esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology  
### Artificial Neural Network Model  
An ANN model, namely WaterNet, proposed by [PERSON] et al. [34] was adopted. As shown in Figure 3, the input and output to the model was an image patch of the size \(7\times 7\times 16\) and an estimated Chla concentration in the center pixel of the patch, respectively. The model is an end-to-end network structure consisting of three phases: that is, band expansion, feature extraction, and Chla concentration estimation. In the band expansion phase, there were three convolutional layers with \(1\times 1\times 3\) kernel filters. The \(1\times 1\times 3\) kernel filters performing convolution on the spectral domain attempt to augment spectral features from the spectral bands of the input image patch, which is also known as spectral feature extraction via band combination [41, 42, 43]. Meanwhile, two convolutional layers containing ten filters of the size \(3\times 3\times 42\), and five filters of the size \(3\times 3\times 10\), were utilized in the feature extraction phase. With those filters, the spatial feature information was extracted. The output to this phase was a feature map of the size \(3\times 3\times 5\), and this output was further flattened and linked to the Chla concentration estimation phase which contained two fully connected layers. A rectified linear unit (ReLU) and sigmoid functions were used as the activation function in convolution and fully connected layers, respectively. In total, this ANN model contained 4753 unknown parameters.
# mmHSE: A Two-Stage Framework for Human Skeleton Estimation Using mmWave FMCW Radar Signals
## 4 Methodology
### Evaluation Metrics  
To evaluate the accuracy of the predicted 3D skeleton, we employ two widely adopted metrics, which provide interpretable and robust measurements of keypoint localization performance:  
* Mean Absolute Error: This metric quantifies the average L1 distance between the predicted keypoints and the ground truth keypoints across all samples, joints, and spatial coordinate dimensions \((x,y,z)\). \[\text{MAE}=\frac{1}{B\cdot N\cdot 3}\sum_{b=1}^{B}\sum_{n=1}^{N}\Big{(}| \hat{y}_{b,n,x}-y_{b,n,x}|+|\hat{y}_{b,n,y}-y_{b,n,y}|+|\hat{y}_{b,n,z}-y_{b,n, z}|\Big{)},\] (19) where \(\hat{\mathbf{y}}_{b,n,x/y/z}\) and \(\mathbf{y}_{b,n,x/y/z}\) denote the predicted and ground truth coordinates of the \(n\)-th keypoint in the \(b\)-th sample, along the \(x\), \(y\), and \(z\) axes, respectively.
* Root Mean Square Error (RMSE): This metric measures the average L2 error between predicted and ground truth coordinates along the three spatial dimensions. \[\text{RMSE}=\sqrt{\frac{1}{B\cdot N\cdot 3}\sum_{b=1}^{B}\sum_{n=1}^{N} \Big{(}(\hat{y}_{b,n,x}-y_{b,n,x})^{2}+(\hat{y}_{b,n,y}-y_{b,n,y})^{2}+(\hat{y} _{b,n,z}-y_{b,n,z})^{2}\Big{)}}.\] (20)  
Each keypoint is defined by its \((x,y,z)\) position in centimeters, with \(x\) indicating the horizontal axis, \(y\) the vertical axis, and \(z\) the depth.
# ICN-Based Enhanced Content Delivery for CDN
## 5 Simulation Study
### Methodology
#### 5.1.1 Topology  
In order to establish a network topology for our proposed design that contains the key components, we developed a mechanism to generate the topology:  
* Routers: We use a real network topology for core routers as follows: 1. Abilene topology (12 nodes, 14 bidirectional links); 2. Geant2 topology (32 nodes, 54 bidirectional links).
* IG and EG: Both IG and EG are selected from routers. Based on our previous assumption, IG is placed close to CDN edge servers. In light of the topology-informed placement strategy for CDN servers proposed in [50], we select IG as the router with the highest out-degrees so that it reaches out to more users with smaller latency. In contrast, EG is selected as the router with the maximum sum of distances to all other routers, reflecting that EG is placed towards the origin server, which is distant from users.
