esa-sceva/satcom-chunk-collection huggingface dataset:
# Biomethane Potential Test: Influence of Inoculum and the Digestion System
## 2 Materials and Methods  
### Digestion Systems  
Four different digestion systems for performing the standardized BMP test according to VDI4630 [5] were compared (Table 2 and Figure 1): Hohenheim biogas yield test (D-HBT), Bergedorf fermentation test (D-BFT), Eudiometer (D-EUD) and an Automatic methane potential test system (D- AMP).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Inoculum** & **Substrate** & **CV \%** & **Reference** \\ \hline Brewery wastewater, animal manure, biological waste, upflow anaerobic sludge blanket & Molasses, bio-refinery waste, manure, A-sludge & 3–33 * & [PERSON] et al. [12] \\ \hline Wastewater treatment plant, sludge chicken manure & Chicken manure, Corn stover & 10–19 & [PERSON] et al. [14] \\ \hline Wastewater treatment plant, slaughterhouse lagoon, municipal solid waste, upflow anaerobic sludge blanket & Animal by products & 17 & [PERSON] et al. [15] \\ \hline Wastewater treatment plant, biogas plant & Food waste, wastewater & 12–74 & [PERSON] et al. [16] \\ \hline Biogas plants Pig manure, para grass & 2–128 & [PERSON] et al. [17] \\ \hline Digestate one adapted to turkey manure, one without adaption & Turkey manure & 1–8 & [PERSON] and [PERSON] [18] \\ \hline Wastewater treatment plant, biogas plant, biowaste plant & Cellulose, food waste, maize, sewage sludge & 1–5 * & [PERSON] et al. [19] \\ \hline \hline \end{tabular}  
* According to [PERSON] [7].  
\end{table}
Table 1: Overview on studies on the effect of different inoculum/substrate combinations on the methane yield compared by the coefficient of variation (CV) of measurement results.
# Charge-Line Dual-FET High-Repetition-Rate Pulsed Laser Driver
###### Abstract  
Most modern pulsed laser systems require versatile laser diode drivers. A state-of-the-art pulsed laser driver should provide precise peak power regulation, high repetition rate, and pulse duration control. A new, charge line dual-FET transistor circuit structure was developed to provide all these features. The pulsed modulation current is adjustable up to \(\mathrm{I_{max}}\) = 1.2 A, with the laser diode forward voltage acceptable up to \(\mathrm{U_{F\max}}\) = 20 V. The maximum repetition rate is limited by a charge line circuit to \(\mathrm{f_{rep\max}}\) = 20 MHz. Compared to the conventional single transistor drivers, the solution proposed in this paper allows a precise, high resolution width regulation to be obtained, whereas a low pulse jitter is ensured. In the solution, two separate, out-of-phase signals are used to trigger the individual Field Effect Transistors (FET). The resultant pulsed modulation current full-width-at-half-maxima (FWHM) is regulated from \(\sim\)200 ps up to 2 ns. All control and timing signals are generated with a popular Field-Programmable Gate Array (FPGA) digital circuitry. The use of standard FPGA devices ensures the low cost and high reliability of the circuit, which are not available in laser drivers consisting of sophisticated analogue adjustable delay circuits.  
pulsed laser diode driver; high repetition rate; low jitter FPGA timing circuit +
Footnote †: journal: Applied Physics Letters
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 9 Smart Port Use Case
### Smart Guided Vehicles (SGV) (URLLC)  
Conventional trucks must be replaced with smart guided vehicles (SGV) in order to improve the safety and port efficiency due to their ability to consume less resources. SGV needs to be equipped with sensors and high resolution cameras. Existing 4G networkcannot meet the low latency requirements of SGV, but it can be achieved by implementing the 5G network.
# Algorithmic Coverage Quantification and Visualization in Range-Free Sensor Networks
## 3 Results
### Scenario 1  
A single SR with detection range and beam opening covering a randomly chosen AoI, and four TRs. The SR is randomly positioned outside the AoI, while the four TRs are randomly positioned within the AoI.  
