esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### FTIR Analysis  
FTIR spectra of the biochar, biochar/rGO, biochar/rGO/DES-CTAB, and biochar/rGO/DES-glycerol are shown in Figure 2. The results showed that biochar/rGO/DES-CTAB and biochar/rGO showed similar and consistent bands. The FTIR spectra of all the materials (biochar, biochar/rGO composite, biochar/rGO/DES-CTAB, and biochar/rGO/DES-glycerol) showed enhancement of peaks and different functional groups, which indicates that the primary material (biochar) has been modified. The DES-CTAB-modifiedadsorbent showed an additional peak at 2848 cm\({}^{-1}\), which collaborates with the CH\({}_{2}\) symmetric stretch.  
The DES-CTAB modified material also showed a broad band at 3530 cm\({}^{-1}\)-3151 cm\({}^{-1}\) belonging to an -OH stretching vibration. The less intense peak at 730 cm\({}^{-1}\) is attributed to C-Cl stretch by the introduction of the DES-CTAB. The biochar was successfully modified with rGO and DES-CTAB. For biochar/rGO and biochar/rGO/DES-CTAB, the peaks at 1607 cm\({}^{-1}\), 1422 cm\({}^{-1}\), 1228 cm\({}^{-1}\), and 1059 cm\({}^{-1}\) correspond to C=C, O=C-O bending, the alkoxy -CO group, and C-O functional groups, respectively. The latter, less intense peaks are attributed to the vibrations of the C-H functional groups.  
The bands at 1044 cm\({}^{-1}\) are assigned to alkoxy C-O-C stretching in the biochar and biochar/rGO [18]. The composite biochar/rGO had no attached functional groups between 1600 cm\({}^{-1}\) and 4000 cm\({}^{-1}\). This confirms the reduction of the most oxygen-containing functional groups by the rGO. The biochar/rGO/DES-glycerol modified adsorbent showed an additional peak at 2848 cm\({}^{-1}\) corresponding to the CH\({}_{2}\) symmetric stretch. The DES-glycerol-modified material also showed a broad band at 3530 cm\({}^{-1}\)-3151 cm\({}^{-1}\) belonging to an -OH stretching resulting from the DES [19]  
The less intense peak at 730 cm\({}^{-1}\) is attributed to C-Cl stretch by introducing the DES-glycerol. This confirms that biochar was successfully modified with rGO and DES-glycerol. For biochar/rGO/DES-glycerol, the peaks at 1607 cm\({}^{-1}\), 1422 cm\({}^{-1}\), 1228 cm\({}^{-1}\), and 1059 cm\({}^{-1}\) correspond to C=C, O=C-O bending, alkoxy -CO group, and C-O functional groups, respectively. The latter less intense peaks at 881 cm\({}^{-1}\) and 792 cm\({}^{-1}\) are attributed to the vibrations of the C-H functional groups.
# Detection of Sleep Apnea from Electrocardiogram and Pulse Oximetry Signals Using Random Forest
## 5 Discussion  
### Comparison among Different Signals  
Reviewing the results of SA detection using either ECG signals or SpO2 signals alone in Section 4.3, we found that the SpO2 feature set had better accuracy, sensitivity, and specificity compared to the ECG feature set. In addition, some previous review works on SA detection also pointed out that SpO2 signals usually performed better than ECG signals [34]. This phenomenon can be attributed to the ability of the signal to characterize on sleep apnea syndrome. Because, when apnea occurs, a decrease in inhaled air flow can directly cause fluctuations in SpO2, and such fluctuations are significant. For the ECG signal, in addition to respiratory events, some cardiovascular diseases such as arrhythmias and heart block may also cause changes in HRV [34]. Therefore, it is challenging to detect apnea using the ECG signal.  
Although excellent results were obtained using SpO2 signals alone, there are still some drawbacks. For example, chronic obstructive pulmonary disease or alveolar hypoventilation can also cause a decrease in oxygen saturation [35]. This means that some non-apnea-induced oxygen desaturations masquerade as apnea-induced oxygen desaturation, which can eventually lead to a decrease in the sensitivity of the model. However, upon comparing Tables 4 and 5, it can be seen that the accuracy and sensitivity were improved by approximately 1% and 2%, respectively, when using the combined signals compared  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Signal** & **Accuracy (\%)** & **Sensitivity (\%)** & **Specificity (\%)** & **AUC** \\ \hline ECG & 88.6\% & 84.5\% & 91.2\% & 0.957 \\ SpO2 & 96.6\% & 93.8\% & 98.4\% & 0.988 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Per-segment classification results based on RF classifier using either ECG or SpO2 feature set.  
