esa-sceva/satcom-chunk-collection huggingface dataset:
# IW-NeRF: Using Implicit Watermarks to Protect the Copyright of Neural Radiation Fields
## 3 Preliminaries  
The neural radiance field (NeRF) model utilizes an MLP network trained with the input of three-dimensional coordinate positions \(\mathrm{x}=(x,y,z)\) and spatial point directions \(\mathrm{d}=(\theta,\varphi)\), where \(\theta\) and \(\varphi\) represent horizontal and vertical azimuths, respectively. The network outputs the color of spatial points \(\mathrm{c}=(r,g,b)\) and the density of corresponding positions (voxels) \(\sigma\). In the specific implementation, the position information of \(\mathrm{x}\) and \(\mathrm{d}\) is encoded initially. Subsequently, \(\mathrm{x}\) is fed into the MLP network to produce \(\sigma\) and a 256-dimensional intermediate feature. This intermediate feature, along with \(\mathrm{d}\), is jointly input into a fully connected layer for color prediction, culminating in the generation of a two-dimensional image through volume rendering. The network structure of the NeRF is depicted in Figure 1.  
Despite the high adaptability and strong learning capabilities of fully connected MLP networks, they exhibit notable parameter and structural redundancies. To validate the substantial redundancy within the NeRF model network, an 8-layer MLP network with a hidden layer size of 128 was trained. Following this, parameters of the trained NeRF model were pruned based on very small absolute values. The experimental outcomes, illustrated in Figure 2, demonstrate that even with a 50% parameter trim, the NeRF model retains high reconstruction quality. Hence, leveraging the inherent redundancy in MLP networks is considered in the design of our proposed solution.  
Figure 1: The network structure of the NeRF model.  
Figure 2: The reconstruction quality of NeRF model under different pruning rates.
# Fully Open-Source Meeting Minutes Generation Tool
## 5 Evaluations
### Limitations and Biases of Recorded Videos  
The recorded videos used in this study were designed to test the system across varied topics, presentation styles, and speaker accents, though some limitations should be noted. Speakers from Vietnam, Congo, Singapore, Malaysia, and Indonesia introduce accent variations. However, this sample set does not fully capture global English diversity. The topics, focusing on technical fields like _AI_ and biotechnology, present structured, content-rich  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Topic** & **Description** & **Description** & **Description** & **Description** & **Description** & **Description** & **Description** & **Description** & **Description** \\ \hline Duration (ms) & 5.50 & 5.29 & 6:44 & 6:37 & 6:53 & 6:05 & 5:28 & 4:32 & 6:13 & 8:10 \\ Bitrate (kbps) & 577 & 632 & 557 & 574 & 497 & 664 & 562 & 846 & 517 & 691 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Recorded presentation videos for evaluations.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**PaddleOCR [58]**} & \multicolumn{2}{c}{**Tesseract [59]**} & \multicolumn{2}{c}{**EasyOCR [26]**} \\ \cline{2-7}
**Size** & \begin{tabular}{c} **Duration** \\ **(s)** \\ \end{tabular} & \begin{tabular}{c} **CER** \\ \end{tabular} & \begin{tabular}{c} **Duration** \\ **(s)** \\ \end{tabular} & \begin{tabular}{c} **CER** \\ \end{tabular} & \
\begin{tabular}{c} **Duration** \\ **(s)** \\ \end{tabular} &
\begin{tabular}{c} **CER** \\ \end{tabular} \\ \hline \(1280\times 720\) & 0.3442 & 0.00\% & 1.2262 & 0.00\% & 4.1956 & 0.00\% \\ \(960\times 540\) & 0.1951 & 0.00\% & 0.9713 & 0.00\% & 2.9337 & 0.00\% \\ \(854\times 480\) & 0.2672 & 0.00\% & 0.8805 & 0.00\% & 2.7653 & 0.00\% \\ \(640\times 360\) & 0.1892 & 0.00\% & 0.6989 & 0.00\% & 2.9206 & 0.00\% \\ \(\mathbf{426\times 240}\) & **0.1278** & **0.00\%** & **0.1531** & **0.00\%** & **3.6544** & **0.00\%** \\ \(320\times 180\) & 0.0991 & 5.22\% & 0.1338 & 4.26\% & 0.9936 & 8.88\% \\ \(256\times 144\) & 0.0773 & 7.02\% & 0.1204 & 100.00\% & 0.2197 & 55.56\% \\ \(160\times 90\) & 0.0594 & 15.40\% & 0.1010 & 100.00\% & 0.3462 & 100.00\% \\ \hline \hline \end{tabular} Note: **bold** values indicate the smallest image size before the CER goes above 0.0%.  
