esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels
## 3 Methods
### Pre-Processing Techniques
#### 3.2.1 Windowed Averaging  
We propose a unique statistical method to enhance local or temporary (specific interval) trends within the dataset's parameters. Despite its utilization for non-sequential analysis, the technique introduces sequential information from the local trend intervals. It calculates an average value (average window, \(\overline{X_{a,k}}\)) for a particular interval (window size, \(a\)) in data points. Then, it replaces all values within the specific estimated window. Applying those processes for the rest of the data points creates isolated window size groups for the entire dataset. We define the required iteration (\(b\)) for any given number (\(n\)) of data points as  
\[b=\left\lceil\frac{n}{a}\right\rceil\,, \tag{3}\]  
and formulate the average window calculation as  
\[\overline{X_{a,k}}=\left\{\begin{array}{ll}\frac{\sum_{a=1}^{k}X_{i}}{a},&k< b\\ \frac{\sum_{a=1}^{k}X_{i}}{n-a(b-1)},&k=b\end{array}\right.\,, \tag{4}\]  
with \(k\) representing the iteration index of a specific data point and \(X\) a variable of interest within the dataset. For each iteration, we define the beginning of variable index \(i\) as  
\[i=a(k-1)+1\,. \tag{5}\]  
The results of feature extraction by windowed averaging cause rapid fluctuations in data changes to be isolated for a specific interval. Figure 9 illustrates how the windowed averaging process results in a voltage dataset from an anomaly-contaminated panel by applying (3)-(5). We decided on the value of 50 as an optimal value of the window interval for the windowed averaging technique.  
Figure 8: Initial voltage data plots: (**a**) normal and (**b**) anomalous.  
Figure 9: Initial voltage data plot after windowed averaging.
## 3 Results
### Submerged vs. Emerged Primary Production and Its Predictors
Figure 5: Net primary production (NPP) (**A**), gross primary production (GPP) (**B**), gross primary production corrected by PARi (GPPi) (**C**), and gross primary production corrected by PARsed and photosynthetic biomass (sediment chl \(a\) content) (GPPSed + chla) (**D**), during emerged (red) and submerged (blue) tidal state in June (T2) and November 2018 (T3). Bars represent means (n = 9) with standard error bars displayed. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{1}{c}{**Predictor**} & \multicolumn{1}{c}{**Marginal Tests**} & \multicolumn{2}{c}{**Full Model**} & \multicolumn{2}{c}{**Variance**} \\  & & & **(\%)** & **Partitioning (\%)** \\ \cline{2-5}  & & **Pseudo-F** & **Proportion** & & \\ \hline
**Submerged** & & & & & 25 (14) \\ NPP & Environment & & & & 2 (14) \\  & OC & 14.05 & 0.10 *** (-) & 2 & \\  & Phaeo & 22.37 & 0.14 *** (-) & & \\  & PAR\({}_{\text{Sed}}\) & 13.42 & 0.09 *** (+) & & \\  & PAR\({}_{\text{Site}}\) & 87.00 & 0.40 *** (+) & 40 & \\  & Community & & & & (13) \\  & _A. stutchburyi_ & 22.08 & 0.14 *** (-) & & \\  & Porewater & & & & (2) \\  & NH\({}_{4}\) (5–7 cm) & 4.14 & 0.03 ** (-) & & \\  & & & Total & 42 & 39 \\ \hline GPP & Environment & & & & 24 (4) \\  & MGS & 3.62 & 0.03 * (+) & & \\  & Phaeo & 9.98 & 0.07 *** (-) & & \\  & PAR\({}_{\text{Sed}}\) & 13.68 & 0.09 *** (+) & & \\  & PAR\({}_{\text{Site}}\) & 27.58 & 0.17 *** (+) & 17 & \\  & Temperature & 11.91 & 0.08 *** (+) & 11 & \\  & Community & & & & (4) \\  & _A. stutchburyi_ & 6.25 & 0.05 ** (-) & & \\  & & & Total & 28 & 28 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Distance-based Linear Model (DistLM) results for submerged and emerged primary productions measures. Marginal tests provide the proportion of variation explained when considered individually. Full model shows the variables included in the stepwise DistLM and the variance attributed to each. Partitioning of variance shows the variance explained solely by environment, community and porewater predictors, and in parentheses, the variance shared by two or more predictor groups. Significance levels are * \(p\leq 0.1\), ** \(p\leq 0.05\), *** \(p\leq 0.01\) and correlation directions are in parentheses. Figure 6: Photosynthesis/respiration ratio (p/r) during submerged (blue) and emerged (red) tidal periods with data pooled across all sampling dates and treatments. Boxes are the 25 th and 75 th percentiles, and whiskers the 5 th and 95 th percentiles. The dotted line is provided as a reference to p/r = 1.
