esa-sceva/satcom-chunk-collection huggingface dataset:
# Area-Efficient Universal Code Generator for GPS L1C and BDS B1C Signals
## 1 Introduction
Lastly, Legendre sequence-based codes show excellent correlation characteristics, and they have recently been used in GPS L1C and BDS BIC signals. Figure 1: Signal acquisition process for a GNSS receiver. As the fields of the application of the GNSS have expanded along with the recent development of the industries, studies to provide high precision and additional functions of GNSS receivers have been extensively conducted [8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. In addition to the simple role of receiving signals and performing navigation calculations, advanced signal technologies, such as anti-jamming [8] and anti-spoofing [9], are applied, and studies on multi-constellation and multi-frequency receivers [10; 11; 12] are being conducted for high-precision high-reliability location calculations. Especially, studies on single code generators that support various codes are actively conducted for the implementation of multi-constellation and multi-frequency receivers so that single receivers can support multiple codes [13; 14; 15; 16; 17]. As shown in Table 1, since LFSR-based code generation is the most widely applied, studies on LFSR-based codes have been steadily conducted [10; 11; 12; 13]. According to one study [13], many LFSR-based PRN codes can be generated using a single LFSR structure. However, there are not so many studies on the Legendre-based code generator structure so far. As representative Legendre-based generator structures, authors of [14; 15] proposed general code generators that use memory to store and read Legendre-based codes. The authors of [16] proposed a code generator structure where Legendre sequences are generated on the fly to generate PRN codes and to the authors of [17] proposed a Weil-generation code generator structure where Legendre sequences are stored in the ROM and Weil sequences are generated on the fly. Although previous studies [14; 15; 16; 17] succeeded in generating multiple Legendre-based codes using single hardware, they still use a lot of unnecessary hardware resources to universally generate Legendre-based codes. Therefore, this paper proposes an area-efficient code generator structure where many Legendre sequence-based codes can be generated using a single universal code generator by removing redundant used hardware resources within a range where the overall system performance is not degraded. The rest of this paper is organized as follows. Section 2 explains the background of Legendre sequence-based PRN codes, and Section 3 describes the previous universal code generators. The proposed universal code generator is proposed in Section 4, the experimental results are discussed in Section 5, and the concluding remarks are made in Section 6.
# Towards Flexible Integration of 5G and IIoT Technologies in Industry 4.0: A Practical Use Case
## 4 Proposed Architecture
Firstly, the NS is designed and described in a way that OSM understands it. The description of the NS reflects its composition in terms of VNFs and Virtual Links between them. In addition, each VNF also requires its own descriptor in order to indicate the requirements of each of its contained VMs, in terms of resources (disk, memory, and CPU), interfaces and internal links, configuration scripts, and other features. This process is known as onboarding in NFV terminology. NSs and VNFs that have been onboard become available in the OSM catalog for instantiation. The NS being instantiated results in OSM negotiating with OpenStack how and where to deploy the VMs with the provided requirements. Once the resulting VMs have been created, OSM can also interact with the ONOS controller in order to install their connectivity requirements into the data plane. A unique VLAN is automatically selected for the NS in order to isolate it from other NSs. Besides, the addition of the WSN introduces an additional dimension to the design of NSs, as services provided by the CDSs can be dynamically allocated to some of these NSs together with the services provided by the VNFs that they are composed of.
# Privacy and Security in Federated Learning: A Survey
## 4 Privacy in Federated Learning
### Privacy Attacks in FL
#### 4.1.1 Unintentional Data Leakage and Model Reconstruction Attacks  
Despite the non-communication of data, [PERSON] et al. [39] have shown that data could be inferred by considering only model weights. Indeed, an honest-but-curious adversary eavesdrops communications of clients and the server, allowing for the reconstruction of the client model. Ref. [40] defines an honest-but-curious client as a client, which aims to store and process data on its own. Unlike other attacks such as data poisoning, the client may not interfere with the collaborative learning algorithm.  
Several studies demonstrated the possibility of reconstructing local data collections by inverting the model gradients sent by the clients to the server [40; 41]. Nonetheless, this attack has some limitations. It performs well on models that have been trained on small batches of data or data points with poor diversity. It is not suitable when multiple stochastic gradient updates have been performed locally before communicating with the server. Other recent studies [42] go further by reconstructing the local client model without interfering in the training process. This attack, called the model reconstruction attack, copes with the previous limits, allowing for high-quality local model reconstruction.
