esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
The microdilution method in 96-well plates proved to be useful for assessing the antimicrobial activity of the test substances, enabling a quantitative analysis of the inhibitory concentrations for the microorganisms [47]. The resazurin stain has a blue/purple color, which is reduced to resorufin when in contact with viable cells, which has a pink color [48]. The data show that the formulations exhibited bacteriostatic activity (MIC) against the microorganisms. Moreover, the formulations with the biosurfactant from _C. quinoa_ exhibited bactericidal activity against _S. aureus_ and _E. faecalis_ at the highest concentrations tested. The use of reagents that act as indicators of cell viability through colorimetric changes has been commonly described in the literature. Tetrazolium bromide (MTT) and resazurin present a colorimetric change, due to an oxidation-reduction reaction, which helps in reading and interpreting the results of in vitro susceptibility tests. Such compounds function as chromogenic substrates for dehydrogenases as oxidation-reduction indicators, related to the transport system in metabolically active cells [49; 50]. The dye resazurin (7-hydroxy-3H-phenoxyazine-3-one 10-oxide) is blue in color, non-fluorescent, and can be reduced to resorufin by oxidoreductase, which is a pink, fluorescent product. The microdilution method using resazurin has the advantage of its increasingly promising application for in vitro susceptibility tests to determine cell viability for bacteria and fungi (yeast and filamentous), as well as not requiring a spectrophotometer to interpret results, since the color change is observed visually [49; 51]. However, among the limitations of this technique, the influence of the density of microbial cells and the composition of the culture medium can be cited as possible factors that can influence the results of colorimetric cell viability tests [50; 51]. The characteristics of the test substance can also influence colorimetric tests, as many compounds can interact and disrupt the reading, giving a non-standard color or delaying/inhibiting the color change. However, the main disadvantage of the resazurin colorimetric test is that it results from the accumulation of a colored or fluorescent product (resorufin) that gradually increases fluorescence over time, making it impossible to detect a decrease in cell viability [51]. The use of biosurfactants as antibiofilm agents could be an effective method for reducing the adhesion of microorganisms, many of which are pathogenic, and combating their possible proliferation on various organic surfaces [52]. Based on the results shown in Figure 13, the formulations containing the highest concentration of the biosurfactant from _C. quinoa_ had inhibition rates >70% against all microorganisms, except _P. aeruginosa_, and even >75% against _S. mutans_, the etiological agent of dental caries. For formulations containing _G. max_, inhibition rates were >25% against all microorganisms tested at CMC (3.5 g/L) and 2 CMC (7.0 g/L), once again with the exception of _P. aeruginosa_.
## 4 Simulation Analysis
### Analysis of Energy Consumption Characteristics  
The total energy consumption of the transmission system, the effective energy consumption of the towing helicopter, and the difference between the total energy consumption and the effective energy consumption can be obtained from Equations (24) and (25), as shown in Figures 10 and 11.  
During 0-2.2 s, the energy consumption of the traction system sharply increases, which is caused by the combined action of internal energy storage components (inertial and capacitive components) and energy consumption components (resistive components) in the system. At the same time, after about 0.2 s, the system enters a steady state, and the difference between total energy consumption and effective energy consumption begins to grow linearly. At this point, the energy storage element is saturated, and the energy is only consumed by the resistive element. Similarly, the proportion of energy consumption in each part of the system can be obtained from the system's state equation and energy consumption equation, as shown in Figure 10. Within 5 s, the energy consumption of inertial components is about 13.1% of the total energy consumption, far exceeding that of resistive and capacitive components. The total energy utilization rate of the system is approximately 84.1%.  
Figure 9: The dynamic characteristic curve of a helicopter and its force profile: (**a**) the traction force on the helicopter; (**b**) helicopter movement speed.
