esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Besides vegetation phenology, GPP\({}_{\text{max}}\) plays a crucial role in controlling the interannual variability of annual GPP [28; 29]. GPP\({}_{\text{max}}\) occurs in summer in northern temperate ecosystems [4] and is positively correlated with summer GPP [30]. Some studies have indicated that GPP\({}_{\text{max}}\) even has a greater impact on variations of GPP\({}_{\text{ann}}\) than phenology [4; 10]. In recent years, the responses of vegetation productivity to shifting phenology in alpine ecosystems across Qinghai-Tibet Plateau (QTP) has been investigated, which mainly focused on the effects of the SOS, EOS and LOS on ecosystem productivity [22; 31]. The alpine ecosystems on the QTP are highly sensitive to climate changes [13]. [PERSON] et al. [31] found that the warming climate in the Tibetan Plateau resulted in a later SOS for alpine meadow in spring due to water limitations but promoted ecosystem carbon uptake in summer with better moisture conditions. [PERSON] et al. [22] found that most of the QTP experienced significant trends in advancing the SOS, delaying the EOS and prolonging the LOS in 2000 and 2012, and vegetation productivity also increased in most parts of the plateau. These studies mainly focused on the changes to SOS, EOS and LOS and their effects on annual productivity in the QTP, but few studies were concerned with the effects of vegetation phenology and photosynthetic capability on the seasonal dynamics of productivity in the region. Moreover, the response relationships between vegetation productivity and phenology were complex and varied within different climatic regimes due to differences of terrain and climate conditions in the QTP [22], and it is necessary to explore the response mechanisms of seasonal vegetation productivity to the phenology dynamics for various climatic zones. The source region of the Yellow River (SRYR) is located in the northeast of the QTP, which is characterized by an alpine climate, and vegetation mainly consists of alpine steppe and alpine meadow [32] with obvious seasonality. Since 2000, both annual temperature and precipitation have seen increasing trends in the SRYR [33]. Moreover, the temperature has had a higher rate increase for the SRYR than that for the whole Tibetan Plateau [34; 35], resulting in changes of the boundaries between seasonally frozen ground and permafrost [36]. An advanced trend of spring phenology in alpine meadow in the SRYR has been reported [37]. This paper aims to study the seasonal responses of vegetation productivity to phenology and physiology changes in alpine ecosystems. There are two objectives for this study: (1) examining the variations of vegetation phenology (SOS, POS, EOS and LOS), photosynthetic carbon uptake capacity (GPP\({}_{\text{max}}\)) and GPP at seasonal and annual scales for the alpine ecosystems in the SRYR from 2001 to 2019 and (2) exploring the seasonal responses of annual GPP to the changes of vegetation phenology and photosynthetic capability. This study can help in understanding the effects of vegetation phenology on the carbon cycle in alpine ecosystems.
## 3 Results and Discussion  
### PXRD  
PXRD data were collected and compared with the calculated pattern for MOF-5 to investigate the crystallinity and phase purity of pristine MOF-5, met@MOF-5 and ceph@MOF-5, as shown in Figure 1. The PXRD patterns of the pristine MOF-5 were found to be consistent with those reported in literature, confirming the phase purity [41].  
The PXRD patterns of met@MOF-5 appeared similar to the pristine MOF and showed all characteristic peaks of the pristine MOF-5, indicating that the crystal structure of the MOF after drug loading remained largely unaffected and the presence of the drug molecules did not alter the lattice parameters of the MOF [42; 43].  
The XRD patterns of ceph@MOF-5 also demonstrated very similar diffraction patterns, as compared to the patterns of the pristine MOF-5 and met@MOF-5. This indicates that the crystallinity of the MOF-5 was unaffected by the presence of the Ceph drug molecules [44]. This showed all the major and minor peaks of the pristine MOF-5 as shown in Figure 1. However, the absence or broadening of PXRD peaks for MOF-5 were noted after exposures of the drug-loaded MOF composites in PBS for 24 h (Figure S6 in Supplementary Materials).
## 1 Introduction  
In the seminiferous tubules of the testis, spermatogonia differentiate into spermatocytes and then spermatids during spermatogenesis, leading to the formation of spermatatozoa. Spermat are intimately associated with the supporting Sertoli cells. The communication between these cells involves the formation of gap junctions, of which Gja1 (Connexin43) is the main unit. Each cell participating in the formation of a gap junction harbors a connexon, made of six connexin protein units, on its plasma membrane. The gap junction is made by the interaction between two connexons from adjacent cells and allows the exchange of different second messengers and small molecules (<1 kDa), including the cyclic nucleotides cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP), inositol trisphosphate, glutathione, and calcium ions.  
