esa-sceva/satcom-chunk-collection huggingface dataset:
# The Importance of Context Awareness in Acoustics-Based Automated Beehive Monitoring
## 4 Recommendations  
As shown above, environmental factors were shown to severely degrade beehive monitoring system performance based on acoustic signals. While the audio segments affected by heavy rain and train rail squealing sounds could potentially be detected based  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Random-Split**} \\ \hline \multirow{2}{*}{Features} & \multicolumn{2}{c}{Clean} & \multicolumn{2}{c}{Urban sound} & \multicolumn{2}{c}{Speech artifacts} & \multicolumn{2}{c}{Heavy rain} \\  & MAE & RMSE & MAE & RMSE & MAE & RMSE & MAE & RMSE \\ \hline Spectrogram & 1.03 & 1.93 & 1.46 & 2.13 & 1.23 & 2.43 & 2.88 & 4.53 \\ MFCCs & 0.86 & 2.34 & 1.17 & 2.72 & 1.86 & 3.93 & 1.00 & 2.52 \\ DWT & 1.42 & 4.43 & 4.84 & 6.52 & 1.92 & 6.12 & 6.47 & 9.01 \\ \hline \multicolumn{8}{c}{**Hive-Independent**} \\ \hline Spectrogram & 4.28 & 5.17 & 6.37 & 6.97 & 7.98 & 8.02 & 7.20 & 7.39 \\ MFCCs & 4.91 & 5.73 & 6.93 & 7.86 & 8.77 & 9.23 & 6.95 & 7.75 \\ DWT & 4.94 & 5.33 & 6.62 & 8.39 & 9.07 & 9.42 & 8.91 & 9.10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison (MAE and RMSE) for beehive monitoring systems based on three feature sets under random-split and hive-independent testing scenarios.  
Figure 15: DWT component comparisons (**a**) without and (**b**) with heavy rainfalls.  
on weather stations' information and/or train schedules, for example, detection of human speech was more challenging. Given the detrimental effect that human speech showed on hive-independent tests and colony behavior, an in-hive speech-detection system would be required. Here, we recommend the use of the modulation spectrum to detect such artifacts. The plots in Figure 16a,b show the modulation spectrogram of the audio data under clean conditions, as well as with background speech, respectively. Here, the original sampling rate of 48,000 Hz was used and a window length of 250 ms and window shift 31.25 ms was used to compute the modulation spectrum. As can be seen, the speech signal changed the dynamics of the audio below 8 Hz modulation frequency (y axis), the region in which speech is known to be in. In future work, automated speech detectors and multimodal hive strength models using numerous sensors will be explored.
## 5 Materials and Methods  
### Bacterial Strains and CHLX Adaptation  
Two clinical strains of _K. pneumoniae_, producers of carbapenemases harbored in plasmids, ST258-KPC3 (high-risk clone) and ST846-OXA48 (low-risk clone) were used in this study. These clinical strains belonging to different sequence types (ST) and are from urine and sputum samples, respectively. The attribution of the ST was carried out in the study of [PERSON] et al. (2017) according to the scheme of the Pasteur Institute ([[http://bigsdb.web.pasteur.fr/klebsiella/](http://bigsdb.web.pasteur.fr/klebsiella/)]([http://bigsdb.web.pasteur.fr/klebsiella/](http://bigsdb.web.pasteur.fr/klebsiella/))) [43].  
In order to obtain the CHLX adapted strains, the clinical strains of _K. pneumoniae_ clinical ST258-KPC3 and ST846-OXA48, were exposed for two weeks to \(\frac{1}{4}\) of their MIC of CHLX (9.8 and 19.5 \(\upmu\)g/mL), respectively in liquid media with aeration. The antibiotic was replaced every 24 h. After these two weeks exposure, the two CA strains were obtained with MIC values of 39.1 and 78.2 \(\upmu\)g/mL, respectively.  
Furthermore, to study the role of the plasmid (Figure S1) encoding the Peml/PemK TA system in the ST846-OXA48 CA strain plasmids were removed from the strain. The cured plasmid ST846-OXA48 CA CP strain of _K. pneumoniae_ was generated following the protocol of [PERSON] et al. (2000) [44], where 3% sodium dodecyl sulfate (SDS) (10% \(w\)/\(v\) pH = 7.4) was used as a curing agent. To check the effective loss of the plasmid, PCR was carried out using the verification primers and, to corroborate the results, plasmid extractions were also performed and subsequently loaded on a 1% agarose gel.  
