esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Combined Standardized Emission and Waste Inventories (stewicombo)  
The _stewicombo_ module utilizes the outputs of _stewi_, _chemicalmatcher_, and _facilitymatcher_ to generate combined inventories that handle duplication and aggregation across inventory sources. In particular, _stewicombo_:  
* Identifies common facilities across datasets;
* Aggregates multiple entities into a single facility;
* Assesses potential duplicate flows from a facility when a flow is reported to be emitted to the same compartment across more than one inventory; and
* Enables custom handling of inventories (e.g., inventory preferences for duplicate flows).  
In _stewicombo_, users have access to three methods for combining inventories (Table 5). These methods allow users to combine flowbyfacility data from one or more inventories. In each case, _stewicombo_ first aligns facilities across inventories using the FRS_ID sourced from _facilitymatcher_ and then aligns flows using the SRS_ID from _chemicalmatcher_.  
The overlap handler module of _stewicombo_ handles the core functions of aggregating and removing overlapping flows within facilities. Under default settings, inventory records are compiled using the following logic:  
* Records that share a common compartment and SRS_ID (i.e., are the same flow) and FRS_ID (i.e., are the same facility) within an inventory are summed. This case typically reflects a single facility reporting to two or more facility IDs within an inventory that need to be aggregated; and
* Records that share a common compartment and SRS_ID (i.e., are the same flow) and FRS_ID (i.e., are the same facility) across multiple inventories are assessed by  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Function** & **Method** \\ \hline Combine Full Inventories & Combines flowbyfacility data for all facilities in the selected inventory(-ies) \\ \hline Combine Inventories for Facilities in & Combines flowbyfacility data for all facilities in the selected inventory(-ies); maintains only those facilities with data present in the base_inventory. \\ \hline Combine Inventories for Facility List & Combines flowbyfacility data for all facilities in the selected inventory(-ies); maintains only those facilities with data present in the base_inventory that are included in the facility_id_list. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Functions for combining inventories in _stewicombo_.  
compartment preference (see Table 6). This case reflects double counting by reporting of the same chemical across two or more inventories.  
Additional steps are taken to avoid overlap of:  
* Nutrient flow releases to water between the TRI and DMR;
* Particulate matter releases to air reflecting PM < 10 and PM < 2.5 in the NEI; and
* Volatile Organic Compound (VOC) releases to air for individually reported VOCs and grouped VOCs.
# Evaluation of Remote Sensing-Based Evapotranspiration
## 3 Methodology
### Evaluation Indices
For KGE, \(r\) is the linear correlation coefficient between observed runoff (remote sensing-based ET datasets) and simulated runoff (simulated ET), \(a\) is the ratio of the standard deviation of simulated runoff (simulated ET) to the standard deviation of observed runoff (remote sensing-based ET datasets), and \(b\) is the ratio of the mean of simulated runoff (simulated ET) to the mean of observed runoff (remote sensing-based ET datasets).
