esa-sceva/satcom-chunk-collection huggingface dataset:
# Vehicle Collision Prediction under Reduced Visibility Conditions
## 4 Experiments
### Test Data  
The Next Generation Simulation (NGSIM) US Route 101 Dataset was used [31]. It was collected from a segment of U.S. Highway 101 in Los Angeles, California, in June 2005. It is provided by tracking individual vehicular movements every 0.1 s, including vehicle location, length, width, velocity, acceleration, the preceding vehicle id, the following vehicle id, space headway, and time headway.  
We tested the model with two special segments of the vehicle data. One is non-congested traffic scenario (LV's speed \(\geq\) 30 ft/s) and the other is congested traffic scenario (LV's speed \(<\) 30 ft/s).  
For the non-congested traffic scenario, the velocity profile and the headway profile are shown in Figure 5. Both vehicles SV and LV were in the same lane before the 71 st time slot. The SV speeded up at the 10 th time slot, 49 th time slot, and 62 nd time slot. After the 71 st time slot, SV passed the LV. We assume that there was a car accident between SV and LV at the 71 st time slot, if SV did not to change the lane when the headway was close to zero. For target visibility, the following assumptions were made. From the 1 st to 19 th time slots, the target visibility is 400 m. From the 20 th to 39 th time slots, the target visibility is 120 m due to a heavy fog. From the 40 th to 71 st time slots, the target visibility is 160 m due to a medium fog. The target visibility was basically assumed; however, its validity was confirmed through weather experts.  
For the congested traffic scenario, the velocity profile and the headway profile are shown in Figure 6. The SV and LV were in the same lane in all time slots. The LV decreased its speed to zero from the 45 th time slot to the 79 th time slot, and speeded up at the 80 th time slot. We assume that there was a car accident between SV and LV at the 115 th time slot, because the headway was close to zero. For target visibility, the following assumptions were made. From the 1 st to 39 th time slots, the target visibility is 400 m. From the 40 th to 79 th time slots, the target visibility is 120 m due to a heavy fog. From the 80 th to 115 th time slots, the target visibility is 160 m due to a medium fog.  
Figure 5: Test data for non-congested traffic scenario. (**a**) Velocity Profile; (**b**) Headway Profile.  
Figure 6: Test data for congested traffic scenario. (**a**) Velocity Profile; (**b**) Headway Profile.
# Multiple-Intelligent Reflective Surfaces (Multi-IRSs)-Based NOMA System
## 5 Simulation and Numerical Results
### Numerical Results of a Two-Reflection-Surfaces-Assisted NOMA Scenario  
The numerical results obtained in the previous scenario already proved that IRS-NOMA is superior to IRS-OMA. Therefore, this section will consider the performance of IRS-NOMA only, but under the proposed multi-IRS-assisted NOMA scenario. The distances in the two-IRSs-based setup are chosen where \(\left(a_{SR}^{b}\right)\) is 100 m, while \(\left(d_{SR}^{b}\right)\) is set  
Figure 5: The required transmission power by IRS-NOMA and IRS-OMA for different destination locations to achieve a target rate of 100 Mbit/s.  
Figure 6: EE-SE trade-off of both IRS-NOMA against IRS-OMA.  
as 120 m; on the other hand, \(\left(d_{RD}^{a}\right)\) and \(\left(d_{RD}^{b}\right)\) were chosen to be 10 m and 5 m, respectively. It is worth mentioning that \(\left(d_{SD}\right)\) is kept the same and it ranges between 100 m to 150 m.  
Figure 7 displays a comparison between the proposed two-IRS-assisted NOMA system against the conventional IRS-NOMA system. The comparison is made in terms of the required transmission power over the distance to achieve a target rate of 100 Mbit/s and for \(N=50\) and \(N=150\) cases. It is obvious from this figure that the proposed two-IRS-assisted NOMA offers better results than the conventional IRS-NOMA. For instance, for the \(N^{a}=N^{b}=N=150\) case, at a distance of 80 m, the proposed two-IRS NOMA requires a transmission power at around 98 dBm, while the conventional IRS-NOMA requires about 103 dBm. It must be noted that it is possible to achieve a higher target rate when the channel power (channel conditions) is good; however, the users with poor channel conditions might face a considerable challenge to maintain a higher target rate without adopting a higher transmission power.  
