esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of the Partitioning of Diffuse and Direct APAR on GPP Estimation
## 4 Discussion  
### Limitations of the Big Leaf Models  
In Figure 4, higher R\({}^{2}\) and lower RMSE values for the diffuse and direct APAR-based GPP compared to the FLUXNET GPP can be noticed, indicating that the partition of diffuse radiation in the GPP estimation is quite necessary. However, the enhancement of model performance was quite limited. The reason may be that the proposed diffuse and direct APAR-based (DDA) method was essentially a big leaf model, which treats the whole canopy as a big uniform leaf and does not separate the leaves into shaded and sunlit parts. Therefore, these simplifications may introduce systematic errors into the calculation of GPP [66; 67; 68]. Such big leaf model limitations might have significantly limited the effects of the partitioning by diffuse and direct APAR on the GPP estimation.
## 3 Results
### Impacts of Climate Change and Human Activities on ANPP  
Climate change and human activities have both influenced the ANPP of the research area. The evident spatial heterogeneity in ANPP at the pixel level indicates that climate change and human activities have different impacts on the spatial patterns and driving mechanisms of ANPP. The combination of climate change and human factors have resulted in an increase in the ANPP in the study area, having a greater influence than each of the two elements individually. In contrast, human activities have had a far greater impact on the decrease of ANPP than each of the two elements alone, being the main reason for the reduction in ANPP.  
Based on the variation (increase/decrease) in ANPP within the research area, 20.44% of the region is influenced only by climate change, 35.99% of the region is influenced solely by human activities, and the remaining 42.79% of the area is influenced by both climate change and human activities (Figure 8). In Figure 8, 'CDI' stands for climate-dominated increase, 'HDI' for human-dominated increase, 'CHI' for climate- and human-dominated increase, 'CDD' for climate-dominated decrease, 'HDD' for human-dominated decrease, and 'CHD' for climate- and human-dominated decrease. Among them, from 2001 to 2018, in the area where ANPP increased, the increase caused by climate change accounted for 25.76%, which was mainly distributed around some mountains in the research area. Human activities accounted for an increase of 20.28%, concentrated in the southern part of Henan Province, the northern part of Hubei Province, central and southern Yunnan Province, central and northern Guizhou Province, central and northern Hunan Province, and central Jiangxi Province. The highest proportion, 53.97%, was influenced by both climate change and human activities, and was widely distributed across various regions of the study area. In the area where ANPP decreased, human activities accounted for an 83.44% decrease in this region, broadly distributed along the coastal provinces, whereas the combined influence of climate change and human activities only accounted for 5.43% and 11.14%, respectively.  
Figure 7: ANPP distribution under elevation gradient (100 m interval).
## 6 Discussion and Limitations of the Study  
The proposed approach shows promising features, yet it is important to acknowledge its open issues. One of the foremost limitations of the architecture lies in the completeness of vulnerability enumeration. In our approach, the AG generated on the controller relies on the information that describes the topology of the network as well as active services and their specific vulnerabilities. If any of that information is missed or not correctly identified, the AG may be incomplete. This incompleteness can lead to a significant loss of information, potentially resulting in a false sense of security. An attacker exploiting a vulnerability that was not accounted for in the AG would go undetected, posing a substantial security risk. For this reason, it is crucial to feed the AG generator with all the necessary information, thus imposing an a priori analysis of the network to recognize them.  
Moreover, the workflow assumes a relatively immutable network environment. However, in some cases, network topology, hosts, and vulnerabilities can change dynamically and frequently. Handling these changes effectively and updating the graph in real time is a challenging task and is one of the main criticisms of AGs in general beyond our proposed use of them. Failure to adapt to dynamic changes could render the AG obsolete, leading to inaccuracies in threat detection.  
Addressing these challenges and potential pitfalls is crucial to the successful implementation of the architecture in real-world network environments. Further research and development are needed to overcome these limitations and make the architecture more robust and effective in enhancing network security.
