esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Degradation and Life Prediction of Polyethylene
## 2 Aging Degradation of Polyethylene  
### The Process of Degradation  
Catalytic peroxide decomposition, direct interactions of metal compounds with organic substrates, oxidation, and energy transfer during photolysis are the primary mechanisms of the chemical degradation of polymers [36]. While the chemical structure of a polymer frequently does not change much, polymer degradation involves a reduction in the polymer's molecular weight [37]. A macromolecular complex called a polymer is made up of big molecules with repeating structural elements. Polymers are typically combinations of substances with various chain lengths or substances with various molecular weights. The properties of polymers are strongly affected by their molecular weight, and this is also one of the processes through which macromolecular substances are created [38].  
The primary source of the polymer degradation process is the continuous interaction between oxygen and the polymer's macromolecules, as well as free radicals created as a consequence of environmental factors such as temperature, humidity, light, mechanical stress, and radiation [39]. In addition, it appears to suggest a decrease in molecular weight, potential branching, and, in a few cases, the formation of cross-linked structures [40]. The unstable oxidized substances formed by degradation gradually converge towards the formation of stable macromolecules with oxidation groups and cause significant changes in the molecular structure, such as molecular weight, polydispersity, branching, etc. While other polymers often migrate in the direction of lower molecular weights, the development of cross-linked structures as mentioned above occurs primarily in the degradation of polyethylene.  
For polyethylene materials, aging is bound to occur with extended use. Both aging and degradation can have a significant impact on the performance of polyethylene. Exposure to numerous environmental variables, such as heat, UV radiation, ozone, chemical attack, mechanical stress, and microbes, can cause polyethylene to degrade, eventually resulting in embrittlement, cracking, discoloration, etc. [41; 42]. Understanding the primary stages of polyethylene breakdown is crucial for this reason.
# Adaptive Path Planning for UAV-Based Pollution Sampling
## 4 Experiments and Results
### Evaluation Criteria--Path Quality Metrics
#### 4.4.1 Path Length  
The length of a path is closely linked to both energy consumption and overall mission duration. In this study, the path length is calculated as the sum of Euclidean distances between consecutive waypoints \(\mathbf{w}\) of the \(\mathbf{W}\) vector. This metric is evaluated using a formula analogous to the minimal length criterion (12), defined as follows:  
\[\text{LEN}=\sum_{n=1}^{N_{w}-1}\|\overline{\mathbf{w}_{n}\mathbf{w}_{n+1}}\|, \mathbf{w}_{n},\mathbf{w}_{n+1}\in\mathbf{W}, \tag{25}\]  
where \(N_{w}\) represents the total number of waypoints in \(\mathbf{W}\).
# Climatic Indices over the Mediterranean Sea: A Review
## 3 Large-Scale Atmospheric Forcing: Climatic Indices
### The East Atlantic-West Russia (EA-WR) Pattern  
The EA-WR pattern comprises a center of high/low SLP anomalies located in the North Sea flanked by two centers of opposite SLP anomalies (low/high) over the Western North Atlantic (between 45\({}^{\circ}\) and 55\({}^{\circ}\) N) and West Russia (Figure 4). Positive phases of EA-WR enhance northerlies over the Eastern Mediterranean and southerlies over the western basin.  
The opposite effect of the EA-WR mode in the wind regime of the Eastern and Western Mediterranean generates a dipolar pattern in the net heat anomalies over the basin. A positive EA-WR mode is associated with higher latent heat losses in the Eastern Mediterranean, with positive anomalies up to 15 Wm\({}^{-2}\) in the Levantine basin, while negative latent heat loss anomalies are simultaneously observed in the Balearic and Adriatic seas [54]. This dipolar pattern can be explained considering the sea level pressure anomaly (SLPA) associated with the positive phase of EA-WR (Figure 4a): the higher SLPA over Northern Europe forces a northerly flow of cold dry air over the eastern basin but a southerly flow of relatively warm moist air over the western basin, this producing net heat anomalies of different signs. In the negative phase, opposite conditions prevail (Figure 4b) and thus sign variations of EA-WR have the potential to induce a see-saw behavior in the heat budgets of the eastern and western sub-basins, a result also presented by other authors [28], who discussed the relevant impact of this mode on winter heat flux.
