esa-sceva/satcom-chunk-collection huggingface dataset:
# Unsupervised Low-Light Image Enhancement in the Fourier Transform Domain
## 4 Experiments
### Noise Experiment  
In order to verify the necessity of our method in the Fourier transform domain, that is, amplitude-domain enhancement does not amplify the noise of the original image. We add noise to the original ten low-light test images; Gaussian noise with a variance of 5 and 20 are added in the two experiments, respectively. The low-light images after noise processing are re-tested, and their PSNR, SSIM, and LPIPS values are calculated. The results are shown in Tables 2 and 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **PSNR** & **SSIM** & **LPIPS** \\ \hline LLNet [14] & 27.97043800 & 0.7468 & 0.2772 \\ RetinexNet [15] & 27.80025249 & 0.2079 & 0.8041 \\ Zero-DCE [24] & 27.70068916 & 0.3056 & 0.6482 \\ Zero-DCE++ [25] & 27.69722996 & 0.2790 & 0.6575 \\ SCI [26] & 27.68119815 & 0.2718 & 0.6620 \\ EnlightenGAN [18] & 27.81145379 & 0.3794 & 0.5937 \\ RUAS [17] & 27.8588932 & 0.2532 & 0.6900 \\ Ours & **28.01347462** & **0.7652** & **0.1873** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative results of adding Gaussian noise with a variance of 5 for each method. The best results are in bold.  
Figure 4: Enhancement results of each low-light face image. (**a**) Noisy image. (**b**) LLNet [14]. (**c**) RetinexNet [15]. (**d**) Zero-DCE [24]. (**e**) Zero-DCE++ [25]. (**f**) SCI [26]. (**g**) EnlightenGAN [18]. (**h**) RUAS [17]. (**i**) Ours.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **PSNR** & **SSIM** & **LPIPS** \\ \hline LLNet [14] & 27.97043800 & 0.7468 & 0.2772 \\ RetinexNet [15] & 27.80025249 & 0.2079 & 0.8041 \\ Zero-DCE [24] & 27.70068916 & 0.3056 & 0.6482 \\ Zero-DCE++ [25] & 27.69722996 & 0.2790 & 0.6575 \\ SCI [26] & 27.68119815 & 0.2718 & 0.6620 \\ EnlightenGAN [18] & 27.81145379 & 0.3794 & 0.5937 \\ RUAS [17] & 27.8588932 & 0.2532 & 0.6900 \\ Ours & **28.01347462** & **0.7652** & **0.1873** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative comparison of the results of each method. The best results are in bold.  
The experimental results of the same test images we selected are shown in Figure 5.  
As can be seen from Figure 5, the LLnet [14] and RetinexNet [15] methods have certain denoising effects, but there are color distortion problems. The noise of Zero-DCE [24], Zero-DCE++ [25], SCI [26], and EnlightenGAN [18] are all significantly amplified and cannot remove the noise well. We can clearly see a lot of noise from the result of RUAS [17]. Our method in Table 2 has also achieved the best results in the three evaluation indicators, achieving image enhancement and image denoising to a certain extent, and achieving better restoration in image details and brightness. The experimental results show that the low-light image processing in the Fourier transform domain can separate the noise, and the noise will not be amplified during the enhancement process. Both qualitative and quantitative results prove the validity of the model.
## 4 Narrowband Interference Suppression Algorithm Based on SVD with Guidance Signal
### SVD with Guidance Signal  
As the narrowband interference amplitude decreases, the corresponding singular value also decreases, and the aliasing area between the narrowband and PD subspaces in-creases, worsening the interference suppression effect. Therefore, how to realize the effective filtering of singular values corresponding to narrowband interference with small amplitude is also an important problem to be studied [29].  
In order to ensure that the small amplitude narrowband interference in the SVD can still be accurately filtered out, this paper introduces the idea of increasing the narrowband interference amplitude by constructing the guidance signal from paper [29]. The specific steps of SVD with guidance signals are as follows:  
(1) Perform FSWT on noisy PD signal \(X\) to adaptively obtain the number of narrowband interference \(m\).  
(2) Use the improved Rife method to accurately estimate the frequency of narrowband interference.  
(3) Construct the guidance signal.  
