esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation Analyses  
We present simulations analyses to provide concrete numerical analyses and characterize the dependency between the flying base station channel capacity (\(C_{FU}\)), the base station's power ratio (\(\eta\)), the stationary base station's channel condition in SNR (\(\mathrm{SNR}_{SU}\)), and the channel condition in the path loss exponent (\(\gamma\)). We also analyze the beneficial region and identify the conditions for which the flying base station is beneficial and provides greater channel capacity than the stationary base station.  
We vary the power ratio (\(\eta\)), the stationary base station's channel performance in SNR (\(\mathrm{SNR}_{SU}\)), and the channel condition in the path attenuation exponent (\(\gamma\)) to simulate different communication environments. We vary \(\eta\), which is the transmission power budget of the flying base station relative to that of the stationary base station, where the base station's power budget is per user, as discussed in Section 3.4. \(\eta\) varies from 0.1 (the flying base station's transmission power for that user is smaller than the stationary base station's to 20 (greater transmission power). The signal-to-noise-ratio of the stationary base station's channel to the user, \(\mathrm{SNR}_{SU}\) varies from 0 dB (1) to 20 dB (100). Furthermore, to simulate different wireless channel conditions, we vary the path-loss/attenuation exponent, \(\gamma\). \(\gamma=2\) simulates the free-space and line-of-sight-dominant channel and increasing \(\gamma\) (to \(\gamma=3\) and then to \(\gamma=4\)) simulates wireless channels with stronger non-line-of-sight paths and greater attenuation, as described in Section 3.3. While we analyze how the channel capacity and the beneficial region behave while varying \(\gamma\), we focus on \(\gamma=2\) when presenting our results in this paper. We focus on \(\gamma=2\), because the flying base station can enablethe line-of-sight communication link (which would be especially important for mmWave communications in 6G), and \(\gamma=2\) is the best to model such communication channel.
# Low-Cost Active Anomaly Detection with Switching Latency
## 3 The DMSC Policy
### Example  
We illustrate the two cases of DMSC policy in Figure 1 with an example that detects \(L=2\) abnormal processes (assume that process 3 and process 4 are abnormal processes) among \(M=5\) processes.  
For Case I, the decision-maker probes the process \(m^{1}(n)\) and terminates the detection procedure once the number of the processes declared as abnormal reaches \(L\). At the beginning of the detection procedure, since the sum-LLRs of all processes are zero, a process is randomly selected to probe at \(n=0\). Suppose that the decision-maker selects process 1. Since process 1 is in a normal state, the sum-LLRs of process 1 will decrease with high probability. Thus, at \(n=1\), when the first observation is finished, the order  
Figure 1: Illustration of the DMSC policy (\(M=5\) and \(L=2\)).  
of sum-LLRs of all processes becomes \(\{2,3,4,5,1\}\). Since the switching across process takes a certain time delay \(s\), the decision-maker randomly chooses a process to probe at \(n=1+s\). Suppose that process 2 is selected. Similarly, the sum-LLRs of process 2 will also decrease since process 2 is also in a normal state, and the order of sum-LLRs of all processes may become \(\{3,4,5,1,2\}\). Then, the decision-maker chooses a process to probe at \(n=3+2s\). Assume that the decision-maker selects process 3. Since process 3 is in an abnormal state, the sum-LLRs of process 3 increases and ranks first with high probability, i.e., \(m^{1}(n)=3\) and \(S_{m^{1}(n)}(n)>0\). From then on, the decision-maker will continue to probe process 3 with high probability until \(S_{m^{1}(n)}(n)\geq B\). Then the decision-maker will stop taking observations from process 3 and declare it as abnormal. At this time, the stage 1 of the entire detection procedure is finished. Then, repeat this procedure until declaring the state of another abnormal process and the entire detection procedure will be terminated.  
