esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Radiation Dose Distribution  
The HI, the CI, the percentage volume that received at least 95% of the prescription dose (V95%), and the maximum target dose (D\({}_{\text{max}}\)) were also compared between the different treatment plans (Tables 6 and 7). The median values (and their ranges) of the HI for the 3D, 4D, and MF-Plans were 1.05 (1.07-1.04), 1.05 (1.06-1.04), and 1.05 (1.07-1.04), respectively; the median values of the CI (and their ranges) of the three plans were 1.00 (1.00-0.90), 1.00 (1.00-0.99), and 1.00 (1.00-0.99), respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Patient Number**} & \multicolumn{3}{c}{**Percentage Difference (\%)**} \\ \cline{2-4}  & **PTV3D vs. PTV4D** & **PTV3D vs. PTVMF** & **PTV4D vs. PTVMF** \\ \hline
1 & 53.1 & 74.1 & 44.7 \\
2 & 23.9 & 28.2 & 5.6 \\
3 & 30.3 & 40.6 & 14.9 \\
4 & 22.3 & 24.4 & 2.7 \\
5 & 30.5 & 31.9 & 2.0 \\
6 & 55.3 & 56.5 & 2.8 \\
7 & 53.5 & 64.8 & 24.3 \\
8 & 38.3 & 47.0 & 14.2 \\
9 & 20.3 & 24.8 & 5.6 \\
10 & 53.7 & 56.0 & 5.1 \\
11 & 37.2 & 44.3 & 11.3 \\
12 & 24.8 & 31.8 & 9.3 \\
13 & 19.6 & 18.6 & 1.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Percentage differences in planning target volumes (PTVs) between treatment plans.  
Figure 2: Planning target volumes (PTVs) delineated on computed tomography (CT) images in different treatment plans for respiratory-gated radiation therapy. (**a**) Transverse, (**b**) coronal, and (**c**) sagittal sections of the chest. The purple line represents the PTV3D of the 3D Plan; the green line represents the PTV4D of the 4D Plan; and the orange line represents the PTVMF of the MF-Plan.  
No statistical difference was seen between the three plans (\(p\) > 0.05). In addition, no differences in the V95% and D\({}_{\text{max}}\) between the plans were observed.
# What Lies Beneath One's Feet? Terrain Classification Using Inertial Data of Human Walk
## 4 Discussion  
### Summary of Findings  
The goal of this work was to classify the type of terrain from the inertial data of the strides collected with a single body mounted IMU. 6D accelerations and angular velocities were recorded with the help of two android based smart phones (on-board MPU-6500 IMU). The sensors were mounted on two different body locations, i.e., chest (smart phone), and lower back (smart phone). A total of 40 volunteers participated in data collection sessions and their gait data were recorded on six different terrains, namely carpet, concrete floor, grass, asphalt, soil, and tiles. A valleys detection method was used to segment low-level 6D gait signals into strides. A total of 194 tempo-spectral features were computed for each of the stride, which included 110 time domain features and 84 spectral domain features. The choice of the predictors was Random Forest and Support Vector Machine and 10-fold cross validation was used as validation model.  
Figure 9 shows the comparison of classification accuracies achieved with SVM and RF for each sensor location. It is observable that the highest classification accuracy was achieved with the lower back sensor data followed by the chest data. Furthermore, it can be seen that SVM performed better with fewer classes, i.e., indoor-outdoor, hard-soft, binary and ternary classification, whereas the RF performed better with more classes, i.e., quaternary, quinary and senary. This is true for the features sets computed from the data collected with the sensors attached on lower back and chest positions. The gradual drop in the classification accuracies as the number of classes increases was because of the natural similarities between different types of surfaces, which caused a significant number of samples to be mis-classified.
