esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Performance Evaluation of INTU  
GRU establishes its superiority in all time intervals in the case of the INTU dataset. Figures 23-25 represent the actual vs. predicted value curves of a one-day interval, Figures 26-28 represent the curves of a three-day interval, and Figures 29-31 represents the curves of a five-day interval. The values of performance metrics are summarized in the Tables 10-12 for one-day, three-day, and five-day intervals, respectively.  
Figure 21: ORCL actual vs. LSTM predicted for five-day interval.  
Figure 22: ORCL actual vs. GRU predicted for five-day interval.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline Model & \(\mathbf{R^{2}}\) & MAE & MAPE & RMSE \\ \hline RNN & 0.90040 & 0.98882 & 1.98959 & 1.48361 \\ \hline
**LSTM** & 0.90153 & 0.98621 & 1.97875 & 1.39695 \\ \hline GRU & 0.90001 & 0.99962 & 2.01920 & 1.40772 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance comparison of ORCL five-day interval.  
Figure 23: INTU actual vs. RNN predicted for one-day interval.  
Figure 24: INTU actual vs. LSTM predicted for one-day interval.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \(\mathbf{R^{2}}\) & **MAE** & **MAPE** & **RMSE** \\ \hline RNN & 0.98555 & 5.26118 & 2.34914 & 7.32498 \\ \hline LSTM & 0.98046 & 6.20282 & 2.78882 & 8.51910 \\ \hline
**GRU** & 0.98983 & 3.70209 & 1.64523 & 6.14465 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Performance comparison of INTU one-day interval.  
Figure 25: INTU actual vs. GRU predicted for one-day interval.  
Figure 28: INTU actual vs. GRU predicted for three-day interval.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \(\mathbf{R^{2}}\) & **MAE** & **MAPE** & **RMSE** \\ \hline RNN & 0.94280 & 10.65475 & 4.46536 & 14.21290 \\ \hline LSTM & 0.97453 & 6.85287 & 2.97133 & 9.48472 \\ \hline
**GRU** & 0.98988 & 3.91155 & 1.82053 & 5.97660 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Performance comparison of INTU three-day interval.  
Figure 26: INTU actual vs. RNN predicted for three-day interval.  
Figure 27: INTU actual vs. LSTM predicted for three-day interval.
# Development of a Prediction Model for Tractor Axle Torque during Tillage Operation
## 3 Results
### Prediction Model
#### 3.3.3 Independence  
A Durbin-Watson test (D.W) was conducted to confirm the results of the independence test of the residuals for all the regression models, as shown in Table 9. The results showed a range of 1.340-2.146 for all the regression models. Among them, Models B-H showed a D.W value close to 2, which means that they guarantee the independence of the residuals. Model A shows a D.W value of less than 1.5, which means that it may have difficulty guaranteeing the independence of the residuals.
## 3 System Model
### Relay Operation  
Relay receives text message from source using USRP kit. The received signal is amplified in PC using LabVIEW. Amplified signal is again forwarded to USRP kit and then retransmitted towards the destination.
# Computer Vision for Fire Detection on UAVs--From Software to Hardware
## 6 Conclusions  
The present work is a product of a literature review, consisting of 72 scientific publications. These publications were Computer Vision implementations with UAVs in the field of fire detection. The time frame of these implementations concerned the last decade. From the beginning of 2016 until the end of 2019, there is a sharp increase in fire detection applications. It should be noted that the time period in which the downward trend of publications begins coincides with the beginning of the COVID-19 pandemic situation.  
In this literature review, different types of UAVs, Computer Vision AI models, sensor types as well as integrated hardware are presented. After a thorough study of the papers, a comparison was made between the various vehicles, models and methods and the proposal for the most efficient solution in fire detection applications were presented. In terms of vehicles, multicopters seem to be the most suitable for such applications. Their vertical take-off/landing and hovering capabilities in combination with their stability and their ability to remain in a constant position allows a 360\({}^{\circ}\)-view of the operational area. In the field of sensors, the combination between visible light and IR sensors increases the effectiveness of early detection of fire or smoke. Additionally, very important auxiliary systems, which are integrated in the vehicle, are the GPS and IMUs, providing information about the location of the vehicle. Furthermore, with regard to the Computer Vision applied methods, when the calculations are made on-board, ERNet provides high accuracy and low power consumption compared to the other high-accuracy models. In case when the calculations are done in a ground-based station, VGG-16 constitutes an effective solution, because alongside its high performance, it implies big computational costs. In any case, CNN models achieve higher performance. Finally, ROS is ideal for path planning and autonomous navigation and Pix4D for photogrammetry and mapping. Both of them are software applications that automate the essential processes of each mission and provide a larger amount of necessary information.  