* Edge servers and origin server: Edge servers are arranged in a ring topology, with each one of them linked to the IG. We set the number of edge servers in proportion to the number of users, with one edge server per ten users. Lastly, the origin server is directly connected to the EG as previously assumed. A topology of our proposed design generated with the Abilene topology is shown in Figure 6.  
Figure 6: The Abilene topology used for IECDN.
# An Optimization Methodology for Adapting Legacy SGX Applications to Use Switchless Calls
## 5 Evaluation
### Micro-Bench Evaluation  
Breakdown of CPU time: For the ECALL that satisfies the switchless efficiency condition, we measure the breakdown of CPU time to compare the traditional ECALL interface and switchless ECALL. For evaluation, we set clients to send 32 long-running flows, and each flow sends 1 KB-sized random packets. Then, we measure the elapsed time of \(\mathtt{ecall\_reproduce\_ssl\_context}\) for a single packet, starting from an SGX-Box thread creation. Note that the elapsed time includes the packet I/O and payload reassembly procedures. Figure 5 shows the result. For the case of a traditional ECALL, the elapsed time consists of time consumed in an untrusted region, time consumed in an enclave region, and an enclave transition, while switchless ECALL does not include the time consumed for enclave transition, respectively. Our result shows that adopting a switchless call to \(\mathtt{ecall\_reproduce\_ssl\_context}\) delivers 10% reduced elapsed time compared to traditional ECALL. This improvement comes from the result that pended time by worker threads (2.6 \(\upmu\)s) is smaller than the total enclave transition latency (5.3 \(\upmu\)s).  
TLS Decryption Throughput: We measure the TLS decryption throughput by increasing the TLS record size from 256 Bytes from 4 KB. As the Figure 6 shows, a naive adoption of switchless call to every ECALL used by SGX-Box degrades the performance by 33% on average. This result supports our claim that it requires an appropriate adoption of switchless calls for SGX applications, which execute synchronously (e.g., run-to-completion model). In contrast, SGX-Box that utilizes switchless SGX based on our strategy gives 5% better throughput than the original SGX-Box with 256 Bytes-sized TLS records, while it delivers 2.7% of improvement for 4 KB record size, respectively. The benefit of adopting switchless calls is reduced for larger record sizes because an execution time for record decryption dominates the time consumed for enclave transition.
## 3 Results and Discussion  
### Thermal Shocking  
Figure 1 shows the variation of the CTE with temperature for Yb0.3 Co4 Sb12, Ti, Mo, and Cu & Ni, which are common electrode materials. Clearly the CTE mismatch between Ti and Yb0.3 Co4 Sb12 (or Cu & Ni) is much less than that between Mo and Yb0.3 Co4 Sb12 (or Cu & Ni). Based on Figure 2a,b, in the as prepared Ti/Mo/Yb0.3 Co4 Sb12 joints, the bonding between Ti, Mo and Yb0.3 Co4 Sb12 is good with the thickness of the Mo layer being about 4.5 \(\upmu\)m and the interfacial diffusion being neglectable at both the Ti/Mo and the Mo/Yb0.3 Co4 Sb12 interfaces. The Ti/Mo/Yb0.3 Co4 Sb12 joints then experienced thermal shocking for 10 cycles between 0 \({}^{\circ}\)C and 600 \({}^{\circ}\)C in vacuum. Figure 2c demonstrates that the bonding between Ti, Mo, and Yb0.3 Co4 Sb12 remains good after 10 cycles of thermal shocking. Besides, the contact resistivity of the joints before and after thermal shocking was measured to be around 3.9 \(\upmu\Omega\cdot\)cm\({}^{2}\) and 3.7 \(\upmu\Omega\cdot\)cm\({}^{2}\), respectively. Based on the above results, it can be concluded that in spite of evident CTE mismatch between Mo and Yb0.3 Co4 Sb12, the interfacial bonding of the Ti/Mo/Yb0.3 Co4 Sb12 joints is quite strong and can survive severe thermal shocking. For these Ti/Mo/Yb0.3 Co4 Sb12 joints, because the Mo layer thickness is very small, the interfacial thermal stress from CTE mismatch is controlled at a relatively low level. Moreover, the Ti layer also helps to balance the CTE mismatch between Mo and Yb0.3 Co4 Sb12 and release the interfacial thermal stress.