## 2 Materials and Methods  
### Data  
First, measurements of free surface profiles during the initial dam-break stages over horizontal dry and wet beds were conducted by [PERSON] and [PERSON] [30]. During this test, the smooth and rectangular horizontal channel was 0.30 m wide, 0.30 m high, and 8.9 m long, as illustrated in Figure 1. The channel was separated by the vertical plate (dam) located 4.65 m from the channel entrance; that is, the lengths of the reservoir, \(L_{0}=4.65\) m, and the downstream channel, \(L_{1}=4.25\) m. The reservoir was located on the left side of the dam and was initially considered inundated; the initial upstream water depth \(h_{0}\) in the reservoir was constant at 0.25 m. On the right side, the initial tailwater depths \(h_{1}\) were 0 m in the case of the dry bed and 0.025 and 0.1 m in the wet bed, so there were three different situations with water depth ratios \(\alpha=h_{1}/h_{0}\) of 0, 0.1, and 0.4. The wet-bed conditions were created by using a low weir at the end of a flume. The water surface profiles were observed at the early stage using three high-speed digital cameras (50 frames/s), and the accuracy of the instrumental measurements was demonstrated in Reference [30]. In the following section, the corresponding numerical results refer to positions \(x=-1\) m (P1), \(-0.5\) m (P2), \(-0.2\) m (P3), \(+0.2\) m (P4), \(+0.5\) m (P5), \(+1\) m (P6), \(+2\) m (P7), and \(+2.85\) m (P8), where the origin of the coordinate system \(x=0\) is at the dam site. The three water depth ratios \(\alpha\) of 0, 0.1, and 0.4, where the \(x,y\) coordinates are normalized by \(h_{0}\).  
Both models are then tested against experimental data designed by [PERSON] et al. [39]. The measurements, taken during the experiment, make up a dataset that can be used for numerical model validation for problems involving free surface profiles during late stages, water depth variations, and averaged velocity evolutions. The Perspex flume was rectangular from the top and horizontal views, as shown in Figure 2, with a Manning coefficient of 0.011. It was 0.51 m wide, 0.50 m high and 12.80 m long. The upstream initial water depth was 0.40 m and the length of the reservoir, \(L_{0}\), was 3.50 m. The initial tailwater depths \(h_{1}\) were 0 m for the dry-bed condition and 0.08 m for the wet-bed condition (i.e., \(\alpha=h_{1}/h_{0}\) of 0 and 0.20). During this test, a high-speed digital camera was used to obtain the measured continuous water surface profiles, and the corresponding water level variations and average velocity variations in different positions were measured for the dry-bed and wet-bed conditions using ultrasonic sensors and ADV, respectively. In the following figure, the measured and computed stage-time hydrographs and velocity variations refer to positions \(x=-0.5\) m (G1), \(-0.1\) m (G2), \(+0.1\) m (G3), \(+0.8\) m (G4), \(+1.0\) m (G5), \(+1.2\) m (G6), \(+3.0\) (G7) and \(+5.5\) m (G8) (see Figure 2).  
Figure 1: Schematic view of the experimental conditions by [PERSON] and [PERSON] [30]: (a) \(\alpha=0\); (b) \(\alpha=0.1\); and (c) \(\alpha=0.4\).
## 3 Methodology  
### Multi-Channel Traffic and Weather Scene Image  
Predicting airport delay levels under convective weather is challenging because traditional numerical features cannot fully capture the complex impact on terminal flight operations [24]. To address this issue, we apply deep learning by converting air traffic scene data into multi-channel images, from which useful features can be extracted. Figure 1 shows the overall framework for constructing the Multi-Channel Traffic and Weather Scene Image (MTWSI).
