esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Positioning for Smart Spaces: Proposal and Evaluation
## 4 Hybrid Positioning Approach  
### Overall Architecture  
A collaborative/hybrid positioning approach has been designed on the basis of usual positioning sources (like cellular, GPS and wireless proximity) and some extra indoor positioning technologies and algorithms coming from smart spaces. This way, the most accurate location source will be chosen or a combination of them, improving the accuracy and availability of the system across different places. A graphical representation of how the main functions of the positioning system work is shown in Figure 1. Data retrieved from radio-frequency communication technologies coming from the same mobile device are combined with data received by smart infrastructures to compute the final position.  
Positioning technologies such as GPS or cellular, which provide a final position, directly input the decision module with the hybridization algorithm. In this case an accuracy approximation is usually provided by the same technology, such as the position quality indicators of GPS using the geometry  
Figure 1: Positioning subsystem.  
of satellite signals. Cell ID carried out using cellular networks, as they are integrated in Android devices at least, also provide an accuracy estimation in meters. However, regarding measurements coming from short and medium-range communication technologies, the signal strength (RSSI) and ID of base stations (Wi-Fi access points and BLE tags) sensed from the surroundings are the input for state-of-the-art fingerprinting and proximity algorithms improved as explained in the next parts. These provide an estimated location together with a position accuracy indicator, which is used to check the most appropriate technology or algorithm.  
Our architecture also puts the focus on energy saving, due to devices like smartphones are power constrained and use short-life batteries. Thanks to the hybridization carried out between GPS/Celular, with good performances outdoors, and WiFi/BLE based algorithms, the positioning system disable or enable GPS/Cellular positioning methods when the user is detected inside a building or near the exit, respectively. This can be detected using special fingerprints/BLE beacons located near the entry points of the building. Another energy saving measure have been added in the fingerprinting matching calculation, which is a CPU-intensive task consuming much energy. It is focused on filtering as many fingerprints as possible using the last reported position to only consider closer fingerprints of the same floor. This introduces the extra issue of detecting floor changes, but this is efficiently solved using special fingerprints/BLE beacons located close to stairs or lifts.  
Once the position and accuracy information coming from all technologies and algorithms and smart infrastructure devices is received, a hybridization process is carried out to compute a final position and provide a combined estimation of the accuracy.
## 3 Method
### Quality Control  
To ensure the high quality and reliability of the Manchu music dataset, quality control is implemented throughout data collection, processing, storage, and publication. This section outlines the specific methods and steps involved in maintaining data quality.  
During data collection, source verification is crucial. Authoritative music academies, cultural research institutions, and Manchu communities are selected to ensure data reliability. Similar data types are cross-verified from multiple independent sources. High-fidelity recording equipment and real-time monitoring by professionals ensure optimal audio and video quality. In data cleaning and preprocessing, noise filtering removes background noise, and segmented audio and video files are manually corrected for accuracy.  
In the data processing phase, consistency checks and quality assessments are key. Uniform processing standards are maintained through periodic checks, while quality assessment tools ensure clarity and sound quality. Multiple algorithms are used for feature extraction, with results cross-validated and manually reviewed for accuracy. Metadata annotations are completed using automated tools and manual corrections, followed by expert reviews to ensure consistency and completeness.  
For data storage, multiple backup strategies prevent data loss, with real-time system monitoring to resolve faults promptly. Regular consistency checks and version control ensure data traceability and reliability.  
Before data publication, a data release review is conducted by experts in musicology and related fields to verify academic value and cultural accuracy. User feedback further ensures usability. Data access is managed securely, and privacy and copyright protections are rigorously enforced to ensure legal compliance.  
Challenges such as data diversity, the large workload of manual reviews, and integrating technology with professional knowledge are addressed through standardized workflows and automated tools, improving efficiency and ensuring high-quality results.  
In summary, the Manchu Music Dataset serves as a comprehensive resource for pre-serving and analyzing traditional Manchu music. To further illustrate its scope and diversity, we present a summary of the dataset's key characteristics in Table 3. This table provides a consolidated view of the dataset's composition, offering insights into its potential for supporting a wide range of research applications.  
