esa-sceva/satcom-chunk-collection huggingface dataset:
# Mutated Channelrhodopsins with Increased Sodium and Calcium Permeability
## 3 Results
### Mutation of the Aspartate in TM4 Influences the Na\({}^{+}\) Permeability  
The potential influence on ion selectivity by mutating ChR2 D156 was not reported. To investigate this, we measured the photocurrent at different potentials and calculated the reversal potential shift of these mutants, when systematically changing bath solutions with different pH and Na\({}^{+}\) or K\({}^{+}\) concentrations.  
ChR2 is a non-selective cation channel which is mostly permeable to H\({}^{+}\)[2]. Both changing extracellular pH from 7.6 to 9.6 and changing extracellular Na\({}^{+}\) concentration from 120 mM to 1 mM altered the ChR2 reversal potential (Figure 3). The ChR2 permeability ratio of Na\({}^{+}\) to H\({}^{+}\) (P\({}_{\text{Na+}}\)/P\({}_{\text{H+}}\)) was determined as P\({}_{\text{Na+}}\)/P\({}_{\text{H+}}\) = 3.1\(\times\)10\({}^{-7}\) (Table 2). Interestingly, D156H (XXM) influenced the Na\({}^{+}\) permeability and increased the P\({}_{\text{Na+}}\)/P\({}_{\text{H+}}\) four times, while D156C (XXL) changed the P\({}_{\text{Na+}}\)/P\({}_{\text{H+}}\) only slightly (Table 2).  
Chronos is more permeable to H\({}^{+}\) than ChR2 with a P\({}_{\text{Na+}}\)/P\({}_{\text{H+}}\) = 2.3\(\times\)10\({}^{-7}\) (Table 2). The D173H mutation did not obviously change this, and D173C increased the P\({}_{\text{Na+}}\)/P\({}_{\text{H+}}\) slightly to 5\(\times\)10\({}^{-7}\) (Figure 3 and Table 2). _P_S_ChR was reported to be highly Na\({}^{+}\) conductive [20]. Changing the outside Na\({}^{+}\) concentration from 120 mM to 1 mM greatly influenced its photocurrent. The inward photocurrent was nearly abolished at 1 mM Na\({}^{+}\) pH 7.6, and we determined the _P_S_ChR P\({}_{\text{Na+}}\)/P\({}_{\text{H+}}\) to be 18\(\times\)10\({}^{-7}\), which was even higher than that of XXM (Figure 3 and Table 2). The D139H mutation increased the P\({}_{\text{Na+}}\)/P\({}_{\text{H+}}\) even five-fold more, to 90\(\times\)10\({}^{-7}\), while the D139C mutation decreased the P\({}_{\text{Na+}}\)/P\({}_{\text{H+}}\) slightly (Figure 3 and Table 2).  
Among the tested constructs, _P_S_ChR D139H was the most Na\({}^{+}\) permeable channelrhodopsin with a large photocurrent and, to our knowledge, the most Na\({}^{+}\)-permeable channelrhodopsin ever reported.
## 3 Results and Discussion
### Characterization of Nanoparticles
Figure 11: **(a)** Antioxidant activity of free extract, rutin, extract-loaded PLGA NPs and extract-loaded PCL NPs (TEAC (μmol/g of sample, mean \(\pm\) S.D., \(n\) \(\geq\) 3, *** \(p\) \(<\) 0.001, when compare free extract with other samples and \(\Delta\Delta\Delta\)\(p\) \(<\) 0.001, when compare rutin with other samples), **(b)** Fold-factor of TEAC when compared with respective extract-loaded NPs, (\(\Box\)) ORAC and (\(\blacksquare\)) HOSC methods. In vitro Coll inhibition activity results are displayed in Figure 12. The free extract presented the highest Coll inhibition activity, even higher than the positive control group. Similar values were observed for the extract-loaded in PLGA and PCL NPs. A fold factor of 1.06 between the free extract and the extract-loaded NPs was observed, suggesting a potential synergistic effect between the extract with the polymers (PLGA and PCL). Coll inhibition activity increased around 10-15% after encapsulation in both types of NPs. Coll is involved in some skin disorders associated to the degradation of collagen-rich extracellular matrix (ECM), as in tumor invasion [6]. Therefore, Coll inhibition might be an important factor to prevent or treat such diseases, where some of the phytocompounds identified in this extract, e.g., naringenin, may play an important role [30].
