esa-sceva/satcom-chunk-collection huggingface dataset:
# Impacts of Diversity Technique Application in Order to Reduce the Fading in IEEE 802.15.4 Networks
## 5 Numerical Results
Figure 6: BER behavior for Rayleigh fading channel using SC and MRC combiners for different values of modulation parameter \(M\). Figure 7: BER behavior for Nakagami-\(m\) fading channel using SC and MRC combiners for different values of modulation parameter \(M\). A graphical representation of the obtained analytical results in (25) and (36) is given in Figures 8 and 9, where the BER behavior when using the SC and MRC combiners with two branches is shown, and for different values of spreading sequence lengths \(L_{c}\) for the Rayleigh and Nakagami-\(m\) fading channels, respectively. The given results show that increasing the spreading sequence lengths \(L_{c}\) leads to a decrease in BER and an increase in the quality of the received signal. The decrease in BER is significantly more pronounced at higher SNR values. Figure 8: BER behavior for Rayleigh fading channel using SC and MRC combiners for different values of spreading sequence lengths \(L_{c}\). Figure 9: BER behavior for Nakagami-\(m\) fading channel using SC and MRC combiners for different values of spreading sequence lengths \(L_{c}\).
## 3 Results
### Therapeutic Drug Monitoring (TDM)  
Using the LC/MS instrument, we documented that the blood immunosuppressant levels were in the normal range during the follow-ups. The retrospective evaluation of TDM recorded in the same patients in the pre-pandemic period showed similar values (Table 2).  
\begin{table}
\begin{tabular}{c c} \hline
**Characteristics** & **Data** \\ \hline Patients (number) & 33 \\ Men (number) & 19 \\ Women (number) & 14 \\ \hline Age (mean \(\pm\) standard deviation) & 55.4 \(\pm\) 12.8 \\ Tacrolimus (number) & 25 \\ Everolimus (number) & 1 \\ Cyclosporine (number) & 7 \\ Mycophenolate (number) & 26 \\ Sirolimus (number) & 1 \\ Prednisone (number) & 24 \\ Dexamethasone (number) & 1 \\ Azathioprine (number) & 1 \\ \hline _Transplant from_ & \\ Cadaver (number) & 30 \\ Living (number) & 3 \\ \hline _lg_G-SARS-CoV-2 _(All/mL; mean \(\pm\) standard deviation)_ & \\ \hline T0 & 0 \\ T1 & 23.83 \(\pm\) 52.14 \\ T2 & 1606.32 \(\pm\) 347.97 \\ T3 & 599.18 \(\pm\) 1025.64 \\ T4 & 325.46 \(\pm\) 568.15 \\ T5 & 9646.33 \(\pm\) 14,176.99 \\ \hline \end{tabular}
\end{table}
Table 1: The demographics of the patients enrolled in the study. T0: admission; T1: II dose of vaccine: 3 weeks after T0; T2: 2 months after T1; T3: 3 months after T2; T4: 6 months after T1, in this time, the patients underwent the III dose of vaccine; T5: 3 weeks after T4, end of the study.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Drug** &
\begin{tabular}{c} **Normal** \\ **Range** **ng/mL** \\ \end{tabular} & **P-p** & **T0** & **T1** & **T2** & **T3** & **T4** & **T5** \\ \hline Tacrolimus & 5–15 & 6.2 \(\pm\) 1.4 & 6.5 \(\pm\) 1.6 & 6.3 \(\pm\) 1.5 & 6.6 \(\pm\) 1.8 & 6.4 \(\pm\) 1.5 & 6.2 \(\pm\) 1.4 & 6.3 \(\pm\) 1.6 \\ Everolimus & 3–8 & 5.2 & 5.6 & 5.4 & 5.6 & 5.7 & 5.5 & 5.6 \\ Cyclosporine & 300–600 & 86 \(\pm\) 21.5 & 92 \(\pm\) 25.3 & 90 \(\pm\) 23.8 & 88 \(\pm\) 23.4 & 87 \(\pm\) 24.6 & 89 \(\pm\) 25.4 & 87 \(\pm\) 23.8 \\ Sirolimus & & 8.4 & 7.8 & 8.1 & 8.3 & 7.9 & 8.2 & 8.3 \\ Mycophenolate & & 2.8 \(\pm\) 0.7 & 2.6 \(\pm\) 0.9 & 2.9 \(\pm\) 1.2 & 2.8 \(\pm\) 1.1 & 3.1 \(\pm\) 1.2 & 3.2 \(\pm\) 1.1 & 3.1 \(\pm\) 1.1 \\ \hline \end{tabular}
\end{table}
Table 2: The therapeutic drug monitoring of the immunosuppressivepressants before and after I dose of vaccine and during the follow-ups. P-p: pre-pandemic. T0: admission; T1: II dose of vaccine: 3 weeks after T0; T2: 2 months after T1; T3: 3 months after T2; T4: 6 months after T1, in this time, the patients underwent the III dose of vaccine; T5: 3 weeks after T4, end of the study. Data are expressed as the mean \(\pm\) standard deviation.
