esa-sceva/satcom-chunk-collection huggingface dataset:
# Autonomous Navigation of Robots: Optimization with DQN
## 5 Design and Execution Strategy for the Control System: A Comprehensive Overview
### Reward System for the DQN Agent
#### 5.3.2 Quantifying Spatial Proximity: The Role of Linear Distance Reward  
The linear distance reward plays a crucial role in assessing the proximity of the mobile robot to its goal. It involves calculating the distance rate \(D_{R}\) by considering both the current distance \(D_{c}\) and the absolute distance \(D_{a}\).  
The current distance refers to the real-time spatial separation between the robot and the goal. As the robot moves and the goal's position changes throughout the training process, this distance continuously varies. To calculate the current distance, trigonometric analysis is required, taking into account the distances of the robot and the goal with respect to the main plane of Gazebo. Figure 10 illustrates this geometric relationship.  
On the other hand, the absolute distance represents the spatial span between the starting point of the Gazebo reference frame and the goal, specifically at the initiation of a  
Figure 10: Real-time geometric analysis: calculating distances of robot and goal.  
training scenario. It serves as a fixed reference for assessing the robot's progress toward the goal.  
By considering both the current distance and the absolute distance, the linear distance reward enables the agent to effectively gauge its proximity to the goal. This reward component provides valuable feedback for the agent's decision-making process, and aids in optimizing its navigation strategy toward achieving the desired objective.  
The distance rate is a crucial factor in determining the reward for the robot's progress towards the goal. This rate is calculated using the equation presented in Equation (14). The purpose of this calculation is to ensure that as the robot approaches the goal, the reward value increases, indicating successful progress. However, if the robot gets too far away from the goal, the reward score is reduced to one, reflecting the need to prioritize proximity to the target. This approach encourages the robot to navigate efficiently and prioritize reaching the goal within a reasonable distance.  
\[R_{d}=2^{(D_{a}/D_{c})} \tag{14}\]  
The constraints in Equations (15)-(17) are established for the calculation of the reward based on the distance from the robot to the goal.  
\[if\ D_{a}>D_{c}, \tag{15}\]  
\[R_{d}>2 \tag{16}\]  
\[else\ 1<R_{d}\leq 2 \tag{17}\]  
If the absolute or initial distance from the goal is greater than the current distance, the value of the reward will always be greater than 2, because this is the base; however, the further the robot moves, the reward will be between 1 and 2.  
Finally, the reward function for the network is presented in (18); three conditions are defined for the system, which will allow for adequate automatic control of the robot's route to the goal.  
\[R=\left\{\begin{array}{ll}200,&\text{if goal is reached}\\ -100,&\text{if collision occurs}\\ R_{d}\cdot R_{\theta},&else\end{array}\right. \tag{18}\]
# Deep Learning-Based Method for Classification and Ripeness Assessment of Fruits and Vegetables
## 5 Discussion
On the other hand, the proposed methodology could be taken as a basis for other types of applications, but it would be necessary to evaluate whether the models proposed in this work are the ones that present the best performance to solve the problem to be addressed, in addition to obtaining the best hyperparameters, as well as having a robust database for training, validation, and testing. Despite the results, there are some limitations; for example, only some classes of fruits and vegetables have data on ripening, and for this reason, models can be improved by adding more classes of ripening states to the rest of fruits and vegetables where the ripening state is not detected. The dataset provides information for only certain types of fruits and vegetables. This constraint is prevalent in several AI models as gathering data for all classes can be challenging and costly. Hence, the model may not precisely recognize the ripeness of fruits and vegetables excluded from the dataset. Performance could be improved by making changes to the hyperparameters, but this depends on the nature of the problem. Tuning the hyperparameters could enhance the model's performance and this could be made with optimization algorithms, such as genetic (GA) and swarm intelligence (SI). Also, the general methodology uses two AI models, one to recognize thirty-two categories of fruits and vegetables, and another model to assess the ripeness of six of the categories. One of the improvements of the system would be to unify in a single model both the classification between fruits and vegetables and the identification of the ripeness stage. On the other hand, the system is not able to detect damaged, obstructed or poorly illuminated fruits and vegetables. Future research will take these points into account in order to create a robust system. Finally, this work can only detect one fruit or vegetable per inference. Therefore, it is necessary to develop a system that allows the detection of more than one element per inference, achieving a more robust system. As part of future work, enhancing the model's performance could involve integrating real-time inference capabilities. This will enable the model to be used in real-world applications, including sorting and grading on farms and quality control in stores. Additionally, incorporating sensors such as hyperspectral and near-infrared cameras could enhance the model's performance. These devices can provide additional information on the chemical composition and structural characteristics of fruits and vegetables.
