esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks
## 3 Self-Similarity Analysis of Satellite Network Traffic  
### LEO Mega-Constellation Networks  
The topology of LEO mega-constellation networks can be demonstrated by \(G=(V,E)\), where \(V\) denotes the satellite nodes and \(E\) denotes the inter-satellite links (ISLs) between satellites. The inter-satellite links include intra-plane ISLs and inter-plane ISLs. The former are utilized to connect front and rear satellites in the same orbit, whereas the latter are utilized to connect adjacent satellites in two adjacent orbits [26]. A LEO mega-constellation network integrated with a terrestrial network is shown in Figure 2, including satellites, ground stations, ground terminals, feeder links and ISLs. The onerous communication between ground gateways may lead to congestion of the shortest path during the communication peak [27]. In this case, the giant mesh topology of the LEO mega-constellation network provides multiple candidate paths. Through reasonable load management based on traffic forecasting, high transmission as well as low latency can be taken into account at the same time.
## 2 Overall Design of the Integrated System
### Working Modes
#### 2.3.1 Periodic Inspection Mode  
Based on the deployment of the WSN nodes showed in Figure 2, the scheduling of the periodic inspection mode [11] is used, as described in Figure 3. In each cycle, the sink node starts to broadcast a parameter collection command to the routing node.  
After receiving the command, the routing node first forwards it, and then determines whether it has children sensor nodes itself. If it has none, the routing node will send an active status report to the sink node after a random time period; otherwise, it will broadcast the parameter collection command to its children sensor nodes, carrying the network address of the children nodes that it has messages to transmit to, since the sensor node can only communicate with its parent node in an indirect manner. In other words, the sensor node needs to poll its parent node to determine whether it has any messages pending. When sensor node receives the command, it will first determine whe  
Figure 3: Scheduling of periodic inspection mode.  
data for it. If yes, it requests the data. After this, it collects environmental parameters according to the command, and sends them in its time slot, then goes to sleep to save power. The routing node gathers the parameters received from its children nodes, and then sends them to the sink node in a multi-hop relay way, and finally to the GMDC via the gateway. At this point, a cycle comes to an end.
# Microgrid Cyber-Security: Review and Challenges toward Resilience
## 3 Definitions and Overview
### Cyber-Physical Security
Interventions may occur with the help of numerous mediums, such as the Universal Serial Bus (USB) thumb drive, viruses, and even software patches and updates [17]. Despite the fact that cyber intrusions on cyber-physical systems (CPSs) can be found under different terms, such as bias injection attack, zero dynamics attack, denial of service (DoS) attacks, eavesdropping attack, replay attack, stealthy attack, covert attack, and dynamic false data injection attacks [18]. These attacks can still be classified according to the one or multiple security criteria they are jeopardizing, as set forth in Figure 2.  
Intentionally introduced faults or malicious attacks triggered by the cyber layer leaving serious impacts on not only the technical aspects, but also on economic and social correlations in power network operations, are the focus of this research. Effects range from tampering smart meters data or manipulating the forecasted load profiles up to reaching equipment damage or even complete blackouts [19]. However, achieving such results is never an easy business. Indeed, physical prerequisites and the current state of the power system architecture with contemporary defense mechanisms, such as controllers prepared to re-examine each input parameter against a selection of acceptable values preventing possible physical damages [20], burden the attacker with the mandatory acquisition of a customized knowledge about the physical nature of the system added to the already required computer-related competencies. But then again, this does not mean that the conventional ways of protection, such as the ones adopted to restrain the spread of fault effects by isolating of a malfunctioning entity, are enough to prevent an attacker from achieving an unacceptable condition in the grid [20]. In that vein, reasonable strategies to fend off such incidence fall into two complementary categories. The first one is about developing measures that tend to detect malicious attacks and tackle down the cause of infection in the system in order to deal with either the compromised unit or entity through isolation or the direct cause from wherein the adversary could have accessed the network. The second important aspect is cyber resiliency, in which we anticipate the behavior of our system under attack and elaborate on what could be done to expeditiously recover from these attacks in a passive protection fashion. At any rate, we must keep in mind that keeping the system utterly safe, over and above maintaining a level of simplicity allowing the intuitive understanding of the entangled operation, is a paradox that preoccupies the power system researchers and engineers.
