esa-sceva/satcom-chunk-collection huggingface dataset:
# ILRS Reference Point Determination Using Close Range Photogrammetry
## 3 Satellite Observing System Wettzell
### Analysis and Results
#### 3.2.1 Bundle Adjustment
Due to the geometry of the building, the markers mounted at the dome wall are cylindrically distributed around the telescope. This configuration yields a stiffer horizontal configuration but leads to a less controlled vertical component. In the horizontal plane, the camera positions surround the telescope, and the horizontal components are restrained by the cylindrical configuration. However, such a configuration can not be realized for the vertical component. For that reason, the standard deviation of the vertical component is slightly overestimated. To overcome this drawback, a spherical configuration of the markers that surrounds the object would be recommended. In practical applications, such an ideal configuration can not be realized due to building restrictions and environment obstructions. As expected, the overall standard deviation \(\bar{\sigma}_{3\text{D}}^{*}\) of the bundle adjustment is slightly smaller than \(\bar{\sigma}_{3D}\). Whereas the marker positions at the telescope are only observed for a dedicated time-span, i.e., for a specific telescope orientation \(k\), the markers at the wall are repeatedly measured during the campaign. For that reason, the markers at the wall are more reliable, and \(\bar{\sigma}_{3\text{D}}^{*}<\bar{\sigma}_{3D}\) occurs.
## 3 Studies of PCMs Incorporated in Passive Systems
### Experimental Studies
#### 3.1.2 At the System Scale (Envelopes and Buildings)
[29] conducted an experimental campaign during summer on a south-facing window shutter containing PCMs. The test cell was located in Aveiro, Portugal, a region with a Mediterranean climate. The south-facing orientation is chosen because it is subjected to discomfort problems, such as indoor temperature asymmetry in winter and overheating in summer. The cell is divided into two internal compartments (of equal dimensions) by an internal partition. The front facade consists of four double-glazedwindows. The reference compartment is equipped with an aluminum shutter; the PCM compartment is equipped with the same shutter filled with paraffin (RT28 HC(r)). The results showed a thermal regulation capacity of the internal temperature of about 18-22% for the compartment equipped with the PCM shutter. The maximum and minimum temperature peaks decreased by 6% and 11%, respectively. In addition, the minimum and maximum temperature peaks are delayed by 45 min and one hour, respectively, compared to the reference compartment. The majority of studies in the literature focus on the short-term thermal stability of PCMs incorporated into buildings. In order to provide reliable data and to go beyond the development stage, it is essential to investigate the stability of building envelopes with integrated PCMs over longer periods in real environmental conditions of a building. To this end, [PERSON] et al. [112] evaluated the thermal performance of a cubic concrete enclosure containing encapsulated PCMs ten years after its construction in a previous study by the same authors [113]. In this previous study, two cubic enclosures were constructed, one serving as a reference made with conventional concrete and the other containing 5 wt% of encapsulated PCMs. The results showed a more stable temperature and a delay in maximum heat flux for the enclosure containing PCMs. Although the experiments in 2005 and ten years later were both performed in summer, the weather conditions were not identical. Indeed, the solar radiation was similar, but the outdoor temperature was higher in 2016 than in 2005. As a result, the indoor temperature was also higher in 2016 than in 2005, which explains a difference in temperatures between conventional concrete and concrete incorporating with and without PCMs (between 1-3 \({}^{\circ}\)C in 2005 and 5-7 \({}^{\circ}\)C in 2016). These results showed that concrete with PCMs is much more effective under extreme conditions (very high temperatures). The compressive strength test performed in 2016 in the same way as in 2005 showed no significant variation, which allows to affirm that the concrete with PCMs shows no change or mechanical degradation.
# Experimental Study on Rockfall Mechanism of Platy Rock on a Complex Slope
## 4 Discussion  
The studies reported in this paper have sought to explore the mechanism of platy rock rockfall motion by the physical model experiment based on 3D printing technology as references for protection against rock fall disasters. This research method could better reproduce the platy rock rockfall process, suggesting that there is merit to the physical model experiment based on 3D printing technology.  
