esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods  
### Hardware System  
In a photovoltaic energy storage system, the low voltage of the photovoltaic PV input board is boosted to a bus voltage of 400 V via an interleaved parallel boost circuit, and a large-capacity electrolytic capacitor is connected in parallel to the busbar for power buffering and decoupling [7; 8; 9]. The 400 V bus voltage then charges the lithium battery through a full-bridge soft-switch circuit for photovoltaic energy storage. External single-phase photovoltaic inverters can be connected to the busbar for post-stage inverter grid connection, or to supply power to the AC load. Alternatively, other DC/DC converters can be connected to supply power to the DC load. When the photovoltaic output is unstable because of weather conditions or during nighttime, the battery starts boosting/discharging the busbar through a push-pull circuit, in order to maintain the bus voltage and to release the stored energy to the post-stage load or the grid. Figure 1 shows a schematic of the charging and discharging system of the lithium titanate battery.  
The control unit employs Texas Instruments (TI)'s TMS320F2809 controller [11]. The sampling and regulating circuit of the controller sends an analog signal to the analog-to-digital converter (ADC) module of the digital signal processor (DSP), which then completes the feedback signal processing and outputs the drive signal adjusted by the control algorithm, thereby achieving a digital closed-loop control of the entire system. The controller has a maximum operating frequency of 100 MHz, and various communication interfaces for data transmission.
## 3 Results and Discussion
### Effect of Pyrolysis Temperature on Biochar Yield and Physicochemical Characteristics
#### 3.1.5 Identification of Functional Groups of Biochar Samples  
The FTIR spectra analysis (Figure 2, Table 3) showed diverse functional groups on the surface of the _M. pyrifera_ biochar samples, and these may be involved in Cu\({}^{2+}\)-biochar adsorption. However, considering the large amount of ash in the samples, this finding must be considered with caution, as some of the functional groups could be shared and/or masked by inorganic compounds. Some characteristic peaks of CaCO\({}_{3}\) coincide with those obtained when analyzing other BCs from seaweeds and other raw materials with a high ash content [35].  
Figure 1: Micrographs obtained by scanning electron microscopy (SEM) of different biochar samples from _Macrocystis pyrifera_ with a total magnification of 30,000\(\times\).  
Figure 2: Fourier transform infrared (FTIR) spectrum of dry biomass of biochar samples from _Macrocystis pyrifera_ (_Mpyr_) generated at 300 (BC300), 450 (BC450), and 600 \({}^{\circ}\)C (BC600) of the final pyrolysis temperature.  
The spectrum of the biochar samples presented, in general, three distinctive peaks at \(\sim\)1440, \(\sim\)870, and \(\sim\)700 cm\({}^{-1}\), similar to carbonate compounds (e.g., calcium, magnesium, and potassium carbonates, amongst others) [39; 40] (Figure 2, Table 3). Several studies indicated that with an increase in temperature, some surface functional groups decreased and, therefore, were not detected in the FTIR spectrum, e.g., [41]. This was observed for the bands at 2920, 1558, and 3400 cm\({}^{-1}\), which disappeared with increasing pyrolysis temperature (Figure 2), indicating the breakdown of bonds, such as the -CH\({}_{2}\) of fatty acid hydrocarbon chains, the C=C bond of secondary aromatic amines, and -OH groups, respectively [16; 31; 41], which can be attributed to the depolymerization and dehydration process [42]. An increase in C=C aromatic stretching band intensity was expected due to the aromatization process as a consequence of the increase in pyrolysis temperature [42], which was observed in the range of 1414-1444 cm\({}^{-1}\); however, this band could also be overlapped by carbonate compounds due to the high ash content in the biochar samples.  
In the thermal conversion process, more oxygenated carbon groups (e.g., -COOH and -CHO, amongst others) were retained, and when the temperature increased, the compounds with carboxyl groups were reduced and the carboxylic groups were deprotonated, generating a more alkaline solution [11]. Therefore, at higher temperatures, a material with a surface composed mainly of oxygen and carbon could be expected, in addition to minerals or inorganic compounds [27; 35].