## 3 Results
### Mean Peak Moments  
The results of the peak moments are summarized in Table 4. There was a significant large effect of load on mean peak moments in both groups (\(p<0.05,\eta\)2p > 0.3), suggesting  
Figure 4: Hip joint range of motion adduction–abduction (in degrees) according to the PFPS, controls and load conditions (right leg).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \multicolumn{2}{c}{**PFPS Group**} & \multicolumn{2}{c}{**Control Group**} & \multicolumn{2}{c}{**\(p\) Value (between**} & \multicolumn{2}{c}{**\(p\) Value (between**} \\  & **(N = 10)** & **(N = 13)** & **(Load vs. No Load)** & **Load vs. No Load)** & **Research Groups)** & **(Interaction Group X Load)** \\ \hline  & \multicolumn{2}{c}{**Without**} & \multicolumn{2}{c}{**With Load**} & \multicolumn{2}{c}{**With Load**} & \multicolumn{2}{c}{**(Partial Eta**} & \multicolumn{2}{c}{**(Partial Eta**} \\  & \(\overline{\textbf{X}}\) (\(\pm\)SD) & \(\overline{\textbf{X}}\) (\(\pm\)SD) & \(\overline{\textbf{X}}\) (\(\pm\)SD) & \(\overline{\textbf{X}}\) (\(\pm\)SD) & **Square n2p**)** & **Square n2p**)** & **Square n2p**)** \\ \hline
**Hip adduction- abduction** & 11.20 (2.13) & 13.21 (2.24) & 11.98 (1.72) & 16.99 (4.38) & \(<\)0.001 \({}^{\star}\) & 0.041 \({}^{\star}\) & 0.025 \({}^{\star}\) \\
**abduction** & & & & & (0.600) & (0.185) & (0.217) \\
**Hip internal– external rotation** & 13.61 (4.77) & 14.00 (3.21) & 11.85 (4.22) & 13.05 (1.79) & 0.252 & 0.331 & 0.561 \\
**Knee flexion– extension Knee** & 29.65 (5.00) & 29.95 (5.24) & 28.30 (6.79) & 29.21 (5.54) & 0.481 & 0.649 & 0.724 \\
**Knee internal– external rotation** & 6.96 (5.38) & 7.21 (3.55) & 4.71 (2.12) & 5.82 (1.67) & 0.202 & 0.174 & 0.421 \\
**Knee internal– external rotation** & 15.08 (4.75) & 16.15 (3.95) & 13.57 (4.36) & 13.06 (3.36) & 0.572 & 0.180 & 0.123 \\
**Foot** & & & & & (0.015) & (0.084) & (0.110) \\
**pronation– supination** & 21.70 (7.74) & 24.14 (6.76) & 18.06 (6.18) & 22.52 (4.77) & 0.001 \({}^{\star}\) & 0.369 & 0.398 \\ \hline  & \multicolumn{2}{c}{* Significant difference (\(p<0.05\)).} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} \\ \end{tabular}
\end{table}
Table 3: Joint range of motion parameters with and without load according to research groups and load condition (right leg, in degrees).  
greater moments of hip adduction, hip internal rotation, knee valgus (Figure 5) and internal rotation with load. No differences between groups were observed (\(p>0.05\)). No interaction between weight carrying and group, weight carrying and dominant leg or weight carrying and leg side (left or right) were found, without a difference between groups.
## 3 Results and Discussion
### Mucoadhesive Property of African Catfish Mucus  
Even though automated and rheological analysis have been used to depict that bio-polymers could be mucoadhesive, studies have revealed that the analysis of spectrum and image analysis could be used to establish the bio-adhesive properties of mucus-based bio-polymers [66; 82; 83]. TEM imaging results of ACM-stabilized soya milk emulsions revealed a form of entrapment by which the mucins in the African catfish entrapped and adhered to the droplets of the soya milk emulsion to confer stability on the ACM-stabilized soya milk emulsion by forming aggregates. Researchers have investigated the aggregation effect of saliva mucus on food emulsions stabilized with protein from whey protein isolate (WPI), b-lacto-lactoglobulin (b-lg), b-casein, and Tween 20 with mucus obtained from humans and pigs using light microscopy [84]. The authors reported that aggregation was irreversible with emulsions stabilized with Tween 20 and undiluted saliva from humans and pigs. However, saliva from humans and pigs coupled with 20:1 dilutions recorded reversible aggregations on emulsions i.e., diluted saliva did not aggregate emulsions because it did not contain mucins. The authors proposed that aggregation was due to the presence of glycoproteins in the mucin because protein is a major part of mucins in saliva. However, irreversible aggregations occurred for WPI- and b-lg-stabilized emulsions even with 20:1 diluted saliva emulsions while reversible aggregations occurred for WPI- and b-lg-stabilized emulsions stabilized with undiluted saliva [84]. The authors suggested that undiluted saliva had a different mechanism for aggregating emulsions and hypothesized that mucin a key protein in saliva accounted for the observed aggregation, but also noted that other salivary components influenced the aggregation. This phenomenon was also observed when [1] investigated the mucoadhesive properties of hagfish mucin aggregated soya milk. Consequently, authors have established that fish, humans, and pig mucin have bio-adhesive properties [1]. Literature has established that mucin interacts strongly with lectins, as lectins selectively adhere to cells and forms precipitates with glycoptides [2]. The authors [2] elucidated that this phenomenon occurred because soybean lectins adhere to hagfish muchin and conferred cohesive stability on hagfish mucus stabilized soya milk emulsions [2]. It can be suggested that the aggregation of the soya milk emulsion is as a result of adhesion between the mucins in African catfish with the soya milk which could be as a result of strong cohesive bonds formed and could confer adhesive properties on the African catfish mucilage as suggested by [2].