## 4 Results and Discussion
### The Impact of HSR Network on Socio-Economic Growth
#### 4.1.3 CR Network (China)  
Chinese HSR services are crucial in distributing industrial resources such as workers, commodities and services in facilitating enterprises. As a result, the services can significantly increase corporate agglomeration [113; 114]. CR network also suits business passengers, who require high-convenience services and need to save on journey time. Because of the service's high earning expenses, low-income parties, students and non-commercial organisations may not likely pay for HSR rates [115; 116]. Since network can sustain only wealthy passengers, the PCC value on the economy (EC1-EC4) is \(-0.13\). Likewise, the results of the PCC for Chinese education (ED1-ED5) are low with a value of \(-0.01\). To sustainably improve the CR network, affordable fares for the middle class should be a priority. In addition, the service should apply new techniques and marketing strategies such as student discounts, advance tickets, and return rates to increase passenger volume.  
The findings show that the network's growth was strongly linked with China's QoL (PD1-PD3), which shows the PCC value at 0.61. The Chinese's conventional rail was the key transportation system in China, but the CR network has offered faster services. Considering the major cities in China, most of them are located long distances apart on the east coast of the mainland. For instance, Beijing and Shanghai are China's leading commercial areas. Both cities are separated by 1200 km. To travel between them, CR services take approximately four hours, instead of 12 h by car. The network has also transformed into a competitor for airplane services. Passengers should factor in extra time for travel and terminal-related activities while travelling, as only the flight itself takes more than two and a half hours [117]. Additionally, air fares are slightly more costly than HSR tickets. Thus, not only do CR services enhance people's lives, but they also catalyse the transportation sector.
## 6 Results
### Adjustment
\begin{table}
\begin{tabular}{c c} \hline
**Modelo** & **RMSE** \\ \hline ARIMA(1,1,1) & 205.38 \\ \hline ARIMA(1,1,31) & 222.66 \\ \hline ARIMA(1,1,32) & 178.8 \\ \hline ARIMA(2,1,24) & 210.47 \\ \hline ARIMA(2,1,31) & 215.46 \\ \hline ARIMA(2,1,32) & 173.62 \\ \hline ARIMA(4,1,31) & 245.36 \\ \hline ARIMA(4,1,32) & 213.77 \\ \hline ARIMA(31,1,24) & 237.69 \\ \hline ARIMA(31,1,31) & 245.66 \\ \hline ARIMA(31,1,32) & 212.53 \\ \hline SARIMA(1,1,1)(0,1,2)(32) & 130.29 \\ \hline SARIMA(1,1,1)(1,1,2)(32) & 128.56 \\ \hline SARIMA(1,1,32)(0,1,2)(32) & 134.45 \\ \hline SARIMA(1,1,32)(1,1,2)(32) & 133.78 \\ \hline SARIMA(2,1,32)(0,1,2)(32) & 132.65 \\ \hline SARIMA(2,1,32)(1,1,2)(32) & 134.12 \\ \hline SARIMA(32,1,32)(1,1,2)(32) & 133.89 \\ \hline \end{tabular}
\end{table} 
Table 4: RMSE (Root-Mean-Square Error) value for various models. Figure 9: Prediction and fit for the ARIMA model (2,1,32). Although this model estimated the random behavior of the original series in a good way, a more adjusted prediction can be observed in the ARIMA model (1,1,1). On the other hand, the SARIMA model that made the best prediction was the SARIMA (1,1,1) (1,1,2) (32), with an RMSE of 128.56 that was previously described in the Table 4. Equation (9) describes the SARIMA model mentioned in the previous sentence:  
\[(1-0.845L)\Big{(}1-L^{32}\Big{)}(1-L)(Y_{t})(1\ -0.845L)\Big{(}1\ -0.09L-0.904L^{2} \Big{)}\Big{(}1-0.99L^{32}\Big{)}u_{t} \tag{9}\]  
The prediction for additional data in the series was made through GRETL, considering the confidence intervals close to 95%, as can be seen in Figure 10.  
