esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Parameters and Existing Model
### Current Empirical Model  
To date, numerous _EMs_ have been developed, each with varying parameters and accuracy. Equations (3)-(7) list several classical models, detailing their names, formulas, and standard deviation:  
* [_PGA_] A&M Model [14] \[\log u=0.90+\log\biggl{[}\Bigl{(}1-\frac{a_{y}}{PGA}\Bigr{)}^{2.53}\Bigl{(} \frac{a_{y}}{PGA}\Bigr{)}^{-1.09}\biggr{]}\pm 0.30\] (3)
* [_Ia_, _PGA_] J07 Model [35] \[\log u=0.561\log I_{a}-3.833\log\Bigl{(}\frac{a_{y}}{PGA}\Bigr{)}-1.474\pm 0.616\] (4)  
Figure 2: Calculation progress of permanent displacement by _NSBM_. **(a)** Acceleration time-history record; **(b)** velocity of sliding mass; **(c)** permanent displacement of sliding mass.  
* \([l_{a}, PGA,a_{y}/PGA]\) R&S08 Model [25] \[\begin{array}{c}\ln u=2.39-5.24\Big{(}\frac{a_{y}}{PGA}\Big{)}-18.78\Big{(} \frac{a_{y}}{PGA}\Big{)}^{2}+42.01\Big{(}\frac{a_{y}}{PGA}\Big{)}^{3}-29.15 \Big{(}\frac{a_{y}}{PGA}\Big{)}^{4}\\ -1.56\ln(PGA)+1.38\ln(I_{a}),\sigma\ln u=0.46+0.56\big{(}a_{y}/PGA\big{)}\end{array}\] (5)
* \([l_{a}]\) H&L Model [22] \[\begin{array}{c}\log u=0.847\log I_{a}-10.62a_{y}+6.587a_{y}\log I_{a}+1.84 \pm 0.295\end{array}\] (6)
* \([l_{a}]\) C14 Model [24] \[\begin{array}{c}\log u=2.228\log I_{a}-2.498\log a_{y}+0.373\log I_{a}\log a _{y}-5.495\pm 0.231\end{array}\] (7)  
where \(u\) is permanent displacement.  
A comparison of these models reveals that logarithmic coordinates are commonly used, and the primary fitting parameters are typically \(I_{a},a_{y},\) and \(PGA\). In this study, the J07 Model and R&S08 Model were selected. Based on the \(N\)_SBM_ and China's time-history records, two models for predicting the permanent displacement of earthquake-induced landslides, specifically suited to sites in China, have been developed (Equations (8) and (9)).  
\[\log u=a_{1}+a_{2}\log I_{a}+a_{3}\log\Big{(}\frac{a_{y}}{PGA}\Big{)}\pm\sigma (\log u) \tag{8}\]  
\[\begin{array}{c}\log u=a_{1}+a_{2}\Big{(}\frac{a_{y}}{PGA}\Big{)}+a_{3}\Big{(} \frac{a_{y}}{PGA}\Big{)}^{2}+a_{4}\Big{(}\frac{a_{y}}{PGA}\Big{)}^{3}+a_{5} \Big{(}\frac{a_{y}}{PGA}\Big{)}^{4}\\ +a_{6}\log I_{a}+a_{7}\log(PGA)\pm\sigma(\log u)\end{array} \tag{9}\]
# The Research of Complex Product Design Process Model under the Concept of Self-Recovery
## 3 The Principle of Functional Self-Recovery
### The Principle of Self-Diagnosis Function Design  
The self-diagnosis function is the section that gives the product the ability to diagnose problems in the design stage. The self-diagnosis function has two main roles in the product design process under the self-recovery concept: first, to detect whether the function has problems; second, to initiate the self-regulation function after diagnosing the product problems. A reasonable self-diagnosis function is a necessary link to construct a functional period oriented to self-recovery and is the key to realizing self-recovery regulation.  
Function expresses the relationship between the input parameters and output parameters of a technical system, which is generally expressed in the form of a verb and a noun [22]. The verb expresses the operation completed by the product, and the noun represents the operation object, in which the noun is measurable. When there are problems with a function, the output parameters of the function will be changed, and by detecting the change in the parameters of the function, it can be judged whether there are problems with the function. Some functions' noun description is parameter information, where the noun can be directly confirmed as the diagnosis object. However, the noun description of some functions hides the parameter information, and thus the designer needs to identify the specific parameter information of the function according to the function's working environment before diagnosing. For example, in the function \"generate pressure\", the verb is \"generate\", and the noun is \"pressure\"; the pressure itself is the parameter information, so it is feasible to diagnose problems with the function \"generating pressure\" by detecting the pressure. As for the function of \"transport goods\", the noun is \"goods\", the designer needs to identify the parameter of the noun \"goods\", which may be \"height\" or \"location\", or other parameters according to the environment of the function. After confirming the parameter hidden in the noun, the designer will find the corresponding detection parameter.  
