esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Comparison between Numerical and Experimental Results  
In order to validate the numerical model, some experimental tests were carried out at the Laboratory of Robotics and Mechatronics of Padova University, which is equipped with the linear vibratory conveyor shown in Figure 19.  
A track (120 mm long) with a stop plate was printed with additive manufacturing and mounted on the conveyor. A single steel cylinder was placed on the track. The motion was monitored by an AVT Pike f-032b camera with a frame-rate of 470 frames/s. Both the track and the cylinder were equipped with markers that were used to reconstruct their trajectories, using the Hough transform [20]. The static friction coefficient of the cylinder on the track was measured as well, and resulted in \(\mu_{s}\) = 0.7.  
Figure 20 makes a comparison between numerical and experimental results. The numerical model is able to capture the most important features of cylinder motion, since the experimental results show a sequence of chaotic rebounds of the cylinder on the stop plate. The largest rebounds predicted by the numerical model have about the same duration (0.5 s) of experimental rebounds, and the traveled distances are only a bit larger (\(\approx\)10%) than the measured values. This phenomenon may occur because, owing to the three-dimensional geometry of the system, the cylinder velocity may have a small component in the transverse direction (\(z\) in Figure 18).  
The main difference between numerical and experimental results is that the numerical model predicts a smaller number of bad impacts, with a short rebound. This happens because the numerical model is two-dimensional and assumes an impact between the cylinder and the stop plate along a line. In reality, the system is three-dimensional, and often the contact begins when a corner of the cylinder impacts the stop plate. The energy transfer from the conveyor to the cylinder is less efficient and leads to a reduced traveled distance.  
These phenomena could be analyzed by means of a 3D numerical model, which would be much more complex and cumbersome than the presented 2D model (a 2D simulation lasting 1 s requires a computation time of about 16 s). Nevertheless, it is worth noticing that a complete agreement between  
Figure 19: Vibratory conveyor for experimental tests.  
Figure 18: Conveyor with saw-teeth track.  
numerical and experimental results could be difficult to achieve even with a 3D simulation, since the system is non-linear and chaotic; thus, small differences between simulation and experimental parameters may lead to large differences in results [19; 21]. In actual conveyors, there are many cylindrical parts, and their collisions further enhance the chaotic behavior of the system [22].
# Current Context and Research Trends in Linear DC-DC Converters
## 2 Switch Mode Power Supplies (SMPS)  
SMPSs are the most adopted method for supplying power to portable DC appliances where high-frequency switching is used for energy conversion [7]. High energy-efficiency, compactness, and the ability to convert a given voltage into any desired output voltage are the key features that make switch-mode power supplies dominant in the field of portable devices. SMPSs are used in a range of low power density applications where buck, boost, and buck-boost converters are used, to the high power density applications where push-pull, half-bridge, and full-bridge versions are used [8].
## 3 Results
### Time Complexity  
Time complexity is another evaluation criterion that shows how long it takes to perform a particular computation or task. In the context of deep learning models for  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **FNR** & **Accuracy** & **FPR** \\ \hline FD-CSM [65] & 0 & 93.6 & 11.7 \\ FD-CV [67] & 0 & 92.9 & 13.3 \\ ANetFire [27] & 2.13 & 94.4 & 9.07 \\ GNetFire [28] & 1.50 & 94.4 & 0.054 \\ CNNFire [29] & 2.12 & 94.5 & 8.87 \\ EMNFire [68] & 0.14 & 95.9 & 0 \\ ICA-K [69] & 4.53 & 95.3 & 4.83 \\ VIT [36] & 1.04 & 97.86 & 2.63 \\ LWCNN [3] & 0.92 & 97.2 & 0 \\ CNN (Anchor) [70] & 1.92 & 94.7 & 8.65 \\ DFAN [2] & 0.58 & 99.6 & 0 \\ SE-CANet [66] & 0.03 & 97.2 & 0.04 \\ CANetB0 [9] & 1.91 & 43.5 & 7.48 \\ E-EFNet & 0.22 & 99.91 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of E-EFNet with state-of-the-art models over the FGG dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Backbone** & **F1-Score** & **Recall** & **Precision** & **Accuracy** \\ \hline EFDNet [22] & 95.00 & 96.00 & 94.11 & 95.00 \\ LW [3] & 95.04 & 94.00 & 95.00 & 94.50 \\ DFAN [2] & 97.00 & 97.00 & 98.00 & 97.50 \\ ResNetFire [25] & 86.00 & 86.00 & 88.00 & 86.67 \\ FCAN [39] & 98.20 & 98.00 & 98.50 & 98.00 \\ E-EFNet & 98.74 & 98.70 & 98.82 & 98.40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of E-EFNet with existing models over the YR dataset.  
