esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Comparison of Different Models' Results
#### 5.2.1 Models Introduction
Additionally, it solves the problem of traditional models being too thorough, or consuming GPU resources and server storage resources. While ensuring prediction accuracy, it reduces the computational complexity and memory usage of self-attention. **Refomer**: Reformer is also a prediction model structure based on Transformer. It uses Axial Positional Embedding to reduce the position encoding matrix and proposes a segmented approach to handle the calculation process of the fully connected layer, calculating only a portion at a time, eliminating the need to load the entire sequence into memory, and using reversible residual connections instead of traditional residual connections. This is a solution for storing extreme compression. The DNN model is composed of linear layers and lacks Feature selection, which also leads to the poor effectiveness of the model for complex tasks. GRU is specifically designed to handle time series problems and performs well in short-term prediction tasks. However, there are issues such as gradient vanishing in medium-long-term prediction tasks. TCN can complete feature extraction with a large Receptive field, but the lack of attention mechanism makes it difficult to select its Receptive field. Vanilla Transformer, as the basic framework of the most classic transformer model, has been widely used in multiple fields. However, it was originally designed to solve NLP problems and lacked optimization for time series. Encoder-Only and Decoder-Only models are optimized on the Transformer framework, with the advantage of halving the number of model parameters and retaining the advantages of the Transformer. The disadvantage is that these models have many layers, and the data efficiency is low because no Feature selection unit is added. The Informer and Reformer models adopt different compression schemes to ensure computational efficiency while ensuring the accuracy of time series prediction. However, this did not bring much accuracy improvement. Especially for the time series with poor autocorrelation such as wind power, the lack of data feature fusion and efficient Feature selection leads to the loss of prediction accuracy. Therefore, in order to solve the above problems, this paper proposes a model based on a gated transformer network.
# Migrating from Developing Asynchronous Multi-Threading Programs to Reactive Programs in Java
## 2 Theoretical Background
### Reactive Programming  
Reactive programming is a paradigm focused on data flows and propagation of changes. It is based on the Observer pattern [18], under which an entity, known as the subject, keeps track of a list of observers, and automatically notifies them about any change in the state of the application. As a result, data flow processing becomes more efficient and flexible, enabling non-blocking and asynchronous data handling.  
Reactive streams are a specification that defines a set of interfaces and methods for asynchronous processing with non-blocking backpressure. The mechanisms employed to take advantage of the programming concepts of reactive streams are implemented within various libraries such as Project Reactor or RxJava [5,6]. These libraries provide a broad set of high-level operations, including filtering, mapping, and reducing, which are employed for dealing with reactive data streams efficiently. These powerful abstractions in Java applications can significantly streamline the development process, improving both performance and scalability.  
From straightforward data processing flows to complex event-driven systems, reactive programming has a wide range of applications. This is going to be the main focus of the research, particularly looking at the benefits and difficulties of transitioning from the conventional solution, the asynchronous multi-threading programming, towards a reactive programming approach.
## 2 Materials and Methods  
### Study Area  
Botswana (18-27\({}^{\circ}\) S; 20-29\({}^{\circ}\) E) is a large semi-arid country with a small population of about 2,379,000 [25], and it covers an area of approximately 581,730 square kilometers [26]. Botswana is landlocked by Namibia to the west and north (the Caprivi Strip), Zambia and Zimbabwe to the north-west, and South Africa to the south-east and south.  
According to the Center for Applied Research of Botswana [27], the country has a mean altitude of 3300 feet (1000 m) and is primarily comprised of a sand-filled basin with gently undulating plains that rise to highlands in neighboring countries. The highest point is 4888 feet (1490 m) in the hills north of Lobate in southeastern Botswana, while the lowest point is 2170 feet (660 m) at the country's easternmost point in the Limpopo Valley. Rainfall in the country is generally low, ranging from 250 mm per annum in the southwest to an annual mean of 650 mm in the northern regions.  
The country is divided into four main environmental regions, as shown in Figure 1. The hardveld region features rocky hill ranges and areas with shallow sand cover in eastern Botswana. The sandveld region consists of deep Kalahari sand found in the western part of the country. Additionally, there are the Mopane savanna and wetlands regions.  
