esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theory  
Particles in a viscous solution under a centrifugal force are subjected to radial centrifugal and viscous drag forces, which act in opposite directions. Therefore, Equation (1), which defines Stokes' law, explains the motion of the particles within the viscous solution [15], where \(\frac{\mathrm{d}^{2}\chi}{\mathrm{d}t^{2}}\) is the acceleration, \(\frac{\mathrm{d}\chi}{\mathrm{d}t}\) is the velocity, m is the mass, D\({}_{\mathrm{P}}\) is the particle diameter, \(\rho_{\mathrm{P}}\) is the density of the particle, \(\rho_{\mathrm{m}}\) is the density of the matrix, \(\eta\) is the viscosity coefficient, and G\({}_{\mathrm{g}}\) is the ratio between the rotational force and gravity (m/s\({}^{2}\)).  
\[\mathrm{m_{P}}\frac{\mathrm{d}^{2}\chi}{\mathrm{d}t^{2}}=\ \Big{|}\rho_{ \mathrm{P}}-\rho_{\mathrm{m}}\Big{|}\frac{4}{3}\pi\bigg{(}\frac{\mathrm{D_{P} }}{2}\bigg{)}^{3}\mathrm{G_{g}}-3\pi\eta\mathrm{D_{P}}\frac{\mathrm{d}\chi}{ \mathrm{d}t} \tag{1}\]  
The value of G\({}_{\mathrm{g}}\) can also be defined as in Equation (2), where r is the diameter of the cast ring (m), N is the rotational velocity (rpm), and g is the gravitational acceleration.  
\[\mathrm{G_{g}}=\frac{\mathrm{r}}{\mathrm{g}}(\frac{2\pi\mathrm{N}}{60})^{2} \tag{2}\]During centrifugal casting, several different events occur simultaneously because of the flow of the molten metal and the surrounding environment in the mold. To express Equation (2) in terms of the centripetal acceleration (a\({}_{\text{p}}\)) and particle densities only, the equation is expressed in a simplified form as shown in Equation (3), ignoring the initial velocity and the radius of the particle. A negative value of a\({}_{\text{p}}\) signifies that the particles move towards the center of rotation.  
\[\text{a}_{\text{p}}=\frac{\rho_{\text{p}}-\rho_{\text{m}}}{\rho_{\text{p}}}4 \pi^{2}\text{N}^{2}\text{r} \tag{3}\]  
To determine the influence of density on the behavior of the solute atoms (Table 1), their densities were used in Equation (3). Si and Mg, which are less dense than Al, move toward the center of rotation, while Fe, which is approximately 2.9 times denser than Al, moves away. The a\({}_{\text{p}}\) of Fe, Si, and Mg were 25.813, \(-\)6.866, and \(-\)21.781 rN\({}^{2}\), respectively. Thus, the a\({}_{\text{p}}\) of Fe is 3.76 and 1.18 times larger in magnitude than those of Si and Mg, respectively.
## 2 Materials and Methods  
The research process was developed using three patients as examples. The selection of patients concerned non-standard pathologies that significantly posed a problem with the choice of surgical technique and implant selection within the hip joint area. Figure 1 presents X-ray images of the analyzed patient cases.  
Patient 1 is a 24-year-old with Down syndrome who was diagnosed with congenital dysplasia of the right hip joint. Despite undergoing multiple surgeries during childhood, the patient's hip components could not be properly aligned. As a result, the patient's acetabulum was vertically positioned and shallow, which led to instability of the femoral head, causing subluxation and mechanical pain. Patient 2, a 75-year-old woman, was diagnosed with post-traumatic acetabular detachment of the right hip endoprosthesis after falling from her bicycle. Despite experiencing pain, the patient continued to walk and did not seek medical attention for many months. During this time, the loose cement-plastic acetabulum damaged the bone acetabulum, enlarging its lumen and thinning its walls. The third patient, a 75-year-old man, is a patient with the most complicated anatomical situation. Crowe type IV developmental dysplasia of the hip is one of the most complex and difficult types of hip deformities to reconstruct. Here, the 3D model was particularly useful. In this patient, the femoral head has always been above its natural position, which significantly shortened the limb, disturbed gait motor skills, and led to degenerative changes with a flat acetabulum and a narrow femoral canal. Surgical treatment involves bringing the hip joint down a few centimeters, where it should be but never was, and reconstructing the correct anatomy after 75 years of the deformity.  
Figure 1: 2D visualizations of the hip joint area for the 3 patients analyzed: (**a**) patient no.1; (**b**) patient no.2; (**c**) patient no.3.