# Self-Ginding Silage Knife Strengthened with Ni-WC Alloy Prepared by Laser Cladding
## 3 Test Results and Discussion
### Wear Resistance Analysis  
Figure 12 shows the mass loss of the substrate and cladding layer from indoor friction tests. The mass loss for the substrate due to the fact of friction was the highest for all specimens, and it had a wear rate of approximately 10.075 mg/h. The wear rates of the cladding layers were 6.025, 2.325, 2.475, and 3.200 mg/h, respectively, at the different laser powers. When the laser power was 1600 W, the wear-resisting property of the cladding layer was the best, which was 4.3 times that of the substrate.  
Figure 11: Charpy impact tests: (**A**) 65 Mn; (**B**) 1200 W; (**C**) 1400 W; (**D**) 1600 W; (**E**) 1800 W.  
Figure 12: Contrast of the wear loss of the specimen.  
Research by [PERSON] and [PERSON] [24; 25] shows that wear resistance is related with the hardness of the material. A large number of hard phases in the cladding layer increases the hardness and wear resistance of the layer material. On the other hand, impact toughness exerts a notable influence on the wear resistance of material. The formula can be concluded similar to (2):  
\[w=C\frac{1}{K_{IC}^{3/4}H^{1/2}} \tag{2}\]  
where:  
\(K_{IC}^{3/4}\)--fracture toughness;  
\(H\)--hardness;  
\(C\)--coefficient of friction.  
When the value of the laser power is between 1200 and 1800 W, a compaction layer of the Ni-WC alloy formed at the surface of the 65 Mn steel. When the laser power was 1600 W, the hardness of the cladding layer was high, and the impact toughness of the cladding layer was the highest for all of the specimens. Therefore, according to Formula (2), the combination of high hardness and high toughness leads to high wear resistance.  
Figure 13 shows the abrasion morphology of the 65 Mn steel and the coat of the Ni-WC alloy using SEM. Under the same abrasive condition, the wear was severe on the surface of the 65 Mn steel. Because the hardness of the 65 Mn steel was lower, a number of scratches and ploughs parallel to the slide direction formed at the material's surface. The wear mechanisms of the specimens was mainly abrasive wear [26]. By contrast, the scratches on the coat's surface were shallow due to the fact of its high hardness.
# Model-Based Design and Simulation of Paraxial Ray Optics Systems
## 2 Ray Transfer Matrix  
According to the theory of ray optics, light propagating in \(yz\)-plane along \(z\)-direction is described by its height \(y\) and orientation \(\theta\). For small angles (sin \(\theta\approx\theta\)), rays can be treated by the paraxial approximation so that the correlation between output and input light due to an optical element is given by a linear system. The response of complex optical systems can be obtained by concatenating \(2\times 2\) ABCD matrices of each basic component like continuous media, mirrors, and lenses.  
Instead of using the \([y\ \theta]^{\mathrm{T}}\) convention from matrix optics, a \(3\times 1\) light array structure \(\mathbf{y}=[y\ \theta\ \ z]^{\mathrm{T}}\) is used to include the position \(z\) for tracing purposes. The input ray \(\mathbf{y}_{n}\) subjected to an optical component described by a \(3\times 3\) transfer matrix \(\mathbf{M}_{n+1,n}\) and a \(3\times 1\) translation vector \(\mathbf{t}_{n+1,n}\) yields the output \(\mathbf{y}_{n+1}\),  
\[\mathbf{y}_{n+1}=\left[\begin{array}{c}y_{n+1}\\ \theta_{n+1}\\ z_{n+1}\end{array}\right]=\mathbf{M}_{n+1,n}\mathbf{y}_{n}+\mathbf{t}_{n+1,n} =\left[\begin{array}{cc}A&B&0\\ C&D&0\\ 0&0&1\end{array}\right]\left[\begin{array}{c}y_{n}\\ \theta_{n}\\ z_{n}\end{array}\right]+\left[\begin{array}{c}0\\ 0\\ E\end{array}\right], \tag{1}\]  
\(A\), \(B\), \(C\), and \(D\) are the elements of the standard ray transfer matrix, and \(E\) updates the output ray position \(z_{n+1}\), as shown in Figure 1. The optical component \([\mathbf{M}_{i}\mathbf{t}]_{n+1,n}\) represents the transformation from the state \(n\) to a subsequent state \(n+1\). Although concatenating components is not mathematically straightforward as in conventional ABCD matrices, the modular design approach allows building and connecting subsystems similarly to block diagrams. For example, in Figure 1c, \(\mathbf{y}_{3}\) is obtained from \(\mathbf{y}_{1}\) by calculating \(\mathbf{y}_{3}=[\mathbf{M}_{i}\mathbf{t}]_{3,2}[\mathbf{M}_{i}\mathbf{t}]_{2, 1}\mathbf{y}_{1}\) which is analogous to the traditional matrix optics procedure.  
