esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Complexity Analysis
### Computational Complexity Analysis
#### 4.2.1 Complexity Formula Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme** & \(\beta=4\) & \(\beta=8\) & \(\beta=12\) & \(\beta=16\) \\ \hline  & & (a) & & \\ \hline SOR & \(2.342\times 10^{-1}\) & \(1.465\times 10^{-1}\) & \(9.432\times 10^{-2}\) & \(6.295\times 10^{-2}\) \\ MSOR & \(2.526\times 10^{-1}\) & \(1.586\times 10^{-1}\) & \(1.015\times 10^{-1}\) & \(6.732\times 10^{-2}\) \\ AOR & \(2.248\times 10^{-1}\) & \(1.372\times 10^{-1}\) & \(8.861\times 10^{-2}\) & \(5.942\times 10^{-2}\) \\ SOR & \(2.002\times 10^{-1}\) & \(7.042\times 10^{-2}\) & \(2.190\times 10^{-2}\) & \(5.719\times 10^{-3}\) \\ SAOR & \(1.886\times 10^{-1}\) & \(6.819\times 10^{-2}\) & \(2.126\times 10^{-2}\) & \(5.519\times 10^{-3}\) \\ CSOR & \(1.461\times 10^{-1}\) & \(3.969\times 10^{-2}\) & \(1.023\times 10^{-2}\) & \(2.243\times 10^{-3}\) \\ Chebyshev-MAOR & \(4.071\times 10^{-1}\) & \(6.498\times 10^{-2}\) & \(2.293\times 10^{-3}\) & \(9.210\times 10^{-5}\) \\ Chebyshev-RI & \(1.981\times 10^{-1}\) & \(1.009\times 10^{-1}\) & \(4.847\times 10^{-2}\) & \(2.002\times 10^{-2}\) \\ CHSOR & \(7.230\times 10^{-3}\) & \(4.010\times 10^{-5}\) & \(6.793\times 10^{-9}\) & \(-8.832\times 10^{-8}\) \\ \hline  & & (b) & & \\ \hline SOR & \(2.521\times 10^{-1}\) & \(1.480\times 10^{-1}\) & \(9.257\times 10^{-2}\) & \(6.061\times 10^{-2}\) \\ MSOR & \(2.708\times 10^{-1}\) & \(1.604\times 10^{-1}\) & \(9.991\times 10^{-2}\) & \(6.501\times 10^{-2}\) \\ AOR & \(2.425\times 10^{-1}\) & \(1.385\times 10^{-1}\) & \(8.672\times 10^{-2}\) & \(5.707\times 10^{-2}\) \\ SSOR & \(2.174\times 10^{-1}\) & \(6.899\times 10^{-2}\) & \(1.803\times 10^{-2}\) & \(3.672\times 10^{-3}\) \\ SAOR & \(2.055\times 10^{-1}\) & \(6.662\times 10^{-2}\) & \(1.733\times 10^{-2}\) & \(3.479\times 10^{-3}\) \\ CSOR & \(1.618\times 10^{-1}\) & \(3.741\times 10^{-2}\) & \(7.688\times 10^{-3}\) & \(1.206\times 10^{-3}\) \\ Chebyshev-MAOR & \(4.261\times 10^{-1}\) & \(6.420\times 10^{-2}\) & \(1.323\times 10^{-3}\) & \(1.610\times 10^{-5}\) \\ Chebyshev-RI & \(2.160\times 10^{-1}\) & \(1.023\times 10^{-1}\) & \(4.607\times 10^{-2}\) & \(1.697\times 10^{-2}\) \\ CHSOR & \(7.884\times 10^{-3}\) & \(8.138\times 10^{-6}\) & \(6.836\times 10^{-8}\) & \(4.185\times 10^{-9}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: BER difference rate between the BER of all detection methods and MMSE at different \(\beta\) when the number of iterations is 2 and the SNR is 30 dB for (a) OFDM and (b) UFMC.
