esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This work studied Gram-positive and weak electricigen _Corynebacterium glutamicum_ for its ability to transfer electrons and to produce bioelectricity in microbial fuel cells (MFCs). The electrochemical and liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS) results revealed that _C. glutamicum_ had the potential to mediate electron transfer to an electrode by emitting its own extracellular electron shuttles such as flavins. To enhance the current collection from _C. glutamicum_, a carbon cloth anode was modified with ferrocene-branched chitosan hydrogel (redox-hydrogel). The maximum current density of the ferrocene-branched chitosan redox hydrogel anode with _C. glutamicum_ was drastically increased to 120 uA cm\({}^{-2}\) relative to a bare carbon cloth electrode with _C. glutamicum_ (261 nA cm\({}^{-2}\)). The power density and polarization curves for the MFC operation with the redox-hydrogel-modified anode showed that _C. glutamicum_ effectively generated bioelectricity by means of the redox-hydrogel anode. The results suggest that, in such an electro-fermentation process, ferrocene-branched chitosan hydrogel grafted onto an anode surface would also facilitate both electron transfer from _C. glutamicum_ to the anode and bioelectricity generation.  
Corynebacterium glutamicum; bioelectricity; redox-hydrogel; carbon cloth electrode +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Quality Control and Data Analysis  
Due to a lack of Rs data during June and September, the analysis included only three periods: 26 April to 8 June (spring), 28 June to 4 September (summer), and 2 October to 3 December (autumn). To reduce the effect of rain on \(\mathrm{R_{s}}\) and sap flow, the data on sunny days were defined as days with no precipitation and with continuous solar irradiation of more than 2.5 kW m\({}^{-2}\) h\({}^{-1}\) between 10:00 and 13:00 h.
# Open Collaborative Platform for Multi-Drones to Support Search and Rescue Operations
## 4 Performance Evaluation
### Field Experiments
#### 4.1.2 Effect on Accuracy of Location Estimation with Dynamic Searching Path  
In our previous work, we discussed that the estimated location will have less error if sensor points were on a greater number of planes. In this experiment, we showed (1) the effect of the number of planes on the accuracy of location estimation and (2) the results after applying the _Dynamic Searching Path_. First, the RSSIs collected from Path 1 and Path 2. The location estimation points were calculated from collected RSSI data. The estimated location points were classified according to the number of planes where RSSI data are used with respect to the mobile phone, see Figure 16.  
Figure 14: Experiment on NTNU’s track field.  
Figure 15: RSSIs collected from (**a**) Path 1 and (**b**) Path 2.  
Figure 17 shows that location estimation using sensing points on three planes results in better accuracy compared to using sensor points on only one or two planes. The location error was less than 10 \(m\) using sensor points on three planes. The location error for using sensor points on only one or two planes was about 40-50 m. Therefore, it is important for Krypto to dynamically adjust its searching path if it sensed a signal source to produce more sensing points on more different planes as proposed in the _Dynamic Searching Path_ in Section 3.3.4.  
Figure 18 shows that more estimated location points were using three or four plans which improved location estimation accuracy when applying the _Dynamic Searching Path_. We can simply solve the _geometric centroid_ to those estimated location points of three or four plans for the estimated location of the signal source (e.g., a mobile phone) to reduce the error to less than 5 m.  
Figure 16: Sensor points at different planes.  
Figure 17: Effect on number of planes to location estimation.
## 4 Results
### Perceived Trust of LDA+ Sentiment Analysis (SA)
#### 4.2.2 ANOVA and Post-Analysis  
The results of ANOVA analysis and post-analysis using SPSS are as follows. As per ANOVA analysis, the influence of the affective level on the perceived trust of the SA evaluation algorithm was not statistically significant (\(p\)-value = 0.101) as shown in Table 3. In other words, LDA+SA perceived trust score can be observed in the same group regardless of the affective level of the paragraph. Figure 3 shows the average LDA+SA perceived trust score based on the affective level of the paragraph.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Sum of Squares**} & \multicolumn{1}{c}{**DF**} & \multicolumn{1}{c}{**Mean Square**} & \multicolumn{1}{c}{**F**} & \multicolumn{1}{c}{\(p\)**-Value**} \\  & & **(Type III)** & & & & & \\ \hline intercept & theory & 4245.003 & 1 & 4245.003 & 447.374 & 0.000 \\  & error & 189.774 & 20 & 9.489 & & \\ \hline affective level & theory & 4.964 & 2 & 2.482 & 2.425 & 0.101 \\  & error & 40.945 & 40 & 1.024 & & \\ \hline subject & theory & 189.774 & 20 & 9.489 & 9.273 & 0.000 \\  & error & 41.242 & 40.305 & 1.023 & & \\ \hline affective level\(\times\)subject & theory & 40.945 & 40 & 1.024 & 1.099 & 0.317 \\  & error & 469.425 & 504 & 0.931 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of analysis of variance (ANOVA) applied to LDA+ sentiment analysis (SA).  
