esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### LEACH Protocol
#### 2.2.2 Steady State Phase  
Nodes transmit data through a single hop to a CH, which aggregates the data and forwards them to the sink. The energy expended by node \(k\) in transmitting \(m\)-sized bits of data is [40]  
\[E_{k}=\begin{cases}E_{\mathtt{elec}}\cdot m+E_{\mathtt{fs}}\cdot m\cdot d^{2}& \text{if }d<d_{\mathtt{th}}\\ E_{\mathtt{elec}}\cdot m+E_{\mathtt{amp}}\cdot m\cdot d^{4},&\text{if }d>d_{ \mathtt{th}},\end{cases} \tag{2}\]  
\(E_{\mathtt{elec}}\) denotes the node electroneg energy, and \(E_{\mathtt{amp}}\) pertains to the energy required for amplification of a signal in a multi-path model. The variables \(d\) and \(d_{\mathtt{th}}\) denote the distance from the transmitter (node or CH) to the receiver (CH or sink), and a predefined threshold distance, respectively. \(E_{\mathtt{fs}}\) represents the energy required for amplification of a signal in open space and is utilized when the distance from the source to the destination is below \(d_{\mathtt{th}}\). The energy expended in receiving \(l\) bits of data is [40]  
\[E_{\mathtt{R}}=(E_{\mathtt{elec}}+E_{\mathtt{DA}})\cdot l \tag{3}\]  
where \(E_{\mathtt{DA}}\) refers to the energy expenditure necessary for aggregating each bit of data.  
Figure 1: Network model [38].
# Balancing Distributed Key-Value Stores with Efficient In-Network Redirecting
## 5 Evaluation  
### Experiment Setups  
**Testbed.** The testbed consists of one 3.2 Tbps Wedge 100 BF-32X switch with a Tofino chip and three Dell PowerEdge R820 servers. Each server has two Xeon E5-4603 CPUs, 256 GB memory, and an Intel XL710-QDA2 network interface card (NIC) with two 40 G ports. The CPU in the Tofino switch is an Intel Pentium D-1517 CPU with four cores running at 1.6 GHz. We assign two servers to be KVS servers and another one to be the client for generating queries; all of them are connected to the switch via two 40 G cables. Limited by the servers we have, we emulate \(M\) storage servers by using \(M/2\) queue on each server machine. Each queue process queries for one KVS partition as a server and drops queries if the received queries exceed its processing rate.  
**Implementation.** The KVSs on the servers are realized by running memcached services. Both the client library and the server agent are implemented in Lua with Moongen [22] for optimized throughput performance. Inside the Switch, functions in the ASIC pipeline are written in P4 and the other parts are written on CPU in Python. The ASIC pipeline communicates with the CPU through Thrift APIs, which are generated by the switch compiler.  
**Traces.** We generated the query traces following the Zipf distribution as skewed workloads, and the items in KVS are partitioned across storage servers. Three different skewness parameters are used for generating, 0.9, 0.95, and 0.99, which are commonly used in the literature [8; 9; 12]. In our experiments, the client keeps sending queries for 5 min at a maximum sending rate of about 30 MQPS (Million Queries Per Second) for every test. Most experiments use read-only workloads, and write queries are tested particularly. By default, \(K\) and \(M\) are set to 10,000 and 32, respectively. The sizes of the key and value are 16 and 128 bytes, respectively.  
**Methodology.** The main evaluation metric we use is the imbalance factor of the rack \(\lambda\) defined in Equation (2), which is also used in the literature [12; 23]:  
\[\lambda=\sum_{j=1}^{M}(\frac{|L_{j}-L|}{L*M}), \tag{2}\]  
where \(L_{j}\) is the KVS load on server \(j\) and \(L\) is the average number of queries across all servers. The value of \(\lambda\) ranges from \([0,2-\frac{2}{M}]\), and a larger \(\lambda\) signifies a more imbalanced load distribution.  
Figure 6: An illustration of HashPipe in KVSwitch. (**a**) Initial state; (**b**) New key is placed with value 1 in first stage; (**c**) B being larger the E; (**d**) L is retained in HashPipe.  
