esa-sceva/satcom-chunk-collection huggingface dataset:
# Searching for Potential Markers of Glomerulopathy in Urine by HS-SPME-GC\(\times\)GC TOFMS
[PERSON] et al. identified the total of 92 substances in samples coming from 24 healthy males [6]. More recently, [PERSON] et al. reviewed and classified 1840 VOCs secreted from a healthy human body. They reported 279 volatiles which are identified in human urine [7]. [PERSON] et al. used GC MS and SPME to study volatile metabolites in urine which are potentially important as cancer biomarkers. Samples were collected from a group of 33 cancer patients and 21 healthy individuals. The authors found 82 different VOCs in the group of cancer patients and in the control group [8]. [PERSON] and coworkers analyzed ketones in urine samples to discriminate between lung cancer patients and healthy controls [9; 10]. Comprehensive two-dimensional gas chromatography-time of flight mass spectrometry (GC \(\times\) GC TOFMS) is a powerful tool which has been successfully used in metabolomics and biomarker discovery. This technique offers high resolution, ordered structure of chromatograms and high peak capacity and is efficient in urine analysis. GC \(\times\) GC quadrupole MS or TOFMS has been used to determine anabolic steroids and their metabolites in human urine samples [11; 12; 13], to quantify salvinorin A in urine [14] and to detect acidic compounds in children's urine [15]. Among the most detailed studies were those conducted by [PERSON] et al. exploring human urine metabolomics. They applied GC \(\times\) GC-TOFMS and SPME to study VOCs in the urine headspace of healthy persons and detected ca. 700 compounds in each sample of which 294 were tentatively identified [16]. A variety of renal injuries may lead to chronic kidney diseases (CKD) [17], which is a group of pathologies, where renal excretion is chronically compromised. Most often, CKD is irreversible and progressive. Patients with end-stage kidney diseases need renal replacement therapy (RRT) such as kidney transplant or dialysis. Worldwide, about 2 million people are receiving RRT [18; 19]. Numerous inflammatory and non-inflammatory diseases affect the renal glomerulus and lead to glomerular kidney diseases [20; 21]. However, searching for specific VOCs in urine can be promising way to find biomarkers of glomerular diseases. The aim of the study was method development based on SPME extraction and GC \(\times\) GC TOFMS analysis of VOCs from human urine. Consequently, our research work focused on identification of VOCs in urine of patients with glomerular diseases and healthy controls. We developed SPME and GC \(\times\) GC TOF MS method for extraction and analysis of volatiles. An automatic method of chromatographic data processing was applied. The ability of the developed method to differentiate between the two investigated groups of subjects was proved and with it the usefulness of GC \(\times\) GC TOFMS in search for glomeruly-specific substances in urine was demonstrated as well.
# Intelligent Handover Decision-Making for Vehicle-to-Everything (V2X) 5G Networks
## 4 Performance Evaluation
### Result Analysis
#### 4.3.3 Performance Comparison in Terms of Packet Losses and Latency Ratio  
At higher speeds, packet loss surges severely, particularly in ACHQ, where frequent HO failures cause data disturbances and remain ineffective at high velocities. Figure 12 shows that CAP noticeably lowers packet loss by guaranteeing more stable connections before starting HOs, as an effective  
Figure 11: Comparative results analysis for different algorithms with diverse SC densities.  
Figure 10: Handover performance results of IHD-V2X algorithm vs. SC density.  
method of integrating TOPSIS with ST in the handover decision. The best performance is notable with the IHD-V2X, which effectively minimizes packet loss by intelligently foreseeing when an HO should be executed, ensuring seamless connection. TOPSIS with ST and Q-Learning, the IHD-V2X method, attains the best results by guaranteeing efficient HO execution and reducing the failed changeovers. This performance improvement benefits video, augmented reality, and audio applications, which require low latency and minimal interruption. Notably, in the context of Q-Learning, both the IHD-V2X and ACHQ outperformed the non-Q-Learning method, the CAP, where the total latency was the lowest for ACHQ handover at all velocity levels. This difference occurs because ACHQ did consider application-specific requirements in its implementation.  
