esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Normal-Hearing Subjects
#### 3.1.2 Statistical Analysis  
The ANOVA showed a significant effect of the SNR [F (4,95) = 519; \(p<10^{-4}\)] and of the coding scheme [F (1,95) = 16; \(p<10^{-4}\)]; there was no significant interaction between them [F (4,95) = 1.95; \(p\) = 0.11]. Consequently, a post-hoc analysis was performed for the coding scheme.  
For each SNR, comparisons were made with paired [PERSON]'s tests (on the 20 subjects who participated in the experiment). In the whole experiment, we had five paired series (one for each SNR); for each paired series we had 20 pairs of values (one per subject). For the extreme SNR values (\(-\)3 dB and +9 dB), the recognition percentages were not significantly different between FC and CP (Table 2). \(P\)-values were below 5% for the SNRs 0 dB, +3 dB and +6 dB.  
Using the Holm-Bonferroni correction, the first corrected decision threshold was 1% and differences become not significant since the lowest \(p\)-value was 0.019. For SNRs +3 and 0 dB, differences were close to significance and worth further investigation; additionally, the [PERSON]'s effect sizes were respectively strong (0.89) and medium (0.68). This coheres with the general ANOVA results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Normal-Hearing Subjects**} & \multicolumn{2}{c}{**Fixed-Channel**} & \multicolumn{2}{c}{**Channel-Picking**} & \multirow{2}{*}{\(p\) (Wilcoxon)} & \multicolumn{2}{c}{**Effect Size**} \\  & & **(n = 20)** & & **(n = 20)** & \\ \hline \multirow{2}{*}{SNR +9 dB} & m & 98.50 & 99.25 & \multirow{2}{*}{0.374} & \multirow{2}{*}{0.31 (small)} \\  & \(\sigma\) & 2.86 & 1.83 & & \\ \hline \multirow{2}{*}{SNR +6 dB} & m & 98.25 & 95.00 & \multirow{2}{*}{0.046} & \multirow{2}{*}{0.60 (medium)} \\  & \(\sigma\) & 3.35 & 6.88 & & \\ \hline \multirow{2}{*}{SNR +3 dB} & m & 92.75 & 84.75 & \multirow{2}{*}{0.019} & \multirow{2}{*}{0.89 (strong)} \\  & \(\sigma\) & 5.95 & 11.29 & & \\ \hline \multirow{2}{*}{SNR 0 dB} & m & 57.25 & 45.50 & \multirow{2}{*}{0.020} & \multirow{2}{*}{0.68 (medium)} \\  & \(\sigma\) & 16.97 & 17.54 & & \\ \hline \multirow{2}{*}{SNR \(-\)3 dB} & m & 7.00 & 2.00 & \multirow{2}{*}{0.065} & \multirow{2}{*}{0.53 (medium)} \\  & \(\sigma\) & 12.50 & 3.40 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Percentage comparisons for normal-hearing subjects between the simulation strategies.  
Figure 3: Syllabe recognition function of the signal-to-noise ratio by NH subjects with the CI simulator using both strategies. Bars indicate the standard deviation. The asterisks indicate the significant differences (5% threshold).
## 4 Experimental Results and Analysis
For the purpose of the better characterization of the error values of different algorithms, the error values obtained by each algorithm are taken as absolute values, and the presented error values are shown in Figure 10. It may be deduced that the BBOBP-EKF approach has an absolute error value of 2000 times, which is basically within 2%. The error value corresponding to a number of times less than 1% is also higher than that of the other algorithms. Overall, it is superior to the BP-EKF algorithm and significantly better than the EKF algorithm. The number of times the error values obtained by different algorithms fall into different intervals is shown in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline
**Error Taking Absolute Value Interval/\%** & **EKF** & **BP-EKF** & **BBOBP-EKF** \\ \hline \(0\leq e\leq 1\) & 459 & 1331 & **1412**1 \\ \(1<e\leq 2\) & 726 & 574 & **554** \\ \(2<e\) & 815 & 95 & **34** \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of the times of different algorithms in the absolute error range. Figure 9: Comparison of different algorithms’ errors. To further illustrate the effects of the three methods, the error results are shown in Table 2. The smaller the index of the SoC estimation error, the better the accuracy of the algorithm. Compared with the BP-EKF algorithm and EKF algorithm, the maximum error of the BBOBP-EKF algorithm is 1% less than that of the BP-EKF algorithm and 2.4% less than that of the EKF algorithm, and its minimum error is also the smallest. Furthermore, the smaller the variance of the error, the more stable the estimation method. The error index in Table 2 intuitively shows that the accuracy of battery SoC estimation based on the BBOBP-EKF method is slightly better than that of the BP-EKF algorithm and obviously better than that of the EKF algorithm.
