esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 State-of-the-Art  
### Fundamentals of Demand-Side Management and Demand-Side Response  
In the past, changes in the electrical load were matched by controlling the power generation of conventional power plants [10]. Currently, due to the intermittent and hardly controllable nature of renewable energy sources, this control mechanism no longer provides a sufficient option anymore. This trend is described by [PERSON] et al. [10] as a \"flexibility gap\". Commonly, four options are available to reintroduce the necessary flexibility into the system [11; 12]:  
* Generation: new flexibility on the supply side
* Transmission: flexibility through the expansion of the power grid
* Storage: flexibility through storage* Sector coupling: flexible conversion of energy between energy sectors
* Consumption: flexibility through DR  
Due to the lack of sufficient deployment to expand the supply side [13], the high social costs of grid expansion [14], the still very high cost of electricity storage [15], and slow progress in interconnecting the energy sectors (power-to-gas, electromobility, etc.) [10], DR is a competitive option.  
DR is a category of DSM and is generally understood as a generic term for measures that influence the level or timing of power consumption. These serve to adapt the electricity demand to the electricity supply, especially to the current generation. Hence, possible DSM measures mainly concern the short term, but also the long term. A very short-term control power could provide balancing energy within seconds [16]. In the long term (several months to years), DSM will also be allocated to programs that promote energy saving or efficiency measures on the part of consumers [17].  
More short-term adjustment effects focus on DR measures, which are understood as a subcategory of DSM. By means of incentive payments or variable electricity prices, DR activities initiated by grid operators and energy providers cause changes in electricity demand [18; 19]. Motivated by such price signals, participating electricity consumers autonomously choose to provide flexibility in their energy demand [16]. However, the consumption adjustment covers a period of minutes to hours. The so-called \"load control\", which includes the load connection, load disconnection, and load shift initiated by the utility company or grid operator, goes one step further with the same term [20]. Especially, in the industrial sector, which holds the largest share of electricity consumption [9], the DR potential can be provided at comparatively low marginal cost [21]. Energy-intensive companies are already using DR, albeit to a small extent [10; 22].
## 3 Fast Channel Information Prediction Based Random Forest  
### Basis for Channel Information Prediction  
The channel quality indication (CQI) is a key index to describe channel information and allocate appropriate MCS. The base station determines which RBs are allocated to the user according to the CQI value fed back by the user and adopts the corresponding adaptive modulation coding (AMC) scheme. This requires obtaining accurate and real-time CQI values. Most downlink resource scheduling schemes of LTE/5G systems are based on receiving ideal CQI, without considering the delay CQI. However, the scheduler actually uses the delayed CQI [26] and the delay of CQI will reduce the rationality of scheduling RBs, which will significantly degrade the system performance.  
The existing channel information prediction methods include the regression model, Kalman filter, artificial neural network, and wavelet support vector machine (wt-svm) [27; 28; 29; 30; 31]. The existing channel information prediction methods mentioned above have problems such as high computational complexity, low prediction accuracy, and inability to process nonlinear data. Moreover, these channel information prediction methods are not used to solve the problem of energy-efficient resources optimal allocation in specific communication scenarios. According to the shortcomings of the existing research, this paper proposes to use random forest to predict SINR to obtain real-time SINR and its corresponding CQI and MCS.
## 5 Case Study
### System Brief  
The experiments were conducted in Dapeng Bay, Shenzhen, China, and the experimental configuration closely matched that described in [53].  
To reduce the cost of sea trials, instead of using equipment such as AUVs or ROVs as UE, an alternative approach was adopted: A beacon was mounted on a steel rod, which was fixed on the port side of a boat and lowered to a fixed depth of 3 m, as illustrated in Figure 11a,b. The beacon was towed to move as the boat maneuvered. Furthermore, a GNSS receiver was installed at the top of the rod above the water surface to obtain the true geodetic coordinates and velocity of the beacon, serving as a benchmark to validate the performance of the positioning and velocity measurement.  
Figure 10: An implementation of the proposed system with the architecture of RC, BS (surface buoy and submerged buoy), and moving node with UE. The communication between RC and surface buoys is via radio signal, and the communication between BS and UE is via acoustic signal.  
During the sea trials, four BSs were deployed in a rectangular configuration, as illustrated in Figure 12, with their precise coordinates annotated in the figure. Upon commencement of the experiment, the beacon-towed boat followed a random-walk trajectory, the details of which were recorded via an onboard GNSS receiver and are presented in Figure 12b.  
