esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things
## 5 Implementation and Results
### Attack Model  
We simulate and evaluate the performance of our proposed detection system against various attacks on IoT networks such as the sinkhole attack, distributed denial-of-service (DDoS) attack, blackhole attack, opportunistic service attack and wormhole attack.  
* Blackhole Attack: In a blackhole attack, the malicious device falsely advertises shortest route to destination and then silently drops all packets on its path creating a blackhole in the network.
* Opportunistic Service Attack: In an opportunistic service attack, the malicious device increases its trust value by providing highly dependable services at first and then later resorts to providing inferior service for its own profit.
* Distributed Denial-of-Service (DDoS) Attack: In a DDoS attack, multiple compromised IoT devices attack a target server or other network resources resulting in denial of service for users of the targeted resource.
* Sinkhole Attack: In a sinkhole attack, the malicious node may announce beneficial route or falsified path to attract all nodes to redirect their packets through it, acting as a sink.
* Wormhole Attack: In a wormhole tunnel attack, pair of attacker devices collude with each other through a virtual private connection. The network packets received by the victim device is first forwarded through the wormhole, and replayed later, resulting in non-optimized routes.
# Link Pruning for Community Detection in Social Networks
## 4 Experiments
### Datasets  
#### 4.1.1 Synthetic Networks  
To demonstrate the effect of our proposed algorithm, we investigated the results on synthetic networks generated by the stochastic block models. We analyzed the networks that have 1000 nodes with the average degree fixed to 20, the number of communities are 4, and varying mixing parameters from 0.1 to 0.7.
# Adaptive and Blind Audio Watermarking Algorithm Based on Chaotic Encryption in Hybrid Domain
## 1 Introduction
Robustness indicates that the algorithm can extract the watermark accurately when the watermarked audio has suffered from external attacks. Capacity means the capability of the watermarked audio to accommodate the necessary information. Security refers to the fact that the user cannot obtain the watermark without the correct key. The watermark algorithm used for copyright protection must have good imperceptibility so as to prevent the audio media from losing its usage value after being embedded in watermarks. In addition, the algorithm must have strong robustness, because the audio media may suffer various attacks in the process of transmission, such as Gaussian noise, format conversion, resampling and other attacks which may cause the watermark to be lost. Security can be achieved through encryption in the process of watermark pretreatment. Capacity is another important index. Under the premise of ensuring the imperceptibility and robustness, the larger the capacity, the more useful information the watermark contains. Most audio watermarking schemes have disadvantages such as poor robustness, low capacity and weak audio quality. Therefore, the audio watermarking algorithm needs further research to improve its overall performance. The purpose of this study is to combine all the useful features of DWT and DCT to design a practical audio watermarking algorithm in order to improve robustness, imperceptibility, security and capacity. The original audio is divided into multiple audio segments, and then each segment is decomposed by DWT to get the detail coefficients which are divided into two packets for carrying a 1 bit watermark. It is helpful to improve the imperceptibility and robustness of the algorithm by taking advantage of the overall average amplitude of each audio segment to adjust the embedding depth. The experimental results show the excellent performance of this algorithm, including large capacity, high imperceptibility, and strong robustness which can withstand 10 common attacks. Since the watermark has been encrypted before it is embedded, only the users who hold the secret key can obtain the watermark accurately, so the algorithm has excellent security performance. This paper is organized as follows: Section 2 describes the proposed watermarking algorithm which consists of four subjects, including the pretreatment to the watermark picture, the principle of watermark embedding, the principle of watermark extraction, and the design of the adaptive embedding depth. The detailed implementation steps of this proposed algorithm are described in Section 3. The experimental results are analyzed and compared with that of some relevant algorithms in recent years in Section 4. The conclusions are presented in Section 5.
## 8 IoMT Security Model
### Blockchain Models
In addition, the simulation of BAKMP-IoMT is performed to demonstrate its impact on performance parameters. [PERSON] et al. [78] proposed a lightweight framework for authentication and permission to complement current blockchain-based IoT networks in the healthcare sector. The authentication method, which is bound by conditional joint probability, employs random numbers. This enables the system to create a secure link between IoT devices for data collection. Extensive simulations with the AVISPA tool and the Cooja simulator are used to assess and evaluate the suggested model. The technology improves access control while also providing excellent mutual authentication. When weighed against each other, it also minimizes transmission costs and computing overheads, as seen in the testing findings. The suggested framework, on the other hand, is not assessed on hardware in a realistic context, making it less efficient (or its practicality is still questionable). [PERSON] et al. [79] proposed a blockchain-based privacy-preservation scheme (Healthchain) for large-scale health data. This scheme encrypts health data using fine-grained access control. In particular, the user transaction is used for key management that can allow users to add or revoke authorized physicians. In addition, it introduces two blockchains to avoid medical disputes, such as physician diagnoses. The IoT data cannot be modified or deleted once stored in the blockchain. The results of the experiment and security assessment confirmed that the system can meet the expected security requirements and can be applied to mobile health systems. However, insider attacks are neglected by the system.