image classification, the time complexity is often expressed in terms of frames per second (FPS), which means the model can process this number of images in one second. Table 4 compares E-EFNet with other state-of-the-art models regarding computational complexity over robust systems and resource-constrained devices. The table shows the FPS achieved by each model on both robust systems and edge devices. As we can see, E-EFNet achieved a high FPS on both the Core-i5 CPU and Raspberry Pi B3+ (RPIB3+) edge devices, with 51 and 8, respectively. This suggests that E-EFNet is a good choice for image classification tasks in IoT environments, where edge devices are often used due to their low cost and energy efficiency. It is worth noting that some other models, such as those of [PERSON] et al. [65] and [PERSON] et al. [18], achieved higher FPS on robust systems because these methods are based on traditional machine learning techniques. The computational complexity of these methods is lower. However, the performance of evaluation metrics is much worse than that of other deep models, as shown in the comparison tables. However, E-EFNet outperforms these models in terms of FPS on more powerful systems. Therefore, the choice of model will depend on the specific requirements of the application and the resources available.
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 8 Evaluation Results
### Micro Benchmarks
#### 8.2.1 Different Amount of APs  
The number of access points (n) primarily affects distance computation in our designs. A key feature of \(\text{Silcom}_{3R}\) is that RSS-based distance computation can be performed locally, eliminating communication overhead. Consequently, the impact of n is confined to m length-n RSS vector computations. As demonstrated in Figure 7, with m held constant, changing n does not significantly affect the server computation time. This consistency stems from the minimal computational overhead of ring arithmetic operations--especially after compiler optimization with -O3 flags; the \(\Pi_{\text{ISED}}\) module contributes insignificantly to the overall end-to-end query time.
## 3 Mobility Management Architecture Using CoAP
### Mobility Management Architecture of CoMP  
Figure 2 shows the mobility management architecture using CoMP. The components of the architecture consist of a CoAP server and CoAP Client nodes and WoT Mobility Management System (WMMS) with a Mobility Management Table (MMT).  
The CoAP client node may request the retrieval of data for the CoAP server node. The WMMS maintains the location address information in MMT, which is necessary to perform mobility management, keeping track of the location of a moving CoAP node. A CoAP node is usually a tiny sensor node that monitors the measured data, and transmits the measured data to the requesting node, CoAP client and Web client.  
In WMMS, P_Addr in MMT is the permanent IP address of the CoAP node that is registered at DNS. T_Addr in MMT is the temporary IP address of the CoAP node, which can be changed as the access point of the CoAP node is changed. H_Flag indicates the handover status of the node. If H_Flag is \"1\", it indicates that the corresponding CoAP node is in handover status; therefore, it may not receive the packet from other nodes. H_Flag is \"0\", it indicates that the corresponding node is not in handover status. A Lifetime is the time to which the binding of P_Addr with T_Addr is effective. The sleep period is sleep time in sleep mode operation of CoAP node. In this time, WMMS does not send a request message to CoAP node until the sleep period expires.  