Building on this dataset, the next chapter delves into its application across various experimental scenarios, including music classification, generation, and emotion analysis. These experiments demonstrate the practical value of the dataset and highlight its versatility in advancing both ethnomusicology and AI-driven music research.
## 3 Results and Discussion
### Cake Studies
#### 3.2.3 FTIR Study  
The FTIR spectra of cake crumbs (Figure 8a) were analyzed to evaluate the components of the cake. The N-H stretching vibration of the amide I region is represented through a spectral peak at 3200 cm\({}^{-1}\), but in the obtained spectra, this peak was masked by the O-H stretching peak of water, which arises nearly at \(\sim\)3300 cm\({}^{-1}\). The spectral band with a maximum at 2927 cm\({}^{-1}\) and 2852 cm\({}^{-1}\) corresponds to the asymmetric and symmetric stretching vibration of CH\({}_{2}\), respectively, which majorly comes from the starch. Few studies have also correlated the peak at 2927 cm\({}^{-1}\) with the starch-lipid interaction [36]. C=O ester group could be confirmed through the spectral band at 1742 cm\({}^{-1}\). The contribution of starch protein is evident from the band having maxima at 1638 cm\({}^{-1}\), which is assigned to the amide I region [44]. A weak transmittance signal at 1463 cm\({}^{-1}\) corresponds to the bending vibrations of methyl. Peaks at 1150 cm\({}^{-1}\) and 1077 cm\({}^{-1}\) is due to the stretching vibration of the C-O [45]. Peaks at 1240 cm\({}^{-1}\), 1150 cm\({}^{-1}\) and 1099 cm\({}^{-1}\) may be due to C=O stretching and C-C stretching of the carbohydrates [46]. The FTIR spectra of all cake crumbs showed the above-mentioned peaks. Interestingly, and similar to batter Sa-C, it showed a reduction in the peak located at 2927 cm\({}^{-1}\), further suggesting the possibility of low starch-lipid interactions. Short crystalline, amorphous, and hydrated starch crystals in the FTIR spectra are denoted by the peaks at 1047 cm\({}^{-1}\), 1022 cm\({}^{-1}\), and 995 cm\({}^{-1}\), respectively [47]. These peaks are extremely sensitive to variations in starch structure. [48].  
Figure 7: Colorimetric parameters: crust (**a**) \(L^{*}\), \(a^{*}\), \(b^{*}\) values; (**b**) \(YI\) values; (**c**) \(\Delta E\) values; crumbs (**d**) \(L^{*}\), \(a^{*}\), \(b^{*}\) values; (**e**) \(YI\) values; (**f**) \(\Delta E\) values. The data are represented as mean \(\pm\) S.D. of triplicates. # represents \(p<0.05\).  
With Origin Pro software, the spectra were deconvoluted in the wavenumber range of 900-1100 cm\({}^{-1}\) using the Lorentz peak fitting tool. Figure 8b shows the peaks obtained after the deconvolution and Lorentz peak fitting. The peak area ratio for the peaks 1047/1022 and 995/1022 cm\({}^{-1}\) were calculated (Figure 8c,d) and are used to predict the short-term order of starch. According to a previous report, proportions of 1047/1022 cm\({}^{-1}\) can be used to describe the degree of order (DO), whereas the spectral intensity ratio at 995/1022 cm\({}^{-1}\) can be used to describe internal structural and conformational changes in the degree of the double helix (DD) [49]. The trend for the DO among the cake crumbs was SpB-C > SaB-C > Sa-C > Control > Sp-C. This suggests that the partial substitution of butter with oloegels might prove beneficial for providing the ordered structure.
