esa-sceva/satcom-chunk-collection huggingface dataset:
# All about Delay-Tolerant Networking (DTN) Contributions to Future Internet
## 3 Related Work
### Information-Centric Networking (ICN) and Named Data Networking (NDN)  
The ICN paradigm represents an alternative approach to traditional IP-based internetworking that focuses on the content itself rather than its (IP) location [59]. Notably, NDN emerges as a leading architecture within ICN, thus gaining increased attention [60; 61]. In these lines, schemes such as NDN-over-DTN (NoD) have been proposed [62]. In particular, NDN accesses the content via its name (i.e., Named Data Objects (NDOs)) as facilitated by Content Identifiers (CIDs) rather than through host-to-host communication. Some key components of NDN include the Forwarding Information Base (FIB) (i.e., similar to routing tables in IP networks), which maps names to interfaces and determines where to forward the interest based on the name it carries, and the Pending Interest Table (PIT), which tem  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Challenges** & **Papers** & **Focus** & **Solution/Protocol** \\ \hline  & [2; 8] & BP \\  & [15; 16] & (B-)DTN7 \\  & [17; 18] & IBR-DTN \\  & [19] & LTP \\  & [24] & ION-DTN \\  & [23] & AOS \\  & [26; 27] & Protocol definition & QUIC and QUICL \\  & [28] & DTPC \\  & [29] & DS-TP \\  & [30] & SPP \\  & [31] & EPP \\  & [32; 33; 34; 35] & Routing protocols \\  & [10] & Testbeds for space & DEN \\  & [11] & SPICE \\  & & \\  & [38] & Multimedia content delivery & BSS \\  & [12] & Microservice-based & A2C and DQN-based \\  & [13; 14] & approaches & HDTN project \\  & [41] & P2P decentralized simulation tool \\  & [42] & Enchantments for scheduling & Modulation techniques for \\  & & electrical pulse generators \\  & [43] & BDTE tool and CGR \\  & [48] & Energy and smart grids & AURA-NMS performance \\  & [49] & SATCOM systems in smart grids \\  & [50; 51; 52] & Maritime & Networking, UAV-enhanced Hybrid Networks \\  & [54; 55; 56] & Agriculture & Precision agriculture with ML and DL \\  & [57; 58] & Military support & Lasers and military satellites \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of space-related literature works.  
porarily stores the corresponding interests. Hence, ICN approaches aim to improve content delivery, caching efficiency [63], and network scalability [64] by directly addressing content and enabling in-network caching and content-based routing. The related works can be found in Table 2.
## 5 Conclusions  
This study made progress in the theory and application of regional heavy metal stress detection in rice paddies based on the satellite-derived VI time series. The temporal trajectories of the rice pixels in the NDVI-NDWI phase-space were explored to comprehensively describe the development of rice through both photosynthetic and metabolic activity. The extraction algorithms of \(D_{ill}\), \(D_{head}\), \(D_{mat}\), \(L_{season}\), \(L_{vcg}\), and \(L_{rep}\) were applied to derive the phenometrics based on the phase-space. Among all the derived phenometrics, \(D_{tilt}\), \(D_{head}\), and \(D_{mat}\) were largely dependent on the transplanting date and \(L_{season}\), \(L_{vcg}\), and \(L_{rep}\) were significantly influenced by the air temperature and solar radiation. The proposed RPI possessed the capability to capture the delayed heading stage caused by the heavy metal stress with reduced effects of human activities and meteorological disturbance. However, this study was limited to the relatively single rice variety in our study area, which is a typical region of a subtropical monsoon climate; therefore, the applicability to other rice varieties or environmental conditions still needs to be explored in our future work. In a word, the greenness-wetness synergetic strategy possessed an advantage in responding to the variations in the whole physiological activity of the plant under heavy metal stress. The phase-space method, as well as the RPI, is recommended for investigation in future phenology research.  
Conceptualization, B.Z. and X.L.; methodology, B.Z.; formal analysis, B.Z.; data curation, Y.M.; writing--original draft preparation, B.Z.; supervision, X.L. and M.L.; funding acquisition, X.L.  
This research was financially supported by the National Natural Science Foundation of China under Grant 41601473 and Grant 41371407, in part by the Fundamental Research Funds for the Central Universities under Grant 2652017122.  
