esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Lossless Transmission Circuit Equations with Terminal Conditions
### Three-Segment Piecewise Linear Resistor  
The three-segment Piecewise Linear (3 SPWL) resistor \(i_{NR}=F_{k}(v_{NR})\), (\(k=N,T\)) in Figure 5 is realized by the op amp based circuit (N) [PERSON]'s 3 SPWL resistor in Figure 1 and Figure 2 or (T) the present authors' Modeled Tunnel Diode (TD) 3 SPWL resistor in Figures 3 and 4. In Figure 1 or Figure 3, \(E_{s}^{+}\) (or \(-E_{s}^{-}\) ) denotes positive (or negative) saturation of the op amp, and \(V_{cc}^{+}\) (or \(-V_{cc}^{-}\)) denotes positive (or negative) power supply. \(E_{d}\) in Figure 3 denotes the forward voltage drop of the ideal switching diode. In Figure 2 or Figure 4, the \(v\)-\(i\) characteristic of \(F_{k}\) is always of the \(N\) type such that \(g_{1},g_{2},\) and \(g_{3}\) are positive, negative, and positive slopes, respectively.  
Figure 5: Transmission line circuit with generally practical side terminal conditions.  
Figure 6: Transmission line circuit with standard side terminal conditions.  
(N) Nagumo's 3 SPWL Resistor \(F_{N}\):  
The slopes \(g_{i}\), \(i=1,\cdots,3\) and break points \(-E_{p}^{-}\) and \(E_{p}^{+}\) in Figure 2 are given as follows:  
\[g_{1} = \frac{1}{R_{1}},\ g_{2}=-\frac{R_{1}}{R_{2}R_{3}}=-\frac{\zeta}{R _{1}},\ g_{3}=\frac{1}{R_{1}},\] \[E_{p}^{-} = \frac{E_{s}^{-}R_{3}}{R_{2}+R_{3}},\ E_{p}^{+}=\frac{E_{s}^{+}R_{3 }}{R_{2}+R_{3}}=\eta E_{p}^{-},\]  
on the condition that \(\eta>-1\), \(\zeta>0\), \(E_{p}^{+}>0>-E_{p}^{-}\), and \(R_{3}=\frac{R_{1}^{2}}{\zeta R_{2}}\). \(E_{p}^{-}\) is freely set up by \(V_{cc}^{-}\). \(\eta\) (i.e., \(E_{p}^{+}\)) is also freely set up by \(V_{cc}^{+}\) because, for any op amps, \(E_{s}^{+}\) (or \(-E_{s}^{-}\) ) is generally in proportion to \(V_{cc}^{+}\) (or \(-V_{cc}^{-}\)).  
(T) TD 3 SPWL Resistor \(F_{7}\):  
The slopes \(g_{i}\), \(i=1,\cdots,3\) and break points \(E_{d}\) and \(E_{p}\) in Figure 4 are given as follows:  
\[g_{1} = \frac{1}{R_{0}},\ g_{2}=\frac{1}{R_{0}}-\frac{R_{1}}{R_{2}R_{3}}= -\frac{\zeta}{R_{0}},\] \[g_{3} = \frac{1}{R_{0}}+\frac{1}{R_{1}}=\frac{2}{R_{0}},\ E_{p}=\frac{E_ {s}^{+}R_{3}}{R_{2}+R_{3}}=\eta E_{d},\]  
on the conditions that \(\eta>1\), \(\zeta>0\), \(E_{p}>E_{d}>0\), \(R_{3}=\frac{R_{0}R_{1}}{R_{2}(\zeta+1)}\), and \(R_{1}=R_{0}\). \(E_{d}\) is freely set up by \(R_{V}\). \(\eta\) (i.e., \(E_{p}\)) is also freely set up by \(V_{cc}^{+}\) because, for any op amps, \(E_{s}^{+}\) is generally in proportion to \(V_{cc}^{+}\).
# An Analytical Survey of WSNs Integration with Cloud and Fog Computing
## 4 Research Methodology
### Data Collection and Analysis  
It involves the processing and analysis of data by using the methodology of the predefined theory. To identify the research papers applicable to our research, we followed these approaches. We perform research on systematic research on the WSN combination with the Fog and cloud computing. In this study, we reveal that how this combination has a great influence on technology. How this combination impacts different domains of research. How this combination provides different facilities to users. This area of research already has the attention of the research community, i.e., introducing new studies for the development of WSN with Fog and cloud computing. From this line of research, we draw some research questions that are adopted during the complete study, as follows:  
**RQ1:**What is WSN with Fog and cloud computing's impact in IoT?  
**RQ2:**What are technologies done by the research community?  
**RQ3:**What are the benefits of WSN with Fog and cloud computing for users?  
Our entire strategy for study methodology relies on search engines such as Google Scholar, Scopus, and Google. A systematic literature review provided numerous elements that are significant in defining the scope of the study. The rest of the elements are related to entities, methods, and processes. The functional dimensions have a significant influence on the technological context plan scope. The method is designed for the division of elements  
Figure 4: Adopted research methodology.  
into parts and sections by CII with categories. For the research review, we used various factors as stated in Table 3. Table 4 presents the searched key strings.  
