esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Network Architecture and System Model
### SAGIN Architecture and Channel Models  
SAGIN is a novel network structure that integrates multiple dimensions. It adopts different protocols and occupies different frequency bands for establishing communications between different dimensions [3]. We selected the rational frequency band for communications by taking into account the distance between ECNs, the influence of current environmental factors on the link, the free-space path loss, tropospheric attenuation, and other factors.  
In the study, tasks were uploaded to the nearest BSs from MTDs by default. As shown in Figure 1, the SAGIN architecture consists of five components: the MTD segment, ground segment, satellite segment, aerial segment, and cloud data center (CDC) segment. Let \(\mathcal{B}=\{1,2,\cdots,B\}\) be used as the set of BSs. Furthermore, UAVs are deployed as an extension of the ground BS process, serving as representatives of the aerial segments. The set of UAVs is denoted by \(\mathcal{U}=\{1,2,\cdots,U\}\). The satellite segment consists of LEO satellites. Due to the utilization of higher communication frequency bands, satellites possess lower propagation delay and free-space attenuation [37]. We consider the displacement of satellites relative to the ground during the task processing time slots to be negligible.  
The SAGIN system provides three task offload strategies based on the task information uploaded by MTDs: Firstly, selecting a BS that possesses sufficient computing resources to calculate the task; secondly, applying for a UAV offload and computing the task; thirdly, selecting a UAV as an intermediary and applying an LEO satellite to offload and compute the task. As a multi-dimensional 6G network, SAGIN integrates several network segments and uses communication protocols to achieve high reliability and high-throughput data transmission [38].  
The maximum achievable transmission rate K [bits/s] for the tasks in the channel can be expressed as follows:  
\[\left\{\begin{array}{l}K=B\text{log}_{2}(1+\frac{P_{\times}10^{-|L_{p}/10|}} {Noise}),\\ L_{p}=32.45+20\lg d(km)+20\lg f(MHz).\end{array}\right. \tag{1}\]  
Within the above equation, \(B(HZ)\), \(P\), and \(Noise\) represent the channel bandwidth, average signal power, and noise power; \(L_{p}\), \(d\), and \(f\) represent the propagation loss in free space, the distance between ECNs, and the signal frequency, respectively.  
Figure 1: SAGIN model.
## 5 Problem Formulation
### User's Comfort in Terms of Waiting Time (\(\tau_{w}\))  
User's comfort in terms of waiting time is important for end-users. Waiting time must be minimized to have a high comfort level so that the end-user's frustration can be avoided. It is that interval of time when a consumer switches on an appliance, and due to the scheduling limitations of the system, he/she has to wait to start its operation. As we have defined the earliest starting time \(\alpha\) and the latest ending time \(\beta\) of an appliance, then another parameter \(\eta\) will be the operational starting time of the same switched-on appliance. This is shown diagrammatically in Figure 3. Here, \(\beta\)-\(\alpha\) is the time span, defined by the consumer, as given in Table 2. Figure 3 shows that a consumer's maximum waiting time could be up to \(\eta_{max}\). Since LOT is already defined by the consumer, so at \(\eta_{max}\), the algorithm will have to start the appliance to complete its operation up to the final time \(\beta\). For example, Dishwasher-1 (Table 2) has a time span of 8 h (from 9:00-17:00) and LOT = 2 h. This means that our proposed algorithm (TG-MFO) must start Dishwasher-1 from 9:00-17:00 to complete its operation of 2 h, with a waiting time ranging from 0-6 h.  
Here, LOT is the length of the operational interval of time, in which an appliance completes its task. In the case of fixed appliances, there is no issue of waiting time, as whatever time the consumer wants to switch it on, he/she can do so. Therefore, we do not include fixed appliances in the scheduling problem.  
Now, since,  
\[(\beta-\alpha)\geq LOT \tag{13}\]  
Therefore, the range of waiting time can be \(\alpha\) to \(\eta_{max}\), as shown in Figure 3.  
