esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In summary, the Ag\({}_{3}\)PO\({}_{4}\)/SnO\({}_{2}\)/CC heterojunction was successfully prepared via a facile two-step method. The structure and morphology characterization results of the as-prepared samples were described in detail. It can be clearly observed that many Ag\({}_{3}\)PO\({}_{4}\) particles with irregular shapes are deposited on the surface of SnO\({}_{2}\)/CC, forming the heterojunction. In the experiments using dye treatments, the Ag\({}_{3}\)PO\({}_{4}\)/SnO\({}_{2}\)/CC heterojunction reveals a higher photocatalytic performance for removing various contaminants (RhB, MB), than that of other as-prepared samples after 70 min under visible irradiation. This is mainly attributed to synergistic effects between the Ag\({}_{3}\)PO\({}_{4}\) and SnO\({}_{2}\) on carbon cloth, which can facilitate charge transfer and suppress the recombination of photogenerated electron-hole pairs, leading to the enhanced photocatalytic performance. Notably, the catalyst stability was maintained after three cycles. The structure and morphology of Ag\({}_{3}\)PO\({}_{4}\)/SnO\({}_{2}\)/CC after photocatalysis was still well-preserved. This indicates that the Ag\({}_{3}\)PO\({}_{4}\)/SnO\({}_{2}\)/CC heterojunction is a competitive candidate as an excellent photocatalyst, which has a high practical significance and application value for the future.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/10/9/3238/s1](http://www.mdpi.com/2076-3417/10/9/3238/s1)]([http://www.mdpi.com/2076-3417/10/9/3238/s1](http://www.mdpi.com/2076-3417/10/9/3238/s1)), Figure S1: The adsorption ability of the as-synthesized samples in the dark, Figure S2. The SEM image of Ag\({}_{3}\)PO\({}_{4}\)/SnO\({}_{2}\)/CC after photocatalysis. Figure S3. PL spectra of Ag\({}_{3}\)PO\({}_{4}\)/CC and Ag\({}_{3}\)PO\({}_{4}\)/SnO\({}_{2}\)/CC, Figure S4. Nitrogen adsorption-desorption isotherms of Ag\({}_{3}\)PO\({}_{4}\)/SnO\({}_{2}\) and Ag\({}_{3}\)PO\({}_{4}\)/SnO\({}_{2}\)/CC, Table S1: Photocatalytic performance comparison of this work versus the previous published results.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] conducted sample characterizations. All authors participated in analysis of the experimental data and discussions of the results, as well as editing the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors gratefully acknowledge financial support from the Foundation of Department of Science and Technology of He'nan Province (Grant No. 182102210295), the Foundation of He'nan Educational Committee  
Figure 10: The schematic illustration of the photocatalytic mechanism of Ag\({}_{3}\)PO\({}_{4}\)/SnO\({}_{2}\)/CC.  
(Grant No. 18B510007) and the Doctoral Scientific Research Foundation of Henan University of Science and Technology (Grant No. 400613480013).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Improving Healthcare Facilities in Remote Areas Using Cutting-Edge Technologies
## 6 Conclusions and Future Work  
Healthcare is one of the most significant fields of study and the nation's top priority. However, even in industrialized nations, individuals living in distant places cannot access timely and adequate healthcare services. Many deaths occurred during the recent COVID-19 outbreak due to the inaccessibility of healthcare services. Therefore, this study suggests a strategy that might provide residents in distant locations with adequate and safe healthcare services without requiring them to relocate. The suggested solution uses new, creative technology and proposes a strategy that would make healthcare facilities accessible 24/7,and from any place, particularly in rural regions where healthcare facilities are few. In the proposed method, patients would receive care at their homes from a remote doctor in rural regions or a nearby local clinic. Remotely accessible healthcare resources (physician, treatment, patient counseling, diagnosis, etc.) would be made available to those who live far away from healthcare facilities. The suggested method is evaluated using mathematical modeling and a case study; the results confirm that the technique is beneficial for enhancing healthcare services for distant patients.  
In the future, we plan to augment the suggested framework with other novel technologies, such as big data and edge computing. In addition, we want to confirm our results by applying the proposed paradigm to a real-world scenario.  
Conceptualization, M.H., M.A. and F.A.-Q.; methodology, M.H. and M.F.A.; validation, M.H., M.A. and F.A.-Q.; formal analysis, S.A.A. and M.A.; investigation, M.A. and M.F.A.; writing--original draft preparation, M.H.; writing--review and editing, S.A.A., M.F.A. and M.A.; visualization, M.A.; funding acquisition, M.A. and S.A.A. All authors have read and agreed to the published version of the manuscript.  
