esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Related Work
Furthermore, the BE is made to start from 1, rather than 0, to avoid having the same third, fourth, and fifth backoff ranges (as was the case with [18]). The performance of TCP-CSMA/CA in terms of the parameters mentioned above was studied via extensive simulations, which showed it to be superior to the 802.15.4 standard. In [22], the authors proposed REO, a reliable and energy-efficient optimization algorithm that optimizes delay, reliability, and energy trade-offs based on traffic and QoS requirements. The authors refer to the delay and energy specifications of medical sensors that are commonly used in WBANs. The best packet size and data rate combination that maximizes the sensor node's battery lifetime was investigated based on these specifications. By properly adjusting the MAC parameters to be consistent with the packet size and data rate selected, QoS requirements could be achieved. Simulation results reflect that REO can outperform comparable works in delay, energy consumption, and reliability. In [23], the authors focused on the fact that 802.15.4 uses a fixed superframe structure without considering the different needs of the nodes. The 802.15.4 does not recognize the demands of different applications. To improve 802.15.4 in this direction, the authors proposed a content-based dynamic superframe adaptation (CDSA) scheme by which to adapt BO and SO based on the content requirements simultaneously. The network coordinator uses five QoS parameters (i.e., data rate, receive ratio, size of the network, number of collisions, and delay) to adjust the superframe and the BE dynamically to achieve the optimal solution. The superframe adjustment scheme aims at achieving higher throughput and reducing end-to-end latency. Furthermore, the nodes' duty cycle is reduced during no activity periods. This way, the network lifetime is enhanced. Performance evaluation through simulations showed that CDSA, compared to 802.15.4 and other schemes that adjust SO and/or BO, can achieve a better packet delivery ratio and throughput, more energy savings, and lower end-to-end delay. In light of reviewing the works above, we can make the following observations:  
1. These are very recent studies that prove the validity of the problem we are tackling in this paper, that is, proposing improvements to the MAC protocol as an important means of enhancing the performance in WBANs. These studies focus on IEEE 802.15.4 MAC, which proves that this protocol is still a potential candidate for deployment in WBANs. * Within the limits of our review, works in the literature are mainly focusing on small-scale WBANs. This fact motivates the investigation of new MAC protocols that maintain promising performance as the WBAN grows in size; that is, studying MAC protocols in large-scale WBANs is validated.
## 1 Introduction
### Effective Proposed Approaches for Classifying Multiple PD Sources
#### 1.3.3 Deep Learning  
A CNN is developed to classify PD sources based on time-frequency data and proves highly effective in identifying multiple PD sources, even amidst overlapping signals.
# Pretreatments Applied to Wheat Straw to Obtain Bioethanol
## 3 Pretreatments Applied to Wheat Straw
### Physical Pretreatment
#### 3.1.2 Irradiation of Wheat Straw  
Pretreatment of wheat straw by irradiation accelerates the enzymatic hydrolysis of the straw [40]. Electron beam accelerator gamma irradiation of wheat straw increases acid hydrolysis of the straw, while the increasing radiation dose results in sugar reduction yields that are higher compared to non-irradiated straw [41].  
The most-used alkaline solution for the pretreatment of wheat straw is sodium hydroxide; thus, by using NaOH/H\({}_{2}\)O\({}_{2}\) for pretreatment at a temperature of 50 \({}^{\circ}\)C, 3-15 h, a sugar reduction of 61.9 g/L was obtained, the delignification rate was 60%, and the yield of bioethanol was 31.1 g/L, for fermentation condition 37 \({}^{\circ}\)C and 96 h.  
According to research carried out by [PERSON] [40], from wheat straw irradiated with ultrasound and pretreated with alkali 0.2% H\({}_{2}\)O\({}_{2}\), for 12 h, hemicellulose was extracted at a percentage of 27.1-28.1% [35].  
Through microwave gamma irradiation, a reduction in the oligosaccharide content from 0.04% to 26.78% and glucose from 0.01% to 0.65% was obtained, while the lignin-hemicellulose matrix was decomposed by the sudden increase in temperature [35].  