## 4 Control System Design
### Fuzzy Neural Network Controller Design  
#### 4.2.1 Controller Structure  
Fuzzy RBF network has the advantages of radial basis neural network and fuzzy control, which improves the reasoning ability of the system [34]. Fuzzy neural network compensators are designed in three attitude channels, respectively, which is used to eliminate the dynamic inverse error caused by external interference of UAV. Based on the real-time output data of the flight control system, the compensator uses the fuzzy rules automatically generated by the neural network to approximate the controlled parameters online [35]. In this paper, the improved fuzzy neural network designed has four layers, including input layer \(L_{1}\), fuzzification layer \(L_{2}\), fuzzy reasoning layer \(L_{3}\) and output layer \(L_{4}\). Figure 3 shows the organizational structure of the fuzzy neural network. The specific functions of each layer are:  
* \(L_{1}\): The fuzzy neural network structure has two inputs, which are the error \(e\) of the state variable in the attitude system and the error rate of change \(ec\). Therefore, the number of neurons in this layer is \(L_{1}=2\). At the same time, the input volume is transferred to the next layer. The input and output of this layer can be expressed as \[L_{1}(i)=x_{i},i=1,2.\] (25)\(L_{2}\): Fuzzy the variables transmitted by the above formula. Give the same fuzzy subset \([\text{NB},\text{NM},\text{NS},\text{ZO},\text{PS},\text{PM},\text{PB}]\) to the two outputs of the \(L_{1}\) layer. The number of nodes in this layer is \(L_{2}=14\), and the membership function is selected as Gaussian.  
\[L_{2}(i,j)=\exp(-\frac{\left(L_{1}(i)-Q_{ij}\right)^{2}}{\left(S_{j}\right)^{2 }}),\ S_{j}=\begin{bmatrix}S_{1}&\cdots&S_{7}\end{bmatrix}^{T},Q_{ij}= \begin{bmatrix}Q_{11}&\cdots&Q_{17}\\ Q_{21}&\ldots&Q_{27}\end{bmatrix} \tag{26}\]  
The \(i\) and \(j\) are the number of input variables and fuzzy sets, respectively. \(Q_{ij}\) and \(S_{j}\) are the center and width of the membership function.  
* \(L_{3}\): This layer is connected with the \(L_{2}\) node to generate corresponding fuzzy rules, and the number of nodes is \(L_{3}=49\). The output of the node is obtained by multiplying all the inputs of the node by fuzzy operation. The nodes of this layer are called rule nodes, and each node has its corresponding fuzzy rules. The output of this layer can be expressed as: \[L_{3}(j)=\prod_{j=1}^{N}L_{2}(i,j).\] (27)
* \(L_{4}\): After the defuzzification is completed, each node variable of this layer is transformed into the network output. The obtained data are transmitted to the flight control system. In this fuzzy neural network structure, \(L_{4}=1\) \[L_{4}=W^{T}L_{3},\] (28) where \(W^{T}\) is the connection weight matrix between the layer and the upper node, and \(W^{T}=\begin{bmatrix}W_{1}&W_{2}&\cdots&W_{49}\end{bmatrix}^{T}\).
## 2 Materials and Methods
### Experimental Procedure  
Prior to any test or intervention and before being evaluated for the study's inclusion criteria, all participants signed a written informed consent approved by the Regional Bioethics Committee at the Lithuanian University of Health Sciences (no. BE-2-106).  
As shown in Figure 1, the evaluation of the proposed CDSS was performed by researchers, resident geriatricians, and expert geriatricians. The researcher's role was taken by doctoral students, who interviewed the patients and entered data into the CDSS. In parallel, resident geriatricians performed diagnostic procedures using comprehensive geriatric assessment methods and laboratory examinations. These procedures were repeated every day within a 10-day period until the final diagnosis was made. The professors of the Clinical Department of Geriatrics of the Lithuanian University of Health Sciences played the role of experts, who reviewed the diagnoses of resident geriatricians, performed additional assessments and laboratory examinations to make the final diagnoses. The efficiency of the proposed CDSS was evaluated by comparing its diagnoses with the diagnoses of resident geriatricians and expert geriatricians (considered the gold standard). The decision-making rules of the proposed CDSS are implemented using fuzzy logic-based XAI, which adapts a set of questionnaires composed according to international practice and supplemented by knowledge accumulated in the Clinical Department of Geriatrics of the Lithuanian University of Health Sciences. In order to assess malnutrition, oropharyngeal dysphagia, eating disorders in dementia, and dehydration, the MNA-SF scale [24], the dysphagia screening tool EAT-10 [25], Edinburgh Feeding Evaluation in Dementia Scale [26], and a set of 10 questions based on the ESPEN guideline on clinical nutrition and hydration in geriatrics [1] were used.  