# Sustainable and Reliable Information and Communication Technology for Resilient Smart Cities
## 4 Reliable ICT
### Definition: Dependability  
The term reliability can also be seen as an attribute of the overarching concept of dependability, which is important in the area of distributed systems and communication networks. Dependability and dependable systems have been discussed in many fundamental research studies [45; 46; 47; 48]. Thereby, dependability encompasses the various types of non-functional requirements for designing high-quality systems as well as the means to achieving these requirements and the major threats or impairments in the ICT context. Ref. [45] provides a comprehensive illustration of the key components comprising dependability (see Figure 3). This includes, among others, key security aspects such as availability, integrity and confidentiality. They play a vital role for urban ICT since only trustworthy processes and infrastructures will be adopted widely.
## 4 Modeling
### Fitting the Model to Experimental Data  
As a result of the optimization in (26) optimal values of \(\lambda\), \(\beta_{T}\), \(\beta_{L}\), \(\sigma_{T}\) and \(\sigma_{L}\) were extracted and presented in Table 3. The estimated SNR values were calculated using the optimal parameter values in (19) at each azimuth and presented in Table 4. The differences between the experimental and estimated SNR values were then calculated for TO, LO and TL (inward and outward) cases as \(TO^{+}\), \(TO^{-}\), \(LO^{+}\), \(LO^{-}\), \(TL^{+}\) and \(TL^{-}\). The experimental and estimated SNR differences are given in Figure 4. All of the SNR values, apart from \(TO^{+}\) (all azimuths) and \(LO^{+}\) (\(20^{\circ}\) and \(30^{\circ}\)), were close to the experimental data.  
Table 3 shows that the ILD cue weight, \(\lambda\), is decreasing with the increasing azimuth and the bias values, \(\beta_{T}\) and \(\beta_{L}\) are increasing as azimuth increases. The increase in azimuth has more influence on ITD than ILD as the \(\beta_{T}\) increased drastically from 0.900 for 0\({}^{\circ}\) to 9.868 for 30\({}^{\circ}\). The increase in azimuth makes the differences in \(\beta_{T}\) more apparent, which shows the inability of the ITD cue to compensate for the ILD cue in larger azimuths.  
In order to double-check the precision in calculations, heat-map plots of the mean square SNR error \(F(\mathbf{x})\) as a function of \(\lambda\) vs. \(\beta_{T}\) and \(\beta_{L}\) are presented in Figures 5 and 6 for azimuth 0\({}^{\circ}\), 10\({}^{\circ}\), 20\({}^{\circ}\) and 30\({}^{\circ}\) values. Figure 5 plots the mean square SNR error \(F(\mathbf{x})\) as a function of \(\lambda\) vs. \(\beta_{T}\) after fixing the \(\beta_{L}\), \(\sigma_{T}\) and \(\sigma_{L}\) at the extracted optimal values as given in Table 3. Similarly, Figure 6 plots the mean square SNR error \(F(\mathbf{x})\) as a function of \(\lambda\) vs. \(\beta_{L}\) after fixing the \(\beta_{T}\), \(\sigma_{T}\) and \(\sigma_{L}\) at the extracted optimal values. The optimal \(\lambda\) and \(\beta\) values for azimuth 0\({}^{\circ}\), 10\({}^{\circ}\), 20\({}^{\circ}\) and 30\({}^{\circ}\) extracted from the proposed model are shown with white dots in Figures 5 and 6.  
Similar to the results given in Table 2, the SNR values of the inward cases are found to be greater than the outward ones in all azimuth values. For the TO, LO, and TL conditions, the difference between the inward and outward SNR values are decreasing with the increasing azimuth.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(SNR_{\overline{TO}}^{+}\) & \(SNR_{\overline{TO}}^{-}\) & \(SNR_{\overline{LO}}^{+}\) & \(SNR_{\overline{LO}}^{-}\) & \(SNR_{\overline{TL}}^{+}\) & \(SNR_{\overline{TL}}^{-}\) \\ \hline
0\({}^{\circ}\) & 0.084 & 0.058 & 0.476 & 0.473 & 0.961 & 0.869 \\
10\({}^{\circ}\) & 0.119 & 0.067 & 0.403 & 0.289 & 0.960 & 0.636 \\
20\({}^{\circ}\) & 0.227 & 0.054 & 0.295 & 0.089 & 1.032 & 0.283 \\
30\({}^{\circ}\) & 0.410 & 0.000 & 0.149 & 0.053 & 1.053 & 0.055 \\ \hline \hline \end{tabular}
\end{table}
Table 4: SNR values calculated from the experimental error rates.