# Geometrical Degrees of Freedom for Cellular Structures Generation: A New Classification Paradigm
## 2 Design and Geometrical Consideration
### RVE Variation Methods
#### 2.3.3 RVE Transformations  
The third column of Table 1 is dedicated to cases where the volume fraction of the RVE undergoes scaling or rotation mathematical transformations. An example is shown in Figure 9. In some cases, RVEs can undergo scaling, rotation or any other type of geometric transformation, while still being recognizable. [PERSON] and [PERSON] [29] have also constructed multiple CSs for which they have varied the scale. There are numerous cases that have been included in that column that might seem a bit more subtle. For example, in many of those case studies, the unit cell that is being scaled and rotated throughout the design space is simply a strut [40, 50].  
The next subsections will tackle the second question--\"why or according to what factor does the RVE vary throughout the design space?\".  
Figure 8: RVE gradient in terms of unit cell density grading across the CS [29]—\(\rho_{\Delta}\) is the difference between the maximum and minimum densities, \(\rho_{avg}\) is the average density.
## 3 Steady-State Error Modeling and Analysis
### Steady-State Error Analysis
When the GNSS signal is attenuated to 30 dB-Hz, with other conditions unchanged, the result is as shown in Figure 9. Similar to the trend in Figure 8, the optimal bandwidth of the normal PLL must be wider than 20 Hz, due to the dynamic stress; and that of PLL with MTI-G or FSAS aiding can be narrower than 3 Hz. In addition, with the GNSS signal attenuated to 30 dB-Hz, the minimum tracking error of the normal PLL increases to 15 degrees; and that of the INS-aided PLL only increases to five degrees. Figure 8: Steady-state tracking errors with the acceleration of 9.8 m/s\({}^{2}\) and CNR of 40 dB-Hz. Figure 9: Steady-state tracking errors with the acceleration of 9.8 m/s\({}^{2}\) and CNR of 30 dB-Hz. Steady-state error analysis shows that, with the assistance of INS information, the dynamic tracking bandwidth of the PLL could be narrower, and the dynamic tracking error can be reduced. It should be note that even the low-grade INS can significantly improve the PLL performance under dynamic conditions. We summarize the steady-state error analysis as follows. The negative effect of the INS aiding, caused by the INS bias-type errors, is small and can be neglected, as shown in Figures 6 and 7. The positive effect of the INS aiding, by compensating for the dynamic stress of vehicle motions, is large and can improves the total steady-state phase tracking significantly, as shown in Figures 8 and 9. Hence, the steady-state error model could be used for optimizing the INS-aided PLL parameters, selecting inertial sensors and analyzing INS-aided PLL performance. Under static conditions with 30-dB-Hz signal power, the optimal bandwidth of the INS-aided PLLs is about 3 Hz, which is almost the same as that of the normal PLLs, and the static tracking error caused by INS bias-type errors is less than one degree. When the vehicle acceleration is 9.8 m/s\({}^{2}\) and the signal power is 40 dB-Hz, the optimal bandwidth of the INS-aided PLLs is still about 3 Hz, which is much narrower than that of the normal PLLs (15 Hz). The dynamic tracking error of the INS-aided PLLs with optimal bandwidth is clearly lower than that of the normal PLLs, especially when the signal power drops to 30 dB-Hz. Therefore, the GNSS signal tracking sensitivity and accuracy of the PLLs can be proven after the INS aiding under dynamic conditions.
