esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 A New NHPP Software Reliability Model  
### Non-Homogeneous Poisson Process  
The software fault detection process has been formulated using a popular counting process. The counting process \(\{\text{N}(t),\,t\geq 0\}\) is a non-homogeneous Poisson process (NHPP) with an intensity function \(\lambda(t)\), if it satisfies the following condition.  
(I) \(\text{N}(0)=0\)  
(II)  Independent increments  
(III) \(\int_{t_{1}}^{t_{2}}\lambda(t)dt\), \((t_{2}\geq t_{1})\): the average of the number of failures in the interval \([t_{1},\,t_{2}]\)  
Assuming that the software failure/defect conforms to the NHPP condition, \(\text{N}(t)(t\geq 0)\) represents the cumulative number of failures up to the point of execution, and \(\text{m}(t)\) is the mean value function. The mean value function \(\text{m}(t)\) and the intensity function \(\lambda(t)\) satisfy the following relationship.  
\[\text{m}(t)=\int_{0}^{t}\lambda(s)ds,\frac{\text{dm}(t)}{dt}=\lambda(t). \tag{1}\]  
\(\text{N}(t)\) is a Poisson distribution involving the mean value function, \(\text{m}(t)\), and can be expressed as:  
\[\text{Pr}\{\text{N}(t)=n\}=\frac{\{\text{m}(t)\}^{n}}{n!}\exp\{-\text{m}(t)\},\,n=0,1,2,3\ldots. \tag{2}\]
## 2 Hypercrosslinked Polystyrene (HPS): Synthesis, Properties, and Applications
For example, hypercrosslinked polystyrene microspheres with a specific surface area of 1161 m\({}^{2}\)/g and a total pore volume of 0.72 cm\({}^{3}\)/g are capable of reversibly storing up to 2.27 wt.% H\({}_{2}\) at 1 bar/77.3 K and 14.8 wt.% CO\({}_{2}\) at 1 bar/273 K [70], which makes them a promising packing material for HPLC, adsorbents for organic compounds, and materials for gas storage. [PERSON] et al. [71] developed an inexpensive and efficient method for the synthesis of amino-functionalized hypercrosslinked polymer nanoparticles (AHCPNPs) with well-defined spherical morphology, high specific surface area (507.64 m\({}^{2}\)/g) and excellent CO\({}_{2}\) adsorption capacity of 53.65 wt.% (12.21 mmol/g). The authors showed the possibility of regulating the adsorption properties of AHCPNPs by selecting diblock copolymers with different molecular weights or adjusting the ratio of the original diblock copolymers to polystyrene. An interesting fact is that in some cases, a decrease in SSA after modification of a hypercrosslinked polymer does not lead to a decrease in its adsorption properties. [PERSON] et al. [72] modified the surface of HPS with amino groups, and the SSA decreased almost twofold--from 806 m\({}^{2}\)/g to 453 m\({}^{2}\)/g--but the ability of the modified polymer to absorb CO\({}_{2}\), on the contrary, increased from 301.67 to 414.41 mg/g. In spite of a slight decrease in the sorption capacities of these adsorbents after recycling, the authors believe that the developed materials are suitable for industry.
## 5 Discussion
### Ablation Study
#### 5.2.3 Network Inputs  
Table 7 summarizes how the input information affects the performance of the rough detection network. In order to simplify the comparison process, we used VGG16 [12] as the encoder and removed the attention blocks in the decoder. The results demonstrate that optical flow information is necessary and using three consecutive frames could obtain a better result than two. We also tested the inputs of five consecutive frames with optical flow, but the network performance for the test set decreased. Finally, three consecutive frames with optical flow were adopted as the input of the rough detection network.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Attention Module Added** & **Video3** & **Video4** & **Video5** & **Video6** \\ \hline None & 71.50\% & 67.75\% & 70.60\% & 69.76\% \\ +SE [14] & 72.08\% & 67.94\% & 71.32\% & 71.51\% \\ +Non Local [24] & 71.73\% & 66.64\% & 69.65\% & 67.81\% \\ +CBAM [25] & 70.75\% & 66.82\% & 70.62\% & 71.25\% \\ +DA [26] & 71.16\% & 66.90\% & 70.80\% & 70.37\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: The influence of different attention modules on the performance of the rough detection network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Architecture Variant** &
\begin{tabular}{c} **The Optimal Value of Mean IoU** \\ **on Validation Set (Video3)** \\ \end{tabular} \\ \hline FlowNetSimple [11] & None & 68.19\% \\ \hline \multirow{3}{*}{Encoder changed} & VGG16 [12] & 71.50\% \\  & Resnet50 [22] & 68.31\% \\  & modified Xception [23] & 64.99\% \\ \hline Attention blocks added & VGG16 [12] + SE [14] & 72.08\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Differing performance of rough detection using different encoder or adding attention blocks.