## 5 Optimizing the Number of Mobile Sinks and Their Paths  
It is not surprising that one mobile sink cannot collect all the generated data in time if the distance between UGVs or the speed of the mobile sink is not ignored. Since the UGV will decide the meeting time according to its cruising and appearance schedule, it is useless whether or not the mobile sink arrives early or late. Once the appearance duration between two UGVs is less than their distance, there is no chance to reach them both. In this section, we try to reduce the number of mobile sinks but still achieve the same performance.  
As seen in Figure 4, even the data collection achieved by the optimal algorithm is still far from collecting all generated data. In the example, the optimal algorithm only achieves less than \(1/3\) of the total generated data, no matter whether the cycle is fixed or subject to the Gaussian distribution. According to the observation, we know that additional mobile sinks must be added. We bring forward some theorems before discussing the algorithm.  
**Lemma 1**: _Each path is independent and has no mutual points with other paths when the number of mobile sinks is optimal._  
If there are mutual points between any paths, we could summarize all the cases into the graph as shown in Figure 5 (note that \(a\) and \(c\) could be the same point just as \(b\) and \(d\) could be the same point). Two paths (\(a\)-\(v\)-\(b\) and \(c\)-\(v\)-\(d\)) pass the same point \(v\), According to the triangle inequality, \(d_{av}+d_{vb}>d_{ab}\) so that \(t_{b}-t_{a}>d_{av}+d_{vb}>d_{ab}\), then there must be an edge between \(a\) and \(b\) which is also true for \(c\) and \(d\). Therefore, if one path already passes the point \(v\), another path could take the direct edge between \(ab\) or \(cd\).  
Figure 3: k-hop search for a better path (k = 3) (the coordinate of the point does not represent the physical location, and the length of an edge does not reflect the distance between two points).  
**Lemma 2**: _Any points that have the same hop number do not have an edge between them._  
**Proof.** According to Algorithm 2, if two points \(q\) and \(r\) have an edge between them and \(r\) is \(q^{\prime}\)s neighbor, then we have \(s_{q}-s_{r}\geq 1\). Therefore, two hop numbers cannot be equal.  
**Definition 6**: _The term **connected point** means that a point \(v\) in the path has both edges on its left and its right while **unconnected point** means points with only one edge or no edges._
## 4 Results  
### Industroyer  
Figure 7 shows the multi-layered attack strategy of Industroyer, referencing the MITRE ATT&CK framework. In the Initial Access phase, techniques such as Valid Accounts (T1078) are utilized, exploiting compromised accounts to gain unauthorized access. Persistence is maintained through Command and Scripting Interpreter (T1059), allowing Industroyer to interact with and disrupt SCADA protocols such as IEC 60870-5-104. The Privilege Escalation phase employs Exploitation for Privilege Escalation (T1068) to exploit known  
Figure 6: Attack vectors and response processes of the security layer.  
vulnerabilities in the grid's OPC servers, allowing targeted manipulation of components such as circuit breakers. Finally, the impact is maximized through the Inhibit Response Function (T0810), which disrupts operator control functions, effectively causing large-scale outages and extending recovery times.
## 2 Study Area and Data  
### Remote Sensing Images and Building Vector Data  
Wenzhou, located in Zhejiang Province, China, is an important coastal commercial and regional center in southeastern China. In recent years, the urban area of Wenzhou has been continuously expanding. The intelligent and automated extraction of building polygons will effectively enhance the efficiency of building supervision. The remote sensing imagery and building vector data used in this study are derived from six regions within the urban area of Wenzhou, captured by unmanned aerial vehicles. Each region's imagery has a size of 20,480 \(\times\) 20,480 pixels with a spatial resolution of 0.2 m, including RGB bands. The study area and the corresponding imagery are shown in Figure 1a. In the high-resolution remote sensing imagery, various buildings with different shapes, arrangements, and heights can be observed in the six regions, as shown in Figure 1b. Moreover, the imagery exhibits challenges in extracting building polygons due to significant issues such as building tilts and shadow occlusions, which can cause confusion between building rooftops, facades, and the background.  
In the data pre-processing stage, we cropped six remote sensing images into 2400 patches of 1024 \(\times\) 1024 size and randomly divided the training and test sets according to the ratio of 7:3. The building contour label data is the building vector data corresponding to the image range, as shown in the sample in Figure 2.
