esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation and Results
### Simulation and Performance Measurements of the aIMF Algorithm  
The objective of this section is to measure the performance of the aIMF algorithm compared with the original datasets, _i.e._, the EUR-USD exchange rates. To test the performance of the proposed aIMF algorithm when filtering the original signals with white Gaussian noise, we applied a variety of loss estimators, _i.e._, MSE, MAE, MAPE, R\({}^{2}\) and Accuracy count.  
At the beginning, we tested the five exchange rates _i.e._, EUR-USD with Normality and Unit Root tests and found that they are nonlinear and nonstationary time series. Next, we simulated the aIMF algorithm using an R Programming that comprised C\(++\) scripts, which followed the logic in Section 2.2. Figure 7 shows that we produced a new signal, which was filtered by the aIMF algorithm.  
Figure 7: Plots of the original datasets, original datasets \(+\) noise, and the aIMF algorithm.  
Having simulated the aIMF algorithm with all noise parameters mentioned in the early of Section 3.1, the results are in the same magnitude and directions. To simplify the graph presentation, we selected the \(x\)-axis representing the data points in the time series of overall data range, and two sets of \(y\)-axis representing the original datasets, EUR-USD exchange rates (Y2) and the original datasets \(+\) noise (Y1) of which shows only the noise's plot of 1 Hz at 10% amplitude (Y1) with 100% random distribution. We analysed the plots and found that the aIMF curve resided within the curve of \"the original datasets \(+\) noise\". The trend of those three plots was in the same direction. In terms of estimation, Tables 1-6 indicate that all loss estimators, namely; MSE, MAE, and MAPE are relatively low ranging from 0.00296-0.00596, 0.04838-0.06618 and 4.08815-5.59678, respectively.  
The R\({}^{2}\) is in the high ranging, from 0.8533-0.9228, whereas, AIC and BIC are high, up to \(-\)6305.47 and \(-\)6974.09, respectively. The Accuracy count showed 57.69%-79.66%. As a result, the performance of the aIMF is acceptable.  
We continued the simulations using 5% of random distribution of the added noise instead of 100%. The results showed that the less numbers of the random noise distributed the better performance of the aIMF algorithm. For example, at 5% random distribution of the added noise at 1 Hz with 10% amplitude, the loss estimators, namely; MSE, MAE, MAPE, R\({}^{2}\), AIC, BIC and Accuracy count were 0.00014, 0.01019, 0.88981, 0.99820, \(-\)15723.93, \(-\)15706.68, and 95.86%, respectively; and it outperformed
## 3 Results  
We investigated the feasibility of the proposed algorithm for CLSM images when applied for the activity assessment of early carious lesions. Figure 2 shows the acquired images of the tooth specimens including the CLSM image with ROIs for calculating the NNPS, CNR, and COV. Figure 3 shows the images obtained with the conventional noise reduction filters and the proposed algorithm. Based on the magnified region in the CLSM images, it can be observed that there is reduced noise in the images obtained with the proposed algorithm.  
Figure 2: (a) White-light image of the tooth specimen obtained using quantitative light-induced fluorescence-digital system (QLF-D), (b) fluorescence image of the tooth specimen obtained with QLF-D, and (c) CLSM images of the tooth specimen showing region of interest (ROI) A (contrast-to-noise ratio (CNR), and coefficient of variation (COV) calculations), ROI B (CNR calculations), and ROI C (normalized noise power spectrum (NNPS) calculations).  
Accurate and early detection of carious lesions from CLSM images is difficult because of additive noise, which is often characterized using the noise power spectrum (a meaningful parameter used to compare different imaging methods or systems). Figure 4 presents the NNPS results for the conventional noise reduction filters and proposed algorithm.  
Figure 4: NNPS results (over ROI C shown in Figure 2) obtained for the noisy image, median filter, Wiener filter, and proposed TV noise reduction algorithm applied to the confocal laser scanning micrograph of the tooth specimen.  
Figure 3: Tooth specimen obtained using CLSM, including the magnified region: (**a**) noisy image, (**b**) conventional median filter, (**c**) conventional Wiener filter, and (**d**) proposed total variation (TV) noise reduction algorithm.  
Figure 5 presents the evaluated CNR and COV results for the conventional noise reduction filters and proposed algorithm. The proposed algorithm achieved the highest CNR, with the CNR of the noisy image \(<\) median filter \(<\) Wiener filter \(<\) proposed algorithm. In addition, the proposed algorithm also exhibited the lowest COV, with the COV of the proposed TV noise reduction algorithm \(<\) conventional Wiener filter \(<\) conventional median filter \(<\) noisy image.
