esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effects of Musical Experience and Hearing Loss on Solving an Audio-Based Gaming Task
## 1 Introduction
### Hearing Loss, Music Listening, and Music Training  
Studies on the relationship between hearing loss and music listening have been performed within several areas. In music therapy, the positive effects of musical interventions on children with hearing loss have been described [32; 33]. Much of the recent research on music with hearing loss has focused on the emerging technologies related to cochlear implants, while some studies look particularly at hearing loss with just hearing aids, such as in the description of how people with hearing aids listen to music from an audiological perspective [34; 35]. Music perception by cochlear implant users has been observed both by otolaryngology laboratories [36; 37; 38] and by psychologists' groups [39; 40].  
Music perception by people with hearing loss has also been explored from various perspectives: which music elements to use in an experiment, ways to propose music, benefits of cochlear implants and hearing aids, and the age and impairment history of participants. Experiments related to the perception of pitch vary from basic pitch discrimination tasks [41] to memorization [36], singing [1], and recognition [40] of melodies. Experiments related to exploring the role of temporal information for melody recognition have included both tempo and rhythm as well as pitch information [42]. It has been shown that pitch and timbre--when parametrically varied in a synthesized tone signal and with music listening history accounted for--interfere and confuse listeners in discrimination tests [43].  
Potentially, musical training can improve timbre perception and identification in cochlear implant patients [44].  
Musical training is typically given to people with hearing loss either in the long or short term. Various experiments have investigated possible long-term effects of informal music activities provided to participants at schools [1], and measured the effectiveness of long-term music lessons for improving the perception of environmental sounds [45]. In short-term training for cochlear implant users, little progress was found in terms of music skills [46]. Effects of the training were found in linguistic identification tests after controlled training of combining the acoustic information of a hearing aid with the electric information from a cochlear implant [47]. Some of these results are related to brain development [1,46].
# Bi-SQUID Versus dc SQUID in Flux-Driven Traveling-Wave Parametric Amplifier
## 4 Bi-SQUID Cell Parameters
Therefore, the inductance parts oppositely influence the current redirection mechanism inside the bi-SQUID cell and hence the 1 dB gain compression points. Figure 8: The current wave amplitude \(I_{p}/I_{c}\) corresponding to the 1 dB compression point of the gain of JTWPA with bi-SQUID cells versus normalized value of the third Josephson junction \(i_{\zeta 3}=L_{c3}/I_{c}\) at \(l=0.75\) and \(\varphi_{dc}=2\pi\Phi_{dc}/\Phi_{0}\). Figure 11: The 1 dB gain compression points versus the normalized inductance component \(l_{m}\) of the second loop inductance of the bi-SQUID cell when no magnetic flux is applied to the loop. Normalized value of the main inductance \(l=0.75\), normalized dc flux applied. Figure 10: The 1 dB gain compression points versus the normalized inductance component \(l_{dc}\) of the second loop inductance of the bi-SQUID cell when no magnetic flux is applied to the loop (red line) and when magnetic flux \(\Phi_{2}=\kappa\Phi_{dc}\) is applied to the loop (black line). In both cases, the normalized value of the main loop inductance is \(l=0.75\), the normalized dc flux applied to the main loop is \(\varphi_{dc}=2\pi\Phi_{dc}/\Phi_{0}=1.6\) and \(l_{m}=0.01\) (normalized value of \(L_{m}\)). Factor \(\kappa=(l_{dc})^{2}/l^{2}\) corresponds approximately to the loop square ratio. Figure 9: Bi-SQUID cell schematic, where \(L\) is the main part of the principal loop inductance of the cell, and \(L_{dc}\) and \(L_{m}\) are parts of the second loop inductance. Pink line on the top is the inductance of the cell of the line, where pump wave propagate. The minimum value of the output signal of the flux-driven JTWPA actually does not change when substituting the dc SQUID cells for bi-SQUID cells. In fact, fluctuations arising in Josephson junctions can be described using the Langevin method [20; 22; 23], i.e., by adding noise current sources connected in parallel to the junctions. The in-phase current fluctuations (in regard to the direction of the transport current) arising in the basic junctions J1 and J2 yield fluctuations in both the transport current and the phase drop \(\Delta\varphi\) on the cell. These are the fluctuations forming the output noise of the flux-driven JTWPA. The antiphase current fluctuations arising in the junctions J1 and J2 cause a circular noise current in the cell loop and hence some small fluctuations in the dc flux setting operation point of the cell. The added Josephson junction J3 can contribute only to the circular noise current; however, this contribution is about compensated by the shunting impact of the junction on the antiphase current fluctuations produced by the junctions J1 and J2. In such a way, the influence of the added junction J3 on the noise characteristics of the JTWPA can be considered negligibly small.
