esa-sceva/satcom-chunk-collection huggingface dataset:
# Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data
## 6 Conclusions  
A simple, but effective method based on an SVM algorithm was developed to map irrigated and rainfed wheat areas in a semi-arid region of China. This method avoids repeated tuning of thresholds in supervised classification. The overall accuracy of the Google-Earth testing samples is 96.0%, indicating that the SVM classification model is robust. The estimated ratio of irrigated-to-rainfeed wheat area in this study is close to that estimated by the agricultural sector of Shanxi Province. The classification of irrigated and rainfed areas for the survey points also agrees with the field level photographs. In addition, maps of maturity dates and sowing dates were generated. It was shown that irrigated and rainfed wheat exhibit distinct maturity dates, although the spatial patterns of sowing dates are similar.  
Accurate training samples, which were obtained from ground surveys in this study, are essential for the presented method. The proposed method can be applied in other arid and semi-arid regions with the availability of accurate training samples and multi-temporal data at high spatial and temporal resolutions similar to HJ-1 A/B (or higher). The HJ-1 A/B has an advantage over Landsat in temporal resolution, although they have the same spatial resolution in the red and near-infrared band. Different temporal resolution may influence the classification result of irrigated and rainfed wheat area, and the comparison of HJ-1 A/B time series with Landsat or Sentinel time series for classifications in partially-irrigated areas is a potential field area of future research. In addition, the performance of the proposed method in areas with more than one major crop type needs to be evaluated in the future.  
The present research was funded by the National Natural Science Foundation of China (Grant Nos. 41171086, 41371119).  
**Author Contributions:** [PERSON] was the main author of the manuscript. [PERSON], [PERSON] and [PERSON] revised the manuscript. [PERSON] provided some ground reference information. [PERSON], [PERSON] and [PERSON] provided some valuable suggestions. All of the authors have read and approved the final manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 7 Discussion  
Table 2 illustrates the privacy enhancement ratios for each proposed mode. Firstly, the privacy enhancement increases with the threshold values. Secondly, the network conditions affect the improvement of privacy, especially the size of the used space. In a small zone, mobile nodes can meet each other more often than in a large zone. Figure 19: The result of the data collection process with the bus model. Figure 20: The result data collection process with the working day model. Thirdly, with some protocols like Spray and Wait, routers are already selective in choosing the next hop nodes. Adding the privacy conditions will make the choosing process more selective, leading to fewer forwarding opportunities and increasing the enhancement ratio. Fourthly, we observe a good privacy enhancement for the stranger then familiar and familiar then stranger modes. Indeed, their privacy enhancement ratios vary between 6% and 71%. The overall effect of the privacy-aware selection methods is minimal concerning the network performance. The energy consumption is minimal since the selection process did not use active transmission or reception. The packet delivery ratio and the packet overhead are also slightly affected. The selection method will find fewer forwarding nodes than the normal routing, leading to fewer used packets in transmitting messages. The average transmission time (ATT) increases because nodes must select acceptable candidates before transferring messages to them, which induces a delay in transmission. The proposed modes ensure a good privacy enhancement without losing the network and message-delivery performance. However, in real-world implementations, encryption is a critical component of privacy and security, especially when sensitive information is transmitted. Even when trust-based routing is used, encryption would still be necessary to protect the content of messages from being accessed by malicious nodes that might be part of the routing path. This paper focuses on the social aspect of privacy, such as selecting trustworthy nodes for message forwarding, rather than on the technical aspects of securing the content of the messages through encryption. Thus, we shift away from encryption to focus on the impact of such social trust and privacy-aware forwarding on network performance on top of routing protocols that do not include encryption as part of their core functionality. However, our proposed modes can be easily integrated with solutions that use encryption as a standard practice in their core functions, mainly when encryption is designed as a complementary layer that secures the content regardless of the routing path. There are several promising directions for future work to build upon the privacy-aware routing techniques presented in this paper. Firstly, the social trust model can be enriched by incorporating additional contextual cues such as user profiles, interests, relationships, and interactions to enable more fine-grained trust evaluations between users. Secondly, exploring encryption, anonymization, and pseudonymization techniques along with social
## 2 Materials and Methods
### Pre-Treatment of Raw Data for the Statistical Analysis  
The raw data (FIDs) relative to the 1D \({}^{1}\)H NOESY NMR and \({}^{1}\)H NMR: experiments were processed by a single operator using Mestrelab and segmented into regular-sized (0.04 ppm) intervals (buckets) in the range of [10.50, 0.50] ppm. The underlying area of each bucket was calculated and normalized to the total intensity. The areas of the buckets in the region [5.13, 4.69] ppm, corresponding to the residual water signal, were set to 0. The data matrices were imported into MetaboAnalyst 5.0, and buckets were subjected to mean-centering and divided by the standard deviation of each variable (Unit Variance scaling). Multivariate statistical analyses such as Principal Component Analysis (PCA), Hierarchical Clustering Dendrogram (HCD), and Partial Least Square-Discriminant Analysis (PLS-DA) were performed. PCA and HCD were used as unsupervised approaches to get an overview of the data. PLS-DA was used as a supervised method that uses multivariate regression techniques to extract via a linear combination of original variables (X) the information that can predict the class membership (Y). The PLS regression was performed using the _plsr_ function provided by _R pls_ package [42]. The classification and cross-validation were performed using the corresponding wrapper function offered by the _caret_ package [43]. To assess the significance of class discrimination, a permutation test was performed. In each permutation, a PLS-DA model was built between the data (X) and the permuted class labels (Y) using the optimal number of components determined by cross-validation for the model based on the original class assignment [44].