## 3 Illustration of the Methodology
### Obtaining the SPD Model  
The initial step of the methodology requires identifying the major electricity customer categories based on their end-use applications. Residential, industrial, commercial, and farm customers were identified as the major categories in the Province of Saskatchewan. Historical hourly variations of the load data for each identified customer sector are shown in Figure 4 per unit of the respective peak values. The annual load profile for each sector was then divided into periods having similar diurnal load profiles. Figure 4 shows that the residential and farm sectors were divided into four seasonal periods since the diurnal load profiles for these customer sectors had reasonable similarity as assessed from (2). The diurnal load profiles of the industrial and commercial sectors were found to be similar throughout the year.  
The next step is to observe the diurnal load variation in each period and further subdivide it into peak and off-peak periods. Figure 5 shows the subdivision using a cut-off factor of 0.9 per unit of the peak load in that period. The figure shows that the commercial sector is divided into two periods that are represented by the peak and average loads in the respective periods calculated using (5) and (8), respectively, and the probabilities of encountering the two periods are calculated using (6) and (9), respectively. Figure 5 also shows that diurnal variation in the industrial load is insignificant, and, therefore, was not subdivided.  
Figure 6 shows the diurnal load profiles for the residential and farm sectors in the four seasonal periods. The seasonal periods for the residential and farm sectors are similarly further subdivided into peak and off-peak periods as shown in the figure. The representative load and its probability are similarly calculated using (5) and (6), respectively.  
The off-load sub-periods in the residential sector are grouped together into one period, and the representative load and its probability are calculated using (8) and (9), respectively. The load and corresponding probabilities for the different periods of the farm sector are obtained similarly. The SPD model thus obtained is shown in Table 1.  
Figure 4: Annual load profiles for the identified customer sectors.  
Figure 5: Diurnal load variation of commercial and industrial sectors.  
Figure 6: Periodic load models for the residential and farm sectors.
## 2 Materials and Methods
### Proposed Model: DNNs and l2,1-Norm Regularization Framework for Robust Features Selection  
A regularization framework is proposed for selecting features, by combining the DNN and \(l2\),1-norm with a new cost function. For a given data, \([X=x_{1},\ x_{2,\...\ },x_{m}]\in\mathrm{R}^{\mathrm{fkm}}\), let \([Y=y_{1},\ y_{2,\...\ },y_{m}]\in\mathrm{R}^{\mathrm{mxc}}\), where \(m\) is the number of training samples, \(f\) is the number of feature dimension, and \(c\) is the number of classes. The goal is to learn a projection matrix \(W\in\mathrm{R}^{\mathrm{fxc}}\). \(W\) is used to select the most robust features from the common space, which is defined by the class labels. The new cost function uses the mean squared error (MSE), sigmoid as an output layer function, and \(l2\),1-norm as a regularizer. The minimization cost function is given, as in Equation (2).  
\[E(y,x)=\left\{\frac{1}{2m}\Big{(}\mathit{sigm}\Big{(}X^{T}W\Big{)}-Y\Big{)}^{ 2}+\lambda\left|\left|W\right|\right|_{2,1}\right\} \tag{2}\]  
where \(\lambda\) is a controlling parameter, \(||.||_{2,1}\) is the \(l2\),1-norm, and \(\mathit{sigm}(z)=\Big{(}\frac{1}{(1+e^{-z})}\Big{)}\).
## 7 Conclusions  
The ES-MBD method proposed in this paper combines the Sobel operator-based binarization method with the background difference binarization method, which effectively addresses the limitations of both methods and reduces the false detection rate. This method can significantly improve the final detection accuracy and the extraction effect of key targets in X-ray images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & \
\begin{tabular}{c} **Precision** \\ **Rate** \\ \end{tabular} & **Recall Rate** & \(\text{F}_{1}-\text{Score}\) &
\begin{tabular}{c} **Accuracy** \\ **Rate** \\ \end{tabular} \\ \hline Gray binarization method & 47.8\% & 48.7\% & 0.482 & 31.8\% \\ Background difference binarization method & 74.8\% & 78.8\% & 0.767 & 62.2\% \\ Sobel operator binarization method & 51.9\% & 61.9\% & 0.565 & 39.3\% \\ ES-MBD binarization method & 97.3\% & 96.5\% & 0.969 & 94.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation metrics of different binarization methods.  
