esa-sceva/satcom-chunk-collection huggingface dataset:
# Genomic Analysis of Response to Neoadjuvant Chemotherapy in Esophageal Adenocarcinoma
## 3 Results
### Mutational Profiles Associated with Response to NAC
#### 3.2.3 Mutated Driver Genes Differ Between Responders and Non-Responders  
Having established the potential importance of chromosomal level structural variation and gene level copy number variation in response to NAC in EAC, we next moved to assess SNVs, starting with known EAC driver genes (Figure 5A). WGS data showed that 96.2% of responders and 94.3% of non-responders carried at least one non-synonymous somatic mutation in these EAC driver genes. As expected, TP53 (65%), CDKN2A (16%), SMAD4 (15%) and ARID1A (8%) were highly mutated in this cohort [25]. NAV3 was exclusively mutated in non-responders (8/36, 22%, [PERSON]'s exact test, \(p=0.01\)) (Figure 5B). We used the Ensembl Variant Effect Predictor ([[https://www.ensembl.org/Tools/VEP](https://www.ensembl.org/Tools/VEP)]([https://www.ensembl.org/Tools/VEP](https://www.ensembl.org/Tools/VEP)), release 75; date accessed: 1 November 2020) [52] to predict mutational consequences and found that several mutations, including NAV3 p.V142G and p.D2366N, are likely to be functionally deleterious (Table S10). We also found mutations in KCNQ3 (4/36 patients, 11%), LRRK2 (4/36 patients, 11%), KRAS (3/36 patients, 8%) and PBRM1 (2/36 patients, 6%) that were unique to non-responders, but these were not statistically significant.  
We then determined which mutations might be pathogenic by cross-referencing them with the database of curated mutations [42]. We identified 33 and 54 curated pathogenic mutations in 74% of responders and 75% of non-responders (Tables S11 and S12) including three exonic mutations in KRAS (p.G12C, p.G12D and p.G13D). In non-responders KRAS was significantly co-mutated with SMAD4 ([PERSON]'s exact test, two-sided, \(p=0.006\)). Evidence from pancreatic cancer suggests that expression of oncogenic KRAS and loss of SMAD4 cooperate to induce the expression of EGFR and to promote invasion [53].
## 3 Johnson-Cook Model
### Johnson-Cook Damage Model Parameters
Afterwards, the \(D_{4}\) strain-rate-dependent parameter was calculated by rewriting Equation (7) complete with the previously calculated \(D_{1}\) to \(D_{3}\) damage parameters according to Equation (11). In detail, when plotting \(\frac{\varepsilon_{f}}{D_{1}+D_{2}\cdot e^{D_{3}\eta^{*}}}\) against \(1+D_{4}\cdot\ln\hat{\varepsilon}^{*}\) (Figure 12) and using a linear regression fitting equation intercepting the vertical axis at a value of 1.0, from the slope of the resulting equation, the final JC damage parameter \(D_{4}\) was calculated. \[\frac{\varepsilon_{f}}{D_{1}+D_{2}\cdot e^{D_{3}\eta^{*}}}=1+D_{4}\cdot\ln \hat{\varepsilon}^{*} \tag{11}\]  
The calculated JC damage model parameters can be seen summarized in Table 4 and can be used in FE software to simulate yield and fracture of high-strength tendons in prestressed concrete applications.