# S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices
## 6 Conclusions  
PDR systems suffer from the inherent drift in INS-based navigation making them reliable for a limited time and requiring aid by other technologies to correct and compensate for the errors. The aim of this article is to present a novel and enhanced PDR system that can provide a positional solution with acceptable accuracy for a prolonged operation time. By enhancing the performance of PDR, the complexity of aiding and frequency by which it is needed can be reduced. The developed system under the name S-PDR utilizes: a robust step detection technique invariant to motion mode and smartphone use case that is capable of detecting steps with high accuracy, account for misdetection, and can validate steps; an attitude correction method that can be applied frequently without imposing usage or motion constraints, that helps eliminate drift in attitude angles by exploiting user motion patterns and behavior; an enhanced PCA-based heading estimation that combines statistical analysis information from the in-step accelerations window, and accelerations outlier rejection to improve the accelerations correlation representative of motion direction. Test results show that S-PDR can successfully track the position changes of the pedestrian with a low final positional error usually below 6 m after 3 min of operation and meter level average positional error through most of the trajectory. This would have not been feasible if not for an accurate step detection, low error attitudes that do not drift over time, and accurate heading estimation that can successfully track the direction of motion of the user. This positional accuracy makes S-PDR a viable short-term solution for positioning and localization indoors and can be used for aiding GNSS in urban canyons where GNSS signals are degraded or blocked. The increased reliability of the solution impacts the complexity of aiding requirements such as Wi-Fi fingerprinting and magnetic map matching where: lower data resolution is needed--fewer data points to be surveyed--which decreases memory requirements and surveying time, less frequent corrections are needed and improves real-time processing.  
[PERSON] developed the methodology, designed the experiments, analyzed the data, and wrote the paper; [PERSON]-[PERSON] reviewed the methodology, verified the results, and revised the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [PERSON] from NSERC and Canada Research Chairs programs.  
Not applicable.  
Data Availability StatementData will be shared upon request.  
Figure 24: Boxplot of the errors of S-PDR vs. Xsens for outdoor test.  
**Acknowledgments:** This research is under the supervision, support and funding of [PERSON]. The authors thank the handling editor and anonymous reviewers for providing constructive remarks.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Biodegradation Studies  
Analysis of the bio-ferrnents' biodegradation was performed following a previously used procedure based on the general method for determining aerobic biodegradation potential in a mineral medium by carbon dioxide production as recommended by the OECD (Organisation for Economic Co-operation and Development). Moreover, the experimental conditions used (carbon content and inoculum volume) were chosen according to the OECD guidelines. The samples of active sludge were taken from the aeration chamber of the Pomorzany sewage treatment plant in Szczecin, Poland. A microbiological test (Schulke Mikrocount Duo) was used to measure the concentration of active sludge suspensions to calculate the total number of microorganisms (CFU/1 mL of active sludge). A microbiological test containing medium and TTC agar with Tergitol-7 was submerged in active sludge for 10 s. The number of bacteria was determined by comparing the test's appearance to that of a standard test after 96 h at room temperature (Figure S2) [26].  
The system for measuring CO2 produced by microorganisms during the 28-day process was described in the last study [14]--Figure S3. The only sources of carbon and energy were bio-ferrnents (B-P, B-E, B-O, and B-S) and reference compounds (SDS) in 40 mg/L organic carbon concentrations. The starting concentrations for the obtained bio-ferrnents and reference compound were as follows: B-P = 83.30 mg/L, B-E = 80.22 mg/L, B-O = 84.53 mg/L, B-S = 79.62 mg/L, and SDS = 81.48 mg/L.  