\end{table}
Table 5: Comparison of OCR algorithms.  
scenarios that may not depict more casual presentations. Audio quality was managed by recording in quiet rooms using standard laptop microphones, minimizing noise but possibly influencing transcription accuracy. Speech patterns were kept natural, though accents may still introduce phonetic challenges for the system. Presentation slides were standardized in the _.pptx_ format without images or complex formatting, ensuring consistency but limiting the representation of real-world design diversity. These constraints provide a controlled testing environment but may not fully reflect broader real-world variability.
## 3 Results
### Production Performances  
The production performance values are shown within Table 4. There were no significant differences concerning the initial or final body weight (\(p\) = 0.580; \(p\) = 0.688) of hens in the CON, CV2%, and SP2% groups. The feed conversion ratio (g feed/g egg) registered the optimum value in the SP2% group compared to CON and CV2% (\(p\) < 0.0001). There was no significant difference in the daily feed intake among the dietary treatments (\(p\) = 0.608). The mean daily feed intake was 112.78 g/day/layer for CON, 112.40 g/day/layer for CV2%, and 113.46 g/day/layer for SP2%. A highly significant difference (\(p\) < 0.0001) was noticed in the feed conversion ratio between all groups. There was a highly significant difference (\(p\) < 0.0001) concerning egg weight among the dietary treatments. The mean egg weight was 59.78 g for CON, 62.42 g for CV2%, and 62.44 g for SP2%. There was a significant difference (\(p\) = 0.046) in the hen day egg production among the dietary treatments; the highest percentage was registered for the SP2% group (95.56%) compared to CON and CV2%. There were significant differences in the percentages of eggs classified into different sizes (XL, L, M, and S) categories among the dietary treatments (\(p\) < 0.0001). The data show that the proportions of eggs in each size category varied among the treatment groups, with significant differences observed for all categories (\(p\) < 0.0001). The CV2% group presented a higher percentage of eggs classified as \"M\" (medium) compared to the SP2% and CON groups. This suggests that chlorella supplementation might have influenced egg size distribution, potentially leading to a significantly higher proportion of medium-sized eggs (\(p\) < 0.0001). The SP2% group showed a significantly higher (\(p\) < 0.0001) percentage of eggs classified as \"L\" (large) compared to the CV2% and CON groups.
# Priority Queues with Fractional Service for Tiered Delay QoS
## 6 Discussion
Thus, even though worst-case delays for higher-priority buffers will not be affected by a larger \(\eta_{i}\), their  
Figure 10: Worst-case delays for buffers when \(M=4\) and \(m=1,2,\dots,5\). probabilities of delay limit violation will be higher. Basically, favoring some classes inevitably affects the other classes. The tradeoff is determined by the setting of \(K_{i}\) and \(\eta_{i}\). The worst-case delay for the \(i\)th class occurs only if all buffers of equal or higher priority are fully occupied, and all packets in these buffers are of the maximum size. Naturally, this is a pessimistic upper bound for the delay. Figure 11 plots worst-case delays and the 95-percentiles of packet delays for the 5-buffer case when \(m=1\). Packet sizes are assumed to follow a truncated exponential distribution with a maximum packet size four times the mean packet size. As can be seen in the graph, worst-case delays, marked by asterisks, are well above their 95-percentiles, marked by circles. Furthermore, the differences between mean packet delays, plotted in dotted line, and worse-case delays are larger for lower priority traffic. This might be caused by the pessimistic assumption that all lower buffers are fully and continuously occupied to reach the worst case. Overall, Figure 11 demonstrates that the majority of packets are protected within their delay-limits promised by the queueing engine.
## 3 Results and Discussion
### Effect of pH  
The pH of the water solution is one of the key factors that control the adsorption process, since it influences the electrostatic interactions between the adsorbent and the adsorbate. The adsorption of m-aminophenol, p-nitrophenol and pentachlorophenol as a function of pH was studied on carbon over a pH range 2-12. The adsorption of pentachlorophenol depending on pH is presented in Figure 5. For all three phenolic substances these results are similar. The desired pH was obtained by adding adequate amounts of NaOH or HCl diluted solutions to the initial non-buffered solution containing the phenolic compound. The maximal uptake of all three pollutant is found to be maximal in the range pH 6-7. As expected, these results show that m-aminophenol and p-nitrophenol are preferentially adsorbed from their neutral form. The effect of pH depends to a large extent on the nature of the adsorbent.  
