esa-sceva/satcom-chunk-collection huggingface dataset:
# A Framework for Constructing a Secure Domain of Sensor Nodes
## 5 Results and Discussion
### Security Evaluation
#### 5.3.1 Node Replication Attack  
An attack involving the addition of a cloned sensor node to the network is quite simple in an unsecured network. In the case of our solution, it is not possible, because each TPM has a unique asymmetrical EK that can not be deleted or regenerated. This key is at the top of the key hierarchy that forms the trust structure. There is also a DK in this structure. Creating a domain-acceptable trust structure on another specimen of the sensor node requires the DK to be obtained. One of the methods of obtaining this key may be the execution of the pre-preparation procedure of the node, but this procedure is performed only in a safe space and requires direct connection of the prepared node to the B node. It should be recognized that such an attempt is unlikely to be carried out by unauthorized persons. Another method might be to obtain a DK key from a functioning node in the domain. However, this approach requires interference in the software and configuration of the sensor node. These types of activities are detected by TPM module mechanisms based on PCRs. If a node's integrity violation is detected during its regular operation, the sensor node sends an appropriate message to the remaining nodes of the domain and is then considered untrusted. If the integrity violation is detected when it is launched, the node's software is blocked, and re-use of such a sensor node is possible after the node\"s pre-preparation procedure is completed, and then the node is registered in the domain.
# Data Structure and Management Protocol to Enhance Name Resolving in Named Data Networking
## 3 Proposed Solution
### Proposed Encoded Component Hash Trie (ECHT) Data Structure  
The main goal of proposing Encoded Component Hash Trie (ECHT) is to have a data structure that stores and organizes entries in the PNPS mapping table in order to access and process them efficiently. An entry is a pair made up of the domain name, which is the entry key, and the delivery location AS number, which is the entry value, i.e., (\"om/edu/squ\", \"/AS2, /AS3, /AS4\"). In ECHT, we focused on speeding up lookup operations (get, getbcm) over modification operations (add, remove). The main characteristics that distinguished our proposed data structure include (1) combining component trie with hashing to rapidly retrieve the delivery location of the requested NDN name, (2) encoding each component as a NameComponent in Type-Length-Value (TLV) format [13], (3) hashing the encoded component with a SipHash proposed in [14] and using the Open Addressing Collision Resolution technique to resolve the hashing collision problem, and finally, (4) proposing a Best Component Match (BCM) mechanism, where the matching is done per component to retrieve the corresponding delivery location instead of using the LPM mechanism.
## 2 Working Resistance Prediction Model for Supports in Ultra-Long Mining Faces
### Results and Analysis  
#### 2.3.1 Hyperparameter Optimization  
(1) Node-embedding dimension optimization  
A key parameter in the AGCRN model is the node-embedding dimension. The optimal dimension for node embedding in the model is determined through hyperparameter optimization. The model performance under different node-embedding dimensions is shown in Figure 5.  
As shown in Figure 5a, the MAE and MAPE values of the model initially decreased with increasing node-embedding dimension, and then gradually increased. When the embedding dimension was 10, the MAE was 1358.21, the MAPE was 10.45%, and the error was the minimum value. As can be seen from Figure 5b, the num  
Figure 5: Performance impact graph of embed dimension, they should be listed as: (**a**) Embedding dimension error effect; (**b**) Number of embedded dimension parameters.  
model exhibited a linear growth trend as the embedding dimension increased. The results indicate that a high-dimensional node-embedding matrix contained more information, which helped the DAGG module to infer more spatial correlation information. Moreover, a higher dimension significantly increased the number of parameters of the NAPL module, making it more difficult to optimize the model. Therefore, when the node-embedding dimension was 10, the error of the AGCRN was the minimum value, and the performance was optimal.  
(2) Time window optimization  
When predicting time series, historical data are usually used to predict the data at the current moment, and the size of the historical data is referred to as the time window. If the time window is too small, the historical data contain incomplete and insufficient information, which affects the prediction results. However, if the time window is too large, the model may not be able to accurately grasp the temporal relationship between the data. Therefore, it is essential to choose an optimal time window. The time window was set to 8, 16, 32, and 64 days, and the other parameters of the model were not changed. The MAE and MAPE were compared. The results are presented in Table 2.  
