esa-sceva/satcom-chunk-collection huggingface dataset:
# Ecotoxicological Assessment of Microplastics in Freshwater Sources--A Review
## 5 Ecotoxicological Effects on Microplastics
### Ecotoxicological Effects on Vibrio fischeri  
The toxicological test using _Vibrio fischeri_ (_V.fischeri_) is considered simple, relatively fast, and cost-effective. Therefore, it is not surprising that, despite the fact that _V.fischeri_ is a marine bacterium, this test is often applied for the ecotoxicological analysis of non-marine samples like wastewaters, freshwaters, leachates, etc. The test is based on measuring the intensity of blue-green luminescence light since _V.fischeri_ has the ability to bioluminescence (associated with the presence of enzyme luciferase). Its wide applicability includes various tested subjects posing adverse effects to the water environment such as metals, antibiotics, herbicides, and other organic and inorganic compounds [128]. Yet, only a few authors used _V.fischeri_ for the determination of MPs toxicological effects, presumably due to insufficient sensitivity [107]. However, [PERSON] [112] exposed _V.fischeri_ to PE beads (1-3 \(\upmu\)m) at 1000 mg/L during 5 and 30 min observing bioluminescence decrease, with EC\({}_{20}\) values of 3600 and 2600 mg/L, respectively (Table 3). Moreover, [PERSON] et al. [107] exposed _V.fischeri_ to oxidized and not oxidized PE particles; size range of 1-500 \(\upmu\)m and concentration range of 0.625-10 mg/L. According to the results after 30 min exposure, PE particles are not toxic to _V.fischeri_ at environmentally relevant concentrations or at even higher concentrations. Besides, the investigated particle sizes did not affect _V.fischeri_, presumably due to the fact that the cell wall acted as a barrier for particles of the used size range [107]. However, although not studied, authors assumed that smaller particles than studied might cause a toxic effect. Issues of _V.fischeri_ for determining MPs toxicity can be associated with the following facts: (i) MPs are solids, thus, may interfere with measuring bioluminescence, and (ii) exposing time (15 min according to ISO 11348-3) is quite short to determine actual MPs effects. MPs may be dissolved in an appropriate solvent, however, solvent interference with toxic effect has to be accounted for as well. Another approach is to use it in the determining toxic effects of MPs constituents (monomers, additives), which may leach during partial MPs degradation; however, filtration prior test to remove solid MPs should be applied.
## 6 Space-Segment Optimization Aspects
### Applicable Optimization Methods for Space Segment
#### 6.8.3 Simulated Annealing (SA)  
SA, while similar to evolutionary algorithms, is a meta-heuristic algorithm that specializes in finding an approximation to the global optimal solution in large search spaces. It was originally introduced in [48] and was inspired by the annealing procedure of metalworking. Similar to evolutionary algorithm counterparts, variants that expend the method exist, such as MOSA[51].  
SA is ideal for computationally hard optimization problems where exact algorithms prove infeasible; even though it only achieves an approximate solution, it is typically enough for many practical problems. In general, SA algorithms assume a system initialized with a positive temperature and a starting solution (represented by point \(X\), typically avector of parameters of interest). At every iteration, the algorithm randomly (or heuristically, in improved versions of the algorithm similar to PSO variants) selects a point close to the current one and decides to accept it according to the acceptance criteria, which is typically a probabilistic objective function that depends on the system temperature. The system's temperature gradually decreases, and the final solution is considered an approximation to the global optimal. We present the procedure in Algorithm 3.  
