esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Bacterial Inactivation Effect and Kinetics Model Analysis
#### 3.1.2 Embedding _S. Typhimurium_ and _S. aureus_ in Liquid Media
Adverse environmental conditions such as extremes of temperature and pH, osmotic shock, anaerobicity and composition of the growth medium can induce stress responses in _S. typhimurium_, which in turn affects its ability to withstand antimicrobial treatments [38]. [PERSON] et al. [10] reported that lower pH rendered _Escherichia coli_ O157:H7, _Listeria monocytogenes_, _S. aureus_, more susceptible to the plasma. Along with the number of stressful environmental factors increasing, microorganism became more vulnerable when treated by plasma. \(D\) values of _S. typhimurium_ and _S. aureus_ planktonic were higher in 12% BSA compared to 1% BSA (\(p<0.05\)). Log reduction of _S. typhimurium_ and _S. aureus_ (total) were significantly different between 1% BSA and 12% BSA (\(p<0.05\)). The inactivation rate of the mixed culture cocktail was slower by comparison to the respective mono culture preparations, which may be due to the greater potential for organic-based quenching of the reactive species generated by DBD-NTP treatment [39]. Higher resistance of bacteria at 12% BSA to plasma may be attributed to BSA reacting with active species of plasma which are supposed to disinfect microorganisms. [PERSON] et al. [30] showed that plasma induced the change of the side chains of the amino acids by hydroxylation, nitration, dehydrogenation and dimerization. [PERSON] et al. [33] showed the He/O\({}_{2}\) plasma jet was used for destruction of BSA proteins and in the case of incomplete protein removal, the treated BSA suffered considerable degradation. This leads to the assumption that existence of BSA could protect microorganisms from being killed by plasma. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Population** & \begin{tabular}{c} **log N\({}_{\rm H}\) dog (CFU/mLJ)** \\ **log N\({}_{\rm H}\) dog (CFU/cm\({}^{2}\))** \\ \end{tabular}  & \(K_{\rm max}\) & \(t_{\rm I}\) & \ \begin{tabular}{c} **log N\({}_{\rm norm}\) (log (CFU/mLJ)** \\ **log N\({}_{\rm norm}\) (Deg (CFU/cm\({}^{2}\)))** \\ \end{tabular}  & \(D\) value 1  & **Log Reduction** \\  & & \begin{tabular}{c} **log N\({}_{\rm H}\) dog (CFU/cm\({}^{2}\))** \\ \end{tabular}  & \(D\) value 1  & **RMSE 3 ** \\ \hline NaCl0.9\% & Total & 7.65 \(\pm\) 0.14  & 0.26 \(\pm\) 0.013  & 74.57 \(\pm\) 2.78 [FOOTNOTE:5]Footnote 5: 5
# ACapMed: Automatic Captioning for Medical Imaging
## 5 Discussion  
We proposed an end-to-end model for medical image captioning where medical images and their associated medical concepts were concatenated and fitted to the LSTM model to generate more accurate captions. A visual encoder based on a pre-trained model was used for visual feature extraction and a multi-label classifier was trained to detect medical concepts based on the visual features. Semantic features obtained from medical concepts and image captions were merged with visual features to predict consecutive words of the captions. The main objective of the study was to investigate the impact of incorporating medical concepts related to images to provide more information on the content of the image and, hence, boost the captioning process performance. It was observed that, as expected, the medical concepts offered more information on the image and the captioning accuracy was improved. To achieve this, the medical concepts were pre-processed, encoded, and fitted to the language generator in accordance with the visual features, and the prediction took into account both aspects. However, the model performance was still relatively low and the captioning still needed to be enhanced. Indeed, the model was able to accurately generate short captions but failed in constructing long captions or captions with more than one sentence. Moreover, multi-label classifier performances have a large impact on the performance of the model. In other words, the number of concepts to be predicted or the fixed probability for selecting concepts per image influenced the outcome of the captioning. So, it was essential to find optimized values for how many concepts were predicted for each image. The method used for the selection of the predicted word by observing the next steps  
Figure 11: Distribution of CUIs per images.  