As there may be a coupling between functions, when the main function of the product has problems, the output parameters of its coupled functions will also be changed. When the complexity of the product is high and it is difficult to diagnose the main function directly, the coupling between functions can be used to diagnose the main function indirectly by diagnosing the functions coupled with the main function.  
In order to visually express the parameter changes within the product from raw materials to goods and to accelerate the efficiency of self-diagnosis function design, the product function structure could be established by combining the function units obtainedby the function decomposition in an orderly manner. The changes in the flow attributes of the function structure could be marked to express the functional output parameters.
# Comparison of the Sentinel-3A and B SLSTR Tandem Phase Data Using Metrological Principles
## 4 Results--Sea Surface Temperature Comparison
### SST Difference and Cloud Probability  
The right-hand panel of Figure 10 shows trends in all algorithms (N2--blue, N3--Orange, D2--Green, D3--Red) as a function of cloud probability (defined as the probability of cloud for the nadir view only) for quality level 5 where any bias at zero cloud probability has been removed. All retrieval algorithms show a small increasing negative bias a function of increasing SLSTR-A nadir cloud probability. We can also look at the SST difference as a function of the difference in the probability of cloud between SLSTR-A and SLSTR-B (right-hand plot) which will reflect real differences in the observed scene and shows that the SST is colder when one sensor has a higher cloud probability. While this may seem obvious, the tandem data are now allowing this trend to be parameterised and modelled. Looking more closely, it also looks like the two channel algorithms (N2--Blue, D2--Green) have a slightly different behaviour to the three channel algorithms and the N2 algorithm is again an outlier.  
Figure 9: SST difference histograms for pixels with an SLSTR-A quality level of 5 and an SLSTR-B quality level of 3.
# Video over DSL with LDGM Codes for Interactive Applications
###### Abstract  
Digital Subscriber Line (DSL) network access is subject to error bursts, which, for interactive video, can introduce unacceptable latencies if video packets need to be re-sent. If the video packets are protected against errors with Forward Error Correction (FEC), calculation of the application-layer channel codes themselves may also introduce additional latency. This paper proposes Low-Density Generator Matrix (LDGM) codes rather than other popular codes because they are more suitable for interactive video streaming, not only for their computational simplicity but also for their licensing advantage. The paper demonstrates that a reduction of up to 4 dB in video distortion is achievable with LDGM Application Layer (AL) FEC. In addition, an extension to the LDGM scheme is demonstrated, which works by rearranging the columns of the parity check matrix so as to make it even more resilient to burst errors. Telemedicine and video conferencing are typical target applications.  
digital subscriber line (DSL); forward error correction (FEC); low density generator matrix (LDGM); low-density parity-check (LDPC); low latency; packet loss; reed-solomon (RS); video streaming +
Footnote †: journal: Computer Physics Communications