Figure 6: Receiver operating characteristic (ROC) curve analysis for RF, KNN, LR, and SVM classifier.  
to using the SpO2 signal alone. This suggests that the two channel signals provide richer SA representation information to the classifier when classifying SA events. Further, from the results of feature selection, the RFECV algorithm selected six features from the ECG feature set and seven features from the SpO2 feature set, respectively. Thus, these features are non-redundant, indicating the complementarity between the two signals.  
Another advantage of using a combination of ECG and SpO2 is better applicability. To the best of our knowledge, SA automatic detection algorithms developed based on single-lead ECG signals are not suitable for cardiac patients, which limits the applicability of the algorithms to some extent. However, the proposed algorithm achieved feature-level fusion. The advantage of using multimodal fusion techniques is that multimodal systems can still operate when one of the modalities is missing [28]. In other words, the proposed algorithm still has the ability to diagnose SA when one of the signals does not work.
# A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks
## 5 Conclusions and Future Work  
In this paper, a novel mobile anchor node assisted RSSI localization scheme for UWSNs is proposed. After one mobile anchor travels along the planned linear trajectory on the water surface, all underwater sensor nodes in the area near the trajectory can locate their positions. We design an SVR-based interpolation method to improve the technique of determining node projection in existing methods. This method increases the accuracy of the nonlinear regression model of noisy measured data and decreases the estimation error caused by the discreteness of measured data, which greatly reduces the error of the determined projection and then improves the location accuracy of the underwater sensor node. By developing a curve matching method to obtain the perpendicular distance from the sensor nodes to the linear trajectory, the location of the sensor node can be calculated based on the  
Figure 16: Comparison of the mean location error of the three schemes.  
Figure 15: Comparison of mean error between node projection and Perpendicular distance.  
determined projection and the obtained perpendicular distance. Compared with existing schemes that require the anchor node to travel at least two trajectories, the proposed scheme only needs one trajectory to locate sensor nodes, and the location time is shortened with the reduction in the number of trajectories. Simulation results show the validity of our proposed localization scheme, the SVR-based interpolation method, and the curve matching method. In the future, we aim to enhance the curve matching method to reduce the error between the obtained perpendicular distance and the actual perpendicular distance, so as to further improve the location accuracy.  
Conceptualization, Y.S. and Y.Y.; Data curation, Y.S.; Funding acquisition, Y.Y.; Investigation, Y.S. and Y.Y.; Methodology, Y.S. and Y.Y.; Software, Y.S.; Supervision, Y.Y.; Validation, Y.S.; Visualization, Y.S.; Writing--original draft, Y.S.; and Writing--review and editing, Q.X., C.H. and X.G.  
This work was supported in part by National Natural Science Foundation of China (61803328) and in part by the Natural Science Foundation of Hebei Province (F201703084).  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Delimitation of Vegetation Types  
Our analysis focused on the four vegetation types accessible to wild and domestic ungulates (i.e., excluding forested stands such as stone pines, cork oaks, and coastal junipers) within the five National Park estates mentioned above. These were saltmarsh, bulrush marsh, shrubland, and grassland. The main vegetation types and their main characteristics are shown in Table 1.  
