esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview of the Seismic Retrofitting Method Using the NBSD-Based WSCS  
Among various seismic control dampers, steel silt dampers are most widely used as seismic structural control systems because they are easy to design and build, cost-effective, structurally stable, and capable of effectively absorbing earthquake input energy. However, [PERSON] and [PERSON] [34] reported that steel silt dampers were highly likely to involve out-of-plane seismic forces and buckling along the axial direction, thereby leading to a decrease in their seismic energy dissipation capacity. In the present study, a novel window-type seismic control system (WSCS) using non-buckling slit dampers (NBSDs) was proposed, as shown in Figure 2. This system is equipped with hinges and buckling prevention plates to prevent the occurrence of buckling, which directly causes its energy dissipation capacity to decrease, so that the applied seismic energy can be effectively dissipated. As such, this system can be suitably applied to existing RC buildings for seismic reinforcement.  
As shown in Figure 2, the NBSD-based WSCs is composed of an H-frame into which steel slit dampers are assembled, along with box modules and hinges to prevent the buckling of these dampers, i.e., preventing the out-of-plane buckling of the struts while only allowing for in-plane lateral deformation to effectively absorb seismic energy.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 2 Technologies for LV Smart Metering--Background Information
### Wireless Technologies
#### 2.2.4 Low Power Wide Area Network (LPWAN)  
Apart from the wireless solutions described above, nowadays there are emerging technologies which would be suitable for the realization of the Internet of Things (IoT). Such technologies are suitable for the Low Power WAN (LPWAN), where transmissions of a few kb over large distances at low speeds in the most power-efficient way are required. Therefore, such a network could be suitable for smart metering applications. Three technologies designed for this scope are namely:  
* SigFox--Its concept is similar to cellular networks; however it is designed to offer services to devices. The transmission is Ultra Narrowband (UNB), while the 868 MHz band is used. There are 400 channels of 100 Hz. Due to the ultra-narrow bandwidth, the noise effect is very low, thus the system is able to recuperate a very low power received signal [34].
* LoRaWAN--It is a specification for Low Power Wide Area Networks and it operates in the 433, 868, 915 MHz band. The channel bandwidth is 125 kHz; the modulation used is either FSK or a chip spread spectrum modulation; the data rates that can be supported are from 0.3 kbps to 50 kbps [34].  
* NB-IoT--It is a narrowband radio technology designed for the IoT, which has been standardized by the Third-Generation Partnership Project (3 GPP) body. It can be deployed in the GSM spectrum and it can support a big number of low throughput devices [35; 36].  
Table 1 gives an overview of the wired and wireless technologies for LV smart metering applications analysed in Section 2.
## 3 Multi-Channel Membrane CDI (MC-MCDI)
### Applications of MC-MCDI  
Besides high saline solution, various types of solutions, such as an organic electrolyte and a redox couple, can be introduced as a supporting electrolyte in the side channels of MC-MCDI [33; 34; 51]. Using various supporting electrolytes, MC-MCDI reaches beyond the confines of its conventional operations, including the extension of cell voltage [33], ion removal assisted by the redox couple reaction [34; 51], and continuous desalination [34]. MC-MCDI with organic electrolyte as the supporting electrolyte could optimize the cell voltage up to 2.4 V which is a two-fold increase in a stable operating cell voltage (V\({}_{\text{cell}}\) = 1.2 V) of CDI for water desalination [33]. For instance, the cation side of the side channel is filled with the aqueous medium, while the anion side of the side channel is filled with the organic electrolyte such as propylene carbonate. As a result, highly enhanced SAC (63.5 mg/g) was achieved with comparable energy consumption and high charge efficiency (Table 3). Since the charge is linearly proportional to the operation voltage (Gouy-Chapman-Stern theory), the extended cell voltage significantly increased the desalination capacity of the porous carbon electrode. In addition, the MC-MCDI could utilize the redox couple such as iodide and ferrocyanide, to further enhance the desalination performance along with the use of the adsorption capacity (Figure 9A) [33; 51]. The system, assisted by the redox reaction, showed a remarkable desalination performance even over long-term operation with its fast kinetics and high reversibility governed by thin-layer electrochemistry [52]. Predominantly, the reversible redox reaction along the side channels allowed sustainable desalination of the treating water. This application highlights that the MC-MCDI with the redox could be developed as a desalination system for highly concentrated water up to seawater level [34].
