esa-sceva/satcom-chunk-collection huggingface dataset:
# Minimum-Cost Offloading for Collaborative Task Execution of MEC-Assisted Platooning
## 3 System Model
### Task Execution Model
#### 3.3.2 MEC Server Execution  
If the task \(k\) is executed on the MEC server (numbered 0), the computing time of the task \(k\) is expressed by  
\[t_{k}^{0}=\frac{\omega_{k}}{f_{0}} \tag{7}\]  
and the cost of offloading the task \(k\) is  
\[b_{k}^{0}=\alpha_{\text{MEC}}f_{0} \tag{8}\]  
where \(f_{0}\) is the computation resource that the MEC server can provide and \(\alpha_{\text{MEC}}\) is the computation resource cost of each unit provided by the MEC server. Note that the MEC server can provide more computation resources than platoon members, i.e., \(f_{0}>f_{1}\), \(f_{0}>f_{2}\),..., \(f_{0}>f_{m}\), that results less computation time for the task.
# Digital Twin-Driven Virtual Network Architecture for Enhanced Extended Reality Capabilities
## 2 Background and Related Work  
This chapter introduces the three necessary pillars considered in the proposal of this paper, which are required for the deployment of extended Reality Functions in Digital Twin Networks. As stated in Section 1, the DTN + XRFs can be supported utilizing network virtualization techniques; then, the key pillars are Digital Twins (DTs), extended Reality (XR) and network virtualization (NV). In addition to the aforementioned technologies, complementary concepts such as space communications and tolerant networks are introduced. The rationale, justification and consistency of these technological pillars will be analyzed and discussed in Section 3. Therefore, the most relevant papers in the related fields of these pillars, as well as their connection with the paper proposal, will be introduced in this section.
# Design and Development of OECT Logic Circuits for Electrical Stimulation Applications
## 5 Conclusions  
With advancement in the wearables sector, there is a growing demand for biomedical electrodes outside of the traditional clinical and research environment. This implies the requirement for a mass-customization approach in smart systems solutions aimed for personal use. The presented work demonstrates a fully printed smart multi-pad electrode based on OECT circuits that can route the current from a single stimulation source to any of the electrode pads. The feasibility of the approach is established with currents in the range of interest for TENS, the most prevalent application of electrical stimulation, and electrotactile stimulation, an emerging technology with great potential in human-machine interface applications. When scaled, this concept would allow an exponential relation between the number of input leads and the number of supported electrode pads, providing a decisive leap in the usability of biomedical electrodes for stimulation.  
Future work will address improvements in the production process to minimize the variability of OECT devices and ensure stable operation regardless of external humidity conditions. In addition, the ease of use of mass-customizable multi-pad electrodes will be addressed through further development of semi-automated calibration algorithms appropriate for lay users.  
Conceptualization, M.K. and M.S.; methodology, M.S., V.K., J.S. and P.A.E.; investigation, S.I., V.K., M.Y.M. and L.H.; validation, M.K., M.S. and P.A.E.; formal analysis, M.S. and M.K.; resources, M.Y.M., J.S. and L.H.; data curation, S.I.; writing--original draft preparation, M.K.; writing--review and editing, M.S., P.A.E. and T.K.; visualization, S.I.; supervision, M.S.; project administration, M.S.; funding acquisition, M.K. and M.S. All authors have read and agreed to the published version of the manuscript.  
This research is part of the WEARPLEX project, funded by the European Commission's Horizon 2020 research program under grant agreement number 825339.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest and that the subject of this work is related to a pending patent submitted under the number EP22382172.9.
# Effect of Bentonite Addition on the Properties of Fly Ash as a Material for Landfill Sealing Layers
## 4 Conclusions  
1. Bentonite addition to fly ash causes a decrease in optimal moisture content (OMC) and an increase in maximum dry density (MDD) of the mixes. The specific density of fly ash and fly ash mixes is lower than for natural soils.
