esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 IoT Deployment Opportunities in Developing Countries
### Leveraging Cellular Networks for IoT Deployments in Nigeria
#### 4.1.2 Spectrum and IoT Issues  
The advent of IoT technologies has pushed for more wireless devices to operate in a different spectrum. Whereas NB-IoT and LTE-M operate in the licensed spectrum with minimal interferences, it uses hybrid automatic repeat request (HARQ) to guarantee data delivery, and narrowband fidelity (NB-FI), SIGFOX and LoRaWAN operate in the unlicensed ISM spectrum with duty cycle limitations. For these reasons, NB-IoT and LTE-M are most suitable for applications with a higher QoS of non-time-critical requirements. Transmissions within the ISM bands are not reliable and require the acknowledgement of uplink and downlink packets, due to their inherent potential for interference and collisions with other users of the spectrum. The higher QoS in NB-IoT and LTE-M networks are at the expense of a higher cost of acquiring a licensed spectrum. The sub-GHz spectrum auction was over USD 500 million/MHz in 2017 [19] and in 2021, an increase was seen in Ofcom's auctioning prices of 700 MHz and 3.6 GHz-3.8 GHz for 5G in the UK [41]. Six lots of 2 \(\times\) 5 MHz (700 MHz band), four lots of 10 MHz (700 MHz band), and twenty-four lots of 5 MHz (3.6 GHz band) amounted to reserved prices of GBP 100 million/slot, GBP 1 million/slot, and GBP 20 million/slot, respectively.  
Spectrum sharing is an essential part of IoT development. Spectrum bands between 1 GHz and 6 GHz that are suitable for IoT development in a cost-effective manner have been allocated for other uses. Finding an unused block of frequencies within a specific area is one easy way of solving spectrum issues or building a contiguous spectrum to meet a particular service requirement if applicable (suitable for the national auction approach). Due to the unavailability of a free spectrum, grey space sharing, as described in [42], is most applicable in situations where there is not a dynamic technology, and a particular frequency is used over a wide geographical area. A secondary user is only possible through established policy regulation and governance frameworks. The white space spectrum (WSS) is generally regarded as the region of unused spectrum within or around existing bands, and presents potential opportunities for IoT deployment in rural areas and developing economies [43].
## 3 Results and Discussion  
### Bacterial Community Profile of Vernicasts  
The bacterial community targeted by the 16S V3-V4 region of the ribosomal DNA gene yielded 327,040 sequences from _P. losbanosensis_ vermicasts (PL) and 200,984 sequences from _E. eugeniaae_ vermicasts (EE) (Table 1). After assembly, preprocessing, and clustering, 4734 and 6111 sequences were obtained and clustered into 280 and 133 OTUs from PL and EE, respectively (Table 2). The Good's coverage of the samples was found to be 1.0 (PL) and 0.9997 (EE) (mean \(\pm\) SD), which implies that most of the diversity was captured. The non-parametric Shannon index showed that more diverse species of bacteria were found in PL (6.6834) than in EE (4.2816).  
A total of 83 bacterial families were present in PL dominated by Vicinamibacteriaceae (12.0%), Cytophagales (8.72%), Thioprofundaceae (5.39%), Gammaproteobacteriaobacteria (4.88%), Geodermatophilaceae (4.63%), Zoogloeaceae (4.14%), and Cytophagaceae (3.91%) (Figure 4). On the other hand, EE was inhabited by 37 families dominated by Flavobacteriumaceae (53.56%), Cellvibrionaceae (9.11%), Moraxellaceae (6.50%), Caulobacteraceae (4.40%), and Pseudomonas (3.80%). Genera _Vicinamibacter_ (9.63%), _Chryscolinea_ (8.72%), _Thioprofundum_ (5.39%), _Acidibacter_ (4.88%), _Blastococcus_ (4.56%), and _Azoarcus_ (4.14%) cover large parts of the 128 bacterial genera in PL, while _Myroides_ (34.32%), _Confluentibacter_ (11.34%), _Cellvibrio_ (9.11%), _Acinetobacter_ (6.35%), and _Flavobacterium_ (4.17%) were relatively more abundant among the 50 genera in EE (Figure 5). In total, 153 and 68 species were present in PL and EE, respectively. Major bacterial species identified in PL were _Vicinamibacter silvestris_ (9.63%), _Chryscolinea serpens_ (8.72%), _Thioprofundum lithotrophicum_ (5.32%), _Acidibacter ferrierducus_ (4.88%), _Blastococcus saxobsidens_ (4.56%), and _Azoarcus olearius_ (4.06%). EE  
Figure 4: Bacterial phyla (**a**) and dominant families (**b**) in _P. loshanosensis_ (PL) and _E. eugeniae_ (EE) vermicasts generated by QIIME-UCLUST.  
