esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Case Analysis  
In the first case (i.e., low price case), the minimum bidding price was reduced to $1019.20 million (5% of the total annual revenue). Figures 9 and 10 represent the bidding price and the number of spectrum blocks over 50 rounds, respectively. Because of the low initial price, all bidders wanted to buy 10 spectrum blocks, which is the maximum number of spectrum blocks sold to the individual bidder (see Figure 10). This high demand on the spectrum blocks results in continuous increase in the bidding price, and the auction could not be ended. Note that blue, purple, and red lines are overlapped in Figures 9 and 10.  
To end the auction, we consider additional demand reduction policy. In this hypothetical policy, each bidder is enabled to reduce its demand during the auction. Figures 11 and 12 show the bidding price and number of spectrum blocks over 50 rounds under the demand reduction policy, respectively. Although most patterns are quite similar to Figures 9 and 10, at the last round, the total demand was for 29 blocks with the total price of $1307.83 million. Interestingly, at the 49 th round, Type 3 reduced its demand to 9 blocks with a bidding price of $416.20 million. Regarding Type 3 offering 10 blocks with a bidding price of $457.86 million at the 48 th round, its critical bidding price was approximately $460 million. This was 9.34% of the annual revenue of Type 3. This implies that, to be completed within the 50 rounds, the minimum bidding price should be higher that 9.34% of annual revenue of the Conservative bidder (i.e., Type 3). Most blue, purple, and red lines are overlapped in Figures 11 and 12.  
Figure 10: Number of spectrum blocks over multiple rounds at Stage 1 (low price case).  
Figure 9: Bidding price over multiple rounds at Stage 1 (low price case).  
In the second case (i.e., high price case), the minimum bidding price was increased to $4076.81 million (20% of the total annual revenue). Figures 13 and 14 reveal the bidding price and number of spectrum blocks over 50 rounds, respectively. Unlike Figures 9 and 10, the Conservative bidder tried to buy the minimum number of spectrum blocks (i.e., 8 blocks) at the beginning of the auction due to its budget shortage (see Figure 13).
# A Comprehensive Review on Music Transcription
## 3 Transcription Techniques: Insights and Advances
### Multi-Pitch Estimation  
Multi-pitch estimation or multi-F0 estimation is a signal processing and music analysis technique used to identify and track multiple simultaneous pitches in an audio signal [34; 46; 47; 48; 49; 50]. Many works have been carried out using this approach. The work in [34] clusters the multi-pitch F0 values into different sources by using probabilistic latent component analysis (PLCA). Similarly, the work in [46] outlines a system developed for the MIREX 2009 contest in Multiple Fundamental Frequency Estimation and Tracking, utilizing frame-by-frame analysis and a tracking mechanism. Additionally, the computational model in [47] involves the human auditory periphery followed by a novel periodicity analysis. This method is computationally efficient and straightforward to implement since the peripheral hearing model needs to be computed only once. Moreover, the proposed algorithm in [48] operates at frame-level for multi-F0 estimation, searching for the set of fundamental frequencies that minimize a spectral distance measure in each audio frame. The spectral distance measure is defined under the assumption that a polyphonic sound can be modeled by a weighted sum of Gaussian spectral models. Similarly, the book and tutorials in [49; 50] outline the fundamental concepts, statistical methods, filtering methods, and subspace methods related to pitch estimation, making it a valuable resource for those interested in this field. Multi-pitch estimation is particularly important in scenarios where multiple sound sources, such as different musical instruments or vocalists, are present together in the same recording [51]. Multi-pitch estimation has applications in various fields, including music transcription, source separation, music analysis, and audio effects processing. In music transcription, it can be used to extract the individual notes played by different instruments in a polyphonic musical piece. In speech processing, it can help separate and analyze multiple speakers in an audio recording.
