esa-sceva/satcom-chunk-collection huggingface dataset:
# A Scalable and Trust-Value-Based Consensus Algorithm for Internet of Vehicles
## 3 System Model  
### System Architecture  
Figure 1 shows the blockchain IoV system model. The communication between vehicles or between vehicles and RSUs is wireless, while the communication between RSUs is wired communication. The model consists of the following main components:  
1. Vehicle: The vehicle is equipped with an intelligent On Board Unit (OBU), which enables it to have functions such as sensing, computing, and storage. The vehicle is responsible for uploading its interaction records, feedback ratings, and other information to the RSUs.
2. RSU: Compared with the vehicles, the RSU has strong computing and storage resources as well as a sufficient energy supply, so the RSU is selected as the node of the blockchain network. In addition, the RSU needs to collect the communication data among vehicles within its communication range as well as the feedback rating data, update the reputation value of vehicles at the end of each cycle according to their behavior, and then package the results into blocks to be added into the blockchain network for global vehicles and other subjects to query.
3. Reputation Center (RC): The reputation center stores the latest reputation of vehicles and RSUs in the network. When the reputation obtained by RSUs from the vehicle side is too different from that stored by RSUs, RSUs will request the latest reputation of the corresponding vehicles from the RC. At the same time, the RC issues certificates for legitimate vehicles and RSUs and distributes public and private keys of vehicles and RSUs through secure channels. The model assumes that the RC is completely credible.
# Commuted PD Controller for Nonlinear Systems: Glucose-Insulin Regulatory Case
## 3 Results
### External Noise Perturbation  
Now, an ingestion is considered an external disturbance. So, an intake meal at 230 min is introduced, after the controller is activated. It corresponds to the case of additive perturbation on (1) or (5), and defined as:  
\[w(t)=5(H(t-230)-H(t-250))+Ae^{-B(t-230)}H(t-230), \tag{20}\]  
where the function \(H(t-a)\) is the well-known Heaviside expression:  
\[H(t-a)=\left\{\begin{array}{ll}0&t<a\\ 1&t\geq a.\end{array}\right.\]  
Due to the additional glucose ingestion, an additional rate of glucose absorption has to be considered (exponential function in (20)). Hence, the classic mathematical model of the pancreatic system is given by [11]:  
\[\dot{x}_{1}(t) = -p_{1}(x_{1}(t)-G_{b})-x_{1}(t)\cdot x_{2}(t)+D(t)+w(t), \tag{21}\] \[\dot{x}_{2}(t) = -p_{2}x_{2}(t)+p_{3}(x_{3}(t)-I_{b}),\] (22) \[\dot{x}_{3}(t) = -n(x_{3}(t)-I_{b})+\gamma[x_{1}(t)-\sigma]^{+}t+u_{c}(t), \tag{23}\]  
which, modified to our controlled system, results in:  
\[\dot{x}_{1}(t) = -p_{1}(x_{1}(t)-G_{b})-x_{1}(t)\cdot x_{2}(t)+D(t)+u_{g}(t)+w(t), \tag{24}\] \[\dot{x}_{2}(t) = -p_{2}x_{2}(t)+p_{3}(x_{3}(t)-I_{b}),\] (25) \[\dot{x}_{3}(t) = -n(x_{3}(t)-I_{b})+\gamma[x_{1}(t)-\sigma]^{+}t+u(t), \tag{26}\]  
with \(u(t)\) defined in (9) and \(u_{g}(t)\) in (10).  
Figures 5 and 6 present the simulations of Patients 1, 2, and 3 under the commuted PD controller action. In these cases, the commuted PD controller has a good performance, because no hypoglycemia range is attained. Hyperglycemia is attained only during ingestion, as expected.  
Figure 5: Simulations of closed-loop system with external perturbation (20). The proposed control strategy performs better than PD control \(u_{c}(t)\)[11], noticing that glucose ingestion is needed to prevent hypoglycemia episodes. (**a**) System (24)–(26) with the commuted PD controller (9) and (10). (**b**) System (21)–(23) with the PD controller \(u_{c}(t)\) (19) in [11].