## 5 Dataset Analysis
### General Dataset Description  
Taking the type of communication used in the proposed scenario into account, a series of important features are selected in the sending of CoAP messages, such as the amount  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Day** & **Modification** & **Interception** & **Duplication** \\ \hline Monday 27 July 2020 & & & \\ Tuesday 28 July 2020 & & ✓ & \\ Wednesday 29 July 2020 & & & ✓ \\ Thursday 30 July 2020 & ✓ & & \\ Friday 31 July 2020 & ✓ & ✓ & ✓ \\ Saturday 1 August 2020 & ✓ & ✓ & ✓ \\ Sunday 2 August 2020 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset anomalies.  
of source and destination bytes, the number of source and destination packets sent, the number of packets for each protocol, and the amount of abnormal and normal packets per day.  
The CIDAD has a total of 88,238 packets, and recalling that the CoAP protocol goes over the UDP, 73% of all the traffic corresponds to UDP packets. Detailed information is presented in Table 3. It contains ARP, IP, and IPv6 traffic. However, the number of packets sent in IPv6 represents only 0.95% of the total packets and 26% of the ARP traffic. All IPv6 packets correspond to ICMPv6 packets, while IPv4 packets contain UDP and ICMP datagrams. All the anomaly packets belong to UDP protocol. Thus, 71.62% of UDP packets are normal, while just 1.20% of all the traffic is labelled as an anomaly. It is important to note that the packets from the sensor that are intercepted do not appear in the dataset because they do not exist anymore. As a consequence, the request packets sent by the client that have no response are labelled as anomalies.  
Knowing how CoAP carries out the exchange of messages and assuming the request/response connection, in normal instances, there should be homogeneity in the number of packets sent from each IP address source, as well as the number of bytes sent. Therefore, alterations in data uniformity are an indication of an anomaly.  
The four sensors belonging to the same InRow share the same IP address. Due to the type of architecture presented, the sensors of the IoT network only send packets to the client and do not create connections between them. A large portion of packets, about 32,249, corresponds to requests made by the client. All abnormal traffic is involved with InRow 13 with the IP address 192.168.0.2, over IPv4.  
The lack of homogeneity in the packets sent by the sensors represents abnormal network behaviour. The frame length determines the number of bytes sent per packet. Taking into account the proposed scenario and the homogeneity that must be presented, given the nature of the CoAP protocol, changes in the density function of the frame length imply anomalies in the packets sent.
# Counteracting Selfish Nodes Using Reputation Based System in Mobile Ad Hoc Networks
## 3 Proposed Reputation-Based Scheme
### Design and Modules
#### 3.2.5 Selfish Node Isolation Unit  
Each node within the network will check the address of the source node of the packets into its selfish list; if the address of the sender node exists in the selfish list, then the packets from that node will be discarded, otherwise the packets will be forwarded to the next node.
###### Abstract  
Accurate prediction of bus arrival times is a challenging problem in the public transportation field. Previous studies have shown that to improve prediction accuracy, more heterogeneous measurements provide better results. So what other factors should be added into the prediction model? Traditional prediction methods mainly use the arrival time and the distance between stations, but do not make full use of dynamic factors such as passenger number, dwell time, bus driving efficiency, etc. We propose a novel approach that takes full advantage of dynamic factors. Our approach is based on a Recurrent Neural Network (RNN). The experimental results indicate that a variety of prediction algorithms (such as Support Vector Machine, Kalman filter, Multilayer Perceptron, and RNN) have significantly improved performance after using dynamic factors. Further, we introduce RNN with an attention mechanism to adaptively select the most relevant input factors. Experiments demonstrate that the prediction accuracy of RNN with an attention mechanism is better than RNN with no attention mechanism when there are heterogeneous input factors. The experimental results show the superior performances of our approach on the data set provided by Jinan Public Transportation Corporation.  