vermicasts were dominated by _Myroides marinus_ (33.87%), _Confluentibacter lentus_ (11.34%), _Cell Vibrio japonicus_ (7.15%), and _Acitetobacter rudis_ (6.35%).
## 4 Results and Discussion
### Influence of Evaporation Temperature  
In order to understand the effect of evaporation temperature on the performance of the ice maker, the ice making characteristics and the performance characteristics of the scraper type slurry type seawater ice-making device were evaluated by varying the evaporation temperature from \(-8\)\({}^{\circ}\)C to \(-15\)\({}^{\circ}\)C.  
Figure 8 shows the temperature of the ice slurry with seawater according to the evaporation temperature of the refrigerant. Figure 9 shows the IPF calculated from the initial salinity of seawater and the temperature of the ice slurry with seawater at the outlet of the evaporator measured above.  
As seen from Figure 8, the temperature of the ice slurry with seawater shows a tendency to decrease as the evaporation temperature decreases and changes from \(-2.29\)\({}^{\circ}\)C to \(-2.14\)\({}^{\circ}\)C depending on the evaporation temperature. Figure 9 shows the IPF of the ice generator according to the evaporation temperature. The IPF is increased from 6.9% to 12.7-19.6% according to the decrease of the evaporator temperature. It appears that the IPF and the refrigerant evaporation temperature are closely related, and the IPF is linearly increased as the evaporation temperature of the refrigerant is decreased. This is because the temperature difference between the two fluids in the evaporator increases as the evaporation temperature of the refrigerant decreases and cooling capacity in the evaporator increases. As the evaporation temperature decreases, the IPF tends to increase and it  
Figure 8: Temperature of ice slurry with seawater according to evaporating temperature.  
Figure 9: IPF with respect to evaporating temperature.  
increases linearly to the evaporation temperature of \(-13\)\({}^{\circ}\)C. However, at the lower temperatures, there is an inflection point where the increase rate of the IPF gradually changes. Therefore, in this system, lowering the evaporation temperature to below \(-13\)\({}^{\circ}\)C seems to have advantage for increasing IPF. When the evaporation temperature is kept under \(-13\)\({}^{\circ}\)C, the seawater flowing down the wall of the tube increases the adhesion with the tube wall, and the seawater ice rises by the shear rather than slip off the tube wall. So it is considered that the ice remaining on the surface acts as heat resistance and the rate of increase of the ice-making amount becomes gradual.  
Figure 10 indicates the cooling capacity in the evaporator according to the evaporation temperature. As the evaporation temperature decreases from \(-8\)\({}^{\circ}\)C to \(-15\)\({}^{\circ}\)C, cooling capacity increases to 1.13\(-\)1.418 kW. The reason why the cooling capacity increases as the evaporation temperature decreases is because the temperature difference between the two fluids in the evaporator becomes larger as the evaporation temperature of the refrigerant decreases, as described above.
# A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification
## 4 Experiment  
### Evaluation of the Identification Model  
The performance of the identification model was evaluated. A Wi-Fi device was set in the corridor, and the experimenter walked at a constant speed in the corridor and room separately with a mobile phone to collect the RSSIs and ranging results. The data collected in the corridor were seen as LOS data, while those collected in the room were seen as NLOS data. A group of data consisted of an RSSI and a ranging result. A total of 8360 groups of data were collected, among which 1800 groups were used for training and 6560 groups were used for testing. The training data acquisition time was only one day away from that of the testing data, and the training data did not participate in the test.  
Figure 9: Single point positioning algorithm.  
