esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
The best performance was achieved by training on SAVEE, EMOVO, and URDU and testing on EMO-DB (68%). The worst performance was observed when training on the same three datasets and testing on EMOVO (61.8%). Regarding the model used, [PERSON] et al. [17], [PERSON] et al. [9], [PERSON] et al. [13], and [PERSON] et al. [15] are all based on SVMs. [PERSON] et al. [16] and [PERSON] et al. [18] utilized DBNs; [PERSON] et al. [20], [PERSON] et al. [23], and [PERSON] et al. [24] applied machine learning and deep learning methods; [PERSON] et al. [22] used ensemble methods; and, lastly, [PERSON] et al. [14] and [PERSON] et al. [19] applied GAN and SMO, respectively. Concerning the earlier studies, we observe that the SVM algorithm performs poorly on large datasets. It also performs poorly in situations with more characteristics per data point, especially in multi-class situations. When attempting to extract features from a DBN plus low-level acoustic information vs. a DBN with eGeMAPS, the latter significantly outperformed the former. Additionally, deep learning models outperform conventional classifiers. However, the model of [PERSON] et al. [20] extracts features quite well but requires a lot of training time. As previously indicated, an ensemble strategy only provided the best performance in one scenario. Furthermore, the existing techniques in SER lack preprocessing operations. We conclude that, across many datasets, a binary performance outperforms multiple classes. Moreover, none of the previous works have focused on the Amharic language. Here, we first present the preprocessing strategy before describing the extraction of features from the signal. Second, we propose an architecture, based on the VGG model, which offers good results. Third, we provide a classification benchmark for Amharic and three non-Amharic languages using VGG and two other models. Finally, we contrast the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref** & **Methods** & **Feature** & **Databases and** & **Expts** & **Classes** \\  & **Employed** & **Extraction** & **Languages** & & \\ \hline \hline \multirow{6}{*}{[23]} & \multirow{6}{*}{CNN and \(\mathrm{Wav2 Vec2-XLSR}\)} & \multirow{6}{*}{prosody} & IEMOCAP, & & \\  & & & CREMA-D (English), & & \\  & & & ESD (English), & & \\  & & & & Synpaflex (French), & X & 4 \\  & & & Oreau (French), & X & 4 \\  & & & EMO-DB, EMOVO, & & \\  & & & emoUERJ & & \\  & & & (Portuguese) & & \\  & & & EMO-DB, eNTERFACE & & \\  & & & (English), & & \\  & & & IITKGP-SEHSC & & \\
[24] & AG-TFNN & MelSpec & (Hindi), & M & 2 \\  & & & IITKGP-ESSC & & \\  & & & & (Telugu), & \\  & & & ShEMO-DB (Persian) & & \\
[22] & SMO, RF, J48, & Spectral Prosodic & SAVEE, URDU, & & \\  & Ensemble & eGeMAPS & EMO-DB, EMOVO & X & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._effectiveness of our novel training scenarios to demonstrate the efficiency of cross-lingual and multilingual approaches.
## 2 Data Quality Characteristic Analysis  
In this section, we followed the method outlined by [PERSON] and [PERSON] [2] to develop an Android application capable of analyzing the full-frequency signal of all satellites observed using smartphones. Real-world datasets were used to evaluate the quality of the BDS-3 B1C/B2a/B1I, GPS L1/L5, and Galileo E1/E5a signals. Data collection was carried out using two smartphones, namely, a Huawei P40 (HP40) and a Xiaomi 11 Lite (Mi11).
## 2 Materials and Methods  
### Study Sample Selection  
We performed a prospective, observational study on 66 children aged between 0 and 17 years in the Pediatric Clinic I in Targu Mures and the Pediatric Cardiology Clinic between January 2019 and March 2022. The children were included in 3 groups: 13 children who underwent the Fontan procedure (\"Fontan group\"), 21 children following the Glenn procedure (\"Glenn group\") and 32 healthy children (\"Control group\").  
