esa-sceva/satcom-chunk-collection huggingface dataset:
# Potentiality of Rod-Type Chitosan Adsorbent Derived from Sewage Sludge
## 1 Introduction  
The use of heavy metals has increased with industrial development, and the possibility of these heavy metals being released into the environment has also increased. Due to their detrimental effects on human health, finding methods to remove heavy metals is very important. Chemical precipitation, ion exchange, evaporation, electroplating, and membrane processes are common commercial methods for removing heavy metals from aqueous solutions [1, 2]. However, these methods are expensive, inefficient, and can produce chemical sludge. New green technologies for removing heavy metals from wastewater are urgently needed.  
Recently, biosorption has been suggested as a possible alternative to conventional methods for heavy metal removal [3]. The main advantages of biosorption are reusability, low operating cost, and no generation of toxic secondary compounds. Biosorbents can be prepared from a variety of biomass such as algae, aquatic plants, moss, and bacteria. Many studies have been carried out to produce a biosorbent using wastewater sludge, which places no limitation on the production amount [4, 5, 6, 7]. Unlike the case of using microorganisms, wastewater sludge does not require cultivation and is advantageous for commercial production because it is generated in large quantities. Despite the advantages, the use of wastewater sludge itself has several drawbacks. The main limitations include solid-liquid separation problems, possible biomass swelling, and the inability to regenerate/reuse it [3, 8]. Therefore, in order to overcome these drawbacks, wastewater sludge must be immobilized. Chitosan, sodium alginate, polysulfone, polyacrylamide, and polyurethane have been used as matrix materials for the immobilization of biosorbents [9, 10]. Specialattention has been given to polysaccharides and natural polymers such as chitosan, which is an emerging matrix material for immobilizing biomass [11].  
In this study, sewage sludge waste was used as a raw material for the manufacture of biosorbents for the removal of heavy metals from aqueous solutions by batch biosorption experiments. To facilitate solid-liquid separation, sludge was immobilized by chitosan, which is known as a common, inexpensive, and environmentally friendly matrix material. Fourier transform infrared spectroscopy (FTIR) analysis was performed and total organic carbon (TOC) measurements were taken to confirm immobilization strength and characteristics. Kinetic and isotherm experiments were conducted to evaluate the removal rate and capacity of the biosorbent.
## 5 Experiments and Results  
We implemented the F&C protocol in the ns-3 network simulator [30]. Various metrics affecting the performance of F&C protocol were studied, including the propagation speed of the initial flooding (\(\Delta_{t}\)), repeal-flooding boundary estimation accuracy, and the operation of F&C under non-ideal conditions. Finally, at the end of this section, a comparison between relevant repeal-based protocols and F&C is also discussed. Table 5 shows the simulations setting used in the experiments.
# A Type of Low-Latency Data Gathering Method with Multi-Sink for Sensor Networks
## 1 Introduction  
For densely deployed Wireless Sensor Networks (WSNs), a typical way to send data is to the static Sink by multi-hop or points-to-point transmission [1; 2; 3; 4]. However, nodes near the Sink tend to consume more energy as they are responsible for receiving and forwarding data from the whole network [5]. This easily leads to network disconnection and causes \"hotspot problem\" [6; 7; 8; 9; 10]. On the other hand, to reduce the end-to-end delay in data collection, Sink should be near the source of the event [11]. As the events occur at different regions within the network area, such an optimization cannot be achieved by using a static Sink [12].  
Recently, how to use one or more mobile elements for data collection has become an important research topic [13]. With the help of one or more mobile Sinks, performance of WSNs in terms of energy consumption and coverage could be greatly improved. [PERSON] _et al._[1] proposed that, due to the limited power of nodes, using one or more mobile Sinks for data collection in WSNs is an effective way to prolong network lifetime. Moreover, owing to the short communication distance between nodes and the mobile Sink, packet loss rate also decreases [14; 15]. Besides, mobile Sinks may collect data from isolated regions which could improve the connectivity of the network.  
A real-time data collection method is proposed in [16]. Trajectories of the mobile Sinks are designed by modeling the problem as the Traveling Salesman Problem (TSP) to achieve a balancebetween energy consumption and data collection latency. While another type of data collection method with the help of multiple mobile Sinks is proposed by [PERSON] _et al._[17] which reduces not only the energy consumption of the whole network but also transmission delays. To further reduce energy consumption of data collection, [PERSON] _et al._[13] divided the sensor nodes into sub-Sink nodes which are in a direct communication area (DCA) or far-away nodes that are within the distance of the multi-hop communication area (MCA). Sink moves along the fixed path to gather as much data as possible. In [18], path constrained Sink mobility is used to improve the energy efficiency of the single-hop sensor networks which may be infeasible due to the limits of the path location and communication power.  
