esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Results for Communication-Efficient Studies  
This subsection illustrates the systematic mapping results of works that present techniques for enhancing communication in FL. We focused on papers that provide efficient communication by reducing the number of updates or the number of bits shared. Out of the papers that we screened, a total of 74 met our selection criteria.  
Figure 12 illustrates the top ten techniques most commonly used in the selected studies, representing a central focus of this study in guiding FL users, researchers, and  
Figure 11: Heatmap between the most commonly used learning models and the respective datasets utilized in non-IID data problem studies.  
Figure 9: Percentage of the ten most commonly used learning models in non-IID data problem studies.  
providers when implementing an FL approach. The results demonstrate that compression techniques such as quantization and sparsification are the most widely used techniques, used in 27% (20 studies) and 15% (11 studies) of the studies, respectively. Furthermore, other techniques are used, like the client selection technique where the server selects the participating clients, used in 9% (seven studies) of the studies. An asynchronous scheme was used in 7% (five studies) of the studies, and two-level aggregation where there is a middle node that aggregates the local models received from some clients before uploading them to the central server is used in 7% (five studies) of the studies. Select model updates where the irrelevant updates will not be uploaded are used in 7% (five studies) of the studies, and over-the-air computation is used in 5% (four studies). Cluster, periodic model averaging, and knowledge distillation are used in 4% (three studies) of the studies. All these techniques can enhance communication in federated learning. The citation of these studies is presented in Appendix A Table A4.  
Figure 13 shows the selected studies' top ten most used learning models. The results indicate that even in the studies that aim to provide communication efficiency in federated learning, the most widely used learning model is the supervised learning model CNN.  
Figure 12: Percentage of the top ten techniques commonly used to provide communication efficiency in FL.  
Figure 13: Percentage of the ten most commonly used learning models in communication-efficient studies.  
The selected studies also utilized different datasets when examining their approach. Figure 14 shows the percentage of the datasets used in more than one study of the 74 selected studies. The figure indicates that the MNIST and Cifar-10 datasets are the most widely used.  
Table A5 in Appendix A presents a mapping between the most used learning models and the respective datasets utilized when employing these models. We highlight only the datasets that are used in more than one study. As illustrated in Figure 15, MNIST and Cifar-10 are widely used among the different local models in the selected studies.
# An Automated End-to-End Side Channel Analysis Based on Probabilistic Model
## Notation and Problem Definition  
### Notations  
* \(y_{t}\): The \(t\)-th signal (time location). Total signal length is \(T\), so we simplify \(\mathbf{y}_{1:T}=(y_{1},y_{2},\cdots,y_{T})\).
* \(r_{t}\): The \(t\)-th random variable which indicates whether \(t\)-th point is change point or not. That is, \[r_{t}=\begin{cases}1,&\text{if $t$ is a change point.}\\ 0,&\text{otherwise.}\end{cases}\]
* \(K\): The number of change points. That is, \(K_{r}=\sum_{t=1}^{T}r_{t}\)
* \(\tau_{k}\): The \(k\)th change point. As \(K_{r}\) is the number of total change points, \(1\leq k\leq K_{r}\) and trivially \(\tau_{1}=1\) and \(\tau_{K_{r}+1}=T+1\).
* \(\lambda\): The hyperparameter that controls the number of change points
* \(\sigma\): The hyperparameter that controls the shape of probability density function
* \(\mu\): The hyperparameter that controls the mean of segments.
* \(V\): The hyperparameter that controls the variance of segments.
* \(\theta\): The set of parameters. That is, \(\theta=\{\lambda,\sigma,\mu,V\}\).
* \(D\): The dimension of feature.
* \(\tilde{\phi}_{k}\): The feature extracted from the \(k\)th segment with dimension of \(D\).
* \(c_{k}\): The cluster assigned to \(k\)th segment.
## 2 The Design Process  
Our design process is based on a dual approach. First, we focused on the internal DM processes within the provider organization that are essential for service delivery. Second, we analyzed users' experiences and insights to align the service with user needs and expectations and facilitate the negotiation process within the MADM process. Specifically, our design process is structured as the following four primary steps (Figure 1):  
* The first step was focused on exploring the provider organization and enhancing the awareness of providers' intentions and boundaries that shaped their DM. By gaining a deeper understanding of the providers' perspectives, we aimed to ensure that their intentions were aligned with the desired outcomes and create a foundation for effective decision support.  