All the bacterial strains and plasmids used in this study shown in the Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Strain or Plasmid** & **Main Characteristics** & **Source or Reference** \\ \hline ST258-KPC3 & _K. pneumoniae_ high-risk clone & This study \\  & carrying \(\beta\)-lactamase KPC3 & This study \\ \hline ST258-KPC3 CA & _K. pneumoniae_ high-risk clone & This study \\  & carrying \(\beta\)-lactamase KPC3 & This study \\  & adapted to CHLX & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of the bacterial strains and plasmids used in this study.
## 3 Materials and Methods
### UAS Set-Ups  
#### 3.1.1 Minimal Set-Up  
For SDEM mapping of Sval1, Sval2 and Green1 we employed a Walkera X350 Pro quadcopter and a lightweight GoPro(r) Hero 3 (payload weight of 150 g in total) (seen in Figure 2a). The GoPro(r)  
Figure 2: **(a)** The minimal set-up during the image acquisition phase for area Sval1 in overcast/fair conditions. **(b)** Global Navigation Satellite System (GNSS) data acquisition phase using the JAVAD antenna and receiver for area Sval2. Overcast and sastrugi sculpted snow can be observed. **(c)** Snow depth probing on a GCP location for area Sval1 during overcast conditions. **(d)** Survey preparation for area Green1 during fair conditions on completely fresh and featureless smooth snow. **(e)** Featureless conditions of area Green2 in clear/fair sky. **(f)** Typical low vegetation shrubs (5 to 30 cm) found in Arctic regions.  
Hero 3 was the silver edition model with an 11 megapixel sensor (3840 \(\times\) 2880 pixels). The camera is mounted on a motorized stabilization gimbal that allows changes to the viewing angle and dampens rotor-induced vibrations.  
GoPro(r) does not allow any customization of its settings and has a fixed lens and aperture with automatic ISO and shutter speed. The total weight of this UAS set-up is 1.5 kg and embodies the minimal equipment (~500 USD) necessary to perform aerial SfM. The flight durability is 15-20 min per standard LiPO battery.
## 2 Background
### Forwarding Information
#### The IEEE 802.11s-Based WMN for WSN  
Wi-Fi technology (or IEEE 802.11) is known to exhibit higher bandwidth compared to Bluetooth or ZigBee [6]. Because IEEE 802.11s is a data link layer implementation, it uses the same PHY layer as common Wi-Fi devices, such as 802.11a/b/g/n. Therefore, the IEEE 802.11s standard will also exhibit a high bandwidth capacity. This capacity means that it can support high levels of traffic and a large number of mesh STAs. This high level of traffic support is appropriate for certain WSN applications that require a large bandwidth. In fact, Wi-Fi technology has already been used for WSN applications, such as privacy-preserving childcare and safety services [26], as well as in sensor systems for the analysis of highly dynamic movement techniques [27].  
The 802.11s standard is compatible with other non-mesh Wi-Fi devices. Normal STAs with mesh functionality can still connect to the WMN by acting as an AP and connecting with the mesh STA. This compatibility means that WSN that utilize the IEEE 802.11s-based WMN will be able to integrate with existing Wi-Fi networks easily. The integration of several wireless systems into the same wireless network is also possible.  
Iera _et al._[28] studied the use of smart phones as a mesh router or gateway. Their results indicated that it is technically possible for 802.11 devices to be converted into mesh STAs through layer-2 implementation (_i.e._, there is no need to modify the device hardware). If the mesh STA is simultaneously connected to the wired LAN, it can be used as a gateway. This connection allows the WSN to be deployed faster because the existing network infrastructure can be utilized. In addition, fewer new network devices are required. Many Wi-Fi devices will likely be converted into mesh STAs to assist in the WMN routing process. Therefore, the effect of having additional mesh STAs on network performance should be investigated.
# Adoption of Multiphase and Variable Flux Motors in Automotive Applications
## 2 Literature Review
### Multiphase Motors
#### 2.1.1 Impact of MP Motors on the Inverter  
One of the advantages of having multiphase motors in EVs is the power segmentation. In automotive converters for three-phase motors, the semiconductors are paralleled to satisfy the electric current requirement (the higher the power, the greater the need for paralleling). The need for paralleling is even greater with new wide band-gap (WBG) devices, as their current rating is lower than in silicon Insulated Gate Bipolar Transistors (IGBTs), mainly because of reduced die size. A clear example is the Tesla Silicon Carbide (SiC) inverter, in which four Metal Oxide Semiconductor Field Effect Transistors (MOSFET) are paralleled in each inverter branch [22]. Paralleling is not an easy task and can lead to a less efficient and less reliable system [23; 24]. For this reason, in [7], an MP drive was proposed to overcome this parallelisation problem in a 50 kW drive. The total power was split into 21 phases, with the result that each semiconductor withstood less current, and avoiding the need for paralleling.  