To define the different vegetation types, we selected the corresponding classes of vegetation maps (Figure 1) elaborated in 2014 by the long-term monitoring program (PSPN; [PERSON] et al., 2014, pp. 37-59) [41] of Donana's Singular Scientific-Technical Facility (ICTS-RBD), and grouped into the four types described above (Figure 1). Subsequently, the polygons occupied by each of these types within each ungulate management unit (see below) were used to calculate their respective area and the variables derived from satellite-obtained NDVI values (see below). These tasks were performed with the ArcGIS 10.1 software [42].  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Vegetation Type** & **Description** \\ \hline Saltmarsh & Halophilous scrub (‘almajar’) on floodplain/marine brackish mudflats, dominated by glaucous glasswort (_Arthrocennium_ _acrossticism_) and shrubby sea-blite (_Suada vera_), interspersed with halophilous grass meadows. \\ \hline Bulrush marsh & Seasonal meadows of tall sedges (Fam. _Cyperacae_) on floodplain/brackish marshes. Dominant species are saltmarsh bulrush (_Bolloscheonus maritimus_), Blysmus bulrush (_Schoenoplectus littoralis_) and somerset rush (_Juncus subulatus_), which may be dominant or co-dominant. \\ \hline Shrub formations on stabilized dunes, sometimes interspersed with sandy grasslands. These formations include a mosaic of two main types, respectively occupying more scric and mesic sites: dry scrubland (‘monte blanco), dominated by _Halimium halimifolium_, _Cistus siliculous, C. Libunotis, Rosmarinus officinalis_, and _Lavandula stocachs_; and wet shrubland (‘monte negro’), dominated by heather (_Erica scoparia, E. umbellata, E. ciliaris, Calluna vulgaris_), _Rubus ulmifolia, Ullex minor_ and _Ulex australis_. \\ \hline Grassland & Wet pasture formations usually spatially associated with lagoons and in the ocotone that forms the marsh and inland areas, usually called “la vera”. Dominated by the association of _Galium palustre_ with _Juncus maritimus_. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vegetation types and description of the characteristics and main species.
## 2 The Proposed Methods
### Extension Neural Network (ENN)
#### 2.3.3 Identification Phase of ENN  
When ENN is trained, it can carry out identification, and the steps are described, as follows. The ENN flow chart is shown in Figure 4.  
Step 1: Read the trained weighting matrix of ENN.  
Step 2: Calculate the median values of each weight, such as Equations (13) and (14).  
Step 3: Read the sample identification data.  
Step 4: Calculate the distance between the identification sample and each cluster by the extension distance, such as Equation (16).  
Step 5: Find \(k\), make \(ED_{ik}=min\{ED_{ik}\}\), and set the output \(O_{ik}=1\) to identify the cluster classification of the samples.  
Step 6: Stop if all identification samples have been classified; otherwise, go back to Step 2.
## 2 Models and Data
### Radiative Transfer Model: AccuRT
#### 2.2.2 Atmospheric Aerosols  
The _aerosols_ material provides a convenient way to specify the aerosol properties as a function of wavelength based on a bi-modal particle size distribution including a fine and a coarse mode. The user specifies the vertical profile of the aerosols in the atmosphere,  
Figure 1: Framework used in data formation, modeling, and algorithm formation. Descriptions of each step are provided in the following sections.  
as well as the refractive index (real and imaginary parts), the effective radii, and the variances of the two modes. Then, a Mie scattering code is used to compute the IOPs of aerosol particles and a numerical integration is employed to integrate over the log-normal size distributions to obtain the absorption and scattering coefficients and the scattering phase function. For our configuration, fine mode aerosols have an optical depth of 0.212 and a single-scattering albedo of 0.948, while coarse mode aerosols have an optical depth of 0.026 and a single-scattering albedo of 1 at a reference wavelength \(\lambda=500\) nm. These values are consistent with values typically measured in the atmosphere [33].
## 3 Sea Surface Sun Glint Suppression and Tiny Human Object Detection Based on Visual Saliency
### Maritime Object Detection Based on Two-Step Clustering
As shown in Figure 6c, the salient points of the objects in the original visible image were effectively separated by using two-step DBSCAN clustering. The area of the objects, especially including tiny human targets, is indicated by the red box. It is essential to appropriately set the parameters \(\varepsilon\) and \(P_{\min}\) in two-step DBSCAN clustering, since objects are generally concealed by sun glint and background noise.