For Case II, the decision-maker probes process \(m^{-1}(n)\) at each given time. Suppose process 5 is randomly selected to probe at the beginning of the detection procedure. Since process 5 is in a normal state, the sum-LLRs of process 5 will decrease and be the lowest among these processes. Thereafter, the decision-maker will continue probing process 5 until \(S_{m^{-1}(n)}(n)<-B\), and then stop taking observations from process 5 and declare it as normal. Repeat this operation until all normal processes are declared. Then, the entire detection procedure terminates with the remaining processes declared as abnormal.
## 3 Results and Discussion
### Characterization of Lentzea sp. JNUCC 0626 as a Novel Strain via ANI Analysis  
The average nucleotide identity (ANI) is a widely accepted metric for evaluating the genomic relatedness of microorganisms. By comparing the average sequence identity of shared genomic regions, ANI is effective in assessing whether two strains belong to the same species. Typically, ANI calculations involve aligning homologous sequences between genomes and measuring the sequence similarity. The commonly accepted threshold for species delineation is 95-96%, with values below this range suggesting that the organisms represent different species [39].  
In this study, we calculated the ANI values for 13 closely related species within the _Lentzea_ genus (Table S6). Using the OrthoANIu algorithm on the EZBioCloud platform, we evaluated the genomic similarity between _Lentzea_ sp. JNUCC 0626 and its closest relative, _L. pudingi_ CGMCC 4.7319. The resulting ANI value was 88.02%, which is significantly below the species boundary threshold, indicating considerable genetic divergence. This result strongly suggests that _Lentzea_ sp. JNUCC 0626 represents a distinct species within the _Lentzea_ genus. The genetic difference between the two strains underscores the novelty of _Lentzea_ sp. JNUCC 0626 and supports its classification as a previously undescribed species.  
Additional phenotypic, biochemical, and ecological studies will be necessary to further validate this classification and provide a comprehensive characterization of this strain.
###### Abstract  
The ability to detect the presence as well as classify the activities of individuals behind visually obscuring structures is of significant benefit to police, security and emergency services in many situations. This paper presents the analysis from a series of experimental results generated using a through-the-wall (TTW) Frequency Modulated Continuous Wave (FMCW) C-Band radar system named _Soprano_. The objective of this analysis was to classify whether an individual was carrying an item in both hands or not using micro-Doppler information from a FMCW sensor. The radar was deployed at a standoff distance, of approximately 0.5 m, outside a residential building and used to detect multiple people walking within a room. Through the application of digital filtering, it was shown that significant suppression of the primary wall reflection is possible, significantly enhancing the target signal to clutter ratio. Singular Value Decomposition (SVD) signal processing techniques were then applied to the micro-Doppler signatures from different individuals. Features from the SVD information have been used to classify whether the person was carrying an item or walking free handed. Excellent performance of the classifier was achieved in this challenging scenario with accuracies up to 94%, suggesting that future through wall radar sensors may have the ability to reliably recognize many different types of activities in TTW scenarios using these techniques.  
micro-Doppler; FMCW radar; through-the-wall; classification +
Footnote †: journal: Sensors
## 3 Results and Discussion
### Results of Scenario 1: Parallel Parking Without Rotation on Parking Area 1  
In this scenario, vehicles were parked in a parallel manner without any rotational freedom. According to the data in Tables 1 and 2, MDE and the CSA achieved the best results from the perspective of the adopted objective function by placing a maximum of 13 vehicles. ABC, GWO, and PPSO managed to place up to 12 vehicles, while HHO and PSO reached a maximum of 11.  
According to Table 3, the MDE algorithm provided the fastest solution, placing 13 vehicles in an average of 19.21 s. In contrast, the CSA achieved the same result in approximately 36.68 s, making it nearly twice as slow as MDE. Notably, MDE demonstrated high consistency, reaching maximum capacity in approximately 50% of the trials, making it the most reliable algorithm in this scenario.  