The expression of Gja1 in spermatatogonia has been confirmed in several studies [1, 2]. Absence of Gja1 in mice results in an important depletion of primordial germ cells in fetal testes [3]. Sertoli cells' specific inactivation of _Gja1_ results in an abnormal formation of the blood-tests barrier, infertility and/or Sertoli cells only syndrome [4, 5, 6, 7]. However, mice with germ cells with specific disruption of _Gja1_ expression are fertile and do not have apparent changes in the assembly of the blood-testis barrier [8], most likely due to the compensatory expression of other connexins in germ cells.  
Regulation of the abundance of Gja1 in spermatatogonia involves various processes, including gene transcription, mRNA maturation, protein synthesis, post-translational modifications, plasma membrane integration and protein degradation. However, gene expression of _Gja1_ is abnormally decreased in most testicular germ cell tumors. Hence, a better understanding of the mechanisms of transcriptional regulation of _Gja1_ in spermatatogonia is essential to understand how the loss of its expression occurs during the development of testicular cancer. As in other cell types, activator protein-1 (AP-1) transcription factors may be involved in such regulatory process. Thus, AP-1 members were overexpressed in GC-1 cells to assess their impact on _Gja1_ expression. We showed that Jun and Fosl2 cooperate to activate the _Gja1_ promoter in GC-1 cells. Furthermore, the recruitment of Jun to the proximal region (-153 to +46 bp) of the _Gja1_ promoter has been confirmed via chromatin immunoprecipitation. Protein kinase A and calcium-calmodulin protein kinase I also contribute to the activation of _Gja1_ expression by improving the cooperation between AP-1 factors. Therefore, the reduction in _Gja1_ expression in testicular germ cell tumors may involve a loss of cooperation between AP-1 factors.  
spermatogonia; connexin43; Gja1; gap junctions; activator protein 1; calcium calmodulin-dependent protein kinases
## 3 Data and Methods
### Data Processing
When the top of a thermally-stratified ABL fell within the sounding range, the pattern of echo-signal was used to determine the ABL height. The latter was determined by visual inspection of echograms and return-signal profiles, as the height where the echo intensity of a pronounced echoing layer sharply decreases. This method was chosen as the echo-intensity is a reliable indicator for mixing, in contrast to the standard deviation of the vertical velocity, \(\sigma_{w}\), that is often wave-dominated in the SBL and therefore, is not a proper indicator for turbulence. The LIDAR data obtained from the Windcube 100s were already filtered for acceptable carrier-to-noise, ratio, i.e., \(\text{CNR}>-23\,\text{dB}\). An additional check was made for the low elevation data, since this also contains clutter from hard targets such as buildings, the shore, etc. A clutter map was used to remove hard targets, which have a very high SNR and a radial velocity of zero. Furthermore, all points with an instrumental wind speed error greater than \(0.5\,\text{m}\,\text{s}^{-1}\) and unphysical wind speed values exceeding \(30\,\text{m}\,\text{s}^{-1}\) were removed. The radial wind speed measurements were used to compute time series of horizontal wind profiles from both PPI scanning patterns, applying the velocity-azimuth-display (VAD) technique [94]. The VAD technique assumes horizontal homogeneity, and the applied method checks this assumption by testing the collinearity. Profile time series of \(\overline{w^{\prime 2}}\) from the SODAR and \(\overline{U}\) from the LIDAR are available as Supplementary Materials (Section 6). Furthermore, deviations from the mean state over one entire scan were used to compute turbulent statistics of the flow.
# Microbial Bioremediation and Biodegradation of Petroleum Products--A Mini Review
## 3 Microbial Diversity and Ecology
### Microbial Influential Factors
#### 3.2.1 Nutrients  
This is an essential element for successfully degrading contaminants, including iron, nitrogen, and phosphorous in some cases. Nonetheless, they can also become an inhibiting factor in the biodegradation process. In freshwater and marine environments, oil spills decrease phosphorous and nitrogen levels and a dramatic increase in carbon levels, which impacts the biodegradation process [38]. Moreover, if phosphorous and nitrogen levels are low, the wetlands cannot provide nutrients due to the intense demands of nutrients by the plants. Consequently, additional nutrients are necessary to aggrandize contaminants' biodegradation [39].