In the ES-MBD method, the Sobel edge detection operator is optimized. Compared with traditional methods, it can extract image gradient features more completely, reduce the loss of local information, and obtain a better detection effect. This method is used in parallel with the background difference binarization method, which can effectively improve the integrity of image extraction and greatly reduce image noise.
2. The ES-MBD method can solve the problem of general methods only being sensitive to strong background differences and insensitive to large areas of the same color.
3. Through the experimental comparison of various binarization methods of X-ray images, the ES-MBD method has the best integrity and the least image noise, reaching a 97.3% accuracy rate and a 96.5% recall rate, while other methods only have the highest effect of a 74.8% accuracy rate and a 78.8% recall rate.  
Conceptualization, X.S.; methodology, X.S. and S.Z.; data curation, S.Z. and J.Y.; writing--original draft preparation, X.S. and S.Z.; writing--review and editing, J.Y. and J.Z.; supervision, X.S., S.Z., J.Y. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (grant no. 51605069).  
Not applicable.  
The data used to support the findings of this study are included within the article.  
The authors declare no conflict of interest.
## Results
### Characteristic of CHC/GO/CNC Nanocomposite Hydrogels  
#### 3.2.1 Sedimentation Rate and Water Retention Analysis of CHC/GO/CNC Nanocomposite Hydrogels  
From the results of the Lumisizer(r) test (Figure 6) of the CHC/GO/CNC nanocomposite suspensions, we found that GO/DI-water (Figure 6a) was not well dispersed in water, and the sedimentation rate was 10.31 mm/h, which was the fastest settlement among the five samples. After addition of CNC, the sedimentation rate of GO/CNC decreased to 0.9 mm/h (Figure 6b), showing significant improvement (the sedimentation rate decreased 11.5 times). Furthermore, the sedimentation rate of CHC/GO/CNC solutions at 0.05 wt%, 0.1 wt%, and 0.2 wt% of CHC addition decreased to 0.66, 0.2, and 0.49 mm/h, respectively (Figure 6c). With the addition of CHC, the hydrogel viscosity rose and thus the sedimentation rate became slower. The optimal addition concentration of CHC was 0.1 wt% (Figure 6d), which gave the slowest sedimentation rate (0.2 mm/h). However, the amount of CHC used was up to 0.2 wt% because as the viscosity became too high, the suspension property decreased (the sedimentation rate was 0.49 mm/h, Figure 6e).  
From the results of the water retention analysis (Figure 7), the water absorption (%) of the CHC/GO/CNC composite hydrogel (-94%) was higher than chitosan and hyaluronic acid (HA) after 48 h incubation with 90% relative humidity (Figure 7a). Further, the water absorption of the CHC/GO/CNC composite hydrogel was about 65% at 23% relative humidity (Figure 7b), and the water retention capacity was significantly higher than that of CS and HA at both humidity. This proves that the CHC/GO/CNC nano-network structure formed by the composite hydrogel exhibits a good water retention effect. Hence, the nano-fillers (GO/CNC) might play an important role in crosslinking the network structure of the nanocomposite hydrogel. In addition, the statistical analysis (Student's \(t\)-test) showed a significant difference (\(p<0.05\)) between the two observed groups (chitosan with HA and CHC/GO/CNC) at the relative humidity of 90% (Figure 7a) and 23% (Figure 7b).  
Figure 5: FTIR spectra of CNC, CNC/GO and pristine cellulose.
## 3 Proposed Framework
### Mechanism for Enhancing Privacy and Security
#### 3.5.2 Comparison with Alternative Cryptographic Methods  
To evaluate the suitability of Salsa20 for federated learning in resource-constrained IoT environments, we refer to prior works [43; 44], which compare Salsa20 and AES in terms of power consumption, encryption latency, and encryption speed. Table 1 summarizes the key metrics.  
As shown in Table 1, Salsa20 consistently demonstrates lower power consumption (2.82 \(\mu\)W vs. 4.01 \(\mu\)W) and higher encryption speeds across different data sizes compared to AES. For instance, when encrypting a 1 GB sequence, Salsa20 achieves a speed of 3624 Mbps, surpassing AES's 3290 Mbps. Meanwhile, Salsa20 excels in handling both small (40 bytes) and large (1500 bytes) data packets, maintaining faster encryption speeds with negligible latency differences (202 vs. 180 cycles).  
