esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Characterising SDN-Based IoT Architecture
### Explanation of Key Operations in SECOD
#### 4.3.3 Controlling Packet_In  
Another characteristic of the attack towards control plane is that most of Packet_In messages are Address Resolution Protocol (ARP). ARP is used to discover the MAC address associated with an IP address; it is essential to the data transmission. However, the Packet_In message generated by the user is usually non-ARP due to the existence of ARP cache in the user itself. In particular for Linux system, an ARP entry will not be erased immediately after a time period, it only updates the state (from REACHABLE to STALE) to wait for a further period. Once it is hit in the state of STALE, it changes back to REACHABLE, and an official ARP request is sent out 5 s later by default. Otherwise, it is removed after a long period without any hit [54]. In this case, most of the Packet_In messages already contain the MAC address of the destination. All they want is a flow entry in the OpenFlow switch. When the official ARP is generated after 5 s, as the flow entry is already created on the switch, there is no need to inquire the controller any more, which means normal Packet_In messages are generally non-ARP. Furthermore, the IP address on the user port could be a public address, which represents multiple private IP addresses. If a suspicious IP address is banned, all the normal users using this public address will lose connections to the network. Thus, filtering packets by both IP address and ARP is a better way to block DDoS attacks, while normal connections are still permitted.  
For periodical IoT data, the configuration of flow entry can be predefined on the controller depends on the period to save system resources. Request and reply between the switch and controller are no longer iterating, because the Time-To-Live (TTL) value of the flow entry will be refreshed before timeout. For random IoT data, reducing TTL value could be a better choice due to its unpredictability.
## 4 Full-Scale Production Verifications
### Controlling Cooling in Full-Scale Experiments  
Two different cooling rates were tested on a single batch of slag with a basicity of 2.8 at Ovako Hofors. One part of the slag was air cooled and the other was cooled by water spraying. The slag was crushed to less than 4 mm before autoclave treatment and the following EN 12457-2 leaching test.  
The air-cooled slag showed chromium leaching of 1.4 mg/kg after autoclave treatment. When water cooling was applied, the chromium leaching decreased to 0.29 mg/kg after autoclave treatment, as seen in Figure 10. Since both the samples were from the same slag ladle, the only difference was the cooling procedure. The result of this full-scale trial reflected the results from the laboratory-scale experiments: faster cooling led to less leaching of chromium. The basicity of the samples was at 2.8, proving a decrease in chromium leaching could be achieved in slag outside of the optimum basicity range by increasing the cooling rate. Water cooling was effective to reduce the chromium leaching but comes with some risk: if the water does not evaporate completely before the next batch is tipped, the remaining water can be trapped in the slag and gasified, resulting in a gas explosion. Other less efficient but safer methods exist for cooling slag faster, for example, spreading the slag over a larger surface and refraining from dumping slag from different batches on top of each other.
# A Protocol Layer Trust-Based Intrusion Detection Scheme for Wireless Sensor Networks
## 3 System Model
### Calculation of Network Layer Trust
The relative deviation of hop count of the monitored node j can be calculated by:  
\[\text{RD}_{\text{hop\_count}}(\text{t})=\frac{\overline{\text{hop\_count}}- \text{hop\_count}_{\text{j}}}{\text{hop\_count}}, \tag{16}\]  
where \(\text{hop\_count}_{\text{j}}\) denotes the value of hop count from node j to its CH. The hop count trust is described as:  
\[\text{T}_{\text{ij}}^{\text{hop\_count}}(\text{t})=\left\{ \begin{array}{ll} 1-\text{RD}_{\text{hop\_count}}(\text{t}),&\text{if $\text{hop\_count}_{\text{j}}<\overline{\text{hop\_count}}$}\\ 1,&\text{else}\end{array} \right. \tag{17}\]  
This means if \(\text{hop\_count}_{\text{j}}\) is less than the average hop count, the more deviation there is, and the lower the trust value will be. In order to obtain the packet forward trust, the monitoring node i can obtain the packet forwarding rate of the monitored node j by:  
\[\text{T}_{\text{ij}}^{\text{PFR}}(\text{t})=\frac{\text{P}_{\text{j}\to \text{k}}(\text{t})}{\text{P}_{\text{i}\to\text{j}\to\text{k}}(\text{t})}, \tag{18}\]  
where \(\text{P}_{\text{i}\to\text{j}\to\text{k}}(\text{t})\) denotes the number of packets that node i wants to transmit to node k with the help of node j and \(\text{P}_{\text{j}\to\text{k}}(\text{t})\) indicates the number of packets that node j has received from node i and forwarded to node k. If node j does not forward packets correctly, its trust degree will decrease. In order to decide whether or not a node is considered compromised, it is necessary to select a system trust threshold, \(\text{Th}_{\text{trust}}\). In a cluster, all of the monitoring nodes will send their trust evaluation results with respect to their neighboring nodes to their CH. The CH then computes the trust value of node j according to:  
\[\text{T}_{\text{j}}(\text{t})=\frac{1}{n}\sum_{\text{i}=1}^{n}\text{T}_{\text {ij}}(\text{t}), \tag{19}\]  
where n denotes the number of neighboring nodes of node j and makes decision by comparing the trust value with \(\text{Th}_{\text{trust}}\). If \(\text{T}_{\text{j}}(\text{t})\) is less than \(\text{Th}_{\text{trust}}\), then node j is regarded as compromised. The method of BS-to-CH trust evaluation is similar to that of CH-to-SN trust evaluation.