Most of the agricultural production in Botswana is from livestock agriculture [28], with traditional livestock-keeping activities dominating commercial production, covering approximately 60 percent of the country's land [29]. However, this traditional animal production system is based on a rain-fed agricultural setting, which faces significant challenges due to a lack of moisture.  
For our study, we selected four locations: Mabutsane, Sehitlwa, Letlhakeng, and Kgagodi, each situated in distinct vegetative regions, as shown in Figure 1. These areas represent different soil types and different types of vegetation. Mabutsane represents the semi-desert region (sandveld), Kgagodi exemplifies the Mopane savanna area (hardveld), Letlhakeng lies in a transitional zone between hardveld and sandveld, and Sehitlwa is characterized as a swamp or wetland area.
# LUMDE: Light-Weight Unsupervised Monocular Depth Estimation via Knowledge Distillation
## 3 Method  
### Overview  
This section describes the unsupervised depth estimation distillation framework. This framework takes a single RGB image as input and produces a dense depth map. The details of our model are shown in Figure 1. In order to achieve unsupervised monocular depth prediction distillation, we follow [PERSON] et al. [15] and propose a dense prediction distillation strategy. This strategy adopts pixel-wise similarity produced by outputs of the teacher and student networks, structured pair-wise similarity produced by intermediate feature maps, and holistic similarity produced by a CNN adversarial module, and generates an integrated loss function based on these similarities. In addition to these elements, [PERSON] et al. [15] also employ the ground truth of related CV tasks. In this work, we abandon the ground truth module and regard the rest of the framework in [15] as our baseline model. Moreover, we introduce a pose network as an extra module to assist the convergence of the student network. Although the pose network is commonly seen in self-supervised monocular depth estimation models to produce photometric difference as supervision, no such design has been reported for the student net during distillation. Following this, the idea of GAN [15; 31] is retained, and we replace the traditional CNN with Transformer [16] as the discriminator, considering its advantages in regard to global information capturing and training parallelism.  
As is shown in Figure 1, our framework is composed of three main blocks: a Knowledge Distillation module, a Pose Network module, and a Transformer module. Transformer [16] is employed as the discriminator of GAN [15; 31]. At the same time, the student network is regarded as the generator to produce fake samples, while the outputs of the teacher network are real samples. More detailed descriptions of these blocks are given in the following three sections.
# Real-Time Seismic Data from the Bottom Sea
## 1 Introduction  
Variations in real-time seismicity provide knowledge of the state of local and regional stresses in the short and medium term, essential information to study the potential seismic risk that may affect infrastructures and population located in the area. Recent seismic activity in 2013 (possible induced by a gas tank) on the coast of Vinaros, or the intense underwater seismic activity associated with the eruption of El Hierro in the archipelago of the Canary Islands (2011-2012), shows the importance of controlling seismic events located in the sea that are not covered by the terrestrial monitoring networks.  
Monitoring the regional seismic events in real time makes it possible to achieve fast estimation of actual earthquake scales, since the measured seismicity directly gives us the true size of the earthquake. In the last decade, various technologies have been proposed and tested to address this challenge, such as cabled Ocean Bottom Seismometers (OBSs) [1; 2; 3; 4], OBSs acoustically linked to surface buoys or surface vehicles [5; 6] and free-floating buoys [7]. Although a permanent array of wired OBSs can be considered as the best technology for seismic surveys, having practically unlimited power and data transmission bandwidth, it is very expensive to implement and its deployment is restricted tothe locations where such cable observatories can be deployed. More affordable solutions are the OBSs acoustically linked to surface buoys or surface vehicles. However, the standalone ocean bottom broadband stations, implementing an acoustic link between the OBS and the surface, are limited by the acoustic communication latency, power consumption, and bandwidth.  
Within this context, we developed and implemented a new technology for standalone ocean bottom broadband systems. First, an overview of the challenges for underwater monitoring of the regional seismic events in real time is presented. Section 2 focuses on the design philosophy of the standalone ocean bottom broadband system including the seafloor unit, the surface buoy, and the mooring and the inductive communication system; it includes the dynamic simulations of the prototype for deployment near the OBSEA observatory at a depth of 20 m and in the Alboran Sea (Western Mediterranean) at a depth of 250-300 m. The actual deployment of the standalone ocean bottom broadband station near the OBSEA observatory and the results are discussed in Section 3. The results include the registration of a seismic event located in the Hautes-Pyrenees, of magnitude 3.7, that occurred on 28 October at 19.06 UTC. Finally, the conclusions drawn are presented in Section 4.