Figure 1: The procedure of experimental evaluation of the proposed CDSS.
## 5 Discussion
### Lessons Learned  
#### 5.1.1. CV Gathering from Students and Data Protection  
In the first step of this pilot's execution, the pilot leader requested through various channels (classroom, social media, university websites, e-mails) CVs from undergraduate and Ph.D. students that would be analyzed to draw conclusions on the average skill level of students. Since the CVs that were requested represented personal data, according to the legal strategy of the project that is compliant with the General Data Protection Regulation (GDPR), they had to be accompanied by a consent form and information sheet. Based on the legal obligations of the project, this consent form required a signature from an official administrative body (such as a police precinct), which was a tedious process for students. For that reason, at the beginning of the pilot's operation, participation of students was low with regard to CV gathering. During the second year of QualiChain's operation and during the first wave of the COVID-19 pandemic, additional digital services were developed for public administrations that allowed this process to be performed online. This shift in administrative processes greatly increased the number of CVs received from students. It is evident that in our modern society, in which ICT technologies provide novel solutions for various everyday problems and tasks, the effort to make a shift from physical to digital administrative processes needs to be intensified. Nowadays, citizens are always \"connected\" either via a personal computer or through a smartphone and national administrative processes need to reflect and leverage that fact to create new and innovative tools. This need was also magnified by the COVID-19 pandemic and the quarantine situation, which made it very difficult to receive a physical signature. In addition, this activity validated QualiChain's legal strategy and provided some perspective regarding the GDPR. It was proven that the GDPR should not be viewed as a barrier when it comes to developing technical solutions but rather as a set of guidelines to protect personal data and inform end users about their rights.
# Design of Surface Plasmon Nanolaser Based on MoS2
## 3 Simulation Analysis
### A Discussion of the Locality and Loss of Modes
#### 3.2.2 Discussion on the Influence of Geometric Structure on the Localization and Loss of Modes  
Figure 6a,c is the effects of the height of the air gap on the normalized mode area and the effective transmission loss. The radii of the Ag nanowires and CdS nanowires are fixed at 80 and 90 nm respectively in Figure 6a,c, and the spacer layer is fixed at 6 nm. Figure 6b,d is the effects of the CdS nanowire and Ag nanowire radius on the normalized mode area and effective transmission loss, in which the heights of the \(\mathrm{h_{u}}\) and \(\mathrm{h_{d}}\) are fixed and are both 10 nm. The structures in Figure 6a,c and Figure 4a are consistent. The structures in Figure 6b,d are consistent with those in Figure 4b, respectively. From Figure 6a,c, the normalized mode area increases as the height of the lower air gap increases, and the effective propagation loss decreases first and then increases, and the trend is upward. The minimums of the \(\mathrm{A_{eff}}\)/\(\mathrm{A_{0}}\) value and \(\mathrm{\alpha_{eff}}\) are, respectively, 0.005 and 0.00013. With the increase of the height of the upper air gap, the normalized mode area and the effective propagation loss all decrease. With the increase of the Ag nanowire radius, the normalized mode area decreases gradually, and the effective propagation loss increases gradually. Through the comparison of the diagrams in Figure 6, it can be seen that the Ag nanowire and CdS nanowire play a more important role in the limited propagation loss. Among them, the Ag nanowire has the greatest impact. The height of the air groove has a greater influence on the normalized mode area, and the upper air groove has the greatest influence.  
Figure 5: The normalized mode area and loss with different materials and the thickness of the space layer (**a**) the normalized mode area (**b**) the propagation loss.  
Figure 6: The normalized mode area and loss of nanolaser versus (**a,c**) the height of air gap, (**b,d**) the radius of CdS and Ag.
