esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
### ROPOS Vehicle
Figure 2: Simulation results for ROPOS QV controller and linear trajectory: (**a**) desired and realized trajectory; (**b**) position error states; (**c**) velocity error states; (**d**) applied force and torque; (**e**) kinetic energy time history; and (**f**) error \(\Delta\xi_{2}\). **Sine trajectory**. Next, the sine trajectory described by (49) for ROPOS and using the QV algorithm was investigated. The same gains (50) were used for simulation. From Figure 4a it is seen that the trajectory is tracked correctly. The state errors are close to zero after about 50 s as it arises from Figure 4b,c. The applied force and torque in Figure 4d have similar values as in Figure 2d. The kinetic energy shown in Figure 4e is consumed mainly by the lateral movement of the vehicle, which also results from dynamic parameters and the error \(\Delta\zeta_{2}\) representing velocity deformation because of the couplings is very small. The kinetic energy average values are now \(K_{m}=2418\) J (and \(K_{m1}=702\), \(K_{m2}=1712\), \(K_{m3}=4\)). Figure 5 shows the test results of the original controller (CL). It can be seen that the trajectory is tracked slightly worse despite the small differences visible in Figure 5a. It is because values of the error states (Figure 5b,c) change during the vehicle motion. The controller works worse than the QV controller despite similar applied force and torque values (cf. Figures 5d and 4d). Figure 4: Simulation results for ROPOS QV controller and sine trajectory: (**a**) desired and realized trajectory; (**b**) position error states; (**c**) velocity error states; (**d**) applied force and torque; (**e**) kinetic energy time history; and (**f**) error \(\Delta\zeta_{2}\).
## 4 Use Cases for Multimedia Analysis in Archiving Community Memories
### Detecting Media about Individuals, Organisations and Places  
Modern web archiving techniques, such as those being created by the ARCOMEM project, are often centred around the main entities (people, places and organisations) that are often integral to the crawl topic. By ensuring that the archived documents mention relevant entities, the quality and accuracy of the archive can be controlled. Storing this metadata alongside the raw archive also provides enhanced browsing and analytic opportunities when the archive is reused. Analysis of non-text media to determine the entity depiction can further enhance the archive's metadata and can be used to help select relevant multimedia documents by looking for the presence of specific entities in a visual sense.
# Formal Modeling and Improvement in the Random Path Routing Network Scheme Using Colored Petri Nets
## 1 Introduction  
One of the major WSN deployment problems is source-location privacy (SPL) [1]. SLP includes the hiding of all source nodes in the network from attackers [1]. All the nodes are present in an open environment, and all the nodes are sending tokens from a source node to a sink node, so if attackers locate the source node, then the source node is at high risk. Providing privacy in sensor networks is very complicated because the nodes have low battery capacity and less computational power [2]. WSNs are deployed in a wide range of domains, such as academia and industry. Lately, the SLP problem has emerged as asignificant issue in WSNs, especially in critical privacy systems. There is a need to address this problem to make the WSNs secure for transmitting messages [3].  
Previous studies have proposed numerous protocols to address SPL, and one of the fundamental protocols is random path routing (RPR) [4]. RPR is used to protect tokens from malicious attackers in the adversary [5]. RPR ensures SLP, however, it is expensive when it starts sending tickets along the longest path each time.  
The proposed calculated random path routing (CRPR) model provides significantly better results in token routing. The proposed module's distance calculation is our contribution. The system first calculates the distances of all available paths, and then randomly routes the token to any of the top three shortest paths. In other words, the model first calculates all the reaches of the known way and determines the shortest path, ensuring the SLP has a low computational cost and lower battery consumption. Additionally, it includes the formal modeling and verification of the CRPR, which authenticates the best tradeoff between computational cost and source-location privacy. The platform used for formal modeling is Colored Petri nets.  
There are four sections in this research paper. The first section presents the related work in which the strengths and weaknesses of the three routing protocols are discussed. The second section of the paper explains the proposed model CRPR. In the third section, this paper outlines the results of the CRPR model. Finally, the last section presents the discussion and future work.