This research received no external funding.  
**Data Availability Statement:** Not applicable, the study does not report any data.  
**Acknowledgments:** This work was supported by the MPhil program \"Advanced Technologies in Informatics and Computers\", hosted by the Department of Computer Science, International Hellenic University, Greece.
# Ras Isoforms from Lab Benches to Lives--What Are We Missing and How Far Are We?
### Ras the Crucial Signal Relay Protein  
The expression pattern of Ras isoforms is tissue-specific as well as developmental stage-specific [16; 17]. The primary role of Ras emerged as a protein that assembles signaling complexes and relays signals to regulate an array of cellular activities. Emerging roles show its involvement in maintaining the integrity of actin cytoskeleton, in cell adhesion and migration, endocytosis, etc. [18]. This brings us to the crucial question--How does one signaling protein regulate counteracting processes like proliferation and apoptosis?  
Ras is a molecular switch that is activated by guanine nucleotide exchange factors (GEFs) that catalyze the exchange of guanosine diphosphate (GDP) with GTP. The counter process--the transition of Ras from active to the resting state--occurs by GTPase activating proteins (GAP) mediated GTP hydrolysis. Ras has a slow intrinsic GTP hydrolysis activity which is accelerated by GAPs by \(\sim\)10\({}^{5}\) folds [19]. Two well-characterized GAPs are neurofibromin (NF1) and Ras P21 Protein Activator 1 (RASA1) (p120 GAP) [20]. If Ras deactivation fails, Ras remains at an active or \"On\" state. Once active, Ras binds to a range of effectors that carry out the downstream signaling. The specificity, as well as the diversity of signaling, arises due to binding with specific activators and effectors [21]. [PERSON] et al. and [PERSON] et al. demonstrated that Ras-induced oncogenic transformation by uncontrolled cell proliferation requires a sustained expression of activated Ras and in an inducible oncogenic system, withdrawal of Ras expression led to tumor regression [22; 23]. The ability of Ras to induce both proapoptotic and antiapoptotic signals may depend on an interplay between the type of receptor(s) activated, strength of activation, the access to activators and effectors, their binding specificities, kinetics, and stoichiometry of signaling.  
Figure 2: The isoforms share sequence similarities in their G-Domain (1–165 aa). This is followed by a Hyper Variable Region (HVR) (166–188/189), which differs in all three isoforms in sequence as well as in the post-translational modifications. In addition to famesylation, a post-translational modification that all the three isoforms undergo, H-Ras and N-Ras further undergo dual and single palmitoylation, respectively. K-Ras undergo the addition of a stretch of polylysine residues.  
The outcome of Ras activation depends on whether it is a normal or an oncogenic cell, as a highly activated Ras in a normal cell will most likely relay a proapoptotic signal, whereas in an oncogenic setup it will relay an antiapoptotic signal [24].
## 2 Material and Methods
### Material  
The meteorological data used in this study were retrieved from the meteorological observation data of the eddy covariance flux tower at 17 flux sites. Figure 2 shows the map representation of the 17 flux sites of cropland over the globe.  
