esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Automatic Estimation of Emotions in Speech
## 2 Emotions
### Emotional Effects on Voice Quality  
Emotion can cause involuntary changes in glottal behavior, e.g., in jitter, shimmer, and the harmonics-to-noise ratio (HNR) [2]. Jitter is the variability of durations of pitch periods in successive vibratory glottal cycles, whereas shimmer is variation in speech waveform amplitude between periods. (Note that the traditional \"pitch period\" is physically realized as quasi-periodicity, as humans do not produce exact repetitions; coarticulation of the VT (movements across phonemes) also renders small changes from period to period). Breathiness may occur in angry and happy speech, and vocal fry in sad and relaxed speech. Harsh voice (irregularity in voicing) has been found in fear speech.  
The HNR measures the relative level of noise in sonorant spectra, i.e., the ratio between amplitudes of periodic and aperiodic components. For speech analysis, there are other pertinent quality measures such as Normalized Amplitude Quotient, Quasi-Open Quotient, and spectral tilt [37]. All of these may affect emotion, but have rarely been directly exploited in recent SER, where recent research is dominated by use of simpler acoustic measures (as many classical phonetic features have been difficult to integrate into standard neural speech classification systems).
## 3 The Integrated Sensing and Communication Platform
### LoRaWAN Communication Platform: LoRa Node, GW and Server
#### 3.2.1 RFM95 Radio Transceiver  
RFM95 LoRa module is a radio transceiver manufactured by HopeRF [26]. It has a receiver sensitivity of \(-148\) dBm and power amplifier of +20 dBm. Consequently, it has a maximum link budget of 168 dBm. It requires 3.3 V of voltage supply and draws a minimum RX current of 10.3 mA. The choice of this transceiver is due to its low cost and its low power consumption with a very good receiver sensitivity, suitable for the proposed application scnario.
# Optimized Deployment of Generalized OCDM in Deep-Sea Shadow-Zone Underwater Acoustic Channels
## 5 Deep-Sea Experiment
Under extremely poor channel conditions, the channel estimation and equalization degrade significantly, and the frequency-selective fading characteristics of OFDM result in a lower BER than OCDM. However, because the low SNR has a reduced impact on GOCDM, its channel estimation and equalization performance are enhanced, enabling it to fully leverage its multipath resistance capabilities. Meanwhile, given that this channel lacks multiple reflection paths and exhibits a low multipath delay, SC-FDE demonstrates a notably high performance with a bit error rate of merely 0.06. In summary, this deep-sea experiment demonstrates that the proposed GOCDM modulation technique effectively reduces the PAPR while improving the resistance to multipath effects. These advantages make GOCDM well-suited for deep-sea shadow-zone communication, where the received signal SNR is low, and multipath conditions are complex. Additionally, we need to determine the optimal value of \(M\) for GOCDM by considering both the communication distance and transmit power in order to achieve the best communication performance. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \begin{tabular}{c} **Transmitter** \\ **and** \\ **Receiver** **Locations** \\ \end{tabular} & \begin{tabular}{c} **Modulation** \\ **Scheme** \\ \end{tabular} & \begin{tabular}{c} **Transmitter** \\ **Vessel** \\ **Speed/kn** \\ \end{tabular} & \begin{tabular}{c} **Receiver** \\ **Vessel** \\ **Speed/kn** \\ \end{tabular} & \begin{tabular}{c} **Transducer** \\ **Depth/m** \\ \end{tabular} & \begin{tabular}{c} **Hydrophone** \\ **Depth/m** \\ \end{tabular} & \
\begin{tabular}{c} **Received** \\ **Signal** \\ **SNR/dB** \\ \end{tabular} &
\begin{tabular}{c} **BER** \\ **SNR/dB** \\ \end{tabular} \\ \hline \multirow{4}{*}{A–B} & OFDM & & & & & & 0.19 \\ \cline{2-8}  & OCDM & & & & & 0.33 \\ \cline{2-8}  & SC-FDE & & & & & 0.06 \\ \cline{2-8}  & GOCDM & & & & & 0.08 \\ \hline \multirow{4}{*}{C–D} & OFDM & & & & & 0.12 \\ \cline{2-8}  & OCDM & & & & & 0.03 \\ \cline{2-8}  & SC-FDE & & & & & 0.04 \\ \cline{2-8}  & GOCDM & & & & & 0 \\ \hline \multirow{4}{*}{E–F} & OFDM & & & & & 0.23 \\ \cline{2-8}  & OCDM & & & & & 0.05 \\ \cline{1-1} \cline{2-8}  & SC-FDE & & & & & 0.01 \\ \cline{1-1} \cline{2-8}  & GOCDM & & & & & 0 \\ \hline \end{tabular}
\end{table} 
Table 2: Deep-sea experiment results.
