esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Nitrogen and Phosphorus Removal
## 4 Discussion  
### Silage Storage Bunker Runoff Composition  
This study contributes to the understanding of nutrient composition of silage Bunker runoff, which is rarely reported in the literature. Leachate composition is highly variable depending on time that elapsed since ensiling and the initial crop moisture content, which in turn leads to variation in runoff composition once it is diluted with precipitation [4]. Total N is comprised of dissolved species, NH4+-N and NO2-+NO3-N and dissolved and particulate organic N. Studies reviewed by [PERSON] [3]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Treatment Tanks with MBBR**} & \multicolumn{2}{c}{**WB**} & \multicolumn{2}{c}{**EB**} \\ \hline  & \multicolumn{2}{c}{**Cumulative Mass Removal**} & \multicolumn{2}{c}{**Average RE**} & \multicolumn{2}{c}{**Cumulative Mass Removal**} & \multicolumn{2}{c}{**Average RE**} & \multicolumn{2}{c}{**Cumulative Mass Removal**} & \multicolumn{2}{c}{**Average RE**} \\  & \multicolumn{2}{c}{**(kg)**} & & \multicolumn{2}{c}{**(kg)**} & \multicolumn{2}{c}{**(kg)**} & \multicolumn{2}{c}{**(kg)**} & \multicolumn{2}{c}{**(\%)**} \\ \hline
**TN** & \(-0.71\) & \(-7.3\) & \(1.81\) & \(57.1\) & \(0.34\) & \(-183.1\) \\
**NH4+-N** & \(-1.20\) & \(-224.0\) & \(0.84\) & \(70.8\) & \(-0.15\) & \(-26.2\) \\
**NO2-+** & \(-0.01\) & \(-2446.7\) & \(0.04\) & \(11.7\) & \(0.001\) & \(270.2\) \\
**NO3-N** & \(-0.71\) & \(-41.0\) & \(0.43\) & \(23.4\) & \(0.15\) & \(7.1\) \\
**NP** & \(0.55\) & \(-33.3\) & \(0.32\) & \(20.7\) & \(0.07\) & \(-16.2\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cumulative nutrient mass load removal and mean removal efficiency for each part of the system (i.e., treatment tanks with MBBR, West bioreactor (WB), and East bioreactor (EB)) for five storm events where no bypass of treatment tanks occurred.  
Figure 6: Nitrogen (N) and phosphorus (P) mass loads for five storm events where no tank bypass occurred, at the System Inflow, Tank Outflow, and Combined Bioreactor Outflows (i.e., from East and West Bioreactor Outflows). TN = total N, NH4+-N = ammonium-N, NO2-+NO3-N = nitrite + nitrate-N, TP = total P, SRP = soluble reactive P.  
suggest that NO\({}_{2}\)\({}^{-}\)+NO\({}_{3}\)\({}^{-}\)-N may only make up 0.02% of the N content of silage leachate with most in the organic form, which is consistent with silage bunker runoff concentrations in this study (Table 1). [PERSON] [6] reported silage bunker runoff EMC concentrations for NH\({}_{4}\)\({}^{+}\)-N of 59 mg L\({}^{-1}\) and NO\({}_{3}\)-N of 4 mg L\({}^{-1}\), both higher than averages reported in this study (Table 1). For P, SRP contributes over 75% of the TP concentration on average (Table 1). Mean P concentrations were less than concentrations measured by [PERSON] [5], for which TP ranged from 26 to 71 mg L\({}^{-1}\) and SRP from 20 to 67 mg L\({}^{-1}\) and the EMC of SRP from [PERSON] [6] was 37 mg L\({}^{-1}\).
# Targeting KRAS in PDAC: A New Way to Cure It?
## 3 KRAS Mutations in PDAC
### Molecular Mechanism of KRAS Mutations  
From the perspective of function, the protein expressed by the KRAS gene is a purine nucleotide binding protein located on the cell membrane and has the activity of GTPase [57].  