# CETD: Counterfactual Explanations by Considering Temporal Dependencies in Sequential Recommendation
## 3 Propose Model
### The CETD Model
#### 3.3.2 Causal Rule Learning Model  
For a user u, we define \(C^{u}\) as the composite dataset that comprises the original pair \((\mathcal{H}^{u},B^{u})\) and the counterfactual input-output pairs \(\{(\widehat{\mathcal{H}}^{u}_{i},\widehat{B}^{u}_{i})\}_{i=1}^{u}\). We can define \(\widehat{\mathcal{H}}^{u}_{i}=[\hat{H}^{u}_{i(1)},\hat{H}^{u}_{i(2)}\cdots, \hat{H}^{u}_{i(T)}]\) as the input sequence of the \(i\)-th record in \(C^{u}\), where \(\hat{H}^{u}_{i(t)}\) is the \(t\)-th item in \(\widehat{\mathcal{H}}^{u}_{i}\). Let \(\widehat{B}^{u}_{i}\) denote the corresponding output for this input sequence. Our goal is to construct a causal model that initially identifies causal relationships between input and output items that are present in the \(C^{u}\), and subsequently selects the causal rule by analyzing the inferred causal dependencies. Our contention is that a single output event can be represented by a logistic regression model that takes into account the causal dependencies of all input items in the sequence. It is necessary for the model to be able to suppose the causal dependency \(\theta_{\hat{H}^{u}_{i(t)},\hat{B}^{u}_{i}}\) that exists between the input item \(\hat{H}^{u}_{i(t)}\) and the output item \(\hat{B}^{u}_{i}\). In recommendation tasks, the proximity of a behavior is a strong predictor of a user's future behaviors, while the impact of past behaviors diminishes over time. Specifically, earlier behaviors are given less weight in the recommendation algorithm compared to more recent behaviors. To account for the temporal effect of behaviors on the recommendation algorithm, we introduce a weight growth parameter \(\lambda\), where \(\lambda\) is a positive value that is less than 1. For a given input-output pair in \(C^{u}\), the probability of its occurrence can be calculated by:  
\[P(\hat{B}^{u}_{i}|\widehat{\mathcal{H}}^{u}_{i})=\sigma\Big{(}\sum_{t=1}^{T} \theta_{\hat{H}^{u}_{i(t)},\hat{B}^{u}_{i}}\cdot\lambda^{T-t}\Big{)}. \tag{4}\]  
To scale the score to a value within the range of 0-1, we utilized the sigmoid function \(\sigma\), which is defined as \(\sigma(x)=(1+exp(-x))^{-1}\). The input value is transformed by the function to produce the desired output. According to Equation (4), the calculated probability should be close to 1. In order to derive the causal dependencies \(\theta\), we maximize the probability over \(C^{u}\). Once all the causal dependencies have been collected, we identify those dependencies \(\theta_{\hat{H}^{u}_{i(t)},\hat{B}^{u}_{i}}\) whose output corresponds to the original input \(B^{u}\). Then, we construct counterfactual explanations based on the items with higher \(\theta\) scores. The counterfactual explanation that is extracted is personalized as a result of the algorithm that is applied to \(C^{u}\), which only consists of records that are centered around the user's original record \((\mathcal{H}^{u},B^{u})\). The complete algorithm is presented in Algorithm 1.