Recalling the paraxial approximation, developing \(\mathbf{M}\) and \(\mathbf{t}\) follows the same procedure of conventional ray optics. Coefficients for selected optical elements are given in Table 1, but complementary examples of ABCD matrices are found in textbooks [15,22]. It is worth noticing that element \(E\) is non-zero only for transmission through a medium or thick component, incrementing the \(z\) value.
## 4 Simulation Results and Analysis
### Nodes Depleted of Battery Power:  
The Figure 8 shows the relative comparison of the proposed DMR and CoDMR protocols with the DBR and CoDBR. The number of nodes which depleted their battery power in DMR and CoDMR is minimum as compared to the counterpart schemes. The reason for this is due to the network division into four equal zones in DMR and CoDMR. Because of which nodes use less energy to forwards information towards the desired minor sink nodes. This helps to balance the energy cost amongst the nodes. In the DBR and CoDBR algorithms, nodes are died quickly as compared to the proposed schemes. The reason is that the DBR and CoDBR protocols transmit heavy information packets over the long transmission path to the upper sink node. Which exhausts high energy and drain their battery quickly. In the CoDBR protocol, two best relays are selected with the destination node for data forwarding, which also exhausts high energy. While the proposed CoDMR scheme uses a single relay with destination node, which also consumes lower energy than CoDBR and saves the battery power. While the DBR algorithm forwards the packets by considering the lowest depth, which makes a high load on the lowest depth nodes and die quickly. Therefore these nodes deplete battery power quickly than the proposed protocols. At round 600, the number of that have depleted battery power in DBR is 450 while CoDBR have more than 450 nodes. At the same instant, the CoDMR protocol has lower than 450 nodes and DMR scheme has the minimum number of nodes which depleted their battery power. After round 600, the depleted power nodes in the DBR and CoDBR algorithms are the same as these are the few nodes that participate in routing without availability of enough neighbor nodes.
## 2 Related Work  
### Applications of Deep Learning in Data-Driven Strategy Modeling  
In recent years, the intersection of artificial intelligence and data-driven modeling has positioned deep learning as a core component in intelligent decision systems [16]. The abundance of heterogeneous data, often characterized by high noise, non-stationarity, nonlinearity, and multi-scale structures, renders traditional linear models insufficient for accurate modeling [17]. DNNs, with their powerful feature extraction capabilities, have demonstrated clear advantages in processing unstructured data and learning complex patterns [18]. Among them, RNNs and their improved variant, LSTM, are most commonly applied to time series modeling [19]. LSTM is particularly effective in capturing long-range dependencies in sequential data and mitigating the vanishing gradient problem [20]. Given a return series \(\{r_{t}\}_{t=1}^{T}\), the hidden state \(h_{t}\) of LSTM is updated through a gated mechanism defined as  
\[f_{t}=\sigma(W_{f}x_{t}+U_{f}h_{t-1}+b_{f}),\quad i_{t}=\sigma(W_{i}x_{t}+U_{i} h_{t-1}+b_{i}) \tag{1}\]  
\[o_{t}=\sigma(W_{o}x_{t}+U_{o}h_{t-1}+b_{o}),\quad\tilde{c}_{t}=\tanh(W_{c}x_{t }+U_{c}h_{t-1}+b_{c}) \tag{2}\]  
\[c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot\tilde{c}_{t},\quad h_{t}=o_{t}\odot\tanh( c_{t}) \tag{3}\]  
Here, \(\sigma(\cdot)\) denotes the sigmoid activation function, and \(\odot\) is the Hadamard product. The Hadamard product \(\odot\) refers to element-wise multiplication, which enables dimension-wise gating within the LSTM mechanism. All terms, including \(f_{t}\), \(i_{t}\), \(o_{t}\), \(\tilde{c}_{t}\), and \(c_{t}\), are vectors of the same dimensionality, and scalar multiplication does not apply in this context. \(x_{t}\) is the input feature vector, and \(h_{t}\) is the current hidden state. While effective in modeling temporal dependencies, LSTM has notable limitations in handling multi-entity interactions, suffers from low computational efficiency, and is challenging to parallelize. More recently, Transformer models have significantly advanced sequential modeling capabilities by discarding recurrence and utilizing the self-attention mechanism to capture global dependencies across the sequence [21]. The abovementioned feature learning capabilities provide the theoretical foundation for constructing multi-modal signal representations in this study, particularly in processing high-dimensional technical indicators and unstructured textual features.