## 2 Materials and Methods
### Sampling and Measurements  
Samples from sites A to M were collected in dark glass vials (24 mL) with a Teflon septum and stored at 2-10 \({}^{\circ}\)C for up to 7 days before their analysis. pH changes between samples were negligible, exhibiting an average pH = 8.0 \(\pm\) 0.3. Samples analysed by fluorescence spectroscopy were previously filtered through Chromafil Xtra PTFE 0.45 \(\upmu\)m filters.  
NPOC and dissolved organic carbon were determined using a Shimadzu TOC-V\({}_{\mathrm{CPH}}\) Total Organic Carbon Analyzer (Kyoto, Japan) equipped with a Shimadzu ASI-V autosampler (Kyoto, Japan). Prior to the analyses, samples were acidified with HCl 2 M (VWR Chemicals, Radnor, PA, USA) and sparged with ultra-pure carrier air to eliminate inorganic carbon. For dissolved organic carbon determinations, samples were previously filtered with 0.45 \(\upmu\)m Whatman cellulose filters. However, since these values presented no significant differences with the NPOC ones, only the last ones are here reported. UVA was measured at 254 nm using a Hach Lange DR6000 spectrophotometer (Loveland, CO, USA). EEMs from both, samples and blanks, were recorded employing a Horiba PTI Quanta Master 400 spectrofluorometer (Kyoto, Japan); excitation range: 250-400 nm, and emission range: 300-600 nm (both recorded within 5 nm intervals).
# Interval Analysis of Vibro-Acoustic Systems by the Enclosing Interval Finite-Element Method
## 4 Numerical Applications in Vibro-Acoustic Coupling Problem
### Numerical Analysis of a 3D Classical Vibro-Acoustic Coupling Model  
The second application is focusing on the classical 3D vibro-acoustic coupling system with uncertain-but-bounded parameters, as this regular-shaped model has been examined by other suitable interval coupling analysis methods [24; 25]. Figure 11 depicts a 3D Mindlin plate vibro-acoustic coupling model: the rectangular plate is located at the top of the cavity, and all edges are supposed to be simple support. The flexible plate is made of aluminum, with material parameters being \(E=71\) Gpa, \(\rho_{s}=2700\) kg/m\({}^{3}\), \(\mu=0.3\), and the thickness of plate is \(t=0.001\) m. The cuboid cavity is filled with air with a density of \(\rho_{f}=1.21\) kg/m\({}^{3}\) and sound speed of \(c=343\) m/s. The other boundary of acoustic cavity is presumed to be rigid, and the external load of the vibro-acoustic system is located on the central point of the plate by a unit force in the norm direction. In total, for the MITC4-element mesh of the plate, the number of elements and nodes are 64 and 81, while for the acoustic mesh with 8-nodes element, the numbers are 640 and 891, respectively. With the same estimation of uncertain level \(\alpha=0.025\) to structural parameters, the Young's modulus \(E\) and the thickness of plate can be regarded as the interval variables  
\[\begin{split}& E^{I}=E^{c}+[-\alpha,\alpha]\times E^{c}\\ & t^{I}=t^{c}+[-\alpha,\alpha]\times t^{c}\end{split} \tag{39}\]  
Based on the previously mentioned IFEMs and Monte Carlo simulation with 10,000 samples, both of the frequency response at fixed response points and deterministic frequency response in the fixed locations are analyzed, and plotted in Figures 12-14, respectively. Unlike other research, the fixed receiving points R1 and R2 are represented for the plate and acoustic differently, though these two nodes share the same coordinate. As such, it may be reasonable to obtain the results with significant difference. The analytical frequency band is from 0 to 200 Hz by the interval of 10 Hz; both the upper and lower bounds of the solutions by different IFEMs will be described in the these figures, together with the large amount of Monte Carlo solution sets, that are represented with gray lines.  
