esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials Used for Restoration and Conservation of Masonry Elements
### Restoration/Conservation of Masorry Materials against Abiotic Factors  
#### 3.3.1. Deterioration of Masonry Materials by Abiotic Factors  
General Considerations  
The alteration of stone (either natural or man-made) is mainly related to two types of abiotic factors [86; 87]:  
* Intrinsic characteristics of the materials:
* Chemical and mineralogical composition (species solubility and their variation, presence of oxidative species, surface and ionic phenomena);
* Structure and texture (mainly the pore distribution, resulting in gelifraction and salt crystallization resistance, water absorption and drying rates).
* Extrinsic factors:
* Water presence;
* Presence of foreign substances altering the pH or the composition;
* Pressure and wind;
* Thermal variations;
* Anthropogenic abiotic factors (mainly related to pollution products).  
The water (in all its forms--solid, liquid or vapors) represents one of the most important factors affecting the integrity of stones: it can interact with the substrate (dissolution, hydrolysis, oxidation-reduction), it can transport other substances (for example sulfates or other pollutants), it represents a medium for chemical reaction (and for the development of microorganisms) or (in case of temperature variation) can produce microcracks in the materials (in its solid form). Closely related to the presence of water, the soluble salts can damage the materials through two pathways (crystallization and hydration).  
The main types of damages induced in stone materials are (as classified by the International Council on Monuments and Sites--ICOMOS [87]):  
* Cracks (including fracture, star cracks, hair cracks, craquele and splitting) and deformation;
* Detachment (blistering, bursting and delamination);
* Material loss (alveolarization, erosion, mechanical damage);
* Discoloration and deposits (crusts, coloration, bleaching, staining, efflorescence and encrustation). The processes necessary for preserving damaged stone materials usually involves three steps [86]:
* Cleaning, often performed mechanically or with dedicated gentle solutions;
* Consolidation (in order to increase the resistance of the material);
* Protection (generally focused on the use of water repellant solutions, as water represents one of the main factors involved in the degradation, as previously presented).  
Some classical and modern materials used for these applications are presented in Table 5, while some representative examples are detailed in the following paragraphs.
# The Influence of Mountain Height and Distance on Shape Factor of Wind Load of Plastic Tunnel
## 4 Model Partition and Simulation Analysis
### Formula for the Shape Factor of Wind Load
#### 4.3.2 Results and Analysis with 400 m Mountain Height  
The influence of a 400 m-high mountain on the surface wind pressure distribution of the greenhouse is simulated, and the ten working conditions of mountain distances \(d\) of 0 m, 250 m, 500 m, 1000 m, 2000 m, 2500 m, 3000 m, 3500 m and 4000 m and the plain area are chosen for analysis. The nephogram of wind pressure distribution on the greenhouse and the value of the shape factor of the wind load are shown in Figures 9 and 10, respectively. The correction factor for the shape factor of wind load is shown in Figure 11.  
Comparing Figure 9 with Figure 6, we see that the surface wind pressure distribution law is consistent. When distance \(d\) is equal to 0 m, negative pressure occurs on the windward side F. This suggests that when the mountain height is large enough, there may be negative pressure on the windward side F. With the increase in the distance \(d\), the pressure on area F changes from negative to positive, and gradually approaches the value in the plain area.  
Figure 8: Correction factor for shape factor of wind load with 200 m mountain height.  
From Figures 10 and 11, it can be inferred that suction is consistently applied on all other areas, which is consistent with the above results. The curve variation trend and the numerical variation trend of shape factors and correction factors on each area are consistent with Figures 7 and 8. When \(d/H<5\), the curve changes sharply, and when \(5<d/H<10\), the valley has little significant influence on shape factors and their correction factors for all areas of the greenhouse.
# A New Hybrid ConvViT Model for Dangerous Farm Insect Detection
## 3 Materials and Methods
### Dataset  
Accurate early detection of agricultural pests is essential in agricultural production processes to protecting crops from damage caused by insects. It is also essential to determining the potential threats that agricultural pests may pose and to detect agricultural pests using artificial intelligence methods for sustainable agriculture. This study used a dataset of images of 15 different species of dangerous farm insects [21]. Image samples of each class in the dataset are shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Study** & **Proposed Algorithm** & **Dataset** & **Performance** \\ \hline
[14] & YoloV4 and CNN & Pest24 & 71.6\% mAP \\ \hline
[15] & EfficientNetB3 & Pest dataset & 97.8\% accuracy \\ \hline
[16] & YoloV3 and Xception & Original & 77\% precision, 36\% recall, and 50\% F-score \\ \hline
[17] & RCNN & Original & 56.4\% mAP and 85.1\% recall \\ \hline
[18] & Entropy ELM and whale & Original & 89.8\% precision, 98.5\% recall, and 99.8\% F-score \\ \hline
[19] & EfficientNetB0 & Original & 97.51\% accuracy \\ \hline
[20] & SVM & Original & MSE 0.09, RMSE 0.3, MAE 0.045, and MPE 2.25 \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: Image samples of each class in the dataset.  