The inclusion criteria for the Fontan and Glenn groups consisted of children who had undergone the Fontan or Glenn surgery for functional unventricular hearts, without other acute or chronic digestive or respiratory illnesses. The malformations for which the Glenn (as an intermediate operative step) and afterward the Fontan operation (the final palliative step) was performed are: double-outlet right ventricle with malposition of the great vessels, large ventricular septal defect with transposition of the great vessels and pulmonary stenosis, pulmonary valve atresia, mitral valve atresia, tricuspid valve atresia with hypoplasia of the right ventricle and complete atrioventricular canal. The inclusion criteria for the control group were: clinically healthy children with a normal weight, without an acute or chronic illness, no history of recent medication, normal ultrasonographic morphology of the liver and normal laboratory parameters.  
The exclusion criteria for the Fontan and Glenn groups were: children with obesity, hepatitis (of viral, autoimmune, metabolic or toxic origin), Gilbert syndrome or hemolysis (both of which can impair the results of the Fibrotest investigation), other acute or chronic respiratory, digestive or renal illnesses. The exclusion criteria for the control group consisted of children with modified laboratory parameters, abnormal liver ultrasound, obesity and acute or chronic illnesses.  
A clinical exam was performed for each patient, determining the weight, height, BMI and oxygen saturations. Medical records were reviewed, and relevant demographic, anatomical, surgical and clinical patient characteristics were extracted.
# SNR Analysis for Quantitative Comparison of Line Detection Methods
## 1 Introduction
Section 3 compares the performance of the ED-based and SD-based line detections based on SNR analysis. Section 4 describes the SNR of the SGAD-based line detection method. The results obtained using real images are described in Section 5 and are followed by the conclusion in Section 6.
## 3 Results
### Results of the Biofilm Production
#### 3.2.5 Results of the Pellicle Test (Air-Liquid Border)  
* Pellicle test in minimal M9 medium  
In the minimal M9 medium at 8 \({}^{\circ}\)C, the largest number of strains, a total of 6, that successfully formed biofilms on the bottom of polystyrene microtiter plates, had no ability to form biofilms at the liquid-air interface in polystyrene tubes (strains F187, F410, F710, F203, F142, F303). Even at a temperature of 25 \({}^{\circ}\)C, seven strains were not able to form a biofilm at the liquid-air interface (strains F187, F410, F710, F142, F303, F262, F458). However, two strains that could not form a biofilm on the bottom of polystyrene plates in minimal M9 medium at 25 \({}^{\circ}\)C (F461, F352), successfully formed a relatively strong biofilm at the liquid-air interface.
* Pellicle test in minimal ABTG medium  
At temperatures of 8 \({}^{\circ}\)C and 16 \({}^{\circ}\)C, most strains produced a good pellicle in ABTG medium, while at 25 \({}^{\circ}\)C, 4 strains lost that ability.
* Pellicle test in TSB medium  
At 8 \({}^{\circ}\)C in TSB, most of the strains produced a good pellicle except for four strains (FN187, F410, F461, and F710) which produced a weak pellicle. At 16 \({}^{\circ}\)C, most strains produced a good pellicle, except strain F142, which lost the ability to produce a pellicle. At 25 \({}^{\circ}\)C, almost all strains produced a good pellicle, except strain F461, which lost the ability to produce a pellicle. The detailed results of the pellicle test are shown in Table 1.  
Figure 6: The ability of each tested _A. salmonicida_ subsp. _salmonicida_ clinical strain to produce biofilms in different media: a comparative view. Legend: M9, ABTG—minimal nutrient media; TSB—rich nutrient medium.
# Wi-Senser: Contactless Head Movement Detection during Sleep Utilizing WiFi Signals
## 3 System Design
### Data Processing  
Due to the strong noise in the original CSI measurements caused by environmental changes, wireless interference, etc., Wi-Senser needs to perform denoising processing on the original CSI measurements to extract fine-grained CSI amplitude information that records human head motion changes. Wi-Senser mainly considers three types of different noise: outliers (i.e., CSI measurements that deviate from the median by several times the standard deviation in a set of CSI measurements), high-frequency environmental noise, and low-frequency interference noise, and uses a filtering channel consisting of a Hampel filter, wavelet filter, and mean filter to filter out the above noise.