# Anomaly Detection and Early Warning Model for Latency in Private 5G Networks
## 4 Analysis of Results  
### Data Set Description  
The dataset was selected from the downlink network status data of a single DU under the exclusive base station of private 5G networks, totaling 16 days, one data point every 15 min, and a total of 1536 data points. The dataset includes PRB, CQI, MCS, etc., the entries of which are listed in Table 1. According to the different pre-processing methods, all the data can be divided into two categories: normalized data, including the total number of PRBs and the total number of CQI reports, which are normalized to the 0-1 range using the maximum and minimum normalization methods, and percentage data, including PRB utilization and retransmission rate. The percentage data were not normalized. The final dataset contained 179 dimensions.  
It is worth noting that, to save resources, the base station sleeps for a period of time every day at regular intervals, and the data collected during the sleep period are blank.
# A Portable Device for the Measurement of Venous Pulse Wave Velocity
## 2 Materials and Methods  
The devised methodology for vPWV measurement requires the acquisition of the following signals:  
* respiratory signal;
* ECG; and
* Doppler shift.  
The flowchart of measurement process is reported in Figure 1, while a schematic representation of the device is shown in Figure 2. Examples of acquired signals are reported in Figure 3, showing typical timing of acquisition. Signals were acquired one at a time to ensure the highest and most stable sampling rate. Raspberry Pi controls the timing and switches the signal acquisition, following the order shown in the list above. The delay introduced by switching the acquisition from one channel to the next was measured and found to be negligible (<2 ms). In order to synchronize the delivery of the compressive stimulus both with respiratory and cardiac cycles, the system first identifies the expiratory phase in the respiratory signal and then the rising edge of the R-wave in the ECG signal. This latter event triggers the delivery of the compressive stimulus, after an optional delay defined by the user, and the acquisition of the haemodynamic response (i.e., the Doppler shift). This signal is then processed to identify the footprint that corresponds to the passage of the pulse wave under the probe. In this way. the transit time of the pulse wave (i.e., the pulse transit time) can be estimated and its velocity calculated on the basis of cuff-probe distance.
# Does Below-Above Canopy Air Mass Decoupling Impact Temperate Floodplain Forest CO2 Exchange?
###### Abstract  
Environmental conditions influence forest ecosystems and consequently, its productivity. Thus, the quantification of forest CO2 exchange is a critical requirement to estimate the CO2 balance of forests on a local and regional scale. Besides interpreting the annual CO2 exchange corresponding to environmental conditions over the studied years (2015-2020) at the floodplain forest in Lanzhot, Czech Republic (48.6815483 N, 16.9463317 E), the influence of below-above canopy air mass decoupling on above canopy derived CO2 exchange is the focus of this study. For this purpose, we applied the eddy covariance (EC) method above and below the forest canopy, assessing different single- and two-level flux filtering strategies. We focused on one example year (2019) of concurrent below and above canopy EC measurements. We hypothesized that conventional single-level EC flux filtering strategies such as the friction velocity (u*) filtering approach might not be sufficient to fully capture the forest CO2 exchange at the studied ecosystem. Results suggest that decoupling occurs regularly, but the implication on the above canopy derived EC CO2 fluxes appears to be negligible on an annual scale. We attribute this to the open canopy and flat EC tower surrounding terrain which inhibits horizontal removal of below-canopy respired CO2.  