As shown in Figure 2, the network architecture of a CoAP node is comprised of CoAP and CoMP at the application layer, UDP at the transport layer, a 6 LoWPAN at the network layer, and IEEE 802.15.4 at MAC layer. CoAP is composed of the message layer and request/response layer. The CoMP makes use of GET, POST, PUT, and DELETE methods at the CoAP Request/Response layer in order to provide mobility management functionality. The CoAP node contains a local binding cache (LBC),  
Figure 2: Mobility management architecture of CoMP.  
which includes P_Addr, T_Addr, Lifetime, and H_Flag. In order to provide the mobility management function, the CoMP refers to the LBC table, whose schema is shown in Figure 3. The meanings of those fields on LBC are the same as those in MMT. The Lifetime value of \"0\" at LBC indicates that an LBC entry for the CoAP node must be deleted and retrieved from the entry of the CoAP node in the MMT of the WMMS. A key feature of CoMP is the use of hold mode, _i.e._, H_Flag, to prevent packet losses while a CoAP node is moving among different wireless networks.
## 2 Semi-Active Control Theory
### LQR Optimal Active Control Algorithm  
Active control algorithm is not only the basis of active control of structural vibration, but also an important part of the construction of semi-active control system. There are mainly the following algorithms: quadratic optimal control algorithm (LQR), instantaneous optimal control algorithm (LQG), robust control algorithm and modal control algorithm, etc. In this paper, the optimal control algorithm (LQR) is used according to the algorithm theory, and the quadratic index performance function is defined as  
\[J=\frac{1}{2}\int_{t_{0}}^{\infty}\left[Z^{T}QZ+U^{T}RU\right]dt \tag{3}\]  
where \(t_{0}\) is the earthquake duration, \(Q\) is a 2n \(\times\) 2n-dimensional semi-positive definite state vector weight matrix, and \(R\) is an r \(\times\) r-dimensional positive definite control force vector weight matrix. The form of the weight matrix \(Q\) and \(R\) can be taken as the form: \(Q=\alpha\begin{bmatrix}K&0\\ 0&M\end{bmatrix}\), \(R=\beta I\), where \(\alpha\) and \(\beta\) are constants to be determined, and the optimal active control force \(U(t)\) can be calculated by regulating the performance target weight matrices \(Q\) and \(R\).  
The objective of classical linear optimal control is to minimize the objective function represented by (3) subject to the constraints of the structural equations of motion (1) and state Equation (2), thus seeking the optimal control force vector \(U(t)\), for which the Hamiltonian function is introduced.  
\[H^{\prime}=\left[Z^{T}QU+U^{T}RU\right]+\lambda^{T}\left[AZ+BU-\dot{Z}\right] \tag{4}\]  
According to the extreme value condition, it can be found that  
\[U(t)=-GZ(t)=-R^{-1}B^{T}PZ(t) \tag{5}\]where \(G\) is the feedback gain matrix. The value of the matrix \(P\) can be obtained by solving the following Riccati equation.  
\[PA+A^{T}P-PBR^{-1}B^{T}P+Q=0 \tag{6}\]  
Substituting the above equation into the equation of state (2) yields:  
\[\dot{Z}=\left(A-BR^{-1}B^{T}P\right)Z(T)+WF(t) \tag{7}\]  
Equation (7) is the equation of state of the structural vibration control system after adopting LQR.
## 6 Evaluation and Experimental Results
### End-to-End Transmission Delay Reduction
#### 6.3.4 Queuing Delay  
The Queuing Delay (Tq) depends on the level of network's congestion. It is the time that a packet waits in the queue until it gets processed. The average of (Tq) is calculated as follows:  
\[\text{T}_{\text{q}}=\frac{(\text{PN}-1)\times L}{2\times\text{R}},\]  
where:  
* Tq is in seconds;  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Results** & **Results** & **P-HIP [36]** & **Slimfit [17]** & **[PERSON] et al. [9]** & **LC-DEX** \\ \hline Initiator & 0.95 & 1.799 & 0.3 & 1.88 & 0.156 & 0.146 \\ Responder & 0.79 & 0.729 & - & 1.895 & 0.156 & 0.146 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Processing delays’ (s) comparison with previous works.  
Figure 11: Processing delays on the initiator and responder sides.  
PN is a number of packets to process;
* L is the packet length;
* R is the transmission rate in b/s. As indicated in Section 6.3.1, R = 250 kb/s.  