# Designing Smart Electromagnetic Environments for Next-Generation Wireless Communications
According to such a vision, smart EM environments have been created by conceiving the use of artificial smart skins deployed/integrated on the walls of buildings to tailor the propagation of the EM waves in complex urban scenarios [16; 17; 18; 19]. These skins consist of passive and/or active fixed/reconfigurable metasurfaces, specifically designed and fabricated, that can be suitable reconfigured by acting on simple electronic devices (e.g., radio-frequency switches, varactors). Thanks to their reconfiguration properties and the consequent need of developing ad-hoc intelligent reconfiguration techniques, the intelligent reflecting surfaces (IRSs) [16] are nowadays opening the doors to a completely new research arena (widely unexplored) for future mobile communications. Although, promising, IRS-based reconfigurable metasructures will not be massively deployed until the introduction of cost-effective solutions. The building of the smart EM environment is envisaged in this work from a different perspective [20]. More specifically, the objects present in the environment are considered as they are, without the introduction of additional coating materials or artificial skins, and their scattering capabilities are opportunistically exploited to obtain the desired EM field distribution in a target region of interest. Towards this end, the DoFs considered in the synthesis are the control points (i.e., amplifiers and phase shifters) of the primary source, namely the BTS antenna array, whose values are properly optimized with the goal of generating the desired complex EM field distribution in the target region thanks to the scattering obtained when properly illuminating the objects present in the scenario through the field generated from the primary source, thus inducing suitable currents on the scatterers. This strategy, although characterized by a reduced number of DoFs as compared to the use of IRSs, does not require additional costs or infrastructures to the current network systems albeit it needs advanced processing tools and optimization-based design techniques as well as the knowledge of the surrounding environment. The paper is organized as follows. Section 2 describes the mathematical formulation of the problem and the proposed design methodology. Some representative numerical results are reported in Section 3 to assess the effectiveness of the proposed method and the idea of the opportunistic exploitation of the surrounding scatterers for building a smart EM environment. Finally, some concluding remarks are drawn (Section 4).
# Structural Behavior of Floor Systems Made by Floor Plates--Mechanical Model Based on Test Results
## 3 Results
### Strains
#### 3.3.3 Moment Curvature Relation  
An even more appropriate way to investigate the ductility is the moment-curvature relationship as given in Figure 9. From the horizontal strains at different levels, the curvature can be derived by dividing the subtraction of the strains at two levels by the distance in between these two levels. In the legend of Figure 9a, different levels (see Section 2.3) are used to obtain the curvature, i.e., 330-170, meaning strains at level (v) and level (iii) are used. In Figure 9b, the overall curvature with strains of level (v) and (i) of series C is given. To evaluate the ductility, the moment curvature relation from the positions (v--top) and (ii--overlap bar) should be used. Indeed, due to small overlap length it might be known that rebar in the floor plate cannot yet develop full plastic strength.  
A curvature ductility factor \(\mu_{\Phi}\) is used mostly as a reference in the assessment of local ductility condition. Here, \(\mu_{\Phi}\) is defined as the ratio of post-yield strength curvature at minimum 85% of the ultimate moment resistance, to the curvature at yield under the assumption of a bilinear relation. Unfortunately, no indication can be found in [4], so reference is made to [29], where for the lowest ductility class (DCL) a value of at least two should be obtained (which corresponds to the upper limit of the behavior factor q = 1.5).  
For series A, results are shown in Figure 9a for A05 (lattice girder on 70 mm--\(\mu_{\Phi}\) = 2), A06 (bended up reinforcement--\(\mu_{\Phi}\) = 3), and A10 (solid slab--\(\mu_{\Phi}\) = 3) are given. Although the curvature of all slabs is sometimes at the limit, the behavior can be described as ductile. Remarkable is the effect of the bended up reinforcement (A06), which ensures that the curvature in the global element is the same for the different levels. Figure 9b shows the moment-curvature relation of series C (C01-C04); values \(\mu_{\Phi}\) of at least three (C03) up to seven (C01) can be found, which corresponds more to medium ductility.  
Figure 9: Moment–curvature relationship for (**a**) A-series (A05, A06, and A10); (**b**) C-series (C01 to C04).