The papers were gathered from Google Scholar and other search engines based on the inclusion and exclusion criteria. This criteria is applied in the bases of the key strings mentioned in Table 4. We only enlisted papers that have knowledge and data related to these aspects. We gathered 250 papers, and after applying the inclusion and exclusion criteria we shortlisted 100 papers for this study.
# Preliminary Study on the Damping Effect of a Lateral Damping Buffer under a Debris Flow Load
## 4 Numerical Simulation
### Calculation Parameters  
In the test, the primary structure with added mass weighs 6.9 kg totally, so \(m_{1}=m_{2}=3.45\) kg, and the mass of the lateral damping buffer \(m_{3}=0.33\) kg. The first and second order frequencies of the structure are \(f_{1}=3.56\) Hz and \(f_{2}=14.07\) Hz. The stiffness of the structure is \(k_{1}=k_{2}=10085\) N/m. Moreover, the stiffness of the buffer is obtained by measurement, \(k_{3}=2321.4\) N/m.  
The damping matrix \([C]\) is determined according to the Rayleigh damping assumption  
\[[C]=\left[\begin{array}{ccc}35.25&-9.14&-1.65\\ -9.14&24.46&0\\ -1.65&0&3.11\end{array}\right]\text{N}\cdot\text{s}\cdot\text{m}^{-1}\]
# High Efficiency Continuous-Variable Quantum Key Distribution Based on ATSC 3.0 LDPC Codes
## 3 Proposed LDPC Codes for Information Reconciliation
### Construction Method of QC-LDPC Codes for Information Reconciliation
Technically, the error-correcting performance of our proposed code is related to its corresponding base matrix. In this work, we adopt the parity-check matrix of ATSC 3.0 standard LDPC code as the base matrix due to its excellent error-correcting performance, which plays a vital role in assisting two parties to obtain symmetrical secret keys in the information reconciliation process. Furthermore, our proposed LDPC codes based on ATSC LDPC codes can achieve a very high reconciliation efficiency under a very low FER, which will be verified in the next section.
## 3 Results and Discussion
### Particle Size Distribution Analysis by Laser Diffraction  
Particle size analysis, in particular, the determined parameter d(0.9) expressed in micrometers (\(\upmu\)m) describing the diameter of 90% of the particles, provides information on the degree of homogeneity of a cosmetic product [22; 23]. Figure 1 presents the particle size distribution curves measured on day 0 and day 30 for the two macroemulsions studied, Figure 1A,B.  
As shown in Figure 1, the particle size distribution curves obtained for the two samples studied shifted towards smaller particles with time, comparing the red and green curves. Moreover, for macroemulsion 1, a deterioration in homogeneity was noted, manifested as the additional maximum in the range of small particles on day 30, Figure 1A. As concerns the parameter d(0.9), for macroemulsion 1, it significantly increased over 30 days, from 30.305 \(\pm\) 6.486 \(\upmu\)m to 47.603 \(\pm\) 4.045 \(\upmu\)m, indicating a formation of agglomerations of the dispersed phase. For macroemulsion 2, the particle size distribution curve shifted with time, but its shape did not change significantly, Figure 1B. This means that the macroemulsion is stable, and this observation is confirmed by the values of the d(0.9) parameter that slightly increased from 8.248 \(\pm\) 0.757 on day 0 to 8.967 \(\pm\) 1.148 on day 30. Analysis of the particle size distribution changes with time for macroemulsion 1 (face cream) indicates a possible destabilization process, not visible to the naked eye, which may be a consequence of the incomplete compatibility of the _Solidago virgaurea_ extract with the other components of this cosmetic product. A potential source of instability is the synthetic emulsifier used in this macroemulsion, which is an ester of benzoic acid and fatty alcohols C12-15, known to be able to undesirably decrease the viscosity of cosmetic emulsions containing it.