canopy decoupling; CO2 fluxes; eddy covariance; floodplain forest
## 3 Calculation Results and Analysis
### Analysis of Calculation Results in Case 1
extracted box girder is shown in Table 4, and the settlement difference between the left and right sides of the beam at different positions is divided by the span to obtain the inclination of each box girder at different settlement amounts as shown in Figure 4. The inclination of the left-span box girder across the ground fissure is the largest, and the inclination is 1.14% when the settlement is 50 cm; the inclination of the middle-span box girder is the second largest, and the inclination is 0.21% when the settlement is 50 cm; the inclination of the right-span box girder is the smallest, and the inclination is 0.052% when the settlement is 50 cm. As shown in Figure 4, the inclination angle of the box girder across the ground fissure is the largest, and the inclination angle of the box girder away from the ground fissure is almost unchanged. The Specification for the Design of Foundations and Foundations of Highway Bridges and Culverts (JTG D63-2019) stipulates that the uneven settlement difference between adjacent peers should not cause the bridge deck to form an additional longitudinal slope of more than 0.2%. When the vertical settlement of the ground fissure \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Vertical**} & \multicolumn{2}{c}{**Left Side Across**} & \multicolumn{2}{c}{**Middle Across**} & \multicolumn{2}{c}{**Right Side Across**} \\
**Settlement** & **Left Side** & **Right Side** & **Left Side** & **Right Side** & **Left Side** & **Right Side** \\
**/cm** & **Displacement** & **Displacement** & **Displacement** & **Displacement** & **Displacement** & **Displacement** \\
**/mm** & **/mm** & **/mm** & **/mm** & **/mm** & **/mm** & **/mm** \\ \hline
10 & \(-\)5.274 & \(-\)10.825 & \(-\)10.825 & \(-\)11.267 & \(-\)11.267 & \(-\)10.058 \\
20 & \(-\)6.272 & \(-\)19.175 & \(-\)19.175 & \(-\)21.252 & \(-\)21.252 & \(-\)20.005 \\
30 & \(-\)7.079 & \(-\)27.575 & \(-\)27.575 & \(-\)31.276 & \(-\)31.276 & \(-\)29.915 \\
40 & \(-\)7.637 & \(-\)36.013 & \(-\)36.013 & \(-\)41.248 & \(-\)41.248 & \(-\)39.740 \\
50 & \(-\)8.086 & \(-\)44.484 & \(-\)44.484 & \(-\)51.224 & \(-\)51.224 & \(-\)49.544 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Vertical displacement of each span of the box girder. Figure 4: The inclination of box girder under different settlement amounts. Figure 3: The inclination of each bridge pier under different settlement amounts. exceeds 10 cm, the left-span inclination across the ground fissure exceeds the specification limit. During the settlement of the hanging wall of the ground fissure, a large longitudinal stress is generated in the box girder, and when the settlement is 10 cm, the stress map is shown in Figure 5. It is observed that the maximum tensile stress in the box girder is 20.5 MPa, while the ultimate tensile stress of C50 concrete is 5 MPa, which means that tensile damage occurs in the box girder concrete when the active settlement of the ground fissure seam is greater than 10 cm.