Figure 12: Model used for ablation study to investigate the impact of incorporating medical concepts for caption generation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **BLEU** \\ \hline ours: LSTM + Argmax & 33.69\% \\ ours: LSTM + Beam Search 3 & **36.28\%** \\ ours: LSTM + Beam Search 5 & 36.23\% \\ ours: LSTM + Beam Search 7 & 33.63\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of our results to some state-of-the-art resultscould also influence the captioning accuracy by predicting the best caption rather than only the word with the highest prediction probability. We reviewed in the following some partially or wrongly generated captions and categorized them according to the possible reasons of misinterpretation.
## 3 Materials and Methods
### Methodology
#### 3.2.1 Stage I: Feature Selection
The goal is to develop a classifier \(f:\mathbb{R}p\rightarrow\{1,2,\ldots,c\}\) that takes a feature vector as input and predicts the true label of x using the formula \(f(x)\). Suppose a randomized classifier that:  
* As the _reference point_ for \(x\), \(Ref(x)\) is chosen at random from S.
* Label x using the reference point's label \(Ref(x)\). This method is comparable to a \(1-NN\) classifier in which the reference point is selected as the new point's close neighbor. Every point in S has a chance of being picked as the reference point, since in NCA, the reference point is chosen at random [11]. The closer \(x_{j}\) is to \(x\), as determined by the distance function \(d_{w}\), the higher the chance \(P\big{(}Ref(x)=x_{j}\mid\text{S}\big{)}\) that point \(x_{j}\) is chosen from S as the reference point for \(x\). Here, the distance function \(d_{w}\) is shown in Equation (2):  
\[d_{w}\big{(}x_{i},x_{j}\big{)}=\sum_{r=1}^{p}w_{r}^{2}\big{|}x_{ir}-x_{jr} \big{|} \tag{2}\]  
where \(w_{r}\) signifies the feature weights. Let us say in Equation (3),  
\[P\big{(}Ref(x)=x_{j}\big{|}s\big{)}\ \alpha\ k\big{(}d_{w}\big{(}x,x_{j}\big{)} \big{)} \tag{3}\]  
where \(k\) signifies a random kernel that assumes big values when \(dw(x,xj)\) is irrelevant (Equation (4)). Presume it is  
\[k(z)=\exp\Bigl{(}-\frac{z}{\sigma}\Bigr{)} \tag{4}\]  
The goal of neighborhood component analysis is to maximize \(F(w)\) concerning \(w\), shown in Equation (5). \[F(w)=\frac{1}{n}\sum_{i=1}^{n}F_{i}(w) \tag{5}\]  
The collection of closest neighbors \(Ci\), however, might alter significantly after all the points have been subjected to a linear transformation. Particularly, any objective function \(f(*)\) based on the neighbors of a point is piecewise-constant and, hence, not differentiable, since the set of neighbors for a point might experience discrete changes in response to smooth changes in the components of A [11].
# Modified Uncertainty Error Aware Estimation Model for Tracking the Path of Unmanned Aerial Vehicles
## 2 Tracking of UAVs Using a Kalman Filter Algorithm with Error and Uncertainty Sensitivity
### Covariance Matrix  
Equation (27) defines the modified updation equation through measuring noise variance impacting measurement update. However, to obtain the covariance matrix of Equation (27), it must satisfy the following constraint.  