## 5 Case Study
### Simulation Settings and Results Analysis
The output of gas-fired units with low carbon emission units under UCT, LCT and RPLCT increases sequentially. (3) The optimal carbon price and carbon trading cost of different carbon trading mechanisms under the same emission reduction requirements  
Case 6 implements UCT; case 7 implements LCT; case 8 implements RPLCT. The carbon emission reduction coefficient of each case is set to 0.15, and P2G is considered in all cases. Carbon trading parameter settings are shown in Appendix A Table A5. It can be seen from Figure 9c that, under the same carbon emission reduction requirements, the optimal carbon prices of UCT, LCT and RPLCT decrease sequentially. Compared  
Figure 10: Power output of units in each case. with case 6 and case 7, under the optimal baseline carbon price, the total scheduling cost of IPGES under UCT is lower than that under LCT, but the higher benchmark carbon price will reduce the enthusiasm of low-emission units. In contrast, under the total carbon emission control requirements of the system, the optimal benchmark carbon price, the total dispatch cost and the carbon trading cost under RPLCT are the lowest. RPLCT can specifically limit the emission of large emission units, reduce the carbon trading cost of small and medium emission units, and stimulate the system to increase the output of clean units and reduce the carbon emission of the system through the form of incentives. It has the best effect in terms of economy and low carbon. (4) The impact of carbon trading prices and free carbon emission quota coefficient on low-carbon economic dispatch of IPGES  
Figure 11 shows the carbon emissions and units dispatching results of IPGES considering unified carbon trading under different carbon trading prices. As the price of carbon trading rises, carbon trading costs account for an increase in the total cost, and the system gradually strengthens the constraints on carbon emissions. The output of high-emission intensity coal-fired units is gradually shifting to low-emission intensity gas-fired units, power generation costs increase, and carbon emissions are reduced. Figure 12 shows the carbon emissions of IPGES considering reward and punishment ladder-type carbon trading under different free carbon emission quota coefficient. Here, the benchmark carbon price is set at 10 $/MW, and other carbon trading parameters refer to case 5. It can be seen from Figure 12 that as the free carbon emission quota coefficient per unit of electricity allocation increases, the system's carbon emissions increase. This is because the increase in the free carbon emission quota allowance coefficient per unit of power generation means that the weakening of the control of the carbon emission intensity of the generator sets will increase the carbon emissions of the system. Figure 11: The carbon emissions and units dispatching results of IPGES under different carbon trading prices.
## 5 Bio-Phytoremediation by Microorganisms  
### Halophilic Microorganisms  
Extremely saline settings are one-of-a-kind habitats for discovering new microbial species. Salt-resistant microbiomes have been found in a variety of saline environments. _Proteobacteria_, _Bacteroidetes_, _Firmicutes_, _Actinobacteria_, _Euryarchaeota_, and _Spirochaetes_ were among the microorganisms found in a variety of salt-resistant microorganisms (Table 1) [77]. In addition to their genetic and physiological adaptations to salt, halophytes' salinity tolerance is influenced by complicated ecological processes. As a result, prokaryotes and fungi that live in the roots and leaves of plants could have a substantial impact on plant development. Archaea and bacteria, as well as members of the fungal kingdom, are designed to be able to acclimatize to a wide range of variations in external osmolarity [78].  
These germs also serve as ideal models for the determination of stress tolerance, adaptability, and response mechanisms, which may then be incorporated into agricultural plants to cope with climate change-related stresses [24]. Worldwide, salt-resistant plant microbiomes (_rhizospheric_, _endophytic_, and _epiphytic_) have been identified and described for their resistance to abiotic salt stress and other favorable characteristics [79].  
