esa-sceva/satcom-chunk-collection huggingface dataset:
# Cyber Resilience Limitations in Space Systems Design Process: Insights from Space Designers
## 4 Results and Findings
### Standards, Regulations, and Architectural Aspects  
The two important themes that emerged were \"Principles-Based Standards and Compliance Hindrance\" and \"Standards Benefits Recognition and Frameworks Local Adjustments.\" These themes highlight the intricate balance between the flexibility provided by principles-based standards and the rigid structure of compliance-based approaches and how the benefits of a cybersecurity and cyber resilience engineering framework can be achieved by providing adjustments based on project requirements. Our investigation showed that the experts understood that principles-based standards offer a flexible framework for cybersecurity and resilience by focusing on desired outcomes rather than prescriptive compliance measures. They also significantly understood how security strategies must be tailored based on threats and not compliance. The experts presented their displeasure with the \"checklist\" approach to current security measures within their organisations. They expressed the urgency around critical changes in maintaining and measuring cybersecurity and resilience. However, despite such views, they recognised the benefits of standards and their criticality for enhancing cyber resilience in space systems. One expert said, \"Standards provide a common framework and language for cybersecurity, facilitating collaboration and information sharing among us engineers and our stakeholders\".  
\"Moving to a principles and risk-based approach has helped to unlock some of that stuffiness. If standards are risk-based and principles-based rather than compliance-based, then that's a good thing.\"--Cybersecurity Lead Engineer, interview participant_  
The further themes, \"Compliance vs Timelines Balance,\" identifies how engineers feared delays during cybersecurity compliance, and \"Monitoring Importance, Holistic Security\" highlighted that the experts deemed it essential to utilise advanced AI technologies for advanced data monitoring to ensure security at all project design, development, and deployment stages.
## 4 Special Case: GPS Unavailability  
When GPS observations have not been available for a few seconds, the Kalman filter, as it was presented in Section 3, cannot be applied any longer. The reasons for this are that the position and velocity errors, resulting from the inertial sensor integration, cannot be bounded without the GPS observations. Furthermore, the accelerometer biases are no longer observable longer. On this account, the system is intended to stop providing position and velocity estimates, and the Kalman filter update switches to a second mode [12], which is referred to as the IMU/Mag solution in Figure 1.  
The Kalman filter in the second mode is a subset of the previously-presented filter (Section 3). The attitude prediction is still determined integrating the angular rates, but the state vector only consists of the attitude and the gyro bias errors. Since the magnetic field observations are not enough information to observe the full orientation (the rotation around the Earth's magnetic field axis would be undetermined), the accelerometer readings are added to the measurement model. The measurement equation for the accelerations is the following [45]:  
\[\tilde{\mathbf{a}}_{ib}^{b} =-\mathbf{C}_{b}^{e,T}\mathbf{g}_{l}^{e}+\vartheta_{a}=-\hat{ \mathbf{C}}_{b}^{e,T}(\mathbf{I}-\mathbf{\Psi})\mathbf{g}_{l}^{e}+\vartheta_ {a} \tag{41}\] \[=-\hat{\mathbf{C}}_{b}^{e,T}\mathbf{g}_{l}^{e}-\hat{\mathbf{C}}_ {b}^{e,T}[\mathbf{g}_{l}^{e}\times]\,\psi+\vartheta_{a}\]  
It is assumed that the main force measured by the accelerometers is the local Earth gravitation vector \(\mathbf{g}_{l}^{e}\). The influence of the trajectory dynamics on the accelerometer measurements is modeled by a large measurement noise with \(\vartheta_{a}\) = 3 m/s\({}^{2}\).
# Deep Learning at the Mobile Edge: Opportunities for 5G Networks
## 2 Mobile Edge Computing
### 5G Network Purpose and Design  
Each generation of mobile network standards has been designed in response to the changing use of mobile communications. 4G and LTE networks enhanced capabilities beyond 3G support for simple mobile browsing and messaging systems. Similarly, 5G networks have been designed with three main goals to improve network performance for the next decade (see Figure 1):  
1. Enhanced mobile broadband (eMBB) will support general consumers applications, such as video streaming, browsing, and cloud-based gaming.
2. Ultra-reliable low-latency communications (URLLC) will support latency-sensitive applications, such as AR/VR, autonomous vehicles and drones, smart city infrastructure, Industry 4.0, and tele-robotics.
