esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of the Climate Change Scenarios for 2050 and 2100  
The effect of climate change slightly varied the application of adaptive models and the possibilities of thermal tolerance. Figures 11 and 12 represent the energy consumption related to the different thermal models in both scenarios. To facilitate the comparisons, box-plots of the current scenario are represented. As can be seen, the progressive external temperature rise had different effects: (i) the heating consumption decreased with the future scenarios; (ii) the cooling consumption increased. In addition, the contribution of the cooling consumption is almost the same as that of the heating consumption at the end of the century. This aspect is important because, in the simulation process, the heating period was greater than the cooling period. A possible progressive increase in cooling periods throughout the 21 st century could therefore imply a greater increase in cooling consumption. However, the adoption of an adaptive model leads to important savings in the total energy consumption with respect to an effective static model due to the progressive increase in saving in cooling degrees. Figure 13 represents the saving distributions in degrees with respect to the static model which were obtained in the different scenarios. The use of adaptive models implies considerable savings in cooling degrees. By using almost any adaptive model, savings in cooling degrees are achieved, although such saving is greater as lower is the level of thermal tolerance. It is important to consider that a rise in the level of thermal tolerance means a narrowing of the comfort zone in a total of 0.5 \({}^{\circ}\)C (i.e., the cooling setpoint value is reduced by 0.25 \({}^{\circ}\)C and the heating setpoint value is increased by 0.25 \({}^{\circ}\)C). Heating and cooling degrees is the difference between the external temperature and the setpoint temperature, in which a value of 20 \({}^{\circ}\)C is generally used. However, this study analyzes the saving in heating and cooling degrees by comparing the difference between the static and the adaptive model. In such way, instead of using 20 \({}^{\circ}\)C as a reference, the static model is used and compared with the adaptive model. A saving in degrees would therefore imply that it could be an energy saving depending on the climate of the zone. Furthermore, this saving in cooling degrees presents a progressive increase throughout the 21 st century due to the rise in external temperatures. This tendency, however, is not reflected in the total energy consumption due to the saving in heating degrees. While a high saving in heating degrees
## 2 Method
### Adaptive Weighting Methods Based on Nsvd  
#### 2.4.1 Coherence Factor and Generalized Coherence Factor Weighting Methods Based on Nsvd  
CF was first presented for the purpose of analyzing echo signals and as a metric for focusing quality in ultrasound imaging. It is obtained by calculating the ratio of the coherent sum to the incoherent sum. Unlike CF, which evaluates signal coherence in the temporal domain, GCF estimates coherence using the spatial frequency spectrum. GCF is computed as the ratio of mainlobe (low-frequency components) energy to the total spectral energy. The mainlobe energy is determined by a cutoff frequency, \(M_{0}\), which is a spatial frequency index and divides the spectrum into low-frequency and high-frequency regions. In general, \(M_{0}\) is selected from 1 to 3 to achieve a good tradeoff between sidelobe suppression and speckle preservation.  
In CPWC imaging, the CF and GCF can be implemented in the angular domain and angular frequency spectrum, respectively. They are generally calculated using DAS outputs of raw echo signals in all steering plane-waves. Instead of DAS, our study uses the output signals from the NSVD filter to calculate CF and GCF, which are respectively expressed as:  
\[CF(x_{p},z_{p})=\frac{|\sum_{n=1}^{N}s_{NSVD}(x_{p},z_{p},\theta_{n})|^{2}}{ N\sum_{n=1}^{N}|s_{NSVD}(x_{p},z_{p},\theta_{n})|^{2}}. \tag{12}\]  
\[GCF(x_{p},z_{p})=\frac{\sum_{j\in[-M_{0},M_{0}]}|\mathbf{P}(j)|^{2}}{\sum_{j=-N/2} ^{N/2-1}|\mathbf{P}(j)|^{2}}, \tag{13}\]where the vector \(\mathbf{P}=[P(-N/2),P(-N/2+1),\dots,P(N/2-1)]\) is the Fourier transform of the vector \([s_{NSVD}(x_{p},z_{p},\theta_{1}),s_{NSVD}(x_{p},z_{p},\theta_{2}),\dots,s_{NSVD }(x_{p},z_{p},\theta_{N})]\). CF and GCF are used to weigh the output of CPWC+NSVD, and the final output can be obtained by:  
\[S_{CF+NSVD}(x_{p},z_{p})=CF(x_{p},z_{p})\times S_{CPWC+NSVD}(x_{p},z_{p}) \tag{14}\]  
\[S_{GCF+NSVD}(x_{p},z_{p})=GCF(x_{p},z_{p})\times S_{CPWC+NSVD}(x_{p},z_{p}). \tag{15}\]
# Wireless Covert Communication with Polarization Dirty Constellation
## 5 Performance Evaluations
### Wireless Covert Channel Testbed  
In this study, we constructed a small-scale experimental testbed, depicted in Figure 8, comprising four devices, including one covert transmitter ([PERSON]), one receiver ([PERSON]), one detector ([PERSON]), and one normal communicator. [PERSON] transmits covert messages to [PERSON], with the positions of [PERSON] and [PERSON] remaining fixed. [PERSON]'s role is to detect the presence of covert communication, with patrols being conducted within the target area to increase the likelihood of detection. To simulate real-world communication scenarios, we designed one normal communicator who utilizes broadcast signals with varying power levels within the target area, thereby changing the signal-to-noise ratio for [PERSON]. It should be noted that the communication activities of the normal communicator can also exert an impact on the detection results.  