Appliances' normalized waiting time (\(\tau_{w}\)) can be calculated as:  
\[\tau_{w}=\frac{\eta-\alpha}{(\beta-LOT)-\alpha} \tag{14}\]  
Equation (14) shows that the normalized waiting time can be from \"0\" (when \(\eta=\alpha\)) to \"1\" (when \((\beta-LOT)=\eta\)). Table 2 illustrates the typical electricity demand of a single home and multiple homes, with different power ratings and types of appliances, their LOTs and (\(\alpha\)) and (\(\beta\)) constraints [31]. Since  
Figure 3: Starting time, ending time, LOT and waiting time.  
different end-users have different habits and life routines, with different sizes and power ratings of appliances, we have assumed four (4) types of homes and randomly selected through the proposed algorithm, to have randomness in the consumed energy when taking multiple (here, 30) homes.
## 4 Simulation Experiment Analysis  
This section describes the test functions used to evaluate the performance of the CG-TSA. The Whale Optimisation Algorithm (WOA) [33], Grey Wolf Optimiser (GWO) [34], Sine Cosine Algorithm (SCA) [35], AOA, and the basic TSA were selected for the function-finding comparison for the CG-TSA. To ensure the fairness of the comparison between the algorithms, the basic parameters of the algorithms were set to the same values including the population size \(N=30\), the maximum number of iterations \(T_{\max}=500\), and the dimensionality \(d=30/500/1000\). The internal parameters of each algorithm were set as shown in Table 1. The experiments were conducted with 10 benchmark test functions for the CG-TSA to compare the search performance, including single-peak test functions F1-F6 and complex multipeak test functions F7-F10, and the description of the benchmark test function information is shown in Table 2. The experimental running environment was Windows 10, the CPU was a 3.4 GHz Intel Core i7-6500U with 8 GB of memory and a 64-bit OS, and the model was built on MATLAB R2021(a).  
\begin{table}
\begin{tabular}{c c} \hline
**Algorithm** & **Parameter** \\ \hline WOA & \\ GWO & \\ SCA & \\ AOA & \(MOP\_Max=1\), \(MOP\_Min=0.2\), \(\alpha=5\), \(\mu=0.49\) \\ TSA & \(P_{\max}=4\), \(P_{\min}=1\) \\ CG-TSA & \(P_{\max}=4\), \(P_{\min}=1\) \\ \hline \end{tabular}
\end{table}
Table 1: Algorithm parameter setting.  
Figure 3: Flowchart of CG-TSA.
# DPIA in Context: Applying DPIA to Assess Privacy Risks of Cyber Physical Systems
## 1 Introduction  
Cyber Physical Systems (CPS) are \"integrations of computation and physical processes\" [1]. Thus, CPS can combine human, physical and technological components and allow these to communicate seamlessly to derive value (see Figure 1). The value generated will depend on the perspective of the user. For example, for the individual who uses their smart phone to communicate with their heating system, the value will be in having a warm home when they get there. For the electricity company, the value may be in providing the power needed on-demand. In addition, the electricity company may also derive value from, for example, being able to remotely read the customer's meter, or use the data generated to predict when the customer will next need additional power to be supplied. However, this also raises a number of security and privacy concerns that need to be explored and addressed.  
While a lot of research has been done around what the security threats, vulnerabilities and implications of CPS might be [2], most of this work considers privacy only as a sub-set of security considerations [3]. We contend that privacy must be considered in its own right and on a more holistic basis. Some work has been done in this area, discussing how privacy might be affected by the use of CPS and highlighting the need for improved decision making to ensure privacy is preservedin CPS [4], a notion that we subscribe to. We contend that, not only does privacy need to be discussed, a practical framework for how to do this in a holistic manner, is also sorely needed.  
This paper seeks to address this gap by introducing the DPIA Data Wheel, a privacy risk assessment framework devised for supporting informed decision making around privacy risk and conducting Data Protection Impact Assessments (DPIA) for any system, project or scenario that involves _high risk_ processing of data. This, we contend, will include most CPS.
# Research on Tracking Technique Based on BPSK-CSK Signals
## 2 Materials and Methods
### A BPSK-CSK Signal Tracking Method Based on Time-Division Multiplexing
#### 2.3.3 Carrier and Code Stripping  
The I and Q signals can be obtained by stripping the carrier and mixing the IF signals. The algorithm is executed as follows:  
Step 1: Save the 4 ms input data (called in this paper) and the local code stripped from the carrier.  