As regards inverter size, DC-link capacitance is one of the bulkiest elements in the converter, and can be around 2/3 of the total size. With MP drives, the capacitance and current requirements of DC-link capacitors are lowered. This allows the use of smaller capacitors and increases the power density of the converter while reducing its cost [10; 11]. [PERSON] el al. mention that a reduction of 50% in capacitance and 10% in DC bus Root Mean Square (RMS) current is achieved by increasing the phase number from 3 to 6 [25].  
The primary challenge with inverters is that three-phase inverters are significantly more common and cheaper than MP inverters. Consequently, three-phase inverters are available in a variety of voltage and power ratings, unlike multiphase inverters. To address this issue, dual three-phase (DTP) drives have been proposed for electric vehicles, offering a more cost-effective solution [26]. Two three-phase inverters can be used in parallel to feed a dual three-phase motor, benefiting from the advantages of MP drives while using conventional three-phase inverters.
# Automatic Generation System for Autonomous Driving Simulation Scenarios Based on PreScan
## 4 Testing and Simulation
### Generation Results  
In this study, the logical scenario in ICW intersection collision warning is selected to show the results, and the scenario is defined as the HV going straight through the intersection, and the RV going straight or turning through the intersection. In this test scenario, the HV speed should be 0-60 km/h, the HV acceleration is 0-4 m/s\({}^{2}\), and the RV speed is: 10 km/h-40 km/h.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**SG Methods** & **Duration Comparison** & **Accuracy** \\ \hline \multirow{6}{*}{Manually generated} & 92 min 24 s & 94\% \\  & 91 min 06 s & 94\% \\ \cline{1-1}  & 98 min 13 s & 100\% \\ \cline{1-1}  & 90 min 46 s & 92\% \\ \cline{1-1}  & 94 min 33 s & 96\% \\ \hline \multirow{6}{*}{Automatic generation} & 37 min 45 s & 100\% \\ \cline{1-1}  & 36 min 47 s & 100\% \\ \cline{1-1}  & 37 min 06 s & 100\% \\ \cline{1-1}  & 38 min 23 s & 100\% \\ \cline{1-1}  & 35 min 31 s & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Manual vs. automatic generation.  
Figure 6: Test results and vehicle routes.  
For example, in the scenario where the HV makes a right turn at the intersection and the RV either goes straight or turns, the parameters are shown as follows:  
\[ICW_{5}:\left\{hv\Big{\{}0_{speed},0.5_{acceleration}\Big{\}},rv1 \Big{\{}4_{speed},\beta_{road}[1]\Big{\}},rv2\Big{\{}4_{speed},\beta_{road}[1] \Big{\}},\right.\] \[\left.rv3\Big{\{}4_{speed},\beta_{road}[1]\Big{\}},\beta_{\rm weather }[1]\right\}\]  
In this scenario, the initial speed of the HV is set to 0 km/h with an acceleration of 0.5 m/s\({}^{2}\). The RV1 has a speed of 4 m/s and is scheduled to turn left at the intersection. Similarly, the RV2 also travels at a speed of 4 m/s and turns left at the intersection. The weather condition for this scenario is set to sunny. Visualizations of the scenario and the corresponding results are shown in Figure 7.  
As shown in Figure 8, the HV's yaw angle gradually changes from 0 to \(-1.5780\) radians after the right turn. Similarly, the HV's yaw rate varies during the turn and then stabilizes at 0 as the vehicle realigns. These visualizations provide a valuable tool for testers, increasing the convenience and accuracy of various assessments, such as the calculation of safety distance.  
At an intersection, when HV turns right, RV can realize four motion states: stationary, straight ahead, left turn, and right turn. With the automatic generation system, simulation scenarios can be generated quickly and accurately with the required data. Compared to manually writing specific scenarios, the automatic generation system saves a lot of time and reduces the risk of errors. In addition, as scenarios become more complex and require more parameters, the efficiency of the automatic generation system becomes even more apparent.  
Figure 7: ICW visualization scenario.