The processes that allow microbes to grow and survive in salt environments are essentially the same across groups. The main strategies comprise avoiding high salt concentrations through specialized membrane or cell wall structures, pumping ions out of the cell via \"salting-out\" mechanisms or adapting their intracellular environment by accumulating non-toxic organic osmolytes, and adapting proteins and enzymes to high levels of solute ions [78]. Exopolysaccharides are excreted in large amounts by various rhizobacterial species, which contribute to reduce salt stress through unknown mechanisms [80].  
A microbe must be able to detect and respond to change in order to survive in a changing environment. In saline conditions, _Chrysobacterium balustinum_, a PGP bacterium, has been shown to improve root surface, total nitrogen composition, and biological nitrogen fixation (BNF) in _Lupinus albus_ seedlings [81]. The plant-associated microbiome has also been shown to be an important component in understanding plant adaptability to a salty environment [82]. Microbes associated with haplotypes in the rhizosphere can withstand salt levels of up to 30%, and this relationship among plants and microbes may help improve soil fertility and quality [18].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
\begin{tabular}{c c} \hline \hline \end{tabular}
\end{table}
Table 1: Halotolerant microorganisms and their beneficial effects on plant growth under saline conditions.
# Using Explainable AI (XAI) for the Prediction of Falls in the Older Population
## 4 Discussion
### Syncope  
Judging by the feature importance scores (\(\geq\)0.1), age was the top predictor for this. In the SHAP plot, having had a fall in the last year, polypharmacy (being on five or more regular medications), their age, being unsteady when getting up from chair, and having osteoporosis, and a myocardial infarction all had clear positive influences in the model. Being a frequent fainter when they were young had a more mixed effect on the model output.  
Polypharmacy was placed second in the SHAP summary plot, which was much higher when it was compared to the other models, underlining the additive contribution of cardiovascular and non-cardiovascular medications towards the risk of orthostatic hypotension as a common cause of syncope in older people [37]. In our model, the feature of antihypertensive had the highest feature importance and SHAP values amongst the individual-type medications that were considered in the syncope model. This is consistent with the literature [38], and it reinforces the importance of careful medication review in the prevention of syncopal falls in older people. The SPRINT trial showed that intensive blood pressure control in adults aged 50 years and above was associated with an increased risk of syncope and hypotension. but not with falls [39]. However, the replication of the trial using the TILDA dataset on adults aged 75 years and older, fulfilling the inclusion criteria for SPRINT, showed that there was a five-fold increase in injurious falls (complex falls) and syncope as compared to the SPRINT group that received standard care [40]. The perils of intensive blood pressure control in older adults aged 65 years and over that are living with frailty, including having a higher risk of future syncope, have been further evidenced in the TILDA dataset [41].  
Unsteadiness when they are getting up from a chair is a feature that showed high importance among the simple falls, complex falls, and syncope models. This may be interpreted differently, where in simple and complex falls, unsteadiness may be associated with musculoskeletal weakness and balance problems, but in syncope, it may be more related to orthostatic intolerance associated with orthostatic hypotension. In orthostatic hypotension, a drop in blood pressure is observed when a person changes, often too quickly, from a sitting to standing position and this results in sudden cerebral hypoperfusion that may precipitate a transient loss of consciousness [18].  
Regarding the mixed effect of them being a frequent fainter when they were young in the syncope model, the clinical implication is that when an older adult with multimorbidity presents with syncope and there is a history of them fainting as a young person, the clinicians should not immediately attribute the current fainting to the old benign tendency to faint (i.e., vasovagal syncope), and regardless of their previous fainting history, they should instigate a comprehensive review of the pathological drivers of the syncope (e.g., orthostatic hypotension or cardiac arrhythmia) [42].
# A Lightweight Entropy-Curvature-Based Attention Mechanism for Meningioma Segmentation in MRI Images
## 4 Experiments  
### Experimental Setup  
#### 4.1.1 Datasets  
In our study, we employed both private and public datasets to validate the performance of the method we proposed.  
Specifically, the private dataset was collected from the Neurosurgery Department of West China Hospital. The dataset contains preoperative T1-weighted MRI scans of 500 meningioma patients; all of the segmentation labels were manually labeled by experienced neurosurgeons. The training set contains 345 cases, the validation set contains 100 cases, and the test set contains 65 cases. All the meningiomas in the dataset have a volume of less than 15 mm\({}^{3}\) for training.  