\[\begin{array}{l}D_{l+1|l}-Q_{l+1}\Big{[}\overline{\delta}_{z}V\Big{(}D_{l+ 1|l+1}\Big{)}\overline{\delta}_{z}^{R}+\delta_{z}X_{l+1}\overline{\delta}_{z} +\overline{\delta}_{z}X_{l+1}\delta_{z}\Big{]}Q_{l+1}^{R}\\ =(J-Q_{l+1}L)D_{l+1|l}(J-Q_{l+1}L)^{R}+Q_{l+1}\widetilde{C}_{l+1}Q_{l+1}^{R}+ \beta_{l+1}\mu_{l}\beta_{l+1}^{R},\end{array} \tag{37}\]  
with  
\[\widetilde{C}_{l+1}=ww^{R}+\delta_{z}\delta_{z}^{R}+\overline{\delta}_{z} \Big{(}\hat{x}_{l+1|l+1}\hat{x}_{l+1|l+1}^{R}\Big{)}\overline{\delta}_{z}^{R}, \tag{38}\]  
\[\beta_{l+1}=\big{[}M_{l+1}(J-Q_{l+1}L)\big{]}, \tag{39}\]  
\[\mu_{l+1}=\begin{bmatrix}\gamma_{l+1}\gamma_{l+1}^{R}&\gamma_{l+1}n_{l+1}^{R} \\ n_{l+1}^{R}\gamma_{l+1}^{R}&0\end{bmatrix}, \tag{40}\]  
\[n_{l+1}=O((J-Q_{l}L)n_{l}+M_{l}\gamma_{l}). \tag{41}\]  
The proposed modified Kalman filter in Equations (20), (24), (32) and (37) for UAV tracking is modeled with an updated parameter \(\delta_{w}=\delta_{z}\), \(\overline{\delta}_{w}=\overline{\delta}_{z}\), and run together with other existing Kalman filter-based tracking models. The performance of the proposed and the existing UAV tracking model, namely DMK, is measured using the RMSE by simulation in a nonlinear and noisy environment. The KFUEA algorithm achieves excellent trackingperformance compared to state-of-the-art KF-based UAV tracking models, which have been experimentally demonstrated.
## 3 Adaptability Analysis of Traditional Current Differential Protection
### Fault Component Current Differential Protection  
The commonly used protection criteria for fault component current differential protection are as follows:  
\[\left\{\begin{array}{rl}&VI_{d\varphi}\textgreater{}&kVI_{r\varphi}\\ &VI_{d\varphi}\textgreater{}&I_{o}\\ &VI_{d\varphi}\textgreater{}&=\left|V\,I_{m\varphi}+VI_{n\varphi}\right|\\ &VI_{r\varphi}\textgreater{}&VI_{n\varphi}\textgreater{}&\end{array}\right. \tag{8}\]  
where \(\Delta I_{d\varphi}\), \(\Delta I_{r\varphi}\) respectively denote the differential and restraint currents for phase \(\varphi\); \(I_{\rm o}\) denotes the action threshold; and \(\Delta I_{m\varphi}\), \(\Delta I_{m\varphi}\) respectively denote the phase fault component currents obtained at the protection installation points on the PV side and grid side of the transmission line.  
In conventional synchronous-generator-based power systems, the fault component current differential protection based on the superposition theorem exhibits high sensitivity due to the elimination of load current influence [27]. However, in grid-forming PV systems, the variable virtual impedance activated during faults introduces a phase shift between the fault component currents on both sides of the transmission line, which results in the loss of in-phase characteristics for the fault component currents derived via the superposition theorem.  
The phasor diagram illustrating fault component currents in a grid-forming PV system with virtual impedance activated is depicted in Figure 4, where \(E_{\rm M}\), \(E_{\rm N}\) represent the EMFs on the PV side and grid side, respectively.  