# Spectrum and Energy Efficiency of Uplink Massive MIMO System with D2D Underlay
###### Abstract  
In this paper, both the spectrum efficiency (SE) and the energy efficiency (EE) are investigated for an uplink massive multiple-input multiple-output (MIMO) system coexisting with an underlay device-to-device (D2D) system. The outage probability and the achievable rates of the cellular user equipments (CUEs) and the D2D link are derived in closed-form, respectively. Constrained by the SE of the D2D link and the CUEs, the EE of the massive MIMO system is maximized by jointly optimizing the transmit power of CUEs and the number of BS antennas. An algorithm with low complexity is proposed to solve the optimization problem. Performance results are provided to validate our derived closed-from results and verify the efficiency of our proposed scheme.  
device-to-device; energy efficiency; massive MIMO; spectrum efficiency; outage probability +
Footnote †: journal:
# A Study of Adversarial Attacks and Detection on Deep Learning-Based Plant Disease Identification
## 3 Experiments and Results
### Results of Adversarial Sample Detection
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**DNN Model** & **Features** & **FGSM** & **BIM** & **PGD** & **CW** \\ \hline \multirow{4}{*}{VGG-16} & KD + BU & **0.987** & **1** & **1** & **0.987** \\  & LID & 0.728 & 0.674 & 0.641 & 0.612 \\  & DeepF & 0.867 & 0.785 & 0.782 & 0.696 \\  & DiscF & 0.91 & 0.864 & 0.889 & 0.756 \\ \hline \multirow{4}{*}{ResNet-101} & KD + BU & **1** & **1** & **1** & **1** \\  & LID & 0.7 & 0.626 & 0.639 & 0.793 \\  & DeepF & 0.844 & 0.801 & 0.756 & 0.874 \\  & DiscF & 0.926 & 0.896 & 0.912 & 0.913 \\ \hline \multirow{4}{*}{Inception V3} & KD + BU & **0.998** & **1** & **1** & **0.997** \\  & LID & 0.74 & 0.719 & 0.663 & 0.675 \\  & DeepF & 0.872 & 0.73 & 0.733 & 0.741 \\  & DiscF & 0.948 & 0.864 & 0.875 & 0.825 \\ \hline \multirow{4}{*}{DenseNet-121} & KD + BU & **1** & **1** & **1** & **1** \\  & LID & 0.691 & 0.629 & 0.604 & 0.675 \\ \cline{1-1}  & DeepF & 0.898 & 0.855 & 0.871 & 0.784 \\ \cline{1-1}  & DiscF & 0.957 & 0.946 & 0.951 & 0.894 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Adversarial sample detection results for multi-class DNN models using different features. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **Source** & **FGSM** & **BIM** & **PGD** & **CW** \\ \hline \multirow{4}{*}{VGG-16} & FGSM & – & 1 & 1 & 0.768 \\  & BIM & 0.846 & – & 0.861 & 0.622 \\  & PGD & 0.988 & 0.994 & – & 0.746 \\  & CW & 1 & 1 & 1 & – \\ \hline \multirow{4}{*}{ResNet-101} & FGSM & – & 1 & 1 & 0.994 \\  & BIM & 1 & – & 1 & 0.994 \\  & PGD & 1 & 1 & – & 0.995 \\  & CW & 1 & 1 & 1 & – \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of detection model transferability for 2-class DNN models.
# A Mathematical Model for Scheduling and Assignment of Customers in Hospital Waste Collection Routes
## 3 Materials and Methods
Indeed, the cluster must have a similar load represented by the number of visited customers avoiding an unequal dispersion. The set of constraints (12) bounds the maximum number of customer visits for the tour performed on the day \(j\) by truck \(k\). The set of constraints (13) and (14) are the domain of variables. The binary variable is set on (13) and the non-negative on the (14). One of the approaches to measure the traveling distance for Vehicle Routing Problems is using Haversine Distance. The Haversine (or great circle) distance is the angular distance between two points on the surface of a sphere. The first distance of each point is assumed to be the latitude, and the second is the longitude, given in radians. As the Earth is nearly spherical, the Haversine formula provides a good approximation of the distance between two points of the Earth's surface, with a less than 1% error on average ([PERSON] et al. [41]). Some examples dealing with polar coordinates for routing problems are considered by [PERSON] et al. [41], [PERSON] [42], and [PERSON] et al. [43]. In our work, the use of polar coordinates based on radians angle is essential due to the consideration of the cities sector (north, south, east, west) and the neighborhoods with unique and special characteristics (sectors with people with high income, sectors with people low income, business sector, among others). Besides, the polar coordinates facilitate the work of the drivers due to the location of sectors. The model's efficiency was tested with real-life data collected from a world leader company in medical and biological infectious waste collection, treatment, and disposal services. The company serves clinics, hospitals, laboratories, medical and dental offices, surgical centers, funeral homes, veterinary clinics, and any establishment that generates needles and potentially infectious waste. The company has been operating in Chile since 2008, with branches in the main cities. The company provides collection, transportation, storage, and disposal services for marine, mining, urban, hazardous, and special waste. The company has a fleet of eight trucks, six for special waste collection and one for hazardous waste collection. The last truck transports the waste to the various final disposal points.
