esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Motivation  
Smart devices may be categorized by their service providence and geographic locations, usually evaluated using a specific analytical system. The collection of detailed data is facilitated by sensor services for specific current initiatives that monitor every day-to-day activity of the populace in smart cities, such as bicycling, car parking, and so on. Many services use an IoT structure based on WSNs to assist noise and air emission control, vehicle mobility, monitoring, and surveillance systems.  
Due to the obvious way a WSN is organized, routing protocols are split into flat and hierarchical structures. A flat routing structure makes each sensor node execute the same routing function and activities. As a result, all the deployed sensing nodes are configured to immediately transmit any detected information to the BS. A hierarchical routing design organizes the deployed sensing nodes into groups named 'clusters'. Nodes in a cluster are defined by their assigned functions and roles or responsibilities. The common hierarchical structure is divided into two layers: in the lower layer, member nodes perceive data from the external phenomena or deployed environment and eventually send the data to their corresponding CH, whereas in the upper layer, CH compresses and relays the received data to the BS [12].  
When developing a smart network, scalability, resilience, and a reduction in network traffic are the primary concerns that should be addressed. In many networking systems, the individual administration of both clustering and routing leads to the prevention of optimal energy utilization and enhancement of network lifespan; thus, this major issue prompted us to develop a distinct clustering process and routing strategy for static regions in smart cities. Moreover, additional FT capabilities are also taken into consideration, especially during data transmission. This study addresses both the categorization of deployed nodes and data routing as a single issue. Static clusters in smart cities are created based on the node's position and communication range. Once the clusters and the controller node have been identified, a routing method is suggested, including FT capability.
## 2 Related Work
### Secure Multi-Party Computation
In healthcare, MPC facilitates collaboration among hospitals to jointly train tumor detection models without exposing sensitive patient data, thus improving diagnostic accuracy while adhering to privacy regulations. In autonomous driving, MPC supports vehicle-to-cloud collaborative computation, such as real-time path optimization and environmental sensing, while preventing the leakage of sensitive data, including driving behaviors and location information. These applications highlight the critical role of MPC in ensuring data privacy and secure computation across various sensitive and distributed data scenarios.
## 3 Case Study (Quseir Road)
### Evaluation of Existing Geometric Alignment of Quseir Road  
The geometric alignment of Quseir Road, depicted in Figure 8, reveals several noteworthy issues that warrant attention in the highway route selection process:  
1. **Sharp Horizontal Curves**: Approximately 21% of the horizontal curves are characterized by sharp radii, indicating speeds below 85 km/h, when back-calculated using the balance equation. Specifically, out of 125 curves, eight curves (6% of the total) feature radii below 135 m, implying speeds between 45 and 55 km/h. Additionally, 11 curves (9%) have radii ranging from 145 m to 210 m, suggesting speeds between 55 and 70 km/h. Moreover, seven curves (6% of the total) exhibit radii between 215 m and 305 m, inferring speeds between 70 and 84 km/h.
2. **Consecutive Reverse Curves:** The existing alignment features severe consecutive reverse curves, with insufficient lengths of tangent between successive curves for the progression of superelevated edges, in accordance with guidelines [20; 21]. This accounts for 4.3% of the total existing alignment.
3. **Short Horizontal Curves:** The alignment includes extremely short horizontal curves, some as short as 15.5 m, resulting in kinks along the road. Research suggests that horizontal curve length (m) should ideally be three to six times the design speed (km/h), with an absolute minimum of 150 m. Within the 90-km section examined in this study, 47 curves (38% of the total curves in the alignment) have lengths less than 150 m.
4. **Small Angles of Deflection:** Curves with angles of deflection less than 5 degrees are not preferable as they lead to short horizontal curves or kinks in the alignment [20; 21]. The existing road alignment includes 18 curves (14.4% of the total alignment) with angles of deflection less than 5 degrees.
5. **Large Angles of Deflection**: Similarly, curves with large angles of deflection are undesirable as they can cause discomfort for drivers, particularly on sharp curves [34; 35]. As an example, the existing alignment consists of two reverse curves between KM 26+994 and KM 28+470. At this distance, the two reverse curves have radii of 540 m and 460 m consecutively, with angles of deflection measuring 160 and 150 degrees, respectively. This configuration results in a reverse curve alignment spanning almost 1.5 km, coupled with sharp curvatures. The application of the Threat Score in this segment yields an average TSExisting of 2.5.