3. Massive machine-type communications (mMTC) will support scalable peer-to-peer networks for IoT applications without high bandwidth.  
5G networks accomplish the high bandwidth, high availability, and low-latency requirements of new Internet services and applications through the adoption of cloud-computing infrastructure. Cloud providers use software-defined networks (SDN) and network function virtualization (NFV) to boost the creation of services to facilitate multi-tenant and multi-service infrastructure.  
The move to SDN infrastructure enables the replacement of proprietary hardware and software for network functions like routers or firewalls with cheaper, standardized, and re-programmable virtual customer premises equipment (vCPE). NFVs are centrally controlled within the cloud. Virtual network functions (VNFs), as one of the key functionalities of NFV, such as load balancers, can run on any generic server to allow the network to scale up resources on demand and be migrated between different parts of the network. However, data processing in large remote cloud data centers based on SDN/NFV functionalities cannot meet the low latency required for real-time data analytics in autonomous vehicles or locally based augmented/virtual reality (AR/VR).  
The increased communication taking place on smartphones, tablets, wearables, and IoT devices can congest the core network communicating the centralized cloud servers. Duplicate requests for popular videos during peak streaming times can overwhelm core networks and lead to a low quality of experience (QoE) for users and costly inefficiencies in network resource usage [2]. Mobile edge computing can help solve these challenges in 5G by creating a decentralized cloud at the network edge.
## 5 Capacity Credit Assessment of Wind Energy by the Secant Method  
The CC of wind energy is frequently defined as the equivalent load carrying capacity (ELCC) [41]. The ELCC is the load increment that is needed to maintain the reliability level unchanged after wind energy integration. The load increment is defined as the CC of the wind energy. The idea of ELCC is expressed as:  
\[I(G,L)=I(G+\Delta G,L+\Delta L), \tag{47}\]  
where \(I(\cdot)\) is LOLE, EENS or other reliability indices. \(G\) and \(L\) are the total generation capacity and load demand of the whole system, respectively.  
\(\Delta G\) stands for the wind energy to be evaluated. \(\Delta L\) is the load increment that the power system can support due to the integration of the wind energy.  
Therefore, we need to find the maximum value of \(\Delta L\), which is usually implemented by the secant method [11]. The schematic diagram of the secant method is shown in Figure 3.  
In the secant method, we need to compute the reliability index of the original system, which excludes the wind energy to be evaluated. The value of this index is denoted as \(R_{0}\). Then we add the wind energy to form the new system and start an iterative process to compute the CC value.  
From the schematic diagram, one can perceive that the points \(X_{r}\) and \(X_{r+1}\) in the secant method are approaching the terminal point, whose ordinate is \(R_{0}\). Actually, once the position of the terminal point is located, its abscissa minus \(L\) is the CC of the wind energy.  
The procedures of the secant method are detailed below.  
**Step 1**: Set \(r=0\). Determine two points \(X_{0}\) and B by computing the reliability index with the load demands of the new system being \(L\) and \(L+\Delta G\), respectively.  
**Step 2**: Connect \(X_{r}\) and \(B\) with a straight line, in which we search for a point whose ordinate is \(R_{0}\). The abscissa of this point is defined as \(L_{r+1}\).  
**Step 3**: Find the new point \(X_{r+1}\) by computing the reliability index with the load of the new system being \(L_{r+1}\).  
Figure 3: Illustration of the secant method. (**a**) schematic diagram of the secant method; (**b**) flowchart of the secant method.  
**Step 4**: If the ordinate of \(X_{r+1}\) is not close enough to \(R_{0}\), set \(r=r+1\) and return to Step 2. Otherwise, end the iteration. Output the value of the last \(L_{r}\) minus \(L\), which is just \(\Delta L\) in (47). \(\Delta L\) is the CC of the wind energy.  
The flowchart of the whole procedures is also presented in Figure 3.
## 4 Results
### Analysis by Serve Origin
#### 4.2.3. Origin and Ball Reception  
Of the 451 reception outcomes recorded, the cross-tabulation of origin (right, left, center) and reception outcomes (in-system, out-of-system, ball return) showed no statistically significant differences (\(\chi^{2}=5.754\), \(p=0.218\), ES = 0.080). Generally, the right, left, and center origins accounted for 41.37%, 25.66%, and 32.96% of all serves, respectively. Meanwhile, in-system receptions were predominant (70.13% of total serves), followed by out-of-system receptions (27.65%), and, to a lesser extent, ball returns (2.21%).  