Nevertheless, how to find out the optimal moving paths for multiple Sinks and how to further reduce energy consumption and time delay on data uploading are still key problems that need to be discussed further.  
The remainder of this paper is organized as follows: the related works, as well as the network model are described in Sections 2 and 3 respectively. In Section 4, we analyze the sleep scheduling and sensing radius adjustment strategies in detail. Trajectories of the Sinks and the transmission path selection algorithm are also described in this section. Experimental results are shown in Section 5 and the conclusion is provided in the last section.
## 3 Experiment Procedure
### Sample Preparation and Experiment  
#### 3.3.1 Full Factorial Test  
In this paper, an independent extrusion 3D printer was used to prepare tensile parts with continuous fiber-reinforced composites inside. The experimental design was carried out according to the full factorial design table of L\({}_{11}\) (\(2^{3}\)) to investigate the effects of print temperature, print layer thickness, and print speed of reinforced fibers on the strength of the tensile specimens. The optimal parameters for printing were also derived. A three-center full factorial test table was generated using the Minitab 19 software, and the test was performed as designed. The tensile test was conducted using a universal testing machine, and the tensile results are shown in Table 4.  
The results were analyzed and simulated using the Minitab software. The fitting results showed that \(p=0.008<0.05\) of the model. \(p=0.258>0.05\). \(p=0.291>0.05\) for bending. It shows that the model is suitable. The designed full factor test table and the maximum tensile stress obtained are shown in Table 4.  
The results were analyzed and simulated using the Minitab software. The fitting results showed that model's \(p=0.008<0.05\), misfit's \(p=0.258>0.05\), and bending's \(p=0.291>0.05\). It shows that the model is suitable. The variance analysis data are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Test** & **Fiber Print** & **Fiber Print Layer** & **Fiber Print** & **Maximum Tensile** \\
**Number** & **Temperature** & **Thickness** & **Speed** & **Stress (Average)** \\ \hline unit & \({}^{\circ}\)C & Mm & mm/min & N \\
1 & 210 & 0.2 & 400 & 87.56 \\
2 & 210 & 0.05 & 250 & 189.52 \\
3 & 230 & 0.2 & 250 & 145.37 \\
4 & 210 & 0.2 & 250 & 134.85 \\
5 & 220 & 0.125 & 325 & 153.76 \\
6 & 230 & 0.2 & 400 & 131.16 \\
7 & 210 & 0.05 & 400 & 180.28 \\
8 & 230 & 0.05 & 400 & 179.21 \\
9 & 230 & 0.05 & 250 & 186.3 \\
10 & 220 & 0.125 & 325 & 164.14 \\
11 & 220 & 0.125 & 325 & 167.04 \\ \multicolumn{4}{c}{Maximum tensile stress at center point (average) (test 5, 10, 11)} & 161.65 \\ \multicolumn{4}{c}{Pure PLA printing} & 23.59 \\ \hline \end{tabular}
\end{table}
Table 4: Experimental scheme and results.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Source** & **DF** & **F-Value** & **\(p\)-Value** \\ \hline Model & 6 & 17.66 & 0.008 \\ Linear & 3 & 32.00 & 0.003 \\ Fiber print temperature & 1 & 3.70 & 0.127 \\ Fiber print layer thickness & 1 & 83.26 & 0.001 \\ Fiber print speed & 1 & 9.030 & 0.040 \\
2-Way Interactions & 3 & 3.32 & 0.138 \\ Fiber print temperature \(\times\) Fiber print layer thickness & 1 & 5.08 & 0.087 \\ Fiber print temperature \(\times\) Fiber print speed & 1 & 1.85 & 0.246 \\ Fiber print layer thickness \(\times\) Fiber print speed & 1 & 3.04 & 0.156 \\ Error & 4 & & \\ Bending & 1 & 1.63 & 0.291 \\ Misfit & 1 & 2.46 & 0.258 \\ Pure Error & 2 & & \\ \hline \end{tabular}
\end{table}
Table 5: Analysis of variance.
