esa-sceva/satcom-chunk-collection huggingface dataset:
# Solution-Processed Memristor Devices Using a Colloidal Quantum Dot-Polymer Composite
## 2 Materials and Methods  
For the fabrication of CdSe/ZnS QD-PMMA memristors, a PMMA powder (M\({}_{\mathrm{w}}\)\(\sim\) 996,000 g/mol, Sigma Aldrich, St. Louis, MO, USA) was first dissolved in toluene (Sigma Aldrich, 99.5%) at a concentration of 4 wt%. After a thorough stirring of the solution, colloidal CdSe/ZnS QDs (NSQDs-HOS-530, Nanosquare, Seoul, Korea) were added to the solution at a concentration of 1, 3 or 5 wt%. The size of CdSe/ZnS QDs with oleic acid ligands was around 6-8 nm with a ZnS shell thickness of 2-3 nm. After mixing the QDs, the solution was sonicated to uniformly disperse the CdSe/ZnS QDs in the solution. To fabricate the memristor device, a glass substrate with a 150 nm-thick indium-tin-oxide (ITO) was used. The ITO layer was used as the bottom electrode. The substrate was sonicated in isopropyl alcohol for 10 min and rinsed with deionized water. Then, an ultraviolet (UV)/ozone treatment was followed for 15 min to remove any organic residues and to enhance the wetting of the CdSe/ZnS QD-PMMA solution. On the ITO-coated glass substrate, the CdSe/ZnS QD-PMMA solution was spin-coated and the resulting assembly was dried on a hot plate for 20 min at \(\sim\)110 \({}^{\circ}\)C to remove the residual solvent and to form a CdSe/ZnS QD-PMMA film. Next, a 150 nm-thick Al electrode was deposited by thermal evaporation as a counter electrode. The active area of the device was 3 \(\times\) 3 mm\({}^{2}\). Finally, to protect the device from the ambient gases during measurements, the device was encapsulated with a glass in a nitrogen ambient glove box. The current-voltage (I-V) characteristics, data retention, and the endurance characteristics of the memristors were analyzed using a semiconductor parameter analyzer (Agilent, Santa Clara, CA, USA, 4155C). The light absorption and the photoluminescence (PL) characteristics of the CdSe/ZnS QDs were analyzed using a UV/vis spectrophotometer (KLAB, Optizen pop) and a PL system equipped with a 500 W Xenon lamp as an excitation light source (PSI, Darsa-5000), respectively. Additionally, the cross-sectional structure of the device was observed using a high-resolution transmission electron microscope (HRTEM) (JEOL, JEM-2100F) to examine the dispersion of CdSe/ZnS QDs in the PMMA matrix.
# Reconfigurable Multi-Beam Receiver Based on Microwave Photonics Technology
## 4 Discussion  
A comparison with related works is given in Table 1. The beamformers proposed in related works are evaluated with a normalized beam pattern to demonstrate the direction of the main lobe and the side lobe depress ratio. However, in this study, the beam pattern is unnormalized. The main lobe's absolute power is given and used to compare under various working modes. To the best of the authors' knowledge, this work is the first research to propose a receiver with a variable beam-count. As shown in Figure 5, the main lobe power in a conventional four-beam receiver is fixed at 12.10 dBm. But in the proposed structure, when the system works in a single-beam mode, the main lobe power is 24.08 dBm, and the gain of the array is 11.98 dB. A higher gain means the receiver can capture the signal in a longer range. When the system works in a dual-beam mode, the main lobe power is 18.06 dBm, and the gain of the array is 5.96 dB. It enables the PAA to balance the distance and number of the received beam.  
In summary, the reconfigurable structure offers the conventional multi-beam receiver a new degree of freedom: a variable beam-count improves the system's energy efficiency and adapts the receiver to various communication scenarios.