KRAS protein, as a binary switch of guanosine diphosphate (GDP)/guanosine triphosphate (GTP), controls important signal transduction from activated membrane receptors to intracellular molecules [58]. In the inactive state, KRAS protein binds to GDP [59]. When stimulated by relevant signal molecules (such as epidermal growth factor receptor EGFR), the binding ability of KRAS protein to GDP is weakened. GTP takes the place of GDP to bind to the RAS protein, and the KRAS protein is, therefore, activated to bind with downstream signal molecules as monomers or dimers for signal transduction. Then, with the effect of GTP-activated proteins (GAPs), the GTPase activity of KRAS is significantly increased, and GTP combined with KRAS is hydrolyzed into GDP, restoring KRAS to its inactivated state [60]. However, in tumor cells, KRAS gene mutation leads to the loss of GTPase activity in the KRAS protein, which makes it unable to hydrolyze GTP into GDP after binding with GTP, entering the inactivation state; this finally leads to the continuous activation of the downstream pathway, resulting in malignant proliferation, metastasis and anti-apoptosis of tumor cells [60; 61]. Intrinsic GTPase and GTP-GDP exchange efficiency can differ between several mutant types of KRAS. For example, KRAS\({}^{\text{GI3}}\) mutation is more sensitive to NF1-GAP-mediated hydrolytic activity, while KRAS\({}^{\text{GI2}}\) and KRAS\({}^{\text{Q61}}\) mutations are insensitive to it [62]. Another example is that the KRAS\({}^{\text{GI2C}}\) mutant type has similar intrinsic GTPase activity to the wild type, whereas other KRAS mutants have lower intrinsic GTPase activity than the wild type. [20]. In fact, the KRAS\({}^{\text{GI2C}}\) inhibitor was designed with this characteristic in mind [10].  
It is also worth mentioning that the oncogenicity and drug resistance of mutant KRAS is related to its dimerization with wild-type KRAS [63]. The exact relationship between them needs to be studied in depth.
## 3 Methods
### Gaussian Mapping  
In Gaussian mapping, in addition to the two windows mentioned above, we randomly select a subset \(W_{random}\) of keyframes during each iteration to participate in the computation of the mapping loss. \(W_{random}\) prevents the scene map from forgetting past observations. The mapping loss is calculated from three aspects: RGB loss \(L_{rgb}\), depth loss \(L_{depth}\), and isotropic regularization \(L_{iso}\):  
\[\begin{split} L_{rgb}=(1-\eta)\parallel(exp(\alpha_{exp})\hat{I }+\beta_{exp})-& I\parallel_{1}+\eta(1-SSIM(exp(\alpha_{exp}) \hat{I}+\beta_{exp}),I)),\\ & L_{depth}=\parallel\hat{D}-D\parallel_{1},\\ & L_{iso}=\sum_{\vec{S}_{i}\in\mathcal{M}}\parallel\vec{S}_{i}- \vec{S}_{i}\parallel_{1},\end{split} \tag{13}\]  
Figure 3: Example for related window.  
where \(SSIM(\cdot)\) refers to the structural similarity index measure [40], \(\eta\) is a hyperparameter used to control its weight, and \(\tilde{S}_{i}\) represents the mean of the scale factors \(S_{i}\) for the 3D Gaussians in the scene map \(M\). The final loss is composed of the RGB and depth losses of the keyframes in all windows \(\mathbf{W}=W_{\text{s}}\bigcup W_{\text{r}}\bigcup W_{random}\) and scale factor isotropic regularization terms:  
\[L=\lambda L_{iso}+\sum_{k\in\mathbf{W}}(\gamma L_{rgb}^{k}+(1-\gamma)L_{depth}^{ k}). \tag{14}\]  
Regarding scene editing, following [18,28], we densify the 3D Gaussians based on gradients and prune them based on their opacity and scale. Additionally, for resetting scene opacity, we adopt the same approach following [28].
# Fabric Defect Detection Based on Improved Lightweight YOLOv8n
## 3 Result and Discussion
### Comparison Experiments
#### 3.5.3 Comparison of Algorithms on Different Datasets  
To validate the generalization capability of the GSL-YOLOv8n algorithm, a subset of 3,563 images from the Tianchi dataset was selected for comparative experiments against different algorithms. The fabric defect types include holes, stains, coarse weft, loose warp, fuzz, knots, and warping defects. The dataset was randomly divided into training, validation, and test sets in a ratio of 8:1:1. The experimental results are shown in Table 6.  
As shown in Table 6, the YOLOv6 algorithm achieves the highest FPS at 303.1, but it suffers from high model complexity and a slower convergence speed. Compared to the other three algorithms, the GSL-YOLOv8n performs best in terms of GFLOPs, Recall (R), and [EMAIL_ADDRESS]/%. Additionally, the GFLOPs of the GSL-YOLOv8n algorithm are reduced by 58.0% compared to the original YOLOv8n model. This indicates that the GSL-YOLOv8n algorithm demonstrates superior performance on this dataset, offering better robustness and generalization capabilities.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **GFLOPs/G** & **R/\%** & **FPS** & **[EMAIL_ADDRESS]/\%** \\ \hline YOLOv5n & 7.1 & 84.26 & 288.6 & 86.75 \\ YOLOv6 & 11.8 & 82.75 & 303.1 & 87.12 \\ YOLOv8n & 8.1 & 82.34 & 300.4 & 88.47 \\ GSL-YOLOv8n & 3.4 & 84.74 & 217.5 & 88.94 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of algorithms on different datasets.  