Figure 11: 3D classical vibro-acoustic model.  
Figure 12: The sound pressure at the point of R1.  
Figure 13: The sound pressure at the point of R2.  
In Table 2, it is clear to see that the IFEMs is more efficient than the traditional probabilistic methods, especially compared to the TN-IPFEM and the new proposed enclosing-IFEM. As the increasing sizes for dyadic product shown in Equation (27), it spends more time for the SMW-IPFEM to calculate the approximate results.
# A Location Privacy Preservation Method Based on Dummy Locations in Internet of Vehicles
###### Abstract
Due to the shortcomings in the existing location privacy-preserving methods in IoV, we investigate the problem of location privacy preservation under road constraints in this paper. In this paper, we propose a vehicle location privacy-preservation method based on dummy locations. In the proposed method, the dummy location selection algorithm is modified based on vehicle location features. The main contributions of this paper as follows:  
* We investigate the problem of vehicle location privacy preservation in IoV and propose a vehicle location privacy-preservation method based on dummy locations. * We define the concept of effective distance to represent the characteristics of vehicle location distribution. Moreover, we improve the dummy location selection algorithm by using anonymous entropy and effective distance. * We analyze the performance of the proposed method in terms of security, computation overhead, and communication overhead, and conduct extensive simulations to evaluate the proposed method. The rest of the paper is organized as follows. The related work about location privacy-preservation methods is overviewed in Section 2. In Section 3, we give some preliminaries and the problem aiming to be solved in this paper. In Section 4, we propose a vehicle location privacy-preservation method based on dummy locations. Performance analysis and simulation results are given to verify the proposed method in Sections 5 and 6, respectively. Finally, we conclude the paper in Section 7.
# A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion
## 3 Methodology  
The architecture of our proposed MIHE-Net is illustrated in Figure 2. The backbone network consists of four stages, each of which is composed of a Multi-kernel Context Interaction (MCI) module and a Hierarchical Expansion Attention (HEA) module. Subsequently, a comprehensive in-depth elaboration is provided for the proposed MCI module, HEA module, overall architecture, and Midpoint Offset Loss Function.
# Context-Based Energy Disaggregation in Smart Homes
## 3 Related Work
### Disaggregation Approaches
Several open data sets are available at this time: high frequency datasets such as BLUED (Building-Level fUly-labeled dataset for Electricity Disaggregation) [37] or REDD (Reference Energy Disaggregation Dataset) [38]; low frequency data sets such as TRACEBASE [10] or ultra-low frequency as AMPds (Almanac of Minutely Power dataset) [39]. As BLUED and REDD include various features for each analyzed appliance, TRACEBASE, provides simple active power data for each monitored appliance. In [40] a detailed comparison among some of the above-mentioned public datasets has been published. The authors also provide semi-automatic labeling algorithm to help researchers in creating fully labeled energy disaggregation datasets. We chose TRACEBASE since it provides public low frequency power consumption traces of various devices gathered in real houses. Moreover, the whole data set is fully labeled and contains temporal information for each power sample.