# New Detection Method for Fungal Infection in Silver Fir Seeds
## 5 Discussion
### Chemical Components That Could Be Used for Detection of Infected Seeds  
One of the goals of GC-MS analysis performed in our research was the identification of chemical components whose presence differed significantly between uninfected and infected samples, and which could be used for assessment of seeds quality by electronic nose measurements. Such information may be used in construction of the next generation of electronic noses, targeted to detect the spoilage of silver fir seeds, using sensors targeted to detection of specific identified VOC.  
Figure 7 shows that \(\alpha\)-terpineol is the primary chemical component differentiating the studied samples. Among other components present in higher proportions in the infected samples, two other chemical also showed potential differentiation: guaila-6.9-diene and \(\gamma\)-cadiene, which showed significant levels of TIC avg_diff. All components whose presence differed between uninfected and infected samples at a \(p\)-value < 0.05 had a TIC pct_diff at 20% or higher. Interesting candidates for the indicators of infection could also be caryophyllia-3.8(13)-diene-6\(\alpha\)-ol and caryophyllia-3.8(13)-diene-6\(\beta\)-ol, as they were detected only in the infected samples, but their relative content in VOC was very low, below 0.5% of TIC, which could be below the detection limit of other types of sensors. The  
components whose content was reduced in the infected samples could also be helpful for differentiation, and these were himachala-2,4-diene, germacrene D, and \(\alpha\)-pinene.
# Rockburst Hazard Evaluation Using an Extended COPRAS Method with Interval-Valued Fuzzy Information
## 3 Methodology
### Phase 3: Obtain the Ranking Results with Extended COPRAS Method  
**Step 1:** Determine the weighted decision-making matrix  
According to Equation (2), the weighted decision-making matrix is obtained as follows [48]:  
\[\widetilde{d}_{ij}=\widetilde{w}_{j}\widetilde{a}_{ij}. \tag{28}\]  
**Step 2:** Calculate the benefit and cost scores  
The benefit and cost scores of each sample can be calculated as follows [48]:  
\[\widetilde{S}_{i}^{+}=\sum\limits_{j\in k^{+}}\widetilde{d}_{ij}, \tag{29}\]  
\[\widetilde{S}_{i}^{-}=\sum\limits_{j\in k^{-}}\widetilde{d}_{ij}, \tag{30}\]  
where \(k^{+}\) and \(k^{-}\) are serial number sets of benefit- and cost-type indicators, respectively.  
**Step 3:** Determine the minimum value of cost scores  
\[\widetilde{S}_{\text{min}}^{-}=\text{min}(\widetilde{S}_{i}^{-}),i=1,2,\dots,m. \tag{31}\]  
**Step 4:** Calculate the significance scores  
The significance scores of each sample can be calculated as follows [53]:  
\[\widetilde{S}_{i}=\widetilde{S}_{i}^{+}+\frac{\exp(\widetilde{S}_{\text{min} }^{-})\sum\limits_{i=1}^{m}\exp(\widetilde{S}_{i}^{-})}{\exp(\widetilde{S}_{ i}^{-})\sum\limits_{i=1}^{m}\frac{\exp(\widetilde{S}_{\text{min}}^{-})}{\exp( \widetilde{S}_{i}^{-})}}=\widetilde{S}_{i}^{+}+\frac{\sum\limits_{i=1}^{m} \exp(\widetilde{S}_{i}^{-})}{\exp(\widetilde{S}_{i}^{-})\sum\limits_{i=1}^{m} \frac{1}{\exp(\widetilde{S}_{i}^{-})}}. \tag{32}\]  
**Step 5:** Rank the samples  
Based on Equations (6) and (7), the complementary matrix for pairwise comparison of significance scores can be constructed as [46]  
\[P=\begin{bmatrix}p_{11}&p_{12}&\dots&p_{1m}\\ p_{21}&p_{22}&\dots&p_{2m}\\ \vdots&\vdots&\ddots&\vdots\\ p_{m1}&p_{m2}&\dots&p_{mm}\end{bmatrix}, \tag{33}\]  
where \(p_{ij}=p(\widetilde{S}_{i}\geq\widetilde{S}_{j}),i,j=1,2,\dots,m\) is the possibility degree of \(\widetilde{S}_{i}\geq\widetilde{S}_{j}\).  