The identification of NLOS/LOS was a binary problem, so precision and recall were utilized to evaluate the model. They were calculated by Equations (21) and (22), respectively, where the TP (true positive) is the number of LOS signals that were accurately identified, FP (false positive) refers to the number of NLOS signals identified as LOS signals, and FN (false negative) points to the number of LOS signals identified as NLOS signals. All the data were divided into different ranges according to the ranging results in order to evaluate the precision and recall at different distances. Here, 3280 groups of LOS data and 3280 groups of NLOS data were used to test the precision, while 3280 groups of LOS data were used to test the recall. The model output the LOS/NLOS recognition results, then TP, FP, and FN were counted to calculate the precision and recall of each interval. In this experiment, the threshold \(\theta\) of the model was set as 0.7.  
\[P=\frac{TP}{TP+FP} \tag{21}\]  
\[R=\frac{TP}{TP+FN} \tag{22}\]  
Figure 10 shows the precision at different ranges. Through calculation, the average precision was 97.08% and the MSE was 0.07. Figure 11 shows the cumulative distribution of precision. All precisions were higher than 70%, 9.09% of which were lower than 85%, and 15.15% of which were lower than 95%. It can be seen that the precision of the model is poor at some ranges, which might be caused by the poor fitting of the ranging result and RSSI.  
Figure 11: Cumulative distribution function of the precisions.  
Figure 10: Precision at different ranging results.  
The recalls and mean probabilities at different ranges are shown in Figure 12, where the value of the blue column is roughly the same as that of the red line. It can be seen that the set of \(\theta\) could distinguish LOS signals and NLOS signals. However, through calculation, the average recall was only 74.97% and the MSE was 0.2. Figure 13 shows the cumulative distribution of recalls. Here, 24.24% of the recalls were lower than 60%, and 51.52% of them were lower than 80%, indicating that the model often misjudges some LOS signals.
# The Noise-Reduction Characteristics of Microstructure of Dragonfly Wing Leading Vein
## 2 Bionic Modeling of Dragonfly Wing Microstructure
### Effect of Serrated Microstructure Width on Noise Reduction  
The size parameters of the leading-edge microstructure of the bionic wing 3 were selected as the reference. By changing the width of leading-edge microstructure serration on the bionic wing and calculating the overall sound pressure level generated by the bionic wing, the influence of the width of microstructure serration on the noise-reduction effect was obtained. The width of the bionic serration microstructure was enlarged by 2, 4, 6, 8, 10, 50, 100 and 200 times, respectively, and numbered r, j, k, L, m, n, o and p in turn. The overall sound pressure level of the bionic wing is shown in Figure 6, and the overall sound-pressure-level reduction is shown in Table 7.  
Figure 6: Pressure nephograms of several bionic wings’ surface: (**a**) wing e, (**b**) basic wing, (**c**) wing c, and (**d**) wing i.  
Figure 8 shows that the sound pressure level of the bionic wing with the noise-reduction effect was lower than the basic airfoil in the whole frequency range; that is to say, noise reduction was achieved in the whole frequency range. By comparing wing k and wing o, it could be found that the reduction of the sound pressure level in the leading edge region had a decisive effect on the overall sound-pressure-level reduction of noise.  
The pressure nephograms of four bionic wings in Figure 9 show that the pressure fluctuation on the upper surface of the three bionic wings was significantly lower than the basic wings, so the overall sound pressure level of the noise generated by the bionic wings was low. The width of the bionic serration microstructure significantly changed the pressure fluctuation distribution at the leading edge and extended the strong pressure fluctuation to the upper surface of the wing, but it had no significant effect on the overall sound pressure level of the bionic wing, that is, the change of the width of the serration microstructure at the leading edge of the bionic wing had no significant effect on the noise.
## 3 Integrated Model  
Equipment with nonlinear characteristics of the degradation trajectory is widely available, such as aero-engines [31], milling tools [32], etc. Neglecting the nonlinearity of the degradation process will lead to increased uncertainty in the degradation model [33]. In the last few decades, many degradation models have been proposed in the field of reliability engineering [34; 35; 36; 37; 38]. In particular, the degradation of each system element is considered in [38] to model the degradation process. Stochastic process methods are used to evaluate system reliability [39; 40]. In order to achieve an accurate description of non-monotonic degenerative processes, the Wiener process began to be introduced into degenerative modeling and has become the most widely used model for stochastic degenerative processes. Since the linear Wiener process is a special form of the nonlinear Wiener process, the nonlinear Wiener process to describe the degradation trajectory of the product has some practical significance and it is more widely used in engineering practice.  