# Towards Lightweight URL-Based Phishing Detection
## 3 Methodology
### Feature Selection  
Our work uses a new combination of features consisting of features (F1-F10) that have been used for phishing detection [11] and proposes F11 and F12. These are summarized in Table 1 along with their descriptions. For the majority of the features, their computation is straightforward based on their description. F8-F10 flag the presence of the most popular suggestive vocabulary in the (i) subdomain, (ii) domain, and (iii) path sections of the URL [12]. To identify the most popular suggestive vocabulary we measured their word frequency with the use of valid phishing URLs from PhishTank. These are split in the format of <subdomain>.<domain>/<path>?<query>. These sections are further probabilistically split using natural language processing (NLP) based on English unigram frequencies. The words that resulted from this process are sorted based on their frequency. It is worth noting that the exploration of the suggestive vocabulary of the query segment showed overlaps with the one used in benign URLs, thus, introducing a lot false positives. As such, our work only considers the lists of suggestive words from the subdomain, domain and path section of the URL. Furthermore, the path section of the URLs is searched for popular domains, as it is a common practice in phishing URLs to use the target domain or brand in the path under the form of \"www.example.com/brand/signin.html\". If no suggestive word is found in the path, the first 2500 domains are searched throughout the path URL section. Finally, F11 and F12 are based on the similarity index between the URL's domain and subdomain, and the top 25K benign domains from [47]. This feature directly targets the use of domain obfuscation techniques, i.e., as those in Table 3. These are commonly used by phishing URLs with the aim to trick the users that they are accessing trusted, i.e., known brand domain. To this end the Levenshtein distance between the target URL and the top 25,000 benign domains is used. To the best of our knowledge, we are the first to use the Levenshtein distance as a feature in phishing detection model. Specifically, based on our analysis, if the Levenshtein distance for the domain section (i.e., F11) is lower than six and greater than zero, then the URL is flagged as suspicious. For the subdomain section (i.e., F12), if the distance is lower than three, then the URL is flagged as suspicious. It is worth noting that our experimentation suggested that subdomains closer to popular domain names are more suspicious than different variations of a domain. Moreover, zero distances
# Evaluating the Flexibility Benefits of Smart Grid Innovations in Transmission Networks
## 3 Use Case Scenario: Battery Integration in the Cypriot Power System
### IRRE with BESS  
In order to investigate the impact of the BESS to the Cyprus power system flexibility for 2020-2025 the IRRE for the six years was calculated again, considering the three different BESSs. Although a BESS is a very expensive asset (especially for BESS with large capacities), it is essential to assess the benefits of such systems to the overall power system operating condition in terms of flexibility improvement and operating cost decrease [24]. The integration of BESS capacity into the network is related to the market incentives provided to the energy actors and the respective business models created (i.e., aggregators, battery-RES installations, ESCO/flexibility providers), a subject very well elaborated in WP3. Note that aim of WP3, entitled \"Regulatory frameworks, market designs and novel business models for flexibility services\" is to develop a proposal for an enhanced market design focusing on the integration of renewables, the promotion of regional collaboration to accelerate market coupling activities in the SEE region (including the establishment of a liberalized electricity market in Cyprus). In addition, WP3 shall explore the suggestion that an enhanced market design incorporating flexibility could help relieve internal congestion issues between SEE region grids and thereby contribute to the available cross-zonal capacity in the region. New business models for market players and TSOs/DSOs where appropriate will be developed, incorporating flexibility services and thereby helping Europe achieve global leadership in renewable energies.  
In this sense, the IRRE for each BESS capacity (40, 80, and 120 MWh) was calculated for the whole period. Figures 6 and 7 illustrate the IRRE (with BESSs) for the years 2020 and 2021, respectively.  
In both years, the IRRE was significantly decreased when a BESS was integrated to the generation portfolio. The behavior of the IRRE is the same for the other 4 years leading to the conclusion that the use of the BESS benefits the system and provides a huge amount of flexibility, due to its ability to provide or absorb large amounts of power almost instantly. Normally, in the absence of a BESS, the required flexibility is provided by dispatchable thermal generation units, which are very expensive to operate for regulation. In this sense, the BESS may limit the use of those generators, while providing that extra flexibility needed, instantly. Furthermore, the RES-battery integration business model can provide multiple revenue streams through the provision of flexibility services, as this is very well described in the activities of FLEXITRANSTORE related with business models. These new market opportunities result in lower cost of operation, lower emissions, and increased stability and power quality.  
