esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Sludge Treatment Reed Bed System and Industrial Sludge
### Industrial Sludge in STRB
#### 2.3.5 Hazardous Organic Compounds  
Sludge may contain a range of hazardous organic compounds, e.g., polyaromatic hydrocarbons (PAHs), di-2-ethylhexyl-phthalates (DEHPs), nonylphenol/nonylphenol ethoxylates (NPEs), and linear alkyl benzene sulfonates (LASs), which originate from coal and tars, lubricating oil additives, and detergents. Sludge treatment in STRBs results in the mineralization of these compounds [6, 11, 12, 52, 53]. The long operation period of STRBs, which can exceed 10 years, provides adequate time for microbiological and abiotic processes to effectively mineralize and decrease the contents of most hazardous organic compounds. Even for a shorter period of 3-6 months, significant reductions have been observed. Such processes have not been detected in conventional mechanical dewatering methods, e.g., centrifugation [11, 45, 53].  
A previous study on the mineralization of LAS and NPE in sludge treated in an STRB reported 98% LAS and 93% NPE degradation under aerobic conditions [11], indicating that limited oxygen availability affects the degradation of organic compounds. Oxygen inflow into the sludge significantly enhanced LAS and NPE mineralization; on the other hand, mineralization under anaerobic conditions was very limited. The same study reported 60% and 32% reductions of DEHP and PAH, respectively [11, 14]. The organic compounds were not only mineralized in the upper parts but through the whole depth of the residual sludge layer. Trials with anaerobically digested sludge (representing sludge after mechanical treatment) indicated a partial degradation of LAS, NPE, DEHP, and PAH in the top sludge layer (0-20 cm), while below this depth, it was observed that these compounds were not degraded [11].
## 3 IBC Time Domain Responses Based on Corrected Transfer Function
In addition, all calculation and measurement rise times (defined as the time spent from 10% to 90% during transition) of output pulses (after demodulation) are around 0.6 \(\upmu\)s at different distances, as shown in the insets of Figure 7(c,d). From the relationship between rise-time and bandwidth, the corresponding system bandwidth would be inversely proportional to 0.6 \(\upmu\)s. This indicates that system bandwidth is in the order of MHz, providing enough bandwidth for possible low distortion in passband transmission. Note that the maximum bandwidth of the transmitted signal is limited by the carrier. In summary, passband transmission with proper choice of carrier frequency would have rather good communication performance in terms of both attenuation and distortion.
## 2 Study Area and Materials
### Data Used in This Study  
#### 2.2.1 Remote Sensing Data  
This study used MODIS NDVI data derived from MOD13A3_V005 (Grade 3 NDVI Vegetation Index Products) with a spatial resolution of 1 km and a temporal resolution of 1 month, covering the period of January 2001 to December 2011. The Chongqing area includes h27v05 and h27v06 strips. The data are mainly used to invert fPAR. All these three types of data came from the NASA-MODIS website ([[http://ladsweb.nascom.nasa.gov](http://ladsweb.nascom.nasa.gov)]([http://ladsweb.nascom.nasa.gov](http://ladsweb.nascom.nasa.gov))) and were openly accessible. The MODIS/NDVI 1 km products were extracted from the MODIS/NDVI data [19] using MODIS Reprojection Tools software. The 264 scene images were synthesized and mosaicked, and the image format and projection were converted. The MODIS stored SIN coordinates were converted to WGS84. The map projection was converted to Lambert conformal conic projection, converting the format of MODIS remote sensing images from HDF scientific data format to TiFF.  
The latest correction method and the correction coefficient for radiation correction are based on remote sensing data. The measured GPS control points on the satellite image for geometric correction are used to ensure that the precision of geometric correction is better than ponepixel. Atmospheric radiation correction and reflectance transformation are conducted using linear transformation.  
Figure 1: Meteorological location and topography of Chongqing.
## 5 Performance Analysis
### Robustness  
In this section, the robustness of the proposed DM schemes about the impacts of imperfect estimation of the directions from all transmit antenna arrays to the LU is investigated.  