Step 2: Accumulate the corresponding sampling points of each code slice using the code NCO to obtain the named data, respectively. Then, an FFT operation is performed to multiply with the local code by the FFT conjugate, followed by an IFFT operation to obtain the index value of the peak.  
Step 3: Generate the local code with the same length by code NCO. The branch local code is obtained by cyclic shifting obtained from step 2. By shifting one code slice obtained, the local code of branch P is shifted by one code slice to generate the E, P, and L codes, respectively, and a correlation operation is performed with the E, P, and L codes, respectively, so as to generate the integral values of I and Q with respect to the E, P, and L codes, which are used as inputs to the carrier loop and the code loop.
## Reliability Analysis Models for Differential Protection Considering Communication Delays and Errors  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 2, [PERSON] 1 and [PERSON] 3**  
1 School of Automation, Nanjing University of Posts and Telecommunications, No. 9, Wenyuan Road, Nanjing 210023, China; E-Mails: limanli824(@163.com (M.L.); [EMAIL_ADDRESS] (R.F.)  
2 School of Electrical Engineering, Southeast University, No. 2 Sipailou, Nanjing 210096, China; E-Mail: [EMAIL_ADDRESS]  
3 NARI Technology Inc., No. 19, Chengxin Road, Nanjing 211106, China; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-25-8586-6511.  
Academic Editor: [PERSON]  
_Received: 24 November 2014 / Accepted: 23 March 2015 / Published: 27 March 2015_  
This paper proposes three probability models to assess the impact of communication delays and bit errors on differential protection. First, the mechanism of relay protection malfunction caused by communication delays and bit errors is introduced. In general, a channel's consistent delay or bit error results in refuse-operations, while a channel's inconsistent delay normally causes false trips. Based on the analysis of the probability distributions of communication delays and bit errors, probabilistic models of false trips and refuse-operations are proposed. Simulation results, using typical parameters, are implemented to investigate the effects of communications on the malfunction probability of differential protection.  
communication delay; bit error; current differential protection; false trip; refuse-operation; probability
## 5 Case Analysis  
### Simulation Parameter Settings  
For parameters related to battery life decay, take the capacity decay rate \(Q_{site}=-0.2\); temperature acceleration coefficient \(T_{acc}=1\); standard depth of discharge \(D_{ref}=60\%\); standard value of state of charge at the beginning of discharge cycle \(SOC_{ref}=80\%\); \(c_{ref}=-2.395\times 10^{-5}\), \(\alpha=2.4106\times 10^{-6}\), \(\beta=2.2227\); Faraday constant \(F=96,485.3383\); and molar gas constant \(R=8.3145\) J/(mol\(\cdot\)K).  
The carbon emission factor of the power generation of grid is \(A_{grid}=0.69\) kg/k\(\cdot\)Wh; the number of EVs is \(N=100\); the maximum battery power is \(E_{max}=50\) kW\(\cdot\)h; the maximum charging and discharging power is \(P_{max}=6\) kW; the guaranteed minimum power is \(E_{ms}=50\%\)\(E_{max}\); the user's expected power is \(E_{exp}=95\%\)\(E_{max}\) when off grid; \(\Delta t=1\) h; the SOC of the EV when it is connected to the power grid obeys the uniform distribution of [0.2, 0.8], and the starting time of the grid connection obeys the uniform distribution; the grid connection duration is 12 h. Suppose the module area of the PV power station is 3000 m\({}^{2}\), the module parameters are 300 WP, and the output of the PV power station \(P_{psum}(k)\) is given by the Pvsyst 7.2 simulation software [33; 34].  
The main source of life-cycle carbon emission of an EV battery is raw material manufacturing, which is 2093.43 kg in total; the emission reduction benefit of battery recycling is 50%; therefore, the total carbon emissions in the battery life cycle is 1046.72 kg, which is used as the basis for estimating the carbon emissions caused by the reduction of battery life [27; 35].
