esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Bloom Filter Architecture
## 3 Proposed Algorithm
### 2-Phase Bloom Filter Architecture
#### 3.2.2 Search
Otherwise, if the \(p\)-BF produces a _negative_ result (meaning that the longest name prefix does not match the input), the search procedure returns to the \(l\)-BF query for the next level. For the NEG (negative) result in the \(l\)-BF querying, the \(p\)-BF is queried from the \(last\_match\_lvl\) while the level of access is decreased until a matching output face is found in the \(p\)-BF. The INDET result occurs when every accessed cell of the \(l\)-BF has a _conflict_ value. Even though the INDET is likely a _positive_, since the type (ORD and PRI) of the node causing the INDET is unknown, the search cannot proceed to the next level. Hence, for the INDET result, we perform \(p\)-BF querying beginning from the longest level of the input name until a matching output face is found in the \(p\)-BF. ```
FunctionqueryPerotBF(\(inName\), \(AccLvl\), \(matchLvl\)) \(matchLvl=\)queryBF(\(levelBF\), \(inName\), \(AccLvl\)); if(\(matchLvl==AccLvl\))then  returnORD; elseif(\(AccLvl<matchLvl\leq C\))then  returnPRI; elseif(\(0\leq matchLvl<AccLvl\))then  returnNEG; elseif(\(matchLvl==X\))then  returnINDET; else  returnNOTHING;  end if  end if
```  
**Algorithm 2**Querying Procedure of the \(l\)-BF  
For the INDET result of the \(p\)-BF shown in Algorithm 3, a matching output face cannot be determined with only 2-phase Bloom filter queries. In order to address this issue, an off-chip hash table is also required. Backtracking should then occur from the indeterminable level in the hash table. ```
FunctionqueryPerotBF(\(nodeType\), \(inName\), \(firstAccLvl\), \(indetLvl\)) if(\(nodeType==PRl\))then matchPort =queryBF(\(portBF\), \(inName\), \(firstAccLvl\)); else // back-tracking for(\(i=firstAccLvl\) to \(1\))do matchPort =queryBF(\(portBF\), \(inName\), \(i\)); if(\(matchPort==X\))then \(indetLvl\) = \(i\);  returnINDET; elseif(\(matchPort>0\))then break;  end if // in case of a negative, continue  end if  end for returnmatchPort;  end for
```  
**Algorithm 3**Querying Procedure of the \(p\)-BF
# Efficient Video Frame Interpolation Using Generative Adversarial Networks
## 4 Experimental Results and Analyses
### Implementation and Training  
We group every three consecutive frames as a patch in sliding windows as shown in Figure 3. The generator network takes the first and third frames from the patch to derive the output (interpolated second frame), while the discriminator network compares the generated output and genuine second frame. The proposed framework takes 32 patches for every training interval and uses the mini-batch gradient descent algorithm to train the network. The input to the network is cropped into blocks of 64 pixels by 64 pixels. It was observed that this size was large enough to capture motions of videos and suitable for training. Cropping positions are randomly chosen to produce diversity.  
We use PyTorch for implementing the proposed method. To train the GAN, we use the Adam optimizer [40] with a decay of the first-order momentum of the gradient of 0.5, and a decay of the second-order momentum of the gradient of 0.999 [4]. A learning rate of 0.0002 is chosen for training. A summary of parameters and the corresponding values used in implementation is listed in Table 4. Besides, the selected values of \(\lambda_{1}\), \(\lambda_{2}\), \(\lambda_{3}\), \(\lambda_{4}\) in the loss function (3) are 0.05, 1.0, 6.0, 1.0. The proposed network is trained on a single NVIDIA GeForce GTX 960 GPU for 400 epochs.  
For the refinement network, we adopt the implementation of the pix2 pix model of [PERSON] et al. [6]. The source code is shared on GitHub under the BSD license [41]. Our implementation that includes all networks is also published on GitHub [42]. The activity diagrams of our implementations are provided in the Appendix A for reference.  
Figure 3: The method for grouping frames into correlated patches. Here, the neighbors share some same frames.