## 6 Structural Performances
### Fire-Resistant Systems  
In PFISB structures, external sheathings, protective claddings, and cavities are adapted to control the spread of fire [6]. The addition of organolay and foam core in FRP/GFRP panels prevents fire from spreading through the facades [123; 124]. The assessment of the post-fire performance was then carried out, and it was revealed that they could resist fire for 90 min and still possessed more than half of the post-fire performance [125]. Further studies on the fire performance of PFISBs and sandwich panels of rock wool revealed that the structural members are least affected due to cavities between the separate buildings' IMUs [126]. Moreover, rock wool sandwich panels can increase the fire resistance by more than 120%. A similar study ensures that the IMU is semi-independent, prevents fire propagation, and provides a sufficient rigidity to guarantee robustness in PFISBs [127]. The author's project tested the PFISBs' steel floor, column, load-bearing wall, and non-load-bearing wall specimens, as shown in Figure 11a-d [128]. The PFISBs' floor, coated with rock wool and cement board, and the ceiling with rock wool and gypsum board, discovered that the steel beams could be effectively protected by gypsum board and rock wool [129]. The PFISBs' column externally covered by gypsum board and internally with rock wool experienced a wide range of fireproof board falling off and could not meet the requirement. The non-load-bearing wall in the PFISB with rock wool and gypsum board encountered an extensive range of gypsum boards falling off. The overall wall fire protection meets the limit, but the fireproof properties of the board's appropriate thickness can ensure the PFISB structure's safety. The load-bearing wall gypsum board fell off, and the non-load-bearing study were deformed, but the load-bearing member's performance was good.
## 3 Components of CRISPR/Cas Systems
### Cas Proteins  
In close proximity to the CRISPR array are genes that encode the CRISPR **a**ssociated (Cas) proteins (Figure 1A) [19, 20, 24, 25, 44]. Cas proteins provide the enzymatic machinery required for the acquisition of new spacers from, and targeting, invading elements.  
CRISPR/Cas systems are currently classified into type I, II and III, based on the phylogeny and presence of particular Cas proteins [10]. There is further division within each type into subtypes (e.g., type I is composed of type I-A to I-F). The Cas proteins are important for the differentiation between both the major CRISPR/Cas types and the subtypes. Two main groups of Cas proteins can be distinguished. The first group, which includes the core Cas proteins, is found across multiple types or subtypes. Cas1 and Cas2 (Cas2 is sometimes fused to another Cas protein) are found in every Cas operon across the three main types, and Cas1 is considered the universal marker of CRISPR/Cas systems [10, 24, 25, 44]. Cas3, Cas9, and Cas10 are each specific for one major type, serving as signature proteins for type I, II, and III, respectively [10].  
The second major group consists of proteins only found within the gene clusters of one particular subtype [10, 25]. For example, the Cse and Csy proteins are subtype-specific for type I-E and type I-F systems, respectively. The subtype specific proteins of some systems form a complex involved in targeting and interference, which in the type I-E system is referred to as Cascade (CRISPR-associated complex for antiviral defense) [45, 46, 47, 48, 49]. These complexes will be discussed in detail below. The systems can be complex since some bacteria contain multiple CRISPR/Cas subtypes, each of which can have multiple CRISPR arrays that function with the appropriate Cas cluster [18]. In summary, the _cas_ cluster for each CRISPR/Cas subtype contains genes encoding Cas1 and Cas2, the signature protein defining the major type and a set of subtype specific proteins. Interestingly, the total number of proteins differs between the types of system, even though the functional principles are similar in the cases studied thus far. It is possible that unexpected functions may reside within Cas proteins that are yet to be characterized.