Figure 8 below compares the behaviors of IRS-NOMA systems through the one-IRS and the proposed two-IRS-assisted NOMA scenarios. The trends clarify that the proposed two-IRS case offers a better EE-SE trade-off than the one IRS case. This is because the extra IRS plane will boost the received signal and improve the reception experience, and this will lead to a better achievable SE, and hence, to a better EE.  
Figure 7: The required transmission power versus the achievable SE for IRS-NOMA.
# COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting
## 2 Materials & Methods
### Training of the Selected Algorithms
#### 2.2.6 ResNet  
The ResNet [38] model comes with a residual learning framework to simplify the training of deeper networks. The architecture is based on network layers' reformulation as a residual block added to the layer inputs. The ResNet network has four stages. It takes as input an image having height and width as multiple of 32 and channel width as 3 (\(224\times 224\times 3\)). Every ResNet architecture performs the initial convolution and max-pooling using \(7\times 7\) and \(3\times 3\) kernel sizes, respectively. Afterward, Stage 1 of the network has 3 Residual blocks. Every Residual block contains three layers. The kernels' size to perform the convolution operation in a residual block of stage 1 is 64, 64, and 128, respectively. The convolution operation in the Residual Block is performed with stride 2. Hence, the input size will be reduced to half in terms of height and width, but the channel width will be doubled. Figure 8 describes the most used ResNet architectures.  
In the current paper, we chose three variants of ResNet (ResNet50-V1, ResNet50-V2, and ResNet11). Two major differences exist between ResNet50-V1 and ResNet50-V2.  
Figure 8: ResNet architectures [38].  
Figure 7: VGG architectures [41].  
The first difference is that ResNet50-V2 has removed the last non-linearity, therefore clearing the input path to output in the form of identity connection. The second difference is that ResNet50-V2 applies Batch Normalization and ReLU activation to the input before the multiplication with the weight matrix (convolution operation), while ResNet50-V1 performs the convolution, followed by Batch Normalization and ReLU activation.  
ResNet11 contains 51 convolutional layers more that ResNet50. It also has 7.6 billion FLOPS (Floating Point Operations per Second) instead of 3.8 billion FLOPS for the ResNet50 model.
## 3 Control Synchronization
Hence, the controller of the DC motor in Figure 2 is divided into two parts, the velocity or phase controller and the PWM calculator as shown in Figure 3.  
Although the vibration system has this self-adjusting capability, it is still bounded from the control perspective. In other words, firstly, Equation (2) is the function of variable \(\varphi_{1}\) and \(\varphi_{2}\) with time, and other symbols of Equation (2) are constant because the system structure parameters are constant. Secondly, because trigonometric functions are bounded, \(T_{Li}\) in Equation (1) are also bounded. Therefore, the role function of the self-adjustment of the vibration system can be considered as an internal disturbance as shown in Figure 3.  
Considering the electromechanical coupling, the disturbance of the self-adjusting of the vibration system is a fast variable with time according to Figure 2 and Equation (2). Therefore, the method of SMC is more suitable for this kind of control synchronization with internal parameter perturbations and disturbances [28; 29]. Defining the velocity of the master motor and the phase difference between motors as the state variables, \(z_{1}=\dot{\varphi}_{1}\), \(z_{2}=2\dot{a}_{1}\) and \(z_{3}=2\dot{a}_{2}\), the state equations of the master motor and the slave motors are rearranged respectively as follows:  
\[\dot{z}_{1}=a_{1}z_{1}+b_{1}u_{1}+W_{1} \tag{3}\]  
\[\dot{z}_{2}=a_{2}z_{2}+b_{1}u_{1}-b_{2}u_{2}+(a_{1}-a_{2})z_{1}+W_{1}-W_{2} \tag{4}\]  
\[\dot{z}_{3}=a_{3}z_{3}+b_{1}u_{1}-b_{3}u_{3}+(a_{1}-a_{3})z_{1}+W_{1}-W_{3} \tag{5}\]  
where \(a_{i}=-\frac{f_{i}R_{i}+K_{i}K_{d}}{j_{i}R_{i}}\), \(b_{i}=\frac{K_{ii}}{j_{i}R_{i}}\), \(W_{i}=\Delta a_{i}+\Delta b_{i}-T_{Li}/J_{i}\), \(i=1,2,3\); \(a_{i}\) and \(b_{i}\) denote the nominal value of parameters, \(\Delta a_{i}\) and \(\Delta b_{i}\) denote the perturbation of parameters, \(W_{i}\) denote the lump uncertainties from the self-adjusting of the vibration system, and it is bounded because of \(T_{Li}\) are also bounded. Since there is master-slave control no matter how many motors, it is a combination of two motors. Due to the length of the paper, the detailed design process of controllers can be seen in reference [34].
