esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Their Outcomes
### Answer to Second RQ
* SVM vs. CNN + BiLSTM: The objectives of this investigation were to evaluate the efficiency of the suggested CNN + BiLSTM model to that of SVM classifier to predict DDoS attacks using historical traffic. SVM classifiers performed worse in terms of precision (74), recall (74), F1-score (74), and accuracy (74), as seen in Table 8. The SVM model's poor performance might be attributed to: (i) long training times, (ii) expensive computation, (iii) increased size requirements for training and testing, and (iv) more complexity [9]. * CNN + BiLSTM vs. Random Forest: the purpose of this experiment was to see how well the suggested CNN + BiLSTM model compared to a random forests (RF) classifier. Table 8 demonstrates that RF classifiers have lower precision (75), recall (75), F1-score (75), and accuracy (75) than the proposed system. The RF model's poor performance is due to the following factors: (i) its legitimate predictions takes time, (ii) it is unreliable for categorical attributes, and (iii) comparable sets of related attributes in the data are favored over bigger sets [39]. * CNN + BiLSTM vs. Logistic Regression: the objective of this experiment was to evaluate the suggested CNN + BiLSTM model against a logistic regression (LR) classifier. As shown in Table 8, LR classifiers provided lower precision (64), recall (64), F1-score (64), and accuracy (64) outcomes (0.64 percent). LR is rated poor because it is prone to overfitting [39] and only makes relatively brief predictions [39]. * CNN + BiLSTM vs. KNN: The objective of this investigation was to evaluate the suggested CNN + BiLSTM model against a k-nearest neighbors (KNN) classifier. Table 8 demonstrates that KNN classifiers have lower precision (71), recall (71), F1-score (71), and accuracy (71). KNN is a low-ranking algorithm because it is (i) time-consuming when working with big data sets, and (ii) sensitive to irrelevant and noisy data [39].
## 3 BeiDou PPP Strategy  
The Position and Navigation Data Analyst (PANDA) software package, developed by the GNSS Research Center at Wuhan University, was used to process the BeiDou/GPS observations of \"BETS\". Since 2003, the functions of the software have been extended continuously. PANDA software can be applied in the precise orbit determination for the GPS, GLONASS, the low-orbit satellites, PPP, PPP-RTK, the network adjustment, the precise relative positioning as well as the gravity field modeling using satellite-to-satellite tracking data [20; 21; 22; 23; 24; 25; 26]. Nowadays, the PANDA software package has been further developed to process the BeiDou data with the capablility of precise orbit and clock determination, as well as precise positioning [3; 5; 27].  
The PPP parameters to be estimated contain the coordinates, the receiver clock biases, the delay of the wet troposphere component and the ambiguities when only processing BeiDou data. When BeiDou/GPS observations are processed in a combined mode, we need to introduce an inter-system bias between multiple GNSS signals in the receiver end [11; 12; 13; 14].  
BeiDou precise orbit and satellite clock bias products used in the contribution are based on the ITRF2008 reference frame, which is the same as used in single GPS data processing [3]. The orbit and satellite clock biases were generated using the strategy proposed by [PERSON] _et al._[27]. In data processing, we used the Turbo-Edit method to detect the major cycle slips and eliminate the major outliers in the first stage. In the second stage, the residuals-editing method is used to detect the minor cycle slips, and eliminate remaining outliers [28]. The cutoff elevation of 7\({}^{\circ}\) and the variance  
Figure 6: Standard deviations of MPCs of C/A and P2 codes of GPS satellites at CHDU station on September 24, 2012.  
component estimation weighting method were applied. We consider corrections related to the satellites (_i.e._, the default satellite antenna phase offset from the BeiDou satellites and the satellite antenna phase offset and PCV of GPS satellites), the corrections related to the propagation path (_i.e._, the zenith troposphere delay, the phase wind up), and the corrections related to the ground stations (_i.e._, the solid earth tide, the ocean loading tide. However, the phase center variation (PCV) of BeiDou satellites and the PCO and PCV corrections in the receiver side are not considered. The reason is that those values are not acquiqustations used below in Table 2, CHDU was not used for orbit and clock determination, so the solutions are independent.
