esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Producing oxygen vacancies via acceptor substitutions is a well-known approach for raising the conductivity of various oxygen ion conductors [23; 24; 25; 26]. The formation of vacancies in La\({}_{2}\)(Hf\({}_{2-x}\)La\({}_{x}\))O\({}_{7-x/2}\) (\(x\) = 0.1) as a result of substitution of the trivalent lanthanide on the tetravalent metal site can be represented as follows:  
\[\text{La}_{2}\text{O}_{3}\overset{\text{HfO}_{2}}{\longrightarrow}\text{ 2 La}^{\prime} _{\text{Hf}}+\text{V}_{\mathbf{O}(48f)}^{\bullet\bullet}+3\text{O}_{\mathbf{O} }^{\text{x}} \tag{1}\]  
The \(Ln_{2}\)O\({}_{3}\)-MO\({}_{2}\) (M = Ti, Zr, Hf) systems have large isomorphism regions around the \(Ln_{2}\)Ti\({}_{2}\)O\({}_{7}\) (\(Ln\) = Sm-Lu), \(Ln_{2}\)Zr\({}_{2}\)O\({}_{7}\) (\(Ln\) = La-Eu), and \(Ln_{2}\)Hf\({}_{2}\)O\({}_{7}\) (\(Ln\) = La-Tb) pyrochlores. In a certain range of \(Ln_{2}\)O\({}_{3}\) concentrations, the pyrochlore solid solutions in these systems can transform into fluorite solid solutions (order-disorder transition) [27; 28; 29; 30; 31; 32]. In the zirconate systems, these order-disorder ranges are symmetric regarding \(Ln_{2}\)Zr\({}_{2}\)O\({}_{7}\), whereas in the titanate systems they are rather broad and are located mainly at high degrees of \(Ln\) substitution for Ti at synthesis temperatures below 1700 \({}^{\circ}\)C. The investigation of the broad isomorphism ranges in the hafnate systems is complicated by the high synthesis temperatures of the solid solutions (T\({}_{\text{sym. }}\)\(\geq\) 1700 \({}^{\circ}\)C) [22; 32]. As shown in a precision XANES spectroscopy study [4], the broad isomorphism range in the Tm\({}_{2}\)(Ti\({}_{2-x}\)Tm\({}_{x}\))O\({}_{7-x/2}\) (\(x\) = 0-0.67) \"stuffed\" titanate system includes three different regions: (1) at \(x\) = 0-0.1, where only a P1 pyrochlore exists; (2) \(x\)-0.13-0.56, where P1 and P2 pyrochlore phases coexist, (P1--thulium-deficient; P2--thulium-enriched), and (3) \(x\)-0.56-0.67, where a defect fluorite F prevails. According to XRD data, in the Yb\({}_{2}\)(Ti\({}_{2-x}\)Yb\({}_{x}\))O\({}_{7-x/2}\) system, two pyrochlore phases coexist in a range of \(x\) = 0.16-0.37 [31]. In this work, ball milling was used to activate a starting oxide mixture for high-temperature synthesis of La\({}_{2}\)Hf\({}_{2}\)O\({}_{7}\) and La\({}_{2}\)(Hf\({}_{1.9}\)La\({}_{0.1}\))O\({}_{6.95}\) solid solution with the aim of ascertaining whether they have proton conductivity. La\({}_{2}\)(Hf\({}_{1.9}\)La\({}_{0.1}\))O\({}_{6.95}\) falls in the La\({}_{2}\)Hf\({}_{2}\)O\({}_{7}\)-La\({}_{2}\)O\({}_{3}\) isomorphous miscibility range, but a low degree of substitution (\(x\) = 0.1) simplifies the situation because in most of the \(Ln_{2}\)M\({}_{2}\)O\({}_{7}\)-\(Ln_{2}\)O\({}_{3}\) systems, only one solid solution exists at such degrees of substitution; in turn, this significantly facilitates interpretation of structural data and allows one to use diffraction techniques, without resorting to spectroscopy (Raman scattering or XANES) [2; 4].