Within the testbed, we assume that [PERSON], [PERSON], and [PERSON] are situated within an indoor line-of-sight (LOS) environment [27]. The distances between [PERSON] and [PERSON], normal and [PERSON], and the normal communicator and detector are established as 2 m, 5 m, and 5 m, correspondingly. Furthermore, the distance between [PERSON] and the detector is varied within the range of 1 m to 10 m. MATLAB is utilized to conduct diverse simulations on the covert communication simulation platform, with the goal of verifying the efficacy of [PERSON]'s detection in the classical model of covert communication. The detection approach adopted  
Figure 7: Decoding process of SCL decoder.  
by [PERSON] is based on both KL divergence and KS tests. In some simulations, WCC-DC with the BCH code is chosen for comparison.
# Early Detection and Stability Assessment of Hazardous Rock Masses in Steep Slopes
## 3 Method  
### Processing of Aerial Survey Image Data  
Currently, the utilization of images and point-cloud models from drone aerial surveys remains rudimentary, with singular processing methods that fail to fully extract geological insights. To address this, this study introduces a method for processing aerial survey image data of high-risk rock masses on high slopes, utilizing unmanned aerial vehicle point clouds. The steps are as follows:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Maximum Value** & **Minimum Value** & **Mean Value** \\ \hline Annual rainfall (mm) & 1125.00 & 285.27 & \textgreater{}600.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical table of average annual climatic characteristics.  
Figure 2: Seasonal rainfall histogram.  
The airworthiness area and optimal flight path were determined through on-site surveys, encompassing a total area of 113,547 square meters and a perimeter of 1400 m, as depicted in Figure 3.  
In order to conduct measurements, two different methods, vertical and oblique photography, were utilized. This led to the gathering of 961 digital orthophoto images and 714 digital oblique images from a total of 7 flights. Subsequently, the image data underwent processes for name point matching and free network adjustment. Following this, air triangulation calculation was carried out to produce three-dimensional dense point-cloud data for the measurement object. The resulting three-dimensional point-cloud data then underwent filtration in order to generate a digital elevation model as depicted in Figure 4.  
The DEM data undergo projection using the WGS4 coordinate system and are subsequently converted to the UTM coordinate system, resulting in the generation of a digital orthophoto. Filtering is applied to point-cloud data to remove ground object points and create an elevation grid for DEM development within the study area. Furthermore, unfiltered point-cloud data can be utilized for DSM generation, as demonstrated in Figure 5.  
Figure 4: Point-cloud model filtering. (**a**) Point-cloud model before filtering; (**b**) point-cloud model after filtering.  
Figure 3: Diagram of route planning (**a**) The airworthiness area (**b**) The optimal flight path.  
The base level is established using the three-dimensional model of the study area to outline the range of hazardous rock masses and calculate their volume. As depicted in Figure 6, structural planes are identified, representative feature points (\(n\geq 3\)) are selected, and the least square method is employed for plane fitting to obtain more precise occurrence information. Based on the theory of plane fitting, MATLAB (MATLAB R2018a) is utilized for data centralization, and the least linear square method is applied to fit the structural plane, further measuring and calculating the risk of railway slope rock masses.