Figure 8: Comparison experiment detection result graph. (**a**) Pilling; (**b**) Stains; (**c**) Pulling Out; (**d**) Float; (**e**) Skips.
# Performance Evaluation of MPTCP on Simultaneous Use of 5G and 4G Networks
## 4 Performance Evaluation
### Performance Evaluations of MPTCP Schedulers When the CCA Is BALIA  
The performance of the considered MPTCP schedulers in terms of total received data for BALIA as a CCA can be observed in Figure 8. As we can follow, although the other schedulers present a similar trend in performances like LIA and OLIA, they could receive the highest amount of data for BALIA. Moreover, BLEU obtains the highest received data of 2093.89 Mb in 5G. In addition to the 4G-received data of 1232.712 Mb, BLEU receives a total of 3326.602 Mb data, which is the highest among all the schedulers. Thus, BLEU in combination with BALIA, provides the best performance in the considered scenario. It can be noted that the significantly higher amount of total received data is received just in those times when the UE has enough signal strength, thus indicating the superiority of the 5G network over 4G. Moreover, this gives a glimpse of the improved performance that can be obtained by implementing a more intelligent scheduler for the combined use of 4G and 5G networks.  
The throughput and \(\Delta\)_PDU_ measured in real-time during the simulation experiment are given in Figure 9. Again, the SINR was the same as Figure 3. We can observe the same trend in the results as we observed for the tests in LIA and OLIA as a CCA. However, it can be noted that BLEU better utilizes the 5G SF in combination with BALIA as a CCA in this test compared to the other schedulers and CCAs. This highly contributed to BLEU  
Figure 8: Performance of the tested MPTCP schedulers in terms of total received data for BALIA as a CCA.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Flow Completion Time (FCT)** \\ \cline{2-3}
**Scheduler** & **ms** \\ \hline Default (SRTT) & 11.502 \\ Round-robin & 11.525 \\ Redundant & 8.195 \\ ECF & 11.785 \\ BLEU & 7.31 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of the tested MPTCP schedulers in terms of FCT for OLIA as a CCA.  
obtaining the highest throughput. We believe that BALIA provides a comparatively better response to network changes than LIA and OLIA. BLEST's intelligent scheduling algorithm takes better advantage of it and yields the highest throughput.  
The FCT observed while the CCA is BALIA for the tested schedulers is listed in Table 4. Similar to the throughput result, BALIA could complete the flows at the lowest time of  
Figure 9: Real-time performance of the tested MPTCP schedulers for BALIA as a CCA in terms of throughput and \(\Delta PDU\) for: (**a,b**) SRTT, (**c,d**) Round-robin, (**e,f**) Redundant, (**g.h**) ECF, and (**i,j**) BLEST, respectively.  
6.012 ms on average. The other schedulers perform in a similar fashion, as observed previously in LIA and OLIA, but with a slightly lower FCT.
## 5 Conclusions  
Our study found that the initial hypotheses regarding the performance of the BiomedCLIP model in detecting scoliosis were not fully confirmed. While the model showed high sensitivity in recognizing advanced scoliosis, its effectiveness in identifying mild cases and different types of scoliosis was limited, particularly in detecting single-curve scoliosis.  
BiomedCLIP performed well in classifying advanced scoliosis, but struggled with early-stage detection and distinguishing between different types of scoliosis, especially when compared to previous results with CLIP models. Though BiomedCLIP demonstrated better differentiation of scoliosis severity, its accuracy in detecting specific types of scoliosis remained inconsistent.  
In conclusion, BiomedCLIP excelled in detecting advanced scoliosis but requires further refinement to improve its performance in early-stage detection and in distinguishing between different scoliosis types. Further optimization and broader data training will be essential to enhance the model's overall accuracy and clinical applicability.  
