esa-sceva/satcom-chunk-collection huggingface dataset:
# Constellation Encryption Design Based on Chaotic Sequence and the RSA Algorithm
## 3 The Proposed Design Method
### Specific Design Steps
#### 3.2.2 Receiver Encryption Steps  
Secondly, the receiving end part is displayed and analyzed, and the encryption block diagram of the sending end is shown in Figure 5.  
A.  
The receiver first uses the RSA algorithm to generate a pair of public key (_n,e_) and secret key (_n,d_), and sends the public key.  
B.  
The receiver uses the key to decrypt the received ciphertext \(c\) and obtains \(m\).  
C.  
The encrypted sequence \(z(n)\) is generated in the same manner as step A on the sender side.  
D.  
After demodulating the received signal, the information is decrypted with the encrypted sequence to obtain the original information.
# Propospheric Attenuation in GeoSurf Satellite Constellations
## 5 Global Clear-Sky Attenuation at 39.6 GHz  
In this section, we consider the total clear-sky attenuation due to water vapor, oxygen, and cloud, calculated with the ITU\(-\)R recommendations [32; 33] at 39.6 GHz. Figure 12 shows the annual probability distributions predicted in the GEO paths on the local meridian of the reference sites. As for rain attenuation, the attenuation depends on site, with drier sites (Pasadena) being less affected than wetter ones (Holmdel).  
Figure 13 shows the difference \(A_{GEO}-A_{Z}\), calculated at the fixed annual probabilities of 1%, 5%, and 10%, between the clear-sky attenuation exceed in the GEO path, \(A_{GEO}\), and that exceeded in the GeoSurf path, \(A_{Z}\), in the latitude range 60\({}^{\circ}\) N to 60\({}^{\circ}\) S along the meridian of Spino d'Adda. We can notice that the Northern hemisphere is more attenuated than the Southern one. Now, even if the attenuation is smaller than that due to rain, it is always present because for probabilities below 1%, the values do not significantly change; therefore, for any link design, the values exceeded at 1% should be considered. Figure 14 shows similar results for the other reference sites.  
Now, for comparing LEO and GeoSurf attenuation at equal probabilities, in Figure 15, we show the difference \(A_{LEO}-A_{Z}\), versus annual probability (%), for the reference sites. From these results, we conclude that the GeoSurf should be affected, as expected, by smaller clear-sky attenuation. Because in system design we are interested in low probabilities and, as recalled, the clear-sky attenuation expected for probabilities below 1% is practically that estimated at 1%, we can assume the values given by the black curves. As for rain attenuation, the Northern hemisphere is more attenuated than the Southern one.  
Figure 12: Annual probability distributions of clear-sky attenuation exceeded in the GEO paths on the local meridian at the reference sites.  
Figure 14: The difference \(A_{GEO}-A_{Z}\), calculated at annual probabilities 1%, 5%, and 10%, between the clear-sky attenuation exceeded in the GEO path, \(A_{GEO}\), and that exceeded in the GeoSurf path, \(A_{Z}\), along the meridian of: (**a**) Robledo (GEO elevation angle 42.8\({}^{\circ}\)), (**b**) Holmdel (GEO elevation angle 43.3\({}^{\circ}\)), (**c**) Pasadena (GEO elevation angle 50.3\({}^{\circ}\)), and (**d**) Canberra (GEO elevation angle 49.0\({}^{\circ}\)), in the latitude range 60\({}^{\circ}\) N to 60\({}^{\circ}\) S.  
Figure 13: The difference \(A_{GEO}-A_{Z}\), calculated at annual probabilities 1%, 5%, and 10%, between the clear-sky attenuation exceeded in the GEO path (elevation angle 37.7\({}^{\circ}\)), \(A_{GEO}\), and that exceeded in the GeoSurf path, \(A_{Z}\), along the meridian of Spino d’Adda (9.5\({}^{\circ}\)), in the latitude range 60\({}^{\circ}\) N to 60\({}^{\circ}\) S.
# YOLOv8-WD: Deep Learning-Based Detection of Defects in Automotive Brake Joint Laser Welds
## 3 Model and Optimization
### C2f Improvement  
Traditional convolution operations use fixed kernels, limiting their ability to adapt to diverse features, particularly in complex tasks like weld defect detection. While increasing the depth, width, or resolution of convolutional neural networks (CNNs) can enhance performance, these approaches may still fail to capture intricate features. To address this, we propose Dynamic Convolution (DyConv), which improves model performance without increasing network size [33].  