The information of the 17 flux sites is shown in Table 1. The 17 flux sites of cropland over the globe were located in different countries (such as Germany, the United States, France, and Italy). DE-Kli and IT-BCi have the lowest (7.77 \({}^{\circ}\)C) and highest (17.88 \({}^{\circ}\)C) mean annual temperatures, respectively. The annual precipitation of these sites varies from 343.1 (US-Tw3) to 2062.25 mm (CH-Oe2). We divide the flux data set into the training set, validation set, and test set, the ratios of which are 60%, 20%, and 20%, respectively, and the three datasets are used to train, validate, and test the ANN model. The vegetation index and reflectance data were retrieved from MODIS MOD9A1 ([[https://modis.ornl.gov/data.html](https://modis.ornl.gov/data.html)]([https://modis.ornl.gov/data.html](https://modis.ornl.gov/data.html)), accessed on 27 February 2020), having a spatial resolution of 500 m. These flux data and MODIS data were used to training the two models of estimating ET. The time series of MODIS data were extracted according to the longitude and latitude coordinates of the flux sites. The spectral index was calculated using the MOD43A4 product, following the formulations shown in Table 2. NDVI is usually used to reflect the information of vegetation coverage and growth. In order to obtain information on a larger regional scale, a new vegetation index NIRv is introduced [19], which can reflect the photosynthetic capacity of surface vegetation better. NIRv is the product of the total near-infrared reflectance (NIRt) (MODIS second band) and NDVI. NIRv is a remote sensing measurement of canopy structure, which can more accurately predict photosynthesis [34]. The shortwave infrared band (SWIR) is usually used to reflect water stress and is calculated by using the reflectance data directly.  
Figure 2: Map representation of 17 eddy covariance flux sites.
# Sustainable Materials from Waste Paper: Thermal and Acoustical Characterization
## 2 Materials and Methodologies
### Sawdust Waste  
Sawdust is a residue obtained from the timber manufacturing process. It is used in several fields since wood is one of the most used raw materials. It can be produced in  
\begin{table}
\begin{tabular}{c c c c c} \hline
**European Directive** & **Directive 94/62/CE** & **Directive 2004/12/CE** & **Directive 2018/852/CE** & **Directive 2018/852/CE** \\ \hline Deadline to & 30 June 2001 & 31 December 2008 & 31 December 2025 & 31 December 2030 \\ \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & General (all materials) & & & \\  & between 25\% and 45\%, & & & \\  & with a minimum & 60\% & 75\% & 85\% \\ \multirow{2}{*}{\
\begin{tabular}{} \end{tabular} } & threshold of 15\% for & & & \\  & each material & & & \\  & & & & \\ \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{50.7\%} & \multirow{2}{*}{62.4\%} & \multirow{2}{*}{80.4\%} & \multirow{2}{*}{87.4\%} \\ \end{tabular}
\end{table}
Table 1: European recycling objectives achieved and to be reached (Adapted from COMIECO, [33]).  
different grain sizes, as small chips or as a fine powder, depending on the timber type and sawing process. [PERSON] [43] affirmed that sawdust has great potential to reduce environmental pollution due to its use as a raw matter for building materials able to comply with international energy standards. Several researchers have investigated the recycling potential of this natural resource, with non-building usage including bedding for poultry and livestock, or, alternatively, soil composting and mulching [44; 45; 46; 47].  
[PERSON] et al. [25; 48], used sawdust as an additive to waste paper pulp for the production of insulating panels [25], and in addition to concrete for the development of wood-crete [48]. They also found that the type of timber can influence the final properties of the composite, with wood-crete made from softwood having a lower thermal conductivity (about 20% less) than the mix developed with hardwood.  
[PERSON] et al. [49] tested the thermal properties of clayey brick with the addition of five different percentages of sawdust. Results demonstrated that sawdust induces a significant enhancement of thermal properties of the final composites, causing a significant drop of about 30%.
# Atmospheric GNSS RO 1D-Var in Use at UCAR: Description and Validation
## 3 Results
### Validation Using NWP Model Data
#### 3.3.2 Comparison of Observation Operators  
Another crucial factor differentiating OLD and NEW is the observation type and operator used in 1D-Var. Figure 10 shows the differences of 1D-Var temperature (upper panels) and specific humidity (lower panels) to the ECMWF analysis in the mean (left) and standard deviation (right) in the tropics (marked T), middle latitudes (M), and polar latitudes (P). The middle latitudes are 30-60\({}^{\circ}\) in both Hemispheres. OLD uses the standard RO refractivity, derived via the Abel inversion, as the observation (denoted as OLD\({}_{\rm N}\) in the legend). While NEW can use the bending angle (NEW\({}_{\rm BA}\)) and the refractivity (NEW\({}_{\rm N}\)), it takes as the default option the two-step approach--variational refractivity inversion followed by the retrieval step. We want to point out that the observation errors of refractivity and bending angle used in this comparison are inter-consistent. We applied the same estimation (Hollingsworth-Lonnberg) method to refractivity and bending angle profiles belonging to an identical data set to ensure the consistency between refractivity and bending angle in the observation error. The consistency is valid between the observation error and the background error. Attaining these consistencies is vital for fair comparisons between observation types and operators.  
