esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Results and Analysis  
### Experimental Setup  
The experimental environment is described as follows. All network models in this experiment are based on the deep learning framework, and each model experiment is completed on a computer i7-11800H 8-core CPU, Windows10 operating system, 16 GB memory and NVIDIA GeForce RTX3060 Laptop GPU.  
Parameter settings are given as follows. The networks in this experiment use the same parameter settings. On the data set, the learning rate size is set to 0.001, and the batch size is set to 100. The training epoch is set to 100, and the stochastic gradient descent optimizer with a momentum of 0.9 is used to optimize the network. All models use the ReLU activation function and use the cross-entropy loss function to calculate the loss.  
Figure 9: Dense Pyramidal Net architecture.  
The datasets are introduced as follows. The datasets used in this experiment are the public datasets of Cifar10 [43] and Cifar100 [44]. The Cifar10 dataset has a total of 60,000 RGB three-channel color images with a resolution of 32 \(\times\) 32 pixels, which are divided into 50,000 training images and 10,000 test images, which can be divided into 10 categories; Cifar100 dataset and Cifar10 dataset are similar but the difference is that the Cifar100 dataset has 100 categories, each category contains 600 images, and each category has 500 training images and 100 testing images.
# Towards a Real-Time Description of the Ionosphere:
## 5 Validation Results for _fo_F2 Based on Ground-Based Ionosonde Observations
### Statistics on the Full Dataset
Mean** & **RMSE** & **NRMSE** & **R** & **Counts** \\
Table 3 instead shows the statistical parameters (1-4) calculated for the _f_/6F2 time series recorded in each of the 40 ionosonde stations listed in Table 1. This table highlights how the IRI _f_/6F2 modeling accuracy and precision are improved by IRTAM in practically every ionosonde station regardless the location. Since the number and distribution of the stations assimilated by IRTAM has changed over the years, and critically depends on the real-time availability of data, it is difficult to distinguish between assimilated and non-assimilated stations. This is why we extended our analysis also to COSMIC data that are completely independent of ionosondes' ones and are not assimilated by IRTAM.
# A Relay Optimization Method for NOMA-Based Power Line Communication Systems
## 3 Optimization Process and Performance Analysis  
Based on the system model established in Section 2 and the results of channel state prediction, this section focuses on how to realize the combined optimization of power distribution and relay position through the LSTM network model and improved simulated annealing algorithm and analyzes the performance. The communication process employs NOMA technology, allowing each user to act as an independent decode-and-forward relay. To ensure system communication quality, if a remote user's path loss due to distance from the signal source is too high, an intermediate user between the remote user and the signal source is selected to act as a relay.  
The intermediate user decodes and retransmits the signal to help the remote user receive it correctly. To ensure reliable communication, the transmitter includes a set of check codes in the transmitted information. Subsequent nodes recognize and verify these check codes. The check codes use cyclic redundancy checks (CRCs), which involve appending a number (the check code) to the frame to be sent, generating a new frame for transmission to the receiver. This appended number ensures that the new frame can be divided (modulo-2) by a specific divisor jointly selected by the transmitter and receiver. At the receiver, the new frame is divided by the selected divisor. If the frame was pre-processed to be divisible without remainder at the transmitter, no remainder should be found at the receiver. If there is a remainder, it indicates an error in transmission. If the check code is correct, the node can communicate normally without relaying. If the check code is incorrect, a feedback request is sent to activate relaying. The relay decodes and forwards the message.  