# Evaluation of Multi-Beam Massive MIMO Considering MAC Layer Using IEEE802.11 ac and FDD-LTE
## 4 Computer Simulation
### IEEE802.11 ac
The proposed modulation results are obtained using the characteristics based on Table 2. At the assumed transmission rate, the full-digital BF configuration achieves 86.7 [Mbps], which is the maximum transmission rate obtainable under IEEE802.11 ac, because it scans a sharp beam width using 64 elements. The median value of multi-beam massive MIMO is approximately 70 [Mbps], and it is asymptotic to the ideal characteristic obtained by utilizing the proposed simple adaptive modulation. The hybrid BF configuration characteristic achieves a low modulation order because beam width is widened when there are eight elements per sub-array. The full-digital configuration can achieve a throughput of only approximately 40 [Mbps] because of the overhead of channel estimation and feedback obtained when there are 64 antenna elements. In hybrid BF, overhead occurs owing to eight elements, which is the number of sub-arrays. As shown in Figure 6, throughput decreases when the transmission rate of even one UT is slow because IEEE802.11 ac requires the synchronization of all UTs, as shown in Figure 6. This reduces the throughput characteristic of the hybrid BF. The multi-beam massive MIMO characteristic is the best among the three configurations because it does not require synchronization and overhead and can effectively utilize the transmission rate obtained in the PHY layer. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Massive MIMO Configuration** & **Precoding** & **Decoding** \\ \hline Multi-beam massive MIMO (Figure 2) & Analog multi-beam & Robust ICA \\ Hybrid BF massive MIMO (Figure 1) & MRC and MMSE & MMSE \\ Full-digital BF massive MIMO (Figure 1) & BD method & MMSE \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Precoding and decoding on downlink. Figure 6: Downlink multi-user (MU)-MIMO procedure under IEEE802.11 ac.
## 4 UbiPriSEQ: The Proposed Framework
### Device Module (DevM)
#### 4.3.1 DevM: Workflow  
DevM observes the current channel state and task buffer, and takes a set of **actions** (i.e.,which tasks to process locally, and which ones to be offloaded, to which fog/cloud node, etc.) based on the current \(Q\)**-value** (obtained from the training phase) or at random with a small probability (see Section 4.3.5). A **reward** (also known as **utility**) is associated with each set of actions taken. The utility could be positive or negative, depending on whether DevM is able to improve the functional parameters, that is, privacy, QoS, and energy-efficiency. Subsequently, DevM computes the new \(Q\)-value using DQN, and based on it decides the set of actions. This process is repeated iteratively until an optimal \(Q\)-value is found. Figure 2 in the left block depicts DevM's process flow.  
We now explain how DevM computes its functional parameters--privacy (Section 4.3.2), QoS and energy-efficiency (Section 4.3.3)--utility (Section 4.3.4), and \(Q\)-value (Section 4.3.5).
## 5 Experimental Results and Simulations  
### Experimental Setup  
In this paper, we examined different types of supervised ML and DL algorithms. This selection is based on the No Free Lunch (NFL) theorem, which suggests that no universal algorithm can be the best-performing for all problems [43]. This motivated our attempts to explore the most well-known ML and DL methods to give the reader a clear image of their performance and determine the most applicable one on the OSA problem.  
Specifically, this paper employed three types of experiments: (1) the preset setting of ML classifiers for the learning process, (2) the hyper-parameters setting while running the ML methods, and (3) the utilization and proposal of the hybrid DL approaches. From those mentioned above, we firstly investigated various predefined ML methods. However, only those classifiers with better performances are reported. Correspondingly, we adopted seven predefined parameter ML classifiers (Medium DT, LDA, LR, Gaussian NB, Medium KNN, BT, and Coarse Gaussian SVM). As well-known, the overall performance of ML algorithms is strongly affected by the used internal parameters. Therefore, after investigating the predefined classifiers, we applied hyper-parameter optimization within _Matlab_ to automate the selection of hyper-parameter values. Accordingly, six optimized classifiers (DT*, NB*, KNN*, DA*, ensemble DT*, and SVM*) were employed for performance validation. Table 4 shows the preset parameters of the predefined parameter classifiers, while Table 5 explores the parameters of optimized classifiers after learning process. The main advantages of hyper-parameters settings are that the classification method will tune its parameters to reduce the classification error and retain the optimal setting for internal parameters. Lastly, we implement four DL models include CNN, LSTM, RNN, and CNNLSTM (hybridization of CNN and LSTM) for performance validation.  
Figure 4: Samples of ECG signals.