## 4 Results
### Algorithms
#### 4.2.4 Advantage Actor-Critic  
The Advantage Actor-Critic (A2C) is a policy-based algorithm and, much like DDQN, uses two neural networks: the actor network and the critic network. The actor network takes the current state as input and outputs a probability distribution over possible actions. It is trained using policy gradient methods, such as the advantage function, to maximize the expected cumulative reward. By exploring the environment and receiving feedback, the actor network adjusts its parameters to improve the policy over time. The critic network in A2C learns the value function, which estimates the expected cumulative reward given a state or state-action pair. The critic network provides a measure of how good or valuable a particular state or state-action pair is. It helps the actor network by providing feedback on the quality of its actions. The critic network is trained using temporal difference learning, where it updates its parameters to minimize the difference between its estimated values and the actual observed rewards. The actor and critic networks are trained simultaneously to improve the learning efficiency and stability.  
The A2C objective function can be defined as:  
\[\begin{split}\mathrm{R}_{t}^{\prime}=\sum\limits_{i=0}^{\mathrm{k }-1}\gamma^{i}\mathrm{r}_{i+i}+\gamma^{k}\mathrm{V}_{\Theta_{v}}(\mathrm{s}_{ t+k}),\\ \mathrm{A}_{\Theta_{v}}(\mathrm{s}_{t},\mathrm{a}_{t})=\mathrm{R }_{t}^{\prime}-\mathrm{V}_{\Theta_{v}}(\mathrm{s}_{t}),\\ \mathrm{J}(\theta)=\mathbb{E}_{\pi}[\sum\limits_{t=0}^{\infty} \mathrm{A}_{\Theta_{v}}(\mathrm{s}_{t},\mathrm{a}_{t})\mathrm{log}\pi_{\Theta} (\mathrm{a}_{t}|\mathrm{s}_{t})+\beta\mathrm{H}_{\Theta}(\pi(\mathrm{s}_{t}))]. \end{split} \tag{2}\]  
\(\theta_{v}\) are the parameters of the value network and \(\mathrm{H}_{\Theta}(\pi(\mathrm{s}_{t}))\) is an entropy term used to encourage exploration during the training process [12].  
The A2C and the DQN algorithms were compared in [10] by using ViZDoom, with results showing that while DQN is better at exploiting the enemy's weaknesses, A2C is better at exploring different play strategies.