# Performance of Millimeter Wave Dense Cellular Network Using Stretched Exponential Path Loss Model
## 1 Introduction
These models assume a single obstruction blocks the LOS path between BS and UE. However, in the case of outdoor environments or systems having antenna arrays, the beamwidths may encounter several obstacles, which can block the LOS path. An alternate approach to model channel behaviour and the presence of blockages in urban areas is to stochastically define the propagation mechanism characterized by a few parameters, such as clutter in the environment and absorption. A channel model that reflects a rich scattering environment and regular city structure with a fixed number of blockages of the same size and orientation is proposed in [27; 28]. The urban area sites are modeled using a random square lattice each having a blockage with some probability. The authors in [29] extend the previous research by considering the random orientation of blockages and signal penetration losses. The work in [22] models blockages with random size, orientation and location using random shape theory considering the height of the BSs, UEs, and buildings. The distribution of blockages is derived and the power loss of a signal that strikes on the blockages is also quantified. All these aforementioned works proposed an exponential path-loss formula as an alternative to the conventional path-loss formula. This is also supported by field measurements where exponential attenuation is shown to be a regular phenomenon due to the cluttered environment [30; 31]. Similarly, the work in [22; 32] developed a path loss formula to incorporate penetration loss on a given link, which indicates that due to the presence of blockages an additional exponential decay component is introduced in the link budget. In [33] SEPLM is proposed, which generalizesvarious exponential path loss models found in the literature [Table I, [33]]. Recently, SEPLM has been used to analyze the uplink of several ultra-dense networks. [34; 35; 36; 37]. Related to coverage analysis of mmwave network analytical frameworks using standard power-law path loss model for LOS and NLOS transmission due to blockage is developed in [23; 24; 25; 38; 39]. Results reported in these works establish that the SINR coverage probability decreases as BS density becomes greater than a certain value and ASE experiences slow growth and falls to zero.\({}^{\rm HM}\)
# Study on Cyber Common Operational Picture Framework for Cyber Situational Awareness
## 2 Related Works
### Cyber Common Operational Pictures Interface
#### 2.3.1 Research on Response Time
\begin{table}
\begin{tabular}{c c c} \hline \hline
**System Interpretation** & **Response Time Definition** & **Time (s)** \\ \hline Key response, including scroll & Key pressed until positive response (e.g., “click”) & 0.1 \\ wheels, optical wheels, mouse clicks & Key pressed until the appearance of the character & 0.2 \\ Key print & Key pressed until the first few lines are visible & 1.0 \\ Page turn & End of the request until the first few lines to scroll & 0.5 \\ Page scan & End of the request until text begins to scroll & 0.5 \\ XY entry & From the selection of field until visual verification & 0.2 \\ Pointing & From the input of point to display point & 0.2 \\ Sketching & From the input of point to display of line & 0.2 \\ Local update & Change to image using the local database (e.g., new & 0.5 \\  & menu list from display buffer) & 0.5 \\ Host update & Change state data is at the host in a readily accessible & 2.0 \\ File update & Image update requires access to a host file & 10 \\ Inquiry (simple) & From command until the display of a commonly used & 2.0 \\  & message & \\ Inquiry (complex) & Response message requires seldom used calculations in & 10 \\  & graphic form & \\ Error feedback & From the entry of input until an error message appears & 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Acceptable System Response Times. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Simple/Complex Response Time** & **Behavior** & **MIL-STD-1472H [37]** & **[PERSON] et al.** [38] & **Shneiderman** [39] & **Common** \\ \hline \hline \multirow{8}{*}{Simple Response Time} & Key response, including scroll & & & & \\  & wheels, optical wheels, mouse clicks & 0.1 & 0.1 & 0.05–0.15 & 0.1 \\ \cline{1-1}  & Key print & 0.2 & 0.1–0.2 & 0.05–0.15 & 0.2 \\ \cline{1-1}  & XY entry, Pointing & 0.2 & & & \\ \cline{1-1}  & Sketching & 0.2 & 2–4 & & \\ \cline{1-1}  & Error feedback & 0.2 & & & 2 \\ \cline{1-1}  & Page scan & 0.5 & & & \\ \cline{1-1}  & Local update & 0.5 & & & \\ \cline{1-1}  & Page move & 1.0 & & & \\ \cline{1-1}  & Host update & 2.0 & & & \\ \cline{1-1}  & Inquiry (simple) & 2.0 & 2.0 & 2–4 & 2.0 \\ \hline \hline Complex Response & Inquiry (complex) & 10 & 5.0 & 8–12 & 10 \\ \cline{1-1} Time & File update & 10 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: User-Acceptable System Response Times Suggested by Relevant Studies. \begin{table}
\begin{tabular}{c c} \hline \hline
**Type of Task** & **Appropriate Response Time (s)** \\ \hline Typing, moving the cursor, clicking the mouse & 0.05–0.15 \\ Simple and frequently used function & 1 \\ Common function & 2–4 \\ Complex function & 8–12 \\ Input confirmation, input error notification & 2–4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: User Preferred System Response Times.