In the downlink, frequency division multiplexing (FDM) is used between 3 MHz and 30 MHz. Once the system confirms the need for relaying, it verifies the Channel State Information (CSI) of user nodes that can correctly receive the information to optimize relay selection. In the optimization process, Channel State Information (CSI) is a critical parameter indicating channel quality. CSI describes the state of the channel, reflecting attenuation and interference that the signal experiences during transmission. Specifically, CSI includes the channel's gain or loss information and evaluates transmission quality on a specific channel. In the optimization process, CSI calculates the signal-to-interference-plus-noise ratio (SINR), determining each user's capacity and total power:  
\[\text{SINR}_{i}=\frac{P_{i}\cdot\text{CSI}_{i}}{\sigma^{2}} \tag{14}\]  
where \(P_{i}\) represents the power allocated to each user. Once the channel state is confirmed, if a user is selected as a relay for decode-and-forward transmission, the channel state will change accordingly. It is necessary to model the altered channel state to predict channel conditions using algorithms. When a user acts as a relay, the received downlink signal is decoded and forwarded via the uplink. The following models are used for the uplink and downlink:
# Automatic Detection System of Deteriorated PV Modules Using Drone with Thermal Camera
## 3 System Configuration
### Drone Hardware Details
#### 3.1.2 Dual Camera Setup (Thermal and RGB Camera)  
The dual camera system consists of a FLIR Vue Pro R thermal camera and a Logitech C270 RGB camera, as shown in Figure 4. FLIR Vue Pro R has a sensor resolution of 336 \(\times\) 256 and uses an uncooled VOx microbolometer as the detector. The thermal camera weighs about 92.13-113.4 g, has dimensions 5.74 \(\times\) 4.44 cm, and operates in a spectral range of 7.5-13.5 \(\upmu\)m, which is enough for our imaging requirements. The Logitech C270 RGB is a fixed focus camera supporting a maximum resolution of 1280 \(\times\) 720 at 30 frames per second.  
Figure 4: Physical structure of dual camera device and image capturing board.  
Figure 3: Block diagram of proposed drone system.
# Distributed Localization of Non-Cooperative Targets in Non-Coplanar Rendezvous Processes
## 4 Numerical Simulation and Analysis
### Configuration Optimization Based on Improved NSGA-III
The figure illustrates that, due to the consistency algorithm, the estimation error trends for the local filters of the three tracking satellites were generally similar, converging around 800 s while achieving high positioning accuracy. The detailed view indicates that after configuration optimization, when the three-satellite positioning system degraded to a two-satellite system, the position estimation error remained essentially unchanged, with a precision loss of only 0.2 m, indicating that the positioning accuracy did not significantly decline.
## 3 Results and Discussion
### Combined Process  
#### 3.3.1 Caffe Removal and Degradation Efficiency  
The caffeine removal and degradation efficiency were evaluated through combined process (CP) cycles. It is important to highlight that the initial caffeine concentration of 20 mg L\({}^{-1}\) was chosen with the objective of simulating the amount of caffeine existing in real effluents [58; 59]. Figure 5 indicates the caffeine removal and degradation values, and Figure 6 illustrates the caffeine C\({}_{\text{F}}\) values obtained throughout the four CP cycles for the three different Fenton reaction times evaluated: 0 min (end of adsorption), 5 min, and 60 min. Average error bars are identified in each column.  
In the analysis of the combined adsorption and Fenton reaction process, the effectiveness of caffeine removal by adsorption remained high in the four cycles, ranging from 89.5% to 97.5%. The application of the Fenton reaction after adsorption proved to be crucial to degrade the residual caffeine in the solution, achieving additional removals of 95.7% to 99.2% in 5 min of the reaction and 95.3% to 99.6% in 60 min of the reaction, in subsequent cycles. The choice between Fenton reaction times of 5 and 60 min must consider the final objectives, assessing the effectiveness in [PERSON] the TOC of the solution and the regeneration capacity of the adsorbent. The results of the fourth cycle showed a degradation efficiency of 98.1% in 5 min and 98.2% in 60 min. [PERSON] et al. [52] degraded 95% of caffeine in the system with a molar ratio of 3:10 Fe\({}^{+2}\)/H\({}_{2}\)O\({}_{2}\) and 30 min of the reaction. [PERSON] et al. [53] obtained 70% degradation of caffeine within the first 10 min of the reaction using pH 5, 0.1 g L\({}^{-1}\) of Fe\({}^{+3}\), and 60 mg L\({}^{-1}\) of H\({}_{2}\)O\({}_{2}\). [PERSON] et al. [60] achieved nearly complete degradation of caffeine within a 60 min timeframe. As seen, other studies in the literature required longer reaction times to achieve maximum caffeine degradation, highlighting that the results obtained in  
Figure 5: Caffeine removal and degradation for Fenton reaction times of 0 (end of adsorption), 5, and 60 min in relation to initial solution of 20 mg L\({}^{-1}\).  
this work are satisfactory and suggest a tertiary treatment with a shorter time period for caffeine removal.