###### Abstract  
In rice paddy soil, biological nitrogen fixation is important for sustaining soil nitrogen fertility and rice growth. _Anneormyobacter_ and _Geobacteriaceae_, iron-reducing bacteria belonging to Deltaproteobacteria, are newly discovered nitrogen-fixing bacteria dominant in paddy soils. They utilize acetate, a straw-derived major carbon compound in paddy soil, as a carbon and energy source, and ferric iron compounds as electron acceptors for anaerobic respiration. In our previous paddy field experiments, a significant increase in soil nitrogen-fixing activity was observed after the application of iron powder to straw-returned paddy field soil. In addition, combining iron application with 60-80% of the conventional nitrogen fertilizer rate could maintain rice yields similar to those with the conventional nitrogen fertilization rate. It was thus suggested that iron application to paddy soil increased the amount of nitrogen fixed in the soil by enhancing nitrogen fixation by diazotrophic iron-reducing bacteria. The present study was conducted to directly verify this suggestion by \({}^{15}\)N-IRMS analysis combined with \({}^{15}\)N-DNA-stable isotope probing of iron-applied and no-iron-applied plot soils in an experimental paddy field. In no-iron-applied native paddy soil, atmospheric \({}^{15}\)N\({}_{2}\) was incorporated into the soil by biological nitrogen fixation, in which diazotrophic iron-reducing bacteria were the most active drivers of nitrogen fixation. In iron-applied paddy soil, the amount of \({}^{15}\)N incorporated into the soil was significantly higher due to enhanced biological nitrogen fixation, especially via diazotrophic iron-reducing bacteria, the most active drivers of nitrogen fixation in the soil. Thus, our previous suggestion was verified. This study provided a novel picture of active nitrogen-fixing microorganisms dominated by diazotrophic iron-reducing bacteria in paddy soil, and directly proved the effectiveness of iron application to enhance their nitrogen fixation and increase the incorporation of atmospheric nitrogen into soil. The enhancement of biological nitrogen fixation in paddy fields by iron application may lead to novel and unique paddy soil management strategies to increase soil nitrogen fertility and ensure rice yields with reduced nitrogen fertilizer input and lower environmental nitrogen burdens.  
nitrogen-fixing bacteria; iron-reducing bacteria; amount of fixed nitrogen; iron application; Article
# Fingerprinting Indoor Positioning Based on Improved Sequential Deep Learning
## 1 Introduction
To address this, methods like the Kalman filter [18; 19] are often combined with Gaussian-based approaches to filter out interference and provide a stable RSSI value for accurate distance estimation. A Fourier-transform Fuzzy C-means Kalman filter [20] has also been proposed to further improve the stability of RSSI fingerprints for indoor positioning. The incorporation of Artificial Intelligence (AI) and Machine Learning (ML) methods has significantly improved indoor positioning systems. [21]. While algorithms like K-Nearest Neighbors (KNN) are widely used for their simplicity, their performance declines in complex, large-scale, multi-floor environments [22]. In contrast, Deep Neural Networks (DNN) can model the complex relationships between RSSI and location, improving accuracy in large-scale settings [23]. Recent advancements have focused on DNNs that learn RSSI distributions rather than directly converting RSSI values into coordinates, with further exploration of Convolutional and Recurrent Neural Networks (CNN and RNN) [24; 25] to improve robustness. A complex neural network [26] based joint estimation of Angle of Arrival (AoA) and Angle of Departure (AoD) has already leveraged advanced deep learning architectures to explore the positioning capabilities within the communication infrastructure of 6G systems. A Wi-Fi Generative Adversarial Networks (GANs) [27; 28] has also been introduced, generating labeled fingerprint data and achieving higher accuracy than standard supervised DNN. Small-scale indoor localization is relatively straightforward, limited to single floors or enclosed spaces with a few access points. However, scaling to multi-building and multi-floor environments introduces significant complexity. Binary classification techniques, as introduced in [29], use features derived from real-world coordinates to partition the location space, applying tree-based classifiers sequentially to progressively narrow down the search area. In this paper, we propose a novel fingerprint-based area localization scheme that applies sequential deep learning to iteratively reduce the search space. By integrating a deep neural network with Weighted K-Nearest Neighbors (WKNN), We enhance location estimates by utilizing the statistical patterns of RSSI samples at each RP. Our method is evaluated using the publicly available UJIIndoorLoc dataset. The rest of this paper is organized as follows: Section 2 covers the fundamentals of the fingerprint-based positioning method, along with an overview of the conventional WKNN algorithm and DNN-based positioning model. Section 3 provides a detailed explanation of the implementation process for our proposed localization algorithm. Section 4 we present the localization results obtained using our method on the _UJ11 IndoorLoc_ validation dataset. Finally, Section 5 concludes this work.