The expression of the guidance signal is shown in (31)  
\[g(t)=2y_{\max}\sum_{i=1}^{m}\sin(2\pi f_{i}^{\prime}t) \tag{31}\]  
In (31), \(y_{\max}\) represents the maximum amplitude of the noise signal and \(f_{i}^{\prime}\) represents the estimated frequency of the narrowband interference obtained by the improved Rife method.  
(4) Add guidance signal \(g(t)\) to the noisy PD signal to obtain the mixed signal \(m(t)\), and \(m(t)\) is constructed within the Hankel matrix for SVD decomposition.  
(5) Set the first \(2m\) singular values to 0, and then the PD signal after narrowband interference suppression can be obtained by reconstructing the signal.  
A noise reduction performance evaluation of traditional SVD methods and the proposed method for the noisy PD signal shown in Figure 1c is shown in Table 5, comparing the suppression effect of the narrowband interference by calculating the three indexes of SNR, NCC and RMSE. On this basis, the narrowband interference amplitude A\({}_{i}\) in the noisy PD signal is transformed equally to verify its impact on the noise reduction method.  
According to Table 5, ISVD is the most ideal in each evaluation index. The specific performance is as follows: the SNR of the noise-reduced PD signal is the largest, the NCC is the closest to 1 and the RMSE is the smallest, which represents the least residual noise content in the noise-reduced PD signal, the smallest error in amplitude and frequency and the highest similarity in waveform with the pure PD signal.  
Additionally, as narrowband interference amplitude decreases, the traditional SVD method's suppression effect worsens, while the proposed method in this paper remains effective.
# Role of Kir4.1 Channel in Auditory Function: Impact on Endocochlear Potential and Hearing Loss
## 4 Discussion
### Kir4.1 Channel
#### 4.3.4 K+ Cochlear Cycle
This process involves the removal of K\({}^{+}\) from the extracellular environment in close proximity to auditory hair cells and its subsequent reintroduction into the perilymph. (3) Kir4.1 channels are expressed--the root cells express Kir4.1 channels, which may mediate potassium fluxes across cellular membranes. These channels are responsible for the resting conductance of root cells and may facilitate the movement of K\({}^{+}\) across the cell membrane. (4) There is a role in maintaining the endocchlear potential, and the potassium transport mediated by root cells may contribute to the generation and maintenance of the endocchlear potential, a critical component for auditory transduction. (5) There is an interconnection via gap junctions, and the root cells are interconnected via gap junctions that allow the passage of molecules such as Lucifer yellow. This interconnection enables root cells to collaborate on ion transport and homeostasis maintenance [31; 32]. Once potassium reaches the outer sulcus cells, it is exchanged with the fibrocytes of the stria vascularis. Kir4.1 is expressed by various cells surrounding the IHCs and OHCs, and it appears that, aided by other channels such as aquaporin-4 (AQP4), it is involved in the recycling and ionic homeostasis of supporting cells [46; 47]. The extraepithelial or medial route follows the potassium flow that, leaving the IHCs, is directed into the interstitial space, where it is subsequently gathered by the inner pillar cells and inner phalangeal cells, which have the Kir4.1 channel expressed in their membrane. In inner sulcus cells in close proximity to the organ of Corti, there is an expression of both Kir4.1 and aquaporin-4 (AQP4) channels [46]. Additionally, a series of gap junctions between these cells facilitate ion exchanges in the area surrounding the basal part of the hair cell. Progressing into the inner sulcus, a series of cells equipped with a gap junction and Kir4.1 facilitate the subsequent removal of K\({}^{+}\) towards the interdental cells while releasing the K\({}^{+}\) stored in the interstitial space between the inner sulcus cells and the fibrocytes present. Potassium returns to flow into the endolymph from both the central and lateral interdental cells in the region where the vectorial membrane attaches. The interdental cells also express a combination of Kir4.1 and AQP4 [47]. In summary, Kir4.1 and AQP4 play complementary roles in K\({}^{+}\) recycling in the cochlea, with Kir4.1 mediating ion flux and AQP4 facilitating the water flow necessary for osmotic balance. Their expression and regulation are important for maintaining ionic homeostasis and preventing hearing loss [46; 47].