Upon fault occurrence and subsequent virtual impedance activation, the typically high inductive/resistive (X/R) ratio of the virtual impedance--implemented to support system inertia, enhance stability, facilitate power control, suppress  
Figure 4: The phasor diagrams of fault component currents on both sides of the transmission line.  
dynamic response--restricts the transient current rate of change during fault inception. This results in delayed current establishment, manifesting as amplitude limitation and phase lag. Furthermore, the increased virtual impedance renders the corresponding load current negligible under fault-point open-circuit conditions. In extreme scenarios where pre-fault and post-fault load current magnitudes remain comparable with a 90\({}^{\circ}\) phase shift. The fault component currents calculated on both sides yield the phasor diagram shown in Figure 4. The resulting maximum phase angle difference reaches 90\({}^{\circ}\), thereby elevating the protection restraint threshold to 0.7.
## 2 Sensory and Communication System Architecture: X-IoCA
### Internet of Robotic Things
#### 2.1.1 Hybrid Heterogeneous Wireless Transceiver Network  
The H2 WTN is composed of low-power networks. These can be low-power wide-area networks (LPWAN), such as LoRa H-WSN, and short-range BLE H-WTN. The purpose of H2 WTN is to unify the characteristics of different low-power networks. The H2 WTN can detect sensor events, such as environmental issues, at short or long distances using BLE or LoRa, respectively. This short-range (SR) and long-range (LR) information can be used by the motion controllers at X-FIS level (see Figure 1) for deciding teleoperation or path-planning strategies for robot motion control.  
The long-range LPWAN network is useful for obtaining information from the area of operations by deploying static and mobile sensor-nodes or concentrator-nodes. On the other hand, the BLE-based network is intended for the detection of agents such as automated guided vehicles (4.0 factory), people (SAR missions), or animals (livestock) [36; 37]. For this, BLE scanners should be embarked on the agents of the X-IoCA architecture.  
Different local edges are available onboard the robots: MQTT brokers and internal databases. This way, LoRa packets can be processed and published in a message queuing telemetry transport (MQTT) broker [38] hosted on any robot connected to the Internet. Local MQTT brokers may be hosted on one or more LoRa concentrator-nodes [26] or on onboard robot PCs. In addition, an internal database for each BLE scanner manages the signals perceived from the close surroundings. The information produced by both types of local edges can be accessed by the X-FIS through the 5G wide area network (WAN) in the X-FIS.
## 3 Original DE Algorithm
### Experiment Results and Analysis
#### 4.3.2 Contrast Experiments for High-Dimensional Segmentation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**D = 10**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{5}{c}{**Evaluation Criterion**} \\ \cline{3-8}  & & **Fitness Value** & **PSNR** & **SSIM** & **Iterations** & **Running Time** \\ \hline \multirow{5}{*}{Image1(Forest)} & DE(DE/rand/1) & 3446.1561 & 29.7778 & 0.8202 & **135.0667** & **3.8724** \\ \cline{2-8}  & DE(DE/best/1) & 3444.6259 & 29.5565 & 0.8127 & **34.2333** & **0.883** \\ \cline{2-8}  & CSDE & **3446.1878** & **29.7858** & **0.8226** & 489.8 & 10.0595 \\ \cline{2-8}  & VNCDE & 3446.1678 & 29.7658 & 0.8206 & 207.0667 & 80.7313 \\ \cline{2-8}  & Our Method & **3446.1771** & **29.8002** & **0.8213** & 136.9333 & 4.2332 \\ \hline \multirow{5}{*}{Image2(Kangaroo)} & DE(DE/rand/1) & 4046.1617 & 29.2401 & 0.8507 & 119.6 & **3.231** \\ \cline{2-8}  & DE(DE/best/1) & **4045.2943** & **29.271** & 0.8628 & **31.2667** & **0.9824** \\ \cline{2-8}  & CSDE & 4046.2345 & 29.2679 & 0.86 & 360.9333 & 7.7758 \\ \cline{2-8}  & VNCDE & 4046.2064 & 29.2415 & **0.9101** & 201.24 & 76.1909 \\ \cline{2-8}  & Our Method & **4046.2481** & **29.2975** & **0.8677** & **114.3333** & 3.248 \\ \hline \multirow{5}{*}{Image3(Actinia)} & DE(DE/rand/1) & 3740.8882 & 28.9319 & 0.8066 & 125.12 & **5.4697** \\ \cline{2-8}  & DE(DE/best/1) & 3739.8085 & 28.9277 & **0.8217** & **31.46** & **1.3898** \\ \cline{1-1} \cline{2-8}  & CSDE & **3741.0143** & **28.9404** & 0.8155 & 387.22 & 14.4902 \\ \cline{1-1} \cline{2-8}  & VNCDE & 3740.9982 & 28.9334 & 0.8092 & 338.84 & 125.4491 \\ \cline{1-1} \cline{2-8}  & Our Method & **3741.0105** & **28.9376** & **0.8156** & **122.98** & 5.576 \\ \hline \end{tabular}
\end{table} 
Table 6: Results comparison of five algorithms for segmentation of images 4–6 on different criteria when D = 10.
