esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Analysis and Statistics  
To assess the performance of the three UNets to upscale images, we used four standard metrics in comparing the reconstructed (reconHQ) to the corresponding ground truth: the pixel-wise Mean Squared Error (MSE), the pixel-wise Mean Intensity Error (MIE), the Mean Structural Similarity Index (SSIM) and the Peak Signal to Noise Ratio (PSNR). In this study, these four metrics formed the response variables. Data collection involves a repeated measure design applied to each of the test images. As image-to-image variation is inevitable, for the purpose of statistical analysis we employ mixed-effects (aka, repeated measure) modeling to account for the fact that the same images are used more than once. In the adequacy checking tests performed on each model, we observed that when we used the MSE and MIE as the response, the standard assumptions of mixed effects model were violated. To overcome this issue, we use the natural logarithm transformation on both MSE and MIE. In summary, the final response variables for the mixed effects modeling analysis were log(MSE), log(MIE), SSIM, and PSNR. We had a total of four fixed variables for the mixed-effect models. These variables were: the type of Unet (DUNet, AUNet, and RUNet), input matrix size (1/8, 1/4, and 1/2), acquisition scenarios (With-Prior and WithOut-Prior), and the number of training epochs (100 and 1000). We observe substantial differences in summary statistics when we compare With-Prior and WithOut-Prior; therefore, we performed mixed effects modeling on them separately. In a sensitivity analysis, we found that the summary statistics from 100 epochs to 1000 epochs did not show a significant change in the actual values; hence, we performed MEM only on results obtained by training Unet models only for 100 epochs. Therefore, two fixed variables are employed in our MEM model, which are the type of Unet (DUNet, AUNet, and RUNet) and the input matrix size (1/8, 1/4, and 1/2). In addition to the above two fixed variables, we also consider their interaction in the mixed effects model. Descriptive statistics were first calculated for each response variable (i.e., the evaluation metrics) for the different fixed effects (aka explanatory) variables, that is the three types of Unets and the three matrix sizes, as shown in Tables 1-3. Each response was tested using mixed effects modeling, with the fixed effects chosen as the explanatory variables, while the 540 test images constituted the random effects. The mixed effects model examined the significance of explanatory variables taking into account the significant image-to-image variation (i.e., incorporating the repeated measure aspect design that we have in this study, where multiple measurements are performed in each of the 540 test images).
## 6 Therapeutic Potential of Cannabinoids in Gut Diseases
### Endocannabinoids  
#### 6.1.1 Endocannabinoids and Diarrhea  
An important point to consider in humans is that a single nucleotide polymorphism in the FAAH gene may downregulate its expression. Therefore, any treatment involving the possible use of this enzyme could depend on the individual susceptibility of each patient. A relationship between the genotypic variation associated with the FAAH gene and the functional GIT disorder-prone phenotype has been noted [101]. It is also worth noting that both reduction of pain perception and alleviation of hyper motility and diarrhea were not possible in FAAH knockout mice [102]. The use of aFAAH inhibitor (PF-3845) in vivo experiments in mice resulted in inhibition of passage in the GIT, both after central (i.c.v.) and peripheral (i.p.) administration. This could be successfully reversed using a CBI antagonist, bringing the transit speed back to the baseline values or even lower. These effects also occurred in tests using mouse models of GIT hypermotility and diarrhea [102]. The same study also tested whether FAAH inhibition would affect nociception. PF-3845 reduced pain-related behaviors in mice. Only the central effect could be reversed using a CBI antagonist, and the antinociceptive effect was partially achieved already at very low doses but only after peripheral administration. The study suggested the relevance and validity of further research into the possibility of using FAAH blockers in the treatment of diseases such as IBS with predominant diarrhea (IBS-D).  
In mice, there was an increase in anandamide (but not 2-AG or PEA) levels in the small intestine after oral administration of cholera toxin (CT). Additionally, there was increased expression of CBI receptors in the intestine. No difference in FAAH activity was observed between animals administered CT and control. Fluid secretion was prevented by administration of the anandamide uptake inhibitor VDM11, but worsened by the CB1 receptor antagonist SR141716A. This suggests that increased CB1 stimulation via increased production of anandamide and upregulation of CB1 receptors exerts a protective effect against cholera toxin-induced hypersecretion [103].
