esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Parametric Study
### Stationary Vehicle Location  
This section investigates the effects of the location of the stationary vehicle and its contact-point response. In the case where the stationary vehicle at 3.18 Hz was located at the mid-span location, its response and contact-point response were presented previously in Figure 5. The first and third modes of frequency of the bridge are visible in the FFT spectra of the stationary vehicle and contact-point responses as shown in Figure 5b. The second mode of frequency of the bridge is not visible in the bridge response spectrum.  
It is known that the maximum value of the second mode shape of a simply supported beam occurs at the first and third quarters of the beam. Consequently, in this section, the stationary vehicle was located at the first quarter of the beam. The stationary vehicle and its contact-point and bridge responses at the first quarter are shown in Figure 11a. Good agreement between the contact-point response and true bridge response can be observed. The acceleration spectra are shown in Figure 11b. The frequency of the vehicle is eliminated in the contact-point response. The first and second modes of frequency were captured by the stationary vehicle and its contact-point response; however, for the first and second modes, there is clear discrepancy of the stationary vehicle spectrum comparing with the true bridge spectrum, while the third mode is not even captured by the stationary vehicle response. On the other hand, the contact-point response coincides almost exactly for the first, second, and third modes of frequency.  
Figure 11: Dynamic responses of an undamped stationary vehicle, along with its contact point and bridge (**a**) acceleration responses when located at the first quarter of the bridge. (**b**) Acceleration spectra.  
Figure 10: Dynamic response of damped stationary vehicle with a filtered contact-point and the bridge (**a**) acceleration response at a mid-span location, along with (**b**) the acceleration spectra.
## 3 Data Source and Data Preprocessing
### Main Magnetic Field and External Magnetic Field Correction  
The non-lithospheric magnetic field must be corrected for the selected data to capture lithospheric magnetic field signal [2]. The corrected magnetic fields cover the main magnetic field, magnetosphere, and ionospheric magnetic field, as well their induced counterparts. Considering the weakness of the ionospheric magnetic field at night, corrections are not conducted using the model. To correct the magnetospheric sources, the long wavelength signals are separated by means of along-track filtering in Section 3.4. To correct the main magnetic field, the POMME-10, EMM2015, and MCO_SHA_2D models were tested [47]. Their mutual differences are small. Therefore, the EMM2015 model (1-15 degrees) is selected as the earth's main magnetic field and its secular variation. The model is removed from the satellite observation value to obtain the so-called residual field. Figure 4 describes the radial components of CHAMP and Swarm satellite observations after deleting the main magnetic field. The MF7 lithospheric magnetic field model is eliminated to comprehensively analyze the along-track filtering data in Section 3.4. Figure 4 shows that before making along-track filtering corrections, the residual magnetic field of CHAMP and Swarm observations range from \(-\)10\(\sim\)10 nT and 0\(\sim\)30 nT, respectively.  
Figure 3: Flow chart showing the sequence of processing steps to separate the lithospheric field contributions from the satellite magnetic data.
## 2 FEM in Partial Discharge Simulation
### Simulation Scenarios
#### 2.2.1 Simulation Scenario I: Single Cavity with Varying Positions  
This section analyzes three simulation scenarios: Near-Positive (cavities close to the high-voltage electrode), Intermediate (cavities centrally positioned), and Near-Negative (cavities near the ground electrode). Each scenario influences the electric field distribution, PD inception voltage, and PD charge patterns. Near-Positive cavities magnify local electric fields, lower PDIV, and facilitate air breakdown, as illustrated in Figure 8, and discharge patterns in Figures 9 and 10, resulting in higher PD charges due to increased potential difference, crucial for insulation reliability. Intermediate cavities, as shown in Figure 11, experience a balanced field, raising PDIV compared to Near-Positive cavities, as shown in Figure 12, with moderate PD charges Figure 13, indicating balanced stress; however, they remain susceptible to PD under AC due to varying field polarities. Near-Negative cavities face the weakest field Figure 14, increasing PDIV Figure 15 and lowering PD charges Figure 16, signifying fewer PD events but potential risk under insulation aging or defects.  
Figure 11: Electric field in Intermediate cavities.  
