esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setup
### IBIF Calibration  
Each subject underwent a session in the laboratory to obtain a subject-specific calibration for the IBIF algorithm. The session involved simultaneous and synchronous recordings of a circumferentially vented mask-based OVV and neck-surface acceleration in an acoustically treated room. Each subject performed a series of sustained vowel gestures (/a/ and /i/) with a constant pitch using comfortable and loud (approximately 6 dB increase) voice. For each gesture, a bandpass-filtered (60-1100 Hz) oral airflow vowel segment was used to perform inverse filtering with a single notch filter (SNF) constrained to unitary gain at DC [35; 36].  
Figure 6: Experimental setup with oral airflow (pneumotachograph) mask and accelerometer sensor (ACC) on neck-surface location.  
Once a glottal airflow approximation was obtained from the OVV signal, the previously introduced \(\mathbf{Q}\) parameters were estimated using the optimization scheme described in [33]. These are the parameters describing the mechanical properties of the neck skin, as well as the length of the trachea and the position of the accelerometer with respect to the glottis [33].
## 4 Roles of PPK2s in Bacterial Physiology and Virulence
### Antibiotic Sensitivity  
With the world facing a rising tide of antibiotic-resistant bacterial infections, an intriguing development has been the connection between polyP, PPKs, and antibiotic sensitivity. A _\(\Delta\)_ppk2A_ strain of _P. aeruginosa_ PAO1 showed increased susceptibility to several clinically relevant antibiotics, including imipenem and ciprofloxacin [80]. Likewise, an _F. tularensis_ _ppk2_ mutant was more susceptible to three distinct classes of antibiotics: the aminoglycosides streptomycin and gentamicin, tetracyclines tetracycline and doxycycline, and the fluoroquinoline ciprofloxacin [52]. _C. jejuni_ _Appk2_ exhibited increased susceptibility to erythromycincin and ciprofloxacin, which are considered drugs of choice for treating _C. jejuni_ infections [48]. _M. tuberculosis_ _ppk2_ mutation yielded increased susceptibility to the L,D-transpeptidase inhibitor antibiotic meropenem, which could potentially be explained by the reduced transcription of L-D-transpeptidase genes observed in the _ppk2_ mutant [74]. In contrast, two independent reports showed that _M. tuberculosis_ _ppk2_ mutants exhibit a fourfold increase in the minimum inhibitory concentration of isoniaizid [41, 55]. While the first four studies suggest the exciting possibility of inhibiting PPK2 enzymes to extend the lifespan of existing antibiotics, the isoniaizd example behooves careful testing of potential PPK2 inhibitors in combination with antibiotics prior to clinical use.
# Dual-Band Shared-Aperture Antenna with Pattern and Polarization Diversity
## 3 Results and Discussion  
The simulated reflection coefficient of the proposed antenna is shown in Figure 11. The low-frequency IMBW of the proposed antenna is similar to that of Design 1 and Design 2 (Design 1 is the monopole antenna, and Design 2 is the monopole antenna loaded with Vivaldi antenna). The Vivaldi antenna and the medium polarization converter have a slight impact on the reflection coefficient of the monopole antenna, and the final working  
Figure 10: Comparison of Vivaldi antennas with polarization converter and without polarization converter in (**a**) reflection coefficients, (**b**) axial ratio and realized gain, and (**c**) efficiency.  
bandwidth is 2.09-11.61 GHz (9.52 GHz, 138.9%), meeting the requirement of the ultra-wideband (Ultra-Wide Band, UWB) frequency range. Figure 11b indicates that the isolation in the low- and high-frequency bands are higher than 46.5 and 58 dB, respectively. The medium polarizer has little effect on the high-frequency impedance matching, and the high-frequency IMBW covers 21.6-29.6 GHz (8 GHz, 31%). The axial ratio of the proposed antenna is shown in Figure 12a. The axial ratio is less than 3 dB within 21.2-29.9 GHz. The overall gain of the proposed antenna is shown in Figure 12b. The peak gain within 2.09-11.61 GHz is from 2.7 to 7.1 dBi, and the broadside gain within 21.6-29.6 GHz is from 16.1 to 18.8 dBi.  