# Sonoporation, a Novel Frontier for Cancer Treatment: A Review of the Literature
Real-time verification is a goal to strive for and a research gap to fill; however, the analysis of the literature reveals, for the most part, post-entry verification techniques of the molecule in the cell. Some researchers investigate sonoporation dynamics with voltage clamp techniques, in which changes in transmembrane current under fixed voltage conditions are evaluated, which directly reflect a change in cell membrane conductance, indicating the opening and closing of pores [10]. A comprehensive understanding of microbubbles necessitates a closer examination of their application. The inspiration to utilize microbubbles stems from their established role as contrast agents in the diagnostic application of ultrasound. These bubbles, when introduced into a medium, enhance the visibility of structures during imaging. It is hypothesized that the phenomenon of cavitation, a crucial aspect in this context, is not solely contingent on whether these microbubbles are injected in situ or incorporated into an in vitro culture. Instead, the cavitation process is believed to be facilitated by the presence of natural gases in the cell. The US Parameters behind Sonoporation: Correlation between Ultrasound Physics and Mechanical Tissue Variables  
In research on sonoporation, it is necessary to standardize physical and acoustic parameters in correlation with the effectiveness and the types of tissues treated, with particular attention to the following:  
* Frequency. * Acoustic intensity. The investigation undertaken in the literature places a notable emphasis on the examination of ISPTA as a pivotal component alongside its concomitant parameters, including but not limited to frequency, duty cycle, and acoustic pressure. In addition, the propertiesof stimulated tumor tissues were found, focusing on the elastic modulus as a characterizing parameter of stiffness. The aim is to derive replicable stimulation values, trying to give direction to this area of research.
###### Abstract  
The summer cultivation of lettuce in greenhouses frequently encounters heat stress challenges. In hydroponic systems, cooling the nutrient solution to reduce root zone temperature is an effective strategy to alleviate heat stress. To address the issue of temperature control instability in hydroponic nutrient solutions under high-temperature conditions, this study developed a nutrient solution temperature control system based on an adaptive DBO-fuzzy PID controller. Firstly, the system integrates high-precision sensor networks and air-source heat pump units, forming the hardware foundation. Simultaneously, a fuzzy PID controller optimized by the Dung Beetle Optimizer (DBO) algorithm was designed for this system, enabling real-time adjustment of quantization and scaling factors in the fuzzy controller. Simulation results showed that the DBO-Fuzzy PID achieved a settling time of 35.23 s, overshoot of 2.18%, and steady-state error of 0.009 \({}^{\circ}\)C. The DBO-Fuzzy PID controller exhibited faster and more stable disturbance rejection compared to traditional PID and fuzzy PID control, demonstrating enhanced stability and robustness. System performance tests in the summer greenhouse demonstrated that with a setpoint of 22 \({}^{\circ}\)C, the DBO-Fuzzy PID optimized nutrient solution temperature control system maintained an average temperature of 21.98 \({}^{\circ}\)C, closer to the target value and exhibiting better adaptability to high-temperature environments compared to traditional PID control. Cultivation experiments confirmed the system's effectiveness in mitigating heat stress and maintaining optimal nutrient solution temperature for lettuce growth. The results can provide a theoretical basis and practical reference for precise and stable temperature control in hydroponic nutrient solutions.  
n
# Satellite-Based Wireless Sensor Development and Deployment Studies for Surface Wave Testing
## 1 Introduction
For long-range wireless communication, cellular communication no matter 3G or 4G LTE protocol highly relies on the coverage and the construction of cellular network. Moreover, for the three-dimensional (3D) outdoor survey for complex geography, GPS-embedded wireless geophones can position themselves, which benefits data mining and processing. Thus, this paper proposed a novel satellite-based communication wireless geophone sensor for surface wave testing of soil, which is easy to deploy, cost effective, and expend \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Protocol Name** & **Data Rate** & **Range** &
\begin{tabular}{c} **Power** \\ **Consumption** \\ **(mJ/Mbyte)** \\ \end{tabular} & **Coverage** \\ \hline \multirow{4}{*}{Short range} & ZigBee & 250 kbps & 50 m & 8–32 & - \\  & Bluetooth & 1–2 Mbps & 100 m & 8 & - \\  & MB-OFDM & 53–200 Mbps & 10–30 m & 8–16 & - \\  & Wi-Fi & 6–54 Mbps & 70–160 m & 120–160 & - \\ \hline \multirow{4}{*}{Long range} & GSM & 384 kbps & 1–2 km & 300,000 & Cellular station \\  & 3G & 2 Mbps & 1–2 km & 80,000 & Cellular station \\ \cline{1-1}  & 4G LTE & 3–8 Mbps & 1–2 km & 40,000 & Cellular station \\ \cline{1-1}  & Satellite & 100–500 kbps & Up to 2000 km & 8000 & Global \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of wireless communication. designed to be conveniently assembled from a standard high-precision accelerometer and low-cost computer that are easy to get. The software is developed and optimized to fit the satellite communication for the surface wave testing. Thanks to the computational core of the computer, tailored coding algorithms are embedded in both the sensing node and server to efficiently prevent data loss. In this paper, hardware assembly and connection are first introduced, followed by software development. A compressive sensing algorithm is presented to encrypt the measurements and prevent information loss. An in-site experiment is designed to validate the functionality of the proposed satellite-based wireless sensing node. The approach will also be applied to future studies on large-area surface wave monitoring strategy.