# Interpretability in Sentiment Analysis: A Self-Supervised Approach to Sentiment Cue Extraction
## 5 Experiments
### Case Study: Comparing SS-SCE with Established Interpretability Methods
SS-SCE\({}_{IMDb}\) achieved the highest precision (0.8571), reflecting its strong ability to accurately identify relevant negative sentiment cues. However, its recall (0.3750) is relatively lower, suggesting that, while it is precise, it may miss some relevant cues. Conversely, SS-SCE\({}_{SST-2}\), with a recall of 0.5000, demonstrates a balanced performance with a precision of 0.5714 and an F1 score of 0.5333. This balance indicates its ability to capture a broader range of relevant cues while maintaining accuracy. SS-SCE\({}_{Yelp}\), despite having the highest precision (0.8333), shows a lower recall (0.3125), indicating a tendency to be very selective in cue extraction, which may lead to missing some pertinent sentiment indicators. In comparison, traditional interpretability methods show lower performance in both precision and recall. LIME and LDS, in particular, demonstrate limited effectiveness in accurately identifying negative sentiment cues. The lower performance of these methods may be attributed to their design, which might not be as fine-tuned for sentiment cue extraction as the SS-SCE approach. Overall, the comparative analysis of sentiment cue extraction presented in Tables 7 and 8 demonstrates the robustness and versatility of the SS-SCE models across both positive and negative text instances. The SS-SCE models, especially SS-SCE\({}_{SST-2}\), consistently exhibit a balanced performance in terms of precision and recall, highlighting their ability to accurately and comprehensively extract sentiment cues. This is particularly evident in SS-SCE\({}_{SST-2}\), which shows strong performance in both positive and negative contexts. While SS-SCE\({}_{IMDb}\) and SS-SCE\({}_{Yelp}\) demonstrate higher precision in specific in
# The Self-Calibration Method for the Vertex Distance of the Elliptical Paraboloid Array
## 1 Introduction
Elliptical paraboloid arrays have considerable application potential in the accurate detection of machine geometry errors. Nowadays, the elliptical paraboloid used in the field of precision measurement has a small surface area and high machining accuracy. To expand the measuring range of displacement, the elliptical paraboloid array has become a research focus. However, the calibration method for the vertex distance of the elliptical paraboloid array has seldom been investigated. There are two reasons accounting for this. On the one hand, contact measurement will destroy the surface properties of the elliptical paraboloid, and they are too small to be detected by probe. On the other hand, traditional visual inspection methods are often affected by specular images and ignore the relative angle between the elliptical paraboloids. To overcome the above difficulties, a self-calibration method for the vertex distance of the elliptical paraboloid array using a non-contact optical slope sensor is proposed. A self-calibration method utilizes the existing displacement measurement system to achieve calibration with a low cost and convenient operation. The relative angle and geometric relationship between elliptical paraboloids was considered in the self-calibration method to improve the calibration accuracy. This self-calibration method has the characteristics of a high accuracy and high repeatability, is non-destructive, and has a long adaptive distance. After calibration, the displacement measurement system error was reduced from 100 \(\upmu\)m to 3 \(\upmu\)m, which satisfies the measuring requirement.
# Analytical TCP Model for Millimeter-Wave 5G NR Systems in Dynamic Human Body Blockage Environment
## 3 TCP in Dynamic Blockage Environments
### Blocked and Non-Blocked Intervals  
To completely parametrize our model, one needs to relate \(T_{0}\) and \(T_{1}\) to the environmental parameters, particularly the speed and spatial density of blockers, \(v_{B}\), and \(\lambda_{B}\).  