```
Step 1: System Initialization  Initialize the system with an initial temperature \(T_{0}>0\) and generate a starting point \(X_{0}\), which will also be initialized as the best known point \(X^{*}\). Step 2: Move to Neighbors  Check a neighboring point \(X_{s}\) and decide if the neighbor is 'better' according to an acceptance criteria. The acceptance criteria must have some probability of accepting 'worse' neighbors related to the temperature of the system (higher-temperature systems have a higher probability of accepting 'worse' neighbors). If the neighbor is accepted, move to it and assign \(X_{s}=X^{*}\); otherwise, stay in the current point. Step 3: Enforce Annealing Schedule Repeat Step 2 until an 'equilibrium condition' is satisfied (in practicality, this condition can be quite complex or very simple. For example; after a number of iterations, or after every point change). When this condition is met, decrease the temperature of the system and move back to Step 2. Step 4: Termination  Repeat steps 2-3 until a termination criteria is met (i.e., total iterations, minimum temperature, etc.).
```  
Overhead-wise, SA is similar to both GA and PSO in the sense that it is typically applied in an offline manner while inherently being an online method. However, since the main strength of the SA method is finding an approximate solution to the global optimum when the exact methods fail, its time and computation properties are mainly an issue of feasibility rather than overhead. Typically, the overhead of the SA method is not a point of critical consideration when SA reaches an acceptable solution, which, in practice, is fine-tuned with the initial starting solution to shorten the computation time.
# A Cognitive Anycast Routing Method for Delay-Tolerant Networks
## 5 Performance Measurements
### Scenario 3: Regular Link Disruptions  
Link disruptions can occur in mobile networks for different reasons in addition to regular link and node failures. It is well known that random graphs show a zero-one phase transition for network connectivity; thus, if the node density drops below the critical threshold, the chances of a network partition greatly increase [44]. Environmental factors, such as obstacles, can also cause link disruptions. Satellite links in the LEO, MEO, or HEO can experience loss of visibility for several minutes or hours and even longer periods beyond the cislunar range.  
Repetitive disruption patterns were dynamically applied to two of the long-haul links of the network as follows. Link g23-g26 was kept active for 11 s and then disabled for 5 s. The active-inactive time lengths for Link g25-g27 were chosen to be 7 s and 13 s, respectively. Given that the experiments were run in real-time, the contact and disruption times were chosen to be small to achieve manageable testing times. The disruption patterns were controlled at runtime by the NEE, which, as explained earlier, runs as a distributed process parallel to the CSG.  
The link disruptions reduce the end-to-end carrying capacity of the network, which the CSG attempts to dynamically discover and exploit. For the given topology and disruption pattern, this task is achieved by using longer paths, as shown in Figure 4b. The penalty of this capacity reduction can be observed through the longer average response times of the flow bundles, as depicted in Figure 4a. Unlike the previous case, the disruptions accentuated the performance differences between the two anycast group members, with  
Figure 3: Scenario 2: Emulated earth–moon network topology. (**a**) Average response time; (**b**) average path length; (**c**) bundle throughput; (**d**) traffic split.  
unicast routing to g30 offering greater bandwidth than to g27. It is interesting to observe that the CGR actually preferred g27 in larger proportion to g30 as the delivery target as it lay along the faster path, as shown in Figure 4d. These observations also apply to the flow throughput that is depicted in Figure 4c.
