esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In summary, hybrid GO-AuNP nanoassemblies were fabricated by a one-step chemical reduction of gold(III) chloride dihydrate with the use of a graphene aqueous dispersion as solvent. The optical properties characterisation of the hybrid nanostructures, scrutinised by UV-visible spectroscopy, highlighted not only the enhancement of the plasmonic sensing features of AuNPs in the GO-AuNP nanocomposite, but also the strong association of the nanogold to the graphene oxide sheets, which resulted in spatial distribution changes for the electric near field of the metal nanoparticles. NMR and AFM analyses confirmed such a strong interaction between the metal and GO, with the finding of most of the citrate molecules on the gold surface replaced by GO sheets and the preferential allocation of AuNP on the defective basal planes of the GO sheets, respectively. _In vitro_ cellular experiments on human neuroblastoma SH-SYSY cell line assessed a synergic effect of AuNP and GO in the hybrid assembly that increased the nanotoxicity of the GO-AuNP in comparison with the bare AuNPs and GO sheets and their effect on mitochondrial dysfunctions, as measured by the mitochondrial O\({}^{2\bullet-}\) production. Such a detected selective ROS increase induced by GO-AuNP is very promising for the potential theranostic application of this system. Indeed, the nanosized character of the nanoplatform would allow the nanoparticles to accumulate in the tumor site, due to the altered structure of its vasculature. Hence, an effective and specific therapeutic action involving the ROS production can occur, followed by the biodegradation of the hybrid into the nontoxic byproducts (including GO and AuNPs) [60]. It is known that ROS concentration plays important role in cancer processes like in apoptosis, cancer survival, autophagy, angiogenesis, metastasis, and inflammation [61]. High levels of ROS in cancer cells compared with normal cells are due to elevated metabolic activity, activation of oncogene, cellular signaling, inherent mitochondrial dysfunction, and higher enzymatic activity of oxidases, cyclooxygenase, lipoxygenases, and thymidine phosphorylates [62]. Therefore, a further increase of ROS selectively in cancer cells may bring level of ROS to a lethal threshold, while resulting safer to normal cells [39]. Intracellular imaging by confocal microscopy highlighted the promising potentialities of these hybrid nanocomposites as a multifunctional platform for imaging/sensing and therapeutic purposes.  
Conceptualization and methodology, C.S.; investigation, L.M.C., I.N. and G.C.; writing--original draft preparation, L.M.C. and I.N.; writing--review and editing, C.S.  
This research was partially supported by MIUR under Grant PRIN 2015 (20152 EKS4Y project) and University of Catania (Piano della Ricerca di Ateneo 2016-2018).  
The Consorzio Interuniversitario di Ricerca in Chimica dei Metalli nei Sistemi Biologici (CIRCMSB) is kindly acknowledged.  
The authors declare no conflict of interest.
# Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer
## 5 Case Study and Result  
To assess the effectiveness of the proposed algorithm, comprehensive case studies on IEEE test systems are carried out. The IEEE 33-bus system is employed to demonstrate the superiority of the proposed algorithm, while the IEEE 69-bus system is used to validate its scalability.