## 3 Results and Discussion
### Low-Temperature Cracking Resistance  
This research used two BBR test indicators to assess the effectiveness of asphalt mastics in subfreezing conditions: the flexural creep strength modulus \(S\) and the slope of the creep curve \(m\). Low \(S\) values in asphalt mastics suggest increased elasticity, increased deformation  
Figure 21: Effect of fiber modification on asphalt mastic \(J_{\text{nr-diff}}\).  
Figure 22: Elastic recovery rate of asphalt mastics.  
tolerance, and improved resistance to low-temperature cracking. The results in Figure 23 are consistent with the conclusion of the previous study by [PERSON] et al. that the addition of fibers elevated the creep stiffness values [41]. This suggests that increasing fiber content may not be beneficial for enhancing the asphalt mastic's performance at low temperatures, since it can lead to poor low-temperature crack resistance. The experimental results also show that as the temperature increases, asphalt mastic's \(S\) value drops quickly. Raising the temperature can make asphalt mastic less likely to crack at low temperatures.  
The creep curve slope, represented by the \(m\) value, is an indicator of the relaxation capability of asphalt mastics. A higher \(m\) value suggests faster stress release and better crack resistance at low temperatures, indicating a greater ability to withstand cracking. Figure 24 illustrates the influence of fiber content on the m value of asphalt mastics. This shows that the addition of basalt fibers slightly reduces the slope value (\(m\)) of the creep curve. This finding is consistent with the results reported by [PERSON] et al. [42], indicating that fiber content has minimal detrimental effects on the low-temperature cracking resistance of asphalt mastics. On the other hand, the inclusion of bamboo fibers increases the \(m\) value, indicating improved resilience to low-temperature cracking as the number of bamboo fibers increases.  
Moreover, the m value of asphalt mastics exhibits a rapid increase with rising temperature, significantly enhancing the material's resistance to low-temperature cracking. In summary, the research findings suggest that the incorporation of bamboo fibers provides more favorable results in terms of the low-temperature properties of asphalt mastics compared to basalt fibers.  
Figure 23: Creep stiffness of fiber-modified asphalt mastics.  
Figure 24: Effects of fiber modification on creep rate of asphalt mastics.
## 2 Molten Salt Mixtures
### Molten Salt Mixtures as Heat Transfer Fluid and Storage Medium
A significant challenge in MS thermal energy storage systems is the risk of the salt freezing, given its high melting point. To mitigate these issues, several strategies are essential to maintain the salt in a liquid state and ensure its effective circulation. First, thermal insulation of storage tanks and piping is crucial to retain heat within the system. Additionally, auxiliary heating systems can prevent the MS from freezing by activating when the temperature approaches its melting point. Continuous circulation of the MS prevents solidification by maintaining uniform heat distribution. Active temperature control systems that monitor and adjust the MS temperature can prevent temperatures from falling below the freezing point. Drainage systems are also beneficial; they allow for the salt to be drained from receivers and pipes during downtime, storing it in thermally insulated tanks to prevent freezing. Pre-heating the system before start-up after a period of inactivity is necessary to ensure the salt returns to a liquid state. Implementing thermal tracing on piping using heating cables or tapes can maintain the salt's temperature above its melting point. Operational strategies, such as pre-heating the system during colder hours or maintaining minimum temperature overnight, are employed to prevent freezing. These measures, individually or in combination, play a key role in the operational reliability and efficiency of CSP systems, addressing the challenges associated with MS thermal management [15].