## 7 IoMT Healthcare Services and Applications
### Rehabilitation  
Smart healthcare services provide patients with access to personalized rehabilitation plans that are tailored to their individual needs. This helps optimize the effectiveness of their rehabilitation and reduces the required time for completion. A home-based rehabilitation program is expected to decrease healthcare costs and enhance patient quality of life. IoT-based smart rehabilitation systems can be developed using an ontology-based automated design method [89]. IoT-based technologies can support effective remote consultations in comprehensive rehabilitation. As a field of medicine dedicated to enhancing and restoring functional ability and quality of life for individuals with disabilities or impairments, physical medicine holds significant importance. Numerous IoT-based rehabilitation systems have been developed, including an integrated application system for correctional facilities, a medical rehabilitation system for smart cities [90; 91], and a language-training system for children with autism [92].
## 5 Conclusions  
Using the inverted U-tube for side gas entrainment is experimentally and numerically investigated in this paper. Various gases can be entrained from the required cavities, but the air is suggested in this paper as it is the dominant gas in most applications. Side entrainment is considered a mixture of air and water vapor with different mass ratios. The CFD model is validated first with experimental data and an acceptable comparison is obtained. The present experiment shows the effect of mixing side-air with water flowing in the inverted U-tube on the created water siphon flow at various water levels between water inlet tanks due to altering the generated vacuum at the tube tip. Additionally, the siphon water mass flow rate is found to be reduced with increasing side-air mass flow rate. Then, different mass ratios for the main water stream and side mixtures in the inverted U-tube are examined. The proposed U-tube is capable to bring down the absolute static pressure to.55.1 kPa in the tube upper part. This low-pressure region motives the suction of the side mixture to the tube cavity. This study proves that, at water streams of 2-4 kg/s, better side entrainment can be found at the water/side mixture (air and water vapor) mass ratio of 461-662. In addition, the side entrainment can still exist at a mass ratio of 1000 for water flowing at 2 kg/s. Moreover, the condensation can magnify the generated vacuum at the tube upper portion at a higher water mass flow rate. This means that circulating flow in the range of 2-4 kg/s in the U-tube enhances the function of the tube in entraining the side mixture with maintaining the siphon flow and undergoing vapor condensation. Increasing the water mass flow rate weakens the side entrainment of gases to the tube upper part and hence reduces the tube function at certain mass ratios. These findings can be applied to a variety of engineering applications for evacuating systems.  
Conceptualization A.H., A.E. and K.Y. formal analysis, K.Y., A.E. and A.H.; funding acquisition A.E.; investigation, A.H., K.Y. and A.E.; methodology, K.Y., A.H. and A.E.; project administration, A.E.; Supervision, A.H. and A.E.; writing--original draft, K.Y.; writing--review and editing, A.H., K.Y. and A.E.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Evaluation Metrics and Statistical Analysis  
Model performance was comprehensively evaluated using a multi-metric approach to address various aspects of classification quality:  
Accuracy: The proportion of correctly classified instances: \(\text{Accuracy}=\frac{TP+TN}{TP+FP+TN+FN}\). Precision: The proportion of true positive predictions among all positive predictions: \(\text{Precision}=\frac{TP}{TP+FP}\). Recall (sensitivity): The proportion of true positives identified among all actual positives: \(\text{Recall}=\frac{TP}{TP+FN}\). F1-scor: The harmonic means of precision and recall: \(\text{F1}=2\cdot\frac{\text{Precision}\cdot\text{Recall}}{\text{Precision}+ \text{Recall}}\).
## 4 Experiments
### Performance Measurement
#### 4.5.2 Retrieval-Augmented Generation Assessment  
We incorporate specialised RAGAS metrics to evaluate the performance of our RAG models. Specifically, we utilise Answer Relevance, Answer Correctness, Answer Similarity, Answer Recall, and Answer Precision [131]. The Answer Relevance metric assesses the relevance of the generated answer to the given prompt, calculated using the cosine similarity between the embeddings of the generated answer and the original question:  
\[\text{Answer Relevance}=\frac{1}{N}\sum_{i=1}^{N}\cos(\mathbf{E}_{i_{i}}, \mathbf{E}_{o}) \tag{19}\]  
where  
* \(N\) is the number of generated answers.