In the analysis of the reliability and validation of the model, the following indicators were obtained that show the appropriate adjustment for the needs raised:  
1. Mean error: 2.9569
2. Root mean square error: 112.53
3. Mean absolute error: 65.64
4. Mean error percentage: -22.235
5. Mean absolute error percentage: 94.45
6. U of Theil: 0.65567
7. Proportion of bias, MU: 0.00019357
8. Disturbance ratio, UD: 0.74771  
Where U, MU, and UD are measures of relative precision that compares the predicted results to the forecast results with minimal historical data.
## 2 Case Study and Data  
The data were drawn from a survey conducted in a western state of the U.S. The data collection was done across 17 counties, and 289 locations in Wyoming in 2019. The data collection process conformed to the criteria highlighted for the state observational seatbelt coalition, which was issued in 2011 by the national highway traffic administration (NHTSA) [13].  
The data include various environmental, and demographic characteristics, and seat belt status of vehicles' occupants to see how those factors motivate the occupants to buckle up. Originally there was information related to 18,286 vehicles. However, as the objective of this study was to evaluate the choice of front-seat passengers, and not all the drivers had a front-seat passenger on board, the data were filtered to include only the vehicles that had a single passenger on board. That reduced the number of observations to 6533. Recall, the data also include only individual-specific characteristics, and no information was collected regarding the alternative-specific preferences.  
In addition to front-seat passengers, there was information regarding the drivers' characteristics which were incorporated in the analysis. That is because a multitude of reasons could be linked to this choice of a seat belt use for front-seat passengers, and those are not necessarily related to passenger-specific characteristics but also drivers' characteristics.  
The individual-specific preference of front-seat passengers, and some of the driver-specific characteristics that were found to impact a choice of passengers in buckling up, were included and highlighted in Table 1. An initial analysis of the dataset indicates that while more than 80% of drivers buckle up, the number is reduced to less than 50% for front-seat passengers.
## 2 Materials and Methods
### Numerical Simulation  
#### 2.2.1 Project Overview  
This paper relies on a tunnel on the Emi-Miyi section of the newly built Chengdu-Kunming Railway, located in Dechang County, Liangshan Prefecture, Sichuan Province. It is one of the high-risk and complex control projects on the Emi line. The total length of the tunnel is 14,280 m, including 5375 m of grade II surrounding rock, 5440 m of grade III surrounding rock, 2070 m of grade IV surrounding rock, 1395 m of grade V surrounding rock, and the maximum buried depth is 1032 m. The stratum lithology in the tunnel area is relatively single, overlying the quaternary holocene slope residual layer of silty clay, Quaternary Middle and Upper Pleistocene (Q\({}_{2+3}\)) silty clay, and pebble soil. The underlying bedrock is mainly tertiary upper Xigeda formation mudstone intercalated sandstone and Pre-Sinian granite.  
The Xigeda Formation mudstone intercalated sandstone is distributed in the inlet and outlet sections of the tunnel. The distribution length of the inlet section is 314.3 m, and of the outlet section is 208.5 m. The physical and mechanical properties of Xigeda formation mudstone intercalated sandstone are poor, especially in moist conditions. Mudding occurs after encountering water, so the rock strength is further weakened and is prone to large deformation during tunnel construction. According to the construction site test, the water content of the Xigeda formation in its natural state is usually 17-24.6%, the saturation is 85-100%, the compressive strength is 1.07-1.73 MPa, the compressive strength in the drying state is 13.2-13.4 MPa, and the compressive strength in the saturated state is 0.79-0.89 MPa.  
The DK494 + 725-DK494 + 775 section of the tunnel was selected as the research scope. The primary rock stratum passing through the tunnel is Xigeda formation mudstone intercalated sandstone, and the three-step temporary inverted arch method is adopted. Figure 2 shows the exposure of the tunnel face. The average buried depth of this section is about 45 m, the net span of the tunnel is 12.47 m, and the net height is 11.30 m.
## 3 Pose Estimation for Multi-Marker Systems
### Correction of Pose Estimation  
After excluding invalid markers and temporal outliers, the developed algorithm refined the final pose estimate using the remaining valid measurements. The correction process involves two primary components: orientation correction and position correction.  