## 5 Conclusions  
In this research, a QKD-aided fronthaul segment supporting low-latency 5G connectivity has been discussed. The integration of a BB84-QKD link supporting the AES-256 encryption of packetized fronthaul operating at 10 Gbps has been thoroughly investigated for both P2P and P2 MP topologies. In the dark fiber case, the secret keys can be distilled even at long fiber distances of P2P and P2 MP links, serving up to 64 users. The shared fiber deployment option to optimize the use of fiber resources in edge optical topologies can be achieved by using the less noisy upconverted CMOS photon counters, allowing for sufficient key distillation for up to 64 Bob stations at 5G terminal nodes interconnected through fiber segments up to 10 km. We also proposed a hybrid Fi-Wi topology supporting the secured P2 MP distribution using mmWave nodes to interconnect several 5G terminal nodes through wireless channels. Compared to the P2 MP shared fiber link case and by letting the symmetric keys which encrypt the necessary functions for the control and management of the radio nodes to be refreshed by a slightly lower rate, the Fi-Wi topology has been verified that could offer a 5-6 km extension of the maximum feasible transmission distance.  
Conceptualization, D.Z., A.N., and G.G.; Data curation, D.Z. and A.N.; Investigation, D.Z., A.N., and G.G.; Methodology, D.Z., A.N., and G.G.; Supervision, G.G. and H.A.; Writing--original draft, D.Z. and G.G.; Writing--review and editing, D.Z. and G.G. All authors have read and agreed to the published version of the manuscript.  
This work was funded by H2020-funded Flagship project UNIQORN (820474).  
Part of the research leading to this work was also supported by the H2020-funded 5G-PPP Phase 3 project 5G-COMPLETE (871900).  
The authors declare no conflict of interest.
## 4 Results  
### Global and Local Moran Indice of Transport Flows  
Using the two spatial weights of flows illustrated in Equation (3), the global Moran Indice was applied to measure the spatial autocorrelation in the flow data. For a fixed distance band (Fixed distance band is 1.39285), the global Moran Indice was 0.066, indicating a pattern of clustering, rather than spatial randomness. This pattern results from the complicated interactions between the expressway network system (Figure 2), and economic distributions (Figure 3). In addition, the local Moran Indice was used to detect four clusters--HH, LL, HL and LH. There were 3104 flows in total, which were statistically significant at 5% level. The majority of these significant flows, 2808 flows, were classified into the LL category, meaning the other three categories each contained a smaller number of clusters, as shown in Figure 5.  
It is clear to see in Figure 5 that the hot clusters (HH) were located in two areas in the south at different spatial scales: one large scale cluster around Suzhou and one small scale cluster around Nanjing. The most southerly HH cluster, full of inter-city flows, takes advantage of active economic development (high-density urban agglomeration including Suzhou, Wuxi and Changzhou cities) and well-connected road networks [38]. The small-scale cluster around Nanjing was dominated by short-distance flows from Nanjing to surrounding counties but still within the urban region. This cluster indicates the diffusion of urban activities.
## 2 Materials and Methods
### Weather and CO\({}_{2}\) Flux Data for the Sugarcane Plantations  
#### 2.2.1 Louisiana, USA Site  
The 10 m tower had an integrated open-path infrared gas analyzer, and climate and CO\({}_{2}\) flux data outputs were produced at a 30 min temporal resolution (Irgason, Campbell Scientific, Logan, UT, USA). The ecosystem carbon uptake was estimated using the difference between the measured net ecosystem exchange (NEE) and daytime ecosystem respiration (R); daytime and night-time R were calculated based on fitted exponential equations [25; 79]. The data covered three growing seasons at the sugarcane plantation (01/2018-12/2020). Multiple sensors at the Chachoula site were recalibrated in the month of April in 2018 and 2019, resulting in major flags in the CO\({}_{2}\) flux data during the subsequent months; for this reason, some of the data were removed from the study.  