6. **Maximum Vertical Grades**: AASHTO [20] specifies that the maximum vertical grades in mountainous regions, designed for speeds of 110 km/h, may reach up to 5%. However, considering the presence of severe combinations of horizontal and vertical curves, it is preferable to limit the maximum grade to 3%. Currently, 1% of the existing alignment consists of grades exceeding 3%, with a maximum grade of 4.5%.
# A Short-Term Forecasting Method of Inter-Frequency Clock Bias for GPS Block IIF Satellites
## 2 Methods  
Taking GPS as an example, we first describe the triple-frequency uncombined PPP models in detail. Subsequently, an IFCB estimation method using three-frequency observations is successively developed.
## 4 Research Results Discussion  
The level of the knowledge absorption capacity required for the transition between successive maturity levels of Logistics 4.0 was determined based on analysis of the answers of experts. Analysisof the results shows the following in all areas: flow of materials (Figure 2), information flow (Figure 3) and management (Figure 4):  
* \"Transition from Ignoring to Defining\" requires having \"Ability to acquire knowledge\".
* \"Transition from Defining to Adopting\" requires having \"Ability to assimilate knowledge\".
* \"Transition from Adopting to Managing\" requires having \"Ability to transform knowledge\".
* \"Transition from Managing to Integrated\" requires having \"Ability to use knowledge\".  
The diagrams with research results present the relationship between the level of knowledge absorption capacity in the organization and the ability to achieve a certain Logistics 4.0 maturity level. The dependence is directly proportional, i.e., the achievement of higher Logistics 4.0 maturity levels in an organization requires achieving ever-higher levels of knowledge absorption capacity in the organization.  
Detailed analysis of the results allows noting the differences between knowledge absorption capacities in the three areas of the logistic system: material flow, information flow, and management. In the area of material flow, the logistics system general requirements to the level of knowledge absorption capacity as a factor limiting the achievement of a certain Logistics 4.0 maturity level are the lowest. These requirements grow for the information flow area to reach the highest level for the management area. To sum up, the research indicates that management is the most demanding area of the logistics system. As an element based on knowledge processing, it requires the greatest absorption capacity to implement Logistics 4.0 solutions.
# 3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video
## 3 Materials and Methods
### Synthetic Data Generation
#### 3.2.2 Signal Formation  
Two-second pulse signals were generated by varying \(\omega\) between 55 and 240 bpm (0.9 and 4 Hz) at regular intervals of 2.5 bpm for the Fourier series model:  
\[y=\sigma\left[a_{0}+\sum_{i=1}^{2}a_{i}\cos\left(i\omega x\right)+b_{i}\sin \left(i\omega x\right)\ \right]+\mu. \tag{2}\]  
\(\sigma\) corresponds to the scaling factor and \(\mu\) to the mean value. We scaled the signal between 0 and 1. \(\omega=2\pi f\) with \(0.9\leqslant f\leqslant 4\) Hz at intervals of 2.5/60 Hz. \(0\leqslant x\leqslant 2\) s. The time sampling was set to 30 Hz. This value corresponds to the typical number of frames per second delivered by standard cameras. Thus, \(y\) corresponds to a vector that integrates 60 (2 s \(\times\) 30 Hz) scalars. Values of \(a_{i}\) and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Fits Periodic** & **Model** & **Number of** \\
**Name** & **Functions?** & **Equation** & **Coefficients** \\ \hline exponential & ✗ & \(y=\sum_{i=1}^{n}a_{i}e^{\mathrm{b}x}\) & \(2n\) \\  & & \(1\leqslant n\leqslant 2\) & \\  & & \(y=\sum_{i=1}^{n+1}a_{i}x^{n+1-i}\) & \\ polynomial & ✗ & \(n\): polynomial degree & \(n+1\) \\  & & \(  
\(b_{i}\) coefficients (Equation (2)) are presented in Table 2. The phase of the produced signal is randomly shifted using a uniform distribution.