## 2 Hybrid Edge-Cloud Approach  
### Motivation for the Edge-Cloud Approach  
Metering devices that produce large volumes of data are in TNs connected to the control center with dedicated optical communication links, which is a very unrealistic scenario for the majority of DNs. Consequently, for such devices, both latency and reliability [12] of data transmission over LTE need to be tested before implementing any kind of corresponding schemes in DNs. Since the aim of this paper is to establish a functional computing framework for fault localization in DNs based on PMU measurements, the evaluation of the QoS for PMU data transmission over an LTE network is of paramount importance. The requirements for the real-time exchange of synchronized phasor measurement data between power system equipment are defined in the IEC 61850-90-5 standard. The standard specifies the acceptable time delays for communication between the PMU and the phasor data concentrator (PDC) for various applications that can be roughly divided into three categories:  
* real-time (time-critical) applications Applications that require selected signals to be available with only a small delay (order of milliseconds) are found in this category since a prompt response is of paramount importance for their proper operation. Some representatives of this group are synchro-check, adaptive relaying and out-of-step protection.
* near real-time (observability) applications In this category, we can find applications, such as situational awareness, state estimation and on-line security assessment, which do not provide any remedial actions, but are merely used for visualization purposes. Therefore, the allowed time delay for those applications is a bit higher (order of seconds).
* archiving applications Archiving applications do not have any time delay requirements as they serve for post-mortem analysis, which is neither real-time nor near real-time. However, those applications face a very strict requirement that the archival process be lossless, i.e., the data measured from PMUs on the system have to be exactly the same as the measurements stored in the archive.  
The rest of this subsection is dedicated to the evaluation of the IEC 61850-90-5-defined QoS for an LTE network.
# On the Rollout of Network Slicing in Carrier Networks: A Technology Radar
## 2 Network Slicing: Concept, System Architecture and Deployment
### Architectural Framework for Network Slicing
This facilitates a decoupled evolution of RAN, CN and TN, and allows the operator to select the technologies and vendor solutions they want for every technical domain. * A clear separation between management (i.e., OAM activities on individual technical domains) and orchestration (i.e., coordination and conflict resolution activities across technical domains). In the proposed solutions, the RAN, CN and TN management domains are focused on management activities, while the NFV and E2E management domains are the ones responsible for orchestration. The interactions across the different management domains are done with a service bus, which features the ZSM cross-domain integration fabric [19]. As seen, it is important for the different domains to make capabilities available for external consumption through standard APIs. Figure 2 captures relevant references for these APIs.
## 2 Methodology
### Objective Function
#### 2.3.3 Regularization Factor  
The Tikhonov regularization method [25] has gained widespread acceptance and application for addressing ill-posed inverse problems. Regularized inversion mitigates the multiplicity of solutions by incorporating model constraints into the objective function. The regularization factor serves as the compromise coefficient between the data fitting function and the model stability function. A higher value of the regularization factor emphasizes model constraint, often resulting in underfitting of the data [26]. Conversely, a lower value of the regularization factor indicates a bias towards data fitting, potentially leading to false structure due to the influence of data noise, commonly known as overfitting. Therefore, the choice of the regularization factor significantly impacts the inversion outcome, playing a pivotal role in the inversion effect [27].  
The symbol \(\beta\) represents the regularization factor of the time-lapse term and mathematically signifies the weight of the time-lapse term's influence on the inversion result. \(\beta\) controls the similarity between models at each time step, representing temporal constraints. In this algorithm, \(\beta\) is set to a fixed scalar value, indicating that the differences between models do not change in the time dimension. A larger \(\beta\) value implies a closer iterative trend model, suggesting minimal differences between models at each time step. Conversely, a smaller \(\beta\) value reduces the weight of the time-lapse term's influence on inversion results, allowing greater differences in model changes at each time during the iteration process.  
When calculating measured data, the value of \(\beta\) can be determined based on the variance of the electric field response section at each time step.  