# A 12-Bit 1-GS/s Pipelined ADC with a Novel Timing Strategy in 40-nm CMOS Process
## 3 Circuit Implementation and Calibration
### Calibration of Mismatches and Errors
#### 3.5.2 Automatic Calibration of Residual Curves  
Automatic calibration of residual curves is proposed in Figure 11. The residual curve of each MDAC is calibrated by the method of plotting the residual curve. As is shown in Figure 12, the red line in the figure is the ideal residual curve, which is uniformly distributed near the threshold values V1-V6. The range of Vres is the difference between the maximum VH and minimum VL. The blue line is a non-ideal residual curve. The distribution of the  
Figure 10: Monte Carlo simulation of the comparator, showing the offset voltage of comparator.  
residual curve obviously exceeds the range of VH and VL due to the imbalance of the threshold values \(\Delta\)V1, \(\Delta\)V2, \(\Delta\)V3, \(\Delta\)V4, \(\Delta\)V5, and \(\Delta\)V6 series of deviations in V6. As shown in Figure 7, the comparator offset of the ADC can be corrected by adjusting the bias current of the preamplifier. In order to avoid the complexity of calibration and improve efficiency, this algorithm mainly conducts an automatic calibration on the output curve of each stage of the pipeline on chip from the perspective of automatic calibration.  
The offset voltage of the comparator is mainly caused by the mismatch of differential pairs. As shown in Figure 12, the flow chart of automatic calibration is presented. After each stage of the pipeline outputs the residual curve, it performs the following automatic calibration steps.  
First, it draws the residual curve VresN and the ideal curve VresN0. The maximum and minimum values of the residual curve are VL and VH, respectively.  
The automatic calibration calculation process involves judging whether the range of the residual curve exceeds the maximum and minimum values of the ideal curve, getting the maximum and minimum values of the residual curve near each threshold V\({}_{\rm{V}}\)-V\({}_{\rm{6}}\). If V\({}_{\rm{1}^{\prime}}\) > V1, the maximum value VH + \(\Delta\)V1 is saved, which exceeds (VL, VH). If V\({}_{\rm{2}^{\prime}}\) < V2, the minimum value VH-\(\Delta\)V1 that exceeds (VL, VH) is saved. When the maximum and minimum values of the residual curve exceed the range (VL and VH), the control code OS_N or OS_P adjusts the bias current of the preamplifier to correct the comparator threshold offset.  
During the working time of the ADC, control code OS_N or OS_P is continuously sent to the register on chip via the SPI interface until the minimum value of V is found, at which point the automatic calibration is considered complete.  
Outputs of Vres after automatic calibration, as shown in Figure 13. Before calibrating the Vres of MDAC1, the first and sixth thresholds deviate from the ideal thresholds severely. After calibration, the Vres of MDAC1 is closer to the ideal Vres. The method of automatic calibration by plotting a residual curve to calibrate the residual curve is effective.  
Figure 11: Automatic calibration of residual curves.  
Figure 12: Algorithm for calibration of residual curves.
# Modified Joint Source-Channel Trellises for Transmitting JPEG Images over Wireless Networks
## 2 The Wireless JPEG Transmission Model  
In this section, a robust model for compressing JPEG images that integrates linear encoding into the JPEG encoding process is proposed. Figure 1 depicts a block diagram of the model's JSCC process. First, an image is sent to a discrete cosine transform (DCT) block. Second, the DCT coefficients are then quantized and rounded to the nearest integers. Third, a trellis is constructed to enable robust transmission of sequences decoded with the modified BCJR algorithm. Fourth, the source and channel encodings are divided into four parts, as illustrated in Figure 1a. Fifth, the original images are encoded in a trellis structure to facilitate expurgation. Sixth, Huffman code is employed to encode the bits that correspond to the DCT coefficients. Seventh, the source and channel encodings are combined and mapped to generate a JPEG image for transmission over a wireless channel mode, i.e., the 802.11 channel mode. Eighth, the receiver is prepared using VLC and random-code decoding by the BCJR algorithm to facilitate the decoding of the encoded sequences, a process illustrated in Figure 1b. Ninth, the BCJR decoding process is integrated with the VLC and linear block JPEG decoding processes. In this step, the JPEG transmission sequence is first decoded with BCJR to recover the JPEG encoding sequence. Next, VLC decoding is used to recover and decode the original image data as bit sequences.