The MOD09A1 data were downloaded from the Level-1 and Atmosphere Archive and Distribution System (LAADS) at the Distributed Active Archive Center (DAAC) at the Goddard Space Flight Center ([[https://ladsweb.nascom.nasa.gov/](https://ladsweb.nascom.nasa.gov/)]([https://ladsweb.nascom.nasa.gov/](https://ladsweb.nascom.nasa.gov/))). The Landsat 8 Level-2 data were downloaded from the Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA) at the U.S. Geological Survey ([[https://espa.cr.usgs.gov/](https://espa.cr.usgs.gov/)]([https://espa.cr.usgs.gov/](https://espa.cr.usgs.gov/))). The authors are especially grateful to the Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences (CAAS), China, for providing help in the research. The authors would also like to thank the Editor-in-Chief and the Associate Editor, who handled the manuscript, and the anonymous reviewers for their insightful comments and suggestions, which greatly helped us to improve the quality of our manuscript.  
The authors declare no conflict of interest.
## 4 Discussion
### Mission Operations Issues and Status  
#### 4.2.1 CPD Operation  
The CPD was tested successfully during the LEOP phase of the satellite operations. The five datasets obtained from the Liulin detector over Japan were measured with an average exposition time of 15.2 s. In comparison, the exposition time for the normal mode missions was 30 s in average. The reduced exposure time caused the early operation (real-time) data to receive fewer counts, and less total dose rate and flux than the normal mode missions data. Variation in the dose and flux for the real-time data comes from two data series (12 November at 05:07:29 UTC and 18 November at 04:34:08 UTC) with most of the hits in the low deposited-energy channels of the detector. The main reason for the decrease in dose rate and flux in the mentioned datasets relates to the rotation of the satellite and the misalignment of the magnetic field lines with respect to the normal direction of the Liulin detector. The effect of the satellite rotation was perceived from the beginning of the operations during the communication sessions with the Kyutech MGS. The rotation of the satellite makes the beacon signal vary in intensity during communication sessions, which was observed during all communication session operations. This highlights the fact that schedule and resource constraints of the project have had a measurable effect on the scientific return of the mission. A similar pattern is  
Figure 15: Ten-Koh and ERG (ARASE) conjunction points: (**a**) world map of the conjunction points for the period between 4 to 30 December 2019, showing both satellites footprints; and (**b**) example of two conjunction points computed for the period between 21 February 2019 and 22 February 2019.  
expected for other satellites that are launched as secondary (piggyback) payloads, without the ability to choose the launch date.  
The Liulin results show an increase in dose rate and flux in regions where the ambient models predict the presence of electrons with more than 2.2 MeV and protons with more than 30 MeV. These energy values correspond to the energy required to penetrate the shielding of Ten-Koh external structure for each particle type. Had the instrument been placed with an unobstructed view of deep-space, lower energies could have also been registered. This, however, would have necessitated a complete redesign of the satellite structure that was not feasible from schedule point of view.
# FPGA-Based Pulse Compressor for Ultra Low Latency Visible Light Communications
## 4 FPGA Implementation
### The Transmitter
performs the summation of the \(Nc\) chirps and produces the output sample. The adder is implemented in four pipelined stages to facilitate the FPGA time closure at the desired clock frequency. The following pseudo-code clarifies how the sequencer calculates its outputs: \begin{tabular}{l} \(\text{ADD\_cnt}=0\) \\ Loop: \\ \(\text{ADD\_cnt}=\text{ADD\_cnt}+1\) \\ \(\text{ADD\_0}=\text{ADD\_cnt}\) \\ \(\text{ADD\_n}=\text{Add\_nn}-1+8\) (\(1\leq\text{n}\leq Nc\)-1) \\ If \(\text{ADD\_n}=Ns\) (\(0\leq\text{n}\leq Nc\)-1) \\ \(\text{ADD\_n}=0\) \\ \(\text{BIT\_n}=\text{Next BIT}\) \\ End if \\ Wait next CK rising edge \\ End Loop \\ \end{tabular}
# Measuring the Performance of Wastewater Treatment in China
## 2 Methodology and Date Sources
### Data and Variables  
This study measures the efficiency of environment deteriorated and economic development of 31 provinces in China from 2011 to 2015. The period completely covers the 12 th Five-Year Plan of China. We consider good output GDP and bad outputs of wastewater discharge and COD discharge of the 31 provinces to estimate the performance of environment efficiency. Each of the 31 regions uses three inputs of capital (K), population (P), and expense (E) to produce one desirable output of gross domestic production (GDP) and two undesirable outputs of wastewater discharged (Wastewater) and chemical oxygen demand (COD).  