# Effects of Microbiota on the Soiling Process of PV Modules in Arid Zones of the Atacama Desert
###### Abstract  
Photovoltaic technology has proven to be a reliable, economical, and clean energy source that is capable of adapting to diverse geographical conditions. However, factors such as soiling overshadow these qualities, thus leading to production losses and affecting the profitability of this technology. For these reasons, soiling is a highly studied topic, which involves considering the physicochemical characterization of the deposited material, mitigation strategies, effect predictions, and cleaning mechanisms. However, there is a relatively unexplored area related to the microbiological contribution to soiling. The surface of photovoltaic modules, along with the deposited material and local atmospheric factors, fosters favorable conditions for the colonization of microorganisms. These microorganisms influence the soiling mechanisms and optical properties of photovoltaic modules. This work presents a detailed characterization of the microbial diversity present in the soiling deposited on photovoltaic modules installed in the Atacama Desert. Two study sites were defined: Andrigasta and the Solar Platform of the Atacama Desert, which have warm and cold desert climates, respectively. Mineralized characterization tests, heavy metal analyses, TOC, and inorganic element analyses were conducted on the deposited material. Additionally, the culturable isolates and the metagenomic DNA of the soiling samples and biofilms grown on standard PV glass were characterized using next-generation sequencing. The results show that the deposited soiling contained a microbiological component that had adapted to extreme desert conditions. The presence of the genera _Arthroducter_, _Kacuria_, and _Dietizu_ were identified in the culturable isolates from Antofagasta, while _Arthroducter_ and _Diezia_ were obtained from the Solar Platform of the Atacama Desert. The metagenomic DNA was mainly represented by the genera _Pontibacter_, _Noviherbaspirillum_, _Massilia_, _Arthrobacter_, _Hymenobacter_, and _Deinococcus_ at Antofagasta. However, at the Solar Platform of the Atacama Desert, the analyzed samples presented DNA concentrations below 0.5 ng/uL, which made their preparation unviable. At the PSDA, the biofilms formed by the genera _Peribacillus_ and _Kacuria_ were identified, whereas the UA showed a greater abundance of bacteria that favored biofilm formation, including those that belonged to the genera _Bacillus_, _Sporascina_, _Burgauea_, _Mesaobacillus_, _Cytokacillus_, _Caldakalihacillus_, and _Plamococcus_. Based on these results, we propose a soiling mechanism that considers the microbiological contribution to material cementation.  
s 2024148591. [[https://doi.org/10.3390/app14198591](https://doi.org/10.3390/app14198591)]([https://doi.org/10.3390/app14198591](https://doi.org/10.3390/app14198591))
# Research on Three-Dimensional Scanning Path Planning of Casing Parts Based on Industrial Robot
## 4 Scanning Simulation Analysis  
### Description of the Scan Trajectory Motion  
In the circular scanning motion, the scanning motion is regarded as the motion of the robot tool coordinate system \(\{T\}\) relative to the workpiece coordinate system \(\{S\}\), both translational motion and rotational motion. As shown in Figure 12, the workpiece coordinate system \(\{S\}\) is used as the reference coordinate system, and the starting point is set to \(P_{0}\). Due to the limited scanning range of the scanner, it is necessary to adjust the attitude when reaching the coordinate point \(P_{1}\). The scanning trajectory of the first circle passes through the coordinate point \(P_{i}\)\(P_{i+1}\),..., \(P_{n}\) in turn. The rising part of the trajectory passes through the coordinate \(P_{j}\), \(P_{j+1}\), the scanner will only have a translational motion until the scan was complete. The spatial pose of the scanner's scanning trajectory coordinates is expressed as:  
\[\begin{array}{l}{}^{S}P_{0}={}^{T}P_{0}+{}^{T}P_{S0};\ {}^{S}P_{1}={}^{0}_{1}T^{S}P_{0};\ {}^{S}P_{2}={}^{0}_{1}T^{S}P_{0};\ {}^{S}P_{3}={}^{0}_{1}T^{ 1}_{2}T^{S}P_{0};\\ {}^{S}P_{i}={}^{0}_{1}T^{1}_{2}T^{2}_{3}T^{i-1}_{i}T^{S}P_{0};\ {}^{S}P_{i+1}={}^{0}_{1}T^{1}_{2}T^{2}_{3}T^{i-1}_{i}T^{i}_{i+1}T^{S}P_{0};\\ {}^{s}P_{n}={}^{0}_{1}T^{2}_{2}T^{3}_{3}T^{i-1}_{i}T^{i-1}_{i+1}T^{n-1}_{n}T^{S }P_{0}={}^{0}_{n}T^{S}P_{0}\\ {}^{s}P_{m}={}^{0}_{0}_{1}TP_{0}\\ \end{array} \tag{19}\]  
Figure 11: Deflection angle and scan corresponding parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & \(\gamma\) & \(A_{1}B_{1}\) & \(A_{1}C_{1}\) & \(A_{1}W_{1}\) & \(L\) \\ \hline
1 & 0\({}^{\circ}\) & 34.43 & 34.43 & 30 & 0 \\
2 & 1\({}^{\circ}\) &  
The coordinate expression of any coordinate point in the scanning trajectory is as follows:  
\[{}^{s}P_{nm}=(^{0}_{n}T^{s}+^{0}_{m}T)P_{0} \tag{20}\]  
where \({}^{0}_{n}T=rot(z,\sum\limits_{i=0}^{n}\gamma_{i})trans(\sum\limits_{i=0}^{n}l_{ i})=\begin{bmatrix}\cos\sum\limits_{i=0}^{n}\gamma_{i}&-\sin\sum\limits_{i=0}^{n} \gamma_{i}&0&0\\ \sin\sum\limits_{i=0}^{n}\gamma_{i}&\cos\sum\limits_{i=0}^{n}\gamma_{i}&0&\sum \limits_{i=0}^{n}l_{i}\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix};^{0}_{n}T=transZ(\sum\limits_{j=2}^{n}k_{j})=\begin{bmatrix} 0\\ 0\\ K_{j}\end{bmatrix}\).  
