esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Chemical Characterization of the Liquids  
Many researchers report strong acidification of the liquid by plasma treatment as long as non-buffered solutions are used [5; 18]. The presence of a phosphate buffer inhibits the acidification [7; 32]. Depending on the treated volume and the plasma source used, the acidification takes only one minute (V = 1.5 mL, [7]) or more than 10 min (V = 1 L, [5]). We treated in our experiments 500 mL of water for 30 min and opted for 10 min interval of measurement to investigate the general behavior of the liquid (Figures 6 and 7). In the slightly acidic (pH = 5.1) saline solution and demineralized water (pH = 4.8), the pH-value decreased to about 3 within 10 min treatment time whereas tap water (pH = 6.6) was not acidified at all. To our knowledge, this is due to the sufficient amount of buffering components like carbonate which corresponds to the high hardness of the local water used in this study (23.3\({}^{\circ}\) dH [33] compared to an average of 16\({}^{\circ}\) dH in Germany [34]).  
Figure 4: Electrical characterization for different treated liquids measured on one electrode: (**a**) tap water \(\sigma\) = 0.648 mS/cm, (**b**) saline solution \(\sigma\) = 16.23 mS/cm, (**c**) distilled water \(\sigma\) = 0.013 mS/cm.  
Figure 7 shows the change of the conductivity \(\sigma\) of the tested liquids. It was determined that the conductivity of the almost non-conductive distilled water increased significantly by around 300 \(\mu\)S/cm, which was in the range of the values reported in [4] (\(\Delta\sigma\) = 150 \(\mu\)S/cm in 10 min). Also, for saline solution with a very high initial conductivity, an increase of about 700 \(\mu\)S/cm was measured. Again, we found different results for tap water. The increase in conductivity was less than 50 \(\mu\)S/cm after 30 min treatment.  
After t\({}_{\text{treat}}\) = 30 min, the concentrations of NO\({}_{2}\)- and NO\({}_{3}\)- were 1.55 mM and 0.77 mM, respectively (data not shown). Contrary to many other reported experiments, the concentration of H\({}_{2}\)O\({}_{2}\) was negligible [6; 7; 8; 32]. The optical emission spectra (Figure 5) show significant emissions of nitrogen and also emissions of nitrogen monoxide. This indicates that the nitration of the liquids is caused by the input of nitrogen species into the liquid by the discharge itself. [PERSON] and [PERSON] [35] reported  
Figure 5: Optical characterization of the discharge for different liquids, the Na-D line is almost missing for liquids not containing sodium.  
Figure 6: Change of pH-value with treatment time.  
positive effects on germination and growth of plants such as radish, tomato, and sweet pepper with plasma treated water containing approximately 0.26 mM of NO3\({}^{-}\). Thus, this kind of application could be an interesting field of research regarding a possible utilization of plasma treated water in agriculture. The temperature of the liquids increased slightly with a rate of \(\Delta\)T \(\approx\) 0.7 K/min. Therefore, also 30 min treatment does not cause notable evaporation of the liquid.
## 3 Results
### Box Plots and ROC Curves  
Based on the box plots (Figure 5) for the identified compounds, it was observed that the mean values of patients without exacerbations (noexac) were higher than the mean values of patients with exacerbations (exac). The box plots also indicated that for the compounds gamma-terpinene (1,4-Cyclohexadiene, 1-methyl-4-(1-methylethylene)) with RT: 8.9 and 2-ethylhexanol with RT: 9.4, the overlap range between the two groups was very narrow. However, for the compounds Heptane,2,2,4,6,6-pentamethyl with RT: 8.3 and Undecane with RT: 10.8, there was some overlap between the two groups.  
The ROC curves (Figure 6) for all the identified compounds showed that the area under the curve (AUC) values were greater than 0.7, indicating satisfactory to excellent separation between the two classes. Additionally, the \(p\)-values for all the curves were significantly lower than the required threshold of 0.05, indicating statistical significance and reliable results.  
Furthermore, from the ROC curves (Figure 7), it appeared that the diagnostic performance of the four substances was better compared to Eosinophils (Eos) of the patients. These findings suggest that the identified compounds have potential as biomarkers for distinguishing between patients with exacerbations and those without exacerbations in COPD.  