As seen in Figure 2, the dataset used consists of different numbers of images of each species for identifying agricultural pests and managing agricultural processes.  
The dataset used in our study contains a total of 1591 images from 15 different species. The number of images from each species is distributed evenly, which increases the ability of the model to learn different species. In addition, the dataset includes both high-resolution and low-resolution images taken in various environments. The images in the dataset consist of both high-quality close-up images taken in a laboratory environment and images obtained in real field conditions and exposed to environmental effects. This diversity increases the ability of the model to generalize in different scenarios. The dataset is diversified to include images with different lighting conditions, perspectives, and ground textures. For example, images taken from different angles for a species allowed the model to learn different visual variations of the same species.  
The dataset includes both distinct features and similarities between species. This helped the model to learn small differences and perform better classifications. For example, distinctive features such as antenna and wing structures in some species played a critical role in increasing the accuracy of the model. In order to better simulate real-world applications, various augmentation techniques were applied to the dataset. These included blurring, brightness variation, rotation, and noise addition. These techniques strengthened the model's ability to work in challenging environmental conditions.
## 2 Materials and Methods
### Methods
#### 2.3.1 Theil-Sen Trend Analysis  
Linear regression trend analysis has been widely used to understand vegetation dynamics [48]; however, it is sensitive to outliers or missing data [49]. Theil-Sen trend analysis, first proposed by [PERSON] et al. [50], is also known as [PERSON]'s slope. It is a non-parametric statistical method, which is robust for calculating the variation trend of a time series, and it is not sensitive to abnormal data [30]. The computational formula used to calculate [PERSON]'s slope is shown below:  
\[\beta=Median\bigg{(}\frac{\text{CI}_{j}-\text{CI}_{i}}{j-i}\bigg{)},\ i\ <\ j \tag{1}\]  
where \(\beta\) indicates the variation trend of the CI times series data from 2000 to 2015; \(\beta<0\) indicates that CI exhibits a downward trend, and \(\beta>0\) indicates an upward trend. _Median_ is a function used to calculate the median of a data set. CI\({}_{j}\) refers to the \(j\)th year CI data, and CI\({}_{i}\) is the CI data in year \(i\).
# The Implementation of Precise Point Positioning (PPP): A Comprehensive Review
## 3 PPP Models
### Standard PPP
Using (39) and (40), the GPS/GLONASS model can be written as  
\[\Phi_{IF} =\rho^{\prime}+c(dt^{\prime}-dt^{\prime})+b^{\prime}_{\Phi_{IF}}- b^{\prime}_{\Phi_{IF}}+T-v+PCV^{s}_{IF}+APC^{r}_{IF}+\lambda_{IF}N_{IF}\]2 \[\Phi_{IF} =\rho^{\prime}+c(dt^{\prime}-dt^{\prime})+b^{\prime}_{\Phi_{IF}}- b^{\prime}_{\Phi_{IF}}+T-v+PCV^{s}_{IF}+APC^{r}_{IF}+\lambda_{IF}N_{IF}\]3 (42) \[\Phi_{IF} =\rho^{\prime}+c(dt^{\prime}-dt^{\prime})+b^{\prime}_{\Phi_{IF}}- b^{\prime}_{\Phi_{IF}}+T-v+PCV^{s}_{IF}+APC^{r}_{IF}+\lambda_{IF}N_{IF}\]4 (43) \[\Phi_{IF} =\rho^{\prime}+c(dt^{\prime}-dt^{\prime})+b^{\prime}_{\Phi_{IF}}- b^{\prime}_{\Phi_{IF}}+T-v+PCV^{s}_{IF}+APC^{r}_{IF}+\lambda_{IF}N_{IF}\]5  
where the subscripts \(G\) and \(R\) denote GPS and GLONASS, respectively. GLONASS observations introduce an additional unknown, the ISB between GLONASS and GPS, in meters. It is also worth mentioning that the IF wavelength for GLONASS can be different for each satellite due to the employment of the FDMA technique in GLONASS legacy signals. The same Multi-GNSS model can be applied to Galileo, Beidou, and other constellations.
