esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Hybrid Charging Scheme
### System Model of the Two-Stage Hybrid Charging Process  
As in the previous studies [10; 17], the infrastructure sensor nodes are considered in this work. The locations of all the sensor nodes are assumed to be known to the mobile charger. It is reasonably assumed that the sensor nodes are capable of measuring their state-of-charging (SoC). The SoC of respective sensor nodes is also known to the charger by the amount of energy transmitted to each sensor node, which can be readily calculated by the power equation (see Equation (2a)). The SoC estimation based on power equation for the mobile charger is also considered in [10] for their charging scheme.  
Figure 1a depicts the situation in the first stage involving the mobile charger and the distribution of cluster heads. Figure 1b shows the initial distribution of seller and buyer nodes when the second stage begins. Unlike the mobile charger in [10], which sojourns at optimal spots without specifying the transition pattern, the mobile charger considered in this work travels while charging. The mobile charger transmits constant RF power during its travel. Furthermore, the path of sequential movements made by the mobile charger is based on the energy level of the nearby cluster heads. The first stage of the hybrid charging scheme is terminated when all the cluster heads become overcharged. Concurrently, a large number of member sensor nodes around the cluster heads also become overcharged by the mobile charger. As a result, a major proportion of the total sensor nodes has surplus energy and can sell the energy to the undercharged buyer nodes. In this second stage,  
Figure 1: Two stages of the hybrid charging scheme: (**a**) charging by mobile charger in the first stage; and (**b**) energy trading between seller and buyer nodes in the second stage.  
the seller nodes transfer their surplus energy to the buyer nodes so that all the sensor nodes meet the target energy level. Thus, the total charging time can be expressed as  
\[T_{tot}=T_{cm}+T_{et} \tag{4}\]  
where \(T_{tot}\), \(T_{cm}\), and \(T_{et}\) represent total charging time, time for charging by the mobile charger, and time for energy trading, respectively.  
Mobile charger provides energy to the rechargeable sensor network. Mobile charger stops charging as soon as all the cluster heads become overcharged. This means that the first stage is over and the second stage of energy trading is due. Note that the energy provided by the charger in the first stage is the resource of energy balancing, e.g., energy trading, in the second stage. Thus, without the mobile charger, there would be no energy to be traded between sensor nodes.
## 2 Materials and Methods
### Bagged Trees  
This model aims to decrease the variability of the tree. It extracts multiple sub-samples from the data collected and trains a decision tree on each. These operations are executed sequentially. A novel tree arises by amalgamating the outcomes of all the individual trees. This tree attempts to generate predictions as the definitive tree. This process occurs in three distinct steps. Sampling, modeling, and consolidation. This decision tree operates on diverse samples, hence diminishing the variance of the model.  
Additionally, it serves as a highly efficient approach for mitigating overfitting in decision trees. Random forest is a type of bagging technique. In these three variants, the attributes used for splitting at each node are chosen randomly. Figure 3 illustrates the operational mechanism of bagging and boosting on trees. Parallel bagging executes each tree concurrently, whereas boosting is carried out sequentially [68].  
The flow diagram illustrating the application of the models in the investigations is provided in Figure 4.  
Data such as temperature, pressure, and humidity measurements obtained from solar panels are used as inputs in our predictive model to estimate PV energy production. These measured environmental parameters are important to estimate the potential production output through the applied methodology using machine learning algorithms.  
Before training the models, several modifications must be implemented to ensure their usability. Table 3 includes details about these nuances. Additionally, it provides information on the duration of training and the specific sections that are stored in memory. The purpose of these decision trees is to enhance performance by augmenting correlation [63].  
Figure 2: Decision trees features comparison.  
Figure 4: Graphical illustration of the predictive algorithm of decision trees.  