The amount adsorbed does not alter too much at acidic pH < 6, whereas the gradual shift at basic media results in a sharp uptake fall of the uptake. As the pH increases, the surface of the activated carbons is being negatively charged until pH > pH\({}_{PZCC}\), (PZC--Point zero charge is the pH value, corresponding to zero net surface charge of adsorbent surface), where the number of negative charges becomes predominant on the carbon surface. At this point, the uptake fall is due to repulsive interactions, that appear between the anionic  
Figure 5: Effect of pH on pentachlorophenol removal. Conditions: time of treatment—60 min; activated carbon amount—0.1 g per 50 mL solution; pentachlorophenol concentration—0.03 g/L.  
form of the adsorbates and the charges on the carbon surfaces. For hydrophobic carbon, the occurrence of negative electrostatic interactions starts at pH > 8.2.
# Lightweight Blockchain Processing. Case Study: Scanned Document Tracking on Tezos Blockchain
## 2 State-of-the-Art
### Video Fingerprinting Basic Concepts  
Video fingerprinting methods are meant to identify duplicated and slightly replicated versions of a given video sequence (query) in a reference video database [17; 18; 19]. They are also commonly referred to as content-based copy detection (CBCD) or near duplicated content detection.  
Any fingerprinting method encompasses two main steps. Firstly, some visual content-based features (fingerprints) that are able to concisely and accurately represent that query are computed. Secondly, such features should be matched with the reference database, according to a similarity metric and a threshold pre-set using functional and/or theoretical criteria.  
Fingerprinting systems are characterized by three main properties. Firstly, the uniqueness property requires for two video sequences with different content to have two different fingerprints (in the sense of the similarity measure and of its underlying threshold). Secondly, the robustness property requires that any query video sequence and its replicas obtained through different distortions have identical fingerprints (here again, in the sense of the similarity measure and of its underlying threshold). Finally, the scalability property relates to the system's ability to deal with large databases.  
From the methodological point of view, fingerprinting computation can be achieved by various means [9]; we mention here-after only a few: 2D-DWT (Discrete Wavelet Transform) coefficients, 3D-DCT (Discrete Cosine Transform) coefficients, pixel differences between consecutive frames, visual attention regions, quantized block motion vectors,MPEG-7 features, such as the color-layout descriptor, invariant moments of frames edge representation, dominant-edge orientation of keyframes. For each and any fingerprint, a different matching rule is devised so as to ensure the check of any potential suspect video sequence against a (massive) prerecorded database.  
Hence, the complexity of visual fingerprinting applications is given both by the fingerprinting computation and by its matching. In the present paper, the focus is set on the way in which such a visual fingerprinting application can be coupled to Blockchain, while ensuring an end-to-end lightweight implementation, demonstrated by a deployment on an embedded platform. Consequently, we will not design a new fingerprinting method, but will integrate an existing one, based on DWT coefficients [9] and designed for tracking camcorder video sequences.
## 3 Results
Insignificant differences (two-sided student \(t\)-test, 95% confidence level) over 2 \(\times\) 2\({}^{\circ}\) latitude–longitude boxes were marked with black crosses. Anisotropy uncertainties were not considered. To investigate whether these large-scale biases were a result of irregular conditions, we examined properties of cross-track samples falling into three regions: SE Atlantic, NE Pacific, and Southern Ocean (highlighted as green rectangles in Figure 8). As shown in Table 4, we found a low median cloud-top effective radius (8.2 um) and low median cloud-topped water vapor (1.6 kg m\({}^{-2}\)) over the SE Atlantic in July. Flux errors of individual samples (\(-\)6.4-7.9 W m\({}^{-2}\)) led to median flux deviation of 0.9 W m\({}^{-2}\). We found slightly larger median flux differences (1.2 W m\({}^{-2}\)) in January over the Southern Ocean. However, the distribution of cloud-top effective radius (a median of 10.5 um) and cloud-topped moisture (a median of 5.9 kg m\({}^{-2}\)) appeared ordinary. We believe that deviations in cloud-top effective radius (7.2-16.2 um) and cloud-topped moisture (2.2-14.1 kg m\({}^{-2}\)) from median conditions paired with a variety of viewing and illumination geometries created flux deviations (\(-\)6.0-7.0 W m\({}^{-2}\)) which failed to cancel out to zero. We repeated the analysis with linear models only dependent on effective radius (of the form: \(\ln\rho_{o}(\theta_{s},\theta_{o},\phi)=a+b\cdot\ln R_{e}\)) and found similar biases; in part reducing the median difference to CERES-like ADMs (Southern Ocean January: 0.6 W m\({}^{-2}\); SE Atlantic July: \(-\)0.1 W m\({}^{-2}\)) and in part increasing it (NE Pacific July: 2.3 W m\({}^{-2}\)). In summary, we show that TOA SW anisotropies of refined ADMs captured departures in cloud-top effective radius and cloud-topped water vapor from respective median conditions. Using mean empirical reflectances of extreme cloud droplet size distributions (cloud-top effective radii of 5 and 20 um) and comparing CERES ADMs, which were insensitive to such extremes, with refined ADMs, we found deviations in estimated fluxes of up to 20 W m\({}^{-2}\) at particular viewing and illuminations geometries. When applied to CERES cross-track observations--serving radiation budget climatologies--we demonstrated that the choice of ADM could introduce significant large-scale biases of 1-2 W m\({}^{-2}\), locally reaching levels of up to 10 W m\({}^{-2}\). In part, we could attribute large-scale biases to regions of persistently small cloud-top effective radii and low cloud-topped water vapor.