The baseline we compare to is the KVS system without any balancing techniques, marked as _Original_. Moreover, we also compare the balancing performance between KVSwitch and NetCache [8], with which the hot items are cached in ASIC pipeline in programmable switches to balance the KVS.
# Design of a Noise Mitigation System Using Lightweight Graded Micro-Porous Material
## 2 Establish Predictive Methods to Calculate Flow Properties of Polydisperse Particles
### Micro-Porous Hollow Bubble (\(\upmu\)-PHB) Composite Material
#### 2.2.2.2 Normal Incidence Impedance Testing  
\(\upmu\)PHB composite material specimens of all three size groups of 25 mm thickness and 100 mm diameter were fabricated. The specimen fabrication was the same as previously explained and is described in detail in references [25; 33]. We measured the absorption coefficient versus frequency using an impedance tube with a two-microphone setup according to the ASTM E1050-12 standard [39] for the frequency range of 250 to 1600 Hz. The incident sound pressure level (SPL) was 100 dB. The measured absorption coefficient that is in the form of signals was smoothened (smoothening factor, RL = 0.15) using a robust locally weighted regression (rLowess) method [40] in MATLAB. A rLowess method that was applied to a fatigue model for data reduction [41] was adapted here.  
For the experimental absorption coefficient versus frequency for the three different size groups, \(\upmu\)PHB composite materials are shown as solid lines in Figure 10 for a specimen thickness of 25 mm. From Figure 10, we see that the absorption coefficient increased with an increase in hollow microbubble size (increased pore size). This is because smaller hollow microbubbles tend to create smaller pores, leading to a material that is too resistive to obtain high absorption; with an increase in the size of the hollow microbubbles, the pore size increases, allowing a good balance between the material's intrinsic loss and leakages and thus a high absorption [49].
# Implementation of an Attribute-Based Encryption Scheme Based on SM9
## 3 Implementation and Evaluation of SM9 Identity-Based Encryption
### ABE Scheme Based on SM9 IBBE
#### 4.2.3 Encrypt (\(\mathrm{M_{pk}}\), \(\mathrm{A}\), \(\mathrm{M}\))  
Given the master public key \(M_{pk}\), an access structure \(A\), and a message \(M\), this algorithm converts the access structure \(A=\{A_{1},A_{2},\ldots,A_{n}\}\) into a set of identities by running the algorithm \(\xi\), and then gets a set of ciphertexts \(C=\{CT_{1},CT_{2},\ldots,CT_{n}\}\) by calling the IBBEncrypt algorithm.  
\[C\leftarrow\textbf{IBBEEncrypt}\big{(}M_{pk},\xi(A,u),M\big{)} \tag{7}\]
###### Abstract  
The objective of this study was to evaluate the capability of machine learning models to accurately predict complex near-critical phase behavior in CO\({}_{2}\)-hydrocarbon systems, which are crucial for enhanced oil recovery and carbon storage applications. We compared the physical Peng-Robinson equation of state model to machine learning algorithms under varying temperatures, pressures, and composition, including challenging near-critical scenarios. We used a direct neural network model and two hybrid model approaches to capture physical behavior in comprehensive compositional space. While all the models showed great performance during training and validation, the Direct Model exhibited unphysical behavior in compositional space, such as fluctuations in equilibrium constants and tie-line crossing. Hybrid Model 1, integrating a single Rachford-Rice iteration for physical constraints, showed an improved consistency in phase predictions. Hybrid Model 2, utilizing logarithmic transformations to better handle nonlinearities in equilibrium constants, further enhanced the accuracy and provided smoother predictions, particularly in the near-critical region. Overall, the hybrid models demonstrated a superior ability to balance computational efficiency and physical accuracy, closely aligning with the reference of the Peng-Robinson equation of state. This study highlights the importance of incorporating physical constraints into machine learning models for reliable phase behavior predictions, especially under near-critical conditions.  
phase behavior; equation-of-state; hybrid modelling; flash calculation; machine learning Article
## 4 Evaluation Metrics  
As proposed in [33], the evaluation framework of covert channels, including channel capacity, robustness, and stealth, can be applied to both storage and timing covert channels, where capacity means the quantity of data that each packet transports, robustness means how easily the covert channel can be eliminated and how robust it is against channel noise, and stealth means how different the covert traffic is from normal traffic. In [34], robustness, rate, and non-detectability refer to the ability of a covert channel to not be indistinguishable from the overt channel, the ability against noise, and the number of bits delivered by each data packet, respectively.  