Figure 5: (**A**) Box plot for Undecane (RT: 10.8); (**B**) Box plot for 2-ethylhexanol (RT: 9.4); (**C**) Box plot for Heptane,2,2,4,6,6-pentamethyl (RT: 8.3); (**D**) Box plot for gamma-terpinene (RT: 8.9).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**RT (min)** & **M/Z** & \(p\)**-Value** & **Volatiles** \\ \hline
8.3 & 57, 58, 67, 112 & 0.0387 & Heptane,2,2,4,6,6-pentamethyl \\
8.9 & 77, 80, 92, 93, 94, 122 & 0.0001 & gamma-terpinene(1-methyl-4-propan-2-ylcyclohexa-1,4-diene) \\
9.4 & 55, 67, 68, 70, 81, 82, 95, 98 & 0.0001 & 2-ethylhexanol \\
10.8 & 41, 44, 56, 118, 123 & 0.0110 & Undecane \\ \hline \hline \end{tabular}
\end{table}
Table 5: Metabolites identified with the NIST library.  
Figure 6: (**A**) ROC Curve for gamma-terpinene (RT: 8.9); (**B**) ROC Curve for 2-ethylhexanol (RT: 9.4); (**C**) Box plot for Heptane,2,2,4,6,6-pentamethyl (RT: 8.3); (**D**) ROC Curve for Undecane (RT: 10.8).  
Figure 7: A summary of ROC Curves for Heptane,2,2,4,6,6-pentamethyl (RT: 8.3), gamma-terpinene (RT: 8.9), 2-ethylhexanol (RT: 9.4), Undecane (RT: 10.8) compared to the ROC Curve of eosinophils (Eos) from each patient.
## 3 Results  
### Comparison of Pigment Concentrations Among Species  
At the leaf level, the dominant pigment was Chl a and total Chl (i.e., Chl a + Chl b), with values of \(7.18\pm 0.29\) and \(9.09\pm 0.40\)\(\upmu\)g/mL for soybean, \(6.10\pm 0.75\) and \(7.91\pm 0.90\)\(\upmu\)g/mL for sweet potato, respectively (Table 2). Leaves from the sweet potato contained Chl a and Chl b at a ratio of \(3.32\pm 0.21\), which was significantly lower than the values of Chl a/Chl b in soybean (i.e., \(3.76\pm 0.09\)). Compared to the content of Chl, Car remained at a relatively low level both in soybean and sweet potato, with values of \(1.13\pm 0.04\) and \(0.89\pm 0.07\)\(\upmu\)g/mL, respectively. However, a higher ratio of Chl to Car was found in sweet potato, while soybean showed relatively lower values of Chl/Car (\(8.80\pm 0.55\) and \(8.09\pm 0.24\), respectively).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fluorescence** & **Definition** & **Physiological Significance** \\
**Parameters** & **Definition** & **Provides an estimate of the maximum efficiency of the PSII** \\ \(F_{\
u}^{*}/F_{m}^{*}\) & PSII maximum efficiency & photochemistry at a given light intensity, which is the PSII \\  & & operating efficiency if all the PSII centers are open (QA oxidized). \\ \hline \multirow{4}{*}{\(F_{\mu}^{\prime}/F_{\
u}^{*}\)} & \multirow{4}{*}{PSII efficiency factor} & Is non-linearly related to the proportion of PSII centers that are open (with QA oxidized). Relates the PSII maximum efficiency to the PSII operating efficiency. Mathematically identical to the coefficient of photochemical quenching, qP. \\ \cline{1-1} \cline{2-3} \multirow{2}{*}{_NPQ_} & \multirow{2}{*}{Non-photochemical quenching} & Estimates the non-photochemical quenching from \(F_{m}\) to \(F_{m}\). Monitors the apparent rate constant for non-radiative decay (heat loss) from PSII and its antennae. \\ \cline{1-1} \cline{2-3}  & & \\ \cline{1-1} \cline{2-3} \multirow{2}{*}{_Note_} & \multirow{2}{*}{Non-photochemical quenching} & Is non-linearly related to the proportion of PSII centers that are open (with QA oxidized). Relates the PSII maximum efficiency to the PSII operating efficiency. Mathematically identical to the coefficient of photochemical quenching, qP. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.  