As the time window increased, the model error initially decreased and then increased. When the time window was 16, the MAE and MAPE values reached the minimum values. As the time window continued to increase, the model error increased further. Therefore, in this study, the time window was set to 16 days to predict the working resistance of the supports during the next 3 days.
_Applied_  
_sciences_  
_Article_  
**High-Efficiency Coupling Method of the**  
**Gradient-Index Fiber Probe and Hollow-Core**  
**Photonic Crystal Fiber**  
**[PERSON] 1,2, [PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 2,* and [PERSON] 3**  
1 Department of Precision Mechanical Engineering, Shanghai University, Shanghai 200444, China; [EMAIL_ADDRESS] (C.W.); [EMAIL_ADDRESS] (Y.Z.); [EMAIL_ADDRESS] (J.S.)  
2 Science and Technology on Near-surface Detection Laboratory, Wuxi 214035, China; [EMAIL_ADDRESS]  
3 School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-137-6473-9726 check for updates  
Received: 22 April 2019; Accepted: 15 May 2019; Published: 20 May 2019  
**Featured Application: Supplying a high-efficiency coupling method of the gradient-index fiber probe and hollow-core photonic crystal fiber for gas detection.**  
**Abstract:** A high-efficiency coupling method using the gradient-index (GRIN) fiber probe and hollow-core photonic crystal fiber (HC-PCF) is proposed to improve the response time and the sensitivity of gas sensors. A coupling efficiency model of the GRIN fiber probe coupled with HC-PCF is analyzed. An optimization method is proposed to guide the design of the probe and five samples of the GRIN fiber probe with different performances are designed, fabricated, and measured. Next, a coupling efficiency experimental system is established. The coupling efficiencies of the probes and single-mode fiber (SMF) are measured and compared. The experimental results corrected by image processing show that the GRIN fiber probe can achieve a coupling efficiency of 80.22% at distances up to 180 \(\upmu\)m, which is obviously superior to the value of 33.45% of SMF at the same distance. Moreover, with the increase of the coupling distance, the coupling efficiency of the probe is still higher than that of SMF.  
**Keywords:** hollow-core photonic crystal fiber; GRIN fiber probe; coupling efficiency; gas sensing
## 4 Signal Combining Schemes for ADR Solutions
### Receiver Scenario with a Single PD (\(N_{pd}\) = 1)
#### 4.2.2 Equal Gain Combining Scheme  
This scheme, proposed in [44], considers for our scenario the combination of the signals received in each PD of the ADRs in the simplest way, assigning a value (weight) to each of these signals. For the VLC system, this scheme must select the best \(T_{x}\), \(T_{x_{top}}\), under the same optimization problem described in (33). The basic principle of this technique is that the signals received by the PDs in the ADR installed in \(R_{x}\), must be combined with the same weight; therefore, the SINR can be expressed as [22; 23; 44]  
\[\gamma_{T_{x_{top}},R_{x}}=\frac{\tau\sigma\xi\left(\sum\limits_{i=1}^{N_{PD}}P_ {a_{top}}H_{T_{x_{top}},PD_{i}}\right)^{2}}{\tau\sigma\xi\sum\limits_{i=1}^{N_{ PD}}\sum\limits_{a=1,A\
eq a_{top}}^{N_{T_{x}}}\left(P_{a}H_{T_{x_{top}},PD_{i}} \right)^{2}+\sum\limits_{i=1}^{N_{PD}}\left(n_{PD_{i}}\right)^{2}}, \tag{37}\]  
where \(\sum\limits_{i=1}^{N_{PD}}H_{T_{x_{top}},PD_{i}}\) is the overall DC channel gain for underground mining VLC systems, which is obtained from the sum of the channel gains of all ADR PDs and \(\sum\limits_{i=1}^{N_{PD}}\sum\limits_{a=1,A\
eq a_{top}}^{N_{T_{x}}}\left(P_ {a}H_{T_{x_{top}},PD_{i}}\right)^{2}\) represents the total interference received by PDs. In terms of physical implementation of this scheme, an adder in the \(R_{x}\) is necessary, which leads to a combiner circuit that must be installed in the ADRs. Although this scheme theoretically exceeds the SBC, when obtaining a better final signal, it is not effective to suppress the interference because each signal received in the PDs is valued with the same weight. Consequently, both the optimal signal and the interfering signals will be processed equally.
