esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Morphological and biochemical responses were assessed in an iceberg (G\({}_{\text{IL}}\)) and butterhead (G\({}_{\text{BL}}\)) lettuce (_Lactuca sativa_ var. capitata) treated with commercially available soluble preparation of mycorrhizal fungi and beneficial soil bacteria (MT) grown in three irrigation treatments considered in greenhouse (continental Croatia, 2022): I\({}_{100}\)--control treatment (100% volumetric water content--VWC); I\({}_{80}\)--80% WVC, moderate drought and I\({}_{60}\)--60% WVC, severe drought), in two growing cycles. MT was applied during lettuce drought-sensitive stages, i.e., transplanting and heading. Study results show that MT improved lettuce growth-related traits, yet the results are genotype and growing-cycle dependent. The beneficial effect of MT was also noted for root length, weight, and diameter which confirms the mycorrhizal role in improving the plant water uptake. Both lettuce genotypes responded to water deficit by overproduction of MDA and proline content, whereby the response of tested variables was growing cycle and genotype-specific. Both genotypes in severe drought treatment (MT-I\({}_{60}\)) responded with higher MDA in the first growing cycle and lower MDA content in the second growing cycle. MT-I\({}_{60}\) treatment reduced proline accumulation in G\({}_{\text{BH}}\) in both growing cycles, while increased accumulation in G\({}_{\text{IL}}\) during the second growing cycle. The responses of lettuce to MT are genotype-specific and shaped by environmental conditions.  
mycorrhizae; drought; lettuce genotype; morphology; MDA; proline Article
## 3 ML Application in O-RAN
### Traffic Steering
Paper [52] proposes a traffic steering use-case in O-RAN systems that exploits the benefits of Non-Orthogonal Multiple Access (NOMA) to improve radio resource efficiency. The authors introduce a resource allocation algorithm that dynamically allocates radio resources based on the traffic demand and channel conditions of the users. The proposed algorithm leverages NOMA to allow multiple users to share the same radio resources, while ensuring a high-quality user experience. Paper [64] proposes a traffic steering approach that ensures the efficient coexistence of eMBB and uRLLC services in O-RAN systems. They introduce a multi-objective optimization problem and a traffic steering algorithm that dynamically steers traffic based on the network load and user demands while ensuring a minimum QoS requirement for both eMBB and uRLLC users.
# R-PreNet: Deraining Network Based on Image Background Prior
###### Abstract  
Single image deraining (SID) has shown its importance in many advanced computer vision tasks. Although many CNN-based image deraining methods have been proposed, how to effectively remove raindrops while maintaining background structure remains a challenge that needs to be overcome. Most of the deraining work focuses on removing rain streaks, but in heavy rain images, the dense accumulation of rainwater or the rain curtain effect significantly interferes with the effective removal of rain streaks, and often introduces some artifacts that make the scene more blurry. In this paper, a novel network architecture, R-PreNet, is introduced for single image denoising with an emphasis on preserving the background structure. The framework effectively exploits the cyclic recursive structure inherent in PreNet. Additionally, the residual channel prior (RCP) and feature fusion modules have been incorporated, enhancing denoising performance by emphasizing background feature information. Compared with the previous methods, this approach offers notable improvement in rainstorm images by reducing artifacts and restoring visual details.  
single image deraining; residual channel prior; interactive fusion +
Footnote †: journal: Medical  
RCP, and the interactive fusion feature module (IFM) [11] to fully utilize RCP information, achieving high-quality rainless image reconstruction.  
Our contributions are summarized as follows:  
This article replicates and tests the PReNet deraining network on three popular image deraining datasets (Rain100H [13], Rain100L [13], Rain14000 [14]) and real rainy image datasets (Practical_by_[PERSON] [13]), and studies the results of deraining.  
This article explores the effectiveness of residual channel prior (RCP) for background protection and proposes an image deraining network structure based on RCP. Numerous experiments have shown that our method outperforms the original method on commonly used rainfall datasets, restoring visually clean images and good detail.  