## 2 Principle and Sample Preparation  
An extinction mechanism of the PLCF can be explained with Figure 1. The molecules of liquid crystal and dichroic dye are surrounded by three-dimensional polymer networks that function as a chamber wall. Figure 1a,b shows a cross-sectional film structure with no voltage and that with a voltage exceeding a threshold, respectively. It is assumed that the voltage is applied via transparent electrodes at the tops and bottoms. Without any voltages, the molecules are aligned randomly, forced by the polymer network shape. This results in light scattering when a light beam is incident from the bottom. In contrast, with the voltage higher than the threshold, the molecules are aligned in parallel to the electric field and, then, the incident light beam goes straight up toward the top. That is, the PLCF becomes transparent.  
Test samples were prepared as follows: First, all the composite materials are mixed and stirred. The materials used are nematic mixture of E7 (Fuji Pigment, Hyogo, Japan) as liquid crystal material, monomer materials of 3,5,5-trimethylhexylacrylate (Aldrich, St. Louis, MO, USA) and 1,6-hexanediolacrylate (Aldrich) as photo-initiator, and dichroic dye. Fractions of the materials are approximately 80 and 20 weight percent for the liquid crystal and the monomer, respectively. The composite materials were injected into a 13-\(\upmu\)m spacing glass cell. Transparent electrodes of indium tin oxide were coated on the inside of the two glasses. Finally, ultraviolet light was incident on the sample to form the three-dimensional polymer network. Table 1 shows sample number, dopant, dye category, and absorption peak wavelength of the dichroic dyes (all from Mitsui Chemical, Tokyo, Japan). The dye quantity in the sample was between 0.1 and 1 weight percent.
# Detecting Cyber Threats in UWF-ZeekDataFall22 Using K-Means Clustering in the Big Data Environment
## 2 Related Works
### Enhanced K-Means Clustering  
[PERSON] et al. (2013) [15] and [PERSON] et al. (2012) [16] explore enhanced versions of K-means clustering as they analyze the combination of K-means (unsupervised) with Naive Bayes classification (supervised). By combining supervised and unsupervised methods, the authors aimed to accurately categorize data before clustering. Although both papers analyzed K-means clustering (KMC) with Naive Bayes Classification (NBC), the prior paper reported better results, claiming that the enhanced KMC + NBC algorithm \"substantially increased the accuracy and detection rate up to 99% and 98.8% respectively, while keeping the false alarm reduced to 2.2%\" [15]. By contrast, [PERSON] et al. (2012) [16] found that the enhanced algorithm allowed for higher detection but generated more false alarms. The difference in results could be attributed to the fact that [PERSON], et al. (2013) [15] use the ISCX 2012 dataset, while [PERSON] et al. (2012) [16] implement the KDD-Cup99 dataset. As discussed earlier, while this dataset contains millions of instances of training and test data, it has high redundancy and therefore contains only a small percent of unique data points. 78% of the training set and 75% of the test set are duplicated, and due to its high redundancy, only a small percent of the KDD-Cup99 dataset has unique data points [14]. In contrast, while the ISCX dataset is smaller, it proves to be better at evaluating IDSs as it is much newer than the KDD-Cup99 dataset and thus contains more recent, realistic attack data. Because a key limitation of K-means is its high sensitivity to the initially chosen centroids, several studies have attempted to address this limitation of K-means by combining it with optimization algorithms.  
[PERSON] et al. (2006) [17] introduces Particle Swarm Optimization (PSO), a classification technique used to find optimal solutions through dimensionality reduction, inspired by the social behavior of swarms. When integrated alongside K-means, PSO enhances the accuracy of IDS by improving centroid selection and accelerating convergence speed. Tested on the KDD-Cup99 dataset, the PSO-KM combination demonstrated improved global search capability and IDS performance. However, it still failed to overcome a disadvantage of K-means: the dependency on the number of clusters.