# Exopolysaccharides from Cyanobacteria: Strategies for Bioprocess Development
## 3 Cyanobacterial Bioprocess (Cyano-EPS) Development
### Production and Optimization
#### 3.2.2 Process Conditions
flagelliforme_[83]. Meta-bibliographic research was conducted to infer which were the cultivation factors that most influence the EPS and glycogen production in _Arthropira platensis_. The results showed a considerable heterogeneity of authors' results explained by different operating conditions and extraction/purification methodologies applied. The authors found light intensity as the most preponderant factor in their desk-research and experimented with different light intensities (100, 400, 800, and 1200 \(\mu\)E). The ratio EPS/glycogen was found higher with lower light intensities. In addition, EPS monosaccharides composition was significantly different among different light intensities applied [86]. For those cases, where there was no correlation between growth and RPS production, a two-stage cultivation might be the most appropriate way to attain high biomass and then obtain a higher amount of the RPS at a second stage. This was already suggested in [51]. Playing with intensities of multiple wavelengths (red, blue, and green) and nitrogen source a strategy was defined and an increase in 66% and 217.3% in _N. flagelliforme_ growth an RPS production was attained, respectively. The growth phase was set with white light for nine days while the EPS phase was followed by nine days of mixed wavelength (red/blue/green = 12:5:5) using urea as a nitrogen source [61]. The use of light as a way to control the production of cyanobacterial products has already been reported [87; 88]. Moreover, lower light intensity was also utilized as a strategy to increase EPS production [61; 89]. _Arthospirasp. was grown in two-stage cultivation wherein biomass production (Zarrouk medium, 30 \({}^{\circ}\)C, 80 \(\upmu\)E) was favored at first. Secondly, the combined effect of light intensity and salinity (NaCl) was found antagonistic on EPS enhancement. Optimal conditions were found (10 \(\upmu\)E and 39 g/L of NaCl) allowing EPS production to have a 1.67-fold increase when compared to optima growth conditions [89]. Applying outdoor cultivation systems can potentially reduce production costs by reducing light and temperature control [62; 90; 91; 92]. Relevance was found in an outdoor pilot scale cultivation of _Spirulina_ sp. LEB-18 on 250 L of open raceways in the summer of Salvador, Bahia, Brazil. The crude RPS demonstrated pseudoplastic behavior and high thermal stability. At day 30, the biomass reached a titer of 1.01 g/L while the crude EPS production was shown to attain 9.5 g/L, wherein the highest productivity happened on day 10 (0.6 g/L/day); however, it is known that outdoor conditions can affect productivities due to variable climate conditions and geographic location [93].
## 2 Results and Discussion
### Application in Biological Samples  
#### 2.4.1 Patient Characteristics  
Altogether, 14 paired blood samples were collected from 11 VAP patients receiving piperacillin, and another 13 paired blood samples were collected from 7 VAP patients receiving impenem. The detailed characteristics of patients recruited for the determination of both antibiotics in plasma is given in Table 6.  
Six paired BAL samples (as described in Section 3.5.) were collected from six VAP patients receiving piperacillin and another five paired BAL samples were collected from three VAP patients receiving impenem. The detailed characteristics of patients recruited for the determination of both antibiotics in bronchoalveolar lavage is given in Table 7.
# Multicriteria Route Planning for In-Operation Mass Transit under Urban Data
## 3 Preliminary
### PF Inference Module  
Given trajectories and stations of a certain arbitrary route, the goal of this module is to infer the PF (which refers to one of the criteria in our targeted route planning problem) for an arbitrary route given by the routing process in Section 4.  
To be more precise, with the grid-based storage for heterogeneous urban data [85], the feature set for each existing route, including points of interest (POIs), entropy of POIs representing the diversity of urban functionalities [89], quantified relationships with existing mass-transit network, route network structure, and population structure, are extracted and integrated as training data. Accordingly, the corresponding ticket data is solicited and set as the training target, which refers to the PF under a certain timestamp. We employ DNN for regression as the inference model with various features treated as the inputs, and the PF values regarded as predictive labels [9]. Meanwhile, a pretrained model is adopted to infer the PF value of a given arbitrary route, which is generated by a route-planning algorithm, once this arbitrary route is deployed in the existing in-operation system.
# Temporally Programmable Hybrid MOPA Laser with Arbitrary Pulse Shape and Frequency Doubling
## 3 MOPA Laser with Arbitrary Pulse Shape  
### Fiber Amplifier Stage  
Due to the narrow spectral linewidth of the semiconductor laser, stimulated Brillouin scattering (SBS) could happen when the pulse energy increases. We carefully increased the pump power of the 2 nd fiber amplifier and observed at the output end. When the average output power reached 22 mW at a pulse repetition rate of 100 kHz with a pulse duration of 168 ns, a strong backward SBS was generated. As shown in Figure 2b, a depression appeared in the middle of the shape, and the pulse shape was also unstable (see the uploaded video from Supplementary Materials). By introducing the optical spectrum analyzer (Agilent 86142B, Palo Alto, CA, USA), the frequency shift caused by SBS was evidently observed as shown in Figure 2a. The spectrum was also unstable (see the uploaded video from Supplementary Materials).  
