esa-sceva/satcom-chunk-collection huggingface dataset:
# The Impact of Pressure on the Fingerprint Impression: Presentation Attack Detection Scheme
## 2 Related Work
### Previous Studies on Fingerprint Pressure
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Technique** & **PAI Species** & \begin{tabular}{c} **Sensing** \\ **Technology** \\ \end{tabular} & \begin{tabular}{c} **APCER =** \\ **BPCER (\%)** \\ \end{tabular} \\ \hline [PERSON] et al. [34] & Optical Flow & \begin{tabular}{c} Gelatin, RTV \\ silicon, white \\ glue, and latex \\ \end{tabular} & Optical & 11.24 \\ [PERSON] et al. [35] & \begin{tabular}{c} Thin-Plate \\ Spline \\ First Order \\ Statistics \\ \end{tabular} & \begin{tabular}{c} Silicon \\ Gelatin \\ \end{tabular} & Optical & 4.5 \\ [PERSON] et al. [36] & \
\begin{tabular}{c} First Order \\ Statistics \\ \end{tabular} &
\begin{tabular}{c} Gelatin \\ \end{tabular} & Capacitive & 4.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dynamic PAD mechanisms based on fingerprint deformation analysis. of the TPS model. The bending energy vector of the TPS model is utilized to distinguish bona fide from attack presentations. Further analysis of fingerprint elasticity was performed by [PERSON] et al. [36], analyzing the variations in the fingerprint area, intensity, and standard deviation. The variations in area and intensity were justified by the applied pressure and skin's moistness. The experiment had shown that genuine fingerprints have an increased size and intensity in the sequence, while artifacts demonstrate a random fluctuation in intensity with increasing size for the area. On the other hand, the standard deviation feature characterizes the skin extension in x and y directions within the deformation process of the fingerprint pattern. The evaluation was reported using a dynamic dataset that was collected by a high frame rate capacitive sensor, while only a gelatin attack was performed. Fisher linear discriminant analysis is used to classify bona fide and attack presentations. Table 1 shows a PAD comparison for the aforementioned methods by highlighting the methods' accuracy, sensing technologies, and used PAI species. There is still considerable ambiguity with regard to the generalizability of some of the previous conclusions. First, dynamic fingerprint patterns comprise all of the natural phenomena of genuine fingerprints and not only an individual characteristic, such as elasticity. Second, experiments in [35; 36] were conducted using one PAI species, so conclusions are limited to those attacks and might not apply to other PAI species. Finally, even though the different PAI species have demonstrated different behaviors, other factors, such as attacking tools and attacker's level of expertise, must be taken into account to completely characterize the interaction over the sensor's surface.
# Energy-Efficient Check-and-Spray Geocast Routing Protocol for Opportunistic Networks
## 2 Related Work
In their scheme, the messages are forwarded to the destination cast, then intelligently flooded inside this cast by using a centrality-based flooding mechanism that relies on the degree centrality of the nodes in the cast. Whenever a node gets a message, it examines whether it is located within the specified area or not. This is achieved by using the Crossing test algorithm [12] (although the Angle summation test algorithm or Triangle test algorithm could have been utilized as well). In [11], [PERSON] et al. proposed a geocasting routing protocol for OppNets (called F-GSAF), which uses fuzzy attributes such as direction and speed, remaining energy, and buffer space of nodes, to determine the relay nodes to carry the message toward its destination. In this scheme, the Mamdani fuzzy controller is considered and the routing of a message from the source to a destination cast follows two steps. First, the messages are relayed to the geocast region following a multi-copying spray approach. Once in that cast region, the messages are intelligently flooded to all the nodes therein. During this flooding step, an acknowledgment mechanism is implemented, which takes care of deleting the messages that have already been delivered and dropping the undelivered copies from the buffers of the nodes that still carry them. Unlike the above discussed work [5; 6; 7; 8; 9; 10; 11], our proposed EECSG protocol involves: (1) predicting the node's movement through acquiring the location information on-the-go, (2) Identifying the destination cast and determining whether the current node's position is located within it, (3) computing a forwarding utility metric that quantifies the routing contribution that can be made by the nodes that are encountered outside the destination cast, and (4) selecting a relay, based on this forwarding utility metric.