The curve circle model is created with (\(R+AW\)) as the radius. The coordinate \(P_{0}\)\(=[R_{i}+AW,l_{i},H_{i}]^{T}\) of the starting point of the scanning path is set. Based on Equation (20), the coordinate position of the coordinate point \(P_{i}\) on the scanning trajectory was solved, and the scanning trajectory was fitted.
## 3 SOH Estimation Methods
### Preprocessing Methods
#### 3.1.2 Discrete Wavelet Transform (DWT)  
In CWT, \(s\) and \(\tau\) have positive real values, and in this case, the computational processes increase because the resulting values of the wavelet transform for the \(f(t)\) signals are highly redundant. To reduce the computational process, dynamic sampling was performed, as defined in Equation (5).  
\[a=2^{j},j\in Z \tag{5}\]  
\[b=k2^{j},\ j,k\ \in Z \tag{6}\]  
The DWT with the changed parameters is defined as follows:  
\[\text{DWT}(j,k)=2^{-\frac{j}{2}}\int_{R}x(t)\frac{1}{\sqrt{a}}\psi^{*}\Big{(} 2^{-j}t-k\Big{)}dt \tag{7}\]  
Figure 4: Result of CWT of reference discharge-RW09.  
Through this process, the original CWT becomes a discrete wavelet function and a scaling function.  
\[\psi_{j,k}(t)=2^{-\frac{j}{2}}\psi\Big{(}2^{-j}t-k\Big{)} \tag{8}\]  
\[\phi_{j,k}(t)=2^{-\frac{j}{2}}\phi\Big{(}2^{-j}t-k\Big{)} \tag{9}\]  
When analyzing signals using the DWT, the wavelet coefficient of the signal can be obtained as follows:  
\[a_{2j}(k)=\int x(t)\phi_{j,k}(t)dt) \tag{10}\]  
\[d_{2j}(k)=\int x(t)\psi_{j,k}^{*}(t)dt \tag{11}\]  
Evidently, the signals are decomposed into approximations and details using Equations (10) and (11). The discrete wavelet and scaling functions act as low- and high-frequency filters, respectively. The results of DWT are shown in Figure 5.
## 4 Impact of Climate Change on Oceans: Leveraging DL for Monitoring and Analysis
### Understanding Impacts on Physical Properties via DL
#### 4.1.1 Applying DL to Estimate Ocean pH Levels  
Ocean acidification refers to the ongoing decrease in ocean pH levels caused by the absorption of excess anthropogenic CO\({}_{2}\) from the atmosphere. Dissolved CO\({}_{2}\) in seawater forms carbonic acid, which dissociates into hydrogen ions, increasing ocean acidity and lowering pH. A recent study by [PERSON] et al. [98] analyzed four decades of global surface ocean acidification (1982-2021), revealing a declining rate of \(-\)0.0166 \(\pm\) 0.0010 per decade. This study also highlighted significant regional variability, with high-latitude regions, such as the Arctic, experiencing the fastest acidification rates.  