# On the Virtual Cell Transmission in Ultra Dense Networks
## 1 Introduction  
With the rapid development of smart phones and all kinds of other mobile computing devices, mobile data traffic volume has grown explosively in recent years. Cisco points out in [1] that by the end of 2015, global mobile data traffic had grown more than 4000-times compared to 2005, and the numberbecomes an amazing 400 million when comparing to the year of 2000. Hence, [1] predicts that there will be an 8.5-times increase in global mobile data traffic by the year of 2020. However, the past decade has also seen considerable development in the wireless communication industry. The long-term evolution (LTE) developed by the 3 rd Generation Partnership Project (3 GPP) has been in wide commercial use. Up to today, more mobile data traffic (almost 47 percent of the total mobile data traffic) goes through LTE than 3G. The LTE networks are seeing their capacity limit soon. As a result, it is a big challenge to realize an 8.5-times network capacity increase within five years for the network operators and vendors.  
The network capacity of future wireless communication systems should be evaluated in an area capacity sense, the unit of which is \(\mathrm{bps/km^{2}}\). Noting that \(\mathrm{bps/km^{2}}=\mathrm{bps/Hz}\times\mathrm{Hz/cell}\times\mathrm{cell/ km^{2}}\), i.e., the area capacity is equal to the product of cell spectral efficiency, system bandwidth and cell density [2]. As a result, we have three ways to boost the network capacity to cope with the 8.5-times increase of global mobile data traffic volume by 2020 [1]; each of the three ways corresponds to some key enablers for 5G [3]; for example, spectrum re-farming and high frequency transmission for bandwidth expanding, massive MIMO [4] and non-orthogonal multiple access [5] for spectral efficiency improvement and ultra dense networks (UDN) for network densification [6]. However, the introduction of new bandwidth is in fact a very costly solution if no additional radio access technologies for unlicensed bands with seamless handover are provided [2]. The capacity gain brought by massive MIMO and non-orthogonal multiple access may be also quite limited due to limited radio frequency chains, pilot contamination, finite and potentially correlated scattering environment and constrained receiver complexity. Hence, neither additional bandwidth nor the improvement of spectral efficiency anticipated from the new air interface can economically efficiently cater to the forecasted traffic volume. Fortunately, we still have the means of network densification. Utilizing the ultra dense deployment of infrastructure equipment and the inherent advantage of extremely proximal transmission, UDN seems promising to provide an ultra high resource reuse factor. This results in hundreds of times capacity improvement in hot-spot areas.
## 3 Results
### Characterization of Switchable Terminators
Positions were named P1, P2, and P3 starting from the bottom of the stem, and the base compositions were adjusted by swapping G and C bases (V1), changing to G-U wobble base pair (V2), and changing to A-U base pair (V3). Three sequence variants were generated for the bottom of the stem, while G-C to A-U changes were also implemented in the other positions, and the resulting GFP fluorescence changes were measured in vivo (Figure 4b,c). Interestingly, the OFF-state GFP fluorescence level was similar to that of T500 original SWT for all sequence variants. However, the ON-state GFP expression levels were reduced for these sequence variants for reasons that are not clear (Figure 4d). Still, we confirmed that certain sequence modifications within the critical terminator hairpin sequence of the T500 SWT could be tolerated, suggesting that further sequence optimization should be possible. Figure 4: Design and Characterization of T500 SWT Variants. (**a**) Schematic of the stem sequence variants of T500 SWT. The positions and types of mutation are indicated by P and V. (**b**) The GFP fluorescence of T500 SWT sequence variants at P1 was measured in the presence of trigger RNA or decoy RNA. Fluorescence levels were expressed on a logarithmic scale. (**c**) The GFP fluorescence of T500 SWT sequence variants (V3: G-C to A-U) was measured in the presence of trigger RNA or decoy RNA. Fluorescence levels were expressed on a logarithmic scale. (**d**) ON/OFF ratios for T500 SWT sequence variants were calculated. Relative errors for the GFP fluorescence are from the s.d. of three biological replicates.