bus arrival time prediction; dynamic factors; recurrent neural network; attention mechanism +
Footnote †: journal:  
_Future Internet_
## 3 Stability Analysis and Discussion
### Single DOF Model of VHCVSS
#### 3.1.3 Model Verification  
Here, the presented method will be verified by comparing with the existing research and time-domain simulation. In order to evaluate the effectiveness of this method, the researches associated with VSS process [7] and with the combination process of VPC and VSS [50] are taken into accout at first. If the comparisons of the stability boundaries plotted by the black lines in Figure 4 with those in Figure 5.17 in [7], and the comparisons of the stability boundaries plotted by the cyan lines in Figure 4 with those in Figure 3 in [50] are respectively carried out, one can see that the results gained by the presented method coincide well with those by the methods in [7; 50]. This illustrates the applicability of the presented method for the stability prediction about Processes I and II.  
For Process IV, to the knowledge of the authors, there is no reference literature at present. Thus, the time-domain simulation is mainly used to carry out the verification. In [53], authors constructed the contour plot of peak-to-peak (PTP) vibration by investigating the vibration amplitude of a cutter. In view of this, based on the method in [53], an improved time-domain program suitable for VHCVSS is proposed. It should be noted that compared with the program in [53], the one in this paper can not only consider the influence of regeneration effect, cutter runout, multimodal of the system, loss of contact, and variable pitch/helix angles in the original method, but also that of variable spindle speed.  
The top diagram of Figure 7 shows the SLDs in Figure 4d between the rotation speed \(\Omega=1500\)-\(5000\) rpm and the axial depth of cut \(a_{p}=0\)-\(14\)mm. The Subgraphs (a)-(d) in Figure 7 respectively show the corresponding contour plot of PTP by the time-domain method for above four processes under the same parameters. It can be seen that the stability boundaries obtained through the presented method are basically consistent with the that through time-domain method, not only for their curve shapes but also for the stability evolution trends caused by the processes differences. This implies the effectiveness of this method in predicting stability for VHCVSS milling. There are little differences between the predicting results and simulation ones. This may attribute to the fact that the presented method can not take into account the influence of loss-of-contact character on cutting stability, but the time domain method can.  
Figure 6: Contribution factor diagram of Processes II and III to the combination effect under different RVF.
# Propagation, Scattering and Defect Detection in a Circular Edge with Quasi-Edge Waves
## 2 Numerical Study
### Three-Dimensional FE Model for Investigating Propagation Characteristics of \(QES_{0}\)
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Part** & **Material** & **Density** & **Elastic Modulus** & **Poisson’s** & **\(\alpha\) Damping** \\  & & **(kg/m\({}^{3}\))** & **(GPa)** & **Ratio** & **\(\alpha\) Damping** \\ \hline Specim & Aluminium & 2704.00 & 70.00 & 0.30 & N/A \\ Wedge & Polystone & 351.40 & 0.90 & 0.40 & 28,783.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Material properties of the FE model. Figure 2: Snapshot of the 3D FE model’s (R/T = 20) simulation results of (**a**) the wedge excitation and (**b**) propagation of \(QES_{0}\). The same modelling procedure outlined in Section 2.1 was also applied to another 3D FE plate model (R/T = \(\infty\)), which simulates the propagation of \(ES_{0}\) in a 5 mm thick flat plate with the same material properties, for comparison purposes. The simulation results from this FE model can be considered as a benchmark as they are not affected by the effect of the curvature (straight edge). This particular 3D model has been analytically and experimentally well validated in previous work [12]. It should be noted that the distance between measurement points and the wedge in this model is approximately the same as the corresponding curved edge model. The excitation frequency is 200 kHz, which has a corresponding \(FTV\) of 2. Then, the 2D FFT results were compared with the dispersion curve of \(ES_{0}\) propagates in an ideal waveguide with the same thickness [20]. Figure 3a,b show the 2D FFT results for the propagation of \(ES_{0}\) in a flat plate and \(QES_{0}\) in a curved edge, respectively. The results indicate that the only propagating wave mode at 200 kHz in curved edge is \(QES_{0}\), which is very similar to the propagation of the \(ES_{0}\) wave mode in terms of wavenumber.