## 5 Implementation of the Flow Rate Control System
### Design of the Reference Trajectory  
In the experimental study, the target flow rate contains two stages, with \(q_{f1}^{*}=0.75\times 10^{-4}\) and \(q_{f2}^{*}=1.50\times 10^{-4}\) m\({}^{3}\)/s. The reference trajectory of the liquid height at the pouring mouth, by which the target flow rate can be achieved, is depicted in Figure 9.  
Figure 9a denotes the reference trajectory of the liquid height, and Figure 9b shows the trajectory of the flow rate, which can be derived by substituting the reference trajectory of the liquid height, presented in Figure 9a, into Equation (6). As can be observed from Figure 9b, the flow rate from 8-10 s and from 17-19 s stays at \(q_{f1}^{*}=0.75\times 10^{-4}\) m\({}^{3}\)/s, and the flow rate from 12-15 s stays at \(q_{f2}^{*}=1.50\times 10^{-4}\) m\({}^{3}\)/s.
# Low-Power Wireless Data Transfer System for Stimulation in an Intracritical Visual Prosthesis
###### Abstract  
There is a growing interest to improve the quality of life of blind people. An implanted intracortical prosthesis could be the last resort in many cases of visual impairment. Technology at this moment is at a stage that implementation is at sight. Making the data communication to and from the implanted electrodes wireless is beneficial to avoid infection and to ease mobility. Here, we focus on the stimulation side, or downlink, for which we propose a low-power non-coherent digital demodulator on the implanted receiver. The experimentally demonstrated downlink is on a scaled-down version at a 1 MHz carrier frequency showing a data rate of 125 kbps. This provides proof of principle for the system with a 12 MHz carrier frequency and a data rate of 4 Mbps, which consumes under 1 mW at the receiver side in integrated circuit (IC) simulation. Due to its digital architecture, the system is easily adjustable to an ISM frequency band with its power consumption scaling linearly with the carrier frequency. The tested system uses off-the-shelf coils, which gave sufficient bandwidth, while staying within safe SAR limits. The digital receiver achieved a reduction in power consumption by skipping clock cycles of redundant bits. The system shows a promising pathway to a low-power wireless-enabled visual prosthesis.  
low-power communication; intracortical visual prosthesis; inductive link; phase shift keying; non-coherent digital demodulator 2021
## 6 White Rot Fungi: An Overview
### Taxonomy and General Characteristics  
Agaricomycetes is a class of Fungi within the Phylum Basidomycota, to which White Rot Fungi (WRF) belongs [45]. The most commonly and vastly studied genera of WRF are Phanerochaete, Trametes, Pleurotus, and Ganoderma. The Fungi from these genera are typically known and characterized by their capability to naturally decompose lignocellulosic material, including lignin, hemicellulose, and cellulose [43].  
White Rot Fungi are filamentous fungi, typically characterized by thread-like structures known as hyphae. Hyphae, when combined, form a structure known as Mycelium. WRF usually exhibit sexual reproduction by producing basidiospores, produced by specialized fungal structures known as basidia [46]. As far as the habitats of WRF are concerned, these can be commonly found in environments like decaying wood, compost piles, and forest soils. In such environments, WRFs play an important role in nutrient cycling and carbon sequestration [47].