Following the recent study in [20], we define a rectangular LoS blockage zone associated with the UE, see Figure 3. The length and width of this zone are given by  
\[w=2r_{B},\,l=d\frac{h_{B}-h_{U}}{h_{A}-h_{U}}+r_{B}. \tag{7}\]  
Recall that the blockers are assumed to move according to the RDM within the coverage area. Using the result in [21], the inter-meeting time of a single blocker with the LoS blockage zone is approximately exponential with the parameter  
\[\gamma_{1}=v_{B}wl\iint\limits_{S_{BS}}f^{2}(s)ds=\frac{4r_{B}}{r}\left(d\frac {h_{B}-h_{U}}{h_{A}-h_{U}}+r_{B}\right), \tag{8}\]  
where \(v_{B}\) is the speed of a moving blocker and \(f(s)=1/\pi r^{2}\) is the stationary distribution of the RDM [14].  
Since the mean number of blockers within coverage of the 5G NR BS is \(\lambda_{B}^{2}\pi r^{2}\) and the inter-meeting time of a single blocker with the LoS blockage zone is exponential, the process of the LoS path blockage is Poisson with the intensity  
\[\gamma=4\pi r_{B}r\lambda_{B}\left[d(h_{B}-h_{U})/(h_{A}-h_{U})+r_{B}\right]. \tag{9}\]  
Consider now the case where a single blocker occludes the LoS path. Due to the properties of the RDM model, the entry point of a blocker is distributed uniformly over the perimeter of the LoS  
Figure 3: Example dynamic line-of-sight (LoS) blockage phenomenon [5].  
blockage zone. However, for any reasonable BS to UE distance, we have \(w<<l\), and thus we may disregard the entry and exit points at the short sides of the LoS blockage zone. With these assumptions, the mean path of a blocker in the LoS blockage zone is [22]  
\[E[L]=\frac{2[w^{3}-(w^{2}+l^{2})^{3/2}]}{3l^{2}}+\frac{w^{2}\ln\left(\frac{l}{w} +\sqrt{1+\frac{l^{2}}{w^{2}}}\right)}{l}. \tag{10}\]  
As shown in [20], the blocked and non-blocked intervals constitute an alternating renewal process, where the non-blocked time follows an exponential distribution with the mean of \(T_{1}=1/\gamma\)[20]. The stochastic properties of the blocked time coincide with the busy period in the \(M/G/\infty\) queue having a Poisson arrival process with the intensity of \(\gamma\) and the service times with the pdf of \(L/v_{B}\). To offer a closed-form approximation for the TCP throughput, we replace the busy period in the \(M/G/\infty\) queue with the one in the \(M/M/\infty\) queue having the same mean service time. Utilizing the results of [23], the mean blocked period is  
\[T_{0}=\frac{1}{\gamma}\sum_{i=1}^{\infty}\frac{1}{i!}\left[\frac{\gamma E[L]}{ v_{B}}\right]^{i}=\frac{e^{\gamma E[L]/v_{B}}-1}{\gamma}, \tag{11}\]  
which completes the parametrization of the TCP model.
## 4 Conclusions
This likely causes an overestimation of the impact of LDA on the precipitation forecast, because no additional observations are considered in this study. The assimilation of other observations (e.g., radar, GNSS-ZTD (Global Navigation Satellite System - Zenit Total Delay), SYNOP) over land would be propagated over the sea by the model, while the assimilation of observations over the sea (e.g., SAR (Synthetic Aperture Radar), water vapor maps) would contribute directly to the precipitation forecast over the sea. Future studies, considering the assimilation of other observations in addition to LDA, will quantify more precisely the contribution of LDA to the improvement of the precipitation forecast over the central Mediterranean Sea. Another limitation of this study is that the impact of LDA is evaluated only for precipitation. The positive impact of LDA in predicting other surface parameters has been shown in some papers [17; 21; 88] and will be considered in future studies. Conceptualization, R.C.T., S.F. and G.P. ; methodology, R.C.T., S.F., G.P. and S.D. ; software, R.C.T., S.F., A.C.P. and L.P.D. ; validation, R.C.T., S.F., A.C.P. and L.P.D. ; writing--original draft preparation, R.C.T., S.F. and A.C.P. ; writing--review and editing, all authors; project administration, G.P. and S.D. ; funding acquisition, S.D. All authors have read and agreed to the published version of the manuscript. This work was partially funded by the project PON ARS01 00405 OT4 CLIMA Sviluppo di Tecnologie innovative di Osservazione della Terra per lo studio del Cambiamento Cimantico e dei suoi impati sull'ambiente (Development of Innovative Earth Observation Technologies for the Study of Climate Change and Its Impacts on the Environment), funded by the Italian Ministry of Education, University and Research (MIUR), in the framework of the National Operational Programme (PON), Research and Innovation 2014-2020, Specialization Area Aerospace. This research was partially funded also by the agreement between the Italian Department of Civil Protection and ISAC-CNR. The support provided to L.P.D. by the Third Continuous Development and Operation Phase (CDOP-3) of the EUMESTA Satellite Application Facility for Hydrology and Water Management (H SAF) program is also acknowledged.