## 4 Discussion
General anesthesia enables the optimization of the endoscopy procedure, shortening it and increasing its efficacy. It also prevents unexpected movements of the child, which could cause traumatic complications of the procedure. Additionally, performing the sialoendoscopy under general anesthesia provides the opportunity to treat both parotid glands in one session. The main limitation of sialoendoscopy is the sensitive technique requirement. The procedure is operator-dependent and requires specific training. Moreover, associated costs are substantial, and the equipment needed is sensitive and prone to damage, which might limit its extensive use [58]. The need for general anesthesia in younger children is also a limitation but can also be of value, as performing the procedure under general anesthesia enables the surgeon to work under more convenient conditions in terms of cooperation, and the sialoendoscopy can be done more thoroughly and bilaterally. Many of the reports in the literature lack a proper follow-up time. The follow-up time described in the literature, both in single studies and in a meta-analysis, is reported to be as much as 36 months or less [8; 26; 31]. Some studies focus on the follow-up period of the first year after the procedure [48; 50]. That is not enough, as it was shown in this study that recurrence of swelling may appear as late as 55 or even 70 months after treatment. In this study, the follow-up time ranged up to 81 months, with a mean follow-up time of 45.17 months. Nevertheless, a recent study claimed that the majority of recurrences occur in the first year after the endoscopy, and, afterward, recurrence rates decreased [27]. The weaknesses in the present research are similar to the majority of other studies in this area and are those of a rather small study population, a retrospective research design, and the absence of a control group for comparison. Since JRP is inclined to spontaneously heal, a control group could be particularly important. Without such a control group, distinguishing the therapeutic effect of the treatment from the ordinary course of the disease is more challenging. Considering the results of this study, it is indicated that bilateral sialoendoscopy under general anesthesia is an effective and safe treatment method and should be anchored as part of a treatment plan for JRP, also in cases of unilateral gland involvement. This study's results showed a clear clinical improvement and even complete resolution and recommend the combination of sialoendoscopy with an intraductal injection of corticosteroids for any child suffering from JRP, both as a diagnostic tool and as a treatment modality.
# JorGPT: Instructor-Aided Grading of Programming Assignments with Large Language Models (LLMs)
## 4 Results
### Execution Time and Cost
#### 4.3.1 Cost Analysis  
The execution cost was calculated based on the number of tokens consumed for both input and output, following the pricing schemes provided by each API. Table 6 summarizes the total cost (in euros) incurred when assessing a batch of 672 student submissions using each model. Notably, some models, such as Gemini 2.0 Flash and Qwen-PLUS, were used under a free-tier plan, resulting in zero cost for this experiment. Among the paid models, DeepSeek proved to be the most cost-effective option (EUR 0.107), while GPT o3-mini showed the highest cost (EUR 2.972), largely due to output token pricing.
## 4 Output Power Modelling for the Switching Process of the DFVSPS Unit
### Pump Mode
#### 4.2.3. Orderly Shutdown Stage  
The method used in the shutdown of the pump mode is the same as that in the generating mode. The method of electromagnetic braking is adopted. The output power of the unit is also 0 at this stage. The equivalent expression is shown in Formula (30):  
\[P_{S4}=0,\ \tau_{2}+N_{PS}\leq t<\tau_{3}, \tag{30}\]  
where \(P_{S4}\) is the power during the shutdown stage, \(\tau_{3}\) is the end time of shutdown stage. The duration calculation method at this stage is consistent with the generating mode. According to the data selected in this paper, the duration of this stage is 50 s.
# Steel Surface Defect Detection Algorithm Based on Improved YOLOv8n
## 1 Introduction
[21] used an efficient swin transformer to detect and classify steel surface defects, an approach which strengthened the connection between the feature mapping channels, reduced the resolution, and solved the image information retention problem. He [22] suggested that an MFN network can effectively improve the ability to obtain information about steel surface defects, and its features are able to integrate information from various levels to detect steel surface defects. A baseline convolutional neural network (CNN) is employed to produce feature maps for subsequent stages. The proposed network (MFN) is capable of fusing multiple features into a single feature and, based on these multilevel features, the region proposal network (RPN) is employed to produce the region of interest (ROI); finally, the baseline network ResNet 34 is used to achieve 74.8% mAP. The above methods have been designed and improved based on transformer and convolutional neural networks, respectively. Although all of the above methods improve the performance of the target detection algorithm to some degree, they still fall short in terms of accuracy and some other aspects. To summarize, we propose the steel surface defect detection algorithm DDI-YOLO, and the contributions made in this essay are the following:  
(1) The resolution of the issue of the original C2f module of YOLOv8 having insufficient ability to extract defective features on the steel surface and being unable to extract multiscale contextual information. Therefore, the dilation-wise residual module is added to the original C2f to further enhance the capacity of the network to extract multiscale contextual information. (2) Since C2f in YOLOv8 cannot detect small-scale patterns during training, the dilated reparam block module is used to enable the detection of defects by C2f on small-scale patterns, thus enhancing the training ability of the model. (3) The C2f_DWR module and the C2f_DRB module are combined to form the C2f_DWR_DRB module, which blends the benefits of every module to improve the model's comprehensiveness. (4) A faster and more accurate convergence by making use of the Inner-IoU loss function to take the place of YOLOv8's original CIoU loss function.