# Forecasting Taxi Demands Using Generative Adversarial Networks with Multi-Source Data
## 6 Results
### Comparison with Baselines
#### 6.1.2 Performance Comparison over the Three Areas Separately  
Our proposed model's performance and the baselines for the three study areas, namely Jiedaokou, Wuhan Railway Station Area, and Guanggu, are shown in Tables 5-7.  
GAN_LSTM_CNN model achieves 15.86% (2.31%, 13.46%) lower MAPE (RMSE, MAE) on Jiedaokou area, 9.22% (3.86%, 6.84%) lower MAPE (RMSE, MAE) on Wuhan railway Station area, and 4.69% (10.02%, 6.84) lower MAPE (RMSE, MAE) and on Guanggu area, respectively. Compared with GAN_CNN, our model reduced MAPE (RMSE, MAE) by 13.79% (4.77%, 12.29%) on the Jiedaokou area, 11.67% (11.45%, 9.93%) on Wuhan railway Station Area, and 4.38% (3.83%, 10.80%) on Guanggu area, respectively.
## 5 Conclusions  
In this paper, we proposed a weighted mean-based spatiotemporal feature extraction technique to build a CNN-based HAR model that recognizes action by processing video data at high speed. Previously, an action was recognized by analyzing motion patterns based on time information through models with complex structures such as 3D CNN, two-stream CNNs using optical flows, and ConvLSTMs. However, the proposed method recognizes an action by extracting frame changes, which are calculated at high speed with temporal features. The temporal feature is used to generate the spatiotemporal features during the entire time by weighing the spatial features extracted from CNNs and through the weighted mean spatial information at the point where motion changes significantly. The generated spatiotemporal features are used to recognize action by entering into MLPs with lower complexity than the prediction model used in the existing HAR, and the proposed model is verified via experiments to recognize action by processing data at a faster speed than the existing CNN-based HAR models. In addition, the efficiency of extracting spatiotemporal features was verified using the frame change rate as time information through higher action recognition performance compared to general average technique. Performance evaluation results according to the number of frames extracted from videos showed that action was recognized with high accuracy even while extracting fewer frames than the HAR model using LSTM. Finally, to assess real-time possibility in embedded systems of low-cost GPUs, the results of performance evaluation in Jetson NANO also show that data are processed at high speed. It was possible to verify the system's utilization value to recognize instantaneous action in an emergency situation.  
Nevertheless, the proposed model still has a weakness in that it is vulnerable to rapid changes of background or obstacles because it recognizes the action by using the changes in image frames over time. Changes in frames caused by background or obstacles, not actions of a person to be recognized, can act as noise and deteriorate action recognition performance. In particular, it is very difficult to recognize an action in a camera that is constantly moving, not a CCTV, which is a fixed type of camera we have adopted, because the frame is continuously changing. Addressing this issue is the direction of research we are pursuing in the future.  
All authors took part in the discussion of the work described in this paper. Conceptualization, J.K.; Data curation, J.K.; Investigation, J.K.; Project administration, J.C.; Writing--original draft, J.C.; Writing--review & editing, J.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MOE) (No.2018R1D1A3B07041729) and the Soonchunhyang University Research Fund.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.
# An IoT-Based Participatory Antitheft System for Public Safety Enhancement in Smart Cities
## 3 Methods and Materials
### System Architecture  
The developed system adopts the system architecture proposed in [36, 37], with a similar structure of M2M systems. Figure 2 illustrates the proposed system architecture, which mainly involves two data transmission mechanisms, via: (i) a BLE/Wi-Fi gateway, and (ii) a smartphone to forward to the BLE device.  
The BLE/Wi-Fi gateway device is used to optimize the efficiency of the application, fulfilling the objectives of the specific project to cover areas--places where people tend to gather or pass through--in order to enhance participatory sensing, while at the same time facilitating BLE remote tag sensor activation.  
Gateway's core platform is Raspberry Pi3 hardware module incorporating internal Wi-Fi and BLE external USB component as illustrated in Figure 3.  