## 4 Results
### Centralized Learning vs. FL Based on Edge-Fog-Cloud Architecture
#### 4.2.1 Centralized Learning Performance  
The results of centralized learning are presented in Table 5. The loss value indicates the error of the model's predictions compared to the true labels. In this case, the loss value is 0.0045648, which suggests that the model's predictions are relatively close to the true labels, indicating good performance. Accuracy measures the proportion of correctly classified samples out of the total number of samples. With an accuracy of 99.36%, the model correctly classifies a high percentage of the samples. Precision measures the proportion of true-positive predictions out of all positive predictions made by the model. With a precision of 99.55%, the model has a high precision, indicating that when it predicts a positive class, it is usually correct. Recall measures the proportion of true-positive predictions out of all actual positive samples in the dataset. With a recall of 99.79%, the model effectively captures a high percentage of the positive samples. The F1-score is the harmonic mean of precision and recall, providing a balance between the two metrics. With an F1-score of 99.67%, the model achieves a high balance between precision and recall, indicating robust performance. Additionally, the training time for the model was substantial, totaling 4041.07 s, suggesting comprehensive training and optimization processes. Despite the extensive training duration, the model's resource utilization remained modest, with an average CPU usage of 4.7% and memory consumption of 40.78 GB.
###### Abstract  
It is difficult for autonomous underwater vehicles (AUVs) to obtain accurate aided position information in many locations because of underwater conditions. The velocity accuracy from the Doppler velocity log (DVL) is a key element in deciding the AUV position accuracy when the integration system of Strapdown Inertial Navigation System/DVL/Magnetic Compass/Press Sensor (SINS/DVL/MCP/PS) is adopted. However, random noise and sudden noise in DVL caused by sound scattering, fishing populations, and seafloor gullies introduce level attitude errors and accumulate as position error. To restrain random noise, a velocity tracing method is designed based on the constant velocity model and the assumption of slow motion of AUV. To address sudden noise, a fault diagnosis method based on the \(\chi^{2}\) rule is introduced to judge sudden changes from innovation. When a sudden change occurs, the time update of the velocity from the tracing model is used for data fusion instead of the velocity from DVL. Simulation test results indicate that with this velocity tracing algorithm, random noise in the DVL can be effectively restrained. The level attitude accuracy and the level position accuracy are also improved with the time update of the velocity when the sudden change occurs.  