## 3 Results and Discussion
### Optimal Waveband Selection Using ANOVA Analysis Results
#### 3.2.1 Development of Single-Waveband Algorithm (SWA)
For honeydew, orange, and watermelon, the optimal wavebands for the original juice and the 1:5, 1:10, and 1:20 dilution samples were in the 470-500 nm range, and the optimal wavebands for the 1:50 and 1:100 dilution samples were in the 660-680 nm range. For apple, the optimal wavebands for original juice, and the 1:5 and 1:10 dilution samples were in the 480-490 nm range, and the optimal wavebands for the 1:20, 1:50, and 1:100 dilution samples were in the 648-660 nm range. The lower the dilution levels on both surfaces, the lower was the accuracy of the selected waveband. In order to determine whether the accuracy was higher when using a single waveband or multiple wavebands, the optimal wavebands were selected using the ratio algorithm (Tables 3 and 4).
## 4 Application of Fe-MOF in Advanced Oxidation Processes
### Photo-Based AOPs
Thus, this fact could be explained by the synergistic effects from several generated radicals or active species, nonradical singlet oxygen yielded (via directly visible light activation of PDS) and direct and indirect electron transfer activation of PDS over PDINH/MIL-88A(Fe)and MIL-88A(Fe), respectively. This study highlighted that chloroquine phosphate was adsorbed on PDINH/MIL-88A(Fe), reducing the number of active sites and decreasing its degradation efficiency. However, following five re-use cycles, PDINH/MIL-88A(Fe)'s degradation efficiency remained around 94%. Moreover, the physicochemical characterization of the composite confirmed that chloroquine phosphatase adsorbed on PDINH/MIL-88A(Fe) and after five cycles PDINH/MIL-88A(Fe) kept its original structure and morphology, showing good stability and reusability. By using a simple solvothermal method, UiO-66 was doped with iron to obtain a solar photocatalyst Fe-UIO-66 and the activation of persulfate under sunlight irradiation to degrade sulfameter in water [189]. This zirconium-based MOF extended the absorption exhibited into sunlight's spectral range by the presence of Fe\({}^{3+}\) when it was doped with iron. As mentioned above, the activation of PS or PMS allowed the recycling of iron due to the presence of the electron donor provided by PS or PMS, allowing the cycling \(\equiv\)Fe\({}^{3+}\)/\(\equiv\)Fe\({}^{2+}\) (Figure 9) in the reaction medium [190]. Thus, the \(\equiv\)Fe\({}^{3+}\) detected in Fe-UIO-66 was reduced to \(\equiv\)Fe\({}^{2+}\) when PS was added which stimulated the production of sulphate radicals (Equations (15) and (16)). In addition, the presence of iron in the MOF caused a charge transfer from the metal to the cluster and this composite had a large specific surface area that provided more active sites improving the photocatalytic performance. In summary, [PERSON] et al. [189] concluded that the combined reactions produced more free radicals as a result of the photocatalytic and PS processes. These last studies provided promising approaches for the removal of persistent organic pollutants through the synergistic effects of photocatalysis and PS, PMS or PDS activation over different photocatalysts based on Fe-MOF, with a high sustainability and promising potential of MOFs for continuous wastewater treatment.
## 3 Results and Discussion
### Adsorption Isotherms  
The adsorption isotherm is essential to describe how the adsorbate interacts with the adsorbent and to reliably predict the adsorption parameters in order to quantitatively compare the behaviour of adsorbents under various experimental conditions.  
To simulate the adsorption isotherms in this study, three commonly used models--the Freundlich model, the Langmuir model, and the Temkin model--were chosen to explain the interaction between the activated carbon and the two pollutants. The adsorption isotherms of CV and Cr (VI) from aqueous solutions on ACSCGs at 303 K are shown in Figure 13 and the parameters of these models are summarised in Table 6. Table 6 shows that the Langmuir model best describes the experimental data, with a high R\({}^{2}\) and a low \(\chi^{2}\), compared with the values of the Freundlich and Temkin models, implying a monolayer adsorption without interactions and on homogeneous sites, where each molecule has a constant enthalpy and activation energy of adsorption.  
Figure 12: Intraparticle diffusion model plots for different temperatures.  