## 2 Design Equations and Limitations  
The joint design equations for hollow structural steel (HSS) members commonly limit the material properties such as the nominal yield strength (\(F_{y}\)) and yield ratio (\(F_{y}/F_{u}\)). In Eurocode 3 [3], the nominal yield strength is limited up to 460 MPa, and a reduction factor of 0.9 should be used when the nominal yield strength exceeds 355 MPa. In addition, the yield ratio should be less than 0.91 and the elongation should be at least 15%. In order to extend the application of high-strength steels, the limitation of the nominal yield strength is increased up to 700 MPa (S700) in Eurocode 3 [29]. For the nominal yield strength of 460 to 700 MPa, the reduction factor of 0.8 should be used instead of 0.9. The yield ratio and the elongation should be less than 0.95 and more than 10%, respectively. Therefore, the nominal yield strength can be used up to 700 MPa by applying the reduction factors to the nominal yield strength.  
In ISO 14346 [4], the nominal yield strength is limited up to 460 MPa, and the reduction factor of 0.9 is given when it is more than 355 MPa, in the same way as Eurocode 3 [3]. In addition, when the yield ratio exceeds 0.8, the nominal yield strength should be \(0.8F_{u}\). This limitation is to ensure the ductility and rotation capability of HSS joints with a high yield ratio. According to the American Institute of Steel Construction (AJSC) [2], the nominal yield strength and yield ratio are limited up to 360 MPa and 0.8, respectively. These are no longer applied to high-strength steel; therefore, the application of high-strength steels to HSS is actually limited.  
Figure 1 shows the geometrical configuration and symbols of the CHS XP-joint studied in this paper. It is designed so that an axial load (\(R_{n}\)) is applied to the X-type longitudinal plate joined to the CHS. The HSS joint strength is commonly determined by the chord plastification and punching shear states. Equation (1) is the joint strength based on the chord punching shear of ISO 14346 [4].  
\[F_{1}=0.58\sigma_{y0}t_{0}h_{1}. \tag{1}\]  
The joint strength based on the chord punching shear is calculated by the chord nominal yield strength (\(0.58\sigma_{y0}\)), the thickness of the chord (\(t_{0}\)), and the longitudinal plate width (\(h_{1}\)). The design strength of the CHS XP-joint is mainly determined by the chord plastification not punching shear, and AISC [2] also suggests that the limit state of the joint strength is HSS plastification. Table 1 shows the design strengths [2; 3; 4] for the CHS XP-joints, the limitations, and the range of validity of configurations such as the longitudinal plate width-to-chord diameter ratio (\(\eta\)) and chord diameter-to-thickness ratio (\(2\gamma\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factors** & **AISC (2016) [2]** & **Eurocode 3 (2005) [3]** & **ISO 14346 (2013) [4]** \\ \hline \(R_{n}\) (\(N_{1,\text{Rd}}\) and \(F_{1}\)) & \(5.5F_{y0}t^{2}(1+0.25\eta)Q_{f}\) & \(5
# Mobility Management Based on Beam-Level Measurement Report in 5G Massive MIMO Cellular Networks
## 3 Unific-Measurement-Based Beam Switching and Handover Scheme
### Proposed Measurement Event for Beam Switching and Handover  
We assume that all cells in the system use the same carrier frequency and the same system bandwidth. In addition, we assume that the hysteresis or frequency offset related parameters are set as zero for the serving cell. Then, we define \"event A3 entering state\" at the \(i\)-th user as  
\[p(k)g_{t}(l)h(k,i)+O_{ctn}(k,l,l_{s})>p(k_{s})g_{t}(l_{s})h(k_{s},i)+O_{ff}(l_ {s}),\forall k\in\mathcal{K},\forall l\in\mathcal{L},\forall i\in\mathcal{I}, \tag{20}\]  
where \(O_{ctn}(k,l,l_{s})\) is the \"beam individual offset\" that indicates the specific offset value for the \(l\)-th neighbor beam of the \(k\)-th cell in the serving beam \(l_{s}\) of the serving cell \(k_{s}\). \(O_{ff}(l_{s})\) is the \"A3-offset\" that indicates the value for all neighboring beams in the serving beam. \(p(k)g_{t}(l)h(k,i)\) and \(p(k_{s})g_{t}(l_{s})h(k_{s},i)\) are RSRP from the \(l\)-th neighbor beam in the serving beam \(l_{s}\) measured at the \(i\)-th user, as specified in Equation (8). After event A3 entering state is triggered, we can determine whether the entering state is a HO or BS.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Description** \\ \hline \(\theta\), \(\theta_{s}\) & Vertical angle, main beam direction of vertical angle \\ \(\phi\), \(\phi_{s}\) & Horizontal angle, main beam direction of horizontal angle \\ \(p\) & Index of the antenna elements in horizontal direction \\ \(r\) & Index of the antenna elements in vertical direction \\ \(\mathcal{I}\) & Set of users in the system \\ \(i\) & Index of the user \\ \(\mathcal{L}\) & Set of main beam candidates \\ \(l\), \(l_{s}\) & Index of the beam, index of the serving beam for \(i\)-th user \\ \(\mathcal{K}\) & Set of cells in the system \\ \(k\), \(k_{s}\) & Index of the cell, index of the serving cell for \(i\)-th user \\ \(d\) & Distance between user and cell in meter \\ \(\mathcal{B}\) & Set of possible beam-pair between user and cell \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of symbols.