# Trends in Quantification of HbA1c Using Electrochemical and Point-of-Care Analyzers
## 5 Biosensors Based on FV Detection
The interface consisted of single-walled carbon nanotubes (SWCNT) that served as the immobilization surface for the bio-recognition element fructosyl amino acid-binding protein (SoCA), isolated from _Rhizobium radiobacter_. The SoCA is a bacterial protein about 29 kDa in molecular weight and has a high specificity towards \(\alpha\)-fructosyl amino acid. The SWCNT also served as a semi-conducting transducing element. The sensor exhibited excellent specificity towards FV with a low detection limit of 1.2 nM [85]. Recently, tungsten disulfide nanoparticle (WS\({}_{2}\) NP)-enhanced MIP-based material was utilized for the HbA1c sensing in diabetic patients. The resultant biosensor showed a detection limit at 0.01 pM and the sensitivity was noted to be 0.27 \(\upmu\)A/pM [86] (Figure 9). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Transducers** & **Detection Range (FV)** & **Sensitivity** & **LoD** & **Ref.** \\ \hline FAO/Carbon paste & 0.2-2.7 mM & 1.1 \(\upmu\)mM\({}^{-1}\) cm\({}^{-2}\) & NA & [71] \\ (in methoxy phenazine methosulfate) & & & & \\ \hline FAO-(PVA-SbQ)/Pt & 0.05–1.8 mM & 0.42 \(\upmu\)A mM\({}^{-1}\) cm\({}^{-2}\) & NA & [71] \\ \hline FAO-POD-Ferroce-(PVA-SbQ)/Pt & 0.1–0.3 mM & 0.4 \(\upmu\)A mM\({}^{-1}\) cm\({}^{-2}\) & NA & [72] \\ \hline FAO-(PVA-SbQ)/PB /GCE & 0.1–0.3 mM & 1.3 \(\upmu\)A mM\({}^{-1}\) cm\({}^{-2}\) & NA & [72] \\ \hline FAO-(PVA-SbQ)/Pt (flow injection analysis) & 0.2–10 mM & 4.6 \(\upmu\)A mM\({}^{-1}\) cm\({}^{-2}\) & NA & [83] \\ \hline FAO/ZnONPs/PPy/Au & 0.1–3.0 mM & 38.42 \(\upmu\)A mM\({}^{-1}\) & 50 \(\upmu\)M & [73] \\ \hline FAO/MNPs/Au & 0–2 mM & NA & 0.1 mM & [74] \\ \hline FAO/GNPs-PTn-TiO\({}_{2}\)/ITO & 0.5–2000 \(\upmu\)M & NA & 0.5 \(\upmu\)M & [75] \\ \hline FAO/AuNPs-PtNPs/PINSCOOH & 0.1–1000 \(\upmu\)M & 0.316 \(\upmu\)M/mA & 0.2 \(\upmu\)M & [76] \\ \hline FAO/AuNPs/GNs/FTO & 0.3–2000 \(\upmu\)M & NA & 0.2 \(\upmu\)M & [77] \\ \hline FAO/PINPs/rGO-MWCNT/Au & 0.05–1000 \(\upmu\)M & 0.562 mA/\(\upmu\)M & 0.1 \(\upmu\)M & [78] \\ \hline FAO/Iridium-Carbon ink & NA & 21.5 \(\upmu\)A mM\({}^{-1}\) cm\({}^{-2}\) & NA & [79] \\ \hline FAO/Graphite ink & 0–8 mM & NA & NA & [80] \\ \hline FPOX/CHIT-GO-AuNPs/FTO & 0.1–2 mM & 8.45 \(\upmu\)A mM\({}^{-1}\) cm\({}^{-2}\) & 0.3 \(\upmu\)M & [81] \\ \hline GCPE/ITO & 0–1 mM & 5.26 \(\upmu\)A mM\({}^{-1}\) & 0.05 mM & [64] \\ \hline Carbon–PVI (in methoxy phenazine methosulfate) & 20–700 \(\upmu\)M & NA & 20 \(\upmu\)M & [65] \\ \hline \hline \end{tabular} NA: Not applicable.  