With ultrasound, the lignin was reduced to 85%, while the crystallinity index and the porosity of the biomass surface increased. The bioethanol yield also increased up to 65.8 mg/L, by increasing the sonication time from 5 to 10 min [41].
# Axial Compression Behavior of Steel Angle-Corrugated Steel Plate-Confined Concrete Columns
## 1 Introduction
In addition, as the internal concrete and corrugated steel tube cannot bear the tensile stress, it is necessary to embed the structural steel or longitudinal bars inside the column to ensure the structure's bending performance, which undoubtedly increases the difficulty of construction. Thus, the research group designed a new type of S-CSC column consisting of corrugated steel plates, steel tubes, core concrete, and concrete in steel tubes. The S-CSC column has been applied in actual projects because of its advantages with respect to its good bending resistance, easy connection, and lack of embedded longitudinal bars, as shown in Figure 1[22]. However, to ensure the compactness of concrete in the steel tubular structure, the S-CSC column must maintain a large section size, which makes it unsuitable for civil buildings. In order to solve this problem, the steel tubular design was replaced by steel angles, and a novel type of steel angle-corrugated steel plate-confined concrete column (SA-CS column) was proposed in this paper. The SA-CS column consists of an outer steel skeleton formed by the welding of the steel angle and corrugated steel plate as well as poured core concrete, as shown in Figure 2. The steel angles are placed outward to provide a plane that can be welded with the corrugated steel plates. Whereas the steel angles projecting inwards and corrugated steel plates cannot form a completely closed cavity. Considering the primary function of the steel angle is to connect with the corrugated steel plate, bearing a small part of the bearing capacity, the effect of lateral buckling on the  
Figure 1: Application of S-CSC column [22]. bearing capacity of the SA-CS column is negligible. Thus, the scheme wherein the steel angles are projecting inward is adopted. The corrugated steel plate is welded horizontally with the steel angle to ensure that the corrugated steel plate's trough and the steel angle's inner edge are in the same plane. Due to the omission of the steel tubular structure, the size limit of the SA-CS column is relaxed, and it is suitable for buildings with small sizes. The purpose of this paper is to propose a new structural form to improve the disadvantages of S-CSC columns that are not suitable for civil buildings. The attempt will provide a new idea for structural design and enable the popularization of structures with corrugated steel plates. In this paper, experiments and finite element simulation evaluated the axial compression behavior of the SA-CS column. On this basis, a parameter analysis was supplemented to explore the primary role of each part of the specimen. Finally, we compare the numerical results with the calculated results by some existing codes and propose a newly designed method for predicting the ultimate bearing capacity of the SA-CS column.
## 4 Proposed QL-Driven ABC-Based Solving Approaches
### QL-Based Employed Bees Phase
#### 4.3.2 Q-Learning Algorithm  
In the proposed IQABC algorithms, a QL algorithm is used to guide the choice of the neighborhood structures in the employed bees phase. The states of the QL model are defined by the set of population individuals, while the six neighborhood structures indicate the set of actions. At each iteration, the QL agent observes the current solution (\(s\)), selects an action (\(a\)) (a neighborhood structure), which generates a new solution (\(s^{\prime}\)), and consequently, it receives a reward/penalty (\(r\)) signal according to the quality of the generated solution (see Formula (6)). The agent records its learning experience by a \(Q\)-value (\(Q\)(\(s\),\(a\))), which is stored in a \(Q\)-table. The update of the \(Q\)-value is given in Formula (7), where \(\alpha_{ql}\) is the \(Q\)-learning rate and \(\gamma\) is the discount factor.  
\[r=1+C_{max}(S)-C_{max}(S^{\prime}) \tag{6}\]\[Q(s,a)=Q(s,a)+\alpha_{ql}\times(r+\gamma\times\max_{a^{\prime}}Q(s^{\prime},a^{ \prime})-Q(s,a)) \tag{7}\]  
The action selection mechanism is implemented using an \(\epsilon\)-greedy strategy, where the agent can choose the best action considering the associated \(Q\)-value (exploitation), or can select one action randomly (exploration) with an \(\epsilon\) probability. Thus, the e-greedy strategy enables both exploitation and exploration. That is, the agent has to exploit what it already knows to obtain a reward, but it also needs to explore in order to make better action selections in the future. The employed bees stage with the QL algorithm is recalled in Algorithm 3.  
