esa-sceva/satcom-chunk-collection huggingface dataset:
# A Design for an Internet Router with a Digital Optical Data Plane
## 8 Discussion
The switches are reconfigured each batch, so existing \(\sim\)1 ns active device switching times should be sufficient. The relationship between transmission formats and processing complexity also affects the feasibility of this approach. Current transmission formats, especially QAM, are optimized for robust transmission, but not necessarily for ease of digital processing. Our \"Optical Turing  
Figure 19: Proposed approach to compute carry-out of a PSK-encoded signal using phase halving and phase squeezing. Figure 20: A binary full-adder from half-adders, using an additional OR gate (**left**); the equivalent for multi-bit data (**right**). Machine\" project [43; 44] is exploring whether other multi-bit encoding formats, notably M-PSK, might concurrently support high-bandwidth transmission while more feasibly enabling digital optical packet processing functions. The major challenges are integration and regeneration [44]. The component devices required by our approach are not individually novel; existing device designs are sufficient, but a practical optical data plane would need to be implemented as an integrated device, including existing designs for laser pumps, coherent comb sources, filters, couplers, and nonlinear wave mixing. Integration is required because it is not practical to support off-chip components, due to phase alignment requirements and signal loss. Combining this set of active and passive devices requires hybrid integration because no single substrate is sufficient. Regeneration is required to compose these separate functions into a processing pipeline, in order to avoid signal degradation that would increase the data error rate. For regeneration, our team has explored a number of approaches, including those requiring active phase stabilization [45] and more recent methods using Brillouin amplification for phase restoration as well as Raman amplification that reduces amplification-induced phase noise, both of which avoid the need for active stabilization [46; 47]. Hybrid integration is feasible but requires maturation of implementation technology, and regeneration will be more efficient and effective when implemented as a hybrid integrated system.
## 2 Materials and Methods
### Data Statistics  
In order to assess the reliability of the system, the data generated during the experiment had to be counted. Four types of data were counted for the experiment, corresponding to the target parameters of the packet loss rate and LQI of the local self-organizing network, and the packet loss rate and SNR of the satellite communication network.  
The packet loss rate can reflect the overall performance of the terminal of the local self-organizing network. In order to obtain the packet loss rate, the number of successful and failed message packets received by the local self-organizing network needs to be collected. Six stumpage environments were selected for the experiment to determine the stumpage per unit area (44,45). The distance from the coordinator node to the forest fire truck was increased from 1 m to 10 m, the terminal node transmitted 60 packets to the coordinator node every 1 m in the different stumpage environments, and the number of received packets was recorded (46).  
The LQI can reflect the channel quality of the local self-organizing network (47,48). In order to obtain the LQI, the data of the network's message packets need to be collected. The experiments measured the number of stumpages per unit area in different environments and packet transmission in these environments, respectively (18). At the end of the experiment, the emberGetLastHopLqi() function was used to parse the LQI of the packets (29,47).  
In the experiment, the elevation angle of the network terminal was set at 10\({}^{\circ}\), 30\({}^{\circ}\), 50\({}^{\circ}\), and 75\({}^{\circ}\), 160 messages were sent and received at each elevation angle, and the numbers of packets successfully and unsuccessfully received and sent were counted.  
At the same time, it was set to maintain line-of-sight propagation between the terminal and the satellite at all times. The signal power and noise power of 10 channels are collected and the signal-to-noise ratio is calculated at the end of the collection (49,50,51).  
Figure 5: (**a**) Fire occurs; (**b**) A arrives at predetermined stationing position; (**c**) A conducts firefighting operations; (**d**) fire extinguished; (**e**) B observes fire site parameters reported by A; (**f**) B monitors fire suppression by A.