In a general way, the implementation of DM is based on the assumption that the a priori directional information is known for the BS. However, in a practical DM system, there always exists an estimated error in the directional information even by adopting accurate direction of arrival (DOA) estimation methods (see, e.g., [46; 47; 48; 49]) and in the range information even by using high-resolution range estimation algorithm (see, e.g., [50]). Therefore, the estimated angle error would result in angle mismatch including the transmit pre-coding vector, i.e., the excitation signal vector \(\mathbf{v}_{k}\) and the AN projection matrix \(\mathbf{T}_{k}\) for the \(k\)th transmit antenna array, which severely degrades the performance of the proposed DM schemes.  
Here, the estimated directional angle of the \(k\)th transmit array can be expressed as  
\[\widehat{\theta}_{dk}=\theta_{dk}+\Delta\theta_{dk}, \tag{47}\]  
where \(\Delta\theta_{dk}\) is the estimated directional angle error.  
It is noted that the angle estimation errors impose negative impact not only on the precoding vector \(\mathbf{v}_{k}\), but also on the AN projection matrix \(\mathbf{T}_{k}\), while the architectures of the BS and the LU remain unchanged. For both proposed DM schemes, the precoding vector vector \(\mathbf{v}_{k}\) and the AN projection matrix \(\mathbf{T}_{k}\) with estimation errors should be rewritten as  
\[\widehat{\mathbf{v}}_{k}=\mathbf{h}(\widehat{\theta}_{dk}), \tag{48}\]  
and  
\[\widehat{\mathbf{T}}_{k}=\mathbf{I}_{N}-\mathbf{h}(\widehat{\theta}_{dk}) \mathbf{h}^{H}(\widehat{\theta}_{dk}), \tag{49}\]  
respectively.  
According to Equation (48), it is easy to obtain that the normalization characteristic in Equation (5) will be affected by the estimation error. Likewise, from Equation (49), it is effortless to derive that the orthogonal property in Equation (7) will also be influenced when an estimation error exists, as analyzed in [51]. Simulations are provided in next Section to illustrate that the security can also be ensured on condition that the estimation errors of the desired directions are within an acceptable limit for the proposed DM schemes.
## 4 Fragility Assessment of Structural Pounding
### Curvature-Based Fragility Curves  
Fragility curves in terms of maximum curvature ductility \(\mu_{\varphi,\text{max}}\) as a function of PGA have been developed based on the five examined procedures. The consistency of the fragility curves is examined for the most critical structural member against bounding. For the examined bounding cases, the local fragility curves of all the external columns at the bounding side of the eight-story RC frame are developed. These results indicate that the column at the fourth floor level is the most critically affected due to the bounding with the adjacent structure (column C20--Figure 1). All the fragility curves are developed at the yield capacity of the column in terms of \(\mu_{\varphi,\text{max}}\) (i.e., damage level equal to \(\mu_{\varphi,\text{max}}=1\)). So, in Figure 7 the local curvature-based fragilities of the column C20 are presented for the cases (i) \(\text{d}_{\text{g}}=0.0\) cm; (ii) \(\text{d}_{\text{g}}=4.5\) cm; (iii) \(\text{d}_{\text{g}}=9.0\) cm; and (iv) without the bounding effect. Results indicate that fragility curves based on the MLE and MM are very close to the probability data points of the empirical CDF. On the other hand, when the IM percentiles method is used, the fragility curve of the column against bounding effect is moved towards greater values of PGA in the case of \(\text{d}_{\text{g}}=0.0\) cm and in comparison to the corresponding curves based on the MLE, the MM, and the empirical CDF. Considering the PSDM-based fragility curves, it can be observed that, for the examined cases, the curvature-based fragilities are shifted to lower values of the PGA in comparison to the other methodologies. However, an important issue here is the type of regression model that was used to produce the PSDM. As discussed in Section 2.5, linear and bilinear regression models are adopted for the PSDM. So, in Figure 7, fragility curves based on both linear (LPSDM) and bilinear PSDM (BPSDM) are presented. These results clearly indicate that the use of a linear PSDM can fail in properly describing the local inelastic demands of the structural member. In the following sections, the assumptions of producing an accurate PSDM are further analyzed to give a better insight on the results of this study.
# Biomechanical Optimization of Lumbar Fusion Cages with a Porous Design: A Finite Element Analysis
## 2 Materials and Methods
### Design of Customized Lumbar Fusion Cage  
The customized lumbar fusion cage was developed by adapting a standard classic cage model to incorporate biomimetic principles, ensuring compatibility with the anatomical features of the lumbar intervertebral space and vertebral bodies. The superior and inferior surfaces of the cage were manually contoured to precisely match the inferior endplate of L3 and the superior endplate of L4, ensuring optimal fit and stability. This precise customization enhances contact with the vertebral surfaces, improving stability and fit within the interbody space.  