AUV; SINS/DVL/MCP/PS integrated navigation system; velocity tracing; constant velocity motion model; Kalman filter
# RIS-Assisted High-Speed Communications with Time-Varying Distance-Dependent Rician Channels
###### Abstract
Compared to previous works, our model contains NLoS (or Rayleigh fading) components, distance-dependent Rician factors, and DS terms;
* We show that when the moving user is far from the BS and the RIS, the Rayleigh fading dominates the channel. However, when they are close, the channel is almost equivalent to the LoS channel;
* For the proposed channel model, we further show that using RIS phase shift optimization, the DS of the cascaded links can be aligned to the DS of the direct link. Furthermore, the DS can be completely eliminated when the direct link is obstructed;
* We derive the closed-form expressions for the ergodic spectral efficiency and outage probability of the proposed system. Besides, it is shown that the deployment strategy has an impact on the system performance. The rest of this paper is arranged as follows. We present the time-varying channel model with dynamic Rician factors for RIS-assisted HSC in Section 2. Based on the proposed system, we optimize the phase shift of the RIS to maximize the received power and show how the RIS aligns with the DS of the cascaded links to the DS of the direct link in Section 3. In Section 4, we derive the analytical expression for the ergodic SE and outage probability. In Section 5, we provide numerical simulations and discussions, followed by the conclusions and future works in Section 6. Notation: \([\cdot]\) and \([\cdot]\) denote the ceiling and the floor of the argument, respectively. \(\mathbb{Z}^{+}\) is the set of positive integers, \(|\cdot|\) refers to the absolute value, \(\|\cdot\|\) represents the \(l_{2}\) norm, Prob denotes the probability, \([\cdot]^{\rm T}\) is the transpose operation for a vector, \((\cdot)^{*}\) is the conjugate operation for a complex number, \(\mathbb{E}\{\cdot\}\) and \(\mathbb{V}\{\cdot\}\) denote respectively the expectation and variance operation. Moreover, \(\mathcal{CN}(\mu,\sigma^{2})\) is the complex Gaussian distribution with expectation \(\mu\) and variance \(\sigma^{2}\), \(\Re\{\cdot\}\) is the real part of a complex number, and \((a\text{ mod }b)\) is the remainder of the division of \(a\) by \(b\). Lastly, \(\max\{\cdot\}\) and \(\min\{\cdot\}\) denote the maximum and minimum values, respectively.
## 2 Materials and Methods  
### Interactions of Intelligent Agents in MASs  
Multi-agent systems (MASs) consist of intelligent agents that interact with both the external environment and each other. In the context of multi-agent system research, a multitude of properties of intelligent agents are outlined, depending on the application domain of such systems (e.g., robotics, social process modeling [11; 12], production scheduling [8], resource management [8; 10]) and the focus of individual research schools (direction, thematic scope).  
Initially, MASs were developed as tools for controlling physical objects, such as robotic systems. However, as the field evolved, MASs also emerged as a modeling methodology for domains where system components can themselves be represented as intelligent agents [4; 8; 10].  
A key feature of MASs is the agents' ability to dynamically reorganize in response to environmental changes. Researchers thus emphasize the multi-agent approach as a framework for designing and describing self-organizing systems [13]. In MASs, interactions serve as the primary mechanism for computation and coordinating the behavior of multiple autonomous software or physical agents. According to existing standards, agents communicate via high-level languages using predefined protocols. These protocols govern asynchronous or synchronous \"dialogues\" between agents, including notifications of changes, proposal exchanges, responses to proposals, and other interactions. Messages from one agent to another must be transmitted at any time, regardless of the recipient's state [4].  
Agent interactions--and their resulting collective behavior--may serve diverse purposes. MAS agents can cooperate to solve complex or large-scale tasks [4; 13]. In such cases, a task is decomposed (e.g., by a user) into simpler subtasks assigned to different agents. Here, interactions aim to coordinate local solutions to achieve a globally optimal outcome, either through fully distributed coordination or varying degrees of centralized control. Alternatively, agents may interact when pursuing individual goals but lacking the capacity to achieve them independently, necessitating assistance from others. While this scenario also involves cooperation, agents may refuse requests based on mutual agreements termed commitments. Under certain conditions, agents may transition between competition and cooperation, a relationship termed coopetition (from cooperation + competition) [4; 13].  
Thus, the following typical agent interaction paradigms can be identified [4; 13; 14]:  
* Cooperation: Agents collaborate toward a shared goal, often exchanging information to improve collective decisions.
* Negotiation/Debate: Agents engage in adversarial interactions, advocating for their own solutions while critiquing others'.
* Competition: Agents pursue conflicting objectives, prioritizing individual goals over collective outcomes.