An RCP extraction module and an interactive fusion module (IFM) are introduced, designated for RCP extraction and guidance, respectively. These aim to attain deep features of the RCP and guide the network to recover more background details.  
The remainder of this paper is structured as follows. Section 2 briefly reviews relevant studies on image denoising methods. Section 3 presents the comprehensive R-PReNet denoising network based on image background prior and delves into the RCP residual channel prior and IFM fusion techniques. Experimental results and comparisons are detailed in Section 4. The conclusion is given in Section 5.
## 3 Risk Identification during Navigation
### Ship's Position Dead Reckoning
\begin{array}{l}\left\{X_{t}^{j}=X_{o}^{j}+v_{j}*t*\sin\left(TC_{j} \right),\;t\in[0,T_{1}]\\ Y_{t}^{j}=Y_{o}^{k}+v_{j}*t*\cos\left(TC_{j}\right),\;t\in[0,T_{1}]\\ \left\{X_{t}^{j}=x_{T_{1}}+v_{j}*(t-T_{1})*\sin\left(\varphi_{s}\right),t\in[T_ {1},T_{2}]\\ Y_{t}^{j}=y_{T_{1}}+v_{j}*(t-T_{1})*\cos\left(\varphi_{s}\right),t\in[T_{1},T_ {2}]\\ \left\{X_{t}^{j}=x_{T_{2}}+v_{j}*(t-T_{2})*\sin\left(\varphi_{s+1}\right),t\in[ T_{2},1800]\\ \left\{Y_{t}^{j}=y_{T_{2}}+v_{j}*(t-T_{2})*\cos\left(\varphi_{s+1}\right),t\in[T_ {2},1800]\right.\end{array} \right.\right. \tag{13}\]  
Figure 4: Schematic diagram of judging the section of the ship. \[ \begin{cases}X_{t}^{j}=X_{o}^{j}+v_{j}*t*\sin\left(TC_{j}\right),\ t\in[0,1800]\\ Y_{t}^{j}=Y_{o}^{j}+v_{j}*t*\cos\left(TC_{j}\right),\ t\in[0,1800]\end{cases}  \tag{14}\]  
where \(v_{j}\) is the speed of the j-th TS, \(v_{0}\) is the speed of the OS, \((x_{T},y_{T})\) is the coordinate of the next segment's starting point, \(T\) is the time when the ship reaches the next segment, \((x_{T_{1}},y_{T_{1}})\), and \(T_{1}\) are the coordinates and time when the ship reaches the current segment's track control boundary line, respectively. The position and time when the ship reaches the track control boundary line of the next segment are \((x_{T_{2}},y_{T_{2}})\)a and \(T_{2}\), respectively.
# Ensemble Learning Traffic Model for Sofia: A Case Study
## 2 Preliminaries: Ensemble Learning Algorithms
### Decision Trees and Random Forests  
ML and AI algorithms perceive the world as a black box model--pretty much as humans do, at least beyond the scale of their everyday experience and scientific knowledge. Both humans and machines tend to learn and improve from their mistakes or wrong predictions--the efficiency of this process (not the absence of errors) is how we usually measure intelligence, be it natural or artificial. This corrective process has different mechanisms to manifest itself--from dopamine cycles in mammals to 'back-propagation' in neural networks. Both units, however, have the tendency to be either too broad (underfitting) or too specific for the training data (overfitting) in their estimates when facing a problem with overwhelming complexity, e.g., making a reliable weather forecast. This phenomenon is known as the 'bias-variance trade-off'. One possible strategy to make the model more inclusive without sacrificing much precision is to allow the major decisions to be taken by some sort of democratic voting process. Typically the atomic constituents of an ensemble model would be decision trees--relatively simple algorithms using straightforward binary logic derived from experience to predict outcomes in unknown situations, e.g., a simple (multi-label) classification tree would rate a person 'overweight' if their body mass index (BMI) exceeds 25 and 'underweight' if is below 18. For more complex decisions, however, single trees tend to overfit drawing absurd conclusions like 'the last time we did not please the gods, there was draught, so we need human sacrifice'. Ensemble learning is a democratic concept that embraces the imperfection of individual trees and turns it into an advantage--a large group of 'weak learners' has better chances of success than a single'strong learner', pretty much like in wild nature. But if all trees are fed the same data