# Influences of Temperature and Substrate Particle Content on Granular Sludge Bed Anaerobic Digestion
## 1 Introduction
### Particle Disintegration and Hydrolysis  
Disintegration is a physical and biological process where complex composite substrates are progressively broken apart before hydrolysis takes place, whereas hydrolysis is a biological enzyme-mediated process where biopolymers are broken down into their respective monomers. Both steps are extracellular processes [5]. Particulate carbohydrates, proteins, and lipids, as well as inert materials, are produced by disintegration of composite substrates. Microbes release enzymes that hydrolyze these biopolymers into smaller components and, given that polymers can hold particles together, hydrolysis contributes to disintegration, making it difficult to distinguish the two steps. Carbohydrates are hydrolyzed into monosaccharides, proteins into amino acids, and lipids into long-chain fatty acids (LCFA) [6]. Disintegration and hydrolysis are often modeled with first-order kinetics with respect to reactor particle content, as a single step or two, such as in anaerobic digestion model no.1 (ADM1) [5]. However, AD experiments with high inoculum-to-substrate ratio resulted in relatively high hydrolysis rates, indicating that microorganisms play a role in the hydrolysis rate. For simple substrates (i.e., substrates with a high proportion of soluble biodegradable components), first-order kinetics is enough to characterize the hydrolysis process. For complex substrates, the presence of solids and fewer biodegradable components makes bioavailability an important factor in determining the hydrolysis kinetics. Since first-order kinetics does not consider microbial influence, its fitness for complex substrates is questioned. Surface-based kinetics were developed that consider the available surface area of particulates for enzymatic action [7]. Inhibition of hydrolysis may occur in substrates with very high solid content (solid-state AD) likely due to diffusion limitation [8]. In addition, hydrolysis could be inhibited by high concentrations of LCFA, H\({}_{2}\), and NH\({}_{3}\) ([PERSON] et al. [6]). In this article, we intend to demonstrate that first-order disintegration and hydrolysis kinetics can accurately model the anaerobic digestion of complex substrates by classifying complex particulates into fast and slow disintegrating sections. We demonstrated the effectiveness of this approach in batch anaerobic reactors [9].
## 3 Communication and Channel Modeling
### Communication Performance
#### 3.2.1 Quality of Service (QoS)  
QoS is a critical communication performance metric that dictates the overall user experience in terms of data rate, latency, and connection reliability. In UAV-based networks, particularly those utilizing UAVs, RIS, and UEs, the primary objective for QoS is often the maximization of the sum rate of the UAV-RIS-UEs LoS communication channels [40]. The sum rate optimization aims to achieve the highest possible throughput while adheringto minimum achievable rate constraints for UEs, ensuring that each device obtains an adequate data rate for its operation.  
In the context of UAVs, RISs can be deployed to enhance signal quality by intelligently reflecting and directing signals to specific UEs, thus improving the communication channel's performance. The UAV serves as a mobile base station or relay, and the RIS acts as an intelligent medium to adjust and optimize the propagation conditions between the UAV and UEs. This approach enables better control over the channel conditions and allows for more efficient utilization of the available spectrum, especially in complex environments with high interference or obstructions.  
The objective function in such optimization problems typically incorporates factors such as the total system throughput, latency, and reliability. Constraints are imposed to ensure that the QoS requirements for UEs, such as minimum data rates or maximum latency, are met, see Table 1.
## 2 Results  
### Isolation of cDNAs Encoding CpGPPS/GGPPS  
From the floral cDNA library of the _C. praecox_ plant, eight cDNAs were found to be annotated as GPPS/GGPPS, of which five cDNAs had full-length open reading frames (ORF). Blast (blastp) results of these ORFs against the NCBI ([[https://www.ncbi.nlm.nih.gov/](https://www.ncbi.nlm.nih.gov/)]([https://www.ncbi.nlm.nih.gov/](https://www.ncbi.nlm.nih.gov/))), TAIR ([[https://www.arabidopsis.org/index.jsp](https://www.arabidopsis.org/index.jsp)]([https://www.arabidopsis.org/index.jsp](https://www.arabidopsis.org/index.jsp))), and Sol genomics ([[https://solgenomics.net/](https://solgenomics.net/)]([https://solgenomics.net/](https://solgenomics.net/))) databases showed that three cDNAs (c81471, c64849, and c90672) have high sequence similarity with the known GPPS.SSU1, GPPS.SSU2, and homodimeric GPPS proteins from other plants, respectively (Table S1). Moreover, the remaining two cDNA (c80517 and c51170) are similar to the GGPPS protein.