## 4 Experimental Setup and Results  
### Experimental Setup  
Multiple modern network architectures including Inception-V3 [22], ResNet-50 [1], DenseNet-121 [46], and ViT [2] are used to verify the generalization of the GRM method. The cross-entropy is used as the cost function for the classification tasks. For the CNNs (ResNet-50, Inception-V3, and DenseNet-121), Adadelta [47] is used as the optimizer to minimize the cost function, where the learning rate is set to 1.0. For the ViT, AdamW [48] is used as the optimizer, coupled with a cosine decay learning rate scheduler and 20 epochs of linear warm-up. The learning rate is set to 0.0001. The size of the image is fixed as 224 for all the experiments.  
The number of training epochs is set to 300, which is long enough for the convergence of training. All networks are implemented by using PyTorch [49]. The experiments are carried out on a server with Linux OS and CPU Intel Xeon E5-2620 @2.4 GHz, four NVIDIA TITAN RTX GPUs, and 64 GB of RAM.
# Formal Analysis of Ratchet Protocols Based on Logic of Events
## 2 Logic of Events Theory
### Fundamental Concepts
#### 2.3.3 Rule Extension  
In LoET, the definition of _has_ appears in the stream relationship. The _Act_ class contains seven basic events, which are referred to as actions. The expression _e has a_ is true only if the action \(e\) contains the atom \(a\). Therefore, it should be expanded after introducing new events \(DH\) and _ratchet_, and the formally defined _Rule_ after the expansion is as follows:  
\[\left\{\begin{array}{c}\mbox{{Rule}}:\;e\;\mbox{{has}}\;a\equiv_{dpf}\;(e \in E(New)\wedge\mbox{{New}}(e)\;\mbox{{has}}\;a)\;\vee\\ \;\;\;\;\;\;\;\;\;\;\;\  
conditions of keys. Incorporating _Clean_ predicates into the Logic of Events theory (_LoET_) can enhance its ability to describe and reason about security protocols, especially those involving key management.  
\[\left\{\begin{array}{c}\textit{Rule3 (definition of Clean)}:\\ ((DH(e_{0})=a\Rightarrow\textit{Clean(e_{0},a))})\vee\\ (Ratchet(e_{1})=a\Rightarrow\textit{Clean(e_{1},a)})\vee\\ (\forall A:Id.\exists e_{2}:\exists E(DH)\lor E(Ratchet).\forall e_{3}:E_{1},\exists e_{4}:E_{2}.\\ loc(e_{2})=loc(e_{3})=loc(e_{4})=A\wedge e_{2}<e_{3}<e_{4}\wedge\\ E_{2}(e_{4})\textit{ has a }\wedge(\
eg(\textit{send(e_{3}) has a) }\vee\
eg(e_{3}=E(\textit{send})))\\ \Rightarrow\textit{Clean(e_{4},a)}\end{array}\right\} \tag{15}\]  
The predicate _Clean(e, a)_ indicates that the atom \(a\) has not been exposed before event \(e\), thereby ensuring its security within the symbolic event sequence. Specifically, _Clean(e, a)_ holds if the following two conditions are satisfied:  
* Prior to event \(e\), all events executed by the same participant (_loc(e)_) must not include atom \(a\) in any attacker-observable operation, such as _Send_, Rcv, or unprotected _Encrypt_. This condition can be verified using the extended has rule in combination with the local order relation (\(<_{loc}\)).
* The occurrence of \(a\) in event \(e\) must originate from secure events (e.g., \(DH\) or _ratchet_) and must not be derivable from any previously exposed message. This corresponds to the _flow relation_ defined in _AxiomF_.  
Therefore, verifying _Clean(e, a)_ essentially involves checking whether atom \(a\) has already been transmitted or leaked through any symbolic path before event \(e\).
# Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements
## 2 Methodology
### Dynamic Data Filtering  
The main assumption of the newly proposed filter approach is based on the self-similarity of a measurement at a point in space. Assuming that the technical integrity of the measuring system is given and the measurement parameters are chosen well, we consider that repetitive measurements--stared or scanned--will not change their behaviour in an unpredictable way in a defined time interval.  