\end{table} 
Table 3: Amperometric biosensors for detecting HbA1c based on FV.
# Optical and Thermal Remote Sensing for Monitoring Agricultural Drought
## 3 Thermal Remote Sensing
### Thermal Inertia as a Drought Indicator  
[PERSON] [167] constructed a simple forward model to simulate the temperature of terrestrial objects. In this model, the attributes of terrestrial objects (thermal inertia, albedo, and emissivity), meteorological factors (transmission, effective air temperature), location (latitude), and season (solar declination) are set as input parameters. Subsequently, [PERSON] et al. [164] proposed an empirical formula based on simulated data to invert the thermal inertia of terrestrial objects based on albedo and diurnal temperature difference. It was found that inversion results are closely related to soil density and interstitial water content and have considerable potential for application in near-surface moisture monitoring. [PERSON] [168] constructed a thermal inertia inversion model using thermal infrared observations from the Synchronous Meteorological Satellite-2 (SMS-2). This model is more universal than the empirical formula developed by [PERSON] et al. [164]. [PERSON] [169] further developed the theoretical model of [PERSON] [168] and successfully applied it to data of the Heat Capacity Mapping Mission (HCMM). By analyzing the relationship of the inversion results with the soil moisture and the evaporation rate, it was revealed that the 24-h mean surface temperature is sensitive to the mean evaporation rate, whereas the diurnal temperature difference is sensitive to the near-surface soil moisture. This finding indicates that space-borne thermal infrared data has potential applications in assessing the surface water budget.  
Nevertheless, most thermal inertia inversion models require meteorological parameters as inputs, which are difficult to acquire through remote sensing [170]. This requirement complicates thermal inertia inversion but can be addressed by apparent thermal inertia (ATI), a simpler alternative to thermal inertia, defined as  
\[\text{ATI}=\frac{C(1-\alpha)}{T_{\text{day}}-T_{\text{night}}} \tag{11}\]  
where \(C\) is a constant coefficient, \(\alpha\) is the surface albedo, and \(T_{\text{day}}\) and \(T_{\text{night}}\) are day/night LST [171].  
ATI products have been used to characterize the diurnal temperature change of the land surface. The algorithmic method of ATI is simpler than that of thermal inertia because it does not require input parameters that satellites are unable to offer. [PERSON] [172] and [PERSON] [173] initially verified the feasibility of ATI products for soil moisture monitoring, and many researchers have followed their footsteps [174; 175; 176; 165]. However, the ATI method assumes that the effect of soil evaporation on temperature changes is negligible. This assumption is invalid in areas experiencing considerable soil evaporation [171].  
The thermal inertia and ATI methods are mainly applied to soil moisture inversion under bare soil or sparse vegetation conditions [168; 177]. Therefore, the thermal inertia and ATI need to be modified under dense vegetation conditions [170; 178; 179].