Figure 2: Perceived trust of LDA according to affective level.
## Results
### Heterologous Expression of a Stranenopile Putative SQE  
Diatoms have been reported to employ an alternative squalene epoxidase (AltSQE) that is different from the conventional SQE found in other eukaryotes [16]. It has been observed that artificially altering the expression of this enzyme in _P. tricrontum_ is particularly challenging [16], suggesting that a strict regulation of endogenous AltSQE may be occurring as is the case for SQE in [33; 34]. Therefore, we hypothesized that the expression of a heterologous, conventional SQE that could override endogenous regulation, would influence final sterol levels. Consequently, a heterologous putative SQE from the Stranenopile _N. oceanica_ (_NoSQE_, Nanoc ID 521007) was expressed in the diatoms _T. pseudonana_ and _P. tricrontum_.  
Confocal microscopy images showed that mVenus fluorescence in NoSQE-mVenus transformants was similarly located to HMGR-mVenus, indicating endoplasmic reticulum, ER, localization in both diatom species (Figure 3). Chlorophyll and mVenus fluorescence intensity were comparable to those of diatoms overexpressing putative HMRG-mVenus and tHMGR-mVenus (see Section 3.2) (Figures S4, S5, S7, and S10).  
Total sterol content of _T. pseudonana_ and _P. tricrontum_ exconyiquants transformed with _NoSQE_-mVenus constructs remained unchanged compared to WT and mVenus controls (Figure 4 and Figure S12). However, in _P. tricrontum_ overexpressing NoSQE-mVenus, we observed significant differences in both end-point sterols and sterol intermediates. Downstream intermediates cycloartenol and obtusifoliol exhibited a 1.8-fold increase compared to WT control (Figure 4), while no differences in squalene were observed. The intermediate 2,3 epoxysqualene was not detected in either diatom species. In contrast to HMGR-mVenus overexpression, campesterol increased by 3-fold (Figure 4). However, the major end-point sterol brassicas sterol remained unchanged.
## 5 Discussion
### Provenance
#### 5.2.2 Geochemical Approach  
The presence of major elements and their ratios can provide reliable information about the origin of the source area [98; 99; 100]. Of the major elements, Al\({}_{2}\)O\({}_{3}\) and TiO\({}_{2}\) are considered immobile during weathering, transport, and diagenesis, so their ratio is used to infer source rock composition [9; 101; 102; 103]. Their ratio in the Early Miocene samples is 28.19 and corresponds to a felsic igneous source cf. [104]. The ratio in the Badenian samples is 18.26 and 16.41 in the Sarmatian samples. Both correspond to an intermediate source (values for granitic, igneous, and sedimentary rocks).  
The discriminant diagram of [PERSON] and [PERSON] [66] separates four major source areas. Samples from the Tunjice Hills plot mostly into the recycled and intermediate fields in diagram A (Figure 8a) and are at the boundary between the recycled and intermediate fields in diagram B (Figure 8b). The differences in provenance according to the diagram by [PERSON] and [PERSON] [66] (Figure 8a) between the Early Miocene and Sarmatian samples of studied rocks are clearly evident.  
Samples that plot into the intermediate igneous provenance field have lower silica content (below 50%). This suggests a higher amount of alumina and CaO [105], and their origin may be associated with parts of the Eastern Alps. Quartzose sedimentary provenance indicated that the sediments were subjected to recycling. Furthermore, the source material was predominantly quartz-rich sedimentary rocks, and to a lesser extent igneous rocks. The rocks are rich in silica and poor in alumina, and the CaO content is low.  