## 3 Results  
### Characterization of M-M-ZIF-67  
The micro-morphologies of the Fe\({}_{3}\)O\({}_{4}\)-MWCNTs and M-M-ZIF-67 were observed by SEM and TEM. As shown in Figure 2A, the Fe\({}_{3}\)O\({}_{4}\) nanoparticles are attached to the MWCNT surface. The SEM image of M-M-ZIF-67 in Figure 2B shows that the composite has a rough surface, indicating that the material has good potential as an adsorbent [27].  
VSM was performed to investigate the magnetic behavior of the magnetic materials, and the results are shown in Figure 3A. The magnetic hysteresis loops show that both the remanence and coercivity values of the three types of magnetic materials are zero, which indicates that they have typical supermagnetic properties and could be separated using an external magnet. The saturation magnetization values of Fe\({}_{3}\)O\({}_{4}\), Fe\({}_{3}\)O\({}_{4}\)-MWCNTs, and M-M-ZIF-67 are 66.8, 59.6, and 53.1 emu g\({}^{-1}\), respectively. As shown in the insert of Figure 3A, well-dispersed M-M-ZIF-67 particles exist in the absence of an external magnet, and they are rapidly attracted to the walls of the vial in a short time (about 20 s) with application of a magnet. The powder XRD patterns of Fe\({}_{3}\)O\({}_{4}\), Fe\({}_{3}\)O\({}_{4}\)-MWCNTs, and M-M-ZIF-67 are shown in Figure 3B. The diffraction patterns of M-M-ZIF-67 are in very close  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Pesticides** & **Retention Time (min)** & **MRM1 1 (\(m\)/z)** & **CE1 2 (eV)** & **MRM2 (\(m\)/z)** & **CE2 (eV)** \\ \hline \(\upmu\)-HCH 2  & 15.32 & 218.90 \(>\) 182.90 & 8 & 218.90 \(>\) 144.90 & 20 \\ \(\upmu\)-HCH 2  & 16.01 & 218.90 \(>\) 182.90 & 8 & 218.90 \(>\) 144.90 & 20 \\ \(\upmu\)-HCH 2  & 19.47 & 246.00 \(>\) 176.00 & 30 & 246.00 \(>\) 211.00 & 22 \\ \(\upmu\)-DDE  & 22 \\ \(\upmu\)-DDE  & 22 \\ \(\upmu\)-DDE  & 22 \\ \(\upmu\)-DDE  & 22 \\ \(\upmu\)-DDE  & 22 \\ \(\upmu\)-DDE  & 22 \\ \(\upmu\)-DDE  & 22 \\ \(\upmu\)-DDE  & 22 \\ \(\upmu\)-DDE  & 22 \\ \(\upmu\)-DDE  & 22 \\ \(\upmu\)-DDE  & 22 \\ \(\upmu\)-DDE [FOOTNOTE:2]Footnote 2: DRM means multiple reaction monitoring transitions; 2 DE means collision energy; 3 HCH means hexachlorocyclohexane; 2 DDD means 1,1-tichloro-2,2-bis(\(p\)-chlorophenyl)ethylene;agreement with the materials of Fe\({}_{3}\)O\({}_{4}\) and Fe\({}_{3}\)O\({}_{4}\)-MWCNTs. This indicates that Fe\({}_{3}\)O\({}_{4}\) is well retained in the Fe\({}_{3}\)O\({}_{4}\)-MWCNTs and M-M-ZIF-67. The FT-IR spectra are shown in Figure 3C. For M-M-ZIF-67, the adsorption band at 577 cm\({}^{-1}\) corresponds to the stretching vibrations of Fe-O, and the band at 1566 cm\({}^{-1}\) corresponds to in-plane bending of CH\({}_{2}\) of the MWCNTs. This suggests that M-M-ZIF-67 is composed of Fe\({}_{3}\)O\({}_{4}\) and MWCNTs. The bands in the region 673 to 1454 cm\({}^{-1}\) are attributed to complete ring stretching or bending vibration of 2-methylimidazole, and the main adsorption bands are preserved in the spectrum of the composite, which indicates that ZIF-67 is successfully synthesized on the surface of M-M-ZIF-67.
