esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Result and Discussion
### Results from the Customer Survey
#### 5.2.4 Part 4: Regulatory Aspects  
About 43% of DSOs thought that existing e-regulations and e-directives from the EU commission were partially clear to foster use of flexibility by DSOs. This lack of clarity was also identified in the report by the EU commission, which stated that the provisions in the policies were too general due to separation of DSOs in two simple categories [28]. About 95% of DSOs agreed that regulations needed to be updated to allow more innovative business models to be adopted by DSOs, while the remaining 5% strongly disagreed on  
Figure 10: Delivery of the RESOLVD solution and DSO preparedness.  
this. Regarding future remuneration approaches for DSOs, views were split; 76% thought that a TOTEX (Total Expenditure)-based approach was appropriate, while 24% did not know what approach would be best in the future.  
The responses from the DSOs in connection to the regulatory aspects clearly showed that a lack of a regulatory framework is a challenge. Typically, with respect to the flexibility of services and markets, the respondents highlighted that the current law did not yet allow flexibility in the network, and there were no prices or conditions for the DSOs for RES integration at LV grids. In this regard, DSOs were not allowed to add flexibility items, e.g., BESS, demand response or other devices/services as part of regulatory asset value. which was the basis for regulated revenue. Only classical DSO grid items were accepted, which did not promote large scale deployment of optimal solutions. Other important aspects which were emphasized by respondents were the disallowance of battery ownership (storage), only partial regulatory frameworks for a flexibility market, missing incentives for DSO transformation (e.g., a trough network usage fee) and missing allowed network fee for reverse direction of electricity flow. In this regard, for example in Germany, operation of energy storage by the DSO is not regulated, and using the smart meter solution to control producing or loads was not released by the IT security authority (BSI) until now. A new law is expected to be passed in 2021/22 about controlling consumer loads in LV grids.
## 5 Experiments
### Experimental Setup  
#### 5.1.1 Environment Settings  
Our experiments were conducted using the FedScale [30] platform, and we ensured a consistent environment setting across different dataset experiments. The client pool consisted of 2800 clients, with 100 clients selected in each round of training. We performed a total of 1000 training rounds. The datasets were divided into non-iid partitions. The computation capability of clients was predetermined prior to the training process, while the communication bandwidth of clients varied during the training process. Furthermore, the client's online/offline status also fluctuated throughout training.
## 4 Results and Discussion
### Comparison with MSC-30/R134a Pair
#### 4.3.2 Isosteric Heat of Adsorption  
Figure 9 shows the isosteric heat of adsorption for the MSC-30/R134a and MSC-30/R1234 yf pairs, respectively. We can see that MSC-30/R134a shows higher values than MSC-30/R1234 yf. Moreover, [PERSON]'s model [21] always demonstrates a higher value in both cases. This increase is attributed to the pressure correction presented in Equation (25) trying to take into account the actual pressure change, which is apparently higher than in the case of an ideal gas and is intrinsic for the phase change from gas to the adsorbed form. Consequently, with the increasing fugacity of the gas, the difference between ideal behavior and experimental finding becomes more obvious, bringing up the necessity for such corrections. This means that these corrections are typically higher with the increasing pressure, as they are affected more by the adsorbed phase as well and are proportional to the adsorption energy increase. In the case of the studied system in this work, MSC-30/R134a and MSC-30/R1234 yf, the pressure dependence is very similar, as is the energy difference, which is partially visible by comparison in Figure 9. Likewise, it can be expected that the COP value calculated using [PERSON]'s model [21] will offer a higher value than the ideal approach, since \(q_{ads}\) is calculated as \(q_{ads}=(w_{max}~{}-~{}w_{min})q_{st}\). Accordingly, if \(q_{st}\) increases, the specific heating power (SHP) increases as well.
# A Heuristic Algorithm for Locating Line-to-Line Faults in Photovoltaic Systems
## 3 Proposed Method
### Module Group Voltage  
A module group voltage is defined by grouping r modules within the same string, where r must be a divisor of the total number of series-connected modules m, meaning m/r must be an integer. Based on the array size in this study, each module group is composed of three PV modules, resulting in 21/3 = 7 module group voltages per string. With two strings, there are a total of 14 module group voltages. These measurements are denoted as \(V_{i,j}\), where \(i\) represents the string number and \(j\) represents the module group number within that string, numbered from 1 to 7 from high to low potential.