## 5 Further Applications  
This section aims at showing that the wearable motion capture system adopted in this paper also allows for evaluating motor imagery (MI), which is an important resource in neurorehabilitation. Defined as a multiprofessional physician led approach to healthcare, it aims at reducing disability and improving functions affected by damaged nervous systems [37]. MI is a cognitive process in which the representation of a given motor movement is executed in working memory without any motor output [38]. This process is embedded in a mental imagery construct--a multimodal cognitive simulation process that enables us to represent perceptual information in our minds in the absence of actual sensory input [39]--and refers specifically to the mental simulation of body movements. The use of MI turns out to be relevant in movement disorders. Indeed, MI has been explored in clinical groups, e.g., patients with movement disorders and specific neurological diseases [40; 41; 42].  
In respect to this, the three AT patients were required to start an additional experiment (see [43]) by standing over a line marked on the floor. In front of them, another line was taped on the ground at a distance (unknown to the subjects) of 9 m (4.5 m for F). They were first asked to stand still and imagine moving towards the visualized target (referred to as MIs). Then, subjects were requested to imagine the same task by mimicking the physical movements of walking (referred to as MIw) (F did not carried out this activity). To verify the ability to perform such imaginary tasks, the time of MI was calculated by a chronometer (referred to as T) and compared to the time spent to reach the target. The results are reported below.  
* A (9 m): T = 6.8 s -- MIs = 6.23 s -- MIw = 5.4 s
* F (4.5 m): T = 35.7 s -- MIs = 23.94 s --MIw = n.a.
* G (9 m): T = 7.8 s -- MIs = 4.96 s -- MIw = 8.18 s The MI tests provide interesting information about the implicit awareness of pathology severity in patients. Patient A shows a good temporal equivalence between the real execution of the task and the two motor imagery tasks. This equivalence is preserved for the dynamic MI of patient G, whereas the imagined time during the static task is shorter, for patients F and G, than the one corresponding to their real walking, according to the idea that patients may often underestimate their deficits [43].
# Impact Force Mechanism of a Trimaran Model and Its Experimental Validation
## 6 Theoretical Analysis of the Experiments  
(i) Pressure measured at the side measuring points  
At the falling stage, as the four measuring points P1, P5, P6, and P10 are on the side, we can use (6) to calculate the slamming pressure. Since they are bilaterally symmetrical, only the pressures on P1 and P5 are calculated using (6), where \(\rho=1003\) kg/m\({}^{3}\) and \(v\) is set to 2 m/s, which is the average speed for H16. For P1, \(t\approx 0.073\) m, and tan\(\beta\approx 2\), we have \(F=1135\) N. We can obtain the result that the pressure on P1 is \(P\approx 57,361\) Pa, which is very close to the actually measured pressure (57,849.5 Pa).  
Similarly, at P5, \(\rho=1003\) kg/m\({}^{3}\), \(v=2\) m/s, \(t\approx 0.163\) m, and tan\(\beta\approx 2\). We have \(F=2523.2\) N and \(P\approx 15,480\) Pa, which is very close to the pressure actually measured (15,017.5 Pa).  
(ii) Pressure of the main hull when entering the water  
Water entry at the main hull can be regarded as the plate's water slamming, and we use (5) for flat water slamming to estimate the slamming pressure, where \(\rho=1003\) kg/m\({}^{3}\), \(C_{\rm s}=5.15\) for the flat, \(A_{\rm p}=0.327\times 0.20=0.0654\) m\({}^{2}\), and \(v=2.7\) m/s according to the data. We have \(F=1227.7\) N and \(P\approx 18,771\) Pa.  
(iii) Pressure of auxiliary hulls when entering water  
Due to the left-right symmetry of the trimaran model, only the left side needs to be calculated. The force is calculated using (6), where \(\rho=1003\) kg/m\({}^{3}\); \(v=2\) m/s, which is the average speed for H16; t = 0.073 m; and tan\(\beta\approx 1\), according to Figure 2. As a result, \(F=2260\) N and \(P\approx 17,000\) Pa  
(iv) Pressure of trimaran when connection bridge entering water  
The connection bridge entering water resembles the plate's water slamming. Thus, we can use (5) to estimate the slamming force. Furthermore, as the model is in left-right symmetry, only values for one side need to be calculated. If we only consider the connection bridge, the slamming force is calculated using (5). As a result, by calculating P07 as an example, we have \(F=233.604\) N and \(P\approx 16,686\) Pa, which is close to the pressure actually measured (19,016.0 Pa).