## 5 Connected Vehicles
### Benefits and Business Opportunities
#### 5.2.7 Traffic Control  
The in-vehicle device can present drivers with real-time traffic conditions nearby and traffic images of major roads and automatically recommend the optimal driving plan to improve the efficiency of traffic operation [101; 102]. Meanwhile, the position and speed of each vehicle will be uploaded to the cloud synchronously, ensuring the real-time and accuracy of traffic information in the area. Especially for vehicles driving on highways, this technology will significantly reduce the probability of accidents.
# Optimisation of Shrinkage and Strength on Thick Plate Part Using Recycled LDPE Materials
## 2 Methods and Materials
### Particle Swarm Optimisation  
In 1995, Professor [PERSON] and Dr [PERSON] developed the PSO method, which was inspired by the social behaviour of flocking birds. Figure 4 shows a flowchart of PSO. The initial settings were made whereby the maximum and minimum range of the input parameters were set [41]. Additionally, the numbers of particle iterations and populations were also defined. Each time the programme evaluated a new solutions, the solution was calculated through the fitness function. The objective function gained from the RSM mathematical model was used as the fitness function to select the best solutions to produce in each iteration. In each iteration, the best solution of each particle was evaluated and recorded as \(P_{i}=(P_{i1},P_{i2},\ldots P_{id})\), also known as the Pbest. Next, the best solution produced by particles in the population were selected and recorded as \(P_{g}=\left(P_{g1},P_{g2},\ldots P_{gd}\right)\), known as the gbest. The new velocity and position of each particle were updated for the particle movement in the next iteration. The velocity and position were updated by applying Equations (1) and (2).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Setting Parameters**} & \multicolumn{4}{c}{**Respenses**} \\
**Run** & **Mould** & **Melt** & **Packing** & **Cooling Time** & **Shrinkage in** & **Shrinkage in** & **Tensile** \\  & **Temperature** (°C) & **Temperature** (°C) & **Pressure (MPa)** & **(s)** & **\(x\) Direction (\%)** & **\(y\) Direction (\%)** & **Strength (MPa)** \\ \hline
1 & 40 & 160 & 12.2 & 19.42 & 2.77 & 2.12 & 8.99 \\
2 & 60 & 160 & 12.2 & 19.42 & 3.03 & 2.20 & 9.34 \\
3 & 40 & 180 & 12.2 & 19.42 & 2.72 & 2.25 & 9.38  
The new gbest may be compared with the previous one, and the gbest with the best solution will be selected as the new gbest, before the iteration continues. The stopping criterion is when the predefined number of iteration sets has been completed or the same solution is produced in multiple iterations (i.e., the optimal solution has been achieved).  
\[{v_{ij}}^{k+1}=\omega{v_{ij}}^{k}+{c_{1}r_{1}}\Big{[}{P_{ij}}-{x_{ij}}^{k} \Big{]}+{c_{2}r_{2}}\Big{[}{P_{gj}}-{x_{ij}}^{k}\Big{]} \tag{1}\]  
\[{x_{ij}}^{k+1}={x_{ij}}^{k}+{v_{ij}}^{k+1} \tag{2}\]
## 3 Results  
### Intra-Session Reliability in IMTP  
Descriptive statistics for the variables analyzed during the first session of IMTP measurements are presented in Table 1. Peak force (PF) and rate of peak force (RPF) demonstrated excellent reliability values for IMTP (ICC = 0.98) and acceptable variability with a coefficient of variation (CV) \(\leq\) 10% (Table 1). However, variables associated with the rate of force development (RFD), force at specific time points, and impulse showed values ranging from low to good reliability across all analyzed time windows, displaying significant variability within the session (CV > 10%). Additionally, PF and RPF exhibited good sensitivity to performance changes, as the SWC0.2 scores were higher than SEM.
