esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Verification of the Proposed Protocols
### Formal Verification Using Proverif  
The modeling of a protocol in ProVerif is composed of declaration, process macros and main processes with queries to verify the security properties of a protocol. The ProVerif code is used to specify the protocol concisely using declaration of types, functions, queries and events. Free names are globally known, whereas bound names are locally known by the process such as the public channel for communication, while [private] excludes names from the attacker [58].  
Specification includes the following:  
* functions: fun encrypt(bitstring, pkey): bitstring., key: type key., private and public names: free rand_a: bitstring[private] free pubChannel: channel.
* Queries: On secrecy, reachability and authentication. A secrecy property is specified as a query of the attacker's knowledge attacker(M). When the fact attacker(M) is derivable, the attacker may have the knowledge of M, but, if it is not derived, there is no way that the attacker can gain the knowledge of M. With reachability, the query attacker(K) is also used to debug the model of the protocol to check a particular branch is reachable or not, query k: bitstring; event(endServer(k)). The authentication properties are specified as correspondence assertions in the form of event(e1(M)) event(e2(M)) which have to be proven. In the case of the DDSec and DDACap protocols, the following is queried to test the secrecy of message attributes such as query attacker (rand_a) for a nonce, query attacker (ua) for id and query attacker (skuea) for key. query C:host,B:host,K:pkey, rand_rc:nonce; event(endUEB(B,C,K,rand_rc)) ==> event(beginUEA(B, C, K, rand_rc)) is used to test the events' relationship.
* Events: Querying events to test their relationship. (i) Event correspondence tested query is a basic correspondence assertion, query x1:t1.., xn:tn; event(e(M1,..,Mj)) ==> event (e'(N1,..,Nk)), for the non-injective correspondence there is at least one earlier occurrence of e'. (ii) Injective correspondence assertions capture the one-to-one relationship, query x1:t1,.., xn:tn; inj-event(e(M1,.., Mj)) ==> inj-event(e'(N1,..,Nk)). The correspondence asserts that, for each occurrence of event e(M1,..,Mj), there is a distinct earlier occurrence of event e'(N1,.,Nk).
* Process: The protocol is encoded using main process and process macros for the participating entities to allow sub-process being defined as (!process). The main process also starts off several copies of the system entities (UEs and gNB) with the relevant parameters representing several sessions of the roles as explained in the message exchange in Sections 5.1 and 5.2.
# Influence of CeO\({}_{2}\) Addition to Ni-Cu/HZSM-5 Catalysts on Hydrodeoxygenation of Bio-Oil
## 3 Results and Discussion
### Characterization of Catalysts
#### 3.2.7 Tg  
A TG analysis is an effective method for obtaining carbon activities and regeneration levels. Figure 10 shows the weight loss of different catalysts after a reaction under the same condition, in which a-d correspond to the 15% CeO\({}_{2}\)-Ni-Cu/HZSM-5, 5% CeO\({}_{2}\)-Ni-Cu/HZSM-5, 20% CeO\({}_{2}\)-Ni-Cu/HZSM-5, and Ni-Cu/HZSM-5 catalysts, respectively. The weight losses were 14, 23, 29, and 41% at 270 \({}^{\circ}\)C after a reaction time of 1 h, indicating that the amount of coke deposition is significantly lower with CeO\({}_{2}\) addition than that without CeO\({}_{2}\) addition. It means that the catalysts added with CeO\({}_{2}\) can exhibit a better carbon deposition resistance. Again, with a 15% CeO\({}_{2}\) addition, the weight loss is the least (14%) or the coke deposition is the least, agreeing with the previous assessments that the 15% CeO\({}_{2}\) is the optimal amount for the HDO reaction of bio-oil. This finding suggests that a catalyst with a proper amount of CeO\({}_{2}\) can improve the carbon resistance of the catalyst, but if excessive CeO\({}_{2}\) is added, the carbon resistance of the catalyst is reduced. An excessive amount of CeO\({}_{2}\) may cover the active site of a part of the Ni, thus reducing the chance of contact between the reactants and the active site, resulting in a large amount of carbon accumulation.  