## 4 Results
### 4D Thermal Model Generation  
As a result of the UAV flight, a total of 1289 RGB and TIR images were obtained. Both sets of images were managed through the SfM photometric process, following the workflow proposed in Figure 1 and using Agisoft Metashape software (version 1.2.7). First, the set of RGB images in JPEG format was used to generate the 3D RGB model and the 3.46 cm/px GSD ortoimage shown in Figure 5, which were georeferenced using the GCPs for better accuracy.  
From the previous 3D RGB model, the 3D thermal model was generated following the workflow shown in Figure 1. The orientation parameters of the RGB images were exported to align the thermal images. Subsequently, the thermal information from the oriented TIR images was projected onto the 3D RGB model, giving false color to each of the points of the representation, enabling acquisition of the thermal model (Figure 6) and the 13.8 cm/px  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Flight Parameters** \\ \hline Height (m) & 100 \\ Speed (m/s) & 3 \\ Frontlap (\%) & 80 \\ Sidelap (\%) & 80 \\ Resolution (cm/px) & 4.77 (RGB) 10.71 (IR) \\ Number of photographs & 1289 \\ Flight time (min) & 87 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Definition of the parameters of the UAV flight plan over the landfill.  
Figure 4: Double grip UAV flight plan designed for the Meruelo MSW landfill.  
GSD thermal orthoimage shown in Figure 7. The set of thermal images in R-JPEG format enabled acquisition of qualitative and quantitative data that represents the temperature gradient in false color.  
The 4D model, unlike single images or 2D orthoimages, enables the interpretation of the morphology of the landfill, providing the analysis with altimetric information that favors the understanding of its berms, slopes, and elevations, given its strong steepness. In addition, relevant thermal information is provided.
# Research on Microhole Processing Technology Based on the Femtosecond-Laser Spiral Trepanning Method
###### Abstract  
Microholes have crucial applications in aerospace, the automotive industry, and other industries. In this study, the microhole processing technology based on the femtosecond-laser spiral trepenning method was investigated. By adjusting the spiral scanning path, laser power distribution, and defocusing amount to control laser energy distribution, an inverted cone hole, straight hole, and normal cone hole were obtained finally. The morphology and element of the microhole were investigated by scanning electron microscopy (SEM) equipped with energy-dispersive X-ray spectroscopy (EDX). The experimental results of the femtosecond-laser spiral trepenning method could achieve fewer impurities. Finally, the formation mechanisms of different microholes are explained in detail. The method is simpler and more efficient than the traditional microhole processing technology. The femtosecond-laser spiral trepenning method with controllable hole roundness, accuracy, and taper has important practical significance in microhole processing.  
femtosecond machining; microholes; spiral trepenning +
Footnote †: journal: Applied Physics Letters
## 2 Downlink Beamforming Design and Uplink Power Allocation  
### Methodology: Convex Optimization-Based Downlink Beamforming Design and Uplink Power Allocation  
In this subsection, we present the conventional approaches for the downlink beamforming design and uplink power allocation via the convex optimization.