Figure 3: The concepts of parallel and sequential processing.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Training Time (Sec)** & **Prediction Speed (Obs/Sec)** & **Model Size (Bytes)** & **Hyperparameters** \\ \hline Custom Tree & 9.207 & 72,806 & 337,475 & Minimum leaf size: 7 \\  & & & Surrogate decision splits: Off. \\ \hline Fine Tree & 7.008 & 67,993 & 475,771 & Minimum leaf size: 4 \\  & & & Surrogate decision splits: Off. \\ \hline Medium Tree & 6.509 & 86,341 & 21,2367 & Minimum leaf size: 12 \\  & & & Surrogate decision splits: Off. \\ \hline Coarse Tree & 5.673 & 90,821 & 75,919 & Minimum leaf size: 36 \\  & & & Surrogate decision splits: Off. \\ \hline Boosted Trees & 11.720 & 95,299 & 152,608 & Minimum leaf size: 8 \\  & & & Number of learners: 30 \\  & & & Learning rate: 0.1 \\  & & & Number of predictors to sample: \\  & & & Select All \\ \hline Bagged Trees & 13.733 & 41,320 & 7,909,624 & Minimum leaf size: 8 \\  & & & Number of learners: 30 \\  & & & Number of predictors to sample: \\  & & & Select All \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameters, training time, and size used for the models.
# Open Collaborative Platform for Multi-Drones to Support Search and Rescue Operations
## 4 Performance Evaluation
### Simulation Study
#### 4.2.3 Results on Number of Drones vs. Coverage Area  
In this experiment, we varied the number of drones from 5 to 50. Each drone started randomly at a random location around the edge of the field. Figure 19 showed the _Centralized_ approach required a smaller number of drones to search the entire area compared with the other two techniques (i.e., _Distributed_ and _Hybrid_). The _Centralized_ approach covered almost the entire area with only 15 drones. The _Hybrid_ approach achieved 95% of coverage with 20 drones. Both the _Distributed_ approach and the _Random_ approach required more than 30 drones to achieve 95% of coverage. In addition, the time required for the _Centralized_ and _Hybrid_ approaches to complete the search of the entire area was far less than the _Distributed_ and _Random_ approaches, see Figure 22.
## 3 Experiments and Results
### Experimental Setup
#### 3.1.1 Experimental Dataset
Figure 8: Normal and abnormal image of rail fasteners (anomalies with red frames): (**a**) normal rail fasteners; (**b**) broken spring strip; (**c**) missing bolt; (**d**) foreign object occlusion. Figure 7: Normal and abnormal image of MNIST (anomalies with red circles): (**a**) normal NIST; (**b**) zero-position anomalies; (**c**) disorder anomalies; (**d**) missing anomalies.
# A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering
## 2 Preliminaries
### Rating Prediction Method  
This section describes the method to predict user ratings for items in the neighborhood-based CF process. The predictions of user ratings for items can be divided into user-based and item-based predictions. User-based predictions are based on the target user's ratings for other items and similar users' ratings for the target item. The item-based prediction is based on the ratings that the target item has received from other users and the ratings that similar items have received from the target user.  
User \(a\)'s neighbor \(N_{u}(a)\) is defined as the set of \(k\) users that have high similarities with \(a\). Similarly, item \(s\)'s neighbor \(N_{i}(s)\) is defined as the set of \(k\) items that have high similarities with \(s\). Equation (3) represents the rating of user \(a\)'s user-based prediction for item \(s\), where \(\overline{u_{x}}\) is the average rating of all item ratings from any given user \(x\). Similarly,Equation (4) represents the rating of user \(a\)'s item-based prediction for item \(s\), where \(\overline{i_{y}}\) is the average rating of all user ratings for any given item \(y\).  