## 3 Strain Gauges and Distributed Optic Fiber Strain Measurements
### Distributed Fibre Optic Sensor vs. Strain Gauge
#### 3.4.1 Berea Core  
The converted strains from the Rayleigh/Brillouin frequency shift measurement are compared with the strain gauge readings from the two channels (Ch5 and Ch7) that are aligned with the optic fiber in Figure 14. In the figure, Rayleigh/Brillouin_4_8 refers to the mean value for monitoring points 4 and 8, which are closest to the strain Ch5 (Figure 2a). Similarly, Rayleigh/Brillouin_6_10 refers to the mean value for monitoring points 6 and 10, which are closest to the strain Ch7 (Figure 2b). It can be seen in Figure 14a that Rayleigh_6_10 agrees well with the strain reading by Ch7, whereas Rayleigh_4_8 starts to deviate from the strain reading by Ch5 as the confining pressure is increased above 4 MPa. The premature levelling-off of the strain by Ch5, as compared with Ch7, suggests that it has most likely suffered malfunction at higher confining pressures. In Figure 14b, the converted strains from Brillouin frequency shift are also much closer to the strain reading by Ch7, though the agreement is not as good compared with that for Rayleigh frequency shift.  
Similarly, in Figure 15, the converted strains from Rayleigh frequency shift are in good agreement with the Ch7 strain reading with increasing pore pressure.  
Figure 14: _Cont._
# A Multi-Node Detection Algorithm Based on Serial and Threshold in Intelligent Sensor Networks
## 8 Analysis of Simulation Results
### Complexity Analysis  
Figure 7 shows the comparison of the computational complexity reduction ratio (CCRR) between the S-T-Max-log-MPA algorithm and the original MPA algorithm when the maximum number of iterations is 5; the complexity reduction ratio of the algorithm is defined as Equation (17). As can be seen from Figure 7, at the threshold of th = 0.01, when E\({}_{\text{b}}\)/N\({}_{\text{o}}\) = 0 dB, the CCRR of the S-T-Max-log-MPA algorithm is 5.74%, which is lower than that of the original MPA algorithm. When E\({}_{\text{b}}\)/N\({}_{\text{o}}\) = 14 dB, the CCRR is 48.18% lower than the original MPA algorithm. At the threshold of th = 0.10, when E\({}_{\text{b}}\)/N\({}_{\text{o}}\) = 0 dB, the CCRR of the S-T-Max-log-MPA algorithm is 18.42% lower than that of the original MPA algorithm. When E\({}_{\text{b}}\)/N\({}_{\text{o}}\) = 14 dB, CCRR is 53.20% lower than the original MPA algorithm. When the threshold is th = 0.60, CCRR is more effective than the threshold values of th = 0.01 and th = 0.10. At E\({}_{\text{b}}\)/N\({}_{\text{o}}\) = 0 dB, CCRR is 44.71% lower than the original MPA algorithm. At E\({}_{\text{b}}\)/N\({}_{\text{o}}\) = 14 dB, CCRR is 60.10% lower than the original MPA algorithm. It can be seen from the figure that the CCRR of the S-T-Max-log-MPA algorithm under different thresholds is lower than the CCRR of the original MPA algorithm, so the S-T-Max-log-MPA algorithm can effectively reduce the CCRR of the SCMA system detection algorithm, and the effect is more obvious with the increase of E\({}_{\text{b}}\)/N\({}_{\text{o}}\) and the threshold.  
\[\text{CCRR}=\frac{\text{Complexity of A algorithm}}{\text{Complexity of B algorithm}} \tag{17}\]
## 2 Experimental Tests
### Test Setup and Loading Devices  
The measurement points of the test model are arranged as shown in Figure 1. Nine piles were set in the subgrade, and since the test was a symmetrical structure, only half of the structure was arranged for the measurement points. As shown in Figure 1a, in order to measure the stress in the pile, the five strain gauges with a distance of 22.5 cm were evenly pasted on the pile. As shown in Figure 1b, nine earth pressure boxes numbered S1 to S9 were arranged to monitor the earth pressure at the top of the pile and the earth pressure between the piles, respectively. Full bridge strain gauges were used to measure the stress on the geogrid numbered A-1 to C-3, and the locations were arranged as shown in Figure 1c. The displacement meters are arranged on the embankment surface loading plate for settlement measurement.  