## 3 Model and Algorithm Design
### Model Optimization Strategies
#### 3.2.2 Model Architecture Optimization  
To address the challenges associated with the CNN-LSTM model architecture, residual networks (ResNets) and densely connected convolutional networks (DenseNets) are introduced to mitigate the issues of vanishing or exploding gradients that typically arise as the depth of the network increases [30]. These architectures enhance the ability of deep networks to train more effectively, especially in the case of complex signal patterns in physiological data. Residual networks (ResNets) introduce residual connections between layers, allowing the network to bypass certain layers, effectively making the network \"skip\" layers when needed. This can significantly help with training deeper networks, as it mitigates the problem of vanishing gradients by allowing gradients to flow more easily through the network during backpropagation. The residual block, which forms the core of the ResNet, can be mathematically represented as follows:  
\[y=F(x,\{W_{i}\})+x \tag{21}\]  
where \(F(x,\{W_{i}\})\) denotes the nonlinear transformation of the convolutional layer. Furthermore, regarding the LSTM component, considering the performance bottleneck of the traditional LSTM when processing long sequences, bidirectional LSTM or Gated Recurrent Unit (GRU) can be employed as alternatives to enhance the model's capability to capture contextual information [31]. The state update formula for GRU is as follows:  
\[\begin{array}{l}z_{t}=\sigma(W_{z}\cdot[h_{t-1},x_{t}])\\ r_{t}=\sigma(W_{r}\cdot[h_{t-1},x_{t}])\\ \overline{h}_{t}=\tanh(W_{h}\cdot[r_{t}\times h_{t-1},x_{t}])\\ h_{t}=(1-z_{t})\times h_{t-1}+z_{t}\times\overline{h}_{t}\end{array} \tag{22}\]  
where \(z_{t}\) and \(r_{t}\) are the update gate and reset gate, respectively, \(\overline{h}_{t}\) is the candidate hidden state, and \(h_{t}\) is the current hidden state.
# Trust Model of Privacy-Concerned, Emotionally Aware Agents in a Cooperative Logistics Problem
## 2 State of the Art
### Computational Representation of Trust  
Agents are intended, by their own nature, to be self-interested, i.e., their behavior cannot be assumed to be altruistic and cooperative [28; 29]. Because they are also dynamic, the past behavior of an agent can only serve as a rough estimation of its future behavior. Agent systems are also, by definition, open, so agents come and go; therefore, different levels of knowledge about the behavior of other agents coexist. Finally, as such systems can be highly populated, the occurrence of interactions with a majority of the other agents in the system is not likely. The joint existence of these features produces a significant difficulty in estimating the expected reliability of potential partners in interactions. In order to overcome such a difficulty, trust models based on reputation computations have been proposed in recent decades [30]. Whereas reputation represents a quantitative evaluation of the expected behavior of another agent, which has to be numerically aggregated and updated from indirect sources and direct experiences (each model suggests a different computation), trust represents the cognitive decision of an agent to participate in a potentially risky or uncertain interaction [31].  
Cognitive decisions of agents are often implemented using the beliefs, desires and intentions (BDI) paradigm and produce intelligent agent behavior through cyclic deliberation about explicit beliefs, desires and intentions of the agent [32]. Such cognitive agents are expected to make use of interaction protocols to communicate meaningful messages with each other according to the IEEE Foundation for Intelligent Physical Agents (FIPA) standard [33]. Such a cognitive trust decision in BDI agents considers factors such as beliefs about the general situation in which the interaction decision is takes place and beliefs about the other agents [34]. Whereas in real life, emotions play a key role in trusting decisions, the beliefs agents use do not represent emotions; agents make decisions unemotionally according to their beliefs about the situation and about the other agents. Even the only properly emotional trust model found in the literature [35] decides with a trust model and an emotional model implemented as independent reasoning blocks. However, the need for an emotional trust model for agents has been recently recognized in the literature [36]. Furthermore, the authors of [37] observed a close relationship between the beliefs of agents and the emotions described by the OCC model of emotions [7]. Finally, underlying some of the purely trust-based models proposed in the literature, such as that proposed in [38], where failures in the trusting decision have a much greater mathematical impact on reputation than success, a hidden implicit emotional process occurs.
## 3 Numerical Validation and Results
### Crack Pattern  
Three crack patterns were selected and are compared in this section: single-sided non-penetrating cracks (Pattern A#), single-sided penetrating cracks (Pattern B#) and both-sided penetrating cracks (Pattern C#), respectively [37]. These chosen patterns are illustrated in Figure 12, with the applied crack position remaining consistent with that shown in Figure 5.  
Figure 10: Modelling approach of reinforcement behavior: (**a**) external prestress tendons modelling approach; (**b**) virtual steel plate method.  