## 4 Proteomics
### Mode of Action  
Proteomics has helped to deconvolute _M.tb_ responses to drug exposure providing insight into mechanisms of drug action and resistance [86]. Recently, [PERSON] et al. used proteomics to explore the metabolic pathways that contribute to the activity of rifampicin [87]. A small percentage of rifampicin resistance occurs through mechanisms other than the well-characterized target, \(\beta\) subunit of RNA polymerase. Proteomic profiling of rifampicin-treated _M.tb_ resulted in the under-expression of four proteins implicated in cell wall biosynthesis (Ino1, FabD, EsxK and PPE60), suggesting that rifampicin also affects cell wall synthesis contributing to bacterial death. The authors used a liquid chromatography-mass spectrometry (LC-MS) approach, assessing small changes to the _M.tb_ proteome temporally. Consideration should be taken when conducting such experiments to minimize the introduction of false-positives as a result of weak signals from an analytical column coupled with a highly sensitive detection platform. Despite these limitations, the authors reported a protein coverage comparable to other studies that applied nano-liquid chromatography. Nano-liquid chromatography is becoming an established tool for advanced peptide separation that uses very narrow columns that are more effective for detecting low abundance compounds [88]. Similarly, proteomics has been utilized to discern the mode of action of repurposed drugs that inhibit _M.tb_ in vitro. Sulfamethoxazole, a broad-spectrum antibiotic that primarily targets the folate biosynthesis pathway, exhibits a synergistic effect when combined with other anti-TB drugs. [PERSON] et al. mapped the _M.tb_ response to sulfamethoxazole exposure using proteomics, identifying induction of oxidative stress and electron transport chain pathways that suggested an additional mode of action for this drug [89]. Proteomics may also be exploited to determine mechanisms of drug resistance. [PERSON] et al. employed a shotgun proteomics system to identify proteins secreted in isoniazid- and rifampicin-resistant _M.tb_ compared to drug-sensitive _M.tb_. Bacterial cultures were filtered through low binding protein-cellulose acetate membranes to collect culture filtrate proteins, before sodium dodecyl sulphate-polyacrylamideimidegel electrophoresis (SDS-PAGE), in-gel digestion and LC-MS. Depending on the aim of the study, consideration in the liquid chromatography (LC) approach should be taken. For example, for high proteome coverage, or for detecting proteins in low abundance, a 1D gradient for 8 h or a 2D LC should be used. However, for analysis of a limited sample volume, or where characterization of low-abundance proteins is not needed, a 1D gradient for 4 h may suffice. In addition, reproducibility and sample requirements will vary greatly depending on the method used [90]. Differential abundance of proteins involved in lipid metabolism, proteasome function and ATP-binding cassette transporters (ABC transporters) between drug-resistant and drug-sensitive strains may reveal novel systems that influence drug efficacy [91].
## 3 Results and Discussion
### Simultaneous Evaluation of Washing Time and Solid-Solution Ratio in Heavy Metal Removal Efficiency
First, Cd was predicted to show the maximum removal efficiency at the solid-solution ratio of 1:24.7 (coded value: \(-\)0.53) and washing time 230.1 min (coded value: 0.83) when washing with 1 M citric acid. The amount of Cd removal under the predicted conditions was 75.7%. Looking at the results of setting the washing conditions in the previous three steps, the maximum removal efficiency was 59.7% at 1 M citric acid, 1:20 solid-solution ratio, and 30 min of washing time in the first step for selecting a washing solution. When the second-step washing time was set, the maximum removal efficiency was 70.1% at 1 M citric acid, a solid-solution ratio of 1:20, and a washing time of 240 min. Finally, in the solid-solution ratio selection step, it was analyzed that the maximum removal efficiency was 80.0% at 1 M citric acid, washing time 360 min, and solid-solution ratio 1:10. It is thought that it would be difficult to directly compare the overall results because each condition is slightly different, but there was no significant difference between the results of the actual CCRD arrangement and the results of the solid-solution ratio selection stage, which showed the maximum removal efficiency among the three-stage analysis results. The CCRD, which is one of the response surface methodologies, has been known to efficiently minimize and simplify the number of experimental steps and their scale [40]. Therefore, in the case of Cd, it is judged that the decision-making process of washing conditions through the three-step evaluation method contains meaningful results to some extent. Copper was predicted to represent the maximum removal efficiency (77.1%) at the solid-solution ratio of 1:21.9 (coded value: \(-\)0.81) and washing time 228.8 min (coded value: 0.81). In the case of Pb, it was predicted to show the maximum removal efficiency (85.3%) at the solid-solution ratio of 1:26.2 (coded value: \(-\)0.38) and washing time 256.4 min (coded value: 1.27). The maximum removal efficiency of Zn was 79.2%, with the solid-solution ratio at 1:26.2 (coded value: \(-\)0.38) and the washing time at 256.4 min (coded value: 1.27). Similar to Cd, it was confirmed that Cu, Pb, and Zn did not show a significant difference from the maximum removal efficiency of the three-step procedure. [PERSON] and  
Figure 10: The effect of the solid-solution ratio and washing time on heavy metal removal efficiency using 1 M citric acid through CCRD using 3D mesh plot. [PERSON] [39] also utilized the CCRD method to optimize Cd and Zn removal from contaminated soil using anionic surfactants. They controlled solution concentration, solution soil ratio and agitation speed, and they also considered the cost of chemicals required for the soil remediation.