# Enhancements and Challenges in CoAP--A Survey
###### Abstract  
The Internet of Engineering Task (IETF) developed a lighter application protocol (Constrained Application Protocol (CoAP)) for the constrained IoT devices operating in lossy environments. Based on UDP, CoAP is a lightweight and efficient protocol compared to other IoT protocols such as HTTP, MQTT, etc. CoAP also provides reliable communication among nodes in wireless sensor networks in addition to features such as resource observation, resource discovery, congestion control, etc. These capabilities of CoAP have enabled the implementation of CoAP in various domains ranging from home automation to health management systems. The use of CoAP has highlighted its shortcomings over the time. To overcome shortcomings of CoAP, numerous enhancements have been made in basic CoAP architecture. This survey highlights the shortcomings of basic CoAP architecture and enhancements made in it throughout the time. Furthermore, existing challenges and issue in the current CoAP architecture are also discussed. Finally, some applications with CoAP implementation are mentioned in order to realize the viability of CoAP in real world use cases.  
CoAP; IoT; WSN; congestion control; enhancements in CoAP +
Footnote †: journal: Sensors
## 5 Conclusions  
Combining an M/M/1/0 queueing model with PPCM, we construct an analytical model of calculating overlap probability between two or more pulses. No matter how many aircraft there are, the analytical model can be used to analyze intra-system and inter-system interference on DME. According to the results mentioned in Section 4, we may reasonably conclude that the M/M/1/0 queueing model can calculate the collision probability between dead time of transponder and duration of subsequent desired signals accurately. Hence, the analytical model is more consistent with simulation than single PPCM.  
With the rise of the number of aircraft, intra-system interference increases, on the contrary, RE decreases. In addition, RE is affected by inter-system interference; for example, JTIDS interference causes RE to decrease about 5% when there are about 100 aircraft. DME capacity is related to reply rate and RE simultaneously, they are interrelated and interact on each other. In accordance with the limits of RE and reply rate recommended by ICAO, the overall performance of DME cannot be improved unless multi parameters are optimized systematically.  
[PERSON] designed and performed the research. [PERSON] and [PERSON] wrote the manuscript. [PERSON] and [PERSON] performed the computation. [PERSON] and [PERSON] analyzed the data. All authors discussed the results and commented on the manuscript.  
This research received no external funding.  
Thanks are due to [PERSON] for the valuable suggestions.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} DME & Distance Measuring Equipment \\ JTIDS & Joint Tactical Information Distribution System \\ RE & Reply Efficiency \\ ICAO & International Civil Aviation Organization \\ ACSS & Airborne Collision Avoidance System \\ IFF & Identification Friend or Foe \\ ATCRBS & Air Traffic Control Radar Beacon System \\ EMC & Electromagnetic Compatibility \\ SNR & signal to noise ratio \\ BER & Bit Error Rate \\ PRF & Pulse Repetition Frequency \\ PPCM & Periodic Pulse Collision Method \\ \end{tabular}
# An Efficient Algorithm for De-Interleaving Staggered PRI Signals
## 2 Problem Formulation and Proposed Algorithm Description
### Improved Square Sine Wave Interpolation Algorithm
#### 2.3.3 A Threshold Criterion
If only one spectrum value exceeds the detection threshold, the period corresponding to the spectrum value is the estimated PRI value; if multiple spectrum values exceed the detection threshold, the period corresponding to the largest spectrum value is selected as the estimated PRI value.