## 3 Methodology
### Feature Database Construction
#### 3.3.1 String Feature Database  
Due to the challenge of string feature duplication, we assign a unique weight to each string feature to reduce the values of frequently-used strings. In BBDetector, we use TF-IDF algorithm, which is a popular algorithm to reflect the importance of words in a document in the corpus, to calculate the weight of each string literal. The weight \(w\) of a string \(i\) to a library \(j\) is shown as Equation (1).  
\[w_{ij}=TF-IDF(i)=TF(i)*IDF(i)=\sum_{k}\frac{n_{ij}}{n_{kj}}*\frac{|L|}{|\{l\in L :i\in l\}|} \tag{1}\]  
where \(n_{ij}\) is the frequency of string \(i\) in library \(j\); \(|L|\) is the total number of libraries and \(|\{l\in L:i\in l\}|\) is the number of libraries which contain the string \(i\).  
After calculating the weight for each string, we build index tables to store the relationship between string features and libraries. We adopt hashing method to generate hash values for each string (\(hash(str1)\)) and use the same column width to store strings which can avoid space waste. Moreover, we adopt an inverted index technique [5,7] to create an inverted mapping of string literal features to the third-party libraries to speed up string literal feature searching and matching. The specific information is as shown in Figure 1, which includes two index tables: the left one uses the inverted index to store the information about the library where the string resides, and the right one stores the information about strings that the library contains to retrieve strings through a library name easily.
# An Overview of the Public Health Challenges in Diagnosing and Controlling Human Foodborne Pathogens
## 4 Diagnostic Approaches of Foodborne Pathogens
### Methods Based on Nucleic Acids  
In order to identify specific gene sequences in an organism, these technologies utilize the genotyping of the target organism. Nucleotides, or sequences of nucleotides, can be used to identify groups, genera, species or strains of microorganisms that belong to a specific family or species [121; 122]. A wide variety of DNA-based assays are available for detecting foodborne pathogens; however, nucleic acid amplification methods and probes seem to be the most popular and widely used methods for detecting foodborne diseases.
# A Multidisciplinary Approach for Improving Resource Efficiency in the Indian Surimi Supply Chain
## 4 SDGs and Impacts
### Environmental Impacts  
There is a magnitude of steps that can go wrong and cause significant environmental harm when it comes to fish supply chains. Organic waste is mainly contributed to food processing industries, such as when RRM is not correctly processed. Unlike chemical waste, where you can see the spillage effects head-on, organic waste usually escapes unnoticed until its impacts have reached uncontrollable proportions [46]. This 2013 study examined one of the biggest seafood processing zones in India, Cochin Corporation, to find that large quantities of seafood solid waste is left unutilised and mismanaged by the private sector. Without regulations for proper seafood disposal, the area was left as an environmental and health hazard. The waste not only affects typically poorer areas, but also the surrounding ecosystems and waterways by reducing biomass and destroying natural food webs [47,48]. It will be vital to implement SDG 12 in ensuring the surimi production and supply chain performs to sustainable standards. If protocols for RRM disposal are not followed, it would, in turn, affect the total revenues and health of a country.  
One of the outcomes of ReValue project is the analysis of the environmental impact of frozen surimi production in the current supply chain where the pre-processing and surimi production operations are not integrated. This was compared to two scenarios that propose supply chain integration. The study found that frozen surimi production generates 4.67 kg CO\({}_{2}\) eq/kg without supply chain integration and 4.43 kg CO\({}_{2}\) eq/kg with the integration of pre-processing and surimi production operations (described in detail in ReValue paper by Sultan et al., 2021 [49]). An overall reduction in the carbon footprint of surimi production will be achieved if the utilisation of RRM is improved.
## 3 Methodology  
### Simulation Model  
To validate the proposed control strategy, the predicted model is simulated in MATLAB/SIMULINK 2019a software (Mathworks). The collected 1000 battery-data are divided into 80% (800), 10% (100) and 10% (100) for training, validation, and testing; respectively. In shown in Figure 4a, different types of hyper-parameters e.g., sigmoid activation function, three neuron, one layer, and 0.1 valued learning rate are instigated to accomplish the training stage. The proposed controlling method to monitor the MPPT scheme and to estimate the SOC of the battery bank by dint of battery management system (BMS) is shown in Figure 4b. The MPPT mode generates maximum possible power from the available solar insolation under any atmospheric condition.  