## 4 Conclusions  
Two spectra of Vis and NIR were fused based on the three different levels, including the spectral data level, the spectral feature level and the model decision level. The feasibility of rapid identification of citrus greening was studied and discussed. The spectral data fusion identification model for citrus greening and classification of the disease was developed through two methods of LS-SVM and PLS-DA. It was concluded after a comparative analysis that the feature level fusion based on PCA variable compression had the best identification results in three level fusion models, and with linear function (Lin_Kernel) being the kernel function, the identification accuracy is the highest. The result of the spectral data level fusion model based on derivative spectrum splicing was second, and the result of decision level fusion model based on the NNLS was the worst. The experiment result proves the feasibility of rapidly detecting citrus greening based on Vis and NIR spectra data fusion. It provides a good reference for the identification of citrus greening with low accuracy and poor stability through a single spectral technique.  
Conceptualization, [PERSON] ([PERSON]); methodology, [PERSON] ([PERSON]) and Y.H.; validation, [PERSON] ([PERSON]), and L.S.; formal analysis, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); investigation, L.S. and Y.L. ([PERSON]); resources, [PERSON] ([PERSON]); data curation, [PERSON] ([PERSON]) and; writing--original draft preparation, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); writing--review and editing, [PERSON] ([PERSON]) and [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON] ([PERSON]); funding acquisition, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
Figure 5: The regression coefficient curve of model on different bands.  
**Funding:** Jiangxi Province Key Laboratory of the Causes and Control of Atmospheric Pollution, East China University of Technology (NO. AE2109), Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System open fund project (No. JETRCNGDSS202102), Doctor initiation research fund project of ECUT (No. DHBK2019074), Jiangxi Key Laboratory for Mass Spectrometry and Instrumentation open fund project (No. JXMS202011), National Natural Science Foundation of China (No. 61866001), Jiangxi Province College Students Innovation and entrepreneurship training program (NO. S202110405004X), and Jiangxi Provincial Natural Science Foundation of China (NO. 20212 BAB204009).  
**Institutional Review Board Statement:** The study did not require ethical approval.  
**Informed Consent Statement:** The study did not involve humans.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to express their gratitude to the Jiangxi Province Key Laboratory of the Causes and Control of Atmospheric Pollution for the venue provision and technical support.  
**Conflicts of Interest:** The authors declare that they have no conflict of interest to report regarding the present study.
# Extensions and Enhancements to \"the Secure Remote Update Protocol\"
## 5 Proof of Identity  
### Joining a System  
The registration operation is distinct from any subsequent request by the device to join and, thus, becomes subordinate to a specific server and subject to control by that server. To perform such a join operation, the device would initiate the process by sending a message containing its identity (and any other parameters that might be required by a given system implementation) and a signature to prove that the device is in possession of that identity. The server can then either accept the request as is; or it can request that the device proves itself via an external observation. Once the server accepts the request, it must then store the device identity in a suitable data structure cataloguing that server's subordinate devices.  
Since identity is conferred by possession of the private key from the key-pair associated with that identity, devices can prove their identity by using the fact that they can demonstrate that they have this private key; for example, by decrypting a validation token encrypted with the public key.  
An observer can be used to challenge a physical device to prove that it possesses the identity it is claiming during the join process. This is accomplished by the C2 server (separately) supplying the device and the observer with an encrypted value and requesting that the observer confirm that the device presents the same value via the observation medium, as it has received via the protocol.  
The mechanism of this presentation will depend on the specifics of the system and the nature of the observer, but it could include displaying a human-readable representation of the value (for a human observer); or using a secondary communications channel to transmit the value to the observer. Such a multi-channel approach might be used in a system where devices in the form of autonomous mobile platforms use a local channel to determine and/or signal their presence in a specific region.