Figure 11 of the DSC profiles (heat loss mW/mg) illustrates the type and distribution of the carbon deposits from the heat release curves of different catalysts under the same reaction conditions. Coke decomposition can be divided into four stages, namely, 100-250, 250-450 450-600, and 600-750 \({}^{\circ}\)C, which correspond to the decomposition temperatures of the small-molecule component results, soft coke, hard coke, graphite and graphite-like carbon, respectively [2; 31]. The catalyst without CeO\({}_{2}\) addition appears with an exothermic peak at 380 \({}^{\circ}\)C. The exothermic peaks of the added CeO\({}_{2}\) catalysts apparently shifted to the left, or with 5%, 15%, and 20% CeO\({}_{2}\), the peaks were 340, 310, and 360 \({}^{\circ}\)C, respectively, which prove that the main deposit on the catalyst is a soft carbon (soluble in organic solvent). The peak temperature reached the lowest (310 \({}^{\circ}\)C) when the CeO\({}_{2}\) load was 15% because the soluble carbon deposited at this time has a relatively small molecular weight derivative (i.e., low boiling point and high solubility). The results indicate that the addition of CeO\({}_{2}\) may lead to a substantial improvement in the resistance of the Ni-Cu/HZSM-5 catalyst against carbon deposition, and the different CeO\({}_{2}\) contents have different effects on the catalyst's resistance to carbon accumulation. In other words, when the CeO\({}_{2}\) load is 15%, the ability of the catalyst to resist carbon may be the strongest.
## 3 Results
### Experimental Validation and Performance Testing of Sensing Materials
Given that the force application duration of the tester is exceedingly brief (<0.5 ms), significantly shorter than the output response time of the digital multimeter, it can be disregarded; thus, the digital multimeter's response time is considered the sensor's reaction time. Figure 10a-c illustrate that the sensor's reaction time and recovery time are 0.21 s and 0.37 s, respectively, \begin{table}
\begin{tabular}{c l c c c c} \hline
**Ref.** & **Materials** & **Hysteresis (\%)** & **Sensitivity (kPa\({}^{-1}\))** & **Range (kPa)** & **Time (ms)** \\ \hline
[57] & PDMS & 4.42 & 3.73 & \textless{}100 & \textless{}50 \\
[58] & CNTs & 7.12 & 3.179 & \textless{}2 & \textless{}100 \\
[59] & Silver nanowire & & & & \\
[59] & Graphene & & & & \\
[60] & CNTs & 6.34 & 0.02 & \textless{}6.5 & - \\
[61] & Single-layer graphene & 4.2 & 0.043 (2–10 kPa) & \textless{}10 & 5000 \\
[61] & Hybridised graphene & 7.5 & 0.032 (0–0.7 kPa) & \textless{}0.7 & - \\
[62] & Bilayer graphene & - & 0.122 (0–5 kPa) & \textless{}20 & 70 \\  & & & 0.077 (5–20 kPa) & & \\ This work & Fe3O4/Graphene & & 2.556 (0–1 kPa) & & \\ CNTs & 3.254 & 1.179 (1–5 kPa) & \textless{}30 & \textless{}370 \\  & & & 0.0054 (5–30 kPa) & & \\ \hline \end{tabular}
\end{table} 
Table 7: Comparative analysis of hysteresis and sensitivity in this study versus those in other studies. Figure 9: Piezoresistive output response curves of flexible sensing materials: (a) hysteresis performance; (b) sensitivity performance. which are enough for practical applications. The durability and stability of the proposed pressure sensing materials were evaluated through loading-unloading force cycles, as depicted in Figure 10d. After 2000 cycles, there was negligible attenuation of the resistance signals, indicating high stability and durability. Figure 10: Additional performance test curves for the flexible sensing material, comprising (**a**) loading and unloading response time; (**b**,**c**) plots that provide magnified details of **a**; (**d**) cycle pressure test.
## 3 Methodology
### Iterative Elastic Bin Refinement
#### 3.2.1 Initialization of Depth Bins  
In the first stage of the iterative refinement process, the full depth range [\(d_{min}\), \(d_{max}\)] is discretized into a fixed number of uniform bins. Each bin represents a potential depth value for the pixel. The initial depth prediction for each pixel is made by selecting the bin that has the highest predicted probability for that pixel. The depth ranges [\(d_{min}\), \(d_{max}\)] is divided into N bins. The center of the \(n\)-th bin is denoted by \(C_{n}\), and the width of each bin is given by  
\[\omega=\frac{d_{max}-d_{min}}{N} \tag{6}\]  
For each pixel \(p\), the initial depth prediction \(D^{(0)}(p)\) is selected as the depth corresponding to the bin center with the highest predicted probability:  
\[D^{(0)}(p)=C_{\text{argmax}_{n}}P_{n}(p) \tag{7}\]  
where \(P_{n}\) (\(p\)) is the predicted probability that pixel \(p\) belongs to bin \(n\). At this stage, the depth prediction is coarse, as the depth range is discretized into uniform bins that may not capture the fine details of the scene. The subsequent stages refine these predictions by narrowing the search space and focusing on areas of high uncertainty.  