# Lipid Fraction Properties of Homemade Raw Cat Foods and Selected Commercial Cat Foods
## 4 Discussion
### Lipid Fraction Properties of Self-Prepared Cat Food
From the nutritional point of view, the distribution of FA in TAG is very important, but even more important, is their composition in the fat. The EFA for cats are LA and AA from the omega-6 group as well as ALA, EPA and DHA belonging to the omega-3 group [40; 41]. The role of omega-6 and omega-3 fatty acids in maintaining health is affected by their ratio. Most fats and oils in cats' foods are richer in omega-6 than omega-3 fatty acids, but it is important for pets to contain an appropriate balance of both. Determining the optimal dietary omega-6 to omega-3 ratio for pet food is still under investigation, but some recommendations indicate ratios of 10:1 to 5:1 [42]. In the case of tested foods, omega-6/omega-3 was in this range (except SP-5, with the ratio below 5:1). Salmon oil has been added to all self-prepared cat food to improve this ratio. Such supplementation seems necessary because the raw diet is intended for cats at any stage of their life, especially during growth and reproduction when they have an increased need for EFA. The addition of salmon oil as well as cod-liver oil and algae meal affects the higher content of EPA, DHA and AA in self-prepared cat food. The added salmon oil was the oil of wild Atlantic salmon, which according to the producer analysis, compared to oil of farmed salmon, has a higher content of EPA and DHA as well as higher ratio of omega-3 to omega-6 (farmed salmon--1:1 and wild salmon--10:1) [43]. FA composition is one of the factors that determine the oxidative stability of fats and oils [24]. As a rule, samples with longer \(\tau_{\text{max}}\) are more stable than those with shorter \(\tau_{\text{max}}\) determined at the same temperature. Extremely short \(\tau_{\text{max}}\), especially for SP-2 and SP-3 (Figure 3), is probably associated with the greatest amount of PUFA in their composition (Figure 1) and a higher PUFA to SFA ratio than in the other tested foods (Table 1). The longest \(\tau_{\text{max}}\) among self-prepared foods was observed for SP-5, for which the content of SFA or MUFA was the highest and PUFA was the lowest. Moreover, it should be mentioned that only SP-5 fat was solid, which was due to the high content of SFA.
# The Strain Heterogeneity and Microstructural Shear Bands in AZ31B Magnesium Alloy
## 3 Results
### Failure Morphology  
Prior to the tensile test, the specimen's surface was carefully mechanically polished to achieve a mirror-like finish, ensuring that any subsequent morphological changes after failure would accurately reflect its plastic deformation behavior. To capture these deformations, both optical microscopy and SEM were employed, with the results presented in Figure 6. Figure 6a demonstrates that in the vicinity of the fracture, severe plastic deformation was evident, concentrated within a cross-distributed area oriented at a 45\({}^{\circ}\) angle to the tensile direction, as highlighted by the red dashed line. This observation aligned perfectly with the strain field analysis of shear bands, reinforcing the notion that the plastic deformation during the tensile process is non-uniform, primarily localized in shear bands oriented at 45\({}^{\circ}\) to the tensile axis. Zooming in on one of the clipped regions in Figure 6b revealed a dense network of slip lines within the shear region, accompanied by numerous \"tongue\"-shaped cracks (indicated by red arrows). These \"tongue\"-shaped cracks are associated with twins. When the crack front encountered twin crystals, it deflected from its original path, separating the twins from the matrix, thereby giving rise to the characteristic \"tongue\" pattern [23]. These characteristics indicate that the twin crystals have a higher density in the shear region.  
To further delve into the microstructural details, a cracking area was magnified, as shown in Figure 6c. Here, it became apparent that cracks predominantly initiated at the intersections of grain boundaries and trace lines. Notably, twin boundaries, being a special type of grain boundary, exhibited limited deformation coordination capabilities within the grain boundary region. Consequently, the junction of twin boundaries and grain boundaries became a preferential site for crack initiation, ultimately impacting the material's plasticity.