# Flocculation Harvesting Techniques for Microalgae: A Review
## 3 Bio-Flocculation
### Algal-Fungal Bio-Flocculation  
Many filamentous fungi have been known to possess self-pelletizing abilities by which algal cells can be entrapped and adsorbed [46]. Fungal biomass is usually produced separately in a fermenter, after which its pellets are applied to algae harvesting (called co-pelletization) [27]. Sometimes bio-flocculations are carried out by the co-cultivation of fungi and algae. However, during the co-culture, fungal cells could have a positive or negative impact on algal growth, depending on their species and culture conditions. Interestingly, co-existing fungi can protect algal culture from external microbial contamination [13]. Harvested fungal-algal biomass (co-pellets) has been successfully utilized as feedstock for various biofuels such as biodiesel, alcohols, and biogas through fermentation and thermochemical methods [47].  
Figure 3: A schematic diagram of bio-flocculation harvesting of algal biomass using bacteria. EPS, extracellular polymeric substances. Reprinted from Reference [27] with permission from Springer Nature.  
The co-pelletizing abilities of four fungal species (_Aspergillus lentulus_, _A. terreus_, _Polyporus_ sp., and _Rhizopus oryzae_) have been evaluated in a bio-flocculation study, by co-culturing with alga _Chroococcus_ sp. in a photosynthetic mineral salt medium augmented with glucose (5 g/L) as an organic carbon substrate. Remarkably, all the tested fungal strains were able to effectively flocculate the targeted alga with high harvesting efficiency of \(\sim\)92% [48]. Similarly, _A. fumigatus_ has been recommended for algae harvesting through co-pelletization, among several self-pelletizing oleaginous fungal strains. This fungal strain showed the highest harvesting efficiency (\(>\)90%) for both freshwater alga _C. protothecodes_ and marine alga _Tetraselmis suecia_ during co-culturing in swine wastewaters within 24 h. It should be noted that, during the co-cultures, the total biomass and lipid production increased synergistically [47]. _A. Fumigatus_ was also reported for successful co-cultivation with the heterotrophic alga _Thraustochytrid_ sp. in an anaerobically digested swine wastewater.  
An efficient harvesting efficiency (\(\sim\)90%) was obtained through the co-pelletization of fungal mycelium and algal cells. Furthermore, they could obtain enhanced biomass and lipid productivity as well as effective nutrient removal from the wastewater [49]. Interestingly, the processing time required for the fungal pellet-based harvesting is much shorter than that needed for spore-based harvesting. For example, harvesting of _Chlorella_ sp. based on co-pelletization with _Penicillium_ sp. require only 2.5 h, while 28 h was required in the case of the spores-based method to obtain the same harvesting efficiency (\(>\)98%) [50].  
The algal-fungal bio-flocculation process can be considered to be highly efficient and sustainable. However, environmental contamination by fungal species and/or spores as well as process scale-up should be properly considered for practical applications [26].