Figure 1: The two sugarcane locations with EC flux tower sites (red doted polygons; (**a**) USA and (**b**) Brazil) displaying the pixel size of the optical data utilized in the study: 10 m (red polygon, Sentinel-2), 30 m (blue polygon, Landsat), and 500 m (green polygon, MODIS).
###### Abstract  
As automated driving technology continues to advance, the question of how users prefer to be driven in their new, more passive role is becoming increasingly relevant. In this paper, a real-world study on a German motorway with 42 participants was conducted to analyze driving style preferences for conditional automated driving, taking the participants' personal driving style into account. In the first part, participants' personal driving style (PDS) was recorded during a manual drive in the first half on a given route. For the second half, participants were asked to demonstrate their desired driving style (DDS) for conditional automated driving. In the second part, participants were driven on the same route in a defensive automated vehicle (AV) while rating driving comfort and safety. Subsequently, the relationship between driving style differences and ratings was analyzed. Furthermore, a comparison between PDS and DDS was performed. The results show that very defensive to moderate drivers perceived the AV's driving style, being similar to their own, as equally safe but significantly more comfortable than moderate to very aggressive drivers. No influence of driving style differences was found on the increase in trust. However, a significant increase in trust after experiencing an automated vehicle has been observed. Furthermore, the rated system characteristics of anthropomorphism, safety, and overall driving strategy had a significant influence on driving style preferences for AVs. This study makes an important contribution to answering the question of how users want to be driven in conditional automated driving.  
autonomous driving; driving style; trust; human-like; driving strategy Article
# Steganalysis of Quantization Index Modulation Steganography in G.723.1 Codec
## 6 Conclusions  
In this paper, a method of steganalysis in G.723.1 speech information based on QIM is proposed. The method analyzes the index distribution characteristics after CNV-QIM steganography. Dimensionality reduction is carried out by PCA which greatly reduces the calculation and makes  
Figure 8: Average detection rate with different embedding rates.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Time (s)**} & \multirow{2}{*}{**Frames**} & \multicolumn{6}{c}{**ACD (\%)**} & \multicolumn{6}{c}{**SCD (\%)**} \\ \cline{3-11}  & & **CM** & **CW** & **EM** & **EW** & **Hybrid** & **CM** & **CW** & **EM** & **EW** & **Hybrid** \\ \hline
0.3 & 10 & 12 & 11 & 11.5 & 11 & 10 & 52 & 54 & 55 & 53.5 & 52.5 \\
0.6 & 20 & 16.5 & 17 & 19 & 18.5 & 15 & 62 & 63.5 & 62 & 63 & 62.5 \\
1.2 & 40 & 56 & 55 & 57.5 & 57 & 56.5 & 79 & 80.5 & 81.5 & 82 & 80 \\
2.4 & 80 & 72 & 70.5 & 69.5 & 68 & 71 & 88 & 87 & 86 & 89 & 87 \\
3 & 100 & 89 & 86 & 87.5 & 90 & 88 & 90.5 & 92 & 91 & 93.5 & 93 \\
6 & 200 & 92 & 91.5 & 93 & 92.5 & 91.5 & 94.5 & 95 & 92.5 & 94.5 & 94 \\
9 & 300 & 95.5 & 93.5 & 94 & 94 & 93 & 94 & 95.5 & 94 & 95.5 & 94.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average detection rates for ACD and SCD (%).  
Figure 9: Average detection rate of speech steganography with different duration.  
the classifier more effective in distinguishing features. Experimental results show that this method improves the accuracy and reliability of CNV-QIM steganography analysis. At the same time, the influence of different embedding rate and different duration on the detection results was compared. The higher the embedding rate, the higher the detection accuracy. The longer the time, the higher the accuracy. In practical application, most of the speech information with different length and different steganographic embedding rate is involved. Therefore, how to improve the accuracy and reliability of speech steganalysis with low length and low embedding rate still faces many challenges. This is also the focus of our next research.  
[PERSON], [PERSON], [PERSON] and [PERSON] conducted the research along with analysis and results. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the joint funds of National Natural Science Foundation of China and Civil Aviation Administration of China (U1933108), the Key Program of Natural Science Foundation of Tianjin (17 CZD)C30900), the Scientific Research Project of Tianjin Municipal Education Commission (2019 KJ117), and the Fundamental Research Funds for the Central Universities of China (3122018D34007, 3122018C003).  