Figure 13 illustrates the deflection curves corresponding to different crack patterns, with the abscissa denoting the distance from point A in Figure 4 to the testing location. It can be observed that distinct modes of cracking correspond to specific characteristics in the deflection curve. A lower distribution of cracks results in shallower cracks and a smoother deflection curve. Furthermore, a comparison can be made between the maximum deflection values with and without cracks. A significant discrepancy can be observed, indicating that cracking has a substantial impact on the overall stiffness of the structure. Specifically, an average degradation of 33% in stiffness was identified in this case. For all three crack patterns, the maximum deflection was observed in the case of a both-sided penetrating crack with a value of 142.7 mm. In pattern A#, the maximum deflection was 132.7 mm, and in pattern B# it was 137.5 mm. Similarly, by comparing the deflection amplitude at the cracking positions (x = 54 m, 216 m), it can be inferred that the difference in deflection is relatively insignificant. Therefore, it can be concluded that the cracking mode of the box girder not only affects its maximum response but also influences the distribution of deflection along the bridge.  
Figure 11: Mid-span deflection comparison between the on-site-measured and FE-predicted deflection.  
Figure 12: Three applied crack patterns in the FE software.
## 4 Discussion  
### Comparative Analysis of Crop Importance in West African Agriculture: Limitations and Key Findings  
Section 3.6 compares the importance of different crop categories within a country's agricultural sector, using two key metrics: crop area proportion and economic value contribution. However, these categorizations introduce some limitations. The data reflects average values from 2000 to 2020, so they represent relative shares rather than absolute values. Additionally, crop categories aligned with the FAO STAT database [57] may include crops not specifically represented in the reviewed literature. The shares do not necessarily sum to 100% because livestock and fallow land are not accounted for.  
Remarkably, despite limited representation in the reviewed literature, vegetables play a substantial economic role. This discrepancy could be due to the relatively small plots occupied by cash crops like vegetables, which makes them challenging to analyze using RS techniques. Also, the review's focus on RS implies that non-RS studies on vegetables are not taken into account, as RS typically favors larger spatial analysis [106; 137; 161]. Only in recent years has very high-resolution RS has been widely used to analyze smaller plots, such as those for vegetable cultivation. One of the few studies addressing vegetables, specifically tomatoes, is [PERSON] et al. [106].  
In contrast, crops such as maize and sorghum are well represented in the reviewed literature and occupy a large share of the agricultural landscape in West Africa [47; 61; 62; 107]. Similarly, cocoa und oil palm are extensively studied and economically important in countries like Ghana and Cote d'Ivoire, aligning with the real-world context described by [PERSON] et al. [145] and [PERSON] et al. [79]. Beans, on the other hand, are covered by only a few studies, primarily focused on Burkina Faso. However, beans are also important in other countries, such as Cote d'Ivoire and Ghana, where the cloud cover poses challenges for RS analyses. In these regions, tree crops can be monitored with Sentinel-1 or other radar instruments that operate effectively despite cloudiness [46; 79].
## 2 Methodology
### Synthetic Waveforms Based on the Reciprocity Theorem
Through the summation of cross-correlation travel time difference \(\Delta\tau(\mathbf{x}_{r},\mathbf{u},\mathbf{d})\) of all windowed waveforms, the target misfit function for waveform inversion location is shown as follows:  
\[\chi(\mathbf{u},\mathbf{d})=\frac{1}{2}\sum_{i=1}^{M}[\Delta\tau(\mathbf{x}_{ r},\mathbf{u},\mathbf{d})] \tag{5}\]where, \(M\) represents the number of windowed waveforms for location. Compared with the ray tracing methods where one can generate a distribution of locations assuming some uncertainty on the arrival picks, the waveform-based location method uses wavefield information with limited frequency band distribution. Many factors will affect the uncertainty of the final location result in a coupled way, such as the recording clock deviation between different sensors, error of velocity model, and spatial distribution of sensor array with respect to microseismic events. In this study, the spatial distribution characteristics of the misfit function around the GSWI location point are used to qualitatively evaluate the uncertainty of location parameters. More sophisticated quantitative uncertainty analysis for waveform-based location is based on second-order Hessian matrix with respect to a target misfit function, which will be an independent future work.