\[\hat{r}_{a,s}^{u}=\overline{u}_{a}+\frac{\sum_{x\in N_{u}(a)}Sim_{u}(a,x)\times (r_{x,s}-\overline{u}_{x})}{\sum_{x\in N_{u}(a)}Sim_{u}(a,x)} \tag{3}\]  
\[\hat{r}_{a,s}^{i}=\overline{i_{s}}+\frac{\sum_{y\in N_{(s)}}Sim_{i}(s,x)\times (r_{a,y}-\overline{i_{y}})}{\sum_{y\in N_{(s)}}Sim_{i}(s,y)} \tag{4}\]  
The rating for item \(s\) predicted by user \(a\) [\(\hat{r}_{a,s}\)] can be obtained by combining user \(a\)'s user-based and item-based rating predictions for item \(s\) [\(\hat{r}_{a,s}^{u}\)] in Equation (3) and [\(\hat{r}_{a,s}^{i}\)] in Equation (4), respectively, using the parameter \(\lambda\), as expressed by Equation (5). Here, \(\lambda\) is a real number in the closed interval \([0,1]\). Specifically, \(\lambda=0\) and \(\lambda=1\) represent the item-based and user-based predictions, respectively, as the user-based prediction is offset to zero by the coefficient value \(\lambda\) and the item-based prediction is offset to zero by the coefficient value \((1-\lambda)\)[18; 19].  
\[\hat{r}_{a,s}=\lambda\hat{r}_{a,s}^{u}+(1-\lambda)\hat{r}_{a,s}^{i} \tag{5}\]  
Recall from Section 1 that if a CF method only relies on the original user-item interaction, we call it a pure CF method [3]. Generally, the pure CF method can achieve high efficiency in recommendation tasks. However, when users rarely express their preferences for items, it is difficult for pure CF methods to gather the required amount of user preference data for the recommendation. In other words, most pure CF methods suffer from poor recommendation accuracy when the user preference data used in the recommendation process is sparse. We refer to this problem as a data sparsity problem. In the next section, we discuss the studies that solved the data sparsity problem using EMDP, AutAI and AdaM methods.
## 4 Hazards Analysis
### Desertification Hazard Assessment
#### 4.4.4 The Management Quality Index (MQI)
\begin{table}
\begin{tabular}{c c} \hline \hline
**Land-Use Intensity** & \\ \hline
**Description** & **Land-Use Intensity Index** \\ High & 1 \\ Medium & 1.5 \\ Low & 2 \\ \hline \hline \end{tabular}
\begin{tabular}{c c} \hline
**Application of Environmental Protection Policy** \\
**Description** & **Application of Environmental Protection** \\
**PolicyIndex** \\ Fully (\(>\)75\% Protected Area) & 1.0 \\ Partially (25–75\% Protected Area) & 1.5 \\ Insufficient (\(<\)25\% Protected Area) & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Parameter ranking used to obtain the MQI [44]. tolerance, although fire risk remains a concern in certain areas. Climatic conditions are moderately favorable, with adequate precipitation and diverse slope orientations, but the island is also affected by a relatively high aridity index, particularly in the southern regions. Management quality is also rated as moderate, with the widespread implementation of environmental policies helping to balance the effects of moderate land-use intensity. Bringing together the results of these four individual indices, the Environmentally Sensitive Areas Index (ESAI) provides an overall assessment of land vulnerability to desertification. It is calculated as the geometric mean of the Soil Quality Index (SQI), the Climate Quality Index (CQI), the Vegetation Quality Index (VQI), and the Management Quality Index (MQI), using the formula:  
ESAI = (SQI \(\times\) CQI \(\times\) VQI \(\times\) MQI)'(1/4)  
The geometric mean was applied to combine the four sub-indices, following the original MEDALUS framework [31]. This method ensures that each component has an equal influence on the final ESAI value, preventing any single variable from disproportionately affecting the result. Based on the ESAI value, land areas are classified as:  
* Not Threatened (N): ESAI < 1.17;
* Potentially Sensitive (P): 1.17-1.22;
* Sensitive (F1-F3): 1.23-1.37;
* Critical (C1-C3): ESAI > 1.38. When applied to Lefkas Island (Figure 18), the ESAI shows that large portions of the island fall within the sensitive and critical categories. This outcome reflects the combined effects of shallow soils, steep terrain, moderate aridity, and human pressures. Although the island benefits from strengths such as dense vegetation cover and active environmental policy implementation, these positive factors are not sufficient to offset the underlying vulnerabilities--particularly in the southern and mountainous zones, where the risk of land degradation remains high.