# Multiparametric Monitoring in Equatorial Tomato Greenhouses (II): Energy Consumption Dynamics
## 4 Experiments and Results
### Setup of Energy Consumption Measurements  
The efficient energy consumption in the nodes of WSNs is relevant for their performance, because it increases the operating time of the batteries and reduces the risk of data loss due to lack of energy in a network node. In this research, the energy consumption measurements of each node were acquired and stored in the laboratory according to the scheme in Figure 1. An experiment was developed for each network, and in all cases the link between the nodes was with line of sight and 2-m length. The battery of each node was connected to the Agilent\({}^{\text{TM}}\) 34410A multimeters for the current consumption measurements, and the recordings were transmitted through a local area network to the computer. The multimeter was configured from the computer using SCPI commands, and current data were transferred to a Microsoft Excel\({}^{\text{TM}}\) database with Agilent IntuilLink\({}^{\text{TM}}\) software, where data were subsequently processed and analyzed with custom-made Matlab\({}^{\text{TM}}\) functions.  
The relevant aspects that were considered for the experimentation phase of the three networks are shown in Table 2. Baud rates were selected according to the technical specifications given by the manufacturer of the Waspmote processing card and the communication modules. The parameters configured on multimeters were static IP address, DHCP protocol disabled, and sample count to fifty thousand. The resolution of the device was also analyzed according to the integration time, which was defined as the period during which the analog-to-digital converter in the multimeter sampled the input signal measurements. This time is typically expressed in number of power line cycles (NPLC) [56]. The integration time is directly related to the resolution of the device and inversely related to the noise and measurement speed. For the first experiment, a low NPLC was selected for the three networksto have a moderate acquisition time of each measurement. However, we observed that the noise in the resulting signals was considerable, especially in the DigiMesh and WiFi networks. The NPLC values were configured differently for each network after the noise level analysis, and shown in Table 2. For the three networks, the experiment was repeated ten times in the same scenario, and fifty thousand samples were acquired every ten milliseconds to statistically verify the similarity of the signals and to determine the behavior pattern of the energy consumption of each node at different speeds.
# Design of an Autonomous Cooperative Drone Swarm for Inspections of Safety Critical Infrastructure
## 6 Communication Services
### Drone-to-Drone (D2D) Communication  
The use of wireless mesh networks for drones has been proposed and demonstrated recently [12; 39; 40; 41]. The D2D communication interface considers a wireless mesh setup with the IEEE 802.11s amendment to the WiFi standard [42]. A mesh network differs from the traditional WiFi infrastructure mode based on a centralized Access Point (AP) to allow a wireless mesh network composed of various ad hoc nodes. Mesh networks dynamically self-organize. To achieve this operation, mesh networks run discovery and peering protocols to locate other nodes and to manage membership of the mesh network. This is handled by a Mesh Peering Management (MPM) mechanism. Essentially, a joining node has to discover the operative wireless mesh network by scanning all radio channels and waiting for beacons (passive mode) or by issuing beacon requests and listening for beacon responses (active mode). Direct communication between neighbor nodes is allowed only when they are peer mesh nodes. After a mesh discovery, two neighbor mesh nodes may agree to establish a mesh peering to each other.
## 5 Innovative Approaches for Enzyme Immobilization
### Nanomaterial-Based Enzyme immobilization
#### 5.1.5 DNA-Directed Enzyme Immobilization  
DNA-directed immobilization (DDI), introduced in the1990s, is a widely used, efficient, and safe method used in diagnostics, biosensing, and research. It leverages DNA-base pairing to immobilize DNA-tagged enzymes onto carriers, with hydrophilic DNA acting as spacer that preserves enzyme structure and improves catalytic performance [143; 144].  
Biomolecules like DNA, antibodies, and enzymes act as recognition elements for specific analytes. DNA identifies nucleic acids, antibodies detect antigens, and enzymes interact with their specific substrates [145]. Effective probe immobilization on electrodes is essential for DNA biosensors, since it has a direct impact on biosensor accuracy, sensitivity, selectivity, and longevity [146]. Nanomaterials improve DNA immobilization due to their large surface area and biocompatibility, preserving biological activity and expanding the sensing interface. Additionally, DNA can act as a bridging molecule, forming covalent and non-covalent linkages between enzymes and nanoparticles to ensure stable immobilization. This concept is illustrated in Figure 5.  
Studies have indicated that a DNA double can serve as a flexible spacer to enhance enzyme flexibility. Enzymes were immobilized on supports such as TiO\({}_{2}\) nanoparticles via a DNA helix and graphene/Au nanorod/polythionine (G/Au NR/PT) [146; 147]. These composites, primarily used as sensors, exhibited excellent catalytic activity, stability, reusability, and sensitivity.  