## 2 Results
### Floral Volatiles in the Eight P. mume Cultivars
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Compounds**} & \multirow{2}{*}{**RI 1**} & \multicolumn{6}{c}{**Emission Amount [mean \(\pm\) s.d. (tgg-5 fresh weight-1)]**} \\ \cline{2-9}  & & **Fub’** & **’Zab’** & **’5 ub’** & **’Zab’** & **’Tep’** & **’Jia’** & **’Fench’** & **’Wbay’** \\ \hline Benzaldehyde & 982 & \(10.1\pm 0.7\) & \(19.3\pm 14.3\) & \(1.5\pm 0.1\) & \(34.1\pm 22.0\) & \(6.0\pm 3.8\) & \(17.6\pm 2.4\) & \(6.0\pm 1.6\) & \(13.4\pm 3.6\) \\ Benzyl alcohol & 1036 & \(108.3\pm 21.2\) & \(149.2\pm 46.9\) & \(38.2\pm 7.6\) & \(221.6\pm 112.3\) & \(2.29\pm 8.3\) & \(1094.0\pm 0.8\) & \(227.0\pm 54.2\) & \(50.6\pm 9.9\) \\ Methyl benzoate & 1060 & \(6.3\pm 4.0\) & \(24.8\pm 11.3\) & \(3.6\pm 2.1\) & \(2.2\pm 0.7\) & \(\rm{tr}^{2}\) & \(0.97\pm 0.46\) & \(6.3\pm 1.3\) & \(0.8\pm 0.3\) \\ Benzyl acetate & 1160 & \(709.1\pm 152.7\) & \(1333.1\pm 360.5\) & \(778.2\pm 131.4\) & \(1691.0\pm 292.8\) & \(239.2\pm 1.9\) & \(7.10\pm 1.85\) & \(620.1\pm 104.6\) & \(0.6\pm \rm{tr}\) \\ Estragole & 1172 & - & - & \(1.5\pm 1.5\) & \(1.4\pm 0.3\) & \(0.4\pm 0.1\) & \(1.0\pm \rm{tr}\) & \(0.3\pm \rm{tr}\) & \(9.6\pm 3.2\) \\ Cininnym alcohol & 1243 & - & - & \(116.1\pm 13.5\) & \(138.5\pm 34.1\) & \(17.8\pm 5.6\) & \\ Eugenol & 1392 & \(88.1\pm 35.6\) & \(145.0\pm 85.6\) & \(166.6\pm 58.5\) & \(406.3\pm 16.3\) & \(274.3\pm 4.6\) & \(325.9\pm 46.9\) & \(168.0\pm 46.1\) & \(336.2\pm 54.1\) \\ Methyleugenol & 1361 & - & - & - & \(5.1\pm 0.9\) & \(3.4\pm 0.7\) & \(7.2\pm 2.7\) & \(\rm{tr}\) & \(32.3\pm 8.9\) \\ Cininnym acetate & 1367 & - & - & - & \(8103.3\pm 177.1\) & \(4.4\pm 1.5\) & \(423.5\pm 139.6\) & - \\ Benzyl benzoate & 1733 & \(95.0\pm 42.4\) & \(119.87\pm 38.9\) & \(157.5\pm 82.5\) & - & - & - & - & - \\ \hline \hline \end{tabular} \({}^{1}\) retention index, \({}^{2}\) trace amount, less than \(0.1\). \({}^{1}\) not detected.  
\end{table} 
Table 2: Emission amount of main floral scent compounds in the eight _P. mmue_ cultivars.
## 4 Trust-Driven Incentive and Aggregation Mechanism
### Stackelberg Game-Based Solution Strategy  
To model the strategic interaction between the server and clients, we adopt a Stackelberg game formulation. The server, as the leader, decides the total budget \(R_{t}\) and reward strategy \(\{r_{i}^{t}\}\); the clients, as followers, choose their resource investment \(x_{i}\) to maximize utility.  
Each client's utility function is defined as follows:  
\[U_{i}(x_{i})=\eta\cdot\tau_{i}^{t}\cdot\frac{x_{i}}{\sum_{j\in\mathcal{S}_{i}}x _{j}}-(a_{i}x_{i}^{2}+b_{i}x_{i}), \tag{8}\]  
where the first term denotes the trust-weighted incentive share, and the second term captures the cost of local resource usage.  
The quadratic cost function \(a_{i}x_{i}^{2}+b_{i}x_{i}\) is widely adopted in economics and resource allocation models [46], as it ensures convexity and reflects diminishing returns--i.e., increasing cost per unit as resource consumption rises. This formulation facilitates closed-form analysis and captures the realistic non-linearity of energy or training time costs on edge devices.  
It is worth noting that each client's best response depends on the global term \(\sum_{j}x_{j}\), which is generally unknown in decentralized settings. We address this by assuming that the server provides an aggregated signal during each communication round. This approximation is consistent with many Stackelberg-based FL mechanisms [47], where clients respond based on coarse-grained information rather than full observability. In future work, distributed best-response estimation or local belief updates could be explored to eliminate this assumption.  
This convex utility yields the following closed-form best-response function via the first-order derivative:  
\[x_{i}^{*}=\frac{\eta\cdot\tau_{i}^{t}-b_{i}\cdot\sum_{j}x_{j}}{2a_{i}\cdot\sum_{ j}x_{j}}. \tag{9}\]  
The server's utility function is defined as the net benefit of the improved model accuracy minus the incentive cost, calculated as follows:  
\[U_{S}=\Delta\mathcal{L}(w_{t})-\lambda\cdot R_{t}, \tag{10}\]  
where \(\Delta\mathcal{L}(w_{t})\) represents the loss reduction after aggregation and \(\lambda\) is a balancing coefficient controlling budget sensitivity. The server's objective is to choose \(R_{t}\) and \(\{r_{i}^{t}\}\) such that \(U_{S}\) is maximized under budget constraints while also encouraging high-trust participation from clients.  