## 3 Analytical Investigations
\begin{array}{ll}f_{4}(a_{1},a_{2},\varphi_{1},\varphi_{2})=&\sigma-\lambda a _{2}^{2}-\frac{1}{4}\lambda a_{1}^{2}+\frac{1}{2}\beta_{1}+\frac{3}{8}\beta_ {5}a_{2}^{2}+\frac{1}{4}\beta_{4}a_{1}^{2}+\frac{3}{8}\beta_{6}a_{2}^{2}+\frac{ 1}{4}\beta_{8}a_{1}^{2}\\ &+\frac{1}{8}(-\lambda+\beta_{4}-\beta_{8}+\beta_{9})a_{1}^{2}\cos(2\varphi_{ 2}-2\varphi_{1})+\frac{1}{8}(-\beta_{7}+\beta_{10})a_{1}^{2}\sin(2\varphi_{2}- 2\varphi_{1})\\ &+\frac{1}{2a_{2}}E(1+\sigma)^{2}\sin(\varphi_{2})=0\end{array}  \tag{47}\]  
Solving Equations (44) to (47) in terms of the system and control parameters (i.e., \(\sigma,\ E,\ \mu,\lambda,\ \delta_{1},\ \delta_{2}\)), we can explore the influence of these parameters on the steady-state vibration amplitudes \(a_{1}\) and \(a_{2}\) of the controlled Jeffcott system as illustrated in Section 4. Moreover, to investigate the stability of the solution given by Equations (42) and (43), one can check the eigenvalues of the non-linear system (38) to (41) using the Lyapunov first method. Accordingly, let \((a_{10},\ a_{20},\ \varphi_{10},\ \varphi_{20})\) be the fixed-point solution of Equations (38) to (41) (i.e., the solution of Equations (44) to (47)) and \((a_{11},\ a_{21},\ \varphi_{11},\ \varphi_{21})\) be a small deviation about that solution. Therefore, we have  
\[a_{j}=a_{j0}+a_{j1},\ \ \varphi_{j}=\varphi_{j0}+\varphi_{j1},\ \ \dot{a}_{j}=\dot{a}_{j1},\ \ \ \dot{\varphi}_{j}=\dot{\varphi}_{j1};\ \ \ \ \ \ \ \ \ \ \ j=1,2 \tag{48}\]  
Inserting Equation (48) into Equations (38) to (41), expanding for a small deviation \(a_{11},\ a_{21},\varphi_{11}\), and \(\varphi_{21}\), keeping the linear terms only, we have  
\[\dot{a}_{11}=\frac{\partial f_{1}}{\partial a_{11}}a_{11}+\frac{\partial f_{1}}{ \partial a_{21}}a_{21}+\frac{\partial f_{1}}{\partial\varphi_{11}}\varphi_{11}+ \frac{\partial f_{1}}{\partial\varphi_{21}}\varphi_{21} \tag{49}\]  
\[\dot{a}_{21}=\frac{\partial f_{2}}{\partial a_{11}}a_{11}+\frac{\partial f_{2}}{ \partial a_{21}}a_{21}+\frac{\partial f_{2}}{\partial\varphi_{11}}\varphi_{11}+ \frac{\partial f_{2}}{\partial\varphi_{21}}\varphi_{21} \tag{50}\]  
\[\dot{\varphi}_{11}=\frac{\partial f_{3}}{\partial a_{11}}a_{11}+\frac{\partial f_{3}}{ \partial a_{21}}a_{21}+\frac{\partial f_{3}}{\partial\varphi_{11}}\varphi_{11}+ \frac{\partial f_{3}}{\partial\varphi_{21}}\varphi_{21} \tag{51}\]\[\dot{\varphi}_{21}=\frac{\partial f_{4}}{\partial a_{11}}a_{11}+\frac{\partial f_{4 }}{\partial a_{21}}a_{21}+\frac{\partial f_{4}}{\partial\varphi_{11}}\varphi_{ 11}+\frac{\partial f_{4}}{\partial\varphi_{21}}\varphi_{21} \tag{52}\]  
According to the Hartman-Grobman theorem, the linear dynamical system (49) to (52) has the same stability behavior as the non-linear system (38) to (41) as long as the equilibrium point of both systems is hyperbolic. Therefore, the stability of the non-linear system (38) to (41) can be checked by investigating the eigenvalues of the linearized system (49) to (52) (see [43]).