Two tests were conducted independently (in two measuring vessels). The amount of produced CO2 was determined using a total organic carbon analysis (TOC-LCSH/CSN, Shimadzu Corporation, Kyoto, Japan). Utilizing the calibration curve y = 4.1187x + 7.1718, R\({}^{2}\) = 0.999, the inorganic carbon (IC) content in the test specimens was determined. The biodegradation degree of the test bio-ferrnents was determined according to the following formula [14]:  
\[\%B=\frac{\left[C_{ICI}\cdot V_{0}+\sum_{i=1}^{n}\left(C_{ICIi+1}+C_{ICIi} \right)\cdot\left(V_{0}-i\cdot V_{P}\right)\right]\cdot R}{m\cdot U}.100\% \tag{4}\]where:  
%B--degree of biodegradation;  
CIC--concentration of inorganic carbon in the test vessel 1.4, obtained by TOC analysis of the test sample corrected by blank (mg/L);  
R--dilution of the sample collected from the test vessel 1.4 (2.5);  
V0--initial volume of NaOH solution in the test vessel 1.4 (0.25 L);  
i--sample number;  
Vp--volume of sample taken from the test vessel 1.4 (0.01 L);  
m--mass of test bio-ferment injected into the test vessel 1.3 (mg);  
U--the proportion of carbon in the test bio-ferment introduced into the test vessel 1.3 (-).
## 3 Image Reconstruction and the MIR Algorithm
### Principle of MIR Algorithm
```
0: Radio Frequency Sensors Data
0: Brain Images
1:for\(A_{n}\gets 1\) to \(A_{t}\)do
2:for x, y \(\leftarrow\) 1 to \(N_{p}\)do
3: Compute \(\tau_{n}\left(\overrightarrow{r}\right)\) using Equation (1)
4:endfor
5:endfor
6:for u, v \(\leftarrow\) 1 to \(N_{p}\)do
7:\(E_{nxn}=0\)
8:for y S \(\leftarrow\)\(S_{s}\)do
9:\(E_{ni}\) = 0
10:for do\(A_{t}\)\(\leftarrow\) 1 to \(A_{t}\)
11:if\(S_{s}\) in range then
12:\(\tilde{E}_{ni}\) = \(E_{ni}\) + \(A_{n}\) + \(\tau_{n}\left(\overrightarrow{r}\right)\) as described in Equation (2)
13:endif
14:endfor
15:\(E_{nxn}\) = \(E_{nxn}\) + (\(E_{ni}\) * \(E_{ni}\))
16:endfor
17:endfor
18:\(I_{nxn}\)= round (\(E_{nxn}\))
```  
**Algorithm 1** MIR Algorithm  
In order to calculate the matrix of pixel values, delay matrix and input signals are used as inputs. The two for loops are used to calculate the matrix of pixels. To calculate the energy for the next pixel, the value of the current pixel is set to zero. The second and third for loop is used to traverse all signal samples to make sure the signal range is valid. Backscattered signals from all antennas are first aligned and then summed in the loop body based on Equation (2). The 'Eni' represents the energy for all samples from all antennas separately. Consequently, it is set to zero after finishing all the antennas. Since the energy level is proportional to signal amplitude, irrespective of positive or negative, energy is therefore accumulated by taking the square of 'Eni'. Next, to store the results, \"round\" function is used to get rounded values, which improve the efficiency of the algorithm without affecting the quality of resulting images. Finally, the energy is stored in a two- dimensional matrix. To optimize the algorithm, the intensive operation needs to be parallelized. The proposed parallelized version of the algorithm can be found in Section 4.
## 1 Introduction
It is also important to note that the choice of different Modulation Coding Scheme (MCS) levels considerably influences the overall network performance of the NB-IoT system in terms of its reliability, its energy efficiency, its scalability, and latency. This is because the higher the modulation coding scheme, the higher the transmission power per modulated bit of information (lower energy efficiency) but the higher the resilience of the transmitted packet to noise and other channel impairments (more transmission reliability). The use of low MCS coupled with high transmitting power has been demonstrated to improve the transmission reliability and therefore, enhance the network coverage in terms of longer transmission distance and immunity to noise [13]. However, this results in the reduction of the network's throughput, and causes the overall energy consumption of the network to be significantly high. Although the number of repeated re-transmissions is significantly cut-down, the transceiver's energy consumption as modeled by Equation (2) remains quite high. This model is based on the fact that the NB-IoT receiving node has three operational states. It is either in synchronization mode or in active mode (handling of the received packets) or otherwise in Idle mode. Most of the energy consumption normally occurs in its active state. Medium energy consumption then occurs in its synchronization state, and the lower amount is consumed during its Idle state [14]. The transceiver's energy consumption during reception \(E_{Rx}\) can be represented as  
\[E_{Rx}=KP_{Rx}t_{synch}+\sum_{k=1}^{K}P_{sleep}t_{sleep}^{k}+P_{Idle}t_{active}^ {K} \tag{2}\]  
where  
* \(t_{synch}\) is the synchronization time,
* \(K\) is the number of cycles or iterations involved in the synchronization process for connection to be effectively established,
* \(t_{sleep}^{k}\) is the time spent in the sleep state in a reception cycle \(k\),
* \(t_{active}^{K}\) is the total active time during all the \(K\) cycles, and
* \(P_{Rx}\), \(P_{sleep}\) and \(P_{Idle}\) are the power value for the receiving, sleeping and idle states, respectively. While the repeating of transmission data or control signals has been selected as a promising approach to enhance coverage of NB-IoT systems according to the 3 GPP rel 13 because a larger repetition number would enhance the transmission reliability, it however results in a significant spectral efficiency loss [15]. To overcome this, this article proposes a 2-dimensional channel coding and link adaptation scheme capable of providing a trade-off between the transmit reliability and the throughput of the system by selecting a suitable MCS on one hand and an appropriate transmission repetition number on the other hand. This is proposed based on the fact that it has been identified that most existing link adaptation schemes found in the literature are solely focused on the selection of a suitable MCS value without consideration of the repetition number which as demonstrated in the paragraphs above is has demonstrated potential benefits towards addressing the energy efficiency of the NB-IoT network.