Although the right origin showed a slight predominance of in-system receptions (29.43% of all servers), the left origin presented a marginally higher proportion of ball returns (1.11%) and the center origin showed an intermediate distribution among these categories. However, these variations were not statistically significant.
# Enhancing Indoor Localization with Room-to-Room Transition Time: A Multi-Dataset Study
## 3 Material and Method
### Offline (Data-Collection) Phase
#### 3.1.2 Mobile-Based Data Collector for Room Transition Time Measurement  
A mobile application was developed to measure the time it took a user to move from one room (source) to another (target) in milliseconds. When the user started moving, he/she pressed the \"Start\" button to start the time measurement, and when he/she reached the target room, he/she pressed the \"Finish\" button to record the transition time between the two rooms. The first author of the study took part in collecting data at a daily walking speed. The Wi-Fi signals obtained from the hardware setup and the room-to-room transition times collected via the mobile application were combined in three different datasets, to assess the general applicability of room-to-room transition time. Table 2 presents a sample from Dataset 2. When the values given in Table 2 were examined, the BSSID measurements were the expression of the signal strengths of the access points obtained as dBm format. If no signal was received from the BSSID, it was recorded as 0 dbm in the dataset. These values are expressed as Not Available (N/A) in Table 2. In addition, the time elapsed during the transition from a source room to a target room was recorded in the dataset as an instantaneous time difference feature. Table 3 summarizes the key characteristics of the three datasets used in this study. Each dataset was collected in a different residential environment with varying spatial layouts and by individuals with different characteristics, such as height, age, and gender. The house sizes ranged from 130 m\({}^{2}\) to 195 m\({}^{2}\), while the data collectors varied in gender, age, and height, which may have introduced additional variability in walking patterns and transition times. This table provides an overview of these differences, supporting the generalizability of the room-to-room transition time analysis. Figure 3: Circuit diagram for ESP32-based Wi-Fi collection module.
# Improved Performance of Li-ion Polymer Batteries Through Improved Pulse Charging Algorithm
###### Abstract  
Pulse charging of lithium-ion polymer batteries (LiPo), when properly implemented, offers increased battery charge and energy efficiencies and improved safety for electronic device consumers. Investigations of the combined impact of pulse charge duty cycle and frequency of the pulse charge current on the performance of lithium-ion polymer (LiPo) batteries used the Taguchi orthogonal arrays (OA) to identify optimal and robust pulse charging parameters that maximize battery charge and energy efficiencies while decreasing charge time. These were confirmed by direct comparison with the commonly applied benchmark constant current-constant voltage (CC-CV) charging method. The operation of a pulse charger using identified optimal parameters resulted in charge time reduction by 49% and increased charge and energy efficiencies of 2% and 12% respectively. Furthermore, when pulse charge current factors, such as frequency and duty cycle were considered, it was found that the duty cycle of the pulse charge current had the most impact on the cycle life of the LiPo battery and that the cycle life could be increased by as much as 100 cycles. Finally, the charging temperature was found to have the most statistically significant impact on the temporarily evolving LiPo battery impedance, a measure of its degradation.  