## 6 Efficiency and Security Analysis
### Security
#### 6.2.1 External Attack
In contrast to the previous types of attack, in this case, [PERSON] refrains from measuring these qubits; instead, she entangles them with her ancilla state before forwarding the relevant qubits to [PERSON] and [PERSON]\({}_{i}\). Following this, [PERSON] remains inactive until the protocol concludes, at which point she measures her qubits, aiming to extract valuable information. However, the result of her efforts leads to a situation where, rather than achieving an even distribution of \(|GHZ_{3}\rangle\) triplets among [PERSON], [PERSON], and [PERSON]\({}_{i}\), she ends up with \(m\)\(|GHZ_{4}\rangle\) quadruples distributed evenly among [PERSON], [PERSON], [PERSON]\({}_{i}\), and herself. Consequently, during the classical portion of the protocol, when [PERSON] and [PERSON]\({}_{i}\) relay their measurements to [PERSON] via the public channel, [PERSON] will be unable to compute the correct modulo 2 sum. This is due to her lack of access to [PERSON]'s private bit vector \(\mathbf{y}_{2}\), which [PERSON] does not transmit through the public channel. As a result, [PERSON] will again be unsuccessful in acquiring any information. **(Attack\({}_{4}\)) PNS**. One of the most potent attack methods that [PERSON] can use against any quantum protocol at the moment is the photon number splitting attack (PNS), which was first described in [83] and subsequently examined in [84; 85]. Contemporary photon sources exhibit a known flaw: at times, they might generate several identical photons rather than a single one. Consequently, [PERSON] could intercept the pulses originating from [PERSON] during the distribution of the \(|GHZ_{3}\rangle\) triplets, retain one photon from the multiphoton pulse for her own use, and transmit the remaining photons to [PERSON] and [PERSON]\({}_{i}\) without being noticed during the transmission phase. Nonetheless, this situation resembles the Entangle and Measure attack analyzed above. Again, instead of \(|GHZ_{3}\rangle\) triplets evenly distributed among the three legitimate actors, there are \(|GHZ_{4}\rangle\) quadruples evenly distributed among [PERSON], [PERSON], [PERSON]\({}_{i}\), and [PERSON]. [PERSON] becomes an active fourth player in the game but fails to uncover any useful information for the reason explained above.
### Filtering of Spatial Spectrum
With the gradual increase of the propagation distance, the polarization order of the anomalous VBB will gradually tend to zero accompanied by the variation of polarization distribution. This characteristic may provide more possibilities and expand the applications in optical trapping, quantum communications, and optical microscopy.
# DDPG-MPCC: An Experience Driven Multipath Performance Oriented Congestion Control
## 2 Experience Driven MPCC Design
### Testbed Setup and Normalization  
We implemented the MPCC kernel and incorporated an offline DDPG module for training and testing using realistic datasets. In contrast to the default MPTCP scheduler, our rate-based approach in DDPG-MPCC signals the scheduler to set a high congestion window, unless unforeseen consequences occur. To maintain tractability, following [5], we adopted the assumption in a rate-based multipath scheduler that a subflow becomes unavailable when 10% of the packets needed to sustain the current sending rate for the current RTT duration are queued for transmission in the path. The DDPG-MPCC scheduler and kernel design extend the work in [5] by importing logs and integrating the DDPG module into our Linux client node. The extraction of kernel logs provides the necessary data for the training and offline learning of DDPG-MPCC.  
Figure 2 illustrates the network topology and the three scenarios employed in evaluating the multipath protocols. Our testbed, created over CloudLab, features a typical client and server connected through two emulators and two routers simulating WiFi and 5G paths. This test bench comprises six nodes, including a client, server, emulators, and routers, as depicted in Figure 3.  
While testing and comparing various congestion controls, we vary the bandwidth of the route by changing the bandwidth of the router's interface. Similarly, we insert a certain loss percentage in our emulator nodes by using the Linux traffic control or utility for the interface in that route. We run five 300s iperf3 tests in multiple network scenarios for varying bandwidth and varying loss percentages where we keep the other environment variables to default.  
Figure 3: CloudLab Testbed Setup with Two paths.  
Figure 2: Three scenarios to quantify the performance of MPCC across varying bandwidth: (**a**) MPTCP connection vs. singlepath connection; (**b**) MPTCP connection vs. two singlepath connections; (**c**) Two MPTCP connections contending with each other in both paths.