# Interference Alignment in Multi-Hop Cognitive Radio Networks under Interference Leakage
## 1 Introduction
To this end, interference alignment (IA) is an important approach for CR to recover the desired signal of the PU or SU by utilizing the precoding and suppression matrices of the channel matrix, which consolidates the interference beam or matrix into one subspace in order to eliminate them. This paper focuses on the interference alignment in CR networks considering multiple hops in the underlay scheme.
# Dynamic Segmentation for Physical Activity Recognition Using a Single Wearable Sensor
## 1 Introduction
Similarly, using cluster analysis for period extraction, [21] proposed a technique to differentiate between basic and transitional activities during segmentation. [PERSON] et al. [26] designed an adaptive time window by using pitch extraction algorithms to divide the data into periodic and non-periodic activities. The study in [27] designed and implemented a segmentation method based on the sliding window autocorrelation technique and the Gaussian model. Using a dataset consisting of readings from an accelerometer embedded in a smartphone, the method successfully divided the data into distinct subsets of activities. Based on a change detection algorithm, an activity segmentation method was presented in [19]. To identify stationary, dynamic, and transitional activities, starting window positions were dynamically detected. The objective of this research is to propose a novel signal segmentation method for physical activity recognition that can enhance classification performance. Unlike previous studies, this method is concerned with the segmentation of physical activities that belong to the same category (i.e., dynamic activities). To achieve this objective, an experiment was conducted to verify and compare the proposed method with the sliding window approach. The comparison demonstrates the effectiveness of our method, particularly in terms of enhancing recognition accuracy. The remainder of this paper is organized as follows: Section 2 presents the set of physical activities applied during the rehabilitation of SCI patients; Section 3 offers an overview of the system; Section 4 describes the proposed segmentation method; Section 5 demonstrates the experimental setup; Sections 6 and 7 present and discuss the results, respectively; and finally, Section 8 concludes the paper.
## 2 Materials and Methods
### Proposed Model
#### 2.5.2 Fine-tuning Stage  
During the pre-training phase, the proposed self-supervised model was trained on audio data from various machines to learn audio representations; we did not utilize the metadata associated with the audio files, such as label or machine type and ID, which can provide valuable information about the machines' states or properties.  
To improve the learning of audio features from machines with different IDs, we employed supervised Contrastive learning methods that leverage machine IDs as labels in order to capture the relationship between audio signals and their corresponding machine IDs. This approach improves the capability to identify sounds from different IDs and strengthens the connection between sounds originating from the same ID. In this stage, we randomly selected machine sound, \(a_{i}\), from a set of \(N\) input machine sounds to serve as the anchor and create contrasts with the remaining (\(N-1\)) machine sounds, and the label for each sample was determined by the associated machine ID. In the next step we derived individual sound embeddings, \(z_{j}\), using a pre-trained model. To effectively capture the relationship among audio embeddings from the same ID and to differentiate audio embeddings from different IDs, we sought to maximize the cosine similarity score for embeddings from the same ID while minimizing the cosine similarity score for embeddings from distinct IDs. The Supervised Contrastive Loss (SCL) [51], defined in Equation (9), encapsulates this process,  
\[L_{SCL}=-\frac{1}{N}\sum_{i=1}^{N}\frac{1}{|P(i)|}\sum_{p\in P(i)}log\frac{ exp\big{(}z_{i};z_{p}/\tau\big{)}}{\sum_{j\
eq i}^{N}exp\big{(}z_{i};z_{j}/ \tau\big{)}} \tag{9}\]  
where the \(\cdot\) symbol denotes the inner dot product, \(\tau\) is the temperature parameter, \(P_{i}\) denotes the samples that have the same machine ID, and \(|P(i)|\) is the cardinalty of \(P_{i}\). To further enhance the distinguishing ability of the learnt audio representation, we used CrossEntorpy loss (CE), so the final combinatorial loss function is as Equation (10),  
\[L_{Satge2}=\lambda_{SCL}L_{SCL}+\lambda_{CE}L_{CE} \tag{10}\]  
where \(\lambda_{SCL}\) and \(\lambda_{CE}\) are the hyperparameters, which show the contribution of each loss of supervised contrast loss (SCL) and CrossEntorpy loss (CE). The primary aim of our proposed method is to detect anomalous sounds in the audio domain by accurately predicting the type and ID of machine sounds. To accomplish this, we calculated an anomaly score, which is determined by taking the negative logarithm of the probability of the current machine sound and its corresponding ID. This methodology relies on the assumption that typical sounds are unlikely to be associated with an inappropriate ID. During the inference stage, if the predicted ID deviates significantly from the actual ID, the sound is classified as an anomaly.