# A Comprehensive Evaluation of Possible RNSS Signals in the S-Band for the KPS
## 1 Introduction
However, they have several drawbacks, such as requiring complex decoders, being highly sensitive to frequency offsets, and so on [19; 20]. Therefore, traditional modulation schemes, such as binary phase-shift keying (BPSK) and binary offset carrier (BOC) modulation, are still attractive signal design options. BPSK and BOC signals were analyzed as new RNSS signal candidates in the S-band [21; 22; 23], but only a few scenarios were considered in the previous studies. The main contribution of this study was to comprehensively evaluate the navigation signal performance of possible BPSK and BOC signals using figures-of-merit (FoMs). The FoMs were determined using RNSS signal design parameters, such as the modulation scheme, chip rate, subcarrier frequency, and so on [24]. Therefore, we clarify the relationship between each FoM and the signal design parameters and analyze the FoMs for each candidate. Since the FoMs have a trade-off relationship [25], we distinguished suitable candidates as S-band signals for KPS by considering the FoMs analysis results together. The rest of this paper consists of the following. Section 2 describes a radio frequency (RF) system using the S-band and adjacent bands. Section 3 introduces several FoMs that quantify navigation signal performance, and then Section 4 provides FoM analysis results for new RNSS signal design candidates. Finally, we discuss the FoM analysis results and findings in Section 5 and conclude the paper in Section 6.
## 2 Overview of Blockchain Technology
### Blockchain Features  
Figure 1 depicts the four main characteristics of blockchain technology.  
* Decentralization: In traditional centralized transaction systems, a third-party agency was always required in order to validate and authenticate each transaction being made between two parties. For example, a bank (central or commercial) would serve as a mediator in a every kind of transaction between two individuals. This would result in an overload to their central systems and servers. In blockchain, due to its utilization of consensus algorithms, third parties are no longer required to act as intermediaries, thereby giving blockchain its decentralized nature [14].
* Anonymity: Due to its decentralized nature, blockchain allows users to execute their transactions under entirely random generated addresses, since there is no centralized authority system to record, monitor, or validate the authenticity of these addresses.
* Auditability (transparency): In blockchain technology, a digital distributed ledger and a digital timestamp are used in order to record and validate each transaction. This means that whoever has access to any node in the network can trace and audit any previous record. In Bitcoin, for example, which is the first and most frequently used cryptocurrency, all transactions can be traced, thus making the data in the blockchain transparent and auditable [15].
* Immutability (persistency): Once a transaction has been recorded in the blockchain, it cannot be tempered with or deleted by any party. This is one of many aspects that makes blockchain networks secure and easily trusted. It works as follows: each block in the blockchain contains a unique piece of code, called a hash, which works like a digital fingerprint. When a new block is created, this hash consists of all the information inside the block, including details of the transaction being made, the hash from previous blocks, and a timestamp, among other information. If anyone attempts to change any information in the block, the hash (fingerprint) will also change, thus signaling that someone has tried to alter something in the blockchain.