A key innovation in the elastic bin refinement strategy is the dynamic adjustment of bin widths based on depth uncertainty. In each iteration, the depth uncertainty for each pixel is estimated. The width of the bin corresponding to the most likely depth prediction is dynamically adjusted to focus the refinement process on areas with high uncertainty. To refine the depth prediction at each pixel, we first estimate the uncertainty of the current depth prediction. The uncertainty is quantified as the variance of the predicted depth distribution over the bins:  
\[\sigma^{2}(p)=\sum_{n=1}^{N}\left(C_{n}-D(p)\right)^{2}P_{n}(p) \tag{8}\]  
where \(D\) (\(p\)) is the current depth estimate for pixel \(p\), \(C_{n}\) is the center of bin \(n\), and \(P_{n}\) (\(p\)) is the predicted probability that pixel \(p\) belongs to bin \(n\). The variance \(\sigma^{2}\) (\(p\)) gives a measure of the uncertainty in the depth prediction, with higher values indicating more uncertain regions. Based on the estimated uncertainty, we adjust the width of the depth bins for the next iteration. Pixels with high uncertainty are given wider bins to cover a larger range of possible depth values. In contrast, pixels with low uncertainty are assigned narrower bins to focus the refinement process on a smaller, more precise range. \(\sigma(p)\) is the standard deviation of the predicted depth distribution at pixel \(p\). The bin width for the next iteration is adjusted as follows:  
\[\omega^{\prime}(p)=\omega\;\cdot\;(1+\kappa\cdot\sigma(p)) \tag{9}\]  
where \(w^{\prime}\) (\(p\)) is the new bin width for pixel \(p\), \(w\) is the original bin width, and \(\kappa\) is a scaling factor that controls the degree of adjustment based on uncertainty. Pixels with higher uncertainty \(\sigma\) (\(p\)) are assigned wider bins, allowing for greater flexibility in refining the depth prediction.
# Noninvasive Magnetic-Marking-Based Flow Metering with Optically Pumped Magnetometers
###### Abstract  
We present a noninvasive procedure that measures the flow velocity of a fluid by using polarized hydrogen nuclei in the fluid. The measurement procedure is based on a time-of-flight method where magnetic information is applied on the fluid with a permanent magnet and an RF-pulse. In contrast to other methods, this magnetic-marking method works without tracers. The read-out of the magnetic information is performed by optically pumped magnetometers downstream. In order to function, the magnetometers have to be operated in a magnetic shield with magnetic field strengths lower than 100 nT, i.e., in the zero-to-ultra-low-field regime. In this regime, the magnetometers are capable of detecting induced magnetic signals of 10 pT or less with an inline-flow setup. The results presented in this paper demonstrate the viability of optically pumped magnetometers for flow metering. The first metering results yielded an average accuracy of 3% at flow velocities between 13 cm/s and 22.4 cm/s.  
ZULF-NMR; flow metering; OPM; time-of-flight measurement; noninvasive; tracer-free; calibration-free; inline measurement; volumetric flow +
Footnote †: journal: Applied
# Chaos Based Frequency Modulation for Joint Monostatic and Bistatic Radar-Communication Systems
## 5 Bistatic Radar Receiver Synchronization and Ambiguity Function
### Signature Analysis for the Bistatic Radar Configuration  
Due to the thumbtack cross-ambiguity surfaces, the CBFM waveforms make an excellent alternative for high-resolution radar imaging. A complex target such as an airplane consists of multiple point reflectors, also called hotspots [63]. Consequently, the received signal from a complex target is given as  
\[r(t)=\sum\limits_{k=1}^{N}s(t-\tau_{k})exp^{[2\pi(f_{c}+f_{D_{k}})t]}. \tag{13}\]  
When the received signal is applied to the matched filter with an impulse response \(s_{rec}(t)\), multiple peaks associated with each hotspot's range and velocity can be obtained, resulting in the signature of the complex target [64]. For example, Figure 12 shows the signature of the BOEING 777 airplane that consists of 24 point reflectors. All these hotspots are accurately resolved, illustrating the high-resolution capability of the proposed CBFM  
Figure 11: Cross–ambiguity surface between \(\{s_{rec}(t),r_{tgt}(t)\}\) for (a) the Lorenz CBFM waveform and (b) the Burke–Shaw CBFM waveform.  
waveform as well as the synchronization approach. A complete study on generating a signature analysis of complex targets is shown in our previous work [59]. As mentioned, we assume the transmitters and receivers are on a non-moving platform. Moving platforms would require, at minimum, adaptive motion compensation to eliminate non-rotational Doppler shifts that would degrade range-Doppler imagery [65]. This analysis will be provided in our future work.  