Figure 6 illustrates the border of the circular AoI and the example SR denoted as \(SR_{1}\). It is evident that the four TRs (\(TR_{j},j=1,\dots,4\)) are within its detection range and obstruct the view of \(SR_{1}\), resulting in four distinct blind zones (\(B_{1}=4\)). These blind zones are, for demonstration purposes, assumed to be of \(\delta_{before}=2\) degrees before and \(\delta_{after}=7\) degrees after the exact bearing of each \(TR_{j}\) in a clockwise direction.  
\begin{table}
\begin{tabular}{l l} \hline \hline
5. & Visualize results \\  & Create a satellite map that includes the AoI \\  & Mark locations of existing SRs \\  & Mark locations of existing TRs \\  & Show the coverage areas of each SR \\  & Display the total area of triangulated coverage \\ \hline \hline
**Output** \\ \hline \hline Satellite map, visualizing the triangulated coverage areas within which new TRs entering the AoI \\ can be detected \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 6: A single sensor \(SR_{1}\) positioned randomly on the map alongside four randomly placed TRs, with clear depiction of \(SR_{1}\)’s blind zones.
# Wireless Sensor Networks for Enabling Smart Production Lines in Industry 4.0
## 3 Algorithm Implementation and Validation via Simulations and Measurements
### Implementation  
#### 3.1.1 Overview  
A multitude of wireless technologies exist, and the choice of which technology to use is often dictated by the availability of certain sensors. For the implementation of the algorithm, we consider the following wireless technologies. As WLAN technologies, offering large data rates, we use IEEE 802.11n and 802.11 ax WiFi, both operational in the 2.4 and 5 GHz frequency bands. Possible use cases of WLAN networks in an industrial environment are video monitoring and remote maintenance. WLAN technology is also present in tablet devices used by workers, for example, to download specific user manuals or register data while performing maintenance. For WSN technologies, we use LoRa and IEEE 802.15.4 ZigBee to transmit low-throughput data from various sensors. Apart from standard ZigBee, we also consider the SDR introduced in [52], which is an IEEE 802.15.4 compliant transceiver with flexible MAC and PHY layers, of which we can modify the channel bandwidth (BW). The characteristics of the SDR are presented in Table 1.  
As workers can also wear on-body devices, for example, to monitor the worker's health, body area networks are also considered. For body area networks, we use Bluetooth Low Energy (BLE). The on-body devices can communicate to a central worn on-body, for example, a cell phone, or to a fixed off-body AP.
# Photonic Integrated Circuits for NGPON2 ONU Transceivers (Invited)
###### Abstract  
The development of photonic integrated circuits (PIC) for access network applications, such as passive optical networks (PON), constitutes a very attractive ecosystem due to PON's potential mass market. The implementation of PIC solutions in this context is expected to facilitate the possibility of increasing the complexity and functionalities of devices at a potentially lower cost. We present a review addressing the prominent access network market requirements and the main restrictions stemming from its specific field of application. Higher focus is given to PON devices for the optical network unit (ONU) and the implications of designing a device ready for market by discussing its various perspectives in terms of technology and cost. The discussed PIC solutions/approaches in this paper are mainly based on indium phosphide (InP) technology, due to its monolithic integration capabilities. A comprehensive set of guidelines considering the current technology limitations, benefits, and processes are presented. Additionally, key current approaches and efforts are analyzed for PON next generations, such as next-generation PON 2 (NGPON2) and high-speed PON (HSP).  
photonic integrated circuits (PIC); access networks; passive optical network (PON); next-generation passive optical network 2 (NGPON2); high-speed PON (HSP); super-PON
# Biocompatible Dispersants for Carbon Nanomaterials
## 3 Methods of Dispersing Carbon Nanomaterials  
Dispersants are a class of molecules that can be added to suspensions of solid particles to prevent agglomeration and settling by increasing repulsion between particles through either steric or electrostatic mechanisms. Surfactants are often used as dispersants; these compounds work by lowering the surface tension between nanoparticles and liquid media. Dispersants are often toxic to humans, as they can denature proteins and destroy lipid membranes [13]. Non-ionic surfactants, in particular, have been found to damage the skin [18]. Therefore, biocompatible alternatives are needed for human use. Fortunately, in nature, there exist many non-toxic and effective surfactant molecules such as bile acid salts (BAS) [19], deoxyribonucle acid (DNA) [20], and cellulose [21]. A range of synthetic biocompatible dispersants also exists, such as Triton X-100 [22] and Pluronic F127 [23]. Many of these dispersants are either not commercially available or quite expensive, and therefore they may be difficult to obtain. It is also known that the structure and charge of many biomolecules such as DNA are sensitive to temperature, pH, and ionic strength of the aqueous phase, among other factors. These variables have to be considered when using them as dispersants. An ideal surfactant should disperse a carbon nanomaterial (CNM) with a minimal concentration of surfactant needed. This dispersant should also have a strong binding affinity for the CNM surface, which is usually facilitated by aromatic groups or long aliphatic chains.  