# Novel Scoring for Energy-Efficient Routing in Multi-Sensored Networks
## 3 The Proposed Mechanism
### Proposed Mechanism
#### 3.2.4 Information Exchange  
Upon each data transmission, the involved nodes consume their energies. These nodes update their \(\lambda\) values according to their knowledge. Using the DSR protocol [43] as a base protocol, each involved node can get the list of all the relay nodes. They update the values of the involved nodes in their routing tables. However, to avoid further energy consumption, these nodes do not broadcast their updated values. The nodes update their routing tables with approximate values by predicting the energy consumption and the number of transmitted packets.  
If a node has not received any updated information about other nodes in a possible route, it selectively sends a route confirmation to a preferred forwarder according to its knowledge. The source node does not proactively update the values, the \(\lambda\) of the relay node, which may be changed due to previous data transmissions. Therefore, the relay node selectively forwards the RREQ of the source node to its sibling neighbor node with the highest \(\lambda\) as shown in Figure 10.  
The node also sends additional information with the RREQ that reflects the values of the nodes involved in previous data transmissions. The nodes in such a case search out the route by contacting upper-hop nodes with an RREQ along with the additional information. In this mechanism, the fundamental DSR-based technique of the RREQ broadcast is not used. The source and relay nodes selectively send the RREQ to the preferred nodes in the upper or similar hops.  
Sometimes the source node does not receive acknowledgment of a selfish node after several attempts. In such a case, the source node either broadcasts the RREQ to its neighbors  
Figure 10: Updating statistics through RREP.  
Figure 9: Stages for managing non-cooperative nodes.  
or selects another node according to its \(\lambda\) score. Each entry in the routing table is associated with a time stamp. This decision is made according to the time stamp attached to the next node's stored \(\lambda\) score in the routing table. Each entry in the routing table is updated along with the current time.
## 3 Results and Discussion  
### Liquid Oxygen Compatibility of EP/BCI/BSEA Composites  
#### 3.1.1 Results of Liquid Oxygen Compatibility  
The LOC of EP thermosets was evaluated by 98J LOT, and the corresponding testing steps were as follows. Firstly, the cured EP specimen was placed into the sample cup and pre-cooled in a container of liquid oxygen along with the cup and impact column. Secondly, the sample cup was placed into the cup holder focused on the anvil section assembly of the machine, and we next placed the pre-cooled impact pin in the center of the cup. Lastly, the hammer plummeted from a height of 1.1 m at free fall onto the column, which transferred the mechanical energy to the samples. Views of all phenomena (including burning, explosion, charring, flash) were recorded during the test, and the LOT sensitivity of the samples was recorded. The sample was considered to be compatible with the liquid oxygen if none of the above phenomena were observed in 20 tests. Moreover, the impact reaction sensitivity coefficient (IRS) was utilized to analyze the LOC of EP. The calculation of IRS was performed according to the following formula [2; 27; 38].  
\[IRS=\frac{\sum_{i}^{4}w_{i}n_{i}}{N} \tag{3}\]in which \(N\) represents the total test time, \(n_{i}\) represents the frequency of the reaction, \(w_{i}\) represents the weight coefficient of different phenomena, namely burning (\(w_{1}\) = 1), explosion (\(w_{2}\) = 0.9), flash (\(w_{3}\) = 0.6), charring (\(w_{4}\) = 0.4). Figure 2 shows the test equipment and the preparation before the impact test, and Figure 3 shows digital photos of all samples after impact in liquid oxygen. As summarized in Table 2, the IRS of pure EP and EP/BCI-25 were both 10.5%, indicating incompatibility with liquid oxygen and high reaction intensity between the two cured EP samples and liquid oxygen. The IRS of the EP/BCI/BSEA-2, with one burning and one charring instance, was 7.0%, and the EP/BCI/BSEA-3 with two charring instances was 4.0%, which indicated that the IRS decreased with the addition of BSEA. The videos of the burning (Video S1) and the lack of reactions of EP/BCI/BSEA-2 during the impact tests (Video S2) are included in the Supporting Information. Furthermore, the EP thermosets exhibited no sensitivity phenomena in 20 impact tests when the BSEA content reached 4 wt% or 5 wt%, indicating that the two cured EP samples achieved LOC. The results are consistent with the flame-retardant performance of BSEA-modified EP [37], revealing that improving the flame retardancy property is a highly effective approach to enhance the LOC of EP.  
