esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Description of Selected Classifiers Implemented in the Python Scikit-Learn Library
### Artificial Neural Network (ANN) Classifier  
ANNs [76; 77] are a collection of machine learning algorithms inspired by the decision-making processes occurring in a human brain. ANNs consist of many interconnected artificial neurons (also called nodes) forming a network called the perceptron, composed of one or more layers of neurons. An artificial neuron is a thresholding processing unit that generates a response (binary or continuous) if a weighted sum of inputs from other neurons exceeds a certain activation level (threshold value). The weights determine the connection strength between neurons, which may express an excitatory (positive) or inhibitory (negative) connection. Through supervised learning techniques, the weights are iteratively adjusted to minimize log-loss cost function. The layers of neurons can be consecutively connected, forming a feed-forward network, or can be arranged in loops (reversely connected), forming a recurrent network. The neurons can be divided into three types: input units, which receive data from outside the neural network; output units, which send data out of the neural network; and hidden units, whose input and output signals remain within the neural network. The distributed structure of an ANN allows for massive computational parallelization provided by modern GPU (graphical processing unit) processors. Optimal parametrization of an ANN might be difficult; however, once achieved the algorithm provides results superior to other machine learning methods for large datasets. The main disadvantage of an ANN is the \"black box\" nature of the derived optimal network configuration, which is not human-interpretable. Consequently, it is difficult to track and justify why a particular output was generated by the algorithm.
Existing localization techniques thus try to combat these challenges by employing static or mobile reference nodes [15; 16]. Hence, the localization mechanisms are dependent on a complex infrastructure of sensor nodes. This work proposes a localization technique that is not dependent on such infrastructure, but rather uses the sun as a reference point. This paper is organized as follows. In Section 2 the proposed localization system is described, outlining hardware requirements and software algorithms. Next, Section 3 shows the simulation results and how they validate the model. Following, Section 4 contains the results of the system working with real sunlight data. In Section 5 the advantages and disadvantages are discussed. Last, Section 6 summarizes the conclusions obtained.
## 2 Materials and Methods
### Proposed Modification in LEACH
#### 2.6.3 Q-Leach  
Q-LEACH is based on Q-table. As a reward, SINR is considered, while states are selected using \(\epsilon\)-greedy. Reward  
The proposed algorithm sets SINR as state and chooses to use transmit power as action. In order to evaluate efficient SINR and transmission power, when the SINR is less than the threshold(\(T\)) and it is greater than the threshold(\(T\)), it is divided into two independent elements. After that, calculate the compensation function \(r_{t+1}\). \(\omega\) is the weighting factor, \(a_{max}\) is the maximum transmission power factor of node \(i\), and at is the power for node \(i\) at the present time. The reward value can have a positive or negative value, depending on \(T\) in Equation (15). \[r_{t+1,i}= \begin{cases}\omega_{1}\text{SINR}+\omega_{2}(a_{max}-a_{t}),&\text {SINR}\leq T\\ \omega_{1}(T-\text{SINR})+\omega_{2}(a_{max}-a_{t}),&\text{SINR}\ >\ T\end{cases} . \tag{15}\]  
Action Selection  
Performed operations in all states are selected using the \(\epsilon\)-greedy method in Equation (16). \[a= \begin{cases}\text{random action}(a),&\text{with probability }\varepsilon\\ maxQ(s,a),&\text{with probability }1-\varepsilon\end{cases} . \tag{16}\]  
\(\varepsilon\) is the exploration factor [0,1]. With probability \(\varepsilon\), the sensor starts an exploration in which the task is chosen at random and performs a random action(\(a\)) to figure out how the environment reacts toother available tasks. Otherwise, the system enters the exploitation and selects the action with the maximum \(Q\) value. One of the reinforcement models, Temporal Difference (TD), is used to estimate the Q-value in Equation (9). The \(Q\)-value is updated as follows in Equation (17). \[Q_{t+1} = Q_{t}(s_{t},a_{t})+\beta(r_{t+1}+\gamma\;max(Q_{t}(s_{t+1},a))-Q_{t}(s_ {t},a_{t})). \tag{17}\]  
\(\beta\) is a learning factor that determines how fast learning occurs (usually set to value between 0 and 1) and \(\gamma\) is a discount factor. The Q-value function is calculated using the existing estimate, not the final return. The Q value of the \(t+1\) state is the value of the current state \(Q(s_{t},a_{t})\) multiplied by the discount factor that controls the importance of the future value by selecting the max value among the action values in the \(t+1\) state. \((s_{t},a_{t})\) is subtracted. It can be obtained by adding the value obtained by multiplying the value by the learning factor to the value obtained by adding the compensation function value \(r_{t+1}\). The closer the discount factor is to 1, the higher the importance for future value, and the closer to 0. This Q-LEACH (Algorithm 2) is as follows.