The experimental results show that the trajectory of platy rock rockfall is different from that of other shapes [44]. Free fall, block sliding and block bouncing are the main movement types of platy rock, while block rolling is generally rare. These results can be explained by the complex and irregular shape of the platy rock.  
The experimental results also confirm that the slope shape has an important influence on the stopping position and rock fall trajectory of platy rock. Our findings are substantial agreement with [PERSON] et al. [45]. The same rule is founded for instantaneous kinetic energy. Therefore, on the exposed rock slope, it is an effective engineering measurement by reducing the slope gradient and design platform in reducing the damage risk of the rock fall of platy rock.  
Many scholars have found that the restitution coefficient is related to the block shape [2; 46; 47], the block mass [2; 48], the Young's modulus [2; 6; 49; 50], the dry density [51], the impact angle [2; 6; 46], the impact energy [2], the slope angle [2; 6; 46] and the drop height [2; 48]. Restitution coefficients over unity have been reported by some researchers and the reason has been recognized by most scholars [5; 36; 37; 38; 39; 40]. Our experimental results have confirmed that the restitution coefficient of platy rock was more likely to be greater than one in the low slope area than in the flat slope area because of the low impacting angle. It was consistent with the research results of other scholars. However, the experimental results also show that the restitution coefficient is also related to the movement type before and after the collision. The combination of various movement types (including the flip motion) and the change of movement types are important reasons bring the normal and tangential restitution coefficient is greater than one.  
This paper only presents a model experiment method and the qualitative description for a single irregular platy rock rockfall mechanism under the conditions of the complex rock slope with a single lithology based on the method. Further work should be performed to quantitative statistical and regression analysis of the uncertain relationship between the movement types of platy rock regarding the fragmentation and the irregular shape with complex lithology based on the experiment method. It would provide a reference for engineering protection against rock fall and the modification of rock fall numerical simulation method.  
Figure 13: Change in the type of motion before and after the collision.
# A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images
## 4 Performance Evaluation
### Evaluation Metrics  
The segmentation results are quantitatively evaluated using parameters such as precision, recall, accuracy (Acc), dice score (DSC), and intersection-over-union (IoU). The equations used for the calculation of Acc, and DSC parameters are given as follows:  
\[\text{Acc}=\frac{(\text{TP}+\text{TN})}{(\text{TP}+\text{TN}+\text{FP}+\text {FN})} \tag{6}\]  
\[\text{DSC}=\frac{2\times\text{TP}}{(2\times\text{TP}+\text{FN}+\text{FP})} \tag{7}\]  
where TP = true positive, TN = true negative, FP = false positive, and FN = false negative cases. Here, note that if the IoU score between the predicted tumor mask and associated GT mask exceeds the threshold value, i.e., 0.7, it will be considered as TP, otherwise it will be considered as FP and it is FN when ground truth mask has no associated predicted tumor mask. Figures 5-7 present the pictorial representation of IOU, precision, and recall parameter, respectively.
# Bearing Capacity of a Concrete Grouting Pad on the Working Surface of a Highway Tunnel Shaft
## 5 Design Method and Engineering Application of Grouting Pad Thickness
### Engineering Applications
#### 5.2.3 Thickness Design of the Grouting Pad  
It is known that the water pressure on the working surface of the shaft is 1.6 MPa, and the average grouting pressure of the hole is 5 MPa, that is, \(P\) = 6.6 MPa, and \(\gamma\) is taken as 1.5. The grouting pad was poured with C40 concrete, and \(r_{2}\)/\(r_{1}\) = 5.5/4.35 = 1.264, which is close to 1.25. Therefore, \(r_{1}\) is still taken as 4.35 m, and the thickness \(B_{0}\) of the grouting pad is calculated, which can be given as:  
\[B_{0}=\sqrt{\frac{3\times 6.6\times 1.5\times(2+1)\left[2\times 2.8^{2}\times \left(2.8/4.35\right)^{2}-5\times 2.8^{2}+4.35^{2}\times 2\right]}{5.5\times 16\times 1.43}}=2.87\;\mathrm{m}\]  
The actual design is 2.9 m.  