# Request Expectation Index Based Cache Replacement Algorithm for Streaming Content Delivery over ICN
###### Abstract  
Since the content delivery unit over Information-Centric Networking (ICN) has shifted from files to the segments of a file named chunks, solely either file-level or chunk-level request probability is insufficient for ICN cache management. In this paper, a Request Expectation Index (RXI) based cache replacement algorithm for streaming content delivery is proposed. In this algorithm, RXI is introduced to serve as a fine-grained and unified estimation criteria of possible future request probability for cached chunks. RXI is customized for streaming content delivery by adopting both file-level and chunk-level request probability and considering the dynamically varied request status at each route as well. Compared to prior work, the proposed algorithm evicts the chunk with the minimum expectation of future request to maintain a high cache utilization. Additionally, simulation results demonstrate that the RXI-based algorithm can remarkably enhance the streaming content delivery performance and can be deployed in complex network scenarios. The proposed results validate that, by taking fine-grained request probability and request status into consideration, the customized in-network caching algorithm can improve the ICN streaming content delivery performance by high cache utilization, fast content delivery, and lower network traffic.  
Keywords:information centric networking; in-network caching; cache replacement; popularity +
Footnote †: journal: Future Internet
# FEA-Based Design Procedure for IPMSM and IM for a Hybrid Electric Vehicle
## 5 Simulation Results and Discussion
### Comparison of the Radial-Flux Designs with Axial-Flux Benchmarks
Using the above approximations, with 80 \({}^{\circ}\)C coolant, the derated output power is estimated at 49 kW at 2800 rpm. The EMRAX 268 CC [63] is another commercial axial-flux PM design and can deliver 105 kW at 4500 rpm with 50 \({}^{\circ}\)C coolant. The derated power for this machine is estimated at 60.8 kW at 2800 rpm. Two different axial-flux PM synchronous machines (AFPMSMs) have been studied in the literature in Refs. [15; 64]. The coolant inlet temperatures for the designed machines in Refs. [15; 64] are 25 \({}^{\circ}\)C and 20 \({}^{\circ}\)C, respectively, and they have rated powers at 2800 rpm of 20 kW and 82 kW, respectively. The winding temperature in Ref. [64] at the rated current is 75 \({}^{\circ}\)C (i.e., the Class A insulation limit). If this insulation level is changed to Class F, the 45 \({}^{\circ}\)C increase in the inlet coolant temperature is compensated and no derating is required. This can also be applied to the machine in Ref. [15]. Table 6 summarizes the above results. It shows that the designed IPMSM and IM have promising performance and outperform some of the commercial designs, especially in output power and efficiency, with the relatively high inlet coolant temperature (80 \({}^{\circ}\)C), although the EMRAX 268 CC remains superior in terms of power density. The designed IM also outperforms the eMobility IM and the GVM210-100P, which show significant reduction in continuous power and efficiency at higher temperatures. The YASA-750 demonstrates strong performance at a lower coolant temperature but has significant power derating when operating with 80 \({}^{\circ}\)C coolant. The two AFPMSM designs differ widely in performance, with one of the designs offering competitive power density and efficiency at 80 \({}^{\circ}\)C.  
Note that the efficiency values for the designed IPMSM and IM do not include bearing/windage losses. Also, the power density shown does not include the housing, shaft, bearings etc. unlike the commercial machines [62; 63] and the AFPMSM in Ref. [64]. The reported weight in Ref. [15] also does not cover the housing, shaft, and bearing weights.
## 4 Finite Element Analysis  
### Establishment of a Finite Element Model  
The hysteretic behavior of the overlapped K-joints was simulated by finite element software ANSYS [47] (ANSYS 10.0, ANSYS-Inc., Pittsburgh, PA, USA, 2006). The load-displacement nonlinear analysis was performed by using the full Newton-Raphson iteration method. In addition, the nonlinear characteristics of materials and geometry were taken into consideration in the finite element models. The three-dimensional four-node elastic-plastic Shell181 was used to simulate the steel tubes, and the Solid65 element was applied concrete. What's more, the encrypted area was divided freely in the model, and the non-encrypted area was divided by mapping. The finite element model is shown in Figure 9. As shown in Figure 10, the boundary conditions of the model were the same as those of the test, that is, one end of the chord was a fixed constraint, the other end could be sliding in a specific direction, and the two end-plate of the brace was hinged. As shown in Figure 11, a bilinear kinematic hardening model (BKIN) was adopted for steel, which included an elastic stage and a strengthening stage, and the modulus magnitude of the strengthening stage was 1% that of the elastic stage, in accordance with [PERSON] et al. [42]. Poisson's ratio (\(\
u\)) of the steel was 0.3. Elastic modulus (\(E\)) and yield strength (\(f_{y}\)) were measured with a material property test. A Multilinear isotropic hardening model (MISO) was used for concrete. Poisson's ratio was 0.2 and the elastic modulus of concrete came from experimental values. Surface-to-Surface contact pairs were established between the chord and concrete. The concrete surface was the target surface, the inner surface of the chord was the contact surface, and the friction coefficient was 0.3.  