The quality of the radar image can be assessed using entropy-like functions [66]. The entropy of an image is given as  
\[E=\frac{-\sum\limits_{m}\sum\limits_{n}I_{mn}log_{2}(I_{mn})}{\sum\limits_{m} \sum\limits_{n}I_{mn}}. \tag{14}\]  
In the above equation, \(m\) and \(n\) are the number of rows and columns of an image, and \(I\) is the normalized ambiguity surface such that the maximum pixel reflectivity is one. For an ideal thumbtack ambiguity function, the pixel value is either 0 or 1. Hence, the entropy value should be zero. Conversely, an increase in the entropy value indicates the presence of sidelobe and noise dominance.  
Using Equation (14), the entropy of the signature generated using the Lorenz CBFM is 1.8605 and that of the Burke-Shaw CBFM waveform is 1.7860. The positive entropy values are apparent due to the increased noise-floor and self-noise of the transmitted waveform. As expected, the entropy values for both the CBFM waveforms are similar due to the same transmitted bandwidth. In either case, the high-resolution imagery of the signature is evident.
# Data Access Based on a Guide Map of the Underwater Wireless Sensor Network
## 5 Performance Analysis of DAGM
### Data Access Performance Analysis  
In the process of data storage, metadata diffusion and data query, the delay includes node processing data time and data packet (DP) transmission delay. The node processing data time mainlyincludes delay in store-and-forwarding of the data, which is much smaller than the data packet transmission delay. Data packet transmission delay is the sum of the sending time and transmission time. Data transmission delay is mainly determined by transmission bandwidth \(b_{t}\), so the greater the bandwidth is, the less sending time is spent; transmission time depends on the velocity \(v_{t}\) and distance \(r_{t}\) of the transmission medium in signal carriers. For the data packet in \(p\) bytes, \(m\) represents the number of retransmissions, and the transmission delay is \(T_{DP}=(p/b_{t})\times(r_{t}/v_{t})\times m\);  
Data storage time \(T_{DStr}\) in DAGM consists of three parts. The first part is that the sensor nodes generate data and transmit it to the storage node to complete storing time \(T_{DTn}\). Another part is that the storage nodes generate metadata and metadata is transmitted to the nearest center ring node time \(T_{MDThn}\). The last part is the time of metadata completing diffusion and synchronization in the center ring, recorded as \(T_{MDsyn}\). Hence, we can obtain Equation (6):  
\[T_{DStr}=T_{DTn}+T_{MDTn}+T_{MDsyn} \tag{6}\]  
In processing the data query, the query sentence is sent to the nearest center ring node. The center ring node will find the metadata set. In the network, there are two possible outcomes: existing data meeting the requirement, and no existing data satisfying the data query. If there is data that meets the query sentence, the center ring node generates data transmission routing according to storage location abstract in the metadata. On one hand, if the query results in _\"ready to receive data\"_ and data transmission routing will be notified to the user; on the other hand, the specific data requests and data transmission routing will be sent to the storage node. After that, the data storage node will transmit the specific application data to the user. If there is no data in the network, the result is returned to user with _failed query_. Therefore, the data query time of no data in the network is \(T_{DQue}=T_{SQue}+T_{RQN}\).  
\(T_{SQue}\) represents the interval from user sending query sentence to the center ring node receiving it. \(T_{RQN}\) represents the interval from center ring node sending query result to the user receiving it.  
When there is existing data meeting the query in the network, we can obtain Equation (7):  
\[T_{DQue}=T_{SQue}+\text{max}(T_{RQN},T_{SMQ}+T_{DT}) \tag{7}\]  
\(T_{SMQ}\) represents the interval from center ring node sending specific data requests and data transmission routing to the data storage node. \(T_{DT}\) represents the interval from the storage node-transmitted specific application data to the user who receives it. Generally, \(T_{SMQ}\) is much less than \(T_{DT}\).