The origin from the recycled orogen fits well with the origin of the sediments in the collision zone, which corresponds to the origin from the Alps. The active Europe convergence throughout the Oligocene and Miocene [106] could lead to the sediments originating from the uplifted Dinaric and Southern Alpine blocks such that most of the sediments could be derived from the locally uplifted blocks of the Southern Alps [38; 107] to the north and the Dinarides to the south of the Tunjice Hills. Considering that the Central Paratethys covered the area south of the Tunjice Hills in the Early and Middle Miocene [38] and that the lowlands with Quaternary sediments to the east still exist [88; 108], the origin from the eastern side is minimal. Lowlands to the west enabled a marine gateway through the so-called Trans-Tethyan Corridor during the Karpatian and the Badenian [38; 44; 109; 110]. The intermediate origin points at the sediments originating from the Alps, more precisely from the Karawanks [111; 112], the Eastern Alps [85] or Triassic igneous rocks [113].  
Since the A-CN-K diagram (Figure 4) shows the weathering tendency of granodiorite and subordinately of tonalite (majority of Badenian samples only), it is possible to conclude that the provenance of the sediments is also in the northeast, from the Eisenkappel magmatic zone [114].
## 2 Materials and Methods
### Datasets  
Two datasets were employed for training the different models: the ICASSP 2021 AEC challenge synthetic dataset [28] and an independently recorded dataset. Unlike the synthetic data, the independent recordings were taken in real-life conditions with low SERs. Various scenarios were considered, including echo-path changes, variations in near-end source positions and distances from the microphone, and varying room sizes. A total of 11 hours of speech data were taken from the LibriSpeech [29] corpus and the TIMIT [30] corpus. Spider MT503 TM or Quattro MT301 TM speakerphones (Shure Inc., Niles, IL, USA) were utilized to simulate low-SER scenarios. The loudspeaker and microphone were positioned 5 cm from each other in these devices. Echo path changes were simulated by moving a Logitech type Z120 TM loudspeaker (Logitech International S.A., Lausanne, Switzerland), playing the echo signal. The loudspeaker's distance from the microphone was either 1, 1.5, or 2 m. The near-end speech was simulated using mouth simulator type 4227-ATM of Bruel&Kjaer (Bruel&Kjaer, Naerum, Denmark). The distance of the mouth simulator from the microphone also varied between recordings and was either 1, 1.5, or 2 m from the microphone. Double-talk utterances always contained two different speakers, so the average overlap between the two was 90%. Rooms of different sizes were used for the recordings (sizes varied between \(3\times 3\times 2.5\) m\({}^{3}\) and \(5\times 5\times 4\) m\({}^{3}\)). Reverberation time (RT\({}_{60}\)) varied between 0.3 and 0.6 seconds. The training data SER was distributed on \([-24,18]\) decibels, and the test data SER was distributed on \([-18,5]\) decibels. Test data speakers were unique and not used in the training set. Further details regarding the data creation can be found in [14].  
The ICASSP 2021 AEC challenge synthetic dataset was used to augment the training data. About 27.7 h of data were generated, with different scenarios, including double-talk, far-end or near-end single-talk, with/without near-end noise, and likewise for far-end. In addition, several nonlinear distortions were applied, with different SERs and signal-to-noise ratios. Further details regarding the dataset can be found in [28].
## 4 Results
### Model Complexity  
In Table 5, we compare the model complexity of WPD-Net with other state-of-the-art denoising networks, including DBDNet, DBDNet2, AD-CNN, LGCT-Net, and LRDUNet, in terms of the number of parameters, floating-point operations (FLOPs), and inference time. WPD-Net demonstrates the lowest parameter count (0.859 million) and the fewest FLOPs (225.1 million), while also achieving the fastest inference time (21.19 ms) on 256 \(\times\) 256 images. This efficiency is achieved through its lightweight design that integrates shallow feature extraction, RDABs, and dense feature fusion, avoiding the need for computationally intensive encoder-decoder architectures or transformer-based modules.  