## 2 Related Work
### Summary of the Surveyed Works  
In Table 1, we compare the above related works pertaining to convergence acceleration. Our main focus lies on the following aspects: whether they are designed for FRSs, their plug-and-play nature, their utilization of nodes with limited resources and the comprehensiveness of client-side training (i.e., no omission of any client in training). \"Plug-and-play\" implies that it has strong compatibility with existing FRSs and that there is no alteration to the structures of client/item models, network topology and aggregation method.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Related Works**}} & \multirow{2}{*}{**Focused on FRSs**} & \multicolumn{1}{c}{\multirow{2}{*}{**Plug-and-Play**}} & \multicolumn{1}{c}{\multirow{2}{*}{\
\begin{tabular}{c} **Exploiting** \\ **Resource-** \\ **Limited Nodes** \\ \end{tabular} }} & \multicolumn{1}{c}{\multirow{2}{*}{
\begin{tabular}{c} **Leaving No** \\ **Client** \\ **Unattended** \\ \end{tabular} }} \\ \hline \multirow{4}{*}{**Selective Client**} & FedFast [19] & ✓ & ✗ & ✗ & ✗ \\  & FLOB [8] & ✗ & ✓ & ✗ & ✗ \\  & Eiffel [29] & ✗ & ✓ & ✓ & ✗ \\  & OCEAN [5] & ✗ & ✓ & ✗ & ✗ \\  & FedMCCS [30] & ✗ & ✓ & ✓ & ✗ \\ \hline \multirow{4}{*}{**Client Clustering**} & FedCor [10] & ✗ & ✗ & ✗ & ✗ \\  & [PERSON] et al. [9] & ✗ & ✗ & ✓ & ✗ \\  & Cali3F [25] & ✓ & ✗ & ✗ & ✗ \\ \hline \multirow{4}{*}{**Upload Dropout**} & [PERSON] et al. [31] & ✗ & ✗ & ✓ & ✗ \\  & FedKD [32] & ✗ & ✗ & ✓ & ✗ \\  & HFCCD [21] & ✓ & ✗ & ✓ & ✗ \\ \hline \multirow{2}{*}{**Network Topology Optimization**} & [PERSON] et al. [33] & ✗ & ✗ & ✓ & ✗ \\  & [PERSON] et al. [6] & ✗ & ✗ & ✗ & ✗ \\ \hline \multirow{3}{*}{**Others**} & [PERSON] et al. [20] & ✓ & ✗ & ✓ & ✓ \\  & PrivFR [22] & ✓ & ✗ & ✓ & ✓ \\ \cline{1-1}  & FedMAvg [23] & ✓ & ✓ & ✗ & ✓ \\ \hline \multicolumn{1}{c}{FedRecI2C} & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the surveyed works.
## 3 Alternative Paths for Message Streams in Cluster-Tree Wireless Sensor Networks
### ARounD Protocol Mechanisms
#### 3.4.2 Closing the ARounD Communication  
As presented in the previous subsection, the ARounD communication is configured to work during a given time, which is specified in the _keep alive_ field. Nevertheless, the ARounD communication also provides an explicit mechanism to close an on-going alternative path.  
The _ARounD communication closing_ mechanism is launched by the PAN coordinator, which sends an _ARounD closing_ frame to all the involved nodes. Figure 15 illustrates the format of this frame. After receiving this frame, the involved nodes immediately finish the ARounD communication. Finally, each involved node sends to PAN coordinator an _ARounD ack_ frame (ACK type field set to 001).  
The ARounD communication also enables the PAN coordinator to verify if all the involved nodes are still alive, using an _ARounD hello_ frame. Figure 16 illustrates the format of this frame. Note that the MAC payload is composed of the _command frame identifier_ field only (with value set to \(0x0f\), which is not used by the IEEE 802.15.4), in order to identify this frame. The PAN coordinator may query a specific node to confirm if it is still alive. This node replies with an _ARounD ack_ frame (_ACK type_ field set to 010).  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Frame Type Value** & **Description** \\ \hline
000 & ACK for an ARounD configuration frame \\
001 & ACK for an ARounD closing frame \\
010 & ACK for an ARounD hello frame \\
011–111 & Reserved \\ \hline \hline \end{tabular}
\end{table}
Table 1: ACK frame types for the ARounD communication scheme.  
Figure 14: Format of the _ARounD auth_ frame.
## 3 Experiments
### Evaluation Metrics  
Following previous point cloud sampling work, we utilize the standard chamfer distance (CD) [9] and Earth mover's distance (EMD) [20] to measure the difference between \(Q\) and \(\hat{Q}\), for which smaller is better. CD can calculate the average of the distance between the generated point cloud point and the nearest point in the ground truth. EMD measures the minimum cost of turning generated point cloud into the ground truth. The noise contained in the input point cloud can interfere with the accuracy of CD and EMD. Because the point cloud data of the blade are too dense and contain many delicate wavy surfaces, it was difficult to reconstruct high-quality mesh data, so we did not choose the commonly used point-to-surface distance.  
