esa-sceva/satcom-chunk-collection huggingface dataset:
# Minimizing the In-Cloud Bandwidth for On-Demand Reactive and Proactive Streaming Applications
## 4 Optimizing Cloud-Based Streaming Internal Bandwidth: Case of Popular Video  
In this section, we study the case of a high SN request rate where it is recommended to use a proactive streaming protocol. Unlike the reactive protocols (such as SASST presented in the previous section), the storage node divides the video into equally-sized segments and broadcasts the them repeatedly on a set of multicast channels regardless of the SN requests using a schedule that allows the SN to obtain all video segments. We aim to determine the optimal segments-to-channels scheduling in terms of consumed bandwidth. This scheduler will be used in the context of NOPP.  
In the rest of the current section, we analyze how SASST could be approximated by the proactive protocol staggered broadcasting. Although it is out of the scope of this paper, this approximation helps in implementing a smooth transition from using reactive protocol a proactive one when the video becomes popular at runtime. Then, we detail the formulation and solving process of the bandwidth optimization problem which leads to NOPP optimal scheduling of segments to broadcasting channels. Two version are considered: (1) minimizing the bandwidth when the streaming period of a given video is known in advance (known time horizon) and (2) when it is not (periodic).
# P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images
## 2 Materials and Methods
### Used Databases
#### 2.1.1 Self-Annotated Database (MIT-BIH AF)  
The MIT-BIH AF database [29; 30] recordings were made at Boston's Beth Israel Hospital (now the Beth Israel Deaconess Medical Center) using ambulatory ECG recorders with a typical recording bandwidth of approximately 0.1 to 40 Hz. This database contains 605 episodes for different rhythms of atrial fibrillation (AFIB), atrial flutter (AFL), junctional rhythms (AV), and other rhythms. This database provides an opportunity to test the robustness of P-wave detection in many different cases: the absence of P-waves in AFIB (f-waves), sawtooth P-waves in AFL, inverted P-waves in AV, and normal P-waves in other rhythms.  
Annotation files for R-peaks in this standard database are available for the evaluation of the QRS detection algorithms, but, this is not the case for P-waves. Thus, in this paper, a cardiologist manually annotated the P-waves in the MIT-BIH AF database [29; 30]. Manual annotation is performed for the first 5 min of each record from Lead II, as sinus P-waves are usually most prominently seen in Lead II [3]. Figures 1 and 2 provide samples of the manual annotations obtained, where P-waves have sometimes been detected for normal and abnormal beats, and other times are absent for atrial fibrillation. The annotation file of P-waves can be found as Supplementary Material.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Database** & **\# Annotated Beats** & **\# Records** & **Record Duration** \\ \hline
**MIT-BIH AF**[29; 30] & 8280 & 23 & 5 min \\
**QT Manual Annotation**[26] & 3194 & 103 & 15 min \\
**QT Automatic Annotation**[26] & 11,201 & 105 & 15 min \\ \hline \hline \end{tabular}
\end{table}
Table 1: Databases used for evaluation and validation.  
Figure 1: Samples of manually annotated P-waves in the MIT-BIH AF database for normal beats. (**a**) 05091 record; (**b**) 08405 record; (**c**) 08455 record; and (**d**) 08215 record, where the light blue areas represent P-wave duration.  
Figure 2: _Cont._
# Risk-Aware Wireless Positioning in
## 4 Our Proposed Risk-Aware Wireless Positioning Scheme  
In this section, we find the optimal positioning signal frequency at each cell by solving the optimization problem (P1'). We start from the following Lemma.  
Lemma: The optimization problem (P1') is a convex problem. \({}_{\blacksquare}\)  
Proof: We recall the objective function of the problem (P1) as follows:  
\[\sum_{i=1}^{N}\left(\frac{p_{i}A_{i}}{f_{i}^{\alpha}}\right) \tag{13}\]  
Then, \((p_{i}A_{i})/f_{i}^{\alpha}\) is the convex function of \(f_{i}\) because all of \(p_{i}\), \(A_{i}\) and \(\alpha\) are positive constants and the term \(1/f_{i}^{\alpha}\) is the convex function of \(f_{i}\) whose second derivative is positive for \(\alpha>0\), that is,  
\[\frac{\alpha(\alpha+1)}{f_{i}^{\alpha+2}}>0. \tag{14}\]  
Since the sum of convex functions is also a convex function [29], (13) is a convex function of \((f_{1},\dots,f_{N})\). Also, the constraint function is convex, so the problem (P1') is a convex problem. \({}_{\blacksquare}\)  
To solve the convex problem, we establish the Lagrangian denoted by \(\mathcal{L}\) as follows:  
\[\mathcal{L}=\sum_{i=1}^{N}\left(\frac{p_{i}A_{i}}{f_{i}^{\alpha}}\right)+ \lambda\left(\sum_{i=1}^{N}p_{i}f_{i}-B\right), \tag{15}\]  
where \(\lambda\) is the Lagrangian multiplier.  