The public dataset is the BraTS2021 dataset [33], which is widely recognized for its comprehensive collection of multimodal MRI scans of glioma patients. This dataset is widely utilized in medical image segmentation. We conducted experimental validation using a dataset of 1237 cases from the BraTS2021 dataset; the training set contains 700 cases, the validation set contains 299 cases, and the test set contains 252 cases. Each case includes the following four modalities: T1, T1 ce, T2, and FLAIR. The annotations consist of the enhancing tumor, the peritumoral edematous, and the necrotic tumor core, which are combined into the following three nested subregions: whole tumor (WT), tumor core (TC), and enhanced tumor (ET).  
Figure 3: The structure of CFA. By applying convolution operations with learnable curvature to enhance the tumor edges, the feature maps obtained from the three planes are finally fused, allowing the information from the three planes to complement each other.
###### Abstract
Equipping multiple transceiver [32] is proven to further enhance throughput performance by handling multiple traffic in parallel allowing additional installation cost. Soft frequency reuse approach has also been proposed in [33] which exploits the capture effect but necessitates a synchronization between multiple nodes with the help of periodic beacon, global positioning system (GPS) information or out-of-band solutions [34]. We should also consider interference from/to other wireless networks on the same frequency band, i.e., inter-system interference. Therefore, throughput performance should be maximized in limited spectrum resources. IPT itself can resolve the hidden terminal problem by establishing the transmission period at the source node, hence, it does not require any handshakes for channel reservation nor control sequence. Figure 1: Multi-hop wireless backhaul network. Periodical transmission approximately realizes synchronization among relay nodes. The most valuable feature of IPT is that it can maximize the throughput upon a single channel constraint. Key idea of our solution is to fully exploit it by introducing multi-channel and multi-interface approaches which can eliminate additional MAC protocols as well as dynamic channel switching but only requires an initial packet scheduling. Our goal is to maximize the system throughput of MWBN via minimized MAC protocol modifications. The major contribution of this paper is to propose a new multi-channel packet scheduling and suitable round robin based IPT forwarding. Computer simulation results exhibit that superior system throughput can be achieved without Bi-IPT on uplink traffic accommodation, especially in heavy offered load situation. The reminder of this paper is organized as follows. Section 2 summarizes related work and justifies the contribution of this paper. Section 3 reviews IPT and its extension, RR-IPT. Section 4 proposes RR-IPT based multi-channel packet scheduling and forwarding. Section 5 describes computer simulation methodologies and Section 6 presents results and discussions. Finally, Section 7 concludes this paper.
## 3 Application to Resistive Driven-Rod Method: Testing with Some Examples  
In the tests that will be described below, a rod is vertically driven into a multilayer soil and the grounding resistance is measured as a function of the length of the rod that is buried in the soil. According to the circuit-based model introduced in Section 2, each branch of the buried rod contributes with a leakage current leakage to the ground and electric potentials are generated at its ends that must be determined by solving (16). The grounding resistance can be calculated from the potential acquired by the injection point of the total current, which is usually the upper end of the rod. This potential depends on the internal resistance of the rod and the resistivity profile of the ground through the resistivity and width of the different layers. These soil parameters can be found by a minimization procedure of (17). In order to work in conditions similar to the real tests, some additional considerations need to be discussed.  
First of all, it is necessary to comment that in many real cases, the rod driven into the soil is composed of rod segments of fixed length assembled by means of small joining pieces. Such connecting pieces provide a contact resistance between the two rods that are joined that makes the whole rod a conductor with non-zero internal resistance. This fact will be modeled by associating a non-zero resistivity to the connecting piece between the adjacent rods, the rods themselves being ideal conductors. Each time a rod segment is assembled with a joining piece and driven into the ground, the grounding resistance of the entire rod is measured. For practical reasons, the rod segments usually have a size ranging from 1 m to 1.5 m while the connecting pieces have a length of a few centimeters. In some of the synthetic cases that will be studied, 0.5 m rod segments will be used although this size is actually fictitious.