## 4 Detailed Design of the Conductive Center Pole Based on the Innovative Design Method
### Analysis of the Functional Requirements of the Conductive Center Pole  
Thermal insulation in the deeper parts of the ground has high requirements for the design of power and signal transmission mechanisms. The conductive function is integrated into the center pole, and the conductive module is required to be connected and screwed with the center pole to complete the conduction of electricity.  
The structure and performance design of the new conductive center pole needs to meet the following requirements:  
1. The conductive module can realize the alignment of the positive and negative electrodes after the center pole is rotated and docked.
2. The conductive module can realize the contact between the positive and negative electrodes after the center pole is rotated and connected.
3. Avoid entanglement of the wire during the screwing process of the center pole.
4. The whole operation process meets the requirements of insulation and 150 \({}^{\circ}\)C temperature resistance.  
A vector of the functional requirements is established, as follows:  
\[\begin{bmatrix}FR_{1}\\ FR_{2}\\ FR_{3}\\ FR_{4}\end{bmatrix}=\left\{\begin{array}{c}\begin{array}{c}\text{Align the electrodes at both ends}\\ \text{Electrode contact at both ends}\\ \text{The wire is not entangled}\\ \text{Insulation and high temperature resistance }(150\ \ ^{\circ}\text{C})\end{array}\right\}\end{array}\right\} \tag{2}\]
# Multi-Path Routing Algorithm Based on Deep Reinforcement Learning for SDN
## 4 Trust-Based Proximal Policy Optimization (TBPPO)  
This section introduces the TBPPO algorithm, which is a multi-objective, multi-path routing planning algorithm providing a secure multi-route scheme designed to provide low delay for real-time communication. The algorithm includes a preprocessing module based on DFS, a trust value calculation module using KL divergence, a Markov process transition mechanism, and a deep reinforcement learning decision module based on PPO. Figure 1 illustrates the overall workflow of the algorithm.  
In this algorithm, the data is divided and transmitted over multiple routes, thereby dispersing traffic across several paths to increase the effective bandwidth of the network, allowing multiple connections to work in parallel. If one path fails, the traffic can automatically switch to another, thereby enhancing network reliability. This method also takes into account security considerations. By dispersing traffic across multiple routes, it enhances network security, as it requires an attacker to compromise several paths at once, thereby raising the complexity and difficulty of network attacks. Our strategy accounts for real-time communication and security along each route, crafting a balanced approach that weighs various metrics including average delay, delay variations, KL trust value, and node diversity.  
Figure 1 depicts the structure of our algorithm. For each time interval, the control panel gathers the current network topology, DDoS attack logs, and node types from the data panel. After processing via the trust value module and the DFS module, these features are concatenated to form a state that enters the DRL module. Within this module, the critic network calculates Q-values while the actor network determines the action. This action is then executed on the data panel and the experience is stored in the replay buffer. Upon completion of an episode, experiences are extracted from the replay buffer in sample batches to update the critic network before proceeding to the next episode.