Accurate monitoring of ocean pH is crucial for understanding acidification trends and mitigating their impacts. However, traditional in situ sensors are costly and limited in spatial and temporal coverage. To address this limitation, data-driven approaches have been increasingly applied to estimate ocean pH levels from different environmental variables. Recent studies have employed ML and DL techniques, such as RF, feedforward neural networks (FFNNs) [99; 100], and the Back Propagation neural network (BP-NN) [101], to map pH distributions with high spatial and temporal resolution by integrating satellite-derived SST, chlorophyll-a concentration, dissolved oxygen, and other in situ observations [102]. These methods have demonstrated strong agreement with reference data, validating their effectiveness for large-scale ocean acidification monitoring [103; 104]. It should be noted that direct measurement of ocean surface pH from space is not possible. Instead, all current approaches rely on indirect estimation, where pH is inferred using models trained on in situ pH measurements and other associated surface variables such as SST, SSS, pCO\({}_{2}\), and chlorophyll concentrations. Some of these variables, like SST and chlorophyll concentration, are retrievable from satellite observations.  
In addition to pH levels, DL has also been leveraged to estimate total alkalinity (TA) [105], which is a key variable in assessing ocean buffer capability, i.e., its ability to resist changes in pH when absorbing CO\({}_{2}\). Models such as Artificial Neural Network (ANN) [106] and TabNet [107] have shown promising accuracy in estimating global TA distributions from remote sensing inputs and in situ measurements [108], further highlighting the potential of advanced DL techniques in capturing TA variations and enhancing our understanding of ocean acidification [109; 110].  
Figure 4: Overview of climate change on ocean physical properties.
## 7 Comparative Analysis
### Detection and Transition Probabilities  
In the first experiment, ASC-RL was tested with other schemes in detection and transition probabilities. At the start of the experiment, at low transition probabilities of 0.0, all schemes work in the same way with an average detection probability of 0.55, as shown in Figure 9. With the increasing trend of transition probability around 0.4, ASC-RL shows better results than other schemes. This increase in detection probability suggests that ASC-RL can detect better, and it dynamically changes the states where other schemes (CAS-IIoT-RL, LSTM-RL and AEM-RL) have no such four-state policy, and their detection probability is lower. In analyzing the average percentage increase in the three schemes with the proposed scheme, as depicted in Table 5, increasing transition probabilities is shown to enhance overall performance by 3.5%. These experiments cover the transition probabilities from 0.0 to 1.0, but do not include the confidence intervals. These experiments are deterministic without repeated sampling. In the future, we want to improve the system's robustness and reproducibility by testing it with repeated trials.  
Figure 8: Prediction accuracy and energy efficiency with combined reward score.  
Figure 9: Detection probability and transition probability comparison of ASC-RL with CAS-IIoT-RL, LSTM-RL, and AEM-RL.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **One-Stage** & **Two-Stage** \\ \hline \multirow{4}{*}{Advantages} & Fast speed; avoids & High precision (localization \\  & background false positives; & and detection rate); anchor \\  & learns generalized features & mechanism; shared \\  & of objects & computation \\ \hline \multirow{2}{*}{Disadvantages} & Low precision (localization & Slow speed; long training \\  & and detection rate); poor & time; relatively high \\  & detection of small objects & false positives \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Object detection algorithm category. in White Muscovy Duck (WMD) flocks under varying lighting conditions. This model meets stringent real-time speed requirements. Despite significant advancements in YOLO-based object detection (a single-stage approach), it still exhibits suboptimal performance in small object detection. This limitation primarily stems from the low-resolution feature maps generated by YOLO's network architecture, leading to inadequate feature extraction for small targets. Compared to region proposal-based methods, YOLO achieves lower recall rates, indicating higher susceptibility to missed detections and false detections. These inherent constraints restrict YOLO's effectiveness in specific application scenarios, necessitating scenario-specific selection and optimization during practical deployment. To address the aforementioned challenges while leveraging YOLO's strengths, this study integrates the existing research and derives optimized improvements for low-light environments through extensive experimentation and analysis. The proposed framework builds upon YOLOv11. To enhance the performance of deep convolutional networks in object detection tasks, we implement three key modifications: replacing standard Conv layers with HetConv [15] in the backbone, incorporating ShuffleAttention (SA) [16] modules, and introducing Inner-SIoU to improve feature extraction for small objects, thereby mitigating missed/false detections. Additionally, we substitute Conv layers with HetConv in the Neck to achieve model lightweighting. Comparative and ablation experiments conducted on both normal-light and low-light datasets demonstrate that our HSS-YOLO outperforms baseline YOLOv11. The subsequent sections detail the model's implementation and optimization strategies.