###### Abstract  
Climate change has recently increased the frequency of landslides in alpine areas. Susceptibility mapping is crucial for anticipating and assessing landslide risk. However, traditional methods focus on static environmental variables to emphasize the spatial distribution of landslides, ignoring temporal dynamics in landslide development in the context of climate change. In this work, we focused on static and dynamic environment factors and utilized the certainty factor-logistic regression (CF-LR) model to assess and predict landslide susceptibility in Taxkorgan County, located in the Karakorum. The assessment and prediction were based on a catalog of climate change-related landslides over the past 20 years, the causative factors, and predicted climatic variables for the Shared Socioeconomic Pathways (SSP1-2.6) scenario. The results indicated that elevation, slope, groundwater, slope length gradient (LS) factor, Topographic Wetness Index (TWI), valley depth, and maximum precipitation were the key causes of slides below the snow line. The key factors causing debris flow above the snow line were elevation, slope, topographic relief, aspect, LS factor, distance to the river, and maximum temperature. The accuracy of slide and debris flow susceptibility was 0.92 and 0.89, respectively. The area of slides with medium, high, and very high susceptibility is 25.5% of the Taxkorgan. In addition, 82.6% of the slides happened in this region, and 49.5% of the entire area is covered by debris flows with medium, high, and very high susceptibility. Moreover, this area accounts for 91.8% of all debris flows. Until 2060, the region's climate is anticipated to become warmer and wetter. Slides below the snow line will gradually decrease and shift eastward, and debris flows above the snow line will expand. Our findings will contribute to the management of landslide risks at the regional scale.  
Keywords:landslides; prediction of susceptibility; climate change; Karakorum +
Footnote †: journal: Applied Sciences
## 3 Verification Method
### Sensor Fusion  
In the fields of wearable technology and motion tracking systems, the integration of accelerometers and gyroscopes plays a critical role in enhancing the accuracy and reliability of data collection [17]. While each sensor independently measures linear acceleration and angular velocity, they both have inherent limitations such as noise and drift errors, which can reduce accuracy when used alone. To mitigate these constraints, it is necessary to monitor the same physical phenomena simultaneously with both sensors and integrate their data. By fusing data from two or more sensors, the limitations of each individual sensor can be compensated for, resulting in more precise measurements.  
Sensor fusion technology is used to combine data collected from different sources to overcome the limitations of individual sensors and provide more reliable information. Specifically, the fusion of accelerometers and gyroscopes plays a key role in compensating for the intrinsic error characteristics of each sensor, enabling a more accurate analysis of motion dynamics. Such fusion is typically implemented using advanced filtering techniques, such as Kalman filters or complementary filters [18; 19].  
Figure 1: Diagram of the proposed method and its validation.  
For instance, in pedestrian dead reckoning (PDR) technology, which utilizes smartphones equipped with both accelerometers and gyroscopes, various filtering techniques including the Kalman filter can be applied to improve location tracking accuracy in indoor environments. In particular, the Kalman filter excels at handling time-varying data and allows for the fusion of multiple sensor inputs in dynamic environments with high uncertainty, thereby reducing measurement errors and enabling more reliable state estimation [18].  
For practical implementation, integrating a three-axis accelerometer and a three-axis gyroscope within a single device is the most effective approach. In this study, we utilized the LSM6 DSOTR module from STMicroelectronics, which includes both a three-axis accelerometer and a three-axis gyroscope. This sensor configuration offers rapid responsiveness to external perturbations, allowing gyroscope data to be prioritized initially to ensure high initial accuracy.  
However, since the drift error of the gyroscope can accumulate during long-term tracking, we use accelerometer data, which is less sensitive to accumulated error, as a reference signal for drift error correction to maintain accuracy over time. This systematic sensor fusion approach ensures continuous and reliable motion tracking, serving as a key component in motion analysis applications that require high reliability.
## 2 System Model
### Complexity and Utility of Resource Allocation  
Performance and complexity of an algorithm is a trade-off problem, as complexity indicates the occupation of system resources (time and memory) when executing the program of the algorithm. As illustrated, the path of the programming problem can be solved by the exhaustive search to obtain the optimal solution whereas accompanied to extremely high complexity. Each D2D pair in the communication system that consists of \(N\) D2D pairs and \(M\) RBs can be allocated with \(M\) possible results. Consequently, all D2D pairs are allocated with \(M^{N}\) possible results, so the complexity of the exhaustive search is denoted by \(O\big{(}M^{N}\big{)}\).  
In the proposed WE-I-A algorithm, the weight value of D2D pairs needs to be calculated for several iterations. For one iteration, BS chooses the available RBs for D2D pairs as far as possible and the number of times calculated is \(C_{M}^{1}+C_{M-1}^{1}+\ldots+C_{M-(N-1)}^{1}=MN-N(N-1)/2\). Without loss of generality, each D2D pair shall traverse the overall RBs, so the number of iterations is \(N\). Definitively the complexity of the proposed algorithm is \(O\big{(}MN^{2}-N^{2}(N-1)/2\big{)}\). It is obvious that \(O\big{(}M^{N}\big{)}>~{}O\big{(}MN^{2}-N^{2}(N-1)/2\big{)}\), so the complexity of the proposed algorithm is much lower.  