## 2 Related work
They propose a multi-hop version of the LEACH protocol (M-LEACH) and show the cases in which M-LEACH outperforms the single-hop version of the protocol. In [8], the authors proposed a hybrid, energy-efficient, distributed clustering algorithm (HEED) which periodically selects cluster head according to a hybrid of the node residual energy and a secondary parameter such as node proximity to its neighbors or node degree. Heed terminates in 0 (1) iterations and incurs low message overhead. It achieves fairly uniform cluster head distribution across the network. The authors in [9] analyze the problem of prolonging the lifetime of a network by determining the optimal cluster size. For a general clustering model, they find the optimal sizes of the cells with which maximum lifetime or minimum energy consumption can be achieved. Based on this result, they propose a location aware hybrid transmission scheme that can further prolong network lifetime. Besides these clustering algorithms mentioned above, there exist several others algorithms such as those described in [10] and [11]. ACE clusters the network in a constant number of iterations using the node degree as the main parameter. [PERSON] _et al._[10] proposed an unequal clustering size model for network organization, which can lead to more uniform energy dissipation among cluster head nodes, thus increasing network lifetime. [PERSON] _et al._[11] proposed a clustering algorithm (EECS) which achieves good cluster head distribution with no iteration and introduces a weighted function for the plain node to make a decision, that which proper cluster should be joined.
## 5 Discussion and Implications
### Generalization and Cross-Dataset Performance  
A notable challenge across all models is generalizing to unseen datasets. When tested on FaceForensics++ without fine tuning, each model experienced an accuracy drop with XCeption exhibiting the most resilience (accuracy decreased from 89.2% to 85.7%). Even minimal fine tuning (10% of target data) significantly boosted accuracy, suggesting data-driven adaptation remains critical for robust deployment. XCeption reached 88.6%, further validating its adaptability and echoing the cross-dataset observations in [11]. Conversely, ResNet-50 showed the largest performance gap, highlighting the importance of domain adaptation techniques and carefully curated training sets that capture a broad range of manipulation artifacts.
## 3 Test and Results
### Simulation Results
The ever-increasing power consumption of a smart home especially for 17:00, 19:00, and 20:00 causes discharging power to increase for the purpose of decreasing transferred power from the grid due to high electricity prices. It is strongly encouraged to utilize distributed generation as much as possible instead of utility grid. In the winter case, the charged battery (at 03:00 and 05:00) is discharged and SOE decreases at the depth-of discharge level (1 kWh). After 21:00, PV output power becomes zero and battery does not have enough energy to be discharged. Therefore, the demand is covered only by the grid. In order to simplify interpretation of the results for the considered winter and summer, Figures 14 and 15 are depicted to clearly show the decomposition of the injected power. Figure 14: The decomposition of injected power to the grid with PV and battery for sample winter case. Figure 8:0 The PV system power decomposition for sample summer case. From the economical evaluation perspective, the proposed scheme decides the requirement of 4 kWh battery storage system and 0.81 kW PV unit. After optimal sizing strategy, \(V_{present}^{income}\) is obtained as Figure 8:11171.741. Total profit of the system is Figure 8:21320.501, Figure 8:3165.4518 is achieved for \(V_{present}^{income}\), \(V_{present}^{outcome}\), and total profit of the hybrid system, respectively. The relevant results are given in Figure 8:4 On the other hand, 0.79 kW PV and 4 kWh battery storage capacity is obtained after simulation results when harmonics are beyond the scope. As a result, it is not wrong to mark that harmonics do not have great effects on sizing strategies especially for one residential end-user. However, as the number of considered households increase, this impact may be far more obvious. Especially if the developed framework can be applied to industrial and commercial end-users which have densely non-linear loads, the impact of sizing can be significantly obvious. Harmonic loads cause the harmonic currents to be drawn from the grid. For this reason, the actual value of current and voltage are rising which increases injected power from the upstream grid. Increased total load requires increasing the sizing of distributed system components. As a result, PV and battery sizes increase comparing both harmonic-based load case study and non-harmonic-based situation. According to the results, it can be concluded that there is no great impact on cost values for two conditions due to only taking one household into account.