* \(\mathbf{E}_{a_{i}}\) is the embedding of the generated answer.
* \(\mathbf{E}_{o}\) is the embedding of the original question.  
The Answer Correctness metric evaluates the accuracy of the generated answer by considering both semantic similarity and factual similarity. The correctness score is calculated as  
\[\text{Answer Correctness}=\alpha\times\text{Semantic Similarity}+(1- \alpha)\cdot\text{Factual Similarity} \tag{20}\]  
where  
* \(\alpha\) is the weight assigned to semantic similarity.
* **Semantic Similarity** is the cosine similarity between the embeddings of the generated answer and the ground truth.
* **Factual Similarity** measures the overlap of factual information between the generated answer and the ground truth.  
These specialised RAGAS metrics enable us to quantify the performance of our RAG system. By evaluating Answer Relevance, Answer Correctness, Answer Similarity, Answer Recall, and Answer Precision, we can assess the quality of the generated responses within the context of sentiment analysis.
## 3 Materials and Methods
### Transformer Encoder Branch  
As inspired by [25], the architecture of the transformer branch adhered to the conventional encoder-decoder design. Initially, the input image is uniformly partitioned into \(N=\frac{H}{S}\times\frac{W}{S},S=16\). Those patches were subsequently flattened and forwarded through a linear embedding layer, yielding an output dimension of \(D_{0}\), resulting in the original embedding sequence \(e\in\mathbb{R}^{N\times D_{0}}\). To incorporate the spatial information, a tunable positional embedding of the same dimension was added to the raw embedding \(e\). These embeddings \(z^{0}\in R^{N\times D_{0}}\) were fed into the transformer branch, which consisted of \(L\) layers of multi headed self-attention (MSA) and multi-layer perception (MLP). The self-attention (SA) mechanism, a key principle of transformer, was essential in updating the states of each embedded patch during every layer of the transformer encoder. SA enabled the model to aggregate information globally by considering the relationships between all patches in the input image. Unlike traditional CNNs that have local receptive fields, the SA mechanism allowed for the network to capture long-range dependencies and contextual information from the whole image.  
\[SA(z_{i})=softmax(\frac{q_{i}k^{T}}{\sqrt{D_{h}}})v \tag{1}\]  
where \([q,k,v]=zW_{qkv},W_{qkv}\in R^{D_{0}\times 3D_{h}}\) is the projection matrix and vector \(z_{i}\in R^{1\times D_{h}}\) are the \(i^{th}\) row of \(z\), respectively.  
MSA is an extension of SA that merges multiple SA operations and projects the latent dimension back to \(R^{D_{0}}\). This enabled the model to capture diverse and complementary patterns and relationships within the input. Additionally, the MLP is a stack of dense layers [20] that processes the outputs of the MSA, further refining and transforming the extracted features. As the global context features can be fused with the corresponding feature maps of convolution encoder branch, we used the progressive upsampling method referred to as SETR [32].
## 3 Analysis Model
### PTO Model  
The PTO force is obtained by the following equation:  
\[F_{PTO}=F_{\text{sup}}+F_{f}+F_{g} \tag{8}\]  
where \(F_{\text{sup}}\) is the supplementary force induced by the added inertial. \(F_{g}\) is the induced force by the generator and the flywheel and \(F_{f}\) is the friction force.  
The supplementary force \(F_{\rm sup}\) is calculated by  
\[F_{\rm sup}=-\frac{I_{s}}{l_{a}}z \tag{9}\]  
\[I_{s}=I_{0}+I_{a} \tag{10}\]  
where \(I_{0}\) and \(I_{a}\) are the initial inertia and the added inertia of the PTO system, respectively. Here, \(I_{a}\) can be calculated by the following equation:  
\[I_{a}=m_{1}{I_{1}}^{2}+m_{2}{I_{2}}^{2} \tag{11}\]  
Figure 3: Hydrodynamic parameters for the heave mode of the floating buoy radius a = 0.325 m; buoy draft b = 0.275 m in a water depth h = 1 m.  