These results demonstrate Salsa20's suitability for IoT environments, where energy efficiency and data transmission rates are crucial for system performance. By minimizing encryption overhead and ensuring high throughput, Salsa20 meets the scalability and efficiency needs of federated learning in heterogeneous networks. The data, adapted from prior works [43; 44], supports the selection of an encryption mechanism, building on established findings to justify Salsa20's inclusion in our framework, without replicating the experiments.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Metric** & **Salsa20** & **AES** \\ \hline Average Power Consumption (\(\mu\)W ) & 2.82 & 4.01 \\ \hline Encryption/Decryption Latency & 202 cycles & 180 cycles \\ \hline Encryption Speed (1 GB data, Mbps) & 3624 & 3290 \\ \hline Encryption Speed (40-byte packets, Mbps) & 2286 & 2240 \\ \hline Encryption Speed (1500-byte packets, Mbps) & 3429 & 3297 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of Salsa20 and AES (adapted from [43; 44]).
# Computational and Communication Infrastructure Challenges for Resilient Cloud Services
## 3 Resilience Challenges
### Computational Infrastructure
#### 3.2.5 Redundancy and Load Balancing  
The services to be implemented should be redundant as a means of improving fault tolerance. When the platform is faced with high traffic demand, the system must distribute the load among the different containers using various balancing algorithms, such as round-robin; for fairly load distribution; least connection, where requests are assigned to the servers with the least active connections; and by source, where requests are distributed based on the source IP address. To achieve this, implementing a system with a frontend that receives requests and multiple backends to fulfill them is suggested.
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 5 Different VANET Technologies Based on Transmission Range
### Short-Range Communication Technologies  
#### 5.1.1 Bluetooth Technology  
The Bluetooth technology based on IEEE 802.15.1 protocol can be used as the short-range communication technology in the vehicular network [20]. It operates in the 2.4 GHz band and can produce the data rate up to 1-4 Mbps with variable coverage ranges [134]. The range of Bluetooth varies due to the propagation conditions, the sensitivity and gain of the transmitting and receiving antennas [39]. Wi-Fi and Bluetooth use the same bandwidth; therefore, to avoid interference, Frequency Hopping Spread Spectrum (FHSS) is used in Bluetooth [135]. Different versions of Bluetooth use different standards. For example, Bluetooth 4.0 (BLE) uses low energy for data transmission, but it is not compatible with older versions of Bluetooth known as \"Bluetooth classic\" (BC) [136].  
Due to the low transmission range of BLE, it is unsuitable for inter-vehicular communication [137]. However, the range and throughput supported by BLE make it a suitable candidate to eliminate the complex wiring within an AV and reduce its weight. BLE is suitable for wireless audio stream transmission and can be used to connect the AV's infotainment system wirelessly to the speakers and microphone. In addition, it may be used to wirelessly connect sensors to the ECU and OBU and reduce the cost and complexity of wiring. In [138], BLE was compared to the CAN bus for intra-vehicular communication  
Figure 6: Different VANET technologies considered for AVs.  
and it was shown that the BLE is a suitable low cost energy efficient solution for communication with less critical sensors having relaxed latency and throughput requirement. However, as BLE takes time to discover devices and establish connection, it is not suitable for critical delay sensitive vehicular applications [139]. In [140], an algorithm utilising the BLE technology is proposed for indoor positioning and exterior localisation for vehicular applications. Furthermore, Bluetooth vulnerabilities are highlighted in [141]. Blueborne, which is identified in [142], can have a serious effect on automotive security. However, detailed Bluetooth and BLE vulnerabilities for AVs are still missing in the literature [143].
## 4 Conclusions  
It is apparent that three methods, ATPRK, S2 Sharp, and S2 SSC, outperform the other methods used in the experiments. ATPRK is easy to use and gives good results without the need for specifically tuning it to the data and may be good enough for many applications. Although improved results can be gained by using S2 Sharp with tuned parameters, this requires parameter tuning for each dataset that is to be processed. It is less practical in general use even though on average it outperforms the other methods quantitatively for the data tested. For 20 m\(\rightarrow\)10 m super-resolution S2 SSC yields the best performance, highlighting the potential of deep learning methods. The tuning method proposed, while somewhat time-consuming, can be easily applied to all S2 super-resolution methods that benefit from parameter tuning. It was shown to greatly improve the results for S2 Sharp and SSSS. The tuning parameters selected for the other methods had little effect on the end results, suggesting their default parameters were already a good fit for the data being tested and that the tuning method can reliably find similarly good parameters using reduced scale data.  