## 4 Simulation Results and Discussion
### KPI Evaluation and Optimization Using Brute Force
#### 4.1.2 Performance Evaluation When GUEs Are Distributed Using TCP  
The AG-HetNet performance is outlined in Figure 11a-c; we plot the 5 pSE variation with respect to positive-biased CRE. The result demonstrates that when 50% of the MBSs are destroyed, the peak values in the case of NIM, eICIC, and FeICIC are observed with lower values of CREs. Moreover, as the CRE increases, the number of GUEs associated with the UABSs increases, and so does the interference experienced by these GUEs, thus decreasing the overall 5 pSE. In a high disaster-struck scenario, when 97.5% of the MBSs are destroyed, a relatively slow degradation of peak values of 5 pSE is observed with the increasing CRE for the NIM, eICIC, and FeICICIC techniques.  
Similarly, we discuss the coverage probability variation for positive-biased CRE. The results in Figure 12a-c demonstrate that when 50% of the MBSs are destroyed, the peak values in the case of NIM, eICIC, and FeICIC are observed for the moderate values of CREs. Moreover, as the CRE increases, the number of GUEs associated with the UABSs increases, and so does the interference experienced by these GUEs, thus decreasing the overall 5 pSE. Whereas in a high disaster-struck scenario with 97.5% of the MBSs destroyed, the peak value of 5 pSE for NIM is observed in the range of 0-3 dB CRE, the eICIC technique observes a peak value around 12 dB CRE, and FeICIC is observed in the range of 0-3 dB CRE.  
Following the comparative analysis of KPIs illustrated in Figure 13a,b for 5 pSE, we observe a moderate deviation in the peak values from NIM to eICIC and a minor deviation in the peak values from eICIC to FeICIC when 50% of MBSs are destroyed. Furthermore, we observe a minor deviation in the peak values of 5 pSE between NIM, eICIC, and FeICICIC when 97.5% of MBSs are destroyed. In comparison, we observe a minor deviation in the peak coverage probability between NIM, eICIC, and FeICIC in both scenarios of 50% and 97.5% of the MBSs being destroyed.
# Strategic Portfolio Optimization Using Simulated, Digital, and Quantum Annealing
## 4 Annealing Methods and the QUBO Model
### Quadratic Unconstrained Binary Optimization (QUBO)  
The quadratic unconstrained binary optimization problem (QUBO) model has emerged as an underpinning of the quantum computing area known as quantum annealing (as well as digital annealing), and has also become a subject of study in neuromorphic computing [3]. Through these connections, QUBO models lie at the heart of experimentation carried out with quantum computers developed by D-Wave Systems and neuromorphic computers developed by IBM [3]. Furthermore, QUBO models can also be used as an input for algorithms such as QAOA [32], which can be executed on quantum-gate computers.  