Comparison models, DBDNet and DBDNet2, utilize dilated convolutional blocks to expand the receptive field without pooling, enabling better handling of high-speckle noise. However, this comes at the cost of increased computational complexity, with DBDNet requiring 298.9 million FLOPs and DBDNet2 slightly increased to 341.3 million. Despite their relatively modest parameter counts, both models are slower than WPD-Net. AD-CNN and LGCT-Net lead to higher complexity, with AD-CNN reaching 421.2 million FLOPs and LGCT-Net the highest among all at 859.7 million, primarily due to their deeper networks and the inclusion of transformer-style attention in LGCT-Net. LRDUNet attempts to achieve a lower parameter count using grouped convolutions and dense skip connections, but still results in 276.8 million FLOPs and longer inference time compared to WPD-Net.  
Our proposed WPD-Net achieves an optimal balance between denoising performance and computational efficiency, making it particularly well-suited for real-time and resource-constrained applications, such as embedded biomedical imaging systems.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **MSE** & **PSNR** & **SSIM** \\ \hline WPD-Net with MSE loss & 0.0298 & 16.335 & 0.816 \\ With RDAB and no DFF module & 0.0173 & 20.108 & 0.884 \\ With RDB and no attention & 0.0149 & 21.834 & 0.913 \\ Our proposed & 0.0076 & 26.164 & 0.982 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of ablation study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Models**} & **Parameters** & **FLOPs** & **Run Time** \\  & **(Millions)** & **(Millions)** & **(ms)** \\ \hline DBDNet [26] & 0.988 & 298.9 & 26.30 \\ DBDNet2 [27] & 0.951 & 341.3 & 27.61 \\ AD-CNN [28] & 1.637 & 421.2 & 29.58 \\ LGCT-Net [29] & 8.796 & 859.7 & 51.39 \\ LRDUNet [34] & 4.213 & 276.8 & 32.74 \\ WPD-Net & 0.859 & 225.1 & 21.19 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model complexity comparison and running time of models on 256 \(\times\) 256 image pixels.
## 7 Conclusions and Further Work  
Facing the problems of occupying extra bandwidth, decreasing the ratio of the effective payload of the packet, and increasing the burden of the switches in the current network congestion detection, this paper proposes a fine-grained network congestion detection method based on flow watermarking, and combines it with the eBPF, which realizes fine-grained efficient network congestion detection with low overhead and easy deployment.  
This paper first proposes a network congestion detection model based on flow watermarking. Then, the theoretical analysis of network congestion detection based on flow watermarking is carried out. And a fine-grained network congestion detection algorithm is designed for the problem that the congestion caused by the micro-burst traffic is difficult to detect.  
The experimental results based on the eBPF environment show that the changes in network status (normal, congested, etc.) can be obtained in real-time and accurately by analyzing the watermark decoding information. The watermark based on the sparsity of the packet can better reflect the network status and the fine-grained network congestion detection algorithm can achieve congestion detection on a small time scale.  
Despite the network congestion detection method based on flow watermarking performing well, there are other challenges that are not addressed. Our future work will investigate the impact of different traffic scheduling algorithms on the network congestion detection method based on flow watermarking. We will also evaluate the effectiveness and overhead of this method in practical implementation.  
Methodology, L.M., G.L. and B.W.; Software, L.M.; Writing--review & editing, L.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 13: The Crps in the different packet loss rate in Figure 7c.