DyConv dynamically adjusts its convolutional parameters to suit task-specific requirements, enhancing the model's ability to detect diverse objects. This is particularly useful in defect detection, where weld defect dimensions and shapes vary. DyConv uses dynamically generated kernels to improve flexibility and efficiency, adapting based on the input image.  
By integrating a coefficient generation module, such as a multilayer perceptron (MLP), DyConv adjusts kernel weights to focus on critical defect regions, minimizing background noise and improving precision, especially in noisy welding environments. This dynamic adjustment enhances both defect recognition and detection accuracy for variable targets, making DyConv effective for complex detection tasks that traditional CNNs struggle with.  
In summary, DyConv's adaptive mechanism enhances robustness and precision, making it particularly suitable for defect detection in challenging industrial environments. It outperforms conventional CNNs by improving focus on key areas, boosting detection accuracy, and reducing noise sensitivity.  
Grad-CAM (Gradient-weighted Class Activation Mapping) is a gradient-based technique that generates class activation maps by weighting gradients from intermediate layers of a convolutional neural network (CNN). It visualizes the regions of an input image most influential to the model's prediction, where red areas indicate high contribution, yellow represents secondary attention, and blue suggests minimal relevance. As shown in Figure 7, the original YOLOv8 model disperses attention across the image. However, with DyConv incorporated, the model focuses more effectively on key features, demonstrating its ability to enhance feature localization in YOLOv8.  
Figure 7: The Grad-CAM comparison diagram (from left to right: original image, YOLOv8, and YOLOv8-WD visualization heatmaps).
# A Survey on Deep Learning in 3D CAD Reconstruction
## 6 CAD Representation
### Constructive Solid Geometry (CSG)  
Constructive Solid Geometry (CSG) is a classic 3D CAD expression form [108] which gradually builds complex 3D structures by applying Boolean operations (including union, intersection, and difference) to basic geometric bodies (such as cubes, spheres, cylinders, etc.) [109]. Among them, the difference operation is particularly important in the modeling process and is especially suitable for designing holes and protrusion structures. Unlike boundary representation (B-Rep), which directly records boundary information, CSG (Constructive Solid Geometry) constructs an expression tree through Boolean primitives and operations, only recording the construction process of the model without storing specific boundary geometry. Therefore, it is necessary to convert it into the final geometry through the Boolean evaluation process. CSG representation consists of Boolean primitives and Boolean operations.  
CSG is usually modeled in a tree structure, with leaf nodes representing basic geometric bodies and intermediate nodes representing Boolean operations or geometric transformations. Among them, protrusion is a typical operation, which is often implemented by union. B-rep describes the boundaries of objects through vertices, edges, faces, and their topological relationships, and pays more attention to the detailed expression of the surface of the shape, which is suitable for scenes that require high-precision modeling. CSG focuses on the construction process, and B-rep emphasizes geometric expression. The two can complement each other.  
CSG and sequence representation are essentially different. CSG combines basic geometric bodies through Boolean operations, focusing on the logical relationship between geometric bodies, and is usually represented by an operation tree. Sequence representation simulates the real design process through sketches and operation sequences, involving sketching, constraint setting, and modeling feature operations. It has stronger expressive ability and is more suitable for complex parametric modeling, which meets actual engineering needs.  
CSG has a concise expression structure and is flexible to modify. It is widely used in many fields such as mechanical design and architectural modeling. Recently, [PERSON] et al. [110] proposed the D2 CSG model, which can learn compact CSG expressions in an unsupervised learning framework for representing three-dimensional CAD shapes.