In addition, the affinity between the carbon and the two adsorbates can be predicted by calculating the Langmuir separation factor (R\({}_{\rm L}\)). Adsorption is more favourable as the value of R\({}_{\rm L}\) is close to 0. Figure 14 shows the R\({}_{\rm L}\) versus C\({}_{0}\) curves in the concentration range studied. The figure shows that the RL values for CV and Cr (VI) are between 0 and 1. It can be concluded that the adsorption process is more favourable at higher concentrations.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & & \multicolumn{4}{c}{**CV**} & \multicolumn{4}{c}{**Cr (VI)**} \\ \cline{3-10}  & & \multicolumn{4}{c}{**T (K)**} & \multicolumn{4}{c}{**T (K)**} \\ \cline{3-10}  & & **298** & **303** & **313** & **323** & **298** & **303** & **313** & **323** \\ \hline \multirow{3}{*}{Step 1} & k\({}_{\rm d1}\) (mg\(\cdot\)min\({}^{2}\)/g) & 33.85 & 34.74 & 36.06 & 41.90 & 27.80 & 27.90 & 27.94 & 28.55 \\  & C\({}_{1}\) (mg/g) & \(-17.20\) & \(-10.01\) & \(-10.03\) & \(-9.04\) & \(-17.57\) & \(-16.99\) & \(-17.35\) & \(-17.68\) \\  & R\({}^{2}\) & 0.972 & 0.972 & 0.975 & 0.977 & 0.980 & 0.988 & 0.989 & 0.989 \\ \hline \multirow{3}{*}{Step 2} & k\({}_{\rm d2}\) (mg\(\cdot\)min\({}^{2}\)/g) & 6.08 & 6.11 & 6.48 & 6.65 & 9.22 & 12.10 & 12.27 & 12.94 \\  & C\({}_{2}\) (mg/g) & 53.38 & 54.62 & 52.27 & 50.22 & 24.15 & 16.18 & 15.02 & 15.35 \\  & R\({}^{2}\) & 0.971 & 0.986 & 0.987 & 0.990 & 0.920 & 0.980 & 0.984 & 0.982 \\ \hline \multirow{3}{*}{Step 3} & k\({}_{\rm d3}\) (mg\(\cdot\)min\({}^{2}\)/g) & 0.008 & 0.092 & 0.151 & 0.319 & 0.459 & 0.567 & 0.744 & 0.751 \\  & C\({}_{3}\) (mg/g) & 96.32 & 94.38 & 91.99 & 88.75 & 56.03 & 53.12 & 53.95 & 53.65 \\ \cline{1-1}  & R\({}^{2}\) & 0.986 & 0.984 & 0.962 & 0.838 & 0.894 & 0.946 & 0.943 & 0.954 \\ \hline \end{tabular}
\end{table}
Table 5: Constants of the intraparticle diffusion model.  
Figure 13: Adsorption isotherm curves for CV and Cr (VI) at 30 \({}^{\circ}\)C.
## 4 Experiments
### Implementation Details  
In our proposed model, the deep feature extraction network consists of six Dense Residual Fused Groups. Each DRFG is composed of five MLLA modules, one GAB, and residual connections. The number of heads is set to six, the input channel number C is configured to sixty, the window size used in the GAB is sixteen, and the interaction interval is set to four. All hyperparameters in our framework were selected through a grid search on the DF2K training set. The selection aims to balance performance and efficiency under lightweight constraints. While these settings work well on standard benchmarks, minor adjustments may be necessary when transferring the model to datasets with significantly different image characteristics.
# Exploiting Blockchain Technology for Enhancing Digital Twins' Security and Transparency
## 3 Proposed Approach
### Implementation
#### 3.1.2 Virtualization  
In alignment with the requirements outlined in the preceding paragraph, the primary objective of our proposed architecture was to establish a secure and isolated security system while enabling the reproducibility of the proposed solution. To fulfill these requirements for the designed backbone blockchain, we decided to introduce an additional layer of abstraction by containerizing each component within the blockchain. This decision was executed through the employment of the Docker engine [36], facilitating the creation of blockchain nodes and the integrated load balancer. The additional system layer ensured a controlled and secure interaction between components while maintaining them in an isolated virtual environment.  