# EAIA: An Efficient and Anonymous Identity-Authentication Scheme in 5G-V2V
## 7 Performance Evaluation
### Discussion  
In this section, we compare the performance and security of EAIA and other related protocols in Table 8. We compare our protocol with [11; 13; 15; 21], denoted as HDMA, PPMA, PPDAS, and SPPC, respectively. The results show that our EAIA has a better performance in terms of security and efficiency. It is worth mentioning that although PPMA has better results in performance comparisons, this solution requires the participation of infrastructure and does not involve session key agreement steps. The proposed scheme not only completes V2V mutual authentication without the participation of RSUs but also requires less computation and communication overhead during authentication. Finally, EAIA can achieve the main security features, including forward secrecy, mutual authentication, and resistance to typical attacks.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Notations** & **Description** & **Energy Consumption** \\ \hline \(E_{me}\) & The energy cost of one exponential operation in G & 9.1 mj \\ \(E_{sig}\) & The energy cost of an ECDSA (160 bits) signature generation & 8.8 mj \\ \(E_{corr}\) & The energy cost of an ECDSA (160 bits) signature verification & 10.9 mj \\ \(E_{bp}\) & The energy cost of one pairing operation & 47.0 mj \\ \(E_{sm}\) & The energy cost of one scalar multiplication & 8.8 mj \\ \(E_{tran}\) & The energy cost for transmitting one bit & 0.66 mj \\ \(E_{rec}\) & The energy cost for receiving one bit & 0.31 mj \\ \hline \hline \end{tabular}
\end{table}
Table 6: Energy consumption of the basic cryptographic operation.  
Figure 8: Compare the results of the energy cost.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme** & **Type** & **Operation** & **Energy Cost** & **Total Energy Cost** \\ \hline HDMA & Computational & \(6T_{me}+2T_{BP}\) & \(148.6\) & \(150.245\) \\  & Transmission & \(1696\) & \(1.645\) & \\ \hline PPMA & Computational & \(0\) & \(0\) & \(14.588\) \\  & Transmission & \(1504\) & \(14.588\) & \(14.588\) \\ \hline PPDAS & Computational & \(2T_{sm}+T_{BP}\) & \(64.6\) & \(66.804\) \\  & Transmission & \(2272\) & \(2.204\) & \\ \hline SPPC & Computational & \(2T_{me}+2T_{sm}+T_{sig}+T_{v\sigma}\) & \(55.5\) & \(58.620\) \\  & Transmission & \(3216\) & \(3.120\) & \\ \hline EAIA & Computational & \(4T_{sm}\) & \(35.2\) & \(36.473\) \\  & Transmission & \(1312\) & \(1.273\) & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of energy cost of different protocols.  
Since this solution is only for direct V2V communication, it is limited to mutual authentication between two vehicles. If possible, we can consider how to complete the mutual authentication of multiple vehicles in an environment without RSUs. At the same time, the internet of vehicles is the research background of this solution, but the solution is applicable to other point-to-point scenarios, and because it does not rely on other facilities, scenarios such as infrastructure in the sea area that require communication can also be considered in the future.
# Pose Measurement for Unmanned Aerial Vehicle Based on Rigid Skeleton
## 3 Research Methodology
### Initialization Stage  
Before tracking and positioning, the tracking target needs to be initially positioned. In the initialization phase, an offline trained classifier (offline model) is used to find possible candidates in the image sequence that is input. Candidates we find are not all the region we want to track. Therefore, the candidate regions need to be filtered [20].  