Figure 2: Configurations of the floating buoy.  
where \(m_{1}\) and \(m_{2}\) are the added mass on the two sides of the center point, and \(l_{1}\) and \(l_{2}\) are the distance from \(m_{1}\) and \(m_{2}\) to the center point, respectively.  
The resistive force from the generator \(F_{g}\), discussed in detail in the previous work [28], is expressed in (12). The resistive force from the generator consists of the flywheel and the generated torque, which is only taken into account when the instantaneous speed \(n\) is larger than the generator speed \(\dot{\theta}\). The instantaneous speed \(n\) caused by the velocity of the floating buoy is obtained in (13).  
\[F_{g}=\left\{\begin{array}{l}\frac{(T_{fl}+T_{g})}{l_{a}}\times i_{GB},\text { for }n\geq\dot{\theta}\\ 0,\text{ otherwise}\end{array}\right. \tag{12}\]  
\[n=\frac{\dot{z}}{l_{a}}i_{GB} \tag{13}\]  
where \(T_{fl}\) represents the torque generated by the flywheel's rotational inertia and is directly proportional to the derivative of \(\theta\); \(l_{a}\) is the arm length; the induced torque of the generator is \(T_{g}\), which is assumed to be proportional to the derivative of \(\theta\); and \(i_{GB}\) is the transmission ratio from the quarter-gear to the output shaft of the gearbox.  
The friction force \(F_{f}\) can be modeled using [PERSON]'s proposed method and presented in  
\[F_{f}=\Big{(}F_{c}+(F_{br}-F_{c})e^{(-c_{F}|\dot{z}|}\Big{)}sign\big{(}\dot{z} \big{)}+f_{v}\dot{z} \tag{14}\]  
where \(f_{v}\) is the viscous friction coefficient; \(F_{br}\) is the breakaway friction force, which is the sum of the Coulomb and static frictions at zero velocity; and \(c_{F}\) is the transition approximation coefficient, which is used to approximate the transition between the static and the Coulomb frictions. \(F_{c}\) is the Coulomb friction that opposes motion with a constant force at any velocity.
# From Cyber-Physical Convergence to Digital Twins: A Review on Edge Computing Use Case Designs
## 9 Network Digital Twin for Vehicle-to-Edge Communication
### Edge Computing-Based Digital Twin for Open RAN  
The disaggregated and open architecture of open RAN is aimed at meeting the high demands for 6G wireless communications. The most important services in a multi-vendor disaggregated network environment require continuous measurements and testing for continuous assurance [138]. How these measurements and tests will be conducted from different vendor infrastructures as well as how they will proceed in tandem with 6G networks is a very important and challenging question. Having some parity between the virtualized network environment and a DT of the network would be crucial. Therefore, building accurate DTs for open RAN environments will be key for network testing to be performed without taking the network offline. MNOs will be called upon to (i) improve computational capability for serving latency-critical applications and (ii) extend coverage to under-served locations and communities. The major challenge here is that a software push from one vendor might potentially cause some disruptions in the mechanisms of other vendors in the radio system [26], since the inter-dependencies among the network equipment from different vendors could be very complex and add too much risk in the operation of the different software.