Figure 9: Finite element model of a joint. (a) K-CC; (b) K-RC.
# Interferometric DEM-Assisted High Precision Imaging Method for ArcSAR
## 6 Experiment  
We used a distributed scenes imaging experiment of ArcSAR to verify the effectiveness of the proposed high-precision imaging method. The experiment contained two parts. First, we used the ArcSAR images of the distributed scenes for interference to extract the DEM image of the scenes. Then, the extracted DEM image was transformed from the slant range to the ground range using the polar coordinate transformation method proposed in Section 4.2. Second, we performed the distributed scenes ArcSAR imaging simulation experiment. The DEM image obtained in Experiment 1 was used for assisted imaging.
## 5 Model Performance Analysis  
To assess the viability of any model and to evaluate its performance, various indicators have been used. Each indicator has its method of inferring the performance of these models. The indicators commonly used include root mean squared error (RMSE), mean absolute error (MAE), relative mean square error (RSE), relative root mean squared error (RRMSE), and coefficient of determination (R\({}^{2}\)). The mathematical expressions for these indicators are given below.  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}\left(ex_{i}-mo_{i}\right)^{2}}{n}} \tag{3}\]  
\[MAE=\frac{\sum_{i=1}^{n}\left|ex_{i}-mo_{i}\right|}{n} \tag{4}\]  
\[RSE=\frac{\sum_{i=1}^{n}(mo_{i}ex_{i})^{2}}{\sum_{i=1}^{n}(\overline{ex}-ex_{ i})^{2}} \tag{5}\]  
\[RRMSE=\frac{1}{e}\sqrt{\frac{\sum_{i=1}^{n}(ex_{i}-mo_{i})^{2}}{n}} \tag{6}\]  
\[R=\frac{\sum_{i=1}^{n}(ex_{i}-\overline{ex}_{i})(mo_{i}-\overline{mo}_{i})}{ \sqrt{\sum_{i=1}^{n}(ex_{i}-\overline{ex}_{i})^{2}\sum_{i=1}^{n}(mo_{i}- \overline{mo}_{i})^{2}}} \tag{7}\]  
\[\rho=\frac{RRMSE}{1+R} \tag{8}\]  
where:  
\(ex_{i}=\) experimental actual strength.  
\(mo_{i}=\) model strength.  
\(\overline{ex}_{i}=\) average value of the experimental outcome.  
\(\overline{mo}_{i}=\) average value of the predicted outcome.  
In this paper, the performance of the model is also evaluated by using the coefficient of determination (R\({}^{2}\)). The model is deemed effective when the value of R\({}^{2}\) is greater than 0.8 and is close to 1 [58]. The value obtained through model is the reflection that shows the correlation between the experimental and predicted outcomes. Lower values of the indicator errors like MAE, RRMSE,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Settings** \\ \hline General & \(f_{c}^{\prime}\) \\ \hline Genes & 4 \\ Chromosomes & 30 \\ Linking function & Addition \\ Head size & 10 \\ Function set & \(+,-,\times,+\) \\ \hline \multicolumn{2}{c}{Numerical constants} \\ \hline Constant per gene & 10 \\ Lower bound & \(-10\) \\ Data type & Floating number \\ Upper bound & 10 \\ \hline \multicolumn{2}{c}{Genetic Operators} \\ \hline Two-point recombination rate & 0.00277 \\ \hline \multicolumn{2}{c}{Gene transposition rate} & 0.00277 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Input parameters assigned in the gene expression programming (GEP) model.  