Therefore, the ranks of samples can be determined using significance scores. The larger \(\widetilde{S}_{i}\) is, the better the sample.  
In addition, to facilitate understanding of the proposed methodology, the symbols used have been compiled as Table A1 in Appendix A.
# A Long Short-Term Memory Network-Based Radio Resource Management for 5G Network
## 3 LSTM-RRM Method  
The LSTM-based RRM is carried out for allocating the bandwidth and power for the desired UEs. The utilization of LSTM in the 5G environment reduces the complexity during decision making, while hyperparameter tuning is done using GSO. In addition, frequency interleaving is developed in the 5G environment to mitigate the burst errors through the network. Then, guard level insertion is enabled before transmitting the data to minimize the ISI. The block diagram of the LSTM-RRM method is shown in Figure 1.
## 2 Radiation in Space and Its Effects on Electronic Components
### Radiation in Space  
LEO is a region in space that extends from approximately 160 to 2000 km above the Earth's surface. There, the space environment is strongly influenced by various sources of radiation that pose a significant threat to human health and the functioning of spacecraft systems, as it implies a transference of energy from charged particles to human tissues and electronic components. The amount of exposure depends on the particle's charge, mass, and energy. It is essential to understand the nature and impacts of these sources to ensure the safety of the astronauts and the correct functioning of the systems.  
In this section, we will examine the main sources of radiation in space and their impacts on human health and technology.  
* Cosmic Radiation: CR is a high-energy particle flux originated outside the Solar System. It consists of high-energy protons, alpha particles, and heavy ions with high linear energy transfer (LET) that can penetrate materials and cause ionizing damage to living organisms and electronic systems. The GCRs (Galactic Cosmic Rays) spectrum is a complex combination of fast-moving ions from various elements in the periodic table, with hydrogen and helium ions as the most abundant. However, predicting the space radiation health risk is a challenge due to the broad disparity in energies and species of ions.
* Solar Particle Events: SPEs are short-lived bursts of high-energy particles that are expelled from the Sun and can reach the Earth in hours. They can pose a significant threat to astronauts and satellite systems, especially during periods of high solar activity.
* Van Allen Belts: Van Allen Belts are two regions of charged particles that are trapped in the Earth's magnetic field. These particles can be a threat to electronics and human health during long-duration missions in LEO.
* Atmospheric Radiation: The Earth's atmosphere acts as a shield against cosmic radiation, but it also contains naturally occurring radioactive isotopes that can contribute to the overall radiation dose in the LEO.  
* Spacecraft-Generated Radiation: The operation of spacecraft systems and their components can also generate radiation, including secondary electrons and electromagnetic interference. This radiation can impact the performance of electronic systems. It is important to note that these sources of radiation can have different energies and interact differently with spacecraft materials and electronics. It is essential to understand their properties to effectively mitigate their effects on space missions. For an in-depth study, see [1; 7; 8] and the references therein.
# Recent Progress in Hybrid Additive Manufacturing of Metallic Materials
## 3 Hybrid Additive Manufacturing as a Joining Technique and Its Challenges
### Alloy Compatibility
#### 3.1.2 Gradient Joining  
Gradient path methods offer a gradual change of composition to minimize mismatches at the interface, which could potentially cause interface cracking. To promote the bonding strength between the dissimilar materials, new chemistries can be developed between the two materials to limit the possibility of secondary brittle phase formation, which can arise from compositionally sharp gradients [52; 57].  