## 2 Materials and Methods
### Materials
#### 2.1.6 High-Frequency Attenuation  
In the stochastic finite-fault method, the total site effect is accounted for by the production of a crustal amplification function and a high-frequency attenuation function. [PERSON] and [PERSON] [49] analyzed seismic data from Eastern Canada and found that the high-frequency attenuation \(\kappa_{0}\) was influenced by site conditions. Even bedrock sites exhibited significant high-frequency attenuation, although the effect was not significant, with values ranging from 0.02 to 0.04 s. [PERSON] [50] estimated the \(\kappa_{0}\) for National Earthquake Hazard Reduction Program (NEHRP) BC site profiles (sediment plus hard rock) in the Mississippi employment and the Atlantic coastal plain. Their results showed that the \(\kappa_{0}\) values increased from about 0.009 to 0.031 s for a sediment thicknesses ranging from 116 to 600 m. [PERSON] and [PERSON] [51] used a database of 1597 accelerograms from more than 500 events recorded at 44 stations in the range of 0-300 km (\(3.3<M_{\rm S}<8.0\)) from the 2008 Wenchuan and 2013 Lushan earthquakes to study the characteristics of \(\kappa\). The \(\kappa_{0}\) values of the JYD, LSJ, and TQD site class A stations were 0.0034, 0.0022, and 0.0027 s, respectively. A total of 334 accelerograms of 42 small-to-moderate earthquakes recorded at 36 strong-motion stations were used to investigate the ground motion characteristics of the southwestern margin of the Sichuan-Yunnan rhombic block [52]. The calculated \(\kappa_{0}\) values of the three site class A stations ranged from 0.0014 to 0.0028 s. There are almost no research results regarding \(\kappa_{0}\) for our study area, so we chose the empirical \(\kappa_{0}\) value mentioned above for hard bedrock sites because major infrastructure sites are generally built on bedrock. Thus, we adopted \(\kappa_{0}\) values of 0.015, 0.025, and 0.035 s by considering the uncertainty with equal weighting in the multi-scheme method.  
Figure 2: Comparison of (**a**) the selected quality factor models and (**b**) the simulation results using the selected quality factor models for a given simulated earthquake scenario [44; 46; 47].
## 4 Results and Analysis
### Cross-Validation
#### 4.2.1 Spatial Consistency  
Spatial consistency was verified by comparing the spatial distribution of FY-FPAR with that of MODIS-FPAR, GEOV1-FPAR, and GLASS-FPAR. A FY-FPAR was randomly selected to display the spatial distribution comparison map (Figure 5). The results show that FY-FPAR maintained a good spatial consistency with MODIS-FPAR, GEOV1-FPAR, and GLASS-FPAR.  
The difference frequency was used to analyze the differences between FY-FPAR and MODIS-FPAR and GEOV1-FPAR, and GLASS-FPAR. Using the FY-FPAR data as the statistical data source, we calculated the difference between the FY-FPAR and the three FPAR products for the MODIS-FPAR, GEOV1-FPAR, and GLASS-FPAR in the study area. The difference results were calculated to obtain the frequency distribution. The difference frequency of the grassland, cropland, shrub, broadleaf forest, and needleleaf forest is shown in Figure 6.  
Figure 6: _Cont._Figure 6 exhibits that the difference frequencies of the grassland, cropland, shrub, broadleaf forest, and needleleaf forest between FY-FPAR, MODIS-FPAR, and GEOV1-FPAR. GLASS-FPAR was concentrated around 0 (in the \(\pm\)0.4 interval), which is a reasonable range. The peak of the difference frequency was almost at 0, and the spatial consistency was relatively good.