There are 6 variables considered in Table 1, including 3 input variables and 3 output variables. These six variables fully explain the changes between economic efficiency and environmental efficiency.
## 4 Validation and Comparison
### Comparative Testing of Baseline Methods  
In this experiment, to fully evaluate the performance of the proposed methods, we implemented six benchmark models: (1) CI, (2) CNN, (3) RNN, (4) GNN, (5) Transformer, (6) AutoML. Additionally, we included two ensemble models optimized using optimization algorithms to adjust the output weights of the models: (7) GA-Optimized Ensemble and (8) PSO-Optimized Ensemble. Suitable hyperparameter tuning techniques were applied to determine the optimal configuration for each model. Below is a detailed description of these models.
# A Novel Deep Learning Approach for Machinery Prognostics Based on Time Windows
## 3 Experiment and Analysis
### Performance Analysis  
In this section, the performance of the proposed deep learning architecture for prognostics is presented. In addition, extensive experiments were performed to investigate the impact of key factors on the results, including the depth of the model and the size of the time window, as well as different optimization algorithms. Compared with the state-of-the-art results on the C-MAPSS dataset, the prediction results demonstrated the superiority of the proposed network.
# LR-MPIBS: A LoRa-Based Maritime Position-Indicating Beacon System
## 2 Materials and Methods
### Workflow and Data-Processing Algorithm  
#### 2.3.1 Workflow of the LR-MPIBS System  
The basic workflow of the LR-MPIBS is shown in Figure 5. PIT starts and initializes each functional modules when it detects someone falling into the water or long pressing the power button for 3 s. The microcontroller receives the NMEA-0813 message sent by the Beidou module, and then analyzes and processes the positioning data in it. The LoRa communication module sends rescue information packets encapsulated according to the rescue information protocol, which contain information such as personnel numbers and positioning data. When SMBS receives rescue data packets, it verifies and analyzes them. After successful verification, SMBS sends a response packet to PIT and forwards the rescue information to the backend server. This is a complete process for the reliable transmission of rescue information. LR-MPIBS operates in a loop according to the above process until PIT stops sending.
## 3 Results and Discussion  
### Transfer-CAP Characterization  
Figure 3a shows the emission of the transferred CAP. Using optical filters with 50% transmission, which were mounted in front of the ICCD camera, hydroxyl, and atomic oxygen radicals were observed as shown in Figure 3b,c, respectively. The dominant species of the spectra measured by OES were a hydroxyl peak (309 nm) and a nitrogen band (300-450 nm) as well as an argon band, as shown in Figure 3d. A similar spectrum related to the conventional argon-based plasma jet was reported for melanoma tumor treatment [27]. Moreover, a small peak of atomic oxygen radical (777.1 nm) was observed in the spectra (Figure 3e). The small peak of atomic oxygen radical was also reported in a radio-frequency plasma jet with argon [28] and argon/oxygen gas feeds [29]. Although the gas feed used in our system was argon, the generated RONS such as hydroxyl and atomic oxygen radicals as well as nitrogen species observed by OES were mainly respectively generated via energetic electron collisions with water molecules, oxygen, and nitrogen of the ambient air [30]. As a result, the presence of RONS generated by the transferred CAP was confirmed by emission and spectroscopic characterizations.
## 3 Simulation Results and Discussion
### Bar-Type Cutoff Probe (BCP)  
Figure 5a shows the S\({}_{21}\) spectra of the BCP with various \(\alpha\) at fixed \(\beta\) and \(\gamma\) in a vacuum condition. An increase in \(\alpha\) led to a slight increase of the S\({}_{21}\) level, which was similar to the RCP case and for the same reason. Here, there were no significant resonance peaks in the S\({}_{21}\) spectra from the BCP, except for case #3, because the length of the radiating and detecting antennae was much smaller than the RCP detecting antenna; the SW resonance took place at high frequencies beyond the interested range (<10 GHz). When the antenna length \(e\) increased, as shown in Figure 5d,g, a low frequency shift of the resonance frequency with low Q-factor took place near 8 GHz (cases #2 and #3), but this could be negligible. The signal level of the BCP, however, was lower than that of the RCP, by as much as 20 dB due to the small size of the BCP antennae.  