Compared with the experimental results in [18], the SBS effect was not observed even when the output power was larger than 150 mW. The main reason is that the spectrum of directly modulated DFB laser is chirped broadened, causing a higher SBS threshold. Therefore, it is important to carefully increase the pump power during amplification in order to protect the optical components from the SBS giant pulse.  
Figure 1: (**a**) Schematic of the fiber stage. OI: optical isolator; WDM: wavelength division multiplex; YDF: Ytterbium doped fiber; BPF: band pass filter (5 nm). (**b**) Photo of setup of solid-state laser amplifiers.
## 4 System Model and Description of the Proposed Protocols
### Transmission and Receiving Energy Calculations in BF-SPR-Three
#### 4.2.6 Binary Tree Generation in BF-SPR-Three  
One of the following two scenarios must be true during packets transmission:  
1. A sensor node is used for linear transmission.
2. There must be a cross-node for generating a binary tree.  
If the node is a simple sensor node, it will unicast the signal. On the contrary, binary tree formation starts from the cross-node for multicasting (using IP broadcasting technology), in which every parent node has almost two child nodes.
# Design and Stability Analysis of Six-Degree-of-Freedom Hydro-Pneumatic Spring Wheel-Leg
## 3 Theoretical Analysis of Hydro-Pneumatic Spring
### Mathematical Derivation of Hydro-Pneumatic Spring
#### 3.2.2 Mathematical Model of Damping Valve  
During the operation of the hydro-pneumatic spring, the hydraulic fluid passes through the damping valve system. Assuming that the pressure of the fluid after passing through the damping valve system is \(P_{2}\), during the compression stroke, the fluid flows through the damping holes and the check valve. According to the thin-walled orifice theory [26], we have  
\[P_{2}-P_{1}=\frac{1}{2}\rho\bigg{[}\frac{Q}{C_{d}\cdot(S_{c}+S_{d})}\bigg{]}^{ 2}sign(\dot{x}). \tag{3}\]  
During the rebound stroke, the fluid flows through the constant-flow damping holes without passing through the check damping holes, which is expressed as  
\[P_{2}-P_{1}=\frac{1}{2}\rho\bigg{(}\frac{Q}{C_{d}\cdot S_{c}}\bigg{)}^{2}sign( \dot{x}), \tag{4}\]  
where \(P_{1}\) represents the pressure of the fluid in the piston cylinder, \(P_{2}\) represents the pressure of the fluid after passing through the damping holes, \(\rho\) represents the density of the fluid, \(C_{d}\) is the flow coefficient of the constant-flow damping holes and check damping holes, \(Q\) represents the volume flow rate of the fluid, \(S_{c}\) represents the equivalent area of the constant-flow damping holes, \(S_{d}\) represents the equivalent area of the check damping holes, \(S_{p}\) represents the equivalent area of the piston, and \(\dot{x}\) represents the relative velocity between the piston and the hydraulic cylinder.
# Matched Filtering for MIMO Coherent Optical Communications with Mode-Dependent Loss Channels
## 5 Numerical Simulation of Linear MIMO FSE Receiver for MDL-Impaired Optical Channel  
In this section, we assess the performance of the ideal MMSE linear receiver when MDL is present. Specifically, we study the SNDR degradation at the receiver output w.r.t. the case when the MDL is negligible. To carry out this study, we will use a FSE-based receiver, as shown in Figure 5, which is the most common implementation of the ideal linear filter in discrete-time systems (see \(\underline{O}(\omega)\)in Figure 1). Note that this scheme is only valid for integer oversampling rates \(r_{\omega 0}\).  
The FSE oversampling rate \(r_{\omega 0}\) has been set to two [1; 12] and the discrete-time equalizer, with a \(\underline{W}_{FSE}(\Omega)\) response, has been designed with a number of taps \(N_{taps}\) large enough so that any further increase does not lead to a significantly better SNDR at the receiver output. The decimated output of \(\underline{W}_{FSE}(\Omega)\), by a \(r_{\omega 0}\) factor, are the estimated symbol \(\underline{s}[n]\).  