Then, we already know that from the Karush-Kuhn-Tucker (KKT) conditions, the optimal solution \((f_{1}^{\star},\dots,f_{N}^{\star})\) satisfies that  
\[\frac{\partial\mathcal{L}(f_{i}^{\star})}{\partial f_{i}}=0\quad\text{for all}\quad i\in\{1,\dots N\}. \tag{16}\]Thus, from the equation  
\[\frac{\partial\mathcal{L}(f_{i}^{\star})}{\partial f_{i}}=\alpha\frac{p_{i}A_{i}} {(f_{i}^{\star})^{\alpha+1}}+\lambda p_{i}=0, \tag{17}\]  
we obtain  
\[f_{i}^{\star}=\left(\frac{\alpha A_{i}}{\lambda}\right)^{\frac{1}{\alpha+1}}. \tag{18}\]  
Also, from the constraint function of the problem (P1'), it should be satisfied that  
\[\sum_{i=1}^{N}p_{i}f_{i}^{\star}=\sum_{i=1}^{N}\bigg{[}p_{i}\left(\frac{\alpha A _{i}}{\lambda}\right)^{\frac{1}{\alpha+1}}\bigg{]}=B, \tag{19}\]  
so we obtain  
\[\lambda^{\frac{1}{\alpha+1}}=\frac{1}{B}\sum_{i=1}^{N}\bigg{[}p_{i}\left( \alpha A_{i}\right)^{\frac{1}{\alpha+1}}\bigg{]}. \tag{20}\]  
Finally, plugging (20) into (18), we obtain  
\[f_{i}^{\star}=\frac{B\cdot\left(\alpha A_{i}\right)^{\frac{1}{\alpha+1}}}{\sum _{n=1}^{N}\bigg{[}p_{n}\left(\alpha A_{n}\right)^{\frac{1}{\alpha+1}}\bigg{]}}. \tag{21}\]
## 2 Materials and Methods
### Data Collection and Presentation  
After each range of motion trial, PFJ load measurements were obtained and the raw data was collected in the form of CSV files which were converted to Microsoft Excel Workbook files (.xlsx) for data processing (Microsoft Excel version 2207, Microsoft, Redmond, WA). Load data was collected over a 12 s time period, during which three trials of knee flexion extension cycling were performed. Load data was recorded initially by the sensor in units of millivolts (mV), which were calibrated or converted into Newtons (N) utilizing a sensor-specific conversion factor of 20:1 (or division by 20). The initial graphs reviewed on the panel PC are shown using mV. Within the 12 s time period the trial is performed, 8785 time units of data were collected, such that there were 8785 corresponding points where load data was measured. Therefore, 1 s of real time contained 732 time units of data. A line graph was created from the raw data for each sensor reading, such that four line plots were visualized representing load plotted against time, corresponding to the four respective pressure sensors. From a qualitative perspective, these plotted graphs could be compared visually in real time by the surgeon to interpret changes in PFJ load measurements for different shim-angle combinations which were obtained.
## 1 Introduction
Therefore, to render a more hydrophobic property with suitable surface charge and to overcome the clogginess, an abundant natural biodegradable and biocompatible chitosan was applied to the Mt-rich clay to form an MCC adsorbent. To simulate the water used in this research work, information of the process water contaminated with dye was supplied from a Thai joss paper factory. The dye that was used in the production of joss papers was reactive red 120 (RR120). The net dye concentration in the water was around \(80\pm 20\) mg L\({}^{-1}\) at the pH \(5\pm 0.5\). Then, the simulated process water was carried out to investigate the decolorization performance of the MCC via batch adsorption. From the preliminary test, a higher performance of dye adsorption was achieved until the weight ratio of Mt-rich clay to chitosan was up to 0.5:1. Further adding more chitosan to the MCC, the performance of the MCC did not change significantly. Therefore, a 0.5:1 (_w/w_) ratio MCC was fabricated and used throughout the experiments. Firstly, the performance of chitosan, Mt-rich clay and the MCC were compared based on their respective performance for dye removal. Then, the adsorbent with the highest performance was selected to investigate by univariate analysis in order to achieve an optimum condition for the maximum removal of dye. However, to be economically and environmentally viable, the adsorbent should be reusable without a decreased performance, and so the reusability of the adsorbent was determined. To understand the insight of adsorption, the isotherms, thermodynamic and kinetics were studied as well. Means of Fourier transform-infrared spectrometry (FT-IR), nitrogen (N\({}_{2}\)) adsorption/desorption,X-ray powder diffraction (XRD), and dynamic light scattering (DLS) on a zetasizer and water contact angle analyses were carried out to characterize the absorbent.