For joint communication and localization, OFDM was adopted as the signal frame of the physical layer, the structure of which is illustrated in Figure 13. The dual-HFM are dedicated for signal detection and synchronization, the CP-OFDM block acts as the preamble which facilitates the parameters estimation and contains information both for decoding, and the ZP-OFDM blocks contain user messages. In the above text, we provide a description related to communication. In terms of localization, along with the dual-HFM detection process, the TOA could be estimated simultaneously. The TOA measurements will then be transmitted to RC, and the localization algorithm will produce a result with these measurements as input. For clarity, the specific algorithmic procedures are omitted here. Readers could refer to the work by [PERSON] et al. for further details [53].  
Figure 11: A beacon was carried by a boat and fixed on a steel rod, with 3 m depth in water. The GNSS installed on boat obtains the precise position and velocity information.  
Figure 12: Sea trial was conducted with 4 BSs and 1 beacon. (**a**) shows the coordinates of the 4 BSs. (**b**) shows the trajectory of the beacon.  
Other details of the experiments are listed Table 3. The depth of the water in the testing area was approximately 10 m. Based on the output of the sound velocity profile, the variation in the sound speed from the surface to the seabed was limited; thus, an effective sound speed of 1530.37 m/s was selected and provided as input to the method.
## 5 Results and Discussion
### Intra-Cluster Communication
#### 5.1.1 Comparison of Residual Energy
By first testing on smaller clusters, the experiments provide foundational  
Figure 10: Residual energy using the proposed cluster-head rotation scheme compared with the traditional method (\(n=3\)); (**a**) Traditional rotation or node roles; (**b**) Proposed rotation of node roles; (**c**) Comparison of energy consumption of node 1; (**d**) Comparison of energy consumption of node 2; (**e**) Comparison of energy consumption of node 3.  
evidence of the method's adaptability, ensuring that the insights gained here translate into larger network scenarios. Figure 11: Residual energy using the proposed cluster-head rotation scheme compared with the traditional rotation (\(n=5\)); (**a**) Traditional rotation of node roles; (**b**) Proposed rotation of node roles; (**c**) Comparison of energy consumption of node 1; (**d**) Comparison of energy consumption of node 2; (**e**) Comparison of energy consumption of node 3; (**f**) Comparison of energy consumption of node 4; (**g**) Comparison of energy consumption of node 4. In contrast, the traditional rotation scheme results in fluctuating energy consumption values, due to early node failures and demonstrating its inefficiency in managing the energy consumption balance. On the other hand, the fixed cluster-head method maintains a steady energy consumption rate until the first node dies, at which point the cluster's overall energy efficiency drops. Notably, both the traditional rotation and fixed cluster-head methods continue to operate even after the first node's death, leading to a complete waste of energy since no further throughput is transmitted to the base station.
## 5 Working Operation of Blockchain in Smart Grid (SG)
The remaining energy units left with Seller Sx (=90) and the remaining BTC tokens left with Buyer Bx (=400) are updated by the smart contract application, and the transaction record and updated state of seller Sx energy units and buyer Bx BTC tokens are reflected too. This block (Block-1, as shown in Figure 6) is cryptographically verified by the miners in the BTC network and verified through the federated consensus mechanism as an authentic transaction. The verified cryptographic signature is sent to the next block (Block-2) as proof of authentication of the previous transaction in Block-1. The transaction recorded in Block-2 includes another deal between the same buyer and seller, Seller Sx and Buyer By, which sees that the energy tokens of the seller are completely exhausted, and the remaining BTC tokens of the buyer are just 50. A fraudulent transaction between the same buyer and seller, as shown in Figure 6 in Block-3, will be rejected as solving its cryptographic puzzle with a verifiable answer (hash) is impossible to compute given its link with previous Block-2. When a certain mandatory number of peers fail to verify Block-3, it will be rejected by the network. The new transaction will first see Seller Sx announce with verifiable claims of more energy units, and Buyer Bx announces with a verifiable claim of more BTC tokens, after which any further transactions involving Buyer Bx and Seller Sx could be verified and endorsed by the BTC network, as in new Block-3. Current research indicates that, in order to successfully propagate a fake (fraudulent) block on the blockchain network, the adversary will have computing power equivalent to at least 51% of the entire hashing (mining) power of all participating nodes on the blockchain network [98]. Figure 6: Blockchain records the SG transactions in a sequential manner. Wrong or fraudulent transactions are rejected. Technological Barriers and Current Challenges in Smart Grid (SG) Operation After Integration of EV Industry  
Although SG architecture is based on previously available and implemented technologies such as blockchain, distributed energy generation, RES, IoT, and cloud computing, their synergy within SG applications, particularly with the integration of EVs, introduces new implementation challenges. These challenges require innovative solutions. In this context, we outline several research challenges that are specific to the integration of EVs into the SG ecosystem.