# On-Board Decentralized Observation Planning for LEO Satellite Constellations
## 2 Problem Description
### Constellation Configuration  
A Walker-\(\delta\) constellation configuration can be represented as the parameter tuple (total number of satellites \(N/\)number of planes \(P/\)inter-plane spacing \(F\), orbit altitude \(h\), orbit inclination \(i\)). The total number of satellites, the number of planes, and the inter plane spacing determine the scale of the whole constellation. Each satellite in the constellation has the same orbit altitude and orbit inclination. In this paper, AGI's STK is introduced to simulate all the constellations. Taking Walker-\(\delta\) (30/3/1, 600 km, 60\({}^{\circ}\)) as an example, its 3D constellation configuration and 2D satellite ground tracks at time \(t=0\) are shown in Figure 1a,b respectively.  
For each satellite in the constellation, it has the same attitude maneuver capability and carries the same observation and communication payload. The observation payload configuration includes its main parameters such as payload type, field of view and resolution. The satellite can only observe the target within the corresponding observation availability masks. The satellite-to-target observation availability masks are jointly determined by attitude maneuver capability, observation payload configuration, satellite position and target position. Communication payloads such as radio or laser payloads enable communication links between a satellite and a ground station or between two satellites to transmit data. However, since the relative positions of any two nodes in the communication network may change over time, they must be within the communication availability masks to establish communication links.
## 3 Protocol Analysis on Modbus and 5G
### Modbus Protocol
#### 3.1.3 5G Protocol  
A wireless channel is used to transmit 5G data, where the ADU of 5G is divided into multiple fields according to the complex protocol stack. The 5G New Radio (NR) protocol stack is divided into the control plane protocol stack and the user plane protocol stack [29]. Therefore, we sent data from terminal side to the service data application protocol (SDAP) sublayer of the data link layer in the user plane protocol stack through the TCP/IP protocol. The uplink frame structure of the data link layer in the user plane protocol stack is illustrated in Figure 3. The SDAP frame is divided into four fields, including 1 bit protocol data unit (PDU) type identifier, 1 bit reserved bit, 6 bits QoS flow identifier, and N bits data. Other sublayer fields are divided in the same way. Then, we make data successively pass through  
Figure 1: The frame structure of the Modbus RTU in the data link layer.  
Figure 2: The frame structure of the Modbus TCP in the data link layer.  
the packet data aggregation protocol (PDCP) sublayer, radio link control (RLC) sublayer, media access control (MAC) sublayer, and physical layer (PHY).  
In general, the physical layer frame structure of wireless communication technology is a fixed structure because the previous application environment does not have high requirements for frame structure. Nowadays, 5G technology is required to be used in various services. In order to cope with various complex scenarios, researchers have redefined the physical layer frame structure of the 5G NR and combined fixed subframes with variable time slots to meet dynamic business development needs and improve network utilization [30].
## 2 System Configuration of AC/DC Hybrid Microgrid
### Hierarchical Control Architecture of AC/DC Microgrid  
Hierarchical control refers to the microgrid control technology that uniformly manages the lower converter and load through the higher central controller [16]. Based on this, for AC/DC hybrid microgrid systems, the factors to be considered in isolate mode and grid-connected mode are often different. For example, in grid-connected operation, due to the support of the public power grid, it is not necessary to consider the voltage and frequency regulation of the AC bus. However, the voltage and frequency of the AC bus must be kept stable when performing isolated island operation. Furthermore, the lower converter often needs to prepare multiple control modes to switch or adjust in different situations. The control architecture adopted in this paper is shown in Figure 2.  
Figure 1: System topology of AC/DC hybrid microgrid.  
In the DC sub-microgrid, when the DC side photovoltaic #1 is connected to the grid, in order to provide more power and make full use of solar energy resources, it generally works at maximum power point tracking (MPPT) mode [17]. During isolate island operation, the DC side photovoltaic #1 can be controlled by MPPT. However, when the generating power of the system is greater than the limit of its own consumable power, it is not suitable to work at the maximum power point. Therefore, the power-DC bus voltage (\(P-U_{dc}\)) droop control is adopted to limit its power output [18]. When the DC side battery #1 is connected to the grid, the method of bus constant voltage control can be adopted to maintain the DC bus voltage stability with the interlinking converter, or it can also be charged at a constant voltage within its own tolerance. When isolating island operation, in addition to constant voltage control, the DC side battery #1 usually adopts droop control and photovoltaic #1 to regulate DC bus voltage and increase system autonomy. In addition, the state of charge (SOC) of the battery is also a key factor to be considered in the design of control strategy and to avoid overcharge or overdischarge [19].  