## 4 Implementation-Process Strategies for Cultured Meat Products
### 3D Printing Process of Cultured Meat
#### 4.2.5 Mimicking the In-Vivo Myogenesis Environment  
The immobile adherent cells--which are embedded into tissue--are the building blocks of muscle. To imitate the 3D structure and the natural environment, a scaffold is necessary. Scaffolds have specific attributes to promote cell adhesion, tissue development, and proliferation, and to reduce carbon footprint and cost.  
Scaffolds are the base used by cells to grow, adhere, and achieve tissue maturity by imitating native 3D tissue and producing structured meat products like meat cuts and steaks. For CUME production, scaffolds should have different properties, including a large surface area. They should also be biologically active, flexible (contractible), and edible, and have no allergic and toxic responses after dissociation or digestion. Lastly, they must have maximum growth medium porosity (diffusion) in order to support tissue development [56; 58].  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Bioreactors** & **Phase of Cultivation** & **Cell Density** & **Specifications/Advantages** & **References** \\ \hline Continuous- & & & - & Retains high O\({}_{2}\) levels \\ Cintronous- & & & - & Prevents bubbling \\ Stirred Tank & Proliferation & \(10^{5}\)–\(10^{6}\) cells/mL & - & Good mixing and mass transfer \\ Bioreactors & & & - & Microcarriers can increase surface area without \\  & & & & needing a larger vessel \\ \hline Rocking Platform & & & - & Can be used as single use bioreactors with \\  & & & & disposable bags (with food-safe polymers), \\  & & & & which are easy to sterilize \\  & & & - & Lower contamination risks, lower energy and \\  & & & & sensor costs, and shorter downtimes \\  & & & & between batches \\ \hline Single Use-Stirred Tank bioreactors & & & - & Supports cell proliferation in cultured \\  & & & - & meat production \\  & & & - & Provides oxygen for cell growth \\  & & & - & Can be used as single-use bioreactors with \\  & & & & disposable bags (with food-safe polymers), \\  & & & & which are easy to sterilize and have lower \\  & & & & contamination risks, lower energy and sensor \\  & & & & costs, and shorter downtimes between batches \\ \hline Rotating Wall & & & - & Production of skeletal muscle tissues \\  & & & - & Low shear stress \\  & & & - & Low contamination \\  & & & - & Saves energy, sterilization costs and time \\ \hline Air-Lift/ Bubble & & & - & Low shear stress \\  & & & - & Low contamination \\  & & & - & Low heat generation \\  & & & - & good homogeneity \\  & & & - & High cell density \\ \hline \end{tabular}
\end{table}
Table 2: Different types of bioreactors used to produce cultured meat.
## 4 Tertiary Water and Wastewater Pollutants
### Pathogens in Drinking Water  
The three groups of pathogens that may be found in drinking water are: bacteria, viruses, and protozoa and helminths.  
When testing for bacterial contamination, the most basic test used is the test for total coliform bacteria [57]. Coliform bacteria are found in water, soil and the digestive tracts of animals [58]. Faecal coliform bacteria are a subset of the total coliform bacteria group. These are found specifically in the gut and faeces of warm-blooded animals. Within this group of faecal coliforms, the major species is _Escherichia coli_. Since _E. coli_ does not reproduce inthe environment, its presence is indicative of faecal contamination. _E. coli_ is also easy and inexpensive to detect, making it historically one of the most common indicator organisms for pathogens [59]. It is also considered to be one of the best tests for the presence of pathogens [57]. These tests are important because while most strains of _E. coli_ are harmless, some strains could result in death [60].  
The second group (viruses) contains a multitude of pathogens. Comparing this list of viruses [61] with a list of the top ten pathogens responsible for outbreaks in public water systems [62] yielded the following result: the third most commonly reported cause for outbreaks, and the first virus on the list, was norovirus. This virus is highly contagious and causes vomiting and diarrhoea [63].  