Investigation, data curation and writing--original draft preparation, S.E.A.; writing--review and editing, M.O.U., J.S. and J.R.S.; software, S.E.A., M.O.U. and H.V.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by The Icelandic Research Fund under Grant 207233-051.  
Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[https://aviris.jpl.nasa.gov/](https://aviris.jpl.nasa.gov/)]([https://aviris.jpl.nasa.gov/](https://aviris.jpl.nasa.gov/)), [[https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)]([https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)), [[https://github.com/lanha/SupReME](https://github.com/lanha/SupReME)]([https://github.com/lanha/SupReME](https://github.com/lanha/SupReME)).  
We would like to thank the authors of Sen2 res, [PERSON] (GEOSTAT team at Inria), and of SupReME and DSen2, [PERSON] (ETH Zurich), for making their code publicly available, as well as [PERSON] (National Cheng Kung University), author of SSSS, [PERSON] (University of Trento), author of MuSA, and [PERSON] (Tongji University), author of ATPRK, for providing code and data upon request.
## 4 Experiments
### Datasets and Evaluation Metrics
#### 4.1.2 Evaluation Metrics  
The main evaluation metric used is the mean average precision (mAP). In our experiments, the mAP was calculated by averaging the areas under the precision-recall (PR) curves across 10 IoU threshold values in the range of [0.50: 0.95) for all categories. AP50 represents the average detection accuracy of all categories at an IoU threshold of 0.5. Additionally, to accurately assess the model's detection performance on small and multi-scale objects, we employed the evaluation metrics \(\text{AP}_{\text{S}}\), \(\text{AP}_{\text{M}}\), and \(\text{AP}_{\text{L}}\) from the COCO dataset [38] as our additional evaluation metrics. Among them, \(\text{AP}_{\text{S}}\) represents the average detection accuracy of small targets (less than 32 \(\times\) 32), \(\text{AP}_{\text{M}}\) represents the average detection accuracy of medium targets (greater than 32 \(\times\) 32 and less than 96 \(\times\) 96), and \(\text{AP}_{\text{L}}\) represents the average detection accuracy of large targets (greater than 96 \(\times\) 96).
## 7 Coal Burst Control and Verification of 76 Working Face in Wuyang Mine
### Practical Application Effect on Site  
#### 7.3.1 Coal Drilling Pressure Relief Effect Test  
To verify the effect of drilling pressure relief in the 76 working face, an online stress monitoring system was employed to monitor the stress changes before and after pressure relief drilling. Two groups of stress monitoring points were arranged in the working face, located 10 m and 15 m away from the coal wall, respectively. Monitoring point I consisted of stressometers 1 and 2, and monitoring point II consisted of stressometers 3 and 4. The arrangement of the monitoring points is shown in Figure 18.  
Field stress monitoring data for the 76 working face are shown in Figure 19. Based on the stress monitoring results, the relative vertical stress at monitoring point 1 (located 10 m from the coal wall) before drilling pressure relief is approximately 8.0 MPa. After pressure relief, the relative vertical stress at this point decreases gradually to about 6.5 MPa, a reduction of approximately 19%. At the same time, the relative vertical stress at monitoring point 2 (located 15 m away from the coal wall) remained basically unchanged before pressure relief, but showed an upward trend after pressure relief. This increase in stress could be attributed to the lack of sufficient local relaxation in this area. While pressure relief  
Figure 17: Numerical simulation of vertical stress curve of 76 working face.  
Figure 18: Monitoring point location diagram.  
drilling reduces stress concentration near the boreholes, the redistribution of stress across the surrounding rock mass may lead to increased stress in areas further from the drilling location. Additionally, mining pressure from the advancing working face can contribute to the stress increase, as the deformation and movement of overlying strata transfer additional load to this region. These factors combined likely caused the observed rise in stress at monitoring point 2.