## 3 Results and Discussion
### Durability Properties
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Property** & **SCLC-EC** & **SCLC-SBA50** & **SCLC-SBA90** & **SCLC-SMR37** &
\begin{tabular}{c} **Optimum** \\ **Mixture** \\ \end{tabular} \\ \hline Slump flow & & & & & \\ (mm) & SF2 (660–750) & SF2 (660–750) & SF1 (550–650) & SF1 (550–650) & SCLC-EC \\ T500 (s) & VS2 & VS2 & VS2 & VS2 & Equal \\ Visual stability & & VSI 0 (Stable) & VSI 0 (Stable) & VSI 0 (Stable) & Equal \\ Index (VSI) & & & & & \\ Passing ability & & & & & \\ (J-ring) & & & & & \\ Fresh bulk & & & & & \\ density (g/cm\({}^{3}\)) & 1.96 & \(>\)2.00 & \(>\)2.00 & 1.97 & SCLC-EC \\ Compressive & & & & & \\ strength 28d & 30.43 & 25.74 & 29.09 & 34.15 & SCLC-SMR37 \\ (MPa) & & & & & \\ Dry bulk density & & & & & \\
28d (g/cm\({}^{3}\)) & \(>\)2.00 & \(>\)2.00 & \(>\)2.00 & \(<\)2.00 & SCLC-SMR37 \\ Efficiency factor & & & & & \\ (MPa.dm\({}^{3}\)/kg) & & & & & \\ Chloride ion & & & & & \\ diffusion (m\({}^{2}\)/s) & 1.26 & 1.83 & 4.24 & 0.58 & SCLC-SMR37 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of the results obtained. significantly improve resistance to chloride ion penetration. This was evident in the performance of SCLC-SMR37 in this study, which demonstrated a lower chloride diffusion rate compared to other mixtures. Overall, the results of the present study align well with international research, reinforcing the potential of using unconventional aggregates such as SBA and SMR for producing sustainable self-compacting lightweight concrete. These aggregates not only offer environmental benefits, but also demonstrate optimized performance in both the fresh and hardened states.
# LPCP: An efficient Privacy-Preserving Protocol for Polynomial Calculation Based on CRT
### Our Contributions  
In this paper, we propose an efficient CRT-based MPC scheme for polynomial computations, which is secure against a semi-honest adversary. As far as we know, our LPCP scheme is the first protocol that introduces the Chinese remainder theorem encryption [18] and the re-encryption techniques into an MPC scheme. The computational complexity of LPCP is reduced to \(\mathcal{O}(1)\cdot T_{trapdoor}+\mathcal{O}(max(log_{2}^{p+q},term_{F})) \cdot T_{multiplication}+\mathcal{O}(1)\cdot T_{hash}\), where \((p,q)\) is the big prime parameters, \(term_{F}\) is the polynomial term number, \(T_{trapdoor}\) is the time of executing a trapdoor permutation encryption, \(T_{multiplication}\) is the time of executing multiplication arithmetic operations, and \(T_{hash}\) is the time of executing a hash function. The communication complexity of LPCP is reduced to \(\mathcal{O}(1)\cdot L_{trapdoor}+\mathcal{O}(1)\cdot L_{ciphertext}+ \mathcal{O}(1)\cdot L_{hash}\), where \(L_{trapdoor}\)is the length of the trapdoor permutation ciphertext, \(L_{ciphertext}\) is the length of the ciphertext, and \(L_{hash}\) is the length of the hash value. For two-party secure computation, compared to the latest two-party secure computation protocol Emp-sh2 pc [19], our experimental result is more than 100 times faster than Emp-sh2 pc in 128-bit and 256-bit additive and multiplicative tests, and more than 100,000 times faster in the 1024-bit modular exponential tests. For multiparty secure computation, our experimental result is much more scalable than those semi-honest protocols from MP-SPDZ [20]. Both the computational and communication overhead of LPCP is affordable for mobile devices with low computation power. As a result, it is feasible for privacy-preserving mobile devices to implement two-party secure computation with our protocol LPCP.