## 3 Selection and Scaling of Ground Motions

Table 3: Five-story models: modal periods (seconds). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & & & & \\ \hline \multirow{2}{*}{**Mode**} & \multicolumn{2}{c}{\(\mathbf{M_{5 ref}}\)} & \multicolumn{2}{c}{\(\mathbf{M_{5 bs5}}\)} & \multicolumn{2}{c}{\(\mathbf{M_{5 ms5}}\)} & \multicolumn{2}{c}{\(\mathbf{M_{5 bs5}}\)} \\ \cline{2-9}  & \(\mathbf{UX}\) & \(\mathbf{UY}\) & \(\mathbf{UX}\) & \(\mathbf{UY}\) & \(\mathbf{UX}\) & \(\mathbf{UY}\) & \(\mathbf{UX}\) & \(\mathbf{UY}\) \\ \hline First Mode (Y) & 0 & 84.9 & 0 & 96.0 & 0 & 76 & 0 & 80.1 \\ Second Mode (X) & 83 & 84.9 & 94.0 & 96.0 & 76.3 & 76 & 78.7 & 80.1 \\ Third Mode (R) & 83 & 84.9 & 94.0 & 96.0 & 76.3 & 76 & 78.7 & 80.1 \\ Fourth Mode (Y) & 83 & 95.1 & 94.0 & 99.5 & 76.3 & 95.6 & 78.7 & 91.2 \\ Fifth Mode (X) & 94 & 95.1 & 99.2 & 99.5 & 94.7 & 95.6 & 90.4 & 91.2 \\ Sixth Mode (R) & 94 & 95.1 & 99.2 & 99.5 & 94.7 & 95.6 & 90.4 & 91.2 \\ Seventh Mode (Y) & 94 & 98.3 & 99.2 & 99.9 & 94.7 & 96.9 & 90.4 & 97.2 \\ Eighth Mode (X) & 94 & 98.3 & 99.2 & 99.9 & 97.0 & 96.9 & 90.4 & 97.2 \\ Ninth Mode (R) & 97.8 & 98.3 & 99.8 & 99.9 & 97.0 & 96.9 & 96.6 & 97.2 \\ Tenth Mode (Y) & 97.8 & 99.6 & 99.8 & 99.9 & 97.0 & 99.6 & 96.6 & 99.9 \\ Eleventh Mode (X) & 97.8 & 100 & 99.8 & 99.9 & 97.0 & 99.6 & 96.6 & 99.9 \\ Twelfth Mode (R) & 97.8 & 100 & 99.8 & 99.9 & 97.0 & 99.6 & 96.6 & 99.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: (a) Cumulative modal mass participation ratio of five-story buildings (soft-story height = 5 m). (b) Cumulative modal mass participation ratio of five-story buildings (soft-story height = 7 m). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**(a)**} \\ \hline \multirow{2}{*}{Mode} & \multicolumn{2}{c}{M\({}_{10\text{ref}}\)} & \multicolumn{2}{c}{**M\({}_{10\text{bs}5}\)**} & \multicolumn{2}{c}{**M\({}_{10\text{ms}5}\)**} & \multicolumn{2}{c}{**M\({}_{10\text{ms}5}\)**} \\ \cline{2-9}  & **UX** & **UY** & **UX** & **UY** & **UX** & **UY** & **UX** & **UY** & **UX** & **UY** \\ \hline First Mode (Y) & 0 & 80.7 & 0 & 90.3 & 0 & 75.5 & 0 & 79.8 \\ Second Mode (X) & 78.6 & 80.7 & 87.0 & 90.3 & 74.5 & 75.5 & 77.8 & 79.8 \\ Third Mode (R) & 78.6 & 80.7 & 87.0 & 90.3 & 74.5 & 75.5 & 77.8 & 79.8 \\ Fourth Mode (Y) & 78.6 & 92.3 & 87.0 & 98.9 & 74.5 & 92.6 & 77.8 & 90.9 \\ Fifth Mode (X) & 91.3 & 92.3 & 97.5 & 98.9 & 91.6 & 92.6 & 90.1 & 90.9 \\ Sixth Mode (Y) & 91.3 & 92.3 & 97.5 & 98.9 & 91.6 & 92.6 & 90.1 & 90.9 \\ Seventh Mode (Y) & 91.3 & 95.8 & 97.5 & 100 & 91.6 & 93.9 & 90.1 & 94.4 \\ Eighth Mode (X) & 95.0 & 95.8 & 99.1 & 100 & 93.4 & 93.9 & 93.9 & 94.4 \\ Tenth Mode (Y) & 95.0 & 97.5 & 99.1 & 100 & 93.4 & 97.6 & 93.9 & 96.8 \\ Eleventh Mode (X) & 95.0 & 98.6 & 99.6 & 100 & 97.1 & 97.6 & 96.3 & 96.8 \\ Twelfth Mode (R) & 97.0 & 98.6 & 99.6 & 100 & 97.1 & 97.6 & 96.3 & 96.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: (a) Cumulative modal mass participation ratio of ten-story buildings (soft-story height = 5 m). (b) Cumulative modal mass participation ratio of ten-story buildings (soft-story height = 7 m).