## 2 Wind Turbine and Control Algorithm
### Control Algorithm
#### 2.2.2 Available Power Estimator  
Available power refers to the maximum power that can be generated under given wind conditions, and an available power estimation algorithm is used to predict this value. Available power serves as a control parameter in situations for which turbine output needs to be intentionally reduced, such as control that generates less power than the planned output, and it is used as a parameter for upstream turbines in wind farm control strategies, including active induction control's demanded power point tracking (DPPT) control [18; 26; 27]. DPPT control is one method of output control aimed at following output commands, operating based on a specific proportion of available power as the output command. Available power can be expressed as shown in Equation (2), where \(C_{p}\) represents the power coefficient of the target wind turbine, adjusted to correspond to wind speed \(V\)[18]. Standard air density is applied, and mechanical losses are set at 94.4%. The available power estimation algorithm is integrated into the control logic of the target wind turbine as an additional module, as depicted in Figure 1.  
\[P_{avail}=\frac{1}{2}\rho_{a}AV_{est}^{3}.C_{p}(V)\eta_{m}\eta_{e} \tag{2}\]  
Figure 3: Overview diagram of the feed-forward control algorithm for validation.
## 2 Results and Discussion  
### Virtual Screening  
We were able to evaluate binding sites and discover interactions that are likely to be relevant for possible competitive inhibitors by docking natural ligands, ATP and GTP, to the constructed structure of SpoT protein third domain. The ligands are located in the common cavity in the 3D visualization of the aforementioned process, which is shown in Figure 1. It should be noted that, despite the fact that the docking box was defined for the whole domain, the ligands appear only in the presented region, although in various conformations. Furthermore, with the exception of ATP's ribose, the structures are in close proximity to one another, suggesting the potential for Pi transfer from ATP to GTP, which corresponds to diphosphokinase activity.  
Figure 2 displays amino acids that interact with ligands and thereby contribute to their binding. Both natural ligands are surrounded by ARG77, ARG51, GLN133, ARG135, and ALA143 amino acids. Hydrogen bonds are formed with ATP via ARG51, ARG77, and ARG48, and with GTP via ARG51 and GLN133. Furthermore, a slew of van der Waals interactions can be seen, which are known to maintain ligands within the pocket.  
To choose the compounds to be submitted for the experimental part of the research, the following approach was adopted. To assert the substance's competitive nature, the preliminary screening results were filtered out of compounds having binding free energies of less than \(-7.6\) kcal/mol, which corresponds to that of GTP (ATP has a binding free energy of \(-6.4\) kcal/mol). They are listed in Table 1, and the whole list is available in Table S1. Furthermore, inhibition constants (\(K_{\text{i}}\)) were calculated, as described in the Section 3. This preliminary screening yielded 105 of the most promising candidates, from which 10 hits were selected based on the following criteria: (1) comparably low binding free energies, (2) amino acid composition interacting similarly to those previously found for natural ligands, (3) visible structural differences, and (4) negligible toxicity to humans. A toxicity profile was established only for selected compounds with favorable parameters (e.g., comparably low binding free energies and/or the ability to interact with a high amount of amino acids  
Figure 1: The 3D structure of SpoT domain exhibiting kinase activity with ATP and GTP bound to the common cavity.  
Figure 2: Amino acids involved in the binding of ATP (**A**) and GTP (**B**). Van der Waals interactions are depicted as wireframed spheres, with red representing oxygen, blue representing nitrogen, and gray representing carbon origin. The surfaces of the ligands are colored in the same way.  
in the target site of SpoT in a way similar to those found for natural ligands). The literature was searched for the toxicity data of these compounds (Table S2).
# HMM Adaptation for Improving a Human Activity Recognition System
## 5 Experiments Carried out in This Work
### Final Experiments and Discussion  
This subsection presents the final results on test datasets. These experiments have been carried out considering the best system configuration obtained from the analyses done on the validation sets (see previous subsection): MMIE HMMs training and user MAP adaptation using 50% of the user session for adaptation (first sub-session) and 50% for testing (second sub-session). The sub-session used for testing is the same in all the experiments. It is important to remark that there is not any overlap between testing and adaptation data. Table 2 shows the final results obtained on validation and test subsets. This table includes the activity segmentation error rates (%), recall (%), precision (%) and confidence intervals at 95%. The results on validation subsets have already been presented in previous subsections.  