The Wi-Fi module is initially configured and automatically enabled upon power up. The BLE module is connected to the USB external interface of the Raspberry Pi3 platform and can be controlled through the relevant command protocol implemented by the manufacturer. The Gateway is served as an intermediate infrastructure component which can enrich and strengthen the participatory sensing concept of the project. Through its BLE component, it is able to transmit the activation signal to the remote tag, thus enabling the tag's beacon mode, which in turn operates in stolen mode, broadcasting its alerting signal.  
Figure 3: Antitheft BLE device and development board.  
Figure 2: Basic architecture of the BLE antitheft device activation.  
In order to secure safe communication ([38, 39]), the following tasks were accomplished:  
* The communication between the mobile application/gateways and the Operation Center is via HTTP and is secured with hash authentication. Only City.Risks application users/gateways can communicate with this endpoint and receive information from it.
* There are only two procedures that can be initiated by the Operation Center, which are activation and discovery. These two procedures can only be initiated after a request from the owner of the device (the one that has registered the device). The request is accompanied by the unique ID of the stolen device and does not contain any personal information about its possessor. Therefore, the tracking procedure focuses on the object and not the owner.
* No mobile application/gateway can initiate a tracking procedure without a corresponding command of the Operation Center (secured with hash authentication).
* No user can report a stolen device that he/she does not own (confirmed with local registration). Therefore, a tracking procedure cannot be initiated by other than the owner.
* The reports received from participatory users during the tracking procedure are not associated with them but with the stolen device. Therefore, the location that accompanies the sighting reports is associated with the ID of the stolen device.
###### Abstract  
During the past dozen years, several mobile mapping systems based on the use of imaging and positioning sensors mounted on terrestrial (and aerial) vehicles have been developed. Recently, systems characterized by an increased portability have been proposed in order to enable mobile mapping in environments that are difficult to access for vehicles, in particular for indoor environments. In this work the performance of a low-cost mobile mapping system is compared with that of: (i) a state-of-the-art terrestrial laser scanning (TLS), considered as the control; (ii) a mobile mapping backpack system (Leica Pegasus), which can be considered as the state-of-the-art of commercial mobile mapping backpack systems. The aim of this paper is two-fold: first, assessing the reconstruction accuracy of the proposed low-cost mobile mapping system, based on photogrammetry and ultra-wide band (UWB) for relative positioning (and a GNSS receiver if georeferencing is needed), with respect to a TLS survey in an indoor environment, where the global navigation satellite system (GNSS) signal is not available; second, comparing such performance with that obtained with the Leica backpack. Both mobile mapping systems are designed to work without any control point, to enable an easy and quick survey (e.g., few minutes) and to be easily portable (relatively low weight and small size). The case study deals with the 3D reconstruction of a medieval bastion in Padua, Italy. Reconstruction using the Leica Pegasus backpack allowed obtaining a smaller absolute error with respect to the UWB-based photogrammetric system. In georeferenced coordinates, the root mean square (RMS) error was respectively 16.1 cm and 50.3 cm; relative error in local coordinates was more similar, respectively 8.2 cm and 6.1 cm. Given the much lower cost (approximately 56k), the proposed photogrammetric-based system can be an interesting alternative when decimetric reconstruction accuracy in georeferenced coordinates is sufficient.  