Figure 6 shows the S\({}_{21}\) spectra of the BCP with various electron densities ranging from \(1\times 10^{9}\) cm\({}^{-3}\) to \(1\times 10^{11}\) cm\({}^{-3}\) and various \(\beta\), with the same conditions as in Figure 4. In Figure 6a, an increase in \(\beta\) produced the same results as the RCP case. With increasing antenna distance, the cutoff frequency saturated to the plasma frequency, which might have resulted from a reduction in capacitive coupling between the antennae for the same reason as in the RCP. Figure 6b,c show smooth spectral shapes, and it is therefore easy to determine the cutoff frequency in each spectrum. They also manifested the same trend as Figure 6a in terms of \(\beta\) dependence.  
Figure 5: S\({}_{21}\) spectra of the BCP from various (**a**,**d**,**g**) antenna widths, (**b**,**e**,**h**) antenna distances, and (**c**,**f**,**i**) dielectric thickness at various antenna lengths: (**a**–**c**) \(e\) = 2.0 mm, (**d**–**f**) \(e\) = 2.5 mm, and (**g**–**i**) \(e\) = 3.0 mm.  
It should be noted for the BCP here that the critical antenna distance (\(d_{c}\)), where the cutoff frequency matched the plasma frequency, was much lower than that in the RCP, at 14 mm compared to 30 mm as shown in Figure 7, which showed the ratio of the cutoff frequency to the plasma frequency by antenna distance. Additionally, the S\({}_{21}\) spectrum of the BCP was much straighter, without any resonance peaks, than that of the RCP. This fact facilitated simple cutoff frequency monitoring by just measuring the minimum S\({}_{21}\) value, which indicated the possibility for a simple plasma monitoring system based on the BCP. In summary, the BCP was more practical than the RCP in terms of miniaturization as well as electron density monitoring, since it was much easier to determine the cutoff frequency in the S\({}_{21}\) spectrum of the BCP than that of the RCP.  
Figure 6: S\({}_{21}\) spectra of the BCP from various normalized antenna distances (\(\beta\)) with electron densities of (**a**) \(n_{\text{e}}=1\times 10^{9}\) cm\({}^{-3}\), (**b**) \(5\times 10^{10}\) cm\({}^{-3}\), and (**c**) \(1\times 10^{11}\) cm\({}^{-3}\).
# Strength Characteristics of Iron Tailings Blended Soil as a Road Base Material
## 4 Conclusions  
A new mixture of iron tailings blended with natural soil was designed as a road base material. The optimal mix ratio was assessed, the mechanical properties and mechanism of ITBS with different materials were investigated. The main findings were as follows:  
* Considering both economy and practical implications, the optimal mix proportion of ITBS is determined as follows, ITS:soil = 50%:50%, and then mixed with 5% cement and 2% soil curing agent in total weight of ITS and soil.
* Under the optimum mix proportion conditions, the compressive strength of ITBS can be effectively exerted when the water content is about 12%. The samples of standard curing and soaking curing increase gradually with the increase of age, and the samples  
Figure 11: SEM images of ITBS with (**a**) ITS:soil = 30:70; (**b**) ITS:soil = 50:50; and (**c**) ITS:soil = 70:30.  
under the condition of long-term soaking curing still have high strength assurance rate, and the ITBS has good water stability.
* The splitting strength and flexural tensile strength of the mixture also increase with the increase of age and increase first and then decrease with the increase of the sand to soil ratio.
* The XRD and SEM results show that the addition of curing agent yielded almost no new substances but the ion exchange reaction between curing agent and soil could occur, which made the aggregate more compact. C-S-H gel and CaCO\({}_{3}\) were produced in the mixed soil due to hydration of cement. These substances filled the pores of aggregate and made the bond between ITS and soil particles more compact and the strength further increased. Using the curing agent in conjunction with the cement can generate a higher strength. In addition, when the contents of the soil and ITS are equal, the combination between the two components is getting closer.  
Conceptualization, J.T., X.N. and Y.L.; methodology, Y.W.; validation, J.T.; formal analysis, J.T.; investigation, X.N.; data curation, J.T. and X.N.; writing--original draft preparation, Y.W. and Y.L.; writing-review and editing, J.T. and X.N.; visualization, J.T.; supervision, X.N.; project administration, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData is contained within the article.  