[PERSON] et al. [58] utilized direct laser deposition of IN625 on SS304L steel to produce a Functionally Graded Material (FGM). Thus, a diffuse gradient zone was produced, with no sharp distinctions and compositional variation. However, cracks were observed in a region containing about 79 wt.% SS304L and 2 wt.% IN625. This was due to the precipitation of a brittle secondary phase [58]. Despite introducing a steady gradient, the appearance of an undesirable phase was unavoidable. Similarly, cracks were also observed in a Ti-6 Al-4V/AlSi10 Mg FGM with 25% gradient increments due to the formation of brittle phases [48]. [PERSON] et al. [59] also reported cracks and a significant fluctuation of elemental composition on a Ti-6 Al-4V/Invar FGM. The distinct segregation of phases was observed with Ti-rich particles enclosed in an Invar matrix [59].  
Figure 8: (**a**) Steel and Cu interface observation using FIB; (**b,c**) zoomed interface. Reprinted with permission from [53]. Copyright 2014 Elsevier Inc.
## 2 Materials and Methods
### Satellite Data
As in the case of emissivity calculation, satellite data averaged within a 3 \(\times\) 3 kernel size centered in the stations were extracted for the study period in order to compare them with station measurements. It should be noted that the EVI has been more frequently used in semi-arid Australia, and previous studies have reported a close correlation between that index and GPP [18; 42]. Thus, the EVI was calculated as a secondary index just for comparison with the results obtained from the LSWI, and it was considered as an established indicator of vegetation growth and vegetation productivity. Cloud cover was masked out for all of the considered data. Figure 2 shows a flowchart of the analysis carried out. Figure 2: Workflow diagram of satellite data (AHI and MODIS) and field data. Both data sources were considered to obtain LST and spectral indices and analyze vegetation response to SM during rainfall wet pulses.
## 3 System Overview
It is important to note that the main purpose of the GQR-tree is to assign the largest possible resident domain (together with as many spatial query ranges as possible) to \(o\). * **Query result update**: The task of query result update is performed mainly in response to the \(UpdateResult\) message sent by a moving object \(o\) that crosses any of the boundary of its assigned spatial query ranges \(q.R\). When receiving the \(UpdateResult\) message from \(o\), the server updates the result of the corresponding query \(q\). For example, the server updates the result of \(q_{2}\) in response to the \(UpdateResult\) message sent by the moving object \(o_{1}\) in Figure 2. As we will describe later, this task may also be performed when the server receives the \(RequestDomain\) message from \(o\).
# Improving Freight Traffic Efficiency at Urban Intersections Using Heavy Vehicle Platooning
## 3 Methodology
### Study Area  
This research leverages the simulation of a real-world intersection--Orange Avenue intersecting Adams Street in Tallahassee, Florida--to assess the performance of the developed algorithm for optimized signal control. This intersection, chosen due to its unique  
Figure 6: Time VS location of HCV platoons within the interest zone.  
geometric design and high traffic volume, including a substantial proportion of heavy connected vehicles (HCVs), offers an ideal environment to test the algorithm's effectiveness in managing HCV platoon departure sequences and reducing intersection delays. The simulation accurately mimics the intersection's actual traffic conditions and traffic signal system, providing a cost-effective, efficient, and safe platform to fine-tune and optimize the algorithm's parameters before potential real-world deployment. This precise representation of real-world traffic flow yields critical insights into the algorithm's likely performance under genuine traffic conditions and its potential in enhancing traffic flow and freight logistics operations at intersections, ultimately contributing to more efficient urban traffic management.  
Field data collection is an essential component of traffic studies as it provides real-world information to improve the accuracy and reliability of simulation models [39]. This study collected data on the number of heavy vehicles on each approach, Traffic-Movement Counts (TMC), queue lengths, and average speeds for each approach. The collected data, including speed, queue length, and TMC information, were used for calibration and as inputs for the simulation model, providing a more realistic representation of traffic conditions and enabling a more robust analysis of the study area.