# MAPSKEW: Metaheuristic Approaches for Partitioning Skew in MapReduce
## 2 Materials and Methods
### Metaheuristics
#### 2.3.2 Local Beam Search  
While in Simulated Annealing only one partitioning candidate is maintained, Local Beam Search (LBS) maintains \(k\) partitions, using more computing resources while covering a larger number of candidate solutions.  
Intuitively, LBS acts as a group search, which group members divide through space in order to find an object. When one of the members suspects that he is close to the object, he calls the others to search in his area. Formally, the algorithm starts by generating a key partitioning is created using the standard Hadoop partitioning function, according to the intermediate data provided as input.  
Then, the algorithm generates \(2*k\) random neighbors of the initial partitioning and selects among those the \(k\) partitions that present less imbalance. The partitions selected in the previous step will be called _current partitions_. From this, the algorithm will generate a random neighbor for each of the current partitions and will select \(k\) partitions that present less imbalance among current and generated partitions. The current partitions will be updated as the \(k\) partitions selected in the previous step. This process is repeated until a maximum number of iterations is reached. Then, the most balanced partitioning will be chosen. The pseudocode of this procedure is presented in Algorithm 2.  
```
1:procedurePartitioner(\(k\),\(D\),\(I\),\(R\))
2:\(init\leftarrow\) partitioning of the keys of \(D\) using \(Hash(HashCode(D)\)\(mod\)\(|R|)\).
3:\(temp\leftarrow\) createArray()
4:\(current\leftarrow\) createArray()
5:for\(q\gets 1\) to \(2*k\)do
6:\(temp\_add(generateNeighbor(init))\)
7:\(current\leftarrow\) selectBest(\(k\),\(temp\))
8:\(i\gets I\)
9:while\(i\
eq 0\)do
10:\(temp\leftarrow createArray()\)
11:\(temp\gets current\)
12:for\(q\gets 1\) to \(k\)do
13:\(temp\_add(generateNeighbor(current[q]))\)
14:\(current\leftarrow\) selectBest(\(k\),\(temp\))
15:\(i=i-1\)
16:return\(selectBest(1,current)\)
```  
**Algorithm 2** Local Beam Search
## 4 Simulations  
### Parameters Used in the Simulation  
In order to evaluate the effect of the degradation of the perovskite layer on the device performance, the SCAPS-1D simulation software, developed by the Department of Electronics and Information Systems (ELIS) of the University of Ghent (Belgium), which has been widely used for the study of PSCs [73; 74; 75; 76; 77; 78; 79], was applied.  