## 5 Conclusions  
The application of an ontology results in a clear structure. It uses a natural assumption that is highly suitable for pervasive computing systems. The accurate expression of knowledge is essential when automatically monitoring, identifying, and processing a WSN working state. SWRL is used to describe the reasoning rules for an OWL instance and to infer new knowledge. By using SWRL, we have improved the ontological reasoning functions of the WSN.  
This paper proposes a method using a middleware based on ontology and SWRL rules for WSN state monitoring and operational decision support. We considered a WSN for lake water quality monitoring. The structure and state information is encoded in the WSN ontology, and can be used for real-time monitoring of the device, data, object observation, and water quality states. A rule set based on SWRL combined with the ontology can help us make intelligent decisions. Our case study shows that the proposed method effectively reduced the energy consumption and improved the reliability of the WSN.  
In the future, we will improve the monitoring functions of the WSN ontology. We will also add more devices, and include more complex strategies in the reasoning rules that dynamically control the energy consumption; for example, topology protocols, power aware routing protocols, and more effective sleeping management protocols. We will conduct more experiments at a larger scale to test rule conflicts and the diagnosis accuracy.  
Figure 21: Test results of durations of two nodes for the same battery consumption with different monitored items.
# Exploring the Evolution of Drought Characteristics in Balochistan, Pakistan
## 2 Materials and Methods  
### Meteorological Data  
The drought index chosen for this study is the Standardized Precipitation Evapotranspiration Index (SPEI) [13]. It can be considered an advanced version of the popular drought indicator, the Standardized Precipitation Index (SPI) [7; 27], as it incorporates the effect of temperature when modeling droughs. SPI assumes either that precipitation has much higher variability than other climatic parameters affecting droughs, such as potential evapotranspiration (PET) and temperature, or that the other climatic parameters are stationary. However, in the context of global warming, assuming temperature to be stationary might lead to misleading conclusions. Therefore, SPEI is better suited for drought assessment, especially under a changing climate where the effect of rising temperatures is significant in drought development [28; 29]. SPEI is based on the calculation of a monthly climatic water balance, which is determined by subtracting PET from precipitation [13; 30]. However, it does have limitations of its own. These include more data requirements than SPI and sensitivity to the calculation method of PET.  
The calculation of SPEI is similar to that of SPI [31] in that the cumulated water balance (for SPEI versus precipitation for SPI) for \(n\) months is standardized to yield SPEI for different timescales. For example, SPEI at a 1-month timescale indicates a normalized water balance for that month as compared to the long-term average for the same month. Similarly, the 12-month SPEI denotes the normalized water balance for the preceding year in comparison to the long-term yearly average.  
The Global SPEI Database, SPEIbase (version 2.5.1), contains monthly SPEI at timescales of 1-48 months and a spatial resolution of 0.5\({}^{\circ}\) for the time period 1901-2015 [32]. The Global SPEI Database is based on gridded monthly precipitation and reference evapotranspiration from the University of East Anglia Climate Research Unit (CRU) (version TS 3.24.01) [33]. The PET is calculated using the Penman-Monteith method. The water balance is transformed to an approximately normal distribution after fitting to its time-series a three-parameter distribution (log-logistic distribution) using the unbiased probability-weighted moments method [30]. SPEI at 3-month and 12-month timescales were acquired from the SPEIbase to evaluate the characteristics of droughts of seasonal and yearly timescales, respectively.