The proposed method can also be potentially applied to other time-series predictions, such as stocks prediction, climate change prediction, and tool life prediction. These examples of applications usually have a huge number of historical datasets, which are difficult to model by typical regression methods and time-series prediction methods. The challenge of the proposed method is the pre-requisite understanding of fundamental fuzzy theory, which sometimes requires an expert to determine its parameters. The PANFIS method also has high complexity in terms of programming language and parameters selection that would be another challenge to embed the proposed method into the real-time application, for example, using LabVIEW. The limitations of the proposed method are the requirement of sufficient historical datasets. Less historical datasets will reduce the prediction accuracy because the model is constructed based on minimum information from the historical datasets.
# Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review
## 3 Background in Programmable Data Plane and 5G and Beyond
### Software Defined Networking, Programmable Data Plane, and Devices
#### 3.1.3 PISA (Protocol Independent Switch Architecture)  
The Protocol-Independent Switch Architecture (PISA) is a programmable switch model that enables granular control over packet processing [17]. As shown in Figure 2, PISA comprises a programmable parser, a programmable match-action pipeline, and a programmable deparser that work together to deal with incoming packets [18].  
Figure 1: SDN general architecture [1].  
Figure 2: PISA programmable switch model.  
The programmable parser is responsible for extracting headers from incoming packets and parsing them based on custom or standard protocol. It can be represented as a state machine, enabling granular control over packet processing. The programmable match-action pipeline is the core component of PISA and executes operations over the packet headers. It is based on the concept of a programmable table and consists of multiple stages that process headers using match-action tables (MATs) and forward them to the next stage. This allows for simultaneous lookups and actions across multiple memory blocks and arithmetic logic units (ALUs). The programmable deparser is responsible for reassembling the packet headers and serializing them for transmission. It receives the processed headers from the pipeline stages and combines them to reconstruct the original packet, ensuring that the packet is correctly formatted and ready to be transmitted.
## 3 Materials and Methods
### APT Detection Time Reduction  
The reduction in detection time provided by the developed methodology can be estimated as follows. We presume that the process is in the behavioural scenario \(b_{1}\) at time \(t_{1}\). Furthermore, we assume a subsequent behaviour \(b_{2}\) that the process will exhibit in the future at time \(t_{2}\). Since observation-based anomaly detection techniques require certain dynamics that would be expected in the transition from \(b_{1}\) to \(b_{2}\), an APT, in the best-case scenario may be detected at the moment of this transition. As a result, in the worst-case scenario, the improvement in the detection time brought by the developed technique is estimated to be at least \(t_{2}-t_{1}\). Obviously, a more precise estimation requires a case-by-case assessment.
## 2 Related Works
### Constrained Application Protocol (CoAP)  
This solution aims to create a generic web protocol that can be used in constrained environments [22]. It partially relies on Representational State Transfer (REST) software style, an architectural style developed by [PERSON] [23]). The message format provided has also some prominent characteristics: to begin with, it offers a 4 byte header where several pieces of information (an identifier for the message, a code, version number of CoAP, etc.) have been included. Secondly, a token value is included right after the header of the message. Lastly, the payload encases the bulk of the message that is transmitted. Data transmissions usually involve an upper layer used to handle the queries and the responses sent from and to the applications; messaging is done to interact with lower levels of communications.  
All in all, this proposal offers several advantages that must be taken into account for any solution used to transfer information at the data level for a specific environment, such as the PDU definition, interchange and purposes. The main disadvantage of this development is that it is expected to be used in environments that, while involving constrained resources, results more reliable that underwater communications. In addition to that, it is likely to work with data received from a regular transport layer, rather than the kind of frames received from acoustic modems, so its applicability for this kind of application domain is limited. If compared to the solution presented in this manuscript, the header of a CoAP message is still longer than the one in MDTP, so we manage to make a better use of the resources available.
# A Test Method for Acoustic Emission Properties of Natural Cellulose Fiber-Reinforced Composites
## 3 Results and Discussion
### Influence of Environmental Relative Humidity  
The two types of composites were conditioned for 24 h in three RHs, namely, 20%, 60%, and 99%, prior to the acoustic vibration test. The results are shown in Figure 4.  