For the purpose of the simulation, a planar structure device made of glass/FTO/ETL/MAPbI\({}_{3}\)/HTL (Figure 10) was implemented using the parameters listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **FTO** & **TiO2 (ETL)** & \
\begin{tabular}{c} **CH3 NH3 PbI3** \\ **(Absorber Film)** \\ \end{tabular} &
\begin{tabular}{c} **Spiro-OMeTAD** \\ **(HTM)** \\ \end{tabular} \\ \hline Thickness (nm) & 354.19 & 89 & 439.07 & 136.50 \\ Bandgap (eV), Eg & 3.89 & 3.4 & 1.59 & 3.17 \\ Electron Affinity (eV), \(\lambda\) & 4 [76; 77; 78] & 4 [76; 79] & 3.9[76; 80] & 2.05[76] \\ Dielectric Permittivity (relative), \(\varepsilon\)r & 9 [76; 77; 78] & 9 [76; 79] & 6.5 [76; 77] & 3 [76] \\ Effective Conduction Band Density (cm\({}^{-3}\)), CB & \(2.2\times 10^{18}\)[76; 78] & \(1.0\times 10^{19}\)[76; 79] & \(1.8\times 10^{18}\)[76] & \(1.0\times 10^{21}\)[76] \\ Effective Valence Band Density (cm\({}^{-3}\)), VB & \(1.8\times 10^{19}\)[76; 78] & \(1.0\times 10^{19}\)[76; 79] & \(1.8\times 10^{18}\)[76] & \(1.0\times 10^{21}\)[76] \\ Electron thermal velocity (cm/S) & \(1.0\times 10^{17}\)[77] & \(1.0\times 10^{17}\)[77] & \(1.0\times 10^{17}\)[77; 80] & \(1.0\times 10^{17}\)[80] \\ Hole thermal velocity (cm/S) & \(1.0\times 10^{17}\)[77] & \(1.0\times 10^{17}\)[77] & \(1.0\times 10^{17}\)[77; 80] & \(1.0\times 10^{17}\)[80] \\ Electron Mobility (cm\({}^{2}\)/V-S), \(\mu\)m & 20 [76; 77; 78] & 0.02 [76; 79] & 0.5 [76] & 2 [76] \\ Hole Mobility (cm\({}^{2}\)/V-S), up & 10 [76; 77; 78] & 2 [76; 79] & 0.5 [76] & 0.01 [76; 80] \\ Donor Concentration ND (cm\({}^{-3}\)) & \(2.0\times 10^{19}\)[76; 78] & \(1.0\times 10^{19}\)[76] & 0 [80] & 0 [80] \\ Acceptor Concentration NA (cm\({}^{-3}\)) & 0 & 0 [79] & \(1.0\times 10^{16}\)[76] & \(1.0\times 10^{19}\)[76] \\ Capture Cross Section for Electrons and Holes (cm\({}^{2}\)) & \(1.0\times 10^{13}\)[80] & \(1.0\times 10^{15}\)[80] & \(1.0\times 10^{15}\)[80] \\ Defect density (cm\({}^{-3}\)) & \(1.0\times 10^{13}\)[76] & \(1.0\times 10^{16}\)[76] & \(1.0\times 10^{18}\)[10] & \(1.0\times 10^{13}\)[76] \\ \hline \hline \end{tabularThe absorption coefficient, the thickness and the band gap values of the layers were obtained from experimental measurements made with the spectroscopic ellipsometer adjusted with the theoretical models proposed by the CompleatEase software [18]. All other parameters have been obtained from the literature.  
Knowing how the band gap and the absorption coefficient curves of the device's layers vary due to the exposure to UV irradiation, an extrapolation of its degradation can be obtained by using the SCAPS 1-D software in terms of its short-circuit current (Jsc), open circuit voltage (Voc), fill factor (FF) and energy efficiency (PCE).
## 4 Numerical Simulations
### Case 1: \(\delta=0.5\) rad  
The simulation results for a steering angle of 0.5 rad are presented in Figures 9-11. Figure 9a-c depict the variations in the coefficient \(z\), \(\rho\), and Type within the adaptive-layer controller under the SEC strategy, while Figure 9d shows the time history of the modified external yaw moment \(M_{z}^{\prime}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Unit** & **Value** \\ \hline \(m\) & kg & 1761 \\ \(h_{g}\) & m & 0.5 \\ \(l_{z}\) & kg \(\cdot\) m\({}^{2}\) & 2700 \\ \(r\) & m & 0.354 \\ \(l_{f}\) & m & 1.497 \\ \(l_{r}\) & m & 1.378 \\ \(b_{f}\) & m & 1.6 \\ \(b_{r}\) & m & 1.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of vehicle dynamics.  
Figure 8: Temporal variation in the vehicle’s steering angles.  
Figure 10: Vehicle stability responses under \(\delta=0.5\) rad: (**a**) yaw rate \(\gamma\), (**b**) side slip angle \(\beta\), (**c**) phase plane diagram (\(\beta\)–\(\beta\)), and (**d**) vehicle trajectory in the \(X\)–\(Y\) plane.  