At higher SC densities, packet loss increases significantly in ACHQ handover, particularly at high densities, owing to excessive failed HOs and frequent network HOs. In Figure 13, CAP noticeably lowers packet loss by ensuring connection stability before initiating HOs. The best performance is shown by IHD-V2X, which minimizes packet loss by guaranteeing an efficient handover implementation and steady data transmission. In a different way, latency is the highest for CAP, while ACHQ and IDH-V2X achieve the lowest latency at all density levels because of their adaptive learning-based methods.
# Microbial Bioremediation and Biodegradation of Petroleum Products--A Mini Review
## 2 Petroleum Products
### Types of Hydrocarbons Degradation
#### 2.1.1 Aliphatic Degradation  
Aliphatics, particularly alkanes, constitute a significant group in crude oil, and their exclusion from oil-contaminated fields has become an ecological concern and is considered beneficial for improving recovery [11]. Microbial degradation of alkanes could occur in several ways. Among them are terminal oxidation, subterminal oxidation, and via alkyl hydroperoxide, the enzymatic and non-enzymatic reactions taking place in these processes.  
Figure 1: Summary of the different categories and subclasses of total petroleum hydrocarbons. Adapted and modified from [PERSON] and [PERSON] [11].  
In general, aliphatic degradation has been classified in the following degree of increasing susceptibility: cyclic alkanes < branched alkanes < n-alkanes.
## 5 Conclusions  
Design and synthesis of peptide-synthetic polymer block copolymers constitute nowadays a highly dynamic field since new applications and properties are sought out for such hybrid materials. Recent developments in peptide-polymer block copolymer synthesis are mainly focused to cover two main goals: i) to increase the molecular level control of the hybrids (_i.e._, control over the block length and molar mass dispersity of both synthetic polymer and peptide segments); and ii) to broaden the available architectures of the hybrid copolymers. The control over intra- and intermolecular interactions of the designed peptide blocks is also crucial to get conjugates able to interact with other (bio)molecules in a well defined manner.  
Just as there are many polymers and peptides with widely differing properties available to make up these chimera molecules, there are likewise many different synthetic approaches to create the hybrid conjugates. Over the past decade, impressive advances have been made in the synthesis of such chimeras. Specifically, new strategies for the ring-opening polymerization of amino acid _N_-carboxy-anhydrides now provide an easy access to peptide-synthetic polymer hybrids and other more complex architectures with a precise control over both polymer molecular weight and chain architecture. The examples given in this paper only supply a flavor of the possibilities offered by these synthetic advances, being obvious that these new developments have not yet been fully explored.  
The importance of producing site-specific modifications on both peptides and synthetic polymers, as well as, controlling the supramolecular organization and the drastic changes or enhancements should be clear when applications related to stimuli-responsiveness, self-assembly and bioactivity are considered. In particular, new peptide-polymer hybrids are crucial for the development of novel biomolecular drugs, new biosensor materials and bioactive hybrid hydrogels that can aid in wound healing and tissue regeneration. The enhanced possibilities given for the highly precise synthesis of peptide-synthetic polymer hybrids provide new and unique opportunities to control the structure and final properties of these materials, which can result in new characteristics with contemporary and unforeseen relevance.  
The capability of peptide-polymer hybrids to spontaneously undergo bioreception-driven self assembly into hydrogel structures is probably the basis of the most promising biomedical applications. Next developments that may be expected concern to the design of hydrogels with multiple functions, hydrogels sensitive to stimuli, hydrogels with programmable responses, and hydrogels translating substrate recognition into mechanical action. Furthermore, peptide-synthetic polymer hybrid hydrogels appear an ideal system to get minimally-invasive injectable tissue engineering scaffolds.  
This work has been supported by grants from MINECO/FEDER and AGAUR (MAT2009-11503, MAT2012-36205, 2009 SGR-1208).