# Three-Dimensional Assessment of Upper Limb Proprioception via a Wearable Exoskeleton
## 4 Discussion
### Proprioceptive Acuity Differences between Single- and Multi-Joint Tasks  
As evidenced by the performance indicators, the multi-joint condition leads to a decrease of proprioceptive acuity for the distal joint.  
We want to support our results with the following considerations: (i) since MJ movements generally involve several processes required for stability, coordination, and neuromuscular control [43; 44], their execution proves to be more complex than SJ ones; (ii) during the single-joint condition, the mechanoreceptors stimulation and the arising sensory information are better encoded by the central nervous system rather than when multiple information comes from different joints (MJ). In SJ condition, muscle spindles can work along their preferred direction by conveying their afferent information to the brain resulting in a population code representing the joint position [22; 23; 24]. Hence, in clinical settings, the sensory evaluation protocol should consider the number of joints simultaneously involved, modulating the obtained outcomes to the task complexity.  
Moreover, our results have been extracted from an experimental setup involving a 3D-workspace and combining information arising from multiple peripersonal spatial components [45], and therefore introducing multiple factors: i.e., the gravity perception, which was involved in the dynamics of the task, and hence introducing extra-information in the sensory channel that complicates feedback integration when a MJ movement is required [25; 26; 27]. In contrast, [PERSON] et al. [28], who implemented a 2D-task, obtained a reverse result, evidencing that the MJ condition leads to lower matching errors than the SJ one. They justified their result, highlighting how MJ movements are more relevant from a \"biological\" point of view, i.e., they are closer to our routines compared to the SJ ones. However, the possibility to implement a 3D-scenario allowed us to cover the whole arm movement. In our study, subjects' performance was compared across different workspaces, overcoming issues about how movements observed at the single-joint can be compared with their projection at the end-effector even though they have different metrics and dimensionality [46].
## 4 Results and Discussion  
### Data Preprocessing  
The datasets used in this study had 28,089 initial features as summarized in Table 2. Preprocessing involved elimination of non-differentially expressed genes and normalization which resulted in 12.2% features for small-cell lung cancer and 43.2% features for non-small-cell lung cancer after preprocessing.
## 5 Errors Compensation  
In the previous section, the influence of the feed offset error and the scanning error on surface shape measurement accuracy was studied. Both errors will affect the surface deformation of the reconstructed antenna in microwave holographic measurement, resulting in a decrease in measurement accuracy. It is very important to reduce or compensate for the influence of error on the measurement accuracy of antenna surface deformation.
# Prediction of the Moisture Content in Corn Straw Compost Based on Their Dielectric Properties
## 3 Results  
### Frequency Dependence of the Dielectric Properties of Samples  
To understand the relationship between frequency and permittivity, the dielectric constant \(\varepsilon^{\prime}\) and the loss factor \(\varepsilon^{\prime\prime}\) of the samples in the frequencies ranging from 1 to 100 kHz were measured at the room temperature of 22.3 \({}^{\circ}\)C and the sample density of level II. The results are shown in Figure 4.  
Figure 4 shows that \(\varepsilon^{\prime}\) and \(\varepsilon^{\prime\prime}\) of the samples decreased with the increase in the test frequency in the frequency range 1-100 kHz. At room temperature, the \(\varepsilon^{\prime}\) and \(\varepsilon^{\prime\prime}\) appeared to decrease sharply when the frequency increases from 1 to 10 kHz. The primary cause of the change in the dielectric properties of the samples could be attributed to the dipole, Maxwell-Wagner effect. With the increase in the frequency, the vibration speed of the dipole lags behind the change of the electric field, which reveals a decreasing trend of  
Figure 4: Frequency dependence of dielectric properties at various moisture contents: (**a**) dielectric constant, (**b**) loss factor.  
with the increase in the frequency. The lower the frequency, the more pronounced the decreasing trend is.