# Pyrophyllite: An Economic Mineral for Different Industrial Applications
###### Abstract  
Pyrophyllite (Al\({}_{2}\)Si\({}_{4}\)O\({}_{10}\)(OH)\({}_{2}\)) is a phyllosilicate often associated with quartz, mica, kaolinite, epidote, and rutile minerals. In its pure state, pyrophyllite exhibits unique properties such as low thermal and electrical conductivity, high refractive behavior, low expansion coefficient, chemical inertness, and high resistance to corrosion by molten metals and gases. These properties make it desirable in different industries such as refractory; ceramic, fiberglass, and cosmetic industries; as filler in the paper, plastic, paint, and pesticide industries; as soil conditioner in the fertilizer industry; and as a dusting agent in the rubber and roofing industries. Pyrophyllite can also serve as an economical alternative in many industrial applications to different minerals as kaolinite, talc, and feldspar. To increase its market value, pyrophyllite must have high alumina (Al\({}_{2}\)O\({}_{3}\)) content, remain free of any impurities, and possess as much whiteness as possible. This paper presented a review of pyrophyllite's industrial applications, its important exploitable properties, and the specifications required for its use in industry. It also presents the most effective and economical techniques for enriching low-grade pyrophyllite ores to make them suitable for various industrial applications.  
pyrophyllite; properties; applications; specifications; enrichment; low-grade +
Footnote †: journal: Applied Sciences
# Speaker recognition Based on Dunge Beetle Optimized CNN
## 2 Background  
### Generation of Spectrogram  
The process of using CNN for speaker recognition includes two parts: the generation of the spectrogram and the calculation of CNN, as shown in Figure 1. Firstly, convert the audio signal into spectrum diagram data, and then import the obtained images into CNN for feature extraction and classification. Finally, accurately identify the speaker.  
Since two-dimensional data is used in CNN, it is necessary to expand the dimension of audio data. The spectrogram is an image that displays the audio frequency spectrum, which denotes the variation of the frequency and amplitude of a speech signal over time. In the spectrogram, the energy of sound is displayed in the form of texture, which is called voiceprint. Voiceprint contains a lot of speaker characteristics, and everyone's voiceprint is different. The essence of speaker recognition is to extract and classify voiceprints.  
As shown in Figure 2, the production process of the spectrogram is divided into five steps: (i) pre-emphasis is used to enhance the high-frequency content of the signal to compensate for the loss during the acquisition process; (ii) due to the short-term stability of the voice signal, the audio signal is decomposed into some small fragments, and then the Hanning window is added to these fragments; (iii) the amplitude-frequency characteristics of the above speech segment sequence are obtained after the fast Fourier transform (FFT) processing, and then the modulus is taken to obtain the transformed linear spectrum; (iv) a linear spectrum is converted into a spectrogram by the logarithmic transformation; (v) color mapping is used to add details and features to the image and resize the image for CNN recognition.  
Figure 1: The process of recognizing the speaker.
## 2 Materials and Methods
### Electrified Power Unit Design
#### 2.1.5 Electric Schematic Overview of the e-HPS  
The complete electric schematic of the proposed e-HPS is shown in Figure 6. The four subsystems can communicate through the Can-Bus link. The two 120 \(\Omega\)-terminations are highlighted and the Anderson connectors are represented to electrically join the battery charger to the battery pack. The color legend is the same as that reported in Section 2.1.2. The BMS block is located next to the battery modules.  