The list mentioned above also revealed that _Giardia_ (from the protozoa and helminths group) is the pathogen responsible for the most disease outbreaks in public water systems. This is likely due to the ineffectiveness of chlorination against protozoans.
## 3 Results
### Test-Retest Reliability Results  
Results of test-retest reliability (intraclass correlation coefficient) in different groups are shown in Table 26. We use test-retest reliability to measure four assessment tests (D-CAT, SAT, TMT-A, and TMT-B) consistency of pre-test scores and post-test scores in different groups. There were eight groups of participants with different game settings in the serious game (Table 26).  
D-CAT results showed a high correlation coefficient in groups 1-6 and 9 (good reliability, \(\geq\)0.8 and <1.0); a medium correlation coefficient in group 7 (acceptable reliability, \(\geq\)0.6 and <0.8). The SAT results showed a high correlation in groups 1-3, 5, 7, 8 (good reliability, \(\geq\)0.8 and <1.0); a medium correlation coefficient in group 6 (acceptable reliability, \(\geq\)0.6 and <0.8); and a low correlation coefficient in group 4 (poor reliability, \(\geq\)0.4 and <0.6). The TMT-A results showed a high correlation in groups 2 and 5-7 (good reliability, \(\geq\)0.8 and <1.0); a medium correlation coefficient in groups 1, 3, and 4 (acceptable reliability, \(\geq\)0.6 and <0.8); and a low correlation coefficient in group 8 (poor reliability, \(\geq\)0.4 and <0.6). The TMT-B results showed a high correlation in groups 3, 5, 6, and 8 (good reliability, \(\geq\)0.8 and <1.0) and a medium correlation coefficient in groups 1, 2, 4, and 7 (acceptable reliability, \(\geq\)0.6 and <0.8).  
In conclusion, the total number of results was 32. Twenty-one results displayed good reliability (\(\geq\)0.8 and <1.0); nine results displayed acceptable reliability (\(\geq\)0.6 and <0.8), and two results showed poor reliability (\(\geq\)0.4 and <0.6).
# An IoT-Based Framework for Smart Water Supply Systems Management
## 4 Background
### Water Supply Systems  
The term water supply system denotes a set of interconnected hydraulic components to collect water from rivers and lakes, to apply water treatment procedures, and to distribute water to the final consumers [1]. A conventional water supply system includes the following elements:  
* Raw water source: water found in the environment.
* Raw water pumps: a set of equipment and installations used to collect water from rivers and lakes.
* Raw water transmissions: transportation raw water to the water treatment facilities.
* Water treatment station: infrastructure for water purification installed close to the final delivery points, to reduce pumping costs and to reduce the chance of new contamination after treatment.
* Water storage tank: a water tank installed on top of a structure, at a height sufficient to pressurize the water distribution system.
* Water distribution system: a set of interconnected pipes and valves used to deliver water to the eventual consumers.  
Figure 5 shows the water life cycle of a water supply system.
# Research on Map-SLAM Fusion Localization Algorithm for Unmanned Vehicle
###### Abstract  
Vision-based localization techniques and detection technologies are key algorithms for the localization and navigation of unmanned vehicles. Especially in scenarios where GPS signals are missing, Simultaneous Localization and Mapping (SLAM) techniques that rely on vision, inertial navigation system (INS) and other sensors have important applications. Among them, vision combined with the IMU SLAM system has the advantage of realistic scale, which is lacking in monocular vision and computational power compared to multi-visual vision, so it is suitable for application in an unmanned vehicle system. In this paper, we propose a fusion localization algorithm that combines a visual-inertial SLAM system and map road information, processing road information in a map under structured roads, and detecting lane lines and locating its local position by a monocular camera, applying a strategy of position prediction and update for map-SLAM fusion localization. It solves the problem of accumulating errors in a pure SLAM system without loopback and provides accurate global-local positioning results for unmanned vehicle positioning and navigation.  
unmanned vehicle; SLAM; visual INS fusion; map localization +
Footnote †: journal: Applied  
and IMU integration lead to increasing SLAM localization errors, while the single movement of an unmanned vehicle rarely passes through repeated positions and cannot be corrected by traditional loopback. We adopt a maximum a posteriori probability approach to represent its own position through a probability distribution; use the localization result output of SLAM as the position update incentive; use the map midway width, lane angle and road position information as a priori; and adopt a prediction-update model to calculate the probability distribution of vehicle position through the recognized lane line perception information. It is worth noting that, different from the general map trajectory mapping [18] which compares a certain segment of motion results with map information, the semantic SLAM [19; 20] method increases the positioning accuracy by adding the inter-frame connection of semantic information. The algorithm flow is shown in Figure 1.