low-cost 3D reconstruction; mobile mapping; UWB; backpack; photogrammetry; terrestrial laser scanning 1
## 5 Discussion
### Isotopic Characteristics and Genesis of Spring Gas
(3) Organic sources (13.0%): minor inputs inferred from residual isotope depletion \(\xi^{13}\)C CO\({}_{2}\) signature. These observations indicate that S1 is influenced by deep-sourced mantle gases, facilitated by hydrothermal alteration and calc-silicate breakdown, with mantle-derived volatiles migrating upward along structurally reactivated zones consistent with active subduction processes beneath the STS orogen [42; 44; 63]. In contrast, S13 exhibits a \(\xi^{13}\)C CO\({}_{2}\) value of \(-\)10.3%, aligning closely with the calcite precipitation trend at approximately 170 \({}^{\circ}\)C. This suggests that CO\({}_{2}\) in S13 arises primarily from thermal decomposition of carbonates and temperature-driven fluid-rock reactions. The alignment with the calcite equilibrium line in Figure 6b further indicates CO\({}_{2}\) fractionation during precipitation processes within a thermally moderated system. Poisson's ratio anomalies offer complementary evidence for fluid presence and mechanical crustal properties. As shown in Figure 7, the high Poisson's ratio (4%) at S1 implies notable fluid enrichment and crustal plasticity, facilitating the ascent of deep-origin gases. S1 is situated at the intersection of the TFF and MDF, a tectonically active zone. Seismic and geophysical studies [54; 55; 72; 73; 74] have identified low p- and s- wave velocities and high conductivity zones at this intersection, supporting the presence of a fluid-rich, deformable crust conducive to deep fluid migration. Elevated \({}^{3}\)He/\({}^{4}\)He ratios at S1 further confirm mantle-derived gas input. In contrast, S13 exhibits a significantly low Poisson's ratio (-8%), indicative of a more rigid crust with limited tectonic activity. The higher wave velocities observed suggest intact rock structures and thinner sedimentary layers, which act as barriers to deep gas ascent. Located solely along the MDF, S13 likely experiences less structural deformation, limiting vertical migration of mantle-derived fluids. Thus, S13's gas composition is dominated by shallow crustal contributions, such as carbonate decomposition and hydrothermal reactions. The isotopic and geophysical data converge to indicate that S1 is strongly influenced by mantle-derived and carbonate-sourced volatiles transported through reactivated fault  
Figure 7: [PERSON]’s ratio anomalies in the study area. Modified from ([PERSON], 2011) [71]. zones, whereas S13 is dominated by crustal helium and thermally evolved CO2 in a more structurally confined setting. These results highlight the importance of combining isotopic signatures with physical crustal properties to distinguish gas origins and infer fault-fluid coupling mechanisms in seismically active regions.
# Outage Performance of SWIPT-D2D-Based Hybrid Satellite-Terrestrial Networks
## 3 Performance Analysis
### PS-D2D Scheme  
The OP of the UT and IoT device for the PS-D2D scheme are derived in the following, respectively.
# Asynchronous Chirp Slope Keying for Underwater Acoustic Communication
## 2 Materials and Methods
### Experimental Set-Up
#### 2.5.2 Experiment Parameters  
The sampling rate of our acquisition unit is limited to \(f_{\mathrm{s}}=88\) kHz. As a result, the received signal is undersampled, i.e., the Nyquist frequency is below the transmission band. While this mixing operation generally results in a leakage of signal power, the band-limited nature of the chirp sequences and the low noise environment limit this aliasing effect. This band-limitation is ensured by an additional analog bandpass-filter. The chirp parameters are listed in Table 2 for the single band and dual-band transmission.  
\begin{table}
\begin{tabular}{l r l} \hline \hline
**Parameter** & **Value** & **Description** \\ \hline \(f_{\mathrm{c}}\) & 67.5 kHz & Center frequency \\ \(\hat{B}\) & 5.0 kHz & Maximal available bandwidth \\ \hline \(f_{\mathrm{s}}\) & 88.0 kHz & Receiver sampling frequency \\ \hline \(N_{\mathrm{res}}\) & 4 & Resampling factor after down-mixing \\ \(N_{\mathrm{res2}}\) & 2 & Resampling factor after signal merge \\ \hline \(f_{\mathrm{s1}}\) & 22 kHz & Sampling frequency after 1 st downsampling \\ \(f_{\mathrm{s2}}\) & 11 kHz & Sampling frequency after 2 nd downsampling \\ \hline \hline \end{tabular}
\end{table}
Table 1: Transmission Band Parameters.  
Figure 7: Schematic experimental set-up in for the acoustic transmission inside a water tank. The tank is filled with fresh water and located inside a closed building. A comparable scenario would be two divers working on a ship’s hull or an UAV inspecting a lake harbor’s foundations.  
The symbol rate and occupied bandwidth of both transmissions is kept constant. Therefore, the single band signal has twice the TB compared to the dual-band one. This implicates the ratio of symbol energy to noise energy to double as well [62],  
\[\frac{E_{\text{sym}}}{E_{\text{n}}}=T\,B\,\gamma, \tag{44}\]  
where \(\gamma\) is the signal-to-noise ratio of the received signal. The expected data rate on the other hand is halved, as each symbol only contains half the bits.