## 1 Introduction
Therefore, effective miniaturization techniques to reduce the antenna's dimensions without adversely affecting the antennas performance parameters, such as radiation, gain, and bandwidth, are critical and vital to realize practical and portable implantable antennas. Bandwidth is also another parameter in the design of implantable antennas. Since the electromagnetic properties of human tissues are variable from person to person, the resonant frequency of antennas when implanted in different individuals may shift and affect the impedance matching of the antenna. Therefore, a wider bandwidth (BW) in implantable antennas is desirable to ameliorate this issue. In [13; 14; 15; 16; 17], some implantable antennas are reported with a lower than 50 MHz BW in each frequency band. Obviously, narrow BW is one of the disadvantages in these designs. Here, we aim to design our antennas smaller than others with a triple band, thus providing larger bandwidths. Another factor in the design procedure of the antenna is the selection of the frequency bands. These bands usually include one or two of the 402 to 405 MHz, 902-928 MHz, and 2.4 to 2.485 GHz bands. The Medical Implant Communications Service (MICS) (402-405 MHz) band is used conventionally for biomedical applications. The Industrial, Scientific, and Medical (ISM) (902-928 MHz, 2.4-2.4835 GHz, and 5.725-5.825 GHz) band is also used for implantable antenna applications. Additionally, the ubiquitous adaption of high-speed communications in medical applications has resulted in a constantly growing demand for high-frequency antennas, with more effective data transmission. As such, we have developed novel triple-band antennas to take advantage of high-speed communications and long-distance data transmission together in one set. Lower frequencies are more desirable for long-distance communications since the wave propagation has less attenuation along the path and high frequencies are more suitable for high-speed communications since they provide a higher effective BW. Therefore, by designing the antennas to operate at 400 MHz from MICS band and 2.4 GHz and 5.7 GHz from ISM bands, better implantable antennas with flexibility for a variety of applications have been achieved. Furthermore, due to the popularity of the Internet of Things (IoT) in daily usage, the proposed antennas are designed at 5.7 GHz band to support both Bluetooth and Wi-Fi bands in addition to the ISM band.
## 2 Materials and Methods  
### Dataset  
The global horizontal irradiation data was collected at a PV power plant in Jiangsu in China. The installed capacity of the power plant was nearly 1.1 megawatts-peak (MWP). The acquired data were recorded every 5 min and the period was from 2018 to 2019, but the dates of 25 and 26 September in 2019 were not considered, due to an error in collection. There were in totally 288 recordings each day. A small amount of abnormal data was generated due to equipment or operation failures, and these unreasonable recordings were regarded as missing entries in this paper. Figure 2 illustrates the solar radiation in 2018 and 2019, respectively, where the blue trend represents lower solar radiation intensity and the yellow trend represents higher solar radiation intensity. It can be seen from two the subfigures that solar radiation is mainly concentrated from 7 a.m. to 6 p.m. each day, and the solar radiation intensity is generally stronger from 12 a.m. to 3 p.m.  
We separated all solar radiation data according to the four seasons: spring, summer, autumn and winter. In total, spring owns 180 days including January, February and March. In summer, there are 182 days in April, May and June. There are 182 days in July, August and September in autumn. Winter has 184 days in October, November and December. Figure 3 further illustrates the solar radiation intensity of the four seasons in 2018. In that year, the average daily maximum value of solar radiation intensity of spring is 592.64 Wh/m\({}^{2}\). In summer and autumn, the average daily maximum solar radiation intensity is 879.88 Wh/m\({}^{2}\) and 949.67 Wh/m\({}^{2}\), respectively. Compared with the other seasons, the average daily maximum solar radiation intensity in winter is only 549.33 Wh/m\({}^{2}\).  
Figure 3: Solar radiation in four seasons in 2018 and 2019. (**a**) Spring; (**b**) Summer; (**c**) Autumn; (**d**) Winter.  
Figure 2: Visualization of the global horizontal irradiation. (**a**) 2018; (**b**) 2019.  
By observing Figures 2 and 3, we can see that these data show no obvious trend or seasonality. What is more, the data are relatively complicated and there are many missing elements. If the original data were directly utilized to perform forecasting, the prediction results would probably have a large error, which would affect the normal operation of the PV power grid. According to the above data characteristics, it is essential to choose the appropriate data processing method. For obtaining higher data quality, we need to recover the missing entries and denoise the completed data. Subsequently, cluster analysis is adopted to reduce the complexity of data. The data of each cluster are chosen to make predictions separately, which can improve the prediction efficiency and accuracy to some extent.
