esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Multi-Server Federated Learning Framework in Vehicular Edge Computing
## 5 Transmission Latency Analysis
### Handover Latency  
As noted in Section 5.1, each round's latency is driven by the slowest client's upload + download time (\(\tau_{\text{MS-FL}}(\text{t})\) in (24)). Since that term already captures the worst-case edge delay, we only need to check whether handovers add any extra latency.  
Under a standard LTE/5G conditional handover--where Radio Resource Control (RRC) commands are pre-installed and the User Equipment (UE) automatically executes them once a neighbor's Reference Signal Received Power (RSRP) exceeds the serving RSRP via configured hysteresis for the Time-to-Trigger [24; 36]--all control-plane signaling (handover request, verify, ACK, and RRC handover command) is effectively instantaneous, and its latency is negligible in comparison to moving the model update itself. In a symmetric setup (where each RSU allocates equal uplink bandwidth to all its clients), a migrating vehicle simply resumes its upload under the same bandwidth allocation. Therefore, the handover upload delay remains bounded by the usual edge delay \(\tau_{\text{MS-FL}}(\text{t})\), and application-level handovers do not introduce any significant amount of additional delay.
## Appendix D Uncertainty--Confidence-Level Based Societal Risk in F-N Curves  
Societal risks for the case studies are presented as F-N curves and constructed using the following equation:  
\[F=k\times N^{-a} \tag{12}\]  
where \(F\) is the cumulative frequency of \(N\) or more fatalities, k is a constant, \(N\) is the number of fatalities, \(a\) is the aversion factor.  
[PERSON] et al. [38] propose a confidence-level-based SR characterize the uncertainty of SR in two dimensions by defining the confidence bounds of the SR given by the F-N diagram. In this method, it is assumed that the events' occurrence in the ET model of PRA follows Poisson distribution. Based on this assumption, the confidence interval of the number of times an event occurs can be determined by:  
\[\begin{array}{c}\varphi_{U}=\frac{X_{1-\omega/2}^{2}(2n+2)}{2}\\ \varphi_{L}=\frac{X_{\omega/2}^{2}(2n)}{2}\end{array} \tag{13}\]  
where \(\varphi_{U}\) and \(\varphi_{L}\) are the upper and lower boundaries of the CI for the mean value of a Poisson distribution, respectively; \(n\) is the number of times an event occurs in an interval (e.g., number of fatalities; \(\omega\) is defined as the significance level of the statistics. \(X_{1-\omega/2}^{2}(2n+2)\) is the \((1-\omega/2)\)th quantile of the chi-squared distribution with \((2n+2)\) degrees of freedom; \(X_{\omega/2}^{2}(2n)\) is the \((w/2)\)th quantile of the chi-squared distribution with \((2n)\) degrees of freedom; and \(X_{1-\omega/2}^{2}(2n+2)\) and \(X_{\omega/2}^{2}(2n)\) can be found in the table of chi-squared distribution. Then, the mean value of event frequencies and the corresponding confidence interval can be determined by:  
\[\begin{array}{c}\theta=n.\frac{1}{5}\\ \theta_{U}=\varphi_{U}.\frac{1}{5}\\ \theta_{L}=\varphi_{L}.\frac{1}{5}\end{array} \tag{14}\]  
where \(\theta\) is defined as the mean value of event frequencies; \(\theta_{U}\) and \(\theta_{L}\) are the upper and lower boundaries of the confidence interval of \(\theta\), respectively; and \(S\) is the product of the number of experiments and an interval of time.  
The reliability of the F-N curve evaluation is examined by the confidence-level-based SR uncertainty in two dimensions in the F-N diagram. Specifically, \(a\)-cuts of F(N) and N are taken as the CI to quantify the SR uncertainty according to the possibility theory by:  
\[\begin{array}{c}\prod(A)=\max_{c\in A}\{\pi(c)\}\\ N(A)=1-\prod(\overline{A})\end{array} \tag{15}\]  
where \(N(A)\) is the necessary measure from the possibilistic distribution \(\pi(c)\) of C, for set A.