# MC-EVM: A Movement-Compensated EVM Algorithm with Face Detection for Remote Pulse Monitoring
To address this, the coordinates of the detected face from each frame are provided to the EVM process. If the face detection model cannot provide exact coordinates for every frame, the EVM uses the last detected coordinates, minimizing errors due to small pixel differences. The SSD model is able to work at a frequency of 19 frames per second (fps) with input images of 512 \(\times\) 512 size [17] but can go down to 12 fps in mobile phones, which means that it can process one frame approximately every 83 milliseconds while the EfficientDet-Lite0 model can work at a frequency of 27 fps, so it can process one frame in 37 milliseconds (measured originally in [18] in a smartphone Pixel 4 with 4 CPUs). These limits exist due to the original implementation and the structure of SSD and EfficientDet due to the size and the internal structure (number and size of the used layers) of these models. We use these models because they provide the best results for smartphone object detection (Samsung A51 with 64-bit octa core CPU). This speed provides the ability to reduce the error of face detection because, even if the user performs a fast movement, the face is moved a very limited distance, and therefore a small number of pixels are considered to be in the accepted error range and are eliminated from the face in the current frame towards the direction of the movement. Our paper builds up as follows: Section 2.1 defines the state-of-the-art for the pulse monitoring and the usage of IT solution to automate the process of pulse monitoring in addition to other types of human bio-signal. Section 2.2 defines the state-of-the-art for the Eulerian Video Magnification and how to use it for pulse extraction. Section 2.3 defines the state-of-the-art for face detection using the SSD and EfficientDet models. Section 3 defines the proposed algorithm together with the solution design. And finally, Section 4 contains the results validation and discussion.
# Deep Learning-Based Signal Detection for Underwater Acoustic OTFS Communication
## 5 Discussion
### Findings and Implications
The results show that the proposed SC-CNN-BiLSTM signal detection method had better performance in multiple communication channels with different scales of multi-path and Doppler effects. The results indicate that the integration of the CNN and RNN can better extract the signal features of time series data. This thought could be employed in various UWA communication scenarios under complex and varying UWA channels.
## 4 Proposed Scheme
### Integration into Distributed Applications  
Integrating the scheme into distributed applications such as blockchains or IoT networks can provide a robust framework for decentralized signature storage and validation. This step extends the applicability of the scheme beyond primary cryptographic processes to a scalable system.
## 2 Algorithm Architecture
### Test Vehicle Configuration  
In this study, the subject vehicle was equipped with ADAS sensors, environment sensors for autonomous driving, and a reference system such as DGPS using the networked transport of RTCM via internet protocol (N-TRIP). The vehicle had long- and mid-range radar and one four-layer lidar mounted on the front bumper. Additionally, on the backside of the vehicle, two single-layer lidar and two rear-side radar were installed in order to detect rear-side objects. A monocular front vision module (Mobileye EyeQ2) was mounted on the center position of the windshield. A U-Blox EVK-M8L GPS, called low-cost GPS, was installed for macrolevel localization, and a DGPS receiver (Oxts RT3002) was also installed for the reference position of the ego-vehicle. The vehicle setups for this paper aimed to use close-to-market sensors for the much faster and more feasible realization of autonomous driving technology in the near future [12]. We used N-TRIP as correction signals for DGPS. In addition, the DGPS is independent of the GPS position input to the system since it only serves as a reference to verify the development of algorithm performance. The throttle, brake, and steering actuators were used for vehicle automation. These systems are connected through the control area network (CAN), and the discretized command signals are transmitted on each cycle. We set up an embedded PC and MicroAutoBox as high- and low-level controllers. Figure 3 depicts the test vehicle and the installed sensors, including detection range and field of view (FOV). For lane-level localization using ADAS commercial sensors, we used low-cost GPS, vehicle proprioceptive sensors (velocity, yaw-rate), and front vision sensors and radars. The specifications of the major sensors utilized in this research are listed in Table 1.  
Figure 2: Localization algorithm architecture. Global Positioning System(GPS), High Definition(HD), Iterative Closest Point (ICP), Dead Reckoning(DR).
## 5 Simulation Results
### Case 2: FC Included  
In this case, the FC-based micro-CHP system is included in the IHEMS. Thermal output of the FC and the boiler satisfy the thermal load demand, while the RES and electrical output of the FC supply the electrical load of the house in combination with the utility.  