## 3 Materials and Methods
### Feature Selection  
To select the best feature from the extracted features, we apply Luca-measure fuzzy entropy (LFE) and Lukasiewicz similarity measure (LS)-based feature selection. It works in such a way that we provide a feature set \(A~{}=~{}(a_{1},~{}a_{2},\dots,~{}a_{n})\) to the algorithm, and after the optimization of an objective function, it provides a subset \(B~{}=~{}(a_{1},~{}a_{2},\dots,~{}a_{m})\) of \(A\) with \(m<n\). The parameters used for the concerned algorithm were LFE and LS. LFE is given by Equation (2), where \(\mu_{A}\big{(}x_{j}\big{)}\) has a value within the range of [0, 1], and LS is defined by Equation (3), where \(f_{r}\) is the feature set. Moreover, \(x\) is the input and \(v\) is the mean vector for a certain class [64].  
\[H(A)~{}=~{}-\sum_{j~{}=~{}1}^{n}\big{(}\mu_{A}\big{(}x_{j}\big{)}\log\mu_{A} \big{(}x_{j}\big{)}+\big{(}1-\mu_{A}\big{(}x_{j}\big{)}\big{)}\log\big{(}1-\mu _{A}\big{(}x_{j}\big{)}\big{)}\big{)} \tag{2}\]  
\[S(x,y)~{}=~{}\frac{1}{t}\sum(1-|x(f_{r})-v(f_{r})|),\quad for~{}[x,~{}v]~{}\in~ {}[0,1] \tag{3}\]  
We experimented using a different number of features, but we obtained the best results with a set of 11 features. In this way, we reduced the size of our feature set by approximately 25%. The 11 features selected using the mentioned feature selection algorithm were Parseval's energy, skewness, kurtosis, Shannon entropy, AR-coefficients, minimum, maximum mode, and median of the time-domain data, FFT minimum and maximum point difference, and FFT minimum and maximum point ratio. The feature selection flow diagram is presented in Figure 6.
# Efficient Design of Reversible Adder and Multiplier Using Peres Gates
## 5 Simulation and Analysis
#### Data Collection  
Data collected during the simulations, including verified truth tables, waveforms, timing diagrams, and quantum cost metrics, were stored and analyzed to draw meaningful insights into the behavior of the RCA using Peres gates. The simulation setup serves as a crucial foundation for the subsequent analysis, providing a detailed understanding of how the proposed reversible circuit performs under different conditions and shedding light on its potential advantages in reversible and quantum computing applications.
# Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
## 4 Proposed Method
### AIDA Method
#### 4.4.4.4 Step 4--SM-Candidate Positions LRP Calculation  
This step calculates the LRP values between each smart meter and the closest candidate positions within a specific pre-established range.  
Considering that the number of smart meters is usually high and knowing that the candidate positions established for each iteration are dispersed throughout the region, the AIDA method seeks to minimize the amount of LRP calculations between meters and CPs. For this, it establishes a theoretical range and calculates the received power only for the positions within that radius.  
Initially, to identify the CPs within the meter range, the distances between each meter and all the CPs of the iteration are calculated. After that, the LRP is calculated and stored in an auxiliary structure only for the relationships in which the distance between the meter and the CP is smaller than \(c_{f}^{c}\) (which corresponds to the communication radius established for the candidate positions). Table 2 describes \(c_{f}^{c}\) and Table 3 presents the gateway/router communication range considered by the method for the experiments.  
In this step, only the LRP values are calculated for all the possible connections. However, no capacity check is performed regarding the number of connections to the CPs. The capacity check is performed by the clustering processes defined in Step 5--SM Clustering  
Figure 4: Initial grid and candidate positions placement.  
Figure 3: Example of MST with edges colored according to the LRP values.  
(Section 4.4.5). For the LRP calculation, Equation (12) is considered. See Section 4.3 for more details.