# Enhancing Heterogeneous Communication for Foggy Highways Using Vehicular Platoons and SDN
## 3 Proposed Scheme: SDVN for Foggy Highways
The maximum \(PT\) is determined by using \(LD\) to calculate several links along a single path between the source and destination platoons. The minimum \(LD\) is chosen for each path because ignoring it could result in the path breaking. Additionally, there are several paths between source and destination. We select \(LD\) with the maximum \(PT\) from all these minimums. Hence, \(LD_{pc}\) indicates the minimum delay at the data layer of the SDVN protocol when platoons communicate, which is calculated as  
\[LD_{pc,ij}=\frac{CR_{\max}\pm d_{ij}}{vel_{ij}}\times\frac{vis_{c}}{vis_{\max}}. \tag{4}\]  
The link with minimum \(LD_{pc}\) is selected as the next hop for establishing an optimal routing path between the source and the destination in two different platoons. When this \(pc\) is the controller, the \(vel_{ij}\) is equal to the speed of light because the communication is LTE-based. When this \(pc\) is the platoon, the \(vel_{ij}\) is the speed between two vehicles or platoons (if vehicles inside the platoon are moving at the same speed). \(vis_{c}\) is between 5 m and 10 m at a certain location, \(\pm\)shows both directions of traffic, and \(vis_{max}\) is the maximum visibility (50 m) we considered. Equation (4) shows how vehicles connect with each other based on current foggy conditions. Now, we explain using Equation (5) how vehicles in platoon B adjust their speed by maintaining a safe distance between each other when they are aware of lower visibility conditions on the next patch of road. \[v_{ft}=v_{ft-1}+\frac{v_{ft-1}-v_{i}}{t^{2}}-\frac{vis_{c}}{t_{s}}, \tag{5}\]where \(v_{f_{t}}\) represents the final velocity (current velocity) a vehicle adjusts to in \(t\) seconds in order to prevent a collision and \(v_{f_{t-1}}\) represents the final velocity in previous time. Meanwhile, \(v_{i}\) denotes the vehicle's initial velocity in previous time (before acceleration), and \(t_{s}\) stands for the simulation (current) time.
# Low-Power Two-Phase Clocking Adiabatic PUF Circuit
## 4 Simulation and Results
### Simulation Result
#### 4.2.2 PUF Evaluation Metric  
We utilize two evaluation metrics to verify the proposed adiabatic PUF behavior.  
1. **Uniqueness** is used to determine the ability of a PUF to distinguish a chip from other chips [29]. The ideal value of the uniqueness metric is 50%, indicating that a uniqueness result closer to 50% has a better performance (where the normalized value of uniqueness is \(0\leq U\leq 1\)). The uniqueness is expressed as \[\mathbf{Uniquness}=\frac{2}{k(k-1)}\sum_{i=1}^{k-1}\sum_{j=i+1}^{k}\frac{HD\big{(} R_{i},\ R_{j}\big{)}}{n}\times 100\] (10) where \(HD(R_{i},R_{j})\) represents the Hamming Distance of the two different PUF instances' responses, with \(k\) number of chips and \(n\)-bit length.  
Figure 8: PUF cell energy dissipation of the proposed adiabatic PUF versus QUALPUF under the PMOS body effect.  
Figure 9: 4-bits PUF energy dissipation of the proposed adiabatic PUF versus QUALPUF under CMOS process variation.  
2. **Reliability** measures how reproducibly the challenge-response pairs of a PUF instance with varying environmental conditions, such as temperature and CMOS process variations. The reliability is mathematically expressed as follows: \[\textbf{Reliability}=100-\frac{1}{k}\sum_{i=1}^{k}\frac{HD\big{(}R_{i},\,R_{i,j}\big{)} }{n}\] (11) where \(k\) represents the total number chips (\(k\) = 100 in this study), \(n\) is the number of PUF bits, and \(HD(R_{i},\,R_{i,j})\) is the Hamming Distance of \(j\)-th sampling of \(R_{i}\). The ideal reliability value is 100%.  
The calculated uniqueness results using Equation (9) are graphically depicted in Figure 10 and Figure 12, and calculated reliabilities (using Equation (10)) are shown in Figure 11 and Figure 13, under V\({}_{\text{TH}}\) and T\({}_{\text{OX}}\) variations, respectively. In this study, we used a bit size of n = 4 and chip number of k = 100. In Figures 10-13, the solid lines indicate the proposed adiabatic PUF, and the dotted lines represent QUALPUF. To facilitate the uniqueness and reliability calculations, Monte Carlo simulations were repeatedly conducted with a varying output load capacitance (CL = 10, 50, 100, and 200 pF) under temperature variations.
# Production Plant and Warehouse Automation with IoT and Industry 5.0
## 7 Challenges on the Platform Shift
### Interoperability  
As the devices that are connected in communication are of different types in IoT as they use different encoding and different protocols, interoperability has always been and continues to be a core essential ideal. Currently, several standards are used to support diverse industries' applications [40]. As a result of the large amounts of data, different interconnected devices also have to adopt standards for collaboration, which is very difficult to perform, as well as within a variety of system restrictions. As a result, IoT systems are being developed to manage increasingly greater levels of interconnection.