# Improving Waste Management System Efficiency and Mobility with Efficient Path MANET
## 2 Related Work
### GA-Based Routing Algorithm
The fitness functions in the two constraints are also combined to form a single mixed constraint function. The existing GA-based routing algorithm uses standard GA operations, namely, crossover and mutation operations, on the chromosomes, and may lead to invalid offspring, thereby increasing the computation and consumption time. Additionally, the newly generated chromosomes in every generation must be applied and verified. Thus, knowing the validity of genes in the chromosomes is essential when using the GA operations. In the next section, the proposed method integrates specific operations to avoid invalid paths and find the efficient path.
## 6 Conclusions and Future Work
### Conclusions
The scope, specific to Turkey's electricity market with its unique regulatory and economic dynamics, may limit external validity, as findings may not generalize to other markets. Moreover, models like XGBoost and ELM pose interpretability challenges, with XGBoost's complex structure complicating transparency, a potential hindrance in contexts where clear model insights are crucial for regulatory or operational decisions. The high computational demands, especially for hyperparameter tuning, can further challenge real-time applications in smaller markets. Addressing these issues through feature attribution for interpretability and approximate optimization to reduce computational load could enhance the models' broader usability. Scalability also presents concerns, as models like XGBoost and LSTM require substantial resources when applied to large datasets. Techniques like batch processing and parallelization are employed to handle larger datasets efficiently, while optimized tree structures in XGBoost reduce iteration time. Additionally, mRMR and MLFS streamline input variables, improving processing speed. These strategies collectively enhance scalability, though highly granular datasets still demand significant resources and extended processing times. This study's findings could be strengthened by linking them more explicitly to economic theories and market behavior frameworks, thereby deepening the theoretical contributions and interpretive value of the forecasting model. For instance, the observed effects of peak demand periods and fluctuations in renewable energy inputs on SMP align with the demand-supply equilibrium theory, which suggests that prices adjust dynamically to shifts in supply and demand, seeking a balance that reflects current market conditions. Additionally, the volatility observed on public holidays and high-demand periods can be contextualized within theories of market efficiency and rational expectations, where price fluctuations represent the collective response of market participants to anticipated future states and newly available information. By embedding these results within established economic principles, the study not only enhances the relevance of its predictive model but also provides a broader theoretical basis for understanding and anticipating price movements in electricity markets, thus offering insights with applicability beyond the empirical scope of this research.