# Towards Federated Learning with Byzantine-Robust Client Weighting
## 1 Introduction  
Federated learning (FL) [1, 2, 3, 4] is a distributed machine learning paradigm where training data reside at autonomous client machines and the learning process is facilitated by a central server. The server maintains a shared model and alternates between requesting clients to try and improve it and integrating their suggested improvements back into that shared model.  
A few challenges arise from this model. First, the need for communication efficiency, both in terms of the size of data transferred and the number of required messages for reaching convergence. Second, clients are outside of the control of the server and as such may be unreliable or even malicious ([PERSON] et al. [5] dubbed this behavior _Byzantine_). Third, whereas classical learning models generally assume that data are homogeneous, here, privacy and the aforementioned communication concerns force us to deal with the data as it is seen by the clients; that is (1) _non-IID_ (identically and independently distributed)--data may depend on the client they reside at, and (2) _unbalanced_--different clients may possess different amounts of data.  
In previous works [6, 7, 8, 9, 10], unbalancedness is either ignored or is represented by a collection of a priori known _client importance weights_ that are usually derived from the amount of data each client has. This work investigates aspects that stem from this unbalancedness. Concretely, we focus on the case where unreliable clients declare the amount of data they have and may thus adversely influence their importance weight. We show that withoutsome mitigation, a single malicious client can obstruct convergence in this manner even in the presence of popular FL defense mechanisms. Our experiments consider protections that replace the server step by a robust mean estimator, such as median [11; 12; 13] and trimmed mean [12].
## 2 Materials and Methods
This open-chamber method measures the evaporation rate and expresses the values in g/m\({}^{2}\) h [16]. The Corneometer CM 825 (Courage + Khazaka GmBH, Cologne, Germany) was used to measure skin hydration. Corneometer(r) is based on capacitance measurements and is well known for its precision and sensitivity in skin hydration measurements [17; 18]. The instrument converts the skin's capacitance values into arbitrary units of skin hydration, which range from 0 to 120 [19]. Skin erythema was measured using the Mexameter MX 18 probe (Courage + Khazaka GmBH, Cologne, Germany). The base of this measurement is the absorption and reflection of light from the skin. A total of 16 light-emitting diodes (LEDs), which are circularly placed on the top of the instrument, emit light at three wavelengths: 568, 660, and 870 nm, which correspond to green, red, and infrared light, respectively. Light that is reflected by the skin is measured by a photo-detector, and the amount of light absorbed by the skin can be calculated. Green and red light are used in erythema measurements to match the hemoglobin's spectral absorption peak [20]. All measurements were conducted in accordance with the guidelines of the Standardization Group of the European Society of Contact Dermatitis [21]. After the objective measurement of skin parameters with the probes, participants were asked about their subjective impressions of using the cream and any potential side effects such as itching, irritation, and similar symptoms. The sites were also examined by a board-certified dermatologist during the study period. The total duration of the study was eleven days, during which seven measurements were taken. On the first day, the baseline values of the skin parameters were measured, after which a Finn chamber with SLS was applied to the skin. On the second day, after removing the chamber, the values of skin damage were measured, and the intervention was introduced. On the third, fourth, fifth, eighth, and eleventh days, the recovery values of the skin on both arms were measured. On other days of the study, therapy was applied without additional measurements. Statistical analysis was conducted using IBM SPSS Statistics software (version 25). Two-way ANOVA with repeated measures was used to compare changes over time between the two groups. An Independent Samples t-Test was utilized to compare the means of the two groups at each specific time point. A Paired Samples t-Test was used to compare values within the same group at the beginning and the end of the study. Statistical significance was set at \(p<0.05\). Data are presented as mean \(\pm\) standard deviation unless stated otherwise.
## 6 Compatibility of the SMA Alloy with the Operational Temperatures  
In the previous section, the performance of the SMA in terms of transmission of twist was investigated. In this section, attention is instead paid to the compatibility of the alloy with the requirements relevant to the temperature.  
According to the requirements, the SMA alloy must start working at a temperature over 90 \({}^{\circ}\)C to avoid any undesired activation due, for instance, to warm environmental conditions; at the same time, it has to finalize the twist transmission within 180 \({}^{\circ}\)C to prevent any overheating safety issue.  