# Markov-Embedded Affinity Learning with Connectivity Constraints for Subspace Clustering
## 5 Optimization
### Complexity Analysis  
In this subsection, we analyze the computational complexity as follows. In Equation (15), the size of \(H\) is \(n\times k\). Therefore, updating \(W\) requires complexity of \(O(kn^{2})\). For \(B\), the computational complexity is \(O(n^{2})\). In Equation (21), the complexity of \(X^{T}X\) is \(O(dn^{2})\). Due to the inversion operation, the complexity of \([X^{T}X+(\zeta+\alpha)I+\beta L_{N}]^{-1}\) is \(O(n^{3})\). Simultaneously, the complexity of \((X^{T}X+\zeta B+\alpha P_{N})\) is \(O(dn^{2})\). Therefore, for \(Z\), the computational complexity is \(O(n^{3}+dn^{2})\). Overall, the total complexity can be written as \(O(n^{3}+kn^{2}+n^{2})\) in each iteration. The dominant complexity arises from the inversion of the \(n\times n\) matrix and the multiplication of the \(n\times n\) matrix, both of which have complexity of \(O(n^{3})\). For multiplication, we can solve this through parallel computation. For inversion, we note the special structure. This part is composed of the sum of the diagonal matrix and the low-rank matrix. Ref. [23] provides a potential method for acceleration.
## 4 Experimental Validation
### Metrics and Implementation Details
#### 4.3.3 Loss  
The loss function adopted to train the CNN is defined in Equation (5) as the Log-Cosh Dice Loss [52]:  
\[coshx=\frac{e^{x}+e^{-x}}{2} \tag{3}\]  
\[DiceLoss=1-\frac{2\sum_{i=1}^{N}\sum_{c=1}^{C}y_{i}^{c}\cdot p_{i}^{c}}{\sum_{ i=1}^{N}\sum_{c=1}^{C}y_{i}^{c2}+\sum_{i=1}^{N}\sum_{c=1}^{C}{p_{i}^{c2}}} \tag{4}\]  
\[Loss=\log(cosh(DiceLoss)) \tag{5}\]  
In Equation (4), \(y_{i}^{c}\) is a binary indicator (0 or 1) indicating whether pixel \(i\) lies in class \(c\) or not, and \(p_{i}^{c}\) is the softmax output of the network for pixel \(i\) and class \(c\).
## 4 Overview and Functioning of the Generator
### Listing and Range of All Parameters
#### 4.2.1 Non-Editable Parameters  
Some parameters are not editable in the configuration file. Firstly, as mentioned before, a caregiver is allowed to work a certain amount of time per day regarding his/her labor agreement. As noticed in the following Tables 1 and 2, the generator sets up the beginning of the day at 8 A.M. and the ending at 8 P.M. A caregiver with a full-time contract is allowed to work between 8 to 10 h a day, and a caregiver with a part-time contract can only work 4 to 6 h a day according to the literature [10; 13; 14; 35; 37; 38]. Compatibilities between patients and caregivers are represented by a score as explained in the previous section. A low score means that the caregiver should not be assigned to this patient due to a low compatibility. The generator ranges compatibility between zero (lowest compatibility) and four (highest compatibility). A score of four is a special feature that allows the generator to set up a reference caregiver to a patient if the configuration file allows it.  
The patient time frame availability on each day starts at 8 A.M. and finishes at 8 P.M. Furthermore, the compatibility scores are set up in the same way. However, as specified in Table 2, a patient has a time window. During this time window, our patient is available to receive his/her care. During the scheduling process, time windows for the patient can be considered as soft or hard constraints depending on how the H  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameters** & **Range** & **References** \\ \hline Minimum and maximum working hours per day (full-time) & 8–10 h & [10; 13; 14] \\ Minimum and maximum working hours per day (part-time) & 4–6 h & [35; 37; 38] \\ Beginning and ending of the day & 8 A.M.–8 P.M. & [23; 39] \\ Compatibility score (lowest to highest) & 0–4 & [13; 17; 24; 33] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Range of non-editable parameters for caregivers.  
Figure 8: UML description of the generator. \([1\dots^{*}]\): one occurrence to multiple occurrences.  
The generator sets up a range of 1 to 4 h, which means that a patient ks at least available for 1 h and at most 4 h to receive his/her care services.  
Lastly, Table 3 sums up the different non-editable parameters for a job. A job has a processing time called duration in the generator. Duration can range from 30 min to an hour and a half. Some jobs in the instance need to be done, and some of them can be postponed to another day. A priority score is set up on all jobs to show how important the job is. Moreover, some jobs must be performed in a certain time frame. Usually, this time frame may not exceed one hour to two hours. Precedence constraints can be found in some particular jobs. The literature shows that the duration between two jobs related by a precedence constraint may not exceed two hours.