In an idealised theoretical experiment without atmospheric and error influence a single point would appear in the \(u_{r}\)-\(\alpha\) diagram for a steady flow. Taking into account the distance dependency of \(\alpha\) adds vertical scattering, while temporal fluctuations of \(u_{r}\) causes horizontal scattering. In reality individual measurements of \(u_{r}\) and \(\alpha\) fluctuate around mean values, which depend on the chosen time interval. Valid measurement points are closer to these mean values, while outliers are characterised by a greater distance. This changes the density of the \(u_{r}\)-\(\alpha\) data distribution.  
In general, it can be said that well parameterised measurements form valid HDDR, which may be overlaid by invalid data. In order to distinguish between those, the dynamic filtering approach is based on two subsequent process steps, temporal & spatial normalisation and data-density calculation. Two different implementations of the density calculation are presented and described in the following sub-sections.
# A Group Decision-Making Approach in MCDM: An Application of the Multichoice Best-Worst Method
## 4 Experimental Studies
### Example 1  
In this example, two DMs provided responses for four criteria. The first and third criteria were considered the best and worst, respectively. The pairwise comparison of the best and worst criterion with other criteria is given in Table 3. The response from DM 1 for \(o_{13}\) is considered a multichoice parameter with two values, 9 and 8. Additionally, for DM 2, the response \(o_{14}=\{3,\ 2\}\). The goal was that, out of all the pairwise comparison options given by DM 1 and 2, the best option or pairwise comparison should be chosen. In Table 3, the last row is the multichoice row, which presents all of the responses of DMs as choices. The MCGBWM model was formulated and solved using the proposed approach.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \(\mathbf{o_{12}}\) & \(\mathbf{o_{13}}\) & \(\mathbf{o_{14}}\) & \(\mathbf{o_{23}}\) & \(\mathbf{o_{43}}\) \\ \hline \(DM1\) & 2 & \(\{8,\ 9\}\) & 3 & 4 & 2 \\ \(DM2\) & 2 & 8 & \(\{2,\ 3\}\) & 4 & 2 \\ \hline Multichoice & 2 & \(\{8,\ 9\}\) & \(\{2,\ 3\}\) & 4 & 2 \\ \hline \end{tabular}
\end{table}
Table 3: Pairwise comparisons data for Example 1.  
Figure 2: Steps of proposed approach.  
We considered the multichoice row in Table 3 as the responses of the DMs. The modeling of the proposed approach using mathematical programming was formulated and is presented below from (29)-(36).  
\[\text{Min }\ \ \xi\] (29) subject to \[W_{1}-W_{2}\times 2\leq\xi,\ \ W_{1}-W_{2}\times 2\geq-\xi\ \ \ \forall j \tag{30}\] \[W_{1}-W_{3}\times(z^{13}+8)\leq\xi,\ \ W_{1}-W_{3}\times(z^{13}+8) \geq-\xi\ \ \ \forall j\] (31) \[W_{1}-W_{4}\times(z^{14}+2)\leq\xi,\ \ W_{1}-W_{4}\times(z^{14}+2) \geq-\xi\ \ \ \forall j\] (32) \[W_{2}-W_{3}\times 4\leq\xi,\ \ W_{2}-W_{3}\times 4\geq-\xi\ \ \ \forall j\] (33) \[W_{4}-W_{3}\times 2\leq\xi,\ \ W_{4}-W_{3}\times 2\geq-\xi\ \ \ \forall j\] (34) \[\sum W_{j}=1,\ \ W_{j}\geq 0,\ \ \forall j,\ \ \xi\geq 0\] (35) \[z^{13}=0,1,\ \ z^{14}=0,1 \tag{36}\]  
Solving the proposed model by considering the multiple choices for Example 1, the optimal value of \(\xi^{*}=0.0266657\); \(z^{13}=1\), i.e., \(o_{13}=8\); and \(z^{14}=0\), i.e., \(o_{14}=3\). The choice of \(o_{BW}\), i.e., \(o_{13}\) is equal to 8. So, CI=4.47 and, hence, CR=0.00059. This implies that the solution is highly consistent. The weight values and rank order of the criteria are provided in Table 4. The optimal weights and CR of the criteria are presented in Figure 3.  