## 4 Discussion
### Practical Implications of This Study  
The forest fire as a major environmental and social issue has attracted widespread attention. It is significant to propose a simple and effective method to estimate the longtime burned area on a large scale. Many fire products can be obtained from the website. However, accurate and longtime burned area distributions are limited. It is necessary to acquire the longtime burned area distribution to analyze the causes of the forest fire and the impacts of forest fire on GPP. In fact, there are many factors affecting GPP, which can be divided into natural and human factors [43]. On the one hand, forest fire as an important disaster will bring a certain loss to GPP. On the other hand, fire as a common and natural disturbance in forest regions plays a critical role in determining the structure and composition of vegetation [44; 45]. Many plants have acquired the adaptive ability to regenerate after a forest fire to keep dynamic disequilibrium [29]. Fires have been found to be important in maintaining vegetation diversity, structure, and functions in fire prone ecosystems such as savannas. Forest fires do not necessarily bring species diversity changes [46]. A recent study showed that savannahs store carbon despite frequent fires [47]. So not all forest fires have a negative impact on ecosystems. In our study, during the revegetation for many years, 70% of the forest fire has no impact on GPP. The result of this study is useful to improve the ecosystem model [48] and forest management [49]. It is significant for promoting carbon neutrality in China. However, we only analyzed the impacts of forest fires on GPP at a national scale ignoring spatial heterogeneity and influence factor differences in different regions. That is the limitation of this study. We will consider these differences in future work.
# Multi-Center Healthcare Data Quality Measurement Model and Assessment Using OMOP CDM
## 2 Materials and Methods
### Healthcare Data Quality Conceptual Model Development
#### 2.1.3 Accuracy  
Accuracy evaluates whether real-world data are accurately represented by the system. For example, (i) the end date of a patient's prescription medication must be equal to one day subtracted from the start date of the medication prescription, and (ii) the medication cannot be prescribed after the patient's date of death.
## 3 Setup  
Figure 2 presents the experimental setup of a UVLC system using different receivers. At the transmitting end, the input data is firstly mapped onto 64 QAM or 64 APSK using GS technology. Then the modulation format of CAP is adopted. In the traditional CAP encoder, the signals are up-sampled by four times to prevent the spectrum from aliasing. Then the I and Q components pass through a pair of orthogonal square root raised cosine (SRRC) filters after I/Q separation. The roll-off factor of SRRC is 0.205. The transmitted signals are generated by adding together these two IQ signals and normalization. Next, the digital signals are loaded into AWG (Tektronix AWG710) to convert to electrical signals. The signals then pass through a hardware pre-equalizer (Eq.) which is used to compensate for the frequency attenuation at high-frequency components [8]. After that, the signals are amplified by an electrical amplifier (ZHL-2-8-s+) and coupled with a DC current through the Bias Tee (Mini-Circuit ZFBT-4R2 GW-FT+), which is used to drive the blue LED. Finally, LED converts the electrical signals into optical signals that will be collimated into parallel light by the lens. The light travels through a 1.2-m water tank and arrives at the receiver.  
At the receiving end, the optical signals are detected by a PIN photodiode (Hamamatsu 10784) which has a pair of differential outputs to suppress common mode noise. The differential signal streams are then amplified by EAs, acquired by OSC (DSO9404A) and synchronized to obtain received waveforms. Three different receivers are experimentally compared. The first one is the traditional CAP decoder, including waveform-level equalization, orthogonal filters, down-sampling, symbol-level equalization and normalization. The second one is a traditional DNN-based waveform-to-symbol converter (DNN converter) [24] that we optimize in this paper. It can find out the complex relationship between received waveforms and symbols with a high-complexity neural network. The last one is the proposed cascaded receiver consisting of a DNN converter with fewer taps or nodes and NN-based DD-LMS proposed in this paper. The DNN converter at the front stage could still handle the majority of ISI and nonlinear distortions. Then modified NN-based DD-LMS is cascaded to mitigate remaining ISI and distortions and further improve signal quality, thus achieving excellent performance with totally low complexity. Finally, the original data could be extracted from GS-64 QAM de-mapping.  
Figure 2: Experimental setup of Underwater VLC system using different receivers. (**a**) shows the overall setup of UVLC system in our experiments. (**b**) shows the details of traditional CAP encoder at the transmitting end. (**c**) shows the details of traditional CAP decoder at the receiving end.