## 2 Establishment of Numerical Simulation Model
### Dynamic Model of Track Structure  
The ANSYS finite element analysis software allows a sufficiently accurate approximation of the track flexibility. Thus, a three-dimensional (3D) finite element model (FEM) of the slab track system was developed using ANSYS, with the slab track in Chinese high-speed railway lines as the object of study. The slab track parameters are presented in Table 1[21]. Figure 4 illustrates the FEM of the slab track system, composed of the rail, rail fastenings, concrete slab layer, cement-asphalt (CA) layer, and support layer. The length of the slab track model is 6.45 m.  
The standard CHN60 rail considered here is modeled using the ANSYS Solid45 3D solid finite elements and divided by a nonuniform mesh according to its nominal geometry. The rail ends are clamped. The loading area around the rail head is densely meshed with small elements (size ~2 cm), and larger elements are used for other components (largest ~6.5 cm). The rail fastenings are modeled as discrete linear elastic springs using the element type COMBIN 14 of ANSYS. This allows for longitudinal displacements, with the other degrees of freedom constrained. The concrete slab layer, CA layer, and support layer are all modeled using the ANSYS Solid185 3D solid finite elements and constructed from uniform meshes with a largest element size of 6.5 cm. The lower surface of the concrete slab layer is connected with the upper surface of the CA layer by coupled nodes. These nodes  
Figure 3: Overview of the vehicle–track coupled system dynamic model.  
are also used as the connection between the lower surface of the CA layer and the upper surface of the support layer. The displacements and deformations are constrained in all three directions at the ends of the concrete slab layer and the bottom of the support layer. As the track structure is assumed to be symmetric with respect to the track centerline, only half of the structure is included in the model to reduce the computation time; this has a negligible influence on the precision of the simulation.
# Leveraging Energy Harvesting and Wake-Up Receivers for Long-Term Wireless Sensor Networks
## 6 Experimental Results
### Energy Consumption of the Sink  
To evaluate the energy consumption of the sink incurred by the evaluated MAC protocols, the energy cost per received packet was evaluated. Using SNW-MAC, the sink only wakes up to poll a sensor node to receive a packet, and therefore, the energy cost per packet received is constant. However, using a pseudo-asynchronous MAC protocol, such as UMPA-X-MAC or PW-MAC, the sink must periodically wake up to check for incoming packets. If the average rate at which the sink receives packets is denoted by \(\gamma\) and if the cost of waking up and receiving a packet is denoted by \(e_{RX}\) and the cost of waking up and not receiving any packet is denoted by \(e_{W}\), then the average power consumption of the sink incurred by packet reception is:  
\[\bar{P}_{C,sink}=e_{RX}\gamma+e_{W}\left(\frac{1}{T_{S}}-\gamma\right). \tag{27}\]  
The average energy cost per received packet is therefore:  
\[\bar{e}_{P}=e_{RX}+e_{W}\left(\frac{1}{\gamma T_{S}}-1\right). \tag{28}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Protocol** & **\(H\) (Joule)** & \(\xi_{\infty}\) **(Packets Per Minute and Per Joule)** \\ \hline UPMA-X-MAC & 0.0125 & 0.666 \\ PW-MAC & 0.00313 & 2.660 \\ SNW-MAC & 0.00135 & 6.156 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Best values of \(H\) and \(\xi_{\infty}\) for the different MAC protocols.  