```
0: Search space, Q-table of the population Begin for Each solution S from the search space do\(\triangleright\) S is the current state  Generate a random number a if\(a\leq\epsilon\)then  Choose the best action (Neighbor structure \(NS\)) associated with the best Q-value of the Q-table. else  Choose the best action \(NS\) randomly endif  Generate New Solution \(Sol^{\prime}\) using \(NS\)\(\triangleright\) Sol' is the next state endfor r\(\leftarrow\) 1+makespan(\(Sol\))-makespan(\(Sol^{\prime}\))\(\triangleright\) reward formula  Update the Q-value of the tuple (\(Sol\),\(NS\)) as specified in Formula (7) ifmakespan(\(Sol^{\prime}\)) \(\leq\) makespan(\(Sol\))then  Replace \(Sol\) with \(Sol^{\prime}\) endif  update the search space End
```  
**Algorithm 3** QL-based Employed bees phase  
To implement the QL algorithm, we used the \"pandas\" python library. The Q-table is stored in a DataFrame structure, which is a two dimensional table where indexes (lines) point to food sources as represented in Section 4.1, and columns refer to a defined list of neighborhood structures as presented in Section 4.3.1.
## 2 Results and Discussion
### Evaluation of Minimum Inhibitory Concentration (MIC)  
In order to establish the metabolic profiling of VOCs emitted by bacteria isolated from pressure ulcers, firstly, the minimum inhibitory concentration (MIC) value was determined. The results indicate that all of the investigated bacterial strains (_E. coli_, _E. faecalis_, _K. pneumoniae_ and _P. mirabilis_) presented similar susceptibility to bio-AgNPs; the MIC value for tested microorganisms was found to be 12.5 ug mL\({}^{-1}\). Moreover, according to previous published results [15; 26; 27] it was observed that the antimicrobial potential of the bio-AgNPs is concentration-dependent; higher concentrations may induce not only the inhibition of bacterial growth but also bacterial death [15]. Therefore, for the investigation of bacterial VOC profiles, chosen concentrations of bio-AgNPs corresponded to MIC value of each strain and two concentration levels above this one (25 and 50 ug mL\({}^{-1}\)).
## 3 Results and Discussion
### Deep Learning Segmentation  
In deep learning segmentation, the sequence of the techniques used are PCS enhancement, SLIC superpixel, and CNN semantic segmentation. Table 2 shows the example of the ALO species captured in three different illuminations with the results obtained when the PCS technique was implemented. Through the enhancement results obtained, the contrast and brightness of the enhanced images were increased significantly. The enhanced image has shown better differentiation between the background and the helminth ovum. However, the background color was slightly changed. Even though this situation happens, it does not affect the next procedure as the helminth ovum can be seen clearly.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Image Illumination** & **Normal Image** & **Over-Exposed Image** & **Under-Exposed Image** \\ \hline MGCS Image & & & \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 2: The results of machine learning segmentation technique on TTO image: (**a**) MGCS on TTO image; (**b**) CMYK_K color model on enhanced TTO image; (**c**) FCM segmentation on TTO image; (**d**) Post-processing on TTO image. Scale bar = 50 \(\upmu\)m.  
After that, the SLIC superpixel technique together with CNN segmentation was applied to the enhanced image. The resultant image obtained was used as the input for the CNN segmentation. Then, the CNN segmentation was applied through three different backbones, which are VGG-16, ResNet-18, and ResNet-34. A clean and clear image has been obtained. Figure 3 shows the resultant image achieved for the deep learning segmentation based on TTO image.
## 5 Use Cases
### Integration with Other Components  
In the previous use case, a dashboard was generated based on a configuration file and a dataset. This generation was straightforward because data were already in the right format for the chosen visualizations (in this case, simple visualizations such as bar charts), and no additional computations were needed.  
However, data are not always in the right format for every visualization [43], and most times, it is necessary to transform the datasets before visualizing them. That is why a complementary data transformation service is included within the ecosystem. As will be discussed, adding this component benefits users in terms of delegating data transformations to an independent component and captures the implicit knowledge that is contained in the execution of data preprocessing tasks.  