Based on the above related work, we also propose three evaluation metrics for the CCES scheme: non-detectability, robustness, and efficiency. Non-detectability refers to the undiscovered ability during covert communication, measured by entropy; robustness refers to the capability against the attacker or hostile network environment, measured by bit error rate (BER); efficiency refers to the number of binary bits transmitted in unit time, measured by bits per second (BPS).
# Effect of Amphiphilic Polymer/Nano-Silica Composite on Shale Stability for Water-Based Muds
## 3 Results and Discussion
### Pore Structure Characterization of Shale  
The plugging capacity of SMA/SiO\({}_{2}\) was further determined through BET analysis, and the surface area and pore volume of shale samples were used as evaluation parameters. As presented in  
Figure 10: SEM images of the shale cores (**a**) before interaction and (**b**) after interaction with the base fluid containing 1% SMA/SiO\({}_{2}\).  
Figure 9: Pressure transmission experiment curves.  
Figure 11, the surface area of original shale without treatment was 15.51 m\({}^{2}\)/g, with a pore volume of 0.02541 cm\({}^{3}\)/g. After being soaked in deionized water, the surface area and pore volume of shale were increased to 19.64 m\({}^{2}\)/g and 0.03107 cm\({}^{3}\)/g respectively. The large increase in the surface area and pore volume indicated that water strongly interacted with shale, resulting in bigger pores and fractures in shale samples. The hydration and capillary effect are essential factors leading to the pores and fractures propagation in shale samples [47]. However, after being soaked in 1.0% SiO\({}_{2}\) solutions, the surface area and pore volume of shale were decreased to 13.42 m\({}^{2}\)/g and 0.02407 cm\({}^{3}\)/g respectively. In especial, a significant decrease was observed after being soaked in 1.0% SMA/SiO\({}_{2}\) solutions, and the surface area and pore volume were reduced to 10.33 m\({}^{2}\)/g and 0.02186 cm\({}^{3}\)/g respectively. Results indicated that SMA/SiO\({}_{2}\) was able to effectively plug the shale pores and fractures, and thus prevent water invading into shale samples.
# Is Microfracture Alone Enough?
## 4 Microfracture Plus Platelet-Rich Plasma  
Although microfracture is a safe and encouraging treatment for musculoskeletal diseases, evidence of its success has varied greatly and is very indication-specific. After setting the MFX holes, the arthroscopic liquid is cleared from the joint in order to create a dry environment for the application of the PRP mixture. The cubital veins can be used to collect 30 to 45 mL of whole blood into a 50 mL syringe that contains sodium citrate. The blood is anticoagulated and centrifuged at 1500 rpm for 10 min. After a second spin of 3000 rpm for 5 min, about 5 mL of PRP is obtained for each person. The PRP is infiltrated into the knee treated by MFX. This procedure can be repeated regularly, and patients follow a standard rehabilitation protocol [26; 27; 28].  
PRP can be used to augment MFX due to the ability of platelets to liberate a series of growth factors that have an important role in building higher-quality cartilage. [PERSON] et al. included the effects of MFX + PRP in their systematic review. They obtained statistically significant improvements in postoperative IDKC and KOOS scores at 2 years. The newest meta-analysis dealing with the evaluation of PRP as an adjuvant for microfracture is by [PERSON] et al. (2020). They analyzed seven studies that met the inclusion criteria and suggested that PRP can improve the outcomes of MFX in knees and ankles at short-termfollow-up. However, these ameliorations did not reach minimal clinically important differences (MCIDs), and thus they were not clinically perceivable by patients [20; 29].  
Recently, [PERSON] et al. conducted a study on 79 patients, divided into a control group of 39 patients treated with MFX + PRP and an observation group of 40 treated by MFX alone. Parameters such as baseline data, pain level, knee range of motion, knee symptoms, motor function, knee function, and complications were evaluated. Significantly statistical results were obtained for IDKC scores (\(p\) < 0.05) and for Tenger and Lysholm scores (\(p\) < 0.05). The rate of complications was also better for the group treated with PRP as an adjuvant (10%, compared to 28% in the control group). Follow-up was done at 1, 2, and 3 months after surgery [30].