## 2 Materials and Methods
### Studied Population  
Patients were recruited consecutively from the patients visiting our departments. The inclusion criteria were as follows: each patient had to have an established diagnosis of psoriasis that could be classified to one of the predefined psoriasis subtypes (large plaque psoriasis, small plaque psoriasis, guttate psoriasis, palmoplantar psoriasis, psoriasis of the scalp, inverse psoriasis, erythrodecrmic psoriasis, PPPP and GPP) and be able to complete the questionnaires. The exclusion criteria included age <16 years, illiteracy, presence of any dermatological or systemic disorder that might cause pruritus, treatment of psoriasis within a period of 2 (for topical agents) or 4 weeks (for systemic therapy or phototherapy) before study inclusion, pregnancy, lactation, and use of drugs with antipruritic potential.  
Out of 338 psoriatic patients enrolled primarily in the study, 295 were included for final analysis. The remaining 43 subjects were excluded because of current or recent (within the prior 4 weeks for systemic agents or phototherapy, or within 2 weeks for topical medications) anti- psoriaticatic treatment or comorbidities of either dermatological or systemic disorders that might cause pruritus.  
Patients were divided into nine groups according to the dominant clinical manifestation of psoriasis: large-plaque psoriasis (\(n=45\)), small-plaque psoriasis (\(n=32\)), guttate psoriasis (\(n=31\)), palmoplantar psoriasis (\(n=33\)), psoriasis of the scalp (\(n=32\)), inverse psoriasis (\(n=23\)), erythrodecrmic psoriasis (\(n=33\)), PPPP (\(n=43\)) and GPP (\(n=23\)) [11]. The majority of the investigated groups were comparable according to gender, age, duration of posoriasis, BMI and coexistence of psoriatic arthritis. However, some statistically significant discrepancies were observed, which can be explained by particular subtype features (e.g., patients with GPP were older, and patients with plaque-type psoriasis were more obese) (Table 1).
## 5 Simulation and Hardware Realization of the Basic Unit of the Proposed GSMLI Topologies
Figure 15b shows the waveform of output voltage and load current when the DC voltage sources were connected across capacitors C1 and C2 in the symmetric configuration. Figure 15c,d show the waveform of voltage across capacitors C1 and C2 and the waveform of the blocked voltage across switches S1 and T1, respectively, in the symmetric configuration. The peak values of voltage across capacitors C1 and C2 are 12 volts. The peak value of the blocked voltage across switches S1 and T1 are 12 and 24 volts, respectively, which verify the equations for maximum blocked voltage by switches. For the experimental results of asymmetric configuration, voltage sources V1 and V2 were taken 12 volts and 36 volts, respectively. This resulted in a 17-level staircase output voltage with a 96-volt peak value, as shown in Figure 15e under the no-load condition. Figure 15f shows the output voltage and load current waveform when a purely resistive load of 100 \(\Omega\) was connected at the output. Figure 15g shows the waveform of output voltage (17 levels) and load current to observe the levels clearly. Figure 15h-j show the waveform of the voltage across capacitors C1 and C2, the waveform of blocked voltage across switches S1 and S3, and the waveform of blocked voltage across switches T1 and T3, respectively, in an asymmetric configuration. The peak values of voltage across  
and 36 volts. The peak values of the blocked voltage across switches S1, S3, T1, and T3 are 12, 12, 24, and 72 volts, respectively, which verify the equations for maximum blocked voltage by switches. It is clear that the experimental results have a close agreement with the simulation results. Figure 12: (**a**) Output voltage and current for change in load from resistive to resistive-inductive, (**b**) output voltage and current for dynamic change in modulation index, and (**c**) THD in output voltage for asymmetric configuration. Figure 13: Voltage across capacitors \(C_{1}\) and \(C_{2}\) for asymmetric configuration. Figure 14: Experimental setup. Figure 15: Waveform of (**a**) output voltage (25 V/div) and load current (1.5 A/div). (**b**) Output voltage (20 V/div) and load current (0.5 A/div) when the voltage supply of 12 V was connected across both capacitors. (**c**) Voltage across capacitors C1 and C2 (12 V/div). (**d**) Blocked voltage across switches S1 and T1 (12 V/div) in symmetric configuration. Waveform of (**e**) output voltage (60 V/div) under the no-load condition. (**f**) Output voltage (20 V/div) and load current (0.75 A/div). (**g**) Output voltage (20 V/div) and load current (0.75 A/div) for showing one complete cycle (**h**). Voltage across capacitors C\({}_{1}\) and C\({}_{2}\) (25 V/div). (**i**) Blocked voltage across switches S1 and S3 (25 V/div). (**j**) Blocked voltage across switches T1 and T3 (24 V/div) in asymmetric configuration.