2. The specific surface area increased with the amount of bentonite addition to the fly ash.
3. With the addition of bentonite to fly ash, hydraulic conductivity of the mixture is decreased compared to that of fly ash alone. Samples with bentonite addition in the amount of 5%, 10% and 15% by dry weight tested at \(i=32\) and \(\sigma^{\prime}=400\) kPa resulted in the decrease in hydraulic conductivity value: about twice, twice and three times in relation to the fly ash, respectively. The hydraulic conductivity values of fly ash may be reduced to the values \(\leq\)1.0 \(\times\) 10\({}^{-9}\) m/s with 15% of bentonite addition and it meets the criteria of a compacted liner and cover materials in terms of hydraulic conductivity.
4. Fly ash and fly ash-bentonite mixtures with a lower amount of bentonite meets the requirement for the sealing layers of landfill of inert wastes, where the hydraulic conductivity \(k\) should have a value equal or lower than 1.0 \(\times\) 10\({}^{-7}\) m/s.
5. Hydraulic conductivity of fly ash and fly ash-bentonite mixtures should be tested under conditions of a fully saturated sample in order to produce reliable results.
6. The lowest values of hydraulic conductivity were obtained for sandy silty clay, which was caused by its higher specific surface area in relation to fly ash-bentonite mixtures.  
This work: carried out at Bialystok University of Technology, was supported by Polish financial resources on science under project no. S/WBiS/2/2018.  
The author declares no conflict of interest.
# An Innovative Hybrid Approach Producing Trial Solutions for Global Optimization
## 3 Experiments  
### Settings and Benchmark Functions  
The benchmark functions used to obtain experimental measurements are presented in Table 1.  
Figure 1: The flowchart of the proposed optimization procedure.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Formula** & **Dimension** \\ \hline ACKLEY & \(f(x)=-a\exp\left(-b\sqrt{\frac{1}{\frac{1}{2}}\sum_{i=1}x_{i}^{2}}\right)-\exp \left(\frac{1}{2}\sum_{i=1}\cos(cx_{i})\right)+a+\exp(1)\) & \(a=20.0\) & 2 \\ \hline BF1 & \(f(x)=-20\exp\left(-b\sqrt{\frac{1}{2}\sum_{i=1}x_{i}^{2}}\right)-\exp\left( \frac{1}{2}\sum_{i=1}\cos(cx_{i})\right)+20+\exp(1)\) & 2 \\ \hline BF1 & \(f(x)=x_{1}^{2}+2x_{1}^{2}-\frac{1}{2}\cos(3 nx_{1})-\frac{1}{2}\cos(4 nx_{2})+ \frac{7}{2}\) & 2 \\ \hline BF2 & \(f(x)=x_{1}^{2}+2x_{1}^{2}-\frac{1}{2}\cos(3 nx_{1})\cos(4 nx_{2})+\frac{3}{20}\) & 2 \\ \hline BF3 & \(f(x)=x_{1}^{2}+2x_{2}^{2}-\frac{1}{20}\cos(3 nx_{1})\cos(4 nx_{2})+\frac{1}{20}\) & 2 \\ \hline DIFFOWER} & \(f(x)=\sum_{i=1}|x_{i}-y_{i}|^{p}\) & \(n=2\)\(p=2,5,10\) \\ \hline CAMEL & \(f(x)=4x_{1}^{2}-2.1x_{1}^{4}+\frac{1}{2}x_{1}^{4}+x_{1}x_{2}-4x_{2}^{2}+4x_{2}^ {2},\quad x\in[-5,5]^{2}\) & 2 \\ \hline EASOM & \(f(x)=-\cos(x_{1})\cos(x_{2})\exp\left((x_{2}
## 3 Results and Discussion
### Equivalent Circuit Model (EQM)
For example, if an impedance arc is composed of two sub-arcs with close characteristic frequencies, but where one of the resistances is significantly smaller than the other one, the small resistance will be estimated with a larger error, although the deviation between the measured and fitted points might still be negligible. For this reason, the uncertainties of determination of fitting parameters must be also taken into consideration. Table 1 shows the fitting results obtained in both wet and low humidity conditions. From the results, we can observe that significant errors occur in the case of the data obtained in low humidity conditions, while considerably lower errors were estimated for the fitting in wet conditions. Hence, the data were further analysed using the distribution function of relaxation times (DFRT), as discussed in the following section.