# Data Privacy and Ethical Considerations in Database Management
## 3 Best Practices
### Ethical Considerations in Anonymization and Pseudonymization  
While anonymization and pseudonymization are crucial for protecting privacy, they also raise several ethical considerations:  
* Re-identification risk: Even with these techniques, there is always a risk of re-identification, especially with advances in data analytics. Ethical database management requires ongoing assessment of these risks [18].
* Data utility vs. privacy: There is often a trade-off between the level of anonymization and the usefulness of the data. Striking the right balance is an ethical challenge [41].
* Informed consent: It is crucial to consider whether data subjects have given informed consent for their data to be anonymized or pseudonymized, especially if the original purpose of data collection changes [42].
* Transparency: Organizations should be transparent about their anonymization and pseudonymization processes to maintain trust with data subjects [43].  
These ethical considerations highlight the need for a thoughtful and balanced approach to implementing anonymization and pseudonymization techniques in database management.
## 4 Application to Classification Problems
### Experiments
The input is an image of subject 10 which was misclassified into subject 8 by WGSC while classified correctly by proposed method. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Training Set Size (\(\mathbf{\alpha}=\text{max}_{i}\{n_{i}\}\), \(\mathbf{\beta}=\text{min}_{i}\{n_{i}\}\))**} \\ \cline{2-7}
**Method** & \(\mathbf{\alpha}\) & **10** & \multicolumn{2}{c}{**25**} & \multicolumn{2}{c}{**50**} \\ \cline{2-7}  & \(\mathbf{\beta}\) & **5** & **10** & **5** & **25** & **25** & **50** \\ \hline GSC(with \(\lambda=1.5\)) & 81.4\% & 86.6\% & 73.6\% & 91.4\% & 88.4\% & 93.2\% \\ Proposed (\(\lambda=1.5\)) & 82.0\% & 87.2\% & 79.0\% & 92.2\% & 89.4\% & 93.0\% \\ Proposed (\(\lambda=3\)) & 82.6\% & 87.8\% & 80.8\% & 92.2\% & 90.6\% & 93.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recognition results on the USPS database. Figure 3: An example of results by WGSC and proposed method. Table 2 summarizes the recognition accuracy of GSC, the proposed method with \(\mathbf{W}=\mathbf{I}_{n}\), WGSC and the proposed method with \(\mathbf{W}\) computed by (9). Training set setting is that 20 subjects have \(\beta\) samples per class and the others have \(\alpha\) samples per class. With the strategically designed matrix (9), WGSC achieves a significant improvement over GSC. By using the proposed method with \(\mathbf{W}\) computed by (9), the performance can be further improved, especially when the training set is unbalanced.
# ML-AKA: An Authentication Protocol for Non-Standalone 5G-Based C-IoT Networks
## 5 D2D Authentication Process for Cellular IoT Network
### Authentication Vector Generation Process in the LTE Framework  
For the generation of AV, the serving network requests access to the home network. The valid request sent by the serving network contains the IMSI, the serving network id (SNID). This SNID is used for the calculation of \(K_{ASME}\) in the home network. By obtaining the AV request, the home network calculates it or retrieve the pre-calculated data.  
In each AV, the AMF is involved in the AUTN. Computation of the AuC occurs as follows, when AuC accepts the request from home subscriber.  
* Message Auth. Code = fun1(\(K\|sqn\|rand\|AMF\)).
* Calculated reply XRES = fun2(\(K\|rand\)).
* Cipher Key, CK = fun3(\(K\|rand\)).
* Mobility Key, NK = fun4(\(K\|rand\)).
* Obscurity Key, OK = fun5(\(K\|rand\)).
* Auth. Token,AUTN =(sqn XOR AK)\(\|AMF\|mac\).  