# Weight Assignment Method and Application of Key Parameters in Shale Gas Resource Evaluation
## 1 Introduction  
Shale gas refers to the accumulation of natural gas mainly in the form of adsorption or free state, located in dark shale or high-carbon shale. Regarding the research on shale gas development in China, [PERSON] and others began research on shale gas generation, enrichment, evaluation, and development as early as 1999. Years of exploration work have shown that China has excellent conditions for shale gas development and enormous resource potential [1,2]. The geological conditions for the development of shale gas in China are variable. Gas-bearing shale in China has the overall characteristics of high organic matter abundance, a high degree of thermal evolution, and a high degree of later changes. Shale varies greatly with the general gas content, but single-well production varies greatly [3]. At the same time, it has hidden characteristics for traditional gas reservoirs. Regarding the enrichment law, shale gas reservoirs are self-generating and self-storing gas reservoirs that are rich in organic matter shale, integrating generation, storage, and sealing. There are various types of gas sources, and the enrichment of shale gas reservoirs is influenced by various factors. Among them, the effective thickness of shale, organic carbon content, and the development degree of pores and fractures have the greatest impact on the enrichment of shale gas reservoirs [4].  
China has abundant shale gas resources. Currently, many experts and scholars in China have conducted detailed and comprehensive research on shale gas resource evaluation, mainly focusing on geological conditions, reservoir formation mechanisms, enrichment patterns, evaluation methods, and other aspects of shale gas development. Over the years, authoritative units have used various means and methods to estimate resource quantities (Table 1), indicating the importance of resource evaluation in resource exploration and development. Evaluating and improving the accuracy of resource evaluation is of utmost importance in resource quantity estimation and evaluation [5; 6; 7; 8]. Therefore, evaluating the key parameters involved in resource evaluation and quantitatively evaluating their accuracy is the focus of research in this field and the main object of this article.
# Multi-Identity Recognition of Darknet Vendors Based on Metric Learning
## 1 Introduction  
The main purpose of anonymous communication networks, such as mix [1], Tor [2], I2P [3], and Freenet [4], is to hide users' identity information and communication data. Cybercriminals often use anonymous communication networks to conceal their identities and carry out online crimes. Dark web marketplaces are a type of hidden service on the dark web; the trading volume in these marketplaces has been increasing in recent years [5, 6]. The anonymity of the dark web makes it difficult to track the identities of the buyers and sellers in these transactions. Additionally, some vendors register accounts on the same market or multiple markets to reach a larger pool of potential buyers [7, 8]. In this context, researching the problem of identifying vendors' multiple identities on the dark web is particularly important.  
Existing studies on identifying vendors' multiple identities on the dark web suffer from several limitations: (1) The existing dark web market datasets, such as DNM (Darknet Market Archives) [9], were collected between 2011 and 2015, and some of the marketplaces included in the dataset, such as Dream Market, have since been shut down. (2) Data labeling for identifying vendors' multiple identities is difficult, and previous studies have usedtwo methods: (1) dividing each vendor account into two parts and treating them as new accounts with shared identities, or (2) determining whether two accounts belong to the same identity based on their account names. However, the first approach is inapplicable for cross-market research and the second method suffers from inaccurate labeling. (3) The problem of identifying vendors' multiple identities on the dark web involves a large number of categories (each vendor identity is considered a category) and a limited number of samples for each category, making it difficult to use traditional multiclass classification models.  
We consider identifying multiple identities of vendors on the dark web as authorship aliasing, which aims to find out which accounts in a given set are controlled by the same real-world vendor. With such an assumption, we propose a method for collecting and labeling data for dark web marketplaces and overcoming the limitations of traditional multiclass classifiers to achieve effective identification of vendors' multiple identities. Our paper has the following contributions:  
1. We collected product data from 21 currently active English dark web marketplaces through a web crawler system and propose a multi-dimensional feature extraction method based on product titles, descriptions, and images, which can comprehensively represent dark web marketplace products.
2. We combined pseudo-labeling technology with manual labeling to improve the accuracy of data labeling.
3. We propose to use a Siamese neural network with metric learning to learn the similarity between vendors and achieve the identification of vendors' multiple identities.