## 4 Experimental Evaluation and Analysis
### Ablation Study
#### Effectiveness of Feature Extraction Modules  
To validate the effectiveness of the two interaction extraction modules in vehicle trajectory prediction, we trained the two variants of VTP-GCN: VTP-GCN without spatial interaction extraction (variant I: W/O SGCN) and VTP-GCN without temporal interaction extraction (variant II: W/O TGCN). We then compared their prediction results with those from the complete model. The RMSE values of the three models are shown in Table 4. It can be seen from Table 4 that the prediction performance of variant II is superior to that of the complete model at the final prediction horizon. However, VTP-GCN exhibits better prediction accuracy than the two variants at the remaining prediction horizons, exhibiting the smallest RMSE average. It is worth noting that the increase in the average prediction error caused by the absence of the spatial interaction extraction module is larger than that caused by the temporal interaction extraction module, indicating that the spatial interaction behavior of the vehicle within the driving scene has a greater impact on the future motion trajectory of the vehicle compared to the temporal interaction. The comparison line chart of the three methods is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Prediction Horizon (s)** & **W/O SGCN** & **W/O TGCN** & **VTP-GCN** \\ \hline
1 & 0.62(+0.07) & 0.67(+0.12) & **0.55** \\
2 & 1.05(+0.06) & 1.16(+0.17) & **0.99** \\
3 & 1.63(+0.07) & 1.69(+0.13) & **1.56** \\
4 & 2.48(+0.12) & 2.40(+0.04) & **2.36** \\
5 & 3.82(+0.23) & **3.49**(\(-0.10\)) & 3.59 \\ \hline Average & 1.92(\(\uparrow\)6\%) & 1.88(\(\uparrow\)4\%) & **1.81** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of RMSE values for the variants of VTP-GCN. Data are converted into meters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Prediction** & **1** & **2** & **3** & **4** & **5** \\
**Horizon (s)** & & & & & \\ \hline Reciprocal of time interval & 0.72(+0.17) & 1.23(+0.24) & 1.79(+0.23) & 2.57(+0.21) & 3.80(+0.21) \\ \hline Self-attention & **0.55** & **0.99** & **1.56** & **2.36** & **3.59** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of RMSE values for models with different temporal weighted adjacency matrices. Data are converted into meters.
## 2 Data
### Auxiliary Data  
The 1-arc Second Digital Elevation Model (DEM) observed by the Shuttle Radar Topography Mission (SRTM) was used for preprocessing. Gaps were filled using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEM. A pre-compiled water vapor database [62; 63] was used for correcting gaseous absorption during atmospheric correction. Similar to [PERSON] et al. [49], we used the CORINE land cover map 2006 ([[http://dataservice.eea.europa.eu](http://dataservice.eea.europa.eu)]([http://dataservice.eea.europa.eu](http://dataservice.eea.europa.eu)), accessed on 18 September 2021) to restrict our analysis to the'semi-natural' class and used the DEM to exclude the alpine zone (>1500 m a.s.l.).
## 4 Conclusions  
In this paper, we presented a reflection power response estimation technique utilizing the spatial correlation of higher-order eigenbeams derived from spherical microphone array measurements. The formulation of the reflection gain as a function of time, frequency, and direction helps in comprehending a faithful room response for a realistic non-diffuse sound field. The experimental results validate the frequency response and temporal response of the reflection power against the theoretical expectations.  
The proposed technique can estimate the resonant frequencies and modal decays caused by directional speakers and complex room environments. Furthermore, the directional decay times and dominant reflection directions facilitate the distinction of early and late reflection features. The insights from this room acoustic evaluation technique will be beneficial in controlling the acoustic quality while designing performance spaces. Particularly, the findings from this method will be more reliable than computational room models while deciding acoustic treatment schemes compatible with the source directivity. Additionally, the room mode features identified from this method can be incorporated in spectral equalization algorithms to improve speech intelligibility and remove audible artifacts. The dominant reflection locations and directional decay spectrum can aid in the inference of room geometry and calibration of the room acoustics in virtual reality-based rendering of heritage sites.  
The method can also be adapted for blind estimation of the discussed characteristics from the direct processing of microphone recordings for any arbitrary source signal, since we can separate the reflected power from the direct path power. Moreover, apart from spherical microphone arrays, any arbitrary array designs that can generate accurate spatial sound field coefficients can be integrated with the proposed algorithm. The future work shall expand the method to include multiple sources in noisy environments to conceive more real-world applications.  
Conceptualization, A.B., T.D.A. and J.Z.; Methodology, A.B., T.D.A. and J.Z.; Software, A.B.; Formal analysis, A.B.; Investigation, A.B.; Validation, A.B., T.D.A. and J.Z.; Resources, A.B., T.D.A. and J.Z.; Writing--original draft preparation, A.B.; Writing--review and editing, T.D.A. and J.Z.; Visualization, A.B.; Supervision, T.D.A. and J.Z.; Project administration, T.D.A.; Funding acquisition, T.D.A.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by Australian Research Council (ARC) Discovery Project Grant No. DP180102375[PERSON]  
Not applicable[PERSON]  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
RIR Room impulse response  
PWD Plane-wave decomposition  
EB-SPRIT Eigenbeam rotational invariance technique  
LTI Linear time invariant