esa-sceva/satcom-chunk-collection huggingface dataset:
# Collision Avoidance Adaptive Data Rate Algorithm for LoRaWAN
## 2 Technological Background
### LoRa Modulation  
The four key LoRa transmission parameters include the transmission power, bandwidth, coding rate, and spreading factor. BW is the frequency range, error correction is provided by CR, and SF represents the data spreading across time. The transmission factors influence the radio range, time-on-air, noise robustness, data rate (DR), and receiver decoding [25]. LoRa modulation utilises spreading factors between 7 and 12 to find a balance between the coverage range and DR. LoRa symbols are made up of 2 SF chirps, distributed across the whole frequency domain. Depending on the regional parameters, LoRaWAN operates at 125 kHz, 250 kHz, or 500 kHz bandwidth [26]. LoRa uses a frequency shift CSS technology that outputs M = 2 SF possible signal waveforms within a given bandwidth BW where the symbol duration is given by T\({}_{\text{s}}=\frac{\text{M}}{\text{BW}}\)[27]. The LoRa modem employs a coding rate, providing improved enhanced interference and decoding error protection. The LoRa specification prescribes CR values of 4/5, 4/6, 4/7, or 4/8. A high CR value indicates more error-correcting bits and, therefore, more robust protection for the data transmitted. The disadvantage is an increase in the time-on-air (ToA) which drains the battery power. The transmission power is a parameter that could also be adjusted to enhance communication performance. The LoRa modulation bit rate \(R_{b}\) would be outlined as shown in (1).  
\[R_{b}=BW*\frac{SF}{2^{SF}}*CR \tag{1}\]  
where \(SF\) = Spreading Factor; \(BW\) = Bandwidth; and \(CR\) = Coding Rate.
###### Abstract  
The poor wear resistance of 316L stainless steel restricts further practical application. In this study, to improve its microhardness and wear resistance, the TiC and NbC ceramic particles were introduced to 316L powder fabricate (Ti, Nb)C ceramics-reinforced composite coatings by laser cladding. The effects of ceramics addition on the phase composition, microstructure, microhardness, and wear properties of the composite coating were investigated with an X-ray diffractometer, optical microscopy, scanning electron microscopy, a Vickers hardness tester, and a multi-functional surface performance tester. Results indicate that the TiC and NbC ceramic particles were distributed at the grain boundaries, effectively inhibiting the grain growth and refining the microstructure. The addition of ceramic particles could have decreased the temperature gradient and promoted the transformation from columnar crystals to equiaxed crystals. In addition, the microhardness was improved due to fine grain strengthening and solid solution strengthening. The friction coefficient and cross-sectional area of the composite coating were 0.381 and 8164.732 \(\upmu\)m\({}^{2}\), which was 0.846 and 0.603 times that of the 316L coating, respectively. Moreover, severe adhesive wear and plastic deformation was transformed into slight adhesive wear and abrasive wear due to the addition of TiC and NbC particles. This study provides new approaches to improving the wear resistance of 316L stainless steel and broadens its application.  
316L stainless steel; laser cladding; composite coating; microstructure; wear resistance Article
# Ciphered BCH Codes for PAPR Reduction in the OFDM in Underwater Acoustic Channels
## 6 Conclusions  
We proposed a low complexity PAPR reduction technique for an underwater acoustic OFDM transceiver. It consists of multiple pre-determined cipher keys, generated using a high-end PC, that are used for XOR ciphering of the BCH-encoded and decoded data at the OFDM transmitter and receiver, respectively. Our findings suggest that while reducing PAPR, the ciphered BCH technique improves the BER performance of the system using channel coding. It is observed that a higher PAPR reduction and a lower BER is achieved when lower code rate BCH codes are used. Using multiple keys, known to both the transmitter and receiver, a level of encryption is achieved as an added advantage. Given the power and processing limitations of the underwater acoustic nodes, the proposed technique is easy to implement and reduces the computational complexity of the transceiver, as most of the processing for the key vector search is performed offline. The most appropriate keys generated are stored in both the transmitter and receiver and are used for ciphering purposes and to reduce the PAPR of the OFDM signal.  