# A Fuzzy-Logic-Based Load Balancing Scheme for a Satellite-Terrestrial Integrated Network
## 2 Materials and Methods
### The Fuzzy-Logic- and Reinforcement-Learning-Based Access Algorithm
Thus, the gradient of critic network is calculated by  
\[\bigtriangledown_{a}Q\Big{(}s,a|\theta^{Q}\Big{)}|_{s=s^{t},a=\mu(s^{t}| \theta^{u})}. \tag{19}\]  
By applying the chain rule to the expected return from the start distribution \(J\) with respect to the actor parameters [31], the actor is updated by Equation (20). \(\theta^{Q}\) and \(\theta^{u}\) can be updated via gradient descent method. \[\bigtriangledown_{\theta^{u}}J\approx\frac{1}{K}\sum_{t}[\bigtriangledown_{ a}Q\Big{(}s,a|\theta^{Q}\Big{)}|_{s=s^{t},a=\mu(s^{t}|\theta^{u})}\bigtriangledown_{ \theta^{u}}\mu(s|\theta^{u})|_{s=s^{t}}]. \tag{20}\]  
The weights of target networks \(\theta^{Q^{\prime}}\) and \(\theta^{u^{\prime}}\) are updated based on the weights of \(\theta^{Q}\) and \(\theta^{u}\), as in Equations (21) and (22). The detailed training process is shown in Algorithm 1. \[\theta^{Q^{\prime}}\leftarrow\theta^{Q}+(1-\tau)\theta^{Q^{\prime}}. \tag{21}\]  
Figure 3: The fuzzy deep deterministic policy gradient training structure. \[\theta^{\mu^{\prime}}\leftarrow\theta^{\mu}+(1-\tau)\theta^{\mu^{\prime}}. \tag{22}\]  
```
0: Training episodes \(E_{max}\), training steps for each episode \(T_{max}\), learning rate of actor network \(\alpha_{A}\), learning rate of critic network \(\alpha_{C}\), initial exploration rate \(v_{init}\), exploration discount \(v_{dis}\), minimum exploration rate \(v_{min}\), replay buffer size \(G\), mini-batch size \(K\), reward discount \(\gamma\), update rate \(\tau\). 1: Randomly initialize the weights of actor network and critic network as \(\theta^{\mu}\) and \(\theta^{Q}\), the initial weight of actor target network \(\theta^{\mu^{\prime}}\) is same as \(\theta^{\mu}\) and the initial weight of critic target network \(\theta^{Q^{\prime}}\) is same as actor network \(\theta^{Q}\)
2: Initialize the empty replay buffer, initialize exploration rate \(var\) as \(v_{init}\)
3:for each episode \(ep\) in range \((1,E_{max})\)do
4: Randomly set users' positions and speeds and data requirements to reset environment
5:for each step \(t\) in range \((1,T_{max})\)do
6: Get fuzzy inputs from the environment, get \(s^{t}\) and \(s^{t}\) from Equations (8) and (16)
7: Get \(a^{t}\) from Equation (17)
8: Perform \(a^{t}\) to the environment and get \(r^{t}\) from Equations (11)-(14)
9: Get \(s^{t+1}\) and \(s^{t+1}\) from Equations (8) and (16)
10: Store \(\left(\hat{s}^{t},a^{t},r^{t},\hat{s}^{t+1}\right)\) in replay buffer
11:if the replay buffer is full then
12: Replace a data randomly by \(\left(\hat{s}^{t},a^{t},r^{t},\hat{s}^{t+1}\right)\)
13: Update exploration rate, \(var=max(var\cdot v_{dis},v_{min})\)
14: Sample a mini-batch of size \(K\) from the replay buffer
15: Update \(\theta^{Q}\) by Equation (19)
16: Update \(\theta^{\mu}\) by Equation (20)
17: Update target networks by Equations (21) and (22)
18:endif
19:endfor
20:endfor
21:Training completed, save the actor network
```  
**Algorithm 1**Fuzzy reinforcement learning training algorithm.
# An Improved YOLO Model for UAV Fuzzy Small Target Image Detection
## 3 Improvement of YOLOv5s Algorithm  
### SPD-Conv Module  
Convolutional neural networks play a significant role in computer vision tasks, such as image classification and object detection. However, their performance decreases rapidly when the image resolution or object size is small. A large number of strided convolutions and pooling layers are present in the feature extraction network of CNNs. These layers result in the loss of a large amount of fine-grained information and the learning of less efficient features. Therefore, in 2022, [PERSON] and [PERSON] from the University of Missouri proposed a novel CNN module called SPD-Conv [24]. It replaced each strided  
Figure 2: Slicing operation.  
convolution and each pooling layer with this module. SPD-Conv consists of an SPD layer and a non-strided convolution layer.  
In the SPD layer, the feature map \(X\) is downsampled, while the information in the channel dimension is preserved, preventing loss of information.  