To reveal the utility function of resource allocation, we have the following definition.  
**Definition 7**.: _The utility of resource allocation represents the integrated efficiency of the number of active D2D pairs and the throughput gain as_  
\[\mathrm{E}=\frac{card\big{(}\beta^{CD}\big{)}\times R_{D2D}}{N\times R_{-opt}} \text{ for }\beta^{CD}_{i,j}\in\beta^{CD}\subseteq B \tag{20}\]  
where \(R_{-opt}\) is the optimal system throughput obtained by exhaustive search with a finite number of D2D pairs and CUs.  
From the expression, as the number of D2D pairs (\(N\)) is determined, it has been revealed that the utility is not only related to the number of active D2D pairs (\(\mathrm{card}\big{(}\beta^{CD}\big{)}\)), but also to the throughput of both D2D pairs and the total system (\(R_{D2D}\) and \(R_{-opt}\)). The number of active D2D pairs can reflect the interference received by the cellular communication system to a certain degree.  
The active D2D pairs are those which are able to communicate successfully by reusing RBs in a communication scenario, so the inactive ones are those that fail in the ability of communication, due to factors like low transmit power, poor uplink state, etc. The utility is an integrated parameter which is formulated to reveal the efficiency of resource allocation. The number of active D2D pairs and their throughput reflect the effect of the resource allocation algorithm in a specific communication environment.
# Gas-Liquid Mixability Study in a Jet-Stirred Tank for Mineral Flotation
## 2 Methodology
### Verification of Grid Independence  
The computational domain for the response was structured and meshed using Fluent Meshing 18.0 software, as illustrated in Figure 4. The Body of Influence method was employed for local mesh refinement. A total of five different meshes were generated, comprising 480,000, 1,150,000, 1,700,000, 2,010,000, and 2,360,000 ortho-hexahedral cells, respectively. A grid-independent analysis was conducted to compare these mesh configurations, and the results are presented in Figure 5.  
For the vertical lines' velocity magnitude, the average velocity deviations between the 1.7 million grid and the coarser grids (480,000 and 1
# Measuring the Performance of Wastewater Treatment in China
## 4 Conclusions and Recommendations
### Conclusions  
According to the efficiency results, we see six regions are located on the efficiency frontier for all five years. Beijing, Tianjin, Shanghai, Jiangsu, Shandong, and Guangdong are economically developed regions, located close to the coast, and developed early under the economic reforms of China, thus achieving faster and more advanced economic growth. The expenses on water treatment and economic growth efficiencies in these regions are strong. Moreover, these six regions already have advanced technological facilities to handle discharged wastewater treatment.  
Tibet, Inner Mongolia, Ningxia, Qinghai, and Hainan are five regions characterized by small populations with less sewage. They are not industrialized with large-scale factories. The end result is a small amount of wastewater pollution, and thus, their environment efficiency is good most of time.  
Fujian, Zhejiang, Liaoning, and Jilin are the other regions close to the coast. The results showed that most of them still have good economic and wastewater discharge treatment efficiencies. Their scores placed them in the second group of efficiency. However, they still have some unstable efficiency conditions that require further understanding.  
Guizhou, Gansu, Anhui, Yunnan, Guangxi, and Jiangxi mostly lagged behind. Moreover, further research is needed to help them to improve their water issues. These inland areas typically developed their economies later than the other regions. Thus, their efficiencies are not so good.  
Chongqing achieved amazing score results during China's 12 th Five-Year Plan. Over the five years, it improved from 27 th to then 24 th, 23 rd, 22 nd, and 11 th. Chongqing improved its efficiency score to 0.9359 in 2015. Because Chongqing continuously improved its wastewater treatment efficiency throughout the 5 years, it should be taken as a good example for other inland regions to follow. Conversely, Shanxi achieved the opposite result even after it spent more on wastewater discharge treatment from 0.354, 0.615, 0.806, 1.028, 2.382 billion RMB during the five-year period. Shanxi has the worst score results during the 12 th Five-Year Plan, dropping from 16 th with an efficiency score of 0.87 to 29 th with an efficiency score of 0.688. Shanxi continuously fell in ranking from 16 th to 21 st, 25 th, 27 th, and 29 th.  