# Flooding Message Mitigation of Wireless Content Centric Networking for Last-Mile Smart-Grid +
## 4 Performance Evaluation  
In this section, we present the performance of our proposed TOP-CCN using the ns-3 simulator [18]. Performance parameters such as reduced content dissemination delay, control message overhead, and increased accuracy of the face information in the PIT and FIB are measured and compared with flooding based CCN and RPL [19] in SG environment. Simulation parameters and smart grid application set used for the simulation are summarized in Table 1. In this table, density level 0.1 represents the one smart meter is placed in 100 m\({}^{2}\) area and 100 smart meters are deployed in 1000 by 1000 m\({}^{2}\) area. When a density level is set to 0.9, then 900 smart meters are installed in a same region. These density level factor can be utilized to control collision rate between devices in a network. For example, Aggregated LMN environment model as apartment environment (aggregated electricity metering box), density of smart meter can be increased to 0.9 level density. As the network density increases, the chance of packet collision and interference are also increased. The performance evaluation is divided into 3 sections. First, we evaluate flooding range control scheme with ns-3 visualization tool. Second evaluation is reliability comparison between IPv6 based routing, CCN and TOP-CCN. This evaluation can show that CCN can be applied to LMN and outperform IP based wireless system. In a last evaluation part, we  
Figure 6: An example of Publisher MPR (PMPR) selection and aggregated prefix information of the multi-hop Content-prefix Announcement (CA) in TOP-CCN.  
evaluated CCN caching performance between traditional CCN schemes and TOP-CCN. In this section we focus on CCN performance as content availability, smart meter firmware downloading time, etc.
# Edge Federated Optimization for Heterogeneous Data
## 2 Related Works
### Main Approaches to Handling Non-IID Data
#### 2.2.3 System-Driven Approach  
For system-driven approaches, two main kinds of secure data similarity evaluation methods are introduced in the literature, namely evaluating the similarity of the loss value and the similarity of model weights. With the first approach, the authors in [16; 17; 18; 19] execute the similarity evaluation by comparing the loss values of different cluster models, where multiple global models are constructed in clusters. Each connected client receives the cluster models for local empirical loss computation. Accordingly, for cluster model aggregation, each client refurbishes the smallest loss-value cluster model, and the server collects these updated models. Instead of considering the loss value, the second approach evaluates the local data similarity and does clustering based on the local model weights. In [11; 20], before clustering the clients, the standard FedAvg algorithm is applied to train the global model. Afterwards, the clients receive the warmed-up global model, perform local updates, and return these local models to the server for calculating the similarity scores of model weights. Thus, based on the received local updated models and the calculated similarity scores, the server can group the clients into clusters.  
Note that client clustering may be applied to handle the issues concerning negative knowledge transfer and performance degradation due to local model aggregation with considerably different training data. Furthermore, generating multiple global models provides a sensible way to enhance the scalability and flexibility of federated learning systems, considering the specific task requirements, at the cost of more communication and computational resource utilization.
## 4 Centralized Algorithm
### CE-Based Scheduling Algorithm  
The objective of the considered RB scheduling problem becomes:  
\[\max_{\beta_{m,k}^{n},p_{m}^{n}} \sum_{k=1}^{K}\sum_{n=1}^{N_{\text{RB}}}\frac{R_{k}^{n}}{ \overline{R}_{k}}\Bigg{/}\sum_{m=1}^{M}\sum_{n=1}^{N_{\text{RB}}}\sum_{k=1}^{ K}\beta_{m,k}^{n}S \tag{8}\]  
where \(S\) is the fixed transmit power. The objective is constrained by C2, C3 and C4 in Equation (7). We first propose a CE-based algorithm to solve the RB scheduling problem described above.  
The CE method is a typical heuristic algorithm to estimate the probabilities of rare events in complex stochastic networks [15] and to deal with linear programming. The basic idea of the CE method is to generate sufficient samples under a given strategy and then update the generating strategy according to samples. After iteratively repeating this procedure, generated samples will converge to the optimal solution. The proposed CE-based scheduling algorithm follows three major stages, including initialization, iteration and a complementary stage to close unfavorable transmissions, as shown in Figure 3.