## 2 The Proposed Approach for Fault Diagnosis of Wheelset Bearing
### The Noise Reduction Signature of AC Process  
The AC process is an adaptive noise reduction strategy, which has been extensively applied. Its advantage mainly benefits from the property that the Gaussian white noise in the signal declines rapidly to 0 as the time lag \(\tau\) increases. The definition of AC of a signal \(x(n)\) can be described as follows:  
\[R(\tau)=\frac{1}{N}\sum_{n=1}^{N}x(n)x(n+\tau) \tag{5}\]  
For bearings with a local fault, its vibration signal, apart from the fault-induced repetitive transients, usually contains multiple harmonic interference components generatedfrom other rotating parts and strong background noise [40]. Therefore, the vibration signal derived from a defective bearing can be expressed as:  
\[y(n)=x(n)+h(n)+w(n) \tag{6}\]  
where \(x(n)\) signifies the fault-induced repetitive transients, \(h(n)\) represents harmonic interference and \(w(n)\) signifies background noise. As a result, the AC of the compound signal \(y(n)\) can be expressed as:  
\[\begin{array}{ll}R_{yy}(\tau)&=\frac{1}{N}\sum_{n=1}^{N}y(n)y(n+\tau)\\ &=\frac{1}{N}\sum_{n=1}^{N}|x(n)+h(n)+w(n)||x(n+\tau)+h(n+\tau)+w(n+\tau)]\\ &=R_{xx}(\tau)+R_{sh}(\tau)+R_{sw}(\tau)+R_{hx}(\tau)+R_{hh}(\tau)+R_{sw}( \tau)+R_{sw}(\tau)+R_{sw}(\tau)\end{array} \tag{7}\]  
Since the Gaussian white noise is random, \(R_{ww}(\tau)\) is approximately 0. Due to the periodic signal \(x(n)\), harmonic interference \(h(t)\) and background noise \(w(t)\) are independent from each other, thus, the cross-correlation among them are can be ignored: \(R_{xh}(\tau)\approx 0\), \(R_{xx}(\tau)\approx 0\), \(R_{lux}(\tau)\approx 0\), \(R_{lux}(\tau)\approx 0\), \(R_{wtx}(\tau)\approx 0\), \(R_{wtx}(\tau)\approx 0\). Consequently, the influential contribution to the final result \(R_{yy}(\tau)\) just remains two terms: \(R_{xx}(\tau)\) and \(R_{hh}(\tau)\):  
\[R_{yy}(\tau)\approx R_{xx}(\tau)+R_{hh}(\tau) \tag{8}\]  
Therefore, the AC process can realize noise cancellation and periodic signal enhancement.
## 3 Optimized Dual-Channel Data Storage and Access Method
### Sub-Matrix Cross-Storage Method  
In the conventional method, the data in the sub-matrix are stored in a row in DDR3 using linear mapping. As DDR3 adopts the burst transmission mode, each read or write operation will complete the access of eight consecutive memory cells in DDR3 when the burst length is eight in a conventional linear mapping strategy. The eight data accessed each time belong to the same range line and eight different azimuth lines. In the SAR imaging system, after the data are read, they need to be cached in the on-chip RAMs first, and then sent to the processing engine such as an FFT processor after the entire row or column of the SAR data matrix is accessed. In range access mode, the eight data transmitted in a single burst belong to one range line, so only one RAM is needed for the cache. However, in azimuth access mode, since the eight data transmitted in a single burst belong to eight azimuth lines, eight RAMs are needed for data caching. In this way, the eight corresponding processing units need to be designed, so that they consume more on-chip resources.  
In this paper, we optimize the mapping of single burst transmission data by using the cross-storage method as shown in Figure 8, which reduces the use of cache RAMs and on-chip computing resources. \"Cross-storage\" means that the data of two adjacent range lines are alternately mapped to one row of DDR3, rather than using the conventional linear mapping method. In the proposed method, the eight data in each burst transmission in DDR3 belong to two adjacent range lines and four adjacent azimuth lines. Therefore, only four RAMs are required for data caching, and the utilization of the RAMs in the range direction is 50% and, in the azimuth direction, 100%, which is significantly improved compared with the linear mapping method. In the SAR real-time imaging system, a reduction in cache resources means that more computing resources can work at the same time, giving the system higher parallel processing capabilities.  
According to the \"cross-storage\" method, the address mapping rule can be obtained. First, determine the sub-matrix \(A_{m,n}\) where the data logical address \((x,y)\) is located:  
\[\left\{\begin{array}{l}m=floor(x,N_{a})\\ n=floor(y,N_{r})\end{array}\right. \tag{7}\]  
where \(0\leq m\leq M-1\), \(0\leq n\leq N-1\).  
Figure 8: Schematic diagram of sub-matrix data cross-storage method. (**a**) The sub-matrix; (**b**) the data in the sub-matrix are mapped to a row of DDR3 by using the cross-mapping method.  
Furthermore, the mapping rule between the logical address \((x,y)\) and the physical storage address \((i,j,k)\) is obtained:  
\[\left\{\begin{array}{c}i=floor((m\cdot N+n)/B_{n})\\ j=floor[(x-m\cdot N_{a})/2]\cdot 2N_{r}+2(y-n\cdot N_{r})+\text{mod}[(x-m\cdot N _{a}),2]\\ k=\text{mod}(m+n,B_{n})\end{array}\right. \tag{8}\]  
where \(i\) is the row address, \(j\) is the column address, \(k\) is the bank address, \"_floor_\" means round down, and \"mod\" means take the remainder.