## 3 Results
### Reusability, Reproducibility, and Carryover Effect of the SBA-15-SPME Arrow  
The SEM images reveal a highly porous and uniform SBA-15 coating (Figure 7), which enhances surface interaction with analytes. This morphology contributes to increased adsorption efficiency, especially in complex matrices like food samples. SEM images (Figure 7a,b) illustrate that the SBA-15-SPME Arrow maintained structural integrity even after 50 extraction and desorption cycles. This robustness is critical for applications where repeat use is necessary, such as in large-scale food safety monitoring programs. The SPME Arrow maintained a uniform SBA-15 coating on the metal rods, with minimal changes in appearance or internal structure, even after extended use. Figure 7c,e further confirmed these observations, showing no discernible changes in the coating after 50 adsorption/desorption cycles. Figure 7f presents the trend in extraction efficiency for clenbuterol and amantadine over 50 cycles, showing a slight decline in efficiency by the 50 th cycle. However, the SPME Arrow remained effective in extracting the target analytes, demonstrating commendable reusability.  
Further reproducibility tests, including intra- and inter-batch reproducibility, were conducted. The relative standard deviation (RSD) within the same batch remained below 7.0%, indicating consistent performance. Similarly, the RSD across different batches was below 9.3%, demonstrating robust reproducibility across batches. Quantification of residual rates was performed by comparing the mass of target compounds in three desorptions against the initial 30 min desorption (Figure S8). The results suggest that 200 uL of acetonitrile for 30 min was sufficient to desorb most analytes from the SBA-15-SPME Arrow.  
The reusability, reproducibility, and carryover test of the SBA-15-SPME Arrow were initially evaluated using standard mixtures to establish a controlled baseline for its mechanical robustness and coating stability. Subsequent quantitative analysis with real samples employed matrix-matched calibration to minimize potential matrix effects and improve quantification accuracy. This ensures that, even in complex matrices, the analytical performance of the SPME Arrow remains reliable. A more comprehensive evaluation of complex food matrices will be explored in future work.  
Figure 6: (**a**) Comparison of extraction efficiency between SBA-15-SPME Arrow and commercial SPME Arrow under optimal conditions for detecting veterinary drugs: (1) SBA-15-SPME Arrow, (2) DVB/Carbon WR/PDMS SPME Arrow, (3) polyacrylate SPME Arrow, (4) DVB/PDMS SPME Arrow, (5) Carbon WR/PDMS SPME Arrow, and (6) PDMS SPME Arrow; (**b**) comparison of SBA-15 and DVB/PDMS SPME Arrow in milk.
## 4 Hardware Design
### Data Collector  
Each node, also called a data collector or collector, has a microprocessor, serial interface flash memory, RF transceiver, linear amplifier, low-noise amplifier, RS-485 transceiver, and IrDA module. The basic hardware structure schematic and a photo of the data collector are shown in Figure 2.  
_Microprocessor_: Usually, one of the three microcontrollers, _i.e._, ATmega128L, MSP430, and Silicon Labs C8051, is used in nodes of the sensor network. After intensive investigations, we chose the ATmega128L microcontroller for our EMMNet platform, because it is a low-power microcontroller based on the AVR-enhanced Reduced Instruction-Set Computer (RISC) architecture. The ATmega128L provides the following features: 128 K bytes of in-system programmable flash with read-while-write capabilities, 4 K bytes Electrically Erasable Programmable Read-Only Memory (EEPROM), 4 K bytes static RAM (SRAM), and 53 general purpose IOs. Making rational use of the microprocessor resources, we leave ample room for expansion. At present, we have used 62.00% of the port, 34.87% of the code memory, 72.75% of the data memory and 88.23% of the EEPROM. Ports assignment is listed in Table 2.  