## 1 Introduction  
A Mobile _Ad hoc_ NETwork (MANET) is a set of mobile nodes which communicate through wireless links. In contrast with conventional networks, a MANET does not need a previous infrastructure, since nodes rely on each other to operate themselves, forming what is called multi-hop communication. Such networks have several disadvantages that a conventional network does not present: the topology of this kind of network may change quickly and in an unpredictable way. Moreover, variations in the capacity of nodes and links, and frequent transmission errors and lack of security could occur. Finally, the limited energy resources of the nodes must be taken into account, since normally an _ad hoc_ network will be formed by devices powered by batteries.  
To communicate with each other [1], the _ad hoc_ nodes need to configure their interfaces with local addresses which are valid inside an _ad hoc_ network. The _ad hoc_ nodes may also need to set routing addresses globally to communicate with other devices on the Internet. From the perspective of the IP layer, an _ad hoc_ network is presented as a multi-hop network of level 3 constituted by a collection of links.  
In an autonomous _ad hoc_ mobile network the nodes can be uniquely identified by an IP address with the only premise that this address must be different from that any other node in the network. The configuration process is the set of steps through which a node obtains its IP address within the network. There are two mechanisms to set addresses: _Stateless_ and _Stateful_.  
The _Stateless_ address configuration proposes its own node to be the one in charge of generating its IP address. The address is obtained from the concatenation of a well-known network prefix and the theoretically unique number inside the network generated by the node. This mechanism may require the inclusion of a module responsible for verifying the uniqueness of the generated address called _Duplicate Address Detection_ (DAD) [2; 3; 4].  
On the other hand, _Stateful_ address configuration is based on using servers which control and assign addresses to all the nodes of the network. The well known _Dynamic Host Configuration Protocol_ (DHCP) [5] is an example of _Stateful_ configuration. However, because of the multi-hop nature of mobile _ad hoc_ networks, this protocol cannot be applied directly.  
This work proposes a _Stateful_-based auto-configuration protocol that guarantees the uniqueness of IP addresses under a wide variety of network conditions such as missing messages and network partitioning. This work is structured in five sections; the first one is the present Introduction. Section 2 shows the obligated references in the auto-configuration protocol scope of Mobile _Ad Hoc_ Networks. Section 3 contains an itemized specification of the so-called _Distributed Dynamic Host Configuration Protocol_ (D2 HCP), a proposal concerning IP addresses auto-configuration for MANETS. Section 4 presents the D2 HCP protocol simulations carried out in NS-3 [6]. Finally, Section 5 discusses the main advantages of the newly developed protocol as well as potential future extensions to the study.
## 3 Challenges and Related Recommendation  
More investigation into numerous topics and avenues of study is necessary before the 6G goal of global reach, complete applications, solid security, all perceptions, and all electronics can be realized. Furthermore, there is an immediate need for innovations in 6G telecommunication concepts. In this section, many challenges will be discussed and analyzed. Furthermore, recommendations and features will be suggested for the discussed challenges.
# POU-SLAM: Scan-to-Model Matching Based on 3D Voxels
###### Abstract  
**Purpose:** Localization and mapping with LiDAR data is a fundamental building block for autonomous vehicles. Though LiDAR point clouds can often encode the scene depth more accurate and steadier compared with visual information, laser-based Simultaneous Localization And Mapping (SLAM) remains challenging as the data is usually sparse, density variable and less discriminative. The purpose of this paper is to propose an accurate and reliable laser-based SLAM solution. **Design/methodology/approach:** The method starts with constructing voxel grids based on the 3D input point cloud. These voxels are then classified into three types to indicate different physical objects according to the spatial distribution of the points contained in each voxel. During the mapping process, a global environment model with Partition of Unity (POU) implicit surface is maintained and the voxels are merged into the model from stage to stage, which is implemented by Levenberg-Marquardt algorithm. **Findings:** We propose a laser-based SLAM method. The method uses POU implicit surface representation to build the model and is evaluated on the KITTI odometry benchmark without loop closure. Our method achieves around 30% translational estimation precision improvement with acceptable sacrifice of efficiency compared to LOAM. Overall, our method uses a more complex and accurate surface representation than LOAM to increase the mapping accuracy at the expense of computational efficiency. Experimental results indicate that the method achieves accuracy comparable to the state-of-the-art methods. **Originality/value:** We propose a novel, low-drift SLAM method that falls into a scan-to-model matching paradigm. The method, which operates on point clouds obtained from Velodyne HDL64, is of value to researchers developing SLAM systems for autonomous vehicles.  