## 4 Discussion and Results
### Impact of Failure Rate on Availability  
For repairable systems, steady state availability is a conventional reliability evaluation index, which indicates the long-term working ability of the system. The steady state availability of the wellhead connector can be obtained by Equation (23). To determine the contribution of every transfer rate to the availability of the subsea wellhead connector, the impacts of the transfer rates on the steady state availability can be obtained by control variate method that is as shown in Figure 11a-e.  
The curve in Figure 11a is the influence of the transfer rates of the wellhead connector during normal operation on the steady state availability of the system. It can be seen that the failure rates of \(\lambda_{17}\) and \(\lambda_{9}\) have the biggest impact on the steady state availability; the failure rate \(\lambda_{16}\) is the second; while other failures have a smaller impact on the steady state availability of the system. Therefore, in order to improve the steady state availability of the system, the probability of collet deformation and seal ring failure during normal operation should be minimized. As shown in Figure 11b, as the maintenance rate \(\lambda_{19}\) increases, the steady state availability first increases rapidly and then slowly. Therefore, for the current maintenance level, reducing the average maintenance time will greatly improve the steady state availability of the system. Figure 11c shows the impacts of the failure rates during the installation test and periodic test on the steady state availability of the system. The failure rate \(\lambda_{24}\) has a bigger impact on the steady state availability, while the failure rate \(\lambda_{20}\) has little impact on steady-state availability. Therefore, reducing the number of failures during periodic testing will greatly increase the steady state availability of the system. As can be seen in Figure 11d, as the periodic test completion rate \(\lambda_{21}\) increases, the steady state availability of the system rapidly rises and then the rate of rise decreases, while the installation test completion rate \(\lambda_{23}\) has a smaller impact on steady  
Figure 9: Reliability and transient availability of subsea wellhead connector.  
state availability. As shown in Figure 11e, the periodic test rate \(\lambda_{22}\) has a small impact on the steady state availability of the wellhead connector.  
Figure 10: Reliability of subsea wellhead connector when pressure ring deformation occurs at t = 10\({}^{9}\) h.
## 2 Theory
### Frequency Response Function (FRF) and Transmissibility Function
Solving the second row of Equation (8) for \(f(\omega)\) and substituting \(f(\omega)\) in the first row yields the following:  
\[\hat{K}_{uu}(\omega)\mathbf{u}_{u}(\omega_{i})+\hat{K}_{uk}(\omega)u_{k}(\omega)= \frac{\mathbf{f}_{s,u}}{f_{s,k}}\hat{K}_{ku}(\omega)\mathbf{u}_{u}(\omega)+\hat{K}_{ kk}(\omega)u_{k}(\omega) \tag{9}\]  
Reorganizing Equation (9) and solving for the ratio of \(\frac{\mathbf{u}_{u}(\omega)}{u_{k}(\omega)}\) results in the definition of the transmissibility function [72]:  
\[T(\omega)=\frac{\mathbf{u}_{u}(\omega)}{u_{k}(\omega)}=\bigg{(}\hat{K}_{uu}(\omega )-\frac{\mathbf{f}_{s,u}}{f_{s,k}}\hat{K}_{ku}(\omega)\bigg{)}^{-1}\bigg{(}\frac{ \mathbf{f}_{s,u}}{f_{s,k}}\hat{K}_{kk}(\omega)-\hat{\mathbf{K}}_{uk}(\omega)\bigg{)} \tag{10}\]  
For discrete frequencies \(\omega_{i}\), the transmissibility function \(\mathbf{T}(\omega_{i})\) can be evaluated numerically:  
\[\mathbf{T}(\omega_{i})=\frac{\mathbf{u}_{u}(\omega_{i})}{u_{k}(\omega_{i})} \tag{11}\]  
while if the transmissibility function is available, the response for any given response at DOF \(k\) can be obtained as follows:  
\[\mathbf{u}_{u}(\omega_{i})=T(\omega_{i})u_{k}(\omega_{i}) \tag{12}\]