The acoustic vibration spectra of three specimens of hemp and ramie composites conditioned in different RHs were somewhat different. It was found that the spectra with RHs of 99% and 20% have a high degree of similarity, but the second characteristic peak of the curve with 60% RH disappeared. However, the specimens in 60% RH had significantly higher third and fourth peaks compared with the specimens conditioned in the other two RHs. The change in the frequency spectra with the change in RHs could be explained as a result of moisture absorption of the composite, since both the cellulose fibers and the epoxy matrix are hygroscopic. The moisture absorption of hygroscopic materials follows an inversed S-shape isotherm [25], in which the moisture absorption is fast at the beginning and then slows down in intermediate RH, due to less and less available intermolecular space for water molecules to diffuse in if the volumetric change is not allowed. As the RH increases to a high level, the replacement of the hydrogen bonds with water molecules will eventually liberate the neighboring molecules and create larger intermolecular spaces, allowing a large number of water molecules to enter, leading to a volumetric increase [26]. Thus, if the RH is around 60%, the density of the composite will be larger than that conditioned at 20%. However, the density of the composite could be lowered when it is conditioned at 99%, due to swelling. The modulus of the composite should always be decreased as the RH is increased, since water is a good plasticizer for hygroscopic materials. The resonant frequency of a composite positively depends on the modulus and negatively on the density of the composite. The net outcome for the change in the frequency spectrum is determined by the balance of the two factors.  
Figure 4: Frequency spectra for composites, conditioned in different RHs, reinforced with: (**a**) hemp: (**b**) ramie. Acoustic vibration performance parameters for composites reinforced with: (**c**) hemp: (**d**) ramie.  
The calculated acoustic parameters are shown in Figure 4. It can be observed that the E\({}_{\text{d}}\)/p, R, and Z of the composites did not change significantly, since they were calculated from the first peak, which did not change much when the RH varied. Among the three RHs, 60% seems to give the most distinctive spectrum, and this is also close to the standard testing conditions for textiles and composites [27]. Therefore, we would recommend using an RH of around 60% for this kind of test.
## 3 Results Analysis
### Tupu Analysis of Land-Use Transitions (LUTs)
#### 3.2.2 Tupu Analysis of LUTs from 2010 to 2020  
In the analysis of LUTs using the Tupu method in Ningde City from 2010 to 2020, a total of 31 types of Tupu units underwent changes, covering a combined area of 298.35 km\({}^{2}\) (Table 5). The distribution pattern of Tupu units exhibited significant differences during the period of 2010-2020 (Figure 4b), with the total number of Tupu units decreasing compared to the previous decade.  
It can be seen from Table 5 and Figure 4b that 151.55 km\({}^{2}\) of cultivated land was converted, of which 35.83% was converted to built-up land (54.30 km\({}^{2}\), code 16), 27.08% was converted to forest land (41.04 km\({}^{2}\), code 12), and 25.49% was converted to grassland (38.64 km\({}^{2}\), code 14); the transferred area of cultivated land was 111.53 km\({}^{2}\), mainly from forest land and grassland. Meanwhile, the conversion area of forest land was 106.22 km\({}^{2}\), and the percentage of conversion to cultivated land, grassland, and orchard land was 74.47%, 18.47%, and 6.38%, respectively. The transfer-in area of grassland was larger than the transfer-out area, with a difference of 20.12 km\({}^{2}\). The transfer-in part mainly came from cultivated land and forest land, while the transferred-out area was mainly cultivated land. Additionally, \"forest land \(\rightarrow\) cultivated land\" (code 21) and \"cultivated land \(\rightarrow\) built-up land\" (code 16) were the important Tupu units, accounting for 26.51% and 18.20%, respectively, of the total land-use types that were converted.