# Performance Analysis of Cooperative Non-Orthogonal Multiple Access in Visible Light Communication
## 1 Introduction
Otherwise, users with good channel conditions require extra time slots to deliver data to those with bad channel conditions [16]. To the best of our knowledge, this paper is the first to investigate the bit error rate (BER) performance of cooperative PD-NOMA in downlink VLC systems and to compare it to non-cooperative PD-NOMA under perfect channel state information (CSI). In this context, we drive a closed-form expression for BER and provide Monte Carlo simulation results to validate the derived analytical BER. Moreover, we calculate the achievable rate of each user for non-cooperative and cooperative PD-NOMA. The remainder of this paper is organized as follows; in Section 2 we give an indoor downlink VLC channel description and system model explanation of non-cooperative and cooperative PD-NOMA. In Section 3, we analyze the BER system performance under perfect CSI. In Section 4, we provide numerical results and related discussions. Finally, we conclude the paper in Section 5.
# Ribosomal Hiberation Factor Links Quorum-Sensing to Acid Resistance in EHEC
## 3 Results
### RMF Serves as the Essential Downstream Executor of AHL-Mediated Acid Resistance  
Genetic evidence demonstrates that RMF is indispensable for AHL-conferred acid protection in EHEC. Blunted AHL efficacy was displayed in \(\Delta\)_rmf_ (Figure 4A). One hundred \(\upmu\)M AHL induced maximal 2.5-fold survival increase (3 h) in \(\Delta\)_rmf_, representing only 28% of the WT response (8.7-fold). Peak protection delayed to 3 h (vs. 2 h in WT), with 2 h efficacy reduced to 22% (2.2-fold vs. WT 9.9-fold). In colony enumeration assays (Figure 4B), \(\Delta\)_rmf_ required 1000\(\times\) higher cell concentrations for detection after 3 h of acid stress. One hundred \(\upmu\)M AHL generated <30 CFU in \(\Delta\)_rmf_ vs. confluent growth in WT at an identical dilution (10\({}^{6}\)). Endogenous AHL showed limited rescue ability (Figure 4C). _yen_l-expressed AHL in \(\Delta\)_rmf_ background provided \(\leq\)3.9-fold protection (vs. 7.0-fold in WT/_pyen_l). Viability reached 0.1% of WT/_pyen_l levels at 3 h. At matched dilutions, \(\Delta\)_rmf_/_pyen_l exhibited sparse colonies vs. confluent growth in WT/_pyen_l (Figure 4D).  
Figure 4: RMF is indispensable for AHL-enhanced acid survival in EHEC. (**A**) Exogenous AHL fails to rescue acid sensitivity in \(\Delta\)_rmf_. The \(\Delta\)_rmf_ mutant exposed to pH 2.5 for 1–3 h showed minimal protection from 3-oxo-C6-AHL. Data: Relative CFU = CFU\({}_{\rm h}\)/CFU\({}_{0}\) (mean \(\pm\) SEM; * \(p\) < 0.05, **_p_ < 0.01, ***_p_ < 0.001 vs. 0 \(\upmu\)M at matched time points). (**B**) Endogenous AHL partially restores  
acid resistance. Complementation with _yen1_ (\(\Delta\)_rmf_/_pyen1_) provided \(\leq\)3.9-fold protection after 3 h at pH 2.5 versus 7.0-fold in WT/_pyen1_ (\"_p_ \(<\) 0.05 vs. \(\Delta\)_rmf_). **(C)** Plate assay demonstrating exogenous AHL efficacy gap. WT maintained confluent growth at 10\({}^{6}\) dilution after 3 h acid stress with 100 \(\upmu\)M AHL, while \(\Delta\)_rmf_ required 1000\(\times\) higher cell concentrations for detection. **(D)** Endogenous AHL production enhances acid survival. WT/_pyen1_ and \(\Delta\)_rmf_/_pyen1_ exhibited denser colonization versus non-AHL-producing counterparts at standardized dilutions (10\({}^{4}\)-10\({}^{6}\)) after 3 h exposure.  