## Appendix D Python Code for Automatic Detection Algorithm  
The main core of the Automatic Detection algorithm lies in the \"find_convex_minima\" procedure. The function first calculates the first derivative (dev1) of the DET time series and then it calculates the second derivative (dev2). This is the rate of change of the first derivative, giving a sense of the curvature of the data. The function then computes the mean (mean_val) and standard deviation (devst) of the second derivative. The function then iterates through the data frame to identify points that are considered the most convex minima.  
The code for the function written in Python code language is reported below.  
import pandas as pd def find_convex_minima(df, x_col, y_col):  \"\"\"  calculate the most convex minima in a dataset.  
Parameters:  df (pd.DataFrame): The dataframe containing the data.  x_col (str): The name of the column representing the independent variable.  y_col (str): The name of the column representing the dependent variable.  
Returns:  pd.DataFrame: A dataframe containing the most convex minima points.  \"\"\"
# A Study of Forest Phenology Prediction Based on GRU Models
## 5 Conclusions  
Based on the study of short-term prediction of phenological events using the GRU model on ROI using forest images collected by low Earth-orbit remote sensing satellites, it can be concluded that the GRU model accurately simulates the process of forest phenological changes by using the GEI time series data in the input forest images. The GRU model has the ability to filter out redundant information when simulating the temporal phenological relationship and shows high accuracy in phenological prediction. The GRU model merges the forget gate and input gate into the update gate, which controls information updating and retention and sets the reset gate to control the contribution of old hidden state to the calculation of new hidden state. Therefore, the GRU structure is simple and can show the trend of changes in forest phenology prediction in advance. In practical applications, the faster the prediction of forest phenology results, the more effective it is to take corresponding forest maintenance measures in advance, and forest phenology prediction is of great significance for studying climate change.  
Conceptualization, Y.Z.; Methodology, L.Z. and PG.; Software, L.Z.; Validation, Y.Z.; Formal analysis, PG.; Investigation, Y.Z.; Writing--original draft, L.Z. and PG.; Writing--review & editing, L.Z., PG. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Research Project of the Jiangxi Provincial Department of Forestry: [2022]38, the Science and Technology Plan Project of Qingyang, Gansu: QY-STK-2022A-034, the Fundamental Research Funds for the Central Universities: 2021 ZY74.  
Not applicable.
## 4 Security Approach
### PKI and the Mapping  
In today's Internet, the PKI is separated from all network services. This means that additional resources for the PKI must be provided despite all the network elements already in place for other functionalities (e.g., DNS server). Contrary to this, we propose to integrate the PKI into the mapping system for the HiiMap architecture. This has the benefit that resources can be shared between functionalities and maintenance can be kept significantly lower compared to operating separate services.  
Each mapping entry consists of the identifier as the primary key and a set of locators by which the node currently can be reached (see Section 3). Further, a timestamp of the last update and a flag, indicating whether the location update was cryptographically signed by the node or not, is stored. Normally any location update must be signed to prevent identifier hijacking. Some devices, however, might not have enough computational power to handle long asymmetric cryptographic operations. To integrate those devices, the owner can request at the regional authority to send unsigned location updates. An unsigned location update is signaled by a set flag and peers can decide whether they want to start a communication with such a node or not.  
To combine the PKI with the mapping system, only the public key of each node must be additionally stored for each mapping entry. This means that no additional protocol or infrastructure has to be provided for querying and storing the public keys. Because the public key is a very static value and not expected to change frequently, the additional burden for the mapping system is limited and the public key databases can be optimized for frequent read requests--contrary to frequent read and write requests for the locators.  
In comparison to today's PKI, it is also not necessary to provide additional lists for key revocation. This is implicitly realized by the loose identifier to public key binding in the mapping system. Whenever a public key for a certain identifier changes, the old public key implicitly becomes invalid.