# Motion Characteristics of Self-Sensing Piezoelectric Actuator for Yarn Micro-Gripper
###### Abstract  
In order to solve the problem of low response frequency and poor consistency of conventional yarn grippers in wet accumulators, in this study, a piezoelectric yarn gripper is used instead of conventional yarn grippers and the motion characteristics of its actuator are studied. This gripper uses a bimorph piezoelectric bending actuator with a low-cost, well integrated self-sensing method based on charge measurement. The modeling of the piezoelectric micromanipulator is based on the piezoelectric and Euler-Bernoulli beam equations. The static and dynamic characteristics of the piezoelectric actuator as well as the self-sensing capability were experimentally tested. The experimental results show that the maximum output displacement at the end of the piezoelectric actuator is 834 \(\upmu\)m, and the maximum output force is 388 \(\upmu\)N at 150 V driving voltage. The stability and consistency of its response are also very good, with a response speed of 24 ms. The self-sensing test of the output force also proved the feasibility of the self-sensing method used, with an error of 0.74%. The piezoelectric yarn gripper studied in this paper is promising for practical clamping applications.  
yarm gripper; bimorph piezoelectric bending actuator; characteristic test; self-sensing Article
# Swin Transformer Assisted Prior Attention Network for Medical Image Segmentation
## 3 Modeling, Methods, and Design
### Dual Supervision Strategy  
Traditional attention mechanism in medical image segmentation automatically generates the attention maps and its internal process of attention is not interpretable, resulting in poor performance of the network that excessively focus on regions not of interest to us. The performance and generalization capacity of the network will be immensely enhanced if the process can be humanly interpretable and regulated. Hence, the strategy of dual supervision learning is proposed to training the network. The loss functions of  
Figure 4: The illustration of enhanced attention block. Different from traditional CAA module that it receives multiple features for aggregating attention information and refining boundaries, which is to utilize a feature fusion between global and local contexts along channel-wise for achieving better performance of attention learning.  
prior attention network and hybrid Transformer network are both Weighted BCE Dice loss function as \(L_{1},L_{2}\). In a multi-lesion segmentation task with \(C\) types of lesions, assumed that \(G_{i},i\in(1,\cdots,C)\) denotes the \(i_{th}\) type of lesion, where the foreground represents the specific type of lesion and the background represents everything except the region of this type of lesion. The binary ground truth map \(G_{b}\) is formulated as:  
\[G_{b}=\sum_{i=1}^{C}G_{i} \tag{14}\]  
The loss function is then utilized to calculate the loss between attention prediction and binary ground truth:  
\[L_{BCE}=-\frac{1}{N}\sum_{i=1}^{N}g_{i}\log(p_{i})+(1-g_{i})\log(1-p_{i}) \tag{15}\]  
\[L_{Dice}=1-2\times\frac{\sum_{i}P_{i}G_{i}}{\sum_{i}P_{i}+\sum_{i}G_{i}} \tag{16}\]  
\[L_{1}=L_{2}=\ell(G_{b},M)=w_{BCE}\cdot L_{BCE}+w_{Dice}\cdot L_{Dice} \tag{17}\]  
where \(\ell(\cdot)\) represents the binary loss function and \(M\) represents the attention prediction from attention guiding decoder. \(g_{i}\in\{0,1\}\) and \(p_{i}\in\{0,1\}\) denote the ground truth of annotation and the probability map, \(P_{i}\) denotes the probability that the \(i_{th}\) pixel contains in the segmentation region, and \(G_{i}\) denotes the ground truth of the \(i_{th}\) pixel; \(w_{1}\) and \(w_{2}\) are uniformly set to 0.5 as default. The computed loss \(L_{1}\) will be used to supervise the parameter update of the attention guiding decoder. Then the attention predictions of the prior attention network will be inserted into the enhanced attention network for guiding final step of segmentation. The dual supervision strategy can enhance the performance and interpretability of the Transformer attention mechanism, and provide a humanly interpretable way to guide the attentional learning in Transformer.