Using the Chinese coal mine experience design method in Equation (12), the grouting termination pressure \(P_{0}\) is taken to be 2-2.5 times the static water pressure of 3.2 MPa, and the thickness \(B_{01}\) of the grouting pad is calculated, which can be seen as:  
\[B_{01}=\frac{3.2\times 5.5}{40}+0.3\times 5.5=2.09\;\mathrm{m}\]  
Therefore, compared with the thickness of the grouting pad designed by the plastic limit method, the thickness of the grouting pad designed by the empirical calculation method of the Chinese coal mine is about less than 40%, which is consistent with the above analysis of the 2-2 shaft grouting pad due to the insufficient bearing capacity that occured through a punching shear failure.
# The Use of Rapeseed Husks to Remove Acidic and Basic Dyes from Aquatic Solutions
###### Abstract  
This study aimed to identify the possibility of using rapeseed husks (RH) as an unconventional sorbent for removing acidic (AR18, AY23) and basic (BR46, BV10) dyes from aqueous solutions. Its scope included, i.e.: sorbent characterization (FTIR, pH\({}_{\text{PZC}}\)), determination of pH effect on dye sorption effectiveness (pH 2-11), analysis of dye sorption kinetics (pseudo-first order model, pseudo-second order model, intraparticular diffusion model), and the determination of the maximum sorption capacity (Langmuir 1 and 2, and Freundlich isotherms). The sorption effectiveness of acidic dyes (AR18, AY23) onto RH was the highest at pH = 2, whereas that of the basic dyes BR46 and BV10 was most effective at pH = 6 and pH = 3, respectively. The time needed to reach the sorption equilibrium of dyes onto RH depended on their initial concentration and ranged from 120 to 150 min for the acidic dyes and from 150 to 180 min for the basic dyes. The maximum sorption capacity (\(Q_{\text{max}}\)) of RH towards AR18 and AY23 was 49.37 mg/g and 41.52 mg/g, respectively, and towards BR46 and BV10 it was 59.07 mg/g and 20.93 mg/g, respectively. The obtained \(Q_{\text{max}}\) values were compared with the results achieved for otherorbents (literature data). This comparison demonstrated that the sorption capacity of rapeseed husks towards the analyzed dyes was higher compared to that of some types of activated carbons.  
rapeseed husks; hulls; unconventional sorbent; sorption; acidic dyes; basic dyes; Acid Red 18; Acid Yellow 23; Basic Red 46; Basic Violet 10 +
Footnote †: journal: Applied Sciences
## 3 Methodology
### Model Selection and Classification Scheme
#### 3.2.3 Light Gradient Boosting Machine (LightGBM)  
Light Gradient Boosting Machine (LightGBM) stands out as a prominent advancement in ML, distinguished by its effectiveness in classification tasks. A key innovation is its unique capability to process categorical features efficiently via Gradient-based One-Side Sampling (GOSS), a technique optimized for large-scale datasets with high-dimensional categorical data. Unlike XGBoost, LightGBM diverges from traditional tree-building methods by employing a leaf-centric growth strategy, where splits focus on nodes that maximize loss reduction. This approach enhances computational efficiency, particularly in datasets with complex feature relationships and substantial size [83].  
The algorithm's iterative process is formalized in Equation (4):  
\[F_{n}(x)=\alpha_{0}f_{0}(x)+\alpha_{1}f_{1}(x)+\cdots+\alpha_{n}f_{n}(x) \tag{4}\]Here, the model initializes with \(n\) decision trees, assigning uniform weights (\(1/n\)) to training samples. Each weak learner \(f(x)\) is trained to determine its optimal coefficient \(\alpha\). The classifier iteratively refines sample weights and integrates new learners until the final ensemble \(F_{n}(x)\) is achieved.
# A Survey on Energy Drainage Attacks and Countermeasures in Wireless Sensor Networks
## 2 Security Issues in WSN
### Literature Review for Specific Attacks
Active security attacks search and destroy information, whereas passive security data involve stealing information. Active attacks include jamming, tempering, routing, and node outage attacks. In particular, some of the routing protocol attacks in WSNs can be classified as selective forwarding, sinkhole attacks, and Sybil attacks, among others. The authors categorized monitoring and eavesdropping, traffic analysis, camouflage adversaries, homing attacks, and passive attacks.