The pigment content at the canopy level was also compared. As shown in Figure 2, the LAI of soybean was significantly higher than that of sweet potato (\(4.18\pm 0.18\) and \(2.67\pm 0.18\), respectively), indicating that more green leaves could be used for photosynthesis in soybean. Due to the higher LAI, the soybean showed a higher content of canopy pigments, with approximately two-fold Chl a and total Chl relative to that of sweet potato (the ratios were 1.84 and 1.80 ug/mL, for LAI*Chl a and LAI* total Chl, respectively). Thus, at the canopy level, the pigment pool size of soybean was much greater than that of sweet potato.
## 2 Code Length Estimation and Blind Synchronization
### Principles of the Proposed Recognition Algorithm in Soft Decision Situations
#### 2.2.1 Calculation of \(p_{i}\) in Soft Decision Situations
We assume that the transmitter is sending a binary sequence of codewords and using a BPSK modulation, _i.e.,_ let \(+1\) and \(-1\) be the modulated symbols of \(0\) and \(1\). The modulation operation from coded bit \(c_{u}\) to modulated symbol \(s_{u}\) can be written as:  
\[s_{u}= 1-2c_{u},\qquad u=1,2,3,... \tag{12}\]  
We assume that the propagation channel is a Binary Symmetry Channel (BSC) which is corrupted by an AWGN with the variance \(\sigma_{n}^{2}=N_{0}/2\). For each configuration, the information symbols in the codes are randomly chosen. A soft decision symbol \(r_{u}\) at the reception can be expressed as:  
\[r_{u}= S_{u}+W_{u},\qquad u=1,2,3,... \tag{13}\]  
where \(\big{(}w_{1},w_{2},w_{3},...\big{)}\) is an AWGN sequence. According to [30], it is easy to prove that the probability \(P_{r}(S_{j,k}=0)\) (\(1\leq k\leq m\)) can be calculated as follows:  
\[P_{r}(S_{j,k}=0)=\frac{1}{2}+\frac{1}{2}\prod_{u=1}^{n_{u}}\tanh(r_{u}/\sigma ^{2}),\ \ 1\leq k\leq m \tag{14}\]  
where \(n_{e}\) is the number of ones in the \(e^{th}\) row of the binary MPCM \(Hb_{\min}(\alpha^{i})\) (\(1\leq i\leq 2^{m}-1\)).
# A Survey on Penetration Path Planning in Automated Penetration Testing
## 5 Planning Methods in Penetration Path Planning
### Intelligent Planning
#### 5.1.2 Heuristic Planning  
Heuristic planning methods treat the planning problem as a state-space search problem, using heuristic functions to guide the search direction based on commonly used search algorithms. This approach improves the efficiency of dealing with large-scale or highly complex planning problems. Several representative heuristic functions have been identified, including the Additive/Max Heuristic [58], Heuristic Based on Relaxed Plan Solution [59], Causal Graph Heuristic [60], and Landmarks Heuristic [61]. Heuristic search-based planning systems, such as Fast-Forward (FF) [59], Fast Downward [62], SGPlan [63], and LAMA [64], have frequently won the International Planning Competition and have become mainstream in current intelligent planning research. In addition, combinatorial optimization algorithms are widely used in heuristic planning, such as simulated annealing, genetic algorithms, particle swarm optimization, and ant colony optimization.  
In the field of penetration path planning, [PERSON] et al. [65] were the first to introduce intelligent planning into penetration path planning, proposing a system called the Behavioral Adversary Modeling System for planning penetration paths in real-world scenarios. [PERSON] et al. [66] enumerated the shortest attack paths to reach different attack targets with the SGPlan planner based on manually constructed domain planning models. [PERSON] et al. [67] developed a device-reachable graph partitioning algorithm based on the aforementioned algorithm. This algorithm is capable of efficiently decomposing a large network topology into some smaller subgraphs. Subsequently, the SGPlan planner is employed to perform penetration path planning for each subgraph in parallel, thereby enhancing the efficiency of penetration path planning. The metric-FF planner [68] incorporates the capacity to process numerical values based on FF and combines the advantages of the A* algorithm. In a recent study, [PERSON] et al. [69] compared the planning efficiencies of metric-FF and SGPlan and integrated metric-FF into the PT framework Core Impact to automate the process of constructing domain planning models and solving them.  