The test loads are divided into two parts: static load and dynamic load, which are loaded on the surface layer of the embankment, and the loading position is shown in the shaded part of Figure 1b. Static load is completed by gravity loading, with reinforced concrete gravity slabs loaded step by step with a total of five levels of loading, and each level of loading is 8.2 kPa after conversion. The form of dynamic loads generated by train operation is affected by more complex factors, and the complete reduction in dynamic train loads in the test is difficult, and there is no unified load form for dynamic train loads by scholars at home and abroad. Therefore, in this study, the simple sinusoidal cyclic load proposed by [PERSON] et al. [37] is used to simulate the train load. The dynamic stresses generated on the subgrade bed during train operation range from 10 to 20 kPa, with a response principal frequency around 10 Hz [38]. The dynamic load device of the test was a 380V electric power rammer, the pressure of the impact loading plate was 11 kPa, the loading frequency was 10 Hz, and the simulated train load schematic was Figure 3. The DH5922D dynamic stress-strain analyzer of China Jiangsu Taizhou Jingjiang Donghua Testing Technology Co. Ltd. was used for test acquisition, and the maximum sampling frequency of the acquisition device is 50 Hz.  
Figure 3: Train simulation load diagram.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Unit** & **Values** \\ \hline Mesh size & mm & 30 \\ Longitudinal tensile strength & kN/m & 30 \\ Poisson ratio & - & 0.39 \\ Elasticity modulus & GPa & 2.32 \\ Transverse tensile strength & kN/m & 30 \\ Longitudinal tensile strength at 2\% strain & kN/m & 10.5 \\ Transverse tensile strength at 2\% strain & kN/m & 10.5 \\ Longitudinal yield elongation & \% & \(\leq\)13 \\ Transverse yield elongation & \% & \(\leq\)16 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Geogrid properties.
## 3 Results and Discussion  
### Effect of the Age of Samples  
Figure 4 describes the electrical resistivity measurements of a saturated specimen over a period of time. A gradual increase in electrical resistivity was observed for the greater age of the specimen.  
At an early age, there are no differences in the electrical measurements among the different pastes, but it was observed that the mixture that had the lowest _w/c_ ratio had the highest electrical resistivity at the end of the experiments. The primary reason should be that the paste that has the lowest _w/c_ ratio has more hydration products and less porosity than the other two mixtures.  
Figure 5 shows the measurements of the electrical resistivity of the air-dried concrete. Until 100 days of air-dried curing, it was observed that the resistivity increased gradually as the age increased. However, the electrical resistivity increased dramatically from an age of 100 days to the completion of the experiment. The electrical resistivity measurement at the end of the experiment was approximately 1400 K\(\Omega\)-cm. According to the guideline value provided by RESI (2002), corrosion is very low if the electrical resistivity  
Figure 4: Electrical resistivity measurements of the saturated concrete without chloride.  
Figure 3: Measurement of the electrical resistivity measurements in concrete.  
is greater than 12 K\(\Omega\)-cm. Therefore, it is expected that the environment for anti-corrosion is driven after 70 days of air-dried and saturated curing.
## 4 Experimentation on the Case Study  
### Data Collection: Project Planning and Equipment  
In particular, a Total Station Leica TCR 1205 R400 (manufactured by Leica Geosystems AG--Part of Hexagon, Heerbrugg, Switzerland) was used to reference the process; this T.S. is characterised by an angular accuracy of 5\" (1.5 mgon) and a distance measurement accuracy of 1 mm + 1.5 ppm under standard conditions.  
For georeferencing processes, a multi-band RTK GNSS receiver module, the South GALAXY G7 (manufactured by South Surveying & Mapping Technology Co., Ltd., Guangzhou, China), was used, with centimetre accuracy.  
API Imager Pro C (manufactured by Zoller + Frohlich GmbH (Z+F)--Baden-Wurttemberg, Wangen im Allgau, Germany) with added thermal camera was used. Specifically, the maximum scanning distance is 180 m with an angular accuracy (horizontal/vertical) less than 0.007\({}^{\circ}\) and point acquisition speed more than 1,000,000 points/s.  