Figure 5: The connection topology of the four battery modules.  
Figure 7 shows the last two subsystems, the battery charger and the battery pack, in addition to the BMS unit, mounted on the waste collection truck. The battery charger is shown in the top left corner of Figure 7, as the black device inside the orange box. The battery pack is located at the lower center of Figure 7, as the four aluminum blocks. The white block on the right side is the BMS unit. The control unit is located inside the white cabin of the vehicle, on the left side in Figure 7. The electric motor and the relative controller are located on the back side of the vehicle, with respect to the one shown in Figure 7.  
Figure 6: The overall electric schematic of the proposed e-HPS.  
Figure 7: The proposed e-HPS: two of the four subsystems are indicated in the picture: the battery charger and the battery pack/battery management system (BMS) unit.  
The overall additional weight of the e-HPS can be estimated to be about 130 kg, that is:  
* 25 kg for each battery module;
* 17 kg for the electric motor;
* 2 kg for the inverter;
* 2 kg for the BMS;
* 9 kg for the battery charger.
## 3 Study Area, Dataset and Evaluation Metrics
### Evaluation Metrics  
To evaluate the proposed hybrid model quantitatively, the following set of statistical metrics are used.  
Mean bias error (MBE):  
\[\text{MBE}=\frac{1}{N}\sum_{i=1}^{N}(fore_{i}-obs_{i}), \tag{8}\]  
where \(i\) is the time point and \(N\) is the total number of verification time points. \(fore\) and \(obs\) represent the forecasts and observations, respectively.  
Mean absolute error (MAE):  
\[\text{MAE}=\frac{1}{N}\sum_{i=1}^{N}|fore_{i}-obs_{i}|, \tag{9}\]  
Root mean square error (RMSE):  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{N}(fore_{i}-obs_{i})^{2}}{N}}. \tag{10}\]  
Figure 3: Domain configurations of the WRF (Weather Research and Forecasting) model for Qinghai (**a**) and Maizuru (**b**) sites in Japan and China, respectively. This image is taken from Google Earth.  
Normalized root mean square error (nRMSE):  
\[\text{nRMSE}=\frac{\sqrt{\frac{1}{N}\sum_{i=1}^{N}(fore_{i}-obs_{i})^{2}}}{Max \{obs\}-Min\{obs\}}. \tag{11}\]
## 2 Results and Discussion  
Although TDM is an indispensable tool to control the effectiveness of therapy, especially among the critically ill, it is still not implemented globally [30]. Over the last decade, researchers have attempted to create analytical methods for TDM [19; 20; 21; 22; 23; 31; 32], but none of them allowed for the preparation of a large number of samples at the same time. For example, in the method developed by [PERSON] et al. [21], the sample preparation technique is protein precipitation, in which each sample should be prepared individually according to the developed procedure. Using TFME, it is possible to prepare as many as 96 samples simultaneously in one 96-well plate, which can be immediately placed in an autosampler for rapid LC-MS/MS analysis [33]. Due to the wide range of available sorbents and the development of sampling equipment, TFME has potential applications in many medical specialties, especially in diagnostics [34]. Importantly, only an unbound fraction of antibiotics present in a sample is extracted with the technique applied here, precluding a bias to appear related to the estimation of unbound concentrations from the literature data. In this study, we describe a method that allows for the simultaneous, fast, quantitative determination of piperacillin and impenem.
# Gyro Error Compensation in Optoelectronic Platform Based on a Hybrid ARIMA-Elman Model
## 4 Hybrid ARIMA-Elman Model
### Specific Steps
As Section 3.2 depicted, in the Elman neural network model, values of the weight matrix \(W^{0}\), \(W^{1}\), \(W^{2}\) and threshold matrix \(B^{0}\), \(B^{1}\) also need to be determined. In fact, the problem can be seen as a combination optimization problem. In detail, it is to seek the best combination of model parameters which make the difference between the predicted outputs and the actual values as small as possible for each sample. Therefore, the intelligent optimization algorithm (i.e., DE algorithm) is first used in handling the problem. Here, as Section 3.4 illustrated, population initialization is initially operated. For both of the parameter optimizations of the ARIMA and Elmanneural network models, the scaling factor \(F\) is set to 0.6 and the crossover probability \(CR\) is set to 0.8. Finally, through mutation operation, cross operation, and selection operation, the optimization values of parameters can be obtained. Step 5: Combine the values of the two predictions. The flow chart of the proposed hybrid ARIMA-Elman model is described in detail, as follows in Figure 8: