esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, we proposed a new path loss model called TYM, which predicted signal strength inside buildings, especially in dead spot areas. The TYM model had new parameters like window size in each floor; weather conditions were also taken into consideration. Moreover, the model 120 total path loss measurements have been included from 35 base stations. We have validated our model over different propagation environments and showed improved results over the Okumura and COST-HATA models. The evaluation analysis proved that the capability of our model over other models compared to real measurements. The TYM model can assist mobile network design engineers to produce stable mobile networks in very accurate and cost-effective ways. Our model enhanced the prediction of path loss by 10% compared to Okumura and by 15% compared to COST-Hata.  
**Author Contributions:** Supervision, N.G. and X.X.; Writing--original draft, Z.N.; Writing--review & editing, N.G., L.A. and X.X.  
**Funding:** This research was funded by the University of Bridgeport, School of Engineering.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Implementation Details
### Project Input Files (Dataset)  
The data input files used in developing the automation workflow were formal, publicly available tax clearance certificates (formal certificates/affidavits) that are normally obtained by registered organizations from the South African Revenue Service. The research decision on which type of image to use in the dataset was based on a typical industry requirement, with the aim of making the process extendible for other similar documents. The dataset consisted of 19 tax clearance certificate images that were scanned copies of the original documents and varied in type (JPEG vs. PNG), quality, and number of fields. An example of the image format is shown in Figure 3. The annotations on the image are explained in Table 1 in order to illustrate the effective data to be derived from the image for key-value matching pairs to be formed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Annotation** & **Description** & **Data Type** & **Relevant (Y/N)** \\ \hline
1 & Watermark/Background noise & String & N \\ \hline
2 & Key/Field name & String & Y \\ \hline
3 & Value/Field value & Numeric/String/Date & Y \\ \hline
4 & Generic text & String & N \\ \hline \hline \end{tabular}
\end{table}
Table 1: Annotation explanations as illustrated in Figure 3.  
Figure 3: An example tax clearance certificate image (masked) illustrating the format of the images. Annotations on the image are explained in Table 1.  
As can be seen from the annotations in Figure 3 and the descriptions in Table 1 the tax clearance certificate images contain both relevant and irrelevant fields and blocks of text. All the data from annotations 1 and 4, although available in the digitized image, were ignored for key-value identification and pairing. The key/field names found in annotation 2 are not fixed to what can be seen in Figure 3 but vary between the images in the dataset. Subsequently, the same then applies for the values/field values as displayed in annotation 3 as a field value would be available for the corresponding key/field name.  
The digitized output result that needs to be derived from the images is the field names (annotation 2) and corresponding field-values (annotation 3) in a dictionary format, that excludes all of the data that is represented by annotations 1 and 4 in Figure 3.  
The keys/field-names are predefined labels of tax characteristics that a company can have. A generated list of such terms had been compiled based on the dataset and was maintained in an external alias file (Figure 1). Values/field-values are the corresponding tax values relating to a specific key. These field-values mostly have a set structure that is successfully representable by regular expressions. Regular expressions were generated by studying the population of tax documents and is also maintained externally in the alias file (Figure 1) where it was subsequently mapped to the corresponding key (Table 2).
## 4 Discussion
This sum of furan concentrations was also relatively high in a few samples in the bakery group (n = 92), mainly in dark biscuits (n = 5), such as pretzels and Speculos (i.e., cinnamon spiced caramelised biscuit), which could be related to the baking conditions as well as the Maillard reaction taking place during the process [49]. In these products, the 2-MeF contributes the most with a concentration range (270-900) ug/kg approximately (Figure S4B). In the composite group (n = 107), the highest amount was found in canned meat stews and liquid soups, especially those containing meat balls (n = 6). In these products, the main contributor was 2-EtF followed by furan, which could again be explained by the effect of the sterilisation process on foods containing unsaturated fatty acids. In the baby food group, the higher contents were observed in snacks for infants followed with jarred baby food and cereals with medians of the total furan equal to 61.7, 47.7, and 10.3 ug/kg, respectively. The levels of furans in homemade food were always near to the LOQ and they were not included in Table S3. Regarding the volatile profiles in coffee (Figure S2), the results suggest that coffee contains about 3.7 times more 2-MeF than furan, indicating that the main exposure to furan derivatives in coffee is due to 2-MeF. Compared to coffee, the main exposure to furans in canned fish is due to 2-EtF, which is frequently 22-23 times higher than furan and 2-MeF respectively (Figure S3). However, the level of 2-EtF in canned fish is at least two orders of magnitude lower than that of 2-MeF in coffee. Based on the previous discussion, the main contributor to furanic compounds is coffee, due to its distinct pattern and the higher concentration of each compound. However, relying only on the frequency of the presence in foods and the concentrations is not sufficient to express the hazard, but the toxicological values should be used to take into account the harmful effects of each compound [50]. According to the hazard index values presented in the Table S4, all food groups exhibit a low hazard (HIs median \(<\) 1.0) except for coffee and coffee imitates where the median is highest. The beverages prepared from coffee beans and ground coffee (with HIs median equal to 0.163 and 0.058 respectively) are the main hazard contributors (Figure 2A). In contrast, the hazard in the remaining coffee sub-groups is significantly lower due to the lower furan content resulting from the loss of volatiles during the process as described above in Figure S5. On the other hand, as 2-MeF and furan are the most abundant furan compounds in coffee, they represent the main hazard contributors according to Figure 2B. considering similar undesirable effects. Based on the discussion of the results presented in this work, the main potential hazard related to furan and alkyl furans in consumer foods are coffee, in particular coffee beans and ground coffee, due to the presence of 2-MeF and the parent furan.
## 3 Mechanical Properties
### Building Atmosphere  
A close look at Table 4 highlights that DED AISI 316L samples can be built in different atmospheres, such as argon or nitrogen. Furthermore, the protective atmosphere can be generated in two main ways. The first is the use of a simple shielding gas that locally protects the molten material from oxidation, while the second involves the usage of a protective chamber filled with an inert gas.  
[PERSON] et al. recently compared the mechanical properties of DED AISI 316L built using a N\({}_{2}\) shielding gas or using a N\({}_{2}\)-filled build chamber. The results showed that very high mechanical  
Figure 14: A summary of the tensile fracture morphologies of AISI 316L stainless steel samples produced by DED using (**a**) fresh powder and (**b**) recycled powder. SEM images of (**c**) fresh and (**d**) recycled AISI 316L powders [37].  
properties can be obtained in both conditions and that the use of the Glove Box (GB) allows the achievement of higher YS, US, and \(\epsilon\) (Table 4). This effect was attributed to the reduced size of the oxides and the higher N content of BC samples, which have a strengthening effect [64].  
The inert gas composition may also have an effect on both the processability of the alloy and its microstructure and mechanical properties. These effects have not yet been investigated in the AM field but were recently studied and reported in the welding literature [88]. In the case of AISI 316L production by DED, it is important to consider that nitrogen is a \(\gamma\)-stabilizer and therefore reduces the high-temperature \(\delta\)-ferrite phase content which, as previously stated, plays a vital role in the determination of mechanical properties of the final parts. Furthermore, N is an important alloying element for austenitic stainless steel and using it as protective gas might increase the mechanical properties of DED AISI 316L [89].
# RDTIDS: Rules and Decision Tree-Based Intrusion Detection System for Internet-of-Things Networks
## 4 Experimentation
### Practical Structure of RDTIDS System  
The RDTIDS system demands a pre-processing of data, different labelling of rows according to the model that is used and creation of artificial features that are outputs of two of the classifiers used. For training the first classifier we need to label the training data set based on the attacks classification provided in Table 2 as \"Attack\" or \"Benign\". For the second classifier, we label each attack as follows: DDoS, DoS slowloris, DoS Slowhittptest, DoS Hulk, DoS GoldenEye, Heartbleed as \"DoS\"; FTP-Patator, SSH-Patator as \"Brute-Force\"; Web Attack--Brute Force, Web Attack--XSS, Web Attack--SQL Injection as \"Web Attack\". Finally, in order to train the third classifier, we use the labeling used for the second classifier. We also add the outputs of the trained classifier 1 and classifier 2 as new features.  
The choice of the three classifiers that compose our hierarchical model is the most important and critical step. To choose the best composition, that gives optimal performance, we tested several combinations with different classifiers. The results for all those combinations are quite lengthy and are not represented in this article. Following this demanding procedure, we opt for the following configuration: classifier 1 is REP Tree [36]; classifier 2 is Jrip [37]; classifier 3 is Forest PA [29].
## 4 Results  
The performance of seven models, evaluated under various hyperparameter configurations, was assessed under seven key metrics. Figure 4 shows the best-performing model configurations for each metric. The ResNet50 architecture demonstrated superior performance in a majority of the metrics. Specifically, ResNet50 with an AdamW optimizer,a learning rate of 0.0001, CosineAnnealingLR scheduler, and FocalLoss achieved the highest accuracy (0.9782), precision (0.9583), F1-score (0.9617), and IOU (0.9262). A different ResNet50 configuration, utilizing an SGD optimizer, a learning rate of 0.01, CosineAnnealingWarmRestarts scheduler, and CrossEntropy loss, achieved the best AUC (0.9961). Meanwhile, MobileNetV3 with an Adam optimizer, a learning rate of 0.01, StepLR scheduler, and CrossEntropy loss excelled in recall (1.0). Multiple models achieved a recall (1.0), so we used the model which took the least training time, as well as for better comparison. ResNet101 with an SGD optimizer, a learning rate of 0.01, StepLR scheduler, and CrossEntropy loss achieved the highest mAP (0.9921). Overall, ResNet50 emerged as the most effective model, particularly with the AdamW and FocalLoss configuration, underscoring its robustness and versatility across multiple performance metrics.
# Joint Resource Allocation for Multiuser Opportunistic Beamforming Systems with OFDM-NOMA
## 3 An Optimization Problem to Maximize SE
### Objective Function  
To achieve the maximum SE of OBON system, the objective function is founded as  
\[\max_{\vartheta,k_{nc},P_{k_{nc}}}E_{\text{total}}=\max_{\vartheta,k_{nc},P_{k_{ nc}}}\frac{1}{N_{c}}\sum_{n_{c}=0}^{N_{c}-1}\sum_{k_{nc}=1}^{K}\log_{2}\left(1+ \frac{P_{k_{nc}}\left|I_{n_{c},k_{nc}}\right|^{2}}{\sum\limits_{k_{nc}^{\prime} =k_{nc}+1}^{K}P_{k_{nc}^{\prime}}\left|I_{n_{c},k_{nc}}\right|^{2}+\sigma^{2}} \right). \tag{17}\]  
Due to both SIC and NOMA, objective function (17) can be rewritten as  
\[\max_{\vartheta,k_{nc},P_{k_{nc}}}\sum_{n_{c}=0}^{N_{c}-1}\left[\log_{2}\left( 1+\frac{P_{1_{n_{c}}}\left|I_{n_{c},k_{nc}}\right|^{2}}{P_{2_{n_{c}}}\left|I_ {n_{c},k_{nc}}\right|^{2}+\sigma^{2}}\right)+\log_{2}\left(1+\frac{P_{2_{n_{c }}}\left|I_{n_{c},k_{nc}}\right|^{2}}{\sigma^{2}}\right)\right]. \tag{18}\]
# Rare Earth Elements Uptake by Synthetic Polymeric and Cellulose-Based Materials: A Review
## 4 Sources and Recovery of REEs
### Recycling and Recovery of REEs  
REE recycling and recovery processes could be also considered secondary sources of REEs and are related to three possible REE sources: electronic devices, industrial processes, and industrial waste. Currently, REE recovery is focused on industrial processes, where magnets, lamps, petroleum cracking catalysts, and batteries are recycled [10; 13]. REE recovery from industrial waste has been proposed due to their economic significance. REE recovery processes from mining and other types of extractive industries have been proposed and implemented [10; 11; 12; 13; 14; 15; 16]. Pilot-scale studies have been developed for REE recovery, and circular economy processes have been proposed for REEs [10]. These processes involve solvent extraction, precipitation, inorganic sorbents, and ion exchange resins [10; 11; 12; 13; 14; 15; 16].  
The main challenge for REE recovery from recycling or secondary sources is related to the low amount of REEs present in those sources, requiring the implementation of new techniques and materials related to preconcentration steps, in which selectivity toward REEs is highly desirable [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. The recovery of REEs from water has the following two main approaches: precipitation and sorption [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. Therefore, adsorbent and ion exchange materials have been considered a practical approach to capture and recover REEs as preconcentration steps [10; 16; 17] due to ease of scalability, low energetic requirements, and high efficiency [18], which could allow selective, rapid capture and recovery of REEs from secondary sources, such as mining wastewaters and AMD. The advantages of REE recovery from secondary sources are the same advantages offered by the usage of sorption or ion exchange materials already described.
# Intelligent Design of Pavement Concrete Based on RSM-NSGA-III-CRITIC-VIKOR
###### Abstract  
Climate-change-induced extreme environments exacerbate pavement degradation in arid regions, where traditional concrete incurs 23-40% higher life-cycle costs due to premature cracking. Particularly in the Gobi Desert, concrete pavements suffer from conflicting performance requirements--high flexural-to-compressive strength ratio (R\({}_{\text{f}}\)/R\({}_{\text{c}}\)), low shrinkage, and controlled porosity--with traditional design methods failing to address multi-objective trade-offs. Existing optimization methods have proven insufficient for such complex environments, with conventional approaches addressing only individual parameters or employing subjective weighting techniques that fail to capture the interrelated nature of critical performance indicators. This study develops an integrated optimization framework combining Response Surface Methodology (RSM), Non-dominated Sorting Genetic Algorithm III (NSGA-III), Criteria Importance Through Intercriteria Correlation (CRITIC) weighting, and VIsekriterijumsko Kompromisno Rangiranje (VIKOR) decision-making to optimize the mix proportions water-cement ratio (W/C), sand ratio, and an air-entraining agent (AEA) for sustainable pavement concrete. Response Surface Methodology (RSM) analysis via Box-Behnken design revealed distinct parameter dominance: AEA exhibited the strongest non-linear effects on R\({}_{\text{f}}\)/R\({}_{\text{c}}\) and porosity, while W/C primarily governed shrinkage. NSGA-III generated 73 Pareto-optimal solutions, with CRITIC selecting an optimal mix (W/C = 0.35), sand ratio = 36%, AEA = 0.200%) validated experimentally (R\({}_{\text{f}}\)/R\({}_{\text{c}}\) = 0.141), shrinkage = 0.0446%, porosity = 2.82%. Microstructural characterization using scanning electron microscopy and low-field nuclear magnetic resonance (SEM/LF-NMR) demonstrated refined pore distribution and enhanced compactness. This framework effectively resolves trade-offs between performance indicators, providing a scientifically robust method for designing durable pavement concrete that reduces shrinkage by 13.0% and porosity by 13.5% compared to conventional mixes, lowering maintenance costs in arid regions.  
pavement concrete; multi-objective optimization; response surface methodology; NSGA-III; VIKOR [[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).
# A Novel Searching Method Using Reinforcement Learning Scheme for Multi-UAVs in Unknown Environments
## 3 Design of Cooperative Search Strategy  
In this section, the Q-Learning method is used to design the yaw angle decision \(\mathbf{u}(k)\) of UAVs. When UAV \(i\) is at the state \(\mathbf{s}_{i}(k)=[x_{i}(k),y_{i}(k),\varphi_{i}(k)]^{\mathrm{T}}\), the corresponding row of the state in the Q-table is set as the \(\mathbf{s}_{i}(k)\) row, in which each value represents the effect of a control decision. The decision corresponding to the maximum value is the optimal decision \(u_{i}(k)\), and the optimal decision set \(\mathbf{u}(k)\) is acquired.