Figure 14: Electric field in Near-Negative cavities.  
Figure 12: PD voltage in Intermediate cavities.  
Figure 13: PD charge in Intermediate cavities.  
Figure 15: PD voltage in Near-Negative cavities.
# Alternative Categorization of Radio Frequency Power Amplifier for Generalized Design Insights
## 3 Alternative Categorization
### Frequency
#### 3.2.1 Step Approach  
The procedure starts by designing the load network for optimal performance at a specific frequency. As a result, the bandwidth of the RFPA is initially limited, so tuning elements [25] need to be incorporated into the load network to mitigate this problem. These elements allow for dynamic adjustments to the load impedance, effectively broadening the RFPA's operating frequency range.  
Tuning elements like varactors [26] or PIN-based diodes offer continuous impedance control, making them ideal for wideband tuning. A capacitor or inductor bank also provides these discrete steps of impedance adjustments, allowing for broader frequency coverage. Alternatively, RF switches [27] have also been used to switch between pre-designed load networks, enabling the RFPA to operate a wide frequency range. However, one must realize that implementing such a design always comes at the cost of a more complex system.
## 2 Related Works
### Task Scheduling and Resource Management  
Scheduling problems are crucial in various domains, with each scenario having unique details and distinct optimization objectives. Properly scheduling tasks (requests, jobs, etc.) not only enhances response speed, but also controls resource utilization and optimizes other performance metrics and costs as per the scenario requirements. Table 1 summarizes the problems addressed by these works and their respective methods. Most of the mainstream methods for scheduling problems are based on heuristic algorithms. [PERSON] et al. [18] proposed a genetic simulated annealing fusion algorithm to achieve task scheduling and resource allocation for cloud-edge collaborative architecture. [PERSON] et al. proposed a two-stage scheduling method based on priority rules in their study [19]. This method involves scheduling among servers and within servers separately, based on the estimated task priorities. Additionally, it adjusts the priorities of subsequent tasks based on the actual start times of previous tasks. [PERSON] et al. [20] presented a series of task allocation strategies for workflow scheduling in edge environments to accommodate different types of tasks. These strategies were combined with a greedy method to construct an improved greedy search algorithm. [PERSON] et al. [21] designed a parallel job scheduling algorithm that combines ant colony algorithm to address edge cloud-based scheduling problems. In recent years, some researchers have applied deep reinforcement learning methods to various scheduling problems. [PERSON] et al. [15] proposed a Policy Gradient-based method that considers both CPU and memory utilization. Task allocation is represented as a two-dimensional tabular image, with resource consumption on the horizontal axis and timestamps on the vertical axis, and task assignment is directly performed based on this table. [PERSON] et al. [22] developed a similar model to [PERSON] et al. [15] for scheduling time-varying workloads in a shared cluster. They added multiple penalty terms to the reward function to avoid unreasonable decisions. [PERSON] et al. [23] designed an RL agent based on the Pointer Network [27] to determine the execution sequence of workflows on heterogeneous cloud servers. They take the task sequence to be scheduled as input and obtain a sequence of machine-task assignments. [PERSON] et al. [24] introduced a rewriter to solve job scheduling. They use a directed acyclic graph to represent the execution order of
## 2 System Architecture and Communication
### Communication  
Fiber network is normally available in urban areas, but it is not readily available in remote rural areas. Due to the rapid development of 4G wireless communication, it is considered for the application of the protection and control system. Special CPE (Customer Premise Equipment) of the wireless communication equipment has been developed for current differential protection with accurate time synchronization. The IRIG B signal from the CPE is used for synchronization of the differential protection.  
The DMS and DTUs are connected by setting up a 4G wireless private network, as shown in Figure 2. A CPE device is connected to each DTU for wireless communication. Communication networks also use data encryption to ensure data reliability and integrity.  