The noise contained in the input point cloud interferes with the accuracy of CD and EMD, so we report an F-score [17] to further evaluate the model performance, for which larger is better. F-score defines point cloud upsampling as a classification problem, evaluating precision and recall by examining the percentage of points in \(Q\) or \(\hat{Q}\) that are able to find another neighbor within a certain threshold \(\tau\).  
Figure 5: The hardware of the experimental setup.  
Deviation [17] is also used to measure the difference between the generated point cloud and the ground truth, and the normalized uniformity coefficient (NUC) [9] is used to measure the uniformity, for which smaller is better.
# Overview on Different Types of Solar Cells: An Update
## 3 Perovskite Solar Cells
### Advantages and the Challenges of Perovskite Solar Cells  
Perovskite photovoltaics have a high light-absorption coefficient (10\({}^{4}\) cm\({}^{-1}\)), which results in an impressive photocurrent generation [131], long carrier diffusion length, and solution processability [132; 133; 134]. Perovskite solar cells are cheaper to produce, owing to their inexpensive and naturally abundant materials (lead, iodine, carbon, and hydrogen) [135]. These solar cells show better defect tolerance than other solar semiconductors on the market [136], which increases their reusability and lowers production costs. Despite  
Figure 6: The efficiency records for perovskite solar cells and monolithic perovskite/Si tandem solar cells compared with other photovoltaic technologies [121].  
their benefits, the commercialization of perovskite solar cells is hampered by another factor. Water by itself (moisture) and other polar solvents provide a serious problem for perovskite solar cells. These solvents can alter the solvated phases of a perovskite and occasionally hydrate a perovskite to produce a monohydrate phase. Polar solvents can considerably be prevented from harming perovskite solar cells by modifying deposition techniques, according to reports. However, environmental moisture is unavoidable [137].  
It is observed that various external factors such as oxygen, light, and heat have expedited the degradation, which can be slowed by choosing a proper composition of the perovskite material (such as FA\({}_{0.85}\)MA\({}_{0.1}\)Cs\({}_{0.05}\)Pb\({}_{2}\),Br\({}_{0.3}\), which show better stability and power conversion efficiency) [138; 139; 140; 141]. In addition, it was noted that the deposition of more hydrophobic and UV-stable ETL/HTL has improved the stability of the device (replacing TiO\({}_{2}\) with SnO\({}_{2}\)). The combination of Ruddlesden-Popper (RP) 2D-layered perovskites with conventional 3D perovskites has improved the stability of the device as 2D perovskite passivate/encapsulate the device [142].  
On the other hand, the use of an excessive amount of lead in the perovskite component is also another big challenge in its commercialization. As an alternative, tin-based perovskites are studied, but the PCE of such devices [143] is poor (PCE -13%). Current-voltage hysteresis between the forward and reverse bias is another major issue. Although the reason for hysteresis is still under debate, it is commonly ascribed to the combination of ion migration with high levels of charge-carrier recombination. To achieve a truly low cost-per-watt and dominate the photovoltaic market, solar cells based on OIHPs should satisfy the much-heralded trio of high-power-conversion efficiency (PCE), yearlong lifetimes, and ultra-low manufacturing costs. Certainly, OIHP-based solar-cell devices are quite farther ahead than other thin-film technologies, as they have shown enormous potential for achieving this.
# A 32-GHz Eight-Way Power Amplifier MMIC in 150 nm GaN HEMT Technology
## 1 Introduction
Due to its high-thermal conductivity and low-thermal resistance, silicon carbide (SiC) is one of the most prevalently used substrates for the epitaxial growth of GaN HEMT. Usually in GaN HEMT technology, a backside via is supported. In high-frequency operations, grounding a source electrode pad with bonding wires results in a reduction in power gain. The degradation of RF performance can be prevented by grounding the electrode. In various microwave sensing and communication systems, such as radar and cellular communication, the radio frequency front-end module (RF FEM) plays a crucial role in overall performance. RF FEM performs an essential function for transmitting and receiving high-frequency signals by boosting the output power using a high-power amplifier (HPA) and reducing the input-referred noise from the receiver chains using a low-noise amplifier (LNA). Hence, high-performance RF REM can significantly enhance the signal-to-noise ratio (SNR) of the wireless communication system, leading to more accurate and reliable communication. Moreover, it helps to effectively remove noise and interference signals from unwanted frequency bands owing to the band-limited frequency response of the amplifiers, resulting in improved overall system performance. A typical RF FEM consists of components such as HPA, driving amplifier (DA), LNA, and switching elements to control the transmit and receive functions. For a high-performance RF FEM, designing a reliable and robust HPA is one of the most critical and challenging issues. This work presents a \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Property** & **Si** & **SiC** & **GaAs** & **InP** & **AlN** & **GaN** \\ \hline Energy bandgap [eV] & 1.12 & 3.2 & 1.43 & 1.34 & 6.2 & 3.5 \\ Breakdown field [10\({}^{6}\) V / cm] & 0.3 & 3.2 & 0.4 & 0.6 & 11.7 & 3 \\ Electron mobility [cm\({}^{2}\)/V:S] & 1450 & 700 & 8500 & 4600 & 300 & 1250 \\ Saturation velocity [10\({}^{5}\) m/s] & 1 & 2.0 & 1.2 & 1.0 & 2.0 & 2.5 \\ Thermal conductivity & 1.31 & 4.9 & 0.46 & 0.77 & 3.4 & 1.5 \\ [W/cm\(\cdot\)K] & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Properties comparison of various semiconductor materials. 32 GHz eight-way HPA for a deep space satellite communication system in 150 nm GaN HEMT technology. In Section 2, the power amplifier design is described. Following this, the simulation result and measurement result are presented in Section 3 followed by the conclusion in Section 4.