## 2 Materials and Methods
### Procedures  
In this study, walking ability (FAC), lower extremity muscle strength (Manual Muscle Testing (MMT)) and muscle tone (Modified Ashworth Scale (MAS)), cognition (Mini-Mental State Examination--Korean version (MMSE-K)), balance ability (Berg Balance Scale (BBS)), and activities of daily living performance (Modified Barthel Index (MBI)) were evaluated to investigate the baseline clinical characteristics of the subjects. An investigation of changes in lower limb muscle activity and gait according to differences in walking speed was conducted in an unobstructed hallway 40 m in length and 5 m wide. Twenty-four patients who had suffered a stroke walked on 20 m over-ground at three different walking speeds (slow (80% of self-selected), self-selected, and maximal speeds), during which changes in lower limb muscle activity and gait were analyzed (Figure 1). In order to minimize potential bias, data on muscle activity and gait variables were collected by researchers who were not involved in this study and were unaware of the specific objectives of the study.  
Lower limb muscle activity was measured using surface electromyography targeting five muscles (rectus femoris, biceps femoris, tibialis anterior, gastrocnemius, and gluteus medius) on the paretic side, and gait ability was measured using a gait analyzer for the stride length, stance and swing phases, and single limb support time on the paretic and non-paretic sides, and the gait asymmetry index was calculated based on the measured parameters. In this study, the self-selected speed was set to the participant's usual walking speed, which was comfortable, and the slow speed was set to 80% of the self-selected speed [16]. The maximum speed was set to the fastest possible speed. All participants underwent the measurements in the following order: using self-selected, slow, and maximum walking speeds. Participants first walked 20 m at their usual comfortable (self-selected) speed. For the slow-speed condition, participants walked on a treadmill for 5 min at a walking speed adjusted to 80% of their self-selected walking speed. Subsequently, measurements were taken while they walked on the ground at the same speed as they did on the treadmill. For the maximum speed condition, participants were instructed to \"walk as fast as possible\" over 20 m. Each measurement was conducted three times, and the average was used for analysis. Two assistants ensured the participants' safety throughout the experiments. Participants wore comfortable clothing and shoes and were given a 5 min break between trials, with additional rest upon request. Verbal encouragement was provided during the maximal speed trial to ensure maximal effort.
## 5 Result and Discussion  
### Data  
The MG contains distributed generation units, including WTs, MTs, and PV panels. The power for the WTs, PV panels, and total load were taken from [28], as shown in Figure 4. Since the load amount is greater than the total renewable energy at most times, other DER sources such as the MT and BESS are necessary to balance the MG demand. In this paper, we assume that the three types of loads (industrial, commercial, and residential customers) have a common characteristic and can be considered as critical and interruptible loads. Therefore, the three types of loads can be added together and considered the total load. Here, the total peak load is 500 kW. The MG structure is equipped with WT whose total installed capacity is 500 kW, PV 300 kW, and MT 500 kW.  
In this study, the capacity of the BESS was 200 kWh, while the charging and discharging ramp-rate limits were both 50 kW/h. The battery efficiencies were 0.9 at any time step during the charging and discharging processes [32]. Furthermore, the renewable energy in the operating cost was assumed to have little effect on the final result. It was also assumed that no energy exchange occurred between MGs. All characteristics of the DERs and other values of the technical parameter are depicted in Table 1. Since we consider not only the scheduling in the MG, but also the operation of the tie-line, we assume that the minimum rated capacity of MT is set to zero in order to clarify the tie-line operation strategy considering the energy band. Figure 5 represents the forecasted real-time pricing (RTP) of the wholesale market and the retail market prices based on the time-of-use (TOU) scheme [33].  