## 5 Note on Incoherent Sampling and Different Estimators
Note that the bias of the estimator has been addressed by the author in [15; 16]. Figure 4: Simulated data points (100 points) for a sinewave with an amplitude of 3 V, an initial phase of \(\pi/3\) rad, and a frequency of 1.1 Hz (\(f_{x}\)). The sampling frequency (\(f_{s}\)) is 100 Hz, and the signal frequency (\(f_{x}\)) is 1.1 Hz. There is no phase noise added. The same can be observed in Figure 6, where the standard deviation of the estimated amplitude is plotted as a function of the number of periods covered by the data points. In this situation of the absence of phase noise or jitter (\(\sigma_{\theta}=0\)), the correct value would be null, as per Equation (27). This is represented by the solid line at the bottom of the figure. The conclusion of this section is that the three-parameter least squares procedure is not meant to be used when the signal frequency is not known since it leads to biased estimations and an increase in estimation uncertainty. There are many other estimators that can be used, some which are more simple and some which are more complex. It is worth repeating, however, that this estimator is still of use in many situations, and the study of its estimation uncertainty in the presence of phase noise or jitter is still valid and has produced an analytical expression, namely Equation (27), that can be used to compute the confidence interval of the estimations of sinewave amplitude made with the three-parameter least squares procedure. Undoubtedly, in the future, a similar study could be made regarding the other estimators mentioned above. Figure 5: Expected value of the estimated sinewave amplitude as a function of the number of periods of the sinusoidal signal. The circles represent the values obtained with the Monte Carlo analysis. The confidence intervals for a confidence level of 99.9% are represented by the vertical bars. The solid line represents the value given by the correct value of estimated amplitude (3 V). The number of repetitions made (R) was 100. Figure 6: Standard deviation of the estimated sinewave amplitude as a function of the number of periods of the sinusoid covered by the data points (incoherent sampling). The circles represent the values obtained with the Monte Carlo analysis. The confidence intervals for a confidence level of 99.9% are represented by the vertical bars. The solid line represents the value given by the theoretical expression (27), which in this case is null since there is no phase noise present. The number of repetitions made (R) was 2000.
## 2 Materials and Methods
### Data Sources
#### 2.2.2 The Environmental Covariates  
In this study, we used a total of 160 covariates to represent environmental conditions (Table 1). The climatic covariates include precipitation, air temperature, and land surface temperature (LST) over 2003-2011. The precipitation and air temperature were obtained from the national Tibetan Plateau data center [42], and LST was obtained from the MOD1A1 and MYD11A1 datasets of moderate resolution imaging spectrometer (MODIS) observations. The daytime and nighttime LST were filtered according to the quality indicators of MOD1A1 and MYD11A1 [43]. Hence, the LST difference was generated as well. The maximum, minimum, and mean of seasonal and annual daytime and nighttime LST, and its difference and mean over nine years, were calculated, as was the maximum, minimum, and mean of seasonal and annual air temperature and precipitation. The digital elevation model (DEM) data were derived from the WorldClim database [44], and the DEM derivatives were computed using SAGA GIS v2. Vegetation conditions were represented by net primary productivity (NDVI), enhanced vegetation index (EVI), and gross primary productivity (GPP), which were derived from the MOD1A3A3, MYD13A3, MOD17A2H, and MYD17A2H datasets over nine years [43]. The annual NDVI, EVI, and GPP were processed by the maximum value composite approach. The vegetation and climate covariates in the sampling year and the multiyear average were extracted to the sampling sites. All the covariate layers were projected to the Albers conic equal area projection and resampled to 1 km by the bilinear method. Besides, a land cover classification dataset, namely the dataset of land cover in Northwest China from 1990 to 2010 [45], was used to mask out water bodies, residential areas, pavement, bare rock, snow cover, and glaciers. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Covariate 1** & **Variable Abbreviation** & **N2** & **Resolution** \\ \hline \multirow{6}{*}{Vegetation} & Enhanced vegetation index of the sampling year and the & EVI\_1/9 & 2 & 1 km \\  & multilayer average & & NDVI\_1/9 & 2 & 1 km \\ \cline{1-1}  & Normalized difference vegetation index of the sampling & & GPP\_1/9 & 2 & 500 m \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Environmental covariates for predicting soil particle size fractions.