To achieve optimal biomechanical performance and biological integration, the cage design incorporated Gyroid and Voronoi lattice structures to mimic the trabecular bone's anisotropic porosity and interconnectivity, enhancing mechanical stability and bone in-growth. This biomimetic lattice was created using Gyroid and Voronoi parametric modeling techniques within Rhinoceros 6 (Robert McNeel & Associates, Seattle, WA, USA) and the Grasshopper plug-in (v.0.9.0076). For the detail modeling process, the region of interest based on the cage geometry was defined, and then distributed random seed points were established in the three-dimensional porous network (recorded in Table 2). After applying a scaling function with an adjustable scaling factor, the structure was refined into a mesh of pores and solid trabeculae, then the mesh was smoothed to form an interconnected lattice in order to mirror natural bone.  
As can be seen from Figure 3, the final biomimetic trabecular porous cage was constructed by integrating the porous lattice with the solid cage geometry using Boolean operations. Target porosity and average pore size were optimized by adjusting the number of cell size (Gyroid) or seed distance (Voronoi), striking a balance between mechanical strength and bone in growth potential. This innovative design enhances both the structural integrity and biological functionality of the cage, positioning it as a promising solution for lumbar interbody fusion applications. All porous structures of G-Cage and V-Cage were developed with a similar relative density of 27% (porosity of 73%), and more details of the parameters used in the modeling are shown in Table 2.  
All cage designs (C-Cage, P-Cage, G-Cage, and V-Cage) were assumed to be made of Magnesium alloy (Mg Alloy), a commonly used material for 3D-printed spinal implants due to its favorable biocompatibility and mechanical properties. For the finite element analysis, the material properties assigned to the cages were as follows: [PERSON]'s Modulus = 42,000 GPa and Poisson's ratio = 0.31.
## 2 Materials and Methods
### Antimicrobial Susceptibility Testing  
Antibiotic susceptibility testing was determined by the Kirby-Bauer diffusimetric method on the Mueller-Hinton standardized medium and the minimum inhibitory concentration method obtained with the Vitek automated system, using a susceptibility card (AST-592) [30; 31; 32]. Each bacterial strain suspension was prepared from pure cultures of bacteria cultivated on plates. The bacterial cells were suspended in 3 mL of a sodium chloride solution. The suspension used in the VITEK 2C system was adjusted to a 0.5 McFarland standard, using a Densicheck (bioMerieux, Marcy-I'Etoile, France). The results of the antimicrobial susceptibility were interpreted according to the Clinical Laboratory Standard Institute [30; 31; 32; 33]. The reference strains used for quality control in the identification and antibiotic sensitivity testing were _S. aureus_ ATCC 25923; ATCC 29213 (quality control for disk diffusions and MIC, according to CISI and VITEK 2C technical insert); and _Enterococcus casselflavus_ ATCC 700327 (quality control for identification, according to VITEK 2C technical insert). Some antibiotics were tested depending on the type of sample (nitrofuranotin and nonfloxacin for testing and reporting of urinary tract isolates only), in accordance with the CLSI recommendations. Isolates of _Enterococcus_ spp. were systematically tested with penicillins (penicillin-10 U, ampicillin-10 ug), fluoroquinolones (ciprofloxacin-5 ug, nonfloxacin-10 ug), macrolides (erythromycin-15 ug), nitrofurans (nitrofuranotin-300 ug), high-level aminoglycosoides (gentamicin-120 ug, streptomycin-300 ug), glycoptides (vancomycin-30 ug), and oxazolidinones (linezolid-30 ug). For the diffusimetric method, Oxoid Antimicrobial Susceptibility Disks, Thermo Scientific, from the USA, were used. In 2019,the samples were tested using the diffusimetric method, and since 2020, the testing has been conducted according to MIC. Antibiogram results were classified as susceptible (S), intermediate (I) or resistant (R). MDR strains were identified according to the European Center for Disease Control (ECDC) [28; 34] as being resistant to at least one agent from at least three antimicrobial categories [28]. Depending on the clinical significance and the demands of the clinician, several MDR strains, such as vancomycin, teicoplanin, and line-z solid, were additionally tested for backup antibiotics. Glycopeptide resistance is mediated by two phenotypes: VanA, with high levels of vancomycin resistance and a variable level of teicoplanin resistance in three hospitals, and VanB, with a variable level of resistance, which was limited to vancomycin in most cases. Only the acquired antimicrobial resistance was accounted for, not the intrinsic one.