In cumulative trials for achieving maximum capacity, MDE required 40.68 s, while the CSA took 86.06 s. Since placing 13 vehicles is more challenging than 12 vehicles, the success rates presented in Table 4 are crucial. The CSA achieved a 93.3% success rate in placing 12 or 13 vehicles, while MDE had a 90% success rate. However, MDE successfully placed 13 vehicles in 50% of trials, whereas the CSA achieved this only 20% of the time. Therefore, MDE was identified as the best optimization method for Scenario 1.  
In conclusion, MDE and CSA were the most robust and consistent methods for this type of application. The other algorithms exhibited lower performance in comparison. Additionally, ABC outperformed GWO, HHO, PSO, and PPSO, demonstrating better optimization capabilities. PPSO also yielded better results than GWO, HHO, and PSO in terms of both vehicle placement and success rates, although it remained less competitive than ABC, the CSA, and MDE.
# Using Graph Partitioning for Scalable Distributed Quantum Molecular Dynamics
## 4 Experiments
### Parameter Choices for METIS and hMETIS  
Using a grid search over sensible values, we tune the parameters of _METIS_ and _hMETIS_, and will keep the set of parameters yielding the best average performance for all systems considered in this section fixed throughout the remainder of the simulations.  
The default multilevel \(k\)-way partitioning as well as the default sorted heavy-edge matching for coarsening the graph were employed to run _METIS_. Importantly, the user can choose to minimize either the _edge cut_ or the _total communication volume_ of the partitioning within the \(k\)-way partitioning routine of _METIS_. As our definition of the _sum of halo nodes_ in Section 2 is equivalent to the definition of the total communication volume of _METIS_, we choose this option.  
We employ _hMETIS_ with the followed parameters: we use recursive bisectioning (instead of \(k\)-way partitioning) with vertex grouping scheme \(\mathit{Ctype}\ =\ 1\) (meaning the hybrid first-choice scheme HFC), Fiduccia-Mattheyses refinement (refinement heuristic parameter set to 1), and \(V\)-cycle refinement on each intermediate solution (\(V\)-cycle refinement parameter set to 3). These parameters are explained in the _hMetis_ manual [21].
## Results and Discussion  
### Prototype of a Wireless Carotid Wearable Ultrasound Doppler System  
The developed prototype of the wireless carotid neckband ultrasound Doppler system is shown in Figure 4. It consists of two single-element ultrasound transducers mounted on the neckband, two coaxial cables for the transducer connection, an ultrasound pre-processing module, a Bluetooth module, a power module, a battery pack, and a smartphone.  
The neckband is used for holding the transducers, and their positions can be adjusted to find the proper locations of the common carotid arteries. The two coaxial cables are used to connect the two 2.5-MHz transducers to the pre-processing module. Figure 4b shows the pre-processing module and the power module. The sizes of the pre-processing module and the power module are 70 mm (width) \(\times\) 50 mm (height) and 80 mm (width) \(\times\) 50 mm (height), respectively. The two modules are interconnected via a 26-pin flat cable. The developed wireless carotid wearable ultrasound Doppler system can be operated by two serially connected, 3.7 V, 750 mAh Li-ion batteries, and can be continuously operated for approximately 1.5 h.
## 3 Results
### AOX Analytical Method
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Laboratory** & **AOX Concentration [mg L\({}^{-1}\)]** \\ \hline \multirow{4}{*}{S1} & Lab-1 & 2.00 \(\pm\) 0.01 \\  & Lab-2 & 6.02 \(\pm\) 0.76 \\  & Lab-3 & 1.55 \(\pm\) 0.36 \\  & Lab-4 & 0.60 \(\pm\) 0.20 \\ \hline \multirow{4}{*}{S2} & Lab-1 & 30.80 \(\pm\) 0.01 \\  & Lab-2 & 86.50 \(\pm\) 0.01 \\ \cline{1-1}  & Lab-3 & 23.80 \(\pm\) 5.50 \\ \cline{1-1}  & Lab-4 & 8.00 \(\pm\) 3.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The AOX concentrations of samples S1 and S2 obtained by the four laboratories. The analytical method used by all laboratories was UNI EN ISO 9562:2004.  