Figure 13 shows the radiation pattern at 2.4 GHz, 6 GHz, and 11 GHz, and Figure 14 depicts the normalized radiation pattern of the proposed antenna at 25 GHz and 29 GHz. It can be seen that the Vivaldi antenna realizes directional radiation across the high-frequency band, while the monopole antenna radiates an omnidirectional pattern across the low-frequency band. The cross-polarization of both the Vivaldi antenna and the monopole antenna is lower than \(-15\) dB, while the front-to-back ratio of the Vivaldi antenna is lower than \(-30\) dB. The proposed antenna possesses a stable radiation pattern.  
A Vivaldi antenna made of metal is designed to be located at the bottom of the cone to work in the high-frequency band. The addition of the Vivaldi antenna has no effect on the radiation performance of the monopole antenna;
3. Design the circular polarization medium converter and adjust the proportion of medium and air to ensure that the axis ratio of the Vivaldi antenna is less than 3 dB. Then, the circular polarization medium converter is placed above the cone monopole antenna.  
Finally, the proposed antenna is compared with other works in Table 2. It can be seen that most antennas exhibit dual LP directional radiation in the dual bands except [5]. The proposed antenna shows wider bandwidths than others, and both polarization and radiation pattern diversities are achieved.
# Use of Lignin, Waste Tire Rubber, and Waste Glass for Soil Stabilization
## 2 Materials and Methods  
### Soil Properties  
The clay soil utilized in this investigation was sourced from a location in the center of Afyonkarahisar, Turkey. The soil was excavated from a depth of approximately 1 to 1.5 m, resulting in disturbed samples. Analysis revealed that 86.2% of the soil particles passed through the US No. 200 sieve (<0.075 mm), classifying the material as low-plasticity clay (CL) under the Unified Soil Classification System (USCS). The clay exhibited a specific gravity of 2.62, a unit weight of 16.5 kN/m\({}^{3}\), a shrinkage limit of 5.8%, a maximum dry density of 1.58 g/cm\({}^{3}\), an optimum moisture content of 21.9%, a liquid limit of 48.6%, and a plastic limit of 23%. Soil characterization tests were performed in accordance with TS 1900-1 [48], ASTM D 854 [49], ASTM D 422 [50], and ASTM D 4318 [51] standards. The particle size distribution, illustrated in Figure 1, was determined using both mechanical sieving and hydrometer analysis. Additionally, Table 1 details the elemental composition of the low-plasticity clay, as identified through X-ray fluorescence (XRF) analysis conducted with the Bruker D8 Advance. It should be noted that the total percentage exceeding 100% in XRFresults is usually due to the combined effects of the conversion of elemental compositions to oxides, matrix effects, and potential inaccuracies in calibration or detection.  
Figure 2 shows the different phases of the mineralogy of each sample as identified by scanning electron microscopy and energy-dispersive X-ray analysis (SEM-EDX). The clay used in the study contains high amounts of silica, as shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Elements** & **Clay (\%)** & **Oxide** & **Clay (\%)** \\ \hline
**Na** & 1.6 & **Na\({}_{2}\)O** & 2.4 \\
**Mg** & 1.3 & **Mg** & 1.4 \\
**Al** & 9.0 & **Al\({}_{2}\)O\({}_{3}\)** & 19.9 \\
**Si** & 24.4 & **SiO\({}_{2}\)** & 60.7 \\
**K** & 2.4 & **K\({}_{2}\)O** & 3.4 \\
**Ca** & 3.1 & **CaO** & 5.0 \\
**Fe** & 1.9 & **Fe\({}_{2}\)O\({}_{3}\)** & 7.2 \\
**O** & 68.8 & **O** & 17.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: XRF data for the low-plasticity clay sample.  
Figure 1: Particle size gradation for low-plasticity clay.  
Figure 2: SEM-EDX results for low-plasticity clay sample.