Figure 5: Schematic representation of DNA serving as a catalytic tool in the modification of nanotubes.
# Graph Convolutional Spectral Clustering for Electricity Market Data Clustering
## 5 Conclusions  
This study addresses the challenge of fluctuating end-user power supply demand within the context of electricity market reform. Leveraging the distributed Internet of Things, along with sensing, data fusion, and intelligent application functions, the study extensively explores methods for collecting end-user electricity demand.  
The paper employs the isolation forest algorithm and RBF neural network algorithm for preprocessing and fusion, achieving data cleaning and mining to overcome redundancy and outliers in load curve data across time and space dimensions. Additionally, the study investigates segmented aggregation approximation based on information entropy for efficient feature extraction and mining of big data. The combination of GCN and spectral clustering is used to comprehensively analyze end-user power consumption data, enhancing anomaly detection and supporting optimization efforts within the power system. The effectiveness of the proposed data fusion and mining method for different types of end-user power data are validated through experiments.  
In summary, the main contribution of this study lies in integrating distribution Internet of Things technology and utilizing advanced data processing and mining methods for in-depth analysis of end-user electricity consumption data in the electricity market. Future research directions may involve further algorithm optimization, improvements in large-scale data processing, and enhancements in real-time response capabilities to meet the increasing demands for power data analysis and adapt to the continuous evolution of the power market.  
Conceptualization, L.H.; methodology, L.H. and M.S.; software, L.H.; validation, L.M.; formal analysis, M.S.; investigation, M.S. and L.M.; resources, L.H.; data curation, L.H.; writing--original draft preparation, L.H. and M.S.; writing--review and editing, L.M.; visualization, M.S.; supervision, L.W.; project administration, L.W.; funding acquisition, L.W. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the Science and Technology Project of SGCC, named Research on Transaction Data Sharing and Integration Service Support Technology for Spatiotemporal Multiscale Analysis of the Electricity Market (5108-202355066A-1-1-ZN).  
Not applicable.  
Data Availability Statement: The data and the code of this study are available from the corresponding author upon request.  
Authors [PERSON] and [PERSON] were employed by the company China Electric Power Research Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Conclusions  
In summary, we present a versatile and straightforward approach to the integration of MOF into pellets and membranes. The processing of HKUST-1 and MMOF-74 (M = Mg\({}^{2+}\), Zn\({}^{2+}\), Co\({}^{2+}\), or Ni\({}^{2+}\)) in pellets and membranes achieved high MOF loadings. Pellet processing was carried out by a simple extrusion method using water as the \"binding agent\". Furthermore, the studied materials were also supported into PMMA membranes with a simple procedure where the materials stay homogeneously distributed over the entire membrane. These are stable up 140 \({}^{\circ}\)C and high RH.  
Over time, the MOFs pellets peel off, and their stability is improved with polymethacrylate polymer coating: no deterioration was observed at 98% RH and 125 \({}^{\circ}\)C. Likewise, the MOF membranes also proved stable in high humidity conditions, without loss of structural integrity and crystallinity.  
The following are available online at [[https://www.mdpi.com/2076-3417/10/3/798/s1](https://www.mdpi.com/2076-3417/10/3/798/s1)]([https://www.mdpi.com/2076-3417/10/3/798/s1](https://www.mdpi.com/2076-3417/10/3/798/s1)), Figure S1--Structures of HKUST and MOF-74 MOFs emphasizing the open metal sites present; Figure S2--Powder X-ray diffractograms of the studied materials, as-synthesized and after processing into pellets; Figure S3--Powder X-ray diffractograms of (a) HKUST and (b) MgMOF-74 after incorporation in a PMMA membrane.  
The work here presented was developed in a highly collaborative environment where, [PERSON] and [PERSON] performed the MOF synthesis, pellet/membrane preparation, characterization, and overall writing of the paper. [PERSON] and [PERSON] performed water adsorption studies in powder and pellet form. [PERSON], [PERSON] and [PERSON] foresaw and supervised the general guidelines of the work, as well as the writing of the paper. All authors have read and agreed to the published version of the manuscript.  
This work was developed within the scope of the project CICECO-Aveiro Institute of Materials, UIDB/50011/2020 & UIDP/50011/2020, financed by national funds through the FCT/MEC and when appropriate co-financed by FEDER under the PT2020 Partnership Agreement and UniRCell (SAICTPAC/0032/2015, POCI-01-0145-FEDER-016422. F.F. and R.F.M. acknowledge the project \"Smart Green Homes--BOSCH\" (POCI-010247-FEDER-007678) for the Postdoctoral and Masters scholarships (refs. BPD/CICECO/5508/2017 and BIM-SGH/CICECO-UA/174-01/2017, respectively). FCT is also gratefully acknowledged for the Junior Research Position CECEIND/00553/2017 (to R.F.M.)  