This study is supported by funding from Prince Sattam bin Abdulaziz University project number (PSAU/2023/R/1444).  
Not applicable.  
This study is supported by funding from Prince Sattam bin Abdulaziz University project number (PSAU/2023/R/1444).  
The authors declare no conflict of interest.
# Co-Firing of Fast Pyrolysis Bio-Oil and Heavy Fuel Oil in a 300-kW\({}_{\text{th}}\) Furnace
## 4 Conclusions  
In this study, combustion characteristics and pollutant emissions of burning fast pyrolysis bio-oil/heavy fuel oil blends in a 300-kW\({}_{\text{th}}\) furnace were investigated. The conclusions are drawn as follows:  
1. At fixed fuel and air flow rates, burning the emulsion of 2.5% bio-oil + 97.5% HFO resulted in a slightly lower temperature in the combustion chamber than burning pure HFO, since the pyrolysis bio-oil has a low calorific value and a high water content of as much as 27%.
2. O\({}_{2}\) and CO\({}_{2}\) concentrations in flue gas for burning the blend of 2.5% bio-oil + 97.5% HFO and pure HFO were nearly the same. Additionally, the CO concentrations in these two burning conditions are close to 0 ppm because of the low excess oxygen operating conditions.
3. Compared with burning pure HFO, an NO emission reduction of around 2.6% was obtained for burning the emulsion of 2.5% bio-oil + 97.5% HFO.
4. SO\({}_{2}\) emission for burning the emulsion of 2.5% bio-oil + 97.5% HFO was slightly lower than that for burning pure HFO. An SO\({}_{2}\) emission reduction of around 7.9% was obtained.  
In view of the furnace tests, due to the complex composition of fast pyrolysis oil, preheating will lead to instability of the fuel supply system in the combustion test. Increasing the mixing ratio of fast pyrolysis bio-oil in HFO will make the combustion unstable during the preheating process. Closing the fuel preheating device will cause the deterioration of oil liquidity. When burning mixed fuel oils and pyrolysis bio-oil, it may be necessary to consider using two separate sets of oil pipeline rather than direct co-firing. Refined purification in the process of oil production is also needed to reduce unnecessary substances and the proportion of volatile substances for practical application.  
This work was supported by the Ministry of Science and Technology, Taiwan, under contract of MOST 105-3113-E-006-004.  
Figure 6: Emissions when burning 100% HFO and the emulsion of 2.5% bio-oil and 97.5% HFO.  
**Author Contributions:** [PERSON] and [PERSON] performed the experiments and analyzed results. [PERSON] generated ideas, designed experiments, analyzed results, and wrote the manuscript. [PERSON] generated ideas, designed experiments, analyzed results, and helped editing the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Improved Durability of Wood Strand-Based Panels Using Guayule
## 3 Results and Discussion
### Durability and Degradation  
#### 3.2.1 Termite Resistance Percent Mass Loss  
Figure 5 shows the average percent mass loss and average termite visual rating assessment damage for the different treatments tested. Following the AWPA E1-23 [31] standard, the percent mass loss for specimens sprayed with guayule had an overall average mass loss of 2.21%, therefore having a 76.66% lower mass loss than the WSB control specimens. The specimens brushed with guayule resin had an overall average mass loss of 2.36%, therefore having a 75.08% lower mass loss than the WSB control specimens. The in-situ specimens had an overall average mass loss of 4.98%, therefore having a 47.41% lower mass loss than the WSB control specimens. The WSB control specimens had an overall average mass loss of 9.47%, which was 75.82% lower than that of the SYP control specimens. The SYP untreated control pine wafer specimens had an overall average mass loss of 39.17%. There were no statistical differences (\(p>0.05\)) between the specimens brushed and specimens sprayed with guayule for mass loss as represented by the letter D (Figure 5). However, there were statistical differences (\(p\leq 0.0001\)) between the specimens of the SYP wafer control untreated specimens, WSB controls, and in-situ specimens for mass loss as represented by the letters A, B, and C, respectively (Figure 5).  
Figure 4: Comparison between (**a**) water absorption and (**b**) thickness swelling of wood strand-based (WSB) control and WSB in-situ specimens being submerged in water during 2 h, 24 h, and 72 h test period. COV = coefficient of variation, which is equal to the ratio between the standard deviation and the mean.