[PERSON] and [PERSON] conceived the idea and designed the system. [PERSON] and [PERSON] implemented the model, ran the simulations and wrote the manuscript. [PERSON] conceived and designed the UWA channel model used and contributed to the discussion section. [PERSON] and [PERSON] reviewed the final version of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a grant from the Spanish Ministry of Science and Innovation in the framework of the project \"NAUTILUS: Swarms of underwater autonomous vehicles guided by artificial intelligence: its time has come\" (PID2020-112502 RB / AEI / 10.13039/501100011033).  
Not applicable.  
The authors express their gratitude to the Escuela Tecnica Superior de Ingenieria de Telecomunicacion and the Instituto de Ingenieria Oceanica, Universidad de Malaga, Malaga, Spain. We would also like to thank College of Computer and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia.  
The authors declare no conflict of interest.
# Spatial Dynamics of Olive Fruit Fly Adults in the Framework of a Monitoring Trap Network
## 2 Materials and Methods
### Samplings  
In all experimental sites, yellow sticky panels that were locally available were used for olive fly trapping. They were provided by Novagrica Biological Products and Solutions (Agios Paraskevi, Greece) for Greece, FASMI (Cercemaggiore, Italy) for Italy, Russell IPM (Deeside, UK) for Lebanon, Econex (Valencia, Spain) for Spain; in case of Tunisia panels were assembled using plastic panels (Societe Ben Jididia Freres, Sfax, Tunisia) and raticolglue (Zapi spa, Conselve, Italy). The size of the panel used was 14 \(\times\) 20 cm. The panels were deployed either baited with solid ammonium carbonate (BT) in vials or without any attractant substance (UT). Ammonium carbonate was provided by Novagrica Biological Products and Solutions (Agios Paraskei, Greece), Novapher (Settimo Milanese, Italy) for Italy, ThermoFisher Scientific (Waltham, MA, USA) for Lebanon, VWR Chemicals (Radnor, PA, USA) for Spain, Sigma-Aldrich (Saint Louis, MO, USA) for Tunisia. In UT case, only visual cues acted in attracting flies, shortening the range of attractivity and minimising the interferences between traps as much as possible, even if the UTs formed a denser grid around the BTs. All traps were placed in the same way, i.e., at 1.8-2 m high, to the south part of the tree canopy. Males and females of the fly were counted and a survey of traps was performed at regular intervals every 4-7 days depending on the country. Dispensers were replaced once per month.  
Traps were deployed in the mid-summer-autumn period, when drupes on the trees are susceptible to female oviposition and monitoring traps are usually deployed in the field; however, this was not the case for Tunisia, where the timing of the trap deployment was anticipated because of the very few olive flies usually trapped in summer-autumn.  
In addition, fruit load and active fruit infestation were sampled in the trees where the traps (either BT or UT) were placed. For fruit damage, 25 fruits per tree were checked at different intervals in the same period that the traps were in the field.
## 4 Design of RT-CCCS
### Packet Scheduling  
The first level of the proposed resource scheduling structure is packet scheduling for SUs' data. In packet scheduling study, we select the Cross-CC PF, following the description as outlined in [13]. The Cross-CC PF takes the statistics from all CCs into consideration, so it can achieve an overall better resource allocation. In this paper, the assignment of the user \(k\) should meet the following scheduling metric:  
\[k^{*}=\arg\max_{k}\left(M_{k}\right) \tag{1}\]  
where \(k^{*}\) is the selected user, and \(M_{k}\) is the priority metric. The priority metric is calculated by dividing the instantaneous transmission data rate of the current dominant CC by its past throughput over all aggregated CCs,  
\[M_{k}=\frac{R_{dominant}}{\tilde{R}_{k}} \tag{2}\]  
where \(R_{dominant}\) is the instantaneous transmission data rate of the current dominant CC, \(N\) is the number of CCs which is used for SA, and \(\tilde{R}_{k}\) is the average delivered throughput for user \(k\) in the past. The difference from Cross-CC PF proposed in [13] is that the packet scheduling is carried out before CC selection.