The corrected orientation was computed by averaging the rotation information from valid markers. Using the pose information from each valid marker, the rotation angle and unit vector of the rotation axis for the representative marker were averaged as follows:  
\[\alpha_{correction} =\frac{\sum_{i=1}^{N}\left|\mathbf{r}_{cR,i}\right|}{N} \tag{11}\] \[\mathbf{u}_{correction} =\frac{\sum_{i=1}^{N}\frac{\mathbf{r}_{cR,i}}{\left|\mathbf{r}_{cR,i} \right|}}{N} \tag{12}\]  
In Equations (11) and (12), \(r_{cR,i}\) denotes the rotation vector of the representative marker relative to the camera coordinate system, calculated using the pose measurements from the \(i\)-\(th\) marker. \(N\) is the number of valid markers.  
The rotation vector of the representative marker relative to the camera coordinate system was then calculated using Equation (13).  
\[\mathbf{r}_{correction}=\mathbf{\alpha}_{correction}\mathbf{u}_{correction} \tag{13}\]  
The resulting rotation vector was converted to a rotation matrix using Equation (5). From the rotation matrix, the roll(\(\varphi_{correction}\)), pitch(\(\theta_{correction}\)), and yaw(\(\psi_{correction}\)) angles were extracted as follows:  
\[\begin{split}\varphi_{correction}&=tan^{-1}\bigg{(} \frac{R_{correction,32}}{R_{correction,33}}\bigg{)}\\ \theta_{correction}&=sin^{-1}(-R_{correction,31})\\ \psi_{correction}&=tan^{-1}\bigg{(}\frac{R_{ correction,21}}{R_{correction,11}}\bigg{)}\end{split} \tag{14}\]  
In Equation (14), \(R_{correction,ij}\) represents the element in the \(i\)-\(th\) row and \(j\)-\(th\) column of the calculated rotation matrix.  
For position correction, the position vector of the representative marker relative to the camera coordinate system was computed by averaging the position vectors calculated from each valid marker.  
\[\overset{\rightarrow}{\mathbf{f}}_{correction}=\frac{\sum_{i=1}^{N}\overset{ \rightarrow}{t}_{cm,i}}{N} \tag{15}\]  
In Equation (15), \(\overset{\rightarrow}{t}_{cm,i}\) represents the position vector of the representative marker relative to the camera coordinate system, calculated using the pose measurements from the \(i\)-\(th\) marker.  
The pose information from multiple valid markers was efficiently merged while mitigating the impact of measurement errors and environmental factors on the final pose estimation using the correction algorithm described in this section.
## 3 Auction-Based Cooperative Rescue Plan
### Bidders
#### 3.2.2 The Cost of Bidders  
In disaster relief, saving the cost should not be put in the first place. However, the cost we introduce in this paper does not refer to investment in equipment or manpower. The cost \(c_{ij}\) of bidder \(B_{j}\) involving in task \(T_{i}\) refers to the distance from \(B_{j}\) to \(T_{i}\), and the time \(t_{ij}\) spent in rescue operation. By introducing the cost in this model, we can take the location of buried sites as a factor to determine the allocation of task, and reduce time spent in moving process.  
The cost \(c_{ij}\) is a function of distance \(d_{ij}\) from \(B_{j}\) to \(T_{i}\) and the time \(t_{ij}\) spent in rescue operation.  
\[c_{ij}=f\big{(}d_{ij},\ t_{ij}\big{)} \tag{6}\]  
When auctioneer \(A_{i}\) estimates the required number of rescue teams \(n_{i}\) as shown in Equation (4), the time \(t_{ij}\)\(B_{j}\) spends in rescue operation is set to be approximately equal to \(t_{need}\), and the same goes with all the other bidders. Therefore, the time spent in rescue operation does not make a difference, and it is ignored in the following model, i.e., \(c_{ij}=f\big{(}d_{ij}\big{)}\).  
When receiving more than one auction message, bidders are supposed to take opportunity cost \(c^{\prime}_{ij}\) into consideration as to determine bid price \(p_{ij}\). Opportunity cost \(c^{\prime}_{ij}\) is the loss of net utility bidder \(B_{j}\) could gain from the second best choice \(T_{s}\) when choosing task \(T_{i}\) to bid on, as shown in Equation (7).  
\[c^{\prime}_{ij}=u_{s}-f(d_{sj}) \tag{7}\]
# Global Power Dynamics in the Contemporary Space System
## 6 Systems Structure--New Balance of Power in Space
### New Repolarization and Rebalance of Space Power  
An examination of the major space powers and their interactions reveals a repolarization and a rebalancing of power among the states in the current space system. The analysis of global power dynamics within the space system, as outlined in Section 5, facilitates the visualization of repolarization and rebalancing of power, manifesting in the formation of novel coalitions and alliances among nations, along with substantial investments in the space domain aimed at enhancing economic, military, and institutional power.  