_RF Transceiver_: We use the Nordic nRF905 transceiver because its carrier frequencies are suitable for automatic electricity meter monitoring and there is no associated proprietary protocol. Its current consumption is very low: in transmit mode, only 9 mA at an output power of \(-10\) dBm, and in receive mode, 12.5 mA. However, EMMNet faces various challenges. Therefore, we add a broadband, fixed-gain, linear amplifier and a low-noise amplifier to the RF circuit.  
\begin{table}
\begin{tabular}{|c|c|} \hline
**Port** & **Function** \\ \hline \hline PB0-PB6 & Connected to RF module \\ \hline PD0-PD3 & Connected to IrDA \\ \hline PD4-PD7 & LED \\ \hline PE0-PE2 & Connected to RS485 transceiver \\ \hline PE3-PE7 & Connected to RF module \\ \hline PF0-PF3 & Connected to external flash \\ \hline PF4-PF7 & JTAG test interface \\ \hline \end{tabular}
\end{table}
Table 2: Ports assignment of ATmega128L.  
Figure 2: (a) Hardware architecture of a data collector. (b) Spot photo of data collector.  
_Memory_: A memory bank with relatively large capacity is believed to be beneficial for data storage and buffering in EMMNet since there can be up to 100 electricity meters connected to a single data collector. Therefore, we chose the AT45 DB041D, a 4-Mbit serial-interface flash memory. In addition to the main memory, the AT45 DB041D also contains two SRAM buffers of 264 bytes each. In our design, we take full advantage of this external SRAM, greatly easing the problem of small SRAM in the ATmega128L.
## 3 Test Results
### Dislocations between Rings  
The results of the dislocations between rings close to the openings under 50 g acceleration of the three buried depths are summarized in Table 3. Noted that the listed results have been converted into the corresponding prototype dislocations through the similarity relationship. The dislocations versus the buried depth are plotted in Figure 12.  
It can be found that the dislocations between rings of the three openings increased approximately linearly with the increase of buried depth. The dislocation close to the big circular opening was the greatest, followed by the small circular opening and rectangular opening. When the buried depth reached 2.0 D, the dislocations close to the three openings all exceeded 5 mm. In practical engineering, excessive locations between segments may cause waterproof failure, and may even lead to the inrush of soil and water, thus inducing  
Figure 12: Variations of the dislocations versus buried depth.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Buried Depth**} & \multicolumn{3}{c}{**Dislocations between Rings (mm)**} \\  & **Rectangular** & **Small Circular** & **Big Circular** \\  & **Opening** & **Opening** & **Opening** \\ \hline
1.0 D & 3.2 & 4.0 & 5.2 \\
1.5 D & 4.3 & 5.2 & 6.7 \\
2.0 D & 5.4 & 6.5 & 8.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dislocations between adjacent rings.  
structural instability. Therefore, attention should be paid to the dislocations during the construction of the cross passages, and reinforcement measures should be taken if necessary.
# Microstructural Changes Influencing the Magnetoresistive Behavior of Bulk Nanocrystalline Materials
## 4 Discussion
### What Is the Optimum Composition and Thermal Treatment after RT HPT?  
Regarding the optimum composition for maximum GMR, the results of this study shall be discussed in combination with results taken from [11]. Therein, the RT GMR of both, Cu\({}_{81}\)Co\({}_{19}\) and Cu\({}_{64}\)Co\({}_{36}\) was approximately 1.5% in the as-deformed state, while the one of as-deformed Cu\({}_{70}\)Co\({}_{30}\) is slightly above. Thus, a compositional region in between \(\sim\)20 at% and \(\sim\)35 at% Co has to be searched for optimum GMR using smaller increments. As a reminder, these GMR values are valid for fields of 22.5 kOe--the maximal attainable magnetic field with the used equipment. The GMR of Cu\({}_{55}\)Co\({}_{45}\) was found to be higher (\(\sim\)3%) at this magnetic field. However, considering literature on optimum ferromagnetic composition [13], the higher GMR-value is simply due to faster saturation.  
For Cu-Fe, while the GMR-effect of Cu\({}_{85}\)Fe\({}_{15}\) is rather small (0.2% [11]), it increases when adding Fe, see Figure 9, and an optimum composition has to be searched for above 15 at% Fe and below the limit of RT HPT-processability. Changing the HPT process parameters, e.g., the deformation temperature opens a wider field of achievable, homogeneously deformed compositions [36]. For comparison, [PERSON] and [PERSON] [13] found maximum GMR in the magnetron sputtered state for Cu\({}_{80}\)Fe\({}_{20}\).  
Regarding the aspect of the absolute value of maximum achievable GMR in higher magnetic fields, it is of importance that RT HPT-processed Cu-Co-materials (\(<\)40 wt%) and Cu-Fe-materials do not show a saturation of magnetization in fields of 7 T [21] and so it will not be the case for GMR. [PERSON] et al. [37] measured \(\sim\)7% GMR for as-melt spun ribbons of Cu\({}_{90}\)Co\({}_{10}\) at approximately 20 kOe, while measuring more than 10% at fields above 60 kOe; the value still was not saturating. Thus, it can be expected for HPT-deformed materials--when considering hysteresis loops from literature [21]--that much higher GMR values are to be expected, when the resistance measurements is performed in higher fields.  