# Multiple Pedestrian Tracking in Dense Crowds Combined with Head Tracking
## 4 Experiment
### Head Detection and Head Tracking Methods  
SCUT-Head. SCUT-Head [9] is a large-scale head detection dataset, with 4405 images and 111,251 head labels in total. The dataset consists of two parts, Part A and Part B. Part A came from the surveillance cameras in certain university classrooms, and Part B was collected from the Internet, so the background of the images in this part is relatively wider. We compared our method with several common detectors on the SCUT-Head dataset, as shown in Table 3. The evaluation indicators like precision, recall and F1 scores were involved. It can be seen from Table 3 that our method is better than other general methods.  
HT21. HT21 [7] is a large-scale pedestrian head tracking dataset in dense scenes. It consists of a training set and a testing set, with a total of 13,410 images and 2,102,385 head bounding boxes, and 6811 head motion trajectories, each frame contains 156.78 goals on average. SORT was mainly composed of Kalman filter and Hungarian algorithm. It detected bounding boxes and then tracked them, and it was a classic multiobject tracker. With the help of high-speed cameras, the IoU value between the same target in the previous and present two frames is considerable. Based on that idea, ref. [71] proposed the tracking algorithm V_IOU. Tracktor++ [6] cleverly used the function of bounding box regression of object detector to achieve target tracking. Comparing the above methods with our method in Table 4, we can see that our method has great advantages in various indicators.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **MOTA\(\uparrow\)** & **IDFI\(\uparrow\)** & **HOTA\(\uparrow\)** \\ \hline ED & 37.89 & 34.46 & 34.52 \\ LD & 51.47 & 46.83 & 39.68 \\
**IoC(Ours)** & **55.07** & **52.18** & **41.12** \\ \hline \hline \end{tabular}
\end{table}
Table 2: After changing the method of measuring the similarities between heads and bodies in TraPeHat, the final performances of TraPeHat on the MOT20 dataset are shown. The directions of arrows indicate smaller or larger values are desired for the metric.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **Precision\%** & **Recall\%** & **F1** \\ \hline FasterRCNN [12] & 87 & 80 & 0.83 \\ RFCN+FRN [9] & 91 & 84 & 0.87 \\ SMD [69] & 93 & 90 & 0.91 \\ HSFA2 Net [70] & 94 & 92 & 0.93 \\
**TraPeHat(ours)** & **95** & **93** & **0.94** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The comparison between different head detection methods.
###### Abstract  
This paper conceptualizes and evaluates the groundwater resource in a coastal urban area hydrologically influenced by peri-urban irrigation agriculture. Adra town in southern Spain was the case study chosen to evaluate the groundwater resource contributed from the northern steep urban sector (NSUS) to the southern flat urban sector (SFUS), which belongs to the Adra River Delta Groundwater Body (ARDGB). The methodology included (1) geological and hydrogeological data compilation; (2) thirteen Multichannel Analysis of Surface Waves (MASW), and eight Ground Penetrating Radar (GPR) profiles to define shallow geological structures and some hydrogeological features; (3) hydrogeological surveys for aquifer hydraulic definition; (4) conceptualization of the hydrogeological functioning; and (5) the NSUS groundwater resource evaluation. All findings were integrated to prepare a 1:5000 scale hydgeological map and cross-sections. Ten hydrogeological formations were defined, four of them (Paleozoic weatherbed rock, Pleistocene littoral facies, Holocene alluvial, and anthropogenic filling) in the NSUS contributing to the SFUS. The NSUS groundwater discharge and recharge are, respectively, around 0.28 Mm\({}^{3}\) year\({}^{-1}\) and 0.31 Mm\({}^{3}\) year\({}^{-1}\), and the actual groundwater storage is around 0.47 Mm\({}^{3}\). The groundwater renewability is high enough to guarantee a durable small exploitation for specific current and future urban water uses which can alleviate the pressure on the ARDGB.  