Using the microbenchmarks presented previously, the values of \(e_{RX}\) and \(e_{W}\) were measured for UPMA-X-MAC and PW-MAC, and the energy cost of receiving a packet using SNW-MAC was also measured. Figure 11 shows the energy cost per received packet for the three evaluated protocols, for different values of \(\gamma\) and for values of \(T_{S}\) of 125 ms and 500 ms. These results were plotted using Equation (28), for values of \(\gamma\) in the range \([0,\frac{1}{T_{S}}]\). It can be seen that if \(\gamma\) is lower than 66 packets per minute, then SNW-MAC enables lower energy consumption on the sink side, despite the higher power needed to transmit WuB and even when the wake-up interval is set to 500 ms. Moreover, when the wake-up interval of pseudo-asynchronous protocols is decreased, which is required if the packet rate is high to reduce collision risk or if low latency is required, the energy cost per packet received significantly increases for UMPA-X-MAC and PW-MAC, as can be seen in Figure 11. In the monitoring applications, which is the focus of this work, low packet rates are expected, and in this case, Figure 11 shows that SNW-MAC enables lower power consumption on the sink side.
# A Survey of Machine Learning-Based System Performance Optimization Techniques
## 2 Background
### Machine Learning Based Cache Management
#### 3.4.2 Perceptron Based Prefetcher  
Prefetching is the process of fetching data from memory into the cache before the processor demands it. If the prediction is wrong, cache pollution occurs, and the efficiency may be reduced because additional work is required. To remove this unnecessary memory request, a two-level prefetching mechanism based on perceptron has been proposed [13]. The first level combines the existing prefetchers (stride prefetcher [4], Markov prefetcher [29]) and Global History Buffer (GHB) [2]. In Figure 10, if a cache miss occurs, the model pushes the block to the GHB stack, and the convolutional prefetcher proposes a block. Next, after finding the proposed block in the index table, it is hashed to the GHB to extract the features related to the proposed block and input it to the perceptron. Perceptron performs prediction based on the input features and does not prefetch the corresponding block if the predicted value is negative. Otherwise, it prefetches the proposed block. Also, depending on the result, the block is added to the Deny Table or Accept Table. Both tables are used as perceptron's training data to adjust memory access patterns. Compared with Stride, Markov Prefetcher, an average of 80.84% of proposed requests by stride prefetcher was rejected, and an average of 49.71% of proposed requests by Markov prefetcher was rejected. However, the cache hit rate was \(-\)1.67 to 2.46% because even useful proposals could be rejected.  
Figure 10: Two Level Prefetcher [13]: In the first level prefetcher, the feature related to the block proposed by the existing prefetcher is transferred to the perceptron, and in the second level prefetcher, the proposal is accepted or denied based on the value predicted by the perceptron.  
Figure 9: Perceptron Reuse Predictor [12]: After XORing the PC and each feature and adding all indexed weights, the predictor predicts the reusability of the block based on the added weights.
# Evaluation of IEEE802.15.4g for Environmental Observations
## 3 Results
### Advanced Metering Infrastructure  
In this scenario, we run the experiment twice during non-consecutive days having similar weather conditions. In the first run, RX nodes are located at 126 m, 180 m and 215 m. In the second run, RX nodes are located at 210 m, 350 m and 400 m. All nodes located within 215 m, even without line of sight, have a PDR close to 100% for almost all radio settings considering packets of 127 B and 2047 B. There are some buildings between the TX node and the RX nodes located at 350 m and 400 m. This severely affects the quality of the radio link. In these two node locations only the O-QPSK radio settings and in less proportion the 2 FSK-FEC-50 are able to get connectivity. These PHYs happen to be the most sensitive of the radio. Table 11 shows these PDR values for all the RX nodes.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **RX at 602 m** & **RX at 685 m** & **RX at 700 m** & **RX at 742 m** & **RX at 779 m** & **RX at 942 m** \\
**PHY Alias** & **PDR** & **PDR** & **PDR** & **PDR** & **PDR** & **PDR** \\  & **127 B-2047 B** & **127 B-2047 B** & **127 B-2047 B** & **127 B-2047 B** & **127 B-2047 B** & **127 B-2047 B** \\ \hline
2 FSK-50 & 0\%–0\% & 99\%–94\% & 2\%–0\
# An Admission-Control-Based Dynamic Query Tree Protocol for Fast Moving RFID Tag Identification
## 6 Simulation Results
### Single Round Identification  
In this subsection, we simulate the identification process in a single round in a static situation. In the evaluation, we first give the numbers of collision and idle slots to identify all tags, respectively. Next, the number of message bits transmitted by the reader and tags are given separately. Then, the average identification time for one tag identification and time efficiency of the comparable algorithms are also exhibited.