The distribution master station transmits the distribution system model data and terminal deployment information to the DMS through TCP (Transmission Control Protocol). Through 4G wireless private network, the DMS uses Web Service to exchange topology change information with DTU terminals, and to enable or disable the current differential protection function. The 4G wireless private communication network is used between the DMS and the terminal, and a discovery/registration mechanism is used between the DMS master station and the terminal, that is, after the terminal is deployed, the terminal is registered in the DMS through a Web Service registration service, and the  
Figure 2: Communication for the differential protection system. DMS: Distribution Management System. CPE: Customer Premise Equipment.  
terminal model information is transmitted through a file transmission service. After successfully registering with the master station, the terminal real-time data is transmitted through IEC 60870-5-104 protocol to the DMS. At the same time, IEEE 1588 protocol is adopted to perform time synchronization service for the terminal. Communication between terminals is also through the wireless private network, but because of the need to transmit sampling values and trip signals, GOOSE/SV protocol based on subscription/publishing mechanism is required to ensure the synchronization efficiency of sampled values.
# Hybrid Precoding Design for Wireless Fading Channels
## 3 Proposed Hybrid Precoding  
Precoding for the users in group 1 was designed based on the average of the data symbols during the coherence time of the channel, and precoding for the users in group 2 was designed based on the actual symbol value of each symbol interval. Since these two types of precoding have very different characteristics, it is very difficult to remove the interference between groups at the receiving end if there are two types of signals together at the received signal. Therefore, it is desirable to implement precoding at the base station to prevent interference between the two user groups. In order to satisfy this condition, in this paper, precoding is implemented so that no signal related to \(\mathbf{x}_{2}\) exists in \(\mathbf{y}_{1}\) and no signal related to \(\mathbf{s  
Equation (10). Therefore, designing \(\mathbf{W}_{1}\) is the same as designing \(\mathbf{G}_{1}\), and the method for designing the matrix \(\mathbf{G}_{1}\) will be explained in detail later.  
In summary, designing the linear precoding matrix, \(\mathbf{W}_{1}\), and the symbol-level precoding vector, \(\mathbf{x}_{2}\), is the same as designing \(\mathbf{G}_{1}\) and \(\mathbf{f}_{2}\), respectively. In this paper, \(\mathbf{G}_{1}\) is referred to as the linear orthogonal MMSE (OMMSE) matrix, and \(\mathbf{f}_{2}\) is referred to as the non-linear orthogonal SLP (OSLP) vector.
# Deep-Learning-Based Framework for PET Image Reconstruction from Sinogram Domain
## 3 Experiments
### Simulation Experiments  
Simulated data of the Zubal thorax phantom with \({}^{64}\)Cu-ATSM and Zubal brain phantom with 11C-Acetate were generated using Monte Carlo simulations, which can simulate the true environment of the PET scan and generate realistic sinogram data by using the GATE toolbox. The simulated PET scanner adopted was the Hamamatsu SHR74000, which contains six rings with 48 detector blocks. The ring diameter is 826 mm. The size of the field of view is 576 mm in the transaxial direction and 18 mm in the axial direction [39]. To obtain the original PET data for the Monte Carlo simulation, a two-compartment model was used to simulate the radiation concentration of different regions of interest (ROIs) in different phantoms. Figure 4 shows that each phantom is composed of several ROIs, in which the kinetic parameters of the two-compartment model are different. Details regarding the kinetic parameter settings can be found in the literature [40; 41; 42]. Each phantom consisted of three data sampling times. For each sampling time, three levels of counting rates were set: 5 \(\times\) 10\({}^{6}\), 1 \(\times\) 10\({}^{7}\), and 5 \(\times\) 10\({}^{7}\). For different counts, each reconstructed dynamic image had 18 frames with a resolution of 64 \(\times\) 64 pixels. Thus, the entire database consisted of two phantoms, and each phantom dataset contained 162 (9 \(\times\) 18) images. One third of each phantom dataset was randomly selected as the testing set, which included 162 images, with the others making up the training set, which included 324 images. The detailed sampling interval of the 18 frames is shown in Table 1. For example, for the 20 min simulated scanning of the Zubal thorax phantom, the first 14 frames were taken every 50 s, then two frames were taken every 100 s, and the last two frames were taken every 150 s.  