# An Optimal Authentication Scheme through Dual Signature for the Internet of Medical Things
## 7 Computational Cost  
A scheme's computational cost is usually measured by the time taken by the scheme's various operations. In this section, we compare our scheme with [PERSON] et al. [21] with respect to the computational cost. For this purpose, we have neglected less expensive operations and only considered costly operations in terms of time. For elliptic curve-based schemes, we have taken into account elliptic curve modular multiplication (\(\mathcal{EMM}\)). For the hyperelliptic curve, we have taken into account the hyperelliptic curve modular multiplication (\(\mathcal{HEMM}\)). According to [27], single \(\mathcal{EMM}\) takes 2.848 milliseconds, and we assume that the single \(\mathcal{HEMM}\) will takes 1.424 milliseconds. The experiment is conducted through the following hardware and software resources:  
* Raspberry PI 4B (2019)
* CPU Architecture: 64 b
* Processor: 1.5 GHz Quad-core
* OS: Ubuntu 20.04.2 LTS with 8 GB memory  
Table 2 depicts a computational cost comparison of [PERSON] et al. [21] with our proposed scheme in terms of major operations. Table 3 provides the cost comparison in seconds and makes it evident that the proposed scheme outperforms the existing scheme. Additionally, it also indicates the cost reduction determined by the formula [28]. A graphical illustration of this comparison is also provided in Figure 3.  
the lengths of elliptic curve points \(|q|\) are 160 bits and hyperelliptic curve points \(|n|\) are 80 bits [29]. Our designed scheme only exchanges two messages between the IoMT device and the edge device and exchanges only two messages between the edge device and the cloud. Table 4 shows the communication overhead of the proposed scheme and [PERSON] et al. [21] and also indicates the cost reduction of our scheme determined by the formula [28]. Figure 4 also shows a graphical illustration of this comparison.
## 5 Results and Analysis
### Correlation Analyses
#### 5.4.2 Biweekly Correlation Analysis
The positive strong correlation between item 11 on Oxford Hip Score and heart rate metrics indicates that the higher the score for item 11 (lower physical health/functional recovery), the higher the participant's heart rate is during the day and during sleep. This relationship can be more clearly seen in Figure 5. Moreover, there is a negative correlation between this item and the motion ratio, indicating that the lower the participant's physical health, the less time they spend in movement (captured by a motion sensor). Further, the Oxford Hip Score has a strong negative correlation with \(l2\)-max, indicating that the lower the participant's physical health (higher value on the Oxford Hip Score), the lower their maximum magnitude of acceleration is, representing less movement. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable 1** & **Variable 2** & **Correlation Value** \\ \hline SIS—Q6 & duration/toseep & 0.68 \\ \hline SIS (Belonging) & step-sum & 0.64 \\ \hline SIS—Q6 & step-sum & 0.62 \\ \hline SIS (Belonging) & lights/keep/duration & 0.60 \\ \hline SIS—Q3 & hr-average & 0.57 \\ \hline SIS—Q4 & distance travelled from home & 0.54 \\ \hline SIS—Q6 & totals/keep/duration & 0.53 \\ \hline SIS (Belonging) & wake/pcount & 0.53 \\ \hline SIS (Belonging) & duration/toseep & 0.51 \\ \hline SIS (Belonging) & distance travelled from home & 0.51 \\ \hline SIS—Q1 & \(l2\)-pct-95 & \(-\)0.52 \\ \hline SIS—Q1 & duration/toseep & \(-\)0.53 \\ \hline SIS—Q6 & snoring & \(-\)0.55 \\ \hline SIS (Connectedness) & \(l2\)-pct-95 & \(-\)0.59 \\ \hline \hline \end{tabular} The different items/questions in the SIS are illustrated in Appendix A in this paper.  
