esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### A Systematic Description of the T3 SD Metamodel
#### 3.1.3 The Buildability Perspective (Wymore's Buildability Cotiyledon)  
The TYR describes the specification of the set of the physical (tangible -hardware, software, etc.) components that are available to build the system. In this sense, **this type of requirements, which can be interpreted as predefined technologies that must be used, is inconsistent with common guidelines for writing good requirements, since they unnecessarily enforce design solutions**[32; 33; 34]**. Nevertheless, we include this type of requirements here to faithfully represent T3 SD. TYR yield a space of buildable system designs. A _Buildable System Design_ (BSD) represents a system formed by the interconnection of physical components and, therefore, is technology dependent. The behavior of the BSD emerges from the way in which its components are inter-connected to each other and allocate the external inputs and outputs, modeled by the _system coupling recipe_ (SCR). This is [PERSON]'s concept of system coupling, defined as \"a specification of the system models to be coupled as components and a specification of which output ports of which components are to be connected to which input ports of which components\" [35]. [PERSON] also introduced the concept of _closure under coupling_[35; 36; 37]. Closure under coupling requires that the set of system models must be closed under the operation of constructing the system resulting from a system coupling recipe, called the _resultant_ (Z@) of the recipe. This is a formal representation of the principal by which system models are able to be hierarchically constructed through coupling of component system models. [PERSON] distinguished between a \"component\" of a system and a \"subsystem,\" where the latter refers to a subset of its state space that is not necessarily associated with any component in a coupling recipe. Consistency of [PERSON]'s basis on the [PERSON] state machine [38] enables measured elaboration through homomorphic relationship from the resultant of a BSD (Z@) to the system model (Z) of the functional design. However, a key aspect in [PERSON]'s framework is that a BSD may or may not implement the functionality in FSD. This is because, as presented, the space of BSDs is only shaped by TYR, with no influence of IOR.  
Selection of a specific BSD within the space of BSDs is driven by the CR. Whereas the TYR defines a space of BSDs, CR defines an order in such space. In [PERSON]'s words, CR is used as a \"means of which any two [alternative BSDs] can be compared consistently\" [20]. These are not limited to traditional financial cost, but may also include acquisition timing restrictions [39]. As with PR, this does not preclude from conducting tests on more elaborate system designs; within T3 SD, system tests are conducted on the ISD, which contains the BSD.  
These relationships are shown as a subset of the metamodel in Figure 6.  
Figure 6: A description of the T3 SD Buildable Perspective showing the buildable designs are defined based on required technology and ordered according to preferred cost of the designs
# Impact of Subsurface Drainage System Design on Nitrate Loss and Crop Production
## 1 Introduction
Nevertheless, there have still been inconsistent outcomes in terms of the relations between tile spacing and depth and crop production. We also found that the previous research was conducted on a comparatively small plot, ranging from 0.1 to 0.6 ha [15; 16; 18; 19]. Therefore, much more research on drainage design is needed, focusing on the preparation of relatively larger-scale study plots, in order to explore the effects on tile flow and nitrogen (N) losses. Distinct operating mechanisms of subsurface drainage systems can lead to varied hydrological processes within the soil, consequently affecting nitrogen (N) losses from a field. Based on studies investigating the impact of subsurface drainage design on nitrogen (N) losses, employing wider and shallower tile installation can result in reductions in nitrogen (N) losses [20; 21]. Ref. [19] showed that wider tile spacings and shallower tile depths result in reduced nitrogen (N) losses based on long-term (1915-1996) modeling studies and field measurements in Minnesota, USA. Ref. [22] also presented similar outcomes, supported by both field-monitored data and modeling results, regarding the response of nitrogen (N) losses. While numerous studies have examined the influence of tile depth and spacing on water quality and crop yields [23; 24; 25], a definitive consensus has remained difficult to achieve. The contrasting outcomes across various research results represent the need for a scientific consensus regarding the impact of tile depth and spacing on nutrient loss from fields and crop yields. Significantly, a comprehensive investigation that examines the trade-offs between the effects of tile depth and spacing on both nitrogen (N) losses and corn/soybean yield in Illinois has yet to be conducted. Therefore, this study aims to bridge this knowledge gap by investigating optimal practices for harmonizing drainage depth and spacing while considering water quality and crop production goals in Illinois. The specific objectives of this study are (a) to determine the impacts of different subsurface drainage design types on nitrogen (N) losses and fertilizer use efficiencies and (b) to evaluate the potential for optimizing subsurface drainage design to mitigate N losses and enhance crop production.