Compared to OLD\({}_{\rm N}\), NEW\({}_{\rm N}\) shows much better agreements with the ECMWF analysis in both mean difference and standard deviation, evincing the overall improvement over OLD. The comparison of NEW\({}_{\rm N}\) with NEW\({}_{\rm BA}\) shows that the standard refractivity is advantageous over the bending angle in the lower troposphere, mainly due to the posterior height determination [38] in the Abel inversion. On the other hand, NEWBA reduces the standard deviation of temperature slightly in the stratosphere. The 1D-Var with the two-step approach (denoted NEW\({}_{\rm VR}\)) outperforms NEW\({}_{\rm N}\) and NEW\({}_{\rm BA}\), strongly supporting using the two-step approach in the 1D-Var. Readers are referred to [38] for more details on the approach.  
Figure 9: Root-mean-square differences of 1 DVAR retrievals from operational ECMWF analysis: (**a,b**) temperature (\({}^{\circ}\)C) and (**c,d**) specific humidity (g kg\({}^{-1}\)). Left panels, (**a,c**), are for the previous 1D-Var version (denoted OLD), while right panels are for the new 1D-Var (denoted NEW) described in this paper.
# A Survey on Penetration Path Planning in Automated Penetration Testing
## 2 Overview
### Penetration Path Planning
#### 2.2.2 Planning Models  
As shown in Figure 2, the planning model is one of the core components of penetration path planning, serving as the foundation upon which effective and efficient penetration strategies are developed. By leveraging planning models, penetration testers can simulate adversarial behavior in a target network environment. The modeling primarily includes the following three components:  
**Network environment information:** this includes critical details such as the topology of the target network, system configurations, service details, attack targets, and known vulnerabilities;  
**Attacker action space:** defining the attacker's action space is a challenging task due to its inherently infinite and evolving nature, encompassing a diverse range of executable actions. The modeling process must consider factors such as the attacker's skill level, tools, available resources, and known exploits. By modeling the attacker's action space, a better understanding of their behavior patterns and attack strategies can be achieved, thereby providing a foundation for planning effective attack paths;  
**Uncertainty factors:** real-world PT is filled with uncertainties, such as dynamic changes in the network environment, the success rate of exploit attempts, and the observability of the environment.  
By establishing such a detailed model, the planning agents can gain a better understanding of the network environment and the attacker's capabilities, thus providing a foundation for planning effective penetration paths. Various modeling techniques, such as a graphical security model, planning domain model, and sequential decision model, can be utilized in this process.  
Figure 2: Literature search strategy.
