esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
DEG analysis group-II, III, and IV, which showed significant higher expression of DEGs in PHS-tolerant cultivar 'Woori' than 'Keumgang', had GO categories related to sugar biosynthetic processing or transport (trehalose biosynthetic process and sugar/proton symporter activity). In particular, 'Woori' showed higher expression of genes related in the trehalose biosynthetic process when PHS induced (group-III and IV). Trehalose is a disaccharide synthesized from two glucose molecules (UDP-GlcNAc + Glc-6-P) [42; 43] and widely used economically in areas such as food industry and medicine [44; 45]. [PERSON] et al. [46] reported, via the effect of sugars in 10 different _Arabidopsis_ accessions, that glucose induced the delay of germination. They also investigated the germination delay effect other sugars (galactose, sucrose, maltose and trehalose) and the interaction with phytohormones. GA or ethylene was independent from the sugars, and ABA contents led the glucose response although ABA seemed to be independent on ABI2, 4, and 5 signaling. The sugar-induced delay of germination is involved in ABI3-mediated ABA signaling, and RGL2 (RGA-like 2) and SPY (SPINDLY) in GA signaling. The effects of exogenous glucose on germination inhibition is studied using _abi3_, _rgl2_, and _spy5 Arabidopsis_ mutants [47]. RNA-seq data revealed two _RGL2_ genes and one _SPY_ gene which showed higher up-regulation in 'Woori' than in 'Keumgang', but these were not selected for investigation due to the gene selection criteria. There were DEGs categorized into flavonoid biosynthetic process and L-phenylalanine metabolic process in group-I and II, respectively, which showed comparatively higher expression in WA than KA whether genes were expressed differentially or not in KP and WP along with drug membrane transport (including ABCG40s in our raw dataset). A total of 6 DEGs were identified as encoding phenylalanine ammonia-lyase 1 (PAL1) in group-I and II (each group had 3 _PAL1_ genes). PAL1 catalyze the phenylpropanol pathway and regulate the content of flavonoids. Trehalose is also known to activate PAL to enhance powdery mildew resistance [48; 49]. Therefore, the majority of categorized DEGs that showed comparatively higher expression in 'Woori' (DEG analysis group-I to IV) were involved in sugar-ABA interactions and consequently the ABI3-mediated ABA signaling.
## 4 Experiment Setup and Data Collection  
In this study, two simulated aircraft trajectories were generated. The trajectories present a high range of dynamics, including but not limited to accelerating to a velocity greater than 200 m/s, deaccelerating, sharp turns, a 300 m to 600 m height change, and a 90\({}^{\circ}\) heading change. The trajectories were simulated to start at latitude 45\({}^{\circ}\)54.744\({}^{\prime}\), longitude \(-\)77\({}^{\circ}\)17.364\({}^{\prime}\), and altitude 100 m on 30 August 2022, midnight. Firstly, the Spirent GSS6700 GNSS simulator, as indicated in Figure 1, was used to simulate the aircraft trajectories due to its ability to simulate high-dynamic vehicle trajectories.  
The simulated trajectories were then imported to the Orolia GSG-8 GNSS simulator, as shown in Figure 2, due to its ability to simulate multi-frequency, multi-constellation GNSS signals. An overview of the imported trajectory to the Orolia simulator is presented as indicated in Figure 3, for further check before starting the simulation experiments.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Combination** & **GNSS Constellation** & **Signals Acquired** \\ \hline
1 & GPS DF & L1 C/A, L1P, L2C, L2P \\ \hline
2 & GPS TF & L1 C/A, L1P, L2C, L2P, L5 \\ \hline
3 & GPS TF + Galileo SF & L1 C/A, L1P, L2C, L2P, L5, E1 \\ \hline
4 & GPS TF + Galileo TF & L1 C/A, L1P, L2C, L2P, L5, E1, E5a, E5b \\ \hline
5 & GPS TF + Galileo TF + & L1 C/A, L1P, L2C, L2P, L5, E1, E5a, E5b, G1 \\ \hline
6 & GPS TF+ Galileo TF+ & L1 C/A, L1P, L2C, L2P, L5, E1, E5a, E5b, G1 \\ \multicolumn{2}{c}{GLONASS DF} & G1, G2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: GNSS combinations.  