# A New Methodology for Estimating Surface Albedo in Heterogeneous Areas from Satellite Imagery
## 4 Methods
### Proposed Method for Estimating Surface Albedo  
According to the values shown in Table 3, in ascending order of their NDVI value, the two methodologies that result in values closer to the ABOVE values (and consequently considered as more accurate) are V01 for intermediate NDVI values (0.385 > NDVI > 0.563) and V03 for high (NDVI \(\geq\) 0.563) and low (NDVI \(\leq\) 0.385) NDVI values. If only ABOVE values were used as for comparison and validation, the conclusion would be to use methodologies V01 and V03 for a new combined methodology, where the NDVI value of each pixel determines the application of one methodology or the other. The same dates and locations used for the comparison with ABOVE were subjected to a study of punctual values for pixels coincident with the position of four SURFAD stations. The results included in Table 5 have shown that the methodology that computes surface albedo values closest to the values measured in the SURFAD stations is methodology V02, for all NDVI values. In this comparison, the fact that all surfaces considered present low vegetation, means NDVI values between 0.20 and 0.50 (Figure 3) must be taken into account, because this comparison with data from SURFRAD stations does not include urban areas or areas with high vegetation. Given the results obtained by both comparisons (ABOVE and SURFRAD), three new methodologies are designed and tested in this work, all of them based on the combination of more than one existing method, using the NDVI value of each pixel as an indicator of the existing surface albedo method to apply (Table 6). In this way, the combined methodology is adaptative to the type of land cover as a function of its NDVI value [62]. The first combined methodology proposed uses the V03 methodology for NDVI values below 0.20 (corresponding to urban areas) and over 0.50 (associated to high vegetation); and the V01 methodology for intermediate NDVI values (low vegetation). The second combined methodology proposed is based on the same principle as the first, using the V02 methodology instead of V01 for intermediate NDVI values. In this way, the second combined methodology incorporates the results from the punctual comparisons with data from SURFRAD stations. The third combined methodology also joins the results from the comparisons with ABOVE and SURFRAD, respectively, using only one inflexion value for NDVI values, taking into account that the V03 methodology was originally designed to be implemented in urban areas (NDVI below 0.20) [32]. Thus, V03 and V02 methodologies are applied if the NDVI of each pixel is below or over 0.20, respectively.
# Blockchain Technology for Intelligent Environments
## 3 Noteworthy Blockchain Implementations
### Public/Permissionless Blockchains
#### 3.1.4 Iota  
IOTA is a rather different type of a distributed ledger, in the sense that it is _not_ based on a blockchain structure. Instead of letting miners confirm transactions in blocks, IOTA decentralizes this process even further, requiring users themselves to confirm each other's transactions. More specifically, each new IOTA transaction has to include _links_ to at least two past transactions, which it _approves directly_. By linking to them, it also _approves indirectly_ all past transactions approved (directly or indirectly) by them, all the way to IOTA's genesis transaction. This creates a directed acyclic graph (DAG) linking newer transactions to older ones. Contrary to other distributed ledgers, this process does not involve any transaction fees, as the approval of the transactions is made by the users themselves.  
Attempting to approve a transaction that is either invalid in itself, or which has approved (directly or indirectly) some invalid transaction, will result in getting the transaction eventually being neglected, and therefore dropped. This constitutes a strong motivation for performing a thorough check when approving past transactions, essentially delegating the task of banning invalid transactions and maintaining consensus to the users themselves. The more new transactions approve (directly or indirectly) a given transaction \(T\), the higher is the confidence on \(T\) being valid. There is no fixed confidence level for considering a transaction definite; it is subject to the risk level each user deems acceptable.  
Currently, the users are not allowed to pick arbitrary transactions to approve. Instead, a centralized third party, known as the _coordinator_, allocates approval tasks to users submitting new transactions. This is considered a temporary measure that is planned to be dropped later on.  
A core advantage of IOTA concerns its scalability. As registering a transaction requires checking and approving two other transactions, the IOTA system enjoys an inherent elasticity: the higher the transaction rate, the higher the collective capacity of _approvers_. An important issue remains: to find the right motivation for ensuring that all pending transactions will be picked for approval, without any of them being left waiting indefinitely. Another unique feature of IOTA is the use of _Winternitz One-Time Signatures_, which are safe even for post-quantum usage. On the other hand, the use of this type of signature results in big transactions, in the order of 10 KB, far larger than Bitcoin's transactions that have an average size of 600 bytes.  