Conceptualization, A.F., R.F. and A.Z.-F.; methodology, A.F., R.F., A.Z.-F. and B.P.; investigation, A.F., B.P., A.Z.-F. and R.F.; data curation, A.F., R.K. and B.P.; writing--original draft preparation, A.F., A.Z.-F., B.P., R.K. and R.F.; writing--review and editing, A.F., A.Z.-F., R.F., B.P., R.K. and E.N.; supervision, B.P. and E.N.; funding acquisition, A.F., E.N. and B.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Data Availability Statement: The data are contained within the article. Many thanks are extended to [PERSON] for linguistic proofreading and to [PERSON] for the substantive support in the field of AI and for assistance in designing the presented study. The authors declare no conflicts of interest.
## 5 Data Communication Phase
### Inter-Cluster Transmission  
The final stage in running a cluster-based algorithm is to route the collected and aggregated data of sensor nodes by their correspondent CHs to the BS, where data are evaluated and processed according to the type of the application. Although single-hop is accepted by LEACH for intra-cluster transmission, many researchers found it infeasible for inter-cluster transmission because of the common long distance communication of CHs with the BS and thus propose implementing multi-hop transmission instead. To implement multi-hop transmission, a backbone should be constructed according to the constraints of WSNs to relay the data towards the BS. The backbone construction should be as simple as possible, energy efficient and applicable, while the total energy consumption of the nodes and the balance distribution of the load among nodes are two other important factors that should be considered. Based on the type of the nodes utilized in creation of the backbone route, we classify multi-hop inter-cluster transmission as multi-hop by CHs and RNs.
# A Convolutional Neural Network as a Potential Tool for Camouflage Assessment
## 1 Introduction  
Camouflage involves the strategic use of materials, colors, or lighting to diminish the visibility of an object against its immediate surroundings. Employed both in the natural world and military practices, the primary aim of camouflage is to enhance survival prospects by lowering the likelihood of detection. Within a military setting, various camouflage techniques, including nets, paints with low emission, and specific patterns, are used to merge crucial assets seamlessly with their environment. Understanding the effectiveness of these camouflage strategies in reducing an object's visibility and discerning the conditions under which they are most effective is crucial. This evaluation necessitates a standard for measuring detectability.  
Over time, various methods for assessing the detectability of visual objects have been established [1, 2, 3, 4, 5]. These methods include experiments with human participants as well as objective, computational (image-based) analyses. Studies involving human observers, which aim to evaluate the effectiveness of camouflage, usually require visual search anddetection tasks. However, these behavioral experiments tend to be costly and time-intensive, necessitating a significant number of participants and repeated trials. Additionally, the inherent complexity of natural scenes, with their high degree of variability, complicates the evaluation of camouflage for moving objects, as their visibility can change depending on their immediate location. Therefore, there is a growing demand for computational models capable of determining camouflage effectiveness using digital images.  
This study investigates the use of a pre-trained convolutional neural network (YOLOv4-tiny) to evaluate camouflage effectiveness from digital imagery, offering a faster and less resource-intensive alternative to traditional human visual search experiments. We find that the detection probability output by YOLO strongly correlates with human search performance and object identification conspicuity, validating its use as a proxy for human detection.  
In the rest of this section, we first discuss the pros and cons of observer and computational object visibility metrics. Then we suggest adopting the YOLO predicted certainty (confidence level) of an object's identity as a measure of camouflage efficiency. In contrast to standard camouflage evaluation methods based on human visual search and detection experiments, which are time-consuming and expensive, a YOLO-based digital camouflage metric aligned with human perception offers a faster and more cost-effective alternative. Figure 1 represents the flowchart of the present study.  
The main contribution of this paper is the finding that CNNs like YOLO can be valuable tools for the automated assessment of camouflage in military and surveillance applications.
## 2 Materials and Methods  
### Protein Expression and Purification  
The proteins used in this study were expressed and purified as follows: BL21 Star (DE3) (Invitrogen, Carlsbad, CA, USA) was transformed with an expression vector. A single colony was inoculated into 5 mL of LB medium containing 100 mg/L ampicillin and 1% glucose at 37 \({}^{\circ}\)C. The pre-culture was inoculated into 250 mL of TB medium containing 100 mg/mL ampicillin, and the cells were grown at 37 \({}^{\circ}\)C until the OD\({}_{600}\) reached approximately 0.4. The culture was continued at 27 \({}^{\circ}\)C overnight in the presence of 1 mM isopropyl-\(\beta\)-D-thiogalactopyranoside (IPTG).  