## 2 Background and Related Work
### ML-Based Methods
In both these studies, the number of variables (14 and 16) was significantly higher than would normally be available in most EHRs. Hospitals supporting the record of these variables may also not have the values for all these variables for maximum patients. This limits the generality or wide applicability of the approaches. The study by [PERSON] et al. [34] used the NHANES dataset and various machine learning algorithms to predict variables that are a major cause for the development of diabetes and cardiovascular diseases. They also considered the prediction of prediabetes and undiagnosed diabetes. Logistic regression, support vector machines, random forest, and gradient boosting algorithms were used to classify the data and predict the outcome for the diseases. The authors used ensemble models by combining the performance of the weaker models to improve accuracy. In diabetes classification, they used 123 variables and achieved good prediction performance. A distinguishing aspect of their work was that the dataset was further categorized into laboratory dataset (containing laboratory results) versus non-laboratory (survey data only) dataset. Laboratory results were any feature variables within the dataset that were obtained via blood or urine tests. The purpose of the non-laboratory dataset was to enable a performance analysis of machine learning models in cases where laboratory results were unavailable for patients, supporting the detection of at-risk patients based only on a survey questionnaire. According to their results, waist size, age, self-reported weight, leg length, and sodium intake were five major predictors for diabetes patients. The study found that machine learning models based on survey questionnaires can give automated identification mechanisms for patients at risk of diabetes. In non-laboratory data, the most important features included waist size, age, weight (self-reported and actual), leg-length, blood pressure, BMI, household income, etc. [34]. The exact number of variables used in non-laboratory data is not reported by the authors, and, thus, it cannot be concluded if their approach can be useful in general situations.
## 4 Evaluation Criteria
In order to compare the results of an ANN, MLR, and ARIMA, the reference [48] employed Wilcoxon signed-rank and paired \(t\)-tests. The ANN had no real advantage over ARIMA or MLR because the \(p\)-values obtained were above \(\alpha\) = 0.05, making the results insignificant. In addition, the importance of these indicators varies depending on the parameters and datasets used. Therefore, it is difficult to compare the results of different methods. Furthermore, there is no task where all algorithms are tested on a single dataset to compare them. The accuracy of the approaches mentioned in this study project is recorded in the following parts of forecasting techniques. To critically evaluate the effectiveness of STLF, especially within the paradigms of modern power systems and smart grids, it is essential to define evaluation metrics that reflect both quantitative and qualitative performance. These metrics provide standardized means to compare and contrast different STLF methodologies and their alignment with the operational requirements of modern power grids. Based on the literature review, the following metrics are considered in this study because most of the forecasting studies in the literature have used these metrics to evaluate the performance of their proposed algorithm. MAPE: Offers a normalized measure of the deviation of forecasted values from actual values, allowing comparisons to across different scales of data. RMSE: Gives higher weight to larger errors, thus being sensitive to outliers which are crucial in peak load forecasting. MAE: Provides a direct average measure of forecasting errors, useful for understanding typical deviations in load predictions. It should be noted that in this paper, the presented performance accuracy of each paper in Sections 5 and 6 is derived from each research paper studied. It is also important to note that developing a comparison between the methodologies is challenging because, in the literature, each research paper considers different protocols, hyperparameters, and input data for the performance analysis of an algorithm in a specific application. The tables and figures presented in Sections 5 and 6 provide a comprehensive review of the papers studied along with their performance, allowing the readers to identify the performance evaluation of each model in relation to the input data for a specific application, which could pave the way for future research.
# Smart Low-Cost Control System for Fish Farm Facilities
## 2 Identification of Critical Factors for Control Systems for Aquaculture
### Factors Affecting Fish Wellbeing and Performance
#### 2.1.4 Factors of the External Biotic Domain  
With regard to the external biotic Domain, it is possible to identify factors linked to animals, plants, and other living beings. The fish surrounding the facilities might have different roles, such as predators [89], opportunistic fish, and vectors [90]. While the predators affect the fish's survival by causing silent mortality, since no cadavers appear, vectors cause mortality due to the transmission of parasites or diseases. Furthermore, opportunistic fish or intruders affect fish performance by consuming part of the provided feed, and this is a silent effect since it might be challenging to identify the intruder fish. They can also become vectors and fish predators. Mammals and birds can mainly interact as predators [89]. Invertebrates can affect aquaculture in two ways. First, they can be parasites of grown fish [91]. Most of the parasites do not kill the fish but affect their swimming and feeding behavior [92]. Moreover, invertebrates can be vectors for other organisms.  
Plants and algae generate biofouling, which affects the fish's performance indirectly [93]. In addition, algae can cause algal blooms [63], which affect the fish's performance directly [94]. Finally, fungi, bacteria, and viruses can provoke several diseases [95], jeopardizing the survival of individuals and affecting the fish's performance. Figure 5 portrays the information above related to the external biotic factors identified for the proposed Smart Low-Cost Control Systems for Aquaculture Facilities.