In Table 4, we can see that the orders of the criteria obtained from the proposed model and using the approach of [PERSON] et al. [36] are the same, i.e., \(C_{1}>C_{2}>C_{4}>C_{3}\). There is a difference in the weights of the criteria. The CR obtained by the proposed method is 0.00059, which is better than that by [PERSON] et al. [36], i.e., 0.096. The smaller the value of CR, the better.  
The optimal weights' ordering and the CR of the criteria are presented in Figure 3.
# Prediction of Emotional Empathy in Intelligent Agents to Facilitate Precise Social Interaction
## 4 Experimental Results
### Support Vector Machine
#### 4.5.7 Optimizable Support Vector Machine  
For an optimizable support vector machine, the 54.8% accuracy rate is achieved; the Gaussian distribution function is used, and the linear kernel type is used (Table 12). This kernel provides one-to-one mapping. As hyper-parameters, both one-vs-one and one-vs-all mapping functions are used; the kernel scaling rate is from 0.001-1000, and a hybrid of four different kernels is utilized with the expectation of obtaining the optimal results. With the focus on optimality, a Bayesian optimizer is used, with 30 iterations, and no time limit for training purposes is provided.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Fine Gaussian** & **Medium Gaussian** & **Coarse Gaussian** \\  & **Support Vector** & **Support Vector** & **Support Vector** \\  & **Machine** & **Machine** & **Machine** \\ \hline Parameters & Values & Values & Values \\ Accuracy & 51.4\% & 55.2\% & 51.4\% \\ Total Misclassification & 102 & 97 & 102 \\ Cost & 102 & 97 & 102 \\ Prediction Speed & \(\sim\)1200 obs/s & \(\sim\)1200 obs/s & \(\sim\)1200 obs/s \\ Training Time & 1.8659 s & 2.1035 s & 2.0044 s \\ Model Type & Fine Gaussian SVM &
\begin{tabular}{c} Medium Gaussian \\ SVM \\ \end{tabular} & Coarse Gaussian SVM \\ Kernel Function & Gaussian & Gaussian & Gaussian \\ Kernel Scale & 2.6 & 11 & 42 \\ Multi-Class Method & One-Vs-One & One-Vs-One & One-Vs-One \\ \hline \hline \end{tabular}
\end{table}
Table 11: Specifications of fine Gaussian support vector machine, medium Gaussian support vector machine, coarse Gaussian support vector machine.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Accuracy & 54.8\% \\ Total Misclassification Cost & 86 \\ Prediction Speed & \(\sim\)1700 obs/s \\ Training Time & 298.91 s \\ Model Type & Optimizable SVM \\ Kernel Scale & 1 \\ Multi-Class Method & One-Vs-One \\ \hline \hline \multicolumn{3}{c}{**Optimized Hyper Parameters**} \\ \hline Kernel Function & Linear \\ Multi-Class Method & One-Vs-One \\ \hline \hline \end{tabular}
\end{table}
Table 12: Specifications of optimizable Gaussian support vector machine.  
Figure 10 presents the classification tracking for the optimizable support vector machine and the 17 th iteration to be the best-point hyper-parameters and the minimum error hyper-parameters. It uses one-to-one mapping alongside a linear kernel function; data standardization is kept accurate (required for parameter's rescaling to obtain the 0 mean value and 1 standard deviation, and it helps in decreasing the ambiguity and equalizing the variability and range of data), and the box constraint level was about 0.1081 (its high value may lead to a high misclassification rate).
# Non-Orthogonal eMBB-URLLC Radio Access for Cloud Radio Access Networks with Analog Fronthauling
## 2 System Model
### Performance Metrics
#### 2.3.1 eMBB  
Capacity enhancement is the main goal of the eMBB service, which is envisioned to provide very high-rate communication to all the UEs. Therefore, for eMBB UEs, we are interested in the per-UE rate defined as  
\[R_{B}=\frac{\log_{2}(M_{B})}{n_{T}n_{F}}, \tag{12}\]  
where \(M_{B}\) is the number of codewords in the codebook of each eMBB UE.