For any intermediate feature map \(X\) with a size of \(\text{S}\times\text{S}\times\text{C1}\), a series of sub-feature maps are cut out, as shown in Formula (3).  
\[f_{0,0}=X[0:S:scale,0:S:scale],f_{1
## 2 Materials and Methods
### Numerical Modelling of Fire Resistance Test  
#### 2.2.1 Fire-Proof Door Characteristics and Test Conditions  
A single-leaf fire-proof marine door with a clear opening of 1200 mm and a height of 2223 mm was considered as a case study. The leaf (1248 mm wide and 2250 mm high) is made of carbon steel sheets with a thickness of 1 mm, containing the insulating material (SEAROX-SL 440, density 150 kg m\({}^{-3}\)) with a thickness of 40 mm. The insulating material was selected by the door manufacturing company due to its lightness, efficiency and proper certifications. The steel sheets were glued to the insulator by a polymer-based glue. The door frame, consisting of steel profiles with a thickness of 4 mm, was screwed on the bulkhead with a spacing of 150 mm. The door was fixed to the frame by means of three hinges on one side and a lock was arranged on the opposite side. Figure 1a exemplifies the model geometry.  
In the fire-test procedure reported in the FTP Code the door was heated from ambient temperature T\({}_{0}\) (in this case 18 \({}^{\circ}\)C) to 945 \({}^{\circ}\)C with a prescribed time-temperature relationship expressed by the following equation:  
\[\text{T }=\text{ }345\text{ }\log_{10}(\text{8t }+\text{ }1)+\text{T}_{0}\text{ }(^{\circ}\text{C}), \tag{2}\]  
where t is the time (min).  
For an A-60 class door, as with that considered here, the fire test lasts 60 min. Temperatures and displacements must be monitored in five positions (Figure 1b). On the fire-unexposed side of the door the following limits are imposed:  
(i) The final temperature of each thermocouple must not exceed 180 \({}^{\circ}\)C above T\({}_{0}\);  
(ii) The medium temperature of the five thermocouples must not exceed 140 \({}^{\circ}\)C above T\({}_{0}\).  
Moreover, only small gaps between the door and the frame are tolerated in order to ensure that no flame passes through the door (detailed procedure and limits are reported in the FTP Code [1]).
# In Vitro Simulation of Human Colonic Fermentation:
## 5 Addressing Colonic In Vitro Digestion Studies
### Faecal Inoculum Collection and Preparation  
The collection of a faecal sample (from one individual) or a faecal pool (from different individuals) is one of the first steps of an in vitro colonic simulation. Faecal donors are selected according to age, gender, body mass index, presence, or absence of illness, etc., depending on the aim of the study. General considerations for donors include not having received antibiotics and prebiotics or probiotics at least some months before the faecal donations. The healthy individuals selected as control or reference groups must follow a standard diet and assure that they are free of any gastrointestinal disease. The faecal collection must be done as quickly as possible in anaerobic conditions, placing the sample in anaerobic tubes, jars, or chambers, or using devices that promote the preservation of anaerobicosis, such as OXOD ones.  
After the collection, an individual faecal sample or faecal pool is mixed with a solution (e.g., phosphate buffered saline or sodium chloride) to obtain a faecal suspension or faecal slurry (e.g., 10% \(w/v\) or 20% \(w/v\)). Then, the suspension is manually homogenised or is homogenised using a stomacher [31; 36; 69; 43; 56]. In addition to the previous homogenisation stage, some protocols include filtering [27; 30; 54], or centrifugation steps [37; 65; 80] so that the supernatant is used as faecal inoculum in the fermentation process. In addition, other protocols include the extraction of the bacterial mass from the rest of the components of the faecal sample by means of the use of iohexol (Nycodenz(r)), which is added to the supernatant of a previously homogenised faecal suspension, causing the isolation of viable cells. After centrifugation, the sample results in a concentrated phase containing the bacteria, which can be recovered and purified with sequential rinsing with sodium chloride. This bacterial extract ensures higher amounts of microbial inoculum, but on the other hand, may cause the loss of some of the species due to the additional processing [105]. Finally, some other authors add a cryoprotective agent (glycerol) to the faecal suspension so that it can be frozen for later use [28; 95; 105; 106].