Figure 3: State of charge (SOC) estimation maneuver from batteries.  
On the other side, BMS receives the battery voltage and current from measuring sensors and estimates the existing SOC by means of BPNN algorithm. The battery is kept in islanding mode if the PV can support the load-demand properly to reduce its utilization and elongate the battery lifetime. Furthermore, the battery section is charged or discharged frequently by following the PV power-supply involvement. From previous research [33; 34; 35], it is noticed that only 40% to 80% charged battery can function actively. More than 70%-80% SOC holding battery meets the over-charging mode at charging period and below the 30%-40% of SOC containing battery meets the deep-discharging mode at discharging period. As a result, this paper keeps the battery charging-discharging range as 40% to 80% SOC to lengthen the battery lifespan. The entire flowchart of the proposed BMS controlling technique is presented in Figure 5. In addition, the battery lifetime is adopted by following the Wohler-curve-based ageing model which can be considered from (8)  
\[Lifetime\ =\ \frac{1}{LL} \tag{8}\]  
where \(LL\) is the lifetime loss (in Ah) of the battery that is the function of DOD as well as DOD is the complementary to SOC.  
Figure 4: (**a**) Collected battery-data training using MATLAB toolbox; (**b**) Schematic diagram of the proposed model.  
Figure 5: Flowchart for the entire system.
# A Systematic Review of Insole Sensor Technology: Recent Studies and Future Directions
###### Abstract  
Background: Integrating diverse sensor technologies into smart insoles offers significant potential for monitoring biomechanical metrics; enhancing sports performance; and managing therapeutic interventions, diseases, disorders, and other health-related issues. The variation in sensor types and applications requires a systematic review to synthesize existing evidence and guide future innovations. Objectives: This review aims to identify, categorize, and critically evaluate the various sensors used in smart insoles, focusing on their technical specifications, application scopes, and validity. Methods: Following the PRISMA guidelines, a search was conducted in three major electronic databases, namely, PubMed, Scopus, and Web of Science, for relevant literature published from 2014 to 2024. Other works not located in the mentioned databases were added manually by parallel searches on related themes and suggestions from the website of the databases. To be eligible, studies were required to describe sensor implementation in insoles, specify the sensor types, and report on either validation experiments or practical outcomes. Results: The search identified 33 qualifying studies. Proper analysis revealed a dominance of pressure sensors, with accelerometers and gyroscopes also being widely used. Critical applications included gait analysis, posture correction, and real-time athletic and rehabilitation feedback. The review also examined the relative effectiveness of different sensor configurations. Conclusions: This systematic review comprehensively classifies sensor technologies within smart insoles and highlights their broad application potential across various fields. Future research should aim to standardize measurement protocols, enhance sensor integration, and advance data processing techniques to boost functionality and clinical applicability.  
wearable sensor; insole; performance; rehabilitation +
Footnote †: journal: Applied Sciences
## 3 Results
### Comparative Evaluation Alteration-Salt Composition  
Principal Component Analysis (PCA) revealed that three principal components accounted for over 70% of the total variance. Figure 7 presents the PCA biplot, which illustrates the distribution of the samples in relation to the analyzed ionic components and alteration phenomenology. The first principal component accounted for 35.59% of the total variance, whereas the second PCA explained 22.53% of the total variance.  
Figure 6: (**a**) Ionic balance, (**b**) trends of calcium and sulphate and (**c**) chloride and sodium of analyzed samples in \(\mu\)Eqs.  
Figure 7: Bi-plot of distribution of samples and ions.  