## 11 Further Research Directions and Future Considerations
### Advancement towards High-Baud-Rate Transmission
It is noteworthy that promising direct-detection-based receivers capable of supporting short-reach systems with 100 Gb/s and beyond should support complex signals that are relatively easier for a high-baud-rate generation. It is highly imperative to support digital chromatic dispersion post-compensation for a considerable reach extension. Furthermore, optical time-division multiplexing (OTDM) can be employed for the Tb/s transmission. However, apart from the need for a precise timing alignment of the tributaries in the OTDM, high-order optical dispersion compensation that is relatively expensive is demanded. Basedon these, its implementation for Tb/s transmission is debatable. On the other hand, optical OFDM offers better performance regarding the SE and receiver sensitivity. It is not only resilient to tributary timing alignment, but is also robust to polarization/channel dispersion. Therefore, a PDM-M-QAM-OFDM scheme is a good candidate for the envisaged Tb/s transmission [131].
## 3 Control Strategy of Motor Control System
### Closed Loop Regulator Design  
As can be seen in Figure 3, the current loop is designed by traditional PI, the speed loop is designed by fuzzy PI control. The dynamic structure of the PMSM vector control system is shown in Figure 4. Where \(K_{te}\) is the electric motor torque coefficient, \(K_{te}=3/\left(2n_{p}\psi_{t}\right)\); \(T_{m}\) is the mechanical time constant, \(T_{m}=J\cdot 2\pi/60\); \(K_{e}\) is the electric motor back-EMF coefficient, \(K_{e}=2\pi/(60n_{p}\psi_{d})\); and \(T_{1}\) is the electric motor armature time constant, \(T_{1}=L_{q}/R\). The inverter is equivalent to the first-order inertial loop with an amplification factor of Ks. ACR is Automatic Current Regulator. ASR is Automatic Speed Regulator.  
In the case of multiple closed-loop control, the design principle of first inner loop and then outer loop is usually used. Therefore, the current regulator is designed first.
###### Abstract  
The association of hypertension or stroke with anthropometric and body composition indices has been evaluated individually but not for patients with both conditions. Here, we compared these indices in patients with both hypertension and stroke and evaluated the best indicators for identifying patients with both diseases in the Korean population. Data were obtained from the Korea National Health and Nutrition Examination Survey (KNHANES) conducted from 2008 to 2011. Data analysis was carried out using a complex sampling design that considered the weighting for personal analysis to represent the whole population in Korea. Binary logistic regression was conducted for evaluating potential associations, and areas under the curve were calculated to compare the predictive power of all variables for identifying patients with hypertension or both hypertension and stroke. Among all hypertension-related factors, waist-to-height ratio (WHtR) exhibited a strong association in men (odds ratio (OR) = 1.390 [1.127-1.714]), whereas trunk-fat mass (OR = 1.613 [1.237-2.104]) and thoracic spine bone mineral density (BMD) (OR = 1.250 [1.044-1.496]) represented the best indicators in women. Comparison of anthropometric and body composition indices in patients with both diseases revealed that left arm BMD and left leg fat mass (LLF) were strongly associated in both men (OR = 0.504 [0.320-0.793]) and women (OR = 0.391 [0.208-0.734]). However, among patients with both hypertension and stroke, WHtR (OR = 1.689 [1.080-2.641]) and LLF (OR = 0.391 [0.208-0.734]) were the best risk predictors in men and women, respectively. Our findings suggested that the best indicators among patients with hypertension or both hypertension and stroke may differ according to men and women.  