account. Their instrument provides the final ug L\({}^{-1}\) value of Cl given the sample volume and the DF. In any case, the key question was, which laboratory provided the most accurate data? Even though they all operated with automatic instrumentation and therefore with a limited amount of human error, it is impossible to establish which of the obtained datasets had a greater accuracy. Figure 1 shows the average value of the AOX content and the normal 95% confidence for the measurements performed by the abovementioned labs on three samples (S1a, S1b and S2). Samples S1a and S1b (S1 samples that were sampled at different times) had AOX concentrations lower than 7 mg L\({}^{-1}\), while S2 had a concentration higher than 7 mg L\({}^{-1}\). The results shown in Figure 1 show a concordant trend in the normalized data, which could argue in favor of a systematic problem. Therefore, further investigations will be necessary. The wide variability in AOX measurements found in this study, from accredited chemical analysis laboratories, may affect regulatory compliance and may raise doubts about the actual efficiency of the analytical method used. Table 5 presents additional information gathered from the laboratories to compare the measurement methods across the different analysis centers. Although the aspects investigated adhered to the expected regulations, variations between the laboratories were observed. This highlights that, despite regulatory compliance, the specific approaches and details may differ from one laboratory to another. In the study carried out by [PERSON] et al., 2023 [31], the standards for AOX determination, such as ISO 9562 and EPA 9020B, and various alternative techniques were discussed. The EPA 1997 method, developed to monitor waters from hypochlorite bleaching in the paper industry, was performance in a single laboratory. This process produces chlorophenols with varying levels of halogenation, which is more stable than mixed industrial effluents, which are more complex. Errors of up to 50% are common, as noted in points 10 and 11 of the EPA 1997 method. Figure 1: Dispersion of analytical AOX measurements. Values S1_a and S1_b are for the S1 samples with a low AOX concentration while value S2 is for the samples with a high AOX concentration.
# Performance Evaluation of LoRa Communications in Harsh Industrial Environments
## 3 LoRa, Channel and Noise
### Channel and Noise
#### 3.2.2 Noise  
In an industrial environment, the quality of wireless communication differs considerably from that in other environments. Unlike other environments, where signals are affected primarily by additive Gaussian noise, in an industrial environment, signals are also disturbed by additional impulse noise generated by motors, controllers, and electrical equipment. The authors of [22] have modeled this industrial noise as a combination of additive Gaussian noise \(w(t)\) and impulse noise \(i(t)\), with zero mean and very high variance, according to Equation (10).  
\[n(t)=w(t)+n(t), \tag{10}\]  
The probability density functions of Gaussian noise and impulse noise are defined in Equations (11) and (12), respectively:  
\[P[w(t)]=\frac{1}{\sqrt{2\pi\sigma^{2}}}exp\left[-\frac{w(t)^{2}}{2\sigma^{2}} \right], \tag{11}\]  
\[P[i(t)]=\frac{1}{\sqrt{2R\pi\sigma^{2}}}exp\left[-\frac{i(t)^{2}}{2R\sigma^{2 }}\right], \tag{12}\]  
All simulations are carried out using a high level of impulse noise with an extremely high variance equivalent to 50 times that of additive Gaussian noise, i.e., a ratio of R = 50.  
Figure 3 provides a visual representation of the simulated channel under two distinct noise modeling scenarios: (a) where noise is represented as additive white Gaussian noise (AWGN) and (b) in the presence of industrial noise.
## 6 Conclusions  
In order to address the problems of low transmission rate and noise effects of DCSK system, this paper proposes a differential chaotic shift keying (NR-ODBR-DCSK) communication system based on noise suppression and orthogonal double rate. The system superimposes two mutually orthogonal signals by Walsh orthogonalization at the transmitter side to achieve double the information transmission rate while avoiding the problem of chaotic synchronization. At the receiving end, the properties of orthogonal signals are utilized to not only increase the information transmission rate, but also reduce the bit error rate (BER), which significantly improves the performance of the system.  