In this study, the bias and variance were adopted for comparison.  
\[bias=\frac{1}{n}\sum_{i=1}^{n}\frac{|\hat{x_{i}}-x_{i}|}{x_{i}} \tag{7}\]  
\[variance=\frac{1}{n-1}\sum_{i=1}^{n}{(\frac{\hat{x_{i}}-\overline{x_{i}}}{x_{i} })}^{2} \tag{8}\]  
where \(n\) denotes the overall number of pixels in the ROI, \(\hat{x_{i}}\) denotes the reconstructed value at voxel \(i\), \(x_{i}\) denotes the true value at voxel \(i\), and \(\overline{x_{i}}\) is the mean value of \(x_{i}\).
## 2 Related Work  
Considering advanced metering and the advanced metering infrastructure (AMI), the iMSys is the German version of an advanced meter. Both central functionalities of advanced meters, collecting measurements and providing a communication interface (cf. [7]), are provided by the iMSys. In general, security is an important subject in the context of advanced metering. For instance, the management of cryptographic keys is surveyed in [7], and a theoretical framework for evaluating the topological vulnerabilities of meshed AMI communication networks is presented in [8]. In contrast to other advanced metering systems, the iMSys is heavily regulated and secured. In [8], the authors state that advanced metering devices are, in general, physically unprotected, and that the use of a public-key-infrastructure (PKI) is rare. The SMGW, however, has specific requirements to protect the device itself and detect attempts of manipulation. In particular, the requirements with respect to security and privacy are specified in [9; 10] and every released iMSys has a certificate from the German _Federal Office for Information Security_ (\"Bundesamt fur Sicherheit in der Informationstechnik,\" BSI) stating that it satisfies all these requirements. Furthermore, there are very strict requirements with respect to the Smart Meter Public-Key-Infrastructure (SM-PKI), which are combined with restrictive data access permissions. Hence, these aspects have to be considered when implementing DSM with iMSys. As, in this paper, we focus on the technical integration, SMGW related publications are the ones mainly considered in the remainder of this section. Even though the diverse technical guidelines specifying the requirements for the German SMGWs and related roles associated with the infrastructure were published years ago, e.g., the core document _BSI TR-03109-1_[6] in the year 2013, all of the infrastructure is still, to this day, under heavy development. This may be one of the driving factors for recent proposals of alternative approaches to iMSys, e.g., on the basis of existing technologies [11; 12]. In the past, the BSI has published [13] and updated [14] an assessment of the functionality implemented by the commercially available SMGWs. The document also outlines possible applications of SMGWs in the fields of sub-metering, smart grids, smart mobility, and smart homes. Overall, the updated assessment was the basis for the start of the SMGW rollout in 2020. Although AMI in general is a broadly researched topic, scientific work considering the German SMGW infrastructure in particular is rather rare and the majority of publications are only available in German. Table 1 provides a brief overview of the available related work. Most work focuses on
## 2 Materials and Methods
### Tools, Data Collection, and Preparation  
The method for the present research was carried out using open-source software (OSS), which has numerous advantages such as accessibility, flexibility, cost-efficiency, and the ability to be run on different operating platforms. The OSS used was QGIS 3.16, PostgreSQL 13, pgAdmin4 as a development and administration platform, and PostGIS Bundle 3 for PostgreSQL. The QGIS plugins used for this work were the QuickOSM plugin, for extracting the roads, railways, buildings, and administrative unit objects from OpenStreetMap (OSM) [51], and the DBManager plugin, for querying and managing the database. PgAdmin was used as an administration platform for PostgreSQL, and PostGIS provided support for the geographic objects, allowing location queries in the PostgreSQL database.  
Bucharest population data were collected from the National Institute of Statistics, 2011 National Census [52], and Bucharest Regional Directorate for Statistics [53]. Data on routes, stations, transport lines, operating schedules, tour start and end times, and the headways of vehicles were retrieved from the website of the main public transport operator and the transport authority [54; 55; 56].  
We used three types of data to develop the methodology: stop locations, the stop sequence for each line and stop arrival times.  
Figure 1: Logical scheme for the methodology development.