and obey identical set of binary rules, they would simply behave as clones, not contributing to the collective advancement. So, weak learners are trained on different datasets, obtained from the original one via bootstrapping (random sampling with replacement), so they have different backgrounds. To ensure even more diversity of opinion, ensemble learning also gives different 'way of thinking' for individual trees by providing them with different subsets of features. In the end, certain trees turn out to be better adapted for certain situations, some turn out to be rather 'brilliant', others-quite 'dumb', but this way the collective decision is well protected against both underfitting and overfitting--this whole process is known as 'bagging'. In the Random Forest algorithm for instance, it is taken by majority vote in the case of classification and simple averaging for regression problems. This seemingly primitive Greek-type democracy provides great results in rather complex problems involving traffic, weather, ecology, social phenomena etc. Speed is a major issue in this setting, but the algorithm is perfectly suited for parallel processing, so it scales nicely.
## 5 Results  
An algorithm was developed for the calculation from Equation (4), through which the mobile application provides the estimation of the load that is mobilized from the initial position.  
Figure 3: Different options for exercise positions.  
For this, the operator must provide information on the initial grip position (out of four options), the subject's height and body mass. Then, the subject must provide the photograph for determination of the working angle (\(\alpha\)). Next, the application calculates the results for different working angles (between 10\({}^{\circ}\) and 55\({}^{\circ}\) with respect to the vertical) and different grip positions for the same subject of mass 60 kg and 1.60 m in height, in relation to % body weight mobilized in each repetition (Table 1 and Figure 4).  
The data indicate that as the working angle (\(\alpha\)) increases, the initial load to be mobilized by the subject decreases and that the position of the initial grip with respect to body height also influences the mobilized load value. With the provided coronal proportionality references, working with a grip at the height of the abdomen implies an increase in the initial load to be mobilized of between 9% and 17%, depending on the angle of work with respect to the work done with the grip at chest height. In addition, a grip at abdomen height implies between 17% and 30% compared to a grip at head height. Therefore the highest loads are mobilized working with the grip at the level of the abdomen. It must be considered that the intensity perception of an exercise can be more related to the abilities of the involved muscles to generate force than with the value of the absolute load mobilized during the execution.  
Data from the present technical note indicate good agreement (\(r=-0.99\)) with those provided by the equations proposed by [PERSON] and [PERSON] [7] for values of 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\) and 75\({}^{\circ}\) (Table 2), taking into account that these authors measured the angle formed by the inclination of the body with respect to the vertical, which is the complementary angle to that recorded with Kinematic Lab Susp.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Angle** & **\% Abdomen Weight** & **\% Pectoral Weight** & **\% Head Weight** \\ \hline
10 & 88 & 72 & 59 \\
15 & 86 & 71 & 58 \\
20 & 84 & 69 & 56 \\
25 & 81 & 66 & 54 \\
30 & 77 & 63 & 52 \\
35 & 73 & 60 & 49 \\
40 & 68 & 56 & 46 \\
45 & 63 & 52 & 42 \\
50 & 57 & 47 & 38 \\
55 & 51 & 42 & 34 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Weight distribution according to working angles and grip positions. Data are for the same subject of mass 60 kg and 1.60 m in height, and expressed as the percentage of body weight mobilized in each repetition.  
Figure 4: Percentage of load depending on the angle and height of the grip.