## 3 Methodology
### Definitions and Problems  
**Definition 1**.: _Ship Trajectory. A ship trajectory consists of a series of timestamped trajectory points, denoted as \(Traj=\{Po_{1},\cdots,Po_{i},\cdots,Po_{L}\}\), \(Traj\in\mathbb{R}^{N_{L}\times N_{D}}\), where \(N_{L}\) represents the number of trajectories, and \(N_{D}\) represents the feature dimensions. Each trajectory point \(Po_{i}=\{cos_{i},so_{gi},lon_{i},lat_{i},ns_{i},t_{i}\}\), \(i=1,\cdots,L\), \(Po_{i}\in\mathbb{R}^{N_{D}}\) represents the ship's attributes at each timestamp. Here, \(cos_{i}\), \(so_{gi}\), \(lon_{i}\), \(lat_{i}\), \(ns_{i}\), and \(t_{i}\), respectively, represent the course over ground, speed over ground, longitude, latitude, navigation states, and timestamp._  
**Definition 2**.: _Ship Trajectory Dataset. Let \(TD=\{D^{1},D^{2},D^{3},\cdots,D^{K}\}\), \(TD\in\mathbb{R}^{N_{K}\times N_{C}\times N_{D}}\), where \(N_{K}\) represents the number of ship trajectory prediction combinations. Each trajectory prediction combination is represented as \(D^{j}=\{X^{j},Y^{j},Z^{j}\}\), \(X^{j}\in\mathbb{R}^{N_{H}\times N_{D4}}\), \(Y^{j
# A Fairness-Aware Peer-to-Peer Decentralized Learning Framework with Heterogeneous Devices
## 6 Discussion
### Trustworthiness in Decentralized Learning  
In decentralized learning, all of the local clients aim to train their local model with a peer-to-peer manner. To increase the learning performance, some research efforts combine both decentralized learning and transfer learning technologies. For example, [PERSON] et al. [16] addressed the problem of decentralized learning in IoT systems. To overcome slow convergence and degraded learning performance, the proposed approach introduces a mutual knowledge transfer algorithm into a decentralized learning framework, where a set of IoT devices optimizes the learning model without a central server. Nonetheless, the lack of consideration for a trustworthy evaluation may result in potential security threats and further reduce accuracy, slow convergence, and the dissemination of unexpected backdoors. As a possible solution, we shall systematically study the techniques to ensure the security trust in the decentralized learning process so that the security risk of all of the components (e.g., model updates, local clients) can be modeled and assessed. By doing this, it enables each task aggregator to collect trust scores from each learning client.  
On the other hand, the evaluation of existing models remains an unsolved issue. We consider the future research works cataloged as two aspects. First, we shall methodically investigate the dissemination of malicious backdoors, which are delivered by some mature (well-trained) learning models. Second, we shall develop the defensive mechanisms to detect the potential backdoors. For instance, the trusted evaluation may record behaviors in both short-term and long-term aspects from each decentralized learning with mechanisms such as a blockchain-based consensus mechanism. Such information can significantly help each client prioritize the received information from other nodes so that the robustness of the decentralized learning framework can be improved.  
Similar to the trustworthy evaluation, we shall conduct further research efforts on designing an incentive scheme in federated (decentralized) learning systems. A proper incentive scheme reveals the relationship between the cost of the training process and the profit of sharing updates. The incentive model can help theoretical models to assess the different effects of node behaviors (both benign and malicious nodes) and further improve the robustness of the decentralized learning framework. Last but not least, we notice that the match mechanism of decentralized learning also remains an open issue. To address this problem, we should fully study the integration of the state-of-the-art networking communication and computing technologies and decentralized learning on an application level.
# Mechanical Properties of Cement Composites Using Modified Plastics by Gamma Irradiation
## 3 Experimental Results and Consideration
### SEM Analysis Results  
The result of SEM analysis, as shown in Figures 8-10, is that a surface of the aggregate itself changed depending on gamma ray irradiation dose, in which a surface change was found through SEM photography. In the case of PP aggregates, a surface change verified through SEM photography was found that as the irradiation dose increased, the surface properties changed more. PE aggregates also showed changes in their surface properties, although they are not as good as PP aggregates. It could be verified changed, but there was no significant change in the surface of ABS aggregate, and it seems that this surface modification was not enough to have an effect on the cement composite. Through this, it was possible to determine why ABS aggregates were not greatly affected by gamma irradiation. According to previous studies, PP and PE among plastic materials were decomposed and crosslinked, respectively, to modify the properties of plastic. However, it was confirmed that PET (polyethylene-terephthalate) or PC (poly-carbonate) was resistant to gamma irradiation effects [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21]. In the case of ABS, there is no case of gamma irradiation effect; thus, as a result of conducting the experiment, it is judged that PC or PE plastics are resistant to gamma irradiation effects.  
Figure 8: SEM images of polypropylene (PP) aggregate: (**a**) 0 kGy; (**b**) 25 kGy; (**c**) 50 kGy; (**d**) 75 kGy; (**e**) 100 kGy.  
to the gamma radiation dose, but at 100 kGy, the crystallinity was rather decreased. In conclusion, it was confirmed that the crystallinity of the plastic material was increased and the strength of the cement composite including the gamma irradiation was improved.  
Figure 11: Results of XRD analysis of PP aggregates according to gamma dose.  
Figure 12: Comparison of crystallinity of PP aggregate according to gamma irradiation dose.