## 3 Results and Discussion
### Analysis of Wetting Properties
#### 3.2.2 Wetting Analysis of Cu-SiC Surfaces Processed by Laser Radiation
Under conditions of spontaneous hydrophobization of Cu-SiC composite materials modified by laser radiation, the dynamics of wetting inversion (change in contact angles over time), the values of contact angles established during stabilization as well as the duration of time required to stabilize the wetting properties, do not depend on the wetting properties of Cu-SiC composite materials before modification. The results and conclusions obtained are important for understanding the mechanism of spontaneous hydrophobization and for identifying the main factors affecting the intensity of the wetting inversion of materials modified by laser radiation. It can be concluded that the wetting properties of materials prior to modification by laser radiation do not affect the intensity, duration of stages, and steady-state values of the contact angles upon the wetting inversion.
## 5 Discussion
### Types of Wi-Fi Signal Parameters Used  
The most commonly used indoor positioning parameter is based on the RSSI; 114 of the reviewed works used RSSI. The second most used Wi-Fi signal is CSI, which was used by 15 papers.  
Wi-Fi is available in many indoor spaces nowadays and is an easily accessible parameter from any device, including mobile and wearable devices. In general, the results obtained with RSSI have an accuracy between 1 and 8 m.  
Nevertheless, these apparent good results can be due to the design of the experiments; therefore, these accuracies cannot be generalized or expected in different environments. The elements that drive to these accuracies can be, among others: (1) experiments withsmall spaces without obstacles and with many reference points, thus avoiding the effect of signal loss when passing through walls and the multipath effect (as explained in [40]); or (2) experiments that use training and validation data with little difference in terms of time and space, or using the data used for training for validation, as in [41].  
On the other hand, there are 15 studies that use CSI [32; 35; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53] from a Wi-Fi signal, generally with better results in terms of accuracy than those obtained with RSSI. CSI is not widely used because the channel state information is not easy to obtain and requires specific network cards and modifications to the original _firmware_[52] (i.e., it cannot be used in smartphones). Despite this, we observed a large increase in the use of this parameter. Before the year 2020, only four papers used this parameter. However, in the last two years up to ten papers have used it, as can be seen in Figure 5. One reason for this may be that the RSSI parameter is reaching its limits, and new mechanisms are being explored as they are becoming more present in common everyday devices.  
Finally, the Signal-to-Noise Ratio (SNR) parameter is beginning to be used, specifically in two papers [9; 54], and in particular in combination with Wi-Fi networks that use _mmWave_ instead of the classical networks that broadcast on traditional frequency channels, i.e., 2.4 GHz and 5 GHz. SNR technologies show better positioning accuracy, and led to the best and the third-best results we found in this review.
They stated that GEP is capable of predicting the considered properties with a high accuracy. [PERSON] et al. [31] used waste foundry sand in the concrete and applied GEP as a tool for predicting the mechanical properties of concrete. They also compared the GEP model with other regression techniques and proved that it has high accuracy and potential for prediction. [PERSON] et al. [32] developed a model for predicting the static modulus of elasticity and uniaxial compressive strength (UCS) by a GEP method, and for this purpose, 42 datasets from previous studies were utilized. Of these, 22 sets were considered for training, and the rest were used for testing. In the other recent study [33], GEP was utilized to predict the compressive strength of geopolymer mortars with ferrosilate basement. Two GEP models were proposed in this research that both have a good fit with real data. According to the lack of an evolutionary intelligence method in previous research studies about the effect of nano-MgO on concrete, the current research utilizes GEP to produce a mathematical formula that is applicable for civil engineers for designing purposes or the prediction of the mechanical properties of concrete containing nano-MgO in the freeze-thaw condition.