The occurrence of changes in the environment may render models and planning results obsolete and no longer applicable. To address this challenge, [PERSON] et al. [70] developed a conditional planning-based PT framework that introduces perceptual actions for different services. In conditional planning mode, the planner FF generates a branch for each potential \"if-then\" condition.
# Deploying Secure Distributed Systems: Comparative Analysis of GNS3 and SEED Internet Emulator
## 5 Network Performance Comparison  
In this section, we discuss the network performance parameters which can be observed in Table 3 of different emulated network models and the hybrid system model. We compared all network platforms based on performance benchmarks, including the maximum, minimum, average, median, and standard deviation in latency, as well as jitter. These measurements for network performance can be further understood through the work of [PERSON] et al. [34], which provides insights into the maximum rate at which data can be transferred, the actual rate at which data are being transferred, any delays between the sender and receiver, and the variation in packet delays. This helped us understand the advantages and disadvantages of each solution and approach, allowing us to assess the efficiency, flexibility, and opportunities associated with each approach.  
We list below some established parameters that are typically used to measure network performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Router** & **P. NICs** & **Physical** & **E. NICs** & **GNS31 SEED Internet Emulator** \\ \hline R1 & 1 & 10.180.1.1 & 3 & 10.150.0.254 \(\mid\)10.150.6.254 \(\mid\)10.150.10.254 \\ R2 & 2 & 10.180.1.2/10.180.2.1 & 4 & 10.150.0.253 \(\mid\)10.150.1.254 \(\mid\)10.150.8.254 \(\mid\)10.150.9.253 \\ R3 & 2 & 10.180.2.2/10.180.3.1 & 3 & 10.150.1.253 \(\mid\)10.150.2.254 \(\mid\)10.150.7.253 \\ R4 & & N/A & 3 & 10.150.2.253 \(\mid\)10.150.3.254 \(\mid\)10.150.8.253 \\ R5 & & N/A & 4 & 10.150.3.253 \(\mid\)10.150.4.254 \(\mid\)10.150.10.253 \(\mid\)10.150.7.254 \\ R6 & & N/A & 3 & 10.150.4.253 \(\mid\)10.150.6.253 \(\mid\)10.150.9.254 \\ \hline \end{tabular}
\end{table}
Table 2: IP Addresses and Network Interface of Routers.  
Figure 3: Hybrid System (Physical and GNS3) Network Model.  
\begin{table}
\begin{tabular}{l l} \hline
**System Benchmarks** & **Explanation** \\ \hline Bandwidth & Measured in bits/second, it represents the maximum rate at which data can be transferred. \\ \hline Throughput & The actual rate at which data are being transferred. \\ \hline Latency & The delay between the sender and receiver in a transmission, which is a function of the signal’s travel and processing time at any nodes the information traverses. \\ \hline Jitter & Variation in packet delay at the receiver end of the communication. \\ \hline Energy Consumption & The energy consumed when running different network models. \\ \hline Routing Protocols & A set of defined rules that allow different devices on the network to communicate with each other. \\ \hline \end{tabular}
\end{table}
Table 3: Performance Benchmark Comparison Criteria.
# Review of Physical Layer Security in 5G Wireless Networks
## 5 Future Research
### Evaluating the Effectiveness of Physical Layer Security Solutions in 5G Networks  
More research is needed to evaluate the effectiveness of physical layer security solutions in 5G wireless networks. This could involve conducting large-scale experiments and simulations to test the performance of different security solutions under various conditions and scenarios.
# Gait Energy Response Functions for Gait Recognition against Various Clothing and Carrying Status
## 2 Related Work  
### Spatial Metric Learning-Based Approaches to Gait Recognition  
The spatial metric learning-based approaches concentrate on learning a more discriminant feature space from original appearance-based features to achieve better performance against the covariates. Additionally, there are two further categories within the spatial metric learning-based approaches: whole-based [11; 12; 13; 24; 25; 26; 27] and part-based approaches [28; 29; 30; 31]. For the whole-based approaches, the holistic appearance-based features are projected into a discriminative space to make them more robust against the covariate conditions. For example, [PERSON] et al. [11] applied linear discriminant analysis (LDA) to real and synthesized templates of GEI to reduce intra-class variations (e.g., clothing variations) to some extent. [PERSON] et al. [25] proposed a matrix representation-based subspace learning algorithm by performing a two-stage scheme composed of concurrent subspace analysis (CSA) to reduce dimension and discriminant analysis with tensor representation (DATER) to obtain discriminant subspace. A random subspace method (RSM) framework that combines multiple inductive biases also has been proposed in [26; 27].  