* The second step involved the exploration of prospective users to gather valuable data about their psychological world. This exploration enabled us to collect relevant information that served as the basis for the subsequent modeling activity. Understanding the needs, motivations, and cognitive processes of prospective users proved to be crucial in designing a user-centered solution.
* In the third step, we engaged in the modeling of DM processes and activities specific to the prospective users and the providers. This comprehensive modeling approach allowed us to gain a holistic view of the different categories of prospective users involved. By capturing the complexities of their DM processes, we aimed to develop a solution that would address their unique needs and challenges.
* The final step involved bridging the users and the providers by aligning their respective DM flows. This step proved to be instrumental in identifying potential issues and developing solutions that facilitated the creation of points of convergence between all the actors involved.
## 3 The Proposed Method
### Modified Adaptive Selective Cuckoo Search Algorithm (MASCSA)
#### 3.2.3 The Modified Adaptive Mutation Mechanism (MAMM)  
In the MAMM, the adaptive mutation mechanism in [24] is applied, together with a proposed condition for determining the use of small size or large size in Equations (15) and (16). The fitness function of each solution is determined and defined as \({\it{FF}}_{s}\). The fitness function is used to calculate the effective index of each solution and the average effective index of the solutions. The effective index of the \(s^{th}\) solution, \({\it{EI}}_{s}\), and the average effective index of the whole population, \({\it{EI}}_{a}\), are calculated as follows:  
\[{\it{EI}}_{s}={\it{FF}}_{\text{best}}/{\it{FF}}_{s} \tag{17}\]  
\[{\it{EI}}_{a}={\it{FF}}_{\text{best}}/{\it{FF}}_{a} \tag{18}\]  
where \({\it{FF}}_{\text{best}}\) and \({\it{FF}}_{a}\) are the fitness function of the best solution and the average fitness function of the whole population. In the case that the effective index of the \(s^{th}\) solution is less than that of the whole population, the \(s^{th}\) solution is still far from the so-far best solution and small size should be used for the \(s^{th}\) solution. On the contrary, the \(s^{th}\) solution may be close to the so-far best solution and the large size is preferred. In summary, the modified adaptive mutation mechanism can be implemented by the five following steps:  
Step 1: Set mutation factor \({\it{MF}}\) to one  
Step 2: Calculate the fitness function of the \(s^{th}\) solution, \({\it{FF}}_{s}\) and determine the lowest one, \({\it{FF}}_{\text{best}}\)  
Step 3: Calculate the mean fitness function of all current solutions, \({\it{FF}}_{a}\)  
Step 4: Calculate \({\it{EI}}_{s}\) and \({\it{EI}}_{a}\) using Equations (17) and (18)  
Step 5: Compare \({\it{EI}}_{s}\) and \({\it{EI}}_{a}\)  
If \({\it{EI}}_{s}<{\it{EI}}_{a}\), apply Equation (15) for the \(s^{th}\) solution.  
Otherwise, apply Equation (16) for the \(s^{th}\) solution.  
Using the AMM [34], ASCSA can jump to promising search zones with appropriate step size, as shown in Equations (15) and (16). However, the condition for applying either Equation (15) or Equation (16) is time-consuming, due to the many values of threshold, including \(10^{-5}\), \(10^{-4}\), \(10^{-3}\), \(10^{-2}\), and \(10^{-1}\). In addition, the mutation factor is also set to the range from 0.1 to 1.0 with ten values. Therefore, it should try (5\(\times\)10) = 50 values for the ASCSA. This becomes a serious issue of ASCSA in finding the best solution. Therefore, the application of the new condition can enable MASCSA to reach high performance, but the shortcomings of the time-consuming manner can be solved easily.
# The Electric Vehicle Routing Problem with Time Windows, Partial Readers, and Parcel Lockers
## 5 Experimental Results
### Results on EVRPTW-PR  
This section reports the results obtained by our proposed algorithm (ALNS) on both EVRPTW-PR small and large instances. These results are then compared with the results achieved by the state-of-the-art algorithms, including those from [PERSON] and Catay [7] (KC), [PERSON] and Walther [52] (SW), and [PERSON] et al. [13] (HGA). Note that optimal solutions for EVRPTW-PR small instances are provided by ref. [7].  