## 1 Introduction
DeepFM [17] is an integration of the FM model structure with the Wide&deep model; however, it ignores either the explicit and implicit feature interactions or does not closely combine the higher-order and lower-order feature interactions, resulting in missing information and making the recommendation results less accurate. In the real world, data are nonlinear and have a complex structure; indeed, different interaction components have varying predictive capabilities. To address these issues, in this paper we propose a new model named EIAFM that incorporates explicit and implicit higher-order feature interaction components to better capture the higher-order features of complex nonlinear structures. Additionally, we introduce an attention mechanism to measure the differential importance of lower-order feature interactions [18]. The probability that a task target in industry invokes an industrial software service component is the target predicted by our model. On a real-world dataset that meets the problem description definition, our comprehensive experiments show that EIAFM outperforms several cutting-edge models in terms of recommendation. In summary, this paper makes the following contributions:  
* The innovation of the EIAFM model lies in its integration of explicit and implicit higher-order feature interactions to accurately capture valuable information from complex data. Furthermore, the introduction of an attention mechanism enables precise measurement of the varying importance within feature interactions, endowing the model with both low-level memory capability and high-level generalization ability. * An industrial software service component recommendation framework based on EIAFM is proposed, which is mainly applicable to typical industrial collaborative manufacturing systems; its application can obtain better service component recommendation results. * Comprehensive experiments are conducted using a real dataset that fits the description of the Service Recommendation of Industrial Software Components problem; the experimental outcomes demonstrate the notable superiority of the proposed EIAFM model over several mainstream recommendation approaches. The rest of this paper is structured as follows: Section 2 introduces related papers in the field of service recommendation; Section 3 develops a recommendation architecture for industrial collaborative manufacturing systems based on EIAFM; Section 4 proposes the EIAFM model, which contains the problem description of industrial software service component recommendation, existing mainstream FM-based service recommendation models (FM, AFM, XDeepFM), and the relationship between EIAFM and the above models; Section 5 conducts an extensive experimental evaluation using a real dataset that fits the problem definition of industrial software service component recommendation; and finally, Section 6 summarizes the contributions of this paper while offering a glimpse into prospective future research directions.
## 5 Review Findings
Another verification tool is a protocol verifier called ProVerif. It offers cryptanalysis primitives such as public-key, symmetric, hash functions, Diffie-Hellman signatures, and key agreements [131]. Cryptanalysis primitives are designed as equations or rules [132]. The Random Oracle Model (ROM) provides rigid security proofing for cryptographic-based schemes [133]. Random Oracle is employed to simulate the hash function and present all possible hash results. All entities, either legitimate or not, have to query the Random Oracle for the hash value. Burrows-Abadi-Needham (BAN) logic is a collection of rules to evaluate protocols against predefined security theories. BAN logic explicitly allows its users to decide if the information shared \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Ref.** & **Year** & **Scheme** & **Computation** & **Communication** & **Storage** \\ \hline [PERSON] et al. [95] & 2017 & ECC & 0.0087 ms & 480 bits & 480 bits \\ [PERSON] et al. [121] & 2018 & ECC & 0.00620 ms & 480 bits & 1456 bits \\ [PERSON] and [PERSON] [77] & 2018 & Hash function & 1.0610 s & 1504 bits & 3008 bits \\ [PERSON] et al. [127] & 2019 & ECC & 92.035 ms & 3456 bits & 1408 bits \\ [PERSON] [66] & 2019 & Hash function & 0.0624 ms & 3904 bits & 1280 bits \\ [PERSON] et al. [113] & 2019 & FCDH & 3.17 ms & 1280 bits & 1472 bits \\ [PERSON] et al. [96] & 2021 & Hash function & 196.02 ms & 7744 bits & 640 bits \\ [PERSON] et al. [128] & 2021 & ECC & 6.6968 ms & 4640 bits & 1568 bits \\ [PERSON] et al. [106] & 2021 & AES, Biohash & 0.00744 ms & 1280 bits & 1824 bits \\ [PERSON] et al. [92] & 2021 & Hash function & 5.312 ms & 98 bytes & 289 bytes \\ [PERSON] et al. [87] & 2021 & Hash function, fuzzy extractor & 0.6663 ms & 1280 bits & 640 bits \\ [PERSON] [129] & 2021 & Hash function & 0.51712 s & 13984 bits & 256 bits \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison among recent authentication schemes according to their performance parameters. Figure 25: Article distribution based on evaluation tools usage. is trusted and secure [134]. BAN logic begins with the presumption that data transfers on media are vulnerable to public monitoring. Figure 26 illustrates the usage distribution of security validation tools. Commonly, researchers use more than one tool or technique to prove an authentication scheme's security. Therefore, the numbers in Figure 26 are overlapped. Using BAN logic for analysis of authentication schemes increased due to its reliable security proofing. Scyther and AVISPA tools are commonly used beside BAN logic, because they offer powerful predefined threat models and ease of user interfaces. Table 5 shows an overall comparison among authentication schemes according to the taxonomy. Figure 26: Article distribution based on security validation tools usage.