# Early Fire Detection on Video Using LBP and Spread Ascending of Smoke
## 2 Proposed System
### Texture Analysis and Classification
#### 2.3.1 Local Binary Pattern  
LBP was originally proposed for texture classification by [PERSON] et al. [12]. This operator compares a central pixel with its neighbors and, if the intensity of the pixel under analysis is smaller than the central pixel of the window, it is assigned a zero, otherwise the central pixel is equal to one, as shown in Equations (13) and (14).  
\[LBP_{P,R}=\sum_{p=0}^{P-1}s(g_{p}-g_{c})2^{p} \tag{13}\]  
Figure 3: Smoke detection results based on the characteristic color of smoke.  
\[s(g_{p}-g_{c})=\left\{\begin{array}{ll}1&\text{if}\quad g_{p}-g_{c}\geq 0\\ 0&\text{if}\quad g_{p}-g_{c}<0\end{array}\right. \tag{14}\]  
where \(g_{c}\) is the value of the central pixel and \(g_{p}(p=0,\cdots,P-1)\) is the value of neighboring pixels equally spaced in a circle of radius \(R(R>0)\). Thus, if we have a neighborhood with eight neighbors, we have 256 different patterns. Typically, the histogram of LBP values, given by Equation (15), is used to obtain the feature vectors used to describe the image under analysis:  
\[H_{P,R}(k)=\sum_{i=1}^{N}\sum_{j=1}^{M}w(LBP_{P,R}^{type}(i,j),k) \tag{15}\]  
where  
\[w(x,y)=\left\{\begin{array}{ll}1&\text{if}\quad x=y\\ 0&\text{otherwise}\end{array}\right. \tag{16}\]  
\(k\in[0,K]\), \(K\) is the number of possible patterns according to the type of LBP used, while \(N\) and \(M\) are the height and width of the image under analysis, respectively.  
Several variations of LBP have been proposed such as uniform LBP, an LBP invariant to rotation, an LBP uniform and invariant to rotation and an LBP that is not redundant [13]. The uniform rotation invariant patterns describe basic structures such as flat areas, points, edges, curves, corners and end of lines, regardless of their orientation. Smoke texture changes constantly without following a definite pattern, therefore does not have a defined orientation and contrast, thus they can be discarded. This is the purpose of using non-redundant LBP patterns that consider identical spatial structures that are complementary in their contrast.
## 2 Comparison of Long-Term Features
### Occurrence Rates  
We examined the seismicity produced by the simulator in a long-term time interval before and after events of larger magnitude. The previously mentioned stacking technique was applied to the number of pairs of events of magnitude exceeding the thresholds listed in Table 3. The time difference and the largest distance between the two events of each pair are also reported in Table 3 for CNAFS and NMTFS, respectively. The results of this analysis are depicted in Figure 3.  
Our findings, concerning the CNAFS, unveil a discernible pattern of seismic activity characterized by a notable increase several centuries preceding pivot events. Additionally, we observe some oscillations starting approximately 100 years prior to the occurrence of the pivot events, followed by a pronounced decrease in seismic activity in the last 50 years, and concluded with a final increase just in the last 5 years. For the NMTFS, a precursory pattern with two maxima of seismic activity 4 years and 2 years before the pivot event can  
Figure 3: (**a**) Stacked number of M 4.2+ earthquakes preceding and following an M 5.2+ earthquake, obtained from the 100,000 years simulation in a long-term scale for the CNAFS. The long-term plot shows acceleration before and quiescence after the strong event. (**b**) As in the top, for the M 5.2+ earthquakes preceding and following an M 5.8+ earthquake in the NMTFS.  
be noted. The complexity of these patterns can be ascribed to the variable slip rate on the different segments composing both fault systems.  
Regarding the seismicity rates observed in the simulations, the two completely different geodynamic contexts exhibit a sharp decrease just at the time of the pivot events, followed by a slow increase lasting about 100 years and 6 years in the CNAFS and the NMTFS, respectively (Figure 3).