## 3 Materials and Methods
### Framework Overview
#### 3.2.2 Bias Correction Model  
While physically based models are effective at simulating natural hydrological processes such as rainfall-runoff generation and streamflow routing, many of them (such as HEC-HMS used in this study) do not account for human behaviors, such as water withdrawals for irrigation. In agricultural regions, particularly during summer months, farmersoften extract significant amounts of water--sometimes through unregulated or informal practices--that alter streamflow in ways the physical model cannot represent. As a result, the discrepancy between simulated and observed discharge is not merely a modeling error but reflects real, unmodeled human influence.  
To address systematic errors introduced by unmodeled human activities, a bias correction model was developed to adjust the synthetic streamflow outputs from HEC-HMS. The correction approach is based on supervised learning, wherein the error between observed and simulated discharge (i.e., residuals) is predicted using a multi-layer perceptron (MLP) regressor.  
The bias correction model utilizes a set of temporal and seasonal features--such as hour, day, month, and one-hot encoded seasons--as inputs, capturing faulty periodic behavior across the year. The model was trained using a sub-set of the training data, specifically targeting the months from July through November, when irrigation activity is most pronounced. This focused training set allowed it to specifically focus on correcting irrigation-driven deviations that are not captured by the physical model.  
The trained MLP model demonstrated strong predictive capability on held-out test data, and its predictions were used to adjust the raw HEC-HMS outputs. Specifically, the predicted error was added to the simulated discharge to produce a corrected streamflow series. Performance was evaluated using standard hydrological metrics such as the Nash-Sutcliffe Efficiency (NSE), modified Kling-Gupta Efficiency (KGE), and RMSE, all of which indicated improved alignment with observed discharge during both calibration and validation periods.
## 5 Conclusions  
* The retrofit solution brings on a substantial increase in strength (minimum +53% at first crack, +85% at the peak and +98% at the ultimate load) of the investigated masonry sample. The failure mode turns to a mixed mechanism, between central diagonal cracking and central bed joint sliding. The proposed solution brings on a significant improvement in the mechanical performance of traditional brick masonry with hydraulic lime mortar joints;
* The improvement in drift capacity of R1 and R2 is clear, but the increase in the ultimate drift after retrofit is not fully clarified by the described testing program. The two tests have produced large differences (i.e., +112% and +303%). The geometric imperfection of sample R1 (out-of-plumb) is deemed responsible for the sudden collapse under testing, and has prevented the sample from reaching an ultimate drift as high as R2 did. Further tests are surely required to explore the proposed retrofit solution in more detail and with increased reliability;
* The design of the fastenings, according to the American Concrete Institute's (ACI) recommendations, is definitely on the safe side, since no sign of masonry rupture, bar plasticization or hole bearing was detected after the tests. However, this aspect deserves more attention in the future steps of the research, because it is crucial in improving the exploitation of the high stiffness and strength of FRP profiles;
* The assumption of the partial half-scale has allowed us to obtain promising results regarding the effectiveness of the proposed retrofit solution, at a limited cost and with relatively manageable laboratory samples; however, a comparison with full-scale behavior would allow for the more detailed design of frame profiles and connections;
* As for the FE model of the case study, an update--consisting of the implementation of experimental data to describe the joint's shear stiffness--was essential to improve the model's capability to catch the experimental load-drift behavior with enough accuracy. In this way, the model has shown rather good agreement with the experimental values of elastic stiffness, maximum load and magnitude of displacements, and most of all the crack pattern and strength gain of the masonry wallette after retrofit (89%, both from the tests and the FE model). This is to be considered the initial state of a predictive model for the next phases of the research, which will be useful to evaluate the pre- and post-retrofit behavior of other case studies. In fact, the residual discrepancy between the FE model and the experimental cases, especially between the first crack and the peak, should be reduced. This could be done by increasing the number of tests on one side, and by accounting for different types of nonlinearities on the other--e.g., plasticity-based models instead of cracking. This task will be duly undertaken in the next steps of the research.