\(\{x_{tj}^{t}\}_{j=0}^{M}\) is the candidate found in the \(t_{th}\) frame of the left image sequence. and \(\{x_{rj}^{t}\}_{j=0}^{N}\) is the candidate region found in the \(t_{th}\) frame of the right image sequence. \(M\) and \(N\) indicate  
Figure 3: Framework of the proposed method.  
the number of candidate regions detected. Each candidate area is \(x^{t}=\left(a^{t},b^{t},w^{t},h^{t}\right)\), where \(a,b,w,h\) denotes the column and row coordinates of the top left corner and the width and height of the candidate, respectively. \(p_{li}\) and \(p_{rj}\) are the center points of \(x^{t}_{li}\) and \(x^{t}_{rj}\), respectively. \(w_{li}\) and \(w_{rj}\) are the widths of \(x^{t}_{li}\) and \(x^{t}_{rj}\), respectively [21].  
In a fixed binocular system, the target is a single-target detection. Therefore, in the corresponding frame, in the \(M\) candidate regions \(\{x^{t}_{li}\}_{i=0}^{M}\) and \(N\) candidate regions \(\{x^{t}_{rj}\}_{j=0}^{N}\) corresponding to all the left and right images, only one pair of candidates should satisfied the following conditions: widths of candidates \(w_{li}\) and \(w_{rj}\) should satisfied \(w_{li}-w_{rj}<0.05\times max(w_{li},w_{rj})\). Based on the epipolar, the constraint shows that the relevant center points \(p_{li}\) and \(p_{rj}\) of should be satisfied \(p^{T}_{rj}Fp_{li}<\epsilon\) (where \(F\) is the fundamental matrix and \(\epsilon\) is a reliable threshold for judging). By applying binocular filtering, candidate in the image can be determined, and the other candidates that are incorrect are filtered out.
# Exploring the Role of Emotions in Arabic Rumor Detection in Social Media
## 2 Background
### Emotion Classification  
Emotions are an important part of language and are regarded as complicated. Emotion classification is a multi-label classification problem that detects the emotion for textual input from pre-defined emotion labels. Several models have been developed to characterize emotions, including the six-category model of [30] of basic emotions: joy, sadness, anger, fear, disgust, and surprise. The model proposed by [31] extended these six categories to include trust and anticipation. Arabic NLP is not as mature as English NLP, and thus fewer research articles have been published on emotion detection in the Arabic language, which is mainly due to resource limitations. Prior studies on emotion recognition for Arabic text varied from traditional Machine Learning (ML) to Deep Learning (DL) advancement. The three major approaches are the lexicon-based approach, the ML approach, and the hybrid approach. The lexicon-based solutions are based on computing the overall intensity for each emotion using a lexicon [32]. On the other hand, various algorithms have been used for emotion recognition, including Support Vector Machine (SVM) [33], Complement Naive Bayes (CNB) [34], Recurrent Neural Networks (RNN) [35], and Convolutional Neural Networks (CNN). The authors in [35] proposed three models for emotion detection in Arabic text: a feature-engineered model, a DL model, and a hybrid model consisting of elements of both. They used various text features in their feature-engineered model, including stylistic, lexical, syntactic, and semantic features. Various deep neural networks were used with word embeddings. The approach was tested on a range of datasets and outperformed state-of-the-art models in a variety of measurements. Recently, a shift toward the advanced pre-trained language model in the study of [36] involved proposing that multilingual BERT build a toolkit for social media processing in Arabic. It focused on several tasks, including delicate, gender, and emotion prediction. Emotions have recently been used as indicators for false information detection. In [10], the authors investigated false and true Twitter rumors and discovered that false rumors tend to elicit emotions such as anxiety, disgust, and surprise, while true rumors evoke emotions like pleasure and anticipation. This finding has inspired researchers to explore the role of emotions in the task of rumor detection.
## 3 Current Techniques for OP Study
### Computed Tomographic Imaging Techniques
#### 3.4.1 Quantitative Computed Tomography (QCT) and High-Resolution Peripheral QCT (HRpQCT)  
Quantitative computed tomography (QCT) is a non-invasive CT technique used in clinics to assess the 3D bone geometry. In QCT, both the X-ray source and the detector rotate synchronously around the patient. By incorporating a mineral standard in the scan, as visible in Figure 10, the volumetric BMD, expressed in g/cm\({}^{3}\), can be additionally calculated [55]. Many studies have shown a correlation between BMD calculated through QCT (expressed in Hounsfield units) and the BMD obtained through DXA, making QCT a potential and alternative tool for osteoporosis screening [56; 57]. However, a general consensus on the superiority of this technique compared to DXA in the prediction of bone fracture risk has not been reached yet [10].  