# Call Blocking Probabilities under a Probabilistic Bandwidth Reservation Policy in Mobile Hotspots
## 3 The Generalized Probabilistic BR Loss Model
### CBP Determination of New and Handover Calls  
The CBP of new calls in the stop phase, \(B_{g,s,new}\), can be determined via:  
\[B_{g,s,new}=(1-p_{s,new})\sum\limits_{n=C-t_{new}}^{C-1}P_{g}(0,n)+P_{g}(0,C) \tag{21}\]  
In the moving phase of the generalized model, call blocking of new calls occurs not only when there are no available b.u., but also probabilistically in states \(P_{g}(1,n)\) where \(C-t_{new}\leq n<C\). Consequently, the CBP computation of new calls in the moving phase,\(B_{g,m,new}\), is based on the following formula:  
\[B_{g,m,new}=(1-p_{m,new})\sum\limits_{n=C-t_{new}}^{C-1}P_{g}(1,n)+P_{g}(1,C) \tag{22}\]  
Based on (21) and (22), the total CBP of new calls, \(B_{g,new}\), can be determined via:  
\[B_{g,new}=B_{g,s,new}+B_{g,m,new} \tag{23}\]  
The CBP of handover calls in the stop phase, \(B_{g,s,h}\), can be determined via:  
\[B_{g,s,h}=P_{g}(0,C) \tag{24}\]  
The CBP of handover calls given stop phase, \(B_{g,s,h}^{*}\), can be determined via:  
\[B_{g,s,h}^{*}=\frac{B_{g,s,h}}{\sum\limits_{n=0}^{C}P_{g}(0,n)} \tag{25}\]Finally, the link utilization, \(U_{g}\), can be computed via the formula:  
\[U_{g}=\sum_{i=0}^{1}\sum_{n=0}^{C}nP_{g}(i,n) \tag{26}\]
# Scalability Analyses of Premium Connections Characterized by a Surface Fractal Function
## 1 Introduction
established a 3D quantitative model utilizing the W-M function to examine the impact of 3D fractals on both contact area and surface roughness [17]. Due to the complex structure of premium connections, the length of the sealing surface is very short. As it is affected by the resolution of the measuring instrument and the sampling length, it cannot accurately reflect all the characteristics of the rough surface. Compared to the traditional method of assuming a smooth sealing surface for premium connection, utilizing fractal functions to describe the rough morphology on the sealing surface of premium connections can reveal the contact behavior of the sealing surface more realistically. First, a fractal rough surface, including the geometric parameters and the material parameters of the premium connections, is constructed with the W-M function. A fractal contact model for rough surfaces is developed by taking into account the variations in actual contact area resulting from elastic, elastoplastic, and fully plastic deformation of asperities. Then, the influence law of the different values of the fractal dimension \(D\) and the scale coefficient \(G\) on the contact area and the contact pressure is analyzed. Secondly, the surface profile of the fractal roughness is created on the sealing surface, and a fractal FEM of full-size premium connection is established. The distribution of the Von Mises stress, contact pressure and effective contact length under different values of the fractal dimension, axial tension and internal pressure are obtained. Finally, based on the theory of sealing strength, an analysis of the sealability of the premium connection is conducted under different loads. The findings of this paper could provide a new method of researching the sealability of premium connections at the microscale, and may serve as a reference for the design and application of premium connections. It may also provide a reference for the sealability analyses of metal sealing parts such as bolts and flanges.
# Post-Quantum Key Exchange and Subscriber Identity Encryption in 5G Using ML-KEM (Kyber) 1
## 6 Experimentation and Performance Analysis
### CPU Processor Analysis  
We analyze CPU performance by measuring the CPU usage and the CPU cycle count. Because we run our experiments and the respective cryptographic operations repeatedly to take the statistical measurements while not running other processes, CPU usage is at 100%.  