Collectively, _rmf_ deletion abolished 72-99.9% of AHL-mediated acid resistance, establishing RMF as the non-redundant effector of quorum sensing signaling.
## 3 Results and Discussion
### Optimal Network
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Change Order**} & \multirow{2}{*}{**Change Type**} & \multicolumn{2}{c}{**Name (Site/TX/Cell)**} & \multirow{2}{*}{**Initial**} & \multirow{2}{*}{**Final**} & \multicolumn{2}{c}{**Quality Improvement Ratio \%**} \\ \hline
1 & Power (W) & Site 2 & 27 & 30 & 100 \\
2 & Azimuth (\({}^{\circ}\)) & Site 2 & 0 & 340 & 100 \\
3 & Site off & Site 3 & on & off & 100 \\
4 & Site off & Site 0 & on & off & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Optimal parameters using ACP. Figure 6: Map showing the signal power coverage (dBm) predicted in ENST campus. Depending on the exact location of Wi-Fi users within the campus, they could connect to different access points providing different levels of signal quality. The transmitter to which the subscriber can connect is classified in descending order according to the quality of the signal that they could obtain.
# An Updated Review of Smac Mimetics, LCL161, Birinapant, and GDC-0152 in Cancer Treatment
## 10 GDC-0152 for Cancer Treatment  
The development of GDC-0152, a pan-IAP antagonist, as an anti-cancer agent was first reported in 2012 [49]. The anti-cancer potency of GDC-0152 was evaluated in glioblastoma, leukemia, osteosarcoma, and glioblastoma [101; 102; 49; 103; 104]. At the molecular level, GDC-0152 induces intrinsic caspase-dependent apoptosis and inhibits the phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) signaling pathway, resulting in cancer cell death [101; 102]. [PERSON] et al. demonstrated that GDC-0152 not only downregulated the protein expression level of cIAP1, cIAP2, and XIAP, but it also downregulates ML-IAP in glioblastoma  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ClinicalTrials.Gov Identifier** & **Phase** & **Condition or Disease (in Patients)** & **Combination Therapy** \\ \hline NCT03803774 & Phase 1 & Head and neck squamous cell & Radiation therapy \\  & & carcinoma & \\ \hline NCT02587962 & \begin{tabular}{c} Phase 1/2 \\ (terminated) \\ \end{tabular} & Solid tumor & Pembrolizumab \\ \hline NCT02147873 & \begin{tabular}{c} Phase 2 \\ (terminated) \\ \end{tabular} & \begin{tabular}{c} Myelodysplastic syndrome \\ Chronic myelomonocytic leukemia \\ \end{tabular} & \begin{tabular}{c} Azactidine \\ Placebo \\ \end{tabular} \\ \hline NCT01940172 & \begin{tabular}{c} Phase 1 \\ (completed) \\ \end{tabular} & \begin{tabular}{c} Relapsed epithelial ovarian cancer \\ Relapsed primary peritoneal cancer \\ Relapsed fallopian tube cancer \\ \end{tabular} & Conatumumab \\ \hline NCT01188499 & \begin{tabular}{c} Phase 1/2 \\ (completed) \\ \end{tabular} & Cancer & \begin{tabular}{c} Carboplatin \\ Paclitaxel \\ Irinotecan \\ Docetaxel \\ Gemcitabine \\ Lipsosomal Doxorubicin \\ \end{tabular} \\ \hline NCT01681368 & \begin{tabular}{c} Phase 2 \\ (terminated) \\ \end{tabular} & Epithelial ovarian cancer & \\  & & Peritoneal neoplasms & - \\  & & Fallopian tube neoplasms & \\ \hline NCT00993239 & \begin{tabular}{c} Phase 1 \\ (completed) \\ \end{tabular} & Cancer & - \\ \hline NCT01573780 & \
\begin{tabular}{c} Phase 1 \\ (terminated) \\ \end{tabular} & Adult solid tumor & Gemcitabine \\ \hline NCT01486784 &
\begin{tabular}{c} Phase 1/2 \\ (terminated) \\ \end{tabular} & Acute myelogenous leukemia & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Clinical status of birinapant (TL32711) for cancer treatment.  