As shown, the final results on test subsets are slightly worse because the system was optimized on the validation subset. Similar to the conclusion obtained in previous subsections, the ARER error decreases significantly when adapting the HMMs to the user considering a MAP approach.  
Table 3 includes the same experiments considering the original dataset partition (70% for HMMs training and 30% for testing) for comparing to previous works. These results show that using a user MAP adaptation, it is possible to significantly improve the segmentation results obtained in previous works on this dataset. When adapting the HMMs to the user, the HAR has better modeling for recognizing the physical activities carried out by this specific user. This adaptation is the main contribution of this paper.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**User Adaptation**} & **Validation** & \multicolumn{2}{c}{**Test**} \\ \cline{2-5}  & **ARER (\%)** & **ARER (\%)** & **Recall (\%)** & **Precision (\%)** \\ \hline Baseline & 3.5\% \(\pm\) 0.4\% & 3.5\% \(\pm\) 0.4\% & 92.9\% \(\pm\) 0.4\% & 92.6\% \(\pm\) 0.4\% \\ MMIE training & 3.3\% \(\pm\) 0.4\% & 3.3\% \(\pm\) 0.4\% & 93.2\% \(\pm\) 0.4\% & 93.1\% \(\pm\) 0.4\% \\ MMIE training + User MAP adaptation & 2.0\% \(\pm\) 0.3\% & 2.1\% \(\pm\) 0.3\% & 95.2\% \(\pm\) 0.3\% & 95.1\% \(\pm\) 0.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Final segmentation results including activity recognition error rate (ARER), recall and precision metrics.
## 1 Introduction
### Kinetics of Interactions with Rocks  
However thermodynamically favourable an scCO\({}_{2}\)-rock reaction may be, its significance and impact will depend upon the speed at which it occurs [19]. When assessing the importance of particular reactions, the rate at which they happen must be compared with the timescales for geological storage, taking into account the changing phases and migration of the CO\({}_{2}\) plume. Most carbonates, and certain sulphates, can react at rates high enough to be measured directly in the laboratory when exposed to CO\({}_{2}\) dissolved in brine. Equilibrium can be reached within a couple of hours at ambient temperatures. Models of dissolution/precipitation rates in CO\({}_{2}\) storage generally follow the simplified law given by [35]:  
\[rate_{m}=A_{m}k(T)_{m}(a_{H^{+}})^{n}\bigg{[}1-\frac{Q_{m}}{K_{m}}\bigg{]} \tag{3}\]  
where \(m\) is the mineral index, \(A\) is the reactive surface per kg water, \(k\)_(T)_ is the temperature-dependent rate constant, \(a_{H^{+}}\) is the proton activity, \(n\) is the order of the reaction (\(0<n<1\)), \(K\) is the equilibrium constant for the mineral water reaction written for the dissolution of 1 mol of mineral, and \(Q\) is the corresponding ion activity product. Positive rate values indicate dissolution and negative values indicate precipitation [19].  
The reactive surface area and kinetic rate constant are both sources of large error. Based on the assumptions regarding the geometry, or basic BET surface area calculations, the mean estimated values of the parameter \(A_{m}\) can vary widely. Methods that provide more accurate surface area measurements for systems as chemically heterogeneous as rocks will be discussed in more detail below. The kinetic rate constant is similarly hard to quantify. Laboratory-based measurements of silicate reactions have been found to be 2-5 orders of magnitude faster than when measured in situ [28].
# Proposal of an Adaptive Fault Tolerance Mechanism to Tolerate Intermittent Faults in RAM
## 4 Assessment of the Adaptive Fault Tolerance Mechanism
### Injection Campaign  
In this section, we present the results of injecting faults under some controlled injection conditions:  
* The set of injection targets are limited in each experiment to those memory addresses that are accessed by the workload, and only if they are accessed (at least) as many times as the maximum number of fault activations of the fault to be injected. That is to say, a memory address that is accessed 15 times during the workload execution will be selected as the target when injecting intermittent faults with a burst length in the range [2; 10], but not in the range [10; 20].
* Faults are injected in each target in a time window where the addresses are accessed. We know these time windows from the workload analysis (see Section 4).  
With these conditions, we prevent injecting faults in memory addresses that will not be accessed after the injection. In this way, we reduce the probability of the fault to become a latent error, increasing the likelihood of becoming an error.  