While the previously described approaches rely on whole-based representation, some studies decompose the holistic appearance-based features into multiple body part-dependent features and enhance the effective parts for recognition while attenuating the parts affected by the covariate conditions. This is because variations such as clothing and carrying status usually affect not the whole but certain parts, and a decline in accuracy is derived mainly from the affected parts. Thus, the part-based approaches have possibilities for achieving better accuracy by appropriate treatment of the affected body parts (e.g., reducing the weights of the affected body parts for recognition). For example, [PERSON] et al. [28] divided the human body into eight sections based on anatomical knowledge and mitigated the effect of clothing variations by adaptively assigning larger and smaller weights to affected and unaffected sections, respectively. [PERSON] et al. [31] divided the human body into several areas equally and then estimated a comparison weight for each area. Weights were based on the similarity between extracted features and those in the database for standard clothing. [PERSON] et al. [29] defined more effective and less effective body parts by analyzing cumulative row-wise recognition rates. The frequency domain-based gait entropy features (EnDFT) of the more effective parts were used for recognition.
## 4 Proposed Method
### Data Integrity Protection Mechanism  
The DI protection mechanism refers to calculating the dynamic integrity digest (LHashDynamic) of each BB when the program is running, and then comparing it with the static digest (LHashStatic) calculated when the program is offline. If the results are inconsistent, the instructions contained in the BB have been tampered with. The hash function can be used to calculate the integrity digest of each BB. However, hardware implementation of the traditional hash function will take up a substantial amount of resources. LHash can meet the DI calculation of embedded systems at a low cost and limited hardware resources [33]. Considering security and area consumption, the LHash calculation module uses a 128-bit internal permutation structure, and the output is a 16-bit binary number as the DI digest of each BB. The size of the output digest can also be adjusted if higher security is desired, but the consumption of on-chip hardware resources will also increase.
# Weather Files for the Calibration of Building Energy Models
## 3 Results and Discussion  
### Comparison of In Situ and Third-Party Weather Files  
The first elements to be analyzed and evaluated were the weather files used in the simulations. For this purpose, the Taylor diagram [51, 52] was used to see which weather parameters were similar and which were not, so that it was possible to predict which of them would have better behavior.  
Figure 6 shows the comparison of the two weather files. Both are the actual meteorological year (AMY) generated with the information of the on-site weather station and a third-party. As can be seen, there were large differences in wind speed and wind direction, which influenced both the baseline and the calibrated model.  
\begin{table}
\begin{tabular}{c} Temperature \\ outputs \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Period features used in the research study of Annex 58.  
Figure 6: The normalized Taylor diagram for Holzkirchen (Germany) weather, comparing on-site and third-party weather data.  
According to the results obtained and shown in the diagram, the temperature and diffuse radiation data were the most similar to the ones provided by the on-site weather station, while the least similar were those related to the wind (direction and speed).  
The following subsections show the results of the model simulations and calibrations using on-site and third-party weather, as well as the combinations of both to obtain more cost-effective weather data.
## 2 Related Work  
It has become necessary to reclassify the localization schemes, divided into two classes as range-based and range-free according to the distance measurement method, considering the mobility of known or unknown nodes. Therefore, if this classification is expanded to consider mobility, four main classes are obtained, as shown in Figure 2[11].  
In the static anchor static node class, both ANs and UNNs are static. This class is the starting point for the other classes shown in Figure 2. Examples of this class can be found in [12; 13]. In the static anchor and mobile nodes class, the estimated location of each UNN will fluctuate around its actual location. As the node will move over time, the old location information will become incorrect. However, the position estimate is revised when updated kernel information is received [14]. An application using UNNs placed in currents along a river and fixed ANs placed on the riverbank to determine these nodes' locations is an example of this type [15]. In the mobile anchor and static node class, which is also the subject of research in this study, the UNNs are static. MAs roam the ROI and periodically share their location information to UNNs to estimate their location. This kind of localization scheme is also divided into two categories: random mobility and planned mobility. This class will be discussed in detail in Section 2.1. In the mobile anchor and mobile node class, both UNNs and ANs are mobile. An example can be found in [16].