Figure 1: Spirent GSS6700 GNSS simulator.  
Figure 2: Orolia GSG-8 GNSS simulator.  
The 3D views of the simulated aircraft trajectories are presented in Figure 4. The first trajectory extended for approximately 27 min, with data logging commencing after 2 min from the start, resulting in 25 min of logged data. The second trajectory extended for approximately 24 min, with data logging commencing after 2 min from the start, resulting in 22 min of logged data. The Orolia GNSS simulator's output was then used for simulated and actual jamming experiments, as described in the following sections.
## 5 Stress-Inducible Genes  
The stress-inducible genes, which are usually activated by any of the stress-inducible transcription factors, play a direct protective role that causes the plant to defend itself against the harmful and damaging effects of the drought and salinity stresses. These target genes increase the tolerance of the plant against salinity and drought by regulating many biological and biochemical activities [35]. For instance, these genes can produce osmoprotectants, proline [78], osmolytes, detoxifying agents, antioxidant enzymes [4] and many other agents that contribute to the adaptation of the plant to drought and salinity stresses.  
Osmolytes are some of the most important molecules involved in abiotic stress tolerance. They are very important endogenous metabolites that are known to carry out many functions in order to maintain the osmotic pressure in the plant [79]. Proline is the most widely known type of osmolyte, and the biosynthesis of the proteinogenic amino acid proline occurs in the cytosol and chloroplast. In _Arabidopsis_, it is controlled by the _P5 CS2_ and _P5 CS1_ genes based on the location of synthesis: cytosol or chloroplast, respectively [80]. Under salt stress, calmodulin (CAM) has an important role in the activation of an MYB family transcription factor (MYB2). The MYB2 binds to a _cis_-acting element in the promoter of the P5 CS1 gene; thus, it plays a significant role in the biosynthesis activity of proline. Moreover, promoter analysis studies of AtP5 CS1, AtP5 CS2 and AtP5 CR indicated that their promoter regions contain ABA-responsive elements as _cis_-regulatory elements, which indicate their connection to the ABA-dependent pathway [24; 69; 81].  
Environmental stress increases the level of reactive oxygen species (ROS), which are useful in signaling at low concentrations but toxic at a higher level. To scavenge and detoxify the ROS molecules, the plant recruits several enzymatic and nonenzymatic antioxidant systems in order to eliminate the harmful effect of the ROS. The major ROS-scavenging enzymes of plants include superoxide dismutase (SOD), ascorbate peroxidase (APX), catalase (CAT), glutathione peroxidase (GPX) [82] and peroxiredoxin (PrxR) [47]. The nonenzymatic antioxidants such as ascorbic acid, tocopherols, flavonoids and carotenoids provide the cells with high-performance machinery for fighting oxidative stress [12; 83]. Those antioxidant genes are regulated during abiotic stress via signaling and transcription factor regulation. For instance, transgenic cotton overexpressing the potato DREB2 TF showed enhanced expression of antioxidant genes (SOD, CAT, etc.) under exposure to water deficit [84]. Studies have shown that there is a relationship between the plant hormones ethylene and JA and the upregulation of several antioxidant-related genes, whether their products areenzymatic or nonenzymatic. The whole transcriptional regulation of the antioxidant genes is not fully clear yet, and studies are still being conducted for the sake of having a complete understanding of the whole regulatory mechanism of the antioxidant activity [83].
## 3 Prediction of Compressor Faults Using Machine Learning Is a Supervised Problem
#### Using Algorithms to Predict the Future  
The creation of predictive algorithms, also known as prognostic algorithms, is the most crucial and perhaps most difficult aspect of predictive maintenance. As a result, a model that takes into account a variety of factors and how they interact and affect one another is created in order to predict machine failures. As many variables as possible should be used to increase the precision of the models. Predictive model development is an iterative process as a result. Data from CMMSs, file cabinets, personal observations, FEMA analyses, and internal sensors such as accelerometers and flow meters are gathered for the initial modeling. Condition-monitoring sensors may need to be installed first and operated for a period of time in order to collect baseline data and create the first predictive model (See Figure 4).  
Over time, as more data are generated by the installed sensors, it is possible to improve the initial models and produce near-perfect failure predictions.  