Using backward induction, the server can derive the optimal reward strategy and establish a closed-loop linkage between incentive allocation and client behavior adaptation.
## 5 Experiments
### Energy Consumption  
Multi-cloud environments typically involve a large amount of data transmission and computation, often resulting in increased energy consumption during service composition. The objective of this paper is to propose an approach that can effectively reduce the energy consumption of cloud service composition. Since the energy consumption of cloud services is closely related to the operating costs of cloud platforms and environmental protection issues, such as energy conservation and emission reduction, reducing the energy consumption of cloud service composition proves extremely important.  
We selected the energy consumption of cloud service composition as our comparative metric for this experiment. We designed four scenarios for this purpose. Each scenarioconsists of 60 subtasks, and for each subtask within the four scenarios, there are 100, 200, 300, and 400 candidate cloud services, respectively. Figure 7 displays the total energy consumption results for each group. Our SkyDRL method achieved an average energy consumption of 1687.50, while PD-DQN consumed 2402.25, Q-learning with skyline consumed 2570.5, SFGA consumed 1829.5, and MAACS consumed 2338.5.  
Compared to the other methods, our SkyDRL method proved superior in terms of reducing energy consumption during the service composition. Specifically, we achieved an average reduction of 29.75% in energy consumption compared to PD-DQN, 34.35% compared to Q-learning with skyline, 7.76% compared to SFGA, and 27.83% compared to MAACS. Notably, our SkyDRL method was not affected by the number of candidate services, demonstrating its effectiveness and robustness in practical, real-world applications.  
In conclusion, our experimental results demonstrate that the proposed SkyDRL method can effectively reduce the energy consumption of cloud service composition. This has significant implications for optimizing cloud platforms, reducing operating costs, and promoting environmental protection through energy conservation and emission reduction.
###### Abstract  
In the context of the internet of things, and particularly within distributed measurement systems that are subject to high privacy risks, it is essential to emphasize the need for increasingly effective privacy protections. The idea presented in this work involves managing critical traffic through an architectural proposal aimed at solving the problem of communications between nodes by optimizing both the confidentiality to be guaranteed to the payload and the transmission speed. Specifically, data such as a typical sensor on/off signal could be sent via a standard encrypted channel, while a sensitive aggregate could be transmitted through a dedicated private channel. Additionally, this work emphasizes the critical importance of optimizing message sizes to 5 k-bytes (small payload messages) for transmission over the reserve channel, enhancing both privacy and system responsiveness, a mandatory requirement in distributed measurement systems. By focusing on small, encrypted payloads, the study facilitates secure, timely updates and summaries of network conditions, maintaining the integrity and privacy of communications in even the most challenging and privacy-sensitive environments. This study provides a comprehensive performance analysis of IoT networks using Dark Net technologies and MQTT protocols, with a focus on privacy and anonymity. It highlights the trade-offs between enhanced security and performance, noting increased latency, reduced bandwidth, and network instability when using TOR, particularly with cipher suites like AES256-GCM-SHA384 and DHE-RSA-CHACHA20-POLY1305. The research emphasizes the need for further exploration of alternative protocols like LWM2M in secure IoT environments and calls for optimization to balance privacy with performance in Dark-Net-based IoT deployments.  
M QTT; mosquito; raspberry 4 gen; linux; OpenWrt; IoT; DMS; TLSv[1.2-1.3]; TOR; dark net; deep web +
Footnote †: journal: Applied sciences
# Provably Secure ECC-Based Anonymous Authentication and Key Agreement for IoT
## 5 Security Analysis
### Descriptive Security Analysis
#### 5.2.3 Anonymity  
In this scheme, \(ID_{s}\) and \(ID_{sp}\) are masked before being transmitted during the authentication process and change dynamically from session to session with the choice of the temporary random numbers \(x_{s}\) and \(x_{sp}\). \(\mathcal{A}\) is incapable of retrieving and tracing the identity from the transmitted messages. That is, the proposal guarantees anonymity.