# Dirac Equation-Based Formulation for the Quantum Conductivity in 2D-Nanomaterials
## 6 Results
### Graphene Conductivity under a Uniform Bias Potential
#### 6.1.1 Conductivity in Function of the Chemical Potential \(\mu\)  
The comparison is performed, for both Equation (17) and the Kubo formula of ref. [33] in terms of (i) the same lifetime (scattering time) \(\tau\) in the band, (ii) the same intrinsic chemical potential (pre-doping) \(\mu_{0}\), and for \(V_{0}\in[-3,3]\) V; We recall that the intrinsic Fermi level is the initial Dirac point of the energy: it can be set to zero, but, realistically, under operative conditions there is a level shift caused by the doping (or gating), [34].  
The plots shown in Figure 4 depict the conductivity in function of the applied external voltage for different values of the pre-doping Fermi level, \(\mu\). We can observe an excellent agreement among the curves, namely, the Kubo-Drude (blue) and Dirac formulation (red) curves. Moreover, the two sets of curves are symmetric with respect to \(\mu_{0}\), thus confirming the ambipolar behaviour, one of the main properties of graphene as nano-structured material.  
Figure 4: _Cont._
## 2 Materials and Methods
External ionization means that ions are formed outside the trap in a specific ionization region inside the mass spectrometer. Although, internal ionization would lead to better limits of detection (LOD), the mass resolution and mass accuracy would strongly decrease simultaneously due to higher amounts of air. External ionization is a compromise between sensitivity and mass resolution for direct sampling of target compounds in an environmental gas matrix. Real-world samples were held in front of the APLD, and the laser was activated. All settings were the same as for standard laboratory measurements. No cleaning was necessary after executed measurements. The LOD is defined by a signal to noise ratio (S/N) of three. Therefore, the signal intensity was not extrapolated to an S/N of three, instead it represents the lowest total amount of substance that was actually measured. The ambient pressure laser desorption, shown in Figure 1, operates by direct desorption of samples from surfaces with short laser pulses. At first, an approximately 5 ns wide Nd:YAG laser pulse with a pulse energy of about 3-5 mJ was guided via a laser fiber to the sampling surface. The laser fiber was mounted at the desorption head with approximately three millimeters distance to the surface. The pulsed laser light ablated the analyte from the surface, and, subsequently, the ablated material was transferred through a capillary into the IT-MS. Desorption head and transfer capillary were heated to 200 \({}^{\circ}\)C to prevent unintended surface adsorption of analytes. Methane as reactant gas was used for chemical ionization, generating primarily protonated quasi molecular ions.
## 3 Interference Mitigation Schemes
### Cognitive Radio Approach
#### 3.3.5 Cognitive Radio WBASN  
The cognitive radio WBASN (CR-WBASN) model is based on UWB technology for medical applications in hospital environments [42]. The architecture model of the CR-WBASN consists of an IR-UWB transceiver with on-off keying modulation and an MB-OFDM transceiver. The frequency band is allocated to the UWB band (3.1 to 10.6 GHz). In [43], a three-tier WBASN architecture for hospital applications is introduced in which cognitive radio can be implemented in the WBASN controller node or the access point to send data to the server. A central controller-based architecture includes three components equipped with cognitive radio capabilities. Applying CR-WBASN can reduce electromagnetic interference and ensure QoS enhancement.
## 3 Test Scheme and Result Analysis
### Analysis of High-Speed Camera Results  
The entire process of uniaxial dynamic impact compression and uniaxial dynamic impact splitting tests of the specimens was filmed using an Y7S2 MotionPro high-speed camera (see Figure 14). It can more intuitively and dynamically reproduce the deformation and damage characteristics of concrete specimens under dynamic impact compression and splitting and compensate for the inability to visualize the development of specimen cracks in the case of rapid impact and a high-strain rate [28; 29; 30; 31]. In order to highlight the advantages of the high-speed camera, a maximum impact rate of 0.5 MPa was adopted to record the damage process of C40 plain concrete and S1 PP0.2 HyFRC under uniaxial dynamic impact compression and uniaxial dynamic impact splitting at 25 \({}^{\circ}\)C (see Figures 15 and 16). In order to compare the damage process at the same moment, one photo was selected at an interval of six photos, and a total of six photos were selected to reproduce the whole destruction process. The direction of the arrows in the Figures 15 and 16 indicates the sequence of the damage process.  