On the \(x\) axis (PC1), a correlation was observed between Na\({}^{+}\) and Cl\({}^{-}\) in the PCA plot. This strong association suggests the presence of sodium chloride, as confirmed by ion chromatography analysis, which is a common salt responsible for the degradation of fresco surfaces. The significant correlation between Ca\({}^{2+}\) and SO\({}_{4}\)\({}^{2-}\) indicates the potential formation of gypsum (CaSO\({}_{4}\) 2H\({}_{2}\)O), a well-documented degradation process in frescoes. This conclusion was supported by ion chromatography analysis. The clustering of Ca\({}^{2+}\), Mg\({}^{2+}\), and PO\({}_{4}\)\({}^{3-}\) suggests a shared geochemical behavior. The negative correlation between Cl\({}^{-}\) and Ca\({}^{2+}\)/Mg\({}^{2+}\) implies that sodium chloride sources are independent of calcium- and magnesium-rich mineral phases. A moderate correlation was observed between NO\({}_{3}\)\({}^{-}\) and PO\({}_{4}\)\({}^{3-}\), whereas a strong correlation was identified between C\({}_{2}\)O\({}_{4}\)\({}^{2-}\) and NO\({}_{2}\)\({}^{-}\) and F\({}^{-}\). K\({}^{+}\) appeared to be relatively independent of other ions. Considering the samples distribution two group were identify, a group formed by S\({}_{11}\) (Light Green), S\({}_{8}\) (White Turf) and S\({}_{9}\) (Hard Black) correlated C\({}_{2}\)O\({}_{4}\)\({}^{2-}\) and NO\({}_{2}\)\({}^{-}\) and F\({}^{-}\) and a second group formed by S\({}_{4}\)-S\({}_{6}\) (\"a\" and \"b\"), representative of pink patinas, which was strongly correlated with NaCl.  
On the \(y\) axis (PC2) a correlation related to the sampling area can be observed. In the upper section of the graph S\({}_{7}\)-S\({}_{11}\) were associated in a cluster in relation to their position in the right side of the altar. In this way S\({}_{2}\) results far from S8, even if have the same microorganisms composition, resulting in different areas of the frescos (Figure 2c). S\({}_{1}\)-S\({}_{3}\) samples were clustered in the low section of the graph being all sampled in the left area of the frescos. S\({}_{4}\)-S\({}_{6}\) samples were clustered separately in relation to their placement underside the altar.
## 4 Real-Scale Vehicle Tests and Result Analysis
### Test and Result Analysis
#### 4.3.2 Damper Failure (Case 2)  
According to the testing program, the damping of the left front suspension damper was adjusted to three different states. It can be seen from the comparison of the modal energy differences in Figure 15a that the MED of the front bounce mode exhibits a significant rise, while the MED of the rear side shows the opposite downward trend. In the pitch mode, the MED of front pitch mode gradually increases as the damping decreases, while the MED of rear pitch mode is slightly reduced.  
Consistent with the simulation predictions in Section 3.3, the MED of rear suspension failure in the bounce mode exhibits a tendency opposite to that of front suspension failure. In the pitch mode, the MED of both the front and rear side increases slightly with the decrease of the damping, which is also consistent with the simulation prediction. This shows that when the rear side damper fails, the reduction of damping will exacerbate the rolling motion of the front suspension. The results identified from the test data are basically consistent with the simulation, indicating that the MED-based monitoring technology is effective for monitoring and diagnosis of the damper faults.
## 3 Results and Discussion
### Trends in PW
This rise of tropospheric water vapor might result in the increase of intensity and frequency of precipitation over land ([PERSON] and [PERSON], 2009; [PERSON] and [PERSON], 2010). [PERSON] and [PERSON] studied the relationship between PW and precipitation over China using the radiosonde observations from 1970 to 1990, and a positive correlation between these two variables is revealed ([PERSON] and [PERSON], 2010). However, in recent research, more complicated relationships were reported between PW and precipitation. [PERSON] et al. studied the variations in PWs and precipitation in mainland China during 1995-2012 and found that the regional trends in PW are not always consistent with the regional trends in precipitation ([PERSON] et al., 2010). These imply that not all changes in precipitation can be explained by the changes in PW. The variation of PW might affect the global precipitation as PW is one of the sources of precipitation. But earlier researches on the relationships between precipitation and PW all show complicated associations of these two and the results vary from region to region. The global relationship of PW and precipitation needs to be further investigated in the future. Figure 8: Long-term trends in PW from radiosonde observations for the period of 1957–2016 (stations with bold black circles are stations with significant trends).