The data transformation component provides a solution for performing data computations and also to format data to different formats. As explained in Section 4, this component is also based on API calls. This example performs calculations on sociodemographic data to offer a Sankey diagram (which can be classified as a \"flow layout\" [43] or \"parallel sets layout\" [45]) in which its links represent the count of each category within each variable (Figure 7).  
As with the dashboard generator service, the HTTP POST is processed by the data transformation service. In this case, the operation specification contains the information needed for the service to perform the data transformations requested by the client. Once the data are processed, they are returned within an HTTP response.  
The API call results can be subsequently used with the dashboard generator service to create a visualization: in this case, a Sankey diagram (Figure 8).
# Learning-Based Online QoE Optimization in Multi-Agent Video Streaming
## 4 Problem Formulation  
In this section, we propose a slice assignment system to distribute the download link bandwidth to users. Let \(\pi(l)=(\pi_{1}(l),\cdots,\pi_{K}(l))\) be a vector in \([0,1]^{K}\) such that \(\sum_{k\in K}\pi_{k}(l)=1\). Each element \(\pi_{k}(l)\) denotes the portion of the total bandwidth that user \(k\) is assigned to. By this definition, user \(k\)'s downloading bandwidth \(d_{k}(l)\) under policy \(\tilde{\pi}(l)\) can be calculated as \(\pi_{k}(l)B\).  
The Multi-Agent Video Streaming (MA-Stream) optimization problem is defined as the following:  
Problem MA-Stream : \[\max \sum_{k\in[K]}\sum_{v\in V_{k}(L)}f\left(\sum_{l\in L_{v}}\frac{T _{s}(l,T_{l},k)}{T_{l}}\right),\] (22) s.t. \[\sum_{k\in[K]}d_{k}(l)=B\;\forall l\in\{1,\cdots,L\},\] (23) var. \[\tilde{\pi}.\] (24)We now discuss the MA-Stream optimization problem described in Equations (22)-(24). Equation (22) denotes the sum of the quality of experience for each user \(k\in[K]\) across each video played in \(L\) time slots. The control variable is the policy \(\pi\) (in Equation (24)), which directly controls the bandwidth allocation. This gives the constraint in Equation (23) where the sum of allocated bandwidths, \(d_{k}(l)\), to each user \(k\in[K]\) can be, at most, the total bandwidth of the system for all slots \(l\in[L]\). Moreover, the QoE for any video is a non-linear function of the cumulative stall durations over each chunk in the video played.  
We utilize the deep reinforcement learning technique to optimize the bandwidth distribution \(\tilde{\pi}(l)\). In the following sub-sections, we define the state, action, and objective for the decision making.
## 3 Method
### Operations
#### 3.3.1 Before  
Scheduling: We discovered that there were three natural break points that remote clinicians could be available: 08:00-09:00 (pre-clinic administrative time), 12:00-13:00 (lunch time) and 16:00-17:00 (end of the day). Experience promptly revealed that the 16:00-17:00 hour was least optimal due to natural end-of-the-day exhaustion; additionally, often clinicians would still be tending to patients (well) past 16:00. We discovered that convening sessions on a fixed schedule (e.g., 3 rd Thursday of the month from 12:00-13:00) facilitated the strongest attendance.  
Reservations: The project manager is responsible for scheduling the videoconference system (MCU), (video) conference room and sending email notifications to all SCAN-ECHO participants detailing the date, time, connection code and referring providers whose cases will be addressed in each session. These emails are sent out one week before each session along with a one-day-before reminder message.  
Specialty team meetings: The specialty team initially convened for one hour per week to formulate and adapt SCAN-ECHO operations and strategies. Subject matter included ideas for improving and refining services, processing findings gathered from the participant surveys, considering new ideas, recommendations and questions that emerged during sessions, identifying relevant didactic subject matter and brainstorming as needed. Additionally, the project manager, a mental health clinician, actively solicited and attended to the emerging impressions and feelings of all involved. As our processes became more refined, we shifted to meeting every other week.  
Website: The project manager regularly updates the website; this typically involves uploading the PowerPoint didactic presentations and case summaries generated by the specialty team.