The comparison of photos in Figures 15 and 16 reveals a significant difference in the damage patterns of C40 plain concrete and S1 PP0.2 HyFRC under uniaxial dynamic impact compression. Since the dynamic tensile capacity of concrete is much smaller than the dynamic compressive capacity, C40 plain concrete is first damaged by spalling along the axial surfaces, followed by core failure. Under the action of the impact compression stress wave, the specimen presents fragmentation failure and crushing failure. S1 PP0.2 HyFRC improved the toughness of concrete, leading to significantly different failure modes from C40 plain concrete. The S1 PP0.2 HyFRC exhibits splitting and tensile damage along the axial direction, dominated by progressive surface spalling and core failure.
# Integrated Autopilot Guidance Based on Zero-Effort-Miss Formulation for Tail-Controlled Missiles
## 3 Integrated Control for Guidance and Autopilot Synthesis
### Zero-Effort-Miss of the Integrated Dynamics  
Based on the equations in (11), the state transition matrix of the integrated dynamics is given by  
\[\Phi(t,t_{0})=\exp(A(t-t_{0})) \tag{13}\]  
which can be used to obtain the integrated ZEM as  
\[z_{f}= I_{5}(1,\cdot)\Phi\Big{(}t_{f},t\Big{)}x\] \[= I_{5}(1,\cdot)\exp\big{(}At_{go}\big{)}x \tag{14}\]  
where \(I_{5}(1,\cdot)\) denotes the first row of the 5-dimensional identity matrix \(I_{5}\). The remaining time-to-go, defined as \(t_{go}\triangleq t_{f}-t\), is applied to the controller design using an estimate of  
\[t_{go}=-r/\dot{r} \tag{15}\]  
Considering the structure of the matrix \(A\) in (12), it is deduced that ZEM in (14) can be rewritten as  
\[z_{f}=z+\dot{z}t_{go}+\Phi_{go}(1,3)a_{M}+\Phi_{go}(1,4)q+\Phi_{go}(1,5)q \tag{16}\]  
where \(\Phi_{go}(i,j)\) represents the \((i,j)\)th component of the state transition matrix \(\Phi_{go}\) defined as \(\Phi_{go}\triangleq\Phi\big{(}t_{go}\big{)}=\exp\big{(}At_{go}\big{)}\). In addition, the first and second terms in (16) are related to the guidance variables and can be rewritten as  
\[z+\dot{z}t_{go}\approx r(\lambda-\lambda_{0})+\dot{r}(\lambda-\lambda_{0})t_{go}+r\dot{ \lambda}t_{go}\] \[= r\dot{\lambda}t_{go} \tag{17}\]  
where the estimation of the time-to-go in (15) is used. Substituting (17) into (16), we have the expression for ZEM as  
\[z_{f}=r\dot{\lambda}t_{go}+\Phi_{go}(1,3)a_{M}+\Phi_{go}(1,4)q+\Phi_{go}(1,5)q. \tag{18}\]  
Figure 1: Two-dimensional engagement geometry for a moving target.  
The proposed ZEM in (18) means the terminal miss distance with the target when the tail fin rate of the missile is maintained at zero. Hence, by stabilizing ZEM at zero, we can achieve target interception while minimizing the terminal rate of the tail fin deflection. To this end, in the next section, we design a controller that can regulate ZEM based on the simple proportional control structure.
## 4 Data  
### Datasets  
Table 1 provides basic information about the datasets used in the evaluation. Since the model used in this work requires a social context with interactions or relations, the list is limited to datasets that either contained this information or that could be extended using other sources (Section 4.2).  
The OMD dataset (Obama-McCain debate) [37] contains tweets about the televised debate between Senator [PERSON] and then-Senator [PERSON]. The tweets were detected by following three hashtags: #_current_,#_tweetdebate_, and #_debate08_. The dataset contained tweets captured during the 97-minute debate, and 53 after it, for a total of 2.5 hours. The dataset included tweet IDs, publication date, text, author name and nickname, and individual annotations of up to seven annotators.  
The Health Care Reform (HCR) [38] dataset contained tweets about the run-up to the signing of the health care bill in the USA on March 23, 2010. It was collected using the #_hcr_ hashtag, from early 2010. A subset of the collected tweets were annotated with polarity (positive, negative, neutral, and irrelevant) and polarity targets (health care reform, [PERSON], Democrats, Republicans, Tea Party, conservatives, liberals, and [PERSON]) by [PERSON] et al. [38]. The tweets were separated into training, dev(HCR-DEV), and test (HCR-TEST) sets. The dataset contained the tweet ID, user ID and username, text of the tweet, sentiment, target of the sentiment, and the annotator and annotator ID.  