# Digestate Management and Processing Practices: A Review
## 5 Digestate Processing
### Treatments of the Liquid Fraction
#### 5.3.3 Ammonia Stripping  
In ammonia stripping, air or steam (or possibly biogas) is introduced into stripping towers equipped with compressors, and the DG flows in the reverse direction, changing NH3 from the liquid to the gas phase [177, 178]. The gas is then transferred to an air scrubber where mass transfer and absorption of the NH3 from the gas to a liquid phase (usually sulfuric or sometimes nitric acid) takes place, followed by the formation and recovery of a concentrated solution of ammonium sulfate (or ammonium nitrate) as an end-product [179, 180]. The process is typically conducted at a pH above 9 and temperatures above 20 \({}^{\circ}\)C [177]. Ammonium sulfate is an inorganic salt, which can be reused as a marketable fertilizer rich in directly available macronutrients, N and S. It can be a valuable substitute for chemical fertilizers based on fossil resources. Alternatively, ammonium nitrate, a concentrated ammonia solution (by adding liquid ammonia), or other ammonia salts can be produced, depending on local legislation and land application options [179].  
The efficiency of the process is dominated by several parameters, such as NH4+ concentration, pH, temperature, retention time, and the flow rates of gas and liquid in the stripping towers. However, pH improves the stripping rate more efficiently than the other parameters which are the reason why alkali addition is adopted in large-scale production. Due to its low cost, lime is often used for that purpose. However, due to the high dosage requirements, the cost may be unexpectedly high [178]. In addition, high dosages of alkali can lead to scaling and fouling of the packing material, which requires periodic cleaning [179].  
This process is widely applied on a full-scale around the world [178] and is the most commonly used method for N recovery [152, 177].
## 3 Validation of the CFD Approach
### Experimental Setup  
The numerical experiments focus on the sloshing of water in a rectangular container at a reduced scale. In [35], the dimensions length \(L=0.590\,\mathrm{m}\) and width \(\mathcal{W}=0.335\,\mathrm{m}\) are used. The following combinations of water heights, excitation amplitudes and frequencies are planned: a constant excitation amplitude \(a=5\,\mathrm{mm}\) with different water levels \(h_{0}\); keeping a constant depth ratio \(h_{0}/L=0.102\) and varying amplitude ratios \(a/L\). The investigations imply a harmonic horizontal forcing motion in time \(x(t)=a\;\sin(\omega_{ext}\;t)\), with the _prescribed_ circular forcing frequency being defined as \(\omega_{ext}=\beta\;\omega_{w}\). The above characteristics need to be studied systematically because they lead to the design of TSDs aimed at mitigating structural vibrations. As the optimal working condition is at harmonic-dominant structural motions, this methodology represents the adequate preparatory phase. The critical outcome of the investigation is the total (dimensionless) sloshing force \(F_{sl}\) (and \(F_{sl}^{\prime}\)) acting on the liquid container, as defined in Equations (7) and (8). The base shear mainly results from the difference in hydrostatic pressure on the two lateral walls (i.e., the water height: right \(h_{R}\) and left \(h_{L}\)). The denominator contains the maximum inertial force of the water treated as a solid mass \(m_{w}\) (being the total mass of liquid),  
\[F_{sl}=\frac{1}{2}\;\rho\;g\;\left(h_{R}^{2}-h_{L}^{2}\right), \tag{7}\]  
\[F_{sl}^{\prime}=\frac{F_{sl}}{m_{w}\;\omega_{ext}^{2}\;a}. \tag{8}\]  
We additionally recorded the physical work; here, the energy E transferred to or from the object. An appropriate relationship considering a forcing cycle is provided in Equation (9). The dimensionless dissipated energy per cycle \(\Delta E^{\prime}\) is also marked, being expressed in Equation (10). This is achieved by normalizing with the kinetic energy of the entire liquid considered as a solid mass,  
\[\Delta E=\int_{t}^{t+T}F_{sl}(t)\;x(t)dt, \tag{9}\]  
\[\Delta E^{\prime}=\frac{\Delta E}{\frac{1}{2}\;m_{w}\;(\omega_{ext}\;a)^{2}}. \tag{10}\]