## 1 Introduction
A DECT study [32] utilized reconstructed NCCT and CCT images at three monochromatic energy levels, 40, 60, and 77 kv, for 15 head and neck cancer patients. Results showed that there were no significant changes in the dose distributions for target volumes and critical structures. A Monte Carlo simulating study [33] examined the magnitude of tumor dose enhancement achieved by contrast media with various beam qualities and indicated that for flattened photon beams, the dose enhancement was less than 5%. However, it was significantly higher for flatting filter free beam, up to 23.1%. A small field stereotactic radiosurgery study (SRS) [15] reported maximum dose difference up to 20% between NCCT- and CCT-based plans and recommended that care should be taken when considering CCT images for dose calculations. Another study [34] suggested a correction strategy with CCT images to overcome up to 5% dose difference. A comparative study [35] for nine lung cancer patients' original treatment plans were recalculated using CCT images. Results showed no significant effect of the contrast agent on dose calculations. The reviewed literature, summarized in Table 1, generally reported limited effect of contrast agents on radiotherapy dose calculations except in a few publications [13; 16; 17; 18; 20; 30]. Nevertheless, there was no consensus on the magnitude of these effects or a clear recommendation to utilize contrast-enhanced CT simulation images for radiotherapy dose calculations. The longitudinal study presented in this work aims to answer the question: Is CCT imaging an acceptable alternative to NCCT for TPS dose calculations? This will be carried out by investigating and then evaluating the effect of the CCT on the treatment planning dose calculations for all concerned critical structures and target volumes using VMAT technique for different anatomical sites: brain, H&N, chest, abdomen, and pelvis.
## 5 Proposed Method
### Problem to Be Optimized  
The case selected to apply the proposed algorithm with Type-2 fuzzy approach is the problem of the autonomous robot [1; 5; 8; 29; 36], the dynamics of this problem is that the robot is able to follow an assigned trajectory with a minimum error value. In the proposed method an FLS is used to move the parameters of the membership functions of the fuzzy controller. The description of the problem and the characteristics of the fuzzy controller are shown below.  
Is an autonomous vehicle capable of following predictable paths in uncertain environments and is illustrated in Figure 10. The robot body is symmetrical around the perpendicular axis and the center of mass is at the geometric center of the body. It has two driving wheels that are fixed to the axis that passes through the center of mass \"C\" represented by {C, Xm, Ym}, and one passive wheel that prevents the robot from tipping over as it moves on a plane [26; 27]. The dynamics of the mobile robot is represented by the following set of Equations (6) and (7), [5; 29; 36].  
\[M(q)\dot{v}+C\big{(}q,\dot{q}\big{)}v+Dv=\tau+P(t) \tag{5}\]  
where  
\(q=(x,y,\theta)^{T}\) is the vector of the configuration coordinates,  
\(v=(v,w)^{T}\) is the vector of velocities,  
\(\tau=(\tau_{1},\tau_{2})\) is the vector of torques applied to the wheels of the robot where \(\tau_{1}\) and \(\tau_{2}\) denote the torques of the right and left wheel,  
\(P\in R^{2}\) is the uniformly bounded disturbance vector,  
\(M(q)\in R^{2\times 2}\) is the positive-definite inertia matrix,  
\(C\big{(}q,\dot{q}\big{)}\theta\) is the vector of centripetal and Coriolis forces, and  
\(D\in R^{2\times 2}\) is a diagonal positive-definite damping matrix.  
The kinematic system is represented by Equation (6)  
\[\dot{q}=\underbrace{\left[\begin{array}{cc}\cos\theta&0\\ \sin\theta&0\\ 0&1\end{array}\right]}_{J(q)}\underbrace{\left[\begin{array}{c}v\\ w\\ v\end{array}\right]}_{v} \tag{6}\]  
Figure 10: Representation of the mobile robot.  
where  
\((x,y)\) is the position in the X-Y (world) reference frame,  
\(\theta\) is the angle between the heading direction and the \(x\)-axis, and  
\(v\) and \(w\) are the linear and angular velocities.  