# A Novel Multi-Server Federated Learning Framework in Vehicular Edge Computing
## 5 Transmission Latency Analysis  
In FL, transmission delays overwhelmingly dictate the overall runtime, rendering local computation times relatively insignificant [5, 6]. Thanks to advances in Central Processing Unit (CPU) and Graphics Processing Unit (GPU) technologies, on-device training has become highly efficient, shifting the primary bottleneck to communication. Contemporary ML models often require hundreds of megabytes--and in some cases gigabytes--of parameters [28, 29, 2], and achieving convergence can demand on the order of hundreds of thousands of communication rounds. Consequently, the aggregate volume of exchanged updates can easily reach the petabyte scale over a full training cycle [27]. Unlike centralized training, FL performance is therefore constrained by its communication overhead--a constraint that becomes even more severe under poor network conditions. In the following section, we evaluate transmission latency across three architectures: our proposed MS-FL framework, a conventional single-server FL setup, and a cloud-based FL system.
# Improving Low-Frequency Panel Absorbers with Two-Dimensional Acoustic Black Hole
## 4 Experimental Validation
### Surface Vibration Measurements  
A set of measurements were performed in TU Dresden's Anechoic chamber for the validation of the numerical models. Therefore, a studio monitor (GENELEC 8250A) was located at two meters' distance (\(d=2\,\mathrm{m}\)) from a specimen in the anechoic chamber. It was used in its full frequency range to send acoustic waves to generate oscillations at the panel surface. Rigid frames were built for holding the manufactured front panels. The front panels were interchanged to obtain the effect of the structural characteristics, while the back volume depth was kept as \(120\,\mathrm{mm}\). The responses of the front panels were measured with an MMF KS95B.100 mini-accelerometer in four positions, as shown in Figure 13.  
In order to check the approaching sound pressure on the front surface, a measurement microphone B&K type 4188 was used. The measured vibration response was divided by the approaching sound pressure in order to obtain the response for the unit sound pressure, as in the simulation case. The experimental results are presented in comparison with the simulation results in Figures 14-16 that follow. The simulation results were achieved through multiple fitting iterations of the model. Ultimately, the geometric parameters in the FEM have been set to panel thickness \(h_{0}=2.9\,\mathrm{mm}\) and residual thickness \(h_{\mathrm{ABH}}=0.35\,\mathrm{mm}\).  
Figures 14-16 show that the predictions of the FEA are in good qualitative agreement with the measurements. While the quality of the quantitative part of the frequency response differs for each measurement position, the prediction of the modal frequencies is sufficient. All of the measurement results show that there is larger damping in the actual structures than the models, since the tip of the frequency peaks are more rounded. The difference in the first frequency for the HPL 3 mm panel was thought to be caused by screwing the front panels tighter, which eventually created a preload on the panel surfaces. Breaking the symmetry effect is noticeable for the designs where the ABH is located off from the center. In general, the prediction quality of the numerical model is more accurate if the selected point is away from the ABH. The differences in the frequency responses can be  
Figure 13: Surface vibration measurement setup.  
caused by topological effects from the manufacturing process that could not be modeled. However, a qualitative description of the acceleration is nevertheless possible for all designs. Furthermore, an agreement of the frequency values can be seen.  
Figure 14: Comparison of the simulated and measured normalised acceleration \(\frac{d}{p}\) at 4 points for the reference panel.  
Figure 15: Comparison of the simulated and measured normalised acceleration \(\frac{d}{p}\) at 4 points for the panel with ABH of radius \(a=0.07\) m at position P22.
## 1 Introduction
They found that inhibition of plant growth by Al was significantly reduced, chiefly due to Al species being converted to the monomers Al(OH)\({}^{+2}\) and Al(OH)\({}_{2}\)+, strongly adsorbed by the biochar, while Al\({}^{+3}\) was transformed to less toxic Al(OH)\({}_{3}\) and Al(OH)\({}_{4}\)-. [PERSON] et al. [17; 18] used batch adsorption experiments to explore the interactive mechanisms of biochar with Al in aqueous solutions. These studies further elucidated the mechanisms for biochar adsorption of Al. In particular, it was found that both, the complexation of Al with organic hydroxyl and carboxyl groups and the surface adsorption and coprecipitation of Al with silicate particles (as KAlSi\({}_{3}\)O\({}_{8}\)) contributed to Al adsorption [17]. Biochar aging introduced carboxylic functional groups on the biochar surfaces, which acted as additional binding sites for Al\({}^{+3}\)[18]. In general, there is a lack of studies conducted in soil. Studies in soil have aimed to unravel the effect of biochar amendment on Al phytotoxicity [19] or to compare the effects of biochar with lime amendments on Al phytotoxicity during soil re-acidification [20]. These studies [19; 20] have so far only considered the effect of biochar in KCl-extractable Al, but not on other fractions. Considering that (i) there are several forms of alkalinity generated by thermal processes, and that (ii) these forms are dependent on the feedstock and the temperature used, it is possible that biochars and hydrochars act on specific aluminium fractions in the soil. The objective of this study was to investigate the influence of biochar and hydrochar on the chemistry of aluminium in an acidic soil. We aimed to compare these more novel amendments with a traditional material, Ca(OH)\({}_{2}\), used by farmers in the region for lining. Thus, similarly to previous experiments with biochars using liming controls [20; 21], Ca(OH)\({}_{2}\) was used to increase the pH of the soil to a value which was the average of all other treatments, allowing to differentiate changes in Al fractionation caused by pH changes from those generated by other mechanisms.