## 1 Introduction
### Spectrum Utilization  
For the HAPS system, it will substantially benefit future development for sharing IMT frequency bands. On the one hand, the scale effect of the worldwide mobile broadband industry can be acquired easily. On the other hand, the spectrum resource is an essential prerequisite for the development of the HAPS industry. The 2023 World Radiocommunication Conference's (WRC-23) agenda item 1.4 is on frequency identification for HIBS (high altitude platform stations as IMT base stations). Agenda item 1.4 focuses on the probable frequency bands below 2.7 GHz for HIBS, which have already been identified for IMT. The conclusion that will be reached at this conference will impact the development of HAPS for the next 5 to 10 years.
## 3 Methodology
### Survey Protocol  
Based on the sections above, we can derive the survey protocol used for the acquisition and analysis of the devices, which is summarized in Figure 2.  
For a proper comparison of the devices' capacities, we define our reference/baseline to compare the measurements. During the process of evaluating the positioning capacities of the receivers, the most obvious benchmarking method is to find the reference coordinates of the surveyed locations. For static surveys, this can be easily defined after or before the survey by surveying the static location with a high-grade GNSS receiver. To minimize device disturbances, we used a tripod as represented in Figure 3a. For dynamic surveys, we used a backpack with an antenna mount on top as represented in Figure 3b. The used receiver was a Novatel PwrPak7 receiver connected to a Novatel GNSS-850 antenna. For the trajectory computations, we opted for Differential GNSS (DGNSS) computations, using the backpack a rover and our local GNSS station located on the university rooftop (Novatel GPS-703-GGG antenna, Sepentrio PolaRx5 receiver) as a base. The total baseline between the devices ranges from a few hundred meters to a few kilometres, depending on the scenario. The DGNSS computations were performed using the open-source software RTKLib [38]; as these computations were needed only for the reference dynamic tracks, the DGNSS algorithms are not a part of our M  
Figure 2: Processing workflow for survey acquisition and processing.
## 1 Introduction  
Person re-identification, abbreviated as \"re-ID\" primarily focuses on identification and recognition of a person re-appearing in multiple views captured across various cameras in the same surveillance system [1, 2]. Person re-ID is crucial to the retrieval of suspects in surveillance camera networks during criminal investigations. Moreover, it is critical in the search for lost people in huge crowds. The mentioned vital applications are indispensable for enhanced public safety and security [3, 4]. As a result, the person re-ID problem in computer vision and machine learning has been receiving growing importance and attention in the recent past [5, 6, 7, 8, 9].  
Typically, a query image is provided to a person re-ID system that searches through a database and returns the matched ones if available. The concept is depicted in Figure 1. In a distributed surveillance system, multiple cameras are capturing the same person from different poses with distinct backgrounds, which may lead to different orientations of the same person [1, 10]. Similarly, people's appearance under different lighting and illumination conditions may also affect the learning capability of re-ID systems [11]. Such situations may lead to degraded performance in estimating the similarity between query and candidate images. In order to enhance the image retrieval task for various computer vision applications including person re-ID, [PERSON] et al. [12] formalized a novel Centroid Triplet Loss function.  
In computer vision and machine learning, person re-ID is regarded as a problem due to the images captured under different lighting conditions, diverse backgrounds, and varying camera views, resulting in considerable intra-class variations. Therefore, the development of robust and stable feature representation methods for re-ID systems has been the center of many researchers' attention in the field.  
The development of effective re-identification systems requires the extraction of discriminative features from pedestrian images. However, complications emerging from complex views in a surveillance system limit the learning capability of re-ID systems. This article aims to develop a deep architecture capable of learning discriminative features, specifically using transformers. In other words, we aim to integrate the transformers in the traditional models and person re-id specific models. The purpose is to explore whether the transformers help improve the performance. We will also look into other aspects such as the effect of running time, number of parameters, computational cost, etc.
For industrial operations, inaccurate or manipulated sensor data can have major repercussions, including malfunctioning equipment, delayed production, and safety risks. As a result, it is crucial to use secure protocols, encrypted data, and authentication methods to guarantee the integrity and validity of sensor data [14].