The authors declare no conflict of interest.
## 5 Conclusions  
* Twelve out of fourteen studies in this systematic review support the use of OAEs as a prognostic indicator for hearing improvement in cases of ISSNHL.
* OAEs are an important component of a diagnostic and monitoring auditory test battery to help determine site of lesion, make proper diagnostic referrals (e.g., for imaging), and predict benefit from aural rehabilitation.
* No unifying OAE protocol was identified in this systematic review. The choice to use TEOAE, DPOAE, or both measures should be based on available equipment, patient age, and pre-existing hearing loss (which, in unilateral cases, may be inferred from the unaffected ear).
* Further research may lead to predictive models of hearing recovery in ISSNHL based on factors such as treatment type, duration from onset to treatment, OAE measures, pure tone thresholds, and audiometric characteristics [26; 36].  
Conceptualization, K.B. and K.T.D.; methodology, K.B. and K.T.D.; validation, K.B. and K.T.D.; formal analysis, K.B.; writing--original draft preparation, K.B.; writing--review and editing, K.T.D.; visualization, K.B. and K.T.D.; supervision, K.T.D.; project administration, K.T.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to thank [PERSON] for feedback during the conceptualization phase of this project, as well as the anonymous reviewers who gave helpful feedback during the preparation of this manuscript.  
The authors declare no conflict of interest.  
ABR = Auditory Brainstem Response; DPOAE = Distortion Product Otoacoustic Emissions; GST = Global Standing Wave Theory; HL = Hearing Loss; ISSNHL = Idiopathic Sudden Sensorineural Hearing Loss; LFSNHL = Low-Frequency Sensorineural Hearing Loss; LOT = Local Oscillator Theory; OAE = Otoacoustic Emissions; OHC = Outer Hair Cells; PTA = Pure Tone Audiometry; SOAE = Spontaneous Otoacoustic Emissions; SNHL = Sensorineural Hearing Loss; SNR = Signal to Noise Ratio; SPL = Sound Pressure Level; SSNHL = Sudden Sensorineural Hearing Loss; TEOAE = Transient Evoked Otoacoustic Emissions; VEMP = Vestibular Evoked Myogenic Potential.
# Quantum Key Distribution for 5G Networks: A Review, State of Art and Future Directions
## 4 Quantum Key Distribution Categories
### QKD Based on Entanglement
* LM05 protocol [108] is similar to BB84 protocol using four states obtained through two common unbiased bases. The protocol works by [PERSON] forwarding the state to [PERSON], where [PERSON] will then encode the state by utilizing unitary transformation. Next, [PERSON] will send the encoded qubit to [PERSON], where the sharp measurement will be produced. A control mode is randomly executed to ensure security where the measurement of the received qubit will be performed. Then the comparison of the results will be conducted through a public channel. Nevertheless, quadratic scale losses occurred for the two-way channel of the protocol [109]. In QKD protocols, the aspect of distance must also be taken into considerations. Most current protocols do not provide a long-distance communication channel. According to [PERSON] et al., the current distance record that the QKD protocol can cover is around 509 km [66].
## 3 Finite Element Modeling and Parametrization  
### Geometry of the Butt Joint  
A schematical representation of the butt joint geometry is given in Figure 1. The butt joint is defined by five parameters: the plate thickness \(t\), the weld toe angle \(\beta\), the weld toe radius \(r\), the ratio of weld seam width to plate thickness \(w/t\), and the undercut \(u\). These parameters are known to be primarily decisive for the SCFs at weld transitions [19, 44, 45, 46] and will therefore be used as input parameter for the machine learning approach based on a neural network. By using a neural network, it is possible to determine SCFs at weld transitions directly from measured weld geometry data without the need for further finite element simulations.  
Figure 1 displays the exact definitions of the parameters except for the undercut. The undercut is set in Figure 2.  
The geometry parameters are varied within the minimum and maximum bounds of each parameter given in Table 1, which were determined by laser measurements. A 3D laser scanner with a spatial resolution of about 0.11 mm and a vertical accuracy of 0.01 mm, attached to a carriage on a stiff frame, were used for the measurements [15]. The lower and upper bound is based on reasonable ranges for these parameters and the resolution of the laser scanning device.