Figure 9: Responses of stability and energy-saving controller under \(\delta=0.5\) rad: (**a**) adaptive coefficient \(z\), (**b**) evaluation index \(\rho\), (**c**) control method in the SEC, and (**d**) external yaw moment \(M_{z}^{\prime}\).  
Figure 9a,b indicate that, under the SEC strategy, the initialization of SOC introduces variations in the adaptive coefficient \(z\), leading to differences in the evaluation index \(\rho\) when SOC \(=0.2\) (SEC-0.2) and SOC \(=0.8\) (SEC-0.8), respectively. In these two cases, the evaluation index \(\rho\) remains within the range \(0\leq\rho<0.25\), corresponding to the energy-saving region described in Section 3.3. And the value of Type remains constant at 0, as shown in Figure 9c. Consequently, no external yaw moment is applied in this region.  
Figure 10a-d show the yaw rate \(\gamma\), side slip angle \(\beta\), phase plane diagram (\(\beta\)-\(\hat{\beta}\)), and vehicle trajectory in the \(X\)-\(Y\) plane, respectively. It is worth noting that under the scenario of \(\delta=0.5\) rad, SEC-0.2 and SEC-0.8 yield equivalent external yaw moments, leading to nearly identical vehicle trajectories, as shown in Figure 10d. The maximum yaw rates for both the EC and SEC strategies are identical when \(\gamma=0.1663\) rad\(/s\), with a corresponding side slip angle of \(\beta=0.009\) rad. In contrast, the SC strategy generates a reduced maximum yaw rate at \(\gamma=0.1279\) rad\(/\)s and a side slip angle at \(\beta=0.0042\) rad, which indicates enhanced stability at the cost of reduced maneuverability (lateral displacement), as demonstrated in Figure 10a-d. The phase plane diagram in Figure 10c further confirms that the phase trajectories of the EC, SEC, and SC are significantly distant from the stability boundary, indicating a highly stable state.  
The overall vehicle energy consumption is summarized in Figure 11, where the EC and SEC achieve energy consumptions of 5.61 kJ and 19.14 kJ, respectively, indicating significant energy savings compared to the SC.
## 2 Materials and Methods  
### Expansion of HCT16 and HT29 Cell Lines and Incubation with Active Compounds  
The HT29 and HCT116 cell lines (obtained originally from the American Type Culture Collection (ATCC, Manassas, VA, USA) were utilized in this study. HT29 cells origin from rectosigmoid part of intestine, whereas HCT116 is adenocarcinoma cells line, however, for simplicity of our manuscript, the cells analyzed from both cell lines we defined as colorectal cancer stem cells. These cells were cultured routinely as a monolayer in McCoy's medium, supplemented with 10% fetal bovine serum (FBS), 1% penicillin-streptomycin and 2 mM-glutamine and incubated at 37\({}^{\circ}\)C under a humidified atmosphere of 5% CO2. The cells were serially subcultured by trypsin treatment when they achieved 80% confluence, and the medium was renewed 2-3 times/week.  
For the current study, HCT116 and HT29 cell lines were cultured in spheroid forms (colonospheres, tumorospheres) that were grown in stem cell medium (SCM) established previously by our group [20; 22; 23]. In brief, cells were maintained in serum-free DMEM-F12 medium supplemented with ITS Liquid Media Complement (1x), bovine serum albumin (BSA, 4 mg/mL), glucose (3 mL/mL), Hepes (5 mM), L-glutamine (2 nM), heparin (4 mg/mL), EGF (20 ng/mL), bFGF (20 ng/mL), and antibiotic antimyotic solution (1\(\times\)). All culture supplements and media were obtained from Sigma-Merck.  