As shown in Figure 8, the FC is idle in the early hours of the day. At 6:00 A.M., when the load demand exceeds the RES power, the FC starts its operation to meet the load demand. Most of the time, no power is purchased from the utility in this mode while selling the surplus power during 01:00-05:00, 11:00-16:00 and 23:00-24:00 time intervals. However, at 18:00 the combined output of the FC and the RES is unable to meet the load demand. Therefore, electric power is purchased from the utility during this hour.  
An important observation in this case is the low power operation of the FC during 11:00-16:00. The reason behind this behavior of the FC is the consideration of startup and shutdown costs of the FC in the developed model. The inherent nature of the RCGA is to move towards an optimal cost; therefore, the algorithm calculates and compares if it is beneficial to shut down the FC or to keep  
Figure 8: Hybrid energy supply with the FC.  
Figure 7: Installation of RES and Utility Bidirectional Power Flow.  
it operating at a lowest optimum level. During this time period, the FC is running at its minimum possible output described as \(P_{FC_{min}}\). The daily operational cost of the SH is $2.3968 in this case which is a decrease of 8% as compared to Case 1.
# Assessing Spatiotemporal LST Variations in Urban Landscapes Using Diurnal UAV Thermography
## 3 Methods
### Statistical Analysis  
Following data pre-processing, statistical analyses were performed to characterize the thermal properties of each LULC type. Descriptive statistics, including the mean, median, standard deviation, minimum, and maximum LST values, were calculated for each LULC class and diurnal period to summarize the central tendency and dispersion of the data. Furthermore, frequency distributions, visualized through histograms overlaid with kernel density estimates, were generated to explore the distributional shapes, providing a foundational understanding of their thermal behavior.
## 3 Method
### FEM Simulation Setup  
Friction and sliding between diaphragms are not considered, and the deformation of diaphragms is assumed to be linear and isotropic. Then, the FEM simulation is developed using the ANSYS 2024 R1 software. Specifically, the domain partitioning is performed in the Design Modeler, and the grids are generated by the meshing module. As shown in Figure 6a, the geometric model used for FEM simulation comprises three diaphragm layers and a cylinder body. Due to the circumferential symmetry of the simplified geometric model, a quarter-section model is employed for reducing computational cost, with symmetric boundary conditions applied at the truncated surfaces. The three diaphragm layers are sequentially labeled as A, B, and C from top to bottom, with the upper surfaces designated as u-faces, the lower surfaces as d-faces, and the circumferential surfaces as c-faces. The cylinder body is labeled as D. To facilitate structured mesh generation, the diaphragms and cylinder body are divided into five meshing blocks, respectively. As shown in Figure 6b, the model is meshed using hexahedral elements, with three layers of mesh discretized in the thickness direction of the diaphragm. Then, the FEM computation and post-processing are conducted in the Mechanical module. The boundary conditions and loads of the FEM model are detailed in Table 2. Furthermore, the diaphragm deformations under varying differential pressures could be obtained through the FEM computation. The stress values extracted along the radial characteristic path of the diaphragm facilitate the calculation of circumferential and radial stresses on both the upper and lower surfaces.
# An Algorithm to Retrieve Precipitable Water Vapor from Sentinel-2 Data
## Materials and Methods  
### Study Area  
The study area is located at the border of Shanxi, Shaanxi, and Inner Mongolia in China. The geographical coordinates of the study area are 107\({}^{\circ}\)28\({}^{\prime}\)48\({}^{\prime\prime}\)-112\({}^{\circ}\)45\({}^{\prime}\)52\({}^{\prime\prime}\)E, 36\({}^{\circ}\)02\({}^{\prime}\)41\({}^{\prime\prime}\)-41\({}^{\circ}\)34\({}^{\prime}\)03\({}^{\prime\prime}\)N, as shown in Figure 1. The study area has a typical arid and semi-arid continental climate with little rainfall, and the rainfall is mainly concentrated between June and September. The average annual rainfall is approximately 360 mm, the average annual sunshine hours are 2875.9 h, and the average yearly temperature is 7.29 \({}^{\circ}\)C. The evaporation is approximately 2000 mm, and the average annual humidity is 56%. The soil type is sandy, primarily soil with loose structure, strong water permeability, poor water, fertilizer retention capacity, and poor soil. The main vegetation types include steppe, deciduous broad-leaved shrub, and sandy vegetation.