Without getting too technical, we here describe how the algorithms work. Based on pre-established rules, their algorithms compare the current behavior of the asset with its expected behavior. A deviation causes a gradual deterioration, which will eventually lead to the failure of the asset. Based on the deviations, current operational circumstances, and historical failure data, the algorithms attempt to predict the points of failure.  
Automated systems achieve the following:  
* Sensors installed in the system monitor operating conditions;
* Analyze anomalous data patterns and predicts them;
* Detect deviations from thresholds and alerts users.
# Airspace Geofencing and Flight Planning for Low-Altitude, Urban, Small Unmanned Aircraft Systems
## 5 Simulation Setup  
Monte Carlo simulations were used to evaluate proposed airspace volumization strategies on the Manhattan map. Figure 11 shows the flowchart of pathfinding logic in our simulation setup. Pathfinding logic comprises four solution modules for the airspace geofencing algorithm. Once the start and end locations were defined, the keep-out geofence ROI polygons (Figure 10) were extracted from post-processed map data. Constant cruise altitude and terrain follower modules were generated by searching the intersection points between the buildings' keep-out geofences and the line that connects UAS start and end waypoints. A pure turn solution was generated if both start and end locations were on the ground. If either start or end location was on the roof of the building (i.e., inside of the keep-out geofence), a constant cruise altitude algorithm was first used to find the flight path from the start/end point to the outside of the keep-out geofence, and the turn module solution was used to calculate the remaining flight path, creating a combined solution.  
Control parameters are shown in Table 1. To offer an experimentally grounded dataset, a prototype quadplane's power consumption model [44] was used per Table 2 to compute \(P_{travel}\) in climb, cruise, and descent segments. A quadplane is a hybrid quadrotor/fixed-wing UAS designed to vertically takeoff and land in an urban environment. For our simulations, the quadrotor motors were active in all phases of flight; cruise power would otherwise be lower. Cost function weighting factors \(\alpha=0.6,\ \beta=0.2,\ \gamma=0.0\) were chosen to prioritize minimum-distance solutions. Note that \(\gamma\) was set to zero because building obstacles have static or permanent geofences.  
Figure 10: Keep-out geofence polygon extraction for UAS flight planning. The initial ROI (green dashed line) is a rectangular box per Figure 5. Keep-out geofences (solid black lines) inside or intersecting the rectangular ROI box are found using polygon intersection and point-in-polygon operations. The final ROI (red dashed line) is the convex hull around these keep-out geofences. For our simulation, \(\delta_{ROI}=150\) m.
# Analyzing the Characteristics of Soil Moisture Using GLDAS Data: A Case Study in Eastern China
## 3 Results and Discussion
### Characteristics of Climate Change in East China
#### 3.1.2 The Seasonal Changes of Soil Moisture  
Each layer of soil moisture is significantly different in eastern China (Figure 7). The layer of soil moisture and precipitation in different months undergo similar changes. Before June, the 0-10 cm layer of soil moisture is low. As rainfall increases in summer, soil moisture gradually rises and reaches its peak value from July to September. In the next month, a decrease in precipitation leads to a gradual decrease in soil moisture and a slightly smaller humidity value. Among them, the 40-100 cm soil moisture layer's seasonal evolution is different from the 0-10 cm, 10-40 cm, and 100-200 cm layers of soil moisture. The 40-100 cm layer of soil moisture is in a drought period from January to June; when the rainy season comes in June, the soil moisture increases with the greater precipitation and reaches a high in the middle of August. The maximum value of surface soil moisture (0-10 cm) coincides with the maximum value of precipitation. In the remaining three layers, maximum soil moisture lags behind the maximum value of precipitation; there are about one to two months of hysteresis. This delay indicates that soil moisture doesn't immediately reflect the real-time dynamic of precipitation.  
Comprehensive analysis of the above shows that the 0-10 cm of soil moisture is largely controlled by precipitation in different seasons. In the time series, the change in precipitation is similar to soil moisture. The deep soil moisture (10-40 cm, 40-100 cm, and 100-200 cm) and the changes in precipitation have a lag of one month in the time series. This shows that, in contrast to surface soil moisture and precipitation consistency, deep soil moisture is less susceptible to real-time precipitation constraints. Deep soil has a certain link to early climate change, can accumulate information on surface hydrological processes information, and is able to respond to the relationship between its ecological environment and climate change.