# Cyber4 Drone: A Systematic Review of Cyber Security and Forensics in Next-Generation Drones
## 4 Threat Models and Attack Scenarios
### Software Based Attack  
Attackers have demonstrated the use of software to hack the video feeds of Predator (and likely Reaper) drones. Software such as SkyGrabber Version 3.2 is popular for offline satellite internet downloads. It intercepts satellite data, including movies, music, pictures, etc., that are downloaded by other users and saves information on the hard disk. It exploits the unencrypted and unauthenticated communication link used for data feeds sent to the ground station using communication satellites. In this attack, the adversary needs to customize the satellite dish for selecting a satellite provider and start grabbing the data packets. The SkyGrabber intercepts data, asserts them into files, and saves the files locally. It is a downloadable computer program that has been used to capture drone images and video recordings. SkyGrabber software can take satellite internet data and assemble it in files such as.avi,.mp3,.mp4, etc., and save this file on hard disk [101]. Attackers use this software, and if the connection between the drone and ground station is unencrypted, they use it to access their videos and other files that are shared between the drone and ground station and can also be used to monitor.  
Figure 4 illustrates image and video capture by sky grabber software in the BLOS scenario. These attacks violate drone confidentiality and integrity because the sky grabber software attack breaks the seceretiveness and violates the access information. These attacks have some prevention techniques; Firstly, using a suitable cryptographic approach. The message sent between the drone and remote control is encrypted and not easily broken by the attacker. Another possible method is to have authentication to allow only authorized access to broadcasted information [101].
# An Energy Efficient Routing Approach for IoT Enabled Underwater WSNs in Smart Cities
## 2 Literature Review  
In the last few decades to deliver reliable communication in underwater and smart environments, different routing protocols have been proposed. All of these routing protocols considered several parameters based on which they generated results in terms of network's lifetime, dead nodes and packets received ratio at sink node. We have created two clusters based on energy that the nodes of a network hold in order to conduct our study. The protocols that consider only homogeneous energy are grouped into one cluster while the protocols that only consider heterogeneous energy are grouped into the other cluster. The protocols under cluster 1 and cluster 2 are discussed below:
## 2 Material and Methods  
### Analytical Methods  
In the method under investigation, the entire SPME injector (Custodion 65 \(\upmu\)m DVB/PDMS, PerkinElmer, Inc., Waltham, MA, USA) was placed directly on top of the grain bed in the kiln without the fiber making contact with the grain (Figure 1). The fiber was then exposed to the grain for one hour with the kiln door closed. The same fiber was used for all in-kiln measurements. After the fiber was exposed for one hour, it was removed from the kiln and immediately introduced into the portable GC/MS instrument (PerkinElmer, Inc., Waltham, MA, USA, Torion T-9).  
Figure 1: Solid Phase Microextraction (SPME) needle positioned on top of the grain bed in the kiln.  