In the AC sub-microgrid, when connected to the grid, the AC side photovoltaic #2 needs to be controlled by MPPT as well as the DC side photovoltaic #1, and the AC side battery #2 can be control charging according to its SOC constant power (P/Q). In isolated island, when the overall power consumption of the system is low, the AC side photovoltaic #2 and battery #2 can adopt P/Q control to achieve the supply and demand balance of AC side power and reduce the working frequency of the interlinking converter.  
In this paper, two parallel interlinking converters are used to exchange power between the AC and DC sub-microgrids. In the grid-connected mode, constant voltage control and DC side energy storage units are used to maintain the DC bus voltage stability. In isolated island mode, constant voltage and constant frequency (V/F) control is adopted to regulate the AC bus voltage and frequency.
## 4 Results
### Virtual Population Experiment: Undertreated Patients  
The virtual population, as described in Section 3, have basal and bolus rates that are sub-optimal, keeping the patients within 110-180 mg/dL at steady state. We consider the virtual patients with high steady state glucose values as patients that are undertreated, i.e., their current treatment regimen does not give the desired blood glucose levels. We show a random sample of four patients in Figure 6 to illustrate the improvements made by letting a TRPO agent train and control each virtual patient. Each figure contains the original sub-optimal basal-bolus treatment as well as the results using TRPO agent superimposed over each other.  
In all four cases, the TRPO agent improves the sub-optimal basal-bolus treatment. For virtual population patient #4, the performance of the basal-bolus is already close to optimal, but we still see a reduction in variance, especially later in the episode, during nighttime.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Treatment** & **Time-in-Range** & **-Hypo** & **-Hyper** & **LBGI** & **HBGI** & **RI** & **Std** & **CoV** \\ \hline Basal-bolus & 83.45 & 2.42 & 14.13 & 0.87 & 4.62 & 5.5 & 40.35 & 0.3 \\ TRPO & 86.12 & **0.1** & 13.78 & 0.46 & **3.17** & **3.62** & **36.55** & **0.27** \\ TRPOe w/ 300 litrs & **86.33** & 0.49 & **13.18** & **0.42** & 4.14 & 4.56 & 36.71 & 0.28 \\ \hline Random skipped boluses: & & & & & & & & \\ Basal-bolus & 79.59 & 2.27 & 18.13 & 0.85 & 5.8 & 6.65 & 50.35 & 0.36 \\ TRPO & 82.91 & **0.0** & 17.09 & **0.2** & 5.55 & 5.75 & 41.06 & **0.29** \\ TRPOe w/ 300 litrs & **84.68** & 0.49 & **14.84** & 0.43 & **4.68** & **5.11** & **40.36** & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of basal-bolus, TRPO and TRPOe results for the [PERSON] patient. low blood glucose indices (LBGI) and high blood glucose indices (HBGI) is low and high blood glucose index respectively, RI is risk index, Std is the overall standard deviation and CoV is the coefficient of variation. All 100 test meal scenarios are included in the performance measures. A lower score is better for all measures, except time-in-range.
# Design of a Digital Array Signal Processing System with Full Array Element
## 2 System Architectural Design
### System Hardware Architecture Design
#### 2.1.3 Clock Network Design  
To achieve synchronous sampling of multiple channels, the transceiver module uses a multi-chip LMK04828 synchronous clock network structure, as shown in Figure 6. This structure consists of a master LMK04828 chip and slave LMK04828 chips. LMK04828 is the master chip in the master control module and the slave chip in the transceiver module. The master chip provides the reference clock and synchronization clock for each slave chip to synchronize the output clock of each slave chip, and the slave chip provides the sampling reference clock and SYSREF for multiple RFSoCs.  
Figure 4: Simulation of inter-channel isolations: (**a**) Simulation between ADC channels; (**b**) Simulation between DAC channels; (**c**) Simulation between ADC channels and clock; (**d**) Simulation between DAC channels and clock.  
To reduce the phase difference provided by the master chip to the slave chip clock, the clock path from the master chip to each slave chip should be strictly equal in length. At the same time, LMK04828 has a clock channel analog delay function that can independently control the output clock delay of each channel. Its minimum delay accuracy of up to 25 ps can make up for the physical delays of different clock paths.  
Figure 5: Simulation of amplitude-phase consistency between channels: (**a**) Simulation between ADC channels; (**b**) Simulation between DAC channels.  
Figure 6: System Clock Network.