From the tuning experiments, we can assert that a latent error may be due to two main reasons:  
1. The injected fault modified a memory address but did not cause any failure.
2. The fault was injected after the last access to that address, or the address was never accessed.  
Next, we will show a subset of the outcomes obtained from the injection campaign.
## 1 Introduction  
The Jiaodong Peninsula is one of the largest gold mineralization regions in China [1]. The three main metallogenic belts in the Jiaodong Peninsula from west to east are Zhaoyuan-Laizhou, Qixia-Penglai, and Muping-Rushan [2]. The gold deposits of the Jiaodong Peninsula were formed mainly during the Early Cretaceous [2; 3; 4]. Previous researchers used stable isotopes of quartz (D-O) and pyrite (S-Pb) and fluid inclusions of quartz in the mineralization stages [5; 6] and observed that ore-forming fluids were sourced from the mantle, meteoric water, magmatic water, and metamorphic water [7; 8; 9; 10; 11; 12]. [PERSON] and [PERSON] (2014) suggested that the ore-forming fluids of the Jiaodong gold deposits primarily originated from the ancient Pacific Plate and the sediments above it [13]. [PERSON] et al. (2020) reported that the ore-forming fluids of the Jiaodong gold deposits were derived from a metasomatized lithospheric mantle [14]. A mineralization genetic model of the Jiaodong gold deposit has been established, including those of orogenic gold [13], decratonic gold deposits [15; 16], and the Jiaodong-type gold deposit [17]. Although many studies have been conducted on gold deposits, the source of ore-forming fluids and the mechanism of gold precipitation remain uncertain.  
Pyrite is one of the most important gold-bearing minerals in Jiaodong gold deposits [2]. Distinct physicochemical environments have led to the development of pyrite with varying crystalline morphologies and trace elements [18; 19]. The chemical composition of pyrite preserves the evolution of ore-forming fluids [19; 20]. Trace elements and stable isotopes of pyrite are also effective for studying the mechanism of gold precipitation [21; 22; 23]. Based on previous studies, this study focused on the Jiaojia gold deposit, a super-large gold deposit in the Jiaodong Peninsula, with a gold reserve of over 300 tons [3; 6]. We conducted an in situ geochemical analysis of gold-bearing pyrite at Jiaojia, including major elements, trace elements, and sulfur isotopes. This study aimed to reveal the source of ore-forming fluids and the mechanism of gold precipitation in the Jiaojia gold deposit.
# Antioxidant, Antibacterial, and Cosmeceutical Potential of Four Common Edible Mushrooms
## 2 Materials and Methods
### Anti-Tyrosinase Activity  
The anti-tyrosinase activity was determined according to a method formerly described [33], with slight changes. The ethanolic extracts of each mushroom were dissolved in 50% DMSO at a final concentration of 0.1, 1, or 10 mg/mL. The assay was executed in a 96-well microplate by adding 40 uL of mushroom tyrosinase enzyme (Sigma Aldrich) (1000 U/mL), 80 uL of phosphate buffer (50 mM, pH 6.5), and 40 uL of the extracts properly diluted (or control). For each sample (or control), a blank containing all the reaction mixture components except the tyrosinase enzyme (replaced by phosphate buffer) was included. In this assay, 50% DMSO instead of sample was used as a negative control and 1 mg/mL Kojic acid (Sigma Aldrich) (dissolved in distilled water) was used as a positive control. The microplates were incubated at 30 \(\pm\) 1 \({}^{\circ}\)C for 10 min, and then 40 uL of L-tyrosine solution (0.2 mg/mL prepared in distilled water and slightly heated in a water bath until complete dissolution) was added. Then, the reaction was incubated in the oven at 30 \(\pm\) 1 \({}^{\circ}\)C for 60 min, and the absorbances were read at 475 nm. The Equation (3) was used to calculate the percentage of tyrosinase inhibition:  
\[\%\ \text{Tyrosinase Inhibition}=\frac{\left(\text{C1}-\text{C2}\right)- \left(\text{S1}-\text{S2}\right)}{\left(\text{C1}-\text{C2}\right)}\times 100 \tag{3}\]  
where C1--absorbance of the control with enzyme, C2--absorbance of the control without enzyme, S1--absorbance of sample with enzyme, S2--absorbance of sample without enzyme.  
The half inhibitory concentration (IC\({}_{50}\)) was calculated by regression of a dose-response curve. Three independent experiments were carried out in triplicate.