# Ten Years of Experience with Scientific TerraSAR-X Data Utilization
## 4 Discussion
Other missions like Sentinel-1 and ALOS-2 strictly follow a systematic and predefined observation plan [4; 10], aiming at global mapping and optimized multi-use at the expense of flexibility. The most requested mode in the TerraSAR-X mission for scientific applications is StripMap (59 percent) in single polarization, followed by the high-resolution Spotlight (28 percent) and ScanSAR modes (13 percent). StripMap data are easy to process and provide the best ratio of resolution versus coverage. The Spotlight modes are appreciated by the user community for very detailed spatial analyses. The introduction of the even higher resolution Staring Spotlight mode in 2013 initially led to a slight increase of the percentage of Spotlight orders, but then stabilized at 30 percent. ScanSAR offers larger area observation capability. A rough estimate based on the coverages per mode of RADARSAT-2 [6] indicate a share of 20 percent for the highest spatial resolution modes, 30 percent for medium and 50 percent for the coarsest ScanSAR modes. However, these numbers are dominated by commercial use and the needs of operational services like the Canadian Ice Service. Often, the X-band is synergistically evaluated with other sensors either by adding high frequency information or by shortening the revisit time for a specific point on ground by for example placing a TerraSAR-X acquisition between two consecutive RADARSAT-2 observations.
# A New Parameter-Free Slope Unit Division Method That Integrates Terrain Factors
## 4 Results, Analysis, and Discussion
### Discussion
#### 4.2.2 Future Improvements of the TFPF-SU Method  
Slope units hold great potential for applications in landslide susceptibility analysis. However, in landslide susceptibility mapping, it is important not only to emphasize the impact of topographic factors but also to comprehensively consider various environmental factors such as geology, hydrology, and vegetation. For example, the strength, degree of weathering, and permeability of soils and rocks directly affect slope stability. Heavy rainfall can increase soil weight, reduce friction between rock and soil, weaken their resistance to sliding, and ultimately trigger landslides. However, our current slope unit subdivision method mainly relies on terrain features and overlooks other key environmental variables. For instance, on low-elevation yet uniform surfaces, it is difficult to accurately identify and delineate boundaries between different landform types. As a result, the extracted slope units may span multiple types of rock and soil or have different surface runoff characteristics, and these factors are not fully incorporated into the analysis, affecting the accuracy and reliability of landslide susceptibility predictions. Therefore, future improvements to the TFPF-SU method should not only optimize the extraction of terrain features. They should also introduce various environmental factors such as geological structures, groundwater dynamics, and vegetation cover. This will enhance the comprehensiveness of the slope unit subdivision and the accuracy of landslide susceptibility mapping. Only by integrating these multidimensional factors can we more comprehensively reflect slope stability and provide a more scientific basis for landslide prediction and prevention.
## 3 Theoretical Analysis  
This paper describes a triple-threshold path-based static power optimization methodology (TPSPOM). This method concentrates on the specific time constraints of each path and considers how each cell affects the overall circuit and the given path. At the same time, this method saves run time by reducing the number of update times. For designs containing hundreds of thousands of cells, the TPSPOM method can efficiently distribute triple-threshold voltages.  
The main steps of the method are as follows:  
1. A simplified path collection is used instead of all paths when considering the circuit's timing.
2. Obtain the timing constraint inequalities for each path based on the information of cells and paths.
3. Quantify the criticality of paths and use them as path weights.
4. Get cell weight based on the weights of all paths associated with the cell and the delay-to-power ratio of the cell.
5. The initial state of the circuit is all-HVT. Path weights are used as the first order and cell weights as the second order to select the cells needing replacement. After each replacement, the path and cell weights are updated using approximate calculations. Repeat swapping cells until all the paths in the path collection meet the timing constraints. This stage saves time by avoiding updating the timing within the STA tools.
6. Apply the assignment results obtained from step 5 in the STA tools. If there are still timing violations, expand the path collection and perform step 5 again until the STA results meet the constraints.