# Interpreted Petri Nets Applied to Autonomous Components within Electric Power Systems
## 6 Discussion
### Implications for Practical Projects  
An advantage of the proposed modeling technique is the application of formal methods [33] which take into consideration the specification as well as the analysis and verification. As a result, the development time and the cost can be reduced, since errors may be detected at an early stage. Having formal specification (in the form of interpreted Petri net), symbolic model checking can be used to verify the mutual relationship between input and output signals.  
An inconvenience, in turn, is that knowledge of interpreted Petri net formalism is needed, as is knowledge of the possible analysis and verification methods that can be applied. However, this barrier can be easily overcome with some interdisciplinary research involving both power and energy systems specialists and automatic control engineers. Moreover, an interpreted Petri net model is always some kind of abstraction. It means that it presents only a view of the system from some chosen perspective, and certain detailed information may intentionally be omitted at this stage of system development.  
Usually, when designing control algorithms in autonomous components within energy systems, no formal methods are used (or at least very rarely [29]), and the proposed solutions are just simulated to check for proper behavior. However, neither simulations or tests can confirm that the designed system will behave correctly in all situations, and these methods take into account only some pre-defined scenarios. The application of formal methods (including model checking) allows for more confidence regarding functionality. The challenge here is therefore to popularize formal methods in the electric power system domain in order to benefit from the diverse possibilities of interpreted Petri nets (as a formal model) that can be easily formally verified using, e.g., a model checking technique.
## 2 Materials and Methods  
### Synthesis and Transfer of CNT and Graphene Films  
Large-area graphene films were grown by CVD using methane as a carbon resource and copper foils as substrates (25 \(\upmu\)m in thickness, Alfa Aesar, Heysham, UK). Graphene transfer was carried out by spin-coating polymethyl methacrylate (PMMA) and the copper foil was etched away in a mixed solution of FeCl\({}_{3}\)/HCl, followed by rinsing with deionized water several times. Then the PMMA-supported graphene films were transferred onto a pre-patterned and cleaned PEN substrate. The PMMA was/was not removed by rinsing with acetone and isopropyl alcohol (IPA) when the graphene film were used as cathodes/anodes. Large-area and free-standing CNT films were synthesized by the CVD method using xylene (carbon source) and ferrocene (catalyst) as precursors, a gas mixture of Ar and H\({}_{2}\) as the carrier gas, and nickel foil as the collector, which is detailed in our previous work [34]. The purification process of CNT films contained three steps: first, it was washed in hydrogen peroxide (H\({}_{2}\)O\({}_{2}\), 30%) for 72 h to remove amorphous carbon, then soaked in hydrochloric acid (HCl, 36.5%) for 4-5 h to remove the iron catalyst residue, and finally washed with deionized (DI) water three times.
# Periodic Monitoring and Filtering Suppression of Signal Interference in Mine 5G Communication
###### Abstract  
Diverse IoT applications, such as unmanned driving, intelligent video, unmanned working face, industrial control, and intelligent robot inspection, are the key technologies in the field of intelligent mines. In order to fully meet the requirements of underground IoT systems of high bandwidth, low latency, and massive connections, it is necessary to study 5G technologies suitable for underground environments to achieve effective deployment in mines. In key areas, such as main transport roadways, fully mechanized mining faces, and underground substations, both spurs and crosstalk in the frequency domain are the dominant factors affecting the stability and reliability of 5G signals. For the purpose of improving the performance of mine 5G, a fusion anti-interference scheme is designed here. Based on a deep complex network and blind source separation, periodic monitoring and filtering suppression of signal interference can be achieved. The test results show that the frequency domain spurs' suppression capability of the proposed method is 20% higher than that of the traditional equalization method. For frequency domain crosstalk, 90% interference elimination could be achieved by the proposed method without additional delays when compared with the conventional blind source separation. The high-bandwidth and low-latency characteristics of 5G communication can be guaranteed by this method.  