## 6 Simulation Results
### Simulation Parameters  
Table 2 presents the nominal LLRR parameters for simulation, considering a subject of height \(h=1.75\,\mathrm{m}\) and weight \(m=75\,\mathrm{kg}\). To define the model parameters reported in this table, we used the CAD design to obtain its masses \(m_{ir}\) and lengths \(\ell_{ir}\). In order to define the lengths \(\ell_{is}\) and weights \(m_{is}\) of the subject's segments, models reported by [PERSON] (2018) and [PERSON] (2018) were used. These models contain anatomical data for analyzing human motion, and we used the average value between male and female subjects. Segment weight \(m_{i}\) was expressed as a percentage of total body weight \(m\) as reported by [PERSON] et al. (2018). Segment length \(\ell_{is}\) were expressed as a percentage of total height \(h\) as reported by [PERSON] et al. (2018). These parameters were used as the nominal values. However, we mentioned before that they are not the actual values when an amputee subject with probeses is analyzed. The backrest \(B_{r}\) of the LLRR is not an actuated joint. However, it is imperative to report the \(B_{r}\) of the robotic system for each test since it determines in which position the robotic system is. Figure 1 presents the conventions for the \(B_{r}\). A counterclockwise movement is the convention for a positive arc of movement, and \(B_{r}\) is measured taking into account the horizontal plane. The ROM of the three joints of the lower limbs during the simulations should be within the values presented by [PERSON] et al. (2018).  
In this work, the viscous friction \(\mathrm{vec}\{v_{i}\}\) was assumed in a heuristic way, taking into account that the simulation showed a three-segment pendulum behavior when the control algorithm is deactivated. It was assumed in this way so that the model behaved as a pendulum that oscillated one time before stabilizing when it was dropped from the position \(\mathbf{q}=(0^{\circ},\ 0^{\circ},\ 90^{\circ})\), as expected in the LLRR. The method consisted of recording a video of the actual LLRR in the laboratory and using Kinovea, free and open-source software used for the motion analysis of sports techniques and gestures, used mainly by sports coaches and athletes to explore, study or comment on performance. Additionally, a heuristic method was used to tune the gains of the PID controller, i.e., the outer loop feedback. These gains were configured to reduce the error and the oscillations. The sampling period was set to \(\mathrm{TS}=0.001\,\mathrm{s}\).  
Regarding the moment of inertia, we define that it would be calculated taking into account a bar centered on the CoM, and with length \(2b_{i}\), where \(b_{i}\) is the distance from the joint \(i\) to the CoM of said segment \(i\). According to [PERSON] et al. (2004) [PERSON] et al. (2004), the moment of inertia for a thin bar is \(I=\frac{1}{3}ML^{2}\). In this case, the thickness of the bar is not taken into account. In summary, for us, the inertia of each segment would be \(I_{ci,zz}=\frac{1}{9}m_{i}(2b_{i})^{2}\). Finally, the position of the CoM \(b_{i}\) was configured as reported by [PERSON] et al. (2018).
## 2 Superconducting Current Leads Development
A low-melting-point soldering alloy (In-Sn) was used for the soldering to minimize the thermal stress on the tapes. To assess the effectiveness of the production step and evaluate the performance of the current leads, these were tested in liquid nitrogen. The results following the subtraction of a linear contribution ascribable to the redistribution of the current at the copper terminals due to the close voltage placement are shown in Figure 2. A rapid voltage rise can be observed above 1500 A, demonstrating the occurrence of a superconducting transition. The critical current was estimated by fitting the experimental curves with a linear contribution over the imposed \(\left(\frac{I}{Tc}\right)^{N}\) formula (1 \(\upmu\)V/cm criterium), resulting in values close to 1750 A for both leads. These results suggest good homogeneity between the two samples and, most importantly, values in line with the expected ones, with approximately 220 A per single tape, as measured in the self-field in liquid nitrogen. The assembled high-current probe is reported in Figure 3, which shows the steel tube covering the resistive leads and the HTS part with a mounted sample holder. For practical use, to protect from potential impacts and to vehiculate helium vapors inside the tubing, during the measurements, the HTS leads are surrounded by a thin G10 high-pressure fiberglass laminate tube, which is not shown here in the picture. During the measurements of the HTS samples, the status of the superconducting leads was assessed by means of voltage measurements carried out along their length, with no evidence of quenches of the superconducting portion occurring at the highest currents and fields. The helium consumption from the bath was drastically reduced following the introduction of the HTS part. While a single measurement run could be performed with the resistive lead, due to both the serious helium consumption during the probe cooling and to the heat leak caused by the large copper area, several samples (i.e., cooling and heating of the probe and high current measurements) could then be measured with the same helium amount.