IOTA is still in a beta phase and has received some negative criticism; for example, MIT's Media Lab criticizes it for incorporating custom cryptographic solutions [43; 44].
# Towards a Scalable Software Defined Network-on-Chip for Next Generation Cloud
## 2 System Overview
### Challenges and State-of-the-Art
Such an approach is inherently power hungry since the majority of the power is dissipated through the crossbar switch and link drivers. Other works, e.g., (2018), is focused on the analysis of fixed hybrid topologies. In (2018), the authors presented their approach to software programming of the NoC topology. Here, the physical substrate is based on a 2D-mesh network, built on top of simple rings running in both the \(x\)- and \(y\)-dimensions of the mesh. Ring stations have been augmented to enable traffic steering from one dimension to the other, while ISA of PE has been extended to control network configuration and dispatching application threads. This work is more in line with our proposed architecture, and, despite its good scalability, the evaluation on the area consumption is lacking. In (2018), the authors proposed a hybrid software-hardware NoC architecture. In contrast to conventional NoCs, the control plane is implemented as a software module running on a dedicated core. In this work, the core (i.e., the actual network manager) can take dynamic decisions on the network configurations. Such a solution can easily adapt to irregular traffic patterns depending on the current state and application demands. However, it still relies on a centralised component that could quickly become the bottleneck and limit the overall scalability. In (2018), the authors propose how to control the packet processing while making the switches agnostic to the hardware implementation. The programmer can decide the functionality to process the packets. Next, a compiler transforms an imperative program into a table dependency graph that can be mapped to multiple specific target switches. This approach first sets the functionality of the switches and then tells how the packets need to be processed. In another work (2018), protocol-oblivious forwarding (POF) based on a generic flow instruction set for programmable SDNs is proposed. The primary idea of this work is to remove the dependency on protocol-specific configurations on the forwarding elements and also enhance the data-path with new stateful instructions. The author claims that the approach can reduce the network cost by using commodity forwarding elements. The open packet processor (OPP) (2018) is proposed to improve the interaction between platform-independent HW configurability and packet-level programming flexibility. It aims at helping programmers to deploy stateful forwarding tasks. The authors claim that the proposed approach shows the viability of extended finite state machines (FSMs) as low-level data plane programming abstraction together with stateful operation and flow-level feature tracking.
## 2 Materials and Methods
### Pit Detection Method
#### 2.3.2 Closed-Loop Crossover Algorithm (CLCA)  
Similarly, the coordinates of point B are obtained, and the edge coordinate sequence is obtained by taking point B as the initial point. The conjugate lines are established at the position of point B:  
\[-x+c_{1}=0 \tag{15}\]  
\[-y+C_{1}=0 \tag{16}\]  
Traverse the coordinate sequence and update the conjugate linear equation at the index point. As shown in Figure 8, we substitute the coordinates of arbitrary index positions \(k\) and \(g\) (\(2\leq k,g\leq n-2\)) into \(-x_{k}+c_{k}=0\) and \(-y_{g}+C_{g}=0\), respectively, and solve \(c_{k}\) and \(C_{g}\). The constraint conditions for judging whether an index point is a pit can be described as  
\[d_{k-2}=-x_{k-2}+c_{k},d_{g-2}=-y_{g-2}+C_{g} \tag{17}\]  
\[d_{k+2}=-x_{k+2}+c_{k},d_{g+2}=-y_{g+2}+C_{g} \tag{18}\]  
\[d_{k-2}\cdot d_{k+2}>0,d_{g-2}\cdot d_{g+2}>0 \tag{19}\]  
Figure 8: Principle of closed-loop crossover algorithm (CLCA).  
Figure 7: The flow chart of the OLCA algorithm.  
Notably, the position of point B should be included in the index. Now, the predecessor point is \(k\), \(g=2\), and the successor point is \(k\), \(g=n-2\). Thus, a set of pit coordinate sequences \(a_{xl}\) is output, the pit sequence detected by the CLCA. The flow chart of the CLCA algorithm is shown in Figure 9.