# Field Trials of SC-FDMA, FBMC and LP-FBMC in Indoor Sub-3.5 GHz Bands
## 1 Introduction
On the other hand, there has not been a field test study on FBMC (or modified version of FBMC) in indoor wireless communication bands, and it has not been verified that FBMC (or modified version of FBMC) can solve the problems of SC-FDMA in the real indoor wireless environment. For reference, there have been few studies on the spectrum shape of FBMC waveforms [16; 17]. In this study, we intensively analyse the performance of SC-FDMA, FBMC, and LP-FBMC via a field test in a real indoor wireless environment. To this end, we implement a test bed utilising a software defined radio (SDR) device. As for the indoor wireless channel, we test the various sub-3.5 GHz bands when considering the different indoor wireless band allocations in the different countries (3.5 GHz band for South Korea; 600 MHz, 2.5 GHz, and 3.5 GHz bands for US; and, 700 MHz and 3.5 GHz bands for Europe [18]). Specifically, the three waveforms (The three uplink schemes in comparison, SC-FDMA, FBMC, and LP-FBMC differ basically in the waveforms. Hence, the terms _'three waveforms'_ and _'three uplink schemes'_ are used interchangeably for referring to {SC-FDMA, FBMC, LP-FBMC} in this paper.) are compared in the following aspects:  
* spectral density of the transmit waveform affected by the RF circuit and the HPA;
* point-to-point link performance according to distance and carrier frequency in two distinct environments: line-of-sight (LoS) and non-line-of-sight (NLoS) environments;
* uplink BER performance according to the time/frequency offset among the users, distance, and carrier frequency; and,
* uplink BER performance according to the subcarrier spacing, and the number of subcarriers. The remainder of this paper is structured, as follows. Section 2 describes the mathematical waveform models of SC-FDMA, FBMC, and LP-FBMC. Section 3 describes the SDR test-bed for the field experiments in indoor wireless channels. The BER performances are intensively tested and compared among the waveforms from various perspectives in Section 4. Finally, we make conclusions in Section 5.
## 3 WDM-PON Network Architectures
### The P2 MP Architecture of the WDM-PON Network  
For creating this P2 MP network architecture, a self-contained wavelength channel from the OLT terminal to each ONU unit must be realized for both downstream and upstream directions. By this, a point-to-point connection is created between OLT and ONU equipment, where each ONU unit can be working on the data rate up to total bit rate of the dedicated wavelength channel. Moreover, different wavelength channels can be working on different bit rates supporting various service variations [21].
## 2 Results and Discussion
### Pca  
To provide more information about differentiation of the origins of SD, PCA was performed based on the 58 common peak areas as new derived variables (principal components, PCs) with eigen values >1, which could adequately summarize the original information. The first two PCs explained approximately 81.6% of the original data variability and the predictive ability of the model (Q2) was 53.5%, which indicated that it was a good model (Q2 > 0.50). According to the PCA score plot model as shown in Figure 3A, one group was the Zhejiang sample point (ZJ1-ZJ11, positive position) and the other was Guizhou sample point (GZ1-GZ13, negative position), which showed that both domains (Zhejiang SD and Guizhou SD) were obviously separated from each other. Moreover, the dots that presented GZ1-GZ13 were close to each other, suggesting the 13 batches of SD from Guizhou were similar. The dots of ZJ1-ZJ11 were relatively scattered, indicating diversification of the 11 batches of Zhejiang SD. It was indicated that the chemical composition of Zhejiang SD was not as stable as Guizhou SD. As shown in the PCA loading plot model (in Figure 3B), the first principal component (PC1) explained 57.2% of the variance and was mainly represented by the linear combination of the following variables: Zingerone (49), 7-tetradecene (26), dodecanal (20), trans,trans-2,4-nonaional (14), tetradecane (27), beta-asarone (44), hexyl hexanoate (25), copaene (28), 2-butyl-2-octenal (24) and trans-2-decenal (10). PC2 identified 24.4% of the variance and was mainly expressed by a linear combination of the variables, including 2,4-di-tert-butylphenol (40), 4-allylanisole (12), carvacrol (21), citral (15), 1,2-epoxyhexadecane (55), 2,6-di-tert-butyl-p-benzoquinone (35), and 1-nonanal (7).