## 3 Results  
### Comparison of MODIS-GPP and Flux Derived GPP  
As shown in Figure 2, MODIS-GPP generally shows seasonal dynamics in different land cover types. For mixed forests (Figure 1(a)), croplands (Figure 1(e)) and grasslands (Figure 1(f)-h), MODIS-GPP presents a high \(R^{2}\) (\(>\)0.5) with flux derived GPP. MODIS-GPP of evergreen forests (Figure 1(b)-d) shows a worse quality than that of the above land cover types with \(R^{2}\) lower than 0.5. For evergreen broadleaf forests (Figure 1(b),d), MODIS-GPP has the worst quality with \(R^{2}\) in a range of 0.03-0.44, bias from \(-\)2.5 gC/m\({}^{2}\)/8-day to \(-\)17.6 gC/m\({}^{2}\)/8-day and RMSE from 14.1 gC/m\({}^{2}\)/8-day to 31.0 gC/m\({}^{2}\)/8-day. Bias in forests is greater than that in other land cover types. Also, RMSE of MODIS-GPP in forests (Figure 1(a)-d) and croplands (Figure 1(e)) are greater than that in grasslands (Figure 1(f)-h). MODIS-GPP underestimates flux derived GPP during the vigorous growth period in alpine meadow (Figure 1(f),h), suggesting that temperature limitation may be a key factor influencing the estimating uncertainty. Additionally, the underestimation of FPAR might also cause the underestimation of estimated GPP. For croplands (Figure 1(e)), although it has higher \(R^{2}\) (0.80) and smaller bias (\(-\)4.8 gC/m\({}^{2}\)/8-day), measured GPP is still underestimated by MODIS-GPP in growing season and overestimated in non-growing season (Figure 2e), leading to a greater RMSE (30.0 gC/m\({}^{2}\)/8-day). At CBS site, MODIS-GPP can well show the seasonal change of flux derived GPP with \(R^{2}\) of 0.76, bias of \(-8.4\) gC/m\({}^{2}\)/8-day and RMSE of 17.3 gC/m\({}^{2}\)/8-day. For evergreen forests (Figure 2b-d), MODIS-GPP has very large fluctuation for each eight-day, resulting in greater RMSE of MODIS-GPP product.
## 2 Materials and Methods
### Method
#### 2.2.3 Hypothesis Test  
To carry out quality control, in the sense of hypothesis testing, statistics and a null hypothesis are needed. First, the number of tolerances \(k\) must be defined, and their metric values must also be determined. These tolerances establish a set of \(k+1\) categories, each with a probability \(\pi_{1}^{0}\), \(\pi_{2}^{0}\), \(\ldots\), \(\pi_{k+1}^{0}\) fixed in the base model. If the quality control is based on a sample of size n, the sampling statistics are \(\
u^{*}=(n_{1},\ n_{2},\ldots,n_{k+1})\) such that \(n_{1}+n_{2}+\ldots+n_{k+1}=n\). The null hypothesis \(\mathbb{H}_{0}\) is that the sampling statistics \(\
u^{*}\) follow a multinomial distribution with parameters \(n,\pi^{0}\) where \(\pi^{0}=(\pi_{1}^{0},\ \pi_{2}^{0},\ldots,\ \pi_{k+1}^{0})\) and \(\pi_{1}^{0}+\pi_{2}^{0}+\ldots+\pi_{k+1}^{0}=1\). The alternative hypothesis, \(\mathbb{H}_{1}\), is that the true distribution of the errors presents larger errors than the specified under \(\mathbb{H}_{0}\). This alternative hypothesis specifies what is considered a worse situation, which is the same as saying that at least one of the following conditions is false in the observed data: \(\frac{n_{1}}{n}\geq\pi_{1}^{0}\) or \(\frac{n_{2}}{n}\leq\pi_{2}^{0}\), \(\ldots\) or \(\frac{n_{k+1}}{n}\leq\pi_{k+1}^{0}\). To make a pass/fail decision about \(\mathbb{H}_{0}\) the \(p\)-value must be calculated and compared with the desired significance level \(\alpha\). In general, \(\alpha=5\%\). This test is an exact test, so the \(p\)-value is calculated by analyzing the space of possible worse cases than the observed one, \(\
u^{*}\). This procedure is performed as follows: the test statistics \(\
u^{*}=(n_{1},n_{2},\ldots n_{k+1})\) are calculated, the probabilities in the multinomial distribution fixed by the null hypothesis for the observed values \(\