## 4 Data Acquisition
### Flight Design  
According to the flight authorization issued by the DGAC (Direction Generale de l'Aviation Civil), the distance between the telepilot and the employed UAV could not surpass 600 m during the acquisition. Therefore, the acquisition field was divided into two segments of 600 m and surveyed separately. The first segment of 600 m consists of the south part of the dike, while the second segment consists of the rest of the dike including the turn at the north end.  
The first segment was surveyed with the routine acquisition configuration, a nadir flight of 3 strips at 50 m (denoted as _s1-n50_). This configuration is simple and economic, whereas not preferable when high photogrammetric accuracy is demanded. It introduces correlations among the camera focal length, the height of camera and the lever-arm. Consequently, parameters could not be accurately estimated. The second segment consists of three flights, a nadir flight of 3 strips at 50 m (denoted as _s2-n50_), an oblique flight of 3 strips at 50 m (denoted as _s2-o50_) and a nadir flight of 2 strips with the first strip at 70 m and the second strip at 30 m (denoted as _s2-n3070_). When mixing oblique and nadir images, images acquired from different flight heights, the above-mentioned correlations could be significantly mitigated and these parameters were better estimated. This configuration of multiple acquisition angles and multiple flight heights is desirable though costly. The objective of the study was to investigate, given a good camera model and a correct lever-arm, the achievable accuracy of a simple while not preferable network configuration. The estimated camera model and lever-arm of the image block of segment 2 (_s2-n50 + s2-o50 + s2-n3070_) was considered of high accuracy and was used for sensor pre-calibration of the image block of Segment 1 (_s1-n50_).  
Figure 3 depicts the conducted flights and the information of flights are given in Table 1.  
Figure 3: An illustration of the conducted flights.
# Research on Communication Stability of Inter-Cannonball Network Based on OPNET
## 4 Modelling Scene Setup
### OPNET Scene Modeling Configuration
Figure 9: Comparative fixed and mobile node network topology in terms of (**a**) fixed node, (**b**) mobile node. In order to observe the failure rate and the impact of different cannonball speeds on network communication, the node movement trajectory in the mobile node simulation is given, and the node speed is used as a variable. It is set that _node_7_, _node_10_, _node_19_, and _node_22_ fail ten minutes after the simulation starts, and the ad hoc network reaches a 10% failure rate at this time. Twenty minutes later, _node_15_, _node_20_, _node_25_, and _node_28_ fails. At this time, the failure rate reaches more than 20%, and the node failure continues until the end of the simulation. We may deduce the link between inter-cannonball network communication stability with fault rate and node shift speed based on these simulation findings.
# Route Prefix Caching Using Bloom Filters in Named Data Networking
## 2 Background
### Route Prefix Caching  
A way to speed up the name lookup process is the route caching technique to store and reuse previous lookup results in the local cache associated with the FIB. The name lookup is to find an entry having the longest matching prefix for a content name. The entry is a pair of the prefix and its output face. The route caching can be classified into the content name caching and the name prefix caching according to the information stored in the cache. The content name caching stores a content name with the corresponding output face. It exploits a sort of temporal locality, but its effectiveness is limited because NDN already provides in-network caching. The name prefix caching stores a name prefix  
Figure 1: Example of packet forwarding when three consumers issue the identical interest packet in named data networking (NDN).  
with the corresponding output face. In this caching scheme, a single name prefix in the cache can be matched as the LPM result for more than one content name. In other words, it also takes advantage of spatial locality. Therefore, the name prefix caching is more effective than the content name caching.  