It should be noted that any physical methods used to aid dispersion may significantly affect the final CNM suspension. Excessive sonication of CNMs may lead to structural damage, loss of sp\({}^{2}\) lattice, or unwanted functionalisation [24; 25]. These undesired effects occur due to the shockwaves formed from ultrasonic cavitation. Although sonication helps in dispersing CNMs, it needs to be applied in a controlled manner. Centrifugation is also widely employed in the study of nanomaterials as it can be used to assess dispersion mechanics [3]. Furthermore, analytical ultracentrifugation can be used to separate CNMs based on size and shape [12]. This technique is also employed in purifying CNM dispersions by removing amorphous carbon, metallic residues and agglomerated CNMs [26]. However, it can also lead to a loss of dispersion in the resulting supernatant. Due to these effects, details involving physical techniques used to aid in the dispersion of CNMs must be rigorously reported to ensure reproducibility.
# Geometric Methods and Computational Tools for Switching Structured Linear Systems
## 4 Control and Observation Problems
### Unknown-Input State Observation  
The unknown-input state observation problem is formulated for a switching structured linear system whose state-space representation is of the form \[\Sigma_{\sigma(t)}\equiv\left\{\begin{array}{rcl}\dot{x}(t)&=&A_{\sigma(t)}\,x(t)+D_ {\sigma(t)}\,d(t),\\ y(t)&=&C_{\sigma(t)}\,x(t),\\ z(t)&=&E_{\sigma(t)}\,x(t),\end{array}\right.\]  
where \(d\in\mathbb{R}^{s}\) is the unknown input, \(y\in\mathbb{R}^{p}\) is the measured output, and \(z\in\mathbb{R}^{r}\) is the to-be-observed (partial) state. With the obvious meaning of the symbols, the corresponding graph representation is  
\[\left((G,\mathcal{E}_{\sigma(t)}),G_{\sigma(t)}^{d},G_{\sigma(t)}^{y}\cup G_{ \sigma(t)}^{z}\right)\]  
and the compact notation  
\[\Sigma_{\sigma}\big{(}(G,\mathcal{E}_{\sigma}),G_{\sigma}^{d},G_{\sigma}^{y} \cup G_{\sigma}^{z}\big{)}\]  
can be adopted. Hence, the unknown-input state observation problem is stated as follows.  
**Problem 2**.: _Let \(\Sigma_{\sigma}((G,\mathcal{E}_{\sigma}),G_{\sigma}^{d},G_{\sigma}^{y}\cup G_ {\sigma}^{z})\) be a switching structured linear system. The problem of observing the subset of states \(G_{\sigma}^{z}\) in the presence of the unknown input directly affecting the subset of states \(G_{\sigma}^{d}\) consists in finding a switching essential output injection \(\mathcal{K}_{\sigma}\), with \(\mathcal{K}_{h}\subseteq G_{h}^{y}\times G\) for all \(h\in\mathcal{H}\), such that, having constructed the switching structured state observer \(\Sigma_{\sigma}^{\mathcal{E}}((G,\mathcal{E}_{\sigma}\setminus\mathcal{K}_{ \sigma}),G_{\sigma}^{\tilde{\sigma}in},G_{\sigma}^{\tilde{\sigma}})\), where \(G_{h}^{\tilde{\sigma}in}=\{x_{i}\colon(x_{j},x_{i})\in\mathcal{K}_{h}\}\) for all \(h\in\mathcal{H}\), the switching structured linear system \(\Sigma_{\sigma}^{\mathcal{E}}((G,\mathcal{E}_{\sigma}\setminus\mathcal{K}_{ \sigma}),G_{\sigma}^{d},G_{\sigma}^{z})\), which models the evolution of the observation error, does not exhibit any switching path \(P_{s}\) in \((G,\mathcal{E}_{\sigma}\setminus\mathcal{K}_{\sigma})\) with the tail and the head in \(G_{h}^{d}\) and \(G_{h}^{z}\), respectively, for any \(h,h^{\prime}\in\mathcal{H}\)._  
**Theorem 2**.: _The following statements are equivalent:_  
* _Problem_ 2 _is solvable;_
* \(S^{*}(\mathcal{E}_{\sigma},G_{\sigma}^{y},\cup_{k\in\mathcal{H}}G_{k}^{d}) \subseteq G\setminus G_{h^{\prime}}^{z}\) _for all_ \(h\in\mathcal{H}\)_._  
A proof of Theorem 2 was given in [19].