The resolution of the equipped thermal camera is 382 \(\times\) 288 pixels with an infrared spectrum between 7.5 and 13 \(\upmu\)m and 32 images for a complete panorama (four rows) with 2500 pixels/360\({}^{\circ}\).  
In addition, a UAS equipped with an RGB camera and a thermal camera was used.  
Specifically, the thermal camera has a resolution of 640 \(\times\) 512 with a Sensor Technology Uncooled VOx Microbolometer, Lens (manufactured by FLIR Systems, Inc.--Wilsonville, OR, USA) (FOV for Full-Sensor Output) of 13 mm; 45\({}^{\circ}\)\(\times\) 37\({}^{\circ}\), spectral band between 7.5 and 13.5 \(\upmu\)m and measurement accuracy of \(\pm\)5 \({}^{\circ}\)C or 5%.
# Efficient Prompt Optimization for Relevance Evaluation via LLM-Based Confusion Matrix Feedback
## 6 Discussion
### Why Did APE Perform Surprisingly Well?  
Our results indicate that APE occasionally outperforms APO and OPRO, despite being a simpler method. This aligns with findings from [PERSON] et al. [28], which suggest that LLM-based reflection-driven prompt optimization may not always be superior to resampling-based approaches.  
One key limitation of APO is that it updates prompts based on individual error cases, leading to localized and potentially biased refinements. Since the updates are driven by specific misclassifications rather than a broader error distribution, this approach risks overfitting to particular cases rather than producing generalizable improvements.  
Similarly, OPRO updates prompts based on previous iterations rather than a direct understanding of the data distribution, which can lead to a reinforcement of past patterns rather than true optimization. Because it lacks a structured way to incorporate overall error tendencies, it may fail to make meaningful global improvements.  
In contrast, APE generates multiple paraphrased variations of a prompt, increasing the likelihood of producing an effective version purely by chance. While this method does not actively refine prompts based on error analysis, the large number of variations increases the probability that some reformulations will perform well, even if they are not intentionally optimized.  
Our results confirm these characteristics: APO and OPRO struggle with targeted optimization, whereas APE benefits from its broader search space. However, APO-CF achieves the most effective updates by leveraging confusion matrix analysis, ensuring that modifications reflect the overall data distribution rather than being influenced by specific cases or past prompt iterations. This suggests that a structured, data-driven refinement process like APO-CF leads to more reliable and generalizable prompt optimizations.
# On the Rollout of Network Slicing in Carrier Networks: A Technology Radar
## 3 Impact of Network Slicing  
The introduction of slicing will impact all the technical domains of the network. In this section, we review the features required on these domains to support slicing. These constitute the basis for the understanding of the different solutions that will be explained later on, in Sections 5-8.
## 4 Literature Review
### Digital Techniques for Field Surveys  
One of the critical steps when dealing with existing buildings is collecting real-world data about the to-be-demolished structures. There are two different approaches for digital field surveys that are investigated: photogrammetry and laser scanning.  
Photogrammetry is a well-established digital image-based survey technique that extracts data from 2D pictures, and places them in 3D spaces [49], providing 3D measurements and producing points clouds for several engineering fields [50]. Close-range photogrammetry considers measurement distances within 300 m between cameras and buildings [51]. Photogrammetry allows the safe and easy analysis of unsafe buildings [52] with a reduced number of on-site field surveys, and enables further analysis of the buildings in remote mode. In addition, it is less expensive than laser scanning techniques [53], and non-experts can conduct surveys using common, portable, and lightweight digital cameras [54]. However, photogrammetry cannot provide automatic modelling of BIM objects; the points cloud can be used as a basis for the creation of the BIM model.  
Laser scanning can be considered as one of the most innovative approaches in the field, and aims to convert real objects into information-rich BIM objects. Modern laser scanning techniques are less expensive than traditional ones, such as terrestrial or aerial laser scanning [55]. However, the conversion process from the points cloud to a 3D plain model and then to an information-rich BIM model is still a time-consuming and expensive task [56]. Recent studies have tested Artificial Intelligence (AI) techniques to achieve an automatic or semi-automatic and therefore faster and less expensive conversion [57; 58].  
In summary, photogrammetry enables safe, low-cost field surveys but lacks automatic BIM object modelling capabilities. On the other hand, laser scanning enables the conversion from points clouds to BIM models, but it is more expensive and is still a developing technique.