Keywords:simultaneous localization and mapping; voxel grids; scan-to-model; partition of unity +
Footnote †: journal: Applied
## 3 Results
### Biomass and Carbon Stock
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Diameter Classes** & **Average Height (m)** & **Average BA m\({}^{2}\) ha\({}^{-1}\)** & **Average Density ha\({}^{-1}\)** & **Volume (m\({}^{3}\) ha\({}^{-1}\))** \\ \hline
6–24 & 3.125 & 1.23 & 46.77 & 35.54 \\
25–44 & 8.032 & 8.94 & 94.71 & 54.12 \\
45–64 & 6.127 & 12.71 & 55.92 & 41.25 \\
65–84 & 3.78 & 12.56 & 29.12 & 31.87 \\ \(\geq\)85 & 1.438 & 11.25 & 20.24 & 26.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average height, Basal area and density of _Monotheca_ in different diameter classes. showing a significant value of R\({}^{2}\) = 0.57 (Table 5). Similarly, the relationship of volume and biomass carbon with Basal area can be explained by the adjusted R\({}^{2}\) values (0.92, 0.79 and 0.76) from lower to higher altitudinal ranges, respectively. In lower ranges, the volume was significantly correlated with Basal area, followed by middle and high elevation ranges. A similar trend was also observed in the relationship between biomass carbon and Basal area using quadratic regression type (R\({}^{2}\) = 0.79). \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multicolumn{1}{c}{**Parameters**} & \multicolumn{1}{c}{**R. Type**} & \multicolumn{1}{c}{**Equation**} & \multicolumn{1}{c}{**y0**} & \multicolumn{1}{c}{**a**} & \multicolumn{1}{c}{**b**} & \multicolumn{1}{c}{**c**} & \multicolumn{1}{c}{**R\({}^{2}\)**} \\ \hline Diameter vs. D EI & P. Cubic & \(d=yo+a\times x+b\times x^{2}+c\times x^{3}\) & 2.67 & 0.62 & \(-\)0.01 & 4.4670 \(\times\) 10\({}^{-5}\) & 0.55 \\ Diameter vs. D EI & P. Cubic & \(d=yo+a\times x+b\times x^{2}+c\times x^{3}\) & 3.44 & 0.29 & \(-\)0.006 & 2.7630 \(\times\) 10\({}^{-5}\) & 0.59 \\ Diameter vs. D EIII & P. Cubic & \(d=yo+a\times x+b\times x^{2}+c\times x^{3}\) & 0.11 & 0.49 & \(-\)0.009 & 4.0638 \(\times\) 10\({}^{-5}\) & 0.51 \\ Diameter vs. M Den & P. Cubic & \(d=yo+a\times x+b\times x^{2}+c\times x^{3}\) & 0.29 & 0.47 & \(-\)0.008 & 3.7646 \(\times\) 10\({}^{-5}\) & 0.57 \\ BA vs. V EI & P. Linear & \(F=yo+a\times x\) & \(-\)0.49 & 1.89 & - & - & 0.92 \\ BA vs. BMC EI & P. Linear & \(F=yo+a\times x\) & \(-\)0.32 & 1.26 & - & - & 0.92 \\ BA vs. V EII & P. Linear & \(F=yo+a\times x\) & \(-\)0.35 & 1.53 & - & - & 0.76 \\ BA vs. BMC EII & P. Linear & \(F=yo+a\times x\) & \(-\)0.23 & 1.02 & - & - & 0.76 \\ BA vs. V EIII & P. Linear & \(F=yo+a\times x\) & \(-\)0.12 & 1.16 & - & - & 0.49 \\ BA vs. BMC EIII & P. Linear & \(F=yo+a\times x\) & \(-\)0.08 & 0.77 & - & - & 0.49 \\ MBA vs. MV & P. Linear & \(F=yo+a\times x\) & \(-\)0.42 & 1.69 & - & - & 0.79 \\ MBA vs. MBMC & P. Linear & \(F=yo+a\times x\) & \(-\)0.28 & 1.13 & - & - & 0.79 \\ \hline \end{tabular}
\end{table} 
Table 5: Regression equation explaining the relation of different parameters. Figure 2: Relation between stem density (ha\({}^{-1}\)) and diameter (cm). Note. DEI = density at elevation zone-I, DEII = density at elevation zone-II, DEIII = density at elevation zone-III, MD = mean density.