## 4 Physical and Chemical Treatments to Improve SvGIs Production
### Alteration in Light and Photoperiod  
Light, in general, has a positive effect on plants growth and development. Studies have shown that high light intensity increases the levels of stevioside and rebaudioside A and, thus, alters the production of SvGIs. High levels of SvGIs were also confirmed by analyzing the gene expression of _SrKA13H_, _SrUGT74G1_, and _SrUGT76G1_, which increased at higher light intensity [25]. The stevia plant is a short-day plant with a critical daylight requirement of 11-12 h to induce flowering. However, supplementing short night interruptions (NI) with low-intensity LED shortens the dark period and, thus, has been shown to have a negative effect on flowering. Delaying the flowering leads to an increase in vegetative growth and biomass of the plant, which is useful as steviol accumulation occurs in a tissue-specific manner in stevia. NI has been shown to increase the yield of harvesting of leaves 4-5 times annually and increase the production of stevioside and Reb A by 21% to 24% (kg ha\({}^{-1}\)) [26]. Different types of LED also affect plant growth and secondary metabolite production. A study on the red/far-red LED and blue LED depicted that R/FR 1.22 and blue light treatment increases the transcripts level of _UGT85C2_ and, hence, increases the SGs content in stevia plant grown in a controlled growth chamber [27]. Recently a combined approach for growing the micro propagated plants with high biomass in less time and enhancing the SvGIs concentration was developed using RITA (Recipient for Automated Temporary Immersion System) and high-red LED. The findings reported an increase in biomass and gene expression of _ent-KO_, _ent-KS_, _ent-KAH13_, _UGT85C2_, _UGT74G1_, and _UGT76G1_ with increases in stevioside and Reb A of up to 37.15% and 22.99%, respectively [28].  
The possible explanation for the enhanced biosynthesis of secondary metabolites under the influence of LEDs may be the activation of photoreceptors, which activate signaling pathways and cause changes in gene expression. The light-absorbing properties are defined by the interaction of a photoreceptor protein and a chromophore. Many studies have suggested the potential use of LED and photoperiod, although the use of light to increase SvGIs is still controversial, and further studies need to be conducted for its effect on SvGIs synthesis.
## 1 Introduction
### Our Contribution  
The paper begins by establishing a system and signal model tailored to the GNSS interference source localization scenario under study. Building upon this foundation, it pro poses a multi-interference sources localization method based on object detection in GNSS C/N\({}_{0}\) distribution maps. The method innovatively constructs C/N\({}_{0}\) distribution maps by exploiting the similarity between GNSS receiver-reported data and image grayscale information. This transforms the GNSS multi-interference sources localization problem into an image processing-based object detection and localization task. To address key challenges in detecting and localizing target interference sources--including estimation of the main lobe direction for directional antennas, multi-target feature recognition and classification, and the detection/localization of low-power GNSS interference sources--the paper proposes an OSF-RCNN framework, an enhanced variant of Faster R-CNN, for processing C/N\({}_{0}\) distribution maps. The OSF-RCNN framework incorporates an Oriented RPN to regress the orientation angles of directional antennas, thereby enhancing both detection accuracy and localization precision for directional interference sources. The framework employs an FPN to achieve multi-scale feature fusion, which improves the method's adaptability to various interference types and different transmission power levels. Additionally, SE blocks are strategically integrated at critical network nodes to intensify focus on low-power interference targets, consequently boosting detection precision for the small low-power targets.  
The study utilized the established system and signal model to construct a dedicated dataset, followed by comprehensive simulation tests to evaluate the proposed method's performance. The experimental validation specifically assessed the method's localization capabilities under the following challenging conditions: (1) directional interference, (2) multiple interference types, and (3) weak interference scenarios. The results demonstrate that the proposed OSF-RCNN method achieves effective detection and accurate localization of multiple GNSS interference sources across all these challenging operational conditions.  
The remainder of this paper is organized as follows: Section 2 introduces the system and signal model of the scenarios discussed. In addition, this section describes the specific steps of the proposed multi-interference sources localization method based on target detection in C/N\({}_{0}\) distribution maps. It also presents the construction method for C/N\({}_{0}\) distribution maps, explains the dataset generation process, and analyzes the remaining challenges in detecting and localizing target interference sources in complex scenarios. In response to these challenges, the OSF-RCNN framework is designed and proposed. Section 3 conducts simulation tests to evaluate the method's performance and analyzes the test results. Section 4 presents the conclusions of this paper.