Here, fun1, fun2, fun3, fun4, and fun5 are the cryptographic functions; fun1 is known as the message authentication function, fun2 is known as the truncated message authentication function, and fun3, fun4 and fun5 are known as key generation functions. For explanation of acronyms and symbols explanation refer Table 2.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{2}{c}{**Acronyms and Symbols**} \\ \hline
**Acronyms** & **Explanation** \\ \hline \(D_{i}/DID_{i}\) & Device i and its identity \\ \(AP_{i}/APID_{i}\) & AP number and its unique ID \\ \(N_{i}\) & Secret key shared between \(D_{i}\) and eNodeB \\ KDF & Cryptographic function (Key Derivation Function) \\ \(H_{i}\) & Session key generated with the help of random nonce values a and b \\ \(HMAC_{i}\) & Hash function based Message Authentication Code \\ eNodeB & Evolved node B \\ SQN & Sequence number \\ SNID & Serving network identity \\ AK/XAK & Anonymity key \\ CK/XCK & Cipher key \\ IK/XIK & Integrity key \\ SK/K & Secret key shared between devices (\(D_{i}\)) and eNodeBs \\ \(KSI_{ASME}\) & Key set identifier for each \(K_{ASME}\) \\ ACK & Acknowledgement \\ \(N_{asme}^{i}\) & Roaming key \\ \(N_{D2D}\)\({}^{i}\) & D2D fun key \\ \(r_{i}\) & Random nonce value \\ \(rand_{i}\) & Random value chosen by eNodeB \\ \(R_{k}\) & Shared random secret key \\ \(S_{ID}\) & Session identity \\ \(K_{CM}\) & Common secret key \\ \(K_{DS}\) & D2D session key \\ \(eKSI\) & Evolved key set identifier \\ \hline \hline \end{tabular}
\end{table}
Table 2: Acronyms and symbols.
# Inhomogeneous Illumination Image Enhancement Under Extremely Low Visibility Condition
## 2 Materials and Methods
### Hmhe
#### 2.2.3 Visual Optimization  
Applying an LPF to an image contributes to the grayscale banding effect. If this phenomenon is not addressed, the final enhanced image displays poor visual perception. The reason for this effect is that the low-frequency components of the image decrease in a stair-stepping manner in the spatial domain, leading to contour-like images. Moreover, when performing global mapping, these bandings will be amplified. Luckily, potential attempts such as noise addition proved to suppress such phenomenon [30]. In order to eliminate the banding effect without affecting the image quality, we eliminated the artifacts by adding a minimum interference to the bands:  
\[U_{illu}^{visual}(x,y)=U_{illu}(x,y)+N_{add}(x,y) \tag{13}\]  
where \(U_{illu}^{visual}\) is the optimized image and \(N_{add}\) is the added Gaussian noise whose standard variance satisfies \(\sigma_{n}=\max\Big{[}gradient\Big{(}I_{filtered}\Big{)}\Big{]}/3\). The comparison between the image before and after the visual optimization algorithm is shown in Figure 4, where Figure 4a-d demonstrates the original filtered images, whereas Figure 4e-h depicts the filtered images after the visual optimization algorithm. Specifically, Figure 4a,d are the processing results in the fog chamber, while Figure 4b,c are outdoor natural low-visibility scenes. It can be seen from the results that the banding effect was visually resolved after applying the visual optimization algorithm.  
Figure 4: Comparison of filtered images before and after the visual optimization algorithm in different scenarios, where yellow represents the highest intensity, transitioning through green for medium intensities, to blue for the lowest intensity regions. The laboratory environment images are placed on the left (**a,b**) for the original images and (**e,f**) for the processed images. The outdoor natural low-visibility scenes are placed on the right side (**c,d**) for the original images and (**g,h**) for the processed images.  
Figure 3: A demonstration of the filtering selection algorithm: (**a,e**) Original image, (**b,f**) estimated illumination image, (**c,g**) the SSIM relation with the kernel size, and (**d,h**) the top-hat transformed image.