# Changes in the Volatile Profile of Wheat Sourdough Produced with the Addition of Cava [PERSON]
## 3 Discussion
### Analysis of Volatile Compounds in Different Sourdough Samples
#### 3.2.2 Acids  
Acids are produced during fermentation throughout the catabolism of long-chain fatty acids [31]. The total concentration of acids was higher with the addition of 2% Cava lees (\(p<0.05\)) (Table 1). In general, high concentrations of organic acids exhibit antimicrobial activity, contributing to the extended shelf-life of bread made with sourdough [3]. In this sense, acetic acid in sourdough has a positive effect because, besides improving its sensory properties, it also possesses anti-ripeness and anti-mold activity [5]. In the same manner, it has also been observed in sparkling wine that acids tend to increase in concentration during biological ageing in contact with lees [36].  
Moreover, there are acids that were not detected in the control sourdoughs that increased in concentration with the addition of lees, as was the case of decanoic acid (Table 3). Decanoic acid has been reported as a major volatile compound found in wine lees surfaces [42]. We identified this compound in Cava lees along with other organic acids, such as acetic, octanoic, dodecanoic, and hexadecanoic acid (Table 3). It can be observed in Table 3 that all of these compounds increased in concentration in the sourdoughs formulated with lees. Since Cava lees can promote the growth and survival of sourdough microbiota [30], this increase may be a consequence of a higher production of microbial metabolites coming from the lees surface, since they are able to retain certain volatile compounds [15; 42].  
Nevertheless, short-chain fatty acids (SCFAs) (butyric and pentanoic acids) decreased in concentration with the addition of lees. In fact, pentanoic acid was not detected in the sourdoughs with Cava lees, even though both SCFAs are volatiles of fermentation origin that have been reported in wheat sourdough [2; 7]. Indeed, butyric acid has been associated with the metabolism of acetic bacteria (such as _Acetobacter cerevisiae_) [7]. Since the sourdoughs produced were fermented spontaneously and analyzed shortly after microbial stabilization, it may be assumed that a greater presence of wild bacterial strains may be conditioned by the addition of lees.
## 5 Validation of RPAL SCAOF in Simulations
### Validation of Energy-Aware Routing Metrics and SCAOF Performance
#### 5.3.2 Network Scenario: 30 LLN Nodes with Runtime Reconfiguration of the Node State  
To evaluate the performance benefits brought by our proposed SCAOF and composite RPAL routing metrics, we have run a specific scenario with different levels of penetration of misbehaving nodes randomly distributed in the grid that is like previous experiments. These misbehaving nodes perform periodic resets of their watchdog that is implemented by controlling the emulator to trigger this behavior. After one system restart time stamp, these nodes will try to rejoin the DODAG, meanwhile they also can be seen as the sources of a \"grey hole attack\" since they will randomly drop the received traffic during the time when the emulated devices start the bootstrap program until they are ready to be a member of their joint DODAG.  
To simplify analysis of the results of this experiment, we provide enough energy to all the nodes for running a 40 min simulation. A JavaScript-based simulation script is used to give the runtime reconfiguration of the node state and generate random restart intervals for those misbehaving nodes. The obtained results regarding the packet loss, latency, number of failed co-operations, and packet transmission cost, are listed in Table 9. From the results, we can observe that the original RPL cannot solve the problems caused by the misbehaving nodes. Its packet loss increases very quickly if the number of misbehaving nodes is increased, and even though the ETX routing metric is able to alleviate the unreliable links its cumulative feature will still cause long-duration delays for carrying out these measurements. Thus, since RPL's MrhOF does not have any mechanisms to promptly detect the misbehaving nodes, more failed packet forwarding will happen, and the cost for the successful delivery of one data packet to the sink node is higher than in RPAL. In the composite routing metrics solution, the Link Color metric is able to reflect the robustness of the RPL router device and SCAOF sets this metric as a precondition when a node selects its parent. Misbehaving nodes can actively broadcast their node state to their neighbors. This evaluation can prove that this is a better method than waiting for the other nodes in the normal state to monitor these dangerous routers. In other words, SCAOF can prevent the A-LLN nodes from connecting the unstable nodes but it needs to select a longer path, and this is the reason why we obtained higher latency results in the performance comparison.  