## 3 Results and Discussion
### Validation on Other Species  
To validate whether the model trained on rice data is applicable to predict the 6 mA sites of other species. We take the datasets including _A. thaliana_, _R. chinensis_, _F. vesca_, _H. sapiens_, _and_D. melanogaster_ as the independent test datasets. We evaluate the performance of 6 mAPred-MSFF, which was trained on the rice 6 mA-rice-Lv dataset and performed the same evaluation for other methods including Deep6 mA, SNNRice6 mA-large, and MM-6 mAPred.  
The prediction results are listed in Table 3. For the two plant species (_F. vesca_ and _R. chinensis_), with the only exception that the value of SP of our proposed method is lower than Deep6 mA, and our proposed method outperforms all other methods in terms of SN, SP, ACC, and MCC. Specifically, for _R. chinensis_, our proposed method achieves an SN of 90.66%, an SP of 94.26%, an ACC of 92.46%, and an MCC of 0.850, where Deep6 mA does have a higher SP of 95.29%. For _F. vesca_, our proposed method achieves an SN of 93.95%, an SP of 95.15%, an ACC of 94.55%, and an MCC of 0.891. However, Deep6 mA does have a higher SP of 96.22%. Compared with the two plant species (_F. vesca_ and _R. chinensis_), the performance of predicting 6 mA sites of the two species (_H. sapiens_ and _D. melanogaster_) is not very good. For the two animal species (_H. sapiens_ and _D. melanogaster_), our proposed method outperforms all other methods in terms of SN, ACC, and MCC;  
Figure 3: The PRC curves (**A–G**) of four models (6 mAPred-MSFF, Deep6 mA, SNNRice6 mA-large, and MM-6 mAPred) based on the seven datasets (6 mA-rice-Lv, 6 mA-rice-Chen, _A. thaliana_, _R. chinensis_, _F. vesca_, _H. sapiens_, and _D. melanogaster_).  
however, the Deep6 mA achieves the best value of SP. Surprisingly, all models trained on the 6 mA-rice-Lv dataset fail in predicting the 6 mA sites of _A. thaliana_, since the best value of SN is 58.66%; although there is a better value of SP, the performance of predicting 6 mA sites of _A. thaliana_ can not meet the requirement in practice. Therefore, we can conclude that the models trained with an 6 mA-rice-Lv dataset can be used to predict the 6 mA sites of other species and the features of 6 mA sites also have certain similarities among different species.
# Estimation of Time-Series Forest Leaf Area Index (LAI) Based on Sentinel-2 and MODIS
## 2 Materials and Methods
### Data and Preprocessing
#### 2.2.3 Forest Type Data  
Using the ArcGIS 10 software and the small-group distribution vector data of the study area, we generated stand type raster data based on its stand type and stand age fields as a reference basis for the stand type and stand age.
## 2 Materials and Methods
### Image Processing  
The capability of creating chemical images is one of the benefits of using hyperspectral imaging in the prediction of chemical components in food and agricultural product. These advantages provide a possibility to visualize the distribution of the evaluated chemical in the examined sample. The chemical image was developed based on the beta coefficient of the created model. The process of creating the chemical image was done by transferring the 3D hyperspectral image to a two-dimensional matrix and multiplying the matrix using the PLS regression coefficient. The resulted matrix was then converted into a 3D image. The visualization of the different anthocyanins concentrations in the samples was carried out by summing the corresponding pixels of all band images. The following equation explains the process to obtain a chemical image (\(I_{chem}\)):  
\[I_{chem}=\sum_{i=1}^{n}I_{i}B_{i}+C \tag{5}\]  
According to the equation, the hypercube image at the particular band (\(i\)) is denoted by \(I_{i}\). The PLS regression coefficient is represented by \(B_{i}\), and the constant value is indicated by \(C\).
# Using Full Pose Measurement for Serial Robot Calibration
## 1 Introduction
Automated fiber placement is another example of industrial application where the orientation of robot end-effector is important [27]. The approach that we introduce in this paper avoids the pitfall of minimization of unbalanced 6D error. First, link twists are determined in the CPA-like procedure from 3D data. Then, using full 6D poses measured by sensors, encoder zero offsets are determined in a separate minimization. The error function used in this minimization does not depend on linear DH parameters (link lengths and linear offsets) nor on the position components of noisy 6D poses acquired by sensors. Once twists and zero offsets are known, they are inserted into another error function, which depends only on position components of sensor data. The remaining linear DH parameters are determined by minimizing this second error function. For comparison, robot calibration based on only 3D sensor data is also performed. Obtained results clearly show that orientation errors of end-effector are smaller when orientation part of 6D data is used. At the same time, the position errors are comparable for both methods.