# A Dynamic Urban Mobility Index from Clustering of Vehicle Speeds in a Tourist-Heavy City
## 1 Introduction
Thus, there is a gap in smart city studies and in the formulations and calculations of subindices in the developing regions where data collection is still in its beginning phases. In regions with inadequate infrastructure and limited data availability but that are highly attractive destinations, such as tourist hotspots, traffic congestion challenges become even more pronounced. The interplay between transportation and tourism has been extensively studied [27; 28]; however, little attention has been given to the costs (time cost and environmental pollution) of road transportation [29]. The highly fluctuating state of mobility, influenced by unpredictable fluctuations in tourist numbers, negatively impacts residents, tourists, and the surrounding environment. Additionally, increased traffic congestion contributes to a deteriorating public perception of the destination [30]. The advent of GPS trackers, which are sensors capable of location awareness, has significantly transformed the potential for gathering detailed information about human movements when compared with conventional methods like travel surveys, space-time diaries, or laborious interviews. The utilization of spatial and temporal data combined and collected from GPS trackers has been extensive and has proven valuable in examining various aspects of mobility, such as addressing contemporary mobile navigation applications [31], assessing urban transport [32; 33], evaluating road network quality [34; 35], and studying population transport [36], among other applications. This paper aims to address the gap in the literature on smart city mobility subindex formulation in the context of developing nations, specifically focusing on regions with limited access to high-resolution data. Additionally, it seeks to contribute to data-driven analyses of vehicle mobility in a highly urbanized tourist hotspot by examining geolocated GPS traces of vehicles and developing a dynamic mobility index that measures the ease of vehicle transport within the city.
# Fuzzy-Based Time Series Forecasting and Modelling: A Bibliometric Analysis
## 5 Analysis and Overview of the Most Cited Articles
### A Technical Overview of the Most Cited Articles
[PERSON] and [PERSON] proposed an FTS based on the fuzzy K-medoid clustering algorithm for the fuzzification step to remove outliers and abnormal observations [47]. The new method was compared to two existing FTS models based on the fuzzy C-means and Gustafson-Kessel [52] clustering algorithms to predict air pollution time series data consisting of weekly SO\({}_{2}\) concentrations measured at 65 monitoring stations in Turkey. RMSE and percent bias were used to assess the performance of the forecasting models. [PERSON] et al. designed a method for multiple time series forecasting using many inputs/many outputs (MIMO) fuzzy aggregation models (FAM) with modular neural networks [48]. In this work, different MIMO-FAM approaches were used: (i) one based on the use of ANFIS with subtractive clustering and fuzzy C-means; (ii) one based on type-1 FIS of Mamdani type; and (iii) one based on interval type-2 FIS. The experiments were carried out over the Mexican stock exchange index, TAIEX and data from the National Association of Securities Dealers Automated Quotation and the performance was evaluated with MAE, RMSE and mean squared error. From these articles, some comments can be made: (i) most works focused on proposing some kind of hybridisation to improve the performance of the FTS model; (ii) the most practical works corresponded to ad hoc solutions for specific problems, ranging from air pollution data to stock index prices and COVID-19 time series; (iii) the most used benchmark datasets were TAIEX and the University of Alabama enrolment; and (iv) many different metrics were applied to performance assessment.