## 3 Modeling of Backup Systems
### Hydrogen Storage System
#### 3.1.3 Fuel Cells  
The FC is responsible for converting stored hydrogen back into electricity when there is a demand for power. This conversion is essential during periods of low RES generation or when energy demand peaks. The efficiency of FCs is crucial in determining how effectively the stored hydrogen can be converted back into usable energy. Various types of FCs, such as PEM, solid oxide FCs (SOFC), and molten carbonate FCs (MCFC), offer different characteristics and suitability for specific applications. Choosing the right fuel cell technology is essential for optimizing the performance of the HSS and ensuring its smooth integration with the HEMS. FCs not only deliver a dependable source of electricity but also help in reducing greenhouse gas emissions, thereby supporting sustainable energy practices. It produces electricity via chemical reactions through providing \(O_{2}\) to the anode and \(H_{2}\) to the cathode. The output voltage of the fuel cells can be calculated using the following equation [53; 54]:  
\[V_{\text{FC}}=V-V_{act}-V_{con}-V_{ohm} \tag{6}\]  
\[\text{V}=1.229-0.85\times 10^{-3}(T_{\text{k}}-298.15)+\frac{R\times T}{2F}\text{ In}\Big{(}P_{H_{2}}\times P_{O_{2}}\text{{}^{\text{1}}}\Big{)} \tag{7}\]  
The activation and concentration losses can be determined using the following equations:\[V_{act}=\frac{RT}{\alpha n_{e}F}\text{ln}\bigg{(}\frac{I_{FC}}{j_{0}\times S} \bigg{)} \tag{8}\]  
\[V_{cont}=\frac{RT}{n_{e}F}\text{ln}\bigg{(}1-\frac{I_{FC}}{i_{1}}\bigg{)} \tag{9}\]  
Ohmic losses are mainly influenced by the membrane resistance (\(R_{m}\)), which can be computed using the following formula:  
\[V_{ohm}=R_{m}\times I_{FC} \tag{10}\]  
To calculate the current produced by the FCs, the following equation can be utilized:  
\[I_{FC}=n_{ex}\times F\times I_{F}\times\dot{m}_{comb} \tag{11}\]  
The energy generated by the FCs can be determined using the following equation:  
\[P_{FC} =V_{FC}\times I_{FC} \tag{12}\]  
\[\eta_{FC}=\frac{V_{FC}\times I_{FC}}{Q_{LHV}\times I_{F}\times\dot{m}_{comb}} \tag{13}\]  
The cost of operating and maintaining the FCs can be determined using the following equation:  
\[OM^{FC}=\phi^{FC}\times S^{FC}\times\sum_{j=1}^{N}\bigg{(}\frac{1+\mu}{1+t_{r }}\bigg{)}^{j} \tag{14}\]  
The integration of these components within the HSS is managed through sophisticated control systems that optimize the production, storage, and utilization of hydrogen. Real-time monitoring and management ensure that the electrolyzer operates when there is excess renewable energy, the hydrogen storage tank maintains optimal pressure levels, and the FC provides electricity as required. This coordinated operation maximizes the efficiency and reliability of the HSS, making it a vital backup system for the HEMS.
# A Combined Approach of Field Data and Earth Observation for Coastal Risk Assessment
## 6 Conclusions  
An integrated approach of remotely sensed and in-situ data has been presented, aimed at coastal observation on both the local and regional scale for short and long-term analysis. A semi-automatic coastal monitoring system, known as COSMO-Beach, has been implemented to detect and analyze the coastline from Cosmo-SkyMed Stripmap HIMAGE data exploiting a mixed radiometric/interferometric approach. The research conducted on the COSMO-SkyMed SAR images has shown that this data satisfactorily meets coastal monitoring requirements: (i) a large archive of low-cost SAR images with a short revisit period and operational continuity; (ii) a segmentation processing can be quite easily implemented with a low computational time and (iii) a good performance in shoreline detection ensuring an accuracy lower than image pixel resolution.  
The segmentation stage adopts a two-step processing procedure: a coarse shoreline, detected using a thresholding method, feeds an Active Contour Model (LGDF) that extracts a fine resolution land-sea interface. A good performance in shoreline detection has been achieved ensuring a high sub-pixel. The whole processing chain is fully automated except the fine segmentation step that requires an operator intervention in cases of anomalies in the extracted features. The integration of remote sensed and in situ data tested in this research has proven to be effective for an accurate estimation of the shoreline and for the study of its dynamics, allowing: (i) selection of high quality SAR images, (ii) precise geocoding, (iii) tidal correction, and (iv) shoreline accuracy assessment.  