## 2 Materials and Methods
### Analysis  
Data analyses were performed using custom written scripts in MATLAB(r). The last minute of the recording was taken into the further analysis. Data from the force pedals were first down-sampled to match the data from the kinematics. All data were filtered using a Butterworth filter, 2 nd order with a low cut-off frequency of 10 Hz [13]. All crank cycles from the recording were cut and averaged to form an ensemble average presented on a crank angle scale from the top dead center (TDC, i.e., 0/360\({}^{\circ}\)) over the bottom dead center (BDC, i.e., 180\({}^{\circ}\)) back to the TDC. Crank and pedal angles were calculated from the kinematics on the pedal.  
The total force (Ftot) was calculated as the vector sum of all three force components. FML was calculated (root mean square) in absolute and relative terms. Normalized FML was expressed as the percentage of the maximal value of Ftot. Kinematic data from the markers on greater trochanter, lateral condyle and lateral malleol were used to calculate Q-angle--the angle between the tibia and femur segments in the frontal plane. This is not a conventional way to calculate the Q-angle, but as this study focuses only on the change in Q-angle throughout the crank cycle, the absolute values are not relevant in this study. Furthermore, the setup used in this study is also the same as used by the majority of cycling-specific 3D kinematics systems (e.g., Retul or STT 3 DMA).  
The data were presented as mean \(\pm\) standard deviation using descriptive statistics. Normality of the data was first checked using Shapiro-Wilk's test. One-way repeated measure ANOVAs were used to examine the differences in absolute and relative FML across the three cadences and across the three workloads. Post hoc pairwise tests with a Bonferroni correction for all ANOVA tests were performed if the main effects were found statistically significant (\(p<0.05\)). Additionally, effect sizes for ANOVA (eta-squared) and 95% confidence intervals (CI) for post hoc tests were calculated. Peak cross-correlation coefficients (zero lag) were calculated between the Q-angle and relative FML. All analyses were carried out in SPSS 22 for Windows and in MATLAB(r). The data was complete for all participants and all trials were included in the further analyses.
## 2 Methods and Data
### Pseudo-Measurement Generation  
DSSE implementations rely heavily on pseudo measurements to make the system observable. Pseudo measurements serve as a substitute for the actual data from digital consumption meter devices. They are commonly devised using historical datasets and load profiles. The proposed DSSE uses solely pseudo measurements as an input, since real-time measured load data are currently not available for the supply areas. These pseudo measurements are checked against the historical total loading of the supplying MV/LV transformer to maintain consistency. Thus, the aim of this approach is primarily not to obtain more and more precise state of the network in the presence of real but incomprehensive measurements, but to perform numerical experiments to evaluate the network models themselves and the potential accuracy in case no real-time measurements are available.  
Figure 1: Flowchart of the Gauss–Newton algorithm.  
Figure 2: Architecture of the DSSE tool. Modules inside the red box perform the run of DSSE, while those outside the red box perform data management and preprocessing.  
(Deployment and integration of such onsite voltage and current measurements are planned in 2022. With these measurements, a greater accuracy and network observability will be assured soon).  
The proposed model of pseudo measurements consists of active and reactive power values provided for each quarter-hour time period of the whole year. The pseudo-measurement database was generated from two sources: SLPs supplied by the Hungarian DSO, E.ON [31], and a collection of samples of actual active power measurements. SLPs represent a realistic consumption profile of Hungarian LV consumers and are traditionally used by the DSO for network upgrade planning and to predict electricity consumption throughout the year. The used SLPs consist of consumption rates for the entire year in a 15 min resolution and include datasets for typical residential and controlled loads. To obtain the actual consumption values, these datasets were scaled by multiplying them with the modeled consumers' AAC. The measurement sample dataset (MSDS) was recorded in a Hungarian distribution network, and it contains electricity consumption time series datasets, characteristic of LV consumers in Hungary. The sample set contains data of 334 residential and 69 controlled consumers, each consisting of 15 min consumption values for the whole course of one year.  
The created pseudo measurements represent five distinct scenarios, each with increasing levels of precision, to test the effects of measurement uncertainty on the estimation. The residential datasets are further classified into four clusters according to their energy volume. Annual consumption values were separated into 52 weeks. Odd weeks serve as input to the estimation, and even weeks as input to the load flow, whose output is regarded as a ground truth to validate the estimation. This system results in 26 weeks' worth of simulation data.