\end{table} 
Table 11: Biweekly strong correlations between SI and sensor data for 8 participants. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable 1** & **Variable 2** & **Correlation Value** \\ \hline Oxford Hip Score—Q11 & hr-average & 0.85 \\ \hline Oxford Hip Score—Q11 & heart rate-mean & 0.63 \\ \hline Oxford Hip Score—Q11 & step-max & 0.58 \\ \hline Oxford Hip Score—Q10 & step-sum & 0.57 \\ \hline Oxford Hip Score—Q4 & deysleep/duration & 0.53 \\ \hline Oxford Hip Score—Q4 & hr-min & 0.52 \\ \hline Oxford Hip Score—Q11 & hr-max & 0.52 \\ \hline Oxford Hip Score—Q3 & motion-ratio & 0.52 \\ \hline Oxford Hip Score—Q3 & lights/keep/duration & 0.51 \\ \hline Oxford Hip Score—Q4 & duration/towakeup & \(-\)0.51 \\ \hline Oxford Hip Score & l2-max & \(-\)0.53 \\ \hline Oxford Hip Score—Q8 & l2-max & \(-\)0.58 \\ \hline Oxford Hip Score—Q4 & motion-sum & \(-\)0.59 \\ \hline Oxford Hip Score—Q2 & heart rate-std & \(-\)0.62 \\ \hline Oxford Hip Score—Q1 & motion-max & \(-\)0.65 \\ \hline Oxford Hip Score—Q11 & motion-ratio & \(-\)0.73 \\ \hline \hline \end{tabular} The different items/questions in the Oxford Hip Score are illustrated in Appendix A in this paper.  
\end{table} 
Table 12: Biweekly strong correlations between Oxford Hip Score and sensor data for 8 participants.
# Physical and Mechanical Properties of Reclaimed Asphalt
## 4 Results and Discussion
### Triaxial Test Results
#### 4.3.2 Permanent Deformation  
From the triaxial test, the developed permanent deformations are determined for each blend. Figure 7 shows the accumulated permanent strains for the blends A, C, D and E as a function of loading cycles.  
It can be seen that the permanent strains for all blends increase as the number of loading cycles increases, with all specimens showing similar trends in their curves every 100 cycles. The accumulated permanent strain after 1500 loading cycles is 0.72, 0.66, 0.69 and 0.59% for blend A, C, D and E, respectively. Therefore, blends with a percentage of RAP develop lower permanent deformations than the blend with 100% VA (blend A). Moreover, blend E (40-60% RAP-VA) develops the lowest permanent strains among all blends, as well as among all blends with a proportion of RAP. In addition, blend D (30-70% RAP-VA) develops a higher permanent strain than blend C (20-80% RAP-VA), which consists of a  
Figure 7: Permanent strains regarding the loading cycles for blends A, C, D and E.  
lower RAP content, an observation that does not allow specific conclusions to be drawn about the behavior of RAP-VA blends as unbound materials. Nevertheless, it can be said that the addition of RAP is acceptable for use in unbound pavement layers, since RAP particles have been already used in pavement layers, so they have been already exposed to some extent to the cumulative permanent deformation that results in a lower rate of the additional cumulative permanent deformation. Overall, the performance of unbound RAP blends is an area that should be investigated, as some controversial results can be found in the international literature. For example, higher permanent deformations have been reported for RAP-VA compared to VA [23; 24; 25], while the opposite has been reported elsewhere [26; 27; 28]. The latter is also confirmed by the current experimental results. Thus, it seems that this is an aspect that needs further investigation.  
Overall, according to the current experimental procedure, it is concluded that a RAP-VA blend containing up to 40% RAP can be incorporated into the base course and subbase layer, since its physical properties are similar to those of pure VA and its mechanical properties appear to be significantly better than those of conventional aggregates. In particular, the corresponding M\({}_{\text{R}}\) values are in the range of 410-470 MPa, which seems acceptable considering that the M\({}_{\text{R}}\) value of the tested pure VA is 406 MPa and the usual range of values for use in unbound layers is between 250 and 600 MPa. As far as permanent deformations are concerned, all tested RAP-VA blends show lower cumulative permanent deformation than pure VA. As for the CBR value, the last observation seems to contradict the decrease in the CBR value with increasing RAP content, but the two parameters are determined by tests of a different nature; the CBR test is a single point penetration, while the triaxial test is based on lateral and axial compression by a variable stress.