Using the Docker-provided images for Hyperledger Besu nodes, our approach involved configuring each node to align with the validation requirements of the network. The abstracted configuration was then encapsulated in Dockerfile format, creating a modular and flexible solution suitable for various network tasks. Following the virtual environment configuration of the security subsystem, an entry point for network tasks was created through port binding on the load balancer's host machine. The configured solution was then deployed as a component in the security solution ready to serve any request coming from the involved actors of the system.
## 3 Results and Discussion
### Characterization of the Inputs and Output Products of the Phosphate Laundries
#### 3.1.4 Water Quality  
The water samples collected from drilling water (DW), recycled water (RW), and sludge water (SW) and recovered from the laundries after natural decantation and filtration, were submitted to chemical analysis in order to determine the anion (F\({}^{-}\), NO\({}_{2}\)\({}^{-}\), NO\({}_{3}\)\({}^{-}\), PO\({}_{4}\)\({}^{3-}\), and SO\({}_{4}\)\({}^{2-}\)) and trace element (Cd, Fe, Pb, Zn, and U) contents. Table 4 presents the results.  
The pH values of the discharge water, which were measured in situ, at the exit of the laundries were 7.87 at M'Dhilla-ZL and 7.07 at Redeyer, reflecting the slight alkaline nature of the wastewater. The feedwater used in the phosphate washing units comes from the various aquifers in the region. At M'Dhilla, the wash (drilling) water shows high loads, exceeding the World Health Organization (WHO) standards, in sulfates (1979 mg/L), fluorine (23.75 mg/L), and nitrites (15.3 mg/L), while the sludge water is heavily loaded with sulfates (2570 mg/L), fluorine (47 mg/L), and cadmium (14.14 mg/L). At Redeyer, the wash water is rich in fluorine (1.9 mg/L), while the sludge water is contaminated with Cd (6.12 mg/L).  
Figure 3: The rare-earth element (REE) patterns normalized to the upper continental crust (UCC) of raw phosphate (RP), marketable phosphate (MP), fine waste (FW), and coarse waste (CW) from M’Dhilla-Zone L (ZL) and Redeyer (R).
## 4 Results and Analysis  
The Signal app uses encryption algorithms to secure data for communication. Due to the encrypted payload, only packet sizes, frequency of the packets, and repetitive patterns of packets can be exploited for analysis. Therefore, certain payload sizes may help to determine the specific activity types. Although the privacy and secrecy are not compromised, extensive inspection of packet sizes (patterns, frequencies) reveals the prominent activities of an app. In this study, an extensive analysis of traffic dumps was performed, which revealed bytes and payload patterns of different activities. Although patterns emerged, as expected they did not help in the reconstruction of actual data. However, through the experimental setup explained in the earlier section, regulatory authorities and law enforcement agencies can determine the behaviors of the Signal app by observing consistent connectivity patterns. The complete summary can be seen at the end of the Results and Analysis section.  
For better understanding, we created the following users with descriptions to conduct the app activities:  
User A: target user, who's entire set of activities are monitored;  
User B: to whom User A communicates.
# Development of a Novel Open Control System Implementation Method under Industrial IoT
## 3 Implementation of Open Control Systems Under Industrial IoT
### Container- and WebAssembly-Based Control Task Virtualization
To be specific, control tasks with high time sensitivity are virtualized by wasm and deployed on edge platforms near actuators (i.e., actuator-level edge platforms). Here, as Figure 8 presents, a real-time operating system is often needed to guarantee high time sensitivity. Parts of the control tasks that do not rely on real-time APIs or I/O APIs (such as mostly compute-intensive tasks) are compiled as wasm modules, while other parts are developed as the host application. The dependence between wasm modules and the host application is implemented via shared memory. In contrast, control tasks that do not have high time sensitivity are virtualized by containers and deployed on cloud platforms or high-level edge platforms (e.g., factory-level edge platforms). The dependence between these containerized control tasks and between containerized control tasks and wasm control tasks is implemented by remote communication over the network, localhost communication, shared files, or others, depending on the dependence's intimacy. Figure 7: WebAssembly-based virtualization.