# BeiDou Short-Message Satellite Resource Allocation Algorithm Based on Deep Reinforcement Learning
## 4 Simulation and Performance Analysis
### Analysis of Simulation Results
#### 4.2.2 The Influences of Different Weight Values on the Optimization Results  
Figure 14 shows the optimization effect of the DRL-SRA on the throughput of the short-message satellite system and \(L_{total}\), \(LI\), and \(SI\) when the weight factors of \(L_{total}\), \(LI\), and \(SI\) were \(\left(\alpha_{1}=\frac{1}{3},\alpha_{2}=\frac{1}{3},\alpha_{3}=\frac{1}{3} \right)\), \(\left(\alpha_{1}  
As shown in Figure 14d, as the number of short-message terminals increased, the total throughput of each algorithm first increased and then stabilized. Because the user service arrival model and service requirements used in the simulation were the same, when the weight parameter was \(\left(\alpha_{1}=\frac{1}{6},\alpha_{2}=\frac{1}{3},\alpha_{3}=\frac{1}{2}\right)\) and the number of terminals was 2250, the throughput of the system was 1790, which was the best because the weight of \(LI\) was relatively high. To improve the quality of service of the short-message terminal as much as possible, the DRL-SRA will have a greater requirement for the response time to the short-message request. This can also be proven by looking at the satellite load balance shown in Figure 14b. To improve the quality of service of the short-message terminal, the utilization of satellite resources needs to be maximized, so when the weight of \(LI\) is less than that of \(SI\), \(LI\) is not significantly reduced relative to other weights.  
When the weight parameter is \(\left(\alpha_{1}=\frac{1}{2},\alpha_{2}=\frac{1}{3},\alpha_{3}=\frac{1}{6}\right)\), the system will reduce \(L_{total}\) as much as possible, which will reduce the efficiency of short-message task processing, so the quality of service of the terminal in this set of weight parameters will be reduced relative to other weights. Additionally, the short-message satellite will respond to the task request as accurately as possible, which will make it challenging to achieve a relative balance in the use of satellite resources in the scenario of an uneven distribution of short-message terminals. \(LI\) is significantly lower than other weights.  
Figure 14: Performance analysis of the DRL-SRA with different weight values.
# A Linear Memory CTC-Based Algorithm for Text-to-Voice Alignment of Very Long Audio Recordings
## 4 Experiments and Results
### Evaluation Metrics  
In order to evaluate the performances of our models, we compute several metrics presented below. Note that we only consider the word onsets as decision boundaries, i.e., we do not compute errors neither on space (character \(\emptyset\)) onsets/offsets nor word offsets.  
The Mean Absolute Alignment Error (MAAE) is the most used metric in alignment evaluations. It reports the average value of all Absolute Alignment Errors (AAE), that measure the absolute difference between the predicted and true times of each word onset.  
The Quantile N (QN%) metric reports, for a fixed N \(\in[0,100]\), the Nth quantile of Absolute Alignment Error (AAE) over all word onsets, indicating that N% of the AAE are below the value QN%. In opposition to the MAAE, this metric is insensitive to outliers. In our evaluations, we will compute the N = 50 (median AAE), N = 95, and N = 99 quantiles.  
The Percentage of Correct Onsets (PCO) metric measures the percentage of onsets that can be considered correctly aligned. A threshold of 300 ms is commonly admitted and chosen by the community for the misaligned/well-aligned binary decision [44; 64], which we choose as well.