Measurements were made throughout the kilning process at Hudson Valley Malt (Germantown, NY, USA). Samples were obtained on six occasions (as the temperature was increased from 50 \({}^{\circ}\)C to 52 \({}^{\circ}\)C, after heating for 40 h at 52 \({}^{\circ}\)C, after 2 h at 65 \({}^{\circ}\)C, at the end of the 65 \({}^{\circ}\)C heating period, after 2 h of heating at 76 \({}^{\circ}\)C, and as the temperature was increased from 76 to 83 \({}^{\circ}\)C) over the three-day kilning period in the production of pale malt from a Conlon variety of barley grown in New York State. The kiln temperature ranged from 50 \({}^{\circ}\)C to 83 \({}^{\circ}\)C. For comparison purposes, a simplified headspace method was also utilized. Three grains were removed from the kiln whenever the SPME fiber was removed. These grains were ground in a mortar and pestle and placed into a 4 mL headspace vial. Approximately 2 mL of 25% (m/v) NaCl solution was added. The vial was capped and allowed to equilibrate for 10 min at room temperature (14 \({}^{\circ}\)C). A separate SPME needle (Torion Custodion) was used to pierce the rubber septum and the SPME fiber was exposed to the headspace for 30 min prior to introduction to the portable GC/MS.  
All samples were evaluated using a portable GC/MS (Perkin Elmer, Torion T9) with an MXT-5 Column. The column temperature was held at 50 \({}^{\circ}\)C for 10 s, then increased to 296 \({}^{\circ}\)C at 2 \({}^{\circ}\)C/s with a final hold time of 47 s. The total run time was 180 s. Significant peaks were not observed at retention times beyond 100 s. The portable GC/MS (Figure 2) is designed specifically for the analysis of volatile and semi-volatile compounds introduced from an SPME fiber or purge and trap needle. The instrument uses an ion trap mass analyzer that has the advantage of being small and therefore easier to maintain under vacuum. It can be made entirely portable with the use of a small helium canister and battery pack. The instrument incorporates a small computer and can also be linked to a laptop to allow more advanced data treatment and library searching. In our case, a NIST (National Institute of Standards and Technology) library (MS Search version 2.0) loaded onto an external laptop computer was used for compound identification.  
Figure 2: The portable Gas Chromatography Mass Spectrometry (GC/MS) instrument as set up for measurements in the malting house.
# Tracking and Registration Technology Based on Panoramic Cameras
## 2 Related Work
Recent advances in deep learning, particularly CNNs, have significantly enhanced visual feature extraction. These capabilities have inspired researchers to integrate learning techniques into visual SLAM and VO. Supervised approaches such as PoseNet [14] and DeepVO [15] leverage labeled data to regress camera poses from image sequences. In contrast, unsupervised methods like SfMLearner [16] and GeoNet [17] jointly estimate depth and pose using geometric consistency, enabling self-supervised training without pose labels. For panoramic imaging, [PERSON] et al. (2018) [18] and [PERSON] et al. [19] adapted SfMLearner to cube maps and cylindrical panoramas, mitigating distortion in Equirectangular Projection (ERP) images. Further improvements include incorporating semantic cues [20] or explicitly modeling panoramic distortions [21; 22], though often at the cost of increased model complexity and reduced efficiency. In summary, marker-based tracking methods offer high accuracy and speed but require prior placement of fiducial markers, limiting their applicability in real-world scenarios. Markerless approaches based on visual SLAM demonstrate strong robustness and accuracy without external markers, yet struggle in low-texture, repetitive-pattern, or dynamic lighting conditions. To address these limitations, wider camera fields of view have been explored to capture more visual cues, though often at the cost of computational efficiency. Recently, research has shifted toward leveraging deep learning to learn rich feature representations directly from image data, aiming for more robust and efficient tracking. While supervised learning achieves high performance, it relies on large-scale pose annotations that are difficult to obtain. Motivated by these challenges, this work focuses on unsupervised deep learning methods for panoramic camera pose estimation, seeking to improve both efficiency and practical applicability in complex environments.
## 2 Related Works  
### Threshold-Based Fault Detection in PV Systems  
Fault detection in PV systems has been investigated from various perspectives to avoid energy losses and accidents [33]. Image-based fault detection schemes were proposed with the rapid development of image sensors and AI approaches, such as convolution neural networks (CNNs) [34; 35; 36; 37; 38; 39; 40]. Despite the accurate diagnostic ability of such schemes, their infrastructural installations are very expensive [41]. On the other hand, model-based detection schemes using thresholds have the advantages of low cost and rapid detection, and they are regarded as leading methods in fault detection, due to their simplicity and decent performance [42].  