## 4 Simulation Analysis of CRPDC
### Coil Currents under Different PWM Duty Cycles  
The driving currents of the proportional coil under the different PWM duty cycles are simulated, as shown in Figure 7. The coil currents calculated by the mathematical model are also shown in Figure 7. The frequencies of input PWM signals are all set to be 4 kHz. The duty cycles of the input PWM signals are set to be 20%, 40%, 60%, and 80%, respectively. The energy-storage capacitance and power voltage are configured to be 10 uF and 24 V. As can be seen from Figure 7, the coil current can be adjusted by changing the duty cycle of the input PWM signal. The average currents of the proportional coil at different PWM duty cycles are evaluated to be 0.81 A, 1.33 A, 1.83 A, and 2.33 A (results from the Multisim model in Figure 4a), as shown in Figure 8a. The current ripples of the proportional coil at different PWM duty cycles are determined to be 31.55 mA, 37.42 mA, 33.42 mA, and 15.28 mA (peak-to-peak values, results from Multisim model), as shown in Figure 8b. As can be seen from the figures, the output average current obviously increases with the increase in the PWM duty cycle. The output current ripple of the proportional coil first increases and then decreases along with the increase in the PWM duty cycle. Therefore, the coil-current amplitude can be regulated by accommodating the duty cycle of the input PWM signal.  
Figure 7: The coil currents of CRPDC under different PWM duty cycles: (**a**) 20% duty cycle; (**b**) 40% duty cycle; (**c**) 60% duty cycle; (**d**) 80% duty cycle.
## 5 Security Analysis
### Privacy, Computation, and Communication Costs  
As stated by [PERSON] et al. [8], the protocol must demonstrate the following characteristics: privacy, costs related to communication, and computation. As for privacy, we have noted that the transmitted data consist of random numbers and partial sums. There is nothing different from that which flows sequentially during the protocol. Therefore, it is necessary for any attacker, regardless of their level of honesty, to have prior knowledge of the protocol being used. This knowledge, even if the middleware conceals it, would still require the attacker to impersonate the parties involved in the connection. A side-channel attack could be avoided by adequate formatting of the transmitted data. Only sums are needed for computational aspects, so we neglect this overload. A technique known as RJID (RandomCode Injection to Mask Power-Analysis-Based Side-Channel Attacks) can protect parties from the exposure of simple and differential power analysis; see [24]. Finally, as mentioned above, communication costs are approximately equal to 70% of a typical three-party sum, as in [14].
# Path Planning for Localization of Radiation Sources Based on Principal Component Analysis
## 3 Experiments
### Simulations
#### 3.1.1 Condition (a) Single Radiation Source
It can be verified that the reconstructed distribution of the radiation source was estimated around the ground truth. In addition, the path was successfully generated, as intended. As the weighted average, the location of the radiation source was estimated as (\(-0.26\) m, \(0.23\) m), while that of the ground truth was (\(0\) m, \(0\) m). The estimation error was approximately \(0.35\) m. Based on this result, it was demonstrated that the proposed method can successfully generate the measurement path and accurately localize a single radiation source.
# A Survey on Blockchain-Based Search Engines
## 3 Search Engines and Their Approaches
### Decentralized Search Engines  
A decentralized search engine does not have a single server. In this kind of search system, the tasks such as crawling, data mining, indexing, and query processing are spread among several peers in a decentralized way, with no single point of control, in contrast to conventional centralized. FAROO [51] and YaCy [52; 53] are two examples of distributed search engines. Both are based on peer-to-peer (P2P) network concepts. In order to address the privacy problems that exist in centralized search systems, distributed search systems have been proposed. The privacy challenges include censorship, the exposure of personal queries to the search, the lack of transparency of proprietary search algorithms and the server, and any third parties with whom the search service operator can share data. There are three critical criteria for a successful search from the user's perspective: a high-quality answer, a quick response time, and a comprehensive web collection available in the index. Distributed search can satisfy the user expectations using the network topology, caching layers, high concurrency, and the local query [41]. The architecture of YaCy, a decentralized search engine, is presented in Figure 3.  