# A Simple Model to Predict Machined Depth and Surface Profile for Picosecond Laser Surface Texturing
## 3 Results and Discussion
### LST of Micro-Channels via Changing Adjacent Interval  
The influence of adjacent interval on the fabrication of the micro-channel was also investigated using Equation (4) by changing the interval from 2 \(\upmu\)m to 5 \(\upmu\)m with a constant scanning speed of 3 mm/s. The other parameters used in simulation were the same as those used in the previous section. Figure 7 shows the predicted and experimental depths of the micro-channels with various adjacent intervals. As can be seen, the machined depth decreases with an increase of the interval from 2 \(\upmu\)m to 5 \(\upmu\)m. An increase in the adjacent interval leads to a decrease of transverse overlapping, resulting in a decrease of the machined depth, which is consistent with previous research [32]. Similar trends are observed for experimental and simulated results, and the differences between them are in the range of 7%.  
Two simulated and experimental micro-channels with adjacent intervals of 2 \(\upmu\)m and 4 \(\upmu\)m are also presented in Figure 8. As can be observed, the machined depth of the micro-channel decreases with an increase of adjacent interval, while the width remains nearly constant at around 50 \(\upmu\)m. The profile made with lower interval has a higher aspect ratio. Furthermore, the simulation provides a very good prediction of the profiles for the experimental results.  
Figure 8: The comparison between experimental and predicted micro-channels for different adjacent intervals: (**a**) measured micro-channels, (**b**) comparison of cross-sectional profiles, and (**c**) predicted micro-channels.  
Figure 7: Effect of adjacent interval on machined depth of micro-channels.
# Study on the Influence of Rock Shape on Rolling Distance
## 1 Introduction
In summary, our understanding of how rockfall motion characteristics are influenced by their shape has been steadily expanding, highlighting the significance of shape as a critical factor in determining their rolling distance. Nonetheless, the prevailing formulas employed for estimating rockfall travel distance primarily comprise empirical models derived from regional rockfall disaster data and kinematic formulas that do not account for the impact of rockfall shape. Consequently, the ongoing research into the mechanism by which rockfall shape influences rolling distance lacks depth, and the correlation between rockfall shape and rolling distance remains unquantifiable. This limitation hampers our ability to assess the extent of rockfall's impact on the ground. This study utilizes a model test device to simulate rockfall scenarios on the ground. By altering the shape of the test block, its rolling speed, and the ground material, we obtain variations in the rolling distance of the test block under the influence of different factors. The experiments reveal that the rolling resistance of the test block is contingent on its interaction with the ground during rolling. This interaction encompasses the two-dimensional cross-sectional shape of the test block during rolling and the extent of ground deformation. The rolling resistance attributable to the shape of the test block is denoted as the coefficient RF, quantifying how rockfall shape impacts the rolling distance. This parameter is integrated into the kinematic formula, yielding formulas for calculating rockfall rolling distance on various terrain features. The accuracy of the formulas is confirmed via a comparative analysis between model test data and results derived from the formulas. Following verification in field tests, these formulas can be used as a reference for rockfall hazard assessment and protective design.
# Transmission Power Adaption for Full-Duplex Relay-Aided Device-to-Device Communication
###### Abstract  
Device-to-device (D2D) communications bring significant improvements of spectral efficiency by underlaying cellular networks. However, they also lead to a more deteriorative interference environment for cellular users, especially the users in severely deep fading or shadowing. In this paper, we investigate a relay-based communication scheme in cellular systems, where the D2D communications are exploited to aid the cellular downlink transmissions by acting as relay nodes with underlaying cellular networks. We modeled two-antenna infrastructure relays employed for D2D relay. The D2D transmitter is able to transmit and receive signals simultaneously over the same frequency band. Then we proposed an efficient power allocation algorithm for the base station (BS) and D2D relay to reduce the loopback interference which is inherent due to the two-antenna infrastructure in full-duplex (FD) mode. We derived the optimal power allocation problem in closed form under the independent power constraint. Simulation results show that the algorithm reduces the power consumption of D2D relay to the greatest extent and also guarantees cellular users' minimum transmit rate. Moreover, it also outperforms the existing half-duplex (HD) relay mode in terms of achievable rate of D2D.  
device-to-device (D2D); relay aided; full-duplex (FD); power allocation; independent power constraint +
Footnote †: journal:  
_Article_
## 3 Results
### Response Time  
Figure 4 shows the aggregated cumulative percentage of crossings as a function of time. It is noticeable that the few crossings with constant speed stimuli all occurred in the initial 2 s after the beginning of the stimuli presentation. For the other speed patterns, there were considerably more crossings. Nevertheless, in both the \"slow down\" and \"stop\" conditions, most responses were given after the vehicle speed began decreasing.  