# PSDFH: A Phase-Space-Based Depth from Hologram Extraction Method
## 4 Experimental Results
### Depth Estimation Results
Figure 9: An illustrative figure of the three experimental configurations: (**a**) the depth value is searched over the whole space; (**b**) regions of interest are used to delimit the search interval; (**c**) the regions of interest are refined, and a fixed size interval of size \(N_{z}+1\) centered on the predicted optimal representation line is used as the search interval. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Piano** & **Table** & **Woods** & **Cars** & **Dice** \\ \hline  & & & & \multicolumn{3}{c}{Without ROI} \\ \hline LAPV & 28.80 & 42.61 & 71.57 & 85.65 & 68.34 \\ \hline WAVV & 18.27 & 43.09 & 12.28 & 34.87 & 17.72 \\ \hline WAVR & 14.99 & 32.84 & 15.89 & 31.96 & 30.03 \\ \hline GLVN & 14.87 & 38.54 & 15.97 & 34.82 & 26.65 \\ \hline GLVA & 14.30 & 39.26 & 12.09 & 33.06 & 18.53 \\ \hline CONT & 22.67 & 54.47 & 42.88 & 59.94 & 74.01 \\ \hline  & & & \multicolumn{3}{c}{With ROI} \\ \hline LAPV & 26.22 & 38.67 & 29.50 & 83.80 & 15.82 \\ \hline WAVV & 11.36 & 23.78 & 25.37 & 31.97 & 23.61 \\ \hline WAVR & 8.63 & 23.68 & 25.04 & 46.17 & 14.73 \\ \hline GLVN & 6.84 & 20.86 & 13.55 & 32.42 & 9.72 \\ \hline GLVA & 11.26 & 23.00 & 28.62 & 41.23 & 22.24 \\ \hline CONT & 24.86 & 35.46 & 36.28 & 65.55 & 29.56 \\ \hline  & & & \multicolumn{3}{c}{ROI Refinement} \\ \hline
11 & 9.28 & 11.04 & 10.42 & 25.58 & 5.31 \\ \hline
21 & 7.44 & 10.22 & 8.65 & 24.84 & 4.05 \\ \hline
31 & 6.33 & 10.03 & 7.53 & 24.28 & 3.8 \\ \hline
41 & 5.65 & 10.32 & 6.97 & 24.18 & 3.93 \\ \hline
51 & 4.94 & 10.79 & 6.90 & 24.03 & 4.37 \\ \hline
61 & 4.86 & 11.08 & 6.76 & 24.13 & 4.98 \\ \hline
71 & 4.73 & 11.48 & 6.68 & 22.87 & 5.83 \\ \hline
81 & 4.81 & 1 Finally, the refinement of the initial ROI and the use of a new fixed-size interval allows an additional gain in performance. From Figure 10, we can observe that optimal performance (except LAPV and CONT operators) can be controlled by changing the parameter \(N_{z}\) in Equation (25), and a minimum value can be reached around \(N_{z}=41\). Then, the performance degrades with a larger choice of \(N_{z}\). For the LAPV and CONT operators, the overall performance degrades as \(N_{z}\) increases which indicates that the operators are not sensitive enough to the slight focus change between different images of the reconstruction volume. For the Cars scene, where the objects that compose the scene are very large, the optimal performances are achieved for the largest value of \(N_{z}\) regardless of the choice of the FM function.