Figure 2: (**a**) 98] LOT machine with high-definition camera, (**b**) samples, specimen cup and striker pin immersed in liquid oxygen and (**c**) liquid oxygen poured into the specimen cup before impact.
## Appendix A  
Define a set \(\mathcal{S}=\{\mathbf{s}^{(p)},p=1,\cdots,2^{N_{\text{s}}-1}|\mathbf{s}_{i}^{(p)}= 1,s_{p\
eq i}^{(p)}\in\{-1,1\}\}\) with alphabet size \(2^{N_{\text{s}}-1}\), where each element of \(\mathcal{S}\) yields the same _a priori_ probability \(1/2^{N_{\text{s}}-1}\). Based on (6), the conditional probability distribution of receiver \(i\)'s received signal \(y_{i}\) given \(s_{j}=1\) is provided by  
\[\Pr\bigl{(}y_{i}|s_{j}=1\bigr{)} = \tag{1}\] \[= \frac{1}{2^{N_{\text{s}}-1}}\sum_{p=1}^{2^{N_{\text{s}}-1}}\frac{ \Bigl{(}\mathbf{g}_{i}\mathbf{W}\mathbf{s}^{(p)}+n_{b}\Bigr{)}^{y_{i}}}{y_{i}!}e^{- \bigl{(}\mathbf{g}_{i}\mathbf{W}\mathbf{s}^{(p)}+n_{b}\bigr{)}}.\]  
Then, we have  
\[\mathbb{E}\bigl{(}y_{i}|s_{j}=1\bigr{)} = \sum_{y_{i}=0}^{\infty}y_{i}\Pr\bigl{(}y_{i}|s_{j}=1\bigr{)} \tag{2}\] \[= \frac{1}{2^{N_{\text{s}}-1}}\sum_{p=1}^{2^{N_{\text{s}}-1}}\sum_{ y_{i}=0}^{\infty}y_{i}\cdot\frac{\Bigl{(}\mathbf{g}_{i}\mathbf{W}\mathbf{s}^{(p)}+n_{b} \Bigr{)}^{y_{i}}}{y_{i}!}e^{-\bigl{(}\mathbf{g}_{i}\mathbf{W}\mathbf{s}^{(p)}+n_{b} \bigr{)}}.\]  
It follows from (2) that \(\mathbb{E}\bigl{(}y_{i}|s_{j}=1\bigr{)}\) is the average of the expected values of \(2^{N_{\text{s}}-1}\) Poisson random variables with means \(\mathbf{g}_{i}\mathbf{W}\mathbf{s}^{(p)}+n_{b},\ p=1,\cdots,2^{N_{\text{s}}-1}\). Hence, we have  
\[\mathbb{E}\bigl{(}y_{i}|s_{j}=1\bigr{)}=\frac{1}{2^{N_{\text{s}}-1}}\mathbf{g}_{i} \mathbf{W}\sum_{p=1}^{2^{N_{\text{s}}-1}}\mathbf{s}^{(p)}+n_{b}. \tag{3}\]  
Note that \(\sum_{p=1}^{2^{N_{\text{s}}-1}}\mathbf{s}^{(p)}=2^{N_{\text{s}}-1}\mathbf{e}_{j}\), where \(\mathbf{e}_{j}\) is the \(j\)-th column of the identity matrix \(\mathbf{I}_{N_{\text{s}}}\). Consequently, (3) is equivalent to  
\[\mathbb{E}\bigl{(}y_{i}|s_{j}=1\bigr{)}=\mathbf{g}_{i}\mathbf{W}\mathbf{e}_{j}+n_{b}. \tag{4}\]Similarly, we can derive  
\[\mathbb{E}\big{(}y_{i}|s_{j}=-1\big{)}=-\mathbf{g}_{i}\mathbf{We}_{j}+n_{b}. \tag{10}\]