As we have four packets in the handshake, we are considering L as the average of packets' lengths. The average handshake packets' lengths in WSN based-IPv4-IoT architecture is 166 bytes. Obtained Tq value is 0.007 s. For calculation of the Tq in the WSN based-6 LoWPAN-IoT architecture, we measured Tq after the proposed compression. The average of the 4 packets' lengths is equal to 226 bytes before compression and 211 bytes after the compression. Tq has the same value before and after the compression, about 0.01 s.
###### Abstract  
Due to the high splitting-gain of dense small cells, Ultra-Dense Network (UDN) is regarded as a promising networking technology to achieve high data rate and low latency in 5G mobile communications. In UDNs, each User Equipment (UE) may receive signals from multiple Base Stations (BSs), which impose severe interference in the networks and in turn motivates the possibility of using Coordinated Multi-Point (CoMP) transmissions to further enhance network capacity. In CoMP-based Ultra-Dense Networks, a great challenge is to tradeoff between the gain of network throughput and the worsening backhaul latency. Caching popular files on BSs has been identified as a promising method to reduce the backhaul traffic load. In this paper, we investigated content placement strategies and user association algorithms for the proactive caching ultra dense networks. The problem has been formulated to maximize network throughput of cell edge UEs under the consideration of backhaul load, which is a constrained non-convex combinatorial optimization problem. To decrease the complexity, the problem is decomposed into two suboptimal problems. We first solved the content placement algorithm based on the cross-entropy (CE) method to minimize the backhaul load of the network. Then, a user association algorithm based on the CE method was employed to pursue larger network throughput of cell edge UEs. Simulation were conducted to validate the performance of the proposed cross-entropy based schemes in terms of network throughput and backhaul load. The simulation results show that the proposed cross-entropy based content placement scheme significantly outperform the conventional random and Most Popular Content placement schemes, with with 50% and 20% backhaul load decrease respectively. Furthermore, the proposed cross-entropy based user association scheme can achieve 30% and 23% throughput gain, compared with the conventional \(N\)-best, No-CoMP, and Threshold based user association schemes.  
Keywords:ultra dense network; cross-entropy; proactive caching; user association; CoMP +
Footnote †: journal:
## 3 Applications to a Waste Separation System
### Elimination of the Incorrect Expert Knowledge  
As declared in Section 3.5, there are some low-weight expert judgments in the case of the parameters and also the system output. To eliminate them from the estimation, an outer iteration circle is suggested to be generated by weighting the initial distributions according to the technique introduced in detail in Section 2.3.  
Figure 16: Goodness values belonging to the outputs.  
Figure 17: Goodness values belonging to the estimable parameters. The labels on the axes mark which parameter the goodness value belongs to.  
At the beginning of the procedure, during the aggregation of the probability distributions of the experts, the judgments of each expert were considered with equal weights. However, as seen before, some assessments seem to be closer to real values than others. By normalizing the goodness values for each variable, weights can be given to the initial uniform distributions of the experts, and new aggregated distributions can be created as the input of the estimation in the inner circle. Therefore, an outer iteration circle can be generated as introduced in Figure 7.  
New aggregated distributions were created for parameters and outputs as well. The convergence of the inner circle outputs in the case of the system outputs (**y**) is introduced in Figure 18 for the first steps of iteration 20 in the outer circle.  
As seen in Figure 18, the system outputs converge, so the estimation is refined. The convergence of the normalized goodness values belonging to the outputs and the number of iterations in the inner circle along the outer iterations can be seen in Figure 19.  
As seen in Figure 19, all the lines start from 0.33, since expert judgments were considered with equal weights in the first iteration. However, after a few iterations, all the lines fluctuate around specific values. Some of them are close to zero, which means that incorrect judgments are largely eliminated from the estimation. It is also seen that the number of iterations in the inner circle decreases after the weights are set. This suggests that the aggregated distributions of the parameters and outputs forming the input of the inner circle become more consistent; thus, less iteration is needed for the convergence in the inner circle.  
As seen from the results, the outer iteration circle helped refine the estimation by decreasing the weights of the less correct expert judgments and increasing the better ones.  