b 2020; 10.895; doi:10.3390/app10030895
# Asymptotic Performance Analysis of the MUSIC Algorithm for Direction-of-Arrival Estimation
## 9 Applications of Derived Analytic Expression of MSE of the MUSIC Algorithm  
### Evaluation of cOvariance Matrix of AOA-Based Localization in Underwater Acoustics  
Once the sound source is detected, localization can be applied to pinpoint the location of an emitter. AOA-based localization can be adopted in this stage. Sensor locations and line-of-bearings (LOBs) from DOA estimation algorithm should be available to implement AOA-based localization algorithm. Any DOA estimation algorithm, including the MUSIC, the maximum-likelihood (ML), the conventional beamforming, the Capon beamforming, can be employed to get the LOBs used for localization. Note that the mean-square-error (MSE) of the location estimation algorithm is highly dependent on the MSE of the DOA estimation algorithm, which implies that, for accurate localization of an emitter location, the MSE of the DOA estimation algorithm should be small. The accuracy of the localization algorithm is quantified in terms of the covariance matrix of the position error \(\delta\mathbf{r}\)[29]:  
\[E\left[\delta\mathbf{r}\delta\mathbf{r}^{\mathbf{T}}\right]=\mathbf{H}^{ \mathbf{\#}}\mathbf{\Lambda}\mathbf{H}^{\mathbf{\#}T} \tag{51}\]  
where \(\mathbf{H}\) and \(\mathbf{\Lambda}\) are defined from  
\[\mathbf{\Lambda} = \left[\begin{array}{ccc}{\sigma_{1}}^{2}{d_{1}}^{2}&\cdots&0\\ \vdots&\ddots&\vdots\\ 0&\cdots&{\sigma_{N}}^{2}{d_{N}}^{2}\end{array}\right] \tag{52}\] \[\mathbf{H} = \left[\begin{array}{ccc}-\sin{\theta_{1}}^{(0)}&\cos{\theta_{1 }}^{(0)}\\ \vdots&\vdots\\ -\sin{\theta_{N}}^{(0)}&\cos{\theta_{N}}^{(0)}\end{array}\right]. \tag{53}\]  
Figure 8: Mean square error of \(\delta\theta\) with respect to the number of repetitions (SNR = 10 dB).  
The superscript # in (51) denotes a pseudo inverse operator. Note that \(\sigma_{i}\), \(d_{i}\) in (52) and \(\theta_{i}{}^{(0)}\) in (53) are standard deviation of the DOA estimate at the \(i\)-th sensor location, the distance from the \(i\)-th sensor location to an emitter, and true DOA at \(i\)-th sensor location, respectively. For an unbiased DOA estimator, \(\theta_{i}{}^{(0)}\) can be obtained from the square root of the MSE of DOA estimate.  
If the MUSIC algorithm is employed to get DOA estimate, the MSE of the estimate in (40) can be used for calculation of \(\sigma_{i}\) in (52).
# Inference Acceleration with Adaptive Distributed DNN Partition over Dynamic Video Stream
## 2 Related Works
### D2C Inference  
The end-to-end collaborative cloud execution of DNNs for model inference is achieved by offloading some of the DNN model computation tasks. [PERSON] [16] split DNNs in a chain topology with layer granularity by weighing the communication time and computation time based on the computation time required for different layers of the model and the size of the data generated. [PERSON] [17] proposed a distributed deep neural network that collaborated with end devices, edge devices, and central servers to achieve distributed inference and designed early exit branches to avoid the need for servers to reason about simple inputs, effectively improving the performance of the model on IoT devices with limited resources. To minimize the computation, [PERSON] [18] considered that servers did not necessarily store models that needed to be offloaded and constructed the model offloading process and inference process as a directed acyclic graph (DAG). IONN solved the optimal offloading policy by solving the shortest path of the DAG while avoiding the unnecessary model structure deployed on the server. This method of solving the optimal partition by constructing a computational graph has received much research attention. [PERSON] [19] considered the problem of solving multiple cut points by constructing the graph with the computation time of the layer as the node and the data transfer time was used as the edge, with energy, latency, QoS, battery consumption, and cloud server blocking as constraints to obtain the offloading strategy for multiple cut points of the DNN model, by solving the shortest path from input to output. [PERSON] [20] adaptively sliced the DNN model based on the available bandwidth between the edge server and the end device to offload more computations to the edge server at a smaller cost of transmission delay, thus reducing both data transmission latency and model computation latency. To avoid suboptimal solutions due to prediction errors in the execution time of each layer of the model, ANS [21] learned by observing the delay and used a contextual gambling machine to predict the optimal cut point.  
[PERSON] [22] raised the priority of key frames to provide differentiation for different data in the learning process. [PERSON] [23] proposed a dynamic DNN decomposition system for synergistic inference without precision loss. A regression model was used to estimate the processing time of DNN layers. The interlayer cut using HPA (heuristic method) was used to achieve collaborative inference at the cloud edge, and the feature maps of the convolutional layers were spatially split into blocks using a vertical separation module, allowing the convolutional layers to execute in parallel at the edge nodes.