For the algorithm's performance analysis, several terms are used: (1) \(f_{b}\) represents the best objective value over 10 replications obtained by each algorithm; (2) \(\Delta f[\%]\) denotes the gap measured by the results of our ALNS against those of the state-of-the-art algorithms; and (3) \(\bar{t}\) denotes the average computational time spent by our ALNS. The details appear in Tables 11-17.  
Regarding the small-sized instances, we compare the results obtained by our ALNS algorithm with the optimal solutions provided in [7]. The details are presented in Table 11. Our ALNS not only achieves optimal solutions for all instances but also operates faster than the GUROBI solver.  
Regarding the large-sized instances, the detailed results of subsets C-1, C-2, R-1, R-2, RC-1, and RC-2 are provided in Tables 11-17, respectively. The tables list the best solution values after 10 runs from all algorithms (i.e., KC, SW, HGA, and our ALNS after) as well as the best-known solutions. Based on these tables, we compare the results of our algorithm against those of the state-of-the-art algorithms by providing the overall average objective value (\(\bar{f}_{b}\)) and the overall average gap (\(\Delta\bar{f}[\%]\)) for each subset, presented in Table 3 and Figure 4.  
Based on Table 3 and Figure 4, our ALNS outperforms the HGA algorithm by improving the solutions by 0.33% on average. It is worth noting that two new best-known solutions (for c107 and r109 instances) are offered. Our ALNS yields solutions that are worse than those achieved by KC, SW, and the BKSs, but the gaps are not significant (ranging from 0.22% to 1.28%). Moreover, our algorithm gives all the best-known solutions for subset C-2, and its solutions for subsets C-1, C-2, and R-1 are better than those obtained by the HGA algorithm by 0.35%, 0.77%, and 1.14%, respectively, on average.  
In conclusion, our ALNS algorithm demonstrates effective performance in solving EVRPTW-PR instances within a reasonable computational time. These results highlight its potential applicability in addressing the EVRPTW-PR-PL problem, as implemented in the subsequent section.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Parameter** & **Value 1** & **Value 2** & **Value 3** & **Value 4** & **Parameter** & **Value 1** & **Value 2** & **Value 3** & **Value 4** \\ \hline \(\omega^{penalty}\) & 1.10 & 1.30 \({}^{(i)}\) & **1.50** & 1.70 & \(\epsilon_{f}\) & 11 & 13 & **15 \({}^{(i)}\)** & 17 \\ \(\Delta\bar{f}_{\text{avg}}[\%]\) & 1.39 & 0.02 & 0.0
# Concept Drift Detection Based on Deep Neural Networks and Autoencoders
## Appendix A  
To demonstrate that the weights in Figure 4 have been updated through error back-propagation, the following presents scatter plots of the mean weights before and after data drift, using the Sine_a and Mixed_a datasets as representatives.  
By observing the below figure, it is evident that there is a noticeable change in the mean values of the hidden layer weights before and after the drift.  
For the experiments, we conducted validation with different drift thresholds, and the results are presented in Table A1.  
The results in Table A1 show that when a broader drift threshold is used, the false detection rate of the DNN+AE-DD algorithm increases accordingly.
## 5 Stopping Distance Simulation Results
### Comparison of Stopping Distances  
Using the bicycle braking model, 5000 emergency braking simulations were run, varying both the center of mass position ratio and the road downslope gradient in order to compare a dual-lever braking system to a single-lever braking system with 70/30 front/rear brake force proportioning (Figure 13). The initial step in each simulation was to determine  
Figure 12: Minimum tire coefficient of friction before front wheel lock occurs for the bike and rider center of mass position ratio and road downslope gradient. Tire coefficient of friction is shown by the colored lines, with values increasing with the color gradient from red to blue.  
the highest brake force achievable before the rear wheel lock occurred. Then the stopping distance was measured for this braking force for a bike and rider system weight of 100 kg and a starting velocity of 25 km/h. Stopping distances would decrease if the rear wheel were allowed to remain locked while the front brake pressure was increased; however, riders do not tend to increase front brake force when the rear wheel is already locked. This is the advantage of proportioning valves; the front brake force is increased relative to the rear wheel force before the rear wheel lock occurs, thus allowing the rider to shorten their stopping distance while still providing a warning signal before tip-over or front wheel lock.  