## 4 Hard Decision Fusion  
For the \(m\)th cluster, we can get its binary decision \(u_{cm}\)\(\in\)\([0,1]\) according to Equation (16) and its corresponding probability of false alarm \(P_{fcm}\) and the probability of detection \(P_{dcm}\). For M clusters, \(\mathbf{U}=[u_{c1},u_{c2},\ldots,u_{cM}]\) is the decision vector of all CHs. Then, the FC combines all binary decisions from all clusters into a final decision. The likelihood ratio test at the FC is:  
\[\mathrm{L}(\mathbf{U})=\frac{P\{\mathbf{U}|H_{1}\}}{P\{\mathbf{U}|H_{0}\}}=\frac{P\{u_{c_{1 }},\ u_{c_{2}},\ldots,u_{c_{M}}|H_{1}\}}{P\{u_{c_{1}},\ u_{c_{2}},\ldots,u_{c_{M}}|H_{ 0}\}} \begin{array}{c}H_{1}\\ >\\ <\\ H_{0}\end{array} \tag{21}\]  
where \(T_{0}\) is determined using the global desired probability of false alarm at the FC.  
Here, the optimal decision rule of the FC can be derived as [2]:  
\[\sum_{m=1}^{M}w_{m}u_{c_{m}} \begin{array}{c}H_{1}\\ >\\ <\\ H_{0}\end{array} \tag{22}\]  
and the coefficients \(\{w_{m}\}\) are determined using the probability of false alarm \(P_{fcm}\) and the probability of detection \(P_{dcm}\):  
\[w_{m}=\left\{\begin{array}{cc}\log\frac{P_{dcm}}{P_{fcm}}&if\ \ u_{c_{m}}=1,\ \ \ m=1,\ 2,\ldots,\ M\\ \log\frac{1-P_{dcm}}{1-P_{fcm}}&if\ \ u_{c_{m}}=0,\ \ \ \ m=1,\ 2,\ldots,\ M \end{array}\right.\]
# Experimental Investigations on the Effects of Fatigue Crack in Urban Metro Welded Bogie Frame
## 2 Experimental Methods
### On-Track Tests
#### 2.2.1 Dynamic Stress Test  
The full-range dynamic stress waveforms of point D02, D11 and D17 are shown in Figure 7. The maximum stress amplitudes of D02, D11 and D17 are 91.5 MPa, 102.4 MPa and 85.6 MPa respectively. According to the stress-time history of each point, the stress spectrum can be obtained by adopting rain-flow counting method. Thus, with _S-N_ curve and Miner's rule of accumulative damage, the damage of each point can be calculated. The results indicate that the damages of these testing points at the weld toe of positioning block are all larger than 1, which provide evidence of initiation and propagation of fatigue crack.  
A typical stress time history signal of point D02 shown in Figure 8. The power spectral density (PSD) analysis and the speed-time-frequency relationship analysis of point D02 in some section are shown in Figures 9 and 10, respectively. It could be observed that the dominant frequencies are 71 Hz, 89 Hz and 94 Hz and strongly relevant to the operating speeds. This demonstrates that these frequencies are not the structural natural frequencies and there is no obvious behavior of the resonate vibration.
## 3 Test Details
### TH DOEs of Modulating Factors
#### 3.3.2 Test Results  
Here, \(R_{partnerage}\) was used for analyses to get rid of the uncertainty caused by LLCR variation. Since it is typical that under TH, chain LLCR maps do not have an obvious pattern. Thus, only analyses based on part average resistance could tell if the improvement is a random kick in or a true stable help. Three ROs were collected at 192 h, 312 h, and 408 h. They were plotted in the variability chart, as shown in Figure 20. It can be seen from the comparison of leg3 vs. leg4 and leg3 vs. leg 5 that the part average was constantly improved by about 1.5 milliohms to 2 milliohms. If compared with the design requirement of \(R_{partnerage}\) of 19 milliohms, the improvement was about 7.9%-10.5%. There were more tests conducted to confirm the improvement by implementing the proposed design, even though they were not described in this paper, the improvement on part average resistance always held.  
There is not much difference between leg 4 and leg5. The increase in Au thickness seems to make a negligible difference. It is believed that it is because the typical industrial plating process is reasonably mature already.  
Besides, it is noticeable from Figure 20 that the resistance, in general, appears to get slightly better with time, especially from 192 h RO to 312 h RO. The author's suspect that this is due to the system creep and contact relaxation that result in contact force redistribution and contacting status  
Figure 20: Variability chart for part average.  
readjustment. Additionally, typically creep and relaxation happen faster within the early stage, which may explain why the drop in resistance is more obvious from 192 h RO to 312 h RO. More study is needed in future work to further investigate this phenomenon.