## 4 Discussion
### The Impact of Initial pH for Value-Added Production from E. gracilis Using STB  
The initial pH is an important factor for _E. gracilis_ cultivation. The photosynthetic efficiency of _E. gracilis_ might decrease under unfavorable pH conditions, such as extreme acidic or alkaline conditions [32]. The activities of enzymes such as laminaribiose phosphorylase in _E. gracilis_ cells can be strongly affected by not only the trophic mode but also the pH [33]. Higher paramylon production and content were observed at pH 5 and 7 than at pH 2 and 3.5, while biomass production showed opposite results [9]. This result is in accordance with that observed in this study, wherein paramylon production at pH 8 was higher than that at pH 3. The optimal pH for cell growth and value-added material production is strongly dependent on the microalgal species [34]. The optimal pH conditions differed according to the maximum titer of biomass and paramylon, probably owing to the change in the dominant species of CO\({}_{2}\) (CO\({}_{3}\)\({}^{2-}\), HCO\({}_{3}\)\({}^{-}\)) in the medium depending on the pH; thus, the type of substances (biomass, protein, polysaccharides, pigments, etc.) accumulated in the cell changed [34].  
Based on the results, the material stream input and output on biomass and paramylon production from _E. gracilis_ using STB are depicted in Figure 5. Preparing hydrolyses by adding 560 L of STB into 440 L of distilled water can make an alternative medium containing 18 g L\({}^{-1}\) of organic carbon source. Setting the initial pH of the process to 3 can produce either 3 kg of biomass or 0.5 kg of paramylon. On the other hand, setting the initial pH of the process to 8 can yield either 2 kg of biomass or 1 kg of paramylon. In summary, it is advantageous to set the initial pH 3 for the efficient production of biomass or the initial pH 8 for the efficient production of paramylon. Since enhanced productivity can be expected from scaled-up cultivation reactors under different operation modes (e.g., semicontinuous) [30], further investigation on _E. gracilis_ cultivation using STB is required.
# Cybersecurity and Medical Imaging: A Simulation-Based Approach to DICOM Communication
## 2 Background
### Picture Archiving and Communication System (PACS)  
PACS is a crucial system utilized in the medical field for the storage, management, and distribution of medical images and associated patient information. It replaces traditional film-based systems by enabling healthcare providers to capture, store, retrieve, and view medical images electronically. With PACS, the cumbersome task of managing physical film records is replaced by a digital infrastructure that facilitates easy image access and promotes efficient collaboration among healthcare professionals. Orthanc [29,41], on the other hand, is an open-source implementation of a PACS server. It provides a lightweight, vendor-neutral, and standards-compliant solution for managing medical images and associated data. Orthanc serves as a software platform that can be utilized to establish a PACS server infrastructure. Its open-source nature offers flexibility and customization options, making it an appealing choice for institutions and developers seeking to create their own PACS environment.
## 4 Generation Scheme
### Direct Generation
#### 4.1.2 Direct Generation with Decomposed Subcarriers  
Figure 4 shows the scheme of direct generation with decomposed subcarriers. First, under the control of the frequency-hopping pattern, the subcarrier generator generates the decomposed subcarriers corresponding to each signal component and IM component. Second, the signal components and IM components are multiplied by the corresponding  
Figure 3: Direct generation with composed subcarriers.  
subcarriers to generate the in-phase part I(\(t\)) and quadrature parts Q(\(t\)). Finally, I(\(t\)) and Q(\(t\)), which are modulated on the cosine-phased and the sine-phased radio-frequency carriers, are combined to obtain \(s_{RF}(t)\).
# Distributed Graph Diameter Approximation
## 1 Introduction  
The analysis of very large (typically sparse) graphs is becoming a central tool in numerous domains, including geographic information systems, social sciences, computational biology, computational linguistics, semantic search and knowledge discovery, and cyber security [1]. A fundamental primitive for graph analytics is the estimation of a graph's diameter, defined as the maximum (weighted) distance between nodes in the same connected component. Computing exactly the diameter of a graph is essentially as expensive as computing All-Pairs Shortest Paths [2], which is too costly for very large graphs. Therefore, when dealing with these graphs, we need to target approximate solutions in order to ensure reasonable running times. Furthermore, large graphs might be too large to fit in the memory of a single machine: in such cases, resorting to distributed algorithms running on large clusters of machines is key to the solution of the problem. Unfortunately, state-of-the-art parallel strategies for diameter estimation are either space inefficient or incur long critical paths. Thus, these strategies are unfeasible for dealing with huge graphs, especially on distributed platforms characterized by limited local memory and high communication costs, (e.g., clusters of loosely coupled commodity servers supporting a MapReduce-like abstraction), which are widely used for big data tasks, and represent the target computational scenario of this paper. In this setting, the challenge is to minimize the number of communication rounds while using linear aggregate space and small (i.e., substantially sublinear) space in the individual processors.  
To cope with the above issues, we develop a novel parallel strategy for approximating the diameter of large undirected graphs, both weighted and unweighted, which is suitable for implementation on MapReduce-like computing frameworks.