There are many well-known reductions of NP-hard and NP-complete problems to QUBO (see [2]), which itself is an NP-hard optimization problem [3]. We proceed to define QUBO as presented in [33]:  
Given a graph \(G=[N,E]\) with node set \(N=\{1,2,\ldots,i,\ldots n\}\) and edge set \(E=\{(i,j):i,j\in N\}\). Denoting the weight of edge \((i,j)\) by \(c_{ij}\), we define QUBO as  
\[Minimize:\sum_{i\in N}c_{ii}x_{i}+\sum_{(i,j)\in E}c_{ij}x_{i}x_{j} \tag{13}\]  
with \(x_{i}=\{0,1\}\); \(i\in N\). An equivalent compact definition, where the coefficients of Equation (13) are represented as a matrix \(Q\) is  
\[Min\;x^{t}Qx \tag{14}\]  
with \(x\in\{0,1\}^{n}\) and \(Q\) being an \(n\times n\) matrix of coefficients [33].
# Indoor Microclimate Analysis of the San Panfilo Church in Tornimparte, Italy
## 3 Results
### Continuous Monitoring
#### 3.2.3 Psychometric Diagrams  
The microclimatic conditions at the different positions are summarized in psychrometric diagrams as shown in Figure 10, where the point cloud represents the air conditions and the red perimeter identifies the values permitted by the UNI standard [3] for frescoes (temperature ranges from 10 \({}^{\circ}\)C to 24 \({}^{\circ}\)C and relative humidity ranges from 55% to 65%). The comparison between the microclimatic conditions at the crucifix top (Figure 10c) and the same physical quantities outside the church below the external portico and at the meteorological station of Colle San Vito (Figure 10a,b) shows the church's ability to limit extreme temperature values and prevent cases of air saturation.  
The ancient stone walls, with their great thermal capacity, adsorb heat in the warm season and release it in the cold one, resulting in moderate excursions in temperature under the external portico with respect to ones at the meteorological station of Colle San Vito, and even more moderate temperature values inside the church.  
Likewise, the church building limits the exchange of moisture. Under the portico, the saturation air condition never occurred, and inside the church, relative and specific humidity were, respectively, never greater than 80% and 12 g/kg. The frescoes under the portico and the [PERSON] and Child in the lunette on the main church door were therefore not affected by condensation events on the surfaces.  
Moreover, the church building limits the microclimatic states close to the intervals recommended by the norms. In fact, in Figure 10c, the points representing the microclimatic condition inside the church are concentrated close to the perimeter that identifies the conditions recommended by the UNI 10828 standard [3].  
The dew point spread is defined as the difference between temperature and dew point temperature; it is an important physical quantity in microclimatic analysis as it gives information about saturation danger. If the dew point spread is close to 0 \({}^{\circ}\)C or lower, condensation can occur; otherwise, its value gives information about the distance from the saturation condition. In the presbytery, the air condition is always far from condensation (Figure 11a), whereas under the portico (Figure 11b), dew point spread is lower but it is always positive.  
Figure 10: Psychometric chart (**a**) at San Vito meteorological station, (**b**) outside San Panfilo church below the external portico and (**c**) inside the church at the crucifix top. The red perimeter identifies the conditions permitted by the UNI 10828 standard.
# Accuracy and Precision of Three-Dimensionally Printed Orthognathic Surgical Splints
### Treatment Planning in Orthognathic Surgery  
#### 1.1.1 Traditional Surgical Planning  
Traditionally, surgical planning for OGS has been based on the findings from clinical examination in conjunction with two-dimensional (2D) cephalometric analysis. However, there are many limitations with the traditional method, which include: errors associated with landmark identification and linear and angular measurement errors when using the 2D cephalometric analysis [1,2], particularly in the presence of facial asymmetries [3,4]; inaccuracies associated with facebow record transfers and bite registration [5]; and lack of precise control over rotational and translational movements of the plaster casts during the model surgery [6]. In addition, traditional methods also require technicians to have a firm understanding of the properties of the dental materials used. These issues mean that the dimensions of the final fabricated OSS may be affected by errors accumulated along sequential stages of production.