## 1 Introduction  
Government strategies to encourage the use of various types of renewable energy to solve global warming and environmental problems are being developed around the world, and a microgrid system that supplies power to a small area has been introduced for the spread of renewable energy. Microgrids using renewable energy resources are increasingly in use, and various design techniques to optimize the facility capacity of the microgrid components (photovoltaic power generation and an energy storage system) have been proposed in many papers [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. In most papers, PV (photovoltaic) power generation or wind power generation was adopted as the main energy source of the microgrid system, and an ESS (energy storage system) was added to increase the utilization rate of the renewable energy. Studies on independent microgrids for islands or isolated areas were initially conducted. Currently, much research is being conducted to apply microgrids to not only isolated areas but also non-isolated areas [11; 12]. We had previously suggested an optimal design method for the facility capacity of microgrids using the loads and load patterns of non-isolated areas. This study aims to improve the feasibility and completeness of the proposed microgrid design by improving the technique [9]. In the referenced study, a self-contained microgrid supplied the customer's power load using only PV power generation and an ESS without receiving energy from electrical power systems. In such a self-contained microgrid, a stable power supply is the top priority [9; 13; 14; 15; 16]. However, PV power generation is affected by the weather and the limited daytime hours, and the stability of the power supply is relatively poor.  
In fact, in microgrids that use PV power as a major energy supply source, there are many cases in which a diesel generator is adopted as a spare power supply source to handle the customer load [17; 18; 19; 20; 21; 22; 23; 24]. Recently, there are many microgrids that use a fuel cell as a spare power supply instead of a diesel generator due to environmental issues [25; 26; 27; 28; 29].  
In this study, we adhere to the concept of the referenced study, which is a stand-alone, microgrid-oriented renewable energy. A renewable energy source (fuel cell) that can be operated for 24 h was added to increase the stability of the power supply. We analyzed and proposed the facility capacity design criteria for three types of renewable energy sources (PV, fuel cell, and ESS) based on the facility cost.  
Fuel cells, a new renewable energy source, provide a stable power supply [30; 31] but are very expensive compared to PV generation, so they are mainly used to support the minimum base load of customers. However, it is necessary to increase the proportion of the fuel cell capacity in the facility to increase the stability of the power supply system. Therefore, we presented a criterion to determine the adopted cost to ensure the competitiveness of fuel cells compared with other energy resources (PV and ESS) when increasing the facility capacity.
# Fault Tolerant DHT-Based Routing in MANET
## 5 Simulation and Results Analysis
### Lookup Success Ratio
However, increasing mobility and network size limits the scope of such approaches because mobility increases the chances of failures (i.e., AN) and network partition (i.e., AN failures); this causes more lookup requests to be buffered and awaited to buffer overflow occurs, and requests are dropped. Similarly, an increase in network size makes said phenomena worst, since the average hop count between a node and it is AN increase with increasing network size, so the periodic updates have to cover longer distances (i.e., updating mapping data); as a result, more and more requests are likely to buffer and be dropped. This tendency is apparent in Figure 5, where DART drops lookup success ratio as network size and mobility increases. However, as shown in Figure 5, DART performs well as compared to VCP. Figure 6 shows success ratio results against mobility as boxplots, indicating median and quartiles, where the small boxes show mean values. The boxplot analysis, along with comparison, shows consistent gains as compared to VCP and DART. Figure 5: Lookup success ratio as a function of network size and speed.
## 2 Mobility Models in FANETs
### Time-Dependent Mobility Model
#### 2.3.4 Enhanced Gauss Markov  
The Enhanced Gauss Markov (EGM) mobility model is a realistic model that relies on the GM model and is dedicated exclusively to FANETs [63]. The uniqueness is that the UAV direction is computed slightly differently from the GM model because it uses direction deviation to limit the sharp turn and sudden stops. Furthermore, EGM includes a novel boundary avoidance mechanism, allowing soft changes and smooth trajectory at the boundaries, as shown in Figure 15. The following is how the EGM works. The node is given a random speed and direction at the start of the experiment. The speed is typically chosen randomly from a uniform distribution in the 30-60 m/s range, suitable for quadcopter UAVs. The angle is chosen randomly from a homogeneous range of 0\({}^{\circ}\)-90\({}^{\circ}\). Several modern FANETs operations use EGM model [64; 65].