# Nitrogen-Related Defects in Crystalline Silicon
###### Abstract  
Defects and impurities play a fundamental role in semiconductors affecting their mechanical, optical, and electronic properties. Nitrogen (N) impurities are almost always present in a silicon (Si) lattice, either unintentionally, due to the growth and processing procedures, or intentionally, as a result of implantation. Nitrogen forms complexes with intrinsic defects (i.e., vacancies and self-interstitials) as well as with other impurities present in the Si lattice such as oxygen and carbon. It is, therefore, necessary to investigate and understand nitrogen-related defects, especially their structures, their energies, and their interaction with intrinsic point defects and impurities. The present review is focused on nitrogen-related defects (for example N\({}_{\text{i}}\), N\({}_{\text{s}}\), N\({}_{\text{i}}\), N\({}_{\text{i}}\), N\({}_{\text{i}}\), N\({}_{\text{s}}\), N\({}_{\text{s}}\)); nitrogen-self-interstitial and nitrogen-vacancy-related complexes (for example N\({}_{\text{s}}\),V, (N\({}_{\text{i}}\),N\({}_{\text{i}}\))Si\({}_{\text{i}}\), (N\({}_{\text{s}}\),N\({}_{\text{s}}\),N\({}_{\text{s}}\))V); nitrogen-oxygen defects (for example NO, NO\({}_{2}\), N\({}_{2}\)O, N\({}_{2}\)O); more extended clusters such as V\({}_{\text{m}}\)N\({}_{2}\)O\({}_{\text{n}}\) (m, n = 1, 2); and nitrogen-carbon defects (for example C\({}_{\text{i}}\)N and C\({}_{\text{i}}\)NO). Both experimental and theoretical investigations are considered as they provide complementary information.  
silicon; nitrogen; intrinsic defects +
Footnote †: journal: Applied
# Physical-Layer Security in Power-Domain NOMA Based on Different Chaotic Maps
## 2 System Model
Thus, the \(m^{\text{th}}\) user's SINR is expressed as follows [13]:  
\[SINR_{m}=\frac{a_{m}\rho_{s}\left|h_{m}\right|^{2}}{\rho_{s}\left|h_{m}\right| ^{2}\sum\
olimits_{i=m+1}^{M}a_{i}\ +1} \tag{4}\]  
Then, the \(M^{\text{th}}\) user's SINR is given with [13]:  
\[SINR_{M}=a_{M}\rho_{s}\left|h_{M}\right|^{2} \tag{5}\]  
After the SINR terms of DL-NOMA are acquired, when all of the symbols (\(x_{1}\), \(x_{2}\),..., \(x_{m-1}\)) have been accurately decoded, the achievable rate at \(U_{m}\) for decoding signal \(x_{m}\) can be gained as follows [13]:  
\[R_{m}^{DL}=\log_{2}(1+SINR_{m})=\log_{2}(1+\frac{a_{m}\rho_{s}\left|h_{m} \right|^{2}}{\rho_{s}\left|h_{m}\right|^{2}\sum\
olimits_{i=m+1}^{M}a_{i}+1}) \tag{6}\]  
Thus, the sum rate of DL-NOMA is described as follows [13]:  
\[ \begin{array}{l}R_{sum}^{DL}=\sum\limits_{m=1}^{M}\log_{2}(1+SINR_{m})\\ =\sum\limits_{m=1}^{M-1}\log_{2}(1+\frac{a_{m}\rho_{s}\left|h_{m}\right|^{2}} {\rho_{s}\left|h_{m}\right|^{2}\sum\
olimits_{i=m+1}^{M}a_{i}+1})+\log_{2} \Big{(}1+a_{M}\rho_{s}\left|h_{M}\right|^{2}\Big{)}\\ =\sum\limits_{m=1}^{M-1}\log_{2}(1+\frac{a_{m}}{\sum\
olimits_{i=m+1}^{M}a_{i }+1/\rho_{s}\left|h_{m}\right|^{2}})+\log_{2}\Big{(}1+a_{M}\rho_{s}\left|h_{M} \right|^{2}\Big{)}\end{array}  \tag{7}\]With a high SNR that is as \(\rho_{s}~{}\rightarrow~{}\infty\), the sum rate of DL-NOMA becomes [13]  
\[R_{sum}^{DL}\approx\sum_{m=1}^{M-1}\log_{2}(1+\frac{a_{m}}{\sum_{i=m+1}^{M}a_{i}} )+\log_{2}\Bigl{(}\rho_{s}|h_{M}|^{2}\Bigr{)}~{}\approx\log_{2}\Bigl{(}\rho_{s }|h_{M}|^{2}\Bigr{)} \tag{8}\]
# Wearable Plasma Pads for Biomedical Applications
## 3 Results and Discussion
### Antibacterial Activity of the Plasma Pad  
The plasma pad antibacterial properties were evaluated using quantitative and qualitative methods that involved counting the number of CFUs and performing inhibition zone assays after plasma treatment, as shown in Figure 7. The antibacterial activity using a quantitative method shows a similar trend to the one shown for the treatment time-dependent antibacterial effect in the qualitative analysis and the plasma pads showed significant killing efficiency toward _S. aureus_ as well as clinical MRSA isolates. The strong antibacterial effects of the plasma pad were also observed in qualitative images. These results indicate that plasma pads can also be used for skin disinfection.  