## 4 Proposed Model
### Open Challenges, Limitations, and Issues
#### 4.2.1 Blockchain Connectivity and Communication Issues  
The HED needs to adopt a blockchain hyperledger-based distributed preservation modular solution for accreditation record management and protection. Because of chain scalability, still, there are no single platforms and no efficient protocols are designed in the centralized system that achieves proper exclusivity. An interoperable solution is surely required at the stage of pervasive education/learning; the efficient distributed blockchain-enabled ecosystems require interconnectivity [36; 37]. Integrity is the main concern required in the design, creation, maintenance, and deployment of distributed accreditation analytical systems for credentials of candidate certificates to be secure and safe. However, the  
Figure 9: Blockchain comparison chart **(A)** Comparison Between Blockchain Permissioned Architecture with Public Permissionless Structure, **(B)** Analytical Relationship.  
scalability delimitation in the blockchain connectivity and communication protocols and the flexible capacity of the size of nodes make ecosystems more reliable in node-time and transaction storage with increased security. Developing a cross-chain platform improves performance in terms of secrecy and reduces operational costs.  
However, the lack of direct cross-chaining ability between nodes of blockchains prevents pervasive learning and academic accreditation transactions using serverless distributed peer-to-peer environments within the proposed model. This interoperable mechanism is used to facilitate blockchain hyperledger sawtooth-enabled P2P network events of node transactions across distinct chains without involving vendor variation. Stateless educational transactions, atomic swaps, relays, consensus, collaborative architecture, and policies for secure and efficient blockchain connectives must be of concern to regional and state education departments, including blockchain connectivity and communication issues as well. The interoperable cross-chain limitation is still an untouched problem in the field of pervasive learning and distributed educational accreditation ledger preservation and management, for example, the safety and nodes of education transaction bottlenecks.
## 5 Verification Experiments and Analysis
### Solving Ability Analysis  
The verification of the solving ability is to check whether this algorithm can find out effective solutions and further some optimal ones when there indeed exist such feasible answers within a solution space. For this purpose, we establish a solution verification mechanism with a traversal algorithm, and the architecture is shown in Figure 13.  
Figure 11: The Performance of this Proposed Planning Mechanism.  
Figure 12: Optimal Fitness Values in 100 Experimental Results.  
Figure 13: Verification Architecture with a Traversal Algorithm.  
With this method, several experiments have been conducted. As we can imagine, with the increase of the number of tasks andrequired sharing resources, the solution space increases dramatically. As shown in Figure 14, the costs of planning mechanism we proposed always keep low and stable, while that of the traversal algorithm increases exponentially. Additionally, with 100 experiments of several different scenes, we can observe that this proposed algorithm can find out feasible solutions correctly, and the average ratio of optimal solution is about 93%, as detailed in Table 5. These data also indicates that this new GA algorithm can guarantee the capability of solving, although there is no well guarantee that the best solution can be found every time. Thus, we can conclude from Figure 14 and Table 5 that this algorithm has good convergence ability and fast convergence speed, which are all vital performance aspects for swarm intelligent systems.
## 4 Design
### Overview
#### 4.1.1 Configuration of the Switches  
The stream gates of the switches in D-ST are always open and their stream gate control list is configured once at deployment time. The frame absolute deadline in the switches is encoded in the VID value, which also associates a frame to a stream gate. In D-ST, the number of used VIDs is equal to the number of stream gates \(N\)The first VID starts with \(VID_{0}\) and the others VIDs are assigned consecutive values, i.e., \(VID_{1}=VID_{0}+1,\ldots,VID_{N-1}=VID_{0}+(N-1)\). The stream gate control list is configured to be modified at a constant interval, called a time unit (\(u\)), within a stream gate cycle. The time unit determines the granularity in encoding of the absolute deadlines. The frames enqueued in the same transmission queue are selected in a first-in first-out (FIFO) order. The number of rules in the stream gate control list is equal to the number of stream gates used for D-ST scheduling. In particular, the stream gate control list is configured so as to change the IPV of each gate over time, according to the following function:  
\[IPV(t,SG)=\left[\left(\left\lfloor\frac{(s(t)+SG-1-VID_{0})\times Q}{N}\right \rfloor\right)\bmod Q\right] \tag{1}\]  
where \(Q\) indicates the number of queues in each port used for D-ST transmissions, \(N\) is the number of stream gates used for EDF scheduling, \(SG\) is the index of the stream gate (starting from \(0\) to \(N-1\)), and \(s(t)\) counts the time units elapsed since a reference time \(t_{0}\), here assumed to be \(t_{0}=0\).  
\[s(t)=\left\lfloor\frac{t}{u}\right\rfloor \tag{2}\]  
In Function (2), \(u\) is the time unit, i.e., a constant interval that determines how often the stream gate control list is modified within a stream gate cycle.  