The cells were harvested by centrifugation at 6000\(\times\)_g_ for 20 min. After resuspension in 50 mL of HisTrap binding buffer (20 mM potassium phosphate buffer, pH 7.4, containing 150 mM KCl and 10 mM imidazole), the cells were disrupted by ultrasonication. After the cell debris was removed by centrifugation at 22,000\(\times\)_g_ for 30 min, the cell lysate was subjected to affinity chromatography using a HisTrap FF crude column (1.6 \(\times\) 2.5 cm, GE Healthcare, Little Chalfont, Buckinghamshire, UK). The column was washed with the HisTrap binding buffer. Then, the protein was eluted with a linear gradient of imidazole (10-400 mM) over 15 column volumes. Fractions containing the recombinant protein were pooled and concentrated using an Amicon Ultra-15 Centrifugal Unit (10 and 50 kDa NMWL, Merck, Darmstadt, Germany). Imidazole was removed using a HiTrap Desalting column (1.6 \(\times\) 2.5 cm, GE Healthcare). The protein was concentrated again and mixed with glycerol (10% (\(v/v\)) final concentration) for long-term storage at \(-\)80 \({}^{\circ}\)C. The purity was checked by SDS-PAGE, and the final protein concentration was determined from the absorbance at 280 nm using its extinction coefficient, which is calculated from those of Trp (5.5 mM\({}^{-1}\) cm\({}^{-1}\)), Tyr (1.49 mM\({}^{-1}\) cm\({}^{-1}\)) and Cys (0.125 mM\({}^{-1}\) cm\({}^{-1}\)).
## 5 Conclusions
### Strengths  
The results from the mathematical model and computer simulation confirmed the manipulator's ability to achieve a volumetric workspace through three actuators, matching or exceeding that of conventional multi-segment SCRaMs employing six or more actuators. The methodology can be used to reduce the number of actuators in a soft continuum manipulator to three. In comparison to SCRaMs reviewed in the literature, the reduction ratio is 25% at minimum for multi-segment SCRaMs employing four actuators, and up to 86% for SCRaMs employing 21 actuators, such as [10]. The reduction in the number of employed actuators is achieved while maintaining or improving the original workspace of the multi-segment SCRaM. In comparison to other actuator reduction methods presented in the literature, the current method offers a 150% improvement over [21], a 400% improvement over [22], and at least a 300% improvement over [23]. The advantages stemming from the application of this approach include cost reduction, design simplification, increased reliability, due to using fewer components, and improved energy efficiency due to a reduced overall weight.
# Glucose Isomerase: Functions, Structures, and Applications
## 4 Application of GI
### High-Fructose Corn Syrup (HFCS)
Among these enzymes, TosGI showed the highest catalytic efficiency toward D-glucose, along with superior thermostability. The optimum temperature of TosGI was 95 \({}^{\circ}\)C, and it retained more than 80% of its activity after 48 h at 85 \({}^{\circ}\)C in the presence of 20 mM Mn\({}^{2+}\)[60]. The kinetic parameters K\({}_{\rm m}\) and k\({}_{\rm cat}\)/K\({}_{\rm m}\) of TosGI were calculated to be 81.46 mM and 21.77 min\({}^{-1}\) mM\({}^{-1}\), respectively [60]. TosGI achieved a maximum yield of 52.16% for the conversion of D-glucose (400 g/L) to D-fructose at 85 \({}^{\circ}\)C [60]. The GI from _Caldicellulosinuptor besci_ (CbeGI), characterized by [PERSON] et al. [57], exhibited the maximum activity at pH 7.0 and 80 \({}^{\circ}\)C and retained good thermostability at 85 \({}^{\circ}\)C. CbeGI showed affinity for D-glucose, with a K\({}_{\text{m}}\) of 42.61 mM, and a conversion efficiency up to 57.3% with 3 M D-glucose [57]. The high catalytic efficiency and affinity of CbeGI make it a valuable enzyme for the direct production of 55% HFCS. [PERSON] et al. reported the characterization of GI from the hyperthermophile _T. neapolitana_ 5068 (TnGI) [61]. The optimal temperature of TnGI was above 95 \({}^{\circ}\)C and the optimum pH was 7.1, but the enzyme also showed high activity over a wide pH range. Kinetics studies showed that the catalytic efficiency (k\({}_{\text{cat}}\)/K\({}_{\text{m}}\)) of TnGI remained constant between 60 and 90 \({}^{\circ}\)C; however, the catalytic efficiency decreased between 90 and 98 \({}^{\circ}\)C, primarily because of a large increase in K\({}_{\text{m}}\)[61]. TnGI had a higher turnover number and a lower Km for glucose compared to other thermophilic GIs [61]. Taken together, thermophilic GIs exhibit various activities according to the species of origin and are expected to be extensively applied in industrial HFCS production.