The authors declare no conflict of interest.
# IoT and Blockchain Integration: Applications, Opportunities, and Challenges
## 7 IoT and Blockchain Integration
### Application Areas of IoT and BCN Integration
#### 7.1.3 Industrial IoT Data Management and Resource Sharing/Trading  
IoT produces a vast amount of highly valuable and sensitive data pertinent to industries, including healthcare, manufacturing, and supply chain management. Both the size and importance of the data demand robust ways to promote the privacy and integrity of the data. Blockchain provides vital features such as integrity and decentralization of data, thus attracting many IoT-based industries to adopt the platform for secure data management. [PERSON] et al. [70] provided an IoT data provenance framework for ensuring trust in the IoT data collection, which could be used in supply chain management and health care management. [PERSON] et al. [71] presented an architecture to manage IoT data on top of blockchain and fog computing, coupled with software-defined networking (SDN) and network functions virtualization (NFV). [PERSON] et al. [72] demonstrated a comparably efficient, secure, authenticated data-sharing model for the IoT based on blockchain. Similarly, [PERSON] et al. [73] proposed a data-sharing framework based on identity authentication and Hyberledger Fabric to ensure data-sharing security. They also proposed a community detection algorithm to segregate the data-sharing clients into different communities based on the similarity of the data label.  
[PERSON] et al. [74] elaborate on the use of the blockchain and e-commerce based on IoT. [PERSON] et al. [75] discuss a decentralized distribution of solar energy between networks using IoT and blockchain networks. [PERSON] et al. [76] proposed a peer-to-peer energy trading framework on blockchain to fully secure energy transactions between a pool of smart IoT devices serving energy suppliers and customers. Similarly, Bitcoin and Ethereum are popular platforms that permit payment for purchasing goods and services [13,14].
## 3 Experiment Setup and Data Acquisition  
### Experiment Setup  
The bottom outlet consists of an inlet section, a pressurized section, and an open channel section. At the tail of the horizontal section, there is a 20 m long pressurized slope section with a slope ratio of 1:5. The cross-sectional size tapers from 5.5 m \(\times\) 12 m (width \(\times\) height) to 5.5 m \(\times\) 8 m, with a radial gate of 5.5 m \(\times\) 8 m (width \(\times\) height) set at the end of the pressurized section. The model arrangement is depicted in Figure 2. In the notation used, H represents the upstream reservoir level minus the height of the bottom plate, while e denotes the relative opening of the gate, defined as e = h/h\({}_{0}\). This ratio is illustrated schematically in Figure 3.  
Figure 2: Model layout.  
The scale ratio of 1:25 is chosen based on the gravity similarity criterion. The scale of this model is 1:25, with a model flow velocity exceeding 6 m/s. The Reynolds number of the gate section significantly surpasses \(1\times 10^{5}\), indicating that the water in the bottom outlet is in a fully turbulent state. Therefore, the scale effect produced by viscous stress can be disregarded [30]. Both the model and the radial gate were constructed from transparent Plexiglas to facilitate the observation of flow details. The results presented in this paper represent prototype values obtained by converting all model test results using the appropriate scaling factors. The input experimental parameters can be found in Table 1.
# Underwater Wireless Communications for Cooperative Robotics with UWSim-NET
## 2 UWSim-NET
### UWSim-NET XML  
All the information required for each experiment is specified by the user in the UWSim-NET XML scene file, whose use is described in detail in [18]. As in UWSim, this file contains the vehicles and their initial positions. The communication channels and the communication devices on board each vehicle are also indicated in the UWSim-NET XML file which includes the following sections:  
* NetTracing Script: It is the block of the xml file that allows the user to specify the NetSimTracing class with the callbacks that will handle NS3 events triggered by UWSim-NET, the library with its implementation and the name of the file where the logs will be stored.