## 3 QoE-Aware Geographic Routing Protocol for Video Streaming over VANETs
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Descriptions** \\ \hline \(G=\{V,E\}\) & Undirected graph \\ \(V\) & Set of vehicles \\ \(u\), \(v\), \(i,j\) & Vehicles \\ \(E\) & Set of communication links \(E:V\times V\) \\ \(l\) & Link \(\in E\) \\ \((u,v)\) & Neighbour relation \\ \(N(u)\) & Set of neighbours of vehicle \(u\) \\ \(v(u,v)\) & Weight vector assigned to a link \((u,v)\) \\ \(w_{i}\) & Individual weight of metric \(i\) \\ \(t\), \(t_{cur}\), \(\Delta t\), \(T_{delay}\) & \(T_{r}\), \(T_{s}\), \(\delta\) & Time \\ \(x\), \(y\), \(x_{p}\), \(y_{p}\), \(x_{d}\), \(y_{d}\), \(x_{i}\), \(y_{i}\), \(x_{j}\), \(y_{j}\) & Position \\ \(s\), \(v_{i}\), \(v_{j}\) & Velocity \\ \(\theta\), \(\theta_{i}\), \(\theta_{j}\) & Direction \\ \(D\), \(Dist_{i}\) & Distance \\ \(LET\), \(LET_{i}\) & Link expiration time \\ \(r\) & The transmission range \\ \(T_{delay}(u)\) & Mean of all vehicle links delays \\ \(card(N(u))\) & Number of neighbours of the vehicle \(u\) \\ \(J(u)\) & Jitter of a vehicle \(u\) \\ \(J(u,v)\) & Jitter between the vehicles \(u\) and \(v\) \\ \(PL(u)\) & Packet loss rate of the vehicle \(u\) \\ \(PKTs_{rc}\) & Number of packets received correctly \\ \(PKTs_{nd}\) & Number of packets received but dropped \\ \(PKTs_{sc}\) & Number of packets sent correctly \\ \(PKTs_{d}\) & Number of packets sent but dropped \\ \(QoE(u)\) & QoE value of a vehicle \(u\) \\ \(MOS_{routing}(u)\), \(MOS_{routing}\), \(MOS_{routing}\), \(MOS_{video}\) & \(MOS_{video}\) \\ \(k_{1}\), \(k_{2}\), \(Q_{MAX}\), \(Q_{MIN}\), \(\alpha\), \(\beta\), \(\gamma\) & Constant values \\ \(p(i)\) & Weight value of vehicle \(i\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Adopted notations. Figure 3: Beacon packet format in GeoQoE-Vanet protocol.
## 4 Methodology
### Machine Learning (ML) Methods
#### 4.2.2 Random Forest  
This method operates based on a structure consisting of numerous regression trees (CARTs) and its output is computed by integrating (or averaging) the output of these CARTs. The principles of ensemble training techniques are based on the assumption that some training methods perform better in certain situations and therefore using a group of these methods can result in good performance in a wide range of situations. In other words, a combination of several models (in our case, prediction models) tends to be more accurate than a single model. A decision tree is a non-parametric statistical model (in the sense that no value is pre-set for its variables) first introduced by [PERSON] et al. [38; 39], which can be described as a structure consisting of many nodes and leaves that grow in the training process. To train a tree, one needs a set of training data like \(L_{n}=\{(x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{n},y_{n})\}\) where \(n\) is the number of observations, \(x\) is the input vector with m features \(X=\{x_{1},x_{2},\ldots,x_{m}\}\), and \(\mathrm{y}\) is the output scalar. During training, the input data are split at each node, beginning with the root node. Each node uses its own splitting operator for this purpose. This process continues until the largest possible number of leaves are obtained. At the end of this training process, the prediction function \(t=(X,L_{n})\) is built on the training data. In the random forest (RF) method, each tree creates a random sample of data (hence the term random in the name). All of the generated decision trees are then combined by a bootstrap-aggregation (bagging) algorithm [40]. A bootstrap dataset is a set of instances like \(\left(L_{n}^{\theta_{1}},\;L_{n}^{\theta_{2}},\;\ldots\;L_{n}^{\theta_{q}}\right)\) that are obtained by random sampling with replacement from the training data. In RF, each decision tree is trained with a set of these values. Using the \(\mathrm{q}\) trees created for the new input, the method obtains \(\mathrm{q}\) predictions in the form of \(\hat{y}_{1}=t\left(x,L_{n}^{\theta_{1}}\right),\hat{y}_{2}=t\left(x,L_{n}^{ \theta_{2}}\right),\ldots,\hat{y}_{q}=t\left(x,L_{n}^{\theta_{q}}\right)\), and then integrates the predictions of individually trained trees by Equation (8) for regression problems [41; 42].  
\[\hat{y}=\frac{1}{q}\sum_{i=1}^{q}\hat{y}_{i}=\frac{1}{q}\sum_{i=1}^{q}t\left( x,L_{n}^{\theta_{i}}\right) \tag{8}\]  
In this equation, \(\hat{y}_{i}\) is the prediction of the i-th tree.  
The main advantage of using a bagging algorithm is that it helps avoid correlation between different trees, which results in the higher diversity of trees created from different samples. Another advantage of this algorithm is its noise resistance, because it produces independent trees based on different training samples, and while a decision tree alone could be noise-sensitive, the combination of several independent decision trees tends to be less noise-sensitive [43].