Taking the difference between the two contour plots in Figure 13 illustrates the advantage of increasing front brake force relative to rear brake force (Figure 14). We can see that there is a greater advantage to a proportioning-type brake system for lower center-of-mass ratios. However, the relative advantage decreases as the gradient of the downslope increases. The difference between proportioning and non-proportioning systems is especially evident on steeper downslopes. The advantage is highest at the point of imminent tip-over because 100% of the brake force must be applied to the front wheel. However, this is also the point where applying too much brake force results in a tip-over. This is the importance of determining the maximum and minimum tire coefficients of friction from Figures 11 and 12.  
Figure 13: Simulated stopping distance without wheel-lock (m) from 25 km/h for a 100 kg bike and rider system comparing the single-lever, prototype proportioning brake system versus a dual-lever brake system.
# Some Thoughts about Infrared Radiation Response Characteristics during Loading of Sandstone Samples
## 5 Discussion of Infrared Radiation Mechanisms during Loading of Rocks  
### Basics of Thermo-Mechanical Coupling  
Under the influence of external loading, the mineral crystals experience dislocation slippage, microcracking and morphology changes. This leads to thermo-mechanical coupled effects including a thermo-elastic effect, gas and fluid compression and expansion in the pore spaces, frictional heat generation and thermal effects due to crack growth. All this leads to changes in the physical temperature of the substance [21]. According to Stefan-Boltzmann law, the infrared radiation varies with the physical temperature of rock samples [22; 23]. The following phenomena have to be considered in general:  
1. Thermo-elastic effect The elastic deformation of a solid under uniaxial compression can be considered as an isentropic reversible process. The relationship between physical temperature change (\(\Delta T\)) and stress change (\(\Delta\sigma_{1}\)) is [14]: \[\Delta T=-\frac{T\beta\delta_{ij}\Delta\varepsilon_{ij}}{C_{p}\rho}=-\frac{T \alpha}{C_{p}\rho}\Delta\sigma_{1}\] (4) where \(\alpha\) is the thermal expansion coefficient, \(\beta\) is the bulk modulus, \(C_{p}\) is the heat capacity at constant pressure, \(\rho\) is the density, and \(T\) is the physical temperature.
2. Adiabatic pore gas process Natural rocks usually contain pores and voids filled with air. During loading and unloading, the air in this space will be compressed or decompressed. The gas expansion or compression within the pores can cause the temperature decrease or increase, which can be described in the case of an adiabatic process according to the following equation [24]: \[\frac{T_{2}}{T_{1}}=\left(\frac{P_{1}}{P_{2}}\right)^{\frac{k-1}{k}}\] (5) where \(T_{1}\) is initial temperature, \(T_{2}\) is the final temperature, \(P_{1}\) is the initial pressure, \(P_{2}\) is the final pressure, and \(k\) is the adiabatic index.
3. Frictional heating During uniaxial compression, already existing or newly initiated microcracks will experience frictional sliding. Friction at the crack contact surfaces generates heat, which results in temperature increase and consequently infrared radiation.
4. Thermal effects of crack growth Crack growth is an irreversible thermodynamic process with energy dissipation. During the crack growth process, the initial stored elastic energy is transformed into fracture energy, seismic energy, and heat. Especially inside the process zone at the crack tips [25; 26], heat is generated, which leads to a local temperature increase. Because the energy release rate is quite different for different fracture modes (energy release rate for Mode-I is much lower than for Mode-II or Model-III) and the fact that Mode-II and Model-III has a strong coupling with frictional heat production, the energy balance for different fracture modes is quite different.
# Comprehensive Survey on VLC in E-Healthcare: Channel Coding Schemes and Modulation Techniques
## 2 Effects of Electromagnetic Fields (EMFs) on Health
Some studies suggest minimal health risks from low-level EMF exposure, while others report increased neuronal excitability at 700 MHz [37]. In contrast, VLC-based systems, which use LEDs for optical data transmission, provide a safe, cost-effective alternative for healthcare environments where RF is restricted.
## 3 Experimental Results and Analysis
### Experiment Design
#### 3.3.1 Basic Experiments Design  
CABIR was compared mainly with a few selected baseline models in terms of the performance indexes achieved over the three public datasets (under the same test condition). A total of four baseline models were selected for this paper, which are detailed below.  
1. VSE++ [34]: a natural image cross-modal retrieval model taking data enhancement and fine-tuning strategies based on an improved triplet loss function.
2. MTFN [40]: a model that has designed a self-defined similarity function and that trains networks via the ranking-loss function.