\(8\times 10^{5}\) cells were seeded in 24-well ultra-low attachment plates and maintained in SCM. After three passages, newly formed spheres were treated with: acetylsalicylic acid (ASA) (Sigma-Aldrich, Poznan, Poland) at following concentrations: 2.2 mM, HCT116 cells or 1.8 mM, for HT29 cells; anti-Fas (BD, IgM, clone EOS9.1) at the concentration 200 ng/mL (or concomitant control antibodies from Thermo Fisher Scientific) or their combinations dissolved in a freshly prepared culture medium. Additionally, for some stimulations, 50 uM 5-fluorouracilc (5-FU) (Sigma-Aldrich) (the most commonly used agent for CRC chemotherapeutic protocols) was used. 5-FU solution was prepared in DMEM/F12 medium, whereas ASA was dissolved in dimethyl sulphoxide (DMSO). In all experiments, the DMSO concentration was never higher than 1% (\(v/v\)) and did not affect cell growth (according to our initial study). All solutions were prepared immediately before use. The control cells were maintained in the SCM. The medium was replaced every 2-3 days to keep antibody and ASA concentration at an equally high level. After 10 days, the cell cultures were analyzed.
# Computer Vision Algorithms on a Raspberry Pi 4 for Automated Depalletizing
## 3 Methodology
### Oriented FAST and Rotated BRIEF (ORB)  
The Oriented FAST and Rotated BRIEF (ORB) algorithm is an efficient method for feature detection and matching in image processing, presented by [PERSON] and colleagues [6]. Compared to the SIFT and SURF, ORB is substantially faster in the usual situation.  
ORB is a robust and efficient method for feature detection in image processing. It starts by identifying key points using the FAST algorithm, which efficiently locates areas with significant intensity changes. Unlike traditional FAST, ORB assigns an orientation to each key point, ensuring rotation invariance. The algorithm then extracts binary descriptors using BRIEF, representing the local intensity patterns around each key point. These descriptors enable efficient matching between key points in different images.  
As in the SIFT, the matching process involves comparing the binary descriptors of key points in the target image with those in a reference template, then filtering out unreliable matches, and finally applying non-maximum suppression. The final result is a representation of detected objects with bounding boxes.  
The ORB implementation in this research  
1. implements the ORB detector for feature extraction in both the template and target image;
2. applies a sliding window approach with a defined step size for efficient detection;
3. employs a Brute-Force Matcher with Hamming distance for descriptor matching;
4. filters matches based on a predefined threshold;
5. performs non-maximum suppression to merge nearby bounding boxes;
6. marks the center of each bounding box and assigns a unique identifier.  
The result of detection is shown in Figure 7.  
Figure 6: SIFT object detection technique tested on the matchboxes image.
# SDebrisNet: A Spatial-Temporal Saliency Network for Space Debris Detection
## 5 Experimental Results and Analysis  
This section describes the experimental results of the SDD dataset. Section 5.1 describes the quantitative comparison results with other space debris detection methods. Section 5.2 analyzes the sensitivity of the proposed method to the video clip length. Section 5.3 describes the ablation study. The robustness of the proposed method is assessed in Section 5.4. Section 5.5 provides the test results on the real space object video sequences. All of the test experiments were performed using a computer equipped with an Intel Xeon W-2123 CPU and NVIDIA RTX2080 TI GPU.
## Appendix C Cross-Walk between Metamodel and Equations of T3 SD  
The image shown in Figure 10 is the metamodel of T3 SD. Most acronyms have been spelled out previously in this article. However, for clarity of the metamodel and completeness of this section, all acronyms are spelled out in Table 10 along with pairing of corresponding equations:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Acronym** & **Meaning** & **Associated Equation(s)** \\ \hline FSD & Buildable system design & Equations (A3) and (A4) \\ CR & Cost requirement & Equation (A8) \\ DSZ & Initial state & Equation (A2) \\ FSD & Functional system design & Equations (A1) and (A2) \\ HI & Homomorphic mapping of inputs & Equations (A5)–(A7) \\ HO & Homomorphic mapping of outputs & Equations (A5)–(A7) \\ HS & Homomorphic mapping of states & Equations (A5)–(A7) \\ IA & Implementation artifacts & Equations (A5)–(A7) \\  & Enables measured elaboration through homomorphic mapping & Equations (A8) and (A9) \\ IOR & Input/output requirement & Equations (A5)–(A7) \\ ISD & Implementable system design & Equation (A1) \\ IZ & Set of inputs & Equation (A1) \\ NZ & Next state function, maps inputs to states & Equation (A1) \\ OZ & Set of outputs & Equation (A1) \\ PR & Performance requirement & Equation (A8) \\ RZ & Readout function, maps outputs to states & Equations (A3) and (A4) \\ SCR & Coupling of system components & Equation (A8) \\ STR & System test requirement & Equation (A1) \\ SZ & Set of states & Equation (A2) \\ TSZ & Discrete timescale & Equation (A8) \\ TYR & Technology requirement & Equations (A1) and (A2) \\ Z & Minimum system model & Equations (A5)–(A7) \\ ZS & System mode, component, or subsystem (Used to represent & Equations (A3) and (A4) \\ Z@ & Resultant system model from coupling of components & Equation (A1) \\ \hline \hline \end{tabular}
\end{table}
Table 10: The acronyms and their associated equations.