Conceptualization, M.F.B., M.G.M., R.N. and D.O.N.; Data curation, M.F.B., M.G.M., R.N. and D.O.N.; Formal analysis, M.F.B., M.G.M., R.N. and D.O.N.; Funding acquisition, M.M.; Investigation, M.F.B., M.G.M., L.P., R.N. and D.O.N.; Methodology, M.F.B., M.G.M., L.P., M.M., R.N., D.O.N., A.M. and M.T.C.; Resources, M.F.B., M.G.M., L.P., R.N., D.O.N. and A.M.; Software, M.F.B., M.G.M., R.N., D.O.N. and A.M.; Supervision, M.M. and M.T.C.; Validation, M.F.B., M.G.M., L.P., R.N., D.O.N. and A.M.; Visualization, M.F.B., M.G.M., R.N. and D.O.N.; Writing--original draft, M.F.B., R.N. and D.O.N.; Writing--review and editing, M.F.B., M.G.M., L.P., M.M., R.N., D.O.N., A.M. and M.T.C..  
This research was partially funded by the FP7 EU projects COCONET ([[http://www.coconet-fp7.eu](http://www.coconet-fp7.eu)]([http://www.coconet-fp7.eu](http://www.coconet-fp7.eu))).  
CSK Products of the Italian Space Agency (ASI), delivered by ASI under a license to use.  
The authors declare no conflict of interest.
###### Abstract  
Compressed sensing based in-network compression methods which minimize data redundancy are critical to cognitive video sensor networks. However, most existing methods require a large number of sensors for each measurement, resulting in significant performance degradation in energy efficiency and quality-of-service satisfaction. In this paper, a cluster-based distributed compressed sensing scheme working together with a quality-of-service aware routing framework is proposed to deliver visual information in cognitive video sensor networks efficiently. First, the correlation among adjacent video sensors determines the member nodes that participate in a cluster. On this basis, a sequential compressed sensing approach is applied to determine whether enough measurements are obtained to limit the reconstruction error between decoded signals and original signals under a specified reconstruction threshold. The goal is to maximize the removal of unnecessary traffic without sacrificing video quality. Lastly, the compressed data is transmitted via a distributed spectrum-aware quality-of-service routing scheme, with an objective of minimizing energy consumption subject to delay and reliability constraints. Simulation results demonstrate that the proposed approach can achieve energy-efficient data delivery and reconstruction accuracy of visual information compared with existing quality-of-service routing schemes.  
spatial correlation; quality-of-service; distributed compressed sensing; information theory; cognitive video sensor networks +
Footnote †: journal:
# Frequency-Shifted Optical Feedback Measurement Technologies Using a Solid-State Microchip Laser
## 3 Applications of the Laser Feedback Interferometer
### Vibration Sensing
[PERSON] [62] presented a signal-processing synthesizing wavelet transform, and a Hilbert transform employed to the micro-vibration measurement based on the semiconductor laser self-mixing technology. The real-time micro vibration with a nanometer resolution and a much wider bandwidth than conventional modulation methods were proved. [PERSON] [63] proposed the self-mixing interferometry in a fiber ring laser and its application for vibration measurement. The maximum error of the amplitude was about \(\lambda/10\), and the maximum relative error of the frequency was about 10%.
# A Review on Anaerobic Digestion of Lignocellulosic Wastes: Pretreatments and Operational Conditions
## 4 AD Stability or Inhibition
### Concentration of Ions  
Some metal ions, including sodium (Na\({}^{+}\)), potassium (K\({}^{+}\)), calcium (Ca\({}^{2+}\)), ferric (Fe\({}^{3+}\)), and magnesium (Mg\({}^{2+}\)), are formed because of the breakdown of organic matter or introduced with the pH adjustment (alkali and acid reagents used in pretreatment). Excessive concentrations of such ions would lead to decreased microbial growth or would even cause toxicity, as they cause dehydration of bacterial cells due to osmotic pressure [149; 150].  
Mg\({}^{2+}\) was found to be inhibitory to methanogenesis when it reaches 720 mg/L. Also, high concentrations (> 100 mM) of the magnesium ion can cause disaggregation of methanogens; thus, the conversion of acetate is inhibited [151]. Nevertheless, Ca\({}^{2+}\) was moderately inhibitory to microorganisms in concentrations above 300 mg/L. However, excessive amounts of Ca\({}^{2+}\) can cause precipitation of carbonates and phosphates, which results in scaling of the reactors, pipes, and biomass; thus, it reduces the specific methanogenic activity and results in a loss of buffer capacity [152; 153; 154]. K\({}^{+}\) concentrations above 3 mg/L are toxic to microorganisms [97], and Fe\({}^{3+}\) was reported to inhibit 52-82% of methanogenesis activities with concentrations of 21 mg/L or above, because Fe\({}^{3+}\) could deactivate enzymes of microorganisms by reacting with their functional groups [155].