To verify the compliance of the alloy with the minimum activation temperature of 90 \({}^{\circ}\)C, the percentage of transmitted twist to a single cell was plotted against the imposed temperature (Figure 18). The activation of the alloy starts at a temperature of 95 \({}^{\circ}\)C, as the curve starts arising, while it achieves a plateau at 180 \({}^{\circ}\)C.  
Thermal analysis investigations were addressed to verify the compliance with the requirement of full activation at 180 \({}^{\circ}\)C. Dedicated finite element models were realized for the WT and the WhT prototypes to support thermal analyses and take into account the different heat transfer conditions characterizing the two environments.  
Figure 19 depicts the model used for the WhT demonstrator, constituted by 141.376 solid elements. The structure of the ribs was replaced by solid elements, whose larger thermal capacity tends to absorb greater heat quantity than the real structure, made of truss-like elements. In addition, the model is naked to further increase the heat transfer with the environment and, thus, the thermal losses.  
Figure 19: The finite element model used for the thermal analysis of the WhT blade segment.  
Figure 18: Percentage of twist transmitted vs. temperature.
## 5 Conclusions  
In this paper, firstly, a novel adaptive AV-to-everything communication approach is proposed with implementation of an optimization technique and a distributed deep learning model in order to enhance the connectivity of vehicular communication. Then, the problem of ensuring a reliable communication between AV and everything is discussed under various environmental conditions. The required inter-vehicle position is initially determined using the Lagrange optimization technique, then simulated using MATLAB. Based on the data provided by MATLAB, the 1D-CNN-based deep learning model was suggested. The goal of employing 1D-CNN is to have lowest computational complexity, which allows for processing energy efficiency and overall achievable data rate, making it perfect for usage in real-time applications. As a result, the deep learning model on AVs was able to determine the correct inter-vehicle location in order to reach a nearly optimal result. Using both techniques, the ideal inter-vehicle position between the AV and any destination, whether it was another vehicle, device, or base station, was predicted. As a result, in order to meet the system's performance requirements, the analytics of the results of forecasting the optimal required inter-vehicle location between the AV and everything were then displayed. Following that, based on the findings obtained in terms of system energy efficiency and achievable data rate, it was discovered that the proposed model can demonstrate the greatest performance under various environmental situations. Furthermore, utilizing analytical and deep learning techniques, it has been proven that the inter-vehicle location can alter depending on a variety of environmental factors. The results reveal that boosting \(P_{A}\) increases energy efficiency and achievable data rate since it helps to overcome any type of interference, whether high or low. Furthermore, the effect of \(\text{SINR}_{\text{th}}\) on the energy efficiency and achievable data rate was investigated. As a result, it has been proven that increasing the SINRth improves overall energy efficiency and achievable data rate for both types of assumed interferences. Finally, the findings reveal that the suggested model may deliver the desired AV performance while maintaining an acceptable level of system efficiency and reliability. Furthermore, another important direction for future work is to assess the effectiveness of the proposed resource allocation technique using experimental data from a hardware implementation or other deep learning techniques.  
This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The author declares no conflict of interest.  
**Glossary**  
List of symbols  
\(B\)System bandwidth  
\(I_{2X}\)Interference at any destination  
\(I_{2 Xmax}\)The maximum permitted interference  
\(C1\), \(C2\)Lagrangian optimization constraints
# Automatic Matching of Multi-Source Satellite Images: A Case Study on ZY-1-02C and ETM+
## 2 Methods
### Initial Matching Point Detection  
Considering image differences, the mathematical model between two satellite images can be described based on three aspects: rotation, scale, and translation. For automatic matching of multi-source satellite images, these aspects are usually unknown before processing.  
Given the inconsistent positioning precision of RPC, directly predicting the initial value of image matching with the RPC model is generally unreliable. In the RPC model, rotation and scale are typically considered as systemic parameters, whereas translation represents the random parameter. The strong correlation among these three parameters requires minimal separation processing. Image matching should be convenient. Thus, the image to be matched is first resampled using the rotation and scale parameters, followed by initial value prediction of the matching points with the translation parameters. Using RPC to obtain relatively stable prediction parameters [36; 37], followed by overall correlation to determine relatively random prediction parameters, is the key to predicting the initial value of image matching.