# A Study of Model Iterations of Fitts' Law and Its Application to Human-Computer Interactions
## 4 Some Studies of Fitts' Law in HCI Scenes
### Touchscreen Scenes
#### 4.1.2 Large Touchscreen  
Users frequently face fixed usage scenarios while using large touchscreens, which necessitates that they take into account both the width and length dimensions while interacting. The angle of motion between the first touch point and the target object is important in 2D target acquisition, and it needs to be carefully assessed for best results.  
Motion angle had a major effect on user performance in a 2D pointing task on big touchscreens, according to [PERSON] et al. They combined motion time, angle, amplitude, and target width to create a nonlinear sinusoidal model [58]. Building on these findings, [PERSON] et al. improved Fitts' law for these kinds of tasks by adding variables like motion angle and target width, demonstrating how effective this method is in considering motion time differences [59]. Furthermore, studies indicate that faster movements in horizontal and vertical directions than in diagonal ones are associated with positioning effects, which are important in touchscreen pointing activities [60]. [PERSON] et al. investigated how beginning and ending positions affected pointing tasks and showed how important they are for task completion [42]. Because of the size of the screens, the size of the icons becomes less important when designing large-screen interfaces. In the two-dimensional plane, designers give priority to elements including target width, motion distance, angle, and positional effects. Designers may efficiently create interactive interfaces that are suited for large touchscreens by integrating these variables.
## 6 Proposal
### Security and Privacy  
The complimentary relationship that characterizes both IoT and cloud computing allows, for example, a user with a mobile device to access information in real time, anytime, anywhere [47]. Despite the numerous advantages this dual technology offers, the challenge remains with regards to security and private data protection for its users. To secure user data and applications, many solutions and frameworks have been suggested and implemented. For instance, the authors in [48] focused on the the human factor of system security by developing a security awareness training framework. The main role of the framework was to train operators of critical infrastructure on various social engineering security threats. The research work [49] suggested a formal probabilistic framework for process learning to compose Service-Specific Overlays (SSO) in cloud networks. An additional work [50] discussed the requirements model for the runtime execution and control of an intention-oriented cloud-based application. Furthermore, a secured objective-driven programming model created automatically at runtime by the PAA (Provisioning-Assurance-Auditing) cloud engine along with the XACML (eXtensible Access Control Markup Language) security annotation representation was presented in [51]. In [52], the authors summarized previous investigatory research that aimed at managing the numerous physical and virtual components in cloud computing.  
In this paper, we used the Spring security framework, which provides both authentication and authorization support to secure Java application. It comes with the implementations of popular security algorithms. On the one hand, we can use either mechanisms provided by the framework or use additional systems for the authentication process. For example, Spring security allows us to authenticate via an LDAP (Lightweight Directory Access Protocol) directory, OpenID, JAAS (Java Authentication and Authorization Service), Kerberos, and many others. On the other hand, Spring security makes it easy to add authorizations to web requests, methods, or even objects.  
\begin{table}
\begin{tabular}{l} \hline \(<\)Pedagogical-scenario\(>\) \\ \(<\)Environments\(>\) \\ \(<\)Environment identifier = “85 ds5 sddfed56s4”\(>\) \\ \(<\)Title\(>\) Physics-PW \(<\)/Title\(>\) \\ \(<\)class-model type = “XML” url = “XML/PW-Environment1.world-model.xml”/\(>\) \\ \(<\)virtual-environment type = “Mascaret” url = “PW-Environment1.world.xml”/\(>\) \\ \(<\)/Environment\(>\) \\ \(<\)/Environments\(>\) \\ \(<\)Activities\(>\) \\ \(<\)Educational-Activities identifier=4d5 fd45s16a6 isvisible = “true”\(>\) \\ \(<\)Title\(>\) Ultrasonic wave’s velocity\(<\)/Title\(>\) \\ \(<\)environment-ref ref = “85 ds5 sddfed56s4/”\(>\) \\ \(<\)Participants id = “6q9 sq54s9” name = “Learner1” Role = “ROLE”/\(>\) \\ \(<\)/Educational-Activities\(>\) \\ \(<\)/Activities\(>\) \\ \(<\)/Pedagogical-scenario\(>\) \\ \hline \end{tabular}
\end{table}
Table 1: An extract of the pedagogical scenario in XML format.