## 3 AoI Minimization in UAV-Assisted WSN/IoT Application Architectures
### UAV Altitude and Scheduling  
Two papers by [PERSON] et al. [8; 9] fit into this category. For the first paper, its motivation lies in the fact that environmental conditions (e.g., height and density of buildings), which affect UAV-based communication quality, vary at different locations. The second considers an environment with obstacles, which motivates the optimization of UAV altitude and the deployment of re-configurable intelligent surfaces.  
In [8], the authors explored a joint study of dynamic UAV altitude control and scheduling policy in a UAV-assisted wireless IoT network. IoTs with limited transmission capabilities are visited by UAVs that relay their data to the base station (in a clustered scenario with blockages shown in Figure 13a). The main objective of the stochastic scheduling and altitude control in the paper was to minimize the expected weighted sum AoI of sampled data, which largely depends on the wireless channel conditions and UAV altitude, to facilitate effective communication. The sampled data of stochastic processes by IoT devices are uploaded to UAVs via unreliable channels. The data are relayed to the base station that processes the packets. UAVs are assumed to have virtual queues to re-transmit undelivered data, thereby improving the efficiency of transmission. The propagation characteristics of the channel between IoT devices and UAVs are affected by the UAV altitude. Particularly, the received signal power is lower due to the long-distance path loss at higher altitudes.  
In a similar architecture characterized by blockages (Figure 13b), the authors in [9] focused on the optimization of RIS configuration for AoI minimization. A wireless network with IoT devices with limited transmission capabilities was used to sample stochastic processes to be processed by the base station. A single UAV enriched with RIS was used as a passive relay for forwarding data to the base station considering different IoT device activation patterns. A successful transfer of sampled data occurs when the signal-to-noise ratio exceeds a predefined threshold, at which the AoI decreases. An optimization problem was used to capture this framework with the objective of minimizing the expected sum AoI considering the signal-to-noise ratio constraints, UAV altitude constraints, and IoT device scheduling constraints.
## 3 Experimental Setup Description  
The exploited experimental setup, shown in Figure 6, has been designed as a proof of concept of the above-described AC-DC boost fed by an almost unknown AC source. It is worth underlining that, since the aim of the paper is to focus on the behavior of the boost converter, in the experimental setup, we considered a transformer in place of the VEH. The iron-core transformer indeed provides a signal compatible with a VEH's output and shows an inductive nature similar to the magnetostrictive devices. This choice allows one to neglect other effects, e.g., non-linearities, typically shown by EH devices based on magnetostrictive materials. The circuit is supplied by a secondary winding of a 150 VA  
Figure 5: Simplified scheme of the real-time code for Arduino. The interrupt is driven by the Schmitt trigger output signal (Figure 6).  
transformer with a peak voltage of 220 mV (V\({}_{\text{in}}\)). The transformer output current is then the inductor one (i\({}_{\text{L}}\)) and is measured by a current clamp (model: Fluke i30s). Moreover, the primary winding is fed by a power amplifier (model: KEPCO BOP 50-20 MG), driven by an arbitrary waveform voltage generator (model: Aim-TTi TGA12104). The Arduino board needs a reference signal for the timing. This is obtained from a Schmitt trigger that acts as a voltage comparator over another secondary winding referenced to ground. The latter is needed since the AC-DC boost input is floating, see Figure 1.  
The boost has a 100 \(\mu\)F capacitor in parallel to a 2.2 k\(\Omega\) resistor as load. The MOSFETs are driven by the two PWM signals outputs of the Arduino board.  
Finally, a DAQ board (model: National Instruments SCXI-1000 + NI 1520 board), at 30 kS/s sampling frequency, measures and acquires the input voltage (V\({}_{\text{in}}\)), the inductor current (i\({}_{\text{L}}\)), the two Arduino PWMs (PWM1 and PWM2) and the voltage over the capacitor-resistor parallel (V\({}_{\text{out}}\)). The voltage V\({}_{\text{in}}\) is measured in differential mode, while all the other voltages are single-ended. Figure 7 reports a picture of the experimental setup. It can be noticed that the AC-DC converter circuit is mounted over a breadboard, the Arduino board and the toroidal transformer.  
Figure 6: Experimental setup: the two controllable parameters “D” and “T\({}_{\text{D}}\)” are in purple; the orange lines are signals exploited for control purposes; and the green lines are the measured signals.