## 4 Deterministic Analysis of the Electricity-Gas-Heat IES  
Steady-state models of an EGH-IES (Equations (1)-(13)), together with the operation modes presented in Section 3, constitute the deterministic energy flow (DEF) problem of the EGH-IES, which can be effectively solved by the Newton-Raphson method. Solution strategies for solving this DEF problem can be divided into two groups: the integrated method and the decomposed method. The integrated method solves all of the DEF equations simultaneously based on a unified Jacobian matrix, while the decomposed method solves the decoupled equations of each subnetwork successively and can deal with the operation modes of the EGH-IES effectively. Therefore, the decomposed method is used in this paper to solve the DEF problem, and different solving sequences are introduced to deal with the subnetworks according to the operation modes. A flowchart of the decomposed method considering the four typical operation modes is shown in Figure 3.  
As shown in Figure 3, when the EGH-IES working in mode 1, mode 2 or mode 4, the three subnetworks are solved sequentially, i.e., the subnetworks are solved one by one. Specifically, in mode 1 and mode 4, the heating and gas networks are solved first, and then the electricity network is solved based on the solved heating and gas networks, while in mode 2, the solving sequence is as follows: the heating network is solved first, then the gas network, and finally the electricity network. Note that in mode 1 and mode 4, the heating and gas networks can be solved in parallel.  
However, the solving sequence of mode 3 is quite different from other modes. In mode 3, the gas and electricity networks are not solved sequentially, but alternately. This is because in this mode, the bus connected to the CHP acts as the electrical slack bus, which means that there is a variation transmission from the electricity network to the gas network; meanwhile, the electricity-driven compressor poses a reverse variation transmission from the gas network to the electricity network. Moreover, the error of \(P_{\mathrm{c}}\) (i.e., the electrical power consumed by the compressor) between two iterations can be used as the convergence criteria of the alternate solution.
## 4 Prediction of 3D Printing Process Parameters Based on GA-BP Neural Network
### GA-BP Neural Network Prediction Method for 3D Printing Process Parameters
In this paper, a simulated binary crossover method is used to perform crossover operation, which is commonly used in genetic algorithms for real number coding, and the final expression is  
\[X_{A}^{t+1}=\alpha X_{B}^{t}+(1-\alpha)X_{A}^{t}, \tag{18}\]  
\[X_{B}^{t+1}=\alpha X_{A}^{t}+(1-\alpha)X_{B}^{t}, \tag{19}\]  
In this equation, \(X_{A}^{t}\) and \(X_{B}^{t}\) denote that the two chromosomes \(A\) and \(B\) crossover at position \(t\), and \(\alpha\) is a random number taken between [0, 1] for the crossover parameter. In this section, the basic bitwise mutation method is employed, which involves executing a random mutation operation based on a predefined mutation probability. Typically, the mutation probability falls within the interval [0.001, 0.2]. For the purposes of this study, the mutation probability is set to \(A\) = 0.2. Prediction process: Initially, individuals with high fitness values from the initial population are selected to advance to the subsequent generation. Subsequently, new individuals are generated through crossover and mutation operations, designed to prevent the algorithm from converging on a local optimum. This evolutionary process is iterated until the optimal combination of process parameters is attained. Assuming the target parameter to be the cortical bone density, denoted as \(\rho\) = 0.963 g/cm\({}^{3}\), the program is executed in accordance with the established GA-BP neural network model. The variation in individual fitness values throughout the prediction process is graphically represented in Figure 15.  
The fitness of the optimal individual exhibits a progressive decline, trending towards stabilization, suggesting that the algorithmic optimization is increasingly converging towards the optimal solution. Upon reaching the 12 th generation, the fitness of the optimal individual plateaus at a low level and remains consistent until the algorithm concludes at the 100 th generation. At this juncture, the optimal combination of process parameters is identified: a filling density of 81.346%, a material flow rate of 94.436%, and a layer thickness of 0.209 mm. To further validate the model, ten sets of cortical and cancellous bone density values are randomly selected and fed into the program to derive the corresponding process parameter values. These parameter combinations are then utilized to 3D print specimens, enabling us to measure the actual densities. The relative errors between the predicted and actual densities are calculated, and to visualize these discrepancies more vividly, the relative error rates are plotted on a line graph, as depicted in Figure 16. The relative errors are found to be within 4%, indicating that the deviations fall within an acceptable margin. This validates the suitability of the BP neural network for serving as the fitness function in a genetic algorithm, thereby facilitating accurate predictions of process parameters.