## 2 Materials and Methods
### Business Understanding
#### 2.1.2 Solution Design and Performance Metrics  
To solve the problem of predicting concrete quality from mixture characteristics, the solution design is shown in Figure 1. The data collected by the RMX company to predict the target variables are prepared by a cleaning process and a feature engineering. Then, the prepared data are used to create and compare the performance of several supervised machine learning methods. The comparison is made through error measurements in the predictions using a 10 fold-cross validation procedure.  
Figure 1: Methodology for model training and evaluation.  
To compare the results of the models, their predictive ability is measured using the mean absolute error (MAE), which measures the average magnitude of the errors in a set of predictions without considering their sign, and the MAPE, which expresses accuracy as a percentage of the error and can be easier to interpret than other accuracy measurement statistics.The mathematical formulae for these two evaluation metrics are shown in Equations (1) and (2).  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|Y_{i}^{real}-Y_{i}^{pred}| \tag{1}\]  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\frac{|Y_{i}^{real}-Y_{i}^{pred}|}{|Y_{i}^{real}|} \tag{2}\]  
The methods with the best performance are selected for a hyperparameter optimization process aimed at improving the predictive performance of the models. This optimization allows for finding the best configuration and selecting the prediction model with the best performance. Finally, the best model is deployed on a web platform, and its predictive capability is validated on a monitoring dataset over a new period of time.  
This solution design is implemented using the Python (version 3.10.12) language and the libraries NumPy, pandas, Matplotlib, seaborn, scikit-learn, XGBoost, Scikit-Optimize, sklearn-genetic, and Joblib.
## 4 Discussion
### Effect of Water Treatment
While a decrease in actual cell numbers in SSF filtrates is typical [45], the concomitant increase in regrowth potential is not and might indicate the die-off of microbes within the accumulated biomass in the upper layers of the SSF and nutrient release by cell lysis. It should be noted that treatment processes do not automatically lead to the desired outcome, but their success depends on the mode of operation. Also, the combination of slow sand filtration with ground passage in the case of river water treatment did not result in a net decrease in maximal growth potential, only in a decrease in day 0 cell concentrations (Figure 1). The increasing discrepancy between the ICC\({}_{\text{day}}\) and ICC\({}_{\text{day}}\) 7 values (i.e., the regrowth factor) can be interpreted as the conversion of biologically fixed nutrients into dissolved nutrients and, thus, the loss of biological stability (Figure 1). Riverbank filtration systems are subject to substantial dynamics regarding water quantity and quality [46]. Most studies have focused on DOC or NOM removal with little information available on AOC. While a decrease in AOC (based on quantification of an increase in ATP content) as an effect of riverbank filtration has been reported [47], the comparability of data is complicated by methodological heterogeneities in the experimental setups and scale of the studies [48]. For this reason, the comparison of removal rates of organic pollutants from different batch and column studies was considered to be nearly impossible [48; 49]. The same might apply to data on the effect of riverbank filtration, which reduced the ICC\({}_{\text{day 0}}\) values but not the maximal growth potential. The effect measured here could possibly be explained by the scenario that microorganisms from a river ecosystem enter in large amounts into a subsurface soil environment, which represents a dramatically different chemical, physical and biological environment for these microbes. The die-off of the microbiome and, thus, a decline in cell numbers, is conceivable, which would be concomitant with the release of previously biologically fixed nutrients. The time to convert these released nutrients into new biomass might not be sufficient during underground passage. Also, saturation effects or limiting factors might play a role and need more clarification. Unfortunately, the individual effects of SSF and ground passage could not be distinguished due to the lack of a sampling site. Rapid sand filtration, on the other hand, in our studied example (Figure 3) resulted in both a decrease in ICC\({}_{\text{day 0}}\) cell numbers and a 21% decline in ICC\({}_{\text{day 7}}\). This finding is consistent with those of other studies [29; 50] and is probably due to the mode of operation. The frequent removal of accumulated biomass turns it into a nutrient sink on an absolute scale. In relative proportions, however, the removal of biomass shifts the relative proportion of nutrients towards the dissolved nutrients (Figure 3C).