Figure 2 shows some examples for the name prefix caching supposing that the FIB contains three prefixes \(p\), \(q\), and \(r\). Assume that the name of the first packet is _name1_ (=/ride/wagon/zo/fx), and its longest matching prefix \(r\) (=/ride/wagon) is cached. Then, the cached lookup result can be reused for fast forwarding when a subsequent content name is any of _name1_, _name4_ (=/ride/wagon/zo/gr), or _name5_ (=/ride/wagon/kw). Now, assume that the first content name is _name2_ (=/ride/bike/du), and its lookup result \(p\) (=/ride) is cached. If the next content name is _name4_ (=/ride/wagon/zo/gr), the prefix \(p\) is misleadingly regarded as its longest matching prefix due to the cache hit. In this case, the correct result for _name4_ should be the prefix \(r\) but not the prefix \(p\). The problem happens because the non-leaf prefix is cached but not all of its descendants are cached. This is called incomplete prefix caching. Note that the non-leaf caching does not always incur the problem. If the second content name was _name3_ (=/ride/cy) in the above, the result prefix \(p\) would be the correct.
# A Fuzzy Based Handover Decision Scheme for Mobile Devices Using Predictive Model
## 4 Proposed Methodology
### Prediction Based Model  
The way toward anticipating UE's next cell area is depicted with a model that is given as follows.  
Consider an array of seven cells with mobile network topology depicted in Figure 2. The BS-1 is at the center which is surrounded by 6 BSs from BS2 to BS-7.  
For each handoff, the cell number and time spent in that cell are considered. UE advancements are recorded and pre-executed to get the case of portability. According to cell-based portability structure, the model for UE1 and UE10 is induced for desire as follows:  
The mobility pattern is given as below for UE 3:  
\[X_{i}=\{(1,t_{1}),(7,t_{2}),(2,t_{3}),(3,t_{4}),(4,t_{5}),(5,t_{6}),(6,t_{7})\} \tag{3}\]  
with seven hands-off.  
For UE13, the mobility pattern is  
\[X_{i}=\{(5,t_{1}),(4,t_{2}),(3,t_{3}),(4,t_{4}),(5,t_{5})\} \tag{4}\]  
with five hands-offs.  
The corresponding sub-patterns are achieved by monitoring each user's pattern which is recorded for a certain period. The obtained sub-patterns are used to train the mobility prediction model. The example of portability of UE 1 is masterminded as in Table 2, which is fitting for training the prescient model. This training informational collection is then taken care of to the predictive model. In the proposed example, consider Pattern 1 and Pattern 2 that represent the training pattern, whereas Pattern 3 represents the test pattern. \(P_{1}\) to \(P_{4}\) are given as input to the predictive model, whereas \(P_{5}\) will be considered as the required output. We accept that over three examples happen for various occasions in the recorded versatility history of UE1. For prediction of mobility, the network is tried on the testing information.  
Figure 2: Representation of Mobility Prediction Model.
## 2 Preliminaries
### Attacks Targeting the Decision Tree  
Generally, a decision tree is constructed from a given dataset; however, it is also possible to partially reconstruct the dataset using the decision tree. This kind of attack is considered in [7]. In this section, we explain the workings of such attacks.  
Figure 4 shows the reconstruction of a dataset from the decision tree shown in Figure 3. As shown, it is impossible to reconstruct the original data completely from a binary tree  
model; however, it is possible to extract some of the data. By exploiting this essential property, it is possible to mount some attacks against reconstructed data, as discussed in Section 2.1. In the following, taking Figure 4 as an example, we present a uniqueness attack, a homogeneous attack, and a background knowledge attack.  
Uniqueness attack: in the dataset (Figure 4) recovered from the model, there is one user whose height is greater than 170 and who is under 15 years of age (as shown in the sixth row (see the 2 nd red box shown on the Figure), where the relevant leaf is converted via \([n_{3}(\texttt{leaf}^{(1)}),n_{3}(\texttt{leaf}^{(0)})]=[1,0]\)); thus, it is possible to target this user with a uniqueness attack.  
Homogeneous attack: similarly, in the fourth and fifth rows (see the 1 st red box shown on the Figure), which are converted from the relevant leaf via \([n_{2}(\texttt{leaf}^{(1)}),\)\(n_{2}(\texttt{leaf}^{(0)})]=[0,2]\), the height \(<170\), weight \(\geq 60\), and health status are the same (i.e., \"unhealthy\"); therefore, a homogeneous attack is possible.  