# Energy-Efficient Controller Placement in Software-Defined Satellite-Terrestrial Integrated Network
## 1 Introduction
In this paper, we propose an energy-efficient controller placement (EECP) scheme in the software-defined STIN architecture. We focus on the green economies of countries along the Belt and Road, which accounts for a large population and urgent demand for traffic flows [11]. In the software-defined STIN architecture we proposed, the controllers are deployed on the LEO satellites, and satellite gateways are located in the countries along the Belt and Road. We consider the demands of global traffic and then build the energy consumption model. Considering the high complexity of the problem in large-scale STIN, we propose a heuristic algorithm, MSAP, to solve the EECP problem. The MSAP algorithm aims at minimizing energy consumption to find the optimal controller placement strategies. The simulations are performed on the Iridium constellation, and the evaluation results also show the outstanding performance of MSAP compared to other algorithms. The main contributions of this paper are summarized as follows:  
* We consider a software-defined STIN architecture, where controllers are deployed on LEO satellites. Based on the architecture, the EECP problem is defined to establish a relationship between economic concerns and traffic demands. The energy consumption model takes the effect of the real-world traffic demands into account. * Considering the high complexity of the problem in large-scale networks, we propose a heuristic-based MSAP algorithm to solve the EECP problem. The MSAP algorithm first adopts the greedy way to find the initial set of controllers and then introduces the simulated annealing (SA) algorithm to obtain the optimal placement strategy. * Extensive simulations are conducted on the simulated STIN with the Iridium constellation and satellite gateways. Simulation results show that the proposed MSAP algorithm outperforms other algorithms in network energy consumption and average latency. The remainder of this paper is organized as follows. Section 2 reviews the related work of controller placement schemes. Section 3 presents the STIN architecture and formulates the EECP problem. The proposed approaches are detailed in Section 4, elaborating the heuristic-based MSAP algorithm. Extensive results are shown and analyses are performed in Section 5. Finally, conclusions are presented in Section 6.
# Fine-Tuning a Personalized OpenBioLLM Using Offline Reinforcement Learning
## 3 Methods
### DTE Module  
The function of this module is to provide accurate prediction of the patient's future physiological state and provide sufficient state representation for personalized treatment decisions in the clinical decision-making process.  
This module, as shown in Figure 3, uses the Transformer architecture and is specifically used to predict and characterize the patient's time series physiological indicators. The module consists of a linear layer, a position encoding layer, Transformer encoder layers, and an output layer. Through the self-attention mechanism, the module can finely capture the long-term dependencies in the time series and achieve high-precision prediction of the patient's future physiological state. In order to maintain the rationality of the causal relationship, the multi-head attention layer in the Transformer encoder uses a masking technique to ensure that the network does not expose future information when processing the current time step.  
Figure 2: The methodology structure of OpenBioLLM modules.  
The trained DTE module extracts the feature vector of the output of the penultimate layer and records it as state_DTE. This vector encodes the information of the patient's current and historical physiological state, and is an important component of the state space in the CQL algorithm, providing sufficient information for subsequent clinical decisions.  
The final output of the DTE module is a prediction of the patient's future physiological state, which is based on the time series pattern deeply analyzed and learned by the encoder layer. The training goal of the module is to ensure that the model can capture and reflect the key changes in the patient's physiological state by minimizing the difference between the predicted physiological indicators and the actual observed values. The pseudo-code for training the DTE module is shown in Algorithm 2.  
```
1:Initialize DTE module network
2:for each epoch do
3: Input: patient trajectory dataset \(P\)
4: DTE module predicts the patient's physiological indicators \(\hat{e}\)
5: Calculate loss: \(\mathcal{L}_{MSE}=\frac{1}{T-1}\sum_{t=1}^{T-1}\frac{1}{m}\sum_{i=1}^{m} \lvert e_{t+1,i}-\hat{e}_{t+1,i}\rvert^{2}\)
6: Update DTE network parameters by gradient descent
7:endfor
```  
**Algorithm 2** Training DTE