The residual energy curves of the BESS for a period of 24 h are shown in Figure 6. The initial state of charge of the BESS was 90 kWh. At 08:00, the BESS was fully charged. During the high-price period, the BESS injected power into the MG. Furthermore, at the end of the day, the residual energy of the BESS decreased to the initial value, which means that the energy of the BESS remained balanced throughout the day.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(\rho_{loss}\) ($\(\$\)KWh) & 250 & \(\rho_{PV1}\) (\(\$\)kWh) & 11 \\ \(P_{M,\max}\) (kW) & 36 & \(\rho_{BESS}\) (\(\$\)\(\$\)KWh) & 8 \\ \(P_{e,\max}\) (kW) & 20 & \(V_{min}\), \(V_{\max}\) (p.u.) & 0.95, 1.05 \\ \(\rho_{min}\) (\(\$\)\(\$\)kWh) & 71 & \(P_{Gini,min}\), \(P_{Gini,max}\) (kW) & 0, 20 \\ \(P_{WT,\min}\), \(P_{WT,\max}\) (kW) & 0, 250 & \(P_{PV,\min}\), \(P_{PV,\max}\) (kW) & 0, 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values of technical parameters.  
Figure 4: Forecasting power of PV panels and WTs and load for test system.
# Evaluating Packaging Design Relative Feature Importance Using an Artificial Neural Network (ANN)
## 3 Results
### Case Study 1: Relative Feature Importance of the Synthetic Dataset
#### 3.1.1 ANN Training Using the Synthetic Dataset  
During the ANN model training process, the synthetic dataset with 3009 data points was randomly divided into two subsets: 70% of the data (2016 data points) for training the model and the remaining 30% (993 data points) for testing the model's accuracy.  
As previously mentioned, the hidden layer neuron number setting was determined through comparing the model errors obtained by the five optimization methods mentioned in Section 2.2 (including Bayesian optimization method, AIC method, BIC method, Hebb's rule, and the OBD rule) to minimize the model error and maximize the computational efficiency. Figure 6 presents the optimal neuron settings for the hidden layer as determined by each method, along with their corresponding model errors in test data over 70 modeling cycles. The results showed that the AIC method achieved the lowest model errors with one hidden layer and 120 neurons. The BIC method produced the second-lowest model error, with one hidden layer and 34 neurons. The error difference between these two methods was no more than 0.0032, considering their 95% confidence intervals (0.0032 \(\pm\) 0.0015 and  
Figure 5: Theoretical BCS feature importance calculated using the simplified McKee formula.  
0.0026 \(\pm\) 0.0011). This indicates that the more complex configuration suggested by the AIC method was not necessary. In contrast, the simple configuration of 34 provides slightly less accuracy but is significantly more efficient in terms of computational resources, making it the optimal choice for balancing model prediction accuracy and computational efficiency. Consequently, the neuron configuration proposed by the BIC rule was applied throughout the study for this dataset. Namely, the ANN model developed in this real dataset includes a single hidden layer with 34 neurons.  
The number of epochs was determined based on the model loss (error) epoch plot (Figure 7), which showed that model error reduction plateaued after 25 epochs, but did not reach zero, meaning the error continued to decrease slowly for both training and test data. To balance high prediction accuracy with computational efficiency, a conservative approach was taken by extending the training for an additional 10 epochs beyond the plateau, resulting in a total of 35 epochs.  
Figure 6: Optimal neuron numbers in the hidden layer(s) determined by different methods for the synthetic dataset.  
Figure 7: Model loss (error) versus epoch plot with 34 neurons in the hidden layer.  
The feature importance of 10 modeling cycles was averaged to obtain a relative reliable average feature importance.