Figure 6: In vivo wound healing effects of plasma pads in the full-thickness excisional wound splitting model. (**a**) Quantification of the wound closure rate following wounding. (\(n=8\), \({}^{*}P<0.05\) compared to control); (**b**) Representative stereomicroscopic images of full-thickness wound sites and wound contraction at Days 0 and 21. Scale bars are 2 mm; (**c**) Histological images of hematoxylin and eosin (H&E) staining of negative control and plasma-treated skin. Scale bars are 500 \(\upmu\)m; (**d**) Quantitative hair follicle assessment in repaired wound sites. Total and hair follicles at each stage (anagen, catagen, and telogen) were determined after H&E staining (\(n=8\), \({}^{*}P<0.05\) compared to control).  
Overall, these results suggest that the biocompatible plasma pad, which causes no cytotoxicity or skin irritation, has a wound healing effect and antibacterial activity against _S. aureus_ and drug-resistant bacteria, such as MRSA, indicating great potential as a method of skin treatment.
# Free-Hand Input and Interaction in Virtual Reality Using a Custom Force-Based Digital Thimble
## 5 User Study 1: Fitts' Law
### Results
#### 5.5.1 Throughput  
An ANOVA identified a significant effect of the device used on the throughput (\(F_{2,22}=18.87\), \(p<0.0001\),\(\eta^{2}=0.1\)). The mouse achieved the highest mean throughput at 3.11 bps, followed by the controller at 2.89 bps and the thimble at 2.61 bps. There was also a significant effect based on selection method (\(F_{1,11}=73.16\), \(p<0.0001\), \(\eta^{2}=0.2\)). The on-press selection method demonstrated a superior performance, yielding a mean throughput of 3.20 bps, whereas the on-release method resulted in a slightly lower mean throughput of 2.54 bps. A Tukey-Kramer test revealed that all input devices were significantly different from each other in terms of performance, but they all exhibited superior performance when using the on-press selection method compared to the on-release selection method. Figure 5 displays the average throughput data categorized by input device and selection method.
# A High-Efficiency Method of Mobile Positioning Based on Commercial Vehicle Operation Data
## 4 Analytical Models with Different Outlier Filtering Criteria  
This section proposes an analytical model and presents numerical results to analyze the feasibility of the proposed mobile positioning method with different outlier filtering criteria.
## 4 Discussion
### Uncertainties and Limitations  
It should be noted that although we used the latest improved GPP and ET products released by MODIS in this study, there are still certain uncertainties in both. There will be some differences in the maximum light rate of plant cover types [63]. At the same time, the input of climatic factors will also introduce certain errors. Uncertainties in ET products are mainly due to errors introduced by inversion algorithms, climate input data and other input variables. Nevertheless, the results of this study still reflect the overall seasonal variation characteristics of eWUE in Inner Mongolia, and more accurate conclusions need to introduce more data and different methods used to carry out multi-channel integration research. This research emphasized the analysis of the influence that precipitation and temperature have on eWUE; however, the impact factors of eWUE are comprehensive and complicated. Since 2000, the Chinese government has implemented several large-scale ecological restoration projects, including the conversion of farmland to forest project (since 1999), the Three-North Shelterbelt Project (since 1978) and the grassland ecological protection subsidy and incentive policy (since 2010), which have had a significant impact on the structure and function of the terrestrial ecosystem [64]. Thus, the impact of other variables such as drought conditions, ecological projects and human activities on eWUE should be further increased. Additionally, its contribution ratio should be quantitatively differentiated according to the degree of its effect.