# A Web of Things-Based Emerging Sensor Network Architecture for Smart Control Systems
## 5 Performance Evaluation  
The proposed framework is simulated for two different type of experiments. In one type of experiment, the entire proposed scheme is tested for the energy consumption of a household appliance. In another type of experiment, the communication of various home users with various household appliances is shown in the context of response time and transmission failure.
# High-Resolution and Wide-Swath 3D Imaging for Urban Areas Based on Distributed Spaceborne SAR
## 2 Materials and Methods
### Range Ambiguity Resolution Algorithm  
Each SAR image is obtained independently in the actual distributed SAR system. Due to geometric differences such as different antenna heights and different viewing angles, the data obtained by each SAR in the same scene on the ground are not completely coincident. There are not only translations in range and azimuth directions but also deformations such as relative compression or stretching. The mismatch of images seriously hinders the performance of the interferometric phase and affects the process of tomography. Therefore, image registration [32] needs to be carried out before tomography to ensure the coherence of multiple SAR images. A general idea of coarse registration-range ambiguity resolution-precise registration is proposed in this paper, as shown in Figure 4.
# P4-KBR: A Key-Based Routing System for P4-Programmable Networks
## 2 Related Work  
Almost two decades ago, key-based routing (KBR) solutions in conjunction with Distributed Hash Tables were highly used to create structured P2P networks such as Chord [8], Pastry [9], Tapestry [10] or Kademlia [11] offering content-sharing solutions in overlay networks. These networks implemented a routing service at application level where a message looking for a particular could be routed towards the node responsible for that key. More recently, key-based solutions have been focused on the proposal of key-value storage systems to give support to datacenter infrastructures or to implement web caching solutions [12; 13; 14].  
Inspired by applicacion level KBR/DHT solutions, we have proposed a key-based routing solution directly implemented at network layer that makes use of the potential of Software Defined Networking and the power and flexibility of programmable data planes. The keys, also named virtual identifiers, are obtained from textual names, whose format can be defined depending on the requirements of the service that is going to make use of the proposed routing solution. Routing tasks become independent from the IP addresses, offering service developers a named-based routing service.  
VIRO (Virtual Id ROuting) [15] proposed a key-based routing solution in the context of SDN/Openflow networks. The idea behind VIRO is the introduction of a topology-aware, structured virtual id space onto which both physical identifiers (e.g., Ethernet MAC addresses) as well as higher layer addresses/names (e.g., IPv4/IPv6) are mapped. Taking advantage of such a topology-aware and structured vid space, VIRO employs a KBR algorithm to build routing tables, look up objects (names, addresses, vids, etc.) and forward packets. In order to implement a required address-name resolution mechanism, each host-node needs to maintain a local table that maps the physical identifiers to virtual identifiers. On the other hand, besides the remote SDN controller responsible for the management plane, VIRO implementation also requires that each node includes a local SDN controller to run the VIRO packets forwarding functions. Another important drawback of this solution is the fact that the standard Ethernet frame structure must be modified to be able to include the ids and the required forwarding directives. Consequently, the authors needed to modify and extend the match and the actions of the Open vSwitch user and kernel spaces to be able to implement their solution.  
Returning to key-value store solutions, the recent work TurboKV [3] presents a distributed key-value store architecture that leverages the power and flexibility of programmable switches. Implemented in P4 using bmv2 switches, this work designs an indexing scheme to manage the directory information records inside the programmable switches, and defines protocols and algorithms to route TurboKV client packets (search queries) to targeted storage nodes.
# Median to Strong Rainfall Intensity Favors Carbon Sink in a Temperate Grassland Ecosystem in China
## 3 Results
### The Relationship Between Abiotic Factors and Carbon Fluxes  
Overall, soil moisture and temperature had significant effects on carbon fluxes (NEE, ER, and GPP) (Tables S2 and S3). In early growing seasons, soil temperature had more significant effects on NEE (explained around 7%-8% of the variation) than soil moisture (0.5%-3% of the variation) (Figure 5a,c). In middle growing seasons, soil moisture had a more significant effect on NEE (explained 14% of the variation) than soil temperature (0.6%) (Figure 5b,d).  