## 2 Signal Model  
### The Geometry of Distributed SAR  
The geometry of the distributed SAR is shown in Figure 1. O-xyz is the Cartesian coordinate system. The \(y\) axis represents the direction of the track, the \(x\) axis represents the direction perpendicular to the track in the horizontal plane, and the \(z\) axis is determined by the right-hand rule. The antennas \(a_{m},m=0,1,\ldots M-1\) are distributed on the platform, where the reference channel \(a_{0}\) is the transmitting channel, and all channels receive the signal simultaneously. The height and velocity of the flight platform are \(H\) and \(v_{\text{p}}\), respectively. The red dashed line indicates the ideal trajectory of the reference channel and the blue curve indicates the actual motion trajectory of the reference channel. The distributed SAR system operates in the side-looking mode. \(\varphi\) denotes the incidence angle. When the slow time \(\eta=0\), the equivalent phase center (EPC) of \(a_{0}\) is denoted as \((0,0,H)\) and the EPC of \(a_{m}\) is \((x_{m},y_{m},H+z_{m})\). \(a_{m}^{\prime}\) is the projection of \(a_{m}\) to the ideal trajectory of \(a_{0}\) and the coordinate is \((0,y_{m},H)\). The line between \(a_{m}^{\prime}\) and \(a_{m}\) is the CTB between the \(m\)th receiver channel and the reference channel. The solid red line in Figure 1 is the difference in slant range from the target \(P\) to \(a_{m}\) and \(a_{m}^{\prime}\). Before signal reconstruction, the signal error between \(a_{m}^{\prime}\) and \(a_{m}\) needs to be compensated.
# Dispatching Strategy for Low-Carbon Flexible Operation of Park-Level Integrated Energy System
## 3 Modeling of PIES
### Cce  
Carbon capture and storage (\(CCS\)) has been a popular low-carbon technology. \(CCS\) separates and compresses the CO\({}_{2}\) component from the gas produced by the emission source, before transporting it to a proper storage site. CCS can be divided into pre-combustion capture, post-combustion capture, and oxygen-enriched combustion [27]. According to [28], post-combustion capture only requires placement of the capture equipment downstream of the gas emission without altering the original system operation process. With simple principles, convenient cooperation with the unit, and mature technology, post-combustion capture has been widely used in practice. The power consumption of carbon capture comes from two sources, namely fixed power consumption and operating power consumption. The fixed power consumption is independent of the operating state of the equipment and can be considered as a constant. The operating power consumption is due to CCE performing solvent regeneration and compression in the process of capturing CO\({}_{2}\), which is proportional to the value of the treated CO\({}_{2}\)[29]; yet, the amount of CO\({}_{2}\) treated does not exceed the total CO\({}_{2}\) emissions [30]. The above process is shown in Equation (11):  
\[\left\{\begin{array}{l}P_{e,t}^{ccs}=P_{e,t}^{ccs,GD}+P_{e,t}^{ccs,YX}\\ P_{e,t}^{ccs,YX}=P_{e}^{ccs}Q_{t}^{LX,CO_{2}}\\ 0\leq Q_{t}^{LX,CO_{2}}\leq Q_{t}^{CO_{2}}\\ Q_{t}^{LJ,CO_{2}}=Q_{t}^{LX,CO_{2}}\mu^{CCS}\end{array}\right. \tag{11}\]  
where in period \(t\), \(P_{e,t}^{ccs}\) is the total electric power consumption in CCE, consisting of fixed power consumption \(P_{e,t}^{ccs,GD}\) and operation power consumption \(P_{e,t}^{ccs,YX}\); \(Q_{t}^{LX,CO_{2}}\) is the ideal value of captured CO\({}_{2}\) at CCE; \(\zeta^{ccs}\) is the power consumption factor for ideal capture; \(Q_{t}^{CO_{2}}\) is the total carbon emission in the system; \(Q_{t}^{SJ,CO_{2}}\) is the final captured CO\({}_{2}\) at CCE; and \(\mu^{CCS}\) is the capture rate of CCE.