# Privacy-Preserving Federated Singular Value Decomposition
## 2 Preliminary  
### Singular Value Decomposition  
Let \(\mathbb{M}\) be a \(s\times d\) matrix with assumption of \(s\leq d\). As shown in Figure 1, the full SVD of \(\mathbb{M}\) is a factorization of the form \(\mathbb{U}\Sigma\mathbb{V}^{T}\), where \(T\) means conjugate transpose. The left-singular vectors are \(\mathbb{U}=[u_{1},u_{2},\ldots,u_{s}]\in\mathbb{R}^{s\times s}\), the right-singular vectors are \(\mathbb{V}=[v_{1},v_{2},\ldots,v_{d}]\in\mathbb{R}^{d\times d}\), and the diagonal matrix with the singular values in decreasing order in its diagonal is \(\Sigma=\text{diag}\{\sigma_{1},\sigma_{1},\ldots,\sigma_{d}\}\in\mathbb{R}^{s \times d}\). The partial or truncated SVD [26; 27] is used to find the top \(k\) (\(k\leq d\)) singular vectors \(\mathbb{U}=[u_{1},u_{2},\ldots,u_{k}]\), \(\mathbb{V}=[v_{1},v_{2},\ldots,v_{k}]\) and singular values \(\Sigma=\text{diag}\{\sigma_{1},\sigma_{1},\ldots,\sigma_{k}\}\).  
If \(\mathbb{M}^{\prime}=\frac{1}{s}\mathbb{M}^{T}\mathbb{M}\in\mathbb{R}^{d\times d}\), then the Power Method [23] could be used to compute the top \(k\) right singular vector of \(\mathbb{M}\) and the top \(k\) eigenvectors of \(\mathbb{M}^{\prime}\). It works by iterating \(\mathbb{Y}=\mathbb{M}^{\prime}\mathbb{Z}\) and \(\mathbb{Z}=\text{orth}(\mathbb{Y})\), where both \(\mathbb{Y}\) and \(\mathbb{Z}\) are \(d\times k\) matrices and \(\text{orth}(\cdot)\) is the orthogonalization of the columns with QR factorization.  
Moreover, if \(\mathbb{M}\) is the composition of \(n\) matrices, then computation of the Power Method can be distributed. So, if \(\mathbb{M}^{T}=[\mathbb{M}_{1}^{T},\mathbb{M}_{2}^{T},\ldots,\mathbb{M}_{n}^ {T}]\in\mathbb{R}^{s\times d}\) with \(s=\sum_{i=1}^{n}s_{i}\), where \(\mathbb{M}_{i}\in\mathbb{R}^{s_{i}\times d}\) and \(\mathbb{M}_{i}^{\prime}=\frac{1}{s_{i}}\mathbb{M}_{i}^{T}\mathbb{M}_{i}\), then Equation (1) holds. Thereby, \(\mathbb{Y}\) can be written as  
Figure 1: Singular value decomposition.  
\(\mathbb{Y}=\sum_{i=1}^{n}\frac{s_{i}}{s}\mathbb{M}_{i}^{t}\mathbb{Z}\in\mathbb{R}^{d \times k}\), which indicates that the Power Method can be processed in parallel by each data holder [18, 28].  
\[\mathbb{M}^{t}=\frac{1}{s}\mathbb{M}^{T}\mathbb{M}=\sum_{i=1}^{n}\frac{1}{s} \mathbb{M}_{i}^{T}\mathbb{M}_{i}=\sum_{i=1}^{n}\frac{s_{i}}{s}\mathbb{M}_{i}^{t }=\sum_{i=1}^{d}p_{i}\mathbb{M}_{i}^{t} \tag{1}\]
# Do We Get What We Need from Clinical Acoustic Voice Measurements?
## 2 Background: Current State of the Art and Challenges in Clinical Acoustic Measurements
### How Technical Solutions May Help to Improve Baseline Data and Application of Techniques  
For a reliable and valid diagnostic application of voice quality measures, an extensive database with normative data for age, gender, training, token, language, and phonetic context effects, considering SPL and \(f_{0}\), are required. Moreover, the clinical usefulness of group comparisons or cut-off values for acoustic voice quality characteristics such as jitter, shimmer, HNR, CPPS, and combined indices must be reviewed. Thus, at present, interindividual comparisons seem far more valid and informative for clinical applications.  