# Facial Privacy Protection with Dynamic Multi-User Access Control for Online Photo Platforms
## 4 Proposed System
### Security Considerations  
#### 4.3.1 Security Implementation for Data Handling  
The system ensures the security of data in transit and stored data through multiple mechanisms. Data in transit is protected using the HTTPS protocol, which encrypts information exchanged between the client application and the server. This ensures that sensitive data, such as user credentials and image data, remain confidential during transmission. Additionally, the server communicates with the CSP through an internal network, isolating data exchanges from external threats and maintaining security.  
To protect stored data, the system does not retain original images or sensitive cryptographic information on the server. Instead, the server stores only face-encrypted images and metadata related to the face encryption processes. Sensitive data, such as private and symmetric keys, are securely managed by the CSP using two primary mechanisms. First, the CSP leverages Hashicorp Vault [31], a dedicated key management system, to store secrets securely. Access to Vault is strictly controlled, and the CSP communicates with it using the AppRole authentication method [32], which enforces specific policies and login constraints to obtain tokens with the required permissions. Second, the CSP employs password-based encryption, where private keys are encrypted with a key derived from the user's password. This ensures that only the user can access their private key, even if the secrets management system is compromised. Finally, although public keys are not sensitive, they are also stored in Vault for consistency.
## 4 Performance and Simulation of 2D Maximum Energy Search Approach  
As the 2D search approach is derived and practiced in EDL carrier detection and tracking for JPL (Jet Propulsion Laboratory)'s Mars explorations [2; 3; 4; 5], we briefly review the carrier detection algorithm adopted in the past missions and the theoretical models used to evaluate the performance. Then, the discussion focuses on the selection of the processing parameters in the 2D search approach, and the influence of the processing parameters on the performance limitation. Finally, a simulation is made to evaluate the tracking performance based on the EDL trajectory of the MSL.  
As described in JPL's report, the 2D search approach is divided into a detection phase and tracking phase [8; 14]. The difference is that all of the possible frequency cells and frequency rate cells trials are made among the full search space in the carrier detection phase, while in the carrier tracking phase, a local search is performed where signal parameter estimates from previous batches are used to reduce the search space for the current data batch. As the strategy on how to determine the local search space after previous estimates compensation needs more discussion, and we focus on the threshold performance of frequency estimation (meaning that the worst case is the most concern), the full signal parameter search space is considered as the common discussion baseline.
## 3 Analysis of Test Results
### Thermogravimetric Analysis  
To study the thermal degradation observed by rheological analysis, PA6 and PBT were mixed with alumina and they were characterized by thermogravimetry (TGA). The results are presented in Figure 3.  
Figure 2: Injection results of (**a**) PBT (60) and (**b**) PA6 (60).  
The weight loss caused by the decomposition of the pure PBT and Al\({}_{2}\)O\({}_{3}\) composites was nearly the same up to a temperature of about 390 \({}^{\circ}\)C. After this point, the thermal degradation was influenced by the presence of 60% Al\({}_{2}\)O\({}_{3}\) in the PBT matrix. The Al\({}_{2}\)O\({}_{3}\) content in the polymer plays a pivotal role in the thermal degradation temperatures. In the case of the PBT containing 60% alumina, more than 60% of the weight is left at 600 \({}^{\circ}\)C. In addition, it was found that the weight reduction rate was lower compared to that of pure PBT.  
In the case of pure PA6, thermal degradation could be observed from 450 \({}^{\circ}\)C and the residual weight was close to 0% at over 600 \({}^{\circ}\)C, making it possible to determine the residual amount according to the alumina content. In the case of PBT (0), it can be observed that the weight reduction occurs sharply from 390 \({}^{\circ}\)C; further, PA6 (0) shows a decrease in weight from 450 \({}^{\circ}\)C. When PBT and PA6 were compared, the thermal stability of PA6 was found to be superior [39].  
In addition, it was discovered that the rate of degradation increased with an increase in the alumina content [40; 41]. The temperature at which the rate of weight reduction starts stopping is beyond 550 and 500 \({}^{\circ}\)C for PA6 (0) and PA6 (50), respectively. This temperature is gradually lowered to 480 and 470 \({}^{\circ}\)C for PA6 (60) and PA6 (50), respectively.