## 4 Computational Aspects and Applications
### Applications to Some Real-World Problems
(**a**) For \(\alpha=1\) ([PERSON]’s method). (**b**) For \(\alpha=0.7669+0.4847i\). By using some conventional methods, the last equation can be separated into two equations called a radial equation and angular equation. It is well known that the angular equation can also be separated into two equations, one of which leading to the associated Legendre equation (see, e.g., (37)):  
\[(1-x^{2})f^{\prime\prime}(x)-2 xf^{\prime}(x)+\bigg{(}l(l+1)-\frac{m^{2}}{1-x^{2 }}\bigg{)}f(x)=0. \tag{52}\]  
In the azimuthally symmetric case \(m=0\), the solution of (52) can be presented in terms of Legendre polynomials. For the purposes of our example, we applied the above mentioned members of the family (10) for the simultaneous computing of all zeros of the polynomial  
\[f(z)=46189\,z^{10}-109395\,z^{8}+90090\,z^{6}-30030\,z^{4}+3465\,z^{2}-63 \tag{53}\]  
that coincide with the zeros of the tenth-order Legendre polynomial. In this example, we used a different kind of initial approximation, namely the following:  
\[x^{(0)} = (-3.343+2.375i,2.690-0.008i,-3.155-1.844i,3.592-1.444i,-2.417+0.8 40i,\] \[0.843-3.729i,-1.468-3.630i,2.031+2.411i,-2.295-1.281i,-1.212-2.5 08i)\]  
whose coordinates are randomly chosen from the square  
\[\{z\in\mathbb{C}\colon|Re(z)|\leq 4\text{ and }\left|Im(z)\right|\leq 4\}.\]  
Despite the 'rough' choice of the initial approximation, it can be seen from Table 3 that, for \(\alpha=1\) ([PERSON]'s method), the stopping criterion (45) is satisfied at the 13 th iteration with an error estimation smaller than \(10^{-18}\), and, at the next step, the roots are found with an accuracy of \(10^{-55}\). It can also be seen that the obtained COC confirms the theoretical order of convergence proven in Section 3. Approximation trajectories for this example calculated for the methods obtained for \(\alpha=1\) ([PERSON]'s method) and \(\alpha=0.766+0.484i\) are plotted in Figure 6a,b. Figure 6: Trajectories of approximations for Example 3. (**a**) For \(\alpha=1\) ([PERSON]’s method). (**b**) For \(\alpha=0.7669+0.4847i\).
## 3 Results and Discussion
### Comparision of Air Pollutant Levels
This delayed effect was mainly seen around 15 to 16 weeks when the wind speed was relatively slow (1.0-2.2 m/s) and the atmosphere was relatively stagnant. The change in NO\({}_{2}\) concentrations was minute compared to PM but was significantly reduced compared to the control period as shown in Figure 6. The sharp decline in traffic since SD has led to a decrease in traffic-related NO\({}_{2}\) in both cities. Particularly in week 8, the average reduction was 38.7 ug/m\({}^{3}\) and a delayed reduction effect was shown two weeks after the implementation of enhanced SD (week 16). The average NO\({}_{2}\) concentration before SD was lower than that of the control period, but higher concentrations were partially observed, resulting in a relatively weaker effect of reduced transboundary pollutants compared to PM. According to a Korea-China-Japan cooperative study (Long-range Transboundary Air Pollutants in North East Asia: LTP), Chinese sources contributed about 40% of Korea's NOx, while the transboundary concentration of PM was up to 80% [57]. This supports our findings that the effects of transboundary NO\({}_{2}\) before SD were weaker than those of PM. Even before the implementation of SD in Korea, Korea's air pollution levels were affected by the neighboring countries, which had already implemented COVID-19 control actions, resulting in significant reductions in concentrations along with the limiting effect of domestic sources due to SD. In general, due to the nature of the Northeast Asian environment, which is dominated by western winds, South Korea is significantly affected by air pollutants from neighboring countries including China and North Korea [58]. According to KMOE, atmospheric PM in Korea from winter to spring is affected 28% to 82% from neighboring countries [59]. The Northeast Asia cooperative study also reported that the transboundary PM\({}_{2.5}\) from China was 32%, and the effect increased to 70% in the period of high concentration. NOx concentrations were also shown to have a transboundary effect of about 40% [55]. These previous research results support that air quality improvements before week  
Figure 5: Changes in weekly PM\({}_{2.5}\)/PM\({}_{10}\) ratios in (**a**) Seoul and (**b**) Daegu. The black dotted line represents the average ratio before SD, and the red dotted line represents the average ratio after SD. The reduction in the PM\({}_{2.5}\)/PM\({}_{10}\) ratio is highlighted. 8, which were not related to social distancing in Korea, were related to strong COVID-19 lockdown measures in China. The average wind speed and direction during the observation period are shown in Figure 7. As expected, the western wind was quite dominant in January and affected measurements in February and March. The results of wind direction and wind speed analysis during the pre- and post-SD periods support the impact of improved air quality in neighboring countries across the borders, especially in Northeast Asia.