# Segmentation of Intracranial Hemorrhage Using Semi-Supervised Multi-Task Attention-Based U-Net
## 1 Introduction
This model was based on a traditional u-net architecture with an additional graph-based segmentation network following the output of the u-net. Their results produced a Dice score of 0.623, marginally beating a conventional u-net. Previous work involving unsupervised image segmentation has proven to perform reasonably well compared to supervised alternatives, especially in the context of medical image segmentation [22]. A paper by [PERSON] et al. [23] proposed a deep representation learning approach of unsupervised segmentation clustering to combat the low amount of available labeled medical image data. This was done by learning deep feature representations of training patches from a given image with joint unsupervised learning. Other unsupervised techniques, including autoencoders [24], restricted Boltzmann machines [25], deep belief networks [26], deep Boltzmann machine [27], and generative adversarial networks [28], have been studied, but not many could reach the level achieved by traditional supervised learning techniques [29; 30]. Semi-supervised learning has become a popular learning technique in recent years. Much analysis has been done on this technique and its usefulness [31]. With the scarcity of labeled medical data, the use of semi-supervised learning has become an attractive alternative. Researchers recently introduced a method called Dynamic Self-Training and Class-Balanced Curriculum (DST-CBC), specifically for semi-supervised semantic segmentation with the ability to exploit all unlabeled data by training with pseudo-labels. This approach was shown to beat narrowly other state-of-the-art models on the PASCAL VOC 2012 and Cityscapes datasets [32]. In some instances, semi-supervised learning techniques performed better than their supervised counterparts. For instance, [PERSON] et al. [33] introduced a semi-supervised segmentation method that consistently learned under transformations, obtaining a higher segmentation accuracy than that of supervised learning. In this paper, we introduce a modified u-net and curriculum learning strategy, using a semi-supervised model based on an earlier work by [PERSON] et al. [34], to perform semantic segmentation on a small dataset of ICH obtained from the Al Hilla Teaching Hospital in Iraq [2]. We also used the RSNA Intracranial Hemorrhage Detection dataset as a collection of unlabeled images to test our semi-supervised learning strategy. The newly-adopted curriculum learning technique augmented [PERSON]'s joint training strategy, which made the algorithm more stable. By employing a modified u-net in the encoder, we also reduced model overfitting due to over-parameterization. The rest of this paper is organized as follows: Sections 2-4 introduce semi-supervised learning, the U-Netarchitecture, and attention, respectively. Section 5 details the experimental methods and procedures. Results are presented in Section 6, and concluding remarks are given in Section 7.
## 5 Results and Discussion
### Ring Shear Results
#### 5.3.3 Effect of Duration of the Contamination  
Figure 10 illustrates the effect of 7, 14, and 21 d duration of contamination on peak shear strength and residual stress ratio of the tested AC, BC, and KC specimens. As can be seen in Figure 10a increasing the duration of the contamination slightly decreased the shear strength of the tested specimens. For instance, the peak shear strength of the AC specimens reduced from 152.7 to 138.5 kPa when the contamination duration continued for 14 and 21 d. This trend was in the range of 148.7 \(\leq\)\(\mathrm{vp}\)\(\leq\) 160 kPa and 102.1 \(\leq\)\(\mathrm{vp}\)\(\leq\) 119.9 kPa for BC and KC specimens. Similar trends were recorded for residual stress ratio of the tested specimens. For instance, a residual stress ratio from 0.76 to 0.69, from 0.80 to 0.74, and from 0.55 to 0.5 were recorded for AC BC and KC specimens when the contamination  
Figure 9: Effect of atrazine contamination on (**a**) Friction angle; (**b**) cohesion.  
was continued for 7 to 21 d. Variations of the peak and residual shear strength were seen by increasing the ageing time after 14 and 21 d for the soil with atrazine percentage of 6% under 200 kPa normal load [see Figure 10b]. It can be clearly seen that increasing the ageing time caused a decrease in peak and residual shear strength of the soil. More reduction in recorded peak shear strength and residual stress ratio of the soil can be attributed to the simulated long-term contamination condition that changed the soil structure and caused a reduction in dielectric constant of the pore fluid and accordingly lower values of shear strength were observed [26].