u^{*}\) are calculated, and the possible worse cases \(m=(m_{1},m_{2},\ldots,m_{k+1})\) are:  
\[\begin{array}{l}m_{1}<n_{1}\ \text{or}\\ m_{1}=n_{1}\ \text{and}\ m_{2}<n_{2}\ \text{or}\\ m_{1}=n_{1};m_{2}=n_{2}\ \text{and}\ m_{3}<n_{3}\ \text{or}\\ \ldots\ldots\ldots\\ m_{1}=n_{1};\ldots\ m_{k-2}=n_{k-2}\ \text{and}\ m_{k-1}<n_{k-1}\end{array} \tag{3}\]  
By summing the probabilities of all the cases that verify these conditions, the \(p\)-value for \(\
u^{*}\) is obtained. If this \(p\)-value is less than or equal to \(\alpha\), then \(\mathbb{H}_{0}\) will be rejected.  
Figure 4: Conceptual example of a control by proportions: 2 tolerances (**a**) and three tolerances (**b**).
# Microbiome-Based Interventions for Food Safety and Environmental Health
## 2 Microbiome-Based Interventions for Ecosystem Sustainability
### Bioremediation for Environmental Health  
The focus on environmental pollution and waste management has fostered interest in microbiome-based strategies, which are cheap, sustainable, and generally eco-friendly. This is because microbial communities possess metabolic versatility that enables them to degrade pollutants, transform toxic molecules, and recycle waste.  
Bioremediation is the utilization of microbes to promote the biodegradation (or detoxification) of harmful pollutants, such as hydrocarbons and heavy metals, that contaminate the soil and water. [PERSON] et al. [35] highlight the use of hydrocarbon-degrading microbes to break down polycyclic aromatic hydrocarbons (PAHs). These bacteria play a pivotal role in the bioremediation of oil spills in the marine environment as they have the potential to degrade PAHs and prevent ecological damage to the environment due to the toxicity of this recalcitrant pollutant. AI takes it further, modeling how fast they will work or where to deploy them [36], while synthetically engineered biology consortia chew through PAHs even quicker [37; 38]. Long-read sequencing helps identify hydrocarbon-degrading bacteria, such as _Pseudomonas_, showing exactly which microbes are involved in breaking down pollutants [22]. For instance, in a recent application by [PERSON] et al. [39], nanopore sequencing technology was used to detect foodborne microorganisms, including _Listeria monocytogenes_. They combined this sequencing approach with machine learning algorithms to enhance the identification and characterization of pathogens in various food samples. This integration  
Figure 2: Microbiome-based interventions for ecosystem sustainability.  
facilitated rapid and accurate detection, demonstrating the potential for real-time monitoring in food safety applications. Similarly, a study by [PERSON] et al. [40] demonstrated that bioremediation strategies at a fuel-contaminated port significantly enhanced the abundance of hydrocarbon-degrading bacteria and achieved over 60% petroleum hydrocarbon removal within 15 days.
# DIKWP-TRIZ: A Revolution on Traditional TRIZ Towards Invention for Artificial Consciousness
## 4 Mapping and Coverage Analysis of TRIZ Rules in DIKWP
### Mapping the 40 TRIZ Principles to the DIKWP Model
#### 4.1.10 Information (I) \(\rightarrow\) Purpose (P)  
Through the analysis and comprehension of Information semantics, specific goals and action purposes are generated based on the semantic differentiation of the Information. The transformation process from Information to Purpose involves extracting key semantic distinctions from the Information and formulating goal-oriented action plans. This process ensures that critical insights are translated into actionable strategies, aligning cognitive understanding with purposeful behavior.  