However, the model-based approaches using thresholds face many challenges, because the performance of fault detection significantly depends on threshold settings [43]. Properly setting thresholds is essential to ensure accurate detection. Thus, many threshold methods have been proposed to improve accuracy [15; 42; 44; 45; 46; 47]. Despite the efforts, there are still some issues to cover. First, fixed threshold methods show difficulties in practical situations, in which volatility and uncertainty exist. For example, [PERSON] et al. proposed a scheme for setting thresholds and detecting shading faults, where faults were detected if there were drops greater than 10% [45]. [PERSON] et al. introduced a shading fault detection method in a grid-connected PV system, in which vertices principal component analysis (VPCA) was compared with a standard PCA [48]. However, a trade-off relation existed between less missed detection and more false alarms, which is a critical problem of fixed threshold methods.  
To deal with this problem, [PERSON] et al. proposed an online fault detection method, in which different dynamic thresholds were used according to solar irradiance intervals [15]. [PERSON] et al. improved setting thresholds, based on solar irradiance, by considering more features, such as current and voltage [49]. [PERSON] et al. introduced a fault diagnosis threshold method based on non-parametric kernel density estimation (NKDE), which obtained thresholds by assigning the confidence values of models [42]. The above approaches require assigning confidence intervals for setting thresholds. Therefore, there is still a limitation in regard to finding proper confidence intervals for the PV system and situations where volatility and uncertainty exist.  
In this paper, we propose a DRL-based dynamic threshold scheme in which a DRL agent automatically finds proper thresholds without assigning confidence intervals (e.g., 3-sigma) for various situations, while considering the various weather conditions, such as ambient temperature, irradiance, humidity, and wind speed. Using the proposed scheme, users have no burdens for manually setting thresholds.
# Asymmetric Dual-Band Tracking Technique for Optimal Joint Processing of B1I and B1C Signals
## 5 Experiment and Discussion
The reason is that the improved ranging performance brought by the steeper ACF of B1C signal is offset by the stronger power of the B1I signal relative to the B1C pilot signal.
# Spatial Data Reconstruction via ADMM and Spatial Spline Regression +
## 3 Patched Estimation and Spatial Spline Regression
### Patched Piece-Wise Constant Estimation  
In our problem, we only have access to the aggregated volumes \(z_{i}\) at locations \(\mathbf{p}_{B_{i}}\). To infer the fine-grained spatial distribution of \(z_{i}\) over subregion \(\Omega_{B_{i}}\) that covers the point \(B_{i}\), a first intuitive heuristic is estimating \(f(\mathbf{p}_{j})\) as the volume \(z_{i}\) divided by its area by assuming the density is distributed uniformly:  
\[\tilde{f}(\mathbf{p}_{j})=\frac{z_{i}}{|\Omega_{B_{i}}|},\text{ for each }\mathbf{p}_{j}\in \Omega_{B_{i}}, \tag{7}\]  
where \(|\Omega_{B_{i}}|\) is the area of \(\Omega_{B_{i}}\). This method gives us a patched piece-wise constant estimation. Note that we use _patch_ to refer to \(\Omega_{B_{i}}\) in this paper, which is the subregion covered \(B_{i}\).  
However, the patched estimation gives an oversimplified solution. The reconstructed spatial field \(\tilde{f}(\mathbf{p}_{j})\) may have jumps on the borders of neighboring patches, which is far from smooth. In reality, the spatial field \(f(\mathbf{p}_{j})\) should change smoothly over the domain, as the underlying characteristics also change smoothly across different regions. Hence, \(f(\mathbf{p}_{j})\) should not be constant within each patch \(\Omega_{B_{i}}\).