## 3 Methods
### AFX Method  
The AFX is defined as the ratio of the white sky albedo (WSA) to the isotropic parameter \(f_{iso}\). The formulation of the model is as follows [26]:  
\[AFX(\lambda)=\frac{WSA}{f_{iso}(\lambda)}=1+\frac{f_{vol}(\lambda)}{f_{iso}( \lambda)}H_{\text{ker\_vol}}+\frac{f_{geo}(\lambda)}{f_{iso}(\lambda)}H_{\text{ ker\_geo}} \tag{4}\]  
where, \(H_{\text{ker\_vol}}\) and \(H_{\text{ker\_geo}}\) are the bi-hemispherical integrals of the volumetric and geometric-optical kernels; here they are 0.189184 and \(-\)1.377622, respectively.  
The AFX is utilized to discern the general characteristics of surface reflectance anisotropy, as outlined in [43]. Based on this attribute, six distinct classes of spectral BRDF prototypes were categorized. To mitigate the discrepancies in spectral reflectance amplitudes across various surfaces, the raw MODIS BRDF parameters were subjected to normalization. This process involved multiplying the parameters by a scaling factor \(K\), where \(K=0.5/f_{iso}\). Table 4 shows the AFX ranges and the corresponding normalized BRDF parameters for the six spectral archetypes, specifically within the green, red, and near-infrared bands.  
Utilizing the AFX method, the angular correction for the Jilin-1 GP01/02 PMS data was executed. Initially, the BRDF parameters derived from the MCD43A1 dataset were resampled to resolutions of 5 m. Subsequently, the AFX values were computed in accordance with Equation (4). The normalized BRDF parameters, \(F_{iso}\), \(F_{vol}\), and \(F_{geo}\) were ascertained based on the distribution of AFX values. Ultimately, the Jilin-1 GP01/02 PMS data were adjusted using these normalized BRDF parameters. The formula for the correction is presented as follows:  
\[R_{\lambda}(\theta_{s},0,\varnothing)=R_{\lambda}(\theta_{s},\theta_{v}, \varnothing)\times\frac{F_{iso}(\lambda)+F_{vol}(\lambda)K_{vol}(\theta_{s},0, \varnothing)+F_{geo}(\lambda)K_{geo}(\theta_{s},0,\varnothing)}{F_{iso}( \lambda)+F_{vol}(\lambda)K_{vol}(\theta_{s},\theta_{v},\varnothing)+F_{geo}( \lambda)K_{geo}(\theta_{s},\theta_{v},\varnothing)} \tag{5}\]
## 1 Introduction
Thus, it was found that for a photocatalytic system with dispersed TiO\({}_{2}\), the photocatalytic degradation efficiency is maximum at a pH of the aqueous solution of 5 [22]. For a photocatalytic system with dispersed TiO\({}_{2}\) intercalated talc nanocomposite, the best results were recorded for neutral conditions of the aqueous solution of 2,4-dichlorophenol [23]. In the case of a photocatalytic system with dispersed Fe/TiO\({}_{2}\), the maximum degradation efficiency of 2,4-dichlorophenol in aqueous solution was recorded at a pH of 4 [24]. For a photocatalytic system with a Fe\({}^{0}\)/TiO\({}_{2}\)/ACF (activated carbon fiber) composite photocatalytic membrane, it has been established that the optimum pH of the aqueous 2,4-dichlorophenol solution subjected to the photocatalytic treatment is 6 [25]. Thus, it becomes clear that for each new photocatalytic system, it is necessary to optimize the operating parameters in order to obtain the highest possible photocatalytic degradation efficiency of specific refractory organic compounds. Therefore, the influence of some operating conditions on the performances of an UV photocatalytic reactor with an immobilized photocatalyst (new photocatalytic membrane --titanium dioxide deposited on the stainless-steel grid support, TiO\({}_{2}\)/stainless steel) for removing 2,4-dichlorophenol from aqueous solutions was studied in this work. The paper aims to identify the optimal pH of the working solution and to establish the need for hydrogen peroxide to be added to the system so that the efficiency of photocatalytic degradation is as high as possible. The influence of the initial concentration of the organic substrate is also investigated.