## 2 Hybrid Framework Description
### ESKF Implementation
#### 2.4.2 Error Measurement Model  
We assume that the measurement is equal to the corresponding true state vector with additive noise. Thus, the measurement model can be defined as:  
\[\mathbf{m}_{k}=\mathbf{H}\mathbf{x}_{k|k}+\mathbf{n}_{k}, \tag{12}\]  
where \(\mathbf{m}\) is the external measurement, \(\mathbf{H}\) is the measurement transition matrix, and \(\mathbf{n}\) is the measurement noise.  
Since the true state vector \(\mathbf{x}_{k|k}=\mathbf{x}_{k|k-1}+\delta\mathbf{x}_{k|k}\) where \(\mathbf{x}_{k|k-1}\) is the predicted state vector, we have  
\[\mathbf{m}_{k}=\mathbf{H}\mathbf{x}_{k|k-1}+\mathbf{H}\delta\mathbf{x}_{k|k}+ \mathbf{n}_{k}. \tag{13}\]  
Hence, the error measurement model is  
\[\mathbf{z}_{k}=\mathbf{m}_{k}-\mathbf{H}\mathbf{x}_{k|k-1}=\mathbf{H}\delta \mathbf{x}_{k|k}+\mathbf{n}_{k}, \tag{14}\]  
where \(\mathbf{z}\) is the error measurement.  
For the ZUPT algorithm, we have  
\[\Delta\mathbf{v}_{k}=\mathbf{0}-\mathbf{v}_{k|k-1}. \tag{15}\]  
For the HDR algorithm, we have  
\[\Delta\psi_{k}^{R}=\bar{\psi}^{S}-\psi_{k|k-1}^{S}, \tag{16}\]  
while for the EC algorithm, we have  
\[\Delta\psi_{k}^{m}=\psi_{k}^{m}-\psi_{k|k-1}^{S}, \tag{17}\]  
where \(\psi^{m}\) represents the compass heading [39].  
As described in Section 2.2.3, the final heading error measurement is  
\[\Delta\psi_{k}=a_{m}\Delta\psi_{k}^{m}+a_{R}\Delta\psi_{k}^{R}. \tag{18}\]  
Thus, the total error measurement is  
\[\mathbf{z}_{k}=\bigg{[}\begin{array}{c}\Delta\psi_{k}\\ \Delta\mathbf{v}_{k}\end{array}\bigg{]}. \tag{19}\]The measurement transition matrix is  
\[\mathbf{H}=\left[\begin{array}{ccc}[001]&0_{1\times 3}&0_{1\times 3}\\ \mathbf{0}_{3\times 3}&\mathbf{0}_{3\times 3}&\mathbf{I}_{3\times 3}\end{array} \right]. \tag{20}\]  
When the measurement update is available, the correction (\(\delta\mathbf{x}_{k|k}\)) can be obtained with the Kalman update equation [6]:  
\[\delta\mathbf{x}_{k|k}=\mathbf{K}_{k}\mathbf{z}_{k}, \tag{21}\]  
where \(\mathbf{K}_{k}\) is the Kalman gain. The ESKF follows the standard process of the KF. For a detailed tutorial on the standard KF applied in a PNS, References [6; 50; 54] are recommended.
# Partial Discharge Inception and Breakdown Voltage Analysis in PCB-Based Electrode Structures
## 2 Experimental Setup and PCB Fabrication  
This section details the equipment used for conducting the PD tests and fabricating the PCB-designed test samples. The testing setup primarily consisted of commercial Haefely standard PD test equipment (Haefely, Basel, Switzerland). The PD experiments were conducted at the high-voltage laboratory of the National Taiwan University of Science and Technology in accordance with the IEC 60270:2000 [28] guidelines for PD measurements. The test circuit used was adapted from a previous study by [PERSON] et al. [29], which provides additional details on their design.