RMSE, and RSE indicate higher performance. Machine learning is a good approach in the prediction of properties. However, overfitting issues in a dataset have a malignant effect in validation and fore casting of mechanical aspect of HSC. Thus, overcoming this problem of overfitting has become a dire need in supervised machine learning algorithms. Researchers used objective function (OBF) for the accuracy of models. OBF uses overall data samples along with the error and regression coefficient. This then provides a more accurate generalized model with adamant higher accuracy and is represented in Equation (8) [59].  
\[OBF=\langle\frac{n_{Train}-n_{Test}}{n}\rangle\rho_{Train}+2(\frac{n_{Test}}{n} )\rho_{Test} \tag{9}\]
# Automated Data Acquisition in Construction with Remote Sensing Technologies
## 3 Standalone Remote Sensing (RS) Technologies
### UWB
In fact, it mitigates the data uncertainties through prediction and update steps which would result in an optimal estimation of the desired parameter by using probability density functions and noise factors. In [36], the authors employed a particle filter to improve the localization accuracy of three different UWB devices by using a Bayesian filtering approach. In their experiment, they used the parameters in line-of-sight (LOS) condition to improve estimated locations in non-line-of-sight (NLOS) condition by using a combined Gaussian and Gamma distribution. In [43], the integration of extended Kalman filter (EKF) and extended unbiased finite impulse response (EFIR) filter was used in order to have more optimized and robust results. The integration of these two filters provided both robustness and accuracy required for localization with UWB sensors in a noisy environment like a construction site. The fusion of these two filters was based on defining a time-varying weighting factor for each of them. In fact, before reaching a specific time in the EFIR, the EKF was the active filter to find the optimized solution. Subsequently, the integration of these two filters comes to play. Therefore, the roving tag position is calculated by the summation of these two weighted factors in each estimated position. Researchers in reference [3] also showed that by attaching more than one tag to each object, and then averaging the positioning data acquired by these tags, the localization accuracy will be improved in comparison with the use of one tag. A higher number of receivers on site also increases the accuracy of this system [16]. The UWB sensors are less influenced by metals and high humidity (a limitation of ordinary RFID systems) and require only a few sensors. Comparing with other technologies such as RFID or ultrasound, UWB has unique advantages including longer range, higher measurement rate, better measurement accuracy, and relative immunity to multipath fading [11; 16]. Besides, UWB offers a wide spectrum and fine time resolution of the signal which results in a more reliable positioning in a harsh environment (e.g., good performance in vicinity of moisture or snow). However, violation of the line-of-sight can lead to a decrement in the signal strength, so an accurate design and sensor placement strategy is needed. Unfortunately, the cost of UWB sensors is still high in comparison with other sensors [31]. Figure 1: \(Cont\). Figure 1: _Cont._
# Inhibitory Effect of Cold Atmospheric Plasma on Chronic Wound-Related Multispecies Biofilms
## 2 Materials and Methods
### Effect of CAP on Multispecies Biofilms  
Multispecies biofilms were formed by MRSA (ATCC 33591), _P. aeruginosa_ (ATCC 27853) and _E. faecalis_ (ATCC 29212) on collagen membranes (Geistlich Bio-Gide(r)), according to the methodology described by [49; 50]. First, standardized suspensions containing 10\({}^{6}\) cells/mL in physiologic solution (NaCl 0.9%) of each microorganism were prepared spectrophotometrically, using the same parameters described before. The collagen membranes were positioned on the surface of tryptic soy agar supplemented by fetal bovine serum solution (50% fetal bovine serum and 50% NaCl 0.9% solution). The smooth layer of the collagen membrane was considered for the growth of multispecies biofilm. Then, aliquots of 10 \(\mu\)L of each bacterial suspension were mixed up in a microtube and the total volume was transferred to the membrane's surface. Plates were incubated at 37 \({}^{\circ}\)C for 24 h. Then, the biofilms were exposed to CAP for 1, 3, 5 and 7 min. The biofilms were mechanically dispersed, and the number of viable cells was determined by plating method. The recovery of each species was carried out by plating the resuspended biofilm in selective culture media (mannitol salt agar, cetrimide agar and Enterococcusol agar, respectively). The experiments were performed in triplicate in three different experiments (n = 9). A negative control (exposed only to helium flow and without ignition) was included for comparative purposes.