When the stream gate control list is calculated, it is configured in the switches only once during deployment.
# Privacy and Security in Digital Health Contact-Tracing: A Narrative Review
## 3 Results
### Psychological Factors
#### 3.6.3 Emotions  
Emotions and anxiety towards the contact-tracing system influence the intention to download contact-tracing app. The anxiety of infecting others was identified as a significant factor that positively influenced the intention to use the contact-tracing apps [39]. In contrast, in Belgium, stress was cited as a reason for not using the contact-tracing app [70]. A study conducted in France reported that the main reason for downloading the app was out of curiosity (36.1%) [100], while in Italy, a small percentage (9.4%) of individuals cited curiosity as a reason for adopting the app [127]. In Germany, a study revealed that adoption intentions for a contact-tracing technology were significantly linked to anticipatory anxiety and anticipated emotion [46]. In a UK study, COVID-19 anxiety associated with contact-tracing app installation was identified as a common barrier to the adoption of contact-tracing apps [97]. It was reported that some individuals believe that the entire concept of digital contact-tracing may generate unnecessary panic among citizens [64]. Similarly, in the United Kingdom, some participants expressed concern about the stigmatising potential of the app. They were concerned that the ability to identify individuals with COVID-19 might result in discrimination, including the possibility of hate crimes [104].  
Another reason cited for not using contact-tracing apps was the fear of becoming overly aware of the situation and its potential consequences, which may lead to unnecessary stress [19]. A study in Belgium found that people did not want to install an app because they were worried it would stress them out [66]. In Germany, individuals expressed objection to living in fear due to the potential mental stress caused by the contact-tracing app [17]. On the other hand, respondents expressed determination that utilising the app would lower their anxiety regarding COVID-19 infection, driving their decision to install it. Half of the participants mentioned that reducing anxiety would have a strong influence on the decision to install the app. Ten percent of the participants mentioned that the app's potential to amplify anxiety about infection would play a role in their decision [109]. Additionally, some participants expressed concerns that interacting with a human tracer during contact-tracing could raise social anxiety, while some participants felt this could trigger emotional anxiety, especially with people who are sensitive about human interaction [115]. People believed that technology provided a sense of anonymity and could enable positive cases or close contacts to avoid judgment by human tracers for their COVID-19 status or background [115].
# An Extended Car-Following Model Considering Generalized Preceding Vehicles in V2X Environment
## 3 Parameter Calibration
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **GPV Model** & **FVD Model** \\ \hline \(a\) & 0.767 & 0.852 \\ \(\lambda\) & 0.301 & 0.389 \\ \(p\) & 0.769 & — \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Calibration results of parameters in the generalized preceding vehicles (GPV) model and the full velocity difference (FVD) model. Figure 2: Data filtering progress of the US101 data set. \[M ARE=\frac{1}{n}{\sum_{i=1}^{n}}\left|\frac{y_{i}-y_{i}^{m}}{y_{i}}\right| \tag{8}\]  
where \(y\) is the acceleration of the object vehicle. \(y_{i}\) and \(y_{i}^{m}\) represent, respectively, the \(i\) th measured value and \(i\) th calculated value with the model. The evaluation results of parameters calibration are as shown in Table 3.  
From Table 3, one can obtain that the calibration results are solid, and all performance indexes of the GPV model are superior to those of the FVD model. According to Table 3, we can obtain that the fitting accuracy of the GPV model to the data measured in the field is 40.497% higher than that of the FVD model. In order to further verify and evaluate the results of parameters calibration and explore the performance of the GPV model in fitting date measured in the field, we calculate acceleration using the GPV model and the FVD model with calibrated parameters and compare the calculation results with the 81 data sets which are randomly selected for verification in previous contents. Part of the comparison results is shown in Figure 3.  
The comparison results reveal that the GPV model has higher fitting accuracy to data measured in the field than the FVD model. It is noteworthy that the fitting acceleration curve of the FVD model has bigger curvature in several places, and there is a certain delay in acceleration calculation results of the FVD model, especially in the deceleration phase. The causation of this phenomenon is that drivers adjust car-following behavior only according to the motion state of their front vehicle in the FVD model and cannot grasp the traffic situation further ahead, which will guide them to take measures in advance and thus reduce reaction delay. The above results suggest that GPV's motion state, such as  
Figure 3: Comparison of computational acceleration between the GPV model and the FVD model with the verification data sets (part). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance Index** & **GPV Model** & **FVD Model** \\ \hline MAE & 1.4746 & 2.495 \\ MARE & 0.1712 & 3.2896 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation Results of the parameter calibrations. average velocity, plays an important role in improving the performance of the car-following model in fitting data measured in the field.