According to the nonlinear degradation model of the Wiener process, the \(X(t)=X(0)+\lambda\Lambda(t,b)+\sigma_{B}B(t)\), \(X(t)\) denotes the magnitude of degradation in the device performance at time \(t\), \(\lambda\) is a drift coefficient. \(\Lambda(t,b)\) is a continuous nonlinear function containing parameters \(b\) and time \(t\), which usually has \(\Lambda(t,b)=t^{b}\) and \(\Lambda(t,b)=\exp(bt)-1\). \(\sigma_{B}\) is the diffusion coefficient, the same value among the same equipment, thus, reflecting the common features among similar products. \(B(t)\) denotes standard Brownian motion. To not lose generality, make \(X(0)=0\), \(\lambda\) and \(B(t)\) independent of each other. Assuming that the drift coefficients corresponding to different stages in the same degradation process are constant, it is easy to obtain \(\lambda_{i+1}=\lambda_{i}\). The Wiener process is an independent incremental process and the degradation increments are \(\Delta X(\Delta t_{s})=X(t_{i+1})-X(t_{i})\). It is assumed that the continuous nonlinear function satisfies \(\Lambda(t,b)=t^{b}\) and degradation increments \(\Delta X(\Delta t_{s})\) approximate to \(\lambda(t_{i+1}^{b}-t_{i}^{b})\), where \(b\) is a constant.  
For \((t_{i},t_{i+1})\) (\(i=0,1,2,\cdots m,\cdots,n-1\), production of the \((i+1)\)_th_ product), assume that the set-up time for each product and the repair time for each degradation state are known, \(t_{i+1}=t_{mt}^{i}+\Delta t_{s}\), \(\Delta t_{s}\) is determined by the productivity \(u\) and the production lot sizing, \(\Delta t_{s}=Q_{s}/(N\cdot u)\), the \(s-\) th (\(s=(i+1)\%m\)) product should be produced within \((t_{mt}^{\prime},t_{i+1})\). The total cost from \(t_{0}\) to \(t_{i}\) is \(\mathrm{C}_{i}\), the number of PM is \(r_{i}\) and the number of CM is \(f_{i}\).
# Application of External Torque Observer and Virtual Force Sensor for a 6-DOF Robot
## Materials and Methods
### System Architecture
#### 3.1.2 Raspberry Pi-Based  
The system architecture which utilizes Raspberry Pi as the computing platform, based on the Linux system environment, is depicted in Figure 2. The user interface was created using the PyQt5 library in Python, while the algorithm program was implemented using the C programming language. Different functions of the program are assigned to independent cores, and data sharing is performed through Shared Memory, as shown in Figure 2. This reduces resource contention issues and improves the performance of real-time tasks. The commands and data transmission of the six-axis robotic arm are completed through IgH EtherCAT. Compared to the Windows system, the Linux system has a more precise system clock, allowing the motion control command update rate to be set at 4 kHz. Considering the computational complexity of the dynamic algorithm, the computation cycle needs to be approximately 0.8 ms. Therefore, the communication cycle is set to 1 ms, which corresponds to a command update rate of 1 kHz.
## 4 Proposed Framework
### Interoperability and Scalability  
Next, in this subsection, we describe how the suggested architecture ensures scalability and interoperability for the easy integration of various types of edge servers and IoT devices. All these features are crucial for the system's flexibility for multi-information sources and dynamic expansion.
# Coal Seam Thickness Prediction Based on Transition Probability of Structural Elements
## 2 RBF-Kriging for borehole Data Interpolation  
### Ordinary Kriging  
The ordinary Kriging interpolation [11] is a spatial interpolation method that was developed based on the Geo-statistical variation function model. Let \(z(x_{i})\) be the value of the variable \(Z\) at a point x. Given the n measurements \(Z(x_{1}),\ldots,Z(x_{n})\) at the known locations \(x_{1},\ldots,x_{2}\), the Kriging estimator interpolates an estimate of \(z^{\prime}(x_{0})\) at an unsampled location \(x_{0}\) by weighed linear combinations of the available samples:  
\[z^{\prime}(x_{0})=\sum_{i=0}^{n}\lambda_{i}z(x_{i}), \tag{1}\]  
where \(\lambda\) is the interpolation weight. Considering the unbiasedness condition yields:  
\[\sum_{i=1}^{n}\lambda_{i}=1. \tag{2}\]Since the weighting procedure depends on both the distance and statistical distribution of the samples, the variation function is adopted to estimate the weight by fitting a spatial model to the data and then defining the effectiveness of each point [12; 13]. The variation function value at point \(x_{i}\) is obtained by  
\[\gamma(h)=\frac{1}{2N(h)}{\sum_{i=1}^{N(h)}}{\left[z(x_{i})-z(x_{i}+h)\right]}^ {2} \tag{3}\]  
where \(h\) is the distance between two points, \(N(h)\) is the number of pairs separated by \(h\). Therefore, the variation function value \(\gamma\big{(}x_{i}-x_{j}\big{)}\) between observation points \(x_{i}\) and \(x_{j}\), and the variation function value \(\gamma\big{(}x_{0}-x_{j}\big{)}\) between the interpolation point \(x_{0}\) and the observation point \(x_{j}\) can be calculated by the fitting function. In general, the fitting function is chosen by experience. The common model includes mainly the linear model, the Gaussian model, and the Exponential model.  