# Shear Band Characterization of Clayey Soils with Particle Image Velocimetry
## 2 Experimental Program
### Preparation of Soil Specimens  
In this study, commercial-grade EPK (Edgar Plastic Kaolin) kaolinite was used for the soil specimens. Table 1 presents the characteristics of the EPK kaolinite. This is a clay with low plasticity and compressibility and is classified as MH (silt of high plasticity, elastic silt) according to the Unified Soil Classification System (USCS).  
The EPK kaolinite was reconstituted to form fully saturated and homogeneous clay specimens based on the slurry consolidation technique suggested by [PERSON] and [PERSON] [25]. The reconstitution process was as follows: (1) The EPK kaolinite powder was mixed with de-aired water to create a kaolinite slurry with 100% moisture content. (2) The prepared slurry was mixed with a rotating blade under vacuum pressure in a mixing chamber to eliminate the air bubbles within. (3) The de-aired EPK kaolinite slurry was poured into a cylinder-shaped consolidometer with a 300 mm diameter, and a 10 kPa vertical load was gradually applied until the target preconsolidation load of 150 kPa was reached. (4) The specimens were consolidated for two weeks. The reconstituted specimens were trimmed and prepared to generate image patterns for DIA. A fretsaw and auxiliary trimming guide were used to trim the cylinder-shaped specimens into a parallelepiped shape with a length of 64.5 mm, a width of 45 mm, and a height of 128 mm. Image patterns can be generated for DIA by artificially adding textures to the soil surface [26, 27] or artificially adding dyed sand or colored particles to the soil [15, 28]. In this study, a method similar to those of [PERSON] et al. [26] and [PERSON] et al. [27] was used, and water-insoluble black paint was added to the soil surface to produce an artificial texture (Figure 2). This method was used because it is believed to have less influence on the shear behavior of the specimen.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**USCS Classification** & **MH** \\ \hline Liquid Limit, _LL_ (\%) & 65.3 \\ Plastic Index, _PI_ (\%) & 23.1 \\ Plastic Limit, _PL_ (\%) & 42.2 \\ Specific Gravity, \(G_{s}\) & 2.62 \\ Percent Finer than \#200 Sieve (\%) & 99.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Index properties of EPK kaolinite used in this study.  
Figure 1: Plane strain testing apparatus.
## 3 Results and Discussion
### Thermal Properties of the Polymers  
Figure 3 shows the thermal properties of the polymers using thermogravimetric analysis (TGA). The thermal parameters of the polymers are listed in Table 3. The decomposition temperatures were 357 \({}^{\circ}\)C, and 394 \({}^{\circ}\)C for the PDTSDTBTDI-DMO and PDTSDTBTDI-8 polymers, respectively. The TGA results show that the polymers' thermal stability was greatly influenced by the substituents' existence on the BTDI moieties. The polymers have thermal stability windows that are well within the range of those used for solar cells and could be stable for such use. In previous studies, TGA was used to evaluate the thermal properties of D-A conjugated polymers for PSC applications [51; 52; 53; 54; 55; 56].  
PDTSDTBTDI-DMO and PDTSDTBTDI-8 were found to have slightly higher decomposition temperatures than those observed for PDTSDTITz-4 and PDTSNTDO (317 \({}^{\circ}\)C and 321 \({}^{\circ}\)C, respectively). PDTSDTBTDI-8 was found to have better thermal stability compared to PDTSDTBTz and PDTSTAZ (368 and 388 \({}^{\circ}\)C, respectively).  
On the other hand, PDTSDTBTDI-DMO proved to be less stable than the polymers mentioned above. However, it is essential to mention that both polymers exhibited less thermal stability than PDTSDTTAZ with \(T_{\text{d}}\) at 415 \({}^{\circ}\)C. The difference in the polymers' thermal stabilities could be attributed to various side chains on the DTS units and different acceptor units.