In the aspect of optimum annealing temperature, 300 \({}^{\circ}\)C were found to be best for medium Co-content. For small Co-content, phase and particle evolution could be slightly different but the optimum temperature is expected not to be far off 300 \({}^{\circ}\)C. For the Cu-Fe system, temperatures below 300 \({}^{\circ}\)C or a shorter duration of annealing treatment should be considered and will be investigated in the future.
# Lessons for Coastal Applications of IMTA as a Way towards Sustainable Development: A Review
## 5 Economic Value and Financial Viability of Coastal IMTA
### Species-Specific IMTA
Related research on the economic viability of IMTA models that incorporate shellfish and/or algae into traditional methods of salmon monoculture also finds potential for increased farm revenues and positive social and environmental outcomes [69; 110]. Salmon is at the core of the most widely used and studied IMTA systems, but the viability of other key IMTA species has also been assessed. An analysis of the financial returns generated by an integrated shrimp-oyster IMTA system that also included a seahorse productioncomponent finds that the benefit per dollar spent was approximately USD 20, with an internal rate of return of 131 percent [111]. Such results appear promising. Farm-level financial returns within a specific regulatory context, rather than overall economic benefits from IMTA, are likely to be the central driver for the entrepreneurial decision of whether to implement IMTA. Most economic research therefore examines the viability of IMTA at the level of the farm. One study constructs a mathematical model on spreadsheet program (Quattro Pro, Borland, Inc.) of an integrated salmon-seaweed system and assesses the financial profitability of growing seaweed near salmon culture operations. The researchers examine two seaweed species, _Saccharina latissima_ and _Nereocystis lutekema_, cultivated in different parts of the farm, either between rows of salmon cages or 30 m from the back of the farm. Both species are reported as financially profitable when co-cultivated with salmon at most locations [112]. IMTA has been found able to boost individual income in favourable market conditions and to provide economic resilience in challenging times [32; 37]. Research to date has focused on markets across North America and Canada, with European markets receiving attention more recently. Consumers looking for IMTA products in Eastern Canada took food safety into consideration and viewed IMTA items as safe; around half of those surveyed in one study indicated their readiness to pay an additional 10% for IMTA-labeled products [24]. It was also observed that mussels produced in IMTA systems may benefit from a price premium, which people in the New York market are willing to pay [113].
# Remote Sensing Scene Classification and Explanation Using RSSCNet and LIME
## 3 Method
### Cyclical Learning Rate  
The learning-rate method proposed by [PERSON] [9] sets cyclical learning rates for the model instead of a fixed value and uses this to train the model. Results show that this can improve the accuracy of the classification and reduce the need for trivial adjustments. During training, the number of iterations used is usually reduced, and there are three different cyclical ways in which the learning-rate loop method can learn: \"triangular\", \"triangular2\", and \"exp_range\". In our research, a two-stage cyclical learning-rate method was used to train the model. In the first stage, the \"triangular\" method was used to quickly find the best solution in the model; using this method, it was possible to avoid falling into a local solution when the learning rate was large. At the second stage, using the traingular2 method, the learning-rate cycle was gradually reduced to confine the model results until, finally, the solution stayed at a fixed position with no large swings (see Figure 6).  
Figure 6: Cyclical learning rate during training.  
The proposed two-stage cyclical learning rate method is calculated as shown in Equation (2).  
\[\left\{\begin{array}{c}z=\left|1+\frac{i}{\Delta}-2*\left[1+\frac{i}{2\Delta} \right]\right|\\ D=\left\{\begin{array}{c}1,\ for\ stage\ 1\\ \left(\frac{lr_{min}}{lr_{max}}\right)^{(\frac{\left[\frac{j}{2\Delta}\right] }{2\Delta})},\ for\ stage\ 2\\ lr=lr_{min}+(D*lr_{max}-lr_{min})*max(0,(1-z))\end{array}\right\},\end{array}\right. \tag{2}\]  
where \(z\) is a dummy variable, \(i_{max}\) is the total number of training epochs \(i\), \(\Delta\) is the step size that is equal to the half cycle length, \(D\) is the damping factor, \(lr\) is the cyclical learning rate, \(lr_{min}\) is the minimum learning rate, and \(lr_{max}\) is the maximum learning rate.