In the NR-ODBR-DCSK system, the noise suppression transmission mechanism is used to effectively reduce the noise variance in the received signal. By analyzing the signal structure of the system, the structure of the transmitter and receiver are designed and theoretically analyzed and simulated in an additive white noise (AWGN) channel. The results show that the NR-ODBR-DCSK system not only has a low bit error rate (BER), but also achieves a good data energy to bit energy ratio (DBR) and exhibits superiority in terms of energy efficiency and transmission performance.  
Finally, this paper verifies the experimental feasibility of the NR-ODBR-DCSK system by performing simulation tests of a semi-physical communication system using two USRP devices. The simulation and analysis results show that the NR-ODBR-DCSK system possesses a higher data transmission rate and lower BER under specific signal-to-noise ratio (SNR) conditions compared with the conventional DCSK and SR-DCSK systems.  
Conceptualization, Y.F.; data curation, Q.Y.; writing--original draft, H.L. All authors have read and agreed to the published version of the manuscript.  
Figure 13: Effect of different numbers of replications \(P\) on the BER performance of NR-ODBR-DCSK system.
## 4 Discussion
In this study, we observed that root inoculation with the four strains induced specific differential metabolic perturbations that were characterized by metabolites from the hydroxycinnamate, benzoate, flavonoid and glycoalkaloid classes. Furthermore, the targeted analysis of aromatic amino acids indicated differential quantitative increases or decreases over two days in response to the four PGPR strains. The observed differences could be a reflection of how effectively the PGPR strains interacted with the tomato roots, as well as the relative activities of specific metabolic pathways that contribute to the annotated VOCs. Comparison and Evaluation of Volatile Organic Compounds Secreted by the Two Pseudomonas Strains, Paeinobacillus alvei (T22) and Lysinibacillus sphaericus (T19)  
The VOC profiles of the two _Pseudomonas_ strains (_Ps. fluorescens_ (N04) and _Ps. koreensis_ (N19)) were subsequently analyzed and compared with two PGPR strains from the _Lysinibacillus_ (_Ly. Sphaericus_ (T19)) and _Paenibacillus_ (_Pa. alvei_ (T22)) genera in order to evaluate the chemical compositions of the volatile blends and to establish whether these strains secrete similar or unique compounds (Figure 4). An assessment of the annotated VOCs of these different strains showed that they have only 13 metabolites in common (Figure 4), with _Pa. alvei_ (T22) having the highest number of unique secreted VOCs under experimental conditions (Figure 4). Moreover, the VOC profiles of the different PGPR strains showed that these species are diverse, as seen by the different combinations of VOCs that were shared by the species and those that were species-specific. The observed differences in the PGPR strains could be an indication that these organisms employ unique mechanisms in order to interact with their neighbors and affect physiological process. Previous studies have shown that PGPR strains secrete a wide range of VOCs that act as info-chemicals within the bacterial community (among the same species or among different species) or with surrounding organisms [15,58,59]. For example, ref. [59] showed that VOCs produced by four strains of _Bacillus_ spp. and _Paenibacillus_ spp. had varying antagonistic effects against soil-borne plant pathogens, including _Ascochyta citrulina_, _Alternaria brassicae_and _Al. solani_. Through headspace sampling and GC-MS analyses, an overlapping and unique volatile pattern could be found among the different species. _Pa. alvei_ (T22) has been studied by us for growth promotion and induced resistance against phytopathogens. Treatment of _Sorghum bicolor_ seedlings with this strain prior to inoculation with _F. pseudograminearum_[28] and _Colletotrichum sublineulum_[29] significantly lowered the progression of disease by the phytopathogens. In addition, this strain was also effective in inducing a primed state against _Phyotrophthora capsaicin_ infection in tomato seedlings [60]. As indicated in Figure 4, _Pa. alvei_ (T22) secreted the highest number (26) of unique VOCs that possibly contribute to its mechanism of action to induce/enhance the aforementioned resistance.