urban hydrogeology; hydrogeological map; multichannel analysis of surface waves; ground penetrating radar; aquifer geometry; groundwater resource evaluation; Adra town; Spain
## 3 Results and Discussion  
### Verification  
The models developed to simulate the behavior of diverse stacking modes were initially tested and calibrated via a simplified scenario where the unsteady-state temperature distribution in the empty refrigerated room at the Harbin University of Commerce was determined theoretically and then compared with previously published experimental data [23]. These data represent the temperature at multiple measurement points in the studied room. Two X-Z planes with y = 1 and 2.2 m heights are chosen for measurements. Temperature cloud diagrams of these two planes are demonstrated in Figure 3a,b, respectively. The locations of measurement points 1-32 are shown in Figure 3c,d. The lengths L17-32 are: L17 = 0.1 m, L18 = L19 = L20 = L21 = L22 = 0.9 m, L23 = L24 = L25 = L26 = 1.74 m, L27 = 0.5 m, L28 = 0.4 m, L29 = 2.48 m, L31 = 2.74 m, L30 = L32 = 0.5 m.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Density** & **Specific Heat Capacity** & **Thermal Conductivity** \\ \hline Unit & kg/m\({}^{3}\) & J/(kg\(\cdot\)K) & W/(m\(\cdot\)K) \\ Value & 1020 & 1400 & 0.4 \\ \hline \end{tabular}
\end{table}
Table 1: Physical and thermophysical parameters of product package units [36; 37; 38].  
The initial temperature of the simulated room was 293.15 K. The experimental and simulated data at 24 h after the beginning of room refrigeration are displayed and compared in Table 2. The maximum absolute and relative errors are 1.647 K and 0.64%, respectively. Therefore, the simulation results are acceptable.
# Development of a Multifunctional Unmanned Boat Platform for Aquaculture Automation
## 3 Experimental Verification
### Comparative Analysis for the Feeding Performance
#### 3.5.3 Cost Analysis  
Table 7 lists the materials used in this project, along with the total costs. Table 8 provides a comparison between daily costs and manual methods. In 2024, the total manufacturing cost of this UBP was approximately 1431 RMB (equivalent to 200 USD), as shown in Table 6. All necessary materials were readily available in local markets. The machine is expected to have a service life of 2 years, and operating the unmanned boat requires only one worker at a daily cost of 100 RMB. By contrast, the manual feeding method incurs a daily cost of 200 RMB, requiring the employment of two workers to distribute the necessary feed across a 10,000 square meter area. By utilizing the unmanned boat in the farming area, the cost can be reduced from 225 RMB per day to 120 RMB per day, a reduction of 46.7%. This demonstrates the economic feasibility of unmanned boats in aquaculture, as they contribute to increased efficiency and higher profits over time.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Treatment**} & \multicolumn{2}{c}{**Initial Weight, Gram**} & \multicolumn{3}{c}{**Total Weight, Gram**} \\  & **Ranges** & **Mean** & **S.D.** & **Ranges** & **Mean** & **S.D.** \\ \hline Manual & 49.7–51.2 & 50.5 & 0.7 & 62.2–68.7 & 65.3 & 5.4 \\ UBP & 49.2–51.3 & 50.4 & 0.6 & 105.3–112.5 & 109.5 & 6.8 \\ \hline \end{tabular}
\end{table}
Table 6: Comparison of sea cucumber weight.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Material**} & **Quantity** & **Unit, RMB** & **Total, RMB** \\ \hline EVA Foam 1  & 3 & 20 & 60 \\ Battery 2  & 1 & 90 & 90 \\ Paddles 2  & 2 & 10 & 20 \\ Motor 2  & 1 & 40 & 40 \\ NodeMCU & 1 & 12 & 12 \\ Water Temp Sensor & 1 & 2 & 2 \\ pH Sensor & 1 & 25 & 25 \\ TDS Sensor & 1 & 12 & 12 \\ NeZha & 1 & 900 & 900 \\ Underwater Camera & 1 & 100 & 100 \\ STM32 & 1 & 150 & 150 \\ BeiDou Antenna & 1 & 20 & 20 \\ Total boat price, RMB & & & 1431 (US S200) \\ \hline \end{tabular}
\end{table}
Table 7: Cost of materials.