QCT can distinguish trabecular and cortical contributions, even though its in-plane low resolution (around 500 \(\upmu\)m) does not allow the characterisation of the trabecular microarchitecture [10; 55]. For this reason, QCT is mainly adopted in clinics for the measurement of the cortical volume and of BMD in the spine and the hip [55].  
To overcome the drawbacks associated with the low resolution obtained with conventional QCT, high-resolution peripheral computed tomography (HRpQCT) has been developed. This non-invasive  
Figure 10: Quantitative computer tomography (QCT) of a vertebral body scanned simultaneously with the calibration phantoms, containing equivalent densities of 0, 50, 100, 150 and 200 mg/cm\({}^{3}\) (reproduced with permission from [12], Elsevier, 2008).  
technique can reach a maximum resolution of 50 \(\upmu\)m, thus allowing the measurement of cancellous bone morphology, even though partial volume effects can still arise [55]. However, HRpQCT has been exclusively adopted for the analysis of peripheral bones (e.g., hand, distal radius and distal tibia) and the actual working resolution depends on the dimensions of the analysed area. Although its application is currently limited to academic medical centres, this technique has shown promising results for the evaluation of the OP-related modification of trabecular and cortical bone architecture in vivo [10]. For this reason, a technological improvement, and a reduction in the cost of HRpQCT equipment, could increase its availability in clinical centres, paving the way for promising applications in OP detection in patients.
## 4 Modeling and Linearization Stage Setup  
The system level model is illustrated in Figure 2 with the designed modeling and system-level stages in Simulink to develop the modeling and linearization for the chain of the PA device under test. The overall structure is flexible up to a nonlinear order of 11 and memory 6. Figure 2a depicts the output string of the DSP builder tool, where the data are controlled with a resolution of 10 bits in the bus address and the amplitude is sampled with the maximum resolution of 14-bit. In this case, the output signals are divided into 16,384 parts. In this scheme, the coefficient calculation is obtained through code based on the LSE method and can be updated taking into account the electrical factor P1 dB that corresponds to the voltage when the linear gain corresponds to a 79.43% decrease.
# Scene Adaptive Segmentation for Crowd Counting in Population Heterogeneous Distribution
###### Abstract  
Crowd counting is an important part of crowd analysis and has been widely applied in the field of public safety and commercial management. Although researchers have proposed many crowd counting methods, there is little research on non-uniform population distribution. In this research, a new scene adaptive segmentation network (SANNet) is proposed that can focus on crowd area to estimate accurately crowd density in population heterogeneous distribution. First, an image segmentation module is designed that can adaptive horizontal segment an image according to different density levels, and then obtains a close-up view image and a distant view image. Second, a dual branches network based on convolution neural network (CNN) is exploited that contains a distant view network (DVNet) and a close-up view network (CVNet), so as to extract different scales of image features and then generate density maps by each branch, respectively, so that the crowd counting module has robustness on different scales of target. Finally, a comparative experiment on three well-known crowd counting datasets shows that SASNet achieved stabilized performance and robustness in population heterogeneous distribution.  
image processing; convolution neural network; crowd counting; heterogeneous distribution +
Footnote †: journal:  
Article
## 3 Results and Discussion
### Effect of the Cathode Current Density on the Microstructure of Carbon Cathodes
The higher were the current densities, the more was the accumulated electrostatic charge in the layers, and this charge accumulation resulted in repulsion between the layers, separation, and exfoliation of the surface layers [23; 24]. Obviously, higher current densities facilitated the exfoliation of graphite flakes from their basal planes in the filler particles, as indicated in Figure 4d. By combined analysis of sodium expansion curves and TEM, the results suggest that high current densities can cause the mechanical failure of carbon cathodes during aluminum electrolysis. In industrial practice, care needs to be taken to limit the maximum current density for optimizing production processes. The microstructure of the fractured surfaces of carbon cathodes was also studied using scanning electron microscope. Figure 5a,b show SEM images of fractured surfaces of the carbon cathodes at testing 120 min with a current density of 0.45 A/cm\({}^{2}\) and 0.7 A/cm\({}^{2}\), respectively. Microscopic rearrangement of graphite flakes can be clearly observed from two groups. Figure 5a shows the deformation in-plane behavior. The edges of the fractured layers show zigzag nature as indicated in Figure 5b. The zigzag nature of the fracture surface reveals the crack developed easily along the sheet edges. This is consistent with the strong bonding nature within the planes and relatively weaker bonding between the sheet edges. Because of vacancies and Stone-Wales defects, the zigzag fracture appears along the sheet edges. These defects play a crucial role for the fracture along the sheet edges.