## 3 Results and Discussion
### Nutrient Removal  
Nitrogen and phosphorous are two important nutrients for agricultural and landscaping reuse. The influent and effluent concentrations of TN and TP were monitored throughout the whole operation period. As shown in Figure 6a,b, the influent concentration of TN fluctuated from 19.6 to 54.2 mg/L throughout the operation period. In spring, with the temperature of 21 \(\pm\) 3.0 \({}^{\circ}\)C (_n_ = 60), the TN concentration in the effluent of the AWFR system was 30.2 \(\pm\) 5.9 mg/L (_n_ = 60), corresponding to a 15.7 \(\pm\) 2.5% (_n_ = 60) TN removal efficiency at an HRT of two days. When the temperature increased to 31 \(\pm\) 3.7 \({}^{\circ}\)C (_n_ = 90) in summer, the TN removal efficiency slightly increased to 19.0 \(\pm\) 2.7% (_n_ = 90) at an HRT of one day. Despite an HRT increase from one day in summer to three days in winter, the average TN removal efficiency decreased to 12.9 \(\pm\) 2.8% (_n_ = 94). Overall, the TN removal was low in this study because TN was probably removed by microbial assimilation only, and not via a nitrification-denitrification pathway. Meanwhile, a similar trend was observed for TP removal (Figure 6c,d). 25.0 \(\pm\) 3.5% (_n_ = 90) of TP was removed in summer at an HRT of one day, corresponding to an average effluent TP concentration of 2.2 \(\pm\) 0.48 mg/L (_n_ = 90). With the decrease of temperature from summer to winter, TP removal efficiency in winter was 16.1 \(\pm\) 4.4% (_n_ = 94) at an HRT of three days, which corresponded to an average TP concentration in the effluent of 2.0 \(\pm\) 0.44 mg/L (_n_ = 94). Most of the TN and TP remained in the effluent of the AWFR system. Therefore, future studies should focus on the ecological post-treatment of the AWFR system.  
Figure 6: Total nitrogen (TN) (**a,b**) and total phosphorus (TP) (**c,d**) concentration in influent, effluent and the removal efficiency throughout the operation period.
# Effect of Postharvest UVB Irradiation on the Fruit of cv. Dottato (_Ficus carica_ L.)
## 2 Materials and Methods
### Chemical Analysis
#### 2.3.4 Polyphenols HPLC-DAD-MS Analysis  
The determination of phenolic compounds was determined according to [PERSON] et al. [39]. The sample extraction procedure was the same as the one used to determine total polyphenols and HPLC analyses were performed using an HP-1100 liquid chromatograph equipped with a DAD detector (Agilent-Technologies, Palo Alto, CA, USA). The column was a Poroshell, 150 \(\times\) 3 mm, 2.7 \(\upmu\)m from Agilent Technologies. The HPLC system was also interfaced with an MSD API Electrospray mass spectrometer (Agilent Technologies, Palo Alto, CA, USA) operating in negative and positive ionization mode. Identification of phenolic compounds was carried out by comparing their UV-vis and mass spectra with literature data and retention times relative to available external standards. Individual polyphenic compounds were quantified directly by HPLC-DAD using a five-point regression curve (r\({}^{2}\geq\) 0.998) based on authentic standards. The calibration was performed at the wavelength of maximum UV-vis absorbance. More specifically, quercetin derivatives were measured at 350 nm, hydroxycinnamic derivatives at 330 nm, and flavones at 330 nm using quercetin-3-O-rutinoside, caffe acid, and apigenin as standard compounds. Each analysis was performed in triplicate.
# Key Technologies for 6G-Enabled Smart Sustainable City
## 4 Key Candidate Technologies for Future SSC
### Discussion on Sustainability of 6G-Enabled SSC  
In Agenda 2030, UN introduced the sustainable development goals (SDGs) to resolve many social issues such as climate change and education inequality [2]. Among all 17 SDGs, SDG 11, sustainable city and communities, will be highly related to 6G-enabled SSC. It is worth noting that the new urban agenda (NUA) was established to serve as an accelerator for SDG 11. The NUA emphasizes the sustainability of future urban planning and development in social, economic, environmental, and spatial sustainability [118]. In particular, the NUA aims to pursue the equal enjoyment of benefits from smart cities for all individuals. Note that spatial sustainability signifies the potential to improve the social, economic, and environmental value of urban spatial conditions. It involves guiding the physical form of urban environments to promote equitable access to jobs, housing, and social interactions.  
A wide deployment of prime technologies for 6G-enabled SSC will play a pivotal role in resolving many social issues. For example, the combination of NTN and advanced mobile edge computing can achieve global coverage and enable remote learning, which could provide a means to reduce regional and social disparities. Sixth-generation-enabled SSC will play an important role in the achievement of UN's SDGs and vastly support the quality and opportunities of humans and everything.
## 4 Discussion
### Limitations  
#### 4.4.1 Exclusion of Overlapping Populations  
To address the issue of overlapping study populations, three studies conducted by the same author on similar cohorts were excluded from the analysis to avoid redundancy and ensure diverse representation [42; 43; 44]. While this decision mitigates the ROB associated with repeated populations, it also reduced the number of included studies, limiting the statistical power of the findings. Future research should prioritize independent replication across diverse populations to enhance the robustness and generalizability of conclusions.