GPP and ER were both significantly impacted by soil moisture in early and middle growing season. In early growing seasons, soil moisture at the depth of 0-20 cm explained around 40% of the changes in GPP and ER (Figure 5a,c). In middle growing seasons, soil moisture explained about 20% more of the changes in GPP and ER than that in the early growing season (Figure 5b,d).  
In early and middle growing seasons, GPP and ER were stimulated with the increasing soil moisture (Figures S4 and S5). Moreover, the response of GPP to soil moisture at a depth of 0-20 cm was more sensitive than that of ER, which was indicated by the greater slope of the regression function in middle growing seasons (Figure 6). Additionally, net ecosystem carbon uptake was stimulated when soil moisture was larger than 8% (v:v), because GPP was more largely stimulated than ER (Figure 6).  
Figure 4: Responses of NEE (net ecosystem exchange), ER (ecosystem respiration), and GPP (gross primary production) to different rainfall treatments over three experiment years in middle growing season. ck: control, 2: 2 mm treatment, 5: 5 mm treatment, 10: 10 mm treatment, 20: 20 mm treatment, 40: 40 mm treatment. The black and grey lines within the box represent the median and mean of the carbon fluxes, respectively, across three sampling years (2015, 2016, and 2017); box limits indicate carbon fluxes in the 25 th–75 th percentile range. Error bars indicate the 10 th and 90 th percentiles. The open dots show the distribution of carbon fluxes data in each treatment.
# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
## 3 OCR Process  
The OCR process refers to identifying and converting printed or handwritten text characters into machine-encoded text. It typically involves several steps, including preprocessing, segmentation, recognition, and postprocessing. During preprocessing, the input image is cleaned up and enhanced to improve the quality of recognition. Segmentation involves breaking the image into individual or groups of letters or characters. Feature extraction is the process of identifying and extracting the relevant features of each character, such as its shape, size, and orientation. In recognition, the characters are classified by comparing them to a set of known characters, and the best match is selected as the recognized character. Finally, postprocessing of recognized text is performed to remove errors from text and improve accuracy and overall results of OCR. OCR recognition accuracy can vary depending on several factors, such as the quality of the input image, the font type and size, and the language being recognized. The flow of the overall OCR process is shown in Figure 4. We have provided a high-level description of the various techniques and methods involved in the OCR process in Table 4.  
Figure 4: The flow of the OCR process along with OCR phases and methods involved.
# Oil Spill Monitoring of Shipborne Radar Image Features Using SVM and Local Adaptive Threshold
## 3 Results and Discussion
### Warning with GIS  
In the case of a practical application, it is necessary to publish the data monitored by radar combined with an electronic chart. Using the ArcGIS Engine, the steps of merging the data of shipborne radar and electronic chart include the following:  
1. The Global Positioning System (GPS) geographic coordinates of the clean-up ship are transformed into plane Cartesian coordinates.
2. Using the range of radar, image size, and ship projection coordinates obtained in step a, the image coordinates of the oil spill boundary points are transformed into the Beijing_1954 projection coordinate system.
3. The projection coordinates of the oil spill boundary points are transformed into the WGS_1984 coordinate system.
4. The target polygon is generated in an electronic chart.  
The result identified in Figure 12b is fused into the electronic chart in Figure 13.
# Temperature Sensing with Nd\({}^{3+}\) Doped YAS Laser Microresonators
## 4 Results and Discussion
### Temperature Calibration  
To be able to determine the temperature of the microspheres, a temperature calibration of the bulk sample was previously performed using the FIR technique. Since the \({}^{4}\)F\({}_{5/2}\) and \({}^{4}\)F\({}_{3/2}\) energy levels of Nd\({}^{3+}\) ions are very close to each other, the \({}^{4}\)F\({}_{5/2}\)\(\rightarrow\)\({}^{4}\)I\({}_{9/2}\) (810 nm) and \({}^{4}\)F\({}_{3/2}\)\(\rightarrow\)\({}^{4}\)I\({}_{9/2}\) (880 nm) transitions are thermally coupled, and their populations follow a Boltzmann distribution law; hence, the ratio of the intensities of these emissions follows the temperature dependence given by Equation (1).  