The effect of \(\lambda\) aligns with that of the Tikhonov regularization factor. To achieve both data fitting and dual constraints of temporal and spatial considerations, \(\lambda\) is gradually decreased when the number of iterations increases and the root-mean-square (RMS) of the data fitting difference no longer decreases. This reduction in \(\lambda\) is expressed by Formula (10):  
\[\lambda_{k+1}=0.1\cdot\lambda_{k} \tag{10}\]
## 2 Materials and Methods
### Data Analysis  
The hip flexion angle was calculated using a trigonometric equation. This equation stated that HFA = Sin \(-\) 1(D1/D2), in which D1 is the distance from the greater trochanter to the fibular head and D2 is the perpendicular distance from the fibular head to the testing table [24]. For measuring the flexibility of the hamstring, the mean of the three trials was estimated and the score was used during the statistical analysis. To normalize the score obtained in mSEBT, the score of the successful trials for each direction was firstly averaged (in cm), then divided by the length of the tested leg and multiplied by 100 [27; 28]. This normalization method takes the differences in limb length into account and permits the inter and intra-subject comparisons [29]. Scores obtained from the three directions were added up, divided by 3 times the leg length, and then multiplied by 100 to create the composite score [27]. For normalizing the score of the strength test, the value of the maximal contraction of the tested muscle was multiplied by the femur length and then divided by the participant's weight [30].  
The mSEBT distance, muscle strength, and flexibility scores from both lower extremities were investigated among female participants. In addition to calculating descriptive statistics for all parameters, Kolmogorov-Smirnov tests were carried out to determine the distribution of the data. Pearson's correlation coefficients were computed to determine the correlations between mSEBT and the muscles' strength and flexibility. To evaluate the degree of correlation between variables, we applied the following guideline for interpreting correlation coefficients: correlation coefficients between 0 and 0.19 were regarded as very weak, those between 0.2 and 0.39 as weak, values from 0.40 to 0.59 were considered moderate, correlations between 0.6 and 0.79 were classified as strong, and coefficients from 0.8 to 1 indicated a very strong and highly pronounced correlation between the variables [31]. The statistical significance threshold was established at \(p\) < 0.05, and the statistical analysis was conducted using Statistical Package for the Social Sciences (SPSS) software, version 26.0, designed for Windows by SPSS Inc. in Chicago, IL, USA.
# Energy Efficient Access Point Placement for Distributed Massive MIMO
## 4 Simulation Results
### Analysis with Perfect Environment Information
#### 4.3.1 Impact of the Number of APs in the D-mMIMO  
We consider a D-mMIMO system with 128 antennas in total (see Table 2). We assume that each AP has the same number of antennas. This section investigates the question of how to decide on the number of APs in the system. We generate optimal placements via the RTS-based model for three cases: (a) four APs, (b) two APs, and (c) one AP.  
The average relative power saving between different configurations over all the maps is illustrated in Figure 11. For example, the full blue line in Figure 11 represents the average relative power saving of using the system with four APs compared with the system with one AP, using isotropic antennas. The results show that the total number of APs has a huge impact on the system's performance. The system achieves the best performance when it has four APs. On average, it saves around 22-32 dB transmit power compared with the one-AP system and 9-15 dB power compared with the two-AP system. In future research, it may be interesting to study potential further enhancements by deploying more APs.  
Figure 11: Impact of the total AP number on the D-mMIMO system’s performance (128 antennas in total) with optimized AP placements on the average relative power saving when the D-mMIMO system is equipped with four APs compared with one or two APs over all the maps.
# Comparative Analysis of Current Control Techniques to Support Virtual Inertia Applications +
## 4 Simulation Results and Comparative Analysis
### PR Controller Simulation Results  
The output current of the grid-connected inverter when a PR controller was used in the current control is shown in Figure 9a. With the PR controller, during the change in the reference current command at 0.05 s, there was no distortion in the shape of the current waveform as in the case of the PI controller. In addition, a closer observation of the current waveform, especially for a lower references, shows lower distortion compared to that with a PI controller. Figure 9b shows the change in the active power of the grid-connected inverter corresponding to the change in the output current. The response time was similar to that of the PI controller, but the overshoot was only 8% in this case. This could be beneficial for virtual inertia implementation, as this could greatly reduce the cost of the components,  
Figure 8: Simulation results with the PI controller. (**a**) Three-phase output current of the inverter. (**b**) Active and reactive power output.  
as less overshoot has to be handled by the inverter. In large systems, even this small reduction in the overshoot can translate to significant economic savings.