We define the receiver performance metric for each mode \(i\), \(L(i)\), as the difference in dB between the output SNR of an ISI and crosstalk-free system with \(D\) parallel AWGN channels (see Figure 3(b)), and the output SNDR of the FSE-based receiver, denoted as \(\underline{SNDR}_{out}\).
# Information Fusion for Multi-Source Material Data: Progress and Challenges
## 2 Background
### Material Data Collection and Data Sharing  
Materials can be roughly divided into composite materials and simple materials. There are many ways to describe materials, such as using a molecular formula [5; 6]. For composite materials, simple representations like a molecular formula are not enough, and new ways need to be devised, e.g., using the combination ratio of various materials in a composite material. Further, as the conditions required for the various chemical reactions of materials are critical, how to describe the composition, ingredient, manufacturing process and properties of materials is very important. The highly specialized data requires manual and standardized definitions by experts in relevant fields, which is time consuming and costly.
# APT Attack Detection Scheme Based on CK Sketch and DNS Traffic
## 5 Conclusions and Future Work  
In this paper, we present a novel APT attack detection approach based on the combination of the CK sketch structure and DNS traffic analysis. Our work allows security personnel to quickly identify and respond to potential APT attacks based on the analysis of DNS traffic. The proposed method leverages the unique characteristics of DNS traffic to identify potential APT attacks and utilizes the CK sketch structure to efficiently compress and summarize the traffic statistics. The experimental results demonstrate the effectiveness of the proposed approach in terms of both processing time and detection accuracy. Specifically, when applied to the ExtraTrees model, the processing time was reduced by approximately 50 times to 0.0638 s while maintaining a high accuracy of 0.97920. This reduction in processing time is expected to be achievable with other machine learning models as well.  
It is important to note that the scope of our current work is limited to the communication control phase of APT attacks and does not take into account the characteristics of other phases. Additionally, the scheme may not provide significant time savings in cases where DNS traffic exhibits a high degree of variability in terms of domain names.  
In future work, our focus will be on expanding the scope of APT attack characterization to encompass not only DNS traffic but also TCP and HTTP traffic. This will provide a more comprehensive understanding of APT attacks and allow for the selection of different attributes as keys in the sketch structure, providing greater adaptability to a wider range of APT attack scenarios. Additionally, we aim to gather more information about the suspect domain names and hosts involved in APT attacks for improved security analysis.  
Conceptualization, D.X.; methodology, D.X.; software, D.X.; validation, D.X., Y.C. and L.Z.; formal analysis, D.X. and B.W.; data curation, L.Z.; writing-original draft preparation, D.X. and B.W.; writing-review and editing, D.X., B.W., and Y.C.; visualization, D.X.; supervision, Y.C.; project administration, Y.C.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Fundamental Research Funds for the Central Universities under Grant No.328202273 and National Key Research and Development Program of China under Grant 2018 YFB1004100.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scheme** & **Training Duration/(s)** \\ \hline Sketch & 0.0647 \\ Non-sketch & 0.0646 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Limitation contrast experiment.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Scheme** & **Accuracy** & **Recall** & **Training Duration/(s)** \\ \hline Scheme of this paper & 0.97981 & 0.97981 & 0.0624 \\ [PERSON] & 0.96711 & 0.96787 & 3.1746 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison with recent studies.
## 5 Proposed Channel Model
### Antenna Loss  
As per ITU-R P.1336-1 [41], the relative gain G(\(\theta\)) of the antenna with respect to the isotropic antenna (dBi) can be given as  
\[G(\theta)=\begin{array}{cc}G_{0}-12\big{(}\frac{\theta}{\theta_{3}}\big{)}^ {2}&0\leq\theta<\theta_{3}\\ G_{0}-12-\ 10 log\frac{\theta}{\theta_{3}}&\theta_{3}\leq\theta<90\end{array} \tag{12}\]  
where \(\theta_{3}=107.6\times 10^{-0.1\times\text{Go}}\); G\({}_{0}\) is the maximum value of gain in or near the horizontal plane in dBi; \(\theta\) is the absolute elevation angles relative to the maximum gain angle in degrees; \(\theta_{3}\) is the 3 dB beam width at the vertical plane in degrees. The path loss due to transmitting and receiving antennas is calculated by  
\[L_{A}=-2*G(\theta) \tag{13}\]