Figure 5: Estimated RES capacity for the years 2020–2025.
## 5 Category I: Energy Transaction
### Transaction Settlement Method
#### 5.3.2 Multi-Signature Algorithm  
Multi-signature algorithm is used to ensure transaction validity. It is basically about finding someone else to prove that neither side has cheated. If the third party fails to satisfy both parties, it may be replaced. The conditions under which a transaction can be redeemed are defined using a multi-key script, the minimum M public keys can be signed to prevent theft [3, 64]. If they only want to trade partial ownership, they could also adopt time locked multi-signature to refund the transaction.  
Similarly, [PERSON] et al. [91] proposed the concept of multi-signature wallet, which acts as a token broker with the buyer's and exchange's signatures. Firstly, participants in energy trading must authorize the exchange to transfer a set of tokens from their blockchain accounts to a multi-signature wallet. Then, an order contract containing information about the buyer and the seller is signed with the private keys of both parties and sent to the exchange. The exchange classifies the orders according to the kind of energy and the delivery time, so that the orders could be settled in the same period. Settlement contracts are created and deployed to the main chain to secure the trader's assets. After the agreement is reached, the smart meter automatically uploads the data to the smart contract and then the token is transferred to the seller. This also guarantees the validity of the transaction and prevents either party from cheating.
# Heart Disease Risk Prediction Using Machine Learning Classifiers with Attribute Evaluators
## 3 Materials and Methods
### Proposed Research Methodology  
The experimental workflow of the proposed methodology is shown in Figure 1. As a first step, we collected the Cleveland heart disease dataset in.csv format from the UCI machine learning repository. Then, we imported the dataset into the software tool and explored the attributes, types, value ranges, and other statistical information. The next step was pre-processing the data, which included tasks such as looking for the missing values in the dataset and replacing missing values, either with the user constant or mean value depending on the type of attribute, to make sure the machine learning classifiers provide better performance. Thereafter, classification was performed with cross-validation using several machine learning algorithms, such as NB, LR, SMO, IBk, AdaBoostM1 + DS, AdaBoostM1 + LR, bagging + REPTree, bagging + LR, JRip, and RF using the full set of attributes. Cross-validation is a resampling method that is used to assess the efficacy of the machine learning model by partitioning the original dataset into a training set to train the model and a test set to evaluate it. The observations in a dataset can be randomly split into k equal-sized groups. We then trained the model using k\(-\)1 folds and validated the models using the remaining kth fold. We repeated this step until all k folds served as a test set and took the average of the recorded values as the performance metric of the model. This work considered k = 10, i.e., a 10-fold cross-validation. Further, we applied attribute evaluators, such as correlation-based feature selection with the BestFirst search method, chi-squared attribute evaluation with Ranker, and ReliefF attribute evaluation with Ranker using a full training set to obtain the optimal set of attributes for predicting heart disease risk and trained the classifiers again using cross-validation. Finally, we tuned the hyperparameter 'k' in the IBk classifier for enhanced performance and analyzed the results.
# Experimental Study on Flexural Properties of Polyurethane-Cement Composites under Temperature Load
## 3 Synthesis Mechanism of Polyurethane Cement Composites
### Interaction between Polyurethane and Silicate Cement
#### 3.3.2 Filling Effect of Cement Particles  
In polyurethane preparation, tiny gaps and bubble impurities can appear on the surface and inside the polyurethane material due to external factors. When polyurethane is subjected to external forces, these tiny gaps can crack and seriously affect the modulus and strength of polyurethane composites. A meaningful way to eliminate cracks is to eliminate the internal stresses in the material. Silicate cement is a mixture containing various mineral particles, and the specific surface area of the mineral admixture is 350-1500 m\({}^{2}\)/kg [43].  
Figure 10: Energy band structure diagram of calcium oxide and silica: (**a**) band structure diagram of CaO; (**b**) band structure diagram of SiO\({}_{2}\).  
The cement particles with a larger specific surface area have a strong adsorption effect on polyurethane colloids and a good affinity with the material. The tiny particles in the cement will fill the space between the hard segment and the soft segment of the polyurethane, making the microphase structure of the polyurethane more compact. The active surface of the microparticles also forms a subvalent cross-linked structure with specific macromolecules around the polyurethane so that when one of the molecular chains is stressed, the stress can be transferred to the other molecules by dispersing the stress through the cross-linked points. The filling of cement particles will reduce the stress concentration inside the polyurethane and retard the fracture process, thus increasing the strength of hydrogen bonding between the polymer and the filler and increasing the mechanical properties of the polyurethane cement composites.