# A Review on the Recovery and Separation of Gallium and Indium from Waste
## 3 Methods of Recovering Gallium and Indium from Secondary Sources
### Sorption and Ion Exchange
#### 3.4.2 Sorption Using Polymer Resins
A very effective solution is the use of macroporous polystyrene-divinylbenzene resins equipped with the following functional groups: (aminomethyl)phosphonic and iminodiacetate. The trade names of these resins are Lewatit TP 260 and Lewatit TP 208. The proposed process of indium recovery from LCD panels using these resins was carried out in the batch system. A resin with N-methylglucamine groups with the trade name Amberlite IRA 743 was also tested, but it gave much worse results. Indium was leached from the separated LCD panels and ground ITO glass using a mixture of hydrochloric and nitric acids, in which their concentrations were 3 M and 0.5 M, respectively. The leaching was carried out for 5 min at a temperature of 70 \({}^{\circ}\)C and an L/S ratio equal to 5 cm\({}^{3}\)/g. Additionally, the system was treated with ultrasound (300 W) and stirred at a speed of 500 rpm. The optimal adsorption conditions proposed by the researchers were as follows: a temperature of 25 \({}^{\circ}\)C, a pH of 2, a contact time of 30 min, and an L/S ratio equal to 50 cm\({}^{3}\)/g of resin. After the process, the resin was filtered and regenerated with 2 M HCl for approximately 5 min. During regeneration, additional mixing was performed and the S/L ratio was 10 cm\({}^{3}\)/g. The concentrations of In in the solutions obtained after regeneration reached up to 120 mg/dm\({}^{3}\). The adsorption and desorption efficiencies were, respectively, 84.7% and 83.3% for Lewatit TP 260, 94.2% and 94.0% for Lewatit TP 208, and 54.7% and 53.3% for Amberlite IRA 743 [43]. The use of Lewatit TP 208 allowed for the highest recovery of indium, although the amine group is present in all three chelating resins. It is assumed that this is related to the symmetry of the iminodiacetate group in Lewatit TP 208 and may provide it with a greater affinity for In(III). Moreover, this group is also characterized by a lower steric hindrance than the other two, which may also contribute to the fact that it is easier for it to interact with indium ions [43]. The structures of all three active groups before and after the addition of indium are shown in Figure 13 [1].
## 4 Results and Discussion
This problem is due to the vanishing or exploding gradient. The large increase in the norm of the gradient during training and the opposite behavior when long term components go exponentially fast to norm zero. To overcome these problems in standard RNN. LSTM and GRU are used and show better results than Vanilla RNN. The best sequence model classifier is LSTM with 90%:10% for the training and testing set with sensitivity, specificity, precision, F1-score, BACC and MCC is 98.49%, 97.97%, 95.67%, 96.32%, 97.56% and 95.32%, respectively (see in Figures 6 and 7). With our proposed sequence model, specifically the LSTM, MI class can be detected properly. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Training:**} & \multirow{2}{*}{**Sequence Model**} & \multicolumn{5}{c}{**Performance Metrics (\%)**} \\ \cline{3-8}  & & **Classifier** & **Sensitivity** & **Specificity** & **Precision** & **F1-score** & **BACC** & **MCC** \\ \hline
90:10 & Vanilla RNN & 85.81 & 87.92 & 89.56 & 84.97 & 88.14 & 89.85 \\  & LSTM & 98.49 & 97.97 & 95.67 & 96.32 & 97.56 & 95.32 \\  & GRU & 87.07 & 98.10 & 94.89 & 94.08 & 98.73 & 93.78 \\ \hline