RTMind [2] contained a set of 62 users and 159 tweets, with positive or negative annotations. To collect this dataset, [PERSON] et al. [2] crawled 2500 Twitter users who tweeted about [PERSON] during two days in May 2013. For each user, their recent tweets (up to 3200, the limit of the API) were collected. At that point, only users that tweeted at least 50 times about [PERSON] were considered. The tweets from those users that relate to [PERSON] were kept and manually labeled by three annotators. Then, a synthetic network of following relations was generated based on a homophily criterion, i.e., users with a similar sentiment were more likely to be connected. The dataset contained the ID of the tweet, the ID of the author, the text of the tweet, the creation time, and the sentiment (positive or negative).
## 4 Insights into Bacterial-Algal Biomass in Different Operational Configurations
### Sequential Batch Reactor (SBR) Systems  
Aerobic photogranules are preferably cultivated in photobioreactors operated in sequencing batches, where the operation can be divided into time cycles. These, in turn, are characterized by the phases of filling, reaction (aerobic, anoxic, or anaerobic), sedimentation, and discharge of treated effluent (supernatant) [105]. The filling phase consists of the raw sewage entering the system by pumping or gravity. This stage can be carried out in an ascending or descending direction. The inclusion of the anaerobic/anoxic phase during the feeding of the effluent to the reactor has shown satisfactory results in denitrification and phosphorus removal [36]. In this stage, the denitrification process takes place due to the remaining nitrite/nitrate from the previous cycle.  
The aeration phase in AGS cultivation takes place solely through the insertion of artificial oxygen so that aerobic biological processes can take place. However, many systems with algal-bacterial consortia use the oxygen produced by the microalgae during photosynthesis to carry out aerobic processes [82]. Due to the significant concentration of photosynthetic microorganisms, the algae-bacteria consortium can photosynthetically produce O\({}_{2}\) necessary for the oxidation of organic matter and nitrification. The occurrence of these processes in systems composed solely of granular sludge is exclusively due to the provision of aeration, resulting in greater energy use [49]. Meanwhile, studies using photosynthetic aeration have shown excellent results, with ammonia nitrogen and phosphorus removals of over 90% [82]. The sedimentation phase consists of separating the biomass from the previously treated effluent while the lighter and less sedimentable biomass remains in suspension.  
Like the granular sludge system, the biomass in the granular consortium has excellent sedimentation capacity, producing an effluent free of suspended solids [51; 87]. In the discharge stage, the treated effluent is discharged, carrying the lower-weight biomass that remained in suspension in that cycle [105]. The operating principle of SBR facilitates the retention of high concentrations of photogranular sludge and eliminates the need for settling tanks to separate the biomass from the treated effluent, as well as the return of the  
Figure 4: Different operational configurations: (**A**) SBR, (**B**) SBP, (**C**) HRP, and (**D**) CFR.  
sludge to the reactor [83], just like sequential batch reactors, but they need to be exposed to lighting.
## 3 Methodology
### Model Selection and Classification Scheme
#### 3.2.1 Random Forest (RF)  
Leveraging decision trees while enhancing their accuracy, Random Forest (RF) stands out as a highly effective ML method employed for both regression and classification purposes [81]. RF is acknowledged as a robust classification approach, and its training is carried out through the bagging method. In the initial phase of training, a bootstrapped dataset is generated. The training process involves the use of a specific subset of variables, where decision trees are constructed, and a data point is assigned a class based on the majority of votes garnered from a varying number of decision trees. The most basic classification model involves the partitioning of variables based on the predictor variables of the parent node, resulting in the refinement of the child nodes. Three core hyperparameters shape the performance of the decision tree model: the number of trees (NT), the number of splits (NS), and the depth (d). NT determines the total trees in the ensemble, where each tree votes to classify data points. While a higher NT can improve accuracy, it also increases computational complexity. NS specifies the minimum samples required to split a node; overly high NS values risk underfitting by restricting splits before nodes achieve purity. In RF models, decision trees generate splits to isolate distinct classes. Although more splits enhance data variability capture, excessive splitting may cause overfitting. Balancing these parameters is essential for optimizing model accuracy and generalization.  
The RF classifier produces its output as follows:  
\[\hat{\gamma}=\frac{1}{q}\sum_{N=1}^{N}h_{k}(X)\]  
where \(h(X)\) represents the prediction of the \(k\)th decision tree in the ensemble, and \(X\) denotes the input feature vector.