The response time was examined with a Linear Mixed Model considering the vehicle speed pattern and the auditory condition variables. The time participants have taken to respond was significantly longer when the vehicle stopped (m = 3.88 s; sd = 0.86 s; \(p\) < 0.01) and when it just slowed down (m = 3.15 s; sd = 0.87 s; \(p\) < 0.01) than when it passed at constant speed (m = 1.36 s; sd = 0.41 s).  
The response time was significantly longer for the approaching gasoline vehicle (m = 3.40 s; sd = 0.79 s; \(p\) = 0.05) than for the electric (m = 3.52 s; sd = 0.99 s). The no sound condition (m = 3.53 s; sd = 1.10 s; \(p\) = 0.44) did not significantly differ from the electric one.  
It is important to mention that existing differences between response times obtained for the gasoline vehicle and the other conditions may be partially explained by a small difference in the speeds used by the model defining movement of the cars, recorded along the passage of the vehicle when the noise was acquired. During the acquisitions, the two cars were driven by the same person to minimize the human error induced in the speed control. However, due to the different sensitivity of  
Figure 4: Percentage of cumulative crossings aggregated for all participants as a function of response time.  
the vehicles' systems and human factors of the professional driver, some differences in the order of 1.70 km/h, on average, were found in the vehicle speed.  
Figure 5 shows the number of crossings and corresponding response time per participant in each condition. Only 2 out of 10 participants felt able to cross the road when the vehicle was approaching them at 30 km/h. Besides also being the only ones to cross during the constant speed condition, these two were also the ones who made crossing decisions more quickly in the other conditions. The results are remarkably consistent, and Figure 5 exhibits the low variability in the responses of all participants.  
In summary, considering all the results of the analysis of the percentage of crossings and the response time, it is noticeable that the participants felt more opportunities to cross the road safely when the vehicle speed varied, namely in stimuli where the car had the pattern of stopping and slowing down. In the responses given by the participants to the stimuli related to the stop speed pattern, it was possible to verify some differences between the different auditory conditions. In these cases, and for the gasoline combustion vehicle, participants made their decision more quickly than in the other two types of stimulus.
## Data and Methods  
### Data  
#### 3.1.1 In-Situ Observations  
To record synoptic scale precipitation data, the Pakistan Meteorological Department (PMD) has installed a network of meteorological observatories across the country as shown in Figure 1b. The density of these stations differs from region to region within the country but the large-scale dominant precipitation features can be determined. The daily observations collected from these stations are already quality controlled and are used for PMD operational purposes. To ensure that the data is homogenous and consistent, a suite of visual and statistical techniques, including a standard normal homogeneity test (SNHT), are adopted here [43]. SNHT is a statistical technique most often used for homogeneity estimation of climate data records, where the purpose of SNHT and other homogenization tests is primarily to detect outliers or spikes in a dataset that could be attributed to non-climatic factors. Such changes might be induced through changes in location, measurement techniques and physical features of the surrounding environment, whereas climate signals are preserved [43; 44; 45]. Only a small amount of data (<4.5%) was missing at a few stations. To make the comparison more accurate, the corresponding values from GPPs were also removed. This procedure did not substantially affect the results of the study.
# Can Walnut Serve as a Magic Bullet for the Management of Non-Alcoholic Fatty Liver Disease?
## 3 Results and Discussion
### Pathogenesis of NAFLD and Progression to NASH, Fibrosis, and Primary Liver Cancer
#### 3.1.1 Lipid Accumulation (Steatosis) and Lipotoxicity  
Lipids are essential for maintaining cellular homeostasis, and therefore intracellular lipid accumulation by dysregulated lipid metabolism can cause lipotoxicity, which leads to organ dysfunction, cell injury, and cell death. Lipotoxicity associated chronic hepatic inflammation is known to trigger the development of NAFLD [19; 20]. Fundamentally, excessive hepatic lipid accumulation results from an imbalance between triglycerides acquisition and disposal; acquisition occurs through increased release of free fatty acids from adipose tissue and intra-hepatic de novo lipogenesis, whereas disposal occurs through export of lipid via very low-density lipoprotein (VLDL) [21].  
Aberrant lipid deposits alter the metabolic and inflammatory signals by modifying the biology and function of intracellular organelles such as the endoplasmic reticulum and mitochondria [22; 23]. Other mechanisms include direct modification of intracellular signaling pathways and interaction with proinflammatory cellular kinases [24]. Toxic lipid species (triglycerides, free fatty acids, lysophosphatidyl choline, ceramides, and free cholesterol) induce mitochondrial dysfunction with oxidative stress through the production of reactive oxygen species (ROS) and endoplasmic reticulum stress, resulting in the activation of inflammatory signal and apoptosis [25; 26; 27; 28; 29; 30].