## 1 Introduction
cereale_ facilitates reduction of soil erosion, retention of soil nutrients, nitrogen fixation, carbon sequestration, and inhibition and suppression of weeds in the cultivating land [40]. Moreover, the residual components of this plant in the soil effectively inhibit seed germination and seed dormancy of weeds [41] and mainly attribute to the exudation of phenolic compounds into the soil, including glycosylated benzoxazinone, 2-(2,4-dihydroxy-1,4-benzoxazin-3-one)-beta-D-glucopyranose (DIBOA-glucose), benzoxazinoids compounds [42], 2-hydroxy-1,4-benzoxazine-3(2H)-one (HBOA), the 2-benzoxazoline (BOA), and 2-amino-3H-phenoxazin-3-one (phenoxazinone APO) [43; 44]. Previous studies on the allelopathic properties of _S. cereale_ have led to the identification of important allelochemicals. However, most of these studies were performed using a single cultivar. It is possible that there are still other allelochemicals present in the several unexplored cultivars. Variation in the allelopathic properties, phenolic compound profiles, and abilities to suppress weeds have been documented for many plant species [45; 46; 47; 48; 49]. Previous studies have shown that allelochemical composition, concentration, and allopatic properties vary significantly among the cultivars [32; 50; 51; 52]. However, the effects of different cultivars on weed control have not been documented yet. In this study, we focused on the comparative allelopathic potential of two _S. cereale_ cultivars: Paldong and Singhi. The main objective of the present study was to explore the possible effects of varying seeding density, high plant density (HPD), and low plant density (LPD), both in tillage and no-tillage systems, on weed growth in field conditions. Furthermore, we documented the major allelochemicals present in the two cultivars of _S. cereale_ leaves extracts. This study provides insight into the mechanism underlying inhibition of weed callus growth by _S. cereale_ leaf extracts. Additionally, correlation between the phenolic compound exudates of cover crop and fresh weight of weed callus was assessed. To the best of our knowledge, the present study is the first report on the allelopathic effects of _S. cereale_ extracts on callus growth and seed germination of different weed species growing in _P._ frutscens fields.
## 2 Background  
Radiomics is an emerging trend that meets artificial intelligence and medical applications with prognostic perspective. Examples are mostly related with oncology, with efforts directed towards glioblastoma [24; 25; 26], oesophageal cancer [27], gastric cancer [28], soft tissue sarcoma [29], and lung tumours [19; 30; 31]. Nevertheless, recent examples outside the oncological field are related to COVID-19 outcome [32; 33], cardiological issues [34], meningioma subtype, pneumonia, and parkinson outcome prediction [35; 36; 37].  
Because of the topic discussed, hereinafter we shortly overview the work concerned with the prediction of the Overall Survival in patients suffering from the NSCLC [19; 20; 30; 38; 39] (Table 1). Furthermore, the interested readers can deepen AI applications in lung cancer starting from two reviews that discuss the prediction of lung cancer prognosis [40; 41].  
The literature explored so far shows a clear predominance of studies that focus on radiomics descriptors that were extracted from the GTV.  
Moreover, the analysis of the literature reveals to us that most of the work computed both 2D and 3D shape and texture descriptors that mostly leverage on the Grey-Level Co-occurrence Matrices or wavelets. This can be justified by the fact that healthy tissue often differs from tumour tissue, mainly at the microstructure level.  
On these grounds, the contribution of this manuscript is twofold: first, we investigate how the use of different volumes of interest impacts the Overall Survival prediction. Second, we introduce the 3D version of the local binary pattern (LBPs), a texture descriptor that is widely used for texture analysis in 2D images in other fields [42], but still not extended to the radiomic field.  
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline
**Author** & **Images** & **S**epepared Volume** & **Study Population** & **Number of Patients** & **Number of Features** & **Features Description** & **Learning Model** & **Performance** \\ \hline [PERSON] et al. & CT & GTV & NSCLC, I and II stages & 113 & 1605 & shape, intensity, training, & Kaplan-Meier method & AUC = 0.67 \\  & & & & & & & & & \\  & & & & & & & & & \\ \hline [PERSON] d.l. & CT & GTV & long and neck corner & 1091 & 440 & humor intensity, & multivariate Cox proportional & AUC = 0.69 \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\ \hline [PERSON] et al. & CT & GTV & NSCLC, I-II/V stages & 464 & 440 & slope, intensity, testing, and & wavelets features & Wilcoxon test for feature selection & AUC = 0.66 \\  & & & & & & & & & & \\ \hline [PERSON] et al. & CT & GTV & NSCLC, III stage & 107 & 212 & slope and feature features & multivariate model & AUC = 0.675 \\  & & & & & & & & & \\ \hline [PERSON] et al. & CT & GTV & NSCLC, I and II stages & 100 & 722 & morphology and feature based features & Cox proportional & AUC = 0.64 \\  & & & & & & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Summary of the background on the radiomics approach to predict the Overall Survival in lung cancer.