# Research of Security Routing Protocol for UAV Communication Network Based on AODV
## 2 Security AODV Routing Protocol
### Protection of Route Error (RERR) Message  
The SAODV protocol uses hop-by-hop signatures to protect all fields of the RERR message. The nodes that generate or transmit RERR messages sign it. The nodes that receive the RERR message verify it. This can ensure the integrity and resistance of the RERR message. However, because the serial number of the destination node is not signed by the corresponding node, in order to ensure the security of the protocol, when processing the RERR message, the node will not update its destination node serial number according to the RERR message.
## 6 Comparative Study  
This section summarizes a comparison of this work with various published FSS designs. A summary of the reviewed FSSs is presented in Table 4 to show the significance of the proposed FSS structure over other works. Therefore, it is observed from the table that the anticipated EM shield is novel and highly selective, with an angular stability up to \(\pm 75^{\circ}\) and polarization-insensitive spectral response with compactness as an additional feature.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref. No.** & **Unit Cell Size (mm)** & **Employed Substrate** & **Operation Band** & **Angular Stability** & **Frequency Ratio** \\ \hline
[12] & 0.087\(\times\) 0.087\(\lambda\) & FR-4 & UWB & 0–60\({}^{\circ}\) & N/A \\
[13] & 0.194\(\lambda\) 0.194\(\lambda\) & Polyethylene terephthalate & GSM & 0–60\({}^{\circ}\) & 2.11 \\
[14] & 0.163\(\lambda\) 0.163\(\lambda\) & Glass & WiFi/WLAN & 0–45\({}^{\circ}\) & 2.24 \\
[15] & 0.21\(\lambda\) 0.21\(\lambda\) & Paper and Polymer & WiFi/WLAN & 0–45\({}^{\circ}\) & 2.22 \\
[16] & 0.161\(\lambda\) 0.161\(\lambda\) & Taconic RF-35 & X & 0–50\({}^{\circ}\) & N/A \\
[17] & 0.167\(\lambda\) 0.167\(\lambda\) & FR-4 & C/X & 0–60\({}^{\circ}\) & 1.32 \\
[18] & 0.26\(\lambda\) 0.26\(\lambda\) & ROA350B & X/Ku & 0–45\({}^{\circ}\) & 1.91 \\
[19] & 0.149\(\lambda\) 0.149\(\lambda\) & FR-4 & C/X & 0–60\({}^{\circ}\) & 1.87 \\
[20] & 0.082\(\lambda\) 0.082\(\lambda\) & FR-4 & WiMAX/X & 0–70\({}^{\circ}\) & 3.3 \\ This Work & 0.184\(\lambda\) 0.184\(\lambda\) & RT/duroid 5880 & X/Ku & 0–75\({}^{\circ}\) & 1.52 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with dual-band FSSs reported in the literature for EM shielding applications.  
Figure 9: (**a**) Performance of the FSS shield at a normal angle of incidence for the TE and TM polarization states. (**b**) SE versus frequency plot as a function of oblique angle for the TE polarization mode. (**c**) SE performance of the FSS for TM mode under various angles of incidence, ensuring angular stability.
# VNF-Enabled 5G Network Orchestration Framework for Slice Creation, Isolation and Management
## 3 Network Model and Mathematical Foundations  
### Physical Infrastructure (PI) Model  
PI's physical structure is illustrated by a weighted undirected graph. The weighted undirected graph is denoted as \(G^{PI}=(N^{PI},E^{PI})\), where \(N^{PI}\) represents a group of physical nodes and \(E^{PI}\) represents a group of physical links. There is a unique set of properties for each graph node and link. Every individual node of a graph should be represented with CPU Capacity (CPU), Link Capacity (LC), Security Level (SL), Delay Rate (DR) and Jitter (JT). As an example, the parameters of the \(i^{th}\) node of a graph should be specified as a subset containing \((CPU_{i}^{PI},LC_{i}^{PI},SL_{i}^{PI},DR_{i}^{PI},JT_{i}^{PI}),i\in N^{PI}\). The values for a node's relevant metrics are developed to match the true features of a 5G mobile network's core nodes. As with nodes, each link of a graph is specified with the subset reflecting the associated network features, \(E_{i}^{PI}(BW_{ij}^{PI},L_{ij}^{PI})\), where \(BW_{ij}^{PI}\) is the bandwidth between the link \(i\) and \(j\) of PI and \(L_{ij}^{PI}\) is the length of the link among the nodes \(i\) and \(j\) of PI.