Reduced COD quantity indicates improvement in industrial wastewater discharge treatment. It was controlled for industrial pollution in 2011-2015.  
The important finding of this study is that wastewater treatment expense is inefficient in many regions. For some areas, their efficiency of wastewater discharge expense is less than 60%. Instead, the efficiency of GDP and wastewater discharge is close, which do not have a big gap between them.
# AOA-Based Three-Dimensional Multi-Target Localization in Industrial WSNs for LOS Conditions
## 3 Network Model
Deploying more ANs results in a higher cost, but the NLOS problem can be avoided. The propagation delay along the LOS path is generally smaller than the MPCs between a pair of Tx and Rx. Hence, the first arriving component (\(l=1\)) travels through the LOS path with the parameter set of \(\omega_{i,k,1}\). The azimuth and elevation direction from \(\mathcal{A}_{i}\) to \(\mathcal{M}_{k}\) is \(\phi_{i,k,1}\) and \(\theta_{i,k,1}\), respectively, as shown in Figure 1. Thus, the relative position of \(\mathcal{M}_{k}\) with respect to \(\mathcal{A}_{i}\) in the coordinate system of \(\mathbb{C}_{i}\) is \((d_{i,k},\phi_{i,k,1},\theta_{i,k,1})\) where \(d_{i,k}\) is the distance. Our purpose is to determine the 3D positions of the \(K\) MSNs in the common network coordinate system, \((x_{M,k},y_{M,k},z_{M,k})_{C_{N}}\) for \(k=1,2,\cdots,K\), simultaneously and accurately. In practical multipath scattering channels, several sub-paths may arrive at the Rx at very close delays (in a small delay bin). These paths belong to a cluster in the delay domain. Because the ATML scheme utilizes the incident angles of the LOS paths for node localization and the parameters of the MPCs are not used, the clustering behavior of the MPCs can be ignored. Figure 2: The MT-SIMO signal flow on the MSNs and ANs in the ATML scheme for 3D node localization.
## 4 Example of Application: Performance Simulations using the EIPS framework  
In this section, our aim is to apply the EIPS framework for performance simulations by conducting a study of analysis of relative information content of current and emerging multi-spectral MW remote sensing systems for temperature and humidity sounding applications.
# BLE RSS Measurements Dataset for Research on Accurate Indoor Positioning
## 4 Usage Examples
### Positioning Accuracy
As explained in Section 2, the fingerprint abstraction provided in the database is the result of grouping the measurements collected during a time window. However, that time window is small and all previously \begin{table}
\begin{tabular}{c c c c} \hline  & **A5** & **BQ** & **S6** \\ \hline Error(m)-All & 3.51 & 1.95 & 2.01 \\ Error(m)-K10 & 3.51 & 1.94 & 2.00 \\ Error(m)-KF10 & 2.63 & 1.88 & 1.83 \\ Ratio-KF10 & 0.44 & 0.82 & 0.83 \\ \hline \end{tabular}
\end{table} 
Table 4: Influence of the number of detectable beacons in WC accuracy. \begin{table}
\begin{tabular}{c c c c} \hline  & **A5** & **BQ** & **S6** \\ \hline Error (m) & 4.43 & 4.61 & 4.88 \\ Max number of coincident beacons & 4 & 12 & 11 \\ \(\rho\) & \(-0.80\) & \(-0.95\) & \(-0.93\) \\ \(p\)-value & 0.2 & \(\approx\) 0 & \(\approx\) 0 \\ \hline \end{tabular}
\end{table} 
Table 5: Influence of the number of detectable beacons in FP accuracy. seen records are discarded. Table 6 present the result of applying a fixed-size moving window over the fingerprints that belong to the same collection point (recalling, unique 2D position and subject facing direction triplet). If a beacon is detected more than once in the fingerprints of a window, the resulting RSS value for that beacon is computed either as the mean value of all the beacon's RSS in the fingerprints inside the window, or as the last detected value. In the results of Table 6, the larger the window size, the better the accuracy, which may be a result of the static collection procedure followed for our database. Also, in some cases, the accuracy difference between the FP and WC is reduced.