The authors declare no conflicts of interest.
# Implementing and Testing a U-Space System: Lessons Learnt
## 3 The U-Space Services
### Emergency Management  
This service was not developed in depth since non-nominal situations were out of the scope of this project. However, warnings were included to inform the pilots of relevant events. A warning message type could be used by pilots or the UAV autonomously to report any problem. If sent, it was broadcast to the traffic nearby and the Web app showed the drone surrounded by a red cube representing a wider separation to provide a margin of time for a tactical deconfriction of close traffic (Figure 17). The same procedure was applied for those contingencies that could be automatically detected by the system, like deviations.  
There was a message to report a link loss of the UAV with the GCS; in that case, if the drone was still able to report to UTM, the position reports were forwarded to the pilots, so that they could be informed of the location of the drone.  
Figure 16: Drone equipped with the module and antenna (both white) of Atmosphere’s satcom solution [30].  
Figure 15: The pilot receives a deconflicted trajectory (red) if affected by a new dynamic geo-fence.
# Edge AI for Real-Time Anomaly Detection in Smart Homes
## 5 Results and Discussion
### Key Findings and Discussion
#### 5.3.1 Justification for the Hybrid Model Approach  
Although both Isolation Forest and LSTM Autoencoder demonstrate strengths in anomaly detection, neither model alone is optimal for all types of anomalies. IF excels in the rapid detection of point anomalies (e.g., sudden spikes in energy consumption) but struggles with temporal anomalies that require understanding sequential patterns. This results in a high false positive rate when normal variations in smart home data are misclassified as anomalies. On the other hand, LSTM-AE captures temporal dependencies, effectively detecting anomalies that develop over time. However, it is computationally expensive and may not be suitable for real-time deployment on constrained edge devices.  
To address these limitations, the hybrid approach combines the strengths of both models:  
* Step 1: Isolation Forest is used as a first-stage anomaly filter, quickly flagging potentially anomalous data points with minimal computational cost.
* Step 2: LSTM Autoencoder is applied only to the flagged data, performing sequential validation to refine anomaly detection and reduce false positives.  
This two-stage process minimizes the number of instances LSTM-AE must process, significantly reducing computational overhead while preserving its high detection accuracy. Using IF as an efficient pre-filter avoids unnecessary deep learning computations, making the hybrid model an optimal balance between speed and accuracy for Edge AI-based anomaly detection.  
Figure 5 illustrates the hybrid anomaly detection workflow, where Isolation Forest rapidly flags anomalies, which the LSTM Autoencoder refines to minimize false positives and enhance sequential anomaly detection.  
Figure 5: Hybrid anomaly detection workflow combining Isolation Forest and LSTM Autoencoder. Isolation Forest rapidly flags anomalies, which the LSTM Autoencoder then refines to minimize false positives and enhance sequential anomaly detection.
## 2 Credible Long-Term Performance and Durability  
**Impact of Temperature Changes and Freeze--Thaw Cycles on the Behaviour of Asphalt Concrete Submerged in Water with Sodium Chloride [1]**  
This paper studies the mechanical behaviour of an asphalt concrete when it is subjected to temperature changes and freeze-thaw cycles. The results show that, although the temperature changes have a negative effect on the mechanical properties, salt water protects the aggregate-binder adhesive, maintains the mechanical strength, increases the number of load cycles for any strain range and reduces the time that the mixture is in contact with frozen water.  
**MSWI Bottom Ash Application to Resist Sulfate Attack on Concrete [2]**  
This research provides a strategy for partially replacing cement with municipal solid waste incineration (MSWI) bottom ash (BA) to improve the performance of concrete against sulphate attack. The results show that the replacement of cement with BA can improve the durability of concrete and actualize the utilization of MSWI residues as a resource.  
**Thermal and Mechanical Properties of Cement Mortar Composite Containing Recycled Expanded Glass Aggregate and Nano Titanium Dioxide [3]**  
This paper aims to investigate the effects of recycled expanded glass aggregates (EGA) on fresh and hardened properties and the thermal insulating performance of cement mortar. The results demonstrate that EGA-mortar can be integrated into the building envelop or non-load-bearing-elements, such as wall partition as a thermal resistance, to reduce the long-term energy consumption in residential buildings.