hypertension; stroke; body composition; body fat mass; bone mineral density; anthropometry; classification model; binary logistic regression; indicator +
Footnote †: journal: Applied Sciences
###### Abstract  
Samples from three different mating stages (before, during and after mating) of the Mediterranean fruit fly _Ceratitis capitata_ were used in this experiment. Samples obtained from whole insects were subjected to extraction with the two mixtures of solvents (acetonitrile/water (A) and methanol/acetonitrile/water (B)) and a comparative study of the extractions using the different solvents was performed. Direct immersion-solid phase microextraction (DI-SPME) was employed, followed by gas chromatographic-mass spectrometry analyses (GC/MS) for the collection, separation and identification of compounds. The method was validated by testing its sensitivity, linearity and reproducibility. The main compounds identified in the three different mating stages were ethyl glycolate, \(\alpha\)-farnesene, decanoic acid octyl ester, 2,6,10,15-tetramethylheptadecane, 11-tricosene, 9,12-(\(Z\),\(Z\))-octadecadienoic acid, methyl stearate, 9-(\(Z\))-tricosene, 9,11-idehydro-lumisterol acetate; 1,54-dibromotetrapentacontane, 9-(\(Z\))-hexadecenoic acid hexadecyl ester, 9-(\(E\))-octadecenoic acid and 9-(\(Z\))-hexadecenoic acid octadecyl ester. The novel findings indicated that compound compositions were not significantly different before and during mating. However, new chemical compounds were generated after mating, such as 1-iodododecane, 9-(\(Z\))-tricosene and 11,13-dimethyl-12-tetradeen-1-acetate which were extracted with both (A) and (B) and dodecanoic acid, (\(Z\))-oleic acid, octadecanoic acid and hentriacontane which were extracted with (A) and ethyl glycolate, 9-hexadecenoic acid hexadecyl ester, palmitoleic acid and 9-(\(E\))-octadecenoic acid, which were extracted with solvent (B). This study has demonstrated that DI-SPME is useful in quantitative insect metabolomics by determining changes in the metabolic compounds in response to mating periods. DI-SPME chemical extraction technology might offer analysis of metabolites that could potentially enhance our understanding on the evolution of the medfly.  
DI-SPME; GC-MS; Mediterranean fruit fly; extraction solvent; metabolites +
Footnote †: journal: molecules  
Article
# Can People Really Do Nothing? Handling Annotation Gaps in ADL Sensor Data
## 7 Conclusions  
We addressed the problem of annotation gap existence in input sensor readings of ADL systems. We investigated three paradigms for handling these gaps. The first paradigm drops the non-annotated gaps from the training data. The second paradigm gives all the instances belonging to these gaps a unique _unknown or do-nothing_ label. Inspired by some repeated patterns in these gaps, the third paradigm attributes every gap by its preceding and successor activities. A hybrid combination of a modified version of the first and the third paradigms was proposed by semantic preprocessing of some annotation gaps. The major comparison criterion between the proposed three paradigms is their impact on the overall activity recognition performance of the adopting supervised recognition model. An HMM was used as the adopted learning model for evaluation purposes. Evaluation results showed the superiority of the third paradigm over the other two paradigms in most cases. Furthermore, a noticeable performance improvement is achieved, if proper semantic preprocessing is conducted. The failure cases of the third paradigm were with gaps whose similar successor activities. The failure cases can be exploited to guide optimal sensor assignment and allocation processes.  
Figure 12: Comparison of accuracy values of paradigm #3 before and after applying semantic preprocessing.  
Figure 13: Comparison of specificity values of paradigm #3 before and after applying semantic preprocessing.  
**Author Contributions:** Conceptualization, A.E.A.H. and W.D.; Methodology, A.E.A.H. and W.D.; Software, A.E.A.H.; Validation, A.E.A.H. and W.D.; Formal Analysis, A.E.A.H.; Investigation, W.D.; Resources, W.D.; Data Creation, A.E.A.H. and W.D.; Writing--Original Draft Preparation, A.E.A.H. and W.D.; Writing--Review and Editing, A.E.A.H. and W.D.; Visualization, A.E.A.H. and W.D.; Project Administration, A.E.A.H. and W.D.; Funding Acquisition, A.E.A.H. and W.D.  
**Funding:** This work was funded by the National Plan for Science, Technology and Innovation (MAARIFAH)--King Abdulaziz City for Science and Technology--the Kingdom of Saudi Arabia, award number (13-ENE2356-10).  
**Acknowledgments:** The authors would like to thank the--King Abdulaziz City for Science and Technology--the Kingdom of Saudi Arabia, (Grant number: 13-ENE2356-10).  
**Conflicts of Interest:** The authors declare no conflict of interest.