From a technical viewpoint, big data analysis techniques may help to gather more profound knowledge about voice function plasticity within and between individuals with and without dysphonia. Natural Language Processing (NLP) techniques are especially targeted at using samples of uninfluenced speech tokens, and have the potential to support the development of the much-needed massive normative databases [63]. Furthermore, machine learning has been applied in scientific databases and has shown great potential in the detection of voice disorders [64].  
Thus, funding and publishing bodies should reinforce the availability of more open-source data to allow further data use and standardization across centers and countries. Again, this will require more research to systematize the procedures, establish minimal requirements, and propose calibration solutions for mobile communication devices to support the broad use of these tools in less economically privileged circumstances. This will help to further develop the huge potential of instrumental acoustic voice quality measures to objectively describe voice function. This would also improve the evidence basis for clinical decisions, as well as research funding and clinical cost coverage applications.
# Effective PSCCH Searching for 5G-NR V2X Sidelink Communications
## 1 Introduction
There are four approaches to select the resources in Mode 2 [13]:  
* Each UE selects its resources autonomously through a sensing-based semi-persistent transmission mechanism;
* A cooperative distributed scheduling is performed, where each UE assists the others to select the resources;
* The UE selects the resources based on the preconfigured scheduling;
* The UE schedules the resources for its neighbors. The sensing-based semi-persistent transmission mechanism is quite explored, however, long intervals with the same allocation may result in persistent collisions, which reduces safety due to consecutive packet losses [14].
## 2 The Problem Statements  
Let us consider a perturbed nonlinear point-to-point control problem:  
\[\dot{x}=F_{0}(t,x,u)+\varepsilon F_{1}(t,x,u),\quad x(t_{0})=x_{0},\quad x(t_{ f})=x_{f},\quad 0<\varepsilon\leq\varepsilon_{0}\ll 1. \tag{1}\]  
Here, \(\varepsilon_{0}\) is the upper bound on the values of the small parameter for which the proximate estimation of the asymptotic approximation takes place. For each specific problem, a specific value for the \(\varepsilon_{0}\) parameter is defined, and in applications it can often be greater than 1.  
The presence of the inequality \(\varepsilon_{0}\ll 1\) in (1) only emphasizes that for small values of the parameter and under fairly general conditions on the right-hand sides, there are corresponding approximation estimates.  
Let us denote an admissible trajectory and admissible a control in problem (1) by \(x(t,\varepsilon)\in\mathbb{R}^{n}\), \(u(t,\varepsilon)\in\mathbb{R}^{r}\). The small parameter \(\varepsilon\) is considered known. We suppose that an admissible open-loop control \(u_{0}(t)\) solving an unperturbed problem with fixed ends  
\[\dot{x}_{0}=F_{0}(t,x_{0},u_{0}),\quad x_{0}(t_{0})=x_{0}^{0},\quad x_{0}(t_{ f})=x_{0}^{f} \tag{2}\]  
is known. It is assumed that the system in (2) is flat and its solution can be found by the covering method, but the perturbed system (1) is not flat.  
**Problem 1**.: _It is necessary to find such an open-loop control \(u_{1}(t,\varepsilon)=u_{0}(t)+\varepsilon u_{1}(t)\), where \(\varepsilon u_{1}(t)\) is the main part of the \(u_{0}(t)\) control correction, which solves perturbed nonlinear control problem (1) more precisely than \(u_{0}(t)\)._  
Experiments show that when solving optimal control problems with a small parameter by traditional methods, asymptotic approximations, which can contain only part of the qualitative information about the exact solution (see in [11], for example), are of great help. This means that even with the help of local procedures, solutions can be found that are close to the points of the global extremum. Therefore, we may state the next problem.  
**Problem 2**.: _It is necessary to find control correction \(\varepsilon\omega(t)\), which solves the problem_  
\[\|x_{f}-x(t,u_{1}(t,\varepsilon)+\varepsilon\omega(t))\|\to\min_{\omega}.\]  
Knowing \(u_{1}(t,\varepsilon)\) means that the first order asymptotic approximation to the admissible open-loop control is known. Finding \(\omega(t)\) actually means finding the missing part of the exact value of the admissible point-to-point control, the main part of which \(u_{1}(t,\varepsilon)\) has already been found.