Figure 18: Convergence of the empirical distributions of the four outputs (**y**) estimated in the inner circle. The lines turn black from orange as the iteration is progressing in the outer iteration circle.
# Cyber Physical Systems Dependability Using CPS-IOT Monitoring
## 5 Performance Evaluation
### The Impact of CTP, RPL, and LIBP on CPS Dependability
#### 5.4.2 Routing Profile  
LIBP's main routing metric consists of the interference defined by the number of supporting children per node. It is also an important parameter that defines not only the reliability of the routing topology resulting from a routing protocol but also the diffusion  
Figure 9: Energy profile: Average power consumption.  
process. The path length is a performance parameter that reveals how efficiently a sensor network has been engineered.  
Figure 9(a) shows the average path interference in terms of the average number of children supported per node. It was obtained by averaging the number of times each node referenced its parent. Smaller values are desirable and indicate balanced load distribution. A balanced load implies a fair and efficient energy distribution in the network and longer lifetime for the entire sensor networks, as all the nodes would most likely have equal battery utilisation. On the other hand, higher interference values are undesirable, as they are indications of congestion that may lead to a higher rate of packet loss across the network. The results presented in the figure show that LIBP does better in this experiment compared to CTP and RPL.  
Depending on the application, a high average path length (leading to a deeper routing tree) may be desirable for better energy distribution. Conversely, a lower average path length (shallow routing tree) can result in lower latency between leaf nodes and sink nodes. The average path lengths depicted in Figure 9(b) were obtained by computing a Time To Live (TTL)-like attribute within the protocol's control plane packets. LIBP and RPL use TTL, while CTP uses \"Time has lived\" (which is \(TTL\_MAX-TTL\)). Once the number of hops was obtained they were averaged to give an average path length metric for each protocol. These results show that on the average, LIBP expands its routing tree by 2 hops compared to the other protocols.
## 4 Analysis of the Anomalous Noise Events Present in the Urban and Suburban Environments
### ANE SNR Distributions  
In this section, the analysis of the ANE contextual SNR is described with the aim of obtaining a more accurate understanding of the salience of anomalous noise events with respect to the surrounding road traffic noise in real-life scenarios. The boxplots of the measured SNR for each type of ANE collected in both urban and suburban locations are depicted in Figure 10.  
Moreover, the majority of median SNR values are located in the range between 0 and +5 dB, except for dog's barks, sirens and trains in the urban scenario. The traffic density during the recordings in the suburban area was generally high or very high, which made it difficult to obtain audio passages where other types of noises surpassed significantly the background traffic noise (i.e., with high salience). The mean value of SNR for the observed anomalous noise events is 1.3 dB and 3.6 dB in the suburban and urban environments, respectively.  
Door or impulsive-like sounds are the ANEs presenting a higher SNR in the suburban context, while in the urban one there is a more uniform distribution of ANEs entailing higher saliencies (i.e., dogs barking, horns, people, sirens, trams and trains and sounds of trucks). In this sense, several ANEs occurred in the urban area over quiet background noise conditions (labelled as BCK), making, for instance, that a dog's barking became a salient ANEs as being recorded in one direction road with very low traffic conditions.  
Figure 10: Boxplots of contextual SNR for each ANE type in each of two site locations (urban: Milan and suburban:Rome).
## 4 Experiment and Result Analysis
### Unsupervised Feature Selection
#### 4.1.2 Dataset  
Five real data sets were used in our experiments [[https://archive.ics.uci.edu/ml/datasets.php](https://archive.ics.uci.edu/ml/datasets.php)]([https://archive.ics.uci.edu/ml/datasets.php](https://archive.ics.uci.edu/ml/datasets.php)) (accessed on 1 January 2021), including CNAE, cifar, connect-4, vehicle, and USPSt. Detailed information on the data sets is provided in Table 2.  
In this work, we defined the incomplete instance ratio as the percentage of incomplete instances out of the total number of samples. To determine the incomplete instance ratio, we randomly marked a portion of the observed information as unobserved information, setting the incomplete instance ratio within a range of 0% to 90%, increasing at an interval of 10%.