The YaCy framework is divided into five parts, namely, YaCy P2P network, crawler, indexing, database, and search interface. YaCy harvests web pages with a web crawler, indexes, parses, and saves the search index locally. When a peer joins a peer network, its local search index is combined with the network's mutual index. When a search is initiated, the local index and a global search index \(f\) both contribute.
# A Survey on the Application of WirelessHART for Industrial Process Monitoring and Control
## 3 Industrial Applications of WirelessHART
### WirelessHART
Wireless handheld: employed for diagnostics, device configuration, and calibration from a remote location. 3. Gateway: acts as a bridging device to connect host applications and field devices. 4. Network manager: accountable for configuring the network, scheduling, routing, and managing communication. 5. Security manager: managing and allocating security encryption keys and keeping track of authorized devices to connect to the network.
## 3 Proposed Approach
### Backbone Architectures
#### 3.2.3 Pyramid Vision Transformer (PVT)  
PVT integrates the pyramidal feature extraction seen in classic CNN-based backbones--where resolution is progressively reduced at multiple stages--into a transformer design (Figure 4). This approach preserves rich multi-scale representations required for object detection, segmentation, or other dense prediction tasks. A key component of PVT is spatial-reduction attention: rather than computing self-attention on the full-resolution feature maps, PVT down-samples the spatial dimensions within the attention layers. This reduces the overall computational load, enabling the network to handle larger images while keeping the complexity more tractable [26].  
Each stage in PVT outputs features at different scales, forming a pyramid-like structure. Early stages retain more detailed spatial information, while deeper stages capture a broader semantic context at a lower resolution. By combining the hierarchical strengths of CNNs with the global attentional capability of transformers, PVT delivers multi-scale feature representations tailored for detecting objects of varying sizes.  
In this study, the combination of YOLOv10 and PVT is referred to as YOLOv10-PVT. Specifically, PVT replaces the standard convolution-based backbone in YOLOv10, supplying the detection head with feature maps at multiple scales that can localize heavy equipment such as loaders and dump trucks. By integrating multi-scale pyramid processing with partial spatial down-sampling, PVT retains many of the global insights character  
Figure 3: Structure of Swin Transformer.  
istic of a transformer while controlling the computational load. This synergy excels in dense prediction tasks--like object detection--where multiple scales must be accurately identified in complex construction-site images. The global attention and pyramid-style feature extraction in PVT enable YOLOv10-PVT to handle diverse object sizes and environmental complexities, capturing both fine details and the overarching context with fewer computational demands than the naive global-attention method.
###### Abstract  
The aim of this study was a comparison of the micro-tensile bond strength (uTBS) to dentin and microleakage of in vitro class V restorations of three different ion-releasing restorative materials under various pH conditions: giomer, a resin-modified glass ionomer (RMGI), and a new alkasite material. A uTBS test was performed using a universal testing machine, immediately and after storage at different pH (4, 7, and 10) buffer solutions (n = 15) over 24 h, and the failure mode was analyzed. For microleakage analysis, class V restorations were performed on extracted premolars, which were sectioned and stored in pH 4-, 7-, and 10-buffered fluorescent 0.02% rhodamine B dye. The specimens were observed under a confocal laser scanning microscope (CLSM) and scored using the acquired images. There were no significant differences in the uTBS according to the type of material (\(p\) = 0.518). The giomer showed a decreased bond strength under the pH 4 condition compared with the immediately tested or pH 7-stored specimens (\(p\)\(\leq\) 0.043). In the microleakage analysis, the class V restoration with giomer showed a higher microleakage than RMGI or alkasite (\(p\) = 0.001). For RMGI and alkasite, the specimens stored at pH 4 showed a significantly lower microleakage than those stored at pH 7 (\(p\) = 0.028). RMGI and alkasite can be adopted as restorative materials in generalized or localized low-pH conditions.  
Keywords: ion-releasing restorative material; giomer; alkasite; micro-tensile bond strength; microleakage; confocal laser scanning microscope +
Footnote †: journal: Applied