m 5G communication; interference suppression; blind source separation; neural network +
Footnote †: journal: Applied  
Article
## 5 Decentralized Decision-Making
### Decentralized Decision-Making Mechanisms
#### 5.3.4 Decentralized Autonomous Organizations (DAOs)  
Decentralized autonomous organizations (DAOs) are a groundbreaking concept within the world of blockchain technology, with the capacity to fundamentally transform corporate governance and enterprise operations. Decentralized autonomous organizations (DAOs) are entities that enable dispersed decision-making and independent performance of organizational functions. These entities function with complete transparency on the blockchain and are regulated by smart contracts. Decentralized autonomous organizations (DAOs) rely on smart contracts, which can contain organizational rules and governance systems. The members of the DAO are granted the authority to make decisions, together with their corresponding rights and responsibilities, in accordance with these smart contracts. DAOs provide openness, immutability, and trustlessness in corporate governance by dispersing these smart contracts throughout a blockchain network [74].  
DAOs are characterized by their decentralized decision-making mechanism, allowing token holders to actively engage in governance processes and shape the organization's trajectory. Token holders have the ability to cast votes on various proposals, including budget allocations, protocol updates, and strategic choices, by utilizing their tokens as voting power. Every token symbolizes ownership in the organization, and the voting power of participants is directly equal to the number of tokens they own. Blockchain technology guarantees transparency and auditability by recording all voting activity and decision results on the blockchain, allowing stakeholders to have a clear view of the governance process. Transparency promotes confidence among members and strengthens the credibility of governance choices inside the DAO [75].  
Decentralized autonomous organizations provide an abundance of advantages as compared to conventional centralized organizations. DAOs mitigate the potential for corruption, censorship, and single points of failure by removing middlemen and centralized decision-makers. Furthermore, DAOs facilitate worldwide involvement, granting membership and the ability to engage in organizational governance to anybody with internet access. Moreover, DAOs foster ingenuity and cooperation by offering a framework for decentralized coordination and distribution of resources. Participants have the ability to suggest ideas, establish working groups, and cooperate on projects within the DAO, which promotes a vibrant and all-encompassing environment of invention [76]. Figure 14 depicts a DAO's architecture.  
DAOs, despite the numerous benefits they offer, are not without their share of difficulties and restrictions. Vulnerabilities in the security of the code of smart contracts can result in exploits or attacks that put the organization's integrity at risk. Questions regarding the legal status, liability, and enforceability of decentralized autonomous organizations (DAOs) are raised in the case of disputes or conflicts because of the legal and regulatory uncertainty that surround them.
## 3 Results
### Change Trend of Land NPP  
The Theil-Sen slope (\(\rho\)) of land NPP ranged from \(-33\) to 20 g C\(\cdot\)m\({}^{-2}\).a\({}^{-1}\) in the Han River Basin from 2001 to 2019. Land with an increasing trend of NPP (\(\rho>0\)) accounted for 92.27% of the basin area, while land with a decreasing trend of NPP (\(\rho<0\)) only accounted for 7.73%. Specifically, land with faster increases in NPP (\(\rho>5\)) was mainly located in Hanzhong, northern Ankang, Fangxian, the Shennonglia Forestry District, regions around the Danjiangkou Reservoir, and parts of the Nanyang Basin, whereas land with faster decreases in NPP (\(\rho<-5\)) accounted for a small proportion and was concentrated in the peri-urban areas (Figure 3a).  
The Mann-Kendall method was used to examine the change trend of land NPP at five significance levels (Figure 3b). Land with highly significant (\(p<0.01\)) and significant (\(p<0.05\)) increases in NPP accounted for 39.25 and 18.57% of the basin area, while land with highly significant (\(p<0.01\)) and significant (\(p<0.05\)) decreases in NPP only accounted for a small proportion of the basin area (0.96% in total). The spatial distribution of areas with significant increases was quite similar to that of areas with high growth rates of land  
Figure 3: Spatial distribution of change trend (**a**) and significance levels (**b**) of land net primary productivity (NPP) in the Han River Basin of China between 2001 and 2019.  
Figure 2: Spatial distribution (**a**) and interannual variation of the multi-year average of land net primary productivity (NPP) in the Han River Basin of China (**b**) and its sub-basins (**c**) from 2001 to 2019.  
NPP. In addition, land NPP in most regions, representing 41.23% of the basin area, did not change significantly.