It should be noticed that considering the robustness of device as a context feature as well as routing metric, a specific multi-core hardware platform like IWoTCore board [71] can provide accurate statistic results of the node state (_i.e._, times of restarting, power management, _etc._), so that SCAOF could obtain the required context-aware data from the hardware level, which is able to greatly increase the efficiency of this OF algorithm.
## 3 Obstacle Avoidance Scenario
### Obstacle Avoidance Techniques
#### 3.4.1 Bug Algorithm  
The simplest obstacle avoidance method among all obstacle avoidance methods is the bug method. [PERSON] and [PERSON] (2008) proposed this method following bug's movement. They made two versions of the bug algorithm: Bug1 and Bug2, which are shown in Figure 9. Here, the robot starts the operation from \"s\" to target \"t\". For the case of Bug1, the robot will move fully around to check the object and will calculate the shortest position towards the target point. For the case of Bug2, the robot will create a line from start to target, and if it finds an obstacle, it will go alongside the obstacle, and when it finds the line, it will keep moving. The Bug1 algorithm travels a long path to reach the goal point, whites Bug2 uses a shorter route. However, to use even a shorter way to reach the goal, some improvements have been made on the bug algorithm, such as tangent bug ([PERSON] and [PERSON], 2017), I-Bug ([PERSON] and [PERSON], 2017), improved bug ([PERSON] and [PERSON], 2017), splitting Bug ([PERSON] and [PERSON], 2017), etc. ([PERSON] and [PERSON], 2017). These bug algorithms are not so reliable in a more complex environment, and in some tricky conditions, one version works better than the other version. However, the generality of the bug algorithms is that it works well with single obstacle avoidance ([PERSON] and [PERSON], 2017).
## 6 Discussion and Future Research Directions  
ML algorithm will play a vital role in the development of the next generation (6G) of communication systems. They provide large-scale optimization solutions in 6G wireless networks. Depending on the different scenarios of wireless networks, choosing the appropriate ML technique is crucial for its success. Technologies that enhance network performance aim to achieve the KPIs for 6G communication networks, and these include high throughput, extended coverage, low latency, high reliability, secure transmission, and energy efficiency [15]. Applications that integrate ML usually further improve the KPIs and lead to the optimization of the overall system. Some of the use case scenarios include handover management, resource allocation, smart mobility management, task offloading, and load distribution for optimal deployment and path planning.
## 3 Results and Discussion
### SEM (Scanning Electron Microscopy)  
Figure 2 shows the Scanning Electron Microscopy (SEM) images of PZT particles before and after 1% volume ratio coupling agent treatment in PZT-PVDF composites. Among them, Figure 2b shows the SEM images of PZT particles treated by KH-570 in the PZT-PVDF composites. It can be seen that the PZT particle is equally distributed. Figure 2c shows the SEM images of PZT particles before and after 1% (volume ratio) titanate coupling agent NDZ-101 treatment in PZT-PVDF composites. From the SEM image, it can be observed that the defects of composite were greatly reduced after treatment by NDZ-101, and the microstructure was compact and dense. Compared with Figure 2c, the spacing between PZT particles in Figure 2b became larger, and their interface faintness increased. This was caused by the silane coupling agent acting as the role of \"molecular bridge\" between ceramic phase and polymer phase, which enhanced adhesion between the two phases so as to be linked closely and weaken the traces of the two phase interface.  
Figure 1: FTIR (Fourier Transform infrared spectroscopy) spectra of piezoelectric ceramic transducer (PZT) with surface treated and untreated by coupling agent.  
Figure 2: SEM (Scanning Electron Microscopy) of PZT-poly (vinylidene fluoride) (PVDF) composites. (**a**) Unmodified; (**b**) 1% KH-570; (**c**) 1% NDZ-101.  
The PZT particles were uniformly distributed in the PVDF without obvious defects. Moreover, PVDF infiltrates into the PZT ceramic material. Although the effect of KH-570 treatment is not obvious, the interface between PVDF and PZT is visible and clear. The results show clearly that there is no infiltration phenomenon between the PZT and PVDF. The interfacial properties in two phases were changed by NDZ-101, and the adhesion between the two phases in the PZT-PVDF composites was effectively improved.