## 3 Data Source  
### Aeromagnetic Data  
We obtained the RTP aeromagnetic data for the northern SCS from the 2021 Aeromagnetic Map of Series of China and its Adjacent Sea areas (scale 1:5,000,000 with specifications), compiled by the China Aero Geophysical Survey and Remote Sensing Center for Natural Resources [25]. This dataset integrates a substantial amount of aeromagnetic survey data collected up to 2021, as well as data from the CCOP East Asia Sea anomaly map (i.e., data from the CCOP GSJ AeroMAG1966 East Asia Sea anomaly map), prioritizing the most recent and highest-precision measurements. Using digital mapping and cartographic techniques, the final data grid has a spacing of 1 km \(\times\) 1 km.  
The RTP aeromagnetic anomalies in the northern SCS predominantly range from \(-\)100 to 100 nT, with positive anomalies being the most common. Low-value anomalies are primarily concentrated in the depression and sedimentary areas. Additionally, an NETrending high-value positive anomaly belt is visible across the region, along with several sporadic high-value anomalies.  
We calculated the magnetic anomaly modulus for the northern SCS (see Appendix A, Figure A1), and the results show that its distribution is generally consistent with that of the RTP anomalies in the high-magnetic anomaly belt region, indicating that the remanent magnetism effect in this area is relatively weak. Therefore, conventional magnetic 3-D inversion methods, including the mixed L\({}_{\rm p}\) norm inversion method discussed above, can be applied for inversion analysis, with scalar values used to represent physical-property distribution.  
Figure 6: Profiles of inversion results using different norms. (**a**) Inversion results along profile of X = 1000 m at depth slice of Z = 350 m; (**b**) Inversion results at the position of X = 1000 m along profile A. The black dashed lines represent the true susceptibility values of the cube.
## 2 Materials and Methods
### Calculation of Corrections--Algorithm
The measuring set's movement velocity is also determined based on the analysis of distances between the measured points and on the measurement performance time. The curvature radius, transverse acceleration, and velocity values calculated in this way provide a basis for the calculation of the cant value (according to Formula 8). The next step is the calculation of the value of the necessary shift (correction) of coordinates in the horizontal zone (according to Formula 1) and the shift direction. Given that the coordinates determined by the satellite method are affected by a known uncertainty (determined by the logger), the equation of the straight line perpendicular to the shift direction was calculated using a weighted regression of a specific number of coordinates (determined by the satellite method) found in the vicinity of this point. \[m_{cor(i)}=\frac{-1}{m_{h(i)}} \tag{17}\]  
\[\frac{dx^{2}}{dm_{h(i)}}=2\cdot\sum_{j=i-2}^{i+2}\frac{1}{\delta_{(j)}^{2}} \cdot Y_{P(j)}\cdot\left(X_{P(j)}-m_{h(i)}\cdot Y_{P(j)}-b_{h(i)}\right)=0 \tag{18}\]  
where:  
\(\chi^{2}\): - Random variable chi-squared distribution [m\({}^{2}\)], \(j\): - Measurement index. The corrected coordinates are calculated using the following relationships:  
\[X_{Por(i)}=X_{P(i)}+\Delta Y_{P(i)}\cdot\cos\left(\frac{\Pi}{2}-atan\Big{(}m_ {cor(i)}\Big{)}\right) \tag{19}\]  
\[Y_{Por(i)}=Y_{P(i)}+\Delta Y_{P(i)}\cdot\sin\left(\frac{\Pi}{2}-atan\Big{(}m_ {cor(i)}\Big{)}\right) \tag{20}\]