We also measure CPU cycles, which helps to select efficient cryptographic algorithms. Lower CPU cycle counts indicate more efficient algorithms, as they result in reduced processing time and energy consumption, which helps to conserve the battery life of the mobile device. In our experiment, we compute the cycle counts for the pre-quantum ciphers (X25519 and secp25r1) described in steps 1, 2, and 5 of Section 5.1. Furthermore, we followed the procedures outlined in steps 1, 2, and 4 of Section 5.2 for the post-quantum cipher (Kyber-512) using both the Mini-PC and Raspberry Pi. Our analysis involves computing the cycle count for 100,000 samples and utilizing a 95% confidence interval to calculate the results.  
Figure 4 shows a CPU cycle count comparison of pre-quantum and post-quantum ciphers on both the Mini-PC and Raspberry Pi. As shown in Figure 4, the Raspberry Pi has a low-performance processor and thus requires more CPU cycles to execute tasks than a Mini PC. The Mini-PC has a higher performance processor for all ciphers compared to the Raspberry Pi.  
For the Raspberry Pi, secp256r1 takes an average of 799,836 cycles during steps 1, 2, and 5 of the existing SUCI calculation as shown in Section 5.1, which is 73.34% higher than Kyber-512, which takes 207,428 cycles for the SUCI calculation, while X25519 takes 702,814 cycles. In the Mini-PC, secp256r1 again takes more cycles than X25519 and Kyber-512. secp256r1 takes an average of 500,109 cycles, which is 75.23% higher than Kyber-512, which takes 123,880 cycles.  
**Summary:** The analysis provides significant differences in cycle counts between pre-quantum (X25519 and secp256r1) and post-quantum (Kyber-512) ciphers on both the Raspberry Pi and Mini-PC. Kyber-512 stands out favorably in the CPU cycle comparison for mobile devices due to its relatively lower cycle count compared to other cryptographic ciphers like secp256r1 and X25519. This lower cycle count indicates that Kyber-512 requires fewer computational resources, making it more efficient and suitable for resource-constrained devices like mobile devices. Lower cycle counts enable Kyber-512 to execute cryptographic operations more efficiently, resulting in reduced processing time and low energy, which helps to conserve battery life, as discussed in Section 6.4.
# Effects of DNA Topology on Transcription from rRNA Promoters in _Bacillus subtilis_
## 4 Discussion
### Transcription with Selected Alternative \(\sigma\) Factors  
Transcription experiments with promoters dependent on alternative \(\sigma\) factors revealed that linear templates are poorer substrates for the majority of them (\(\sigma^{\mathrm{B}}\), \(\sigma^{\mathrm{D}}\), \(\sigma^{\mathrm{E}}\), \(\sigma^{\mathrm{F}}\), and \(\sigma^{\mathrm{H}}\)). This trend was previously reported also for RNAP:\(\sigma^{\mathrm{H}}\) transcribing from the _spoIIA_ promoter [66]. For forespore-specific \(\sigma^{\mathrm{F}}\), this is consistent with the DNA supercoiling increase in the forespore [40]. For \(\sigma^{\mathrm{H}}\) and \(\sigma^{\mathrm{E}}\) that are active in stationary phase, although activities of respective promoters strongly decreased with reduced supercoiling in vitro, this likely reflects the physiologically relevant requirements for their activities in the cell in stationary phase. Also, the decrease in the level of supercoiling in stationary phase is likely not as extreme as in our in vitro experiments where it was used to better visualize the effects.  
The exception was \(\sigma^{\mathrm{N}}\), where transcription (SC vs. LIN) is either relatively unaffected or even increased on linear templates. This is likely physiologically important as mitomycin, which induces \(\sigma^{\mathrm{N}}\) expression [24], causes also DNA relaxation and \(\sigma^{\mathrm{N}}\) may have evolved to be most active under such conditions. The proficiency of RNAP:\(\sigma^{\mathrm{N}}\) on linear templates then may stem from the relatively short spacers of \(\sigma^{\mathrm{N}}\) dependent promoters (15 bp compared to 17 bp for \(\sigma^{\mathrm{A}}\), [67]), analogously to \(\sigma^{\mathrm{70}}\) and \(\sigma^{\mathrm{S}}\) of _E. coli_ where the different \(\sigma\) activities were proposed to be due to preferences for differently DNA supercoiled templates [68; 69; 70].