The interpolation weight in Equation (1) can be computed by combining Equations (2) and (4):  
\[\sum_{i=1}^{n}{\lambda_{i}\gamma\big{(}x_{i}-x_{j}\big{)}}+\varepsilon=\gamma \big{(}x_{0}-x_{j}\big{)},j=1,\ldots,n. \tag{4}\]  
where \(\varepsilon\) is the Lagrange multiplier, which can be calculated as well. \(\varepsilon\) is introduced to make the variance minimum for the estimate in Equation (1). Subsequently, \(\mathrm{z}^{\prime}(x_{0})\) can be obtained by Equation (1).  
Although the Kriging performs well in the coal seam thickness prediction, the variation function model, which is often chosen by experience, may not accurately express the cutting data. In this paper, calculating the interpolation weight using efficient samples and fitting the variation function through RBF improves the Kriging method.
## 6 Numerical Results  
Numerical results were obtained in MATLAB. We created a network that consists of videos of different lengths segmented over fog nodes and accessed by users. The following comparisons were performed to evaluate the performance of the proposed algorithm when different parameters are changed. To this aim, we considered changing the number of users, while keeping the number of fog nodes and videos fixed, changing the number of fog nodes while keeping the number of users and videos fixed, and changing the number of videos while keeping the number of users and fog nodes fixed.  
We hypothesized having a working area with a size of 100 \(\times\) 100 m. The data rate among nodes was supposed to be equal to 150 Mb/s at a distance of 10 m, though it decreased as the distance increased according to (4). The data rate for downloading from the cloud was set at a fixed rate equal to 15 Mb/s.  
There were 5 quality levels corresponding to different video rates, equal to 1 MB/s, 2 MB/s, 5 MB/s, 10 MB/s and 20 MB/s, respectively. The transcoding time was considered a function of the difference between the target and the original quality level. In particular, we set \(\delta_{tc}(d_{pn}^{\bar{d}},d_{pn}^{\bar{d}})=0.1(q-\bar{q})\), corresponding to say that we need 0.1 s for each quality level to be changed. The duration of each segment was a random value between 1 s and 7 s [29]. The system was evaluated through the utility function:  
\[U(x)=log(x+1) \tag{10}\]  
where \(x\) maps the quality of the requesting user. The users were then clustered based on the quality of the segments they request. The higher the number returned by the function, the better the performance of the system [28]. Since the grouping is performed on the quality, \(U(x)\), it corresponds to measuring how well the system is performing and is affected by the change in quality. The utility function was used since it is a simple way to assess the behaviour of the system.  
Three scenarios for evaluating the performance were set up, as detailed in Table 1. When varying the users, an evaluation was performed by having a network that consisted of 10 fog nodes and 10 videos; the number of users increased from 2 users to 100 users. When varying the nodes, the evaluation was performed by having a network that consisted of 20 users and 10 videos; the number of fog nodes increased from 3 fog nodes to 20 fog nodes. When varying the videos, the evaluation was performed by having a network that consisted of 20 users and 10 fog nodes; the number of videos increased from 5 videos to 25 videos. In all these cases, the videos can be short (i.e., 10 segments), medium (i.e., 100 segments) or long (i.e., 1000 segments). In order to have a fair comparison among all the three video length cases, we fixed the capacity of each for g node equal to 20% of all the segments of all the videos present in the system.