80:20 & Vanilla RNN & 86.86 & 87.28 & 88.37 & 82.40 & 81.66 & 89.64 \\  & LSTM & 92.47 & 97.62 & 90.11 & 88.57 & 89.81 & 79.62 \\  & GRU & 87.17 & 88.49 & 90.60 & 86.69 & 88.90 & 87.90 \\ \hline
70:30 & Vanilla RNN & 81.88 & 90.78 & 60.46 & 63.60 & 75.00 & 67.08 \\  & LSTM & 88.18 & 93.61 & 71.51 & 82.55 & 83.33 & 78.78 \\  & GRU & 92.59 & 93.60 & 71.51 & 82.27 & 83.33 & 78.78 \\ \hline
60:40 & Vanilla RNN & 67.09 & 91.12 & 60.46 & 69.56 & 75.00 & 67.08 \\  & LSTM & 97.61 & 92.16 & 65.11 & 74.91 & 75.00 & 67.08 \\  & GRU & 96.85 & 93.79 & 72.67 & 81.43 & 83.33 & 78.78 \\ \hline
50:50 & Vanilla RNN & 31.44 & 88.70 & 64.53 & 42.28 & 73.14 & 54.71 \\  & LSTM & 88.14 & 93.00 & 69.18 & 77.52 & 83.33 & 78.78 \\  & GRU & 51.02 & 87.80 & 43.41 & 46.91 & 67.06 & 48.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The result of the sequence model classifier performance.
# Hands-On Deformation of Volumetric Anatomical Images on a Touchscreen
## 4 Methods: User-Guided Handle Selection
### Mapping of Selection Cues  
The problem of handle selection can be posed as selecting a subset of the voxels in the volume dataset, based on some user input. This problem has been tackled before in medical imaging, as part of image segmentation. In that context, user control can be maximized by displaying supplementary cross sections, and letting the user draw directly on such cross sections. However, with cross sections, interaction does not mimic real life actions.  
Instead, we wish to let users select handles in a tangible manner, letting them express their intent directly on the volume image. To this end, we design a two-finger selection metaphor that exploits isosurface raycasting to map the 2D user's actions into 3D cues in a natural manner. In the following, we define the 2D and 3D positions associated with the two fingers, and we describe the mapping between finger actions and selection parameters.  
We assume that user interaction is carried out using the index finger and the thumb. We define as \(\mathbf{p}_{i}\) and \(\mathbf{p}_{t}\) the screen-space positions of the index and the thumb, respectively. These points map along their corresponding viewing rays to 3D finger points \(\mathbf{q}_{i}\) and \(\mathbf{q}_{t}\) on the visible isosurface, respectively. In our implementation, we find the two 3D finger points through independent ray casting, but a graph-based optimization could provide more robust results [31].  
When the user first touches the screen with both fingers, we define the handle seed \(\mathbf{s}\) as the average of the corresponding 3D finger point positions, \(\mathbf{s}=\frac{\mathbf{q}_{i}+\mathbf{q}_{i}}{2}\). Based on the 3D depth of the seed and the current perspective projection, we also compute the scale factor \(s\) between screen-space displacements and 3D displacements.  
The user may then touch the screen with both fingers, and vary the distance between their screen-space positions, indicating a grow or zoom action. At any time, we define the handle extent \(d\) by scaling the distance between the finger screen-space positions, i.e., \(d=s\left\|\mathbf{p}_{i}-\mathbf{p}_{t}\right\|\).  
The various components of the mapping of selection cues are depicted in Figure 3. As soon as the user lifts one finger off the screen, the selection process is terminated. As shown in Figure 4, the selection is displayed at all times using a color mask.  
Figure 3: For handle selection, the 2D positions of the thumb and index on the touchscreen (\(\mathbf{p}_{t}\) and \(\mathbf{p}_{i}\), respectively) are mapped to 3D positions (\(\mathbf{q}_{i}\) and \(\mathbf{q}_{i}\), respectively) through isosurface raycasting. The selection is initialized at the seed \(\mathbf{s}\).