Furthermore Equation (7) shows the non-holonomic constraint, which this system has, which corresponds to a no-slip wheel condition preventing the robot from moving sideways.  
\[\dot{y}\cos\theta-\dot{x}\sin\theta=0 \tag{7}\]  
The system fails to meet [PERSON]'s necessary condition for feedback stabilization, which implies that no continuous static state-feedback controller exists that can stabilize the closed-loop system around the equilibrium point.
## 3 Results and Discussion
### Composition of the Seed Oil  
GC-MS is widely used to identify and analyze different compounds, such as hydrophilic, hydrophobic, and volatile compounds [89]. In this study, GC-MS analysis of both PMSOEPE and PMSOENH identified seventeen FAs with different quantities. Oleic acid had the highest percentage of PMSOEPE (37.91%), followed by (32.04%) of 6,9,11-octadecatrienoic acid, and (24.47%) of linoleic acid. The main FAs in PMSOENH were 6,9,11-octadecatrienoic acid (34%), oleic acid (32.11%), and linoleic acid (25.63%), respectively. The remaining FAs had approximately the same yields for both samples, except stearic acid, which was (1.45%) for PMSOEPE and (1.96%) for PMSOENH. Furthermore, palmitoleic acid was unavailable in PMSOEPE, but it was (0.25%) in PMSOENH. The total  
Figure 2: Color appearance of PMSOENH and PMSOEPE. PUFAs content of PMOENH was higher (59.82%) than PMSOEPE (56.69%), but the MUFAs content of PMSOENH was lower (32.7%) than PMSOEPE (38.27%). The list of FAs with their retention time (RT) and yield percentage is presented in Table 3.  
According to the literature, the FA constituents and yield percentages of PMSOE are significantly different. For example, [PERSON] et al. (2011) used the cold press method to extract the major constituents of Turkish PMSOE. The main FAs obtained were (35.8%) oleic acid, (24.9%) linoleic acid, and (22.6%) linoleicalidic acid (43). Another example is the study performed by [PERSON] et al., (2009) on Sudanese PMSOE using petroleum ether as a solvent at 40-60 \({}^{\circ}\)C and Soxhlet for extraction. The reported essential yields of FAs were (45%) oleic acid, (47%) linoleic acid, (5.7%) palmitic acid, and (1.3%) stearic acid (45). However, the main reported FAs contents of Syrian PMSO using \(n\)-hexane solvent and Soxhlet apparatus were (40.7%) alpha-eleostearic acid, (29.8%) oleic acid, and (26.6%) linoleic acid (41). Furthermore, [PERSON] et al., (2016) extracted Egyptian PMSOE using the maceration technique and petroleum ether as a solvent at 60-80 \({}^{\circ}\)C. The results showed timmodonic acid as a major component (33.1%), followed by oleic acid (28.7%), and linoleic acid (24.4%) (90). The results of our study showed three FAs (palmitvaccenic acid, gadelaidic acid, and 6,9,11-octadecatrienoic acid) that were not obtained in earlier studies. Moreover, the highest constituent was 6,9,11-octadecatrienoic acid. In addition, PMSOENH contained a higher quantity of PUFA (59.82%) relative to the Sudanese, Turkish, and Egyptian PMSOE (47.1%, 56.1%, and 57.79%, respectively) (Table 4).
## 3 Model Structure
### Auto-Correlation Mechanism and Correction Module
#### 3.2.2 Correction Module  
The correction module is improved on the basis of residual structure. In deep learning research, many scholars adopt the residual structure to protect the integrity of data and mitigate the gradient disappearance problem; additionally, some scholars also adopt the convolution method for time series prediction. Inspired by this, we perform the convolution operation on the features extracted by the auto-correlation mechanism as new features to be added to the results of the residual structure; the steps are shown in Figure 3. The correction module complements and optimizes the residual structure, the process of which can be defined as:  
\[\begin{split} R(X)&=\text{Conv}(F(X)),\\ E(X)&=F(X)+X+R(X).\end{split} \tag{6}\]  
Figure 2: Auto-correlation mechanism (left) and time delay aggregation (right).  
In the above equation, X represents the data variable before entering the auto-correlation mechanism, and F(X) represents the output after the operation of the auto-correlation mechanism. R(X) is the result of convolution of F(X); X,F(X) and R(X) are added together to obtain E(X) as the output of the correction module.