The current space industry structure indicates that the United States is the leader in this sector. The U.S. accounts for nearly two-thirds of the total spending on space activities and possesses more than half of the world's top space companies, including the top 10 companies by revenue, with SpaceX being the leader among them [94].  
However, these indicators do not reveal the underlying trends behind the numbers. The United States emerged as the predominant space power in the 1990s, following the conclusion of the Cold War, establishing a unipolar global power structure. The United States has maintained its leadership position in this sector, but the analysis of global power dynamics suggests a gradual and consistent decline in the nation's monopoly over the space industry. This transition from a unipolar to a more diverse global power structure is underway.
###### Abstract  
Quality of service (QoS)-aware data gathering in static-channel based underwater wireless sensor networks (UWSNs) is severely limited due to location and time-dependent acoustic channel communication characteristics. This paper proposes a novel cross-layer QoS-aware multichannel routing protocol called QoSRP for the internet of UWSNs-based time-critical marine monitoring applications. The proposed QoSRP scheme considers the unique characteristics of the acoustic communication in highly dynamic network topology during gathering and relaying events data towards the sink. The proposed QoSRP scheme during the time-critical events data-gathering process employs three basic mechanisms, namely underwater channel detection (UWCD), underwater channel assignment (UWCA) and underwater packets forwarding (UWPF). The UWCD mechanism finds the vacant channels with a high probability of detection and low probability of missed detection and false alarms. The UWCA scheme assigns high data rates channels to acoustic sensor nodes (ASNs) with longer idle probability in a robust manner. Lastly, the UWPF mechanism during conveying information avoids congestion, data path loops and balances the data traffic load in UWSNs. The QoSRP scheme is validated through extensive simulations conducted by NS2 and AquaSim 2.0 in underwater environments (UWEs). The simulation results reveal that the QoSRP protocol performs better compared to existing routing schemes in UWSNs.  
Internet of underwater things; channel aware; multichannel; acoustic sensor networks; underwater wireless sensor network +
Footnote †: journal: Sensors
## 2 Related Work
In this work, using rectangular planes and boxes, a kitchen environment is reconstructed in order to provide to the robot a map with more information about, i.e., how to use or open a particular piece of furniture. A set of planar structures to represent pieces of furniture was presented in [14], which stated that their planar representations \"have a certain size, orientation, height above ground and spatial relation to each other\". This method was used in [15] to create semantic maps of furniture. This method is similar to our framework; however, their method used a set of rules, while our method uses a probabilistic framework. Our method is more flexible, more able to deal with uncertainty, more able to process partial information and can be easily incorporated into many SLAM methods. In relation to plane extraction, a faster alternative than the common methods for plane segmentation was presented in [16]. They used integral images, taking advantage of the structured point cloud from RGB-D cameras. Another option is the use of semantic information from the environment to improve the furniture detection. For example in [17], geometrical properties of the 3D world and the contextual relations between the objects were used to detect objects and understand the environment. By using a Conditional Random Field (CRF) model, they integrated object appearance, geometry and relationships with the environment. This tackled some of the problems with feature-based approaches, including pose variation, object occlusion or illumination changes. In [18], the use of the visual appearance, shape features and contextual relations such as object co-occurrence was proposed to semantically label a full 3D point cloud scene. To use this information, they proposed a graphical model isomorphic to a Markov random field. The main idea of our approach is to improve the understanding of the environment by identifying the pieces of furniture, which is still a very challenging task, as stated in [19]. These pieces of furniture will be represented by a graph structure as a combination of geometrical entities. Using graphs to represent environment relations was done in [20; 21]. Particularly, in [20], a semantic model of the scene based on objects was created; where each node in the graph represented an object and the edges represented their relationship, which were also used to improve the object's detection. The work in [21] used graphs to describe the configuration of basic shapes for the detection of features over a large point cloud. In this case, the nodes represent geometric primitives, such as planes, cylinders, spheres, etc. Our approach uses a representation similar to [21]. Each object is decomposed into geometric primitives and represented by a graph. However, our approach differs because it processes only one point cloud at a time, and it is a probabilistic framework.