# Biomass from Green Areas and Its Use for Energy Purposes
## 3 Results and Discussion
### Measurement of Mechanical Properties
The addition of coniferous (especially spruce) sawdust during the production of pellets has a positive effect on the durability of solid fuels, which increases the pellets' resistance to mechanical damage [37]. Pellets made of waste biomass produced during the cultivation of sunflower and tobacco were distinguished by high resistance to mechanical damage. For the analyzed pellets, the destructive force oscillated in the range from 601.53 to 788.71 N. The addition of fir sawdust from coniferous trees during the production process of pellets from waste biomass of sunflower and tobacco improved the strength parameters of the tested pellets [33]. Analyzing the destructive force for pellets made of coniferous and deciduous sawdust and been wood, it was found that 166 N to 654 N was needed to destroy different types of pellets [38]. The mechanical \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pellet Type** & \begin{tabular}{c} **F\({}_{\text{max}}\)** \\ **(N)** \\ \end{tabular} & \
\begin{tabular}{c} **W to F\({}_{\text{max}}\)** \\ **(MJ)** \\ \end{tabular} &
\begin{tabular}{c} **Relative Deformation** \\ \(\varepsilon\) \\ \end{tabular} \\ \hline P1 & 249.00 \({}^{\text{a}}\)\(\pm\) 67.40 & 34.90 \({}^{\text{a}}\)\(\pm\) 5.79 & 0.311 \({}^{\text{a}}\)\(\pm\) 0.055 \\ P2 & 282.00 \({}^{\text{a}}\)\(\pm\) 66.30 & 77.60 \({}^{\text{b}}\)\(\pm\) 19.70 & 0.562 \({}^{\text{b}}\)\(\pm\) 0.055 \\ P3 & 74.60 \({}^{\text{a}}\)\(\pm\) 11.10 & 15.70 \({}^{\text{a}}\)\(\pm\) 0.932 & 0.311 \({}^{\text{a}}\)\(\pm\) 0.074 \\ P4 & 63.60 \({}^{\text{a}}\)\(\pm\) 18.80 & 11.30 \({}^{\text{a}}\)\(\pm\) 0.582 & 0.262 \({}^{\text{a}}\)\(\pm\) 0.074 \\ P5 & 408.00 \({}^{\text{a}}\)\(\pm\) 95.70 & 74.80 \({}^{\text{a}}\)\(\pm\) 11.40 & 0.351 \({}^{\text{a}}\)\(\pm\) 0.054 \\ P6 & 451.00 \({}^{\text{a}}\)\(\pm\) 141.00 & 108.00 \({}^{\text{b}}\)\(\pm\) 35.40 & 0.441 \({}^{\text{a}}\)\(\pm\) 0.141 \\ P7 & 439.00 \({}^{\text{a}}\)\(\pm\) 66.80 & 104.00 \({}^{\text{a}}\)\(\pm\) 34.30 & 0.472 \({}^{\text{a}}\)\(\pm\) 0.111 \\ P8 & 493.00 \({}^{\text{a}}\)\(\pm\) 135.00 & 119.00 \({}^{\text{a}}\)\(\pm\) 39.40 & 0.511 \({}^{\text{a}}\)\(\pm\) 0.131 \\ \hline \hline \end{tabular} Statistically significant differences marked by different letters (\(p\leq\) 0.05). The data were analyzed separately for each type of material.  
\end{table} 
Table 4: Mechanical parameters of the tested pellets in the horizontal axis compression process. strength of pellets produced from straw increases as a result of the use of an additive in the form of coniferous sawdust during the production process [39]. It is advantageous to use different biomass mixtures for the production of pellets. Pellets made of more than one raw material are characterized by greater resistance to mechanical damage compared to pellets from a single type of biomass. Wood biomass pellets are more resistant to damage during transport and storage compared to agricultural biomass pellets [40].
## 8 Results of the Parametric Analyses Carried Out with the Proposed Tool
### Impact of the Cable's Voltage Level
Specifically, the AC losses in the superconductor, which were the largest contribution to the total losses, were 4.3 times higher than in the 220 kV case when \(V_{rms}\) was 115 kV, 2.4 times higher when \(V_{rms}\) was 132 kV, and 1.8 times higher when \(V_{rms}\) was 150 kV. Moreover, in the 115 kV case, setting the frequency to 60 Hz led to a proportional higher \(Q_{AC}\) compared with the 50 Hz cases, which also affected the insulation losses. As for \(Q_{ins}\), these values decreased as the voltage decreased. However, their impact was limited, ranging from 4% to 11% between 115 kV and 220 kV. Figure 15: Capital costs per kilometer of cable, varying the voltage level and the frequency. The cases considered had \(P_{max}\) = 2 GW, \(cos\varphi\) = 0.9, and \(L_{line}\) = 50 km. Adding the capital and the operating costs resulted in a total cost per unit of length sized for \(P_{max}\) = 2 GW ranging from EUR 10.3 M/km for the 115 kV scenario to EUR 6.6 M/km for the 220 kV scenario. This would suggest that moving toward higher voltages is also advantageous for superconducting cables, similar to conventional high-power cables. However, it is important to remember that the presented results do not include the cost of transformers.