## 3 Model
### Multi-View Attention Network
#### 3.2.1 Context Matching Module  
Generally, the semantic intent of a sentence not only relies on particular words that implicitly point to the topic of the sentence but also tends to be determined by the context of the entire sequence. Therefore, we build the Context Matching module that adaptively integrates the question and its relevant history at sentence level. Contextual representation is constructed by concatenating the last hidden states of the forward and backward LSTMs  
Figure 2: Model architecture of Multi-View Attention Network (MVAN). Bi-Directional Long Short-Term Memory (BiLSTM) layers for the question (blue) and the dialog history (green) are shared in the Topic Aggregation Module and Context Matching Module, respectively.  
for the question and dialog history, denoted as \(s^{q}=[\overrightarrow{u}_{1}^{q}\overleftarrow{u}_{n_{q}}^{q}]\) and \(s^{h}_{r}=[\overrightarrow{u}_{r,1}^{h},\overleftarrow{u}_{r,n_{h_{r}}}^{h}]\), respectively. We then apply an attention mechanism to focus on question-relevant history. The Context Matching module takes contextual representation of the question \(s^{q}\in\mathbb{R}^{d_{s}\times 1}\) and dialog history \(s^{h}=\{s^{h}_{0},s^{h}_{1},...,s^{h}_{t-1}\}\in\mathbb{R}^{d_{s}\times l}\) and outputs question-relevant history features as follows:  
\[z^{\mathcal{S}}_{r} =\mathbf{W}^{\top}(f^{\mathcal{S}}_{q}(s^{q})\circ f^{\mathcal{S }}_{h}(s^{h}_{r}))+b \tag{2a}\] \[a^{\mathcal{S}}_{r} =\text{softmax}(z^{\mathcal{S}}_{r})\] (2b) \[\tilde{s}^{h} =\sum_{r=0}^{t-1}a^{\mathcal{S}}_{r}s^{h}_{r}, \tag{2c}\]  
where \(\circ\) is element-wise multiplication, \(\mathbf{W}\in\mathbb{R}^{d_{f}\times 1}\) is a projection matrix, and \(f^{\mathcal{S}}_{q}(\cdot)\) and \(f^{\mathcal{S}}_{h}(\cdot)\) denote non-linear transformation functions which convert \(d_{s}\) to \(d_{f}\) dimensions. We apply a gate function to automatically filter out dialog history that is irrelevant to current question as follows:  
\[gate^{\mathcal{C}} =\sigma(\mathbf{W}^{\mathcal{C}}_{gate}[s^{q},s^{h}]+b^{\mathcal{ C}}_{gate}) \tag{3a}\] \[e^{\mathcal{C}} =gate^{\mathcal{C}}\circ[s^{q},s^{h}], \tag{3b}\]  
where \(\sigma(\cdot)\) is a sigmoid function and \(\mathbf{W}^{\mathcal{C}}_{gate}\in\mathbb{R}^{2d_{s}\times 2d_{s}}\) and \(b^{\mathcal{C}}_{gate}\in\mathbb{R}^{2d_{s}\times 1}\) are trainable parameters. Note that \(e^{\mathcal{C}}\in\mathbb{R}^{2d_{s}\times 1}\) is a context-matching representation that selectively combines the contextual information of the question and question-relevant dialog history.
## 3 Compact Model Validation  
For the validation of the model scalability and affirming model versatility, both type I and II [21] UTC-PDs were measured. More specifically, two varieties of type I UTC-PDs have been characterized: (i) an InGaAs/InGaAsP UTC-PD with rectangular waveguides, through which the light is guided evanescently into the active region [22]; (ii) an InGaAs/InP UTC-PD with a simple top-illuminated structure. The type II device consist of a GaInAsSb/InP UTC-PD also with a simple top-illuminated structure [23]. Several geometries of the UTC-PDs were investigated in the study, with their active areas listed in Table 1. Dedicated test structures associated with the UTC-PDs were available for extraction of their intrinsic parameters.  
Figure 3: Methodology flow of the measurements and the parameter extraction of the UTC-PD.
# Methodology for CubeSat Debris Collision Avoidance Based on Its Active ADCS System
## 2 State of the Art
### CubeSats and Space Debris
These drag sails expedite the descent of spacecrafts into Earth's atmosphere, where they disintegrate upon reentry once their missions conclude. This approach reduces the risk of potential collisions and opens up valuable orbital space for the deployment of additional satellites [20].