* Communication Channels: Each channel, RF, VLC or acoustic, available in the scene will be configured in a _CustomCommsChannel_ block of the xml file. For each channel in the scene, the user is expected to specify a unique identifier and the propagation speed.
* Communication Devices: They are defined in the block of the vehicle, or ROV, and are onboarded. Each vehicle can have as many communication devices as would be required. The user is required to indicate the _DCComms identifier_ that has to coincide with that specified in the user application that communicates with the device via the DCComms API. The user is also expected to provide the channel identifiers the device sends and receives messages, the bitrate, the jitter, the intrinsic delay and the mathematical expression that relates the BER with the distance. The user can also select the medium access control (MAC) protocol to be used by the device.
# Transmissive Polarizer Metasurfaces: From Microwave to Optical Regimes
## 4 Polarization Manipulation Using MSs  
As discussed in Section 2, the polarization of electromagnetic waves possesses a very important role in radio and optical communication systems. They have been designed and explored for applications, such as dual-polarized radars, fiber optic communications, and MIMO systems. These devices are called polarization manipulators. Their most important type of polarization is a linear polarization wave which can be rotated from one polarization direction to another, converted from one state to another, or selected depending upon their polarization state for reflection or transmission.  
Polarization rotators are also termed polarization twisters or twist-polarizers. Initially, they were used for satellite ground stations in dual-polarized antennas [48]. In optoelectronics applications, they are used for displays [49]. Optical activity of natural materials as observed in the Nicol prism, the lenses of polarized sunglasses and proteins are found to be weaker [49], which means rotation of polarization plane per wavelength is low. Therefore MSs are advantageous in this manner. Polarization rotators were designed for the first time using dense wire grids with a multilayer structure whose wire rotates from one layer to another [48; 50]. They can be designed in such a way that they miniaturize the return losses. However, such devices only work for a particular linear polarization. Chirality in the optical regime was reported in [51]; the device shows the rotation of \(250^{\circ}/\lambda\). Similarly, another polarization rotator was reported [52] which had only \(\lambda/30\) thickness and consisted of a complementary split-ring resonator (SRR). MSs can also be employed to possibly engineer the sensitivity to polarization states, such as linear or circular. Devices in line with such properties to improve the properties of microwave antennas were proposed [53; 54]. These devices control circular polarization, as they allow for one form of circularly polarized waves, termed circularly polarized selective surfaces (CPSS). These surfaces are further categorized as symmetric and asymmetric CPSSs where the symmetric allows one type of CP wave to transmit through them, irrespective of the direction.  
Polarization manipulation has also been explored for non-contact Hall measurements by the magneto-optic effect, where [55; 56] study the chiral molecular structure of proteins and DNA [57; 58]. One of the most important functions of MSs under polarization manipulation is linear polarization to circular polarization conversion (LP-to-CP conversion) in transmission mode. In addition to many applications of LP-to-CP converters in optical regimes [59], these converters are being used in microwave and millimeter-wave systems, including imaging systems and reflectors [60; 61].
## 4 The Proposed Scheme
### System Structure  
For the energy trading scenario in the IIoT, this research proposes a Paillier homomorphic encryption and IBS-based energy trading scheme. The following role information is included in the scheme:  
* Energy nodes: the users can become energy nodes after registering, and the KGC then distributes the key pair. Energy nodes can engage in energy transactions to complete the blockchain's point-to-point transaction information transfer procedure.
* Transaction broadcast nodes (TBN): as full nodes in the blockchain, they do not directly participate in transactions and are only responsible for broadcasting transaction information to other energy nodes.
* Energy station (ES): the energy station is accountable for powering energy nodes and has the authority to choose qualified participants in the energy trading system.
* Cloud service provider (CSP): stores complete transaction information to reduce data writing on the blockchain.
* Key generation center (KGC): for system nodes, the KGC serves as the authority for key generation and distribution. It also offers services for pseudonym generation, identity authentication, and transaction correctness checking.  
The privacy protection of transactions in this scheme is reflected in two aspects: user anonymity and encryption of transaction amounts. The system architecture is shown in Figure 3.