3. SCAN [27]: a model with the Stacked Cross Attention mechanism proposed for exploring fine-grained semantic correspondence between images and texts, signifying a successful application of the cross-attention mechanism in the field of natural images. In this paper, the model CABIR was compared with both SCAN i2t and t2i.
4. AMFMN [18]: See Section 1 for a detailed introduction. In this paper, the CABIR model was compared simultaneously with AMFMN-soft, AMFMN-fusion, and AMFMN-sim, three versions of AMFMN.
## 2 Materials and Methods
### Feedstocks, Moisture and Carbon Content  
Feedstocks included maize cobs (_Zea mays_), sesame stems (_Sesamum indicum_), elephant grass (_Cenchrus purpureus_), coffee husks (Robusta, _Coffea anephora_), and a 50/50 \(w\)/\(w\) mixture of elephant grass and woody twigs from mixed local sources (predominantly _Combretum_ wooded grassland with the characteristic species being _Combretum_ _mobile_[22]). The sizes of the feedstocks were as follows: maize cobs 15-20 cm in length; sesame stems30-50 cm in length and 5 mm thick; grass 80-100 cm in length and 2-3 mm thick; twigs 80-100 cm in length and 10-20 mm thick; coffee husk 10 mm diameter. Feedback moisture was measured with a Protimeter Timbermaster BLD5609 (Amphenol Thermometrics, Taunton, U.K.; 1% accuracy) for maize cobs, sesame stems and twigs and by weight loss at 60 \({}^{\circ}\)C in a muffle furnace overnight for grass and coffee husk. Moisture contents were 11.2 \(\pm\) 2.4% (\(n\) = 25) for maize cobs, 9.6 \(\pm\) 1.5% (\(n\) = 12) for twigs, 10.0 \(\pm\) 1.5% (\(n\) = 25) for sesame stems, 8.0 \(\pm\) 1.0% (\(n\) = 3) for grass, and 12.1 \(\pm\) 1.1% (\(n\) = 3) for coffee husk (Table 1). The carbon, hydrogen, and nitrogen contents of the biochars were measured by element analysis after combustion at 1050 \({}^{\circ}\)C and chromatographic detection in a Leco 836 element analyzer, LECO Corporation, St. Joseph, MI, USA [7].
## 3 Multicarrier CDMA Based Modulation Schemes
The authors have also proposed two new spectrum sensing schemes based on interference limit and compared their performance with existing adaptive spectrum sensing scheme. In [43], the authors investigated the performance of CDMA based cognitive radio networks in the presence of receive beamforming at the base stations, for uplink communication. Using the simulation results the authors have shown that, beamforming results in significant performance improvements in terms of probability of outage. In [44], a modified greedy algorithm was proposed to vary the number of activated secondary users based on the pre-determined interference threshold, in a DS-CDMA based cognitive radio network. Using simulation results it was shown that, the proposed CR system exhibits an acceptable BER performance at low SNR level when compared to conventional CDMA system without Greedy algorithm. In this section, various multicarrier CDMA (combination of OFDM and CDMA) based modulation schemes are discussed.
## 2 Materials and Methods
### Oversampling Methods
#### 2.3.2. Smote  
The Synthetic Minority Oversampling Technique (SMOTE) is an algorithm which is used to oversample the minority class or multiple classes by creating synthetic samples based on real data. The algorithm execution starts by taking a difference between the sample and its nearest neighbor. Then the difference is multiplied by a random number in 0 to 1 range and added to the sample under consideration. By doing so the random point is created along the line segment between two specific features.  
After application of the SMOTE algorithm the Kernel PCA was applied just for visualization purposes to get better perspective of synthetically generated samples using SMOTE algorithm. The binary and multiclass datasets balanced with SMOTE are shown in Figure 6.  
Figure 5: The results of random oversampling technique application on binary and multiclass dataset. (**a**) Binary scatter plot after random oversampling; (**b**) Multiclass scatter plot after random oversampling.  
Figure 6: _Cont._In Figure 6 the results of SMOTE over-sampling technique to both binary and multi-class datasets are shown. As seen in the binary case the minority class (Hepatitis-C) is oversampled. The region of this class is now much larger although both classes overlap. In the case of the multiclass problem, three minority classes (Hepatitis C, fibrosis, and cirrhosis) are oversampled. The number of samples is much larger than in binary case and the overlapping occurs between all 4 classes.