The experimental values obtained for the intensity ratio of the emission bands of the bulk sample are plotted in Figure 4 and fitted to Equation (1), obtaining a value of 956 cm\({}^{-1}\) for the energy gap \(E_{32}\) and a pre-exponential parameter B with a value of 2.85. The value for the energy gap is similar to the value of 1053 cm\({}^{-1}\) obtained by calculating the energy difference between the maximums of the bands in the absorption spectrum of the sample (see Supplementary Figure S1).  
In order to characterize the performance of the FIR technique, the sensitivity was calculated applying Equation (5). A maximum value of \(S_{FIR}\) = 1.6 \(\times\) 10\({}^{-2}\) K\({}^{-1}\) at the lowest temperature in the measured range was obtained. This yields to a temperature resolution in the order of 3 K using Equation (7) if an error of 5% in the calculation of the ratio is  
Figure 4: Experimental values of the ratio of the intensities between the thermalized bands of Nd\({}^{3+}\) obtained from the bulk sample inside an electrical furnace (blue squares), and fitted curve to Equation (1) (solid red line).  
Figure 3: Spectra showing the whispering gallery modes overlapped with the Nd\({}^{3+}\) emission under 532 nm laser excitation and 119 mW pump power and sample temperature at 399 K.  
assumed. This error is due to the inaccuracy of the calculated areas because of the overlap between the thermalized bands.  
Using this calibration, the temperature of the microspheres were obtained in the incoming results by calculating the ratio of the intensities of the emission bands from the thermally coupled levels. This was possible because the microspheres were made of the same material as the bulk sample used for the temperature calibration, and hence the 810 nm and 880 nm emission bands of Nd\({}^{3+}\) ions in the microspheres presented the same changes with temperature.
# Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?
## 3 Experimental Setup  
To test whether non-expert annotated data sets hold any value for cardiac MRI segmentation, the following two cardiac cine MRI data sets were used:  
* Automated Cardiac Diagnosis Challenge (ACDC) data set [2]: This data set comprises 150 exams acquired at the University Hospital of Dijon (all from different patients). It is divided into 5 evenly distributed subgroups (4 pathological plus 1 healthy subject groups) and split into 100 exams for training, and 50 are held out set for testing. The exams were acquired using two MRI scanners with different magnetic strengths (1.5 T and 3 T). The pixel spacing varies from 0.7 mm to 1.9 mm with a slice spacing varying between 5 mm and 10 mm. An example of images with the different expert and non-expert annotations is shown in Figure 1.
* Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Image Segmentation (M&M) data set [19]: This data set consists of 375 cases from 3 different countries (Spain, Germany and Canada) totaling 6 different centers with 4 different MRI manufacturers (Siemens, General Electric, Philips and Canon). The cohort is composed of patients with hypertrophic and dilated cardiomyopathies as well as healthy subjects. The cine MR images were annotated by experienced clinicians from the respective centers.  
We trained the segmentation models on the 100 ACDC training subjects on either the expert or non-expert ground truth data. Training was done with a fixed set of hyper-parameters, chosen through a cross-validated hyper-parameters search to best fit the 3 annotators, without tuning it further. The networks were trained three times in order to reduce the effect of the stochastic nature of the training process on the results.  
As mentioned before, we first trained the neural networks on non-expert data with exactly the same setup as for the expert annotations. Then, we retrained from scratch the neural networks (U-Net, Attention U-Net and ENet) with a L1 loss, which was shown to be robust to noisy labels [18].  
We then tested in turn on the 50 ACDC test subjects and the 150 M&Ms training data. The M&Ms data set constitutes data with ground truth that are not biased toward either of the annotators of the training set, be it the expert or the non-expert. Moreover, testing on different data sets provided an inter-expert variability range as well as a domain generalization setup.