cells in vitro and in vivo [104]. [PERSON] et al. found that the pharmacological properties of GDC-0152 are correlated with pH [105]. They demonstrated that the presence of succinin acid and hydroxypropyl-\(\beta\)-cyclodextrinrin can increase the solubility of GDC-0152, thereby improving blood compatibility, reducing hemolysis, and increasing the maximum tolerated dose [105]. Additionally, they remain stable in human plasma for up to 25 h [106]. Only one phase 1 clinical trial has studied the safety and pharmacokinetics of GDC-0152 (NCT00977067); however, it was terminated for reasons unrelated to patient safety or anti-cancer activity in 2017.
# Production of Sustainable Postbiotics from Sugarcane Straw for Potential Food Applications
## 2 Materials and Methods
### Postbiotic Extract Biological Characterization
#### 2.5.2 Radical Scavenging Activity  
The 2.2\({}^{\prime}\)-azino-bis (3-ethylbenzothazoline-6-sulphonic acid) (ABTS\({}^{+}\)) radical cation test was used to calculate the total antioxidant capacity. Potassium persulfate (2.45 mM) and ABTS (7 mmol/L) were combined in a 1:1 (\(v/v\)) ratio to create the ABTS\({}^{+}\) solution. On a 96-well microplate, the reaction was carried out in five minutes at 30 \({}^{\circ}\)C with diluted ABTS (OD of 0.700 \(\pm\) 0.020 at 734 nm) and the samples (15 \(\mu\)L of each sample in duplicated reacted with 200 \(\mu\)L of ABTS). After that, OD was measured in a microplate reader (Synergy H1 microplate reader, Biotek, Winooksi, Vermont, EUA) at 734 nm [29]. For the calibration curve, Trolox was used (0.0075-0.075 mg/mL). A serial of sample dilutions were performed (0.39-12.5 mg/mL) in methanol to evaluate the IC\({}_{50}\) (mg/g dw).  
DPPH Radical Cation  
The 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical test was used to calculate the DPPH. The working solution was created up to a final OD of 0.600 \(\pm\) 0.100 at 515 nm after the DPPH solution (600 \(\mu\)M) was made in methanol. After mixing the sample (25 \(\mu\)L) and DPPH (175 \(\mu\)L) in duplicate, the OD was measured at 515 nm in a microplate reader after 30 min at 25 \({}^{\circ}\)C (Synergy H1 microplate reader, Biotek, Winooski, Vermont, EUA) [29]. For the calibration curve, Trolox was used (0.0075-0.075 mg/mL). A serial of sample dilutions was performed (0.14-12.5 mg/mL) in methanol to evaluate the IC\({}_{50}\) (mg/g dw).  
Oxygen Radical Absorption Capacity (ORAC)  
The antioxidant capacity was evaluated using the Oxygen Radical Absorbance Capacity (ORAC) assay [30]. In summary, the reaction took place at 40 \({}^{\circ}\)C in 75 mM phosphate buffer (pH 7.4), and the final test mixture (200 \(\mu\)L) contained the following compounds: Trolox (10-80 \(\mu\)M) or sample (0.08-1.667 mg/mL), fluorescein (70 nM), AAPH (14 mM). For 137 min (104 cycles), the fluorescence was captured using a 485 nm excitation and 580 nm emission wavelength (Synergy H1; BioTek, Winooski, VT, USA). We utilized 96-well black polystyrene microplates from Nunc in Denmark. Daily, AAPH and Trolox solutions were prepared, and fluorescein stock solution (1.17 mM) in 75 mM phosphate buffer was diluted (pH 7.4). At least three separate runs were carried out for each sample, and all reaction mixes were prepared in duplicate. The results were expressed as \(\mu\)M of Trolox equivalent per g dry weight.