Background knowledge attack: Similarly, in the seventh, eighth, and ninth rows, there are three users whose data match in both height \(\geq 170\) and age \(\geq 15\). Among these users, one is healthy (yes) and two are unhealthy (no). As an attacker, we can consider the following:  
(Background knowledge of user A) height: 173, age: 33, healthy;  
(Background knowledge of target user B) height: 171, age: 19.  
In this case, if the adversary knows that user A is healthy, he/she can identify that user B is unhealthy.
# Impact of Deep Eutectic Solvents on Extraction of Polyphenols from Grape Seeds and Skin
## 4 Conclusions  
Considering a diverse array of biologically active compounds present in grape, valorization of grape pomace by extracting potent antioxidants represents a great challenge. This study investigated antioxidant potential and polyphenol composition of seeds and skin obtained from some widely grown grapes with an emphasis on Serbian old autochthonous varieties. International grapes did not surpass autochthonous ones concerning parameters of interest. Moreover, some Serbian varieties distinguished as potent sources of polyphenols. For example, high polyphenol concentrations were determined in 'Prokupac' and 'Black Tamjanika' seeds. Furthermore, variety 'Zupljanka' was outstanding with significantly higher amounts of catechins in seeds. When it comes to skin, noteworthy TPC values were determined for 'Zacinak' variety. Therefore, broaden exploitation of seeds and skin from Serbian traditional varieties is strongly recommended.  
Additionally, because of a growing awareness about environmental pollution, there is a need to minimize and even eliminate the use of hazardous chemicals in extraction processes. Within this research, it was shown that conventional solvents could be replaced with natural deep eutectic solvents, yielding the same or better polyphenol content. NADES were reported as safe for human consumption, and they could be used in the industry without difficult and expensive downstream purification steps.  
Since ChCit exhibited high extraction efficiency towards polyphenols from grape seeds and skin, the present work strongly emphasizes its utilization for green and sustainable extraction of biologically active compounds from wine industry pomace.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/14/4830/s1](http://www.mdpi.com/2076-3417/10/14/4830/s1)]([http://www.mdpi.com/2076-3417/10/14/4830/s1](http://www.mdpi.com/2076-3417/10/14/4830/s1)), Table S1: Antioxidant activity of grape seed extracts, Table S2: Antioxidant activity of grape skin extracts, Figure S1: HPLC chromatogram of polyphenols in a sample of grape seeds, Figure S2: HPLC chromatogram of polyphenols in a sample of grape skin.  
Conceptualization, I.R.R. and S.S.; methodology, V.T. and M.P.; investigation, N.D. and M.P.; writing-original draft preparation, N.D.; writing--review and editing, N.D., V.T.; visualization, I.R.R. and S.S.; supervision, I.R.R. and S.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Serbian Ministry of Education, Science, and Technological Development (451-03-68/2020-14/200161).  
We gratefully thank winery Milosavljevic Bucje, winery Matalj Negotin, and Agricultural High School Rajko Bosnic Negotin for their kind donation of grape samples.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Numerical Experiments and Results
### Simulation Experiment and Results
As shown in Figure 3, when n = 15 and \(\theta\) = 0.2, 0.4, 0.6, 0.8, the maximum values of the objective value using RS were 5808.1, 7061.3, 8156.1, and 9755.3, respectively, and those using NS were 6755.3, 8104.5, 9522.5, and 11,007.7, respectively. These results prove that the robust solution is the best worst-case solution. This is a very meaningful result considering uncertain war situations. Because enemies are always attempting to create disadvantageous conditions for opponents as much as possible, a solution that considers the worst case is required when determining firing scheduling and this solution must be excellent. The solution searched by the methodology proposed in this study always provides better results in the worst case than the NS. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{8}{c}{**Objective Values**} \\ \cline{3-10} \multirow{3}{*}{**n**} & \multirow{3}{*}{\(\boldsymbol{\theta}\)} & \multicolumn{2}{c}{**N**} & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**Min**} & \multicolumn{2}{c}{**Max**} & \multicolumn{2}{c}{**SD**} \\ \cline{3-10}  & & **RS** & **NS** & **RS** & **NS** & **RS** & **NS** & **RS** & **NS** & **RS** & **NS** \\ \hline \multirow{4}{