a BH on the SiO\({}_{2}\)/Si substrate. The 150-nm-thick and 800-nm-wide active region was successfully buried in InP. Figure 5e shows an atomic force microscope (AFM) image of the surface of a regrown InP layer on a SiO\({}_{2}\)/Si substrate. Clear atomic steps were observed, and the root-mean-square roughness was 0.22 nm, which are comparable to the surface morphology of a standard InP epitaxial layer on an InP substrate.  
Figure 4: (**a–c**) PL spectra for MQW-DHs with different total thicknesses on SiO\({}_{2}\)/Si substrates before (solid lines) and after (dashed lines) annealing; (**d–f**) PL-intensity map measured with PL microscope after annealing.
# Mars to Earth Data Downloading: A Directory Synchronization Approach
## 4 Comparison of Direct and DTNbox Approaches for Bulk Data Transfers
### DTNbox Approach (Alternative Way)  
When data are generated as large bulk data files, an alternative approach is to decouple file generation from file sending, by letting DTNbox carry out the file transfer task. It is enough to establish a bilateral synchronization between the source and the destination nodes. The two directories must be devoted to a specific application. Once a new file is generated (e.g., an image on the Lander), DTNbox will transfer it to the synchronized directory on the destination node (e.g., PDGS). Here, the application can read the file and move it onto another directory for long-term storage. In this way, the file is deleted form the \"local copy\" directory, and, if synchronization is bidirectional, after a while the file will be deleted on the owner directory, on the source node, thus completing the file transfer process.  
The advantage of the DTNbox approach is that by decoupling data generation from dispatch, scientific appliances (e.g., cameras, data loggers, etc.) can be directly used, with no need to build custom versions directly interfaced with the API of the Bundle Protocol. We can also avoid the use of custom scripts, which would lack the features of DTNbox, to download files via BP. There are only two requirements: data must be generated as files, the application must be delay-tolerant, i.e., it must allow for possible additional delay due to DTNbox processing. This is why this approach is particularly appealing for bulk data, which are usually generated as files and are low priority.
# Evaluation of Eco-Efficiency of Two Alternative Agricultural Biogas Plants
## Materials and Methods
### Eco-Efficiency Analysis  
According to the definition presented in ISO 14045, eco-efficiency is an \"aspect of sustainability relating the environmental performance of a product system to its product system value\" [17]. The product system value can be assessed from different perspectives, including, for example, functional, monetary, or aesthetic aspects. Therefore, within the framework of the conducted analysis, an eco-efficiency indicator was determined that can be described by the relation [18]:  
\[I_{EE\ (x)}=\frac{LCA_{(x)}}{LCA_{(x)}+LCA_{(y)}}+\ \frac{LCOE_{(x)}}{LCOE_{(x)}+ LCOE_{(y)}} \tag{3}\]  
where:  
\(I_{EE(x)}\)--eco-efficiency indicator of product system \(x\);  
\(LCA_{(x)}\)--environmental indicator of product system \(x\);  
\(LCA_{(y)}\)--environmental indicator of product system \(y\);  
\(LCOE_{(x)}\)--cost indicator of product system \(x\);\(LCOE_{(y)}\)--cost indicator of product system \(y\).  
Product systems \(x\) and \(y\) represent objects that are compared in terms of eco-efficiency. Therefore, the \(I_{EE}\) indicator presents the normalised value and its value for the particular product system is estimated in relation to other one. Thus, it must not indicate a zero value. The presented eco-efficiency assessment algorithm allows the determination of the dimensionless index that includes at the same time environmental impacts and costs assigned to an identical functional unit which facilitates the interpretation. Equation (3) allows both positive and negative values of the analysed aspects to be taken into account.
# Hybrid NHPSO-JTVAC-SVM Model to Predict Production Lead Time
## 3 Lead-Time Prediction Based on NHPSO-JIVAC-SVM
### Data and Preparation
#### 3.1.1 Data Normalization  
To eliminate the effect of significant differences between the different scales on the learning speed, prediction accuracy, and generalizability of the SVM, we performed normalization preprocessing on the training and test samples, and the data were normalized to [0, 1]. The normalization formula was as follows:  
\[x_{scaled}=\frac{x-x_{m_{in}}}{x_{m_{xx}}-x_{m_{in}}} \tag{18}\]
# Advancement of the DRPE Encryption Algorithm for Phase CGHs by Random Pixel Shuffling
## 4 Results Discussion and Performance Analysis
### Pixel Distribution  
Another property of the phase information stored in holograms is that it has a pixel distribution in which it is difficult to identify structures, correlations, or patterns (much more so than in images obtained directly from scenes). The reason, once again, is that the sum of multiple contributions from the scene to each pixel makes it virtually impossible to predict the value of adjacent pixels based on the value of any specific pixel. Figure 7b shows the histogram of the encrypted CGH, where it is observed that the phase value distribution is nearly flat, which helps to hinder statistical analysis-based hacking attempts.
## 2 WBANs for Life and Safety Critical Applications
### WBANs--The Key Enabling Wearable Technology  
With the recent advances in electronics and the development of short range and ultra-low power radio technologies, e.g., Zigbee, Bluetooth Low Energy (BLE), Ultra Wideband (UWB), and so on, Wireless Body Area Networks (WBANs) have recently emerged as a key enabling technology for many applications.  
A WBAN consists of multiple miniaturized, smart and self-powered sensor devices that can be attached, or even implanted into, humans to monitor their physiological parameters (e.g., temperature,blood pressure, heart pulse rate, _etc._), body motion (e.g., posture, orientation, location, _etc._) as well as their surrounding environment (e.g., toxic gases, humidity, heat, _etc._). All the monitored parameters are then transmitted wirelessly, using short-range on-body or Intra-WBAN communications, from the on/in-body sensors to the WBAN's coordinator for further data processing and analysis.  
The WBAN coordinator is generally considered as a more resource-rich device that can wirelessly interconnect the WBAN sensors to external network infrastructures, using off-body or beyond-WBAN communications, such as static Wireless Sensor Networks (WSNs), WiFi Access Points or Broadband Cellular Networks (e.g., GSM, GPRS, 3G, LTE, _etc._). In case of unavailable or out-of-range network infrastructures, the WBANs coordinators and/or WBANs sensors can exploit cooperative and multi-hop body-to-body or inter-WBANs communications to extend the end-to-end network connectivity, thus forming a self-organizing and dynamic _Wireless Body-to-Body Network_ (WBBN).  
As shown in Figure 1, the resulting WBANs communication architecture is based on three maintiers: (i) _Tier-1_: which correspond to the Intra-WBANs communications between the on-body sensors and the WBAN coordinator; (ii) _Tier-2_: which correspond to the beyond-WBANs communications between the WBANs coordinators and the outside world using, for example, available cellular networks or network infrastructures (e.g., WiFi, WSNs, _etc._); and (iii) _Tier-3_: which correspond to the inter-WBANs communications between neighbor WBANs.