## 1 Introduction
Additionally, they are particularly effective in handling large volumes of data at high speeds, can continuously improve with more data, and can make predictions without explicit programming, making them highly efficient [14]. Furthermore, they can solve complex real-world problems and provide automatic problem-solving approaches. Non-parametric machine learning algorithms have lately attracted significant attention in utility infrastructure risk modeling. [PERSON] et al. [15] used non-parametric methods such as bootstrapping, bagging, and gradient boosting to improve the prediction performance in utility learning frameworks. [PERSON] et al. [16] reviewed the application of parametric and non-parametric machine learning techniques to power system reliability, highlighting the predictive capabilities of non-parametric algorithms in maintenance-related aspects. [PERSON] et al. [17] further emphasized the importance of non-parametric methods in predicting health and safety hazards in power infrastructure operations, achieving near-perfect predictions. When examining the literature, it becomes apparent that most studies on vegetation-related power outage modeling have been conducted at coarser resolutions, with limited research available that delves into the probability of tree-related outage risks on distribution power lines at a finer spatial granularity. Furthermore, the majority of these assessments have depended on the random forest (RF) algorithm [18; 19]. To the best of our knowledge, no previous studies have been undertaken to predict tree-related outage risks at a finer spatial granularity using a wide range of non-parametric machine learning algorithms. Utilizing non-parametric machine learning modeling is crucial in this context, as it allows for the exploration of complex, nonlinear relationships within the data, which are often inherent in vegetation-related outage risk factors, thus enabling more accurate predictions at a finer spatial scale. Accurate identification and localization of vegetation-risk-prone areas are essential for improving grid reliability by aiding utility professionals in making informed decisions to implement appropriate tree-trimming and grid-hardening practices. Therefore, the central objective of our study is to systematically evaluate the effectiveness of decision tree (DT), support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), and k-Nearest Neighbor (k-NN) algorithms in identifying the risk of tree-related power outages to distribution power lines at a finer spatial resolution. This work is an extension of our previous study [19], in which we developed a vegetation risk model to assess the impact of local environmental variables on the outage probability along distribution power lines using the RF algorithm.
# Effects of On-Site PV Generation and Residential Demand Response on Distribution System Reliability
## 1 Introduction
### Motivation and Background
A conceptual definition of demand response (DR) by US Energy Information Administration viewpoint is as follows: _\"Demand-side management (DSM) programs consist of the planning, implementing, and monitoring activities of electric utilities which are designed to encourage consumers to modify their level and pattern of electricity usage\"_[7]. It is worthy to indicate that new approaches play a critical role in electrical system planning while considering its sophisticated structure, and the last decade has seen a growing trend towards incorporating DR strategies into operational stages to increase network performance and quality of services. Here, smart households that can alter their internal operation in an optimized way, especially if a home energy management system (HEM) exists, have further capability to enhance effective implementation of residential DR, which is an area where more implementations have been provided recently.
## 4 Signal Pretreatment
### Error Correction
In addition, some of the error cases mentioned above are marked by the vertical red lines. It can be clearly seen that the emerging voltage spikes after binarization are effectively eliminated using this method, and a pure and stable Morse-encoded signal interpreting \"SOS\" is derived after the designed pretreatment stage.
# Study on Vibration Characteristics of Multi-Beam Structures with Stick and Slip at Joints
###### Abstract  
Stick-slip phenomena may manifest at the joints during cyclic vibrations in beam structures connected by some forms of joint. This work incorporates the sticking-slip effect of joint connections into the dynamic analysis framework of multi-beam structures through changes in friction forces. The system characteristic equation is solved using the incremental harmonic balance method, the vibration characteristics of the connected structure are explored through the dynamic response, and the accuracy of the model established in this paper is verified through experiments. The equivalent stiffness and damping changes of a connecting beam under different connection states are investigated for the first time. The research indicates that the \"tracking\" phenomenon, induced by abrupt damping and resonance frequency variations due to low contact pressure and a low friction coefficient, leads to a relatively stable vibration response amplitude across an extended frequency range. This results in the gradual attenuation of resonance peaks within the frequency response curve, giving rise to a defined resonance frequency range. As connection stiffness diminishes, the system demonstrates characteristics of internal resonance. In addition, the influence characteristics of external excitation and connection joint position on the vibration response of multi-beam structures are also explored. This model provides an effective method for studying the vibration problems of complex beam frame structures.  
multi-beam structure; Fourier series; friction damping; sticking slip; response analysis Article