* **Principle 16: Partial or Excessive Actions** Adjust the level of effort in Information processing according to the specific requirements of the objective, ensuring that the Information is neither excessive nor insufficient. This means determining the scope and depth of Information collection and analysis based on the importance and required detail of the objective. For example, in software project management, if the goal is to evaluate the cost-effectiveness of a project, efforts should be focused on collecting Data directly related to costs and benefits, avoiding the collection of excessive unrelated Data to improve the efficiency and relevance of Information processing [77].
* **Principle 32: Color Changes** We extend the semantics of color to the realm of presentation. Modify the presentation of Information according to the objective to enhance the clarity and impact of Information transmission. This principle emphasizes the importance of presentation format, suggesting that Information should be expressed or visually represented in a way that better aligns with the needs of the target audience. For example, in corporate annual reports, to better convey the company's performance and future outlook to shareholders, key financial Data can be presented using charts, timelines, and other visual tools to make the Information more intuitive and understandable [78; 79; 80].
# Inland Water Level Monitoring from Satellite Observations:
###### Abstract  
Inland water level and its dynamics are key components in the global water cycle and land surface hydrology, significantly influencing climate variability and water resource management. Satellite observations, in particular altimetry missions, provide inland water level time series for nearly three decades. Space-based remote sensing is regarded as a cost-effective technique that provides measurements of global coverage and homogeneous accuracy in contrast to in-situ sensors. The advent of Open-Loop Tracking Command (OLTC), and Synthetic Aperture Radar (SAR) mode strengthened the use of altimetry missions for inland water level monitoring. However, it is still very challenging to obtain accurate measurements of water level over narrow rivers and small lakes. This scoping systematic literature review summarizes and disseminates the research findings, highlights major results, and presents the limitations regarding inland water level monitoring from satellite observations between 2018 and 2022. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline and through a double screening process, 48 scientific publications were selected meeting the eligibility criteria. To summarize the achievements of the previous 5 years, we present fundamental statistical results of the publications, such as the annual number of publications, scientific journals, keywords, and study regions per continent and type of inland water body. Also, publications associated with specific satellite missions were analyzed. The findings show that Sentinel-3 is the dominant satellite mission, while the ICESat-2 laser altimetry mission has exhibited a high growth trend. Furthermore, publications including radar altimetry missions were charted based on the retracking algorithms, presenting the novel and improved methods of the last five years. Moreover, this review confirms that there is a lack of research on the collaboration of altimetry data with machine learning techniques.  
inland water level; inland water bodies; satellite; altimetry; Sentinel-3; SAR; retracking algorithms; ICESat-2; PRISMA 2024
## 2 Study Area and Data  
### Study Area  
As a study basin, the Fuji River basin was selected as it is a well-gauged basin, i.e., 28 ground observation stations in a 3570 km\({}^{2}\) area (the average observation density: 127.5 km\({}^{2}\)/ station). Although it is beyond this study's scope, this basin can eventually serve to identify the effect of ground observation density on the accuracy of SPPs' bias correction.  
The Fuji River, located in the middle of Honshu Island, Japan, is one of the steepest rivers in Japan (Figure 1). The river comes from the Nokogiri Mountains with an altitude of about 2600 m in the northwest of Yamanashi Prefecture. It runs from the mountainous region to the Kofu (the capital of Yamanashi Prefecture) plain and then runs through the mountainous region before discharging out into the Pacific Ocean. The mainstream length is about 128 km. Summer is hot and humid, and winter is cold, with an average temperature of 26 \({}^{\circ}\)C and 3 \({}^{\circ}\)C, respectively. Furthermore, the mean annual precipitation of the basin and mean annual runoff at Kitamatsuno discharge station are 2100 mm and 69.2 m\({}^{3}\)/s, respectively [38]. The rainy season is from the end of August to the beginning of October, during which typhoons often occur, and the risk of heavy rain is high.  
Figure 1: Digital Elevation Model (DEM) and gauge stations in the Fuji River basin. In total, twenty-eight rain gauges and forty-seven SPP meshes were involved in the calculation. The capital of Yamanashi Prefecture, Kofu city, is located in the Kofu Basin.