## 4 Advancements in Industry 4.0 and Pandemic Management
### Internet of Things (IoT)  
The Internet of things (IoT) has emerged as a revolutionary concept that connects physical devices and objects to the internet, enabling them to collect and exchange data autonomously. In recent years, IoT has found extensive applications in various fields, with Industry 4.0 being one of its prominent domains. Industry 4.0 represents the Fourth Industrial Revolution, characterized by the integration of advanced technologies into manufacturing and production processes to create smart, efficient, and interconnected factories. In this group of papers, we explore several key aspects of IoT in the context of Industry 4.0. The authors delve into innovative approaches, systems, and architectures to enhance the implementation and utilization of IoT technologies [51] in transforming industries and optimizing various processes. The papers address challenges faced by small- to medium-sized factories, data engineers, and city managers in deploying and utilizing IoT solutions. [PERSON] et al. [52] propose an automatic machine status prediction system for Industry 4.0. Recognizing the financial constraints of smaller factories, they introduce the use of inexpensive add-on triaxial sensors for machinery monitoring. This cost-effective approach, coupled with a neural network and dimensionality reduction method, allows for automatic analysis of collected data, making Industry 4.0 more accessible to smaller enterprises. [PERSON] et al. [53] present the Interface for Time Series Reduction System (I4 TSRS), which assists data engineers in reducing the dimensionality of time-series data for Industry 4.0 scenarios. The system recommends techniques to obtain reduced representations of industrial time-series data while preserving their main characteristics. This reduction in data size lowers storage and transmission costs without compromising data exploitation in various processes. Same authors, in their later work, [PERSON] et al. [54] propose a three-level hierarchical architecture for efficient storage of Industry 4.0 time-series data in cloud environments. By categorizing and storing data at different levels, the architecture optimizes storage resources, allowing for cost-effective and scalable data management. [PERSON] et al. [55] explore data dissemination in the context of Industry 4.0 applications within the Internet of vehicles (IoV). Their novel data dissemination scheme relies on short-term traffic prediction using a deep learning network. This scheme empowers city managers to regulate traffic flow efficiently and fosters the development of various Industry 4.0 applications in IoV, such as smart logistics and autonomous driving. Finally, [PERSON] et al. [56] introduce a pipeline architecture for feature-based unsupervised clustering using multivariate time series from high-performance computing (HPC) jobs. The pipeline's scalability and unsupervised nature make it suitable for a wide range of industrial and research applications, enabling meaningful clustering of large-scale data sets.
## 4 Discussion
### Limitations  
The usability testing was limited in scope due to resource constraints, while the results presented regarding collaborative cross-site usage are based on the first cross-site practical application of CoAT in a very limited test scenario, which in the case of the evaluation performed in Leipzig, was performed in a teaching context, preventing a more detailed statistical analysis of evaluation results. This naturally limits the generalizability of our results, which will have to be verified in larger application scenarios with direct integration into production data provisioning pipelines.
## 4 Discussion  
The consequent experts had a 0.612 Kendall inter-ranker reliability coefficient, which while being higher than 0.6, i.e., considered a good concordance, is still not very high. The reason for this could be the following:  
According to our study, the experts use the density of the lesions as the most important attribute in the case of incidental findings of liver nodules in the abdominal CT examinations, and the size is the least important property. These quantities had a rather low variance (0.37 and 0.57) among the experts' rankings, all the experts ranked the density as one of the first two places, and almost all the experts ranked the size as one of the last two places.  
From Figure 5, as well as from their approximately 1 variance values, it can be also be seen that the other four attributes are ranked without a high concordance. This might be attributed to the fact that the borderline between the attributes contour, shape, homogeneity, and \"other features\" is different for each individual, even though the questionnaire gives a short description about the meaning of \"other features\". More probably the ranking of the homogeneity, contour, shape, and other features depends on the actual case and the actual manifestation of the disease.  
The attributes of density and shape are easy to quantify as soon as the lesion is marked in the CT images.  
The other studied properties, namely the shape, contour, and homogeneity are not so clearly ordered; moreover, their quantification is also challenging. Several quantifying formulae were suggested and compared with the experts' opinion regarding the regularity of the shape, the sharpness of the contour, and the homogeneity of the density distribution within the lesion. The shape can be quite well characterized by the roundness of the object. For the contour sharpness, the averaged density difference between the two sides of the borderline of the lesion versus the density difference of the lesion and the liver seems to be plausible, but the numerical results suggest this quantity is not good enough, and further study about this quantity is needed. Homogeneity is very hard to capture, not only because the CT images have a native, fine-grained inhomogeneity, but also because the experts understand different things under the same label \"inhomogeneity\". Further research will be directed toward the more detailed quantization of the various meanings of inhomogeneity.  
This implies that the attributes have two categories: a rather clear and easily represented class containing size and shape, and a class consisting of less clear and less easily interpreted expressions such as contour, shape, and homogeneity that cannot really be measured on a scale in one dimension.