# Easing DApp Interaction for Non-Blockchain Users from a Conceptual Modelling Approach
## 5 Middleware to Ease DApp User Interaction  
With the aim of tackling the DApp interaction problem questioned by RQ2, we propose a technique based on a middleware that will \"translate\" the blockchain's mechanisms into concepts users are familiar with. In other words, the middleware will implement the required functionality to transform the DApp model shown on Figure 1 to the App-equivalent one. From now on, we will exemplify the proposed middleware by using Ethereum since it currently is the most used blockchain to deploy smart contracts.  
In a common Ethereum DApp, the software architecture is distributed between a JavaScript-based web application featuring the Web3 library and a wallet [30] as frontend and a set of Ethereum's smart contracts as backend (top of Figure 2). This architecture, for starters, makes users unable to interact if they do not have an Ethereum wallet (e.g., Metamask [32]) embedded into their browsers. Furthermore, since the wallet is running and properly installed, they will be able to log in and interact with the DApp by signing transactions with their private keys. What is more, for those transactions involving the modification of the blockchain, the user will have to put some gas into the transaction (i.e., pay for it) in order for it to be successfully committed into the blockchain (Figure 3). Finally, users will also have to take another decision before releasing the transaction: deciding how much gas they are willing to put on in. Hence, putting not enough gas could make the transaction unsuccessful, since no miner will mine the transaction into a block due to the low reward to be received. This process gets even more complicated if the user deals with a private Ethereum-based blockchain, such as Quorum, since the transaction will have to be manually adjusted to use no gas.  
Putting everything together leads us to a straightforward conclusion: A non-expert user interaction with a DApp will probable fail due to the considerable number of concepts he/she needs to know. Therefore, we propose using a middleware (bottom of Figure 2) that will relieve the user of the need of mastering blockchain concepts and technologies when interacting with a DApp for the first time. To demonstrate our proposal, we will use a Python implementation and a Firebase database. Nevertheless, any technology could be used provided it has the required libraries (e.g., Web3 for the Ethereum) to interact with the blockchain. In the following, the different components of our proposed middleware will be shown.  
Figure 2: Ethereum software architecture comparison.
## 2 Materials and Methods
### System Description
#### 2.3.3 Client Socket  
The client socket implementation on the RPi0-2W system, as elucidated in Section 2.3.1, encompasses key functionalities such as capturing vibration data with precise time synchronization, storing it in local CSV files, and transmitting it to the server socket via a TCP/IP socket connection. Each client, dedicated to an individual RPi0-2W end node device, diligently acquires continuous data from three axes ADXL345 accelerometers and obtains precise time readings from the DS3231 RTC module. This data acquisition process is initiated upon receiving a'start' message from the server.  
Upon the server-side entering the accept() process, as illustrated in Figure 2, the client socket is initiated, and the client socket creation is finalized during the initial stage on the client-side. Following this, the program prompts the user to provide the essential server IP address and port number. Specifically, the user is expected to input the server's IP address and port number. These details are crucial for establishing a seamless connection between the client socket and the server socket. The connect() function is subsequently employed to establish the connection with the server socket, utilizing the provided server IP address and port number.  
Similar to the server-side functionalities, an acceptance message is transmitted from the server to the connected client upon successful establishment of the connection. Subsequently, the user is prompted once again to provide the sensor name, which is then forwarded to the server. Concurrently, the client generates a corresponding CSV file named \"sensor_name.csv\" to locally store the acquired sensor data readings, as denoted by the assignment statement (a) a local.csv file is created.  
Within the client-side implementation, a while loop is employed, utilizing the recv\(0\) function to wait for the receipt of the'start' message from the server. Upon receiving the'start' message, a new thread is spawned to retrieve sensor data and precise time, ensuring thread synchronization through the use of a lock. Additionally, another thread is created and initiated to manage the local CSV file and stream the acquired data to the server, as denoted by the assignment statements (b) A new thread is created to retrieve the sensor data and store into a sensor_data list and (c) Another thread is created to manage the local.csv file, respectively.