## 3 5G and Beyond
### Cloud-Based 5G/6G Systems
#### 3.2.2 Fog RAN  
Fog computing can be integrated into the mobile network in the form of RAN, named Fog-RAN (F-RAN). In F-RANs, fog computing resources may be used for caching at the network edge. It can provide faster retrieval of content and lower burden on the front-haul. It is possible to implement F-RAN through 5G related mobile technologies [18].
# An Unambiguous Delay-And-Multiply Acquisition Scheme for GPS L1C Signals
## 3 Results
### Results of the Performance Analysis for the Proposed DM Acquisition Method  
In this section we simulate the acquisition process for both conventional and proposed DM methods. Assume that the intermediate frequency is \(f_{IF}=6.2\) MHz, the sampling frequency is \(f_{s}=60\) MHz, and the Doppler frequency \(f_{D}=0\). The coherent integration time is \(T_{c}=10\) ms and the number of samples is \(M=f_{s}T_{c}\) = 600,000.
# Low Complexity Equalization of Orthogonal Chirp Division Multiplexing in Doubly-Selective Channels
## 5 Conclusions  
In this paper, we proposed an OCDM based modulation scheme termed UP-OCDM system for high rate communication over doubly selective channels. Compared with the OCDM system, UP-OCDM is able to reduce the storage overhead while maintaining the advantages of high spectral efficiency, diversity gaining, and simple receiver structure. Furthermore, with the circulant property of the transform matrix in UP-OCDM, two low complexity equalization algorithms are given, which are based on banded channel approximation and the LSQR algorithm, respectively. Compared with the MMSE equalization algorithm with a computational complexity of \(\mathcal{O}(N^{3})\), the computational complexities of equalization are reduced to \(\mathcal{O}(NQ^{2})\) and \(\mathcal{O}(iNMlogN)\) operations, respectively, where \(Q\) is the bandwidth parameter, \(i\) is the iterative number, and \(M\) is the order of the basis expansion model of the doubly selective channel. Simulation results show that the performance of UP-OCDM and OCDM outperform OFDM, especially in 4 QAM and 16 QAM scenarios. Besides, the validity of the low complexity methods are confirmed by numerical simulations. Further examination of the impacts of Doppler spread, bandwidth and iteration number on the BER performance is proposed, as well. Finally, a comparison of the proposed low complexity equalization algorithms is given. The results suggest that UP-OCDM has the potential to be equipped for the high-rate wireless communication applications over doubly selective channels.  
Conceptualization, X.W., X.-H.S. and Z.J.; Methodology, X.W. and X.-H.S.; Software, X.W.; Validation, X.W. and X.-H.S.; Formal Analysis, X.W. and Z.J.; Investigation, X.W., X.-H.S. and Z.J.; Resources, X.W. and X.-H.S.; Writing--Original Draft Preparation, X.W.; Writing--Review & Editing, X.W.; Visualization, X.W., X.-H.S. and Z.J.; Supervision, X.-H.S. and Z.J.; Project Administration, X.-H.S.; Funding Acquisition, X.-H.S. and Z.J. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (61771401) and the National Key Research and Development Program of China under Grant (2016 YFC1400200). This research was funded by the National Natural Science Foundation of China (61771401) and the National Key Research and Development Program of China under Grant (2016 YFC1400200). These financial supports are gratefully acknowledged. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.  
Figure 5: BER performance of the MMSE block equalizer, the band MMSE equalizer, and the iterative LSQR equalizer under different \(Q\) and \(i\) with the normalized Doppler spread of 0.1 (**a**) and 0.3 (**b**).